pax_global_header00006660000000000000000000000064126606264710014524gustar00rootroot0000000000000052 comment=994896361eb0789c97803c853ffca9dae942e414 brian-1.4.3/000077500000000000000000000000001266062647100126245ustar00rootroot00000000000000brian-1.4.3/.coveragerc000066400000000000000000000012011266062647100147370ustar00rootroot00000000000000# .coveragerc to control coverage.py # following the example at http://nedbatchelder.com/code/coverage/config.html [run] branch = True source = brian omit = brian/experimental/* brian/deprecated/* brian/unused/* brian/tests/* [report] # Regexes for lines to exclude from consideration exclude_lines = # Have to re-enable the standard pragma pragma: no cover # Don't complain if tests don't hit defensive assertion code: raise AssertionError raise NotImplementedError # Don't complain if non-runnable code isn't run: if 0: if __name__ == .__main__.: if __name__==.__main__.: ignore_errors = True brian-1.4.3/.coveragerc_brian2000066400000000000000000000011661266062647100162060ustar00rootroot00000000000000# .coveragerc to control coverage.py # following the example at http://nedbatchelder.com/code/coverage/config.html [run] branch = True source = brian2 omit = brian2/codegen/old/* brian2/codegen/temp/* brian2/tests/* [report] # Regexes for lines to exclude from consideration exclude_lines = # Have to re-enable the standard pragma pragma: no cover # Don't complain if tests don't hit defensive assertion code: raise AssertionError raise NotImplementedError # Don't complain if non-runnable code isn't run: if 0: if __name__ == .__main__.: if __name__==.__main__.: ignore_errors = True brian-1.4.3/.svnauthors000066400000000000000000000007361266062647100150470ustar00rootroot00000000000000thesamovar = Dan Goodman romainbrette = Romain Brette crossant = Cyrille Rossant bfontaine = Bertrand Fontaine bgour = Boris Gourevitch emuller = Eilif Muller vbenich = Victor Benichoux jlaudan = Jonathan Laudanski mstimberg = Marcel Stimberg brian-1.4.3/MANIFEST.in000066400000000000000000000001331266062647100143570ustar00rootroot00000000000000recursive-include brian *.py *.c *.h *.i *.cpp *.cxx *.bat include brian_setup_info.py brian-1.4.3/README.txt000066400000000000000000000416141266062647100143300ustar00rootroot00000000000000============ B R I A N ============================= A clock-driven simulator for spiking neural networks ==================================================== Version: 1.4.3 Authors: Romain Brette http://audition.ens.fr/brette/ Dan Goodman http://thesamovar.net/neuroscience Team: Cyrille Rossant http://cyrille.rossant.net/ Bertrand Fontaine Victor Benichoux Marcel Stimberg Jonathan Laudanski ==== Installation ========================================================== Requirements: Python (version 2.5-7), the following modules: * numpy (version >=1.4.1) * scipy (version >= 0.7) * matplotlib (version >=0.90.1, optional, necessary for plotting ) * sympy (optional, necessary for the "event-based" feature in Synapses) All operating systems: run 'python setup.py install' from the download folder. Windows: You can run the installer exe file. ==== Extras ================================================================ Included in the extras download are: docs Documentation for Brian including tutorials. examples Examples of using Brian, these serve as supplementary documentation. tutorials Fully worked through tutorials on using Brian. These can be read through in the documentation too. ==== Usage and Documentation =============================================== See the documentation in the extras download, or online: http://www.briansimulator.org/docs ==== Changes =============================================================== Version 1.4.2 to 1.4.3 ---------------------- A minor bugfix release to fix incompatibilities with the upcoming numpy release. Also prepares for the removal of the `scipy.weave` package (replaced by the `weave` package). Version 1.4.1 to 1.4.2 ---------------------- This is a bugfix release that does not add any major features. See the commit log at http://neuralensemble.org/trac/brian/log/ for details. Note that our development efforts are now entirely focused on Brian 2 (https://github.com/brian-team/brian2), this will most likely be the last release in the 1.x series. Version 1.4.0 to 1.4.1 ---------------------- Major features: * C extensions are compiled by default during installation (with a fallback to the Python version if compilation fails) -- this might lead to a considerable speedup for users who did not compile those extensions manually before Minor features: * Convenience methods for the Synapses class, allowing to save and load the connectivity and to convert the weights into a matrix * A new openmp option to switch on the use of OpenMP pragmas in generated C code * Brian hears: Two new models, MiddleEar (filtering by the middle ear) and ZhangSynapse (model of the IHC-AN synapse) * Brian hears: New convenience functions to get reasonable axis ticks for logarithmic axes Improvements: * Brian's documentation is now also available under brian.readthedocs.org * ProgressReporter has context manager support (i.e. can be used in "with" statements) * NeuronGroup and Synapses work with empty model specifications. * C version of SpikeContainer is now picklable Bug fixes: * Synaptic equations referring to variables in the pre- or postsynaptic group are never considered as being linear (fixes ticket #83) * Fix issue with static equations in synaptic models (see https://groups.google.com/d/msg/briansupport/-/uqxLK_yoqKUJ ) * Make LinearStateUpdater pickable, even if array B is "NotImplemented". * Fixed the bug in which the StateSpikeMonitor didn't record variables defined with a static equation. * Important bug fixes for brian hears, all users are encouraged to update: * Make sure that LinearFilterbank copies it source and therefore not changes it (when not using weave) (fixes ticket #73) * Fix some bugs in the TanCarney model * Fix shifting multi-channel sounds with fractional=True (fixes ticket #80) Experimental features: * A C version of SpikeQueue (used in the Synapses class), which can lead to a considerable speedup (see "Advanced concepts/Compiled code" for instructions how to use it). * Delays can be specified as a parameters of the Synapses model and then be changed dynamically. Version 1.3.1 to 1.4.0 ---------------------- Major features: * New Synapses class (plasticity, gap junctions, nonlinear synapses, etc) Minor features: * New AERSpikeMonitor class * Several updates to library.electrophysiology Improvements: * Units should work better with static code analysers now * Added Network.remove * SpikeMonitor has a new .it attribute (returns pair i, t of arrays of spike times) * Many new examples Bug fixes: * Assigning to a static variable (equation) now raise an error * Fixed issues for TimedArrays with explicitly set times (fixes ticket #81) * Fixed bug, repr and str didn't work for Sound * Fixed bug where tone(array_of_frequencies, ...) * Fixed SparseConnectionMatrix bug suggested by Owen Mackwood * Fixed bug in Parameters reported by Jimmy Bonaiuto * Fixed bug with contained_objects reported by Oleg Sinyavskiy * Units __repr__ and __str__ fixes * Sound.spectrum, Sound.pinknoise, brownnoise * t wasn't available in StringReset and PythonThreshold Deprecated or removed features: * MultipleSpikeGeneratorGroup * experimental.coincidence_detection Experimental features: * Generating model documentation automatically (experimental.model_documentation) Version 1.3.0 to 1.3.1 ---------------------- Minor features: * New PoissonInput class * New auditory model: TanCarney (brian.hears) * Many more examples from papers * New electrode compensation module (in library.electrophysiology) * New trace analysis module (in library.electrophysiology) * Added new brian.tools.taskfarm.run_tasks function to use multiple CPUs to perform multiple runs of a simulation and save results to a DataManager, with an optional GUI interface. * Added FractionalDelay filterbank to brian.hears, fractional itds to HeadlessDatabase and fractional shifts to Sound.shifted. * Added vowel function to brian.hears for creating artificial vowel sounds * New spike_triggered_average function * Added maxlevel and atmaxlevel to Sound * New IRNS/IRNO noise functions Improvements: * SpikeGeneratorGroup is much faster. * Added RemoteControlClient.set(var, name) to allow sending data to the server from the client (previously you could only receive data from the server but not send it, except in string form). * Monitors do not process empty spike arrays when there have not been any spikes, increases speed for monitored networks with sparse firing (#78) * Various speed optimisations Bug fixes: * Fixed bug with frozen equations and time variable in equations * Fixed bug with loading sounds using Sound('filename.wav') * SpikeMonitor now clears spiketimes correctly on reinit (#75) * MultiConnection now propagates reinit (important for monitors) (#76) * Fixed bug in realtime plotting * Fixed various bugs in Sound * Fixed bugs in STDP * Bow propagates spikes only if spikes exist (#78) Version 1.2.1 to 1.3.0 ---------------------- Major features: * Added Brian.hears auditory library Minor features: * Added new brian.tools.datamanager.DataManager, moved from brian.experimental * reinit(states=False) will now not reset NeuronGroup state variables to 0. * modelfitting now has support for refractoriness * New examples in misc: after_potential, non_reliability, reliability, van_rossum_metric, remotecontrolserver, remotecontrolclient * New experimental.neuromorphic package * Van Rossum metric added Improvements: * SpikeGeneratorGroup is faster for large number of events ("gather" option). * Speed improvement for pure Python version of sparse matrix preparation * Speed improvements for spike propagation weave code (50-100% faster). * Clocks have been changed and should now behave more predictably. In addition, you can now specify an order attribute for clocks. * modelfitting is now based on playdoh 0.3 * modelfitting can now use euler/exp.euler or RK2 integration schemes * Loading AER data is much faster * Freezing now uses higher precision (used to only use 12sf now uses 17sf) Bug fixes: * Bug in STDP with small values for wmin/wmax fixed (ticket #63) * Equations/aliases now work correctly in STDP (ticket #56) * Bug in sparse matrices introduced in scipy 0.8.0 fixed * Bug in TimedArray when dt keyword is used now fixed (thanks to Adrien Wohrer for pointing out the bug). * Units now work correctly in STDP (ticket #60) * STDP raises an error if operations are reordered (ticket #57) * linked_var works with static vars (equations) (ticket #68) * Changing clock.t during a run won't end the run * Fixed ticket #66 (unit bug) * Fixed ticket #64 (bug with freeze) * Can now run a network with no group * Exception handling now works properly for C version of circiular spike container * ccircular now builds correctly on linux and 64 bit Internal changes: * brian.connection deprecated and replaced by subpackage brian.connections, making the code structure much more straightforward and setting up for future work on code generation, etc. Version 1.2.0 to 1.2.1 ---------------------- Major features: * New remote controlling of running Brian scripts via RemoteControlServer and RemoteControlClient. Minor features: * New module tools.io * weight and sparseness can now both be functions in connect_random * New StateHistogramMonitor object * clear now has a new keyword all which allows you to destroy all Brian objects regardless of whether or not they would be found by MagicNetwork. In addition, garbage collection is called after a clear. * New method StateMonitor.insert_spikes to have spikes on voltage traces. Improvements * The sparseness keyword in connect_random can be a function * Added 'wmin' to STDP * You can now access STDP internal variables, e.g. stdp.A_pre, and monitor them by doing e.g. StateMonitor(stdp.pre_group, 'A_pre') * STDP now supports nonlinear equations and parameters * refractory can now be a vector (see docstring for NeuronGroup) for constant resets. * modelfitting now uses playdoh library * C++ compiled code is now much faster thanks to adding -ffast-math switch to gcc, and there is an option which allows you to set your own compiler switches, for example -march=native on gcc 4.2+. * SpikeGeneratorGroup now has a spiketimes attribute to reset the list of spike times. * StateMonitor now caches values in an array, improving speed for M[i] operation and resolving ticket #53 Bug fixes * Sparse matrices with some versions of scipy * Weave now works on 64 bit platforms with 64 bit Python * Fixed bug introduced in 1.2.0 where dense DelayConnection structures would not propagate any spikes * Fixed bug where connect* functions on DelayConnection didn't work with subgroups but only with the whole group. * Fixed bug with linked_var from subgroups not working * Fixed bug with adding Equations objects together using a shared base equation (ticket #9 on the trac) * unit_checking=False now works (didn't do anything before) * Fixed bug with using Equations object twice (for two different NeuronGroups) * Fixed unit checking bug and ZeroDivisionError (ticket #38) * Fixed rare problems with spikes being lost due to wrong size of SpikeContainer, it now dynamically adapts to the number of spikes. * Fixed ticket #5, ionic_currents did not work with units off * Fixed ticket #6, Current+MembraneEquation now works * Fixed bug in modelfitting : the fitness was not computed right with CPUs. * Fixed bug in modelfitting with random seeds on Unix systems. * brian.hears.filtering now works correctly on 64 bit systems Removed features * Model has now been removed from Brian (it was deprecated in 1.1). Version 1.1.3 to 1.2.0 ---------------------- Major features: * Model fitting toolbox (library.modelfitting) Minor features: * New real-time ``refresh=`` options added to plotting functions * Gamma factor in utils.statistics * New RegularClock object * Added brian_sample_run function to test installation in place of nose tests Improvements: * Speed improvements to monitors and plotting functions * Sparse matrix support improved, should work with scipy versions up to 0.7.1 * Various improvements to brian.hears (still experimental though) * Parameters now picklable * Made Equations picklable Bug fixes: * Fixed major bug with subgroups and connections (announced on webpage) * Fixed major bug with multiple clocks (announced on webpage) * No warnings with Python 2.6 * Minor bugfix to TimedArray caused by floating point comparisons * Bugfix: refractory neurons could fire in very extreme circumstances * Fixed bug with DelayConnection not setting max_delay * Fixed bug with STP * Fixed bug with weight=lambda i,j:rand() New examples: * New multiprocessing examples * Added polychronisation example * Added modelfitting examples * Added examples of TimedArray and linked_var * Added examples of using derived classes with Brian * Realtime plotting example Version 1.1.2 to 1.1.3 ---------------------- * STDP now works with DelayConnection * Added EventClock * Added RecentStateMonitor * Added colormap option to StateMonitor.plot * Added timed array module, see TimedArray class for details. * Added optional progress reporting to run() * New recall() function (converse to forget()) * Added progress reporting module (brian.utils.progressreporting) * Added SpikeMonitor.spiketimes * Added developer's guide to docs * Early version of brian.hears subpackage for auditory modelling * Various bug fixes Version 1.1.1 to 1.1.2 ---------------------- * Standard functions rand() and randn() can now be used in string resets. * New forget() function. * Major bugfix for STP Version 1.1.0 to 1.1.1 ---------------------- * New statistical function: vector_strength * Bugfix for one line string thresholds/resets Version 1.0.0 to 1.1.0 ---------------------- * STDP * Short-term plasticity (Tsodyks-Markram model) * New DelayConnection for heterogeneous delays * New code for Connections, including new 'dynamic' connection matrix type * Reset and threshold can be specified with strings (Python expressions) * Much improved documentation * clear() function added for ipython users * Simplified initialisation of Connection objects * Optional unit checking in NeuronGroup * Spike train statistics (utils.statistics) * Miscellaneous optimisations * New MultiStateMonitor class * New Group, MultiGroup objects (for convenience of people writing extensions mostly) * Improved contained_objects protocol with ObjectContainer class in brian.base * UserComputed* classes removed for this version (they will return in another form). Version 1.0.0 RC5 to version 1.0.0 ---------------------------------- * 2nd order Runge-Kutta method (use order=2) * Quantity arrays are disabled (units only for scalars) * brian_global_config added * UserComputedConnectionMatrix and UserComputedSparseConnectionMatrix * SimpleCustomRefractoriness, CustomRefractoriness Version 1.0.0 RC4 to version 1.0.0 RC5 -------------------------------------- * Bugfix of sparse matrix problems * Compiled version of spike propagation (much faster for networks with lots of spikes) * Assorted small improvements Version 1.0.0 RC3 to version 1.0.0 RC4 -------------------------------------- * Added StateSpikeMonitor * Changed QuantityArray behaviour to work better with numpy, scipy and pylab Version 1.0.0 RC2 to version 1.0.0 RC3 -------------------------------------- * Small bugfixes Version 1.0.0 RC1 to version 1.0.0 RC2 -------------------------------------- * Documentation system now much better, using Sphinx, includes cross references, index, etc. * Added VariableReset * Added run_all_tests() * numpywrappers module added, but not in global namespace * Quantity comparison to zero doesn't check units (positivity/negativity) Version 1.0.0 beta to version 1.0.0 RC1 --------------------------------------- * Connection: connect_full allows a functional weight argument (like connect_random) * Short-term plasticity: In Connection: 'modulation' argument allows modulating weights by a state variable from the source group (see examples). * HomogeneousCorrelatedSpikeTrains: input spike trains with exponential correlations. * Network.stop(): stops the simulation (can be called by a user script) * PopulationRateMonitor: smooth_rate method * Optimisation of Euler code: use compile=True when initialising NeuronGroup * More examples * Pickling now works (saving and loading states) * dot(a,b) now works correctly with qarray's with homogeneous units * Parallel simulations using Parallel Python (independent simulations only) * Example of html inferfaces to Brian scripts using CherryPy * Time dependence in equations (see phase_locking example) * SpikeCounter and PopulationSpikeCounter brian-1.4.3/brian/000077500000000000000000000000001266062647100137175ustar00rootroot00000000000000brian-1.4.3/brian/__init__.py000066400000000000000000000173711266062647100160410ustar00rootroot00000000000000# ---------------------------------------------------------------------------------- # Copyright ENS, INRIA, CNRS # Contributors: Romain Brette (brette@di.ens.fr) and Dan Goodman (goodman@di.ens.fr) # # Brian is a computer program whose purpose is to simulate models # of biological neural networks. # # This software is governed by the CeCILL license under French law and # abiding by the rules of distribution of free software. You can use, # modify and/ or redistribute the software under the terms of the CeCILL # license as circulated by CEA, CNRS and INRIA at the following URL # "http://www.cecill.info". # # As a counterpart to the access to the source code and rights to copy, # modify and redistribute granted by the license, users are provided only # with a limited warranty and the software's author, the holder of the # economic rights, and the successive licensors have only limited # liability. # # In this respect, the user's attention is drawn to the risks associated # with loading, using, modifying and/or developing or reproducing the # software by the user in light of its specific status of free software, # that may mean that it is complicated to manipulate, and that also # therefore means that it is reserved for developers and experienced # professionals having in-depth computer knowledge. Users are therefore # encouraged to load and test the software's suitability as regards their # requirements in conditions enabling the security of their systems and/or # data to be ensured and, more generally, to use and operate it in the # same conditions as regards security. # # The fact that you are presently reading this means that you have had # knowledge of the CeCILL license and that you accept its terms. # ---------------------------------------------------------------------------------- # """ Brian """ __docformat__ = "restructuredtext en" import warnings as _warnings from scipy import * try: from pylab import * except: _warnings.warn("Couldn't import pylab.") if 'x' in globals(): del x # for some reason x is defined as 'symlog' by pylab! if 'f' in globals(): del f from clock import * from connections import * from synapses import * from directcontrol import * from stateupdater import * from monitor import * from network import * from neurongroup import * from plotting import * from reset import * from threshold import * from units import * from tools import * from equations import * from globalprefs import * from unitsafefunctions import * from stdunits import * from membrane_equations import * from compartments import * from log import * from magic import * from stdp import * from stp import * from timedarray import * from deprecated.multiplespikegeneratorgroup import * from tests.simpletest import * __version__ = '1.4.3' __release_date__ = '2016-02-16' ### Define global preferences which are not defined anywhere else define_global_preference( 'useweave', 'False', desc=""" Defines whether or not functions should use inlined compiled C code where defined. Requires a compatible C++ compiler. The ``gcc`` and ``g++`` compilers are probably the easiest option (use Cygwin on Windows machines). See also the ``weavecompiler`` global preference. """) set_global_preferences(useweave=False) define_global_preference( 'weavecompiler', 'gcc', desc=''' Defines the compiler to use for weave compilation. On Windows machines, installing Cygwin is the easiest way to get access to the gcc compiler. ''') set_global_preferences(weavecompiler='gcc') define_global_preference( 'gcc_options', "['-ffast-math']", desc=''' Defines the compiler switches passed to the gcc compiler. For gcc versions 4.2+ we recommend using ``-march=native``. By default, the ``-ffast-math`` optimisations are turned on - if you need IEEE guaranteed results, turn this switch off. ''') set_global_preferences(gcc_options=['-ffast-math']) define_global_preference( 'openmp', 'False', desc=''' Whether or not to use OpenMP pragmas in generated C code. If supported on your compiler (gcc 4.2+) it will use multiple CPUs and can run substantially faster. However, if you are already running several simulations in parallel this will not improve the speed and may even slow it down. In addition, for smaller networks or for simpler neuron models the parallelisation overheads can make it take longer. ''') set_global_preferences(openmp=False) define_global_preference( 'usecodegen', 'False', desc=''' Whether or not to use experimental code generation support. ''') set_global_preferences(usecodegen=False) define_global_preference( 'usecodegenweave', 'False', desc=''' Whether or not to use C with experimental code generation support. ''') set_global_preferences(usecodegenweave=False) define_global_preference( 'usecodegenstateupdate', 'True', desc=''' Whether or not to use experimental code generation support on state updaters. ''') set_global_preferences(usecodegenstateupdate=True) define_global_preference( 'usecodegenreset', 'False', desc=''' Whether or not to use experimental code generation support on resets. Typically slower due to weave overheads, so usually leave this off. ''') set_global_preferences(usecodegenreset=False) define_global_preference( 'usecodegenthreshold', 'True', desc=''' Whether or not to use experimental code generation support on thresholds. ''') set_global_preferences(usecodegenthreshold=True) define_global_preference( 'usenewpropagate', 'False', desc=''' Whether or not to use experimental new C propagation functions. ''') set_global_preferences(usenewpropagate=False) define_global_preference( 'usecstdp', 'False', desc=''' Whether or not to use experimental new C STDP. ''') set_global_preferences(usecstdp=False) define_global_preference( 'brianhears_usegpu', 'False', desc=''' Whether or not to use the GPU (if available) in Brian.hears. Support is experimental at the moment, and requires the PyCUDA package to be installed. ''') set_global_preferences(brianhears_usegpu=False) # check if we were run from a file or some other source, and set the default # behaviour for magic functions accordingly import inspect as _inspect import os as _os _of = _inspect.getouterframes(_inspect.currentframe()) if len(_of) > 1 and _os.path.exists(_of[1][1]): _magic_useframes = True else: _magic_useframes = False define_global_preference( 'magic_useframes', str(_magic_useframes), desc=""" Defines whether or not the magic functions should search for objects defined only in the calling frame or if they should find all objects defined in any frame. This should be set to ``False`` if you are using Brian from an interactive shell like IDLE or IPython where each command has its own frame, otherwise set it to ``True``. """) set_global_preferences(magic_useframes=_magic_useframes) ### Update documentation for global preferences import globalprefs as _gp _gp.__doc__ += _gp.globalprefdocs try: import brian_global_config except ImportError: pass try: import nose @nose.tools.nottest def run_all_tests(): import tests tests.go() except ImportError: def run_all_tests(): print "Brian test framework requires 'nose' package." brian-1.4.3/brian/base.py000066400000000000000000000035421266062647100152070ustar00rootroot00000000000000''' Various base classes for Brian ''' __all__ = ['ObjectContainer'] class ObjectContainer(object): ''' Implements the contained_objects protocol The object contains an attribute _contained_objects and a property contained_objects whose getter just returns _contained_objects or an empty list, and whose setter appends _contained_objects with the objects. This makes classes which set the value of contained_objects without thinking about things like inheritance work correctly. You can still directly manipulate _contained_objects or do something like:: co = obj.contained_objects co[:] = [] Note that when extending the list, duplicate objects are removed. ''' def get_contained_objects(self): if hasattr(self, '_contained_objects'): return self._contained_objects self._contained_objects = [] return self._contained_objects def set_contained_objects(self, newobjs): self._contained_objects = self.get_contained_objects() ids = set(id(o) for o in self._contained_objects) newobjs = [o for o in newobjs if id(o) not in ids] self._contained_objects.extend(newobjs) contained_objects = property(fget=get_contained_objects, fset=set_contained_objects) if __name__ == '__main__': from brian import * class A(NetworkOperation): def __init__(self): x = NetworkOperation(lambda:None) print 'A:', id(x) self.contained_objects = [x] class B(A): def __init__(self): super(B, self).__init__() x = NetworkOperation(lambda:None) print 'B:', id(x) self.contained_objects = [x] b = B() print map(id, b.contained_objects) brian-1.4.3/brian/clock.py000066400000000000000000000350771266062647100154000ustar00rootroot00000000000000# ---------------------------------------------------------------------------------- # Copyright ENS, INRIA, CNRS # Contributors: Romain Brette (brette@di.ens.fr) and Dan Goodman (goodman@di.ens.fr) # # Brian is a computer program whose purpose is to simulate models # of biological neural networks. # # This software is governed by the CeCILL license under French law and # abiding by the rules of distribution of free software. You can use, # modify and/ or redistribute the software under the terms of the CeCILL # license as circulated by CEA, CNRS and INRIA at the following URL # "http://www.cecill.info". # # As a counterpart to the access to the source code and rights to copy, # modify and redistribute granted by the license, users are provided only # with a limited warranty and the software's author, the holder of the # economic rights, and the successive licensors have only limited # liability. # # In this respect, the user's attention is drawn to the risks associated # with loading, using, modifying and/or developing or reproducing the # software by the user in light of its specific status of free software, # that may mean that it is complicated to manipulate, and that also # therefore means that it is reserved for developers and experienced # professionals having in-depth computer knowledge. Users are therefore # encouraged to load and test the software's suitability as regards their # requirements in conditions enabling the security of their systems and/or # data to be ensured and, more generally, to use and operate it in the # same conditions as regards security. # # The fact that you are presently reading this means that you have had # knowledge of the CeCILL license and that you accept its terms. # ---------------------------------------------------------------------------------- # """ Clocks for the simulator """ __docformat__ = "restructuredtext en" __all__ = ['Clock', 'defaultclock', 'guess_clock', 'define_default_clock', 'reinit_default_clock', 'get_default_clock', 'EventClock', 'RegularClock', 'FloatClock', 'NaiveClock'] from inspect import stack from units import * from globalprefs import * import magic from time import time from numpy import ceil class Clock(magic.InstanceTracker): ''' An object that holds the simulation time and the time step. Initialisation arguments: ``dt`` The time step of the simulation. ``t`` The current time of the clock. ``order`` If two clocks have the same time, the order of the clock is used to resolve which clock is processed first, lower orders first. ``makedefaultclock`` Set to ``True`` to make this clock the default clock. The times returned by this clock are always off the form ``n*dt+offset`` for integer ``n`` and float ``dt`` and ``offset``. For example, for a clock with ``dt=10*ms``, setting ``t=25*ms`` will set ``n=2`` and ``offset=5*ms``. For a clock that uses true float values for ``t`` rather than underlying integers, use :class:`FloatClock` (although see the caveats there). In order to make sure that certain operations happen in the correct sequence, you can use the ``order`` attribute, clocks with a lower order will be processed first if the time is the same. The condition for two clocks to be considered as having the same time is ``abs(t1-t2) 1: # several clocks: ambiguous # What type of error? raise TypeError("Clock is ambiguous. Please specify it explicitly.") if len(clocks) == 1: return clocks[0] # Fall back on default clock if exists_global_preference('defaultclock'): return get_global_preference('defaultclock') # No clock found raise TypeError("No clock found. Please define a clock.") class EventClock(Clock): ''' Clock that is used for events. Works the same as a :class:`Clock` except that it is never guessed as a clock to use by :class:`NeuronGroup`, etc. These clocks can be used to make multiple clock simulations without causing ambiguous clock problems. ''' @staticmethod def _track_instances(): return False # Do not track the default clock class DefaultClock(Clock): @staticmethod def _track_instances(): return False defaultclock = DefaultClock(dt=0.1 * msecond) define_global_preference( 'defaultclock', 'Clock(dt=0.1*msecond)', desc=""" The default clock to use if none is provided or defined in any enclosing scope. """) def define_default_clock(**kwds): ''' Create a new default clock Uses the keywords of the :class:`Clock` initialiser. Sample usage:: define_default_clock(dt=1*ms) ''' kwds['makedefaultclock'] = True newdefaultclock = Clock(**kwds) def reinit_default_clock(t=0 * msecond): ''' Reinitialise the default clock (to zero or a specified time) ''' get_default_clock().reinit(t) def get_default_clock(): ''' Returns the default clock object. ''' return get_global_preference('defaultclock') if __name__ == '__main__': print id(guess_clock()), id(defaultclock) brian-1.4.3/brian/compartments.py000066400000000000000000000061261266062647100170120ustar00rootroot00000000000000# ---------------------------------------------------------------------------------- # Copyright ENS, INRIA, CNRS # Contributors: Romain Brette (brette@di.ens.fr) and Dan Goodman (goodman@di.ens.fr) # # Brian is a computer program whose purpose is to simulate models # of biological neural networks. # # This software is governed by the CeCILL license under French law and # abiding by the rules of distribution of free software. You can use, # modify and/ or redistribute the software under the terms of the CeCILL # license as circulated by CEA, CNRS and INRIA at the following URL # "http://www.cecill.info". # # As a counterpart to the access to the source code and rights to copy, # modify and redistribute granted by the license, users are provided only # with a limited warranty and the software's author, the holder of the # economic rights, and the successive licensors have only limited # liability. # # In this respect, the user's attention is drawn to the risks associated # with loading, using, modifying and/or developing or reproducing the # software by the user in light of its specific status of free software, # that may mean that it is complicated to manipulate, and that also # therefore means that it is reserved for developers and experienced # professionals having in-depth computer knowledge. Users are therefore # encouraged to load and test the software's suitability as regards their # requirements in conditions enabling the security of their systems and/or # data to be ensured and, more generally, to use and operate it in the # same conditions as regards security. # # The fact that you are presently reading this means that you have had # knowledge of the CeCILL license and that you accept its terms. # ---------------------------------------------------------------------------------- # ''' Compartmental models for Brian. ''' from equations import * from membrane_equations import * from units import check_units, ohm #TODO: add MembraneEquation class Compartments(Equations): """ Creates a compartmental model from a dictionary of MembraneEquation objects. Compartments are initially unconnected. Caution: the original objects are modified. """ def __init__(self, comps): Equations.__init__(self, '') for name, eqs in comps.iteritems(): name = str(name) # Change variable names vars = eqs._units.keys() for var in vars: eqs.substitute(var, var + '_' + name) self += eqs @check_units(Ra=ohm) def connect(self, a, b, Ra): """ Connects compartment a to compartment b with axial resistance Ra. """ a, b = str(a), str(b) # Axial current from a to b Ia_name = 'Ia_' + a + '_' + b self += Equations('Ia=(va-vb)*invRa : amp', Ia=Ia_name, invRa=1. / Ra, va='vm_' + a, vb='vm_' + b) # Add the current to both compartments self._string['__membrane_Im_' + a] += '-' + Ia_name self._string['__membrane_Im_' + b] += '+' + Ia_name brian-1.4.3/brian/connection.py000066400000000000000000000005431266062647100164320ustar00rootroot00000000000000''' This module is here only to maintain backward compatibility with old code that references brian.connection rather than brian.connections. It should be removed at some point in the future ''' from connections import * import log as _log _log.log_warn('brian', 'Module "connection" in Brian is deprecated, use "brian.connections" instead.') brian-1.4.3/brian/connections/000077500000000000000000000000001266062647100162415ustar00rootroot00000000000000brian-1.4.3/brian/connections/__init__.py000066400000000000000000000003271266062647100203540ustar00rootroot00000000000000from sparsematrix import * from connectionvector import * from constructionmatrix import * from connectionmatrix import * from connection import * from delayconnection import * from otherconnections import * brian-1.4.3/brian/connections/base.py000066400000000000000000000017051266062647100175300ustar00rootroot00000000000000import copy from itertools import izip import itertools from random import sample import bisect from ..units import second, msecond, check_units, DimensionMismatchError import types from .. import magic from ..log import log_warn, log_info, log_debug from numpy import * try: import weave except ImportError: from scipy import weave from scipy import sparse, stats, rand, linalg import scipy import scipy.sparse import numpy from numpy.random import binomial, exponential import random as pyrandom from scipy import random as scirandom from ..utils.approximatecomparisons import is_within_absolute_tolerance from ..globalprefs import get_global_preference from ..base import ObjectContainer from ..stdunits import ms from operator import isSequenceType effective_zero = 1e-40 colon_slice = slice(None, None, None) def todense(x): if hasattr(x, 'todense'): return x.todense() return array(x, copy=False) brian-1.4.3/brian/connections/connection.py000066400000000000000000000533111266062647100207550ustar00rootroot00000000000000from base import * from sparsematrix import * from connectionvector import * from constructionmatrix import * from connectionmatrix import * from construction import * from propagation_c_code import * from scipy.sparse import issparse import gc # we do this at the bottom because of order of import issues #from delayconnection import * __all__ = ['Connection'] class Connection(magic.InstanceTracker, ObjectContainer): ''' Mechanism for propagating spikes from one group to another A Connection object declares that when spikes in a source group are generated, certain neurons in the target group should have a value added to specific states. See Tutorial 2: Connections to understand this better. With arguments: ``source`` The group from which spikes will be propagated. ``target`` The group to which spikes will be propagated. ``state`` The state variable name or number that spikes will be propagated to in the target group. ``delay`` The delay between a spike being generated at the source and received at the target. Depending on the type of ``delay`` it has different effects. If ``delay`` is a scalar value, then the connection will be initialised with all neurons having that delay. For very long delays, this may raise an error. If ``delay=True`` then the connection will be initialised as a :class:`DelayConnection`, allowing heterogeneous delays (a different delay for each synapse). ``delay`` can also be a pair ``(min,max)`` or a function of one or two variables, in both cases it will be initialised as a :class:`DelayConnection`, see the documentation for that class for details. Note that in these cases, initialisation of delays will only have the intended effect if used with the ``weight`` and ``sparseness`` arguments below. ``max_delay`` If you are using a connection with heterogeneous delays, specify this to set the maximum allowed delay (smaller values use less memory). The default is 5ms. ``modulation`` The state variable name from the source group that scales the synaptic weights (for short-term synaptic plasticity). ``structure`` Data structure: ``sparse`` (default), ``dense`` or ``dynamic``. See below for more information on structures. ``weight`` If specified, the connection matrix will be initialised with values specified by ``weight``, which can be any of the values allowed in the methods `connect*`` below. ``sparseness`` If ``weight`` is specified and ``sparseness`` is not, a full connection is assumed, otherwise random connectivity with this level of sparseness is assumed. **Methods** ``connect_random(P,Q,p[,weight=1[,fixed=False[,seed=None]]])`` Connects each neuron in ``P`` to each neuron in ``Q`` with independent probability ``p`` and weight ``weight`` (this is the amount that gets added to the target state variable). If ``fixed`` is True, then the number of presynaptic neurons per neuron is constant. If ``seed`` is given, it is used as the seed to the random number generators, for exactly repeatable results. ``connect_full(P,Q[,weight=1])`` Connect every neuron in ``P`` to every neuron in ``Q`` with the given weight. ``connect_one_to_one(P,Q)`` If ``P`` and ``Q`` have the same number of neurons then neuron ``i`` in ``P`` will be connected to neuron ``i`` in ``Q`` with weight 1. ``connect(P,Q,W)`` You can specify a matrix of weights directly (can be in any format recognised by NumPy). Note that due to internal implementation details, passing a full matrix rather than a sparse one may slow down your code (because zeros will be propagated as well as nonzero values). **WARNING:** No unit checking is done at the moment. For a more efficient low-level method see :meth:`connect_from_sparse`. Additionally, you can directly access the matrix of weights by writing:: C = Connection(P,Q) print C[i,j] C[i,j] = ... Where here ``i`` is the source neuron and ``j`` is the target neuron. Note: if ``C[i,j]`` should be zero, it is more efficient not to write ``C[i,j]=0``, if you write this then when neuron ``i`` fires all the targets will have the value 0 added to them rather than just the nonzero ones. **WARNING:** No unit checking is currently done if you use this method. Take care to set the right units. **Connection matrix structures** Brian currently features three types of connection matrix structures, each of which is suited for different situations. Brian has two stages of connection matrix. The first is the construction stage, used for building a weight matrix. This stage is optimised for the construction of matrices, with lots of features, but would be slow for runtime behaviour. Consequently, the second stage is the connection stage, used when Brian is being run. The connection stage is optimised for run time behaviour, but many features which are useful for construction are absent (e.g. the ability to add or remove synapses). Conversion between construction and connection stages is done by the ``compress()`` method of :class:`Connection` which is called automatically when it is used for the first time. The structures are: ``dense`` A dense matrix. Allows runtime modification of all values. If connectivity is close to being dense this is probably the most efficient, but in most cases it is less efficient. In addition, a dense connection matrix will often do the wrong thing if using STDP. Because a synapse will be considered to exist but with weight 0, STDP will be able to create new synapses where there were previously none. Memory requirements are ``8NM`` bytes where ``(N,M)`` are the dimensions. (A ``double`` float value uses 8 bytes.) ``sparse`` A sparse matrix. See :class:`SparseConnectionMatrix` for details on implementation. This class features very fast row access, and slower column access if the ``column_access=True`` keyword is specified (making it suitable for learning algorithms such as STDP which require this). Memory requirements are 12 bytes per nonzero entry for row access only, or 20 bytes per nonzero entry if column access is specified. Synapses cannot be created or deleted at runtime with this class (although weights can be set to zero). ``dynamic`` A sparse matrix which allows runtime insertion and removal of synapses. See :class:`DynamicConnectionMatrix` for implementation details. This class features row and column access. The row access is slower than for ``sparse`` so this class should only be used when insertion and removal of synapses is crucial. Memory requirements are 24 bytes per nonzero entry. However, note that more memory than this may be required because memory is allocated using a dynamic array which grows by doubling its size when it runs out. If you know the maximum number of nonzero entries you will have in advance, specify the ``nnzmax`` keyword to set the initial size of the array. **Low level methods** .. automethod:: connect_from_sparse **Advanced information** The following methods are also defined and used internally, if you are writing your own derived connection class you need to understand what these do. ``propagate(spikes)`` Action to take when source neurons with indices in ``spikes`` fired. ``do_propagate()`` The method called by the :class:`Network` ``update()`` step, typically just propagates the spikes obtained by calling the ``get_spikes`` method of the ``source`` :class:`NeuronGroup`. ''' #@check_units(delay=second) def __init__(self, source, target, state=0, delay=0 * msecond, modulation=None, structure='sparse', weight=None, sparseness=None, max_delay=5 * ms, **kwds): if not isinstance(delay, float): if delay is True: delay = None # this instructs us to use DelayConnection, but not initialise any delays self.__class__ = DelayConnection self.__init__(source, target, state=state, modulation=modulation, structure=structure, weight=weight, sparseness=sparseness, delay=delay, max_delay=max_delay, **kwds) return self.source = source # pointer to source group self.target = target # pointer to target group self.state = state if isinstance(state, str): # named state variable self.nstate = target.get_var_index(state) else: self.nstate = state # target state index if isinstance(modulation, str): # named state variable self._nstate_mod = source.get_var_index(modulation) else: self._nstate_mod = modulation # source state index if isinstance(structure, str): structure = construction_matrix_register[structure] self.W = structure((len(source), len(target)), **kwds) self.iscompressed = False # True if compress() has been called source.set_max_delay(delay) if not isinstance(self, DelayConnection): self.delay = int(delay / source.clock.dt) # Synaptic delay in time bins self._useaccel = get_global_preference('useweave') self._cpp_compiler = get_global_preference('weavecompiler') self._extra_compile_args = ['-O3'] if self._cpp_compiler == 'gcc': self._extra_compile_args += get_global_preference('gcc_options') # ['-march=native', '-ffast-math'] self._keyword_based_init(weight=weight, sparseness=sparseness) def _keyword_based_init(self, weight=None, sparseness=None, **kwds): # Initialisation of weights # TODO: check consistency of weight and sparseness # TODO: select dense or sparse according to sparseness if weight is not None or sparseness is not None: if weight is None: weight = 1.0 if sparseness is None: sparseness = 1 if issparse(weight) or isinstance(weight, ndarray): self.connect(W=weight) elif sparseness == 1: self.connect_full(weight=weight) else: self.connect_random(weight=weight, p=sparseness) def propagate(self, spikes): if not self.iscompressed: self.compress() if len(spikes): # Target state variable sv = self.target._S[self.nstate] # If specified, modulation state variable if self._nstate_mod is not None: sv_pre = self.source._S[self._nstate_mod] # Get the rows of the connection matrix, each row will be either a # DenseConnectionVector or a SparseConnectionVector. rows = self.W.get_rows(spikes) if not self._useaccel: # Pure Python version is easier to understand, but slower than C++ version below if isinstance(rows[0], SparseConnectionVector): if self._nstate_mod is None: # Rows stored as sparse vectors without modulation for row in rows: sv[row.ind] += row else: # Rows stored as sparse vectors with modulation for i, row in izip(spikes, rows): # note we call the numpy __mul__ directly because row is # a SparseConnectionVector with different mul semantics sv[row.ind] += numpy.ndarray.__mul__(row, sv_pre[i]) else: if self._nstate_mod is None: # Rows stored as dense vectors without modulation for row in rows: sv += row else: # Rows stored as dense vectors with modulation for i, row in izip(spikes, rows): sv += numpy.ndarray.__mul__(row, sv_pre[i]) else: # C++ accelerated code, does the same as the code above but faster and less pretty nspikes = len(spikes) if isinstance(rows[0], SparseConnectionVector): rowinds = [r.ind for r in rows] datas = rows if self._nstate_mod is None: code = propagate_weave_code_sparse codevars = propagate_weave_code_sparse_vars else: code = propagate_weave_code_sparse_modulation codevars = propagate_weave_code_sparse_modulation_vars else: if not isinstance(spikes, numpy.ndarray): spikes = array(spikes, dtype=int) N = len(sv) if self._nstate_mod is None: code = propagate_weave_code_dense codevars = propagate_weave_code_dense_vars else: code = propagate_weave_code_dense_modulation codevars = propagate_weave_code_dense_modulation_vars weave.inline(code, codevars, compiler=self._cpp_compiler, #type_converters=weave.converters.blitz, extra_compile_args=self._extra_compile_args) def compress(self): if not self.iscompressed: self.W = self.W.connection_matrix() self.iscompressed = True gc.collect() # collect after changing matrix type to free up mem def reinit(self): ''' Resets the variables. ''' pass def do_propagate(self): self.propagate(self.source.get_spikes(self.delay)) def origin(self, P, Q): ''' Returns the starting coordinate of the given groups in the connection matrix W. ''' return (P._origin - self.source._origin, Q._origin - self.target._origin) # TODO: rewrite all the connection functions to work row by row for memory and time efficiency # TODO: change this def connect(self, source=None, target=None, W=None): ''' Connects (sub)groups P and Q with the weight matrix W (any type). Internally: inserts W as a submatrix. TODO: checks if the submatrix has already been specified. ''' P = source or self.source Q = target or self.target i0, j0 = self.origin(P, Q) self.W[i0:i0 + len(P), j0:j0 + len(Q)] = W def connect_random(self, source=None, target=None, p=1., weight=1., fixed=False, seed=None, sparseness=None): ''' Connects the neurons in group P to neurons in group Q with probability p, with given weight (default 1). The weight can be a quantity or a function of i (in P) and j (in Q). If ``fixed`` is True, then the number of presynaptic neurons per neuron is constant. ''' P = source or self.source Q = target or self.target if sparseness is not None: p = sparseness # synonym if seed is not None: random_state = numpy.random.get_state() pyrandom_state = pyrandom.getstate() numpy.random.seed(seed) # numpy's random number seed pyrandom.seed(seed) # Python's random number seed if fixed: random_matrix_function = random_matrix_fixed_column else: random_matrix_function = random_matrix if callable(weight): # Check units try: if weight.func_code.co_argcount == 2: weight(0, 0) + Q._S0[self.nstate] else: weight() + Q._S0[self.nstate] except DimensionMismatchError, inst: raise DimensionMismatchError("Incorrects unit for the synaptic weights.", *inst._dims) self.connect(P, Q, random_matrix_function(len(P), len(Q), p, value=weight)) else: # Check units try: weight + Q._S0[self.nstate] except DimensionMismatchError, inst: raise DimensionMismatchError("Incorrects unit for the synaptic weights.", *inst._dims) self.connect(P, Q, random_matrix_function(len(P), len(Q), p, value=float(weight))) # Reset the random generator state after connection matrix was generated if seed is not None: numpy.random.set_state(random_state) pyrandom.setstate(pyrandom_state) def connect_full(self, source=None, target=None, weight=1.): ''' Connects the neurons in group P to all neurons in group Q, with given weight (default 1). The weight can be a quantity or a function of i (in P) and j (in Q). ''' P = source or self.source Q = target or self.target # TODO: check units if callable(weight): # Check units try: weight(0, 0) + Q._S0[self.nstate] except DimensionMismatchError, inst: raise DimensionMismatchError("Incorrects unit for the synaptic weights.", *inst._dims) W = zeros((len(P), len(Q))) try: x = weight(0, 1. * arange(0, len(Q))) failed = False if array(x).size != len(Q): failed = True except: failed = True if failed: # vector-based not possible log_debug('connections', 'Cannot build the connection matrix by rows') for i in range(len(P)): for j in range(len(Q)): w = float(weight(i, j)) #if not is_within_absolute_tolerance(w,0.,effective_zero): # for sparse matrices W[i, j] = w else: for i in range(len(P)): # build W row by row #Below: for sparse matrices (?) #w = weight(i,1.*arange(0,len(Q))) #I = (abs(w)>effective_zero).nonzero()[0] #print w, I, w[I] #W[i,I] = w[I] W[i, :] = weight(i, 1. * arange(0, len(Q))) self.connect(P, Q, W) else: try: weight + Q._S0[self.nstate] except DimensionMismatchError, inst: raise DimensionMismatchError("Incorrect unit for the synaptic weights.", *inst._dims) self.connect(P, Q, float(weight) * ones((len(P), len(Q)))) def connect_one_to_one(self, source=None, target=None, weight=1): ''' Connects source[i] to target[i] with weights 1 (or weight). ''' P = source or self.source Q = target or self.target if (len(P) != len(Q)): raise AttributeError, 'The connected (sub)groups must have the same size.' # TODO: unit checking self.connect(P, Q, float(weight) * eye_lil_matrix(len(P))) def connect_from_sparse(self, W, delay=None, column_access=True): ''' Bypasses the usual Brian mechanisms for constructing and compressing matrices, and allows you to directly set the weight (and optionally delay) matrices from scipy sparse matrix objects. This can be more memory efficient, because the default sparse matrix used for construction is ``scipy.sparse.lil_matrix`` which is very memory inefficient. Arguments: ``W`` The weight matrix ``delay`` Optional delay matrix if you are using heterogeneous delays. ``column_access=True`` Set to ``False`` to save memory if you are not using STDP. .. warning:: This is a low-level method that bypasses the usual checks on the data. In particular, make sure that if you pass a weight and delay matrix, that they have the same structure. ''' set_connection_from_sparse(self, W, delay=delay, column_access=column_access) def __getitem__(self, i): return self.W.__getitem__(i) def __setitem__(self, i, x): # TODO: unit checking self.W.__setitem__(i, x) def __repr__(self): return '%s(%s, %s, state=%s)' % (self.__class__.__name__, repr(self.source), repr(self.target), repr(self.nstate)) def __str__(self): return '%s from %s to %s, acting on state variable %d' % (self.__class__.__name__, str(self.source), str(self.target), self.nstate) from delayconnection import * brian-1.4.3/brian/connections/connectionmatrix.py000066400000000000000000001034011266062647100221760ustar00rootroot00000000000000from base import * from sparsematrix import * from connectionvector import * import gc __all__ = [ 'ConnectionMatrix', 'SparseConnectionMatrix', 'DenseConnectionMatrix', 'DynamicConnectionMatrix', 'set_connection_from_sparse', ] class ConnectionMatrix(object): ''' Base class for connection matrix objects Connection matrix objects support a subset of the following methods: ``get_row(i)``, ``get_col(i)`` Returns row/col ``i`` as a :class:`DenseConnectionVector` or :class:`SparseConnectionVector` as appropriate for the class. ``set_row(i, val)``, ``set_col(i, val)`` Sets row/col with an array, :class:`DenseConnectionVector` or :class:`SparseConnectionVector` (if supported). ``get_element(i, j)``, ``set_element(i, j, val)`` Gets or sets a single value. ``get_rows(rows)`` Returns a list of rows, should be implemented without Python function calls for efficiency if possible. ``get_cols(cols)`` Returns a list of cols, should be implemented without Python function calls for efficiency if possible. ``insert(i,j,x)``, ``remove(i,j)`` For sparse connection matrices which support it, insert a new entry or remove an existing one. ``getnnz()`` Return the number of nonzero entries. ``todense()`` Return the matrix as a dense array. The ``__getitem__`` and ``__setitem__`` methods are implemented by default, and automatically select the appropriate methods from the above in the cases where the item to be got or set is of the form ``:``, ``i,:``, ``:,j`` or ``i,j``. ''' # methods to be implemented by subclass def get_row(self, i): return NotImplemented def get_col(self, i): return NotImplemented def set_row(self, i, x): return NotImplemented def set_col(self, i, x): return NotImplemented def set_element(self, i, j, x): return NotImplemented def get_element(self, i, j): return NotImplemented def get_rows(self, rows): return [self.get_row(i) for i in rows] def get_cols(self, cols): return [self.get_col(i) for i in cols] def insert(self, i, j, x): return NotImplemented def remove(self, i, j): return NotImplemented def getnnz(self): return NotImplemented def todense(self): return array([todense(r) for r in self]) # we support the following indexing schemes: # - s[:] # - s[i,:] # - s[:,i] # - s[i,j] def __getitem__(self, item): if isinstance(item, tuple) and isinstance(item[0], int) and item[1] == colon_slice: return self.get_row(item[0]) if isinstance(item, slice): if item == colon_slice: return self else: raise ValueError(str(item) + ' not supported.') if isinstance(item, int): return self.get_row(item) if isinstance(item, tuple): if len(item) != 2: raise TypeError('Only 2D indexing supported.') item_i, item_j = item if isinstance(item_i, int) and isinstance(item_j, slice): if item_j == colon_slice: return self.get_row(item_i) raise ValueError('Only ":" indexing supported.') if isinstance(item_i, slice) and isinstance(item_j, int): if item_i == colon_slice: return self.get_col(item_j) raise ValueError('Only ":" indexing supported.') if isinstance(item_i, int) and isinstance(item_j, int): return self.get_element(item_i, item_j) raise TypeError('Only (i,:), (:,j), (i,j) indexing supported.') raise TypeError('Can only get items of type slice or tuple') def __setitem__(self, item, value): if isinstance(item, tuple) and isinstance(item[0], int) and item[1] == colon_slice: return self.set_row(item[0], value) if isinstance(item, slice): raise ValueError(str(item) + ' not supported.') if isinstance(item, int): return self.set_row(item, value) if isinstance(item, tuple): if len(item) != 2: raise TypeError('Only 2D indexing supported.') item_i, item_j = item if isinstance(item_i, int) and isinstance(item_j, slice): if item_j == colon_slice: return self.set_row(item_i, value) raise ValueError('Only ":" indexing supported.') if isinstance(item_i, slice) and isinstance(item_j, int): if item_i == colon_slice: return self.set_col(item_j, value) raise ValueError('Only ":" indexing supported.') if isinstance(item_i, int) and isinstance(item_j, int): return self.set_element(item_i, item_j, value) raise TypeError('Only (i,:), (:,j), (i,j) indexing supported.') raise TypeError('Can only set items of type slice or tuple') class DenseConnectionMatrix(ConnectionMatrix, numpy.ndarray): ''' Dense connection matrix See documentation for :class:`ConnectionMatrix` for details on connection matrix types. This matrix implements a dense connection matrix. It is just a numpy array. The ``get_row`` and ``get_col`` methods return :class:`DenseConnectionVector`` objects. ''' def __new__(subtype, data, **kwds): if 'copy' not in kwds: kwds = dict(kwds.iteritems()) kwds['copy'] = False return numpy.array(data, **kwds).view(subtype) def __init__(self, val, **kwds): # precompute rows and cols for fast returns by get_rows etc. self.rows = [DenseConnectionVector(numpy.ndarray.__getitem__(self, i)) for i in xrange(val.shape[0])] self.cols = [DenseConnectionVector(numpy.ndarray.__getitem__(self, (slice(None), i))) for i in xrange(val.shape[1])] def get_rows(self, rows): return [self.rows[i] for i in rows] def get_cols(self, cols): return [self.cols[i] for i in cols] def get_row(self, i): return self.rows[i] def get_col(self, i): return self.cols[i] def set_row(self, i, x): numpy.ndarray.__setitem__(self, i, todense(x)) def set_col(self, i, x): numpy.ndarray.__setitem__(self, (colon_slice, i), todense(x)) #self[:, i] = todense(x) def get_element(self, i, j): return numpy.ndarray.__getitem__(self, (i, j)) #return self[i,j] def set_element(self, i, j, val): numpy.ndarray.__setitem__(self, (i, j), val) #self[i,j] = val insert = set_element def remove(self, i, j): numpy.ndarray.__setitem__(self, (i, j), 0) #self[i, j] = 0 class SparseConnectionMatrix(ConnectionMatrix): ''' Sparse connection matrix See documentation for :class:`ConnectionMatrix` for details on connection matrix types. This class implements a sparse matrix with a fixed number of nonzero entries. Row access is very fast, and if the ``column_access`` keyword is ``True`` then column access is also supported (but is not as fast as row access). If the ``use_minimal_indices`` keyword is ``True`` then the neuron and synapse indices will use the smallest possible integer type (16 bits for neuron indices if the number of neurons is less than ``2**16``, otherwise 32 bits). Otherwise, it will use the word size for the CPU architecture (32 or 64 bits). The matrix should be initialised with a scipy sparse matrix. The ``get_row`` and ``get_col`` methods return :class:`SparseConnectionVector` objects. In addition to the usual slicing operations supported, ``M[:]=val`` is supported, where ``val`` must be a scalar or an array of length ``nnz``. Implementation details: The values are stored in an array ``alldata`` of length ``nnz`` (number of nonzero entries). The slice ``alldata[rowind[i]:rowind[i+1]]`` gives the values for row ``i``. These slices are stored in the list ``rowdata`` so that ``rowdata[i]`` is the data for row ``i``. The array ``rowj[i]`` gives the corresponding column ``j`` indices. For row access, the memory requirements are 12 bytes per entry (8 bytes for the float value, and 4 bytes for the column indices). The array ``allj`` of length ``nnz`` gives the column ``j`` coordinates for each element in ``alldata`` (the elements of ``rowj`` are slices of this array so no extra memory is used). If column access is being used, then in addition to the above there are lists ``coli`` and ``coldataindices``. For column ``j``, the array ``coli[j]`` gives the row indices for the data values in column ``j``, while ``coldataindices[j]`` gives the indices in the array ``alldata`` for the values in column ``j``. Column access therefore involves a copy operation rather than a slice operation. Column access increases the memory requirements to 20 bytes per entry (4 extra bytes for the row indices and 4 extra bytes for the data indices). TODO: update size numbers when use_minimal_indices=True for different architectures. ''' def __init__(self, val, column_access=True, use_minimal_indices=False, **kwds): self._useaccel = get_global_preference('useweave') self._cpp_compiler = get_global_preference('weavecompiler') self._extra_compile_args = ['-O3'] if self._cpp_compiler == 'gcc': self._extra_compile_args += get_global_preference('gcc_options') # ['-march=native', '-ffast-math'] self.nnz = nnz = val.getnnz()# nnz stands for number of nonzero entries alldata = numpy.zeros(nnz) self.neuron_index_dtype = int self.synapse_index_dtype = int if use_minimal_indices: if max(val.shape)<2**16: self.neuron_index_dtype = uint16 else: self.neuron_index_dtype = uint32 self.synapse_index_dtype = uint32 if column_access: colind = numpy.zeros(val.shape[1] + 1, dtype=self.synapse_index_dtype) allj = numpy.zeros(nnz, dtype=self.neuron_index_dtype) rowind = numpy.zeros(val.shape[0] + 1, dtype=self.synapse_index_dtype) rowdata = [] rowj = [] if column_access: coli = [] coldataindices = [] i = 0 # i points to the current index in the alldata array as we go through row by row for c in xrange(val.shape[0]): # extra the row values and column indices of row c of the initialising matrix # this works for any of the scipy sparse matrix formats if isinstance(val, sparse.lil_matrix): r = val.rows[c] d = val.data[c] else: sr = val[c, :] sr = sr.tolil() r = sr.rows[0] d = sr.data[0] # copy the values into the alldata array, the indices into the allj array, and # so forth rowind[c] = i alldata[i:i + len(d)] = d allj[i:i + len(r)] = r rowdata.append(alldata[i:i + len(d)]) rowj.append(allj[i:i + len(r)]) i = i + len(r) rowind[val.shape[0]] = i if column_access: # counts the number of nonzero elements in each column counts = zeros(val.shape[1], dtype=self.synapse_index_dtype) if len(allj): bincounts = numpy.bincount(allj) else: bincounts = numpy.array([], dtype=int) counts[:len(bincounts)] = bincounts # ensure that counts is the right length # two algorithms depending on whether weave is available if self._useaccel: # this algorithm just goes through one by one adding each # element to the appropriate bin whose sizes we have # precomputed. alldi will contain all the data indices # in blocks alldi[s[i]:s[i+1]] of length counts[i], and # curcdi[i] is the current offset into each block. s is # therefore just the cumulative sum of counts. curcdi = numpy.zeros(val.shape[1], dtype=int) allcoldataindices = numpy.zeros(nnz, dtype=self.synapse_index_dtype) colind[:] = numpy.hstack(([0], cumsum(counts))) colalli = numpy.zeros(nnz, dtype=self.neuron_index_dtype) numrows = val.shape[0] code = ''' int i = 0; for(int k=0;k=rowind[i+1]) i++; int j = allj[k]; allcoldataindices[colind[j]+curcdi[j]] = k; colalli[colind[j]+curcdi[j]] = i; curcdi[j]++; } ''' weave.inline(code, ['nnz', 'allj', 'allcoldataindices', 'rowind', 'numrows', 'curcdi', 'colind', 'colalli'], compiler=self._cpp_compiler, extra_compile_args=self._extra_compile_args, ) # now store the blocks of allcoldataindices in coldataindices and update coli too for i in xrange(len(colind) - 1): D = allcoldataindices[colind[i]:colind[i + 1]] I = colalli[colind[i]:colind[i + 1]] coldataindices.append(D) coli.append(I) else: # now allj[a] will be the columns in order, so that # the first counts[0] elements of allj[a] will be 0, # or in other words the first counts[0] elements of a # will be the data indices of the elements (i,j) with j==0 # mergesort is necessary because we want the relative ordering # of the elements of a within a block to be maintained allcoldataindices = a = argsort(allj, kind='mergesort') # this defines colind so that a[colind[i]:colind[i+1]] are the data # indices where j==i colind[:] = numpy.hstack(([0], cumsum(counts))) # this computes the row index of each entry by first generating # expanded_row_indices which gives the corresponding row index # for each entry enumerated row-by-row, and then using the # array allcoldataindices to index this array to convert into # the corresponding row index for each entry enumerated # col-by-col. if len(a): expanded_row_indices = empty(len(a), dtype=self.neuron_index_dtype) for k, (i, j) in enumerate(zip(rowind[:-1], rowind[1:])): expanded_row_indices[i:j] = k colalli = expanded_row_indices[a] else: colalli = numpy.zeros(nnz, dtype=self.neuron_index_dtype) # in this loop, I are the data indices where j==i # and alli[I} are the corresponding i coordinates for i in xrange(len(colind) - 1): D = a[colind[i]:colind[i + 1]] I = colalli[colind[i]:colind[i + 1]] coldataindices.append(D) coli.append(I) self.alldata = alldata self.rowdata = rowdata self.allj = allj self.rowj = rowj self.rowind = rowind self.shape = val.shape self.column_access = column_access if column_access: self.colalli = colalli self.coli = coli self.coldataindices = coldataindices self.allcoldataindices = allcoldataindices self.colind = colind self.rows = [SparseConnectionVector(self.shape[1], self.rowj[i], self.rowdata[i]) for i in xrange(self.shape[0])] def getnnz(self): return self.nnz def get_element(self, i, j): n = searchsorted(self.rowj[i], j) if n >= len(self.rowj[i]) or self.rowj[i][n] != j: return 0 return self.rowdata[i][n] def set_element(self, i, j, x): n = searchsorted(self.rowj[i], j) if n >= len(self.rowj[i]) or self.rowj[i][n] != j: raise ValueError('Insertion of new elements not supported for SparseConnectionMatrix.') self.rowdata[i][n] = x def get_row(self, i): return self.rows[i] def get_rows(self, rows): return [self.rows[i] for i in rows] def get_col(self, j): if self.column_access: return SparseConnectionVector(self.shape[0], self.coli[j], self.alldata[self.coldataindices[j]]) else: raise TypeError('No column access.') def get_cols(self, cols): if self.column_access: return [SparseConnectionVector(self.shape[0], self.coli[j], self.alldata[self.coldataindices[j]]) for j in cols] else: raise TypeError('No column access.') def set_row(self, i, val): if isinstance(val, SparseConnectionVector): if val.ind is not self.rowj[i]: if not equal(val.ind, self.rowj[i]).all(): raise ValueError('Sparse row setting must use same indices.') self.rowdata[i][:] = val else: if isinstance(val, numpy.ndarray): val = asarray(val) self.rowdata[i][:] = val[self.rowj[i]] else: self.rowdata[i][:] = val def set_col(self, j, val): if self.column_access: if isinstance(val, SparseConnectionVector): if val.ind is not self.coli[j]: if not (val.ind == self.coli[j]).all(): raise ValueError('Sparse col setting must use same indices.') self.alldata[self.coldataindices[j]] = val else: if isinstance(val, numpy.ndarray): val = asarray(val) self.alldata[self.coldataindices[j]] = val[self.coli[j]] else: self.alldata[self.coldataindices[j]] = val else: raise TypeError('No column access.') def __setitem__(self, item, value): if item == colon_slice: self.alldata[:] = value else: ConnectionMatrix.__setitem__(self, item, value) class DynamicConnectionMatrix(ConnectionMatrix): ''' Dynamic (sparse) connection matrix See documentation for :class:`ConnectionMatrix` for details on connection matrix types. This class implements a sparse matrix with a variable number of nonzero entries. Row access and column access are provided, but are not as fast as for :class:`SparseConnectionMatrix`. The matrix should be initialised with a scipy sparse matrix. The ``get_row`` and ``get_col`` methods return :class:`SparseConnectionVector` objects. In addition to the usual slicing operations supported, ``M[:]=val`` is supported, where ``val`` must be a scalar or an array of length ``nnz``. **Implementation details** The values are stored in an array ``alldata`` of length ``nnzmax`` (maximum number of nonzero entries). This is a dynamic array, see: http://en.wikipedia.org/wiki/Dynamic_array You can set the resizing constant with the argument ``dynamic_array_const``. Normally the default value 2 is fine but if memory is a worry it could be made smaller. Rows and column point in to this data array, and the list ``rowj`` consists of an array of column indices for each row, with ``coli`` containing arrays of row indices for each column. Similarly, ``rowdataind`` and ``coldataind`` consist of arrays of pointers to the indices in the ``alldata`` array. ''' def __init__(self, val, nnzmax=None, dynamic_array_const=2, **kwds): self.shape = val.shape self.dynamic_array_const = dynamic_array_const if nnzmax is None or nnzmax < val.getnnz(): nnzmax = val.getnnz() self.nnzmax = nnzmax self.nnz = val.getnnz() self.alldata = numpy.zeros(nnzmax) self.unusedinds = range(self.nnz, self.nnzmax) i = 0 self.rowj = [] self.rowdataind = [] alli = zeros(self.nnz, dtype=int) allj = zeros(self.nnz, dtype=int) for c in xrange(val.shape[0]): # extra the row values and column indices of row c of the initialising matrix # this works for any of the scipy sparse matrix formats if isinstance(val, sparse.lil_matrix): r = val.rows[c] d = val.data[c] else: sr = val[c, :] sr = sr.tolil() r = sr.rows[0] d = sr.data[0] self.alldata[i:i + len(d)] = d self.rowj.append(array(r, dtype=int)) self.rowdataind.append(arange(i, i + len(d))) allj[i:i + len(d)] = r alli[i:i + len(d)] = c i += len(d) # now update the coli and coldataind variables self.coli = [] self.coldataind = [] # counts the number of nonzero elements in each column counts = numpy.histogram(allj, numpy.arange(val.shape[1] + 1, dtype=int))[0] # now we have to go through one by one unfortunately, and so we keep curcdi, the # current column data index for each column curcdi = numpy.zeros(val.shape[1], dtype=int) # initialise the memory for the column data indices for j in xrange(val.shape[1]): self.coldataind.append(numpy.zeros(counts[j], dtype=int)) # one by one for every element, update the dataindices and curcdi data pointers for i, j in enumerate(allj): self.coldataind[j][curcdi[j]] = i curcdi[j] += 1 for j in xrange(val.shape[1]): self.coli.append(alli[self.coldataind[j]]) def getnnz(self): return self.nnz def insert(self, i, j, x): n = searchsorted(self.rowj[i], j) if n < len(self.rowj[i]) and self.rowj[i][n] == j: self.alldata[self.rowdataind[i][n]] = x return m = searchsorted(self.coli[j], i) if self.nnz == self.nnzmax: # reallocate memory using a dynamic array structure (amortized O(1) cost for append) newnnzmax = int(self.nnzmax * self.dynamic_array_const) if newnnzmax <= self.nnzmax: newnnzmax += 1 if newnnzmax > self.shape[0] * self.shape[1]: newnnzmax = self.shape[0] * self.shape[1] self.alldata = hstack((self.alldata, numpy.zeros(newnnzmax - self.nnzmax, dtype=self.alldata.dtype))) self.unusedinds.extend(range(self.nnz, newnnzmax)) self.nnzmax = newnnzmax newind = self.unusedinds.pop(-1) self.alldata[newind] = x self.nnz += 1 # update row newrowj = numpy.zeros(len(self.rowj[i]) + 1, dtype=int) newrowj[:n] = self.rowj[i][:n] newrowj[n] = j newrowj[n + 1:] = self.rowj[i][n:] self.rowj[i] = newrowj newrowdataind = numpy.zeros(len(self.rowdataind[i]) + 1, dtype=int) newrowdataind[:n] = self.rowdataind[i][:n] newrowdataind[n] = newind newrowdataind[n + 1:] = self.rowdataind[i][n:] self.rowdataind[i] = newrowdataind # update col newcoli = numpy.zeros(len(self.coli[j]) + 1, dtype=int) newcoli[:m] = self.coli[j][:m] newcoli[m] = i newcoli[m + 1:] = self.coli[j][m:] self.coli[j] = newcoli newcoldataind = numpy.zeros(len(self.coldataind[j]) + 1, dtype=int) newcoldataind[:m] = self.coldataind[j][:m] newcoldataind[m] = newind newcoldataind[m + 1:] = self.coldataind[j][m:] self.coldataind[j] = newcoldataind def remove(self, i, j): n = searchsorted(self.rowj[i], j) if n >= len(self.rowj[i]) or self.rowj[i][n] != j: raise ValueError('No element to remove at position ' + str((i, j))) oldind = self.rowdataind[i][n] self.unusedinds.append(oldind) self.nnz -= 1 m = searchsorted(self.coli[j], i) # update row newrowj = numpy.zeros(len(self.rowj[i]) - 1, dtype=int) newrowj[:n] = self.rowj[i][:n] newrowj[n:] = self.rowj[i][n + 1:] self.rowj[i] = newrowj newrowdataind = numpy.zeros(len(self.rowdataind[i]) - 1, dtype=int) newrowdataind[:n] = self.rowdataind[i][:n] newrowdataind[n:] = self.rowdataind[i][n + 1:] self.rowdataind[i] = newrowdataind # update col newcoli = numpy.zeros(len(self.coli[j]) - 1, dtype=int) newcoli[:m] = self.coli[j][:m] newcoli[m:] = self.coli[j][m + 1:] self.coli[j] = newcoli newcoldataind = numpy.zeros(len(self.coldataind[j]) - 1, dtype=int) newcoldataind[:m] = self.coldataind[j][:m] newcoldataind[m:] = self.coldataind[j][m + 1:] self.coldataind[j] = newcoldataind def get_element(self, i, j): n = searchsorted(self.rowj[i], j) if n >= len(self.rowj[i]) or self.rowj[i][n] != j: return 0 return self.alldata[self.rowdataind[i][n]] set_element = insert def get_row(self, i): return SparseConnectionVector(self.shape[1], self.rowj[i], self.alldata[self.rowdataind[i]]) def get_rows(self, rows): return [SparseConnectionVector(self.shape[1], self.rowj[i], self.alldata[self.rowdataind[i]]) for i in rows] def get_col(self, j): return SparseConnectionVector(self.shape[0], self.coli[j], self.alldata[self.coldataind[j]]) def get_cols(self, cols): return [SparseConnectionVector(self.shape[0], self.coli[j], self.alldata[self.coldataind[j]]) for j in cols] def set_row(self, i, val): if isinstance(val, SparseConnectionVector): if val.ind is not self.rowj[i]: if not (val.ind == self.rowj[i]).all(): raise ValueError('Sparse row setting must use same indices.') self.alldata[self.rowdataind[i]] = val else: if isinstance(val, numpy.ndarray): val = asarray(val) self.alldata[self.rowdataind[i]] = val[self.rowj[i]] else: self.alldata[self.rowdataind[i]] = val def set_col(self, j, val): if isinstance(val, SparseConnectionVector): if val.ind is not self.coli[j]: if not (val.ind == self.coli[j]).all(): raise ValueError('Sparse row setting must use same indices.') self.alldata[self.coldataind[j]] = val else: if isinstance(val, numpy.ndarray): val = asarray(val) self.alldata[self.coldataind[j]] = val[self.coli[j]] else: self.alldata[self.coldataind[j]] = val def __setitem__(self, item, value): if item == colon_slice: self.alldata[:self.nnz] = value else: ConnectionMatrix.__setitem__(self, item, value) class UnconstructedMatrix(object): pass def make_sparse_connection_matrix(x, column_access=True): x = x.tocsr() if not x.has_sorted_indices: x.sort_indices() y = UnconstructedMatrix() y.__class__ = SparseConnectionMatrix y._useaccel = get_global_preference('useweave') y._cpp_compiler = get_global_preference('weavecompiler') y._extra_compile_args = ['-O3'] if y._cpp_compiler == 'gcc': y._extra_compile_args += get_global_preference('gcc_options') # ['-march=native', '-ffast-math'] y.nnz = nnz = int(x.getnnz())# nnz stands for number of nonzero entries y.alldata = alldata = x.data y.rowind = rowind = array(x.indptr, dtype=int, copy=False) y.allj = allj = array(x.indices, dtype=int, copy=False) if column_access: colind = numpy.zeros(x.shape[1]+1, dtype=int) coli = [] coldataindices = [] rowdata = [] rowj = [] i = 0 # i points to the current index in the alldata array as we go through row by row for c in xrange(x.shape[0]): k = y.rowind[c+1]-y.rowind[c] rowdata.append(y.alldata[i:i+k]) rowj.append(y.allj[i:i+k]) i += k if column_access: # counts the number of nonzero elements in each column counts = zeros(x.shape[1], dtype=int) if len(allj): bincounts = numpy.bincount(allj) else: bincounts = numpy.array([], dtype=int) counts[:len(bincounts)] = bincounts # ensure that counts is the right length # two algorithms depending on whether weave is available if y._useaccel: # this algorithm just goes through one by one adding each # element to the appropriate bin whose sizes we have # precomputed. alldi will contain all the data indices # in blocks alldi[s[i]:s[i+1]] of length counts[i], and # curcdi[i] is the current offset into each block. s is # therefore just the cumulative sum of counts. curcdi = numpy.zeros(x.shape[1], dtype=int) allcoldataindices = numpy.zeros(nnz, dtype=int) colind[:] = numpy.hstack(([0], cumsum(counts))) colalli = numpy.zeros(nnz, dtype=int) numrows = x.shape[0] code = ''' int i = 0; for(int k=0;k=rowind[i+1]) i++; int j = allj[k]; allcoldataindices[colind[j]+curcdi[j]] = k; colalli[colind[j]+curcdi[j]] = i; curcdi[j]++; } ''' weave.inline(code, ['nnz', 'allj', 'allcoldataindices', 'rowind', 'numrows', 'curcdi', 'colind', 'colalli'], compiler=y._cpp_compiler, extra_compile_args=y._extra_compile_args, ) # now store the blocks of allcoldataindices in coldataindices and update coli too for i in xrange(len(colind) - 1): D = allcoldataindices[colind[i]:colind[i + 1]] I = colalli[colind[i]:colind[i + 1]] coldataindices.append(D) coli.append(I) else: # now allj[a] will be the columns in order, so that # the first counts[0] elements of allj[a] will be 0, # or in other words the first counts[0] elements of a # will be the data indices of the elements (i,j) with j==0 # mergesort is necessary because we want the relative ordering # of the elements of a within a block to be maintained allcoldataindices = a = argsort(allj, kind='mergesort') # this defines colind so that a[colind[i]:colind[i+1]] are the data # indices where j==i colind[:] = numpy.hstack(([0], cumsum(counts))) # this computes the row index of each entry by first generating # expanded_row_indices which gives the corresponding row index # for each entry enumerated row-by-row, and then using the # array allcoldataindices to index this array to convert into # the corresponding row index for each entry enumerated # col-by-col. if len(a): expanded_row_indices = empty(len(a), dtype=int) for k, (i, j) in enumerate(zip(rowind[:-1], rowind[1:])): expanded_row_indices[i:j] = k colalli = expanded_row_indices[a] else: colalli = numpy.zeros(nnz, dtype=int) # in this loop, I are the data indices where j==i # and alli[I} are the corresponding i coordinates for i in xrange(len(colind) - 1): D = a[colind[i]:colind[i + 1]] I = colalli[colind[i]:colind[i + 1]] coldataindices.append(D) coli.append(I) y.rowdata = rowdata y.rowj = rowj y.shape = x.shape y.column_access = column_access if column_access: y.colalli = colalli y.coli = coli y.coldataindices = coldataindices y.allcoldataindices = allcoldataindices y.colind = colind y.rows = [SparseConnectionVector(y.shape[1], y.rowj[i], y.rowdata[i]) for i in xrange(y.shape[0])] return y def set_connection_from_sparse(C, W, delay=None, column_access=True): C.W = make_sparse_connection_matrix(W, column_access=column_access) if delay is not None: C.delay = make_sparse_connection_matrix(delay, column_access=column_access) C.iscompressed = True gc.collect() brian-1.4.3/brian/connections/connectionvector.py000066400000000000000000000131011266062647100221710ustar00rootroot00000000000000from base import * __all__ = [ 'ConnectionVector', 'SparseConnectionVector', 'DenseConnectionVector', ] class ConnectionVector(object): ''' Base class for connection vectors, just used for defining the interface ConnectionVector objects are returned by ConnectionMatrix objects when they retrieve rows or columns. At the moment, there are two choices, sparse or dense. This class has no real function at the moment. ''' def todense(self): return NotImplemented def tosparse(self): return NotImplemented class DenseConnectionVector(ConnectionVector, numpy.ndarray): ''' Just a numpy array. ''' def __new__(subtype, arr): return numpy.array(arr, copy=False).view(subtype) def todense(self): return self def tosparse(self): return SparseConnectionVector(len(self), self.nonzero(), self) class SparseConnectionVector(ConnectionVector, numpy.ndarray): ''' Sparse vector class A sparse vector is typically a row or column of a sparse matrix. This class can be treated in many cases as if it were just a vector without worrying about the fact that it is sparse. For example, if you write ``2*v`` it will evaluate to a new sparse vector. There is one aspect of the semantics which is potentially confusing. In a binary operation with a dense vector such as ``sv+dv`` where ``sv`` is sparse and ``dv`` is dense, the result will be a sparse vector with zeros where ``sv`` has zeros, the potentially nonzero elements of ``dv`` where ``sv`` has no entry will be simply ignored. It is for this reason that it is a ``SparseConnectionVector`` and not a general ``SparseVector``, because these semantics make sense for rows and columns of connection matrices but not in general. Implementation details: The underlying numpy array contains the values, the attribute ``n`` is the length of the sparse vector, and ``ind`` is an array of the indices of the nonzero elements. ''' def __new__(subtype, n, ind, data): x = numpy.array(data, copy=False).view(subtype) x.n = n x.ind = ind return x def __array_finalize__(self, orig): # the array is passed through this function after standard numpy operations, # this ensures that the indices are kept from the original array. This makes, # for example, sin(x) do the right thing for x a sparse vector. try: self.ind = orig.ind self.n = orig.n except AttributeError: pass return self def __reduce__(self): object_state = list(numpy.ndarray.__reduce__(self)) object_state[2] = (object_state[2], self.n, self.ind) return tuple(object_state) def __setstate__(self, state): nd_state, n, ind = state numpy.ndarray.__setstate__(self, nd_state) self.n = n self.ind = ind def todense(self): x = zeros(self.n) x[self.ind] = self return x def tosparse(self): return self # This is a list of the binary operations that numpy arrays support. modifymeths = ['__add__', '__and__', '__div__', '__divmod__', '__eq__', '__floordiv__', '__ge__', '__gt__', '__iadd__', '__iand__', '__idiv__', '__ifloordiv__', '__ilshift__', '__imod__', '__imul__', '__ior__', '__ipow__', '__irshift__', '__isub__', '__itruediv__', '__ixor__', '__le__', '__lshift__', '__lt__', '__mod__', '__mul__', '__ne__', '__or__', '__pow__', '__radd__', '__rand__', '__rdiv__', '__rdivmod__', '__rfloordiv__', '__rlshift__', '__rmod__', '__rmul__', '__ror__', '__rpow__', '__rrshift__', '__rshift__', '__rsub__', '__rtruediv__', '__rxor__', '__sub__', '__truediv__', '__xor__'] # This template function (where __add__ is replaced by any of the methods above) implements # the semantics described in this class' docstring when operating with a dense vector. template = ''' def __add__(self, other): if isinstance(other, SparseConnectionVector): # Note that removing this check is potentially dangerous, but only in weird circumstances would it cause # any problems, and leaving it in causes problems for STDP with DelayConnection (because the indices are # not the same, but should be presumed to be equal). #if other.ind is not self.ind: # raise TypeError('__add__(SparseConnectionVector, SparseConnectionVector) only defined if indices are the same') return SparseConnectionVector(self.n, self.ind, numpy.ndarray.__add__(asarray(self), asarray(other))) if isinstance(other, numpy.ndarray): return SparseConnectionVector(self.n, self.ind, numpy.ndarray.__add__(asarray(self), other[self.ind])) return SparseConnectionVector(self.n, self.ind, numpy.ndarray.__add__(asarray(self), other)) '''.strip() # this substitutes any of the method names in the modifymeths list for __add__ in the template # above and then executes them, i.e. adding them as methods to the class. When the behaviour is # stable, this can be replaced by the explicit definitions but it may as well be left as it is for # the moment (it's slower at import time, but not at run time, and errors are more difficult to # catch when it is done like this). for m in modifymeths: s = template.replace('__add__', m) exec s del modifymeths, template brian-1.4.3/brian/connections/construction.py000066400000000000000000000164221266062647100213520ustar00rootroot00000000000000from base import * from sparsematrix import * __all__ = ['random_row_func', 'random_matrix', 'random_matrix_fixed_column', 'eye_lil_matrix', ] def random_row_func(N, p, weight=1., initseed=None): ''' Returns a random connectivity ``row_func`` for use with :class:`UserComputedConnectionMatrix` Gives equivalent output to the :meth:`Connection.connect_random` method. Arguments: ``N`` The number of target neurons. ``p`` The probability of a synapse. ``weight`` The connection weight (must be a single value). ``initseed`` The initial seed value (for reproducible results). ''' if initseed is None: initseed = pyrandom.randint(100000, 1000000) # replace this cur_row = numpy.zeros(N) myrange = numpy.arange(N, dtype=int) def row_func(i): pyrandom.seed(initseed + int(i)) scirandom.seed(initseed + int(i)) k = scirandom.binomial(N, p, 1)[0] cur_row[:] = 0.0 cur_row[pyrandom.sample(myrange, k)] = weight return cur_row return row_func # Generation of matrices def random_matrix(n, m, p, value=1.): ''' Generates a sparse random matrix with size (n,m). Entries are 1 (or optionnally value) with probability p. If value is a function, then that function is called for each non zero element as value() or value(i,j). ''' # TODO: # Simplify (by using valuef) W = sparse.lil_matrix((n, m)) if callable(value) and callable(p): if value.func_code.co_argcount == 0: valuef = lambda i, j:[value() for _ in j] # value function elif value.func_code.co_argcount == 2: try: failed = (array(value(0, arange(m))).size != m) except: failed = True if failed: # vector-based not possible log_debug('connections', 'Cannot build the connection matrix by rows') valuef = lambda i, j:[value(i, k) for k in j] else: valuef = value else: raise AttributeError, "Bad number of arguments in value function (should be 0 or 2)" if p.func_code.co_argcount == 2: # Check if p(i,j) is vectorisable try: failed = (array(p(0, arange(m))).size != m) except: failed = True if failed: # vector-based not possible log_debug('connections', 'Cannot build the connection matrix by rows') for i in xrange(n): W.rows[i] = [j for j in range(m) if rand() < p(i, j)] W.data[i] = list(valuef(i, array(W.rows[i]))) else: # vector-based possible for i in xrange(n): W.rows[i] = list((rand(m) < p(i, arange(m))).nonzero()[0]) W.data[i] = list(valuef(i, array(W.rows[i]))) elif p.func_code.co_argcount == 0: for i in xrange(n): W.rows[i] = [j for j in range(m) if rand() < p()] W.data[i] = list(valuef(i, array(W.rows[i]))) else: raise AttributeError, "Bad number of arguments in p function (should be 2)" elif callable(value): if value.func_code.co_argcount == 0: # TODO: should work with partial objects for i in xrange(n): k = random.binomial(m, p, 1)[0] W.rows[i] = sample(xrange(m), k) W.rows[i].sort() W.data[i] = [value() for _ in xrange(k)] elif value.func_code.co_argcount == 2: try: failed = (array(value(0, arange(m))).size != m) except: failed = True if failed: # vector-based not possible log_debug('connections', 'Cannot build the connection matrix by rows') for i in xrange(n): k = random.binomial(m, p, 1)[0] W.rows[i] = sample(xrange(m), k) W.rows[i].sort() W.data[i] = [value(i, j) for j in W.rows[i]] else: for i in xrange(n): k = random.binomial(m, p, 1)[0] W.rows[i] = sample(xrange(m), k) W.rows[i].sort() W.data[i] = list(value(i, array(W.rows[i]))) else: raise AttributeError, "Bad number of arguments in value function (should be 0 or 2)" elif callable(p): if p.func_code.co_argcount == 2: # Check if p(i,j) is vectorisable try: failed = (array(p(0, arange(m))).size != m) except: failed = True if failed: # vector-based not possible log_debug('connections', 'Cannot build the connection matrix by rows') for i in xrange(n): W.rows[i] = [j for j in range(m) if rand() < p(i, j)] W.data[i] = [value] * len(W.rows[i]) else: # vector-based possible for i in xrange(n): W.rows[i] = list((rand(m) < p(i, arange(m))).nonzero()[0]) W.data[i] = [value] * len(W.rows[i]) elif p.func_code.co_argcount == 0: for i in xrange(n): W.rows[i] = [j for j in range(m) if rand() < p()] W.data[i] = [value] * len(W.rows[i]) else: raise AttributeError, "Bad number of arguments in p function (should be 2)" else: for i in xrange(n): k = random.binomial(m, p, 1)[0] # Not significantly faster to generate all random numbers in one pass # N.B.: the sample method is implemented in Python and it is not in Scipy W.rows[i] = sample(xrange(m), k) W.rows[i].sort() W.data[i] = [value] * k return W def random_matrix_fixed_column(n, m, p, value=1.): ''' Generates a sparse random matrix with size (n,m). Entries are 1 (or optionnally value) with probability p. The number of non-zero entries by per column is fixed: (int)(p*n) If value is a function, then that function is called for each non zero element as value() or value(i,j). ''' W = sparse.lil_matrix((n, m)) k = (int)(p * n) for j in xrange(m): # N.B.: the sample method is implemented in Python and it is not in Scipy for i in sample(xrange(n), k): W.rows[i].append(j) if callable(value): if value.func_code.co_argcount == 0: for i in xrange(n): W.data[i] = [value() for _ in xrange(len(W.rows[i]))] elif value.func_code.co_argcount == 2: for i in xrange(n): W.data[i] = [value(i, j) for j in W.rows[i]] else: raise AttributeError, "Bad number of arguments in value function (should be 0 or 2)" else: for i in xrange(n): W.data[i] = [value] * len(W.rows[i]) return W def eye_lil_matrix(n): ''' Returns the identity matrix of size n as a lil_matrix (sparse matrix). ''' M = sparse.lil_matrix((n, n)) M.setdiag([1.] * n) return M brian-1.4.3/brian/connections/constructionmatrix.py000066400000000000000000000054461266062647100226030ustar00rootroot00000000000000from base import * from sparsematrix import * from connectionmatrix import * __all__ = [ 'ConstructionMatrix', 'SparseConstructionMatrix', 'DenseConstructionMatrix', 'DynamicConstructionMatrix', 'construction_matrix_register', ] class ConstructionMatrix(object): ''' Base class for construction matrices A construction matrix is used to initialise and build connection matrices. A ``ConstructionMatrix`` class has to implement a method ``connection_matrix(*args, **kwds)`` which returns a :class:`ConnectionMatrix` object of the appropriate type. ''' def connection_matrix(self, *args, **kwds): return NotImplemented class DenseConstructionMatrix(ConstructionMatrix, numpy.ndarray): ''' Dense construction matrix. Essentially just numpy.ndarray. The ``connection_matrix`` method returns a :class:`DenseConnectionMatrix` object. The ``__setitem__`` method is overloaded so that you can set values with a sparse matrix. ''' def __init__(self, val, **kwds): self[:] = 0 self.init_kwds = kwds def connection_matrix(self, **additional_kwds): self.init_kwds.update(additional_kwds) kwds = self.init_kwds return DenseConnectionMatrix(self, **kwds) def __setitem__(self, index, W): # Make it work for sparse matrices #if isinstance(W,sparse.spmatrix): if isinstance(W, sparse.spmatrix): ndarray.__setitem__(self, index, W.todense()) else: ndarray.__setitem__(self, index, W) def todense(self): return asarray(self) class SparseConstructionMatrix(ConstructionMatrix, SparseMatrix): ''' SparseConstructionMatrix is converted to SparseConnectionMatrix. ''' def __init__(self, arg, **kwds): SparseMatrix.__init__(self, arg) self.init_kwds = kwds def connection_matrix(self, **additional_kwds): self.init_kwds.update(additional_kwds) return SparseConnectionMatrix(self, **self.init_kwds) class DynamicConstructionMatrix(ConstructionMatrix, SparseMatrix): ''' DynamicConstructionMatrix is converted to DynamicConnectionMatrix. ''' def __init__(self, arg, **kwds): SparseMatrix.__init__(self, arg) self.init_kwds = kwds def connection_matrix(self, **additional_kwds): self.init_kwds.update(additional_kwds) return DynamicConnectionMatrix(self, **self.init_kwds) # this is used to look up str->class conversions for structure=... keyword construction_matrix_register = { 'dense':DenseConstructionMatrix, 'sparse':SparseConstructionMatrix, 'dynamic':DynamicConstructionMatrix, } brian-1.4.3/brian/connections/delayconnection.py000066400000000000000000000426401266062647100217770ustar00rootroot00000000000000from base import * from sparsematrix import * from connectionvector import * from constructionmatrix import * from connectionmatrix import * from construction import * from connection import * from propagation_c_code import * import warnings network_operation = None # we import this when we need to because of order of import issues __all__ = [ 'DelayConnection', ] class DelayConnection(Connection): ''' Connection which implements heterogeneous postsynaptic delays Initialised as for a :class:`Connection`, but with the additional keyword: ``max_delay`` Specifies the maximum delay time for any neuron. Note, the smaller you make this the less memory will be used. Overrides the following attribute of :class:`Connection`: .. attribute:: delay A matrix of delays. This array can be changed during a run, but at no point should it be greater than ``max_delay``. In addition, the methods ``connect``, ``connect_random``, ``connect_full``, and ``connect_one_to_one`` have a new keyword ``delay=...`` for setting the initial values of the delays, where ``delay`` can be one of: * A float, all delays will be set to this value * A pair (min, max), delays will be uniform between these two values. * A function of no arguments, will be called for each nonzero entry in the weight matrix. * A function of two argument ``(i,j)`` will be called for each nonzero entry in the weight matrix. * A matrix of an appropriate type (e.g. ndarray or lil_matrix). Finally, there is a method: ``set_delays(source, target, delay)`` Where ``delay`` must be of one of the types above. **Notes** This class implements post-synaptic delays. This means that the spike is propagated immediately from the presynaptic neuron with the synaptic weight at the time of the spike, but arrives at the postsynaptic neuron with the given delay. At the moment, Brian only provides support for presynaptic delays if they are homogeneous, using the ``delay`` keyword of a standard ``Connection``. **Implementation** :class:`DelayConnection` stores an array of size ``(n,m)`` where ``n`` is ``max_delay/dt`` for ``dt`` of the target :class:`NeuronGroup`'s clock, and ``m`` is the number of neurons in the target. This array can potentially be quite large. Each row in this array represents the array that should be added to the target state variable at some particular future time. Which row corresponds to which time is tracked using a circular indexing scheme. When a spike from neuron ``i`` in the source is encountered, the delay time of neuron ``i`` is looked up, the row corresponding to the current time plus that delay time is found using the circular indexing scheme, and then the spike is propagated to that row as for a standard connection (although this won't be propagated to the target until a later time). **Warning** If you are using a dynamic connection matrix, it is your responsibility to ensure that the nonzero entries of the weight matrix and the delay matrix exactly coincide. This is not an issue for sparse or dense matrices. ''' def __init__(self, source, target, state=0, modulation=None, structure='sparse', weight=None, sparseness=None, delay=None, max_delay=5 * msecond, **kwds): global network_operation if network_operation is None: from ..network import network_operation Connection.__init__(self, source, target, state=state, modulation=modulation, structure=structure, weight=weight, sparseness=sparseness, **kwds) self._max_delay = int(max_delay / target.clock.dt) + 1 source.set_max_delay(max_delay) # Each row of the following array stores the cumulative effect of spikes at some # particular time, defined by a circular indexing scheme. The _cur_delay_ind attribute # stores the row corresponding to the current time, so that _cur_delay_ind+1 corresponds # to that time + target.clock.dt, and so on. When _cur_delay_ind reaches _max_delay it # resets to zero. self._delayedreaction = numpy.zeros((self._max_delay, len(target))) # vector of delay times, can be changed during a run if isinstance(structure, str): structure = construction_matrix_register[structure] self.delayvec = structure((len(source), len(target)), **kwds) self._cur_delay_ind = 0 # this network operation is added to the Network object via the contained_objects # protocol (see the line after the function definition). The standard Connection.propagate # function propagates spikes to _delayedreaction rather than the target, and this # function which is called after the usual propagations propagates that data from # _delayedreaction to the target. It only needs to be called each target.clock update. @network_operation(clock=target.clock, when='after_connections') def delayed_propagate(): # propagate from _delayedreaction -> target group target._S[self.nstate] += self._delayedreaction[self._cur_delay_ind, :] # reset the current row of _delayedreaction self._delayedreaction[self._cur_delay_ind, :] = 0.0 # increase the index for the circular indexing scheme self._cur_delay_ind = (self._cur_delay_ind + 1) % self._max_delay self.delayed_propagate = delayed_propagate self.contained_objects = [delayed_propagate] # this is just used to convert delayvec's which are in ms to integers, precalculating it makes it faster self._invtargetdt = 1 / self.target.clock._dt self._useaccel = get_global_preference('useweave') self._cpp_compiler = get_global_preference('weavecompiler') self._extra_compile_args = ['-O3'] if self._cpp_compiler == 'gcc': self._extra_compile_args += get_global_preference('gcc_options') # ['-march=native', '-ffast-math'] if delay is not None: self.set_delays(delay=delay) def propagate(self, spikes): if not self.iscompressed: self.compress() if len(spikes): # Target state variable dr = self._delayedreaction # If specified, modulation state variable if self._nstate_mod is not None: sv_pre = self.source._S[self._nstate_mod] # Get the rows of the connection matrix, each row will be either a # DenseConnectionVector or a SparseConnectionVector. rows = self.W.get_rows(spikes) dvecrows = self.delayvec.get_rows(spikes) if not self._useaccel: # Pure Python version is easier to understand, but slower than C++ version below if isinstance(rows[0], SparseConnectionVector): if self._nstate_mod is None: # Rows stored as sparse vectors without modulation for row, dvecrow in izip(rows, dvecrows): if not len(row.ind) == len(dvecrow.ind): raise RuntimeError('Weight and delay matrices must be kept in synchrony for sparse matrices.') drind = (self._cur_delay_ind + numpy.array(self._invtargetdt * dvecrow, dtype=int)) % self._max_delay dr[drind, dvecrow.ind] += row else: # Rows stored as sparse vectors with modulation for i, row, dvecrow in izip(spikes, rows, dvecrows): if not len(row.ind) == len(dvecrow.ind): raise RuntimeError('Weight and delay matrices must be kept in synchrony for sparse matrices.') drind = (self._cur_delay_ind + numpy.array(self._invtargetdt * dvecrow, dtype=int)) % self._max_delay # note we call the numpy __mul__ directly because row is # a SparseConnectionVector with different mul semantics dr[drind, dvecrow.ind] += numpy.ndarray.__mul__(row, sv_pre[i]) else: if self._nstate_mod is None: # Rows stored as dense vectors without modulation drjind = numpy.arange(len(self.target), dtype=int) for row, dvecrow in izip(rows, dvecrows): drind = (self._cur_delay_ind + numpy.array(self._invtargetdt * dvecrow, dtype=int)) % self._max_delay dr[drind, drjind[:len(drind)]] += row else: # Rows stored as dense vectors with modulation drjind = numpy.arange(len(self.target), dtype=int) for i, row, dvecrow in izip(spikes, rows, dvecrows): drind = (self._cur_delay_ind + numpy.array(self._invtargetdt * dvecrow, dtype=int)) % self._max_delay dr[drind, drjind[:len(drind)]] += numpy.ndarray.__mul__(row, sv_pre[i]) else: # C++ accelerated code, does the same as the code above but faster and less pretty nspikes = len(spikes) cdi = self._cur_delay_ind idt = self._invtargetdt md = self._max_delay if isinstance(rows[0], SparseConnectionVector): rowinds = [r.ind for r in rows] datas = rows if self._nstate_mod is None: code = delay_propagate_weave_code_sparse codevars = delay_propagate_weave_code_sparse_vars else: code = delay_propagate_weave_code_sparse_modulation codevars = delay_propagate_weave_code_sparse_modulation_vars else: if not isinstance(spikes, numpy.ndarray): spikes = array(spikes, dtype=int) N = len(self.target) if self._nstate_mod is None: code = delay_propagate_weave_code_dense codevars = delay_propagate_weave_code_dense_vars else: code = delay_propagate_weave_code_dense_modulation codevars = delay_propagate_weave_code_dense_modulation_vars weave.inline(code, codevars, compiler=self._cpp_compiler, type_converters=weave.converters.blitz, extra_compile_args=self._extra_compile_args) def do_propagate(self): self.propagate(self.source.get_spikes(0)) def _set_delay_property(self, val): self.delayvec[:] = val delay = property(fget=lambda self:self.delayvec, fset=_set_delay_property) def compress(self): if not self.iscompressed: # We want delayvec to have nonzero entries at the same places as # W does, so we use W to initialise the compressed version of # delayvec, and then copy the values from the old delayvec to # the new compressed one, allowing delayvec and W to not have # to be perfectly intersected at the initialisation stage. If # the structure is dynamic, it will be the user's # responsibility to update them in sequence delayvec = self.delayvec # Special optimisation for the lil_matrix case if the indices # already match. Note that this special optimisation also allows # you to use multiple synapses via a sort of hack (editing the # rows and data attributes of the lil_matrix explicitly) using_lil_matrix = False if isinstance(delayvec, sparse.lil_matrix): using_lil_matrix = True self.delayvec = self.W.connection_matrix(copy=True) repeated_index_hack = False for i in xrange(self.W.shape[0]): if using_lil_matrix: try: row = SparseConnectionVector(self.W.shape[1], array(delayvec.rows[i], dtype=int), array(delayvec.data[i])) if sum(diff(row.ind)==0)>0: warnings.warn("You are using the repeated index hack, be careful!") repeated_index_hack = True self.delayvec.set_row(i, row) except ValueError: if repeated_index_hack: warnings.warn("You are using the repeated index " "hack and not ensuring that weight " "and delay indices correspond, this " "is almost certainly an error!") using_lil_matrix = False # The delayvec[i,:] operation for sparse.lil_matrix format # is VERY slow, but the CSR format is fine. delayvec = delayvec.tocsr() if not using_lil_matrix: self.delayvec.set_row(i, array(todense(delayvec[i, :]), copy=False).flatten()) Connection.compress(self) def set_delays(self, source=None, target=None, delay=None): ''' Set the delays corresponding to the weight matrix ``delay`` must be one of: * A float, all delays will be set to this value * A pair (min, max), delays will be uniform between these two values. * A function of no arguments, will be called for each nonzero entry in the weight matrix. * A function of two argument ``(i,j)`` will be called for each nonzero entry in the weight matrix. * A matrix of an appropriate type (e.g. ndarray or lil_matrix). ''' if delay is None: return W = self.W P = source or self.source Q = target or self.target i0, j0 = self.origin(P, Q) i1 = i0 + len(P) j1 = j0 + len(Q) if isinstance(W, sparse.lil_matrix): def getrow(i): inds = array(W.rows[i], dtype=int) inds = inds[logical_and(inds >= j0, inds < j1)] return inds, len(inds) else: def getrow(i): inds = (W[i, j0:j1] != 0).nonzero()[0] + j0 return inds, len(inds) #return slice(j0, j1), j1-j0 if isinstance(delay, (float, int)): for i in xrange(i0, i1): inds, L = getrow(i) self.delayvec[i, inds] = delay elif isinstance(delay, (tuple, list)) and len(delay) == 2: delaymin, delaymax = delay for i in xrange(i0, i1): inds, L = getrow(i) rowdelay = rand(L) * (delaymax - delaymin) + delaymin self.delayvec[i, inds] = rowdelay elif callable(delay) and delay.func_code.co_argcount == 0: for i in xrange(i0, i1): inds, L = getrow(i) rowdelay = [delay() for _ in xrange(L)] self.delayvec[i, inds] = rowdelay elif callable(delay) and delay.func_code.co_argcount == 2: for i in xrange(i0, i1): inds, L = getrow(i) if isinstance(inds, slice): inds = numpy.arange(inds.start, inds.stop) self.delayvec[i, inds] = delay(i - i0, inds - j0) else: #raise TypeError('delays must be float, pair or function of 0 or 2 arguments') self.delayvec[i0:i1, j0:j1] = delay # probably won't work, but then it will raise an error def connect(self, source=None, target=None, W=None, delay=None): Connection.connect(self, source=source, target=target, W=W) if delay is not None: self.set_delays(source, target, delay) def connect_random(self, source=None, target=None, p=1.0, weight=1.0, fixed=False, seed=None, sparseness=None, delay=None): Connection.connect_random(self, source=source, target=target, p=p, weight=weight, fixed=fixed, seed=seed, sparseness=sparseness) if delay is not None: self.set_delays(source, target, delay) def connect_full(self, source=None, target=None, weight=1.0, delay=None): Connection.connect_full(self, source=source, target=target, weight=weight) if delay is not None: self.set_delays(source, target, delay) def connect_one_to_one(self, source=None, target=None, weight=1.0, delay=None): Connection.connect_one_to_one(self, source=source, target=target, weight=weight) if delay is not None: self.set_delays(source, target, delay) brian-1.4.3/brian/connections/otherconnections.py000066400000000000000000000054171266062647100222060ustar00rootroot00000000000000from base import * from connection import * __all__ = [ 'IdentityConnection', 'MultiConnection', ] class IdentityConnection(Connection): ''' A :class:`Connection` between two groups of the same size, where neuron ``i`` in the source group is connected to neuron ``i`` in the target group. Initialised with arguments: ``source``, ``target`` The source and target :class:`NeuronGroup` objects. ``state`` The target state variable. ``weight`` The weight of the synapse, must be a scalar. ``delay`` Only homogeneous delays are allowed. The benefit of this class is that it has no storage requirements and is optimised for this special case. ''' @check_units(delay=second) def __init__(self, source, target, state=0, weight=1, delay=0 * msecond): if (len(source) != len(target)): raise AttributeError, 'The connected (sub)groups must have the same size.' self.source = source # pointer to source group self.target = target # pointer to target group if type(state) == types.StringType: # named state variable self.nstate = target.get_var_index(state) else: self.nstate = state # target state index self.W = float(weight) # weight source.set_max_delay(delay) self.delay = int(delay / source.clock.dt) # Synaptic delay in time bins def propagate(self, spikes): ''' Propagates the spikes to the target. ''' self.target._S[self.nstate, spikes] += self.W def compress(self): pass class MultiConnection(Connection): ''' A hub for multiple connections with a common source group. ''' def __init__(self, source, connections=[]): self.source = source self.connections = connections self.iscompressed = False self.delay = connections[0].delay def propagate(self, spikes): ''' Propagates the spikes to the targets. ''' for C in self.connections: C.propagate(spikes) def compress(self): if not self.iscompressed: for C in self.connections: C.compress() self.iscompressed = True def reinit(self): for C in self.connections: # this does nothing for normal connections but is important for # SpikeMonitors C.reinit() def __repr__(self): return '<%s, containing %d connections from %s>' % (self.__class__.__name__, len(self.connections), repr(self.source)) brian-1.4.3/brian/connections/propagation_c_code.py000066400000000000000000000226731266062647100224440ustar00rootroot00000000000000################################################################################ ################## NO DELAYS ################################################### ################################################################################ ################## SPARSE ###################################################### propagate_weave_code_sparse_vars = ['sv', 'rowinds', 'datas', 'spikes', 'nspikes'] propagate_weave_code_sparse = ''' for(int j=0;j row = convert_to_blitz(_row,"row"); long* row = (long*)_row->data; PyObject* _datasj = datas[j]; PyArrayObject* _data = convert_to_numpy(_datasj, "data"); conversion_numpy_check_type(_data, PyArray_DOUBLE, "data"); conversion_numpy_check_size(_data, 1, "data"); //blitz::Array data = convert_to_blitz(_data,"data"); double* data = (double*)_data->data; //int m = row.numElements(); int m = _row->dimensions[0]; for(int k=0;k row = convert_to_blitz(_row,"row"); long* row = (long*)_row->data; PyObject* _datasj = datas[j]; PyArrayObject* _data = convert_to_numpy(_datasj, "data"); conversion_numpy_check_type(_data, PyArray_DOUBLE, "data"); conversion_numpy_check_size(_data, 1, "data"); //blitz::Array data = convert_to_blitz(_data,"data"); double* data = (double*)_data->data; //int m = row.numElements(); int m = _row->dimensions[0]; //double mod = sv_pre(spikes(j)); double mod = sv_pre[spikes[j]]; for(int k=0;k row = convert_to_blitz(_row,"row"); double *row = (double *)_row->data; for(int k=0;k row = convert_to_blitz(_row,"row"); //double mod = sv_pre(spikes(j)); double *row = (double *)_row->data; double mod = sv_pre[spikes[j]]; for(int k=0;k row = convert_to_blitz(_row,"row"); PyObject* _datasj = datas[j]; PyArrayObject* _data = convert_to_numpy(_datasj, "data"); conversion_numpy_check_type(_data, PyArray_DOUBLE, "data"); conversion_numpy_check_size(_data, 1, "data"); blitz::Array data = convert_to_blitz(_data,"data"); PyObject* _dvecrowsj = dvecrows[j]; PyArrayObject* _dvecrow = convert_to_numpy(_dvecrowsj, "dvecrow"); conversion_numpy_check_type(_dvecrow, PyArray_DOUBLE, "dvecrow"); conversion_numpy_check_size(_dvecrow, 1, "dvecrow"); blitz::Array dvecrow = convert_to_blitz(_dvecrow,"dvecrow"); int m = row.numElements(); for(int k=0;k row = convert_to_blitz(_row,"row"); PyObject* _datasj = datas[j]; PyArrayObject* _data = convert_to_numpy(_datasj, "data"); conversion_numpy_check_type(_data, PyArray_DOUBLE, "data"); conversion_numpy_check_size(_data, 1, "data"); blitz::Array data = convert_to_blitz(_data,"data"); PyObject* _dvecrowsj = dvecrows[j]; PyArrayObject* _dvecrow = convert_to_numpy(_dvecrowsj, "dvecrow"); conversion_numpy_check_type(_dvecrow, PyArray_DOUBLE, "dvecrow"); conversion_numpy_check_size(_dvecrow, 1, "dvecrow"); blitz::Array dvecrow = convert_to_blitz(_dvecrow,"dvecrow"); int m = row.numElements(); double mod = sv_pre(spikes(j)); for(int k=0;k row = convert_to_blitz(_row,"row"); PyObject* _dvecrowsj = dvecrows[j]; PyArrayObject* _dvecrow = convert_to_numpy(_dvecrowsj, "dvecrow"); conversion_numpy_check_type(_dvecrow, PyArray_DOUBLE, "dvecrow"); conversion_numpy_check_size(_dvecrow, 1, "dvecrow"); blitz::Array dvecrow = convert_to_blitz(_dvecrow,"dvecrow"); for(int k=0;k row = convert_to_blitz(_row,"row"); PyObject* _dvecrowsj = dvecrows[j]; PyArrayObject* _dvecrow = convert_to_numpy(_dvecrowsj, "dvecrow"); conversion_numpy_check_type(_dvecrow, PyArray_DOUBLE, "dvecrow"); conversion_numpy_check_size(_dvecrow, 1, "dvecrow"); blitz::Array dvecrow = convert_to_blitz(_dvecrow,"dvecrow"); double mod = sv_pre(spikes(j)); for(int k=0;k self.curperiod: self.reinit() self.curperiod = cp t = t - cp * self.period # it is the iterator for neuron i, and nextspiketime is the stored time of the next spike for i, (it, nextspiketime) in enumerate(izip(self.spiketimeiter, self.nextspiketime)): # Note we use approximate equality testing because the clock t+=dt mechanism accumulates errors if isinstance(self.spiketimes[i], numpy.ndarray): curt = float(t) else: curt = t if nextspiketime is not None and is_approx_less_than_or_equal(nextspiketime, curt): firing[i] = 1 try: nextspiketime = it.next() if is_approx_less_than_or_equal(nextspiketime, curt): log_warn('brian.MultipleSpikeGeneratorThreshold', 'Stacking multiple firing times') except StopIteration: nextspiketime = None self.nextspiketime[i] = nextspiketime return where(firing)[0] brian-1.4.3/brian/directcontrol.py000066400000000000000000000622521266062647100171530ustar00rootroot00000000000000# ---------------------------------------------------------------------------------- # Copyright ENS, INRIA, CNRS # Contributors: Romain Brette (brette@di.ens.fr) and Dan Goodman (goodman@di.ens.fr) # # Brian is a computer program whose purpose is to simulate models # of biological neural networks. # # This software is governed by the CeCILL license under French law and # abiding by the rules of distribution of free software. You can use, # modify and/ or redistribute the software under the terms of the CeCILL # license as circulated by CEA, CNRS and INRIA at the following URL # "http://www.cecill.info". # # As a counterpart to the access to the source code and rights to copy, # modify and redistribute granted by the license, users are provided only # with a limited warranty and the software's author, the holder of the # economic rights, and the successive licensors have only limited # liability. # # In this respect, the user's attention is drawn to the risks associated # with loading, using, modifying and/or developing or reproducing the # software by the user in light of its specific status of free software, # that may mean that it is complicated to manipulate, and that also # therefore means that it is reserved for developers and experienced # professionals having in-depth computer knowledge. Users are therefore # encouraged to load and test the software's suitability as regards their # requirements in conditions enabling the security of their systems and/or # data to be ensured and, more generally, to use and operate it in the # same conditions as regards security. # # The fact that you are presently reading this means that you have had # knowledge of the CeCILL license and that you accept its terms. # ---------------------------------------------------------------------------------- # """Direct controlling mechanisms NeuronGroups and callable objects which allow direct control over the behaviour of neurons. """ __all__ = ['SpikeGeneratorGroup', 'PulsePacket', 'PoissonGroup', 'OfflinePoissonGroup', 'PoissonInput'] from neurongroup import * from threshold import * from stateupdater import * from units import * import random as pyrandom from numpy import where, array, zeros, ones, inf, nonzero, tile, sum, isscalar,\ cumsum, hstack, bincount, ceil, ndarray, ascontiguousarray from copy import copy from clock import guess_clock from utils.approximatecomparisons import * import warnings from operator import itemgetter from log import * import numpy from numpy.random import exponential, randint, binomial from connections import Connection from itertools import izip class SpikeGeneratorGroup(NeuronGroup): """Emits spikes at given times Initialised as:: SpikeGeneratorGroup(N,spiketimes[,clock[,period]]) with arguments: ``N`` The number of neurons in the group. ``spiketimes`` An object specifying which neurons should fire and when. It can be a container such as a ``list``, containing tuples ``(i,t)`` meaning neuron ``i`` fires at time ``t``, or a callable object which returns such a container (which allows you to use generator objects even though this is slower, see below). ``i`` can be an integer or an array (list of neurons that spike at the same time). If ``spiketimes`` is not a list or tuple, the pairs ``(i,t)`` need to be sorted in time. You can also pass a numpy array ``spiketimes`` where the first column of the array is the neuron indices, and the second column is the times in seconds. Alternatively you can pass a tuple with two arrays, the first one being the neuron indices and the second one times. WARNING: units are not checked in this case, the time array should be in seconds. ``clock`` An optional clock to update with (omit to use the default clock). ``period`` Optionally makes the spikes recur periodically with the given period. Note that iterator objects cannot be used as the ``spikelist`` with a period as they cannot be reinitialised. ``gather=False`` Set to True if you want to gather spike events that fall in the same timestep. (Deprecated since Brian 1.3.1) ``sort=True`` Set to False if your spike events are already sorted. Has an attribute: ``spiketimes`` This can be used to reset the list of spike times, however the values of ``N``, ``clock`` and ``period`` cannot be changed. **Sample usages** The simplest usage would be a list of pairs ``(i,t)``:: spiketimes = [(0,1*ms), (1,2*ms)] SpikeGeneratorGroup(N,spiketimes) A more complicated example would be to pass a generator:: import random def nextspike(): nexttime = random.uniform(0*ms,10*ms) while True: yield (random.randint(0,9),nexttime) nexttime = nexttime + random.uniform(0*ms,10*ms) P = SpikeGeneratorGroup(10,nextspike()) This would give a neuron group ``P`` with 10 neurons, where a random one of the neurons fires at an average rate of one every 5ms. Please note that as of 1.3.1, this behavior is preserved but will run slower than initializing with arrays, or lists. **Notes** Note that if a neuron fires more than one spike in a given interval ``dt``, additional spikes will be discarded. A warning will be issued if this is detected. Also, if you want to use a SpikeGeneratorGroup with many spikes and/or neurons, please use an initialization with arrays. Also note that if you pass a generator, then reinitialising the group will not have the expected effect because a generator object cannot be reinitialised. Instead, you should pass a callable object which returns a generator. In the example above, that would be done by calling:: P = SpikeGeneratorGroup(10,nextspike) Whenever P is reinitialised, it will call ``nextspike()`` to create the required spike container. """ def __init__(self, N, spiketimes, clock=None, period=None, sort=True, gather=None): clock = guess_clock(clock) self.N = N self.period = period if gather: log_warn('brian.SpikeGeneratorGroup', 'SpikeGeneratorGroup\'s gather keyword use is deprecated') fallback = False # fall back on old SpikeGeneratorThreshold or not if isinstance(spiketimes, list): # spiketimes is a list of (i,t) if len(spiketimes): idx, times = zip(*spiketimes) else: idx, times = [], [] # the following try ... handles the case where spiketimes has index arrays # e.g spiketimes = [([0, 1], 0 * msecond), ([0, 1, 2], 2 * msecond)] # Notes: # - if there is always the same number of indices by array, its simple, it's just a matter of flattening # - if not, then it requires a for loop, and it's done in the except try: idx = array(idx, dtype = float) times = array(times, dtype = float) if idx.ndim > 1: # simple case times = tile(times.reshape((len(times), 1)), (idx.shape[1], 1)).flatten() idx = idx.flatten() except ValueError: new_idx = [] new_times = [] for k, item in enumerate(idx): if isinstance(item, list): new_idx += item # append indices new_times += [times[k]]*len(item) else: new_times += [times[k]] new_idx += [item] idx = array(new_idx, dtype = float) times = new_times times = array(times, dtype = float) elif isinstance(spiketimes, tuple): # spike times is a tuple with idx, times in arrays idx = spiketimes[0] times = spiketimes[1] elif isinstance(spiketimes, ndarray): # spiketimes is a ndarray, with first col is index and second time idx = spiketimes[:,0] times = spiketimes[:,1] else: log_warn('brian.SpikeGeneratorGroup', 'Using (slow) threshold because spiketimes is assumed to be a generator/iterator') # spiketimes is a callable object, so falling back on old SpikeGeneratorThreshold fallback = True if not fallback: thresh = FastSpikeGeneratorThreshold(N, idx, times, dt=clock.dt, period=period) else: thresh = SpikeGeneratorThreshold(N, spiketimes, period=period, sort=sort) if not hasattr(self, '_initialized'): NeuronGroup.__init__(self, N, model=LazyStateUpdater(), threshold=thresh, clock=clock) self._initialized = True else: self._threshold = thresh def reinit(self): super(SpikeGeneratorGroup, self).reinit() self._threshold.reinit() def get_spiketimes(self): return self._threshold.spiketimes def set_spiketimes(self, values): self.__init__(self.N, values, period = self.period) # changed due to the 2.5 issue spiketimes = property(get_spiketimes, set_spiketimes) class FastSpikeGeneratorThreshold(Threshold): ''' A faster version of the SpikeGeneratorThreshold where spikes are processed prior to the run (offline). It replaces the SpikeGeneratorThreshold as of 1.3.1. ''' ## Notes: # - N is ignored (should it not?) def __init__(self, N, addr, timestamps, dt = None, period=None): self.set_offsets(addr, timestamps, dt = dt) self.period = period self.dt = dt self.reinit() def set_offsets(self, I, T, dt = 1000): # Convert times into integers T = array(ceil(T/dt), dtype=int) # Put them into order # We use a field array to sort first by time and then by neuron index spikes = zeros(len(I), dtype=[('t', int), ('i', int)]) spikes['t'] = T spikes['i'] = I spikes.sort(order=('t', 'i')) T = ascontiguousarray(spikes['t']) self.I = ascontiguousarray(spikes['i']) # Now for each timestep, we find the corresponding segment of I with # the spike indices for that timestep. # The idea of offsets is that the segment offsets[t]:offsets[t+1] # should give the spikes with time t, i.e. T[offsets[t]:offsets[t+1]] # should all be equal to t, and so then later we can return # I[offsets[t]:offsets[t+1]] at time t. It might take a bit of thinking # to see why this works. Since T is sorted, and bincount[i] returns the # number of elements of T equal to i, then j=cumsum(bincount(T))[t] # gives the first index in T where T[j]=t. if len(T): self.offsets = hstack((0, cumsum(bincount(T)))) else: self.offsets = array([]) def __call__(self, P): t = P.clock.t if self.period is not None: cp = int(t / self.period) if cp > self.curperiod: self.reinit() self.curperiod = cp t = t - cp * self.period dt = P.clock.dt t = int(round(t/dt)) if t+1>=len(self.offsets): return array([], dtype=int) return self.I[self.offsets[t]:self.offsets[t+1]] def reinit(self): self.curperiod = -1 @property def spiketimes(self): # this is a pain to do! retrieve spike times from offsets res = [] for k in range(len(self.offsets)-1): idx = self.I[self.offsets[k]:self.offsets[k+1]] ts = [k*self.dt]*len(idx) res += zip(idx, ts) return res def __repr__(self): return '' def __str__(self): return 'Fast threshold mechanism for the SpikeGenerator group' class SpikeGeneratorThreshold(Threshold): """ Old threshold object for the SpikeGeneratorGroup **Notes** This version of the SpikeGeneratorThreshold object is deprecated, since version 1.3.1 of Brian it has been replaced in most cases by the FastSpikeGeneratorThreshold. This is kept only as a fallback object for when a SpikeGeneratorGroup object is initialized with a generator or an iterator object (see the doc for SpikeGeneratorGroup for more details). Please note that since this implementation is slower, using a static data structure as an input to a SpikeGeneratorGroup is advised. """ def __init__(self, N, spiketimes, period=None, sort=True): self.set_spike_times(N, spiketimes, period=period, sort=sort) def reinit(self): def makeiter(obj): if callable(obj): return iter(obj()) return iter(obj) self.spiketimeiter = makeiter(self.spiketimes) try: self.nextspikenumber, self.nextspiketime = self.spiketimeiter.next() except StopIteration: self.nextspiketime = None self.nextspikenumber = 0 self.curperiod = -1 def set_spike_times(self, N, spiketimes, period=None, sort=True): # N is the number of neurons, spiketimes is an iterable object of tuples (i,t) where # t is the spike time, and i is the neuron number. If spiketimes is a list or tuple, # then it will be sorted here. if isinstance(spiketimes, (list, tuple)) and sort: spiketimes = sorted(spiketimes, key=itemgetter(1)) self.spiketimes = spiketimes self.N = N self.period = period self.reinit() def __call__(self, P): firing = zeros(self.N) t = P.clock.t if self.period is not None: cp = int(t / self.period) if cp > self.curperiod: self.reinit() self.curperiod = cp t = t - cp * self.period if isinstance(self.spiketimes, numpy.ndarray): t = float(t) while self.nextspiketime is not None and is_approx_less_than_or_equal(self.nextspiketime, t): if type(self.nextspikenumber)==int and firing[self.nextspikenumber]: log_warn('brian.SpikeGeneratorThreshold', 'Discarding multiple overlapping spikes') firing[self.nextspikenumber] = 1 try: self.nextspikenumber, self.nextspiketime = self.spiketimeiter.next() except StopIteration: self.nextspiketime = None return where(firing)[0] def __repr__(self): return '' def __str__(self): return 'Threshold mechanism for the SpikeGenerator group' # The output of this function is fed into SpikeGeneratorGroup, consisting of # time sorted pairs (t,i) where t is when neuron i fires @check_units(t=second, n=1, sigma=second) def PulsePacketGenerator(t, n, sigma): times = [pyrandom.gauss(t, sigma) for i in range(n)] times.sort() neuron = range(n) pyrandom.shuffle(neuron) return zip(neuron, times) class PulsePacket(SpikeGeneratorGroup): """ Fires a Gaussian distributed packet of n spikes with given spread **Initialised as:** :: PulsePacket(t,n,sigma[,clock]) with arguments: ``t`` The mean firing time ``n`` The number of spikes in the packet ``sigma`` The standard deviation of the firing times. ``clock`` The clock to use (omit to use default or local clock) **Methods** This class is derived from :class:`SpikeGeneratorGroup` and has all its methods as well as one additional method: .. method:: generate(t,n,sigma) Change the parameters and/or generate a new pulse packet. """ @check_units(t=second, n=1, sigma=second) def __init__(self, t, n, sigma, clock=None): self.clock = guess_clock(clock) self.generate(t, n, sigma) def reinit(self): super(PulsePacket, self).reinit() self._threshold.reinit() @check_units(t=second, n=1, sigma=second) def generate(self, t, n, sigma): SpikeGeneratorGroup.__init__(self, n, PulsePacketGenerator(t, n, sigma), self.clock) def __repr__(self): return "Pulse packet neuron group" class PoissonGroup(NeuronGroup): ''' A group that generates independent Poisson spike trains. **Initialised as:** :: PoissonGroup(N,rates[,clock]) with arguments: ``N`` The number of neurons in the group ``rates`` A scalar, array or function returning a scalar or array. The array should have the same length as the number of neurons in the group. The function should take one argument ``t`` the current simulation time. ``clock`` The clock which the group will update with, do not specify to use the default clock. ''' def __init__(self, N, rates=0 * hertz, clock=None): ''' Initializes the group. P.rates gives the rates. ''' NeuronGroup.__init__(self, N, model=LazyStateUpdater(), threshold=PoissonThreshold(), clock=clock) if callable(rates): # a function is passed self._variable_rate = True self.rates = rates self._S0[0] = self.rates(self.clock.t) else: self._variable_rate = False self._S[0, :] = rates self._S0[0] = rates self.var_index = {'rate':0} def update(self): if self._variable_rate: self._S[0, :] = self.rates(self.clock.t) NeuronGroup.update(self) class OfflinePoissonGroup(object): # This is weird, there is only an init method def __init__(self, N, rates, T): """ Generates a Poisson group with N spike trains and given rates over the time window [0,T]. """ if isscalar(rates): rates = rates * ones(N) totalrate = sum(rates) isi = exponential(1 / totalrate, T * totalrate * 2) spikes = cumsum(isi) spikes = spikes[spikes <= T] neurons = randint(0, N, len(spikes)) self.spiketimes = zip(neurons, spikes) # Used in PoissonInput below class EmptyGroup(object): def __init__(self, clock): self.clock = clock def get_spikes(self, delay): return None class PoissonInput(Connection): """ Adds a Poisson input to a NeuronGroup. Allows to efficiently simulate a large number of independent Poisson inputs to a NeuronGroup variable, without simulating every synapse individually. The synaptic events are generated randomly during the simulation and are not preloaded and stored in memory (unless record=True is used). All the inputs must target the same variable, have the same frequency and same synaptic weight. You can use as many PoissonInput objects as you want, even targetting a same NeuronGroup. There is the possibility to consider time jitter in the presynaptic spikes, and synaptic unreliability. The inputs can also be recorded if needed. Finally, all neurons from the NeuronGroup receive independent realizations of Poisson spike trains, except if the keyword freeze=True is used, in which case all neurons receive the same Poisson input. **Initialised as:** :: PoissonInput(target[, N][, rate][, weight][, state][, jitter][, reliability][, copies][, record][, freeze]) with arguments: ``target`` The target :class:`NeuronGroup` ``N`` The number of independent Poisson inputs ``rate`` The rate of each Poisson process ``weight`` The synaptic weight ``state`` The name or the index of the synaptic variable of the :class:`NeuronGroup` ``jitter`` is ``None`` by default. There is the possibility to consider ``copies`` presynaptic spikes at each Poisson event, randomly shifted according to an exponential law with parameter ``jitter=taujitter`` (in second). ``reliability`` is ``None`` by default. There is the possibility to consider ``copies`` presynaptic spikes at each Poisson event, where each of these spikes is unreliable, i.e. it occurs with probability ``jitter=alpha`` (between 0 and 1). ``copies`` The number of copies of each Poisson event. This is identical to ``weight=copies*w``, except if ``jitter`` or ``reliability`` are specified. ``record`` ``True`` if the input has to be recorded. In this case, the recorded events are stored in the ``recorded_events`` attribute, as a list of pairs ``(i,t)`` where ``i`` is the neuron index and ``t`` is the event time. ``freeze`` ``True`` if the input must be the same for all neurons of the :class:`NeuronGroup` """ _record = [] def __init__(self, target, N=None, rate=None, weight=None, state=None, jitter=None, reliability=None, copies=1, record=False, freeze=False): self.source = EmptyGroup(target.clock) self.target = target self.N = len(self.target) self.clock = target.clock self.delay = None self.iscompressed = True self.delays = None # delay to wait for the j-th synchronous spike to occur after the last sync event, for target neuron i self.lastevent = -inf * ones(self.N) # time of the last event for target neuron i self.events = [] self.recorded_events = [] self.n = N self.rate = rate self.w = weight self.var = state self._jitter = jitter if jitter is not None: self.delays = zeros((copies, self.N)) self.reliability = reliability self.copies = copies self.record = record self.frozen = freeze if (jitter is not None) and (reliability is not None): raise Exception("Specifying both jitter and reliability is currently not supported.") if isinstance(state, str): # named state variable self.index = self.target.get_var_index(state) else: self.index = state def get_jitter(self): return self._jitter def set_jitter(self, value): self._jitter = value if value is not None: self.delays = zeros((self.copies, self.N)) # changed due to the 2.5 issue jitter = property(get_jitter, set_jitter) def propagate(self, spikes): i = 0 n = self.n f = self.rate w = self.w var = self.var jitter = self.jitter reliability = self.reliability record = self.record frozen = self.frozen state = self.index if (jitter==None) and (reliability==None): if frozen: rnd = binomial(n=n, p=f * self.clock.dt) self.target._S[state, :] += w * rnd if rnd > 0: self.events.append(self.clock.t) else: rnd = binomial(n=n, p=f * self.clock.dt, size=(self.N)) self.target._S[state, :] += w * rnd ind = nonzero(rnd>0)[0] if record and len(ind)>0: self.recorded_events.append((ind[0], self.clock.t)) elif (jitter is not None): p = self.copies taujitter = jitter if (p > 0) & (f > 0): k = binomial(n=n, p=f * self.clock.dt, size=(self.N)) # number of synchronous events here, for every target neuron syncneurons = (k > 0) # neurons with a syncronous event here self.lastevent[syncneurons] = self.clock.t if taujitter == 0.0: self.delays[:, syncneurons] = zeros((p, sum(syncneurons))) else: self.delays[:, syncneurons] = exponential(scale=taujitter, size=(p, sum(syncneurons))) # Delayed spikes occur now lastevent = tile(self.lastevent, (p, 1)) b = (abs(self.clock.t - (lastevent + self.delays)) <= (self.clock.dt / 2) * ones((p, self.N))) # delayed spikes occurring now weff = sum(b, axis=0) * w self.target._S[state, :] += weff elif (reliability is not None): p = self.copies alpha = reliability if (p > 0) & (alpha > 0): weff = w * binomial(n=p, p=alpha) self.target._S[state, :] += weff * binomial(n=n, p=f * self.clock.dt, size=(self.N)) def _test(): import doctest doctest.testmod() if __name__ == "__main__": _test() brian-1.4.3/brian/equations.py000066400000000000000000001236471266062647100163160ustar00rootroot00000000000000# ---------------------------------------------------------------------------------- # Copyright ENS, INRIA, CNRS # Contributors: Romain Brette (brette@di.ens.fr) and Dan Goodman (goodman@di.ens.fr) # # Brian is a computer program whose purpose is to simulate models # of biological neural networks. # # This software is governed by the CeCILL license under French law and # abiding by the rules of distribution of free software. You can use, # modify and/ or redistribute the software under the terms of the CeCILL # license as circulated by CEA, CNRS and INRIA at the following URL # "http://www.cecill.info". # # As a counterpart to the access to the source code and rights to copy, # modify and redistribute granted by the license, users are provided only # with a limited warranty and the software's author, the holder of the # economic rights, and the successive licensors have only limited # liability. # # In this respect, the user's attention is drawn to the risks associated # with loading, using, modifying and/or developing or reproducing the # software by the user in light of its specific status of free software, # that may mean that it is complicated to manipulate, and that also # therefore means that it is reserved for developers and experienced # professionals having in-depth computer knowledge. Users are therefore # encouraged to load and test the software's suitability as regards their # requirements in conditions enabling the security of their systems and/or # data to be ensured and, more generally, to use and operate it in the # same conditions as regards security. # # The fact that you are presently reading this means that you have had # knowledge of the CeCILL license and that you accept its terms. # ---------------------------------------------------------------------------------- # ''' Differential equations for Brian models. ''' from operator import isSequenceType import types from units import * from stdunits import * from inspection import * from scipy import exp try: import weave except ImportError: from scipy import weave from globalprefs import * import re import inspect import optimiser import warnings import uuid from numpy import zeros, ones import numpy from log import * from optimiser import * from scipy import optimize import unitsafefunctions import copy try: import sympy use_sympy = True except: warnings.warn('sympy not installed') use_sympy = False __all__ = ['Equations', 'unique_id'] # TODO: write interface for equations.py def unique_id(): """ Returns a unique name (e.g. for internal hidden variables). """ return '_' + str(uuid.uuid1().int) class Equations(object): """Container that stores equations from which models can be created Initialised as:: Equations(expr[,level=0[,keywords...]]) with arguments: ``expr`` An expression, which can each be a string representing equations, an :class:`Equations` objects, or a list of strings and :class:`Equations` objects. See below for details of the string format. ``level`` Indicates how many levels back in the stack the namespace for string equations is found, so that e.g. ``level=0`` looks in the namespace of the function where the :class:`Equations` object was created, ``level=1`` would look in the namespace of the function that called the function where the :class:`Equations` object was created, etc. Normally you can just leave this out. ``keywords`` Any sequence of keyword pairs ``key=value`` where the string ``key`` in the string equations will be replaced with ``value`` which can be either a string, value or ``None``, in the latter case a unique name will be generated automatically (but it won't be pretty). Systems of equations can be defined by passing lists of :class:`Equations` to a new :class:`Equations` object, or by adding :class:`Equations` objects together (the usage is similar to that of a Python ``list``). **String equations** String equations can be of any of the following forms: (1) ``dx/dt = f : unit`` (differential equation) (2) ``x = f : unit`` (equation) (3) ``x = y`` (alias) (4) ``x : unit`` (parameter) Here each of ``x`` and ``y`` can be any valid Python variable name, ``f`` can be any valid Python expression, and ``unit`` should be the unit of the corresponding ``x``. You can also include multi-line expressions by appending a ``\`` character at the end of each line which is continued on the next line (following the Python standard), or comments by including a ``#`` symbol. These forms mean: *Differential equation* A differential equation with variable ``x`` which has physical units ``unit``. The variable ``x`` will become one of the state variables of the model. *Equation* An equation defining the meaning of ``x`` can be used for building systems of complicated differential equations. *Alias* The variable ``x`` becomes equivalent to the variable ``y``, useful for connecting two separate systems of equations together. *Parameter* The variable ``x`` will have physical units ``unit`` and will be one of the state variables of the model (but will not evolve dynamically, instead it should be set by the user). .. index:: single: xi pair: xi; noise single: white noise single: gaussian noise single: noise single: noise; gaussian single: noise; white **Noise** String equations can also use the reserved term ``xi`` for a Gaussian white noise with mean 0 and variance 1. **Example usage** :: eqs=Equations(''' dv/dt=(u-v)/tau : volt u=3*v : volt w=v ''') **Details** For more details, see :ref:`moreonequations` in the user manual. """ def __init__(self, expr='', level=0, **kwds): # Empty object self._Vm = None # name of variable with membrane potential self._eq_names = [] # equations names self._diffeq_names = [] # differential equations names self._diffeq_names_nonzero = [] # differential equations names self._function = {} # dictionary of functions self._string = {} # dictionary of strings (defining the functions) self._namespace = {} # dictionary of namespaces for the strings (globals,locals) self._alias = {} # aliases (mapping name1 -> name2) self._units = {'t':second} # dictionary of units self._dependencies = {} # dictionary of dependencies (on static equations) self._useweave = get_global_preference('useweave') self._cpp_compiler = get_global_preference('weavecompiler') self._extra_compile_args = ['-O3'] if self._cpp_compiler == 'gcc': self._extra_compile_args += get_global_preference('gcc_options') # ['-march=native', '-ffast-math'] self._frozen = False # True if all units and parameters are gone self._prepared = False if expr is None: expr = '' if not isinstance(expr, str): # assume it is a sequence of Equations objects for eqs in expr: if not isinstance(eqs, Equations): eqs = Equations(eqs, level=level + 1) self += eqs elif expr != '': # Check keyword arguments param_dict = {} for name, value in kwds.iteritems(): if value is None: # name is not important: choose unique name value = unique_id() if isinstance(value, str): # variable name substitution expr = re.sub('\\b' + name + '\\b', value, expr) expr = re.sub('\\bd' + name + '\\b', 'd' + value, expr) # derivative else: param_dict[name] = value if kwds == {}: # weird: changed from param_dict on 18/06/08 self.parse_string_equations(expr, level=level + 1) else: self.parse_string_equations(expr, namespace=param_dict, level=level + 1) """ ----------------------------------------------------------------------- PARSING AND BUILDING NAMESPACES ----------------------------------------------------------------------- """ def parse_string_equations(self, eqns, level=1, namespace=None): """ Parses a string defining equations and builds an Equations object. Uses the namespace in the given level of the stack. """ diffeq_pattern = re.compile('\s*d(\w+)\s*/\s*dt\s*=\s*(.+?)\s*:\s*(.*)') eq_pattern = re.compile('\s*(\w+)\s*=\s*(.+?)\s*:\s*(.*)') alias_pattern = re.compile('\s*(\w+)\s*=\s*(\w+)\s*$') param_pattern = re.compile('\s*(\w+)\s*:\s*(.*)') empty_pattern = re.compile('\s*$') patterns = [diffeq_pattern, eq_pattern, alias_pattern, param_pattern, empty_pattern] # Merge multi-line statements eqns = re.sub('\\\s*?\n', ' ', eqns) # Namespace of the functions ns_global, ns_local = namespace, namespace if namespace is None: frame = inspect.stack()[level + 1][0] ns_global, ns_local = frame.f_globals, frame.f_locals #print frame.f_code.co_filename #useful for debugging which file the namespace came from for line in eqns.splitlines(): line = re.sub('#.*', '', line) # remove comments result = None for pattern in patterns: result = pattern.match(line) if result: break if result == None: raise TypeError, "Invalid equation string: " + line if pattern == eq_pattern: name, eq, unit = result.groups() self.add_eq(name, eq, unit, ns_global, ns_local) elif pattern == diffeq_pattern: name, eq, unit = result.groups() self.add_diffeq(name, eq, unit, ns_global, ns_local) elif pattern == alias_pattern: name1, name2 = result.groups() self.add_alias(name1, name2) elif pattern == param_pattern: name, unit = result.groups() self.add_param(name, unit, ns_global, ns_local) def add_eq(self, name, eq, unit, global_namespace={}, local_namespace={}): """ Inserts an equation. name = variable name eq = string definition unit = unit of the variable (possibly a string) *_namespace = namespaces associated to the string """ # Find external objects vars = list(get_identifiers(eq)) if type(unit) == types.StringType: vars.extend(list(get_identifiers(unit))) self._namespace[name] = {} for var in vars: if var in local_namespace: #local self._namespace[name][var] = local_namespace[var] elif var in global_namespace: #global self._namespace[name][var] = global_namespace[var] elif var in globals(): # typically units self._namespace[name][var] = globals()[var] self._eq_names.append(name) if type(unit) == types.StringType: self._units[name] = eval(unit, self._namespace[name].copy()) else: self._units[name] = unit self._string[name] = eq def add_diffeq(self, name, eq, unit, global_namespace={}, local_namespace={}, nonzero=True): """ Inserts a differential equation. name = variable name eq = string definition unit = unit of the variable (possibly a string) *_namespace = namespaces associated to the string nonzero = False if dx/dt=0 (parameter) """ # Find external objects vars = list(get_identifiers(eq)) if type(unit) == types.StringType: vars.extend(list(get_identifiers(unit))) self._namespace[name] = {} for var in vars: if var in local_namespace: #local self._namespace[name][var] = local_namespace[var] elif var in global_namespace: #global self._namespace[name][var] = global_namespace[var] elif var in globals(): # typically units self._namespace[name][var] = globals()[var] self._diffeq_names.append(name) if type(unit) == types.StringType: self._units[name] = eval(unit, self._namespace[name].copy()) else: self._units[name] = unit self._string[name] = eq if nonzero: self._diffeq_names_nonzero.append(name) def add_alias(self, name1, name2): """ Inserts an alias. name1 = new name name2 = old name """ self._alias[name1] = name2 # TODO: what if name2 is not defined yet? self.add_eq(name1, name2, self._units[name2]) def add_param(self, name, unit, global_namespace={}, local_namespace={}): """ Inserts a parameter. name = variable name eq = string definition unit = unit of the variable (possibly a string) *_namespace = namespaces associated to the string """ if isinstance(unit, Quantity): unit = scalar_representation(unit) self.add_diffeq(name, '0*' + unit + '/second', unit, global_namespace, local_namespace, nonzero=False) """ ----------------------------------------------------------------------- FINALISATION ----------------------------------------------------------------------- """ def prepare(self, check_units=True): ''' Do a number of checks (units) and preparation of the object. ''' if self._prepared: return # Let Vm be the first differential equation vm_name = self.get_Vm() if vm_name: # TODO: INFO logging i = self._diffeq_names.index(vm_name) self._diffeq_names[0], self._diffeq_names[i] = self._diffeq_names[i], self._diffeq_names[0] else: pass # TODO: WARNING log that a potential problem has occurred here? # Clean namespace (avoids conflicts between variables and external variables) self.clean_namespace() # Compile strings to functions self.compile_functions() # Check units if check_units: self.check_units() # Set the update order of (static) variables self.set_eq_order() # Replace static variables by their value in differential equations self.substitute_eq() self.compile_functions() # Check free variables free_vars = self.free_variables() if free_vars != []: log_info('brian.equations', 'Free variables: ' + str(free_vars)) self._prepared = True def get_Vm(self): ''' Finds the variable that is most likely to be the membrane potential. ''' if self._Vm: return self._Vm vm_names = ['v', 'V', 'vm', 'Vm'] guesses = [var for var in self._diffeq_names if var in vm_names] if len(guesses) == 1: # Unambiguous return guesses[0] else: # Ambiguous or not found return None def clean_namespace(self): ''' Removes all variable names from namespaces ''' all_variables = self._eq_names + self._diffeq_names + self._alias.keys() + ['t'] for name in self._namespace: for var in all_variables: if var in self._namespace[name]: log_warn('brian.equations', 'Equation variable ' + var + ' also exists in the namespace') del self._namespace[name][var] def compile_functions(self, freeze=False): """ Compile all functions defined as strings. If freeze is True, all external parameters and units are replaced by their value. ALL FUNCTIONS MUST HAVE STRINGS. """ all_variables = self._eq_names + self._diffeq_names + self._alias.keys() + ['t'] # Check if freezable freeze = freeze and all([optimiser.freeze(expr, all_variables, self._namespace[name])\ for name, expr in self._string.iteritems()]) self._frozen = freeze # Compile strings to functions for name, expr in self._string.iteritems(): namespace = self._namespace[name] # name space of the function # Find variables vars = [var for var in get_identifiers(expr) if var in all_variables] if freeze: expr = optimiser.freeze(expr, all_variables, namespace) #self._string[name]=expr # should we? #namespace={} s = "lambda " + ','.join(vars) + ":" + expr self._function[name] = eval(s, namespace) def check_units(self): ''' Checks the units of the differential equations, using the units of x. dx_i/dt must have units of x_i / time. ''' self.set_eq_order() # Better: replace xi in the string, or in the namespace try: for var in self._eq_names: f = self._function[var] old_func_globals = copy.copy(f.func_globals) f.func_globals['xi'] = 0 * second ** -.5 # Noise f.func_globals.update(namespace_replace_quantity_with_pure(f.func_globals)) units = namespace_replace_quantity_with_pure(self._units) self.apply(var, units) + self._units[var] # Check that the two terms have the same dimension f.func_globals.update(old_func_globals) for var in self._diffeq_names: f = self._function[var] old_func_globals = copy.copy(f.func_globals) f.func_globals['xi'] = 0 * second ** -.5 # Noise f.func_globals.update(namespace_replace_quantity_with_pure(f.func_globals)) units = namespace_replace_quantity_with_pure(self._units) self.apply(var, units) + (self._units[var] / second) # Check that the two terms have the same dimension f.func_globals.update(old_func_globals) except DimensionMismatchError, inst: raise DimensionMismatchError("The differential equation of " + var + " is not homogeneous", *inst._dims) except: warnings.warn("Unexpected exception in checking units of " + var) raise def set_eq_order(self): ''' Computes the internal depency graph of static variables and deduces the update order. Sets the list of dependencies of dynamic variables on static variables. This is called by check_units() ''' if len(self._eq_names) > 0: # Internal dependency dictionary dependency = {} for key in self._eq_names: f = self._function[key] dependency[key] = [var for var in f.func_code.co_varnames if var in self._eq_names] # Sets the order staticvars_list = [] no_dep = None while (len(staticvars_list) < len(self._eq_names)) and (no_dep != []): no_dep = [key for key, value in dependency.iteritems() if value == []] staticvars_list += no_dep # Clear dependency list for key in no_dep: del dependency[key] for key, value in dependency.iteritems(): dependency[key] = [var for var in value if not(var in staticvars_list)] if no_dep == []: # The dependency graph has cycles! raise ReferenceError, "The static variables are referring to each other" else: staticvars_list = [] # Calculate dependencies on static variables self._dependencies = {} for key in staticvars_list: self._dependencies[key] = [] for var in self._function[key].func_code.co_varnames: if var in self._eq_names: self._dependencies[key] += [var] + self._dependencies[var] for key in self._diffeq_names: f = self._function[key] self._dependencies[key] = [] for var in f.func_code.co_varnames: if var in self._eq_names: self._dependencies[key] += [var] + self._dependencies[var] # Sort the dependency lists for key in self._dependencies: staticdep = [(staticvars_list.index(var), var) for var in self._dependencies[key]] staticdep.sort() self._dependencies[key] = [x[1] for x in staticdep] # Update _eq self._eq_names = staticvars_list def substitute_eq(self, name=None): """ Replaces the static variable 'name' by its value in differential equations. If None: substitute all static variables. """ if name is None: for var in self._eq_names[-1::-1]: # reverse order self.substitute_eq(var) else: self.add_prefix_namespace(name) #print name for var in self._diffeq_names_nonzero: # String self._string[var] = re.sub("\\b" + name + "\\b", '(' + self._string[name] + ')', self._string[var]) # Namespace self._namespace[var].update(self._namespace[name]) #print self def add_prefix_namespace(self, name): """ Make the variables in the namespace associated to variable name specific to that variable by inserting the prefix name_. """ vars = self._namespace[name].keys() untransformed_funcs = set(getattr(unitsafefunctions, v) for v in unitsafefunctions.quantity_versions) untransformed_funcs.update(set([numpy.clip])) for var in vars: v = self._namespace[name][var] addprefix = True if isinstance(v, numpy.ufunc): addprefix = False try: if v in untransformed_funcs: addprefix = False except TypeError: #unhashable types pass if addprefix: # String self._string[name] = re.sub("\\b" + var + "\\b", name + '_' + var, self._string[name]) # Namespace self._namespace[name][name + '_' + var] = self._namespace[name][var] del self._namespace[name][var] def free_variables(self): """ Returns the list of free variables (i.e., which are not defined within the equation string). """ all_variables = self._eq_names + self._diffeq_names + self._alias.keys() + ['t'] free_vars = [] for expr in self._string.itervalues(): free_vars += [name for name in get_identifiers(expr) if name not in all_variables] return list(set(free_vars)) """ ----------------------------------------------------------------------- CALCULATING RHS OF DIFF EQUATIONS ----------------------------------------------------------------------- """ def apply(self, state, vardict): ''' Calculates self._function[state] with arguments in vardict and static variables. The dictionary is filled with the required static variables. ''' f = self._function[state] # Calculate static variables for var in self._dependencies[state]: # could add something like: if var not in vardict: this would allow you to override the dependencies if you wanted to - worth doing? vardict[var] = call_with_dict(self._function[var], vardict) return f(*[vardict[var] for var in f.func_code.co_varnames]) """ ----------------------------------------------------------------------- EQUATION INSPECTION ----------------------------------------------------------------------- """ def is_stochastic(self, var=None): ''' Returns True if the equation for var is stochastic, or if all equations are stochastic (var=None). ''' if var: return 'xi' in get_identifiers(self._string[var]) else: return any([self.is_stochastic(name) for name in self._diffeq_names_nonzero]) def is_time_dependent(self, var=None): ''' Returns True if the equation for var is time dependent, or if all equations are time dependent (var=None). ''' if var: return 't' in get_identifiers(self._string[var]) else: return any([self.is_time_dependent(name) for name in self._diffeq_names_nonzero]) # return any([self.is_time_dependent(name) for name in self._diffeq_names_nonzero+self._eq_names]) def is_linear(self): ''' Returns True if all equations are linear. If the equations are time dependent, then returns False. If the equations depend on external functions, then returns False. ''' if self.is_time_dependent(): return False for f in self._namespace.iterkeys(): if any([type(key) == types.FunctionType for key in self._namespace[f].itervalues()]): return False return all([is_affine(f) for f in self._function.itervalues()]) def is_conditionally_linear(self): ''' Returns True if the differential equations are linear with respect to the state variable. ''' # Equations have to be prepared for it to work. for var in self._diffeq_names: S = self._units.copy() S[var] = AffineFunction() try: self.apply(var, S) except: return False return True """ ----------------------------------------------------------------------- NUMERICAL INTEGRATION (to be replaced by code generation) ----------------------------------------------------------------------- """ def forward_euler(self, S, dt): ''' Updates the value of the state variables in dictionary S with the forward Euler algorithm over step dt. ''' # Calculate all static variables (or do that after?) #for var in self._eq_names: # S[var]=call_with_dict(self._function[var],S) # Calculate derivatives buffer = {} for varname in self._diffeq_names_nonzero: f = self._function[varname] buffer[varname] = f(*[S[var] for var in f.func_code.co_varnames]) # Update variables for var in self._diffeq_names_nonzero: S[var] += dt * buffer[var] def forward_euler_code_string(self): ''' Generates Python code for a forward Euler step. ''' # TODO: check if it can really be frozen # TODO: change /a to *(1/a) with precalculation (use parser) all_variables = self._eq_names + self._diffeq_names + self._alias.keys() + ['t'] # nonzero? insert dt? vars_tmp = [name + '__tmp' for name in self._diffeq_names] lines = ','.join(self._diffeq_names) + '=P._S\n' lines += ','.join(vars_tmp) + '=P._dS\n' for name in self._diffeq_names_nonzero: namespace = self._namespace[name] expr = optimiser.freeze(self._string[name], all_variables, namespace) lines += name + '__tmp[:]=' + expr + '\n' lines += 'P._S+=dt*P._dS\n' #print lines return lines # Return a function f(P) or a namespace (exec code in namespace) # 1st option: include directly in neurongroup._state_updater (good?) def forward_euler_code(self): ''' Generates Python code for a forward Euler step. ''' # TODO: check if it can really be frozen # TODO: change /a to *(1/a) with precalculation (use parser) all_variables = self._eq_names + self._diffeq_names + self._alias.keys() + ['t'] # nonzero? insert dt? vars_tmp = [name + '__tmp' for name in self._diffeq_names] lines = ','.join(self._diffeq_names) + '=P._S\n' lines += ','.join(vars_tmp) + '=P._dS\n' for name in self._diffeq_names_nonzero: namespace = self._namespace[name] expr = optimiser.freeze(self._string[name], all_variables, namespace) lines += name + '__tmp[:]=' + expr + '\n' lines += 'P._S+=dt*P._dS\n' #print lines return compile(lines, 'Euler update code', 'exec') # Return a function f(P) or a namespace (exec code in namespace) # 1st option: include directly in neurongroup._state_updater (good?) def Runge_Kutta2(self, S, dt): ''' Updates the value of the state variables in dictionary S with the 2nd order Runge-Kutta algorithm over step dt (midpoint). ''' # Calculate all static variables (or do that after?) #for var in self._eq_names: # S[var]=call_with_dict(self._function[var],S) # Calculate derivatives buffer = {} S_half = S.copy() # Half a step for varname in self._diffeq_names_nonzero: f = self._function[varname] buffer[varname] = f(*[S[var] for var in f.func_code.co_varnames]) # Update variables for var in self._diffeq_names_nonzero: S_half[var] = S[var] + .5 * dt * buffer[var] # Whole step for varname in self._diffeq_names_nonzero: f = self._function[varname] buffer[varname] = f(*[S_half[var] for var in f.func_code.co_varnames]) # Update variables for var in self._diffeq_names_nonzero: S[var] += dt * buffer[var] def exponential_euler(self, S, dt): ''' Updates the value of the state variables in dictionary S with an exponential Euler algorithm over step dt. Test with is_conditionally_linear first. Same as default integration method in Genesis. Close to the implicit Euler method in Neuron. ''' # Calculate all static variables (BAD: INSERT IT BELOW) #for var in self._eq_names: # S[var]=call_with_dict(self._function[var],S) n = len(S[self._diffeq_names_nonzero[0]]) # Calculate the coefficients of the affine function Z = zeros(n) O = ones(n) A = {} B = {} for varname in self._diffeq_names_nonzero: f = self._function[varname] oldval = S[varname] S[varname] = Z B[varname] = f(*[S[var] for var in f.func_code.co_varnames]).copy() # important if compiled S[varname] = O A[varname] = f(*[S[var] for var in f.func_code.co_varnames]) - B[varname] B[varname] /= A[varname] S[varname] = oldval # Integrate for varname in self._diffeq_names_nonzero: f = self._function[varname] if self._useweave: Bx = B[varname] Ax = A[varname] Sx = S[varname] # Compilation with blitz: we need an approximation because exp is not understood #weave.blitz('Sx[:]=-Bx+(Sx+Bx)*(1.+Ax*dt*(1.+.5*Ax*dt))',check_size=0) code = """ for(int k=0;k<=]?=', line) # lines of the form w = ..., w *= ..., etc. if m: num_persynapse += 1 per_synapse_lines.append(line) else: num_perneuron += 1 if num_persynapse!=0 and not reordering_warning: log_warn('brian.experimental.cstdp', 'STDP operations are being re-ordered, results may be wrong.') reordering_warning = True per_neuron_lines.append(line) return per_neuron_lines, per_synapse_lines per_neuron_pre, per_synapse_pre = splitcode(pre) per_neuron_post, per_synapse_post = splitcode(post) all_vars = vars_pre + vars_post + ['w'] per_neuron_pre = [c_single_statement(freeze(line, all_vars, pre_namespace)) for line in per_neuron_pre] per_neuron_post = [c_single_statement(freeze(line, all_vars, post_namespace)) for line in per_neuron_post] per_synapse_pre = [c_single_statement(freeze(line, all_vars, pre_namespace)) for line in per_synapse_pre] per_synapse_post = [c_single_statement(freeze(line, all_vars, post_namespace)) for line in per_synapse_post] per_neuron_pre = '\n'.join(per_neuron_pre) per_neuron_post = '\n'.join(per_neuron_post) per_synapse_pre = '\n'.join(per_synapse_pre) per_synapse_post = '\n'.join(per_synapse_post) # Neuron groups G_pre = NeuronGroup(len(C.source), model=sep_pre, clock=self.clock) G_post = NeuronGroup(len(C.target), model=sep_post, clock=self.clock) G_pre._S[:] = 0 G_post._S[:] = 0 self.pre_group = G_pre self.post_group = G_post var_group = {} for i, v in enumerate(vars_pre): var_group[v] = G_pre for i, v in enumerate(vars_post): var_group[v] = G_post self.var_group = var_group self.contained_objects += [G_pre, G_post] vars_pre_ind = {} for i, var in enumerate(vars_pre): vars_pre_ind[var] = i vars_post_ind = {} for i, var in enumerate(vars_post): vars_post_ind[var] = i prevars_dict = dict((k, G_pre.state(k)) for k in vars_pre) postvars_dict = dict((k, G_post.state(k)) for k in vars_post) clipcode = '' if isfinite(wmin): clipcode += 'if(w<%wmin%) w = %wmin%;\n'.replace('%wmin%', repr(float(wmin))) if isfinite(wmax): clipcode += 'if(w>%wmax%) w = %wmax%;\n'.replace('%wmax%', repr(float(wmax))) if not isinstance(C, DelayConnection): precode = iterate_over_spikes('_j', '_spikes', (load_required_variables('_j', prevars_dict), transform_code(per_neuron_pre), iterate_over_row('_k', 'w', C.W, '_j', (load_required_variables('_k', postvars_dict), transform_code(per_synapse_pre), ConnectionCode(clipcode))))) postcode = iterate_over_spikes('_j', '_spikes', (load_required_variables('_j', postvars_dict), transform_code(per_neuron_post), iterate_over_col('_i', 'w', C.W, '_j', (load_required_variables('_i', prevars_dict), transform_code(per_synapse_post), ConnectionCode(clipcode))))) log_debug('brian.experimental.c_stdp', 'CSTDP Pre code:\n' + str(precode)) log_debug('brian.experimental.c_stdp', 'CSTDP Post code:\n' + str(postcode)) connection_delay = C.delay * C.source.clock.dt if (delay_pre is None) and (delay_post is None): # same delays as the Connnection C delay_pre = connection_delay delay_post = 0 * ms elif delay_pre is None: delay_pre = connection_delay - delay_post if delay_pre < 0 * ms: raise AttributeError, "Postsynaptic delay is too large" elif delay_post is None: delay_post = connection_delay - delay_pre if delay_post < 0 * ms: raise AttributeError, "Postsynaptic delay is too large" # create forward and backward Connection objects or SpikeMonitor objects pre_updater = SpikeMonitor(C.source, function=precode, delay=delay_pre) post_updater = SpikeMonitor(C.target, function=postcode, delay=delay_post) updaters = [pre_updater, post_updater] self.contained_objects += [pre_updater, post_updater] else: if delay_pre is not None or delay_post is not None: raise ValueError("Must use delay_pre=delay_post=None for the moment.") max_delay = C._max_delay * C.target.clock.dt # Ensure that the source and target neuron spikes are kept for at least the # DelayConnection's maximum delay C.source.set_max_delay(max_delay) C.target.set_max_delay(max_delay) self.G_pre_monitors = {} self.G_post_monitors = {} self.G_pre_monitors.update(((var, RecentStateMonitor(G_pre, vars_pre_ind[var], duration=(C._max_delay + 1) * C.target.clock.dt, clock=G_pre.clock)) for var in vars_pre)) self.G_post_monitors.update(((var, RecentStateMonitor(G_post, vars_post_ind[var], duration=(C._max_delay + 1) * C.target.clock.dt, clock=G_post.clock)) for var in vars_post)) self.contained_objects += self.G_pre_monitors.values() self.contained_objects += self.G_post_monitors.values() prevars_dict_delayed = dict((k, self.G_pre_monitors[k]) for k in prevars_dict.keys()) postvars_dict_delayed = dict((k, self.G_post_monitors[k]) for k in postvars_dict.keys()) precode_immediate = iterate_over_spikes('_j', '_spikes', (load_required_variables('_j', prevars_dict), transform_code(per_neuron_pre))) precode_delayed = iterate_over_spikes('_j', '_spikes', iterate_over_row('_k', 'w', C.W, '_j', extravars={'_delay':C.delayvec}, code=( ConnectionCode('double _t_past = _max_delay-_delay;', vars={'_max_delay':float(max_delay)}), load_required_variables_pastvalue('_k', '_t_past', postvars_dict_delayed), transform_code(per_synapse_pre), ConnectionCode(clipcode)))) postcode = iterate_over_spikes('_j', '_spikes', (load_required_variables('_j', postvars_dict), transform_code(per_neuron_post), iterate_over_col('_i', 'w', C.W, '_j', extravars={'_delay':C.delayvec}, code=( load_required_variables_pastvalue('_i', '_delay', prevars_dict_delayed), transform_code(per_synapse_post), ConnectionCode(clipcode))))) log_debug('brian.experimental.c_stdp', 'CSTDP Pre code (immediate):\n' + str(precode_immediate)) log_debug('brian.experimental.c_stdp', 'CSTDP Pre code (delayed):\n' + str(precode_delayed)) log_debug('brian.experimental.c_stdp', 'CSTDP Post code:\n' + str(postcode)) pre_updater_immediate = SpikeMonitor(C.source, function=precode_immediate) pre_updater_delayed = SpikeMonitor(C.source, function=precode_delayed, delay=max_delay) post_updater = SpikeMonitor(C.target, function=postcode) updaters = [pre_updater_immediate, pre_updater_delayed, post_updater] self.contained_objects += updaters def __call__(self): pass def __getattr__(self, name): if name == 'var_group': # this seems mad - the reason is that getattr is only called if the thing hasn't # been found using the standard methods of finding attributes, which for var_index # should have worked, this is important because the next line looks for var_index # and if we haven't got a var_index we don't want to get stuck in an infinite # loop raise AttributeError if not hasattr(self, 'var_group'): # only provide lookup of variable names if we have some variable names, i.e. # if the var_index attribute exists raise AttributeError G = self.var_group[name] return G.state_(name) def __setattr__(self, name, val): if not hasattr(self, 'var_group') or name not in self.var_group: object.__setattr__(self, name, val) else: G = self.var_group[name] G.state_(name)[:] = val if __name__ == '__main__': from time import time log_level_debug() structure = 'dense' delay = False if not delay: delay = 0 * ms max_delay = 5 * ms N = 1000 taum = 10 * ms tau_pre = 20 * ms tau_post = tau_pre Ee = 0 * mV vt = -54 * mV vr = -60 * mV El = -74 * mV taue = 5 * ms F = 15 * Hz gmax = .01 dA_pre = .01 dA_post = -dA_pre * tau_pre / tau_post * 1.05 eqs_neurons = ''' dv/dt=(ge*(Ee-vr)+El-v)/taum : volt # the synaptic current is linearized dge/dt=-ge/taue : 1 ''' input = PoissonGroup(N, rates=F) neurons = NeuronGroup(1, model=eqs_neurons, threshold=vt, reset=vr) synapses = Connection(input, neurons, 'ge', weight=rand(len(input), len(neurons)) * gmax, structure=structure, delay=delay, max_delay=max_delay) neurons.v = vr #stdp=ExponentialSTDP(synapses,tau_pre,tau_post,dA_pre,dA_post,wmax=gmax) ## Explicit STDP rule eqs_stdp = ''' dA_pre/dt=-A_pre/tau_pre : 1 dA_post/dt=-A_post/tau_post : 1 ''' dA_post *= gmax dA_pre *= gmax stdp = CSTDP(synapses, eqs=eqs_stdp, pre='A_pre+=dA_pre;w+=A_post', post='A_post+=dA_post;w+=A_pre', wmax=gmax) rate = PopulationRateMonitor(neurons) start_time = time() run(100 * second, report='text') print "Simulation time:", time() - start_time subplot(311) plot(rate.times / second, rate.smooth_rate(100 * ms)) subplot(312) plot(synapses.W.todense() / gmax, '.') subplot(313) hist(synapses.W.todense() / gmax, 20) show() brian-1.4.3/brian/experimental/codegen/000077500000000000000000000000001266062647100200205ustar00rootroot00000000000000brian-1.4.3/brian/experimental/codegen/__init__.py000066400000000000000000000016511266062647100221340ustar00rootroot00000000000000from codegen import * from integration_schemes import * from codegen_python import * from codegen_c import * from codegen_gpu import * if __name__ == '__main__': if True: from brian import * eqs = Equations(''' dV/dt = -W*V/(10*second) : volt dW/dt = -V**2/(1*second) : volt ''') scheme = exp_euler_scheme print 'Equations' print '=========' print eqs print 'Scheme' print '======' for block_specifier, block_code in scheme: print block_specifier print block_code print 'Python code' print '===========' print PythonCodeGenerator().generate(eqs, scheme) print 'C code' print '======' print CCodeGenerator().generate(eqs, scheme) print 'GPU code' print '======' print GPUCodeGenerator().generate(eqs, scheme) brian-1.4.3/brian/experimental/codegen/c_support_code.py000066400000000000000000000002561266062647100234050ustar00rootroot00000000000000c_support_code = ''' double clip(double x, double low, double high) { if(xhigh) return high; return x; } #define inf INFINITY ''' brian-1.4.3/brian/experimental/codegen/codegen.py000066400000000000000000000224521266062647100220030ustar00rootroot00000000000000from ...optimiser import freeze from string import Template import re from rewriting import * __all__ = ['CodeGenerator'] class CodeGenerator(object): ''' CodeGenerator base class for generating code in a variety of languages from mathematical expressions Base classes can define their own behaviour, but the following scheme is expected to be fairly standard. The initialiser takes at least one argument ``sympy_rewrite``, which defaults to ``True``, which specifies whether or not expressions should be simplified by passing them through sympy, so that e.g. ``V/(10*0.1)`` gets simplified to ``V*1.0`` (but not to ``V`` for some reason). There are also the following methods:: .. method:: generate(eqs, scheme) Returns code in the target language for the set of equations ``eqs`` according to the integration scheme ``scheme``. The following schemes are included by default: * ``euler_scheme`` * ``rk2_scheme`` * ``exp_euler_scheme`` This method may also call the :meth:`initialisation` and :meth:`finalisation` methods for code to include at the start and end. By default, it will call the :meth:`scheme` method to fill in the code in between. .. method:: initialisation(eqs) Returns initialisation code. .. method:: finalisation(eqs) Returns finalisation code. .. method:: scheme(eqs, scheme) Returns code for the given set of equations integrated according to the given scheme. See below for an explanation of integration schemes. In the process of generating code from the schemes, this method may call any of the following methods: :meth:`single_statement` to convert a single statement into code; :meth:`single_expr` to convert a single expression into code; :meth:`substitute` to substitute different values for specific variable names in given expressions; :meth:`vartype` to get the variable type specifier if necessary for that language. .. method:: single_statement(expr) Convert the given statement into code. By default, this looks for statements of the form ``A = B``, ``A += B``, etc. and applies the method :meth:`single_expr` to ``B``. Classes deriving from the base class typically only need to write a :meth:`single_expr` method and not a :meth:`single_statement` one. .. method:: single_expr(expr) Convert the given expression into code. For example, for Python you can do nothing, but for C, for example the expression ``A**B`` should be converted to ``pow(A, B)``, and this is done using sympy replacement. By default, this method will simplify the expression using sympy if the code generator was initialised with ``sympy_rewrite=True``. .. method:: substitute(var_expr, substitutions) Makes the given substitutions into the expression. ``substitutions`` should be a dictionary with keys the names of the variables to substitute, and values the value to substitute for that expression. By default these values are passed to the :meth:`single_substitute` method to check if there are any language specific things that need to be done to it, for example something substituting ``1`` should be replaced by ``ones(num_neurons)`` for Python which is vectorised. Typically this method shouldn't need to be rewritten for derived classes, whereas :meth:`single_substitute` might. .. method:: single_substitute(s) Replaces ``s``, a substitution value from :meth:`substitute`, with a language specific version if necessary. Should return a string. **Schemes** A scheme consists of a sequence of pairs ``(block_specifier, block_code)``. The ``block_specifier`` is currently unused, the schemes available at the moment all use ``('foreachvar', 'all')`` in this space - other specifiers are possible, e.g. ``'all_nonzero'`` instead of ``'all'``. ``block_code`` is a multi-line template string expressing that stage of the scheme (templating explained below). For each specifier, code pair, a block of code is generated in that order - multiple pairs can be used for separating stages in an integration scheme (i.e. do something for all variables, then do something else for all variables, etc.). Templating has two features, the first is standard Python templating replacements, e.g. the template string ``'${var}__buf'`` would be replaced by ``'x__buf'`` where the Python variable ``var='x'``. The available variables are: ``var`` The current variable in the loop over each variable. ``var_expr`` The right hand side of the differential equation, f(x) for the equation dx/dt=f(x). ``vartype`` The data type (or ``''`` if none). Should be used at the start of the statement if the statement is declaring a new variable. The other feature of templating is that expressions of the form ``'@func(args)'`` will call the method ``func`` of the code generator with the given ``args`` and be replaced by that expression. Typically, this is used for the :meth:`substitute` method. Example scheme:: rk2_scheme = [ (('foreachvar', 'all'), """ $vartype ${var}__buf = $var_expr $vartype ${var}__half = $var+dt*${var}__buf """), (('foreachvar', 'all'), """ ${var}__buf = @substitute(var_expr, {var:var+'__buf'}) $var += dt*${var}__buf """) ] ''' def __init__(self, sympy_rewrite=True): self.sympy_rewrite = sympy_rewrite def single_statement(self, expr): m = re.search(r'[^><=]=', expr) if m: return expr[:m.end()] + ' ' + self.single_expr(expr[m.end():]) return expr def single_expr(self, expr): if self.sympy_rewrite is not False: if self.sympy_rewrite is True: #rewriters = [floatify_numbers] rewriters = [] else: rewriters = self.sympy_rewrite return sympy_rewrite(expr.strip(), rewriters) return expr.strip() def vartype(self): return '' def initialisation(self, eqs): return '' def finalisation(self, eqs): return '' def generate(self, eqs, scheme): code = self.initialisation(eqs) code += self.scheme(eqs, scheme) code += self.finalisation(eqs) return code def scheme(self, eqs, scheme): code = '' all_variables = eqs._eq_names + eqs._diffeq_names + eqs._alias.keys() + ['t'] vartype = self.vartype() for block_specifier, block_code in scheme: # for the moment, processing of block_specifier is very crude if block_specifier == ('foreachvar', 'all'): vars_to_use = eqs._diffeq_names elif block_specifier == ('foreachvar', 'nonzero'): vars_to_use = eqs._diffeq_names_nonzero for line in block_code.split('\n'): line = line.strip() if line: origline = line for var in vars_to_use: vars = eqs._diffeq_names line = origline namespace = eqs._namespace[var] var_expr = freeze(eqs._string[var], all_variables, namespace) while 1: m = re.search(r'\@(\w+)\(', line) if not m: break methname = m.group(1) start, end = m.span() numopen = 1 for i in xrange(end, len(line)): if line[i] == '(': numopen += 1 if line[i] == ')': numopen -= 1 if numopen == 0: break if numopen != 0: raise SyntaxError('Parentheses unmatching.') args = line[start + 1:i + 1] #print args exec 'line = line[:start]+self.' + args + '+line[i+1:]' substitutions = {'vartype':vartype, 'var':var, 'var_expr':var_expr} t = Template(line) code += self.single_statement(t.substitute(**substitutions)) + '\n' return code def single_substitute(self, s): return str(s) def substitute(self, var_expr, substitutions): for var, replace_var in substitutions.iteritems(): var_expr = re.sub(r'\b' + var + r'\b', self.single_substitute(replace_var), var_expr) return var_expr brian-1.4.3/brian/experimental/codegen/codegen_c.py000066400000000000000000000032631266062647100223040ustar00rootroot00000000000000from codegen import * from rewriting import * __all__ = ['CCodeGenerator'] class CCodeGenerator(CodeGenerator): def __init__(self, dtype='double', sympy_rewrite=True, openmp=True): CodeGenerator.__init__(self, sympy_rewrite=sympy_rewrite) self._dtype = dtype self._openmp = openmp def vartype(self): return self._dtype def initialisation(self, eqs): vartype = self.vartype() code = '' for j, name in enumerate(eqs._diffeq_names): code += vartype + ' *' + name + '__Sbase2 = _S+' + str(j) + '*num_neurons;\n' for j, name in enumerate(eqs._diffeq_names): code += vartype + ' *' + name + '__Sbase = '+name + '__Sbase2;\n' return code def generate(self, eqs, scheme): vartype = self.vartype() code = self.initialisation(eqs) if self._openmp: code += '#pragma omp parallel for\n' code += 'for(int _i=0;_i<=]=', expr) if m: return expr[:m.end()] + ' ' + single_expr(expr[m.end():]) return expr def c_single_expr(expr): return rewrite_to_c_expression(single_expr(expr.strip())).strip() def c_single_statement(expr): return single_statement(expr, single_expr=c_single_expr) + ';' python_single_expr = single_expr python_single_statement = single_statement gpu_single_expr = c_single_expr gpu_single_statement = c_single_statement brian-1.4.3/brian/experimental/codegen/integration_schemes.py000066400000000000000000000021401266062647100244210ustar00rootroot00000000000000__all__ = ['euler_scheme', 'rk2_scheme', 'exp_euler_scheme'] euler_scheme = [ (('foreachvar', 'nonzero'), ''' $vartype ${var}__tmp = $var_expr '''), (('foreachvar', 'nonzero'), ''' $var += ${var}__tmp*dt ''') ] rk2_scheme = [ (('foreachvar', 'all'), ''' $vartype ${var}__buf = $var_expr $vartype ${var}__half = (.5*dt)*${var}__buf ${var}__half += $var '''), (('foreachvar', 'nonzero'), ''' ${var}__buf = @substitute(var_expr, dict((var, var+'__half') for var in vars)) $var += dt*${var}__buf ''') ] exp_euler_scheme = [ (('foreachvar', 'nonzero'), ''' $vartype ${var}__B = @substitute(var_expr, {var:0}) $vartype ${var}__A = @substitute(var_expr, {var:1}) ${var}__A -= ${var}__B ${var}__B /= ${var}__A ${var}__A *= dt '''), (('foreachvar', 'nonzero'), ''' $var += ${var}__B $var *= exp(${var}__A) $var -= ${var}__B ''') ] brian-1.4.3/brian/experimental/codegen/known_issues.txt000066400000000000000000000002721266062647100233110ustar00rootroot00000000000000KNOWN ISSUES (roughly in order of importance): TODO: Python functions can't be used in Equations (allow for specification of C code somehow? workaround with network operation?)brian-1.4.3/brian/experimental/codegen/reset.py000066400000000000000000000120311266062647100215110ustar00rootroot00000000000000import re from rewriting import * from ...inspection import namespace from ...optimiser import freeze from ...reset import Reset from ...equations import Equations from ...globalprefs import get_global_preference from ...log import log_warn from expressions import * try: import weave except ImportError: from scipy import weave from c_support_code import * __all__ = ['generate_c_reset', 'generate_python_reset', 'CReset', 'PythonReset'] def generate_c_reset(eqs, inputcode, vartype='double', level=0, ns=None, openmp=True): if ns is None: ns, unknowns = namespace(inputcode, level=level + 1, return_unknowns=True) all_variables = eqs._eq_names + eqs._diffeq_names + eqs._alias.keys() + ['t'] code = '' for j, name in enumerate(eqs._diffeq_names): code += vartype + ' *' + name + '__Sbase2 = _S+' + str(j) + '*num_neurons;\n' for j, name in enumerate(eqs._diffeq_names): code += vartype + ' *' + name + '__Sbase = '+name + '__Sbase2;\n' # for j, name in enumerate(eqs._diffeq_names): # code += vartype + ' *' + name + '__Sbase = _S+' + str(j) + '*_num_neurons;\n' if openmp: code += '#pragma omp parallel for\n' code += 'for(int _i=0;_i<_nspikes;_i++){\n' code += ' long _j = _spikes[_i];\n' for j, name in enumerate(eqs._diffeq_names): code += ' ' + vartype + ' &' + name + ' = ' + name + '__Sbase[_j];\n' for line in inputcode.split('\n'): line = line.strip() if line: # handle multiple statements in one line statements = line.split(';') for statement in statements: statement = freeze(statement.strip(), all_variables, ns) statement = c_single_statement(statement) code += ' ' + line + '\n' code += '}\n' return code def generate_python_reset(eqs, inputcode, level=0, ns=None): if ns is None: ns, unknowns = namespace(inputcode, level=level + 1, return_unknowns=True) all_variables = eqs._eq_names + eqs._diffeq_names + eqs._alias.keys() + ['t', '_spikes'] code = '' for var in eqs._diffeq_names: inputcode = re.sub("\\b" + var + "\\b", var + '[_spikes]', inputcode) for line in inputcode.split('\n'): line = line.strip() if line: line = freeze(line, all_variables, ns) line = python_single_statement(line) code += line + '\n' return code class PythonReset(Reset): def __init__(self, inputcode, level=0): self._ns, unknowns = namespace(inputcode, level=level + 1, return_unknowns=True) self._inputcode = inputcode self._prepared = False def __call__(self, P): ns = self._ns if not self._prepared: vars = [var for var in P.var_index if isinstance(var, str)] eqs = P._eqs outputcode = generate_python_reset(eqs, self._inputcode, ns=ns) self._compiled_code = compile(outputcode, "PythonReset", "exec") for var in vars: ns[var] = P.state(var) self._prepared = True cc = self._compiled_code spikes = P.LS.lastspikes() ns['_spikes'] = spikes exec cc in ns class CReset(Reset): def __init__(self, inputcode, level=0): self._ns, unknowns = namespace(inputcode, level=level + 1, return_unknowns=True) self._inputcode = inputcode self._prepared = False self._weave_compiler = get_global_preference('weavecompiler') self._extra_compile_args = ['-O3'] if self._weave_compiler == 'gcc': self._extra_compile_args += get_global_preference('gcc_options') # ['-march=native', '-ffast-math'] self._openmp = get_global_preference('openmp') if self._openmp: self._extra_compile_args.append('-fopenmp') self._extra_link_args = ['-fopenmp'] else: self._extra_link_args = [] def __call__(self, P): if not self._prepared: vars = [var for var in P.var_index if isinstance(var, str)] eqs = P._eqs self._outputcode = generate_c_reset(eqs, self._inputcode, ns=self._ns) _spikes = P.LS.lastspikes() dt = P.clock._dt t = P.clock._t _nspikes = len(_spikes) _S = P._S _num_neurons = len(P) try: weave.inline(self._outputcode, ['_S', '_nspikes', 'dt', 't', '_spikes', '_num_neurons'], c_support_code=c_support_code, compiler=self._weave_compiler, extra_compile_args=self._extra_compile_args, extra_link_args=self._extra_link_args) except: log_warn('brian.experimental.codegen.reset', 'C compilation failed, falling back on Python.') self.__class__ = PythonReset self._prepared = False return self.__call__(P) brian-1.4.3/brian/experimental/codegen/rewriting.py000066400000000000000000000107721266062647100224130ustar00rootroot00000000000000try: from sympy.printing.ccode import CCodePrinter from sympy.printing.precedence import precedence import sympy except ImportError: sympy = None CCodePrinter = object from ...optimiser import symbolic_eval def boolean_printer(origop, newop, start=''): def f(self, expr): PREC = precedence(expr) return start + newop.join(self.parenthesize(a, PREC) for a in expr.args) f.__name__ = '_print_' + origop return f class NewCCodePrinter(CCodePrinter): _print_And = boolean_printer('And', '&&') _print_Or = boolean_printer('Or', '||') _print_Not = boolean_printer('Not', '', '!') __all__ = ['rewrite_to_c_expression', 'sympy_rewrite', 'rewrite_pow', 'floatify_numbers'] if sympy is not None: pow = sympy.Function('pow') else: pow = None def rewrite_pow(e): if not len(e.args): return e newargs = tuple(rewrite_pow(a) for a in e.args) if isinstance(e, sympy.Pow) and e.args[1] != -1: return pow(*newargs) else: return e.new(*newargs) def floatify_numbers(e): if not len(e.args): if e.is_number: return sympy.Number(float(e)) return e newargs = tuple(floatify_numbers(a) for a in e.args) return e.new(*newargs) def make_sympy_expressions(eqs): exprs = {} for name in eqs._diffeq_names + eqs._eq_names: exprs[name] = symbolic_eval(eqs._string[name]) return exprs def sympy_rewrite(s, rewriters=None): if rewriters is not None: if callable(rewriters): rewriters = [rewriters] else: rewriters = [] expr = symbolic_eval(s) if not hasattr(expr, 'args'): return str(expr) for f in rewriters: expr = f(expr) return str(expr) def rewrite_to_c_expression(s): if sympy is None: raise ImportError('sympy package required for code generation.') e = symbolic_eval(s) return NewCCodePrinter().doprint(e) def generate_c_expressions(eqs): exprs = make_sympy_expressions(eqs) cexprs = {} for name, expr in exprs.iteritems(): cexprs[name] = str(rewrite_pow(expr)) return cexprs if __name__ == '__main__': from brian import * if True: s = '-V**2/(10*0.001)' print sympy_rewrite(s) print sympy_rewrite(s, rewrite_pow) print sympy_rewrite(s, floatify_numbers) print sympy_rewrite(s, [rewrite_pow, floatify_numbers]) if False: area = 20000 * umetre ** 2 Cm = (1 * ufarad * cm ** -2) * area gl = (5e-5 * siemens * cm ** -2) * area El = -60 * mV EK = -90 * mV ENa = 50 * mV g_na = (100 * msiemens * cm ** -2) * area g_kd = (30 * msiemens * cm ** -2) * area VT = -63 * mV # Time constants taue = 5 * ms taui = 10 * ms # Reversal potentials Ee = 0 * mV Ei = -80 * mV we = 6 * nS # excitatory synaptic weight (voltage) wi = 67 * nS # inhibitory synaptic weight # The model eqs = Equations(''' dv/dt = (gl*(El-v)+ge*(Ee-v)+gi*(Ei-v)-g_na*(m*m*m)*h*(v-ENa)-g_kd*(n*n*n*n)*(v-EK))/Cm : volt dm/dt = alpham*(1-m)-betam*m : 1 dn/dt = alphan*(1-n)-betan*n : 1 dh/dt = alphah*(1-h)-betah*h : 1 dge/dt = -ge*(1./taue) : siemens dgi/dt = -gi*(1./taui) : siemens alpham = 0.32*(mV**-1)*(13*mV-v+VT)/(exp((13*mV-v+VT)/(4*mV))-1.)/ms : Hz betam = 0.28*(mV**-1)*(v-VT-40*mV)/(exp((v-VT-40*mV)/(5*mV))-1)/ms : Hz alphah = 0.128*exp((17*mV-v+VT)/(18*mV))/ms : Hz betah = 4./(1+exp((40*mV-v+VT)/(5*mV)))/ms : Hz alphan = 0.032*(mV**-1)*(15*mV-v+VT)/(exp((15*mV-v+VT)/(5*mV))-1.)/ms : Hz betan = .5*exp((10*mV-v+VT)/(40*mV))/ms : Hz ''') cexprs = generate_c_expressions(eqs) for k, v in cexprs.iteritems(): print k, ':', v if False: taum = 20 * msecond taue = 5 * msecond taui = 10 * msecond Ee = (0. + 60.) * mvolt Ei = (-80. + 60.) * mvolt eqs = Equations(''' dv/dt = (-v+ge*(Ee-v)+gi*(Ei-v))*(1./taum) : volt dge/dt = -ge*(1./taue) : 1 dgi/dt = -gi*(1./taui) : 1 ''') cexprs = generate_c_expressions(eqs) for k, v in cexprs.iteritems(): print k, ':', v if False: x = sympy.Symbol('x') y = x ** 2 + 3 * x print y print rewrite_pow(y) brian-1.4.3/brian/experimental/codegen/stateupdaters.py000066400000000000000000000165521266062647100232730ustar00rootroot00000000000000from ...stateupdater import StateUpdater from ...globalprefs import get_global_preference from ...clock import guess_clock from ...log import log_debug, log_warn from codegen_c import * from codegen_python import * from integration_schemes import * import time try: import weave except ImportError: from scipy import weave import numpy, scipy import re from c_support_code import * try: import numexpr as numexpr except ImportError: numexpr = None __all__ = ['CStateUpdater', 'PythonStateUpdater'] class CStateUpdater(StateUpdater): def __init__(self, eqs, scheme, clock=None, freeze=False): self.clock = guess_clock(clock) self.eqs = eqs self.scheme = scheme self.freeze = freeze self._openmp = get_global_preference('openmp') self.code_c = CCodeGenerator(openmp=self._openmp).generate(eqs, scheme) log_debug('brian.experimental.codegen.stateupdaters', 'C state updater code:\n' + self.code_c) self._weave_compiler = get_global_preference('weavecompiler') self._extra_compile_args = ['-O3'] if self._weave_compiler == 'gcc': self._extra_compile_args += get_global_preference('gcc_options') # ['-march=native', '-ffast-math'] if self._openmp: self._extra_compile_args.append('-fopenmp') self._extra_link_args = ['-fopenmp'] else: self._extra_link_args = [] self.namespace = {} code_vars = re.findall(r'\b\w+\b', self.code_c) self._arrays_to_check = [] for varname in code_vars: if varname not in eqs._eq_names + eqs._diffeq_names + eqs._alias.keys() + ['t', 'dt', '_S', 'num_neurons']: # this is kind of a hack, but since we're going to be writing a new # and more sensible Equations module anyway, it can stand for name in eqs._namespace: if varname in eqs._namespace[name]: varval = eqs._namespace[name][varname] if isinstance(varval, numpy.ndarray): self.namespace[varname] = varval self._arrays_to_check.append((varname, varval)) self.code_c = re.sub(r'\b'+varname+r'\b', varname+'[_i]', self.code_c) break def __call__(self, P): if self._arrays_to_check is not None: N = len(P) for name, X in self._arrays_to_check: if len(X)!=N: raise ValueError('Array '+name+' has wrong size ('+str(len(X))+' instead of '+str(N)+')') self._arrays_to_check = None self.namespace['dt'] = P.clock._dt self.namespace['t'] = P.clock._t self.namespace['num_neurons'] = len(P) self.namespace['_S'] = P._S try: weave.inline(self.code_c, self.namespace.keys(),#['_S', 'num_neurons', 'dt', 't'], local_dict = self.namespace, support_code=c_support_code, compiler=self._weave_compiler, extra_compile_args=self._extra_compile_args, extra_link_args=self._extra_link_args) except: log_warn('brian.experimental.codegen.stateupdaters', 'C compilation failed, falling back on Python.') self.__class__ = PythonStateUpdater self.__init__(self.eqs, self.scheme, self.clock, self.freeze) self.__call__(P) class PythonStateUpdater(StateUpdater): def __init__(self, eqs, scheme, clock=None, freeze=False): eqs.prepare() self.clock = guess_clock(clock) self.code_python = PythonCodeGenerator().generate(eqs, scheme) self.compiled_code = compile(self.code_python, 'StateUpdater code', 'exec') if False and numexpr is not None: # This only improves things for large N, in which case Python speed # is close to C speed anyway, so less valuable newcode = '' for line in self.code_python.split('\n'): m = re.search(r'(\b\w*\b\s*[^><=]?=\s*)(.*)', line) # lines of the form w = ..., w *= ..., etc. if m: if '*' in m.group(2) or '+' in m.group(2) or '/' in m.group(2) or \ '-' in m.group(2) or '**' in m.group(2) or '(' in m.group(2): if '[' not in m.group(2): line = m.group(1) + "_numexpr.evaluate('" + m.group(2) + "')" newcode += line + '\n' self.code_python = newcode self.code_python = 'def _stateupdate(_S, dt, t, num_neurons):\n' + '\n'.join([' ' + line for line in self.code_python.split('\n')]) + '\n' self.namespace = {'_numexpr':numexpr} for varname in self.compiled_code.co_names: if varname not in eqs._eq_names + eqs._diffeq_names + eqs._alias.keys() + ['t', 'dt', '_S', 'num_neurons']: # this is kind of a hack, but since we're going to be writing a new # and more sensible Equations module anyway, it can stand for name in eqs._namespace: if varname in eqs._namespace[name]: self.namespace[varname] = eqs._namespace[name][varname] break if varname not in self.namespace: if hasattr(numpy, varname): self.namespace[varname] = getattr(numpy, varname) log_debug('brian.experimental.codegen.stateupdaters', 'Python state updater code:\n' + self.code_python) exec self.code_python in self.namespace self.state_update_func = self.namespace['_stateupdate'] def __call__(self, P): self.state_update_func(P._S, P.clock._dt, P.clock._t, len(P)) if __name__ == '__main__': from brian import * duration = 1 * second N = 10000 domonitor = False # duration = 100*ms # N = 10 # domonitor = True eqs = Equations(''' #dV/dt = -V*V/(10*ms) : 1 #dV/dt = cos(2*pi*t/(100*ms))/(10*ms) : 1 dV/dt = -W*V/(100*ms) : 1 dW/dt = -W/(100*ms) : 1 #dV/dt = cos(2*pi*t/(100*ms))/(10*ms) : 1 #dW/dt = cos(2*pi*t/(100*ms))/(10*ms) : 1 #dW2/dt = cos(2*pi*t/(100*ms))/(10*ms) : 1 #dV/dt = h/(10*ms) : 1 #h = -V*V : 1 ''') print eqs G = NeuronGroup(N, eqs, compile=True, freeze=True, implicit=True) print G._state_updater.__class__ # su = PythonStateUpdater(eqs, euler_scheme, clock=G.clock) # print 'Python code:' # print su.code_python su = CStateUpdater(eqs, exp_euler_scheme, clock=G.clock) print 'C++ loop code:' print su.code_c G.V = 1 G.W = 0.1 if domonitor: M = StateMonitor(G, 'V', record=True) S = copy(G._S) run(1 * ms) start = time.time() run(duration) print 'Original code:', (time.time() - start) * second if domonitor: M_V = M[0] reinit() G._S[:] = S G._state_updater = su run(1 * ms) start = time.time() run(duration) print 'New code:', (time.time() - start) * second if domonitor: M_V2 = M[0] if domonitor: print amax(abs(M_V - M_V2)) plot(M.times, M_V) plot(M.times, M_V2) show() brian-1.4.3/brian/experimental/codegen/test_hh.py000066400000000000000000000037641266062647100220420ustar00rootroot00000000000000from brian import * from brian.library.ionic_currents import * from brian.experimental.codegen import * import time try: import weave except ImportError: from scipy import weave N = 1000 record_and_plot = N == 1 El = 10.6 * mV EK = -12 * mV ENa = 120 * mV eqs = MembraneEquation(1 * uF) + leak_current(.3 * msiemens, El) eqs += K_current_HH(36 * msiemens, EK) + Na_current_HH(120 * msiemens, ENa) eqs += Current('I:amp') eqs.prepare() print eqs print '.............................' pycode = PythonCodeGenerator().generate(eqs, exp_euler_scheme) print pycode print '.............................' ccode = CCodeGenerator().generate(eqs, exp_euler_scheme) print ccode neuron = NeuronGroup(N, eqs, implicit=True, freeze=True) if record_and_plot: trace = StateMonitor(neuron, 'vm', record=True) neuron.I = 10 * uA _S_python = array(neuron._S) _S = array(neuron._S) ns = {'_S':_S_python, 'exp':exp, 'dt':defaultclock._dt} pycode_comp = compile(pycode, '', 'exec') start = time.time() run(100 * ms) print 'N:', N print 'Brian:', time.time() - start start = time.time() hand_trace_python = [] for T in xrange(int(100 * ms / defaultclock.dt)): exec pycode_comp in ns if record_and_plot: hand_trace_python.append(copy(_S_python[0])) print 'Codegen Python:', time.time() - start start = time.time() hand_trace_c = [] for T in xrange(int(100 * ms / defaultclock.dt)): dt = defaultclock._dt t = T * defaultclock._dt num_neurons = len(neuron) weave.inline(ccode, ['_S', 'num_neurons', 'dt', 't'], compiler='gcc', #type_converters=weave.converters.blitz, extra_compile_args=['-O3', '-march=native', '-ffast-math'])#O2 seems to be faster than O3 here if record_and_plot: hand_trace_c.append(copy(_S[0])) print 'Codegen C:', time.time() - start if record_and_plot: plot(trace[0]) plot(array(hand_trace_python)) plot(array(hand_trace_c)) show() brian-1.4.3/brian/experimental/codegen/test_reset.py000066400000000000000000000013301266062647100225500ustar00rootroot00000000000000from brian import * from brian.experimental.codegen.reset import * eqs = Equations(''' #dV/dt = -V/(100*second) : volt V : volt Vt : volt ''') dVt = 2 * mV reset = ''' V = 0*volt Vt += dVt ''' print '**** C CODE *****' print generate_c_reset(eqs, reset) print '**** PYTHON CODE ****' print generate_python_reset(eqs, reset) GP = NeuronGroup(10, eqs, threshold=1 * volt, reset=PythonReset(reset)) GC = NeuronGroup(10, eqs, threshold=1 * volt, reset=CReset(reset)) GP.V = 0.5 GP.Vt = 0.1 GP.V[[2, 4, 8]] = 2 GC.V = 0.5 GC.Vt = 0.1 GC.V[[2, 4, 8]] = 2 run(1 * ms) print 'GP.V =', GP.V print 'GP.Vt =', GP.Vt print 'GC.V =', GC.V print 'GC.Vt =', GC.Vt brian-1.4.3/brian/experimental/codegen/test_threshold.py000066400000000000000000000013671266062647100234340ustar00rootroot00000000000000from brian import * from brian.experimental.codegen.threshold import * eqs = Equations(''' V : volt Vt : volt ''') threshold = ''' (V>Vt)&(V>Vt) ''' print '**** C CODE *****' print generate_c_threshold(eqs, threshold) # print '**** PYTHON CODE ****' print generate_python_threshold(eqs, threshold) GP = NeuronGroup(10, eqs, threshold=PythonThreshold(threshold), reset=0 * volt) GC = NeuronGroup(10, eqs, threshold=CThreshold(threshold), reset=0 * volt) MP = SpikeMonitor(GP) MC = SpikeMonitor(GC) GP.V[[1, 2, 4, 8]] = 2 GP.Vt = 1 * volt GP.Vt[[2, 8]] = 3 GC.V[[1, 2, 4, 8]] = 2 GC.Vt = 1 * volt GC.Vt[[2, 8]] = 3 run(1 * ms) print GP.V print GP.Vt print MP.spikes print print GC.V print GC.Vt print MC.spikes brian-1.4.3/brian/experimental/codegen/threshold.py000066400000000000000000000111131266062647100223630ustar00rootroot00000000000000import re from rewriting import * from ...inspection import namespace from ...optimiser import freeze from ...threshold import Threshold from ...equations import Equations from ...globalprefs import get_global_preference from ...log import log_warn from expressions import * try: import weave except ImportError: from scipy import weave from c_support_code import * __all__ = ['generate_c_threshold', 'generate_python_threshold', 'CThreshold', 'PythonThreshold'] def generate_c_threshold(eqs, inputcode, vartype='double', level=0, ns=None): inputcode = inputcode.strip() if ns is None: ns, unknowns = namespace(inputcode, level=level + 1, return_unknowns=True) all_variables = eqs._eq_names + eqs._diffeq_names + eqs._alias.keys() + ['t'] code = 'int _numspikes = 0;\n' for j, name in enumerate(eqs._diffeq_names): code += vartype + ' *' + name + '__Sbase = _S+' + str(j) + '*_num_neurons;\n' code += 'for(int _i=0;_i<_num_neurons;_i++){\n' for j, name in enumerate(eqs._diffeq_names): code += ' ' + vartype + ' &' + name + ' = ' + name + '__Sbase[_i];\n' inputcode = freeze(inputcode.strip(), all_variables, ns) inputcode = c_single_expr(inputcode) code += ' if(' + inputcode + ')\n' code += ' _spikes[_numspikes++] = _i;\n' code += '}\n' code += 'return_val = _numspikes;\n' return code def generate_python_threshold(eqs, inputcode, level=0, ns=None): inputcode = inputcode.strip() if ns is None: ns, unknowns = namespace(inputcode, level=level + 1, return_unknowns=True) all_variables = eqs._eq_names + eqs._diffeq_names + eqs._alias.keys() + ['t'] inputcode = inputcode.strip() inputcode = freeze(inputcode, all_variables, ns) inputcode = python_single_expr(inputcode) return inputcode class PythonThreshold(Threshold): def __init__(self, inputcode, level=0): inputcode = inputcode.strip() self._ns, unknowns = namespace(inputcode, level=level + 1, return_unknowns=True) self._inputcode = inputcode self._prepared = False def __call__(self, P): if not self._prepared: ns = self._ns vars = [var for var in P.var_index if isinstance(var, str)] eqs = P._eqs outputcode = generate_python_threshold(eqs, self._inputcode, ns=ns) for var in vars: ns[var] = P.state(var) self._compiled_code = compile(outputcode, "PythonThreshold", "eval") self._prepared = True self._ns['t'] = P.clock._t return eval(self._compiled_code, self._ns).nonzero()[0] def __repr__(self): return 'PythonThreshold(%r)' % self._inputcode __str__ = __repr__ class CThreshold(Threshold): def __init__(self, inputcode, level=0): inputcode = inputcode.strip() self._ns, unknowns = namespace(inputcode, level=level + 1, return_unknowns=True) if 'rand' in self._ns or 'randn' in self._ns: raise NotImplementedError('rand and randn not supported in thresholds with C code generation.') self._inputcode = inputcode self._prepared = False self._weave_compiler = get_global_preference('weavecompiler') self._extra_compile_args = ['-O3'] if self._weave_compiler == 'gcc': self._extra_compile_args += get_global_preference('gcc_options') # ['-march=native', '-ffast-math'] def __call__(self, P): if not self._prepared: vars = [var for var in P.var_index if isinstance(var, str)] eqs = P._eqs self._outputcode = generate_c_threshold(eqs, self._inputcode, ns=self._ns) _spikes = P._spikesarray t = P.clock._t _S = P._S _num_neurons = len(P) try: _numspikes = weave.inline(self._outputcode, ['_S', 't', '_spikes', '_num_neurons'], support_code=c_support_code, compiler=self._weave_compiler, extra_compile_args=self._extra_compile_args) except: log_warn('brian.experimental.codegen.threshold', 'C compilation failed, falling back on Python.') self.__class__ = PythonThreshold self._prepared = False return self.__call__(P) return _spikes[0:_numspikes] def __repr__(self): return 'CThreshold(%r)' % self._inputcode __str__ = __repr__brian-1.4.3/brian/experimental/codegen2/000077500000000000000000000000001266062647100201025ustar00rootroot00000000000000brian-1.4.3/brian/experimental/codegen2/__init__.py000066400000000000000000000007071266062647100222170ustar00rootroot00000000000000from blocks import * from codeitems import * from codeobject import * from connection import * from dependencies import * from equations import * from expressions import * from formatting import * from gpu import * from integration import * from languages import * from makeintegrator import * from reset import * from resolution import * from statements import * from stateupdater import * from symbols import * from threshold import * brian-1.4.3/brian/experimental/codegen2/blocks.py000066400000000000000000000151611266062647100217350ustar00rootroot00000000000000''' ''' from brian import * from statements import * from dependencies import * from codeitems import * from languages import * from formatting import * __all__ = [ 'Block', 'ControlBlock', 'ForBlock', 'PythonForBlock', 'CForBlock', 'IfBlock', 'PythonIfBlock', 'CIfBlock', ] class Block(CodeItem): ''' Contains a list of :class:`CodeItem` objects which are considered to be executed in serial order. The list is passed as arguments to the init method, so if you want to pass a list you can initialise as:: block = Block(*items) ''' def __init__(self, *args): self.contents = list(args) # we only need to implement __iter__ because the default behaviour of # CodeItem will concatenate the elements contained within def __iter__(self): return iter(self.contents) class ControlBlock(Block): ''' Helper class used as the base for various control structures such as for loops, if statements. These are typically not language-invariant and should only be output in the resolution process by symbols (which know the language they are resolving to). Consists of strings ``start`` and ``end``, a list of ``contents`` (as for :class:`Block`), and explicit sets of ``dependencies`` and ``resolved`` (these are self-dependencies/resolved). The output code consists of the start string, the indented converted contents, and then the end string. For example, for a C for loop, we would have ``start='for(...){`` and ``end='}'``. ''' def __init__(self, start, end, contents, dependencies, resolved): self.selfdependencies = dependencies self.selfresolved = resolved self.start = start self.end = end if isinstance(contents, CodeItem): contents = [contents] self.contents = contents def convert_to(self, language, symbols={}, namespace={}): contentcode = Block.convert_to(self, language, symbols=symbols, namespace=namespace) s = self.start+'\n'+indent_string(contentcode)+'\n'+self.end return strip_empty_lines(s) class ForBlock(ControlBlock): ''' Simply a base class, does nothing. ''' pass class PythonForBlock(ForBlock): ''' A for loop in Python, the structure is:: for var in container: content Where ``var`` and ``container`` are strings, and ``content`` is a :class:`CodeItem` or list of items. Dependencies can be given explicitly, or by default they are ``Read(x)`` for each word ``x`` in ``container``. Resolved can be given explicitly, or by default it is ``set(var)``. ''' def __init__(self, var, container, content, dependencies=None, resolved=None): if dependencies is None: dependencies = set([Read(x) for x in get_identifiers(container)]) if resolved is None: resolved = set([var]) self.var = var self.container = container start = 'for {var} in {container}:'.format(var=var, container=container) end = '' ControlBlock.__init__(self, start, end, content, dependencies, resolved) class CForBlock(ForBlock): ''' A for loop in C, the structure is:: for(spec) { content } You specify a string ``var`` which is the variable the loop is iterating over, and a string ``spec`` should be of the form ``'int i=0; iVt Reset (for each neuron in _spikes): V = 0 Propagation (for each synapse): V += w*mod We then proceed to construct code to evaluate this for each of the required indices (neurons, spikes, synapses), vectorising in Python when possible, or inserting loops. This has several components: Code object =========== The Code object is responsible for storing and executing the finished code. It consists of a code string and a namespace, and the code is executed inside the namespace. There are PythonCode, CCode and GPUCode subclasses. The __call__ mechanism is used to execute the code, and the keyword arguments provided to __call__ are inserted into the namespace. Expressions and MathematicalStatement ===================================== An expression is a mathematical expression such as 'x**2+y', given in Python syntax. A statement is something like 'z = x**2+y' or 'z += 1'. We use the standard Python syntax with one addition, a new operator := which is used to define a new variable. We need this because in both Python and C, defining a variable is different to writing to it. When converting to C, we need to transform the expression into 'pow(x,2)+y' and this is handled by sympy (which also does some algebraic simplification). Similarly, when defining a variable in C we do something like 'double z = pow(x,2)+y'. CodeItem, Block, Statement ========================== Each of this is an item of code. A Statement is like a single line or unit of code, a CodeItem can be anything, and a Block is a sequence of several code items. Loops and if statements are types of blocks too. The key method that each of these share is convert_to to return a code string in different languages. Symbols and Dependencies ======================== The key element in the whole scheme is the idea of Symbols and Dependencies. To illustrate this, in the example of state update we have: _temp_V = -V/tau V += _temp_V*dt Here, the symbol V 'depends on' the symbol neuron_index. That is, for each neuron_index, we have a corresponding value of V. In turn, neuron_index takes on multiple values. In Python, we go through the multiple values of neuron_index by vectorisation, whereas in C we do it through a loop. We generate the final code, by going through all the symbols in a code item and 'resolving' the symbol, which means transforming the code. For example, resolving 'V' in Python would mean 'V = _arr_V[neuron_index]' and in C it would be 'double &V = _arr_V[neuron_index];'. Having resolved V it introduces a new symbol neuron_index that needs to be resolved. In the case of Python this would be 'neuron_index = slice(None)' whereas in C it would involve a loop. There are two types of dependencies, Read and Write depending on how the symbol is used. So, in Python we can read a numpy array just using the symbol name directly, whereas to write to it we need to write 'V[:] = ...' to make the modification in-place. A symbol can say whether it is single or multiple valued, so that V is single valued because it has a single value for a single value of neuron_index which is the variable that takes multiple values. A symbol can also say whether or not it needs a loop to resolve it or not, this information can be used to optimise the code (we prefer to put as much stuff outside loops as possible). In resolving a symbol, we specify whether or not we want to read or write (or both), and whether or not we can vectorise. The reason for this is that we can only vectorise over one loop, so if we have two loops, then we have to loop over one and vectorise over the other. This is determined by the symbol resolution algorithm (see below). In resolving a variable we also have a code item and a namespace. The symbol resolution returns a new code item and can modify the namespace (e.g. adding new values to it, such as _arr_V). We can also change the read/write names of the variable, for example when writing to V we want to do _arr_V[:]=... whereas when reading from it we want to just use _arr_V, so the Symbol object has read() and write() methods which give the names to use when reading to or writing to a symbol. In this way, we build up a complete code string iteratively by resolving one variable at a time. For example for state update above, we would start by resolving V, which would mean: Python: V = _arr_V[neuron_index] _temp_V = -V/tau _arr_V[neuron_index] += _temp_V*dt C: double &V = _arr_V[neuron_index]; double _temp_V = -V/tau; V += _temp_V*dt; This introduces a new symbol neuron_index which we need to resolve: Python: neuron_index = slice(None) V = _arr_V[neuron_index] _temp_V = -V/tau _arr_V[neuron_index] += _temp_V*dt C: for(int neuron_index=0; neuron_indexVt' in the context of thresholding. Brian decodes: 'V': neuron variable 'Vt': neuron variable Dependency for 'V' as above but with target_index->neuron_index. Dependency for 'Vt' as for 'V' Code template: Python: return (%EXPRESSION%).nonzero()[0] C: C block: int &numspikes = _numspikes[0]; for(int neuron_index=0; neuron_indexVt).nonzero()[0] C: namespace = { 'V_arr':G.V, 'Vt_arr':G.Vt, } Code: C block: int &numspikes = _numspikes[0]; for(int neuron_index=0; neuron_indexVt) { spikes[numspikes++] = neuron_index; } } Python block: return spikes[:_numspikes[0]] ===================================== Numerical integration with extensions ===================================== User specifies: 'dV/dt = (-V+I(t))/tau : 1' where I is a TimedArray. Brian decodes: 'V': '(-V+I(t))/tau' diff eq 'I': TimedArray has GPU properties 't': time ('tau' is a constant) 'V' will have dependencies as above. 't' will insert a value for time in the namespace Dependency for 'I' (only C shown as Python is trivial): - Namespace C: 'I_arr':asarray(I), 'I_t_init':I._t_init, 'I_dt':I._dt, 'I_len':len(I), - Support code C: inline double I_get(double t, double *I_arr, double t_init, double dt, int I_len) { int ti = (int)(nearbyint((t-t_init)/dt)); if(ti<0) ti = 0; if(ti>=I_len) ti = I_len-1; return I_arr[t]; } #define I(t) I_get(t, I_arr, I_t_init, I_dt, I_len) The support code replaces the symbol I with a macro which expands to the I_get function which works as expected.brian-1.4.3/brian/experimental/codegen2/expressions.py000066400000000000000000000042051266062647100230370ustar00rootroot00000000000000from brian import * from brian.optimiser import symbolic_eval from sympy.printing.ccode import CCodePrinter import sympy from formatting import * from dependencies import * import re __all__ = ['Expression', ] def substitute_symbols(expr, symbols): ''' For each symbol which appears in the dict ``symbols`` and as a word in the expression string ``expr``, replace the symbol with ``sym.read()``. ''' substitutions = dict((name, sym.read()) for name, sym in symbols.iteritems()) return word_substitute(expr, substitutions) class Expression(object): ''' A mathematical expression such as ``x*y+z``. Has an attribute ``dependencies`` which is ``Read(var)`` for all words ``var`` in ``expr``. Has a method :meth:`convert_to` defined the same way as :meth:`CodeItem.convert_to`. ''' def __init__(self, expr): self.expr = expr self.sympy_expr = symbolic_eval(self.expr) self.dependencies = set(Read(x) for x in get_identifiers(expr)) self.resolved = set() def __str__(self): return self.expr def convert_to(self, language, symbols={}, namespace={}): ''' Converts expression into a string for the given ``language`` using the given set of ``symbols``. Replaces each :class:`Symbol` appearing in the expression with ``sym.read()``, and if the language is C++ or GPU then uses ``sympy.CCodePrinter().doprint()`` to convert the syntax, e.g. ``x**y`` becomes ``pow(x,y)``. ''' if language.name=='python': return substitute_symbols(self.expr, symbols) elif language.name=='c' or language.name=='gpu': return substitute_symbols( CCodePrinter().doprint(self.sympy_expr), symbols) if __name__=='__main__': from languages import * python = PythonLanguage() c = CLanguage() expr = Expression('3*x**2+(y=N) return; if(indices[thread]!=indices[thread+1]) out[indices[thread]] = thread; } '''.replace('INT', index_typestr) scatter_module = compiler.SourceModule(scatter_code) self.scatter_func = scatter_module.get_function('scatter') self.block, self.grid = compute_block_grid(maxblocksize, N) self.N = index_dtype(N) self.scatter_func.prepare((intp, intp, index_dtype), self.block) self.gpu_compacted = gpuarray.zeros(N, index_dtype) self.compacted = zeros(N, index_dtype) def get_gpu(self, cond): N = self.N if len(cond)!=N+1: raise ValueError("Should have len(cond)=N+1") self.cumsum_kernel(cond) driver.memcpy_dtoh(self.nspiked_arr, int(int(cond.gpudata)+N*self.index_dtype(0).nbytes)) nspiked = self.nspiked_arr[0] self.scatter_func.prepared_call(self.grid, intp(self.gpu_compacted.gpudata), intp(cond.gpudata), N) return self.gpu_compacted, nspiked def __call__(self, cond): gpucomp, nspiked = self.get_gpu(cond) compacted = self.compacted[:nspiked] driver.memcpy_dtoh(compacted, gpucomp.gpudata) return compacted class GPUAtomicCompactor(object): def __init__(self, N, maxblocksize=None, sorted=True, index_dtype=int32): from ..statements import c_data_type if maxblocksize is None: maxblocksize = pycuda.autoinit.device.max_threads_per_block self.index_dtype = index_dtype self.index_typestr = index_typestr = c_data_type(index_dtype) self.block, self.grid = compute_block_grid(maxblocksize, N) self.N = index_dtype(N) self.compact_code = ''' __global__ void compact(INT *out, INT *cond, INT N, unsigned int *counter) { const int thread = threadIdx.x+blockIdx.x*blockDim.x; if(thread>=N) return; if(cond[thread]) out[atomicAdd(counter, 1)] = thread; } '''.replace('INT', index_typestr) self.module = compiler.SourceModule(self.compact_code) self.gpu_compact = self.module.get_function('compact') self.gpu_compact.prepare((intp, intp, index_dtype, intp), self.block) self.gpu_compacted = gpuarray.zeros(N, index_dtype) self.compacted = zeros(N, index_dtype) self.counter = gpuarray.zeros(1, uint32) self.sorted = sorted def __call__(self, cond): N = self.N if len(cond)!=N+1: raise ValueError("Should have len(cond)=N+1") self.counter.set(array([0], dtype=uint32)) self.gpu_compact.prepared_call(self.grid, intp(self.gpu_compacted.gpudata), intp(cond.gpudata), self.N, intp(self.counter.gpudata)) nspiked = self.counter.get()[0] #print nspiked compacted = self.compacted[:nspiked] driver.memcpy_dtoh(compacted, self.gpu_compacted.gpudata) if self.sorted: compacted.sort() return compacted # TODO: could make this an option, or switch based on runtime profiling, but # in most realistic cases, atomic seems to be slightly faster than scan # where realistic means firing rates lower than 100 Hz and N<1M GPUCompactor = GPUAtomicCompactor brian-1.4.3/brian/experimental/codegen2/gpu/importpycuda.py000066400000000000000000000003521266062647100237670ustar00rootroot00000000000000try: import pycuda import pycuda.autoinit as autoinit import pycuda.driver as driver import pycuda.compiler as compiler from pycuda import gpuarray from pycuda import scan except ImportError: pycuda = None brian-1.4.3/brian/experimental/codegen2/gpu/management.py000066400000000000000000000464601266062647100233750ustar00rootroot00000000000000''' GPUManager handles: - Copying memory to/from the GPU - Holds multiple kernels - Compiles kernels and returns them - Combines kernels with the same vectorisation index and range ''' from brian import * from ..codeobject import * from ..formatting import * from ..languages import * from importpycuda import * import re __all__ = ['GPUKernel', 'GPUManager', 'GPUSymbolMemoryManager', 'GPUCode', 'GPULanguage', 'compute_block_grid_size', 'compute_block_grid', ] def compute_block_grid_size(maxblocksize, numinds): blocksize = maxblocksize if numinds=({end}))) return; {code_str} }} We also compute the block size and grid size using the user provided maximum block size. ''' from ..statements import c_data_type if self.prepared: return self.prepared = True ns = self.namespace code_str = self.code self.numinds = numinds = ns.pop('_num_gpu_indices') self.func_args = [] memman_items = [] for name, value in ns.iteritems(): if isinstance(value, ndarray): # TODO: we shouldself._gpu_func_is_prepared have access to the symbol name so that the # memory manager can be used by the user to get/set values symname = devname = name hostarr = value memman_items.append((symname, hostarr, devname)) else: dtype = array(value).dtype.type if dtype==float64 and self.scalar=='float': dtype = float32 dtype_c = c_data_type(dtype) self.func_args.append((name, dtype, dtype_c)) # This allocates memory on the GPU self.mem_man.add_symbols(memman_items) func_args_str = ', '.join(dtype_c+' '+name for name, _, dtype_c in self.func_args) code_str_template = ''' __global__ void {name}({funcargs}) {{ int {vector_index} = blockIdx.x * blockDim.x + threadIdx.x; if(({vector_index}<({start}))||({vector_index}>=({end}))) return; {code_str} }} ''' code_str = flattened_docstring(code_str_template).format( vector_index=self.index, start=self.start, end=self.end, code_str=indent_string(code_str), name=self.name, funcargs=func_args_str) self.kernel_code = code_str self.blocksize, self.gridsize = compute_block_grid_size(self.maxblocksize, numinds) self.grid = (self.gridsize, 1) def prepare_gpu_func(self): ''' Calls the ``pycuda`` GPU function ``prepare()`` method for low-overhead function calls. ''' self.gpu_func.prepare(tuple(dtype for _, dtype, _ in self.func_args), (self.blocksize, 1, 1)) self._gpu_func_is_prepared = True def run(self): ''' Calls the function on the GPU, extracting the scalar variables in the argument list from the namespace. ''' if not self._gpu_func_is_prepared: self.prepare_gpu_func() ns = self.namespace args = [dtype(ns[name]) for name, dtype, _ in self.func_args] # print self.gpu_func.local_size_bytes, self.gpu_func.shared_size_bytes, self.gpu_func.num_regs # print self.grid, self.blocksize self.gpu_func.prepared_call(self.grid, *args) class GPUManager(object): ''' This object controls everything on the GPU. It uses a :class:`GPUKernel` object for managing kernels, and a :class:`GPUSymbolMemoryManager` object for managing symbol memory. The class is used by: 1. Adding several kernels using :meth:`add_kernel` 2. Calling :meth:`prepare` (see method documentation for details) 3. Run code with :meth:`run` Memory is mirrored on GPU and CPU. In the present implementation, in the development phase only, each call to :meth:`run` will copy all symbols from CPU to GPU before running the GPU kernel, and afterwards copy all symbols from GPU back to CPU. In the future, this will be disabled and symbol memory copies will be handled explicitly by calls to methods :meth:`copy_to_device` and :meth:`copy_to_host`. ''' def __init__(self, force_sync=True, usefloat=False): self.numkernels = 0 self.kernels = {} self.combined_kernels = [] self.mem_man = GPUSymbolMemoryManager(usefloat=usefloat) self.prepared = False self.force_sync = force_sync self.usefloat = usefloat def add_kernel(self, name, code, namespace): ''' Adds a kernel with the given name, code and namespace. Creates a :class:`GPUKernel` object. ''' basename = name suffix = 2 while name in self.kernels: name = basename+str(suffix) suffix += 1 if self.usefloat: scalar = 'float' else: scalar = 'double' self.kernels[name] = GPUKernel(name, code, namespace, self.mem_man, force_sync=self.force_sync, scalar=scalar) return name def make_combined_kernel(self, *names): ''' Not used at present. Will be used to combine multiple kernels with the same vectorisation index for efficiency. ''' self.combined_kernels.append(names) def prepare(self): ''' Compiles code and initialises memory. Performs the following steps: 1. :meth:`GPUKernel.prepare` is called for each kernel, converting the partial code into a complete kernel, and adds symbols to the :class:`GPUSymbolMemoryManager`, which allocates space on the GPU and copies data to it from the CPU. 2. :meth:`generate_code` is called, combining individual kernels into one source file, and adding memory management kernels and declarations. 3. :meth:`compile` is called, which JIT-compiles the code using ``pycuda``. 4. :meth:`initialise_memory` is called, which allocates memory ''' if self.prepared: return self.prepared = True for name, kernel in self.kernels.iteritems(): kernel.prepare() self.generate_code() self.compile() self.initialise_memory() def generate_code(self): ''' Combines kernel source into one source file, and adds memory management kernel functions. These simple kernels simply copy a pointer to a previously specified name. This is necessary because when ``pycuda`` is used to allocate memory, it doesn't give it a name only a pointer, and the kernel functions use a named array. Calls :meth:`GPUSymbolMemoryManager.generate_code`. ''' self.prepare() code = '' # global memory arrays code += self.mem_man.generate_code() # individual kernels for name, kernel in self.kernels.iteritems(): code += kernel.kernel_code # combined kernels # for names in self.combined_kernels: # combined_name = 'combined_'+'_'.join(names) # for name in names: # code, ns, index, _ = self.kernels[name] # if necessary, convert double to float if self.usefloat: code = re.sub(r'\bdouble\b', 'float', code) log_info('brian.codegen2.gpu.GPUManager', 'GENERATED GPU SOURCE CODE:\n'+code) self.code = code def compile(self): ''' Compiles code using :class:`pycuda.compiler.SourceModule` and extracts kernel functions with :meth:`pycuda.compiler.SourceModule.get_function`. The :attr:`GPUKernel.gpu_func` attribute is set for each kernel. ''' self.gpu_mod = compiler.SourceModule(self.code) for name, kernel in self.kernels.iteritems(): kernel.gpu_func = self.gpu_mod.get_function(name) def initialise_memory(self): ''' Copies allocated memory pointers to named global memory pointer variables so that kernels can use them. The kernel names to do this are in the :attr:`GPUSymbolMemoryManager.symbol_upload_funcnames` dict (keys are symbol names), and the allocated pointers are in the :attr:`GPUSymbolMemoryManager.device` dict. ''' for symname in self.mem_man.names: fname = self.mem_man.symbol_upload_funcnames[symname] f = self.gpu_mod.get_function(fname) f(self.mem_man.device[symname], block=(1,1,1)) def run(self, name): ''' Runs the named kernel. Calls :meth:`GPUKernel.run`. Note that all symbols are copied to and from the GPU before and after the kernel run, although this is only for the development phase and will change later. ''' kernel = self.kernels[name] if self.force_sync: self.copy_to_device(True) # TODO: temporary kernel.run() if self.force_sync: self.copy_to_host(True) # TODO: temporary # TODO: combined kernels # proxies to mem_man def add_symbols(self, items): ''' Proxy to :meth:`GPUSymbolMemoryManager.add_symbols`. ''' self.mem_man.add_symbols(items) def copy_to_device(self, symname): ''' Proxy to :meth:`GPUSymbolMemoryManager.copy_to_device`. ''' self.mem_man.copy_to_device(symname) def copy_to_host(self, symname): ''' Proxy to :meth:`GPUSymbolMemoryManager.copy_to_host`. ''' self.mem_man.copy_to_host(symname) class GPUSymbolMemoryManager(object): ''' Manages symbol memory on the GPU. Stores an attribute ``device`` and ``host`` which are dicts, with keys the symbol names, and values :class:`pycuda.gpuarray.GPUArray` and :class:`numpy.ndarray` respectively. Add symbols with :meth:`add_symbols`, which will allocate memory. ''' def __init__(self, usefloat=False): self.device = {} self.host = {} self.usefloat = usefloat def add_symbols(self, items): ''' Adds a collection of symbols. Each item in ``items`` is of the form ``(symname, hostarr, devname)`` where ``symname`` is the symbol name, ``hostarr`` is the :class:`numpy.ndarray` containing the data, and ``devname`` is the name the array pointer should have on the device. Allocates memory on the device, and copies data to the GPU. ''' # if pycuda is None: # raise ImportError("Cannot import pycuda.") for symname, hostarr, devname in items: if symname not in self.device: if pycuda is not None: if self.usefloat and hostarr.dtype==float64: devarr = pycuda.gpuarray.to_gpu(array(hostarr, dtype=float32)) else: devarr = pycuda.gpuarray.to_gpu(hostarr) else: devarr = None self.device[symname] = devarr self.host[symname] = hostarr def generate_code(self): ''' Generates declarations for array pointer names on the device, and kernels to copy device pointers to the array pointers. General form is:: __device__ {dtypestr} *{name}; __global__ void set_array_{name}({dtypestr} *_{name}) { {name} = _{name}; } Stores the kernel function names in attribute :attr:`symbol_upload_funcnames` (dict with keys being symbol names). Returns a string with declarations and kernels combined. ''' all_init_arr = '' all_arr_ptr = '' self.symbol_upload_funcnames = {} for name, value in self.host.iteritems(): if isinstance(value, ndarray): from ..statements import c_data_type dtypestr = c_data_type(value.dtype) if self.usefloat and dtypestr=='double': dtypestr = 'float' init_arr_template = ''' __global__ void set_array_{name}({dtypestr} *_{name}) {{ {name} = _{name}; }} ''' self.symbol_upload_funcnames[name] = 'set_array_'+name # TODO: handle double/float init_arr = flattened_docstring(init_arr_template).format( dtypestr=dtypestr, name=name) arr_ptr_template = ''' __device__ {dtypestr} *{name}; ''' arr_ptr = flattened_docstring(arr_ptr_template).format( dtypestr=dtypestr, name=name) all_init_arr += init_arr all_arr_ptr += arr_ptr return all_arr_ptr+all_init_arr def copy_to_device(self, symname): ''' Copy the memory in the :class:`numpy.ndarray` for ``symname`` to the allocated device memory. If ``symname==True``, do this for all symbols. You can also pass a list for ``symname``. ''' if symname is True: self.copy_to_device(self.device.keys()) return if not isinstance(symname, str): for name in symname: self.copy_to_device(name) return devarr = self.device[symname] hostarr = self.host[symname] if self.usefloat and hostarr.dtype==float64: # TODO: make this pagelocked/cached for speed? hostarr = array(hostarr, dtype=float32) devarr.set(hostarr) def copy_to_host(self, symname): ''' As for :meth:`copy_to_device` but copies memory from device to host. ''' if symname is True: self.copy_to_host(self.device.keys()) return if not isinstance(symname, str): for name in symname: self.copy_to_host(name) return devarr = self.device[symname] hostarr = self.host[symname] if devarr.dtype==float32 and hostarr.dtype==float64: # TODO: cache the float32 intermediate array for speed #hostarr[:] = array(devarr.get(pagelocked=True), dtype=float64) hostarr[:] = devarr.get(pagelocked=True) else: devarr.get(hostarr) @property def names(self): ''' The list of symbol names managed. ''' return self.device.keys() class GPUCode(Code): ''' :class:`Code` object for GPU. For the user, works as the same as any other :class:`Code` object. Behind the scenes, source code is passed to the :class:`GPUManager` ``gpu_man`` from the :class:`GPULanguage` object, via :meth:`GPUManager.add_kernel`. Compilation is handled by :meth:`GPUManager.prepare`, and running code by :meth:`GPUManager.run`. ''' def __init__(self, name, code_str, namespace, pre_code=None, post_code=None, language=None): if language is None: raise ValueError("Must provide a language argument to GPUCode.") self.gpu_man = language.gpu_man name = self.gpu_man.add_kernel(name, code_str, namespace) Code.__init__(self, name, code_str, namespace, pre_code=pre_code, post_code=post_code, language=language) def compile(self): ''' Simply calls :meth:`GPUManager.prepare`. ''' self.gpu_man.prepare() self.code_compiled = True def run(self): ''' Simply runs the kernel via :meth:`GPUManager.run`. ''' self.gpu_man.run(self.name) class GPULanguage(CLanguage): ''' :class:`Language` object for GPU. Has an attribute ``gpu_man``, the :class:`GPUManager` object responsible for allocating, copying memory, etc. One is created if you do not specify one. ''' CodeObjectClass = GPUCode def __init__(self, scalar='double', gpu_man=None, force_sync=True): Language.__init__(self, 'gpu') self.scalar = scalar if gpu_man is None: gpu_man = GPUManager(force_sync=force_sync, usefloat=(scalar=='float')) self.gpu_man = gpu_man self.force_sync = force_sync brian-1.4.3/brian/experimental/codegen2/integration.py000066400000000000000000000101761266062647100230040ustar00rootroot00000000000000from brian import * from expressions import * from formatting import * from equations import * from statements import * __all__ = ['EquationsContainer', 'make_integration_step', 'euler', 'rk2', 'exp_euler', ] class EquationsContainer(object): ''' Utility class for defining numerical integration scheme Initialise with a set of equations ``eqs``. You can now iterate over this object in two ways, firstly over all the differential equations:: for var, expr in eqscontainer: yield f(expr) Or over just the differential equations with nonzero expressions (i.e. not including ``dx/dt=0`` for parameters):: for var, expr in eqscontainer.nonzero: yield f(expr) Here ``var`` is the name of the symbol, and ``expr`` is a string, the right hand side of the differential equation ``dvar/dt=expr``. Also has attributes: ``names`` The symbol names for all the differential equations ``names_nonzero`` The symbol names for all the nonzero differential equations ''' def __init__(self, eqs): frozen = self.frozen = frozen_equations(eqs) self.all = [(var, frozen[var]) for var in eqs._diffeq_names] self.nonzero = [(var, frozen[var]) for var in eqs._diffeq_names_nonzero] self.names = eqs._diffeq_names self.names_nonzero = eqs._diffeq_names_nonzero def __iter__(self): return iter(self.all) def make_integration_step(method, eqs): ''' Return an integration step from a method and a set of equations. The ``method`` should be a function ``method(eqs)`` which receives a :class:`EquationsContainer` object as its argument, and ``yield`` s statements. For example, the :func:`euler` integration step is defined as:: def euler(eqs): for var, expr in eqs.nonzero: yield '_temp_{var} := {expr}'.format(var=var, expr=expr) for var, expr in eqs.nonzero: yield '{var} += _temp_{var}*dt'.format(var=var, expr=expr) ''' eqs_container = EquationsContainer(eqs) statements = [] for s in method(eqs_container): statements.extend(statements_from_codestring(s)) return statements def euler(eqs): ''' Euler integration ''' for var, expr in eqs.nonzero: yield '_temp_{var} := {expr}'.format(var=var, expr=expr) for var, expr in eqs.nonzero: yield '{var} += _temp_{var}*dt'.format(var=var, expr=expr) def rk2(eqs): ''' 2nd order Runge-Kutta integration ''' for var, expr in eqs: yield ''' _buf_{var} := {expr} _half_{var} := .5*dt*_buf_{var} _half_{var} += {var} '''.format(var=var, expr=expr) for var, expr in eqs.nonzero: half_subst = dict((var, '_half_'+var) for var in eqs.names) expr = word_substitute(expr, half_subst) yield ''' _buf_{var} = {expr} {var} += dt*_buf_{var} '''.format(var=var, expr=expr) def exp_euler(eqs): ''' Exponential-Euler integration ''' for var, expr in eqs.nonzero: subs = { 'expr_B': word_substitute(expr, {var:0}), 'expr_A': word_substitute(expr, {var:1}), 'var_B': '_B_'+var, 'var_A': '_A_'+var, } yield ''' {var_B} := {expr_B} {var_A} := {expr_A} {var_A} -= {var_B} {var_B} /= {var_A} {var_A} *= dt '''.format(**subs) for var, expr in eqs.nonzero: yield ''' {var} += _B_{var} {var} *= exp(_A_{var}) {var} -= _B_{var} '''.format(var=var) if __name__=='__main__': tau = 10*ms Vt0 = 1.0 taut = 100*ms eqs = Equations(''' dV/dt = (-V+I)/tau : 1 dI/dt = -I/tau : 1 dVt/dt = (Vt0-Vt)/taut : 1 ''') eqs.prepare() for stmt in make_integration_step(rk2, eqs): print str(stmt) brian-1.4.3/brian/experimental/codegen2/languages.py000066400000000000000000000026721266062647100224310ustar00rootroot00000000000000from brian import * from codeobject import * __all__ = ['Language', 'PythonLanguage', 'CLanguage', ] class Language(object): ''' Base class for languages, each should provide a ``name`` attribute, and a method :meth:`~Language.code_object`. ''' def __init__(self, name): self.name = name.strip().lower() def code_object(self, name, code_str, namespace): ''' Return a :class:`Code` object from a given ``name``, code string ``code_str`` and ``namespace``. If the class has a class attribute ``CodeObjectClass``, the default implementation returns:: CodeObjectClass(name, code_str, namespace, language=self) ''' return self.CodeObjectClass(name, code_str, namespace, language=self) class PythonLanguage(Language): ''' Python language. ''' CodeObjectClass = PythonCode def __init__(self): Language.__init__(self, 'python') class CLanguage(Language): ''' C language. Has an attribute ``scalar='double'`` which gives the default type of scalar values (used when ``dtype`` is not specified). This can be used, for example, on the GPU where ``double`` may not be available. ''' CodeObjectClass = CCode def __init__(self, scalar='double'): Language.__init__(self, 'c') self.scalar = scalar brian-1.4.3/brian/experimental/codegen2/makeintegrator.py000066400000000000000000000112171266062647100234720ustar00rootroot00000000000000from brian import * from languages import * from integration import * from symbols import * class ValueSymbol(Symbol): ''' This class is simply used to replace some known symbols with constant values. In :func:`make_c_integrator` it is just used for setting the value of dt. We just change :meth:`Symbol.read` to return ``'0.001'`` or whatever. ''' supported_languages = ['c'] def __init__(self, name, language, value): Symbol.__init__(self, name, language) if isinstance(value, float): value = float(value) if isinstance(value, int): value = int(value) self.value = value def read(self): return str(self.value) class TimeSymbol(Symbol): ''' This symbol is used to redefine the Brian symbol 't' to use a different name (like T or time) and to potentially give it a different scale (e.g. ms instead of second). We simply replace :meth:`Symbol.read` with ``'(scale*name)'``, the parentheses so that it works as part of a larger expression. ''' supported_languages = ['c'] def __init__(self, name, language, timeunit): Symbol.__init__(self, name, language) self.timeunit = timeunit def read(self): if self.timeunit is second: return self.name else: return '('+str(float(self.timeunit))+'*'+self.name+')' @check_units(dt=second) def make_c_integrator(eqs, method, dt, values=None, scalar='double', timeunit=second, timename='t'): ''' Gives C/C++ format code for the integration step of a differential equation. ``eqs`` The equations, can be an :class:`brian.Equations` object or a multiline string in Brian equations format. ``method`` The integration method, typically :func:`euler`, :func:`rk2` or :func:`exp_euler`, although you can pass your own integration method, see :func:`make_integration_step` for details. ``dt`` The value of the timestep dt (in Brian units, e.g. ``0.1*ms``) ``values`` Optional, dictionary of mappings variable->value, these values will be inserted into the generated code. ``scalar`` By default it is ``'double'`` but if you want to use float as your scalar type, set this to ``'float'``. ``timename`` The name of the time variable (if used). In Brian this is 't', but you can change it to 'T' or 'time' or whatever. This can be used if you want users to specify time in Brian form ('t') but the context in which this code will be used (e.g. another simulator) specifies time with a different variable name (e.g. 'T'). ``timeunit`` The unit of the time variable, scaled because Brian expects time to be in seconds. Returns a triple ``(code, vars, params)``: ``code`` The C/C++ code to perform the update step (string). ``vars`` A list of variable names. ``params`` A list of per-neuron parameter names. ''' if values is None: values = {} if not isinstance(eqs, Equations): # update the namespace so that names like 'sin', 'exp' are included ns = globals() ns.update(locals()) ns.update(values) eqs = Equations(eqs, **ns) language = CLanguage(scalar=scalar) symbols = { 'dt': ValueSymbol('dt', language, float(dt)), 't': TimeSymbol(timename, language, timeunit), } steps = make_integration_step(method, eqs) code = '\n'.join([s.convert_to(language, symbols=symbols) for s in steps]) vars = eqs._diffeq_names_nonzero allvars = eqs._diffeq_names params = [p for p in allvars if not p in vars] return code, vars, params if __name__=='__main__': if 1: eqs = ''' dv/dt = (ge+gi-(v+49*mV))/(20*ms) : volt dge/dt = -ge/(5*ms) : volt dgi/dt = -gi/(10*ms) : volt ''' code, vars, params = make_c_integrator(eqs, method=euler, dt=0.1*ms) print code if 0: eqs = ''' dv/dt = (ge*sin(2*pi*500*Hz*t)+gi-(v+49*mV))/(20*ms) : volt dge/dt = -ge/tau : volt dgi/dt = -gi/tau_i : volt tau : second ''' code, vars, params = make_c_integrator(eqs, method=euler, dt=0.1*ms, values={'tau_i':10*ms}, scalar='float', timeunit=1*ms, timename='T') print 'vars =', vars print 'params =', params print print code brian-1.4.3/brian/experimental/codegen2/newdesign.txt000066400000000000000000000211071266062647100226270ustar00rootroot00000000000000Introduction ============ Code generation works using code blocks, each code block has a set of used symbols and a set of dependencies, namely a set of symbols which have to be handled before the current code can run. For example, if we were computing V += 1 for a whole NeuronGroup, we would start with that statement, then we would see that there was a symbol V not yet resolved. The block is then: V += 1 Dependencies: V Depending on the language, we can resolve that symbol in many different ways. In Python, we would just copy the symbol V directly into the namespace. However, when we modify V we need to do an in-place modification, so the Python code would look like: V[:] += 1 In C, we would need to do a loop over the neuron indices. We would handle this by resolving the dependency of V by adding a statement as follows, but also adding a new dependency: double &V = _arr_V[neuron_index]; V += 1; Dependencies: _arr_V, neuron_index The _arr_V is loaded directly into the namespace, so that dependency is easily resolved. The neuron_index dependency is handled by looping: for(int neuron_index=0; neuron_index pow(x,2)). Statements are overall in Python syntax except that := is not part of Python. We need := for full flexibility, but we can infer it in many cases (e.g. when LHS = RHS and LHS is a variable not yet defined). We could handle this by requiring its use or by inference. Statements and code strings can be analysed, their symbols extracted and looked up in a table or by value in the calling/defining code namespace, and the appropriate actions to resolve them extracted. The appropriate action depends on how the symbol is used: read symbol appears in expression write symbol appears on LHS of statement In each case, we may need to apply a context sensitive transformation to the symbol. For example, in-place modification of a Python array is done by: V = ... -> V[:] = ... Or, we might be operating on only a subset of the indices, in which case: V = ... -> V[indices] = ... There is some space here to think about optimisations, e.g. if I write V*V I don't want it transformed to V[indices]*V[indices] but instead have a statement V_indices = V[indices] added before, and then V_indices*V_indices. Code transformations that might be necessary are: - Introduce a new variable into the namespace - Rewrite read/write in a statement, e.g. V->V[:] - Add load/save code after statements, e.g. V_indices=V[indices] - Introduce a loop, and indent existing statements - Move statements out of loops for optimisation reasons? Note that this requires having a notion of a loop doesn't it? And also maintaining slightly finer control over the idea of a statement, e.g. code such as double &V = _arr_V[neuron_index]; would have to be movable, which means keeping an idea of the purpose of the statement (resolves 'V', introduces dependency on 'neuron_index'). May also consider having a set of standard variable names with context-sensitive resolution rules, namely: neuron_index Either an int in C, or in Python either a slice or array of ints. Used when iterating over neurons. source_index As neuron_index but used in synaptic event handling, and refers to the source neuron. target_index As neuron_index but used in synaptic event handling, and refers to the target neuron. synapse_index As neuron_index but referring to the index of the synapse in synaptic event handling. Dependencies can be: read write We have to be careful about the order in which symbols are resolved, because the resolution of one symbol can make the resolution of another symbol impossible. For example, if we have a synaptic propagation statement: V += w*mod where V is a target variable, w is a synaptic variable, and mod is a source variable, then V depends on target_index, w depends on synapse_index and mod depends on source_index. Other dependencies are: target_index depends on synapse_index, synapse_index depends on source_index. So the dependency graph looks something like this (where a->b means that resolving a introduces a new symbol b to be resolved): 'V' 'w' 'mod' | | | v v v 'tgt' -> 'syn' -> 'src' If we chose to resolve mod, then src first, then when we came to resolve syn we wouldn't be able to, because when we resolve a variable we can only introduce changes before or after a block of code, and resolving syn would mean reintroducing a dependency on src which is not possible (remember, we are working outwards from inner loops to outer loops). The solution to this problem is to construct the whole dependency graph (which means each Symbol has to have a method giving the set of dependencies it introduces in its resolution), and then find the resolution order backwards (i.e. we find the last symbol to be resolved first) at each stage choosing only nodes in the graph with no outgoing edges. We can also perform an optimisation by asking each node whether it requires a loop to resolve it, and always preferring statements which don't require a loop first. In the above example, the reverse order we get is then: src mod syn w tgt V (the last three are interchangeable) So we resolve in the order: V tgt w syn mod src Which has this loop structure: for src in spikes: mod = ... for syn in row[src]: w = w(syn) tgt = tgt(syn) V = V(tgt) V += w The dependency graph is a directed acyclic graph (DAG) and therefore it is guaranteed that we will find a suitable order (a topological sorting always exists for DAGs). Types ===== Expression > convert to language > extract symbols (dependencies) > substitute words CodeItem Statement, ControlBlock or Block > extract dependencies > extract resolved symbol name (LHS) Statement MathematicalStatement + var op expr > convert to language CodeStatement + contents: e.g. double &V = _arr_V[neuron_index]; > extract dependencies > extract resolved symbol name (LHS) > substitutions ControlBlock (introduces a level of indentation) + string definition (start, end) + contents + optimisation properties (e.g. prefer to be inside if blocks, but outside for blocks) > extract dependencies > extract resolved symbol names > extract contents Block + contents: sequence of Statements, Blocks and ControlBlocks > extract dependencies > extract resolved symbol names > iterate over contents Symbol + name, other properties > extract read/write substitutions > extract load/save for read/write situations > update namespace > resolve dependency (returns CodeItem) Standard Symbol types: Neuron group variable When iterating over neurons When iterating over synapses When iterating over subset of neurons (reset, refractory, etc.) Synaptic variable When iterating over synapses Indices Defined by context (see above), including: neuron_index source_index target_index synapse_index Extensions, e.g. TimedArray noise for stochastic DE integration and other situations? ==================================== TODO ==================================== - Dependencies from C/Python keywords and namespace (e.g. int, exp) brian-1.4.3/brian/experimental/codegen2/notes_romain.txt000066400000000000000000000024031266062647100233370ustar00rootroot00000000000000Notes ----- There are various aspects in code generation, which can be conceptually separated. 1) Linking variables to their values or meaning. In an equation, there are names that correspond to neuron variables and others that correspond to external variables. At this stage, one may want to freeze the values of external variables, for example. 2) Inspection of equations. This includes units checking, identification of stochastic terms, of linear equations, etc. It may also include some equation rewriting. One question is whether we should make more use of sympy to do this. 3) (For equations only) Producing target-independent state-update code from the equations. The nature of this intermediate can be discussed. It could be a string (good for debugging). But it could also be an object, for example an iterable object. At this stage, the code is not vectorised, it is local (either to a neuron or to a synapse). 4) Producing target-specific code. Essentially, what we want to do at this stage is to produce vectorized code from the non-vectorised statements, in a way that takes into account the different target platforms. To me, the term "resolving" correspond to the operation #1, not to operation #4 (as seems to be Dan's usage?). brian-1.4.3/brian/experimental/codegen2/plan.txt000066400000000000000000000014351266062647100216000ustar00rootroot00000000000000Main parts: * Basic Code object, handles the generated strings for different languages, compiling and running the code, namespaces * Common code generation tools: - String analysis tools, e.g. extracting variable names, substitutions, dedenting Python code, etc. - Templating tools * Equations tools - Expression evaluation and rewriting - Analysis of Equations objects - Dependency graphs * Numerical integration - Schemes * Vectorisation tools, how to convert loops to/from vector operations. Don't try to do it generally, but some tools for this might be useful (maybe do this in an emergent way). * Extensibility tools - Method for provision of C/GPU versions of Python methods (e.g. for TimedArray.__call__, or user provided code)brian-1.4.3/brian/experimental/codegen2/reset.py000066400000000000000000000046041266062647100216020ustar00rootroot00000000000000from brian import * from statements import * from symbols import * from equations import * from blocks import * from brian.optimiser import freeze from brian.inspection import namespace __all__ = ['CodeGenReset'] class CodeGenReset(Reset): def __init__(self, group, inputcode, language, level=0): self.namespace = namespace(inputcode, level=level+1) self.inputcode = inputcode self.language = language P = group ns = self.namespace self.inputcode = freeze_with_equations(self.inputcode, P._eqs, ns) statements = statements_from_codestring(self.inputcode, P._eqs, infer_definitions=True) block = Block(*statements) symbols = get_neuron_group_symbols(P, self.language, index='_neuron_index') if language.name=='python' or language.name=='c': symbols['_neuron_index'] = ArrayIndex('_neuron_index', '_spikes', self.language, array_len='_numspikes') if language.name=='gpu': symbols['_neuron_index'] = SliceIndex('_neuron_index', '0', '_num_neurons', self.language, all=True) _spiked_symbol = group._threshold._spiked_symbol symbols['_spiked'] = _spiked_symbol block = CIfBlock('_spiked', [block]) self.namespace['_num_gpu_indices'] = len(P) self.namespace['t'] = 1.0 # dummy value self.namespace['_num_neurons'] = len(P) self.code = block.generate('reset', self.language, symbols, namespace=ns) ns = self.code.namespace ns['dt'] = P.clock._dt log_info('brian.codegen2.CodeGenReset', 'CODE:\n'+self.code.code_str) log_info('brian.codegen2.CodeGenReset', 'KEYS:\n'+str(ns.keys())) def __call__(self, P): ns = self.code.namespace ns['_spikes'] = spikes = P.LS.lastspikes() ns['_numspikes'] = len(spikes) ns['t'] = P.clock._t self.code() brian-1.4.3/brian/experimental/codegen2/resolution.py000066400000000000000000000117721266062647100226670ustar00rootroot00000000000000from brian import * from symbols import * from blocks import * from codeitems import * from statements import * from formatting import * from languages import * from dependencies import * __all__ = ['resolve'] def resolve(item, symbols, namespace=None): ''' Resolves ``symbols`` in ``item`` in the optimal order. The first stage of this algorithm is to construct a dependency graph on the symbols. The optimal order is resolve loops as late as possible. We actually construct the inverse of the resolution order, which is the intuitive order (i.e. if the first thing we do is loop over a variable, then that variable is the last symbol we resolve). We start by finding the set of symbols which have no dependencies. The graph is acyclic so this always possible. Then, among those candidates, if possible we choose loopless symbols first (this corresponds to executing loops as late as possible). With this symbol removed from the graph we repeat until all symbols are placed in order. We then resolve in reverse order (because we start with the inner loop and add code outwards). At the beginning of this stage, vectorisable is set to ``True``. But after we encounter the first multi-valued symbol we set ``vectorisable`` to ``False`` (we can only vectorise one loop, and it has to be the innermost one). This vectorisation is used by both Python and GPU but not C++. Each resolution step calls :meth:`CodeItem.resolve` on the output of the previous stage. ''' if namespace is None: namespace = {} # stage 1, build the dependency graph depgraph = {} toprocess = get_read_or_write_dependencies(item.dependencies) while len(toprocess): var = toprocess.pop() if var not in symbols: # what should we do in this situation? hope that it will work # because the var is actually resolved or is a symbol like exp # that will be in the compiler and python namespaces? continue if var in depgraph: continue sym = symbols[var] deps = get_read_or_write_dependencies(sym.dependencies()) # we are only interested in resolvable symbols, i.e. those in the # symbols dict, this should be all of them but possibly some might # not be (e.g. exp, as above) deps = [dep for dep in deps if dep in symbols] depgraph[var] = set(deps) toprocess.update(deps) # stage 2, compute the resolution order resolution_order = [] while(len(depgraph)): # the candidates for resolution are those with no outgoing edges (and # since the graph is directed and acyclic there must be one) candidates = set(var for var, edges in depgraph.iteritems() if len(edges)==0) # just in case the graph isn't DA, raise an error if len(candidates)==0: raise ValueError("Graph is not acyclic!") # optimisation: try to resolve symbols without loops first loopless_candidates = set(var for var in candidates if not symbols[var].resolution_requires_loop()) if len(loopless_candidates): candidates = loopless_candidates var = candidates.pop() for node, edges in depgraph.iteritems(): edges.discard(var) del depgraph[var] resolution_order.append(var) resolution_order = resolution_order[::-1] # stage 3, do the resolving vectorisable = True for var in resolution_order: sym = symbols[var] read = Read(var) in item.dependencies write = Write(var) in item.dependencies log_msg = 'Resolving '+var+' ' if read: log_msg += '(read) ' if write: log_msg += '(write) ' if vectorisable: log_msg += '(vectorisable) ' log_msg += 'dependencies '+str(item.dependencies) log_info('brian.codegen2.resolve', log_msg) item = sym.resolve(read, write, vectorisable, item, namespace) if sym.multi_valued(): vectorisable = False return item, namespace if __name__=='__main__': item = MathematicalStatement('x', ':=', 'y*y') y = zeros(10) #idx = array([1, 3, 5]) subset = False #language = PythonLanguage() language = CLanguage() sym_y = ArraySymbol(y, 'y', language, index='idx', subset=subset, ) if subset: N = 'idx_arr_len' sym_idx = IndexSymbol('idx', N, language, index_array='idx_arr') else: N = 'y_len' sym_idx = IndexSymbol('idx', N, language) symbols = {'y':sym_y, 'idx':sym_idx} item, namespace = resolve(item, symbols) print 'Code:\n', indent_string(item.convert_to(language, symbols)), print 'Dependencies:', item.dependencies print 'Resolved:', item.resolved print print 'Namespace:', namespace.keys() brian-1.4.3/brian/experimental/codegen2/statements.py000066400000000000000000000235121266062647100226460ustar00rootroot00000000000000from brian import * from brian.optimiser import symbolic_eval from sympy.printing.ccode import CCodePrinter import sympy from formatting import * from expressions import * from codeitems import * from dependencies import * from languages import * import re __all__ = [ 'Statement', 'CodeStatement', 'CDefineFromArray', 'MathematicalStatement', 'statements_from_codestring', 'c_data_type', ] class Statement(CodeItem): ''' Just a base class, supposed to indicate single line statements. ''' pass class CodeStatement(Statement): ''' A language-specific single line of code, which should only be used in the resolution step by a :class:`Symbol` which knows the language it is resolving to. The string ``code`` and the set of ``dependencies`` and ``resolved`` have to be given explicitly. ''' def __init__(self, code, dependencies, resolved): self.dependencies = dependencies self.resolved = resolved if '\n' in code: code = flattened_docstring(code) self.code = code def __str__(self): return self.code __repr__ = __str__ def convert_to(self, language, symbols={}, namespace={}): return self.code def c_data_type(dtype): ''' Gives the C language specifier for numpy data types. For example, ``numpy.int32`` maps to ``int32_t`` in C. Perhaps this method is given somewhere in numpy, but I couldn't find it. ''' if dtype is None: dtype = float64 if dtype is int: dtype = array([1]).dtype.type if dtype==float32: dtype = 'float' elif dtype==float64: dtype = 'double' elif dtype==int32: dtype = 'int32_t' elif dtype==int64: dtype = 'int64_t' elif dtype==uint16: dtype = 'uint16_t' elif dtype==uint32: dtype = 'uint32_t' elif dtype==bool_ or dtype is bool: dtype = 'bool' return dtype class CDefineFromArray(CodeStatement): ''' Define a variable from an array and an index in C. For example:: double &V = __arr_V[neuron_index]; Initialisation arguments are: ``var`` The variable being defined, a string. ``arr`` A string representing the array. ``index`` A string giving the index. ``dependencies`` Given explicitly, or by default use ``set([Read(arr), Read(index)])``. ``resolved`` Given explicitly, or by default use ``set([var])``. ``dtype`` The numpy data type of the variable being defined. ``reference`` Whether the variable should be treated as a C++ reference (e.g. ``double &V = ...`` rather than ``double V = ...``. If the variable is being written to as well as read from, use ``reference=True``. ``const`` Whether the variable can be defined as const, specify this if only reading the value and not writing to it. ''' def __init__(self, var, arr, index, dependencies=None, resolved=None, dtype=None, reference=True, const=False): dtype = c_data_type(dtype) if reference: ref = '&' else: ref = '' if const: const = 'const ' else: const = '' code = '{const}{dtype} {ref}{var} = {arr}[{index}];'.format( dtype=dtype, ref=ref, const=const, var=var, arr=arr, index=index) if dependencies is None: dependencies = set([Read(arr), Read(index)]) if resolved is None: resolved = set([var]) CodeStatement.__init__(self, code, dependencies, resolved) class MathematicalStatement(Statement): ''' A single line mathematical statement. The structure is ``var op expr``. ``var`` The left hand side of the statement, the value being written to, a string. ``op`` The operation, can be any of the standard Python operators (including ``+=`` etc.) or a special operator ``:=`` which means you are defining a new symbol (whereas ``=`` means you are setting the value of an existing symbol). ``expr`` A string or an :class:`Expression` object, giving the right hand side of the statement. ``dtype`` If you are defining a new variable, you need to specify its numpy dtype. If ``op==':='`` then this statement will resolve ``var``, otherwise it will add a :class:`Write` dependency for ``var``. The other dependencies come from ``expr``. ''' def __init__(self, var, op, expr, dtype=None): self.var = var.strip() self.op = op.strip() if isinstance(expr, str): expr = Expression(expr) self.expr = expr if dtype is not None: dtype = c_data_type(dtype) self.dtype = dtype self.dependencies = self.expr.dependencies if self.op==':=': self.resolved = set([self.var]) else: self.dependencies.add(Write(self.var)) self.resolved = set() def __str__(self): return self.var+' '+self.op+' '+str(self.expr) __repr__ = __str__ def convert_to(self, language, symbols={}, tabs=0, namespace={}): ''' When converting to a code string, the following takes place: * If the LHS variable is in the set of ``symbols``, then the LHS is replaced by ``sym.write()`` * The expression is converted with :meth:`Expression.convert_to`. * If the operation is definition ``op==':='`` then the output is language dependent. For Python it is ``lhs = rhs`` and for C or GPU it is ``dtype lhs = rhs``. * If the operation is not definition, the statement is converted to ``lhs op rhs``. * If the language is C/GPU the statement has ``;`` appended. ''' if self.var in symbols: sym = symbols[self.var] lhs_name = sym.write() else: lhs_name = self.var if self.op==':=': if lhs_name in namespace: raise ValueError("Trying to redefine existing Symbol "+self.var) if language.name=='python': initial = lhs_name+' = ' elif language.name=='c' or language.name=='gpu': dtype = self.dtype if dtype is None: dtype = language.scalar initial = dtype+' '+lhs_name+' = ' else: initial = lhs_name+' '+self.op+' ' statementstr = initial+self.expr.convert_to(language, symbols, namespace=namespace) if language.name=='c' or language.name=='gpu': statementstr += ';' return TAB*tabs+statementstr def statements_from_codestring(code, eqs=None, defined=None, infer_definitions=False): ''' Generate a list of statements from a user-defined string. ``code`` The input code string, a multi-line string which should be flat, no indents. ``eqs`` A Brian :class:`~brian.Equations` object, which is used to specify a set of already defined variable names if you are using ``infer_definitions``. ``defined`` A set of symbol names which are already defined, if you are using ``infer_definitions``. ``infer_definitions`` Set to ``True`` to guess when a line of the form ``a=b`` should be inferred to be of type ``a:=b``, as user-specified code may not make the distinction between ``a=b`` and ``a:=b``. The rule for definition inference is that you scan through the lines, and a set of already defined symbols is maintained (starting from ``eqs`` and ``defined`` if provided), and an ``=`` op is changed to ``:=`` if the name on the LHS is not already in the set of symbols already defined. ''' lines = [line.strip() for line in code.split('\n') if line.strip()] statements = [] if defined is None: defined = set() if eqs is not None: defined.update(set(eqs._diffeq_names)) for line in lines: m = re.search(r'[^><=]=', line) if not m: raise ValueError("Could not extract statement from line: "+line) start, end = m.start(), m.end() op = line[start:end].strip() var = line[:start].strip() expr = line[end:].strip() # var should be a single word if len(re.findall(r'\b\w+\b', var))!=1: raise ValueError("LHS in statement must be single variable name, line: "+line) if op=='=' and infer_definitions and var not in defined: op = ':=' defined.add(var) stmt = MathematicalStatement(var, op, expr) statements.append(stmt) return statements if __name__=='__main__': statements = statements_from_codestring(''' x := y + z y = 7 z += 3 x = 2 ''') for stmt in statements: print stmt print ' Deps, Resolved:', stmt.dependencies, stmt.resolved deps = set().union(*[stmt.dependencies for stmt in statements]) res = set().union(*[stmt.resolved for stmt in statements]) for r in res: deps.discard(Read(r)) deps.discard(Write(r)) print 'Deps:', deps print 'R/W:', get_read_or_write_dependencies(deps) print 'Resolved:', res print language = CLanguage() stmt = CDefineFromArray('V', '_arr_V', '_neuron_index', language) print stmt print 'Deps:', stmt.dependencies print 'Resolved:', stmt.resolved brian-1.4.3/brian/experimental/codegen2/stateupdater.py000066400000000000000000000045541266062647100231710ustar00rootroot00000000000000from brian import * from integration import * from symbols import * from blocks import * from resolution import * __all__ = ['CodeGenStateUpdater'] class CodeGenStateUpdater(StateUpdater): ''' State updater using code generation, supports Python, C++, GPU. Initialised with: ``group`` The :class:`~brian.NeuronGroup` that this will be used in. ``method`` The integration method, currently one of :func:`euler`, :func:`rk2` or :func:`exp_euler`, but you can define your own too. See :func:`make_integration_step` for details. ``language`` The :class:`Language` object. Creates a :class:`Block` from the equations and the ``method``, gets a set of :class:`Symbol` objects from :func:`get_neuron_group_symbols`, and defines the symbol ``_neuron_index`` as a :class:`SliceIndex`. Then calls :meth:`CodeItem.generate` to get the :class:`Code` object. Inserts ``t`` and ``dt`` into the namespace, and ``_num_neurons`` and ``_num_gpu_indices`` in case they are needed. ''' def __init__(self, group, method, language, clock=None): self.clock = guess_clock(clock) self.group = group eqs = group._eqs self.eqs = eqs self.method = method self.language = language block = Block(*make_integration_step(self.method, self.eqs)) symbols = get_neuron_group_symbols(group, self.language) symbols['_neuron_index'] = SliceIndex('_neuron_index', '0', '_num_neurons', self.language, all=True) self.code = block.generate('stateupdate', self.language, symbols) log_info('brian.codegen2.CodeGenStateUpdater', 'STATE UPDATE CODE:\n'+self.code.code_str) log_info('brian.codegen2.CodeGenStateUpdater', 'STATE UPDATE NAMESPACE KEYS:\n'+str(self.code.namespace.keys())) ns = self.code.namespace ns['t'] = 1.0 # dummy value ns['dt'] = group.clock._dt ns['_num_neurons'] = len(group) ns['_num_gpu_indices'] = len(group) def __call__(self, G): code = self.code ns = code.namespace ns['t'] = G.clock._t code() brian-1.4.3/brian/experimental/codegen2/symbols.py000066400000000000000000000521121266062647100221450ustar00rootroot00000000000000from brian import * from statements import * from expressions import * from codeitems import * from blocks import * from languages import * from dependencies import * from formatting import * __all__ = [ 'language_invariant_symbol_method', 'Symbol', 'RuntimeSymbol', 'ArraySymbol', 'NeuronGroupStateVariableSymbol', 'SliceIndex', 'ArrayIndex', 'get_neuron_group_symbols', ] def language_invariant_symbol_method(basemethname, langs, fallback=None, doc=None): ''' Helper function to create methods for :class:`Symbol` classes. Sometimes it is clearer to write a separate method for each language the :class:`Symbol` supports. This function can generate a method that can take any language, and calls the desired method. For example, if you had defined two methods ``load_python`` and ``load_c`` then you would define the ``load`` method as follows:: load = language_invariant_symbol_method('load', {'python':load_python, 'c':load_c}) The ``fallback`` gives a method to call if no language-specific method was found. A docstring can be provided to ``doc``. ''' def meth(self, *args, **kwds): langname = self.language.name if langname in langs: meth = langs[langname] return meth(self, *args, **kwds) if fallback is not None: return fallback(self, *args, **kwds) meth.__name__ = basemethname if doc is None: try: doc = ''' Method generated by :func:`language_invariant_symbol_method`. Languages and methods follow: ''' for langname, langmeth in langs.iteritems(): doc += '\n'+strip_empty_lines(''' ``{langname}`` :meth:`{langmeth.__name__}` '''.format(langname=langname, langmeth=langmeth)) if fallback is not None: if hasattr(fallback, 'im_class'): fbname = fallback.im_class.__name__+'.'+fallback.__name__ else: fbname = fallback.__name__ doc += '\n'+strip_empty_lines(''' ``fallback`` :meth:`{fbname}` '''.format(fbname=fbname)) except: doc = ''' Method generated by :func:`language_invariant_symbol_method`. Automatic generation of language list failed. ''' meth.__doc__ = doc return meth class Symbol(object): ''' Base class for all symbols. Every symbol has attributes ``name`` and ``language`` which should be a string and :class:`Language` object respectively. The symbol class should define some or all of the methods below. ''' supported_languages = [] def __init__(self, name, language): self.name = name self.language = language if not self.supported(): raise NotImplementedError( "Language "+language.name+" not supported for symbol "+name) def supported(self): ''' Returns ``True`` if the language specified at initialisation is supported. By default, checks if the language name is in the class attribute ``supported_languages`` (list), however can be overridden. ''' return self.language.name in self.supported_languages def update_namespace(self, read, write, vectorisable, namespace): ''' Called by :meth:`resolve`, can be overridden to modify the namespace, e.g. adding data. ''' pass def load(self, read, write, vectorisable): ''' Called by :meth:`resolve`, can be overridden to perform more complicated loading code. By default, returns an empty :class:`Block`. ''' return Block() def save(self, read, write, vectorisable): ''' Called by :meth:`resolve`, can be overridden to perform more complicated saving code. By default, returns an empty :class:`Block`. ''' return Block() def read(self): ''' The string that should be used when this symbol is read, by default just the symbol name. ''' return self.name def write(self): ''' The string that should be used when this symbol is written, by default just the symbol name. ''' return self.name def resolve(self, read, write, vectorisable, item, namespace): ''' Creates a modified item in which the symbol has been resolved. For example, if we started from the expression:: x += 1 and we wanted to produce the following C++ code:: for(int i=0; i target_index w -> synapse_index mod -> source_index target_index -> synapse_index synapse_index -> source_index The issue with mod is that it only depends on source_index and we don't want to vectorise in Python over source_index because it will be used inside another loop which is doing its own vectorisation, i.e. for source_index in spikes: for synapse_index in f(source_index): target_index = g(synapse_index) V += w*mod We want to vectorise this like this: for source_index in spikes: mod = _mod[source_index] synapse_index = f(source_index) target_index = g(source_index) V, w = ... V += w*mod and not like this (which would be an error): mod = _mod[spikes] for source_index in spikes: synapse_index = f(source_index) target_index = g(source_index) V, w = ... V += w*mod Because here we are using mod in the statement V += w*mod where the vectorisation is being done over target_index not source_index. So, essentially we want to allow vectorisation over only a single variable, and it should be the innermost one. However, there is a slight issue in that we are vectorising in a way over both synapse_index AND target_index in the above example, but we can resolve this by saying that target_index = f(synapse_index) is itself vectorised over synapse_index so we are only really vectorising over synapse_index. This gives us the following dependency graph: V -> target_index (single-value) w -> synapse_index (single-value) mod -> source_index (single-value) target_index -> synapse_index (single-value) synapse_index -> source_index (multi-value) source_index -> spikes (multi-value) So there are two loops here, and we vectorise only over synapse_index, the innermost loop. This drops out of the dependency graph resolution because we sequence the resolution order as V w target_index synapse_index(multi) mod source_index(multi) We just add the condition vectorisable=True to all the variables up to the first multi-valued variable, and False afterwards: Vectorisable: V w target_index synapse_index(multi) Not vectorisable: mod source_index(multi) brian-1.4.3/brian/experimental/codegen2/test/000077500000000000000000000000001266062647100210615ustar00rootroot00000000000000brian-1.4.3/brian/experimental/codegen2/test/test1.py000066400000000000000000000021431266062647100224730ustar00rootroot00000000000000from brian import * from brian.experimental.codegen2 import * language = CLanguage() #language = PythonLanguage() structure = 'sparse' #structure = 'dense' tau = 10*ms Vt0 = 1.0 taut = 100*ms eqs = Equations(''' dV/dt = (-V+I)/tau : 1 dI/dt = -I/tau : 1 dVt/dt = (Vt0-Vt)/taut : 1 ''') threshold = 'V>Vt' reset = ''' Vt += 0.5 V = 0 I = 0 ''' G = NeuronGroup(3, eqs, threshold=threshold, reset=reset) G.Vt = Vt0 H = NeuronGroup(len(G), 'V:1\nmod:1', reset=0, threshold=1) P = PoissonGroup(1, rates=300*Hz) Ci = Connection(P, H, 'V', weight=2) H.mod = [1.0, 0.9, 0.1] #C = Connection(H, G, 'I', modulation='mod', structure=structure) C = CodeGenConnection(H, G, 'I', modulation='mod', structure=structure, language=language) for i in xrange(len(G)): C[i, i] = 1 G._state_updater = CodeGenStateUpdater(G, euler, language, clock=G.clock) G._threshold = CodeGenThreshold(G, threshold, language) G._resetfun = CodeGenReset(G, reset, language) M = MultiStateMonitor(G, record=True) Msp = SpikeMonitor(G) run(100*ms) print Msp.spikes M.plot() show() brian-1.4.3/brian/experimental/codegen2/test/test_gpu.py000066400000000000000000000021551266062647100232700ustar00rootroot00000000000000from brian import * from brian.experimental.codegen2 import * from brian.experimental.codegen2.gpu import * log_level_info() #language = GPULanguage(scalar='float') language = GPULanguage() structure = 'sparse' #structure = 'dense' tau = 10*ms Vt0 = 1.0 taut = 100*ms eqs = Equations(''' dV/dt = (-V+I)/tau : 1 dI/dt = -I/tau : 1 dVt/dt = (Vt0-Vt)/taut : 1 ''') threshold = 'V>Vt' reset = ''' Vt += 0.5 V = 0 I = 0 ''' G = NeuronGroup(3, eqs, threshold=threshold, reset=reset) G.Vt = Vt0 H = NeuronGroup(len(G), 'V:1\nmod:1', reset=0, threshold=1) P = PoissonGroup(1, rates=300*Hz) Ci = Connection(P, H, 'V', weight=2) H.mod = [1.0, 0.9, 0.1] C = Connection(H, G, 'I', modulation='mod', structure=structure) for i in xrange(len(G)): C[i, i] = 1 G._state_updater = CodeGenStateUpdater(G, euler, language, clock=G.clock) G._threshold = CodeGenThreshold(G, threshold, language) G._resetfun = CodeGenReset(G, reset, language) M = MultiStateMonitor(G, record=True) Msp = SpikeMonitor(G) #language.gpu_man.prepare() #exit() run(100*ms) print Msp.spikes M.plot() show() brian-1.4.3/brian/experimental/codegen2/threshold.py000066400000000000000000000116631266062647100224570ustar00rootroot00000000000000from brian import * from symbols import * from equations import * from statements import * from blocks import * from dependencies import * from brian.inspection import namespace from gpu import GPUCompactor __all__ = ['CodeGenThreshold'] class CodeGenThreshold(Threshold): def __init__(self, group, inputcode, language, level=0): inputcode = inputcode.strip() self.namespace = namespace(inputcode, level=level+1) self.inputcode = inputcode self.language = language self.prepared = False P = group ns = self.namespace self.inputcode = freeze_with_equations(self.inputcode, P._eqs, ns) block = make_threshold_block(P, self.inputcode, self.language) symbols = get_neuron_group_symbols(P, self.language) symbols['_neuron_index'] = SliceIndex('_neuron_index', '0', '_num_neurons', self.language, all=True) if self.language.name=='c': symbols['_numspikes'] = NumSpikesSymbol('_numspikes', self.language) if self.language.name=='gpu': _arr_spiked_bool = zeros(len(P)+1, dtype=int32) symbols['_spiked'] = ArraySymbol(_arr_spiked_bool, '_spiked', self.language, index='_neuron_index', array_name='_arr_spiked_bool', ) self._spiked_symbol = symbols['_spiked'] self._arr_spiked_bool = _arr_spiked_bool code = self.code = block.generate('threshold', self.language, symbols) log_info('brian.codegen2.CodeGenThreshold', 'CODE:\n'+self.code.code_str) log_info('brian.codegen2.CodeGenThreshold', 'KEYS:\n'+str(ns.keys())) ns = self.code.namespace ns['t'] = 1.0 # dummy value ns['dt'] = P.clock._dt ns['_num_neurons'] = len(P) if self.language.name=='python': def threshold_func(P): code() return ns['_spikes_bool'].nonzero()[0] elif self.language.name=='c': ns['_spikes'] = zeros(len(P), dtype=int) def threshold_func(P): code() return ns['_spikes'][:ns['_arr__numspikes'][0]] elif self.language.name=='gpu': ns['_arr_spiked_bool'] = _arr_spiked_bool ns['_num_gpu_indices'] = len(P) # TODO: this threshold func should do nothing on GPU unless # we want to force sync, or alternatively we can do a # compaction on the GPU and then return that compactor = GPUCompactor(len(P), index_dtype=int32) device_syms = code.gpu_man.mem_man.device def threshold_func(P): code() return compactor(device_syms['_arr_spiked_bool']) #if not language.force_sync: # code.gpu_man.copy_to_host('_arr_spiked_bool') #return ns['_arr_spiked_bool'].nonzero()[0] self.threshold_func = threshold_func def __call__(self, P): ns = self.code.namespace ns['t'] = P.clock._t return self.threshold_func(P) def make_threshold_block(group, threshold, language): if language.name=='python': # NOTE: this doesn't work unless the statement is vectorised, but # that's OK because in thresholding we're always vectorised here return MathematicalStatement('_spikes_bool', ':=', threshold) elif language.name=='gpu': return MathematicalStatement('_spiked', '=', threshold) elif language.name=='c': return Block( MathematicalStatement('_spiked', ':=', threshold, dtype=bool), CodeStatement('if(_spiked) _spikes[_numspikes++] = _neuron_index;', set([Write('_spikes'), Read('_numspikes'), Write('_numspikes'), Read('_neuron_index') ]), set()) ) class NumSpikesSymbol(Symbol): supported_languages = ['c'] def update_namespace(self, read, write, vectorisable, namespace): namespace['_arr_'+self.name] = zeros(1, dtype=int) def load(self, read, write, vectorisable): code = ''' long int &{name} = _arr_{name}[0]; {name} = 0; '''.format(name=self.name) return CodeStatement(code, set([Read('_arr_'+self.name)]), set()) def dependencies(self): return set([Read('_arr_'+self.name)]) brian-1.4.3/brian/experimental/codegen2/todo.txt000066400000000000000000000002431266062647100216070ustar00rootroot00000000000000For vectorised Python arrays, a write invalidates the previous read cache, so after we write we need to regenerate the read names. Random numbers and symbol xibrian-1.4.3/brian/experimental/compensation/000077500000000000000000000000001266062647100211135ustar00rootroot00000000000000brian-1.4.3/brian/experimental/compensation/__init__.py000066400000000000000000000000001266062647100232120ustar00rootroot00000000000000brian-1.4.3/brian/experimental/compensation/compare.py000077500000000000000000000043131266062647100231170ustar00rootroot00000000000000import brian_no_units from brian import * import numpy import brian import brian.experimental.modelfitting as gpu import fast_compensation as cpu #brian.log_level_error() debug_level() def compensate_gpu(input, trace, p=1.0): equations = Equations(''' dV0/dt=(R*Iinj-V0+Vr)/tau : 1 Iinj=(V-V0)/Re : 1 dV/dt=Re*(I-Iinj)/taue : 1 Ve=V-V0 : 1 I : 1 R : 1 Re : 1 Vr : 1 tau : second taue : second ''') params = dict( R = [1.0e3, 1.0e6, 1000.0e6, 1.0e12], tau = [.1*ms, 1*ms, 30*ms, 200*ms], Vr = [-100.0e-3, -80e-3, -40e-3, -10.0e-3], Re = [1.0e3, 1.0e6, 1000.0e6, 1.0e12], taue = [.01*ms, .1*ms, 5*ms, 20*ms]) tracecomp, traceV, traceVe, results = gpu.compensate(input, trace, gpu=1, p=p, equations=equations, maxiter=10, popsize=10, slice_duration=1*second, **params) return traceV, traceVe, results if __name__ == '__main__': I = numpy.load("current1.npy")[:10000] * 25 # because current1[2010_12_09_0006] * 25 Vraw = numpy.load("trace1.npy")[:10000] R = 600*Mohm tau = 10*ms Vr = -70*mV Re = 100*Mohm taue = 1*ms # CPU COMPENSATION #Vcomp, params = cpu.compensate(I, Vraw, dt=.1*ms, # p = 1.0, # durslice=10*second, # R=R, tau=tau, Vr=Vr, Re=Re, taue=taue) # GPU COMPENSATION traceV, traceVe, results = compensate_gpu(I, Vraw) Vcomp_gpu = Vraw - traceVe subplot(211) plot(I) subplot(212) plot(Vraw) plot(Vcomp_gpu) show() brian-1.4.3/brian/experimental/compensation/fast_compensation.py000066400000000000000000000106501266062647100252030ustar00rootroot00000000000000import brian_no_units from brian import * from filter import * from scipy.optimize import * class ElectrodeCompensation (object): eqs = """ dV/dt=Re*(-Iinj)/taue : volt dV0/dt=(R*Iinj-V0+Vr)/tau : volt Iinj=(V-V0)/Re : amp """ def __init__(self, I, Vraw, dt=defaultclock.dt, durslice=1*second, p=1.0, *params): self.I = I self.Vraw = Vraw self.p = p self.dt = dt self.x0 = self.params_to_vector(*params) self.duration = len(I) * dt self.durslice = min(durslice, self.duration) self.slicesteps = int(durslice/dt) self.nslices = int(ceil(len(I)*dt/durslice)) self.islice = 0 self.I_list = [I[self.slicesteps*i:self.slicesteps*(i+1)] for i in range(self.nslices)] self.Vraw_list = [Vraw[self.slicesteps*i:self.slicesteps*(i+1)] for i in range(self.nslices)] def vector_to_params(self, *x): R,tau,Vr,Re,taue = x R = R*R tau = tau*tau Re = Re*Re taue = taue*taue return R,tau,Vr,Re,taue def params_to_vector(self, *params): x = params x = [sqrt(params[0]), sqrt(params[1]), params[2], sqrt(params[3]), sqrt(params[4])] return list(x) def get_model_trace(self, row, *x): R, tau, Vr, Re, taue = self.vector_to_params(*x) eqs = Equations(self.eqs) eqs.prepare() self._eqs = eqs y = simulate(eqs, self.I_list[self.islice] * Re/taue, self.dt, row=row) return y def fitness(self, x): R, tau, Vr, Re, taue = self.vector_to_params(*x) y = self.get_model_trace(0, *x) e = self.dt*sum(abs(self.Vraw_list[self.islice]-y)**self.p) return e def compensate_slice(self, x0): fun = lambda x: self.fitness(x) x = fmin(fun, x0, maxiter=10000, maxfun=10000) return x def compensate(self): self.params_list = [] self.xlist = [self.x0] t0 = time.clock() for self.islice in range(self.nslices): newx = self.compensate_slice(self.xlist[self.islice]) self.xlist.append(newx) self.params_list.append(self.vector_to_params(*newx)) print "Slice %d/%d compensated in %.2f seconds" % \ (self.islice+1, self.nslices, time.clock()-t0) t0 = time.clock() self.xlist = self.xlist[1:] return self.xlist def get_compensated_trace(self): Vcomp_list = [] Vmodel_list = [] for self.islice in range(self.nslices): x = self.xlist[self.islice] V = self.get_model_trace(0, *x) V0 = self.get_model_trace(1, *x) Velec = V-V0 Vcomp_list.append(self.Vraw_list[self.islice] - Velec) Vmodel_list.append(V) self.Vcomp = hstack(Vcomp_list) self.Vmodel = hstack(Vmodel_list) return self.Vcomp, self.Vmodel def compensate(I, Vraw, dt=defaultclock.dt, durslice=1*second, p=1.0, **initial_params): R = initial_params.get("R", 100*Mohm) tau = initial_params.get("tau", 20*ms) Vr = initial_params.get("Vr", -70*mV) Re = initial_params.get("Re", 50*Mohm) taue = initial_params.get("taue", .5*ms) comp = ElectrodeCompensation(I, Vraw, dt, durslice, p, R, tau, Vr, Re, taue) comp.compensate() Vcomp, Vmodel = comp.get_compensated_trace() params = comp.params_list return Vcomp, Vmodel, params if __name__ == '__main__': I = numpy.load("current1.npy")[:100000] * 80 Vraw = numpy.load("trace1.npy")[:100000] R = 200*Mohm tau = 20*ms Vr = -70*mV Re = 50*Mohm taue = .5*ms Vcomp, Vmodel, params = compensate(I, Vraw, dt=.1*ms, p = 1.0, durslice=10*second, R=R, tau=tau, Vr=Vr, Re=Re, taue=taue) #subplot(211) #plot(I) #subplot(212) plot(Vraw, 'k') plot(Vmodel, 'g') plot(Vcomp, 'r') show() brian-1.4.3/brian/experimental/compensation/filter.py000066400000000000000000000062511266062647100227560ustar00rootroot00000000000000import brian_no_units from brian import * from itertools import count from scipy.signal import lfilter from scipy import linalg from numpy.linalg import inv, matrix_power import numpy import time def get_linear_equations(eqs): ''' Returns the matrices A and C for the linear model dX/dt = M(X-B), where eqs is an Equations object. ''' # Otherwise assumes it is given in functional form n = len(eqs._diffeq_names) # number of state variables dynamicvars = eqs._diffeq_names #print eqs._diffeq_names # Calculate B AB = zeros((n, 1)) d = dict.fromkeys(dynamicvars) #print d for j in range(n): d[dynamicvars[j]] = 0. * eqs._units[dynamicvars[j]] for var, i in zip(dynamicvars, count()): #print i, var AB[i] = -eqs.apply(var, d) # Calculate A M = zeros((n, n)) for i in range(n): for j in range(n): d[dynamicvars[j]] = 0. * eqs._units[dynamicvars[j]] if isinstance(eqs._units[dynamicvars[i]], Quantity): d[dynamicvars[i]] = Quantity.with_dimensions(1., eqs._units[dynamicvars[i]].get_dimensions()) else: d[dynamicvars[i]] = 1. for var, j in zip(dynamicvars, count()): M[j, i] = eqs.apply(var, d) + AB[j] #M-=eye(n)*1e-10 # quick dirty fix for problem of constant derivatives; dimension = Hz #B=linalg.lstsq(M,AB)[0] # We use this instead of solve in case M is degenerate try: B = linalg.solve(M, AB) # We use this instead of solve in case M is degenerate except: B = None return M, B def compute_filter(A, row=0): d = len(A) # compute a a = poly(A) # directly vector a of the filter, a[0]=1 # compute b recursively b = zeros(d+1) T = eye(d) b[0] = T[row, 0] for i in range(1, d+1): T = a[i]*eye(d) + dot(A, T) b[i] = T[row, 0] return b, a def simulate(eqs, I, dt, row=0): """ I must be normalized (I*Re/taue for example) """ M, B = get_linear_equations(eqs) A = linalg.expm(M * dt) b, a = compute_filter(A, row=row) y = lfilter(b, a, I*dt) + B[row] return y if __name__ == '__main__': R = 500*Mohm Re = 400*Mohm tau = 10*ms taue = 1.0*ms Vr = -70*mV dt = .1*ms # +Re*I/taue eqs = Equations(""" dV/dt=Re*(-Iinj)/taue : volt dV0/dt=(R*Iinj-V0+Vr)/tau : volt Iinj=(V-V0)/R : amp """) eqs.prepare() Is = numpy.load("current1.npy")[:50000] eqs2 = Equations(""" dV/dt=Re*(I-Iinj)/taue : volt dV0/dt=(R*Iinj-V0+Vr)/tau : volt Iinj=(V-V0)/R : amp I : amp """) G = NeuronGroup(1, eqs2) G.V = Vr G.I = TimedArray(Is) stm = StateMonitor(G, 'V', record=True) t0 = time.clock() run(len(Is)*dt) t1 = time.clock()-t0 y0 = stm.values[0] t0 = time.clock() y = simulate(eqs, Is * Re/taue, dt, row=0) t2 = time.clock()-t0 print t1, t2, t1/t2 print max(abs(y-y0)) subplot(211) plot(Is) subplot(212) plot(y0) plot(y) show() brian-1.4.3/brian/experimental/connectionmonitor.py000066400000000000000000000403341266062647100225410ustar00rootroot00000000000000''' Ideas for weight monitor: Should have following features: * Record from a subset of weights * Convert to Brian or scipy sparse matrix format * Callback feature * Record to file * Display in GUI ''' from brian import * from random import sample import matplotlib.cm as cm try: import pygame except ImportError: pygame = None __all__ = ['ConnectionMonitor'] class ConnectionMonitor(NetworkOperation): ''' Monitors synaptic connection weights Initialisation arguments: ``C`` The connection from which to monitor weight values. ``matrix='W'`` Which matrix to record from, 'W' is weights but you can also record delays with ``matrix='delay'``. ``synapses=None`` Specify which synapses to record. Can take the following values: An integer Will record this many randomly selected synapses (or less if there are less). A value between 0 and 1 Will record this fraction of the synapses, chosen at random. ``True``, ``None`` Records all the synapses A list The list should consist of pairs ``(i, j)`` to record those synapses only. Note that if an integer or fraction is specified, for a sparse matrix, the set of synapses that will be recorded will be chosen at random from the nonzero values at the beginning of the run. If you are using a :class:`DynamicConnectionMatrix` you might want to specify the synapses to record from explicitly. ``callback=None`` A callback function, each time a matrix ``M`` is recorded, ``callback(M)`` will be called. This could be used for saving matrices to a file, for example, or for runtime analysis. See below for recorded matrix format. ``store=False`` Whether or not to keep a copy of the recorded matrices in memory, specifically in the ``values`` attribute (see below). ``display=False`` Whether or not to display images at runtime. Can take any of the following values: ``'pygame'`` Displays the matrix using the pygame module, which must be installed if you want to use this. Only one matrix can be recorded at a time using pygame. Note that if ``display`` is not ``False`` it will replace the image callback provided (if any). ``image_callback=None`` Each time an image ``I`` is recorded, ``image_callback(I)`` is called. See below for image format. ``size=None`` Specifies the dimension of the image. A fairly crude scaling will be used if the dimensions of the image are different to those of the matrix. ``cmap=jet`` The colourmap to use. Various values are available in ``matplotlib.cm``. ``wmin=None, wmax=None`` The minimum and maximum weights for use with the colourmap. If not specified, for each recorded matrix the minimum and maximum values will be used. ``clock=None`` By default, matrices will be recorded every 1s of simulated time. ``when='end'`` When the matrices are recorded (see :class:`NetworkOperation`). **Matrix format** Matrices are recorded in one of two formats, either a numpy ``ndarray`` if the matrix to be recorded is dense and all synapses are being recorded, or a Brian :class:`SparseMatrix` (which is derived from ``scipy.sparse.lil_matrix``) in all other cases. If a subset of synapses to record has been selected, the sparse matrix will consist only of those synapses. **Image format** The images are numpy arrays with shape ``(width, height, 4)`` and ``dtype=uint8``. The last axis is the colour in the format RGBA, with values between 0 and 255. The A component is alpha, and will always be 255. **Attributes** ``values`` If ``store=True`` this consists of a list of pairs ``(t, M)`` giving the recorded matrix ``M`` at time ``t``. ''' def __init__(self, C, matrix='W', synapses=None, callback=None, store=False, display=False, image_callback=None, size=None, cmap=cm.jet, wmin=None, wmax=None, clock=None, when='end', ): self.C = C self.matrix = matrix self.synapses = synapses self.synapse_set = None self.callback = callback self.display = display self.store = store self.image_callback = image_callback Wh, Ww = getattr(C, matrix).shape if size is None: size = Ww, Wh w, h = size self.size = (h, w) self.wmin = wmin self.wmax = wmax self.cmap = cmap if clock is None: clock = EventClock(dt=1*second) if display=='pygame': if pygame is None: warnings.warn('Need pygame module to use pygame display mode.') else: self.screen = pygame.display.set_mode(self.size[::-1]) self.screen_arr = pygame.surfarray.pixels3d(self.screen) self.image_callback = self.pygame_image_callback NetworkOperation.__init__(self, lambda:None, clock=clock, when=when) self.reinit() def reinit(self): self.values = [] def get_matrix(self): W = getattr(self.C, self.matrix) sparseW = isinstance(W.get_row(0), SparseConnectionVector) if not(self.synapses is None or self.synapses is True): if self.synapse_set is None: # The first time we run we generate the synapse set # We don't do this on initialisation because the user # might initialise the matrix values before defining the # monitor. if sparseW: nnz = W.getnnz() else: nnz = W.shape[0]*W.shape[1] synapses = self.synapses if isinstance(synapses, float): synapses = int(synapses*nnz) if isinstance(synapses, int): if synapses>=nnz: # recording all synapses, so use the code below self.synapses = None else: # We want to pick a sample of indices at random, so # we pick a subset of [0...nnz-1] and pick out the # corresponding synapses (i,j) if they are arranged # in order indices = sample(xrange(nnz), synapses) indices.sort() indices = array(indices, dtype=int) if not sparseW: # For dense matrices there is a simple conversion # from 1D indices to 2D indices i = indices%W.shape[0] j = indices/W.shape[0] synapses = zip(i, j) else: # For sparse matrices, we need to go through row # by row and pick out the corresponding indices # Note that we don't return a list of pairs # (i,j) in this case, but directly create the # synapse_set as it is more efficient ss = SparseMatrix(W.shape) for i in xrange(W.shape[0]): row = W.get_row(i) I = indicesWw: w = Ww if h>Wh: h = Wh image = zeros((w, h)) # We're going to do some crude scaling by adding all the weights and # dividing by the number of itmes, for each pixel of the image image_numitems = zeros((w, h), dtype=int) ind = arange(W.shape[1]) # We will do histograms for each row, adding up the corresponding # weights for indices in these bins bins = hstack(((arange(w)*Ww)/w, Ww)) for i in xrange(W.shape[0]): u = h-1-((i*h)//Wh) # target coordinate, scaled row = W.get_row(i) if isinstance(row, SparseConnectionVector): ind = row.ind numitems, _ = histogram(ind, bins) vals, _ = histogram(ind, bins, weights=row) image[:, u] += vals image_numitems[:, u] += numitems image_numitems[image_numitems==0] = 1 image = image/image_numitems if wmin is None: wmin = amin(image) if wmax is None: wmax = amax(image) if wmax - wmin < 1e-20: wmax = wmin + 1e-20 image = self.cmap(clip((image - wmin) / (wmax - wmin), 0, 1), bytes=True) # Scale up if the requested image size is larger than the matrix ih, iw = self.size if ih>h or iw>w: yind = (arange(ih)*h)/ih xind = (arange(iw)*w)/iw xind, yind = meshgrid(xind, yind) image = image[xind.T, yind.T, :] return image def pygame_image_callback(self, image): self.screen_arr[:, :, :3] = image[:, :, :3] pygame.display.flip() pygame.event.pump() def __call__(self): W = self.get_matrix() if self.callback is not None: self.callback(W) if self.store: self.values.append((self.clock.t, W)) if self.image_callback is not None: self.image_callback(self.get_image()) if __name__=='__main__': if 0: N = 1000 taum = 10 * ms tau_pre = 20 * ms tau_post = tau_pre Ee = 0 * mV vt = -54 * mV vr = -60 * mV El = -74 * mV taue = 5 * ms F = 15 * Hz gmax = .01 dA_pre = .01 dA_post = -dA_pre * tau_pre / tau_post * 1.05 eqs_neurons = ''' dv/dt=(ge*(Ee-vr)+El-v)/taum : volt # the synaptic current is linearized dge/dt=-ge/taue : 1 ''' input = PoissonGroup(N, rates=F) neurons = NeuronGroup(1, model=eqs_neurons, threshold=vt, reset=vr) synapses = Connection(input, neurons, 'ge', weight=rand(len(input), len(neurons)) * gmax) neurons.v = vr #stdp=ExponentialSTDP(synapses,tau_pre,tau_post,dA_pre,dA_post,wmax=gmax) ## Explicit STDP rule eqs_stdp = ''' dA_pre/dt=-A_pre/tau_pre : 1 dA_post/dt=-A_post/tau_post : 1 ''' dA_post *= gmax dA_pre *= gmax stdp = STDP(synapses, eqs=eqs_stdp, pre='A_pre+=dA_pre;w+=A_post', post='A_post+=dA_post;w+=A_pre', wmax=gmax) rate = PopulationRateMonitor(neurons) M = ConnectionMonitor(synapses, store=True, synapses=200) run(100 * second, report='text') Z = zeros((100, 1000)) for i, (_, m) in enumerate(M.values): #print m[:, 0].__class__ Z[i, :] = m.todense().flatten() imshow(Z, aspect='auto') show() else: Nin = 200 Nout = 200 duration = 40 * second Fmax = 50 * Hz tau = 10 * ms taue = 2 * ms taui = 5 * ms sigma = 0.#0.4 # Note that factor (tau/taue) makes integral(v(t)) the same when the connection # acts on ge as if it acted directly on v. eqs = Equations(''' #dv/dt = -v/tau + sigma*xi/(2*tau)**.5 : 1 dv/dt = (-v+(tau/taue)*ge-(tau/taui)*gi)/tau + sigma*xi/(2*tau)**.5 : 1 dge/dt = -ge/taue : 1 dgi/dt = -gi/taui : 1 excitatory = ge inhibitory = gi ''') reset = 0 threshold = 1 refractory = 0 * ms taup = 5 * ms taud = 5 * ms Ap = .1 Ad = -Ap * taup / taud * 1.2 wmax_ff = 0.1 wmax_rec = wmax_ff wmax_inh = wmax_rec width = 0.2 recwidth = 0.2 Gin = PoissonGroup(Nin) Gout = NeuronGroup(Nout, eqs, reset=reset, threshold=threshold, refractory=refractory) ff = Connection(Gin, Gout, 'excitatory', structure='dense') for i in xrange(Nin): ff[i, :] = (rand(Nout) > .5) * wmax_ff rec = Connection(Gout, Gout, 'excitatory') for i in xrange(Nout): d = abs(float(i) / Nout - linspace(0, 1, Nout)) d[d > .5] = 1. - d[d > .5] dsquared = d ** 2 prob = exp(-dsquared / (2 * recwidth ** 2)) prob[i] = -1 inds = (rand(Nout) < prob).nonzero()[0] w = rand(len(inds)) * wmax_rec rec[i, inds] = w inh = Connection(Gout, Gout, 'inhibitory', sparseness=1, weight=wmax_inh) stdp_ff = ExponentialSTDP(ff, taup, taud, Ap, Ad, wmax=wmax_ff) stdp_rec = ExponentialSTDP(rec, taup, taud, Ap, Ad, wmax=wmax_rec) M = ConnectionMonitor(ff, display='pygame', #size=(500,500), #synapses=10000, ) run(0 * ms) @network_operation(clock=EventClock(dt=20 * ms)) def stimulation(): Gin.rate = Fmax * exp(-(linspace(0, 1, Nin) - rand())**2 / (2 * width ** 2)) run(40*second, report='stderr') if 0: for i, (_, m) in enumerate(M.values): if i%16==0: figure() subplot(4, 4, i%16+1) imshow(array(m.todense()), aspect='auto', #interpolation='nearest' ) show()brian-1.4.3/brian/experimental/cspikequeue/000077500000000000000000000000001266062647100207375ustar00rootroot00000000000000brian-1.4.3/brian/experimental/cspikequeue/__init__.py000066400000000000000000000000001266062647100230360ustar00rootroot00000000000000brian-1.4.3/brian/experimental/cspikequeue/cspikequeue.py000066400000000000000000000121231266062647100236330ustar00rootroot00000000000000# This file was automatically generated by SWIG (http://www.swig.org). # Version 1.3.40 # # Do not make changes to this file unless you know what you are doing--modify # the SWIG interface file instead. # This file is compatible with both classic and new-style classes. from sys import version_info if version_info >= (2,6,0): def swig_import_helper(): from os.path import dirname import imp fp = None try: fp, pathname, description = imp.find_module('_cspikequeue', [dirname(__file__)]) except ImportError: import _cspikequeue return _cspikequeue if fp is not None: try: _mod = imp.load_module('_cspikequeue', fp, pathname, description) finally: fp.close() return _mod _cspikequeue = swig_import_helper() del swig_import_helper else: import _cspikequeue del version_info try: _swig_property = property except NameError: pass # Python < 2.2 doesn't have 'property'. def _swig_setattr_nondynamic(self,class_type,name,value,static=1): if (name == "thisown"): return self.this.own(value) if (name == "this"): if type(value).__name__ == 'SwigPyObject': self.__dict__[name] = value return method = class_type.__swig_setmethods__.get(name,None) if method: return method(self,value) if (not static) or hasattr(self,name): self.__dict__[name] = value else: raise AttributeError("You cannot add attributes to %s" % self) def _swig_setattr(self,class_type,name,value): return _swig_setattr_nondynamic(self,class_type,name,value,0) def _swig_getattr(self,class_type,name): if (name == "thisown"): return self.this.own() method = class_type.__swig_getmethods__.get(name,None) if method: return method(self) raise AttributeError(name) def _swig_repr(self): try: strthis = "proxy of " + self.this.__repr__() except: strthis = "" return "<%s.%s; %s >" % (self.__class__.__module__, self.__class__.__name__, strthis,) try: _object = object _newclass = 1 except AttributeError: class _object : pass _newclass = 0 class SpikeQueue(_object): __swig_setmethods__ = {} __setattr__ = lambda self, name, value: _swig_setattr(self, SpikeQueue, name, value) __swig_getmethods__ = {} __getattr__ = lambda self, name: _swig_getattr(self, SpikeQueue, name) def __init__(self, *args): this = _cspikequeue.new_SpikeQueue(*args) try: self.this.append(this) except: self.this = this __swig_destroy__ = _cspikequeue.delete_SpikeQueue __del__ = lambda self : None; __swig_setmethods__["X"] = _cspikequeue.SpikeQueue_X_set __swig_getmethods__["X"] = _cspikequeue.SpikeQueue_X_get if _newclass:X = _swig_property(_cspikequeue.SpikeQueue_X_get, _cspikequeue.SpikeQueue_X_set) __swig_setmethods__["n"] = _cspikequeue.SpikeQueue_n_set __swig_getmethods__["n"] = _cspikequeue.SpikeQueue_n_get if _newclass:n = _swig_property(_cspikequeue.SpikeQueue_n_get, _cspikequeue.SpikeQueue_n_set) __swig_setmethods__["n_delays"] = _cspikequeue.SpikeQueue_n_delays_set __swig_getmethods__["n_delays"] = _cspikequeue.SpikeQueue_n_delays_get if _newclass:n_delays = _swig_property(_cspikequeue.SpikeQueue_n_delays_get, _cspikequeue.SpikeQueue_n_delays_set) __swig_setmethods__["n_maxevents"] = _cspikequeue.SpikeQueue_n_maxevents_set __swig_getmethods__["n_maxevents"] = _cspikequeue.SpikeQueue_n_maxevents_get if _newclass:n_maxevents = _swig_property(_cspikequeue.SpikeQueue_n_maxevents_get, _cspikequeue.SpikeQueue_n_maxevents_set) __swig_setmethods__["currenttime"] = _cspikequeue.SpikeQueue_currenttime_set __swig_getmethods__["currenttime"] = _cspikequeue.SpikeQueue_currenttime_get if _newclass:currenttime = _swig_property(_cspikequeue.SpikeQueue_currenttime_get, _cspikequeue.SpikeQueue_currenttime_set) __swig_setmethods__["something"] = _cspikequeue.SpikeQueue_something_set __swig_getmethods__["something"] = _cspikequeue.SpikeQueue_something_get if _newclass:something = _swig_property(_cspikequeue.SpikeQueue_something_get, _cspikequeue.SpikeQueue_something_set) __swig_setmethods__["retarray"] = _cspikequeue.SpikeQueue_retarray_set __swig_getmethods__["retarray"] = _cspikequeue.SpikeQueue_retarray_get if _newclass:retarray = _swig_property(_cspikequeue.SpikeQueue_retarray_get, _cspikequeue.SpikeQueue_retarray_set) def expand(self, *args): return _cspikequeue.SpikeQueue_expand(self, *args) def next(self): return _cspikequeue.SpikeQueue_next(self) def _peek(self, *args): return _cspikequeue.SpikeQueue__peek(self, *args) def peek(self): return _cspikequeue.SpikeQueue_peek(self) def insert(self, *args): return _cspikequeue.SpikeQueue_insert(self, *args) def __repr__(self): return _cspikequeue.SpikeQueue___repr__(self) def __str__(self): return _cspikequeue.SpikeQueue___str__(self) def print_summary(self): return _cspikequeue.SpikeQueue_print_summary(self) SpikeQueue_swigregister = _cspikequeue.SpikeQueue_swigregister SpikeQueue_swigregister(SpikeQueue) brian-1.4.3/brian/experimental/cspikequeue/numpy.i000066400000000000000000001523631266062647100222730ustar00rootroot00000000000000/* -*- C -*- (not really, but good for syntax highlighting) */ #ifdef SWIGPYTHON %{ #ifndef SWIG_FILE_WITH_INIT # define NO_IMPORT_ARRAY #endif #include "stdio.h" #include %} /**********************************************************************/ %fragment("NumPy_Backward_Compatibility", "header") { /* Support older NumPy data type names */ %#if NDARRAY_VERSION < 0x01000000 %#define NPY_BOOL PyArray_BOOL %#define NPY_BYTE PyArray_BYTE %#define NPY_UBYTE PyArray_UBYTE %#define NPY_SHORT PyArray_SHORT %#define NPY_USHORT PyArray_USHORT %#define NPY_INT PyArray_INT %#define NPY_UINT PyArray_UINT %#define NPY_LONG PyArray_LONG %#define NPY_ULONG PyArray_ULONG %#define NPY_LONGLONG PyArray_LONGLONG %#define NPY_ULONGLONG PyArray_ULONGLONG %#define NPY_FLOAT PyArray_FLOAT %#define NPY_DOUBLE PyArray_DOUBLE %#define NPY_LONGDOUBLE PyArray_LONGDOUBLE %#define NPY_CFLOAT PyArray_CFLOAT %#define NPY_CDOUBLE PyArray_CDOUBLE %#define NPY_CLONGDOUBLE PyArray_CLONGDOUBLE %#define NPY_OBJECT PyArray_OBJECT %#define NPY_STRING PyArray_STRING %#define NPY_UNICODE PyArray_UNICODE %#define NPY_VOID PyArray_VOID %#define NPY_NTYPES PyArray_NTYPES %#define NPY_NOTYPE PyArray_NOTYPE %#define NPY_CHAR PyArray_CHAR %#define NPY_USERDEF PyArray_USERDEF %#define npy_intp intp %#define NPY_MAX_BYTE MAX_BYTE %#define NPY_MIN_BYTE MIN_BYTE %#define NPY_MAX_UBYTE MAX_UBYTE %#define NPY_MAX_SHORT MAX_SHORT %#define NPY_MIN_SHORT MIN_SHORT %#define NPY_MAX_USHORT MAX_USHORT %#define NPY_MAX_INT MAX_INT %#define NPY_MIN_INT MIN_INT %#define NPY_MAX_UINT MAX_UINT %#define NPY_MAX_LONG MAX_LONG %#define NPY_MIN_LONG MIN_LONG %#define NPY_MAX_ULONG MAX_ULONG %#define NPY_MAX_LONGLONG MAX_LONGLONG %#define NPY_MIN_LONGLONG MIN_LONGLONG %#define NPY_MAX_ULONGLONG MAX_ULONGLONG %#define NPY_MAX_INTP MAX_INTP %#define NPY_MIN_INTP MIN_INTP %#define NPY_FARRAY FARRAY %#define NPY_F_CONTIGUOUS F_CONTIGUOUS %#endif } /**********************************************************************/ /* The following code originally appeared in * enthought/kiva/agg/src/numeric.i written by Eric Jones. It was * translated from C++ to C by John Hunter. Bill Spotz has modified * it to fix some minor bugs, upgrade from Numeric to numpy (all * versions), add some comments and functionality, and convert from * direct code insertion to SWIG fragments. */ %fragment("NumPy_Macros", "header") { /* Macros to extract array attributes. */ %#define is_array(a) ((a) && PyArray_Check((PyArrayObject *)a)) %#define array_type(a) (int)(PyArray_TYPE(a)) %#define array_numdims(a) (((PyArrayObject *)a)->nd) %#define array_dimensions(a) (((PyArrayObject *)a)->dimensions) %#define array_size(a,i) (((PyArrayObject *)a)->dimensions[i]) %#define array_data(a) (((PyArrayObject *)a)->data) %#define array_is_contiguous(a) (PyArray_ISCONTIGUOUS(a)) %#define array_is_native(a) (PyArray_ISNOTSWAPPED(a)) %#define array_is_fortran(a) (PyArray_ISFORTRAN(a)) } /**********************************************************************/ %fragment("NumPy_Utilities", "header") { /* Given a PyObject, return a string describing its type. */ char* pytype_string(PyObject* py_obj) { if (py_obj == NULL ) return "C NULL value"; if (py_obj == Py_None ) return "Python None" ; if (PyCallable_Check(py_obj)) return "callable" ; if (PyString_Check( py_obj)) return "string" ; if (PyInt_Check( py_obj)) return "int" ; if (PyFloat_Check( py_obj)) return "float" ; if (PyDict_Check( py_obj)) return "dict" ; if (PyList_Check( py_obj)) return "list" ; if (PyTuple_Check( py_obj)) return "tuple" ; if (PyFile_Check( py_obj)) return "file" ; if (PyModule_Check( py_obj)) return "module" ; if (PyInstance_Check(py_obj)) return "instance" ; return "unkown type"; } /* Given a NumPy typecode, return a string describing the type. */ char* typecode_string(int typecode) { static char* type_names[25] = {"bool", "byte", "unsigned byte", "short", "unsigned short", "int", "unsigned int", "long", "unsigned long", "long long", "unsigned long long", "float", "double", "long double", "complex float", "complex double", "complex long double", "object", "string", "unicode", "void", "ntypes", "notype", "char", "unknown"}; return typecode < 24 ? type_names[typecode] : type_names[24]; } /* Make sure input has correct numpy type. Allow character and byte * to match. Also allow int and long to match. This is deprecated. * You should use PyArray_EquivTypenums() instead. */ int type_match(int actual_type, int desired_type) { return PyArray_EquivTypenums(actual_type, desired_type); } } /**********************************************************************/ %fragment("NumPy_Object_to_Array", "header", fragment="NumPy_Backward_Compatibility", fragment="NumPy_Macros", fragment="NumPy_Utilities") { /* Given a PyObject pointer, cast it to a PyArrayObject pointer if * legal. If not, set the python error string appropriately and * return NULL. */ PyArrayObject* obj_to_array_no_conversion(PyObject* input, int typecode) { PyArrayObject* ary = NULL; if (is_array(input) && (typecode == NPY_NOTYPE || PyArray_EquivTypenums(array_type(input), typecode))) { ary = (PyArrayObject*) input; } else if is_array(input) { char* desired_type = typecode_string(typecode); char* actual_type = typecode_string(array_type(input)); PyErr_Format(PyExc_TypeError, "Array of type '%s' required. Array of type '%s' given", desired_type, actual_type); ary = NULL; } else { char * desired_type = typecode_string(typecode); char * actual_type = pytype_string(input); PyErr_Format(PyExc_TypeError, "Array of type '%s' required. A '%s' was given", desired_type, actual_type); ary = NULL; } return ary; } /* Convert the given PyObject to a NumPy array with the given * typecode. On success, return a valid PyArrayObject* with the * correct type. On failure, the python error string will be set and * the routine returns NULL. */ PyArrayObject* obj_to_array_allow_conversion(PyObject* input, int typecode, int* is_new_object) { PyArrayObject* ary = NULL; PyObject* py_obj; if (is_array(input) && (typecode == NPY_NOTYPE || PyArray_EquivTypenums(array_type(input),typecode))) { ary = (PyArrayObject*) input; *is_new_object = 0; } else { py_obj = PyArray_FromObject(input, typecode, 0, 0); /* If NULL, PyArray_FromObject will have set python error value.*/ ary = (PyArrayObject*) py_obj; *is_new_object = 1; } return ary; } /* Given a PyArrayObject, check to see if it is contiguous. If so, * return the input pointer and flag it as not a new object. If it is * not contiguous, create a new PyArrayObject using the original data, * flag it as a new object and return the pointer. */ PyArrayObject* make_contiguous(PyArrayObject* ary, int* is_new_object, int min_dims, int max_dims) { PyArrayObject* result; if (array_is_contiguous(ary)) { result = ary; *is_new_object = 0; } else { result = (PyArrayObject*) PyArray_ContiguousFromObject((PyObject*)ary, array_type(ary), min_dims, max_dims); *is_new_object = 1; } return result; } /* Convert a given PyObject to a contiguous PyArrayObject of the * specified type. If the input object is not a contiguous * PyArrayObject, a new one will be created and the new object flag * will be set. */ PyArrayObject* obj_to_array_contiguous_allow_conversion(PyObject* input, int typecode, int* is_new_object) { int is_new1 = 0; int is_new2 = 0; PyArrayObject* ary2; PyArrayObject* ary1 = obj_to_array_allow_conversion(input, typecode, &is_new1); if (ary1) { ary2 = make_contiguous(ary1, &is_new2, 0, 0); if ( is_new1 && is_new2) { Py_DECREF(ary1); } ary1 = ary2; } *is_new_object = is_new1 || is_new2; return ary1; } } /**********************************************************************/ %fragment("NumPy_Array_Requirements", "header", fragment="NumPy_Backward_Compatibility", fragment="NumPy_Macros") { /* Test whether a python object is contiguous. If array is * contiguous, return 1. Otherwise, set the python error string and * return 0. */ int require_contiguous(PyArrayObject* ary) { int contiguous = 1; if (!array_is_contiguous(ary)) { PyErr_SetString(PyExc_TypeError, "Array must be contiguous. A non-contiguous array was given"); contiguous = 0; } return contiguous; } /* Require that a numpy array is not byte-swapped. If the array is * not byte-swapped, return 1. Otherwise, set the python error string * and return 0. */ int require_native(PyArrayObject* ary) { int native = 1; if (!array_is_native(ary)) { PyErr_SetString(PyExc_TypeError, "Array must have native byteorder. " "A byte-swapped array was given"); native = 0; } return native; } /* Require the given PyArrayObject to have a specified number of * dimensions. If the array has the specified number of dimensions, * return 1. Otherwise, set the python error string and return 0. */ int require_dimensions(PyArrayObject* ary, int exact_dimensions) { int success = 1; if (array_numdims(ary) != exact_dimensions) { PyErr_Format(PyExc_TypeError, "Array must have %d dimensions. Given array has %d dimensions", exact_dimensions, array_numdims(ary)); success = 0; } return success; } /* Require the given PyArrayObject to have one of a list of specified * number of dimensions. If the array has one of the specified number * of dimensions, return 1. Otherwise, set the python error string * and return 0. */ int require_dimensions_n(PyArrayObject* ary, int* exact_dimensions, int n) { int success = 0; int i; char dims_str[255] = ""; char s[255]; for (i = 0; i < n && !success; i++) { if (array_numdims(ary) == exact_dimensions[i]) { success = 1; } } if (!success) { for (i = 0; i < n-1; i++) { sprintf(s, "%d, ", exact_dimensions[i]); strcat(dims_str,s); } sprintf(s, " or %d", exact_dimensions[n-1]); strcat(dims_str,s); PyErr_Format(PyExc_TypeError, "Array must have %s dimensions. Given array has %d dimensions", dims_str, array_numdims(ary)); } return success; } /* Require the given PyArrayObject to have a specified shape. If the * array has the specified shape, return 1. Otherwise, set the python * error string and return 0. */ int require_size(PyArrayObject* ary, npy_intp* size, int n) { int i; int success = 1; int len; char desired_dims[255] = "["; char s[255]; char actual_dims[255] = "["; for(i=0; i < n;i++) { if (size[i] != -1 && size[i] != array_size(ary,i)) { success = 0; } } if (!success) { for (i = 0; i < n; i++) { if (size[i] == -1) { sprintf(s, "*,"); } else { sprintf(s, "%ld,", (long int)size[i]); } strcat(desired_dims,s); } len = strlen(desired_dims); desired_dims[len-1] = ']'; for (i = 0; i < n; i++) { sprintf(s, "%ld,", (long int)array_size(ary,i)); strcat(actual_dims,s); } len = strlen(actual_dims); actual_dims[len-1] = ']'; PyErr_Format(PyExc_TypeError, "Array must have shape of %s. Given array has shape of %s", desired_dims, actual_dims); } return success; } /* Require the given PyArrayObject to to be FORTRAN ordered. If the * the PyArrayObject is already FORTRAN ordered, do nothing. Else, * set the FORTRAN ordering flag and recompute the strides. */ int require_fortran(PyArrayObject* ary) { int success = 1; int nd = array_numdims(ary); int i; if (array_is_fortran(ary)) return success; /* Set the FORTRAN ordered flag */ ary->flags = NPY_FARRAY; /* Recompute the strides */ ary->strides[0] = ary->strides[nd-1]; for (i=1; i < nd; ++i) ary->strides[i] = ary->strides[i-1] * array_size(ary,i-1); return success; } } /* Combine all NumPy fragments into one for convenience */ %fragment("NumPy_Fragments", "header", fragment="NumPy_Backward_Compatibility", fragment="NumPy_Macros", fragment="NumPy_Utilities", fragment="NumPy_Object_to_Array", fragment="NumPy_Array_Requirements") { } /* End John Hunter translation (with modifications by Bill Spotz) */ /* %numpy_typemaps() macro * * This macro defines a family of 41 typemaps that allow C arguments * of the form * * (DATA_TYPE IN_ARRAY1[ANY]) * (DATA_TYPE* IN_ARRAY1, DIM_TYPE DIM1) * (DIM_TYPE DIM1, DATA_TYPE* IN_ARRAY1) * * (DATA_TYPE IN_ARRAY2[ANY][ANY]) * (DATA_TYPE* IN_ARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2) * (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* IN_ARRAY2) * (DATA_TYPE* IN_FARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2) * (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* IN_FARRAY2) * * (DATA_TYPE IN_ARRAY3[ANY][ANY][ANY]) * (DATA_TYPE* IN_ARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3) * (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DATA_TYPE* IN_ARRAY3) * (DATA_TYPE* IN_FARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3) * (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DATA_TYPE* IN_FARRAY3) * * (DATA_TYPE INPLACE_ARRAY1[ANY]) * (DATA_TYPE* INPLACE_ARRAY1, DIM_TYPE DIM1) * (DIM_TYPE DIM1, DATA_TYPE* INPLACE_ARRAY1) * * (DATA_TYPE INPLACE_ARRAY2[ANY][ANY]) * (DATA_TYPE* INPLACE_ARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2) * (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* INPLACE_ARRAY2) * (DATA_TYPE* INPLACE_FARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2) * (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* INPLACE_FARRAY2) * * (DATA_TYPE INPLACE_ARRAY3[ANY][ANY][ANY]) * (DATA_TYPE* INPLACE_ARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3) * (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DATA_TYPE* INPLACE_ARRAY3) * (DATA_TYPE* INPLACE_FARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3) * (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DATA_TYPE* INPLACE_FARRAY3) * * (DATA_TYPE ARGOUT_ARRAY1[ANY]) * (DATA_TYPE* ARGOUT_ARRAY1, DIM_TYPE DIM1) * (DIM_TYPE DIM1, DATA_TYPE* ARGOUT_ARRAY1) * * (DATA_TYPE ARGOUT_ARRAY2[ANY][ANY]) * * (DATA_TYPE ARGOUT_ARRAY3[ANY][ANY][ANY]) * * (DATA_TYPE** ARGOUTVIEW_ARRAY1, DIM_TYPE* DIM1) * (DIM_TYPE* DIM1, DATA_TYPE** ARGOUTVIEW_ARRAY1) * * (DATA_TYPE** ARGOUTVIEW_ARRAY2, DIM_TYPE* DIM1, DIM_TYPE* DIM2) * (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DATA_TYPE** ARGOUTVIEW_ARRAY2) * (DATA_TYPE** ARGOUTVIEW_FARRAY2, DIM_TYPE* DIM1, DIM_TYPE* DIM2) * (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DATA_TYPE** ARGOUTVIEW_FARRAY2) * * (DATA_TYPE** ARGOUTVIEW_ARRAY3, DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3) * (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DATA_TYPE** ARGOUTVIEW_ARRAY3) * (DATA_TYPE** ARGOUTVIEW_FARRAY3, DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3) * (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DATA_TYPE** ARGOUTVIEW_FARRAY3) * * where "DATA_TYPE" is any type supported by the NumPy module, and * "DIM_TYPE" is any int-like type suitable for specifying dimensions. * The difference between "ARRAY" typemaps and "FARRAY" typemaps is * that the "FARRAY" typemaps expect FORTRAN ordering of * multidimensional arrays. In python, the dimensions will not need * to be specified (except for the "DATA_TYPE* ARGOUT_ARRAY1" * typemaps). The IN_ARRAYs can be a numpy array or any sequence that * can be converted to a numpy array of the specified type. The * INPLACE_ARRAYs must be numpy arrays of the appropriate type. The * ARGOUT_ARRAYs will be returned as new numpy arrays of the * appropriate type. * * These typemaps can be applied to existing functions using the * %apply directive. For example: * * %apply (double* IN_ARRAY1, int DIM1) {(double* series, int length)}; * double prod(double* series, int length); * * %apply (int DIM1, int DIM2, double* INPLACE_ARRAY2) * {(int rows, int cols, double* matrix )}; * void floor(int rows, int cols, double* matrix, double f); * * %apply (double IN_ARRAY3[ANY][ANY][ANY]) * {(double tensor[2][2][2] )}; * %apply (double ARGOUT_ARRAY3[ANY][ANY][ANY]) * {(double low[2][2][2] )}; * %apply (double ARGOUT_ARRAY3[ANY][ANY][ANY]) * {(double upp[2][2][2] )}; * void luSplit(double tensor[2][2][2], * double low[2][2][2], * double upp[2][2][2] ); * * or directly with * * double prod(double* IN_ARRAY1, int DIM1); * * void floor(int DIM1, int DIM2, double* INPLACE_ARRAY2, double f); * * void luSplit(double IN_ARRAY3[ANY][ANY][ANY], * double ARGOUT_ARRAY3[ANY][ANY][ANY], * double ARGOUT_ARRAY3[ANY][ANY][ANY]); */ %define %numpy_typemaps(DATA_TYPE, DATA_TYPECODE, DIM_TYPE) /************************/ /* Input Array Typemaps */ /************************/ /* Typemap suite for (DATA_TYPE IN_ARRAY1[ANY]) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DATA_TYPE IN_ARRAY1[ANY]) { $1 = is_array($input) || PySequence_Check($input); } %typemap(in, fragment="NumPy_Fragments") (DATA_TYPE IN_ARRAY1[ANY]) (PyArrayObject* array=NULL, int is_new_object=0) { npy_intp size[1] = { $1_dim0 }; array = obj_to_array_contiguous_allow_conversion($input, DATA_TYPECODE, &is_new_object); if (!array || !require_dimensions(array, 1) || !require_size(array, size, 1)) SWIG_fail; $1 = ($1_ltype) array_data(array); } %typemap(freearg) (DATA_TYPE IN_ARRAY1[ANY]) { if (is_new_object$argnum && array$argnum) { Py_DECREF(array$argnum); } } /* Typemap suite for (DATA_TYPE* IN_ARRAY1, DIM_TYPE DIM1) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DATA_TYPE* IN_ARRAY1, DIM_TYPE DIM1) { $1 = is_array($input) || PySequence_Check($input); } %typemap(in, fragment="NumPy_Fragments") (DATA_TYPE* IN_ARRAY1, DIM_TYPE DIM1) (PyArrayObject* array=NULL, int is_new_object=0) { npy_intp size[1] = { -1 }; array = obj_to_array_contiguous_allow_conversion($input, DATA_TYPECODE, &is_new_object); if (!array || !require_dimensions(array, 1) || !require_size(array, size, 1)) SWIG_fail; $1 = (DATA_TYPE*) array_data(array); $2 = (DIM_TYPE) array_size(array,0); } %typemap(freearg) (DATA_TYPE* IN_ARRAY1, DIM_TYPE DIM1) { if (is_new_object$argnum && array$argnum) { Py_DECREF(array$argnum); } } /* Typemap suite for (DIM_TYPE DIM1, DATA_TYPE* IN_ARRAY1) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DIM_TYPE DIM1, DATA_TYPE* IN_ARRAY1) { $1 = is_array($input) || PySequence_Check($input); } %typemap(in, fragment="NumPy_Fragments") (DIM_TYPE DIM1, DATA_TYPE* IN_ARRAY1) (PyArrayObject* array=NULL, int is_new_object=0) { npy_intp size[1] = {-1}; array = obj_to_array_contiguous_allow_conversion($input, DATA_TYPECODE, &is_new_object); if (!array || !require_dimensions(array, 1) || !require_size(array, size, 1)) SWIG_fail; $1 = (DIM_TYPE) array_size(array,0); $2 = (DATA_TYPE*) array_data(array); } %typemap(freearg) (DIM_TYPE DIM1, DATA_TYPE* IN_ARRAY1) { if (is_new_object$argnum && array$argnum) { Py_DECREF(array$argnum); } } /* Typemap suite for (DATA_TYPE IN_ARRAY2[ANY][ANY]) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DATA_TYPE IN_ARRAY2[ANY][ANY]) { $1 = is_array($input) || PySequence_Check($input); } %typemap(in, fragment="NumPy_Fragments") (DATA_TYPE IN_ARRAY2[ANY][ANY]) (PyArrayObject* array=NULL, int is_new_object=0) { npy_intp size[2] = { $1_dim0, $1_dim1 }; array = obj_to_array_contiguous_allow_conversion($input, DATA_TYPECODE, &is_new_object); if (!array || !require_dimensions(array, 2) || !require_size(array, size, 2)) SWIG_fail; $1 = ($1_ltype) array_data(array); } %typemap(freearg) (DATA_TYPE IN_ARRAY2[ANY][ANY]) { if (is_new_object$argnum && array$argnum) { Py_DECREF(array$argnum); } } /* Typemap suite for (DATA_TYPE* IN_ARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DATA_TYPE* IN_ARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2) { $1 = is_array($input) || PySequence_Check($input); } %typemap(in, fragment="NumPy_Fragments") (DATA_TYPE* IN_ARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2) (PyArrayObject* array=NULL, int is_new_object=0) { npy_intp size[2] = { -1, -1 }; array = obj_to_array_contiguous_allow_conversion($input, DATA_TYPECODE, &is_new_object); if (!array || !require_dimensions(array, 2) || !require_size(array, size, 2)) SWIG_fail; $1 = (DATA_TYPE*) array_data(array); $2 = (DIM_TYPE) array_size(array,0); $3 = (DIM_TYPE) array_size(array,1); } %typemap(freearg) (DATA_TYPE* IN_ARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2) { if (is_new_object$argnum && array$argnum) { Py_DECREF(array$argnum); } } /* Typemap suite for (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* IN_ARRAY2) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* IN_ARRAY2) { $1 = is_array($input) || PySequence_Check($input); } %typemap(in, fragment="NumPy_Fragments") (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* IN_ARRAY2) (PyArrayObject* array=NULL, int is_new_object=0) { npy_intp size[2] = { -1, -1 }; array = obj_to_array_contiguous_allow_conversion($input, DATA_TYPECODE, &is_new_object); if (!array || !require_dimensions(array, 2) || !require_size(array, size, 2)) SWIG_fail; $1 = (DIM_TYPE) array_size(array,0); $2 = (DIM_TYPE) array_size(array,1); $3 = (DATA_TYPE*) array_data(array); } %typemap(freearg) (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* IN_ARRAY2) { if (is_new_object$argnum && array$argnum) { Py_DECREF(array$argnum); } } /* Typemap suite for (DATA_TYPE* IN_FARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DATA_TYPE* IN_FARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2) { $1 = is_array($input) || PySequence_Check($input); } %typemap(in, fragment="NumPy_Fragments") (DATA_TYPE* IN_FARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2) (PyArrayObject* array=NULL, int is_new_object=0) { npy_intp size[2] = { -1, -1 }; array = obj_to_array_contiguous_allow_conversion($input, DATA_TYPECODE, &is_new_object); if (!array || !require_dimensions(array, 2) || !require_size(array, size, 2) || !require_fortran(array)) SWIG_fail; $1 = (DATA_TYPE*) array_data(array); $2 = (DIM_TYPE) array_size(array,0); $3 = (DIM_TYPE) array_size(array,1); } %typemap(freearg) (DATA_TYPE* IN_FARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2) { if (is_new_object$argnum && array$argnum) { Py_DECREF(array$argnum); } } /* Typemap suite for (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* IN_FARRAY2) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* IN_FARRAY2) { $1 = is_array($input) || PySequence_Check($input); } %typemap(in, fragment="NumPy_Fragments") (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* IN_FARRAY2) (PyArrayObject* array=NULL, int is_new_object=0) { npy_intp size[2] = { -1, -1 }; array = obj_to_array_contiguous_allow_conversion($input, DATA_TYPECODE, &is_new_object); if (!array || !require_dimensions(array, 2) || !require_size(array, size, 2) || !require_fortran(array)) SWIG_fail; $1 = (DIM_TYPE) array_size(array,0); $2 = (DIM_TYPE) array_size(array,1); $3 = (DATA_TYPE*) array_data(array); } %typemap(freearg) (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* IN_FARRAY2) { if (is_new_object$argnum && array$argnum) { Py_DECREF(array$argnum); } } /* Typemap suite for (DATA_TYPE IN_ARRAY3[ANY][ANY][ANY]) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DATA_TYPE IN_ARRAY3[ANY][ANY][ANY]) { $1 = is_array($input) || PySequence_Check($input); } %typemap(in, fragment="NumPy_Fragments") (DATA_TYPE IN_ARRAY3[ANY][ANY][ANY]) (PyArrayObject* array=NULL, int is_new_object=0) { npy_intp size[3] = { $1_dim0, $1_dim1, $1_dim2 }; array = obj_to_array_contiguous_allow_conversion($input, DATA_TYPECODE, &is_new_object); if (!array || !require_dimensions(array, 3) || !require_size(array, size, 3)) SWIG_fail; $1 = ($1_ltype) array_data(array); } %typemap(freearg) (DATA_TYPE IN_ARRAY3[ANY][ANY][ANY]) { if (is_new_object$argnum && array$argnum) { Py_DECREF(array$argnum); } } /* Typemap suite for (DATA_TYPE* IN_ARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, * DIM_TYPE DIM3) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DATA_TYPE* IN_ARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3) { $1 = is_array($input) || PySequence_Check($input); } %typemap(in, fragment="NumPy_Fragments") (DATA_TYPE* IN_ARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3) (PyArrayObject* array=NULL, int is_new_object=0) { npy_intp size[3] = { -1, -1, -1 }; array = obj_to_array_contiguous_allow_conversion($input, DATA_TYPECODE, &is_new_object); if (!array || !require_dimensions(array, 3) || !require_size(array, size, 3)) SWIG_fail; $1 = (DATA_TYPE*) array_data(array); $2 = (DIM_TYPE) array_size(array,0); $3 = (DIM_TYPE) array_size(array,1); $4 = (DIM_TYPE) array_size(array,2); } %typemap(freearg) (DATA_TYPE* IN_ARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3) { if (is_new_object$argnum && array$argnum) { Py_DECREF(array$argnum); } } /* Typemap suite for (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, * DATA_TYPE* IN_ARRAY3) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DATA_TYPE* IN_ARRAY3) { $1 = is_array($input) || PySequence_Check($input); } %typemap(in, fragment="NumPy_Fragments") (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DATA_TYPE* IN_ARRAY3) (PyArrayObject* array=NULL, int is_new_object=0) { npy_intp size[3] = { -1, -1, -1 }; array = obj_to_array_contiguous_allow_conversion($input, DATA_TYPECODE, &is_new_object); if (!array || !require_dimensions(array, 3) || !require_size(array, size, 3)) SWIG_fail; $1 = (DIM_TYPE) array_size(array,0); $2 = (DIM_TYPE) array_size(array,1); $3 = (DIM_TYPE) array_size(array,2); $4 = (DATA_TYPE*) array_data(array); } %typemap(freearg) (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DATA_TYPE* IN_ARRAY3) { if (is_new_object$argnum && array$argnum) { Py_DECREF(array$argnum); } } /* Typemap suite for (DATA_TYPE* IN_FARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, * DIM_TYPE DIM3) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DATA_TYPE* IN_FARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3) { $1 = is_array($input) || PySequence_Check($input); } %typemap(in, fragment="NumPy_Fragments") (DATA_TYPE* IN_FARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3) (PyArrayObject* array=NULL, int is_new_object=0) { npy_intp size[3] = { -1, -1, -1 }; array = obj_to_array_contiguous_allow_conversion($input, DATA_TYPECODE, &is_new_object); if (!array || !require_dimensions(array, 3) || !require_size(array, size, 3) | !require_fortran(array)) SWIG_fail; $1 = (DATA_TYPE*) array_data(array); $2 = (DIM_TYPE) array_size(array,0); $3 = (DIM_TYPE) array_size(array,1); $4 = (DIM_TYPE) array_size(array,2); } %typemap(freearg) (DATA_TYPE* IN_FARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3) { if (is_new_object$argnum && array$argnum) { Py_DECREF(array$argnum); } } /* Typemap suite for (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, * DATA_TYPE* IN_FARRAY3) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DATA_TYPE* IN_FARRAY3) { $1 = is_array($input) || PySequence_Check($input); } %typemap(in, fragment="NumPy_Fragments") (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DATA_TYPE* IN_FARRAY3) (PyArrayObject* array=NULL, int is_new_object=0) { npy_intp size[3] = { -1, -1, -1 }; array = obj_to_array_contiguous_allow_conversion($input, DATA_TYPECODE, &is_new_object); if (!array || !require_dimensions(array, 3) || !require_size(array, size, 3) || !require_fortran(array)) SWIG_fail; $1 = (DIM_TYPE) array_size(array,0); $2 = (DIM_TYPE) array_size(array,1); $3 = (DIM_TYPE) array_size(array,2); $4 = (DATA_TYPE*) array_data(array); } %typemap(freearg) (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DATA_TYPE* IN_FARRAY3) { if (is_new_object$argnum && array$argnum) { Py_DECREF(array$argnum); } } /***************************/ /* In-Place Array Typemaps */ /***************************/ /* Typemap suite for (DATA_TYPE INPLACE_ARRAY1[ANY]) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DATA_TYPE INPLACE_ARRAY1[ANY]) { $1 = is_array($input) && PyArray_EquivTypenums(array_type($input), DATA_TYPECODE); } %typemap(in, fragment="NumPy_Fragments") (DATA_TYPE INPLACE_ARRAY1[ANY]) (PyArrayObject* array=NULL) { npy_intp size[1] = { $1_dim0 }; array = obj_to_array_no_conversion($input, DATA_TYPECODE); if (!array || !require_dimensions(array,1) || !require_size(array, size, 1) || !require_contiguous(array) || !require_native(array)) SWIG_fail; $1 = ($1_ltype) array_data(array); } /* Typemap suite for (DATA_TYPE* INPLACE_ARRAY1, DIM_TYPE DIM1) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DATA_TYPE* INPLACE_ARRAY1, DIM_TYPE DIM1) { $1 = is_array($input) && PyArray_EquivTypenums(array_type($input), DATA_TYPECODE); } %typemap(in, fragment="NumPy_Fragments") (DATA_TYPE* INPLACE_ARRAY1, DIM_TYPE DIM1) (PyArrayObject* array=NULL, int i=1) { array = obj_to_array_no_conversion($input, DATA_TYPECODE); if (!array || !require_dimensions(array,1) || !require_contiguous(array) || !require_native(array)) SWIG_fail; $1 = (DATA_TYPE*) array_data(array); $2 = 1; for (i=0; i < array_numdims(array); ++i) $2 *= array_size(array,i); } /* Typemap suite for (DIM_TYPE DIM1, DATA_TYPE* INPLACE_ARRAY1) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DIM_TYPE DIM1, DATA_TYPE* INPLACE_ARRAY1) { $1 = is_array($input) && PyArray_EquivTypenums(array_type($input), DATA_TYPECODE); } %typemap(in, fragment="NumPy_Fragments") (DIM_TYPE DIM1, DATA_TYPE* INPLACE_ARRAY1) (PyArrayObject* array=NULL, int i=0) { array = obj_to_array_no_conversion($input, DATA_TYPECODE); if (!array || !require_dimensions(array,1) || !require_contiguous(array) || !require_native(array)) SWIG_fail; $1 = 1; for (i=0; i < array_numdims(array); ++i) $1 *= array_size(array,i); $2 = (DATA_TYPE*) array_data(array); } /* Typemap suite for (DATA_TYPE INPLACE_ARRAY2[ANY][ANY]) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DATA_TYPE INPLACE_ARRAY2[ANY][ANY]) { $1 = is_array($input) && PyArray_EquivTypenums(array_type($input), DATA_TYPECODE); } %typemap(in, fragment="NumPy_Fragments") (DATA_TYPE INPLACE_ARRAY2[ANY][ANY]) (PyArrayObject* array=NULL) { npy_intp size[2] = { $1_dim0, $1_dim1 }; array = obj_to_array_no_conversion($input, DATA_TYPECODE); if (!array || !require_dimensions(array,2) || !require_size(array, size, 2) || !require_contiguous(array) || !require_native(array)) SWIG_fail; $1 = ($1_ltype) array_data(array); } /* Typemap suite for (DATA_TYPE* INPLACE_ARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DATA_TYPE* INPLACE_ARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2) { $1 = is_array($input) && PyArray_EquivTypenums(array_type($input), DATA_TYPECODE); } %typemap(in, fragment="NumPy_Fragments") (DATA_TYPE* INPLACE_ARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2) (PyArrayObject* array=NULL) { array = obj_to_array_no_conversion($input, DATA_TYPECODE); if (!array || !require_dimensions(array,2) || !require_contiguous(array) || !require_native(array)) SWIG_fail; $1 = (DATA_TYPE*) array_data(array); $2 = (DIM_TYPE) array_size(array,0); $3 = (DIM_TYPE) array_size(array,1); } /* Typemap suite for (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* INPLACE_ARRAY2) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* INPLACE_ARRAY2) { $1 = is_array($input) && PyArray_EquivTypenums(array_type($input), DATA_TYPECODE); } %typemap(in, fragment="NumPy_Fragments") (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* INPLACE_ARRAY2) (PyArrayObject* array=NULL) { array = obj_to_array_no_conversion($input, DATA_TYPECODE); if (!array || !require_dimensions(array,2) || !require_contiguous(array) || !require_native(array)) SWIG_fail; $1 = (DIM_TYPE) array_size(array,0); $2 = (DIM_TYPE) array_size(array,1); $3 = (DATA_TYPE*) array_data(array); } /* Typemap suite for (DATA_TYPE* INPLACE_FARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DATA_TYPE* INPLACE_FARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2) { $1 = is_array($input) && PyArray_EquivTypenums(array_type($input), DATA_TYPECODE); } %typemap(in, fragment="NumPy_Fragments") (DATA_TYPE* INPLACE_FARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2) (PyArrayObject* array=NULL) { array = obj_to_array_no_conversion($input, DATA_TYPECODE); if (!array || !require_dimensions(array,2) || !require_contiguous(array) || !require_native(array) || !require_fortran(array)) SWIG_fail; $1 = (DATA_TYPE*) array_data(array); $2 = (DIM_TYPE) array_size(array,0); $3 = (DIM_TYPE) array_size(array,1); } /* Typemap suite for (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* INPLACE_FARRAY2) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* INPLACE_FARRAY2) { $1 = is_array($input) && PyArray_EquivTypenums(array_type($input), DATA_TYPECODE); } %typemap(in, fragment="NumPy_Fragments") (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* INPLACE_FARRAY2) (PyArrayObject* array=NULL) { array = obj_to_array_no_conversion($input, DATA_TYPECODE); if (!array || !require_dimensions(array,2) || !require_contiguous(array) || !require_native(array) || !require_fortran(array)) SWIG_fail; $1 = (DIM_TYPE) array_size(array,0); $2 = (DIM_TYPE) array_size(array,1); $3 = (DATA_TYPE*) array_data(array); } /* Typemap suite for (DATA_TYPE INPLACE_ARRAY3[ANY][ANY][ANY]) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DATA_TYPE INPLACE_ARRAY3[ANY][ANY][ANY]) { $1 = is_array($input) && PyArray_EquivTypenums(array_type($input), DATA_TYPECODE); } %typemap(in, fragment="NumPy_Fragments") (DATA_TYPE INPLACE_ARRAY3[ANY][ANY][ANY]) (PyArrayObject* array=NULL) { npy_intp size[3] = { $1_dim0, $1_dim1, $1_dim2 }; array = obj_to_array_no_conversion($input, DATA_TYPECODE); if (!array || !require_dimensions(array,3) || !require_size(array, size, 3) || !require_contiguous(array) || !require_native(array)) SWIG_fail; $1 = ($1_ltype) array_data(array); } /* Typemap suite for (DATA_TYPE* INPLACE_ARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, * DIM_TYPE DIM3) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DATA_TYPE* INPLACE_ARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3) { $1 = is_array($input) && PyArray_EquivTypenums(array_type($input), DATA_TYPECODE); } %typemap(in, fragment="NumPy_Fragments") (DATA_TYPE* INPLACE_ARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3) (PyArrayObject* array=NULL) { array = obj_to_array_no_conversion($input, DATA_TYPECODE); if (!array || !require_dimensions(array,3) || !require_contiguous(array) || !require_native(array)) SWIG_fail; $1 = (DATA_TYPE*) array_data(array); $2 = (DIM_TYPE) array_size(array,0); $3 = (DIM_TYPE) array_size(array,1); $4 = (DIM_TYPE) array_size(array,2); } /* Typemap suite for (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, * DATA_TYPE* INPLACE_ARRAY3) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DATA_TYPE* INPLACE_ARRAY3) { $1 = is_array($input) && PyArray_EquivTypenums(array_type($input), DATA_TYPECODE); } %typemap(in, fragment="NumPy_Fragments") (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DATA_TYPE* INPLACE_ARRAY3) (PyArrayObject* array=NULL) { array = obj_to_array_no_conversion($input, DATA_TYPECODE); if (!array || !require_dimensions(array,3) || !require_contiguous(array) || !require_native(array)) SWIG_fail; $1 = (DIM_TYPE) array_size(array,0); $2 = (DIM_TYPE) array_size(array,1); $3 = (DIM_TYPE) array_size(array,2); $4 = (DATA_TYPE*) array_data(array); } /* Typemap suite for (DATA_TYPE* INPLACE_FARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, * DIM_TYPE DIM3) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DATA_TYPE* INPLACE_FARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3) { $1 = is_array($input) && PyArray_EquivTypenums(array_type($input), DATA_TYPECODE); } %typemap(in, fragment="NumPy_Fragments") (DATA_TYPE* INPLACE_FARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3) (PyArrayObject* array=NULL) { array = obj_to_array_no_conversion($input, DATA_TYPECODE); if (!array || !require_dimensions(array,3) || !require_contiguous(array) || !require_native(array) || !require_fortran(array)) SWIG_fail; $1 = (DATA_TYPE*) array_data(array); $2 = (DIM_TYPE) array_size(array,0); $3 = (DIM_TYPE) array_size(array,1); $4 = (DIM_TYPE) array_size(array,2); } /* Typemap suite for (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, * DATA_TYPE* INPLACE_FARRAY3) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DATA_TYPE* INPLACE_FARRAY3) { $1 = is_array($input) && PyArray_EquivTypenums(array_type($input), DATA_TYPECODE); } %typemap(in, fragment="NumPy_Fragments") (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DATA_TYPE* INPLACE_FARRAY3) (PyArrayObject* array=NULL) { array = obj_to_array_no_conversion($input, DATA_TYPECODE); if (!array || !require_dimensions(array,3) || !require_contiguous(array) || !require_native(array) || !require_fortran(array)) SWIG_fail; $1 = (DIM_TYPE) array_size(array,0); $2 = (DIM_TYPE) array_size(array,1); $3 = (DIM_TYPE) array_size(array,2); $4 = (DATA_TYPE*) array_data(array); } /*************************/ /* Argout Array Typemaps */ /*************************/ /* Typemap suite for (DATA_TYPE ARGOUT_ARRAY1[ANY]) */ %typemap(in,numinputs=0, fragment="NumPy_Backward_Compatibility,NumPy_Macros") (DATA_TYPE ARGOUT_ARRAY1[ANY]) (PyObject * array = NULL) { npy_intp dims[1] = { $1_dim0 }; array = PyArray_SimpleNew(1, dims, DATA_TYPECODE); if (!array) SWIG_fail; $1 = ($1_ltype) array_data(array); } %typemap(argout) (DATA_TYPE ARGOUT_ARRAY1[ANY]) { $result = SWIG_Python_AppendOutput($result,array$argnum); } /* Typemap suite for (DATA_TYPE* ARGOUT_ARRAY1, DIM_TYPE DIM1) */ %typemap(in,numinputs=1, fragment="NumPy_Fragments") (DATA_TYPE* ARGOUT_ARRAY1, DIM_TYPE DIM1) (PyObject * array = NULL) { npy_intp dims[1]; if (!PyInt_Check($input)) { char* typestring = pytype_string($input); PyErr_Format(PyExc_TypeError, "Int dimension expected. '%s' given.", typestring); SWIG_fail; } $2 = (DIM_TYPE) PyInt_AsLong($input); dims[0] = (npy_intp) $2; array = PyArray_SimpleNew(1, dims, DATA_TYPECODE); if (!array) SWIG_fail; $1 = (DATA_TYPE*) array_data(array); } %typemap(argout) (DATA_TYPE* ARGOUT_ARRAY1, DIM_TYPE DIM1) { $result = SWIG_Python_AppendOutput($result,array$argnum); } /* Typemap suite for (DIM_TYPE DIM1, DATA_TYPE* ARGOUT_ARRAY1) */ %typemap(in,numinputs=1, fragment="NumPy_Fragments") (DIM_TYPE DIM1, DATA_TYPE* ARGOUT_ARRAY1) (PyObject * array = NULL) { npy_intp dims[1]; if (!PyInt_Check($input)) { char* typestring = pytype_string($input); PyErr_Format(PyExc_TypeError, "Int dimension expected. '%s' given.", typestring); SWIG_fail; } $1 = (DIM_TYPE) PyInt_AsLong($input); dims[0] = (npy_intp) $1; array = PyArray_SimpleNew(1, dims, DATA_TYPECODE); if (!array) SWIG_fail; $2 = (DATA_TYPE*) array_data(array); } %typemap(argout) (DIM_TYPE DIM1, DATA_TYPE* ARGOUT_ARRAY1) { $result = SWIG_Python_AppendOutput($result,array$argnum); } /* Typemap suite for (DATA_TYPE ARGOUT_ARRAY2[ANY][ANY]) */ %typemap(in,numinputs=0, fragment="NumPy_Backward_Compatibility,NumPy_Macros") (DATA_TYPE ARGOUT_ARRAY2[ANY][ANY]) (PyObject * array = NULL) { npy_intp dims[2] = { $1_dim0, $1_dim1 }; array = PyArray_SimpleNew(2, dims, DATA_TYPECODE); if (!array) SWIG_fail; $1 = ($1_ltype) array_data(array); } %typemap(argout) (DATA_TYPE ARGOUT_ARRAY2[ANY][ANY]) { $result = SWIG_Python_AppendOutput($result,array$argnum); } /* Typemap suite for (DATA_TYPE ARGOUT_ARRAY3[ANY][ANY][ANY]) */ %typemap(in,numinputs=0, fragment="NumPy_Backward_Compatibility,NumPy_Macros") (DATA_TYPE ARGOUT_ARRAY3[ANY][ANY][ANY]) (PyObject * array = NULL) { npy_intp dims[3] = { $1_dim0, $1_dim1, $1_dim2 }; array = PyArray_SimpleNew(3, dims, DATA_TYPECODE); if (!array) SWIG_fail; $1 = ($1_ltype) array_data(array); } %typemap(argout) (DATA_TYPE ARGOUT_ARRAY3[ANY][ANY][ANY]) { $result = SWIG_Python_AppendOutput($result,array$argnum); } /*****************************/ /* Argoutview Array Typemaps */ /*****************************/ /* Typemap suite for (DATA_TYPE** ARGOUTVIEW_ARRAY1, DIM_TYPE* DIM1) */ %typemap(in,numinputs=0) (DATA_TYPE** ARGOUTVIEW_ARRAY1, DIM_TYPE* DIM1 ) (DATA_TYPE* data_temp , DIM_TYPE dim_temp) { $1 = &data_temp; $2 = &dim_temp; } %typemap(argout, fragment="NumPy_Backward_Compatibility") (DATA_TYPE** ARGOUTVIEW_ARRAY1, DIM_TYPE* DIM1) { npy_intp dims[1] = { *$2 }; PyObject * array = PyArray_SimpleNewFromData(1, dims, DATA_TYPECODE, (void*)(*$1)); if (!array) SWIG_fail; $result = SWIG_Python_AppendOutput($result,array); } /* Typemap suite for (DIM_TYPE* DIM1, DATA_TYPE** ARGOUTVIEW_ARRAY1) */ %typemap(in,numinputs=0) (DIM_TYPE* DIM1 , DATA_TYPE** ARGOUTVIEW_ARRAY1) (DIM_TYPE dim_temp, DATA_TYPE* data_temp ) { $1 = &dim_temp; $2 = &data_temp; } %typemap(argout, fragment="NumPy_Backward_Compatibility") (DIM_TYPE* DIM1, DATA_TYPE** ARGOUTVIEW_ARRAY1) { npy_intp dims[1] = { *$1 }; PyObject * array = PyArray_SimpleNewFromData(1, dims, DATA_TYPECODE, (void*)(*$2)); if (!array) SWIG_fail; $result = SWIG_Python_AppendOutput($result,array); } /* Typemap suite for (DATA_TYPE** ARGOUTVIEW_ARRAY2, DIM_TYPE* DIM1, DIM_TYPE* DIM2) */ %typemap(in,numinputs=0) (DATA_TYPE** ARGOUTVIEW_ARRAY2, DIM_TYPE* DIM1 , DIM_TYPE* DIM2 ) (DATA_TYPE* data_temp , DIM_TYPE dim1_temp, DIM_TYPE dim2_temp) { $1 = &data_temp; $2 = &dim1_temp; $3 = &dim2_temp; } %typemap(argout, fragment="NumPy_Backward_Compatibility") (DATA_TYPE** ARGOUTVIEW_ARRAY2, DIM_TYPE* DIM1, DIM_TYPE* DIM2) { npy_intp dims[2] = { *$2, *$3 }; PyObject * array = PyArray_SimpleNewFromData(2, dims, DATA_TYPECODE, (void*)(*$1)); if (!array) SWIG_fail; $result = SWIG_Python_AppendOutput($result,array); } /* Typemap suite for (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DATA_TYPE** ARGOUTVIEW_ARRAY2) */ %typemap(in,numinputs=0) (DIM_TYPE* DIM1 , DIM_TYPE* DIM2 , DATA_TYPE** ARGOUTVIEW_ARRAY2) (DIM_TYPE dim1_temp, DIM_TYPE dim2_temp, DATA_TYPE* data_temp ) { $1 = &dim1_temp; $2 = &dim2_temp; $3 = &data_temp; } %typemap(argout, fragment="NumPy_Backward_Compatibility") (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DATA_TYPE** ARGOUTVIEW_ARRAY2) { npy_intp dims[2] = { *$1, *$2 }; PyObject * array = PyArray_SimpleNewFromData(2, dims, DATA_TYPECODE, (void*)(*$3)); if (!array) SWIG_fail; $result = SWIG_Python_AppendOutput($result,array); } /* Typemap suite for (DATA_TYPE** ARGOUTVIEW_FARRAY2, DIM_TYPE* DIM1, DIM_TYPE* DIM2) */ %typemap(in,numinputs=0) (DATA_TYPE** ARGOUTVIEW_FARRAY2, DIM_TYPE* DIM1 , DIM_TYPE* DIM2 ) (DATA_TYPE* data_temp , DIM_TYPE dim1_temp, DIM_TYPE dim2_temp) { $1 = &data_temp; $2 = &dim1_temp; $3 = &dim2_temp; } %typemap(argout, fragment="NumPy_Backward_Compatibility,NumPy_Array_Requirements") (DATA_TYPE** ARGOUTVIEW_FARRAY2, DIM_TYPE* DIM1, DIM_TYPE* DIM2) { npy_intp dims[2] = { *$2, *$3 }; PyObject * obj = PyArray_SimpleNewFromData(2, dims, DATA_TYPECODE, (void*)(*$1)); PyArrayObject * array = (PyArrayObject*) obj; if (!array || !require_fortran(array)) SWIG_fail; $result = SWIG_Python_AppendOutput($result,obj); } /* Typemap suite for (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DATA_TYPE** ARGOUTVIEW_FARRAY2) */ %typemap(in,numinputs=0) (DIM_TYPE* DIM1 , DIM_TYPE* DIM2 , DATA_TYPE** ARGOUTVIEW_FARRAY2) (DIM_TYPE dim1_temp, DIM_TYPE dim2_temp, DATA_TYPE* data_temp ) { $1 = &dim1_temp; $2 = &dim2_temp; $3 = &data_temp; } %typemap(argout, fragment="NumPy_Backward_Compatibility,NumPy_Array_Requirements") (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DATA_TYPE** ARGOUTVIEW_FARRAY2) { npy_intp dims[2] = { *$1, *$2 }; PyObject * obj = PyArray_SimpleNewFromData(2, dims, DATA_TYPECODE, (void*)(*$3)); PyArrayObject * array = (PyArrayObject*) obj; if (!array || !require_fortran(array)) SWIG_fail; $result = SWIG_Python_AppendOutput($result,obj); } /* Typemap suite for (DATA_TYPE** ARGOUTVIEW_ARRAY3, DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3) */ %typemap(in,numinputs=0) (DATA_TYPE** ARGOUTVIEW_ARRAY3, DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3) (DATA_TYPE* data_temp, DIM_TYPE dim1_temp, DIM_TYPE dim2_temp, DIM_TYPE dim3_temp) { $1 = &data_temp; $2 = &dim1_temp; $3 = &dim2_temp; $4 = &dim3_temp; } %typemap(argout, fragment="NumPy_Backward_Compatibility") (DATA_TYPE** ARGOUTVIEW_ARRAY3, DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3) { npy_intp dims[3] = { *$2, *$3, *$4 }; PyObject * array = PyArray_SimpleNewFromData(3, dims, DATA_TYPECODE, (void*)(*$1)); if (!array) SWIG_fail; $result = SWIG_Python_AppendOutput($result,array); } /* Typemap suite for (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DATA_TYPE** ARGOUTVIEW_ARRAY3) */ %typemap(in,numinputs=0) (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DATA_TYPE** ARGOUTVIEW_ARRAY3) (DIM_TYPE dim1_temp, DIM_TYPE dim2_temp, DIM_TYPE dim3_temp, DATA_TYPE* data_temp) { $1 = &dim1_temp; $2 = &dim2_temp; $3 = &dim3_temp; $4 = &data_temp; } %typemap(argout, fragment="NumPy_Backward_Compatibility") (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DATA_TYPE** ARGOUTVIEW_ARRAY3) { npy_intp dims[3] = { *$1, *$2, *$3 }; PyObject * array = PyArray_SimpleNewFromData(3, dims, DATA_TYPECODE, (void*)(*$3)); if (!array) SWIG_fail; $result = SWIG_Python_AppendOutput($result,array); } /* Typemap suite for (DATA_TYPE** ARGOUTVIEW_FARRAY3, DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3) */ %typemap(in,numinputs=0) (DATA_TYPE** ARGOUTVIEW_FARRAY3, DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3) (DATA_TYPE* data_temp, DIM_TYPE dim1_temp, DIM_TYPE dim2_temp, DIM_TYPE dim3_temp) { $1 = &data_temp; $2 = &dim1_temp; $3 = &dim2_temp; $4 = &dim3_temp; } %typemap(argout, fragment="NumPy_Backward_Compatibility,NumPy_Array_Requirements") (DATA_TYPE** ARGOUTVIEW_FARRAY3, DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3) { npy_intp dims[3] = { *$2, *$3, *$4 }; PyObject * obj = PyArray_SimpleNewFromData(3, dims, DATA_TYPECODE, (void*)(*$1)); PyArrayObject * array = (PyArrayObject*) obj; if (!array || require_fortran(array)) SWIG_fail; $result = SWIG_Python_AppendOutput($result,obj); } /* Typemap suite for (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DATA_TYPE** ARGOUTVIEW_FARRAY3) */ %typemap(in,numinputs=0) (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DATA_TYPE** ARGOUTVIEW_FARRAY3) (DIM_TYPE dim1_temp, DIM_TYPE dim2_temp, DIM_TYPE dim3_temp, DATA_TYPE* data_temp) { $1 = &dim1_temp; $2 = &dim2_temp; $3 = &dim3_temp; $4 = &data_temp; } %typemap(argout, fragment="NumPy_Backward_Compatibility,NumPy_Array_Requirements") (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DATA_TYPE** ARGOUTVIEW_FARRAY3) { npy_intp dims[3] = { *$1, *$2, *$3 }; PyObject * obj = PyArray_SimpleNewFromData(3, dims, DATA_TYPECODE, (void*)(*$3)); PyArrayObject * array = (PyArrayObject*) obj; if (!array || require_fortran(array)) SWIG_fail; $result = SWIG_Python_AppendOutput($result,obj); } %enddef /* %numpy_typemaps() macro */ /* *************************************************************** */ /* Concrete instances of the %numpy_typemaps() macro: Each invocation * below applies all of the typemaps above to the specified data type. */ %numpy_typemaps(signed char , NPY_BYTE , int) %numpy_typemaps(unsigned char , NPY_UBYTE , int) %numpy_typemaps(short , NPY_SHORT , int) %numpy_typemaps(unsigned short , NPY_USHORT , int) %numpy_typemaps(int , NPY_INT , int) %numpy_typemaps(unsigned int , NPY_UINT , int) %numpy_typemaps(long , NPY_LONG , int) %numpy_typemaps(unsigned long , NPY_ULONG , int) %numpy_typemaps(long long , NPY_LONGLONG , int) %numpy_typemaps(unsigned long long, NPY_ULONGLONG, int) %numpy_typemaps(float , NPY_FLOAT , int) %numpy_typemaps(double , NPY_DOUBLE , int) /* *************************************************************** * The follow macro expansion does not work, because C++ bool is 4 * bytes and NPY_BOOL is 1 byte * * %numpy_typemaps(bool, NPY_BOOL, int) */ /* *************************************************************** * On my Mac, I get the following warning for this macro expansion: * 'swig/python detected a memory leak of type 'long double *', no destructor found.' * * %numpy_typemaps(long double, NPY_LONGDOUBLE, int) */ /* *************************************************************** * Swig complains about a syntax error for the following macro * expansions: * * %numpy_typemaps(complex float, NPY_CFLOAT , int) * * %numpy_typemaps(complex double, NPY_CDOUBLE, int) * * %numpy_typemaps(complex long double, NPY_CLONGDOUBLE, int) */ #endif /* SWIGPYTHON */ brian-1.4.3/brian/experimental/cspikequeue/old/000077500000000000000000000000001266062647100215155ustar00rootroot00000000000000brian-1.4.3/brian/experimental/cspikequeue/old/cspikequeue_pythonmodule.py000066400000000000000000000037461266062647100272330ustar00rootroot00000000000000import brian.experimental.cspikequeue.cspikequeue as _cspikequeue from brian.monitor import SpikeMonitor from brian.stdunits import ms import warnings import numpy as np import sys __all__=['CSpikeQueueWrapper'] INITIAL_MAXSPIKESPER_DT = 1 class CSpikeQueueWrapper(SpikeMonitor): def __init__(self, source, synapses, delays, max_delay = 60*ms, maxevents = INITIAL_MAXSPIKESPER_DT, precompute_offsets = True): super(CSpikeQueueWrapper, self).__init__(source, record = False) nsteps = int(np.floor(max_delay/self.source.clock.dt))+1 print "Initialized with %d steps and %d events" % (nsteps, maxevents) self._max_delay = max_delay self.synapses = synapses self.delays = delays # Delay handling should also be in C self._spikequeue = _cspikequeue.SpikeQueue(nsteps, int(maxevents)) def compress(self): nsteps=max(self.delays)+1 # Check whether some delays are too long if (nsteps>self._spikequeue.n_delays): desired_max_delay = nsteps * self.source.clock.dt print desired_max_delay raise ValueError,"Synaptic delays exceed maximum delay, set max_delay to %.1f ms" % (desired_max_delay/ms) if hasattr(self, '_iscompressed') and self._iscompressed: return self._iscompressed = True # Adjust the maximum delay and number of events per timestep if necessary maxevents=self._spikequeue.n_maxevents if maxevents==INITIAL_MAXSPIKESPER_DT: # automatic resize maxevents=max(INITIAL_MAXSPIKESPER_DT, max([len(targets) for targets in self.synapses])) # Resize self._spikequeue.expand(int(maxevents))# Resize def insert(self, *args, **kargs): self._spikequeue.insert(*args) def next(self): return self._spikequeue.next() def peek(self): return self._spikequeue.peek() brian-1.4.3/brian/experimental/cspikequeue/old/test_all.py000066400000000000000000000022621266062647100237000ustar00rootroot00000000000000import os, subprocess SEPARATOR = '/'*20 def run_and_print(command, print_out = True): p = subprocess.Popen(command, shell=True, stdout=subprocess.PIPE, stderr=subprocess.STDOUT) if print_out: for line in p.stdout.readlines(): print line, retval = p.wait() return retval run_and_print("rm spikequeue") if True: # The c++ test CPP_BUILD = 'g++ spikequeue.cpp -O3 -o spikequeue' if run_and_print(CPP_BUILD): print SEPARATOR+' C++ file built' if False: CPP_RUN= './spikequeue' if run_and_print(CPP_RUN): print SEPARATOR+' Run successful' if True: run_and_print("rm _cspikequeue.so") # the swig test SWIG_BUILD = 'swig -c++ -python spikequeue.i' run_and_print(SWIG_BUILD) print SEPARATOR+' SWIG files built' PYTHON_BUILD = 'python setup.py build_ext --inplace' run_and_print(PYTHON_BUILD) print SEPARATOR+'Python files built' if False: PYTHON_TEST = 'python test_python.py' run_and_print(PYTHON_TEST) print SEPARATOR+' Python test successful' if True: PYTHON_TEST = 'python test_two_synapses.py' run_and_print(PYTHON_TEST) print SEPARATOR+' Python test successful' brian-1.4.3/brian/experimental/cspikequeue/old/test_benchmark.py000066400000000000000000000033511266062647100250620ustar00rootroot00000000000000# coding: latin-1 """ CUBA example with delays. Connection (no delay): 3.5 s DelayConnection: 5.7 s Synapses (with precomputed offsets): 6.6 s # 6.9 s Synapses with weave: 6.4 s Synapses with zero delays: 5.2 s """ from brian import * import time import random as pyrandom set_global_preferences(useweave = False) def run_test(usecspikequeue): seed(120937) pyrandom.seed(120937) start_time = time.time() taum = 20 * ms taue = 5 * ms taui = 10 * ms Vt = -50 * mV Vr = -60 * mV El = -49 * mV eqs = Equations(''' dv/dt = (ge+gi-(v-El))/taum : volt dge/dt = -ge/taue : volt dgi/dt = -gi/taui : volt ''') P = NeuronGroup(4000, model=eqs, threshold=Vt, reset=Vr, refractory=5 * ms) P.v = Vr P.ge = 0 * mV P.gi = 0 * mV Pe = P.subgroup(3200) Pi = P.subgroup(800) we = (60 * 0.27 / 10) * mV # excitatory synaptic weight (voltage) wi = (-20 * 4.5 / 10) * mV # inhibitory synaptic weight Se = Synapses(Pe, P, model = 'w : 1', pre = 'ge += we', max_delay = 1*ms) Si = Synapses(Pi, P, model = 'w : 1', pre = 'gi += wi', max_delay = 1*ms) Se[:,:]=0.02 Si[:,:]=0.02 Se.delay='rand()*ms' Si.delay='rand()*ms' P.v = Vr + rand(len(P)) * (Vt - Vr) print "Network construction time:", time.time() - start_time, "seconds" print len(P), "neurons in the network" print "Simulation running..." run(1 * msecond) start_time = time.time() run(1 * second) duration = time.time() - start_time print P.v return duration if __name__ == '__main__': durC = run_test(True) durPython = run_test(False) print "(CPP) Simulation time:", durC, "seconds" print "(Python) Simulation time:", durPython, "seconds" brian-1.4.3/brian/experimental/cspikequeue/old/test_python.py000066400000000000000000000014471266062647100244550ustar00rootroot00000000000000print "Imports ok" import sys if not '.' in sys.path: sys.path.append('.') from cspikequeue import * #from cspikequeue.cspikequeue_pythonmodule import CSpikeQueueWrapper import numpy as np if True: queue = SpikeQueue(20, 10) #print queue print "time", queue.currenttime sys.stdout.flush() q = queue.peek() spikes = np.array([0, 1, 2, 3], dtype = int) delays = np.array([2, 3, 4, 5], dtype = int) queue.insert(spikes, delays) queue.expand(100) queue.expand(100) queue.expand(100) queue.expand(100) queue.expand(100) queue.expand(100) queue.insert(spikes+1, delays) print queue # print "Queue expanded" for k in range(10): print "time", queue.currenttime print "current content", queue.peek() queue.next() brian-1.4.3/brian/experimental/cspikequeue/old/test_two_synapses.py000066400000000000000000000022231266062647100256630ustar00rootroot00000000000000''' One synapse within several possibilities. Synapse from 0->2,3. ''' from brian import * def test(use_cspikequeue): reinit_default_clock() clear(all = True) P=SpikeGeneratorGroup(3, [(0, 5*ms), (2, 10*ms), (1, 15*ms)]) Q=NeuronGroup(4,model='v:1') S=Synapses(P,Q,model='w:1',pre='v+=w', max_delay = 4*ms) S[0,1]=True S.w[0,1]=1. S[0,2]=True S.w[0,2]=2. S[0,3]=True S.w[0,3]=3. S[1,0] = True S.w[1,0]= 4. S.delay[0,:]=np.array([0*ms, 2*ms, 4*ms]) M=StateMonitor(Q,'v',record=True) Ms=SpikeMonitor(P,record=True) run(40*ms) return M, Ms, S def plot_mons(M, Ms): for i in range(4): plot(M.times/ms,M[i],'k') for i in range(3): plot(Ms.spiketimes[i]/ms,-0.01*np.ones(len(Ms.spiketimes[i])),'ok') ylim(-.5, 5) if __name__ == '__main__': subplot(212) M, Ms, S = test(True) plot_mons(M, Ms) xlabel('With Cspikequeue') print 'With' print S.queues[0] subplot(211) M, Ms, S = test(False) plot_mons(M, Ms) xlabel('Without Cspikequeue') print 'Without' print S.queues[0] print 'Done' show() #1079574528 brian-1.4.3/brian/experimental/cspikequeue/setup.py000066400000000000000000000013341266062647100224520ustar00rootroot00000000000000#! /usr/bin/env python from distutils.core import * from distutils import sysconfig import numpy try: numpy_include = numpy.get_include() except AttributeError: numpy_include = numpy.get_numpy_include() # spikequeue extension module cspikequeue = Extension("_cspikequeue", ["spikequeue_wrap.cxx", "spikequeue.cpp"], include_dirs = [numpy_include] # swig_opts = ['-c++'] ) # setup setup( name = "CSpikeQueue", description = "Manages spikes and such", author = "Victor Benichoux", version = "1.0", ext_modules = [cspikequeue], py_modules = ["cspikequeue"] ) brian-1.4.3/brian/experimental/cspikequeue/spikequeue.cpp000066400000000000000000000155511266062647100236320ustar00rootroot00000000000000#include "spikequeue.h" #include #include #include using namespace std; SpikeQueue::SpikeQueue(int n_delays, int n_maxevents) { this->n_delays = n_delays; // first dimension of X (nsteps) this->n_maxevents = n_maxevents; // second dimension of X this->currenttime = 0; this->retarray = NULL; this->something = -1; this->retarray = new long[n_maxevents]; this->n = new int[n_delays]; this->X = new long*[n_delays]; if(!this->X || !this->n || !this->retarray){ if(this->X) { delete [] this->X; this->X = 0; } if(this->n) { delete [] this->n; this->n = 0; } if(this->retarray) { delete [] this->retarray; this->retarray = 0; } throw BrianException("Not enough memory in creating SpikeQueue."); } for (int i = 0 ; i < n_delays ; i++) { (this->X)[i] = new long[n_maxevents]; (this->n)[i] = 0; for (int j = 0 ; j < n_maxevents; j++) { (this->X)[i][j] = 0; } if (!((this->X)[i])) { throw BrianException("Not enough memory in creating SpikeQueue (X)."); } } } SpikeQueue::~SpikeQueue() { for (int i = 0 ; i < this->n_delays ; i++) { delete [] (this->X)[i]; } if(this->X) delete [] this->X; if(this->retarray) delete [] this->retarray; if(this->n) delete [] this->n; this->X = NULL; this->retarray = NULL; this->n = NULL; } // Spike Queue data structure void SpikeQueue::expand(int maxevents = -1) { if (maxevents != -1){ this->n_maxevents += maxevents; // we add maxevents } else { int orig_n_maxevents = this->n_maxevents; this->n_maxevents += orig_n_maxevents; // we multiply by 2. } // declare and allocate long *new_retarray = new long[this->n_maxevents]; long **new_X = new long*[this->n_delays]; // expand the 2D array structure for (int i = 0 ; i < n_delays ; i++) { // allocate new memory new_X[i] = new long[this->n_maxevents]; // check allocation success if (!(new_X[i])) { stringstream err; err << "Not enough memory in expanding SpikeQueue to size ("; err << this->n_delays << ","; err << this->n_maxevents << ")"; throw BrianException(err.str()); } // copy old contents of X memcpy((void *)new_X[i], (void *)(this->X[i]), sizeof(long)*(this->n)[i]); // delete old contents delete [] (this->X)[i]; // zero out newly allocated data for (int j = (this->n)[i] ; j < this->n_maxevents; j++) { new_X[i][j] = 0; } } if(!new_X || !retarray){ if(new_X) delete [] new_X; throw BrianException("Not enough memory in expanding SpikeQueue."); } // delete delete [] this->X; // assign this->X = new_X; // re-delete delete [] this->retarray; // re-assign this->retarray = new_retarray; } void SpikeQueue::next() { this->n[this->currenttime] = 0; // erase this->currenttime = ((this->currenttime + 1) % (this->n_delays)); } void SpikeQueue::_peek(int nevents) { for (int i = 0; i < nevents; i++) { this->retarray[i] = (this->X)[this->currenttime][i]; } } // my two attempts at returning a numpy array void SpikeQueue::peek(long **ret, int *ret_n) { int nevents = (this->n)[this->currenttime]; this->_peek(nevents); *ret = this->retarray; *ret_n = nevents; } void SpikeQueue::insert(int len1, long *vec1, int len2, long *vec2) { // vec1 = targets // vec2 = delays if (len1!=len2) { throw BrianException("Inputs to insert have non matching lengths"); } for (int k = 0; k < len1; k ++) { // get the timebin of this spike int d = (this->currenttime + vec2[k]) % (this->n_delays); if ((this->n)[d] == this->n_maxevents) { this->expand(); } // place it in the 2D array (this->X)[d][(this->n)[d]] = vec1[k]; (this->n)[d]++; // check that we can fit the events } } // DEBUG/PRINTING void SpikeQueue::print_summary() { cout << "SpikeQueue" << endl; cout << "n_maxevents: " << this->n_maxevents << endl; cout << "n_delays: " << this->n_delays << endl; cout << "currenttime: " << this->currenttime << endl; cout << "Contents" << endl; for (int i = 0; i < (this->n_delays); i ++){ cout << '(' << (this->n)[i] << ')'; for (int j = 0; j < this->n[i]; j ++){ cout << (this->X)[i][j] << ','; } cout << endl; } } // Even this simple thing doesnt work. string SpikeQueue::__repr__() { stringstream out; out << "SpikeQueue" << endl; out << "n_delays = " << this->n_delays << endl; out << "n_maxevents = " << this->n_maxevents << endl; out << "currenttime = " << this->currenttime << endl; int n = 0; for (int k=0; kn_delays; k++){ n += (this->n)[k]; out << "(" << k << ")" << (this->n)[k] << endl; } out << "Contains " << n << " spikes" << endl; return out.str(); } string SpikeQueue::__str__() { return this->__repr__(); } ///////////////////// MAIN /////////////////////// int main(void){ /* int N = 5; SpikeQueue x (10, N); cout << "////////////////" << endl; cout << "///// INIT /////" << endl; cout << "////////////////" << endl; x.print_summary(); cout << "////////////////" << endl; cout << "Inserting spikes" << endl; cout << "////////////////" << endl; long delay[4] = {1, 2, 2, 3}; long target[4] = {45, 45, 46, 46}; // x.insert(delay, target, 4); cout << "////////////////" << endl; cout << "///// CHECK /////" << endl; cout << "////////////////" << endl; // We should see some spikes x.print_summary(); cout << "////////////////" << endl; cout << "///// NEXT /////" << endl; cout << "////////////////" << endl; // We should see currenttime = 1 x.next(); x.print_summary(); cout << "////////////////" << endl; cout << "///// EXPND ////" << endl; cout << "////////////////" << endl; x.expand(); // We should not see any difference x.print_summary(); cout << "////////////////" << endl; cout << "///// PEEK /////" << endl; cout << "////////////////" << endl; x._peek(N); // I have to print manually, we should see the numbers corresponding to the right line in the data. for (int i = 0; i < N; i++) { cout << x.retarray[i] << ','; } cout << endl; cout << "////////////////" << endl; cout << "/ NEXT + PEEK //" << endl; cout << "////////////////" << endl; x.next(); x._peek(N); for (int i = 0; i < N; i++) { cout << x.retarray[i] << ','; } cout << endl; cout << "////////////////" << endl; cout << "/ NEXT + PEEK 2/" << endl; cout << "////////////////" << endl; x.next(); x._peek(N); for (int i = 0; i < N; i++) { cout << x.retarray[i] << ','; } cout << endl; int n; long *ret; x.peek(&ret, &n); cout << "////////////////" << endl; cout << "/ LAST /" << endl; cout << "////////////////" << endl; for (int i = 0; i < n; i++) { cout << ret[i] << ','; } cout << endl; return 1; */ } brian-1.4.3/brian/experimental/cspikequeue/spikequeue.h000066400000000000000000000014411266062647100232700ustar00rootroot00000000000000#ifndef _BRIAN_LIB_H #define _BRIAN_LIB_H #include #include #include #include #include using namespace std; #define neuron_value(group, neuron, state) group->S[neuron+state*(group->num_neurons)] #define BrianException std::runtime_error class SpikeQueue { private: public: SpikeQueue(int n0, int n1); ~SpikeQueue(); long **X; int *n; int n_delays, n_maxevents, currenttime, something; long *retarray; void expand(int maxevents); void next(); void _peek(int nevents); void peek(long **ret, int *ret_n); void insert(int len1, long *vec1, int len2, long *vec2); string __repr__(); /* doesn't work, but doesn't work in ccircular either. */ string __str__(); /* //printout c++ */ void print_summary(); }; #endif brian-1.4.3/brian/experimental/cspikequeue/spikequeue.i000066400000000000000000000010451266062647100232710ustar00rootroot00000000000000%module cspikequeue %include "exception.i" %include "std_string.i" %exception { try { $action } catch( std::runtime_error &e ) { PyErr_SetString(PyExc_RuntimeError, const_cast(e.what())); return NULL; } } %allowexception; %{ #define SWIG_FILE_WITH_INIT #include "spikequeue.h" %} %include "numpy.i" %init %{ import_array(); %} %apply (int DIM1, long* IN_ARRAY1) {(int len1, long* vec1), (int len2, long* vec2)} %apply (long** ARGOUTVIEW_ARRAY1, int* DIM1 ) {(long **ret, int *ret_n)}; %include "spikequeue.h" brian-1.4.3/brian/experimental/cspikequeue/spikequeue_wrap.cxx000066400000000000000000004330721266062647100247050ustar00rootroot00000000000000/* ---------------------------------------------------------------------------- * This file was automatically generated by SWIG (http://www.swig.org). * Version 1.3.40 * * This file is not intended to be easily readable and contains a number of * coding conventions designed to improve portability and efficiency. Do not make * changes to this file unless you know what you are doing--modify the SWIG * interface file instead. * ----------------------------------------------------------------------------- */ #define SWIGPYTHON #define SWIG_PYTHON_DIRECTOR_NO_VTABLE #ifdef __cplusplus /* SwigValueWrapper is described in swig.swg */ template class SwigValueWrapper { struct SwigMovePointer { T *ptr; SwigMovePointer(T *p) : ptr(p) { } ~SwigMovePointer() { delete ptr; } SwigMovePointer& operator=(SwigMovePointer& rhs) { T* oldptr = ptr; ptr = 0; delete oldptr; ptr = rhs.ptr; rhs.ptr = 0; return *this; } } pointer; SwigValueWrapper& operator=(const SwigValueWrapper& rhs); SwigValueWrapper(const SwigValueWrapper& rhs); public: SwigValueWrapper() : pointer(0) { } SwigValueWrapper& operator=(const T& t) { SwigMovePointer tmp(new T(t)); pointer = tmp; return *this; } operator T&() const { return *pointer.ptr; } T *operator&() { return pointer.ptr; } }; template T SwigValueInit() { return T(); } #endif /* ----------------------------------------------------------------------------- * This section contains generic SWIG labels for method/variable * declarations/attributes, and other compiler dependent labels. * ----------------------------------------------------------------------------- */ /* template workaround for compilers that cannot correctly implement the C++ standard */ #ifndef SWIGTEMPLATEDISAMBIGUATOR # if defined(__SUNPRO_CC) && (__SUNPRO_CC <= 0x560) # define SWIGTEMPLATEDISAMBIGUATOR template # elif defined(__HP_aCC) /* Needed even with `aCC -AA' when `aCC -V' reports HP ANSI C++ B3910B A.03.55 */ /* If we find a maximum version that requires this, the test would be __HP_aCC <= 35500 for A.03.55 */ # define SWIGTEMPLATEDISAMBIGUATOR template # else # define SWIGTEMPLATEDISAMBIGUATOR # endif #endif /* inline attribute */ #ifndef SWIGINLINE # if defined(__cplusplus) || (defined(__GNUC__) && !defined(__STRICT_ANSI__)) # define SWIGINLINE inline # else # define SWIGINLINE # endif #endif /* attribute recognised by some compilers to avoid 'unused' warnings */ #ifndef SWIGUNUSED # if defined(__GNUC__) # if !(defined(__cplusplus)) || (__GNUC__ > 3 || (__GNUC__ == 3 && __GNUC_MINOR__ >= 4)) # define SWIGUNUSED __attribute__ ((__unused__)) # else # define SWIGUNUSED # endif # elif defined(__ICC) # define SWIGUNUSED __attribute__ ((__unused__)) # else # define SWIGUNUSED # endif #endif #ifndef SWIG_MSC_UNSUPPRESS_4505 # if defined(_MSC_VER) # pragma warning(disable : 4505) /* unreferenced local function has been removed */ # endif #endif #ifndef SWIGUNUSEDPARM # ifdef __cplusplus # define SWIGUNUSEDPARM(p) # else # define SWIGUNUSEDPARM(p) p SWIGUNUSED # endif #endif /* internal SWIG method */ #ifndef SWIGINTERN # define SWIGINTERN static SWIGUNUSED #endif /* internal inline SWIG method */ #ifndef SWIGINTERNINLINE # define SWIGINTERNINLINE SWIGINTERN SWIGINLINE #endif /* exporting methods */ #if (__GNUC__ >= 4) || (__GNUC__ == 3 && __GNUC_MINOR__ >= 4) # ifndef GCC_HASCLASSVISIBILITY # define GCC_HASCLASSVISIBILITY # endif #endif #ifndef SWIGEXPORT # if defined(_WIN32) || defined(__WIN32__) || defined(__CYGWIN__) # if defined(STATIC_LINKED) # define SWIGEXPORT # else # define SWIGEXPORT __declspec(dllexport) # endif # else # if defined(__GNUC__) && defined(GCC_HASCLASSVISIBILITY) # define SWIGEXPORT __attribute__ ((visibility("default"))) # else # define SWIGEXPORT # endif # endif #endif /* calling conventions for Windows */ #ifndef SWIGSTDCALL # if defined(_WIN32) || defined(__WIN32__) || defined(__CYGWIN__) # define SWIGSTDCALL __stdcall # else # define SWIGSTDCALL # endif #endif /* Deal with Microsoft's attempt at deprecating C standard runtime functions */ #if !defined(SWIG_NO_CRT_SECURE_NO_DEPRECATE) && defined(_MSC_VER) && !defined(_CRT_SECURE_NO_DEPRECATE) # define _CRT_SECURE_NO_DEPRECATE #endif /* Deal with Microsoft's attempt at deprecating methods in the standard C++ library */ #if !defined(SWIG_NO_SCL_SECURE_NO_DEPRECATE) && defined(_MSC_VER) && !defined(_SCL_SECURE_NO_DEPRECATE) # define _SCL_SECURE_NO_DEPRECATE #endif /* Python.h has to appear first */ #include #include /* ----------------------------------------------------------------------------- * swigrun.swg * * This file contains generic C API SWIG runtime support for pointer * type checking. * ----------------------------------------------------------------------------- */ /* This should only be incremented when either the layout of swig_type_info changes, or for whatever reason, the runtime changes incompatibly */ #define SWIG_RUNTIME_VERSION "4" /* define SWIG_TYPE_TABLE_NAME as "SWIG_TYPE_TABLE" */ #ifdef SWIG_TYPE_TABLE # define SWIG_QUOTE_STRING(x) #x # define SWIG_EXPAND_AND_QUOTE_STRING(x) SWIG_QUOTE_STRING(x) # define SWIG_TYPE_TABLE_NAME SWIG_EXPAND_AND_QUOTE_STRING(SWIG_TYPE_TABLE) #else # define SWIG_TYPE_TABLE_NAME #endif /* You can use the SWIGRUNTIME and SWIGRUNTIMEINLINE macros for creating a static or dynamic library from the SWIG runtime code. In 99.9% of the cases, SWIG just needs to declare them as 'static'. But only do this if strictly necessary, ie, if you have problems with your compiler or suchlike. */ #ifndef SWIGRUNTIME # define SWIGRUNTIME SWIGINTERN #endif #ifndef SWIGRUNTIMEINLINE # define SWIGRUNTIMEINLINE SWIGRUNTIME SWIGINLINE #endif /* Generic buffer size */ #ifndef SWIG_BUFFER_SIZE # define SWIG_BUFFER_SIZE 1024 #endif /* Flags for pointer conversions */ #define SWIG_POINTER_DISOWN 0x1 #define SWIG_CAST_NEW_MEMORY 0x2 /* Flags for new pointer objects */ #define SWIG_POINTER_OWN 0x1 /* Flags/methods for returning states. The SWIG conversion methods, as ConvertPtr, return and integer that tells if the conversion was successful or not. And if not, an error code can be returned (see swigerrors.swg for the codes). Use the following macros/flags to set or process the returning states. In old versions of SWIG, code such as the following was usually written: if (SWIG_ConvertPtr(obj,vptr,ty.flags) != -1) { // success code } else { //fail code } Now you can be more explicit: int res = SWIG_ConvertPtr(obj,vptr,ty.flags); if (SWIG_IsOK(res)) { // success code } else { // fail code } which is the same really, but now you can also do Type *ptr; int res = SWIG_ConvertPtr(obj,(void **)(&ptr),ty.flags); if (SWIG_IsOK(res)) { // success code if (SWIG_IsNewObj(res) { ... delete *ptr; } else { ... } } else { // fail code } I.e., now SWIG_ConvertPtr can return new objects and you can identify the case and take care of the deallocation. Of course that also requires SWIG_ConvertPtr to return new result values, such as int SWIG_ConvertPtr(obj, ptr,...) { if () { if () { *ptr = ; return SWIG_NEWOBJ; } else { *ptr = ; return SWIG_OLDOBJ; } } else { return SWIG_BADOBJ; } } Of course, returning the plain '0(success)/-1(fail)' still works, but you can be more explicit by returning SWIG_BADOBJ, SWIG_ERROR or any of the SWIG errors code. Finally, if the SWIG_CASTRANK_MODE is enabled, the result code allows to return the 'cast rank', for example, if you have this int food(double) int fooi(int); and you call food(1) // cast rank '1' (1 -> 1.0) fooi(1) // cast rank '0' just use the SWIG_AddCast()/SWIG_CheckState() */ #define SWIG_OK (0) #define SWIG_ERROR (-1) #define SWIG_IsOK(r) (r >= 0) #define SWIG_ArgError(r) ((r != SWIG_ERROR) ? r : SWIG_TypeError) /* The CastRankLimit says how many bits are used for the cast rank */ #define SWIG_CASTRANKLIMIT (1 << 8) /* The NewMask denotes the object was created (using new/malloc) */ #define SWIG_NEWOBJMASK (SWIG_CASTRANKLIMIT << 1) /* The TmpMask is for in/out typemaps that use temporal objects */ #define SWIG_TMPOBJMASK (SWIG_NEWOBJMASK << 1) /* Simple returning values */ #define SWIG_BADOBJ (SWIG_ERROR) #define SWIG_OLDOBJ (SWIG_OK) #define SWIG_NEWOBJ (SWIG_OK | SWIG_NEWOBJMASK) #define SWIG_TMPOBJ (SWIG_OK | SWIG_TMPOBJMASK) /* Check, add and del mask methods */ #define SWIG_AddNewMask(r) (SWIG_IsOK(r) ? (r | SWIG_NEWOBJMASK) : r) #define SWIG_DelNewMask(r) (SWIG_IsOK(r) ? (r & ~SWIG_NEWOBJMASK) : r) #define SWIG_IsNewObj(r) (SWIG_IsOK(r) && (r & SWIG_NEWOBJMASK)) #define SWIG_AddTmpMask(r) (SWIG_IsOK(r) ? (r | SWIG_TMPOBJMASK) : r) #define SWIG_DelTmpMask(r) (SWIG_IsOK(r) ? (r & ~SWIG_TMPOBJMASK) : r) #define SWIG_IsTmpObj(r) (SWIG_IsOK(r) && (r & SWIG_TMPOBJMASK)) /* Cast-Rank Mode */ #if defined(SWIG_CASTRANK_MODE) # ifndef SWIG_TypeRank # define SWIG_TypeRank unsigned long # endif # ifndef SWIG_MAXCASTRANK /* Default cast allowed */ # define SWIG_MAXCASTRANK (2) # endif # define SWIG_CASTRANKMASK ((SWIG_CASTRANKLIMIT) -1) # define SWIG_CastRank(r) (r & SWIG_CASTRANKMASK) SWIGINTERNINLINE int SWIG_AddCast(int r) { return SWIG_IsOK(r) ? ((SWIG_CastRank(r) < SWIG_MAXCASTRANK) ? (r + 1) : SWIG_ERROR) : r; } SWIGINTERNINLINE int SWIG_CheckState(int r) { return SWIG_IsOK(r) ? SWIG_CastRank(r) + 1 : 0; } #else /* no cast-rank mode */ # define SWIG_AddCast # define SWIG_CheckState(r) (SWIG_IsOK(r) ? 1 : 0) #endif #include #ifdef __cplusplus extern "C" { #endif typedef void *(*swig_converter_func)(void *, int *); typedef struct swig_type_info *(*swig_dycast_func)(void **); /* Structure to store information on one type */ typedef struct swig_type_info { const char *name; /* mangled name of this type */ const char *str; /* human readable name of this type */ swig_dycast_func dcast; /* dynamic cast function down a hierarchy */ struct swig_cast_info *cast; /* linked list of types that can cast into this type */ void *clientdata; /* language specific type data */ int owndata; /* flag if the structure owns the clientdata */ } swig_type_info; /* Structure to store a type and conversion function used for casting */ typedef struct swig_cast_info { swig_type_info *type; /* pointer to type that is equivalent to this type */ swig_converter_func converter; /* function to cast the void pointers */ struct swig_cast_info *next; /* pointer to next cast in linked list */ struct swig_cast_info *prev; /* pointer to the previous cast */ } swig_cast_info; /* Structure used to store module information * Each module generates one structure like this, and the runtime collects * all of these structures and stores them in a circularly linked list.*/ typedef struct swig_module_info { swig_type_info **types; /* Array of pointers to swig_type_info structures that are in this module */ size_t size; /* Number of types in this module */ struct swig_module_info *next; /* Pointer to next element in circularly linked list */ swig_type_info **type_initial; /* Array of initially generated type structures */ swig_cast_info **cast_initial; /* Array of initially generated casting structures */ void *clientdata; /* Language specific module data */ } swig_module_info; /* Compare two type names skipping the space characters, therefore "char*" == "char *" and "Class" == "Class", etc. Return 0 when the two name types are equivalent, as in strncmp, but skipping ' '. */ SWIGRUNTIME int SWIG_TypeNameComp(const char *f1, const char *l1, const char *f2, const char *l2) { for (;(f1 != l1) && (f2 != l2); ++f1, ++f2) { while ((*f1 == ' ') && (f1 != l1)) ++f1; while ((*f2 == ' ') && (f2 != l2)) ++f2; if (*f1 != *f2) return (*f1 > *f2) ? 1 : -1; } return (int)((l1 - f1) - (l2 - f2)); } /* Check type equivalence in a name list like ||... Return 0 if not equal, 1 if equal */ SWIGRUNTIME int SWIG_TypeEquiv(const char *nb, const char *tb) { int equiv = 0; const char* te = tb + strlen(tb); const char* ne = nb; while (!equiv && *ne) { for (nb = ne; *ne; ++ne) { if (*ne == '|') break; } equiv = (SWIG_TypeNameComp(nb, ne, tb, te) == 0) ? 1 : 0; if (*ne) ++ne; } return equiv; } /* Check type equivalence in a name list like ||... Return 0 if equal, -1 if nb < tb, 1 if nb > tb */ SWIGRUNTIME int SWIG_TypeCompare(const char *nb, const char *tb) { int equiv = 0; const char* te = tb + strlen(tb); const char* ne = nb; while (!equiv && *ne) { for (nb = ne; *ne; ++ne) { if (*ne == '|') break; } equiv = (SWIG_TypeNameComp(nb, ne, tb, te) == 0) ? 1 : 0; if (*ne) ++ne; } return equiv; } /* Check the typename */ SWIGRUNTIME swig_cast_info * SWIG_TypeCheck(const char *c, swig_type_info *ty) { if (ty) { swig_cast_info *iter = ty->cast; while (iter) { if (strcmp(iter->type->name, c) == 0) { if (iter == ty->cast) return iter; /* Move iter to the top of the linked list */ iter->prev->next = iter->next; if (iter->next) iter->next->prev = iter->prev; iter->next = ty->cast; iter->prev = 0; if (ty->cast) ty->cast->prev = iter; ty->cast = iter; return iter; } iter = iter->next; } } return 0; } /* Identical to SWIG_TypeCheck, except strcmp is replaced with a pointer comparison */ SWIGRUNTIME swig_cast_info * SWIG_TypeCheckStruct(swig_type_info *from, swig_type_info *ty) { if (ty) { swig_cast_info *iter = ty->cast; while (iter) { if (iter->type == from) { if (iter == ty->cast) return iter; /* Move iter to the top of the linked list */ iter->prev->next = iter->next; if (iter->next) iter->next->prev = iter->prev; iter->next = ty->cast; iter->prev = 0; if (ty->cast) ty->cast->prev = iter; ty->cast = iter; return iter; } iter = iter->next; } } return 0; } /* Cast a pointer up an inheritance hierarchy */ SWIGRUNTIMEINLINE void * SWIG_TypeCast(swig_cast_info *ty, void *ptr, int *newmemory) { return ((!ty) || (!ty->converter)) ? ptr : (*ty->converter)(ptr, newmemory); } /* Dynamic pointer casting. Down an inheritance hierarchy */ SWIGRUNTIME swig_type_info * SWIG_TypeDynamicCast(swig_type_info *ty, void **ptr) { swig_type_info *lastty = ty; if (!ty || !ty->dcast) return ty; while (ty && (ty->dcast)) { ty = (*ty->dcast)(ptr); if (ty) lastty = ty; } return lastty; } /* Return the name associated with this type */ SWIGRUNTIMEINLINE const char * SWIG_TypeName(const swig_type_info *ty) { return ty->name; } /* Return the pretty name associated with this type, that is an unmangled type name in a form presentable to the user. */ SWIGRUNTIME const char * SWIG_TypePrettyName(const swig_type_info *type) { /* The "str" field contains the equivalent pretty names of the type, separated by vertical-bar characters. We choose to print the last name, as it is often (?) the most specific. */ if (!type) return NULL; if (type->str != NULL) { const char *last_name = type->str; const char *s; for (s = type->str; *s; s++) if (*s == '|') last_name = s+1; return last_name; } else return type->name; } /* Set the clientdata field for a type */ SWIGRUNTIME void SWIG_TypeClientData(swig_type_info *ti, void *clientdata) { swig_cast_info *cast = ti->cast; /* if (ti->clientdata == clientdata) return; */ ti->clientdata = clientdata; while (cast) { if (!cast->converter) { swig_type_info *tc = cast->type; if (!tc->clientdata) { SWIG_TypeClientData(tc, clientdata); } } cast = cast->next; } } SWIGRUNTIME void SWIG_TypeNewClientData(swig_type_info *ti, void *clientdata) { SWIG_TypeClientData(ti, clientdata); ti->owndata = 1; } /* Search for a swig_type_info structure only by mangled name Search is a O(log #types) We start searching at module start, and finish searching when start == end. Note: if start == end at the beginning of the function, we go all the way around the circular list. */ SWIGRUNTIME swig_type_info * SWIG_MangledTypeQueryModule(swig_module_info *start, swig_module_info *end, const char *name) { swig_module_info *iter = start; do { if (iter->size) { register size_t l = 0; register size_t r = iter->size - 1; do { /* since l+r >= 0, we can (>> 1) instead (/ 2) */ register size_t i = (l + r) >> 1; const char *iname = iter->types[i]->name; if (iname) { register int compare = strcmp(name, iname); if (compare == 0) { return iter->types[i]; } else if (compare < 0) { if (i) { r = i - 1; } else { break; } } else if (compare > 0) { l = i + 1; } } else { break; /* should never happen */ } } while (l <= r); } iter = iter->next; } while (iter != end); return 0; } /* Search for a swig_type_info structure for either a mangled name or a human readable name. It first searches the mangled names of the types, which is a O(log #types) If a type is not found it then searches the human readable names, which is O(#types). We start searching at module start, and finish searching when start == end. Note: if start == end at the beginning of the function, we go all the way around the circular list. */ SWIGRUNTIME swig_type_info * SWIG_TypeQueryModule(swig_module_info *start, swig_module_info *end, const char *name) { /* STEP 1: Search the name field using binary search */ swig_type_info *ret = SWIG_MangledTypeQueryModule(start, end, name); if (ret) { return ret; } else { /* STEP 2: If the type hasn't been found, do a complete search of the str field (the human readable name) */ swig_module_info *iter = start; do { register size_t i = 0; for (; i < iter->size; ++i) { if (iter->types[i]->str && (SWIG_TypeEquiv(iter->types[i]->str, name))) return iter->types[i]; } iter = iter->next; } while (iter != end); } /* neither found a match */ return 0; } /* Pack binary data into a string */ SWIGRUNTIME char * SWIG_PackData(char *c, void *ptr, size_t sz) { static const char hex[17] = "0123456789abcdef"; register const unsigned char *u = (unsigned char *) ptr; register const unsigned char *eu = u + sz; for (; u != eu; ++u) { register unsigned char uu = *u; *(c++) = hex[(uu & 0xf0) >> 4]; *(c++) = hex[uu & 0xf]; } return c; } /* Unpack binary data from a string */ SWIGRUNTIME const char * SWIG_UnpackData(const char *c, void *ptr, size_t sz) { register unsigned char *u = (unsigned char *) ptr; register const unsigned char *eu = u + sz; for (; u != eu; ++u) { register char d = *(c++); register unsigned char uu; if ((d >= '0') && (d <= '9')) uu = ((d - '0') << 4); else if ((d >= 'a') && (d <= 'f')) uu = ((d - ('a'-10)) << 4); else return (char *) 0; d = *(c++); if ((d >= '0') && (d <= '9')) uu |= (d - '0'); else if ((d >= 'a') && (d <= 'f')) uu |= (d - ('a'-10)); else return (char *) 0; *u = uu; } return c; } /* Pack 'void *' into a string buffer. */ SWIGRUNTIME char * SWIG_PackVoidPtr(char *buff, void *ptr, const char *name, size_t bsz) { char *r = buff; if ((2*sizeof(void *) + 2) > bsz) return 0; *(r++) = '_'; r = SWIG_PackData(r,&ptr,sizeof(void *)); if (strlen(name) + 1 > (bsz - (r - buff))) return 0; strcpy(r,name); return buff; } SWIGRUNTIME const char * SWIG_UnpackVoidPtr(const char *c, void **ptr, const char *name) { if (*c != '_') { if (strcmp(c,"NULL") == 0) { *ptr = (void *) 0; return name; } else { return 0; } } return SWIG_UnpackData(++c,ptr,sizeof(void *)); } SWIGRUNTIME char * SWIG_PackDataName(char *buff, void *ptr, size_t sz, const char *name, size_t bsz) { char *r = buff; size_t lname = (name ? strlen(name) : 0); if ((2*sz + 2 + lname) > bsz) return 0; *(r++) = '_'; r = SWIG_PackData(r,ptr,sz); if (lname) { strncpy(r,name,lname+1); } else { *r = 0; } return buff; } SWIGRUNTIME const char * SWIG_UnpackDataName(const char *c, void *ptr, size_t sz, const char *name) { if (*c != '_') { if (strcmp(c,"NULL") == 0) { memset(ptr,0,sz); return name; } else { return 0; } } return SWIG_UnpackData(++c,ptr,sz); } #ifdef __cplusplus } #endif /* Errors in SWIG */ #define SWIG_UnknownError -1 #define SWIG_IOError -2 #define SWIG_RuntimeError -3 #define SWIG_IndexError -4 #define SWIG_TypeError -5 #define SWIG_DivisionByZero -6 #define SWIG_OverflowError -7 #define SWIG_SyntaxError -8 #define SWIG_ValueError -9 #define SWIG_SystemError -10 #define SWIG_AttributeError -11 #define SWIG_MemoryError -12 #define SWIG_NullReferenceError -13 /* Compatibility macros for Python 3 */ #if PY_VERSION_HEX >= 0x03000000 #define PyClass_Check(obj) PyObject_IsInstance(obj, (PyObject *)&PyType_Type) #define PyInt_Check(x) PyLong_Check(x) #define PyInt_AsLong(x) PyLong_AsLong(x) #define PyInt_FromLong(x) PyLong_FromLong(x) #define PyString_Format(fmt, args) PyUnicode_Format(fmt, args) #endif #ifndef Py_TYPE # define Py_TYPE(op) ((op)->ob_type) #endif /* SWIG APIs for compatibility of both Python 2 & 3 */ #if PY_VERSION_HEX >= 0x03000000 # define SWIG_Python_str_FromFormat PyUnicode_FromFormat #else # define SWIG_Python_str_FromFormat PyString_FromFormat #endif /* Warning: This function will allocate a new string in Python 3, * so please call SWIG_Python_str_DelForPy3(x) to free the space. */ SWIGINTERN char* SWIG_Python_str_AsChar(PyObject *str) { #if PY_VERSION_HEX >= 0x03000000 char *cstr; char *newstr; Py_ssize_t len; str = PyUnicode_AsUTF8String(str); PyBytes_AsStringAndSize(str, &cstr, &len); newstr = (char *) malloc(len+1); memcpy(newstr, cstr, len+1); Py_XDECREF(str); return newstr; #else return PyString_AsString(str); #endif } #if PY_VERSION_HEX >= 0x03000000 # define SWIG_Python_str_DelForPy3(x) free( (void*) (x) ) #else # define SWIG_Python_str_DelForPy3(x) #endif SWIGINTERN PyObject* SWIG_Python_str_FromChar(const char *c) { #if PY_VERSION_HEX >= 0x03000000 return PyUnicode_FromString(c); #else return PyString_FromString(c); #endif } /* Add PyOS_snprintf for old Pythons */ #if PY_VERSION_HEX < 0x02020000 # if defined(_MSC_VER) || defined(__BORLANDC__) || defined(_WATCOM) # define PyOS_snprintf _snprintf # else # define PyOS_snprintf snprintf # endif #endif /* A crude PyString_FromFormat implementation for old Pythons */ #if PY_VERSION_HEX < 0x02020000 #ifndef SWIG_PYBUFFER_SIZE # define SWIG_PYBUFFER_SIZE 1024 #endif static PyObject * PyString_FromFormat(const char *fmt, ...) { va_list ap; char buf[SWIG_PYBUFFER_SIZE * 2]; int res; va_start(ap, fmt); res = vsnprintf(buf, sizeof(buf), fmt, ap); va_end(ap); return (res < 0 || res >= (int)sizeof(buf)) ? 0 : PyString_FromString(buf); } #endif /* Add PyObject_Del for old Pythons */ #if PY_VERSION_HEX < 0x01060000 # define PyObject_Del(op) PyMem_DEL((op)) #endif #ifndef PyObject_DEL # define PyObject_DEL PyObject_Del #endif /* A crude PyExc_StopIteration exception for old Pythons */ #if PY_VERSION_HEX < 0x02020000 # ifndef PyExc_StopIteration # define PyExc_StopIteration PyExc_RuntimeError # endif # ifndef PyObject_GenericGetAttr # define PyObject_GenericGetAttr 0 # endif #endif /* Py_NotImplemented is defined in 2.1 and up. */ #if PY_VERSION_HEX < 0x02010000 # ifndef Py_NotImplemented # define Py_NotImplemented PyExc_RuntimeError # endif #endif /* A crude PyString_AsStringAndSize implementation for old Pythons */ #if PY_VERSION_HEX < 0x02010000 # ifndef PyString_AsStringAndSize # define PyString_AsStringAndSize(obj, s, len) {*s = PyString_AsString(obj); *len = *s ? strlen(*s) : 0;} # endif #endif /* PySequence_Size for old Pythons */ #if PY_VERSION_HEX < 0x02000000 # ifndef PySequence_Size # define PySequence_Size PySequence_Length # endif #endif /* PyBool_FromLong for old Pythons */ #if PY_VERSION_HEX < 0x02030000 static PyObject *PyBool_FromLong(long ok) { PyObject *result = ok ? Py_True : Py_False; Py_INCREF(result); return result; } #endif /* Py_ssize_t for old Pythons */ /* This code is as recommended by: */ /* http://www.python.org/dev/peps/pep-0353/#conversion-guidelines */ #if PY_VERSION_HEX < 0x02050000 && !defined(PY_SSIZE_T_MIN) typedef int Py_ssize_t; # define PY_SSIZE_T_MAX INT_MAX # define PY_SSIZE_T_MIN INT_MIN #endif /* ----------------------------------------------------------------------------- * error manipulation * ----------------------------------------------------------------------------- */ SWIGRUNTIME PyObject* SWIG_Python_ErrorType(int code) { PyObject* type = 0; switch(code) { case SWIG_MemoryError: type = PyExc_MemoryError; break; case SWIG_IOError: type = PyExc_IOError; break; case SWIG_RuntimeError: type = PyExc_RuntimeError; break; case SWIG_IndexError: type = PyExc_IndexError; break; case SWIG_TypeError: type = PyExc_TypeError; break; case SWIG_DivisionByZero: type = PyExc_ZeroDivisionError; break; case SWIG_OverflowError: type = PyExc_OverflowError; break; case SWIG_SyntaxError: type = PyExc_SyntaxError; break; case SWIG_ValueError: type = PyExc_ValueError; break; case SWIG_SystemError: type = PyExc_SystemError; break; case SWIG_AttributeError: type = PyExc_AttributeError; break; default: type = PyExc_RuntimeError; } return type; } SWIGRUNTIME void SWIG_Python_AddErrorMsg(const char* mesg) { PyObject *type = 0; PyObject *value = 0; PyObject *traceback = 0; if (PyErr_Occurred()) PyErr_Fetch(&type, &value, &traceback); if (value) { char *tmp; PyObject *old_str = PyObject_Str(value); PyErr_Clear(); Py_XINCREF(type); PyErr_Format(type, "%s %s", tmp = SWIG_Python_str_AsChar(old_str), mesg); SWIG_Python_str_DelForPy3(tmp); Py_DECREF(old_str); Py_DECREF(value); } else { PyErr_SetString(PyExc_RuntimeError, mesg); } } #if defined(SWIG_PYTHON_NO_THREADS) # if defined(SWIG_PYTHON_THREADS) # undef SWIG_PYTHON_THREADS # endif #endif #if defined(SWIG_PYTHON_THREADS) /* Threading support is enabled */ # if !defined(SWIG_PYTHON_USE_GIL) && !defined(SWIG_PYTHON_NO_USE_GIL) # if (PY_VERSION_HEX >= 0x02030000) /* For 2.3 or later, use the PyGILState calls */ # define SWIG_PYTHON_USE_GIL # endif # endif # if defined(SWIG_PYTHON_USE_GIL) /* Use PyGILState threads calls */ # ifndef SWIG_PYTHON_INITIALIZE_THREADS # define SWIG_PYTHON_INITIALIZE_THREADS PyEval_InitThreads() # endif # ifdef __cplusplus /* C++ code */ class SWIG_Python_Thread_Block { bool status; PyGILState_STATE state; public: void end() { if (status) { PyGILState_Release(state); status = false;} } SWIG_Python_Thread_Block() : status(true), state(PyGILState_Ensure()) {} ~SWIG_Python_Thread_Block() { end(); } }; class SWIG_Python_Thread_Allow { bool status; PyThreadState *save; public: void end() { if (status) { PyEval_RestoreThread(save); status = false; }} SWIG_Python_Thread_Allow() : status(true), save(PyEval_SaveThread()) {} ~SWIG_Python_Thread_Allow() { end(); } }; # define SWIG_PYTHON_THREAD_BEGIN_BLOCK SWIG_Python_Thread_Block _swig_thread_block # define SWIG_PYTHON_THREAD_END_BLOCK _swig_thread_block.end() # define SWIG_PYTHON_THREAD_BEGIN_ALLOW SWIG_Python_Thread_Allow _swig_thread_allow # define SWIG_PYTHON_THREAD_END_ALLOW _swig_thread_allow.end() # else /* C code */ # define SWIG_PYTHON_THREAD_BEGIN_BLOCK PyGILState_STATE _swig_thread_block = PyGILState_Ensure() # define SWIG_PYTHON_THREAD_END_BLOCK PyGILState_Release(_swig_thread_block) # define SWIG_PYTHON_THREAD_BEGIN_ALLOW PyThreadState *_swig_thread_allow = PyEval_SaveThread() # define SWIG_PYTHON_THREAD_END_ALLOW PyEval_RestoreThread(_swig_thread_allow) # endif # else /* Old thread way, not implemented, user must provide it */ # if !defined(SWIG_PYTHON_INITIALIZE_THREADS) # define SWIG_PYTHON_INITIALIZE_THREADS # endif # if !defined(SWIG_PYTHON_THREAD_BEGIN_BLOCK) # define SWIG_PYTHON_THREAD_BEGIN_BLOCK # endif # if !defined(SWIG_PYTHON_THREAD_END_BLOCK) # define SWIG_PYTHON_THREAD_END_BLOCK # endif # if !defined(SWIG_PYTHON_THREAD_BEGIN_ALLOW) # define SWIG_PYTHON_THREAD_BEGIN_ALLOW # endif # if !defined(SWIG_PYTHON_THREAD_END_ALLOW) # define SWIG_PYTHON_THREAD_END_ALLOW # endif # endif #else /* No thread support */ # define SWIG_PYTHON_INITIALIZE_THREADS # define SWIG_PYTHON_THREAD_BEGIN_BLOCK # define SWIG_PYTHON_THREAD_END_BLOCK # define SWIG_PYTHON_THREAD_BEGIN_ALLOW # define SWIG_PYTHON_THREAD_END_ALLOW #endif /* ----------------------------------------------------------------------------- * Python API portion that goes into the runtime * ----------------------------------------------------------------------------- */ #ifdef __cplusplus extern "C" { #if 0 } /* cc-mode */ #endif #endif /* ----------------------------------------------------------------------------- * Constant declarations * ----------------------------------------------------------------------------- */ /* Constant Types */ #define SWIG_PY_POINTER 4 #define SWIG_PY_BINARY 5 /* Constant information structure */ typedef struct swig_const_info { int type; char *name; long lvalue; double dvalue; void *pvalue; swig_type_info **ptype; } swig_const_info; /* ----------------------------------------------------------------------------- * Wrapper of PyInstanceMethod_New() used in Python 3 * It is exported to the generated module, used for -fastproxy * ----------------------------------------------------------------------------- */ SWIGRUNTIME PyObject* SWIG_PyInstanceMethod_New(PyObject *self, PyObject *func) { #if PY_VERSION_HEX >= 0x03000000 return PyInstanceMethod_New(func); #else return NULL; #endif } #ifdef __cplusplus #if 0 { /* cc-mode */ #endif } #endif /* ----------------------------------------------------------------------------- * See the LICENSE file for information on copyright, usage and redistribution * of SWIG, and the README file for authors - http://www.swig.org/release.html. * * pyrun.swg * * This file contains the runtime support for Python modules * and includes code for managing global variables and pointer * type checking. * * ----------------------------------------------------------------------------- */ /* Common SWIG API */ /* for raw pointers */ #define SWIG_Python_ConvertPtr(obj, pptr, type, flags) SWIG_Python_ConvertPtrAndOwn(obj, pptr, type, flags, 0) #define SWIG_ConvertPtr(obj, pptr, type, flags) SWIG_Python_ConvertPtr(obj, pptr, type, flags) #define SWIG_ConvertPtrAndOwn(obj,pptr,type,flags,own) SWIG_Python_ConvertPtrAndOwn(obj, pptr, type, flags, own) #define SWIG_NewPointerObj(ptr, type, flags) SWIG_Python_NewPointerObj(ptr, type, flags) #define SWIG_CheckImplicit(ty) SWIG_Python_CheckImplicit(ty) #define SWIG_AcquirePtr(ptr, src) SWIG_Python_AcquirePtr(ptr, src) #define swig_owntype int /* for raw packed data */ #define SWIG_ConvertPacked(obj, ptr, sz, ty) SWIG_Python_ConvertPacked(obj, ptr, sz, ty) #define SWIG_NewPackedObj(ptr, sz, type) SWIG_Python_NewPackedObj(ptr, sz, type) /* for class or struct pointers */ #define SWIG_ConvertInstance(obj, pptr, type, flags) SWIG_ConvertPtr(obj, pptr, type, flags) #define SWIG_NewInstanceObj(ptr, type, flags) SWIG_NewPointerObj(ptr, type, flags) /* for C or C++ function pointers */ #define SWIG_ConvertFunctionPtr(obj, pptr, type) SWIG_Python_ConvertFunctionPtr(obj, pptr, type) #define SWIG_NewFunctionPtrObj(ptr, type) SWIG_Python_NewPointerObj(ptr, type, 0) /* for C++ member pointers, ie, member methods */ #define SWIG_ConvertMember(obj, ptr, sz, ty) SWIG_Python_ConvertPacked(obj, ptr, sz, ty) #define SWIG_NewMemberObj(ptr, sz, type) SWIG_Python_NewPackedObj(ptr, sz, type) /* Runtime API */ #define SWIG_GetModule(clientdata) SWIG_Python_GetModule() #define SWIG_SetModule(clientdata, pointer) SWIG_Python_SetModule(pointer) #define SWIG_NewClientData(obj) SwigPyClientData_New(obj) #define SWIG_SetErrorObj SWIG_Python_SetErrorObj #define SWIG_SetErrorMsg SWIG_Python_SetErrorMsg #define SWIG_ErrorType(code) SWIG_Python_ErrorType(code) #define SWIG_Error(code, msg) SWIG_Python_SetErrorMsg(SWIG_ErrorType(code), msg) #define SWIG_fail goto fail /* Runtime API implementation */ /* Error manipulation */ SWIGINTERN void SWIG_Python_SetErrorObj(PyObject *errtype, PyObject *obj) { SWIG_PYTHON_THREAD_BEGIN_BLOCK; PyErr_SetObject(errtype, obj); Py_DECREF(obj); SWIG_PYTHON_THREAD_END_BLOCK; } SWIGINTERN void SWIG_Python_SetErrorMsg(PyObject *errtype, const char *msg) { SWIG_PYTHON_THREAD_BEGIN_BLOCK; PyErr_SetString(errtype, (char *) msg); SWIG_PYTHON_THREAD_END_BLOCK; } #define SWIG_Python_Raise(obj, type, desc) SWIG_Python_SetErrorObj(SWIG_Python_ExceptionType(desc), obj) /* Set a constant value */ SWIGINTERN void SWIG_Python_SetConstant(PyObject *d, const char *name, PyObject *obj) { PyDict_SetItemString(d, (char*) name, obj); Py_DECREF(obj); } /* Append a value to the result obj */ SWIGINTERN PyObject* SWIG_Python_AppendOutput(PyObject* result, PyObject* obj) { #if !defined(SWIG_PYTHON_OUTPUT_TUPLE) if (!result) { result = obj; } else if (result == Py_None) { Py_DECREF(result); result = obj; } else { if (!PyList_Check(result)) { PyObject *o2 = result; result = PyList_New(1); PyList_SetItem(result, 0, o2); } PyList_Append(result,obj); Py_DECREF(obj); } return result; #else PyObject* o2; PyObject* o3; if (!result) { result = obj; } else if (result == Py_None) { Py_DECREF(result); result = obj; } else { if (!PyTuple_Check(result)) { o2 = result; result = PyTuple_New(1); PyTuple_SET_ITEM(result, 0, o2); } o3 = PyTuple_New(1); PyTuple_SET_ITEM(o3, 0, obj); o2 = result; result = PySequence_Concat(o2, o3); Py_DECREF(o2); Py_DECREF(o3); } return result; #endif } /* Unpack the argument tuple */ SWIGINTERN int SWIG_Python_UnpackTuple(PyObject *args, const char *name, Py_ssize_t min, Py_ssize_t max, PyObject **objs) { if (!args) { if (!min && !max) { return 1; } else { PyErr_Format(PyExc_TypeError, "%s expected %s%d arguments, got none", name, (min == max ? "" : "at least "), (int)min); return 0; } } if (!PyTuple_Check(args)) { PyErr_SetString(PyExc_SystemError, "UnpackTuple() argument list is not a tuple"); return 0; } else { register Py_ssize_t l = PyTuple_GET_SIZE(args); if (l < min) { PyErr_Format(PyExc_TypeError, "%s expected %s%d arguments, got %d", name, (min == max ? "" : "at least "), (int)min, (int)l); return 0; } else if (l > max) { PyErr_Format(PyExc_TypeError, "%s expected %s%d arguments, got %d", name, (min == max ? "" : "at most "), (int)max, (int)l); return 0; } else { register int i; for (i = 0; i < l; ++i) { objs[i] = PyTuple_GET_ITEM(args, i); } for (; l < max; ++l) { objs[l] = 0; } return i + 1; } } } /* A functor is a function object with one single object argument */ #if PY_VERSION_HEX >= 0x02020000 #define SWIG_Python_CallFunctor(functor, obj) PyObject_CallFunctionObjArgs(functor, obj, NULL); #else #define SWIG_Python_CallFunctor(functor, obj) PyObject_CallFunction(functor, "O", obj); #endif /* Helper for static pointer initialization for both C and C++ code, for example static PyObject *SWIG_STATIC_POINTER(MyVar) = NewSomething(...); */ #ifdef __cplusplus #define SWIG_STATIC_POINTER(var) var #else #define SWIG_STATIC_POINTER(var) var = 0; if (!var) var #endif /* ----------------------------------------------------------------------------- * Pointer declarations * ----------------------------------------------------------------------------- */ /* Flags for new pointer objects */ #define SWIG_POINTER_NOSHADOW (SWIG_POINTER_OWN << 1) #define SWIG_POINTER_NEW (SWIG_POINTER_NOSHADOW | SWIG_POINTER_OWN) #define SWIG_POINTER_IMPLICIT_CONV (SWIG_POINTER_DISOWN << 1) #ifdef __cplusplus extern "C" { #if 0 } /* cc-mode */ #endif #endif /* How to access Py_None */ #if defined(_WIN32) || defined(__WIN32__) || defined(__CYGWIN__) # ifndef SWIG_PYTHON_NO_BUILD_NONE # ifndef SWIG_PYTHON_BUILD_NONE # define SWIG_PYTHON_BUILD_NONE # endif # endif #endif #ifdef SWIG_PYTHON_BUILD_NONE # ifdef Py_None # undef Py_None # define Py_None SWIG_Py_None() # endif SWIGRUNTIMEINLINE PyObject * _SWIG_Py_None(void) { PyObject *none = Py_BuildValue((char*)""); Py_DECREF(none); return none; } SWIGRUNTIME PyObject * SWIG_Py_None(void) { static PyObject *SWIG_STATIC_POINTER(none) = _SWIG_Py_None(); return none; } #endif /* The python void return value */ SWIGRUNTIMEINLINE PyObject * SWIG_Py_Void(void) { PyObject *none = Py_None; Py_INCREF(none); return none; } /* SwigPyClientData */ typedef struct { PyObject *klass; PyObject *newraw; PyObject *newargs; PyObject *destroy; int delargs; int implicitconv; } SwigPyClientData; SWIGRUNTIMEINLINE int SWIG_Python_CheckImplicit(swig_type_info *ty) { SwigPyClientData *data = (SwigPyClientData *)ty->clientdata; return data ? data->implicitconv : 0; } SWIGRUNTIMEINLINE PyObject * SWIG_Python_ExceptionType(swig_type_info *desc) { SwigPyClientData *data = desc ? (SwigPyClientData *) desc->clientdata : 0; PyObject *klass = data ? data->klass : 0; return (klass ? klass : PyExc_RuntimeError); } SWIGRUNTIME SwigPyClientData * SwigPyClientData_New(PyObject* obj) { if (!obj) { return 0; } else { SwigPyClientData *data = (SwigPyClientData *)malloc(sizeof(SwigPyClientData)); /* the klass element */ data->klass = obj; Py_INCREF(data->klass); /* the newraw method and newargs arguments used to create a new raw instance */ if (PyClass_Check(obj)) { data->newraw = 0; data->newargs = obj; Py_INCREF(obj); } else { #if (PY_VERSION_HEX < 0x02020000) data->newraw = 0; #else data->newraw = PyObject_GetAttrString(data->klass, (char *)"__new__"); #endif if (data->newraw) { Py_INCREF(data->newraw); data->newargs = PyTuple_New(1); PyTuple_SetItem(data->newargs, 0, obj); } else { data->newargs = obj; } Py_INCREF(data->newargs); } /* the destroy method, aka as the C++ delete method */ data->destroy = PyObject_GetAttrString(data->klass, (char *)"__swig_destroy__"); if (PyErr_Occurred()) { PyErr_Clear(); data->destroy = 0; } if (data->destroy) { int flags; Py_INCREF(data->destroy); flags = PyCFunction_GET_FLAGS(data->destroy); #ifdef METH_O data->delargs = !(flags & (METH_O)); #else data->delargs = 0; #endif } else { data->delargs = 0; } data->implicitconv = 0; return data; } } SWIGRUNTIME void SwigPyClientData_Del(SwigPyClientData* data) { Py_XDECREF(data->newraw); Py_XDECREF(data->newargs); Py_XDECREF(data->destroy); } /* =============== SwigPyObject =====================*/ typedef struct { PyObject_HEAD void *ptr; swig_type_info *ty; int own; PyObject *next; } SwigPyObject; SWIGRUNTIME PyObject * SwigPyObject_long(SwigPyObject *v) { return PyLong_FromVoidPtr(v->ptr); } SWIGRUNTIME PyObject * SwigPyObject_format(const char* fmt, SwigPyObject *v) { PyObject *res = NULL; PyObject *args = PyTuple_New(1); if (args) { if (PyTuple_SetItem(args, 0, SwigPyObject_long(v)) == 0) { PyObject *ofmt = SWIG_Python_str_FromChar(fmt); if (ofmt) { #if PY_VERSION_HEX >= 0x03000000 res = PyUnicode_Format(ofmt,args); #else res = PyString_Format(ofmt,args); #endif Py_DECREF(ofmt); } Py_DECREF(args); } } return res; } SWIGRUNTIME PyObject * SwigPyObject_oct(SwigPyObject *v) { return SwigPyObject_format("%o",v); } SWIGRUNTIME PyObject * SwigPyObject_hex(SwigPyObject *v) { return SwigPyObject_format("%x",v); } SWIGRUNTIME PyObject * #ifdef METH_NOARGS SwigPyObject_repr(SwigPyObject *v) #else SwigPyObject_repr(SwigPyObject *v, PyObject *args) #endif { const char *name = SWIG_TypePrettyName(v->ty); PyObject *repr = SWIG_Python_str_FromFormat("", name, v); if (v->next) { #ifdef METH_NOARGS PyObject *nrep = SwigPyObject_repr((SwigPyObject *)v->next); #else PyObject *nrep = SwigPyObject_repr((SwigPyObject *)v->next, args); #endif #if PY_VERSION_HEX >= 0x03000000 PyObject *joined = PyUnicode_Concat(repr, nrep); Py_DecRef(repr); Py_DecRef(nrep); repr = joined; #else PyString_ConcatAndDel(&repr,nrep); #endif } return repr; } SWIGRUNTIME int SwigPyObject_print(SwigPyObject *v, FILE *fp, int SWIGUNUSEDPARM(flags)) { char *str; #ifdef METH_NOARGS PyObject *repr = SwigPyObject_repr(v); #else PyObject *repr = SwigPyObject_repr(v, NULL); #endif if (repr) { str = SWIG_Python_str_AsChar(repr); fputs(str, fp); SWIG_Python_str_DelForPy3(str); Py_DECREF(repr); return 0; } else { return 1; } } SWIGRUNTIME PyObject * SwigPyObject_str(SwigPyObject *v) { char result[SWIG_BUFFER_SIZE]; return SWIG_PackVoidPtr(result, v->ptr, v->ty->name, sizeof(result)) ? SWIG_Python_str_FromChar(result) : 0; } SWIGRUNTIME int SwigPyObject_compare(SwigPyObject *v, SwigPyObject *w) { void *i = v->ptr; void *j = w->ptr; return (i < j) ? -1 : ((i > j) ? 1 : 0); } /* Added for Python 3.x, would it also be useful for Python 2.x? */ SWIGRUNTIME PyObject* SwigPyObject_richcompare(SwigPyObject *v, SwigPyObject *w, int op) { PyObject* res; if( op != Py_EQ && op != Py_NE ) { Py_INCREF(Py_NotImplemented); return Py_NotImplemented; } if( (SwigPyObject_compare(v, w)==0) == (op == Py_EQ) ) res = Py_True; else res = Py_False; Py_INCREF(res); return res; } SWIGRUNTIME PyTypeObject* _PySwigObject_type(void); SWIGRUNTIME PyTypeObject* SwigPyObject_type(void) { static PyTypeObject *SWIG_STATIC_POINTER(type) = _PySwigObject_type(); return type; } SWIGRUNTIMEINLINE int SwigPyObject_Check(PyObject *op) { return (Py_TYPE(op) == SwigPyObject_type()) || (strcmp(Py_TYPE(op)->tp_name,"SwigPyObject") == 0); } SWIGRUNTIME PyObject * SwigPyObject_New(void *ptr, swig_type_info *ty, int own); SWIGRUNTIME void SwigPyObject_dealloc(PyObject *v) { SwigPyObject *sobj = (SwigPyObject *) v; PyObject *next = sobj->next; if (sobj->own == SWIG_POINTER_OWN) { swig_type_info *ty = sobj->ty; SwigPyClientData *data = ty ? (SwigPyClientData *) ty->clientdata : 0; PyObject *destroy = data ? data->destroy : 0; if (destroy) { /* destroy is always a VARARGS method */ PyObject *res; if (data->delargs) { /* we need to create a temporary object to carry the destroy operation */ PyObject *tmp = SwigPyObject_New(sobj->ptr, ty, 0); res = SWIG_Python_CallFunctor(destroy, tmp); Py_DECREF(tmp); } else { PyCFunction meth = PyCFunction_GET_FUNCTION(destroy); PyObject *mself = PyCFunction_GET_SELF(destroy); res = ((*meth)(mself, v)); } Py_XDECREF(res); } #if !defined(SWIG_PYTHON_SILENT_MEMLEAK) else { const char *name = SWIG_TypePrettyName(ty); printf("swig/python detected a memory leak of type '%s', no destructor found.\n", (name ? name : "unknown")); } #endif } Py_XDECREF(next); PyObject_DEL(v); } SWIGRUNTIME PyObject* SwigPyObject_append(PyObject* v, PyObject* next) { SwigPyObject *sobj = (SwigPyObject *) v; #ifndef METH_O PyObject *tmp = 0; if (!PyArg_ParseTuple(next,(char *)"O:append", &tmp)) return NULL; next = tmp; #endif if (!SwigPyObject_Check(next)) { return NULL; } sobj->next = next; Py_INCREF(next); return SWIG_Py_Void(); } SWIGRUNTIME PyObject* #ifdef METH_NOARGS SwigPyObject_next(PyObject* v) #else SwigPyObject_next(PyObject* v, PyObject *SWIGUNUSEDPARM(args)) #endif { SwigPyObject *sobj = (SwigPyObject *) v; if (sobj->next) { Py_INCREF(sobj->next); return sobj->next; } else { return SWIG_Py_Void(); } } SWIGINTERN PyObject* #ifdef METH_NOARGS SwigPyObject_disown(PyObject *v) #else SwigPyObject_disown(PyObject* v, PyObject *SWIGUNUSEDPARM(args)) #endif { SwigPyObject *sobj = (SwigPyObject *)v; sobj->own = 0; return SWIG_Py_Void(); } SWIGINTERN PyObject* #ifdef METH_NOARGS SwigPyObject_acquire(PyObject *v) #else SwigPyObject_acquire(PyObject* v, PyObject *SWIGUNUSEDPARM(args)) #endif { SwigPyObject *sobj = (SwigPyObject *)v; sobj->own = SWIG_POINTER_OWN; return SWIG_Py_Void(); } SWIGINTERN PyObject* SwigPyObject_own(PyObject *v, PyObject *args) { PyObject *val = 0; #if (PY_VERSION_HEX < 0x02020000) if (!PyArg_ParseTuple(args,(char *)"|O:own",&val)) #else if (!PyArg_UnpackTuple(args, (char *)"own", 0, 1, &val)) #endif { return NULL; } else { SwigPyObject *sobj = (SwigPyObject *)v; PyObject *obj = PyBool_FromLong(sobj->own); if (val) { #ifdef METH_NOARGS if (PyObject_IsTrue(val)) { SwigPyObject_acquire(v); } else { SwigPyObject_disown(v); } #else if (PyObject_IsTrue(val)) { SwigPyObject_acquire(v,args); } else { SwigPyObject_disown(v,args); } #endif } return obj; } } #ifdef METH_O static PyMethodDef swigobject_methods[] = { {(char *)"disown", (PyCFunction)SwigPyObject_disown, METH_NOARGS, (char *)"releases ownership of the pointer"}, {(char *)"acquire", (PyCFunction)SwigPyObject_acquire, METH_NOARGS, (char *)"aquires ownership of the pointer"}, {(char *)"own", (PyCFunction)SwigPyObject_own, METH_VARARGS, (char *)"returns/sets ownership of the pointer"}, {(char *)"append", (PyCFunction)SwigPyObject_append, METH_O, (char *)"appends another 'this' object"}, {(char *)"next", (PyCFunction)SwigPyObject_next, METH_NOARGS, (char *)"returns the next 'this' object"}, {(char *)"__repr__",(PyCFunction)SwigPyObject_repr, METH_NOARGS, (char *)"returns object representation"}, {0, 0, 0, 0} }; #else static PyMethodDef swigobject_methods[] = { {(char *)"disown", (PyCFunction)SwigPyObject_disown, METH_VARARGS, (char *)"releases ownership of the pointer"}, {(char *)"acquire", (PyCFunction)SwigPyObject_acquire, METH_VARARGS, (char *)"aquires ownership of the pointer"}, {(char *)"own", (PyCFunction)SwigPyObject_own, METH_VARARGS, (char *)"returns/sets ownership of the pointer"}, {(char *)"append", (PyCFunction)SwigPyObject_append, METH_VARARGS, (char *)"appends another 'this' object"}, {(char *)"next", (PyCFunction)SwigPyObject_next, METH_VARARGS, (char *)"returns the next 'this' object"}, {(char *)"__repr__",(PyCFunction)SwigPyObject_repr, METH_VARARGS, (char *)"returns object representation"}, {0, 0, 0, 0} }; #endif #if PY_VERSION_HEX < 0x02020000 SWIGINTERN PyObject * SwigPyObject_getattr(SwigPyObject *sobj,char *name) { return Py_FindMethod(swigobject_methods, (PyObject *)sobj, name); } #endif SWIGRUNTIME PyTypeObject* _PySwigObject_type(void) { static char swigobject_doc[] = "Swig object carries a C/C++ instance pointer"; static PyNumberMethods SwigPyObject_as_number = { (binaryfunc)0, /*nb_add*/ (binaryfunc)0, /*nb_subtract*/ (binaryfunc)0, /*nb_multiply*/ /* nb_divide removed in Python 3 */ #if PY_VERSION_HEX < 0x03000000 (binaryfunc)0, /*nb_divide*/ #endif (binaryfunc)0, /*nb_remainder*/ (binaryfunc)0, /*nb_divmod*/ (ternaryfunc)0,/*nb_power*/ (unaryfunc)0, /*nb_negative*/ (unaryfunc)0, /*nb_positive*/ (unaryfunc)0, /*nb_absolute*/ (inquiry)0, /*nb_nonzero*/ 0, /*nb_invert*/ 0, /*nb_lshift*/ 0, /*nb_rshift*/ 0, /*nb_and*/ 0, /*nb_xor*/ 0, /*nb_or*/ #if PY_VERSION_HEX < 0x03000000 0, /*nb_coerce*/ #endif (unaryfunc)SwigPyObject_long, /*nb_int*/ #if PY_VERSION_HEX < 0x03000000 (unaryfunc)SwigPyObject_long, /*nb_long*/ #else 0, /*nb_reserved*/ #endif (unaryfunc)0, /*nb_float*/ #if PY_VERSION_HEX < 0x03000000 (unaryfunc)SwigPyObject_oct, /*nb_oct*/ (unaryfunc)SwigPyObject_hex, /*nb_hex*/ #endif #if PY_VERSION_HEX >= 0x03000000 /* 3.0 */ 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 /* nb_inplace_add -> nb_index, nb_inplace_divide removed */ #elif PY_VERSION_HEX >= 0x02050000 /* 2.5.0 */ 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 /* nb_inplace_add -> nb_index */ #elif PY_VERSION_HEX >= 0x02020000 /* 2.2.0 */ 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 /* nb_inplace_add -> nb_inplace_true_divide */ #elif PY_VERSION_HEX >= 0x02000000 /* 2.0.0 */ 0,0,0,0,0,0,0,0,0,0,0 /* nb_inplace_add -> nb_inplace_or */ #endif }; static PyTypeObject swigpyobject_type; static int type_init = 0; if (!type_init) { const PyTypeObject tmp = { /* PyObject header changed in Python 3 */ #if PY_VERSION_HEX >= 0x03000000 PyVarObject_HEAD_INIT(&PyType_Type, 0) #else PyObject_HEAD_INIT(NULL) 0, /* ob_size */ #endif (char *)"SwigPyObject", /* tp_name */ sizeof(SwigPyObject), /* tp_basicsize */ 0, /* tp_itemsize */ (destructor)SwigPyObject_dealloc, /* tp_dealloc */ (printfunc)SwigPyObject_print, /* tp_print */ #if PY_VERSION_HEX < 0x02020000 (getattrfunc)SwigPyObject_getattr, /* tp_getattr */ #else (getattrfunc)0, /* tp_getattr */ #endif (setattrfunc)0, /* tp_setattr */ #if PY_VERSION_HEX >= 0x03000000 0, /* tp_reserved in 3.0.1, tp_compare in 3.0.0 but not used */ #else (cmpfunc)SwigPyObject_compare, /* tp_compare */ #endif (reprfunc)SwigPyObject_repr, /* tp_repr */ &SwigPyObject_as_number, /* tp_as_number */ 0, /* tp_as_sequence */ 0, /* tp_as_mapping */ (hashfunc)0, /* tp_hash */ (ternaryfunc)0, /* tp_call */ (reprfunc)SwigPyObject_str, /* tp_str */ PyObject_GenericGetAttr, /* tp_getattro */ 0, /* tp_setattro */ 0, /* tp_as_buffer */ Py_TPFLAGS_DEFAULT, /* tp_flags */ swigobject_doc, /* tp_doc */ 0, /* tp_traverse */ 0, /* tp_clear */ (richcmpfunc)SwigPyObject_richcompare, /* tp_richcompare */ 0, /* tp_weaklistoffset */ #if PY_VERSION_HEX >= 0x02020000 0, /* tp_iter */ 0, /* tp_iternext */ swigobject_methods, /* tp_methods */ 0, /* tp_members */ 0, /* tp_getset */ 0, /* tp_base */ 0, /* tp_dict */ 0, /* tp_descr_get */ 0, /* tp_descr_set */ 0, /* tp_dictoffset */ 0, /* tp_init */ 0, /* tp_alloc */ 0, /* tp_new */ 0, /* tp_free */ 0, /* tp_is_gc */ 0, /* tp_bases */ 0, /* tp_mro */ 0, /* tp_cache */ 0, /* tp_subclasses */ 0, /* tp_weaklist */ #endif #if PY_VERSION_HEX >= 0x02030000 0, /* tp_del */ #endif #ifdef COUNT_ALLOCS 0,0,0,0 /* tp_alloc -> tp_next */ #endif }; swigpyobject_type = tmp; /* for Python 3 we already assigned ob_type in PyVarObject_HEAD_INIT() */ #if PY_VERSION_HEX < 0x03000000 swigpyobject_type.ob_type = &PyType_Type; #endif type_init = 1; } return &swigpyobject_type; } SWIGRUNTIME PyObject * SwigPyObject_New(void *ptr, swig_type_info *ty, int own) { SwigPyObject *sobj = PyObject_NEW(SwigPyObject, SwigPyObject_type()); if (sobj) { sobj->ptr = ptr; sobj->ty = ty; sobj->own = own; sobj->next = 0; } return (PyObject *)sobj; } /* ----------------------------------------------------------------------------- * Implements a simple Swig Packed type, and use it instead of string * ----------------------------------------------------------------------------- */ typedef struct { PyObject_HEAD void *pack; swig_type_info *ty; size_t size; } SwigPyPacked; SWIGRUNTIME int SwigPyPacked_print(SwigPyPacked *v, FILE *fp, int SWIGUNUSEDPARM(flags)) { char result[SWIG_BUFFER_SIZE]; fputs("pack, v->size, 0, sizeof(result))) { fputs("at ", fp); fputs(result, fp); } fputs(v->ty->name,fp); fputs(">", fp); return 0; } SWIGRUNTIME PyObject * SwigPyPacked_repr(SwigPyPacked *v) { char result[SWIG_BUFFER_SIZE]; if (SWIG_PackDataName(result, v->pack, v->size, 0, sizeof(result))) { return SWIG_Python_str_FromFormat("", result, v->ty->name); } else { return SWIG_Python_str_FromFormat("", v->ty->name); } } SWIGRUNTIME PyObject * SwigPyPacked_str(SwigPyPacked *v) { char result[SWIG_BUFFER_SIZE]; if (SWIG_PackDataName(result, v->pack, v->size, 0, sizeof(result))){ return SWIG_Python_str_FromFormat("%s%s", result, v->ty->name); } else { return SWIG_Python_str_FromChar(v->ty->name); } } SWIGRUNTIME int SwigPyPacked_compare(SwigPyPacked *v, SwigPyPacked *w) { size_t i = v->size; size_t j = w->size; int s = (i < j) ? -1 : ((i > j) ? 1 : 0); return s ? s : strncmp((char *)v->pack, (char *)w->pack, 2*v->size); } SWIGRUNTIME PyTypeObject* _PySwigPacked_type(void); SWIGRUNTIME PyTypeObject* SwigPyPacked_type(void) { static PyTypeObject *SWIG_STATIC_POINTER(type) = _PySwigPacked_type(); return type; } SWIGRUNTIMEINLINE int SwigPyPacked_Check(PyObject *op) { return ((op)->ob_type == _PySwigPacked_type()) || (strcmp((op)->ob_type->tp_name,"SwigPyPacked") == 0); } SWIGRUNTIME void SwigPyPacked_dealloc(PyObject *v) { if (SwigPyPacked_Check(v)) { SwigPyPacked *sobj = (SwigPyPacked *) v; free(sobj->pack); } PyObject_DEL(v); } SWIGRUNTIME PyTypeObject* _PySwigPacked_type(void) { static char swigpacked_doc[] = "Swig object carries a C/C++ instance pointer"; static PyTypeObject swigpypacked_type; static int type_init = 0; if (!type_init) { const PyTypeObject tmp = { /* PyObject header changed in Python 3 */ #if PY_VERSION_HEX>=0x03000000 PyVarObject_HEAD_INIT(&PyType_Type, 0) #else PyObject_HEAD_INIT(NULL) 0, /* ob_size */ #endif (char *)"SwigPyPacked", /* tp_name */ sizeof(SwigPyPacked), /* tp_basicsize */ 0, /* tp_itemsize */ (destructor)SwigPyPacked_dealloc, /* tp_dealloc */ (printfunc)SwigPyPacked_print, /* tp_print */ (getattrfunc)0, /* tp_getattr */ (setattrfunc)0, /* tp_setattr */ #if PY_VERSION_HEX>=0x03000000 0, /* tp_reserved in 3.0.1 */ #else (cmpfunc)SwigPyPacked_compare, /* tp_compare */ #endif (reprfunc)SwigPyPacked_repr, /* tp_repr */ 0, /* tp_as_number */ 0, /* tp_as_sequence */ 0, /* tp_as_mapping */ (hashfunc)0, /* tp_hash */ (ternaryfunc)0, /* tp_call */ (reprfunc)SwigPyPacked_str, /* tp_str */ PyObject_GenericGetAttr, /* tp_getattro */ 0, /* tp_setattro */ 0, /* tp_as_buffer */ Py_TPFLAGS_DEFAULT, /* tp_flags */ swigpacked_doc, /* tp_doc */ 0, /* tp_traverse */ 0, /* tp_clear */ 0, /* tp_richcompare */ 0, /* tp_weaklistoffset */ #if PY_VERSION_HEX >= 0x02020000 0, /* tp_iter */ 0, /* tp_iternext */ 0, /* tp_methods */ 0, /* tp_members */ 0, /* tp_getset */ 0, /* tp_base */ 0, /* tp_dict */ 0, /* tp_descr_get */ 0, /* tp_descr_set */ 0, /* tp_dictoffset */ 0, /* tp_init */ 0, /* tp_alloc */ 0, /* tp_new */ 0, /* tp_free */ 0, /* tp_is_gc */ 0, /* tp_bases */ 0, /* tp_mro */ 0, /* tp_cache */ 0, /* tp_subclasses */ 0, /* tp_weaklist */ #endif #if PY_VERSION_HEX >= 0x02030000 0, /* tp_del */ #endif #ifdef COUNT_ALLOCS 0,0,0,0 /* tp_alloc -> tp_next */ #endif }; swigpypacked_type = tmp; /* for Python 3 the ob_type already assigned in PyVarObject_HEAD_INIT() */ #if PY_VERSION_HEX < 0x03000000 swigpypacked_type.ob_type = &PyType_Type; #endif type_init = 1; } return &swigpypacked_type; } SWIGRUNTIME PyObject * SwigPyPacked_New(void *ptr, size_t size, swig_type_info *ty) { SwigPyPacked *sobj = PyObject_NEW(SwigPyPacked, SwigPyPacked_type()); if (sobj) { void *pack = malloc(size); if (pack) { memcpy(pack, ptr, size); sobj->pack = pack; sobj->ty = ty; sobj->size = size; } else { PyObject_DEL((PyObject *) sobj); sobj = 0; } } return (PyObject *) sobj; } SWIGRUNTIME swig_type_info * SwigPyPacked_UnpackData(PyObject *obj, void *ptr, size_t size) { if (SwigPyPacked_Check(obj)) { SwigPyPacked *sobj = (SwigPyPacked *)obj; if (sobj->size != size) return 0; memcpy(ptr, sobj->pack, size); return sobj->ty; } else { return 0; } } /* ----------------------------------------------------------------------------- * pointers/data manipulation * ----------------------------------------------------------------------------- */ SWIGRUNTIMEINLINE PyObject * _SWIG_This(void) { return SWIG_Python_str_FromChar("this"); } SWIGRUNTIME PyObject * SWIG_This(void) { static PyObject *SWIG_STATIC_POINTER(swig_this) = _SWIG_This(); return swig_this; } /* #define SWIG_PYTHON_SLOW_GETSET_THIS */ /* TODO: I don't know how to implement the fast getset in Python 3 right now */ #if PY_VERSION_HEX>=0x03000000 #define SWIG_PYTHON_SLOW_GETSET_THIS #endif SWIGRUNTIME SwigPyObject * SWIG_Python_GetSwigThis(PyObject *pyobj) { if (SwigPyObject_Check(pyobj)) { return (SwigPyObject *) pyobj; } else { PyObject *obj = 0; #if (!defined(SWIG_PYTHON_SLOW_GETSET_THIS) && (PY_VERSION_HEX >= 0x02030000)) if (PyInstance_Check(pyobj)) { obj = _PyInstance_Lookup(pyobj, SWIG_This()); } else { PyObject **dictptr = _PyObject_GetDictPtr(pyobj); if (dictptr != NULL) { PyObject *dict = *dictptr; obj = dict ? PyDict_GetItem(dict, SWIG_This()) : 0; } else { #ifdef PyWeakref_CheckProxy if (PyWeakref_CheckProxy(pyobj)) { PyObject *wobj = PyWeakref_GET_OBJECT(pyobj); return wobj ? SWIG_Python_GetSwigThis(wobj) : 0; } #endif obj = PyObject_GetAttr(pyobj,SWIG_This()); if (obj) { Py_DECREF(obj); } else { if (PyErr_Occurred()) PyErr_Clear(); return 0; } } } #else obj = PyObject_GetAttr(pyobj,SWIG_This()); if (obj) { Py_DECREF(obj); } else { if (PyErr_Occurred()) PyErr_Clear(); return 0; } #endif if (obj && !SwigPyObject_Check(obj)) { /* a PyObject is called 'this', try to get the 'real this' SwigPyObject from it */ return SWIG_Python_GetSwigThis(obj); } return (SwigPyObject *)obj; } } /* Acquire a pointer value */ SWIGRUNTIME int SWIG_Python_AcquirePtr(PyObject *obj, int own) { if (own == SWIG_POINTER_OWN) { SwigPyObject *sobj = SWIG_Python_GetSwigThis(obj); if (sobj) { int oldown = sobj->own; sobj->own = own; return oldown; } } return 0; } /* Convert a pointer value */ SWIGRUNTIME int SWIG_Python_ConvertPtrAndOwn(PyObject *obj, void **ptr, swig_type_info *ty, int flags, int *own) { if (!obj) return SWIG_ERROR; if (obj == Py_None) { if (ptr) *ptr = 0; return SWIG_OK; } else { SwigPyObject *sobj = SWIG_Python_GetSwigThis(obj); if (own) *own = 0; while (sobj) { void *vptr = sobj->ptr; if (ty) { swig_type_info *to = sobj->ty; if (to == ty) { /* no type cast needed */ if (ptr) *ptr = vptr; break; } else { swig_cast_info *tc = SWIG_TypeCheck(to->name,ty); if (!tc) { sobj = (SwigPyObject *)sobj->next; } else { if (ptr) { int newmemory = 0; *ptr = SWIG_TypeCast(tc,vptr,&newmemory); if (newmemory == SWIG_CAST_NEW_MEMORY) { assert(own); if (own) *own = *own | SWIG_CAST_NEW_MEMORY; } } break; } } } else { if (ptr) *ptr = vptr; break; } } if (sobj) { if (own) *own = *own | sobj->own; if (flags & SWIG_POINTER_DISOWN) { sobj->own = 0; } return SWIG_OK; } else { int res = SWIG_ERROR; if (flags & SWIG_POINTER_IMPLICIT_CONV) { SwigPyClientData *data = ty ? (SwigPyClientData *) ty->clientdata : 0; if (data && !data->implicitconv) { PyObject *klass = data->klass; if (klass) { PyObject *impconv; data->implicitconv = 1; /* avoid recursion and call 'explicit' constructors*/ impconv = SWIG_Python_CallFunctor(klass, obj); data->implicitconv = 0; if (PyErr_Occurred()) { PyErr_Clear(); impconv = 0; } if (impconv) { SwigPyObject *iobj = SWIG_Python_GetSwigThis(impconv); if (iobj) { void *vptr; res = SWIG_Python_ConvertPtrAndOwn((PyObject*)iobj, &vptr, ty, 0, 0); if (SWIG_IsOK(res)) { if (ptr) { *ptr = vptr; /* transfer the ownership to 'ptr' */ iobj->own = 0; res = SWIG_AddCast(res); res = SWIG_AddNewMask(res); } else { res = SWIG_AddCast(res); } } } Py_DECREF(impconv); } } } } return res; } } } /* Convert a function ptr value */ SWIGRUNTIME int SWIG_Python_ConvertFunctionPtr(PyObject *obj, void **ptr, swig_type_info *ty) { if (!PyCFunction_Check(obj)) { return SWIG_ConvertPtr(obj, ptr, ty, 0); } else { void *vptr = 0; /* here we get the method pointer for callbacks */ const char *doc = (((PyCFunctionObject *)obj) -> m_ml -> ml_doc); const char *desc = doc ? strstr(doc, "swig_ptr: ") : 0; if (desc) desc = ty ? SWIG_UnpackVoidPtr(desc + 10, &vptr, ty->name) : 0; if (!desc) return SWIG_ERROR; if (ty) { swig_cast_info *tc = SWIG_TypeCheck(desc,ty); if (tc) { int newmemory = 0; *ptr = SWIG_TypeCast(tc,vptr,&newmemory); assert(!newmemory); /* newmemory handling not yet implemented */ } else { return SWIG_ERROR; } } else { *ptr = vptr; } return SWIG_OK; } } /* Convert a packed value value */ SWIGRUNTIME int SWIG_Python_ConvertPacked(PyObject *obj, void *ptr, size_t sz, swig_type_info *ty) { swig_type_info *to = SwigPyPacked_UnpackData(obj, ptr, sz); if (!to) return SWIG_ERROR; if (ty) { if (to != ty) { /* check type cast? */ swig_cast_info *tc = SWIG_TypeCheck(to->name,ty); if (!tc) return SWIG_ERROR; } } return SWIG_OK; } /* ----------------------------------------------------------------------------- * Create a new pointer object * ----------------------------------------------------------------------------- */ /* Create a new instance object, without calling __init__, and set the 'this' attribute. */ SWIGRUNTIME PyObject* SWIG_Python_NewShadowInstance(SwigPyClientData *data, PyObject *swig_this) { #if (PY_VERSION_HEX >= 0x02020000) PyObject *inst = 0; PyObject *newraw = data->newraw; if (newraw) { inst = PyObject_Call(newraw, data->newargs, NULL); if (inst) { #if !defined(SWIG_PYTHON_SLOW_GETSET_THIS) PyObject **dictptr = _PyObject_GetDictPtr(inst); if (dictptr != NULL) { PyObject *dict = *dictptr; if (dict == NULL) { dict = PyDict_New(); *dictptr = dict; PyDict_SetItem(dict, SWIG_This(), swig_this); } } #else PyObject *key = SWIG_This(); PyObject_SetAttr(inst, key, swig_this); #endif } } else { #if PY_VERSION_HEX >= 0x03000000 inst = PyBaseObject_Type.tp_new((PyTypeObject*) data->newargs, Py_None, Py_None); PyObject_SetAttr(inst, SWIG_This(), swig_this); Py_TYPE(inst)->tp_flags &= ~Py_TPFLAGS_VALID_VERSION_TAG; #else PyObject *dict = PyDict_New(); PyDict_SetItem(dict, SWIG_This(), swig_this); inst = PyInstance_NewRaw(data->newargs, dict); Py_DECREF(dict); #endif } return inst; #else #if (PY_VERSION_HEX >= 0x02010000) PyObject *inst; PyObject *dict = PyDict_New(); PyDict_SetItem(dict, SWIG_This(), swig_this); inst = PyInstance_NewRaw(data->newargs, dict); Py_DECREF(dict); return (PyObject *) inst; #else PyInstanceObject *inst = PyObject_NEW(PyInstanceObject, &PyInstance_Type); if (inst == NULL) { return NULL; } inst->in_class = (PyClassObject *)data->newargs; Py_INCREF(inst->in_class); inst->in_dict = PyDict_New(); if (inst->in_dict == NULL) { Py_DECREF(inst); return NULL; } #ifdef Py_TPFLAGS_HAVE_WEAKREFS inst->in_weakreflist = NULL; #endif #ifdef Py_TPFLAGS_GC PyObject_GC_Init(inst); #endif PyDict_SetItem(inst->in_dict, SWIG_This(), swig_this); return (PyObject *) inst; #endif #endif } SWIGRUNTIME void SWIG_Python_SetSwigThis(PyObject *inst, PyObject *swig_this) { PyObject *dict; #if (PY_VERSION_HEX >= 0x02020000) && !defined(SWIG_PYTHON_SLOW_GETSET_THIS) PyObject **dictptr = _PyObject_GetDictPtr(inst); if (dictptr != NULL) { dict = *dictptr; if (dict == NULL) { dict = PyDict_New(); *dictptr = dict; } PyDict_SetItem(dict, SWIG_This(), swig_this); return; } #endif dict = PyObject_GetAttrString(inst, (char*)"__dict__"); PyDict_SetItem(dict, SWIG_This(), swig_this); Py_DECREF(dict); } SWIGINTERN PyObject * SWIG_Python_InitShadowInstance(PyObject *args) { PyObject *obj[2]; if (!SWIG_Python_UnpackTuple(args,(char*)"swiginit", 2, 2, obj)) { return NULL; } else { SwigPyObject *sthis = SWIG_Python_GetSwigThis(obj[0]); if (sthis) { SwigPyObject_append((PyObject*) sthis, obj[1]); } else { SWIG_Python_SetSwigThis(obj[0], obj[1]); } return SWIG_Py_Void(); } } /* Create a new pointer object */ SWIGRUNTIME PyObject * SWIG_Python_NewPointerObj(void *ptr, swig_type_info *type, int flags) { if (!ptr) { return SWIG_Py_Void(); } else { int own = (flags & SWIG_POINTER_OWN) ? SWIG_POINTER_OWN : 0; PyObject *robj = SwigPyObject_New(ptr, type, own); SwigPyClientData *clientdata = type ? (SwigPyClientData *)(type->clientdata) : 0; if (clientdata && !(flags & SWIG_POINTER_NOSHADOW)) { PyObject *inst = SWIG_Python_NewShadowInstance(clientdata, robj); if (inst) { Py_DECREF(robj); robj = inst; } } return robj; } } /* Create a new packed object */ SWIGRUNTIMEINLINE PyObject * SWIG_Python_NewPackedObj(void *ptr, size_t sz, swig_type_info *type) { return ptr ? SwigPyPacked_New((void *) ptr, sz, type) : SWIG_Py_Void(); } /* -----------------------------------------------------------------------------* * Get type list * -----------------------------------------------------------------------------*/ #ifdef SWIG_LINK_RUNTIME void *SWIG_ReturnGlobalTypeList(void *); #endif SWIGRUNTIME swig_module_info * SWIG_Python_GetModule(void) { static void *type_pointer = (void *)0; /* first check if module already created */ if (!type_pointer) { #ifdef SWIG_LINK_RUNTIME type_pointer = SWIG_ReturnGlobalTypeList((void *)0); #else type_pointer = PyCObject_Import((char*)"swig_runtime_data" SWIG_RUNTIME_VERSION, (char*)"type_pointer" SWIG_TYPE_TABLE_NAME); if (PyErr_Occurred()) { PyErr_Clear(); type_pointer = (void *)0; } #endif } return (swig_module_info *) type_pointer; } #if PY_MAJOR_VERSION < 2 /* PyModule_AddObject function was introduced in Python 2.0. The following function is copied out of Python/modsupport.c in python version 2.3.4 */ SWIGINTERN int PyModule_AddObject(PyObject *m, char *name, PyObject *o) { PyObject *dict; if (!PyModule_Check(m)) { PyErr_SetString(PyExc_TypeError, "PyModule_AddObject() needs module as first arg"); return SWIG_ERROR; } if (!o) { PyErr_SetString(PyExc_TypeError, "PyModule_AddObject() needs non-NULL value"); return SWIG_ERROR; } dict = PyModule_GetDict(m); if (dict == NULL) { /* Internal error -- modules must have a dict! */ PyErr_Format(PyExc_SystemError, "module '%s' has no __dict__", PyModule_GetName(m)); return SWIG_ERROR; } if (PyDict_SetItemString(dict, name, o)) return SWIG_ERROR; Py_DECREF(o); return SWIG_OK; } #endif SWIGRUNTIME void SWIG_Python_DestroyModule(void *vptr) { swig_module_info *swig_module = (swig_module_info *) vptr; swig_type_info **types = swig_module->types; size_t i; for (i =0; i < swig_module->size; ++i) { swig_type_info *ty = types[i]; if (ty->owndata) { SwigPyClientData *data = (SwigPyClientData *) ty->clientdata; if (data) SwigPyClientData_Del(data); } } Py_DECREF(SWIG_This()); } SWIGRUNTIME void SWIG_Python_SetModule(swig_module_info *swig_module) { static PyMethodDef swig_empty_runtime_method_table[] = { {NULL, NULL, 0, NULL} };/* Sentinel */ #if PY_VERSION_HEX >= 0x03000000 /* Add a dummy module object into sys.modules */ PyObject *module = PyImport_AddModule((char*)"swig_runtime_data" SWIG_RUNTIME_VERSION); #else PyObject *module = Py_InitModule((char*)"swig_runtime_data" SWIG_RUNTIME_VERSION, swig_empty_runtime_method_table); #endif PyObject *pointer = PyCObject_FromVoidPtr((void *) swig_module, SWIG_Python_DestroyModule); if (pointer && module) { PyModule_AddObject(module, (char*)"type_pointer" SWIG_TYPE_TABLE_NAME, pointer); } else { Py_XDECREF(pointer); } } /* The python cached type query */ SWIGRUNTIME PyObject * SWIG_Python_TypeCache(void) { static PyObject *SWIG_STATIC_POINTER(cache) = PyDict_New(); return cache; } SWIGRUNTIME swig_type_info * SWIG_Python_TypeQuery(const char *type) { PyObject *cache = SWIG_Python_TypeCache(); PyObject *key = SWIG_Python_str_FromChar(type); PyObject *obj = PyDict_GetItem(cache, key); swig_type_info *descriptor; if (obj) { descriptor = (swig_type_info *) PyCObject_AsVoidPtr(obj); } else { swig_module_info *swig_module = SWIG_Python_GetModule(); descriptor = SWIG_TypeQueryModule(swig_module, swig_module, type); if (descriptor) { obj = PyCObject_FromVoidPtr(descriptor, NULL); PyDict_SetItem(cache, key, obj); Py_DECREF(obj); } } Py_DECREF(key); return descriptor; } /* For backward compatibility only */ #define SWIG_POINTER_EXCEPTION 0 #define SWIG_arg_fail(arg) SWIG_Python_ArgFail(arg) #define SWIG_MustGetPtr(p, type, argnum, flags) SWIG_Python_MustGetPtr(p, type, argnum, flags) SWIGRUNTIME int SWIG_Python_AddErrMesg(const char* mesg, int infront) { if (PyErr_Occurred()) { PyObject *type = 0; PyObject *value = 0; PyObject *traceback = 0; PyErr_Fetch(&type, &value, &traceback); if (value) { char *tmp; PyObject *old_str = PyObject_Str(value); Py_XINCREF(type); PyErr_Clear(); if (infront) { PyErr_Format(type, "%s %s", mesg, tmp = SWIG_Python_str_AsChar(old_str)); } else { PyErr_Format(type, "%s %s", tmp = SWIG_Python_str_AsChar(old_str), mesg); } SWIG_Python_str_DelForPy3(tmp); Py_DECREF(old_str); } return 1; } else { return 0; } } SWIGRUNTIME int SWIG_Python_ArgFail(int argnum) { if (PyErr_Occurred()) { /* add information about failing argument */ char mesg[256]; PyOS_snprintf(mesg, sizeof(mesg), "argument number %d:", argnum); return SWIG_Python_AddErrMesg(mesg, 1); } else { return 0; } } SWIGRUNTIMEINLINE const char * SwigPyObject_GetDesc(PyObject *self) { SwigPyObject *v = (SwigPyObject *)self; swig_type_info *ty = v ? v->ty : 0; return ty ? ty->str : (char*)""; } SWIGRUNTIME void SWIG_Python_TypeError(const char *type, PyObject *obj) { if (type) { #if defined(SWIG_COBJECT_TYPES) if (obj && SwigPyObject_Check(obj)) { const char *otype = (const char *) SwigPyObject_GetDesc(obj); if (otype) { PyErr_Format(PyExc_TypeError, "a '%s' is expected, 'SwigPyObject(%s)' is received", type, otype); return; } } else #endif { const char *otype = (obj ? obj->ob_type->tp_name : 0); if (otype) { PyObject *str = PyObject_Str(obj); const char *cstr = str ? SWIG_Python_str_AsChar(str) : 0; if (cstr) { PyErr_Format(PyExc_TypeError, "a '%s' is expected, '%s(%s)' is received", type, otype, cstr); SWIG_Python_str_DelForPy3(cstr); } else { PyErr_Format(PyExc_TypeError, "a '%s' is expected, '%s' is received", type, otype); } Py_XDECREF(str); return; } } PyErr_Format(PyExc_TypeError, "a '%s' is expected", type); } else { PyErr_Format(PyExc_TypeError, "unexpected type is received"); } } /* Convert a pointer value, signal an exception on a type mismatch */ SWIGRUNTIME void * SWIG_Python_MustGetPtr(PyObject *obj, swig_type_info *ty, int argnum, int flags) { void *result; if (SWIG_Python_ConvertPtr(obj, &result, ty, flags) == -1) { PyErr_Clear(); #if SWIG_POINTER_EXCEPTION if (flags) { SWIG_Python_TypeError(SWIG_TypePrettyName(ty), obj); SWIG_Python_ArgFail(argnum); } #endif } return result; } #ifdef __cplusplus #if 0 { /* cc-mode */ #endif } #endif #define SWIG_exception_fail(code, msg) do { SWIG_Error(code, msg); SWIG_fail; } while(0) #define SWIG_contract_assert(expr, msg) if (!(expr)) { SWIG_Error(SWIG_RuntimeError, msg); SWIG_fail; } else #define SWIG_exception(code, msg) do { SWIG_Error(code, msg); SWIG_fail;; } while(0) /* -------- TYPES TABLE (BEGIN) -------- */ #define SWIGTYPE_p_SpikeQueue swig_types[0] #define SWIGTYPE_p_char swig_types[1] #define SWIGTYPE_p_int swig_types[2] #define SWIGTYPE_p_long swig_types[3] #define SWIGTYPE_p_p_long swig_types[4] static swig_type_info *swig_types[6]; static swig_module_info swig_module = {swig_types, 5, 0, 0, 0, 0}; #define SWIG_TypeQuery(name) SWIG_TypeQueryModule(&swig_module, &swig_module, name) #define SWIG_MangledTypeQuery(name) SWIG_MangledTypeQueryModule(&swig_module, &swig_module, name) /* -------- TYPES TABLE (END) -------- */ #if (PY_VERSION_HEX <= 0x02000000) # if !defined(SWIG_PYTHON_CLASSIC) # error "This python version requires swig to be run with the '-classic' option" # endif #endif /*----------------------------------------------- @(target):= _cspikequeue.so ------------------------------------------------*/ #if PY_VERSION_HEX >= 0x03000000 # define SWIG_init PyInit__cspikequeue #else # define SWIG_init init_cspikequeue #endif #define SWIG_name "_cspikequeue" #define SWIGVERSION 0x010340 #define SWIG_VERSION SWIGVERSION #define SWIG_as_voidptr(a) const_cast< void * >(static_cast< const void * >(a)) #define SWIG_as_voidptrptr(a) ((void)SWIG_as_voidptr(*a),reinterpret_cast< void** >(a)) #include namespace swig { class SwigPtr_PyObject { protected: PyObject *_obj; public: SwigPtr_PyObject() :_obj(0) { } SwigPtr_PyObject(const SwigPtr_PyObject& item) : _obj(item._obj) { Py_XINCREF(_obj); } SwigPtr_PyObject(PyObject *obj, bool initial_ref = true) :_obj(obj) { if (initial_ref) { Py_XINCREF(_obj); } } SwigPtr_PyObject & operator=(const SwigPtr_PyObject& item) { Py_XINCREF(item._obj); Py_XDECREF(_obj); _obj = item._obj; return *this; } ~SwigPtr_PyObject() { Py_XDECREF(_obj); } operator PyObject *() const { return _obj; } PyObject *operator->() const { return _obj; } }; } namespace swig { struct SwigVar_PyObject : SwigPtr_PyObject { SwigVar_PyObject(PyObject* obj = 0) : SwigPtr_PyObject(obj, false) { } SwigVar_PyObject & operator = (PyObject* obj) { Py_XDECREF(_obj); _obj = obj; return *this; } }; } #include #define SWIG_FILE_WITH_INIT #include "spikequeue.h" #ifndef SWIG_FILE_WITH_INIT # define NO_IMPORT_ARRAY #endif #include "stdio.h" #include #include #if !defined(SWIG_NO_LLONG_MAX) # if !defined(LLONG_MAX) && defined(__GNUC__) && defined (__LONG_LONG_MAX__) # define LLONG_MAX __LONG_LONG_MAX__ # define LLONG_MIN (-LLONG_MAX - 1LL) # define ULLONG_MAX (LLONG_MAX * 2ULL + 1ULL) # endif #endif SWIGINTERN int SWIG_AsVal_double (PyObject *obj, double *val) { int res = SWIG_TypeError; if (PyFloat_Check(obj)) { if (val) *val = PyFloat_AsDouble(obj); return SWIG_OK; } else if (PyInt_Check(obj)) { if (val) *val = PyInt_AsLong(obj); return SWIG_OK; } else if (PyLong_Check(obj)) { double v = PyLong_AsDouble(obj); if (!PyErr_Occurred()) { if (val) *val = v; return SWIG_OK; } else { PyErr_Clear(); } } #ifdef SWIG_PYTHON_CAST_MODE { int dispatch = 0; double d = PyFloat_AsDouble(obj); if (!PyErr_Occurred()) { if (val) *val = d; return SWIG_AddCast(SWIG_OK); } else { PyErr_Clear(); } if (!dispatch) { long v = PyLong_AsLong(obj); if (!PyErr_Occurred()) { if (val) *val = v; return SWIG_AddCast(SWIG_AddCast(SWIG_OK)); } else { PyErr_Clear(); } } } #endif return res; } #include #include SWIGINTERNINLINE int SWIG_CanCastAsInteger(double *d, double min, double max) { double x = *d; if ((min <= x && x <= max)) { double fx = floor(x); double cx = ceil(x); double rd = ((x - fx) < 0.5) ? fx : cx; /* simple rint */ if ((errno == EDOM) || (errno == ERANGE)) { errno = 0; } else { double summ, reps, diff; if (rd < x) { diff = x - rd; } else if (rd > x) { diff = rd - x; } else { return 1; } summ = rd + x; reps = diff/summ; if (reps < 8*DBL_EPSILON) { *d = rd; return 1; } } } return 0; } SWIGINTERN int SWIG_AsVal_long (PyObject *obj, long* val) { if (PyInt_Check(obj)) { if (val) *val = PyInt_AsLong(obj); return SWIG_OK; } else if (PyLong_Check(obj)) { long v = PyLong_AsLong(obj); if (!PyErr_Occurred()) { if (val) *val = v; return SWIG_OK; } else { PyErr_Clear(); } } #ifdef SWIG_PYTHON_CAST_MODE { int dispatch = 0; long v = PyInt_AsLong(obj); if (!PyErr_Occurred()) { if (val) *val = v; return SWIG_AddCast(SWIG_OK); } else { PyErr_Clear(); } if (!dispatch) { double d; int res = SWIG_AddCast(SWIG_AsVal_double (obj,&d)); if (SWIG_IsOK(res) && SWIG_CanCastAsInteger(&d, LONG_MIN, LONG_MAX)) { if (val) *val = (long)(d); return res; } } } #endif return SWIG_TypeError; } SWIGINTERN int SWIG_AsVal_int (PyObject * obj, int *val) { long v; int res = SWIG_AsVal_long (obj, &v); if (SWIG_IsOK(res)) { if ((v < INT_MIN || v > INT_MAX)) { return SWIG_OverflowError; } else { if (val) *val = static_cast< int >(v); } } return res; } #define SWIG_From_long PyInt_FromLong SWIGINTERNINLINE PyObject * SWIG_From_int (int value) { return SWIG_From_long (value); } /* Support older NumPy data type names */ #if NDARRAY_VERSION < 0x01000000 #define NPY_BOOL PyArray_BOOL #define NPY_BYTE PyArray_BYTE #define NPY_UBYTE PyArray_UBYTE #define NPY_SHORT PyArray_SHORT #define NPY_USHORT PyArray_USHORT #define NPY_INT PyArray_INT #define NPY_UINT PyArray_UINT #define NPY_LONG PyArray_LONG #define NPY_ULONG PyArray_ULONG #define NPY_LONGLONG PyArray_LONGLONG #define NPY_ULONGLONG PyArray_ULONGLONG #define NPY_FLOAT PyArray_FLOAT #define NPY_DOUBLE PyArray_DOUBLE #define NPY_LONGDOUBLE PyArray_LONGDOUBLE #define NPY_CFLOAT PyArray_CFLOAT #define NPY_CDOUBLE PyArray_CDOUBLE #define NPY_CLONGDOUBLE PyArray_CLONGDOUBLE #define NPY_OBJECT PyArray_OBJECT #define NPY_STRING PyArray_STRING #define NPY_UNICODE PyArray_UNICODE #define NPY_VOID PyArray_VOID #define NPY_NTYPES PyArray_NTYPES #define NPY_NOTYPE PyArray_NOTYPE #define NPY_CHAR PyArray_CHAR #define NPY_USERDEF PyArray_USERDEF #define npy_intp intp #define NPY_MAX_BYTE MAX_BYTE #define NPY_MIN_BYTE MIN_BYTE #define NPY_MAX_UBYTE MAX_UBYTE #define NPY_MAX_SHORT MAX_SHORT #define NPY_MIN_SHORT MIN_SHORT #define NPY_MAX_USHORT MAX_USHORT #define NPY_MAX_INT MAX_INT #define NPY_MIN_INT MIN_INT #define NPY_MAX_UINT MAX_UINT #define NPY_MAX_LONG MAX_LONG #define NPY_MIN_LONG MIN_LONG #define NPY_MAX_ULONG MAX_ULONG #define NPY_MAX_LONGLONG MAX_LONGLONG #define NPY_MIN_LONGLONG MIN_LONGLONG #define NPY_MAX_ULONGLONG MAX_ULONGLONG #define NPY_MAX_INTP MAX_INTP #define NPY_MIN_INTP MIN_INTP #define NPY_FARRAY FARRAY #define NPY_F_CONTIGUOUS F_CONTIGUOUS #endif /* Macros to extract array attributes. */ #define is_array(a) ((a) && PyArray_Check((PyArrayObject *)a)) #define array_type(a) (int)(PyArray_TYPE(a)) #define array_numdims(a) (((PyArrayObject *)a)->nd) #define array_dimensions(a) (((PyArrayObject *)a)->dimensions) #define array_size(a,i) (((PyArrayObject *)a)->dimensions[i]) #define array_data(a) (((PyArrayObject *)a)->data) #define array_is_contiguous(a) (PyArray_ISCONTIGUOUS(a)) #define array_is_native(a) (PyArray_ISNOTSWAPPED(a)) #define array_is_fortran(a) (PyArray_ISFORTRAN(a)) /* Given a PyObject, return a string describing its type. */ char* pytype_string(PyObject* py_obj) { if (py_obj == NULL ) return "C NULL value"; if (py_obj == Py_None ) return "Python None" ; if (PyCallable_Check(py_obj)) return "callable" ; if (PyString_Check( py_obj)) return "string" ; if (PyInt_Check( py_obj)) return "int" ; if (PyFloat_Check( py_obj)) return "float" ; if (PyDict_Check( py_obj)) return "dict" ; if (PyList_Check( py_obj)) return "list" ; if (PyTuple_Check( py_obj)) return "tuple" ; if (PyFile_Check( py_obj)) return "file" ; if (PyModule_Check( py_obj)) return "module" ; if (PyInstance_Check(py_obj)) return "instance" ; return "unkown type"; } /* Given a NumPy typecode, return a string describing the type. */ char* typecode_string(int typecode) { static char* type_names[25] = {"bool", "byte", "unsigned byte", "short", "unsigned short", "int", "unsigned int", "long", "unsigned long", "long long", "unsigned long long", "float", "double", "long double", "complex float", "complex double", "complex long double", "object", "string", "unicode", "void", "ntypes", "notype", "char", "unknown"}; return typecode < 24 ? type_names[typecode] : type_names[24]; } /* Make sure input has correct numpy type. Allow character and byte * to match. Also allow int and long to match. This is deprecated. * You should use PyArray_EquivTypenums() instead. */ int type_match(int actual_type, int desired_type) { return PyArray_EquivTypenums(actual_type, desired_type); } /* Given a PyObject pointer, cast it to a PyArrayObject pointer if * legal. If not, set the python error string appropriately and * return NULL. */ PyArrayObject* obj_to_array_no_conversion(PyObject* input, int typecode) { PyArrayObject* ary = NULL; if (is_array(input) && (typecode == NPY_NOTYPE || PyArray_EquivTypenums(array_type(input), typecode))) { ary = (PyArrayObject*) input; } else if is_array(input) { char* desired_type = typecode_string(typecode); char* actual_type = typecode_string(array_type(input)); PyErr_Format(PyExc_TypeError, "Array of type '%s' required. Array of type '%s' given", desired_type, actual_type); ary = NULL; } else { char * desired_type = typecode_string(typecode); char * actual_type = pytype_string(input); PyErr_Format(PyExc_TypeError, "Array of type '%s' required. A '%s' was given", desired_type, actual_type); ary = NULL; } return ary; } /* Convert the given PyObject to a NumPy array with the given * typecode. On success, return a valid PyArrayObject* with the * correct type. On failure, the python error string will be set and * the routine returns NULL. */ PyArrayObject* obj_to_array_allow_conversion(PyObject* input, int typecode, int* is_new_object) { PyArrayObject* ary = NULL; PyObject* py_obj; if (is_array(input) && (typecode == NPY_NOTYPE || PyArray_EquivTypenums(array_type(input),typecode))) { ary = (PyArrayObject*) input; *is_new_object = 0; } else { py_obj = PyArray_FromObject(input, typecode, 0, 0); /* If NULL, PyArray_FromObject will have set python error value.*/ ary = (PyArrayObject*) py_obj; *is_new_object = 1; } return ary; } /* Given a PyArrayObject, check to see if it is contiguous. If so, * return the input pointer and flag it as not a new object. If it is * not contiguous, create a new PyArrayObject using the original data, * flag it as a new object and return the pointer. */ PyArrayObject* make_contiguous(PyArrayObject* ary, int* is_new_object, int min_dims, int max_dims) { PyArrayObject* result; if (array_is_contiguous(ary)) { result = ary; *is_new_object = 0; } else { result = (PyArrayObject*) PyArray_ContiguousFromObject((PyObject*)ary, array_type(ary), min_dims, max_dims); *is_new_object = 1; } return result; } /* Convert a given PyObject to a contiguous PyArrayObject of the * specified type. If the input object is not a contiguous * PyArrayObject, a new one will be created and the new object flag * will be set. */ PyArrayObject* obj_to_array_contiguous_allow_conversion(PyObject* input, int typecode, int* is_new_object) { int is_new1 = 0; int is_new2 = 0; PyArrayObject* ary2; PyArrayObject* ary1 = obj_to_array_allow_conversion(input, typecode, &is_new1); if (ary1) { ary2 = make_contiguous(ary1, &is_new2, 0, 0); if ( is_new1 && is_new2) { Py_DECREF(ary1); } ary1 = ary2; } *is_new_object = is_new1 || is_new2; return ary1; } /* Test whether a python object is contiguous. If array is * contiguous, return 1. Otherwise, set the python error string and * return 0. */ int require_contiguous(PyArrayObject* ary) { int contiguous = 1; if (!array_is_contiguous(ary)) { PyErr_SetString(PyExc_TypeError, "Array must be contiguous. A non-contiguous array was given"); contiguous = 0; } return contiguous; } /* Require that a numpy array is not byte-swapped. If the array is * not byte-swapped, return 1. Otherwise, set the python error string * and return 0. */ int require_native(PyArrayObject* ary) { int native = 1; if (!array_is_native(ary)) { PyErr_SetString(PyExc_TypeError, "Array must have native byteorder. " "A byte-swapped array was given"); native = 0; } return native; } /* Require the given PyArrayObject to have a specified number of * dimensions. If the array has the specified number of dimensions, * return 1. Otherwise, set the python error string and return 0. */ int require_dimensions(PyArrayObject* ary, int exact_dimensions) { int success = 1; if (array_numdims(ary) != exact_dimensions) { PyErr_Format(PyExc_TypeError, "Array must have %d dimensions. Given array has %d dimensions", exact_dimensions, array_numdims(ary)); success = 0; } return success; } /* Require the given PyArrayObject to have one of a list of specified * number of dimensions. If the array has one of the specified number * of dimensions, return 1. Otherwise, set the python error string * and return 0. */ int require_dimensions_n(PyArrayObject* ary, int* exact_dimensions, int n) { int success = 0; int i; char dims_str[255] = ""; char s[255]; for (i = 0; i < n && !success; i++) { if (array_numdims(ary) == exact_dimensions[i]) { success = 1; } } if (!success) { for (i = 0; i < n-1; i++) { sprintf(s, "%d, ", exact_dimensions[i]); strcat(dims_str,s); } sprintf(s, " or %d", exact_dimensions[n-1]); strcat(dims_str,s); PyErr_Format(PyExc_TypeError, "Array must have %s dimensions. Given array has %d dimensions", dims_str, array_numdims(ary)); } return success; } /* Require the given PyArrayObject to have a specified shape. If the * array has the specified shape, return 1. Otherwise, set the python * error string and return 0. */ int require_size(PyArrayObject* ary, npy_intp* size, int n) { int i; int success = 1; int len; char desired_dims[255] = "["; char s[255]; char actual_dims[255] = "["; for(i=0; i < n;i++) { if (size[i] != -1 && size[i] != array_size(ary,i)) { success = 0; } } if (!success) { for (i = 0; i < n; i++) { if (size[i] == -1) { sprintf(s, "*,"); } else { sprintf(s, "%ld,", (long int)size[i]); } strcat(desired_dims,s); } len = strlen(desired_dims); desired_dims[len-1] = ']'; for (i = 0; i < n; i++) { sprintf(s, "%ld,", (long int)array_size(ary,i)); strcat(actual_dims,s); } len = strlen(actual_dims); actual_dims[len-1] = ']'; PyErr_Format(PyExc_TypeError, "Array must have shape of %s. Given array has shape of %s", desired_dims, actual_dims); } return success; } /* Require the given PyArrayObject to to be FORTRAN ordered. If the * the PyArrayObject is already FORTRAN ordered, do nothing. Else, * set the FORTRAN ordering flag and recompute the strides. */ int require_fortran(PyArrayObject* ary) { int success = 1; int nd = array_numdims(ary); int i; if (array_is_fortran(ary)) return success; /* Set the FORTRAN ordered flag */ ary->flags = NPY_FARRAY; /* Recompute the strides */ ary->strides[0] = ary->strides[nd-1]; for (i=1; i < nd; ++i) ary->strides[i] = ary->strides[i-1] * array_size(ary,i-1); return success; } SWIGINTERN swig_type_info* SWIG_pchar_descriptor(void) { static int init = 0; static swig_type_info* info = 0; if (!init) { info = SWIG_TypeQuery("_p_char"); init = 1; } return info; } SWIGINTERNINLINE PyObject * SWIG_FromCharPtrAndSize(const char* carray, size_t size) { if (carray) { if (size > INT_MAX) { swig_type_info* pchar_descriptor = SWIG_pchar_descriptor(); return pchar_descriptor ? SWIG_NewPointerObj(const_cast< char * >(carray), pchar_descriptor, 0) : SWIG_Py_Void(); } else { #if PY_VERSION_HEX >= 0x03000000 return PyUnicode_FromStringAndSize(carray, static_cast< int >(size)); #else return PyString_FromStringAndSize(carray, static_cast< int >(size)); #endif } } else { return SWIG_Py_Void(); } } SWIGINTERNINLINE PyObject * SWIG_From_std_string (const std::string& s) { if (s.size()) { return SWIG_FromCharPtrAndSize(s.data(), s.size()); } else { return SWIG_FromCharPtrAndSize(s.c_str(), 0); } } #ifdef __cplusplus extern "C" { #endif SWIGINTERN PyObject *_wrap_new_SpikeQueue(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; int arg1 ; int arg2 ; int val1 ; int ecode1 = 0 ; int val2 ; int ecode2 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; SpikeQueue *result = 0 ; if (!PyArg_ParseTuple(args,(char *)"OO:new_SpikeQueue",&obj0,&obj1)) SWIG_fail; ecode1 = SWIG_AsVal_int(obj0, &val1); if (!SWIG_IsOK(ecode1)) { SWIG_exception_fail(SWIG_ArgError(ecode1), "in method '" "new_SpikeQueue" "', argument " "1"" of type '" "int""'"); } arg1 = static_cast< int >(val1); ecode2 = SWIG_AsVal_int(obj1, &val2); if (!SWIG_IsOK(ecode2)) { SWIG_exception_fail(SWIG_ArgError(ecode2), "in method '" "new_SpikeQueue" "', argument " "2"" of type '" "int""'"); } arg2 = static_cast< int >(val2); { try { result = (SpikeQueue *)new SpikeQueue(arg1,arg2); } catch( std::runtime_error &e ) { PyErr_SetString(PyExc_RuntimeError, const_cast(e.what())); return NULL; } } resultobj = SWIG_NewPointerObj(SWIG_as_voidptr(result), SWIGTYPE_p_SpikeQueue, SWIG_POINTER_NEW | 0 ); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_delete_SpikeQueue(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; SpikeQueue *arg1 = (SpikeQueue *) 0 ; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:delete_SpikeQueue",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_SpikeQueue, SWIG_POINTER_DISOWN | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "delete_SpikeQueue" "', argument " "1"" of type '" "SpikeQueue *""'"); } arg1 = reinterpret_cast< SpikeQueue * >(argp1); { try { delete arg1; } catch( std::runtime_error &e ) { PyErr_SetString(PyExc_RuntimeError, const_cast(e.what())); return NULL; } } resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_SpikeQueue_X_set(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; SpikeQueue *arg1 = (SpikeQueue *) 0 ; long **arg2 = (long **) 0 ; void *argp1 = 0 ; int res1 = 0 ; void *argp2 = 0 ; int res2 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OO:SpikeQueue_X_set",&obj0,&obj1)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_SpikeQueue, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "SpikeQueue_X_set" "', argument " "1"" of type '" "SpikeQueue *""'"); } arg1 = reinterpret_cast< SpikeQueue * >(argp1); res2 = SWIG_ConvertPtr(obj1, &argp2,SWIGTYPE_p_p_long, 0 | 0 ); if (!SWIG_IsOK(res2)) { SWIG_exception_fail(SWIG_ArgError(res2), "in method '" "SpikeQueue_X_set" "', argument " "2"" of type '" "long **""'"); } arg2 = reinterpret_cast< long ** >(argp2); { try { if (arg1) (arg1)->X = arg2; } catch( std::runtime_error &e ) { PyErr_SetString(PyExc_RuntimeError, const_cast(e.what())); return NULL; } } resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_SpikeQueue_X_get(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; SpikeQueue *arg1 = (SpikeQueue *) 0 ; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; long **result = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:SpikeQueue_X_get",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_SpikeQueue, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "SpikeQueue_X_get" "', argument " "1"" of type '" "SpikeQueue *""'"); } arg1 = reinterpret_cast< SpikeQueue * >(argp1); { try { result = (long **) ((arg1)->X); } catch( std::runtime_error &e ) { PyErr_SetString(PyExc_RuntimeError, const_cast(e.what())); return NULL; } } resultobj = SWIG_NewPointerObj(SWIG_as_voidptr(result), SWIGTYPE_p_p_long, 0 | 0 ); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_SpikeQueue_n_set(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; SpikeQueue *arg1 = (SpikeQueue *) 0 ; int *arg2 = (int *) 0 ; void *argp1 = 0 ; int res1 = 0 ; void *argp2 = 0 ; int res2 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OO:SpikeQueue_n_set",&obj0,&obj1)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_SpikeQueue, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "SpikeQueue_n_set" "', argument " "1"" of type '" "SpikeQueue *""'"); } arg1 = reinterpret_cast< SpikeQueue * >(argp1); res2 = SWIG_ConvertPtr(obj1, &argp2,SWIGTYPE_p_int, SWIG_POINTER_DISOWN | 0 ); if (!SWIG_IsOK(res2)) { SWIG_exception_fail(SWIG_ArgError(res2), "in method '" "SpikeQueue_n_set" "', argument " "2"" of type '" "int *""'"); } arg2 = reinterpret_cast< int * >(argp2); { try { if (arg1) (arg1)->n = arg2; } catch( std::runtime_error &e ) { PyErr_SetString(PyExc_RuntimeError, const_cast(e.what())); return NULL; } } resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_SpikeQueue_n_get(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; SpikeQueue *arg1 = (SpikeQueue *) 0 ; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; int *result = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:SpikeQueue_n_get",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_SpikeQueue, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "SpikeQueue_n_get" "', argument " "1"" of type '" "SpikeQueue *""'"); } arg1 = reinterpret_cast< SpikeQueue * >(argp1); { try { result = (int *) ((arg1)->n); } catch( std::runtime_error &e ) { PyErr_SetString(PyExc_RuntimeError, const_cast(e.what())); return NULL; } } resultobj = SWIG_NewPointerObj(SWIG_as_voidptr(result), SWIGTYPE_p_int, 0 | 0 ); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_SpikeQueue_n_delays_set(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; SpikeQueue *arg1 = (SpikeQueue *) 0 ; int arg2 ; void *argp1 = 0 ; int res1 = 0 ; int val2 ; int ecode2 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OO:SpikeQueue_n_delays_set",&obj0,&obj1)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_SpikeQueue, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "SpikeQueue_n_delays_set" "', argument " "1"" of type '" "SpikeQueue *""'"); } arg1 = reinterpret_cast< SpikeQueue * >(argp1); ecode2 = SWIG_AsVal_int(obj1, &val2); if (!SWIG_IsOK(ecode2)) { SWIG_exception_fail(SWIG_ArgError(ecode2), "in method '" "SpikeQueue_n_delays_set" "', argument " "2"" of type '" "int""'"); } arg2 = static_cast< int >(val2); { try { if (arg1) (arg1)->n_delays = arg2; } catch( std::runtime_error &e ) { PyErr_SetString(PyExc_RuntimeError, const_cast(e.what())); return NULL; } } resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_SpikeQueue_n_delays_get(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; SpikeQueue *arg1 = (SpikeQueue *) 0 ; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; int result; if (!PyArg_ParseTuple(args,(char *)"O:SpikeQueue_n_delays_get",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_SpikeQueue, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "SpikeQueue_n_delays_get" "', argument " "1"" of type '" "SpikeQueue *""'"); } arg1 = reinterpret_cast< SpikeQueue * >(argp1); { try { result = (int) ((arg1)->n_delays); } catch( std::runtime_error &e ) { PyErr_SetString(PyExc_RuntimeError, const_cast(e.what())); return NULL; } } resultobj = SWIG_From_int(static_cast< int >(result)); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_SpikeQueue_n_maxevents_set(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; SpikeQueue *arg1 = (SpikeQueue *) 0 ; int arg2 ; void *argp1 = 0 ; int res1 = 0 ; int val2 ; int ecode2 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OO:SpikeQueue_n_maxevents_set",&obj0,&obj1)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_SpikeQueue, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "SpikeQueue_n_maxevents_set" "', argument " "1"" of type '" "SpikeQueue *""'"); } arg1 = reinterpret_cast< SpikeQueue * >(argp1); ecode2 = SWIG_AsVal_int(obj1, &val2); if (!SWIG_IsOK(ecode2)) { SWIG_exception_fail(SWIG_ArgError(ecode2), "in method '" "SpikeQueue_n_maxevents_set" "', argument " "2"" of type '" "int""'"); } arg2 = static_cast< int >(val2); { try { if (arg1) (arg1)->n_maxevents = arg2; } catch( std::runtime_error &e ) { PyErr_SetString(PyExc_RuntimeError, const_cast(e.what())); return NULL; } } resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_SpikeQueue_n_maxevents_get(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; SpikeQueue *arg1 = (SpikeQueue *) 0 ; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; int result; if (!PyArg_ParseTuple(args,(char *)"O:SpikeQueue_n_maxevents_get",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_SpikeQueue, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "SpikeQueue_n_maxevents_get" "', argument " "1"" of type '" "SpikeQueue *""'"); } arg1 = reinterpret_cast< SpikeQueue * >(argp1); { try { result = (int) ((arg1)->n_maxevents); } catch( std::runtime_error &e ) { PyErr_SetString(PyExc_RuntimeError, const_cast(e.what())); return NULL; } } resultobj = SWIG_From_int(static_cast< int >(result)); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_SpikeQueue_currenttime_set(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; SpikeQueue *arg1 = (SpikeQueue *) 0 ; int arg2 ; void *argp1 = 0 ; int res1 = 0 ; int val2 ; int ecode2 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OO:SpikeQueue_currenttime_set",&obj0,&obj1)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_SpikeQueue, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "SpikeQueue_currenttime_set" "', argument " "1"" of type '" "SpikeQueue *""'"); } arg1 = reinterpret_cast< SpikeQueue * >(argp1); ecode2 = SWIG_AsVal_int(obj1, &val2); if (!SWIG_IsOK(ecode2)) { SWIG_exception_fail(SWIG_ArgError(ecode2), "in method '" "SpikeQueue_currenttime_set" "', argument " "2"" of type '" "int""'"); } arg2 = static_cast< int >(val2); { try { if (arg1) (arg1)->currenttime = arg2; } catch( std::runtime_error &e ) { PyErr_SetString(PyExc_RuntimeError, const_cast(e.what())); return NULL; } } resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_SpikeQueue_currenttime_get(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; SpikeQueue *arg1 = (SpikeQueue *) 0 ; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; int result; if (!PyArg_ParseTuple(args,(char *)"O:SpikeQueue_currenttime_get",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_SpikeQueue, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "SpikeQueue_currenttime_get" "', argument " "1"" of type '" "SpikeQueue *""'"); } arg1 = reinterpret_cast< SpikeQueue * >(argp1); { try { result = (int) ((arg1)->currenttime); } catch( std::runtime_error &e ) { PyErr_SetString(PyExc_RuntimeError, const_cast(e.what())); return NULL; } } resultobj = SWIG_From_int(static_cast< int >(result)); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_SpikeQueue_something_set(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; SpikeQueue *arg1 = (SpikeQueue *) 0 ; int arg2 ; void *argp1 = 0 ; int res1 = 0 ; int val2 ; int ecode2 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OO:SpikeQueue_something_set",&obj0,&obj1)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_SpikeQueue, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "SpikeQueue_something_set" "', argument " "1"" of type '" "SpikeQueue *""'"); } arg1 = reinterpret_cast< SpikeQueue * >(argp1); ecode2 = SWIG_AsVal_int(obj1, &val2); if (!SWIG_IsOK(ecode2)) { SWIG_exception_fail(SWIG_ArgError(ecode2), "in method '" "SpikeQueue_something_set" "', argument " "2"" of type '" "int""'"); } arg2 = static_cast< int >(val2); { try { if (arg1) (arg1)->something = arg2; } catch( std::runtime_error &e ) { PyErr_SetString(PyExc_RuntimeError, const_cast(e.what())); return NULL; } } resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_SpikeQueue_something_get(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; SpikeQueue *arg1 = (SpikeQueue *) 0 ; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; int result; if (!PyArg_ParseTuple(args,(char *)"O:SpikeQueue_something_get",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_SpikeQueue, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "SpikeQueue_something_get" "', argument " "1"" of type '" "SpikeQueue *""'"); } arg1 = reinterpret_cast< SpikeQueue * >(argp1); { try { result = (int) ((arg1)->something); } catch( std::runtime_error &e ) { PyErr_SetString(PyExc_RuntimeError, const_cast(e.what())); return NULL; } } resultobj = SWIG_From_int(static_cast< int >(result)); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_SpikeQueue_retarray_set(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; SpikeQueue *arg1 = (SpikeQueue *) 0 ; long *arg2 = (long *) 0 ; void *argp1 = 0 ; int res1 = 0 ; void *argp2 = 0 ; int res2 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OO:SpikeQueue_retarray_set",&obj0,&obj1)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_SpikeQueue, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "SpikeQueue_retarray_set" "', argument " "1"" of type '" "SpikeQueue *""'"); } arg1 = reinterpret_cast< SpikeQueue * >(argp1); res2 = SWIG_ConvertPtr(obj1, &argp2,SWIGTYPE_p_long, SWIG_POINTER_DISOWN | 0 ); if (!SWIG_IsOK(res2)) { SWIG_exception_fail(SWIG_ArgError(res2), "in method '" "SpikeQueue_retarray_set" "', argument " "2"" of type '" "long *""'"); } arg2 = reinterpret_cast< long * >(argp2); { try { if (arg1) (arg1)->retarray = arg2; } catch( std::runtime_error &e ) { PyErr_SetString(PyExc_RuntimeError, const_cast(e.what())); return NULL; } } resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_SpikeQueue_retarray_get(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; SpikeQueue *arg1 = (SpikeQueue *) 0 ; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; long *result = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:SpikeQueue_retarray_get",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_SpikeQueue, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "SpikeQueue_retarray_get" "', argument " "1"" of type '" "SpikeQueue *""'"); } arg1 = reinterpret_cast< SpikeQueue * >(argp1); { try { result = (long *) ((arg1)->retarray); } catch( std::runtime_error &e ) { PyErr_SetString(PyExc_RuntimeError, const_cast(e.what())); return NULL; } } resultobj = SWIG_NewPointerObj(SWIG_as_voidptr(result), SWIGTYPE_p_long, 0 | 0 ); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_SpikeQueue_expand(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; SpikeQueue *arg1 = (SpikeQueue *) 0 ; int arg2 ; void *argp1 = 0 ; int res1 = 0 ; int val2 ; int ecode2 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OO:SpikeQueue_expand",&obj0,&obj1)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_SpikeQueue, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "SpikeQueue_expand" "', argument " "1"" of type '" "SpikeQueue *""'"); } arg1 = reinterpret_cast< SpikeQueue * >(argp1); ecode2 = SWIG_AsVal_int(obj1, &val2); if (!SWIG_IsOK(ecode2)) { SWIG_exception_fail(SWIG_ArgError(ecode2), "in method '" "SpikeQueue_expand" "', argument " "2"" of type '" "int""'"); } arg2 = static_cast< int >(val2); { try { (arg1)->expand(arg2); } catch( std::runtime_error &e ) { PyErr_SetString(PyExc_RuntimeError, const_cast(e.what())); return NULL; } } resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_SpikeQueue_next(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; SpikeQueue *arg1 = (SpikeQueue *) 0 ; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:SpikeQueue_next",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_SpikeQueue, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "SpikeQueue_next" "', argument " "1"" of type '" "SpikeQueue *""'"); } arg1 = reinterpret_cast< SpikeQueue * >(argp1); { try { (arg1)->next(); } catch( std::runtime_error &e ) { PyErr_SetString(PyExc_RuntimeError, const_cast(e.what())); return NULL; } } resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_SpikeQueue__peek(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; SpikeQueue *arg1 = (SpikeQueue *) 0 ; int arg2 ; void *argp1 = 0 ; int res1 = 0 ; int val2 ; int ecode2 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OO:SpikeQueue__peek",&obj0,&obj1)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_SpikeQueue, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "SpikeQueue__peek" "', argument " "1"" of type '" "SpikeQueue *""'"); } arg1 = reinterpret_cast< SpikeQueue * >(argp1); ecode2 = SWIG_AsVal_int(obj1, &val2); if (!SWIG_IsOK(ecode2)) { SWIG_exception_fail(SWIG_ArgError(ecode2), "in method '" "SpikeQueue__peek" "', argument " "2"" of type '" "int""'"); } arg2 = static_cast< int >(val2); { try { (arg1)->_peek(arg2); } catch( std::runtime_error &e ) { PyErr_SetString(PyExc_RuntimeError, const_cast(e.what())); return NULL; } } resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_SpikeQueue_peek(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; SpikeQueue *arg1 = (SpikeQueue *) 0 ; long **arg2 = (long **) 0 ; int *arg3 = (int *) 0 ; void *argp1 = 0 ; int res1 = 0 ; long *data_temp2 ; int dim_temp2 ; PyObject * obj0 = 0 ; { arg2 = &data_temp2; arg3 = &dim_temp2; } if (!PyArg_ParseTuple(args,(char *)"O:SpikeQueue_peek",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_SpikeQueue, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "SpikeQueue_peek" "', argument " "1"" of type '" "SpikeQueue *""'"); } arg1 = reinterpret_cast< SpikeQueue * >(argp1); { try { (arg1)->peek(arg2,arg3); } catch( std::runtime_error &e ) { PyErr_SetString(PyExc_RuntimeError, const_cast(e.what())); return NULL; } } resultobj = SWIG_Py_Void(); { npy_intp dims[1] = { *arg3 }; PyObject * array = PyArray_SimpleNewFromData(1, dims, NPY_LONG, (void*)(*arg2)); if (!array) SWIG_fail; resultobj = SWIG_Python_AppendOutput(resultobj,array); } return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_SpikeQueue_insert(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; SpikeQueue *arg1 = (SpikeQueue *) 0 ; int arg2 ; long *arg3 = (long *) 0 ; int arg4 ; long *arg5 = (long *) 0 ; void *argp1 = 0 ; int res1 = 0 ; PyArrayObject *array2 = NULL ; int is_new_object2 = 0 ; PyArrayObject *array4 = NULL ; int is_new_object4 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; PyObject * obj2 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OOO:SpikeQueue_insert",&obj0,&obj1,&obj2)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_SpikeQueue, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "SpikeQueue_insert" "', argument " "1"" of type '" "SpikeQueue *""'"); } arg1 = reinterpret_cast< SpikeQueue * >(argp1); { npy_intp size[1] = { -1 }; array2 = obj_to_array_contiguous_allow_conversion(obj1, NPY_LONG, &is_new_object2); if (!array2 || !require_dimensions(array2, 1) || !require_size(array2, size, 1)) SWIG_fail; arg2 = (int) array_size(array2,0); arg3 = (long*) array_data(array2); } { npy_intp size[1] = { -1 }; array4 = obj_to_array_contiguous_allow_conversion(obj2, NPY_LONG, &is_new_object4); if (!array4 || !require_dimensions(array4, 1) || !require_size(array4, size, 1)) SWIG_fail; arg4 = (int) array_size(array4,0); arg5 = (long*) array_data(array4); } { try { (arg1)->insert(arg2,arg3,arg4,arg5); } catch( std::runtime_error &e ) { PyErr_SetString(PyExc_RuntimeError, const_cast(e.what())); return NULL; } } resultobj = SWIG_Py_Void(); { if (is_new_object2 && array2) { Py_DECREF(array2); } } { if (is_new_object4 && array4) { Py_DECREF(array4); } } return resultobj; fail: { if (is_new_object2 && array2) { Py_DECREF(array2); } } { if (is_new_object4 && array4) { Py_DECREF(array4); } } return NULL; } SWIGINTERN PyObject *_wrap_SpikeQueue___repr__(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; SpikeQueue *arg1 = (SpikeQueue *) 0 ; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; std::string result; if (!PyArg_ParseTuple(args,(char *)"O:SpikeQueue___repr__",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_SpikeQueue, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "SpikeQueue___repr__" "', argument " "1"" of type '" "SpikeQueue *""'"); } arg1 = reinterpret_cast< SpikeQueue * >(argp1); { try { result = (arg1)->__repr__(); } catch( std::runtime_error &e ) { PyErr_SetString(PyExc_RuntimeError, const_cast(e.what())); return NULL; } } resultobj = SWIG_From_std_string(static_cast< std::string >(result)); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_SpikeQueue___str__(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; SpikeQueue *arg1 = (SpikeQueue *) 0 ; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; std::string result; if (!PyArg_ParseTuple(args,(char *)"O:SpikeQueue___str__",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_SpikeQueue, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "SpikeQueue___str__" "', argument " "1"" of type '" "SpikeQueue *""'"); } arg1 = reinterpret_cast< SpikeQueue * >(argp1); { try { result = (arg1)->__str__(); } catch( std::runtime_error &e ) { PyErr_SetString(PyExc_RuntimeError, const_cast(e.what())); return NULL; } } resultobj = SWIG_From_std_string(static_cast< std::string >(result)); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_SpikeQueue_print_summary(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; SpikeQueue *arg1 = (SpikeQueue *) 0 ; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:SpikeQueue_print_summary",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_SpikeQueue, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "SpikeQueue_print_summary" "', argument " "1"" of type '" "SpikeQueue *""'"); } arg1 = reinterpret_cast< SpikeQueue * >(argp1); { try { (arg1)->print_summary(); } catch( std::runtime_error &e ) { PyErr_SetString(PyExc_RuntimeError, const_cast(e.what())); return NULL; } } resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *SpikeQueue_swigregister(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *obj; if (!PyArg_ParseTuple(args,(char*)"O:swigregister", &obj)) return NULL; SWIG_TypeNewClientData(SWIGTYPE_p_SpikeQueue, SWIG_NewClientData(obj)); return SWIG_Py_Void(); } static PyMethodDef SwigMethods[] = { { (char *)"SWIG_PyInstanceMethod_New", (PyCFunction)SWIG_PyInstanceMethod_New, METH_O, NULL}, { (char *)"new_SpikeQueue", _wrap_new_SpikeQueue, METH_VARARGS, NULL}, { (char *)"delete_SpikeQueue", _wrap_delete_SpikeQueue, METH_VARARGS, NULL}, { (char *)"SpikeQueue_X_set", _wrap_SpikeQueue_X_set, METH_VARARGS, NULL}, { (char *)"SpikeQueue_X_get", _wrap_SpikeQueue_X_get, METH_VARARGS, NULL}, { (char *)"SpikeQueue_n_set", _wrap_SpikeQueue_n_set, METH_VARARGS, NULL}, { (char *)"SpikeQueue_n_get", _wrap_SpikeQueue_n_get, METH_VARARGS, NULL}, { (char *)"SpikeQueue_n_delays_set", _wrap_SpikeQueue_n_delays_set, METH_VARARGS, NULL}, { (char *)"SpikeQueue_n_delays_get", _wrap_SpikeQueue_n_delays_get, METH_VARARGS, NULL}, { (char *)"SpikeQueue_n_maxevents_set", _wrap_SpikeQueue_n_maxevents_set, METH_VARARGS, NULL}, { (char *)"SpikeQueue_n_maxevents_get", _wrap_SpikeQueue_n_maxevents_get, METH_VARARGS, NULL}, { (char *)"SpikeQueue_currenttime_set", _wrap_SpikeQueue_currenttime_set, METH_VARARGS, NULL}, { (char *)"SpikeQueue_currenttime_get", _wrap_SpikeQueue_currenttime_get, METH_VARARGS, NULL}, { (char *)"SpikeQueue_something_set", _wrap_SpikeQueue_something_set, METH_VARARGS, NULL}, { (char *)"SpikeQueue_something_get", _wrap_SpikeQueue_something_get, METH_VARARGS, NULL}, { (char *)"SpikeQueue_retarray_set", _wrap_SpikeQueue_retarray_set, METH_VARARGS, NULL}, { (char *)"SpikeQueue_retarray_get", _wrap_SpikeQueue_retarray_get, METH_VARARGS, NULL}, { (char *)"SpikeQueue_expand", _wrap_SpikeQueue_expand, METH_VARARGS, NULL}, { (char *)"SpikeQueue_next", _wrap_SpikeQueue_next, METH_VARARGS, NULL}, { (char *)"SpikeQueue__peek", _wrap_SpikeQueue__peek, METH_VARARGS, NULL}, { (char *)"SpikeQueue_peek", _wrap_SpikeQueue_peek, METH_VARARGS, NULL}, { (char *)"SpikeQueue_insert", _wrap_SpikeQueue_insert, METH_VARARGS, NULL}, { (char *)"SpikeQueue___repr__", _wrap_SpikeQueue___repr__, METH_VARARGS, NULL}, { (char *)"SpikeQueue___str__", _wrap_SpikeQueue___str__, METH_VARARGS, NULL}, { (char *)"SpikeQueue_print_summary", _wrap_SpikeQueue_print_summary, METH_VARARGS, NULL}, { (char *)"SpikeQueue_swigregister", SpikeQueue_swigregister, METH_VARARGS, NULL}, { NULL, NULL, 0, NULL } }; /* -------- TYPE CONVERSION AND EQUIVALENCE RULES (BEGIN) -------- */ static swig_type_info _swigt__p_SpikeQueue = {"_p_SpikeQueue", "SpikeQueue *", 0, 0, (void*)0, 0}; static swig_type_info _swigt__p_char = {"_p_char", "char *", 0, 0, (void*)0, 0}; static swig_type_info _swigt__p_int = {"_p_int", "int *", 0, 0, (void*)0, 0}; static swig_type_info _swigt__p_long = {"_p_long", "long *", 0, 0, (void*)0, 0}; static swig_type_info _swigt__p_p_long = {"_p_p_long", "long **", 0, 0, (void*)0, 0}; static swig_type_info *swig_type_initial[] = { &_swigt__p_SpikeQueue, &_swigt__p_char, &_swigt__p_int, &_swigt__p_long, &_swigt__p_p_long, }; static swig_cast_info _swigc__p_SpikeQueue[] = { {&_swigt__p_SpikeQueue, 0, 0, 0},{0, 0, 0, 0}}; static swig_cast_info _swigc__p_char[] = { {&_swigt__p_char, 0, 0, 0},{0, 0, 0, 0}}; static swig_cast_info _swigc__p_int[] = { {&_swigt__p_int, 0, 0, 0},{0, 0, 0, 0}}; static swig_cast_info _swigc__p_long[] = { {&_swigt__p_long, 0, 0, 0},{0, 0, 0, 0}}; static swig_cast_info _swigc__p_p_long[] = { {&_swigt__p_p_long, 0, 0, 0},{0, 0, 0, 0}}; static swig_cast_info *swig_cast_initial[] = { _swigc__p_SpikeQueue, _swigc__p_char, _swigc__p_int, _swigc__p_long, _swigc__p_p_long, }; /* -------- TYPE CONVERSION AND EQUIVALENCE RULES (END) -------- */ static swig_const_info swig_const_table[] = { {0, 0, 0, 0.0, 0, 0}}; #ifdef __cplusplus } #endif /* ----------------------------------------------------------------------------- * Type initialization: * This problem is tough by the requirement that no dynamic * memory is used. Also, since swig_type_info structures store pointers to * swig_cast_info structures and swig_cast_info structures store pointers back * to swig_type_info structures, we need some lookup code at initialization. * The idea is that swig generates all the structures that are needed. * The runtime then collects these partially filled structures. * The SWIG_InitializeModule function takes these initial arrays out of * swig_module, and does all the lookup, filling in the swig_module.types * array with the correct data and linking the correct swig_cast_info * structures together. * * The generated swig_type_info structures are assigned staticly to an initial * array. We just loop through that array, and handle each type individually. * First we lookup if this type has been already loaded, and if so, use the * loaded structure instead of the generated one. Then we have to fill in the * cast linked list. The cast data is initially stored in something like a * two-dimensional array. Each row corresponds to a type (there are the same * number of rows as there are in the swig_type_initial array). Each entry in * a column is one of the swig_cast_info structures for that type. * The cast_initial array is actually an array of arrays, because each row has * a variable number of columns. So to actually build the cast linked list, * we find the array of casts associated with the type, and loop through it * adding the casts to the list. The one last trick we need to do is making * sure the type pointer in the swig_cast_info struct is correct. * * First off, we lookup the cast->type name to see if it is already loaded. * There are three cases to handle: * 1) If the cast->type has already been loaded AND the type we are adding * casting info to has not been loaded (it is in this module), THEN we * replace the cast->type pointer with the type pointer that has already * been loaded. * 2) If BOTH types (the one we are adding casting info to, and the * cast->type) are loaded, THEN the cast info has already been loaded by * the previous module so we just ignore it. * 3) Finally, if cast->type has not already been loaded, then we add that * swig_cast_info to the linked list (because the cast->type) pointer will * be correct. * ----------------------------------------------------------------------------- */ #ifdef __cplusplus extern "C" { #if 0 } /* c-mode */ #endif #endif #if 0 #define SWIGRUNTIME_DEBUG #endif SWIGRUNTIME void SWIG_InitializeModule(void *clientdata) { size_t i; swig_module_info *module_head, *iter; int found, init; clientdata = clientdata; /* check to see if the circular list has been setup, if not, set it up */ if (swig_module.next==0) { /* Initialize the swig_module */ swig_module.type_initial = swig_type_initial; swig_module.cast_initial = swig_cast_initial; swig_module.next = &swig_module; init = 1; } else { init = 0; } /* Try and load any already created modules */ module_head = SWIG_GetModule(clientdata); if (!module_head) { /* This is the first module loaded for this interpreter */ /* so set the swig module into the interpreter */ SWIG_SetModule(clientdata, &swig_module); module_head = &swig_module; } else { /* the interpreter has loaded a SWIG module, but has it loaded this one? */ found=0; iter=module_head; do { if (iter==&swig_module) { found=1; break; } iter=iter->next; } while (iter!= module_head); /* if the is found in the list, then all is done and we may leave */ if (found) return; /* otherwise we must add out module into the list */ swig_module.next = module_head->next; module_head->next = &swig_module; } /* When multiple interpeters are used, a module could have already been initialized in a different interpreter, but not yet have a pointer in this interpreter. In this case, we do not want to continue adding types... everything should be set up already */ if (init == 0) return; /* Now work on filling in swig_module.types */ #ifdef SWIGRUNTIME_DEBUG printf("SWIG_InitializeModule: size %d\n", swig_module.size); #endif for (i = 0; i < swig_module.size; ++i) { swig_type_info *type = 0; swig_type_info *ret; swig_cast_info *cast; #ifdef SWIGRUNTIME_DEBUG printf("SWIG_InitializeModule: type %d %s\n", i, swig_module.type_initial[i]->name); #endif /* if there is another module already loaded */ if (swig_module.next != &swig_module) { type = SWIG_MangledTypeQueryModule(swig_module.next, &swig_module, swig_module.type_initial[i]->name); } if (type) { /* Overwrite clientdata field */ #ifdef SWIGRUNTIME_DEBUG printf("SWIG_InitializeModule: found type %s\n", type->name); #endif if (swig_module.type_initial[i]->clientdata) { type->clientdata = swig_module.type_initial[i]->clientdata; #ifdef SWIGRUNTIME_DEBUG printf("SWIG_InitializeModule: found and overwrite type %s \n", type->name); #endif } } else { type = swig_module.type_initial[i]; } /* Insert casting types */ cast = swig_module.cast_initial[i]; while (cast->type) { /* Don't need to add information already in the list */ ret = 0; #ifdef SWIGRUNTIME_DEBUG printf("SWIG_InitializeModule: look cast %s\n", cast->type->name); #endif if (swig_module.next != &swig_module) { ret = SWIG_MangledTypeQueryModule(swig_module.next, &swig_module, cast->type->name); #ifdef SWIGRUNTIME_DEBUG if (ret) printf("SWIG_InitializeModule: found cast %s\n", ret->name); #endif } if (ret) { if (type == swig_module.type_initial[i]) { #ifdef SWIGRUNTIME_DEBUG printf("SWIG_InitializeModule: skip old type %s\n", ret->name); #endif cast->type = ret; ret = 0; } else { /* Check for casting already in the list */ swig_cast_info *ocast = SWIG_TypeCheck(ret->name, type); #ifdef SWIGRUNTIME_DEBUG if (ocast) printf("SWIG_InitializeModule: skip old cast %s\n", ret->name); #endif if (!ocast) ret = 0; } } if (!ret) { #ifdef SWIGRUNTIME_DEBUG printf("SWIG_InitializeModule: adding cast %s\n", cast->type->name); #endif if (type->cast) { type->cast->prev = cast; cast->next = type->cast; } type->cast = cast; } cast++; } /* Set entry in modules->types array equal to the type */ swig_module.types[i] = type; } swig_module.types[i] = 0; #ifdef SWIGRUNTIME_DEBUG printf("**** SWIG_InitializeModule: Cast List ******\n"); for (i = 0; i < swig_module.size; ++i) { int j = 0; swig_cast_info *cast = swig_module.cast_initial[i]; printf("SWIG_InitializeModule: type %d %s\n", i, swig_module.type_initial[i]->name); while (cast->type) { printf("SWIG_InitializeModule: cast type %s\n", cast->type->name); cast++; ++j; } printf("---- Total casts: %d\n",j); } printf("**** SWIG_InitializeModule: Cast List ******\n"); #endif } /* This function will propagate the clientdata field of type to * any new swig_type_info structures that have been added into the list * of equivalent types. It is like calling * SWIG_TypeClientData(type, clientdata) a second time. */ SWIGRUNTIME void SWIG_PropagateClientData(void) { size_t i; swig_cast_info *equiv; static int init_run = 0; if (init_run) return; init_run = 1; for (i = 0; i < swig_module.size; i++) { if (swig_module.types[i]->clientdata) { equiv = swig_module.types[i]->cast; while (equiv) { if (!equiv->converter) { if (equiv->type && !equiv->type->clientdata) SWIG_TypeClientData(equiv->type, swig_module.types[i]->clientdata); } equiv = equiv->next; } } } } #ifdef __cplusplus #if 0 { /* c-mode */ #endif } #endif #ifdef __cplusplus extern "C" { #endif /* Python-specific SWIG API */ #define SWIG_newvarlink() SWIG_Python_newvarlink() #define SWIG_addvarlink(p, name, get_attr, set_attr) SWIG_Python_addvarlink(p, name, get_attr, set_attr) #define SWIG_InstallConstants(d, constants) SWIG_Python_InstallConstants(d, constants) /* ----------------------------------------------------------------------------- * global variable support code. * ----------------------------------------------------------------------------- */ typedef struct swig_globalvar { char *name; /* Name of global variable */ PyObject *(*get_attr)(void); /* Return the current value */ int (*set_attr)(PyObject *); /* Set the value */ struct swig_globalvar *next; } swig_globalvar; typedef struct swig_varlinkobject { PyObject_HEAD swig_globalvar *vars; } swig_varlinkobject; SWIGINTERN PyObject * swig_varlink_repr(swig_varlinkobject *SWIGUNUSEDPARM(v)) { #if PY_VERSION_HEX >= 0x03000000 return PyUnicode_InternFromString(""); #else return PyString_FromString(""); #endif } SWIGINTERN PyObject * swig_varlink_str(swig_varlinkobject *v) { #if PY_VERSION_HEX >= 0x03000000 PyObject *str = PyUnicode_InternFromString("("); PyObject *tail; PyObject *joined; swig_globalvar *var; for (var = v->vars; var; var=var->next) { tail = PyUnicode_FromString(var->name); joined = PyUnicode_Concat(str, tail); Py_DecRef(str); Py_DecRef(tail); str = joined; if (var->next) { tail = PyUnicode_InternFromString(", "); joined = PyUnicode_Concat(str, tail); Py_DecRef(str); Py_DecRef(tail); str = joined; } } tail = PyUnicode_InternFromString(")"); joined = PyUnicode_Concat(str, tail); Py_DecRef(str); Py_DecRef(tail); str = joined; #else PyObject *str = PyString_FromString("("); swig_globalvar *var; for (var = v->vars; var; var=var->next) { PyString_ConcatAndDel(&str,PyString_FromString(var->name)); if (var->next) PyString_ConcatAndDel(&str,PyString_FromString(", ")); } PyString_ConcatAndDel(&str,PyString_FromString(")")); #endif return str; } SWIGINTERN int swig_varlink_print(swig_varlinkobject *v, FILE *fp, int SWIGUNUSEDPARM(flags)) { char *tmp; PyObject *str = swig_varlink_str(v); fprintf(fp,"Swig global variables "); fprintf(fp,"%s\n", tmp = SWIG_Python_str_AsChar(str)); SWIG_Python_str_DelForPy3(tmp); Py_DECREF(str); return 0; } SWIGINTERN void swig_varlink_dealloc(swig_varlinkobject *v) { swig_globalvar *var = v->vars; while (var) { swig_globalvar *n = var->next; free(var->name); free(var); var = n; } } SWIGINTERN PyObject * swig_varlink_getattr(swig_varlinkobject *v, char *n) { PyObject *res = NULL; swig_globalvar *var = v->vars; while (var) { if (strcmp(var->name,n) == 0) { res = (*var->get_attr)(); break; } var = var->next; } if (res == NULL && !PyErr_Occurred()) { PyErr_SetString(PyExc_NameError,"Unknown C global variable"); } return res; } SWIGINTERN int swig_varlink_setattr(swig_varlinkobject *v, char *n, PyObject *p) { int res = 1; swig_globalvar *var = v->vars; while (var) { if (strcmp(var->name,n) == 0) { res = (*var->set_attr)(p); break; } var = var->next; } if (res == 1 && !PyErr_Occurred()) { PyErr_SetString(PyExc_NameError,"Unknown C global variable"); } return res; } SWIGINTERN PyTypeObject* swig_varlink_type(void) { static char varlink__doc__[] = "Swig var link object"; static PyTypeObject varlink_type; static int type_init = 0; if (!type_init) { const PyTypeObject tmp = { /* PyObject header changed in Python 3 */ #if PY_VERSION_HEX >= 0x03000000 PyVarObject_HEAD_INIT(&PyType_Type, 0) #else PyObject_HEAD_INIT(NULL) 0, /* Number of items in variable part (ob_size) */ #endif (char *)"swigvarlink", /* Type name (tp_name) */ sizeof(swig_varlinkobject), /* Basic size (tp_basicsize) */ 0, /* Itemsize (tp_itemsize) */ (destructor) swig_varlink_dealloc, /* Deallocator (tp_dealloc) */ (printfunc) swig_varlink_print, /* Print (tp_print) */ (getattrfunc) swig_varlink_getattr, /* get attr (tp_getattr) */ (setattrfunc) swig_varlink_setattr, /* Set attr (tp_setattr) */ 0, /* tp_compare */ (reprfunc) swig_varlink_repr, /* tp_repr */ 0, /* tp_as_number */ 0, /* tp_as_sequence */ 0, /* tp_as_mapping */ 0, /* tp_hash */ 0, /* tp_call */ (reprfunc) swig_varlink_str, /* tp_str */ 0, /* tp_getattro */ 0, /* tp_setattro */ 0, /* tp_as_buffer */ 0, /* tp_flags */ varlink__doc__, /* tp_doc */ 0, /* tp_traverse */ 0, /* tp_clear */ 0, /* tp_richcompare */ 0, /* tp_weaklistoffset */ #if PY_VERSION_HEX >= 0x02020000 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, /* tp_iter -> tp_weaklist */ #endif #if PY_VERSION_HEX >= 0x02030000 0, /* tp_del */ #endif #ifdef COUNT_ALLOCS 0,0,0,0 /* tp_alloc -> tp_next */ #endif }; varlink_type = tmp; /* for Python 3 we already assigned ob_type in PyVarObject_HEAD_INIT() */ #if PY_VERSION_HEX < 0x03000000 varlink_type.ob_type = &PyType_Type; #endif type_init = 1; } return &varlink_type; } /* Create a variable linking object for use later */ SWIGINTERN PyObject * SWIG_Python_newvarlink(void) { swig_varlinkobject *result = PyObject_NEW(swig_varlinkobject, swig_varlink_type()); if (result) { result->vars = 0; } return ((PyObject*) result); } SWIGINTERN void SWIG_Python_addvarlink(PyObject *p, char *name, PyObject *(*get_attr)(void), int (*set_attr)(PyObject *p)) { swig_varlinkobject *v = (swig_varlinkobject *) p; swig_globalvar *gv = (swig_globalvar *) malloc(sizeof(swig_globalvar)); if (gv) { size_t size = strlen(name)+1; gv->name = (char *)malloc(size); if (gv->name) { strncpy(gv->name,name,size); gv->get_attr = get_attr; gv->set_attr = set_attr; gv->next = v->vars; } } v->vars = gv; } SWIGINTERN PyObject * SWIG_globals(void) { static PyObject *_SWIG_globals = 0; if (!_SWIG_globals) _SWIG_globals = SWIG_newvarlink(); return _SWIG_globals; } /* ----------------------------------------------------------------------------- * constants/methods manipulation * ----------------------------------------------------------------------------- */ /* Install Constants */ SWIGINTERN void SWIG_Python_InstallConstants(PyObject *d, swig_const_info constants[]) { PyObject *obj = 0; size_t i; for (i = 0; constants[i].type; ++i) { switch(constants[i].type) { case SWIG_PY_POINTER: obj = SWIG_NewPointerObj(constants[i].pvalue, *(constants[i]).ptype,0); break; case SWIG_PY_BINARY: obj = SWIG_NewPackedObj(constants[i].pvalue, constants[i].lvalue, *(constants[i].ptype)); break; default: obj = 0; break; } if (obj) { PyDict_SetItemString(d, constants[i].name, obj); Py_DECREF(obj); } } } /* -----------------------------------------------------------------------------*/ /* Fix SwigMethods to carry the callback ptrs when needed */ /* -----------------------------------------------------------------------------*/ SWIGINTERN void SWIG_Python_FixMethods(PyMethodDef *methods, swig_const_info *const_table, swig_type_info **types, swig_type_info **types_initial) { size_t i; for (i = 0; methods[i].ml_name; ++i) { const char *c = methods[i].ml_doc; if (c && (c = strstr(c, "swig_ptr: "))) { int j; swig_const_info *ci = 0; const char *name = c + 10; for (j = 0; const_table[j].type; ++j) { if (strncmp(const_table[j].name, name, strlen(const_table[j].name)) == 0) { ci = &(const_table[j]); break; } } if (ci) { size_t shift = (ci->ptype) - types; swig_type_info *ty = types_initial[shift]; size_t ldoc = (c - methods[i].ml_doc); size_t lptr = strlen(ty->name)+2*sizeof(void*)+2; char *ndoc = (char*)malloc(ldoc + lptr + 10); if (ndoc) { char *buff = ndoc; void *ptr = (ci->type == SWIG_PY_POINTER) ? ci->pvalue : 0; if (ptr) { strncpy(buff, methods[i].ml_doc, ldoc); buff += ldoc; strncpy(buff, "swig_ptr: ", 10); buff += 10; SWIG_PackVoidPtr(buff, ptr, ty->name, lptr); methods[i].ml_doc = ndoc; } } } } } } #ifdef __cplusplus } #endif /* -----------------------------------------------------------------------------* * Partial Init method * -----------------------------------------------------------------------------*/ #ifdef __cplusplus extern "C" #endif SWIGEXPORT #if PY_VERSION_HEX >= 0x03000000 PyObject* #else void #endif SWIG_init(void) { PyObject *m, *d; #if PY_VERSION_HEX >= 0x03000000 static struct PyModuleDef SWIG_module = { PyModuleDef_HEAD_INIT, (char *) SWIG_name, NULL, -1, SwigMethods, NULL, NULL, NULL, NULL }; #endif /* Fix SwigMethods to carry the callback ptrs when needed */ SWIG_Python_FixMethods(SwigMethods, swig_const_table, swig_types, swig_type_initial); #if PY_VERSION_HEX >= 0x03000000 m = PyModule_Create(&SWIG_module); #else m = Py_InitModule((char *) SWIG_name, SwigMethods); #endif d = PyModule_GetDict(m); SWIG_InitializeModule(0); SWIG_InstallConstants(d,swig_const_table); import_array(); #if PY_VERSION_HEX >= 0x03000000 return m; #else return; #endif } brian-1.4.3/brian/experimental/cuda/000077500000000000000000000000001266062647100173305ustar00rootroot00000000000000brian-1.4.3/brian/experimental/cuda/__init__.py000066400000000000000000000000321266062647100214340ustar00rootroot00000000000000from gpucodegen import * brian-1.4.3/brian/experimental/cuda/briantonemo.py000066400000000000000000000406131266062647100222230ustar00rootroot00000000000000from brian import * from brian.utils.dynamicarray import * import new import nemo import ctypes try: import pycuda import pycuda.autoinit as autoinit import pycuda.driver as drv import pycuda.compiler as compiler from pycuda import gpuarray except ImportError, e: pycuda = None from brian.experimental.codegen.rewriting import rewrite_to_c_expression use_delay_connection = False __all__ = ['NemoConnection', 'NemoNetwork'] def numpy_array_from_memory(ptr, N, dtype): ''' Creates a numpy array of length N and given dtype from the pointer ptr. ''' buffer_from_memory = ctypes.pythonapi.PyBuffer_FromMemory buffer_from_memory.restype = ctypes.py_object buffer = buffer_from_memory(ptr, dtype().itemsize*N) return frombuffer(buffer, dtype=dtype) _created_connection = False _created_network = False class NemoConnection(DelayConnection): def compress(self): global _created_connection DelayConnection.compress(self) # check that limitations are met if _created_connection: raise NotImplementedError("Current version only supports a single connection.") _created_connection = True if self.source is not self.target: raise NotImplementedError("Current version only supports a single group.") if not isinstance(self.W[0, :], SparseConnectionVector): raise NotImplementedError("Current version only supports sparse matrix types.") # now upload to nemo self.nemo_net = nemo.Network() if pycuda is not None: self.nemo_use_gpu = True else: self.nemo_use_gpu = False # create dummy neurons self.nemo_input_neuron_idx = self.nemo_net.add_neuron_type('Input') self.nemo_net.add_neuron(self.nemo_input_neuron_idx, range(len(self.source))) # create synapses for i in xrange(len(self.source)): Wrow = self.W[i, :] Wdelay = self.delay[i, :] ind = asarray(Wrow.ind) delay = asarray(Wdelay/ms, dtype=int)+1 weight = asarray(Wrow, dtype=float32) if len(ind): #self.nemo_net.add_synapse(i, ind, delay, weight, False) self.nemo_net.add_synapse(i, ind.tolist(), delay.tolist(), weight.tolist(), False) # configure self.nemo_conf = nemo.Configuration() if self.nemo_use_gpu: self.nemo_conf.set_cuda_backend(0) else: self.nemo_conf.set_cpu_backend() # simulation object self.nemo_sim = nemo.Simulation(self.nemo_net, self.nemo_conf) def do_propagate(self): if use_delay_connection: DelayConnection.do_propagate(self) return if not self.iscompressed: self.compress() if self.nemo_use_gpu: self.propagate(self.source.get_spikes(0)) else: self.propagate(self.source.get_spikes(0)) def propagate(self, spikes): if use_delay_connection: DelayConnection.propagate(self, spikes) return if self.nemo_use_gpu: spikes_bool = zeros(len(self.source), dtype=uint32) spikes_bool[spikes] = True spikes_gpu = pycuda.gpuarray.to_gpu(spikes_bool) spikes_gpu_ptr = int(int(spikes_gpu.gpudata)) exc_ptr, inh_ptr = tuple(self.nemo_sim.propagate(spikes_gpu_ptr, len(self.source))) exc = zeros(len(self.source), dtype=float32) inh = zeros(len(self.source), dtype=float32) pycuda.driver.memcpy_dtoh(exc, exc_ptr) pycuda.driver.memcpy_dtoh(inh, inh_ptr) self.target._S[self.nstate] += exc self.target._S[self.nstate] += inh else: spikes = array(spikes, dtype=uint32) spikes_ptr = spikes.ctypes.data spikes_len = len(spikes) exc_ptr, inh_ptr = tuple(self.nemo_sim.propagate(spikes_ptr, spikes_len)) exc = numpy_array_from_memory(exc_ptr, len(self.source), float32) inh = numpy_array_from_memory(inh_ptr, len(self.source), float32) self.target._S[self.nstate] += exc self.target._S[self.nstate] += inh class NemoNetworkPropagate(NetworkOperation): def __init__(self, net, clock): NetworkOperation.__init__(self, when='after_connections', clock=clock) self.net = net self.collected_spikes = DynamicArray(0, dtype=uint32) self.nspikes = 0 self.use_gpu = net.nemo_use_gpu def addspikes(self, spikes): if self.nspikes+len(spikes)>len(self.collected_spikes): self.collected_spikes.resize(self.nspikes+len(spikes)) self.collected_spikes[self.nspikes:self.nspikes+len(spikes)] = spikes self.nspikes += len(spikes) def __call__(self): spikes = self.collected_spikes[:self.nspikes] total_neurons = self.net.total_neurons if self.use_gpu: if not hasattr(self, 'spikes_gpu'): spikes_bool = drv.pagelocked_zeros(total_neurons, dtype=uint32) spikes_bool[spikes] = True spikes_gpu = pycuda.gpuarray.to_gpu(spikes_bool) spikes_gpu_ptr = int(int(spikes_gpu.gpudata)) self.spikes_bool = spikes_bool self.spikes_gpu = spikes_gpu self.spikes_gpu_ptr = spikes_gpu_ptr else: spikes_bool = self.spikes_bool spikes_bool[:] = False spikes_bool[spikes] = True spikes_gpu = self.spikes_gpu pycuda.driver.memcpy_htod(spikes_gpu.gpudata, spikes_bool) spikes_gpu_ptr = self.spikes_gpu_ptr acc_ptr = self.net.nemo_sim.propagate(self.synapse_type, spikes_gpu_ptr, total_neurons) if not hasattr(self, 'acc'): self.acc = acc = drv.pagelocked_zeros(total_neurons, dtype=float32) else: acc = self.acc pycuda.driver.memcpy_dtoh(acc, acc_ptr) else: spikes_ptr = spikes.ctypes.data spikes_len = len(spikes) acc_ptr = self.net.nemo_sim.propagate(self.synapse_type, spikes_ptr, spikes_len) acc = numpy_array_from_memory(acc_ptr, total_neurons, float32) for _, targetvar, targetslice in self.net.nemo_propagate_targets: targetvar += acc[targetslice] self.nspikes = 0 class NemoNetworkConnectionPropagate(object): def __init__(self, net, source_offset): self.net = net self.source_offset = source_offset def __call__(self, spikes): self.net.nemo_propagate.addspikes(spikes+self.source_offset) def get_connection_variable(C): varname = str(C.nstate) for k, v in C.target.var_index.iteritems(): if v==C.nstate and isinstance(k, str): varname = k return varname class NemoNetwork(Network): def prepare(self): global _created_network if _created_network: raise NotImplementedError("Current version only supports a single network object.") _created_network = True Network.prepare(self) if hasattr(self, 'clocks') and len(self.clocks)>1: raise NotImplementedError("Current version only supports a single clock.") if pycuda is not None: self.nemo_use_gpu = True # self.nemo_use_gpu = False # log_warn('brian.experimental.cuda.briantonemo', # 'GPU available but not yet supported, using CPU.') else: self.nemo_use_gpu = False # add a NetworkOperation that will be used to carry out the propagation # by NeMo. The individual Connection objects push their spikes into a # global queue (this is done by NemoNetworkConnectionPropagate) and then # the NemoNetworkPropagate NetworkOperation object propagates the # accumulated effect of the spikes to the target NeuronGroup objects. nemo_propagate = NemoNetworkPropagate(self, self.clock) self.nemo_propagate = nemo_propagate # we add this object to the network and don't need to prepare() again # because we rebuild the update schedule below. self.add(nemo_propagate) # create virtual neurons for Nemo, the virtual neurons are either source # neurons or target neurons. For each Connection, we create a group of # source neurons (or re-use if it has already been created), and a # group of target neurons. Each target group and target variable has # a corresponding virtual group. In all cases, we work with the _owner # of the NeuronGroup so that we don't create multiple groups for each # subgroup. print 'Nemo network virtual neuron groups:' self.group_offset = group_offset = {} total_neurons = 0 self.nemo_managed_connections = [] self.nemo_propagate_targets = [] network_ops_to_remove = [] all_connections = [] for C in self.connections: if isinstance(C, MultiConnection): all_connections.extend(C.connections) else: all_connections.append(C) for C in all_connections: # We handle only Connection and DelayConnection objects. For the CPU # version at least, we can rely on Brian to fall back to its # default behaviour otherwise. if C.__class__ is not DelayConnection and C.__class__ is not Connection: continue # Nemo cannot handle modulation, so we check for this case if C._nstate_mod is not None: log_warn("brian.experimental.cuda.briantonemo", "Synaptic modulation is not supported in Nemo, skipped this Connection.") continue # Add the source neuron group G = C.source._owner if id(G) not in group_offset: group_offset[id(G)] = total_neurons print '- Source group, length', len(G), 'indices %d:%d'%(total_neurons, total_neurons+len(G)) total_neurons += len(G) # Add the (target neuron group, target variable) virtual group key = (id(C.target._owner), C.nstate) if key not in group_offset: group_offset[key] = total_neurons varname = get_connection_variable(C) print '- Target group, length', len(G), 'variable', varname, 'indices %d:%d'%(total_neurons, total_neurons+len(G)) target_offset = total_neurons targetslice = slice(target_offset, target_offset+len(C.target)) targetvar = C.target._S[C.nstate] self.nemo_propagate_targets.append((varname, targetvar, targetslice)) total_neurons += len(C.target) self.nemo_managed_connections.append(C) if C.__class__ is DelayConnection: network_ops_to_remove.append(C.delayed_propagate) self.total_neurons = total_neurons # now upload to nemo self.nemo_net = nemo.Network() # create dummy synapse type self.synapse_type = self.nemo_net.add_synapse_type() self.nemo_propagate.synapse_type = self.synapse_type # create dummy neurons self.nemo_input_neuron_idx = self.nemo_net.add_neuron_type('Input', [self.synapse_type]) self.nemo_net.add_neuron(self.nemo_input_neuron_idx, range(total_neurons)) # add connections and upload synapses to nemo total_synapses = 0 print 'Nemo network synapses:' for C in self.nemo_managed_connections: # We handle subgrouping using the source and target offset source_offset = group_offset[id(C.source._owner)]+C.source._origin target_offset = group_offset[(id(C.target._owner), C.nstate)]+C.target._origin dt = C.source.clock.dt # We replace the propagate method of the Connection with a nemo # version, which adds the spikes to a dynamic stack in the # NemoNetworkPropagate object, which is called after the connections # have been processed by Brian. Note that this way of doing things # only works for the CPU, for the GPU we will need to replace # do_propagate instead. C.propagate = NemoNetworkConnectionPropagate(self, source_offset) # create synapses this_connection_synapses = 0 for i in xrange(len(C.source)): # Need to handle Connection/DelayConnection, and sparse/dense # matrix types Wrow = C.W[i, :] if C.__class__ is DelayConnection: Wdelay = C.delay[i, :] else: Wdelay = zeros(len(Wrow)) if isinstance(Wrow, SparseConnectionVector): ind = (Wrow.ind+target_offset) else: ind = range(target_offset, target_offset+len(Wrow)) delay = (1+asarray(Wdelay/dt, dtype=int)) # Need to update this when NeMo gets support for longer delays if self.nemo_use_gpu and amax(delay)>512: raise NotImplementedError("Current version of NeMo with GPU backend has a maximum delay of 512 steps.") if not self.nemo_use_gpu and amax(delay)>64: raise NotImplementedError("Current version of NeMo with CPU backend has a maximum delay of 64 steps.") weight = asarray(Wrow, dtype=float32) total_synapses += len(weight) this_connection_synapses += len(weight) if len(ind): self.nemo_net.add_synapse(self.synapse_type, i+source_offset, ind, delay, weight, False) print '-', C.__class__.__name__, print 'from source group of', len(C.source), 'neurons', print 'to target group of', len(C.target), 'neurons,', print 'variable %s,'%get_connection_variable(C), print 'matrix type %s,'%C.W.__class__.__name__, print 'number of synapses', this_connection_synapses # debug print the propagation targets print 'Nemo network propagation targets:' for varname, targetvar, targetslice in self.nemo_propagate_targets: print '- Variable', varname, 'indices %d:%d'%(targetslice.start, targetslice.stop) # remove the delayed_propagate functions which are used by # DelayConnection and will already have been inserted into the network # at this point (as they are in the contained_objects of # DelayConnection). for k, v in self._operations_dict.iteritems(): v = [f for f in v if not f in network_ops_to_remove] self._operations_dict[k] = v # We changed lots of propagate functions so we need to rebuild the # update schedule to make use of them self._build_update_schedule() print 'Nemo network total neurons:', total_neurons print 'Nemo network total synapses:', total_synapses # configure self.nemo_conf = nemo.Configuration() if self.nemo_use_gpu: self.nemo_conf.set_cuda_backend(0) else: self.nemo_conf.set_cpu_backend() # simulation object self.nemo_sim = nemo.Simulation(self.nemo_net, self.nemo_conf) # This switches the default behaviour of Brian's Network object to NemoNetwork # i.e. if you import this module, then Nemo will be used def switch_to_nemo_network(self, *args, **kwds): self.__class__ = NemoNetwork Network_init(self, *args, **kwds) Network_init = Network.__init__ Network.__init__ = new.instancemethod(switch_to_nemo_network, None, Network) brian-1.4.3/brian/experimental/cuda/buffering.py000066400000000000000000000322431266062647100216550ustar00rootroot00000000000000''' GPU Buffering class See docstring for GPUBufferedArray for details. ''' from brian import * import pycuda import pycuda.gpuarray import numpy from numpy import intp __all__ = ['SynchronisationError', 'GPUBufferedArray'] if __name__ == '__main__': DEBUG_BUFFER_CACHE = True else: DEBUG_BUFFER_CACHE = False numpy_inplace_methods = set([ '__iadd__', '__iand__', '__idiv__', '__ifloordiv__', '__ilshift__', '__imod__', '__imul__', '__ior__', '__ipow__', '__irshift__', '__isub__', '__itruediv__', '__ixor__', '__setitem__', '__setslice__', 'byteswap', 'fill', 'put', 'sort', ]) numpy_access_methods = set([ '__abs__', '__add__', '__and__', '__array__', '__contains__', '__copy__', '__deepcopy__', '__delattr__', '__delitem__', '__delslice__', '__div__', '__divmod__', '__eq__', '__float__', '__floordiv__', '__ge__', '__getitem__', '__getslice__', '__gt__', '__hash__', '__hex__', '__iadd__', '__iand__', '__idiv__', '__ifloordiv__', '__ilshift__', '__imod__', '__imul__', '__index__', '__int__', '__invert__', '__ior__', '__ipow__', '__irshift__', '__isub__', '__iter__', '__itruediv__', '__ixor__', '__le__', '__long__', '__lshift__', '__lt__', '__mod__', '__mul__', '__ne__', '__neg__', '__nonzero__', '__oct__', '__or__', '__pos__', '__pow__', '__radd__', '__rand__', '__rdiv__', '__rdivmod__', '__reduce__', '__reduce_ex__', '__repr__', '__rfloordiv__', '__rlshift__', '__rmod__', '__rmul__', '__ror__', '__rpow__', '__rrshift__', '__rshift__', '__rsub__', '__rtruediv__', '__rxor__', '__setitem__', '__setslice__', '__setstate__', '__str__', '__sub__', '__truediv__', '__xor__', 'all', 'any', 'argmax', 'argmin', 'argsort', 'astype', 'byteswap', 'choose', 'clip', 'compress', 'conj', 'conjugate', 'copy', 'cumprod', 'cumsum', 'diagonal', 'dump', 'dumps', 'fill', 'flatten', 'getfield', 'item', 'itemset', 'max', 'mean', 'min', 'nonzero', 'prod', 'ptp', 'put', 'ravel', 'repeat', 'reshape', 'resize', 'round', 'searchsorted', 'setfield', 'setflags', 'sort', 'squeeze', 'std', 'sum', 'swapaxes', 'take', 'tofile', 'tolist', 'tostring', 'trace', 'transpose', 'var', 'view']) - numpy_inplace_methods class SynchronisationError(RuntimeError): ''' Indicates that the GPU and CPU data are in conflict (i.e. both have changed) ''' pass # These two functions are decorators to be applied to the numpy # methods. The inplace version sets the cpu data changed flag # after it has run, the access version only makes sure that # the data has been synchronised to the CPU. def gpu_buffered_array_inplace_method(func): def new_func(self, *args, **kwds): self.sync_to_cpu() rval = func(self, *args, **kwds) self._cpu_data_changed = True return rval new_func.__name__ = func.__name__ new_func.__doc__ = func.__doc__ return new_func def gpu_buffered_array_access_method(func): def new_func(self, *args, **kwds): self.sync_to_cpu() rval = func(self, *args, **kwds) return rval new_func.__name__ = func.__name__ new_func.__doc__ = func.__doc__ return new_func class DecoupledGPUBufferedArray(numpy.ndarray): pass class GPUBufferedArray(numpy.ndarray): ''' An object that automatically manages a numpy array that is stored on the CPU and GPU The idea is that whenever data is accessed on either the GPU or CPU, this object performs any synchronisation operations. So for example if you change a value in the array on the CPU and then run a function on the GPU, the data from the CPU side needs to be copied to the GPU before calling the GPU function. The best practice is to use the ``cpu_array`` and ``gpu_array`` or ``gpu_dev_alloc`` attributes explicitly, and call the ``changed_cpu_data()`` and ``changed_gpu_data()`` methods after modifying data. However, the class does its best to be intelligent about when changes to data have happened so these arrays can be used in code that is not GPU aware to some extent. See implementation notes below for details on when this will fail. **Initialisation** Initialising with a numpy array creates a ``pycuda.gpuarray.GPUArray`` instance to store the data on the GPU. If you want, you can specify a pre-existing ``GPUArray`` or ``pycuda.DeviceAllocation`` instance. Initialising with a pagelocked numpy array will make copies faster. **Attributes** Each of these attributes returns a reference to data on the CPU or GPU (and like everything in this class, will synchronise if necessary). Obtaining a reference to CPU/GPU data is taken as implying that the data will be changed by subsequent code, and so the CPU/GPU data is marked as changed. To obtain references without marking the data as changed, use the attribute with ``_nomodify`` appended. If you are obtaining and storing a reference, you need to explicitly mark it as modified when you make a change (see the ``changed_gpu_data()`` and ``changed_cpu_data()`` methods below). ``cpu_array``, ``cpu_array_nomodify`` A reference to the array on the CPU. ``gpu_array``, ``gpu_array_nomodify`` If the GPU data is a ``GPUArray``, this will return it, otherwise it will throw an error. ``gpu_dev_alloc``, ``gpu_dev_alloc_nomodify`` Return the ``DeviceAllocation`` for the GPU data. ``gpu_pointer``, ``gpu_pointer_nomodify`` Returns an integer pointer to the GPU data. **Methods** ``changed_gpu_data()``, ``changed_cpu_data()`` Mark the data on the GPU or CPU as modified. You need to do this explicitly if you obtained and stored a reference to the CPU or GPU data. ``sync_to_gpu()``, ``sync_to_cpu()`` Explicitly make sure that the data on the CPU/GPU has been copied to the GPU/CPU respectively if they are marked as changed. ``sync()`` Explicitly synchronise the CPU and GPU. **Implementation** The implementation is not clever about copying data, it either copies all the data or none at all. Internally it stores two flags to indicate whether data on the CPU or GPU has changed, which it tries its best to be accurate but cannot guarantee (see below), and performs a copy when needed (subsequently updating these flags). Results of numpy functions, except for a handful of explicitly inplace operators, do not mark the data as changed. So doing, for example, ``clip(y,0,1,out=y)`` will not mark the data in ``y`` as changed. ''' def __new__(subtype, cpu_arr, gpu_arr=None): return numpy.array(cpu_arr, copy=False).view(subtype) def __init__(self, cpu_arr, gpu_arr=None): if gpu_arr is None: if DEBUG_BUFFER_CACHE: print 'Initialising and copying GPU memory' self._gpu_arr = pycuda.gpuarray.to_gpu(self) self._gpu_data_changed = False self._cpu_data_changed = False else: self._gpu_arr = gpu_arr self._gpu_data_changed = False self._cpu_data_changed = True def _check_synchronisation(self): if not hasattr(self, '_cpu_data_changed'): # This happens if you do, for example, y[0,:][:]=1 because the y[0,:] will # return an uninitialised GPUBufferedArray. At the moment, we don't # handle this case (although perhaps in the case of slicing we could?) so # maybe we should throw an error here? The alternative is to try to minimise # the impact on code (although it will have decoupled the array from the # GPU data, which may not be expected). self.__class__ = DecoupledGPUBufferedArray self._cpu_data_changed = False self._gpu_data_changed = False return if self._cpu_data_changed and self._gpu_data_changed: raise SynchronisationError('GPU and CPU data desynchronised.') def sync(self): self.sync_to_gpu() self.sync_to_cpu() def sync_to_gpu(self): self._check_synchronisation() if self._cpu_data_changed: gpu_arr = self._gpu_arr if isinstance(gpu_arr, pycuda.gpuarray.GPUArray): gpu_arr.set(self) else: pycuda.driver.memcpy_htod(gpu_arr, self) self._cpu_data_changed = False if DEBUG_BUFFER_CACHE: print 'Synchronised to GPU' def sync_to_cpu(self): self._check_synchronisation() if self._gpu_data_changed: gpu_arr = self._gpu_arr if isinstance(gpu_arr, pycuda.gpuarray.GPUArray): gpu_arr.get(self) else: pycuda.driver.memcpy_dtoh(self, gpu_arr) self._gpu_data_changed = False if DEBUG_BUFFER_CACHE: print 'Synchronised to CPU' def changed_gpu_data(self): self._gpu_data_changed = True self._check_synchronisation() def changed_cpu_data(self): self._cpu_data_changed = True self._check_synchronisation() def get_cpu_array(self, modify=True): self.sync_to_cpu() if modify: # assume that the user is going to modify the data self._cpu_data_changed = True return self def get_gpu_array(self, modify=True): self.sync_to_gpu() if modify: # assume that the user is going to modify the data self._gpu_data_changed = True if isinstance(self._gpu_arr, pycuda.gpuarray.GPUArray): return self._gpu_arr raise TypeError('GPU buffer is not a GPUArray') def get_gpu_dev_alloc(self, modify=True): self.sync_to_gpu() if modify: # assume that the user is going to modify the data self._gpu_data_changed = True if isinstance(self._gpu_arr, pycuda.gpuarray.GPUArray): return self._gpu_arr.gpudata elif isinstance(self._gpu_arr, pycuda.driver.DeviceAllocation): return self._gpu_arr raise TypeError('gpu_arr should be a DeviceAllocation or GPUArray.') def get_gpu_pointer(self): return intp(self.gpu_dev_alloc) cpu_array = property(fget=get_cpu_array) gpu_array = property(fget=get_gpu_array) gpu_dev_alloc = property(fget=get_gpu_dev_alloc) gpu_pointer = property(fget=get_gpu_pointer) cpu_array_nomodify = property(fget=lambda self:self.get_cpu_array(False)) gpu_array_nomodify = property(fget=lambda self:self.get_gpu_array(False)) gpu_dev_alloc_nomodify = property(fget=lambda self:self.get_gpu_dev_alloc(False)) gpu_pointer_nomodify = property(fget=lambda self:self.get_gpu_pointer(False)) for __name in numpy_inplace_methods: exec __name + ' = gpu_buffered_array_inplace_method(numpy.ndarray.' + __name + ')' for __name in numpy_access_methods: exec __name + ' = gpu_buffered_array_access_method(numpy.ndarray.' + __name + ')' def __array_wrap__(self, obj, context=None): # normally, __array_wrap__ calls __array_finalize__ to convert the result obj into an object of its own type # but here, we don't want that, we want it to be a straight numpy array which doesn't maintain a connection # to the GPU array. return obj if __name__ == '__main__': import pycuda.autoinit from pycuda import driver as drv def gpuarrstatus(z): return '(cpu_changed=' + str(z._cpu_data_changed) + ', gpu_changed=' + str(z._gpu_data_changed) + ')' x = array([1, 2, 3, 4], dtype=numpy.float32) print '+ About to initialise GPUBufferedArray' y = GPUBufferedArray(x) print '- Initialised GPUBufferedArray', gpuarrstatus(y) print '+ About to set item on CPU array', gpuarrstatus(y) y[3] = 5 print '- Set item on CPU array', gpuarrstatus(y) mod = drv.SourceModule(''' __global__ void doubleit(float *y) { int i = threadIdx.x; y[i] *= 2.0; } ''') doubleit = mod.get_function("doubleit") print '+ About to call GPU function', gpuarrstatus(y) doubleit(y.gpu_array, block=(4, 1, 1)) print '- Called GPU function', gpuarrstatus(y) print '+ About to print CPU array', gpuarrstatus(y) print y print '- Printed CPU array', gpuarrstatus(y) brian-1.4.3/brian/experimental/cuda/gpucodegen.py000066400000000000000000000331611266062647100220260ustar00rootroot00000000000000#import brian_no_units from brian import * from brian import optimiser try: import weave except ImportError: from scipy import weave try: import pycuda import pycuda.autoinit as autoinit import pycuda.driver as drv import pycuda.compiler as compiler from pycuda import gpuarray from buffering import * except ImportError: log_warn('brian.experimental.cuda.gpucodegen', 'Cannot import pycuda') import time from brian.experimental.codegen.rewriting import rewrite_to_c_expression __all__ = ['GPUNonlinearStateUpdater', 'UserControlledGPUNonlinearStateUpdater', 'GPUNeuronGroup'] #DEBUG_BUFFER_CACHE = False class GPUNonlinearStateUpdater(NonlinearStateUpdater): def __init__(self, eqs, clock=None, freeze=False, precision='double', maxblocksize=512, forcesync=False): NonlinearStateUpdater.__init__(self, eqs, clock, compile=False, freeze=freeze) self.precision = precision if self.precision == 'double': self.precision_dtype = float64 else: self.precision_dtype = float32 self.clock_dt = float(guess_clock(clock).dt) self.code_gpu = self.generate_forward_euler_code() self.maxblocksize = maxblocksize self.forcesync = forcesync self._prepared = False def generate_forward_euler_code(self): eqs = self.eqs M = len(eqs._diffeq_names) all_variables = eqs._eq_names + eqs._diffeq_names + eqs._alias.keys() + ['t'] clines = '__global__ void stateupdate(int N, SCALAR t, SCALAR *S)\n' clines += '{\n' clines += ' int i = blockIdx.x * blockDim.x + threadIdx.x;\n' clines += ' if(i>=N) return;\n' for j, name in enumerate(eqs._diffeq_names): clines += ' int _index_' + name + ' = i+' + str(j) + '*N;\n' for j, name in enumerate(eqs._diffeq_names): # clines += ' SCALAR &' + name + ' = S[i+'+str(j)+'*N];\n' clines += ' SCALAR ' + name + ' = S[_index_' + name + '];\n' for j, name in enumerate(eqs._diffeq_names): namespace = eqs._namespace[name] expr = optimiser.freeze(eqs._string[name], all_variables, namespace) expr = rewrite_to_c_expression(expr) print expr if name in eqs._diffeq_names_nonzero: clines += ' SCALAR ' + name + '__tmp = ' + expr + ';\n' for name in eqs._diffeq_names_nonzero: # clines += ' '+name+' += '+str(self.clock_dt)+'*'+name+'__tmp;\n' clines += ' S[_index_' + name + '] = ' + name + '+' + str(self.clock_dt) + '*' + name + '__tmp;\n' clines += '}\n' clines = clines.replace('SCALAR', self.precision) self.gpu_mod = compiler.SourceModule(clines) self.gpu_func = self.gpu_mod.get_function("stateupdate") return clines def _prepare(self, P): blocksize = self.maxblocksize if len(P) < blocksize: blocksize = len(P) if len(P) % blocksize == 0: gridsize = len(P) / blocksize else: gridsize = len(P) / blocksize + 1 self._prepared = True self.gpu_func.prepare((int32, self.precision_dtype, 'i'), (blocksize, 1, 1)) self._S_gpu_addr = P._S.gpu_pointer self._gpu_N = int32(len(P)) self._gpu_grid = (gridsize, 1) def __call__(self, P): if not self._prepared: self._prepare(P) P._S.sync_to_gpu() self.gpu_func.prepared_call(self._gpu_grid, self._gpu_N, self.precision_dtype(P.clock._t), self._S_gpu_addr) P._S.changed_gpu_data() if self.forcesync: P._S.sync_to_cpu() P._S.changed_cpu_data() class UserControlledGPUNonlinearStateUpdater(GPUNonlinearStateUpdater): def __init__(self, eqs, clock=None, freeze=False, precision='double', maxblocksize=512, gpu_to_cpu_vars=None, cpu_to_gpu_vars=None): GPUNonlinearStateUpdater.__init__(self, eqs, clock=clock, freeze=freeze, precision=precision, maxblocksize=maxblocksize, forcesync=False) self.gpu_to_cpu_vars = gpu_to_cpu_vars self.cpu_to_gpu_vars = cpu_to_gpu_vars def _prepare(self, P): GPUNonlinearStateUpdater._prepare(self, P) if isinstance(P._S._gpu_arr, pycuda.gpuarray.GPUArray): self._gpuoffset = int(P._S._gpu_arr.gpudata) elif isinstance(P._S._gpu_arr, pycuda.driver.DeviceAllocation): self._gpuoffset = int(P._S._gpu_arr) self._cpuflat = array(P._S, copy=False) self._cpuflat.shape = self._cpuflat.size def __call__(self, P): if not self._prepared: self._prepare(P) # copy from CPU to GPU for i, j, k in self.cpu_to_gpu_vars: pycuda.driver.memcpy_htod(self._gpuoffset + i, self._cpuflat[j:k]) self.gpu_func.prepared_call(self._gpu_grid, self._gpu_N, self.precision_dtype(P.clock._t), self._S_gpu_addr) # copy from GPU back to CPU for i, j, k in self.gpu_to_cpu_vars: pycuda.driver.memcpy_dtoh(self._cpuflat[j:k], self._gpuoffset + i) P._S._cpu_data_changed = False # override any buffering P._S._gpu_data_changed = False # override any buffering class GPUNeuronGroup(NeuronGroup): ''' Neuron group which performs numerical integration on the GPU. .. warning:: This class is still experimental, not supported and subject to change in future versions of Brian. Initialised with arguments as for :class:`NeuronGroup` and additionally: ``precision='double'`` The GPU scalar precision to use, older models can only use ``precision='float'``. ``maxblocksize=512`` If GPU compilation fails, reduce this value. ``forcesync=False`` Whether or not to force copying of state variables to and from the GPU each time step. This is slow, so it is better to specify precisely which variables should be copied to and from using ``gpu_to_cpu_vars`` and ``cpu_to_gpu_vars``. ``pagelocked_mem=True`` Whether to store state variables in pagelocked memory on the CPU, which makes copying data to/from the GPU twice as fast. ``cpu_to_gpu_vars=None``, ``gpu_to_cpu_vars=None`` Which variables should be copied each time step from the CPU to the GPU (before state update) and from the GPU to the CPU (after state update). The point of the copying of variables to and from the GPU is that the GPU maintains a separate memory from the CPU, and so changes made on either the CPU or GPU won't automatically be reflected in the other. Since only numerical integration is done on the GPU, any state variable that is modified by incoming synapses, for example, should be copied to and from the GPU each time step. In addition, any variables used for thresholding or resetting need to be appropriately copied (GPU->CPU for thresholding, and both for resetting). ''' def __init__(self, N, model, threshold=None, reset=NoReset(), init=None, refractory=0 * msecond, level=0, clock=None, order=1, implicit=False, unit_checking=True, max_delay=0 * msecond, compile=False, freeze=False, method=None, precision='double', maxblocksize=512, forcesync=False, pagelocked_mem=True, gpu_to_cpu_vars=None, cpu_to_gpu_vars=None): eqs = model eqs.prepare() NeuronGroup.__init__(self, N, eqs, threshold=threshold, reset=reset, init=init, refractory=refractory, level=level, clock=clock, order=order, compile=compile, freeze=freeze, method=method) self.precision = precision if self.precision == 'double': self.precision_dtype = float64 self.precision_nbytes = 8 else: self.precision_dtype = float32 self.precision_nbytes = 4 self.clock = guess_clock(clock) if gpu_to_cpu_vars is None and cpu_to_gpu_vars is None: self._state_updater = GPUNonlinearStateUpdater(eqs, clock=self.clock, precision=precision, maxblocksize=maxblocksize, forcesync=forcesync) else: cpu_to_gpu_vars = [(self.get_var_index(var) * len(self) * self.precision_nbytes, self.get_var_index(var) * len(self), (self.get_var_index(var) + 1) * len(self)) for var in cpu_to_gpu_vars] gpu_to_cpu_vars = [(self.get_var_index(var) * len(self) * self.precision_nbytes, self.get_var_index(var) * len(self), (self.get_var_index(var) + 1) * len(self)) for var in gpu_to_cpu_vars] self._state_updater = UserControlledGPUNonlinearStateUpdater(eqs, clock=self.clock, precision=precision, maxblocksize=maxblocksize, gpu_to_cpu_vars=gpu_to_cpu_vars, cpu_to_gpu_vars=cpu_to_gpu_vars) if pagelocked_mem: self._S = GPUBufferedArray(drv.pagelocked_zeros(self._S.shape, dtype=self.precision_dtype)) else: self._S = GPUBufferedArray(array(self._S, dtype=self.precision_dtype)) self._gpuneurongroup_init_finished = True def copyvar_cpu_to_gpu(self, var): i, j, k = (self.get_var_index(var) * len(self) * self.precision_nbytes, self.get_var_index(var) * len(self), (self.get_var_index(var) + 1) * len(self)) pycuda.driver.memcpy_htod(self._state_updater._gpuoffset + i, self._state_updater._cpuflat[j:k]) def copyvar_gpu_to_cpu(self, var): i, j, k = (self.get_var_index(var) * len(self) * self.precision_nbytes, self.get_var_index(var) * len(self), (self.get_var_index(var) + 1) * len(self)) pycuda.driver.memcpy_dtoh(self._state_updater._cpuflat[j:k], self._state_updater._gpuoffset + i) def __setattr__(self, name, val): try: self._gpuneurongroup_init_finished NeuronGroup.__setattr__(self, name, val) if name in self.var_index: self._S.changed_cpu_data() except AttributeError: object.__setattr__(self, name, val) if __name__ == '__main__': #from brian.experimental.ccodegen import AutoCompiledNonlinearStateUpdater set_global_preferences(usecodegen=False) #duration = 10*second #N = 1000 #domonitor = False duration = 1000 * ms N = 100 domonitor = False showfinal = False forcesync = True method = 'gpu' # methods are 'c', 'python' and 'gpu' if drv.get_version() == (2, 0, 0): # cuda version precision = 'float' elif drv.get_version() > (2, 0, 0): precision = 'double' else: raise Exception, "CUDA 2.0 required" #precision = 'float' import buffering buffering.DEBUG_BUFFER_CACHE = False # eqs = Equations(''' # #dV/dt = -V*V/(10*ms) : 1 # dV/dt = cos(2*pi*t/(100*ms))/(10*ms) : 1 # #dV/dt = -V*V*V*V*V/(100*ms) : 1 # #dW/dt = -W*W*W*W*W/(100*ms) : 1 # #dV/dt = cos(2*pi*t/(100*ms))/(10*ms) : 1 # #dW/dt = cos(2*pi*t/(100*ms))/(10*ms) : 1 # #dW2/dt = cos(2*pi*t/(100*ms))/(10*ms) : 1 # #dV/dt = h/(10*ms) : 1 # #h = -V*V : 1 # ''') #eqs = Equations('\n'.join('dv'+str(i)+'/dt=-v'+str(i)+'/second:1' for i in range(20))) #10 works 11 is too much #print eqs # taum=20*ms # taue=5*ms # taui=10*ms # Vt=-50*mV # Vr=-60*mV # El=-49*mV # # eqs= Equations(''' # dV/dt = (ge+gi-(V-El))/taum : volt # dge/dt = -ge/taue : volt # dgi/dt = -gi/taui : volt # ''') from brian.library.ionic_currents import * El = 10.6 * mV EK = -12 * mV ENa = 120 * mV eqs = MembraneEquation(1 * uF) + leak_current(.3 * msiemens, El) eqs += K_current_HH(36 * msiemens, EK) + Na_current_HH(120 * msiemens, ENa) eqs += Current('I:amp') #eqs.prepare() # #for n in eqs._string.keys(): # eqs._string[n] = rewrite_to_c_expression(eqs._string[n]) #print eqs if method == 'gpu': G = GPUNeuronGroup(N, eqs, precision=precision, maxblocksize=256, forcesync=forcesync) print 'GPU loop code:' print G._state_updater.code_gpu gf = G._state_updater.gpu_func print '(lmem, smem, registers) = ', (gf.local_size_bytes, gf.shared_size_bytes, gf.num_regs) devdata = pycuda.tools.DeviceData() orec = pycuda.tools.OccupancyRecord(devdata, 256) print 'tb_per_mp', orec.tb_per_mp print 'limited_by', orec.limited_by print 'warps_per_mp', orec.warps_per_mp print 'occupancy', orec.occupancy elif method == 'c': G = NeuronGroup(N, eqs, compile=True, freeze=True) su = AutoCompiledNonlinearStateUpdater(eqs, G.clock, freeze=True) G._state_updater = su elif method == 'python': #G = NeuronGroup(N, eqs, freeze=True)#, compile=True, freeze=True) G = NeuronGroup(N, eqs, compile=True, freeze=True) G.V = 1 if domonitor: M = StateMonitor(G, 'V', record=True) start = time.time() run(duration) autoinit.context.synchronize() print method, 'code:', (time.time() - start) if domonitor: M_V = M[0] if domonitor: plot(M.times, M_V) if showfinal: figure() plot(G.V) if domonitor or showfinal: show() brian-1.4.3/brian/experimental/dana/000077500000000000000000000000001266062647100173175ustar00rootroot00000000000000brian-1.4.3/brian/experimental/dana/__init__.py000066400000000000000000000000001266062647100214160ustar00rootroot00000000000000brian-1.4.3/brian/experimental/dana/convolution_matrix.py000066400000000000000000000076721266062647100236500ustar00rootroot00000000000000def convolution_matrix(src, dst, kernel, toric=False): ''' Build a sparse convolution matrix M such that: (M*src.ravel()).reshape(src.shape) = convolve2d(src,kernel) You can specify whether convolution is toric or not and specify a different output shape. If output (dst) is different, convolution is only applied at corresponding normalized location within the src array. Building the matrix can be pretty long if your kernel is big but it can nonetheless saves you some time if you need to apply several convolution compared to fft convolution (no need to go to the Fourier domain). Parameters: ----------- src : n-dimensional numpy array Source shape dst : n-dimensional numpy array Destination shape kernel : n-dimensional numpy array Kernel to be used for convolution Returns: -------- A sparse convolution matrix Examples: --------- >>> Z = np.ones((3,3)) >>> M = convolution_matrix(Z,Z,Z,True) >>> print (M*Z.ravel()).reshape(Z.shape) [[ 9. 9. 9.] [ 9. 9. 9.] [ 9. 9. 9.]] >>> M = convolution_matrix(Z,Z,Z,False) >>> print (M*Z.ravel()).reshape(Z.shape) [[ 4. 6. 4.] [ 6. 9. 6.] [ 4. 6. 4.]] ''' # For a toric connection, it is wrong to have a kernel larger # than the source # if toric: # shape = np.minimum(np.array(src.shape), np.array(kernel.shape)) # kernel = extract(kernel, shape, np.rint(np.array(kernel.shape)/2.)) # Get non NaN value from kernel and their indices. nz = (1 - np.isnan(kernel)).nonzero() data = kernel[nz].ravel() indices = [0,]*(len(kernel.shape)+1) indices[0] = np.array(nz) indices[0] += np.atleast_2d((np.array(src.shape)//2 - np.array(kernel.shape)//2)).T # Generate an array A for a given shape such that given an index tuple I, # we can translate into a flat index F = (I*A).sum() to_flat_index = np.ones((len(src.shape),1), dtype=int) if len(src.shape) > 1: to_flat_index[:-1] = src.shape[1] R, C, D = [], [], [] dst_index = 0 src_indices = [] # Translate target tuple indices into source tuple indices taking care of # possible scaling (this is done by normalizing indices) for i in range(len(src.shape)): z = np.rint((np.linspace(0,1,dst.shape[i])*(src.shape[i]-1))).astype(int) src_indices.append(z) nd = [0,]*(len(kernel.shape)) for index in np.ndindex(dst.shape): dims = [] # Are we starting a new dimension ? if index[-1] == 0: for i in range(len(index)-1,0,-1): if index[i]: break dims.insert(0,i-1) dims.append(len(dst.shape)-1) for dim in dims: i = index[dim] if toric: z = (indices[dim][dim] - src.shape[dim]//2 +(kernel.shape[dim]+1)%2 + src_indices[dim][i]) % src.shape[dim] else: z = (indices[dim][dim] - src.shape[dim]//2 +(kernel.shape[dim]+1)%2 + src_indices[dim][i]) # if toric: # z = (indices[dim][dim] - src.shape[dim]/2.0 -(kernel.shape[dim]+1)%2 + src_indices[dim][i]) % src.shape[dim] # else: # z = (indices[dim][dim] - src.shape[dim]/2.0 -(kernel.shape[dim]+1)%2+ src_indices[dim][i]) n = np.where((z >= 0)*(z < src.shape[dim]))[0] if dim == 0: nd[dim] = n.copy() else: nd[dim] = nd[dim-1][n] indices[dim+1] = np.take(indices[dim], n, 1) indices[dim+1][dim] = z[n] dim = len(dst.shape)-1 z = indices[dim+1] R.extend( [dst_index,]*len(z[0]) ) C.extend( (z*to_flat_index).sum(0).tolist() ) D.extend( data[nd[-1]].tolist() ) dst_index += 1 return sp.coo_matrix( (D,(R,C)), (dst.size,src.size)) brian-1.4.3/brian/experimental/genn/000077500000000000000000000000001266062647100173435ustar00rootroot00000000000000brian-1.4.3/brian/experimental/genn/__init__.py000066400000000000000000000000251266062647100214510ustar00rootroot00000000000000from model import * brian-1.4.3/brian/experimental/genn/model.py000066400000000000000000000031411266062647100210140ustar00rootroot00000000000000''' MODEL DEFINITION LOOKS LIKE: // variables (including consts possibly?) n.varNames.push_back(tS("V")); n.varNames.push_back(tS("float")); (for each) // compile time constants n.pNames.push_back(tS("gNa")); (for each) n.simCode = tS("code with \n"); Inserted into the model definition template below. ''' from brian import * from brian.experimental.codegen2 import * __all__ = 'genn_model_code' model_definition_template = ''' void prepareUserModels() {{ neuronModel n; {code} nModels.push_back(n); }} ''' def genn_model_code(eqs, method, dt, values=None, scalar='double'): code, vars, params = make_c_integrator(eqs, method, dt, values=values, scalar=scalar, timeunit=ms, timename='T') print code print s = [] for v in vars: s.append('n.varNames.push_back(tS("{v}"));'.format(v=v)) s.append('n.varNames.push_back(tS("{scalar}"));'.format(scalar=scalar)) for p in params: s.append('n.pNames.push_back(tS("{p}"));'.format(p=p)) code = code.replace('\n', '\\n') s.append('n.simCode = tS("{code}");'.format(code=code)) code = '\n'.join(' '+line for line in s) return model_definition_template.format(code=code) if __name__=='__main__': eqs = ''' dv/dt = (ge*sin(2*pi*500*Hz*t)+gi-(v+49*mV))/(20*ms) : volt dge/dt = -ge/tau : volt dgi/dt = -gi/tau_i : volt tau : second tau_i : second ''' print genn_model_code(eqs, euler, dt=0.1*ms) brian-1.4.3/brian/experimental/genn/runtime_api.txt000066400000000000000000000010331266062647100224150ustar00rootroot00000000000000COMPILE - Create model code for each set of equations - Create neuron populations (NeuronGroup) - Create synapse populations (Connection) (but not weights, etc.) (- Constant values are passed here (but not needed)) - Compile everything (use a script that does that) RUNTIME - Create dense array in the correct format (float/double), pass pointer via API - Use API calls to get/send data to state variables (and subset of variables) - API initialisation function - API run function - API get spike times - API deallocate brian-1.4.3/brian/experimental/integrodiff.py000066400000000000000000000125151266062647100212720ustar00rootroot00000000000000''' Conversion from integral form to differential form. See BEP-5. TODO: * maximum rank * better function name * discrete time version * rescale X0 to avoid numerical problems * automatic determination of T? ''' import re import inspect from brian.units import * from brian.stdunits import * from brian.inspection import get_identifiers from brian.utils.autodiff import * from brian.equations import * from scipy import linalg def integral2differential(expr, T=20 * ms, level=0, N=20, suffix=None, matrix_output=False): ''' Example: eqs,w=integral2differential('g(t)=t*exp(-t/tau)') M,nvar,w=integral2differential('g(t)=t*exp(-t/tau)',matrix_output=True) Returns an Equations object corresponding to the time-invariant linear system specified by the impulse response g(t), and the value w to generate the impulse response: g_in->g_in+w. If matrix_output is True, returns the matrix of the corresponding differential system, the index nvar of the variable and the initial condition w=x_nvar(0). T is the interval over which the function is calculated. N is the number of points chosen in that interval. level is the frame level where the expression is defined. suffix is a string added to internal variable names (default: unique string). ''' # Expression matching varname, time, RHS = re.search('\s*(\w+)\s*\(\s*(\w+)\s*\)\s*=\s*(.+)\s*', expr).groups() # Build the namespace frame = inspect.stack()[level + 1][0] global_namespace, local_namespace = frame.f_globals, frame.f_locals # Find external objects vars = list(get_identifiers(RHS)) namespace = {} for var in vars: if var == time: # time variable pass elif var in local_namespace: #local namespace[var] = local_namespace[var] elif var in global_namespace: #global namespace[var] = global_namespace[var] elif var in globals(): # typically units namespace[var] = globals()[var] # Convert to a function f = eval('lambda ' + time + ':' + RHS, namespace) # Unit unit = get_unit(f(rand()*second)).name # Pick N points t = rand(N) * T # Calculate derivatives and find rank n = 0 rank = 0 M = f(t).reshape(N, 1) while rank == n: n += 1 dfn = differentiate(f, t, order=n).reshape(N, 1) x, _, rank, _ = linalg.lstsq(M, dfn) if rank == n: M = hstack([M, dfn]) oldx = x # oldx expresses dfn as a function of df0,..,dfn-1 (n=rank) # Find initial condition X0 = array([differentiate(f, 0 * ms, order=n) for n in range(rank)]) # Rescaling DOES NOT WORK #R=ones(rank) #for i in range(rank): # if X0[i]!=0.: # R[i]=1./X0[i] # else: # R[i]=1. #R=diag(R) #X0=dot(R,X0) #oldx=dot(R,oldx) # Build A A = diag(ones(rank - 1), 1) A[-1, :] = oldx.reshape(1, rank) # Find Q=P^{-1} Q = eye(rank) if X0[0] == 0.: # continuous g, spikes act on last variable: x->x+1 Q[:, -1] = X0 nvar = rank - 1 w = 1. # Exact inversion P = eye(rank) P[:-1, -1] = -X0[:-1] / X0[-1] # Has to be !=0 !! P[-1, -1] = 1. / X0[-1] else: # discontinuous g, spikes act on first variable: x->x+g(0) Q[:, 0] = X0 nvar = 0 w = X0[0] P = linalg.inv(Q) M = dot(dot(P, A), Q) #M=dot(linalg.inv(R),dot(M,R)) # Turn into string # Set variable names if rank < 5: names = [varname] + ['x', 'y', 'z'][:rank - 1] else: names = [varname] + ['x' + str(i) for i in range(rank - 1)] # Add suffix if suffix is None: suffix = unique_id() names[1:] = [name + suffix for name in names[1:]] # Build string eqs = [] for i in range(rank): eqs.append('d' + names[i] + '/dt=' + '+'.join([str(x) + '*' + name for x, name in zip(M[i, :], names) if x != 0.]) + ' : ' + str(unit)) eqs.append(varname + '_in=' + names[nvar]) # alias eq_string = '\n'.join(eqs).replace('+-', '-') if matrix_output: return M, nvar, w else: return Equations(eq_string), w if __name__ == '__main__': from brian import * from scipy import linalg tau = 10 * ms tau2 = 5 * ms freq = 350 * Hz # The gammatone example does not seem to work for higher orders # probably a numerical problem; use a rescaling matrix for X0? f = lambda t:(t / tau) ** 1 * exp(-t / tau) * cos(2 * pi * freq * t) A, nvar, w = integral2differential('g(t)=(t/tau)**1*exp(-t/tau)*cos(2*pi*freq*t)', suffix='', matrix_output=True) eq, w = integral2differential('g(t)=(t/tau)**1*exp(-t/tau)*cos(2*pi*freq*t)', suffix='', matrix_output=False) print eq #f=lambda t:exp(-t/tau)-exp(-t/tau2)*cos(2*pi*t/tau) #A,nvar,w=integral2differential('g(t)=exp(-t/tau)-exp(-t/tau2)*cos(2*pi*t/tau)', # matrix_output=True) print A, nvar, w for t in range(10): t = t * 1 * ms print linalg.expm(A * t)[0, nvar] * w, f(t) #t=arange(50)*.5*ms #plot(t,f(t)) #show() brian-1.4.3/brian/experimental/model_documentation.py000066400000000000000000000435041266062647100230250ustar00rootroot00000000000000import itertools import string from sympy import Symbol, Eq, Derivative from sympy.printing import latex from sympy.printing.pretty.pretty import PrettyPrinter from brian import * from brian.inspection import get_identifiers __all__ = ['DocumentWriter', 'TextDocumentWriter', 'LaTeXDocumentWriter', 'document_network', 'labels_from_namespace'] class DocumentWriter(object): ''' Base class for documenting a network. Already takes care of assigning labels to objects if none are given. ''' def document_network(self, net=None, labels=None, sim=True, groups=True, connections=True, operations=True, graph_connections=False): ''' Documents the network `net`, if not network is given, a :class:`MagicNetwork` is automatically created and documented, i.e. if you are running scripts without any explicit network (you use `run(...)` instead of `net.run(...)`), you can simply call `writer.document_network()`. ''' if net is None: net = MagicNetwork(level=2) if labels is None: labels = {} self.do_graph_connections = graph_connections letters = string.uppercase letter_count = 0 self.short_labels = {} self.long_labels = {} for group in net.groups: if group._owner is group: if group in labels: self.short_labels[group], self.long_labels[group] = labels[group] else: self.short_labels[group] = letters[letter_count] self.long_labels[group] = str(group) letter_count += 1 # process subgroups subgroup_counter = 0 for subgroup_ref in group._subgroup_set: subgroup = subgroup_ref() # (the saved subgroups are only weak references) if subgroup in labels: self.short_labels[subgroup], self.long_labels[subgroup] = labels[subgroup] else: self.short_labels[subgroup] = self.short_labels[group] + str(subgroup_counter) self.long_labels[subgroup] = str(subgroup) for other in itertools.chain(net.connections, net.operations): if other in labels: self.short_labels[other], self.long_labels[other] = labels[other] else: self.short_labels[other] = letters[letter_count] self.long_labels[other] = str(other) letter_count += 1 self.intro() if sim: self.document_sim(net) if groups: self.document_groups(net.groups) if connections: self.document_connections(net.connections) if graph_connections: self.graph_connections(net.connections, net.groups) if operations: self.document_operations(net.operations) self.outro() def intro(self): """ Is called before any other output function, useful for the start of an HTML or LaTeX document. """ pass def outro(self): """ Is called after all other output function, useful for the end of an HTML or LaTeX document. """ pass def document_sim(self, network): """ Document some general properties of the network, e.g. the timestep used in the simulation. """ pass def document_connections(self, connections): """ Document the connections of the network (including :class:`SpikeMonitor` etc. as they are modeled as :class:`Connection` objects). """ pass def graph_connections(self, connections, groups): """ Draw a graph visualizing the connection structure of the network. """ pass def document_group(self, group): """ Document a single :class:`NeuronGroup` `group`. Will normally be called for every group by :meth:`document_groups`. """ pass def document_groups(self, groups): """ Document all :class:`NeuronGroup` `groups`. Should normally call :meth:`document_group` for every group. """ for group in groups: self.document_group(group) def document_operations(self, operations): """ Document all :class:`NetworkOperation` `operations` (including :class:`StateMonitor` etc. as they are modeled as :class:`NetworkOperation` objects). Should normally call :meth:`document_operation` for every operation. """ for op in operations: self.document_operation(op) def document_operation(self, operation): """ Document a single :class:`NetworkOperation` `operation`. Should normally be called by :meth:`document_operations` for every operation. """ pass @staticmethod def to_sympy_expression(eq_string): """ Simple helper function for converting an Equation string `eq_string` (only the right hand side of an equation) into a `sympy` expression by calling `x = Symbol('x')` for every variable `x` in the equation. """ l_namespace = {'Symbol': Symbol} # add all variables as sympy symbols to namespace for identifier in get_identifiers(eq_string): exec '%s = Symbol("%s")' % (identifier, identifier) in {}, l_namespace return eval(eq_string, {}, l_namespace) class TextDocumentWriter(DocumentWriter): """ Documents the network by printing to stdout, uses `sympy` for formatting the equations (including nice Unicode symbols) """ def __init__(self, **kwargs): DocumentWriter.__init__(self, **kwargs) self.pp = PrettyPrinter(settings={'wrap_line': False}) def document_sim(self, network): clock = guess_clock(network.clock) print 'Simulating with dt = ' + str(clock.dt) def document_connections(self, connections): print 'Connections and SpikeMonitors: ' for con in connections: if con.target: print '\t%s (%s -> %s)' % (con, self.short_labels[con.source], self.short_labels[con.target]) # Monitors are connections without a target for con in connections: if con.target is None: print '\t%s (%s, monitoring %s)' % (con, self.short_labels[con], self.short_labels[con.source]) def document_groups(self, groups): if not groups: return print 'Groups: ' DocumentWriter.document_groups(self, groups) def document_group(self, group): print '\t%s (%s)' % (self.long_labels[group], self.short_labels[group]) sympy_time = Symbol('t') try: eqs = group._eqs print '\t\tEquations: ' equations = '' for varname in eqs._diffeq_names: sympy_var = DocumentWriter.to_sympy_expression(varname) eq_string = eqs._string[varname] sympy_string = self.pp.doprint(Eq(Derivative(sympy_var, sympy_time), DocumentWriter.to_sympy_expression(eq_string))) for line in sympy_string.split('\n'): equations += '\t\t\t' + line + '\n' equations += '\n' for varname in eqs._eq_names: sympy_var = DocumentWriter.to_sympy_expression(varname) eq_string = eqs._string[varname] sympy_string = self.pp.doprint(Eq(sympy_var, DocumentWriter.to_sympy_expression(eq_string))) for line in sympy_string.split('\n'): equations += '\t\t\t' + line + '\n' equations += '\n' print equations except AttributeError: pass if group._subgroup_set: print '\t\tSubgroups:' for subgroup_ref in group._subgroup_set: subgroup = subgroup_ref() print '\t\t\t%s (%s)' % (self.long_labels[subgroup], self.short_labels[subgroup]) def document_operations(self, operations): if not operations: return print 'NetworkOperations and StateMonitors:' for op in operations: if isinstance(op, StateMonitor): print '\t' + str(op) + ' (monitoring: %s)' % self.short_labels[op.P] else: print '\t' + str(op) class LaTeXDocumentWriter(DocumentWriter): """ Documents the network by printing LaTeX code to stdout. Prints a full document (i.e. including preamble etc.). The resulting LaTeX file needs the `amsmath` package. Note that if you use the `graph_connections=True` option, you additionally need the `tikz` and `dot2texi` packages (part of `texlive-core` and `texlive-pictures` on Linux systems) and the `dot2tex` tool. To make the conversion from dot code to LaTeX automatically happen on document creation you have to pass the `--shell-escape` option to your LaTeX call, e.g. `pdflatex --shell-escape network_doc.tex` """ def __init__(self, **kwargs): DocumentWriter.__init__(self) self.pp = PrettyPrinter(settings={'wrap_line': False}) def intro(self): print r''' \documentclass{article} \usepackage{amsmath,textcomp} ''' if self.do_graph_connections: print r''' \usepackage{dot2texi} \usepackage{tikz} \usetikzlibrary{shapes,arrows} ''' print r'\begin{document}' + '\n' def outro(self): print r'\end{document}' def document_sim(self, network): print r'\section{Simulation settings}' clock = guess_clock(network.clock) print 'Simulating with dt = ' + str(clock.dt) def document_connections(self, connections): if not connections: return print r'\section{Connections and SpikeMonitors}' print r'\begin{itemize}' for con in connections: if con.target: print r'\item %s (%s \textrightarrow{} %s)' % (con, self.short_labels[con.source], self.short_labels[con.target]) # Monitors are connections without a target for con in connections: if con.target is None: print '\item %s (monitoring: %s)' % (con, self.short_labels[con.source]) print r'\end{itemize}' def document_groups(self, groups): if not groups: return print r'\section{Groups}' for group in groups: self.document_group(group) def document_group(self, group): print r'\subsection{%s (%s)}' % (self.long_labels[group], self.short_labels[group]) sympy_time = Symbol('t') try: eqs = group._eqs print r'\subsubsection*{Equations}' print LaTeXDocumentWriter.latex_equations(eqs) except AttributeError: pass if group._subgroup_set: print '\t\tSubgroups:' for subgroup_ref in group._subgroup_set: subgroup = subgroup_ref() print '\t\t\t%s (%s)' % (self.long_labels[subgroup], self.short_labels[subgroup]) def graph_connections(self, connections, groups): dot_text = generate_dot_text(connections, groups, self.short_labels) print r'\begin{dot2tex}' + '\n' + dot_text + '\n' + r'\end{dot2tex}' @staticmethod def latex_equation(eq_string): """ Helper function to convert the right hand side of an equation string `eq_string` to a LaTeX expression (not a full equation in LaTeX terms) using `sympy`. """ # convert equation to latex latex_eq = latex(DocumentWriter.to_sympy_expression(eq_string), mode='plain') return latex_eq @staticmethod def format_equation(lhs, rhs, unit): """ Helper function to convert an equation consisting of two strings for the left respectively the right hand side of an equation (`lhs` and `rhs`) and a Brian :class:`Unit` `unit` into a LaTeX expression aligning on the equality sign for `amsmath`'s `align` environment. """ return r'%s &= %s &(\text{unit: }%s)\\' % (lhs, rhs, unit) + '\n' @staticmethod def latex_equations(eqs): """ Convert Brian equations (either a -- possibly multi-line -- string or an :class:`Equation` object) `eqs` into a LaTeX equation using `amsmath`'s `align` environment. """ if isinstance(eqs, str): eqs = Equations(eqs) equations = r'\begin{align*}' + '\n' for varname in eqs._diffeq_names: eq_string = eqs._string[varname] rhs = LaTeXDocumentWriter.latex_equation(eq_string) lhs = r'\frac{\mathrm{d}%s}{\mathrm{d}t}' % (varname) equations += LaTeXDocumentWriter.format_equation(lhs, rhs, eqs._units[varname]) for varname in eqs._eq_names: eq_string = eqs._string[varname] rhs = LaTeXDocumentWriter.latex_equation(eq_string) equations += LaTeXDocumentWriter.format_equation(varname, rhs, eqs._units[varname]) equations += r'\end{align*}' + '\n' return equations def document_network(net=None, output='text', **kwargs): ''' Convenience method for documenting a network without having to construct a :class:`DocumentWriter` object first. If no network `net` is given, a :class:`MagicNetwork` is automatically created and documented. The `output` argument should be either `text` (the default) or `latex`. Any further keyword arguments are passed on to the respective :class:`DocumentWriter`. ''' if output.lower() == 'text': writer = TextDocumentWriter() elif output.lower() == 'latex': writer = LaTeXDocumentWriter() else: raise ValueError('Unknown output mode "%s"' % output) writer.document_network(net, **kwargs) def labels_from_namespace(namespace): ''' Creates a labels dictionary that can be handed over to the `document_network` from a given namespace. Would typically be called like this: `net.document_network(labels=labels_from_namespace(locals()))` This allows `document_network` to use the variable names used in the Python code as the short labels, making it easier to link the model description and the actual code. ''' labels = {} for name, obj in namespace.iteritems(): if isinstance(obj, (NeuronGroup, NetworkOperation, Monitor, Connection)): labels[obj] = (name, str(obj)) return labels def generate_dot_text(connections, groups, labels): dot_description = 'digraph network {\n' conn_strings = [] used_groups = set() for c in connections: if c.target is None: # a monitor conn_strings.append('%s -> %s [style=dotted, label="monitoring"];' % (labels[c], labels[c.source])) else: #TODO: Label connections with info about connections (e.g. 1:1) # possible to label arrowhead with target variable? conn_strings.append('%s -> %s;' % (labels[c.source], labels[c.target])) used_groups.add(c.target) used_groups.add(c.source) # Assure that every group is shown (or all subgroups of a group are shown) for group in groups: if group not in used_groups: all_subgroups_used = True for subgroup in group._subgroup_set: if subgroup() not in used_groups: all_subgroups_used = False break if not all_subgroups_used: dot_description += '%s;\n' % (label[group]) dot_description += '\n'.join(conn_strings) dot_description += '\n}' return dot_description if __name__ == '__main__': # Test with some example code defaultclock.dt=0.01*ms C=281*pF g_L=30*nS E_L=-70.6*mV V__T=-50.4*mV Delta__T=2*mV tau_w=40*ms a=4*nS b=0.08*nA I=.8*nA V_cut=V__T+5*Delta__T # practical threshold condition N=500 eqs=""" dvm/dt=(g_L*(E_L-vm)+g_L*Delta__T*exp((vm-V__T)/Delta__T)+I-w)/C : volt dw/dt=(a*(vm-E_L)-w)/tau_w : amp Vr:volt """ neuron=NeuronGroup(N,model=eqs,threshold=V_cut,reset="vm=Vr;w+=b") neuron.vm=E_L neuron.w=a*(neuron.vm-E_L) neuron.Vr=linspace(-48.3*mV,-47.7*mV,N) # bifurcation parameter M=StateSpikeMonitor(neuron,("Vr","w")) # record Vr and w at spike times # Document the network document_network(output='latex', labels=labels_from_namespace(locals()), graph_connections=True) brian-1.4.3/brian/experimental/modelfitting/000077500000000000000000000000001266062647100211015ustar00rootroot00000000000000brian-1.4.3/brian/experimental/modelfitting/__init__.py000077500000000000000000000000651266062647100232160ustar00rootroot00000000000000from modelfitting import * from compensation import *brian-1.4.3/brian/experimental/modelfitting/benchmark.py000077500000000000000000000025541266062647100234160ustar00rootroot00000000000000from brian import * from brian.experimental.modelfitting import * from numpy.random import * import time if __name__ == '__main__': equations = Equations(''' dV/dt=(R*I-V)/tau : 1 I : 1 R : 1 tau : second ''') input = loadtxt('current.txt') trace = loadtxt('trace_artificial.txt') groups = 1 overlap = 0*ms # input, trace = slice_trace(input, trace, slices = groups, overlap = overlap) neurons = 25600 R = 3e9*ones(neurons) tau = 25*ms*ones(neurons) criterion = LpError(p=2, varname='V') t0 = time.clock() criterion_values = simulate( model = equations, reset = 0, threshold = 1, data = trace, input = input, use_gpu = True, groups = groups, overlap = overlap, stepsize = 128*ms, dt = .1*ms, criterion = criterion, neurons = neurons, R = R, tau = tau) dur = time.clock()-t0 print dur print min(criterion_values), max(criterion_values) brian-1.4.3/brian/experimental/modelfitting/compensation.py000077500000000000000000000102521266062647100241550ustar00rootroot00000000000000from brian import * from modelfitting import * import time, os dt = defaultclock.dt def compensate(current, trace, popsize = 100, maxiter = 10, equations = None, reset = None, threshold = None, slice_duration = 1 * second, overlap = 100*ms, initial = None, dt = defaultclock.dt, cpu = None, gpu = 1, record=['V','Ve'], cut = None, cut_length = 0.0, p = 0.5, best_params=None, **params): trace0 = trace.copy() if initial is None: initial = trace[0] if slice_duration is None: slices = 1 else: slices = max(int(len(current)*dt/slice_duration), 1) current0 = current current, trace = slice_trace(current, trace, slices=slices, overlap=overlap, dt=dt) if cut is not None: cut, cut_indices = transform_spikes(popsize, current0, [(0, float(c)) for c in cut], slices=slices, overlap=overlap, dt=dt) cut_steps = int32(cut_length/dt) cut = array(cut/dt, dtype=int32) cut_indices = array(cut_indices, dtype=int32) else: cut_indices = None cut_steps = 0 initial_values = {'V0': initial, 'Ve': 0.} if equations is None: equations = Equations(''' dV0/dt=(R*Iinj-V0+Vr)/tau : 1 Iinj=(V-V0)/Re : 1 dV/dt=Re*(I-Iinj)/taue : 1 Ve=V-V0 : 1 I : 1 R : 1 Re : 1 Vr : 1 tau : second taue : second ''') if threshold is None: threshold = "V>100000" if reset is None: reset = "" if len(params) == 0: params = dict(R = [1.0e3, 1.0e6, 1000.0e6, 1.0e12], Re = [1.0e3, 1.0e6, 1000.0e6, 1.0e12], Vr = [-100.0e-3, -80e-3, -40e-3, -10.0e-3], tau = [.1*ms, 1*ms, 30*ms, 200*ms], taue = [.01*ms, .1*ms, 5*ms, 20*ms]) criterion = LpError(p=p, varname='V') if best_params is None: results = modelfitting( model = equations, reset = reset, threshold = threshold, data = trace, input = current, cpu = cpu, gpu = gpu, dt = dt, popsize = popsize, maxiter = maxiter, onset = overlap, criterion = criterion, initial_values = initial_values, **params ) print_table(results) best_params = results.best_params else: results = best_params for key in best_params.keys(): best_params[key] = [best_params[key]] criterion_values, record_values = simulate( model = equations, reset = reset, threshold = threshold, data = trace, input = current, dt = dt, neurons = slices, # 1 neuron/slice onset = overlap, criterion = criterion, use_gpu = True, record = record, initial_values = initial_values, **best_params ) traceV = record_values[0][:,int(overlap/dt):].flatten() traceVe = record_values[1][:,int(overlap/dt):].flatten() # return traceV, traceVe, results return trace0 - traceVe, traceV, traceVe, results brian-1.4.3/brian/experimental/modelfitting/criteria.py000077500000000000000000001452201266062647100232640ustar00rootroot00000000000000from brian import Equations, NeuronGroup, Clock, CoincidenceCounter, Network, zeros, array, \ ones, kron, ms, second, concatenate, hstack, sort, nonzero, diff, TimedArray, \ reshape, sum, log, Monitor, NetworkOperation, defaultclock, linspace, vstack, \ arange, sort_spikes, rint, SpikeMonitor, Connection, int32, double,SpikeGeneratorGroup,DelayConnection,\ SpikeCounter,forget,diagflat,inf,sqrt,ones_like,isnan,append,repeat,tile,mean,logical_and,where from brian.tools.statistics import firing_rate, get_gamma_factor from playdoh import * try: import pycuda import pycuda.driver as drv import pycuda from pycuda import gpuarray can_use_gpu = True except ImportError: can_use_gpu = False from brian.experimental.codegen.integration_schemes import * import sys, cPickle class Criterion(Monitor, NetworkOperation): """ Abstract class from which modelfitting criterions should derive. Derived classes should implement the following methods: ``initialize(self, **params)`` Called once before the simulation. ```params`` is a dictionary with criterion-specific parameters. ``timestep_call(self)`` Called at every timestep. ``spike_call(self, neurons)`` Called at every spike, with the spiking neurons as argument. ``get_values(self)`` Called at the end, returns the criterion values. You have access to the following methods: ``self.get_value(self, varname)`` Returns the value of the specified variable for all neurons (vector). You have access to the following attributes: ``self.step`` The time step (integer) ``self.group`` The NeuronGroup ``self.traces=None`` Target traces. A 2-dimensional K*T array where K is the number of targets, and T the total number of timesteps. It is still a 2-dimensional array when K=1. It is None if not specified. ``self.spikes=None`` A list of target spike trains : [(i,t)..] where i is the target index and t the spike time. ``self.N`` The number of neurons in the NeuronGroup ``self.K=1`` The number of targets. ``self.duration`` The total duration of the simulation, in seconds. ``self.total_steps`` The total number of time steps. ``self.dt`` The timestep duration, in seconds. ``self.delays=zeros(self.n)`` The delays for every neuron, in seconds. The delay is relative to the target. ``self.onset=0*ms`` The onset, in seconds. The first timesteps, before onset, should be discarded in the criterion. ``self.intdelays=0`` The delays, but in number of timesteps (``int(delays/dt)``). NOTE: traces and spikes have all been sliced before being passed to the criterion object """ def __init__(self, group, traces=None, spikes=None, targets_count=1, duration=None, onset=0*ms, spikes_inline=None, spikes_offset=None,trials_offset=None, traces_inline=None, traces_offset=None, delays=None, when='end', **params): NetworkOperation.__init__(self, None, clock=group.clock, when=when) self.group = group # needed by SpikeMonitor self.source = group self.source.set_max_delay(0) self.delay = 0 self.trials_offset=trials_offset self.N = len(group) # number of neurons self.K = targets_count # number of targets self.dt = self.clock.dt if traces is not None: self.traces = array(traces) # KxT array if self.traces.ndim == 1: self.traces = self.traces.reshape((1,-1)) assert targets_count==self.traces.shape[0] self.spikes = spikes # get the duration from the traces if duration is not specified in the constructor if duration is None: duration = self.traces.shape[1] # total number of steps self.duration = duration self.total_steps = int(duration/self.dt) if delays is None: delays = zeros(self.n) self.delays = delays self.onset = int(onset/self.dt) self.intdelays = array(self.delays/self.clock.dt, dtype=int) self.mindelay = min(delays) self.maxdelay = max(delays) # the following data is sliced self.spikes_inline = spikes_inline self.spikes_offset = spikes_offset self.traces_inline = traces_inline self.traces_offset = traces_offset if self.spikes is not None: # target spike count and rates self.target_spikes_count = self.get_spikes_count(self.spikes) self.target_spikes_rate = self.get_spikes_rate(self.spikes) self.initialize(**params) def step(self): """ Return the current time step """ return int(round(self.clock.t/self.dt)) def get_spikes_count(self, spikes): count = zeros(self.K) for (i,t) in spikes: count[i] += 1 return count def get_spikes_rate(self, spikes): count = self.get_spikes_count(spikes) return count*1.0/self.duration def get_gpu_code(self): # TO IMPLEMENT """ Returns CUDA code snippets to put at various places in the CUDA code template. It must return a dictionary with the following items: %CRITERION_DECLARATION%: kernel declaration code %CRITERION_INIT%: initialization code %CRITERION_TIMESTEP%: main code, called at every time step %CRITERION_END%: finalization code """ log_warn("GPU code not implemented by the derived criterion class") def initialize_cuda_variables(self): # TO IMPLEMENT """ Initialize criterion-specific CUDA variables here. """ pass def get_kernel_arguments(self): # TO IMPLEMENT """ Return a list of objects to pass to the CUDA kernel. """ pass def initialize(self): # TO IMPLEMENT """ Override this method to initialize the criterion before the simulation """ pass def timestep_call(self): # TO IMPLEMENT """ Override this method to do something at every time step """ pass def spike_call(self, neurons): # TO IMPLEMENT """ Override this method to do something at every time spike. neurons contains the list of neurons that just spiked """ pass def get_value(self, varname): return self.group.state_(varname) def __call__(self): self.timestep_call() neurons = self.group.get_spikes() self.spike_call(neurons) # def propagate(self, neurons): # self.spike_call(neurons) def get_values(self): # TO IMPLEMENT """ Override this method to return the criterion values at the end. It must return one (or several, as a tuple) additive values, i.e., values corresponding to slices. The method normalize can normalize combined values. """ pass additive_values = property(get_values) def normalize(self, values): # TO IMPLEMENT """ Values contains combined criterion values. It is either a vector of values (as many values as neurons), or a tuple with different vector of as many values as neurons. """ pass class CriterionStruct(object): type = None # 'trace', 'spike' or 'both' def get_name(self): return self.__class__.__name__ name = property(get_name) class LpErrorCriterion(Criterion): type = 'traces' def initialize(self, p=2, varname='v', method='all',insets=None,outsets=None,points=None): """ Called at the beginning of every iteration. The keyword arguments here are specified in modelfitting.initialize_criterion(). """ self.method=method if insets is not None: self.insets=array(append(insets,insets[-1]+100000000),dtype=int) self.outsets=array(append(outsets,outsets[-1]+100000000),dtype=int) if points is not None: self.points=array(append(points,points[-1]+100000000),dtype=int) self.next_index=0 self.p = double(p) self.varname = varname self._error = zeros((self.K, self.N)) def timestep_call(self): if self.method=='all': v = self.get_value(self.varname) t = self.step()+1 if t= self.duration: return d = self.intdelays indices = (t-d>=0)&(t-d= self.duration: return if t>=self.insets[self.next_index] and t<=self.outsets[self.next_index]: if t==self.outsets[self.next_index]: self.next_index+=1 d = self.intdelays indices = (t-d>=0)&(t-d= self.duration: return if t==self.points[self.next_index]: self.next_index+=1 d = self.intdelays indices = (t-d>=0)&(t-d= onset)&(Tdelay= onset)&(Tdelay= inset)&(T <= outset)) { if (T == outset) {next_index+=1; inset=insets[next_index]; outset=outsets[next_index]; } error = error + pow(abs(trace_value - %s), %.4f); } """ %(self.varname, self.p) # FINALIZATION code['%CRITERION_END%'] = """ error_arr[neuron_index] = error; *next_index_past=next_index; """ if self.method=='points': # DECLARATION code['%CRITERION_DECLARE%'] = """ double *error_arr, int *points, int *next_index_past,""" # INITIALIZATION code['%CRITERION_INIT%'] = """ double error = error_arr[neuron_index]; int next_index=*next_index_past; int point=points[next_index]; """ # TIMESTEP #(T >= onset)&(Tdelayilevel*self.level_duration,temp<=(ilevel+1)*self.level_duration) # print where(ind)[0] self.target_count_level[ilevel,itrial]=len(where(ind)[0]) # First target spikes (needed for the computation of # the target train firing rates) # self.first_target_spike = zeros(self.N) self.last_spike_allowed = ones(self.N, dtype='bool') self.next_spike_allowed = ones(self.N, dtype='bool') if 1: #threadIdx.x+blockDim.x*itrial def get_cuda_code(self): block_size=32 code = {} # DECLARATION code['%CRITERION_DECLARE%'] = """ int *spikecount, // Number of spikes produced by each neuron int *num_coincidences, // Count of coincidences for each neuron """ if self.algorithm == 'exclusive': code['%CRITERION_DECLARE%'] += """ int *sp_trial_indices, // int *last_spike_time_arr, // pass zero array int *next_spike_time_arr, bool *last_spike_allowed_arr, bool *next_spike_allowed_arr, int *spike_count_level, int *current_level_index, """ # INITIALIZATION code['%CRITERION_INIT%'] = """ int ntrials=%i;//*ntrial; int nlevel=%i; int level_duration=%i; int level_ind = current_level_index[neuron_index]; int nspikes_level = spike_count_level[neuron_index*nlevel+level_ind]; int nspikes = spikecount[neuron_index]; __shared__ int ncoinc[%i]; __shared__ int sp_trial_indices_temp[%i]; __shared__ int last_spike_time[%i]; __shared__ int next_spike_time[%i]; __shared__ bool last_spike_allowed[%i]; __shared__ bool next_spike_allowed[%i]; for(int itrial=0;itrial=Tspike; bool near_next_spike = next_spike_time[threadIdx.x*ntrials+itrial]-%d<=Tspike; near_last_spike = near_last_spike && has_spiked; near_next_spike = near_next_spike && has_spiked; ncoinc[threadIdx.x*ntrials+itrial] +=(near_last_spike&&last_spike_allowed[threadIdx.x*ntrials+itrial]) || (near_next_spike&&next_spike_allowed[threadIdx.x*ntrials+itrial]); bool near_both_allowed = (near_last_spike&&last_spike_allowed[threadIdx.x*ntrials+itrial]) && (near_next_spike&&next_spike_allowed[threadIdx.x*ntrials+itrial]); last_spike_allowed[threadIdx.x*ntrials+itrial] = last_spike_allowed[threadIdx.x*ntrials+itrial] && !near_last_spike; next_spike_allowed[threadIdx.x*ntrials+itrial] = (next_spike_allowed[threadIdx.x*ntrials+itrial] && !near_next_spike) || near_both_allowed; if(Tspike>=next_spike_time[threadIdx.x*ntrials+itrial]){ sp_trial_indices_temp[threadIdx.x*ntrials+itrial]++; last_spike_time[threadIdx.x*ntrials+itrial] = next_spike_time[threadIdx.x*ntrials+itrial]; next_spike_time[threadIdx.x*ntrials+itrial] = spiketimes[sp_trial_indices_temp[threadIdx.x*ntrials+itrial]+1]; } last_spike_allowed[threadIdx.x*ntrials+itrial] = next_spike_allowed[threadIdx.x*ntrials+itrial]; next_spike_allowed[threadIdx.x*ntrials+itrial] = true; } nspikes += has_spiked*(T>=onset); if (T>(level_ind+1)*level_duration) { spike_count_level[neuron_index*nlevel+level_ind]=nspikes_level; level_ind+=1; nspikes_level=spike_count_level[neuron_index*nlevel+level_ind]; } nspikes_level += has_spiked*(T>=onset); """% (self.delta, self.delta) # FINALIZATION code['%CRITERION_END%'] = """ spike_count_level[neuron_index*nlevel+level_ind]=nspikes_level; if (Tend>(level_ind+1)*level_duration) { level_ind+=1; } for(int itrial=0;itrial=Tspike; bool near_next_spike = next_spike_time_arr[neuron_index*ntrials+itrial]-%d<=Tspike; near_last_spike = near_last_spike && has_spiked; near_next_spike = near_next_spike && has_spiked; num_coincidences[neuron_index*ntrials+itrial] +=(near_last_spike&&last_spike_allowed_arr[neuron_index*ntrials+itrial]) || (near_next_spike&&next_spike_allowed_arr[neuron_index*ntrials+itrial]); bool near_both_allowed = (near_last_spike&&last_spike_allowed_arr[neuron_index*ntrials+itrial]) && (near_next_spike&&next_spike_allowed_arr[neuron_index*ntrials+itrial]); last_spike_allowed_arr[neuron_index*ntrials+itrial] = last_spike_allowed_arr[neuron_index*ntrials+itrial] && !near_last_spike; next_spike_allowed_arr[neuron_index*ntrials+itrial] = (next_spike_allowed_arr[neuron_index*ntrials+itrial] && !near_next_spike) || near_both_allowed; if(Tspike>=next_spike_time_arr[neuron_index*ntrials+itrial]){ sp_trial_indices[neuron_index*ntrials+itrial]+=1; last_spike_time_arr[neuron_index*ntrials+itrial] = next_spike_time_arr[neuron_index*ntrials+itrial]; next_spike_time_arr[neuron_index*ntrials+itrial] = spiketimes[sp_trial_indices[neuron_index*ntrials+itrial]+1]; } last_spike_allowed_arr[neuron_index*ntrials+itrial] = next_spike_allowed_arr[neuron_index*ntrials+itrial]; next_spike_allowed_arr[neuron_index*ntrials+itrial] = true; } nspikes += has_spiked*(T>=onset); """% (self.delta, self.delta) # FINALIZATION code['%CRITERION_END%'] = """ spikecount[neuron_index] = nspikes; """ return code def initialize_cuda_variables(self): """ Initialize GPU variables to pass to the kernel """ self.spike_count_gpu = gpuarray.to_gpu(zeros(self.N, dtype=int32)) self.coincidences_gpu = gpuarray.to_gpu(zeros(self.N*self.ntrials, dtype=int32)) self.sp_trial_indices_gpu = gpuarray.to_gpu(array(self.trials_offset,dtype=int32)) self.last_spike_time_gpu = gpuarray.to_gpu(zeros(self.N*self.ntrials, dtype=int32)) self.next_spike_time_gpu = gpuarray.to_gpu(zeros(self.N*self.ntrials, dtype=int32)) self.last_spike_allowed_arr = gpuarray.to_gpu(zeros(self.N*self.ntrials, dtype=bool)) self.next_spike_allowed_arr = gpuarray.to_gpu(ones(self.N*self.ntrials, dtype=bool)) self.spike_count_level_gpu = gpuarray.to_gpu(zeros(self.N*self.nlevels, dtype=int32)) self.current_level_index_gpu = gpuarray.to_gpu(zeros(self.N, dtype=int32)) def get_kernel_arguments(self): """ Return a list of objects to pass to the CUDA kernel. """ args = [self.spike_count_gpu, self.coincidences_gpu,self.sp_trial_indices_gpu,self.last_spike_time_gpu,self.next_spike_time_gpu] args += [self.last_spike_allowed_arr,self.next_spike_allowed_arr,self.spike_count_level_gpu,self.current_level_index_gpu] return args def update_gpu_values(self): """ Call gpuarray.get() on final values, so that get_values() returns updated values. """ self.coincidences = self.coincidences_gpu.get() self.spike_count = self.spike_count_gpu.get() self.spike_count_level = self.spike_count_level_gpu.get() # print self.sp_trial_indices_gpu.get() def get_values(self): return (self.coincidences, self.spike_count) def normalize(self, values): coincidence_count = values[0] spike_count = values[1] spike_count_temp = repeat(spike_count,self.ntrials) # self.target_count_temp = tile(self.target_count,self.N) delta = self.delta*self.dt if self.ntrials>1: coincidence_count = sum(reshape(coincidence_count,(self.N,self.ntrials)),axis=1) gamma = get_gamma_factor(coincidence_count, spike_count*self.ntrials, sum(self.target_count), array(mean(self.target_rate)), delta) self.spike_count_level=reshape(self.spike_count_level,(self.N,self.nlevels)) # print self.target_count_level.shape,sum(self.target_count_level,axis=1) # print self.spike_count_level-sum(self.target_count_level,axis=1) # print abs(self.spike_count_level*self.ntrials-sum(self.target_count_level,axis=1))/sum(self.target_count_level,axis=1) # print self.target_count_level if self.fr_weight!=0: gamma = gamma - self.fr_weight*mean(abs(self.spike_count_level*self.ntrials-sum(self.target_count_level,axis=1))/sum(self.target_count_level,axis=1),axis=1) return gamma class GammaFactor2(CriterionStruct): def __init__(self, delta = 4*ms,fr_weight=0, coincidence_count_algorithm = 'exclusive',nlevels=1,level_duration=100): self.type = 'spikes' self.delta = delta self.nlevels = nlevels self.fr_weight = fr_weight self.level_duration=level_duration self.coincidence_count_algorithm = coincidence_count_algorithm class GammaFactorCriterion(Criterion): """ Coincidence counter class. Counts the number of coincidences between the spikes of the neurons in the network (model spikes), and some user-specified data spike trains (target spikes). This number is defined as the number of target spikes such that there is at least one model spike within +- ``delta``, where ``delta`` is the half-width of the time window. Initialised as:: cc = CoincidenceCounter(source, data, delta = 4*ms) with the following arguments: ``source`` A :class:`NeuronGroup` object which neurons are being monitored. ``data`` The list of spike times. Several spike trains can be passed in the following way. Define a single 1D array ``data`` which contains all the target spike times one after the other. Now define an array ``spiketimes_offset`` of integers so that neuron ``i`` should be linked to target train: ``data[spiketimes_offset[i]], data[spiketimes_offset[i]+1]``, etc. It is essential that each spike train with the spiketimes array should begin with a spike at a large negative time (e.g. -1*second) and end with a spike that is a long time after the duration of the run (e.g. duration+1*second). ``delta=4*ms`` The half-width of the time window for the coincidence counting algorithm. ``spiketimes_offset`` A 1D array, ``spiketimes_offset[i]`` is the index of the first spike of the target train associated to neuron i. ``spikedelays`` A 1D array with spike delays for each neuron. All spikes from the target train associated to neuron i are shifted by ``spikedelays[i]``. ``coincidence_count_algorithm`` If set to ``'exclusive'``, the algorithm cannot count more than one coincidence for each model spike. If set to ``'inclusive'``, the algorithm can count several coincidences for a single model spike. ``onset`` A scalar value in seconds giving the start of the counting: no coincidences are counted before ``onset``. Has three attributes: ``coincidences`` The number of coincidences for each neuron of the :class:`NeuronGroup`. ``coincidences[i]`` is the number oflength[i]`` is the spike count for n i. ``target_length`` The number of spikes in the target spike train associated to each neuron. """ type = 'spikes' def initialize(self, delta=4*ms, fr_weight=0,coincidence_count_algorithm='exclusive'): self.algorithm = coincidence_count_algorithm self.delta = int(rint(delta / self.dt)) self.fr_weight = fr_weight self.spike_count = zeros(self.N, dtype='int') self.coincidences = zeros(self.N, dtype='int') self.spiketime_index = self.spikes_offset self.last_spike_time = array(rint(self.spikes_inline[self.spiketime_index] / self.dt), dtype=int) self.next_spike_time = array(rint(self.spikes_inline[self.spiketime_index + 1] / self.dt), dtype=int) # First target spikes (needed for the computation of # the target train firing rates) # self.first_target_spike = zeros(self.N) self.last_spike_allowed = ones(self.N, dtype='bool') self.next_spike_allowed = ones(self.N, dtype='bool') def spike_call(self, spiking_neurons): dt = self.dt t = self.step()*dt spiking_neurons = array(spiking_neurons) if len(spiking_neurons)>0: if t >= self.onset: self.spike_count[spiking_neurons] += 1 T_spiking = array(rint((t + self.delays[spiking_neurons]) / dt), dtype=int) remaining_neurons = spiking_neurons remaining_T_spiking = T_spiking while True: remaining_indices, = (remaining_T_spiking > self.next_spike_time[remaining_neurons]).nonzero() if len(remaining_indices): indices = remaining_neurons[remaining_indices] self.spiketime_index[indices] += 1 self.last_spike_time[indices] = self.next_spike_time[indices] self.next_spike_time[indices] = array(rint(self.spikes_inline[self.spiketime_index[indices] + 1] / dt), dtype=int) if self.algorithm == 'exclusive': self.last_spike_allowed[indices] = self.next_spike_allowed[indices] self.next_spike_allowed[indices] = True remaining_neurons = remaining_neurons[remaining_indices] remaining_T_spiking = remaining_T_spiking[remaining_indices] else: break # Updates coincidences count near_last_spike = self.last_spike_time[spiking_neurons] + self.delta >= T_spiking near_next_spike = self.next_spike_time[spiking_neurons] - self.delta <= T_spiking last_spike_allowed = self.last_spike_allowed[spiking_neurons] next_spike_allowed = self.next_spike_allowed[spiking_neurons] I = (near_last_spike & last_spike_allowed) | (near_next_spike & next_spike_allowed) if t >= self.onset: self.coincidences[spiking_neurons[I]] += 1 if self.algorithm == 'exclusive': near_both_allowed = (near_last_spike & last_spike_allowed) & (near_next_spike & next_spike_allowed) self.last_spike_allowed[spiking_neurons] = last_spike_allowed & -near_last_spike self.next_spike_allowed[spiking_neurons] = (next_spike_allowed & -near_next_spike) | near_both_allowed def get_cuda_code(self): code = {} # DECLARATION code['%CRITERION_DECLARE%'] = """ int *spikecount, // Number of spikes produced by each neuron int *num_coincidences, // Count of coincidences for each neuron """ if self.algorithm == 'exclusive': code['%CRITERION_DECLARE%'] += """ bool *last_spike_allowed_arr, bool *next_spike_allowed_arr, """ # INITIALIZATION code['%CRITERION_INIT%'] = """ int ncoinc = num_coincidences[neuron_index]; int nspikes = spikecount[neuron_index]; int last_spike_time = spiketimes[spiketime_index]; int next_spike_time = spiketimes[spiketime_index+1]; """ if self.algorithm == 'exclusive': code['%CRITERION_INIT%'] += """ bool last_spike_allowed = last_spike_allowed_arr[neuron_index]; bool next_spike_allowed = next_spike_allowed_arr[neuron_index]; """ # TIMESTEP code['%CRITERION_TIMESTEP%'] = """ const int Tspike = T+spikedelay; """ if self.algorithm == 'inclusive': code['%CRITERION_TIMESTEP%'] += """ ncoinc += has_spiked && (((last_spike_time+%d)>=Tspike) || ((next_spike_time-%d)<=Tspike)); """ % (self.delta, self.delta) if self.algorithm == 'exclusive': code['%CRITERION_TIMESTEP%'] += """ bool near_last_spike = last_spike_time+%d>=Tspike; bool near_next_spike = next_spike_time-%d<=Tspike; near_last_spike = near_last_spike && has_spiked; near_next_spike = near_next_spike && has_spiked; ncoinc += (near_last_spike&&last_spike_allowed) || (near_next_spike&&next_spike_allowed); bool near_both_allowed = (near_last_spike&&last_spike_allowed) && (near_next_spike&&next_spike_allowed); last_spike_allowed = last_spike_allowed && !near_last_spike; next_spike_allowed = (next_spike_allowed && !near_next_spike) || near_both_allowed; """ % (self.delta, self.delta) code['%CRITERION_TIMESTEP%'] += """ nspikes += has_spiked*(T>=onset); if(Tspike>=next_spike_time){ spiketime_index++; last_spike_time = next_spike_time; next_spike_time = spiketimes[spiketime_index+1]; """ if self.algorithm == 'exclusive': code['%CRITERION_TIMESTEP%'] += """ last_spike_allowed = next_spike_allowed; next_spike_allowed = true; """ code['%CRITERION_TIMESTEP%'] += """ } """ # FINALIZATION code['%CRITERION_END%'] = """ num_coincidences[neuron_index] = ncoinc; spikecount[neuron_index] = nspikes; """ if self.algorithm == 'exclusive': code['%CRITERION_END%'] += """ last_spike_allowed_arr[neuron_index] = last_spike_allowed; next_spike_allowed_arr[neuron_index] = next_spike_allowed; """ return code def initialize_cuda_variables(self): """ Initialize GPU variables to pass to the kernel """ self.spike_count_gpu = gpuarray.to_gpu(zeros(self.N, dtype=int32)) self.coincidences_gpu = gpuarray.to_gpu(zeros(self.N, dtype=int32)) if self.algorithm == 'exclusive': self.last_spike_allowed_arr = gpuarray.to_gpu(zeros(self.N, dtype=bool)) self.next_spike_allowed_arr = gpuarray.to_gpu(ones(self.N, dtype=bool)) def get_kernel_arguments(self): """ Return a list of objects to pass to the CUDA kernel. """ args = [self.spike_count_gpu, self.coincidences_gpu] if self.algorithm == 'exclusive': args += [self.last_spike_allowed_arr, self.next_spike_allowed_arr] return args def update_gpu_values(self): """ Call gpuarray.get() on final values, so that get_values() returns updated values. """ self.coincidences = self.coincidences_gpu.get() self.spike_count = self.spike_count_gpu.get() def get_values(self): return (self.coincidences, self.spike_count) def normalize(self, values): coincidence_count = values[0] spike_count = values[1] delta = self.delta*self.dt gamma = get_gamma_factor(coincidence_count, spike_count, self.target_spikes_count, self.target_spikes_rate, delta) gamma = gamma - self.fr_weight*abs(spike_count-self.target_spikes_count)/self.target_spikes_count # print 'fr',abs(spike_count-self.target_spikes_count)/self.target_spikes_count,'fr' return gamma class GammaFactor(CriterionStruct): def __init__(self, delta = 4*ms,fr_weight=0, coincidence_count_algorithm = 'exclusive'): self.type = 'spikes' self.delta = delta self.fr_weight = fr_weight self.coincidence_count_algorithm = coincidence_count_algorithm class VanRossumCriterion(Criterion): type = 'spikes' def initialize(self, tau): self.delay_range =max(self.delays)- min(self.delays)#delay range self.min_delay = abs(min(self.delays))#minimum of possible delay self.distance_vector=zeros(self.N) self.nbr_neurons_group = self.N/self.K eqs=""" dv/dt=(-v)/tau: volt """ # network to convolve target spikes with the kernel self.input_target=SpikeGeneratorGroup(self.K,self.spikes,clock=self.group.clock) self.kernel_target=NeuronGroup(self.K,model=eqs,clock=self.group.clock) self.C_target = DelayConnection(self.input_target, self.kernel_target, 'v', structure='dense', max_delay=self.min_delay) self.C_target.connect_one_to_one(self.input_target,self.kernel_target) self.C_target.delay = self.min_delay*ones_like(self.C_target.delay) # network to convolve population spikes with the kernel self.kernel_population=NeuronGroup(self.N,model=eqs,clock=self.group.clock) self.C_population = DelayConnection(self.group, self.kernel_population, 'v', structure='sparse', max_delay=self.delay_range) for iN in xrange(self.N): self.C_population.delay[iN,iN] = diagflat(self.min_delay + self.delays[iN]) self.C_population.connect_one_to_one(self.group,self.kernel_population) self.spikecount_mon = SpikeCounter(self.group) self.contained_objects = [self.kernel_population,self.C_population,self.spikecount_mon,self.input_target,self.C_target,self.kernel_target] self.dt=self.group.clock.dt self.tau = tau def timestep_call(self): trace_population = self.kernel_population.state_('v') trace_target = self.kernel_target.state_('v') for igroup in xrange(self.K): self.distance_vector[igroup*self.nbr_neurons_group:(1+igroup)*self.nbr_neurons_group] += (trace_population[igroup*self.nbr_neurons_group:(1+igroup)*self.nbr_neurons_group]-trace_target[igroup])**2 self.spikecount=self.spikecount_mon.count def get_values(self): return (self.distance_vector) def normalize(self, distance_vector): distance_vector[nonzero(self.spikecount==0)] = inf #distance_vector[nonzero(self.spikecount==1)] = inf return 1-distance_vector*self.group.clock.dt def get_cuda_code(self): code = {} # DECLARATION code['%CRITERION_DECLARE%'] = """ double *error_arr, int *count_arr, """ # INITIALIZATION code['%CRITERION_INIT%'] = """ double error = error_arr[neuron_index]; double V_kernel =0; double V_target =0; int count=count_arr[neuron_index]; int next_spike_time = spiketimes[spiketime_index+1]; """ # TIMESTEP # code['%CRITERION_TIMESTEP%'] = """ # """ code['%CRITERION_TIMESTEP%'] = """ const int Tspike = T+spikedelay; if(has_spiked){ // If the neuron has spiked V_kernel+=1; } count += has_spiked*(T>=onset); if(Tspike>=next_spike_time){ V_target+=1; spiketime_index++; next_spike_time = spiketimes[spiketime_index+1]; } V_kernel=V_kernel*exp(-%.8f/%.6f); V_target=V_target*exp(-%.8f/%.6f); error+=pow(V_kernel-V_target,2); """ % (self.dt,self.tau,self.dt,self.tau) # FINALIZATION code['%CRITERION_END%'] = """ error_arr[neuron_index] = error; count_arr[neuron_index] = count; """ return code def initialize_cuda_variables(self): """ Initialize GPU variables to pass to the kernel """ self.error_gpu = gpuarray.to_gpu(zeros(self.N, dtype=double)) self.count_gpu = gpuarray.to_gpu(zeros(self.N, dtype=int32)) def get_kernel_arguments(self): """ Return a list of objects to pass to the CUDA kernel. """ args = [self.error_gpu,self.count_gpu] return args def update_gpu_values(self): """ Call gpuarray.get() on final values, so that get_values() returns updated values. """ self.distance_vector = self.error_gpu.get() self.spikecount = self.count_gpu.get() class VanRossum(CriterionStruct): def __init__(self, tau): self.type = 'spikes' self.tau = tau class BretteCriterion(Criterion): type = 'spikes' def initialize(self,tau_metric): # tau_metric=tau_metric** self.delay_range =max(self.delays)- min(self.delays)#delay range self.min_delay = abs(min(self.delays))#minimum of possible delay self.corr_vector=zeros(self.N) self.norm_pop = zeros(self.N) self.norm_target = zeros(self.N) self.nbr_neurons_group = self.N/self.K eqs=""" tau:second dv/dt=(-v)/tau: volt """ # network to convolve target spikes with the kernel self.input_target=SpikeGeneratorGroup(self.K,self.spikes,clock=self.group.clock) self.kernel_target=NeuronGroup(self.N,model=eqs,clock=self.group.clock) self.C_target = DelayConnection(self.input_target, self.kernel_target, 'v', structure='sparse', max_delay=self.min_delay) self.kernel_target.tau=tau_metric for igroup in xrange(self.K): self.C_target.W[igroup,igroup*self.nbr_neurons_group:(1+igroup)*self.nbr_neurons_group] = ones(self.nbr_neurons_group) self.C_target.delay[igroup,igroup*self.nbr_neurons_group:(1+igroup)*self.nbr_neurons_group] = self.min_delay * ones(self.nbr_neurons_group) # network to convolve population spikes with the kernel self.kernel_population=NeuronGroup(self.N,model=eqs,clock=self.group.clock) self.C_population = DelayConnection(self.group, self.kernel_population, 'v', structure='sparse', max_delay=self.delay_range) for iN in xrange(self.N): self.C_population.delay[iN,iN] = diagflat(self.min_delay + self.delays[iN]) self.C_population.connect_one_to_one(self.group,self.kernel_population) self.kernel_population.tau=tau_metric self.spikecount_mon = SpikeCounter(self.group) self.contained_objects = [self.kernel_population,self.C_population,self.spikecount_mon,self.input_target,self.C_target,self.kernel_target] self.tau_metric=tau_metric self.dt=self.group.clock.dt def timestep_call(self): trace_population = self.kernel_population.state_('v') trace_target = self.kernel_target.state_('v') self.corr_vector += trace_population*trace_target self.norm_pop += trace_population**2 self.norm_target += trace_target**2 self.spikecount=self.spikecount_mon.count def get_values(self): #print self.corr_vector,self.norm_pop,self.norm_target return (self.corr_vector,self.norm_pop,self.norm_target) def normalize(self, values): corr_vector=values[0] norm_pop=values[1] norm_target=values[2] corr_vector[nonzero(self.spikecount==0)] = -inf temp=self.corr_vector/self.tau_metric/sqrt(norm_pop)/sqrt(norm_target) temp[isnan(temp)] = -inf return temp def get_cuda_code(self): code = {} # DECLARATION code['%CRITERION_DECLARE%'] = """ double *error_arr, int *count_arr, double *tau_metric_arr, double *norm_pop_arr, double *norm_target_arr, """ # INITIALIZATION code['%CRITERION_INIT%'] = """ double error = error_arr[neuron_index]; double V_kernel =0; double V_target =0; double tau_metric=tau_metric_arr[neuron_index]; int count=count_arr[neuron_index]; int next_spike_time = spiketimes[spiketime_index+1]; double norm_pop=norm_pop_arr[neuron_index]; double norm_target=norm_target_arr[neuron_index]; """ # TIMESTEP # code['%CRITERION_TIMESTEP%'] = """ # """ code['%CRITERION_TIMESTEP%'] = """ const int Tspike = T+spikedelay; if(has_spiked){ // If the neuron has spiked V_kernel+=1; } count += has_spiked*(T>=onset); if(Tspike>=next_spike_time){ V_target+=1; spiketime_index++; next_spike_time = spiketimes[spiketime_index+1]; } V_kernel=V_kernel*exp(-%.8f/tau_metric); V_target=V_target*exp(-%.8f/tau_metric); error+=V_kernel*V_target; norm_pop+=pow(V_kernel,2); norm_target +=pow(V_target,2); """ % (self.dt,self.dt) # FINALIZATION code['%CRITERION_END%'] = """ error_arr[neuron_index] = error; count_arr[neuron_index] = count; norm_pop_arr[neuron_index] = norm_pop; norm_target_arr[neuron_index] = norm_target; """ return code def initialize_cuda_variables(self): """ Initialize GPU variables to pass to the kernel """ self.error_gpu = gpuarray.to_gpu(zeros(self.N, dtype=double)) self.count_gpu = gpuarray.to_gpu(zeros(self.N, dtype=int32)) self.tau_metric_gpu = gpuarray.to_gpu(array(self.tau_metric, dtype=double)) self.norm_pop_gpu = gpuarray.to_gpu(zeros(self.N, dtype=double)) self.norm_target_gpu = gpuarray.to_gpu(zeros(self.N, dtype=double)) def get_kernel_arguments(self): """ Return a list of objects to pass to the CUDA kernel. """ args = [self.error_gpu,self.count_gpu,self.tau_metric_gpu,self.norm_pop_gpu,self.norm_target_gpu] return args def update_gpu_values(self): """ Call gpuarray.get() on final values, so that get_values() returns updated values. """ self.corr_vector = self.error_gpu.get() self.spikecount = self.count_gpu.get() self.norm_pop = self.norm_pop_gpu.get() self.norm_target = self.norm_target_gpu.get() class Brette(CriterionStruct): def __init__(self): self.type = 'spikes' brian-1.4.3/brian/experimental/modelfitting/gpu_modelfitting.py000077500000000000000000000614151266062647100250250ustar00rootroot00000000000000from brian import Equations, NeuronGroup, Clock, CoincidenceCounter, Network, zeros, array, \ ones, kron, ms, second, concatenate, hstack, sort, nonzero, diff, TimedArray, \ reshape, sum, log, Monitor, NetworkOperation, defaultclock, linspace, vstack, \ arange, sort_spikes, rint, SpikeMonitor, Connection, Threshold, Reset, \ int32, double, VariableReset, StringReset, VariableThreshold, StringThreshold, \ Refractoriness from brian.tools.statistics import firing_rate, get_gamma_factor from playdoh import * import brian.optimiser as optimiser import pycuda.driver as drv import pycuda from pycuda.gpuarray import GPUArray from pycuda import gpuarray try: from pycuda.compiler import SourceModule except ImportError: from pycuda.driver import SourceModule from numpy import * from brian.experimental.codegen.integration_schemes import * from brian.experimental.codegen.codegen_gpu import * import re __all__ = ['GPUModelFitting', 'euler_scheme', 'rk2_scheme', 'exp_euler_scheme','close_cuda' ] if drv.get_version() == (2, 0, 0): # cuda version default_precision = 'float' elif drv.get_version() > (2, 0, 0): default_precision = 'double' else: raise Exception, "CUDA 2.0 required" class ModelfittingGPUCodeGenerator(GPUCodeGenerator): def generate(self, eqs, scheme): vartype = self.vartype() code = '' for line in self.scheme(eqs, scheme).split('\n'): line = line.strip() if line: code += ' ' + line + '\n' return code BLOCKSIZE = 256 def get_cuda_template(): return """ __global__ void runsim( // ITERATIONS int Tstart, int Tend, // Start, end time as integer (t=T*dt) int duration, // Total duration as integer // STATE VARIABLES %SCALAR% *state_vars, // State variables are offset from this // INPUT PARAMETERS double *I_arr, // Input current int *I_arr_offset, // Input current offset (for separate input // currents for each neuron) // DELAYS PARAMETERS int *spikedelay_arr, // Integer delay for each spike // REFRACTORY PARAMETERS int *refractory_arr, // Integer refractory times int *next_allowed_spiketime_arr, // Integer time of the next allowed spike (for refractoriness) // CRITERION SPECIFIC PARAMETERS %CRITERION_DECLARE% // DATA DECLARE %DATA_DECLARE% // STATEMONITOR DECLARE %STATEMONITOR_DECLARE% // SPIKEMONITOR DECLARE %SPIKEMONITOR_DECLARE% // MISC PARAMETERS int onset // Time onset (only count spikes from here onwards) ) { // NEURON INDEX const int neuron_index = blockIdx.x * blockDim.x + threadIdx.x; if(neuron_index>=%NUM_NEURONS%) return; // EXTRACT STATE VARIABLES %EXTRACT_STATE_VARIABLES% // LOAD VARIABLES %LOAD_VARIABLES% // DATA INIT %DATA_INIT% // STATEMONITOR INIT %STATEMONITOR_INIT% // SPIKEMONITOR INIT %SPIKEMONITOR_INIT% // CRITERION INITIALIZATION %CRITERION_INIT% // INPUT INITIALIZATION int I_offset0 = I_arr_offset[blockIdx.x * blockDim.x]; // I_offset of the first thread in the block int I_offset = I_arr_offset[neuron_index]; // DELAYS INITIALIZATION int spikedelay = spikedelay_arr[neuron_index]; // REFRACTORY INITIALIZATION const int refractory = refractory_arr[neuron_index]; int next_allowed_spiketime = next_allowed_spiketime_arr[neuron_index]; %SCALAR% ${input_var} = 0; for(int T=Tstart; T=onset for neurons in the first group if ((neuron_index>=%NUM_NEURONS_FIRSTGROUP%)|(T>=onset)) { %STATE_UPDATE% } // THRESHOLD const bool is_refractory = (T<=next_allowed_spiketime); const bool has_spiked = (%THRESHOLD%)&&!is_refractory; // RESET if(has_spiked||is_refractory) { %RESET1% } if(has_spiked) { next_allowed_spiketime = T+refractory; %RESET2% %SPIKEMONITOR_UPDATE% } // DATA UPDATE %DATA_UPDATE% // STATEMONITOR UPDATE %STATEMONITOR_UPDATE% // CRITERION TIMESTEP %CRITERION_TIMESTEP% } // STORE VARIABLES %STORE_VARIABLES% // STATEMONITOR END %STATEMONITOR_END% // SPIKEMONITOR END %SPIKEMONITOR_END% // CRITERION END %CRITERION_END% next_allowed_spiketime_arr[neuron_index] = next_allowed_spiketime; // END %DATA_END% } """ class GPUModelFitting(object): ''' Model fitting class to interface with GPU Initialisation arguments: ``G`` The initialised NeuronGroup to work from. ``eqs`` The equations defining the NeuronGroup. ``I``, ``I_offset`` The current array and offsets (see below). ``spiketimes``, ``spiketimes_offset`` The spike times array and offsets (see below). ``spikedelays``, Array of delays for each neuron. ``refractory``, Array of refractory periods, or a single value. ``delta`` The half-width of the coincidence window. ``precision`` Should be 'float' or 'double' - by default the highest precision your GPU supports. ``coincidence_count_algorithm`` Should be 'inclusive' if multiple predicted spikes can match one target spike, or 'exclusive' (default) if multiple predicted spikes can match only one target spike (earliest spikes are matched first). Methods: ``reinit_vars(I, I_offset, spiketimes, spiketimes_offset, spikedelays)`` Reinitialises all the variables, counters, etc. The state variable values are copied from the NeuronGroup G again, and the variables I, I_offset, etc. are copied from the method arguments. ``launch(duration[, stepsize])`` Runs the kernel on the GPU for simulation time duration. If ``stepsize`` is given, the simulation is broken into pieces of that size. This is useful on Windows because driver limitations mean that individual GPU kernel launches cannot last more than a few seconds without causing a crash. Attributes: ``coincidence_count`` An array of the number of coincidences counted for each neuron. ``spike_count`` An array of the number of spikes counted for each neuron. **Details** The equations for the NeuronGroup can be anything, but they will be solved with the Euler method. One restriction is that there must be a parameter named I which is the time varying input current. (TODO: support for multiple input currents? multiple names?) The current I is passed to the GPU in the following way. Define a single 1D array I which contains all the time varying current arrays one after the other. Now define an array I_offset of integers so that neuron i should see currents: I[I_offset[i]], I[I_offset[i]+1], I[I_offset[i]+2], etc. The experimentally recorded spike times should be passed in a similar way, put all the spike times in a single array and pass an offsets array spiketimes_offset. One difference is that it is essential that each spike train with the spiketimes array should begin with a spike at a large negative time (e.g. -1*second) and end with a spike that is a long time after the duration of the run (e.g. duration+1*second). The GPU uses this to mark the beginning and end of the train rather than storing the number of spikes for each train. ''' def __init__(self, G, eqs, criterion, # Criterion object input_var, subpopsize, onset=0*ms, precision=default_precision, statemonitor_var=None, spikemonitor = False, nbr_spikes = 200, duration=None, scheme=euler_scheme, stand_alone=False ): eqs.prepare() self.precision = precision self.scheme = scheme if precision == 'double': self.mydtype = float64 else: self.mydtype = float32 self.stand_alone=stand_alone self.N = len(G) self.dt = G.clock.dt self.onset = onset self.eqs = eqs self.G = G self.spikemonitor = spikemonitor self.nbr_spikes = nbr_spikes self.subpopsize = subpopsize self.duration = int(ceil(duration/self.dt)) self.input_var = input_var self.statemonitor_var = statemonitor_var self.criterion = criterion self.generate_code() def generate_threshold_code(self, src): eqs = self.eqs threshold = self.G._threshold if threshold.__class__ is Threshold: state = threshold.state if isinstance(state, int): state = eqs._diffeq_names[state] threshold = state + '>' + str(float(threshold.threshold)) elif isinstance(threshold, VariableThreshold): state = threshold.state if isinstance(state, int): state = eqs._diffeq_names[state] threshold = state + '>' + threshold.threshold_state elif isinstance(threshold, StringThreshold): namespace = threshold._namespace expr = threshold._expr all_variables = eqs._eq_names + eqs._diffeq_names + eqs._alias.keys() + ['t'] expr = optimiser.freeze(expr, all_variables, namespace) threshold = expr else: raise ValueError('Threshold must be constant, VariableThreshold or StringThreshold.') # Substitute threshold src = src.replace('%THRESHOLD%', threshold) return src def generate_reset_code(self, src): eqs = self.eqs reset = self.G._resetfun # print eqs if reset.__class__ is Refractoriness: state = reset.state if isinstance(state, int): state = eqs._diffeq_names[state] reset = state + ' = ' + str(float(reset.resetvalue)) elif reset.__class__ is Reset: state = reset.state if isinstance(state, int): state = eqs._diffeq_names[state] reset = state + ' = ' + str(float(reset.resetvalue)) elif isinstance(reset, VariableReset): state = reset.state if isinstance(state, int): state = eqs._diffeq_names[state] reset = state + ' = ' + reset.resetvaluestate elif isinstance(reset, StringReset): namespace = reset._namespace expr = reset._expr all_variables = eqs._eq_names + eqs._diffeq_names + eqs._alias.keys() + ['t'] expr = optimiser.freeze(expr, all_variables, namespace) reset = expr # self.reset = reset # Substitute reset reset = '\n '.join(line.strip() + ';' for line in reset.split('\n') if line.strip()) reset=reset.split('\n') src = src.replace('%RESET1%', reset[0]) reset = '\n '.join(line.strip()+'\n' for line in reset[1:]) src = src.replace('%RESET2%', reset) # print src return src def generate_data_code(self, src): # Substitute spikes/traces declare if self.criterion.type == 'spikes': data_declare = """ int *spiketimes, // Array of all spike times as integers (begin and // end each train with large negative value) int *spiketime_indices, // Pointer into above array for each neuron """ if self.criterion.type == 'traces': data_declare = """ double *traces_arr, int *traces_arr_offset, """ src = src.replace('%DATA_DECLARE%', data_declare) # Substitute spikes/traces init if self.criterion.type == 'spikes': data_init = """ int spiketime_index = spiketime_indices[neuron_index]; """ if self.criterion.type == 'traces': data_init = """ int trace_offset = traces_arr_offset[neuron_index]; double trace_value = 0.0; int Tdelay = 0; """ src = src.replace('%DATA_INIT%', data_init) # Substitute spikes/traces update if self.criterion.type == 'spikes': data_update = """ """ if self.criterion.type == 'traces': data_update = """ Tdelay = T+spikedelay; if ((Tdelay>=0)&(Tdelay=2.2 # pass self.block = (blocksize, 1, 1) self.grid = (int(ceil(float(self.N) / blocksize)), 1) self.kernel_func_kwds = {'block':self.block, 'grid':self.grid} mydtype = self.mydtype N = self.N eqs = self.eqs statevars_arr = gpuarray.to_gpu(array(self.G._S.flatten(), dtype=mydtype)) self.I = gpuarray.to_gpu(array(I, dtype=mydtype)) self.statevars_arr = statevars_arr self.I_offset = gpuarray.to_gpu(array(I_offset, dtype=int32)) # SPIKES if self.criterion.type == 'spikes': self.initialize_spikes(spiketimes, spiketimes_offset) # TRACES if self.criterion.type == 'traces': self.initialize_traces(traces, traces_offset) self.criterion.initialize_cuda_variables() self.initialize_delays(spikedelays) self.initialize_refractory(refractory) if self.statemonitor_var is not None: self.initialize_statemonitor() if self.spikemonitor is True: self.initialize_spikemonitor() self.initialize_kernel_arguments() def launch(self, duration, stepsize=128*ms): if stepsize is None: self.kernel_func(int32(0), int32(duration / self.dt), *self.kernel_func_args, **self.kernel_func_kwds) pycuda.context.synchronize() else: stepsize = int(stepsize / self.dt) duration = int(duration / self.dt) for Tstart in xrange(0, duration, stepsize): Tend = Tstart + min(stepsize, duration - Tstart) self.kernel_func(int32(Tstart), int32(Tend), int32(duration), *self.kernel_func_args, **self.kernel_func_kwds) pycuda.context.synchronize() # if self.stand_alone and 1: ## print 'close stand alone' ## drv.init() # pycuda.context.pop() # pycuda.context = None def get_statemonitor_values(self): values = [val.get().reshape((self.N, -1)) for val in self.statemonitor_values] if len(self.statemonitor_var)==1: return values[0] else: return values def get_spikemonitor_values(self): values = self.spikemonitor_values.get().reshape((self.N, -1)) return values def close_cuda(): """ Closes the current PyCUDA context. MUST be called at the end of the script. """ print 'close' log_debug("Trying to close current PyCUDA context") if pycuda.context is not None: try: log_debug("Closing current PyCUDA context") pycuda.context.pop() pycuda.context = None except: log_warn("A problem occurred when closing PyCUDA context") brian-1.4.3/brian/experimental/modelfitting/lp2bert.py000066400000000000000000000076731266062647100230420ustar00rootroot00000000000000 if criterion_name == 'LpError2': params['p'] = self.criterion.p params['varname'] = self.criterion.varname params['insets'] = self.criterion.insets params['outsets'] = self.criterion.outsets self.criterion_object = LpErrorCriterion2(**params) class LpErrorCriterion2(Criterion): type = 'traces' def initialize(self, p=2, varname='v',insets=None,outsets=None): """ Called at the beginning of every iteration. The keyword arguments here are specified in modelfitting.initialize_criterion(). """ self.p = double(p) self.varname = varname self._error = zeros((self.K, self.N)) self.insets = insets self.outsets = outsets self.next_index=0 self.insets = array(append(insets,inf),dtype=int) self.outsets = array(append(outsets,inf),dtype=int) def timestep_call(self): v = self.get_value(self.varname) t = self.step()+1 if t= self.duration: return if t>=self.insets[self.next_index]and t<=self.outsets[self.next_index]: if t==self.outsets[self.next_index]: self.next_index+=1 d = self.intdelays indices = (t-d>=0)&(t-d= onset)&(Tdelay= insets[next_index])&(T <= outsets[next_index])) { if (T == outsets[next_index]) {next_index+=1;} error = error + pow(abs(trace_value - %s), %.4f); } """ % (self.varname, self.p) # FINALIZATION code['%CRITERION_END%'] = """ error_arr[neuron_index] = error; """ return code def initialize_cuda_variables(self): """ Initialize GPU variables to pass to the kernel """ self.error_gpu = gpuarray.to_gpu(zeros(self.N, dtype=double)) self.insets_gpu = gpuarray.to_gpu(self.insets) self.outsets_gpu = gpuarray.to_gpu(self.outsets) def get_kernel_arguments(self): """ Return a list of objects to pass to the CUDA kernel. """ args = [self.error_gpu,self.insets_gpu,self.outsets_gpu] return args def update_gpu_values(self): """ Call gpuarray.get() on final values, so that get_values() returns updated values. """ self._error = self.error_gpu.get() print self._error def get_values(self): if self.K == 1: error = self._error.flatten() else: error = self._error return error # just the integral, for every slice def normalize(self, error): # error is now the combined error on the whole duration (sum on the slices) # HACK: 1- because modelfitting MAXIMIZES for now... # self._norm = sum(self.traces**self.p, axis=1)**(1./self.p) # norm of every trace return 1-(self.dt*error)**(1./self.p)#/self._norm class LpError2(CriterionStruct): """ Structure used by the users to specify a criterion """ def __init__(self, p = 2, varname = 'v',insets=None,outsets=None): self.type = 'trace' self.p = p self.insets = insets self.outsets = outsets self.varname = varname brian-1.4.3/brian/experimental/modelfitting/modelfitting.py000077500000000000000000000507041266062647100241510ustar00rootroot00000000000000from brian import Equations, NeuronGroup, Clock, CoincidenceCounter, Network, zeros, array, \ ones, kron, ms, second, concatenate, hstack, sort, nonzero, diff, TimedArray, \ reshape, sum, log, Monitor, NetworkOperation, defaultclock, linspace, vstack, \ arange, sort_spikes, rint, SpikeMonitor, Connection from brian.tools.statistics import firing_rate, get_gamma_factor try: from playdoh import * except Exception, e: print e raise ImportError("Playdoh must be installed (https://code.google.com/p/playdoh/)") try: import pycuda from gpu_modelfitting import GPUModelFitting can_use_gpu = True except ImportError: can_use_gpu = False from brian.experimental.codegen.integration_schemes import * from criteria import * from simulator import * import sys, cPickle __all__ = ['modelfitting', 'print_table', 'PSO', 'GA', 'CMAES', 'slice_trace', 'transform_spikes', 'MAXCPU', 'MAXGPU', 'GammaFactor','GammaFactor2', 'LpError','VanRossum','Brette', 'simulate', 'debug_level', 'info_level', 'warning_level', 'open_server'] class ModelFitting(Fitness): def initialize(self, **kwds): # Gets the key,value pairs in shared_data for key, val in self.shared_data.iteritems(): setattr(self, key, val) # Gets the key,value pairs in **kwds for key, val in kwds.iteritems(): setattr(self, key, val) self.model = cPickle.loads(self.model) if type(self.model) is str: self.model = Equations(self.model) self.simulator = Simulator(self.model, self.reset, self.threshold, inputs = self.inputs, input_var = self.input_var, dt = self.dt, refractory = self.refractory, max_refractory = self.max_refractory, spikes = self.spikes, traces = self.traces, groups = self.groups, slices = self.slices, overlap = self.overlap, onset = self.onset, neurons = self.nodesize, initial_values = self.initial_values, unit_type = self.unit_type, stepsize = self.stepsize, precision = self.precision, criterion = self.criterion, ntrials=self.ntrials, method = self.method ) def evaluate(self, **param_values): """ Use fitparams['delays'] to take delays into account Use fitparams['refractory'] to take refractory into account """ values = self.simulator.run(**param_values) return values def modelfitting(model=None, reset=None, threshold=None, refractory=0*ms, data=None, input_var='I', input=None, dt=None, popsize=1000, maxiter=10, slices=1, overlap=None, onset=None, initial_values=None, stepsize=100 * ms, unit_type=None, total_units=None, ntrials=1, cpu=None, gpu=None, precision='double', # set to 'float' or 'double' to specify single or double precision on the GPU machines=[], allocation=None, returninfo=False, scaling=None, algorithm=CMAES, async = None, criterion=None, optparams={}, method='Euler', **params): """ Model fitting function. Fits a spiking neuron model to electrophysiological data (injected current and spikes). See also the section :ref:`model-fitting-library` in the user manual. **Arguments** ``model`` An :class:`~brian.Equations` object containing the equations defining the model. ``reset`` A reset value for the membrane potential, or a string containing the reset equations. ``threshold`` A threshold value for the membrane potential, or a string containing the threshold equations. ``refractory`` The refractory period in second. If it's a single value, the same refractory will be used in all the simulations. If it's a list or a tuple, the fitting will also optimize the refractory period (see ``**params`` below). Warning: when using a refractory period, you can't use a custom reset, only a fixed one. ``data`` A list of spike times, or a list of several spike trains as a list of pairs (index, spike time) if the fit must be performed in parallel over several target spike trains. In this case, the modelfitting function returns as many parameters sets as target spike trains. ``input_var='I'`` The variable name used in the equations for the input current. ``input`` A vector of values containing the time-varying signal the neuron responds to (generally an injected current). ``dt`` The time step of the input (the inverse of the sampling frequency). ``**params`` The list of parameters to fit the model with. Each parameter must be set as follows: ``param_name=[bound_min, min, max, bound_max]`` where ``bound_min`` and ``bound_max`` are the boundaries, and ``min`` and ``max`` specify the interval from which the parameter values are uniformly sampled at the beginning of the optimization algorithm. If not using boundaries, set ``param_name=[min, max]``. Also, you can add a fit parameter which is a spike delay for all spikes : add the special parameter ``delays`` in ``**params``, for example ``modelfitting(..., delays=[-10*ms, 10*ms])``. You can also add fit the refractory period by specifying ``modelfitting(..., refractory=[-10*ms, 10*ms])``. ``popsize`` Size of the population (number of particles) per target train used by the optimization algorithm. ``maxiter`` Number of iterations in the optimization algorithm. ``optparams`` Optimization algorithm parameters. It is a dictionary: keys are parameter names, values are parameter values or lists of parameters (one value per group). This argument is specific to the optimization algorithm used. See :class:`PSO`, :class:`GA`, :class:`CMAES`. ``delta=4*ms`` The precision factor delta (a scalar value in second). ``slices=1`` The number of time slices to use. ``overlap=0*ms`` When using several time slices, the overlap between consecutive slices, in seconds. ``initial_values`` A dictionary containing the initial values for the state variables. ``cpu`` The number of CPUs to use in parallel. It is set to the number of CPUs in the machine by default. ``gpu`` The number of GPUs to use in parallel. It is set to the number of GPUs in the machine by default. ``precision`` GPU only: a string set to either ``float`` or ``double`` to specify whether to use single or double precision on the GPU. If it is not specified, it will use the best precision available. ``returninfo=False`` Boolean indicating whether the modelfitting function should return technical information about the optimization. ``scaling=None`` Specify the scaling used for the parameters during the optimization. It can be ``None`` or ``'mapminmax'``. It is ``None`` by default (no scaling), and ``mapminmax`` by default for the CMAES algorithm. ``algorithm=CMAES`` The optimization algorithm. It can be :class:`PSO`, :class:`GA` or :class:`CMAES`. ``optparams={}`` Optimization parameters. See ``method='Euler'`` Integration scheme used on the CPU and GPU: ``'Euler'`` (default), ``RK``, or ``exponential_Euler``. See also :ref:`numerical-integration`. ``machines=[]`` A list of machine names to use in parallel. See :ref:`modelfitting-clusters`. **Return values** Return an :class:`OptimizationResult` object with the following attributes: ``best_pos`` Minimizing position found by the algorithm. For array-like fitness functions, it is a single vector if there is one group, or a list of vectors. For keyword-like fitness functions, it is a dictionary where keys are parameter names and values are numeric values. If there are several groups, it is a list of dictionaries. ``best_fit`` The value of the fitness function for the best positions. It is a single value if there is one group, or it is a list if there are several groups. ``info`` A dictionary containing various information about the optimization. Also, the following syntax is possible with an ``OptimizationResult`` instance ``or``. The ``key`` is either an optimizing parameter name for keyword-like fitness functions, or a dimension index for array-like fitness functions. ``or[key]`` it is the best ``key`` parameter found (single value), or the list of the best parameters ``key`` found for all groups. ``or[i]`` where ``i`` is a group index. This object has attributes ``best_pos``, ``best_fit``, ``info`` but only for group ``i``. ``or[i][key]`` where ``i`` is a group index, is the same as ``or[i].best_pos[key]``. For more details on the gamma factor, see `Jolivet et al. 2008, "A benchmark test for a quantitative assessment of simple neuron models", J. Neurosci. Methods `__ (available in PDF `here `__). """ for param in params.keys(): if (param not in model._diffeq_names) and (param != 'delays') and (param != 'tau_metric'): raise Exception("Parameter %s must be defined as a parameter in the model" % param) if criterion is None: criterion = GammaFactor() data = array(data) if criterion.type == 'spikes': # Make sure that 'data' is a N*2-array if data.ndim == 1: data = concatenate((zeros((len(data), 1)), data.reshape((-1, 1))), axis=1) spikes = data traces = None if ntrials>1: groups = 1 else: groups = int(array(data)[:, 0].max() + 1) # number of target trains elif criterion.type == 'trace': if data.ndim == 1: data = data.reshape((1,-1)) spikes = None traces = data groups = data.shape[0] elif criterion.type == 'both': # TODO log_warn("Not implemented yet") pass inputs = input if inputs.ndim==1: inputs = inputs.reshape((1,-1)) # dt must be set if dt is None: raise Exception('dt (sampling frequency of the input) must be set') # default overlap when no time slicing if overlap is None: overlap = 0*ms if onset is None: onset = overlap # if slices == 1: # overlap = 0*ms # onset = overlap # default allocation if cpu is None and gpu is None and unit_type is None: if CANUSEGPU: unit_type = 'GPU' else: unit_type = 'CPU' # check numerical integration method if (gpu>0 or unit_type == 'GPU') and method not in ['Euler', 'RK', 'exponential_Euler']: raise Exception("The method can only be 'Euler', 'RK', or 'exponential_Euler' when using the GPU") if method not in ['Euler', 'RK', 'exponential_Euler', 'linear', 'nonlinear']: raise Exception("The method can only be 'Euler', 'RK', 'exponential_Euler', 'linear', or 'nonlinear'") if (algorithm == CMAES) & (scaling is None): scaling = 'mapminmax' # determines whether optimization over refractoriness or not if type(refractory) is tuple or type(refractory) is list: params['refractory'] = refractory max_refractory = refractory[-1] else: max_refractory = None # duration = len(input) * dt # duration of the input # keyword arguments for Modelfitting initialize kwds = dict( model=cPickle.dumps(model), threshold=threshold, reset=reset, refractory=refractory, max_refractory=max_refractory, input_var=input_var, dt=dt, criterion=criterion, slices=slices, overlap=overlap, returninfo=returninfo, precision=precision, stepsize=stepsize, ntrials=ntrials, method=method, onset=onset) shared_data = dict(inputs=inputs, traces=traces, spikes=spikes, initial_values=initial_values) if async: r = maximize_async( ModelFitting, shared_data=shared_data, kwds = kwds, groups=groups, popsize=popsize, maxiter=maxiter, optparams=optparams, unit_type = unit_type, machines=machines, allocation=allocation, total_units = total_units, cpu=cpu, gpu=gpu, returninfo=returninfo, codedependencies=[], algorithm=algorithm, scaling=scaling, **params) else: r = maximize( ModelFitting, shared_data=shared_data, kwds = kwds, groups=groups, popsize=popsize, maxiter=maxiter, optparams=optparams, unit_type = unit_type, machines=machines, allocation=allocation, total_units = total_units, cpu=cpu, gpu=gpu, returninfo=returninfo, codedependencies=[], algorithm=algorithm, scaling=scaling, **params) # r is (results, fitinfo) or (results) return r #def get_spikes(model=None, reset=None, threshold=None, # input=None, input_var='I', dt=None, # **params): # """ # Retrieves the spike times corresponding to the best parameters found by # the modelfitting function. # # **Arguments** # # ``model``, ``reset``, ``threshold``, ``input``, ``input_var``, ``dt`` # Same parameters as for the ``modelfitting`` function. # # ``**params`` # The best parameters returned by the ``modelfitting`` function. # # **Returns** # # ``spiketimes`` # The spike times of the model with the given input and parameters. # """ # duration = len(input) * dt # ngroups = len(params[params.keys()[0]]) # # group = NeuronGroup(N=ngroups, model=model, reset=reset, threshold=threshold, # clock=Clock(dt=dt)) # group.set_var_by_array(input_var, TimedArray(input, clock=group.clock)) # for param, values in params.iteritems(): # if (param == 'delays') | (param == 'fitness'): # continue # group.state(param)[:] = values # # M = SpikeMonitor(group) # net = Network(group, M) # net.run(duration) # reinit_default_clock() # return M.spikes # #def predict(model=None, reset=None, threshold=None, # data=None, delta=4 * ms, # input=None, input_var='I', dt=None, # **params): # """ # Predicts the gamma factor of a fitted model with respect to the data with # a different input current. # # **Arguments** # # ``model``, ``reset``, ``threshold``, ``input_var``, ``dt`` # Same parameters as for the ``modelfitting`` function. # # ``input`` # The input current, that can be different from the current used for the fitting # procedure. # # ``data`` # The experimental spike times to compute the gamma factor against. They have # been obtained with the current ``input``. # # ``**params`` # The best parameters returned by the ``modelfitting`` function. # # **Returns** # # ``gamma`` # The gamma factor of the model spike trains against the data. # If there were several groups in the fitting procedure, it is a vector # containing the gamma factor for each group. # """ # spikes = get_spikes(model=model, reset=reset, threshold=threshold, # input=input, input_var=input_var, dt=dt, # **params) # # ngroups = len(params[params.keys()[0]]) # gamma = zeros(ngroups) # for i in xrange(ngroups): # spk = [t for j, t in spikes if j == i] # gamma[i] = gamma_factor(spk, data, delta, normalize=True, dt=dt) # if len(gamma) == 1: # return gamma[0] # else: # return gamma if __name__ == '__main__': from brian import loadtxt, ms, savetxt, loadtxt, Equations, NeuronGroup, run, SpikeMonitor,\ StateMonitor, Network from pylab import * def generate_data(): g = NeuronGroup(1, model=equations, reset=0, threshold=1) g.I = TimedArray(input, dt=.1*ms) g.tau = 25*ms g.R = 3e9 SpM = SpikeMonitor(g) StM = StateMonitor(g, 'V', record=True) net = Network(g, SpM, StM) net.run(1*second) return StM.values[0], SpM.spikes equations = Equations(''' dV/dt=(R*I-V)/tau : 1 I : 1 R : 1 tau : second #tau_metric : second ''') input = loadtxt('current.txt') # ARTIFICIAL DATA: R=3e9, tau=25*ms # spikes = loadtxt('spikes.txt') # real data trace, spikes = generate_data() # savetxt('trace_artificial.txt', trace) # savetxt('spikes_artificial.txt', spikes) # trace = loadtxt('trace_artificial.txt') overlap = 10*ms slices = 1 dt = .1*ms input, trace = slice_trace(input, trace, slices = slices, dt=dt, overlap = overlap) # GAMMA FACTOR # criterion = GammaFactor(delta=2*ms) # spikes= loadtxt('spikes_artificial.txt') # data = spikes # data[:,1] += 50*ms # # LP ERROR criterion = LpError(p=2, varname='V') data = trace print data.shape print input.shape # # Van Rossum # criterion = VanRossum(tau=2*ms) # data = spikes #Brette # criterion = Brette() # data = spikes results = modelfitting( model = equations, reset = 0, threshold = 1, data = data, input = input, #onset = overlap, gpu = 1, #cpu=4, dt = dt, popsize = 20000, maxiter = 10, criterion = criterion, R = [1.0e9,1.0e9, 9.0e9, 9.0e9], tau = [10*ms,10*ms, 40*ms, 40*ms], algorithm=CMAES, tau_metric= [0.5*ms,0.5*ms, 4*ms, 4*ms], #delays=[-1*ms, 1*ms] ) print_table(results)brian-1.4.3/brian/experimental/modelfitting/modelfitting_old.py000077500000000000000000001406741266062647100250150ustar00rootroot00000000000000 from brian import Equations, NeuronGroup, Clock, CoincidenceCounter, Network, zeros, array, \ ones, kron, ms, second, concatenate, hstack, sort, nonzero, diff, TimedArray, \ reshape, sum, log, Monitor, NetworkOperation, defaultclock, linspace, vstack, \ arange, sort_spikes, rint, SpikeMonitor, Connection,SpikeGeneratorGroup,DelayConnection,\ SpikeCounter,forget,diagflat,inf,sqrt from scipy.sparse import dia_matrix,lil_matrix from brian.tools.statistics import firing_rate, get_gamma_factor try: from playdoh import * except Exception, e: print e raise ImportError("Playdoh must be installed (https://code.google.com/p/playdoh/)") try: import pycuda from gpu_modelfitting import GPUModelFitting can_use_gpu = True except ImportError: can_use_gpu = False from brian.experimental.codegen.integration_schemes import * import sys, cPickle __all__ = ['modelfitting', 'print_table', 'get_spikes', 'predict', 'PSO', 'GA','CMAES','Brette','VanRossum', 'MAXCPU', 'MAXGPU', 'GammaFactor', 'LpError', 'debug_level', 'info_level', 'warning_level', 'open_server'] class DataTransformer(object): """ Transform spike, input and trace data from user-friendly data structures, like 2 dimensional arrays or lists of spikes, into algorithm- and GPU-friendly structures, i.e. inline vectors (1 dimensional) easily parallelizable. """ def __init__(self, neurons, input, spikes = None, traces = None, dt = defaultclock.dt, slices = 1, overlap = 0*ms, groups = 1): self.neurons = neurons # number of particles on the node self.input = input # a IxT array self.spikes = spikes # a list of spikes [(i,t)...] self.traces = traces # a KxT array self.slices = slices self.overlap = overlap self.groups = groups self.dt = dt # ensure 2 dimensions if self.input.ndim == 1: self.input = self.input.reshape((1,-1)) if self.traces is not None: if self.traces.ndim == 1: self.traces = self.traces.reshape((1,-1)) self.inputs_count = self.input.shape[0] self.T = self.input.shape[1] # number of steps self.duration = self.T*self.dt self.subpopsize = self.neurons/self.groups # number of neurons per group: nodesize/groups self.input = self.input[:,0:self.slices * (self.T / self.slices)] # makes sure that len(input) is a multiple of slices self.sliced_steps = self.T / self.slices # timesteps per slice self.overlap_steps = int(self.overlap / self.dt) # timesteps during the overlap self.total_steps = self.sliced_steps + self.overlap_steps # total number of timesteps self.sliced_duration = self.overlap + self.duration / self.slices # duration of the vectorized simulation self.N = self.neurons * self.slices # TOTAL number of neurons on this node self.input = hstack((zeros((self.inputs_count, self.overlap_steps)), self.input)) # add zeros at the beginning because there is no overlap from the previous slice def slice_spikes(self, spikes): # from standard structure to standard structure sliced_spikes = [] slice_length = self.sliced_steps*self.dt for (i,t) in spikes: slice = int(t/slice_length) newt = self.overlap + (t % slice_length)*second newi = i + self.groups*slice sliced_spikes.append((newi, newt)) sliced_spikes = sort_spikes(sliced_spikes) return sliced_spikes def slice_traces(self, traces): # from standard structure to standard structure k = traces.shape[0] sliced_traces = zeros((k*self.slices, self.total_steps)) for slice in xrange(self.slices): i0 = slice*k i1 = (slice+1)*k j0 = slice*self.sliced_steps j1 = (slice+1)*self.sliced_steps sliced_traces[i0:i1,self.overlap_steps:] = traces[:,j0:j1] if slice>0: sliced_traces[i0:i1,:self.overlap_steps] = traces[:,j0-self.overlap_steps:j0] return sliced_traces def transform_spikes(self, spikes): # from standard structure to inline structure i, t = zip(*spikes) i = array(i) t = array(t) alls = [] n = 0 pointers = [] model_target = [] for j in xrange(self.groups): s = sort(t[i == j]) s = hstack((-1 * second, s, self.duration + 1 * second)) model_target.extend([j] * self.subpopsize) alls.append(s) pointers.append(n) n += len(s) pointers = array(pointers, dtype=int) model_target = array(hstack(model_target), dtype=int) spikes_inline = hstack(alls) spikes_offset = pointers[model_target] return spikes_inline, spikes_offset def transform_traces(self, traces): # from standard structure to inline structure K, T = traces.shape traces_inline = traces.flatten() traces_offset = array(kron(arange(K), T*ones(self.subpopsize)), dtype=int) return traces_inline, traces_offset class Criterion(Monitor, NetworkOperation): """ Abstract class from which modelfitting criterions should derive. Derived classes should implement the following methods: ``initialize(self, **params)`` Called once before the simulation. ```params`` is a dictionary with criterion-specific parameters. ``timestep_call(self)`` Called at every timestep. ``spike_call(self, neurons)`` Called at every spike, with the spiking neurons as argument. ``get_values(self)`` Called at the end, returns the criterion values. You have access to the following methods: ``self.get_value(self, varname)`` Returns the value of the specified variable for all neurons (vector). You have access to the following attributes: ``self.step`` The time step (integer) ``self.group`` The NeuronGroup ``self.traces=None`` Target traces. A 2-dimensional K*T array where K is the number of targets, and T the total number of timesteps. It is still a 2-dimensional array when K=1. It is None if not specified. ``self.spikes=None`` A list of target spike trains : [(i,t)..] where i is the target index and t the spike time. ``self.N`` The number of neurons in the NeuronGroup ``self.K=1`` The number of targets. ``self.duration`` The total duration of the simulation, in seconds. ``self.total_steps`` The total number of time steps. ``self.dt`` The timestep duration, in seconds. ``self.delays=zeros(self.n)`` The delays for every neuron, in seconds. The delay is relative to the target. ``self.onset=0*ms`` The onset, in seconds. The first timesteps, before onset, should be discarded in the criterion. ``self.intdelays=0`` The delays, but in number of timesteps (``int(delays/dt)``). NOTE: traces and spikes have all been sliced before being passed to the criterion object """ def __init__(self, group, traces=None, spikes=None, targets_count=1, duration=None, onset=0*ms, spikes_inline=None, spikes_offset=None, traces_inline=None, traces_offset=None, delays=None, when='end', **params): NetworkOperation.__init__(self, None, clock=group.clock, when=when) self.group = group # needed by SpikeMonitor self.source = group self.source.set_max_delay(0) self.delay = 0 self.N = len(group) # number of neurons self.K = targets_count # number of targets self.dt = self.clock.dt if traces is not None: self.traces = array(traces) # KxT array if self.traces.ndim == 1: self.traces = self.traces.reshape((1,-1)) assert targets_count==self.traces.shape[0] self.spikes = spikes # get the duration from the traces if duration is not specified in the constructor if duration is None: duration = self.traces.shape[1] # total number of steps self.duration = duration self.total_steps = int(duration/self.dt) if delays is None: delays = zeros(self.n) self.delays = delays self.onset = int(onset/self.dt) self.intdelays = array(self.delays/self.clock.dt, dtype=int) self.mindelay = min(delays) self.maxdelay = max(delays) # the following data is sliced self.spikes_inline = spikes_inline self.spikes_offset = spikes_offset self.traces_inline = traces_inline self.traces_offset = traces_offset if self.spikes is not None: # target spike count and rates self.target_spikes_count = self.get_spikes_count(self.spikes) self.target_spikes_rate = self.get_spikes_rate(self.spikes) self.initialize(**params) def step(self): """ Return the current time step """ return int(self.clock.t/self.dt) def get_spikes_count(self, spikes): count = zeros(self.K) for (i,t) in spikes: count[i] += 1 return count def get_spikes_rate(self, spikes): count = self.get_spikes_count(spikes) return count*1.0/self.duration def initialize(self): # TO IMPLEMENT """ Override this method to initialize the criterion before the simulation """ pass def timestep_call(self): # TO IMPLEMENT """ Override this method to do something at every time step """ pass def spike_call(self, neurons): # TO IMPLEMENT """ Override this method to do something at every time spike. neurons contains the list of neurons that just spiked """ pass def get_value(self, varname): return self.group.state_(varname) def __call__(self): self.timestep_call() def propagate(self, neurons): self.spike_call(neurons) def get_values(self): # TO IMPLEMENT """ Override this method to return the criterion values at the end. It must return one (or several, as a tuple) additive values, i.e., values corresponding to slices. The method normalize can normalize combined values. """ pass additive_values = property(get_values) def normalize(self, values): # TO IMPLEMENT """ Values contains combined criterion values. It is either a vector of values (as many values as neurons), or a tuple with different vector of as many values as neurons. """ pass class CriterionStruct(object): type = None # 'trace', 'spike' or 'both' def get_name(self): return self.__class__.__name__ name = property(get_name) class LpErrorCriterion(Criterion): def initialize(self, p=2, varname='v'): self.p = p self.varname = varname self._error = zeros((self.K, self.N)) def timestep_call(self): v = self.get_value(self.varname) t = self.step() if t=0)&(t-d= self.onset: self.spike_count[spiking_neurons] += 1 T_spiking = array(rint((t + self.delays[spiking_neurons]) / dt), dtype=int) remaining_neurons = spiking_neurons remaining_T_spiking = T_spiking while True: remaining_indices, = (remaining_T_spiking > self.next_spike_time[remaining_neurons]).nonzero() if len(remaining_indices): indices = remaining_neurons[remaining_indices] self.spiketime_index[indices] += 1 self.last_spike_time[indices] = self.next_spike_time[indices] self.next_spike_time[indices] = array(rint(self.spikes_inline[self.spiketime_index[indices] + 1] / dt), dtype=int) if self.coincidence_count_algorithm == 'exclusive': self.last_spike_allowed[indices] = self.next_spike_allowed[indices] self.next_spike_allowed[indices] = True remaining_neurons = remaining_neurons[remaining_indices] remaining_T_spiking = remaining_T_spiking[remaining_indices] else: break # Updates coincidences count near_last_spike = self.last_spike_time[spiking_neurons] + self.delta >= T_spiking near_next_spike = self.next_spike_time[spiking_neurons] - self.delta <= T_spiking last_spike_allowed = self.last_spike_allowed[spiking_neurons] next_spike_allowed = self.next_spike_allowed[spiking_neurons] I = (near_last_spike & last_spike_allowed) | (near_next_spike & next_spike_allowed) if t >= self.onset: self.coincidences[spiking_neurons[I]] += 1 if self.coincidence_count_algorithm == 'exclusive': near_both_allowed = (near_last_spike & last_spike_allowed) & (near_next_spike & next_spike_allowed) self.last_spike_allowed[spiking_neurons] = last_spike_allowed & -near_last_spike self.next_spike_allowed[spiking_neurons] = (next_spike_allowed & -near_next_spike) | near_both_allowed def get_values(self): return (self.coincidences, self.spike_count) def normalize(self, values): coincidence_count = values[0] spike_count = values[1] delta = self.delta*self.dt gamma = get_gamma_factor(coincidence_count, spike_count, self.target_spikes_count, self.target_spikes_rate, delta) return gamma class GammaFactor(CriterionStruct): def __init__(self, delta = 4*ms, coincidence_count_algorithm = 'exclusive'): self.type = 'spikes' self.delta = delta self.coincidence_count_algorithm = coincidence_count_algorithm class VanRossumCriterion(Criterion): def initialize(self, tau): self.delay_range =max(self.delays)- min(self.delays)#delay range self.min_delay = abs(min(self.delays))#minimum of possible delay self.distance_vector=zeros(self.N) self.nbr_neurons_group = self.N/self.K eqs=""" dv/dt=(-v)/tau: volt """ # network to convolve target spikes with the kernel self.input_target=SpikeGeneratorGroup(self.K,self.spikes,clock=self.group.clock) self.kernel_target=NeuronGroup(self.K,model=eqs,clock=self.group.clock) self.C_target = DelayConnection(self.input_target, self.kernel_target, 'v', structure='dense', max_delay=self.min_delay) self.C_target.connect_one_to_one(self.input_target,self.kernel_target) self.C_target.delay = self.min_delay*ones_like(self.C_target.delay) # network to convolve population spikes with the kernel self.kernel_population=NeuronGroup(self.N,model=eqs,clock=self.group.clock) self.C_population = DelayConnection(self.group, self.kernel_population, 'v', structure='sparse', max_delay=self.delay_range) for iN in xrange(self.N): self.C_population.delay[iN,iN] = diagflat(self.min_delay + self.delays[iN]) self.C_population.connect_one_to_one(self.group,self.kernel_population) self.spikecount = SpikeCounter(self.group) self.contained_objects = [self.kernel_population,self.C_population,self.spikecount,self.input_target,self.C_target,self.kernel_target] def __call__(self): trace_population = self.kernel_population.state_('v') trace_target = self.kernel_target.state_('v') for igroup in xrange(self.K): self.distance_vector[igroup*self.nbr_neurons_group:(1+igroup)*self.nbr_neurons_group] += (trace_population[igroup*self.nbr_neurons_group:(1+igroup)*self.nbr_neurons_group]-trace_target[igroup])**2 def get_values(self): return (self.distance_vector) def normalize(self, distance_vector): distance_vector[nonzero(self.spikecount.count==0)] = inf return -self.distance_vector*self.group.clock.dt class VanRossum(CriterionStruct): def __init__(self, tau): self.type = 'spikes' self.tau = tau class BretteCriterion(Criterion): def initialize(self,tau_metric): self.delay_range =max(self.delays)- min(self.delays)#delay range self.min_delay = abs(min(self.delays))#minimum of possible delay self.corr_vector=zeros(self.N) self.norm_pop = zeros(self.N) self.norm_target = zeros(self.N) self.nbr_neurons_group = self.N/self.K eqs=""" tau:second dv/dt=(-v)/tau: volt """ # network to convolve target spikes with the kernel self.input_target=SpikeGeneratorGroup(self.K,self.spikes,clock=self.group.clock) self.kernel_target=NeuronGroup(self.N,model=eqs,clock=self.group.clock) self.C_target = DelayConnection(self.input_target, self.kernel_target, 'v', structure='sparse', max_delay=self.min_delay) self.kernel_target.tau=tau_metric for igroup in xrange(self.K): self.C_target.W[igroup,igroup*self.nbr_neurons_group:(1+igroup)*self.nbr_neurons_group] = ones(self.nbr_neurons_group) self.C_target.delay[igroup,igroup*self.nbr_neurons_group:(1+igroup)*self.nbr_neurons_group] = self.min_delay * ones(self.nbr_neurons_group) # network to convolve population spikes with the kernel self.kernel_population=NeuronGroup(self.N,model=eqs,clock=self.group.clock) self.C_population = DelayConnection(self.group, self.kernel_population, 'v', structure='sparse', max_delay=self.delay_range) for iN in xrange(self.N): self.C_population.delay[iN,iN] = diagflat(self.min_delay + self.delays[iN]) self.C_population.connect_one_to_one(self.group,self.kernel_population) self.kernel_population.tau=tau_metric self.spikecount = SpikeCounter(self.group) self.contained_objects = [self.kernel_population,self.C_population,self.spikecount,self.input_target,self.C_target,self.kernel_target] def __call__(self): trace_population = self.kernel_population.state_('v') trace_target = self.kernel_target.state_('v') self.corr_vector += trace_population*trace_target self.norm_pop += trace_population**2 self.norm_target += trace_target**2 def get_values(self): return (self.corr_vector,self.norm_pop,self.norm_target) def normalize(self, values): corr_vector=values[0] norm_pop=values[1] norm_target=values[2] corr_vector[nonzero(self.spikecount.count==0)] = -inf #print self.corr_vector/sqrt(norm_pop)/sqrt(norm_target) return self.corr_vector/sqrt(norm_pop)/sqrt(norm_target) class Brette(CriterionStruct): def __init__(self): self.type = 'spikes' class ModelFitting(Fitness): def initialize(self, **kwds): # Initialization of variables self.use_gpu = self.unit_type=='GPU' # Gets the key,value pairs in shared_data for key, val in self.shared_data.iteritems(): setattr(self, key, val) # Gets the key,value pairs in **kwds for key, val in kwds.iteritems(): setattr(self, key, val) self.neurons = self.nodesize self.groups = self.groups self.model = cPickle.loads(self.model) if type(self.model) is str: self.model = Equations(self.model) self.initialize_neurongroup() self.transform_data() self.inject_input() # if self.use_gpu: # ######## # # TODO # ######## # # Select integration scheme according to method # if self.method == 'Euler': scheme = euler_scheme # elif self.method == 'RK': scheme = rk2_scheme # elif self.method == 'exponential_Euler': scheme = exp_euler_scheme # else: raise Exception("The numerical integration method is not valid") # # self.mf = GPUModelFitting(self.group, self.model, self.input, self.I_offset, # self.spiketimes, self.spiketimes_offset, zeros(self.neurons), 0*ms, self.delta, # precision=self.precision, scheme=scheme) # else: # self.cc = CoincidenceCounter(self.group, self.spiketimes, self.spiketimes_offset, # onset=self.onset, delta=self.delta) def initialize_neurongroup(self): # Add 'refractory' parameter on the CPU only if not self.use_gpu: if self.max_refractory is not None: refractory = 'refractory' self.model.add_param('refractory', second) else: refractory = self.refractory else: if self.max_refractory is not None: refractory = 0*ms else: refractory = self.refractory # Must recompile the Equations : the functions are not transfered after pickling/unpickling self.model.compile_functions() self.group = NeuronGroup(self.neurons, model=self.model, reset=self.reset, threshold=self.threshold, refractory=refractory, max_refractory = self.max_refractory, method = self.method, clock=Clock(dt=self.dt)) if self.initial_values is not None: for param, value in self.initial_values.iteritems(): self.group.state(param)[:] = value def transform_data(self): self.transformer = DataTransformer(self.neurons, self.inputs, spikes = self.spikes, traces = self.traces, dt = self.dt, slices = self.slices, overlap = self.overlap, groups = self.groups) self.total_steps = self.transformer.total_steps self.sliced_duration = self.transformer.sliced_duration self.sliced_inputs = self.transformer.slice_traces(self.inputs) self.inputs_inline, self.inputs_offset = self.transformer.transform_traces(self.sliced_inputs) if self.traces is not None: self.sliced_traces = self.transformer.slice_traces(self.traces) self.traces_inline, self.traces_offset = self.transformer.transform_traces(self.sliced_traces) else: self.sliced_traces, self.traces_inline, self.traces_offset = None, None, None if self.spikes is not None: self.sliced_spikes = self.transformer.slice_spikes(self.spikes) self.spikes_inline, self.spikes_offset = self.transformer.transform_spikes(self.sliced_spikes) else: self.sliced_spikes, self.spikes_inline, self.spikes_offset = None, None, None def inject_input(self): # Injects current in consecutive subgroups, where I_offset have the same value # on successive intervals I_offset = self.inputs_offset k = -1 for i in hstack((nonzero(diff(I_offset))[0], len(I_offset) - 1)): I_offset_subgroup_value = I_offset[i] I_offset_subgroup_length = i - k sliced_subgroup = self.group.subgroup(I_offset_subgroup_length) input_sliced_values = self.inputs_inline[I_offset_subgroup_value:I_offset_subgroup_value + self.total_steps] sliced_subgroup.set_var_by_array(self.input_var, TimedArray(input_sliced_values, clock=self.group.clock)) k = i def initialize_criterion(self, delays,tau_metric = None): # general criterion parameters params = dict(group=self.group, traces=self.sliced_traces, spikes=self.sliced_spikes, targets_count=self.groups*self.slices, duration=self.sliced_duration, onset=self.onset, spikes_inline=self.spikes_inline, spikes_offset=self.spikes_offset, traces_inline=self.traces_inline, traces_offset=self.traces_offset, delays=delays, when='start') criterion_name = self.criterion.__class__.__name__ # criterion-specific parameters if criterion_name == 'GammaFactor': params['delta'] = self.criterion.delta params['coincidence_count_algorithm'] = self.criterion.coincidence_count_algorithm self.criterion_object = GammaFactorCriterion(**params) if criterion_name == 'LpError': params['p'] = self.criterion.p params['varname'] = self.criterion.varname self.criterion_object = LpErrorCriterion(**params) if criterion_name == 'VanRossum': params['tau'] = self.criterion.tau self.criterion_object = VanRossumCriterion(**params) if criterion_name == 'Brette': params['tau_metric'] = tau_metric self.criterion_object = BretteCriterion(**params) def update_neurongroup(self, **param_values): """ Inject fitting parameters into the NeuronGroup """ # Sets the parameter values in the NeuronGroup object self.group.reinit() for param, value in param_values.iteritems(): self.group.state(param)[:] = kron(value, ones(self.slices)) # kron param_values if slicing # Reinitializes the model variables if self.initial_values is not None: for param, value in self.initial_values.iteritems(): self.group.state(param)[:] = value def combine_sliced_values(self, values): if type(values) is tuple: combined_values = tuple([sum(reshape(v, (self.slices, -1)), axis=0) for v in values]) else: combined_values = sum(reshape(values, (self.slices, -1)), axis=0) return combined_values def evaluate(self, **param_values): """ Use fitparams['delays'] to take delays into account Use fitparams['refractory'] to take refractory into account """ delays = param_values.pop('delays', zeros(self.neurons)) refractory = param_values.pop('refractory', zeros(self.neurons)) tau_metric = param_values.pop('tau_metric', zeros(self.neurons)) # repeat spike delays and refractory to take slices into account delays = kron(delays, ones(self.slices)) refractory = kron(refractory, ones(self.slices)) tau_metric = kron(tau_metric, ones(self.slices)) self.update_neurongroup(**param_values) if self.criterion.__class__.__name__ == 'Brette': self.initialize_criterion(delays,tau_metric) else: self.initialize_criterion(delays) if self.use_gpu: pass ######### # TODO ######### # # Reinitializes the simulation object # self.mf.reinit_vars(self.input, self.I_offset, self.spiketimes, self.spiketimes_offset, delays, refractory) # # LAUNCHES the simulation on the GPU # self.mf.launch(self.duration, self.stepsize) # coincidence_count = self.mf.coincidence_count # spike_count = self.mf.spike_count else: # set the refractory period if self.max_refractory is not None: self.group.refractory = refractory # Launch the simulation on the CPU self.group.clock.reinit() net = Network(self.group, self.criterion_object) net.run(self.duration) sliced_values = self.criterion_object.get_values() combined_values = self.combine_sliced_values(sliced_values) values = self.criterion_object.normalize(combined_values) return values def modelfitting(model=None, reset=None, threshold=None, refractory=0*ms, data=None, input_var='I', input=None, dt=None, popsize=1000, maxiter=10, slices=1, overlap=0*second, initial_values=None, stepsize=100 * ms, unit_type='CPU', total_units=None, cpu=None, gpu=None, precision='double', # set to 'float' or 'double' to specify single or double precision on the GPU machines=[], allocation=None, returninfo=False, scaling=None, algorithm=CMAES, criterion=None, optparams={}, method='Euler', **params): """ Model fitting function. Fits a spiking neuron model to electrophysiological data (injected current and spikes). See also the section :ref:`model-fitting-library` in the user manual. **Arguments** ``model`` An :class:`~brian.Equations` object containing the equations defining the model. ``reset`` A reset value for the membrane potential, or a string containing the reset equations. ``threshold`` A threshold value for the membrane potential, or a string containing the threshold equations. ``refractory`` The refractory period in second. If it's a single value, the same refractory will be used in all the simulations. If it's a list or a tuple, the fitting will also optimize the refractory period (see ``**params`` below). Warning: when using a refractory period, you can't use a custom reset, only a fixed one. ``data`` A list of spike times, or a list of several spike trains as a list of pairs (index, spike time) if the fit must be performed in parallel over several target spike trains. In this case, the modelfitting function returns as many parameters sets as target spike trains. ``input_var='I'`` The variable name used in the equations for the input current. ``input`` A vector of values containing the time-varying signal the neuron responds to (generally an injected current). ``dt`` The time step of the input (the inverse of the sampling frequency). ``**params`` The list of parameters to fit the model with. Each parameter must be set as follows: ``param_name=[bound_min, min, max, bound_max]`` where ``bound_min`` and ``bound_max`` are the boundaries, and ``min`` and ``max`` specify the interval from which the parameter values are uniformly sampled at the beginning of the optimization algorithm. If not using boundaries, set ``param_name=[min, max]``. Also, you can add a fit parameter which is a spike delay for all spikes : add the special parameter ``delays`` in ``**params``, for example ``modelfitting(..., delays=[-10*ms, 10*ms])``. You can also add fit the refractory period by specifying ``modelfitting(..., refractory=[-10*ms, 10*ms])``. ``popsize`` Size of the population (number of particles) per target train used by the optimization algorithm. ``maxiter`` Number of iterations in the optimization algorithm. ``optparams`` Optimization algorithm parameters. It is a dictionary: keys are parameter names, values are parameter values or lists of parameters (one value per group). This argument is specific to the optimization algorithm used. See :class:`PSO`, :class:`GA`, :class:`CMAES`. ``delta=4*ms`` The precision factor delta (a scalar value in second). ``slices=1`` The number of time slices to use. ``overlap=0*ms`` When using several time slices, the overlap between consecutive slices, in seconds. ``initial_values`` A dictionary containing the initial values for the state variables. ``cpu`` The number of CPUs to use in parallel. It is set to the number of CPUs in the machine by default. ``gpu`` The number of GPUs to use in parallel. It is set to the number of GPUs in the machine by default. ``precision`` GPU only: a string set to either ``float`` or ``double`` to specify whether to use single or double precision on the GPU. If it is not specified, it will use the best precision available. ``returninfo=False`` Boolean indicating whether the modelfitting function should return technical information about the optimization. ``scaling=None`` Specify the scaling used for the parameters during the optimization. It can be ``None`` or ``'mapminmax'``. It is ``None`` by default (no scaling), and ``mapminmax`` by default for the CMAES algorithm. ``algorithm=CMAES`` ``optparams={}`` Optimization parameters. See ``method='Euler'`` Integration scheme used on the CPU and GPU: ``'Euler'`` (default), ``RK``, or ``exponential_Euler``. See also :ref:`numerical-integration`. ``machines=[]`` A list of machine names to use in parallel. See :ref:`modelfitting-clusters`. **Return values** Return an :class:`OptimizationResult` object with the following attributes: ``best_pos`` Minimizing position found by the algorithm. For array-like fitness functions, it is a single vector if there is one group, or a list of vectors. For keyword-like fitness functions, it is a dictionary where keys are parameter names and values are numeric values. If there are several groups, it is a list of dictionaries. ``best_fit`` The value of the fitness function for the best positions. It is a single value if there is one group, or it is a list if there are several groups. ``info`` A dictionary containing various information about the optimization. Also, the following syntax is possible with an ``OptimizationResult`` instance ``or``. The ``key`` is either an optimizing parameter name for keyword-like fitness functions, or a dimension index for array-like fitness functions. ``or[key]`` it is the best ``key`` parameter found (single value), or the list of the best parameters ``key`` found for all groups. ``or[i]`` where ``i`` is a group index. This object has attributes ``best_pos``, ``best_fit``, ``info`` but only for group ``i``. ``or[i][key]`` where ``i`` is a group index, is the same as ``or[i].best_pos[key]``. For more details on the gamma factor, see `Jolivet et al. 2008, "A benchmark test for a quantitative assessment of simple neuron models", J. Neurosci. Methods `__ (available in PDF `here `__). """ if criterion is None: criterion = GammaFactor() data = array(data) if criterion.type == 'spikes': # Make sure that 'data' is a N*2-array if data.ndim == 1: data = concatenate((zeros((len(data), 1)), data.reshape((-1, 1))), axis=1) spikes = data traces = None elif criterion.type == 'trace': if data.ndim == 1: data = data.reshape((1,-1)) spikes = None traces = data elif criterion.type == 'both': # TODO log_warn("Not implemented yet") pass inputs = input if inputs.ndim==1: inputs = inputs.reshape((1,-1)) # dt must be set if dt is None: raise Exception('dt (sampling frequency of the input) must be set') # default overlap when no time slicing if slices == 1: overlap = 0*ms # default allocation if cpu is None and gpu is None: if CANUSEGPU: gpu = 1 else: cpu = MAXCPU-1 # check numerical integration method if (gpu>0 or unit_type == 'GPU') and method not in ['Euler', 'RK', 'exponential_Euler']: raise Exception("The method can only be 'Euler', 'RK', or 'exponential_Euler' when using the GPU") if method not in ['Euler', 'RK', 'exponential_Euler', 'linear', 'nonlinear']: raise Exception("The method can only be 'Euler', 'RK', 'exponential_Euler', 'linear', or 'nonlinear'") if (algorithm == CMAES) & (scaling is None): scaling = 'mapminmax' # determines whether optimization over refractoriness or not if type(refractory) is tuple or type(refractory) is list: params['refractory'] = refractory max_refractory = refractory[-1] # refractory = 'refractory' else: max_refractory = None # common values # group_size = particles # Number of particles per target train groups = int(array(data)[:, 0].max() + 1) # number of target trains # N = group_size * group_count # number of neurons duration = len(input) * dt # duration of the input # keyword arguments for Modelfitting initialize kwds = dict( model=cPickle.dumps(model), threshold=threshold, reset=reset, refractory=refractory, max_refractory=max_refractory, input_var=input_var, dt=dt, duration=duration, criterion=criterion, slices=slices, overlap=overlap, returninfo=returninfo, precision=precision, stepsize=stepsize, method=method, onset=overlap) shared_data = dict(inputs=inputs, traces=traces, spikes=spikes, initial_values=initial_values) r = maximize( ModelFitting, shared_data=shared_data, kwds = kwds, groups=groups, popsize=popsize, maxiter=maxiter, optparams=optparams, unit_type = unit_type, machines=machines, allocation=allocation, cpu=cpu, gpu=gpu, returninfo=returninfo, codedependencies=[], algorithm=algorithm, scaling=scaling, **params) # r is (results, fitinfo) or (results) return r def get_spikes(model=None, reset=None, threshold=None, input=None, input_var='I', dt=None, **params): """ Retrieves the spike times corresponding to the best parameters found by the modelfitting function. **Arguments** ``model``, ``reset``, ``threshold``, ``input``, ``input_var``, ``dt`` Same parameters as for the ``modelfitting`` function. ``**params`` The best parameters returned by the ``modelfitting`` function. **Returns** ``spiketimes`` The spike times of the model with the given input and parameters. """ duration = len(input) * dt ngroups = len(params[params.keys()[0]]) group = NeuronGroup(N=ngroups, model=model, reset=reset, threshold=threshold, clock=Clock(dt=dt)) group.set_var_by_array(input_var, TimedArray(input, clock=group.clock)) for param, values in params.iteritems(): if (param == 'delays') | (param == 'fitness'): continue group.state(param)[:] = values M = SpikeMonitor(group) net = Network(group, M) net.run(duration) reinit_default_clock() return M.spikes def predict(model=None, reset=None, threshold=None, data=None, delta=4 * ms, input=None, input_var='I', dt=None, **params): """ Predicts the gamma factor of a fitted model with respect to the data with a different input current. **Arguments** ``model``, ``reset``, ``threshold``, ``input_var``, ``dt`` Same parameters as for the ``modelfitting`` function. ``input`` The input current, that can be different from the current used for the fitting procedure. ``data`` The experimental spike times to compute the gamma factor against. They have been obtained with the current ``input``. ``**params`` The best parameters returned by the ``modelfitting`` function. **Returns** ``gamma`` The gamma factor of the model spike trains against the data. If there were several groups in the fitting procedure, it is a vector containing the gamma factor for each group. """ spikes = get_spikes(model=model, reset=reset, threshold=threshold, input=input, input_var=input_var, dt=dt, **params) ngroups = len(params[params.keys()[0]]) gamma = zeros(ngroups) for i in xrange(ngroups): spk = [t for j, t in spikes if j == i] gamma[i] = gamma_factor(spk, data, delta, normalize=True, dt=dt) if len(gamma) == 1: return gamma[0] else: return gamma if __name__ == '__main__': from brian import loadtxt, ms, savetxt, loadtxt, Equations, NeuronGroup, run, SpikeMonitor,\ StateMonitor, Network from pylab import * def generate_data(): g = NeuronGroup(1, model=equations, reset=0, threshold=1) g.I = TimedArray(input, dt=.1*ms) g.tau = 25*ms g.R = 3e9 SpM = SpikeMonitor(g) StM = StateMonitor(g, 'V', record=True) net = Network(g, SpM, StM) net.run(1*second) return StM.values[0], SpM.spikes equations = Equations(''' dV/dt=(R*I-V)/tau : 1 I : 1 R : 1 tau : second ''') input = loadtxt('current.txt') # ARTIFICIAL DATA: R=3e9, tau=25*ms # spikes = loadtxt('spikes.txt') # real data # trace, spikes = generate_data() # savetxt('trace_artificial.txt', trace) # savetxt('spikes_artificial.txt', spikes) trace = loadtxt('trace_artificial.txt') spikes= loadtxt('spikes_artificial.txt') # GAMMA FACTOR # criterion = GammaFactor(delta=4*ms) # data = spikes # LP ERROR # criterion = LpError(p=2, varname='V') # data = trace ##Van Rossum ERROR # criterion = VanRossum(tau=4*ms) # data = spikes equations = Equations(''' dV/dt=(R*I-V)/tau : 1 I : 1 R : 1 tau_metric:second tau : second ''') # Brette ERROR criterion = Brette() data = spikes # results = modelfitting( model = equations, reset = 0, threshold = 1, data = data, input = input, cpu = 1, dt = .1*ms, popsize = 1000, maxiter = 5, criterion = criterion, R = [1.0e9, 9.0e9], tau = [10*ms, 40*ms], tau_metric=[1*ms,10*ms] # delays = [-5*ms, 5*ms], # refractory = [0*ms, 0*ms, 10*ms, 10*ms] ) print_table(results) brian-1.4.3/brian/experimental/modelfitting/simulator.py000077500000000000000000000662171266062647100235110ustar00rootroot00000000000000from brian import Equations, NeuronGroup, Clock, CoincidenceCounter, Network, zeros, array, \ ones, kron, ms, second, concatenate, hstack, sort, nonzero, diff, TimedArray, \ reshape, sum, log, Monitor, NetworkOperation, defaultclock, linspace, vstack, \ arange, sort_spikes, rint, SpikeMonitor, Connection, StateMonitor,where from brian.tools.statistics import firing_rate, get_gamma_factor try: from playdoh import * except Exception, e: print e raise ImportError("Playdoh must be installed (https://code.google.com/p/playdoh/)") try: import pycuda from gpu_modelfitting import GPUModelFitting can_use_gpu = True except ImportError: can_use_gpu = False from brian.experimental.codegen.integration_schemes import * from criteria import * import sys, cPickle class DataTransformer(object): """ Transform spike, input and trace data from user-friendly data structures, like 2 dimensional arrays or lists of spikes, into algorithm- and GPU-friendly structures, i.e. inline vectors (1 dimensional) easily parallelizable. """ def __init__(self, neurons, input, spikes = None, traces = None, dt = defaultclock.dt, slices = 1, overlap = 0*ms, groups = 1,ntrials=1): self.neurons = neurons # number of particles on the node self.input = input # a IxT array self.spikes = spikes # a list of spikes [(i,t)...] self.traces = traces # a KxT array self.slices = slices self.overlap = overlap self.groups = groups self.ntrials=ntrials self.dt = dt # ensure 2 dimensions if self.input.ndim == 1: self.input = self.input.reshape((1,-1)) if self.traces is not None: if self.traces.ndim == 1: self.traces = self.traces.reshape((1,-1)) self.inputs_count = self.input.shape[0] self.T = self.input.shape[1] # number of steps self.duration = self.T*self.dt self.subpopsize = self.neurons/self.groups # number of neurons per group: nodesize/groups self.input = self.input[:,0:self.slices * (self.T / self.slices)] # makes sure that len(input) is a multiple of slices self.sliced_steps = self.T / self.slices # timesteps per slice self.overlap_steps = int(self.overlap / self.dt) # timesteps during the overlap self.total_steps = self.sliced_steps + self.overlap_steps # total number of timesteps self.sliced_duration = self.overlap + self.duration / self.slices # duration of the vectorized simulation self.N = self.neurons * self.slices # TOTAL number of neurons on this node self.input = hstack((zeros((self.inputs_count, self.overlap_steps)), self.input)) # add zeros at the beginning because there is no overlap from the previous slice def slice_spikes(self, spikes): # slice the spike trains into different chunks and assign new index sliced_spikes = [] slice_length = self.sliced_steps*self.dt # print self.groups,self.slices, slice_length for (i,t) in spikes: slice = int(t/slice_length) newt = self.overlap + (t % slice_length)*second newi = i + self.groups*slice # print i,t,slice,newt,newi # discard unreachable spikes if newi >= (self.slices*self.groups): continue sliced_spikes.append((newi, newt)) sliced_spikes = sort_spikes(sliced_spikes) return sliced_spikes def slice_traces(self, traces): # from standard structure to standard structure k = traces.shape[0] sliced_traces = zeros((k*self.slices, self.total_steps)) for slice in xrange(self.slices): i0 = slice*k i1 = (slice+1)*k j0 = slice*self.sliced_steps j1 = (slice+1)*self.sliced_steps sliced_traces[i0:i1,self.overlap_steps:] = traces[:,j0:j1] if slice>0: sliced_traces[i0:i1,:self.overlap_steps] = traces[:,j0-self.overlap_steps:j0] return sliced_traces def transform_trials(self, spikes): # from standard structure to inline structure i, t = zip(*spikes) i = array(i) t = array(t) alls = [] n = 0 pointers = [] model_target = [] pointers.append(0) for j in xrange(self.ntrials): s = sort(t[i == j]) s = hstack((-1 * second, s, self.duration + 1 * second)) alls.append(s) pointers.append(pointers[j]+len(s)) pointers.pop() spikes_inline = hstack(alls) # print pointers # print nonzero(spikes_inline==-1) # show() return spikes_inline, pointers def transform_spikes(self, spikes): # from standard structure to inline structure i, t = zip(*spikes) i = array(i) t = array(t) alls = [] n = 0 pointers = [] model_target = [] for j in xrange(self.groups): s = sort(t[i == j]) s = hstack((-1 * second, s, self.duration + 1 * second)) model_target.extend([j] * self.subpopsize) alls.append(s) pointers.append(n) n += len(s) pointers = array(pointers, dtype=int) model_target = array(hstack(model_target), dtype=int) spikes_inline = hstack(alls) spikes_offset = pointers[model_target] return spikes_inline, spikes_offset def transform_traces(self, traces): # from standard structure to inline structure K, T = traces.shape traces_inline = traces.flatten() traces_offset = array(kron(arange(K), T*ones(self.subpopsize)), dtype=int) return traces_inline, traces_offset def slice_trace(input, trace, slices = 1, dt = defaultclock.dt, overlap = 0*ms): input = input.reshape((1,-1)) trace = trace.reshape((1,-1)) dt = DataTransformer(1, input, trace, dt = dt, slices = slices, overlap = overlap, groups = slices) sliced_traces = dt.slice_traces(trace) sliced_input = dt.slice_traces(input) return sliced_input, sliced_traces def transform_spikes(n, input, spikes, slices = 1, dt = defaultclock.dt, overlap = 0*ms): input = input.reshape((1,-1)) dt = DataTransformer(n, input, spikes = spikes, dt = dt, slices = slices, overlap = overlap) sliced_spikes = dt.slice_spikes(spikes) dt.groups = slices spikes_inline, spike_offset = dt.transform_spikes(sliced_spikes) return spikes_inline, spike_offset class Simulator(object): def __init__(self, model, reset, threshold, inputs, input_var = 'I', dt = defaultclock.dt, refractory = 0*ms, max_refractory = None, spikes = None, traces = None, groups = 1, slices = 1, overlap = 0*second, onset = 0*second, neurons = 1000, # = nodesize = number of neurons on this node = (total number of neurons on this node)/(number of slices) initial_values = None, unit_type = 'CPU', stepsize = 128*ms, precision = 'double', criterion = None, statemonitor_var=None, spikemonitor = False, nbr_spikes = 200, ntrials=1, method = 'Euler', # stand_alone=False, # neuron_group=None, # given_neuron_group=False ): # print refractory, max_refractory # self.neuron_group = neuron_group # self.given_neuron_group = False # self.stand_alone = given_neuron_group self.model = model self.reset = reset self.threshold = threshold self.inputs = inputs self.input_var = input_var self.dt = dt self.refractory = refractory self.max_refractory = max_refractory self.spikes = spikes self.traces = traces self.initial_values = initial_values self.groups = groups self.slices = slices self.overlap = overlap self.ntrials=ntrials self.onset = onset self.neurons = neurons self.unit_type = unit_type if type(statemonitor_var) is not list and statemonitor_var is not None: statemonitor_var = [statemonitor_var] self.statemonitor_var = statemonitor_var self.spikemonitor=spikemonitor self.nbr_spikes = nbr_spikes self.stepsize = stepsize self.precision = precision self.criterion = criterion self.method = method self.use_gpu = self.unit_type=='GPU' if self.statemonitor_var is not None: self.statemonitor_values = [zeros(self.neurons)]*len(statemonitor_var) self.initialize_neurongroup() self.transform_data() self.inject_input() if self.criterion.__class__.__name__ == 'Brette': self.initialize_criterion(delays=zeros(self.neurons),tau_metric=zeros(self.neurons)) else: self.initialize_criterion(delays=zeros(self.neurons)) if self.use_gpu: self.initialize_gpu() def initialize_neurongroup(self): # Add 'refractory' parameter on the CPU only if not self.use_gpu: if self.max_refractory is not None: refractory = 'refractory' self.model.add_param('refractory', second) else: refractory = self.refractory else: if self.max_refractory is not None: refractory = 0*ms else: refractory = self.refractory # Must recompile the Equations : the functions are not transfered after pickling/unpickling self.model.compile_functions() # print refractory, self.max_refractory if type(refractory) is double: refractory=refractory*second # if self.give_neuron_group == False: self.group = NeuronGroup(self.neurons, # TODO: * slices? model=self.model, reset=self.reset, threshold=self.threshold, refractory=refractory, max_refractory = self.max_refractory, method = self.method, clock=Clock(dt=self.dt)) if self.initial_values is not None: for param, value in self.initial_values.iteritems(): self.group.state(param)[:] = value # else: # self.group = self.neuron_group def initialize_gpu(self): # Select integration scheme according to method if self.method == 'Euler': scheme = euler_scheme elif self.method == 'RK': scheme = rk2_scheme elif self.method == 'exponential_Euler': scheme = exp_euler_scheme else: raise Exception("The numerical integration method is not valid") self.mf = GPUModelFitting(self.group, self.model, self.criterion_object, self.input_var, self.neurons/self.groups, self.onset, statemonitor_var = self.statemonitor_var, spikemonitor = self.spikemonitor, nbr_spikes = self.nbr_spikes, duration = self.sliced_duration, precision=self.precision, scheme=scheme) def transform_data(self): self.transformer = DataTransformer(self.neurons, self.inputs, spikes = self.spikes, traces = self.traces, dt = self.dt, slices = self.slices, overlap = self.overlap, groups = self.groups,ntrials=self.ntrials) self.total_steps = self.transformer.total_steps self.sliced_duration = self.transformer.sliced_duration if self.ntrials>1: self.inputs_inline = self.inputs.flatten() self.sliced_inputs = self.inputs self.inputs_offset = zeros(self.neurons) else: self.sliced_inputs = self.transformer.slice_traces(self.inputs) self.inputs_inline, self.inputs_offset = self.transformer.transform_traces(self.sliced_inputs) if self.traces is not None: self.sliced_traces = self.transformer.slice_traces(self.traces) self.traces_inline, self.traces_offset = self.transformer.transform_traces(self.sliced_traces) else: self.sliced_traces, self.traces_inline, self.traces_offset = None, None, None if self.spikes is not None: if self.ntrials>1: self.sliced_spikes = self.transformer.slice_spikes(self.spikes) self.spikes_inline, self.trials_offset = self.transformer.transform_trials(self.spikes) self.spikes_offset = zeros((self.neurons),dtype=int) else: self.sliced_spikes = self.transformer.slice_spikes(self.spikes) self.spikes_inline, self.spikes_offset = self.transformer.transform_spikes(self.sliced_spikes) self.trials_offset=[0] else: self.sliced_spikes, self.spikes_inline, self.spikes_offset,self.trials_offset = None, None, None, None def inject_input(self): # Injects current in consecutive subgroups, where I_offset have the same value # on successive intervals I_offset = self.inputs_offset k = -1 for i in hstack((nonzero(diff(I_offset))[0], len(I_offset) - 1)): I_offset_subgroup_value = I_offset[i] I_offset_subgroup_length = i - k sliced_subgroup = self.group.subgroup(I_offset_subgroup_length) input_sliced_values = self.inputs_inline[I_offset_subgroup_value:I_offset_subgroup_value + self.total_steps] sliced_subgroup.set_var_by_array(self.input_var, TimedArray(input_sliced_values, clock=self.group.clock)) k = i def initialize_criterion(self, **criterion_params): # general criterion parameters params = dict(group=self.group, traces=self.sliced_traces, spikes=self.sliced_spikes, targets_count=self.groups*self.slices, duration=self.sliced_duration, onset=self.onset, spikes_inline=self.spikes_inline, spikes_offset=self.spikes_offset, traces_inline=self.traces_inline, traces_offset=self.traces_offset,trials_offset=self.trials_offset) for key,val in criterion_params.iteritems(): params[key] = val criterion_name = self.criterion.__class__.__name__ # criterion-specific parameters if criterion_name == 'GammaFactor': params['delta'] = self.criterion.delta params['coincidence_count_algorithm'] = self.criterion.coincidence_count_algorithm params['fr_weight'] = self.criterion.fr_weight self.criterion_object = GammaFactorCriterion(**params) if criterion_name == 'GammaFactor2': params['delta'] = self.criterion.delta params['coincidence_count_algorithm'] = self.criterion.coincidence_count_algorithm params['fr_weight'] = self.criterion.fr_weight params['nlevels'] = self.criterion.nlevels params['level_duration'] = self.criterion.level_duration self.criterion_object = GammaFactorCriterion2(**params) if criterion_name == 'LpError': params['p'] = self.criterion.p params['varname'] = self.criterion.varname params['method'] = self.criterion.method params['insets'] = self.criterion.insets params['outsets'] = self.criterion.outsets params['points'] = self.criterion.points self.criterion_object = LpErrorCriterion(**params) if criterion_name == 'VanRossum': params['tau'] = self.criterion.tau self.criterion_object = VanRossumCriterion(**params) if criterion_name == 'Brette': self.criterion_object = BretteCriterion(**params) def update_neurongroup(self, **param_values): """ Inject fitting parameters into the NeuronGroup """ # Sets the parameter values in the NeuronGroup object self.group.reinit() for param, value in param_values.iteritems(): self.group.state(param)[:] = kron(value, ones(self.slices)) # kron param_values if slicing # Reinitializes the model variables if self.initial_values is not None: for param, value in self.initial_values.iteritems(): self.group.state(param)[:] = value def combine_sliced_values(self, values): if type(values) is tuple: combined_values = tuple([sum(reshape(v, (self.slices, -1)), axis=0) for v in values]) else: combined_values = sum(reshape(values, (self.slices, -1)), axis=0) return combined_values def run(self, **param_values): delays = param_values.pop('delays', zeros(self.neurons)) # print self.refractory,self.max_refractory if self.max_refractory is not None: refractory = param_values.pop('refractory', zeros(self.neurons)) else: refractory = self.refractory*ones(self.neurons) tau_metric = param_values.pop('tau_metric', zeros(self.neurons)) self.update_neurongroup(**param_values) # repeat spike delays and refractory to take slices into account delays = kron(delays, ones(self.slices)) refractory = kron(refractory, ones(self.slices)) tau_metric = kron(tau_metric, ones(self.slices)) # TODO: add here parameters to criterion_params if a criterion must use some parameters criterion_params = dict(delays=delays) if self.criterion.__class__.__name__ == 'Brette': criterion_params['tau_metric'] = tau_metric self.update_neurongroup(**param_values) self.initialize_criterion(**criterion_params) if self.use_gpu: # Reinitializes the simulation object self.mf.reinit_vars(self.criterion_object, self.inputs_inline, self.inputs_offset, self.spikes_inline, self.spikes_offset, self.traces_inline, self.traces_offset, delays, refractory ) # LAUNCHES the simulation on the GPU self.mf.launch(self.sliced_duration, self.stepsize) # Synchronize the GPU values with a call to gpuarray.get() self.criterion_object.update_gpu_values() else: # set the refractory period if self.max_refractory is not None: self.group.refractory = refractory # Launch the simulation on the CPU self.group.clock.reinit() net = Network(self.group, self.criterion_object) if self.statemonitor_var is not None: self.statemonitors = [] for state in self.statemonitor_var: monitor = StateMonitor(self.group, state, record=True) self.statemonitors.append(monitor) net.add(monitor) net.run(self.sliced_duration) sliced_values = self.criterion_object.get_values() combined_values = self.combine_sliced_values(sliced_values) values = self.criterion_object.normalize(combined_values) return values def get_statemonitor_values(self): if not self.use_gpu: return [monitor.values for monitor in self.statemonitors] else: return self.mf.get_statemonitor_values() def get_spikemonitor_values(self): if not self.use_gpu: return [monitor.values for monitor in self.statemonitors] else: return self.mf.get_spikemonitor_values() def simulate( model, reset = None, threshold = None, input = None, input_var = 'I', dt = defaultclock.dt, refractory = 0*ms, max_refractory = None, data = None, groups = 1, slices = 1, overlap = 0*second, onset = None, neurons = 1, initial_values = None, use_gpu = False, stepsize = 128*ms, precision = 'double', criterion = None, ntrials=1, record = None, spikemonitor = False, nbr_spikes = 200, method = 'Euler', stand_alone=False, # neuron_group = none, **params): unit_type = 'CPU' if use_gpu: unit_type = 'GPU' for param in params.keys(): if (param not in model._diffeq_names) and (param != 'delays') and (param != 'tau_metric'): raise Exception("Parameter %s must be defined as a parameter in the model" % param) if criterion is None: criterion = GammaFactor() data = array(data) if criterion.type == 'spikes': # Make sure that 'data' is a N*2-array if data.ndim == 1: data = concatenate((zeros((len(data), 1)), data.reshape((-1, 1))), axis=1) spikes = data traces = None groups = int(array(data)[:, 0].max() + 1) # number of target trains elif criterion.type == 'trace': if data.ndim == 1: data = data.reshape((1,-1)) spikes = None traces = data groups = data.shape[0] elif criterion.type == 'both': # TODO log_warn("Not implemented yet") pass inputs = input if inputs.ndim==1: inputs = inputs.reshape((1,-1)) # dt must be set if dt is None: raise Exception('dt (sampling frequency of the input) must be set') # default overlap when no time slicing if slices == 1: overlap = 0*ms if onset is None: onset = overlap # check numerical integration method if use_gpu and method not in ['Euler', 'RK', 'exponential_Euler']: raise Exception("The method can only be 'Euler', 'RK', or 'exponential_Euler' when using the GPU") if method not in ['Euler', 'RK', 'exponential_Euler', 'linear', 'nonlinear']: raise Exception("The method can only be 'Euler', 'RK', 'exponential_Euler', 'linear', or 'nonlinear'") # determines whether optimization over refractoriness or not if type(refractory) is tuple or type(refractory) is list: params['refractory'] = refractory max_refractory = refractory[-1] else: max_refractory = None # Initialize GPU if use_gpu: set_gpu_device(0) # if neuron_group is not None: # self.neuron_group = neuron_group # self.given_neuron_group = True # else: # self.given_neuron_group = False simulator = Simulator(model, reset, threshold, inputs, input_var = input_var, dt = dt, refractory = refractory, max_refractory = max_refractory, spikes = spikes, traces = traces, groups = groups, slices = slices, overlap = overlap, onset = onset, neurons = neurons, initial_values = initial_values, unit_type = unit_type, stepsize = stepsize, precision = precision, ntrials=ntrials, criterion = criterion, statemonitor_var = record, spikemonitor = spikemonitor, nbr_spikes = nbr_spikes, method = method, # stand_alone=stand_alone, # self.neuron_group, # self.given_neuron_group ) criterion_values = simulator.run(**params) if record is not None and spikemonitor is False: record_values = simulator.get_statemonitor_values() return criterion_values, record_values elif record is not None and spikemonitor is True: record_values = simulator.get_statemonitor_values() spike_times = simulator.get_spikemonitor_values() return criterion_values, record_values,spike_times elif record is None and spikemonitor is True: spike_times = simulator.get_spikemonitor_values() return criterion_values,spike_times else: return criterion_values if __name__ == '__main__': from brian import loadtxt, ms, savetxt, loadtxt, Equations, NeuronGroup, run, SpikeMonitor,\ StateMonitor, Network from pylab import * equations = Equations(''' dV/dt=(R*I-V)/tau : 1 I : 1 R : 1 tau : second ''') input = loadtxt('current.txt') trace = loadtxt('trace_artificial.txt') neurons = 1 groups = 4 overlap = 250*ms R = [3e9]*neurons tau = linspace(25*ms, 30*ms, neurons) # GAMMA FACTOR criterion = GammaFactor(delta=2*ms) spikes= loadtxt('spikes_artificial.txt') data = spikes # LP ERROR # criterion = LpError(p=2, varname='V') # input, trace = slice_trace(input, trace, slices = groups, overlap = overlap) # data = trace # SIMULATE, EVALUATE CRITERION AND RECORD TRACES ON GPU criterion_values, record_values = simulate( model = equations, reset = 0, threshold = 1, data = trace, input = input, use_gpu = True, dt = .1*ms, criterion = criterion, record = 'V', onset = overlap, neurons = neurons*groups, R = R, tau = tau, ) print criterion_values print record_values.shape n = data.shape[0] print data.shape for i in xrange(n): subplot(100*n+10+(i+1)) plot(trace[i,:]) plot(record_values[i,:]) show() brian-1.4.3/brian/experimental/morphology/000077500000000000000000000000001266062647100206135ustar00rootroot00000000000000brian-1.4.3/brian/experimental/morphology/TODO.txt000066400000000000000000000025131266062647100221220ustar00rootroot00000000000000TODO ---- 1) Maybe: first test with cylinders in Neuron, check access resistance and all. - Cylinder = fine - Tapered cylinder = almost fine (slight difference at the start, maybe different boundary condition?) 1b) Remy's stuff: does *not* work (maybe the gpu version?) 1c) Write it myself, use Remy's examples to check. - Works for 1 branch. - Impulse response works for sequence of branches. - Ball and stick works! (same as Neuron). - Hack for soma works. - Several branches: seems to work. (- Then add soma more cleanly.) 2) Check with active currents. - HH on cylinder, matches nicely. - HH on soma+axon+dendrite works. * Threshold, reset and refractoriness: threshold should be able to return a single boolean, in this case reset should be given the full index array. * Cm should be (optionally) a distributed variable (for myeline). It could be done simply by a check that Cm is in the equations (if Cm=None, then we use the distributed variable). * Write examples, including point processes * Think about point processes. Use the Synapses object? * Morphology: adding morphologies (at least cylinders)? * One thing is missing in the Morphology: the position of the current branch in the big matrix (Note: all other variables, length etc, are views on the big matrix, for the current branch). * GPU brian-1.4.3/brian/experimental/morphology/__init__.py000066400000000000000000000000671266062647100227270ustar00rootroot00000000000000from morphology import * from spatialneuron import * brian-1.4.3/brian/experimental/morphology/algorithms.txt000066400000000000000000000175531266062647100235400ustar00rootroot00000000000000Algorithms for simulating branched cable equations -------------------------------------------------- The state update for the cable equation can be done with Hines method: Int J Biomed Comput. 1984 Jan-Feb;15(1):69-76. Efficient computation of branched nerve equations. Hines M. Available on Neuron's website: http://www.neuron.yale.edu/neuron/nrnpubs Another method here: http://www.jstor.org/stable/pdfplus/2157690.pdf The Backward Euler Method for Numerical Solution of the Hodgkin-Huxley Equations of Nerve Conduction. Mascagni M. 1990 And: Digital computer solutions for excitation and propagation of the nerve impulse. Cooley & Dodge 1966 In Hines method, values of ionic currents are calculated at midpoints of time steps as a function of V(t), and the values of the conductances are considered as constant within a timestep for the integration of the cable equation. The cable equation uses a simple implicit scheme followed by an explicit step. As I understand it, the update of Im (IHH in the paper) and the integration of the cable equation are completely independent. Therefore, we can have separate stateupdaters which are called in the following order: 1) Update the spatial equation with Hines method. 2) Update the membrane current with a standard vectorized updater (2nd order?). 3) Update the point processes with a standard vectorized updater. (actually I think 2 should be before 1, but it won't make a difference) Mascagni's method is backward Euler, with the advantage that convergence is guaranteed. The algorithm is as follows: 1) Solve the discrete system for V(t+dt), where the other variables are constant and assumed to be known at time t+dt (first iteration: use value at t). This is a sparse linear system (could be computed with scipy.sparse.linalg?). 2) Solve for the other variables at t+dt using a backward Euler step with the values of V from step 1. In principle this is a simple 1D linear system (assuming conditional linearity for these variables). 3) Repeat until convergence (how many times?). In comparison, Hines method uses a single iteration, which is solving a sparse linear system as well (in fact the same one, but at time t+dt/2). 1) Calculate other variables at t+dt/2, by an Euler step from time t-dt/2, that uses the values at time t for V, and estimated at time t for the other variables using the average between t-dt/2 and t+dt/2 (this makes it implicit). 2) Solve for V(t+dt/2) then do a forward step to t+dt. This looks to me like one step of the backward Euler method (except it's 2 then 1). There is pseudo-code in Hines paper. Both methods rely on the conditional linearity of the equations, therefore it cannot work for all types of models (e.g. exponential model). To deal with branches, there are two options: 1) Use Hines method. 2) Use domain decomposition. This means using the linearity of the problem (it is linear once the other variables considered fixed, by conditional linearity) to solve the equation separately on each branch and then combine them linearly. The big advantage is that this way we can still use the solve_banded method of scipy. ---- The simulation is in two stages: 1) Solve the discrete system for V(t+dt), where the other variables are constant and assumed to be known at time t+dt (first iteration: use value at t). This is a sparse linear system (could be computed with scipy.sparse.linalg?). 2) Calculate Im at t+dt using a backward Euler step with the values of V from step 1. That is, do one step of implicit Euler. And possibly repeat (until convergence) (how many times?). The discretized longitudinal current is: a/(2*R)*(V[i+1]-2*V[i]+V[i-1])/dx**2 Artificial gridpoints are used so that: (V[1]-V[-1])/(2*dx) = dV/dt(0) # =0 in general (V[N+1]-V[N-1])/(2*dx) = dV/dt(L) # =0 in general ** This is taken from Mascagni but does not seem to be adapted to Neuron's convention ** ** Neuron's convention below would be: ** With Neuron's convention, we should add two endpoints with coordinates 0 and 1, i.e., on the boundaries of the cable. If i goes from 0 to N-1 (included), we define points -1 and N, at half a compartment from the ends. These nodes have zero area, and coefficients Aplus and Aminus are calculated based on half a compartment length. There is no membrane current. Voltage at endpoints seems to be Genesis's convention too. ** Soma ** The soma should be treated specially, with no axial resistance. Consider also the capacitive current (and leak?). Actually in Mascagni it's more complicated, because we use the full membrane equation (conductances in front V in particular). Therefore perhaps we would need sympy to reorganize the equation first as dV/dt=aV+b (see the Synapses class). x=scipy.linalg.solve_banded((lower,upper),ab,b) lower = number of lower diagonals upper = number of upper diagonals ab = array(l+u+1,M) each row is one diagonal a[i,j]=ab[u+i-j,j] -------------------- Domain decomposition -------------------- Consider two connected cables, with the junction point at distance L. The linear equations (that is, assuming fixed variables other than v) are: L_A v^A = I^A L_B v^B = I^B for the two cables A and B. Continuity requirements are: * v^A(L)=v^B(0) # potential * d/dx v^A(L)=d/dx v^B(0) # current There are homogeneous boundary conditions on the two ends (zero current). Consider the first equation, with boundary condition at 0 and free (unknown) voltage at L. Then the set of solutions is a linear one-dimensional manifold: v^A = k*v_0^A + v_*^A where v_0^A is a solution to the homogeneous equation, and v_*^A is a particular solution. For example, choose: * v_0(L)=1 and v_*^A(L)=0 Then v^A(L)=k. The same can be done for cable B. The two continuity requirements mean that: * the constants (k) are identical * they are determined by a linear equation expressing the continuity of current at L: k (d/dx v_0^A - d/dx v_0^B) = d/dx v_*^B - d/dx v_*^A Therefore, an algorithm is: * Solve 2 tridiagonal systems per branch. * Solve the unknowns by a linear system with the dimension of the order of the number of branches * Do linear combinations. The first and third steps can be done in parallel. When the branches connect on both ends, we need two unknown constants per branch. This is solved as follows: * Particular solution v_*(0)=v_*(L)=0 * Homogeneous solution v_0(0)=1 and v_0(L)=0 * Homogeneous solution v_1(0)=0 and v_0(L)=1 * Combination: v = v_*(0) + v(0)v_0 + v(L)v_L (v(0) and v(L) are the two unknowns) Alternatively, boundary conditions could be expressed as currents rather than potentials. When there are several branches, we simply change to current continuity condition to Kirchoff's law. -------------- Implementation -------------- ** Branches ** We use domain decomposition. Each branch consists on n compartments. The voltage v(i) is placed at the middle of the compartment, as in Neuron and Genesis. Each compartment has: 1) a membrane current proportional to the area of the compartment, 2) an axial current to the right, through half the length of the compartment, 3) an axial current to the left, through half the length of the compartment. TO CHECK: axial resistances We add a starting point (-1) and an end point (n), through which: - there is no membrane current, - there is an axial current towards the branch. The soma is a special case, with no axial current. ** Computation ** For each branch, we have Lv=I, where L and I are updated at every timestep. 1) Calculate particular solution with v_*(-1)=v_*(n)=0. 2) Homogeneous solution v_0(-1)=1 and v_0(n)=0 3) Homogeneous solution v_1(-1)=0 and v_0(n)=1 4) Combination: v = v_*(0) + v(0)v_0 + v(L)v_L (v(0) and v(L) are the two unknowns) It is seen here that the two additional endpoints do not actually need to be explicitly present, since they are in fact constant in the calculations. brian-1.4.3/brian/experimental/morphology/morphology.py000066400000000000000000000417231266062647100233730ustar00rootroot00000000000000''' Neuronal morphology module for Brian. TODO: * Maybe str: should give a text representation of the tree * set_length etc.: should be recursive * check_consistency: check that lengths, area and coordinates are consistent (and fix) * rescale: change number of compartments ''' from brian.group import Group from scipy import rand from numpy import * from brian.units import meter from brian.stdunits import um import warnings from pylab import figure import copy try: from mpl_toolkits.mplot3d import Axes3D except: warnings.warn('Pylab 0.99.1 is required for 3D plots') __all__ = ['Morphology', 'Cylinder', 'Soma'] class Morphology(object): ''' Neuronal morphology (=tree of branches). ''' def __init__(self, filename=None, n=None): self.children = [] self._namedkid = {} self.iscompressed = False if filename is not None: self.loadswc(filename) elif n is not None: # Creates a branch with n compartments # The problem here is that these parameters should have some self-consistency self.x, self.y, self.z, self.diameter, self.length, self.area = [zeros(n) for _ in range(6)] def set_length(self): ''' Sets the length of compartments according to their coordinates ''' x = hstack((0 * um, self.x)) y = hstack((0 * um, self.y)) z = hstack((0 * um, self.z)) self.length = sum((x[1:] - x[:-1]) ** 2 + (y[1:] - y[:-1]) ** 2 + (z[1:] - z[:-1]) ** 2) ** .5 def set_area(self): ''' Sets the area of compartments according to diameter and length (assuming cylinders) ''' self.area = pi * self.diameter * self.length def set_coordinates(self): ''' Sets the coordinates of compartments according to their lengths (taking a random direction) ''' l = cumsum(self.length) theta = rand()*2 * pi phi = rand()*2 * pi self.x = l * sin(theta) * cos(phi) self.y = l * sin(theta) * sin(phi) self.z = l * cos(theta) def loadswc(self, filename): ''' Reads a SWC file containing a neuronal morphology. Large database at http://neuromorpho.org/neuroMorpho Information below from http://www.mssm.edu/cnic/swc.html SWC File Format The format of an SWC file is fairly simple. It is a text file consisting of a header with various fields beginning with a # character, and a series of three dimensional points containing an index, radius, type, and connectivity information. The lines in the text file representing points have the following layout. n T x y z R P n is an integer label that identifies the current point and increments by one from one line to the next. T is an integer representing the type of neuronal segment, such as soma, axon, apical dendrite, etc. The standard accepted integer values are given below. * 0 = undefined * 1 = soma * 2 = axon * 3 = dendrite * 4 = apical dendrite * 5 = fork point * 6 = end point * 7 = custom x, y, z gives the cartesian coordinates of each node. R is the radius at that node. P indicates the parent (the integer label) of the current point or -1 to indicate an origin (soma). ''' # 1) Create the list of segments, each segment has a list of children lines = open(filename).read().splitlines() segment = [] # list of segments types = ['undefined', 'soma', 'axon', 'dendrite', 'apical', 'fork', 'end', 'custom'] previousn = -1 for line in lines: if line[0] != '#': # comment numbers = line.split() n = int(numbers[0]) - 1 T = types[int(numbers[1])] x = float(numbers[2]) * um y = float(numbers[3]) * um z = float(numbers[4]) * um R = float(numbers[5]) * um P = int(numbers[6]) - 1 # 0-based indexing if (n != previousn + 1): raise ValueError, "Bad format in file " + filename seg = dict(x=x, y=y, z=z, T=T, diameter=2 * R, parent=P, children=[]) location = (x, y, z) if T == 'soma': seg['area'] = 4 * pi * R ** 2 seg['length'] = 0 * um else: # dendrite locationP = (segment[P]['x'], segment[P]['y'], segment[P]['z']) seg['length'] = (sum((array(location) - array(locationP)) ** 2)) ** .5 * meter seg['area'] = seg['length'] * 2 * pi * R if P >= 0: segment[P]['children'].append(n) segment.append(seg) previousn = n # We assume that the first segment is the root self.create_from_segments(segment) def create_from_segments(self, segment, origin=0): """ Recursively create the morphology from a list of segments. Each segment has attributes: x,y,z,diameter,area,length (vectors) and children (list). It also creates a dictionary of names (_namedkid). """ n = origin if segment[origin]['T'] != 'soma': # if it's a soma, only one compartment while (len(segment[n]['children']) == 1) and (segment[n]['T'] != 'soma'): # Go to the end of the branch n += 1 # End of branch branch = segment[origin:n + 1] # Set attributes self.diameter, self.length, self.area, self.x, self.y, self.z = \ zip(*[(seg['diameter'], seg['length'], seg['area'], seg['x'], seg['y'], seg['z']) for seg in branch]) self.diameter, self.length, self.area, self.x, self.y, self.z = array(self.diameter), array(self.length), \ array(self.area), array(self.x), array(self.y), array(self.z) self.type = segment[n]['T'] # normally same type for all compartments in the branch # Create children (list) self.children = [Morphology().create_from_segments(segment, origin=c) for c in segment[n]['children']] # Create dictionary of names (enumerates children from number 1) for i, child in enumerate(self.children): self._namedkid[str(i + 1)] = child # Name the child if possible if child.type in ['soma', 'axon', 'dendrite']: if child.type in self._namedkid: self._namedkid[child.type] = None # two children with the same name: erase (see next block) else: self._namedkid[child.type] = child # Erase useless names for k in self._namedkid.keys(): if self._namedkid[k] is None: del self._namedkid[k] # If two kids, name them L (left) and R (right) if len(self.children) == 2: self._namedkid['L'] = self._namedkid['1'] self._namedkid['R'] = self._namedkid['2'] return self def branch(self): ''' Returns the current branch without the children. ''' morpho = copy.copy(self) morpho.children = [] morpho._namedkid = {} return morpho def __getitem__(self, x): """ Returns the subtree named x. Ex.: neuron['axon'] or neuron['11213'] neuron[10*um:20*um] returns the subbranch from 10 um to 20 um. neuron[10*um] returns one compartment. neuron[5] returns compartment number 5. TODO: neuron[:] returns the full branch. """ if type(x) == type(0): # int: returns one compartment morpho = self.branch() i = x j = i + 1 morpho.diameter = morpho.diameter[i:j] morpho.length = morpho.length[i:j] morpho.area = morpho.area[i:j] morpho.x = morpho.x[i:j] morpho.y = morpho.y[i:j] morpho.z = morpho.z[i:j] if hasattr(morpho, '_origin'): morpho._origin += i return morpho elif isinstance(x, slice): # neuron[10*um:20*um] morpho = self.branch() start, stop = x.start, x.stop l = cumsum(morpho.length) # coordinate on the branch i = searchsorted(l, start) j = searchsorted(l, stop) morpho.diameter = morpho.diameter[i:j] morpho.length = morpho.length[i:j] morpho.area = morpho.area[i:j] morpho.x = morpho.x[i:j] morpho.y = morpho.y[i:j] morpho.z = morpho.z[i:j] if hasattr(morpho, '_origin'): morpho._origin += i return morpho elif isinstance(x, float): # neuron[10*um] morpho = self.branch() l = cumsum(morpho.length) # coordinate on the branch i = searchsorted(l, x) j = i + 1 morpho.diameter = morpho.diameter[i:j] morpho.length = morpho.length[i:j] morpho.area = morpho.area[i:j] morpho.x = morpho.x[i:j] morpho.y = morpho.y[i:j] morpho.z = morpho.z[i:j] if hasattr(morpho, '_origin'): morpho._origin += i return morpho else: x = str(x) # convert int to string if (len(x) > 1) and all([c in 'LR123456789' for c in x]): # binary string of the form LLLRLR or 1213 (or mixed) return self._namedkid[x[0]][x[1:]] elif x in self._namedkid: return self._namedkid[x] else: raise AttributeError, "The subtree " + x + " does not exist" def __setitem__(self, x, kid): """ Inserts the subtree and name it x. Ex.: neuron['axon'] or neuron['11213'] If the tree already exists with another name, then it creates a synonym for this tree. The coordinates of the subtree are relative before function call, and are absolute after function call. """ x = str(x) # convert int to string if (len(x) > 1) and all([c in 'LR123456789' for c in x]): # binary string of the form LLLRLR or 1213 (or mixed) self._namedkid[x[0]][x[1:]] = kid elif x in self._namedkid: raise AttributeError, "The subtree " + x + " already exists" else: # Update coordinates kid.x += self.x[-1] kid.y += self.y[-1] kid.z += self.z[-1] if kid not in self.children: self.children.append(kid) self._namedkid[str(len(self.children))] = kid # numbered child self._namedkid[x] = kid def __delitem__(self, x): """ Removes the subtree x. """ x = str(x) # convert int to string if (len(x) > 1) and all([c in 'LR123456789' for c in x]): # binary string of the form LLLRLR or 1213 (or mixed) del self._namedkid[x[0]][x[1:]] elif x in self._namedkid: child = self._namedkid[x] # Delete from name dictionary for name, kid in self._namedkid.items(): if kid is child: del self._namedkid[name] # Delete from list of children for i, kid in enumerate(self.children): if kid is child: del self.children[i] else: raise AttributeError, "The subtree " + x + " does not exist" def __getattr__(self, x): """ Returns the subtree named x. Ex.: axon=neuron.axon """ return self[x] def __setattr__(self, x, kid): """ Attach a subtree and named it x. If the subtree is None then the subtree x is deleted. Ex.: neuron.axon=Soma(diameter=10*um) Ex.: neuron.axon=None """ if isinstance(kid, Morphology): if kid is None: del self[x] else: self[x] = kid else: # If it is not a subtree, then it's a normal class attribute object.__setattr__(self, x, kid) def __len__(self): """ Returns the total number of compartments. """ return len(self.x) + sum(len(child) for child in self.children) def compress(self, diameter=None, length=None, area=None, x=None, y=None, z=None, origin=0): """ Compresses the tree by changing the compartment vectors to views on a matrix (or vectors). The morphology cannot be changed anymore but all other functions should work normally. origin : offset in the base matrix """ self._origin = origin n = len(self.x) # Update values of vectors diameter[:n] = self.diameter length[:n] = self.length area[:n] = self.area x[:n] = self.x y[:n] = self.y z[:n] = self.z # Attributes are now views on these vectors self.diameter = diameter[:n] self.length = length[:n] self.area = area[:n] self.x = x[:n] self.y = y[:n] self.z = z[:n] for kid in self.children: kid.compress(diameter=diameter[n:], length=length[n:], area=area[n:], x=x[n:], y=y[n:], z=z[n:], origin=origin+n) n += len(kid) self.iscompressed = True def plot(self, axes=None, simple=True, origin=None): """ Plots the morphology in 3D. Units are um. axes : the figure axes (new figure if not given) simple : if True, the diameter of branches is ignored """ if axes is None: # new figure fig = figure() axes = Axes3D(fig) x, y, z, d = self.x / um, self.y / um, self.z / um, self.diameter / um if origin is not None: x0, y0, z0 = origin x = hstack((x0, x)) y = hstack((y0, y)) z = hstack((z0, z)) if len(x) == 1: # root with a single compartment: probably just the soma axes.plot(x, y, z, "r.", linewidth=d[0]) else: if simple: axes.plot(x, y, z, "k") else: # linewidth reflects compartment diameter for n in range(1, len(x)): axes.plot([x[n - 1], x[n]], [y[n - 1], y[n]], [z[n - 1], z[n]], 'k', linewidth=d[n - 1]) for c in self.children: c.plot(origin=(x[-1], y[-1], z[-1]), axes=axes, simple=simple) class Cylinder(Morphology): """ A cylinder. """ def __init__(self, length=None, diameter=None, n=1, type=None, x=None, y=None, z=None): """ Creates a cylinder. n: number of compartments. type : 'soma', 'axon' or 'dendrite' x,y,z : end point (relative to origin of cylinder) length is optional (and ignored) if x,y,z is specified If x,y,z unspecified: random direction """ Morphology.__init__(self) if x is None: theta = rand()*2 * pi phi = rand()*2 * pi x = length * sin(theta) * cos(phi) y = length * sin(theta) * sin(phi) z = length * cos(theta) else: length = (sum(array((x, y, z)) ** 2)) ** .5 scale = arange(1, n + 1) * 1. / n self.x, self.y, self.z = x * scale, y * scale, z * scale self.length = ones(n) * length / n self.diameter = ones(n) * diameter self.area = ones(n) * pi * diameter * length / n self.type = type class Soma(Morphology): # or Sphere? """ A spherical soma. """ def __init__(self, diameter=None): Morphology.__init__(self) self.diameter = ones(1) * diameter self.area = ones(1) * pi * diameter ** 2 self.length = zeros(1) self.x, self.y, self.z = zeros(1), zeros(1), zeros(1) self.type = 'soma' if __name__ == '__main__': from pylab import show #morpho=Morphology('mp_ma_40984_gc2.CNG.swc') # retinal ganglion cell morpho = Morphology('oi24rpy1.CNG.swc') # visual L3 pyramidal cell print len(morpho), "compartments" morpho.axon = None morpho.plot() #morpho=Cylinder(length=10*um,diameter=1*um,n=10) #morpho.plot(simple=True) morpho = Soma(diameter=10 * um) morpho.dendrite = Cylinder(length=3 * um, diameter=1 * um, n=10) morpho.dendrite.L = Cylinder(length=5 * um, diameter=1 * um, n=10) morpho.dendrite.R = Cylinder(length=7 * um, diameter=1 * um, n=10) morpho.dendrite.LL = Cylinder(length=3 * um, diameter=1 * um, n=10) morpho.axon = Morphology(n=5) morpho.axon.diameter = ones(5) * 1 * um morpho.axon.length = [1 * um, 2 * um, 1 * um, 3 * um, 1 * um] morpho.axon.set_coordinates() morpho.axon.set_area() morpho.plot(simple=True) show() brian-1.4.3/brian/experimental/morphology/mp_ma_40984_gc2.CNG.swc000066400000000000000000000267671266062647100243550ustar00rootroot00000000000000# Original file mp.ma.40984.gc2.asc.swc edited by Duncan Donohue using SWCfix version 1.21 on 8/31/05. # Errors and fixes documented in mp.ma.40984.gc2.asc.swc.err. See SWCfix1.21.doc for more information. # # Amaral to SWC conversion from L-Measure. R. Scorcioni: rscorcio@gmu.edu # Original fileName:C:\Documents and Settings\Admin\Desktop\Miller-Eutectic\mp.ma.40984.gc2.asc # # ORIGINAL_SOURCE # CREATURE # REGION # FIELD/LAYER # TYPE # CONTRIBUTOR # REFERENCE # RAW # EXTRAS # SOMA_AREA # SHRINKAGE_CORRECTION # VERSION_NUMBER # VERSION_DATE # ********************************************* # SCALE 1.0 1.0 1.0 1 1 0.2917 0.04167 -0.1458 12.030 -1 2 3 12. 6.5 1. 0.850 1 3 3 15. 9. 1.5 0.75 2 4 3 18.5 10. 2.5 0.65 3 5 3 17. 8. 3. 0.15 4 6 3 18. 7. 2.5 0.15 5 7 3 16. 4. 8. 0.15 6 8 3 14. 0.5 8. 0.15 7 9 3 7. -11.5 9. 0.09 8 10 3 1.5 -19. 8. 0.09 9 11 3 -3.5 -22. 9. 0.09 10 12 3 -9.5 -24.5 9. 0.09 11 13 3 -17.5 -24. 9. 0.09 12 14 3 -21.5 -23. 10.5 0.09 13 15 3 -25. -22.5 10.5 0.09 14 16 3 21.5 10. 2. 0.45 4 17 3 24.5 11.5 2.5 0.4 16 18 3 27. 10.5 4. 0.3 17 19 3 28.5 10. 6. 0.2 18 20 3 29.5 11. 7. 0.2 19 21 3 31. 9.5 8.5 0.25 20 22 3 36. 11.5 9. 0.2 21 23 3 37.5 9. 10.5 0.2 22 24 3 38.5 4. 10. 0.25 23 25 3 42. 3.5 11. 0.2 24 26 3 44. 0.5 10. 0.2 25 27 3 48.5 -0.5 10.5 0.2 26 28 3 60. -2. 9. 0.2 27 29 3 68. -1.5 9.5 0.2 28 30 3 72. -3. 9.5 0.25 29 31 3 75.5 -6.5 9. 0.15 30 32 3 79. -9.5 9.5 0.15 31 33 3 84.5 -12. 9. 0.15 32 34 3 96. -13. 8.5 0.15 33 35 3 101.5 -16.5 10. 0.15 34 36 3 112. -17. 10. 0.15 35 37 3 119.5 -18.5 10. 0.15 36 38 3 126. -20. 10. 0.15 37 39 3 133.5 -21.5 10. 0.09 38 40 3 137.5 -21.5 10. 0.09 39 41 3 140.5 -25. 9.5 0.09 40 42 3 139.5 -30. 10.5 0.09 41 43 3 141.5 -34.5 10.5 0.09 42 44 3 141.5 -38. 10.5 0.09 43 45 3 142. -41. 10.5 0.09 44 46 3 145.5 -44. 10.5 0.09 45 47 3 149.5 -48.5 9.5 0.09 46 48 3 154.5 -52. 11. 0.09 47 49 3 158. -58.5 11. 0.09 48 50 3 157.5 -65. 12.5 0.09 49 51 3 156. -70.5 12.5 0.09 50 52 3 155. -80. 12.5 0.09 51 53 3 155. -84. 13.5 0.09 52 54 3 158.5 -84. 13.5 0.09 53 55 3 160.5 -85.5 13.5 0.09 54 56 3 10. -4. 3. 1.95 1 57 3 10.5 -6. 4. 1.85 56 58 3 14.5 -7. 3.5 1.45 57 59 3 15. -10.5 3.5 1.45 58 60 3 18.5 -12.5 3.5 1.45 59 61 3 20.5 -15.5 3.5 1.45 60 62 3 20.5 -18. 3.5 1.45 61 63 3 19.5 -22.5 7.5 0.8 62 64 3 17. -30. 8.5 0.8 63 65 3 15.5 -35. 8.5 0.9 64 66 3 14. -40. 9. 0.9 65 67 3 14. -46. 8.5 0.9 66 68 3 16. -52.5 9. 1.75 67 69 3 13.5 -56.5 9. 0.65 68 70 3 12.5 -59. 8. 0.75 69 71 3 10. -61. 8.5 0.15 70 72 3 5. -65. 7. 0.15 71 73 3 -0.5 -69.5 6.5 0.15 72 74 3 -3.5 -73.5 7. 0.09 73 75 3 -6.5 -78.5 7.5 0.09 74 76 3 -9. -82. 7.5 0.09 75 77 3 -8.5 -89. 7.5 0.09 76 78 3 -7.5 -95. 7. 0.09 77 79 3 -6. -101. 7. 0.09 78 80 3 -8. -113. 6.5 0.09 79 81 3 -11.5 -123. 7. 0.09 80 82 3 -12. -130.5 7.5 0.09 81 83 3 -11.5 -141. 7. 0.09 82 84 3 -13.5 -150.5 7.5 0.09 83 85 3 -15. -157.5 7. 0.09 84 86 3 -17.5 -162.5 7.5 0.09 85 87 3 -18.5 -168. 8. 0.09 86 88 3 -20.5 -170.5 8. 0.09 87 89 3 12. -63.5 9. 0.45 70 90 3 13. -68.5 9. 0.4 89 91 3 14.5 -73. 8.5 0.4 90 92 3 17.5 -78. 9.5 0.4 91 93 3 19. -84.5 10. 0.4 92 94 3 21.5 -92.5 9.5 0.4 93 95 3 24. -99.5 9. 0.4 94 96 3 25. -103. 9. 0.4 95 97 3 25.5 -104.5 10. 0.4 96 98 3 27. -107. 9.5 0.4 97 99 3 29. -109. 10.5 0.4 98 100 3 31.5 -114.5 10.5 0.4 99 101 3 33. -120.5 10. 0.4 100 102 3 32. -123.5 10. 0.4 101 103 3 30. -127. 9. 0.15 102 104 3 28. -131.5 8.5 0.15 103 105 3 26.5 -133.5 9.5 0.09 104 106 3 31.5 -133. 8. 0.09 104 107 3 35.5 -134.5 7. 0.09 106 108 3 33.5 -124. 9. 0.3 102 109 3 37. -125.5 9. 0.2 108 110 3 39. -126.5 8.5 0.15 109 111 3 42.5 -129.5 8.5 0.15 110 112 3 46. -132. 7. 0.15 111 113 3 49. -135.5 6.5 0.09 112 114 3 49. -137. 7. 0.09 113 115 3 53.5 -141. 6. 0.09 114 116 3 57. -145.5 6. 0.09 115 117 3 60.5 -150. 6. 0.09 116 118 3 61.5 -154. 6. 0.09 117 119 3 62. -158.5 7.5 0.09 118 120 3 67.5 -165. 7.5 0.09 119 121 3 72.5 -170.5 8. 0.09 120 122 3 72.5 -176.5 7.5 0.09 121 123 3 76.5 -186. 8.5 0.09 122 124 3 76.5 -187. 8.5 0.09 123 125 3 21.5 -53.5 9. 0.65 68 126 3 25.5 -55.5 9.5 0.55 125 127 3 30.5 -58. 10. 0.45 126 128 3 33. -58.5 9.5 0.45 127 129 3 33. -62. 9. 0.15 128 130 3 35.5 -66.5 7.5 0.15 129 131 3 38. -70. 8.5 0.15 130 132 3 39.5 -75. 9. 0.15 131 133 3 40. -78.5 8. 0.15 132 134 3 42. -81. 8.5 0.09 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152. -89. 11. 0.09 338 340 3 154.5 -94. 12.5 0.09 339 341 3 47. -36.5 8.5 0.15 307 342 3 52.5 -36. 10.5 0.09 341 343 3 55. -36. 9.5 0.09 342 344 3 59.5 -36. 9.5 0.09 343 345 3 61. -39. 9.5 0.09 344 346 3 63. -41. 9.5 0.09 345 347 3 65. -45.5 9.5 0.09 346 348 3 68.5 -46.5 8.5 0.049 347 349 3 70. -49. 8.5 0.049 348 350 3 68.5 -52. 10. 0.049 349 351 3 68.5 -55.5 8.5 0.049 350 352 3 71.5 -61.5 10. 0.049 351 353 3 76.5 -62.5 9. 0.049 352 brian-1.4.3/brian/experimental/morphology/oi24rpy1.CNG.swc000066400000000000000000023030641266062647100233400ustar00rootroot00000000000000# Original file oi24rpy1.asc.swc edited by Duncan Donohue using StdSwc version 1.21 on 8/22/05. # Irregularities and fixes documented in oi24rpy1.asc.swc.std. See StdSwc1.21.doc for more information. # # Neurolucida to SWC conversion from L-Measure. R. Scorcioni: rscorcio@gmu.edu # Original fileName:C:\Documents and Settings\Admin\Desktop\eysel\oi24rpy1.asc # # ORIGINAL_SOURCE # CREATURE # REGION # FIELD/LAYER # TYPE # CONTRIBUTOR # REFERENCE # RAW # EXTRAS # SOMA_AREA # SHRINKAGE_CORRECTION # VERSION_NUMBER # VERSION_DATE # ********************************************* # SCALE 1.0 1.0 1.0 1 1 -21910.8 -7388.66 216.957 10.485 -1 2 4 -21911.8 -7390.21 232.92 3.22 1 3 4 -21912.1 -7388.21 236.59 2.725 2 4 4 -21912.8 -7385.5 239.71 2.355 3 5 4 -21913 -7384.13 247.16 1.86 4 6 4 -21913.1 -7383.92 247.21 1.86 5 7 4 -21913.7 -7382.31 250 1.86 6 8 4 -21913.7 -7382.43 250.69 1.86 7 9 4 -21914.4 -7380.22 248.55 1.86 8 10 4 -21914.6 -7378.42 248.87 1.86 9 11 4 -21918.9 -7372.62 250.23 1.985 10 12 4 -21919.6 -7371.91 250.41 1.985 11 13 4 -21920.5 -7369.79 251.08 1.985 12 14 4 -21920.5 -7369.83 251.31 1.985 13 15 4 -21920.4 -7369.68 253.82 1.985 14 16 4 -21921 -7368.54 260.46 1.985 15 17 4 -21921 -7368.59 260.76 1.985 16 18 4 -21921.9 -7366.92 260.97 1.985 17 19 4 -21921.7 -7366.84 259.37 1.985 18 20 4 -21921.4 -7367.07 259.3 1.735 19 21 4 -21921 -7369.1 258.93 1.61 20 22 4 -21921.1 -7368.88 258.98 1.61 21 23 4 -21920.6 -7369.83 258.77 1.61 22 24 4 -21920.1 -7371 259.89 1.61 23 25 4 -21919.3 -7371.39 261.27 1.485 24 26 4 -21918.9 -7371.87 261.15 1.365 25 27 4 -21918.3 -7372.27 261.03 1.24 26 28 4 -21917.3 -7372.9 260.83 1.24 27 29 4 -21916.8 -7374.15 260.58 1.24 28 30 4 -21917 -7374.91 260.46 1.115 29 31 4 -21916.7 -7376.02 260.87 0.865 30 32 4 -21916.4 -7378.19 263.6 0.865 31 33 4 -21916.5 -7379.25 263.43 0.745 32 34 4 -21916.8 -7380.85 263.19 0.745 33 35 4 -21916.3 -7383.44 262.73 0.745 34 36 4 -21915.4 -7385.42 262.31 0.745 35 37 4 -21914.5 -7387.1 261.95 0.745 36 38 4 -21914 -7387.84 263.33 0.745 37 39 4 -21913.5 -7389.03 263.08 0.745 38 40 4 -21912.5 -7389.92 262.84 0.62 39 41 4 -21913.1 -7391.34 262.66 0.62 40 42 4 -21912.6 -7392.06 262.5 0.62 41 43 4 -21912.3 -7392.02 262.48 0.62 42 44 4 -21911.5 -7393.15 262.22 0.62 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4 -21876.6 -7328.13 315.04 0.495 995 997 4 -21876.7 -7327.93 315.08 0.495 996 998 4 -21876 -7326.54 315.51 0.495 997 999 4 -21876 -7326.62 315.98 0.495 998 1000 4 -21874.5 -7324.89 317.14 0.495 999 1001 4 -21874.6 -7324.62 317.19 0.495 1000 1002 4 -21873.1 -7323.66 320.64 0.495 1001 1003 4 -21871.9 -7322.61 320.69 0.495 1002 1004 4 -21870.5 -7320.96 320.83 0.495 1003 1005 4 -21868.7 -7318.91 318.95 0.495 1004 1006 4 -21867.4 -7318.15 318.96 0.495 1005 1007 4 -21866.9 -7317.78 318.97 0.495 1006 1008 4 -21865.6 -7317.24 318.94 0.495 1007 1009 4 -21864.5 -7316.76 318.92 0.495 1008 1010 4 -21862.7 -7315.9 318.89 0.495 1009 1011 4 -21862.5 -7315.81 318.88 0.495 1010 1012 4 -21859.8 -7314.32 318.88 0.495 1011 1013 4 -21859.7 -7313.96 318.93 0.495 1012 1014 4 -21856.5 -7311.5 317.55 0.495 1013 1015 4 -21853.1 -7309.79 317.51 0.495 1014 1016 4 -21852.6 -7309.43 317.53 0.495 1015 1017 4 -21849.5 -7307.46 317.41 0.495 1016 1018 4 -21849.3 -7307.14 317.44 0.495 1017 1019 4 -21844.6 -7304.6 314.24 0.495 1018 1020 4 -21844 -7304.45 314.21 0.495 1019 1021 4 -21841.5 -7303 313.22 0.495 1020 1022 4 -21841.3 -7302.91 312.86 0.495 1021 1023 4 -21838.2 -7300.89 312.48 0.495 1022 1024 4 -21838 -7300.6 312.5 0.495 1023 1025 4 -21837.8 -7300.27 312.54 0.495 1024 1026 4 -21835.5 -7298.72 312.52 0.495 1025 1027 4 -21835 -7298.38 312.53 0.495 1026 1028 4 -21832.6 -7296.19 311.6 0.495 1027 1029 4 -21832.4 -7295.82 311.64 0.495 1028 1030 4 -21832 -7295.24 311.7 0.495 1029 1031 4 -21831.2 -7293.21 311.96 0.495 1030 1032 4 -21830.6 -7292.02 312.1 0.495 1031 1033 4 -21830.3 -7291.96 312.08 0.495 1032 1034 4 -21828.4 -7289.35 313.23 0.37 1033 1035 4 -21827.2 -7286.97 313.5 0.37 1034 1036 4 -21826.9 -7286.93 313.48 0.37 1035 1037 4 -21824.2 -7283.04 314.45 0.37 1036 1038 4 -21821.6 -7281.8 317.81 0.37 1037 1039 4 -21820 -7279.68 318.01 0.37 1038 1040 4 -21817 -7277.7 318.05 0.37 1039 1041 4 -21815.4 -7276.88 318.04 0.37 1040 1042 4 -21815.1 -7276.82 318.03 0.37 1041 1043 4 -21812 -7275.05 318.94 0.37 1042 1044 4 -21806.7 -7272.47 323.81 0.37 1043 1045 4 -21881.5 -7338.52 309.69 0.745 987 1046 4 -21879.4 -7337.87 309.59 0.745 1045 1047 4 -21879.1 -7337.77 309.58 0.745 1046 1048 4 -21875.8 -7336.3 312.25 0.495 1047 1049 4 -21872.9 -7336.29 312.13 0.495 1048 1050 4 -21870.3 -7335.54 315.32 0.495 1049 1051 4 -21867.5 -7334.51 316.87 0.495 1050 1052 4 -21864.5 -7335.81 319.6 0.495 1051 1053 4 -21864.3 -7335.82 319.58 0.495 1052 1054 4 -21862.6 -7337.08 319.26 0.495 1053 1055 4 -21861.6 -7337.66 319.08 0.495 1054 1056 4 -21857.9 -7340.47 318.62 0.495 1055 1057 4 -21857.3 -7340.61 318.54 0.495 1056 1058 4 -21855.1 -7341.71 319.1 0.495 1057 1059 4 -21855 -7341.8 319.59 0.495 1058 1060 4 -21851.9 -7341.93 319.28 0.495 1059 1061 4 -21852.2 -7341.95 319.3 0.495 1060 1062 4 -21848.7 -7342.18 318.94 0.495 1061 1063 4 -21846.3 -7342.55 318.66 0.495 1062 1064 4 -21843.8 -7342.11 318.49 0.495 1063 1065 4 -21843.8 -7341.83 318.54 0.495 1064 1066 4 -21841.4 -7340.61 318.52 0.495 1065 1067 4 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1113 1115 4 -21860.3 -7351.43 324.38 0.62 1114 1116 4 -21857.5 -7350.95 324.2 0.495 1115 1117 4 -21856 -7351.2 324.02 0.495 1116 1118 4 -21853.9 -7351.22 325.97 0.495 1117 1119 4 -21852.5 -7350.99 325.87 0.495 1118 1120 4 -21851.2 -7350.19 325.88 0.495 1119 1121 4 -21851.2 -7349.93 325.93 0.495 1120 1122 4 -21849.3 -7351.15 329.81 0.495 1121 1123 4 -21849.1 -7350.85 329.84 0.495 1122 1124 4 -21845.8 -7349.72 329.72 0.495 1123 1125 4 -21842.5 -7349.35 332.19 0.495 1124 1126 4 -21842.4 -7349.43 332.69 0.495 1125 1127 4 -21840.3 -7348.86 336.16 0.495 1126 1128 4 -21840.1 -7348.55 336.19 0.495 1127 1129 4 -21838.4 -7348.27 336.08 0.495 1128 1130 4 -21835.5 -7347.67 336.96 0.495 1129 1131 4 -21832.3 -7347.14 338.59 0.495 1130 1132 4 -21830.1 -7346.5 338.49 0.495 1131 1133 4 -21829.8 -7346.19 338.51 0.495 1132 1134 4 -21829 -7346.04 338.47 0.495 1133 1135 4 -21825.6 -7346.39 341.88 0.495 1134 1136 4 -21822.6 -7344.98 342.96 0.495 1135 1137 4 -21819.8 -7345.56 342.61 0.495 1136 1138 4 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1.61 1160 1162 4 -21928.9 -7372.63 292.36 1.115 1161 1163 4 -21930.4 -7372.37 292.54 0.865 1162 1164 4 -21932.1 -7372.1 293.99 0.865 1163 1165 4 -21932 -7372.16 294.32 0.865 1164 1166 4 -21933.9 -7372.04 297.81 0.865 1165 1167 4 -21934.7 -7370.63 298.12 0.865 1166 1168 4 -21935.9 -7369.85 300.21 0.745 1167 1169 4 -21937.1 -7369.12 305.29 0.745 1168 1170 4 -21937.8 -7368.71 305.45 0.745 1169 1171 4 -21938.5 -7367.78 305.73 0.745 1170 1172 4 -21939.3 -7365.87 309.59 0.745 1171 1173 4 -21940.6 -7364.39 310.76 0.62 1172 1174 4 -21940.5 -7364.46 311.18 0.62 1173 1175 4 -21941.4 -7363.57 314.3 0.495 1174 1176 4 -21942.3 -7362.83 315.26 0.495 1175 1177 4 -21943.1 -7362.18 318.45 0.495 1176 1178 4 -21944.3 -7361.33 320.11 0.495 1177 1179 4 -21945.4 -7359.77 321.14 0.495 1178 1180 4 -21947.8 -7359.46 321.45 0.495 1179 1181 4 -21947.8 -7359.49 321.62 0.495 1180 1182 4 -21948.3 -7358.75 323.14 0.495 1181 1183 4 -21949.8 -7357.54 322.2 0.495 1182 1184 4 -21949.9 -7357.25 322.25 0.495 1183 1185 4 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4 -22000.6 -7326.61 379.76 0.37 1208 1210 4 -22003.9 -7325.86 380.32 0.37 1209 1211 4 -22005.2 -7325.48 384.65 0.37 1210 1212 4 -22000.3 -7325.79 385.19 0.37 1211 1213 4 -22002.6 -7324.13 387.22 0.37 1212 1214 4 -22004.2 -7323.82 389.78 0.37 1213 1215 4 -22004.1 -7323.95 390.56 0.37 1214 1216 4 -22007 -7322.93 393.73 0.37 1215 1217 4 -22009.2 -7322.79 396.63 0.25 1216 1218 4 -22012 -7321.3 398.84 0.25 1217 1219 4 -22012.2 -7321.62 398.8 0.25 1218 1220 4 -22013.6 -7321 405.79 0.25 1219 1221 4 -22015.3 -7319.76 410.67 0.25 1220 1222 4 -22017.8 -7318.03 414.91 0.25 1221 1223 4 -22019 -7316.5 424.63 0.25 1222 1224 4 -22021.3 -7314.27 432.43 0.25 1223 1225 4 -21939.3 -7370.74 314.95 0.62 1171 1226 4 -21941.2 -7370.5 315.15 0.62 1225 1227 4 -21942.5 -7370.8 315.57 0.62 1226 1228 4 -21942.4 -7370.9 316.18 0.62 1227 1229 4 -21944.2 -7372.21 319.88 0.495 1228 1230 4 -21946.1 -7372.85 319.95 0.495 1229 1231 4 -21948.5 -7374.6 320.92 0.495 1230 1232 4 -21950.7 -7376.73 321.5 0.495 1231 1233 4 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338.19 0.495 1255 1257 4 -21990.4 -7402.38 339.75 0.495 1256 1258 4 -21990.7 -7402.39 339.78 0.495 1257 1259 4 -21994.8 -7405.34 339.77 0.495 1258 1260 4 -21998 -7408.51 339.35 0.37 1259 1261 4 -21998 -7408.74 339.31 0.37 1260 1262 4 -21999.6 -7410.97 339.1 0.37 1261 1263 4 -22001.7 -7413.14 341.68 0.37 1262 1264 4 -22004.5 -7415 341.64 0.37 1263 1265 4 -22004.8 -7415.06 341.65 0.37 1264 1266 4 -22007.2 -7415.55 343.56 0.37 1265 1267 4 -22009.5 -7415.67 343.76 0.37 1266 1268 4 -22012.4 -7416.19 347.21 0.37 1267 1269 4 -22012.7 -7416.24 347.23 0.37 1268 1270 4 -22015.7 -7417.6 345.8 0.37 1269 1271 4 -22016 -7417.73 344.76 0.37 1270 1272 4 -22020 -7420.28 342.99 0.37 1271 1273 4 -22023.3 -7421.72 343.06 0.37 1272 1274 4 -22023.5 -7421.73 343.08 0.37 1273 1275 4 -22025.5 -7421.84 343.25 0.37 1274 1276 4 -22025.9 -7421.59 343.32 0.37 1275 1277 4 -22028.9 -7423.47 349.26 0.25 1276 1278 4 -22033.4 -7424.22 341.65 0.25 1277 1279 4 -22034 -7424.3 341.69 0.37 1278 1280 4 -21955.8 -7376.75 322.51 0.495 1236 1281 4 -21957.1 -7377.05 322.59 0.495 1280 1282 4 -21958.9 -7377.06 322.75 0.495 1281 1283 4 -21959.2 -7377.19 322.75 0.495 1282 1284 4 -21960.3 -7377.12 322.87 0.495 1283 1285 4 -21961.9 -7377.97 322.88 0.495 1284 1286 4 -21963.3 -7377.45 323.1 0.495 1285 1287 4 -21963.6 -7377.49 323.12 0.495 1286 1288 4 -21964.6 -7377.67 324.66 0.37 1287 1289 4 -21966 -7378.2 324.7 0.37 1288 1290 4 -21967.2 -7378.17 324.82 0.37 1289 1291 4 -21967.7 -7378.07 324.89 0.37 1290 1292 4 -21968.4 -7377.33 325.07 0.37 1291 1293 4 -21969.8 -7377.36 325.2 0.37 1292 1294 4 -21971.4 -7376.97 323.98 0.37 1293 1295 4 -21972.5 -7377.13 324.06 0.37 1294 1296 4 -21972.7 -7374.71 326.52 0.37 1295 1297 4 -21972.9 -7374.82 326.52 0.37 1296 1298 4 -21973.9 -7373.89 326.77 0.37 1297 1299 4 -21975.7 -7373.94 326.93 0.37 1298 1300 4 -21977.8 -7374.03 330.03 0.37 1299 1301 4 -21978.1 -7374.08 330.05 0.37 1300 1302 4 -21980.1 -7373.88 330.27 0.37 1301 1303 4 -21980.8 -7373.75 330.36 0.37 1302 1304 4 -21981 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-7391.42 183.93 0.495 1588 1590 2 -21899.8 -7391.56 178.43 0.495 1589 1591 2 -21895.5 -7390.62 178.19 0.495 1590 1592 2 -21894.9 -7391.04 178.06 0.495 1591 1593 2 -21894.3 -7392.2 175.95 0.495 1592 1594 2 -21894.2 -7392.74 171.79 0.495 1593 1595 2 -21894.3 -7392.58 170.86 0.495 1594 1596 2 -21893.8 -7393.42 169.72 0.495 1595 1597 2 -21893.4 -7393.85 168.13 0.495 1596 1598 2 -21893.5 -7393.65 166.94 0.495 1597 1599 2 -21892.7 -7394.39 164.42 0.495 1598 1600 2 -21892 -7394.96 163.44 0.495 1599 1601 2 -21892.1 -7394.92 163.22 0.495 1600 1602 2 -21891.5 -7395.32 162.92 0.495 1601 1603 2 -21891.5 -7395.05 162.97 0.495 1602 1604 2 -21890.5 -7394.73 157.58 0.495 1603 1605 2 -21892.5 -7392.47 153.72 0.495 1604 1606 2 -21892.6 -7392.24 153.77 0.495 1605 1607 2 -21894.3 -7391.1 152.14 0.495 1606 1608 2 -21894.5 -7390.64 150.85 0.495 1607 1609 2 -21894.5 -7389.12 149.86 0.495 1608 1610 2 -21894.2 -7389.04 149.85 0.495 1609 1611 2 -21893.3 -7387.57 147.11 0.495 1610 1612 2 -21893.1 -7387.27 147.01 0.495 1611 1613 2 -21892 -7386.48 146.73 0.495 1612 1614 2 -21892.2 -7386.19 146.6 0.495 1613 1615 2 -21890.7 -7384.82 146.27 0.495 1614 1616 2 -21890.2 -7384.86 145.39 0.495 1615 1617 2 -21889.3 -7384.72 145.27 0.495 1616 1618 2 -21889.1 -7384.15 145.34 0.495 1617 1619 2 -21888.9 -7384.08 145.33 0.495 1618 1620 2 -21889 -7381.29 138.72 0.495 1619 1621 2 -21887.4 -7380.45 138.52 0.495 1620 1622 2 -21887.6 -7380.24 137.23 0.495 1621 1623 2 -21886.5 -7378.7 134.18 0.495 1622 1624 2 -21886.6 -7378.56 133.34 0.495 1623 1625 2 -21885.8 -7378.04 131.09 0.495 1624 1626 2 -21885.8 -7378 130.84 0.495 1625 1627 2 -21885.5 -7378.1 127.35 0.495 1626 1628 2 -21885 -7378.59 124.27 0.495 1627 1629 2 -21885 -7378.52 123.84 0.495 1628 1630 2 -21884.7 -7379.46 122.23 0.495 1629 1631 2 -21884.5 -7378.99 121.28 0.495 1630 1632 2 -21883.8 -7379.08 119.34 0.495 1631 1633 2 -21883.6 -7378.74 119.38 0.495 1632 1634 2 -21883.2 -7379.06 115.26 0.495 1633 1635 2 -21882.4 -7378.54 111.96 0.495 1634 1636 2 -21882.6 -7378.08 110.61 0.495 1635 1637 2 -21882 -7378.53 112.42 0.495 1636 1638 2 -21881.8 -7377.38 107.36 0.495 1637 1639 2 -21881.8 -7377.1 107.41 0.495 1638 1640 2 -21880 -7378.54 104.92 0.495 1639 1641 2 -21879.7 -7378.28 104.94 0.495 1640 1642 2 -21877.3 -7378.83 104.62 0.495 1641 1643 2 -21876.1 -7379.25 100.06 0.495 1642 1644 2 -21876.3 -7379.24 100.09 0.495 1643 1645 2 -21873.8 -7378.19 97.86 0.495 1644 1646 2 -21872.4 -7378.57 92.25 0.495 1645 1647 2 -21872.8 -7378.24 91.53 0.495 1646 1648 2 -21872.5 -7375.5 85.58 0.495 1647 1649 2 -21871.8 -7374.61 85.65 0.37 1648 1650 2 -21871.8 -7374.33 85.71 0.37 1649 1651 2 -21871.1 -7372.29 85.62 0.37 1650 1652 2 -21870.4 -7371.15 85.75 0.37 1651 1653 2 -21871.2 -7369.18 86.15 0.37 1652 1654 2 -21872.3 -7367.48 86.53 0.37 1653 1655 2 -21872.3 -7365.66 86.84 0.37 1654 1656 2 -21872.5 -7363.77 88.38 0.37 1655 1657 2 -21873.1 -7363.06 89.88 0.37 1656 1658 2 -21873.6 -7361.82 90.13 0.37 1657 1659 2 -21875.4 -7361.06 90.42 0.37 1658 1660 2 -21875.4 -7360.76 90.47 0.37 1659 1661 2 -21876.7 -7359.85 94.79 0.37 1660 1662 2 -21878 -7358.95 94.95 0.37 1661 1663 2 -21878 -7358.93 94.78 0.37 1662 1664 2 -21880.5 -7357.66 94.57 0.37 1663 1665 2 -21880.6 -7357.49 93.52 0.37 1664 1666 2 -21882.5 -7356.82 91.07 0.37 1665 1667 2 -21884.3 -7358.03 91.68 0.37 1666 1668 2 -21886.6 -7358.36 91.84 0.37 1667 1669 2 -21887.7 -7358.27 91.96 0.37 1668 1670 2 -21888.1 -7357.26 92.17 0.37 1669 1671 2 -21890.1 -7357.35 92.34 0.37 1670 1672 2 -21892 -7356.97 90.39 0.37 1671 1673 2 -21894 -7356.8 90.6 0.37 1672 1674 2 -21894 -7356.54 90.65 0.37 1673 1675 2 -21895.5 -7355.99 90.89 0.37 1674 1676 2 -21897.2 -7356.66 90.1 0.37 1675 1677 2 -21899.4 -7357.25 90.2 0.37 1676 1678 2 -21902.6 -7355.84 91.8 0.37 1677 1679 2 -21904 -7355.83 91.93 0.37 1678 1680 2 -21907 -7355.26 92.31 0.37 1679 1681 2 -21907.8 -7355.37 92.36 0.37 1680 1682 2 -21911.2 -7355.11 92.73 0.37 1681 1683 2 -21911.4 -7354.08 92.92 0.37 1682 1684 2 -21912.8 -7354.06 93.05 0.37 1683 1685 2 -21914.2 -7354.28 93.14 0.37 1684 1686 2 -21916.6 -7353.66 93.47 0.37 1685 1687 2 -21916.8 -7353.65 93.5 0.37 1686 1688 2 -21919 -7353 92.59 0.37 1687 1689 2 -21919 -7352.76 92.63 0.37 1688 1690 2 -21921.4 -7352.03 92.98 0.37 1689 1691 2 -21921.6 -7352.14 92.98 0.37 1690 1692 2 -21925.6 -7352.22 93.34 0.37 1691 1693 2 -21926 -7352.02 93.4 0.37 1692 1694 2 -21927.9 -7352.04 93.58 0.37 1693 1695 2 -21928.2 -7351.86 93.64 0.37 1694 1696 2 -21929.7 -7350.79 93.96 0.37 1695 1697 2 -21930.4 -7350.4 94.09 0.37 1696 1698 2 -21932.5 -7349.38 94.46 0.37 1697 1699 2 -21936.6 -7348.46 94.99 0.37 1698 1700 2 -21939.1 -7348.03 93.46 0.37 1699 1701 2 -21943 -7346.8 94.02 0.37 1700 1702 2 -21946.3 -7346.54 91.32 0.37 1701 1703 2 -21950.1 -7345.87 91.79 0.37 1702 1704 2 -21952.9 -7343.92 92.37 0.37 1703 1705 2 -21952.9 -7343.64 92.42 0.37 1704 1706 2 -21955.8 -7342 92.95 0.37 1705 1707 2 -21956 -7342.03 92.98 0.37 1706 1708 2 -21958.5 -7341.53 90.97 0.25 1707 1709 2 -21960 -7341.25 91.15 0.25 1708 1710 2 -21960.3 -7341.01 91.23 0.25 1709 1711 2 -21962 -7341.29 91.34 0.25 1710 1712 2 -21962.3 -7340.81 91.45 0.25 1711 1713 2 -21964.6 -7340.6 91.7 0.25 1712 1714 2 -21965 -7339.93 91.84 0.25 1713 1715 2 -21968.2 -7339.14 92.28 0.25 1714 1716 2 -21968.6 -7338.91 92.35 0.25 1715 1717 2 -21970.3 -7338.68 92.55 0.25 1716 1718 2 -21970.6 -7338.46 92.61 0.25 1717 1719 2 -21971.3 -7339.09 94.02 0.25 1718 1720 2 -21972.3 -7337.92 94.31 0.25 1719 1721 2 -21973.2 -7337.78 94.42 0.25 1720 1722 2 -21975.3 -7337.31 93.08 0.25 1721 1723 2 -21975.7 -7336.89 93.18 0.25 1722 1724 2 -21975.8 -7335.86 93.36 0.25 1723 1725 2 -21976.1 -7335.62 93.44 0.25 1724 1726 2 -21977.8 -7335.63 93.59 0.25 1725 1727 2 -21980 -7335.52 94.13 0.25 1726 1728 2 -21982.1 -7335.36 94.35 0.25 1727 1729 2 -21983.3 -7334.97 94.53 0.25 1728 1730 2 -21983.4 -7334.72 94.58 0.25 1729 1731 2 -21984.5 -7332.9 93.84 0.25 1730 1732 2 -21986.1 -7333.7 93.85 0.25 1731 1733 2 -21988.2 -7332.95 94.18 0.25 1732 1734 2 -21990.2 -7331.53 93.95 0.25 1733 1735 2 -21992.6 -7330.61 94.33 0.25 1734 1736 2 -21997.1 -7328.71 95.06 0.25 1735 1737 2 -21997.4 -7328.76 95.08 0.25 1736 1738 2 -21999.7 -7328.11 95.41 0.25 1737 1739 2 -21999.8 -7327.58 95.51 0.25 1738 1740 2 -22001 -7327.27 95.67 0.25 1739 1741 2 -22001.3 -7327.05 95.73 0.25 1740 1742 2 -22004 -7326.11 96.14 0.25 1741 1743 2 -22007.4 -7325.45 94.02 0.25 1742 1744 2 -22010.1 -7324.33 94.46 0.25 1743 1745 2 -22010.9 -7323.94 96.08 0.25 1744 1746 2 -22012.9 -7323.71 96.31 0.25 1745 1747 2 -22015.2 -7323.54 96.56 0.25 1746 1748 2 -22015.2 -7323.33 96.59 0.25 1747 1749 2 -22017 -7321.88 94.74 0.25 1748 1750 2 -22017 -7322.18 94.69 0.25 1749 1751 2 -22018.1 -7322.31 94.77 0.25 1750 1752 2 -22018.3 -7322.34 94.8 0.25 1751 1753 2 -22019.5 -7322.29 94.91 0.25 1752 1754 2 -22020.1 -7322.11 95 0.25 1753 1755 2 -22021.5 -7322.34 95.09 0.25 1754 1756 2 -22023.5 -7322.15 95.31 0.25 1755 1757 2 -22025.7 -7320.97 94.42 0.25 1756 1758 2 -22026.7 -7320.03 94.67 0.25 1757 1759 2 -22027.2 -7319.07 94.87 0.25 1758 1760 2 -22028.4 -7318.22 95.13 0.25 1759 1761 2 -22028.5 -7317.97 95.17 0.25 1760 1762 2 -22030.6 -7318.57 95.28 0.25 1761 1763 2 -22030.9 -7318.62 95.3 0.25 1762 1764 2 -22031 -7317.86 95.43 0.25 1763 1765 2 -22033.1 -7317.14 95.75 0.25 1764 1766 2 -22033.2 -7316.33 95.89 0.25 1765 1767 2 -22033.6 -7316.16 95.95 0.25 1766 1768 2 -22033.6 -7315.62 96.05 0.25 1767 1769 2 -22034 -7315.13 96.16 0.25 1768 1770 2 -22034.9 -7314.73 96.31 0.25 1769 1771 2 -22035.5 -7315.22 98.2 0.25 1770 1772 2 -22037 -7314.91 98.4 0.25 1771 1773 2 -22037.2 -7314.96 98.41 0.25 1772 1774 2 -22042.3 -7312.73 96.75 0.125 1773 1775 2 -21904.7 -7356.81 85.01 0.25 1677 1776 2 -21904.7 -7356.71 84.36 0.25 1775 1777 2 -21908.1 -7357.01 84.7 0.25 1776 1778 2 -21910.9 -7356.87 81.57 0.25 1777 1779 2 -21913 -7358.66 79.16 0.25 1778 1780 2 -21913 -7358.6 78.79 0.25 1779 1781 2 -21915.7 -7360.1 77.34 0.25 1780 1782 2 -21918.3 -7360.86 74.63 0.25 1781 1783 2 -21920.7 -7362.03 74.66 0.25 1782 1784 2 -21922.6 -7362.36 74.79 0.25 1783 1785 2 -21960.7 -7355.05 55.17 0.25 1784 1786 2 -21962.8 -7356.2 55.18 0.25 1785 1787 2 -21966.3 -7357.66 54.43 0.25 1786 1788 2 -21969 -7358.85 54.48 0.25 1787 1789 2 -21969.8 -7358.96 54.54 0.25 1788 1790 2 -21973.1 -7359.53 54.75 0.25 1789 1791 2 -21974 -7359.18 54.89 0.25 1790 1792 2 -21974.6 -7359 54.98 0.25 1791 1793 2 -21974.9 -7358.79 55.04 0.25 1792 1794 2 -21976.7 -7358.03 55.34 0.25 1793 1795 2 -21977.7 -7357.11 55.58 0.25 1794 1796 2 -21978 -7356.9 55.65 0.25 1795 1797 2 -21981 -7355.82 56.11 0.25 1796 1798 2 -21981.4 -7355.32 56.22 0.25 1797 1799 2 -21981.8 -7354.93 56.32 0.25 1798 1800 2 -21981.8 -7354.37 56.42 0.25 1799 1801 2 -21982.1 -7354.15 56.49 0.25 1800 1802 2 -21985.1 -7353.3 52.31 0.25 1801 1803 2 -21985.4 -7353.32 52.31 0.25 1802 1804 2 -21987.1 -7353.55 52.17 0.25 1803 1805 2 -21987.4 -7353.48 51.69 0.25 1804 1806 2 -21988.4 -7353.57 49.68 0.25 1805 1807 2 -21988.9 -7353.69 49.71 0.25 1806 1808 2 -21990.1 -7353.55 49.84 0.25 1807 1809 2 -21991.2 -7353.77 49.9 0.25 1808 1810 2 -21997.8 -7349.59 51.21 0.37 1809 1811 2 -21998 -7349.93 51.18 0.37 1810 1812 2 -21998.7 -7349.24 51.36 0.37 1811 1813 2 -22000.8 -7348.59 51.66 0.37 1812 1814 2 -22002.3 -7347.86 51.93 0.37 1813 1815 2 -22004.5 -7347.96 49.08 0.37 1814 1816 2 -22006.7 -7348.11 49.19 0.37 1815 1817 2 -22008.8 -7348.97 48.82 0.37 1816 1818 2 -22008.8 -7348.94 48.63 0.37 1817 1819 2 -22010.7 -7349.96 47.71 0.37 1818 1820 2 -22012.6 -7350.54 47.78 0.37 1819 1821 2 -22012.6 -7350.31 47.83 0.37 1820 1822 2 -22013.2 -7349.85 47.96 0.37 1821 1823 2 -22014.1 -7350.46 45.92 0.37 1822 1824 2 -22015.5 -7350.76 46 0.37 1823 1825 2 -22016.3 -7351.14 46.01 0.37 1824 1826 2 -22016.6 -7350.96 46.07 0.37 1825 1827 2 -22016.9 -7350.74 46.13 0.37 1826 1828 2 -22017 -7350.49 46.18 0.37 1827 1829 2 -22019 -7350.35 44.98 0.37 1828 1830 2 -22020.8 -7350.44 45.13 0.37 1829 1831 2 -22022.2 -7350.16 45.31 0.37 1830 1832 2 -22022.6 -7349.74 45.42 0.37 1831 1833 2 -22023.6 -7350.44 45.4 0.37 1832 1834 2 -22025.2 -7350.04 42.99 0.37 1833 1835 2 -22026.5 -7350.54 43.03 0.37 1834 1836 2 -22027.1 -7350.62 43.07 0.37 1835 1837 2 -22027.1 -7350.93 43.02 0.37 1836 1838 2 -22028.4 -7351.16 43.11 0.37 1837 1839 2 -22028.7 -7351.46 43.08 0.37 1838 1840 2 -22032.2 -7351.81 39.89 0.37 1839 1841 2 -22053.3 -7350.96 37.18 0.37 1840 1842 2 -22055 -7351.06 37.32 0.37 1841 1843 2 -22057.4 -7350.43 37.65 0.37 1842 1844 2 -22058.2 -7350.56 37.7 0.37 1843 1845 2 -22059.1 -7350.22 37.84 0.37 1844 1846 2 -22059.4 -7350.26 37.86 0.37 1845 1847 2 -22060.4 -7350.47 37.93 0.37 1846 1848 2 -22060.4 -7350.73 37.88 0.37 1847 1849 2 -22064.1 -7350.72 34.28 0.37 1848 1850 2 -22065.7 -7351.06 34.37 0.37 1849 1851 2 -22067.1 -7351.04 34.51 0.37 1850 1852 2 -22068 -7350.69 34.65 0.37 1851 1853 2 -22068.4 -7350.24 34.76 0.37 1852 1854 2 -22069.2 -7350.64 34.77 0.37 1853 1855 2 -22069.5 -7350.71 34.79 0.37 1854 1856 2 -22070.6 -7350.63 34.9 0.37 1855 1857 2 -22071.2 -7350.26 35.02 0.37 1856 1858 2 -22076.2 -7349.34 35.64 0.37 1857 1859 2 -22078.4 -7348.93 32.83 0.37 1858 1860 2 -22079.8 -7347.65 33.17 0.37 1859 1861 2 -22081 -7348.64 33.12 0.37 1860 1862 2 -22081.3 -7348.49 33.18 0.37 1861 1863 2 -22083.1 -7348.41 32.61 0.37 1862 1864 2 -22083 -7348.62 32.57 0.37 1863 1865 2 -22085.2 -7349.03 32.71 0.37 1864 1866 2 -22087 -7349.13 32.85 0.37 1865 1867 2 -22089.2 -7349 33.09 0.37 1866 1868 2 -22090.1 -7349.29 30.73 0.37 1867 1869 2 -22091.2 -7348.18 31.01 0.37 1868 1870 2 -22092.5 -7348.69 31.05 0.37 1869 1871 2 -22092.8 -7348.73 31.07 0.37 1870 1872 2 -22095.5 -7349.53 31.19 0.37 1871 1873 2 -22095.7 -7349.59 31.2 0.37 1872 1874 2 -22097.5 -7349.07 28.31 0.37 1873 1875 2 -22098.3 -7349.22 28.36 0.37 1874 1876 2 -22098.8 -7349.32 28.4 0.37 1875 1877 2 -22099.6 -7349.72 28.4 0.37 1876 1878 2 -22100.2 -7349.36 28.52 0.37 1877 1879 2 -22100.5 -7349.14 28.59 0.37 1878 1880 2 -22101.2 -7348.99 28.67 0.37 1879 1881 2 -22104.6 -7350.16 28.8 0.37 1880 1882 2 -22106.1 -7349.94 28.96 0.37 1881 1883 2 -22106.8 -7349.24 29.14 0.37 1882 1884 2 -22107.9 -7348.28 27.18 0.37 1883 1885 2 -22108.4 -7347.07 27.43 0.37 1884 1886 2 -22110 -7346.06 23.36 0.37 1885 1887 2 -22110 -7346.32 23.31 0.37 1886 1888 2 -22111.2 -7346.48 21.75 0.37 1887 1889 2 -22111.1 -7346.76 21.69 0.37 1888 1890 2 -22112.2 -7347.25 21.71 0.37 1889 1891 2 -22112.4 -7347.55 21.68 0.37 1890 1892 2 -22114 -7348.09 21.74 0.37 1891 1893 2 -22115.5 -7349.44 21.66 0.37 1892 1894 2 -22116.2 -7350.1 21.61 0.37 1893 1895 2 -22118 -7350.37 19.83 0.37 1894 1896 2 -22119.3 -7350.64 19.91 0.37 1895 1897 2 -22121.7 -7350.42 19.38 0.37 1896 1898 2 -22123.7 -7350.28 19.59 0.37 1897 1899 2 -22125.2 -7349.73 19.7 0.37 1898 1900 2 -22126.3 -7348.34 20.03 0.37 1899 1901 2 -22127.3 -7347.71 18.33 0.37 1900 1902 2 -22129 -7347.76 18.48 0.37 1901 1903 2 -22129.6 -7347.84 18.52 0.37 1902 1904 2 -22130.9 -7347.93 16.23 0.37 1903 1905 2 -22132 -7347.64 16.39 0.37 1904 1906 2 -22132.3 -7347.67 16.4 0.37 1905 1907 2 -22133.3 -7347.31 16.56 0.37 1906 1908 2 -22133.5 -7347.38 16.57 0.37 1907 1909 2 -22135.5 -7347.74 16.69 0.37 1908 1910 2 -22135.7 -7347.82 16.7 0.37 1909 1911 2 -22137.5 -7347.08 17 0.37 1910 1912 2 -22137.6 -7346.84 17.04 0.37 1911 1913 2 -22138.2 -7346.68 17.12 0.37 1912 1914 2 -22138.5 -7346.72 17.14 0.37 1913 1915 2 -22139 -7346.59 17.22 0.37 1914 1916 2 -22139.3 -7346.4 17.28 0.37 1915 1917 2 -22141.7 -7346.73 15.52 0.37 1916 1918 2 -22142 -7346.8 15.54 0.37 1917 1919 2 -22142.7 -7346.42 14.44 0.37 1918 1920 2 -22144.3 -7346.14 12.8 0.37 1919 1921 2 -22144.2 -7346.33 12.31 0.37 1920 1922 2 -22145 -7346.69 11.96 0.37 1921 1923 2 -22145.3 -7346.74 11.98 0.37 1922 1924 2 -22146.8 -7346.47 12.16 0.37 1923 1925 2 -22148.9 -7346.72 9.78 0.25 1924 1926 2 -22150.5 -7346.76 9.93 0.25 1925 1927 2 -22151.4 -7346.92 9.98 0.25 1926 1928 2 -22153.5 -7346.02 10.33 0.25 1927 1929 2 -22153.5 -7346.28 10.28 0.25 1928 1930 2 -22156.5 -7345.81 9.46 0.25 1929 1931 2 -22165.2 -7346.55 4.94 0.25 1930 1932 2 -22165.5 -7346.57 4.67 0.25 1931 1933 2 -22166.9 -7346.54 4.8 0.25 1932 1934 2 -22168.2 -7347.15 5.12 0.25 1933 1935 2 -22168.1 -7347.4 5.07 0.25 1934 1936 2 -22169 -7347.7 3.03 0.25 1935 1937 2 -22170.9 -7348.61 3.05 0.25 1936 1938 2 -22172.5 -7348.89 3.16 0.25 1937 1939 2 -22174.7 -7348.24 1.87 0.25 1938 1940 2 -22177 -7348.11 2.11 0.25 1939 1941 2 -22177.6 -7347.73 2.23 0.25 1940 1942 2 -22178.7 -7347.95 2.29 0.25 1941 1943 2 -22179.5 -7346.78 2.56 0.25 1942 1944 2 -22179.8 -7346.34 2.67 0.25 1943 1945 2 -22181.9 -7345.63 2.98 0.25 1944 1946 2 -22183.1 -7344.56 1.88 0.25 1945 1947 2 -22183.3 -7344.62 1.89 0.25 1946 1948 2 -22184.5 -7342.98 2.27 0.25 1947 1949 2 -22184.4 -7343.24 2.22 0.25 1948 1950 2 -22187.2 -7343 -0.3 0.25 1949 1951 2 -22187.2 -7343.27 -0.35 0.25 1950 1952 2 -22190.3 -7343.59 -1.52 0.25 1951 1953 2 -22190.5 -7343.65 -1.51 0.25 1952 1954 2 -22192.4 -7344.74 -1.51 0.25 1953 1955 2 -22193.8 -7344.06 -3.25 0.25 1954 1956 2 -22194.4 -7343.89 -3.16 0.25 1955 1957 2 -22196.1 -7343.91 -3.01 0.25 1956 1958 2 -22196.9 -7344.32 -3.12 0.25 1957 1959 2 -22197.7 -7343.85 -4.87 0.25 1958 1960 2 -22198.1 -7344.69 -4.97 0.25 1959 1961 2 -22198.7 -7344.53 -4.89 0.25 1960 1962 2 -22198.9 -7344.59 -4.88 0.25 1961 1963 2 -22199.5 -7344.36 -4.78 0.25 1962 1964 2 -22199.6 -7344.12 -4.74 0.25 1963 1965 2 -22201.3 -7343.02 -6.42 0.25 1964 1966 2 -22201.6 -7342.7 -5.49 0.25 1965 1967 2 -22203.8 -7343.04 -5.33 0.25 1966 1968 2 -22204.2 -7342.86 -5.27 0.25 1967 1969 2 -22206.6 -7343.78 -5.2 0.25 1968 1970 2 -22209.1 -7344.45 -5.08 0.25 1969 1971 2 -22209.3 -7344.51 -5.06 0.25 1970 1972 2 -22211.6 -7344.6 -4.87 0.25 1971 1973 2 -22214.2 -7344.49 -4.6 0.25 1972 1974 2 -22214.4 -7344.57 -4.59 0.25 1973 1975 2 -22216.7 -7344.39 -4.35 0.25 1974 1976 2 -22218.2 -7343.83 -4.11 0.25 1975 1977 2 -22219.8 -7344.65 -4.1 0.25 1976 1978 2 -22221.9 -7344.9 -5.8 0.25 1977 1979 2 -22223.9 -7346.18 -7.95 0.25 1978 1980 2 -22224.6 -7346.6 -7.85 0.25 1979 1981 2 -22225.8 -7346.81 -7.78 0.25 1980 1982 2 -22226 -7346.59 -7.71 0.25 1981 1983 2 -22226.4 -7345.85 -7.55 0.25 1982 1984 2 -22226.7 -7345.87 -7.53 0.25 1983 1985 2 -22228.4 -7345.85 -10.61 0.25 1984 1986 2 -22230.4 -7345.95 -10.44 0.25 1985 1987 2 -22230.7 -7345.46 -10.33 0.25 1986 1988 2 -22231 -7345.53 -10.31 0.25 1987 1989 2 -22232.1 -7345.42 -10.19 0.25 1988 1990 2 -22232.5 -7344.95 -10.07 0.25 1989 1991 2 -22232.8 -7345 -10.06 0.25 1990 1992 2 -22233.1 -7344.8 -10 0.25 1991 1993 2 -22233.7 -7344.32 -9.86 0.25 1992 1994 2 -22234 -7344.13 -9.8 0.25 1993 1995 2 -22235.4 -7344.37 -9.7 0.25 1994 1996 2 -22235.7 -7344.43 -9.69 0.25 1995 1997 2 -22236.8 -7344.32 -9.56 0.25 1996 1998 2 -22237.4 -7344.18 -9.48 0.25 1997 1999 2 -22238 -7344.03 -9.4 0.25 1998 2000 2 -22238.4 -7343.82 -9.34 0.25 1999 2001 2 -22239 -7343.65 -9.25 0.25 2000 2002 2 -22239.8 -7343.79 -9.2 0.25 2001 2003 2 -22240.4 -7343.59 -9.11 0.25 2002 2004 2 -22241 -7342.93 -8.94 0.25 2003 2005 2 -22241.1 -7342.66 -8.89 0.25 2004 2006 2 -22242.4 -7343.4 -8.89 0.25 2005 2007 2 -22243.8 -7344.39 -10.74 0.25 2006 2008 2 -22243.7 -7344.63 -10.78 0.25 2007 2009 2 -22245.9 -7345.25 -10.68 0.25 2008 2010 2 -22247.4 -7345.01 -10.5 0.25 2009 2011 2 -22247.7 -7344.76 -10.43 0.25 2010 2012 2 -22248 -7344.53 -10.37 0.25 2011 2013 2 -22248.5 -7343.56 -10.16 0.25 2012 2014 2 -22249.1 -7343.38 -10.08 0.25 2013 2015 2 -22249.7 -7342.94 -9.95 0.25 2014 2016 2 -22250.9 -7342.64 -9.79 0.25 2015 2017 2 -22251.5 -7342.49 -9.71 0.25 2016 2018 2 -22251.5 -7342.22 -9.66 0.25 2017 2019 2 -22251.8 -7342.03 -9.6 0.25 2018 2020 2 -22252.2 -7341.77 -9.52 0.25 2019 2021 2 -22278.5 -7349.83 -25.11 0.25 2020 2022 2 -22285.3 -7351.06 -27.24 0.25 2021 2023 2 -22286.1 -7351.19 -27.18 0.25 2022 2024 2 -22287.4 -7351.95 -27.19 0.25 2023 2025 2 -22287.7 -7351.99 -27.17 0.25 2024 2026 2 -22289.5 -7351.78 -28.36 0.25 2025 2027 2 -22291 -7351.22 -28.14 0.25 2026 2028 2 -22291.3 -7351.26 -28.12 0.25 2027 2029 2 -22292.5 -7351.43 -28.04 0.25 2028 2030 2 -22292.4 -7351.68 -28.09 0.25 2029 2031 2 -22294.4 -7352.15 -28.83 0.25 2030 2032 2 -22294.7 -7352.22 -28.75 0.25 2031 2033 2 -22296.2 -7351.91 -28.56 0.25 2032 2034 2 -22296.2 -7351.65 -28.52 0.25 2033 2035 2 -22297.2 -7352.07 -28.49 0.25 2034 2036 2 -22297.5 -7352.39 -28.52 0.25 2035 2037 2 -22298.5 -7352.61 -28.45 0.25 2036 2038 2 -22301.2 -7352.11 -31.65 0.25 2037 2039 2 -22301.1 -7352.4 -31.7 0.25 2038 2040 2 -22302.1 -7353.09 -31.72 0.25 2039 2041 2 -22302.9 -7353.98 -31.8 0.25 2040 2042 2 -22303.2 -7354.05 -31.78 0.25 2041 2043 2 -22304 -7354.2 -31.73 0.25 2042 2044 2 -22304.8 -7354.32 -31.67 0.25 2043 2045 2 -22305.6 -7354.47 -31.62 0.25 2044 2046 2 -22305.6 -7354.2 -31.57 0.25 2045 2047 2 -22307 -7355.24 -31.62 0.25 2046 2048 2 -22308.8 -7354.96 -33.14 0.25 2047 2049 2 -22309.3 -7355.06 -33.1 0.25 2048 2050 2 -22310.2 -7355.17 -33.04 0.25 2049 2051 2 -22310.7 -7355.54 -33.05 0.25 2050 2052 2 -22312.6 -7356.37 -33.01 0.25 2051 2053 2 -22312.9 -7356.42 -32.99 0.25 2052 2054 2 -22313.4 -7356.23 -32.91 0.25 2053 2055 2 -22313.5 -7356.02 -32.87 0.25 2054 2056 2 -22315.5 -7356.68 -35.27 0.25 2055 2057 2 -22316.9 -7357.19 -35.28 0.25 2056 2058 2 -22316.9 -7357.17 -35.44 0.25 2057 2059 2 -22319.2 -7357.83 -36.7 0.25 2058 2060 2 -22319.5 -7357.56 -37.09 0.25 2059 2061 2 -22321.3 -7357.65 -37.86 0.25 2060 2062 2 -22322.4 -7357.87 -37.79 0.25 2061 2063 2 -22324.1 -7357.86 -37.63 0.25 2062 2064 2 -22324.4 -7357.91 -37.61 0.25 2063 2065 2 -22326.5 -7357.95 -39.06 0.25 2064 2066 2 -22326.5 -7357.93 -39.21 0.25 2065 2067 2 -22334 -7359.18 -43.43 0.25 2066 2068 2 -22335.7 -7359.49 -44.5 0.25 2067 2069 2 -22335.8 -7359.4 -45.07 0.25 2068 2070 2 -22337.2 -7359.97 -46.18 0.25 2069 2071 2 -22338 -7360.45 -47.37 0.25 2070 2072 2 -22338.5 -7361.21 -48.03 0.25 2071 2073 2 -22338.7 -7361.04 -49.02 0.25 2072 2074 2 -22339.7 -7361.5 -49 0.25 2073 2075 2 -22339.8 -7361.37 -49.8 0.25 2074 2076 2 -22341.5 -7361.13 -49.6 0.25 2075 2077 2 -22341.6 -7361.07 -50.01 0.25 2076 2078 2 -22344.2 -7362.02 -50.12 0.25 2077 2079 2 -22346.8 -7362.57 -50.78 0.25 2078 2080 2 -22346.7 -7362.81 -50.83 0.25 2079 2081 2 -22349.2 -7362.88 -52.38 0.25 2080 2082 2 -22349.9 -7363.53 -52.41 0.25 2081 2083 2 -22350.2 -7363.58 -52.4 0.25 2082 2084 2 -22350.9 -7364.21 -52.43 0.25 2083 2085 2 -22351.5 -7364.07 -52.35 0.25 2084 2086 2 -22352.4 -7362.98 -54.54 0.25 2085 2087 2 -22352.7 -7363.01 -54.52 0.25 2086 2088 2 -22354.4 -7363.07 -54.39 0.25 2087 2089 2 -22356 -7362.74 -56.11 0.25 2088 2090 2 -22357.9 -7363.33 -55.87 0.25 2089 2091 2 -22358.2 -7363.6 -55.89 0.25 2090 2092 2 -22361.1 -7364.7 -57.16 0.125 2091 2093 2 -22361.9 -7364.83 -57.1 0.125 2092 2094 2 -22363.5 -7364.41 -59.04 0.125 2093 2095 2 -22364.6 -7365.08 -59.05 0.125 2094 2096 2 -22364.9 -7365.07 -59.02 0.125 2095 2097 2 -22365.8 -7365 -60.24 0.125 2096 2098 2 -22366.9 -7365.15 -60.16 0.125 2097 2099 2 -22367.2 -7365.22 -60.14 0.125 2098 2100 2 -22368.1 -7364.81 -59.99 0.125 2099 2101 2 -22369.2 -7365.58 -60.72 0.125 2100 2102 2 -22370.3 -7365.19 -60.54 0.125 2101 2103 2 -22371.4 -7365.63 -60.52 0.125 2102 2104 2 -22371.7 -7365.65 -60.49 0.125 2103 2105 2 -22372.5 -7365.76 -60.43 0.125 2104 2106 2 -22372.9 -7365.58 -60.37 0.125 2105 2107 2 -22374.6 -7365.57 -61.3 0.125 2106 2108 2 -22375.1 -7366.09 -61.93 0.125 2107 2109 2 -22375.3 -7366.91 -62.04 0.125 2108 2110 2 -22376.6 -7367.2 -64.1 0.125 2109 2111 2 -22377.5 -7367.71 -66.3 0.125 2110 2112 2 -22378.3 -7368.36 -66.34 0.125 2111 2113 2 -22379.4 -7369.01 -66.42 0.125 2112 2114 2 -22379.7 -7369.68 -67.55 0.125 2113 2115 2 -22380 -7369.71 -67.76 0.125 2114 2116 2 -22381.7 -7369.92 -69.1 0.125 2115 2117 2 -22382.8 -7370.56 -69.11 0.125 2116 2118 2 -22384.2 -7370.24 -68.92 0.125 2117 2119 2 -22384.5 -7370.31 -68.91 0.125 2118 2120 2 -22385.3 -7370.4 -68.84 0.125 2119 2121 2 -22387.6 -7370.29 -72.33 0.125 2120 2122 2 -22387.8 -7370.32 -72.31 0.125 2121 2123 2 -22388.8 -7369.99 -73.32 0.125 2122 2124 2 -22389.1 -7370.02 -73.3 0.125 2123 2125 2 -22390.6 -7369.38 -73.51 0.125 2124 2126 2 -22390.9 -7369.4 -73.49 0.125 2125 2127 2 -22392.9 -7368.8 -73.56 0.125 2126 2128 2 -22395.4 -7369.14 -73.38 0.125 2127 2129 2 -22397.9 -7370.55 -77.83 0.125 2128 2130 2 -22398.1 -7370.85 -77.86 0.125 2129 2131 2 -22399.8 -7372.9 -78.04 0.125 2130 2132 2 -22402.4 -7372.63 -81.18 0.125 2131 2133 2 -22440.9 -7376.04 -98.4 0.125 2132 2134 2 -22443.3 -7376.62 -98.27 0.125 2133 2135 2 -22444.2 -7376.74 -98.21 0.125 2134 2136 2 -22446.9 -7375.49 -97.75 0.125 2135 2137 2 -22447.2 -7375 -97.64 0.125 2136 2138 2 -22447.6 -7374.26 -97.48 0.125 2137 2139 2 -22447.9 -7374.04 -97.42 0.125 2138 2140 2 -22450.2 -7373.82 -97.17 0.125 2139 2141 2 -22450.4 -7374.09 -97.19 0.125 2140 2142 2 -22451.4 -7375.27 -97.29 0.125 2141 2143 2 -22451.6 -7375.57 -97.32 0.125 2142 2144 2 -22451.8 -7376.11 -97.39 0.125 2143 2145 2 -22453.4 -7377.37 -97.45 0.125 2144 2146 2 -22454.8 -7377.75 -97.79 0.125 2145 2147 2 -22456.8 -7378.28 -99.32 0.125 2146 2148 2 -22457.1 -7378.77 -99.71 0.125 2147 2149 2 -22456.9 -7380.24 -100.59 0.125 2148 2150 2 -22457 -7380.41 -101.33 0.125 2149 2151 2 -22456.9 -7381.26 -102.19 0.125 2150 2152 2 -22457.5 -7382.5 -108 0.125 2151 2153 2 -22457.2 -7383.08 -109.28 0.125 2152 2154 2 -22457.5 -7383.08 -109.26 0.125 2153 2155 2 -22458.7 -7383.96 -111.52 0.125 2154 2156 2 -22459.9 -7383.93 -113.84 0.125 2155 2157 2 -22459.9 -7384.2 -113.88 0.125 2156 2158 2 -22460.6 -7385.45 -113.35 0.125 2157 2159 2 -22466.4 -7381.97 -112.23 0.125 2158 2160 2 -22468.7 -7382.23 -111.45 0.125 2159 2161 2 -22470.5 -7382.45 -113.21 0.125 2160 2162 2 -22470.4 -7382.45 -113.22 0.125 2161 2163 2 -22471.9 -7382.46 -113.09 0.125 2162 2164 2 -22472.8 -7382.34 -112.99 0.125 2163 2165 2 -22473.8 -7382.77 -112.99 0.125 2164 2166 2 -22474.7 -7382.35 -116.02 0.125 2165 2167 2 -22475 -7382.19 -115.96 0.125 2166 2168 2 -22476.9 -7382.8 -115.88 0.125 2167 2169 2 -22478.1 -7382.72 -115.76 0.125 2168 2170 2 -22478.6 -7381.21 -115.46 0.125 2169 2171 2 -22479.6 -7382.35 -116.24 0.125 2170 2172 2 -22481.5 -7383.16 -117.84 0.125 2171 2173 2 -22481.6 -7383.09 -118.27 0.125 2172 2174 2 -22483.1 -7381.95 -120.1 0.125 2173 2175 2 -22483.8 -7382.88 -120.28 0.125 2174 2176 2 -22483.8 -7382.86 -120.41 0.125 2175 2177 2 -22484.9 -7382.45 -123.68 0.125 2176 2178 2 -22485.5 -7382.54 -123.64 0.125 2177 2179 2 -22486.6 -7382.75 -123.57 0.125 2178 2180 2 -22486.9 -7382.28 -123.46 0.125 2179 2181 2 -22488.8 -7381.88 -128.87 0.125 2180 2182 2 -22515 -7388.77 -154.34 0.125 2181 2183 2 -22518.5 -7389.2 -156.99 0.125 2182 2184 2 -22518.8 -7389.24 -156.97 0.125 2183 2185 2 -22523.3 -7389.58 -160.89 0.125 2184 2186 2 -22524.5 -7389.24 -160.72 0.125 2185 2187 2 -22525.9 -7388.5 -161.36 0.125 2186 2188 2 -22527.3 -7388.43 -161.65 0.125 2187 2189 2 -22528 -7388.2 -163.35 0.125 2188 2190 2 -22530.6 -7388.64 -165.29 0.125 2189 2191 2 -22531.3 -7388.61 -165.75 0.125 2190 2192 2 -22532.9 -7389.52 -162.55 0.125 2191 2193 2 -22533.5 -7389.64 -162.25 0.125 2192 2194 2 -22535.6 -7390.31 -162.16 0.125 2193 2195 2 -22536.1 -7390.64 -162.16 0.125 2194 2196 2 -22537.7 -7391.9 -162.9 0.125 2195 2197 2 -22537.8 -7391.59 -164.74 0.125 2196 2198 2 -22539.3 -7393.67 -166.55 0.125 2197 2199 2 -22539.6 -7393.71 -166.53 0.125 2198 2200 2 -22541.2 -7394.23 -166.89 0.125 2199 2201 2 -22541.2 -7394.45 -167.05 0.125 2200 2202 2 -22543.1 -7396.12 -170.33 0.125 2201 2203 2 -22543.4 -7396.16 -170.3 0.125 2202 2204 2 -22544.2 -7397.13 -171.67 0.125 2203 2205 2 -22544.5 -7397.07 -172.26 0.125 2204 2206 2 -22546.7 -7397.82 -173.17 0.125 2205 2207 2 -22546.8 -7397.71 -173.83 0.125 2206 2208 2 -22548.2 -7397.67 -173.77 0.125 2207 2209 2 -22548.2 -7397.63 -174.01 0.125 2208 2210 2 -22549.5 -7398.84 -174.9 0.125 2209 2211 2 -22549.9 -7398.78 -175.31 0.125 2210 2212 2 -22551.8 -7399.67 -176.86 0.125 2211 2213 2 -22552.1 -7400.17 -174.58 0.125 2212 2214 2 -22552.9 -7400.84 -174.62 0.125 2213 2215 2 -22553.4 -7401.17 -174.62 0.125 2214 2216 2 -22554.7 -7401.67 -174.58 0.125 2215 2217 2 -22556.4 -7402.06 -178.69 0.125 2216 2218 2 -22557.4 -7402.73 -178.71 0.125 2217 2219 2 -22559.5 -7403.34 -182.01 0.125 2218 2220 2 -22559.9 -7403.18 -183.26 0.125 2219 2221 2 -22548.7 -7403.92 -184.44 0.125 2220 2222 2 -22549.9 -7405.68 -184.03 0.125 2221 2223 2 -22550.1 -7405.57 -186.22 0.125 2222 2224 2 -22553.8 -7406.77 -190.55 0.125 2223 2225 2 -22554 -7406.83 -190.54 0.125 2224 2226 2 -22555.5 -7407.96 -190.59 0.125 2225 2227 2 -22556 -7408.1 -190.56 0.125 2226 2228 2 -22557.6 -7408.53 -190.48 0.125 2227 2229 2 -22557.6 -7408.27 -190.43 0.125 2228 2230 2 -22559.5 -7408.12 -192.09 0.125 2229 2231 2 -22561.5 -7408.62 -193.41 0.125 2230 2232 2 -22562 -7408.75 -193.38 0.125 2231 2233 2 -22563.7 -7408.87 -194.87 0.125 2232 2234 2 -22566.9 -7409.19 -197.32 0.125 2233 2235 2 -22567.9 -7410.74 -200.13 0.125 2234 2236 2 -22570.1 -7412.17 -201.59 0.125 2235 2237 2 -22571.9 -7413.45 -202.82 0.125 2236 2238 2 -22574.1 -7415.01 -203.03 0.125 2237 2239 2 -22574.8 -7415.48 -203.04 0.125 2238 2240 2 -22576.7 -7415.91 -202.94 0.125 2239 2241 2 -22576.7 -7416.22 -203.01 0.125 2240 2242 2 -22578 -7416.01 -205.5 0.125 2241 2243 2 -22578.3 -7416.08 -205.49 0.125 2242 2244 2 -22579 -7418.17 -206.94 0.125 2243 2245 2 -22579.5 -7418.33 -206.92 0.125 2244 2246 2 -22579.7 -7418.65 -206.95 0.125 2245 2247 2 -22582.1 -7418.59 -208.64 0.125 2246 2248 2 -22582.4 -7418.64 -208.74 0.125 2247 2249 2 -22585.2 -7418.06 -209.55 0.125 2248 2250 2 -22585.2 -7417.99 -210.02 0.125 2249 2251 2 -22587.8 -7418.29 -211.68 0.125 2250 2252 2 -22588.1 -7418.39 -211.67 0.125 2251 2253 2 -22590.2 -7417.5 -216.07 0.125 2252 2254 2 -22590.3 -7417.38 -216.8 0.125 2253 2255 2 -22594 -7418.83 -222.67 0.125 2254 2256 2 -22594.3 -7418.87 -222.66 0.125 2255 2257 2 -22594.6 -7421.41 -219.78 0.125 2256 2258 2 -22594.7 -7421.17 -221.28 0.125 2257 2259 2 -22598.7 -7422.99 -224.08 0.125 2258 2260 2 -22605.5 -7426.45 -232.92 0.125 2259 2261 2 -22605.7 -7426.59 -232.92 0.125 2260 2262 2 -22606.6 -7427.65 -232.62 0.125 2261 2263 2 -22606.9 -7427.66 -232.85 0.125 2262 2264 2 -22609.3 -7427.91 -236.09 0.125 2263 2265 2 -22609.2 -7428.15 -236.22 0.125 2264 2266 2 -22611.5 -7428.35 -237.92 0.125 2265 2267 2 -22614.1 -7429.5 -238.14 0.125 2266 2268 2 -22614.2 -7429.45 -238.45 0.125 2267 2269 2 -22615.3 -7429.07 -242.02 0.125 2268 2270 2 -22615.6 -7429.11 -242 0.125 2269 2271 2 -22617.5 -7429.32 -242.03 0.125 2270 2272 2 -22617.6 -7429.24 -242.52 0.125 2271 2273 2 -22620.2 -7428.63 -244.49 0.125 2272 2274 2 -22622.4 -7429.15 -246.01 0.125 2273 2275 2 -22622.7 -7428.97 -246.01 0.125 2274 2276 2 -22624 -7428.49 -245.84 0.125 2275 2277 2 -22624.6 -7428.72 -248.41 0.125 2276 2278 2 -22629.8 -7437.44 -249.37 0.125 2277 2279 2 -22630.9 -7437.39 -249.26 0.125 2278 2280 2 -22631.2 -7437.44 -249.24 0.125 2279 2281 2 -22632.8 -7437.98 -249.18 0.125 2280 2282 2 -22634.5 -7438.48 -250.98 0.125 2281 2283 2 -22637.3 -7438.77 -253.78 0.125 2282 2284 2 -22638.1 -7438.68 -253.69 0.125 2283 2285 2 -22640.2 -7439.8 -253.68 0.125 2284 2286 2 -22642 -7440.03 -257.19 0.125 2285 2287 2 -22642.3 -7440.38 -257.22 0.125 2286 2288 2 -22643.5 -7440.1 -257.06 0.125 2287 2289 2 -22644.1 -7439.68 -256.93 0.125 2288 2290 2 -22644.8 -7438.71 -259.82 0.125 2289 2291 2 -22645.9 -7438.9 -259.74 0.125 2290 2292 2 -22646.1 -7438.94 -259.73 0.125 2291 2293 2 -22647.3 -7438.95 -259.62 0.125 2292 2294 2 -22649.7 -7439.99 -265.12 0.125 2293 2295 2 -22649.8 -7439.94 -265.44 0.125 2294 2296 2 -22651.7 -7439.62 -267.5 0.125 2295 2297 2 -22651.6 -7439.85 -267.53 0.125 2296 2298 2 -22653.5 -7441.14 -270.01 0.125 2297 2299 2 -22655 -7440.99 -270.6 0.125 2298 2300 2 -22656.5 -7442.3 -271.08 0.125 2299 2301 2 -22658.8 -7441.69 -273.6 0.125 2300 2302 2 -22659.1 -7441.72 -273.58 0.125 2301 2303 2 -22662.6 -7441.39 -278.78 0.125 2302 2304 2 -22663.6 -7442.15 -278.81 0.125 2303 2305 2 -22665.9 -7443.54 -282.31 0.125 2304 2306 2 -22667.3 -7445.09 -282.89 0.125 2305 2307 2 -22667.6 -7445.1 -282.87 0.125 2306 2308 2 -22668.1 -7445.47 -282.88 0.125 2307 2309 2 -22668.4 -7445.53 -282.86 0.125 2308 2310 2 -22669.4 -7445.45 -285.7 0.125 2309 2311 2 -22669.5 -7445.24 -285.65 0.125 2310 2312 2 -22671.2 -7444.7 -288.72 0.125 2311 2313 2 -22672.9 -7445.41 -289.45 0.125 2312 2314 2 -22676.8 -7445.27 -296.96 0.125 2313 2315 2 -22680.4 -7447.21 -297.38 0.125 2314 2316 2 -22681.6 -7447.01 -298.05 0.125 2315 2317 2 -22684.1 -7446.33 -302.68 0.125 2316 2318 2 -22684.6 -7446.43 -302.65 0.125 2317 2319 2 -22685.3 -7446.11 -303.52 0.125 2318 2320 2 -22685.8 -7446.5 -303.53 0.125 2319 2321 2 -22686.8 -7446.96 -303.51 0.125 2320 2322 2 -22687.1 -7447.01 -303.49 0.125 2321 2323 2 -22687.8 -7446.35 -303.36 0.125 2322 2324 2 -22687.8 -7446.33 -303.51 0.125 2323 2325 2 -22688.9 -7445.84 -305.58 0.125 2324 2326 2 -22689.2 -7445.87 -305.56 0.125 2325 2327 2 -22690.8 -7446.46 -305.51 0.125 2326 2328 2 -22692.3 -7447.5 -305.54 0.125 2327 2329 2 -22693.9 -7448.06 -310.07 0.125 2328 2330 2 -22696.5 -7448.8 -313.08 0.125 2329 2331 2 -22697.8 -7449.87 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2469 2 -22869.3 -7441.23 -455.17 0.125 2468 2470 2 -22870.9 -7441.36 -457.67 0.125 2469 2471 2 -22871.7 -7441.49 -457.61 0.125 2470 2472 2 -22873.1 -7441.71 -459.22 0.125 2471 2473 2 -22874.3 -7441.71 -459.15 0.125 2472 2474 2 -22874.3 -7441.68 -459.34 0.125 2473 2475 2 -22876.3 -7442.02 -461.09 0.125 2474 2476 2 -22876.3 -7442.25 -461.14 0.125 2475 2477 2 -22876.7 -7442.25 -463.21 0.125 2476 2478 2 -22877.5 -7441.04 -462.93 0.125 2477 2479 2 -22878.7 -7441.18 -464.96 0.125 2478 2480 2 -22879.8 -7441.37 -464.89 0.125 2479 2481 2 -22880.1 -7441.17 -464.83 0.125 2480 2482 2 -22881.6 -7440.41 -467.53 0.125 2481 2483 2 -22882.8 -7440.09 -467.54 0.125 2482 2484 2 -22884.1 -7440.13 -470.04 0.125 2483 2485 2 -22887.9 -7440.06 -471.33 0.125 2484 2486 2 -22890.4 -7439.7 -474.43 0.125 2485 2487 2 -22890.7 -7439.78 -474.42 0.125 2486 2488 2 -22892.1 -7440.08 -475.63 0.125 2487 2489 2 -22892.4 -7440.1 -475.61 0.125 2488 2490 2 -22893.7 -7440.04 -477.43 0.125 2489 2491 2 -22894 -7439.81 -477.37 0.125 2490 2492 2 -22895.1 -7439.99 -477.39 0.125 2491 2493 2 -22895.4 -7440.02 -477.57 0.125 2492 2494 2 -22897.8 -7440.1 -479.42 0.125 2493 2495 2 -22898 -7440.18 -479.41 0.125 2494 2496 2 -22899.8 -7440.02 -480.59 0.125 2495 2497 2 -22899.9 -7439.74 -480.85 0.125 2496 2498 2 -22902 -7439.74 -482.84 0.125 2497 2499 2 -22903.7 -7440.64 -484.02 0.125 2498 2500 2 -22904.1 -7440.1 -484.47 0.125 2499 2501 2 -22904.4 -7439.77 -484.97 0.125 2500 2502 2 -22904.5 -7439.68 -485.54 0.125 2501 2503 2 -22905.8 -7439.62 -487.21 0.125 2502 2504 2 -22905.7 -7439.88 -487.26 0.125 2503 2505 2 -22907.3 -7439.63 -488.73 0.125 2504 2506 2 -22908.9 -7440.1 -489.26 0.125 2505 2507 2 -22909 -7439.93 -490.28 0.125 2506 2508 2 -22910.4 -7440.11 -490.77 0.125 2507 2509 2 -22910.7 -7440.15 -490.75 0.125 2508 2510 2 -22912.7 -7440.53 -492.29 0.125 2509 2511 2 -22915.2 -7441.07 -493.37 0.125 2510 2512 2 -22915.2 -7441.33 -493.41 0.125 2511 2513 2 -22916.8 -7442.33 -493.91 0.125 2512 2514 2 -22918.2 -7443.9 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2 -22994.2 -7453.56 -549.37 0.125 2582 2584 2 -22994.7 -7453.67 -549.34 0.125 2583 2585 2 -22996 -7453.16 -549.13 0.125 2584 2586 2 -22996 -7452.91 -549.09 0.125 2585 2587 2 -22997.5 -7452.38 -553.13 0.125 2586 2588 2 -22997.9 -7452.78 -553.15 0.125 2587 2589 2 -22998.4 -7453.43 -553.22 0.125 2588 2590 2 -23000.3 -7452.66 -553.55 0.125 2589 2591 2 -23000.5 -7452.71 -553.54 0.125 2590 2592 2 -23002.3 -7452.22 -556.64 0.125 2591 2593 2 -23002.6 -7452.31 -556.62 0.125 2592 2594 2 -23003.7 -7452.51 -556.55 0.125 2593 2595 2 -23004.7 -7451.42 -556.28 0.125 2594 2596 2 -23006.9 -7450.79 -557.92 0.125 2595 2597 2 -23007 -7450.52 -557.87 0.125 2596 2598 2 -23008.9 -7450.59 -561.02 0.125 2597 2599 2 -23008.9 -7450.56 -561.2 0.125 2598 2600 2 -23010.3 -7451.32 -562.96 0.125 2599 2601 2 -23012 -7451.63 -564.68 0.125 2600 2602 2 -23013.5 -7451.15 -564.47 0.125 2601 2603 2 -23015.1 -7451.5 -564.42 0.125 2602 2604 2 -23019.2 -7450.86 -563.93 0.125 2603 2605 2 -23021.2 -7451.12 -564.52 0.125 2604 2606 2 -23022.9 -7450.9 -564.32 0.125 2605 2607 2 -23022.8 -7451.2 -564.38 0.125 2606 2608 2 -23024.4 -7450.38 -564.09 0.125 2607 2609 2 -23024.5 -7449.88 -564 0.125 2608 2610 2 -23026.1 -7449.14 -563.73 0.125 2609 2611 2 -23029.6 -7449.5 -565.39 0.125 2610 2612 2 -23030.3 -7450.16 -565.43 0.125 2611 2613 2 -23033.2 -7450.33 -565.96 0.125 2612 2614 2 -23037 -7449.92 -570.39 0.125 2613 2615 2 -23040.2 -7450.9 -574.25 0.125 2614 2616 2 -23042.4 -7452.1 -577.8 0.125 2615 2617 2 -23046.2 -7450.76 -578.14 0.125 2616 2618 2 -23046.2 -7451.04 -578.19 0.125 2617 2619 2 -23047.2 -7451.52 -578.17 0.125 2618 2620 2 -23047.5 -7451.57 -578.15 0.125 2619 2621 2 -23050.6 -7452.42 -579.77 0.125 2620 2622 2 -23050.5 -7452.65 -579.82 0.125 2621 2623 2 -23052.7 -7452.02 -579.51 0.125 2622 2624 2 -23055.6 -7452.88 -579.38 0.125 2623 2625 2 -23057.4 -7452.93 -579.22 0.125 2624 2626 2 -23058.3 -7452.34 -579.04 0.125 2625 2627 2 -23062.8 -7450.57 -594.5 0.125 2626 2628 2 -23063.1 -7450.61 -594.48 0.125 2627 2629 2 -23066.9 -7450.11 -599.68 0.125 2628 2630 2 -23068.3 -7450.63 -599.64 0.125 2629 2631 2 -23072.6 -7450.31 -604.62 0.125 2630 2632 2 -23074.1 -7448.72 -604.22 0.125 2631 2633 2 -23077.9 -7447.76 -611.68 0.125 2632 2634 2 -23079.9 -7448.29 -612.24 0.125 2633 2635 2 -23085.4 -7448.95 -620.17 0.125 2634 2636 2 -23086 -7447.65 -622.11 0.125 2635 2637 2 -23087.3 -7446.76 -621.87 0.125 2636 2638 2 -23088.1 -7445.96 -624.08 0.125 2637 2639 2 -23088.4 -7445.97 -624.06 0.125 2638 2640 2 -23091 -7446.75 -627.63 0.125 2639 2641 2 -23092.1 -7447.15 -627.59 0.125 2640 2642 2 -23094.2 -7447.36 -629.72 0.125 2641 2643 2 -23094.5 -7446.64 -629.57 0.125 2642 2644 2 -23099.3 -7448.05 -632.39 0.125 2643 2645 2 -23101.9 -7448.08 -634.22 0.125 2644 2646 2 -23103.7 -7447.82 -634.02 0.125 2645 2647 2 -23103.9 -7447.6 -633.95 0.125 2646 2648 2 -23103.9 -7445.77 -633.65 0.125 2647 2649 2 -23104 -7445.24 -633.56 0.125 2648 2650 2 -23104.6 -7444.78 -633.42 0.125 2649 2651 2 -23105.6 -7442.75 -635.41 0.125 2650 2652 2 -23107.2 -7440.76 -638.33 0.125 2651 2653 2 -23107.2 -7440.74 -638.44 0.125 2652 2654 2 -23106.7 -7438.69 -642.32 0.125 2653 2655 2 -23105.5 -7436.38 -645.54 0.125 2654 2656 2 -23104 -7435.08 -648.47 0.125 2655 2657 2 -23104.2 -7435.15 -648.46 0.125 2656 2658 2 -23105 -7432.15 -653.88 0.125 2657 2659 2 -23105.2 -7430.52 -657.03 0.125 2658 2660 2 -23105.2 -7430.5 -657.2 0.125 2659 2661 2 -23104.2 -7428.15 -657.81 0.125 2660 2662 2 -23104.2 -7428.4 -657.86 0.125 2661 2663 2 -23104.3 -7425.21 -662.19 0.125 2662 2664 2 -23104.7 -7424.73 -662.08 0.125 2663 2665 2 -23104.7 -7424.49 -662.04 0.125 2664 2666 2 -23105.1 -7422.72 -664.71 0.125 2665 2667 2 -23105.8 -7422.15 -664.21 0.125 2666 2668 2 -23106.8 -7419.67 -669.48 0.125 2667 2669 2 -23106.8 -7419.44 -669.44 0.125 2668 2670 2 -23106.2 -7416.01 -669.98 0.125 2669 2671 2 -23105.3 -7411 -675.19 0.125 2670 2672 2 -23105.6 -7411.05 -675.18 0.125 2671 2673 2 -23105.1 -7408.47 -678.71 0.125 2672 2674 2 -23105.1 -7408.23 -678.67 0.125 2673 2675 2 -23105.9 -7405.34 -680.36 0.125 2674 2676 2 -23106.2 -7405.37 -680.34 0.125 2675 2677 2 -23105.1 -7405.12 -679.6 0.125 2676 2678 2 -23115.4 -7406.42 -678.85 0.125 2677 2679 2 -23116.6 -7400.33 -680.59 0.125 2678 2680 2 -23116.5 -7400.61 -680.64 0.125 2679 2681 2 -23116.1 -7398.63 -682.35 0.125 2680 2682 2 -23116.2 -7398.37 -682.3 0.125 2681 2683 2 -23116.4 -7393.36 -686.17 0.125 2682 2684 2 -23117 -7389.84 -690.95 0.125 2683 2685 2 -23117.3 -7389.92 -690.94 0.125 2684 2686 2 -23117.7 -7387.62 -690.57 0.125 2685 2687 2 -23118 -7387.64 -690.54 0.125 2686 2688 2 -23117.2 -7384.79 -693.72 0.125 2687 2689 2 -23117.1 -7383.72 -693.55 0.125 2688 2690 2 -23117.3 -7381.77 -695.28 0.125 2689 2691 2 -23116.6 -7379.78 -697.11 0.125 2690 2692 2 -23116.9 -7377.93 -696.93 0.125 2691 2693 2 -23116.6 -7375.67 -700.03 0.125 2692 2694 2 -23116.9 -7373.91 -699.71 0.125 2693 2695 2 -23117 -7373.65 -699.66 0.125 2694 2696 2 -23116.9 -7368.95 -704.52 0.125 2695 2697 2 -23117.3 -7365.15 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2766 2 -23062.6 -7438.47 -682.48 0.125 2765 2767 2 -23064.3 -7439.22 -688.81 0.125 2766 2768 2 -23064.1 -7439.66 -686.16 0.125 2767 2769 2 -23064.8 -7438.94 -686.56 0.125 2768 2770 2 -23066.1 -7438.01 -691.04 0.125 2769 2771 2 -23068.5 -7436.07 -696.65 0.125 2770 2772 2 -23068.5 -7435.81 -696.61 0.125 2771 2773 2 -23070.3 -7434.96 -704.27 0.125 2772 2774 2 -23071.4 -7436.78 -704.01 0.125 2773 2775 2 -23074 -7438.19 -707.5 0.125 2774 2776 2 -23074.2 -7438.84 -708.68 0.125 2775 2777 2 -23078.7 -7443.54 -715.18 0.125 2776 2778 2 -23080.9 -7446.81 -718.42 0.125 2777 2779 2 -23081.2 -7446.87 -718.4 0.125 2778 2780 2 -23082.3 -7449.88 -722.34 0.125 2779 2781 2 -23082.6 -7449.91 -722.31 0.125 2780 2782 2 -23083.8 -7452.04 -723.66 0.125 2781 2783 2 -23083.8 -7452.2 -724.23 0.125 2782 2784 2 -23085.8 -7453.94 -725.41 0.125 2783 2785 2 -23085.9 -7453.78 -726.36 0.125 2784 2786 2 -23085.9 -7456.42 -728.11 0.125 2785 2787 2 -23085.8 -7456.68 -728.15 0.125 2786 2788 2 -23086.1 -7458.59 -728.45 0.125 2787 2789 2 -23086.3 -7458.6 -728.42 0.125 2788 2790 2 -23086 -7462.77 -733.67 0.125 2789 2791 2 -23086 -7462.55 -733.62 0.125 2790 2792 2 -23084.9 -7465.18 -736.04 0.125 2791 2793 2 -23082.5 -7467.65 -736.69 0.125 2792 2794 2 -23082.4 -7470.67 -737.92 0.125 2793 2795 2 -23083.6 -7477.05 -742.79 0.125 2794 2796 2 -23085.7 -7479.38 -749.44 0.125 2795 2797 2 -23084.9 -7483.13 -756.71 0.125 2796 2798 2 -23080.9 -7483.19 -757.1 0.125 2797 2799 2 -23081.2 -7483.27 -757.08 0.125 2798 2800 2 -23080.6 -7484.45 -757.33 0.125 2799 2801 2 -23080 -7485.2 -759.86 0.125 2800 2802 2 -23080.3 -7485.21 -760.39 0.125 2801 2803 2 -23079.3 -7488.43 -761.04 0.125 2802 2804 2 -23079.2 -7490.38 -766.64 0.125 2803 2805 2 -23079.5 -7491.5 -766.79 0.125 2804 2806 2 -23078.5 -7491 -766.81 0.125 2805 2807 2 -23077.7 -7491.83 -771.93 0.125 2806 2808 2 -23077.4 -7492.51 -773.76 0.125 2807 2809 2 -23076.2 -7494.14 -779.8 0.125 2808 2810 2 -23076.3 -7493.88 -779.76 0.125 2809 2811 2 -23075.5 -7495.16 -782.4 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129.92 0.37 2927 2929 2 -21974.9 -7300.85 131.01 0.37 2928 2930 2 -21975.9 -7299.48 131.36 0.37 2929 2931 2 -21975.9 -7299.76 131.31 0.37 2930 2932 2 -21977.6 -7298 132.2 0.37 2931 2933 2 -21979.5 -7297.92 133.14 0.37 2932 2934 2 -21980.5 -7296.51 134.74 0.37 2933 2935 2 -21980.7 -7295.74 134.88 0.37 2934 2936 2 -21981.9 -7294.89 135.29 0.37 2935 2937 2 -21984 -7293.69 137.15 0.37 2936 2938 2 -21985 -7292.64 137.9 0.37 2937 2939 2 -21985.3 -7292.44 137.96 0.37 2938 2940 2 -21986.3 -7291.37 138.72 0.37 2939 2941 2 -21986.6 -7291.45 138.73 0.37 2940 2942 2 -21986.9 -7291.07 139.37 0.37 2941 2943 2 -21988.2 -7289.74 139.72 0.37 2942 2944 2 -21988.5 -7289.78 139.74 0.37 2943 2945 2 -21990.2 -7288.32 140.5 0.37 2944 2946 2 -21990.1 -7288.49 141.52 0.37 2945 2947 2 -21990.8 -7287.55 141.74 0.37 2946 2948 2 -21990.7 -7287.56 141.82 0.37 2947 2949 2 -21992 -7286.71 142.12 0.37 2948 2950 2 -21993.2 -7286.42 142.33 0.37 2949 2951 2 -21993.2 -7286.44 142.43 0.37 2950 2952 2 -21994.7 -7284.91 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368.07 0.125 3189 3191 2 -22237.6 -7086.88 370.33 0.125 3190 3192 2 -22237.8 -7087.85 378.32 0.125 3191 3193 2 -22238.6 -7087.22 381.51 0.125 3192 3194 2 -22238.5 -7087.26 381.76 0.125 3193 3195 2 -22239 -7085.8 384.08 0.125 3194 3196 2 -22238.9 -7085.86 385.76 0.125 3195 3197 2 -22224.7 -7075.84 349.8 0.125 3181 3198 2 -22226 -7074.61 350.71 0.125 3197 3199 2 -22226.7 -7074.2 350.78 0.125 3198 3200 2 -22226.7 -7073.96 350.82 0.125 3199 3201 2 -22227.3 -7073.73 352.28 0.125 3200 3202 2 -22227.4 -7073.8 354.07 0.125 3201 3203 2 -22228.3 -7072.37 354.38 0.125 3202 3204 2 -22229.9 -7071.11 354.75 0.125 3203 3205 2 -22230.6 -7070.48 354.92 0.125 3204 3206 2 -22231 -7070.24 354.99 0.125 3205 3207 2 -22232.2 -7069.45 355.24 0.125 3206 3208 2 -22232.6 -7068.97 355.36 0.125 3207 3209 2 -22233.2 -7068.6 355.48 0.125 3208 3210 2 -22233.3 -7068.06 355.58 0.125 3209 3211 2 -22233.7 -7067.51 360.97 0.125 3210 3212 2 -22234 -7067.03 361.27 0.125 3211 3213 2 -22234.5 -7066.31 361.43 0.125 3212 3214 2 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377.18 0.125 3236 3238 2 -22249.8 -7050.4 377.23 0.125 3237 3239 2 -22251.6 -7049.92 377.26 0.125 3238 3240 2 -22254.4 -7048.19 380.17 0.125 3239 3241 2 -22255.7 -7046.65 380.55 0.125 3240 3242 2 -22255.8 -7046.41 380.76 0.125 3241 3243 2 -22256.2 -7046.27 384.17 0.125 3242 3244 2 -22256.1 -7046.51 385.58 0.125 3243 3245 2 -22257.3 -7045.56 386.62 0.125 3244 3246 2 -22257.3 -7045.43 387.44 0.125 3245 3247 2 -22259.3 -7044.93 388.75 0.125 3246 3248 2 -22259.5 -7044.16 388.9 0.125 3247 3249 2 -22260.5 -7043.27 389.27 0.125 3248 3250 2 -22260.8 -7042.87 389.53 0.125 3249 3251 2 -22261.5 -7042.52 389.83 0.125 3250 3252 2 -22264 -7041.77 392.19 0.125 3251 3253 2 -22264.3 -7041.73 391.93 0.125 3252 3254 2 -22265.8 -7041.77 392.12 0.125 3253 3255 2 -22265.8 -7041.26 392.21 0.125 3254 3256 2 -22266.2 -7041.09 392.27 0.125 3255 3257 2 -22267.3 -7040.86 393.08 0.125 3256 3258 2 -22267.5 -7040.94 394.76 0.125 3257 3259 2 -22267.5 -7040.71 394.8 0.125 3258 3260 2 -22267.8 -7040.67 394.33 0.125 3259 3261 2 -22267.6 -7041.08 396.83 0.125 3260 3262 2 -22268 -7040.66 397.24 0.125 3261 3263 2 -22268.3 -7040.02 397.82 0.125 3262 3264 2 -22268.4 -7039.79 397.86 0.125 3263 3265 2 -22268.4 -7039.36 398.5 0.125 3264 3266 2 -22269.3 -7037.09 400.27 0.125 3265 3267 2 -22271.2 -7036.03 401.74 0.125 3266 3268 2 -22273.5 -7033.67 399.74 0.125 3267 3269 2 -22276.8 -7032.27 402.15 0.125 3268 3270 2 -22278.6 -7031.81 406.98 0.125 3269 3271 2 -22279.8 -7031.03 407.38 0.125 3270 3272 2 -22279.9 -7030.74 407.43 0.125 3271 3273 2 -22281.4 -7029.25 411.02 0.125 3272 3274 2 -22282.3 -7028.63 411.26 0.125 3273 3275 2 -22284.7 -7028.12 414.94 0.125 3274 3276 2 -22285.6 -7027.47 416.8 0.125 3275 3277 2 -22286.8 -7025.29 417.28 0.125 3276 3278 2 -22287.5 -7022.76 417.84 0.125 3277 3279 2 -22288.7 -7021.05 418.23 0.125 3278 3280 2 -22288.8 -7020.54 418.32 0.125 3279 3281 2 -22290.7 -7018.76 420.49 0.125 3280 3282 2 -22292.8 -7017.57 421.33 0.125 3281 3283 2 -22292.8 -7017.45 422.03 0.125 3282 3284 2 -22295.2 -7016.55 424.12 0.125 3283 3285 2 -22295.2 -7016.29 424.27 0.125 3284 3286 2 -22296.4 -7015.98 424.44 0.125 3285 3287 2 -22298.9 -7014.47 426.56 0.125 3286 3288 2 -22299.9 -7011.75 427.11 0.125 3287 3289 2 -22300 -7011.23 427.2 0.125 3288 3290 2 -22300.9 -7009.51 427.57 0.125 3289 3291 2 -22300.9 -7009.23 427.62 0.125 3290 3292 2 -22301.8 -7008.23 430.5 0.125 3291 3293 2 -22301.8 -7007.98 430.54 0.125 3292 3294 2 -22303.3 -7007.41 430.78 0.125 3293 3295 2 -22304.8 -7007.15 430.96 0.125 3294 3296 2 -22305.8 -7006.92 430.58 0.125 3295 3297 2 -22308 -7007.79 430.02 0.125 3296 3298 2 -22310.2 -7009.08 427.8 0.125 3297 3299 2 -22312.5 -7010.85 427.59 0.125 3298 3300 2 -22312.7 -7010.93 427.6 0.125 3299 3301 2 -22316.5 -7011.66 422.31 0.125 3300 3302 2 -22316.7 -7011.7 422.33 0.125 3301 3303 2 -22318.6 -7012.82 422.26 0.125 3302 3304 2 -22321.5 -7013.86 419.09 0.125 3303 3305 2 -22323.7 -7015.02 419.1 0.125 3304 3306 2 -22323.7 -7014.99 418.95 0.125 3305 3307 2 -22325.2 -7014.09 418.5 0.125 3306 3308 2 -22327.3 -7014.87 416.3 0.125 3307 3309 2 -22332.3 -7017.85 413.95 0.125 3308 3310 2 -22335 -7020.26 413.91 0.125 3309 3311 2 -22338.4 -7020.72 413.02 0.125 3310 3312 2 -22339.5 -7020.92 413.09 0.125 3311 3313 2 -22342.1 -7021.76 410.58 0.125 3312 3314 2 -22343.4 -7022.31 410.61 0.125 3313 3315 2 -22345 -7023.14 410.62 0.125 3314 3316 2 -22345.6 -7024.29 410.48 0.125 3315 3317 2 -22346.9 -7024.82 410.52 0.125 3316 3318 2 -22348.7 -7025.8 410.82 0.125 3317 3319 2 -22351.3 -7026.21 409.03 0.125 3318 3320 2 -22352.9 -7027.42 408.02 0.125 3319 3321 2 -22356 -7030.86 407.09 0.125 3320 3322 2 -22357.9 -7033.21 406.28 0.125 3321 3323 2 -22359.1 -7035.63 405.99 0.125 3322 3324 2 -22360.5 -7037.74 405.9 0.125 3323 3325 2 -22361.5 -7038.67 403.93 0.125 3324 3326 2 -22362.8 -7040.51 402.29 0.125 3325 3327 2 -22363.9 -7042.1 402.13 0.125 3326 3328 2 -22365.6 -7043.2 402.11 0.125 3327 3329 2 -22367.7 -7044.26 402.82 0.125 3328 3330 2 -22307 -7003.86 433.71 0.125 3296 3331 2 -22307 -7003.58 433.76 0.125 3330 3332 2 -22307.6 -6998 434.77 0.125 3331 3333 2 -22309.7 -6996.19 439.35 0.125 3332 3334 2 -22310 -6996.18 439.38 0.125 3333 3335 2 -22311.7 -6995.96 439.58 0.125 3334 3336 2 -22314.3 -6994.28 440.1 0.125 3335 3337 2 -22316 -6992.94 440.48 0.125 3336 3338 2 -22315.2 -6992.53 440.47 0.125 3337 3339 2 -22315.7 -6991.29 440.72 0.125 3338 3340 2 -22316.4 -6988.73 441.21 0.125 3339 3341 2 -22317.4 -6988.08 441.41 0.125 3340 3342 2 -22318.9 -6985.55 438.51 0.125 3341 3343 2 -22320.2 -6983.18 437.64 0.125 3342 3344 2 -22323.1 -6981.6 436.99 0.125 3343 3345 2 -22323.4 -6981.61 437.02 0.125 3344 3346 2 -22325.9 -6978.05 435 0.125 3345 3347 2 -22326.2 -6978.37 434.97 0.125 3346 3348 2 -22327.2 -6977.47 435.21 0.125 3347 3349 2 -22327.5 -6977.54 435.23 0.125 3348 3350 2 -22327.4 -6975.61 435.55 0.125 3349 3351 2 -22329.2 -6974.08 435.96 0.125 3350 3352 2 -22330.7 -6973.54 436.19 0.125 3351 3353 2 -22330.8 -6972.75 436.34 0.125 3352 3354 2 -22331 -6971.96 436.48 0.125 3353 3355 2 -22331 -6971.71 436.52 0.125 3354 3356 2 -22332.7 -6969.27 435.21 0.125 3355 3357 2 -22333 -6968.77 435.32 0.125 3356 3358 2 -22333.1 -6968.52 435.37 0.125 3357 3359 2 -22334.7 -6966.77 433.5 0.125 3358 3360 2 -22335 -6966.56 433.56 0.125 3359 3361 2 -22335.9 -6966.21 433.71 0.125 3360 3362 2 -22336 -6965.96 433.75 0.125 3361 3363 2 -22336.3 -6964.07 434.09 0.125 3362 3364 2 -22336.6 -6963.64 434.19 0.125 3363 3365 2 -22338.2 -6963.05 434.44 0.125 3364 3366 2 -22339.1 -6961.72 436.77 0.125 3365 3367 2 -22339.3 -6960.38 437.02 0.125 3366 3368 2 -22339.4 -6960.13 437.06 0.125 3367 3369 2 -22339.8 -6959.4 437.22 0.125 3368 3370 2 -22340.8 -6958.49 437.47 0.125 3369 3371 2 -22342.5 -6957.18 437.84 0.125 3370 3372 2 -22344.6 -6955.94 439.73 0.125 3371 3373 2 -22344.6 -6955.69 439.78 0.125 3372 3374 2 -22345.1 -6954.41 444.71 0.125 3373 3375 2 -22347.2 -6953.85 450.61 0.125 3374 3376 2 -22349.9 -6953.65 454.61 0.125 3375 3377 2 -22348.9 -6952.87 457.06 0.125 3376 3378 2 -22348.9 -6952.69 462.47 0.125 3377 3379 2 -22349.9 -6949.76 468.22 0.125 3378 3380 2 -22346.5 -6954.07 438.36 0.125 3373 3381 2 -22348 -6953.81 438.54 0.125 3380 3382 2 -22348.2 -6953.86 438.55 0.125 3381 3383 2 -22348.7 -6953.11 438.66 0.125 3382 3384 2 -22352.5 -6949.73 438.21 0.125 3383 3385 2 -22352.7 -6949.79 438.23 0.125 3384 3386 2 -22354.4 -6948.74 438.55 0.125 3385 3387 2 -22355.6 -6948.14 438.77 0.125 3386 3388 2 -22355.7 -6947.36 438.91 0.125 3387 3389 2 -22358.9 -6945.8 439.46 0.125 3388 3390 2 -22358.9 -6945.48 439.52 0.125 3389 3391 2 -22359.6 -6945.08 439.65 0.125 3390 3392 2 -22361.3 -6942.13 438.77 0.125 3391 3393 2 -22361.3 -6941.87 438.81 0.125 3392 3394 2 -22361.6 -6940.58 439.05 0.125 3393 3395 2 -22361.8 -6940.4 439.11 0.125 3394 3396 2 -22362.9 -6939.47 439.36 0.125 3395 3397 2 -22363.2 -6939.48 439.38 0.125 3396 3398 2 -22363.5 -6939.05 439.49 0.125 3397 3399 2 -22363.6 -6938.79 439.53 0.125 3398 3400 2 -22365.7 -6936.44 440.02 0.125 3399 3401 2 -22366 -6936.5 440.04 0.125 3400 3402 2 -22365.9 -6935.11 440.21 0.125 3401 3403 2 -22366 -6934.85 440.26 0.125 3402 3404 2 -22369.1 -6931.99 439.67 0.125 3403 3405 2 -22369.4 -6932.04 439.68 0.125 3404 3406 2 -22370.7 -6931.14 439.96 0.125 3405 3407 2 -22371.6 -6930.41 441.26 0.125 3406 3408 2 -22373.2 -6929.28 442.61 0.125 3407 3409 2 -22373.2 -6929.01 442.65 0.125 3408 3410 2 -22375.4 -6926.17 443.33 0.125 3409 3411 2 -22376.2 -6924.51 444.23 0.125 3410 3412 2 -22376.2 -6924.6 444.81 0.125 3411 3413 2 -22378.6 -6922.32 445.16 0.125 3412 3414 2 -22380 -6920.67 445.57 0.125 3413 3415 2 -22380.8 -6919.46 445.84 0.125 3414 3416 2 -22382.7 -6917.59 447.78 0.125 3415 3417 2 -22383.8 -6916.47 448.04 0.125 3416 3418 2 -22383.9 -6915.93 448.14 0.125 3417 3419 2 -22384.2 -6915.72 448.2 0.125 3418 3420 2 -22386.3 -6913.69 448.73 0.125 3419 3421 2 -22388.6 -6911.94 449.24 0.125 3420 3422 2 -22391 -6911.01 454.06 0.125 3421 3423 2 -22392.7 -6909.94 454.44 0.125 3422 3424 2 -22392.6 -6910.02 454.94 0.125 3423 3425 2 -22393.3 -6908.76 456.53 0.125 3424 3426 2 -22393.5 -6908.84 456.62 0.125 3425 3427 2 -22396 -6906.71 459.62 0.125 3426 3428 2 -22398 -6904.22 464.08 0.125 3427 3429 2 -22397.9 -6904.26 464.36 0.125 3428 3430 2 -22401.4 -6899.44 465.01 0.125 3429 3431 2 -22406.8 -6892.78 470.64 0.125 3430 3432 2 -22401.2 -6904.61 466.37 0.125 3429 3433 2 -22402.7 -6904.64 466.5 0.125 3432 3434 2 -22404.1 -6902.96 466.91 0.125 3433 3435 2 -22407.1 -6902.97 470.07 0.125 3434 3436 2 -22407 -6903.04 470.48 0.125 3435 3437 2 -22410 -6901.86 472.15 0.125 3436 3438 2 -22411 -6901.37 476.35 0.125 3437 3439 2 -22413 -6899.9 476.78 0.125 3438 3440 2 -22413.3 -6899.42 476.89 0.125 3439 3441 2 -22413.4 -6898.54 478.14 0.125 3440 3442 2 -22413.7 -6898.57 478.16 0.125 3441 3443 2 -22414.1 -6897.1 480.49 0.125 3442 3444 2 -22414.2 -6896.56 480.59 0.125 3443 3445 2 -22415.2 -6894.07 481.18 0.125 3444 3446 2 -22415.2 -6893.85 481.22 0.125 3445 3447 2 -22416 -6892.19 482.36 0.125 3446 3448 2 -22417.9 -6890.55 485.39 0.125 3447 3449 2 -22418.8 -6890.18 485.54 0.125 3448 3450 2 -22418.9 -6889.67 485.63 0.125 3449 3451 2 -22420.5 -6887.34 488.11 0.125 3450 3452 2 -22420.5 -6887.5 489.11 0.125 3451 3453 2 -22421.9 -6886.44 487.83 0.125 3452 3454 2 -22422.6 -6883.87 492.92 0.125 3453 3455 2 -22417.6 -6884.59 492.34 0.125 3454 3456 2 -22418.6 -6882.85 496.67 0.125 3455 3457 2 -22419.6 -6879.46 500.99 0.125 3456 3458 2 -22135.1 -7160.19 250.3 0.125 3090 3459 2 -22136.3 -7159.28 252.03 0.125 3458 3460 2 -22138.2 -7157.34 249.03 0.25 3459 3461 2 -22139.7 -7156.51 249.26 0.25 3460 3462 2 -22142.2 -7154.69 249.53 0.25 3461 3463 2 -22145.6 -7153.97 247.66 0.25 3462 3464 2 -22147.1 -7152.91 247.97 0.25 3463 3465 2 -22149.8 -7150.62 247.13 0.25 3464 3466 2 -22150 -7150.6 247.16 0.25 3465 3467 2 -22152.5 -7149.39 247.59 0.25 3466 3468 2 -22154.1 -7147.73 248.02 0.25 3467 3469 2 -22155.4 -7146.86 248.28 0.25 3468 3470 2 -22157.2 -7145.04 248.89 0.25 3469 3471 2 -22159.7 -7144.41 248 0.25 3470 3472 2 -22161.5 -7143.83 248.26 0.25 3471 3473 2 -22163.9 -7142.75 247.97 0.25 3472 3474 2 -22165.6 -7141.1 248.21 0.25 3473 3475 2 -22165.7 -7140.03 248.35 0.25 3474 3476 2 -22165.8 -7139.74 248.4 0.25 3475 3477 2 -22167 -7139.09 248.41 0.25 3476 3478 2 -22167 -7139.08 248.3 0.25 3477 3479 2 -22168.5 -7138.46 248.39 0.25 3478 3480 2 -22170.2 -7137.13 247.25 0.25 3479 3481 2 -22172 -7136.32 247.55 0.25 3480 3482 2 -22172 -7136.05 247.6 0.25 3481 3483 2 -22172.4 -7135.08 247.8 0.25 3482 3484 2 -22165.3 -7136.36 246.92 0.37 3483 3485 2 -22168.8 -7135.27 240.62 0.37 3484 3486 2 -22169.5 -7133.83 240.93 0.37 3485 3487 2 -22170.5 -7131.33 241.44 0.37 3486 3488 2 -22172.3 -7129.41 237.94 0.37 3487 3489 2 -22172.7 -7128.42 238.14 0.37 3488 3490 2 -22175 -7126.94 236.91 0.37 3489 3491 2 -22176.8 -7124.79 236.89 0.37 3490 3492 2 -22178.1 -7123.02 234.91 0.37 3491 3493 2 -22180.1 -7123.13 235.08 0.37 3492 3494 2 -22182.2 -7122.44 235.39 0.37 3493 3495 2 -22185 -7120.73 232.28 0.37 3494 3496 2 -22186.6 -7118.04 231 0.37 3495 3497 2 -22187.7 -7117.1 230.69 0.37 3496 3498 2 -22189.7 -7115.38 225.56 0.37 3497 3499 2 -22190.7 -7114.49 225.54 0.37 3498 3500 2 -22191.5 -7113.29 225.81 0.37 3499 3501 2 -22193.3 -7111.52 224.85 0.37 3500 3502 2 -22195.6 -7109.77 224.99 0.37 3501 3503 2 -22197.6 -7108 220.81 0.37 3502 3504 2 -22199.1 -7105.64 221.33 0.37 3503 3505 2 -22200.7 -7104.55 221.48 0.37 3504 3506 2 -22206.5 -7100.54 213.36 0.37 3505 3507 2 -22209.8 -7099.01 210.98 0.37 3506 3508 2 -22210.3 -7097.5 211.27 0.37 3507 3509 2 -22213.3 -7095.11 211.7 0.37 3508 3510 2 -22216 -7092.83 207.19 0.37 3509 3511 2 -22218.9 -7092.23 207.34 0.37 3510 3512 2 -22221.8 -7089.18 201.15 0.25 3511 3513 2 -22224.8 -7087.24 199.87 0.25 3512 3514 2 -22226.5 -7086 198.57 0.25 3513 3515 2 -22228.7 -7085.3 197.33 0.25 3514 3516 2 -22230.7 -7082.14 192.94 0.25 3515 3517 2 -22232 -7079.65 191.7 0.25 3516 3518 2 -22234.4 -7077.49 188.01 0.25 3517 3519 2 -22235.6 -7075.65 187.17 0.25 3518 3520 2 -22236.7 -7075.09 184.71 0.25 3519 3521 2 -22238.7 -7074.15 185.05 0.125 3520 3522 2 -22243 -7068.55 180.69 0.125 3521 3523 2 -22245.9 -7063.5 175.92 0.125 3522 3524 2 -22248.3 -7059.32 167.21 0.125 3523 3525 2 -22248.8 -7057.5 167.35 0.125 3524 3526 2 -22252 -7054.46 161.28 0.125 3525 3527 2 -22253.2 -7054.53 160.7 0.125 3526 3528 2 -22253.8 -7052.46 160.73 0.125 3527 3529 2 -22253.8 -7050.78 160.49 0.125 3528 3530 2 -22256.3 -7048.33 158.51 0.125 3529 3531 2 -22257.9 -7046.32 156.69 0.125 3530 3532 2 -22259.4 -7044.73 155.58 0.125 3531 3533 2 -22261.3 -7042.28 154.03 0.125 3532 3534 2 -22263.6 -7040.42 153.79 0.125 3533 3535 2 -22265.8 -7037.74 149.16 0.125 3534 3536 2 -22266.6 -7037.6 149.25 0.125 3535 3537 2 -22267.4 -7036.15 149.41 0.125 3536 3538 2 -22268.5 -7033.52 149.07 0.125 3537 3539 2 -22271.9 -7032.09 142.98 0.125 3538 3540 2 -22273.1 -7030.53 142.22 0.125 3539 3541 2 -22273.8 -7028.79 141.11 0.125 3540 3542 2 -22275.7 -7026.06 136.49 0.125 3541 3543 2 -22276.1 -7024.81 136.74 0.125 3542 3544 2 -22277.2 -7022.88 137.16 0.125 3543 3545 2 -22280 -7018.77 131.75 0.125 3544 3546 2 -22282.7 -7016.66 131.24 0.125 3545 3547 2 -22290.3 -7019.22 131.53 0.125 3546 3548 2 -22292 -7017.44 131.99 0.125 3547 3549 2 -22293.2 -7015.57 132.41 0.125 3548 3550 2 -22295.3 -7012.03 132.06 0.125 3549 3551 2 -22297.2 -7011.08 132.32 0.125 3550 3552 2 -22298.2 -7009.53 130.4 0.125 3551 3553 2 -22299 -7008.12 130.68 0.125 3552 3554 2 -22300.8 -7007.4 130.95 0.125 3553 3555 2 -22303.2 -7005.75 125.57 0.125 3554 3556 2 -22304.5 -7004.95 125.82 0.125 3555 3557 2 -22306.8 -7002.9 121.31 0.125 3556 3558 2 -22307.6 -7001.7 121.58 0.125 3557 3559 2 -22309.1 -7000.96 119.05 0.125 3558 3560 2 -22309.1 -6999.15 119.35 0.125 3559 3561 2 -22309.3 -6997.86 119.59 0.125 3560 3562 2 -22311.4 -6995.6 120.16 0.125 3561 3563 2 -22313.9 -6993.65 118.83 0.125 3562 3564 2 -22314.9 -6991.78 116.68 0.125 3563 3565 2 -22314.8 -6990.07 114.88 0.125 3564 3566 2 -22316.4 -6988.26 113.82 0.125 3565 3567 2 -22317.6 -6986.75 111.88 0.125 3566 3568 2 -22318.5 -6986.41 112.02 0.125 3567 3569 2 -22318.7 -6985.11 112.25 0.125 3568 3570 2 -22320.1 -6983.38 108.64 0.125 3569 3571 2 -22320.4 -6982.09 108.87 0.125 3570 3572 2 -22321.3 -6981.07 106.97 0.125 3571 3573 2 -22321.2 -6979.92 106.88 0.125 3572 3574 2 -22322.9 -6979.35 104.82 0.125 3573 3575 2 -22323.3 -6978.65 104.97 0.125 3574 3576 2 -22321.8 -6973.33 102.61 0.125 3575 3577 2 -22323.7 -6972.94 101.68 0.125 3576 3578 2 -22324.9 -6970.96 101.68 0.125 3577 3579 2 -22329.5 -6962.81 100.13 0.125 3578 3580 2 -22334.1 -6956.31 100.39 0.125 3579 3581 2 -22336.8 -6953.9 101.04 0.125 3580 3582 2 -22338 -6952.04 101.46 0.125 3581 3583 2 -22338.4 -6948.15 102.14 0.125 3582 3584 2 -22340.5 -6945.93 102.7 0.125 3583 3585 2 -22342.2 -6944.09 103.17 0.125 3584 3586 2 -22342.7 -6943.15 103.37 0.125 3585 3587 2 -22344.1 -6939.86 103.34 0.125 3586 3588 2 -22346.1 -6937.11 102.71 0.125 3587 3589 2 -22350.3 -6933.67 100.51 0.125 3588 3590 2 -22353.2 -6931.84 101.09 0.125 3589 3591 2 -22357.2 -6927.74 100.22 0.125 3590 3592 2 -22358.7 -6925.63 100.71 0.125 3591 3593 2 -22360.9 -6921.3 101.64 0.125 3592 3594 2 -22362.7 -6918.75 102.23 0.125 3593 3595 2 -22365.8 -6915.61 98.46 0.125 3594 3596 2 -22368.2 -6911.61 99.35 0.125 3595 3597 2 -22371.9 -6907.72 98.42 0.125 3596 3598 2 -22373.2 -6905.34 98.93 0.125 3597 3599 2 -22376.6 -6900.03 97.88 0.125 3598 3600 2 -22378.6 -6895.94 98.76 0.125 3599 3601 2 -22379.4 -6895.01 98.98 0.125 3600 3602 2 -22380.9 -6891.77 98.9 0.125 3601 3603 2 -22380.7 -6887.9 99.52 0.125 3602 3604 2 -22379.6 -6883.58 96.17 0.125 3603 3605 2 -22379.9 -6881.7 96.39 0.125 3604 3606 2 -22379 -6879.93 96.44 0.125 3605 3607 2 -22378.2 -6877.94 96.7 0.125 3606 3608 2 -22378.6 -6875.15 97.2 0.125 3607 3609 2 -22379.4 -6871.18 96.23 0.125 3608 3610 2 -22379.3 -6867.07 94.62 0.125 3609 3611 2 -22379 -6865.42 94.86 0.125 3610 3612 2 -22379.7 -6864.19 95.13 0.125 3611 3613 2 -22381.4 -6861.05 91.42 0.125 3612 3614 2 -22382.3 -6859.92 91.7 0.125 3613 3615 2 -22382.3 -6858.28 91.97 0.125 3614 3616 2 -22381.8 -6857.71 92.02 0.125 3615 3617 2 -22381.9 -6855.82 92.33 0.125 3616 3618 2 -22381 -6853.86 92.58 0.125 3617 3619 2 -22379.5 -6850.07 89.6 0.125 3618 3620 2 -22378.5 -6847.51 89.93 0.125 3619 3621 2 -22377.2 -6844.59 87.06 0.125 3620 3622 2 -22376.3 -6842.09 84.6 0.125 3621 3623 2 -22375.8 -6840.39 84.83 0.125 3622 3624 2 -22375.9 -6839.12 85.06 0.125 3623 3625 2 -22376.5 -6838.4 82.21 0.125 3624 3626 2 -21870.3 -7372.4 83.95 0.37 1650 3627 2 -21870.5 -7370.37 84.31 0.37 3626 3628 2 -21870.3 -7369.13 86.56 0.37 3627 3629 2 -21870.7 -7366.77 86.99 0.37 3628 3630 2 -21870.7 -7366.55 87.03 0.37 3629 3631 2 -21870.1 -7364.8 87.27 0.37 3630 3632 2 -21869.8 -7364.57 87.28 0.37 3631 3633 2 -21869.3 -7362.61 87.55 0.37 3632 3634 2 -21869.1 -7362.03 87.62 0.37 3633 3635 2 -21869.9 -7360.58 87.94 0.37 3634 3636 2 -21870 -7359.53 88.13 0.37 3635 3637 2 -21869.6 -7358.68 88.23 0.37 3636 3638 2 -21869.8 -7357.64 88.42 0.37 3637 3639 2 -21870.1 -7356 92.69 0.37 3638 3640 2 -21871.1 -7354.64 93.27 0.37 3639 3641 2 -21871.1 -7354.42 93.57 0.37 3640 3642 2 -21870.9 -7352.09 94.47 0.37 3641 3643 2 -21870.9 -7351.82 94.52 0.37 3642 3644 2 -21871.4 -7350.14 95.13 0.37 3643 3645 2 -21871.5 -7349.64 95.49 0.37 3644 3646 2 -21872.3 -7347.51 96.6 0.37 3645 3647 2 -21872.7 -7346.54 96.8 0.37 3646 3648 2 -21873.6 -7343.74 98.6 0.37 3647 3649 2 -21874.7 -7342.07 98.98 0.37 3648 3650 2 -21875 -7341.83 99.05 0.37 3649 3651 2 -21876.3 -7339.67 99.59 0.37 3650 3652 2 -21876.3 -7339.4 99.64 0.37 3651 3653 2 -21878.1 -7337.91 101.89 0.37 3652 3654 2 -21880.2 -7335.36 102.64 0.37 3653 3655 2 -21880.1 -7335.11 102.67 0.37 3654 3656 2 -21881.5 -7331.71 104.02 0.37 3655 3657 2 -21881.3 -7331.82 106.17 0.37 3656 3658 2 -21881.2 -7329.52 105.63 0.37 3657 3659 2 -21881.2 -7329.26 105.68 0.37 3658 3660 2 -21881 -7328.38 106.87 0.37 3659 3661 2 -21881 -7327.86 106.97 0.37 3660 3662 2 -21880 -7325.79 110.02 0.37 3661 3663 2 -21879.9 -7325.45 110.06 0.37 3662 3664 2 -21878.3 -7324.68 110.01 0.37 3663 3665 2 -21878.2 -7323.35 110.21 0.37 3664 3666 2 -21878 -7322.83 110.28 0.37 3665 3667 2 -21876.5 -7321.18 110.42 0.37 3666 3668 2 -21876.6 -7321.01 110.45 0.37 3667 3669 2 -21876.9 -7318.92 110.82 0.37 3668 3670 2 -21877.5 -7316.63 111.26 0.37 3669 3671 2 -21879.9 -7315.47 111.68 0.37 3670 3672 2 -21881.3 -7314.1 112.03 0.37 3671 3673 2 -21882.5 -7311.62 112.56 0.37 3672 3674 2 -21882.6 -7311.13 112.65 0.37 3673 3675 2 -21883.4 -7310.38 115.45 0.37 3674 3676 2 -21883.7 -7308.07 115.86 0.37 3675 3677 2 -21884 -7305.52 118.23 0.25 3676 3678 2 -21884 -7305.26 118.28 0.25 3677 3679 2 -21884.4 -7302.68 118.75 0.25 3678 3680 2 -21884.5 -7302.43 118.79 0.25 3679 3681 2 -21885.1 -7300.13 119.23 0.25 3680 3682 2 -21885.4 -7299.67 119.34 0.25 3681 3683 2 -21886.4 -7298.75 119.58 0.25 3682 3684 2 -21887.2 -7297.3 119.9 0.25 3683 3685 2 -21887.5 -7296.87 120 0.25 3684 3686 2 -21886.9 -7293.24 120.54 0.37 3685 3687 2 -21887.3 -7290.92 120.96 0.37 3686 3688 2 -21889.4 -7286.12 119.38 0.37 3687 3689 2 -21889.3 -7286.2 119.88 0.37 3688 3690 2 -21890.2 -7284.11 121.01 0.37 3689 3691 2 -21892.2 -7278.85 126.04 0.37 3690 3692 2 -21892.3 -7275.45 126.6 0.37 3691 3693 2 -21892.4 -7271.26 127.49 0.37 3692 3694 2 -21894.1 -7269.72 127.91 0.37 3693 3695 2 -21895.9 -7267.82 126.23 0.37 3694 3696 2 -21897.4 -7265.97 126.67 0.37 3695 3697 2 -21897.6 -7266.03 126.68 0.37 3696 3698 2 -21898.3 -7264.41 129.13 0.37 3697 3699 2 -21898.6 -7261.02 129.71 0.37 3698 3700 2 -21898.8 -7259.98 129.91 0.37 3699 3701 2 -21899.8 -7257.51 130.41 0.37 3700 3702 2 -21899.8 -7257.27 130.45 0.37 3701 3703 2 -21900.9 -7256.79 135.71 0.37 3702 3704 2 -21901 -7256.53 135.76 0.37 3703 3705 2 -21902.8 -7252.9 136.53 0.37 3704 3706 2 -21902.6 -7248.22 139.16 0.37 3705 3707 2 -21903.2 -7244.85 139.77 0.37 3706 3708 2 -21902.7 -7243.19 142.81 0.25 3707 3709 2 -21903.7 -7241.76 143.14 0.25 3708 3710 2 -21903.7 -7241.56 143.17 0.25 3709 3711 2 -21904.1 -7239.48 143.55 0.25 3710 3712 2 -21904 -7238.45 143.71 0.25 3711 3713 2 -21905.1 -7237.69 141.7 0.25 3712 3714 2 -21905.4 -7237.5 141.76 0.25 3713 3715 2 -21905.9 -7236.24 142.02 0.25 3714 3716 2 -21906 -7235.98 142.06 0.25 3715 3717 2 -21906.2 -7234.95 142.25 0.25 3716 3718 2 -21906.8 -7234.03 143.98 0.25 3717 3719 2 -21908.3 -7233.24 144.26 0.25 3718 3720 2 -21908.4 -7232.98 144.3 0.25 3719 3721 2 -21909 -7232.58 144.43 0.25 3720 3722 2 -21909.9 -7228.94 144.78 0.25 3721 3723 2 -21910.4 -7227.48 145.07 0.25 3722 3724 2 -21910.5 -7227.24 145.11 0.25 3723 3725 2 -21911 -7224.17 145.67 0.25 3724 3726 2 -21911.3 -7220.5 146.31 0.25 3725 3727 2 -21910.2 -7216.34 145.04 0.25 3726 3728 2 -21910 -7211.8 145.78 0.25 3727 3729 2 -21910 -7211.53 145.82 0.25 3728 3730 2 -21910.5 -7208.53 143.61 0.25 3729 3731 2 -21910.6 -7207.74 143.75 0.25 3730 3732 2 -21910.5 -7204.11 146.32 0.25 3731 3733 2 -21910.6 -7203.85 146.4 0.25 3732 3734 2 -21910.8 -7202.61 146.63 0.25 3733 3735 2 -21911.8 -7200.14 147.12 0.25 3734 3736 2 -21911.6 -7199.56 147.2 0.25 3735 3737 2 -21911.7 -7199.31 147.25 0.25 3736 3738 2 -21910.7 -7196.71 149.65 0.25 3737 3739 2 -21911.3 -7196.31 149.78 0.25 3738 3740 2 -21911.4 -7196.05 149.83 0.25 3739 3741 2 -21911.7 -7194.29 150.14 0.25 3740 3742 2 -21911.5 -7193.98 150.17 0.25 3741 3743 2 -21912 -7192.75 150.42 0.25 3742 3744 2 -21911.9 -7192.49 150.47 0.25 3743 3745 2 -21911.4 -7191.59 153.31 0.25 3744 3746 2 -21911.3 -7191.73 154.15 0.25 3745 3747 2 -21911.4 -7188.24 154.52 0.25 3746 3748 2 -21911.4 -7187.99 154.56 0.25 3747 3749 2 -21912.7 -7186.94 154.86 0.25 3748 3750 2 -21913.5 -7183.42 153 0.25 3749 3751 2 -21913.5 -7183.19 153.04 0.25 3750 3752 2 -21914.1 -7182.49 153.22 0.25 3751 3753 2 -21913.9 -7182.22 153.25 0.25 3752 3754 2 -21914 -7181.42 153.39 0.25 3753 3755 2 -21914.1 -7181.17 153.44 0.25 3754 3756 2 -21914.5 -7180.46 153.59 0.25 3755 3757 2 -21914.4 -7179.39 153.76 0.25 3756 3758 2 -21914.5 -7179.11 153.81 0.25 3757 3759 2 -21914.2 -7178.83 153.84 0.25 3758 3760 2 -21914.6 -7175.97 155.77 0.25 3759 3761 2 -21914 -7173.96 157.09 0.25 3760 3762 2 -21913.3 -7172.31 158.88 0.25 3761 3763 2 -21913.4 -7172 158.94 0.25 3762 3764 2 -21913.7 -7166.89 156.01 0.25 3763 3765 2 -21913.4 -7166.59 156.03 0.25 3764 3766 2 -21913.8 -7160.68 162.15 0.25 3765 3767 2 -21915 -7156.83 163.68 0.25 3766 3768 2 -21915.9 -7154.64 164.13 0.25 3767 3769 2 -21916.2 -7152.6 164.5 0.25 3768 3770 2 -21916.3 -7152.36 164.55 0.25 3769 3771 2 -21915.8 -7150.34 164.3 0.25 3770 3772 2 -21915.7 -7149.61 163.52 0.25 3771 3773 2 -21915.5 -7149.27 163.55 0.25 3772 3774 2 -21916.7 -7145.57 164.28 0.25 3773 3775 2 -21915.6 -7141.85 159.85 0.25 3774 3776 2 -21915.9 -7141.92 159.86 0.25 3775 3777 2 -21915.8 -7140.32 160.12 0.25 3776 3778 2 -21915.9 -7140.05 160.17 0.25 3777 3779 2 -21916.7 -7136.98 160.26 0.25 3778 3780 2 -21916.8 -7136.69 160.11 0.25 3779 3781 2 -21917.7 -7134.24 160.6 0.25 3780 3782 2 -21917.8 -7134 160.65 0.25 3781 3783 2 -21916.8 -7131.43 160.98 0.25 3782 3784 2 -21916.6 -7130.85 161.06 0.25 3783 3785 2 -21917.5 -7129.15 161.3 0.25 3784 3786 2 -21917.5 -7128.88 161.35 0.25 3785 3787 2 -21917.4 -7126.94 164.25 0.25 3786 3788 2 -21917.5 -7125.91 164.44 0.25 3787 3789 2 -21917.2 -7124.77 164.59 0.25 3788 3790 2 -21917 -7124.51 164.61 0.25 3789 3791 2 -21916.6 -7123.15 164.81 0.25 3790 3792 2 -21916.3 -7123.07 164.79 0.25 3791 3793 2 -21916.4 -7121.45 167.83 0.25 3792 3794 2 -21916.3 -7120.65 167.95 0.25 3793 3795 2 -21916.4 -7119.87 168.09 0.25 3794 3796 2 -21916.5 -7119.13 168.22 0.25 3795 3797 2 -21916.6 -7118.85 168.27 0.25 3796 3798 2 -21917 -7115.4 166.77 0.25 3797 3799 2 -21917.7 -7113.19 167.58 0.25 3798 3800 2 -21917.9 -7111.91 167.81 0.25 3799 3801 2 -21917.9 -7111.66 167.86 0.25 3800 3802 2 -21917.9 -7110.07 168.12 0.25 3801 3803 2 -21917.9 -7109.83 168.16 0.25 3802 3804 2 -21917.7 -7107.21 170.48 0.25 3803 3805 2 -21918.2 -7105.75 170.78 0.25 3804 3806 2 -21917 -7104.73 171.02 0.25 3805 3807 2 -21916.3 -7103.34 171.19 0.25 3806 3808 2 -21916.1 -7102.77 171.26 0.25 3807 3809 2 -21915.2 -7101.24 172.72 0.25 3808 3810 2 -21915.2 -7100.96 172.78 0.25 3809 3811 2 -21916.1 -7099.12 173.68 0.25 3810 3812 2 -21916.1 -7096.88 176.2 0.25 3811 3813 2 -21916.1 -7096.6 176.25 0.25 3812 3814 2 -21916.5 -7095.36 176.66 0.25 3813 3815 2 -21917.1 -7093.62 177 0.25 3814 3816 2 -21917.2 -7093.38 177.05 0.25 3815 3817 2 -21917.3 -7091.98 177.95 0.25 3816 3818 2 -21917.7 -7090.93 179.33 0.25 3817 3819 2 -21918.1 -7088.65 180.07 0.25 3818 3820 2 -21918.9 -7086.93 181.75 0.25 3819 3821 2 -21918.9 -7086.41 181.84 0.25 3820 3822 2 -21920.8 -7084.57 184.89 0.25 3821 3823 2 -21920.8 -7084.59 185.04 0.25 3822 3824 2 -21922.5 -7082.51 185.55 0.25 3823 3825 2 -21922.5 -7082.27 185.59 0.25 3824 3826 2 -21923 -7080.57 189.22 0.25 3825 3827 2 -21923 -7080.27 189.27 0.25 3826 3828 2 -21922.4 -7078.87 189.44 0.25 3827 3829 2 -21922 -7078 189.55 0.25 3828 3830 2 -21921.5 -7075.79 189.87 0.25 3829 3831 2 -21921.2 -7075.49 189.9 0.25 3830 3832 2 -21920.4 -7073.93 194.12 0.25 3831 3833 2 -21920.3 -7072.86 194.29 0.25 3832 3834 2 -21919.9 -7071.99 194.39 0.25 3833 3835 2 -21919.7 -7071.69 194.42 0.25 3834 3836 2 -21919.7 -7071.42 194.47 0.25 3835 3837 2 -21920 -7069.9 194.75 0.25 3836 3838 2 -21920 -7069.36 194.84 0.25 3837 3839 2 -21920.5 -7067.91 195.13 0.25 3838 3840 2 -21920.6 -7067.65 195.18 0.25 3839 3841 2 -21921.5 -7065 197.88 0.25 3840 3842 2 -21921.7 -7063.23 198.19 0.25 3841 3843 2 -21921.7 -7062.21 201.7 0.25 3842 3844 2 -21921.8 -7061.99 201.74 0.25 3843 3845 2 -21922.1 -7060.83 204.02 0.25 3844 3846 2 -21922.1 -7060.87 204.28 0.25 3845 3847 2 -21922.3 -7058.28 207.92 0.25 3846 3848 2 -21922.3 -7058.03 207.97 0.25 3847 3849 2 -21923.6 -7054.21 212.7 0.25 3848 3850 2 -21926.5 -7052.08 213.32 0.125 3849 3851 2 -21927.5 -7049.65 213.82 0.125 3850 3852 2 -21927.7 -7049.66 213.84 0.125 3851 3853 2 -21927.1 -7048.52 213.97 0.125 3852 3854 2 -21928.7 -7047.06 214.95 0.125 3853 3855 2 -21928.7 -7046.85 215.33 0.125 3854 3856 2 -21929.9 -7045.99 216.94 0.125 3855 3857 2 -21930.8 -7044.84 218.84 0.125 3856 3858 2 -21932.1 -7041.3 217.52 0.125 3857 3859 2 -21933.4 -7040.19 217.86 0.125 3858 3860 2 -21933.7 -7038.55 218.7 0.125 3859 3861 2 -21933.7 -7036.69 219.01 0.125 3860 3862 2 -21934 -7036.75 219.02 0.125 3861 3863 2 -21934.2 -7035.18 219.31 0.125 3862 3864 2 -21934.3 -7034.91 219.36 0.125 3863 3865 2 -21934.3 -7032.84 219.71 0.125 3864 3866 2 -21934.4 -7032.59 219.75 0.125 3865 3867 2 -21935.3 -7031.23 221.87 0.125 3866 3868 2 -21935.3 -7031.21 221.7 0.125 3867 3869 2 -21936.7 -7029.56 221.98 0.125 3868 3870 2 -21937.8 -7026.85 222.53 0.125 3869 3871 2 -21938.3 -7025.63 222.78 0.125 3870 3872 2 -21938.3 -7025.38 222.82 0.125 3871 3873 2 -21937.3 -7023.75 224.92 0.125 3872 3874 2 -21937 -7022.11 225.17 0.125 3873 3875 2 -21937.3 -7019.96 226.86 0.125 3874 3876 2 -21938.1 -7016.74 227.63 0.125 3875 3877 2 -21938.8 -7015.52 227.96 0.125 3876 3878 2 -21938.8 -7015.54 228.1 0.125 3877 3879 2 -21940.2 -7014.34 227.43 0.125 3878 3880 2 -21941.2 -7013.96 227.58 0.125 3879 3881 2 -21942.5 -7012.39 227.97 0.125 3880 3882 2 -21944.4 -7009.8 228.57 0.125 3881 3883 2 -21944.5 -7009.54 228.62 0.125 3882 3884 2 -21945.3 -7007.25 230.17 0.125 3883 3885 2 -21945.6 -7007.29 230.19 0.125 3884 3886 2 -21946.9 -7006.19 230.27 0.125 3885 3887 2 -21948.6 -7004.65 230.68 0.125 3886 3888 2 -21949.3 -7002.12 231.07 0.125 3887 3889 2 -21950.5 -6999.95 226.91 0.125 3888 3890 2 -21950.5 -6999.71 226.95 0.125 3889 3891 2 -21952.3 -6999.42 227.05 0.125 3890 3892 2 -21953.3 -6997.53 225.84 0.125 3891 3893 2 -21953.5 -6995.21 228.05 0.125 3892 3894 2 -21953.5 -6993.9 228.26 0.125 3893 3895 2 -21953.6 -6993.64 228.31 0.125 3894 3896 2 -21953.9 -6991.34 228.73 0.125 3895 3897 2 -21954 -6991.07 228.78 0.125 3896 3898 2 -21954.2 -6989.06 230.9 0.125 3897 3899 2 -21954.2 -6988.8 230.95 0.125 3898 3900 2 -21955.3 -6987.67 231.23 0.125 3899 3901 2 -21955.6 -6987.46 231.3 0.125 3900 3902 2 -21957.8 -6986.28 231.69 0.125 3901 3903 2 -21958.7 -6984.3 232.11 0.125 3902 3904 2 -21958.7 -6983.79 232.2 0.125 3903 3905 2 -21958.5 -6983.5 232.22 0.125 3904 3906 2 -21958.4 -6982.69 232.35 0.125 3905 3907 2 -21958.2 -6982.11 232.42 0.125 3906 3908 2 -21957.6 -6979.41 231.63 0.125 3907 3909 2 -21957.9 -6979.21 231.69 0.125 3908 3910 2 -21958.2 -6977.18 232.06 0.125 3909 3911 2 -21959.8 -6974.19 233.47 0.125 3910 3912 2 -21961.2 -6972.59 234.03 0.125 3911 3913 2 -21964.5 -6971.14 234.95 0.125 3912 3914 2 -21966.3 -6968.88 235.96 0.125 3913 3915 2 -21966.4 -6968.39 236.05 0.125 3914 3916 2 -21968.6 -6966.49 236.92 0.125 3915 3917 2 -21968.8 -6965.45 237.1 0.125 3916 3918 2 -21968.5 -6963.54 237.48 0.125 3917 3919 2 -21968.6 -6963.3 237.53 0.125 3918 3920 2 -21966.6 -6960.87 234.85 0.125 3919 3921 2 -21966.7 -6960.63 234.89 0.125 3920 3922 2 -21966.3 -6958.33 234.52 0.125 3921 3923 2 -21966.2 -6957.28 234.69 0.125 3922 3924 2 -21966.3 -6956.22 234.88 0.125 3923 3925 2 -21967.7 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2 -22024.6 -6572.13 253.59 0.125 4229 4231 2 -22024.3 -6571.83 253.62 0.125 4230 4232 2 -22024.6 -6570.58 253.85 0.125 4231 4233 2 -22024.6 -6570.34 253.89 0.125 4232 4234 2 -22025.5 -6567.32 253.14 0.125 4233 4235 2 -22027.9 -6565.3 251.55 0.125 4234 4236 2 -22028.2 -6564.37 253.66 0.125 4235 4237 2 -22028.5 -6564.4 253.68 0.125 4236 4238 2 -22030.2 -6562.6 253.88 0.125 4237 4239 2 -22030.3 -6562.53 253.49 0.125 4238 4240 2 -22030.4 -6560.1 253.86 0.125 4239 4241 2 -22030.5 -6559.9 253.9 0.125 4240 4242 2 -22030.6 -6555.44 251.49 0.125 4241 4243 2 -22032.5 -6553.08 250.41 0.125 4242 4244 2 -22032.5 -6552.84 250.45 0.125 4243 4245 2 -22032.7 -6551.29 250.73 0.125 4244 4246 2 -22032.9 -6550.52 250.87 0.125 4245 4247 2 -22033 -6547.86 251.33 0.125 4246 4248 2 -22032.9 -6547.12 251.44 0.125 4247 4249 2 -22033.1 -6545.8 251.67 0.125 4248 4250 2 -22033.9 -6543.3 250.48 0.125 4249 4251 2 -22033.9 -6543.03 250.53 0.125 4250 4252 2 -22034.5 -6541.31 250.87 0.125 4251 4253 2 -22034.7 -6540.52 251.01 0.125 4252 4254 2 -22035 -6538.75 251.33 0.125 4253 4255 2 -22034.7 -6538.42 251.36 0.125 4254 4256 2 -22036 -6534.33 251.71 0.125 4255 4257 2 -22035.7 -6532.71 251.95 0.125 4256 4258 2 -22035.7 -6532.46 252 0.125 4257 4259 2 -22035.4 -6531.34 252.15 0.125 4258 4260 2 -22035.4 -6531.09 252.19 0.125 4259 4261 2 -22031.8 -6527.77 252.14 0.125 4260 4262 2 -22028.1 -6526.83 258.2 0.125 4261 4263 2 -22027.3 -6524.56 264.02 0.125 4262 4264 2 -22026.7 -6522.85 264.25 0.125 4263 4265 2 -22027 -6522.68 264.49 0.125 4264 4266 2 -21870.6 -7377.64 83.98 0.745 1647 4267 2 -21869.3 -7378.44 80.14 0.745 4266 4268 2 -21867.3 -7378.45 75.84 0.745 4267 4269 2 -21867.5 -7378.47 75.86 0.745 4268 4270 2 -21865.7 -7378.47 69.65 0.745 4269 4271 2 -21864.5 -7379.02 65.03 0.745 4270 4272 2 -21863.8 -7379.23 62.09 0.745 4271 4273 2 -21862.3 -7379.23 58.69 0.745 4272 4274 2 -21860.3 -7378.64 55.54 0.745 4273 4275 2 -21859.7 -7378.45 48.98 0.745 4274 4276 2 -21859.7 -7378.37 48.46 0.745 4275 4277 2 -21859.9 -7380.1 53.41 0.745 4276 4278 2 -21857.7 -7379.98 53.14 0.37 4277 4279 2 -21856.1 -7377.63 53.38 0.37 4278 4280 2 -21852.3 -7376.05 58.42 0.37 4279 4281 2 -21852.3 -7376.08 58.64 0.37 4280 4282 2 -21850.7 -7375.08 63.66 0.37 4281 4283 2 -21849.2 -7374.51 70.61 0.37 4282 4284 2 -21849.1 -7372.28 72.1 0.37 4283 4285 2 -21849.1 -7372.15 72.74 0.37 4284 4286 2 -21848.9 -7370.81 76.04 0.25 4285 4287 2 -21849.2 -7370.59 76.11 0.25 4286 4288 2 -21849.8 -7370.22 76.93 0.25 4287 4289 2 -21849.6 -7370.48 78.48 0.25 4288 4290 2 -21849.8 -7369.16 78.32 0.25 4289 4291 2 -21850.7 -7368.52 78.71 0.25 4290 4292 2 -21851.6 -7367.34 81.74 0.25 4291 4293 2 -21851.7 -7366.79 86.31 0.25 4292 4294 2 -21852.4 -7364.87 88.63 0.25 4293 4295 2 -21852.9 -7364.48 90.66 0.25 4294 4296 2 -21852.8 -7361.71 94.86 0.25 4295 4297 2 -21852.4 -7359.31 97.03 0.25 4296 4298 2 -21852.4 -7359.38 97.44 0.25 4297 4299 2 -21851.8 -7358.33 101.42 0.25 4298 4300 2 -21852.1 -7357.63 101.59 0.25 4299 4301 2 -21852.3 -7356.74 105.61 0.25 4300 4302 2 -21852.6 -7356.79 105.63 0.25 4301 4303 2 -21852.6 -7354.61 110.23 0.25 4302 4304 2 -21852.9 -7354.42 110.28 0.25 4303 4305 2 -21850.7 -7351.64 110.53 0.37 4304 4306 2 -21852.7 -7349.22 107.13 0.125 4305 4307 2 -21852.5 -7348.93 107.16 0.125 4306 4308 2 -21851.3 -7347.71 110.61 0.125 4307 4309 2 -21850.6 -7345.5 110.95 0.125 4308 4310 2 -21849 -7343.42 115.82 0.125 4309 4311 2 -21849 -7343.12 115.86 0.125 4310 4312 2 -21848.2 -7341.17 119.48 0.125 4311 4313 2 -21848.2 -7341.19 119.59 0.125 4312 4314 2 -21848 -7339.08 119.94 0.125 4313 4315 2 -21847 -7337.86 126.14 0.125 4314 4316 2 -21849.2 -7335.97 136.02 0.125 4315 4317 2 -21849.5 -7335.42 141.8 0.125 4316 4318 2 -21849.5 -7333.48 147.62 0.125 4317 4319 2 -21849.8 -7333.52 147.64 0.125 4318 4320 2 -21848.8 -7331.17 151.91 0.125 4319 4321 2 -21848.2 -7329.43 157.99 0.125 4320 4322 2 -21846.7 -7327.62 162.76 0.125 4321 4323 2 -21847.2 -7326.56 171.62 0.125 4322 4324 2 -21847.4 -7324.78 174.88 0.125 4323 4325 2 -21847.8 -7323.75 179.32 0.125 4324 4326 2 -21847.9 -7322.95 183.92 0.125 4325 4327 2 -21848.3 -7321.04 184.27 0.25 4326 4328 2 -21848.7 -7319.51 184.57 0.25 4327 4329 2 -21854.3 -7322.44 184.61 0.25 4328 4330 2 -21853.1 -7321.87 194.65 0.25 4329 4331 2 -21852.5 -7321.29 196.46 0.25 4330 4332 2 -21852.2 -7320.84 201.71 0.25 4331 4333 2 -21852.7 -7320.02 204.38 0.25 4332 4334 2 -21852.5 -7318.45 209.6 0.25 4333 4335 2 -21852.5 -7318.41 214.21 0.25 4334 4336 2 -21852.5 -7318.31 215.13 0.25 4335 4337 2 -21851.8 -7318.29 216.87 0.25 4336 4338 2 -21850.9 -7317.78 217.69 0.25 4337 4339 2 -21850.2 -7317.42 220.7 0.25 4338 4340 2 -21850.1 -7317.48 221.04 0.25 4339 4341 2 -21850.4 -7316.42 224.27 0.25 4340 4342 2 -21850.5 -7315.99 227.72 0.25 4341 4343 2 -21850.5 -7315.73 227.77 0.25 4342 4344 2 -21849.7 -7316.03 229.98 0.25 4343 4345 2 -21849.8 -7315.79 230.03 0.25 4344 4346 2 -21850.4 -7315.62 233.35 0.25 4345 4347 2 -21850.4 -7315.32 233.39 0.25 4346 4348 2 -21850.7 -7313.78 236.69 0.25 4347 4349 2 -21851.4 -7314.05 242.53 0.25 4348 4350 2 -21852.8 -7314.14 246.51 0.25 4349 4351 2 -21851.7 -7312.9 249.39 0.25 4350 4352 2 -21851.5 -7312.71 256.27 0.25 4351 4353 2 -21853.6 -7304.2 259.49 0.125 4352 4354 2 -21853.7 -7303.19 259.99 0.125 4353 4355 2 -21853.7 -7303.22 260.15 0.125 4354 4356 2 -21852.9 -7302.35 266.63 0.125 4355 4357 2 -21853 -7302.09 266.68 0.125 4356 4358 2 -21853.2 -7301.78 269.16 0.125 4357 4359 2 -21853.2 -7301.59 269.53 0.125 4358 4360 2 -21853.5 -7300.66 276.2 0.125 4359 4361 2 -21853.5 -7298.72 279.18 0.125 4360 4362 2 -21853.7 -7298.75 279.2 0.125 4361 4363 2 -21854.9 -7298.19 279.41 0.125 4362 4364 2 -21855 -7297.99 279.45 0.125 4363 4365 2 -21855.7 -7297.06 284.43 0.125 4364 4366 2 -21855.7 -7297.32 284.39 0.125 4365 4367 2 -21855.7 -7297.08 288.86 0.125 4366 4368 2 -21855.6 -7296.76 291.91 0.125 4367 4369 2 -21855.6 -7296.52 291.96 0.125 4368 4370 2 -21856.6 -7296.87 298.99 0.125 4369 4371 2 -21856.7 -7296.12 299.13 0.125 4370 4372 2 -21856.9 -7295.77 292.69 0.125 4371 4373 2 -21857 -7295.51 292.74 0.125 4372 4374 2 -21857.4 -7296.57 300.77 0.125 4373 4375 2 -21857.5 -7296.27 300.82 0.125 4374 4376 2 -21856.8 -7295.23 304.66 0.125 4375 4377 2 -21856.8 -7295.26 304.81 0.125 4376 4378 2 -21858.5 -7296.1 307.79 0.125 4377 4379 2 -21858.2 -7295.89 312.79 0.125 4378 4380 2 -21858.3 -7295.61 312.84 0.125 4379 4381 2 -21857.5 -7295.61 316.53 0.125 4380 4382 2 -21859.4 -7296.07 321.53 0.125 4381 4383 2 -21859.4 -7296.1 321.76 0.125 4382 4384 2 -21860.9 -7295.39 326.93 0.125 4383 4385 2 -21863.2 -7295.12 332.07 0.125 4384 4386 2 -21867.3 -7295.91 332.31 0.125 4385 4387 2 -21868.5 -7297.39 336.42 0.125 4386 4388 2 -21870.7 -7298.06 336.51 0.125 4387 4389 2 -21871.3 -7298.13 336.55 0.125 4388 4390 2 -21871.6 -7297.93 336.62 0.125 4389 4391 2 -21872.4 -7298.1 336.67 0.125 4390 4392 2 -21875.4 -7297.51 341.72 0.125 4391 4393 2 -21877.1 -7298.38 347.91 0.125 4392 4394 2 -21878.6 -7298.31 352.38 0.125 4393 4395 2 -21878.5 -7298.59 352.33 0.125 4394 4396 2 -21881.8 -7298.53 356.67 0.125 4395 4397 2 -21884.5 -7299.19 362.72 0.125 4396 4398 2 -21884.5 -7298.91 362.77 0.125 4397 4399 2 -21885.4 -7299.61 363.87 0.125 4398 4400 2 -21885.7 -7299.61 363.89 0.125 4399 4401 2 -21887.3 -7299.59 371.21 0.125 4400 4402 2 -21887.5 -7299.82 372.26 0.125 4401 4403 2 -21890.8 -7300.21 379.46 0.125 4402 4404 2 -21891 -7300.29 379.47 0.125 4403 4405 2 -21886.1 -7297.48 379.47 0.125 4404 4406 2 -21888 -7297.52 387.81 0.125 4405 4407 2 -21888.2 -7297.61 387.82 0.125 4406 4408 2 -21889.6 -7297.11 389.73 0.125 4407 4409 2 -21889.5 -7297.38 391.35 0.125 4408 4410 2 -21894.8 -7296.28 399.09 0.125 4409 4411 2 -21896.8 -7296.43 399.25 0.125 4410 4412 2 -21897 -7296.47 399.26 0.125 4411 4413 2 -21898.4 -7295.45 399.56 0.125 4412 4414 2 -21901 -7294.92 409.96 0.125 4413 4415 2 -21903.4 -7294.57 414.53 0.125 4414 4416 2 -21906 -7292.88 421.86 0.125 4415 4417 2 -21906.9 -7291.15 422.24 0.125 4416 4418 2 -21906.9 -7290.91 422.28 0.125 4417 4419 2 -21908.8 -7287.73 422.43 0.125 4418 4420 2 -21908.9 -7285.39 422.83 0.125 4419 4421 2 -21908.2 -7283.77 426.86 0.125 4420 4422 2 -21908.1 -7283.87 427.48 0.125 4421 4423 2 -21907.1 -7277.89 427.53 0.125 4422 4424 2 -21905.3 -7275.12 429.43 0.125 4423 4425 2 -21903.7 -7272.95 429.64 0.125 4424 4426 2 -21903.5 -7272.59 429.68 0.125 4425 4427 2 -21902.5 -7270 430.02 0.125 4426 4428 2 -21902.4 -7269.17 430.15 0.125 4427 4429 2 -21902.8 -7267.39 430.48 0.125 4428 4430 2 -21902.6 -7267.11 430.51 0.125 4429 4431 2 -21900.9 -7258.69 436.35 0.125 4430 4432 2 -21897.6 -7250.46 448.71 0.125 4431 4433 2 -21898.5 -7247.36 450.34 0.125 4432 4434 2 -21898.5 -7246 450.57 0.125 4433 4435 2 -21898 -7241.4 455.91 0.125 4434 4436 2 -21898.3 -7241.47 455.92 0.125 4435 4437 2 -21899.4 -7239.69 457.01 0.125 4436 4438 2 -21900.4 -7234.61 464.24 0.125 4437 4439 2 -21905 -7234.28 464.73 0.125 4438 4440 2 -21905.5 -7231.12 465.3 0.125 4439 4441 2 -21905.5 -7231.33 465.27 0.125 4440 4442 2 -21904.4 -7225.62 473.04 0.125 4441 4443 2 -21902.6 -7221.79 479.58 0.125 4442 4444 2 -21902.3 -7221.45 479.61 0.125 4443 4445 2 -21899.5 -7217.53 483.3 0.125 4444 4446 2 -21899.4 -7217.82 485.07 0.125 4445 4447 2 -21896.7 -7216.3 489.46 0.125 4446 4448 2 -21897 -7216.33 489.48 0.125 4447 4449 2 -21891.1 -7211.95 496.5 0.125 4448 4450 2 -21890.6 -7210.3 499.93 0.125 4449 4451 2 -21893 -7210.72 501.95 0.125 4450 4452 2 -21896.8 -7211.7 508.7 0.125 4451 4453 2 -21897.1 -7211.75 508.71 0.125 4452 4454 2 -21898 -7211.06 508.92 0.125 4453 4455 2 -21899.6 -7207.13 507.3 0.125 4454 4456 2 -21900.1 -7205.52 530.44 0.125 4455 4457 2 -21900.2 -7205.28 530.48 0.125 4456 4458 2 -21846.5 -7336.01 129.92 0.125 4315 4459 2 -21846.7 -7333.92 133.05 0.125 4458 4460 2 -21846.8 -7332.94 133.39 0.125 4459 4461 2 -21847.1 -7330.44 140.66 0.125 4460 4462 2 -21846.4 -7327.7 145.28 0.125 4461 4463 2 -21845.3 -7326.2 148.83 0.125 4462 4464 2 -21843.7 -7322.75 149.25 0.125 4463 4465 2 -21842.3 -7322.47 153.34 0.125 4464 4466 2 -21842.8 -7319.64 153.86 0.125 4465 4467 2 -21842.8 -7319.16 158.77 0.125 4466 4468 2 -21842.8 -7319.18 158.88 0.125 4467 4469 2 -21842.8 -7317.35 159.2 0.125 4468 4470 2 -21842.6 -7316.09 162.85 0.125 4469 4471 2 -21842.3 -7314.51 163.51 0.125 4470 4472 2 -21842.2 -7314.31 163.98 0.125 4471 4473 2 -21842 -7313.07 167.79 0.125 4472 4474 2 -21841.3 -7311.4 169.56 0.125 4473 4475 2 -21842.1 -7310.56 171.81 0.125 4474 4476 2 -21842.7 -7308.81 172.16 0.125 4475 4477 2 -21841.8 -7308.25 174.19 0.125 4476 4478 2 -21840.9 -7306.93 175.13 0.125 4477 4479 2 -21841.3 -7305.53 176.24 0.125 4478 4480 2 -21840.6 -7303.87 178.13 0.125 4479 4481 2 -21840.6 -7303.02 180.77 0.125 4480 4482 2 -21840.5 -7303.21 181.98 0.125 4481 4483 2 -21840.7 -7301.95 182.61 0.125 4482 4484 2 -21840.2 -7301.73 186.42 0.125 4483 4485 2 -21840 -7299.59 186.75 0.125 4484 4486 2 -21839.8 -7297.18 187.14 0.125 4485 4487 2 -21846.2 -7300.04 187.25 0.125 4486 4488 2 -21845.2 -7299.22 191.06 0.125 4487 4489 2 -21844.8 -7297.07 191.38 0.125 4488 4490 2 -21843.8 -7296.45 194.74 0.125 4489 4491 2 -21843.6 -7296.4 194.72 0.125 4490 4492 2 -21842.8 -7294.54 197.04 0.125 4491 4493 2 -21841.5 -7291.65 200.1 0.125 4492 4494 2 -21840.4 -7289.98 203.88 0.125 4493 4495 2 -21839 -7285.84 207.37 0.125 4494 4496 2 -21837.9 -7283.5 211.94 0.125 4495 4497 2 -21837.8 -7282.56 214.51 0.125 4496 4498 2 -21837.7 -7279.92 217.93 0.125 4497 4499 2 -21837.2 -7278.78 218.08 0.125 4498 4500 2 -21837 -7277.61 219.19 0.125 4499 4501 2 -21838.1 -7275.14 221.25 0.125 4500 4502 2 -21840.3 -7274.23 224.04 0.125 4501 4503 2 -21842.1 -7273.56 228.43 0.125 4502 4504 2 -21844.8 -7272.67 228.97 0.125 4503 4505 2 -21847.7 -7272.2 231.82 0.125 4504 4506 2 -21849.6 -7270.9 232.57 0.08 4505 4507 2 -21851.6 -7270.66 236.12 0.08 4506 4508 2 -21852.2 -7271.63 239.25 0.08 4507 4509 2 -21853.1 -7272.25 243.05 0.125 4508 4510 2 -21854.6 -7272.45 243.15 0.125 4509 4511 2 -21856.3 -7272.15 243.37 0.125 4510 4512 2 -21856.1 -7271.47 247.4 0.125 4511 4513 2 -21855.1 -7270.27 247.42 0.125 4512 4514 2 -21854.2 -7269.23 248.48 0.08 4513 4515 2 -21854 -7268.43 248.59 0.08 4514 4516 2 -21854 -7268.65 248.55 0.08 4515 4517 2 -21853.6 -7266.21 250.44 0.08 4516 4518 2 -21853.2 -7264.82 250.63 0.08 4517 4519 2 -21853 -7262.75 254.24 0.08 4518 4520 2 -21853.2 -7261.15 254.51 0.08 4519 4521 2 -21853.2 -7260.9 254.55 0.08 4520 4522 2 -21853.8 -7260.17 259.32 0.08 4521 4523 2 -21854.7 -7259.81 259.47 0.08 4522 4524 2 -21855.1 -7259.34 259.58 0.08 4523 4525 2 -21855.7 -7258.88 259.72 0.08 4524 4526 2 -21856.4 -7257.88 259.94 0.08 4525 4527 2 -21857 -7257.46 260.07 0.08 4526 4528 2 -21857.4 -7256.73 260.22 0.08 4527 4529 2 -21859.4 -7255.05 263.22 0.08 4528 4530 2 -21860.3 -7252.53 263.72 0.08 4529 4531 2 -21860.3 -7252.25 263.77 0.08 4530 4532 2 -21861.3 -7244.18 263.37 0.08 4531 4533 2 -21861.9 -7242.7 263.67 0.08 4532 4534 2 -21862 -7240.33 264.07 0.08 4533 4535 2 -21862.3 -7238.34 266.57 0.08 4534 4536 2 -21863.8 -7234.94 262.89 0.08 4535 4537 2 -21863.8 -7234.66 262.94 0.08 4536 4538 2 -21864.2 -7234.23 263.04 0.08 4537 4539 2 -21864.2 -7233.96 263.09 0.08 4538 4540 2 -21864.7 -7233.13 271.5 0.08 4539 4541 2 -21865 -7233.17 271.45 0.26 4540 4542 2 -21865 -7231.8 271.67 0.135 4541 4543 2 -21866.1 -7230.45 271.99 0.135 4542 4544 2 -21866.1 -7230.2 272.04 0.135 4543 4545 2 -21866.4 -7228.66 272.32 0.08 4544 4546 2 -21866.4 -7228.37 272.37 0.08 4545 4547 2 -21867.1 -7227.51 274.28 0.08 4546 4548 2 -21867.8 -7225.22 279 0.08 4547 4549 2 -21868.5 -7224.3 279.47 0.08 4548 4550 2 -21869.8 -7218.98 285.07 0.08 4549 4551 2 -21869.9 -7218.75 285.11 0.08 4550 4552 2 -21872.9 -7211.57 290.93 0.08 4551 4553 2 -21874.6 -7208.62 296.96 0.08 4552 4554 2 -21874.5 -7208.69 297.39 0.08 4553 4555 2 -21876.3 -7206.9 297.85 0.08 4554 4556 2 -21876.3 -7206.63 297.9 0.08 4555 4557 2 -21879.8 -7201.59 304.33 0.08 4556 4558 2 -21879.8 -7201.35 304.37 0.08 4557 4559 2 -21881.7 -7197.12 310.8 0.08 4558 4560 2 -21883.9 -7194.58 313.46 0.08 4559 4561 2 -21884.3 -7193.25 320.64 0.08 4560 4562 2 -21884.6 -7193.27 320.66 0.08 4561 4563 2 -21881.6 -7186.79 339.25 0.08 4562 4564 2 -21881.8 -7186.86 339.26 0.08 4563 4565 2 -21881.4 -7186.33 339.8 0.08 4564 4566 2 -21887 -7189.95 319.35 0.08 4562 4567 2 -21887.6 -7188.68 319.61 0.08 4566 4568 2 -21887.6 -7188.42 319.65 0.08 4567 4569 2 -21888.2 -7188.81 326.97 0.08 4568 4570 2 -21888.2 -7189.05 326.92 0.08 4569 4571 2 -21888.8 -7188.66 327.05 0.08 4570 4572 2 -21854.5 -7377.37 51.88 0.25 4278 4573 2 -21851.3 -7376.8 54.53 0.25 4572 4574 2 -21848.8 -7376.48 54.73 0.25 4573 4575 2 -21848.5 -7376.43 54.71 0.25 4574 4576 2 -21844.5 -7376.1 56.33 0.25 4575 4577 2 -21841.9 -7375.68 57.73 0.25 4576 4578 2 -21837.9 -7374.95 59.74 0.25 4577 4579 2 -21837.3 -7374.82 59.71 0.25 4578 4580 2 -21833.6 -7375.14 59.94 0.25 4579 4581 2 -21833 -7374.73 59.96 0.25 4580 4582 2 -21832.8 -7374.72 59.93 0.25 4581 4583 2 -21829.2 -7371.87 60.73 0.25 4582 4584 2 -21825.9 -7369.96 63.3 0.25 4583 4585 2 -21826 -7369.72 63.34 0.25 4584 4586 2 -21824.1 -7368.6 63.35 0.25 4585 4587 2 -21823.4 -7368.14 63.35 0.25 4586 4588 2 -21823.1 -7367.89 63.38 0.25 4587 4589 2 -21820.7 -7366.99 63.3 0.25 4588 4590 2 -21820.5 -7366.94 63.28 0.25 4589 4591 2 -21818.7 -7366.92 63.12 0.25 4590 4592 2 -21818 -7366.26 63.16 0.25 4591 4593 2 -21816.7 -7366.42 65.15 0.25 4592 4594 2 -21814.9 -7366.66 64.99 0.25 4593 4595 2 -21812 -7366.48 70.8 0.25 4594 4596 2 -21812.1 -7366.22 70.88 0.25 4595 4597 2 -21809.6 -7365.62 71.04 0.25 4596 4598 2 -21809.3 -7365.36 71.29 0.25 4597 4599 2 -21806.6 -7364.46 71.73 0.25 4598 4600 2 -21806.1 -7364.36 71.7 0.25 4599 4601 2 -21801.5 -7363.19 73.27 0.25 4600 4602 2 -21795.3 -7362.33 79.77 0.25 4601 4603 2 -21795.1 -7362.02 79.8 0.25 4602 4604 2 -21793 -7356.71 80.96 0.25 4603 4605 2 -21789.3 -7350.21 88.87 0.25 4604 4606 2 -21787.3 -7349.8 89.72 0.25 4605 4607 2 -21786.8 -7349.49 89.72 0.25 4606 4608 2 -21785.5 -7350.06 91.07 0.25 4607 4609 2 -21784.6 -7349.92 91.02 0.25 4608 4610 2 -21782.2 -7350.69 91.42 0.25 4609 4611 2 -21781.9 -7350.7 91.39 0.25 4610 4612 2 -21780.2 -7350.67 91.37 0.25 4611 4613 2 -21779.7 -7350.59 91.33 0.25 4612 4614 2 -21778.1 -7349.32 91.61 0.25 4613 4615 2 -21777.7 -7348.73 91.67 0.25 4614 4616 2 -21777.1 -7348.65 91.63 0.25 4615 4617 2 -21773.8 -7347.21 92.43 0.25 4616 4618 2 -21773 -7346.82 92.42 0.25 4617 4619 2 -21770.8 -7345.16 91.35 0.25 4618 4620 2 -21769.9 -7345.35 91.23 0.25 4619 4621 2 -21768.3 -7345.02 91.13 0.25 4620 4622 2 -21767.8 -7344.69 91.14 0.25 4621 4623 2 -21767.8 -7344.41 91.19 0.25 4622 4624 2 -21766.6 -7343.43 91.24 0.25 4623 4625 2 -21766.3 -7343.13 91.26 0.25 4624 4626 2 -21764.1 -7342.74 91.12 0.25 4625 4627 2 -21763 -7342.58 91.04 0.25 4626 4628 2 -21763.1 -7342.3 91.09 0.25 4627 4629 2 -21761.4 -7342.29 90.94 0.25 4628 4630 2 -21761.1 -7342.22 90.92 0.25 4629 4631 2 -21758.9 -7342.26 94.32 0.25 4630 4632 2 -21758.8 -7341.78 93.11 0.25 4631 4633 2 -21757.5 -7340.79 93.31 0.25 4632 4634 2 -21756.9 -7340.67 94.58 0.25 4633 4635 2 -21756.6 -7340.34 94.61 0.25 4634 4636 2 -21750.8 -7339.42 98.85 0.25 4635 4637 2 -21750.6 -7338.87 98.93 0.25 4636 4638 2 -21750.3 -7338.57 98.96 0.25 4637 4639 2 -21748.1 -7337.79 99.66 0.25 4638 4640 2 -21747.8 -7337.8 99.63 0.25 4639 4641 2 -21744.5 -7336.3 101.93 0.25 4640 4642 2 -21744.3 -7335.74 102.01 0.25 4641 4643 2 -21740.6 -7332.98 100.2 0.25 4642 4644 2 -21738.4 -7331.8 100.19 0.37 4643 4645 2 -21737 -7329.72 100.41 0.37 4644 4646 2 -21736.8 -7329.4 100.44 0.37 4645 4647 2 -21735 -7328.28 100.45 0.37 4646 4648 2 -21734.1 -7327.09 100.57 0.37 4647 4649 2 -21732.4 -7326.76 100.46 0.37 4648 4650 2 -21731.1 -7326.3 100.42 0.37 4649 4651 2 -21730.9 -7325.94 100.45 0.37 4650 4652 2 -21730.5 -7325.1 100.56 0.37 4651 4653 2 -21730.3 -7324.53 100.63 0.37 4652 4654 2 -21730 -7323.98 100.7 0.37 4653 4655 2 -21729.3 -7323.59 100.7 0.37 4654 4656 2 -21727 -7321.89 100.76 0.37 4655 4657 2 -21726.3 -7321.73 100.72 0.37 4656 4658 2 -21723.7 -7320.93 98.79 0.37 4657 4659 2 -21721 -7319.68 98.75 0.37 4658 4660 2 -21720.7 -7319.61 98.74 0.37 4659 4661 2 -21719 -7318.54 98.75 0.37 4660 4662 2 -21718.4 -7318.42 98.72 0.37 4661 4663 2 -21716.6 -7317.31 98.73 0.37 4662 4664 2 -21716.7 -7317.08 98.78 0.37 4663 4665 2 -21715.2 -7315.75 98.85 0.37 4664 4666 2 -21714.4 -7315.6 98.81 0.37 4665 4667 2 -21712.3 -7316 98.55 0.37 4666 4668 2 -21711.8 -7315.93 98.51 0.37 4667 4669 2 -21710.5 -7315.19 98.51 0.37 4668 4670 2 -21710 -7315.06 98.48 0.37 4669 4671 2 -21710 -7314.78 98.53 0.37 4670 4672 2 -21707.8 -7313.63 100.1 0.37 4671 4673 2 -21707.5 -7313.58 100.09 0.37 4672 4674 2 -21706.8 -7312.94 100.13 0.37 4673 4675 2 -21706.6 -7312.9 100.11 0.37 4674 4676 2 -21704 -7312.96 99.85 0.37 4675 4677 2 -21703.7 -7313.14 99.79 0.37 4676 4678 2 -21703.4 -7313.13 99.77 0.37 4677 4679 2 -21702.2 -7313.71 99.56 0.37 4678 4680 2 -21701.1 -7313.49 99.5 0.37 4679 4681 2 -21697.7 -7312.86 102.02 0.37 4680 4682 2 -21696.1 -7310.93 100.4 0.37 4681 4683 2 -21693.8 -7309.47 100.42 0.37 4682 4684 2 -21693.3 -7309.36 100.39 0.37 4683 4685 2 -21691 -7309.23 100.2 0.37 4684 4686 2 -21690.7 -7309.19 100.18 0.37 4685 4687 2 -21687.9 -7308.34 102.54 0.37 4686 4688 2 -21687.2 -7306.3 102.8 0.37 4687 4689 2 -21686.1 -7305.61 102.82 0.37 4688 4690 2 -21684.2 -7303.67 102.96 0.37 4689 4691 2 -21683.1 -7303.22 102.93 0.37 4690 4692 2 -21679.2 -7301.5 102.85 0.37 4691 4693 2 -21678.3 -7301.33 102.8 0.37 4692 4694 2 -21673.5 -7300.01 102.92 0.37 4693 4695 2 -21671.1 -7299.73 105.05 0.25 4694 4696 2 -21669.1 -7298.3 105.09 0.25 4695 4697 2 -21666 -7296.69 105.07 0.25 4696 4698 2 -21662.2 -7294.97 106.54 0.25 4697 4699 2 -21659.4 -7293.67 106.49 0.25 4698 4700 2 -21657.8 -7292.64 106.52 0.25 4699 4701 2 -21657.7 -7291.55 106.69 0.25 4700 4702 2 -21655.2 -7290.59 109.58 0.25 4701 4703 2 -21655.2 -7290.33 109.63 0.25 4702 4704 2 -21652.9 -7288.84 109.66 0.25 4703 4705 2 -21652.3 -7287.41 109.85 0.25 4704 4706 2 -21651 -7286.86 109.81 0.25 4705 4707 2 -21650.4 -7286.82 109.76 0.25 4706 4708 2 -21648.1 -7286.95 109.53 0.25 4707 4709 2 -21647.6 -7286.61 109.54 0.25 4708 4710 2 -21645.2 -7285.95 109.85 0.25 4709 4711 2 -21644.9 -7285.63 109.93 0.25 4710 4712 2 -21644 -7285.53 111.64 0.25 4711 4713 2 -21643.2 -7285.4 111.59 0.25 4712 4714 2 -21641.2 -7285.56 111.37 0.25 4713 4715 2 -21641.2 -7285.29 111.42 0.25 4714 4716 2 -21640.5 -7284.39 111.5 0.25 4715 4717 2 -21640 -7284.04 111.51 0.25 4716 4718 2 -21638.6 -7284.28 111.34 0.25 4717 4719 2 -21638.3 -7283.99 111.36 0.25 4718 4720 2 -21636.8 -7282.69 111.44 0.25 4719 4721 2 -21635.8 -7282.23 111.42 0.25 4720 4722 2 -21635.6 -7281.9 111.45 0.25 4721 4723 2 -21635.4 -7281.62 111.48 0.25 4722 4724 2 -21633.7 -7281.3 111.38 0.25 4723 4725 2 -21633.5 -7280.73 111.45 0.25 4724 4726 2 -21632.5 -7279.82 111.51 0.25 4725 4727 2 -21632 -7279.45 111.61 0.25 4726 4728 2 -21630.9 -7278.92 112.57 0.25 4727 4729 2 -21630.2 -7279.2 113.4 0.25 4728 4730 2 -21629.4 -7279.07 113.35 0.25 4729 4731 2 -21629.2 -7278.76 113.51 0.25 4730 4732 2 -21627.8 -7278.05 113.55 0.25 4731 4733 2 -21627.4 -7277.7 113.57 0.25 4732 4734 2 -21626.6 -7277.55 113.52 0.25 4733 4735 2 -21626 -7277.47 113.48 0.25 4734 4736 2 -21625.8 -7277.36 113.47 0.25 4735 4737 2 -21624.7 -7276.69 113.49 0.25 4736 4738 2 -21623.9 -7276.54 113.43 0.25 4737 4739 2 -21622.5 -7276.58 113.37 0.25 4738 4740 2 -21621.4 -7276.16 113.64 0.25 4739 4741 2 -21621.1 -7276.1 113.62 0.25 4740 4742 2 -21620.1 -7275.22 114.12 0.25 4741 4743 2 -21619 -7274.84 114.44 0.25 4742 4744 2 -21615.8 -7273.8 114.6 0.25 4743 4745 2 -21614.7 -7273.33 114.62 0.25 4744 4746 2 -21613.3 -7273.35 114.51 0.25 4745 4747 2 -21613.1 -7273.03 114.58 0.25 4746 4748 2 -21611.3 -7271.85 114.23 0.25 4747 4749 2 -21609.1 -7271.97 113.99 0.25 4748 4750 2 -21608 -7271.52 113.97 0.25 4749 4751 2 -21607.8 -7271.5 113.95 0.25 4750 4752 2 -21606.8 -7270.28 114.06 0.25 4751 4753 2 -21606 -7270.1 114.02 0.25 4752 4754 2 -21603.8 -7269.98 113.83 0.25 4753 4755 2 -21603.5 -7269.92 113.81 0.25 4754 4756 2 -21594.2 -7266.8 112.7 0.25 4755 4757 2 -21591.1 -7265.98 113.94 0.25 4756 4758 2 -21590.9 -7265.94 113.92 0.25 4757 4759 2 -21589 -7265.31 113.85 0.25 4758 4760 2 -21588.4 -7265.24 113.81 0.25 4759 4761 2 -21588.5 -7264.99 113.85 0.25 4760 4762 2 -21586.2 -7265.11 113.62 0.25 4761 4763 2 -21583.3 -7265.02 114.08 0.25 4762 4764 2 -21582.8 -7264.92 114.05 0.25 4763 4765 2 -21580.9 -7264.32 113.97 0.25 4764 4766 2 -21579.8 -7263.85 113.94 0.25 4765 4767 2 -21579.6 -7263.79 113.93 0.25 4766 4768 2 -21578 -7262.76 113.96 0.25 4767 4769 2 -21577 -7262 113.99 0.25 4768 4770 2 -21576.8 -7261.99 113.97 0.25 4769 4771 2 -21575.8 -7261.53 113.95 0.25 4770 4772 2 -21574.7 -7260.81 113.97 0.25 4771 4773 2 -21572.8 -7261.06 117.04 0.25 4772 4774 2 -21572.5 -7261.02 117.02 0.25 4773 4775 2 -21570 -7260.84 116.81 0.25 4774 4776 2 -21569.8 -7260.26 116.89 0.25 4775 4777 2 -21566.5 -7259.59 117.64 0.25 4776 4778 2 -21564.3 -7259.18 117.5 0.25 4777 4779 2 -21563.8 -7258.85 117.51 0.25 4778 4780 2 -21562.4 -7258.61 117.42 0.25 4779 4781 2 -21561 -7258.35 117.34 0.25 4780 4782 2 -21559.3 -7257.27 117.35 0.25 4781 4783 2 -21557.6 -7256.91 117.26 0.25 4782 4784 2 -21555.3 -7257.36 116.97 0.25 4783 4785 2 -21553.6 -7257.27 116.82 0.25 4784 4786 2 -21549.5 -7255.63 114.38 0.25 4785 4787 2 -21546.8 -7254.31 115.57 0.25 4786 4788 2 -21545.9 -7254.93 115.38 0.25 4787 4789 2 -21544.6 -7254.16 115.38 0.25 4788 4790 2 -21544.3 -7253.86 115.41 0.25 4789 4791 2 -21544.1 -7253.82 115.4 0.25 4790 4792 2 -21541.8 -7252.95 117.53 0.25 4791 4793 2 -21541.4 -7252.65 117.53 0.25 4792 4794 2 -21538.1 -7253.12 117.15 0.25 4793 4795 2 -21537 -7252.94 117.08 0.25 4794 4796 2 -21534.8 -7252.53 116.94 0.25 4795 4797 2 -21534.1 -7252.13 116.94 0.25 4796 4798 2 -21531.6 -7251.42 116.82 0.25 4797 4799 2 -21530.6 -7250.98 116.8 0.25 4798 4800 2 -21530.3 -7250.94 116.78 0.25 4799 4801 2 -21526.2 -7250.46 116.47 0.25 4800 4802 2 -21524.5 -7250.16 116.37 0.25 4801 4803 2 -21520.4 -7249.69 116.06 0.25 4802 4804 2 -21519.5 -7249.8 115.96 0.25 4803 4805 2 -21516.5 -7248.66 118.5 0.25 4804 4806 2 -21511.8 -7246.41 117.89 0.125 4805 4807 2 -21508.6 -7245.08 117.81 0.125 4806 4808 2 -21508.4 -7244.76 117.84 0.125 4807 4809 2 -21503.9 -7242.89 117.73 0.125 4808 4810 2 -21503.7 -7242.62 117.75 0.125 4809 4811 2 -21503 -7240.1 118.1 0.125 4810 4812 2 -21501.7 -7237.24 120.09 0.125 4811 4813 2 -21500.6 -7236.54 120.11 0.125 4812 4814 2 -21500.1 -7236.17 120.13 0.125 4813 4815 2 -21499.5 -7236.09 120.09 0.125 4814 4816 2 -21496.6 -7235.05 119.98 0.125 4815 4817 2 -21495.3 -7234.79 119.9 0.125 4816 4818 2 -21495.2 -7235.09 119.85 0.125 4817 4819 2 -21492.1 -7235.24 119.53 0.125 4818 4820 2 -21491.5 -7235.16 119.49 0.125 4819 4821 2 -21487.9 -7235.32 119.12 0.125 4820 4822 2 -21487.6 -7235.26 119.1 0.125 4821 4823 2 -21485.9 -7235.46 118.91 0.125 4822 4824 2 -21483.3 -7235.2 119.61 0.125 4823 4825 2 -21481.3 -7235.07 119.44 0.125 4824 4826 2 -21480.1 -7233.85 119.54 0.125 4825 4827 2 -21479.9 -7233.79 119.52 0.125 4826 4828 2 -21477.2 -7232.77 119.44 0.125 4827 4829 2 -21476.6 -7232.66 119.41 0.125 4828 4830 2 -21473.6 -7232.18 119.21 0.125 4829 4831 2 -21473.4 -7231.86 119.24 0.125 4830 4832 2 -21471 -7231.55 122.72 0.125 4831 4833 2 -21470.2 -7231.4 122.66 0.125 4832 4834 2 -21468.1 -7231.56 122.45 0.125 4833 4835 2 -21467 -7231.36 122.38 0.125 4834 4836 2 -21464.9 -7231 122.23 0.125 4835 4837 2 -21464.6 -7230.66 122.27 0.125 4836 4838 2 -21463.1 -7229.39 122.34 0.125 4837 4839 2 -21462.7 -7229.01 122.36 0.125 4838 4840 2 -21461.6 -7228.58 122.33 0.125 4839 4841 2 -21460.5 -7228.08 122.31 0.125 4840 4842 2 -21456.4 -7225.76 122.3 0.125 4841 4843 2 -21456.1 -7225.7 122.29 0.125 4842 4844 2 -21453.4 -7225.87 124.03 0.125 4843 4845 2 -21452.6 -7225.72 123.98 0.125 4844 4846 2 -21448.9 -7224.77 123.79 0.125 4845 4847 2 -21448.8 -7224.49 123.83 0.125 4846 4848 2 -21447.9 -7223.78 123.69 0.125 4847 4849 2 -21447.6 -7223.7 123.67 0.125 4848 4850 2 -21445.4 -7223.56 123.49 0.125 4849 4851 2 -21445.1 -7223.52 123.47 0.125 4850 4852 2 -21443.5 -7222.99 123.41 0.125 4851 4853 2 -21443 -7222.88 123.37 0.125 4852 4854 2 -21442.2 -7222.49 123.36 0.125 4853 4855 2 -21441 -7222.28 123.29 0.125 4854 4856 2 -21440.2 -7222.15 123.24 0.125 4855 4857 2 -21439 -7221.42 123.24 0.125 4856 4858 2 -21438.2 -7221 123.24 0.125 4857 4859 2 -21437 -7219.98 123.29 0.125 4858 4860 2 -21436.7 -7219.66 123.32 0.125 4859 4861 2 -21436.4 -7219.6 123.31 0.125 4860 4862 2 -21432.7 -7218.99 126.24 0.125 4861 4863 2 -21432.9 -7219.04 126.25 0.125 4862 4864 2 -21430.5 -7217.37 128.35 0.125 4863 4865 2 -21430.8 -7217.44 128.27 0.125 4864 4866 2 -21428.2 -7216.52 131.57 0.125 4865 4867 2 -21425.5 -7215.76 131.57 0.125 4866 4868 2 -21425.4 -7215.85 132.12 0.125 4867 4869 2 -21422.7 -7215.94 134.93 0.125 4868 4870 2 -21419.3 -7214.51 134.85 0.125 4869 4871 2 -21418.9 -7213.62 134.96 0.125 4870 4872 2 -21417.8 -7213.2 134.93 0.125 4871 4873 2 -21417.5 -7213.12 134.91 0.125 4872 4874 2 -21415 -7211.91 134.87 0.125 4873 4875 2 -21414.4 -7211.51 134.89 0.125 4874 4876 2 -21412.9 -7210.47 134.92 0.125 4875 4877 2 -21412.1 -7210.35 134.86 0.125 4876 4878 2 -21410.7 -7209.23 137.7 0.125 4877 4879 2 -21410.2 -7208.9 137.7 0.125 4878 4880 2 -21408.9 -7208.13 137.71 0.125 4879 4881 2 -21407.8 -7207.93 137.64 0.125 4880 4882 2 -21406.5 -7207.49 137.59 0.125 4881 4883 2 -21406.2 -7207.15 137.62 0.125 4882 4884 2 -21405.3 -7206.19 137.69 0.125 4883 4885 2 -21404.8 -7205.83 137.7 0.125 4884 4886 2 -21402.5 -7204.76 139.79 0.125 4885 4887 2 -21401.2 -7204.53 139.7 0.125 4886 4888 2 -21400.1 -7203.83 139.72 0.125 4887 4889 2 -21399 -7202.22 139.88 0.125 4888 4890 2 -21397 -7201.54 142.15 0.125 4889 4891 2 -21395.5 -7200.74 142.15 0.125 4890 4892 2 -21394.7 -7200.35 142.19 0.125 4891 4893 2 -21394.4 -7200.35 142.17 0.125 4892 4894 2 -21393 -7199.65 144.26 0.125 4893 4895 2 -21392.7 -7199.33 144.29 0.125 4894 4896 2 -21391.4 -7198.15 146.74 0.125 4895 4897 2 -21391 -7198.09 146.72 0.125 4896 4898 2 -21387.7 -7196.59 145.86 0.125 4897 4899 2 -21387.6 -7196.74 146.77 0.125 4898 4900 2 -21385.5 -7195.24 147.69 0.125 4899 4901 2 -21383 -7194.22 150.61 0.125 4900 4902 2 -21383 -7194.42 150.58 0.125 4901 4903 2 -21379.3 -7192.72 150.28 0.125 4902 4904 2 -21379 -7192.68 150.26 0.125 4903 4905 2 -21377.3 -7191.54 150.28 0.125 4904 4906 2 -21376.3 -7190.85 150.3 0.125 4905 4907 2 -21371.9 -7190.13 154.7 0.125 4906 4908 2 -21371.5 -7190.05 154.62 0.125 4907 4909 2 -21370 -7189.46 154.52 0.125 4908 4910 2 -21369.8 -7189.23 154.61 0.125 4909 4911 2 -21366.4 -7188.17 156.77 0.125 4910 4912 2 -21365.3 -7188 156.8 0.125 4911 4913 2 -21362.8 -7187.37 157.14 0.125 4912 4914 2 -21362.5 -7187.08 157.16 0.125 4913 4915 2 -21359.1 -7186.64 159.24 0.125 4914 4916 2 -21358 -7186.19 159.22 0.125 4915 4917 2 -21354.9 -7184.56 159.21 0.125 4916 4918 2 -21354 -7183.83 159.24 0.125 4917 4919 2 -21350.4 -7182.52 159.67 0.125 4918 4920 2 -21345.5 -7181.39 160.88 0.125 4919 4921 2 -21342.6 -7181.09 161.94 0.125 4920 4922 2 -21340.7 -7180.32 157.87 0.125 4921 4923 2 -21337.7 -7179.79 157.67 0.125 4922 4924 2 -21334.5 -7177.72 159.63 0.125 4923 4925 2 -21341.2 -7183.7 159.27 0.125 4924 4926 2 -21338 -7181.91 159.26 0.125 4925 4927 2 -21332.6 -7179.55 159.15 0.125 4926 4928 2 -21331.9 -7179.2 159.14 0.125 4927 4929 2 -21330.3 -7177.91 159.21 0.125 4928 4930 2 -21329.8 -7177.59 159.21 0.125 4929 4931 2 -21327.7 -7176.19 159.25 0.125 4930 4932 2 -21326.6 -7175.84 159.2 0.125 4931 4933 2 -21326 -7175.69 159.17 0.125 4932 4934 2 -21324.8 -7174.71 159.22 0.125 4933 4935 2 -21323.8 -7173.56 159.31 0.125 4934 4936 2 -21322.8 -7172.37 159.42 0.125 4935 4937 2 -21321.8 -7171.43 159.48 0.125 4936 4938 2 -21319.1 -7170.34 159.8 0.125 4937 4939 2 -21318.6 -7169.75 159.85 0.125 4938 4940 2 -21316.7 -7168.94 159.81 0.125 4939 4941 2 -21315.8 -7168.82 159.75 0.125 4940 4942 2 -21314 -7167.54 159.79 0.125 4941 4943 2 -21313.8 -7167.2 159.82 0.125 4942 4944 2 -21309.8 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0.125 4966 4968 2 -21267.3 -7157.06 168.06 0.125 4967 4969 2 -21266.8 -7156.68 168.08 0.125 4968 4970 2 -21264.1 -7155.28 168.06 0.125 4969 4971 2 -21263.6 -7154.95 168.07 0.125 4970 4972 2 -21263.4 -7154.64 168.1 0.125 4971 4973 2 -21261.2 -7154.02 172.14 0.125 4972 4974 2 -21260.1 -7153.59 172.11 0.125 4973 4975 2 -21258 -7152.7 172.06 0.125 4974 4976 2 -21257.4 -7152.15 172.1 0.125 4975 4977 2 -21255.9 -7150.61 172.21 0.125 4976 4978 2 -21255.4 -7150.27 172.22 0.125 4977 4979 2 -21255.2 -7149.98 172.25 0.125 4978 4980 2 -21253.1 -7149.55 174.5 0.125 4979 4981 2 -21252.5 -7149.22 174.5 0.125 4980 4982 2 -21252 -7148.91 174.52 0.125 4981 4983 2 -21251 -7148.45 174.5 0.125 4982 4984 2 -21249.5 -7148.3 174.39 0.125 4983 4985 2 -21248.7 -7148.18 174.33 0.125 4984 4986 2 -21248.2 -7147.85 174.34 0.125 4985 4987 2 -21247.9 -7147.58 174.36 0.125 4986 4988 2 -21245.8 -7146.35 178.24 0.125 4987 4989 2 -21245.1 -7145.7 178.28 0.125 4988 4990 2 -21244.8 -7145.66 178.26 0.125 4989 4991 2 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5013 5015 2 -21219.5 -7115.66 199.15 0.125 5014 5016 2 -21217.2 -7113.87 200.05 0.125 5015 5017 2 -21216.1 -7113.03 200.88 0.125 5016 5018 2 -21213.4 -7111.61 200.86 0.125 5017 5019 2 -21212.5 -7110.65 200.93 0.125 5018 5020 2 -21211.9 -7110.07 200.98 0.125 5019 5021 2 -21206.7 -7102.7 201.73 0.125 5020 5022 2 -21206.7 -7102.45 201.77 0.125 5021 5023 2 -21202.8 -7097.06 203.19 0.125 5022 5024 2 -21202.8 -7096.77 203.24 0.125 5023 5025 2 -21200.7 -7095.42 203.27 0.125 5024 5026 2 -21200.5 -7095.12 203.3 0.125 5025 5027 2 -21198.1 -7094.62 205.17 0.125 5026 5028 2 -21198.3 -7093.58 205.36 0.125 5027 5029 2 -21197 -7092.1 205.49 0.125 5028 5030 2 -21196.8 -7091.82 205.51 0.125 5029 5031 2 -21196.3 -7091.18 205.57 0.125 5030 5032 2 -21196 -7091.15 205.55 0.125 5031 5033 2 -21192.5 -7089.67 210.49 0.125 5032 5034 2 -21192.2 -7089.4 210.51 0.125 5033 5035 2 -21192.3 -7089.12 210.56 0.125 5034 5036 2 -21187.2 -7085.56 212.55 0.125 5035 5037 2 -21186.9 -7085.23 212.58 0.125 5036 5038 2 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0.125 5060 5062 2 -21162.7 -7068.27 221.54 0.125 5061 5063 2 -21161.7 -7067.58 221.56 0.125 5062 5064 2 -21160.9 -7066.87 221.6 0.125 5063 5065 2 -21160 -7065.56 225.35 0.125 5064 5066 2 -21159.4 -7065.23 225.36 0.125 5065 5067 2 -21158.4 -7064.3 225.42 0.125 5066 5068 2 -21157.4 -7063.63 225.43 0.125 5067 5069 2 -21156.6 -7063.25 225.42 0.125 5068 5070 2 -21155.1 -7061.29 226.45 0.125 5069 5071 2 -21155.1 -7061.05 226.54 0.125 5070 5072 2 -21153.9 -7060.34 227.93 0.125 5071 5073 2 -21153.6 -7060.19 228.89 0.125 5072 5074 2 -21151.4 -7059.8 232.79 0.125 5073 5075 2 -21150 -7056.03 228.06 0.125 5074 5076 2 -21148 -7055.23 229.41 0.125 5075 5077 2 -21147.6 -7054.11 229.55 0.125 5076 5078 2 -21145 -7051.86 229.49 0.125 5077 5079 2 -21143.2 -7050.52 230.78 0.125 5078 5080 2 -21142.6 -7050.44 230.74 0.125 5079 5081 2 -21141.2 -7050.49 230.6 0.125 5080 5082 2 -21140.6 -7050.41 230.56 0.125 5081 5083 2 -21140.7 -7050.15 230.6 0.125 5082 5084 2 -21139.3 -7049.7 230.55 0.125 5083 5085 2 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5107 5109 2 -21113.9 -7015.53 234.42 0.125 5108 5110 2 -21114 -7015.31 234.46 0.125 5109 5111 2 -21112.6 -7013.41 234.65 0.125 5110 5112 2 -21112.1 -7012.74 234.71 0.125 5111 5113 2 -21111.2 -7011.78 234.79 0.125 5112 5114 2 -21111 -7011.23 234.87 0.125 5113 5115 2 -21109.1 -7010.06 236.31 0.125 5114 5116 2 -21108.8 -7009.97 236.29 0.125 5115 5117 2 -21107.2 -7009.54 236.7 0.125 5116 5118 2 -21106.5 -7008.58 236.79 0.125 5117 5119 2 -21105.9 -7007.34 236.93 0.125 5118 5120 2 -21105.6 -7007.32 236.91 0.125 5119 5121 2 -21103.8 -7006.16 236.94 0.125 5120 5122 2 -21101.4 -7003.86 237.09 0.125 5121 5123 2 -21100.9 -7003.49 237.11 0.125 5122 5124 2 -21097.6 -7001.96 239.66 0.125 5123 5125 2 -21095.5 -7001.03 239.62 0.125 5124 5126 2 -21095.2 -7000.95 239.61 0.125 5125 5127 2 -21094.3 -7000 239.68 0.125 5126 5128 2 -21094.1 -6999.42 239.75 0.125 5127 5129 2 -21093.9 -6999.11 239.79 0.125 5128 5130 2 -21093.1 -6998.68 239.78 0.125 5129 5131 2 -21092 -6998.2 239.77 0.125 5130 5132 2 -21089.3 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5614 2 -21728 -7350.85 80.38 0.37 5613 5615 2 -21727.4 -7350.74 80.35 0.37 5614 5616 2 -21726.8 -7350.65 80.31 0.37 5615 5617 2 -21725.4 -7350.63 81.36 0.37 5616 5618 2 -21724.5 -7350.45 81.31 0.37 5617 5619 2 -21723.6 -7349.51 81.38 0.37 5618 5620 2 -21723 -7349.42 81.34 0.37 5619 5621 2 -21720.9 -7349.42 83.17 0.37 5620 5622 2 -21719.9 -7350.33 82.94 0.37 5621 5623 2 -21716.4 -7350.12 84.6 0.37 5622 5624 2 -21713.5 -7349.54 86.84 0.37 5623 5625 2 -21712.8 -7348.68 87.23 0.37 5624 5626 2 -21712 -7348.55 87.23 0.37 5625 5627 2 -21711.7 -7348.51 87.27 0.37 5626 5628 2 -21709.1 -7347.86 88.78 0.37 5627 5629 2 -21709.1 -7347.63 88.91 0.37 5628 5630 2 -21708.3 -7347.36 89.76 0.37 5629 5631 2 -21708 -7347.07 89.78 0.37 5630 5632 2 -21705.7 -7346.83 90.47 0.37 5631 5633 2 -21705.5 -7346.39 91.09 0.37 5632 5634 2 -21704.4 -7345.98 91.39 0.37 5633 5635 2 -21703.5 -7345.86 91.65 0.37 5634 5636 2 -21702 -7346.26 91.77 0.37 5635 5637 2 -21701.8 -7346.03 92.1 0.37 5636 5638 2 -21700.5 -7345.25 92.1 0.37 5637 5639 2 -21699.3 -7345.4 92.56 0.37 5638 5640 2 -21699 -7345.35 92.54 0.37 5639 5641 2 -21698.4 -7345.36 93.07 0.37 5640 5642 2 -21697.8 -7345.27 93.04 0.37 5641 5643 2 -21696.4 -7344.96 93.96 0.37 5642 5644 2 -21695.9 -7344.65 93.96 0.37 5643 5645 2 -21693.4 -7343.8 94.6 0.37 5644 5646 2 -21693.2 -7343.58 94.61 0.37 5645 5647 2 -21691.2 -7342.78 95.3 0.37 5646 5648 2 -21691 -7342.74 95.28 0.37 5647 5649 2 -21688.5 -7342.08 95.16 0.37 5648 5650 2 -21688.3 -7342.06 95.14 0.37 5649 5651 2 -21687.5 -7341.16 95.22 0.37 5650 5652 2 -21687.3 -7340.86 95.25 0.37 5651 5653 2 -21685 -7340.62 95.88 0.37 5652 5654 2 -21682 -7338.7 95.91 0.37 5653 5655 2 -21678.2 -7337.79 95.71 0.37 5654 5656 2 -21675.7 -7338.03 96.32 0.37 5655 5657 2 -21674 -7337.45 96.27 0.37 5656 5658 2 -21673.4 -7337.55 96.19 0.37 5657 5659 2 -21672.6 -7337.73 96.08 0.37 5658 5660 2 -21670 -7337.78 95.84 0.37 5659 5661 2 -21669.8 -7337.2 95.92 0.37 5660 5662 2 -21668 -7336.37 95.88 0.37 5661 5663 2 -21666 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0.37 5686 5688 2 -21604.9 -7329.4 108.05 0.37 5687 5689 2 -21603.8 -7329.44 107.95 0.37 5688 5690 2 -21603.2 -7329.36 107.97 0.37 5689 5691 2 -21602.4 -7329.23 107.99 0.37 5690 5692 2 -21601.5 -7328.89 108.31 0.37 5691 5693 2 -21600.9 -7329.03 109.77 0.37 5692 5694 2 -21600.1 -7328.63 109.76 0.37 5693 5695 2 -21599.2 -7328.79 109.9 0.37 5694 5696 2 -21598.3 -7328.89 109.81 0.37 5695 5697 2 -21597.1 -7328.98 109.82 0.37 5696 5698 2 -21596.4 -7328.84 109.77 0.37 5697 5699 2 -21595 -7328.4 110.27 0.37 5698 5700 2 -21593.1 -7328.91 111.8 0.25 5699 5701 2 -21591.3 -7329.44 111.55 0.25 5700 5702 2 -21590.8 -7329.33 111.51 0.25 5701 5703 2 -21590 -7328.89 111.51 0.25 5702 5704 2 -21589.4 -7328.8 111.48 0.25 5703 5705 2 -21589.1 -7329 111.42 0.25 5704 5706 2 -21587.4 -7329.27 111.21 0.25 5705 5707 2 -21586.6 -7329.1 111.16 0.25 5706 5708 2 -21586.3 -7328.74 111.19 0.25 5707 5709 2 -21584.8 -7328.25 111.13 0.25 5708 5710 2 -21584.1 -7328.41 111.05 0.25 5709 5711 2 -21583.3 -7328.49 110.95 0.25 5710 5712 2 -21583 -7328.18 110.98 0.25 5711 5713 2 -21582 -7327.99 110.91 0.25 5712 5714 2 -21580.6 -7327.76 110.82 0.25 5713 5715 2 -21580 -7327.68 110.79 0.25 5714 5716 2 -21578.3 -7326.8 113.6 0.25 5715 5717 2 -21578.2 -7325.95 113.73 0.25 5716 5718 2 -21578.2 -7325.71 113.77 0.25 5717 5719 2 -21577 -7324.96 113.78 0.25 5718 5720 2 -21576.7 -7324.88 113.76 0.25 5719 5721 2 -21574.6 -7323.51 113.8 0.25 5720 5722 2 -21574.4 -7323.2 113.83 0.25 5721 5723 2 -21572.8 -7321.24 113.32 0.25 5722 5724 2 -21573.1 -7318.17 112.68 0.25 5723 5725 2 -21572.8 -7316.75 112.89 0.25 5724 5726 2 -21572.8 -7316.51 112.92 0.25 5725 5727 2 -21572 -7316.1 112.92 0.25 5726 5728 2 -21508.2 -7300.79 129.05 0.25 5727 5729 2 -21508.2 -7300.53 129.09 0.25 5728 5730 2 -21506.3 -7300.14 128.78 0.25 5729 5731 2 -21505.8 -7299.79 128.79 0.25 5730 5732 2 -21504 -7300.27 128.55 0.25 5731 5733 2 -21503.8 -7300.2 128.53 0.25 5732 5734 2 -21502.7 -7299.48 128.55 0.25 5733 5735 2 -21502.2 -7299.39 128.52 0.25 5734 5736 2 -21502.2 -7299.66 128.47 0.25 5735 5737 2 -21500.7 -7299.69 128.33 0.25 5736 5738 2 -21499.7 -7299.5 128.27 0.25 5737 5739 2 -21499.1 -7299.43 128.22 0.25 5738 5740 2 -21498.6 -7298.99 128.25 0.25 5739 5741 2 -21498.3 -7298.96 128.23 0.25 5740 5742 2 -21497.5 -7299.13 128.12 0.25 5741 5743 2 -21496.6 -7298.95 128.07 0.25 5742 5744 2 -21495.5 -7299.03 127.95 0.25 5743 5745 2 -21495.2 -7298.93 127.85 0.25 5744 5746 2 -21494.7 -7299.15 130.92 0.25 5745 5747 2 -21494.3 -7299.22 132.95 0.25 5746 5748 2 -21494.1 -7298.88 132.98 0.25 5747 5749 2 -21493.6 -7298.24 133.05 0.25 5748 5750 2 -21493.1 -7297.9 133.05 0.25 5749 5751 2 -21492.8 -7297.8 132.83 0.25 5750 5752 2 -21492.3 -7297.73 132.63 0.25 5751 5753 2 -21491.7 -7297.62 132.59 0.25 5752 5754 2 -21490.9 -7297.47 132.54 0.25 5753 5755 2 -21489.6 -7296.98 132.56 0.25 5754 5756 2 -21489.1 -7296.58 132.57 0.25 5755 5757 2 -21486.3 -7294.53 132.66 0.25 5756 5758 2 -21485.8 -7294.43 132.63 0.25 5757 5759 2 -21484.7 -7294.24 132.55 0.25 5758 5760 2 -21483 -7293.95 132.45 0.25 5759 5761 2 -21482.8 -7294.14 132.39 0.25 5760 5762 2 -21480.6 -7293.29 132.33 0.25 5761 5763 2 -21479.3 -7292.73 132.3 0.25 5762 5764 2 -21477.1 -7292.52 136.04 0.25 5763 5765 2 -21476.9 -7292.19 136.07 0.25 5764 5766 2 -21476.6 -7292.15 136.05 0.25 5765 5767 2 -21475.5 -7291.73 136.13 0.25 5766 5768 2 -21474.7 -7291.61 136.28 0.25 5767 5769 2 -21473.5 -7290.19 136.99 0.25 5768 5770 2 -21473.3 -7289.86 137.02 0.25 5769 5771 2 -21472 -7289.59 139.63 0.25 5770 5772 2 -21393.8 -7270.14 138.63 0.25 5771 5773 2 -21391.7 -7268.95 135.64 0.25 5772 5774 2 -21390.6 -7268.73 135.58 0.25 5773 5775 2 -21390.3 -7268.68 135.56 0.25 5774 5776 2 -21389.4 -7268.8 135.45 0.25 5775 5777 2 -21388.3 -7268.61 135.38 0.25 5776 5778 2 -21387.2 -7268.94 135.22 0.25 5777 5779 2 -21386.9 -7268.88 135.21 0.25 5778 5780 2 -21385.1 -7268.84 135.05 0.25 5779 5781 2 -21384.6 -7268.77 135.01 0.25 5780 5782 2 -21383 -7268.19 134.96 0.25 5781 5783 2 -21381.9 -7268.26 134.84 0.25 5782 5784 2 -21381.7 -7267.91 134.88 0.25 5783 5785 2 -21381.1 -7267.85 134.83 0.25 5784 5786 2 -21380.8 -7267.81 134.81 0.25 5785 5787 2 -21380 -7267.68 134.76 0.25 5786 5788 2 -21379.1 -7267.8 134.66 0.25 5787 5789 2 -21378.3 -7267.66 134.61 0.25 5788 5790 2 -21377.5 -7267.18 134.61 0.25 5789 5791 2 -21375.5 -7266.84 135.9 0.25 5790 5792 2 -21374.4 -7266.65 135.83 0.25 5791 5793 2 -21374.2 -7266.35 135.86 0.25 5792 5794 2 -21373.4 -7265.97 135.85 0.25 5793 5795 2 -21372.6 -7265.79 135.8 0.25 5794 5796 2 -21371.4 -7266.35 135.6 0.25 5795 5797 2 -21371.1 -7266.32 135.57 0.25 5796 5798 2 -21370.1 -7265.87 135.55 0.25 5797 5799 2 -21369.2 -7265.45 135.54 0.25 5798 5800 2 -21365.6 -7265.49 138.78 0.25 5799 5801 2 -21365.1 -7265.35 138.75 0.25 5800 5802 2 -21362.3 -7265.12 138.53 0.25 5801 5803 2 -21361.8 -7264.99 138.5 0.25 5802 5804 2 -21361.8 -7264.73 138.54 0.25 5803 5805 2 -21356.3 -7263.75 138.04 0.25 5804 5806 2 -21355.8 -7263.63 138.01 0.25 5805 5807 2 -21354.4 -7263.47 137.91 0.25 5806 5808 2 -21353.6 -7263.27 137.87 0.25 5807 5809 2 -21352.7 -7263.41 137.76 0.25 5808 5810 2 -21352.3 -7263.04 137.78 0.25 5809 5811 2 -21352 -7262.98 137.76 0.25 5810 5812 2 -21350 -7262.9 137.59 0.25 5811 5813 2 -21349.2 -7263 137.49 0.25 5812 5814 2 -21348.9 -7262.94 137.48 0.25 5813 5815 2 -21347.7 -7263.28 137.31 0.25 5814 5816 2 -21347.3 -7264.1 136.2 0.25 5815 5817 2 -21347.2 -7265.13 136.01 0.25 5816 5818 2 -21346.2 -7267.68 135.81 0.25 5817 5819 2 -21345.4 -7268.82 135.55 0.25 5818 5820 2 -21346.1 -7268.79 128.81 0.25 5819 5821 2 -21345.4 -7270.61 127.28 0.25 5820 5822 2 -21345.3 -7270.87 127.23 0.25 5821 5823 2 -21343.5 -7271.57 126.95 0.25 5822 5824 2 -21342.5 -7270.35 127.06 0.25 5823 5825 2 -21342.7 -7268.82 124.53 0.25 5824 5826 2 -21341.4 -7268.31 124.49 0.25 5825 5827 2 -21340.3 -7269.72 124.15 0.25 5826 5828 2 -21340.3 -7269.93 124.12 0.25 5827 5829 2 -21344.5 -7263.18 138.19 0.25 5815 5830 2 -21342.2 -7262.69 136.12 0.25 5829 5831 2 -21341.4 -7262.55 136.06 0.25 5830 5832 2 -21332.4 -7260.45 135.82 0.25 5831 5833 2 -21331.9 -7260.11 135.83 0.25 5832 5834 2 -21331.6 -7260.04 135.81 0.25 5833 5835 2 -21329.3 -7259.1 135.75 0.25 5834 5836 2 -21326.3 -7258.31 135.66 0.25 5835 5837 2 -21325.8 -7257.68 135.72 0.25 5836 5838 2 -21325.1 -7257.76 136.83 0.25 5837 5839 2 -21322.5 -7257.81 137.75 0.25 5838 5840 2 -21321.1 -7257.7 138.53 0.25 5839 5841 2 -21319.1 -7257.87 138.32 0.25 5840 5842 2 -21318.3 -7257.72 138.26 0.25 5841 5843 2 -21316 -7257.35 138.12 0.25 5842 5844 2 -21313.9 -7256.92 137.98 0.25 5843 5845 2 -21308.4 -7255.54 138.37 0.25 5844 5846 2 -21307.6 -7255.41 138.31 0.25 5845 5847 2 -21305.1 -7255.05 138.55 0.25 5846 5848 2 -21304.8 -7255.2 138.5 0.25 5847 5849 2 -21304.5 -7255.45 138.43 0.25 5848 5850 2 -21302.1 -7256.08 138.1 0.25 5849 5851 2 -21301.8 -7255.8 138.12 0.25 5850 5852 2 -21299.8 -7254.87 138.12 0.25 5851 5853 2 -21298.4 -7254.62 138.03 0.25 5852 5854 2 -21296.5 -7254.05 137.98 0.25 5853 5855 2 -21294.3 -7253.38 138.07 0.25 5854 5856 2 -21294.3 -7253.66 138.02 0.25 5855 5857 2 -21292.7 -7252.73 138.51 0.25 5856 5858 2 -21292.8 -7252.44 138.69 0.25 5857 5859 2 -21291.9 -7252.28 139.94 0.25 5858 5860 2 -21291 -7252.41 139.94 0.25 5859 5861 2 -21289.8 -7252.45 139.83 0.25 5860 5862 2 -21289.3 -7252.37 139.79 0.25 5861 5863 2 -21289 -7252.08 139.82 0.25 5862 5864 2 -21288.8 -7252.03 139.8 0.25 5863 5865 2 -21287.8 -7251.58 139.78 0.25 5864 5866 2 -21287.5 -7251.49 139.77 0.25 5865 5867 2 -21286.7 -7251.1 139.76 0.25 5866 5868 2 -21285.7 -7250.39 139.78 0.25 5867 5869 2 -21285.1 -7250.23 139.76 0.25 5868 5870 2 -21282.8 -7250.39 139.52 0.25 5869 5871 2 -21281.8 -7250.19 139.45 0.25 5870 5872 2 -21281.4 -7250.44 139.38 0.25 5871 5873 2 -21279.8 -7249.85 139.32 0.25 5872 5874 2 -21278.8 -7249.41 139.3 0.25 5873 5875 2 -21278.5 -7249.12 139.33 0.25 5874 5876 2 -21277.9 -7248.22 139.41 0.25 5875 5877 2 -21277.3 -7248.06 139.39 0.25 5876 5878 2 -21277.1 -7247.84 139.4 0.25 5877 5879 2 -21275.2 -7247.64 141.77 0.25 5878 5880 2 -21274.7 -7247.81 141.69 0.25 5879 5881 2 -21272.4 -7247.68 141.51 0.25 5880 5882 2 -21272.1 -7247.64 141.49 0.25 5881 5883 2 -21270.9 -7246.59 141.54 0.25 5882 5884 2 -21270.7 -7246.34 141.56 0.25 5883 5885 2 -21269.9 -7245.9 141.57 0.25 5884 5886 2 -21269.4 -7245.57 141.57 0.25 5885 5887 2 -21266.6 -7245.34 141.35 0.25 5886 5888 2 -21265 -7244.77 141.29 0.25 5887 5889 2 -21264.7 -7244.72 141.27 0.25 5888 5890 2 -21260.8 -7243.76 142.69 0.25 5889 5891 2 -21260 -7243.88 142.59 0.25 5890 5892 2 -21258.6 -7243.63 142.5 0.25 5891 5893 2 -21258.3 -7243.61 142.48 0.25 5892 5894 2 -21257.2 -7243.65 142.36 0.25 5893 5895 2 -21256.3 -7243.76 142.27 0.25 5894 5896 2 -21259.2 -7247.37 141.93 0.125 5895 5897 2 -21256.6 -7247.32 141.7 0.125 5896 5898 2 -21254.7 -7247.05 141.57 0.125 5897 5899 2 -21254.1 -7246.71 141.57 0.125 5898 5900 2 -21251.8 -7246.67 141.37 0.125 5899 5901 2 -21251.6 -7246.64 141.35 0.125 5900 5902 2 -21249.3 -7246.83 141.1 0.125 5901 5903 2 -21248.8 -7246.77 141.06 0.125 5902 5904 2 -21247.8 -7247.72 140.81 0.125 5903 5905 2 -21247.2 -7247.6 140.78 0.125 5904 5906 2 -21246.1 -7247.17 140.75 0.125 5905 5907 2 -21245.6 -7246.87 140.75 0.125 5906 5908 2 -21245.1 -7246.79 140.71 0.125 5907 5909 2 -21244.2 -7246.68 140.65 0.125 5908 5910 2 -21243.7 -7246.32 140.66 0.125 5909 5911 2 -21242.3 -7246.41 140.51 0.125 5910 5912 2 -21242 -7246.36 140.49 0.125 5911 5913 2 -21241.3 -7246.1 142.66 0.125 5912 5914 2 -21241.4 -7245.77 142.28 0.125 5913 5915 2 -21239.9 -7246.37 142.05 0.125 5914 5916 2 -21238.2 -7246.14 141.93 0.125 5915 5917 2 -21238 -7245.88 141.94 0.125 5916 5918 2 -21236.9 -7244.92 142.01 0.125 5917 5919 2 -21237 -7244.63 142.06 0.125 5918 5920 2 -21234.8 -7244.11 141.94 0.125 5919 5921 2 -21233.2 -7243.31 141.93 0.125 5920 5922 2 -21233 -7243.02 141.95 0.125 5921 5923 2 -21232.7 -7242.93 141.94 0.125 5922 5924 2 -21230 -7242.06 141.83 0.125 5923 5925 2 -21227.3 -7241.45 140.49 0.125 5924 5926 2 -21227.3 -7241.36 139.96 0.125 5925 5927 2 -21225.3 -7241.4 139.77 0.125 5926 5928 2 -21224.5 -7241.27 139.71 0.125 5927 5929 2 -21222.5 -7241.04 139.89 0.125 5928 5930 2 -21222.5 -7241.15 140.53 0.125 5929 5931 2 -21220.8 -7240.65 140.53 0.125 5930 5932 2 -21220.6 -7240.61 140.62 0.125 5931 5933 2 -21218.9 -7240.15 140.54 0.125 5932 5934 2 -21218.3 -7240.09 140.5 0.125 5933 5935 2 -21217.8 -7239.76 140.5 0.125 5934 5936 2 -21216.7 -7239.85 140.38 0.125 5935 5937 2 -21216.2 -7239.5 140.39 0.125 5936 5938 2 -21215.9 -7239.46 140.37 0.125 5937 5939 2 -21214.8 -7238.79 140.39 0.125 5938 5940 2 -21214.6 -7238.28 140.45 0.125 5939 5941 2 -21214.3 -7238.18 140.44 0.125 5940 5942 2 -21213.6 -7237.31 140.51 0.125 5941 5943 2 -21213.1 -7237.01 140.51 0.125 5942 5944 2 -21212.8 -7236.92 140.51 0.125 5943 5945 2 -21212 -7236.79 140.45 0.125 5944 5946 2 -21211.1 -7236.72 140.38 0.125 5945 5947 2 -21210.3 -7236.57 140.33 0.125 5946 5948 2 -21210 -7236.55 140.3 0.125 5947 5949 2 -21208.3 -7236.31 140.19 0.125 5948 5950 2 -21208.1 -7236 140.22 0.125 5949 5951 2 -21206.5 -7235.55 140.14 0.125 5950 5952 2 -21205.9 -7235.15 140.15 0.125 5951 5953 2 -21205.6 -7235.16 140.12 0.125 5952 5954 2 -21205.2 -7234.83 140.13 0.125 5953 5955 2 -21204.3 -7234.68 140.08 0.125 5954 5956 2 -21203.2 -7234.56 139.99 0.125 5955 5957 2 -21202.6 -7234.72 139.91 0.125 5956 5958 2 -21202.3 -7234.67 139.89 0.125 5957 5959 2 -21198.3 -7234.82 140.33 0.125 5958 5960 2 -21197 -7235.66 140.08 0.125 5959 5961 2 -21196.7 -7236.16 139.96 0.125 5960 5962 2 -21196.5 -7236.16 139.94 0.125 5961 5963 2 -21195.8 -7236.3 139.86 0.125 5962 5964 2 -21194.7 -7236.14 139.78 0.125 5963 5965 2 -21193.1 -7235.67 139.7 0.125 5964 5966 2 -21192.3 -7235.29 139.7 0.125 5965 5967 2 -21191.3 -7234.62 139.71 0.125 5966 5968 2 -21190.5 -7234.23 139.7 0.125 5967 5969 2 -21189.3 -7233.7 140.74 0.125 5968 5970 2 -21187.5 -7232.68 140.74 0.125 5969 5971 2 -21186.8 -7230.95 140.96 0.125 5970 5972 2 -21185.7 -7230.6 140.92 0.125 5971 5973 2 -21185.5 -7230.29 140.94 0.125 5972 5974 2 -21184.9 -7229.91 140.96 0.125 5973 5975 2 -21183.9 -7229.24 140.97 0.125 5974 5976 2 -21182.9 -7228.37 141.02 0.125 5975 5977 2 -21181.6 -7227.61 141.02 0.125 5976 5978 2 -21181 -7227.52 140.99 0.125 5977 5979 2 -21179.9 -7226.88 140.99 0.125 5978 5980 2 -21179 -7225.89 141.06 0.125 5979 5981 2 -21178.7 -7225.91 141.03 0.125 5980 5982 2 -21176.7 -7223.48 141.25 0.125 5981 5983 2 -21174.2 -7223.01 145.04 0.125 5982 5984 2 -21173.3 -7224.13 147.28 0.125 5983 5985 2 -21172.8 -7224.32 147.2 0.125 5984 5986 2 -21172.7 -7224.57 147.15 0.125 5985 5987 2 -21171.6 -7224.82 146.02 0.125 5986 5988 2 -21171.3 -7225.01 145.96 0.125 5987 5989 2 -21167.6 -7225.04 145.61 0.125 5988 5990 2 -21166.2 -7224.84 145.52 0.125 5989 5991 2 -21164.6 -7224.32 145.45 0.125 5990 5992 2 -21164 -7224.02 145.45 0.125 5991 5993 2 -21161.9 -7223.2 145.38 0.125 5992 5994 2 -21160.8 -7223.04 145.3 0.125 5993 5995 2 -21159.3 -7223.07 145.16 0.125 5994 5996 2 -21158.5 -7223.2 145.06 0.125 5995 5997 2 -21157.4 -7223.33 144.94 0.125 5996 5998 2 -21156.8 -7223.23 144.9 0.125 5997 5999 2 -21155.1 -7223.3 144.73 0.125 5998 6000 2 -21154 -7223.1 144.66 0.125 5999 6001 2 -21153.4 -7223.08 144.61 0.125 6000 6002 2 -21152.3 -7222.89 144.54 0.125 6001 6003 2 -21150.9 -7223.22 144.34 0.125 6002 6004 2 -21150 -7223.35 144.24 0.125 6003 6005 2 -21148.7 -7224.51 143.93 0.125 6004 6006 2 -21148.1 -7224.65 143.85 0.125 6005 6007 2 -21147.6 -7224.6 143.81 0.125 6006 6008 2 -21146.7 -7224.5 143.74 0.125 6007 6009 2 -21146.2 -7224.43 143.7 0.125 6008 6010 2 -21144.7 -7223.92 145.06 0.125 6009 6011 2 -21142.3 -7222.94 145.87 0.125 6010 6012 2 -21142 -7222.92 145.85 0.125 6011 6013 2 -21140 -7222.64 145.71 0.125 6012 6014 2 -21139.7 -7222.58 145.69 0.125 6013 6015 2 -21138.4 -7221.74 146.39 0.125 6014 6016 2 -21138.1 -7221.43 146.42 0.125 6015 6017 2 -21136.5 -7220.91 146.17 0.125 6016 6018 2 -21136.5 -7220.87 145.96 0.125 6017 6019 2 -21135.6 -7221.29 145.8 0.125 6018 6020 2 -21133.4 -7220.78 146.15 0.125 6019 6021 2 -21133.2 -7221.08 147.9 0.125 6020 6022 2 -21131.5 -7220.72 148.73 0.125 6021 6023 2 -21130.4 -7221.09 148.56 0.125 6022 6024 2 -21129.3 -7220.65 148.53 0.125 6023 6025 2 -21128.4 -7220.38 149.02 0.125 6024 6026 2 -21128 -7220.39 149.43 0.125 6025 6027 2 -21126.3 -7220.79 149.74 0.125 6026 6028 2 -21125.9 -7220.83 150.28 0.125 6027 6029 2 -21123.6 -7221.41 150.74 0.125 6028 6030 2 -21123.3 -7221.64 150.67 0.125 6029 6031 2 -21121.4 -7222.71 150.33 0.125 6030 6032 2 -21121.2 -7222.6 149.71 0.125 6031 6033 2 -21119.1 -7222.1 148.47 0.125 6032 6034 2 -21118.8 -7221.77 148.46 0.125 6033 6035 2 -21117.7 -7221.85 148.17 0.125 6034 6036 2 -21116.6 -7221.68 147.9 0.125 6035 6037 2 -21116.6 -7221.95 147.85 0.125 6036 6038 2 -21114.5 -7222.58 146.75 0.125 6037 6039 2 -21113.5 -7222.35 146.2 0.125 6038 6040 2 -21111.5 -7221.04 144.89 0.125 6039 6041 2 -21078.3 -7216.84 141.95 0.125 6040 6042 2 -21074.8 -7216.07 141.65 0.125 6041 6043 2 -21074.4 -7216.38 142.08 0.125 6042 6044 2 -21071.9 -7216.27 141.91 0.125 6043 6045 2 -21071.8 -7216.56 141.86 0.125 6044 6046 2 -21070.7 -7216.64 141.74 0.125 6045 6047 2 -21069.3 -7216.17 141.69 0.125 6046 6048 2 -21068.8 -7216.1 141.65 0.125 6047 6049 2 -21068.5 -7216.34 141.58 0.125 6048 6050 2 -21068 -7216.01 141.59 0.125 6049 6051 2 -21067.7 -7215.72 141.61 0.125 6050 6052 2 -21067.5 -7215.42 141.64 0.125 6051 6053 2 -21066.3 -7215.26 141.56 0.125 6052 6054 2 -21065.8 -7215.17 141.52 0.125 6053 6055 2 -21064.9 -7215.33 141.42 0.125 6054 6056 2 -21064.6 -7215.3 141.4 0.125 6055 6057 2 -21063.6 -7214.58 141.42 0.125 6056 6058 2 -21063.4 -7214.27 141.45 0.125 6057 6059 2 -21062.2 -7214.67 141.27 0.125 6058 6060 2 -21061.9 -7214.64 141.25 0.125 6059 6061 2 -21061.4 -7214.03 141.3 0.125 6060 6062 2 -21060.1 -7213.34 141.29 0.125 6061 6063 2 -21059.2 -7213.19 141.24 0.125 6062 6064 2 -21058.7 -7213.16 141.19 0.125 6063 6065 2 -21058.2 -7212.83 141.2 0.125 6064 6066 2 -21057.1 -7212.42 141.17 0.125 6065 6067 2 -21056.2 -7212.29 141.11 0.125 6066 6068 2 -21054 -7211.85 141.91 0.125 6067 6069 2 -21053 -7211.95 143.28 0.125 6068 6070 2 -21052.1 -7212.09 143.19 0.125 6069 6071 2 -21051.6 -7212.03 143.14 0.125 6070 6072 2 -21050.7 -7211.88 143.09 0.125 6071 6073 2 -21049.1 -7211.44 143.02 0.125 6072 6074 2 -21048.5 -7211.37 142.98 0.125 6073 6075 2 -21047.7 -7210.95 142.97 0.125 6074 6076 2 -21046.9 -7210.58 142.99 0.125 6075 6077 2 -21045.2 -7210.88 142.78 0.125 6076 6078 2 -21044.5 -7209.71 142.91 0.125 6077 6079 2 -21044 -7209.38 142.91 0.125 6078 6080 2 -21042.1 -7210.44 142.56 0.125 6079 6081 2 -21039.9 -7210.16 142.41 0.125 6080 6082 2 -21039.3 -7209.8 142.41 0.125 6081 6083 2 -21037.1 -7209.77 142.21 0.125 6082 6084 2 -21036 -7209.85 142.09 0.125 6083 6085 2 -21034.5 -7210.21 141.89 0.125 6084 6086 2 -21033.1 -7210.93 139.67 0.125 6085 6087 2 -21030.8 -7211.62 138.74 0.125 6086 6088 2 -21029.7 -7211.46 138.66 0.125 6087 6089 2 -21029.2 -7211.39 138.62 0.125 6088 6090 2 -21027.8 -7211.17 138.53 0.125 6089 6091 2 -21023 -7210.75 137.8 0.125 6090 6092 2 -21021.9 -7210.57 137.79 0.125 6091 6093 2 -21019.7 -7210.28 137.63 0.125 6092 6094 2 -21018.3 -7209.86 137.71 0.125 6093 6095 2 -21017.8 -7209.75 137.68 0.125 6094 6096 2 -20988.9 -7218.75 130.67 0.25 6095 6097 2 -20987.6 -7220.41 130.27 0.25 6096 6098 2 -20986 -7220.72 130.08 0.25 6097 6099 2 -20983.7 -7221.48 129.73 0.25 6098 6100 2 -20982.9 -7221.36 129.67 0.25 6099 6101 2 -20980.5 -7221.35 131.47 0.25 6100 6102 2 -20979.9 -7221.26 131.43 0.25 6101 6103 2 -20978.9 -7220.31 131.28 0.25 6102 6104 2 -20977.3 -7220.23 130.57 0.25 6103 6105 2 -20977 -7220.22 130.54 0.25 6104 6106 2 -20974.4 -7220.67 133.56 0.25 6105 6107 2 -20974.2 -7220.62 133.36 0.25 6106 6108 2 -20971.3 -7220.8 133.3 0.25 6107 6109 2 -20971 -7220.75 133.28 0.25 6108 6110 2 -20968.4 -7220.86 134.15 0.25 6109 6111 2 -20967.8 -7220.78 134.22 0.25 6110 6112 2 -20965.8 -7221.35 134.08 0.25 6111 6113 2 -20965.2 -7221.22 134.11 0.25 6112 6114 2 -20965.2 -7221.52 134.05 0.25 6113 6115 2 -20963.6 -7222.61 133.82 0.25 6114 6116 2 -20962.8 -7222.5 133.74 0.25 6115 6117 2 -20962.5 -7222.21 133.76 0.25 6116 6118 2 -20962.3 -7222.05 133.03 0.25 6117 6119 2 -20962.1 -7221.75 133.06 0.25 6118 6120 2 -20962.1 -7221.25 130.14 0.25 6119 6121 2 -20959 -7221.77 132.32 0.25 6120 6122 2 -20958.4 -7221.44 132.32 0.25 6121 6123 2 -20956.2 -7221.12 132.16 0.25 6122 6124 2 -20955.2 -7220.71 132.13 0.25 6123 6125 2 -20954.6 -7220.38 132.14 0.25 6124 6126 2 -20954.1 -7220.29 132.1 0.25 6125 6127 2 -20952.1 -7220.5 131.51 0.25 6126 6128 2 -20950.6 -7220.81 131.32 0.25 6127 6129 2 -20950.4 -7220.47 131.35 0.25 6128 6130 2 -20948.8 -7219.54 131.36 0.25 6129 6131 2 -20948.5 -7219.18 131.39 0.25 6130 6132 2 -20948.4 -7218.63 131.47 0.25 6131 6133 2 -20947.5 -7218.52 131.41 0.25 6132 6134 2 -20946.1 -7218.35 131.3 0.25 6133 6135 2 -20945 -7218.21 131.22 0.25 6134 6136 2 -20944 -7217.76 131.2 0.25 6135 6137 2 -20943.4 -7217.69 131.16 0.25 6136 6138 2 -20941.6 -7218.93 130.12 0.25 6137 6139 2 -20941.3 -7218.84 130.11 0.25 6138 6140 2 -20940.9 -7219.37 131.79 0.25 6139 6141 2 -20940.3 -7219.33 131.74 0.25 6140 6142 2 -20938.8 -7220.29 133.63 0.25 6141 6143 2 -20938.6 -7220.25 133.61 0.25 6142 6144 2 -20937.8 -7219.9 133.83 0.25 6143 6145 2 -20937.5 -7219.85 133.81 0.25 6144 6146 2 -20936.7 -7219.73 133.76 0.25 6145 6147 2 -20936.3 -7219.75 133.72 0.25 6146 6148 2 -20934.8 -7220.06 135.08 0.25 6147 6149 2 -20934.5 -7219.98 135.07 0.25 6148 6150 2 -20933.6 -7220.44 135.08 0.25 6149 6151 2 -20932.7 -7220.59 134.97 0.25 6150 6152 2 -20931.6 -7220.16 134.99 0.25 6151 6153 2 -20931.1 -7220.08 134.96 0.25 6152 6154 2 -20929.9 -7220.18 134.84 0.25 6153 6155 2 -20929.4 -7220.11 134.9 0.25 6154 6156 2 -20927.7 -7220.16 134.74 0.25 6155 6157 2 -20927.3 -7220.34 134.67 0.25 6156 6158 2 -20927.1 -7220.33 134.65 0.25 6157 6159 2 -20926 -7219.91 134.62 0.25 6158 6160 2 -20924.9 -7219.77 134.54 0.25 6159 6161 2 -20923.9 -7218.55 134.65 0.25 6160 6162 2 -20923.7 -7218.22 134.68 0.25 6161 6163 2 -20922.1 -7218.62 136.39 0.25 6162 6164 2 -20921.8 -7218.59 136.37 0.25 6163 6165 2 -20919.6 -7218.86 134.98 0.25 6164 6166 2 -20919.3 -7218.85 134.96 0.25 6165 6167 2 -20917.6 -7219.12 134.75 0.25 6166 6168 2 -20917 -7219.08 134.7 0.25 6167 6169 2 -20915.3 -7219.09 134.54 0.25 6168 6170 2 -20915 -7219.05 134.52 0.25 6169 6171 2 -20914.5 -7218.47 134.57 0.25 6170 6172 2 -20913.7 -7218.06 134.56 0.25 6171 6173 2 -20912.9 -7217.39 134.6 0.25 6172 6174 2 -20911.8 -7217.27 134.52 0.125 6173 6175 2 -20911 -7217.44 134.41 0.125 6174 6176 2 -20910.7 -7217.39 134.39 0.125 6175 6177 2 -20909.2 -7218.23 134.11 0.125 6176 6178 2 -20907.9 -7218.51 134.79 0.125 6177 6179 2 -20907.3 -7218.44 135.12 0.125 6178 6180 2 -20907.3 -7218.47 135.3 0.125 6179 6181 2 -20907 -7218.42 135.28 0.125 6180 6182 2 -20906.7 -7218.11 136.78 0.125 6181 6183 2 -20906.4 -7218.14 136.74 0.125 6182 6184 2 -20905 -7217.92 136.83 0.125 6183 6185 2 -20904.8 -7217.6 136.87 0.125 6184 6186 2 -20904.3 -7217.56 136.82 0.125 6185 6187 2 -20903.7 -7217.72 136.74 0.125 6186 6188 2 -20903.1 -7217.62 136.7 0.125 6187 6189 2 -20902.8 -7217.59 136.68 0.125 6188 6190 2 -20902.4 -7218.34 136.52 0.125 6189 6191 2 -20901.9 -7218.26 136.48 0.125 6190 6192 2 -20901.3 -7218.19 136.44 0.125 6191 6193 2 -20901 -7218.12 136.43 0.125 6192 6194 2 -20900.2 -7218.26 136.32 0.125 6193 6195 2 -20899.3 -7218.19 136.27 0.125 6194 6196 2 -20898.8 -7218.07 136.24 0.125 6195 6197 2 -20898.5 -7217.79 136.26 0.125 6196 6198 2 -20898.3 -7217.74 136.24 0.125 6197 6199 2 -20898 -7217.74 136.21 0.125 6198 6200 2 -20897.4 -7218.16 136.09 0.125 6199 6201 2 -20896.8 -7217.84 136.09 0.125 6200 6202 2 -20895.2 -7217.35 136.02 0.125 6201 6203 2 -20894 -7217.21 135.93 0.125 6202 6204 2 -20894 -7217.41 135.9 0.125 6203 6205 2 -20893.1 -7217.86 135.74 0.125 6204 6206 2 -20892.8 -7218.05 135.68 0.125 6205 6207 2 -20892.5 -7218.02 135.66 0.125 6206 6208 2 -20892.4 -7218.2 136.75 0.125 6207 6209 2 -20892.1 -7218.49 137.02 0.125 6208 6210 2 -20891.8 -7218.45 137 0.125 6209 6211 2 -20890.4 -7218.22 136.9 0.125 6210 6212 2 -20890.1 -7218.2 136.88 0.125 6211 6213 2 -20888.9 -7219.08 136.62 0.125 6212 6214 2 -20888.6 -7218.79 136.64 0.125 6213 6215 2 -20887.4 -7218.36 136.59 0.125 6214 6216 2 -20886.8 -7218 136.6 0.125 6215 6217 2 -20886.8 -7217.48 136.69 0.125 6216 6218 2 -20886.3 -7217.43 136.65 0.125 6217 6219 2 -20886 -7217.39 136.63 0.125 6218 6220 2 -20884.8 -7217.07 137.4 0.125 6219 6221 2 -20884.3 -7216.84 138.13 0.125 6220 6222 2 -20883.2 -7216.44 138.13 0.125 6221 6223 2 -20882.7 -7216.13 138.13 0.125 6222 6224 2 -20882.7 -7215.86 138.18 0.125 6223 6225 2 -20879.1 -7216.28 140.17 0.125 6224 6226 2 -20879 -7216.66 140.74 0.125 6225 6227 2 -20875.9 -7216.15 141.58 0.125 6226 6228 2 -20875.6 -7216.11 141.56 0.125 6227 6229 2 -20870.3 -7215.19 141.81 0.125 6228 6230 2 -20870 -7214.9 141.83 0.125 6229 6231 2 -20864.5 -7214.37 144.42 0.125 6230 6232 2 -20864.2 -7214.34 144.4 0.125 6231 6233 2 -20860.8 -7214.63 144.03 0.125 6232 6234 2 -20859.7 -7214.52 143.95 0.125 6233 6235 2 -20856.9 -7214.39 143.7 0.125 6234 6236 2 -20856.7 -7214.09 143.73 0.125 6235 6237 2 -20855.2 -7213.88 143.63 0.125 6236 6238 2 -20852.2 -7212.9 143.39 0.125 6237 6239 2 -20852 -7212.87 143.37 0.125 6238 6240 2 -20848.2 -7212.9 143.01 0.125 6239 6241 2 -20847.4 -7213.05 142.91 0.125 6240 6242 2 -20846 -7212.81 142.82 0.125 6241 6243 2 -20846 -7212.56 142.86 0.125 6242 6244 2 -20843.2 -7211.41 144.45 0.125 6243 6245 2 -20842.7 -7211.04 144.46 0.125 6244 6246 2 -20841.5 -7211.12 145.51 0.125 6245 6247 2 -20841 -7210.75 145.52 0.125 6246 6248 2 -20841 -7210.51 145.56 0.125 6247 6249 2 -20839.3 -7210.14 146.09 0.125 6248 6250 2 -20838.5 -7210 146.03 0.125 6249 6251 2 -20835.4 -7210.61 143.95 0.125 6250 6252 2 -20834.8 -7210.51 143.91 0.125 6251 6253 2 -20830.1 -7209.89 143.57 0.125 6252 6254 2 -20829.8 -7209.84 143.55 0.125 6253 6255 2 -20826.9 -7208.87 143.44 0.125 6254 6256 2 -20826.6 -7208.33 143.51 0.125 6255 6257 2 -20826.1 -7207.98 143.51 0.125 6256 6258 2 -20823.2 -7206.77 143.44 0.125 6257 6259 2 -20823 -7205.97 143.55 0.125 6258 6260 2 -20816.6 -7205.15 146.55 0.125 6259 6261 2 -20816.3 -7205.11 146.53 0.125 6260 6262 2 -20812.2 -7204.02 146.33 0.125 6261 6263 2 -20811.7 -7203.66 146.34 0.125 6262 6264 2 -20807.2 -7203.88 149.21 0.125 6263 6265 2 -20806 -7201.85 149.44 0.125 6264 6266 2 -20803.8 -7200.19 152.42 0.125 6265 6267 2 -20801 -7199.79 152.22 0.125 6266 6268 2 -20800.7 -7199.5 152.24 0.125 6267 6269 2 -20799.1 -7198.99 152.18 0.125 6268 6270 2 -20793.8 -7197.41 153.03 0.125 6269 6271 2 -20788.8 -7197.33 156.23 0.125 6270 6272 2 -20784.8 -7195.88 155.63 0.125 6271 6273 2 -20782.2 -7195.45 156.46 0.125 6272 6274 2 -20781.9 -7195.41 156.44 0.125 6273 6275 2 -20780 -7194.3 156.45 0.125 6274 6276 2 -20778.1 -7193.12 157.03 0.125 6275 6277 2 -20776.5 -7192.87 156.91 0.125 6276 6278 2 -20776 -7193.07 156.83 0.125 6277 6279 2 -20772.4 -7193.91 156.36 0.125 6278 6280 2 -20771.8 -7193.84 156.32 0.125 6279 6281 2 -20771.6 -7194.1 156.25 0.125 6280 6282 2 -20768.1 -7194.64 155.83 0.125 6281 6283 2 -20767.8 -7194.31 155.86 0.125 6282 6284 2 -20767.6 -7194.31 155.84 0.125 6283 6285 2 -20765.9 -7193.56 155.81 0.125 6284 6286 2 -20765.7 -7193.27 155.84 0.125 6285 6287 2 -20765.4 -7193.19 155.82 0.125 6286 6288 2 -20764.4 -7192.49 155.84 0.125 6287 6289 2 -20763.5 -7192.4 155.78 0.125 6288 6290 2 -20761.3 -7192.13 155.61 0.125 6289 6291 2 -20760 -7191.89 155.53 0.125 6290 6292 2 -20756.5 -7191.68 155.24 0.125 6291 6293 2 -20756.6 -7191.45 155.28 0.125 6292 6294 2 -20754.2 -7190.35 155.57 0.125 6293 6295 2 -20752.9 -7190.51 157.43 0.125 6294 6296 2 -20752.5 -7190.5 157.4 0.125 6295 6297 2 -20749.8 -7189.99 157.94 0.125 6296 6298 2 -20749.8 -7189.69 157.99 0.125 6297 6299 2 -20748 -7188.62 157.99 0.125 6298 6300 2 -20747.4 -7188.29 158 0.125 6299 6301 2 -20747.1 -7188.24 157.98 0.125 6300 6302 2 -20745.6 -7187 158.05 0.125 6301 6303 2 -20745.1 -7186.41 158.1 0.125 6302 6304 2 -20744.1 -7185.45 158.16 0.125 6303 6305 2 -20743.9 -7185.17 158.18 0.125 6304 6306 2 -20742.1 -7184.12 156.51 0.125 6305 6307 2 -20741.1 -7183.41 156.53 0.125 6306 6308 2 -20740.8 -7183.37 156.51 0.125 6307 6309 2 -20740.2 -7181.71 156.73 0.125 6308 6310 2 -20739.9 -7181.41 156.75 0.125 6309 6311 2 -20738.4 -7180.14 156.82 0.125 6310 6312 2 -20738.2 -7179.3 156.94 0.125 6311 6313 2 -20732.9 -7175.22 156.01 0.125 6312 6314 2 -20732.9 -7174.92 156.06 0.125 6313 6315 2 -20730.7 -7174.61 155.9 0.125 6314 6316 2 -20730.1 -7174.51 155.87 0.125 6315 6317 2 -20726.3 -7173.47 155.68 0.125 6316 6318 2 -20724.8 -7172.04 154.75 0.125 6317 6319 2 -20724.8 -7172.11 155.14 0.125 6318 6320 2 -20723.8 -7170.62 155.3 0.125 6319 6321 2 -20720 -7169.68 155.43 0.125 6320 6322 2 -20719.7 -7169.66 155.59 0.125 6321 6323 2 -20713.9 -7166.26 156.09 0.125 6322 6324 2 -20713.9 -7166.01 156.13 0.125 6323 6325 2 -20709.3 -7165.52 156.62 0.125 6324 6326 2 -20707.5 -7164.31 157.33 0.125 6325 6327 2 -20705.1 -7162.95 157.81 0.125 6326 6328 2 -20704.6 -7162.67 157.86 0.125 6327 6329 2 -20701.9 -7161.94 160.2 0.125 6328 6330 2 -20701.8 -7162.23 160.15 0.125 6329 6331 2 -20699.9 -7161.94 160.01 0.125 6330 6332 2 -20699.1 -7161.8 159.96 0.125 6331 6333 2 -20694.1 -7160.56 159.7 0.125 6332 6334 2 -20689 -7157.3 158.48 0.125 6333 6335 2 -20686.1 -7157.3 159.28 0.125 6334 6336 2 -20684.1 -7157.27 159.1 0.125 6335 6337 2 -20683.3 -7157.13 159.04 0.125 6336 6338 2 -20682.2 -7156.27 159.09 0.125 6337 6339 2 -20681.8 -7155.69 159.14 0.125 6338 6340 2 -20681.8 -7155.38 159.19 0.125 6339 6341 2 -20679.5 -7152.17 161.14 0.125 6340 6342 2 -20676.2 -7149.61 164.43 0.125 6341 6343 2 -20675.6 -7149.57 164.43 0.125 6342 6344 2 -20673.7 -7149.26 164.39 0.125 6343 6345 2 -20673.4 -7149.26 164.41 0.125 6344 6346 2 -20670.7 -7148.64 164.3 0.125 6345 6347 2 -20670.1 -7148.55 164.3 0.125 6346 6348 2 -20665.7 -7147.66 164.12 0.125 6347 6349 2 -20664.9 -7147.33 164.15 0.125 6348 6350 2 -20663.3 -7146.31 164.21 0.125 6349 6351 2 -20661.9 -7146.11 164.15 0.125 6350 6352 2 -20661.6 -7146.31 164.09 0.125 6351 6353 2 -20655.1 -7143.67 164.23 0.125 6352 6354 2 -20648.8 -7142.33 167.61 0.125 6353 6355 2 -20649.2 -7136.31 168.65 0.125 6354 6356 2 -20639.9 -7133.74 170.14 0.125 6355 6357 2 -20635.9 -7132.83 171.91 0.125 6356 6358 2 -20632.7 -7131.74 172.53 0.125 6357 6359 2 -20632.5 -7131.7 172.51 0.125 6358 6360 2 -20626.7 -7129.41 174.78 0.125 6359 6361 2 -20621.4 -7128.39 176.03 0.125 6360 6362 2 -20621.4 -7128.14 176.07 0.125 6361 6363 2 -20618.7 -7126.3 176.12 0.125 6362 6364 2 -20618.5 -7125.97 176.18 0.125 6363 6365 2 -20618.2 -7125.66 176.2 0.125 6364 6366 2 -20615.7 -7125 176.34 0.125 6365 6367 2 -20615.8 -7124.7 176.4 0.125 6366 6368 2 -20613.8 -7124.64 176.22 0.125 6367 6369 2 -20613.6 -7124.23 176.27 0.125 6368 6370 2 -20612 -7123.17 176.17 0.125 6369 6371 2 -20611.6 -7122.79 176.19 0.125 6370 6372 2 -20611.1 -7122.41 176.21 0.125 6371 6373 2 -20609.2 -7121.77 176.14 0.125 6372 6374 2 -20609 -7121.47 176.16 0.125 6373 6375 2 -20607.9 -7121.04 176.14 0.125 6374 6376 2 -20607.4 -7120.88 176.11 0.125 6375 6377 2 -20603 -7117.4 179.61 0.125 6376 6378 2 -20601.2 -7117.91 179.36 0.125 6377 6379 2 -20600.9 -7117.57 179.39 0.125 6378 6380 2 -20599.7 -7116.55 179.45 0.125 6379 6381 2 -20599.4 -7116.51 179.43 0.125 6380 6382 2 -20598.9 -7116.14 179.44 0.125 6381 6383 2 -20598.5 -7115.77 179.46 0.125 6382 6384 2 -20598.2 -7115.71 179.44 0.125 6383 6385 2 -20597.9 -7115.66 179.42 0.125 6384 6386 2 -20595.4 -7114.95 179.52 0.125 6385 6387 2 -20593.5 -7114.39 180 0.125 6386 6388 2 -20593.2 -7114.36 179.98 0.125 6387 6389 2 -20591.8 -7113.97 180.72 0.125 6388 6390 2 -20591.5 -7113.71 181.26 0.125 6389 6391 2 -20589.3 -7113.33 181.12 0.125 6390 6392 2 -20586.9 -7111.54 180.08 0.125 6391 6393 2 -20586.7 -7111.22 180.11 0.125 6392 6394 2 -20585 -7111.19 179.96 0.125 6393 6395 2 -20584.5 -7111.06 179.93 0.125 6394 6396 2 -20583.2 -7111.63 179.72 0.125 6395 6397 2 -20582.6 -7111.53 179.68 0.125 6396 6398 2 -20581.8 -7111.37 179.63 0.125 6397 6399 2 -20581.3 -7110.78 179.69 0.125 6398 6400 2 -20580.6 -7110.06 179.74 0.125 6399 6401 2 -20580.4 -7109.73 179.77 0.125 6400 6402 2 -20578.5 -7109.38 179.65 0.125 6401 6403 2 -20577.9 -7109.26 179.62 0.125 6402 6404 2 -20578 -7109 179.67 0.125 6403 6405 2 -20575.7 -7108.18 181.99 0.125 6404 6406 2 -20574 -7107.87 181.89 0.125 6405 6407 2 -20572.6 -7107.87 181.76 0.125 6406 6408 2 -20571.8 -7107.72 181.7 0.125 6407 6409 2 -20570.4 -7107.14 181.68 0.125 6408 6410 2 -20569.6 -7106.96 181.63 0.125 6409 6411 2 -20569.7 -7106.72 181.67 0.125 6410 6412 2 -20566.5 -7105.85 181.55 0.125 6411 6413 2 -20566.2 -7105.78 181.53 0.125 6412 6414 2 -20564.7 -7105.81 181.39 0.125 6413 6415 2 -20564.4 -7105.71 181.38 0.125 6414 6416 2 -20563.3 -7105.52 181.31 0.125 6415 6417 2 -20562.3 -7105.31 181.25 0.125 6416 6418 2 -20562 -7104.99 181.28 0.125 6417 6419 2 -20558.7 -7104.74 184.84 0.125 6418 6420 2 -20558.5 -7104.46 184.86 0.125 6419 6421 2 -20557.4 -7104.19 184.8 0.125 6420 6422 2 -20557.4 -7103.93 184.85 0.125 6421 6423 2 -20555.3 -7102.57 185.34 0.125 6422 6424 2 -20554.3 -7102.11 185.32 0.125 6423 6425 2 -20553.5 -7101.93 185.27 0.125 6424 6426 2 -20552.1 -7101.67 185.19 0.125 6425 6427 2 -20551 -7101.46 185.12 0.125 6426 6428 2 -20550.3 -7100.81 185.15 0.125 6427 6429 2 -20549.8 -7100.17 185.22 0.125 6428 6430 2 -20549.7 -7099.35 185.35 0.125 6429 6431 2 -20549 -7098.39 185.44 0.125 6430 6432 2 -20547.7 -7097.62 185.45 0.125 6431 6433 2 -20545.8 -7097.78 188.13 0.125 6432 6434 2 -20545.8 -7097.83 188.43 0.125 6433 6435 2 -20543 -7097.42 188.87 0.125 6434 6436 2 -20538.2 -7096.19 191.85 0.125 6435 6437 2 -20534.5 -7095.2 193.05 0.125 6436 6438 2 -20533.7 -7093.97 193.17 0.125 6437 6439 2 -20531.8 -7093.35 193.09 0.125 6438 6440 2 -20531.1 -7092.39 193.18 0.125 6439 6441 2 -20526.8 -7091.79 195.71 0.125 6440 6442 2 -20525.7 -7091.36 195.69 0.125 6441 6443 2 -20518.2 -7090.66 198.2 0.125 6442 6444 2 -20517.7 -7090.32 198.2 0.125 6443 6445 2 -20517.4 -7090.28 198.19 0.125 6444 6446 2 -20516.1 -7089.73 198.15 0.125 6445 6447 2 -20515.9 -7089.19 198.22 0.125 6446 6448 2 -20515.2 -7088.48 198.27 0.125 6447 6449 2 -20514.7 -7088.1 198.29 0.125 6448 6450 2 -20514.5 -7088.1 198.27 0.125 6449 6451 2 -20512.5 -7087.42 198.2 0.125 6450 6452 2 -20512.3 -7087.11 198.23 0.125 6451 6453 2 -20511 -7084.98 198.46 0.125 6452 6454 2 -20510.8 -7084.71 198.49 0.125 6453 6455 2 -20510.5 -7084.66 198.47 0.125 6454 6456 2 -20509.1 -7084.4 198.38 0.125 6455 6457 2 -20508.9 -7084.06 198.42 0.125 6456 6458 2 -20504.9 -7082.38 199.17 0.125 6457 6459 2 -20491.9 -7078.32 203.8 0.125 6458 6460 2 -20489.6 -7077.11 203.78 0.125 6459 6461 2 -20480.8 -7074.79 204.3 0.125 6460 6462 2 -20480 -7074.35 204.3 0.125 6461 6463 2 -20479.8 -7074.04 204.33 0.125 6462 6464 2 -20473.5 -7071.98 207.04 0.125 6463 6465 2 -20468.6 -7069.47 205.61 0.125 6464 6466 2 -20468.3 -7069.39 205.6 0.125 6465 6467 2 -20467.5 -7068.17 205.72 0.125 6466 6468 2 -20467.2 -7067.87 205.74 0.125 6467 6469 2 -20465 -7067.43 205.61 0.125 6468 6470 2 -20464.7 -7067.37 205.59 0.125 6469 6471 2 -20463.1 -7067.01 205.37 0.125 6470 6472 2 -21138.1 -7221.17 151.06 0.125 6016 6473 2 -21136.3 -7221.14 157.04 0.125 6472 6474 2 -21134.1 -7221.5 163.48 0.125 6473 6475 2 -21134.3 -7222.56 170.82 0.125 6474 6476 2 -21134.1 -7222.85 172.53 0.125 6475 6477 2 -21134.5 -7222.33 175.76 0.125 6476 6478 2 -21134.5 -7222.08 175.81 0.125 6477 6479 2 -21131.5 -7221.01 176.37 0.125 6478 6480 2 -21131.5 -7220.74 176.41 0.125 6479 6481 2 -21130.8 -7220.32 176.41 0.125 6480 6482 2 -21130.5 -7220.03 176.44 0.125 6481 6483 2 -21130 -7219.45 176.49 0.125 6482 6484 2 -21129.8 -7219.16 176.51 0.125 6483 6485 2 -21129 -7218.79 176.5 0.125 6484 6486 2 -21128.8 -7217.71 176.66 0.125 6485 6487 2 -21128.9 -7216.89 176.8 0.125 6486 6488 2 -21127.7 -7215.69 176.89 0.125 6487 6489 2 -21126.4 -7214.15 177.03 0.125 6488 6490 2 -21126.2 -7213.9 177.05 0.125 6489 6491 2 -21124.6 -7213.13 177.02 0.125 6490 6492 2 -21124.1 -7212.52 177.07 0.125 6491 6493 2 -21134 -7221.04 181.14 0.125 6478 6494 2 -21132.5 -7220.76 186.61 0.125 6493 6495 2 -21132.5 -7220.71 188.04 0.125 6494 6496 2 -21132.3 -7220.94 189.43 0.125 6495 6497 2 -21130 -7220.77 194.71 0.125 6496 6498 2 -21129.7 -7220.72 194.69 0.125 6497 6499 2 -21128.3 -7221.49 199.87 0.125 6498 6500 2 -21128 -7221.67 201.01 0.125 6499 6501 2 -21126.3 -7221.51 204.67 0.125 6500 6502 2 -21187.9 -7237.25 141.75 0.125 5968 6503 2 -21186.4 -7237.85 141.51 0.125 6502 6504 2 -21182.9 -7239.62 143.37 0.125 6503 6505 2 -21181.3 -7240.18 144.69 0.125 6504 6506 2 -21180.2 -7240.03 144.61 0.125 6505 6507 2 -21179.1 -7240.52 146.29 0.125 6506 6508 2 -21179 -7240.7 147.39 0.125 6507 6509 2 -21178.1 -7242.05 149.56 0.125 6508 6510 2 -21177.9 -7242.98 150.59 0.125 6509 6511 2 -21169.1 -7259.06 158.25 0.125 6510 6512 2 -21166.6 -7261.12 157.68 0.125 6511 6513 2 -21166.5 -7261.61 157.59 0.125 6512 6514 2 -21166.2 -7261.83 157.52 0.125 6513 6515 2 -21164.2 -7264.23 156.94 0.125 6514 6516 2 -21163.2 -7265.42 156.65 0.125 6515 6517 2 -21160.7 -7266.42 158.05 0.125 6516 6518 2 -21159.7 -7267.84 157.71 0.125 6517 6519 2 -21159.1 -7268.06 157.62 0.125 6518 6520 2 -21158.7 -7268.27 157.55 0.125 6519 6521 2 -21157.8 -7269.18 157.32 0.125 6520 6522 2 -21157.2 -7269.36 157.26 0.125 6521 6523 2 -21156.3 -7269.55 157.25 0.125 6522 6524 2 -21156 -7269.53 157.43 0.125 6523 6525 2 -21149.9 -7270.24 161.28 0.125 6524 6526 2 -21148.9 -7272.84 162.7 0.125 6525 6527 2 -21148.6 -7273.06 162.64 0.125 6526 6528 2 -21146.5 -7275.56 162.91 0.125 6527 6529 2 -21145.8 -7276.12 163.65 0.125 6528 6530 2 -21145.5 -7276.11 163.63 0.125 6529 6531 2 -21144.9 -7276.69 164.29 0.125 6530 6532 2 -21144.6 -7276.89 164.23 0.125 6531 6533 2 -21144.5 -7277.44 164.13 0.125 6532 6534 2 -21144.2 -7277.66 164.06 0.125 6533 6535 2 -21143.7 -7278.23 164.76 0.125 6534 6536 2 -21143.3 -7278.84 165.47 0.125 6535 6537 2 -21141.8 -7280.51 168.11 0.125 6536 6538 2 -21140 -7280.46 169.24 0.125 6537 6539 2 -21139.5 -7280.9 170.42 0.125 6538 6540 2 -21139.4 -7281.94 170.24 0.125 6539 6541 2 -21139 -7282.6 171.34 0.125 6540 6542 2 -21138.1 -7283.75 172.26 0.125 6541 6543 2 -21137.9 -7283.78 172.23 0.125 6542 6544 2 -21136.3 -7284.76 173.13 0.125 6543 6545 2 -21135.3 -7284.82 173.88 0.125 6544 6546 2 -21134.7 -7285.49 173.72 0.125 6545 6547 2 -21134 -7286.52 173.78 0.125 6546 6548 2 -21132.6 -7286.35 173.68 0.125 6547 6549 2 -21132.3 -7286.34 173.99 0.125 6548 6550 2 -21130.3 -7288.9 177.6 0.125 6549 6551 2 -21129.4 -7288.8 177.54 0.125 6550 6552 2 -21127.4 -7290.81 181 0.125 6551 6553 2 -21127.1 -7291.54 180.85 0.125 6552 6554 2 -21125.9 -7291.64 180.73 0.125 6553 6555 2 -21125.2 -7291.83 182.43 0.125 6554 6556 2 -21124.9 -7292.03 182.37 0.125 6555 6557 2 -21124.2 -7292.42 183.26 0.125 6556 6558 2 -21123.6 -7292.6 183.17 0.125 6557 6559 2 -21123.2 -7293.01 184.1 0.125 6558 6560 2 -21122.1 -7294.34 184.81 0.125 6559 6561 2 -21121.8 -7294.54 184.75 0.125 6560 6562 2 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0.125 6584 6586 2 -21111 -7298.34 198.25 0.125 6585 6587 2 -21111 -7298.62 198.23 0.125 6586 6588 2 -21110.5 -7299.89 198.35 0.125 6587 6589 2 -21110.1 -7300.08 198.28 0.125 6588 6590 2 -21109.4 -7300.82 198.41 0.125 6589 6591 2 -21109.1 -7300.79 198.39 0.125 6590 6592 2 -21108.2 -7300.86 198.46 0.125 6591 6593 2 -21108.2 -7301.4 198.36 0.125 6592 6594 2 -21107.5 -7302.12 198.45 0.125 6593 6595 2 -21107.2 -7302.08 198.59 0.125 6594 6596 2 -21104.8 -7303.02 201.81 0.125 6595 6597 2 -21104.4 -7303.23 201.74 0.125 6596 6598 2 -21103.5 -7303.58 201.6 0.125 6597 6599 2 -21101.2 -7305.33 205.92 0.125 6598 6600 2 -21099.9 -7305.82 206.65 0.125 6599 6601 2 -21097.7 -7308.01 207.58 0.125 6600 6602 2 -21096.5 -7309.22 209.53 0.125 6601 6603 2 -21096 -7309.65 210.5 0.125 6602 6604 2 -21094.8 -7310.2 210.42 0.125 6603 6605 2 -21093.7 -7310.22 210.31 0.125 6604 6606 2 -21092.4 -7311.05 210.05 0.125 6605 6607 2 -21091.7 -7311.21 209.98 0.125 6606 6608 2 -21090.5 -7311.76 209.78 0.125 6607 6609 2 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0.25 7564 7566 2 -21702.9 -7436.16 164.67 0.25 7565 7567 2 -21702.5 -7436.34 164.62 0.25 7566 7568 2 -21702.2 -7436.58 164.55 0.25 7567 7569 2 -21701.9 -7436.82 164.48 0.25 7568 7570 2 -21701.2 -7437.74 164.26 0.25 7569 7571 2 -21700.9 -7437.71 164.24 0.25 7570 7572 2 -21701.7 -7438.87 164.12 0.25 7571 7573 2 -21701.8 -7439.43 164.04 0.25 7572 7574 2 -21701.2 -7439.65 163.95 0.25 7573 7575 2 -21701 -7439.84 163.89 0.25 7574 7576 2 -21700.1 -7440.01 163.78 0.25 7575 7577 2 -21699.7 -7440.48 163.67 0.25 7576 7578 2 -21699.3 -7441.75 163.41 0.25 7577 7579 2 -21699 -7441.99 163.34 0.25 7578 7580 2 -21699 -7444.02 164.36 0.25 7579 7581 2 -21698.1 -7445.25 165.51 0.25 7580 7582 2 -21698.1 -7445.32 165.93 0.25 7581 7583 2 -21697.4 -7445.64 166.63 0.25 7582 7584 2 -21697.1 -7445.54 166.62 0.25 7583 7585 2 -21696.7 -7446.59 166.41 0.25 7584 7586 2 -21697.5 -7447.65 165.72 0.25 7585 7587 2 -21696.5 -7448.29 165.86 0.25 7586 7588 2 -21695.8 -7448.88 166.15 0.25 7587 7589 2 -21694.2 -7450.16 166.88 0.25 7588 7590 2 -21693.7 -7451.43 166.77 0.25 7589 7591 2 -21692.3 -7452.72 168.85 0.25 7590 7592 2 -21692.4 -7453.51 168.73 0.25 7591 7593 2 -21691.3 -7454.12 168.52 0.25 7592 7594 2 -21690.8 -7454.83 168.01 0.25 7593 7595 2 -21690.8 -7454.87 168.23 0.25 7594 7596 2 -21690.5 -7455.62 168.07 0.25 7595 7597 2 -21689.4 -7456.15 170.29 0.25 7596 7598 2 -21689.2 -7457.62 171.02 0.25 7597 7599 2 -21688.3 -7457.5 170.95 0.25 7598 7600 2 -21687.4 -7457.62 170.86 0.25 7599 7601 2 -21687 -7458.92 170.6 0.25 7600 7602 2 -21686.9 -7459.11 170.56 0.25 7601 7603 2 -21687.1 -7460.5 170.35 0.25 7602 7604 2 -21685.4 -7462.36 174.32 0.25 7603 7605 2 -21684.8 -7462.27 174.29 0.25 7604 7606 2 -21684.5 -7462.47 174.22 0.25 7605 7607 2 -21683.8 -7463.44 174 0.25 7606 7608 2 -21683 -7465.22 173.69 0.25 7607 7609 2 -21682.9 -7465.73 173.67 0.25 7608 7610 2 -21682.5 -7468.36 174.79 0.25 7609 7611 2 -21682.2 -7468.26 174.77 0.25 7610 7612 2 -21681.3 -7468.69 174.61 0.25 7611 7613 2 -21681.4 -7468.43 174.66 0.25 7612 7614 2 -21680.5 -7468.06 174.64 0.25 7613 7615 2 -21680.3 -7468.01 174.63 0.25 7614 7616 2 -21679.3 -7468.39 175.8 0.25 7615 7617 2 -21679.2 -7469.87 175.15 0.25 7616 7618 2 -21677.5 -7470.48 176.55 0.25 7617 7619 2 -21675.8 -7472.88 176.16 0.25 7618 7620 2 -21675.1 -7474.1 175.89 0.25 7619 7621 2 -21675 -7474.35 175.84 0.25 7620 7622 2 -21674.9 -7475.17 175.69 0.25 7621 7623 2 -21674.7 -7475.11 175.68 0.25 7622 7624 2 -21673.7 -7475.66 176.52 0.25 7623 7625 2 -21672.5 -7475.81 176.39 0.25 7624 7626 2 -21671.4 -7475.92 176.27 0.25 7625 7627 2 -21670.6 -7477.75 174.91 0.25 7626 7628 2 -21669.9 -7478.95 174.65 0.25 7627 7629 2 -21668.6 -7479.5 176.91 0.25 7628 7630 2 -21667.9 -7479.92 176.78 0.25 7629 7631 2 -21667.6 -7480.62 176.63 0.25 7630 7632 2 -21666.9 -7481.03 177.52 0.25 7631 7633 2 -21667 -7483.48 179.11 0.25 7632 7634 2 -21666.3 -7483.93 179.02 0.25 7633 7635 2 -21665.4 -7483.98 176.84 0.25 7634 7636 2 -21665 -7483.65 175.45 0.25 7635 7637 2 -21663.5 -7483.73 175.3 0.25 7636 7638 2 -21663.2 -7483.67 175.28 0.25 7637 7639 2 -21661.7 -7484.52 174.99 0.25 7638 7640 2 -21661.2 -7486.33 174.64 0.25 7639 7641 2 -21660.9 -7489.16 174.14 0.25 7640 7642 2 -21660.1 -7489.61 175.53 0.25 7641 7643 2 -21659 -7489.76 175.59 0.25 7642 7644 2 -21658.9 -7490.03 175.54 0.25 7643 7645 2 -21658.4 -7490.93 176.56 0.25 7644 7646 2 -21658.6 -7491.18 177.91 0.25 7645 7647 2 -21659.2 -7491.84 179.69 0.25 7646 7648 2 -21659.4 -7492.71 179.61 0.25 7647 7649 2 -21659.2 -7493.56 180.07 0.25 7648 7650 2 -21658.9 -7493.79 180.01 0.25 7649 7651 2 -21657.4 -7494.63 179.72 0.25 7650 7652 2 -21657 -7495.14 179.61 0.25 7651 7653 2 -21656.6 -7496.13 179.41 0.25 7652 7654 2 -21656.6 -7496.4 179.36 0.25 7653 7655 2 -21656.1 -7498.19 182.28 0.25 7654 7656 2 -21656.1 -7498.22 182.44 0.25 7655 7657 2 -21655.8 -7500.57 182.02 0.25 7656 7658 2 -21655.6 -7500.54 182 0.25 7657 7659 2 -21654.1 -7501.37 181.72 0.25 7658 7660 2 -21652.9 -7501.47 181.6 0.25 7659 7661 2 -21652.1 -7503.24 181.23 0.25 7660 7662 2 -21650.6 -7503.79 181 0.25 7661 7663 2 -21650.5 -7504.06 180.95 0.25 7662 7664 2 -21650.9 -7505.98 180.67 0.25 7663 7665 2 -21650.9 -7506.25 180.62 0.25 7664 7666 2 -21649.3 -7507.16 180.79 0.25 7665 7667 2 -21649 -7507.13 180.77 0.25 7666 7668 2 -21648.7 -7507.6 180.76 0.25 7667 7669 2 -21648 -7508.83 180.48 0.25 7668 7670 2 -21647.7 -7508.8 180.47 0.25 7669 7671 2 -21646.5 -7509.14 180.29 0.25 7670 7672 2 -21646.6 -7510.17 181.47 0.25 7671 7673 2 -21645.5 -7511.17 181.47 0.25 7672 7674 2 -21644.8 -7512.65 181.15 0.25 7673 7675 2 -21644.8 -7512.9 181.11 0.25 7674 7676 2 -21644.1 -7514.94 182.39 0.25 7675 7677 2 -21644 -7515.73 182.25 0.25 7676 7678 2 -21642.5 -7518.07 182.63 0.25 7677 7679 2 -21642.3 -7518.02 182.62 0.25 7678 7680 2 -21639.8 -7518.49 183.93 0.25 7679 7681 2 -21639.3 -7518.88 181.63 0.25 7680 7682 2 -21639.2 -7519.08 182.88 0.25 7681 7683 2 -21638.5 -7520.09 182.89 0.25 7682 7684 2 -21638.3 -7521.9 182.57 0.25 7683 7685 2 -21638 -7521.82 182.55 0.25 7684 7686 2 -21636.5 -7522.7 182.27 0.25 7685 7687 2 -21636.5 -7522.93 182.23 0.25 7686 7688 2 -21635.5 -7523.87 181.98 0.25 7687 7689 2 -21635.2 -7524.1 181.92 0.25 7688 7690 2 -21634.5 -7525.05 181.7 0.25 7689 7691 2 -21634.2 -7525.27 181.63 0.25 7690 7692 2 -21633.1 -7528.09 183.1 0.25 7691 7693 2 -21633.2 -7528.28 180.92 0.25 7692 7694 2 -21633 -7529.6 180.68 0.25 7693 7695 2 -21632.8 -7529.58 180.66 0.25 7694 7696 2 -21632.4 -7530.31 180.51 0.25 7695 7697 2 -21632.1 -7530.5 180.3 0.25 7696 7698 2 -21631.6 -7532.03 179.86 0.25 7697 7699 2 -21631.6 -7532.25 179.59 0.25 7698 7700 2 -21631.2 -7533.27 176.35 0.25 7699 7701 2 -21630.8 -7533.41 176.3 0.25 7700 7702 2 -21630.8 -7533.7 176.25 0.25 7701 7703 2 -21630.1 -7534.4 176.07 0.25 7702 7704 2 -21629.8 -7534.89 175.96 0.25 7703 7705 2 -21629.6 -7536.71 175.64 0.25 7704 7706 2 -21629.6 -7536.97 175.59 0.25 7705 7707 2 -21627.1 -7537.98 175.2 0.25 7706 7708 2 -21626.5 -7538.17 175.11 0.25 7707 7709 2 -21626 -7538.33 175.03 0.25 7708 7710 2 -21625 -7539.25 174.79 0.25 7709 7711 2 -21624.7 -7539.24 174.76 0.25 7710 7712 2 -21623.4 -7540.7 174.96 0.25 7711 7713 2 -21622.5 -7541.29 174.25 0.25 7712 7714 2 -21622.5 -7541.53 174.21 0.25 7713 7715 2 -21622 -7542.58 174.32 0.25 7714 7716 2 -21621.9 -7543.05 175.34 0.25 7715 7717 2 -21620.4 -7545.32 175.28 0.25 7716 7718 2 -21620.1 -7545.57 175.21 0.25 7717 7719 2 -21619.3 -7545.39 175.16 0.25 7718 7720 2 -21619 -7545.11 175.19 0.25 7719 7721 2 -21619 -7545.8 174.72 0.25 7720 7722 2 -21618.9 -7546.09 174.67 0.25 7721 7723 2 -21618.8 -7547.45 173.37 0.25 7722 7724 2 -21618.8 -7547.72 173.32 0.25 7723 7725 2 -21618.5 -7549.29 173.04 0.25 7724 7726 2 -21618.6 -7549.54 173 0.25 7725 7727 2 -21617.9 -7550.52 172.77 0.25 7726 7728 2 -21617.6 -7550.74 172.71 0.25 7727 7729 2 -21616.2 -7552.91 172.21 0.25 7728 7730 2 -21615.2 -7554.01 171.38 0.25 7729 7731 2 -21614.9 -7554.01 171.35 0.25 7730 7732 2 -21614.5 -7554.97 171.15 0.25 7731 7733 2 -21614.2 -7555.2 171.08 0.25 7732 7734 2 -21613.1 -7555.32 170.96 0.25 7733 7735 2 -21612.5 -7555.5 170.87 0.25 7734 7736 2 -21612.1 -7556.51 170.67 0.25 7735 7737 2 -21612.3 -7557.34 170.54 0.25 7736 7738 2 -21612.5 -7557.7 171.19 0.25 7737 7739 2 -21612.1 -7558.51 171.03 0.25 7738 7740 2 -21612 -7558.71 170.99 0.25 7739 7741 2 -21611.9 -7559.28 170.88 0.25 7740 7742 2 -21609.9 -7562.09 174.73 0.25 7741 7743 2 -21609.6 -7562.08 174.7 0.25 7742 7744 2 -21608.9 -7562.61 174.99 0.25 7743 7745 2 -21608.6 -7562.54 174.98 0.25 7744 7746 2 -21608.3 -7563.35 175.03 0.25 7745 7747 2 -21608 -7563.32 175.01 0.25 7746 7748 2 -21607.3 -7563.36 172.83 0.25 7747 7749 2 -21607.2 -7563.84 172.75 0.25 7748 7750 2 -21606.9 -7563.82 172.73 0.25 7749 7751 2 -21606.3 -7564.25 172.6 0.25 7750 7752 2 -21606.3 -7564.53 172.56 0.25 7751 7753 2 -21605.5 -7565.85 172.91 0.25 7752 7754 2 -21605.2 -7566.06 172.85 0.25 7753 7755 2 -21604.5 -7566.57 172.81 0.25 7754 7756 2 -21604.2 -7567.06 172.75 0.25 7755 7757 2 -21603.9 -7567.01 172.73 0.25 7756 7758 2 -21603.2 -7567.71 172.55 0.25 7757 7759 2 -21602.9 -7567.92 172.57 0.25 7758 7760 2 -21601.5 -7568.29 172.44 0.25 7759 7761 2 -21600.9 -7568.2 172.4 0.25 7760 7762 2 -21600.1 -7571.87 176.82 0.25 7761 7763 2 -21598.1 -7571.98 180.4 0.25 7762 7764 2 -21596.7 -7572.23 179.55 0.25 7763 7765 2 -21596.4 -7572.16 179.54 0.25 7764 7766 2 -21594.6 -7572.51 179.62 0.25 7765 7767 2 -21594.3 -7572.7 179.56 0.25 7766 7768 2 -21593.6 -7575.14 181.6 0.25 7767 7769 2 -21593.6 -7574.89 181.61 0.25 7768 7770 2 -21592.5 -7574.21 178.52 0.25 7769 7771 2 -21591.8 -7577.48 182.2 0.25 7770 7772 2 -21591.5 -7577.73 182.13 0.25 7771 7773 2 -21590.9 -7579.43 182.82 0.25 7772 7774 2 -21589.8 -7580.58 184.32 0.25 7773 7775 2 -21589.5 -7580.82 184.25 0.25 7774 7776 2 -21588.3 -7581.18 184.08 0.25 7775 7777 2 -21588 -7581.4 184.01 0.25 7776 7778 2 -21587.7 -7581.66 183.94 0.25 7777 7779 2 -21586.8 -7583.64 183.53 0.25 7778 7780 2 -21588.9 -7570.71 185.87 0.125 7779 7781 2 -21587.5 -7572.34 185.46 0.125 7780 7782 2 -21586.2 -7574.25 185.4 0.125 7781 7783 2 -21586.2 -7574.51 185.35 0.125 7782 7784 2 -21585.5 -7575.17 185.18 0.125 7783 7785 2 -21584 -7575.65 184.57 0.125 7784 7786 2 -21583.3 -7576.24 184.01 0.125 7785 7787 2 -21583.3 -7576.45 183.97 0.125 7786 7788 2 -21583.2 -7578.6 183.61 0.125 7787 7789 2 -21583 -7579.69 183.83 0.125 7788 7790 2 -21582.5 -7580.92 183.57 0.125 7789 7791 2 -21582.5 -7580.95 183.73 0.125 7790 7792 2 -21581.4 -7582.9 184.83 0.125 7791 7793 2 -21581.3 -7584.68 185.93 0.125 7792 7794 2 -21580.3 -7585.55 185.69 0.125 7793 7795 2 -21579.2 -7586.64 185.41 0.125 7794 7796 2 -21579.2 -7586.93 185.36 0.125 7795 7797 2 -21578.6 -7588.42 185.06 0.125 7796 7798 2 -21577.5 -7588.46 184.94 0.125 7797 7799 2 -21577.3 -7589.17 186.12 0.125 7798 7800 2 -21576.8 -7589.87 185.96 0.125 7799 7801 2 -21576.9 -7590.97 185.79 0.125 7800 7802 2 -21575.9 -7591.86 185.54 0.125 7801 7803 2 -21574.2 -7594.06 185.79 0.125 7802 7804 2 -21573 -7596.73 185.23 0.125 7803 7805 2 -21572.5 -7598.61 186.93 0.125 7804 7806 2 -21572 -7599.86 186.72 0.125 7805 7807 2 -21571.8 -7600.33 186.63 0.125 7806 7808 2 -21571.5 -7600.57 186.56 0.125 7807 7809 2 -21571.7 -7601.11 186.49 0.125 7808 7810 2 -21571.5 -7602.41 186.25 0.125 7809 7811 2 -21570.3 -7602.49 186.13 0.125 7810 7812 2 -21569.1 -7603.01 185.93 0.125 7811 7813 2 -21568.8 -7603.22 185.86 0.125 7812 7814 2 -21568.4 -7604.82 187.49 0.125 7813 7815 2 -21568.3 -7605.07 187.44 0.125 7814 7816 2 -21568.1 -7606.07 187.26 0.125 7815 7817 2 -21567.8 -7606.29 187.19 0.125 7816 7818 2 -21567.5 -7606.24 187.17 0.125 7817 7819 2 -21566.3 -7606.78 186.97 0.125 7818 7820 2 -21566 -7608.04 186.76 0.125 7819 7821 2 -21566 -7608.3 186.71 0.125 7820 7822 2 -21566 -7609.39 187.74 0.125 7821 7823 2 -21565.5 -7611.3 185.55 0.125 7822 7824 2 -21565.4 -7611.57 185.5 0.125 7823 7825 2 -21565 -7613.18 187.35 0.125 7824 7826 2 -21564.3 -7614.96 188.49 0.125 7825 7827 2 -21563.9 -7615.39 188.39 0.125 7826 7828 2 -21563.6 -7615.95 187.26 0.125 7827 7829 2 -21563.3 -7616.14 187.2 0.125 7828 7830 2 -21561.9 -7617.7 186.78 0.125 7829 7831 2 -21561.8 -7617.98 186.73 0.125 7830 7832 2 -21561.5 -7619.5 186.45 0.125 7831 7833 2 -21561.2 -7619.98 186.34 0.125 7832 7834 2 -21561.1 -7620.23 186.29 0.125 7833 7835 2 -21560.4 -7621.47 189.32 0.125 7834 7836 2 -21560.4 -7621.69 189.14 0.125 7835 7837 2 -21559.7 -7623.72 188.75 0.125 7836 7838 2 -21559.6 -7624.21 188.64 0.125 7837 7839 2 -21559 -7624.66 188.51 0.125 7838 7840 2 -21558.7 -7624.86 188.45 0.125 7839 7841 2 -21557.8 -7625.82 190.13 0.125 7840 7842 2 -21557.1 -7626.35 190.8 0.125 7841 7843 2 -21557 -7626.88 190.7 0.125 7842 7844 2 -21556.4 -7628.35 190.4 0.125 7843 7845 2 -21556.2 -7629.36 190.22 0.125 7844 7846 2 -21556 -7630.41 190.02 0.125 7845 7847 2 -21555.6 -7631.14 189.87 0.125 7846 7848 2 -21554.2 -7633.42 191.93 0.125 7847 7849 2 -21554.4 -7635.11 191.67 0.125 7848 7850 2 -21554.5 -7636.41 191.49 0.125 7849 7851 2 -21554.4 -7636.68 191.44 0.125 7850 7852 2 -21553.1 -7639.11 191.15 0.125 7851 7853 2 -21553 -7639.63 191.05 0.125 7852 7854 2 -21552.5 -7640.64 190.84 0.125 7853 7855 2 -21552.5 -7640.88 190.8 0.125 7854 7856 2 -21551.7 -7643.62 196.14 0.125 7855 7857 2 -21551.2 -7644.56 195.94 0.125 7856 7858 2 -21550.9 -7644.76 195.88 0.125 7857 7859 2 -21549.3 -7645.55 195.6 0.125 7858 7860 2 -21549 -7645.47 195.58 0.125 7859 7861 2 -21549 -7647.01 196.46 0.125 7860 7862 2 -21548.6 -7647.44 196.36 0.125 7861 7863 2 -21547.8 -7648.38 196.17 0.125 7862 7864 2 -21547.8 -7648.65 196.12 0.125 7863 7865 2 -21547.4 -7649.35 196.02 0.125 7864 7866 2 -21546.6 -7650.47 199.73 0.125 7865 7867 2 -21546.6 -7650.73 199.68 0.125 7866 7868 2 -21545 -7653.06 199.15 0.125 7867 7869 2 -21544.7 -7653.29 199.08 0.125 7868 7870 2 -21544.6 -7654.01 198.95 0.125 7869 7871 2 -21544.9 -7655.39 198.75 0.125 7870 7872 2 -21545.3 -7657.41 198.51 0.125 7871 7873 2 -21545.5 -7659.83 198.13 0.125 7872 7874 2 -21545.1 -7660.54 197.97 0.125 7873 7875 2 -21544.8 -7660.49 197.95 0.125 7874 7876 2 -21543.5 -7661.31 197.7 0.125 7875 7877 2 -21542.3 -7663.45 197.23 0.125 7876 7878 2 -21542 -7664.73 196.99 0.125 7877 7879 2 -21541.2 -7667.78 196.41 0.125 7878 7880 2 -21540.8 -7667.97 196.35 0.125 7879 7881 2 -21540.8 -7668.22 196.3 0.125 7880 7882 2 -21540.3 -7668.95 196.14 0.125 7881 7883 2 -21540.3 -7669.2 196.09 0.125 7882 7884 2 -21540.5 -7670.07 195.96 0.125 7883 7885 2 -21540.4 -7670.28 195.92 0.125 7884 7886 2 -21539.7 -7670.97 195.75 0.125 7885 7887 2 -21539.2 -7672.39 195.46 0.125 7886 7888 2 -21538.9 -7672.61 195.39 0.125 7887 7889 2 -21538.2 -7673.33 195.21 0.125 7888 7890 2 -21537.8 -7673.73 195.11 0.125 7889 7891 2 -21537.3 -7674.71 194.9 0.125 7890 7892 2 -21537 -7676.16 195.49 0.125 7891 7893 2 -21536.3 -7676.83 195.31 0.125 7892 7894 2 -21535.3 -7677.26 195.63 0.125 7893 7895 2 -21534 -7677.64 197.72 0.125 7894 7896 2 -21533.4 -7677.82 197.64 0.125 7895 7897 2 -21532.3 -7677.79 197.54 0.125 7896 7898 2 -21532.2 -7678.35 197.43 0.125 7897 7899 2 -21531.4 -7679.51 197.17 0.125 7898 7900 2 -21531.1 -7679.5 197.15 0.125 7899 7901 2 -21530.5 -7679.9 197.02 0.125 7900 7902 2 -21530.2 -7679.81 197.01 0.125 7901 7903 2 -21529.3 -7680.94 197.99 0.125 7902 7904 2 -21529 -7682.55 197.9 0.125 7903 7905 2 -21528.5 -7683.61 198.49 0.125 7904 7906 2 -21528 -7684.85 198.24 0.125 7905 7907 2 -21527.5 -7685.85 198.03 0.125 7906 7908 2 -21527.2 -7686.3 197.93 0.125 7907 7909 2 -21525.8 -7685.87 197.06 0.125 7908 7910 2 -21524.5 -7687.79 198.37 0.125 7909 7911 2 -21524 -7688.24 196.59 0.125 7910 7912 2 -21523.6 -7690 196.27 0.125 7911 7913 2 -21523.1 -7692.59 195.8 0.125 7912 7914 2 -21522 -7694.5 198.68 0.125 7913 7915 2 -21522 -7694.45 198.41 0.125 7914 7916 2 -21521.3 -7696.82 198.45 0.125 7915 7917 2 -21521 -7697.01 198.39 0.125 7916 7918 2 -21519.2 -7699.86 199.23 0.125 7917 7919 2 -21518 -7701.61 200.93 0.125 7918 7920 2 -21516.3 -7702.95 200.91 0.125 7919 7921 2 -21516.2 -7702.99 201.15 0.125 7920 7922 2 -21515.6 -7704.78 201.08 0.125 7921 7923 2 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9152 2 -22017 -8039.33 38.36 0.25 9151 9153 2 -22019.2 -8043.17 33.35 0.25 9152 9154 2 -22022.4 -8048.29 31.84 0.25 9153 9155 2 -22024 -8051.42 28.03 0.25 9154 9156 2 -22026.7 -8054.47 27.46 0.25 9155 9157 2 -22027.8 -8056.23 27.27 0.25 9156 9158 2 -22033.5 -8072.68 24.21 0.25 9157 9159 2 -22036 -8074.7 19.81 0.25 9158 9160 2 -22036.5 -8077.13 19.22 0.25 9159 9161 2 -22037.2 -8080.58 16.53 0.25 9160 9162 2 -22040.3 -8084.4 13.8 0.25 9161 9163 2 -22041.7 -8086.73 13.28 0.25 9162 9164 2 -22042.7 -8090.04 11.12 0.25 9163 9165 2 -22042.8 -8093.19 8.99 0.25 9164 9166 2 -22042.6 -8097.18 5.67 0.25 9165 9167 2 -22042.3 -8100.28 5.17 0.125 9166 9168 2 -22043 -8103.55 3.07 0.125 9167 9169 2 -22044.7 -8105.33 -0.74 0.125 9168 9170 2 -22045.2 -8109.08 -1.32 0.125 9169 9171 2 -22045.3 -8111.72 -1.86 0.125 9170 9172 2 -22045.5 -8113.06 -3.62 0.125 9171 9173 2 -22045.2 -8114.42 -3.48 0.125 9172 9174 2 -22046.4 -8117.44 -4.31 0.125 9173 9175 2 -22046.2 -8119.5 -6.3 0.125 9174 9176 2 -22047.2 -8122.38 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56.16 0.125 9199 9201 2 -21999.9 -7991.85 55.95 0.125 9200 9202 2 -21999.5 -7994.52 54.5 0.125 9201 9203 2 -21999.6 -7996.77 56.51 0.125 9202 9204 2 -21999.2 -7998.72 56.83 0.125 9203 9205 2 -21998.3 -8002.06 56.76 0.125 9204 9206 2 -21997.1 -8003.86 57.44 0.125 9205 9207 2 -21996.7 -8006.04 57.77 0.125 9206 9208 2 -21995.4 -8008.22 58.98 0.125 9207 9209 2 -21995.2 -8009.56 59.11 0.125 9208 9210 2 -21987.6 -8015.72 58.21 0.125 9209 9211 2 -21996.4 -8010.85 53.14 0.125 9209 9212 2 -21996.5 -8014.17 51.89 0.125 9211 9213 2 -21998.3 -8016.61 51.66 0.125 9212 9214 2 -21999.8 -8017.5 49.32 0.125 9213 9215 2 -22001 -8019.08 47.89 0.125 9214 9216 2 -22000.8 -8020.47 46.71 0.125 9215 9217 2 -22000.9 -8021.93 45.69 0.125 9216 9218 2 -21999.8 -8025.56 44.4 0.125 9217 9219 2 -21999.2 -8022.98 42.59 0.125 9218 9220 2 -21999.1 -8021.66 42.8 0.125 9219 9221 2 -21998.8 -7974.52 64.48 0.125 8854 9222 2 -21997.7 -7976.93 65.44 0.125 9221 9223 2 -21998 -7975.68 64.16 0.125 9222 9224 2 -21997 -7977.12 66.98 0.125 9223 9225 2 -21995.8 -7981.33 70.49 0.125 9224 9226 2 -21995.2 -7982.82 70.19 0.125 9225 9227 2 -21995 -7985.47 76.18 0.125 9226 9228 2 -21994 -7988.38 82.63 0.125 9227 9229 2 -21997.3 -7987.65 84.02 0.125 9228 9230 2 -21997.1 -7989.2 83.75 0.125 9229 9231 2 -21996.6 -7990.49 83.61 0.125 9230 9232 2 -21996.5 -7992.78 84.48 0.125 9231 9233 2 -21996.2 -7995.35 88.36 0.125 9232 9234 2 -21996 -7997.15 87.81 0.125 9233 9235 2 -21996.7 -8001.86 92.28 0.125 9234 9236 2 -21997.4 -8004.48 94.33 0.125 9235 9237 2 -21998.3 -8006.86 96.32 0.125 9236 9238 2 -21998.8 -8008.66 98.47 0.125 9237 9239 2 -21999.4 -8008.68 93.68 0.125 9238 9240 2 -21999 -8010.67 95.79 0.125 9239 9241 2 -21999.5 -8012.71 96.14 0.125 9240 9242 2 -22000.5 -8013.44 96.57 0.125 9241 9243 2 -22000.2 -8015.42 100.2 0.125 9242 9244 2 -22001.7 -8016.36 102.93 0.125 9243 9245 2 -22003.3 -8016.59 103.05 0.125 9244 9246 2 -22005.3 -8016.86 103.19 0.125 9245 9247 2 -22007.7 -8016.87 104.48 0.125 9246 9248 2 -22010.5 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132.67 0.37 9915 9917 3 -22018.1 -7352.67 132.72 0.37 9916 9918 3 -22019.5 -7352.84 132.63 0.37 9917 9919 3 -22020.8 -7352.3 132.84 0.37 9918 9920 3 -22022.7 -7350.84 133.26 0.37 9919 9921 3 -22022.6 -7348.69 132.15 0.125 9920 9922 3 -22022.2 -7347.3 132.35 0.125 9921 9923 3 -22022.3 -7347.02 132.4 0.125 9922 9924 3 -22022 -7345.43 132.64 0.125 9923 9925 3 -22022 -7345.17 132.69 0.125 9924 9926 3 -22021.5 -7343.43 132.64 0.125 9925 9927 3 -22021.2 -7342.1 132.83 0.125 9926 9928 3 -22021.4 -7340.84 133.06 0.125 9927 9929 3 -22021.7 -7340.54 133.13 0.125 9928 9930 3 -22022.4 -7339.89 133.3 0.125 9929 9931 3 -21927 -7393.03 202.75 0.865 9575 9932 3 -21928.2 -7393.8 199.97 0.865 9931 9933 3 -21928.3 -7393.58 198.63 0.865 9932 9934 3 -21929.7 -7395.3 195.02 0.865 9933 9935 3 -21929.8 -7395.28 194.95 0.865 9934 9936 3 -21932 -7397.47 194.76 0.865 9935 9937 3 -21933.8 -7399.34 193.24 0.865 9936 9938 3 -21935.7 -7400.31 192.87 0.865 9937 9939 3 -21936.1 -7400.19 191.97 0.865 9938 9940 3 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9964 3 -21951.2 -7387.32 177.23 0.37 9963 9965 3 -21952.7 -7386 176.22 0.37 9964 9966 3 -21952.9 -7386.06 176.23 0.37 9965 9967 3 -21954.4 -7385.79 176.36 0.37 9966 9968 3 -21956.3 -7386.36 176.42 0.37 9967 9969 3 -21956.8 -7386.23 176.5 0.37 9968 9970 3 -21958.9 -7385.51 176.8 0.37 9969 9971 3 -21961.1 -7386.77 175.65 0.37 9970 9972 3 -21963.3 -7386.86 175.64 0.37 9971 9973 3 -21963.3 -7386.77 175.14 0.37 9972 9974 3 -21967 -7386.27 169.04 0.37 9973 9975 3 -21969 -7386.09 169.26 0.37 9974 9976 3 -21971.3 -7386.4 167.39 0.37 9975 9977 3 -21972.5 -7386.09 167.55 0.37 9976 9978 3 -21974.4 -7385.76 165.36 0.37 9977 9979 3 -21975.4 -7385.5 167.5 0.37 9978 9980 3 -21978.2 -7385.1 165.63 0.25 9979 9981 3 -21979.9 -7385.02 165.28 0.37 9980 9982 3 -21982 -7384.27 162.34 0.37 9981 9983 3 -21984.3 -7384.27 159.83 0.25 9982 9984 3 -21986 -7384.52 159.91 0.37 9983 9985 3 -21987.9 -7385.14 159.97 0.37 9984 9986 3 -21990.5 -7385.01 155.37 0.37 9985 9987 3 -21990.6 -7384.76 153.87 0.37 9986 9988 3 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141.17 0.37 10010 10012 3 -22031.6 -7378.99 141.35 0.37 10011 10013 3 -22031.6 -7379.27 141.3 0.37 10012 10014 3 -22032.9 -7379.52 141.34 0.37 10013 10015 3 -22034.1 -7379.43 141.42 0.37 10014 10016 3 -22035 -7378.83 141.61 0.37 10015 10017 3 -22037.6 -7378.07 139.77 0.37 10016 10018 3 -22038.6 -7376.95 140.05 0.37 10017 10019 3 -22038.9 -7377.01 140.06 0.37 10018 10020 3 -22040.5 -7375.99 140.38 0.37 10019 10021 3 -22040.5 -7376.22 140.34 0.37 10020 10022 3 -22043.1 -7375.49 140.9 0.25 10021 10023 3 -22043.4 -7375.5 140.93 0.25 10022 10024 3 -22046.2 -7375.44 141.2 0.25 10023 10025 3 -22046.5 -7375.23 141.26 0.25 10024 10026 3 -22049.5 -7376.06 141.57 0.25 10025 10027 3 -22049.5 -7375.79 141.62 0.25 10026 10028 3 -22054.6 -7375.76 141.21 0.25 10027 10029 3 -22054.6 -7375.78 141.35 0.25 10028 10030 3 -22055.9 -7376.41 142.21 0.25 10029 10031 3 -22058.1 -7376.53 142.4 0.25 10030 10032 3 -22059.2 -7376.83 142.96 0.125 10031 10033 3 -21941.3 -7388.02 179.77 0.37 9953 10034 3 -21943.2 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0.37 10101 10103 3 -21967.7 -7447.91 175.67 0.37 10102 10104 3 -21968.5 -7448.54 175.64 0.37 10103 10105 3 -21969.3 -7448.65 175.7 0.37 10104 10106 3 -21970 -7450.34 175.48 0.37 10105 10107 3 -21970.6 -7452.03 175.26 0.37 10106 10108 3 -21970.8 -7454.44 175.23 0.37 10107 10109 3 -21971.1 -7454.51 175.25 0.37 10108 10110 3 -21972.1 -7456.35 172.81 0.37 10109 10111 3 -21972.8 -7457.83 172.63 0.37 10110 10112 3 -21973.5 -7459.26 172.4 0.37 10111 10113 3 -21965.8 -7459.63 171.62 0.37 10112 10114 3 -21965.9 -7460.7 171.45 0.37 10113 10115 3 -21967.1 -7462.22 171.31 0.37 10114 10116 3 -21967.2 -7463.27 171.15 0.37 10115 10117 3 -21967.1 -7464.2 168.78 0.37 10116 10118 3 -21966.7 -7465.93 168.2 0.37 10117 10119 3 -21966.5 -7466.9 168 0.37 10118 10120 3 -21966.3 -7467.92 167.59 0.37 10119 10121 3 -21966.1 -7467.79 167.06 0.37 10120 10122 3 -21965.7 -7468.52 166.9 0.37 10121 10123 3 -21965.7 -7468.47 166.63 0.37 10122 10124 3 -21965.1 -7469.17 164.99 0.37 10123 10125 3 -21964.7 -7471.78 165.07 0.37 10124 10126 3 -21963.9 -7473.84 161.95 0.37 10125 10127 3 -21963 -7475.38 162.19 0.37 10126 10128 3 -21962.6 -7478.12 161.27 0.37 10127 10129 3 -21962.3 -7478.08 161.25 0.37 10128 10130 3 -21962.2 -7479.08 161 0.37 10129 10131 3 -21962.3 -7480.36 160.62 0.37 10130 10132 3 -21962.1 -7481.61 160.05 0.37 10131 10133 3 -21962.1 -7481.86 160.01 0.37 10132 10134 3 -21962.4 -7482.98 159.86 0.37 10133 10135 3 -21962.8 -7484.09 159.71 0.37 10134 10136 3 -21963 -7485.98 159.42 0.37 10135 10137 3 -21963.2 -7486.55 159.34 0.37 10136 10138 3 -21963.2 -7488.36 159.04 0.37 10137 10139 3 -21963.2 -7488.6 158.77 0.37 10138 10140 3 -21963.9 -7490.51 155.39 0.37 10139 10141 3 -21963.9 -7490.82 155.33 0.37 10140 10142 3 -21963.8 -7493.25 153.93 0.37 10141 10143 3 -21964.5 -7495.48 150.56 0.37 10142 10144 3 -21964.2 -7497.46 149.49 0.37 10143 10145 3 -21964.3 -7497.55 148.67 0.37 10144 10146 3 -21964.3 -7498.88 148.46 0.37 10145 10147 3 -21964.3 -7499.12 148.41 0.37 10146 10148 3 -21965.3 -7501.94 146.6 0.37 10147 10149 3 -21965.3 -7502.1 145.85 0.37 10148 10150 3 -21965.3 -7504.24 144.29 0.37 10149 10151 3 -21965.2 -7504.76 144.2 0.37 10150 10152 3 -21964.6 -7507.14 142.63 0.37 10151 10153 3 -21964.7 -7507.05 142.09 0.37 10152 10154 3 -21964.5 -7508.93 143.86 0.37 10153 10155 3 -21964.9 -7508.71 142.39 0.37 10154 10156 3 -21964.6 -7510.75 141.98 0.37 10155 10157 3 -21965 -7511.88 141.83 0.25 10156 10158 3 -21966.1 -7513.37 141.69 0.25 10157 10159 3 -21966.3 -7513.73 141.65 0.25 10158 10160 3 -21966.5 -7514.54 141.53 0.25 10159 10161 3 -21966.8 -7514.32 141.6 0.37 10160 10162 3 -21967 -7516.22 141.31 0.37 10161 10163 3 -21967.5 -7516.64 137.22 0.37 10162 10164 3 -21967.7 -7517.18 137.15 0.37 10163 10165 3 -21968.1 -7518.09 137.04 0.25 10164 10166 3 -21968.1 -7519.64 136.78 0.25 10165 10167 3 -21967.9 -7520.93 136.55 0.37 10166 10168 3 -21968.1 -7522 134.87 0.37 10167 10169 3 -21968.8 -7523.13 134.43 0.37 10168 10170 3 -21969 -7523.95 134.15 0.25 10169 10171 3 -21969 -7523.86 133.6 0.25 10170 10172 3 -21969.7 -7525.39 132.62 0.25 10171 10173 3 -21970.1 -7526.58 131.3 0.25 10172 10174 3 -21971.2 -7437.49 179.1 0.62 10094 10175 3 -21972.6 -7437.15 179.29 0.495 10174 10176 3 -21974.4 -7437.72 177.81 0.37 10175 10177 3 -21976.1 -7439 179.31 0.37 10176 10178 3 -21976.3 -7439.79 179.2 0.37 10177 10179 3 -21976.4 -7440.88 179.03 0.37 10178 10180 3 -21977.2 -7441.78 178.95 0.37 10179 10181 3 -21977.9 -7444.48 178.58 0.37 10180 10182 3 -21977.9 -7444.79 178.52 0.37 10181 10183 3 -21978 -7446.11 178.31 0.37 10182 10184 3 -21979.1 -7448.38 178.04 0.37 10183 10185 3 -21980.2 -7449.35 177.98 0.37 10184 10186 3 -21981.7 -7450.77 177.38 0.37 10185 10187 3 -21982.3 -7452.44 176.85 0.37 10186 10188 3 -21982.5 -7453.23 176.74 0.37 10187 10189 3 -21983.3 -7454.09 176.29 0.25 10188 10190 3 -21983.3 -7454.33 176.24 0.37 10189 10191 3 -21983.7 -7455.22 176.14 0.37 10190 10192 3 -21984.4 -7456.85 175.68 0.25 10191 10193 3 -21985.1 -7458.26 175.49 0.25 10192 10194 3 -21985.1 -7457.98 175.54 0.25 10193 10195 3 -21985.8 -7458.87 175.46 0.37 10194 10196 3 -21987.6 -7460.29 176.01 0.25 10195 10197 3 -21989 -7460.74 176.06 0.25 10196 10198 3 -21991.4 -7461.57 176.15 0.37 10197 10199 3 -21975.2 -7437.14 183.54 0.37 10175 10200 3 -21976.6 -7437.09 183.68 0.37 10199 10201 3 -21977.3 -7438.28 183.55 0.37 10200 10202 3 -21978 -7439.72 186.73 0.37 10201 10203 3 -21978.7 -7439.01 186.9 0.37 10202 10204 3 -21980 -7438.56 189.39 0.37 10203 10205 3 -21980.3 -7438.61 189.41 0.37 10204 10206 3 -21981.3 -7439.54 189.35 0.37 10205 10207 3 -21983.8 -7440.15 189.48 0.37 10206 10208 3 -21985.2 -7441.64 193.89 0.37 10207 10209 3 -21987.2 -7441.64 194.08 0.37 10208 10210 3 -21988.6 -7441.58 194.22 0.37 10209 10211 3 -21990.2 -7441.02 197.61 0.37 10210 10212 3 -21991.5 -7441.48 197.66 0.37 10211 10213 3 -21992.3 -7442.37 197.58 0.37 10212 10214 3 -21993.2 -7443.83 197.43 0.37 10213 10215 3 -21994 -7446.48 201.14 0.37 10214 10216 3 -21995.3 -7447.2 201.14 0.37 10215 10217 3 -21996.6 -7447.86 202.34 0.37 10216 10218 3 -21996.8 -7448.95 202.17 0.37 10217 10219 3 -21998.2 -7448.87 202.33 0.37 10218 10220 3 -21998.2 -7448.58 202.39 0.37 10219 10221 3 -22000 -7449.85 206.71 0.37 10220 10222 3 -22000.4 -7450.69 206.61 0.37 10221 10223 3 -22002.6 -7451.79 206.63 0.25 10222 10224 3 -22004.3 -7451.53 206.84 0.37 10223 10225 3 -22006 -7451.68 203.38 0.25 10224 10226 3 -22007.2 -7451.3 203.55 0.25 10225 10227 3 -22008.2 -7452 203.53 0.37 10226 10228 3 -22009.7 -7453.93 210.32 0.37 10227 10229 3 -22009.7 -7453.63 210.37 0.37 10228 10230 3 -22011.6 -7454.45 210.59 0.37 10229 10231 3 -22013.9 -7454.78 210.75 0.37 10230 10232 3 -22016.4 -7455.14 210.88 0.25 10231 10233 3 -22016.3 -7455.17 211.06 0.25 10232 10234 3 -22019.4 -7455.65 211.59 0.25 10233 10235 3 -22019.4 -7455.4 211.63 0.25 10234 10236 3 -22022.7 -7456.26 211.17 0.25 10235 10237 3 -22027.8 -7456.43 211.62 0.25 10236 10238 3 -22028.1 -7456.46 211.65 0.25 10237 10239 3 -22032.8 -7455.63 211.01 0.25 10238 10240 3 -22032.8 -7455.34 211.06 0.25 10239 10241 3 -22037.5 -7455.43 209.93 0.25 10240 10242 3 -21921.7 -7383.42 213.66 1.24 1 10243 3 -21923.2 -7382.39 209.65 1.24 10242 10244 3 -21924.1 -7381.74 209.85 1.24 10243 10245 3 -21925.6 -7380.85 210.14 1.24 10244 10246 3 -21926.9 -7379.96 210.4 1.24 10245 10247 3 -21928.8 -7378.34 207.58 0.99 10246 10248 3 -21930.9 -7377.29 207.91 0.99 10247 10249 3 -21932.4 -7375.64 205.16 1.24 10248 10250 3 -21934 -7374.55 205.45 1.115 10249 10251 3 -21935.5 -7373.21 202.78 0.745 10250 10252 3 -21936.7 -7372.54 203 0.495 10251 10253 3 -21938.6 -7372 201.76 0.62 10252 10254 3 -21939.5 -7371.59 201.91 1.115 10253 10255 3 -21939.5 -7371.88 201.86 1.115 10254 10256 3 -21940.8 -7372.35 201.91 1.115 10255 10257 3 -21942 -7371.97 202.08 0.865 10256 10258 3 -21943.4 -7370.76 200.38 0.745 10257 10259 3 -21944.8 -7370.95 200.48 0.745 10258 10260 3 -21946.8 -7370.94 200.66 0.62 10259 10261 3 -21948.3 -7370.37 200.9 0.62 10260 10262 3 -21950.1 -7369.7 197.33 0.62 10261 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132.48 0.37 10284 10286 3 -21971.9 -7371.04 132.03 0.37 10285 10287 3 -21972.8 -7369.79 130.31 0.37 10286 10288 3 -21974.7 -7368.19 122.62 0.25 10287 10289 3 -21976 -7366.19 113.44 0.25 10288 10290 3 -21976 -7366.76 119.81 0.25 10289 10291 3 -21974.5 -7365.27 119.92 0.495 10290 10292 3 -21974.8 -7362.64 114.04 0.495 10291 10293 3 -21974.6 -7362.33 114.07 0.495 10292 10294 3 -21973.6 -7359.71 109.52 0.37 10293 10295 3 -21972.6 -7360.64 109.27 0.37 10294 10296 3 -21972.3 -7358 102.4 0.37 10295 10297 3 -21971 -7356.06 97.33 0.37 10296 10298 3 -21970.5 -7354.05 97.36 0.37 10297 10299 3 -21970.7 -7352.83 97.96 0.125 10298 10300 3 -21952.5 -7368.46 197.76 0.62 10263 10301 3 -21954.2 -7368.42 197.93 0.62 10300 10302 3 -21956.2 -7368.43 198.11 0.62 10301 10303 3 -21957.5 -7367.27 198.42 0.62 10302 10304 3 -21957.5 -7367.01 198.47 0.62 10303 10305 3 -21958.7 -7366.29 194.9 0.62 10304 10306 3 -21959.3 -7367.69 196.16 0.495 10305 10307 3 -21960.6 -7368.11 196.22 0.495 10306 10308 3 -21961.9 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10331 3 -21981.9 -7376.27 148.63 0.25 10330 10332 3 -21981.8 -7376.51 148.58 0.25 10331 10333 3 -21983.1 -7377.19 148.15 0.25 10332 10334 3 -21984.6 -7377.83 145.87 0.25 10333 10335 3 -21985 -7378.95 145.61 0.25 10334 10336 3 -21986.8 -7379.68 145.09 0.25 10335 10337 3 -21988.2 -7379 142.85 0.25 10336 10338 3 -21990.1 -7378.81 140.32 0.25 10337 10339 3 -21991.8 -7379.96 137.5 0.25 10338 10340 3 -21993 -7381.56 136.39 0.25 10339 10341 3 -21993 -7381.52 136.15 0.25 10340 10342 3 -21994.7 -7382.21 135.12 0.25 10341 10343 3 -21995.7 -7382.21 132.88 0.25 10342 10344 3 -21995.8 -7382.03 131.81 0.25 10343 10345 3 -21997.7 -7382.79 129.67 0.25 10344 10346 3 -21999.2 -7383.45 127.58 0.25 10345 10347 3 -22000.2 -7384.72 130.65 0.25 10346 10348 3 -22000.3 -7384.59 129.82 0.25 10347 10349 3 -22000.1 -7386.16 128.06 0.25 10348 10350 3 -21999.8 -7387.92 127.74 0.25 10349 10351 3 -22001.9 -7381.27 117.38 0.25 10348 10352 3 -22002.7 -7380.12 117.64 0.25 10351 10353 3 -22003 -7380.16 117.66 0.25 10352 10354 3 -21966.1 -7369.48 192.55 0.62 10310 10355 3 -21968.1 -7369.54 192.73 0.495 10354 10356 3 -21969.8 -7369.24 192.94 0.495 10355 10357 3 -21971.6 -7368.7 193.2 0.495 10356 10358 3 -21972.8 -7368.64 193.31 0.495 10357 10359 3 -21973 -7368.61 193.35 0.495 10358 10360 3 -21975 -7368.91 193.48 0.495 10359 10361 3 -21975.6 -7368.1 190.18 0.495 10360 10362 3 -21976.9 -7368.85 190.18 0.37 10361 10363 3 -21977.3 -7369.14 188.54 0.37 10362 10364 3 -21975.1 -7369.05 188.35 0.37 10363 10365 3 -21977.5 -7368.88 186.63 0.37 10364 10366 3 -21977.6 -7368.8 186.17 0.37 10365 10367 3 -21977.4 -7367.18 184.9 0.37 10366 10368 3 -21978.4 -7366.74 182.89 0.37 10367 10369 3 -21978.5 -7366.66 182.36 0.37 10368 10370 3 -21978.8 -7367.48 179.28 0.37 10369 10371 3 -21980.9 -7367.29 177.78 0.37 10370 10372 3 -21981 -7367.13 176.83 0.37 10371 10373 3 -21982.5 -7367.43 175.63 0.37 10372 10374 3 -21982.7 -7367.5 175.64 0.37 10373 10375 3 -21984.5 -7366.58 173.5 0.37 10374 10376 3 -21986.2 -7366.63 173.65 0.37 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148.91 0.37 10398 10400 3 -22012.5 -7356.46 148.93 0.37 10399 10401 3 -22013.7 -7356.16 149.1 0.37 10400 10402 3 -22014.1 -7355.42 149.26 0.37 10401 10403 3 -22014.1 -7355.16 149.3 0.37 10402 10404 3 -22015.4 -7354.31 146.19 0.37 10403 10405 3 -22015.3 -7354.52 146.15 0.37 10404 10406 3 -22016.5 -7353.97 146.35 0.37 10405 10407 3 -22018.8 -7353.85 146.59 0.37 10406 10408 3 -22019.5 -7354.53 146.54 0.37 10407 10409 3 -22020.6 -7353.22 143.07 0.37 10408 10410 3 -22022 -7352.92 143.26 0.37 10409 10411 3 -22023.3 -7352.57 136.94 0.37 10410 10412 3 -22023.6 -7352.65 136.96 0.37 10411 10413 3 -22024.9 -7352.86 134.16 0.37 10412 10414 3 -22026.2 -7352.44 129.25 0.37 10413 10415 3 -22026.2 -7352.41 129.1 0.37 10414 10416 3 -22022.3 -7352.32 131.79 0.37 10415 10417 3 -22022.4 -7352.3 131.71 0.37 10416 10418 3 -22021.4 -7350.79 131.66 0.37 10417 10419 3 -22019.1 -7350.01 126.05 0.37 10418 10420 3 -22018.7 -7350.18 122.85 0.37 10419 10421 3 -21977.4 -7368.37 191.9 0.495 10361 10422 3 -21979.2 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3 -22049.2 -7370.77 187.51 0.37 10467 10469 3 -22051.3 -7372.7 185.75 0.25 10468 10470 3 -22052.7 -7374.3 185.62 0.25 10469 10471 3 -22056.3 -7377.22 183.38 0.25 10470 10472 3 -22057 -7377.88 183.33 0.25 10471 10473 3 -22057.3 -7378.12 183.32 0.25 10472 10474 3 -22057.4 -7379.47 183.1 0.25 10473 10475 3 -22059 -7379.69 178.35 0.25 10474 10476 3 -22061.1 -7380.84 178.36 0.25 10475 10477 3 -22061.1 -7380.76 177.86 0.25 10476 10478 3 -22064 -7380.94 173.24 0.25 10477 10479 3 -22067.8 -7381.66 173.48 0.25 10478 10480 3 -22072 -7383.21 173.62 0.25 10479 10481 3 -22074.4 -7384.43 173.64 0.25 10480 10482 3 -21960.3 -7366.73 199.27 0.495 10305 10483 3 -21962.1 -7366.37 199.49 0.495 10482 10484 3 -21962.9 -7364.75 198.7 0.495 10483 10485 3 -21963.6 -7364.05 198.88 0.495 10484 10486 3 -21964.5 -7363.13 199.12 0.495 10485 10487 3 -21964.6 -7362.92 199.16 0.495 10486 10488 3 -21966.5 -7363.13 199.31 0.495 10487 10489 3 -21968.4 -7361.92 198.77 0.37 10488 10490 3 -21968.5 -7361.18 198.9 0.37 10489 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0.37 10512 10514 3 -21975.8 -7343.71 202.99 0.37 10513 10515 3 -21976.2 -7342.69 203.14 0.37 10514 10516 3 -21978.5 -7341.02 206.06 0.37 10515 10517 3 -21978.9 -7340.32 206.21 0.37 10516 10518 3 -21980.2 -7339.42 206.48 0.37 10517 10519 3 -21980.8 -7338.98 206.61 0.37 10518 10520 3 -21980.8 -7338.73 206.65 0.37 10519 10521 3 -21981.2 -7338.01 206.81 0.37 10520 10522 3 -21983 -7336.92 207.16 0.37 10521 10523 3 -21984.6 -7337.66 207.18 0.37 10522 10524 3 -21984.7 -7337.41 207.23 0.37 10523 10525 3 -21985 -7337.21 207.29 0.37 10524 10526 3 -21985 -7336.95 207.34 0.37 10525 10527 3 -21985.8 -7335.97 209.25 0.37 10526 10528 3 -21986 -7334.97 209.43 0.37 10527 10529 3 -21987.8 -7333.93 209.78 0.37 10528 10530 3 -21989 -7333.81 209.9 0.37 10529 10531 3 -21990.3 -7332.81 209.43 0.37 10530 10532 3 -21991.5 -7331.9 209.7 0.37 10531 10533 3 -21994.3 -7330.44 210.2 0.37 10532 10534 3 -21996 -7329.9 210.46 0.37 10533 10535 3 -21998 -7328.98 212.89 0.37 10534 10536 3 -22000 -7328.94 213.08 0.37 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243.26 0.37 10581 10583 3 -22048.4 -7289.02 243.53 0.37 10582 10584 3 -22048.7 -7288.75 243.6 0.37 10583 10585 3 -22050 -7288.69 245.45 0.37 10584 10586 3 -22052.3 -7288.09 246.58 0.125 10585 10587 3 -22052.2 -7288.15 246.99 0.125 10586 10588 3 -22055.5 -7286.96 251.63 0.125 10587 10589 3 -21972.6 -7352.61 197.08 0.37 10500 10590 3 -21973.4 -7350.85 197.26 0.37 10589 10591 3 -21974 -7348.45 192.56 0.37 10590 10592 3 -21969.2 -7347.82 192.22 0.37 10591 10593 3 -21969.3 -7347 192.36 0.37 10592 10594 3 -21970.2 -7345.4 192.71 0.37 10593 10595 3 -21970.2 -7345.1 192.76 0.37 10594 10596 3 -21971.8 -7343.47 189.84 0.37 10595 10597 3 -21973.1 -7342.92 190.05 0.25 10596 10598 3 -21973.4 -7341.12 190.38 0.37 10597 10599 3 -21975.5 -7339.59 187.65 0.37 10598 10600 3 -21975.9 -7339.18 187.75 0.37 10599 10601 3 -21975.7 -7338.61 187.83 0.37 10600 10602 3 -21975.7 -7338.37 187.87 0.37 10601 10603 3 -21976.1 -7337.36 188.08 0.37 10602 10604 3 -21977.3 -7337.05 188.24 0.37 10603 10605 3 -21978 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153.53 0.37 10764 10766 3 -21876.5 -7257.07 150.8 0.37 10765 10767 3 -21876.1 -7256.22 150.96 0.37 10766 10768 3 -21874.1 -7256.4 150.77 0.37 10767 10769 3 -21873.9 -7256.36 150.77 0.37 10768 10770 3 -21872.9 -7254.47 148.61 0.25 10769 10771 3 -21870.8 -7253.28 148.61 0.25 10770 10772 3 -21865.5 -7250.49 142.71 0.25 10771 10773 3 -21865 -7250.54 141.71 0.37 10772 10774 3 -21909.1 -7312.57 171.07 0.37 10714 10775 3 -21909.3 -7312.58 171.09 0.37 10774 10776 3 -21909.9 -7310.83 168.26 0.37 10775 10777 3 -21910 -7310.08 168.39 0.37 10776 10778 3 -21910.9 -7309.18 165.61 0.37 10777 10779 3 -21911.2 -7307.91 164.3 0.37 10778 10780 3 -21912.4 -7307.56 164.46 0.37 10779 10781 3 -21912.4 -7307.33 164.51 0.37 10780 10782 3 -21911.9 -7305.92 163.08 0.37 10781 10783 3 -21912.3 -7303.9 157.67 0.37 10782 10784 3 -21913.6 -7303.7 156.76 0.37 10783 10785 3 -21913.5 -7302.46 156.09 0.37 10784 10786 3 -21914 -7300.07 151.59 0.37 10785 10787 3 -21913.7 -7300.04 151.57 0.37 10786 10788 3 -21913.4 -7298.88 154.2 0.37 10787 10789 3 -21912.8 -7297.13 151.28 0.37 10788 10790 3 -21912.9 -7296.89 151.32 0.37 10789 10791 3 -21913.7 -7296.76 151.42 0.37 10790 10792 3 -21915.1 -7295.42 151.77 0.37 10791 10793 3 -21915.2 -7295.09 151.4 0.37 10792 10794 3 -21915.4 -7293.38 152.37 0.37 10793 10795 3 -21915 -7292.57 152.66 0.37 10794 10796 3 -21914.7 -7290.09 146.62 0.25 10795 10797 3 -21914.8 -7289.13 147.08 0.25 10796 10798 3 -21915.1 -7288.71 147.65 0.37 10797 10799 3 -21915.2 -7288.45 147.71 0.37 10798 10800 3 -21915 -7286.38 142.44 0.37 10799 10801 3 -21915 -7285.11 142.65 0.37 10800 10802 3 -21914.7 -7284.75 142.69 0.37 10801 10803 3 -21914.7 -7283.03 139 0.25 10802 10804 3 -21914.8 -7282.75 139.05 0.25 10803 10805 3 -21914.8 -7282.51 139.09 0.25 10804 10806 3 -21914 -7280.89 138.35 0.37 10805 10807 3 -21914.4 -7279.98 139.09 0.37 10806 10808 3 -21914.4 -7279.73 139.14 0.37 10807 10809 3 -21915 -7276.27 136.14 0.37 10808 10810 3 -21916 -7275.17 136.41 0.37 10809 10811 3 -21916.8 -7275.29 136.47 0.37 10810 10812 3 -21916.8 -7275.01 136.52 0.37 10811 10813 3 -21917.3 -7274.29 136.68 0.37 10812 10814 3 -21917.3 -7274.01 136.73 0.37 10813 10815 3 -21918.4 -7273.16 135.17 0.37 10814 10816 3 -21918.4 -7273.42 135.12 0.37 10815 10817 3 -21919.6 -7272.6 135.38 0.37 10816 10818 3 -21920.3 -7271.91 135.56 0.37 10817 10819 3 -21920.7 -7271.73 135.62 0.37 10818 10820 3 -21922.2 -7270.22 132.06 0.37 10819 10821 3 -21923.3 -7269.69 134.03 0.25 10820 10822 3 -21923.3 -7269.73 134.28 0.25 10821 10823 3 -21925.1 -7267.93 128.52 0.25 10822 10824 3 -21927.3 -7268.84 128.57 0.25 10823 10825 3 -21929.2 -7269.16 128.7 0.25 10824 10826 3 -21930.5 -7267.73 130.18 0.25 10825 10827 3 -21932.5 -7267.26 125.57 0.25 10826 10828 3 -21934.3 -7266.77 125.52 0.25 10827 10829 3 -21934.4 -7266.54 125.57 0.25 10828 10830 3 -21936.1 -7265.13 125.45 0.25 10829 10831 3 -21903.8 -7333.31 173.46 0.37 10699 10832 3 -21905.1 -7332.5 169.32 0.37 10831 10833 3 -21907.1 -7331.77 166.52 0.37 10832 10834 3 -21907.4 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225.65 0.37 11107 11109 3 -21855.7 -7307.33 225.6 0.37 11108 11110 3 -21855.7 -7307.06 225.65 0.37 11109 11111 3 -21854.2 -7306.46 227.81 0.25 11110 11112 3 -21853.6 -7304.51 228.08 0.25 11111 11113 3 -21853.1 -7303.65 228.17 0.25 11112 11114 3 -21853.1 -7303.34 228.23 0.25 11113 11115 3 -21851.6 -7303.12 230.86 0.25 11114 11116 3 -21851.1 -7301.98 231.01 0.25 11115 11117 3 -21850.6 -7301.62 231.02 0.37 11116 11118 3 -21849.8 -7301.22 231.01 0.37 11117 11119 3 -21849.1 -7300.39 231.08 0.25 11118 11120 3 -21849.1 -7300.12 231.13 0.25 11119 11121 3 -21848.4 -7299.72 231.12 0.37 11120 11122 3 -21848.1 -7299.16 231.2 0.37 11121 11123 3 -21846.5 -7298.72 231.11 0.25 11122 11124 3 -21845.9 -7298.34 231.12 0.25 11123 11125 3 -21845.4 -7298 231.13 0.495 11124 11126 3 -21844.6 -7297.58 231.13 0.495 11125 11127 3 -21843.7 -7296.42 231.23 0.25 11126 11128 3 -21842.3 -7295.68 231.23 0.25 11127 11129 3 -21840.5 -7295.6 233.33 0.37 11128 11130 3 -21839.5 -7294.67 233.39 0.25 11129 11131 3 -21837.8 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233.59 0.37 11381 11383 3 -21892.4 -7289.16 233.71 0.37 11382 11384 3 -21892.4 -7288.87 233.76 0.37 11383 11385 3 -21892.3 -7288.09 233.88 0.37 11384 11386 3 -21892 -7288.01 233.86 0.37 11385 11387 3 -21891.8 -7286.88 233.64 0.37 11386 11388 3 -21891.9 -7286.62 233.69 0.37 11387 11389 3 -21891.7 -7285.56 233.85 0.37 11388 11390 3 -21891.9 -7284.49 234.04 0.37 11389 11391 3 -21892.3 -7283.23 234.29 0.37 11390 11392 3 -21892.3 -7283.01 234.33 0.37 11391 11393 3 -21892 -7281.7 236.57 0.37 11392 11394 3 -21891.9 -7280.88 236.69 0.37 11393 11395 3 -21891.9 -7280.61 236.74 0.37 11394 11396 3 -21892.1 -7279.57 236.92 0.37 11395 11397 3 -21892.1 -7279.32 236.97 0.37 11396 11398 3 -21891.5 -7277.4 237.22 0.37 11397 11399 3 -21891.3 -7277.08 237.26 0.37 11398 11400 3 -21890.7 -7276.5 237.3 0.37 11399 11401 3 -21890.8 -7276.2 237.36 0.37 11400 11402 3 -21891 -7274.48 238.12 0.37 11401 11403 3 -21890.8 -7273.43 238.28 0.37 11402 11404 3 -21890.9 -7273.16 238.33 0.37 11403 11405 3 -21891.2 -7272.19 238.52 0.37 11404 11406 3 -21891.9 -7271.74 238.66 0.37 11405 11407 3 -21892.2 -7271.23 238.77 0.37 11406 11408 3 -21892.3 -7270.47 238.91 0.37 11407 11409 3 -21892.1 -7269.65 239.03 0.37 11408 11410 3 -21891.9 -7269.36 239.06 0.37 11409 11411 3 -21891.7 -7268.5 239.18 0.37 11410 11412 3 -21891.5 -7268.24 239.2 0.37 11411 11413 3 -21891.6 -7267.42 239.34 0.37 11412 11414 3 -21891.7 -7266.93 239.44 0.37 11413 11415 3 -21891.5 -7266.37 239.51 0.37 11414 11416 3 -21891.2 -7266.06 239.53 0.37 11415 11417 3 -21891.1 -7264.96 239.71 0.37 11416 11418 3 -21891 -7262.62 240.3 0.37 11417 11419 3 -21891 -7260.78 240.6 0.37 11418 11420 3 -21891 -7260.47 240.65 0.37 11419 11421 3 -21891.1 -7259.95 240.75 0.37 11420 11422 3 -21890.7 -7258.36 240.97 0.37 11421 11423 3 -21890.5 -7257.24 242.81 0.25 11422 11424 3 -21890.5 -7257.22 242.71 0.25 11423 11425 3 -21890.4 -7255.44 242.98 0.25 11424 11426 3 -21890.6 -7254.37 243.17 0.25 11425 11427 3 -21890.4 -7253.81 243.25 0.25 11426 11428 3 -21890.4 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3 -21897.5 -7395.97 206.15 1.61 11588 11590 3 -21895.8 -7397.28 199.43 1.985 11589 11591 3 -21894.4 -7397.32 199.2 1.985 11590 11592 3 -21894.7 -7397.62 194.88 1.735 11591 11593 3 -21895.1 -7398.91 194.04 1.24 11592 11594 3 -21894.3 -7400.4 193.72 0.99 11593 11595 3 -21894.8 -7401.01 190.73 0.865 11594 11596 3 -21894.2 -7402.31 192.34 0.62 11595 11597 3 -21893.7 -7403.13 189.44 0.495 11596 11598 3 -21893.9 -7404.29 188.23 0.495 11597 11599 3 -21893.6 -7405.13 187.12 0.495 11598 11600 3 -21893.5 -7405.97 185.71 0.62 11599 11601 3 -21893.8 -7405.98 185.74 0.62 11600 11602 3 -21894 -7406.96 181.74 0.495 11601 11603 3 -21894.2 -7407.24 181.72 0.495 11602 11604 3 -21894.2 -7408.37 180.54 0.495 11603 11605 3 -21893.4 -7409.89 180.22 0.495 11604 11606 3 -21893.4 -7411.14 178.05 0.495 11605 11607 3 -21893.2 -7412.3 174.08 0.495 11606 11608 3 -21893.1 -7413.37 173.89 0.495 11607 11609 3 -21889.1 -7413.55 175.81 0.495 11608 11610 3 -21889 -7413.89 171.84 0.495 11609 11611 3 -21887.9 -7415.27 171.51 0.495 11610 11612 3 -21887.9 -7416.88 171.25 0.495 11611 11613 3 -21887.6 -7417.26 166.42 0.495 11612 11614 3 -21886.9 -7418.67 165.62 0.495 11613 11615 3 -21887.2 -7418.67 165.65 0.495 11614 11616 3 -21886.6 -7420.14 165.35 0.495 11615 11617 3 -21886 -7421.07 158.94 0.495 11616 11618 3 -21885.4 -7422.18 156.48 0.495 11617 11619 3 -21884 -7423.17 157.29 0.37 11618 11620 3 -21883 -7424.32 157.01 0.37 11619 11621 3 -21882.2 -7425.28 156.78 0.37 11620 11622 3 -21881.1 -7426.88 156.41 0.37 11621 11623 3 -21880.1 -7427.74 156.17 0.37 11622 11624 3 -21879.9 -7429.02 155.95 0.37 11623 11625 3 -21879.5 -7429.79 155.78 0.37 11624 11626 3 -21878.6 -7430.1 155.64 0.37 11625 11627 3 -21878.1 -7431.57 155.3 0.37 11626 11628 3 -21877.6 -7432.85 155.04 0.37 11627 11629 3 -21876.8 -7434.51 154.69 0.37 11628 11630 3 -21876.3 -7435.7 154.14 0.37 11629 11631 3 -21875.7 -7435.85 154.06 0.37 11630 11632 3 -21875.7 -7436.13 154.01 0.37 11631 11633 3 -21875.3 -7436.59 153.9 0.37 11632 11634 3 -21875 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-7456.01 136.69 0.37 11656 11658 3 -21859.5 -7456.3 136.3 0.37 11657 11659 3 -21858.7 -7457.41 134.35 0.37 11658 11660 3 -21858.9 -7459.55 134.02 0.37 11659 11661 3 -21858.1 -7460.57 129.91 0.37 11660 11662 3 -21857.2 -7462.79 129.45 0.37 11661 11663 3 -21856 -7464.95 124.42 0.37 11662 11664 3 -21854.9 -7468.11 123.79 0.37 11663 11665 3 -21854 -7469.71 119.5 0.37 11664 11666 3 -21852.6 -7471.04 119.16 0.37 11665 11667 3 -21851.9 -7472.05 114.83 0.37 11666 11668 3 -21850.8 -7473.44 114.5 0.25 11667 11669 3 -21849.9 -7474.03 114.32 0.25 11668 11670 3 -21848.7 -7475.68 113.93 0.37 11669 11671 3 -21848.3 -7476.39 113.78 0.37 11670 11672 3 -21847.6 -7477.85 113.47 0.25 11671 11673 3 -21847.1 -7478.82 113.26 0.25 11672 11674 3 -21846.7 -7479.79 113.04 0.37 11673 11675 3 -21846 -7480.19 112.91 0.37 11674 11676 3 -21844.1 -7481.43 112.53 0.37 11675 11677 3 -21844.4 -7482.67 110.22 0.37 11676 11678 3 -21843.8 -7483.75 106.11 0.37 11677 11679 3 -21843.2 -7484.44 105.94 0.37 11678 11680 3 -21842.9 -7484.56 101.12 0.25 11679 11681 3 -21842.3 -7484.77 101.03 0.25 11680 11682 3 -21841.8 -7485.71 100.83 0.37 11681 11683 3 -21840.9 -7486.34 100.64 0.37 11682 11684 3 -21840 -7486.99 100.45 0.25 11683 11685 3 -21839.4 -7487.43 100.32 0.25 11684 11686 3 -21838.3 -7488.27 100.08 0.37 11685 11687 3 -21837.9 -7488.45 96.8 0.25 11686 11688 3 -21836.8 -7488.71 96.64 0.25 11687 11689 3 -21835.4 -7490.35 96.24 0.37 11688 11690 3 -21834.4 -7491.15 95.51 0.37 11689 11691 3 -21833.4 -7491.26 92.67 0.25 11690 11692 3 -21832.3 -7492.91 92.3 0.25 11691 11693 3 -21829 -7496.95 89.16 0.25 11692 11694 3 -21827.9 -7497.84 88.91 0.25 11693 11695 3 -21884 -7424.46 153.69 0.495 11618 11696 3 -21883.7 -7425.7 150.52 0.495 11695 11697 3 -21883.2 -7427.22 148.93 0.495 11696 11698 3 -21882.7 -7429.25 148.54 0.495 11697 11699 3 -21882.9 -7430.62 145.5 0.495 11698 11700 3 -21883 -7431.88 144.83 0.495 11699 11701 3 -21882.2 -7432.25 141.89 0.495 11700 11702 3 -21882 -7434.12 141.55 0.495 11701 11703 3 -21881.8 -7435.03 136.3 0.495 11702 11704 3 -21881.5 -7437.06 135.83 0.495 11703 11705 3 -21881.6 -7437.79 132.41 0.495 11704 11706 3 -21881.2 -7440.25 131.13 0.495 11705 11707 3 -21881.5 -7441.41 128.21 0.495 11706 11708 3 -21881.2 -7442.9 127.94 0.495 11707 11709 3 -21881 -7444.18 127.7 0.495 11708 11710 3 -21881 -7444.83 120.63 0.495 11709 11711 3 -21880.7 -7446.85 120.19 0.495 11710 11712 3 -21880.8 -7446.83 120.07 0.495 11711 11713 3 -21880.9 -7449.03 115.46 0.495 11712 11714 3 -21880.7 -7450.32 115.23 0.495 11713 11715 3 -21880.4 -7452.53 109.97 0.495 11714 11716 3 -21880.1 -7454.36 109.64 0.495 11715 11717 3 -21880.3 -7455.92 105.97 0.495 11716 11718 3 -21880.3 -7457.14 103.87 0.37 11717 11719 3 -21880.9 -7457.81 101.15 0.37 11718 11720 3 -21880.9 -7457.98 100.34 0.37 11719 11721 3 -21881 -7459.23 100.14 0.37 11720 11722 3 -21880.8 -7459.84 100.03 0.37 11721 11723 3 -21880.1 -7461.2 99.73 0.37 11722 11724 3 -21879.8 -7463.78 96.17 0.37 11723 11725 3 -21879.8 -7466.32 93.51 0.37 11724 11726 3 -21879.3 -7468.33 93.12 0.37 11725 11727 3 -21879.5 -7468.35 93.14 0.37 11726 11728 3 -21879 -7470.45 90.03 0.37 11727 11729 3 -21879 -7470.66 89.99 0.37 11728 11730 3 -21879.5 -7471.94 90.32 0.25 11729 11731 3 -21880 -7472.54 88.57 0.25 11730 11732 3 -21880.6 -7473.61 86.33 0.25 11731 11733 3 -21881.3 -7474.88 85.28 0.25 11732 11734 3 -21881.4 -7474.77 84.67 0.25 11733 11735 3 -21881.9 -7475.75 82.16 0.25 11734 11736 3 -21882 -7477.25 81.32 0.25 11735 11737 3 -21882.2 -7477.32 81.33 0.25 11736 11738 3 -21882.9 -7477.83 76.01 0.25 11737 11739 3 -21882.6 -7477.75 76 0.25 11738 11740 3 -21882.5 -7477.93 72.51 0.25 11739 11741 3 -21882 -7477.96 67.07 0.25 11740 11742 3 -21880.6 -7480.65 65.01 0.25 11741 11743 3 -21879.7 -7482.08 61.54 0.25 11742 11744 3 -21879.4 -7482.55 61.43 0.25 11743 11745 3 -21879.3 -7482.85 61.37 0.25 11744 11746 3 -21879.5 -7484.48 60.02 0.25 11745 11747 3 -21879.6 -7484.23 58.5 0.25 11746 11748 3 -21880.1 -7485.79 56.33 0.25 11747 11749 3 -21880.2 -7485.59 55.13 0.25 11748 11750 3 -21880.9 -7488.1 49.94 0.25 11749 11751 3 -21881 -7491.27 49.43 0.25 11750 11752 3 -21892.2 -7397.93 196.67 1.61 11592 11753 3 -21890.7 -7398.52 196.44 1.61 11752 11754 3 -21889.6 -7398.59 196.32 1.485 11753 11755 3 -21888.7 -7399.04 196.16 1.24 11754 11756 3 -21887.3 -7399.49 195.11 1.24 11755 11757 3 -21887.3 -7399.24 195.16 1.24 11756 11758 3 -21886.5 -7400.68 194.84 0.62 11757 11759 3 -21885.3 -7401.88 194.53 0.62 11758 11760 3 -21884.7 -7402.7 192.01 0.495 11759 11761 3 -21885.1 -7402.46 192.08 0.495 11760 11762 3 -21884.5 -7403.69 190.02 0.495 11761 11763 3 -21884.5 -7403.39 190.07 0.495 11762 11764 3 -21883.8 -7404.4 189.84 0.495 11763 11765 3 -21883.7 -7405.68 189.61 0.495 11764 11766 3 -21883.6 -7406.08 188.88 0.495 11765 11767 3 -21882.9 -7407.01 188.52 0.495 11766 11768 3 -21882.7 -7408.13 187.19 0.495 11767 11769 3 -21882.7 -7408.03 186.6 0.495 11768 11770 3 -21880.8 -7409.32 185.89 0.495 11769 11771 3 -21880.9 -7409.22 185.32 0.495 11770 11772 3 -21879.8 -7409.42 184.24 0.495 11771 11773 3 -21878.6 -7408.51 183.29 0.495 11772 11774 3 -21877.8 -7408.17 183.27 0.495 11773 11775 3 -21876 -7407.91 183.15 0.495 11774 11776 3 -21875 -7407.47 183.13 0.495 11775 11777 3 -21873.8 -7407.35 183.04 0.495 11776 11778 3 -21873.6 -7407.09 183.06 0.495 11777 11779 3 -21872 -7406.95 182.09 0.495 11778 11780 3 -21870.7 -7407 180.29 0.495 11779 11781 3 -21869.8 -7406.95 177.67 0.495 11780 11782 3 -21869.8 -7406.84 176.96 0.495 11781 11783 3 -21869.1 -7407.26 173.79 0.495 11782 11784 3 -21869.1 -7407.52 173.74 0.495 11783 11785 3 -21867.2 -7409.14 173.29 0.495 11784 11786 3 -21865.7 -7409.04 170.8 0.495 11785 11787 3 -21866 -7409.14 170.82 0.495 11786 11788 3 -21860.7 -7409.17 170.31 0.495 11787 11789 3 -21860 -7409.6 170.18 0.495 11788 11790 3 -21858.8 -7410.24 166.34 0.37 11789 11791 3 -21858.2 -7411.76 166.03 0.37 11790 11792 3 -21857.3 -7412.61 165.39 0.37 11791 11793 3 -21855.9 -7413.44 162.31 0.37 11792 11794 3 -21855.4 -7415.16 161.91 0.37 11793 11795 3 -21854.3 -7415.92 159.45 0.37 11794 11796 3 -21853.6 -7416.78 157.5 0.37 11795 11797 3 -21853.7 -7416.54 156.06 0.37 11796 11798 3 -21853.7 -7417.47 154.15 0.37 11797 11799 3 -21852.8 -7418.01 154.44 0.37 11798 11800 3 -21851.6 -7420.16 156.99 0.25 11799 11801 3 -21851.3 -7420.38 156.93 0.37 11800 11802 3 -21850.3 -7421.76 156.58 0.37 11801 11803 3 -21849.3 -7422.45 155.39 0.37 11802 11804 3 -21848.6 -7422.93 147.98 0.37 11803 11805 3 -21848.6 -7422.68 148.03 0.37 11804 11806 3 -21847.2 -7424.22 147.49 0.37 11805 11807 3 -21847.4 -7423.84 146.93 0.37 11806 11808 3 -21846.1 -7424.77 145.38 0.37 11807 11809 3 -21843.8 -7426.14 143.36 0.37 11808 11810 3 -21843 -7427.82 142.76 0.37 11809 11811 3 -21842 -7429.32 139.99 0.37 11810 11812 3 -21840.6 -7430.72 136.92 0.37 11811 11813 3 -21839.3 -7431.74 136.54 0.37 11812 11814 3 -21838.2 -7431.54 130.43 0.37 11813 11815 3 -21837 -7432.55 129.5 0.37 11814 11816 3 -21835.6 -7433.37 126.28 0.37 11815 11817 3 -21834.2 -7434.55 123.42 0.37 11816 11818 3 -21832.8 -7435.71 119.12 0.37 11817 11819 3 -21831.9 -7435.85 116.5 0.37 11818 11820 3 -21830.5 -7437.8 114.88 0.37 11819 11821 3 -21829.2 -7439.52 112.19 0.37 11820 11822 3 -21827.8 -7440.81 108.67 0.37 11821 11823 3 -21827.2 -7441.95 104.77 0.37 11822 11824 3 -21825.4 -7443.26 107.83 0.37 11823 11825 3 -21824.8 -7443.71 103.14 0.25 11824 11826 3 -21824.1 -7444.33 102.97 0.25 11825 11827 3 -21823.2 -7444.55 99.01 0.25 11826 11828 3 -21823.2 -7444.49 98.62 0.25 11827 11829 3 -21822.8 -7445.34 94.59 0.25 11828 11830 3 -21820.3 -7446.49 98.82 0.25 11829 11831 3 -21819.6 -7446.99 94.9 0.25 11830 11832 3 -21818.2 -7448.85 94.37 0.25 11831 11833 3 -21817.1 -7449.85 90.36 0.25 11832 11834 3 -21815 -7452.5 88.84 0.25 11833 11835 3 -21814 -7454.27 86.37 0.25 11834 11836 3 -21813.3 -7456.81 85.88 0.25 11835 11837 3 -21812.6 -7457.73 85.66 0.25 11836 11838 3 -21810.1 -7459.04 85.67 0.125 11837 11839 3 -21809.5 -7460.14 83.18 0.125 11838 11840 3 -21808.8 -7461.44 81.96 0.125 11839 11841 3 -21807.9 -7461.98 78.04 0.125 11840 11842 3 -21807.8 -7462.24 78 0.125 11841 11843 3 -21806.7 -7464.33 76.95 0.125 11842 11844 3 -21805.9 -7465.42 74.65 0.125 11843 11845 3 -21804.4 -7466.85 74.76 0.125 11844 11846 3 -21803.3 -7467.03 70.68 0.125 11845 11847 3 -21803 -7467.25 70.62 0.125 11846 11848 3 -21802.7 -7467.21 70.59 0.125 11847 11849 3 -21800.2 -7469.1 69.78 0.125 11848 11850 3 -21799.9 -7469.06 69.77 0.125 11849 11851 3 -21798.7 -7469.69 69.55 0.125 11850 11852 3 -21798 -7469.84 69.46 0.125 11851 11853 3 -21796.8 -7470.87 68.8 0.125 11852 11854 3 -21796.3 -7470.82 68.76 0.125 11853 11855 3 -21795.2 -7470.87 66.94 0.125 11854 11856 3 -21794.9 -7470.81 66.92 0.125 11855 11857 3 -21794.1 -7471.44 64.95 0.125 11856 11858 3 -21850.8 -7417.48 153.77 0.37 11798 11859 3 -21849 -7416.81 148.74 0.37 11858 11860 3 -21846.6 -7416.85 142.41 0.37 11859 11861 3 -21843.7 -7417.29 136.68 0.37 11860 11862 3 -21842.4 -7416.46 131.72 0.37 11861 11863 3 -21840.4 -7417.4 131.37 0.37 11862 11864 3 -21840.1 -7417.32 131.36 0.37 11863 11865 3 -21837 -7417.32 126.67 0.37 11864 11866 3 -21836.6 -7418.31 126.47 0.37 11865 11867 3 -21835.8 -7418.3 119.69 0.37 11866 11868 3 -21835 -7419.04 117.06 0.37 11867 11869 3 -21834.2 -7419.46 111.11 0.37 11868 11870 3 -21833.6 -7420.03 105.65 0.37 11869 11871 3 -21833.7 -7419.77 105.7 0.37 11870 11872 3 -21832.8 -7421.68 105.3 0.37 11871 11873 3 -21831.6 -7421.24 102.29 0.25 11872 11874 3 -21831.1 -7421.94 99.28 0.25 11873 11875 3 -21830.2 -7422.61 94.66 0.25 11874 11876 3 -21829.6 -7421.27 89.21 0.25 11875 11877 3 -21828.3 -7420.5 89.22 0.25 11876 11878 3 -21826.8 -7421.49 90.14 0.125 11877 11879 3 -21826.8 -7421.23 90.18 0.125 11878 11880 3 -21824.6 -7419.96 83.38 0.125 11879 11881 3 -21822.9 -7418.71 82.35 0.125 11880 11882 3 -21822.7 -7417.91 82.47 0.125 11881 11883 3 -21823.2 -7415.28 77.96 0.125 11882 11884 3 -21824.8 -7414.51 72.56 0.125 11883 11885 3 -21824.8 -7414.25 72.61 0.125 11884 11886 3 -21826.6 -7412.7 67.09 0.125 11885 11887 3 -21826.8 -7411.4 67.32 0.125 11886 11888 3 -21826.6 -7410.86 67.39 0.125 11887 11889 3 -21828 -7408.78 63.58 0.125 11888 11890 3 -21828 -7408.76 63.43 0.125 11889 11891 3 -21828.1 -7408.08 64.08 0.125 11890 11892 3 -21884.8 -7399.42 198.13 1.115 11757 11893 3 -21882.8 -7398.88 198.03 1.115 11892 11894 3 -21883.1 -7398.96 198.05 1.115 11893 11895 3 -21881.4 -7398.92 197.9 1.115 11894 11896 3 -21880 -7399.28 197.7 1.115 11895 11897 3 -21877.4 -7398.93 197.52 1.115 11896 11898 3 -21875.9 -7399.77 197.24 1.115 11897 11899 3 -21874.1 -7400.01 195.11 1.115 11898 11900 3 -21872.8 -7400.61 194.9 1.115 11899 11901 3 -21871.4 -7401.24 194.66 1.115 11900 11902 3 -21870 -7403.44 197.64 1.115 11901 11903 3 -21870 -7403.21 197.68 1.115 11902 11904 3 -21869.2 -7403.6 197.54 1.115 11903 11905 3 -21869.3 -7404.72 197.36 1.115 11904 11906 3 -21869.2 -7404.96 197.32 1.115 11905 11907 3 -21868.7 -7406.72 196.98 0.99 11906 11908 3 -21868.3 -7408.24 196.68 0.865 11907 11909 3 -21867.2 -7410.31 195.33 0.865 11908 11910 3 -21867.3 -7411.68 195.12 0.865 11909 11911 3 -21866.3 -7413.36 194.74 0.865 11910 11912 3 -21866.2 -7413.66 194.69 0.865 11911 11913 3 -21864.9 -7415.03 194.28 0.745 11912 11914 3 -21864.2 -7416.08 197.52 0.745 11913 11915 3 -21864.1 -7417.07 197.35 0.745 11914 11916 3 -21863.9 -7418.11 193.9 0.745 11915 11917 3 -21864.2 -7418.13 193.92 0.745 11916 11918 3 -21863.6 -7418.85 193.75 0.745 11917 11919 3 -21862.8 -7420.1 193.47 0.62 11918 11920 3 -21862 -7421.58 193.15 0.62 11919 11921 3 -21861.6 -7422.55 192.95 0.62 11920 11922 3 -21860.8 -7423.24 192.76 0.62 11921 11923 3 -21860.2 -7423.38 192.68 0.495 11922 11924 3 -21860.1 -7424.3 191.75 0.495 11923 11925 3 -21859.7 -7426 193.94 0.495 11924 11926 3 -21859.7 -7425.84 189.74 0.495 11925 11927 3 -21858.7 -7427.42 193.23 0.495 11926 11928 3 -21858.6 -7427.65 193.18 0.495 11927 11929 3 -21858.3 -7428.92 191.3 0.495 11928 11930 3 -21858.6 -7428.96 191.32 0.495 11929 11931 3 -21858.2 -7430.12 190.33 0.495 11930 11932 3 -21858 -7431.89 189.99 0.495 11931 11933 3 -21857.7 -7432.34 189.49 0.495 11932 11934 3 -21857.4 -7432.59 189.41 0.495 11933 11935 3 -21856.7 -7433.27 189.24 0.495 11934 11936 3 -21856.4 -7434.01 189.09 0.495 11935 11937 3 -21857.4 -7435.28 187.75 0.495 11936 11938 3 -21856.8 -7436.81 189.31 0.495 11937 11939 3 -21856.8 -7437.07 189.26 0.495 11938 11940 3 -21856.5 -7437.54 189.15 0.495 11939 11941 3 -21855.8 -7439.49 189.72 0.495 11940 11942 3 -21855.3 -7441.27 189.38 0.495 11941 11943 3 -21855.2 -7443.77 191.46 0.495 11942 11944 3 -21855 -7444.59 191.31 0.495 11943 11945 3 -21854.6 -7445.54 191.11 0.495 11944 11946 3 -21854.8 -7446.36 190.99 0.495 11945 11947 3 -21854.8 -7446.67 190.94 0.495 11946 11948 3 -21854.9 -7447.5 190.82 0.37 11947 11949 3 -21854.6 -7447.95 190.71 0.37 11948 11950 3 -21854.5 -7448.21 190.66 0.37 11949 11951 3 -21854.2 -7449.33 189.52 0.37 11950 11952 3 -21853.7 -7450.61 189.26 0.37 11951 11953 3 -21853.3 -7452.73 190.74 0.37 11952 11954 3 -21852.6 -7454.47 190.38 0.37 11953 11955 3 -21851.5 -7456.66 189.93 0.37 11954 11956 3 -21851.2 -7458.71 189.59 0.37 11955 11957 3 -21849.7 -7458.83 189.47 0.37 11956 11958 3 -21848.8 -7459.24 189.32 0.37 11957 11959 3 -21847.9 -7459.95 189.12 0.37 11958 11960 3 -21848.2 -7459.95 189.15 0.37 11959 11961 3 -21847.8 -7461.25 188.89 0.62 11960 11962 3 -21846.9 -7461.35 188.79 0.37 11961 11963 3 -21846 -7461.55 188.9 0.37 11962 11964 3 -21845.4 -7462.02 188.77 0.37 11963 11965 3 -21845.4 -7461.72 188.82 0.37 11964 11966 3 -21844.1 -7463.11 188.47 0.37 11965 11967 3 -21843.5 -7463.31 188.38 0.37 11966 11968 3 -21843.6 -7464.88 188.13 0.37 11967 11969 3 -21843.3 -7465.4 188.01 0.37 11968 11970 3 -21842.8 -7466.15 187.85 0.37 11969 11971 3 -21842.2 -7466.63 187.71 0.37 11970 11972 3 -21841.3 -7466.51 189.6 0.37 11971 11973 3 -21840.7 -7466.66 189.52 0.37 11972 11974 3 -21840.2 -7468.22 189.22 0.37 11973 11975 3 -21839.5 -7468.65 189.08 0.37 11974 11976 3 -21838.9 -7468.81 189 0.37 11975 11977 3 -21837.8 -7469.43 188.79 0.37 11976 11978 3 -21837.7 -7469.76 188.73 0.37 11977 11979 3 -21837.2 -7470.21 187.21 0.37 11978 11980 3 -21836.6 -7470.61 187.08 0.37 11979 11981 3 -21836.2 -7471.34 186.93 0.37 11980 11982 3 -21835.7 -7473.17 186.58 0.37 11981 11983 3 -21834.8 -7473.59 186.42 0.37 11982 11984 3 -21834 -7474.41 188.54 0.37 11983 11985 3 -21833.8 -7475.42 188.35 0.37 11984 11986 3 -21833.6 -7477 188.08 0.37 11985 11987 3 -21833.3 -7477.97 187.58 0.37 11986 11988 3 -21832.5 -7479.17 187.31 0.37 11987 11989 3 -21832.8 -7479.2 187.33 0.37 11988 11990 3 -21832 -7479.34 187.23 0.37 11989 11991 3 -21831.3 -7479.8 187.09 0.37 11990 11992 3 -21830.5 -7479.92 186.99 0.37 11991 11993 3 -21829.2 -7480.3 186.81 0.37 11992 11994 3 -21829.2 -7480.56 186.77 0.37 11993 11995 3 -21829 -7481.85 186.54 0.37 11994 11996 3 -21828.8 -7482.08 186.48 0.37 11995 11997 3 -21828.4 -7482.85 186.31 0.37 11996 11998 3 -21828.4 -7483.08 186.27 0.37 11997 11999 3 -21828.2 -7484.15 186.08 0.37 11998 12000 3 -21828.2 -7484.38 186.04 0.37 11999 12001 3 -21828 -7485.67 185.81 0.37 12000 12002 3 -21827.2 -7487.39 186.65 0.37 12001 12003 3 -21826.7 -7488.88 186.36 0.37 12002 12004 3 -21827 -7488.89 186.38 0.37 12003 12005 3 -21825.7 -7489.79 186.12 0.37 12004 12006 3 -21824.5 -7490.43 185.9 0.37 12005 12007 3 -21824.6 -7491.81 185.68 0.37 12006 12008 3 -21824.5 -7493.07 185.47 0.25 12007 12009 3 -21823.6 -7492.98 185.4 0.495 12008 12010 3 -21823.1 -7492.9 185.37 0.495 12009 12011 3 -21822.8 -7493.12 185.3 0.495 12010 12012 3 -21821.8 -7493.77 185.1 0.25 12011 12013 3 -21822.1 -7493.82 185.12 0.25 12012 12014 3 -21821.4 -7495.01 184.86 0.37 12013 12015 3 -21821.3 -7495.8 184.72 0.37 12014 12016 3 -21821.6 -7495.83 184.74 0.37 12015 12017 3 -21820.4 -7496.99 185.86 0.37 12016 12018 3 -21819.8 -7497.99 185.63 0.37 12017 12019 3 -21819.8 -7497.7 185.69 0.37 12018 12020 3 -21819.2 -7498.19 185.55 0.125 12019 12021 3 -21818.6 -7498.63 185.42 0.125 12020 12022 3 -21818.8 -7498.63 185.44 0.125 12021 12023 3 -21818.2 -7498.83 185.35 0.37 12022 12024 3 -21817.6 -7499.53 185.18 0.37 12023 12025 3 -21817.5 -7500.29 185.04 0.37 12024 12026 3 -21817.5 -7500.08 185.08 0.37 12025 12027 3 -21816.8 -7501.21 184.56 0.125 12026 12028 3 -21816.6 -7501.52 183.43 0.37 12027 12029 3 -21816.5 -7501.61 183.95 0.37 12028 12030 3 -21816.1 -7503.15 183.65 0.37 12029 12031 3 -21815.9 -7504.17 183.47 0.125 12030 12032 3 -21815.5 -7505.15 183.27 0.125 12031 12033 3 -21815.2 -7505.46 183.18 0.125 12032 12034 3 -21815.2 -7505.14 183.24 0.125 12033 12035 3 -21814.8 -7506.67 182.94 0.495 12034 12036 3 -21815.1 -7506.71 182.96 0.495 12035 12037 3 -21815.2 -7507.79 182.8 0.495 12036 12038 3 -21814.3 -7509.66 183.31 0.25 12037 12039 3 -21813.6 -7510.63 183.09 0.25 12038 12040 3 -21813 -7511.35 182.91 0.495 12039 12041 3 -21812.6 -7511.83 182.8 0.495 12040 12042 3 -21811.9 -7512.8 182.57 0.25 12041 12043 3 -21811.6 -7513.28 182.46 0.25 12042 12044 3 -21810.8 -7514.78 182.14 0.495 12043 12045 3 -21810.5 -7515.63 181.48 0.125 12044 12046 3 -21810.7 -7515.72 181.49 0.125 12045 12047 3 -21810.9 -7516.53 181.38 0.125 12046 12048 3 -21811.1 -7517.35 181.39 0.37 12047 12049 3 -21810.9 -7518.89 181.12 0.125 12048 12050 3 -21811.3 -7519.78 181.01 0.125 12049 12051 3 -21811.1 -7519.73 180.99 0.495 12050 12052 3 -21811.1 -7519.97 180.95 0.495 12051 12053 3 -21810.9 -7521.08 180.76 0.25 12052 12054 3 -21810.9 -7521.31 180.71 0.25 12053 12055 3 -21811.1 -7521.73 181.35 0.25 12054 12056 3 -21811.3 -7524.15 180.98 0.25 12055 12057 3 -21811.3 -7526.53 180.58 0.25 12056 12058 3 -21811.1 -7528.1 180.3 0.25 12057 12059 3 -21851.5 -7460.51 190.08 0.495 11956 12060 3 -21851.5 -7461.94 190.35 0.495 12059 12061 3 -21851.2 -7461.9 190.33 0.495 12060 12062 3 -21851.1 -7462.67 190.19 0.495 12061 12063 3 -21850.8 -7463.2 190.08 0.37 12062 12064 3 -21850.4 -7464.44 189.83 0.37 12063 12065 3 -21850.1 -7465.84 190.49 0.37 12064 12066 3 -21850.3 -7466.96 190.33 0.37 12065 12067 3 -21849.6 -7467.68 190.14 0.37 12066 12068 3 -21849.5 -7468.85 192.41 0.37 12067 12069 3 -21850 -7469.72 192.32 0.37 12068 12070 3 -21850.3 -7471.54 193.1 0.37 12069 12071 3 -21850.5 -7472.61 192.94 0.37 12070 12072 3 -21850.7 -7474.09 194.96 0.37 12071 12073 3 -21850.9 -7474.62 194.89 0.37 12072 12074 3 -21850.8 -7474.92 194.83 0.37 12073 12075 3 -21850.1 -7475.78 195.68 0.37 12074 12076 3 -21850.2 -7476.78 196.54 0.25 12075 12077 3 -21850.3 -7477.49 197.41 0.25 12076 12078 3 -21850.2 -7477.65 198.39 0.25 12077 12079 3 -21850.6 -7477.71 196.8 0.495 12078 12080 3 -21850.3 -7478.87 199.14 0.495 12079 12081 3 -21851 -7479.57 199.3 0.25 12080 12082 3 -21851.5 -7479.86 199.3 0.495 12081 12083 3 -21851.1 -7481.39 201.81 0.25 12082 12084 3 -21850.8 -7482.13 201.65 0.25 12083 12085 3 -21850.7 -7483.15 201.48 0.495 12084 12086 3 -21849.8 -7484.14 203.11 0.125 12085 12087 3 -21849.2 -7484.35 203.02 0.125 12086 12088 3 -21849.2 -7484.64 202.97 0.125 12087 12089 3 -21848.9 -7484.85 202.91 0.37 12088 12090 3 -21849.8 -7485.62 203.6 0.125 12089 12091 3 -21850 -7486.44 203.53 0.125 12090 12092 3 -21849.6 -7487.45 207.88 0.37 12091 12093 3 -21850.4 -7488.62 209.54 0.37 12092 12094 3 -21850.3 -7489.93 209.31 0.125 12093 12095 3 -21850.1 -7490.99 209.12 0.37 12094 12096 3 -21849.5 -7492.7 214.53 0.125 12095 12097 3 -21849.4 -7493.74 214.35 0.37 12096 12098 3 -21850 -7495.13 215.56 0.37 12097 12099 3 -21850.8 -7495.23 215.62 0.37 12098 12100 3 -21851.7 -7496.07 213.59 0.125 12099 12101 3 -21851.9 -7496.36 213.56 0.125 12100 12102 3 -21852.3 -7497.41 214.62 0.37 12101 12103 3 -21854 -7499.5 214.41 0.37 12102 12104 3 -21854.6 -7501.01 219.42 0.125 12103 12105 3 -21854.8 -7501.87 219.29 0.125 12104 12106 3 -21854.6 -7503.42 219.02 0.125 12105 12107 3 -21854.7 -7504.72 218.81 0.125 12106 12108 3 -21854.2 -7505.55 219.23 0.125 12107 12109 3 -21854.4 -7506.63 219.07 0.37 12108 12110 3 -21854.8 -7507.75 218.93 0.37 12109 12111 3 -21854.8 -7436.4 185.45 0.37 11936 12112 3 -21853.5 -7437.85 185.09 0.37 12111 12113 3 -21853.2 -7438.61 184.93 0.37 12112 12114 3 -21849.7 -7441.33 179.91 0.37 12113 12115 3 -21847.3 -7444.77 173.57 0.37 12114 12116 3 -21845.9 -7445.7 173.28 0.37 12115 12117 3 -21845.6 -7445.37 173.32 0.37 12116 12118 3 -21843.7 -7446.97 172.87 0.37 12117 12119 3 -21843.5 -7446.92 172.85 0.37 12118 12120 3 -21841.9 -7447.33 170.11 0.37 12119 12121 3 -21840.7 -7448.1 168.9 0.37 12120 12122 3 -21838.4 -7449.39 170.48 0.37 12121 12123 3 -21837.4 -7450.59 170.18 0.37 12122 12124 3 -21836.6 -7450.78 170.07 0.125 12123 12125 3 -21835.8 -7450.86 169.98 0.125 12124 12126 3 -21834.8 -7451.02 169.87 0.37 12125 12127 3 -21834.1 -7452.53 169.55 0.37 12126 12128 3 -21834.1 -7452.24 169.6 0.37 12127 12129 3 -21833.7 -7453.26 169.39 0.125 12128 12130 3 -21833.1 -7453.42 169.31 0.125 12129 12131 3 -21832.9 -7453.19 168.18 0.37 12130 12132 3 -21832.3 -7453.93 168 0.37 12131 12133 3 -21832.2 -7454.7 167.86 0.125 12132 12134 3 -21831.8 -7455.68 167.66 0.37 12133 12135 3 -21830.2 -7456.55 167.37 0.125 12134 12136 3 -21829.7 -7456.53 167.7 0.125 12135 12137 3 -21829 -7457.25 167.51 0.37 12136 12138 3 -21829 -7457.49 167.47 0.37 12137 12139 3 -21828.8 -7458.1 164.91 0.125 12138 12140 3 -21828.5 -7458.34 164.85 0.37 12139 12141 3 -21827.6 -7458.49 164.74 0.37 12140 12142 3 -21826.9 -7459.12 164.57 0.125 12141 12143 3 -21826.8 -7459.28 165.57 0.125 12142 12144 3 -21826 -7459.75 165.41 0.125 12143 12145 3 -21825.2 -7460.43 162.04 0.37 12144 12146 3 -21824 -7461.34 161.78 0.125 12145 12147 3 -21824.3 -7461.39 161.79 0.37 12146 12148 3 -21823 -7462.22 161.43 0.37 12147 12149 3 -21822.6 -7463.2 161.23 0.37 12148 12150 3 -21821.7 -7464.14 160.99 0.25 12149 12151 3 -21820.9 -7465.33 160.72 0.25 12150 12152 3 -21819.3 -7466.04 158.11 0.25 12151 12153 3 -21818.6 -7466.7 159.03 0.25 12152 12154 3 -21817.2 -7466.49 158.93 0.25 12153 12155 3 -21811.7 -7465.6 158.31 0.125 12154 12156 3 -21809.3 -7466.67 157.33 0.37 12155 12157 3 -21808.8 -7466.3 157.34 0.37 12156 12158 3 -21807.4 -7466.93 154.67 0.125 12157 12159 3 -21806.8 -7467.37 154.54 0.125 12158 12160 3 -21806.5 -7467.55 154.48 0.125 12159 12161 3 -21806.1 -7467.76 154.41 0.125 12160 12162 3 -21804.6 -7469.44 156.18 0.37 12161 12163 3 -21804 -7469.36 156.14 0.37 12162 12164 3 -21802.4 -7469.31 155.99 0.125 12163 12165 3 -21801.7 -7469.77 155.86 0.125 12164 12166 3 -21801.4 -7470.2 155.75 0.25 12165 12167 3 -21800.8 -7471.68 155.46 0.37 12166 12168 3 -21800.9 -7471.42 155.5 0.37 12167 12169 3 -21800.6 -7473.2 155.18 0.25 12168 12170 3 -21799.9 -7474.57 152.87 0.25 12169 12171 3 -21799.6 -7476.14 152.59 0.25 12170 12172 3 -21799.2 -7477.65 150.82 0.37 12171 12173 3 -21799.1 -7478.17 150.72 0.25 12172 12174 3 -21798.5 -7478.8 150.56 0.25 12173 12175 3 -21797.5 -7479.69 150.32 0.37 12174 12176 3 -21797.7 -7479.72 150.34 0.37 12175 12177 3 -21796.5 -7480.33 150.12 0.25 12176 12178 3 -21795.2 -7481.42 149.82 0.37 12177 12179 3 -21795.3 -7481.15 149.87 0.37 12178 12180 3 -21794.2 -7482.34 150.16 0.37 12179 12181 3 -21793.6 -7484.15 149.81 0.37 12180 12182 3 -21793.9 -7484.16 149.83 0.37 12181 12183 3 -21794.3 -7485.02 149.72 0.125 12182 12184 3 -21794.6 -7486.42 149.53 0.125 12183 12185 3 -21794.2 -7487.13 149.37 0.25 12184 12186 3 -21795.2 -7487.78 149.36 0.25 12185 12187 3 -21795.4 -7488.36 149.28 0.37 12186 12188 3 -21796.2 -7489.82 150.6 0.25 12187 12189 3 -21796.6 -7490.46 150.53 0.495 12188 12190 3 -21797.8 -7491.71 150.44 0.37 12189 12191 3 -21798.8 -7492.96 150.32 0.125 12190 12192 3 -21799.9 -7493.13 150.4 0.125 12191 12193 3 -21800.1 -7493.47 150.36 0.125 12192 12194 3 -21801.6 -7494.05 152.29 0.25 12193 12195 3 -21803.6 -7493.34 152.6 0.25 12194 12196 3 -21803.9 -7493.41 152.61 0.37 12195 12197 3 -21803.3 -7495.4 152.18 0.125 12194 12198 3 -21804.1 -7495.8 152.19 0.37 12197 12199 3 -21857.6 -7426.2 187.15 0.495 11926 12200 3 -21856.2 -7426.81 185.58 0.37 12199 12201 3 -21853.7 -7427.15 183.11 0.37 12200 12202 3 -21852 -7427.89 182.08 0.495 12201 12203 3 -21850.3 -7428.32 181.21 0.495 12202 12204 3 -21848.8 -7428.64 181.02 0.495 12203 12205 3 -21846.9 -7428.89 180.8 0.495 12204 12206 3 -21845.6 -7429.71 180.54 0.495 12205 12207 3 -21844.9 -7430.2 180.4 0.495 12206 12208 3 -21844.7 -7430.17 180.38 0.495 12207 12209 3 -21843.8 -7430.56 180.23 0.37 12208 12210 3 -21842.8 -7430.7 180.12 0.37 12209 12211 3 -21842.3 -7431.15 179.93 0.37 12210 12212 3 -21841.6 -7431.83 179.75 0.37 12211 12213 3 -21841 -7432.55 179.57 0.25 12212 12214 3 -21840.8 -7432.77 177.83 0.495 12213 12215 3 -21839.9 -7432.91 177.73 0.495 12214 12216 3 -21838.7 -7434.25 180.25 0.37 12215 12217 3 -21837.8 -7434.97 180.04 0.37 12216 12218 3 -21836.8 -7435.61 179.85 0.37 12217 12219 3 -21836 -7435.82 178.46 0.37 12218 12220 3 -21835.4 -7436.25 178.33 0.37 12219 12221 3 -21834.7 -7437.77 178.23 0.37 12220 12222 3 -21834 -7438.72 178.01 0.37 12221 12223 3 -21833 -7439.42 177.8 0.25 12222 12224 3 -21832.7 -7439.91 177.69 0.37 12223 12225 3 -21831.4 -7441.02 177.35 0.37 12224 12226 3 -21830.8 -7441.19 177.27 0.37 12225 12227 3 -21829.3 -7441.64 177.59 0.37 12226 12228 3 -21828.1 -7442.26 177.38 0.37 12227 12229 3 -21827.2 -7442.91 177.18 0.37 12228 12230 3 -21825.5 -7444.29 178.38 0.37 12229 12231 3 -21824.3 -7445.13 178.07 0.37 12230 12232 3 -21823.8 -7446.09 174.57 0.25 12231 12233 3 -21823.2 -7446.55 174.44 0.25 12232 12234 3 -21821.9 -7447.17 174.22 0.25 12233 12235 3 -21820.5 -7447.69 173.79 0.25 12234 12236 3 -21820.2 -7448.48 172.04 0.25 12235 12237 3 -21819.3 -7449.15 171.84 0.25 12236 12238 3 -21818.4 -7449.31 171.73 0.25 12237 12239 3 -21817.6 -7449.9 171.03 0.37 12238 12240 3 -21816.3 -7450.48 170.82 0.37 12239 12241 3 -21815.1 -7451.11 170.72 0.25 12240 12242 3 -21814.5 -7451.59 170.6 0.25 12241 12243 3 -21813.3 -7452.24 170.38 0.37 12242 12244 3 -21812.1 -7452.05 170.31 0.37 12243 12245 3 -21811.5 -7451.72 167.39 0.37 12244 12246 3 -21810.9 -7452.45 167.2 0.37 12245 12247 3 -21810 -7452.83 167.06 0.37 12246 12248 3 -21809.3 -7453.26 166.93 0.25 12247 12249 3 -21808.8 -7453.18 166.89 0.25 12248 12250 3 -21808.2 -7453.65 166.75 0.25 12249 12251 3 -21807.5 -7454.32 166.58 0.37 12250 12252 3 -21805.8 -7454.64 166.36 0.37 12251 12253 3 -21804 -7454.91 166.16 0.37 12252 12254 3 -21803.4 -7455.33 162.81 0.37 12253 12255 3 -21802.6 -7456.6 162.53 0.25 12254 12256 3 -21802.1 -7456.74 162.45 0.37 12255 12257 3 -21801.5 -7456.87 162.38 0.37 12256 12258 3 -21801.4 -7457.14 162.33 0.37 12257 12259 3 -21800.2 -7458.06 162.06 0.37 12258 12260 3 -21799.9 -7458.32 161.99 0.495 12259 12261 3 -21799.3 -7458.74 161.86 0.495 12260 12262 3 -21797.5 -7459.4 160.8 0.37 12261 12263 3 -21796.2 -7461.1 160.4 0.37 12262 12264 3 -21795.5 -7462.3 160.13 0.37 12263 12265 3 -21795.7 -7462.35 160.14 0.37 12264 12266 3 -21794.5 -7462.73 160.35 0.125 12265 12267 3 -21793.9 -7463.5 160.17 0.37 12266 12268 3 -21793.2 -7464.18 159.99 0.37 12267 12269 3 -21792.3 -7465.13 159.74 0.125 12268 12270 3 -21790.8 -7464.91 159.65 0.125 12269 12271 3 -21790.5 -7465.39 159.54 0.37 12270 12272 3 -21788.3 -7465.1 159.37 0.37 12271 12273 3 -21786.8 -7465.67 159.14 0.37 12272 12274 3 -21785.3 -7466 158.95 0.37 12273 12275 3 -21785.6 -7466.02 158.97 0.37 12274 12276 3 -21784.6 -7466.7 158.77 0.37 12275 12277 3 -21783.6 -7466.8 154.15 0.25 12276 12278 3 -21782.5 -7467.76 155.54 0.125 12277 12279 3 -21781.8 -7468.74 155.31 0.37 12278 12280 3 -21781.2 -7469.8 154.18 0.125 12279 12281 3 -21780.3 -7470.18 154.03 0.125 12280 12282 3 -21780.2 -7470.47 153.98 0.125 12281 12283 3 -21778.8 -7470.8 153.79 0.37 12282 12284 3 -21778.7 -7471.3 153.7 0.37 12283 12285 3 -21777.6 -7472.77 153.36 0.125 12284 12286 3 -21776.2 -7473.08 153.17 0.125 12285 12287 3 -21775.3 -7473.23 153.07 0.125 12286 12288 3 -21774.2 -7473.66 151.8 0.495 12287 12289 3 -21774.2 -7473.95 151.75 0.495 12288 12290 3 -21861.8 -7416.23 198.81 0.62 11914 12291 3 -21861.8 -7416.52 198.76 0.62 12290 12292 3 -21859.8 -7417.11 202.21 0.62 12291 12293 3 -21857.9 -7418.66 206 0.495 12292 12294 3 -21856.8 -7420.05 207 0.495 12293 12295 3 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0.495 12316 12318 3 -21836.8 -7442.66 222.76 0.37 12317 12319 3 -21835.9 -7442.8 222.66 0.37 12318 12320 3 -21835 -7443.2 222.5 0.495 12319 12321 3 -21834.2 -7443.37 222.39 0.495 12320 12322 3 -21833.3 -7443.8 222.24 0.495 12321 12323 3 -21831.8 -7444.63 221.96 0.495 12322 12324 3 -21829.8 -7446.69 226.05 0.495 12323 12325 3 -21828.8 -7447.62 225.8 0.25 12324 12326 3 -21828.4 -7448.4 225.64 0.495 12325 12327 3 -21827.1 -7449 225.42 0.495 12326 12328 3 -21825.2 -7450.59 226.39 0.495 12327 12329 3 -21824.6 -7452.07 227.76 0.495 12328 12330 3 -21824.5 -7452.21 228.62 0.495 12329 12331 3 -21826.8 -7442.29 228.6 0.37 12330 12332 3 -21825.1 -7443.31 228.27 0.37 12331 12333 3 -21823.9 -7444.42 231.05 0.37 12332 12334 3 -21823.6 -7444.35 231.03 0.37 12333 12335 3 -21823.7 -7445.68 230.82 0.37 12334 12336 3 -21822.6 -7447.06 233.25 0.37 12335 12337 3 -21821.7 -7448.78 232.88 0.37 12336 12338 3 -21821.5 -7448.72 232.87 0.37 12337 12339 3 -21820.5 -7449.52 232.64 0.37 12338 12340 3 -21820.4 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13320 3 -21878.2 -7505.42 138.59 0.25 13319 13321 3 -21877 -7505.98 138.39 0.25 13320 13322 3 -21875 -7506.16 138.17 0.37 13321 13323 3 -21874.4 -7506.59 138.04 0.37 13322 13324 3 -21873.5 -7507.83 135.29 0.37 13323 13325 3 -21872.6 -7509.52 134.93 0.37 13324 13326 3 -21871.8 -7510.73 134.65 0.25 13325 13327 3 -21870.9 -7511.36 134.46 0.25 13326 13328 3 -21870.1 -7511.64 132.61 0.37 13327 13329 3 -21868.6 -7512.33 131.69 0.25 13328 13330 3 -21867.8 -7512.9 129.54 0.37 13329 13331 3 -21867.8 -7513.11 129.51 0.37 13330 13332 3 -21866.8 -7513.76 129.31 0.37 13331 13333 3 -21865.9 -7513.69 126.8 0.25 13332 13334 3 -21864 -7514.75 126.45 0.25 13333 13335 3 -21924.4 -7454.09 186.36 0.62 13177 13336 3 -21924.2 -7455.63 182.74 0.62 13335 13337 3 -21924.2 -7455.57 182.37 0.62 13336 13338 3 -21924.5 -7457.46 179.2 0.62 13337 13339 3 -21924.2 -7458.88 181 0.495 13338 13340 3 -21923.8 -7459.6 180.85 0.495 13339 13341 3 -21923.6 -7460.93 180.62 0.495 13340 13342 3 -21924.4 -7461.89 179.11 0.495 13341 13343 3 -21924 -7462.82 178.92 0.62 13342 13344 3 -21924.2 -7464.29 177.74 0.62 13343 13345 3 -21924.1 -7465.31 177.56 0.62 13344 13346 3 -21923.1 -7466.56 177.45 0.62 13345 13347 3 -21922.7 -7467.82 177.19 0.62 13346 13348 3 -21922.5 -7468.79 177.02 0.62 13347 13349 3 -21922.3 -7470.63 176.69 0.495 13348 13350 3 -21922 -7471.72 173.59 0.495 13349 13351 3 -21922 -7472.49 173.46 0.62 13350 13352 3 -21922.1 -7473.57 173.29 0.62 13351 13353 3 -21921.7 -7474.32 173.13 0.495 13352 13354 3 -21921 -7475.57 172.86 0.495 13353 13355 3 -21921 -7476.75 170.62 0.495 13354 13356 3 -21921.7 -7478.1 169.97 0.62 13355 13357 3 -21921.8 -7479.96 169.7 0.62 13356 13358 3 -21921.9 -7481.03 169.54 0.62 13357 13359 3 -21922 -7482.41 169.32 0.495 13358 13360 3 -21921.8 -7483.74 169.18 0.495 13359 13361 3 -21922 -7484.93 168.13 0.495 13360 13362 3 -21922.8 -7485.32 168.14 0.495 13361 13363 3 -21922.4 -7486.33 168.22 0.495 13362 13364 3 -21922.3 -7487.11 168.08 0.495 13363 13365 3 -21922.4 -7488.54 168.23 0.495 13364 13366 3 -21922.5 -7489.58 168.07 0.495 13365 13367 3 -21921.9 -7490.71 167.13 0.495 13366 13368 3 -21921.9 -7492.29 166.87 0.495 13367 13369 3 -21921.8 -7493.87 166.57 0.37 13368 13370 3 -21920.8 -7494.69 165.86 0.37 13369 13371 3 -21920.7 -7495.48 165.72 0.37 13370 13372 3 -21919.7 -7496.41 165.47 0.37 13371 13373 3 -21919.1 -7497.38 165.25 0.37 13372 13374 3 -21918.1 -7498.5 164.54 0.37 13373 13375 3 -21917.2 -7499.17 164.34 0.37 13374 13376 3 -21917 -7500.73 164.07 0.37 13375 13377 3 -21916.9 -7501.23 163.97 0.37 13376 13378 3 -21916.9 -7501.5 163.93 0.37 13377 13379 3 -21916 -7502.17 163.73 0.37 13378 13380 3 -21915.5 -7503.12 163.53 0.37 13379 13381 3 -21916.1 -7504.02 161.94 0.37 13380 13382 3 -21915.7 -7505.28 161.69 0.37 13381 13383 3 -21915.3 -7507.67 161.46 0.37 13382 13384 3 -21910.3 -7506.92 161.11 0.37 13383 13385 3 -21909.5 -7508.22 161.41 0.37 13384 13386 3 -21908.9 -7509.72 161.11 0.37 13385 13387 3 -21908.7 -7511.01 160.88 0.37 13386 13388 3 -21908.8 -7511.8 160.76 0.37 13387 13389 3 -21909.2 -7512.95 160.6 0.37 13388 13390 3 -21909.1 -7513.69 160.47 0.37 13389 13391 3 -21908.5 -7515.49 160.25 0.37 13390 13392 3 -21908.2 -7517.24 159.92 0.37 13391 13393 3 -21908.2 -7517.52 159.87 0.37 13392 13394 3 -21908.1 -7519.36 159.56 0.37 13393 13395 3 -21908.2 -7520.93 159.3 0.37 13394 13396 3 -21908.1 -7521.18 159.26 0.37 13395 13397 3 -21907.9 -7522.72 158.98 0.37 13396 13398 3 -21907.8 -7523 158.93 0.37 13397 13399 3 -21907.3 -7524.93 158.11 0.37 13398 13400 3 -21907.7 -7527.38 157.74 0.37 13399 13401 3 -21908.4 -7529.87 157.4 0.37 13400 13402 3 -21909.5 -7531.62 157.21 0.25 13401 13403 3 -21910 -7532.51 157.11 0.37 13402 13404 3 -21909.9 -7534.36 156.8 0.37 13403 13405 3 -21910.4 -7535 155.44 0.25 13404 13406 3 -21909.9 -7536.48 155.15 0.25 13405 13407 3 -21909.7 -7538 154.87 0.37 13406 13408 3 -21909.5 -7539.04 154.68 0.37 13407 13409 3 -21908.8 -7540.63 151.99 0.25 13408 13410 3 -21908.6 -7542.15 151.71 0.25 13409 13411 3 -21908.3 -7543.44 151.47 0.37 13410 13412 3 -21908.4 -7543.18 151.52 0.37 13411 13413 3 -21908.6 -7545.57 151.14 0.37 13412 13414 3 -21908.6 -7545.32 151.19 0.37 13413 13415 3 -21907.3 -7547.76 146.4 0.25 13414 13416 3 -21907.1 -7549.05 146.15 0.37 13415 13417 3 -21906.9 -7550.54 145.88 0.37 13416 13418 3 -21906.8 -7552.5 145.98 0.37 13417 13419 3 -21907 -7554.37 145.69 0.25 13418 13420 3 -21907 -7554.59 145.66 0.25 13419 13421 3 -21906.9 -7555.41 145.51 0.25 13420 13422 3 -21906.5 -7555.88 145.39 0.495 13421 13423 3 -21906.9 -7556.99 145.25 0.495 13422 13424 3 -21907.6 -7558.17 145.11 0.495 13423 13425 3 -21907.6 -7559.75 144.85 0.37 13424 13426 3 -21907.6 -7561.31 144.59 0.37 13425 13427 3 -21907.4 -7562.66 144.35 0.37 13426 13428 3 -21907.9 -7564.1 141.61 0.37 13427 13429 3 -21909.1 -7565.16 141.55 0.37 13428 13430 3 -21909.3 -7566.69 139.68 0.25 13429 13431 3 -21909.5 -7568.58 139.39 0.37 13430 13432 3 -21909.8 -7569.98 139.19 0.25 13431 13433 3 -21910.5 -7571.1 139.07 0.37 13432 13434 3 -21910.5 -7572.73 138.8 0.37 13433 13435 3 -21910.3 -7573.75 138.61 0.37 13434 13436 3 -21910.8 -7575.6 134.93 0.25 13435 13437 3 -21910.7 -7575.74 135.79 0.25 13436 13438 3 -21910.8 -7576.61 135.93 0.25 13437 13439 3 -21912 -7578.38 134.19 0.25 13438 13440 3 -21911.5 -7580.12 133.85 0.25 13439 13441 3 -21911.4 -7580.66 133.75 0.25 13440 13442 3 -21911.2 -7583.47 133.27 0.25 13441 13443 3 -21911.1 -7583.73 133.22 0.25 13442 13444 3 -21910.2 -7589.38 132.2 0.25 13443 13445 3 -21910.5 -7589.43 132.21 0.25 13444 13446 3 -21911.3 -7591.88 129.95 0.25 13445 13447 3 -21912.1 -7594.09 129.66 0.25 13446 13448 3 -21912.3 -7596.31 129.31 0.25 13447 13449 3 -21912.3 -7598.46 126.51 0.25 13448 13450 3 -21912.3 -7600.3 126.2 0.25 13449 13451 3 -21912.5 -7601.14 126.12 0.37 13450 13452 3 -21912.4 -7603.03 125.81 0.25 13451 13453 3 -21912.5 -7604.59 125.55 0.37 13452 13454 3 -21912.2 -7606.11 125.28 0.25 13453 13455 3 -21912.5 -7606.17 125.29 0.25 13454 13456 3 -21912 -7607.13 125.09 0.37 13455 13457 3 -21912.1 -7608.74 124.83 0.25 13456 13458 3 -21911.8 -7610.24 124.55 0.37 13457 13459 3 -21911.8 -7611.82 124.29 0.25 13458 13460 3 -21911.7 -7613.94 123.93 0.37 13459 13461 3 -21910.9 -7619.5 120.92 0.125 13460 brian-1.4.3/brian/experimental/morphology/spatialneuron.py000066400000000000000000000413601266062647100240550ustar00rootroot00000000000000''' Compartmental neurons See BEP-15 TODO: * Threshold and reset are special (not as normal NeuronGroup because only 1 spike) * Hines method * Point processes * StateMonitor * neuron.plot('gl') * Iteration (over the branch or the entire tree?) ''' from morphology import * from brian.stdunits import * from brian.units import * from brian.reset import NoReset from brian.stateupdater import StateUpdater from brian.equations import Equations from brian.inspection import * from brian.optimiser import * from brian.group import Group from brian.clock import guess_clock from itertools import count from brian.neurongroup import NeuronGroup from scipy.linalg import solve_banded from numpy import zeros, ones, isscalar, diag_indices, pi from numpy.linalg import solve import numpy try: import sympy use_sympy = True except: warnings.warn('sympy not installed: some features in SpatialNeuron will not be available') use_sympy = False __all__ = ['SpatialNeuron', 'CompartmentalNeuron'] class SpatialNeuron(NeuronGroup): """ Compartmental model with morphology. """ def __init__(self, morphology=None, model=None, threshold=None, reset=NoReset(), refractory=0 * ms, level=0, clock=None, unit_checking=True, compile=False, freeze=False, Cm=0.9 * uF / cm ** 2, Ri=150 * ohm * cm): clock = guess_clock(clock) N = len(morphology) # number of compartments if isinstance(model, str): model = Equations(model, level=level + 1) model += Equations(''' v:volt # membrane potential ''') # Process model equations (Im) to extract total conductance and the remaining current if use_sympy: try: membrane_eq=model._string['Im'] # the membrane equation except: raise TypeError,"The transmembrane current Im must be defined" # Check conditional linearity ids=get_identifiers(membrane_eq) _namespace=dict.fromkeys(ids,1.) # there is a possibility of problems here (division by zero) _namespace['v']=AffineFunction() eval(membrane_eq,model._namespace['v'],_namespace) try: eval(membrane_eq,model._namespace['v'],_namespace) except: # not linear raise TypeError,"The membrane current must be linear with respect to v" # Extracts the total conductance from Im, and the remaining current z=symbolic_eval(membrane_eq) symbol_v=sympy.Symbol('v') b=z.subs(symbol_v,0) a=-sympy.simplify(z.subs(symbol_v,1)-b) gtot_str="_gtot="+str(a)+": siemens/cm**2" I0_str="_I0="+str(b)+": amp/cm**2" model+=Equations(gtot_str+"\n"+I0_str,level=level+1) # better: explicit insertion with namespace of v else: raise TypeError,"The Sympy package must be installed for SpatialNeuron" # Equations for morphology (isn't it a duplicate??) eqs_morphology = Equations(""" diameter : um length : um x : um y : um z : um area : um**2 """) full_model = model + eqs_morphology # Create the state updater NeuronGroup.__init__(self, N, model=full_model, threshold=threshold, reset=reset, refractory=refractory, level=level + 1, clock=clock, unit_checking=unit_checking, implicit=True) #Group.__init__(self, full_model, N, unit_checking=unit_checking, level=level+1) #self._eqs = model #var_names = full_model._diffeq_names #self.Cm = Cm # could be a vector? self.Cm = ones(len(self))*Cm # Temporary hack - so that it can be a vector, later self.Ri = Ri self._state_updater = SpatialStateUpdater(self, clock) #S0 = {} # Fill missing units #for key, value in full_model._units.iteritems(): # if not key in S0: # S0[key] = 0 * value #self._S0 = [0] * len(var_names) #for var, i in zip(var_names, count()): # self._S0[i] = S0[var] # Insert morphology self.morphology = morphology self.morphology.compress(diameter=self.diameter, length=self.length, x=self.x, y=self.y, z=self.z, area=self.area) def subgroup(self, N): # Subgrouping cannot be done in this way raise NotImplementedError def __getitem__(self, x): ''' Subgrouping mechanism. self['axon'] returns the subtree named "axon". TODO: self[:] returns the full branch. ''' morpho = self.morphology[x] N = self[morpho._origin:morpho._origin + len(morpho)] N.morphology = morpho return N def __getattr__(self, x): if (x != 'morphology') and ((x in self.morphology._namedkid) or all([c in 'LR123456789' for c in x])): # subtree return self[x] else: return NeuronGroup.__getattr__(self, x) class SpatialStateUpdater_old(StateUpdater): """ State updater for compartmental models. For the moment I assume there is a single branch (=axon). """ def __init__(self, neuron, clock=None): self.eqs = neuron._eqs self.neuron = neuron self._isprepared = False self._state_updater=neuron._state_updater # to update the currents def prepare(self): ''' From Hines 1984 paper, discrete formula is: A_plus*V(i+1)-(A_plus+A_minus)*V(i)+A_minus*V(i-1)=Cm/dt*(V(i,t+dt)-V(i,t))+gtot(i)*V(i)-I0(i) A_plus: i->i+1 A_minus: i->i-1 ** Note: why are the coefficients not symmetrical then? ** This gives the following tridiagonal system: A_plus*V(i+1)-(Cm/dt+gtot(i)+A_plus+A_minus)*V(i)+A_minus*V(i-1)=-Cm/dt*V(i,t)-I0(i) Boundaries, one simple possibility (sealed ends): -(Cm/dt+gtot(n)+A_minus)*V(n)+A_minus*V(n-1)=-Cm/dt*V(n,t)-I0(n) A_plus*V(1)-(Cm/dt+gtot(0)+A_plus)*V(0)=-Cm/dt*V(0,t)-I0(0) For the domain decomposition idea, most can be precalculated, and only the diagonal elements (gtot(i)) and the RHS will change. ''' mid_diameter=.5*(self.neuron.diameter[:-1]+self.neuron.diameter[1:]) # i -> i+1 self.Aplus=mid_diameter**2/(4*self.neuron.diameter[:-1]*self.neuron.length[:-1]**2*self.neuron.Ri) self.Aminus=mid_diameter**2/(4*self.neuron.diameter[1:]*self.neuron.length[1:]**2*self.neuron.Ri) # It seems that Aminus(1) = Aminus[0] def __call__(self, neuron): ''' Updates the state variables. ''' if not self._isprepared: self.prepare() self._isprepared=True # Update the membrane currents (should it be after (no real difference though)) self._state_updater(neuron) ''' x=solve_banded((lower,upper),ab,b) lower = number of lower diagonals = 1 upper = number of upper diagonals = 1 ab = array(l+u+1,M) each row is one diagonal a[i,j]=ab[u+i-j,j] ''' b=-neuron.Cm/neuron.clock.dt*neuron.v-neuron._I0 ab = zeros((3,len(neuron))) # part of it could be precomputed # a[i,j]=ab[1+i-j,j] # ab[1,:]=main diagonal # ab[0,1:]=upper diagonal # ab[2,:-1]=lower diagonal ab[0,1:]=self.Aplus ab[2,:-1]=self.Aminus ab[1,:]=-neuron.Cm/neuron.clock.dt-neuron._gtot ab[1,1:]-=self.Aminus ab[1,:-1]-=self.Aplus neuron.v=solve_banded((1,1),ab,b,overwrite_ab=True,overwrite_b=True) def __len__(self): ''' Number of state variables ''' return len(self.eqs) class SpatialStateUpdater_new(StateUpdater): """ State updater for compartmental models, with several branches. """ def __init__(self, neuron, clock=None): self.eqs = neuron._eqs self.neuron = neuron self._isprepared = False self._state_updater=neuron._state_updater # to update the currents def cut_branches(self,morphology): ''' Recursively cut the branches by setting zero axial resistances. ''' self.invr[morphology._origin]=0 for kid in (morphology.children): self.cut_branches(kid) def number_branches(self,morphology,n=0,parent=-1): ''' Recursively number the branches and return their total number. n is the index number of the current branch. parent is the index number of the parent branch. ''' morphology.index=n morphology.parent=parent nbranches=1 for kid in (morphology.children): nbranches+=self.number_branches(kid,n+nbranches,n) return nbranches def prepare(self): ''' Preparation of data structures. See the relevant document. ''' # Correction for soma (a bit of a hack), so that it has negligible axial resistance if self.neuron.morphology.type=='soma': self.neuron.length[0]=self.neuron.diameter[0]*0.01 # Inverse axial resistance self.invr=zeros(len(self.neuron)) self.invr[1:]=pi/(2*self.neuron.Ri)*(self.neuron.diameter[:-1]*self.neuron.diameter[1:])/\ (self.neuron.length[:-1]+self.neuron.length[1:]) # Note: this would give nan for the soma self.cut_branches(self.neuron.morphology) # Linear systems # The particular solution '''a[i,j]=ab[u+i-j,j]''' # u is the number of upper diagonals = 1 self.ab_star=zeros((3,len(self.neuron))) self.ab_star[0,1:]=self.invr[1:]/self.neuron.area[:-1] self.ab_star[2,:-1]=self.invr[1:]/self.neuron.area[1:] self.ab_star[1,:]=-self.neuron.Cm/self.neuron.clock.dt-self.invr/self.neuron.area self.ab_star[1,:-1]-=self.invr[1:]/self.neuron.area[:-1] # Homogeneous solutions self.ab_plus=zeros((3,len(self.neuron))) self.ab_minus=zeros((3,len(self.neuron))) self.ab_plus[:]=self.ab_star self.ab_minus[:]=self.ab_star self.b_plus=zeros(len(self.neuron)) self.b_minus=zeros(len(self.neuron)) # Solutions self.v_star=zeros(len(self.neuron)) self.u_plus=zeros(len(self.neuron)) self.u_minus=zeros(len(self.neuron)) # Boundary conditions self.boundary_conditions(self.neuron.morphology) # Linear system for connecting branches n=1+self.number_branches(self.neuron.morphology) # number of nodes (2 for the root) self.P=zeros((n,n)) # matrix self.B=zeros(n) # vector RHS self.V=zeros(n) # solution = voltages at nodes def boundary_conditions(self,morphology): ''' Recursively sets the boundary conditions in the linear systems. ''' first=morphology._origin # first compartment last=first+len(morphology.x)-1 # last compartment # Inverse axial resistances at the ends: r0 and rn morphology.invr0=float(pi/(2*self.neuron.Ri)*self.neuron.diameter[first]**2/self.neuron.length[first]) morphology.invrn=float(pi/(2*self.neuron.Ri)*self.neuron.diameter[last]**2/self.neuron.length[last]) # Correction for boundary conditions self.ab_star[1,first]-=float(morphology.invr0/self.neuron.area[first]) # because of units problems self.ab_star[1,last]-=float(morphology.invrn/self.neuron.area[last]) self.ab_plus[1,first]-=float(morphology.invr0/self.neuron.area[first]) # because of units problems self.ab_plus[1,last]-=float(morphology.invrn/self.neuron.area[last]) self.ab_minus[1,first]-=float(morphology.invr0/self.neuron.area[first]) # because of units problems self.ab_minus[1,last]-=float(morphology.invrn/self.neuron.area[last]) # RHS for homogeneous solutions self.b_plus[last]=-float(morphology.invrn/self.neuron.area[last]) self.b_minus[first]=-float(morphology.invr0/self.neuron.area[first]) # Recursive call for kid in (morphology.children): self.boundary_conditions(kid) def __call__(self, neuron): ''' Updates the state variables. ''' if not self._isprepared: self.prepare() self._isprepared=True # Update the membrane currents (should it be after (no real difference though)) self._state_updater(neuron) ''' x=solve_banded((lower,upper),ab,b) lower = number of lower diagonals = 1 upper = number of upper diagonals = 1 ab = array(l+u+1,M) each row is one diagonal a[i,j]=ab[u+i-j,j] That is: a[i,j]=ab[1+i-j,j] ab[1,:]=main diagonal ab[0,1:]=upper diagonal ab[2,:-1]=lower diagonal ''' # Particular solution b=-neuron.Cm/neuron.clock.dt*neuron.v-neuron._I0 ab = zeros((3,len(neuron))) ab[:]=self.ab_star ab[1,:]-=neuron._gtot self.v_star[:]=solve_banded((1,1),ab,b,overwrite_ab=True,overwrite_b=True) # Homogeneous solutions b[:]=self.b_plus ab[:]=self.ab_plus ab[1,:]-=neuron._gtot self.u_plus[:]=solve_banded((1,1),ab,b,overwrite_ab=True,overwrite_b=True) b[:]=self.b_minus ab[:]=self.ab_minus ab[1,:]-=neuron._gtot self.u_minus[:]=solve_banded((1,1),ab,b,overwrite_ab=True,overwrite_b=True) # Solve the linear system connecting branches self.P[:]=0 self.B[:]=0 self.fill_matrix(self.neuron.morphology) self.V = solve(self.P,self.B) # Calculate solutions by linear combination self.linear_combination(self.neuron.morphology) def linear_combination(self,morphology): ''' Calculates solutions by linear combination ''' first=morphology._origin # first compartment last=first+len(morphology.x)-1 # last compartment i=morphology.index+1 i_parent=morphology.parent+1 self.neuron.v[first:last+1]=self.v_star[first:last+1]+self.V[i_parent]*self.u_minus[first:last+1]\ +self.V[i]*self.u_plus[first:last+1] # Recursive call for kid in (morphology.children): self.linear_combination(kid) def fill_matrix(self,morphology): ''' Recursively fills the matrix of the linear system that connects branches together. ''' first=morphology._origin # first compartment last=first+len(morphology.x)-1 # last compartment i=morphology.index+1 i_parent=morphology.parent+1 # Towards parent if i==1: # first branch, sealed end self.P[0,0]=self.u_minus[first]-1 self.P[0,1]=self.u_plus[first] self.B[0]=-self.v_star[first] else: self.P[i_parent,i_parent]+=(1-self.u_minus[first])*morphology.invr0 self.P[i_parent,i]-=self.u_plus[first]*morphology.invr0 self.B[i_parent]+=self.v_star[first]*morphology.invr0 # Towards children self.P[i,i]=(1-self.u_plus[last])*morphology.invrn self.P[i,i_parent]=-self.u_minus[last]*morphology.invrn self.B[i]=self.v_star[last]*morphology.invrn # Recursive call for kid in (morphology.children): self.fill_matrix(kid) def __len__(self): ''' Number of state variables ''' return len(self.eqs) SpatialStateUpdater = SpatialStateUpdater_new CompartmentalNeuron = SpatialNeuron if __name__ == '__main__': from brian import * morpho = Morphology('oi24rpy1.CNG.swc') # visual L3 pyramidal cell print len(morpho), "compartments" El = -70 * mV ENa = 50 * mV eqs = ''' # The same equations for the whole neuron, but possibly different parameter values Im=gl*(El-v)+gNa*m**3*(ENa-v) : amp/cm**2 # distributed transmembrane current gl : siemens/cm**2 # spatially distributed conductance gNa : siemens/cm**2 dm/dt=(minf-m)/(0.3*ms) : 1 minf=1./(1+exp(-(v+30*mV)/(6*mV))) : 1 ''' neuron = SpatialNeuron(morphology=morpho, threshold="axon[50*um].v>0*mV", model=eqs, refractory=4 * ms, Cm=0.9 * uF / cm ** 2, Ri=150 * ohm * cm) neuron.axon[0 * um:50 * um].gl = 1e-3 * siemens / cm ** 2 print sum(neuron.axon.gl) print neuron.axon[40 * um].gl #branch=neuron.axon[0*um:50*um] neuron.morphology.plot() show() brian-1.4.3/brian/experimental/morphology/spatialneuron_remy.py000066400000000000000000000410351266062647100251100ustar00rootroot00000000000000''' Compartmental neurons ''' from brian.experimental.morphology import Morphology from brian.stdunits import * from brian.units import * from brian.reset import NoReset from brian.stateupdater import StateUpdater from brian.inspection import * from brian.optimiser import * from itertools import count from brian.neurongroup import NeuronGroup from scipy.linalg import solve_banded from numpy import zeros, ones, isscalar, diag_indices from numpy.linalg import solve from brian.clock import guess_clock from brian.equations import Equations import functools import warnings from math import ceil, log try: import weave except ImportError: from scipy import weave from time import time import trace import numpy try: import sympy use_sympy = True except: warnings.warn('sympy not installed: some features in SpatialNeuron will not be available') use_sympy = False __all__ = ['SpatialNeuron', 'CompartmentalNeuron'] class SpatialNeuron(NeuronGroup): """ Compartmental model with morphology. """ def __init__(self, morphology=None, model=None, threshold=None, reset=NoReset(), refractory=0 * ms, level=0, clock=None, unit_checking=True, compile=False, freeze=False, implicit=True, Cm=0.9 * uF / cm ** 2, Ri=150 * ohm * cm, bc_type = 2, diffeq_nonzero=True): clock = guess_clock(clock) N = len(morphology) # number of compartments if isinstance(model, str): model = Equations(model, level=level + 1) model += Equations(''' v:volt # membrane potential ''') # Process model equations (Im) to extract total conductance and the remaining current if use_sympy: try: membrane_eq=model._string['Im'] # the membrane equation except: raise TypeError,"The transmembrane current Im must be defined" # Check conditional linearity ids=get_identifiers(membrane_eq) _namespace=dict.fromkeys(ids,1.) # there is a possibility of problems here (division by zero) _namespace['v']=AffineFunction() eval(membrane_eq,model._namespace['v'],_namespace) try: eval(membrane_eq,model._namespace['v'],_namespace) except: # not linear raise TypeError,"The membrane current must be linear with respect to v" # Extracts the total conductance from Im, and the remaining current z=symbolic_eval(membrane_eq) symbol_v=sympy.Symbol('v') b=z.subs(symbol_v,0) a=-sympy.simplify(z.subs(symbol_v,1)-b) gtot_str="_gtot="+str(a)+": siemens/cm**2" I0_str="_I0="+str(b)+": amp/cm**2" model+=Equations(gtot_str+"\n"+I0_str,level=level+1) # better: explicit insertion with namespace of v else: raise TypeError,"The Sympy package must be installed for SpatialNeuron" # Equations for morphology (isn't it a duplicate??) eqs_morphology = Equations(""" diameter : um length : um x : um y : um z : um area : um**2 """) full_model = model + eqs_morphology NeuronGroup.__init__(self, N, model=full_model, threshold=threshold, reset=reset, refractory=refractory, level=level + 1, clock=clock, unit_checking=unit_checking, implicit=implicit) self.model_with_diffeq_nonzero = diffeq_nonzero self._state_updater = SpatialStateUpdater(self, clock) self.Cm = ones(len(self))*Cm self.Ri = Ri self.bc_type = bc_type #default boundary condition on leaves self.bc = ones(len(self)) # boundary conditions on branch points self.changed = True # Insert morphology self.morphology = morphology self.morphology.compress(diameter=self.diameter, length=self.length, x=self.x, y=self.y, z=self.z, area=self.area) def subgroup(self, N): # Subgrouping cannot be done in this way raise NotImplementedError def __getitem__(self, x): ''' Subgrouping mechanism. self['axon'] returns the subtree named "axon". TODO: self[:] returns the full branch. ''' morpho = self.morphology[x] N = self[morpho._origin:morpho._origin + len(morpho)] N.morphology = morpho return N def __getattr__(self, x): if (x != 'morphology') and ((x in self.morphology._namedkid) or all([c in 'LR123456789' for c in x])): # subtree return self[x] else: return NeuronGroup.__getattr__(self, x) class SpatialStateUpdater(StateUpdater): """ State updater for compartmental models. """ def __init__(self, neuron, clock=None): self.eqs = neuron._eqs self.neuron = neuron self._isprepared = False self._state_updater=neuron._state_updater # to update the currents self.first_test_gtot=True self.callcount=0 def prepare_branch(self, morphology, mid_diameter,ante=0): ''' 1) fill neuron.branches and neuron.index with information about the morphology of the neuron 2) change some wrong values in Aplus and Aminus. Indeed these were correct only if the neuron is a linear cable. Knowledge of the morphology gives correct values. 3) fill neuron.bc (boundary conditions) ''' branch = morphology.branch() i=branch._origin j= i + len(branch) - 2 endpoint = j + 1 self.neuron.index[i:endpoint+1] = self.neuron.BPcount children_number = 0 #connections between branches for x in (morphology.children):#parent of segment n isn't always n-1 at branch points. We need to change Aplus and Aminus gc = 2 * msiemens/cm**2 startpoint = x._origin mid_diameter[startpoint] = .5*(self.neuron.diameter[endpoint]+self.neuron.diameter[startpoint]) self.Aminus[startpoint]=mid_diameter[startpoint]**2/(4*self.neuron.diameter[startpoint]*self.neuron.length[startpoint]**2*self.neuron.Ri) if endpoint>0: self.Aplus[startpoint]=mid_diameter[startpoint]**2/(4*self.neuron.diameter[endpoint]*self.neuron.length[endpoint]**2*self.neuron.Ri) else : self.Aplus[startpoint]=gc self.Aminus[startpoint]=gc children_number+=1 #boundary conditions pointType = self.neuron.bc[endpoint] hasChild = (children_number>0) if (not hasChild) and (pointType == 1): #if the branch point is a leaf of the tree : apply default boundary condition self.neuron.bc[endpoint] = self.neuron.bc_type #extract informations about the branches index_ante = self.neuron.index[ante] bp = endpoint index = self.neuron.BPcount self.i_list.append(i) self.j_list.append(j) self.bp_list.append(bp) self.pointType_list.append(max(1,pointType)) self.pointTypeAnte_list.append(max(1,self.neuron.bc[ante])) self.temp[index] = index_ante self.id.append(index) self.test_list.append((j-i+2)>1) for x in xrange(j-i+2): self.ante_list.append(ante) self.post_list.append(bp) if index_ante == 0: self.ind0.append(index) if pointType==0 : self.ind_bctype_0.append(bp) #initialize the parts of the linear systems that will not change if (j-i+2)>1: #j-i+2 = len(branch) #initialize ab self.ab[0,i:j]= self.Aplus[i:j] self.ab[2,i:j]= self.Aminus[i+1:j+1] #initialize bL VL0 = 1 * volt self.bL[i] = (- VL0 * self.Aminus[i]) #initialize bR VR0 = 1 * volt self.bR[j] = (- VR0 * self.Aplus[j+1]) self.neuron.BPcount += 1 for x in (morphology.children): self.prepare_branch(x,mid_diameter,endpoint) def prepare(self): ''' From Hines 1984 paper, discrete formula is: A_plus*V(i+1)-(A_plus+A_minus)*V(i)+A_minus*V(i-1)=Cm/dt*(V(i,t+dt)-V(i,t))+gtot(i)*V(i)-I0(i) A_plus: i->i+1 A_minus: i->i-1 This gives the following tridiagonal system: A_plus*V(i+1)-(Cm/dt+gtot(i)+A_plus+A_minus)*V(i)+A_minus*V(i-1)=-Cm/dt*V(i,t)-I0(i) ''' mid_diameter = zeros(len(self.neuron)) # mid(i) : (i-1) <-> i mid_diameter[1:] = .5*(self.neuron.diameter[:-1]+self.neuron.diameter[1:]) self.Aplus = zeros(len(self.neuron)) # A+ i -> j = Aplus(j) self.Aminus = zeros(len(self.neuron)) # A- i <- j = Aminus(j) self.Aplus[1]= mid_diameter[1]**2/(4*self.neuron.diameter[1]*self.neuron.length[1]**2*self.neuron.Ri) self.Aplus[2:]=mid_diameter[2:]**2/(4*self.neuron.diameter[1:-1]*self.neuron.length[1:-1]**2*self.neuron.Ri) self.Aminus[1:]=mid_diameter[1:]**2/(4*self.neuron.diameter[1:]*self.neuron.length[1:]**2*self.neuron.Ri) self.neuron.index = zeros(len(self.neuron),int) # gives the index of the branch containing the current compartment self.neuron.BPcount = 0 # number of branch points (or branches). = len(self.neuron.branches) #the three solutions for V on a branch self.vL = zeros((len(self.neuron)),numpy.float64) self.vR = zeros((len(self.neuron)),numpy.float64) self.d = zeros((len(self.neuron)),numpy.float64) #matrix and right hand in the tridiagonal systems that we solve to find vL, vR and d. self.bL = zeros((len(self.neuron)),numpy.float64) self.bR = zeros((len(self.neuron)),numpy.float64) self.bd = zeros((len(self.neuron)),numpy.float64) self.ab = zeros((3,len(self.neuron))) self.ab1_base = zeros(len(self.neuron)) self.gtot = zeros(len(self.neuron)) self.I0 = zeros(len(self.neuron)) self.i_list = [] #the indexes of the first points of the branches in the neuron. len = neuron.BPcount self.j_list = [] #the indexes of the last points of the branches in the neuron. len = neuron.BPcount self.bp_list = [] #the indexes of the branch points in the neuron. len = neuron.BPcount self.pointType_list = [] #boundary condition on bp. len = neuron.BPcount self.pointTypeAnte_list = [] #boundary condition on ante. len = neuron.BPcount self.index_ante_list1 = [] #index of the parent branch of the current branch. index is in [0,neuron.BPcount] self.index_ante_list2 = [] self.ante_list = [] #the indexes in the neuron of the branch points connected to i, for every compartment. len = len(self.neuron) self.post_list = [] #for every compartment, contains the index of the branch point. len = len(self.neuron) self.test_list = [] #for each branch : 1 if the branch has more than 3 compartments, else 0 self.id = [] #list of every integer in [0,neuron.BPcount]. used in step to change some values in a matrix self.temp = zeros(len(self.neuron)) #used to construct index_ante_list0, 1, 2. self.ind0 = [] #indexes (in [0,neuron.BPcount]) of the branches connected to compartment 0 self.ind_bctype_0 = [] #indexes of the branch point with boundary condition 0 (constant V) # prepare_branch : fill the lists, changes Aplus & Aminus self.prepare_branch(self.neuron.morphology, mid_diameter,0) self.index_ante_list1, self.ind1 = numpy.unique(numpy.array(self.temp,int),return_index=True) self.ind1 = numpy.sort(self.ind1) self.index_ante_list1 = self.temp[self.ind1] self.index_ante_list1 = list(self.index_ante_list1) self.ind2 = [] for x in xrange(self.neuron.BPcount): self.ind2.append(x) self.ind2 = numpy.delete(self.ind2,self.ind1,None) self.ind2 = numpy.setdiff1d(self.ind2, self.ind0, assume_unique=True) self.index_ante_list2 = self.temp[self.ind2] self.index_ante_list2 = list(self.index_ante_list2) self.index_ante_list = [] for idx in xrange(self.neuron.BPcount): self.index_ante_list.append(self.temp[idx]) # linear system P V = B used to deal with the voltage at branch points and take boundary conditions into account. self.P = zeros((self.neuron.BPcount,self.neuron.BPcount)) self.B = zeros(self.neuron.BPcount) self.solution_bp = zeros(self.neuron.BPcount) #in case of a sealed end, Aminus and Aplus are doubled : self.Aminus_bp = self.Aminus[self.bp_list] self.Aminus_bp [:] *= self.pointType_list[:] self.Aplus_i = self.Aplus[self.i_list] self.Aplus_i[:] *= self.pointTypeAnte_list[:] def step(self, neuron): if self.first_test_gtot and isscalar(neuron._gtot): self.first_test_gtot=False #neuron._gtot = ones(len(neuron)) * neuron._gtot self.gtot[:] = neuron._gtot #this compute the value of neuron._gtot. #if we call neuron._gtot[1] and then neuron._gtot[2] it does 2 computations #here we call it only one time on the whole array. this is much faster self.I0 = neuron._I0 #------------------------------------solve tridiagonal systems on the branchs------------------------- #ab is the matrix in the tridiagonal systems describing the branches. #bd is a right hand in one of these tridiagonal systems. if self.neuron.changed : # neuron.changed = True <=> there was a new input somewhere. example : the user does neuron.I[x] = y self.update_ab_base() self.update_ab_gtot() self.update_bd() self.calculate_vd_vL_vR() self.neuron.changed = False #-----------fill P and B, matrix and right hand used to find the voltage at the branch points----------------- self.P[:,:] = 0 self.B[:] = 0 Cm = neuron.Cm[self.bp_list] dt = neuron.clock.dt gtot = self.gtot[self.bp_list] I0 = self.I0[self.bp_list] v_bp = neuron.v[self.bp_list] vLleft = self.vL[self.i_list] vLright = self.vL[self.j_list] vRleft = self.vR[self.i_list] vRright = self.vR[self.j_list] dleft = self.d[self.i_list] dright = self.d[self.j_list] vLleft[:] *= self.test_list[:] #if a branch has less than 3 compartments, this equals 0. #thus we can do the same work on every branch point. vLright[:] *= self.test_list[:] vRleft[:] *= self.test_list[:] vRright[:] *= self.test_list[:] dleft[:] *= self.test_list[:] dright[:] *= self.test_list[:] self.B[self.index_ante_list1] += - self.Aplus_i[self.ind1[:]] * dleft[self.ind1[:]] self.B[self.index_ante_list2] += - self.Aplus_i[self.ind2[:]] * dleft[self.ind2[:]] self.B[0] += sum(- self.Aplus_i[self.ind0[:]] * dleft[self.ind0[:]]) self.P[(self.index_ante_list1,self.index_ante_list1)] += self.Aplus_i[self.ind1[:]] * (vLleft[self.ind1[:]] - 1) self.P[(self.index_ante_list2,self.index_ante_list2)] += self.Aplus_i[self.ind2[:]] * (vLleft[self.ind2[:]] - 1) self.P[0,0] += sum(self.Aplus_i[self.ind0[:]] * (vLleft[self.ind0[:]] - 1)) di = diag_indices(neuron.BPcount) self.B[:] += - Cm[:]/dt * second * v_bp[:] - I0[:] - self.Aminus_bp[:] * dright[:] self.P[di] += - Cm[:]/dt * second - gtot[:] + self.Aminus_bp[:] * (vRright[:] - 1) self.P[(self.id,self.index_ante_list)] += self.Aminus_bp[:] *vLright[:] self.P[(self.index_ante_list,self.id)] += self.Aplus_i[:] *vRleft[:] self.P[self.ind_bctype_0,:] = 0 self.P[(self.ind_bctype_0,self.ind_bctype_0)] = 1 self.B[self.ind_bctype_0] = neuron.v[self.ind_bctype_0] #------------------------------------------------------solve PV=B----------------------------------- self.solution_bp = solve(self.P,self.B) neuron.v[self.bp_list] = self.solution_bp[:] #-------------------------------------------------------update v------------------------------------- self.finalize_v_global() def update_ab_base(self): #part of ab that doesn't change if there is no prompt from the operator. self.ab1_base[:-1] = (- self.neuron.Cm[:-1] / self.neuron.clock.dt * second - self.Aminus[:-1] - self.Aplus[1:]) self.ab1_base[-1] = (- self.neuron.Cm[-1] / self.neuron.clock.dt * second - self.Aminus[-1]) def update_ab_gtot(self): #this is called every step. changing part of ab. self.ab[1,:] = self.ab1_base[:] - self.neuron._gtot def update_bd(self): #bd is a right hand side in a tridiagonal system self.bd[:] = -self.neuron.Cm[:] / self.neuron.clock.dt * self.neuron.v[:] - self.neuron._I0[:] def calculate_vd_vL_vR(self): for index in xrange(self.neuron.BPcount) : if self.test_list[index] : i = self.i_list[index] j = self.j_list[index] self.vL[i:j+1] = solve_banded((1,1),self.ab[:,i:j+1],self.bL[i:j+1],overwrite_ab=False,overwrite_b=False) self.vR[i:j+1] = solve_banded((1,1),self.ab[:,i:j+1],self.bR[i:j+1],overwrite_ab=False,overwrite_b=False) self.d[i:j+1] = solve_banded((1,1),self.ab[:,i:j+1],self.bd[i:j+1],overwrite_ab=False,overwrite_b=False) def finalize_v_global(self): #V(x) = V(left) * vL(x) + V(right) * vR(x) + d(x) self.neuron.v[:] = self.vL[:] * self.neuron.v[self.ante_list[:]] + self.vR[:] * self.neuron.v[self.post_list[:]] + self.d[:] self.neuron.v[self.bp_list] = self.solution_bp[:] def __call__(self, neuron): ''' Updates the state variables. ''' if not self._isprepared: self.prepare() self._isprepared=True print "state updater prepared" self.callcount+=1 print self.callcount #Update I,V if neuron.changed : self._state_updater.changed = True self._state_updater(neuron) #update the currents self.step(neuron) #update V def __len__(self): ''' Number of state variables ''' return len(self.eqs) CompartmentalNeuron = SpatialNeuron brian-1.4.3/brian/experimental/multilinearstateupdater.py000066400000000000000000000213631266062647100237460ustar00rootroot00000000000000from brian import * from brian.stateupdater import get_linear_equations_solution_numerically, get_linear_equations try: import weave except ImportError: from scipy import weave from scipy import linalg import numpy import inspect from itertools import count import re __all__ = ['MultiLinearStateUpdater', 'get_multilinear_state_updater', 'MultiLinearNeuronGroup'] __all__ = ['MultiLinearStateUpdater', 'get_multilinear_state_updater', 'MultiLinearNeuronGroup'] def get_multilinear_matrices(eqs, subs, clock=None): ''' Returns the matrices M and B for the linear model dX/dt = M(X-B), where eqs is an Equations object. ''' if clock is None: clock = guess_clock() nsubs = len(subs[subs.keys()[0]]) # Otherwise assumes it is given in functional form n = len(eqs._diffeq_names) # number of state variables dynamicvars = eqs._diffeq_names # Calculate B AB = zeros((n, 1, nsubs)) d = dict.fromkeys(dynamicvars) for j in range(n): if dynamicvars[j] in subs: d[dynamicvars[j]] = subs[j] else: d[dynamicvars[j]] = 0. * eqs._units[dynamicvars[j]] for var, i in zip(dynamicvars, count()): AB[i, 0, :] = -eqs.apply(var, d) # Calculate A M = zeros((n, n, nsubs)) for i in range(n): for j in range(n): if dynamicvars[j] in subs: d[dynamicvars[j]] = subs[j] else: d[dynamicvars[j]] = 0. * eqs._units[dynamicvars[j]] if dynamicvars[i] in subs: d[dynamicvars[i]] = subs[i] else: if isinstance(eqs._units[dynamicvars[i]], Quantity): d[dynamicvars[i]] = Quantity.with_dimensions(1., eqs._units[dynamicvars[i]].get_dimensions()) else: d[dynamicvars[i]] = 1. for var, j in zip(dynamicvars, count()): M[j, i, :] = eqs.apply(var, d) + AB[j] allM = M allAB = AB AiBi = [] for i in xrange(nsubs): try: M = reshape(allM[:, :, i], allM.shape[:2]) AB = reshape(allAB[:, :, i], allAB.shape[:2]) C = linalg.solve(M, AB) A = linalg.expm(M * clock.dt) B = -dot(A, C) + C except LinAlgError: numeulersteps = 100 deltat = clock.dt / numeulersteps E = eye(n) + deltat * M C = eye(n) D = zeros((n, 1)) for step in xrange(numeulersteps): C, D = dot(E, C), dot(E, D) - AB * deltat A, B = C, D AiBi.append((A, B)) return AiBi def get_multilinear_state_updater(eqs, subs, level=0, clock=None): ''' Make a multilinear state updater Arguments: ``eqs`` should be the equations, and must be a string not an :class:`Equations` object. ``subs`` A dictionary of arrays, each key k is a variable name, each value is an equally sized array of values it takes, this array should have the size of the number of neurons in the group. ``level`` How many levels up to look for the equations' namespace. ``clock`` If you want. ''' neweqs = Equations(eqs, level=level + 1) neweqs.prepare() AiBi = get_multilinear_matrices(neweqs, subs, clock=clock) n = AiBi[0][0].shape[0] nsubs = len(subs[subs.keys()[0]]) A = numpy.zeros((n, n, nsubs)) B = numpy.zeros((n, nsubs)) for i, (Ai, Bi) in enumerate(AiBi): A[:, :, i] = Ai B[:, i] = Bi.squeeze() return MultiLinearStateUpdater(A, B) class MultiLinearStateUpdater(StateUpdater): ''' A StateUpdater with one differential equation for each neuron Initialise with: ``A``, ``B`` Arrays, see below. The update step for neuron i is S[:,i] = dot(A[:,:,i], S[:,i]) + B[:, i]. ''' def __init__(self, A, B=None): self.A = A self.B = B self._useaccel = get_global_preference('useweave') self._cpp_compiler = get_global_preference('weavecompiler') self._extra_compile_args = ['-O3'] if self._cpp_compiler == 'gcc': self._extra_compile_args += get_global_preference('gcc_options') # ['-march=native', '-ffast-math'] def __call__(self, P): if self._useaccel: n = len(P) m = len(self) S = P._S A = self.A B = self.B code = ''' double x[m]; for(int i=0;i=len(self.offsets): return array([], dtype=int) return self.I[self.offsets[t]:self.offsets[t+1]] ########### AER loading stuff ###################### def load_multiple_AER(filename, check_sorted = False, relative_time = False, directory = '.'): f=open(filename,'rb') line = f.readline() res = [] line = line.strip('\n') while not line == '': res.append(load_AER(os.path.join(directory, line), check_sorted = check_sorted, relative_time = relative_time)) line = f.readline() f.close() return res def load_AER(filename, check_sorted = False, relative_time = True): ''' Loads AER data files for use in Brian. Returns a list containing tuples with a vector of addresses and a vector of timestamps (ints, unit is usually usecond). It can load any kind of .dat, or .aedat files. Note: For index files (that point to multiple .(ae)dat files) it will return a list containing tuples as for single files. Keyword Arguments: If check_sorted is True, checks if timestamps are sorted, and sort them if necessary. If relative_time is True, it will set the first spike time to zero and all others relatively to that precise time (avoid negative timestamps, is definitely a good idea). Hence to use those data files in Brian, one should do: addr, timestamp = load_AER(filename, relative_time = True) G = AERSpikeGeneratorGroup((addr, timestamps)) ''' l = filename.split('.') ext = l[-1].strip('\n') filename = filename.strip('\n') directory = os.path.dirname(filename) if ext == 'aeidx': #AER data points to different AER files return load_multiple_AER(filename, check_sorted = check_sorted, relative_time = relative_time, directory = directory) elif not (ext == 'dat' or ext == 'aedat'): raise ValueError('Wrong extension for AER data, should be dat, or aedat, it was '+ext) # This is inspired by the following Matlab script: # http://jaer.svn.sourceforge.net/viewvc/jaer/trunk/host/matlab/loadaerdat.m?revision=2001&content-type=text%2Fplain f=open(filename,'rb') version=1 # default (if not found in the file) # Skip header and look for version number line = f.readline() while line[0] == '#': if line[:9] == "#!AER-DAT": version = int(float(line[9:-1])) line = f.readline() line += f.read() f.close() if version==1: print 'Loading version 1 file '+filename ''' Format is: sequence of (addr = 2 bytes,timestamp = 4 bytes) Number format is big endian ('>') ''' ## This commented paragraph is the non-vectorized version #nevents=len(line)/6 #for n in range(nevents): # events.append(unpack('>HI',line[n*6:(n+1)*6])) # address,timestamp x=fromstring(line, dtype=int16) # or uint16? x=x.reshape((len(x)/3,3)) addr=x[:,0].newbyteorder('>') timestamp=x[:,1:].copy() timestamp.dtype=int32 timestamp=timestamp.newbyteorder('>').flatten() else: # version==2 print 'Loading version 2 file '+filename ''' Format is: sequence of (addr = 4 bytes,timestamp = 4 bytes) Number format is big endian ('>') ''' ## This commented paragraph is the non-vectorized version #nevents=len(line)/8 #for n in range(nevents): # events.append(unpack('>II',line[n*8:(n+1)*8])) # address,timestamp x = fromstring(line, dtype=int32).newbyteorder('>') addr = x[::2] if len(addr) == len(x[1::2]): timestamp = x[1::2] else: print """It seems there was a problem with the AER file, timestamps and addr don't have the same length!""" timestamp = x[1::2] if check_sorted: # Sorts the events if necessary if any(diff(timestamp)<0): # not sorted ind = argsort(timestamp) addr,timestamp = addr[ind],timestamp[ind] if (timestamp<0).all(): print 'Negative timestamps' if relative_time: t0 = min(timestamp) timestamp -= t0 return addr,timestamp HEADER = """#!AER-DAT2.0\n# This is a raw AE data file - do not edit\n# Data format is int32 address, int32 timestamp (8 bytes total), repeated for each event\n# Timestamps tick is 1 us\n# created with the Brian simulator on """ def save_AER(spikemonitor, f): ''' Saves the SpikeMonitor's contents to a file in aedat format. File should have 'aedat' extension. One can specify an open file, or, alternatively the filename as a string. Usage: save_AER(spikemonitor, file) ''' if isinstance(spikemonitor, SpikeMonitor): spikes = spikemonitor.spikes else: spikes = spikemonitor if isinstance(f, str): strinput = True f = open(f, 'wb') l = f.name.split('.') if not l[-1] == 'aedat': raise ValueError('File should have aedat extension') header = HEADER header += str(datetime.datetime.now()) + '\n' f.write(header) # i,t=zip(*spikes) for (i,t) in spikes: addr = struct.pack('>i', i) f.write(addr) time = struct.pack('>i', int(ceil(float(t/usecond)))) f.write(time) if strinput: f.close() class AERSpikeMonitor(FileSpikeMonitor): """Records spikes to an AER file Initialised as:: FileSpikeMonitor(source, filename[, record=False]) Does everything that a :class:`SpikeMonitor` does except ONLY records the spikes to the named file in AER format. Has one additional method: ``close_file()`` Closes the file manually (will happen automatically when the program ends). """ def __init__(self, source, filename, record=False, delay=0): super(FileSpikeMonitor, self).__init__(source, record, delay) self.filename = filename self.f = open(filename, 'wb') header = HEADER header += str(datetime.datetime.now()) + '\n' self.f.write(header) def propagate(self, spikes): # super(AERSpikeMonitor, self).propagate(spikes) # TODO do it better, no struct.pack! check numpy doc for # addr = array(spikes).newbyteorder('>') for i in spikes: addr = struct.pack('>i', i) self.f.write(addr) time = struct.pack('>i', int(ceil(float(self.source.clock.t/usecond)))) self.f.write(time) ########### AER addressing stuff ###################### def extract_DVS_event(addr): ''' Extracts retina event from an address or a vector of addresses. Chip: Digital Vision Sensor (DVS) http://siliconretina.ini.uzh.ch/wiki/index.php Returns: x, y, polarity (ON/OFF: 1/-1) ''' retina_size=128 xmask = 0xfE # x are 7 bits (64 cols) ranging from bit 1-8 ymask = 0x7f00 # y are also 7 bits xshift=1 # bits to shift x to right yshift=8 # bits to shift y to right polmask=1 # polarity bit is LSB x = retina_size - 1 - ((addr & xmask) >> xshift) y = (addr & ymask) >> yshift pol = 1 - 2*(addr & polmask) # 1 for ON, -1 for OFF return x,y,pol def extract_AMS_event(addr): ''' Extracts cochlea event from an address or a vector of addresses Chip: Silicon Cochlea (AMS) Returns: side, channel, filternature More precisely: side: 0 is left, 1 is right channel: apex (LF) is 63, base (HF) is 0 filternature: 0 is lowpass, 1 is bandpass ''' # Reference: # ch.unizh.ini.jaer.chip.cochlea.CochleaAMSNoBiasgen.Extractor in the jAER package (look in the javadoc) # also the cochlea directory in jAER/host/matlab has interesting stuff # the matlab code was used to write this function. I don't understand the javadoc stuff #cochlea_size = 64 xmask = 31 # x are 5 bits 32 channels) ranging from bit 1-5 ymask = 32 # y (one bit) determines left or right cochlea xshift=0 # bits to shift x to right yshift=5 # bits to shift y to right channel = 1 + ((addr & xmask) >> xshift) side = (addr & ymask) >> yshift lpfBpf = mod(addr, 2) # leftRight = mod(addr, 4) return (lpfBpf, side, channel) if __name__=='__main__': path=r'C:Users\Romain\Desktop\jaerSampleData\DVS128' filename=r'\Tmpdiff128-2006-02-03T14-39-45-0800-0 tobi eye.dat' addr,timestamp=load_AER(path+filename) brian-1.4.3/brian/experimental/neuromorphic/__init__.py000066400000000000000000000001031266062647100232310ustar00rootroot00000000000000from realtime import * from AER import * from spikequeue import *brian-1.4.3/brian/experimental/neuromorphic/example_AER.py000066400000000000000000000026441266062647100236300ustar00rootroot00000000000000''' This example demonstrates how to load AER data files and use them as a SpikeGeneratorGroup in Brian For more information either check the documentation in the package, post to the google group, or send me an email victor.benichoux@ens.fr ''' from brian import * from brian.experimental.neuromorphic import * filename = '/path/to/file' # support .aedat and .aeidx files addr, timestamps = load_AER(filename) # load the data # addr contains a list of addresses # timestamps the list of spike times (the first spike comes in at t = 0, but you may change that # Note: In the case of an aeidx file, the value returned is a list of tuples (addr, timestamp) group = AERSpikeGeneratorGroup((addr, timestamps))# Create an AER group # at that point you can do whatever you want, it is a regular Brian neuron group # * Events addressing * # # We provide with two event extraction functions that fetch the # specialized addresses of the DVS retina and the AMS cochlea. # For example: (x, y, pol) = extract_DVS_event(addr) # returns the x and y positions of the neuron, alongside the polarity # (on/off) of the event # See the documentation for more on this. M = SpikeMonitor(group)# monitor it run(group.maxtime) # this group has an additional attribute maxtime # that gives you the last spike time # plot it raster_plot(M) show() # Additionally you can save any SpikeMonitor to and aedat file format as follows save(M, './dummy.aedat') brian-1.4.3/brian/experimental/neuromorphic/notes.txt000066400000000000000000000012531266062647100230200ustar00rootroot00000000000000Current state ------------- AER.py: * AERSpikeGeneratorGroup: see with Dan for integrating in SpikeGeneratorGroup? * FastDCThreshold: other name? * load_AER: ok * load_multiple_AER: ok, hidden * save_AER: mapping, vectorisation, argument possible (addr,timestamp) * extract_DVS_event: ok * extract_AMS_event: ok realtime.py: -> network.py spikequeue.py: unused Others/TODO: * SpikeMonitor.save -> AER? (see with Dan+Tobi). Pb possible avec dt=1 µs. idea: SpikeMonitor.save(filename,dt=1*us) see with PyNN guys et al? * Send UDP spikes to jAER * Docs * (someday) online spike input via AERSpikeGeneratorGroup (use SpikeContainer) * A few examples brian-1.4.3/brian/experimental/neuromorphic/realtime.py000066400000000000000000000037401266062647100233060ustar00rootroot00000000000000""" Real-time Brian See BEP-20-AER interface """ from brian.network import NetworkOperation from brian.clock import EventClock from brian.stdunits import ms from time import sleep,time __all__=['RealtimeController'] class RealtimeController(NetworkOperation): ''' Pauses are inserted so that Brian runs always between real time and about 50 ms ahead of it (probably a bit more). Principle: every 50 ms, so we insert a sleep() to synchronize. Typical use:: R=RealtimeController() run(1*second) R.reinit() # will resynchronise real time at next run run(2*second) ''' def __init__(self,dt=50*ms,verbose=False): ''' dt is the resynchronisation period ''' self.clock = EventClock(dt=dt) # this could be a global preference self.when = 'end' self.first_time=True self.verbose=verbose # for debugging def __call__(self): # First time: synchronise Brian and real time if self.first_time: # stores the start time self.synchronise() self.first_time=False real_time=time()+self.offset if self.verbose: print self.clock.t,real_time if self.clock._treal_time: # synchronize real time and clock sleep(self.clock._t-real_time) def synchronise(self): ''' Sets the offset to the real clock so that Brian and real time are synchronised. ''' self.offset=self.clock._t-time() def reinit(self): ''' The clock synchronise with real time at the next call. ''' self.first_time=True if __name__=='__main__': from brian import * R=RealtimeController(dt=100*ms,verbose=True) run(3*second) sleep(1*second) R.reinit() # try to comment it! run(2*second) brian-1.4.3/brian/experimental/neuromorphic/spikequeue.py000066400000000000000000000064051266062647100236650ustar00rootroot00000000000000""" SpikeQueue This is really a sketch. """ #from brian.utils.circular import SpikeContainer #from brian.neurongroup import NeuronGroup #from brian.directcontrol import SpikeGeneratorGroup from brian import * from numpy import unique from time import time __all__=['SpikeQueue'] class SpikeQueue(NeuronGroup): ''' A group that sends spikes like SpikeGeneratorGroup, but in a vectorised way, forgetting past events. Initialised with a vector of spike times and a vector of corresponding neuron indices. ''' def __init__(self, N, spiketimes, neurons, clock=None, check_sorted=True): clock = guess_clock(clock) NeuronGroup.__init__(self, N, model=LazyStateUpdater(), clock=clock) # Check if spike times are sorted if check_sorted: # Sorts the events if necessary if any(diff(spiketimes)<0): # not sorted ind=argsort(spiketimes) neurons,spiketimes=neurons[ind],spiketimes[ind] # Create the spike queue self.set_max_delay(max(spiketimes)) # This leaves space for the next spikes # Push the spikes self.LS.push(neurons) # This takes a bit of time (not sure why but this could be enhanced) # Set the cursors back self.LS.ind.advance(-1) self.LS.S.cursor=self.LS.ind[0] # Discretize spike times and make them relative to current time step spiketimes=array((spiketimes-clock.t)/clock.dt,dtype=int) # in units of dt # Calculate indices of spike groups u,indices=unique(spiketimes,return_index=True) # determine repeated time indices # Build vector of indices with -1 meaning: same index as previously x=-ones(max(u)+2) # maximum time index x[-1]=len(spiketimes) # last entry ## This is vectorized yet incredibly inefficient #x[u]=indices #empty_ind=where(x<0)[0] # time bins with no spikes # This is really slow: #x[empty_ind]=indices[digitize(empty_ind,u)] # -1 are replaced with next positive entry # As a loop (This now takes about 30% of the whole construction time): # Perhaps it could be written in C x[u]=indices for i in where(x<0)[0][::-1]: # -1 are replaced with next positive entry x[i]=x[i+1] # x[0] is always 0; maybe this should be dropped self.LS.ind[0:len(x)]=x self.LS.ind[len(x)]=-1 # no more spike at that point self._stopped=False # True when no more spike def reinit(self): super(SpikeQueueGroup, self).reinit() def push_spike_times(self,spiketimes,neurons): ''' Inserts future spike times For this we need to store the end position of future spikes ''' pass def update(self): # LS.S contains the data (neurons that spike) # LS.ind is a circular vector with pointers to locations in LS.S, # one for each future time bin if (self.LS.ind[1]>=0) & (not self._stopped): ns=self.LS.ind[1]-self.LS.ind[0] # number of spikes in next bin self.LS.S.advance(ns) else: self._stopped=True self.LS.ind[1]=self.LS.ind[0] self.LS.ind.advance(1) brian-1.4.3/brian/experimental/neuromorphic/test_AERSpikeMonitor.py000066400000000000000000000012121266062647100255060ustar00rootroot00000000000000from brian import * from brian.experimental.neuromorphic import * defaultclock.dt = 1*ms runtime = 100*ms g = PoissonGroup(100, 100*Hz) #g = SpikeGeneratorGroup(1, [(0,t*ms) for t in range(int(runtime/defaultclock.dt))]) Maer = AERSpikeMonitor(g, './dummy.aedat') M = SpikeMonitor(g) run(100*ms) Maer.close_file() addr, timestamps = load_AER('./dummy.aedat') if len(addr) == M.nspikes: print 'looks good' else: print 'glub' print 'addr, M.spikes',len(addr),M.nspikes # Check for NewSpikeGeneratorGroup N = max(addr) group = NewSpikeGeneratorGroup(N, (addr, timestamps), time_unit = 1*usecond) plot(timestamps, addr, '.') show() brian-1.4.3/brian/experimental/neuromorphic/test_newspikegengroup.py000066400000000000000000000004621266062647100261350ustar00rootroot00000000000000from brian import * from brian.experimental.neuromorphic import * defaultclock.dt = 1*ms runtime = 100*ms addr, timestamps = load_AER('./dummy.aedat') N = max(addr) group = NewSpikeGeneratorGroup(N, (addr, timestamps), time_unit = 1*usecond) M = SpikeMonitor(group) run(100*ms) raster_plot(M) show() brian-1.4.3/brian/experimental/neuromorphic/test_struct_vs_np.py000066400000000000000000000007031266062647100252700ustar00rootroot00000000000000from brian import * import struct x = array(rand(10)*1000, dtype = int16) print 'x',x x_n = x.newbyteorder('>') print 'x_n',x_n s_struct = '' for v in x_n: s_struct += struct.pack('>i', v) s_np =x_n.tostring() if s_np == s_struct: print 'yay' else: print s_np print s_struct xre=fromstring(s_struct, dtype=int16) # or uint16? xre = xre[:,0].newbyteorder('>') print 'xre', xre if (x == xre).all(): print 'YAY' brian-1.4.3/brian/experimental/new_c_propagate.py000066400000000000000000000463631266062647100221370ustar00rootroot00000000000000''' NOTES: General scheme for propagation code is something like: Iterate over spikes (spike index i): Set source neuron index j=spikes[i] Load required source neuron variables for neuron index j Iterate over row j of W: Set target neuron index k Load weight variable Load required target neuron variables for neuron index k Execute propagation code Functionally: iterate_over_spikes('j', 'spikes', (load_required_variables('j', {'modulation':modulation_var}), iterate_over_row('k', 'w', W, 'j', (load_required_variables('k', {'V':V}), transform_code('V += w*modulation') ) ) ) ) With some options like: * load_required_variables('j', {}) * iterate_over_row('k', 'w', W, 'j', delayvec=delayvec, delayvar='delay', ...) * load_required_variables_delayedreaction('k', {'V':{'dr':dr, 'cdi':cdi, 'idt':idt, 'md':md}}) We could also have: * iterate_over_col('k', 'w', W, 'j', ...) And STDP could be implemented as follows, for pre spikes: iterate_over_spikes('j', 'spikes', (load_required_variables('j', {'A_pre':A_pre}), """ A_pre += dA_pre """, iterate_over_row('k', 'w', W, 'j', (load_required_variables('k', {'A_post':A_post}), transform_code('w += A_post') ) ) ) ) And for post spikes: iterate_over_spikes('j', 'spikes', (load_required_variables('j', {'A_post':A_post}), transform_code('A_post += dA_post'), iterate_over_col('k', 'w', W, 'j', (load_required_variables('k', {'A_pre':A_pre}), transform_code('w += A_pre') ) ) ) ) To do STDP with delays, we need also: * load_required_variables_pastvalue('k', {'A_post':A_post_monitor}) Maybe for future-proofing STDP against having multiple per-synapse variables in the future, and generally having per-synapse dynamics and linked matrices which we want to jointly iterate over, could improve iterate_over_row/col to iterate over several synaptic variables with the same underlying matrix structure as the main weight matrix. Then, instead of having delayvec=delayvec, delayvar='delay' as a special case, we'd have a list of additional linked matrices. TODO: * Have ConnectionCode object feature a C and Python code and namespace to be executed. The Python namespace can be used for grabbing some parameters such as the _cdi variables that currently have to be set in by hand. ''' if __name__ == '__main__': from brian import * from brian.utils.documentation import * from brian.experimental.codegen.c_support_code import * else: from ..connections import Connection, DelayConnection, MultiConnection, \ DenseConnectionMatrix, DenseConstructionMatrix, \ SparseConnectionMatrix, SparseConstructionMatrix, \ DynamicConnectionMatrix, DynamicConstructionMatrix from ..log import log_debug, log_warn, log_info from ..utils.documentation import flattened_docstring from ..globalprefs import get_global_preference from codegen.c_support_code import * import numpy try: import weave except ImportError: from scipy import weave import new __all__ = ['make_new_connection', 'expand_code', 'transform_code', 'iterate_over_spikes', 'load_required_variables', 'load_required_variables_delayedreaction', 'load_required_variables_pastvalue', 'iterate_over_row', 'iterate_over_col', 'ConnectionCode', ] class ConnectionCode(object): def __init__(self, codestr, vars=None, pycodestr=None, pyvars=None): if pyvars is None: pyvars = {} if pycodestr is None: pycodestr = '' if vars is None: vars = {} self.vars = vars self.codestr = codestr if len([line for line in pycodestr.split('\n') if line]) > 1: pycodestr = flattened_docstring(pycodestr) else: pycodestr = pycodestr.strip() self.pycodestr = pycodestr self.pyvars = pyvars self.prepared = False self._weave_compiler = get_global_preference('weavecompiler') self._extra_compile_args = ['-O3'] if self._weave_compiler == 'gcc': self._extra_compile_args += get_global_preference('gcc_options') # ['-march=native', '-ffast-math'] def prepare(self): self.pyvars['vars'] = self.vars self.vars['_spikes'] = None self.vars['_spikes_len'] = None self.vars_list = self.vars.keys() if len(self.pycodestr): self.compiled_pycode = compile(self.pycodestr, 'ConnectionCode', 'exec') else: self.compiled_pycode = None self.prepared = True def __call__(self, _spikes): if not self.prepared: self.prepare() if len(_spikes): if not isinstance(_spikes, numpy.ndarray): _spikes = array(_spikes, dtype=int) vars = self.vars # print '****' # print self.codestr # for k, v in vars.iteritems(): # if isinstance(v, numpy.ndarray): # print k, ': shape =', v.shape # else: # print k, ':', v # import sys # sys.stdout.flush() vars['_spikes'] = _spikes vars['_spikes_len'] = len(_spikes) if self.compiled_pycode is not None: exec self.compiled_pycode in self.pyvars weave.inline(self.codestr, self.vars_list, local_dict=self.vars, support_code=c_support_code, compiler=self._weave_compiler, extra_compile_args=self._extra_compile_args) def __str__(self): s = 'C code:\n' spaces = 0 for line in self.codestr.split('\n'): if line.strip(): if '}' in line: spaces -= 4 s += ' ' * spaces + line.strip() + '\n' if '{' in line: spaces += 4 s += 'Python code:\n' s += self.pycodestr return s __repr__ = __str__ def expand_code(code): if isinstance(code, ConnectionCode): return code elif isinstance(code, (tuple, list)): codestr = '\n'.join([expand_code(c).codestr for c in code]) vars = {} pyvars = {} for c in code: vars.update(c.vars) pyvars.update(c.pyvars) pycodestr = '\n'.join([expand_code(c).pycodestr for c in code]) return ConnectionCode(codestr, vars, pycodestr, pyvars) else: raise TypeError('ConnectionCode should be string or tuple') def transform_code(codestr, vars=None): # TODO: replace with something more sophisticated than this return ConnectionCode('\n'.join(line + ';' for line in codestr.split('\n') if line.strip()), vars) def iterate_over_spikes(neuron_index, spikes, code): outcode = ''' for(int _spike_index=0; _spike_index<%SPIKES_LEN%; _spike_index++) { const int %NEURON_INDEX% = %SPIKES%[_spike_index]; %CODE% } ''' code = expand_code(code) outcode = outcode.replace('%SPIKES%', spikes) outcode = outcode.replace('%SPIKES_LEN%', spikes + '_len') outcode = outcode.replace('%NEURON_INDEX%', neuron_index) outcode = outcode.replace('%CODE%', code.codestr) return ConnectionCode(outcode, code.vars, code.pycodestr, code.pyvars) def load_required_variables(neuron_index, neuron_vars): vars = {} codestr = '' for k, v in neuron_vars.iteritems(): vars[k + '__array'] = v codestr += 'double &' + k + ' = ' + k + '__array[' + neuron_index + '];\n' return ConnectionCode(codestr, vars) def load_required_variables_delayedreaction(neuron_index, delay, delay_index, neuron_var, C): vars = {} codestr = 'double &%Z% = _dr[((%CDI%+(int)(_idt*%D%))%_md)*%N%+%I%];' codestr = codestr.replace('%CDI%', delay_index) codestr = codestr.replace('%Z%', neuron_var) codestr = codestr.replace('%D%', delay) codestr = codestr.replace('%N%', '_num_target_neurons') codestr = codestr.replace('%I%', neuron_index) vars['_num_target_neurons'] = len(C.target) vars['_dr'] = C._delayedreaction vars[delay_index] = None # filled in by Python code (below) vars['_idt'] = C._invtargetdt vars['_md'] = C._max_delay pyvars = {'conn':C} pycodestr = "vars['_cdi'] = conn._cur_delay_ind" return ConnectionCode(codestr, vars, pycodestr, pyvars) def load_required_variables_pastvalue(neuron_index, time, neuron_vars): vars = {} pyvars = {} pycodestr = '' codestr = '' for k, M in neuron_vars.iteritems(): vars[k + '__values'] = M._values vars[k + '__arraylen'] = M._values.shape[1] vars[k + '__cti'] = None # current_time_index filled in by propagation function vars[k + '__idt'] = M._invtargetdt vars[k + '__nd'] = M.num_duration pyvars[k + '__RecentStateMonitor'] = M pycodestr += "vars['%k%__cti'] = %k%__RecentStateMonitor.current_time_index\n" .replace('%k%', k) newcodestr = 'double &%var% = %var%__values[((%var%__nd+%var%__cti-1-(int)(%var%__idt*%time%))%%var%__nd)*%var%__arraylen+%i%];\n' newcodestr = newcodestr.replace('%var%', k) newcodestr = newcodestr.replace('%time%', time) newcodestr = newcodestr.replace('%i%', neuron_index) codestr += newcodestr; return ConnectionCode(codestr, vars, pycodestr, pyvars) def iterate_over_row(target_index, weight_variable, weight_matrix, source_index, code, extravars={}): code = expand_code(code) vars = {} vars.update(code.vars) if isinstance(weight_matrix, DenseConnectionMatrix): outcode = ''' double *_weight_arr_row = _weight_arr+%SOURCEINDEX%*_num_target_neurons; for(int %TARGETINDEX%=0; %TARGETINDEX%<_num_target_neurons; %TARGETINDEX%++) { double &%WEIGHT% = _weight_arr_row[%TARGETINDEX%]; %EXTRAVARS% %CODE% } ''' extravarscode = '' for k, v in extravars.iteritems(): extracodetmp = 'double &%V% = %V%__array[%TARGETINDEX%+%SOURCEINDEX%*_num_target_neurons];' extracodetmp = extracodetmp.replace('%V%', k) extracodetmp = extracodetmp.replace('%TARGETINDEX%', target_index) extracodetmp = extracodetmp.replace('%SOURCEINDEX%', source_index) vars[k + '__array'] = numpy.asarray(v) extravarscode += extracodetmp outcode = outcode.replace('%EXTRAVARS%', extravarscode) vars['_weight_arr'] = numpy.asarray(weight_matrix) vars['_num_target_neurons'] = weight_matrix.shape[1] elif isinstance(weight_matrix, SparseConnectionMatrix): outcode = ''' for(int _p=_rowind[%SOURCEINDEX%]; _p<_rowind[%SOURCEINDEX%+1]; _p++) { int %TARGETINDEX% = _allj[_p]; double &%WEIGHT% = _alldata[_p]; %EXTRAVARS% %CODE% } ''' extravarscode = '' for k, v in extravars.iteritems(): extracodetmp = 'double &%V% = %V%__alldata[_p];' extracodetmp = extracodetmp.replace('%V%', k) vars[k + '__alldata'] = v.alldata extravarscode += extracodetmp outcode = outcode.replace('%EXTRAVARS%', extravarscode) vars['_rowind'] = weight_matrix.rowind vars['_allj'] = weight_matrix.allj vars['_alldata'] = weight_matrix.alldata elif isinstance(weight_matrix, DynamicConnectionMatrix): # TODO: support dynamic matrix structure # the best way to support dynamic matrix type would be to # reorganise dynamic matrix data structure. Ideally, it should consist # of numpy arrays only. Maybe some sort of linked list structure? # Otherwise, we can use code that accesses the Python lists, but it's # probably less efficient (maybe this is anyway not a big issue with # the dynamic matrix type?) raise TypeError('Dynamic matrix not supported.') else: raise TypeError('Must be dense/sparse/dynamic matrix.') outcode = outcode.replace('%TARGETINDEX%', target_index) outcode = outcode.replace('%SOURCEINDEX%', source_index) outcode = outcode.replace('%WEIGHT%', weight_variable) outcode = outcode.replace('%CODE%', code.codestr) return ConnectionCode(outcode, vars, code.pycodestr, code.pyvars) def iterate_over_col(source_index, weight_variable, weight_matrix, target_index, code, extravars={}): code = expand_code(code) vars = {} vars.update(code.vars) if isinstance(weight_matrix, DenseConnectionMatrix): outcode = ''' double *_weight_arr_row = _weight_arr+%TARGETINDEX%; for(int %SOURCEINDEX%=0; %SOURCEINDEX%<_num_source_neurons; %SOURCEINDEX%++) { double &%WEIGHT% = _weight_arr_row[%SOURCEINDEX%*_num_target_neurons]; %EXTRAVARS% %CODE% } ''' extravarscode = '' for k, v in extravars.iteritems(): extracodetmp = 'double &%V% = %V%__array[%SOURCEINDEX%+%TARGETINDEX%*_num_target_neurons];' extracodetmp = extracodetmp.replace('%V%', k) extracodetmp = extracodetmp.replace('%SOURCEINDEX%', source_index) extracodetmp = extracodetmp.replace('%TARGETINDEX%', target_index) vars[k + '__array'] = numpy.asarray(v) extravarscode += extracodetmp outcode = outcode.replace('%EXTRAVARS%', extravarscode) vars['_weight_arr'] = numpy.asarray(weight_matrix) vars['_num_source_neurons'] = weight_matrix.shape[0] vars['_num_target_neurons'] = weight_matrix.shape[1] elif isinstance(weight_matrix, SparseConnectionMatrix): outcode = ''' for(int _q=_colind[%TARGETINDEX%]; _q<_colind[%TARGETINDEX%+1]; _q++) { int _p = _allcoldataindices[_q]; int %SOURCEINDEX% = _colalli[_q]; double &%WEIGHT% = _alldata[_p]; %EXTRAVARS% %CODE% } ''' extravarscode = '' for k, v in extravars.iteritems(): extracodetmp = 'double &%V% = %V%__alldata[_p];' extracodetmp = extracodetmp.replace('%V%', k) vars[k + '__alldata'] = v.alldata extravarscode += extracodetmp outcode = outcode.replace('%EXTRAVARS%', extravarscode) vars['_colind'] = weight_matrix.colind vars['_colalli'] = weight_matrix.colalli vars['_allcoldataindices'] = weight_matrix.allcoldataindices vars['_alldata'] = weight_matrix.alldata elif isinstance(weight_matrix, DynamicConnectionMatrix): # TODO: support dynamic matrix structure # the best way to support dynamic matrix type would be to # reorganise dynamic matrix data structure. Ideally, it should consist # of numpy arrays only. Maybe some sort of linked list structure? # Otherwise, we can use code that accesses the Python lists, but it's # probably less efficient (maybe this is anyway not a big issue with # the dynamic matrix type?) raise TypeError('Dynamic matrix not supported.') else: raise TypeError('Must be dense/sparse/dynamic matrix.') outcode = outcode.replace('%SOURCEINDEX%', source_index) outcode = outcode.replace('%TARGETINDEX%', target_index) outcode = outcode.replace('%WEIGHT%', weight_variable) outcode = outcode.replace('%CODE%', code.codestr) return ConnectionCode(outcode, vars, code.pycodestr, code.pyvars) # TODO: # * TEST iterate_over_col # * TEST load_required_variables_pastvalue def generate_connection_code(C): modulation = C._nstate_mod is not None delay = isinstance(C, DelayConnection) if not modulation and not delay: code = iterate_over_spikes('_j', '_spikes', iterate_over_row('_k', 'w', C.W, '_j', (load_required_variables('_k', {'V':C.target.state(C.nstate)}), transform_code('V += w')))) elif modulation and not delay: code = iterate_over_spikes('_j', '_spikes', (load_required_variables('_j', {'modulation':C.source.state(C._nstate_mod)}), iterate_over_row('_k', 'w', C.W, '_j', (load_required_variables('_k', {'V':C.target.state(C.nstate)}), transform_code('V += w*modulation'))))) elif not modulation and delay: code = iterate_over_spikes('_j', '_spikes', iterate_over_row('_k', 'w', C.W, '_j', extravars={'_delay':C.delayvec}, code=(load_required_variables_delayedreaction('_k', '_delay', '_cdi', 'V', C), transform_code('V += w')))) elif modulation and delay: code = iterate_over_spikes('_j', '_spikes', (load_required_variables('_j', {'modulation':C.source.state(C._nstate_mod)}), iterate_over_row('_k', 'w', C.W, '_j', extravars={'_delay':C.delayvec}, code=(load_required_variables_delayedreaction('_k', '_delay', '_cdi', 'V', C), transform_code('V += w*modulation'))))) else: raise TypeError('Not supported.') return code def new_propagate(self, _spikes): if not self.iscompressed: self.compress() if not hasattr(self, '_connection_code'): self._connection_code = generate_connection_code(self) log_warn('brian.experimental.new_c_propagate', 'Using new C based propagation function.') log_debug('brian.experimental.new_c_propagate', 'C based propagation code:\n' + str(self._connection_code)) if len(_spikes): self._connection_code(_spikes) def make_new_connection(C): if C.__class__ is MultiConnection: for c in C.connections: make_new_connection(c) if C.__class__ is Connection or C.__class__ is DelayConnection: if C.W.__class__ is SparseConnectionMatrix or \ C.W.__class__ is DenseConnectionMatrix or \ C.W.__class__ is SparseConstructionMatrix or \ C.W.__class__ is DenseConstructionMatrix: C.propagate = new.instancemethod(new_propagate, C, C.__class__) if __name__ == '__main__': log_level_debug() structure = 'sparse' delay = 0 * ms#0*ms or True modulation = False G = NeuronGroup(1, 'V:1\nmod:1', reset=0, threshold=1) G.V = 2 G.mod = 5 H = NeuronGroup(10, 'V:1') if modulation: modulation = 'mod' else: modulation = None C = Connection(G, H, 'V', structure=structure, delay=delay, modulation=modulation) C[0, :] = linspace(0, 1, 10) if delay: C.delay[0, :] = linspace(0, 1, 10) * ms M = StateMonitor(H, 'V', record=True) make_new_connection(C) run(2 * ms) M.plot() legend() show() brian-1.4.3/brian/experimental/realtime_monitor.py000066400000000000000000000121461266062647100223430ustar00rootroot00000000000000from brian import * from brian.log import log_info try: import pygame except ImportError: # as this is an experimental package, don't bother the user with a warning log_info(__name__, 'Could not import pygame. The RealtimeConnectionMonitor ' 'class depends on it.') import matplotlib.cm as cm __all__ = ['RealtimeConnectionMonitor'] class RealtimeConnectionMonitor(NetworkOperation): ''' Realtime monitoring of weight matrix Short docs: ``C`` Connection to monitor ``size`` Dimensions (width, height) of output window, leave as ``None`` to use ``C.W.shape``. ``scaling`` If output window dimensions are different to connection matrix, scaling is used, options are ``'fast'`` for no interpolation, or ``'smooth'`` for (slower) smooth interpolation. ``wmin, wmax`` Minimum and maximum weight matrix values, if left to ``None`` then the min/max of the weight matrix at each moment is used (and this scaling can change over time). ``clock`` Leave to ``None`` for an update every 100ms. ``cmap`` Colour map to use, black and white by default. Get other values from ``matplotlib.cm.*``. Note that this class uses PyGame and due to a limitation with pygame, there can be only one window using it. Other options are being considered. ''' def __init__(self, C, size=None, scaling='fast', wmin=None, wmax=None, clock=None, cmap=cm.gray): self.C = C self.wmin = wmin self.wmax = wmax self.cmap = cmap if clock is None: clock = EventClock(dt=100 * ms) NetworkOperation.__init__(self, lambda:None, clock=clock) pygame.init() if size is None: width, height = C.W.shape self.scaling = None else: width, height = size self.scaling = scaling self.width, self.height = width, height self.screen = pygame.display.set_mode((width, height)) self.screen_arr = pygame.surfarray.pixels3d(self.screen) def __call__(self): W = self.C.W.todense() wmin, wmax = self.wmin, self.wmax if wmin is None: wmin = amin(W) if wmax is None: wmax = amax(W) if wmax - wmin < 1e-20: wmax = wmin + 1e-20 W = self.cmap(clip((W - wmin) / (wmax - wmin), 0, 1), bytes=True) if self.scaling is None: self.screen_arr[:, :, :] = W[:, :, :3] elif self.scaling == 'fast': srf = pygame.surfarray.make_surface(W[:, :, :3]) pygame.transform.scale(srf, (self.width, self.height), self.screen) elif self.scaling == 'smooth': srf = pygame.surfarray.make_surface(W[:, :, :3]) pygame.transform.smoothscale(srf, (self.width, self.height), self.screen) pygame.display.flip() pygame.event.pump() if __name__ == '__main__': Nin = 200 Nout = 200 duration = 40 * second Fmax = 50 * Hz tau = 10 * ms taue = 2 * ms taui = 5 * ms sigma = 0.#0.4 # Note that factor (tau/taue) makes integral(v(t)) the same when the connection # acts on ge as if it acted directly on v. eqs = Equations(''' #dv/dt = -v/tau + sigma*xi/(2*tau)**.5 : 1 dv/dt = (-v+(tau/taue)*ge-(tau/taui)*gi)/tau + sigma*xi/(2*tau)**.5 : 1 dge/dt = -ge/taue : 1 dgi/dt = -gi/taui : 1 excitatory = ge inhibitory = gi ''') reset = 0 threshold = 1 refractory = 0 * ms taup = 5 * ms taud = 5 * ms Ap = .1 Ad = -Ap * taup / taud * 1.2 wmax_ff = 0.1 wmax_rec = wmax_ff wmax_inh = wmax_rec width = 0.2 recwidth = 0.2 Gin = PoissonGroup(Nin) Gout = NeuronGroup(Nout, eqs, reset=reset, threshold=threshold, refractory=refractory) ff = Connection(Gin, Gout, 'excitatory', structure='dense', weight=rand(Nin, Nout) * wmax_ff) for i in xrange(Nin): ff[i, :] = (rand(Nout) > .5) * wmax_ff rec = Connection(Gout, Gout, 'excitatory') for i in xrange(Nout): d = abs(float(i) / Nout - linspace(0, 1, Nout)) d[d > .5] = 1. - d[d > .5] dsquared = d ** 2 prob = exp(-dsquared / (2 * recwidth ** 2)) prob[i] = -1 inds = (rand(Nout) < prob).nonzero()[0] w = rand(len(inds)) * wmax_rec rec[i, inds] = w inh = Connection(Gout, Gout, 'inhibitory', sparseness=1, weight=wmax_inh) stdp_ff = ExponentialSTDP(ff, taup, taud, Ap, Ad, wmax=wmax_ff) stdp_rec = ExponentialSTDP(rec, taup, taud, Ap, Ad, wmax=wmax_rec) M = RealtimeConnectionMonitor(ff, size=(500, 500), scaling='smooth', wmin=0., wmax=wmax_ff, clock=EventClock(dt=20 * ms), cmap=cm.jet) run(0 * ms) @network_operation(clock=EventClock(dt=20 * ms)) def stimulation(): Gin.rate = Fmax * exp(-(linspace(0, 1, Nin) - rand())**2 / (2 * width ** 2)) run(duration, report='stderr') brian-1.4.3/brian/experimental/stochasticconnection.py000077500000000000000000000034431266062647100232210ustar00rootroot00000000000000from ..connections.base import * from ..connections.sparsematrix import * from ..connections.connectionvector import * from ..connections.constructionmatrix import * from ..connections.connectionmatrix import * from ..connections.construction import * from ..connections.connection import * from ..connections.delayconnection import * from ..connections.propagation_c_code import * import warnings import numpy.random network_operation = None # we import this when we need to because of order of import issues __all__ = [ 'StochasticConnection', ] class StochasticConnection(Connection): ''' Connection which implements probabilistic (unreliable) synapses Initialised as for a :class:`Connection`, but with the additional keyword: ``reliability`` Specifies the probability that a presynaptic spike triggers a postsynaptic current in the postsynaptic neuron. ''' @check_units(delay=second) def __init__(self, source, target, state=0, delay=0 * msecond, structure='dense', reliability=1.0, **kwds): Connection.__init__(self, source, target, state=state, delay=delay, structure=structure, **kwds) if (len(source) != len(target)): raise AttributeError, 'The connected (sub)groups must have the same size.' source.set_max_delay(delay) self.reliability = reliability def propagate(self, spikes): ''' Propagates the spikes to the target. ''' if len(spikes): rows = self.W[spikes, :] * (numpy.random.rand(len(spikes), self.W.shape[1]) <= self.reliability) for row in rows: self.target._S[self.nstate, :] += array(row, dtype=float32) def compress(self): pass brian-1.4.3/brian/experimental/trace_analysis.py000066400000000000000000000111141266062647100217650ustar00rootroot00000000000000''' Analysis of voltage traces (current-clamp experiments or HH models). ''' from brian import * from numpy import * #from brian.stdunits import mV from brian.tools.io import * from time import time from scipy import optimize from scipy import stats from scipy.signal import lfilter __all__ = ['fit_EIF', 'IV_curve', 'threshold_model', 'estimate_capacitance'] """ TODO: * A better fit_EIF function (conjugate gradient? Check also Badel et al., 2008. Or maybe just calculate gradient) * Fit subthreshold kernel (least squares, see also Jolivet) * Standard threshold methods """ """ The good strategy: * optimise for every tau for best threshold prediction * find the tau that minimises misses """ def threshold_model(v, onsets=None, dt=1., tau=None): ''' Fits adaptive threshold model. Model: tau*dvt/dt=vt0+a*[v-vi]^+ tau can be fixed or found by optimization. Returns vt0, vi, a[, tau] (tau in timesteps by default) ''' if onsets is None: onsets = spike_onsets(v) if tau is None: spikes = spike_mask(v, onsets) def f(tau): vt0, vi, a = threshold_model(v, onsets=onsets, dt=dt, tau=tau) theta = vt0 + a * lowpass(clip(v - vi, 0, inf), tau, dt=0.05) return mean(theta[-spikes] < v[-spikes]) # false alarm rate tau = float(optimize.fmin(lambda x:f(*x), 10., disp=0)) return list(threshold_model(v, onsets=onsets, dt=dt, tau=tau)) + [tau] else: threshold = v[onsets] def f(vt0, vi, a, tau): return vt0 + a * lowpass(clip(v - vi, 0, inf), tau, dt=dt) return optimize.leastsq(lambda x:f(x[0], x[1], x[2], tau)[onsets] - threshold, [-55., -65., 1.])[0] def estimate_capacitance(i, v, dt=1, guess=100.): ''' Estimates capacitance from current-clamp recording with white noise (see Badel et al., 2008). Hint for units: if v and dt are in ms, then the capacitance has the same relationship to F than the current to A (pA -> pF). ''' dv = diff(v) / dt i, v = i[:-1], v[:-1] mask = -spike_mask(v) # subthreshold trace dv, v, i = dv[mask], v[mask], i[mask] return optimize.fmin(lambda C:var(dv - i / C), guess, disp=0)[0] def IV_curve(i, v, dt=1, C=None, bins=None, T=0): ''' Dynamic I-V curve (see Badel et al., 2008) E[C*dV/dt-I]=f(V) T: time after spike onset to include in estimation. bins: bins for v (default: 20 bins between min and max). ''' C = C or estimate_capacitance(i, v, dt) if bins is None: bins = linspace(min(v), max(v), 20) dv = diff(v) / dt v, i = v[:-1], i[:-1] mask = -spike_mask(v, spike_onsets(v) + T) # subthreshold trace dv, v, i = dv[mask], v[mask], i[mask] fv = i - C * dv # intrinsic current return array([mean(fv[(v >= vmin) & (v < vmax)]) for vmin, vmax in zip(bins[:-1], bins[1:])]) def fit_EIF(i, v, dt=1, C=None, T=0): ''' Fits the exponential model of spike initiation in the phase plane (v,dv). T: time after spike onset to include in estimation. Returns gl, El, deltat, vt (leak conductance, leak reversal potential, slope factor, threshold) The result does not seem very reliable (deltat depends critically on T). Hint for units: if v is in mV, dt in ms, i in pA, then gl is in nS ''' C = C or estimate_capacitance(i, v, dt) dv = diff(v) / dt v, i = v[:-1], i[:-1] mask = -spike_mask(v, spike_onsets(v) + T) # subthreshold trace dv, v, i = dv[mask], v[mask], i[mask] f = lambda gl, El, deltat, vt:C * dv - i - (gl * (El - v) + gl * deltat * exp((v - vt) / deltat)) x, _ = optimize.leastsq(lambda x:f(*x), [50., -60., 3., -55.]) return x if __name__ == '__main__': filename = r"D:\My Dropbox\Neuron\Hu\recordings_Ifluct\I0_1_std_1_tau_10_sampling_20\vs.dat" #filename=r'D:\Anna\2010_03_0020_random_noise_200pA.atf' #filename2=r'D:\Anna\input_file.atf' # in pF from pylab import * #M=loadtxt(filename) #t,vs=M[:,0],M[:,1] t, vs = read_neuron_dat(filename) #t,vs=read_atf(filename) #print array(spike_duration(vs,full=True))*.05 #vt0,vi,a,tau=threshold_model(vs,dt=0.05) #print vt0,vi,a,tau #theta=vt0+a*lowpass(clip(vs-vi,0,inf),tau,dt=0.05) spikes = spike_onsets(vs) print std(vs[spikes]) #spikes2=spike_onsets_dv2(vs) #spikes3=spike_onsets_dv3(vs) plot(t, vs, 'k') #plot(t,theta,'b') plot(t[spikes], vs[spikes], '.r') #plot(t[spikes2],vs[spikes2],'.g') #plot(t[spikes3],vs[spikes3],'.k') show() brian-1.4.3/brian/fundamentalunits.py000066400000000000000000001574531266062647100176710ustar00rootroot00000000000000# ---------------------------------------------------------------------------------- # Copyright ENS, INRIA, CNRS # Contributors: Romain Brette (brette@di.ens.fr) and Dan Goodman (goodman@di.ens.fr) # # Brian is a computer program whose purpose is to simulate models # of biological neural networks. # # This software is governed by the CeCILL license under French law and # abiding by the rules of distribution of free software. You can use, # modify and/ or redistribute the software under the terms of the CeCILL # license as circulated by CEA, CNRS and INRIA at the following URL # "http://www.cecill.info". # # As a counterpart to the access to the source code and rights to copy, # modify and redistribute granted by the license, users are provided only # with a limited warranty and the software's author, the holder of the # economic rights, and the successive licensors have only limited # liability. # # In this respect, the user's attention is drawn to the risks associated # with loading, using, modifying and/or developing or reproducing the # software by the user in light of its specific status of free software, # that may mean that it is complicated to manipulate, and that also # therefore means that it is reserved for developers and experienced # professionals having in-depth computer knowledge. Users are therefore # encouraged to load and test the software's suitability as regards their # requirements in conditions enabling the security of their systems and/or # data to be ensured and, more generally, to use and operate it in the # same conditions as regards security. # # The fact that you are presently reading this means that you have had # knowledge of the CeCILL license and that you accept its terms. # ---------------------------------------------------------------------------------- # """Defines physical units and quantities The standard way to use this class is as follows: V = 3 * volt I = 2 * amp R=V/I print R will return 1.5 ohm The following fundamental units are defined: metre, kilogram, second, amp, kelvin, mole, candle And these additional basic units: radian, steradian, hertz, newton, pascal, joule, watt, coulomb, volt, farad, ohm, siemens, weber, tesla, henry, celsius, lumen, lux, becquerel, gray, sievert, katal, gram, gramme Additionally, it includes all scaled versions of these units using the standard SI prefixes (see the documentation for the Unit class for more details), e.g. uamp, mmetre, etc. It also includes the second and third powers of each of these units, e.g. mvolt2 = mvolt*mvolt, metre3 = metre**3, etc. The module also defines these classes: -- Dimension Stores the physical dimensions (length, mass, etc.) -- DimensionMismatchError Exception raised if you try to add inconsistent units, etc. -- Quantity The class of a value with a unit -- Unit The class of the defined units like mvolt, etc. -- UnitRegistry Stores 'known' units for printing These functions: -- get_dimensions(x) Returns the dimensions of a quantity or number x -- have_same_dimensions(x,y) Tests if x and y have the same dimensions -- is_dimensionless(x) Tests if x is dimensionless -- display_in_unit(x,u) Displays quantity x in unit u -- register_new_unit(u) Add a new unit u to the list of 'known' units for printing purposes -- get_unit(x) Returns the fundamental unit of value x if one is known, or simply the value 1 with dimensions of x if none is known And this decorator for function argument checking: -- check_units(...) If you want to use shorter named units, import the stdunits module, which defines things like mV for mvolt, etc. They are not included by default in the units module because of the potential for variable name clashes. """ __all__ = [ 'Dimension', 'Scale', 'DimensionMismatchError', 'get_dimensions', 'is_dimensionless', 'have_same_dimensions', 'display_in_unit', 'Quantity', 'Unit', 'register_new_unit', 'check_units', 'is_scalar_type', 'get_unit', 'get_unit_fast', 'scalar_representation', 'quantity_with_dimensions', ] from brian_unit_prefs import bup from operator import isNumberType, isSequenceType from itertools import izip import math, numpy from utils.approximatecomparisons import * import types from functools import * import sys # Note that the decorator module below is used to provide signature preserving # decorators, but it has the unfortunate side effect of messing up the tracebacks # because it uses eval, so we only use it when we want to generate documentation, # i.e. if 'sphinx' or 'docutils' or 'epydoc' are loaded. # TODO: this has stopped working anyway, so it's now removed #try: # import decorator # use_decorator = 'sphinx' in sys.modules or 'docutils' in sys.modules or 'epydoc' in sys.modules #except: # use_decorator = False # SI dimensions (see table at end of file) and various descriptions, # each description maps to an index i, and the power of each dimension # is stored in the variable dims[i] _di = { "Length":0, "length": 0, "metre":0, "metres":0, "metre": 0, "metres":0, "metre":0, "metres":0, "metre": 0, "metres":0, "m": 0, \ "Mass":1, "mass": 1, "kilogram":1, "kilograms":1, "kilogram": 1, "kilograms":1, "kg": 1, \ "Time":2, "time": 2, "second":2, "seconds":2, "second": 2, "seconds":2, "s": 2, \ "Electric Current":3, "Electric Current":3, "electric current": 3, "Current":3, "current":3, "ampere":3, "amperes":3, "ampere": 3, "amperes":3, "A": 3, \ "Temperature":4, "temperature": 4, "kelvin":4, "kelvins":4, "kelvin": 4, "kelvins":4, "K": 4, \ "Quantity of Substance":5, "Quantity of substance": 5, "quantity of substance": 5, "Substance":5, "substance":5, "mole":5, "moles":5, "mole": 5, "moles":5, "mol": 5, \ "Luminosity":6, "luminosity": 6, "candle":6, "candles":6, "candle": 6, "candles":6, "cd": 6 } _ilabel = ["m", "kg", "s", "A", "K", "mol", "cd"] # The same labels with the names used for constructing them in Python code _iclass_label = ["metre", "kilogram", "second", "amp", "kelvin", "mole", "candle"] # SI unit _prefixes, see table at end of file _siprefixes = {"y":1e-24, "z":1e-21, "a":1e-18, "f":1e-15, "p":1e-12, "n":1e-9, "u":1e-6, "m":1e-3, "c":1e-2, "d":1e-1, \ "":1, \ "da":1e1, "h":1e2, "k":1e3, "M":1e6, "G":1e9, "T":1e12, "P":1e15, "E":1e18, "Z":1e21, "Y":1e24} class Dimension(object): '''Stores the indices of the 7 basic SI unit dimension (length, mass, etc.) Provides a subset of arithmetic operations appropriate to dimensions: multiplication, division and powers, and equality testing. Methods: is_dimensionless() returns Boolean value Notes: Most users shouldn't use this class directly, but instead write things like: x = 3 * mvolt, etc. ''' __slots__ = ["_dims"] #### INITIALISATION #### def __init__(self, *args, **keywords): """Initialise Dimension object with a vector or keywords Call as Dimension(list), Dimension(keywords) or Dimension(dim) list -- a list with the indices of the 7 elements of an SI dimension keywords -- a sequence of keyword=value pairs where the keywords are the names of the SI dimensions, or the standard unit dim -- a dimension object to copy Examples: The following are all definitions of the dimensions of force Dimension(length=1, mass=1, time=-2) Dimension(m=1, kg=1, s=-2) Dimension([1,1,-2,0,0,0,0]) The 7 units are (in order): Length, Mass, Time, Electric Current, Temperature, Quantity of Substance, Luminosity and can be referred to either by these names or their SI unit names, e.g. length, metre, and m all refer to the same thing here. """ if len(args): if isSequenceType(args[0]) and len(args[0]) == 7: # initialisation by list self._dims = args[0] elif isinstance(args[0], Dimension): # initialisation by another dimension object self._dims = args[0]._dims else: # initialisation by keywords self._dims = [0, 0, 0, 0, 0, 0, 0] for k in keywords.keys(): # _di stores the index of the dimension with name 'k' self._dims[_di[k]] = keywords[k] #### METHODS #### def get_dimension(self, d): """Returns the list of dimension indices. See documentation for __init__. """ return self._dims[_di[d]] def set_dimension(self, d, value): """Sets the list of dimension indices. See documentation for __init__. """ self._dims[_di[d]] = value def is_dimensionless(self): """Tells you whether the object is dimensionless.""" return sum([x == 0 for x in self._dims]) == 7 #### REPRESENTATION #### def _str_representation(self, python_code=False): """String representation in basic SI units, or 1 for dimensionless. Use `python_code=False` for display purposes and `True` for valid Python code.""" if python_code: power_operator = " ** " else: power_operator = "^" parts = [] for i in range(len(self._dims)): if self._dims[i]: if python_code: s = _iclass_label[i] else: s = _ilabel[i] if self._dims[i] != 1: s += power_operator + str(self._dims[i]) parts.append(s) if python_code: s = " * ".join(parts) if not len(s): return "%s()" % self.__class__.__name__ else: s = " ".join(parts) if not len(s): return "1" return s.strip() def __repr__(self): return self._str_representation(python_code=True) def __str__(self): return self._str_representation(python_code=False) #### ARITHMETIC #### # Note that none of the dimension arithmetic objects do sanity checking # on their inputs, although most will throw an exception if you pass the # wrong sort of input def __mul__(self, value): return Dimension([x + y for x, y in izip(self._dims, value._dims)]) def __div__(self, value): return Dimension([x - y for x, y in izip(self._dims, value._dims)]) def __truediv__(self, value): return self.__div__(value) def __pow__(self, value): return Dimension([x * value for x in self._dims]) def __imul__(self, value): self._dims = [x + y for x, y in izip(self._dims, value._dims)] return self def __idiv__(self, value): self._dims = [x - y for x, y in izip(self._dims, value._dims)] return self def __itruediv__(self, value): return self.__idiv__(value) def __ipow__(self, value): self._dims = [x * value for x in self._dims] return self #### COMPARISON #### def __eq__(self, value): #return sum([x==y for x,y in izip(self._dims,value._dims)])==7 return sum([is_within_absolute_tolerance(x, y) for x, y in izip(self._dims, value._dims)]) == 7 def __ne__(self, value): return not self.__eq__(value) #### MAKE DIMENSION PICKABLE #### def __getstate__(self): return self._dims def __setstate__(self, state): self._dims = state class Scale(object): """Stores the scale factor for each SI dimension. Probably would only be very rarely used by a user, but might conceivably be useful in certain circumstances. Methods: -- Initialisation by list of keywords -- scale_factor(dim) gives the overall scaling for a value in dimension dim -- unit_representation(dim) gives a string representation of the unit defined by the Scale object applied to dimension dim """ __slots__ = ["scale"] def __init__(self, *args, **keywords): """Initialise by list of scales or keywords, see Dimension documentation e.g. Scale(length="m", time="u") = Scale(["m","","u","","","",""]) corresponds to measuring the unit of length at the milli scale and the unit of time at the u scale. """ self.scale = [ "", "", "", "", "", "", "" ] for k in keywords: self.scale[_di[k]] = keywords[k] def scale_factor(self, dim): """Returns the scaling factor for dimension dim For example, if the scale factor of length is milli, and the dimensions of dim are length^2 then the scale factor will be 0.001^2. """ sf = 1 for s, i in izip(self.scale, dim._dims): if i: sf *= _siprefixes[s] ** i return sf def unit_representation(self, dim): """Returns a representation of the dimension dim at this scale For example, if the scale factor of length is milli, and the dimensions of dim are length^2 then this will return mm^2. """ s = "" for i in range(7): if dim._dims[i]: s += self.scale[i] + _ilabel[i] if dim._dims[i] != 1: s += "^" + str(dim._dims[i]) s += " " return s.strip() class DimensionMismatchError(Exception): """Exception class for attempted operations with inconsistent dimensions For example, ``3*mvolt + 2*amp`` raises this exception. The purpose of this class is to help catch errors based on incorrect units. The exception will print a representation of the dimensions of the two inconsistent objects that were operated on. If you want to check for inconsistent units in your code, do something like:: try: ... your code here ... except DimensionMismatchError, inst: ... cleanup code here, e.g. print "Found dimension mismatch, details:", inst ... """ def __init__(self, description, *dims): """Raise as DimensionMismatchError(desc,dim1,dim2,...) desc -- a description of the type of operation being performed, e.g. Addition, Multiplication, etc. dim -- the dimensions of the objects involved in the operation, any number of them is possible """ # Call the base class constructor to make Exception pickable, see: # http://bugs.python.org/issue1692335 Exception.__init__(self, description, *dims) self._dims = dims self.desc = description def __repr__(self): return self.__str__() def __str__(self): s = self.desc + ", dimensions were " for d in self._dims: s += "(" + str(d) + ") " return s def is_scalar_type(obj): """Tells you if the object is a 1d number type This function is mostly used internally by the module for argument type checking. A scalar type can be considered a dimensionless quantity (see the documentation for Quantity for more information). """ return isNumberType(obj) and not isSequenceType(obj) def get_dimensions(obj): """Returns the dimensions of any object that has them. Slightly more general than obj.get_dimensions() because it will return a new dimensionless Dimension() object if the object is of number type but not a Quantity (e.g. a float or int). """ if isNumberType(obj) and not isinstance(obj, Quantity): return Dimension() return obj.get_dimensions() def is_dimensionless(obj): """Tests if a scalar value is dimensionless or not, returns a ``bool``. Note that the syntax may change in later releases of Brian, with tighter integration of scalar and array valued quantities. """ return get_dimensions(obj) == Dimension() def have_same_dimensions(obj1, obj2): """Tests if two scalar values have the same dimensions, returns a ``bool``. Note that the syntax may change in later releases of Brian, with tighter integration of scalar and array valued quantities. """ return get_dimensions(obj1) == get_dimensions(obj2) def display_in_unit(x, u): """String representation of the object x in unit u. """ if not have_same_dimensions(x, u): raise DimensionMismatchError("Non-matching unit for function display_in_unit", get_dimensions(x), get_dimensions(u)) s = str(float(x / u)) + " " if not is_dimensionless(u): if isinstance(u, Unit): s += str(u) else: s += str(u.dim) return s.strip() def quantity_with_dimensions(floatval, dims): return Quantity.with_dimensions(floatval, dims) class Quantity(numpy.float64): """A number with an associated physical dimension. In most cases, it is not necessary to create a :class:`Quantity` object by hand, instead use the constant unit names ``second``, ``kilogram``, etc. The details of how :class:`Quantity` objects work is subject to change in future releases of Brian, as we plan to reimplement it in a more efficient manner, more tightly integrated with numpy. The following can be safely used: * :class:`Quantity`, this name will not change, and the usage ``isinstance(x,Quantity)`` should be safe. * The standard unit objects, ``second``, ``kilogram``, etc. documented in the main documentation will not be subject to change (as they are based on SI standardisation). * Scalar arithmetic will work with future implementations. """ # This documentation is subject to change. """ This is the main user class for the units module, although in most cases it is not necessary to initialise a new quantity by hand (see construction below for details). The Quantity class defines arithmetic operations which check for consistency of dimensions and raise the DimensionMismatchError exception if they are inconsistent. The class also defines default and other representations of a number for printing purposes. Typical usage: I = 3 * amp # I is a Quantity object R = 2 * ohm # same for R print I*R # displays "6 V" print (I*R).in_unit(mvolt) # displays "6000 mV" print (I*R)/mvolt # displays "6000" x = I + R # raises DimensionMismatchError See the documentation on the Unit class for more details about the available unit names like mvolt, etc. Casting rules: The three rules that define the casting operations for Quantity object are: 1. Quantity op Quantity = Quantity - Performs dimension checking if appropriate 2. Scalar op Quantity = Quantity - Assumes that the scalar is dimensionless 3. other op Quantity = other - The Quantity object is downcast to a float Scalar types are 1 dimensional number types, including float, int, etc. but not array. The Quantity class is a derived class of float, so many other operations will also downcast to float. For example, sin(x) where x is a quantity will return sin(float(x)) without doing any dimension checking. Construction details: x = Quantity(value) returns a dimensionless object, you can then set the dimensions via x.set_dimensions(dim) x = Quantity.with_dimensions(value,dim) returns an object with floating point value value, and dimensions dim, see the documentation for Quantity.with_dimensions(...) for more. Static constructors: -- with_dimensions(dim) -- with_dimensions(keywords...) Methods: -- get_dimensions() return Dimension -- set_dimensions(dim) -- is_dimensionless() return boolean -- at_scale(scale) return string -- has_same_dimensions(other) return boolean -- in_unit(unit) return string -- in_best_unit() return string """ __slots__ = ["dim"] #### CONSTRUCTION #### def __init__(self, value): """Initialises as dimensionless """ super(Quantity, self).__init__() self.dim = Dimension() @staticmethod def with_dimensions(value, *args, **keywords): """Static method to create a Quantity object with dimensions Use as Quantity.with_dimensions(value,dim), Quantity.with_dimensions(value,dimlist) or Quantity.with_dimensions(value,keywords...) -- value is a float or other scalar type -- dim is a dimension object -- dimlist, keywords (see the Dimension constructor) e.g. x = Quantity.with_dimensions(2,Dimension(length=1)) x = Quantity.with_dimensions(2,length=1) x = 2 * metre all define the same object. """ x = Quantity(value) if len(args) and isinstance(args[0], Dimension): x.set_dimensions(args[0]) else: x.set_dimensions(Dimension(*args, **keywords)) return x #### METHODS #### def get_dimensions(self): """Returns the dimensions of this object """ return self.dim def set_dimensions(self, dim): """Set the dimensions of this object """ self.dim = dim def is_dimensionless(self): """Tells you whether this is a dimensionless object """ return self.dim.is_dimensionless() def at_scale(self, scale): """Returns a string representation at given scale """ return str(float(self) / scale.scale_factor(self.dim)) + " " + scale.unit_representation(self.dim) def has_same_dimensions(self, other): """Tells you if this object has the same dimensions as another. """ return self.dim == get_dimensions(other) def in_unit(self, u, python_code=False): """String representation of the object in unit `u`. If `python_code` is `True`, this will return valid python code, i.e. a string like `5.0 * um ** 2`instead of `5.0 um^2` """ if not self.has_same_dimensions(u): raise DimensionMismatchError("Non-matching unit for method in_unit", self.dim, u.dim) s = str(float(self / u)) + " " if not u.is_dimensionless(): if isinstance(u, Unit): if python_code: s += '* ' + repr(u) else: s += str(u) else: if python_code: s += "* " + repr(u.dim) else: s += str(u.dim) elif python_code == True: # A quantity without unit is not recognisable otherwise return '%s(%s)' % (self.__class__.__name__, s.strip()) return s.strip() def in_best_unit(self, python_code=False, *regs): """String representation of the object in the 'best unit' If `python_code` is `True`, this will return valid python code, i.e. a string like `5.0 * um ** 2`instead of `5.0 um^2` For more information, see the documentation for the UnitRegistry class. Essentially, this looks at the value of the quantity for all 'known' matching units (e.g. mvolt, namp, etc.) and returns the one with the most compact representation. Standard units are built in, but you can register new units for consideration. """ u = _get_best_unit(self, *regs) return self.in_unit(u, python_code) #### METHODS (NUMERICAL) #### def sqrt(self): return self ** 0.5 def log(self): if self.is_dimensionless(): return Quantity.with_dimensions(math.log(float(self)), self.dim) raise DimensionMismatchError('log', self.dim) def exp(self): if self.is_dimensionless(): return Quantity.with_dimensions(math.exp(float(self)), self.dim) raise DimensionMismatchError('exp', self.dim) def sin(self): if self.is_dimensionless(): return Quantity.with_dimensions(math.sin(float(self)), self.dim) raise DimensionMismatchError('sin', self.dim) def cos(self): if self.is_dimensionless(): return Quantity.with_dimensions(math.cos(float(self)), self.dim) raise DimensionMismatchError('cos', self.dim) def tan(self): if self.is_dimensionless(): return Quantity.with_dimensions(math.tan(float(self)), self.dim) raise DimensionMismatchError('tan', self.dim) def asin(self): if self.is_dimensionless(): return Quantity.with_dimensions(math.asin(float(self)), self.dim) raise DimensionMismatchError('asin', self.dim) def acos(self): if self.is_dimensionless(): return Quantity.with_dimensions(math.acos(float(self)), self.dim) raise DimensionMismatchError('acos', self.dim) def atan(self): if self.is_dimensionless(): return Quantity.with_dimensions(math.atan(float(self)), self.dim) raise DimensionMismatchError('atan', self.dim) arcsin = asin arccos = cos arctan = tan def sinh(self): if self.is_dimensionless(): return Quantity.with_dimensions(math.sinh(float(self)), self.dim) raise DimensionMismatchError('sinh', self.dim) def cosh(self): if self.is_dimensionless(): return Quantity.with_dimensions(math.cosh(float(self)), self.dim) raise DimensionMismatchError('cosh', self.dim) def tanh(self): if self.is_dimensionless(): return Quantity.with_dimensions(math.tanh(float(self)), self.dim) raise DimensionMismatchError('tanh', self.dim) def arcsinh(self): if self.is_dimensionless(): return Quantity.with_dimensions(numpy.arcsinh(float(self)), self.dim) raise DimensionMismatchError('sinh', self.dim) def arccosh(self): if self.is_dimensionless(): return Quantity.with_dimensions(numpy.arccosh(float(self)), self.dim) raise DimensionMismatchError('cosh', self.dim) def arctanh(self): if self.is_dimensionless(): return Quantity.with_dimensions(numpy.arctanh(float(self)), self.dim) raise DimensionMismatchError('tanh', self.dim) #### REPRESENTATION #### def __repr__(self): return self.in_best_unit(python_code=True) def __str__(self): #s = super(Quantity,self).__str__() #if not self.is_dimensionless(): s += " " + str(self.dim) #return s return self.in_best_unit() #return str(float(self))+'*'+str(get_unit(self)) #### ARITHMETIC #### # Arithmetic operations implement the following set of rules for # determining casting: # 1. Quantity op Quantity returns Quantity (and performs dimension checking if appropriate) # 2. Scalar op Quantity returns Quantity (and performs dimension checking assuming Scalar is dimensionless) # 3. other op Quantity returns other (Quantity is downcast to float) # Scalar types are those for which is_scalar_type() returns True, including float, int, long, complex but not array def __mul__(self, other): # This code, like all the other arithmetic code below, implements the casting rules # defined above. if isinstance(other, Quantity): return Quantity.with_dimensions(float(self) * float(other), self.dim * other.dim) elif is_scalar_type(other): return Quantity.with_dimensions(float(self) * other, self.dim) else: return NotImplemented #return super(Quantity,self).__mul__(other) def __rmul__(self, other): return self.__mul__(other) def __div__(self, other): if isinstance(other, Quantity): return Quantity.with_dimensions(float(self) / float(other), self.dim / other.dim) elif is_scalar_type(other): return Quantity.with_dimensions(float(self) / other, self.dim) else: return NotImplemented #return super(Quantity,self).__div__(other) def __truediv__(self, other): if isinstance(other, Quantity): return Quantity.with_dimensions(float(self) / float(other), self.dim / other.dim) elif is_scalar_type(other): return Quantity.with_dimensions(float(self) / other, self.dim) else: return NotImplemented #return super(Quantity,self).__truediv__(other) def __rdiv__(self, other): if isinstance(other, Quantity): return Quantity.with_dimensions(float(other) / float(self), other.dim / self.dim) elif is_scalar_type(other): return Quantity.with_dimensions(other / float(self), [-x for x in self.dim._dims]) else: return NotImplemented #return super(Quantity,self).__rdiv__(other) def __rtruediv__(self, other): if isinstance(other, Quantity): return Quantity.with_dimensions(float(other) / float(self), other.dim / self.dim) elif is_scalar_type(other): return Quantity.with_dimensions(other / float(self), [-x for x in self.dim._dims]) else: return NotImplemented #return super(Quantity,self).__rtruediv__(other) def __mod__(self, other): if isinstance(other, Quantity) or is_scalar_type(other): dim = get_dimensions(other) if dim == self.dim: return Quantity.with_dimensions(float(self) % float(other), self.dim) else: raise DimensionMismatchError("Addition", self.dim, dim) else: return NotImplemented #return super(Quantity,self).__add__(other) def __add__(self, other): if isinstance(other, Quantity) or is_scalar_type(other): dim = get_dimensions(other) if dim == self.dim: return Quantity.with_dimensions(float(self) + float(other), self.dim) else: raise DimensionMismatchError("Addition", self.dim, dim) else: return NotImplemented #return super(Quantity,self).__add__(other) def __radd__(self, other): return self.__add__(other) def __sub__(self, other): if isinstance(other, Quantity) or is_scalar_type(other): dim = get_dimensions(other) if dim == self.dim: return Quantity.with_dimensions(float(self) - float(other), self.dim) else: raise DimensionMismatchError("Subtraction", self.dim, dim) else: return NotImplemented #return super(Quantity,self).__sub__(other) def __rsub__(self, other): if isinstance(other, Quantity) or is_scalar_type(other): dim = get_dimensions(other) if dim == self.dim: return Quantity.with_dimensions(float(other) - float(self), self.dim) else: raise DimensionMismatchError("Subtraction(R)", self.dim, dim) else: return NotImplemented #return super(Quantity,self).__rsub__(other) def __pow__(self, other): if isinstance(other, Quantity): if other.is_dimensionless(): # WARNING: because dimension consistency is checked by exact comparison of dimensions, # this may lead to unexpected behaviour (e.g. (x**2)**0.5 may not have the same dimensions as x) return Quantity.with_dimensions(float(self) ** float(other), self.dim ** float(other)) else: raise DimensionMismatchError("Power", self.dim, other.dim) elif is_scalar_type(other): return Quantity.with_dimensions(float(self) ** other, self.dim ** other) else: return NotImplemented #return super(Quantity,self).__pow__(other) def __rpow__(self, other): if self.is_dimensionless(): if isinstance(other, Quantity): return Quantity.with_dimensions(float(other) ** float(self), other.dim ** float(self)) elif is_scalar_type(other): return Quantity(other ** float(self)) else: return NotImplemented #return super(Quantity,self).__pow__(other) else: raise DimensionMismatchError("Power(R)", self.dim) def __neg__(self): return Quantity.with_dimensions(-float(self), self.dim) def __pos__(self): return self def __abs__(self): return Quantity.with_dimensions(abs(float(self)), self.dim) #### COMPARISONS #### def __lt__(self, other): if isinstance(other, Quantity): if self.dim == other.dim: return float(self) < float(other) else: raise DimensionMismatchError("LessThan", self.dim, other.dim) elif is_scalar_type(other): if other == 0 or other == 0.: return float(self) < other if numpy.isposinf(other): return True if numpy.isneginf(other): return False if self.is_dimensionless(): return float(self) < other else: raise DimensionMismatchError("LessThan", self.dim, Dimension()) else: return NotImplemented #return super(Quantity,self).__lt__(other) def __le__(self, other): if isinstance(other, Quantity): if self.dim == other.dim: return float(self) <= float(other) else: raise DimensionMismatchError("LessThanOrEquals", self.dim, other.dim) elif is_scalar_type(other): if other == 0 or other == 0.: return float(self) <= other if numpy.isposinf(other): return True if numpy.isneginf(other): return False if self.is_dimensionless(): return float(self) <= other else: raise DimensionMismatchError("LessThanOrEquals", self.dim, Dimension()) else: return NotImplemented #return super(Quantity,self).__le__(other) def __gt__(self, other): if isinstance(other, Quantity): if self.dim == other.dim: return float(self) > float(other) else: raise DimensionMismatchError("GreaterThan", self.dim, other.dim) elif is_scalar_type(other): if other == 0 or other == 0.: return float(self) > other if numpy.isneginf(other): return True if numpy.isposinf(other): return False if self.is_dimensionless(): return float(self) > other else: raise DimensionMismatchError("GreaterThan", self.dim, Dimension()) else: return NotImplemented #return super(Quantity,self).__gt__(other) def __ge__(self, other): if isinstance(other, Quantity): if self.dim == other.dim: return float(self) >= float(other) else: raise DimensionMismatchError("GreaterThanOrEquals", self.dim, other.dim) elif is_scalar_type(other): if other == 0 or other == 0.: return float(self) >= other if numpy.isneginf(other): return True if numpy.isposinf(other): return False if self.is_dimensionless(): return float(self) >= other else: raise DimensionMismatchError("GreaterThanOrEquals", self.dim, Dimension()) else: return NotImplemented #return super(Quantity,self).__ge__(other) def __eq__(self, other): if isinstance(other, Quantity): if self.dim == other.dim: return float(self) == float(other) else: raise DimensionMismatchError("Equals", self.dim, other.dim) elif is_scalar_type(other): if other == 0 or other == 0. or numpy.isinf(other): return float(self) == other if self.dim.is_dimensionless(): return float(self) == other else: raise DimensionMismatchError("Equals", self.dim, Dimension()) else: return NotImplemented #return super(Quantity,self).__eq__(other) def __ne__(self, other): if isinstance(other, Quantity): if self.dim == other.dim: return float(self) != float(other) else: raise DimensionMismatchError("Equals", self.dim, other.dim) elif is_scalar_type(other): if other == 0 or other == 0. or numpy.isinf(other): return float(self) != other if self.dim.is_dimensionless(): return float(self) != other else: raise DimensionMismatchError("NotEquals", self.dim, Dimension()) else: return NotImplemented #return super(Quantity,self).__ne__(other) #### MAKE QUANTITY PICKABLE #### def __reduce__(self): return (quantity_with_dimensions, (float(self), self.dim)) class Unit(Quantity): ''' A physical unit Normally, you do not need to worry about the implementation of units. They are derived from the :class:`Quantity` object with some additional information (name and string representation). You can define new units which will be used when generating string representations of quantities simply by doing an arithmetical operation with only units, for example:: Nm = newton * metre Note that operations with units are slower than operations with :class:`Quantity` objects, so for efficiency if you do not need the extra information that a :class:`Unit` object carries around, write ``1*second`` in preference to ``second``. ''' # original documentation """A physical unit Basically, a unit is just a quantity with given dimensions, e.g. mvolt = 0.001 with the dimensions of voltage. The units module defines a large number of standard units, and you can also define your own (see below). The unit class also keeps track of various things that were used to define it so as to generate a nice string representation of it. See Representation below. Typical usage: x = 3 * mvolt # returns a quantity print x.in_unit(uvolt) # returns 3000 uV Standard units: The units class has the following fundamental units: metre, kilogram, second, amp, kelvin, mole, candle And these additional basic units: radian, steradian, hertz, newton, pascal, joule, watt, coulomb, volt, farad, ohm, siemens, weber, tesla, henry, celsius, lumen, lux, becquerel, gray, sievert, katal And additionally, it includes all scaled versions of these units using the following prefixes Factor Name Prefix ----- ---- ------ 10^24 yotta Y 10^21 zetta Z 10^18 exa E 10^15 peta P 10^12 tera T 10^9 giga G 10^6 mega M 10^3 kilo k 10^2 hecto h 10^1 deka da 1 10^-1 deci d 10^-2 centi c 10^-3 milli m 10^-6 micro u (\mu in SI) 10^-9 nano n 10^-12 pico p 10^-15 femto f 10^-18 atto a 10^-21 zepto z 10^-24 yocto y So for example nohm, ytesla, etc. are all defined. Defining your own: It can be useful to define your own units for printing purposes. So for example, to define the newton metre, you write: Nm = newton * metre Writing: print (1*Nm).in_unit(Nm) will return "1 Nm" because the Unit class generates a new display name of "Nm" from the display names "N" and "m" for newtons and metres automatically (see Representation below). To register this unit for use in the automatic printing of the Quantity.in_best_unit() method, see the documentation for the UnitRegistry class. Construction: The best way to construct a new unit is to use standard units already defined and arithmetic operations, e.g. newton*metre. See the documentation for __init__ and the static methods create(...) and create_scaled_units(...) for more details. If you don't like the automatically generated display name for the unit, use the set_display_name(name) method. Representation: A new unit defined by multiplication, division or taking powers generates a name for the unit automatically, so that for example the name for pfarad/mmetre**2 is "pF/mm^2", etc. If you don't like the automatically generated name, use the set_display_name(name) method. """ __slots__ = ["dim", "scale", "scalefactor", "dispname", "name", "iscompound"] #### CONSTRUCTION #### def __init__(self, value): """Initialises a dimensionless unit """ super(Unit, self).__init__(value) self.dim = Dimension() self.scale = [ "", "", "", "", "", "", "" ] self.scalefactor = "" self.name = "" self.dispname = "" self.iscompound = False def __new__(typ, *args, **kw): obj = super(Unit, typ).__new__(typ, *args, **kw) global automatically_register_units if automatically_register_units: register_new_unit(obj) return obj @staticmethod def create(dim, name="", dispname="", scalefactor="", **keywords): """Creates a new named unit dim -- the dimensions of the unit name -- the full name of the unit, e.g. volt dispname -- the display name, e.g. V scalefactor -- scaling factor, e.g. m for mvolt keywords -- the scaling for each SI dimension, e.g. length="m", mass="-1", etc. """ scale = [ "", "", "", "", "", "", "" ] for k in keywords: scale[_di[k]] = keywords[k] v = 1.0 for s, i in izip(scale, dim._dims): if i: v *= _siprefixes[s] ** i u = Unit(v * _siprefixes[scalefactor]) u.dim = dim u.scale = scale u.scalefactor = scalefactor + "" u.name = name + "" u.dispname = dispname + "" u.iscompound = False return u @staticmethod def create_scaled_unit(baseunit, scalefactor): """Create a scaled unit from a base unit baseunit -- e.g. volt, amp scalefactor -- e.g. "m" for mvolt, mamp """ u = Unit(float(baseunit) * _siprefixes[scalefactor]) u.dim = baseunit.dim u.scale = baseunit.scale u.scalefactor = scalefactor u.name = scalefactor + baseunit.name u.dispname = scalefactor + baseunit.dispname u.iscompound = False return u #### METHODS #### def set_name(self, name): """Sets the name for the unit """ self.name = name def set_display_name(self, name): """Sets the display name for the unit """ self.dispname = name #### REPRESENTATION #### def __repr__(self): if self.name == "": if self.scalefactor: parts = [repr(_siprefixes[self.scalefactor])] else: parts = [] for i in range(7): if self.dim._dims[i]: s = self.scale[i] + _iclass_label[i] if self.dim._dims[i] != 1: s += ' ** ' + str(self.dim._dims[i]) parts.append(s) s = " * ".join(parts) if not len(s): return "%s(1)" % self.__class__.__name__ return s.strip() else: return self.name def __str__(self): if self.dispname == "": s = self.scalefactor + " " for i in range(7): if self.dim._dims[i]: s += self.scale[i] + _ilabel[i] if self.dim._dims[i] != 1: s += "^" + str(self.dim._dims[i]) s += " " if not len(s): return "1" return s.strip() else: return self.dispname #### ARITHMETIC #### def __mul__(self, other): if isinstance(other, Unit): u = Unit(float(self) * float(other)) u.name = self.name + " * " + other.name u.dispname = self.dispname + ' ' + other.dispname u.dim = self.dim * other.dim u.iscompound = True return u else: return super(Unit, self).__mul__(other) def __div__(self, other): if isinstance(other, Unit): u = Unit(float(self) / float(other)) if other.iscompound: u.dispname = '(' + self.dispname + ')' u.name = '(' + self.name + ')' else: u.dispname = self.dispname u.name = self.name u.dispname += '/' u.name += ' / ' if other.iscompound: u.dispname += '(' + other.dispname + ')' u.name += '(' + other.name + ')' else: u.dispname += other.dispname u.name += other.name u.dim = self.dim / other.dim u.iscompound = True return u else: return super(Unit, self).__div__(other) def __pow__(self, other): if is_scalar_type(other): u = Unit(float(self) ** other) if self.iscompound: u.dispname = '(' + self.dispname + ')' u.name = '(' + self.name + ')' else: u.dispname = self.dispname u.name = self.name u.dispname += '^' + str(other) u.name += ' ** ' + repr(other) u.dim = self.dim ** other return u else: return super(Unit, self).__mul__(other) automatically_register_units = True class UnitRegistry(object): """Stores known units for printing in best units All a user needs to do is to use the register_new_unit(u) function. Default registries: The units module defines three registries, the standard units, user units, and additional units. Finding best units is done by first checking standard, then user, then additional. New user units are added by using the register_new_unit(u) function. Standard units includes all the basic non-compound unit names built in to the module, including volt, amp, etc. Additional units defines some compound units like newton metre (Nm) etc. Methods: add(u) - add a new unit __getitem__(x) - get the best unit for quantity x e.g. UnitRegistry ur; ur[3*mvolt] returns mvolt """ def __init__(self): self.objs = [] def add(self, u): """Add a unit to the registry """ self.objs.append(u) def __getitem__(self, x): """Returns the best unit for quantity x The algorithm is to consider the value: m=abs(x/u) for all matching units u. If there is a unit u with a value of m in [1,1000) then we select that unit. Otherwise, we select the first matching unit (which will typically be the unscaled version). """ matching = filter(lambda o: have_same_dimensions(o, x), self.objs) if len(matching) == 0: raise KeyError("Unit not found in registry.") floatrep = filter(lambda o: 0.1 <= abs(float(x / o)) < 100, matching) if len(floatrep): return floatrep[0] else: return matching[0] def register_new_unit(u): """Register a new unit for automatic displaying of quantities Example usage: 2.0*farad/metre**2 = 2.0 m^-4 kg^-1 s^4 A^2 register_new_unit(pfarad / mmetre**2) 2.0*farad/metre**2 = 2000000.0 pF/mm^2 """ UserUnitRegister.add(u) standard_unit_register = UnitRegistry() additional_unit_register = UnitRegistry() UserUnitRegister = UnitRegistry() def all_registered_units(*regs): """Returns all registered units in the correct order """ if not len(regs): regs = [ standard_unit_register, UserUnitRegister, additional_unit_register] for r in regs: for u in r.objs: yield u def _get_best_unit(x, *regs): """Returns the best unit for quantity x Checks the registries regs, unless none are provided in which case it will check the standard, user and additional unit registers in turn. """ if get_dimensions(x) == Dimension(): return Quantity(1) if len(regs): for r in regs: try: return r[x] except KeyError: pass return Quantity.with_dimensions(1, x.dim) else: return _get_best_unit(x, standard_unit_register, UserUnitRegister, additional_unit_register) def get_unit(x, *regs): ''' Find the most appropriate consistent unit from the unit registries, or just return a Quantity with the same dimensions and value 1 ''' for u in all_registered_units(*regs): if is_equal(float(u), 1) and have_same_dimensions(u, x): return u return Quantity.with_dimensions(1, get_dimensions(x)) def get_unit_fast(x): ''' Return a quantity with value 1 and the same dimensions ''' return Quantity.with_dimensions(1, get_dimensions(x)) #### DECORATORS def check_units(**au): """Decorator to check units of arguments passed to a function **Sample usage:** :: @check_units(I=amp,R=ohm,wibble=metre,result=volt) def getvoltage(I,R,**k): return I*R You don't have to check the units of every variable in the function, and you can define what the units should be for variables that aren't explicitly named in the definition of the function. For example, the code above checks that the variable wibble should be a length, so writing:: getvoltage(1*amp,1*ohm,wibble=1) would fail, but:: getvoltage(1*amp,1*ohm,wibble=1*metre) would pass. String arguments are not checked (e.g. ``getvoltage(wibble='hello')`` would pass). The special name ``result`` is for the return value of the function. An error in the input value raises a :exc:`DimensionMismatchError`, and an error in the return value raises an ``AssertionError`` (because it is a code problem rather than a value problem). **Notes** This decorator will destroy the signature of the original function, and replace it with the signature ``(*args, **kwds)``. Other decorators will do the same thing, and this decorator critically needs to know the signature of the function it is acting on, so it is important that it is the first decorator to act on a function. It cannot be used in combination with another decorator that also needs to know the signature of the function. """ def do_check_units(f): @wraps(f) def new_f(*args, **kwds): newkeyset = kwds.copy() arg_names = f.func_code.co_varnames[0:f.func_code.co_argcount] for (n, v) in zip(arg_names, args[0:f.func_code.co_argcount]): newkeyset[n] = v for k in newkeyset.iterkeys(): # string variables are allowed to pass, the presumption is they # name another variable. None is also allowed, useful for # default parameters if (k in au.keys() and not isinstance(newkeyset[k], str) and not newkeyset[k] is None and not isinstance(newkeyset[k], numpy.ndarray)): if not have_same_dimensions(newkeyset[k], au[k]): raise DimensionMismatchError("Function " + f.__name__ + " variable " + k + " should have dimensions of " + str(au[k]), get_dimensions(newkeyset[k])) result = f(*args, **kwds) if "result" in au: assert have_same_dimensions(result, au["result"]), \ ("Function " + f.__name__ + " should return a value with unit " + str(au["result"]) + " but has returned " + str(get_dimensions(result))) return result # new_f.__name__ = f.__name__ # new_f.__doc__ = f.__doc__ # new_f.__dict__.update(f.__dict__) return new_f return do_check_units ## Note: do not normally call this, see note on importing of decorator module at the top of this module #if use_decorator: # old_check_units = check_units # def check_units(**au): # return lambda f : decorator.new_wrapper(old_check_units(**au)(f), f) # check_units.__doc__ = old_check_units.__doc__ def _check_nounits(**au): """Don't bother checking units decorator """ def dont_check_units(f): return f return dont_check_units if 'sphinx' in sys.modules: check_units = _check_nounits def scalar_representation(x): if isinstance(x, Unit): return x.name u = get_unit(x) if isinstance(u, Unit): return '(' + repr(float(x)) + '*' + u.name + ')' if isinstance(x, Quantity): return '(Quantity.with_dimensions(' + repr(float(x)) + ',' + repr(x.dim._dims) + '))' return repr(x) # Remove all units if not bup.use_units: check_units = _check_nounits def get_dimensions(obj): return Dimension() def is_dimensionless(obj): return True def have_same_dimensions(obj1, obj2): return True def get_unit(x, *regs): return 1. def scalar_representation(x): return '1.0' ################################################### ##### ADDITIONAL INFORMATION #SI DIMENSIONS #------------- #Quantity Unit Symbol #-------- ---- ------ #Length metre m #Mass kilogram kg #Time second s #Electric current ampere A #Temperature kelvin K #Quantity of substance mole mol #Luminosity candle cd # SI UNIT PREFIXES # ---------------- # Factor Name Prefix # ----- ---- ------ # 10^24 yotta Y # 10^21 zetta Z # 10^18 exa E # 10^15 peta P # 10^12 tera T # 10^9 giga G # 10^6 mega M # 10^3 kilo k # 10^2 hecto h # 10^1 deka da # 1 # 10^-1 deci d # 10^-2 centi c # 10^-3 milli m # 10^-6 micro u (\mu in SI) # 10^-9 nano n # 10^-12 pico p # 10^-15 femto f # 10^-18 atto a # 10^-21 zepto z # 10^-24 yocto y brian-1.4.3/brian/globalprefs.py000066400000000000000000000072451266062647100166010ustar00rootroot00000000000000# ---------------------------------------------------------------------------------- # Copyright ENS, INRIA, CNRS # Contributors: Romain Brette (brette@di.ens.fr) and Dan Goodman (goodman@di.ens.fr) # # Brian is a computer program whose purpose is to simulate models # of biological neural networks. # # This software is governed by the CeCILL license under French law and # abiding by the rules of distribution of free software. You can use, # modify and/ or redistribute the software under the terms of the CeCILL # license as circulated by CEA, CNRS and INRIA at the following URL # "http://www.cecill.info". # # As a counterpart to the access to the source code and rights to copy, # modify and redistribute granted by the license, users are provided only # with a limited warranty and the software's author, the holder of the # economic rights, and the successive licensors have only limited # liability. # # In this respect, the user's attention is drawn to the risks associated # with loading, using, modifying and/or developing or reproducing the # software by the user in light of its specific status of free software, # that may mean that it is complicated to manipulate, and that also # therefore means that it is reserved for developers and experienced # professionals having in-depth computer knowledge. Users are therefore # encouraged to load and test the software's suitability as regards their # requirements in conditions enabling the security of their systems and/or # data to be ensured and, more generally, to use and operate it in the # same conditions as regards security. # # The fact that you are presently reading this means that you have had # knowledge of the CeCILL license and that you accept its terms. # ---------------------------------------------------------------------------------- # """ Global preferences for Brian ---------------------------- The following global preferences have been defined: """ # the global preferences referred to above are automatically # added to the docstring when they are defined via # define_global_preference __docformat__ = "restructuredtext en" __all__ = ['set_global_preferences', 'get_global_preference', 'get_global_preferences', 'exists_global_preference', 'define_global_preference'] import sys from utils.documentation import * globalprefdocs = "" class BrianGlobalPreferences: pass g_prefs = BrianGlobalPreferences() def set_global_preferences(**kwds): """Set global preferences for Brian Usage:: ``set_global_preferences(...)`` where ... is a list of keyword assignments. """ for k, v in kwds.iteritems(): g_prefs.__dict__[k] = v def get_global_preference(k): """Get the value of the named global preference """ return g_prefs.__dict__[k] def get_global_preferences(): """ Get a dictionary containing all the currently set preferences. """ return g_prefs.__dict__.copy() def exists_global_preference(k): """Determine if named global preference exists """ return hasattr(g_prefs, k) def define_global_preference(k, defaultvaluedesc, desc): """Define documentation for a new global preference Arguments: ``k`` The name of the preference (a string) ``defaultvaluedesc`` A string description of the default value ``desc`` A multiline description of the preference in docstring format """ global globalprefdocs globalprefdocs += '``' + k + ' = ' + defaultvaluedesc + '``\n' globalprefdocs += flattened_docstring(desc, numtabs=1) brian-1.4.3/brian/group.py000066400000000000000000000147231266062647100154340ustar00rootroot00000000000000from brian import * __all__ = ['Group', 'MultiGroup'] class Group(object): ''' Generic fixed-length variable values container class Used internally by Brian to store values associated with variables. Primary use is for NeuronGroup to store a state matrix of values associated to the different variables. Each differential equation in ``equations`` is allocated an array of length ``N`` in the attribute ``_S`` of the object. Unit consistency checking is performed. ''' def __init__(self, equations, N, level=0, unit_checking=True): if isinstance(equations, str): equations = Equations(equations, level=level + 1) equations.prepare(check_units=unit_checking) var_names = equations._diffeq_names M = len(var_names) self._S = zeros((M, N)) self.staticvars = dict([(name, equations._function[name]) for name in equations._eq_names]) self.var_index = dict(zip(var_names, range(M))) self.var_index.update(zip(range(M), range(M))) # name integer i -> state variable i for var1, var2 in equations._alias.iteritems(): self.var_index[var1] = self.var_index[var2] def get_var_index(self, name): ''' Returns the index of state variable "name". ''' return self.var_index[name] def __len__(self): ''' Number of neurons in the group. ''' return self._S.shape[1] def num_states(self): return self._S.shape[0] def state_(self, name): ''' Gets the state variable named "name" as a reference to the underlying array ''' if isinstance(name, int): return self._S[name] if name in self.staticvars: f = self.staticvars[name] return f(*[self.state_(var) for var in f.func_code.co_varnames]) i = self.var_index[name] return self._S[i] state = state_ def __getattr__(self, name): if name == 'var_index': # this seems mad - the reason is that getattr is only called if the thing hasn't # been found using the standard methods of finding attributes, which for var_index # should have worked, this is important because the next line looks for var_index # and if we haven't got a var_index we don't want to get stuck in an infinite # loop raise AttributeError('var_index') if not hasattr(self, 'var_index'): # only provide lookup of variable names if we have some variable names, i.e. # if the var_index attribute exists raise AttributeError('var_index') try: return self.state(name) except KeyError: if len(name) and name[-1] == '_': try: origname = name[:-1] return self.state_(origname) except KeyError: raise AttributeError(origname) raise AttributeError(name) def __setattr__(self, name, val): origname = name if len(name) and name[-1] == '_': origname = name[:-1] if not hasattr(self, 'var_index') or (name not in self.var_index and origname not in self.var_index): object.__setattr__(self, name, val) else: if name in self.var_index: self.state(name)[:] = val else: self.state_(origname)[:] = val class MultiGroup(object): ''' Generic variable-length variable values container class Used internally by Brian to store values associated with variables. See :class:`Group` for more details. This class takes a list of groups or of ``(equations, N)`` pairs. The ``state()`` and ``state_()`` methods work as if it were a single :class:`Group` but return arrays of different lengths from different state matrix structures depending on where the state value is defined. Similarly for attribute access. ''' def __init__(self, groups, level=0): newgroups = [] self.var_index = dict() for g in groups: if not isinstance(g, Group): eqs, N = g g = Group(eqs, N, level=level + 1) newgroups.append(g) self.var_index.update(dict((k, g) for k in g.var_index.keys())) self.var_index.update(dict((k, g) for k in g.staticvars.keys())) self.groups = newgroups def state_(self, name): return self.var_index[name].state_(name) state = state_ def __getattr__(self, name): try: return self.state(name) except KeyError: if len(name) and name[-1] == '_': try: origname = name[:-1] return self.state_(origname) except KeyError: raise AttributeError raise AttributeError def __setattr__(self, name, val): origname = name if len(name) and name[-1] == '_': origname = name[:-1] if not hasattr(self, 'var_index') or (name not in self.var_index and origname not in self.var_index): object.__setattr__(self, name, val) else: if name in self.var_index: self.state(name)[:] = val else: self.state_(origname)[:] = val if __name__ == '__main__': if True: eqs_A = Equations(''' dV/dt = -V/(1*second) : volt W = V*V : volt2 x = V ''') eqs_B = Equations(''' dVb/dt = -Vb/(1*second) : volt Wb = Vb*Vb : volt2 xb = Vb ''') g = MultiGroup(((eqs_A, 10), (eqs_B, 5))) for h in g.groups: h._S[:] = rand(*h._S.shape) print g.V print g.V ** 2 print g.W print g.x print g.Vb print g.Vb ** 2 print g.Wb print g.xb if False: eqs = Equations(''' dV/dt = -V/(1*second) : volt W = V*V : volt2 x = V ''') g = Group(eqs, 10) print g._S g._S[:] = rand(*g._S.shape) print g._S print g.V print g.V ** 2 print g.W print g.x g.V = -1 print g.V print g.W print g.x brian-1.4.3/brian/hears/000077500000000000000000000000001266062647100150215ustar00rootroot00000000000000brian-1.4.3/brian/hears/__init__.py000066400000000000000000000003131266062647100171270ustar00rootroot00000000000000from prefs import * from bufferable import * from sounds import * from onlinesounds import * from erb import * from filtering import * from hrtf import * from db import * from plotting import * brian-1.4.3/brian/hears/bufferable.py000066400000000000000000000143041266062647100174720ustar00rootroot00000000000000''' The Bufferable class serves as a base for all the other Brian.hears classes ''' from numpy import zeros, empty, hstack, vstack, arange, diff class Bufferable(object): ''' Base class for Brian.hears classes Defines a buffering interface of two methods: ``buffer_init()`` Initialise the buffer, should set the time pointer to zero and do any other initialisation that the object needs. ``buffer_fetch(start, end)`` Fetch the next samples ``start:end`` from the buffer. Value returned should be an array of shape ``(end-start, nchannels)``. Can throw an ``IndexError`` exception if it is outside the possible range. In addition, bufferable objects should define attributes: ``nchannels`` The number of channels in the buffer. ``samplerate`` The sample rate in Hz. By default, the class will define a default buffering mechanism which can easily be extended. To extend the default buffering mechanism, simply implement the method: ``buffer_fetch_next(samples)`` Returns the next ``samples`` from the buffer. The default methods for ``buffer_init()`` and ``buffer_fetch()`` will define a buffer cache which will get larger if it needs to to accommodate a ``buffer_fetch(start, end)`` where ``end-start`` is larger than the current cache. If the filterbank has a ``minimum_buffer_size`` attribute, the internal cache will always have at least this size, and the ``buffer_fetch_next(samples)`` method will always get called with ``samples>=minimum_buffer_size``. This can be useful to ensure that the buffering is done efficiently internally, even if the user request buffered chunks that are too small. If the filterbank has a ``maximum_buffer_size`` attribute then ``buffer_fetch_next(samples)`` will always be called with ``samples<=maximum_buffer_size`` - this can be useful for either memory consumption reasons or for implementing time varying filters that need to update on a shorter time window than the overall buffer size. The following attributes will automatically be maintained: ``self.cached_buffer_start``, ``self.cached_buffer_end`` The start and end of the cached segment of the buffer ``self.cached_buffer_output`` An array of shape ``((cached_buffer_end-cached_buffer_start, nchannels)`` with the current cached segment of the buffer. Note that this array can change size. ''' def buffer_fetch(self, start, end): if not hasattr(self, 'cached_buffer_start'): self.buffer_init() # optimisations for the most typical cases, which are when start:end is # the current cached segment, or when start:end is the next cached # segment of the same size as the current one if start==self.cached_buffer_start and end==self.cached_buffer_end: return self.cached_buffer_output if start==self.cached_buffer_end and end-start==self.cached_buffer_output.shape[0]: self.cached_buffer_output = self._buffer_fetch_next(end-start) self.cached_buffer_start = start self.cached_buffer_end = end return self.cached_buffer_output # handle bad inputs if endfloat(other) def __ge__(self, other): if not isinstance(other, dB_type): raise dB_error('Can only compare with another dB') return float(self)>=float(other) def __eq__(self, other): if not isinstance(other, dB_type): raise dB_error('Can only compare with another dB') return float(self)==float(other) def __ne__(self, other): if not isinstance(other, dB_type): raise dB_error('Can only compare with another dB') return float(self)!=float(other) def __reduce__(self): return (dB_type, (float(self),)) def gain(self): return 10**(float(self)/20.0) dB = dB_type(1.0) def gain(level): ''' Returns the gain factor associated to a level in dB. The formula is: gain = 10**(level/20.0) ''' if not isinstance(level, dB_type): raise dB_error('Level must be in dB') return level.gain() brian-1.4.3/brian/hears/erb.py000066400000000000000000000015341266062647100161460ustar00rootroot00000000000000''' Utility functions adapted from MAP ''' from brian import * __all__ = ['erbspace'] @check_units(low=Hz, high=Hz) def erbspace(low, high, N, earQ=9.26449, minBW=24.7, order=1): ''' Returns the centre frequencies on an ERB scale. ``low``, ``high`` Lower and upper frequencies ``N`` Number of channels ``earQ=9.26449``, ``minBW=24.7``, ``order=1`` Default Glasberg and Moore parameters. ''' low = float(low) high = float(high) cf = -(earQ * minBW) + exp((arange(N)) * (-log(high + earQ * minBW) + \ log(low + earQ * minBW)) / (N-1)) * (high + earQ * minBW) cf = cf[::-1] return cf # Testing if __name__ == '__main__': cf = erbspace(20 * Hz, 20 * kHz, 3000) print amin(cf), amax(cf) print diff(cf)[-5:] plot(cf) show() brian-1.4.3/brian/hears/filtering/000077500000000000000000000000001266062647100170045ustar00rootroot00000000000000brian-1.4.3/brian/hears/filtering/__init__.py000066400000000000000000000004301266062647100211120ustar00rootroot00000000000000from filterbank import * from linearfilterbank import * from firfilterbank import * from filterbanklibrary import * from filterbankgroup import * from drnl import DRNL from dcgc import DCGC from fractionaldelay import * #from zilany import ZILANY from tan_carney import *brian-1.4.3/brian/hears/filtering/dcgc.py000066400000000000000000000165021266062647100202620ustar00rootroot00000000000000from brian import * from filterbank import Filterbank,FunctionFilterbank,ControlFilterbank,CombinedFilterbank,RestructureFilterbank from filterbanklibrary import * from linearfilterbank import * __all__ = ['DCGC'] def set_parameters(cf,param): parameters=dict() parameters['b1'] = 1.81 parameters['c1'] = -2.96 parameters['b2'] = 2.17 parameters['c2'] = 2.2 parameters['decay_tcst'] = .5*ms parameters['lev_weight'] = .5 parameters['level_ref'] = 50. parameters['level_pwr1'] = 1.5 parameters['level_pwr2'] = .5 parameters['RMStoSPL'] = 30. parameters['frat0'] = .2330 parameters['frat1'] = .005 parameters['lct_ERB'] = 1.5 #value of the shift in ERB frequencies parameters['frat_control'] = 1.08 parameters['order_gc']=4 parameters['ERBrate']= 21.4*log10(4.37*cf/1000+1) parameters['ERBwidth']= 24.7*(4.37*cf/1000 + 1) if param: if not isinstance(param, dict): raise Error('given parameters must be a dict') for key in param.keys(): if not parameters.has_key(key): raise Exception(key + ' is invalid key entry for given parameters') parameters[key] = param[key] return parameters #defition of the controler class class AsymCompUpdate: def __init__(self,target,samplerate,fp1,param): fp1=atleast_1d(fp1) self.iteration=0 self.target=target self.samplerate=samplerate self.fp1=fp1 self.exp_deca_val = exp(-1/(param['decay_tcst'] *samplerate)*log(2)) self.level_min = 10**(- param['RMStoSPL']/20) self.level_ref = 10**(( param['level_ref'] - param['RMStoSPL'])/20) self.b=param['b2'] self.c=param['c2'] self.lev_weight=param['lev_weight'] self.level_ref=param['level_ref'] self.level_pwr1=param['level_pwr1'] self.level_pwr2=param['level_pwr2'] self.RMStoSPL=param['RMStoSPL'] self.frat0=param['frat0'] self.frat1=param['frat1'] self.level1_prev=-100 self.level2_prev=-100 self.p0=2 self.p1=1.7818*(1-0.0791*self.b)*(1-0.1655*abs(self.c)) self.p2=0.5689*(1-0.1620*self.b)*(1-0.0857*abs(self.c)) self.p3=0.2523*(1-0.0244*self.b)*(1+0.0574*abs(self.c)) self.p4=1.0724 def __call__(self,*input): value1=input[0][-1,:] value2=input[1][-1,:] level1 = maximum(maximum(value1,0),self.level1_prev*self.exp_deca_val) level2 = maximum(maximum(value2,0),self.level2_prev*self.exp_deca_val) self.level1_prev=level1 self.level2_prev=level2 level_total=self.lev_weight*self.level_ref*(level1/self.level_ref)**self.level_pwr1+(1-self.lev_weight)*self.level_ref*(level2/self.level_ref)**self.level_pwr2 level_dB=20*log10(maximum(level_total,self.level_min))+self.RMStoSPL frat = self.frat0 + self.frat1*level_dB fr2 = self.fp1*frat self.iteration+=1 self.target.filt_b, self.target.filt_a=asymmetric_compensation_coeffs(self.samplerate,fr2,self.target.filt_b,self.target.filt_a,self.b,self.c,self.p0,self.p1,self.p2,self.p3,self.p4) class DCGC(CombinedFilterbank): ''' The compressive gammachirp auditory filter as described in Irino, T. and Patterson R., "A compressive gammachirp auditory filter for both physiological and psychophysical data", JASA 2001. Technical implementation details and notation can be found in Irino, T. and Patterson R., "A Dynamic Compressive Gammachirp Auditory Filterbank", IEEE Trans Audio Speech Lang Processing. The model consists of a control pathway and a signal pathway in parallel. The control pathway consists of a bank of bandpass filters followed by a bank of highpass filters (this chain yields a bank of gammachirp filters). The signal pathway consist of a bank of fix bandpass filters followed by a bank of highpass filters with variable cutoff frequencies (this chain yields a bank of gammachirp filters with a level-dependent bandwidth). The highpass filters of the signal pathway are controlled by the output levels of the two stages of the control pathway. Initialised with arguments: ``source`` Source of the cochlear model. ``cf`` List or array of center frequencies. ``update_interval`` Interval in samples controlling how often the band pass filter of the signal pathway is updated. Smaller values are more accurate, but give longer computation times. ``param`` Dictionary used to overwrite the default parameters given in the original paper. The possible parameters to change and their default values (see Irino, T. and Patterson R., "A Dynamic Compressive Gammachirp Auditory Filterbank", IEEE Trans Audio Speech Lang Processing) are:: param['b1'] = 1.81 param['c1'] = -2.96 param['b2'] = 2.17 param['c2'] = 2.2 param['decay_tcst'] = .5*ms param['lev_weight'] = .5 param['level_ref'] = 50. param['level_pwr1'] = 1.5 param['level_pwr2'] = .5 param['RMStoSPL'] = 30. param['frat0'] = .2330 param['frat1'] = .005 param['lct_ERB'] = 1.5 #value of the shift in ERB frequencies param['frat_control'] = 1.08 param['order_gc']=4 param['ERBrate']= 21.4*log10(4.37*cf/1000+1) # cf is the center frequency param['ERBwidth']= 24.7*(4.37*cf/1000 + 1) ''' def __init__(self, source,cf,update_interval=1,param={}): CombinedFilterbank.__init__(self, source) source = self.get_modified_source() parameters=set_parameters(cf,param) ERBspace = mean(diff(parameters['ERBrate'])) cf=atleast_1d(cf) #bank of passive gammachirp filters. As the control path uses the same passive filterbank than the signal path (buth shifted in frequency) #this filterbanl is used by both pathway. pGc=LogGammachirp(source,cf,b=parameters['b1'], c=parameters['c1']) # self.gc.filt_b=pGc.filt_b # self.gc.filt_a=pGc.filt_a fp1 = cf + parameters['c1']*parameters['ERBwidth']*parameters['b1']/parameters['order_gc'] nbr_cf=len(cf) #### Control Path #### n_ch_shift = round(parameters['lct_ERB']/ERBspace); #value of the shift in channels indch1_control = minimum(maximum(1, arange(1,nbr_cf+1)+n_ch_shift),nbr_cf).astype(int)-1 fp1_control = fp1[indch1_control] pGc_control=RestructureFilterbank(pGc,indexmapping=indch1_control) fr2_control = parameters['frat_control']*fp1_control asym_comp_control=AsymmetricCompensation(pGc_control, fr2_control,b=parameters['b2'], c=parameters['c2']) #### Signal Path #### fr1=fp1*parameters['frat0'] signal_path= AsymmetricCompensation(pGc, fr1,b=parameters['b2'], c=parameters['c2']) # self.asym_comp.filt_b=signal_path.filt_b # self.asym_comp.filt_a=signal_path.filt_a #### Controler #### updater = AsymCompUpdate(signal_path,source.samplerate,fp1,parameters) #the updater control = ControlFilterbank(signal_path, [pGc_control,asym_comp_control], signal_path, updater, update_interval) self.set_output(control)brian-1.4.3/brian/hears/filtering/drnl.py000077500000000000000000000176321266062647100203310ustar00rootroot00000000000000from brian import * from filterbank import Filterbank, FunctionFilterbank, CombinedFilterbank from filterbanklibrary import * __all__ = ['DRNL'] def set_parameters(cf,type,param): parameters=dict() parameters['stape_scale']=0.00014 parameters['order_linear']=3 parameters['order_nonlinear']=3 if type=='guinea pig': parameters['cf_lin_p0']=0.339 parameters['cf_lin_m']=0.339 parameters['bw_lin_p0']=1.3 parameters['bw_lin_m']=0.53 parameters['cf_nl_p0']=0 parameters['cf_nl_m']=1. parameters['bw_nl_p0']=0.8 parameters['bw_nl_m']=0.58 parameters['a_p0']=1.87 parameters['a_m']=0.45 parameters['b_p0']=-5.65 parameters['b_m']=0.875 parameters['c_p0']=-1. parameters['c_m']=0 parameters['g_p0']=5.68 parameters['g_m']=-0.97 parameters['lp_lin_cutoff_p0']=0.339 parameters['lp_lin_cutoff_m']=0.339 parameters['lp_nl_cutoff_p0']=0 parameters['lp_nl_cutoff_m']=1. elif type=='human': parameters['cf_lin_p0']=-0.067 parameters['cf_lin_m']=1.016 parameters['bw_lin_p0']=0.037 parameters['bw_lin_m']=0.785 parameters['cf_nl_p0']=-0.052 parameters['cf_nl_m']=1.016 parameters['bw_nl_p0']=-0.031 parameters['bw_nl_m']=0.774 parameters['a_p0']=1.402 parameters['a_m']=0.819 parameters['b_p0']=1.619 parameters['b_m']=-0.818 parameters['c_p0']=-0.602 parameters['c_m']=0 parameters['g_p0']=4.2 parameters['g_m']=0.48 parameters['lp_lin_cutoff_p0']=-0.067 parameters['lp_lin_cutoff_m']=1.016 parameters['lp_nl_cutoff_p0']=-0.052 parameters['lp_nl_cutoff_m']=1.016 if param: if not isinstance(param, dict): raise Error('given parameters must be a dict') for key in param.keys(): if not parameters.has_key(key): raise Exception(key + ' is invalid key entry for given parameters') parameters[key] = param[key] return parameters class DRNL(CombinedFilterbank): r''' Implementation of the dual resonance nonlinear (DRNL) filter as described in Lopez-Paveda, E. and Meddis, R., "A human nonlinear cochlear filterbank", JASA 2001. The entire pathway consists of the sum of a linear and a nonlinear pathway. The linear path consists of a bank of bandpass filters (second order gammatone), a low pass function, and a gain/attenuation factor, g, in a cascade. The nonlinear path is a cascade consisting of a bank of gammatone filters, a compression function, a second bank of gammatone filters, and a low pass function, in that order. Initialised with arguments: ``source`` Source of the cochlear model. ``cf`` List or array of center frequencies. ``type`` defines the parameters set corresponding to a certain fit. It can be either: ``type='human'`` The parameters come from Lopez-Paveda, E. and Meddis, R.., "A human nonlinear cochlear filterbank", JASA 2001. ``type ='guinea pig'`` The parameters come from Summer et al., "A nonlinear filter-bank model of the guinea-pig cochlear nerve: Rate responses", JASA 2003. ``param`` Dictionary used to overwrite the default parameters given in the original papers. The possible parameters to change and their default values for humans (see Lopez-Paveda, E. and Meddis, R.,"A human nonlinear cochlear filterbank", JASA 2001. for notation) are:: param['stape_scale']=0.00014 param['order_linear']=3 param['order_nonlinear']=3 from there on the parameters are given in the form :math:`x=10^{\mathrm{p0}+m\log_{10}(\mathrm{cf})}` where ``cf`` is the center frequency:: param['cf_lin_p0']=-0.067 param['cf_lin_m']=1.016 param['bw_lin_p0']=0.037 param['bw_lin_m']=0.785 param['cf_nl_p0']=-0.052 param['cf_nl_m']=1.016 param['bw_nl_p0']=-0.031 param['bw_nl_m']=0.774 param['a_p0']=1.402 param['a_m']=0.819 param['b_p0']=1.619 param['b_m']=-0.818 param['c_p0']=-0.602 param['c_m']=0 param['g_p0']=4.2 param['g_m']=0.48 param['lp_lin_cutoff_p0']=-0.067 param['lp_lin_cutoff_m']=1.016 param['lp_nl_cutoff_p0']=-0.052 param['lp_nl_cutoff_m']=1.016 ''' def __init__(self, source, cf, type='human', param={}): CombinedFilterbank.__init__(self, source) source = self.get_modified_source() cf = atleast_1d(cf) nbr_cf=len(cf) parameters=set_parameters(cf,type,param) #conversion to stape velocity (which are the units needed for the further centres) source=source*parameters['stape_scale'] #### Linear Pathway #### #bandpass filter (second order gammatone filter) cf_linear=10**(parameters['cf_lin_p0']+parameters['cf_lin_m']*log10(cf)) bandwidth_linear=10**(parameters['bw_lin_p0']+parameters['bw_lin_m']*log10(cf)) gammatone=ApproximateGammatone(source, cf_linear, bandwidth_linear, order=parameters['order_linear']) #linear gain g=10**(parameters['g_p0']+parameters['g_m']*log10(cf)) func_gain=lambda x:g*x gain= FunctionFilterbank(gammatone,func_gain) #low pass filter(cascade of 4 second order lowpass butterworth filters) cutoff_frequencies_linear=10**(parameters['lp_lin_cutoff_p0']+parameters['lp_lin_cutoff_m']*log10(cf)) order_lowpass_linear=2 lp_l=LowPass(gain,cutoff_frequencies_linear) lowpass_linear=Cascade(gain,lp_l,4) #### Nonlinear Pathway #### #bandpass filter (third order gammatone filters) cf_nonlinear=10**(parameters['cf_nl_p0']+parameters['cf_nl_m']*log10(cf)) bandwidth_nonlinear=10**(parameters['bw_nl_p0']+parameters['bw_nl_m']*log10(cf)) bandpass_nonlinear1=ApproximateGammatone(source, cf_nonlinear, bandwidth_nonlinear, order=parameters['order_nonlinear']) #compression (linear at low level, compress at high level) a=10**(parameters['a_p0']+parameters['a_m']*log10(cf)) #linear gain b=10**(parameters['b_p0']+parameters['b_m']*log10(cf)) v=10**(parameters['c_p0']+parameters['c_m']*log10(cf))#compression exponent func_compression=lambda x:sign(x)*minimum(a*abs(x),b*abs(x)**v) compression=FunctionFilterbank(bandpass_nonlinear1, func_compression) #bandpass filter (third order gammatone filters) bandpass_nonlinear2=ApproximateGammatone(compression, cf_nonlinear, bandwidth_nonlinear, order=parameters['order_nonlinear']) #low pass filter cutoff_frequencies_nonlinear=10**(parameters['lp_nl_cutoff_p0']+parameters['lp_nl_cutoff_m']*log10(cf)) order_lowpass_nonlinear=2 lp_nl=LowPass(bandpass_nonlinear2,cutoff_frequencies_nonlinear) lowpass_nonlinear=Cascade(bandpass_nonlinear2,lp_nl,3) #adding the two pathways drnl_filter=lowpass_linear+lowpass_nonlinear self.set_output(drnl_filter) if __name__ == '__main__': from brian import * set_global_preferences(usenewbrianhears=True, useweave=False) from brian.hears import * dBlevel=60*dB # dB level in rms dB SPL sound=Sound.load('/home/bertrand/Data/Toolboxes/AIM2006-1.40/Sounds/aimmat.wav') samplerate=sound.samplerate sound=sound.atlevel(dBlevel) simulation_duration=len(sound)/samplerate nbr_center_frequencies=50 center_frequencies=log_space(100*Hz, 1000*Hz, nbr_center_frequencies) dnrl_filter=DRNL(sound,center_frequencies) dnrl_filter.buffer_init() dnrl=dnrl_filter.buffer_fetch(0, len(sound))brian-1.4.3/brian/hears/filtering/filterbank.py000066400000000000000000001014641266062647100215050ustar00rootroot00000000000000from brian import * try: import weave except ImportError: from scipy import weave from scipy import signal, random from ..bufferable import Bufferable from operator import isSequenceType from __builtin__ import all __all__ = ['Filterbank', 'RestructureFilterbank', 'Repeat', 'Tile', 'Join', 'Interleave', 'FunctionFilterbank', 'SumFilterbank', 'DoNothingFilterbank', 'ControlFilterbank', 'CombinedFilterbank', ] class Filterbank(Bufferable): ''' Generalised filterbank object **Documentation common to all filterbanks** Filterbanks all share a few basic attributes: .. autoattribute:: source .. attribute:: nchannels The number of channels. .. attribute:: samplerate The sample rate. .. autoattribute:: duration To process the output of a filterbank, the following method can be used: .. automethod:: process Alternatively, the buffer interface can be used, which is described in more detail below. Filterbank also defines arithmetical operations for +, -, ``*``, / where the other operand can be a filterbank or scalar. **Details on the class** This class is a base class not designed to be instantiated. A Filterbank object should define the interface of :class:`Bufferable`, as well as defining a ``source`` attribute. This is normally a :class:`Bufferable` object, but could be an iterable of sources (for example, for filterbanks that mix or add multiple inputs). The ``buffer_fetch_next(samples)`` method has a default implementation that fetches the next input, and calls the ``buffer_apply(input)`` method on it, which can be overridden by a derived class. This is typically the easiest way to implement a new filterbank. Filterbanks with multiple sources will need to override this default implementation. There is a default ``__init__`` method that can be called by a derived class that sets the ``source``, ``nchannels`` and ``samplerate`` from that of the ``source`` object. For multiple sources, the default implementation will check that each source has the same number of channels and samplerate and will raise an error if not. There is a default ``buffer_init()`` method that calls ``buffer_init()`` on the ``source`` (or list of sources). **Example of deriving a class** The following class takes N input channels and sums them to a single output channel:: class AccumulateFilterbank(Filterbank): def __init__(self, source): Filterbank.__init__(self, source) self.nchannels = 1 def buffer_apply(self, input): return reshape(sum(input, axis=1), (input.shape[0], 1)) Note that the default ``Filterbank.__init__`` will set the number of channels equal to the number of source channels, but we want to change it to have a single output channel. We use the ``buffer_apply`` method which automatically handles the efficient cacheing of the buffer for us. The method receives the array ``input`` which has shape ``(bufsize, nchannels)`` and sums over the channels (``axis=1``). It's important to reshape the output so that it has shape ``(bufsize, outputnchannels)`` so that it can be used as the input to subsequent filterbanks. ''' def __init__(self, source): if isinstance(source, Bufferable): self.source = source self.nchannels = source.nchannels self.samplerate = source.samplerate else: self.nchannels = source[0].nchannels self.samplerate = source[0].samplerate for s in source: if s.nchannels!=self.nchannels: raise ValueError('All sources must have the same number of channels.') if int(s.samplerate)!=int(self.samplerate): raise ValueError('All sources must have the same samplerate.') self.source = source def change_source(self, source): if not hasattr(self, '_source') or self._source is None: self._source = source return if isinstance(source, tuple): for s in source: if int(s.samplerate)!=int(self.samplerate): raise ValueError('source samplerate is wrong.') for news, olds in zip(source, self._source): if news.nchannels!=olds.nchannels: raise ValueError('New sources have different numbers of channels to old sources.') self._source = source return if source.nchannels==self.nchannels: self._source = source return if source.nchannels==1: self._source = Repeat(source, self.nchannels) else: raise ValueError('New source must have the same number of channels as old source.') source = property(fget=lambda self:self._source, fset=lambda self, source:self.change_source(source), doc=''' The source of the filterbank, a :class:`Bufferable` object, e.g. another :class:`Filterbank` or a :class:`Sound`. It can also be a tuple of sources. Can be changed after the object is created, although note that for some filterbanks this may cause problems if they do make assumptions about the input based on the first source object they were passed. If this is causing problems, you can insert a dummy filterbank (:class:`DoNothingFilterbank`) which is guaranteed to work if you change the source. ''') def get_duration(self): if hasattr(self, '_duration'): return self._duration else: source = self.source if isinstance(source, Bufferable): source = [source] try: durations = [s.duration for s in source] duration = max(durations) return duration except KeyError: raise KeyError('Cannot compute duration from sources.') def set_duration(self, duration): self._duration = duration duration = property(fget=get_duration, fset=set_duration, doc=''' The duration of the filterbank. If it is not specified by the user, it is computed by finding the maximum of its source durations. If these are not specified a :class:`KeyError` will be raised (for example, using :class:`OnlineSound` as a source). ''') def process(self, func=None, duration=None, buffersize=32): ''' Returns the output of the filterbank for the given duration. ``func`` If a function is specified, it should be a function of one or two arguments that will be called on each filtered buffered segment (of shape ``(buffersize, nchannels)`` in order. If the function has one argument, the argument should be buffered segment. If it has two arguments, the second argument is the value returned by the previous application of the function (or 0 for the first application). In this case, the method will return the final value returned by the function. See example below. ``duration=None`` The length of time (in seconds) or number of samples to process. If no ``func`` is specified, the method will return an array of shape ``(duration, nchannels)`` with the filtered outputs. Note that in many cases, this will be too large to fit in memory, in which you will want to process the filtered outputs online, by providing a function ``func`` (see example below). If no duration is specified, the maximum duration of the inputs to the filterbank will be used, or an error raised if they do not have durations (e.g. in the case of :class:`OnlineSound`). ``buffersize=32`` The size of the buffered segments to fetch, as a length of time or number of samples. 32 samples typically gives reasonably good performance. For example, to compute the RMS of each channel in a filterbank, you would do:: def sum_of_squares(input, running_sum_of_squares): return running_sum_of_squares+sum(input**2, axis=0) rms = sqrt(fb.process(sum_of_squares)/nsamples) ''' if duration is None: duration = self.duration if not isinstance(duration, int): duration = int(duration*self.samplerate) if not isinstance(buffersize, int): buffersize = int(buffersize*self.samplerate) self.buffer_init() endpoints = hstack((arange(0, duration, buffersize), duration)) zendpoints = zip(endpoints[:-1], endpoints[1:]) #sizes = diff(endpoints) if func is None: return vstack(tuple(self.buffer_fetch(start, end) for start, end in zendpoints)) else: if func.func_code.co_argcount==1: for start, end in zendpoints: func(self.buffer_fetch(start, end)) else: runningval = 0 for start, end in zendpoints: runningval = func(self.buffer_fetch(start, end), runningval) return runningval def buffer_init(self): Bufferable.buffer_init(self) if isinstance(self.source, Bufferable): self.source.buffer_init() else: for s in self.source: s.buffer_init() self.next_sample = 0 def buffer_apply(self, input): raise NotImplementedError def buffer_fetch_next(self, samples): start = self.next_sample self.next_sample += samples end = start+samples input = self.source.buffer_fetch(start, end) return self.buffer_apply(input) def __add__ (self, other): if isinstance(other, Bufferable): return SumFilterbank((self, other)) else: func = lambda x: other+x return FunctionFilterbank(self, func) __radd__ = __add__ def __sub__ (self, other): if isinstance(other, Bufferable): return SumFilterbank((self, other), (1, -1)) else: func = lambda x: x-other return FunctionFilterbank(self, func) def __rsub__ (self, other): # Note that __rsub__ should return other-self if isinstance(other, Bufferable): return SumFilterbank((self, other), (-1, 1)) else: func = lambda x: other-x return FunctionFilterbank(self, func) def __mul__(self, other): if isinstance(other, Bufferable): func = lambda x, y: x*y return FunctionFilterbank((self, other), func) else: func = lambda x: x*other return FunctionFilterbank(self, func) __rmul__ = __mul__ def __div__(self, other): if isinstance(other, Bufferable): func = lambda x, y: x/y return FunctionFilterbank((self, other), func) else: func = lambda x: x/other return FunctionFilterbank(self, func) def __rdiv__(self, other): # Note __rdiv__ returns other/self if isinstance(other, Bufferable): func = lambda x, y: x/y return FunctionFilterbank((other, self), func) else: func = lambda x: other/x return FunctionFilterbank(self, func) class RestructureFilterbank(Filterbank): ''' Filterbank used to restructure channels, including repeating and interleaving. **Standard forms of usage:** Repeat mono source N times:: RestructureFilterbank(source, N) For a stereo source, N copies of the left channel followed by N copies of the right channel:: RestructureFilterbank(source, N) For a stereo source, N copies of the channels tiled as LRLRLR...LR:: RestructureFilterbank(source, numtile=N) For two stereo sources AB and CD, join them together in serial to form the output channels in order ABCD:: RestructureFilterbank((AB, CD)) For two stereo sources AB and CD, join them together interleaved to form the output channels in order ACBD:: RestructureFilterbank((AB, CD), type='interleave') These arguments can also be combined together, for example to AB and CD into output channels AABBCCDDAABBCCDDAABBCCDD:: RestructureFilterbank((AB, CD), 2, 'serial', 3) The three arguments are the number of repeats before joining, the joining type ('serial' or 'interleave') and the number of tilings after joining. See below for details. **Initialise arguments:** ``source`` Input source or list of sources. ``numrepeat=1`` Number of times each channel in each of the input sources is repeated before mixing the source channels. For example, with repeat=2 an input source with channels ``AB`` will be repeated to form ``AABB`` ``type='serial'`` The method for joining the source channels, the options are ``'serial'`` to join the channels in series, or ``'interleave'`` to interleave them. In the case of ``'interleave'``, each source must have the same number of channels. An example of serial, if the input sources are ``abc`` and ``def`` the output would be ``abcdef``. For interleave, the output would be ``adbecf``. ``numtile=1`` The number of times the joined channels are tiled, so if the joined channels are ``ABC`` and ``numtile=3`` the output will be ``ABCABCABC``. ``indexmapping=None`` Instead of specifying the restructuring via ``numrepeat, type, numtile`` you can directly give the mapping of input indices to output indices. So for a single stereo source input, ``indexmapping=[1,0]`` would reverse left and right. Similarly, with two mono sources, ``indexmapping=[1,0]`` would have channel 0 of the output correspond to source 1 and channel 1 of the output corresponding to source 0. This is because the indices are counted in order of channels starting from the first source and continuing to the last. For example, suppose you had two sources, each consisting of a stereo sound, say source 0 was ``AB`` and source 1 was ``CD`` then ``indexmapping=[1, 0, 3, 2]`` would swap the left and right of each source, but leave the order of the sources the same, i.e. the output would be ``BADC``. ''' def __init__(self, source, numrepeat=1, type='serial', numtile=1, indexmapping=None): self._has_been_optimised = False self._reinit(source, numrepeat, type, numtile, indexmapping) def _do_reinit(self): self._reinit(*self._original_init_arguments) if self._has_been_optimised: self._optimisation_target._do_reinit() def _reinit(self, source, numrepeat, type, numtile, indexmapping): self._original_init_arguments = (source, numrepeat, type, numtile, indexmapping) if isinstance(source, Bufferable): source = (source,) if indexmapping is None: nchannels = array([s.nchannels for s in source]) idx = hstack(([0], cumsum(nchannels))) I = [arange(start, stop) for start, stop in zip(idx[:-1], idx[1:])] I = tuple(repeat(i, numrepeat) for i in I) if type=='serial': indexmapping = hstack(I) elif type=='interleave': if len(unique(nchannels))!=1: raise ValueError('For interleaving, all inputs must have an equal number of channels.') I0 = len(I[0]) indexmapping = zeros(I0*len(I), dtype=int) for j, i in enumerate(I): indexmapping[j::len(I)] = i else: raise ValueError('Type must be "serial" or "interleave"') indexmapping = tile(indexmapping, numtile) if not isinstance(indexmapping, ndarray): indexmapping = array(indexmapping, dtype=int) # optimisation to reduce multiple RestructureFilterbanks into a single # one, by collating the sources and reconstructing the indexmapping # from the individual indexmappings if all(isinstance(s, RestructureFilterbank) for s in source): newsource = () newsourcesizes = () for s in source: s._has_been_optimised = True s._optimisation_target = self newsource += s.source inputsourcesize = sum(inpsource.nchannels for inpsource in s.source) newsourcesizes += (inputsourcesize,) newsourcesizes = array(newsourcesizes) newsourceoffsets = hstack((0, cumsum(newsourcesizes))) new_indexmapping = zeros_like(indexmapping) sourcesizes = array(tuple(s.nchannels for s in source)) sourceoffsets = hstack((0, cumsum(sourcesizes))) # gives the index of the source of each element of indexmapping sourceindices = digitize(indexmapping, cumsum(sourcesizes)) for i in xrange(len(indexmapping)): source_index = sourceindices[i] s = source[source_index] relative_index = indexmapping[i]-sourceoffsets[source_index] source_relative_index = s.indexmapping[relative_index] new_index = source_relative_index+newsourceoffsets[source_index] new_indexmapping[i] = new_index source = newsource indexmapping = new_indexmapping self.indexmapping = indexmapping self.nchannels = len(indexmapping) self.samplerate = source[0].samplerate for s in source: if int(s.samplerate)!=int(self.samplerate): raise ValueError('All sources must have the same samplerate.') self._source = source def buffer_fetch_next(self, samples): start = self.next_sample self.next_sample += samples end = start+samples inputs = tuple(s.buffer_fetch(start, end) for s in self.source) input = hstack(inputs) input = input[:, self.indexmapping] return input def change_source(self, source): if not hasattr(self, '_source') or self._source is None: self._source = source return oldsource, numrepeat, type, numtile, indexmapping = self._original_init_arguments self._original_init_arguments = source, numrepeat, type, numtile, indexmapping self._do_reinit() # self._reinit(source, numrepeat, type, numtile, indexmapping) # if self._has_been_optimised: # target = self._optimisation_target # target._reinit(*target._original_init_arguments) class Repeat(RestructureFilterbank): ''' Filterbank that repeats each channel from its input, e.g. with 3 repeats channels ABC would map to AAABBBCCC. ''' def __init__(self, source, numrepeat): RestructureFilterbank.__init__(self, source, numrepeat) class Tile(RestructureFilterbank): ''' Filterbank that tiles the channels from its input, e.g. with 3 tiles channels ABC would map to ABCABCABC. ''' def __init__(self, source, numtile): RestructureFilterbank.__init__(self, source, numtile=numtile) class Join(RestructureFilterbank): ''' Filterbank that joins the channels of its inputs in series, e.g. with two input sources with channels AB and CD respectively, the output would have channels ABCD. You can initialise with multiple sources separated by commas, or by passing a list of sources. ''' def __init__(self, *sources): source = [] for s in sources: if isinstance(s, Bufferable): source.append(s) else: source.extend(s) RestructureFilterbank.__init__(self, tuple(source), type='serial') class Interleave(RestructureFilterbank): ''' Filterbank that interleaves the channels of its inputs, e.g. with two input sources with channels AB and CD respectively, the output would have channels ACBD. You can initialise with multiple sources separated by commas, or by passing a list of sources. ''' def __init__(self, *sources): source = [] for s in sources: if isinstance(s, Bufferable): source.append(s) else: source.extend(s) RestructureFilterbank.__init__(self, tuple(source), type='interleave') class FunctionFilterbank(Filterbank): ''' Filterbank that just applies a given function. The function should take as many arguments as there are sources. For example, to half-wave rectify inputs:: FunctionFilterbank(source, lambda x: clip(x, 0, Inf)) The syntax ``lambda x: clip(x, 0, Inf)`` defines a function object that takes a single argument ``x`` and returns ``clip(x, 0, Inf)``. The numpy function ``clip(x, low, high)`` returns the values of ``x`` clipped between ``low`` and ``high`` (so if ``xhigh`` it returns ``high``, otherwise it returns ``x``). The symbol ``Inf`` means infinity, i.e. no clipping of positive values. **Technical details** Note that functions should operate on arrays, in particular on 2D buffered segments, which are arrays of shape ``(bufsize, nchannels)``. Typically, most standard functions from numpy will work element-wise. If you want a filterbank that changes the shape of the input (e.g. changes the number of channels), set the ``nchannels`` keyword argument to the number of output channels. ''' def __init__(self, source, func, nchannels=None,**params): if isinstance(source, Bufferable): source = (source,) Filterbank.__init__(self, source) self.func = func if nchannels is not None: self.nchannels = nchannels self.params = params def buffer_fetch_next(self, samples): start = self.cached_buffer_end end = start+samples inputs = tuple(s.buffer_fetch(start, end) for s in self.source) # print inputs,self.params return self.func(*inputs,**self.params) class SumFilterbank(FunctionFilterbank): ''' Sum filterbanks together with given weight vectors. For example, to take the sum of two filterbanks:: SumFilterbank((fb1, fb2)) To take the difference:: SumFilterbank((fb1, fb2), (1, -1)) ''' def __init__(self, source, weights=None): if weights is None: weights = ones(len(source)) self.weights = weights func = lambda *inputs: sum(input*w for input, w in zip(inputs, weights)) FunctionFilterbank.__init__(self, source, func) class DoNothingFilterbank(Filterbank): ''' Filterbank that does nothing to its input. Useful for removing a set of filters without having to rewrite your code. Can also be used for simply writing compound derived classes. For example, if you want a compound Filterbank that does AFilterbank and then BFilterbank, but you want to encapsulate that into a single class, you could do:: class ABFilterbank(DoNothingFilterbank): def __init__(self, source): a = AFilterbank(source) b = BFilterbank(a) DoNothingFilterbank.__init__(self, b) However, a more general way of writing compound filterbanks is to use :class:`CombinedFilterbank`. ''' def buffer_apply(self, input): return input class ControlFilterbank(Filterbank): ''' Filterbank that can be used for controlling behaviour at runtime Typically, this class is used to implement a control path in an auditory model, modifying some filterbank parameters based on the output of other filterbanks (or the same ones). The controller has a set of input filterbanks whose output values are used to modify a set of output filterbanks. The update is done by a user specified function or class which is passed these output values. The controller should be inserted as the last bank in a chain. Initialisation arguments: ``source`` The source filterbank, the values from this are used unmodified as the output of this filterbank. ``inputs`` Either a single filterbank, or sequence of filterbanks which are used as inputs to the ``updater``. ``targets`` The filterbank or sequence of filterbanks that are modified by the updater. ``updater`` The function or class which does the updating, see below. ``max_interval`` If specified, ensures that the updater is called at least as often as this interval (but it may be called more often). Can be specified as a time or a number of samples. **The updater** The ``updater`` argument can be either a function or class instance. If it is a function, it should have a form like:: # A single input def updater(input): ... # Two inputs def updater(input1, input2): ... # Arbitrary number of inputs def updater(*inputs): ... Each argument ``input`` to the function is a numpy array of shape ``(numsamples, numchannels)`` where ``numsamples`` is the number of samples just computed, and ``numchannels`` is the number of channels in the corresponding filterbank. The function is not restricted in what it can do with these inputs. Functions can be used to implement relatively simple controllers, but for more complicated situations you may want to maintain some state variables for example, and in this case you can use a class. The object ``updater`` should be an instance of a class that defines the ``__call__`` method (with the same syntax as above for functions). In addition, you can define a reinitialisation method ``reinit()`` which will be called when the ``buffer_init()`` method is called on the filterbank, although this is entirely optional. **Example** The following will do a simple form of gain control, where the gain parameter will drift exponentially towards target_rms/rms with a given time constant:: # This class implements the gain (see Filterbank for details) class GainFilterbank(Filterbank): def __init__(self, source, gain=1.0): Filterbank.__init__(self, source) self.gain = gain def buffer_apply(self, input): return self.gain*input # This is the class for the updater object class GainController(object): def __init__(self, target, target_rms, time_constant): self.target = target self.target_rms = target_rms self.time_constant = time_constant def reinit(self): self.sumsquare = 0 self.numsamples = 0 def __call__(self, input): T = input.shape[0]/self.target.samplerate self.sumsquare += sum(input**2) self.numsamples += input.size rms = sqrt(self.sumsquare/self.numsamples) g = self.target.gain g_tgt = self.target_rms/rms tau = self.time_constant self.target.gain = g_tgt+exp(-T/tau)*(g-g_tgt) And an example of using this with an input ``source``, a target RMS of 0.2 and a time constant of 50 ms, updating every 10 ms:: gain_fb = GainFilterbank(source) updater = GainController(gain_fb, 0.2, 50*ms) control = ControlFilterbank(gain_fb, source, gain_fb, updater, 10*ms) ''' def __init__(self, source, inputs, targets, updater, max_interval=None): Filterbank.__init__(self, source) if not isinstance(inputs, (list, tuple)): inputs = [inputs] if not isinstance(targets, (list, tuple)): targets = [targets] self.inputs = inputs self.updater = updater if max_interval is not None: if not isinstance(max_interval, int): max_interval = int(max_interval*source.samplerate) for x in inputs+targets: x.maximum_buffer_size = max_interval self.maximum_buffer_size = max_interval def buffer_init(self): Filterbank.buffer_init(self) if hasattr(self.updater, 'reinit'): self.updater.reinit() def buffer_fetch_next(self, samples): start = self.next_sample self.next_sample += samples end = start+samples source_input = self.source.buffer_fetch(start, end) input_buffers = [x.buffer_fetch(start, end) for x in self.inputs] self.updater(*input_buffers) return source_input class CombinedFilterbank(Filterbank): ''' Filterbank that encapsulates a chain of filterbanks internally. This class should mostly be used by people writing extensions to Brian hears rather than by users directly. The purpose is to take an existing chain of filterbanks and wrap them up so they appear to the user as a single filterbank which can be used exactly as any other filterbank. In order to do this, derive from this class and in your initialisation follow this pattern:: class RectifiedGammatone(CombinedFilterbank): def __init__(self, source, cf): CombinedFilterbank.__init__(self, source) source = self.get_modified_source() # At this point, insert your chain of filterbanks acting on # the modified source object gfb = Gammatone(source, cf) rectified = FunctionFilterbank(gfb, lambda input: clip(input, 0, Inf)) # Finally, set the output filterbank to be the last in your chain self.set_output(fb) This combination of a :class:`Gammatone` and a rectification via a :class:`FunctionFilterbank` can now be used as a single filterbank, for example:: x = whitenoise(100*ms) fb = RectifiedGammatone(x, [1*kHz, 1.5*kHz]) y = fb.process() **Details** The reason for the ``get_modified_source()`` call is that the source attribute of a filterbank can be changed after creation. The modified source provides a buffer (in fact, a :class:`DoNothingFilterbank`) so that the input to the chain of filters defined by the derived class doesn't need to be changed. ''' def __init__(self, source): Filterbank.__init__(self, source) def get_duration(self): if hasattr(self, '_duration'): return self._duration else: return max(Filterbank.get_duration(self), self.output.duration) source = property(fget=lambda self:self._source, fset=lambda self, source:self.change_source(source)) def change_source(self, source): Filterbank.change_source(self, source) if hasattr(self, '_modified_source'): self._modified_source.source = source def get_modified_source(self): self._modified_source = DoNothingFilterbank(self.source) return self._modified_source def set_output(self, output): self.output = output self.nchannels = output.nchannels def buffer_init(self): Filterbank.buffer_init(self) self.output.buffer_init() def buffer_fetch(self, start, end): return self.output.buffer_fetch(start, end) brian-1.4.3/brian/hears/filtering/filterbankgroup.py000066400000000000000000000060041266062647100225540ustar00rootroot00000000000000from brian import StateUpdater, NeuronGroup, Equations, Clock, network_operation __all__ = ['FilterbankGroup'] class FilterbankGroup(NeuronGroup): ''' Allows a Filterbank object to be used as a NeuronGroup Initialised as a standard :class:`NeuronGroup` object, but with two additional arguments at the beginning, and no ``N`` (number of neurons) argument. The number of neurons in the group will be the number of channels in the filterbank. (TODO: add reference to interleave/serial channel stuff here.) ``filterbank`` The Filterbank object to be used by the group. In fact, any Bufferable object can be used. ``targetvar`` The target variable to put the filterbank output into. One additional keyword is available beyond that of :class:`NeuronGroup`: ``buffersize=32`` The size of the buffered segments to fetch each time. The efficiency depends on this in an unpredictable way, larger values mean more time spent in optimised code, but are worse for the cache. In many cases, the default value is a good tradeoff. Values can be given as a number of samples, or a length of time in seconds. Note that if you specify your own :class:`Clock`, it should have 1/dt=samplerate. ''' def __init__(self, filterbank, targetvar, *args, **kwds): self.targetvar = targetvar self.filterbank = filterbank filterbank.buffer_init() # update level keyword kwds['level'] = kwds.get('level', 0)+1 # Sanitize the clock - does it have the right dt value? if 'clock' in kwds: if int(1/kwds['clock'].dt)!=int(filterbank.samplerate): raise ValueError('Clock should have 1/dt=samplerate') else: kwds['clock'] = Clock(dt=1/filterbank.samplerate) buffersize = kwds.pop('buffersize', 32) if not isinstance(buffersize, int): buffersize = int(buffersize*self.samplerate) self.buffersize = buffersize self.buffer_pointer = buffersize self.buffer_start = -buffersize NeuronGroup.__init__(self, filterbank.nchannels, *args, **kwds) @network_operation(when='start', clock=self.clock) def apply_filterbank_output(): if self.buffer_pointer>=self.buffersize: self.buffer_pointer = 0 self.buffer_start += self.buffersize self.buffer = self.filterbank.buffer_fetch(self.buffer_start, self.buffer_start+self.buffersize) setattr(self, targetvar, self.buffer[self.buffer_pointer, :]) self.buffer_pointer += 1 self.contained_objects.append(apply_filterbank_output) def reinit(self): NeuronGroup.reinit(self) self.filterbank.buffer_init() self.buffer_pointer = self.buffersize self.buffer_start = -self.buffersize brian-1.4.3/brian/hears/filtering/filterbanklibrary.py000066400000000000000000001036301266062647100230670ustar00rootroot00000000000000from brian import * try: import weave except ImportError: from scipy import weave from scipy import signal, random from operator import isSequenceType from filterbank import Filterbank,RestructureFilterbank from linearfilterbank import * from firfilterbank import * __all__ = ['Cascade', 'Gammatone', 'ApproximateGammatone', 'LogGammachirp', 'LinearGammachirp', 'LinearGaborchirp', 'IIRFilterbank', 'Butterworth', 'AsymmetricCompensation', 'LowPass', 'asymmetric_compensation_coeffs', 'BiQuadratic' ] class Gammatone(LinearFilterbank): ''' Bank of gammatone filters. They are implemented as cascades of four 2nd-order IIR filters (this 8th-order digital filter corresponds to a 4th-order gammatone filter). The approximated impulse response :math:`\\mathrm{IR}` is defined as follow :math:`\\mathrm{IR}(t)=t^3\\exp(-2\\pi b \\mathrm{ERB}(f)t)\\cos(2\\pi f t)` where :math:`\\mathrm{ERB}(f)=24.7+0.108 f` [Hz] is the equivalent rectangular bandwidth of the filter centered at :math:`f`. It comes from Slaney's exact gammatone implementation (Slaney, M., 1993, "An Efficient Implementation of the Patterson-Holdsworth Auditory Filter Bank". Apple Computer Technical Report #35). The code is based on `Slaney's Matlab implementation `__. Initialised with arguments: ``source`` Source of the filterbank. ``cf`` List or array of center frequencies. ``b=1.019`` parameter which determines the bandwidth of the filters (and reciprocally the duration of its impulse response). In particular, the bandwidth = b.ERB(cf), where ERB(cf) is the equivalent bandwidth at frequency ``cf``. The default value of ``b`` to a best fit (Patterson et al., 1992). ``b`` can either be a scalar and will be the same for every channel or an array of the same length as ``cf``. ``erb_order=1``, ``ear_Q=9.26449``, ``min_bw=24.7`` Parameters used to compute the ERB bandwidth. :math:`\\mathrm{ERB} = ((\mathrm{cf}/\mathrm{ear\\_Q})^{\\mathrm{erb}\\_\\mathrm{order}} + \\mathrm{min\\_bw}^{\\mathrm{erb}\\_\\mathrm{order}})^{(1/\\mathrm{erb}\\_\\mathrm{order})}`. Their default values are the ones recommended in Glasberg and Moore, 1990. ``cascade=None`` Specify 1 or 2 to use a cascade of 1 or 2 order 8 or 4 filters instead of 4 2nd order filters. Note that this is more efficient but may induce numerical stability issues. ''' def __init__(self, source, cf, b=1.019, erb_order=1, ear_Q=9.26449, min_bw=24.7, cascade=None): cf = atleast_1d(cf) self.cf = cf self.samplerate = source.samplerate T = 1/self.samplerate self.b,self.erb_order,self.EarQ,self.min_bw=b,erb_order,ear_Q,min_bw erb = ((cf/ear_Q)**erb_order + min_bw**erb_order)**(1/erb_order) B = b*2*pi*erb # B = 2*pi*b A0 = T A2 = 0 B0 = 1 B1 = -2*cos(2*cf*pi*T)/exp(B*T) B2 = exp(-2*B*T) A11 = -(2*T*cos(2*cf*pi*T)/exp(B*T) + 2*sqrt(3+2**1.5)*T*sin(2*cf*pi*T) / \ exp(B*T))/2 A12=-(2*T*cos(2*cf*pi*T)/exp(B*T)-2*sqrt(3+2**1.5)*T*sin(2*cf*pi*T)/\ exp(B*T))/2 A13=-(2*T*cos(2*cf*pi*T)/exp(B*T)+2*sqrt(3-2**1.5)*T*sin(2*cf*pi*T)/\ exp(B*T))/2 A14=-(2*T*cos(2*cf*pi*T)/exp(B*T)-2*sqrt(3-2**1.5)*T*sin(2*cf*pi*T)/\ exp(B*T))/2 i=1j gain=abs((-2*exp(4*i*cf*pi*T)*T+\ 2*exp(-(B*T)+2*i*cf*pi*T)*T*\ (cos(2*cf*pi*T)-sqrt(3-2**(3./2))*\ sin(2*cf*pi*T)))*\ (-2*exp(4*i*cf*pi*T)*T+\ 2*exp(-(B*T)+2*i*cf*pi*T)*T*\ (cos(2*cf*pi*T)+sqrt(3-2**(3./2))*\ sin(2*cf*pi*T)))*\ (-2*exp(4*i*cf*pi*T)*T+\ 2*exp(-(B*T)+2*i*cf*pi*T)*T*\ (cos(2*cf*pi*T)-\ sqrt(3+2**(3./2))*sin(2*cf*pi*T)))*\ (-2*exp(4*i*cf*pi*T)*T+2*exp(-(B*T)+2*i*cf*pi*T)*T*\ (cos(2*cf*pi*T)+sqrt(3+2**(3./2))*sin(2*cf*pi*T)))/\ (-2/exp(2*B*T)-2*exp(4*i*cf*pi*T)+\ 2*(1+exp(4*i*cf*pi*T))/exp(B*T))**4) allfilts=ones(len(cf)) self.A0, self.A11, self.A12, self.A13, self.A14, self.A2, self.B0, self.B1, self.B2, self.gain=\ A0*allfilts, A11, A12, A13, A14, A2*allfilts, B0*allfilts, B1, B2, gain self.filt_a=dstack((array([ones(len(cf)), B1, B2]).T,)*4) self.filt_b=dstack((array([A0/gain, A11/gain, A2/gain]).T, array([A0*ones(len(cf)), A12, zeros(len(cf))]).T, array([A0*ones(len(cf)), A13, zeros(len(cf))]).T, array([A0*ones(len(cf)), A14, zeros(len(cf))]).T)) LinearFilterbank.__init__(self, source, self.filt_b, self.filt_a) if cascade is not None: self.decascade(cascade) class ApproximateGammatone(LinearFilterbank): r''' Bank of approximate gammatone filters implemented as a cascade of ``order`` IIR gammatone filters. The filter is derived from the sampled version of the complex analog gammatone impulse response :math:`g_{\gamma}(t)=t^{\gamma-1} (\lambda e^{i \eta t})^{\gamma}` where :math:`\gamma` corresponds to ``order``, :math:`\eta` defines the oscillation frequency ``cf``, and :math:`\lambda` defines the bandwidth parameter. The design is based on the Hohmann implementation as described in Hohmann, V., 2002, "Frequency analysis and synthesis using a Gammatone filterbank", Acta Acustica United with Acustica. The code is based on the Matlab gammatone implementation from `Meddis' toolbox `__. Initialised with arguments: ``source`` Source of the filterbank. ``cf`` List or array of center frequencies. ``bandwidth`` List or array of filters bandwidth corresponding, one for each cf. ``order=4`` The number of 1st-order gammatone filters put in cascade, and therefore the order the resulting gammatone filters. ''' def __init__(self, source, cf, bandwidth,order=4): cf = atleast_1d(cf) bandwidth = atleast_1d(bandwidth) self.cf = cf self.samplerate = source.samplerate dt = 1/self.samplerate phi = 2 * pi * bandwidth * dt theta = 2 * pi * cf * dt cos_theta = cos(theta) sin_theta = sin(theta) alpha = -exp(-phi) * cos_theta b0 = ones(len(cf)) b1 = 2 * alpha b2 = exp(-2 * phi) z1 = (1 + alpha * cos_theta) - (alpha * sin_theta) * 1j z2 = (1 + b1 * cos_theta) - (b1 * sin_theta) * 1j z3 = (b2 * cos(2 * theta)) - (b2 * sin(2 * theta)) * 1j tf = (z2 + z3) / z1 a0 = abs(tf) a1 = alpha * a0 # we apply the same filters order times so we just duplicate them in the 3rd axis for the parallel_lfilter_step command self.filt_a = dstack((array([b0, b1, b2]).T,)*order) self.filt_b = dstack((array([a0, a1, zeros(len(cf))]).T,)*order) self.order = order LinearFilterbank.__init__(self,source, self.filt_b, self.filt_a) class LogGammachirp(LinearFilterbank): r''' Bank of gammachirp filters with a logarithmic frequency sweep. The approximated impulse response :math:`\mathrm{IR}` is defined as follows: :math:`\mathrm{IR}(t)=t^3e^{-2\pi b \mathrm{ERB}(f)t}\cos(2\pi (f t +c\cdot\ln(t))` where :math:`\mathrm{ERB}(f)=24.7+0.108 f` [Hz] is the equivalent rectangular bandwidth of the filter centered at :math:`f`. The implementation is a cascade of 4 2nd-order IIR gammatone filters followed by a cascade of ncascades 2nd-order asymmetric compensation filters as introduced in Unoki et al. 2001, "Improvement of an IIR asymmetric compensation gammachirp filter". Initialisation parameters: ``source`` Source sound or filterbank. ``f`` List or array of the sweep ending frequencies (:math:`f_{\mathrm{instantaneous}}=f+c/t`). ``b=1.019`` Parameters which determine the duration of the impulse response. ``b`` can either be a scalar and will be the same for every channel or an array with the same length as ``f``. ``c=1`` The glide slope (or sweep rate) given in Hz/second. The trajectory of the instantaneous frequency towards f is an upchirp when c<0 and a downchirp when c>0. ``c`` can either be a scalar and will be the same for every channel or an array with the same length as ``f``. ``ncascades=4`` Number of times the asymmetric compensation filter is put in cascade. The default value comes from Unoki et al. 2001. ''' def __init__(self, source, f,b=1.019,c=1,ncascades=4): f = atleast_1d(f) self.f = f self.samplerate= source.samplerate self.c=c self.b=b gammatone=Gammatone(source, f,b) self.gammatone_filt_b=gammatone.filt_b self.gammatone_filt_a=gammatone.filt_a ERBw=24.7*(4.37e-3*f+1.) p0=2 p1=1.7818*(1-0.0791*b)*(1-0.1655*abs(c)) p2=0.5689*(1-0.1620*b)*(1-0.0857*abs(c)) p3=0.2523*(1-0.0244*b)*(1+0.0574*abs(c)) p4=1.0724 self.asymmetric_filt_b=zeros((len(f),3, ncascades)) self.asymmetric_filt_a=zeros((len(f),3, ncascades)) self.asymmetric_filt_b,self.asymmetric_filt_a=asymmetric_compensation_coeffs(self.samplerate,f,self.asymmetric_filt_b,self.asymmetric_filt_a,b,c,p0,p1,p2,p3,p4) #concatenate the gammatone filter coefficients so that everything is in cascade in each frequency channel self.filt_b=concatenate([self.gammatone_filt_b, self.asymmetric_filt_b],axis=2) self.filt_a=concatenate([self.gammatone_filt_a, self.asymmetric_filt_a],axis=2) LinearFilterbank.__init__(self, source, self.filt_b,self.filt_a) class LinearGammachirp(FIRFilterbank): r''' Bank of gammachirp filters with linear frequency sweeps and gamma envelope as described in Wagner et al. 2009, "Auditory responses in the barn owl's nucleus laminaris to clicks: impulse response and signal analysis of neurophonic potential", J. Neurophysiol. The impulse response :math:`\mathrm{IR}` is defined as follow :math:`\mathrm{IR}(t)=t^3e^{-t/\sigma}\cos(2\pi (f t +c/2 t^2)+\phi)` where :math:`\sigma` corresponds to ``time_constant`` and :math:`\phi` to ``phase`` (see definition of parameters). Those filters are implemented as FIR filters using truncated time representations of gammachirp functions as the impulse response. The impulse responses, which need to have the same length for every channel, have a duration of 15 times the biggest time constant. The length of the impulse response is therefore ``15*max(time_constant)*sampling_rate``. The impulse responses are normalized with respect to the transmitted power, i.e. the rms of the filter taps is 1. Initialisation parameters: ``source`` Source sound or filterbank. ``f`` List or array of the sweep starting frequencies (:math:`f_{\mathrm{instantaneous}}=f+ct`). ``time_constant`` Determines the duration of the envelope and consequently the length of the impulse response. ``c=1`` The glide slope (or sweep rate) given in Hz/second. The time-dependent instantaneous frequency is ``f+c*t`` and is therefore going upward when c>0 and downward when c<0. ``c`` can either be a scalar and will be the same for every channel or an array with the same length as ``f``. ``phase=0`` Phase shift of the carrier. Has attributes: ``length_impulse_response`` Number of samples in the impulse responses. ``impulse_response`` Array of shape ``(nchannels, length_impulse_response)`` with each row being an impulse response for the corresponding channel. ''' def __init__(self,source, f, time_constant, c, phase=0): self.f=f=atleast_1d(f) self.c=c=atleast_1d(c) self.phase=phase=atleast_1d(phase) self.time_constant=time_constant=atleast_1d(time_constant) if len(time_constant)==1: time_constant=time_constant*ones(len(f)) if len(c)==1: c=c*ones(len(f)) if len(phase)==1: phase=phase*ones(len(f)) self.samplerate= source.samplerate Tcst_max=max(time_constant) t_start=-Tcst_max*3*second t=arange(t_start,-4*t_start,1./self.samplerate) self.impulse_response=zeros((len(f),len(t))) for ich in xrange(len(f)): env=(t-t_start)**3*exp(-(t-t_start)/time_constant[ich]) self.impulse_response[ich,:]=env*cos(2*pi*(f[ich]*t+c[ich]/2*t**2)+phase[ich]) # self.impulse_response[ich,:]=self.impulse_response[ich,:]/sqrt(sum(self.impulse_response[ich,:]**2)) self.impulse_response[ich,:]=self.impulse_response[ich,:]/sum(abs(self.impulse_response[ich,:])) FIRFilterbank.__init__(self,source, self.impulse_response) class LinearGaborchirp(FIRFilterbank): r''' Bank of gammachirp filters with linear frequency sweeps and gaussian envelope as described in Wagner et al. 2009, "Auditory responses in the barn owl's nucleus laminaris to clicks: impulse response and signal analysis of neurophonic potential", J. Neurophysiol. The impulse response :math:`\mathrm{IR}` is defined as follows: :math:`\mathrm{IR}(t)=e^{-t/2\sigma^2}\cos(2\pi (f t +c/2 t^2)+\phi)`, where :math:`\sigma` corresponds to ``time_constant`` and :math:`\phi` to ``phase`` (see definition of parameters). These filters are implemented as FIR filters using truncated time representations of gammachirp functions as the impulse response. The impulse responses, which need to have the same length for every channel, have a duration of 12 times the biggest time constant. The length of the impulse response is therefore ``12*max(time_constant)*sampling_rate``. The envelope is a gaussian function (Gabor filter). The impulse responses are normalized with respect to the transmitted power, i.e. the rms of the filter taps is 1. Initialisation parameters: ``source`` Source sound or filterbank. ``f`` List or array of the sweep starting frequencies (:math:`f_{\mathrm{instantaneous}}=f+c*t`). ``time_constant`` Determines the duration of the envelope and consequently the length of the impluse response. ``c=1`` The glide slope (or sweep rate) given ins Hz/second. The time-dependent instantaneous frequency is ``f+c*t`` and is therefore going upward when c>0 and downward when c<0. ``c`` can either be a scalar and will be the same for every channel or an array with the same length as ``f``. ``phase=0`` Phase shift of the carrier. Has attributes: ``length_impulse_response`` Number of sample in the impulse responses. ``impulse_response`` Array of shape ``(nchannels, length_impulse_response)`` with each row being an impulse response for the corresponding channel. ''' def __init__(self,source, f, time_constant, c, phase=0): self.f=f=atleast_1d(f) self.c=c=atleast_1d(c) self.phase=phase=atleast_1d(phase) self.time_constant=time_constant=atleast_1d(time_constant) if len(time_constant)==1: time_constant=time_constant*ones(len(f)) if len(c)==1: c=c*ones(len(f)) if len(phase)==1: phase=phase*ones(len(f)) self.samplerate = source.samplerate Tcst_max=max(time_constant) t_start=-Tcst*6*second t=arange(t_start,-t_start,1./self.samplerate) self.impulse_response=zeros((len(f),len(t))) for ich in xrange(len(f)): env=exp(-(t/(2*time_constant[ich]))**2) self.impulse_response[ich,:]=env*cos(2*pi*(f[ich]*t+c[ich]/2*t**2)+phase[ich]) self.impulse_response[ich,:]=self.impulse_response[ich,:]/sqrt(sum(self.impulse_response[ich,:]**2)) FIRFilterbank.__init__(self, source, self.impulse_response) class IIRFilterbank(LinearFilterbank): ''' Filterbank of IIR filters. The filters can be low, high, bandstop or bandpass and be of type Elliptic, Butterworth, Chebyshev etc. The ``passband`` and ``stopband`` can be scalars (for low or high pass) or pairs of parameters (for stopband and passband) yielding similar filters for every channel. They can also be arrays of shape ``(1, nchannels)`` for low and high pass or ``(2, nchannels)`` for stopband and passband yielding different filters along channels. This class uses the scipy iirdesign function to generate filter coefficients for every channel. See the documentation for scipy.signal.iirdesign for more details. Initialisation parameters: ``samplerate`` The sample rate in Hz. ``nchannels`` The number of channels in the bank ``passband``, ``stopband`` The edges of the pass and stop bands in Hz. For lowpass and highpass filters, in the case of similar filters for each channel, they are scalars and passbandpassband for a highpass. For a bandpass or bandstop filter, in the case of similar filters for each channel, make passband and stopband a list with two elements, e.g. for a bandpass have ``passband=[200*Hz, 500*Hz]`` and ``stopband=[100*Hz, 600*Hz]``. ``passband`` and ``stopband`` can also be arrays of shape ``(1, nchannels)`` for low and high pass or ``(2, nchannels)`` for stopband and passband yielding different filters along channels. ``gpass`` The maximum loss in the passband in dB. Can be a scalar or an array of length ``nchannels``. ``gstop`` The minimum attenuation in the stopband in dB. Can be a scalar or an array of length ``nchannels``. ``btype`` One of 'low', 'high', 'bandpass' or 'bandstop'. ``ftype`` The type of IIR filter to design: 'ellip' (elliptic), 'butter' (Butterworth), 'cheby1' (Chebyshev I), 'cheby2' (Chebyshev II), 'bessel' (Bessel). ''' def __init__(self, source, nchannels, passband, stopband, gpass, gstop, btype, ftype): Wpassband = passband.copy() Wstopband = stopband.copy() Wpassband = atleast_1d(Wpassband) Wstopband = atleast_1d(Wstopband) gpass = atleast_1d(gpass) gstop = atleast_1d(gstop) self.samplerate=source.samplerate if Wpassband.shape != Wstopband.shape: raise Exception('passband and stopband must contain the same number of ent') try: Wpassband=Wpassband/self.samplerate*2+0.0 # wn=1 corresponding to half the sample rate Wstopband=Wstopband/self.samplerate*2+0.0 except DimensionMismatchError: raise DimensionMismatchError('IIRFilterbank passband, stopband parameters must be in Hz') # now design filterbank if btype=='low' or btype=='high': if len(Wpassband)==1: #if there is only one Wn value for all channel just repeat it self.filt_b, self.filt_a = signal.iirdesign(Wpassband, Wstopband, gpass, gstop, ftype=ftype) self.filt_b=kron(ones((nchannels,1)),self.filt_b) self.filt_a=kron(ones((nchannels,1)),self.filt_a) else: #else make nchannels different filters if len(gstop) != nchannels: #if the ripple parameters are scalar make them as long as the number of channels gpass=repeat(gpass,nchannels) if len(gstop) != nchannels: gstop=repeat(gstop,nchannels) order=0 filt_b, filt_a =[1]*nchannels,[1]*nchannels for i in xrange((nchannels)): #generate the different filter coeffcients filt_b[i], filt_a[i] = signal.iirdesign(Wpassband[i], Wstopband[i], gpass[i], gstop[i], ftype=ftype) if len(filt_b[i])>order: #take the highst order of them to be the size of the filter coefficient matrix order=len(filt_b[i]) self.filt_b=zeros((nchannels,order)) self.filt_a=zeros((nchannels,order)) for i in xrange((nchannels)): #fill the coefficient matrix self.filt_b[i,:len(filt_b[i])], self.filt_a[i,:len(filt_a[i])] = filt_b[i],filt_a[i] else: if Wpassband.ndim==1: #if there is only one Wn pair of values for all channel just repeat it self.filt_b, self.filt_a = signal.iirdesign(Wpassband, Wstopband, gpass, gstop, ftype=ftype) self.filt_b=kron(ones((nchannels,1)),self.filt_b) self.filt_a=kron(ones((nchannels,1)),self.filt_a) else: if len(gstop) != nchannels:#if the ripple parameters are scalar make them as long as the number of channels gpass=repeat(gpass,nchannels) if len(gstop) != nchannels: gstop=repeat(gstop,nchannels) order=0 filt_b, filt_a =[1]*nchannels,[1]*nchannels for i in xrange((nchannels)):#take the highst order of them to be the size of the filter coefficient matrix filt_b[i], filt_a[i] = signal.iirdesign(Wpassband[:,i], Wstopband[:,i], gpass[i], gstop[i], ftype=ftype) if len(filt_b[i])>order: order=len(filt_b[i]) self.filt_b=zeros((nchannels,order)) self.filt_a=zeros((nchannels,order)) for i in xrange((nchannels)):#fill the coefficient matrix self.filt_b[i,:len(filt_b[i])], self.filt_a[i,:len(filt_a[i])] = filt_b[i],filt_a[i] self.filt_a=self.filt_a.reshape(self.filt_a.shape[0],self.filt_a.shape[1],1) self.filt_b=self.filt_b.reshape(self.filt_b.shape[0],self.filt_b.shape[1],1) self.nchannels = nchannels self.passband = passband self.stopband = stopband self.gpass = gpass self.gstop = gstop self.ftype= ftype self.order= self.filt_a.shape[1]-1 LinearFilterbank.__init__(self,source, self.filt_b, self.filt_a) class Butterworth(LinearFilterbank): ''' Filterbank of low, high, bandstop or bandpass Butterworth filters. The cut-off frequencies or the band frequencies can either be the same for each channel or different along channels. Initialisation parameters: ``samplerate`` Sample rate. ``nchannels`` Number of filters in the bank. ``order`` Order of the filters. ``fc`` Cutoff parameter(s) in Hz. For the case of a lowpass or highpass filterbank, ``fc`` is either a scalar (thus the same value for all of the channels) or an array of length ``nchannels``. For the case of a bandpass or bandstop, ``fc`` is either a pair of scalar defining the bandpass or bandstop (thus the same values for all of the channels) or an array of shape ``(2, nchannels)`` to define a pair for every channel. ``btype`` One of 'low', 'high', 'bandpass' or 'bandstop'. ''' def __init__(self,source, nchannels, order, fc, btype='low'): Wn=fc.copy() Wn=atleast_1d(Wn) #Scalar inputs are converted to 1-dimensional arrays self.samplerate = source.samplerate try: Wn= Wn/self.samplerate *2+0.0 # wn=1 corresponding to half the sample rate except DimensionMismatchError: raise DimensionMismatchError('Wn must be in Hz') if btype=='low' or btype=='high': self.filt_b=zeros((nchannels,order+1)) self.filt_a=zeros((nchannels,order+1)) if len(Wn)==1: #if there is only one Wn value for all channel just repeat it self.filt_b, self.filt_a = signal.butter(order, Wn, btype=btype) self.filt_b=kron(ones((nchannels,1)),self.filt_b) self.filt_a=kron(ones((nchannels,1)),self.filt_a) else: #else make nchannels different filters for i in xrange((nchannels)): self.filt_b[i,:], self.filt_a[i,:] = signal.butter(order, Wn[i], btype=btype) else: self.filt_b=zeros((nchannels,2*order+1)) self.filt_a=zeros((nchannels,2*order+1)) if Wn.ndim==1: #if there is only one Wn pair of values for all channel just repeat it self.filt_b, self.filt_a = signal.butter(order, Wn, btype=btype) self.filt_b=kron(ones((nchannels,1)),self.filt_b) self.filt_a=kron(ones((nchannels,1)),self.filt_a) else: for i in xrange((nchannels)): self.filt_b[i,:], self.filt_a[i,:] = signal.butter(order, Wn[:,i], btype=btype) self.filt_a=self.filt_a.reshape(self.filt_a.shape[0],self.filt_a.shape[1],1) self.filt_b=self.filt_b.reshape(self.filt_b.shape[0],self.filt_b.shape[1],1) self.nchannels = nchannels LinearFilterbank.__init__(self,source, self.filt_b, self.filt_a) class BiQuadratic(LinearFilterbank): ''' Bank of biquadratic bandpass filters The transfer function of the filters are like the ones of all second-order linear filters :math:`H(s)=\frac{Kw_{0}^{2}}{s_{2}+w_{0}/Qs+w_{0}^{2}}` where :math:`w_{0}` is the centre frequency and :math:`Q` the quality factor of the filter The implementation is a 2nd-order IIR filter with a tranfer function being the ratio of two quadratic functions. Initialisation parameters: ``source`` Source sound or filterbank. ``f`` List or array of the centre frequencies. (:math:`w_{0}^{2}/2\pi`) ``Q`` Quality factor of the filters (dimensionless). It can be a scalar (the same for every channel) or a list/array.``Q`` defines the bandwidth such that ``BW`` Alternativl ''' def __init__(self, source, cf,Q): cf=cf[0] Q=Q[0] # Q=1 # cf = atleast_1d(cf) self.samplerate= source.samplerate w0 = 2*pi*cf/self.samplerate/second BW = 2./log(2)*arcsinh((1./2/Q)) alpha = sin(w0)*sinh(log(2)/2 * BW * w0/sin(w0) ) b_temp = array([alpha,0,-alpha])/(1 + alpha) a_temp = array([1 + alpha,-2*cos(w0),1 - alpha])/(1 + alpha) print b_temp,a_temp self.filt_b = tile(b_temp.reshape([3,1]),[1,1,1]) self.filt_a = tile(a_temp.reshape([3,1]),[1,1,1]) LinearFilterbank.__init__(self, source, self.filt_b,self.filt_a) class LowPass(LinearFilterbank): ''' Bank of 1st-order lowpass filters The code is based on the code found in the `Meddis toolbox `__. It was implemented here to be used in the DRNL cochlear model implementation. Initialised with arguments: ``source`` Source of the filterbank. ``fc`` Value, list or array (with length = number of channels) of cutoff frequencies. ''' def __init__(self,source,fc): if not isSequenceType(fc): fc = fc*ones(source.nchannels) nchannels=len(fc) self.samplerate= source.samplerate dt=1./self.samplerate self.filt_b=zeros((nchannels, 2, 1)) self.filt_a=zeros((nchannels, 2, 1)) tau=1/(2*pi*fc) self.filt_b[:,0,0]=dt/tau self.filt_b[:,1,0]=0*ones(nchannels) self.filt_a[:,0,0]=1*ones(nchannels) self.filt_a[:,1,0]=-(1-dt/tau) LinearFilterbank.__init__(self,source, self.filt_b, self.filt_a) class Cascade(LinearFilterbank): ''' Cascade of ``n`` times a linear filterbank. Initialised with arguments: ``source`` Source of the new filterbank. ``filterbank`` Filterbank object to be put in cascade ``n`` Number of cascades ''' def __init__(self,source, filterbank,n): b=filterbank.filt_b a=filterbank.filt_a self.samplerate = source.samplerate self.nchannels=filterbank.nchannels self.filt_b=zeros((b.shape[0], b.shape[1],n)) self.filt_a=zeros((a.shape[0], a.shape[1],n)) for i in range((n)): self.filt_b[:,:,i]=b[:,:,0] self.filt_a[:,:,i]=a[:,:,0] LinearFilterbank.__init__(self, source,self.filt_b, self.filt_a) class AsymmetricCompensation(LinearFilterbank): ''' Bank of asymmetric compensation filters. Those filters are meant to be used in cascade with gammatone filters to approximate gammachirp filters (Unoki et al., 2001, Improvement of an IIR asymmetric compensation gammachirp filter, Acoust. Sci. & Tech.). They are implemented a a cascade of low order filters. The code is based on the implementation found in the `AIM-MAT toolbox `__. Initialised with arguments: ``source`` Source of the filterbank. ``f`` List or array of the cut off frequencies. ``b=1.019`` Determines the duration of the impulse response. Can either be a scalar and will be the same for every channel or an array with the same length as ``cf``. ``c=1`` The glide slope when this filter is used to implement a gammachirp. Can either be a scalar and will be the same for every channel or an array with the same length as ``cf``. ``ncascades=4`` The number of time the basic filter is put in cascade. ''' def __init__(self, source, f,b=1.019, c=1,ncascades=4): f = atleast_1d(f) self.f = f self.samplerate = source.samplerate ERBw=24.7*(4.37e-3*f+1.) p0=2 p1=1.7818*(1-0.0791*b)*(1-0.1655*abs(c)) p2=0.5689*(1-0.1620*b)*(1-0.0857*abs(c)) p3=0.2523*(1-0.0244*b)*(1+0.0574*abs(c)) p4=1.0724 self.filt_b=zeros((len(f), 3, ncascades)) self.filt_a=zeros((len(f), 3, ncascades)) for k in arange(ncascades): r=exp(-p1*(p0/p4)**(k)*2*pi*b*ERBw/self.samplerate) #k instead of k-1 because range 0 N-1 Df=(p0*p4)**(k)*p2*c*b*ERBw phi=2*pi*maximum((f+Df), 0)/self.samplerate psy=2*pi*maximum((f-Df), 0)/self.samplerate ap=vstack((ones(r.shape),-2*r*cos(phi), r**2)).T bz=vstack((ones(r.shape),-2*r*cos(psy), r**2)).T fn=f#+ compensation_filter_order* p3 *c *b *ERBw/4; vwr=exp(1j*2*pi*fn/self.samplerate) vwrs=vstack((ones(vwr.shape), vwr, vwr**2)).T ##normilization stuff nrm=abs(sum(vwrs*ap, 1)/sum(vwrs*bz, 1)) bz=bz*tile(nrm,[3,1]).T self.filt_b[:, :, k]=bz self.filt_a[:, :, k]=ap LinearFilterbank.__init__(self, source, self.filt_b, self.filt_a) def asymmetric_compensation_coeffs(samplerate,fr,filt_b,filt_a,b,c,p0,p1,p2,p3,p4): ''' This function is used to generated the coefficient of the asymmetric compensation filter used for the gammachirp implementation. ''' ERBw=24.7*(4.37e-3*fr+1.) nbr_cascade=4 for k in arange(nbr_cascade): r=exp(-p1*(p0/p4)**(k)*2*pi*b*ERBw/samplerate) #k instead of k-1 because range 0 N-1 Dfr=(p0*p4)**(k)*p2*c*b*ERBw phi=2*pi*maximum((fr+Dfr), 0)/samplerate psy=2*pi*maximum((fr-Dfr), 0)/samplerate ap=vstack((ones(r.shape),-2*r*cos(phi), r**2)).T bz=vstack((ones(r.shape),-2*r*cos(psy), r**2)).T vwr=exp(1j*2*pi*fr/samplerate) vwrs=vstack((ones(vwr.shape), vwr, vwr**2)).T ##normilization stuff nrm=abs(sum(vwrs*ap, 1)/sum(vwrs*bz, 1)) bz=bz*tile(nrm,[3,1]).T filt_b[:, :, k]=bz filt_a[:, :, k]=ap return filt_b,filt_a def factorial(n): return prod(arange(1, n+1)) brian-1.4.3/brian/hears/filtering/firfilterbank.py000066400000000000000000000200521266062647100221770ustar00rootroot00000000000000''' FIR filterbank, can be treated as a special case of LinearFilterbank, but an optimisation is possible using buffered output by using FFT based convolution as in HRTF.apply. To do this is slightly tricky because it needs to cache previous inputs. For the moment, we implement it as a special case of LinearFilterbank but later this will change to using the FFT method. ''' from brian import * from filterbank import * from linearfilterbank import * __all__ = ['FIRFilterbank', 'LinearFIRFilterbank', 'FFTFIRFilterbank'] class LinearFIRFilterbank(LinearFilterbank): def __init__(self, source, impulse_response, minimum_buffer_size=None): # if a 1D impulse response is given we apply it to every channel # Note that because we are using LinearFilterbank at the moment, this # means duplicating the impulse response. However, it could be stored # just once when we move to using FFT based convolution and in fact this # will save a lot of computation as the FFT only needs to be computed # once then. if len(impulse_response.shape)==1: impulse_response = repeat(reshape(impulse_response, (1, len(impulse_response))), source.nchannels, axis=0) # Automatically duplicate mono input to fit the desired output shape if impulse_response.shape[0]!=source.nchannels: if source.nchannels!=1: raise ValueError('Can only automatically duplicate source channels for mono sources, use RestructureFilterbank.') source = RestructureFilterbank(source, impulse_response.shape[0]) # Implement it as a LinearFilterbank b = reshape(impulse_response, impulse_response.shape+(1,)) a = zeros_like(b) a[:, 0, :] = 1 LinearFilterbank.__init__(self, source, b, a) if minimum_buffer_size is not None: self.minimum_buffer_size = minimum_buffer_size class FFTFIRFilterbank(Filterbank): def __init__(self, source, impulse_response, minimum_buffer_size=None): # if a 1D impulse response is given we apply it to every channel # Note that because we are using LinearFilterbank at the moment, this # means duplicating the impulse response. However, it could be stored # just once when we move to using FFT based convolution and in fact this # will save a lot of computation as the FFT only needs to be computed # once then. if len(impulse_response.shape)==1: impulse_response = repeat(reshape(impulse_response, (1, len(impulse_response))), source.nchannels, axis=0) # Automatically duplicate mono input to fit the desired output shape if impulse_response.shape[0]!=source.nchannels: if source.nchannels!=1: raise ValueError('Can only automatically duplicate source channels for mono sources, use RestructureFilterbank.') source = RestructureFilterbank(source, impulse_response.shape[0]) Filterbank.__init__(self, source) self.input_cache = zeros((impulse_response.shape[1], self.nchannels)) self.impulse_response = impulse_response self.fftcache_nmax = -1 if minimum_buffer_size is None: minimum_buffer_size = 3*impulse_response.shape[1] self.minimum_buffer_size = minimum_buffer_size def buffer_init(self): Filterbank.buffer_init(self) self.input_cache[:] = 0 # This version uses a single FFT/IFFT call, using the axis keyword, but it # doesn't appear to be any more efficient than looping, and uses much more # memory, although my tests weren't exhaustive. # def buffer_apply(self, input): # nmax = max(self.input_cache.shape[0]+input.shape[0], self.impulse_response.shape[1]) # nmax = 2**int(ceil(log2(nmax))) # if self.fftcache_nmax!=nmax: # # impulse response: (nchannels, ir_length) # ir = zeros((self.nchannels, nmax)) # ir[:, :self.impulse_response.shape[1]] = self.impulse_response # # fftcache: (ir_length, nchannels) # self.fftcache = fft(ir, n=nmax, axis=1).T # self.fftcache_nmax = nmax # fullinput = vstack((self.input_cache, input)) # fullinput = vstack((fullinput, zeros((nmax-fullinput.shape[1], self.nchannels)))) # # fullinput: (ir_length, nchannels) # fullinput_fft = fft(fullinput, n=nmax, axis=0) # fulloutput_fft = fullinput_fft*self.fftcache # fulloutput = ifft(fulloutput_fft, n=nmax, axis=0).real # output = fulloutput[self.input_cache.shape[0]:self.input_cache.shape[0]+input.shape[0]] # # update input cache # nic = self.input_cache.shape[0] # ni = input.shape[0] # #print ni, nic # if ni>=nic: # self.input_cache[:, :] = input[-nic:, :] # else: # self.input_cache[:-ni, :] = self.input_cache[ni:, :] # self.input_cache[-ni:, :] = input # return output def buffer_apply(self, input): output = zeros_like(input) nmax = max(self.input_cache.shape[0]+input.shape[0], self.impulse_response.shape[1]) nmax = 2**int(ceil(log2(nmax))) if self.fftcache_nmax!=nmax: self.fftcache = [] for i, (previnput, curinput, ir) in enumerate(zip(self.input_cache.T, input.T, self.impulse_response)): fullinput = hstack((previnput, curinput)) # pad fullinput = hstack((fullinput, zeros(nmax-len(fullinput)))) # apply fft if self.fftcache_nmax!=nmax: # recompute IR fft, first pad, then take fft, then store ir = hstack((ir, zeros(nmax-len(ir)))) ir_fft = fft(ir, n=nmax) self.fftcache.append(ir_fft) else: ir_fft = self.fftcache[i] fullinput_fft = fft(fullinput, n=nmax) curoutput_fft = fullinput_fft*ir_fft curoutput = ifft(curoutput_fft) # unpad curoutput = curoutput[len(previnput):len(previnput)+len(curinput)] output[:, i] = curoutput.real if self.fftcache_nmax!=nmax: self.fftcache_nmax = nmax # update input cache nic = self.input_cache.shape[0] ni = input.shape[0] #print ni, nic if ni>=nic: self.input_cache[:, :] = input[-nic:, :] else: self.input_cache[:-ni, :] = self.input_cache[ni:, :] self.input_cache[-ni:, :] = input return output class FIRFilterbank(Filterbank): ''' Finite impulse response filterbank Initialisation parameters: ``source`` Source sound or filterbank. ``impulse_response`` Either a 1D array providing a single impulse response applied to every input channel, or a 2D array of shape ``(nchannels, ir_length)`` for ``ir_length`` the number of samples in the impulse response. Note that if you are using a multichannel sound ``x`` as a set of impulse responses, the array should be ``impulse_response=array(x.T)``. ``minimum_buffer_size=None`` If specified, gives a minimum size to the buffer. By default, for the FFT convolution based implementation of ``FIRFilterbank``, the minimum buffer size will be ``3*ir_length``. For maximum efficiency with FFTs, ``buffer_size+ir_length`` should be a power of 2 (otherwise there will be some zero padding), and ``buffer_size`` should be as large as possible. ''' def __init__(self, source, impulse_response, use_linearfilterbank=False, minimum_buffer_size=None): if use_linearfilterbank: self.__class__ = LinearFIRFilterbank else: self.__class__ = FFTFIRFilterbank self.__init__(source, impulse_response, minimum_buffer_size=minimum_buffer_size) brian-1.4.3/brian/hears/filtering/fractionaldelay.py000066400000000000000000000074661266062647100225340ustar00rootroot00000000000000from brian import * from filterbank import * from firfilterbank import * __all__ = ['FractionalDelay'] class FractionalDelay(FIRFilterbank): ''' Filterbank for applying delays which are fractional multiples of the timestep Initialised with arguments: ``source`` Source sound or filterbank. ``delays`` A list or array of delays to apply (the number of channels in the filterbank will be equal to the length of this). ``filter_length=None`` Use this to explicitly set the length of the impulse response, should be odd. If not specified, it will be automatically determined from the delays. See notes below. ``**args`` Arguments to pass to :class:`FIRFilterbank` (from which this class is derived). **Attributes** .. attribute:: delay_offset The global delay offset. If the specified delay in a given channel is ``delay`` the actual delay will be ``delay_offset+delay``. It is equal to ``(filter_length/2)/source.samplerate``. .. attribute:: filter_length The length of the filter to use. This is automatically determined from the delays. Note that ``delay_offset`` should be larger than the maximum positive or negative delay. The minimum filter length is by default 2048 samples, which allows for good accuracy for signals with power above 20 Hz. For low frequency analysis, longer filters will be necessary. For high frequency analysis, a shorter filter length could be used for a more efficient computation. **Notes** Inducing a delay for a sound that is an integer multiple of the timestep (1/samplerate) can be done simply by offsetting the samples, e.g. ``sound[3:]`` is ``sound`` delayed by ``3/sound.samplerate``. However, for fractional multiples of the timestep, the sound needs to be filtered. The theory and code for this was adapted from `http://www.labbookpages.co.uk/audio/beamforming/fractionalDelay.html `__. The filters induce a delay of ``delay_offset+delay`` where ``delay_offset`` is a positive value larger than the maximum positive or negative delay. This value is available as the attribute ``delay_offset``. ''' def __init__(self, source, delays, filter_length=None, **args): delays = array(delays) delay_max = amax(abs(delays)) delay_max_int = int(ceil(source.samplerate*delay_max)) if filter_length is None: filter_length = 2*int(delay_max_int*1.25)+1 if filter_length<2048: filter_length = 2048 if filter_length/2<=delay_max_int: raise ValueError('Filter length not long enough for selected delays.') self.delay_offset = (filter_length//2)/source.samplerate self.filter_length = filter_length self.delays = delays irs = [fractional_delay_ir(delay, source.samplerate, filter_length=filter_length) for delay in delays] irs = array(irs) self.impulse_response = irs FIRFilterbank.__init__(self, source, irs, **args) # Adapted from # http://www.labbookpages.co.uk/audio/beamforming/fractionalDelay.html def fractional_delay_ir(delay, samplerate, filter_length=151): delay = delay*samplerate centre_tap = filter_length // 2 t = arange(filter_length) x = t-delay if abs(round(delay)-float(delay))<1e-10: return array(x==centre_tap, dtype=float) sinc = sin(pi*(x-centre_tap))/(pi*(x-centre_tap)) window = 0.54-0.46*cos(2.0*pi*(x+0.5)/filter_length) # Hamming window tap_weight = window*sinc return tap_weight brian-1.4.3/brian/hears/filtering/gpulinearfilterbank.py000066400000000000000000000205501266062647100234100ustar00rootroot00000000000000''' ''' # TODO: support for GPUBufferedArray? from numpy import * import pycuda #import pycuda.autoinit as autoinit import pycuda.driver as drv import pycuda.compiler from pycuda import gpuarray from brian.experimental.cuda.buffering import * import re from filterbank import Filterbank, RestructureFilterbank from gputools import * import gc __all__ = ['LinearFilterbank'] class LinearFilterbank(Filterbank): ''' Generalised parallel linear filterbank This filterbank allows you to construct a chain of linear filters in a bank so that each channel in the bank has its own filters. You pass the (b,a) parameters for the filter in the format specified in the function ``apply_linear_filterbank``. Note that there are additional GPU specific options: ``precision`` Should be single for older GPUs. ``forcesync`` Should be set to true to force a copy of GPU->CPU after each filter step, but not if the output is being used in ongoing GPU computations. By default this is ``True`` for compatibility. ``pagelocked_mem`` Allocate faster pagelocked memory for GPU->CPU copies (doubles copy rate). ``unroll_filterorder`` Whether or not to unroll the loop for the filter order, normally this should be done for small filter orders. By default, it is done if the filter order is less than or equal to 32. ''' def __init__(self, source, b, a, samplerate=None, precision='double', forcesync=True, pagelocked_mem=True, unroll_filterorder=None): # Automatically duplicate mono input to fit the desired output shape if b.shape[0]!=source.nchannels: if source.nchannels!=1: raise ValueError('Can only automatically duplicate source channels for mono sources, use RestructureFilterbank.') source = RestructureFilterbank(source, b.shape[0]) Filterbank.__init__(self, source) if pycuda.context is None: set_gpu_device(0) self.precision=precision if self.precision=='double': self.precision_dtype=float64 else: self.precision_dtype=float32 self.forcesync=forcesync self.pagelocked_mem=pagelocked_mem n, m, p=b.shape self.filt_b=b self.filt_a=a filt_b_gpu=array(b, dtype=self.precision_dtype) filt_a_gpu=array(a, dtype=self.precision_dtype) filt_state=zeros((n, m-1, p), dtype=self.precision_dtype) if pagelocked_mem: filt_y=drv.pagelocked_zeros((n,), dtype=self.precision_dtype) self.pre_x=drv.pagelocked_zeros((n,), dtype=self.precision_dtype) else: filt_y=zeros(n, dtype=self.precision_dtype) self.pre_x=zeros(n, dtype=self.precision_dtype) self.filt_b_gpu=gpuarray.to_gpu(filt_b_gpu.T.flatten()) # transform to Fortran order for better GPU mem self.filt_a_gpu=gpuarray.to_gpu(filt_a_gpu.T.flatten()) # access speeds self.filt_state=gpuarray.to_gpu(filt_state.T.flatten()) self.unroll_filterorder = unroll_filterorder if unroll_filterorder is None: if m<=32: unroll_filterorder = True else: unroll_filterorder = False # TODO: improve code, check memory access patterns, maybe use local memory code=''' #define x(s,i) _x[(s)*n+(i)] #define y(s,i) _y[(s)*n+(i)] #define a(i,j,k) _a[(i)+(j)*n+(k)*n*m] #define b(i,j,k) _b[(i)+(j)*n+(k)*n*m] #define zi(i,j,k) _zi[(i)+(j)*n+(k)*n*(m-1)] __global__ void filt(SCALAR *_b, SCALAR *_a, SCALAR *_x, SCALAR *_zi, SCALAR *_y, int numsamples) { int j = blockIdx.x * blockDim.x + threadIdx.x; if(j>=n) return; for(int s=0; s1: # we need to do this so as not to alter the values in x in the C code below # but if zi.shape[2] is 1 there is only one filter in the chain and the # copy operation at the end of the C code will never happen. x = array(x, copy=True, order='C') else: # make sure that the array is in C-order x = asarray(x, order='C') y = empty_like(x) n, m, p = b.shape n1, m1, p1 = a.shape numsamples = x.shape[0] if n1!=n or m1!=m or p1!=p or x.shape!=(numsamples, n) or zi.shape!=(n, m, p): raise ValueError('Data has wrong shape.') if numsamples>1 and not x.flags['C_CONTIGUOUS']: raise ValueError('Input data must be C_CONTIGUOUS') if not b.flags['F_CONTIGUOUS'] or not a.flags['F_CONTIGUOUS'] or not zi.flags['F_CONTIGUOUS']: raise ValueError('Filter parameters must be F_CONTIGUOUS') code = ''' #define X(s,i) x[(s)*n+(i)] #define Y(s,i) y[(s)*n+(i)] #define A(i,j,k) a[(i)+(j)*n+(k)*n*m] #define B(i,j,k) b[(i)+(j)*n+(k)*n*m] #define Zi(i,j,k) zi[(i)+(j)*n+(k)*n*(m-1)] for(int s=0; s 1.5)*(V_sat2 - 1.5) + 1.5) : 1 V_sat2 = 2 + 3*np.log10(CF_param / 1000.0) : 1 # Equation 17 P_I_exponent = p_2 * V_ihc : 1 # avoid infinity values P_I = p_1 * np.clip(np.log(1 + np.exp(P_I_exponent)), -np.inf, np.abs(P_I_exponent) + 1): 1 # Following Equation A16 (p_2 is the same as P_ST) p_2_exponent = np.log(2) * V_sat / P_rest : 1 p_2 = np.clip(np.log(np.exp(p_2_exponent) - 1), -np.inf, np.abs(p_2_exponent)) : 1 # Equation A18-A19 # Concentration in the stores (as differential instead of difference equation) dC_I/dt = (-P_I*C_I + P_L*(C_L - C_I))/V_I : Hz dC_L/dt = (-P_L*(C_L - C_I) + P_G*(C_G - C_L))/V_I : Hz # time-varying discharge rate (ignoring refractory effects), equation A20 s = C_I * P_I : Hz ''' FilterbankGroup.__init__(self, source, 'V_ihc', model=eqs) self.CF_param = CF self.C_I = C_Irest self.C_L = C_Lrest def set_parameters(cf,param): parameters=dict() parameters['fc_LP_control']=800 #Hz parameters['fc_LP_fb']=500 #Hz parameters['fp1']=1.0854*cf-106.0034 parameters['ta']=10**(np.log10(cf)*1.0230 + 0.1607) parameters['tb']=10**(np.log10(cf)*1.4292 - 1.1550) - 1000 parameters['gain80']=10**(np.log10(cf)*0.5732 + 1.5220) parameters['rgain']=10**( np.log10(cf)*0.4 + 1.9) parameters['average_control']=0.3357 parameters['zero_r']= np.array(-10**( np.log10(cf)*1.5-0.9 )) if param: if not isinstance(param, dict): raise TypeError('given parameters must be a dict') for key in param.keys(): if key != 'nlgain' and not parameters.has_key(key): raise KeyError(key + ' is invalid key entry for given parameters') parameters[key] = param[key] parameters['nlgain']= (parameters['gain80'] - parameters['rgain'])/parameters['average_control'] return parameters class Control_Coefficients: def __init__(self,cf,samplerate): self.cf = cf self.PI2 = 2.*3.14159265358979 self.nch=len(cf) self.fs_bilinear = 2.0*samplerate#*ones(self.nch) # self.fs_bilinear =tile(self.fs_bilinear.reshape(self.nch,-1),3) self.x_cf=11.9*np.log10(0.8+cf/456); self.f_shift=(pow(10,((self.x_cf+1.2)/11.9))-0.8)*456-cf self.wbw=cf/4.0 self.filt_a = np.zeros((len(cf),3,5), order='F') #8 5 # self.filt_a[:,0,:] = 1 self.filt_b = np.zeros((len(cf),3,5), order='F') self.control_signal = 0 self.preal = np.zeros((self.nch,6)) self.pimg = np.zeros((self.nch,6)) self.preal,self.pimg = self.analog_poles() def return_coefficients(self,control_signal): self.wbw=-(self.preal[:,0] - control_signal)/self.PI2 self.gain_norm_bp=((self.PI2**2 * np.sqrt(self.wbw**2 + self.f_shift**2) * np.sqrt((2*self.cf+self.f_shift)**2 + self.wbw**2) )**3)/np.sqrt(self.PI2**2*self.cf**2)# iord = [1,3,5] preal = self.preal[:,iord]-control_signal.T #actually control_signal is the same for the three channels temp=(self.fs_bilinear-(preal))**2 + self.pimg[:,iord]**2 self.filt_a[:,0,0:3] = 1. self.filt_a[:,1,0:3]= -2*(self.fs_bilinear**2-(preal)**2-self.pimg[:,iord]**2)/temp self.filt_a[:,2,0:3] = ((self.fs_bilinear+(preal))**2+self.pimg[:,iord]**2)/temp self.filt_b[:,0,0:3] = 1./temp self.filt_b[:,1,0:3] = 2./temp self.filt_b[:,2,0:3] = 1./temp self.filt_a[:,0,3] = 1. self.filt_a[:,1,3]= 1. ## changed from 1 to 0 self.filt_a[:,2,3] = 0. self.filt_b[:,0,3] = self.fs_bilinear self.filt_b[:,1,3] = -self.fs_bilinear self.filt_b[:,2,3] = 0 # self.filt_b[:,:,3] = self.gain_norm_bp*self.filt_b[:,:,3] self.filt_a[:,0,4] = 1. self.filt_b[:,0,4] = self.gain_norm_bp return self.filt_b,self.filt_a def analog_poles(self): self.preal[:,0] = -self.PI2*self.wbw #that should be -, actually there are never used self.preal[:,1] = -self.PI2*self.wbw self.preal[:,2] = self.preal[:,0] self.preal[:,3] = self.preal[:,1] self.preal[:,4] = self.preal[:,0] self.preal[:,5] = self.preal[:,1] self.pimg[:,0] = self.PI2*(self.cf+self.f_shift) self.pimg[:,1] = -self.PI2*(self.cf+self.f_shift) self.pimg[:,2] = self.pimg[:,0] self.pimg[:,3] = self.pimg[:,1] self.pimg[:,4] = self.pimg[:,0] self.pimg[:,5] = self.pimg[:,1] return self.preal,self.pimg class Signal_Coefficients: def __init__(self,cf,samplerate,parameters): self.t = 0 self.cf = cf self.PI2 = 2*3.14159265358979 self.nch=len(cf) self.fs_bilinear = 2.0*samplerate#*ones(self.nch) self.order_of_pole = 20 self.half_order_pole = self.order_of_pole/2 self.order_of_zero = self.half_order_pole self.filt_a = np.zeros((len(cf),3,11), order='F') self.filt_b = np.zeros((len(cf),3,11), order='F') self.preal = np.zeros((self.nch,20)) self.pimg = np.zeros((self.nch,20)) self.control_signal = 0 self.rgain =parameters['rgain']# 10**(log10(cf)*0.4 + 1.9) self.fp1=parameters['fp1']#1.0854*cf-106.0034 self.ta= parameters['ta']# 10**(log10(cf)*1.0230 + 0.1607) self.tb= parameters['tb']# 10**(log10(cf)*1.4292 - 1.1550) - 1000 self.zeroa = parameters['zero_r']# -10**(log10(cf)*1.5-0.9 ) self.zeroamat = np.tile(self.zeroa.reshape(self.nch,-1),10) self.preal,self.pimg = self.analog_poles(0) self.cfmat = np.tile(self.cf.reshape(self.nch,-1),20) self.gain_norm = np.sqrt(np.prod((2*pi*self.cfmat-self.pimg[:,0:20])**2+self.preal[:,0:20]**2,axis=1)) self.gain_norm = self.gain_norm /(np.sqrt((2*pi*self.cf)**2+self.zeroa**2))**self.order_of_zero def return_coefficients(self,control_signal): self.preal,self.pimg = self.analog_poles(control_signal) iord = np.arange(2,22,2)-1 temp=(self.fs_bilinear-self.preal[:,iord])**2 + self.pimg[:,iord]**2 self.filt_a[:,0,:10] = 1 self.filt_a[:,1,:10] = -2*(self.fs_bilinear**2-self.preal[:,iord]**2-self.pimg[:,iord]**2)/temp self.filt_a[:,2,:10] = ((self.fs_bilinear+self.preal[:,iord])**2+self.pimg[:,iord]**2)/temp self.filt_b[:,0,:10] = (-self.zeroamat+self.fs_bilinear)/temp self.filt_b[:,1,:10] = (-2*self.zeroamat)/temp self.filt_b[:,2,:10] = (-self.zeroamat-self.fs_bilinear)/temp self.filt_a[:,0,10] = 1. self.filt_b[:,0,10] = self.gain_norm/3. return self.filt_b,self.filt_a def analog_poles(self,control_signal): aa = -self.rgain-control_signal aa[aa>=0] = 100 self.preal[:,0] = -self.rgain-control_signal self.preal[:,4] = self.preal[:,0]-self.ta self.preal[:,2] = (self.preal[:,0]+self.preal[:,4])*0.5 self.preal[:,1] = self.preal[:,0] self.preal[:,3] = self.preal[:,2] self.preal[:,5] = self.preal[:,4] self.preal[:,6] = self.preal[:,0] self.preal[:,7] = self.preal[:,1] self.preal[:,8] = self.preal[:,4] self.preal[:,9] = self.preal[:,5] self.preal[:,10:] = self.preal[:,:10] self.pimg[:,0] = self.PI2*self.fp1 self.pimg[:,4] = self.pimg[:,0]-self.tb self.pimg[:,2] = (self.pimg[:,0]+self.pimg[:,4])*0.5 self.pimg[:,1] = -self.pimg[:,0] self.pimg[:,3] = -self.pimg[:,2] self.pimg[:,5] = -self.pimg[:,4] self.pimg[:,6] = self.pimg[:,0] self.pimg[:,7] = self.pimg[:,1] self.pimg[:,8] = self.pimg[:,4] self.pimg[:,9] = self.pimg[:,5] self.pimg[:,10:] = self.pimg[:,:10] return self.preal,self.pimg class Filter_Update: def __init__(self, target,coef): self.coef = coef self.target = target self.param = [] def __call__(self, input): reshaped_input = input[-1,:].reshape(1,-1) self.target.filt_b,self.target.filt_a = self.coef.return_coefficients(reshaped_input) self.param.append(self.coef.control_signal) class LowPass_IHC(LinearFilterbank): def __init__(self,source,cf,fc,gain,order): nch = len(cf) TWOPI = 2*pi self.samplerate = source.samplerate c = 2.0 * self.samplerate c1LP = ( c/Hz - TWOPI*fc ) / ( c/Hz + TWOPI*fc ) c2LP = TWOPI*fc/Hz / (TWOPI*fc + c/Hz) b_temp = np.array([c2LP,c2LP]) a_temp = np.array([1,-c1LP]) filt_b = np.tile(b_temp.reshape([2,1]),[nch,1,order]) filt_a = np.tile(a_temp.reshape([2,1]),[nch,1,order]) filt_b[:,:,0] = filt_b[:,:,0]*gain LinearFilterbank.__init__(self, source, filt_b, filt_a) class LowPass_filter(LinearFilterbank): def __init__(self,source,cf,fc,gain,order): nch = len(cf) TWOPI = 2*pi self.samplerate = source.samplerate c = 2.0 * self.samplerate c1LP = ( c/Hz - TWOPI*fc ) b_temp = np.array([1,1])/ ( c/Hz + TWOPI*fc ) a_temp = np.array([1,-c1LP/ ( c/Hz + TWOPI*fc )]) filt_b = np.tile(b_temp.reshape([2,1]),[nch,1,order]) filt_a = np.tile(a_temp.reshape([2,1]),[nch,1,order]) filt_b[:,:,order-1] = filt_b[:,:,order-1]*gain LinearFilterbank.__init__(self, source, filt_b, filt_a) def saturation_fc(x,A0=1,B=1,C=1,D=1): ind = x>=0 x[ind]=A0*np.log(x[ind]*B+1.0) ind = x<0 dtemp = (-x[ind])**C tempA = -A0*(dtemp+D)/(3*dtemp+D) x[ind]=tempA*np.log(abs(x[ind])*B+1.0) return x class TanCarneyIHC(CombinedFilterbank): def __init__(self, source, cf): CombinedFilterbank.__init__(self, source) source = self.get_modified_source() ## Saturation saturation = FunctionFilterbank(source, saturation_fc, A0=0.1, B=2000, C=1.74, D=6.87e-9) ## low pass IHC ihc = LowPass_IHC(saturation, cf, 3800, 1, 7) self.set_output(ihc) class TanCarneyControl(CombinedFilterbank): def __init__(self, source, cf, update_interval, param=None): CombinedFilterbank.__init__(self, source) source = self.get_modified_source() cf = np.atleast_1d(cf) samplerate=source.samplerate parameters = set_parameters(cf, param) ##### Control Path #### # band pass filter control_coef = Control_Coefficients(cf, samplerate) [filt_b,filt_a] = control_coef.return_coefficients(np.zeros((1,len(cf)))) BP_control = LinearFilterbank(source,filt_b,filt_a) # first non linearity of control path Acp,Bcp,Ccp=100.,2.5,0.60 func_NL1_control=lambda x:np.sign(x)*Bcp*np.log(1.+Acp*abs(x)**Ccp) NL1_control=FunctionFilterbank(BP_control,func_NL1_control) # second non linearity of control path asym,s0,x1,s1=7.,8.,5.,3. shift = 1./(1.+asym) x0 = s0*np.log((1.0/shift-1)/(1+np.exp(x1/s1))) func_NL2_control=lambda x:(1.0/(1.0+np.exp(-(x-x0)/s0)*(1.0+np.exp(-(x-x1)/s1)))-shift)*parameters['nlgain'] NL2_control=FunctionFilterbank(NL1_control,func_NL2_control) #control low pass filter (its output will be used to control the signal path) gain_lp_con = (2*pi*parameters['fc_LP_control'])**3*1.5 LP_control = LowPass_filter(NL2_control,cf,parameters['fc_LP_control'],gain_lp_con,3) #low pass filter for feedback to control band pass (its output will be used to control the control path) gain_lp_fb = parameters['fc_LP_fb']*2*pi*10 LP_feed_back = LowPass_filter(LP_control,cf,parameters['fc_LP_fb'],gain_lp_fb,1) updater = Filter_Update(BP_control, control_coef) #instantiation of the updater for the control path output = ControlFilterbank(LP_control, LP_feed_back, BP_control, updater, update_interval) #controler for the band pass filter of the control path self.set_output(output) class TanCarneySignal(CombinedFilterbank): def __init__(self, source, cf, update_interval, param=None): CombinedFilterbank.__init__(self, source) source = self.get_modified_source() cf = np.atleast_1d(cf) parameters = set_parameters(cf, param) samplerate=source.samplerate if int(source.samplerate)!=50000: warnings.warn('To use the TanCarney cochlear model the sample rate should be 50kHz') # band pass filter signal_coef = Signal_Coefficients(cf, samplerate,parameters) [filt_b,filt_a] = signal_coef.return_coefficients(np.zeros((1,len(cf)))) BP_signal = LinearFilterbank(source,filt_b,filt_a) control_output = TanCarneyControl(source, cf, update_interval, parameters) updater = Filter_Update(BP_signal, signal_coef) #instantiation of the updater for the signal path output = ControlFilterbank(BP_signal, control_output, BP_signal, updater, update_interval) #controler for the band pass filter of the signal path self.set_output(output) class TanCarney(CombinedFilterbank): ''' Class implementing the nonlinear auditory filterbank model as described in Tan, G. and Carney, L., "A phenomenological model for the responses of auditory-nerve fibers. II. Nonlinear tuning with a frequency glide", JASA 2003. The model consists of a control path and a signal path. The control path controls both its own bandwidth via a feedback loop and also the bandwidth of the signal path. Initialised with arguments: ``source`` Source of the cochlear model. ``cf`` List or array of center frequencies. ``update_interval`` Interval in samples controlling how often the band pass filter of the signal pathway is updated. Smaller values are more accurate but increase the computation time. ``param`` Dictionary used to overwrite the default parameters given in the original paper. ''' def __init__(self, source, cf, update_interval=1, param=None): CombinedFilterbank.__init__(self, source) source = self.get_modified_source() cf = np.atleast_1d(cf) parameters=set_parameters(cf,param) signal = TanCarneySignal(source, cf, update_interval, parameters) ihc = TanCarneyIHC(signal, cf) self.set_output(ihc) brian-1.4.3/brian/hears/filtering/zilany.py000066400000000000000000000365321266062647100206750ustar00rootroot00000000000000from brian import * from filterbank import Filterbank,FunctionFilterbank,ControlFilterbank, CombinedFilterbank from filterbanklibrary import * from linearfilterbank import * import warnings from scipy.io import loadmat,savemat from brian.hears import * try: from scikits.samplerate import resample have_scikits_samplerate = True except (ImportError, ValueError): have_scikits_samplerate = False #print have_scikits_samplerate def set_parameters(cf,param): parameters=dict() parameters['fc_LP_control']=800*Hz parameters['fc_LP_fb']=500*Hz parameters['fp1']=1.0854*cf-106.0034 parameters['ta']=10**(log10(cf)*1.0230 + 0.1607) parameters['tb']=10**(log10(cf)*1.4292 - 1.1550) - 1000 parameters['gain80']=10**(log10(cf)*0.5732 + 1.5220) parameters['rgain']=10**( log10(cf)*0.4 + 1.9) parameters['average_control']=0.3357 parameters['zero_r']= array(-10**( log10(cf)*1.5-0.9 )) if param: if not isinstance(param, dict): raise Error('given parameters must be a dict') for key in param.keys(): if not parameters.has_key(key): raise Exception(key + ' is invalid key entry for given parameters') parameters[key] = param[key] parameters['nlgain']= (parameters['gain80'] - parameters['rgain'])/parameters['average_control'] return parameters ## def gain_groupdelay(binwidth,centerfreq,cf,tau): tmpcos = cos(2*pi*(centerfreq-cf)*binwidth) dtmp2 = tau*2.0/binwidth c1LP = (dtmp2-1)/(dtmp2+1) c2LP = 1.0/(dtmp2+1) tmp1 = 1+c1LP*c1LP-2*c1LP*tmpcos tmp2 = 2*c2LP*c2LP*(1+tmpcos) wb_gain = sqrt(tmp1/tmp2) grdelay = floor((0.5-(c1LP*c1LP-c1LP*tmpcos)/(1+c1LP*c1LP-2*c1LP*tmpcos))).astype(int) return wb_gain,grdelay def get_taubm(cf,CAgain,taumax): bwfactor = 0.7; factor = 2.5; ratio = 10**(-CAgain/(20.0*factor)) bmTaumax = taumax/bwfactor; bmTaumin = bmTaumax*ratio; return bmTaumax,bmTaumin,ratio def get_tauwb(cf,CAgain,order): #### ratio = 10**(-CAgain/(20.0*order)) #ratio of TauMin/TauMax according to the gain, order */ ##Q10 = pow(10,0.4708*log10(cf/1e3)+0.5469); */ /* 75th percentile */ Q10 = 10**(0.4708*log10(cf/1e3)+0.4664) #/* 50th percentile */ ##Q10 = pow(10,0.4708*log10(cf/1e3)+0.3934); */ /* 25th percentile */ bw = cf/Q10; taumax = 2.0/(2*pi*bw); taumin = taumax*ratio; return taumax,taumin ### function to initialize the chirp filters class Chirp_Coefficients: def __init__(self,cf,taumax,samplerate,rsigma,fcohc): self.nch=len(cf) self.T = 1./ samplerate self.TWOPI = 2*pi self.sigma0 = 1/taumax self.rsigma = rsigma self.fcohc = fcohc self.ipw = 1.01*cf*self.TWOPI-50 self.ipb = 0.2343*self.TWOPI*cf-1104 self.rpa = pow(10, log10(cf)*0.9 + 0.55)+ 2000 self.pzero = pow(10,log10(cf)*0.7+1.6)+500 self.order_of_pole = 10 self.half_order_pole = self.order_of_pole/2 self.order_of_zero = self.half_order_pole self.fs_bilinear = self.TWOPI*cf/tan(self.TWOPI*cf*self.T/2) self.fs_bilinear =tile(self.fs_bilinear.reshape(self.nch,-1),5) self.rzero = -self.pzero self.CF = self.TWOPI*cf self.nch=len(self.CF) self.filt_a = zeros((len(cf),3,self.half_order_pole), order='F') self.filt_a[:,0,:] = 1 self.filt_b = zeros((len(cf),3,self.half_order_pole), order='F') self.preal = zeros((self.nch,3)) self.pimg = zeros((self.nch,6)) self.preal = self.analog_poles_real(0*ones(self.nch),1*ones(self.nch)) self.pimg = self.analog_poles_img() self.CFmat = tile(self.CF.reshape(self.nch,-1),5) self.rzeromat = tile(self.rzero.reshape(self.nch,-1),5) self.Cinitphase = sum(arctan(self.CFmat/(-self.rzeromat))\ -arctan((self.CFmat-self.pimg[:,[0,2,1,0,1]])/(-self.preal[:,[0,2,1,0,1]]))\ -arctan((self.CFmat+self.pimg[:,[0,2,1,0,1]])/(-self.preal[:,[0,2,1,0,1]])),axis=1) self.CFmat10 = tile(self.CF.reshape(self.nch,-1),10) self.Cgain_norm = prod((self.CFmat10-self.pimg[:,[0,1,2,3,4,5,0,3,1,5]])**2+self.preal[:,[0,1,2,0,2,1,0,0,1,1]]**2,axis=1) self.norm_gain = sqrt(self.Cgain_norm)/sqrt(self.CF**2+self.rzero**2)**5 self.norm_gain= tile(self.norm_gain.reshape(self.nch,-1),3) def return_coefficients(self): self.preal = self.analog_poles_real(self.rsigma,self.fcohc) self.phase = sum(-arctan((self.CFmat-self.pimg[:,[0,2,1,0,1]])/(-self.preal[:,[0,2,1,0,1]]))\ -arctan((self.CFmat+self.pimg[:,[0,2,1,0,1]])/(-self.preal[:,[0,2,1,0,1]])),axis=1) self.rzero = -self.CF/tan((self.Cinitphase-self.phase)/self.order_of_zero) self.rzero = tile(self.rzero.reshape(self.nch,-1),5) iord = [0,2,1,0,1] temp = (self.fs_bilinear-self.preal[:,iord])**2+self.pimg[:,iord]**2 self.filt_a[:,0,:] = 1 self.filt_a[:,1,:] = -2*(self.fs_bilinear**2-self.preal[:,iord]**2-self.pimg[:,iord]**2)/temp self.filt_a[:,2,:] = ((self.fs_bilinear+self.preal[:,iord])**2+self.pimg[:,iord]**2)/temp self.filt_b[:,0,:] = (-self.rzero+self.fs_bilinear)/temp self.filt_b[:,1,:] = (-2*self.rzero)/temp self.filt_b[:,2,:] = (-self.rzero-self.fs_bilinear)/temp self.filt_b[:,:,4] = self.norm_gain/4.*self.filt_b[:,:,4] return self.filt_b,self.filt_a def analog_poles_real(self,rsigma,fcohc): self.preal[:,0] = -self.sigma0*fcohc-rsigma #0 self.preal[:,1] = self.preal[:,0] - self.rpa #4 self.preal[:,2] = (self.preal[:,0]+self.preal[:,1])*0.5 #2 return self.preal def analog_poles_img(self): self.pimg[:,0] = self.ipw #0 self.pimg[:,1] = self.pimg[:,0] - self.ipb #4 self.pimg[:,2] = (self.pimg[:,0]+self.pimg[:,1])*0.5 #2 self.pimg[:,3] = -self.pimg[:,0] #1 self.pimg[:,4] = -self.pimg[:,2] #3 self.pimg[:,5] = -self.pimg[:,1] #5 return self.pimg #### controlers ##### #definition of the class updater for the signal path bandpass filter class Filter_Update: def __init__(self, target,c1_coefficients,samplerate,cf,bmTaumax,bmTaumin,cohc,TauWBMax,TauWBMin): self.bmTaumax = bmTaumax self.bmTaumin = bmTaumin self.target=target self.samplerate=samplerate self.cf=atleast_1d(cf) self.cohc = cohc self.TauWBMax = TauWBMax self.TauWBMin = TauWBMin bmplace = 11.9 * log10(0.80 + cf / 456.0) self.centerfreq = 456.0*(pow(10,(bmplace+1.2)/11.9)-0.80) self.c1_coefficients=c1_coefficients self.rsigma=[] def __call__(self,input): tmptauc1 = input[-1,:] tauc1 = self.cohc*(tmptauc1-self.bmTaumin)+self.bmTaumin #signal path update self.c1_coefficients.rsigma = 1/tauc1-1/self.bmTaumax # self.target[0].filt_b,self.target[0].filt_a = self.c1_coefficients.return_coefficients() # #control path update # tauwb = self.TauWBMax+(tauc1-self.bmTaumax)*(self.TauWBMax-self.TauWBMin)/(self.bmTaumax-self.bmTaumin) # [wb_gain,self.grdelay] = gain_groupdelay(1./self.samplerate,self.centerfreq,self.cf,tauwb); # # grd[n] = grdelay # if ((grd[n]+n)taumax out[ix,ind] = taumax[ind] return out class LowPass_filter(LinearFilterbank): def __init__(self,source,cf,fc,gain,order): nch = len(cf) TWOPI = 2*pi self.samplerate = source.samplerate c = 2.0 * self.samplerate c1LP = ( c/Hz - TWOPI*fc ) / ( c/Hz + TWOPI*fc ) c2LP = TWOPI*fc/Hz / (TWOPI*fc + c/Hz) b_temp = array([c2LP,c2LP]) a_temp = array([1,-c1LP]) filt_b = tile(b_temp.reshape([2,1]),[nch,1,order]) filt_a = tile(a_temp.reshape([2,1]),[nch,1,order]) filt_b[:,:,0] = filt_b[:,:,0]*gain LinearFilterbank.__init__(self, source, filt_b, filt_a) class ZILANY(CombinedFilterbank): ''' Class implementing the nonlinear auditory filterbank model as described in Tan, G. and Carney, L., "A phenomenological model for the responses of auditory-nerve fibers. II. Nonlinear tuning with a frequency glide", JASA 2003. The model consists of a control path and a signal path. The control path controls both its own bandwidth via a feedback loop and also the bandwidth of the signal path. Initialised with arguments: ``source`` Source of the cochlear model. ``cf`` List or array of center frequencies. ``update_interval`` Interval in samples controlling how often the band pass filter of the signal pathway is updated. Smaller values are more accurate but increase the computation time. ``param`` Dictionary used to overwrite the default parameters given in the original paper. ''' def __init__(self, source,cf,update_interval,param={}): file="/home/bertrand/Data/MatlabProg/brian_hears/ZilanyCarney-JASAcode-2009/wbout.mat" X=loadmat(file,struct_as_record=False) wbout = Sound(X['wbout'].flatten()) wbout.samplerate = 100*kHz CombinedFilterbank.__init__(self, source) # source = self.get_modified_source() cf = atleast_1d(cf) nbr_cf=len(cf) samplerate=source.samplerate parameters=set_parameters(cf,param) # if int(source.samplerate)!=50000: # warnings.warn('To use the PMFR cochlear model the sample rate should be 50kHz') # if not have_scikits_samplerate: # raise ImportError('To use the PMFR cochlear model the sample rate should be 50kHz and scikits.samplerate package is needed for resampling') # #source=source.resample(50*kHz) # warnings.warn('The input to the PMFR cochlear model has been resampled to 50kHz') cohc =1 # ohc scaling factor: 1 is normal OHC function; 0 is complete OHC dysfunction cihc = 1 # i ihc scaling factor: 1 is normal IHC function; 0 is complete IHC dysfunction bmplace = 11.9 * log10(0.80 + cf / 456.0) centerfreq = 456.0*(pow(10,(bmplace+1.2)/11.9)-0.80) CAgain = minimum(60,maximum(15*ones(len(cf)),52/2*(tanh(2.2*log10(cf/600)+0.15)+1))) # Parameters for the control-path wideband filter =======*/ bmorder = 3; Taumax,Taumin = get_tauwb(cf,CAgain,bmorder) taubm = cohc*(Taumax-Taumin)+Taumin; ratiowb = Taumin/Taumax; #====== Parameters for the signal-path C1 filter ======*/ bmTaumax,bmTaumin,ratiobm = get_taubm(cf,CAgain,Taumax) bmTaubm = cohc*(bmTaumax-bmTaumin)+bmTaumin; fcohc = bmTaumax/bmTaubm; # Parameters for the control-path wideband filter =======*/ wborder = 3 TauWBMax = Taumin+0.2*(Taumax-Taumin); TauWBMin = TauWBMax/Taumax*Taumin; tauwb = TauWBMax+(bmTaubm-bmTaumax)*(TauWBMax-TauWBMin)/(bmTaumax-bmTaumin); [wbgain,grdelay] = gain_groupdelay(1./self.samplerate,centerfreq,cf,tauwb) # Nonlinear asymmetry of OHC function and IHC C1 transduction function*/ ohcasym = 7.0 ihcasym = 3.0 # ##### Control Path #### # print tauwb*pi*centerfreq # gt_control = BiQuadratic(source, centerfreq,tauwb*2*pi*centerfreq) gt_control = ApproximateGammatone(source, centerfreq, 1./(2*pi*tauwb), order=3) # gt_control = LinearGammachirp(source, centerfreq,tauwb, c=0) # gt_control = Gammatone(source, centerfreq, b=1./(pi*tauwb)) # def wb_gain(x,cf=array([1000]),tauwb=array([.0003]),TauWBMax=array([.0003]),wborder=3): # print x.shape,cf.shape # out=zeros_like(x) # for ix in xrange(len(x)): # out[ix,:] = (tauwb/TauWBMax)**wborder*x[ix,:]*10e3*maximum(ones(len(cf)),cf/5e3) # return out # gt_control1 = FunctionFilterbank(gt_control,wb_gain,cf=cf,tauwb=tauwb,TauWBMax=TauWBMax,wborder=wborder) max_temp = (tauwb/TauWBMax)**wborder*10e3*maximum(ones(len(cf)),cf/5e3) gt_control1 = FunctionFilterbank(gt_control,lambda x:x*max_temp) # first non linearity of control path NL1_control=FunctionFilterbank(gt_control1,boltzman,asym=ohcasym,s0=12.0,s1=5.0,x1=5.0) # #control low pass filter (its output will be used to control the signal path) LP_control = LowPass_filter(NL1_control,cf,600,1.0,2) # # second non linearity of control path NL2_control=FunctionFilterbank(LP_control,OHC_transduction,taumin=bmTaumin, taumax=bmTaumax, asym=ohcasym) # # #### C1 #### rsigma = zeros(len(cf)) c1_coefficients = Chirp_Coefficients(cf, bmTaumax, samplerate, rsigma,1*ones(len(cf))) [filt_b,filt_a] = c1_coefficients.return_coefficients() C1_filter = LinearFilterbank(source,filt_b,filt_a) C1_IHC = FunctionFilterbank(C1_filter,IHC_transduction,slope = 0.1,asym = ihcasym,sign=1) #### C2 #### c2_coefficients = Chirp_Coefficients(cf, bmTaumax, samplerate, 0*ones(len(cf)),1./ratiobm) [filt_b,filt_a] = c2_coefficients.return_coefficients() C2_filter = LinearFilterbank(source,filt_b,filt_a) gain_temp = cf**2/2e4 C2_IHC_pre = FunctionFilterbank(C2_filter,lambda x:x*abs(x)*gain_temp) C2_IHC = FunctionFilterbank(C2_IHC_pre,IHC_transduction,slope = 0.2,asym = 1.0,sign=-1) # C_IHC = C1_IHC + C2_IHC # C_IHC_lp = LowPass_filter(C_IHC,cf,3000,1.0,7) #controlers definition updater=Filter_Update([C1_filter],c1_coefficients,samplerate,cf,bmTaumax,bmTaumin,cohc,TauWBMax,TauWBMin) #instantiation of the updater for the control path output = ControlFilterbank(C1_filter, NL2_control, [C1_filter],updater, update_interval) #controler for the band pass filter of the control path self.set_output(output) brian-1.4.3/brian/hears/hrtf/000077500000000000000000000000001266062647100157645ustar00rootroot00000000000000brian-1.4.3/brian/hears/hrtf/__init__.py000066400000000000000000000000741266062647100200760ustar00rootroot00000000000000from hrtf import * from ircam import * from itd import * brian-1.4.3/brian/hears/hrtf/hrtf.py000066400000000000000000000247771266062647100173220ustar00rootroot00000000000000from brian import * from ..sounds import Sound from ..filtering import FIRFilterbank from copy import copy __all__ = ['HRTF', 'HRTFSet', 'HRTFDatabase', 'make_coordinates'] class HRTF(object): ''' Head related transfer function. **Attributes** ``impulse_response`` The pair of impulse responses (as stereo :class:`Sound` objects) ``fir`` The impulse responses in a format suitable for using with :class:`FIRFilterbank` (the transpose of ``impulse_response``). ``left``, ``right`` The two HRTFs (mono :class:`Sound` objects) ``samplerate`` The sample rate of the HRTFs. **Methods** .. automethod:: apply .. automethod:: filterbank You can get the number of samples in the impulse response with ``len(hrtf)``. ''' def __init__(self, hrir_l, hrir_r=None): if hrir_r is None: hrir = hrir_l else: hrir = Sound((hrir_l, hrir_r), samplerate=hrir_l.samplerate) self.samplerate = hrir.samplerate self.impulse_response = hrir self.left = hrir.left self.right = hrir.right def apply(self, sound): ''' Returns a stereo :class:`Sound` object formed by applying the pair of HRTFs to the mono ``sound`` input. Equivalently, you can write ``hrtf(sound)`` for ``hrtf`` an :class:`HRTF` object. ''' # Note we use an FFT based method for applying HRTFs that is # mathematically equivalent to using convolution (accurate to 1e-15 # in practice) and around 100x faster. if not sound.nchannels==1: raise ValueError('HRTF can only be applied to mono sounds') if len(unique(array([self.samplerate, sound.samplerate], dtype=int)))>1: raise ValueError('HRTF and sound samplerates do not match.') sound = asarray(sound).flatten() # Pad left/right/sound with zeros of length max(impulse response length) # at the beginning, and at the end so that they are all the same length # which should be a power of 2 for efficiency. The reason to pad at # the beginning is that the first output samples are not guaranteed to # be equal because of the delays in the impulse response, but they # exactly equalise after the length of the impulse response, so we just # zero pad. The reason for padding at the end is so that for the FFT we # can just multiply the arrays, which should have the same shape. left = asarray(self.left).flatten() right = asarray(self.right).flatten() ir_nmax = max(len(left), len(right)) nmax = max(ir_nmax, len(sound))+ir_nmax nmax = 2**int(ceil(log2(nmax))) leftpad = hstack((left, zeros(nmax-len(left)))) rightpad = hstack((right, zeros(nmax-len(right)))) soundpad = hstack((zeros(ir_nmax), sound, zeros(nmax-ir_nmax-len(sound)))) # Compute FFTs, multiply and compute IFFT left_fft = fft(leftpad, n=nmax) right_fft = fft(rightpad, n=nmax) sound_fft = fft(soundpad, n=nmax) left_sound_fft = left_fft*sound_fft right_sound_fft = right_fft*sound_fft left_sound = ifft(left_sound_fft).real right_sound = ifft(right_sound_fft).real # finally, we take only the unpadded parts of these left_sound = left_sound[ir_nmax:ir_nmax+len(sound)] right_sound = right_sound[ir_nmax:ir_nmax+len(sound)] return Sound((left_sound, right_sound), samplerate=self.samplerate) __call__ = apply def get_fir(self): return array(self.impulse_response.T, copy=True) fir = property(fget=get_fir) def filterbank(self, source, **kwds): ''' Returns an :class:`FIRFilterbank` object that can be used to apply the HRTF as part of a chain of filterbanks. ''' return FIRFilterbank(source, self.fir, **kwds) def __len__(self): return self.impulse_response.shape[0] def make_coordinates(**kwds): ''' Creates a numpy record array from the keywords passed to the function. Each keyword/value pair should be the name of the coordinate the array of values of that coordinate for each location. Returns a numpy record array. For example:: coords = make_coordinates(azimuth=[0, 30, 60, 0, 30, 60], elevation=[0, 0, 0, 30, 30, 30]) print coords['azimuth'] ''' dtype = [(name, float) for name in kwds.keys()] n = len(kwds.values()[0]) x = zeros(n, dtype=dtype) for name, values in kwds.items(): x[name] = values return x class HRTFSet(object): ''' A collection of HRTFs, typically for a single individual. Normally this object is created automatically by an :class:`HRTFDatabase`. **Attributes** ``hrtf`` A list of ``HRTF`` objects for each index. ``num_indices`` The number of HRTF locations. You can also use ``len(hrtfset)``. ``num_samples`` The sample length of each HRTF. ``fir_serial``, ``fir_interleaved`` The impulse responses in a format suitable for using with :class:`FIRFilterbank`, in serial (LLLLL...RRRRR....) or interleaved (LRLRLR...). **Methods** .. automethod:: subset .. automethod:: filterbank .. automethod:: get_index You can access an HRTF by index via ``hrtfset[index]``, or by its coordinates via ``hrtfset(coord1=val1, coord2=val2)``. **Initialisation** ``data`` An array of shape (2, num_indices, num_samples) where data[0,:,:] is the left ear and data[1,:,:] is the right ear, num_indices is the number of HRTFs for each ear, and num_samples is the length of the HRTF. ``samplerate`` The sample rate for the HRTFs (should have units of Hz). ``coordinates`` A record array of length ``num_indices`` giving the coordinates of each HRTF. You can use :func:`make_coordinates` to help with this. ''' def __init__(self, data, samplerate, coordinates): self.data = data self.samplerate = samplerate self.coordinates = coordinates self.hrtf = [] for i in xrange(self.num_indices): l = Sound(self.data[0, i, :], samplerate=self.samplerate) r = Sound(self.data[1, i, :], samplerate=self.samplerate) self.hrtf.append(HRTF(l, r)) def __getitem__(self, key): return self.hrtf[key] def get_index(self, **kwds): ''' Return the index of the HRTF with the coords specified by keyword. ''' I = ones(self.num_indices, dtype=bool) for key, value in kwds.items(): I = logical_and(I, abs(self.coordinates[key]-value)<1e-10) indices = I.nonzero()[0] if len(indices)==0: raise IndexError('No HRTF exists with those coordinates') if len(indices)>1: raise IndexError('More than one HRTF exists with those coordinates') return indices[0] def __call__(self, **kwds): return self.hrtf[self.get_index(**kwds)] def subset(self, condition): ''' Generates the subset of the set of HRTFs whose coordinates satisfy the ``condition``. This should be one of: a boolean array of length the number of HRTFs in the set, with values of True/False to indicate if the corresponding HRTF should be included or not; an integer array with the indices of the HRTFs to keep; or a function whose argument names are names of the parameters of the coordinate system, e.g. ``condition=lambda azim:azim`__. The database object can be initialised with the following arguments: ``basedir`` The directory where the database has been downloaded and extracted, e.g. ``r'D:\HRTF\IRCAM'``. Multiple directories in a list can be provided as well (e.g IRCAM and IRCAM New). ``compensated=False`` Whether to use the raw or compensated impulse responses. ``samplerate=None`` If specified, you can resample the impulse responses to a different samplerate, otherwise uses the default 44.1 kHz. The coordinates are pairs ``(azim, elev)`` where ``azim`` ranges from 0 to 345 degrees in steps of 15 degrees, and elev ranges from -45 to 90 in steps of 15 degrees. After loading the database, the attribute 'subjects' gives all the subjects number that were detected as installed. **Obtaining the database** The database can be downloaded `here `__. Each subject archive should be extracted to a folder (e.g. IRCAM) with the names of the subject, e.g. IRCAM/IRC_1002, etc. ''' def __init__(self, basedir, compensated=False, samplerate=None): if not isinstance(basedir, (list, tuple)): basedir = [basedir] self.basedir = basedir self.compensated = compensated names = [] for basedir in self.basedir: names += glob(os.path.join(basedir, 'IRC_*')) splitnames = [os.path.split(name) for name in names] p = re.compile('IRC_\d{4,4}') self.subjects = [int(name[4:8]) for base, name in splitnames if not (p.match(name[-8:]) is None)] if samplerate is not None: raise ValueError('Custom samplerate not supported.') self.samplerate = samplerate def load_subject(self, subject, rounddot5 = False): subject = str(subject) if subject[0] == '3': # this is the case only for stuffed animals recordings # IRC_30.. samplerate = 192*kHz else: samplerate = 44.1*kHz ok = False k = 0 while k < len(self.basedir) and not ok: try: filename = os.path.join(self.basedir[k], 'IRC_' + subject) if self.compensated: filename = os.path.join(filename, 'COMPENSATED/MAT/HRIR/IRC_' + subject + '_C_HRIR.mat') else: filename = os.path.join(filename, 'RAW/MAT/HRIR/IRC_' + subject + '_R_HRIR.mat') m = loadmat(filename, struct_as_record=True) ok = True except IOError: ok = False k += 1 if not ok: raise IOError("Couldn't find the HRTF files for subject "+str(subject)) if 'l_hrir_S' in m.keys(): # RAW DATA affix = '_hrir_S' else: # COMPENSATED DATA affix = '_eq_hrir_S' l, r = m['l' + affix], m['r' + affix] azim = l['azim_v'][0][0][:, 0] elev = l['elev_v'][0][0][:, 0] if len(azim) == len(elev) and len(azim) == 1: # it is the case with IRCAM_New db # - the coordinates are 1xN instead of Nx1 # - some measures that should be at the same elevation are # at very close but different elevations (7.47 # vs. 7.5). This is annoying for interpolation. Hence I # allow one to round the elevations conv = lambda x : x if rounddot5: conv = lambda x: np.round(2*x)/2 azim = conv(l['azim_v'][0][0][0, :]) elev = l['elev_v'][0][0][0, :] coords = make_coordinates(azim=azim, elev=elev) l = l['content_m'][0][0] r = r['content_m'][0][0] # self.data has shape (num_ears=2, num_indices, hrir_length) data = vstack((reshape(l, (1,) + l.shape), reshape(r, (1,) + r.shape))) hrtfset = HRTFSet(data, samplerate, coords) hrtfset.name = 'IRCAM_'+subject return hrtfset brian-1.4.3/brian/hears/hrtf/itd.py000066400000000000000000000076471266062647100171340ustar00rootroot00000000000000from brian import * from hrtf import * from ..prefs import get_samplerate from ..filtering.fractionaldelay import FractionalDelay from ..sounds import silence __all__ = ['HeadlessDatabase'] speed_of_sound_in_air = 343.2*metre/second class HeadlessDatabase(HRTFDatabase): ''' Database for creating HRTFSet with artificial interaural time-differences Initialisation keywords: ``n``, ``azim_max``, ``diameter`` Specify the ITDs for two ears separated by distance ``diameter`` with no head. ITDs corresponding to ``n`` angles equally spaced between ``-azim_max`` and ``azim_max`` are used. The default diameter is that which gives the maximum ITD as 650 microseconds. The ITDs are computed with the formula ``diameter*sin(azim)/speed_of_sound_in_air``. In this case, the generated :class:`HRTFSet` will have coordinates of ``azim`` and ``itd``. ``itd`` Instead of specifying the keywords above, just give the ITDs directly. In this case, the generated :class:`HRTFSet` will have coordinates of ``itd`` only. ``fractional_itds=False`` Set this to ``True`` to allow ITDs with a fractional multiple of the timestep ``1/samplerate``. Note that the filters used to do this are not perfect and so this will introduce a small amount of numerical error, and so shouldn't be used unless this level of timing precision is required. See :class:`FractionalDelay` for more details. To get the HRTFSet, the simplest thing to do is just:: hrtfset = HeadlessDatabase(13).load_subject() The generated ITDs can be returned using the ``itd`` attribute of the :class:`HeadlessDatabase` object. If ``fractional_itds=False`` then Note that the delays induced in the left and right channels are not symmetric as making them so wastes half the samplerate (if the delay to the left channel is itd/2 and the delay to the right channel is -itd/2). Instead, for each channel either the left channel delay is 0 and the right channel delay is -itd (if itd<0) or the left channel delay is itd and the right channel delay is 0 (if itd>0). If ``fractional_itds=True`` then delays in the left and right channels will be symmetric around a global offset of ``delay_offset``. ''' def __init__(self, n=None, azim_max=pi/2, diameter=speed_of_sound_in_air*650*usecond, itd=None, samplerate=None, fractional_itds=False): if itd is None: azim = linspace(-azim_max, azim_max, n) itd = diameter*sin(azim)/speed_of_sound_in_air coords = make_coordinates(azim=azim, itd=itd) else: coords = make_coordinates(itd=itd) self.itd = itd samplerate = self.samplerate = get_samplerate(samplerate) if not fractional_itds: dl = itd.copy() dr = -itd dl[dl<0] = 0 dr[dr<0] = 0 dl = array(rint(dl*samplerate), dtype=int) dr = array(rint(dr*samplerate), dtype=int) idxmax = max(amax(dl), amax(dr)) data = zeros((2, len(itd), idxmax+1)) data[0, arange(len(itd)), dl] = 1 data[1, arange(len(itd)), dr] = 1 else: delays = hstack((itd/2, -itd/2)) fd = FractionalDelay(silence(1*ms, samplerate=samplerate), delays) ir = fd.impulse_response data = zeros((2, len(itd), fd.filter_length)) data[0, :, :] = ir[:len(itd), :] data[1, :, :] = ir[len(itd):, :] self.delay_offset = fd.delay_offset self.hrtfset = HRTFSet(data, samplerate, coords) self.hrtfset.name = 'ITDDatabaseSubject' self.subjects = ['0'] def load_subject(self, subject='0'): return self.hrtfset brian-1.4.3/brian/hears/onlinesounds.py000066400000000000000000000066621266062647100201250ustar00rootroot00000000000000# TODO: update all of this with the new interface/buffering mechanism # TODO: decide on a good interface for online sounds, that is general from brian import * from numpy import * import numpy import wave import array as pyarray import time try: import pygame have_pygame = True except ImportError: have_pygame = False try: from scikits.samplerate import resample have_scikits_samplerate = True except (ImportError, ValueError): have_scikits_samplerate = False from bufferable import Bufferable from sounds import Sound, BaseSound __all__ = ['OnlineSound', 'OnlineWhiteNoise', 'OnlineWhiteNoiseBuffered', 'OnlineWhiteNoiseShifted', ] class OnlineSound(BaseSound): def __init__(self): pass def update(self): pass class OnlineWhiteNoise(OnlineSound): ''' Noise generator which produces one sample at a time online input parameters are the mean mu and the variance sigma default mu=0, sigma=1 ''' def __init__(self,mu=None,sigma=None,tomux=1): if mu==None: self.mu=0 if sigma==None: self.sigma=1 self.tomux=tomux self.nchannels = 1 def buffer_init(self): pass def buffer_fetch(self, start, end): samples = end-start temp = (self.mu+sqrt(self.sigma)*randn(samples))*self.tomux temp = temp.reshape(len(temp),1) return temp class OnlineWhiteNoiseBuffered(OnlineSound): def __init__(self,samplerate,mu,sigma,max_abs_itd): self.samplerate=samplerate self.length_buffer=int(max_abs_itd * self.samplerate) self.mu=mu self.sigma=sigma self.buffer=[0]*(2*self.length_buffer+1) def update(self): self.buffer.pop() self.buffer.insert(0,self.mu+self.sigma*randn(1)) return self.buffer[self.length_buffer] class OnlineWhiteNoiseShifted(OnlineSound): def __init__(self,samplerate,online_white_noise_buffered,shift=lambda:randn(1)*ms,time_interval=-1*ms): #self.shift_applied=[] self.samplerate=samplerate self.interval_in_sample= int(time_interval *self.samplerate) #print self.interval_in_sample self.count=0 self.shift=shift self.length_buffer=online_white_noise_buffered.length_buffer self.shift_in_sample=int(shift()* self.samplerate) if abs(self.shift_in_sample) > self.length_buffer: self.shift_in_sample=sign(self.shift_in_sample)*self.length_buffer self.ITDused=[] self.ITDused.append(self.shift_in_sample/self.samplerate) self.reference=online_white_noise_buffered def update(self): if self.count ==self.interval_in_sample: self.shift_in_sample=int(self.shift()* self.samplerate) if abs(self.shift_in_sample) > self.length_buffer: self.shift_in_sample=sign(self.shift_in_sample)*self.length_buffer self.ITDused.append(self.shift_in_sample/self.samplerate) #print self.shift() self.count=0 self.count=self.count+1 # print self.length_buffer # print self.shift_in_sample # print self.length_buffer+1+self.shift_in_sample return self.reference.buffer[self.length_buffer+self.shift_in_sample] brian-1.4.3/brian/hears/plotting.py000066400000000000000000000062051266062647100172360ustar00rootroot00000000000000from brian import * from erb import * __all__ = ['log_frequency_xaxis_labels', 'log_frequency_yaxis_labels'] def log_frequency_xaxis_labels(ax=None, freqs=None): ''' Sets tick positions for log-scale frequency x-axis at sensible locations. Also uses scalar representation rather than exponential (i.e. 100 rather than 10^2). ``ax=None`` The axis to set, or uses ``gca()`` if ``None``. ``freqs=None`` Override the default frequency locations with your preferred tick locations. See also: :func:`log_frequency_yaxis_labels`. Note: with log scaled axes, it can be useful to call ``axis('tight')`` before setting the ticks. ''' if ax is None: ax = gca() return log_frequency_axis_labels(ax.xaxis, freqs=freqs) def log_frequency_yaxis_labels(ax=None, freqs=None): ''' Sets tick positions for log-scale frequency x-axis at sensible locations. Also uses scalar representation rather than exponential (i.e. 100 rather than 10^2). ``ax=None`` The axis to set, or uses ``gca()`` if ``None``. ``freqs=None`` Override the default frequency locations with your preferred tick locations. See also: :func:`log_frequency_yaxis_labels`. Note: with log scaled axes, it can be useful to call ``axis('tight')`` before setting the ticks. ''' if ax is None: ax = gca() return log_frequency_axis_labels(ax.yaxis, freqs=freqs) def log_frequency_axis_labels(ax, freqs=None): if freqs is not None: ax.set_major_locator(FixedLocator(freqs)) ax.set_major_formatter(ScalarFormatter()) ax.set_minor_locator(NullLocator()) return xmin, xmax = ax.get_view_interval() # we use the first of these ranges that the data fits within allowed_ranges = [[1, 2, 4, 8, 16, 32, 64], [50, 75, 100, 150, 200, 300, 400], [10, 20, 40, 80, 160, 320], [1, 2, 4, 8, 16, 32, 64, 100, 200, 400, 800], [125, 250, 500, 1000, 2000, 4000, 8000, 16000], [100, 250, 500, 1000, 2500, 5000, 10000, 25000, 50000], ] found = False for R in allowed_ranges: if xmin>=amin(R)*0.9999 and xmax<=amax(R)*1.0001: found = True break if not found: ax.set_major_locator(LogLocator(base=2)) else: ax.set_major_locator(FixedLocator(R)) ax.set_major_formatter(ScalarFormatter()) ax.set_minor_locator(NullLocator()) if __name__=='__main__': for i, cfs in enumerate([erbspace(150*Hz, 5*kHz, 100), erbspace(2*Hz, 64*Hz, 100), erbspace(100*Hz, 10*kHz, 100), erbspace(100*Hz, 400*Hz, 100), ]): subplot(2, 2, i+1) #cfs = erbspace(150*Hz, 5*kHz, 100) semilogx(cfs, 1-((arange(len(cfs))-len(cfs)/2.0)/(len(cfs)/2.0))**2) axis('tight') log_frequency_xaxis_labels() show() brian-1.4.3/brian/hears/prefs.py000066400000000000000000000007141266062647100165140ustar00rootroot00000000000000from brian import * __all__ = ['get_samplerate', 'set_default_samplerate'] default_samplerate = 44.1*kHz def get_samplerate(samplerate): if samplerate is None: return default_samplerate else: return samplerate def set_default_samplerate(samplerate): ''' Sets the default samplerate for Brian hears objects, by default 44.1 kHz. ''' global default_samplerate default_samplerate = samplerate brian-1.4.3/brian/hears/sounds.py000066400000000000000000001361411266062647100167140ustar00rootroot00000000000000from brian import * from numpy import * import numpy import array as pyarray import time import struct try: import pygame have_pygame = True except ImportError: have_pygame = False try: from scikits.samplerate import resample have_scikits_samplerate = True except (ImportError, ValueError): have_scikits_samplerate = False from bufferable import Bufferable from prefs import get_samplerate from db import dB, dB_type, dB_error, gain from scipy.signal import fftconvolve, lfilter from scipy.misc import factorial __all__ = ['BaseSound', 'Sound', 'pinknoise','brownnoise','powerlawnoise', 'whitenoise', 'irns', 'irno', 'tone', 'click', 'clicks', 'silence', 'sequence', 'harmoniccomplex', 'loadsound', 'savesound', 'play', 'vowel' ] _mixer_status = [-1,-1] class BaseSound(Bufferable): ''' Base class for Sound and OnlineSound ''' pass class Sound(BaseSound, numpy.ndarray): ''' Class for working with sounds, including loading/saving, manipulating and playing. For an overview, see :ref:`sounds_overview`. **Initialisation** The following arguments are used to initialise a sound object ``data`` Can be a filename, an array, a function or a sequence (list or tuple). If its a filename, the sound file (WAV or AIFF) will be loaded. If its an array, it should have shape ``(nsamples, nchannels)``. If its a function, it should be a function f(t). If its a sequence, the items in the sequence can be filenames, functions, arrays or Sound objects. The output will be a multi-channel sound with channels the corresponding sound for each element of the sequence. ``samplerate=None`` The samplerate, if necessary, will use the default (for an array or function) or the samplerate of the data (for a filename). ``duration=None`` The duration of the sound, if initialising with a function. **Loading, saving and playing** .. automethod:: load .. automethod:: save .. automethod:: play **Properties** .. autoattribute:: duration .. autoattribute:: nsamples .. autoattribute:: nchannels .. autoattribute:: times .. autoattribute:: left .. autoattribute:: right .. automethod:: channel **Generating sounds** All sound generating methods can be used with durations arguments in samples (int) or units (e.g. 500*ms). One can also set the number of channels by setting the keyword argument nchannels to the desired value. Notice that for noise the channels will be generated independantly. .. automethod:: tone .. automethod:: whitenoise .. automethod:: powerlawnoise .. automethod:: brownnoise .. automethod:: pinknoise .. automethod:: silence .. automethod:: click .. automethod:: clicks .. automethod:: harmoniccomplex .. automethod:: vowel **Timing and sequencing** .. automethod:: sequence(*sounds, samplerate=None) .. automethod:: repeat .. automethod:: extended .. automethod:: shifted .. automethod:: resized **Slicing** One can slice sound objects in various ways, for example ``sound[100*ms:200*ms]`` returns the part of the sound between 100 ms and 200 ms (not including the right hand end point). If the sound is less than 200 ms long it will be zero padded. You can also set values using slicing, e.g. ``sound[:50*ms] = 0`` will silence the first 50 ms of the sound. The syntax is the same as usual for Python slicing. In addition, you can select a subset of the channels by doing, for example, ``sound[:, -5:]`` would be the last 5 channels. For time indices, either times or samples can be given, e.g. ``sound[:100]`` gives the first 100 samples. In addition, steps can be used for example to reverse a sound as ``sound[::-1]``. **Arithmetic operations** Standard arithemetical operations and numpy functions work as you would expect with sounds, e.g. ``sound1+sound2``, ``3*sound`` or ``abs(sound)``. **Level** .. autoattribute:: level .. automethod:: atlevel .. autoattribute:: maxlevel .. automethod:: atmaxlevel **Ramping** .. automethod:: ramp .. automethod:: ramped **Plotting** .. automethod:: spectrogram .. automethod:: spectrum ''' duration = property(fget=lambda self:len(self) / self.samplerate, doc='The length of the sound in seconds.') nsamples = property(fget=lambda self:len(self), doc='The number of samples in the sound.') times = property(fget=lambda self:arange(len(self), dtype=float) / self.samplerate, doc='An array of times (in seconds) corresponding to each sample.') nchannels = property(fget=lambda self:self.shape[1], doc='The number of channels in the sound.') left = property(fget=lambda self:self.channel(0), doc='The left channel for a stereo sound.') right = property(fget=lambda self:self.channel(1), doc='The right channel for a stereo sound.') @check_units(samplerate=Hz, duration=second) def __new__(cls, data, samplerate=None, duration=None): if isinstance(data, numpy.ndarray): samplerate = get_samplerate(samplerate) # if samplerate is None: # raise ValueError('Must specify samplerate to initialise Sound with array.') if duration is not None: raise ValueError('Cannot specify duration when initialising Sound with array.') x = array(data, dtype=float) elif isinstance(data, str): if duration is not None: raise ValueError('Cannot specify duration when initialising Sound from file.') if samplerate is not None: raise ValueError('Cannot specify samplerate when initialising Sound from a file.') x = Sound.load(data) samplerate = x.samplerate elif callable(data): samplerate = get_samplerate(samplerate) # if samplerate is None: # raise ValueError('Must specify samplerate to initialise Sound with function.') if duration is None: raise ValueError('Must specify duration to initialise Sound with function.') L = int(rint(duration * samplerate)) t = arange(L, dtype=float) / samplerate x = data(t) elif isinstance(data, (list, tuple)): kwds = {} if samplerate is not None: kwds['samplerate'] = samplerate if duration is not None: kwds['duration'] = duration channels = tuple(Sound(c, **kwds) for c in data) x = hstack(channels) samplerate = channels[0].samplerate else: raise TypeError('Cannot initialise Sound with data of class ' + str(data.__class__)) if len(x.shape)==1: x.shape = (len(x), 1) x = x.view(cls) x.samplerate = samplerate x.buffer_init() return x def __array_wrap__(self, obj, context=None): handled = False x = numpy.ndarray.__array_wrap__(self, obj, context) if not hasattr(x, 'samplerate') and hasattr(self, 'samplerate'): x.samplerate = self.samplerate if context is not None: ufunc = context[0] args = context[1] return x def __array_finalize__(self,obj): if obj is None: return self.samplerate = getattr(obj, 'samplerate', None) def buffer_init(self): pass def buffer_fetch(self, start, end): if start<0: raise IndexError('Can only use positive indices in buffer.') samples = end-start X = asarray(self)[start:end, :] if X.shape[0] int(self.samplerate): self = self.resample(other.samplerate) elif int(other.samplerate) < int(self.samplerate): other = other.resample(self.samplerate) if len(self) > len(other): other = other.resized(len(self)) elif len(self) < len(other): self = self.resized(len(other)) return Sound(numpy.ndarray.__add__(self, other), samplerate=self.samplerate) else: x = numpy.ndarray.__add__(self, other) return Sound(x, self.samplerate) __radd__ = __add__ # getslice and setslice need to be implemented for compatibility reasons, # but __getitem__ covers all the functionality so we just use that def __getslice__(self, start, stop): return self.__getitem__(slice(start, stop)) def __setslice__(self, start, stop, seq): return self.__setitem__(slice(start, stop), seq) def __getitem__(self,key): channel = slice(None) if isinstance(key, tuple): channel = key[1] key = key[0] if isinstance(key, int): return np.ndarray.__getitem__(self, key) if isinstance(key, float): return np.ndarray.__getitem__(self, round(key*self.samplerate)) sliceattr = [v for v in [key.start, key.stop] if v is not None] slicedims = array([units.have_same_dimensions(flag, second) for flag in sliceattr]) attrisint = array([isinstance(v, int) for v in sliceattr]) s = sum(attrisint) if s!=0 and s!=len(sliceattr): raise ValueError('Slice attributes must be all ints or all times') if s==len(sliceattr): # all ints start = key.start or 0 stop = key.stop or self.shape[0] step = key.step or 1 if start>=0 and stop<=self.shape[0]: return Sound(np.ndarray.__getitem__(self, (key, channel)), self.samplerate) else: startpad = max(-start, 0) endpad = max(stop-self.shape[0], 0) startmid = max(start, 0) endmid = min(stop, self.shape[0]) atstart = zeros((startpad, self.shape[1])) atend = zeros((endpad, self.shape[1])) return Sound(vstack((atstart, asarray(self)[startmid:endmid:step], atend)), self.samplerate) if not slicedims.all(): raise DimensionMismatchError('Slicing', *[units.get_unit(d) for d in sliceattr]) start = key.start or 0*msecond stop = key.stop or self.duration step = key.step or 1 if int(step)!=step: #resampling raise NotImplementedError start = int(rint(start*self.samplerate)) stop = int(rint(stop*self.samplerate)) return self.__getitem__((slice(start,stop,step),channel)) def __setitem__(self,key,value): channel=slice(None) if isinstance(key,tuple): channel=key[1] key=key[0] if isinstance(key,int): return np.ndarray.__setitem__(self,(key,channel),value) if isinstance(key,float): return np.ndarray.__setitem__(self,(int(rint(key*self.samplerate)),channel),value) sliceattr = [v for v in [key.start, key.step, key.stop] if v is not None] slicedims = array([units.have_same_dimensions(flag, second) for flag in sliceattr]) attrisint = array([isinstance(v, int) for v in sliceattr]) s = sum(attrisint) if s!=0 and s!=len(sliceattr): raise ValueError('Slice attributes must be all ints or all times') if s==len(sliceattr): # all ints # If value is a mono sound its shape will be (N, 1) but the numpy # setitem will have shape (N,) so in this case it's a shape mismatch # so we squeeze the array to make sure this doesn't happen. if isinstance(value,Sound) and channel!=slice(None): value=value.squeeze() return asarray(self).__setitem__((key,channel),value) # returns None if not slicedims.all(): raise DimensionMismatchError('Slicing',*[units.get_unit(d) for d in sliceattr]) if key.__getattribute__('step') is not None: # resampling? raise NotImplementedError start = key.start stop = key.stop or self.duration if (start is not None and start<0*ms) or stop > self.duration: raise IndexError('Slice bigger than Sound object') if start is not None: start = int(rint(start*self.samplerate)) stop = int(rint(stop*self.samplerate)) return self.__setitem__((slice(start,stop),channel),value) def extended(self, duration): ''' Returns the Sound with length extended by the given duration, which can be the number of samples or a length of time in seconds. ''' duration = get_duration(duration, self.samplerate) return self[:self.nsamples+duration] def resized(self, L): ''' Returns the Sound with length extended (or contracted) to have L samples. ''' if L == len(self): return self elif L < len(self): return Sound(self[:L, :], samplerate=self.samplerate) else: padding = zeros((L - len(self), self.nchannels)) return Sound(concatenate((self, padding)), samplerate=self.samplerate) def shifted(self, duration, fractional=False, filter_length=2048): ''' Returns the sound delayed by duration, which can be the number of samples or a length of time in seconds. Normally, only integer numbers of samples will be used, but if ``fractional=True`` then the filtering method from `http://www.labbookpages.co.uk/audio/beamforming/fractionalDelay.html `__ will be used (introducing some small numerical errors). With this method, you can specify the ``filter_length``, larger values are slower but more accurate, especially at higher frequencies. The large default value of 2048 samples provides good accuracy for sounds with frequencies above 20 Hz, but not for lower frequency sounds. If you are restricted to high frequency sounds, a smaller value will be more efficient. Note that if ``fractional=True`` then ``duration`` is assumed to be a time not a number of samples. ''' if not fractional: if not isinstance(duration, int): duration = int(rint(duration*self.samplerate)) if duration>=0: y = vstack((zeros((duration, self.nchannels)), self)) return Sound(y, samplerate=self.samplerate) else: return self[-duration:, :] else: if self.nchannels>1: sounds = [self.channel(i).shifted(duration, fractional=True, filter_length=filter_length) for i in xrange(self.nchannels)] return Sound(hstack(sounds), samplerate=self.samplerate) # Adapted from # http://www.labbookpages.co.uk/audio/beamforming/fractionalDelay.html delay = duration*self.samplerate if delay>=0: idelay = int(delay) elif delay<0: idelay = -int(-delay) delay -= idelay centre_tap = filter_length // 2 t = arange(filter_length) x = t-delay if abs(round(delay)-delay)<1e-10: tap_weight = array(x==centre_tap, dtype=float) else: sinc = sin(pi*(x-centre_tap))/(pi*(x-centre_tap)) window = 0.54-0.46*cos(2.0*pi*(x+0.5)/filter_length) # Hamming window tap_weight = window*sinc if filter_length<256: y = convolve(tap_weight, self.flatten()) else: y = fftconvolve(tap_weight, self.flatten()) y = y[filter_length/2:-filter_length/2] sound = Sound(y, self.samplerate) sound = sound.shifted(idelay) return sound def repeat(self, n): ''' Repeats the sound n times ''' x = vstack((self,)*n) return Sound(x, samplerate=self.samplerate) ### TODO: test this - I haven't installed scikits.samplerate on windows # it should work, according to the documentation 2D arrays are acceptable # in the format we use fof sounds here @check_units(samplerate=Hz) def resample(self, samplerate, resample_type='sinc_best'): ''' Returns a resampled version of the sound. ''' if not have_scikits_samplerate: raise ImportError('Need scikits.samplerate package for resampling') y = array(resample(self, float(samplerate / self.samplerate), resample_type), dtype=float64) return Sound(y, samplerate=samplerate) def _init_mixer(self): global _mixer_status if _mixer_status==[-1,-1] or _mixer_status[0]!= self.nchannels or _mixer_status[1] != self.samplerate: pygame.mixer.quit() pygame.mixer.init(int(self.samplerate), -16, self.nchannels) _mixer_status=[self.nchannels,self.samplerate] def play(self, normalise=False, sleep=False): ''' Plays the sound (normalised to avoid clipping if required). If sleep=True then the function will wait until the sound has finished playing before returning. ''' if self.nchannels>2: raise ValueError("Can only play sounds with 1 or 2 channels.") self._init_mixer() if normalise: a = amax(abs(self)) else: a = 1 x = array((2 ** 15 - 1) * clip(self / a, -1, 1), dtype=int16) if self.nchannels==1: x.shape = x.size # Make sure pygame receives an array in C-order x = pygame.sndarray.make_sound(ascontiguousarray(x)) x.play() if sleep: time.sleep(self.duration) def spectrogram(self, low=None, high=None, log_power=True, other = None, **kwds): ''' Plots a spectrogram of the sound Arguments: ``low=None``, ``high=None`` If these are left unspecified, it shows the full spectrogram, otherwise it shows only between ``low`` and ``high`` in Hz. ``log_power=True`` If True the colour represents the log of the power. ``**kwds`` Are passed to Pylab's ``specgram`` command. Returns the values returned by pylab's ``specgram``, namely ``(pxx, freqs, bins, im)`` where ``pxx`` is a 2D array of powers, ``freqs`` is the corresponding frequencies, ``bins`` are the time bins, and ``im`` is the image axis. ''' if self.nchannels>1: raise ValueError('Can only plot spectrograms for mono sounds.') if other is not None: x = self.flatten()-other.flatten() else: x = self.flatten() pxx, freqs, bins, im = specgram(x, Fs=self.samplerate, **kwds) if low is not None or high is not None: restricted = True if low is None: low = 0*Hz if high is None: high = amax(freqs)*Hz I = logical_and(low <= freqs, freqs <= high) I2 = where(I)[0] I2 = [max(min(I2) - 1, 0), min(max(I2) + 1, len(freqs) - 1)] Z = pxx[I2[0]:I2[-1], :] else: restricted = False Z = pxx if log_power: Z[Z < 1e-20] = 1e-20 # no zeros because we take logs Z = 10 * log10(Z) Z = flipud(Z) if restricted: imshow(Z, extent=(0, amax(bins), freqs[I2[0]], freqs[I2[-1]]), origin='upper', aspect='auto') else: imshow(Z, extent=(0, amax(bins), freqs[0], freqs[-1]), origin='upper', aspect='auto') xlabel('Time (s)') ylabel('Frequency (Hz)') return (pxx, freqs, bins, im) @check_units(low=Hz, high=Hz) def spectrum(self, low=None, high=None, log_power=True, display=False): ''' Returns the spectrum of the sound and optionally plots it. Arguments: ``low``, ``high`` If these are left unspecified, it shows the full spectrum, otherwise it shows only between ``low`` and ``high`` in Hz. ``log_power=True`` If True it returns the log of the power. ``display=False`` Whether to plot the output. Returns ``(Z, freqs, phase)`` where ``Z`` is a 1D array of powers, ``freqs`` is the corresponding frequencies, ``phase`` is the unwrapped phase of spectrum. ''' if self.nchannels>1: raise ValueError('Can only plot spectrum for mono sounds.') # Flatten array, fft operates on the last axis by default sp = numpy.fft.fft(array(self).flatten()) freqs = array(range(len(sp)), dtype=float64) / len(sp) * float64(self.samplerate) pxx = abs(sp) ** 2 phase = unwrap(mod(angle(sp), 2 * pi)) if low is not None or high is not None: restricted = True if low is None: low = 0*Hz if high is None: high = amax(freqs)*Hz I = logical_and(low <= freqs, freqs <= high) I2 = where(I)[0] Z = pxx[I2] freqs = freqs[I2] phase = phase[I2] else: restricted = False Z = pxx if log_power: Z[Z < 1e-20] = 1e-20 # no zeros because we take logs Z = 10 * log10(Z) if display: subplot(211) semilogx(freqs, Z) ticks_freqs = 32000 * 2 ** -array(range(18), dtype=float64) xticks(ticks_freqs, map(str, ticks_freqs)) grid() xlim((freqs[0], freqs[-1])) xlabel('Frequency (Hz)') ylabel('Power (dB/Hz)') if log_power else ylabel('Power') subplot(212) semilogx(freqs, phase) ticks_freqs = 32000 * 2 ** -array(range(18), dtype=float64) xticks(ticks_freqs, map(str, ticks_freqs)) grid() xlim((freqs[0], freqs[-1])) xlabel('Frequency (Hz)') ylabel('Phase (rad)') show() return (Z, freqs, phase) def get_level(self): ''' Returns level in dB SPL (RMS) assuming array is in Pascals. In the case of multi-channel sounds, returns an array of levels for each channel, otherwise returns a float. ''' if self.nchannels==1: rms_value = sqrt(mean((asarray(self)-mean(asarray(self)))**2)) rms_dB = 20.0*log10(rms_value/2e-5) return rms_dB*dB else: return array(tuple(self.channel(i).get_level() for i in xrange(self.nchannels))) def set_level(self, level): ''' Sets level in dB SPL (RMS) assuming array is in Pascals. ``level`` should be a value in dB, or a tuple of levels, one for each channel. ''' rms_dB = self.get_level() if self.nchannels>1: level = array(level) if level.size==1: level = level.repeat(self.nchannels) level = reshape(level, (1, self.nchannels)) rms_dB = reshape(rms_dB, (1, self.nchannels)) else: if not isinstance(level, dB_type): raise dB_error('Must specify level in dB') rms_dB = float(rms_dB) level = float(level) gain = 10**((level-rms_dB)/20.) self *= gain level = property(fget=get_level, fset=set_level, doc=''' Can be used to get or set the level of a sound, which should be in dB. For single channel sounds a value in dB is used, for multiple channel sounds a value in dB can be used for setting the level (all channels will be set to the same level), or a list/tuple/array of levels. It is assumed that the unit of the sound is Pascals. ''') def atlevel(self, level): ''' Returns the sound at the given level in dB SPL (RMS) assuming array is in Pascals. ``level`` should be a value in dB, or a tuple of levels, one for each channel. ''' newsound = self.copy() newsound.level = level return newsound def get_maxlevel(self): return amax(self.level)*dB def set_maxlevel(self, level): self.level += level-self.maxlevel maxlevel = property(fget=get_maxlevel, fset=set_maxlevel, doc=''' Can be used to set or get the maximum level of a sound. For mono sounds, this is the same as the level, but for multichannel sounds it is the maximum level across the channels. Relative level differences will be preserved. The specified level should be a value in dB, and it is assumed that the unit of the sound is Pascals. ''') def atmaxlevel(self, level): ''' Returns the sound with the maximum level across channels set to the given level. Relative level differences will be preserved. The specified level should be a value in dB and it is assumed that the unit of the sound is Pascals. ''' newsound = self.copy() newsound.maxlevel = level return newsound def ramp(self, when='onset', duration=10*ms, envelope=None, inplace=True): ''' Adds a ramp on/off to the sound ``when='onset'`` Can take values 'onset', 'offset' or 'both' ``duration=10*ms`` The time over which the ramping happens ``envelope`` A ramping function, if not specified uses ``sin(pi*t/2)**2``. The function should be a function of one variable ``t`` ranging from 0 to 1, and should increase from ``f(0)=0`` to ``f(0)=1``. The reverse is applied for the offset ramp. ``inplace`` Whether to apply ramping to current sound or return a new array. ''' when = when.lower().strip() if envelope is None: envelope = lambda t:sin(pi * t / 2) ** 2 if not isinstance(duration, int): sz = int(rint(duration * self.samplerate)) else: sz = duration multiplier = envelope(reshape(linspace(0.0, 1.0, sz), (sz, 1))) if inplace: target = self else: target = Sound(copy(self), self.samplerate) if when == 'onset' or when == 'both': target[:sz, :] *= multiplier if when == 'offset' or when == 'both': target[target.nsamples-sz:, :] *= multiplier[::-1] return target @check_units(duration=second) def ramped(self, when='onset', duration=10*ms, envelope=None): ''' Returns a ramped version of the sound (see :meth:`Sound.ramp`). ''' return self.ramp(when=when, duration=duration, envelope=envelope, inplace=False) def fft(self,n=None): ''' Performs an n-point FFT on the sound object, that is an array of the same size containing the DFT of each channel. n defaults to the number of samples of the sound, but can be changed manually setting the ``n`` keyword argument ''' if n is None: n=self.shape[0] res=zeros(n,self.nchannels) for i in range(self.nchannels): res[:,i]=fft(asarray(self)[:,i].flatten(),n=n) return res @staticmethod @check_units(frequency=Hz, duration=second, samplerate=Hz) def tone(frequency, duration, phase=0, samplerate=None, nchannels=1): ''' Returns a pure tone at frequency for duration, using the default samplerate or the given one. The ``frequency`` and ``phase`` parameters can be single values, in which case multiple channels can be specified with the ``nchannels`` argument, or they can be sequences (lists/tuples/arrays) in which case there is one frequency or phase for each channel. ''' samplerate = get_samplerate(samplerate) duration = get_duration(duration,samplerate) frequency = array(frequency) phase = array(phase) if frequency.size>nchannels and nchannels==1: nchannels = frequency.size if phase.size>nchannels and nchannels==1: nchannels = phase.size if frequency.size==nchannels: frequency.shape = (1, nchannels) if phase.size==nchannels: phase.shape =(nchannels, 1) t = arange(0, duration, 1)/samplerate t.shape = (t.size, 1) # ensures C-order (in contrast to tile(...).T ) x = sin(phase + 2.0 * pi * frequency * tile(t, (1, nchannels))) return Sound(x, samplerate) @staticmethod @check_units(f0=Hz, duration=second, samplerate=Hz) def harmoniccomplex(f0, duration, amplitude=1, phase=0, samplerate=None, nchannels=1): ''' Returns a harmonic complex composed of pure tones at integer multiples of the fundamental frequency ``f0``. The ``amplitude`` and ``phase`` keywords can be set to either a single value or an array of values. In the former case the value is set for all harmonics, and harmonics up to the sampling frequency are generated. In the latter each harmonic parameter is set separately, and the number of harmonics generated corresponds to the length of the array. ''' samplerate=get_samplerate(samplerate) phases = np.array(phase).flatten() amplitudes = np.array(amplitude).flatten() if len(phases)>1 or len(amplitudes)>1: if (len(phases)>1 and len(amplitudes)>1) and (len(phases) != len(amplitudes)): raise ValueError('Please specify the same number of phases and amplitudes') Nharmonics = max(len(phases),len(amplitudes)) else: Nharmonics = int(np.floor( samplerate/(2*f0) ) ) if len(phases) == 1: phases = np.tile(phase, Nharmonics) if len(amplitudes) == 1: amplitudes = np.tile(amplitude, Nharmonics) x = amplitudes[0]*tone(f0, duration, phase = phases[0], samplerate = samplerate, nchannels = nchannels) for i in range(1,Nharmonics): x += amplitudes[i]*tone((i+1)*f0, duration, phase = phases[i], samplerate = samplerate, nchannels = nchannels) return Sound(x,samplerate) @staticmethod @check_units(duration=second, samplerate=Hz) def whitenoise(duration, samplerate=None, nchannels=1): ''' Returns a white noise. If the samplerate is not specified, the global default value will be used. ''' samplerate = get_samplerate(samplerate) duration = get_duration(duration,samplerate) x = randn(duration,nchannels) return Sound(x, samplerate) @staticmethod @check_units(duration=second, samplerate=Hz) def powerlawnoise(duration, alpha, samplerate=None, nchannels=1,normalise=False): ''' Returns a power-law noise for the given duration. Spectral density per unit of bandwidth scales as 1/(f**alpha). Sample usage:: noise = powerlawnoise(200*ms, 1, samplerate=44100*Hz) Arguments: ``duration`` Duration of the desired output. ``alpha`` Power law exponent. ``samplerate`` Desired output samplerate ''' samplerate = get_samplerate(samplerate) duration = get_duration(duration,samplerate) # Adapted from http://www.eng.ox.ac.uk/samp/software/powernoise/powernoise.m # Little MA et al. (2007), "Exploiting nonlinear recurrence and fractal # scaling properties for voice disorder detection", Biomed Eng Online, 6:23 n=duration n2=int(n/2) f=array(fftfreq(n,d=1.0/samplerate), dtype=complex) f.shape=(len(f),1) f=tile(f,(1,nchannels)) if n%2==1: z=(randn(n2,nchannels)+1j*randn(n2,nchannels)) a2=1.0/( f[1:(n2+1),:]**(alpha/2.0)) else: z=(randn(n2-1,nchannels)+1j*randn(n2-1,nchannels)) a2=1.0/(f[1:n2,:]**(alpha/2.0)) a2*=z if n%2==1: d=vstack((ones((1,nchannels)),a2, flipud(conj(a2)))) else: d=vstack((ones((1,nchannels)),a2, 1.0/( abs(f[n2])**(alpha/2.0) )* randn(1,nchannels), flipud(conj(a2)))) x=real(ifft(d.flatten())) x.shape=(n,nchannels) if normalise: for i in range(nchannels): #x[:,i]=normalise_rms(x[:,i]) x[:,i] = ((x[:,i] - amin(x[:,i]))/(amax(x[:,i]) - amin(x[:,i])) - 0.5) * 2; return Sound(x,samplerate) @staticmethod @check_units(duration=second, samplerate=Hz) def pinknoise(duration, samplerate=None, nchannels=1, normalise=False): ''' Returns pink noise, i.e :func:`powerlawnoise` with alpha=1 ''' return Sound.powerlawnoise(duration, 1.0, samplerate=samplerate, nchannels=nchannels, normalise=normalise) @staticmethod @check_units(duration=second, samplerate=Hz) def brownnoise(duration, samplerate=None, nchannels=1, normalise=False): ''' Returns brown noise, i.e :func:`powerlawnoise` with alpha=2 ''' return Sound.powerlawnoise(duration, 2.0, samplerate=samplerate, nchannels=nchannels, normalise=normalise) @staticmethod @check_units(duration=second, samplerate=Hz) def irns(delay, gain, niter, duration, samplerate=None, nchannels=1): ''' Returns an IRN_S noise. The iterated ripple noise is obtained trough a cascade of gain and delay filtering. For more details: see Yost 1996 or chapter 15 in Hartman Sound Signal Sensation. ''' if nchannels!=1: raise ValueError("nchannels!=1 not supported.") samplerate = get_samplerate(samplerate) noise=Sound.whitenoise(duration) splrate=noise.samplerate x=array(noise.T)[0] IRNfft=np.fft.fft(x) Nspl,spl_dur=len(IRNfft),float(1.0/splrate) w=2*pi*fftfreq(Nspl,spl_dur) d=float(delay) for k in range(1,niter+1): nchoosek=factorial(niter)/(factorial(niter-k)*factorial(k)) IRNfft+=nchoosek*(gain**k)*IRNfft*exp(-1j*w*k*d) IRNadd = np.fft.ifft(IRNfft) x=real(IRNadd) return Sound(x,samplerate) @staticmethod @check_units(duration=second, samplerate=Hz) def irno(delay, gain, niter, duration, samplerate=None, nchannels=1): ''' Returns an IRN_O noise. The iterated ripple noise is obtained many attenuated and delayed version of the original broadband noise. For more details: see Yost 1996 or chapter 15 in Hartman Sound Signal Sensation. ''' samplerate = get_samplerate(samplerate) noise=Sound.whitenoise(duration) splrate=noise.samplerate x=array(noise.T)[0] IRNadd=np.fft.fft(x) Nspl,spl_dur=len(IRNadd),float(1.0/splrate) w=2*pi*fftfreq(Nspl,spl_dur) d=float(delay) for k in range(1,niter+1): IRNadd+=(gain**k)*IRNadd*exp(-1j*w*k*d) IRNadd = np.fft.ifft(IRNadd) x=real(IRNadd) return Sound(x, samplerate) @staticmethod @check_units(duration=second, samplerate=Hz) def click(duration, peak=None, samplerate=None, nchannels=1): ''' Returns a click of the given duration. If ``peak`` is not specified, the amplitude will be 1, otherwise ``peak`` refers to the peak dB SPL of the click, according to the formula ``28e-6*10**(peak/20.)``. ''' samplerate = get_samplerate(samplerate) duration = get_duration(duration,samplerate) if peak is not None: if not isinstance(peak, dB_type): raise dB_error('Peak must be given in dB') amplitude = 28e-6*10**(float(peak)/20.) else: amplitude = 1 x = amplitude*ones((duration,nchannels)) return Sound(x, samplerate) @staticmethod @check_units(duration=second, samplerate=Hz) def clicks(duration, n, interval, peak=None, samplerate=None, nchannels=1): ''' Returns a series of n clicks (see :func:`click`) separated by interval. ''' oneclick = Sound.click(duration, peak=peak, samplerate=samplerate) return oneclick[:interval].repeat(n) @staticmethod @check_units(duration=second, samplerate=Hz) def silence(duration, samplerate=None, nchannels=1): ''' Returns a silent, zero sound for the given duration. Set nchannels to set the number of channels. ''' samplerate = get_samplerate(samplerate) duration = get_duration(duration,samplerate) x=numpy.zeros((duration,nchannels)) return Sound(x, samplerate) @staticmethod @check_units(pitch=Hz, duration=second, samplerate=Hz) def vowel(vowel=None, formants=None, pitch=100*Hz, duration=1*second, samplerate=None, nchannels=1): ''' Returns an artifically created spoken vowel sound (following the source-filter model of speech production) with a given ``pitch``. The vowel can be specified by either providing ``vowel`` as a string ('a', 'i' or 'u') or by setting ``formants`` to a sequence of formant frequencies. The returned sound is normalized to a maximum amplitude of 1. The implementation is based on the MakeVowel function written by Richard O. Duda, part of the Auditory Toolbox for Matlab by Malcolm Slaney: http://cobweb.ecn.purdue.edu/~malcolm/interval/1998-010/ ''' samplerate = get_samplerate(samplerate) duration = get_duration(duration, samplerate) if not (vowel or formants): raise ValueError('Need either a vowel or a list of formants') elif (vowel and formants): raise ValueError('Cannot use both vowel and formants') if vowel: if vowel == 'a' or vowel == '/a/': formants = (730.0*Hz, 1090.0*Hz, 2440.0*Hz) elif vowel == 'i' or vowel == '/i/': formants = (270.0*Hz, 2290.0*Hz, 3010.0*Hz) elif vowel == 'u' or vowel == '/u/': formants = (300.0*Hz, 870.0*Hz, 2240.0*Hz) else: raise ValueError('Unknown vowel: "%s"' % (vowel)) points = np.arange(0, duration - 1, samplerate / pitch) indices = np.floor(points).astype(int) y = np.zeros(duration) y[indices] = (indices + 1) - points y[indices + 1] = points - indices # model the sound source (periodic glottal excitation) a = np.exp(-250.*Hz * 2 * np.pi / samplerate) y = lfilter([1],[1, 0, -a * a], y.copy()) # model the filtering by the vocal tract bandwidth = 50.*Hz for f in formants: cft = f / samplerate q = f / bandwidth rho = np.exp(-np.pi * cft / q) theta = 2 * np.pi * cft * np.sqrt(1 - 1/(4.0 * q * q)) a2 = -2 * rho * np.cos(theta) a3 = rho * rho y = lfilter([1 + a2 + a3], [1, a2, a3], y.copy()) #normalize sound data = y / np.max(np.abs(y), axis=0) data.shape = (data.size, 1) return Sound(np.tile(data, (nchannels, 1)), samplerate=samplerate) @staticmethod def sequence(*args, **kwds): ''' Returns the sequence of sounds in the list sounds joined together ''' samplerate = kwds.pop('samplerate', None) if len(kwds): raise TypeError('Unexpected keywords to function sequence()') sounds = [] for arg in args: if isinstance(arg, (list, tuple)): sounds.extend(arg) else: sounds.append(arg) if samplerate is None: samplerate = max(s.samplerate for s in sounds) rates = unique([int(s.samplerate) for s in sounds]) if len(rates)>1: sounds = tuple(s.resample(samplerate) for s in sounds) x = vstack(sounds) return Sound(x, samplerate) def save(self, filename, normalise=False, samplewidth=2): ''' Save the sound as a WAV. If the normalise keyword is set to True, the amplitude of the sound will be normalised to 1. The samplewidth keyword can be 1 or 2 to save the data as 8 or 16 bit samples. ''' ext = filename.split('.')[-1].lower() if ext=='wav': import wave as sndmodule elif ext=='aiff' or ext=='aifc': import aifc as sndmodule raise NotImplementedError('Can only save as wav soundfiles') else: raise NotImplementedError('Can only save as wav soundfiles') if samplewidth != 1 and samplewidth != 2: raise ValueError('Sample width must be 1 or 2 bytes.') scale = {2:2 ** 15, 1:2 ** 7-1}[samplewidth] if ext=='wav': meanval = {2:0, 1:2**7}[samplewidth] dtype = {2:int16, 1:uint8}[samplewidth] typecode = {2:'h', 1:'B'}[samplewidth] else: meanval = {2:0, 1:2**7}[samplewidth] dtype = {2:int16, 1:uint8}[samplewidth] typecode = {2:'h', 1:'B'}[samplewidth] w = sndmodule.open(filename, 'wb') w.setnchannels(self.nchannels) w.setsampwidth(samplewidth) w.setframerate(int(self.samplerate)) x = array(self,copy=True) am=amax(x) z = zeros(x.shape[0]*self.nchannels, dtype=x.dtype) x.shape=(x.shape[0],self.nchannels) for i in range(self.nchannels): if normalise: x[:,i] /= am x[:,i] = (x[:,i]) * scale + meanval z[i::self.nchannels] = x[::1,i] data = array(z, dtype=dtype) data = pyarray.array(typecode, data) w.writeframes(data.tostring()) w.close() @staticmethod def load(filename): ''' Load the file given by filename and returns a Sound object. Sound file can be either a .wav or a .aif file. ''' ext = filename.split('.')[-1].lower() if ext=='wav': import wave as sndmodule elif ext=='aif' or ext=='aiff': import aifc as sndmodule else: raise NotImplementedError('Can only load aif or wav soundfiles') wav = sndmodule.open(filename, "r") nchannels, sampwidth, framerate, nframes, comptype, compname = wav.getparams() frames = wav.readframes(nframes * nchannels) typecode = {2:'h', 1:'B'}[sampwidth] out = frombuffer(frames, dtype=dtype(typecode)) scale = {2:2 ** 15, 1:2 ** 7-1}[sampwidth] meanval = {2:0, 1:2**7}[sampwidth] data = zeros((nframes, nchannels)) for i in range(nchannels): data[:, i] = out[i::nchannels] data[:, i] /= scale data[:, i] -= meanval return Sound(data, samplerate=framerate*Hz) def __repr__(self): arrayrep = repr(asarray(self)) arrayrep = '\n'.join(' '+l for l in arrayrep.split('\n')) return 'Sound(\n'+arrayrep+',\n '+repr(self.samplerate)+')' def __str__(self): return 'Sound duration %s, channels %s, samplerate %s' % (self.duration, self.nchannels, self.samplerate) def __reduce__(self): return (_load_Sound_from_pickle, (asarray(self), float(self.samplerate))) def _load_Sound_from_pickle(arr, samplerate): return Sound(arr, samplerate=samplerate*Hz) def play(*sounds, **kwds): ''' Plays a sound or sequence of sounds. For example:: play(sound) play(sound1, sound2) play([sound1, sound2, sound3]) If ``normalise=True``, the sequence of sounds will be normalised to the maximum range (-1 to 1), and if ``sleep=True`` the function will wait until the sounds have finished playing before returning. ''' normalise = kwds.pop('normalise', False) sleep = kwds.pop('sleep', False) if len(kwds): raise TypeError('Unexpected keyword arguments to function play()') sound = sequence(*sounds) sound.play(normalise=normalise, sleep=sleep) play.__doc__ = Sound.play.__doc__ def savesound(sound, filename, normalise=False, samplewidth=2): sound.save(filename, normalise=normalise, samplewidth=samplewidth) savesound.__doc__ = Sound.save.__doc__ def get_duration(duration,samplerate): if not isinstance(duration, int): duration = int(rint(duration * samplerate)) return duration whitenoise = Sound.whitenoise powerlawnoise = Sound.powerlawnoise pinknoise = Sound.pinknoise brownnoise = Sound.brownnoise irns = Sound.irns irno = Sound.irno tone = Sound.tone harmoniccomplex = Sound.harmoniccomplex click = Sound.click clicks = Sound.clicks silence = Sound.silence sequence = Sound.sequence vowel = Sound.vowel loadsound = Sound.load brian-1.4.3/brian/inspection.py000066400000000000000000000355451266062647100164600ustar00rootroot00000000000000# ---------------------------------------------------------------------------------- # Copyright ENS, INRIA, CNRS # Contributors: Romain Brette (brette@di.ens.fr) and Dan Goodman (goodman@di.ens.fr) # # Brian is a computer program whose purpose is to simulate models # of biological neural networks. # # This software is governed by the CeCILL license under French law and # abiding by the rules of distribution of free software. You can use, # modify and/ or redistribute the software under the terms of the CeCILL # license as circulated by CEA, CNRS and INRIA at the following URL # "http://www.cecill.info". # # As a counterpart to the access to the source code and rights to copy, # modify and redistribute granted by the license, users are provided only # with a limited warranty and the software's author, the holder of the # economic rights, and the successive licensors have only limited # liability. # # In this respect, the user's attention is drawn to the risks associated # with loading, using, modifying and/or developing or reproducing the # software by the user in light of its specific status of free software, # that may mean that it is complicated to manipulate, and that also # therefore means that it is reserved for developers and experienced # professionals having in-depth computer knowledge. Users are therefore # encouraged to load and test the software's suitability as regards their # requirements in conditions enabling the security of their systems and/or # data to be ensured and, more generally, to use and operate it in the # same conditions as regards security. # # The fact that you are presently reading this means that you have had # knowledge of the CeCILL license and that you accept its terms. # ---------------------------------------------------------------------------------- # ''' Inspection of strings with python statements and models defined by differential equations. TODO: some of the module is obsolete ''' __all__ = ['is_affine', 'depends_on', 'Term', 'get_global_term', 'get_var_names', 'check_equations_units', 'fill_vars', 'AffineFunction', 'get_identifiers', 'modified_variables', 'namespace', 'clean_text', 'namespace_replace_quantity_with_pure', 'list_replace_quantity_with_pure'] import numpy from numpy import array from units import * import parser import re import inspect from copy import copy class PureQuantityBase(Quantity): def __init__(self, value): self.dim = value.dim def __div__(self, other): if not isinstance(other, Quantity) and not is_scalar_type(other): return NotImplemented try: return Quantity.__div__(self, other) except ZeroDivisionError: try: odim = other.dim except AttributeError: odim = Dimension() return Quantity.with_dimensions(0, self.dim / odim) __truediv__ = __div__ def __rdiv__(self, other): if not isinstance(other, Quantity) and not is_scalar_type(other): return NotImplemented try: return Quantity.__rdiv__(self, other) except ZeroDivisionError: try: odim = other.dim except AttributeError: odim = Dimension() return Quantity.with_dimensions(0, odim / self.dim) __rtruediv__ = __rdiv__ def __mod__(self, other): if not isinstance(other, Quantity) and not is_scalar_type(other): return NotImplemented try: return Quantity.__mod__(self, other) except ZeroDivisionError: return Quantity.with_dimensions(0, self.dim) def returnpure(meth): def f(*args, **kwds): x = meth(*args, **kwds) if isinstance(x, Quantity): return PureQuantity(x) else: return x return f class PureQuantity(PureQuantityBase): ''' Use this class for unit checking. The idea is that operations should always work if they are dimensionally consistent regardless of the values. The key one is that division by zero does not raise an error but returns a value with the correct dimensions (in fact, it returns 0 with the correct dimensions). This is important for Brian because we do unit checking by substituting zeros into the equations, which sometimes gives a divide by zero error. The way it works is that it derives from Quantity, but for division it wraps a try: except ZeroDivisionError: around the operation, and returns a zero with the correct units if it encounters it. In addition to that, it wraps every method of Quantity so that they return PureQuantity objects instead of Quantity objects (otherwise e.g. a*b would be a Quantity not a PureQuantity even if a, b were PureQuantity). Finally, the *_replace_quantity_with_pure functions are just designed to scan through a dict or list of variables and replace Quantity objects by PureQuantity objects. You need to do this when evaluating some code in a user namespace, for example. ''' for methname in dir(PureQuantityBase): meth = getattr(PureQuantityBase, methname) try: meth2 = getattr(numpy.float64, methname) except AttributeError: meth2 = meth if callable(meth) and meth is not meth2: exec methname + '=returnpure(PureQuantityBase.' + methname + ')' del meth, meth2, methname def namespace_replace_quantity_with_pure(ns): newns = {} for k, v in ns.iteritems(): if isinstance(v, Quantity): v = PureQuantity(v) newns[k] = v return newns def list_replace_quantity_with_pure(L): newL = [] for v in L: if isinstance(v, Quantity): v = PureQuantity(v) newL.append(v) return newL def namespace(expr, level=0, return_unknowns=False): ''' Returns a namespace with the values of identifiers in expr, taking from: * local namespace * global namespace * units ''' # Build the namespace frame = inspect.stack()[level + 1][0] global_namespace, local_namespace = frame.f_globals, frame.f_locals # Find external objects space = {} unknowns = [] for var in get_identifiers(expr): if var in local_namespace: #local space[var] = local_namespace[var] elif var in global_namespace: #global space[var] = global_namespace[var] elif var in globals(): # typically units space[var] = globals()[var] else: unknowns.append(var) if return_unknowns: return space, unknowns else: return space def get_identifiers(expr): ''' Returns the list of identifiers (variables or functions) in the Python expression (string). ''' # cleaner: parser.expr(expr).tolist() then find leaves of the form [1,name] return parser.suite(expr).compile().co_names def clean_text(expr): ''' Cleans a Python expression or statement: * Remove comments (# comment) * Merge multi-line statements (\) * Split at semi-columns (careful: indentation is ignored) ''' # Merge multi-line statements expr = re.sub('\\\s*?\n', ' ', expr) # Remove comments expr = re.sub('#.*', '', expr) # Split at semi-columns expr = re.sub(';', '\n', expr) return expr def modified_variables(expr): ''' Returns the list of variables or functions in expr that are in left-hand sides, e.g.: x+=5 expr can be a multiline statement. Multiline comments are not allowed but multiline statements are. Functions may also be returned as in: do(something) # here do is returned, not something TODO: maybe functions should be removed? TODO: better handling of semi-columns (;) ''' vars = get_identifiers(expr) expr = clean_text(expr) # Find lines that start by an identifier mod_vars = [] for line in expr.splitlines(): s = re.search(r'^\s*(\w+)\b', line) if s and (s.group(1) in vars): mod_vars.append(s.group(1)) return mod_vars def fill_vars(f, keepnamespace=False, *varnames): ''' Returns a function with arguments given by varnames (list or tuple), given that the arguments of f are in varnames. If keepnamespace is True, then the original func_globals dictionary of the function is kept. Purpose: changing the syntax for function calls. Example: f=lambda x:2*x g=fill_vars(f,'y','x') Then g(1,2) returns f(2). This is somehow the inverse of partial (in module functools). N.B.: the order of variables matters. ''' if list(f.func_code.co_varnames) == varnames: return f varstring = varnames[0] for name in varnames[1:]: varstring += ',' + name shortvarstring = f.func_code.co_varnames[0] for name in f.func_code.co_varnames[1:]: shortvarstring += ',' + name if keepnamespace: # Create a unique name fname = 'fill_vars_function' + id(f) f.func_globals[fname] = f return eval('lambda ' + varstring + ': ' + fname + '(' + shortvarstring + ')', f.func_globals) else: return eval('lambda ' + varstring + ': f(' + shortvarstring + ')', {'f':f}) def check_equations_units(eqs, x): ''' Check the units of the differential equations, using the units of x. df_i/dt must have units of x_i / time. ''' try: for f, x_i in zip(eqs, x): f.func_globals['xi'] = 0 * second ** -.5 # Noise f(*x) + (x_i / second) # Check that the two terms have the same dimension except DimensionMismatchError, inst: raise DimensionMismatchError("The differential equations are not homogeneous!", *inst._dims) def get_var_names(eqs): ''' Get the variable names from the set of equations. Returns a list. N.B.: the order is preserved. ''' names = list(eqs[0].func_code.co_varnames) for eq in eqs: for name in eq.func_code.co_varnames: if not(name in names): names.append(name) return names class AffineFunction(object): ''' An object that can be added and multiplied by a float (or array or int). ''' def __init__(self, a=1., b=0.): ''' Defines an affine function as a*x+b. ''' self.a = a self.b = b def __add__(self, y): if isinstance(y, AffineFunction): return AffineFunction(self.a + y.a, self.b + y.b) else: return AffineFunction(self.a, self.b + array(y)) def __radd__(self, x): if isinstance(x, AffineFunction): return AffineFunction(self.a + x.a, self.b + x.b) else: return AffineFunction(self.a, self.b + array(x)) def __neg__(self): return AffineFunction(-self.a, -self.b) def __sub__(self, y): if isinstance(y, AffineFunction): return AffineFunction(self.a - y.a, self.b - y.b) else: return AffineFunction(self.a, self.b - array(y)) def __rsub__(self, x): if isinstance(x, AffineFunction): return AffineFunction(x.a - self.a, x.b - self.b) else: return AffineFunction(-self.a, array(x) - self.b) def __mul__(self, y): if isinstance(y, float) or isinstance(y, int) or isinstance(y, array): return AffineFunction(self.a * array(y), self.b * array(y)) else: return y.__rmul__(self) def __rmul__(self, x): if isinstance(x, float) or isinstance(x, int) or isinstance(x, array): return AffineFunction(array(x) * self.a, array(x) * self.b) else: return x.__mul__(self) def __div__(self, y): if isinstance(y, float) or isinstance(y, int) or isinstance(y, array): return AffineFunction(self.a / array(y), self.b / array(y)) else: return y.__rdiv__(self) def __repr__(self): return str(self.a) + '*x+' + str(self.b) class Term(object): ''' A variable that can be used to isolate terms in a function, e.g.: f=lambda x:a+3*x f(Term()) --> 3*Term() Idea: a+x(z) = x(z) + a = x(z) a*x(z) = x(z)*a = x(a*z) ''' def __init__(self, x=1.): self.x = x def __add__(self, y): return self def __radd__(self, y): return self def __mul__(self, y): return Term(self.x * y) def __rmul__(self, y): return Term(self.x * y) def __div__(self, y): return Term(self.x / y) def __neg__(self): return Term(-self.x) def __repr__(self): return str(self.x) + '*Term()' def __print__(self): return str(self.x) + '*Term()' def is_affine(f): ''' Tests whether f is an affine function. ''' nargs = f.func_code.co_argcount try: f(*([AffineFunction()]*nargs)) except: return False return True #def is_affine1st(f,x0): # ''' # Tests whether f is affine in its 1st variable. # ''' # nargs=f.func_code.co_argcount # return is_affine(lambda x:f(x,*x0)) def depends_on(f, x, x0): ''' Tests whether f depends on global variable x. N.B.: returns True also if f generates an error (e.g. with undefined variables). x0 is the test value for the variables (tuple). Other idea: use 'xi' in f.func_code.co_names (but not working for nested functions) ''' #return x in f.func_code.co_names # if x in f.func_globals: # oldx=f.func_globals[x] # else: # oldx=None old_func_globals = copy(f.func_globals) x0 = list_replace_quantity_with_pure(x0) f.func_globals.update(namespace_replace_quantity_with_pure(f.func_globals)) result = False f.func_globals[x] = None nargs = f.func_code.co_argcount try: f(*x0) except: result = True # if oldx==None: # del f.func_globals[x] # x was not defined # else: # f.func_globals[x]=oldx # previous value f.func_globals.update(old_func_globals) return result def get_global_term(f, x, x0): ''' Extract the term in global variable x from function x, returns a float. x0 is the test value for the variables (tuple). Example: getterm(lambda x:2*x+5*y,'y',(0,0))) --> 5 ''' old_func_globals = copy(f.func_globals) x0 = list_replace_quantity_with_pure(x0) f.func_globals.update(namespace_replace_quantity_with_pure(f.func_globals)) f.func_globals[x] = Term() result = f(*x0).x f.func_globals.update(old_func_globals) return result brian-1.4.3/brian/library/000077500000000000000000000000001266062647100153635ustar00rootroot00000000000000brian-1.4.3/brian/library/IF.py000066400000000000000000000066341266062647100162440ustar00rootroot00000000000000''' Integrate-and-Fire models. ''' from brian.units import * from brian.stdunits import * from brian.membrane_equations import * from numpy import exp __all__ = ['leaky_IF', 'perfect_IF', 'exp_IF', 'quadratic_IF', 'Brette_Gerstner', 'Izhikevich', \ 'AdaptiveReset', 'aEIF'] __credits__ = dict(author='Romain Brette (brette@di.ens.fr)', date='April 2008') #TODO: specific integration methods """ ****************************************** One-dimensional integrate-and-fire models ****************************************** """ @check_units(tau=second, El=volt) def leaky_IF(tau, El): ''' A leaky integrate-and-fire model (membrane equation). tau dvm/dt = EL - vm ''' return MembraneEquation(tau) + \ Current('Im=El-vm:volt', current_name='Im', El=El) @check_units(tau=second) def perfect_IF(tau): ''' A perfect integrator. tau dvm/dt = ... ''' return MembraneEquation(tau) @check_units(C=farad, gL=siemens, EL=volt, VT=volt, DeltaT=volt) def exp_IF(C, gL, EL, VT, DeltaT): ''' An exponential integrate-and-fire model (membrane equation). ''' return MembraneEquation(C) + \ Current('Im=gL*(EL-vm)+gL*DeltaT*exp((vm-VT)/DeltaT):amp', \ gL=gL, EL=EL, DeltaT=DeltaT, exp=exp, VT=VT) @check_units(C=farad, a=siemens / volt, EL=volt, VT=volt) def quadratic_IF(C, a, EL, VT): ''' Quadratic integrate-and-fire model. C*dvm/dt=a*(vm-EL)*(vm-VT) ''' return MembraneEquation(C) + \ Current('Im=a*(vm-EL)*(vm-VT):amp', \ a=a, EL=EL, exp=exp, VT=VT) """ ****************************************** Two-dimensional integrate-and-fire models ****************************************** """ @check_units(a=1 / second, b=1 / second) def Izhikevich(a=0.02 / ms, b=0.2 / ms): ''' Returns a membrane equation for the Izhikevich model (variables: vm and w). ''' return MembraneEquation(1.) + \ Current(''' Im=(0.04/ms/mV)*vm**2+(5/ms)*vm+140*mV/ms-w : volt/second dw/dt=a*(b*vm-w) : volt/second ''', current_name='Im') @check_units(C=farad, gL=siemens, EL=volt, VT=volt, DeltaT=volt, \ tauw=second, a=siemens) def Brette_Gerstner(C=281 * pF, gL=30 * nS, EL= -70.6 * mV, VT= -50.4 * mV, \ DeltaT=2 * mV, tauw=144 * ms, a=4 * nS): ''' Returns a membrane equation for the Brette-Gerstner model. Default: a regular spiking cortical cell. Brette, R. and W. Gerstner (2005). Adaptive exponential integrate-and-fire model as an effective description of neuronal activity. Journal of Neurophysiology 94: 3637-3642. ''' return exp_IF(C, gL, EL, VT, DeltaT) + \ IonicCurrent('dw/dt=(a*(vm-EL)-w)/tauw:amp', \ a=a, EL=EL, tauw=tauw) aEIF = Brette_Gerstner # synonym AdEx = aEIF class AdaptiveReset(object): ''' A two-variable reset: V<-Vr w<-w+b (used in Izhikevich and Brette-Gerstner models) ''' def __init__(self, Vr= -70.6 * mvolt, b=0.0805 * nA): self.Vr = Vr self.b = b def __call__(self, P): ''' Clamps membrane potential at reset value. ''' spikes = P.LS.lastspikes() P.vm_[spikes] = self.Vr P.w_[spikes] += self.b brian-1.4.3/brian/library/__init__.py000066400000000000000000000000021266062647100174640ustar00rootroot00000000000000 brian-1.4.3/brian/library/electrophysiology/000077500000000000000000000000001266062647100211475ustar00rootroot00000000000000brian-1.4.3/brian/library/electrophysiology/__init__.py000066400000000000000000000002241266062647100232560ustar00rootroot00000000000000from electrophysiology_models import * from trace_analysis import * from electrode_compensation import * from lp_electrodecompensation import * brian-1.4.3/brian/library/electrophysiology/electrode_compensation.py000066400000000000000000000173641266062647100262610ustar00rootroot00000000000000""" Electrode compensation ---------------------- Active Electrode Compensation (AEC) """ from brian.stateupdater import get_linear_equations from brian.log import log_info from brian import second, Mohm, mV, ms, Equations, ohm, volt, second from scipy.optimize import fmin from scipy.signal import lfilter from scipy import linalg from numpy import sqrt, ceil, zeros, eye, poly, dot, hstack, array from scipy import zeros, array, optimize, mean, arange, diff, rand, exp, sum, convolve, eye, linalg, sqrt import time __all__=['full_kernel', 'full_kernel_from_step', 'electrode_kernel_soma', 'electrode_kernel_dendrite', 'solve_convolution', 'electrode_kernel', 'AEC_compensate'] ''' Active Electrode Compensation ----------------------------- From: Brette et al (2008). High-resolution intracellular recordings using a real-time computational model of the electrode. Neuron 59(3):379-91. ''' def full_kernel(v, i, ksize, full_output=False): ''' Calculates the full kernel from the recording v and the input current i. The last ksize steps of v should be null. ksize = size of the resulting kernel full_output = returns K,v0 if True (v0 is the resting potential) ''' # Calculate the correlation vector # and the autocorrelation vector vi = zeros(ksize) ii = zeros(ksize) vref = mean(v) # taking as the reference potential simplifies the formulas for k in range(ksize): vi[k] = mean((v[k:] - vref) * i[:len(i) - k]) ii[k] = mean(i[k:] * i[:len(i) - k]) vi -= mean(i) ** 2 K = levinson_durbin(ii, vi) if full_output: v0 = vref - mean(i) * sum(K) return K, v0 else: return K def full_kernel_from_step(V, I): ''' Calculates the full kernel from the response (V) to a step input (I, constant). ''' return diff(V) / I def solve_convolution(K, Km): ''' Solves Ke = K - Km * Ke/Re Linear problem ''' Re = sum(K) - sum(Km) n = len(Km) A = eye(n) * (1 + Km[0] / Re) for k in range(n): for m in range(k): A[k, m] = Km[k - m] / Re return linalg.lstsq(A, K)[0] def electrode_kernel_dendrite(Karg, start_tail, full_output=False): ''' (For dendritic recordings) Extracts the electrode kernel Ke from the raw kernel K by removing the membrane kernel, estimated from the indexes >= start_tail of the raw kernel. full_output = returns Ke,Km if True (otherwise Ke) (Ke=electrode filter, Km=membrane filter) ''' K = Karg.copy() def remove_km(RawK, Km): ''' Solves Ke = RawK - Km * Ke/Re for a dendritic Km. ''' Kel = RawK - Km # DOES NOT CONVERGE!! for _ in range(5): # Iterative solution Kel = RawK - convolve(Km, Kel)[:len(Km)] / sum(Kel) # NB: Re=sum(Kel) increases after every iteration return Kel # Fit of the tail to a dendritic kernel to find the membrane time constant t = arange(len(K)) tail = arange(start_tail, len(K)) Ktail = K[tail] f = lambda params:params[0] * ((tail + 1) ** -.5) * exp(-params[1] ** 2 * (tail + 1)) - Ktail #Rtail=sum(Ktail) #g=lambda tau:sum((tail+1)**(-.5)*exp(-(tail+1)/tau)) #J=lambda tau:sum(((tail+1)**(-.5)*exp(-(tail+1)/tau)/g(tau)-Ktail/Rtail)**2) p, _ = optimize.leastsq(f, array([1., .03])) #p=optimize.fminbound(J,.1,10000.) #p=optimize.golden(J) #print "tau_dend=",p*.1 #Km=(t+1)**(-.5)*exp(-(t+1)/p)*Rtail/g(p) print "tau_dend=", .1 / (p[1] ** 2) Km = p[0] * ((t + 1) ** -.5) * exp(-p[1] ** 2 * (t + 1)) K[tail] = Km[tail] # Find the minimum z = optimize.fminbound(lambda x:sum(solve_convolution(K, x * Km)[tail] ** 2), .5, 1.) Ke = solve_convolution(K, z * Km) if full_output: return Ke[:start_tail], z * Km else: return Ke[:start_tail] def electrode_kernel_soma(Karg, start_tail, full_output=False): ''' (For somatic recordings - alternative method) Extracts the electrode kernel Ke from the raw kernel K by removing the membrane kernel, estimated from the indexes >= start_tail of the raw kernel. full_output = returns Ke,Km if True (otherwise Ke) (Ke=electrode filter, Km=membrane filter) ''' K = Karg.copy() def remove_km(RawK, Km): ''' Solves Ke = RawK - Km * Ke/Re for a dendritic Km. ''' Kel = RawK - Km for _ in range(5): # Iterative solution Kel = RawK - convolve(Km, Kel)[:len(Km)] / sum(Kel) # NB: Re=sum(Kel) increases after every iteration return Kel # Fit of the tail to a somatic kernel to find the membrane time constant t = arange(len(K)) tail = arange(start_tail, len(K)) Ktail = K[tail] f = lambda params:params[0] * exp(-params[1] ** 2 * (tail + 1)) - Ktail p, _ = optimize.leastsq(f, array([1., .3])) Km = p[0] * exp(-p[1] ** 2 * (t + 1)) print "tau_soma=", .1 / (p[1] ** 2) K[tail] = Km[tail] # Find the minimum z = optimize.fminbound(lambda x:sum(solve_convolution(K, x * Km)[tail] ** 2), .5, 1.) Ke = solve_convolution(K, z * Km) print "R=", sum(z * p[0] * exp(-p[1] ** 2 * (arange(1000) + 1))) if full_output: return Ke[:start_tail], z * Km else: return Ke[:start_tail] def electrode_kernel(Karg, start_tail, full_output=False): ''' Extracts the electrode kernel Ke from the raw kernel K by removing the membrane kernel, estimated from the indexes >= start_tail of the raw kernel. full_output = returns Ke,Km if True (otherwise Ke) (Ke=electrode filter, Km=membrane filter) Finds automatically whether to use dendritic or somatic kernel. ''' K = Karg.copy() # Fit of the tail to a somatic kernel to find the membrane time constant t = arange(len(K)) tail = arange(start_tail, len(K)) Ktail = K[tail] f = lambda params:params[0] * exp(-params[1] ** 2 * (tail + 1)) - Ktail p, _ = optimize.leastsq(f, array([1., .3])) Km_soma = p[0] * exp(-p[1] ** 2 * (t + 1)) f = lambda params:params[0] * ((tail + 1) ** -.5) * exp(-params[1] ** 2 * (tail + 1)) - Ktail p, _ = optimize.leastsq(f, array([1., .03])) Km_dend = p[0] * ((t + 1) ** -.5) * exp(-p[1] ** 2 * (t + 1)) if sum((Km_soma[tail] - Ktail) ** 2) < sum((Km_dend[tail] - Ktail) ** 2): print "Somatic kernel" Km = Km_soma else: print "Dendritic kernel" Km = Km_dend K[tail] = Km[tail] # Find the minimum z = optimize.fminbound(lambda x:sum(solve_convolution(K, x * Km)[tail] ** 2), .5, 1.) Ke = solve_convolution(K, z * Km) if full_output: return Ke[:start_tail], z * Km else: return Ke[:start_tail] def AEC_compensate(v, i, ke): ''' Active Electrode Compensation, done offline. v = recorded potential i = injected current ke = electrode kernel Returns the compensated potential. ''' return v - convolve(ke, i)[:-(len(ke) - 1)] def levinson_durbin(a, y): ''' Solves AX=Y where A is a symetrical Toeplitz matrix with coefficients given by the vector a (a = first row = first column of A). ''' b = 0 * a x = 0 * a b[0] = 1. / a[0] x[0] = y[0] * b[0] for i in range(1, len(a)): alpha = sum(a[1:i + 1] * b[:i]) u = 1. / (1 - alpha ** 2) v = -alpha * u tmp = b[i - 1] if i > 1: b[1:i] = v * b[i - 2::-1] + u * b[:i - 1] b[0] = v * tmp b[i] = u * tmp beta = y[i] - sum(a[i:0:-1] * x[:i]) x += beta * b return x brian-1.4.3/brian/library/electrophysiology/electrophysiology_models.py000066400000000000000000000277021266062647100266600ustar00rootroot00000000000000''' Electrophysiology models for Brian. R. Brette 2008. Contains: * Electrode models * Current clamp and voltage amplifiers * DCC and SEVC amplifiers (discontinuous) * AEC (Active Electrode Compensation) * Acquisition board ''' from brian.units import amp, check_units, volt, farad from brian.equations import Equations, unique_id from operator import isSequenceType from brian.units import ohm, Mohm from brian.stdunits import pF, ms, nA, mV, nS from brian.neurongroup import NeuronGroup from scipy import zeros, array, optimize, mean, arange, diff, rand, exp, sum, convolve, eye, linalg, sqrt from brian.clock import Clock from electrode_compensation import * __all__ = ['electrode', 'current_clamp', 'voltage_clamp', 'DCC', 'SEVC', 'AcquisitionBoard', 'AEC', 'VC_AEC'] ''' ------------ Electrodes ------------ ''' # TODO: sharp electrode model (cone) # No unit checking of Re and Ce because they can be lists @check_units(v_rec=volt, vm=volt, i_inj=amp, i_cmd=amp) def electrode(Re, Ce, v_el='v_el', vm='vm', i_inj='i_inj', i_cmd='i_cmd'): ''' An intracellular electrode modeled as an RC circuit, or multiple RC circuits in series (if Re, Ce are lists). v_el = electrode (=recording) potential vm = membrane potential i_inj = current entering the membrane i_cmd = electrode command current (None = no injection) Returns an Equations() object. ''' if isSequenceType(Re): if len(Re) != len(Ce) or len(Re) < 2: raise TypeError, "Re and Ce must have the same length" v_mid, i_mid = [], [] for i in range(len(Re) - 1): v_mid.append('v_mid_' + str(i) + unique_id()) i_mid.append('i_mid_' + str(i) + unique_id()) eqs = electrode(Re[0], Ce[0], v_mid[0], vm, i_inj, i_mid[0]) for i in range(1, len(Re) - 1): eqs + electrode(Re[i], Ce[i], v_mid[i], v_mid[i - 1], i_mid[i - 1], i_mid[i]) eqs += electrode(Re[-1], Ce[-1], v_el, v_mid[-1], i_mid[-1], i_cmd) return eqs else: if Ce > 0 * farad: return Equations(''' dvr/dt = ((vm-vr)/Re+ic)/Ce : mV ie = (vr-vm)/Re : nA''', vr=v_el, vm=vm, ic=i_cmd, ie=i_inj, \ Re=Re, Ce=Ce) else: # ideal electrode - pb here return Equations(''' vr = vm+Re*ic : volt ie = ic : amp''', vr=v_el, vm=vm, ic=i_cmd, ie=i_inj) ''' ------------ Amplifiers ------------ ''' def voltage_clamp(vm='vm', v_cmd='v_cmd', i_rec='i_rec', i_inj='i_inj', Re=20 * Mohm, Rs=0 * Mohm, tau_u=1 * ms): ''' Continuous voltage-clamp amplifier + ideal electrode (= input capacitance is already neutralized). vm = membrane potential (or electrode) variable v_cmd = command potential i_inj = injected current (into the neuron) i_rec = recorded current (= -i_inj) Re = electrode resistance Rs = series resistance compensation tau_u = delay of series compensation (for stability) ''' return Equations(''' Irec=-I : amp I=(Vc+U-Vr)/Re : amp dU/dt=(Rs*I-U)/tau : volt ''', Vr=vm, Vc=v_cmd, I=i_inj, Rs=Rs, tau=tau_u, Irec=i_rec, Re=Re) # TODO: Re, Ce as lists def current_clamp(vm='vm', i_inj='i_inj', v_rec='v_rec', i_cmd='i_cmd', Re=80 * Mohm, Ce=4 * pF, bridge=0 * ohm, capa_comp=0 * farad, v_uncomp=None): ''' Continuous current-clamp amplifier + electrode. vm = membrane potential (or electrode) variable i_inj = injected current (into the neuron) v_rec = recorded potential i_cmd = command current bridge = bridge resistance compensation capa_comp = capacitance neutralization Re = electrode resistance Ce = electrode capacitance (input capacitance) v_uncomp = uncompensated potential (raw measured potential) ''' if capa_comp != Ce: return Equations(''' Vr=U-R*Ic : volt I=(U-V)/Re : amp dU/dt=(Ic-I)/(Ce-CC) : volt ''', Vr=v_rec, V=vm, I=i_inj, Ic=i_cmd, R=bridge, Ce=Ce, CC=capa_comp, U=v_uncomp, Re=Re) else: return Equations(''' Vr=V+(Re-R)*I : volt I=Ic : amp # not exactly an alias because the units of Ic is unknown ''', Vr=v_rec, V=vm, I=i_inj, Ic=i_cmd, R=bridge, Re=Re) class AcquisitionBoard(NeuronGroup): ''' Digital acquisition board (DSP). Use: board=AcquisitionBoard(P=neuron,V='V',I='I',clock) where P = neuron group V = potential variable name (in P) I = current variable name (in P) clock = acquisition clock Recording: vm=board.record Injecting: board.command=... Injects I, records V. ''' def __init__(self, P, V, I, clock=None): eqs = Equations(''' record : units_record command : units_command ''', units_record=P.unit(V), units_command=P.unit(I)) NeuronGroup.__init__(self, len(P), model=eqs, clock=clock) self._P = P self._V = V self._I = I def update(self): self.record = self._P.state(self._V) # Record self._P.state(self._I)[:] = self.command # Inject class DCC(AcquisitionBoard): ''' Discontinuous current-clamp. Use: board=DCC(P=neuron,V='V',I='I',frequency=2*kHz) where P = neuron group V = potential variable name (in P) I = current variable name (in P) frequency = sampling frequency Recording: vm=board.record Injecting: board.command=I ''' def __init__(self, P, V, I, frequency): self.clock = Clock(dt=1. / (3. * frequency)) AcquisitionBoard.__init__(self, P, V, I, self.clock) self._cycle = 0 def set_frequency(self, frequency): ''' Sets the sampling frequency. ''' self.clock.dt = 1. / (3. * frequency) def update(self): if self._cycle == 0: self.record = self._P.state(self._V) # Record self._P.state(self._I)[:] = 3 * self.command # Inject else: self._P.state(self._I)[:] = 0 #*nA self._cycle = (self._cycle + 1) % 3 class SEVC(DCC): ''' Discontinuous voltage-clamp. Use: board=SEVC(P=neuron,record='V',command='I',frequency=2*kHz,gain=10*nS) where P = neuron group V = potential variable name (in P) I = current variable name (in P) frequency = sampling frequency gain = feedback gain gain2 = control gain (integral controller) Recording: i=board.record Setting the clamp potential: board.command=-20*mV ''' def __init__(self, P, V, I, frequency, gain=100 * nS, gain2=0 * nS / ms): DCC.__init__(self, P, V, I, frequency) self._J = zeros(len(P)) # finer control self._gain = gain self._gain2 = gain2 def update(self): if self._cycle == 0: self._J += self.clock._dt * self._gain2 * (self._P.state(self._V) - self.command) self.record = self._gain * (self._P.state(self._V) - self.command) + self._J self._P.state(self._I)[:] = -3 * self.record # Inject else: self._P.state(self._I)[:] = 0 #*nA self._cycle = (self._cycle + 1) % 3 ''' ------------------------------------- Active Electrode Compensation (AEC) The technique was presented in the following paper: High-resolution intracellular recordings using a real-time computational model of the electrode R. Brette, Z. Piwkowska, C. Monier, M. Rudolph-Lilith, J. Fournier, M. Levy, Y. Fregnac, T. Bal, A. Destexhe Neuron (2008) 59(3):379-91. ------------------------------------- ''' class AEC(AcquisitionBoard): """ An acquisition board with AEC. Warning: only works with 1 neuron (not N). Use: board=AEC(neuron,'V','I',clock) where P = neuron group V = potential variable name (in P) I = current variable name (in P) clock = acquisition clock Recording: vm=board.V Injecting: board.command(I) """ def __init__(self, P, V, I, clock=None): AcquisitionBoard.__init__(self, P, V, I, clock=clock) self._estimation = False self._compensation = False self.Ke = None self._Vrec = [] self._Irec = [] def start_injection(self, amp=.5 * nA, DC=0 * nA): ''' Start white noise injection for kernel estimation. amp = current amplitude DC = additional DC current ''' self._amp, self._DC = amp, DC self._estimation = True def stop_injection(self): ''' Stop white noise injection. ''' self._estimation = False self.command = 0 #*nA def estimate(self, ksize=150, ktail=50, dendritic=False): ''' Estimate electrode kernel Ke (after injection) ksize = kernel size (in bins) ktail = tail parameter (in bins), indicates the end of the electrode kernel ''' self._ksize = ksize self._ktail = ktail # Calculate Ke vrec = array(self._Vrec) / mV irec = array(self._Irec) / nA K = full_kernel(vrec, irec, self._ksize) self._K = K * Mohm if dendritic: self.Ke = electrode_kernel_dendrite(K, ktail) * Mohm else: self.Ke = electrode_kernel_soma(K, ktail) * Mohm self._Vrec = [] self._Irec = [] def switch_on(self, Ke=None): ''' Switch compensation on, with kernel Ke. (If not given: use last kernel) ''' self._compensation = True self._lastI = zeros(self._ktail) self._posI = 0 def switch_off(self): ''' Switch compensation off. ''' self._compensation = False def update(self): AcquisitionBoard.update(self) if self._estimation: I = 2. * (rand() - .5) * self._amp + self._DC self.command = I # Record self._Vrec.append(self.record[0]) self._Irec.append(I) if self._compensation: # Compensate self._lastI[self._posI] = self.command[0] self._posI = (self._posI - 1) % self._ktail self.record[0] = self.record[0] - sum(self.Ke * self._lastI[range(self._posI, self._ktail) + range(0, self._posI)]) class VC_AEC(AEC): def __init__(self, P, V, I, gain=50 * nS, gain2=0 * nS / ms, clock=None): AEC.__init__(self, P, V, I, clock=clock) self._gain = gain self._gain2 = gain2 self._J = zeros(len(P)) def stop_injection(self): ''' Stop white noise injection. ''' self._estimation = False self.record = 0 #*nA def update(self): V = self._P.state(self._V) # Record self._P.state(self._I)[:] = -self.record # Inject if self._estimation: I = 2. * (rand() - .5) * self._amp + self._DC self.record = -I # Record self._Vrec.append(V[0]) self._Irec.append(I) if self._compensation: # Compensate self._lastI[self._posI] = -self.record[0] self._posI = (self._posI - 1) % self._ktail V[0] = V[0] - sum(self.Ke * self._lastI[range(self._posI, self._ktail) + range(0, self._posI)]) self._J += self.clock._dt * self._gain2 * (self.command - V) self.record = -(self._gain * (self.command - V) + self._J) if __name__ == '__main__': from brian import * taum = 20 * ms gl = 20 * nS Cm = taum * gl eqs = Equations('dv/dt=(-gl*v+i_inj)/Cm : volt') + electrode(50 * Mohm, 10 * pF, vm='v', i_cmd=.5 * nA) neuron = NeuronGroup(1, model=eqs) M = StateMonitor(neuron, 'v_el', record=True) run(100 * ms) plot(M.times / ms, M[0] / mV) show() brian-1.4.3/brian/library/electrophysiology/lp_electrodecompensation.py000066400000000000000000000556431266062647100266170ustar00rootroot00000000000000""" Lp offline electrode compensation method. Rossant et al., "A calibration-free electrode compensation method" J. Neurophysiol 2012 """ from brian import * from scipy.optimize import fmin, fmin_powell, fmin_cg, fmin_bfgs, fmin_ncg from scipy.signal import lfilter from scipy import linalg # ndtr is the cumulative distribution function of a standard Gaussian law from scipy.special import ndtr import time __all__ = ["ElectrodeCompensation", "Lp_compensate", "find_peak_threshold", "find_spikes", "confusion_matrix", "get_scores", "get_trace_quality"] ''' Equivalent linear filter ------------------------ ''' def compute_filter(A, row=0): """ Compute the equivalent digital filter of a system Y(n+1)=AY(n)+X(n). From a linear discrete-time multidimensional system Y(n+1)=AY(n)+X(n), with X(n) a vector with X(n)[1:]==0, this function compute the vectors b and a of an equivalent linear 1D filter for simulating the variable indexed by "row". The two vectors b, a can be used in the Scipy function lfilter. """ d = len(A) # compute a: characteristic polynomial of the matrix a = poly(A) # with a[0]=1 # compute b recursively b = zeros(d+1) T = eye(d) b[0] = T[row, 0] for i in range(1, d+1): T = a[i]*eye(d) + dot(A, T) b[i] = T[row, 0] return b, a def simulate(eqs, I, dt, row=0): """ Simulate a linear neuron model in response to an injected current using an equivalent linear filter. Simulate a system of neuron equations in response to an injected current I with sampling frequency 1/dt, using a Scipy linear filter rather than a Brian integrator (it's more efficient since there are no spikes). The variable indexed by "row" only is simulated. "I" must not appear in the equations, rather, it will be injected in the *first* variable only. That is, if the equations correspond to the differential system dY/dt=MY+I, where only the first component of I is nonzero, then the equations must correspond to the matrix M. For instance, if the equations were dv/dt=(R*I-v)/tau, then the following equation must be given: "dv/dt=-v/tau", and the input current will be "I*R/tau". """ # get M and B such that the system is dY/dy=M(Y-B) # NOTE: it only works if the system is invertible (det(M)!=0) ! M, B = get_linear_equations(eqs) # discretization of the system: Y(n+1)=AY(n), were A=exp(M*dt). A = linalg.expm(M * dt) # compute the filter b, a = compute_filter(A, row=row) # apply the filter to the injected current y = lfilter(b, a, I*dt) + B[row] return y ''' Lp Electrode Compensation method -------------------------------- ''' class ElectrodeCompensation (object): # 1RC # the term "+ (Re/taue) * I" in the first equation is removed # the coefficient behind "I" is in the variable "I_coeff" eqs = """ dV/dt=Re*(-Iinj)/taue : volt dV0/dt=(R*Iinj-V0+Vr)/tau : volt Iinj=(V-V0)/Re : amp """ # the parameters tuple is: R, tau, Vr, Re, taue # this function returns Re/taue, the coefficient behind "I" I_coeff = lambda self, p: p[3]/p[4] def __init__(self, I, Vraw, dt, durslice=1*second, p=1.0, criterion=None, *params): """ Initialize an ElectrodeCompensation instance, used to store the model parameters during the optimization. * I: injected current, 1D vector. * Vraw: raw (uncompensated) voltage trace, 1D vector, same length as I. * dt: sampling period (inverse of the sampling frequency), in second. * durslice=1*second: duration of each time slice, where the fit is performed independently * p=1.0: parameter of the Lp error. p should be less than 2. Experimenting with this parameter is recommended. Use p~=1 at first, especially with difficult recordings Use p~0.5 with good recordings (less noise) or with biophysical model simulations without noise. * criterion: a custom error function used in the optimization. If None, it is the Lp error. Otherwise, it should be a function of the form "lambda raw, model: error", where raw and model are the raw and linear model membrane potential traces. For instance, the function for the Lp error is: "lambda raw, model: sum(abs(raw-model)**self.p)". It can also be a function of the form: "lambda raw, model, electrode: error" in the case when one needs the electrode response to compute the error. * *params: a list of initial parameters for the optimization, in the following order: R, tau, Vr, Re, taue. """ self.I = I self.Vraw = Vraw self.p = p self.dt = dt self.dt_ = float(dt) self.x0 = self.params_to_vector(*params) self.duration = len(I) * dt self.durslice = min(durslice, self.duration) self.slicesteps = int(durslice/dt) self.nslices = int(ceil(len(I)*dt/durslice)) if criterion is None: self.criterion = lambda raw, model: self.Lp_error(raw, model) else: self.criterion = criterion self.islice = 0 self.I_list = [I[self.slicesteps*i:self.slicesteps*(i+1)] for i in range(self.nslices)] self.Vraw_list = [Vraw[self.slicesteps*i:self.slicesteps*(i+1)] for i in range(self.nslices)] def vector_to_params(self, *x): """ Convert a vector of parameters (used for the optimization) to a tuple of actual parameters. """ R,tau,Vr,Re,taue = x # parameter transformation to force all parameters to be positive # and tau>taue R = R*R Re = Re*Re taue = taue*taue tau = taue + tau*tau # taudiff return R*ohm,tau*second,Vr*volt,Re*ohm,taue*second def params_to_vector(self, *params): """ Inverse function of vector_to_params: from a tuple of actual parameters, return a vector of parameters in the optimization space coordinates. """ x = [sqrt(params[0]), sqrt(params[1]-params[4]), # taudiff params[2], sqrt(params[3]), sqrt(params[4]), ] return list(x) def get_model_trace(self, row, *x): """ Compute the model response (variable index "row") to the injected current, at a specific slice (stored in self.islice), with model parameters specified with the vector x. """ # get the actual model parameters params = self.vector_to_params(*x) R, tau, Vr, Re, taue = params # get the coefficient behind I in the equations coeff = self.I_coeff(params) # compile the neuron equations, i.e., inject the parameters in them eqs = Equations(self.eqs) eqs.prepare() self._eqs = eqs # simulate the neuron response y = simulate(eqs, self.I_list[self.islice] * coeff, self.dt, row=row) return y def get_trace(self, islice, *params): """ Get the neuron and electrode traces, in slice number "islice", with the list of parameters in "params" given in the order: R, tau, Vr, Re, taue. """ x = self.params_to_vector(*params) # full model trace (neuron and electrode) V = self.get_model_trace(0, *x) # neuron voltage V0 = self.get_model_trace(1, *x) # electrode voltage Velec = V-V0 return V0, Velec def Lp_error(self, raw, model): """ Default error function: return the L^p error between the raw trace, and the full model trace (neuron and electrode) """ return (self.dt_*sum(abs(raw-model)**self.p))**(1./self.p) def fitness(self, x): """ fitness function provided to the fmin optimization procedure. Simulate the model and compute the error between the full model response (neuron and electrode) and the raw trace. """ # get the parameters R, tau, Vr, Re, taue = self.vector_to_params(*x) # compute the full model trace vmodel = self.get_model_trace(0, *x) # check if the error function requests the electrode trace if self.criterion.func_code.co_argcount>=3: v0 = self.get_model_trace(1, *x) velec = vmodel - v0 # call the error function with the parameters: raw, model, electrode e = self.criterion(self.Vraw_list[self.islice], vmodel, velec) else: # call the error function with the parameters: raw, model e = self.criterion(self.Vraw_list[self.islice], vmodel) return e def compensate_slice(self, x0): """ Compensate on the current slice, by calling fmin on the fitness function. """ fun = lambda x: self.fitness(x) x = fmin(fun, x0, maxiter=10000, maxfun=10000, disp=True) return x def compensate(self): """ Compute compensate_slice for all slices. """ # params_list contains the best parameters for each slice self.params_list = [] # xlist contains the best vector for each slice self.xlist = [self.x0] t0 = time.clock() for self.islice in range(self.nslices): newx = self.compensate_slice(self.x0) self.xlist.append(newx) self.params_list.append(self.vector_to_params(*newx)) msg = "Slice %d/%d compensated in %.2f seconds" % \ (self.islice+1, self.nslices, time.clock()-t0) log_info("electrode_compensation", msg) t0 = time.clock() self.xlist = self.xlist[1:] return self.xlist def get_compensated_trace(self): """ Once the optimization is done, compute all the model traces (full, neuron, electrode voltages). This function returns the compensated trace (raw - neuron model voltage). """ Vcomp_list = [] Vneuron_list = [] Velec_list = [] for self.islice in range(self.nslices): x = self.xlist[self.islice] V = self.get_model_trace(0, *x) V0 = self.get_model_trace(1, *x) Velec = V-V0 Vneuron_list.append(V0) Velec_list.append(Velec) Vcomp_list.append(self.Vraw_list[self.islice] - Velec) self.Vcomp = hstack(Vcomp_list) self.Vneuron = hstack(Vneuron_list) self.Velec = hstack(Velec_list) return self.Vcomp def Lp_compensate(I, Vraw, dt, slice_duration=1*second, p=1.0, criterion=None, full=False, docompensation=True, **initial_params): """ Perform the L^p electrode compensation technique on a recorded membrane potential. * I: injected current, 1D vector. * Vraw: raw (uncompensated) voltage trace, 1D vector, same length as I. * dt: sampling period (inverse of the sampling frequency), in second. * slice_duration=1*second: duration of each time slice, where the fit is performed independently * p=1.0: parameter of the Lp error. p should be less than 2. Experimenting with this parameter is recommended. Use p~1 at first, especially with difficult recordings Use p~0.5 with good recordings (less noise) or with biophysical model simulations without noise. * criterion: a custom error function used in the optimization. If None, it is the Lp error. Otherwise, it should be a function of the form "lambda raw, model: error", where raw and model are the raw and linear model membrane potential traces. For instance, the function for the Lp error is: "lambda raw, model: sum(abs(raw-model)**self.p)". It can also be a function of the form: "lambda raw, model, electrode: error" in the case when one needs the electrode response to compute the error. * full=False: if False, return a tuple (compensated_trace, parameters) where parameters is an array of the best parameters (one column/slice) If True, return a dict with the following keys: Vcompensated, Vneuron, Velectrode, params=params, instance where instance in the ElectrodeCompensation object. * docompensation=True: if False, does not perform the optimization and only return an ElectrodeCompensation object instance, to take full control over the optimization procedure. * params: a list of initial parameters for the optimization, in the following order: R, tau, Vr, Re, taue. Best results are obtained when reasonable estimates of the parameters are given. """ R = initial_params.get("R", 100*Mohm) tau = initial_params.get("tau", 20*ms) Vr = initial_params.get("Vr", -70*mV) Re = initial_params.get("Re", 50*Mohm) taue = initial_params.get("taue", .5*ms) comp = ElectrodeCompensation(I, Vraw, dt, slice_duration, p, criterion, R, tau, Vr, Re, taue, ) if docompensation: comp.compensate() Vcomp = comp.get_compensated_trace() params = array(comp.params_list).transpose() if not full: return Vcomp, params else: return dict(Vcompensated=Vcomp, Vneuron=comp.Vneuron, Vfull=comp.Vneuron+comp.Velec, Velectrode=comp.Velec, params=params, instance=comp) else: return dict(instance=comp) ''' Automatic spike detection ------------------------- ''' def find_peaks(v): """ Return the indices of the local extrema on a trace v. """ dv = diff(v) peaks = ((dv[1:] * dv[:-1]) <= 0).nonzero()[0] + 1 return peaks def find_peak_threshold(v, nbins=20, full=False): """ Find an adequate separatrix for spike detection in an intracellular trace. This threshold is found through the following procedure: * find all peaks in the trace, corresponding to a crossing of dv=0 in the phase space. Those peaks approximately follow a mixture of two gaussian distributions, one containg all spikes, the other one containing non relevant peaks in the subthreshold trace. * compute an histogram of the peak values and find the boundary between the two gaussian distributions, by finding a relevant local minimum in this histogram. Possible improvements: * use a kernel density estimation * try the mean-shift algorithm for the clustering step Parameters: * nbins=20: number of bins in the histogram * full=False: if True, return a dict with the following keys: separatrix, peak_values, peaks, allminima, histogram, bins. else, return """ peaks = find_peaks(v) vc = v[peaks] # peak values vc0 = vc.copy() m = median(vc) # histogram of the peaks bins = linspace(m, max(vc), nbins) h0,b = histogram(vc, bins) h = h0[:-5] dh = diff(h) # all local minima candidates = nonzero((dh[:-1]<=0) & (dh[1:]>=0))[0]+1 try: if len(candidates)>1: # find the longest sequence of successive integers in candidates dcandidates = diff(candidates) dcandidates[dcandidates!=1] = 0 # the last index of the longest sequence i1 = argmax(cumsum(dcandidates))+1 # first index of this sequence i0 = nonzero(dcandidates[:i1+1]!=1)[0] if len(i0)>0: i0 = i0[0] else: i0 = 0 vc = mean(b[candidates[i0:i1+1]]) else: vc = b[candidates[0]] except: vc = (m+max(vc))/2 if full: # return all variables return dict(separatrix=vc, peak_values=vc0, allminima=candidates, histogram=h0, bins=b,peaks=peaks) # return just the separatrix return vc def find_spikes(v, vc=None, dt=0.1*ms, refractory=5*ms, check_quality=False): """ Find spikes in an intracellular trace. * vc=None: separatrix (in volt). If None, a separatrix will be automatically detected using the method described in the paper. * dt=0.1*ms: timestep in the trace (inverse of the sampling frequency) * refractory=5*ms: refractory period: minimal duration between two successive spikes * check_quality=False: if True, will check spike detection quality using signal detection theory. The function then returns a tuple (spikes,scores) where scores is a dict. """ # compute the refractory period in number of time steps refractory = int(refractory/dt) # determine the separatrix automatically if vc is None: vc = find_peak_threshold(v) dv = diff(v) spikes = ((v[1:] > vc) & (v[:-1] < vc)).nonzero()[0] spikepeaks = [] if len(spikes) > 0: for i in range(len(spikes) - 1): # the peak is the max of [spike, spike+refractory] # where spike is the time when v crosses vc spike = spikes[i] + argmax(v[spikes[i]:spikes[i]+refractory]) # refractory period: discard spurious spikes if len(spikepeaks)>0: if (spike-spikepeaks[-1] 0: spikepeaks.append(spikes[-1] + decreasing[0]) else: spikepeaks.append(len(dv)) # last element spikes = array(spikepeaks) if check_quality: TP, FP, FN, TN = confusion_matrix(v, vc) scores = get_scores(TP, FP, FN, TN) MCC = scores['MCC'] print "MCC: %.8f" % MCC return spikes, scores else: return spikes def confusion_matrix(v, vc, full=False): """ Compute the confusion matrix of detected spikes vs. non-spike local extrema. * v: intracellular trace * vc: separatrix * full=False: if True, return a dict with keys: TP, FP, FN, TN, mu1, s1, mu2, s2 """ sign_changes = find_peaks(v) peaks = v[sign_changes] # peak values peaks = sort(peaks) # sort values v1 = v2 = vc # first cluster mu1 = mean(peaks[peaks<=v1]) s1 = std(peaks[peaks<=v1]) # second cluster mu2 = mean(peaks[peaks>=v2]) s2 = std(peaks[peaks>=v2]) # compute the confusion matrix TP = 1-ndtr((v2-mu2)/s2) FP = 1-ndtr((v2-mu1)/s1) FN = ndtr((v1-mu2)/s2) TN = ndtr((v1-mu1)/s1) if full: return dict(TP=TP,FP=FP,FN=FN,TN=TN,mu1=mu1,s1=s1,mu2=mu2,s2=s2) return TP, FP, FN, TN def get_scores(TP, FP, FN, TN): """ Compute various scores associated with the confusion matrix. Return a dict with values: TP, FP, FN, TN, TPR, FPR, ACC, MCC, F1 """ P = TP+FN N = FP+TN P2 = TP+FP N2 = FN+TN TPR = TP/P # true positive rate FPR = FP/N # false positive rate C = array([[TP,FP],[FN,TN]]) # confusion matrix # ACCuracy ACC = (TP+TN)/(P+N) # MCC (coef de Matthews) MCC = (TP*TN-FP*FN)/sqrt(P*N*P2*N2) # F1 score F1 = 2*TP/(P+P2) return dict(TP=TP, FP=FP, FN=FN, TN=TN, TPR=TPR, FPR=FPR,ACC=ACC,MCC=MCC,F1=F1) ''' Quality criterion ----------------- ''' def current_before_spikes(v, I, spikes, full=False): """ Compute the voltage and current before each spikes. * v: raw voltage * I: injected current * spikes: spikes indices * full=False: if True, return a dict with keys: coefficients, after_onsets, spike_before, spike_onset, spike_after """ # pre-spike phase between spike_before and spike_after, relative to spike # peak. spike_before = -100 spike_after = 10 # onset of spike relative to spike peak spike_onset = -20 t_before = arange(spike_onset-spike_before) after_onsets = [] # list of pairs (a, b) of the linear regression of voltage depolarization # before each spike cs = [] for spike in spikes: bef = spike + spike_before onset = spike + spike_onset after = spike + spike_after # v just before the spike before = v[bef:onset] # mean i during the AP after_onset = mean(I[onset:after]) after_onsets.append(after_onset) # linear regression I -> peak c = polyfit(t_before, before, 1) cs.append(c) cs = array(cs) after_onsets = array(after_onsets) if not(full): return cs, after_onsets else: return dict(coefficients=cs, after_onsets=after_onsets, spike_before=spike_before, spike_onset=spike_onset, spike_after=spike_after,) def best_peak_prediction(cs, peaks): """ Compute the best linear prediction of the peak from the linear regression c """ A = array([[sum(cs[:,0]**2), sum(cs[:,0]*cs[:,1])], \ [sum(cs[:,0]*cs[:,1]), sum(cs[:,1]**2)]]) B = array([[sum(cs[:,0]*peaks)], [sum(cs[:,1]*peaks)]]) u = linalg.solve(A, B) peaks_prediction = dot(u.reshape((1, -1)), cs.transpose()) return peaks_prediction.flatten() def get_trace_quality(v, I, full=False): """ Compute the quality of a compensated trace. * v: a compensated intracellular trace * I: injected current * full=False: if True, return a dict with the following keys: correlation, spikes, coefficients, after_onsets, peaks_prediction, after_onsets, spike_before, spike_onset, spike_after """ peaks = find_peaks(v) vc = find_peak_threshold(v) spikes = find_spikes(v,vc) # linear regression of current before spikes r = current_before_spikes(v, I, spikes, True) cs, after_onsets = r["coefficients"], r["after_onsets"] # compute the best linear prediction of the peak from the linear regression peaks_prediction = best_peak_prediction(cs, v[spikes]) c = abs(corrcoef(v[spikes] - peaks_prediction, after_onsets)[0,1]) if not(full): return c else: return dict(dict(correlation=c, spikes=spikes, cs=cs, after_onsets=after_onsets, peaks_prediction=peaks_prediction).items()+r.items()) brian-1.4.3/brian/library/electrophysiology/trace_analysis.py000066400000000000000000000204501266062647100245230ustar00rootroot00000000000000""" Analysis of voltage traces. Mainly about analysis of spike shapes. """ from numpy import * from scipy import optimize from scipy.signal import lfilter __all__ = ['find_spike_criterion', 'spike_peaks', 'spike_onsets', 'find_onset_criterion', 'slope_threshold', 'vm_threshold', 'spike_shape', 'spike_duration', 'reset_potential', 'spike_mask', 'lowpass', 'spike_onsets_dv2', 'spike_onsets_dv3'] def lowpass(x, tau, dt=1.): """ Low-pass filters x(t) with time constant tau. """ a = exp(-dt / tau) return lfilter([1. - a], [1., -a], x) def spike_duration(v, onsets=None, full=False): ''' Average spike duration. Default: time from onset to next minimum. If full is True: * Time from onset to peak * Time from onset down to same value (spike width) * Total duration from onset to next minimum * Standard deviations for these 3 values ''' if onsets is None: onsets = spike_onsets(v) dv = diff(v) total_duration = [] time_to_peak = [] spike_width = [] for i, spike in enumerate(onsets): if i == len(onsets) - 1: next_spike = len(dv) else: next_spike = onsets[i + 1] total_duration.append(((dv[spike:next_spike - 1] <= 0) & (dv[spike + 1:next_spike] > 0)).argmax()) time_to_peak.append((dv[spike:next_spike] <= 0).argmax()) spike_width.append((v[spike + 1:next_spike] <= v[spike]).argmax()) if full: return mean(time_to_peak), mean(spike_width), mean(total_duration), \ std(time_to_peak), std(spike_width), std(total_duration) else: return mean(total_duration) def reset_potential(v, peaks=None, full=False): ''' Average reset potential, calculated as next minimum after spike peak. If full is True, also returns the standard deviation. ''' if peaks is None: peaks = spike_peaks(v) dv = diff(v) reset = [] for i, spike in enumerate(peaks): if i == len(peaks) - 1: next_spike = len(dv) else: next_spike = peaks[i + 1] reset.append(v[spike + ((dv[spike:next_spike - 1] <= 0) & (dv[spike + 1:next_spike] > 0)).argmax() + 1]) if full: return mean(reset), std(reset) else: return mean(reset) def find_spike_criterion(v): ''' This is a rather complex method to determine above which voltage vc one should consider that a spike is produced. We look in phase space (v,dv/dt), at the horizontal axis dv/dt=0. We look for a voltage for which the voltage cannot be still. Algorithm: find the largest interval of voltages for which there is no sign change of dv/dt, and pick the middle. ''' # Rather: find the lowest maximum? dv = diff(v) sign_changes = ((dv[1:] * dv[:-1]) <= 0).nonzero()[0] vc = v[sign_changes + 1] i = argmax(diff(vc)) # I think there is a mistake, I should sort vc first return .5 * (vc[i] + vc[i + 1]) def spike_peaks(v, vc=None): ''' Returns the indexes of spike peaks. vc is the spike criterion (voltage above which we consider we have a spike) ''' # Possibly: add refractory criterion if vc is None: vc = find_spike_criterion(v) dv = diff(v) spikes = ((v[1:] > vc) & (v[:-1] < vc)).nonzero()[0] peaks = [] if len(spikes) > 0: for i in range(len(spikes) - 1): peaks.append(spikes[i] + (dv[spikes[i]:spikes[i + 1]] <= 0).nonzero()[0][0]) decreasing = (dv[spikes[-1]:] <= 0).nonzero()[0] if len(decreasing) > 0: peaks.append(spikes[-1] + decreasing[0]) else: peaks.append(len(dv)) # last element (maybe should be deleted?) return array(peaks) def spike_onsets(v, criterion=None, vc=None): ''' Returns the indexes of spike onsets. vc is the spike criterion (voltage above which we consider we have a spike). First derivative criterion (dv>criterion). ''' if vc is None: vc = find_spike_criterion(v) if criterion is None: criterion = find_onset_criterion(v, vc=vc) peaks = spike_peaks(v, vc) dv = diff(v) d2v = diff(dv) previous_i = 0 j = 0 l = [] for i in peaks: # Find last peak of derivative (commented: point where derivative is largest) # inflexion = previous_i + argmax(dv[previous_i:i]) inflexion=where(d2v[previous_i:i-2]*d2v[previous_i+1:i-1]<0)[0][-1]+2+previous_i j += max((dv[j:inflexion] < criterion).nonzero()[0]) + 1 l.append(j) previous_i = i return array(l) def spike_onsets_dv2(v, vc=None): ''' Returns the indexes of spike onsets. vc is the spike criterion (voltage above which we consider we have a spike). Maximum of 2nd derivative. DOESN'T SEEM GOOD ''' if vc is None: vc = find_spike_criterion(v) peaks = spike_peaks(v, vc) d2v = diff(diff(v)) d3v = diff(d2v) # I'm guessing you have to shift v by 1/2 per differentiation j = 0 l = [] previous_i=0 for i in peaks: # Find peak of derivative inflexion=where(d2v[previous_i:i-1]*d2v[previous_i+1:i]<0)[0][-1]+2+previous_i j += max(((d3v[j:inflexion - 1] > 0) & (d3v[j + 1:inflexion] < 0)).nonzero()[0]) # +2? l.append(j) previous_i=i return array(l) def spike_onsets_dv3(v, vc=None): ''' Returns the indexes of spike onsets. vc is the spike criterion (voltage above which we consider we have a spike). Maximum of 3rd derivative. DOESN'T SEEM GOOD ''' if vc is None: vc = find_spike_criterion(v) peaks = spike_peaks(v, vc) dv4 = diff(diff(diff(diff(v)))) j = 0 l = [] for i in peaks: # Find peak of derivative (alternatively: last sign change of d2v, i.e. last local peak) j += max(((dv4[j:i - 1] > 0) & (dv4[j + 1:i] < 0)).nonzero()[0]) + 3 l.append(j) return array(l) def find_onset_criterion(v, guess=0.0001, vc=None): ''' Finds the best criterion on dv/dt to determine spike onsets, based on minimum threshold variability. ''' if vc is None: vc = find_spike_criterion(v) return float(optimize.fmin(lambda x:std(v[spike_onsets(v, x, vc)]), guess, disp=0)) def spike_shape(v, onsets=None, before=100, after=100): ''' Spike shape (before peaks). Aligned on spike onset by default (to align on peaks, just pass onsets=peaks). onsets: spike onset times before: number of timesteps before onset after: number of timesteps after onset ''' if onsets is None: onsets = spike_onsets(v) shape = zeros(after + before) for i in onsets: v0 = v[max(0, i - before):i + after] shape[len(shape) - len(v0):] += v0 return shape / len(onsets) def vm_threshold(v, onsets=None, T=None): ''' Average membrane potential before spike threshold (T steps). ''' if onsets is None: onsets = spike_onsets(v) l = [] for i in onsets: l.append(mean(v[max(0, i - T):i])) return array(l) def slope_threshold(v, onsets=None, T=None): ''' Slope of membrane potential before spike threshold (T steps). Returns all slopes as an array. ''' if onsets is None: onsets = spike_onsets(v) l = [] for i in onsets: v0 = v[max(0, i - T):i] M = len(v0) x = arange(M) - M + 1 slope = sum((v0 - v[i]) * x) / sum(x ** 2) l.append(slope) return array(l) def spike_mask(v, spikes=None, T=None): ''' Returns an array of booleans which are True in spikes. spikes: starting points of spikes (default: onsets) T: duration (default: next minimum) Ex: v=v[spike_mask(v)] # only spikes v=v[-spike_mask(v)] # subthreshold trace ''' if spikes is None: spikes = spike_onsets(v) ind = (v == 1e9) if T is None: dv = diff(v) for i, spike in enumerate(spikes): if i == len(spikes) - 1: next_spike = len(dv) else: next_spike = spikes[i + 1] T = ((dv[spike:next_spike - 1] <= 0) & (dv[spike + 1:next_spike] > 0)).argmax() ind[spike:spike + T + 1] = True else: # fixed duration for i in spikes: ind[i:i + T] = True return ind brian-1.4.3/brian/library/ionic_currents.py000066400000000000000000000035561266062647100207740ustar00rootroot00000000000000''' Ionic currents for Brian ''' from brian.units import check_units, siemens, volt from brian.membrane_equations import Current from brian.equations import unique_id @check_units(El=volt) def leak_current(gl, El, current_name=None): ''' Leak current: gl*(El-vm) ''' current_name = current_name or unique_id() return Current('I=gl*(El-vm) : amp', gl=gl, El=El, I=current_name, current_name=current_name) #check_units(EK=volt) def K_current_HH(gmax, EK, current_name=None): ''' Hodkin-Huxley K+ current. Resting potential is 0 mV. ''' current_name = current_name or unique_id() return Current(''' I=gmax*n**4*(EK-vm) : amp dn/dt=alphan*(1-n)-betan*n : 1 alphan=.01*(10*mV-vm)/(exp(1-.1*vm/mV)-1)/mV/ms : Hz betan=.125*exp(-.0125*vm/mV)/ms : Hz ''', gmax=gmax, EK=EK, I=current_name, current_name=current_name) # 2 problems here: # * The current variable is determined thanks to the units, # so it doesn't work if units are off. # * Variables n, alphan and betan may conflict with other variables # (e.g. in other currents). # # Solutions: # * Set current_name explicitly. If None, generate an id or use I_K. # * Anonymise names with unique id, on option. This could be an option in # Current(). #check_units(ENa=volt) def Na_current_HH(gmax, ENa, current_name=None): ''' Hodkin-Huxley Na+ current. ''' current_name = current_name or unique_id() return Current(''' I=gmax*m**3*h*(ENa-vm) : amp dm/dt=alpham*(1-m)-betam*m : 1 dh/dt=alphah*(1-h)-betah*h : 1 alpham=.1*(25*mV-vm)/(exp(2.5-.1*vm/mV)-1)/mV/ms : Hz betam=4*exp(-.0556*vm/mV)/ms : Hz alphah=.07*exp(-.05*vm/mV)/ms : Hz betah=1./(1+exp(3.-.1*vm/mV))/ms : Hz ''', gmax=gmax, ENa=ENa, I=current_name, current_name=current_name) brian-1.4.3/brian/library/modelfitting/000077500000000000000000000000001266062647100200505ustar00rootroot00000000000000brian-1.4.3/brian/library/modelfitting/__init__.py000066400000000000000000000004201266062647100221550ustar00rootroot00000000000000from modelfitting import * from brian.experimental.codegen.integration_schemes import * #__all__ = ['modelfitting', 'print_table', 'get_spikes', 'predict', # 'PSO', 'GA','CMAES', # 'euler_scheme', 'rk2_scheme', 'exp_euler_scheme' # ] brian-1.4.3/brian/library/modelfitting/gpu_modelfitting.py000066400000000000000000000667561266062647100240060ustar00rootroot00000000000000from brian import * from playdoh import * import brian.optimiser as optimiser #import pycuda.autoinit as autoinit import pycuda.driver as drv import pycuda from pycuda.gpuarray import GPUArray from pycuda import gpuarray try: from pycuda.compiler import SourceModule except ImportError: from pycuda.driver import SourceModule from numpy import * from brian.experimental.codegen.integration_schemes import * from brian.experimental.codegen.codegen_gpu import * import re __all__ = ['GPUModelFitting', 'euler_scheme', 'rk2_scheme', 'exp_euler_scheme' ] if drv.get_version() == (2, 0, 0): # cuda version default_precision = 'float' elif drv.get_version() > (2, 0, 0): default_precision = 'double' else: raise Exception, "CUDA 2.0 required" class ModelfittingGPUCodeGenerator(GPUCodeGenerator): def generate(self, eqs, scheme): vartype = self.vartype() code = '' for line in self.scheme(eqs, scheme).split('\n'): line = line.strip() if line: code += ' ' + line + '\n' return code modelfitting_kernel_template = """ __global__ void runsim( int Tstart, int Tend, // Start, end time as integer (t=T*dt) // State variables %SCALAR% *state_vars, // State variables are offset from this double *I_arr, // Input current int *I_arr_offset, // Input current offset (for separate input // currents for each neuron) int *spikecount, // Number of spikes produced by each neuron int *num_coincidences, // Count of coincidences for each neuron int *spiketimes, // Array of all spike times as integers (begin and // end each train with large negative value) int *spiketime_indices, // Pointer into above array for each neuron int *spikedelay_arr, // Integer delay for each spike int *refractory_arr, // Integer refractory times int *next_allowed_spiketime_arr,// Integer time of the next allowed spike (for refractoriness) int onset // Time onset (only count spikes from here onwards) %COINCIDENCE_COUNT_DECLARE_EXTRA_STATE_VARIABLES% ) { const int neuron_index = blockIdx.x * blockDim.x + threadIdx.x; if(neuron_index>=%NUM_NEURONS%) return; %EXTRACT_STATE_VARIABLES% // Load variables at start %LOAD_VARIABLES% int spiketime_index = spiketime_indices[neuron_index]; int last_spike_time = spiketimes[spiketime_index]; int next_spike_time = spiketimes[spiketime_index+1]; %COINCIDENCE_COUNT_INIT% int ncoinc = num_coincidences[neuron_index]; int nspikes = spikecount[neuron_index]; int I_offset = I_arr_offset[neuron_index]; int spikedelay = spikedelay_arr[neuron_index]; const int refractory = refractory_arr[neuron_index]; int next_allowed_spiketime = next_allowed_spiketime_arr[neuron_index]; for(int T=Tstart; T=onset); // Reset if(has_spiked||is_refractory) { %RESET% } if(has_spiked) next_allowed_spiketime = T+refractory; // Coincidence counter const int Tspike = T+spikedelay; %COINCIDENCE_COUNT_TEST% if(Tspike>=next_spike_time){ spiketime_index++; last_spike_time = next_spike_time; next_spike_time = spiketimes[spiketime_index+1]; %COINCIDENCE_COUNT_NEXT% } } // Store variables at end %STORE_VARIABLES% %COINCIDENCE_COUNT_STORE_VARIABLES% next_allowed_spiketime_arr[neuron_index] = next_allowed_spiketime; spiketime_indices[neuron_index] = spiketime_index; num_coincidences[neuron_index] = ncoinc; spikecount[neuron_index] = nspikes; } """ coincidence_counting_algorithm_src = { 'inclusive':{ '%COINCIDENCE_COUNT_DECLARE_EXTRA_STATE_VARIABLES%':'', '%COINCIDENCE_COUNT_INIT%':'', '%COINCIDENCE_COUNT_TEST%':''' ncoinc += has_spiked && (((last_spike_time+%DELTA%)>=Tspike) || ((next_spike_time-%DELTA%)<=Tspike)); ''', '%COINCIDENCE_COUNT_NEXT%':'', '%COINCIDENCE_COUNT_STORE_VARIABLES%':'', }, 'exclusive':{ '%COINCIDENCE_COUNT_DECLARE_EXTRA_STATE_VARIABLES%':''', bool *last_spike_allowed_arr, bool *next_spike_allowed_arr ''', '%COINCIDENCE_COUNT_INIT%':''' bool last_spike_allowed = last_spike_allowed_arr[neuron_index]; bool next_spike_allowed = next_spike_allowed_arr[neuron_index]; ''', '%COINCIDENCE_COUNT_TEST%':''' bool near_last_spike = last_spike_time+%DELTA%>=Tspike; bool near_next_spike = next_spike_time-%DELTA%<=Tspike; near_last_spike = near_last_spike && has_spiked; near_next_spike = near_next_spike && has_spiked; ncoinc += (near_last_spike&&last_spike_allowed) || (near_next_spike&&next_spike_allowed); bool near_both_allowed = (near_last_spike&&last_spike_allowed) && (near_next_spike&&next_spike_allowed); last_spike_allowed = last_spike_allowed && !near_last_spike; next_spike_allowed = (next_spike_allowed && !near_next_spike) || near_both_allowed; ''', '%COINCIDENCE_COUNT_NEXT%':''' last_spike_allowed = next_spike_allowed; next_spike_allowed = true; ''', '%COINCIDENCE_COUNT_STORE_VARIABLES%':''' last_spike_allowed_arr[neuron_index] = last_spike_allowed; next_spike_allowed_arr[neuron_index] = next_spike_allowed; ''', }, } def generate_modelfitting_kernel_src(G, eqs, threshold, reset, dt, num_neurons, delta, coincidence_count_algorithm='exclusive', precision=default_precision, scheme=euler_scheme ): eqs.prepare() src = modelfitting_kernel_template # Substitute state variable declarations indexvar = dict((v, k) for k, v in G.var_index.iteritems() if isinstance(k, str) and k!='I') extractions = '\n '.join('%SCALAR% *'+name+'_arr = state_vars+'+str(i*num_neurons)+';' for i, name in indexvar.iteritems()) src = src.replace('%EXTRACT_STATE_VARIABLES%', extractions) # Substitute load variables loadvar_names = eqs._diffeq_names + [] loadvar_names.remove('I') # I is assumed to be a parameter and loaded per time step loadvars = '\n '.join('%SCALAR% ' + name + ' = ' + name + '_arr[neuron_index];' for name in loadvar_names) src = src.replace('%LOAD_VARIABLES%', loadvars) # Substitute save variables savevars = '\n '.join(name + '_arr[neuron_index] = ' + name + ';' for name in loadvar_names) src = src.replace('%STORE_VARIABLES%', savevars) # Substitute threshold src = src.replace('%THRESHOLD%', threshold) # Substitute reset reset = '\n '.join(line.strip() + ';' for line in reset.split('\n') if line.strip()) src = src.replace('%RESET%', reset) # Substitute state update sulines = ModelfittingGPUCodeGenerator(dtype=precision).generate(eqs, scheme) sulines = re.sub(r'\bdt\b', '%DT%', sulines) src = src.replace('%STATE_UPDATE%', sulines.strip()) # Substitute coincidence counting algorithm ccalgo = coincidence_counting_algorithm_src[coincidence_count_algorithm] for search, replace in ccalgo.iteritems(): src = src.replace(search, replace) # Substitute dt src = src.replace('%DT%', str(float(dt))) # Substitute SCALAR src = src.replace('%SCALAR%', precision) # Substitute number of neurons src = src.replace('%NUM_NEURONS%', str(num_neurons)) # Substitute delta, the coincidence window half-width src = src.replace('%DELTA%', str(int(rint(delta / dt)))) return src class GPUModelFitting(object): ''' Model fitting class to interface with GPU Initialisation arguments: ``G`` The initialised NeuronGroup to work from. ``eqs`` The equations defining the NeuronGroup. ``I``, ``I_offset`` The current array and offsets (see below). ``spiketimes``, ``spiketimes_offset`` The spike times array and offsets (see below). ``spikedelays``, Array of delays for each neuron. ``refractory``, Array of refractory periods, or a single value. ``delta`` The half-width of the coincidence window. ``precision`` Should be 'float' or 'double' - by default the highest precision your GPU supports. ``coincidence_count_algorithm`` Should be 'inclusive' if multiple predicted spikes can match one target spike, or 'exclusive' (default) if multiple predicted spikes can match only one target spike (earliest spikes are matched first). Methods: ``reinit_vars(I, I_offset, spiketimes, spiketimes_offset, spikedelays)`` Reinitialises all the variables, counters, etc. The state variable values are copied from the NeuronGroup G again, and the variables I, I_offset, etc. are copied from the method arguments. ``launch(duration[, stepsize])`` Runs the kernel on the GPU for simulation time duration. If ``stepsize`` is given, the simulation is broken into pieces of that size. This is useful on Windows because driver limitations mean that individual GPU kernel launches cannot last more than a few seconds without causing a crash. Attributes: ``coincidence_count`` An array of the number of coincidences counted for each neuron. ``spike_count`` An array of the number of spikes counted for each neuron. **Details** The equations for the NeuronGroup can be anything, but they will be solved with the Euler method. One restriction is that there must be a parameter named I which is the time varying input current. (TODO: support for multiple input currents? multiple names?) The current I is passed to the GPU in the following way. Define a single 1D array I which contains all the time varying current arrays one after the other. Now define an array I_offset of integers so that neuron i should see currents: I[I_offset[i]], I[I_offset[i]+1], I[I_offset[i]+2], etc. The experimentally recorded spike times should be passed in a similar way, put all the spike times in a single array and pass an offsets array spiketimes_offset. One difference is that it is essential that each spike train with the spiketimes array should begin with a spike at a large negative time (e.g. -1*second) and end with a spike that is a long time after the duration of the run (e.g. duration+1*second). The GPU uses this to mark the beginning and end of the train rather than storing the number of spikes for each train. ''' def __init__(self, G, eqs, I, I_offset, spiketimes, spiketimes_offset, spikedelays, refractory, delta, onset=0 * ms, coincidence_count_algorithm='exclusive', precision=default_precision, scheme=euler_scheme ): eqs.prepare() self.precision = precision if precision == 'double': self.mydtype = float64 else: self.mydtype = float32 self.N = N = len(G) self.dt = dt = G.clock.dt self.delta = delta self.onset = onset self.eqs = eqs self.G = G self.coincidence_count_algorithm = coincidence_count_algorithm threshold = G._threshold if threshold.__class__ is Threshold: state = threshold.state if isinstance(state, int): state = eqs._diffeq_names[state] threshold = state + '>' + str(float(threshold.threshold)) elif isinstance(threshold, VariableThreshold): state = threshold.state if isinstance(state, int): state = eqs._diffeq_names[state] threshold = state + '>' + threshold.threshold_state elif isinstance(threshold, StringThreshold): namespace = threshold._namespace expr = threshold._expr all_variables = eqs._eq_names + eqs._diffeq_names + eqs._alias.keys() + ['t'] expr = optimiser.freeze(expr, all_variables, namespace) threshold = expr else: raise ValueError('Threshold must be constant, VariableThreshold or StringThreshold.') self.threshold = threshold reset = G._resetfun if reset.__class__ is Reset: state = reset.state if isinstance(state, int): state = eqs._diffeq_names[state] reset = state + ' = ' + str(float(reset.resetvalue)) elif isinstance(reset, VariableReset): state = reset.state if isinstance(state, int): state = eqs._diffeq_names[state] reset = state + ' = ' + reset.resetvaluestate elif isinstance(reset, StringReset): namespace = reset._namespace expr = reset._expr all_variables = eqs._eq_names + eqs._diffeq_names + eqs._alias.keys() + ['t'] expr = optimiser.freeze(expr, all_variables, namespace) reset = expr self.reset = reset self.kernel_src = generate_modelfitting_kernel_src( self.G, eqs, threshold, reset, dt, N, delta, coincidence_count_algorithm=coincidence_count_algorithm, precision=precision, scheme=scheme) def reinit_vars(self, I, I_offset, spiketimes, spiketimes_offset, spikedelays, refractory): self.kernel_module = SourceModule(self.kernel_src) self.kernel_func = self.kernel_module.get_function('runsim') blocksize = 128 try: blocksize = self.kernel_func.get_attribute(pycuda.driver.function_attribute.MAX_THREADS_PER_BLOCK) except: # above won't work unless CUDA>=2.2 pass self.block = (blocksize, 1, 1) self.grid = (int(ceil(float(self.N) / blocksize)), 1) self.kernel_func_kwds = {'block':self.block, 'grid':self.grid} mydtype = self.mydtype N = self.N eqs = self.eqs statevars_arr = gpuarray.to_gpu(array(self.G._S.flatten(), dtype=mydtype)) self.I = gpuarray.to_gpu(array(I, dtype=mydtype)) self.statevars_arr = statevars_arr self.I_offset = gpuarray.to_gpu(array(I_offset, dtype=int32)) self.spiketimes = gpuarray.to_gpu(array(rint(spiketimes / self.dt), dtype=int32)) self.spiketime_indices = gpuarray.to_gpu(array(spiketimes_offset, dtype=int32)) self.num_coincidences = gpuarray.to_gpu(zeros(N, dtype=int32)) self.spikecount = gpuarray.to_gpu(zeros(N, dtype=int32)) self.spikedelay_arr = gpuarray.to_gpu(array(rint(spikedelays / self.dt), dtype=int32)) if isinstance(refractory, float): refractory = refractory*ones(N) self.refractory_arr = gpuarray.to_gpu(array(rint(refractory / self.dt), dtype=int32)) self.next_allowed_spiketime_arr = gpuarray.to_gpu(-ones(N, dtype=int32)) self.next_spike_allowed_arr = gpuarray.to_gpu(ones(N, dtype=bool)) self.last_spike_allowed_arr = gpuarray.to_gpu(zeros(N, dtype=bool)) self.kernel_func_args = [self.statevars_arr, self.I, self.I_offset, self.spikecount, self.num_coincidences, self.spiketimes, self.spiketime_indices, self.spikedelay_arr, self.refractory_arr, self.next_allowed_spiketime_arr, int32(rint(self.onset / self.dt))] if self.coincidence_count_algorithm == 'exclusive': self.kernel_func_args += [self.last_spike_allowed_arr, self.next_spike_allowed_arr] def launch(self, duration, stepsize=1 * second): if stepsize is None: self.kernel_func(int32(0), int32(duration / self.dt), *self.kernel_func_args, **self.kernel_func_kwds) pycuda.context.synchronize() else: stepsize = int(stepsize / self.dt) duration = int(duration / self.dt) for Tstart in xrange(0, duration, stepsize): Tend = Tstart + min(stepsize, duration - Tstart) self.kernel_func(int32(Tstart), int32(Tend), *self.kernel_func_args, **self.kernel_func_kwds) pycuda.context.synchronize() def get_coincidence_count(self): return self.num_coincidences.get() def get_spike_count(self): return self.spikecount.get() coincidence_count = property(fget=lambda self:self.get_coincidence_count()) spike_count = property(fget=lambda self:self.get_spike_count()) if __name__ == '__main__': import time from matplotlib.cm import jet from playdoh import * initialise_cuda() set_gpu_device(0) if 1: set_global_preferences(usecodegenthreshold=False) N = 10000 nvar = 100 delta = 4 * ms doplot = True eqs = ''' dV/dt = (-V+R*I)/tau : volt Vt : volt tau : second R : ohm I : amp ''' eqs += '\n'.join(['var'+str(i)+':0' for i in range(nvar)]) eqs = Equations(eqs) Vr = 0.0 * volt Vt = 1.0 * volt I = loadtxt('../../../dev/ideas/cuda/modelfitting/current.txt') spiketimes = loadtxt('../../../dev/ideas/cuda/modelfitting/spikes.txt') spiketimes -= int(min(spiketimes)) I_offset = zeros(N, dtype=int) spiketimes_offset = zeros(N, dtype=int) G = NeuronGroup(N, eqs, reset='V=0*volt; Vt+=1*volt', threshold='V>1*volt') G.R = rand(N) * 2e9 + 1e9 G.tau = rand(N) * 49 * ms + 1 * ms spikedelays = rand(N) * 5 * ms duration = len(I) * G.clock.dt spiketimes = hstack((-1, spiketimes, float(duration) + 1)) mf = GPUModelFitting(G, eqs, I, I_offset, spiketimes, spiketimes_offset, spikedelays, 0*ms, delta, coincidence_count_algorithm='exclusive') mf.reinit_vars(I, I_offset, spiketimes, spiketimes_offset, spikedelays, 0*ms) print mf.kernel_src start_time = time.time() mf.launch(duration) running_time = time.time() - start_time print 'N:', N print 'Duration:', duration print 'Total running time:', running_time if doplot: spikecount = mf.spike_count num_coincidences = mf.coincidence_count R = G.R tau = G.tau print 'Spike count varies between', spikecount.min(), 'and', spikecount.max() print 'Num coincidences varies between', num_coincidences.min(), 'and', num_coincidences.max() subplot(221) scatter(R, tau, color=jet(spikecount / float(spikecount.max()))) xlabel('R') ylabel('tau') title('Spike count, max = ' + str(spikecount.max())) axis('tight') subplot(222) scatter(R, tau, color=jet(num_coincidences / float(num_coincidences.max()))) xlabel('R') ylabel('tau') title('Num coincidences, max = ' + str(num_coincidences.max())) axis('tight') spikecount -= num_coincidences num_coincidences -= spikecount num_coincidences[num_coincidences < 0] = 0 maxcoinc = num_coincidences.max() num_coincidences = (1. * num_coincidences) / maxcoinc subplot(212) scatter(R, tau, color=jet(num_coincidences)) xlabel('R') ylabel('tau') title('Hot = ' + str(maxcoinc) + ' excess coincidences, cool = 0 or less') axis('tight') show() if 0: N = 10000 eqs = Equations(''' dV/dt = (-V+R*I)/tau : volt dVt/dt = -(V-1*volt)/tau_t : volt R : ohm tau : second tau_t : second Vt_delta : volt I : amp ''') threshold = 'V>Vt' reset = ''' V = 0*volt Vt += Vt_delta ''' delta = 4 * ms src, declarations_seq = generate_modelfitting_kernel_src(eqs, threshold, reset, defaultclock.dt, N, delta) print src print print declarations_seq if 0: # test traces #clk = RegularClock(makedefaultclock=True) clk = defaultclock N = 1 duration = 200 * ms delta = 4 * ms Ntarg = 20 randspikes = hstack(([-1 * second], sort(rand(Ntarg) * duration * .9 + duration * 0.05), [duration + 1 * second])) #randspikes = sort(unique(array(randspikes/(2*delta), dtype=int)))*2*delta randspikes = sort(unique(array(randspikes / clk.dt, dtype=int))) * clk.dt + 1e-10 eqs = Equations(''' dV/dt = (-V+I)/(10*ms) : 1 I : 1 ''') threshold = 'V>1' reset = 'V=0' G = NeuronGroup(N, eqs, threshold=threshold, reset=reset, method='Euler', clock=clk) #from brian.experimental.ccodegen import * #su = AutoCompiledNonlinearStateUpdater(eqs, G.clock, freeze=True) #G._state_updater = su #I = 1.1*ones(int(duration/defaultclock.dt)) I = 5.0 * rand(int(duration / defaultclock.dt)) #I = hstack((zeros(100), 10*ones(int(duration/defaultclock.dt)))) #I = hstack((zeros(100), 10*ones(100))*(int(duration/defaultclock.dt)/200)) #I = hstack((zeros(100), 10*exp(-linspace(0,2,100)))*(int(duration/defaultclock.dt)/200)) #G.I = TimedArray(hstack((0, I))) #G.I = TimedArray(I[1:], clock=clk) G.I = TimedArray(I, clock=clk) M = StateMonitor(G, 'V', record=True, when='end', clock=clk) MS = SpikeMonitor(G) cc_ex = CoincidenceCounter(source=G, data=randspikes, delta=delta, coincidence_count_algorithm='exclusive') cc_in = CoincidenceCounter(source=G, data=randspikes, delta=delta, coincidence_count_algorithm='inclusive') # cc2 = CoincidenceCounter(source=G, data=randspikes[1:-1], delta=delta) run(duration) #spiketimes = array([-1*second, duration+1*second]) #spiketimes_offset = zeros(N, dtype=int) #spiketimes = [-1*second]+MS[0]+[duration+1*second] spiketimes = randspikes spiketimes_offset = zeros(N, dtype=int) #I = array([0]+I) I_offset = zeros(N, dtype=int) spikedelays = zeros(N) reinit_default_clock() G.V = 0 mf = GPUModelFitting(G, eqs, I, I_offset, spiketimes, spiketimes_offset, spikedelays, delta, coincidence_count_algorithm='exclusive') allV = [] oldnc = 0 oldsc = 0 allcoinc = [] all_pst = [] all_nst = [] allspike = [] all_nsa = [] all_lsa = [] if 0: for i in xrange(len(M.times)): mf.kernel_func(int32(i), int32(i + 1), *mf.kernel_func_args, **mf.kernel_func_kwds) #autoinit.context.synchronize() pycuda.context.synchronize() allV.append(mf.state_vars['V'].get()) all_pst.append(mf.spiketimes.get()[mf.spiketime_indices.get()]) all_nst.append(mf.spiketimes.get()[mf.spiketime_indices.get() + 1]) all_nsa.append(mf.next_spike_allowed_arr.get()[0]) all_lsa.append(mf.last_spike_allowed_arr.get()[0]) # self.next_spike_allowed_arr = gpuarray.to_gpu(ones(N, dtype=bool)) # self.last_spike_allowed_arr = gpuarray.to_gpu(zeros(N, dtype=bool)) nc = mf.coincidence_count[0] if nc > oldnc: oldnc = nc allcoinc.append(i * clk.dt) sc = mf.spike_count[0] if sc > oldsc: oldsc = sc allspike.append(i * clk.dt) else: mf.launch(duration, stepsize=None) print 'Num target spikes:', len(randspikes) - 2 print 'Predicted spike counts:', MS.nspikes, mf.spike_count[0] print 'Coincidences:' print 'GPU', mf.coincidence_count # print 'CPU bis inc', cc_in.coincidences print 'CPU bis exc', cc_ex.coincidences # print 'CPU', cc2.coincidences # for t in randspikes[1:-1]: # plot([t*second-delta, t*second+delta], [0, 0], lw=5, color=(.9,.9,.9)) # plot(randspikes[1:-1], zeros(len(randspikes)-2), '+', ms=15) # plot(M.times, M[0]) if len(allV): # plot(M.times, allV) # plot(allcoinc, zeros(len(allcoinc)), 'o') # figure() plot(M.times, array(all_pst) * clk.dt) plot(M.times, array(all_nst) * clk.dt) plot(randspikes[1:-1], randspikes[1:-1], 'o') plot(allspike, allspike, 'x') plot(allcoinc, allcoinc, '+') plot(M.times, array(all_nsa) * M.times, '--') plot(M.times, array(all_lsa) * M.times, '-.') predicted_spikes = allspike target_spikes = [t * second for t in randspikes] i = 0 truecoinc = [] for pred_t in predicted_spikes: while target_spikes[i] < pred_t + delta: if abs(target_spikes[i] - pred_t) < delta: truecoinc.append((pred_t, target_spikes[i])) i += 1 break i += 1 print 'Truecoinc:', len(truecoinc) for t1, t2 in truecoinc: plot([t1, t2], [t1, t2], ':', color=(0.5, 0, 0), lw=3) # show() brian-1.4.3/brian/library/modelfitting/gputools.py000066400000000000000000000026741266062647100223070ustar00rootroot00000000000000#from ..log import * import atexit __all__ = ['initialise_cuda', 'set_gpu_device', 'close_cuda'] try: import pycuda import pycuda.driver as drv pycuda.context = None pycuda.isinitialised = False MAXGPU = 0 # drv.Device.count() # def initialise_cuda(): global MAXGPU if not pycuda.isinitialised: drv.init() pycuda.isinitialised = True MAXGPU = drv.Device.count() def set_gpu_device(n): """ This function makes pycuda use GPU number n in the system. """ initialise_cuda() #log_debug('brian.hears', "Setting PyCUDA context number %d" % n) try: pycuda.context.detach() except: pass pycuda.context = drv.Device(n).make_context() def close_cuda(): """ Closes the current context. MUST be called at the end of the script. """ if pycuda.context is not None: #log_debug('brian.hears', "Closing current PyCUDA context") try: pycuda.context.pop() pycuda.context = None except: pass atexit.register(close_cuda) except: MAXGPU = 0 def initialise_cuda(): pass def set_gpu_device(n): raise Exception("PyCUDA not available") pass def close_cuda(): pass brian-1.4.3/brian/library/modelfitting/modelfitting.py000066400000000000000000000620751266062647100231210ustar00rootroot00000000000000#from brian import Equations, NeuronGroup, Clock, CoincidenceCounter, Network, zeros, array, \ # ones, kron, ms, second, concatenate, hstack, sort, nonzero, diff, TimedArray, \ # reshape, sum, log from brian import * from brian.tools.statistics import firing_rate, get_gamma_factor try: from playdoh import * except Exception, e: print e raise ImportError("Playdoh must be installed (https://code.google.com/p/playdoh/)") try: import pycuda from brian.library.modelfitting.gpu_modelfitting import GPUModelFitting can_use_gpu = True except ImportError: can_use_gpu = False from brian.experimental.codegen.integration_schemes import * import sys, cPickle __all__ = ['modelfitting', 'print_table', 'get_spikes', 'predict', 'PSO', 'GA','CMAES', 'MAXCPU', 'MAXGPU', 'debug_level', 'info_level', 'warning_level', 'open_server'] class ModelFitting(Fitness): def initialize(self, **kwds): self.use_gpu = self.unit_type=='GPU' # Gets the key,value pairs in shared_data for key, val in self.shared_data.iteritems(): setattr(self, key, val) # Gets the key,value pairs in **kwds for key, val in kwds.iteritems(): setattr(self, key, val) # log_info(self.model) self.model = cPickle.loads(self.model) # log_info(self.model) # if model is a string if type(self.model) is str: self.model = Equations(self.model) self.total_steps = int(self.duration / self.dt) self.neurons = self.nodesize self.groups = self.groups # Time slicing self.input = self.input[0:self.slices * (len(self.input) / self.slices)] # makes sure that len(input) is a multiple of slices self.duration = len(self.input) * self.dt # duration of the input self.sliced_steps = len(self.input) / self.slices # timesteps per slice self.overlap_steps = int(self.overlap / self.dt) # timesteps during the overlap self.total_steps = self.sliced_steps + self.overlap_steps # total number of timesteps self.sliced_duration = self.overlap + self.duration / self.slices # duration of the vectorized simulation self.N = self.neurons * self.slices # TOTAL number of neurons in this worker self.input = hstack((zeros(self.overlap_steps), self.input)) # add zeros at the beginning because there is no overlap from the previous slice # Prepares data (generates I_offset, spiketimes, spiketimes_offset) self.prepare_data() # Add 'refractory' parameter on the CPU on the CPU only if not self.use_gpu: if self.max_refractory is not None: refractory = 'refractory' self.model.add_param('refractory', second) else: refractory = self.refractory else: if self.max_refractory is not None: refractory = 0*ms else: refractory = self.refractory # Must recompile the Equations : the functions are not transfered after pickling/unpickling self.model.compile_functions() self.group = NeuronGroup(self.N, model=self.model, reset=self.reset, threshold=self.threshold, refractory=refractory, max_refractory = self.max_refractory, method = self.method, clock=Clock(dt=self.dt)) if self.initial_values is not None: for param, value in self.initial_values.iteritems(): self.group.state(param)[:] = value # Injects current in consecutive subgroups, where I_offset have the same value # on successive intervals k = -1 for i in hstack((nonzero(diff(self.I_offset))[0], len(self.I_offset) - 1)): I_offset_subgroup_value = self.I_offset[i] I_offset_subgroup_length = i - k sliced_subgroup = self.group.subgroup(I_offset_subgroup_length) input_sliced_values = self.input[I_offset_subgroup_value:I_offset_subgroup_value + self.total_steps] sliced_subgroup.set_var_by_array(self.input_var, TimedArray(input_sliced_values, clock=self.group.clock)) k = i if self.use_gpu: # Select integration scheme according to method if self.method == 'Euler': scheme = euler_scheme elif self.method == 'RK': scheme = rk2_scheme elif self.method == 'exponential_Euler': scheme = exp_euler_scheme else: raise Exception("The numerical integration method is not valid") self.mf = GPUModelFitting(self.group, self.model, self.input, self.I_offset, self.spiketimes, self.spiketimes_offset, zeros(self.neurons), 0*ms, self.delta, precision=self.precision, scheme=scheme) else: self.cc = CoincidenceCounter(self.group, self.spiketimes, self.spiketimes_offset, onset=self.onset, delta=self.delta) def prepare_data(self): """ Generates I_offset, spiketimes, spiketimes_offset from data, and also target_length and target_rates. The neurons are first grouped by time slice : there are group_size*group_count per group/time slice Within each time slice, the neurons are grouped by target train : there are group_size neurons per group/target train """ # Generates I_offset self.I_offset = zeros(self.N, dtype=int) for slice in range(self.slices): self.I_offset[self.neurons * slice:self.neurons * (slice + 1)] = self.sliced_steps * slice # Generates spiketimes, spiketimes_offset, target_length, target_rates i, t = zip(*self.data) i = array(i) t = array(t) alls = [] n = 0 pointers = [] dt = self.dt target_length = [] target_rates = [] model_target = [] group_index = 0 neurons_in_group = self.subpopsize for j in xrange(self.groups): # neurons_in_group = self.groups[j] # number of neurons in the current group and current worker s = sort(t[i == j]) target_length.extend([len(s)] * neurons_in_group) target_rates.extend([firing_rate(s)] * neurons_in_group) for k in xrange(self.slices): # first sliced group : 0...0, second_train...second_train, ... # second sliced group : first_train_second_slice...first_train_second_slice, second_train_second_slice... spikeindices = (s >= k * self.sliced_steps * dt) & (s < (k + 1) * self.sliced_steps * dt) # spikes targeted by sliced neuron number k, for target j targeted_spikes = s[spikeindices] - k * self.sliced_steps * dt + self.overlap_steps * dt # targeted spikes in the "local clock" for sliced neuron k targeted_spikes = hstack((-1 * second, targeted_spikes, self.sliced_duration + 1 * second)) model_target.extend([k + group_index * self.slices] * neurons_in_group) alls.append(targeted_spikes) pointers.append(n) n += len(targeted_spikes) group_index += 1 pointers = array(pointers, dtype=int) model_target = array(hstack(model_target), dtype=int) self.spiketimes = hstack(alls) self.spiketimes_offset = pointers[model_target] # [pointers[i] for i in model_target] # Duplicates each target_length value 'group_size' times so that target_length[i] # is the length of the train targeted by neuron i self.target_length = array(target_length, dtype=int) self.target_rates = array(target_rates) def evaluate(self, **param_values): """ Use fitparams['delays'] to take delays into account Use fitparams['refractory'] to take refractory into account """ delays = param_values.pop('delays', zeros(self.neurons)) refractory = param_values.pop('refractory', zeros(self.neurons)) # kron spike delays delays = kron(delays, ones(self.slices)) refractory = kron(refractory, ones(self.slices)) # Sets the parameter values in the NeuronGroup object self.group.reinit() for param, value in param_values.iteritems(): self.group.state(param)[:] = kron(value, ones(self.slices)) # kron param_values if slicing # Reinitializes the model variables if self.initial_values is not None: for param, value in self.initial_values.iteritems(): self.group.state(param)[:] = value if self.use_gpu: # Reinitializes the simulation object self.mf.reinit_vars(self.input, self.I_offset, self.spiketimes, self.spiketimes_offset, delays, refractory) # LAUNCHES the simulation on the GPU self.mf.launch(self.duration, self.stepsize) coincidence_count = self.mf.coincidence_count spike_count = self.mf.spike_count else: # set the refractory period if self.max_refractory is not None: self.group.refractory = refractory self.cc = CoincidenceCounter(self.group, self.spiketimes, self.spiketimes_offset, onset=self.onset, delta=self.delta) # Sets the spike delay values self.cc.spikedelays = delays # Reinitializes the simulation objects self.group.clock.reinit() # self.cc.reinit() net = Network(self.group, self.cc) # LAUNCHES the simulation on the CPU net.run(self.duration) coincidence_count = self.cc.coincidences spike_count = self.cc.model_length coincidence_count = sum(reshape(coincidence_count, (self.slices, -1)), axis=0) spike_count = sum(reshape(spike_count, (self.slices, -1)), axis=0) gamma = get_gamma_factor(coincidence_count, spike_count, self.target_length, self.target_rates, self.delta) return gamma def modelfitting(model=None, reset=None, threshold=None, refractory=0*ms, data=None, input_var='I', input=None, dt=None, popsize=1000, maxiter=10, delta=4*ms, slices=1, overlap=0*second, initial_values=None, stepsize=100 * ms, unit_type=None, total_units=None, cpu=None, gpu=None, precision='double', # set to 'float' or 'double' to specify single or double precision on the GPU machines=[], allocation=None, returninfo=False, scaling=None, algorithm=CMAES, async = None, optparams={}, method='Euler', **params): """ Model fitting function. Fits a spiking neuron model to electrophysiological data (injected current and spikes). See also the section :ref:`model-fitting-library` in the user manual. **Arguments** ``model`` An :class:`~brian.Equations` object containing the equations defining the model. ``reset`` A reset value for the membrane potential, or a string containing the reset equations. ``threshold`` A threshold value for the membrane potential, or a string containing the threshold equations. ``refractory`` The refractory period in second. If it's a single value, the same refractory will be used in all the simulations. If it's a list or a tuple, the fitting will also optimize the refractory period (see ``**params`` below). Warning: when using a refractory period, you can't use a custom reset, only a fixed one. ``data`` A list of spike times, or a list of several spike trains as a list of pairs (index, spike time) if the fit must be performed in parallel over several target spike trains. In this case, the modelfitting function returns as many parameters sets as target spike trains. ``input_var='I'`` The variable name used in the equations for the input current. ``input`` A vector of values containing the time-varying signal the neuron responds to (generally an injected current). ``dt`` The time step of the input (the inverse of the sampling frequency). ``**params`` The list of parameters to fit the model with. Each parameter must be set as follows: ``param_name=[bound_min, min, max, bound_max]`` where ``bound_min`` and ``bound_max`` are the boundaries, and ``min`` and ``max`` specify the interval from which the parameter values are uniformly sampled at the beginning of the optimization algorithm. If not using boundaries, set ``param_name=[min, max]``. Also, you can add a fit parameter which is a spike delay for all spikes : add the special parameter ``delays`` in ``**params``, for example ``modelfitting(..., delays=[-10*ms, 10*ms])``. You can also add fit the refractory period by specifying ``modelfitting(..., refractory=[-10*ms, 10*ms])``. ``popsize`` Size of the population (number of particles) per target train used by the optimization algorithm. ``maxiter`` Number of iterations in the optimization algorithm. ``optparams`` Optimization algorithm parameters. It is a dictionary: keys are parameter names, values are parameter values or lists of parameters (one value per group). This argument is specific to the optimization algorithm used. See :class:`PSO`, :class:`GA`, :class:`CMAES`. ``delta=4*ms`` The precision factor delta (a scalar value in second). ``slices=1`` The number of time slices to use. ``overlap=0*ms`` When using several time slices, the overlap between consecutive slices, in seconds. ``initial_values`` A dictionary containing the initial values for the state variables. ``cpu`` The number of CPUs to use in parallel. It is set to the number of CPUs in the machine by default. ``gpu`` The number of GPUs to use in parallel. It is set to the number of GPUs in the machine by default. ``precision`` GPU only: a string set to either ``float`` or ``double`` to specify whether to use single or double precision on the GPU. If it is not specified, it will use the best precision available. ``returninfo=False`` Boolean indicating whether the modelfitting function should return technical information about the optimization. ``scaling=None`` Specify the scaling used for the parameters during the optimization. It can be ``None`` or ``'mapminmax'``. It is ``None`` by default (no scaling), and ``mapminmax`` by default for the CMAES algorithm. ``algorithm=CMAES`` The optimization algorithm. It can be :class:`PSO`, :class:`GA` or :class:`CMAES`. ``optparams={}`` Optimization parameters. See ``method='Euler'`` Integration scheme used on the CPU and GPU: ``'Euler'`` (default), ``RK``, or ``exponential_Euler``. See also :ref:`numerical-integration`. ``machines=[]`` A list of machine names to use in parallel. See :ref:`modelfitting-clusters`. **Return values** Return an :class:`OptimizationResult` object with the following attributes: ``best_pos`` Minimizing position found by the algorithm. For array-like fitness functions, it is a single vector if there is one group, or a list of vectors. For keyword-like fitness functions, it is a dictionary where keys are parameter names and values are numeric values. If there are several groups, it is a list of dictionaries. ``best_fit`` The value of the fitness function for the best positions. It is a single value if there is one group, or it is a list if there are several groups. ``info`` A dictionary containing various information about the optimization. Also, the following syntax is possible with an ``OptimizationResult`` instance ``or``. The ``key`` is either an optimizing parameter name for keyword-like fitness functions, or a dimension index for array-like fitness functions. ``or[key]`` it is the best ``key`` parameter found (single value), or the list of the best parameters ``key`` found for all groups. ``or[i]`` where ``i`` is a group index. This object has attributes ``best_pos``, ``best_fit``, ``info`` but only for group ``i``. ``or[i][key]`` where ``i`` is a group index, is the same as ``or[i].best_pos[key]``. For more details on the gamma factor, see `Jolivet et al. 2008, "A benchmark test for a quantitative assessment of simple neuron models", J. Neurosci. Methods `__ (available in PDF `here `__). """ for param in params.keys(): if (param not in model._diffeq_names) and (param != 'delays'): raise Exception("Parameter %s must be defined as a parameter in the model" % param) # Make sure that 'data' is a N*2-array data = array(data) if data.ndim == 1: data = concatenate((zeros((len(data), 1)), data.reshape((-1, 1))), axis=1) # dt must be set if dt is None: raise Exception('dt (sampling frequency of the input) must be set') # default overlap when no time slicing if slices == 1: overlap = 0 * ms # default allocation if cpu is None and gpu is None and unit_type is None: if CANUSEGPU: unit_type = 'GPU' else: unit_type = 'CPU' # check numerical integration method if (gpu>0 or unit_type == 'GPU') and method not in ['Euler', 'RK', 'exponential_Euler']: raise Exception("The method can only be 'Euler', 'RK', or 'exponential_Euler' when using the GPU") if method not in ['Euler', 'RK', 'exponential_Euler', 'linear', 'nonlinear']: raise Exception("The method can only be 'Euler', 'RK', 'exponential_Euler', 'linear', or 'nonlinear'") if (algorithm == CMAES) & (scaling is None): scaling = 'mapminmax' # determines whether optimization over refractoriness or not if type(refractory) is tuple or type(refractory) is list: params['refractory'] = refractory max_refractory = refractory[-1] # refractory = 'refractory' else: max_refractory = None # common values # group_size = particles # Number of particles per target train groups = int(array(data)[:, 0].max() + 1) # number of target trains # N = group_size * group_count # number of neurons duration = len(input) * dt # duration of the input # keyword arguments for Modelfitting initialize kwds = dict( model=cPickle.dumps(model), threshold=threshold, reset=reset, refractory=refractory, max_refractory=max_refractory, input_var=input_var,dt=dt, duration=duration,delta=delta, slices=slices, overlap=overlap, returninfo=returninfo, precision=precision, stepsize=stepsize, method=method, onset=0 * ms) shared_data = dict(input=input, data=data, initial_values=initial_values) if async: r = maximize_async( ModelFitting, shared_data=shared_data, kwds = kwds, groups=groups, popsize=popsize, maxiter=maxiter, optparams=optparams, unit_type = unit_type, total_units = total_units, machines=machines, allocation=allocation, cpu=cpu, gpu=gpu, returninfo=returninfo, codedependencies=[], algorithm=algorithm, scaling=scaling, **params) else: r = maximize( ModelFitting, shared_data=shared_data, kwds = kwds, groups=groups, popsize=popsize, maxiter=maxiter, optparams=optparams, unit_type = unit_type, total_units = total_units, machines=machines, allocation=allocation, cpu=cpu, gpu=gpu, returninfo=returninfo, codedependencies=[], algorithm=algorithm, scaling=scaling, **params) return r def get_spikes(model=None, reset=None, threshold=None, input=None, input_var='I', dt=None, initial_values=None, **params): """ Retrieves the spike times corresponding to the best parameters found by the modelfitting function. **Arguments** ``model``, ``reset``, ``threshold``, ``input``, ``input_var``, ``dt``, ``initial_values`` Same parameters as for the ``modelfitting`` function. ``**params`` The best parameters returned by the ``modelfitting`` function. **Returns** ``spiketimes`` The spike times of the model with the given input and parameters. """ duration = len(input) * dt ngroups = len(params[params.keys()[0]]) group = NeuronGroup(N=ngroups, model=model, reset=reset, threshold=threshold, clock=Clock(dt=dt)) group.set_var_by_array(input_var, TimedArray(input, clock=group.clock)) for param, values in params.iteritems(): if (param == 'delays') | (param == 'fitness'): continue group.state(param)[:] = values if initial_values is not None: for param, value in initial_values.iteritems(): group.state(param)[:] = value M = SpikeMonitor(group) net = Network(group, M) net.run(duration) reinit_default_clock() return M.spikes def predict(model=None, reset=None, threshold=None, data=None, delta=4 * ms, input=None, input_var='I', dt=None, **params): """ Predicts the gamma factor of a fitted model with respect to the data with a different input current. **Arguments** ``model``, ``reset``, ``threshold``, ``input_var``, ``dt`` Same parameters as for the ``modelfitting`` function. ``input`` The input current, that can be different from the current used for the fitting procedure. ``data`` The experimental spike times to compute the gamma factor against. They have been obtained with the current ``input``. ``**params`` The best parameters returned by the ``modelfitting`` function. **Returns** ``gamma`` The gamma factor of the model spike trains against the data. If there were several groups in the fitting procedure, it is a vector containing the gamma factor for each group. """ spikes = get_spikes(model=model, reset=reset, threshold=threshold, input=input, input_var=input_var, dt=dt, **params) ngroups = len(params[params.keys()[0]]) gamma = zeros(ngroups) for i in xrange(ngroups): spk = [t for j, t in spikes if j == i] gamma[i] = gamma_factor(spk, data, delta, normalize=True, dt=dt) if len(gamma) == 1: return gamma[0] else: return gamma brian-1.4.3/brian/library/random_processes.py000066400000000000000000000031401266062647100213010ustar00rootroot00000000000000''' Random processes for Brian. ''' from brian.equations import Equations from brian.units import get_unit from scipy.signal import lfilter from numpy import exp, sqrt from numpy.random import randn from brian.stdunits import ms __all__ = ['OrnsteinUhlenbeck', 'white_noise', 'colored_noise'] def OrnsteinUhlenbeck(x, mu, sigma, tau): ''' An Ornstein-Uhlenbeck process. mu = mean sigma = standard deviation tau = time constant x = name of the variable Returns an Equations() object ''' return Equations('dx/dt=(mu-x)*invtau+sigma*((2.*invtau)**.5)*xi : unit', \ x=x, mu=mu, sigma=sigma, invtau=1. / tau, unit=get_unit(mu)) def white_noise(dt, duration): n = int(duration/dt) noise = randn(n) return noise def colored_noise(tau, dt, duration): noise = white_noise(dt, duration) a = [1., -exp(-dt/tau)] b = [1.] fnoise = sqrt(2*dt/tau)*lfilter(b, a, noise) return fnoise if __name__ == '__main__': tau = 10*ms dt = .1*ms duration = 1000*ms noise = white_noise(dt, duration) cnoise = colored_noise(tau, dt, duration) from numpy import linspace t = linspace(0*ms, duration, len(noise)) from pylab import acorr, show, subplot, plot, ylabel, xlim subplot(221) plot(t, noise) ylabel('white noise') subplot(222) acorr(noise, maxlags=200) xlim(-200,200) subplot(223) plot(t, cnoise) ylabel('colored noise') subplot(224) acorr(cnoise, maxlags=200) xlim(-200,200) show()brian-1.4.3/brian/library/synapses.py000066400000000000000000000117051266062647100176060ustar00rootroot00000000000000''' Synapse models for Brian (no plasticity here). ''' from brian.equations import * from brian.units import check_units, second, amp, siemens from brian.membrane_equations import Current __all__ = ['exp_current', 'alpha_current', 'biexp_current', \ 'exp_conductance', 'alpha_conductance', 'biexp_conductance', \ 'exp_synapse', 'alpha_synapse', 'biexp_synapse'] __credits__ = dict(author='Romain Brette (brette@di.ens.fr)', date='April 2008') # ----------------- # Synaptic currents # ----------------- @check_units(tau=second) def exp_current(input, tau, current_name=None, unit=amp): ''' Exponential synaptic current. input = name of input variable (where presynaptic spikes act). current_name = name of current variable ''' current_name = current_name or unique_id() current = Current() + exp_synapse(input, tau, unit, current_name) current.set_current_name(current_name) return current @check_units(tau=second) def alpha_current(input, tau, current_name=None, unit=amp): ''' Alpha synaptic current. current_name = name of current variable ''' current_name = current_name or unique_id() current = Current() + alpha_synapse(input, tau, unit, current_name) current.set_current_name(current_name) return current @check_units(tau1=second, tau2=second) def biexp_current(input, tau1, tau2, current_name=None, unit=amp): ''' Biexponential synaptic current. current_name = name of current variable ''' current_name = current_name or unique_id() current = Current() + biexp_synapse(input, tau1, tau2, unit, current_name) current.set_current_name(current_name) return current # --------------------- # Synaptic conductances # --------------------- @check_units(tau=second) def exp_conductance(input, E, tau, conductance_name=None, unit=siemens): ''' Exponential synaptic conductance. conductance_name = name of conductance variable E = synaptic reversal potential ''' conductance_name = conductance_name or unique_id() return Current('I=g*(E-vm): amp', I=input + '_current', g=conductance_name, E=E) + \ exp_synapse(input, tau, unit, conductance_name) @check_units(tau=second) def alpha_conductance(input, E, tau, conductance_name=None, unit=siemens): ''' Alpha synaptic conductance. conductance_name = name of conductance variable E = synaptic reversal potential ''' conductance_name = conductance_name or unique_id() return Current('I=g*(E-vm): amp', I=input + '_current', g=conductance_name, E=E) + \ alpha_synapse(input, tau, unit, conductance_name) @check_units(tau1=second, tau2=second) def biexp_conductance(input, E, tau1, tau2, conductance_name=None, unit=siemens): ''' Exponential synaptic conductance. conductance_name = name of conductance variable E = synaptic reversal potential ''' conductance_name = conductance_name or unique_id() return Current('I=g*(E-vm): amp', I=input + '_current', g=conductance_name, E=E) + \ biexp_synapse(input, tau1, tau2, unit, conductance_name) # --------------- # Synaptic inputs # --------------- @check_units(tau=second) def exp_synapse(input, tau, unit, output=None): ''' Exponentially decaying synaptic current/conductance: g(t)=exp(-t/tau) output = output variable name (plugged into the membrane equation). input = input variable name (where spikes are received). ''' if output is None: output = input + '_out' return Equations(''' dx/dt = -x*invtau : unit y=x''', x=output, y=input, unit=unit, invtau=1. / tau) @check_units(tau=second) def alpha_synapse(input, tau, unit, output=None): ''' Alpha synaptic current/conductance: g(t)=(t/tau)*exp(1-t/tau) output = output variable name (plugged into the membrane equation). input = input variable name (where spikes are received). The peak is 1 at time t=tau. ''' if output is None: output = input + '_out' return Equations(''' dx/dt = (y-x)*invtau : unit dy/dt = -y*invtau : unit ''', x=output, y=input, unit=unit, invtau=1. / tau) @check_units(tau1=second, tau2=second) def biexp_synapse(input, tau1, tau2, unit, output=None): ''' Biexponential synaptic current/conductance: g(t)=(tau2/(tau2-tau1))*(exp(-t/tau1)-exp(-t/tau2)) output = output variable name (plugged into the membrane equation). input = input variable name (where spikes are received). The peak is 1 at time t=tau1*tau2/(tau2-tau1)*log(tau2/tau1) ''' if output is None: output = input + '_out' invpeak = (tau2 / tau1) ** (tau1 / (tau2 - tau1)) return Equations(''' dx/dt = (invpeak*y-x)*invtau1 : unit dy/dt = -y*invtau2 : unit ''', x=output, y=input, unit=unit, invtau1=1. / tau1, invtau2=1. / tau2, invpeak=invpeak) brian-1.4.3/brian/license.txt000066400000000000000000000034341266062647100161060ustar00rootroot00000000000000---------------------------------------------------------------------------------- Copyright ENS, INRIA, CNRS Contributors: Romain Brette (romain.brette@ens.fr) and Dan Goodman (dan.goodman@ens.fr) Brian is a computer program whose purpose is to simulate models of biological neural networks. This software is governed by the CeCILL license under French law and abiding by the rules of distribution of free software. You can use, modify and/ or redistribute the software under the terms of the CeCILL license as circulated by CEA, CNRS and INRIA at the following URL "http://www.cecill.info". As a counterpart to the access to the source code and rights to copy, modify and redistribute granted by the license, users are provided only with a limited warranty and the software's author, the holder of the economic rights, and the successive licensors have only limited liability. In this respect, the user's attention is drawn to the risks associated with loading, using, modifying and/or developing or reproducing the software by the user in light of its specific status of free software, that may mean that it is complicated to manipulate, and that also therefore means that it is reserved for developers and experienced professionals having in-depth computer knowledge. Users are therefore encouraged to load and test the software's suitability as regards their requirements in conditions enabling the security of their systems and/or data to be ensured and, more generally, to use and operate it in the same conditions as regards security. The fact that you are presently reading this means that you have had knowledge of the CeCILL license and that you accept its terms. ---------------------------------------------------------------------------------- brian-1.4.3/brian/log.py000066400000000000000000000071631266062647100150610ustar00rootroot00000000000000# ---------------------------------------------------------------------------------- # Copyright ENS, INRIA, CNRS # Contributors: Romain Brette (brette@di.ens.fr) and Dan Goodman (goodman@di.ens.fr) # # Brian is a computer program whose purpose is to simulate models # of biological neural networks. # # This software is governed by the CeCILL license under French law and # abiding by the rules of distribution of free software. You can use, # modify and/ or redistribute the software under the terms of the CeCILL # license as circulated by CEA, CNRS and INRIA at the following URL # "http://www.cecill.info". # # As a counterpart to the access to the source code and rights to copy, # modify and redistribute granted by the license, users are provided only # with a limited warranty and the software's author, the holder of the # economic rights, and the successive licensors have only limited # liability. # # In this respect, the user's attention is drawn to the risks associated # with loading, using, modifying and/or developing or reproducing the # software by the user in light of its specific status of free software, # that may mean that it is complicated to manipulate, and that also # therefore means that it is reserved for developers and experienced # professionals having in-depth computer knowledge. Users are therefore # encouraged to load and test the software's suitability as regards their # requirements in conditions enabling the security of their systems and/or # data to be ensured and, more generally, to use and operate it in the # same conditions as regards security. # # The fact that you are presently reading this means that you have had # knowledge of the CeCILL license and that you accept its terms. # ---------------------------------------------------------------------------------- # ''' Logging information for Brian ''' import logging import sys __all__ = [ 'log_warn', 'log_info', 'log_debug', 'log_level_error', 'log_level_warn', 'log_level_info', 'log_level_debug' ] console = logging.StreamHandler(sys.stderr) formatter = logging.Formatter('%(name)-18s: %(levelname)-8s %(message)s') console.setFormatter(formatter) logging.getLogger('brian').addHandler(console) #brian_loggers = {} get_log = logging.getLogger #def get_log(logname): # if logname in brian_loggers: # return brian_loggers[logname] # newlog = logging.getLogger(logname) # #newlog.addHandler(console) # brian_loggers[logname] = newlog # return newlog def log_warn(logname, message): get_log(logname).warn(message) def log_info(logname, message): get_log(logname).info(message) def log_debug(logname, message): get_log(logname).debug(message) def log_level_error(): '''Shows log messages only of level ERROR or higher. ''' logging.getLogger('brian').setLevel(logging.ERROR) def log_level_warn(): '''Shows log messages only of level WARNING or higher (including ERROR level). ''' logging.getLogger('brian').setLevel(logging.WARN) def log_level_info(): '''Shows log messages only of level INFO or higher (including WARNING and ERROR levels). ''' logging.getLogger('brian').setLevel(logging.INFO) def log_level_debug(): '''Shows log messages only of level DEBUG or higher (including INFO, WARNING and ERROR levels). ''' logging.getLogger('brian').setLevel(logging.DEBUG) if __name__ == '__main__': #log_level_info() log_warn('brian.fish', 'Warning') log_warn('brian.monkey', 'Ook') log_info('brian.moose', 'Mook') log_debug('brian.goat', 'Bah') brian-1.4.3/brian/magic.py000066400000000000000000000307261266062647100153610ustar00rootroot00000000000000# ---------------------------------------------------------------------------------- # Copyright ENS, INRIA, CNRS # Contributors: Romain Brette (brette@di.ens.fr) and Dan Goodman (goodman@di.ens.fr) # # Brian is a computer program whose purpose is to simulate models # of biological neural networks. # # This software is governed by the CeCILL license under French law and # abiding by the rules of distribution of free software. You can use, # modify and/ or redistribute the software under the terms of the CeCILL # license as circulated by CEA, CNRS and INRIA at the following URL # "http://www.cecill.info". # # As a counterpart to the access to the source code and rights to copy, # modify and redistribute granted by the license, users are provided only # with a limited warranty and the software's author, the holder of the # economic rights, and the successive licensors have only limited # liability. # # In this respect, the user's attention is drawn to the risks associated # with loading, using, modifying and/or developing or reproducing the # software by the user in light of its specific status of free software, # that may mean that it is complicated to manipulate, and that also # therefore means that it is reserved for developers and experienced # professionals having in-depth computer knowledge. Users are therefore # encouraged to load and test the software's suitability as regards their # requirements in conditions enabling the security of their systems and/or # data to be ensured and, more generally, to use and operate it in the # same conditions as regards security. # # The fact that you are presently reading this means that you have had # knowledge of the CeCILL license and that you accept its terms. # ---------------------------------------------------------------------------------- # """Magic function tools Use these functions to automagically find objects of a particular class. The way it works is that whenever a new object is created from a class derived from InstanceTracker, it is stored along with the 'frame' it was called from (loosely speaking, the function where the object is defined). When you call one of the functions below, it picks out all the objects of the required type in a frame a specified number of levels before the current one (e.g. in the frame of the calling function). Functions --------- get_instances(instancetype, level=1, all=False) This function finds all instances at the given level in the sequence of calling frames. If all is True then levels will not be used and all instances will be found. find_instances(instancetype, startlevel=1, all=False) This function searches the frames starting from the given startlevel until it finds at least one object of the required type, at which point it will return all objects of that type from that level If all is True then levels will not be used and all instances will be found. find_all_instances(instancetype,startlevel=1, all=False): This function searches the frames starting from the given startlevel, and returns all objects of the required type from all levels. Noe that includeglobals is set to False by default so as not to pick up multiple copies of objects If all is True then levels will not be used and all instances will be found. Variables: instancetype A class (must be derived from InstanceTracker) level The level in the sequence of calling frames. So, for a function f, calling with level=0 will find variables defined in that function f, whereas calling with level=1 will find variables defined in the function which called f. The latter is the default value because magic functions are usually used within Brian functions to find variables defined by the user. Return values: All the functions return a tuple (objects, names) where objects is the list of matching objects, and names is a list of strings giving the objects' names if they are defined. At the moment, the only name returned is the id of the object. Notes: These functions return each object at most once. Classes ------- InstanceTracker Derive your class from this one to automagically keep track of instances of it. If you want a subclass of a tracked class not to be tracked, define the method _track_instances to return False. """ __docformat__ = "restructuredtext en" from weakref import * from inspect import * from globalprefs import * __all__ = [ 'get_instances', 'find_instances', 'find_all_instances', 'magic_register', 'magic_return', 'InstanceTracker' ] class ExtendedRef(ref): """A weak reference which also defines an optional id """ def __init__(self, ob, callback=None, **annotations): super(ExtendedRef, self).__init__(ob, callback) self.__id = 0 def set_i_d(self, id): self.__id = id def get_i_d(self): return self.__id class WeakSet(set): """A set of extended references Removes references from the set when they are destroyed.""" def add(self, value, id=0): wr = ExtendedRef(value, self.remove) wr.set_i_d(id) set.add(self, wr) def set_i_d(self, value, id): for _ in self: if _() is value: _.set_i_d(id) return def get_i_d(self, value): for _ in self: if _() is value: return _.get_i_d() def get(self, id=None): if id is None: return [ _() for _ in self if _.get_i_d() != -1 ] else: return [ _() for _ in self if _.get_i_d() == id] class InstanceFollower(object): """Keep track of all instances of classes derived from InstanceTracker The variable __instancesets__ is a dictionary with keys which are class objects, and values which are WeakSets, so __instanceset__[cls] is a weak set tracking all of the instances of class cls (or a subclass). """ __instancesets__ = {} def add(self, value, id=0): for cls in value.__class__.__mro__: # MRO is the Method Resolution Order which contains all the superclasses of a class if cls not in self.__instancesets__: self.__instancesets__[cls] = WeakSet() self.__instancesets__[cls].add(value, id) def set_i_d(self, value, id): for cls in value.__class__.__mro__: # MRO is the Method Resolution Order which contains all the superclasses of a class if cls in self.__instancesets__: self.__instancesets__[cls].set_i_d(value, id) def get_i_d(self, value): for cls in value.__class__.__mro__: if cls in self.__instancesets__: return self.__instancesets__[cls].get_i_d(value) def get(self, cls, id=None): if not cls in self.__instancesets__: return [] return self.__instancesets__[cls].get(id) class InstanceTracker(object): """Base class for all classes whose instances are to be tracked Derive your class from this one to automagically keep track of instances of it. If you want a subclass of a tracked class not to be tracked, define the method _track_instances to return False. """ __instancefollower__ = InstanceFollower() # static property of all objects of class derived from InstanceTracker @staticmethod def _track_instances(): return True def set_instance_id(self, idvalue=None, level=1): if idvalue is None: idvalue = id(getouterframes(currentframe())[level + 1][0]) self.__instancefollower__.set_i_d(self, idvalue) def get_instance_id(self): return self.__instancefollower__.get_i_d(self) def __new__(typ, *args, **kw): obj = object.__new__(typ)#, *args, **kw) outer_frame = id(getouterframes(currentframe())[1][0]) # the id is the id of the calling frame if obj._track_instances(): obj.__instancefollower__.add(obj, outer_frame) return obj def magic_register(*args, **kwds): '''Declare that a magically tracked object should be put in a particular frame **Standard usage** If ``A`` is a tracked class (derived from :class:`InstanceTracker`), then the following wouldn't work:: def f(): x = A('x') return x objs = f() print get_instances(A,0)[0] Instead you write:: def f(): x = A('x') magic_register(x) return x objs = f() print get_instances(A,0)[0] **Definition** Call as:: magic_register(...[,level=1]) The ``...`` can be any sequence of tracked objects or containers of tracked objects, and each tracked object will have its instance id (the execution frame in which it was created) set to that of its parent (or to its parent at the given level). This is equivalent to calling:: x.set_instance_id(level=level) For each object ``x`` passed to :func:`magic_register`. ''' level = kwds.get('level', 1) for x in args: if isinstance(x, InstanceTracker): x.set_instance_id(level=level + 1) else: magic_register(*x, **{'level':level + 1}) def magic_return(f): ''' Decorator to ensure that the returned object from a function is recognised by magic functions **Usage example:** :: @magic_return def f(): return PulsePacket(50*ms, 100, 10*ms) **Explanation** Normally, code like the following wouldn't work:: def f(): return PulsePacket(50*ms, 100, 10*ms) pp = f() M = SpikeMonitor(pp) run(100*ms) raster_plot() show() The reason is that the magic function :func:`run()` only recognises objects created in the same execution frame that it is run from. The :func:`magic_return` decorator corrects this, it registers the return value of a function with the magic module. The following code will work as expected:: @magic_return def f(): return PulsePacket(50*ms, 100, 10*ms) pp = f() M = SpikeMonitor(pp) run(100*ms) raster_plot() show() **Technical details** The :func:`magic_return` function uses :func:`magic_register` with the default ``level=1`` on just the object returned by a function. See details for :func:`magic_register`. ''' def new_f(*args, **kwds): obj = f(*args, **kwds) magic_register(obj) return obj new_f.__name__ = f.__name__ new_f.__doc__ = f.__doc__ return new_f def get_instances(instancetype, level=1, all=False): """Find all instances of a given class at a given level in the stack See documentation for module Brian.magic """ try: instancetype.__instancefollower__ except AttributeError: raise InstanceTrackerError('Cannot track instances of type ', instancetype) target_frame = id(getouterframes(currentframe())[level + 1][0]) if all or not get_global_preference('magic_useframes'): target_frame = None objs = instancetype.__instancefollower__.get(instancetype, target_frame) return (objs, map(str, map(id, objs))) def find_instances(instancetype, startlevel=1, all=False): """Find first instances of a given class in the stack See documentation for module Brian.magic """ # Note that we start from startlevel+1 because startlevel means from the calling function's point of view for level in range(startlevel + 1, len(getouterframes(currentframe()))): objs, names = get_instances(instancetype, level, all=all) if len(objs): return (objs, names) return ([], []) def find_all_instances(instancetype, startlevel=1, all=False): """Find all instances of a given class in the stack See documentation for module Brian.magic """ objs = [] names = [] # Note that we start from startlevel+1 because startlevel means from the calling function's point of view for level in range(startlevel + 1, len(getouterframes(currentframe()))): newobjs, newnames = get_instances(instancetype, level, all=all) objs += newobjs names += newnames return (objs, names) if __name__ == '__main__': print __doc__ brian-1.4.3/brian/membrane_equations.py000066400000000000000000000140471266062647100201550ustar00rootroot00000000000000# ---------------------------------------------------------------------------------- # Copyright ENS, INRIA, CNRS # Contributors: Romain Brette (brette@di.ens.fr) and Dan Goodman (goodman@di.ens.fr) # # Brian is a computer program whose purpose is to simulate models # of biological neural networks. # # This software is governed by the CeCILL license under French law and # abiding by the rules of distribution of free software. You can use, # modify and/ or redistribute the software under the terms of the CeCILL # license as circulated by CEA, CNRS and INRIA at the following URL # "http://www.cecill.info". # # As a counterpart to the access to the source code and rights to copy, # modify and redistribute granted by the license, users are provided only # with a limited warranty and the software's author, the holder of the # economic rights, and the successive licensors have only limited # liability. # # In this respect, the user's attention is drawn to the risks associated # with loading, using, modifying and/or developing or reproducing the # software by the user in light of its specific status of free software, # that may mean that it is complicated to manipulate, and that also # therefore means that it is reserved for developers and experienced # professionals having in-depth computer knowledge. Users are therefore # encouraged to load and test the software's suitability as regards their # requirements in conditions enabling the security of their systems and/or # data to be ensured and, more generally, to use and operate it in the # same conditions as regards security. # # The fact that you are presently reading this means that you have had # knowledge of the CeCILL license and that you accept its terms. # ---------------------------------------------------------------------------------- # ''' Membrane equations for Brian models. ''' __all__ = ['Current', 'IonicCurrent', 'InjectedCurrent', 'MembraneEquation'] from equations import * from units import have_same_dimensions, get_unit, second, volt, amp from warnings import warn class Current(Equations): ''' A set of equations defining a current. current_name is the name of the variable that must be added as a membrane current to the membrane equation. ''' def __init__(self, expr='', current_name=None, level=0, surfacic=False, **kwd): Equations.__init__(self, expr, level=level + 1, **kwd) if surfacic: # A surfacic current is multiplied by membrane area in a MembraneEquation self._prefix = '__scurrent_' else: self._prefix = '__current_' # Find which variable is the current if current_name: # Explicitly given self.set_current_name(current_name) elif expr != '': # Guess if len(self._units) == 2: # only one variable (the other one is t) # Only 1 variable: it's the current correct_names = [name for name in self._units.keys() if name != 't'] if len(correct_names) != 1: raise NameError, "The equations do not include time (variable t)" name, = correct_names self.set_current_name(name) else: # Look for variables with dimensions of current: won't work with units off! current_names = [name for name, unit in self._units.iteritems()\ if have_same_dimensions(unit, amp)] if len(current_names) == 1: # only one current self.set_current_name(current_names[0]) else: warn("The current variable could not be found!") def set_current_name(self, name): if name != 't': if name is None: name = unique_id() current_name = self._prefix + name self.add_eq(current_name, name, self._units[name]) # not an alias because read-only def __iadd__(self, other): # Adding a MembraneEquation if isinstance(other, MembraneEquation): return other.__iadd__(self) else: return Equations.__iadd__(self, other) class IonicCurrent(Current): ''' A ionic current; current direction is defined from intracellular to extracellular. ''' def set_current_name(self, name): if name != 't': if name is None: name = unique_id() current_name = self._prefix + name self.add_eq(current_name, '-' + name, self._units[name]) InjectedCurrent = Current class MembraneEquation(Equations): ''' A membrane equation, defined by a capacitance C and a sum of currents. Ex: eq=MembraneEquation(200*pF) eq=MembraneEquation(200*pF,vm='V') No more than one membrane equation allowed per system of equations. ''' def __init__(self, C=None, **kwd): if C is not None: expr = ''' dvm/dt=__membrane_Im/C : volt __membrane_Im=0*unit : unit ''' Equations.__init__(self, expr, C=C, unit=get_unit(C * volt / second), **kwd) else: Equations.__init__(self) def __iadd__(self, other): Equations.__iadd__(self, other) self.set_membrane_current() return self def set_membrane_current(self): current_vars = [name for name in self._eq_names if name.startswith('__current_')] # point current scurrent_vars = [name for name in self._eq_names if name.startswith('__scurrent_')] # surfacic current if scurrent_vars != []: if current_vars != []: self._string['__membrane_Im'] = '+'.join(current_vars) + '+__area*(' + '+'.join(scurrent_vars) + ')' else: self._string['__membrane_Im'] = '__area*(' + '+'.join(scurrent_vars) + ')' self._namespace[name]['__area'] = self.area elif current_vars != []: self._string['__membrane_Im'] = '+'.join(current_vars) brian-1.4.3/brian/monitor.py000066400000000000000000001771061266062647100157740ustar00rootroot00000000000000# ---------------------------------------------------------------------------------- # Copyright ENS, INRIA, CNRS # Contributors: Romain Brette (brette@di.ens.fr) and Dan Goodman (goodman@di.ens.fr) # # Brian is a computer program whose purpose is to simulate models # of biological neural networks. # # This software is governed by the CeCILL license under French law and # abiding by the rules of distribution of free software. You can use, # modify and/ or redistribute the software under the terms of the CeCILL # license as circulated by CEA, CNRS and INRIA at the following URL # "http://www.cecill.info". # # As a counterpart to the access to the source code and rights to copy, # modify and redistribute granted by the license, users are provided only # with a limited warranty and the software's author, the holder of the # economic rights, and the successive licensors have only limited # liability. # # In this respect, the user's attention is drawn to the risks associated # with loading, using, modifying and/or developing or reproducing the # software by the user in light of its specific status of free software, # that may mean that it is complicated to manipulate, and that also # therefore means that it is reserved for developers and experienced # professionals having in-depth computer knowledge. Users are therefore # encouraged to load and test the software's suitability as regards their # requirements in conditions enabling the security of their systems and/or # data to be ensured and, more generally, to use and operate it in the # same conditions as regards security. # # The fact that you are presently reading this means that you have had # knowledge of the CeCILL license and that you accept its terms. # ---------------------------------------------------------------------------------- # ''' Monitors (spikes and state variables). * Tip: Spike monitors should have non significant impact on simulation time if properly coded. ''' __all__ = ['VanRossumMetric','SpikeMonitor', 'PopulationSpikeCounter', 'SpikeCounter', 'FileSpikeMonitor', 'StateMonitor', 'ISIHistogramMonitor', 'Monitor', 'PopulationRateMonitor', 'StateSpikeMonitor', 'MultiStateMonitor', 'RecentStateMonitor', 'CoincidenceCounter', 'CoincidenceMatrixCounter', 'StateHistogramMonitor', 'AERSpikeMonitor'] from units import * from connections import Connection, SparseConnectionVector from numpy import array, zeros, mean, histogram, linspace, tile, digitize, \ copy, ones, rint, exp, arange, convolve, argsort, mod, floor, asarray, \ maximum, Inf, amin, amax, sort, nonzero, setdiff1d, diag, hstack, resize,\ inf, var, tril, empty, float64, array, sum, int32, ceil from scipy.spatial.distance import sqeuclidean from itertools import repeat, izip from clock import guess_clock, EventClock, Clock from network import NetworkOperation, network_operation from stdunits import ms, Hz from collections import defaultdict import types from operator import isSequenceType from tools.statistics import firing_rate from neurongroup import NeuronGroup import bisect from base import * from time import time import datetime try: import pylab, matplotlib except: pass from globalprefs import * try: import weave except ImportError: from scipy import weave class Monitor(object): pass class SpikeMonitor(Connection, Monitor): ''' Counts or records spikes from a :class:`NeuronGroup` Initialised as one of:: SpikeMonitor(source(,record=True)) SpikeMonitor(source,function=function) Where: ``source`` A :class:`NeuronGroup` to record from ``record`` ``True`` or ``False`` to record all the spikes or just summary statistics. ``function`` A function ``f(spikes)`` which is passed the array of neuron numbers that have fired called each step, to define custom spike monitoring. Has attributes: ``nspikes`` The number of recorded spikes ``spikes`` A time ordered list of pairs ``(i,t)`` where neuron ``i`` fired at time ``t``. ``spiketimes`` A dictionary with keys the indices of the neurons, and values an array of the spike times of that neuron. For example, ``t=M.spiketimes[3]`` gives the spike times for neuron 3. ``it`` Return a tuple ``(i, t)`` where ``i`` and ``t`` are the arrays of spike indices and corresponding spike times (int and float). For ``M`` a :class:`SpikeMonitor`, you can also write: ``M[i]`` An array of the spike times of neuron ``i``. Notes: :class:`SpikeMonitor` is subclassed from :class:`Connection`. To define a custom monitor, either define a subclass and rewrite the ``propagate`` method, or pass the monitoring function as an argument (``function=myfunction``, with ``def myfunction(spikes):...``) ''' # isn't there a units problem here for delay? def __init__(self, source, record=True, delay=0, function=None): # recordspikes > record? self.source = source # pointer to source group self.target = None self.nspikes = 0 self.spikes = [] self.record = record self.W = None # should we just remove this variable? source.set_max_delay(delay) self.delay = int(delay / source.clock.dt) # Synaptic delay in time bins self._newspikes = True self.custom_function = function is not None if self.custom_function: self.propagate = function def reinit(self): """ Clears all monitored spikes """ self.nspikes = 0 self.spikes = [] self._newspikes = True #recreate self._spiketimes on next access def propagate(self, spikes): ''' Deals with the spikes. Overload this function to store or process spikes. Default: counts the spikes (variable nspikes) ''' if len(spikes): self._newspikes = True self.nspikes += len(spikes) if self.record: self.spikes += zip(spikes, repeat(self.source.clock.t)) def origin(self, P, Q): ''' Returns the starting coordinate of the given groups in the connection matrix W. ''' return (P.origin - self.source.origin, 0) def compress(self): pass def __getitem__(self, i): return self.getspiketimes()[i] def getspiketimes(self): if self._newspikes: self._newspikes = False self._spiketimes = {} for i in xrange(len(self.source)): self._spiketimes[i] = [] for i, t in self.spikes: self._spiketimes[i].append(float(t)) for i in xrange(len(self.source)): self._spiketimes[i] = array(self._spiketimes[i]) return self._spiketimes spiketimes = property(fget=getspiketimes) @property def it(self): if len(self.spikes)==0: return array([], dtype=int), array([], dtype=float) i, t = zip(*self.spikes) return array(i, dtype=int), array(t, dtype=float) def __repr__(self): repr_str = 'SpikeMonitor(%s, record=%s' % (repr(self.source), repr(self.record)) if self.delay > 0: repr_str += ', delay=%s' % repr(self.delay * self.source.clock.dt) if self.custom_function: repr_str += ', function_%s' % repr(self.propagate) repr_str += ')' return repr_str def __str__(self): description = "%s monitoring %s" % (self.__class__.__name__, str(self.source)) if self.delay > 0: description += ' with a delay of %s' % str(self.delay * self.source.clock.dt) return description # def getvspikes(self): # if isinstance(self.source, VectorizedNeuronGroup): # N = self.source.neuron_number # overlap = self.source.overlap # duration = self.source.duration # vspikes = [(mod(i,N),(t-overlap)+i/N*(duration-overlap)*second) for (i,t) in self.spikes if t >= overlap] # vspikes.sort(cmp=lambda x,y:2*int(x[1]>y[1])-1) # return vspikes # concatenated_spikes = property(fget=getvspikes) class AutoCorrelogram(SpikeMonitor): ''' Calculates autocorrelograms for the selected neurons (online). Initialised as:: AutoCorrelogram(source,record=[1,2,3], delay=10*ms) where ``delay`` is the size of the autocorrelogram. NOT FINISHED ''' def __init__(self, source, record=True, delay=0): SpikeMonitor.__init__(self, source, record=record, delay=delay) self.reinit() if record is not False: if record is not True and not isinstance(record, int): self.recordindex = dict((i, j) for i, j in zip(self.record, range(len(self.record)))) def reinit(self): if self.record == True: self._autocorrelogram = zeros((len(self.record), len(self.source))) else: self._autocorrelogram = zeros((len(self.record), self.delay)) def propagate(self, spikes): spikes_set = set(spikes) if self.record == True: for i in xrange(self.delay): # Not a brilliant implementation self._autocorrelogram[spikes_set.intersection(self.source.LS[i]), i] += 1 def __getitem__(self, i): # TODO: returns the autocorrelogram of neuron i pass class PopulationSpikeCounter(SpikeMonitor): ''' Counts spikes from a :class:`NeuronGroup` Initialised as:: PopulationSpikeCounter(source) With argument: ``source`` A :class:`NeuronGroup` to record from Has one attribute: ``nspikes`` The number of recorded spikes ''' def __init__(self, source, delay=0): SpikeMonitor.__init__(self, source, record=False, delay=delay) def __repr__(self): repr_str = '%s(%s' % (self.__class__.__name__, repr(self.source)) if self.delay > 0: repr_str += ', delay=%s' % repr(self.delay * self.source.clock.dt) repr_str += ')' return repr_str class SpikeCounter(PopulationSpikeCounter): ''' Counts spikes from a :class:`NeuronGroup` Initialised as:: SpikeCounter(source) With argument: ``source`` A :class:`NeuronGroup` to record from Has two attributes: ``nspikes`` The number of recorded spikes ``count`` An array of spike counts for each neuron For a :class:`SpikeCounter` ``M`` you can also write ``M[i]`` for the number of spikes counted for neuron ``i``. ''' def __init__(self, source): PopulationSpikeCounter.__init__(self, source) self.count = zeros(len(source), dtype=int) def __getitem__(self, i): return int(self.count[i]) def propagate(self, spikes): if len(spikes): PopulationSpikeCounter.propagate(self, spikes) self.count[spikes] += 1 def reinit(self): self.count[:] = 0 PopulationSpikeCounter.reinit(self) class StateSpikeMonitor(SpikeMonitor): ''' Counts or records spikes and state variables at spike times from a :class:`NeuronGroup` Initialised as:: StateSpikeMonitor(source, var) Where: ``source`` A :class:`NeuronGroup` to record from ``var`` The variable name or number to record from, or a tuple of variable names or numbers if you want to record multiple variables for each spike. Has two attributes: .. attribute:: nspikes The number of recorded spikes .. attribute:: spikes A time ordered list of tuples ``(i,t,v)`` where neuron ``i`` fired at time ``t`` and the specified variable had value ``v``. If you specify multiple variables, each tuple will be of the form ``(i,t,v0,v1,v2,...)`` where the ``vi`` are the values corresponding in order to the variables you specified in the ``var`` keyword. And two methods: .. method:: times(i=None) Returns an array of the spike times for the whole monitored group, or just for neuron ``i`` if specified. .. method:: values(var, i=None) Returns an array of the values of variable ``var`` for the whole monitored group, or just for neuron ``i`` if specified. ''' def __init__(self, source, var): SpikeMonitor.__init__(self, source) if isinstance(var, (str, int)) or not isSequenceType(var): var = (var,) self._varnames = var self._vars = [source.state_(v) for v in var] self._varindex = dict((v, i + 2) for i, v in enumerate(var)) self._units = [source.unit(v) for v in var] def propagate(self, spikes): if len(spikes): self.nspikes += len(spikes) recordedstate = [ [ x * u for x in self.source.state_(varname)[spikes]] for varname, u in izip(self._varnames, self._units)] # the above line used to be this one: # recordedstate = [ [x * u for x in v[spikes]] for v, u in izip(self._vars, self._units) ] # which didn't take the values of the variables if they were defined by a static eqn # a = b + c : 1 self.spikes += zip(spikes, repeat(self.source.clock.t), *recordedstate) def __getitem__(self, i): return NotImplemented # don't use the version from SpikeMonitor def times(self, i=None): '''Returns the spike times (of neuron ``i`` if specified)''' if i is not None: return array([x[1] for x in self.spikes if x[0] == i]) else: return array([x[1] for x in self.spikes]) def values(self, var, i=None): '''Returns the recorded values of ``var`` (for spikes from neuron ``i`` if specified)''' v = self._varindex[var] if i is not None: return array([x[v] for x in self.spikes if x[0] == i]) else: return array([x[v] for x in self.spikes]) def __repr__(self): return 'StateSpikeMonitor(%s, var=%s)' % (repr(self.source), repr(self._varnames)) def __str__(self): return 'StateSpikeMonitor monitoring variable(s) %s of: %s' % (str(self._varnames), str(self.source)) class HistogramMonitorBase(SpikeMonitor): pass class ISIHistogramMonitor(HistogramMonitorBase): ''' Records the interspike interval histograms of a group. Initialised as:: ISIHistogramMonitor(source, bins) ``source`` The source group to record from. ``bins`` The lower bounds for each bin, so that e.g. ``bins = [0*ms, 10*ms, 20*ms]`` would correspond to bins with intervals 0-10ms, 10-20ms and 20+ms. Has properties: ``bins`` The ``bins`` array passed at initialisation. ``count`` An array of length ``len(bins)`` counting how many ISIs were in each bin. This object can be passed directly to the plotting function :func:`hist_plot`. ''' def __init__(self, source, bins, delay=0): SpikeMonitor.__init__(self, source, delay) self.bins = array(bins) self.reinit() def reinit(self): super(ISIHistogramMonitor, self).reinit() self.count = zeros(len(self.bins)) self.LS = 1000 * second * ones(len(self.source)) def propagate(self, spikes): if len(spikes): super(ISIHistogramMonitor, self).propagate(spikes) isi = self.source.clock.t - self.LS[spikes] self.LS[spikes] = self.source.clock.t # all this nonsense is necessary to deal with the fact that # numpy changed the semantics of histogram in 1.2.0 or thereabouts try: h, a = histogram(isi, self.bins, new=True) except TypeError: h, a = histogram(isi, self.bins) if len(h) == len(self.count): self.count += h else: self.count[:-1] += h self.count[-1] += len(isi) - sum(h) def __repr__(self): repr_str = "%s(%s, bins=%s" % (self.__class__.__name__, repr(self.source), repr(self.bins)) if self.delay > 0: repr_str += ', delay=%s' % repr(self.delay * self.source.clock.dt) repr_str += ')' return repr_str def __str__(self): description = '%s monitoring %s' % (self.__class__.__name__, str(self.source)) if self.delay > 0: description += ' with a delay of %s' % str(self.delay * self.source.clock.dt) return description class FileSpikeMonitor(SpikeMonitor): """Records spikes to a file Initialised as:: FileSpikeMonitor(source, filename[, record=False]) Does everything that a :class:`SpikeMonitor` does except also records the spikes to the named file. note that spikes are recorded as an ASCII file of lines each of the form: ``i, t`` Where ``i`` is the neuron that fired, and ``t`` is the time in seconds. Has one additional method: ``close()`` Closes the file manually (will happen automatically when the program ends). """ def __init__(self, source, filename, record=False, delay=0): super(FileSpikeMonitor, self).__init__(source, record, delay) self.filename = filename self.f = open(filename, 'w') def reinit(self): self.close_file() self.f = open(self.filename, 'w') def propagate(self, spikes): if len(spikes): super(FileSpikeMonitor, self).propagate(spikes) for i in spikes: self.f.write(str(i) + ", " + str(float(self.source.clock.t)) + "\n") def close(self): self.f.close() # The default header for AER files (see AERSpikeMonitor init): AER_HEADER = """#!AER-DAT2.0 # This is a raw AE data file - do not edit # Data format is int32 address, int32 timestamp (8 bytes total), repeated for each event # Timestamps tick is %s # created with the Brian simulator on """ class AERSpikeMonitor(FileSpikeMonitor): """Records spikes to an AER file Initialised as:: FileSpikeMonitor(source, filename[, record=False]) Does everything that a :class:`SpikeMonitor` does except ONLY records the spikes to the named file in AER format, during the simulation. These spikes can then be reloaded via the io.load_aer function and used later into a SpikeGeneratorGroup. It is about three times faster than the :class:`FileSpikeMonitor` and creates smaller files. On the other hand those files are not human-readable because they are in binary format. Has one additional method: ``close()`` Closes the file manually (will happen automatically when the program ends). """ def __init__(self, source, filename, record=False, delay=0): super(FileSpikeMonitor, self).__init__(source, record, delay) self.filename = filename self.f = open(filename, 'wb') header = AER_HEADER % self.source.clock.dt.__repr__() header += str(datetime.datetime.now()) + '\n' self.f.write(header) def propagate(self, spikes): # super(AERSpikeMonitor, self).propagate(spikes) addr = array(spikes, dtype = int32) x = zeros(2*len(addr), dtype = int32) x[1::2] = addr[::-1] x[::2] = int(ceil(float(self.source.clock.t/self.source.clock.dt))) s = x.tostring()[::-1] self.f.write(s) class PopulationRateMonitor(SpikeMonitor): ''' Monitors and stores the (time-varying) population rate Initialised as:: PopulationRateMonitor(source,bin) Records the average activity of the group for every bin. Properties: ``rate``, ``rate_`` An array of the rates in Hz. ``times``, ``times_`` The times of the bins. ``bin`` The duration of a bin (in second). ''' times = property(fget=lambda self:array(self._times)) times_ = times rate = property(fget=lambda self:array(self._rate)) rate_ = rate def __init__(self, source, bin=None): SpikeMonitor.__init__(self, source) if bin: self._bin = int(bin / source.clock.dt) else: self._bin = 1 # bin size in number self._rate = [] self._times = [] self._curstep = 0 self._clock = source.clock self._factor = 1. / float(self._bin * source.clock.dt * len(source)) def reinit(self): SpikeMonitor.reinit(self) self._rate = [] self._times = [] self._curstep = 0 def propagate(self, spikes): if self._curstep == 0: self._rate.append(0.) self._times.append(self._clock._t) # +.5*bin? self._curstep = self._bin self._rate[-1] += len(spikes) * self._factor self._curstep -= 1 def smooth_rate(self, width=None, filter='gaussian'): """ Returns a smoothed version of the vector of rates, convolving the rates with a filter (gaussian or flat) with the given width. """ if width is None: # automatic with Shinomoto's algorithms if filter=='flat': """ (Experimental) If width is not given and the filter is flat, then the bin size is automatically chosen using Shimazaki and Shinomoto's method: Shimazaki and Shinomoto, A method for selecting the bin size of a time histogram Neural Computation 19(6), 1503-1527, 2007 http://dx.doi.org/10.1162/neco.2007.19.6.1503 """ # Shinomoto's method to find the optimal bin size. Adapted from: # Shimazaki and Shinomoto, A method for selecting the bin size of a time histogram # Neural Computation 19(6), 1503-1527, 2007 # http://dx.doi.org/10.1162/neco.2007.19.6.1503 counts=array(self._rate)/self._factor best_value=inf for nbins in range(2,500): # possible number of bins (maybe a less brutal optimization?) binsize=len(counts)/nbins x=resize(counts,(len(counts)/binsize,binsize)) #x.reshape((x.size,1))[len(counts):]=0 # unnecessary because smaller x=x.sum(1) # x is the histogram with nbins bins K=mean(x) # average number of spikes per recording bin value=(2*K-var(x))/binsize**2 if value= self.onset: self.model_length[spiking_neurons] += 1 T_spiking = array(rint((self.source.clock.t + self.spikedelays[spiking_neurons]) / dt), dtype=int) remaining_neurons = spiking_neurons remaining_T_spiking = T_spiking while True: remaining_indices, = (remaining_T_spiking > self.next_spike_time[remaining_neurons]).nonzero() if len(remaining_indices): indices = remaining_neurons[remaining_indices] self.target_length[indices] += 1 self.spiketime_index[indices] += 1 self.last_spike_time[indices] = self.next_spike_time[indices] self.next_spike_time[indices] = array(rint(self.data[self.spiketime_index[indices] + 1] / dt), dtype=int) if self.coincidence_count_algorithm == 'exclusive': self.last_spike_allowed[indices] = self.next_spike_allowed[indices] self.next_spike_allowed[indices] = True remaining_neurons = remaining_neurons[remaining_indices] remaining_T_spiking = remaining_T_spiking[remaining_indices] else: break # Updates coincidences count near_last_spike = self.last_spike_time[spiking_neurons] + self.delta >= T_spiking near_next_spike = self.next_spike_time[spiking_neurons] - self.delta <= T_spiking last_spike_allowed = self.last_spike_allowed[spiking_neurons] next_spike_allowed = self.next_spike_allowed[spiking_neurons] I = (near_last_spike & last_spike_allowed) | (near_next_spike & next_spike_allowed) if self.source.clock.t >= self.onset: self.coincidences[spiking_neurons[I]] += 1 if self.coincidence_count_algorithm == 'exclusive': near_both_allowed = (near_last_spike & last_spike_allowed) & (near_next_spike & next_spike_allowed) self.last_spike_allowed[spiking_neurons] = last_spike_allowed & ~near_last_spike self.next_spike_allowed[spiking_neurons] = (next_spike_allowed & ~near_next_spike) | near_both_allowed class VanRossumMetric(StateMonitor): """ van Rossum spike train metric. From M. van Rossum (2001): A novel spike distance (Neural Computation). Compute the van Rossum distance between every spike train from the source population. Arguments: ``source`` The group to compute the distances for. ``tau`` Time constant of the kernel (low pass filter). Has one attribute: ``distance`` A square symmetric matrix containing the distances. """ def __init__(self, source, tau=2*ms): self.dt = source.clock.dt self.source = source self.nbr_neurons = len(source) self.tau=tau eqs=""" dv/dt=(-v)/tau: volt """ kernel=NeuronGroup(self.nbr_neurons,model=eqs) C = Connection(source, kernel, 'v') C.connect_one_to_one(source,kernel) StateMonitor.__init__(self,kernel, 'v', record=True) self.contained_objects=[kernel,C] #self.distance_matrix=zeros((self.nbr_neurons,self.nbr_neurons)) def reinit(self): StateMonitor.reinit(self) # def define(self): # @network_operation(clock=EventClock(dt=self.dt)) # def get_distance_online(): # tt=time() # for neuron_idx1 in range(self.nbr_neurons): # for neuron_idx2 in range((neuron_idx1+1)): # self.distance_matrix[neuron_idx1,neuron_idx2]=self.distance_matrix[neuron_idx1,neuron_idx2]+self.dt/self.tau*abs(self[neuron_idx1][-1]-self[neuron_idx2][-1])**2 # print time()-tt # self.contained_objects.append(get_distance_online) def get_distance(self): if get_global_preference('useweave'): _cpp_compiler=get_global_preference('weavecompiler') _extra_compile_args=['-O3'] if _cpp_compiler=='gcc': _extra_compile_args+=get_global_preference('gcc_options') # ['-march=native', '-ffast-math'] nbr_neurons=int(self.nbr_neurons) distance_matrix=zeros((nbr_neurons,nbr_neurons),dtype=float64) nbr_time_step=int(len(self[0])) dt=float(self.dt) traces=self.values tau=float(self.tau) code=''' for(int k1=0;k1200: for neuron_idx1 in xrange(self.nbr_neurons): vidx1 = values[neuron_idx1] for neuron_idx2 in xrange((neuron_idx1+1)): self.distance_matrix[neuron_idx1,neuron_idx2]=self.dt/self.tau*sum((vidx1-values[neuron_idx2])**2) else: for neuron_idx1 in xrange(self.nbr_neurons): Vi = values[neuron_idx1].reshape((1, nbr_time_step)) Vj = values.reshape((self.nbr_neurons, nbr_time_step)) self.distance_matrix[neuron_idx1, :] = (self.dt/self.tau)*sum((Vi-Vj)**2, axis=1) #print time()-tt return tril(self.distance_matrix,k=0)+tril(self.distance_matrix,k=0).T distance = property(fget=get_distance) class CoincidenceMatrixCounter(SpikeMonitor): """ Coincidence counter matrix class. Counts the number of coincidences between the spikes of the neurons in the network (model spikes). This yields a matrix with the coincidence counts between every pair of neurons in the network Initialised as:: cc = CoincidenceCounter(source, delta = 4*ms) with the following arguments: ``source`` A :class:`NeuronGroup` object which neurons are being monitored. ``delta=4*ms`` The half-width of the time window for the coincidence counting algorithm. ``onset`` A scalar value in seconds giving the start of the counting: no coincidences are counted before ``onset``. Has three attributes: ``coincidences`` The matrix containg the number of coincidences between each neuron of the :class:`NeuronGroup`. ``coincidences[i,j]`` is the number of coincidences between neuron i and j. ``model_length`` The number of spikes for each neuron. ``model_length[i]`` is the spike count for neuron i. """ def __init__(self, source, onset=None, delta=4 * ms): source.set_max_delay(0) self.source = source self.delay = 0 if onset is None: onset = 0 * ms self.onset = onset self.N = len(source) dt = self.source.clock.dt self.delta = array(rint(delta / dt), dtype=int) self.reinit() def reinit(self): dt = self.source.clock.dt # does not seem to be used # Number of spikes for each neuron self.model_length = zeros(self.N, dtype='int') self.target_length = zeros(self.N, dtype='int') self._coincidences = zeros((self.N, self.N), dtype='int') self.last_spike_time = -100 * ones(self.N, dtype='int') def get_coincidences(self): M = array(self._coincidences, dtype=float) M -= diag(M.diagonal() / 2) return M coincidences = property(fget=get_coincidences) def propagate(self, spiking_neurons): dt = self.source.clock.dt spiking_neurons = array(spiking_neurons) if len(spiking_neurons): if self.source.clock.t >= self.onset: self.model_length[spiking_neurons] += 1 tint = array(rint(self.source.clock.t / dt), dtype=int) self.last_spike_time[spiking_neurons] = tint I, = (abs(self.last_spike_time - tint) <= self.delta).nonzero() if self.source.clock.t >= self.onset: for ispike in spiking_neurons: #self.coincidences[ispike,setdiff1d(I,ispike)]+=1 self._coincidences[ispike, I[ispike <= I]] += 1 self._coincidences[I[ispike <= I], ispike] += 1 # if self.source.clock.t == self.onset: # self.coincidences=(self.coincidences+self.coincidences.T)/2 class StateHistogramMonitor(NetworkOperation, Monitor): ''' Records the histogram of a state variable from a :class:`NeuronGroup`. Initialise as:: StateHistogramMonitor(P,varname,range(,period=1*ms)(,nbins=20)) Where: ``P`` The group to be recorded from ``varname`` The state variable name or number to be recorded ``range`` The minimum and maximum values for the state variable. A 2-tuple of floats. ``period`` When to record. ``nbins`` Number of bins for the histogram. The :class:`StateHistogramMonitor` object has the following properties: ``mean`` The mean value of the state variable for every neuron in the group ``var`` The unbiased estimate of the variances, as in ``mean`` ``std`` The square root of ``var``, as in ``mean`` ``hist`` A 2D array of the histogram values of all the neurons, each row is a single neuron's histogram. ``bins`` A 1D array of the bin centers used to compute the histogram ``bin_edges`` A 1D array of the bin edges used to compute the histogram In addition, if :class:`M`` is a :class:`StateHistogramMonitor` object, you write:: M[i] for the histogram of neuron ``i``. ''' def __init__(self, group, varname, range, period=1 * ms, nbins=20): self.clock = Clock(period) NetworkOperation.__init__(self, None, clock=self.clock) self.group = group self.len = len(group) self.varname = varname self.nbins = nbins self.n = 0 self.bin_edges = linspace(range[0], range[1], self.nbins + 1) self._hist = zeros((self.len, self.nbins + 2)) self.sum = zeros(self.len) self.sum2 = zeros(self.len) def __call__(self): x = self.group.state_(self.varname) self.sum += x self.sum2 += x ** 2 inds = digitize(x, self.bin_edges) for i in xrange(self.len): self._hist[i, inds[i]] += 1 self.n += 1 def __getitem__(self, i): return self.hist[i, :] hist = property(fget=lambda self:self._hist[:, 1:-1] / self.n) bins = property(fget=lambda self:(self.bin_edges[:-1] + self.bin_edges[1:]) / 2) mean = property(fget=lambda self:self.sum / self.n) var = property(fget=lambda self:(self.sum2 / self.n - self.mean ** 2)) std = property(fget=lambda self:self.var ** .5) brian-1.4.3/brian/network.py000066400000000000000000001250041266062647100157640ustar00rootroot00000000000000# ---------------------------------------------------------------------------------- # Copyright ENS, INRIA, CNRS # Contributors: Romain Brette (brette@di.ens.fr) and Dan Goodman (goodman@di.ens.fr) # # Brian is a computer program whose purpose is to simulate models # of biological neural networks. # # This software is governed by the CeCILL license under French law and # abiding by the rules of distribution of free software. You can use, # modify and/ or redistribute the software under the terms of the CeCILL # license as circulated by CEA, CNRS and INRIA at the following URL # "http://www.cecill.info". # # As a counterpart to the access to the source code and rights to copy, # modify and redistribute granted by the license, users are provided only # with a limited warranty and the software's author, the holder of the # economic rights, and the successive licensors have only limited # liability. # # In this respect, the user's attention is drawn to the risks associated # with loading, using, modifying and/or developing or reproducing the # software by the user in light of its specific status of free software, # that may mean that it is complicated to manipulate, and that also # therefore means that it is reserved for developers and experienced # professionals having in-depth computer knowledge. Users are therefore # encouraged to load and test the software's suitability as regards their # requirements in conditions enabling the security of their systems and/or # data to be ensured and, more generally, to use and operate it in the # same conditions as regards security. # # The fact that you are presently reading this means that you have had # knowledge of the CeCILL license and that you accept its terms. # ---------------------------------------------------------------------------------- # import copy import gc import magic import time from collections import defaultdict from itertools import chain from operator import isSequenceType from inspect import * from brian.base import * from brian.clock import guess_clock, Clock from brian.connections import * from brian.globalprefs import * from brian.neurongroup import NeuronGroup from brian.units import second from brian.utils.progressreporting import * ''' Network class ''' __all__ = ['Network', 'MagicNetwork', 'NetworkOperation', 'network_operation', 'run', 'reinit', 'stop', 'clear', 'forget', 'recall'] globally_stopped = False class Network(object): ''' Contains simulation objects and runs simulations **Initialised as:** :: Network(...) with ``...`` any collection of objects that should be added to the :class:`Network`. You can also pass lists of objects, lists of lists of objects, etc. Objects that need to passed to the :class:`Network` object are: * :class:`NeuronGroup` and anything derived from it such as :class:`PoissonGroup`. * :class:`Connection` and anything derived from it. * Any monitor such as :class:`SpikeMonitor` or :class:`StateMonitor`. * Any network operation defined with the :func:`network_operation` decorator. Models, equations, etc. do not need to be passed to the :class:`Network` object. The most important method is the ``run(duration)`` method which runs the simulation for the given length of time (see below for details about what happens when you do this). **Example usage:** :: G = NeuronGroup(...) C = Connection(...) net = Network(G,C) net.run(1*second) **Methods** ``add(...)`` Add additional objects after initialisation, works the same way as initialisation. ``remove(...)`` Remove objects from the Network. ``run(duration[, report[, report_period]])`` Runs the network for the given duration. See below for details about what happens when you do this. See documentation for :func:`run` for an explanation of the ``report`` and ``report_period`` keywords. ``reinit(states=True)`` Reinitialises the network, runs each object's ``reinit()`` and each clock's ``reinit()`` method (resetting them to 0). If ``states=False`` then it will not reinitialise the :class:`NeuronGroup` state variables. ``stop()`` Can be called from a :func:`network_operation` for example to stop the network from running. ``__len__()`` Returns the number of neurons in the network. ``__call__(obj)`` Similar to ``add``, but you can only pass one object and that object is returned. You would only need this in obscure circumstances where objects needed to be added to the network but were either not stored elsewhere or were stored in a way that made them difficult to extract, for example below the NeuronGroup object is only added to the network if certain conditions hold:: net = Network(...) if some_condition: x = net(NeuronGroup(...)) **What happens when you run** For an overview, see the Concepts chapter of the main documentation. When you run the network, the first thing that happens is that it checks if it has been prepared and calls the ``prepare()`` method if not. This just does various housekeeping tasks and optimisations to make the simulation run faster. Also, an update schedule is built at this point (see below). Now the ``update()`` method is repeatedly called until every clock has run for the given length of time. After each call of the ``update()`` method, the clock is advanced by one tick, and if multiple clocks are being used, the next clock is determined (this is the clock whose value of ``t`` is minimal amongst all the clocks). For example, if you had two clocks in operation, say ``clock1`` with ``dt=3*ms`` and ``clock2`` with ``dt=5*ms`` then this will happen: 1. ``update()`` for ``clock1``, tick ``clock1`` to ``t=3*ms``, next clock is ``clock2`` with ``t=0*ms``. 2. ``update()`` for ``clock2``, tick ``clock2`` to ``t=5*ms``, next clock is ``clock1`` with ``t=3*ms``. 3. ``update()`` for ``clock1``, tick ``clock1`` to ``t=6*ms``, next clock is ``clock2`` with ``t=5*ms``. 4. ``update()`` for ``clock2``, tick ``clock2`` to ``t=10*ms``, next clock is ``clock1`` with ``t=6*ms``. 5. ``update()`` for ``clock1``, tick ``clock1`` to ``t=9*ms``, next clock is ``clock1`` with ``t=9*ms``. 6. ``update()`` for ``clock1``, tick ``clock1`` to ``t=12*ms``, next clock is ``clock2`` with ``t=10*ms``. etc. The ``update()`` method simply runs each operation in the current clock's update schedule. See below for details on the update schedule. **Update schedules** An update schedule is the sequence of operations that are called for each ``update()`` step. The standard update schedule is: * Network operations with ``when = 'start'`` * Network operations with ``when = 'before_groups'`` * Call ``update()`` method for each :class:`NeuronGroup`, this typically performs an integration time step for the differential equations defining the neuron model. * Network operations with ``when = 'after_groups'`` * Network operations with ``when = 'middle'`` * Network operations with ``when = 'before_connections'`` * Call ``do_propagate()`` method for each :class:`Connection`, this typically adds a value to the target state variable of each neuron that a neuron that has fired is connected to. See Tutorial 2: Connections for a more detailed explanation of this. * Network operations with ``when = 'after_connections'`` * Network operations with ``when = 'before_resets'`` * Call ``reset()`` method for each :class:`NeuronGroup`, typically resets a given state variable to a given reset value for each neuron that fired in this update step. * Network operations with ``when = 'after_resets'`` * Network operations with ``when = 'end'`` There is one predefined alternative schedule, which you can choose by calling the ``update_schedule_groups_resets_connections()`` method before running the network for the first time. As the name suggests, the reset operations are done before connections (and the appropriately named network operations are called relative to this rearrangement). You can also define your own update schedule with the ``set_update_schedule`` method (see that method's API documentation for details). This might be useful for example if you have a sequence of network operations which need to be run in a given order. ''' operations = property(fget=lambda self:self._all_operations) def __init__(self, *args, **kwds): self.clock = None # Initialized later self.groups = [] self.connections = [] # The following dict keeps a copy of which operations are in which slot self._operations_dict = defaultdict(list) self._all_operations = [] self.update_schedule_standard() self.prepared = False self._added_objects = [] for o in chain(args, kwds.itervalues()): self.add(o) def unprepare(self): if self.prepared: # _added_objects keeps track of everything the user has added in # order, and can be used when re-preparing a Network, but the way # we do it is to undo the effect of the previous prepare() by # calling __init__ again and then adding the previously added # objects _added_objects = self._added_objects self.prepared = False Network.__init__(self) for o in _added_objects: self.add(o) def add(self, *objs): """ Add an object or container of objects to the network """ self.unprepare() for obj in objs: if isinstance(obj, (NeuronGroup, Connection, NetworkOperation)): self._added_objects.append(obj) if isinstance(obj, NeuronGroup): if obj not in self.groups: self.groups.append(obj) elif isinstance(obj, Connection): if obj not in self.connections: self.connections.append(obj) elif isinstance(obj, NetworkOperation): if obj not in self._all_operations: self._operations_dict[obj.when].append(obj) self._all_operations.append(obj) elif isSequenceType(obj): for o in obj: self.add(o) else: raise TypeError('Only the following types of objects can be added to a network: NeuronGroup, Connection or NetworkOperation') try: gco = obj.contained_objects if gco is not None: self.add(gco) except AttributeError: pass def remove(self, *objs, **kwds): ''' Remove an object or sequence of objects from a Network (note that this will force the Network to be prepared from scratch). ''' unprepare = kwds.pop('unprepare', True) for obj in objs: if isinstance(obj, (NeuronGroup, Connection, NetworkOperation)): self._added_objects = [o for o in self._added_objects if o is not obj] if isinstance(obj, NeuronGroup): self.groups = [o for o in self.groups if o is not obj] if isinstance(obj, Connection): self.connections = [o for o in self.connections if o is not obj] if isinstance(obj, NetworkOperation): self._all_operations = [o for o in self._all_operations if o is not obj] self._operations_dict[obj.when] = [o for o in self._operations_dict[obj.when] if o is not obj] elif isSequenceType(obj): for o in obj: self.remove(o, unprepare=False) else: raise TypeError('Only the following types of objects can be removed from a network: NeuronGroup, Connection or NetworkOperation') try: gco = obj.contained_objects if gco is not None: self.remove(gco, unprepare=False) except AttributeError: pass if unprepare: self.unprepare() def __call__(self, obj): """ Add an object to the network and return it """ self.add(obj) return obj def reinit(self, states=True): ''' Resets the objects and clocks. If ``states=False`` it will not reinit the state variables. ''' objs = self.groups + self.connections + self.operations if self.clock is not None: objs.append(self.clock) else: guess_clock(None).reinit() if hasattr(self, 'clocks'): objs.extend(self.clocks) for P in objs: if hasattr(P, 'reinit'): if isinstance(P, NeuronGroup): try: P.reinit(states=states) except TypeError: P.reinit() else: P.reinit() def prepare(self): ''' Prepares the network for simulation: + Checks the clocks of the neuron groups + Gather connections with identical subgroups + Compresses the connection matrices for faster simulation Calling this function is not mandatory but speeds up the simulation. ''' self.unprepare() # Set the clock if self.same_clocks(): self.set_clock() else: self.set_many_clocks() # Gather connections with identical subgroups # 'subgroups' maps subgroups to connections (initialize with immutable object (not [])!) # Use ids of the objects here, to avoid hashing problems subgroups = dict.fromkeys([(id(C.source), id(C.delay)) for C in self.connections], None) for C in self.connections: if subgroups[(id(C.source), id(C.delay))] is None: subgroups[(id(C.source), id(C.delay))] = [C] else: subgroups[(id(C.source), id(C.delay))].append(C) self.connections = subgroups.values() cons = self.connections # just for readability for i in range(len(cons)): if len(cons[i]) > 1: # at least 2 connections with the same subgroup cons[i] = MultiConnection(cons[i][0].source, cons[i]) else: cons[i] = cons[i][0] # Compress connections for C in self.connections: C.compress() # Compress groups that can be compressed for N in self.groups: if hasattr(N,'compress'): N.compress() # Experimental support for new propagation code if get_global_preference('usenewpropagate') and get_global_preference('useweave'): from experimental.new_c_propagate import make_new_connection for C in self.connections: make_new_connection(C) # build operations list for each clock self._build_update_schedule() self.prepared = True def update_schedule_standard(self): self._schedule = ['ops start', 'ops before_groups', 'groups', 'ops after_groups', 'ops middle', 'ops before_connections', 'connections', 'ops after_connections', 'synapses', 'ops before_resets', 'resets', 'ops after_resets', 'ops end' ] self._build_update_schedule() def update_schedule_groups_resets_connections(self): self._schedule = ['ops start', 'ops before_groups', 'groups', 'ops after_groups', 'ops middle', 'ops before_resets', 'resets', 'ops after_resets', 'ops before_connections', 'connections', 'ops after_connections', 'synapses', 'ops end' ] self._build_update_schedule() def set_update_schedule(self, schedule): """ Defines a custom update schedule A custom update schedule is a list of schedule items. Each update step of the network, the schedule items will be run in turn. A schedule item can be defined as a string or tuple. The following string definitions are possible: 'groups' Calls the 'update' function of each group in turn, this is typically the integration step of the simulation. 'connections' Calls the 'do_propagate' function of each connection in turn, this is typically propagating spikes forward (and backward in the case of STDP). 'synapses' Calls the 'update' function of each synapse in turn, this may include both updating the state of the synapse as well as altering the state of groups targeted by the synapse. 'resets' Calls the 'reset' function of each group in turn. 'ops '+name Calls each operation in turn whose 'when' parameter is set to 'name'. The standard set of 'when' names is start, before_groups, after_groups, before_resets, after_resets, before_connections, after_connections, end, but you can use any you like. If a tuple is provided, it should be of the form: (objset, func, allclocks) with: objset a list of objects to be processed func Either None or a string. In the case of none, each object in objset must be callable and will be called. In the case of a string, obj.func will be called for each obj in objset. allclocks Either True or False. If it's set to True, then the object will be placed in the update schedule of every clock in the network. If False, it will be placed in the update schedule only of the clock obj.clock. """ self._schedule = schedule self._build_update_schedule() def _build_update_schedule(self): ''' Defines what the update step does For each clock we build a list self._update_schedule[id(clock)] of functions which are called at the update step if that clock is active. This is generic and works for single or multiple clocks. See documentation for set_update_schedule for an explanation of the self._schedule object. ''' self._update_schedule = defaultdict(list) if hasattr(self, 'clocks'): clocks = self.clocks else: clocks = [self.clock] clockset = clocks for item in self._schedule: # we define some simple names for common schedule items if isinstance(item, str): if item == 'groups': # we can't directly do something like checking for # isinstance(group, Synapses) here, as this would lead to # a circular import objset = [group for group in self.groups if not hasattr(group, 'presynaptic')] objfun = 'update' allclocks = False elif item == 'synapses': objset = [group for group in self.groups if hasattr(group, 'presynaptic')] objfun = 'update' allclocks = False elif item == 'resets': objset = self.groups objfun = 'reset' allclocks = False elif item == 'connections': objset = self.connections objfun = 'do_propagate' allclocks = False # Connections do not define their own clock, but they should # be updated on the schedule of their source group for obj in objset: obj.clock = obj.source.clock elif len(item) > 4 and item[0:3] == 'ops': # the item is of the forms 'ops when' objset = self._operations_dict[item[4:]] objfun = None allclocks = False else: # we allow the more general form of usage as well objset, objfun, allclocks = item for obj in objset: if objfun is None: f = obj else: f = getattr(obj, objfun) if not allclocks: useclockset = [obj.clock] else: useclockset = clockset for clock in useclockset: self._update_schedule[id(clock)].append(f) def update(self): for f in self._update_schedule[id(self.clock)]: f() """ def update_threaded(self,queue): ''' EXPERIMENTAL (not useful for the moment) Parallel update of the network (using threads). ''' # Update groups: one group = one thread for P in self.groups: queue.put(P) queue.join() # Wait until job is done # The following is done serially # Propagate spikes for C in self.connections: C.propagate(C.source.get_spikes(C.delay)) # Miscellanous operations for op in self.operations: op() """ def run(self, duration, threads=1, report=None, report_period=10 * second): ''' Runs the simulation for the given duration. ''' global globally_stopped self.stopped = False globally_stopped = False if not self.prepared: self.prepare() self.clock.set_duration(duration) try: for c in self.clocks: c.set_duration(duration) except AttributeError: pass if report is not None: start_time = time.time() if not isinstance(report, ProgressReporter): report = ProgressReporter(report, report_period) next_report_time = start_time + float(report_period) else: report_period = report.period next_report_time = report.next_report_time if self.clock.still_running() and not self.stopped and not globally_stopped: not_same_clocks = not self.same_clocks() clk = self.clock while clk.still_running() and not self.stopped and not globally_stopped: if report is not None: cur_time = time.time() if cur_time > next_report_time: next_report_time = cur_time + float(report_period) report.update((self.clock.t - self.clock.start) / duration) self.update() clk.tick() if not_same_clocks: # Find the next clock to update #self.clock = min([(clock.t, id(clock), clock) for clock in self.clocks])[2] clk = self.clock = min(self.clocks) #heapq.heappush(self.clocks, self.clock) #clk = self.clock = heapq.heappop(self.clocks) #clk = self.clock = heapq.heappushpop(self.clocks, self.clock) #if not clk0: clock = groups_and_operations[0].clock return all([obj.clock == clock for obj in groups_and_operations]) else: return True def set_clock(self): ''' Sets the clock and checks that clocks of all groups are synchronized. ''' if self.same_clocks(): groups_and_operations=self.groups + self.operations if len(groups_and_operations)>0: self.clock = groups_and_operations[0].clock else: self.clock = guess_clock() else: raise TypeError, 'Clocks are not synchronized!' # other error type? def set_many_clocks(self): ''' Sets a list of clocks. self.clock points to the current clock between considered. ''' self.clocks = list(set([obj.clock for obj in self.groups + self.operations])) #self.clocks.sort(key=lambda c:-c._dt) #heapq.heapify(self.clocks) #self.clock = min([(clock.t, clock) for clock in self.clocks])[1] self.clock = min(self.clocks) #self.clock = heapq.heappop(self.clocks) def __len__(self): ''' Number of neurons in the network ''' n = 0 for P in self.groups: n += len(P) # use compact iterator function? return n def __repr__(self): return '<%s of %d groups with a total of %d neurons>' % (self.__class__.__name__, len(self.groups), len(self)) def __str__(self): return '%s of %d groups with a total of %d neurons' % (self.__class__.__name__, len(self.groups), len(self)) # TODO: obscure custom update schedules might still lead to unpicklable Network object def __reduce__(self): # This code might need some explanation: # # The problem with pickling the Network object is that you cannot pickle # 'instance methods', that is a copy of a method of an instance. The # Network object does this because the _update_schedule attribute stores # a copy of all the functions that need to be called each time step, and # these are all instance methods (of NeuronGroup, Reset, etc.). So, we # solve the problem by deleting this attribute at pickling time, and then # rebuilding it at unpickling time. The function unpickle_network defined # below does the unpickling. # # We basically want to make a copy of the current object, and delete the # update schedule from it, and then pickle that. Some weird recursive # stuff happens if you try to do this in the obvious way, so we take the # seemingly mad step of converting the object to a general 'heap' class # (that is, a new-style class with no methods or anything, in this case # the NetworkNoMethods class defined below), do all our operations on # this, store a copy of the actual class of the object (which may not be # Network for derived classes), work with this, and then restore # everything back to the way it was when everything is done. # oldclass = self.__class__ # class may be derived from Network self.__class__ = NetworkNoMethods # stops recursion in copy.copy net = copy.copy(self) # we make a copy because after returning from this function we can't restore the class self.__class__ = oldclass # restore the class of the original, which is now back in its original state net._update_schedule = None # remove the problematic element from the copy return (unpickle_network, (oldclass, net)) # the unpickle_network function called with arguments oldclass, net restores it as it was # This class just used as a general 'heap' class - has no methods but can have attributes class NetworkNoMethods(object): pass def unpickle_network(oldclass, net): # See Network.__reduce__ for an explanation, basically the _update_schedule # cannot be pickled because it contains instance methods, but it can just be # rebuilt. net.__class__ = oldclass net._build_update_schedule() return net class NetworkOperation(magic.InstanceTracker, ObjectContainer): """Callable class for operations that should be called every update step Typically, you should just use the :func:`network_operation` decorator, but if you can't for whatever reason, use this. Note: current implementation only works for functions, not any callable object. **Initialisation:** :: NetworkOperation(function[,clock]) If your function takes an argument, the clock will be passed as that argument. """ def __init__(self, function=None, clock=None, when='end'): self.clock = guess_clock(clock) self.when = when self.function = function if hasattr(function, 'func_code'): self._has_arg = (self.function.func_code.co_argcount==1) def __call__(self): if not hasattr(self, 'function') or self.function is None: return if self._has_arg: self.function(self.clock) else: self.function() def network_operation(*args, **kwds): """Decorator to make a function into a :class:`NetworkOperation` A :class:`NetworkOperation` is a callable class which is called every time step by the :class:`Network` ``run`` method. Sometimes it is useful to just define a function which is to be run every update step. This decorator can be used to turn a function into a :class:`NetworkOperation` to be added to a :class:`Network` object. **Example usages** Operation doesn't need a clock:: @network_operation def f(): ... Automagically detect clock:: @network_operation def f(clock): ... Specify a clock:: @network_operation(specifiedclock) def f(clock): ... Specify when the network operation is run (default is ``'end'``):: @network_operation(when='start') def f(): ... Then add to a network as follows:: net = Network(f,...) """ # Notes on this decorator: # Normally, a decorator comes in two types, with or without arguments. If # it has no arguments, e.g. # @decorator # def f(): # ... # then the decorator function is defined with an argument, and that # argument is the function f. In this case, the decorator function # returns a new function in place of f. # # However, you can also define: # @decorator(arg) # def f(): # ... # in which case the argument to the decorator function is arg, and the # decorator function returns a 'function factory', that is a callable # object that takes a function as argument and returns a new function. # # It might be clearer just to note that the first form above is equivalent # to: # f = decorator(f) # and the second to: # f = decorator(arg)(f) # # In this case, we're allowing the decorator to be called either with or # without an argument, so we have to look at the arguments and determine # if it's a function argument (in which case we do the first case above), # or if the arguments are arguments to the decorator, in which case we # do the second case above. # # Here, the 'function factory' is the locally defined class # do_network_operation, which is a callable object that takes a function # as argument and returns a NetworkOperation object. class do_network_operation(object): def __init__(self, clock=None, when='end'): self.clock = clock self.when = when def __call__(self, f, level=1): new_network_operation = NetworkOperation(f, self.clock, self.when) # Depending on whether we were called as @network_operation or # @network_operation(...) we need different levels, the level is # 2 in the first case and 1 in the second case (because in the # first case we go originalcaller->network_operation->do_network_operation # and in the second case we go originalcaller->do_network_operation # at the time when this method is called). new_network_operation.set_instance_id(level=level) new_network_operation.__name__ = f.__name__ new_network_operation.__doc__ = f.__doc__ new_network_operation.__dict__.update(f.__dict__) return new_network_operation if len(args) == 1 and callable(args[0]): # We're in case (1), the user has written: # @network_operation # def f(): # ... # and the single argument to the decorator is the function f return do_network_operation()(args[0], level=2) else: # We're in case (2), the user has written: # @network_operation(...) # def f(): # ... # and the arguments might be clocks or strings, and may have been # called with or without names, so we check both the variable length # argument list *args, and the keyword dictionary **kwds, falling # back on the default values if nothing is given. clk = None when = 'end' for arg in args: if isinstance(arg, Clock): clk = arg elif isinstance(arg, str): when = arg for key, val in kwds.iteritems(): if key == 'clock': clk = val if key == 'when': when = val return do_network_operation(clock=clk, when=when) #raise TypeError, "Decorator must be used as @network_operation or @network_operation(clock)" class MagicNetwork(Network): ''' Creates a :class:`Network` object from any suitable objects **Initialised as:** :: MagicNetwork() The object returned can then be used just as a regular :class:`Network` object. It works by finding any object in the ''execution frame'' (i.e. in the same function, script or section of module code where the :class:`MagicNetwork` was created) derived from :class:`NeuronGroup`, :class:`Connection` or :class:`NetworkOperation`. **Sample usage:** :: G = NeuronGroup(...) C = Connection(...) @network_operation def f(): ... net = MagicNetwork() Each of the objects ``G``, ``C`` and ``f`` are added to ``net``. **Advanced usage:** :: MagicNetwork([verbose=False[,level=1]]) with arguments: ``verbose`` Set to ``True`` to print out a list of objects that were added to the network, for debugging purposes. ``level`` Where to find objects. ``level=1`` means look for objects where the :class:`MagicNetwork` object was created. The ``level`` argument says how many steps back in the stack to look. ''' def __init__(self, verbose=False, level=1): ''' Set verbose=False to turn off comments. The level variable contains the location of the namespace. ''' (groups, groupnames) = magic.find_instances(NeuronGroup) groups = [g for g in groups if g._owner is g] groupnames = [gn for g, gn in zip(groups, groupnames) if g._owner is g] (connections, connectionnames) = magic.find_instances(Connection) (operations, operationnames) = magic.find_instances(NetworkOperation) if verbose: print "[MagicNetwork] Groups:", groupnames print "[MagicNetwork] Connections:", connectionnames print "[MagicNetwork] Operations:", operationnames # Use set() to discard duplicates Network.__init__(self, list(set(groups)), list(set(connections)), list(set(operations))) def run(duration, threads=1, report=None, report_period=10 * second): ''' Run a network created from any suitable objects that can be found Arguments: ``duration`` the length of time to run the network for. ``report`` How to report progress, the default ``None`` doesn't report the progress. Some standard values for ``report``: ``text``, ``stdout`` Prints progress to the standard output. ``stderr`` Prints progress to the standard error output stderr. ``graphical``, ``tkinter`` Uses the Tkinter module to show a graphical progress bar, this may interfere with any other GUI code you have. Alternatively, you can provide your own callback function by setting ``report`` to be a function ``report(elapsed, complete)`` of two variables ``elapsed``, the amount of time elapsed in seconds, and ``complete`` the proportion of the run duration simulated (between 0 and 1). The ``report`` function is guaranteed to be called at the end of the run with ``complete=1.0`` so this can be used as a condition for reporting that the computation is finished. ``report_period`` How often the progress is reported (by default, every 10s). Works by constructing a :class:`MagicNetwork` object from all the suitable objects that could be found (:class:`NeuronGroup`, :class:`Connection`, etc.) and then running that network. Not suitable for repeated runs or situations in which you need precise control. ''' MagicNetwork(verbose=False, level=2).run(duration, threads=threads, report=report, report_period=report_period) def reinit(states=True): ''' Reinitialises any suitable objects that can be found **Usage:** :: reinit(states=True) Works by constructing a :class:`MagicNetwork` object from all the suitable objects that could be found (:class:`NeuronGroup`, :class:`Connection`, etc.) and then calling ``reinit()`` for each of them. Not suitable for repeated runs or situations in which you need precise control. If ``states=False`` then :class:`NeuronGroup` state variables will not be reinitialised. ''' MagicNetwork(verbose=False, level=2).reinit(states=states) def stop(): ''' Globally stops any running network, this is reset the next time a network is run ''' global globally_stopped globally_stopped = True def clear(erase=True, all=False): ''' Clears all Brian objects. Specifically, it stops all existing Brian objects from being collected by :class:`MagicNetwork` (objects created after clearing will still be collected). If ``erase`` is ``True`` then it will also delete all data from these objects. This is useful in, for example, ``ipython`` which stores persistent references to objects in any given session, stopping the data and memory from being freed up. If ``all=True`` then all Brian objects will be cleared. See also :func:`forget`. ''' if all is False: net = MagicNetwork(level=2) objs = net.groups + net.connections + net.operations else: groups, _ = magic.find_instances(NeuronGroup, all=True) connections, _ = magic.find_instances(Connection, all=True) operations, _ = magic.find_instances(NetworkOperation, all=True) objs = groups+connections+operations for o in objs: o.set_instance_id(-1) if erase: for k, v in o.__dict__.iteritems(): object.__setattr__(o, k, None) gc.collect() def forget(*objs): ''' Forgets the list of objects passed Forgetting means that :class:`MagicNetwork` will not pick up these objects, but all data is retained. You can pass objects or lists of objects. Forgotten objects can be recalled with :func:`recall`. See also :func:`clear`. ''' for obj in objs: if isinstance(obj, (NeuronGroup, Connection, NetworkOperation)): obj._forgotten_instance_id = obj.get_instance_id() obj.set_instance_id(-1) elif isSequenceType(obj): for o in obj: forget(o) else: raise TypeError('Only the following types of objects can be forgotten: NeuronGroup, Connection or NetworkOperation') def recall(*objs): ''' Recalls previously forgotten objects See :func:`forget` and :func:`clear`. ''' for obj in objs: if isinstance(obj, (NeuronGroup, Connection, NetworkOperation)): if hasattr(obj, '_forgotten_instance_id'): obj.set_instance_id(obj._forgotten_instance_id) elif isSequenceType(obj): for o in obj: recall(o) else: raise TypeError('Only the following types of objects can be recalled: NeuronGroup, Connection or NetworkOperation') brian-1.4.3/brian/neurongroup.py000066400000000000000000000760601266062647100166650ustar00rootroot00000000000000# ---------------------------------------------------------------------------------- # Copyright ENS, INRIA, CNRS # Contributors: Romain Brette (brette@di.ens.fr) and Dan Goodman (goodman@di.ens.fr) # # Brian is a computer program whose purpose is to simulate models # of biological neural networks. # # This software is governed by the CeCILL license under French law and # abiding by the rules of distribution of free software. You can use, # modify and/ or redistribute the software under the terms of the CeCILL # license as circulated by CEA, CNRS and INRIA at the following URL # "http://www.cecill.info". # # As a counterpart to the access to the source code and rights to copy, # modify and redistribute granted by the license, users are provided only # with a limited warranty and the software's author, the holder of the # economic rights, and the successive licensors have only limited # liability. # # In this respect, the user's attention is drawn to the risks associated # with loading, using, modifying and/or developing or reproducing the # software by the user in light of its specific status of free software, # that may mean that it is complicated to manipulate, and that also # therefore means that it is reserved for developers and experienced # professionals having in-depth computer knowledge. Users are therefore # encouraged to load and test the software's suitability as regards their # requirements in conditions enabling the security of their systems and/or # data to be ensured and, more generally, to use and operate it in the # same conditions as regards security. # # The fact that you are presently reading this means that you have had # knowledge of the CeCILL license and that you accept its terms. # ---------------------------------------------------------------------------------- # from brian.stateupdater import LazyStateUpdater ''' Neuron groups ''' __all__ = ['NeuronGroup', 'linked_var'] from numpy import * try: import weave except ImportError: from scipy import weave from scipy import rand, linalg, random from numpy.random import exponential, randint import copy from units import * from threshold import * import bisect from reset import * from clock import * from stateupdater import * from inspection import * from operator import isSequenceType import types from utils.circular import * import magic from itertools import count from equations import * from globalprefs import * import sys from brian_unit_prefs import bup import numpy from base import * from group import * from threshold import select_threshold from collections import defaultdict from log import log_info timedarray = None # ugly hack: import this module when it is needed, can't do it here because of order of imports network = None # ugly hack: import this module when it is needed, can't do it here because of order of imports class TArray(numpy.ndarray): ''' This internal class is just used for when Brian sends an array t to an object. All the elements will be the same in this case, and you can check for isinstance(arr, TArray) to do optimisations based on this. This behaviour may change in the future. ''' def __new__(subtype, arr): # All numpy.ndarray subclasses need something like this, see # http://www.scipy.org/Subclasses return numpy.array(arr, copy=False).view(subtype) class LinkedVar(object): def __init__(self, source, var=0, func=None, when='start', clock=None): self.source = source self.var = var self.func = func self.when = when self.clock = clock def linked_var(source, var=0, func=None, when='start', clock=None): """ Used for linking one :class:`NeuronGroup` variable to another. Sample usage:: G = NeuronGroup(...) H = NeuronGroup(...) G.V = linked_var(H, 'W') In this scenario, the variable V in group G will always be updated with the values from variable W in group H. The groups G and H must be the same size (although subgroups can be used if they are not the same size). Arguments: ``source`` The group from which values will be taken. ``var`` The state variable of the source group to take values from. ``func`` An additional function of one argument to pass the source variable values through, e.g. ``func=lambda x:clip(x,0,Inf)`` to half rectify the values. ``when`` The time in the main Brian loop at which the copy operation is performed, as explained in :class:`Network`. ``clock`` The update clock for the copy operation, by default it will use the clock of the target group. """ return LinkedVar(source, var, func, when, clock) class NeuronGroup(magic.InstanceTracker, ObjectContainer, Group): """Group of neurons Initialised with arguments: ``N`` The number of neurons in the group. ``model`` An object defining the neuron model. It can be an :class:`Equations` object, a string defining an :class:`Equations` object, a :class:`StateUpdater` object, or a list or tuple of :class:`Equations` and strings. ``threshold=None`` A :class:`Threshold` object, a function, a scalar quantity or a string. If ``threshold`` is a function with one argument, it will be converted to a :class:`SimpleFunThreshold`, otherwise it will be a :class:`FunThreshold`. If ``threshold`` is a scalar, then a constant single valued threshold with that value will be used. In this case, the variable to apply the threshold to will be guessed. If there is only one variable, or if you have a variable named one of ``V``, ``Vm``, ``v`` or ``vm`` it will be used. If ``threshold`` is a string then the appropriate threshold type will be chosen, for example you could do ``threshold='V>10*mV'``. The string must be a one line string. ``reset=None`` A :class:`Reset` object, a function, a scalar quantity or a string. If it's a function, it will be converted to a :class:`FunReset` object. If it's a scalar, then a constant single valued reset with that value will be used. In this case, the variable to apply the reset to will be guessed. If there is only one variable, or if you have a variable named one of ``V``, ``Vm``, ``v`` or ``vm`` it will be used. If ``reset`` is a string it should be a series of expressions which are evaluated for each neuron that is resetting. The series of expressions can be multiline or separated by a semicolon. For example, ``reset=`Vt+=5*mV; V=Vt'``. Statements involving ``if`` constructions will often not work because the code is automatically vectorised. For such constructions, use a function instead of a string. ``refractory=0*ms``, ``min_refractory``, ``max_refractory`` A refractory period, used in combination with the ``reset`` value if it is a scalar. For constant resets only, you can specify refractory as an array of length the number of elements in the group, or as a string, giving the name of a state variable in the group. In the case of these variable refractory periods, you should specify ``min_refractory`` (optional) and ``max_refractory`` (required). ``level=0`` See :class:`Equations` for details. ``clock`` A clock to use for scheduling this :class:`NeuronGroup`, if omitted the default clock will be used. ``order=1`` The order to use for nonlinear differential equation solvers. TODO: more details. ``implicit=False`` Whether to use an implicit method for solving the differential equations. TODO: more details. ``max_delay=0*ms`` The maximum allowable delay (larger values use more memory). This doesn't usually need to be specified because Connections will update it. ``compile=False`` Whether or not to attempt to compile the differential equation solvers (into Python code). Typically, for best performance, both ``compile`` and ``freeze`` should be set to ``True`` for nonlinear differential equations. ``freeze=False`` If True, parameters are replaced by their values at the time of initialization. ``method=None`` If not None, the integration method is forced. Possible values are linear, nonlinear, Euler, exponential_Euler (overrides implicit and order keywords). ``unit_checking=True`` Set to ``False`` to bypass unit-checking. **Methods** .. method:: subgroup(N) Returns the next sequential subgroup of ``N`` neurons. See the section on subgroups below. .. method:: state(var) Returns the array of values for state variable ``var``, with length the number of neurons in the group. .. method:: rest() Sets the neuron state values at rest for their differential equations. The following usages are also possible for a group ``G``: ``G[i:j]`` Returns the subgroup of neurons from ``i`` to ``j``. ``len(G)`` Returns the number of neurons in ``G``. ``G.x`` For any valid Python variable name ``x`` corresponding to a state variable of the the :class:`NeuronGroup`, this returns the array of values for the state variable ``x``, as for the :meth:`state` method above. Writing ``G.x = arr`` for ``arr`` a :class:`TimedArray` will set the values of variable x to be ``arr(t)`` at time t. See :class:`TimedArraySetter` for details. **Subgroups** A subgroup is a view on a group. It isn't a new group, it's just a convenient way of referring to a subset of the neurons in an already defined group. The subset has to be a continguous set of neurons. They can be overlapping if defined with the slice notation, or consecutive if defined with the :meth:`subgroup` method. Subgroups can themselves be subgrouped. Subgroups can be used in almost all situations exactly as if they were groups, except that they cannot be passed to the :class:`Network` object. **Details** TODO: details of other methods and properties for people wanting to write extensions? """ @check_units(max_delay=second) def __init__(self, N, model=None, threshold=None, reset=NoReset(), init=None, refractory=0 * msecond, level=0, clock=None, order=1, implicit=False, unit_checking=True, max_delay=0 * msecond, compile=False, freeze=False, method=None, max_refractory=None, ):#**args): # any reason why **args was included here? ''' Initializes the group. ''' self._useweave = get_global_preference('useweave') if self._useweave: self._cpp_compiler = get_global_preference('weavecompiler') self._extra_compile_args = ['-O3'] if self._cpp_compiler == 'gcc': self._extra_compile_args += get_global_preference('gcc_options') self._spiking = True # by default, produces spikes if bup.use_units: # one additional frame level induced by the decorator level += 1 # If it is a string, convert to Equations object if isinstance(model, (str, list, tuple)): model = Equations(model, level=level + 1) if isinstance(threshold, str): if isinstance(model, Equations): threshold = select_threshold(threshold, model, level=level + 1) else: threshold = StringThreshold(threshold, level=level + 1) if isinstance(reset, str): if isinstance(model, Equations): reset = select_reset(reset, model, level=level + 1) else: reset = StringReset(reset, level=level + 1) # Clock clock = guess_clock(clock)#not needed with protocol checking self.clock = clock # Initial state self._S0 = init self.staticvars = [] # StateUpdater if isinstance(model, StateUpdater): self._state_updater = model # Update mechanism self._all_units = defaultdict() elif isinstance(model, Equations): if len(model) == 0: self._eqs = model self._state_updater = LazyStateUpdater(0) self._all_units = defaultdict() else: self._eqs = model if (init == None) and (model._units == {}): raise AttributeError, "The group must be initialized." self._state_updater, var_names = magic_state_updater(model, clock=clock, order=order, check_units=unit_checking, implicit=implicit, compile=compile, freeze=freeze, method=method) Group.__init__(self, model, N, unit_checking=unit_checking) self._all_units = model._units # Converts S0 from dictionary to tuple if self._S0 == None: # No initialization: 0 with units S0 = {} else: S0 = self._S0.copy() # Fill missing units for key, value in model._units.iteritems(): if not key in S0: S0[key] = 0 * value self._S0 = [0] * len(var_names) for var, i in zip(var_names, count()): self._S0[i] = S0[var] else: raise TypeError, "StateUpdater must be specified at initialization." # TODO: remove temporary unit hack, this makes all state variables dimensionless if no units are specified # What is this?? if self._S0 is None: self._S0 = dict((i, 1.) for i in range(len(self._state_updater))) # Threshold if isinstance(threshold, Threshold): self._threshold = threshold elif type(threshold) == types.FunctionType: if threshold.func_code.co_argcount == 1: self._threshold = SimpleFunThreshold(threshold) else: self._threshold = FunThreshold(threshold) elif is_scalar_type(threshold): # Check unit if self._S0 != None: try: threshold + self._S0[0] except DimensionMismatchError, inst: raise DimensionMismatchError("The threshold does not have correct units.", *inst._dims) self._threshold = Threshold(threshold=threshold) else: # maybe raise an error? self._threshold = NoThreshold() self._spiking = False # Initialization of the state matrix if not hasattr(self, '_S'): self._S = zeros((len(self._state_updater), N)) if self._S0 != None: for i in range(len(self._state_updater)): self._S[i, :] = self._S0[i] # Reset and refractory period self._variable_refractory_time = False period_max = 0 if is_scalar_type(reset) or reset.__class__ is Reset: if reset.__class__ is Reset: if isinstance(reset.state, str): numstate = self.get_var_index(reset.state) else: numstate = reset.state reset = reset.resetvalue else: numstate = 0 # Check unit if self._S0 != None: try: reset + self._S0[numstate] except DimensionMismatchError, inst: raise DimensionMismatchError("The reset does not have correct units.", *inst._dims) if isinstance(refractory, float): max_refractory = refractory else: if isinstance(refractory, str): if max_refractory is None: raise ValueError('Must specify max_refractory if using variable refractoriness.') self._refractory_variable = refractory self._refractory_array = None else: max_refractory = amax(refractory) * second self._refractory_variable = None self._refractory_array = refractory self._variable_refractory_time = True # What is this 0.9 ?!! Answer: it's just to check that the refractory period is at least clock.dt otherwise don't bother if max_refractory > 0.9 * clock.dt: # Refractory period - unit checking is done here self._resetfun = Refractoriness(period=max_refractory, resetvalue=reset, state=numstate) period_max = int(max_refractory / clock.dt) + 1 else: # Simple reset self._resetfun = Reset(reset, state=numstate) elif type(reset) == types.FunctionType: self._resetfun = FunReset(reset) if refractory > 0.9 * clock.dt: raise ValueError('Refractoriness for custom reset functions not yet implemented, see http://groups.google.fr/group/briansupport/browse_thread/thread/182aaf1af3499a63?hl=en for some options.') elif hasattr(reset, 'period'): # A reset with refractoriness # TODO: check unit (in Reset()) self._resetfun = reset # reset function period_max = int(reset.period / clock.dt) + 1 else: # No reset? self._resetfun = reset if hasattr(threshold, 'refractory'): # A threshold with refractoriness period_max = max(period_max, threshold.refractory + 1) if max_refractory is None: max_refractory = refractory if max_delay < period_max * clock.dt: max_delay = period_max * clock.dt self._max_delay = 0 self.set_max_delay(max_delay) self._next_allowed_spiketime = -ones(N) self._refractory_time = float(max_refractory) - 0.5 * clock._dt if not self._variable_refractory_time and max_refractory < 0.9 * clock.dt: self._use_next_allowed_spiketime_refractoriness = False else: self._use_next_allowed_spiketime_refractoriness = True self._owner = self # owner (for subgroups) self._subgroup_set = magic.WeakSet() self._origin = 0 # start index from owner if subgroup self._next_subgroup = 0 # start index of next subgroup # ensure that var_index has all the 0,...,N-1 integers as names if not hasattr(self, 'var_index'): self.var_index = {} for i in range(self.num_states()): self.var_index[i] = i # these are here for the weave accelerated version of the threshold # call mechanism. self._spikesarray = zeros(N, dtype=int) # various things for optimising self.__t = TArray(zeros(N)) self._var_array = {} for i in range(self.num_states()): self._var_array[i] = self._S[i] for kk, i in self.var_index.iteritems(): sv = self.state_(i) if sv.base is self._S: self._var_array[kk] = sv # todo: should we have a guarantee that var_index exists (even if it just # consists of mappings i->i)? def set_max_delay(self, max_delay): if hasattr(self, '_owner') and self._owner is not self: self._owner.set_max_delay(max_delay) return _max_delay = int(max_delay / self.clock.dt) + 2 # in time bins if _max_delay > self._max_delay: self._max_delay = _max_delay self.LS = SpikeContainer(self._max_delay, useweave=get_global_preference('useweave'), compiler=get_global_preference('weavecompiler')) # Spike storage # update all subgroups if any exist if hasattr(self, '_subgroup_set'): # the first time set_max_delay is called this is false for G in self._owner._subgroup_set.get(): G._max_delay = self._max_delay G.LS = self.LS def rest(self): ''' Sets the variables at rest. ''' self._state_updater.rest(self) def reinit(self, states=True): ''' Resets the variables. ''' if self._owner is self: if states: if self._S0 is not None and self._state_updater is not None: for i in range(len(self._state_updater)): self._S[i, :] = self._S0[i] else: self._S[:] = 0 # State matrix self._next_allowed_spiketime[:] = -1 self.LS.reinit() def update(self): ''' Updates the state variables. ''' self._state_updater(self) # update the variables if self._spiking: spikes = self._threshold(self) # get spikes if not isinstance(spikes, numpy.ndarray): spikes = array(spikes, dtype=int) if not spikes.flags.contiguous: spikes = array(spikes) if self._use_next_allowed_spiketime_refractoriness: spikes = spikes[self._next_allowed_spiketime[spikes] <= self.clock._t] if self._variable_refractory_time: if self._refractory_variable is not None: refractime = self.state_(self._refractory_variable) else: refractime = self._refractory_array self._next_allowed_spiketime[spikes] = self.clock._t + refractime[spikes] else: self._next_allowed_spiketime[spikes] = self.clock._t + self._refractory_time self.LS.push(spikes) # Store spikes def get_refractory_indices(self): return (self._next_allowed_spiketime > self.clock._t).nonzero()[0] def get_spikes(self, delay=0): ''' Returns indexes of neurons that spiked at time t-delay*dt. ''' if self._owner == self: # Group # if delay==0: # return self.LS.lastspikes() #return self.LS[delay] # last spikes return self.LS.get_spikes(delay, 0, len(self)) else: # Subgroup return self.LS.get_spikes(delay, self._origin, len(self)) # if delay==0: # ls = self.LS.lastspikes() # else: # ls = self.LS[delay] #ls = self.LS[delay] # spikes = ls-self._origin # return spikes[bisect.bisect_left(spikes,0):\ # bisect.bisect_left(spikes,len(self))] # return ls[bisect.bisect_left(ls,self._origin):\ # bisect.bisect_left(ls,len(self)+self._origin)]-self._origin def reset(self): ''' Resets the neurons. ''' self._resetfun(self) def subgroup(self, N): if self._next_subgroup + N > len(self): raise IndexError, "Subgroup is too large." P = self[self._next_subgroup:self._next_subgroup + N] self._next_subgroup += N; return P def unit(self, name): ''' Returns the unit of variable name ''' if name in self._all_units: return self._all_units[name] elif name in self.staticvars: f = self.staticvars[name] print f.func_code.co_varnames print [(var, self.unit(var)) for var in f.func_code.co_varnames] return get_unit(f(*[1. * self.unit(var) for var in f.func_code.co_varnames])) elif name == 't': # time return second else: return get_unit(self._S0[self.get_var_index(name)]) def state_(self, name): if name == 't': self.__t[:] = self.clock._t return self.__t else: return Group.state_(self, name) state = state_ def __getitem__(self, i): if i == -1: return self[self._S.shape[1] - 1:] else: return self[i:i + 1] def __getslice__(self, i, j): ''' Creates subgroup (view). TODO: views for all arrays. ''' Q = copy.copy(self) Q._S = self._S[:, i:j] Q.N = Q._S.shape[1] Q._origin = self._origin + i Q._next_subgroup = 0 self._subgroup_set.add(Q) return Q def same(self, Q): ''' Tests if the two groups (subgroups) are of the same kind, i.e., if they can be added. This is not used at the moment. OBSOLETE ''' # Same class? if self.__class__ != Q.__class__: return False # Check all variables except arrays and a few ones exceptvar = ['owner', 'nextsubgroup', 'origin'] for v, val in self.__dict__.iteritems(): if not(v in Q.__dict__): return False if (not(isinstance(val, ndarray)) and (not v in exceptvar) and (val != Q.__dict__[v])): return False for v in Q.__dict__.iterkeys(): if not(v in self.__dict__): return False return True def __repr__(self): if self._owner == self: return '%s(%d, model=%s, threshold=%s, reset=%s)' % (self.__class__.__name__, len(self), repr(self._state_updater), repr(self._threshold), repr(self._resetfun)) else: return '' % (len(self), repr(self._owner)) def __str__(self): if self._owner == self: return '%s of %d neurons' % (self.__class__.__name__, len(self)) else: return 'Size %d subgroup of %s' % (len(self), self.__class__.__name__) def __setattr__(self, name, val): global timedarray if timedarray is None: import timedarray # make sure we are only handing resolved names if hasattr(self, '_eqs') and name in self._eqs._alias: self.__setattr__(self._eqs._alias[name], val) return if isinstance(val, timedarray.TimedArray): self.set_var_by_array(name, val) elif hasattr(self, 'staticvars') and name in self.staticvars: raise ValueError("Cannot assign static variable "+name) elif isinstance(val, LinkedVar): self.link_var(name, val.source, val.var, val.func, val.when, val.clock) elif hasattr(val,"synapses") and not isinstance(val,Equations): # a synaptic variable # This above is a hack because we can't import SynapticVariable (infinite recursion) S=val.synapses if S.target!=self: raise AttributeError,"Can only link a synaptic variable to the target group" global network if network is None: import network selfarr = self.state_(name) s_name = val.name #Faster version using weave if self._useweave: log_info('brian.neurongroup', 'Using weave for lumped variable.') compiler = self._cpp_compiler extra_args = self._extra_compile_args @network.network_operation(clock=S.clock) def update_link_var(): s_state = S.state_(s_name) code = ''' for (int i=0; i 1: log_info('brian.hist_plot', "Found more than one histogram monitor, using first one.") histmon = histmons[0] # OPTIONS # Defaults myopts = {"title":"", "xlabel":"Time (ms)", "ylabel":"Count", "showplot":False, 'newfigure':True } # User options _take_options(myopts, plotoptions) # PLOTTING ROUTINE if myopts['newfigure']: pylab.figure() pylab.bar(histmon.bins[:-1] / ms, histmon.count[:-1], (histmon.bins[1:] - histmon.bins[:-1]) / ms, **plotoptions) pylab.ylabel(myopts['ylabel']) pylab.xlabel(myopts['xlabel']) pylab.title(myopts["title"]) if myopts["showplot"]: pylab.show() brian-1.4.3/brian/reset.py000066400000000000000000000413101266062647100154120ustar00rootroot00000000000000# ---------------------------------------------------------------------------------- # Copyright ENS, INRIA, CNRS # Contributors: Romain Brette (brette@di.ens.fr) and Dan Goodman (goodman@di.ens.fr) # # Brian is a computer program whose purpose is to simulate models # of biological neural networks. # # This software is governed by the CeCILL license under French law and # abiding by the rules of distribution of free software. You can use, # modify and/ or redistribute the software under the terms of the CeCILL # license as circulated by CEA, CNRS and INRIA at the following URL # "http://www.cecill.info". # # As a counterpart to the access to the source code and rights to copy, # modify and redistribute granted by the license, users are provided only # with a limited warranty and the software's author, the holder of the # economic rights, and the successive licensors have only limited # liability. # # In this respect, the user's attention is drawn to the risks associated # with loading, using, modifying and/or developing or reproducing the # software by the user in light of its specific status of free software, # that may mean that it is complicated to manipulate, and that also # therefore means that it is reserved for developers and experienced # professionals having in-depth computer knowledge. Users are therefore # encouraged to load and test the software's suitability as regards their # requirements in conditions enabling the security of their systems and/or # data to be ensured and, more generally, to use and operate it in the # same conditions as regards security. # # The fact that you are presently reading this means that you have had # knowledge of the CeCILL license and that you accept its terms. # ---------------------------------------------------------------------------------- # ''' Reset mechanisms ''' __all__ = ['Reset', 'VariableReset', 'Refractoriness', 'NoReset', 'FunReset', 'CustomRefractoriness', 'SimpleCustomRefractoriness', 'StringReset', 'select_reset'] from numpy import where, zeros from units import * from clock import * import inspect import re import numpy from inspection import * from utils.documentation import flattened_docstring from globalprefs import * from log import * CReset = PythonReset = None def select_reset(expr, eqs, level=0): ''' Automatically selects the appropriate Reset object from a string. Matches the following patterns if expr is a one liner: var_name = const : Reset var_name = var_name : VariableReset others : StringReset ''' # plan: # - strip it and see if it is one line, if not select StringReset # - see if it matches A = B, if not select StringReset # - check if A, B both match diffeq variable names, and if so # select VariableReset # - check that A is a variable name, if not select StringReset # - extract all the identifiers from B, and if none of them are # callable, assume it is a constant, try to eval it and then use # Reset. If not, or if eval fails, use StringReset # This misses the case of e.g. V=10*mV*exp(1) because exp will be # callable, but in general a callable means that it could be # non-constant. global CReset, PythonReset use_codegen = get_global_preference('usecodegen') and get_global_preference('usecodegenreset') use_weave = get_global_preference('useweave') and get_global_preference('usecodegenweave') if use_codegen: if CReset is None: from experimental.codegen.reset import CReset, PythonReset if use_weave: log_warn('brian.reset', 'Using codegen CReset') return CReset(expr, level=level + 1) else: log_warn('brian.reset', 'Using codegen PythonReset') return PythonReset(expr, level=level + 1) expr = expr.strip() if '\n' in expr: return StringReset(expr, level=level + 1) eqs.prepare() ns = namespace(expr, level=level + 1) s = re.search(r'\s*(\w+)\s*=(.+)', expr) if not s: return StringReset(expr, level=level + 1) A = s.group(1) B = s.group(2).strip() if A not in eqs._diffeq_names: return StringReset(expr, level=level + 1) if B in eqs._diffeq_names: return VariableReset(B, A) vars = get_identifiers(B) all_vars = eqs._eq_names + eqs._diffeq_names + eqs._alias.keys() + ['t'] for v in vars: if v not in ns or v in all_vars or callable(ns[v]): return StringReset(expr, level=level + 1) try: val = eval(B, ns) except: return StringReset(expr, level=level + 1) return Reset(val, A) class Reset(object): ''' Resets specified state variable to a fixed value **Initialise as:** :: R = Reset([resetvalue=0*mvolt[, state=0]]) with arguments: ``resetvalue`` The value to reset to. ``state`` The name or number of the state variable to reset. This will reset all of the neurons that have just spiked. The given state variable of the neuron group will be set to value ``resetvalue``. ''' def __init__(self, resetvalue=0 * mvolt, state=0): self.resetvalue = resetvalue self.state = state self.statevectors = {} def __call__(self, P): ''' Clamps membrane potential at reset value. ''' V = self.statevectors.get(id(P), None) if V is None: V = P.state_(self.state) self.statevectors[id(P)] = V V[P.LS.lastspikes()] = self.resetvalue def __repr__(self): return '%s(resetvalue=%s, state=%s)' % (self.__class__.__name__, repr(self.resetvalue), repr(self.state)) class StringReset(Reset): ''' Reset defined by a string Initialised with arguments: ``expr`` The string expression used to reset. This can include multiple lines or statements separated by a semicolon. For example, ``'V=-70*mV'`` or ``'V=-70*mV; Vt+=10*mV'``. Some standard functions are provided, see below. ``level`` How many levels up in the calling sequence to look for names in the namespace. Usually 0 for user code. Standard functions for expressions: ``rand()`` A uniform random number between 0 and 1. ``randn()`` A Gaussian random number with mean 0 and standard deviation 1. For example, these could be used to implement an adaptive model with random reset noise with the following string:: E -= 1*mV V = Vr+rand()*5*mV ''' def __init__(self, expr, level=0): expr = flattened_docstring(expr) self._namespace, unknowns = namespace(expr, level=level + 1, return_unknowns=True) self._prepared = False self._expr = expr class Replacer(object): def __init__(self, func, n): self.n = n self.func = func def __call__(self): return self.func(self.n) self._Replacer = Replacer def __call__(self, P): if not self._prepared: unknowns = [var for var in P.var_index if isinstance(var, str)] expr = self._expr for var in unknowns: expr = re.sub("\\b" + var + "\\b", var + '[_spikes_]', expr) self._code = compile(expr, "StringReset", "exec") unknowns.extend(P.staticvars) # make non-differential equations accessible self._vars = unknowns self._prepared = True spikes = P.LS.lastspikes() self._namespace['_spikes_'] = spikes self._namespace['rand'] = self._Replacer(numpy.random.rand, len(spikes)) self._namespace['randn'] = self._Replacer(numpy.random.randn, len(spikes)) self._namespace['t'] = P.clock._t for var in self._vars: self._namespace[var] = P.state(var) exec self._code in self._namespace def __repr__(self): return "%s(%s)" % (self.__class__.__name__, repr(self._expr)) class VariableReset(Reset): ''' Resets specified state variable to the value of another state variable Initialised with arguments: ``resetvaluestate`` The state variable which contains the value to reset to. ``state`` The name or number of the state variable to reset. This will reset all of the neurons that have just spiked. The given state variable of the neuron group will be set to the value of the state variable ``resetvaluestate``. ''' def __init__(self, resetvaluestate=1, state=0): self.resetvaluestate = resetvaluestate self.state = state self.resetstatevectors = {} self.statevectors = {} def __call__(self, P): ''' Clamps membrane potential at reset value. ''' V = self.statevectors.get(id(P), None) if V is None: V = P.state_(self.state) self.statevectors[id(P)] = V Vr = self.resetstatevectors.get(id(P), None) if Vr is None: Vr = P.state_(self.resetvaluestate) self.resetstatevectors[id(P)] = Vr lastspikes = P.LS.lastspikes() V[lastspikes] = Vr[lastspikes] def __repr__(self): return 'VariableReset(' + str(self.resetvaluestate) + ', ' + str(self.state) + ')' class FunReset(Reset): ''' A reset with a user-defined function. **Initialised as:** :: FunReset(resetfun) with argument: ``resetfun`` A function ``f(G,spikes)`` where ``G`` is the :class:`NeuronGroup` and ``spikes`` is an array of the indexes of the neurons to be reset. ''' def __init__(self, resetfun): self.resetfun = resetfun def __call__(self, P): self.resetfun(P, P.LS.lastspikes()) def __repr__(self): return '%s(%r)' % (self.__class__.__name__, self.resetfun) def __str__(self): return 'Functional threshold mechanism' class Refractoriness(Reset): ''' Holds the state variable at the reset value for a fixed time after a spike. Initialised with arguments: ``resetvalue`` The value to reset and hold to. ``period`` The length of time to hold at the reset value. If using variable refractoriness, this is the maximum period. ``state`` The name or number of the state variable to reset and hold. ''' @check_units(period=second) def __init__(self, resetvalue=0 * mvolt, period=5 * msecond, state=0): self.period = period self.resetvalue = resetvalue self.state = state self._periods = {} # a dictionary mapping group IDs to periods self.statevectors = {} def __call__(self, P): ''' Clamps state variable at reset value. ''' # if we haven't computed the integer period for this group yet. # do so now if id(P) in self._periods: period = self._periods[id(P)] else: period = int(self.period / P.clock.dt) + 1 self._periods[id(P)] = period V = self.statevectors.get(id(P), None) if V is None: V = P.state_(self.state) self.statevectors[id(P)] = V neuronindices = P.LS[0:period] if P._variable_refractory_time: neuronindices = neuronindices[P._next_allowed_spiketime[neuronindices] > (P.clock._t - P.clock._dt * 0.25)] V[neuronindices] = self.resetvalue def __repr__(self): return 'Refractory period, ' + str(self.period) class SimpleCustomRefractoriness(Refractoriness): ''' Holds the state variable at the custom reset value for a fixed time after a spike. **Initialised as:** :: SimpleCustomRefractoriness(resetfunc[,period=5*ms[,state=0]]) with arguments: ``resetfun`` The custom reset function ``resetfun(P, spikes)`` for ``P`` a :class:`NeuronGroup` and ``spikes`` a list of neurons that fired spikes. ``period`` The length of time to hold at the reset value. ``state`` The name or number of the state variable to reset and hold, it is your responsibility to check that this corresponds to the custom reset function. The assumption is that ``resetfun(P, spikes)`` will reset the state variable ``state`` on the group ``P`` for the spikes with indices ``spikes``. The values assigned by the custom reset function are stored by this object, and they are clamped at these values for ``period``. This object does not introduce refractoriness for more than the one specified variable ``state`` or for spike indices other than those in the variable ``spikes`` passed to the custom reset function. ''' @check_units(period=second) def __init__(self, resetfun, period=5 * msecond, state=0): self.period = period self.resetfun = resetfun self.state = state self._periods = {} # a dictionary mapping group IDs to periods self.statevectors = {} self.lastresetvalues = {} def __call__(self, P): ''' Clamps state variable at reset value. ''' # if we haven't computed the integer period for this group yet. # do so now if id(P) in self._periods: period = self._periods[id(P)] else: period = int(self.period / P.clock.dt) + 1 self._periods[id(P)] = period V = self.statevectors.get(id(P), None) if V is None: V = P.state_(self.state) self.statevectors[id(P)] = V LRV = self.lastresetvalues.get(id(P), None) if LRV is None: LRV = zeros(len(V)) self.lastresetvalues[id(P)] = LRV lastspikes = P.LS.lastspikes() self.resetfun(P, lastspikes) # call custom reset function LRV[lastspikes] = V[lastspikes] # store a copy of the custom resetted values clampedindices = P.LS[0:period] V[clampedindices] = LRV[clampedindices] # clamp at custom resetted values def __repr__(self): return 'Custom refractory period, ' + str(self.period) class CustomRefractoriness(Refractoriness): ''' Holds the state variable at the custom reset value for a fixed time after a spike. **Initialised as:** :: CustomRefractoriness(resetfunc[,period=5*ms[,refracfunc=resetfunc]]) with arguments: ``resetfunc`` The custom reset function ``resetfunc(P, spikes)`` for ``P`` a :class:`NeuronGroup` and ``spikes`` a list of neurons that fired spikes. ``refracfunc`` The custom refractoriness function ``refracfunc(P, indices)`` for ``P`` a :class:`NeuronGroup` and ``indices`` a list of neurons that are in their refractory periods. In some cases, you can choose not to specify this, and it will use the reset function. ``period`` The length of time to hold at the reset value. ''' @check_units(period=second) def __init__(self, resetfun, period=5 * msecond, refracfunc=None): self.period = period self.resetfun = resetfun if refracfunc is None: refracfunc = resetfun self.refracfunc = refracfunc self._periods = {} # a dictionary mapping group IDs to periods def __call__(self, P): ''' Clamps state variable at reset value. ''' # if we haven't computed the integer period for this group yet. # do so now if id(P) in self._periods: period = self._periods[id(P)] else: period = int(self.period / P.clock.dt) + 1 self._periods[id(P)] = period lastspikes = P.LS.lastspikes() self.resetfun(P, lastspikes) # call custom reset function clampedindices = P.LS[0:period] self.refracfunc(P, clampedindices) def __repr__(self): return 'Custom refractory period, ' + str(self.period) class NoReset(Reset): ''' Absence of reset mechanism. **Initialised as:** :: NoReset() ''' def __init__(self): pass def __call__(self, P): pass def __repr__(self): return 'NoReset()' def __str__(self): return 'No reset' brian-1.4.3/brian/stateupdater.py000066400000000000000000000671411266062647100170070ustar00rootroot00000000000000# ---------------------------------------------------------------------------------- # Copyright ENS, INRIA, CNRS # Contributors: Romain Brette (brette@di.ens.fr) and Dan Goodman (goodman@di.ens.fr) # # Brian is a computer program whose purpose is to simulate models # of biological neural networks. # # This software is governed by the CeCILL license under French law and # abiding by the rules of distribution of free software. You can use, # modify and/ or redistribute the software under the terms of the CeCILL # license as circulated by CEA, CNRS and INRIA at the following URL # "http://www.cecill.info". # # As a counterpart to the access to the source code and rights to copy, # modify and redistribute granted by the license, users are provided only # with a limited warranty and the software's author, the holder of the # economic rights, and the successive licensors have only limited # liability. # # In this respect, the user's attention is drawn to the risks associated # with loading, using, modifying and/or developing or reproducing the # software by the user in light of its specific status of free software, # that may mean that it is complicated to manipulate, and that also # therefore means that it is reserved for developers and experienced # professionals having in-depth computer knowledge. Users are therefore # encouraged to load and test the software's suitability as regards their # requirements in conditions enabling the security of their systems and/or # data to be ensured and, more generally, to use and operate it in the # same conditions as regards security. # # The fact that you are presently reading this means that you have had # knowledge of the CeCILL license and that you accept its terms. # ---------------------------------------------------------------------------------- # ''' Neuron StateUpdaters ''' __all__ = ['StateUpdater', 'LinearStateUpdater', 'NonlinearStateUpdater', 'SynapticNoise', 'LazyStateUpdater', 'magic_state_updater', 'FunStateUpdater', 'get_linear_equations'] #from scipy.weave import blitz from numpy import * from scipy import linalg from scipy.linalg import LinAlgError try: import weave except ImportError: from scipy import weave from scipy.optimize import fsolve import copy from operator import isSequenceType from inspection import * from units import second, mvolt from clock import guess_clock import magic from equations import * from itertools import count from units import Quantity import warnings from log import * from globalprefs import * from experimental.codegen import * CStateUpdater = PythonStateUpdater = None def magic_state_updater(model, clock=None, order=1, implicit=False, compile=False, freeze=False, \ method=None, check_units=True): ''' Examines the set of differential equations in 'model' (Equations object) and returns a StateUpdater object and the list of dynamic variables. For example, the magic_state_updater function can determine if it is linear or nonlinear. Available methods: * None: the method is automatically selected * linear * Euler * RK (Runge-Kutta, second order) * exponential_Euler * nonlinear: automatic selection, but not linear ''' global CStateUpdater, PythonStateUpdater if method == 'exponential_Euler': implicit = True order = 1 elif method == 'Euler': implicit = False order = 1 elif method == 'RK': implicit = False order = 2 elif method == 'linear' or method is None: pass else: raise AttributeError, "Unknown integration method!" # All the first below should go in Equations if not(isinstance(model, Equations)): # a set of equations? raise TypeError, "An Equations object must be passed." model.prepare(check_units=check_units) # check units and other things dynamicvars = model._diffeq_names # Dynamic variables # Identify stochastic equations noiselist = [] for statevar in model._diffeq_names: f = model._function[statevar] x0 = [model._units[var] for var in f.func_code.co_varnames] # init variables if depends_on(f, 'xi', x0): noiselist.append((statevar, get_global_term(f, 'xi', x0))) # s.d. of noise f.func_globals['xi'] = 0 * second ** -.5 # better: remove in string use_codegen = get_global_preference('usecodegen') and get_global_preference('usecodegenstateupdate') use_weave = get_global_preference('useweave') and get_global_preference('usecodegenweave') if CStateUpdater is None: from experimental.codegen.stateupdaters import CStateUpdater, PythonStateUpdater # Linearity test # insert this in equations allow_linear = (method is None) or (method == 'linear') if allow_linear and model.is_linear(): log_info('brian.stateupdater', "Linear model: using exact updates") stateupdaterobj = LinearStateUpdater(model, clock=clock) else: # Nonlinear model - check order of the method if implicit: # implicit integration schemes if model.is_conditionally_linear(): log_info('brian.stateupdater', "Using exponential Euler") if not use_codegen: stateupdaterobj = ExponentialEulerStateUpdater(model, clock=clock, compile=compile, freeze=freeze) elif use_weave: stateupdaterobj = CStateUpdater(model, exp_euler_scheme, clock=clock, freeze=freeze) log_warn('brian.stateupdater', 'Using codegen CStateUpdater') else: stateupdaterobj = PythonStateUpdater(model, exp_euler_scheme, clock=clock, freeze=freeze) log_warn('brian.stateupdater', 'Using codegen PythonStateUpdater') else: raise TypeError, "General implicit methods are not implemented yet." else: # explicit method if order == 1: if not use_codegen: stateupdaterobj = NonlinearStateUpdater(model, clock=clock, compile=compile, freeze=freeze) elif use_weave: stateupdaterobj = CStateUpdater(model, euler_scheme, clock=clock, freeze=freeze) log_warn('brian.stateupdater', 'Using codegen CStateUpdater') else: stateupdaterobj = PythonStateUpdater(model, euler_scheme, clock=clock, freeze=freeze) log_warn('brian.stateupdater', 'Using codegen PythonStateUpdater') elif order == 2: if not use_codegen: stateupdaterobj = RK2StateUpdater(model, clock=clock, compile=compile, freeze=freeze) elif use_weave: stateupdaterobj = CStateUpdater(model, rk2_scheme, clock=clock, freeze=freeze) log_warn('brian.stateupdater', 'Using codegen CStateUpdater') else: stateupdaterobj = PythonStateUpdater(model, rk2_scheme, clock=clock, freeze=freeze) log_warn('brian.stateupdater', 'Using codegen PythonStateUpdater') else: raise TypeError, "Methods with order greater than 2 are not implemented yet." # Insert noise for var, sigma in noiselist: # TODO: noise with mu = 0 i = dynamicvars.index(var) stateupdaterobj = SynapticNoise(stateupdaterobj, i, 0 * model._units[var] / second, sigma, clock) return stateupdaterobj, dynamicvars # TODO: StateUpdater should be lazy by default class StateUpdater(object): ''' A callable state update mechanism. By default, a leaky integrate-and-fire model with zero resting potential and unit time constant. Warning: to update the state matrix, use the slice operation, e.g. S[:]=0 (not S=0) otherwise operations are not done in place (a new object is created), so that all views are compromised (the reference to the data changes). ''' def __init__(self, clock=None): ''' Default model: dv/dt=-v ''' if clock == None: self.update_factor = exp(-clock.dt) # The update matrix else: raise TypeError, "A time reference must be passed." def rest(self, P): ''' Sets the variables at rest. P is the neuron group. ''' warnings.warn('Rest is not implemented for this model') def __call__(self, P): ''' Updates the state variables. Careful here: always use the slice operation for affectations. P is the neuron group. ''' P._S[:] *= self.update_factor def __str__(self): return '%s with %d state variables' % (self.__class__.__name__, len(self)) def __repr__(self): return '<%s with %d state variables>' % (self.__class__.__name__, len(self)) def __len__(self): ''' Number of state variables ''' return 1 #def get_linear_equations(eqs): # ''' # Returns the matrices M and B for the linear model dX/dt = M(X-B), # where eqs is an Equations object. # ''' # # Otherwise assumes it is given in functional form # n=len(eqs._diffeq_names) # number of state variables # dynamicvars=eqs._diffeq_names # # Calculate B # AB=zeros((n,1)) # d=dict.fromkeys(dynamicvars) # for j in range(n): # d[dynamicvars[j]]=0.*eqs._units[dynamicvars[j]] # for var,i in zip(dynamicvars,count()): # AB[i]=-eqs.apply(var,d) # # Calculate A # M=zeros((n,n)) # for i in range(n): # for j in range(n): # d[dynamicvars[j]]=0.*eqs._units[dynamicvars[j]] # if isinstance(eqs._units[dynamicvars[i]],Quantity): # d[dynamicvars[i]]=Quantity.with_dimensions(1.,eqs._units[dynamicvars[i]].get_dimensions()) # else: # d[dynamicvars[i]]=1. # for var,j in zip(dynamicvars,count()): # M[j,i]=eqs.apply(var,d)+AB[j] # M-=eye(n)*1e-10 # quick dirty fix for problem of constant derivatives; dimension = Hz # B=linalg.lstsq(M,AB)[0] # We use this instead of solve in case M is degenerate # return M,B def get_linear_equations(eqs): ''' Returns the matrices M and B for the linear model dX/dt = M(X-B), where eqs is an Equations object. ''' # Otherwise assumes it is given in functional form n = len(eqs._diffeq_names) # number of state variables dynamicvars = eqs._diffeq_names # Calculate B AB = zeros((n, 1)) d = dict.fromkeys(dynamicvars) for j in range(n): d[dynamicvars[j]] = 0. * eqs._units[dynamicvars[j]] for var, i in zip(dynamicvars, count()): AB[i] = -eqs.apply(var, d) # Calculate A M = zeros((n, n)) for i in range(n): for j in range(n): d[dynamicvars[j]] = 0. * eqs._units[dynamicvars[j]] if isinstance(eqs._units[dynamicvars[i]], Quantity): d[dynamicvars[i]] = Quantity.with_dimensions(1., eqs._units[dynamicvars[i]].get_dimensions()) else: d[dynamicvars[i]] = 1. for var, j in zip(dynamicvars, count()): M[j, i] = eqs.apply(var, d) + AB[j] #M-=eye(n)*1e-10 # quick dirty fix for problem of constant derivatives; dimension = Hz #B=linalg.lstsq(M,AB)[0] # We use this instead of solve in case M is degenerate B = linalg.solve(M, AB) # We use this instead of solve in case M is degenerate return M, B def get_linear_equations_solution_numerically(eqs, dt): # Otherwise assumes it is given in functional form n = len(eqs._diffeq_names) # number of state variables dynamicvars = eqs._diffeq_names # Calculate B AB = zeros((n, 1)) d = dict.fromkeys(dynamicvars) for j in range(n): d[dynamicvars[j]] = 0. * eqs._units[dynamicvars[j]] for var, i in zip(dynamicvars, count()): AB[i] = -eqs.apply(var, d) # Calculate A M = zeros((n, n)) for i in range(n): for j in range(n): d[dynamicvars[j]] = 0. * eqs._units[dynamicvars[j]] if isinstance(eqs._units[dynamicvars[i]], Quantity): d[dynamicvars[i]] = Quantity.with_dimensions(1., eqs._units[dynamicvars[i]].get_dimensions()) else: d[dynamicvars[i]] = 1. for var, j in zip(dynamicvars, count()): M[j, i] = eqs.apply(var, d) + AB[j] #B=linalg.solve(M,AB) numeulersteps = 100 deltat = dt / numeulersteps E = eye(n) + deltat * M C = eye(n) D = zeros((n, 1)) for step in xrange(numeulersteps): C, D = dot(E, C), dot(E, D) - AB * deltat return C, D #return M,B set_global_preferences(useweave_linear_diffeq=False) define_global_preference('useweave_linear_diffeq', 'False', desc=""" Whether to use weave C++ acceleration for the solution of linear differential equations. Note that on some platforms, typically older ones, this is faster and on some platforms, typically new ones, this is actually slower. """) class LinearStateUpdater(StateUpdater): ''' A linear model with dynamics dX/dt = M(X-B) or dX/dt = MX. **Initialised as:** :: LinearStateUpdater(M[,B[,clock]]) with arguments: ``M`` Matrix defining the differential equation. ``B`` Optional linear term in the differential equation. ``clock`` Optional clock. Computes an update matrix A=exp(M dt) for the linear system, and performs the update step. TODO: more mathematical details? ''' #TODO: sparse linear models (e.g. cable equations) def __init__(self, M, B=None, clock=None): ''' Initialize a linear model with dynamics dX/dt = M(X-B) or dX/dt = MX, where B is a column vector. TODO: more checks TODO: rest ''' self._useaccel = get_global_preference('useweave_linear_diffeq') self._cpp_compiler = get_global_preference('weavecompiler') self._extra_compile_args = ['-O3'] if self._cpp_compiler == 'gcc': self._extra_compile_args += get_global_preference('gcc_options') # ['-march=native', '-ffast-math'] self._useB = False if clock == None: clock = guess_clock() if isinstance(M, ndarray): self.A = linalg.expm(M * clock.dt) self.B = B elif isinstance(M, Equations): try: M, self.B = get_linear_equations(M) self.A = linalg.expm(M * clock.dt) #self.A=array(self.A,single) if self.B is not None: self._C = -dot(self.A, self.B) + self.B #self._C=array(self._C,single) self._useB = True else: self._useB = False except LinAlgError: log_info('brian.stateupdater', 'Solving linear equations numerically') self.A, self._C = get_linear_equations_solution_numerically(M, clock.dt) self.B = NotImplemented # raises error on trying to use this self._useB = True # note the numpy dot command works faster if self.A has C ordering compared # to fortran ordering (although maybe this depends on which implementation # of BLAS you're using). The difference is only significant in small # calculations because making a copy of self.A is usually not serious, its # size is only the number of variables, not the number of neurons. self.A = array(self.A, order='C') if self._useB: self._C = array(self._C, order='C') def rest(self, P): if self._useB: if self.B is NotImplemented: raise NotImplementedError, \ "The resting potential cannot be found because the equations are degenerate " + \ "(most likely because they include a parameter)" P._S[:] = self.B else: P._S[:] = 0 def __call__(self, P): ''' Updates the state variables. Careful here: always use the slice operation for affectations. P is the neuron group. ''' if self._useB: # This could be removed if not self._useaccel: #P._S[:]=dot(self.A,P._S)+self._C P._S[:] = dot(self.A, P._S) #P._S = dot(self.A,P._S) #P._S += self._C add(P._S, self._C, P._S) #P._S[:]=dot(self.A,P._S-self.B)+self.B else: m = len(self) S = P._S n = S.shape[1] #n = len(P) A = self.A c = self._C code = ''' double x[m]; for(int i=0;i0: # additional_deps.append('__pre_deps='+'+'.join(vars_pre)) #if len(vars_post)>0: # additional_deps.append('__post_deps='+'+'.join(vars_post)) additional_deps = ['__pre_deps='+'+'.join(vars_pre), '__post_deps='+'+'.join(vars_post)] separated_equations = separate_equations(eqs_obj, additional_deps) if not len(separated_equations) == 2: raise ValueError('Equations should separate into pre and postsynaptic variables.') sep_pre, sep_post = separated_equations for v in vars_pre: if v in sep_post._diffeq_names: sep_pre, sep_post = sep_post, sep_pre break index_pre = [i for i in range(len(vars)) if vars[i] in vars_pre or vars[i] in sep_pre._diffeq_names] index_post = [i for i in range(len(vars)) if vars[i] in vars_post or vars[i] in sep_post._diffeq_names] vars_pre = array(vars)[index_pre] vars_post = array(vars)[index_post] # Check pre/post consistency shared_vars = set(vars_pre).intersection(vars_post) if shared_vars != set([]): raise Exception, str(list(shared_vars)) + " are both presynaptic and postsynaptic!" # Substitute equations/aliases into pre/post code def substitute_eqs(code): for name in sep_pre._eq_names[-1::-1]+sep_post._eq_names[-1::-1]: # reverse order, as in equations.py if name in sep_pre._eq_names: expr = sep_pre._string[name] else: expr = sep_post._string[name] code = re.sub("\\b" + name + "\\b", '(' + expr + ')', code) return code pre = substitute_eqs(pre) post = substitute_eqs(post) # Create namespaces for pre and post codes pre_namespace = namespace(pre, level=level + 1) post_namespace = namespace(post, level=level + 1) pre_namespace['clip'] = clip post_namespace['clip'] = clip pre_namespace['Inf'] = Inf post_namespace['Inf'] = Inf pre_namespace['enumerate'] = enumerate post_namespace['enumerate'] = enumerate # freeze pre and post (otherwise units will cause problems) all_vars = list(vars_pre) + list(vars_post) + ['w'] pre = '\n'.join(freeze(line.strip(), all_vars, pre_namespace) for line in pre.split('\n')) post = '\n'.join(freeze(line.strip(), all_vars, post_namespace) for line in post.split('\n')) # Neuron groups G_pre = NeuronGroup(len(C.source), model=sep_pre, clock=self.clock) G_post = NeuronGroup(len(C.target), model=sep_post, clock=self.clock) G_pre._S[:] = 0 G_post._S[:] = 0 self.pre_group = G_pre self.post_group = G_post var_group = {} # maps variable name to group for v in vars_pre: var_group[v] = G_pre for v in vars_post: var_group[v] = G_post self.var_group = var_group # Create updaters and monitors if isinstance(C, DelayConnection): G_pre_monitors = {} # these get values put in them later G_post_monitors = {} max_delay = C._max_delay * C.target.clock.dt def gencode(incode, vars, other_vars, wreplacement): num_immediate = num_delayed = 0 reordering_warning = False incode_lines = [line.strip() for line in incode.split('\n')] outcode_immediate = 'for _i in spikes:\n' # delayed variables outcode_delayed = 'for _j, _i in enumerate(spikes):\n' for var in other_vars: outcode_delayed += ' ' + var + '__delayed = ' + var + '__delayed_values_seq[_j]\n' for line in incode_lines: if not line.strip(): continue m = re.search(r'\bw\b\s*[^><=]?=', line) # lines of the form w = ..., w *= ..., etc. for var in vars: line = re.sub(r'\b' + var + r'\b', var + '[_i]', line) for var in other_vars: line = re.sub(r'\b' + var + r'\b', var + '__delayed', line) if m: num_delayed += 1 outcode_delayed += ' ' + line + '\n' else: if num_delayed!=0 and not reordering_warning: log_warn('brian.stdp', 'STDP operations are being re-ordered for delay connection, results may be wrong.') reordering_warning = True num_immediate += 1 outcode_immediate += ' ' + line + '\n' outcode_delayed = re.sub(r'\bw\b', wreplacement, outcode_delayed) outcode_delayed += '\n %(w)s = clip(%(w)s, %(min)e, %(max)e)' % {'min':wmin, 'max':wmax, 'w':wreplacement} return (outcode_immediate, outcode_delayed) pre_immediate, pre_delayed = gencode(pre, vars_pre, vars_post, 'w[_i,:]') post_immediate, post_delayed = gencode(post, vars_post, vars_pre, 'w[:,_i]') log_debug('brian.stdp', 'PRE CODE IMMEDIATE:\n'+pre_immediate) log_debug('brian.stdp', 'PRE CODE DELAYED:\n'+pre_delayed) log_debug('brian.stdp', 'POST CODE:\n'+post_immediate+post_delayed) pre_delay_expr = 'max_delay-d' post_delay_expr = 'd' pre_code_immediate = compile(pre_immediate, "Presynaptic code immediate", "exec") pre_code_delayed = compile(pre_delayed, "Presynaptic code delayed", "exec") post_code = compile(post_immediate + post_delayed, "Postsynaptic code", "exec") if delay_pre is not None or delay_post is not None: raise ValueError("Must use delay_pre=delay_post=None for the moment.") max_delay = C._max_delay * C.target.clock.dt # Ensure that the source and target neuron spikes are kept for at least the # DelayConnection's maximum delay C.source.set_max_delay(max_delay) C.target.set_max_delay(max_delay) # create forward and backward Connection objects or SpikeMonitor objects pre_updater_immediate = STDPUpdater(C.source, C, vars=vars_pre, code=pre_code_immediate, namespace=pre_namespace, delay=0 * ms) pre_updater_delayed = DelayedSTDPUpdater(C, reverse=False, delay_expr=pre_delay_expr, max_delay=max_delay, vars=vars_pre, other_vars=vars_post, varmon=G_pre_monitors, othervarmon=G_post_monitors, code=pre_code_delayed, namespace=pre_namespace, delay=max_delay) post_updater = DelayedSTDPUpdater(C, reverse=True, delay_expr=post_delay_expr, max_delay=max_delay, vars=vars_post, other_vars=vars_pre, varmon=G_post_monitors, othervarmon=G_pre_monitors, code=post_code, namespace=post_namespace, delay=0 * ms) updaters = [pre_updater_immediate, pre_updater_delayed, post_updater] self.contained_objects += updaters vars_pre_ind = dict((var, i) for i, var in enumerate(vars_pre)) vars_post_ind = dict((var, i) for i, var in enumerate(vars_post)) self.G_pre_monitors = G_pre_monitors self.G_post_monitors = G_post_monitors self.G_pre_monitors.update(((var, RecentStateMonitor(G_pre, vars_pre_ind[var], duration=(C._max_delay + 1) * C.target.clock.dt, clock=G_pre.clock)) for var in vars_pre)) self.G_post_monitors.update(((var, RecentStateMonitor(G_post, vars_post_ind[var], duration=(C._max_delay + 1) * C.target.clock.dt, clock=G_post.clock)) for var in vars_post)) self.contained_objects += self.G_pre_monitors.values() self.contained_objects += self.G_post_monitors.values() else: # Indent and loop pre = re.compile('^', re.M).sub(' ', pre) post = re.compile('^', re.M).sub(' ', post) pre = 'for _i in spikes:\n' + pre post = 'for _i in spikes:\n' + post # Pre code for var in vars_pre: # presynaptic variables (vectorisation) pre = re.sub(r'\b' + var + r'\b', var + '[_i]', pre) pre = re.sub(r'\bw\b', 'w[_i,:]', pre) # synaptic weight # Post code for var in vars_post: # postsynaptic variables (vectorisation) post = re.sub(r'\b' + var + r'\b', var + '[_i]', post) post = re.sub(r'\bw\b', 'w[:,_i]', post) # synaptic weight # Bounds: add one line to pre/post code (clip(w,min,max,w)) # or actual code? (rather than compiled string) if wmax==Inf: pre += '\n w[_i,:]=clip(w[_i,:],%(min)e,Inf)' % {'min':wmin} post += '\n w[:,_i]=clip(w[:,_i],%(min)e,Inf)' % {'min':wmin} else: pre += '\n w[_i,:]=clip(w[_i,:],%(min)e,%(max)e)' % {'min':wmin, 'max':wmax} post += '\n w[:,_i]=clip(w[:,_i],%(min)e,%(max)e)' % {'min':wmin, 'max':wmax} log_debug('brian.stdp', 'PRE CODE:\n'+pre) log_debug('brian.stdp', 'POST CODE:\n'+post) # Compile code pre_code = compile(pre, "Presynaptic code", "exec") post_code = compile(post, "Postsynaptic code", "exec") connection_delay = C.delay * C.source.clock.dt if (delay_pre is None) and (delay_post is None): # same delays as the Connnection C delay_pre = connection_delay delay_post = 0 * ms elif delay_pre is None: delay_pre = connection_delay - delay_post if delay_pre < 0 * ms: raise AttributeError, "Presynaptic delay is too large" elif delay_post is None: delay_post = connection_delay - delay_pre if delay_post < 0 * ms: raise AttributeError, "Postsynaptic delay is too large" # create forward and backward Connection objects or SpikeMonitor objects pre_updater = STDPUpdater(C.source, C, vars=vars_pre, code=pre_code, namespace=pre_namespace, delay=delay_pre) post_updater = STDPUpdater(C.target, C, vars=vars_post, code=post_code, namespace=post_namespace, delay=delay_post) updaters = [pre_updater, post_updater] self.contained_objects += [pre_updater, post_updater] # Put variables in namespaces for i, var in enumerate(vars_pre): for updater in updaters: updater._namespace[var] = G_pre._S[i] for i, var in enumerate(vars_post): for updater in updaters: updater._namespace[var] = G_post._S[i] self.contained_objects += [G_pre, G_post] def __call__(self): pass def __getattr__(self, name): if name == 'var_group': # this seems mad - the reason is that getattr is only called if the thing hasn't # been found using the standard methods of finding attributes, which for var_index # should have worked, this is important because the next line looks for var_index # and if we haven't got a var_index we don't want to get stuck in an infinite # loop raise AttributeError if not hasattr(self, 'var_group'): # only provide lookup of variable names if we have some variable names, i.e. # if the var_index attribute exists raise AttributeError G = self.var_group[name] return G.state_(name) def __setattr__(self, name, val): if not hasattr(self, 'var_group') or name not in self.var_group: object.__setattr__(self, name, val) else: G = self.var_group[name] G.state_(name)[:] = val class ExponentialSTDP(STDP): ''' Exponential STDP. Initialised with the following arguments: ``taup``, ``taum``, ``Ap``, ``Am`` Synaptic weight change (relative to the maximum weight wmax):: f(s) = Ap*exp(-s/taup) if s >0 f(s) = Am*exp(s/taum) if s <0 ``interactions`` * 'all': contributions from all pre-post pairs are added * 'nearest': only nearest-neighbour pairs are considered * 'nearest_pre': nearest presynaptic spike, all postsynaptic spikes * 'nearest_post': nearest postsynaptic spike, all presynaptic spikes ``wmin=0`` minimum synaptic weight ``wmax`` maximum synaptic weight ``update`` * 'additive': modifications are additive (independent of synaptic weight) (or "hard bounds") * 'multiplicative': modifications are multiplicative (proportional to w) (or "soft bounds") * 'mixed': depression is multiplicative, potentiation is additive See documentation for :class:`STDP` for more details. ''' def __init__(self, C, taup, taum, Ap, Am, interactions='all', wmin=0, wmax=None, update='additive', delay_pre=None, delay_post=None, clock=None): if wmax is None: raise AttributeError, "You must specify the maximum synaptic weight" wmax = float(wmax) # removes units eqs = Equations(''' dA_pre/dt=-A_pre/taup : 1 dA_post/dt=-A_post/taum : 1''', taup=taup, taum=taum, wmax=wmax) if interactions == 'all': pre = 'A_pre+=Ap' post = 'A_post+=Am' elif interactions == 'nearest': pre = 'A_pre=Ap' post = 'A_post=Am' elif interactions == 'nearest_pre': pre = 'A_pre=Ap' post = 'A_post=+Am' elif interactions == 'nearest_post': pre = 'A_pre+=Ap' post = 'A_post=Am' else: raise AttributeError, "Unknown interaction type " + interactions if update == 'additive': Ap *= wmax Am *= wmax pre += '\nw+=A_post' post += '\nw+=A_pre' elif update == 'multiplicative': if Am < 0: pre += '\nw*=(1+A_post)' else: pre += '\nw+=(wmax-w)*A_post' if Ap < 0: post += '\nw*=(1+A_pre)' else: post += '\nw+=(wmax-w)*A_pre' elif update == 'mixed': if Am < 0 and Ap > 0: Ap *= wmax pre += '\nw*=(1+A_post)' post += '\nw+=A_pre' elif Am > 0 and Ap < 0: Am *= wmax post += '\nw*=(1+A_pre)' pre += '\nw+=A_post' else: if Am > 0: raise AttributeError, "There is no depression in STDP rule" else: raise AttributeError, "There is no potentiation in STDP rule" else: raise AttributeError, "Unknown update type " + update STDP.__init__(self, C, eqs=eqs, pre=pre, post=post, wmin=wmin, wmax=wmax, delay_pre=delay_pre, delay_post=delay_post, clock=clock) if __name__ == '__main__': pass brian-1.4.3/brian/stdunits.py000066400000000000000000000056551266062647100161610ustar00rootroot00000000000000# ---------------------------------------------------------------------------------- # Copyright ENS, INRIA, CNRS # Contributors: Romain Brette (brette@di.ens.fr) and Dan Goodman (goodman@di.ens.fr) # # Brian is a computer program whose purpose is to simulate models # of biological neural networks. # # This software is governed by the CeCILL license under French law and # abiding by the rules of distribution of free software. You can use, # modify and/ or redistribute the software under the terms of the CeCILL # license as circulated by CEA, CNRS and INRIA at the following URL # "http://www.cecill.info". # # As a counterpart to the access to the source code and rights to copy, # modify and redistribute granted by the license, users are provided only # with a limited warranty and the software's author, the holder of the # economic rights, and the successive licensors have only limited # liability. # # In this respect, the user's attention is drawn to the risks associated # with loading, using, modifying and/or developing or reproducing the # software by the user in light of its specific status of free software, # that may mean that it is complicated to manipulate, and that also # therefore means that it is reserved for developers and experienced # professionals having in-depth computer knowledge. Users are therefore # encouraged to load and test the software's suitability as regards their # requirements in conditions enabling the security of their systems and/or # data to be ensured and, more generally, to use and operate it in the # same conditions as regards security. # # The fact that you are presently reading this means that you have had # knowledge of the CeCILL license and that you accept its terms. # ---------------------------------------------------------------------------------- # ######### PHYSICAL UNIT NAMES ##################### #------------------------------------ Dan Goodman - # These are optional shorthand unit names which in # most circumstances shouldn't clash with local names """Optional short unit names This module defines the following short unit names: mV, mA, uA (micro_amp), nA, pA, mF, uF, nF, mS, uS, ms, Hz, kHz, MHz, cm, cm2, cm3, mm, mm2, mm3, um, um2, um3 """ import units as _units import fundamentalunits as _fundamentalunits mV = _units.mvolt mA = _units.mamp uA = _units.uamp nA = _units.namp pA = _units.pamp pF = _units.pfarad uF = _units.ufarad nF = _units.nfarad nS = _units.nsiemens uS = _units.usiemens ms = _units.msecond Hz = _units.hertz kHz = _units.khertz MHz = _units.Mhertz cm = _units.cmetre cm2 = _units.cmetre2 cm3 = _units.cmetre3 mm = _units.mmetre mm2 = _units.mmetre2 mm3 = _units.mmetre3 um = _units.umetre um2 = _units.umetre2 um3 = _units.umetre3 _units.all_units.extend([mV, mA, uA, nA, pA, pF, uF, nF, nS, uS, ms, Hz, kHz, MHz, cm, cm2, cm3, mm, mm2, mm3, um, um2, um3]) brian-1.4.3/brian/stp.py000066400000000000000000000130741266062647100151040ustar00rootroot00000000000000''' Short-term synaptic plasticity. Implements the short-term plasticity model described in: Markram et al (1998). Differential signaling via the same axon of neocortical pyramidal neurons, PNAS. Synaptic dynamics is described by two variables x and u, which follows the following differential equations:: dx/dt=(1-x)/taud (depression) du/dt=(U-u)/tauf (facilitation) where taud, tauf are time constants and U is a parameter in 0..1. Each presynaptic spike triggers modifications of the variables:: x<-x*(1-u) u<-u+U*(1-u) Synaptic weights are modulated by the product u*x (in 0..1) (before update). ''' # See BEP-1 from network import NetworkOperation from neurongroup import NeuronGroup from monitor import SpikeMonitor from scipy import zeros, exp, isscalar from connections import DelayConnection __all__ = ['STP'] class STPGroup(NeuronGroup): ''' Neuron group forwarding spikes with short term plasticity modulation. ''' def __init__(self, N, clock=None): eqs = ''' ux : 1 x : 1 u : 1 ''' NeuronGroup.__init__(self, N, model=eqs, clock=clock) def update(self): pass class STPUpdater(SpikeMonitor): ''' Event-driven updates of STP variables. ''' def __init__(self, source, P, taud, tauf, U, delay=0): SpikeMonitor.__init__(self, source, record=False, delay=delay) # P is the group with the STP variables N = len(P) self.P = P self.minvtaud = -1. / taud self.minvtauf = -1. / tauf self.U = U self.ux = P.ux self.x = P.x self.u = P.u self.lastt = zeros(N) # last update self.clock = P.clock def propagate(self, spikes): interval = self.clock.t - self.lastt[spikes] self.u[spikes] = self.U + (self.u[spikes] - self.U) * exp(interval * self.minvtauf) tmp = 1 - self.u[spikes] self.x[spikes] = 1 + (self.x[spikes] - 1) * exp(interval * self.minvtaud) self.ux[spikes] = self.u[spikes] * self.x[spikes] self.x[spikes] *= tmp self.u[spikes] += self.U * tmp self.lastt[spikes] = self.clock.t self.P.LS.push(spikes) class STPUpdater2(STPUpdater): ''' STP Updater where U, taud and tauf are vectors ''' def propagate(self, spikes): interval = self.clock.t - self.lastt[spikes] self.u[spikes] = self.U[spikes] + (self.u[spikes] - self.U[spikes]) * exp(interval * self.minvtauf[spikes]) tmp = 1 - self.u[spikes] self.x[spikes] = 1 + (self.x[spikes] - 1) * exp(interval * self.minvtaud[spikes]) self.ux[spikes] = self.u[spikes] * self.x[spikes] self.x[spikes] *= tmp self.u[spikes] += self.U[spikes] * tmp self.lastt[spikes] = self.clock.t self.P.LS.push(spikes) class SynapticDepressionUpdater(SpikeMonitor): ''' Event-driven updates of STP variables. Special case: tauf=0*ms (synaptic depression). dx/dt=(1-x)/taud (depression) x<-x*(1-U) NOT FINISHED ''' def __init__(self, source, P, taud, tauf, U, delay=0): SpikeMonitor.__init__(self, source, record=False, delay=delay) # P is the group with the STP variables N = len(P) self.P = P self.minvtaud = -1. / taud self.U = U self.ux = P.ux self.x = P.x self.lastt = zeros(N) # last update self.clock = P.clock def propagate(self, spikes): interval = self.clock.t - self.lastt[spikes] self.x[spikes] = 1 + (self.x[spikes] - 1) * exp(interval * self.minvtaud) self.ux[spikes] = self.U * self.x[spikes] self.x[spikes] *= 1 - self.U self.lastt[spikes] = self.clock.t self.P.LS.push(spikes) class STP(NetworkOperation): ''' Short-term synaptic plasticity, following the Tsodyks-Markram model. Implements the short-term plasticity model described in Markram et al (1998). Differential signaling via the same axon of neocortical pyramidal neurons, PNAS. Synaptic dynamics is described by two variables x and u, which follow the following differential equations:: dx/dt=(1-x)/taud (depression) du/dt=(U-u)/tauf (facilitation) where taud, tauf are time constants and U is a parameter in 0..1. Each presynaptic spike triggers modifications of the variables:: u<-u+U*(1-u) x<-x*(1-u) Synaptic weights are modulated by the product ``u*x`` (in 0..1) (before update). Reference: * Markram et al (1998). "Differential signaling via the same axon of neocortical pyramidal neurons", PNAS. ''' def __init__(self, C, taud, tauf, U): if isinstance(C, DelayConnection): raise AttributeError, "STP does not handle heterogeneous connections yet." NetworkOperation.__init__(self, lambda:None, clock=C.source.clock) N = len(C.source) P = STPGroup(N, clock=C.source.clock) P.x = 1 P.u = U P.ux = U if (isscalar(taud) & isscalar(tauf) & isscalar(U)): updater = STPUpdater(C.source, P, taud, tauf, U, delay=C.delay * C.source.clock.dt) else: updater = STPUpdater2(C.source, P, taud, tauf, U, delay=C.delay * C.source.clock.dt) self.contained_objects = [updater] C.source = P C.delay = 0 C._nstate_mod = 0 # modulation of synaptic weights self.vars = P def __call__(self): pass brian-1.4.3/brian/synapses/000077500000000000000000000000001266062647100155645ustar00rootroot00000000000000brian-1.4.3/brian/synapses/TODO.txt000066400000000000000000000021641266062647100170750ustar00rootroot00000000000000TODO (Synapses) --------------- * Check that delays do not exceed maximum delay in SynapticDelayVariable * S.w[P,Q]='...' : shift origin of i,j * S[:,:]='i variables (w) (floats) could be separated between continuously updated variables (i.e., not parameters) to be put in _S and other ones This should be a dynamic state matrix (extendable 2D array). For now it's in _S. * synapse -> presynaptic i (int32 or smaller, based on the size of the presynaptic group) This should be a dynamic vector. I call it 'presynaptic' for now. * synapse -> postsynaptic j (same) I call it 'postsynaptic' for now. * synapse -> presynaptic delay (depending on max delay? but this is not known at the beginning) This is better than presynaptic i -> delay for construction. * synapse -> postsynaptic delay The synaptic mappings should be addressable by (pre,post) number (2D or 3D). This could be done using separate objects, one per synaptic variable. There would be a dictionary of these objects. Pre/post mappings (useful for construction and spike queues): * presynaptic i -> synapse indexes (int32 or more? depending on len(pre)*len(post)) List of dynamic arrays * postsynaptic j -> synapse indexes Code: * presynaptic/postsynaptic code and namespace (pre_code,pre_namespace) Accessing synaptic variables ---------------------------- To do something like S.w[1,2]=1*nS we need to have a special vector class that can be indexed with pre/post indexes. First, the setattr method can be directly inherited from NeuronGroup, except for the delays. It allows things like: S.w=[1*nS,2*nS] although maybe this should not be allowed. The import method is getattr. It returns an object with a getitem that allows 2 and 3D access to an underlying sparse matrix. brian-1.4.3/brian/synapses/interface.txt000066400000000000000000000072751266062647100203000ustar00rootroot00000000000000Creation -------- (done) S[2,3:5]=True # or an array S[2,3:5]=(2,3) # 2 synapses from 2 to 3, 3 from 2 to 4 (done) S[group1,group2]='rand()<0.2' # careful this is highly inefficient Modification and access ----------------------- (done) S.w[2,5]=1*nS (done) S.w[1,:]=2*nS (done) S.w[2,3,1] # 2nd synapse from 2->3 (done) S.w[2,3]=(1*nS,2*nS) (done) S.w[group1,group2]="(1+cos(i-j))*2*nS" # or pre/post High-level functions -------------------- (done) S.connect_random(group1,group2,sparseness=0.1) (done) S.connect_one_to_one(group1,group2) Equations --------- (done) S=Synapses(source,target,model="w : 1",pre="v+=w") # standard synapse (done) S=Synapses(source,target, model='''dg/dt=-a*g+b*x*(1-g) : 1 dx/dt=-c*x : 1 w : 1 # synaptic weight ''', pre='x+=w') # NMDA synapses (done) S=Synapses(source,target, model='''dA_pre/dt=-A_pre/tau_pre : 1 dA_post/dt=-A_post/tau_post : 1 w : 1 # synaptic weight ''', pre='''x+=w A_pre+=dA_pre;w+=A_post''' post='''A_post+=dA_post;w+=A_pre''') # STDP (done) S=Synapses(source,target, model='''dA_pre/dt=-A_pre/tau_pre : 1 dA_post/dt=-A_post/tau_post : 1 w : 1 # synaptic weight ''', pre='''v+=w A_pre+=dA_pre;w=clip(w+A_post,0,inf)''' post='''A_post+=dA_post;w=clip(w+A_pre,0,inf)''') # STDP with clipping (done) S=Synapses(source,target, model='''dx/dt=(1-x)/taud : 1 du/dt=(U-u)/tauf : 1 w : 1''', pre='''v=v+w*u*x x=x*(1-u) u=u+U*(1-u) ''') # STP (done) S=Synapses(source,target, model='''x : 1 u : 1 w : 1''', pre='''u=U+(u-U)*exp(-(t-lastspike)/tauf) x=1+(x-1)*exp(-(t-lastspike)/taud) v=v+w*u*x x*=(1-u) u+=U*(1-u) ''') # Event-driven STP (done) S=Synapses(source,target,model="""w : 1 p : 1 # transmission probability""", pre="v+=w*(rand() neuron state variables 2) identify spikes: neuron state variables -> spikes 3) push spikes to queues: spikes -> queue 4) update synaptic variables and target groups: queue, synaptic variables, neuron state variables -> synaptic variables, neuron state variables 5) reset neuron groups: neuron state variables,spikes -> neuron state variables First, all spike queues can be run concurrently. The following sequences must be respected: 1 -> 2 -> (3, 5) 1 -> 4 In other words, we can 1) do the resets, then run neuron group updates in parallel 2) identify spikes and push them to the queues, in parallel with synaptic updates We cannot merge 1 and 2. So each core could: * run one queue (there are typically 2 queues in an STDP) * run one continuous synapse updater * then: run one code (pre/post), but not concurrently Option 2: dividing the Synapses object --------------------------------------- Alternatively, the Synapses object (including queues) is divided into smaller pieces, each one running in one core. Each core would take care of a subset of postsynaptic indexes. This requires splitting the data structure and the spike queues in pieces. brian-1.4.3/brian/synapses/notes.txt000066400000000000000000000164461266062647100174700ustar00rootroot00000000000000Notes ===== Synapses variables ------------------ * presynaptic i -> synapse indexes * presynaptic i -> delay * synapse -> variables (w) * synapse -> presynaptic i (int32 or smaller, based on the size of the presynaptic group) * synapse -> postsynaptic j These two are currently in existing_synapses All synaptic mappings are handled by a SparseStateVector (deals with multiple types) Maybe we don't need a StateVector (just dynamic arrays) and for backpropagation (STDP): * postsynaptic j -> synapse indexes * postsynaptic j -> delay (do we need synapse -> delay?) Also * presynaptic queue (pre_queue) * postsynaptic queue * presynaptic/postsynaptic code and namespace (pre_code,pre_namespace) Data structure -------------- Currently, synapses are pushed: there is no order (meaning synapse indexes for neuron i may not be contiguous). Operations to optimize: * search * insertion: push values (O(1)) * deletion (should it be allowed?) we use a mask for deleted synapses There are also two mappings: * presynaptic i -> synapse indexes (sorted by synapse index) * postsynaptic j -> synapse indexes Current search (i,j): * get synapse indexes for pre i and post j * calculate intersection O(p) (p = number of synapses per neuron) Alternative: use a hash table of synapses for each presynaptic neuron: synapse[i][j] The structure must be dynamic. Maybe we don't need to compress the structure. Initialisation -------------- Currently: * parse model equations and create a state updater * clean pre/post strings * create variables (state vector) * create and compile pre code pre code deals with v+=w (problem with repeated postsynaptic indexes) Call ---- * Synapses object gets the synaptic events and apply the pre code * Call state updater StateVector ----------- A state matrix for variables with different dtypes. ConstructionSparseStateVector -> StateVector. ParameterVector = 1 row of a StateVector. Required for S.w[1,2]. Clock ----- Currently, we initialize the object with the clock of the synaptic state updater. At this time, it will not work if the clocks of the neuron groups are different. However, it should be possible in principle (but more complex). Event-driven updates --------------------------- (done) A simple start would be to deal with 1D linear differential equations. Steps: 1) identify isolated 1D linear differential equations in model, 2) remove them from the model string, 3) compute the update string, 4) insert at the beginning of pre and post. Here is how to deal with 3) using sympy. Assuming RHS=a*x+b: 1) z=brian.optimiser.symbolic_eval(RHS) 2) z.coeff(Symbol('x')) -> a 3) z.subs(Symbol('x'),0) or z.coeff(1) -> b 4) str(...) for the string representation For step 1): 1) Look for variable names that are defined by differential equations. 2) If there is only the one on the LHS, then this is a 1D equation. 3) Check linearity, either using sympy (I don't know what exactly), or the utilities in inspection (is_affine; setting external variables to 0). All this can be done after parsing by NeuronGroup. Issues ^^^^^^ Event-driven code can be problematic in some cases. In all these cases, updates should be continuous. 1) The equation depends on pre or postsynaptic variables. 2) Another equation depends on the synaptic variable. 3) The synaptic variable corresponds to a lumped variable (see below), defined later by P.ge=S.ge. Issues #1 and #2 can be solved by examining the equations. Issue #3 is more problematic because it is not known at the time when equations are examined. Several possibilities to solve this problem: 1) Explicitly ask for event-driven code using some local syntax, e.g. 'dx/dt=-x : 1 (event-driven)'. 2) Explicitly allow event-driven code using a global keyword, e.g. eventdriven=True. 3) Examine the equations only at run time, through the compress() method. Then the instruction P.ge=S.ge should inform S. I think the global syntax, although simpler, cannot satisfactorily handle all cases, such as NMDA synapses with plasticity. So option #2 seems bad. Option #1 seems good because it is explicit, but it implies going into the details of parsing Equations. A simple solution is to have a class derived from Equations, for which the parsing changes. This would be my first choice. Lumped variables ---------------- With synaptic nonlinearities, we want to simulate differential equations for all synaptic variables, then (in general) sum them per postsynaptic neuron and add the value to one variable, or set it to some value (e.g. total conductance). To do this is simple: * loop over postsynaptic neurons j * ge[j]=sum(S.ge[synapses_post[j]]) The main issue is then the syntax. We could use something like linked_var, for example: P.ge=lumped_var(synapses.ge) where P is the NeuronGroup, and ge must have been defined as a parameter in P. Or, in fact, we could just use linked_var directly! P.ge=linked_var(synapses,"ge") Of course we need to modify linked_var a bit. Or: P.ge=S.ge This is possible given that S.ge is a SynapticVariable, and therefore has all the necessary information. We then use link_var with a function that does the sum. Gap junctions ------------- It could be simple, for example: model=''' g : siemens I=g*(V_post-V_pre) : amp ''' P.I=S.I The only limitation here is that it won't be possible to have delays in these connections. Delayed STDP ------------ When we consider delayed STDP, we have 3 delays instead of just two: pre->synapse, synapse->post (for STDP variables), pre->post (for v+=w). I can see two ways to deal with this: 1) allow a Synapses object to share state variables with another one, 2) have multiple codes and delays for the pre (and perhaps post) side. I think the most elegant, and perhaps simplest, way is #2. Here is how to do it: * the pre keyword accepts sequences (tuples/lists): pre=('v+=w','Apre+=dApre') * we have a list of presynaptic queues and codes, * delay_pre is now a list of delay arrays, * setattr fails on delay_pre Since there is no fundamental difference between pre and post sides, we could have a unified treatment of queues and codes. How to deal with pre and post suffixes -------------------------------------- For gap junctions and some STDP rules, we need to access pre and post synaptic variables. A simple idea to deal with this is to use static variables. For example: v_pre=source.v[presynaptic[:]]. How to deal with heterosynaptic modifications --------------------------------------------- I can think of two examples: 1) Synaptic scaling 2) Axonal propagation (Kempter et al.) Specific examples: 1) A postsynaptic spike modifies all weights for that neuron. This is actually what happens in STDP, so it is not a problem. 2) A presynaptic spike modifies all weights for the target neuron. 3) A presynaptic spike modifies all weights for synapses with the same source neuron (Kempter et al). 1 is already implemented, 2 and 3 are more complex. For each synaptic event, we need to change a whole set of variables, and there can be overlaps between synaptic events. This is a similar problem as for modifying postsynaptic variables. Various notes ------------- * I decided to use signed rather than unsigned ints for indexes, because there were some conversion problems. brian-1.4.3/brian/synapses/spikequeue.py000066400000000000000000000500061266062647100203170ustar00rootroot00000000000000""" Spike queues following BEP-21. The spike queue class stores future synaptic events produced by a given presynaptic neuron group (or postsynaptic for backward propagation in STDP). The structure X is a 2D array, where row is the time bin and column is the position in that bin (each row is a stack) . The array is circular in the time dimension. There is a 1D array (n) giving the position of the last added event in each time bin. The 2D array is dynamic in the column direction. The row corresponding to current time is stored in currenttime. X_flat is a flattened view of X. Main methods: * peek() Outputs the current events: we simply get the row corresponding to currenttime, so this is fast. We then shift the cursor of the circular array by one row: next(). * insert(delay, target, offset=None) Insert events in the queue. Each presynaptic neuron has a corresponding array of target synapses and corresponding delays. We must push each target synapse (index) on top of the stack (row) corresponding to the delay. If all synapses have different delays, this is relatively easy to vectorise. It is a bit more difficult if there are synapses with the same delays. For a given presynaptic neuron, each synaptic delay corresponds to coordinates (i,j) in the circular array of stacks, where i is the delay (stack index and j is index relative to the top of the stack (0=top, 1=1 above top). The absolute location in the structure is then calculated as n[i]+j, where n[i] is the location of the top of stack i. The only difficulty is to calculate j, and in Python this requires sorting (see development mailing list). It can be preprocessed if event feeding involves a loop over presynaptic spikes (if it's vectorised then it's not possible anymore). In this case it takes K*4 bytes. * offsets(delay) This calculates the offsets j mentionned above, for a given array of delays. * precompute_offsets() This precomputes all offsets for all presynaptic neurons. * propagate() The class is implemented as a SpikeMonitor, which means the propagate() function is called at each timestep with the spikes produced by the neuron group. The function executes different codes (different strategies) depending on whether offsets are precomputed or not, and on whether delays are heterogeneous or homogeneous. """ import numpy as np try: import pylab except: pass try: import weave except ImportError: from scipy import weave from brian.globalprefs import get_global_preference, exists_global_preference, define_global_preference from brian.monitor import SpikeMonitor from brian.stdunits import ms import warnings __all__=['SpikeQueue'] INITIAL_MAXSPIKESPER_DT = 1 class SpikeQueue(SpikeMonitor): '''Spike queue Initialised with arguments: ``source`` The neuron group that sends spikes. ``synapses`` A list of synapses (synapses[i]=array of synapse indices for neuron i). ``delays`` An array of delays (delays[k]=delay of synapse k). ``max_delay=0*ms`` The maximum delay (in second) of synaptic events. At run time, the structure is resized to the maximum delay in ``delays``, and thus the ``max_delay`` should only be specified if delays can change during the simulation (in which case offsets should not be precomputed). ``maxevents = INITIAL_MAXSPIKESPER_DT`` The initial size of the queue for each timestep. Note that the data structure automatically grows to the required size, and therefore this option is generally not useful. ``precompute_offsets = True`` A flag to precompute offsets. By default, offsets (an internal array derived from ``delays``, used to insert events in the data structure, see below) are precomputed for all neurons, the first time the object is run. This usually results in a speed up but takes memory, which is why it can be disabled. **Data structure** A spike queue is implemented as a 2D array ``X`` that is circular in the time direction (rows) and dynamic in the events direction (columns). The row index corresponding to the current timestep is ``currentime``. Each element contains the target synapse index. The class is implemented as a :class:`SpikeMonitor`, so that the propagate() method is called at each timestep (of the monitored group). **Methods** .. method:: next() Advances by one timestep. .. method:: peek() Returns the all the synaptic events corresponding to the current time, as an array of synapse indexes. .. method:: precompute_offsets() Precompute all offsets corresponding to delays. This assumes that delays will not change during the simulation. If they do (between two runs for example), then this method can be called. **Offsets** Offsets are used to solve the problem of inserting multiple synaptic events with the same delay. This is difficult to vectorise. If there are n synaptic events with the same delay, these events are given an offset between 0 and n-1, corresponding to their relative position in the data structure. They can be either precalculated (faster), or determined at run time (saves memory). Note that if they are determined at run time, then it is possible to also vectorise over presynaptic spikes. ''' def __init__(self, source, synapses, delays, max_delay = 0*ms, maxevents = INITIAL_MAXSPIKESPER_DT, precompute_offsets = True): self.source = source #NeuronGroup self.delays = delays self.synapses = synapses self._precompute_offsets=precompute_offsets self._max_delay=max_delay if max_delay>0: # do not precompute offsets if delays can change self._precompute_offsets=False # number of time steps, maximum number of spikes per time step nsteps = int(np.floor((max_delay)/(self.source.clock.dt)))+1 self.X = np.zeros((nsteps, maxevents), dtype = self.synapses[0].dtype) # target synapses self.X_flat = self.X.reshape(nsteps*maxevents,) self.currenttime = 0 self.n = np.zeros(nsteps, dtype = int) # number of events in each time step self._offsets = None # precalculated offsets # Compiled version self._useweave = get_global_preference('useweave') self._cpp_compiler = get_global_preference('weavecompiler') self._extra_compile_args = ['-O3'] if self._cpp_compiler == 'gcc': self._extra_compile_args += get_global_preference('gcc_options') # ['-march=native', '-ffast-math'] if self._useweave: # no need to precompute offsets if weave is used self._precompute_offsets=False super(SpikeQueue, self).__init__(source, record = False) #useweave=get_global_preference('useweave') #compiler=get_global_preference('weavecompiler') def compress(self): ''' This is called the first time the network is run. The size of the of the data structure (number of rows) is adjusted to fit the maximum delay in ``delays'', if necessary. Offsets are calculated, unless the option ``precompute_offsets'' is set to False. A flag is set if delays are homogeneous, in which case insertion will use a faster method. ''' nsteps=max(self.delays)+1 # Check whether some delays are too long if (self._max_delay>0) and (nsteps>self.X.shape[0]): raise ValueError,"Synaptic delays exceed maximum delay" if hasattr(self, '_iscompressed') and self._iscompressed: return self._iscompressed = True # Adjust the maximum delay and number of events per timestep if necessary maxevents=self.X.shape[1] if maxevents==INITIAL_MAXSPIKESPER_DT: # automatic resize maxevents=max(INITIAL_MAXSPIKESPER_DT,max([len(targets) for targets in self.synapses])) # Check if homogeneous delays if self._max_delay>0: self._homogeneous=False else: self._homogeneous=(nsteps==min(self.delays)+1) # Resize if (nsteps>self.X.shape[0]) or (maxevents>self.X.shape[1]): # Resize nsteps=max(nsteps,self.X.shape[0]) # Choose max_delay if is is larger than the maximum delay maxevents=max(maxevents,self.X.shape[1]) self.X = np.zeros((nsteps, maxevents), dtype = self.synapses[0].dtype) # target synapses self.X_flat = self.X.reshape(nsteps*maxevents,) self.n = np.zeros(nsteps, dtype = int) # number of events in each time step # Precompute offsets if (self._offsets is None) and self._precompute_offsets: self.precompute_offsets() ################################ SPIKE QUEUE DATASTRUCTURE ###################### def next(self): ''' Advances by one timestep ''' self.n[self.currenttime]=0 # erase self.currenttime=(self.currenttime+1) % len(self.n) def peek(self): ''' Returns the all the synaptic events corresponding to the current time, as an array of synapse indexes. ''' return self.X[self.currenttime,:self.n[self.currenttime]] def _update_delays(self, delays): ''' Internal method to update the delays, used by the Synapses class when the delays are dynamically varied. Delays are assumed to be represented as floating values in second, hence the conversion to "timestep delays" is handled here. ''' #log_debug('brian.synapses.spikequeue', 'Updating delays...') self.delays = np.array(np.floor(delays/self.source.clock.dt), dtype = int)+1 def precompute_offsets(self): ''' Precompute all offsets corresponding to delays. This assumes that delays will not change during the simulation. If they do (between two runs for example), then this method can be called. ''' self._offsets=[] for i in range(len(self.synapses)): delays=self.delays[self.synapses[i].data] self._offsets.append(self.offsets(delays)) def offsets(self, delay): ''' Calculates offsets corresponding to a delay array. If there n identical delays, there are given offsets between 0 and n-1. Example: [7,5,7,3,7,5] -> [0,0,1,0,2,1] The code is complex because tricks are needed for vectorisation. ''' if self._useweave: return self.offsets_C(delay) # We use merge sort because it preserves the input order of equal # elements in the sorted output I = np.argsort(delay,kind='mergesort') xs = delay[I] J = xs[1:] != xs[:-1] #K = xs[1:]==xs[:-1] A = np.hstack((0, np.cumsum(J))) B = np.hstack((0, np.cumsum(np.logical_not(J)))) BJ = np.hstack((0, B[:-1][J])) ei = B-BJ[A] ofs = np.zeros_like(delay) ofs[I] = np.array(ei, dtype=ofs.dtype) # maybe types should be signed? return ofs def insert(self, delay, target, offset=None): ''' Vectorised insertion of spike events. ``delay`` Delays in timesteps (array). ``target`` Target synaptic indexes (array). ``offset`` Offsets within timestep (array). If unspecified, they are calculated from the delay array. ''' delay=np.array(delay,dtype=int) if self._useweave: # C-optimised insertion (minor speed up) self.insert_C(delay,target) return if offset is None: offset=self.offsets(delay) # Calculate row indexes in the data structure timesteps = (self.currenttime + delay) % len(self.n) # (Over)estimate the number of events to be stored, to resize the array # It's an overestimation for the current time, but I believe a good one # for future events m=max(self.n)+len(target) if (m >= self.X.shape[1]): # overflow self.resize(m+1) self.X_flat[timesteps*self.X.shape[1]+offset+self.n[timesteps]]=target self.n[timesteps] += offset+1 # that's a trick (to update stack size) # Note: the trick can only work if offsets are ordered in the right way def insert_homogeneous(self,delay,target): ''' Inserts events at a fixed delay. ``delay`` Delay in timesteps (scalar). ``target`` Target synaptic indexes (array). ''' timestep = (self.currenttime + delay) % len(self.n) nevents=len(target) m = self.n[timestep]+nevents+1 # If overflow, then at least one self.n is bigger than the size if (m >= self.X.shape[1]): self.resize(m+1) # was m previously (not enough) k=timestep*self.X.shape[1]+self.n[timestep] self.X_flat[k:k+nevents]=target self.n[timestep]+=nevents def resize(self, maxevents): ''' Resizes the underlying data structure (number of columns = spikes per dt). ``maxevents`` The new number of columns.It will be rounded to the closest power of 2. ''' # old and new sizes old_maxevents = self.X.shape[1] new_maxevents = int(2**np.ceil(np.log2(maxevents))) # maybe 2 is too large # new array newX = np.zeros((self.X.shape[0], new_maxevents), dtype = self.X.dtype) newX[:, :old_maxevents] = self.X[:, :old_maxevents] # copy old data self.X = newX self.X_flat = self.X.reshape(self.X.shape[0]*new_maxevents,) def propagate(self, spikes): ''' Called by the network object at every timestep. Spikes produce synaptic events that are inserted in the queue. ''' if len(spikes): # print '(Python) In propagate: spikes = ', spikes if self._homogeneous: # homogeneous delays synaptic_events=np.hstack([self.synapses[i].data for i in spikes]) # could be not efficient self.insert_homogeneous(self.delays[0],synaptic_events) elif self._offsets is None: # vectorise over synaptic events # there are no precomputed offsets, this is the case (in particular) when there are dynamic delays synaptic_events=np.hstack([self.synapses[i].data for i in spikes]) if len(synaptic_events): delay = self.delays[synaptic_events] self.insert(delay, synaptic_events) else: # offsets are precomputed for i in spikes: synaptic_events=self.synapses[i].data # assuming a dynamic array: could change at run time? if len(synaptic_events): delay = self.delays[synaptic_events] offsets = self._offsets[i] self.insert(delay, synaptic_events, offsets) ######################################## C optimised versions def insert_C(self,delay,target): ''' Insertion of events using weave. ``delay`` Delays in timesteps (array). ``target`` Target synaptic indexes (array). ''' # Check if we can fit the events (crude check) nevents=len(target) m=max(self.n)+nevents if m>self.X.shape[1]: self.resize(m) Xflat=self.X_flat n=self.n ncols=self.X.shape[1] currentt=self.currenttime ndelays=len(self.n) code=''' for(int k=0;kself.n_delays): desired_max_delay = nsteps * self.source.clock.dt raise ValueError,"Synaptic delays exceed maximum delay, set max_delay to %.1f ms" % (desired_max_delay/ms) if hasattr(self, '_iscompressed') and self._iscompressed: return self._iscompressed = True # Adjust the maximum delay and number of events per timestep if necessary maxevents=self.n_maxevents if maxevents==INITIAL_MAXSPIKESPER_DT: # automatic resize maxevents=max(INITIAL_MAXSPIKESPER_DT, max([len(targets) for targets in self.synapses])) # Resize self.expand(int(maxevents)) def propagate(self, spikes): ''' Called by the network object at every timestep. Spikes produce synaptic events that are inserted in the queue. ''' if len(spikes): synaptic_events=np.hstack([self.synapses[i].data for i in spikes]) # could be not efficient self.insert(synaptic_events, self.delays[synaptic_events]) warnings.warn('Using C++ SpikeQueue') except ImportError: has_cspikequeue = False brian-1.4.3/brian/synapses/synapses.py000066400000000000000000001474271266062647100200220ustar00rootroot00000000000000''' The Synapses class - see BEP-21 ''' import re import warnings from operator import isSequenceType import copy import numpy as np from numpy.random import binomial from random import sample from scipy import rand, randn from brian.inspection import get_identifiers, namespace from brian.log import log_debug, log_warn from brian.neurongroup import NeuronGroup from brian.optimiser import AffineFunction, symbolic_eval from brian.stdunits import ms from brian.synapses.spikequeue import SpikeQueue from brian.synapses.synaptic_equations import SynapticEquations from brian.synapses.synapticvariable import (SynapticDelayVariable, SynapticVariable, slice_to_array) from brian.utils.documentation import flattened_docstring from brian.utils.dynamicarray import DynamicArray, DynamicArray1D try: import sympy use_sympy = True except: warnings.warn('sympy not installed: some features in Synapses will not be available') use_sympy = False __all__ = ['Synapses','invert_array'] def _binomial_wrapper(n, p): ''' A simple wrapper around numpy's binomial function for use in synaptic code. ''' return np.random.binomial(np.array(n, dtype=int), p) class Synapses(NeuronGroup): # This way we inherit a lot of useful stuff '''Set of synapses between two neuron groups Initialised with arguments: ``source`` The source :class:`NeuronGroup`. ``target=None`` The target :class:`NeuronGroup`. By default, target=source. ``model=None`` The equations that defined the synaptic variables, as an Equations object or a string. The syntax is the same as for a :class:`NeuronGroup`. ``pre=None`` The code executed when presynaptic spikes arrive at the synapses. There can be multiple presynaptic codes, passed as a list or tuple of strings. ``post=None`` The code executed when postsynaptic spikes arrive at the synapses. ``max_delay=0*ms`` The maximum pre and postsynaptic delay. This is only useful if the delays can change during the simulation. ``level=0`` See :class:`Equations` for details. ``clock=None`` The clock for updating synaptic state variables according to ``model``. Currently, this must be identical to both the source and target clocks. ``compile=False`` Whether or not to attempt to compile the differential equation solvers (into Python code). Typically, for best performance, both ``compile`` and ``freeze`` should be set to ``True`` for nonlinear differential equations. ``freeze=False`` If True, parameters are replaced by their values at the time of initialization. ``method=None`` If not None, the integration method is forced. Possible values are linear, nonlinear, Euler, exponential_Euler (overrides implicit and order keywords). ``unit_checking=True`` Set to ``False`` to bypass unit-checking. ``order=1`` The order to use for nonlinear differential equation solvers. TODO: more details. ``implicit=False`` Whether to use an implicit method for solving the differential equations. TODO: more details. ``code_namespace=None`` Namespace for the pre and post codes. **Methods** .. method:: state(var) Returns the vector of values for state variable ``var``, with length the number of synapses. The vector is an instance of class :class:`SynapticVariable`. .. method:: synapse_index(i) Returns the synapse indexes correspond to i, which can be a tuple or a slice. If i is a tuple (m,n), m and n can be an integer, an array, a slice or a subgroup. .. automethod:: save_connectivity *The following usages are also possible for a Synapses object ``S``*: ``len(S)`` Returns the number of synapses in ``S``. **Attributes**: ``delay`` The presynaptic delays for all synapses (synapse->delay). If there are multiple presynaptic delays (multiple pre codes), this is a list. ``delay_pre`` Same as ``delay``. ``delay_post`` The postsynaptic delays for all synapses (synapse->delay post). ``lastupdate`` The time of last update of all synapses (synapse->last update). This only exists if there are dynamic synaptic variables. *Internal attributes*: ``source`` The source neuron group. ``target`` The target neuron group. ``_S`` The state matrix (a 2D dynamical array with values of synaptic variables). At run time, it is transformed into a static 2D array (with compress()). ``presynaptic`` The (dynamic) array of presynaptic neuron indexes for all synapses (synapse->i). ``postsynaptic`` The array of postsynaptic neuron indexes for all synapses (synapse->j). ``synapses_pre`` A list of (dynamic) arrays giving the set of synapse indexes for each presynaptic neuron i (i->synapses) ``synapses_post`` A list of (dynamic) arrays giving the set of synapse indexes for each postsynaptic neuron j (j->synapses) ``queues`` List of SpikeQueues for pre and postsynaptic spikes. ``codes`` The compiled codes to be executed on pre and postsynaptic spikes. ``namespaces`` The namespaces for the pre and postsynaptic codes. ''' def __init__(self, source, target = None, model = None, pre = None, post = None, max_delay = 0*ms, level = 0, clock = None, code_namespace=None, unit_checking = True, method = None, freeze = False, implicit = False, order = 1): # model (state updater) related target=target or source # default is target=source # Check clocks. For the moment we enforce the same clocks for all objects clock = clock or source.clock if source.clock!=target.clock: raise ValueError,"Source and target groups must have the same clock" if pre is None: pre_list=[] elif isSequenceType(pre) and not isinstance(pre,str): # a list of pre codes pre_list=pre else: pre_list=[pre] pre_list=[flattened_docstring(pre) for pre in pre_list] if post is not None: post=flattened_docstring(post) # Pre and postsynaptic indexes (synapse -> pre/post) self.presynaptic=DynamicArray1D(0,dtype=smallest_inttype(len(source))) # this should depend on number of neurons self.postsynaptic=DynamicArray1D(0,dtype=smallest_inttype(len(target))) # this should depend on number of neurons if not isinstance(model,SynapticEquations): model=SynapticEquations(model,level=level+1) # Insert the lastupdate variable if necessary (if it is mentioned in pre/post, or if there is event-driven code) expr=re.compile(r'\blastupdate\b') if (len(model._eventdriven)>0) or \ any([expr.search(pre) for pre in pre_list]) or \ (post is not None and expr.search(post) is not None): model+='\nlastupdate : second\n' pre_list=[pre+'\nlastupdate=t\n' for pre in pre_list] if post is not None: post=post+'\nlastupdate=t\n' # Identify pre and post variables in the model string # They are identified by _pre and _post suffixes # or no suffix for postsynaptic variables ids=set() for RHS in model._string.itervalues(): ids.update(get_identifiers(RHS)) pre_ids = [id[:-4] for id in ids if id[-4:]=='_pre'] post_ids = [id[:-5] for id in ids if id[-5:]=='_post'] post_vars = [var for var in source.var_index if isinstance(var,str)] # postsynaptic variables post_ids2 = list(ids.intersection(set(post_vars))) # post variables without the _post suffix # remember whether our equations refer to any variables in the pre- or # postsynaptic group. This is important for the state-updater, e.g. the # equations can no longer be solved as linear equations. model.refers_others = (len(pre_ids) + len(post_ids) + len(post_ids2) > 0) # Insert static equations for pre and post variables S=self for name in pre_ids: model.add_eq(name+'_pre', 'S.source.'+name+'[S.presynaptic[:]]', source.unit(name), global_namespace={'S':S}) for name in post_ids: model.add_eq(name+'_post', 'S.target.'+name+'[S.postsynaptic[:]]', target.unit(name), global_namespace={'S':S}) for name in post_ids2: # we have to change the name of the variable to avoid problems with equation processing if name not in model._string: # check that it is not already defined model.add_eq(name, 'S.target.state_(__'+name+')[S.postsynaptic[:]]', target.unit(name), global_namespace={'S':S,'__'+name:name}) self.source=source self.target=target NeuronGroup.__init__(self, 0, model=model, clock=clock, level=level+1, unit_checking=unit_checking, method=method, freeze=freeze, implicit=implicit, order=order) # Dynamical delays if "delay" in self.var_index: # if there is a "delay" variable specified in the model eqns self.has_variable_delays = True # remember it log_warn('brian.synapses', 'Variable delays (presynaptic) detected ' '-- note that this feature is still experimental') # tell the user else: self.has_variable_delays = False ''' At this point we have: * a state matrix _S with all variables * units, state dictionary with each value being a row of _S + the static equations * subgroups of synapses * link_var (i.e. we can link two synapses objects) * __len__ * __setattr__: we can write S.w=array of values * var_index is a dictionary from names to row index in _S * num_states() Things we have that we don't want: * LS structure (but it will not be filled since the object does not spike) * (from Group) __getattr_ needs to be rewritten * a complete state updater, but we need to extract parameters and event-driven parts * The state matrix is not dynamic Things we may need to add: * _pre and _post suffixes ''' self._iscompressed=False # True if compress() has already been called # Look for event-driven code in the differential equations if use_sympy: eqs=self._eqs # an Equations object #vars=eqs._diffeq_names_nonzero # Dynamic variables vars=eqs._eventdriven.keys() var_set=set(vars) for var,RHS in eqs._eventdriven.iteritems(): ids=get_identifiers(RHS) if len(set(list(ids)+[var]).intersection(var_set))==1: # no external dynamic variable # Now we test if it is a linear equation _namespace=dict.fromkeys(ids,1.) # there is a possibility of problems here (division by zero) # plus units problems? (maybe not since these are identifiers too) # another option is to use random numbers, but that doesn't solve all problems _namespace[var]=AffineFunction() try: eval(RHS,eqs._namespace[var],_namespace) except: # not linear raise TypeError,"Cannot turn equation for "+var+" into event-driven code" z=symbolic_eval(RHS) symbol_var=sympy.Symbol(var) symbol_t=sympy.Symbol('t')-sympy.Symbol('lastupdate') b=z.subs(symbol_var,0) a=sympy.simplify(z.subs(symbol_var,1)-b) if a==0: expr=symbol_var+b*symbol_t else: expr=-b/a+sympy.exp(a*symbol_t)*(symbol_var+b/a) expr=var+'='+str(expr) # Replace pre and post code # N.B.: the differential equations are kept, we will probably want to remove them! pre_list=[expr+'\n'+pre for pre in pre_list] if post is not None: post=expr+'\n'+post else: raise TypeError,"Cannot turn equation for "+var+" into event-driven code" elif len(self._eqs._eventdriven)>0: raise TypeError,"The Sympy package must be installed to produce event-driven code" if len(self._eqs._diffeq_names_nonzero)==0: self._state_updater=None # Set last spike to -infinity if 'lastupdate' in self.var_index: self.lastupdate=-1e6 # _S is turned to a dynamic array - OK this is probably not good! we may lose references at this point S=self._S self._S=DynamicArray(S.shape) self._S[:]=S # Pre and postsynaptic delays (synapse -> delay_pre/delay_post) self._delay_pre=[DynamicArray1D(len(self),dtype=np.int16) for _ in pre_list] # max 32767 delays self._delay_post=DynamicArray1D(len(self),dtype=np.int16) # Actually only useful if there is a post code! # Pre and postsynaptic synapses (i->synapse indexes) max_synapses=2147483647 # it could be explicitly reduced by a keyword # We use a loop instead of *, otherwise only 1 dynamic array is created self.synapses_pre=[DynamicArray1D(0,dtype=smallest_inttype(max_synapses)) for _ in range(len(self.source))] self.synapses_post=[DynamicArray1D(0,dtype=smallest_inttype(max_synapses)) for _ in range(len(self.target))] # Code generation self._binomial = _binomial_wrapper self.contained_objects = [] self.codes=[] self.namespaces=[] self.queues=[] for i,pre in enumerate(pre_list): code,_namespace=self.generate_code(pre,level+1,code_namespace=code_namespace) self.codes.append(code) self.namespaces.append(_namespace) if self.has_variable_delays: _precompute_offsets = False else: _precompute_offsets = True self.queues.append(SpikeQueue(self.source, self.synapses_pre, self._delay_pre[i], max_delay = max_delay, precompute_offsets = _precompute_offsets)) if post is not None: code,_namespace=self.generate_code(post,level+1,direct=True,code_namespace=code_namespace) self.codes.append(code) self.namespaces.append(_namespace) self.queues.append(SpikeQueue(self.target, self.synapses_post, self._delay_post, max_delay = max_delay)) self.contained_objects+=self.queues def generate_code(self,code,level,direct=False,code_namespace=None): ''' Generates pre and post code. ``code'' The code as a string. ``level'' The namespace level in which the code is executed. ``direct=False'' If True, the code is generated assuming that postsynaptic variables are not modified. This makes the code faster. ``code_namespace'' Additional namespace (highest priority) TODO: * include static variables (substitution) * have a list of variable names ''' # Handle multi-line pre, post equations and multi-statement equations separated by ; # (this should probably be factored) if '\n' in code: code = flattened_docstring(code) elif ';' in code: code = '\n'.join([line.strip() for line in code.split(';')]) # Create namespaces _namespace = namespace(code, level = level + 1) if code_namespace is not None: _namespace.update(code_namespace) # Explicitly set builtins to prevent adding all the builtins to the # namespace (which leads to problems with pickling) _namespace['__builtins__'] = {} _namespace['len'] = len _namespace['bool'] = np.bool _namespace['target'] = self.target # maybe we could save one indirection here _namespace['unique'] = np.unique _namespace['nonzero'] = np.nonzero _namespace['empty'] = np.empty _namespace['logical_not'] = np.logical_not _namespace['not_equal'] = np.not_equal _namespace['take'] = np.take _namespace['extract'] = np.extract _namespace['add'] = np.add _namespace['hstack'] = np.hstack code = re.sub(r'\b' + 'rand\(\)', 'rand(n)', code) code = re.sub(r'\b' + 'randn\(\)', 'randn(n)', code) # Generate the code def update_code(code, indices, postinds): res = code # given the synapse indices, write the update code, # this is here because in the code we generate we need to write this twice (because of the multiple presyn spikes for the same postsyn neuron problem) # Replace synaptic variables by their value for var in self.var_index: # static variables are not included here if isinstance(var, str): res = re.sub(r'\b' + var + r'\b', var + '['+indices+']', res) # synaptic variable, indexed by the synapse number # Replace postsynaptic variables by their value for postsyn_var in self.target.var_index: # static variables are not included here if isinstance(postsyn_var, str): #res = re.sub(r'\b' + postsyn_var + r'_post\b', 'target.' + postsyn_var + '['+postinds+']', res)# postsyn variable, indexed by post syn neuron numbers #res = re.sub(r'\b' + postsyn_var + r'\b', 'target.' + postsyn_var + '['+postinds+']', res)# postsyn variable, indexed by post syn neuron numbers res = re.sub(r'\b' + postsyn_var + r'_post\b', '_target_' + postsyn_var + '['+postinds+']', res)# postsyn variable, indexed by post syn neuron numbers res = re.sub(r'\b' + postsyn_var + r'\b', '_target_' + postsyn_var + '['+postinds+']', res)# postsyn variable, indexed by post syn neuron numbers _namespace['_target_' + postsyn_var] = self.target.state_(postsyn_var) # Replace presynaptic variables by their value for presyn_var in self.source.var_index: # static variables are not included here if isinstance(presyn_var, str): #res = re.sub(r'\b' + presyn_var + r'_pre\b', 'source.' + presyn_var + '[_pre['+indices+']]', res)# postsyn variable, indexed by post syn neuron numbers res = re.sub(r'\b' + presyn_var + r'_pre\b', '_source_' + presyn_var + '[_pre['+indices+']]', res)# postsyn variable, indexed by post syn neuron numbers _namespace['_source_' + presyn_var] = self.source.state_(presyn_var) # Replace n by number of synapses being updated res = re.sub(r'\bn\b','len('+indices+')', res) return res # Check whether the pre/post-code writes to a pre-synaptic variable # (which is not supported in general, with possibly several synapses # sharing the same pre-synaptic cell) for line in code.split('\n'): split_line = line.split('=') if len(split_line) > 1: lhs = split_line[0] if '_pre' in lhs: raise ValueError(('The synaptic code "%s" changes the ' 'value of a pre-synaptic variable. This ' 'is not allowed, since it can lead to ' 'incorrect results if a pre-synaptic ' 'neuron has more than one outgoing ' 'connection. Please upgrade to Brian 2 ' 'to get this functionality or re-write ' 'your model.') % line) if direct: # direct update code, not caring about multiple accesses to postsynaptic variables code_str = '_post_neurons = _post[_synapses]\n'+update_code(code, '_synapses', '_post_neurons') + "\n" else: algo = 3 if algo==0: ## Old version using numpy's unique() code_str = "_post_neurons = _post[_synapses]\n" # not necessary to do a copy because _synapses is not a slice code_str += "_u, _i = unique(_post_neurons, return_index = True)\n" #code_str += update_code(code, '_synapses[_i]', '_u') + "\n" code_str += update_code(code, '_synapses[_i]', '_post[_synapses[_i]]') + "\n" code_str += "if len(_u) < len(_post_neurons):\n" code_str += " _post_neurons[_i] = -1\n" code_str += " while (len(_u) < len(_post_neurons)) & (_post_neurons>-1).any():\n" # !! the any() is time consuming (len(u)>=1??) #code_str += " while (len(_u) < len(_post_neurons)) & (len(_u)>1):\n" # !! the any() is time consuming (len(u)>=1??) code_str += " _u, _i = unique(_post_neurons, return_index = True)\n" code_str += indent(update_code(code, '_synapses[_i[1:]]', '_post[_synapses[_i[1:]]]'),2) + "\n" code_str += " _post_neurons[_i[1:]] = -1 \n" elif algo==1: code_str = "_post_neurons = _post[_synapses]\n" # not necessary to do a copy because _synapses is not a slice code_str += "_perm = _post_neurons.argsort()\n" code_str += "_aux = _post_neurons[_perm]\n" code_str += "_flag = empty(len(_aux) + 1, dtype = bool)\n" code_str += "_flag[0] = _flag[-1] = True\n" code_str += "not_equal(_aux[1:], _aux[:-1], _flag[1:-1])\n" code_str += "_F = _flag.nonzero()[0][:-1]\n" code_str += "logical_not(_flag, _flag)\n" code_str += "while len(_F):\n" code_str += " _u = _aux[_F]\n" code_str += " _i = _perm[_F]\n" code_str += indent(update_code(code, '_synapses[_i]', '_u'), 1) + "\n" code_str += " _F += 1\n" code_str += " _F = _F[_flag[_F]]\n" elif algo==2: code_str = ''' _post_neurons = _post.data.take(_synapses) _perm = _post_neurons.argsort() _aux = _post_neurons.take(_perm) _flag = empty(len(_aux)+1, dtype=bool) _flag[0] = _flag[-1] = 1 not_equal(_aux[1:], _aux[:-1], _flag[1:-1]) if 0:#_flag.sum()==len(_aux)+1: %(code1)s else: _F = _flag.nonzero()[0][:-1] logical_not(_flag, _flag) while len(_F): _u = _aux.take(_F) _i = _perm.take(_F) %(code2)s _F += 1 _F = extract(_flag.take(_F), _F) ''' code_str = flattened_docstring(code_str) % {'code1': indent(update_code(code, '_synapses','_post_neurons'), 1), 'code2': indent(update_code(code, '_synapses[_i]', '_u'), 2)} elif algo==3: code_str = ''' _post_neurons = _post.data.take(_synapses) _perm = _post_neurons.argsort() _aux = _post_neurons.take(_perm) _flag = empty(len(_aux)+1, dtype=bool) _flag[0] = _flag[-1] = 1 not_equal(_aux[1:], _aux[:-1], _flag[1:-1]) _F = _flag.nonzero()[0][:-1] logical_not(_flag, _flag) while len(_F): _u = _aux.take(_F) _i = _perm.take(_F) %(code)s _F += 1 _F = extract(_flag.take(_F), _F) ''' code_str = flattened_docstring(code_str) % {'code': indent(update_code(code, '_synapses[_i]', '_u'), 1)} elif algo==4: code_str = ''' _post_neurons = _post[_synapses] _perm = _post_neurons.argsort() _aux = _post_neurons[_perm] _flag = empty(len(_aux)+1, dtype=bool) _flag[0] = _flag[-1] = 1 not_equal(_aux[1:], _aux[:-1], _flag[1:-1]) _F = _flag.nonzero()[0][:-1] logical_not(_flag, _flag) while len(_F): _u = _aux[_F] _i = _perm[_F] %(code)s _F += 1 _F = _F[_flag[_F]] ''' code_str = flattened_docstring(code_str) % {'code': indent(update_code(code, '_synapses[_i]', '_u'), 1)} # print code_str log_debug('brian.synapses', '\nCODE:\n'+code_str) # Compile compiled_code = compile(code_str, "Synaptic code", "exec") _namespace['_original_code_string'] = code_str return compiled_code,_namespace # Pickling support def __getstate__(self): # code objects cannot be pickled, we therefore delete them and later # reconstruct them from the code strings (stored in the namespace) state = copy.copy(self.__dict__) state['codes'] = [None] * len(self.codes) # We cannot pickle module objects and numpy is included as 'np' for ns in state['namespaces']: for k, v in ns.iteritems(): if v is np: ns[k] = None return state def __setstate__(self, state): self.__dict__ = state # Reconstruct the codes from the code string for idx, (queue, namespace) in enumerate(zip(self.queues, self.namespaces)): self.codes[idx] = compile(namespace['_original_code_string'], "Synaptic code", "exec") # I"m not quite sure why, but this seems to be necessary for postsyn_var in self.target.var_index: if isinstance(postsyn_var, str): namespace['_target_' + postsyn_var] = self.target.state_(postsyn_var) for presyn_var in self.source.var_index: if isinstance(presyn_var, str): namespace['_source_' + presyn_var] = self.source.state_(presyn_var) namespace['target'] = self.target # numpy array views don't survive pickling queue.X_flat = queue.X.reshape(queue.X.size, ) # Re-compress the Synapses object (this also adds "np" to the namespaces) self._iscompressed = False self.compress() def __setitem__(self, key, value): ''' Creates new synapses. Synapse indexes are created such that synapses with the same presynaptic neuron and delay have contiguous indexes. Caution: 1) there is no deletion 2) synapses are added, not replaced (e.g. S[1,2]=True;S[1,2]=True creates 2 synapses) TODO: * S[:,:]=array (boolean or int) ''' if self._iscompressed: raise AttributeError,"Synapses cannot be added after they have been run" if not isinstance(key, tuple): # we should check that number of elements is 2 as well raise AttributeError,'Synapses behave as 2-D objects' pre,post=key # pre and post indexes (can be slices) ''' Each of these sets of statements creates: * synapses_pre: a mapping from presynaptic neuron to synapse indexes * synapses_post: same * presynaptic: an array of presynaptic neuron indexes (synapse->pre) * postsynaptic: same ''' pre_slice = self.presynaptic_indexes(pre) post_slice = self.postsynaptic_indexes(post) # Bound checks if pre_slice[-1]>=len(self.source): raise ValueError('Presynaptic index %d greater than number of '\ 'presynaptic neurons (%d)' % (pre_slice[-1], len(self.source))) if post_slice[-1]>=len(self.target): raise ValueError('Postsynaptic index %d greater than number of '\ 'postsynaptic neurons (%d)' % (post_slice[-1], len(self.target))) if isinstance(value,float): self.connect_random(pre,post,value) return elif isinstance(value, (int, bool)): # ex. S[1,7]=True # Simple case, either one or multiple synapses between different neurons if value is False: raise ValueError('Synapses cannot be deleted') elif value is True: nsynapses = 1 else: nsynapses = value postsynaptic,presynaptic=np.meshgrid(post_slice,pre_slice) # synapse -> pre, synapse -> post # Flatten presynaptic.shape=(presynaptic.size,) postsynaptic.shape=(postsynaptic.size,) # pre,post -> synapse index, relative to last synapse # (that's a complex vectorised one!) synapses_pre=np.arange(len(presynaptic)).reshape((len(pre_slice),len(post_slice))) synapses_post=np.ones((len(post_slice),1),dtype=int)*np.arange(0,len(presynaptic),len(post_slice))+\ np.arange(len(post_slice)).reshape((len(post_slice),1)) # Repeat if nsynapses>1: synapses_pre=np.hstack([synapses_pre+k*len(presynaptic) for k in range(nsynapses)]) # could be vectorised synapses_post=np.hstack([synapses_post+k*len(presynaptic) for k in range(nsynapses)]) # could be vectorised presynaptic=np.tile(presynaptic,nsynapses) postsynaptic=np.tile(postsynaptic,nsynapses) # Make sure the type is correct synapses_pre=np.array(synapses_pre,dtype=self.synapses_pre[0].dtype) synapses_post=np.array(synapses_post,dtype=self.synapses_post[0].dtype) # Turn into dictionaries synapses_pre=dict(zip(pre_slice,synapses_pre)) synapses_post=dict(zip(post_slice,synapses_post)) elif isinstance(value, str): # string code assignment # For subgroups, origin of i and j are shifted to subgroup origin if isinstance(pre,NeuronGroup): pre_shift=pre_slice[0] else: pre_shift=0 if isinstance(post,NeuronGroup): post_shift=post_slice[0] else: post_shift=0 code = re.sub(r'\b' + 'rand\(\)', 'rand(n)', value) # replacing rand() code = re.sub(r'\b' + 'randn\(\)', 'randn(n)', code) # replacing randn() _namespace = namespace(value, level=1) _namespace.update({'j' : post_slice-post_shift, 'n' : len(post_slice), 'rand': np.random.rand, 'randn': np.random.randn}) # try: # Vectorise over all indexes: not faster! # post,pre=np.meshgrid(post_slice-post_shift,pre_slice-pre_shift) # pre=pre.flatten() # post=post.flatten() # _namespace['i']=array(pre,dtype=self.presynaptic.dtype) # _namespace['j']=array(post,dtype=self.postsynaptic.dtype) # _namespace['n']=len(post) # result = eval(code, _namespace) # mask on synapses # if result.dtype==float: # random number generation # result=rand(len(post))pre) * postsynaptic: same If synapses_pre or synapses_post is not specified, it is calculated from presynaptic or postsynaptic. ''' # Resize dynamic arrays and push new values newsynapses=len(presynaptic) # number of new synapses nvars,nsynapses_all=self._S.shape self._S.resize((nvars,nsynapses_all+newsynapses)) self.presynaptic.resize(nsynapses_all+newsynapses) self.presynaptic[nsynapses_all:]=presynaptic self.postsynaptic.resize(nsynapses_all+newsynapses) self.postsynaptic[nsynapses_all:]=postsynaptic for delay_pre in self._delay_pre: delay_pre.resize(nsynapses_all+newsynapses) self._delay_post.resize(nsynapses_all+newsynapses) if synapses_pre is None: synapses_pre=invert_array(presynaptic,dtype=self.synapses_post[0].dtype) for i,synapses in synapses_pre.iteritems(): nsynapses=len(self.synapses_pre[i]) self.synapses_pre[i].resize(nsynapses+len(synapses)) self.synapses_pre[i][nsynapses:]=synapses+nsynapses_all # synapse indexes are shifted if synapses_post is None: synapses_post=invert_array(postsynaptic,dtype=self.synapses_post[0].dtype) for j,synapses in synapses_post.iteritems(): nsynapses=len(self.synapses_post[j]) self.synapses_post[j].resize(nsynapses+len(synapses)) self.synapses_post[j][nsynapses:]=synapses+nsynapses_all def __getattr__(self, name): if name == 'var_index': raise AttributeError if not hasattr(self, 'var_index'): raise AttributeError if (name=='delay_pre') or (name=='delay'): # default: delay is presynaptic delay if name == 'delay' and self.has_variable_delays: # handle variable delays return SynapticVariable(self.state(name), self, name) # stored as floats for update (i.e not SynapticDelayVar) if len(self._delay_pre) > 1: return [SynapticDelayVariable(delay_pre,self,name) for delay_pre in self._delay_pre] else: return SynapticDelayVariable(self._delay_pre[0],self,name) elif name=='delay_post': return SynapticDelayVariable(self._delay_post,self,name) try: x=self.state(name) return SynapticVariable(x,self,name) except KeyError: return NeuronGroup.__getattr__(self,name) def __setattr__(self, name, val): if ((name=='delay_pre') or (name=='delay') or (name=='delay_post')) and (not self.has_variable_delays): # if only constant delays, then delays are held in the _delay_pre (list of array) and _delay_post (array) data if name=='delay_post': SynapticDelayVariable(self._delay_post,self,name)[:]=val else: #i.e (name=='delay_pre') or (name=='delay'): if len(self._delay_pre)==1: SynapticDelayVariable(self._delay_pre[0], self, name)[:]=val else: raise NotImplementedError,"Cannot assign multiple delays at the same time" else: # copied from Group origname = name if len(name) and name[-1] == '_': origname = name[:-1] if not hasattr(self, 'var_index') or (name not in self.var_index and origname not in self.var_index): object.__setattr__(self, name, val) else: if name in self.var_index: x=self.state(name) else: x=self.state_(origname) SynapticVariable(x,self,name).__setitem__(slice(None,None,None),val,level=2) def update(self): # this is called at every timestep ''' Updates the synaptic variables. TODO: * Deal with static variables ''' if self._state_updater is not None: self._state_updater(self) for queue, _namespace, code in zip(self.queues, self.namespaces, self.codes): synaptic_events = queue.peek() if len(synaptic_events): # Build the namespace - Here we don't consider static equations _namespace['_synapses'] = synaptic_events _namespace['t'] = self.clock._t exec code in _namespace queue.next() if self.has_variable_delays: queue._update_delays(_namespace['delay'])#self._S[self.var_index['delay'],:]) def connect_one_to_one(self,pre=None,post=None): ''' Connects each neuron in the ``pre'' group to each corresponding one in the ``post'' group. ''' if pre is None: pre = self.source if post is None: post = self.target pre, post = self.presynaptic_indexes(pre), self.postsynaptic_indexes(post) if len(pre) != len(post): raise TypeError,"Source and target groups do not have the same size" for i,j in zip(pre,post): self[i,j]=True def connect_random(self,pre=None,post=None,sparseness=None): ''' Creates random connections between pre and post neurons (default: all neurons). This is equivalent to:: S[pre,post]=sparseness ``pre=None'' The set of presynaptic neurons, defined as an integer, an array, a slice or a subgroup. ``post=None'' The set of presynaptic neurons, defined as an integer, an array, a slice or a subgroup. ``sparseness=None'' The probability of connection of a pair of pre/post-synaptic neurons. ''' if pre is None: pre=self.source if post is None: post=self.target pre,post=self.presynaptic_indexes(pre),self.postsynaptic_indexes(post) m=len(post) synapses_pre={} nsynapses=0 presynaptic,postsynaptic=[],[] for i in pre: # vectorised over post neurons k = binomial(m, sparseness, 1)[0] # number of postsynaptic neurons synapses_pre[i]=nsynapses+np.arange(k) presynaptic.append(i*np.ones(k,dtype=int)) # Not significantly faster to generate all random numbers in one pass # N.B.: the sample method is implemented in Python and it is not in Scipy postneurons = sample(xrange(m), k) #postneurons.sort() # sorting is unnecessary postsynaptic.append(post[postneurons]) nsynapses+=k presynaptic=np.hstack(presynaptic) postsynaptic=np.hstack(postsynaptic) synapses_post=None # we ask for automatic calculation of (post->synapse) # this is more or less given by unique self.create_synapses(presynaptic,postsynaptic,synapses_pre,synapses_post) def presynaptic_indexes(self,x): ''' Returns the array of presynaptic neuron indexes corresponding to x, which can be a integer, an array, a slice or a subgroup. ''' return neuron_indexes(x,self.source) def postsynaptic_indexes(self,x): ''' Returns the array of postsynaptic neuron indexes corresponding to x, which can be a integer, an array, a slice or a subgroup. ''' return neuron_indexes(x,self.target) def uncompress(self): # make the state matrix a dynamic array again S = self._S self._S = DynamicArray(S.shape) self._S[:] = S # now isn't compressed self._iscompressed = False def compress(self): ''' * Checks that the object is not empty. * Make the state array non-dynamical (important for the state updater). * Updates namespaces of pre and post code. ''' if hasattr(self, '_iscompressed') and self._iscompressed: return self._iscompressed = True # Check that the object is not empty if len(self)==0: warnings.warn("Empty Synapses object") self._S=self._S[:,:] # Update namespaces of pre/post code for _namespace in self.namespaces: for var,i in self.var_index.iteritems(): # no static variables here if isinstance(var, str): _namespace[var]=self._S[i,:] for var,i in self.source.var_index.iteritems(): if isinstance(var, str): _namespace[var+'_pre']=self.source._S[i,:] for var,i in self.target.var_index.iteritems(): if isinstance(var, str): _namespace[var+'_post']=self.target._S[i,:] _namespace[var]=self.target._S[i,:] _namespace['_pre']=self.presynaptic _namespace['_post']=self.postsynaptic _namespace['np']=np _namespace['binomial']=self._binomial _namespace['rand']=rand _namespace['randn']=randn _namespace['zeros']=np.zeros _namespace['sum']=sum self._iscompressed=True def synapse_index(self,i): ''' Returns the synapse indexes correspond to i, which is a tuple. If i is a tuple (m,n), m and n can be an integer, an array, a slice or a subgroup. Searching synapse indexes for synapse (i,j) is implemented as follows. If i or j is an integer or a slice, they are converted to a boolean test. Then the following is executed: 1) get indexes of target synapses of presynaptic neuron(s) i 2) test whether postsynaptic neurons of these synapses correspond to j 3) return synapses that passed the test or the symmetrical operations (depending on what is possible and faster). Otherwise, the following is executed: 1) get indexes of target synapses of presynaptic neuron(s) i 2) get indexes of source synapses of postsynaptic neuron(s) j 3) calculate the intersection This will generally be ok for vectorised searches, but not for searching single elements (i,j). In this case, one might want to use a dictionary (i,j)->synapse index (not implemented). This is fast but 1) cannot be vectorised, 2) is very memory expensive. ''' if not isinstance(i,tuple): # we assume it is directly a synapse index return i if len(i)==2: i,j=i # We use boolean tests if possible (faster) if isinstance(i,slice) or isinstance(i,int): test_i=slice_to_test(i) else: test_i=None if isinstance(j,slice) or isinstance(j,int): test_j=slice_to_test(j) else: test_j=None i=neuron_indexes(i,self.source) j=neuron_indexes(j,self.target) synapsetype=self.synapses_pre[0].dtype if (test_i is None) and (test_j is None): # no speed-up is possible synapses_pre=np.array(np.hstack([self.synapses_pre[k] for k in i]),dtype=synapsetype) synapses_post=np.array(np.hstack([self.synapses_post[k] for k in j]),dtype=synapsetype) return np.intersect1d(synapses_pre, synapses_post,assume_unique=True) elif ((len(i)=start) & ((y-start)%step==0) else: return lambda y:(y>=start) & (y Vmax: Vmax = V[i] Vi = i Vmaxtime = M.times[Vi] * second Vmax = Vmax * volt assert abs(Vmax - expected_vmax) < desired_vmaxaccuracy assert abs(Vmaxtime - expected_vmaxtime) < desired_vmaxtimeaccuracy if __name__ == '__main__': testepsp() brian-1.4.3/brian/tests/testcorrectness/test_exponential_current.py000066400000000000000000000034411266062647100260160ustar00rootroot00000000000000from brian import * def testexponentialcurrent(): '''Tests whether an exponential current works as predicted From Tutorial 2b. The scheme we implement is the following diffential equations: | taum dV/dt = -V + ge - gi | taue dge/dt = -ge | taui dgi/dt = -gi An excitatory neuron connects to state ge, and an inhibitory neuron connects to state gi. When an excitatory spike arrives, ge instantaneously increases, then decays exponentially. Consequently, V will initially but continuously rise and then fall. Solving these equations, if V(0)=0, ge(0)=g0 corresponding to an excitatory spike arriving at time 0, and gi(0)=0 then: | gi = 0 | ge = g0 exp(-t/taue) | V = (exp(-t/taum) - exp(-t/taue)) taue g0 / (taum-taue) ''' reinit_default_clock() taum = 20 * ms taue = 1 * ms taui = 10 * ms Vt = 10 * mV Vr = 0 * mV spiketimes = [(0, 0 * ms)] G1 = SpikeGeneratorGroup(2, spiketimes) G2 = NeuronGroup(N=1, model=''' dV/dt = (-V+ge-gi)/taum : volt dge/dt = -ge/taue : volt dgi/dt = -gi/taui : volt ''', threshold=Vt, reset=Vr) G2.V = Vr C1 = Connection(G1, G2, 'ge') C2 = Connection(G1, G2, 'gi') C1[0, 0] = 3 * mV C2[1, 0] = 3 * mV Mv = StateMonitor(G2, 'V', record=True) Mge = StateMonitor(G2, 'ge', record=True) Mgi = StateMonitor(G2, 'gi', record=True) run(100 * ms) t = Mv.times Vpredicted = (exp(-t / taum) - exp(-t / taue)) * taue * (3 * mV) / (taum - taue) Vdiff = abs(Vpredicted - Mv[0]) assert max(Vdiff) < 0.00001 if __name__ == '__main__': testexponentialcurrent() brian-1.4.3/brian/tests/testcorrectness/test_from_tutorial1c.py000066400000000000000000000022631266062647100250410ustar00rootroot00000000000000from brian import * def testfromtutorial1c(): '''Tests a behaviour from Tutorial 1c Solving the differential equation gives: V = El + (Vr-El) exp (-t/tau) Setting V=Vt at time t gives: t = tau log( (Vr-El) / (Vt-El) ) If the simulator runs for time T, and fires a spike immediately at the beginning of the run it will then generate n spikes, where: n = [T/t] + 1 If you have m neurons all doing the same thing, you get nm spikes. This calculation with the parameters above gives: t = 48.0 ms n = 21 nm = 840 As predicted. ''' reinit_default_clock() tau = 20 * msecond # membrane time constant Vt = -50 * mvolt # spike threshold Vr = -60 * mvolt # reset value El = -49 * mvolt # resting potential (same as the reset) dV = 'dV/dt = -(V-El)/tau : volt # membrane potential' G = NeuronGroup(N=40, model=dV, threshold=Vt, reset=Vr) G.V = El M = SpikeMonitor(G) run(1 * second) assert M.nspikes == 840 if __name__ == '__main__': testfromtutorial1c() brian-1.4.3/brian/tests/testcorrectness/test_synapses_vs_connection.py000066400000000000000000000067061266062647100265310ustar00rootroot00000000000000''' Make sure that the Synapses class does the same thing as the Connection class. ''' from brian import * from brian.tests import repeat_with_global_opts def assert_same_voltage(mon, N): ''' Make sure that the first N and last N of the recorded voltage traces at exactly the same. ''' for index in range(N): assert (mon[index] == mon[N + index]).all() @repeat_with_global_opts([{'useweave': False}, {'useweave': True}]) def test_without_delays(): ''' Compare connection via Connection class and via Synapses class.''' reinit_default_clock() N = 10 duration = (N + 1) * ms spikes = [(i, i * ms) for i in range(N)] source = SpikeGeneratorGroup(N, spikes) # one to one connecting targets = NeuronGroup(2 * N, model='dv/dt = -v / (1 * ms) : 1', threshold=10, reset=0) target_syn = targets[:N] target_conn = targets[N:] conn = Connection(source, target_conn) conn.connect_one_to_one(source, target_conn, weight=1.1) syn = Synapses(source, target_syn, model='w:1', pre='v+=w') syn.connect_one_to_one(source, target_syn) syn.w = 1.1 mon = StateMonitor(targets, 'v', record=True) run(duration) assert_same_voltage(mon, N) @repeat_with_global_opts([{'useweave': False}, {'useweave': True}]) def test_with_constant_delays(): ''' Compare connection via Connection class and via Synapses class using homogeneous delays. ''' reinit_default_clock() N = 10 delay = 5 * ms duration = (N + 1) * ms + delay spikes = [(i, i * ms) for i in range(N)] source = SpikeGeneratorGroup(N, spikes) # one to one connecting targets = NeuronGroup(2 * N, model='dv/dt = -v / (1 * ms) : 1', threshold=10, reset=0) target_syn = targets[:N] target_conn = targets[N:] conn = Connection(source, target_conn, delay=delay) conn.connect_one_to_one(source, target_conn, weight=1.1) syn = Synapses(source, target_syn, model='w:1', pre='v+=w', max_delay=delay) syn.connect_one_to_one(source, target_syn) syn.w = 1.1 syn.delay = delay mon = StateMonitor(targets, 'v', record=True) run(duration) assert_same_voltage(mon, N) @repeat_with_global_opts([{'useweave': False}, {'useweave': True}]) def test_with_variable_delays(): ''' Compare connection via Connection class and via Synapses class using heterogeneous delays. ''' reinit_default_clock() N = 10 flat_delays = array([i * ms for i in range(N)]) delays = eye(N) * flat_delays duration = (N + 1) * ms + delays.max() * second spikes = [(i, i * ms) for i in range(N)] source = SpikeGeneratorGroup(N, spikes) # one to one connecting targets = NeuronGroup(2 * N, model='dv/dt = -v / (1 * ms) : 1', threshold=10, reset=0) target_syn = targets[:N] target_conn = targets[N:] conn = Connection(source, target_conn, delay=True, max_delay=flat_delays.max()) conn.connect_one_to_one(source, target_conn, weight=1.1, delay=delays) syn = Synapses(source, target_syn, model='w:1', pre='v+=w', max_delay=flat_delays.max()) syn.connect_one_to_one(source, target_syn) syn.w = 1.1 syn.delay = flat_delays mon = StateMonitor(targets, 'v', record=True) run(duration) assert_same_voltage(mon, N) if __name__ == '__main__': test_without_delays() test_with_constant_delays() test_with_variable_delays()brian-1.4.3/brian/tests/testfeatures/000077500000000000000000000000001266062647100175775ustar00rootroot00000000000000brian-1.4.3/brian/tests/testfeatures/__init__.py000066400000000000000000000000021266062647100217000ustar00rootroot00000000000000 brian-1.4.3/brian/tests/testfeatures/test_connect.py000066400000000000000000000170611266062647100226460ustar00rootroot00000000000000from numpy.testing.utils import assert_equal from brian import * def test_delay_connect_with_subgroups(): G = NeuronGroup(4, 'V:1') # test connect method C1 = Connection(G, G, 'V', delay=True, structure='dense') C2 = Connection(G, G, 'V', delay=True, structure='sparse') C3 = Connection(G, G, 'V', delay=True, structure='dynamic') for i, C in enumerate([C1, C2, C3]): C.connect(G[0:2], G[0:2], W=ones((2, 2)), delay=1) C.connect(G[0:2], G[2:4], W=ones((2, 2)), delay=(2.1, 2.2)) C.connect(G[2:4], G[0:2], W=ones((2, 2)), delay=lambda:4) C.connect(G[2:4], G[2:4], W=ones((2, 2)), delay=lambda i, j:i + 10 * j) assert (C.W.todense() == 1).all(), 'Problem with connection C' + str(i + 1) D = C.delayvec.todense() assert (array(D, dtype=int) == array([[1, 1, 2, 2], [1, 1, 2, 2], [4, 4, 0, 10], [4, 4, 1, 11]], dtype=int)).all(), 'Problem with connection C' + str(i + 1) # test connect_random method C1 = Connection(G, G, 'V', delay=True, structure='dense') C2 = Connection(G, G, 'V', delay=True, structure='sparse') C3 = Connection(G, G, 'V', delay=True, structure='dynamic') for i, C in enumerate([C1, C2, C3]): C.connect_random(G[0:2], G[0:2], p=1, weight=1, delay=1) C.connect_random(G[0:2], G[2:4], p=1, weight=1, delay=(2.1, 2.2)) C.connect_random(G[2:4], G[0:2], p=1, weight=1, delay=lambda:4) C.connect_random(G[2:4], G[2:4], p=1, weight=1, delay=lambda i, j:i + 10 * j) assert (C.W.todense() == 1).all(), 'Problem with connection C' + str(i + 1) D = C.delayvec.todense() assert (array(D, dtype=int) == array([[1, 1, 2, 2], [1, 1, 2, 2], [4, 4, 0, 10], [4, 4, 1, 11]], dtype=int)).all(), 'Problem with connection C' + str(i + 1) # test connect_full method C1 = Connection(G, G, 'V', delay=True, structure='dense') C2 = Connection(G, G, 'V', delay=True, structure='sparse') C3 = Connection(G, G, 'V', delay=True, structure='dynamic') for i, C in enumerate([C1, C2, C3]): C.connect_full(G[0:2], G[0:2], weight=1, delay=1) C.connect_full(G[0:2], G[2:4], weight=1, delay=(2.1, 2.2)) C.connect_full(G[2:4], G[0:2], weight=1, delay=lambda:4) C.connect_full(G[2:4], G[2:4], weight=1, delay=lambda i, j:i + 10 * j) assert (C.W.todense() == 1).all(), 'Problem with connection C' + str(i + 1) D = C.delayvec.todense() assert (array(D, dtype=int) == array([[1, 1, 2, 2], [1, 1, 2, 2], [4, 4, 0, 10], [4, 4, 1, 11]], dtype=int)).all(), 'Problem with connection C' + str(i + 1) # test connect_one_to_one method C1 = Connection(G, G, 'V', delay=True, structure='dense') C2 = Connection(G, G, 'V', delay=True, structure='sparse') C3 = Connection(G, G, 'V', delay=True, structure='dynamic') for i, C in enumerate([C1, C2, C3]): C.connect_one_to_one(G[0:2], G[0:2], weight=1, delay=1) C.connect_one_to_one(G[0:2], G[2:4], weight=1, delay=(2.1, 2.2)) C.connect_one_to_one(G[2:4], G[0:2], weight=1, delay=lambda:4) C.connect_one_to_one(G[2:4], G[2:4], weight=1, delay=lambda i, j:i + 10 * j) D = C.delayvec.todense() assert (array(D, dtype=int) == array([[1, 0, 2, 0], [0, 1, 0, 2], [4, 0, 0, 0], [0, 4, 0, 11]], dtype=int)).all(), 'Problem with connection C' + str(i + 1) def test_connect_with_seed(): G1 = NeuronGroup(10, 'v:1') G2 = NeuronGroup(10, 'v:1') C1 = Connection(G1, G2, 'v') C2 = Connection(G1, G2, 'v') # We check two things: # 1. Providing a seed to connect_random should produce exactly the same # connection pattern every time # 2. This seed argument does not affect the general random number generation np.random.seed(12345) numbers = np.random.rand(5) np.random.seed(12345) C1.connect_random(p=0.5, seed=54321) C2.connect_random(p=0.5, seed=54321) assert_equal(C1.W.todense(), C2.W.todense()) # The general random number generation should be unaffected and use the seed # specified above new_numbers = np.random.rand(5) assert_equal(numbers, new_numbers) def test_connect_with_subgroups(): G = NeuronGroup(4, 'V:1') # test connect method C1 = Connection(G, G, 'V', structure='dense') C2 = Connection(G, G, 'V', structure='sparse') C3 = Connection(G, G, 'V', structure='dynamic') for i, C in enumerate([C1, C2, C3]): C.connect(G[0:2], G[0:2], W=ones((2, 2))) C.connect(G[0:2], G[2:4], W=2 * ones((2, 2))) C.connect(G[2:4], G[0:2], W=4 * ones((2, 2))) C.connect(G[2:4], G[2:4], W=array([[0, 10], [1, 11]])) W = C.W.todense() assert (array(W, dtype=int) == array([[1, 1, 2, 2], [1, 1, 2, 2], [4, 4, 0, 10], [4, 4, 1, 11]], dtype=int)).all(), 'Problem with connection C' + str(i + 1) # test connect_random method C1 = Connection(G, G, 'V', structure='dense') C2 = Connection(G, G, 'V', structure='sparse') C3 = Connection(G, G, 'V', structure='dynamic') for i, C in enumerate([C1, C2, C3]): C.connect_random(G[0:2], G[0:2], p=1, weight=1) C.connect_random(G[2:4], G[2:4], p=1, weight=lambda i, j:i + 10 * j) W = C.W.todense() assert (array(W, dtype=int) == array([[1, 1, 0, 0], [1, 1, 0, 0], [0, 0, 0, 10], [0, 0, 1, 11]], dtype=int)).all(), 'Problem with connection C' + str(i + 1) # test connect_full method C1 = Connection(G, G, 'V', structure='dense') C2 = Connection(G, G, 'V', structure='sparse') C3 = Connection(G, G, 'V', structure='dynamic') for i, C in enumerate([C1, C2, C3]): C.connect_full(G[0:2], G[0:2], weight=1) C.connect_full(G[2:4], G[2:4], weight=lambda i, j:i + 10 * j) W = C.W.todense() assert (array(W, dtype=int) == array([[1, 1, 0, 0], [1, 1, 0, 0], [0, 0, 0, 10], [0, 0, 1, 11]], dtype=int)).all(), 'Problem with connection C' + str(i + 1) # test connect_one_to_one method C1 = Connection(G, G, 'V', structure='dense') C2 = Connection(G, G, 'V', structure='sparse') C3 = Connection(G, G, 'V', structure='dynamic') for i, C in enumerate([C1, C2, C3]): C.connect_one_to_one(G[0:2], G[0:2], weight=1) W = C.W.todense() assert (array(W, dtype=int) == array([[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]], dtype=int)).all(), 'Problem with connection C' + str(i + 1) if __name__ == '__main__': test_delay_connect_with_subgroups() test_connect_with_seed() test_connect_with_subgroups() brian-1.4.3/brian/tests/testfeatures/test_io_spikes.py000066400000000000000000000051441266062647100232010ustar00rootroot00000000000000''' This test script is meant to test the AERSpikeMonitor and the load_aer functions to do efficient spikes i/o. Good news: - loading/saving AER files works Bad news: - stacking of spikes makes this test fail if dt is too big. In this case in the SpikeGeneratorGroup some spikes are discarded. ''' import time from brian import * from brian.tools.io import * N = 1000 def do_spikeio_test(test = 'save', dt = .1*ms): ''' This test function can test two aspects of spike io. ``test = 'save'`` tests generating a lot of Poisson spikes and saving them. It is then reloaded and the number of spikes is checked for. ``test = 'reload'`` tests loading the spikes and putting them directly in a SpikeGeneratorGroup. It's a more thorough test that also tests for the integration with SpikeGeneratorGroup ''' reinit_default_clock() clear(all = True) defaultclock.dt = dt if test == 'save': # first generate some spikes g = PoissonGroup(N, 200*Hz) # with a monitor Maer = AERSpikeMonitor(g, './dummy.aedat') M = SpikeMonitor(g) run(100*ms) Maer.close() # reload the spikes addr, timestamps = load_aer('./dummy.aedat') # compare the recorded spikes number etc... if len(addr) == M.nspikes: print 'Saving is a success' else: print 'Saving... failed' print 'addr, M.spikes', len(addr),M.nspikes elif test == 'reload': addr, timestamps = load_aer('./dummy.aedat') # check interface with SpikeGeneratorGroup group = SpikeGeneratorGroup(N, (addr, timestamps)) newM = SpikeMonitor(group, record = True) run(100*ms) if len(addr) == newM.nspikes: print 'Re-loading is a success' else: print 'Re-loading... failed' print 'addr, M.spikes', len(addr), newM.nspikes def do_speedtest_saving(): t0 = time.time() g = PoissonGroup(N, 200*Hz) # with a monitor Maer = AERSpikeMonitor(g, './dummy.aedat') run(1000*ms) print "AERSpikeMonitor ", time.time()-t0 reinit_default_clock() clear() t0 = time.time() g = PoissonGroup(N, 200*Hz) # with a monitor Maer = FileSpikeMonitor(g, './dummy.txt') run(1000*ms) print "FileSpikeMonitor ", time.time()-t0 if __name__ == '__main__': print '@dt = 1ms' do_spikeio_test(test = 'save', dt = 1*ms) do_spikeio_test(test = 'reload', dt = 1*ms) print '@dt = 0.1ms' do_spikeio_test(test = 'save', dt = .1*ms) do_spikeio_test(test = 'reload', dt = .1*ms) do_speedtest_saving() brian-1.4.3/brian/tests/testfeatures/test_pickling.py000066400000000000000000000061021266062647100230070ustar00rootroot00000000000000import pickle import itertools from numpy.testing import assert_equal from brian import * def test_timed_array_pickling(): ''' Tests pickling of brian objects ''' def timed_array_correctly_pickled(obj): ''' Pickles and unpickles a TimedArray object (using a string, i.e. not writing to a file. Returns whether the two objects are identical or not. ''' pickled = pickle.dumps(obj) unpickled = pickle.loads(pickled) assert((unpickled == obj).all()) # tests the array content assert(unpickled.clock.t == obj.clock.t and unpickled.clock.dt == obj.clock.dt) assert(unpickled.guessed_clock == obj.guessed_clock) assert((unpickled.times is None and obj.times is None) or (unpickled.times == obj.times).all()) assert(unpickled._dt == obj._dt) assert(unpickled._t_init == obj._t_init) # Test TimedArray with a clock ta = TimedArray([0, 1], clock=Clock(dt=0.2*ms)) timed_array_correctly_pickled(ta) # Test TimedArray with the default clock ta = TimedArray([0, 1]) timed_array_correctly_pickled(ta) def test_neurongroup_pickling(): #very simple test of pickling for a NeuronGroup and a Network G = NeuronGroup(42, model=LazyStateUpdater()) net = Network(G) pickled = pickle.dumps(net) unpickled = pickle.loads(pickled) assert(len(unpickled) == 42) def test_linearstateupdater_pickling(): G = NeuronGroup(10, model='''dv/dt = -(v + I)/ (10 * ms) : volt I : volt''') pickled = pickle.dumps(G) unpickled = pickle.loads(pickled) assert len(unpickled) == 10 assert_equal(G._state_updater.A, unpickled._state_updater.A) assert G._state_updater.B == unpickled._state_updater.B def test_synapses_pickling(): # Test pickling a Network with a Synapses object G1 = NeuronGroup(42, model='v:1', threshold='v>1', reset='v=0') G1.name = 'G1' G2 = NeuronGroup(42, model='v:1') G2.name = 'G2' G1.v = 1.1 S = Synapses(G1, G2, model='w:1', pre='v+=w') S[:, :] = 'i==j' S.w = 'i' net = Network(G1, G2, S) pickled = pickle.dumps(net) net.run(defaultclock.dt) assert_equal(G2.v[:], np.arange(len(G1))) unpickled_net = pickle.loads(pickled) unpickled_G1 = next(group for group in unpickled_net.groups if getattr(group, 'name', None) == 'G1') unpickled_G2 = next(group for group in unpickled_net.groups if getattr(group, 'name', None) == 'G2') unpickled_S = next(group for group in unpickled_net.groups if isinstance(group, Synapses)) defaultclock.t = 0*ms assert len(unpickled_S) == len(S) assert len(unpickled_G1) == len(G1) assert len(unpickled_G2) == len(G2) unpickled_net.run(defaultclock.dt) assert_equal(unpickled_G2.v[:], np.arange(len(unpickled_G1))) if __name__ == '__main__': test_timed_array_pickling() test_neurongroup_pickling() test_linearstateupdater_pickling() test_synapses_pickling() brian-1.4.3/brian/tests/testfeatures/test_propagation.py000066400000000000000000000035341266062647100235400ustar00rootroot00000000000000from brian import * def test_structures(): reinit_default_clock() H = NeuronGroup(1, 'V:1\nmod:1', reset=0, threshold=1) H.V = 2 H.mod = 1 G = [NeuronGroup(10, 'V:1', reset=0, threshold=1) for _ in range(12)] M = [SpikeMonitor(g) for g in G] C0 = Connection(H, G[0], 'V', weight=2) C1 = Connection(H, G[1], 'V', weight=2, structure='dense') C2 = Connection(H, G[2], 'V', weight=2, structure='dynamic') C3 = Connection(H, G[3], 'V', weight=2, modulation='mod') C4 = Connection(H, G[4], 'V', weight=2, structure='dense', modulation='mod') C5 = Connection(H, G[5], 'V', weight=2, structure='dynamic', modulation='mod') C6 = Connection(H, G[6], 'V', weight=2, delay=True) C7 = Connection(H, G[7], 'V', weight=2, delay=True, structure='dense') C8 = Connection(H, G[8], 'V', weight=2, delay=True, structure='dynamic') C9 = Connection(H, G[9], 'V', weight=2, delay=True, modulation='mod') C10 = Connection(H, G[10], 'V', weight=2, delay=True, structure='dense', modulation='mod') C11 = Connection(H, G[11], 'V', weight=2, delay=True, structure='dynamic', modulation='mod') for c in [C6, C7, C8, C9, C10, C11]: c.delay[0, :] = arange(10) * defaultclock.dt + defaultclock.dt / 2 run(2 * ms) for k, m in enumerate(M): assert len(m.spikes), 'Problem with connection ' + str(k) i, j = zip(*m.spikes) i = array(i) j = array(j) j = array((j + defaultclock.dt / 2) / defaultclock.dt, dtype=int) assert (i == arange(10)).all(), 'Problem with connection ' + str(k) if k < 6: assert (j == 1).all(), 'Problem with connection ' + str(k) else: assert (j == (1 + arange(10))).all(), 'Problem with connection ' + str(k) + ': j=' + str(j) if __name__ == '__main__': test_structures() brian-1.4.3/brian/tests/testfeatures/test_synapses_tomatrix.py000066400000000000000000000045731266062647100250150ustar00rootroot00000000000000''' This test covers the to_matrix feature of SynapticVariable ''' from brian.synapses import * from brian.neurongroup import * import numpy as np import cStringIO from matplotlib.pyplot import * from nose.tools import * def test_first_last(): # prepare neurongroups for the creation of a synapse object N0 = 1 g0 = NeuronGroup(N0, model = 'dv/dt = 0/ms : 1') N1 = 1 g1 = NeuronGroup(N1, model = 'dv/dt = 0/ms : 1') # create two synapses between the same neurons synapses = Synapses(g0, g1, model = 'w:1', pre = 'v+=w') synapses[0,0] = 2 synapses.w[0,0,0] = 1 synapses.w[0,0,1] = 2 # check the first/last behavior w_dense = synapses.w.to_matrix(multiple_synapses = 'first') assert w_dense[0,0] == 1 w_dense = synapses.w.to_matrix(multiple_synapses = 'last') assert w_dense[0,0] == 2 def test_min_max_sum(): # prepare neurongroups for the creation of a synapse object N0 = 1 g0 = NeuronGroup(N0, model = 'dv/dt = 0/ms : 1') N1 = 1 g1 = NeuronGroup(N1, model = 'dv/dt = 0/ms : 1') # create two synapses between the same neurons synapses = Synapses(g0, g1, model = 'w:1', pre = 'v+=w') synapses[0,0] = 3 synapses.w[0,0,0] = 1 synapses.w[0,0,1] = 2 synapses.w[0,0,2] = 3 # check the first/last behavior w_dense = synapses.w.to_matrix(multiple_synapses = 'sum') assert w_dense[0,0] == 6 w_dense = synapses.w.to_matrix(multiple_synapses = 'max') assert w_dense[0,0] == 3 w_dense = synapses.w.to_matrix(multiple_synapses = 'min') assert w_dense[0,0] == 1 def test_min_max_sum(): # prepare neurongroups for the creation of a synapse object N0 = 10 g0 = NeuronGroup(N0, model = 'dv/dt = 0/ms : 1') N1 = 10 g1 = NeuronGroup(N1, model = 'dv/dt = 0/ms : 1') # create two synapses between the same neurons synapses = Synapses(g0, g1, model = 'w:1', pre = 'v+=w') synapses[0,0] = 3 synapses.w[0,0,0] = 1 synapses.w[0,0,1] = 2 synapses.w[0,0,2] = 3 # check the first/last behavior w_dense = synapses.w.to_matrix(multiple_synapses = 'sum') assert w_dense[0,0] == 6 w_dense = synapses.w.to_matrix(multiple_synapses = 'max') assert w_dense[0,0] == 3 w_dense = synapses.w.to_matrix(multiple_synapses = 'min') assert w_dense[0,0] == 1 if __name__ == '__main__': test_first_last() test_min_max_sum() print "done" brian-1.4.3/brian/tests/testinterface/000077500000000000000000000000001266062647100177215ustar00rootroot00000000000000brian-1.4.3/brian/tests/testinterface/__init__.py000066400000000000000000000000671266062647100220350ustar00rootroot00000000000000''' Created on 4 sept. 2009 @author: goodman ''' brian-1.4.3/brian/tests/testinterface/test_clock.py000066400000000000000000000106501266062647100224270ustar00rootroot00000000000000from brian import * from nose.tools import * def test(): """ The :class:`Clock` object ~~~~~~~~~~~~~~~~~~~~~~~~~ A :class:`Clock` object is initialised via:: c = Clock(dt=0.1*msecond, t=0*msecond, makedefaultclock=False) In particular, the following will work and do the same thing:: c = Clock() c = Clock(t=0*second) c = Clock(dt=0.1*msecond) c = Clock(0.1*msecond,0*second) Setting the ``makedefaultclock=True`` argument sets the newly created clock as the default one. The default clock ~~~~~~~~~~~~~~~~~ The default clock can be found using the :func:`get_default_clock` function, and redefined using the :func:`define_default_clock` function, where the arguments passed to :func:`define_default_clock` are the same as the initialising arguments to the ``Clock(...)`` statement. The default clock can be reinitialised by calling :func:`reinit_default_clock`. A less safe way to access the default clock is to refer directly to the variable :data:`defaultclock`. If the default clock has been redefined, this won't work. The :func:`guess_clock` function ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ The function :func:`guess_clock` should return (in this order of priority): * The clock passed as an argument, if one was passed * A clock defined in the calling function, if there was one * The default clock otherwise If more than one clock was defined in the calling function, it should raise a ``TypeError`` exception. """ reinit_default_clock() # check that 'defaultclock' default clock exists and starts at t=0 assert defaultclock.t < 0.001 * msecond # check that default clock exists and starts at t = 0 c = guess_clock() assert c.t < 0.001 * msecond # check that passing no arguments works c = Clock() # check that passing t argument works c = Clock(t=1 * second) assert c.t > 0.9 * second # check that passing dt argument works c = Clock(dt=10 * msecond) assert c.dt > 9 * msecond # check that passing t and dt arguments works c = Clock(t=2 * second, dt=1 * msecond) assert c.t > 1.9 * second assert 0.5 * msecond < c.dt < 2 * msecond # check that making this the default clock works assert get_global_preference('defaultclock').dt < 9 * msecond c = Clock(dt=10 * msecond, makedefaultclock=True) assert get_global_preference('defaultclock').dt > 9 * msecond # check that the other ways of defining a default clock work define_default_clock(dt=3 * msecond) assert 2.9 * msecond < get_global_preference('defaultclock').dt < 3.1 * msecond # check that the get_default_clock function works assert 2.9 * msecond < get_default_clock().dt < 3.1 * msecond # check that passing unnamed arguments in the order dt, t works c = Clock(10 * msecond, 1 * second) assert c.t > 0.9 * second assert c.dt > 9 * msecond # check that reinit() sets t=0 c = Clock(t=1 * second) c.reinit() assert c.t < 0.001 * second # check that tick() works c = Clock(t=1 * second, dt=1 * msecond) for i in range(10): c.tick() assert c.t > 9 * msecond # check that reinit_default_clock works for i in range(10): get_default_clock().tick() reinit_default_clock() assert get_default_clock().t < 0.0001 * second # check that guess_clock passed a clock returns that clock assert 0.6 * second < guess_clock(Clock(t=0.7 * second)).t < 0.8 * second # check that guess_clock passed no clock returns the only clock we've defined in this function so far c = Clock() assert guess_clock() is c # check that if no clock is defined, guess_clock returns the default clock del c assert guess_clock() is get_default_clock() # check that if two or more clocks are defined, guess_clock raises a TypeError c = Clock() d = Clock() assert_raises(TypeError, guess_clock) del d # check that if we have a calling stack, the innermost clock is found c = Clock() def f(): d = Clock() assert guess_clock() is d del d return guess_clock() assert f() is c # cleanup: reset the default clock to its default state set_global_preferences(defaultclock=defaultclock) brian-1.4.3/brian/tests/testinterface/test_connection.py000066400000000000000000000223521266062647100234750ustar00rootroot00000000000000from brian import * from nose.tools import * from brian.utils.approximatecomparisons import is_approx_equal from brian.tests import repeat_with_global_opts from brian.connections.construction import (random_matrix, random_matrix_fixed_column) from scipy.sparse import issparse def test_utility_functions(): ''' Test the (non-public) random_matrix and random_matrix_fixed_column functions. ''' # Convenience functions to make the testing a bit easier def assert_size_and_value(matrix, shape, value): assert issparse(matrix) assert matrix.shape == shape rows, cols = matrix.nonzero() for row, col in zip(rows, cols): if callable(value): if value.func_code.co_argcount == 0: assert matrix[row, col] == value() elif value.func_code.co_argcount == 2: assert matrix[row, col] == value(row, col) else: raise AssertionError('Illegal value argument') else: assert matrix[row, col] == value def assert_entries_per_column(matrix, n_entries): for col_idx in range(matrix.shape[1]): entries = matrix[:, col_idx].nonzero()[0] assert len(entries) == n_entries, 'number of entries is %d not %d!' % (len(entries), n_entries) # with fixed probability p = 1. r_m = random_matrix(2, 4, p=p) assert_size_and_value(r_m, (2, 4), 1.) # with fixed probability and value p, value = 1., 2. r_m = random_matrix(2, 4, p=p, value=value) assert_size_and_value(r_m, (2, 4), 2.) # Note: p can also be a function (like value), but as this is not # documented, this is also not tested # with a constant function for value value = lambda : 2. r_m = random_matrix(2, 4, p=1., value=value) assert_size_and_value(r_m, (2, 4), value) # with an index dependent function for value value = lambda i, j : (i != j) * 2. r_m = random_matrix(2, 4, p=1., value=value) assert_size_and_value(r_m, (2, 4), value) # Test random matrix with fixed number of entries per column # with fixed probability p = 0.5 r_m = random_matrix_fixed_column(100, 4, p=p) assert_size_and_value(r_m, (100, 4), 1.) assert_entries_per_column(r_m, 50) # with fixed probability and value p, value = 0.5, 2. r_m = random_matrix_fixed_column(100, 4, p=p, value=value) assert_size_and_value(r_m, (100, 4), 2.) assert_entries_per_column(r_m, 50) # Note: p can also be a function (like value), but this is not documented # with a constant function for value value = lambda : 2. r_m = random_matrix_fixed_column(100, 4, p=0.5, value=value) assert_size_and_value(r_m, (100, 4), value) assert_entries_per_column(r_m, 50) # with an index dependent function for value value = lambda i, j : (i != j) * 2. + 1 r_m = random_matrix_fixed_column(100, 4, p=0.5, value=value) assert_size_and_value(r_m, (100, 4), value) assert_entries_per_column(r_m, 50) @repeat_with_global_opts([{'useweave': False}, {'useweave': True}]) def test_construction(): ''' :class:`Connection` ~~~~~~~~~~~~~~~~~~~ **Initialised as:** :: Connection(source, target[, state=0[, delay=0*ms]]) With arguments: ``source`` The group from which spikes will be propagated. ``target`` The group to which spikes will be propagated. ``state`` The state variable name or number that spikes will be propagated to in the target group. ``delay`` The delay between a spike being generated at the source and received at the target. At the moment, the mechanism for delays only works for relatively short delays (an error will be generated for delays that are too long), but this is subject to change. The exact behaviour then is not part of the assured interface, although it is very likely that the syntax will not change (or will at least be backwards compatible). **Methods** ``connect_random(P,Q,p[,weight=1])`` Connects each neuron in ``P`` to each neuron in ``Q``. ``connect_full(P,Q[,weight=1])`` Connect every neuron in ``P`` to every neuron in ``Q``. ``connect_one_to_one(P,Q)`` If ``P`` and ``Q`` have the same number of neurons then neuron ``i`` in ``P`` will be connected to neuron ``i`` in ``Q`` with weight 1. Additionally, you can directly access the matrix of weights by writing:: C = Connection(P,Q) print C[i,j] C[i,j] = ... Where here ``i`` is the source neuron and ``j`` is the target neuron. Note: No unit checking is currently done if you use this method, but this is subject to change for future releases. The behaviour when a list of neuron ``spikes`` is received is to add ``W[i,:]`` to the target state variable for each ``i`` in ``spikes``. ''' reinit_default_clock() # test Connection object eqs = ''' da/dt = 0.*hertz : 1. db/dt = 0.*hertz : 1. ''' spikes = [(0, 1 * msecond), (1, 3 * msecond)] G1 = SpikeGeneratorGroup(2, spikes) G2 = NeuronGroup(2, model=eqs, threshold=10., reset=0.) # first test the methods # connect_full C = Connection(G1, G2) C.connect_full(G1, G2, weight=2.) for i in range(2): for j in range(2): assert (is_approx_equal(C[i, j], 2.)) # connect_random C = Connection(G1, G2) C.connect_random(G1, G2, 0.5, weight=2.) # can't assert anything about that # connect_one_to_one C = Connection(G1, G2) C.connect_one_to_one(G1, G2) for i in range(2): for j in range(2): if i == j: assert (is_approx_equal(C[i, j], 1.)) else: assert (is_approx_equal(C[i, j], 0.)) del C # and we will use a specific set of connections in the next part Ca = Connection(G1, G2, 'a') Cb = Connection(G1, G2, 'b') Ca[0, 0] = 1. Ca[0, 1] = 1. Ca[1, 0] = 1. #Ca[1,1]=0 by default #Cb[0,0]=0 by default Cb[0, 1] = 1. Cb[1, 0] = 1. Cb[1, 1] = 1. net = Network(G1, G2, Ca, Cb) net.run(2 * msecond) # after 2 ms, neuron 0 will have fired, so a 0 and 1 should # have increased by 1 to [1,1], and b 1 should have increased # by 1 to 1 assert (is_approx_equal(G2.a[0], 1.)) assert (is_approx_equal(G2.a[1], 1.)) assert (is_approx_equal(G2.b[0], 0.)) assert (is_approx_equal(G2.b[1], 1.)) net.run(2 * msecond) # after 4 ms, neuron 1 will have fired, so a 0 should have # increased by 1 to 2, and b 0 and 1 should have increased # by 1 to [1, 2] assert (is_approx_equal(G2.a[0], 2.)) assert (is_approx_equal(G2.a[1], 1.)) assert (is_approx_equal(G2.b[0], 1.)) assert (is_approx_equal(G2.b[1], 2.)) reinit_default_clock() @repeat_with_global_opts([{'useweave': False}, {'useweave': True}]) def test_access(): 'Test accessing the contents of a ConnectionMatrix after construction' G = NeuronGroup(3, model=LazyStateUpdater()) # sparse matrix C = Connection(G, G, structure='sparse') C[0, 0] = 1. C[0, 2] = 1. assert(C[0, 0] == 1. and C[0, 2] == 1.) assert(C.W[0, 0] == 1. and C.W[0, 2] == 1.) assert(all(C.W.todense() == array([[1., 0., 1.], [0., 0., 0.], [0., 0., 0.]]))) C.compress() assert(C.W[0, 0] == 1. and C.W[0, 2] == 1.) # Note that this is a sparse matrix assert(all(C.W[0, :] == [1., 1.])) assert(size(C.W[1, :]) == 0) assert(all(C.W[:, 0] == [1.])) assert(size(C.W[:, 1]) == 0) assert(all(C.W.todense() == array([[1., 0., 1.], [0., 0., 0.], [0., 0., 0.]]))) # dense matrix C = Connection(G, G, structure='dense') C[0, 0] = 1. C[0, 2] = 1. assert(C[0, 0] == 1. and C[0, 2] == 1.) assert(C.W[0, 0] == 1. and C.W[0, 2] == 1.) C.compress() assert(C.W[0, 0] == 1. and C.W[0, 2] == 1.) assert(all(C.W[0, :] == [1., 0., 1.])) assert(all(C.W[:, 0] == [1., 0., 0.])) assert(all(C.W == C.W.todense())) # dynamic matrix C = Connection(G, G, structure='dynamic') C[0, 0] = 1. C[0, 2] = 1. assert(C[0, 0] == 1. and C[0, 2] == 1.) assert(C.W[0, 0] == 1. and C.W[0, 2] == 1.) C.compress() assert(C.W[0, 0] == 1. and C.W[0, 2] == 1.) # Note that this is a sparse matrix assert(all(C.W[0, :] == [1., 1.])) assert(size(C.W[1, :]) == 0) assert(all(C.W[:, 0] == [1.])) assert(size(C.W[:, 1]) == 0) assert(all(C.W.todense() == array([[1., 0., 1.], [0., 0., 0.], [0., 0., 0.]]))) if __name__ == '__main__': test_construction() test_access() test_utility_functions() brian-1.4.3/brian/tests/testinterface/test_directcontrol.py000066400000000000000000000247721266062647100242210ustar00rootroot00000000000000from brian import * from brian.utils.approximatecomparisons import * from nose.tools import * def test(): """ Spike containers ~~~~~~~~~~~~~~~~ A spike container is either an iterable object or a callable object which returns an iterable object. For example, a list is a spike container, as is a generator, as is a function which returns a list, or a generator function. :class:`SpikeGeneratorGroup` ~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Called as:: SpikeGeneratorGroup(N,spiketimes[,clock]) where N is the number of neurons in the group, and spiketimes is a spike container, whose elements are tuples (i,t) meaning neuron i fires at time t. Pairs (i,t) need to be sorted in time unless spiketimes is a tuple or list. For example:: from math import random def spikefirer(N,lower,upper): nexttime = random.uniform(lower,upper) while True: yield (random.randint(0,N-1),nexttime) nexttime = nexttime + random.uniform(lower,upper) G = SpikeGeneratorGroup(10,uniform_isi(10,0*msecond,10*msecond)) would give a neuron group P with 10 neurons, where a random one of the neurons fires with an interval between spikes which is uniform in (0ms, 10ms). If spiketimes is callable, it will be called when the group is reinitialised. If you provide a generator rather than a callable object, reinitialising the group will not reinitialise the generator. Note that if a neuron fires more than one spike in a given interval dt, additional spikes will be discarded. If a clock is provided, updates of the group will be synchronised with that clock, otherwise the standard clock guessing procedure will be used (see :func:`~brian.clock.guess_clock` in the :mod:`~brian.clock` module). :class:`PulsePacket` ~~~~~~~~~~~~~~~~~~~~ Fires a Gaussian distributed packet of n spikes with given spread, called as:: PulsePacket(t,n,sigma[,clock]) You can change the parameters by calling the method ``generate(t,n,sigma)``. If a clock is provided, updates of the group will be synchronised with that clock, otherwise the standard clock guessing procedure will be used (see :func:`~brian.clock.guess_clock` in the :mod:`~brian.clock` module). """ reinit_default_clock() # helper function for running a simple network def mininet(grouptype, *args, **kwds): reinit_default_clock() G = grouptype(*args, **kwds) M = SpikeMonitor(G, True) net = Network(G, M) net.run(5 * msecond) reinit_default_clock() return M.spikes # run mininet twice to see if it handles reinit() correctly def mininet2(grouptype, *args, **kwds): reinit_default_clock() G = grouptype(*args, **kwds) M = SpikeMonitor(G, True) net = Network(G, M) net.run(5 * msecond) spikes1 = M.spikes net.reinit() net.run(5 * msecond) spikes2 = M.spikes reinit_default_clock() return (spikes1, spikes2) def test1(spikes): assert len(spikes) == 5 i, t = zip(*spikes) # zip(*...) is the inverse of zip, so i is the ordered list of neurons that fired, and t is the ordered list of times assert i == (0, 1, 0, 1, 0) # check that the order of neuron firings is correct for s1, s2 in enumerate(t): assert is_approx_equal(s1 * msecond, s2) # the firing times are (0,1,2,3,4)ms # spike generator with list spiketimes = [(0, 0 * msecond), (1, 1 * msecond), (0, 2 * msecond), (1, 3 * msecond), (0, 4 * msecond) ] spikes = mininet(SpikeGeneratorGroup, 2, spiketimes) test1(spikes) # spike generator with list (already sorted so pass sort=False) spiketimes = [(0, 0 * msecond), (1, 1 * msecond), (0, 2 * msecond), (1, 3 * msecond), (0, 4 * msecond) ] spikes = mininet(SpikeGeneratorGroup, 2, spiketimes, sort=False) test1(spikes) # spike generator with unsorted (inversely sorted) list (sort=True is default) spiketimes = [(0, 4 * msecond), (1, 3 * msecond), (0, 2 * msecond), (1, 1 * msecond), (0, 0 * msecond) ] spikes = mininet(SpikeGeneratorGroup, 2, spiketimes) test1(spikes) # check that it works with a clock def testwithclock(): spikes = mininet(SpikeGeneratorGroup, 2, spiketimes, clock=Clock(dt=0.1 * msecond)) test1(spikes) spikes = mininet(SpikeGeneratorGroup, 2, spiketimes, clock=Clock(dt=2 * msecond)) assert len(spikes) == 5 i, t = zip(*spikes) # zip(*...) is the inverse of zip, so i is the ordered list of neurons that fired, and t is the ordered list of times for s1, s2 in zip([0, 2, 2, 4, 4], t): assert is_approx_equal(s1 * msecond, s2) # the firing times are (0,2,2,4,4)ms testwithclock() # spike generator with a function returning a list def return_spikes(): return [(0, 0 * msecond), (1, 1 * msecond), (0, 2 * msecond), (1, 3 * msecond), (0, 4 * msecond) ] spikes = mininet(SpikeGeneratorGroup, 2, return_spikes) test1(spikes) # spike generator with a list of spikes with simultaneous spikes across neurons spiketimes = [(0, 0 * msecond), (1, 0 * msecond), (0, 2 * msecond), (1, 2 * msecond), (0, 4 * msecond), (1, 4 * msecond) ] def test2(spikes): assert len(spikes) == 6 #check both neurons spiked at the correct times for neuron in [0, 1]: for s1, s2 in zip([0, 2, 4], [t for i, t in spikes if i==neuron]): assert is_approx_equal(s1 * msecond, s2) spikes = mininet(SpikeGeneratorGroup, 2, spiketimes) test2(spikes) # same but using the gather=True option spikes = mininet(SpikeGeneratorGroup, 2, spiketimes, gather=True) test2(spikes) # same but with index arrays instead of single neuron indices spiketimes = [([0, 1], 0 * msecond), ([0, 1], 2 * msecond), ([0, 1], 4 * msecond)] spikes = mininet(SpikeGeneratorGroup, 2, spiketimes) test2(spikes) # spike generator with single indices and index arrays of varying length spiketimes = [([0, 1], 0 * msecond), (0, 1 * msecond), (1, 2 * msecond), ([0], 3 * msecond) ] spikes = mininet(SpikeGeneratorGroup, 2, spiketimes) def test3(spikes): assert len(spikes) == 5 #check both neurons spiked at the correct times for s1, s2 in zip([0, 1, 3], [t for i, t in spikes if i==0]): assert is_approx_equal(s1 * msecond, s2) for s1, s2 in zip([0, 2], [t for i, t in spikes if i==1]): assert is_approx_equal(s1 * msecond, s2) test3(spikes) # spike generator with an array of (non-simultaneous) spikes # NOTE: For an array, the times have to be in seconds and sorted spiketimes = array([[0, 0.0], [1, 0.001], [0, 0.002], [1, 0.003], [0, 0.004]]) spikes = mininet(SpikeGeneratorGroup, 2, spiketimes) test1(spikes) # spike generator with an array of (simultaneous) spikes spiketimes = array([[0, 0.0], [1, 0.0], [0, 0.002], [1, 0.002], [0, 0.004], [1, 0.004]]) spikes = mininet(SpikeGeneratorGroup, 2, spiketimes) test2(spikes) # spike generator with an array of (simultaneous) spikes, using gather=True spiketimes = array([[0, 0.0], [1, 0.0], [0, 0.002], [1, 0.002], [0, 0.004], [1, 0.004]]) spikes = mininet(SpikeGeneratorGroup, 2, spiketimes, gather=True) test2(spikes) # test the handling of an empty initialization and direct setting of spiketimes def test_attribute_setting(): reinit_default_clock() G = SpikeGeneratorGroup(2, []) M = SpikeMonitor(G, True) net = Network(G, M) net.run(5 * msecond) assert len(M.spikes) == 0 reinit_default_clock() net.reinit() G.spiketimes = [(0, 0 * msecond), (1, 1 * msecond), (0, 2 * msecond), (1, 3 * msecond), (0, 4 * msecond)] net.run(5 * msecond) test1(M.spikes) test_attribute_setting() # tests a subtle difficulty when setting spiketimes and using a subgroup def test_attribute_setting_subgroup(): reinit_default_clock() G = SpikeGeneratorGroup(2, []) subG = G.subgroup(2) M = SpikeMonitor(subG, True) G.spiketimes = [(0, 0 * msecond), (1, 1 * msecond), (0, 2 * msecond), (1, 3 * msecond), (0, 4 * msecond)] G.spiketimes = [(0, 0 * msecond), (1, 1 * msecond), (0, 2 * msecond), (1, 3 * msecond), (0, 4 * msecond)] net = Network(G, M) net.run(5 * msecond) test1(M.spikes) test_attribute_setting_subgroup() # spike generator with generator def sg(): yield (0, 0 * msecond) yield (1, 1 * msecond) yield (0, 2 * msecond) yield (1, 3 * msecond) yield (0, 4 * msecond) spikes = mininet(SpikeGeneratorGroup, 2, sg()) test1(spikes) # spike generator reinit spikes1, spikes2 = mininet2(SpikeGeneratorGroup, 2, sg) test1(spikes1) test1(spikes2) # spike generator group with generator and period def gen(): yield (0, 0 * msecond) yield (1, 1 * msecond) spikes = mininet(SpikeGeneratorGroup, 2, gen, period=2*ms) test1(spikes) # spike generator group with list and period spiketimes = [(0, 0 * msecond), (1, 1 * msecond)] spikes = mininet(SpikeGeneratorGroup, 2, spiketimes, period=2*ms) test1(spikes) # pulse packet with 0 spread spikes = mininet(PulsePacket, 2.5 * msecond, 10, 0 * msecond) assert len(spikes) == 10 i, t = zip(*spikes) for s in t: assert is_approx_equal(2.5 * msecond, s) # do not attempt to verify the behaviour of PulsePacket here, this is # an interface test only def test_poissoninput(): eqs = Equations("dv/dt=(1-v)/(1*second) : 1") group = NeuronGroup(N=1, model=eqs, reset=0, threshold=1) input = PoissonInput(group, N = 10, rate=50 * Hz, weight = .11, state='v') m = SpikeCounter(group) net = Network(group, input, m) net.run(500 * ms) #only checks that there some spikes assert (m.nspikes >= 1) if __name__ == '__main__': test() test_poissoninput() brian-1.4.3/brian/tests/testinterface/test_equations.py000066400000000000000000000072701266062647100233500ustar00rootroot00000000000000from brian import * from nose.tools import * def test(): ''' Equations module. ''' reinit_default_clock() tau = 2 * ms # Test Parsing and building def test_build_and_parse(eqs): assert set(eqs._eq_names) == set(['x', 'y']) # An alias is also a static equation assert set(eqs._diffeq_names) == set(['v', 'z']) # A parameter is a differential equation dz/dt=0 assert eqs._diffeq_names_nonzero == ['v'] assert eqs._alias == {'y':'x'} assert eqs._units == {'t':second, 'x':volt ** 2, 'z':amp, 'y':volt ** 2, 'v':volt} assert eqs._string == {'y':'x', 'x':'v**2', 'z':'0*amp/second', 'v':'(y-v)/tau'} assert eqs._namespace['v']['tau'] == 2 * ms assert 'tau' not in eqs._namespace['x'] # Multiline string eqs = Equations(''' x=v**2 : volt**2 y=x # a comment dv/dt=(y-v)/tau : volt z : amp''') test_build_and_parse(eqs) # Concatenated Equation objects eqs = (Equations('''x=v**2 : volt**2 y=x # a comment''') + Equations('dv/dt=(y-v)/tau : volt') + Equations('z : amp')) test_build_and_parse(eqs) # List of Equation objects eqs = Equations([Equations('''x=v**2 : volt**2 y=x # a comment'''), Equations('dv/dt=(y-v)/tau : volt'), Equations('z : amp')]) test_build_and_parse(eqs) # List of strings eqs = Equations(['''x=v**2 : volt**2 y=x # a comment''', 'dv/dt=(y-v)/tau : volt', 'z : amp']) test_build_and_parse(eqs) # Name substitutions assert Equations('dx/dt=-x/(2*ms) : 1', x='y')._diffeq_names == ['y'] assert Equations('''dx/dt=-x/(2*ms) : 1 z = x ** 2 : 1''', z='y')._eq_names == ['y'] # Explicit namespace eqs2 = Equations('dx/dt=-x/tau:volt', tau=1 * ms) assert eqs2._namespace['x']['tau'] == 1 * ms assert eqs2._namespace['x'] == {'tau':1 * ms, 'volt':volt} # Find membrane potential assert eqs.get_Vm() == 'v' assert Equations('v=x**2 : 1').get_Vm() == None # must be a differential equation assert Equations('dx/dt=1/(2*ms) : 1').get_Vm() == None assert Equations('dvm/dt=1/(2*ms) : 1').get_Vm() == 'vm' assert Equations('''dvm/dt=1/(2*ms) : 1 dv/dt=1/(2*ms) : 1 ''').get_Vm() == None # ambiguous # Unit checking eqs = Equations('dv/dt=-v : volt') assert_raises(DimensionMismatchError, eqs.prepare) # Free variables eqs = Equations('dv/dt=(freevar-v)/tau : volt') assert eqs.free_variables() == ['tau', 'freevar'] # Equation types: stochastic, time dependent, linear, conditionally linear eqs = Equations(''' dv/dt=(f(t)-v)/tau : volt dw/dt=-w/tau+3*tau**-.5*xi : 1''') assert eqs.is_stochastic() assert eqs.is_stochastic(var='v') is False assert eqs.is_stochastic(var='w') assert eqs.is_time_dependent() assert eqs.is_time_dependent(var='v') assert eqs.is_time_dependent(var='w') is False assert eqs.is_linear() is False # not linear if time-dependent eqs = Equations('dv/dt=-v/tau : volt') assert eqs.is_linear() eqs.prepare() assert eqs.is_conditionally_linear() eqs = Equations(''' dv/dt=(w**3-v)/tau : 1 dw/dt=(v**2-w)/tau : 1 ''') eqs.prepare() assert eqs.is_conditionally_linear() eqs = Equations(''' dv/dt=(w**3-v**2)/tau : 1 dw/dt=(v**2-w)/tau : 1 ''') eqs.prepare() assert eqs.is_conditionally_linear() is False if __name__ == '__main__': test() brian-1.4.3/brian/tests/testinterface/test_hears.py000066400000000000000000000233671266062647100224470ustar00rootroot00000000000000from numpy.testing.utils import assert_raises, assert_equal from brian import * from brian.tests import repeat_with_global_opts from brian.hears import * def assert_sound_properties(snd, duration, samplerate, nchannels): ''' Simple utility function to check whether all properties of a sound are as expected. ''' assert(isinstance(snd, Sound)) assert(snd.duration == duration), '%s != %s' % (snd.duration, duration) assert(snd.samplerate == get_samplerate(samplerate)), '%s != %s' % (snd.samplerate, samplerate) assert(snd.nchannels == nchannels), '%s != %s' % (snd.nchannels, nchannels) def test_sound_construction(): ''' Tests various ways of instantiating sounds. ''' # Test everything for default samplerate and a given one for samplerate in [None, 16 * kHz]: samplerate = get_samplerate(samplerate) # sound from array snd = Sound(array([-1, 0, 1]), samplerate=samplerate) assert_sound_properties(snd, 3 / samplerate, samplerate, 1) # stereo sound from array snd = Sound(array([[-1, 0, 1], [0, 1, 0]]).T, samplerate=samplerate) assert_sound_properties(snd, 3 / samplerate, samplerate, 2) # stereo sound from sequence snd = Sound([array([-1, 0, 1]), array([0, 1, 0])], samplerate=samplerate) assert_sound_properties(snd, 3 / samplerate, samplerate, 2) # sound from a function sound_func = lambda t: sin(2 * pi * 400 * Hz * t) snd = Sound(sound_func, samplerate=samplerate, duration=1*second) assert_sound_properties(snd, 1 * second, samplerate, 1) # Test everything for mono and stereo # # Note (do not use itertools.product here as this does not exist in # python 2.5) for samplerate, nchannels in [(None, 1), (None, 2), (16*kHz, 1), (16*kHz, 2)]: samplerate = get_samplerate(samplerate) # tone snd = tone(400 * Hz, duration=1*second, samplerate=samplerate, nchannels=nchannels) assert_sound_properties(snd, 1 * second, samplerate, nchannels) # complex tone snd = harmoniccomplex(400 * Hz, duration=1*second, samplerate=samplerate, nchannels=nchannels) assert_sound_properties(snd, 1 * second, samplerate, nchannels) # whitenoise snd = whitenoise(duration=1*second, samplerate=samplerate, nchannels=nchannels) assert_sound_properties(snd, 1 * second, samplerate, nchannels) # pinknoise snd = pinknoise(duration=1*second, samplerate=samplerate, nchannels=nchannels) assert_sound_properties(snd, 1 * second, samplerate, nchannels) # brownnoise snd = brownnoise(duration=1*second, samplerate=samplerate, nchannels=nchannels) assert_sound_properties(snd, 1 * second, samplerate, nchannels) # powerlawnoise snd = powerlawnoise(1*second, 1, samplerate=samplerate, nchannels=nchannels) assert_sound_properties(snd, 1 * second, samplerate, nchannels) # currently fails, the duration is not correct # # irno # snd = irno(1 / (400 * Hz), 1, 3, duration=1*second, # samplerate=samplerate, nchannels=nchannels) # assert_sound_properties(snd, 1 * second, samplerate, nchannels) # click snd = click(3 / samplerate, samplerate=samplerate, nchannels=nchannels) assert_sound_properties(snd, 3 / samplerate, samplerate, nchannels) # silence snd = silence(1 * second, samplerate=samplerate, nchannels=nchannels) assert_sound_properties(snd, 1 * second, samplerate, nchannels) # artificial vowel snd = vowel('a', duration=1 * second, samplerate=samplerate, nchannels=nchannels) def test_sound_access(): ''' Test accessing attributes and parts of a sound in various ways. ''' for nchannels in [1, 2]: if nchannels == 1: snd = tone(400*Hz, duration=1*second) else: snd = Sound([tone(400*Hz, duration=1*second), tone(600*Hz, duration=1*second)]) assert(snd.nsamples == int(snd.duration * snd.samplerate)) assert(len(snd.times) == snd.nsamples) if nchannels == 1: assert(isinstance(snd.level, dB_type)) if nchannels == 2: assert(np.all(snd.left == snd.channel(0))) assert(np.all(snd[:, 0] == snd.channel(0))) assert(np.all(snd.right == snd.channel(1))) assert(np.all(snd[:, 1] == snd.channel(1))) snd.level = 60.0 * dB # Changing level should not change other properties assert_sound_properties(snd.atlevel(70*dB), snd.duration, snd.samplerate, snd.nchannels) assert_sound_properties(snd.atmaxlevel(80*dB), snd.duration, snd.samplerate, snd.nchannels) assert_sound_properties(snd.ramped(), snd.duration, snd.samplerate, snd.nchannels) assert(len(snd[:100]) == 100) assert(len(snd[:snd.duration]) == len(snd)) assert_raises(DimensionMismatchError, lambda :snd[:3*Hz]) snd[:100, :] = 0 assert(np.all(snd[:100, :] == 0)) # repeat snd_repeated = snd.repeat(3) assert_sound_properties(snd_repeated, snd.duration * 3, snd.samplerate, snd.nchannels) snd_sequence = sequence([snd, snd]) assert_sound_properties(snd_sequence, snd.duration * 2, snd.samplerate, snd.nchannels) def test_shifting(): # one channel snd = tone(1000*Hz, duration=100*ms, samplerate=10*kHz) shifted = snd.shifted(10*ms) assert shifted.duration == 110*ms assert_equal(shifted[:10*ms], silence(duration=10*ms, samplerate=10*kHz)) assert_equal(shifted[10*ms:], snd) #fractional shift snd = tone(1000*Hz, duration=100*ms, samplerate=10*kHz) shifted = snd.shifted(10.0*ms, fractional=True) # actually that leads to a sound with a total length of 109.9ms, # I think for multiples of the sample rate, using fractional or not should # not make a difference for the length... assert_equal(shifted[:10*ms], silence(duration=10*ms, samplerate=10*kHz)) # several channels snd = Sound([tone(1000*Hz, duration=100*ms, samplerate=10*kHz), tone(2000*Hz, duration=100*ms, samplerate=10*kHz)], samplerate=10*kHz) shifted = snd.shifted(10*ms) assert shifted.nchannels == snd.nchannels assert shifted.duration == 110*ms for channel in xrange(shifted.nchannels): assert_equal(shifted[:10*ms, channel], silence(duration=10*ms, samplerate=10*kHz)) assert_equal(shifted[10*ms:, channel], snd[:, channel]) #fractional shift shifted = snd.shifted(10.0*ms, fractional=True) assert shifted.nchannels == snd.nchannels for channel in xrange(shifted.nchannels): assert_equal(shifted[:10*ms, channel], silence(duration=10*ms, samplerate=10*kHz)) @repeat_with_global_opts([{'useweave': False}, {'useweave': True}]) def test_linear_filtering(): ''' Test that linear filtering does not change the source ''' original_sound = Sound(linspace(-1, 1, 1000)) sound = original_sound.copy() filtered = Gammatone(sound, 1000*Hz).process() assert_equal(np.asarray(original_sound), np.asarray(sound)) @repeat_with_global_opts([{'useweave': False}, {'useweave': True}]) def test_multichannel_processing(): '''Make sure that processing multiple channels gives the same results as processing single channels. ''' set_default_samplerate(50*kHz) freqs = np.array([100, 1000, 5000]) sounds = Sound([harmoniccomplex(float(freq)*Hz, duration=5*ms) for freq in freqs]) # test some filters that only need a single 'CF' argument in addition # to the source filters = [Gammatone, DRNL, TanCarney] for one_filter in filters: print one_filter.__name__ # process all channels in parallel all_channels = one_filter(sounds, freqs).process() all_channels_array = np.asarray(all_channels) # process each channel separately for idx, freq in enumerate(freqs): one_channel = one_filter(sounds.channel(idx), freq).process() assert_equal(np.asarray(one_channel).flatten(), all_channels_array[:, idx]) @repeat_with_global_opts([{'useweave': False}, {'useweave': True}]) def test_middleear(): '''Make sure that using multiple gain values in the middle ear model do the expected thing. ''' gains = np.arange(1., 5.) snd = harmoniccomplex(1000*Hz, duration=5*ms) # use multiple gains for the same sound all_gains = MiddleEar(snd, gain=gains).process() # same but with RestructureFilterbank all_gains2 = MiddleEar(RestructureFilterbank(snd, len(gains)), gain=gains).process() assert_equal(np.asarray(all_gains), np.asarray(all_gains2)) # use single gain values for idx, gain in enumerate(gains): one_gain = MiddleEar(snd, gain=gain).process() assert_equal(np.asarray(all_gains)[:, idx].flatten(), np.asarray(one_gain).flatten()) if __name__ == '__main__': test_sound_construction() test_sound_access() test_shifting() test_linear_filtering() test_multichannel_processing() test_middleear() brian-1.4.3/brian/tests/testinterface/test_inspection.py000066400000000000000000000007551266062647100235140ustar00rootroot00000000000000from brian import * from brian.inspection import * def test(): ''' Inspection module ''' reinit_default_clock() expr = ''' x_12+=y*12 # comment pour(water,\ "on desk")''' # Check that identifiers are correctly extracted assert (get_identifiers(expr) == ('x_12', 'y', 'pour', 'water')) # Check that modified variables are correctly extracted assert (modified_variables(expr) == ['x_12', 'pour']) if __name__ == '__main__': test() brian-1.4.3/brian/tests/testinterface/test_magic.py000066400000000000000000000156301266062647100224170ustar00rootroot00000000000000from brian import * from nose.tools import * def test(): """ See the main documentation or the API documentation for details of the purpose, main functions and classes of the magic module. Functions ~~~~~~~~~ * :func:`get_instances(instancetype,level=1)` * :func:`find_instances(instancetype,startlevel=1)` * :func:`find_all_instances(instancetype,startlevel=1)` Here instancetype is a class derived from :class:`InstanceTracker`, including :class:`Clock`, :class:`NeuronGroup`, :class:`Connection`, :class:`NetworkOperation`. ``level`` is an integer greater than 0 that tells the function how far back it should search in the sequence of called functions. ``level=0`` means it should find instances from the function calling one of the magic functions, ``level=1`` means it should find instances from the function calling the function calling one of the magic functions, etc. :func:`get_instances` returns all instances at a specified level. :func:`find_instances` searches increasing levels starting from the given ``startlevel`` until it finds a nonzero number of instances, and then returns those. :func:`find_all_instances` finds all instances from a given level onwards. Classes ~~~~~~~ An object of a class ``cls`` derived from ``InstanceTracker`` will be tracked if ``cls._track_instances()`` returns ``True``. The default behaviour of ``InstanceTracker`` is to always return ``True``, but this method can be redefined if you do not want to track instances of a particular subclass of a tracked class. Note that this method is a static method of a class, and cannot be used to stop particular instances being tracked. Redefine it using something like:: class A(InstanceTracker): pass class B(A): @staticmethod def _track_instances(): return False A warning (technical detail) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~ The current implementation of instance tracking will return every variable that was created in a given execution frame (i.e. in a given function) that is still alive (i.e. a reference still exists to it somewhere). So for example, the following will not find any instance:: class A(InstanceTracker): pass def f(A): return A() a = f(A) print get_instances(A,level=0) The reason is that the object a was created in the function ``f``, but the :func:`get_instances` statement is run in the main body. Similarly, the following will return two instances rather than one as you might expect:: class A(InstanceTracker): pass def f(A): a = A() return get_instances(A,level=0) insts1 = f(A) isnts2 = f(A) print insts2 The reason is that the first call to ``f`` defines an instance of ``A`` and returns (via :func:`get_instances`) an object which contains a reference to it. The object is still therefore alive. The second call to ``f`` creates a new ``A`` object, but the :func:`get_instances` call returns both ``A`` objects, because they are both still alive (the first is stored in ``insts1``) and both created in the function ``f``. This behaviour is not part of the assured interface of the magic module, and you shouldn't rely on it. """ reinit_default_clock() # Define a heirarchy of classes A, B, C, D, E and track instances of them # Do not track instances of D or E class A(InstanceTracker): gval = 0 def __init__(self): self.value = A.gval A.gval += 1 def __repr__(self): return str(self) def __str__(self): return str(self.value) class B(A): pass class C(B): pass class D(C): @staticmethod def _track_instances(): return False class E(D): pass # Create some sample objects of each type a1 = A() # object 0 a2 = A() # object 1 b = B() # object 2 c = C() # object 3 d = D() # object 4 e = E() # object 5 # Find the instances of each type on this level instA, names = get_instances(A, level=0) instB, names = get_instances(B, level=0) instC, names = get_instances(C, level=0) instD, names = get_instances(D, level=0) instE, names = get_instances(E, level=0) # This is the expected behaviour: # instA = [a1, a2, b, c] assert all(o in instA for o in [a1, a2, b, c]) assert all(o not in instA for o in [d, e]) # instB = [b, c] assert all(o in instB for o in [b, c]) assert all(o not in instB for o in [a1, a2, d, e]) # instC = [c] assert c in instC assert all(o not in instC for o in [a1, a2, b, d, e]) # instD = instE = [] assert len(instD) == 0 assert len(instE) == 0 # Check that level=0 and level=1 work as expected def f1(vars, A, B, C, D, E): a3 = A() # object 6 inst_ahere, names = get_instances(A, level=0) # level=0 should refer to definitions inside f inst_abefore, names = get_instances(A, level=1) # level=1 should refer to definitions inside the function calling f # inst_abefore = [a1, a2, b, c] assert all(o in instA for o in vars[0:4]) assert all(o not in instA for o in vars[4:]) # inst_ahere = [a3] assert len(inst_ahere) == 1 and a3 in inst_ahere f1([a1, a2, b, c, d, e], A, B, C, D, E) # Check that nested function calling works as expected def f2(A): a4 = A() # object 7 return [get_instances(A, level) for level in range(2)] inst = f2(A) # inst[0][0] = [a4] assert str(inst[0][0]) == '[7]' # inst[1][0] = [a1,a2,c,b] assert len(inst[1][0]) == 4 and all(o in inst[1][0] for o in [a1, a2, b, c]) def f3(A): a5 = A() # object 9 return [get_instances(A, level) for level in range(2)] def f4(A): a6 = A() # object 8 return f3(A) inst = f4(A) # inst[0][0] = [a5] assert str(inst[0][0]) == '[9]' # inst[1][0] = [a6] assert str(inst[1][0]) == '[8]' # check that find_instances works as expected def f5(A): return f6(A) def f6(A): a = A() # object 10 return f7(A) def f7(A): return find_instances(A, startlevel=0) inst = f5(A)[0][0] assert str(inst) == '10' # check that find_all_instances works as expected def f8(A): a = A() # object 11 return find_all_instances(A, startlevel=0) insts = f8(A)[0] # should be objects 0,1,2,3 and 11 s = map(str, insts) s.sort() assert str(s) == "['0', '1', '11', '2', '3']" if __name__ == '__main__': test() brian-1.4.3/brian/tests/testinterface/test_monitor.py000066400000000000000000000414631266062647100230310ustar00rootroot00000000000000from brian import * from nose.tools import * from brian.utils.approximatecomparisons import is_approx_equal, is_within_absolute_tolerance try: from brian.experimental.cuda.gpu_modelfitting import * import pycuda.autoinit as autoinit use_gpu = True except ImportError: use_gpu = False def test_spikemonitor(): ''' :class:`SpikeMonitor` ~~~~~~~~~~~~~~~~~~~~~ Records spikes from a :class:`NeuronGroup`. Initialised as one of:: SpikeMonitor(source(,record=True)) SpikeMonitor(source,function=function) Where: source A :class:`NeuronGroup` to record from record True or False to record all the spikes or just summary statistics. function A function f(spikes) which is passed the array of spikes numbers that have fired called each step, to define custom spike monitoring. Has two attributes: nspikes The number of recorded spikes spikes A time ordered list of pairs (i,t) where neuron i fired at time t. :class:`StateMonitor` ~~~~~~~~~~~~~~~~~~~~~ Records the values of a state variable from a :class:`NeuronGroup`. Initialise as:: StateMonitor(P,varname(,record=False) (,when='end)(,timestep=1)(,clock=clock)) Where: P The group to be recorded from varname The state variable name or number to be recorded record What to record. The default value is False and the monitor will only record summary statistics for the variable. You can choose record=integer to record every value of the neuron with that number, record=list of integers to record every value of each of those neurons, or record=True to record every value of every neuron (although beware that this may use a lot of memory). when When the recording should be made in the :class:`Network` update, possible values are any of the strings: 'start', 'before_groups', 'after_groups', 'before_connections', 'after_connections', 'before_resets', 'after_resets', 'end' (in order of when they are run). timestep A recording will be made each timestep clock updates (so timestep should be an integer). clock A clock for the update schedule, use this if you have specified a clock other than the default one in your network, or to update at a lower frequency than the update cycle. Note though that if the clock here is different from the main clock, the when parameter will not be taken into account, as network updates are done clock by clock. Use the timestep parameter if you need recordings to be made at a precise point in the network update step. The :class:`StateMonitor` object has the following properties: times The times at which recordings were made mean The mean value of the state variable for every neuron in the group (not just the ones specified in the record keyword) var The unbiased estimate of the variances, as in mean std The square root of var, as in mean In addition, if M is a :class:`StateMonitor` object, you write:: M[i] for the recorded values of neuron i (if it was specified with the record keyword). It returns an array object. Others ~~~~~~ The following monitors also exist, but are not part of the assured interface because their syntax is subject to change. See the documentation for each class for more details. * :class:`Monitor` (base class) * :class:`ISIHistogramMonitor` * :class:`FileSpikeMonitor` * :class:`PopulationRateMonitor` ''' reinit_default_clock() # test that SpikeMonitor retrieves the spikes generator by SpikeGeneratorGroup spikes = [(0, 3 * ms), (1, 4 * ms), (0, 7 * ms)] G = SpikeGeneratorGroup(2, spikes, clock=defaultclock) M = SpikeMonitor(G) net = Network(G, M) net.run(10 * ms) assert (M.nspikes == 3) for (mi, mt), (i, t) in zip(M.spikes, spikes): assert (mi == i) assert (is_approx_equal(mt, t)) # test that the spiketimes are saved and accessed correctly assert(len(M[0]) == len(M.spiketimes[0]) == 2 and len(M[1]) == len(M.spiketimes[1]) == 1) assert((M.spiketimes[0] == M[0]).all() and (M.spiketimes[1] == M[1]).all()) # test that spiketimes are cleared on reinit M.reinit() assert (M.nspikes == 0) assert (len(M.spikes) == 0) assert (len(M[0]) == 0 and len(M[1]) == 0) assert (len(M.spiketimes[0]) == 0 and len(M.spiketimes[1]) == 0) # test that SpikeMonitor function calling usage does what you'd expect f_spikes = [] def f(spikes): if len(spikes): f_spikes.extend(spikes) G = SpikeGeneratorGroup(2, spikes, clock=defaultclock) M = SpikeMonitor(G, function=f) net = Network(G, M) reinit_default_clock() net.run(10 * ms) assert (f_spikes == [0, 1, 0]) # test that SpikeMonitors in MultiConnection objects do reinitialize # properly G = SpikeGeneratorGroup(2, spikes, clock=defaultclock) G2 = NeuronGroup(1, model='dv/dt = -v / (5*ms) : 1') C = Connection(G, G2, 'v', weight=0) M = SpikeMonitor(G) # Note: Because M and C share the source (G), they are replaced by a # MultiConnection net = Network(G, G2, C, M) net.run(10 * ms) net.reinit() # make sure that the reinit propagates to the SpikeMonitor assert (M.nspikes == 0) assert (len(M.spikes) == 0) assert (len(M[0]) == 0 and len(M[1]) == 0) assert (len(M.spiketimes[0]) == 0 and len(M.spiketimes[1]) == 0) # test interface for StateMonitor object dV = 'dV/dt = 0*Hz : 1.' G = NeuronGroup(3, model=dV, reset=0., threshold=10.) @network_operation(when='start') def f(clock): if clock.t >= 1 * ms: G.V = [1., 2., 3.] M1 = StateMonitor(G, 'V') M2 = StateMonitor(G, 'V', record=0) M3 = StateMonitor(G, 'V', record=[0, 1]) M4 = StateMonitor(G, 'V', record=True) reinit_default_clock() net = Network(G, f, M1, M2, M3, M4) net.run(2 * ms) assert (is_within_absolute_tolerance(M2[0][0], 0.)) assert (is_within_absolute_tolerance(M2[0][-1], 1.)) assert (is_within_absolute_tolerance(M3[1][0], 0.)) assert (is_within_absolute_tolerance(M3[1][-1], 2.)) assert (is_within_absolute_tolerance(M4[2][0], 0.)) assert (is_within_absolute_tolerance(M4[2][-1], 3.)) assert_raises(IndexError, M1.__getitem__, 0) assert_raises(IndexError, M2.__getitem__, 1) assert_raises(IndexError, M3.__getitem__, 2) assert_raises(IndexError, M4.__getitem__, 3) for M in [M3, M4]: assert (is_within_absolute_tolerance(float(max(abs(M.times - M2.times))), float(0 * ms))) assert (is_within_absolute_tolerance(float(max(abs(M.times_ - M2.times_))), 0.)) assert (is_within_absolute_tolerance(float(M2.times[0]), float(0 * ms))) d = diff(M2.times) assert (is_within_absolute_tolerance(max(d), min(d))) assert (is_within_absolute_tolerance(float(max(d)), float(get_default_clock().dt))) # construct unbiased estimator from variances of recorded arrays v = array([ var(M4[0]), var(M4[1]), var(M4[2]) ]) * float(len(M4[0])) / float(len(M4[0]) - 1) m = array([0.5, 1.0, 1.5]) assert (is_within_absolute_tolerance(abs(max(M1.mean - m)), 0.)) assert (is_within_absolute_tolerance(abs(max(M1.var - v)), 0.)) assert (is_within_absolute_tolerance(abs(max(M1.std - v ** 0.5)), 0.)) # test when, timestep, clock for StateMonitor c = Clock(dt=0.1 * ms) cslow = Clock(dt=0.2 * ms) dV = 'dV/dt = 0*Hz : 1.' G = NeuronGroup(1, model=dV, reset=0., threshold=1., clock=c) @network_operation(when='start', clock=c) def f(): G.V = 2. M1 = StateMonitor(G, 'V', record=True, clock=cslow) M2 = StateMonitor(G, 'V', record=True, timestep=2, clock=c) M3 = StateMonitor(G, 'V', record=True, when='before_groups', clock=c) net = Network(G, f, M1, M2, M3, M4) net.run(2 * ms) print M1[0], M3[0] assert (2 * len(M1[0]) == len(M3[0])) assert (len(M1[0]) == len(M2[0])) for i in range(len(M1[0])): assert (is_within_absolute_tolerance(M1[0][i], M2[0][i])) assert (is_within_absolute_tolerance(M1[0][i], 0.)) for x in M3[0]: assert (is_within_absolute_tolerance(x, 2.)) reinit_default_clock() # for next test def test_counter(): ''' Tests the consistency of :class:`SpikeCounter`, :class:`PopulationSpikeCounter` and :class:`SpikeMonitor`. ''' reinit_default_clock() # Test whether all monitors count the same number of spikes spikes = [(0, 3 * ms), (1, 4 * ms), (0, 7 * ms)] G = SpikeGeneratorGroup(2, spikes, clock=defaultclock) M = SpikeMonitor(G) C = SpikeCounter(G) P = PopulationSpikeCounter(G) net = Network(G, M, C, P) net.run(10 * ms) # total number of spikes assert(M.nspikes == C.nspikes == P.nspikes == 3) # spikes per neuron assert(len(M[0]) == C[0] == C.count[0] == 2) assert(len(M[1]) == C[1] == C.count[1] == 1) # check for correct reinit net.reinit() assert(M.nspikes == C.nspikes == P.nspikes == 0) assert(len(M[0]) == C[0] == C.count[0] == 0) assert(len(M[1]) == C[1] == C.count[1] == 0) reinit_default_clock() # for next test def test_coincidencecounter(): """ Simulates an IF model with constant input current and checks the total number of coincidences with prediction. """ eqs = """ dV/dt = (-V+R*I)/tau : 1 I : 1 R : 1 tau : second """ reset = 0 threshold = 1 duration = 500 * ms input = 1.2 + .2 * randn(int(duration / defaultclock._dt)) delta = 4 * ms n = 10 def get_data(n): # Generates data from an IF neuron group = NeuronGroup(N=1, model=eqs, reset=reset, threshold=threshold, method='Euler', refractory=3 * delta) group.I = TimedArray(input, start=0 * second, dt=defaultclock.dt) group.R = 1.0 group.tau = 20 * ms M = SpikeMonitor(group) stM = StateMonitor(group, 'V', record=True) net = Network(group, M, stM) net.run(duration) data = M.spikes # train0 = M.spiketimes[0] reinit_default_clock() # trains = [] # for i in range(n): # trains += zip(i*ones(len(train0), dtype='int'), (array(train0) + (delta*c) * rand(len(train0)))) # trains.sort(lambda x,y: (2*int(x[1]>y[1])-1)) # trains = [(i,t*second) for i,t in trains] return data, stM.values#, trains data, data_voltage = get_data(n=n) train0 = [t for i, t in data] group = NeuronGroup(n, eqs, reset=reset, threshold=threshold, method='Euler') group.I = TimedArray(input, start=0 * second, dt=defaultclock.dt) group.R = 1.0 * ones(n) group.tau = 20 * ms * (1 + .1 * (2 * rand(n) - 1)) cc = CoincidenceCounter(source=group, data=([-1 * second] + train0 + [duration + 1 * second]), delta=delta) sm = SpikeMonitor(group) statem = StateMonitor(group, 'V', record=True) net = Network(group, cc, sm, statem) net.run(duration) reinit_default_clock() cpu_voltage = statem.values online_coincidences = cc.coincidences cpu_spike_count = array([len(sm[i]) for i in range(n)]) offline_coincidences = array([gamma_factor(sm[i], train0, delta=delta, normalize=False, dt=defaultclock.dt) for i in range(n)]) if use_gpu: # Compute gamma factor with GPU inp = array(input) I_offset = zeros(n, dtype=int) #spiketimes = array(hstack(([-1*second],train0,[data[-1][1]+1*second]))) spiketimes = array(hstack(([-1 * second], train0, [duration + 1 * second]))) spiketimes_offset = zeros(n, dtype=int) spikedelays = zeros(n) cd = CoincidenceCounter(source=group, data=data, delta=delta) group.V = 0.0 mf = GPUModelFitting(group, Equations(eqs), inp, I_offset, spiketimes, spiketimes_offset, spikedelays, delta) # Normal GPU launch mf.launch(duration) # GPU record of voltage and spikes # allV = [] # oldnc = 0 # oldsc = 0 # allcoinc = [] # all_pst = [] # all_nst = [] # allspike = [] # all_nsa = [] # all_lsa = [] # # for i in xrange(int(duration/defaultclock.dt)): # mf.kernel_func(int32(i), int32(i+1), # *mf.kernel_func_args, **mf.kernel_func_kwds) # autoinit.context.synchronize() # allV.append(mf.state_vars['V'].get()) # all_pst.append(mf.spiketimes.get()[mf.spiketime_indices.get()]) # all_nst.append(mf.spiketimes.get()[mf.spiketime_indices.get()+1]) # all_nsa.append(mf.next_spike_allowed_arr.get()[0]) # all_lsa.append(mf.last_spike_allowed_arr.get()[0]) # # self.next_spike_allowed_arr = gpuarray.to_gpu(ones(N, dtype=bool)) # # self.last_spike_allowed_arr = gpuarray.to_gpu(zeros(N, dtype=bool)) # nc = mf.coincidence_count[0] # if nc>oldnc: # oldnc = nc # allcoinc.append(i*defaultclock.dt) # sc = mf.spike_count[0] # if sc>oldsc: # oldsc = sc # allspike.append(i*defaultclock.dt) # # gpu_voltage = array(allV) cc = mf.coincidence_count gpu_spike_count = mf.spike_count cd._model_length = gpu_spike_count cd._coincidences = cc gpu_coincidences = cc print "Spike count" print "Data", len(data) print "CPU", cpu_spike_count if use_gpu: print "GPU", gpu_spike_count print "max error : %.1f" % max(abs(cpu_spike_count - gpu_spike_count)) print print "Offline" print offline_coincidences print print "Online" print online_coincidences print "max error : %.6f" % max(abs(online_coincidences - offline_coincidences)) if use_gpu: print print "GPU" print gpu_coincidences print "max error : %.6f" % max(abs(gpu_coincidences - offline_coincidences)) bad_neuron = nonzero(abs(gpu_coincidences - offline_coincidences) > 1e-10)[0] if len(bad_neuron) > 0: print "Bad neuron", bad_neuron, group.tau[bad_neuron[0]] print print return # plot(linspace(0,duration/second,len(data_voltage[0])), data_voltage[0], 'k', linewidth=.5) # plot(linspace(0,duration/second,len(cpu_voltage[0])), cpu_voltage[0], 'b') # plot(linspace(0,duration/second,len(gpu_voltage[:,0])), gpu_voltage[:,0], 'g') # show() times = linspace(0, duration / second, len(all_pst)) figure() plot(times, array(all_pst) * defaultclock.dt) plot(times, array(all_nst) * defaultclock.dt) plot(train0, train0, 'o') plot(allspike, allspike, 'x') plot(allcoinc, allcoinc, '+') plot(times, array(all_nsa) * times, '--') plot(times, array(all_lsa) * times, '-.') predicted_spikes = allspike target_spikes = train0 + [duration + 1 * second] i = 0 truecoinc = [] for pred_t in predicted_spikes: while target_spikes[i] <= pred_t + delta - 1e-10 * second: if abs(target_spikes[i] - pred_t) <= delta + 1e-10 * second: truecoinc.append((pred_t, target_spikes[i])) i += 1 break i += 1 print 'Truecoinc:', len(truecoinc) for t1, t2 in truecoinc: plot([t1, t2], [t1, t2], ':', color=(0.5, 0, 0), lw=3) show() # assert is_within_absolute_tolerance(online_gamma1,offline_gamma1) # assert is_within_absolute_tolerance(online_gamma2,offline_gamma2) #def test_vectorized_spikemonitor(): # eqs = """ # dV/dt = (-V+I)/tau : 1 # tau : second # I : 1 # """ # N = 30 # taus = 10*ms + 90*ms * rand(N) # duration = 1000*ms # input = 2.0 + 3.0 * rand(int(duration/defaultclock._dt)) # vgroup = VectorizedNeuronGroup(model=eqs, reset=0, threshold=1, # input=input, slices=2, overlap=200*ms, tau=taus) # M = SpikeMonitor(vgroup) # run(vgroup.duration) # raster_plot(M) # show() if __name__ == '__main__': test_spikemonitor() test_counter() # test_coincidencecounter() brian-1.4.3/brian/tests/testinterface/test_network.py000066400000000000000000000133041266062647100230240ustar00rootroot00000000000000import sys from StringIO import StringIO from numpy.testing.utils import assert_raises from brian import * from brian.utils.progressreporting import ProgressReporter def test_progressreporting(): ''' Tests whether calling for progress reporting works -- the actual output is not checked, only that something is printed to the correct output. ''' real_stdout = sys.stdout real_stderr = sys.stderr G = NeuronGroup(1, model=LazyStateUpdater()) net = Network(G) # Tests various ways to get a (textual) progress report string_output = StringIO() sys.stdout = string_output net.run(defaultclock.dt, report='text') assert(len(string_output.getvalue()) > 0) net.run(defaultclock.dt, report='print') string_output = StringIO() sys.stdout = string_output net.run(defaultclock.dt, report='stdout') assert(len(string_output.getvalue()) > 0) string_output = StringIO() sys.stdout = string_output string_err = StringIO() sys.stderr = string_err net.run(defaultclock.dt, report='stderr') assert(len(string_output.getvalue()) == 0 and len(string_err.getvalue()) > 0) net.run(defaultclock.dt, report=string_output) assert(len(string_output.getvalue()) > 0) net.run(defaultclock.dt, report='text', report_period=5*second) #explicitely construct a ProgressReporter string_output = StringIO() sys.stdout = string_output reporter = ProgressReporter('text') reporter.start() net.run(defaultclock.dt, report=reporter) assert(len(string_output.getvalue()) > 0) string_output = StringIO() sys.stdout = string_output string_err = StringIO() sys.stderr = string_err reporter = ProgressReporter('stderr') net.run(defaultclock.dt, report=reporter) assert(len(string_output.getvalue()) == 0 and len(string_err.getvalue()) > 0) # use MagicNetwork implicitly string_output = StringIO() sys.stdout = string_output run(defaultclock.dt, report='text', report_period=5*second) assert(len(string_output.getvalue()) > 0) sys.stdout = real_stdout sys.stderr = real_stderr def test_network_generation(): ''' Tests various ways of creating a network ''' G = NeuronGroup(42, model=LazyStateUpdater()) net = Network(G) assert(len(net) == 42) net = Network() net.add(G) assert(len(net) == 42) #try adding a group twice net.add(G) assert(len(net) == 42) #try adding something that should not be possible to add assert_raises(TypeError, lambda: net.add(1 * msecond)) net = Network() # the network's call function adds an object and returns it assert(net(G) is G) assert(len(net) == 42) net = MagicNetwork() assert(len(net) == 42) net = MagicNetwork(verbose=True) assert(len(net) == 42) # Test that 'forgetting' a group works forget(G) net = MagicNetwork(verbose=True) assert(len(net) == 0) # Test that 'recalling' works recall(G) net = MagicNetwork(verbose=True) assert(len(net) == 42) # Repeat the test with a list of objects # Test that 'forgetting' a group works forget([G]) net = MagicNetwork(verbose=True) assert(len(net) == 0) # Test that 'recalling' works recall([G]) net = MagicNetwork(verbose=True) assert(len(net) == 42) def test_reinit(): ''' Test whether reinitializing works correctly and only resets what it is supposed to reset. ''' # setup a simple network eqs = '''dv/dt = -v / (1 * ms) : 1 # a variable p : 1 # a parameter ''' G = NeuronGroup(1, model=eqs) G.v = 0.1 G.p = 1 net = Network(G) net.run(defaultclock.dt) assert(G.v > 0 and G.p == 1) #reinit(False) should only reset the clock net.reinit(False) assert(defaultclock.t == 0) #reinit(True) should reset everyting net.run(defaultclock.dt) net.reinit(True) assert(defaultclock.t == 0) assert(G.v == 0 and G.p == 0) def test_network_clocks(): ''' Test whether setting and checking clocks works ''' # Two groups using the same (default) clock G1 = NeuronGroup(42, model=LazyStateUpdater()) G2 = NeuronGroup(42, model=LazyStateUpdater()) net = Network(G1, G2) assert(net.same_clocks()) # Two groups using the same clocks clock = Clock(dt=0.333*ms) G1 = NeuronGroup(42, model=LazyStateUpdater(), clock=clock) G2 = NeuronGroup(42, model=LazyStateUpdater(), clock=clock) net = Network(G1, G2) assert(net.same_clocks()) # Two groups using different clocks clock2 = Clock(dt=0.5*ms) G1 = NeuronGroup(42, model=LazyStateUpdater(), clock=clock) G2 = NeuronGroup(42, model=LazyStateUpdater(), clock=clock2) net = Network(G1, G2) assert(not net.same_clocks()) def test_network_operation(): reinit_default_clock() G = NeuronGroup(1, model='dv/dt = -v / (1 * ms) : 1') G.v = 1 @network_operation(when='end') def keep_voltage(clock): #reset voltage to 1 after the update step G.v = 1 mon = StateMonitor(G, 'v', record=True) net = Network(G, keep_voltage, mon) net.run(5 * defaultclock.dt) assert(all(mon[0] == 1.0)) #test a dummy NetworkOperation that does nothing mon.reinit() @network_operation() def do_nothing(clock): pass net = Network(G, do_nothing, mon) net.run(5 * defaultclock.dt) # test that there is some decay assert(all(mon[0] < 1.0)) if __name__ == '__main__': test_progressreporting() test_network_generation() test_network_clocks() test_network_operation() test_reinit()brian-1.4.3/brian/tests/testinterface/test_neurongroup.py000066400000000000000000000072661266062647100237300ustar00rootroot00000000000000from brian import * from brian.utils.approximatecomparisons import * from nose.tools import * def test_poissongroup(): ''' Test PoissonGroup ''' reinit_default_clock() # PoissonGroup with fixed rate, test that basic state setting works G = PoissonGroup(10, rates=10 * Hz) G1 = G[:5] G2 = G[5:] G1.rate = 5 * Hz G2.rate = 20 * Hz assert(is_approx_equal(G.rate[0], float(5 * Hz))) assert(is_approx_equal(G1.rate[0], float(5 * Hz))) assert(is_approx_equal(G2.rate[0], float(20 * Hz))) # PoissonGroup with fixed rates, test 0 and 1/dt rate (the only # deterministic rates) G = PoissonGroup(2, rates=array([0.0, 1.0/defaultclock.dt])) counter = SpikeCounter(G) runtime = 10 * ms run(runtime) assert(counter.count[0] == 0) assert(counter.count[1] == runtime/defaultclock.dt) # Same, but with a function returning the rates def rate_fun(t): return array([0.0, 1.0/defaultclock.dt]) G = PoissonGroup(2, rates=rate_fun) counter = SpikeCounter(G) runtime = 10 * ms run(runtime) assert(counter.count[0] == 0) assert(counter.count[1] == runtime/defaultclock.dt) def test_linked_var(): ''' Tests whether linking variables between groups works. ''' # Test the basic linked_var behaviour reinit_default_clock() G1 = NeuronGroup(1, model='dv/dt = -v/(10*ms) : 1') G2 = NeuronGroup(1, model='v: 1') #link the two variables G2.v = linked_var(G1, 'v') G1.v = 1 mon1 = StateMonitor(G1, 'v', record=True) mon2 = StateMonitor(G2, 'v', record=True) run(50*ms) # The linked variable is updated of the beginning of a time step, before the # update hapenns --> The values are shifted by one assert(sum(abs(mon1[0][:-1] - mon2[0][1:])) == 0) # Test the basic linked_var behaviour with when='middle' reinit_default_clock() #link the two variables, copy after the update step G2.v = linked_var(G1, 'v', when='middle') G1.v = 1 mon1 = StateMonitor(G1, 'v', record=True) mon2 = StateMonitor(G2, 'v', record=True) run(50*ms) assert(sum(abs(mon1[0] - mon2[0])) == 0) # Test the basic linked_var behaviour with an applied function reinit_default_clock() #link the two variables, copy after the update step G2.v = linked_var(G1, 'v', when='middle', func=lambda x: x * 2) G1.v = 1 mon1 = StateMonitor(G1, 'v', record=True) mon2 = StateMonitor(G2, 'v', record=True) run(50*ms) assert(sum(abs(2 * mon1[0] - mon2[0])) == 0) def test_variable_setting(): ''' Test that assigning to state variables and parameters works but assigning to static equations is forbidden. ''' eqs = ''' dv/dt = -v / tau : 1 vsquared = v ** 2 : 1 v_alias = v tau : second tau_alias = tau ''' G = NeuronGroup(1, model=eqs) # Assigning to a state (differential equation) G.v = 0.25 assert G.v == 0.25 and G.v_alias == 0.25 # indirectly via an alias G.v_alias = 0.5 assert G.v == 0.5 and G.v_alias == 0.5 # Assigning to a parameter G.tau = 5 * ms assert G.tau == 5 * ms and G.tau_alias == 5 * ms # via an alias G.tau_alias = 50 * ms assert G.tau == 50 * ms and G.tau_alias == 50 * ms # assert that assigning to a static equation does not work def assign_to_static(): G.vsquared = 2 assert_raises(ValueError, assign_to_static) if __name__ == '__main__': test_poissongroup() test_linked_var() test_variable_setting()brian-1.4.3/brian/tests/testinterface/test_reset.py000066400000000000000000000122501266062647100224540ustar00rootroot00000000000000from brian import * from nose.tools import * from brian.utils.approximatecomparisons import is_approx_equal from brian.tests import repeat_with_global_opts @repeat_with_global_opts([ # no C code or code generation, {'useweave': False, 'usecodegen': False}, # # use weave but no code generation {'useweave': True, 'usecodegen': False}, # use Python code generation {'useweave': False, 'usecodegen': True, 'usecodegenreset': True}, # use C code generation {'useweave': True, 'usecodegen': True, 'usecodegenreset': True, 'usecodegenweave': True} ]) def test(): """ :class:`Reset` ~~~~~~~~~~~~~~ Initialised as:: R = Reset(resetvalue=0*mvolt, state=0) After a neuron from a group with this reset fires, it will set the specified state variable to the given value. State variable 0 is customarily the membrane voltage, but this isn't required. :class:`FunReset` ~~~~~~~~~~~~~~~~~ Initialised as:: R = FunReset(resetfun) Where resetfun is a function taking two arguments, the group it is acting on, and the indices of the spikes to be reset. The following is an example reset function:: def f(P,spikeindices): P._S[0,spikeindices]=array([i/10. for i in range(len(spikeindices))]) :class:`Refractoriness` ~~~~~~~~~~~~~~~~~~~~~~~ Initialised as:: R = Refractoriness(resetvalue=0*mvolt,period=5*msecond,state=0) After a neuron from a group with this reset fires, the specified state variable of the neuron will be set to the specified resetvalue for the specified period. :class:`NoReset` ~~~~~~~~~~~~~~~~ Initialised as:: R = NoReset() Does nothing. """ reinit_default_clock() # test that reset works as expected # the setup below is that group G starts with state values (1,1,1,1,1,0,0,0,0,0) threshold # value 0.5 (which should be initiated for the first 5 neurons) and reset 0.2 so that the # final state should be (0.2,0.2,0.2,0.2,0.2,0,0,0,0,0) G = NeuronGroup(10, model=LazyStateUpdater(), reset=Reset(0.2), threshold=Threshold(0.5), init=(0.,)) G1 = G.subgroup(5) G2 = G.subgroup(5) G1.state(0)[:] = array([1.] * 5) G2.state(0)[:] = array([0.] * 5) net = Network(G) net.run(1 * msecond) assert (all(G1.state(0) < 0.21) and all(0.19 < G1.state(0)) and all(G2.state(0) < 0.01)) # recreate the same behaviour with a VariableReset eqs = ''' v : 1 r : 1 ''' G = NeuronGroup(10, model=eqs, reset=VariableReset(resetvaluestate='r', state='v'), threshold=Threshold(0.5)) G.r = 0.2 G1 = G.subgroup(5) G2 = G.subgroup(5) G1.v[:] = array([1.] * 5) G2.v[:] = array([0.] * 5) net = Network(G) net.run(1 * msecond) assert (all(G1.state(0) < 0.21) and all(0.19 < G1.state(0)) and all(G2.state(0) < 0.01)) # test string reset that resets two variables eqs = '''v : 1 w : 1 ''' G = NeuronGroup(2, model=eqs, reset='v = 0; w = -1', threshold=Threshold(0.5)) G.v =[0.25, 1] G.w =[0, 0] # only second group crosses threshold net = Network(G) net.run(defaultclock.dt) # Check that the states of the first group are unchanged assert(G.v[0] == 0.25 and G.w[0] == 0) # Check that the states of the second group are reset correctly assert(G.v[1] == 0 and G.w[1] == -1) # check that function reset works as expected def f(P, spikeindices): P._S[0, spikeindices] = array([i / 10. for i in range(len(spikeindices))]) P.called_f = True G = NeuronGroup(10, model=LazyStateUpdater(), reset=FunReset(f), threshold=Threshold(2.), init=(3.,)) G.called_f = False net = Network(G) net.run(1 * msecond) assert (G.called_f) for i, v in enumerate(G.state(0)): assert (is_approx_equal(i / 10., v)) # check that refractoriness works as expected # the network below should start at V=15, immediately spike as it is above threshold=1, # then should be clamped at V=-.5 until t=1ms at which point it should quickly evolve # via the DE to a value near 0 (and certainly between -.5 and 0). We test that the # value at t=0.5 is exactly -.5 and the value at t=1.5 is between -0.4 and 0.1 (to # avoid floating point problems) dV = 'dV/dt=-V/(.1*msecond):1.' G = NeuronGroup(1, model=dV, threshold=1., reset=Refractoriness(-.5, 1 * msecond)) G.V = 15. net = Network(G) net.run(0.5 * msecond) for v in G.state(0): assert (is_approx_equal(v, -.5)) net.run(1 * msecond) for v in G.state(0): assert (-0.4 < v < 0.1) get_default_clock().reinit() if __name__ == '__main__': test() brian-1.4.3/brian/tests/testinterface/test_statistics.py000066400000000000000000000004441266062647100235260ustar00rootroot00000000000000from brian import * from nose.tools import * def test(): """ Statistics module """ assert total_correlation([],[]) is NaN assert correlogram([],[]) is NaN assert firing_rate([]) is NaN assert CV([]) is NaN if __name__ == '__main__': test() brian-1.4.3/brian/tests/testinterface/test_stdp.py000066400000000000000000000025561266062647100223140ustar00rootroot00000000000000from brian import * from brian.tests import repeat_with_global_opts @repeat_with_global_opts([{'usecstdp': False, 'useweave': False}, {'usecstdp': True, 'useweave': True}]) def test_stdp(): ''' Test that the basic STDP mechanism works. Does not really test for correctness, only asserts that weight increases after the spike. ''' reinit() # Set up a very simple network that receives an input spike and spikes # about 2ms later inp = SpikeGeneratorGroup(1, [(0, 1 * ms)]) Ee, El = 0 * mV, - 80 * mV eqs_neurons = ''' dv/dt = (ge * (Ee - v) + (El - v)) / (5 * ms) : volt dge/dt = -ge / (5 * ms) : 1 ''' G = NeuronGroup(1, model=eqs_neurons, threshold=-50 * mV, reset=El) G.v = -51 * mV con = Connection(inp, G, 'ge', weight=1) # Set up the STDP eqs_stdp = """ dA_pre/dt = -A_pre/(10 * ms) : 1 dA_post/dt = -A_post/(10 * ms) : 1 """ delta_A_pre, delta_A_post = 0.1, 0.1 stdp = STDP(con, eqs=eqs_stdp, pre='A_pre+=delta_A_pre; w+=A_post', post='A_post+=delta_A_post; w+=A_pre') net = Network(inp, G, con, stdp) net.run(4 * ms) # Postsynaptic spike came after presynaptic spike: weight should increase assert(con.W[0, 0] > 1) if __name__ == '__main__': test_stdp()brian-1.4.3/brian/tests/testinterface/test_str_repr.py000066400000000000000000000063451266062647100232020ustar00rootroot00000000000000from brian import * def test_str_repr(): ''' Tests that the __str__ and __repr__ methods work for the most important Brian classes. This only tests whether they return any string at all. (For units this is tested in test_units.py) ''' def assert_str_repr(obj): assert(len(str(obj))) assert(len(repr(obj))) # Clocks clock = Clock(dt=0.2*ms) assert_str_repr(clock) assert_str_repr(defaultclock) # Equations and NeuronGroups eqs = Equations('dv/dt=-v/(5 * ms) : volt') group = NeuronGroup(100, model=eqs, reset=0*mV, threshold=10*mV) assert_str_repr(eqs) assert_str_repr(group) subg = group.subgroup(50) assert_str_repr(subg) pgroup = PoissonGroup(1, rates=100*Hz) assert_str_repr(pgroup) sgroup = SpikeGeneratorGroup(1, []) assert_str_repr(sgroup) # Thresholds and Resets thresholds = [Threshold(1), StringThreshold('v > 1'), NoThreshold(), VariableThreshold('v', 'w'), EmpiricalThreshold(), FunThreshold(lambda v: v > 1), SimpleFunThreshold(lambda v: v > 1), PoissonThreshold(), HomogeneousPoissonThreshold()] for threshold in thresholds: assert_str_repr(threshold) resets = [Reset(0), StringReset('v = 0'), VariableReset(), FunReset(lambda G, spikes: None), NoReset(), Refractoriness(), SimpleCustomRefractoriness(lambda G, spikes: None), CustomRefractoriness(lambda G, spikes: None)] for reset in resets: assert_str_repr(reset) # Connections con = Connection(group, group, 'v') con.connect_full(group, group, 1*mvolt) assert_str_repr(con) con_delay = Connection(group, group, 'v', delay=True) con_delay.connect_full(group, group, weight=1*mvolt, delay=5*ms) assert_str_repr(con_delay) # Monitors and counters monitors = [SpikeMonitor(group), SpikeMonitor(group, delay=1*ms), SpikeMonitor(group, delay=1*ms, function=lambda spikes:None), StateMonitor(group, 'v'), SpikeCounter(group), StateSpikeMonitor(group, 'v'), RecentStateMonitor(group, 'v'), MultiStateMonitor(group), PopulationRateMonitor(group), PopulationSpikeCounter(group), PopulationSpikeCounter(group, delay=1*ms), ISIHistogramMonitor(group, bins=[0*ms, 10*ms, 20*ms]), ISIHistogramMonitor(group, bins=[0*ms, 10*ms, 20*ms], delay=1*ms), CoincidenceCounter(group, [1*ms, 2*ms]), VanRossumMetric(group)] for monitor in monitors: assert_str_repr(monitor) # Networks net = MagicNetwork() assert_str_repr(net) net = Network(group, con) assert_str_repr(net) # Equations eqs = Equations('''dv/dt = (-v + I) / (10 * ms) : volt #diffeq I = sin(2 * pi * f * t/second)**2 : volt #eq I2 = I # alias f : Hz # parameter''') assert_str_repr(eqs) # Synapses syn = Synapses(group, model='w:1', pre='v+=w') assert_str_repr(syn) if __name__ == '__main__': test_str_repr() brian-1.4.3/brian/tests/testinterface/test_synapses.py000066400000000000000000000511251266062647100232030ustar00rootroot00000000000000''' Tests various ways of constructing synapses. ''' # Needed so that python 2.5 does not complain about a SyntaxError from __future__ import with_statement import warnings import sys from nose.tools import assert_raises import numpy as np from brian.network import Network from brian.clock import defaultclock, reinit_default_clock from brian.synapses import Synapses, SynapticEquations from brian.neurongroup import NeuronGroup from brian.directcontrol import SpikeGeneratorGroup from brian.monitor import StateMonitor from brian.threshold import NoThreshold from brian.stdunits import ms, mV, nS, nF def test_construction_single_synapses(): ''' Test the construction of synapses with a single synapse per connection. ''' G = NeuronGroup(20, model='v:1', threshold=NoThreshold()) # specifying only one group should use it as source and target syn = Synapses(G, model='w:1') assert syn.source is syn.target # specifying source and target with subgroups subgroup1, subgroup2 = G[:10], G[10:] syn = Synapses(subgroup1, subgroup2, model='w:1', pre='v += w') assert syn.source is subgroup1 assert syn.target is subgroup2 # create all-to-all connections syn[:, :] = True assert len(syn) == 10 * 10 # deleting a synapse should not work def delete_synapse(): syn[0, 0] = False assert_raises(ValueError, delete_synapse) # set the weights syn.w[:, :] = 2 # set the delays syn.delay[:, :] = 1 * ms all_weights = np.array([syn.w[i, j] for i in xrange(len(subgroup1)) for j in xrange(len(subgroup2))]) assert (all_weights == 2).all() all_delays = np.array([syn.delay[i, j] for i in xrange(len(subgroup1)) for j in xrange(len(subgroup2))]) assert (all_delays == 1 * ms).all() # create one-to-one connections syn = Synapses(subgroup1, subgroup2, model='w:1', pre='v += w') syn[:, :] = 'i == j' syn.w[:, :] = 2 assert len(syn) == len(subgroup1) for i in xrange(len(subgroup1)): for j in xrange(len(subgroup2)): if i == j: assert syn.w[i, j] == 2 else: assert len(syn.w[i, j]) == 0 net = Network(G, syn) net.run(defaultclock.dt) # adding synapses should not work after the simulation has already been run def adding_a_synapse(): syn[0, 1] = True assert_raises(AttributeError, adding_a_synapse) syn = Synapses(subgroup1, subgroup2, model='w:1', pre='v += w') syn.connect_one_to_one(subgroup1, subgroup2) syn.w[:, :] = 2 assert len(syn) == len(subgroup1) for i in xrange(len(subgroup1)): for j in xrange(len(subgroup2)): if i == j: assert syn.w[i, j] == 2 else: assert len(syn.w[i, j]) == 0 # Calling connect_one_to_one without specifying groups should use the full # source or target group syn = Synapses(subgroup1, subgroup2, model='w:1', pre='v += w') syn.connect_one_to_one(subgroup1) assert len(syn) == len(subgroup1) syn = Synapses(subgroup1, subgroup2, model='w:1', pre='v += w') syn.connect_one_to_one(post=subgroup2) assert len(syn) == len(subgroup1) syn = Synapses(subgroup1, subgroup2, model='w:1', pre='v += w') syn.connect_one_to_one() assert len(syn) == len(subgroup1) # connect_one_to_one should only work with subgroups of the same size assert_raises(TypeError, lambda : syn.connect_one_to_one(G[:2], G[:1])) # create random connections # the only two cases that can be tested exactly are 0 and 100% connection # probability syn = Synapses(subgroup1, subgroup2, model='w:1', pre='v += w') # floats are interpreted as connection probabilities syn[:, :] = 0.0 assert len(syn) == 0 syn = Synapses(subgroup1, subgroup2, model='w:1', pre='v += w') syn.connect_random(subgroup1, subgroup2, 0.0) assert len(syn) == 0 syn = Synapses(subgroup1, subgroup2, model='w:1', pre='v += w') syn[:, :] = 1.0 assert len(syn) == 10 * 10 # set the weights syn.w[:, :] = 2 # set the delays syn.delay[:, :] = 1 * ms all_weights = np.array([syn.w[i, j] for i in xrange(len(subgroup1)) for j in xrange(len(subgroup2))]) assert (all_weights == 2).all() all_delays = np.array([syn.delay[i, j] for i in xrange(len(subgroup1)) for j in xrange(len(subgroup2))]) assert (all_delays == 1 * ms).all() syn = Synapses(subgroup1, subgroup2, model='w:1', pre='v += w') syn.connect_random(subgroup1, subgroup2, 1.0) assert len(syn) == 10 * 10 # set the weights syn.w[:, :] = 2 # set the delays syn.delay[:, :] = 1 * ms all_weights = np.array([syn.w[i, j] for i in xrange(len(subgroup1)) for j in xrange(len(subgroup2))]) assert (all_weights == 2).all() all_delays = np.array([syn.delay[i, j] for i in xrange(len(subgroup1)) for j in xrange(len(subgroup2))]) assert (all_delays == 1 * ms).all() # Calling connect_random without specifying groups should use the full # source or target group syn = Synapses(subgroup1, subgroup2, model='w:1', pre='v += w') syn.connect_random(subgroup1, sparseness=1.0) assert len(syn) == 10 * 10 syn = Synapses(subgroup1, subgroup2, model='w:1', pre='v += w') syn.connect_random(post=subgroup2, sparseness=1.0) assert len(syn) == 10 * 10 syn = Synapses(subgroup1, subgroup2, model='w:1', pre='v += w') syn.connect_random(sparseness=1.0) assert len(syn) == 10 * 10 # Just test that probabilities between zero and one work at all syn = Synapses(subgroup1, subgroup2, model='w:1', pre='v += w') syn.connect_random(subgroup1, subgroup2, 0.3) syn = Synapses(subgroup1, subgroup2, model='w:1', pre='v += w') syn[:, :] = 0.3 # Test that probabilities outside of the legal range raise an error syn = Synapses(subgroup1, subgroup2, model='w:1', pre='v += w') assert_raises(ValueError, lambda : syn.connect_random(subgroup1, subgroup2, 1.3)) assert_raises(ValueError, lambda : syn.connect_random(subgroup1, subgroup2, -.3)) def wrong_probability(): syn[:, :] = -0.3 assert_raises(ValueError, wrong_probability) def wrong_probability(): #@DuplicatedSignature syn[:, :] = 1.3 assert_raises(ValueError, wrong_probability) # this test requires python 2.6 if sys.version_info[0] >= 2 and sys.version_info[1] >=6: # Running a model with a synapses object with 0 synapses should raise a warning syn = Synapses(subgroup1, subgroup2, model='w:1', pre='v += w') with warnings.catch_warnings(record=True) as w: # Cause all warnings to always be triggered. warnings.simplefilter("always") # Trigger a warning. syn.compress() # Verify some things assert len(w) == 1 # use arrays as neuron indices instead of slices or ints syn = Synapses(subgroup1, subgroup2, model='w:1', pre='v += w') syn[np.arange(5), np.arange(5)] = True syn.w[np.arange(5), np.arange(5)] = 2 syn.delay[np.arange(5), np.arange(5)] = 5 * ms assert len(syn) == 5 * 5 assert all([syn.w[i, j] == 2 for i in xrange(5) for j in xrange(5)]) assert all([syn.delay[i, j] == 5 * ms for i in xrange(5) for j in xrange(5)]) assert all([len(syn.w[i, j]) == 0 for i in xrange(5, len(subgroup1)) for j in xrange(5, len(subgroup2))]) assert all([len(syn.delay[i, j]) == 0 for i in xrange(5, len(subgroup1)) for j in xrange(5, len(subgroup2))]) # Create a synapse without a model (e.g. a simple connection with constant # weights syn = Synapses(subgroup1, subgroup2, model='', pre='v+=1') def test_construction_multiple_synapses(): ''' Test the construction of synapses with multiple synapses per connection. ''' G = NeuronGroup(20, model='v:1', threshold=NoThreshold()) subgroup1, subgroup2 = G[:10], G[10:] syn = Synapses(subgroup1, subgroup2, model='w:1', pre='v += w') # create all-to-all connections with three synapses per connection syn[:, :] = 3 assert len(syn) == 3 * 10 * 10 # set the weights to the same value for all synapses syn.w[:, :] = 2 # set the delays syn.delay[:, :] = 1 * ms all_weights = np.array([syn.w[i, j, syn_idx] for i in xrange(len(subgroup1)) for j in xrange(len(subgroup2)) for syn_idx in xrange(3)]) assert (all_weights == 2).all() all_delays = np.array([syn.delay[i, j, syn_idx] for i in xrange(len(subgroup1)) for j in xrange(len(subgroup2)) for syn_idx in xrange(3)]) assert (all_delays == 1 * ms).all() def test_construction_and_access(): ''' Test various ways of constructing and accessing synapses. ''' G = NeuronGroup(20, model='v:1', threshold=NoThreshold()) subgroup1, subgroup2 = G[:10], G[10:] syn = Synapses(subgroup1, subgroup2, model='w:1', pre='v += w') # single synaptic indices syn[3, 4] = True syn.w[3, 4] = 0.25 syn.delay[3, 4] = 1 * ms for i in xrange(len(subgroup1)): for j in xrange(len(subgroup2)): if i == 3 and j == 4: assert syn.w[i, j] == 0.25 assert syn.delay[i, j] == 1 * ms else: assert len(syn.w[i, j]) == 0 assert len(syn.delay[i, j]) == 0 # illegal target index def illegal_index(): syn[3, 10] = True assert_raises(ValueError, illegal_index) # illegal source index def illegal_index(): #@DuplicatedSignature syn[10, 4] = True assert_raises(ValueError, illegal_index) # target slice syn = Synapses(subgroup1, subgroup2, model='w:1', pre='v += w') syn[3, :] = True syn.w[3, :] = 0.25 syn.delay[3, :] = 1 * ms for i in xrange(len(subgroup1)): for j in xrange(len(subgroup2)): if i == 3: assert syn.w[i, j] == 0.25 assert syn.delay[i, j] == 1 * ms else: assert len(syn.w[i, j]) == 0 assert len(syn.delay[i, j]) == 0 # access with slice assert (syn.w[3, :] == 0.25).all() assert (syn.delay[3, :] == 1 * ms).all() # source slice syn = Synapses(subgroup1, subgroup2, model='w:1', pre='v += w') syn[:, 4] = True syn.w[:, 4] = 0.25 syn.delay[:, 4] = 1 * ms for i in xrange(len(subgroup1)): for j in xrange(len(subgroup2)): if j == 4: assert syn.w[i, j] == 0.25 assert syn.delay[i, j] == 1 * ms else: assert len(syn.w[i, j]) == 0 assert len(syn.delay[i, j]) == 0 # access with slice assert (syn.w[:, 4] == 0.25).all() assert (syn.delay[:, 4] == 1 * ms).all() # Use string initialization for synaptic variable syn = Synapses(subgroup1, subgroup2, model='w:1', pre='v += w') syn[:, 4] = True # synapses from even source index have weight 1, uneven --> weight 0.5 # same for delays (1ms and 0.5ms) syn.w[:, 4] = '(i % 2 == 0) * 0.5 + 0.5' syn.delay[:, 4] = '(i % 2 == 0) * 0.5 * ms + 0.5 * ms' assert (syn.w[::2, 4] == 1).all() assert (syn.w[1::2, 4] == 0.5).all() assert (syn.delay[::2, 4] == 1 * ms).all() assert (syn.delay[1::2, 4] == 0.5 * ms).all() # Use string initialization with a numpy function and constant syn = Synapses(subgroup1, subgroup2, model='w:1', pre='v += w') syn[:, 4] = True syn.w[:, 4] = '0.5 + 0.5 * np.cos(i / (1.0 * n) * np.pi)' syn.delay[:, 4] = '0.0005 + 0.0005 * np.cos(i / (1.0 * n) * np.pi)' assert (syn.w[:, 4] == 0.5 + 0.5 * np.cos(np.arange(len(subgroup1)) / (1.0 * len(subgroup1)) * np.pi)).all() # possible delays are always multiples of dt delays = 0.5 * ms + 0.5 * ms * np.cos(np.arange(len(subgroup1)) / (1.0 * len(subgroup1)) * np.pi) delays_rounded_to_dt = (delays / defaultclock.dt).astype(np.int) * defaultclock.dt assert (syn.delay[:, 4] == delays_rounded_to_dt).all() def test_model_definition(): ''' Tests various ways of defining models. Note that this test only checks for whether the interface accepts what it is supposed to accept, not whether the Synapses class actually does what it is supposed to do... ''' G = NeuronGroup(1, model='v:1', threshold=NoThreshold()) # model, pre and post string syn = Synapses(G, model='w:1', pre='v+=w', post='v+=w') # list of pre codes syn = Synapses(G, model='w:1', pre=['v+=w', 'v+=1']) # an equation object model = SynapticEquations('w:1') syn = Synapses(G, model=model, pre='v+=w') ############################################################################ # Some more complex examples from the docs ############################################################################ # event-driven simulations model='''w:1 dApre/dt=-Apre/taupre : 1 (event-driven) dApost/dt=-Apost/taupost : 1 (event-driven)''' syn = Synapses(G, model=model) model ='''w : 1 p : 1''' syn = Synapses(G, model=model, pre="v+=w*(rand()= 6 * ms] == 1).all() assert (mon[0][mon.times < 6 * ms] == 0).all() # same as above but with two synapses reinit_default_clock() mon.reinit() G.reinit() syn = Synapses(inp, G, model='w:1', pre='v+=w', max_delay=5*ms) syn[:, :] = 2 syn.w[:, :] = 1 syn.delay[:, :] = [0 * ms, 0 * ms] net = Network(inp, G, syn, mon) net.run(defaultclock.dt) syn.delay[:, :] = [5 * ms, 5 * ms] net.run(6.5*ms) # spike should arrive at 5 + 1 ms assert (mon[0][mon.times >= 6 * ms] == 2).all() assert (mon[0][mon.times < 6 * ms] == 0).all() def test_incorrect_pre_reference(): G = NeuronGroup(1, '''v : 1 x : 1''', threshold='v>1', reset='v=0') G.v = 1.1 # spikes G2 = NeuronGroup(10, '') # The pre-synaptic x variable should be 10 in the end, because 10 synapses # were created and all of them increase the pre-synaptic variable by 1. # This does not work in Brian 1 (but it does work in Brian 2), we therefore # only throw an error to avoid this problem. assert_raises(ValueError, lambda: Synapses(G, G2, pre='x_pre += 1')) ################################################################################ # Low level unit tests, test single helper functions from brian.synapses.synapticvariable import slice_to_array # avoid nose picking up on slice_to_test as a test_function from brian.synapses.synapses import slice_to_test as slice_to_t slice_to_t.__name__ = 'slice_to_t' from brian.synapses.synapses import invert_array, smallest_inttype, indent def test_slice_to_array(): ''' Test the slice_to_array function (converts a slice to the corresponding array of integers). ''' # test special cases: array, sequence, int assert (slice_to_array(42) == np.array([42])).all() assert (slice_to_array([23, 42]) == np.array([23, 42])).all() assert (slice_to_array(np.array([23, 42])) == np.array([23, 42])).all() assert (slice_to_array(slice(0, 5)) == np.arange(0, 5)).all() assert (slice_to_array(slice(2, 5)) == np.arange(2, 5)).all() assert (slice_to_array(slice(2, None), N=5) == np.arange(2, 5)).all() assert (slice_to_array(slice(2, 5, 2)) == np.arange(2, 5, 2)).all() assert (slice_to_array(slice(2, -1, 2), N=10) == np.arange(2, 9, 2)).all() def test_slice_to_test(): ''' Test the slice_to_test function (converts a slice to a corresponding function, returning True in case an index is in the given slice). ''' testfun = slice_to_t(5) assert testfun(5) and not testfun(4) testfun = slice_to_t(slice(None, 5)) assert all([testfun(x) for x in xrange(5)]) assert not testfun(5) testfun = slice_to_t(slice(0, 5)) assert all([testfun(x) for x in xrange(5)]) assert not testfun(5) testfun = slice_to_t(slice(0, 10, 2)) assert all([testfun(x) for x in xrange(0, 10, 2)]) assert not testfun(1) assert not testfun(10) testfun = slice_to_t(slice(0, None, 2)) assert all([testfun(x) for x in xrange(0, 10, 2)]) assert not testfun(1) def test_invert_array(): ''' Test the invert_array function. ''' assert invert_array(np.array([])) == {} test_ar = np.array([0, 4, 4, 1, 0, 0]) inverted_array = invert_array(test_ar) # The inverted_array function does not guarantee sorted arrays for key in inverted_array: inverted_array[key].sort() assert len(inverted_array) == 3 assert (inverted_array[0] == np.array([0, 4, 5])).all() assert (inverted_array[4] == np.array([1, 2])).all() assert (inverted_array[1] == np.array([3])).all() def test_smallest_inttype(): ''' Test the smallest_inttype function (returning the smallest signed integer type that can represent a certain number) ''' values = [2**n-1 for n in xrange(1, 36)] values.extend([2**n for n in xrange(1, 36)]) for value in values: # only checks for whether the type is big enough, not whether it is the # smallest possible type inttype = smallest_inttype(value) assert inttype(value) == value def test_indent(): ''' Tests the indent function. ''' assert indent('some text') == ' some text' assert indent('some text', 2) == ' some text' assert indent(' some text') == ' some text' before_text='''some text more text''' after_text=''' some text more text''' assert indent(before_text) == after_text if __name__ == '__main__': test_construction_single_synapses() test_construction_multiple_synapses() test_construction_and_access() test_model_definition() test_incorrect_pre_reference() test_slice_to_array() test_slice_to_test() test_smallest_inttype() test_indent() test_max_delay() brian-1.4.3/brian/tests/testinterface/test_threshold.py000066400000000000000000000220731266062647100233320ustar00rootroot00000000000000from brian import * from nose.tools import * from operator import itemgetter from brian.utils.approximatecomparisons import is_approx_equal from brian.tests import repeat_with_global_opts from brian.globalprefs import get_global_preference @repeat_with_global_opts([ # no C code or code generation, {'useweave': False, 'usecodegen': False}, # # use weave but no code generation {'useweave': True, 'usecodegen': False}, # use Python code generation {'useweave': False, 'usecodegen': True, 'usecodegenthreshold': True}, # use C code generation {'useweave': True, 'usecodegen': True, 'usecodegenthreshold': True, 'usecodegenweave': True} ]) def test(): """ :class:`Threshold` ~~~~~~~~~~~~~~~~~~ Initialised as ``Threshold(threshold[,state=0])`` Causes a spike whenever the given state variable is above the threshold value. :class:`NoThreshold` ~~~~~~~~~~~~~~~~~~~~ Does nothing, initialised as ``NoThreshold()`` Functional thresholds ~~~~~~~~~~~~~~~~~~~~~ Initialised as:: FunThreshold(thresholdfun) SimpleFunThreshold(thresholdfun[,state=0]) Threshold functions return a boolean array the same size as the number of neurons in the group, where if the returned array is True at index i then neuron i fires. The arguments passed to the :class:`FunThreshold` function are the full array of state variables for the group in order. The argument passed to the :class:`SimpleFunThreshold` function is the array of length N corresponding to the given state variable. :class:`VariableThreshold` ~~~~~~~~~~~~~~~~~~~~~~~~~~ Initialised as ``VariableThreshold(threshold_state[,state=0])`` Causes a spike whenever the state variable defined by state is above the state variable defined by threshold_state. :class:`EmpiricalThreshold` ~~~~~~~~~~~~~~~~~~~~~~~~~~~ Initialised as:: EmpiricalThreshold(threshold[,refractory=1*msecond[,state=0[,clock]]]) Causes a spike when the given state variable exceeds the threshold value, but only if there has been no spike within the refractory period. Will use the given clock if specified, otherwise the standard guessing procedure is used. Poisson thresholds ~~~~~~~~~~~~~~~~~~ Initialised as:: PoissonThreshold([state=0]) HomogeneousPoissonThreshold([state=0]) The Poisson process gets the rates from the specified state variable, the homogeneous version uses the rates from the specified variable of the first neuron in the group. """ reinit_default_clock() # test that Threshold works as expected with default state G = NeuronGroup(3, model=LazyStateUpdater(), reset=Reset(0.), threshold=Threshold(1.), init=(0.,)) M = SpikeMonitor(G, True) net = Network(G, M) net.run(1 * msecond) assert (len(M.spikes) == 0) G.state(0)[:] = array([0.5, 1.5, 2.5]) net.run(1 * msecond) i, t = zip(*sorted(M.spikes, key=itemgetter(0))) assert (i == (1, 2)) for s in t: assert (is_approx_equal(s, 1 * msecond)) # test that Threshold works as expected with specified state def test_specified_state(G): M = SpikeMonitor(G, True) net = Network(G, M) net.run(1 * msecond) assert (len(M.spikes) == 0) net.reinit() G.state(0)[:] = array([0.5, 1.5, 2.5]) net.run(1 * msecond) assert (len(M.spikes) == 0) net.reinit() G.state(1)[:] = array([0.5, 1.5, 2.5]) net.run(1 * msecond) i, t = zip(*sorted(M.spikes, key=itemgetter(0))) assert (i == (1, 2)) for s in t: assert (is_approx_equal(s, 0 * msecond)) G = NeuronGroup(3, model=LazyStateUpdater(numstatevariables=2), reset=Reset(0., state=1), threshold=Threshold(1., state=1), init=(0., 0.)) test_specified_state(G) # use string threshold eqs = '''v : 1 w : 1 ''' G = NeuronGroup(3, model=eqs, reset=Reset(0., state=1), threshold='w > 1') test_specified_state(G) # test that VariableThreshold works as expected def test_variable_threshold(G): M = SpikeMonitor(G, True) net = Network(G, M) get_default_clock().reinit() G.state(2)[:] = array([1., 2., 3.]) # the thresholds G.state(1)[:] = array([4., 1., 2.]) # the values net.run(1 * msecond) i, t = zip(*sorted(M.spikes, key=itemgetter(0))) assert (i == (0,)) assert (is_approx_equal(t[0], 0 * second)) G = NeuronGroup(3, model=LazyStateUpdater(numstatevariables=3), reset=Reset(0., state=1), threshold=VariableThreshold(2, state=1), init=(0., 0., 0.)) test_variable_threshold(G) # use string threshold eqs = '''v : 1 w : 1 x : 1 ''' G = NeuronGroup(3, model=eqs, reset=Reset(0., state=1), threshold='w > x') test_variable_threshold(G) # test that FunThreshold works as expected def f(S0, S1): return S0 > S1 * S1 G = NeuronGroup(3, model=LazyStateUpdater(numstatevariables=2), reset=Reset(0.), threshold=FunThreshold(f), init=(0., 0.)) G.state(0)[:] = array([2., 3., 10.]) G.state(1)[:] = array([1., 2., 3.]) # the square root of the threshold values M = SpikeMonitor(G, True) net = Network(G, M) get_default_clock().reinit() net.run(1 * msecond) i, t = zip(*sorted(M.spikes, key=itemgetter(0))) assert (i == (0, 2)) for s in t: assert (is_approx_equal(s, 0 * msecond)) # test that SimpleFunThreshold works as expected def f(S): return S > 1. G = NeuronGroup(3, model=LazyStateUpdater(), reset=Reset(0.), threshold=SimpleFunThreshold(f), init=(0.,)) G.state(0)[:] = array([0.5, 1.5, 2.5]) M = SpikeMonitor(G, True) net = Network(G, M) get_default_clock().reinit() net.run(1 * msecond) i, t = zip(*sorted(M.spikes, key=itemgetter(0))) assert (i == (1, 2)) for s in t: assert (is_approx_equal(s, 0 * msecond)) # test that EmpiricalThreshold works as expected G = NeuronGroup(1, model=LazyStateUpdater(numstatevariables=2), reset=NoReset(), threshold=EmpiricalThreshold(1., refractory=0.5 * msecond, state=1), init=(0., 2.)) M = SpikeMonitor(G, True) net = Network(G, M) get_default_clock().reinit() net.run(1.6 * msecond) i, t = zip(*sorted(M.spikes, key=itemgetter(1))) assert (i == (0, 0, 0, 0)) for i, s in enumerate(t): assert (is_approx_equal(s, i * 0.5 * msecond)) # test that PoissonThreshold works def test_poisson_threshold(G): init = float(1. / get_default_clock().dt) # should cause spiking at every time interval G.state(0)[:] = array([0., init, 0.]) M = SpikeMonitor(G, True) net = Network(G, M) net.run(1 * msecond) assert (len(M.spikes)) i, t = zip(*sorted(M.spikes, key=itemgetter(1))) assert (all(j == 1 for j in i)) G = NeuronGroup(3, model=LazyStateUpdater(), reset=NoReset(), threshold=PoissonThreshold()) test_poisson_threshold(G) # Poisson threshold via a string threshold using the rand() function eqs = '''v : 1 w : 1 x : 1 ''' # A threshold with rand in it is not supported by CThreshold if not (get_global_preference('usecodegen') and get_global_preference('usecodegenthreshold') and get_global_preference('useweave') and get_global_preference('usecodegenweave')): G = NeuronGroup(3, model=eqs, reset=NoReset(), threshold='rand() < v') test_poisson_threshold(G) G = NeuronGroup(3, model=eqs, reset=NoReset(), threshold=StringThreshold('rand() < v')) test_poisson_threshold(G) # test that HomogeneousPoissonThreshold works init = float(1. / get_default_clock().dt) # should cause spiking at every time interval G = NeuronGroup(3, model=LazyStateUpdater(), reset=NoReset(), threshold=HomogeneousPoissonThreshold()) M = SpikeMonitor(G, True) net = Network(G, M) G.state(0)[:] = array([0., init, 0.]) # should do nothing, because only first neuron is looked at net.run(1 * msecond) assert (len(M.spikes) == 0) G.state(0)[:] = array([init, 0., 0.]) # should do nothing, because only first neuron is looked at net.run(1 * msecond) # we actually cannot make any assertion about the behaviour of this system, other than # that it should run correctly if __name__ == '__main__': test() brian-1.4.3/brian/tests/testinterface/test_timedarray.py000066400000000000000000000076621266062647100235060ustar00rootroot00000000000000from brian import * from nose.tools import assert_raises def test_construction(): ''' Test various ways of constructing a TimedArray. ''' reinit_default_clock() ar = array([0, 2, 4, 6]) def assert_values(ta, ar, start, dt): # check values at timepoints for index, val in enumerate(ar): assert ta(start + dt * index) == val # check values between timepoints for index, val in enumerate(ar[:-1]): assert ta(start + dt * (index + 0.4)) == val # defaultclock ta = TimedArray(ar) assert ta.clock == defaultclock assert_values(ta, ar, 0 * second, defaultclock.dt) # a given clock clock = Clock(dt=0.25 * ms) ta = TimedArray(ar, clock=clock) assert ta.clock == clock assert_values(ta, ar, 0 * second, clock.dt) # given dt dt = 0.5 * ms ta = TimedArray(ar, dt=dt) assert_values(ta, ar, 0 * second, dt=dt) # given start and dt start, dt = 1 * ms, 0.5 * ms ta = TimedArray(ar, start=start, dt=dt) assert_values(ta, ar, start=start, dt=dt) # giving start, dt and clock should not work assert_raises(ValueError, lambda : TimedArray(ar, start=start, dt=dt, clock=Clock())) # a list of times dt = 0.25 * ms times = arange(len(ar)) * dt ta = TimedArray(ar, times=times) # list of times + clock should not work assert_raises(ValueError, lambda : TimedArray(ar, times=times, clock=Clock())) def test_access(): ''' Test various ways of accessing a TimedArray. ''' reinit_default_clock() # 1-D array ar = array([0, 2, 4, 6]) ta = TimedArray(ar) assert ta[1] == array(ar[1]) assert (ta[:] == ar[:]).all() and (ta.times == ta[:].times).all() assert (ta[:2] == ar[:2]).all() assert (ta[:2].times == ta.times[:2]).all() index_array = array([0, 2]) assert (ta[index_array] == ar[index_array]).all() assert (ta[index_array].times == ta.times[index_array]).all() times = arange(len(ar)) * defaultclock.dt assert (ta(times) == ar).all() # values beyond the last timepoint should default to the last value assert ta(1 * second) == ar[-1] # 2-D array ar = array([[0, 1], [2, 3], [4, 5], [6, 7]]) ta = TimedArray(ar) assert len(ta(0 * second)) == 2 # last values assert (ta(1 * second) == ar[-1, :]).all() # this should use the first time for the first "neuron" and the second time # for the second one assert (ta(array([0 * second, 2 * defaultclock.dt])) == array([ar[0, 0], ar[2, 1]])).all() def test_neurongroup_integration(): ''' Test using a TimedArray for setting values of a NeuronGroup. ''' reinit_default_clock() duration = 5 * ms ar = arange(0, int(duration / defaultclock.dt)) * 3 ta = TimedArray(ar) # Link variable to TimedArray via TimedArraySetter G = NeuronGroup(1, model='p : 1') G.p = ta mon = StateMonitor(G, 'p', record=0) run(duration) assert (mon[0] == ar).all() reinit_default_clock() # Using explicit times ta = TimedArray(ar, times=arange(int(duration / defaultclock.dt)) * defaultclock.dt) G = NeuronGroup(1, model='p : 1') G.p = ta mon = StateMonitor(G, 'p', record=0) run(duration) assert (mon[0] == ar).all() reinit_default_clock() # use an array with custom, irregular times ar = array([1, 5]) ta = TimedArray(ar, times=array([0*ms, 3 * ms])) G = NeuronGroup(1, model='p : 1') G.p = ta mon = StateMonitor(G, 'p', record=0) run(duration) assert (mon[0][mon.times < 3 * ms] == 1).all() assert (mon[0][mon.times >= 3 * ms] == 5).all() # TODO: Check 2-D arrays as well if __name__ == '__main__': test_construction() test_access() test_neurongroup_integration() brian-1.4.3/brian/tests/testinterface/test_units.py000066400000000000000000000164251266062647100225040ustar00rootroot00000000000000import itertools from brian import * from nose.tools import * import numpy def test(): """ Names ~~~~~ The following units should exist: metre, kilogram, second, amp, kelvin, mole, candle radian, steradian, hertz, newton, pascal, joule, watt, coulomb, volt, farad, ohm, siemens, weber, tesla, henry, celsius, lumen, lux, becquerel, gray, sievert, katal, gram, gramme In addition, all versions of these units scaled by the following prefixes should exist (in descending order of size): Y, Z, E, P, T, G, M, k, h, da, d, c, m, u, n, p, f, a, z, y And, all of the above units with the suffixes 2 and 3 exist, and refer to the unit to the power of 2 and 3, e.g. ``metre3 = metre**3``. Arithmetic ~~~~~~~~~~ The following operations on :class:`Quantity` objects require that the operands have the same dimensions: +, -, <, <=, >, >=, ==, != The following operations on :class:`Quantity` objects work with any pair of operands: / and * In addition, ``-x`` and ``abs(x)`` will work on any :class:`Quantity` ``x``, and will return values with the same dimension as their argument. The power operation ``x**y`` requires that ``y`` be dimensionless. Casting ~~~~~~~ The three rules that define the casting operations for :class:`Quantity` object are: 1. :class:`Quantity` op :class:`Quantity` = :class:`Quantity`: Performs dimension consistency check if appropriate. 2. Scalar op :class:`Quantity` = :class:`Quantity`: Assumes that the scalar is dimensionless 3. other op :class:`Quantity` = other: The :class:`Quantity` object is downcast to a ``float`` Scalar types are 1 dimensional number types, including ``float``, ``int``, etc. but not ``array``. The :class:`Quantity` class is a derived class of ``float``, so many other operations will also downcast to ``float``. For example, ``sin(x)`` where ``x`` is a quantity will return ``sin(float(x))`` without doing any dimension checking. Although see the Brian.unitsafefunctions module for a way round this. It is better to be explicit about casting if you can be. TODO: more details on ``numpy.array``/:class:`Quantity` operations? :func:`check_units` decorator ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ The decorator :func:`check_units` can be used to check that the arguments passed to a function have the right units, e.g.:: @check_units(I=amp,V=volt) def find_resistance(I,V): return V/I will work if you try ``find_resistance(1*amp,1*volt)`` but raise an exception if you try ``find_resistance(1,1)`` say. """ reinit_default_clock() # the following units should exist: units_which_should_exist = [ metre, meter, kilogram, second, amp, kelvin, mole, candle, radian, steradian, hertz, newton, pascal, joule, watt, coulomb, volt, farad, ohm, siemens, weber, tesla, henry, celsius, lumen, lux, becquerel, gray, sievert, katal, gram, gramme ] # scaled versions of all these units should exist (we just check farad as an example) some_scaled_units = [ Yfarad, Zfarad, Efarad, Pfarad, Tfarad, Gfarad, Mfarad, kfarad, hfarad, dafarad, dfarad, cfarad, mfarad, ufarad, nfarad, pfarad, ffarad, afarad, zfarad, yfarad ] # and check that the above is in descending order of size import copy sorted_scaled_units = copy.copy(some_scaled_units) sorted_scaled_units.sort(reverse=True) assert some_scaled_units == sorted_scaled_units # some powered units powered_units = [ cmetre2, Yfarad3 ] # check that operations requiring consistent units work with consistent units a = 1 * kilogram b = 2 * kilogram c = [ a + b, a - b, a < b, a <= b, a > b, a >= b, a == b, a != b ] # check that given inconsistent units they raise an exception from operator import add, sub, mul, div, lt, le, gt, ge, eq, ne tryops = [add, sub, lt, le, gt, ge, eq, ne] a = 1 * kilogram b = 1 * second def inconsistent_operation(a, b, op): return op(a, b) for op in tryops: assert_raises(DimensionMismatchError, inconsistent_operation, a, b, op) # check that comparisons to integer zero (no units) work a = 1 * kilogram assert(all([not (a < 0), not (a <= 0), a > 0, a >= 0, not (a == 0), a != 0])) # check that comparisons to float zero (no units) work a = 1 * kilogram assert(all([not (a < 0.), not (a <= 0.), a > 0., a >= 0., not (a == 0.), a != 0.])) # check that comparisons to inf and -inf work a = 1 * kilogram assert(all([a < inf, a <= inf, a > -inf, a >= -inf, a != inf, a != -inf])) # check that comparisons for dimensionless units to scalars work a = 1 * kilogram / kilogram assert(all([a < 2, a <= 2, a > 0, a >= 0, a == 1, a != 2])) # check that operations not requiring consistent units work a = 1 * kilogram b = 1 * second c = [ a * b, a / b ] # check that - and abs give results with the same dimensions assert (-a).has_same_dimensions(a) assert abs(a).has_same_dimensions(a) assert - abs(a) < abs(a) # well why not, this should be true # check that pow requires the index to be dimensionless a = (1 * kilogram) ** 0.352 def inconsistent_power(a, b): return a ** b a = 1 * kilogram b = 1 * second assert_raises(DimensionMismatchError, inconsistent_power, a, b) # check casting rule 1 a = 1 * kilogram b = 2 * kilogram for op in [add, sub, mul, div]: assert isinstance(op(a, b), Quantity) # check casting rule 2 a = 1 b = 1 * kilogram for op in [mul, div]: assert isinstance(op(a, b), Quantity) and isinstance(op(b, a), Quantity) for op in [add, sub]: assert_raises(DimensionMismatchError, inconsistent_operation, a, b, op) for op in [add, sub, mul, div]: assert isinstance(op(a, b / b), Quantity) and isinstance(op(b / b, a), Quantity) # check casting rule 3 assert isinstance(numpy.array([1, 2]) * (1 * kilogram), numpy.ndarray) assert isinstance((1 * kilogram) * numpy.array([1, 2]), numpy.ndarray) # check_units decorator @check_units(I=amp, V=volt) def find_resistance(I, V): return V / I R = find_resistance(1 * amp, 1 * volt) assert_raises(DimensionMismatchError, find_resistance, 1, 1) # check that str works (ignoring the result) and that repr returns a # consistent representation, i.e. eval(repr(x)) == x # Combined units complex_units = [(kgram * metre2)/(amp * second3), 5 * (kgram * metre2)/(amp * second3), metre * second**-1, 10 * metre * second**-1] for u in itertools.chain(units_which_should_exist, some_scaled_units, powered_units, complex_units): str(u) assert(eval(repr(u)) == u) if __name__ == '__main__': test() brian-1.4.3/brian/tests/testinterface/test_unitsafefunctions.py000066400000000000000000000024621266062647100251050ustar00rootroot00000000000000from brian import * from nose.tools import * import numpy def test(): """ Each of the following functions f(x) should use units if they are passed a :class:`Quantity` object or fall back on their numpy versions otherwise. sqrt, log, exp, sin, cos, tan, arcsin, arccos, arctan, sinh, cosh, tanh, arcsinh, arccosh, arctanh """ reinit_default_clock() # check sqrt behaves as expected x = 3 * second z = numpy.array([3., 2.]) assert (have_same_dimensions(sqrt(x), second ** 0.5)) assert (isinstance(sqrt(z), numpy.ndarray)) # check the return types are right for all other functions x = 0.5 * second / second funcs = [ sqrt, log, exp, sin, cos, tan, arcsin, arccos, arctan, sinh, cosh, tanh, arcsinh, arccosh, arctanh ] for f in funcs: assert (isinstance(f(x), Quantity)) assert (isinstance(f(z), numpy.ndarray)) # check that attempting to use these functions on something with units fails funcs = [ log, exp, sin, cos, tan, arcsin, arccos, arctan, sinh, cosh, tanh, arcsinh, arccosh, arctanh ] x = 3 * second for f in funcs: assert_raises(DimensionMismatchError, f, x) if __name__ == '__main__': test() brian-1.4.3/brian/tests/testutils/000077500000000000000000000000001266062647100171215ustar00rootroot00000000000000brian-1.4.3/brian/tests/testutils/__init__.py000066400000000000000000000000021266062647100212220ustar00rootroot00000000000000 brian-1.4.3/brian/tests/testutils/test_dynamicarray.py000066400000000000000000000044101266062647100232140ustar00rootroot00000000000000from numpy import array from brian.utils.dynamicarray import DynamicArray, DynamicArray1D def test_dynamicarray(): # Perform the test mentioned in the docstring of the DynamicArray class. # TODO: This could be a doctest directly... x = DynamicArray((2, 3), dtype=int) x[:] = 1 x.resize((3, 3)) x[:] += 1 x.resize((3, 4)) x[:] += 1 x.resize((4, 4)) x[:] += 1 x.data[:] = x.data**2 # Do a resize that changes nothing x.resize((4, 4)) # This is the expected result y = array([[16, 16, 16, 4], [16, 16, 16, 4], [ 9, 9, 9, 4], [ 1, 1, 1, 1]]) assert(len(x) == len(y)) assert(len(x[0]) == len(y[0])) assert(x.shape == (4, 4)) assert((x == y).all()) # Do the same with use_numpy_resize=True x = DynamicArray((2, 3), dtype=int, use_numpy_resize=True) x[:] = 1 x.resize((3, 3)) x[:] += 1 x.resize((3, 4)) x[:] += 1 x.resize((4, 4)) x[:] += 1 x.data[:] = x.data**2 # Do a resize that changes nothing x.resize((4, 4)) y = array([[16, 16, 16, 4], [16, 16, 16, 4], [ 9, 9, 9, 4], [ 1, 1, 1, 1]]) assert(len(x) == len(y)) assert(len(x[0]) == len(y[0])) assert(x.shape == (4, 4)) assert((x == y).all()) # Test shrinking x = DynamicArray((2, 3), dtype=int) x[:] = 1 x.shrink((1, 2)) # This should not do anything x.shrink((2, 3)) y = array([[1, 1]]) assert(x.shape == y.shape) assert((x == y).all()) # Test DynamicArray1D x = DynamicArray1D(2, dtype=int) x[:] = 1 x.resize(3) x[:] += 1 x.resize(4) x.data[:] = x.data**2 # Expected result y = array([4, 4, 1, 0]) assert(len(x) == len(y)) assert(x.shape == y.shape) assert((x == y).all()) # Test DynamicArray1D with use_numpy_resize=True x = DynamicArray1D(2, dtype=int, use_numpy_resize=True) x[:] = 1 x.resize(3) x[:] += 1 x.resize(4) x.data[:] = x.data**2 # Expected result y = array([4, 4, 1, 0]) assert(len(x) == len(y)) assert(x.shape == y.shape) assert((x == y).all()) if __name__ == '__main__': test_dynamicarray()brian-1.4.3/brian/tests/testutils/test_savesynapses.py000066400000000000000000000027621266062647100232650ustar00rootroot00000000000000''' This file is a test for the save/load connectivity options of the Synapses class. See the doc to the save_connectivity method of Synapses for further details on what it does. This test covers the use case as described in the docstring ''' from brian.synapses import * from brian.neurongroup import * import numpy as np import cStringIO from nose.tools import * def test_save_load_builtin(): # prepare neurongroups for the creation of a synapse object N0 = 10 g0 = NeuronGroup(N0, model = 'dv/dt = 0/ms : 1') N1 = 20 g1 = NeuronGroup(N1, model = 'dv/dt = 0/ms : 1') # create synapses synapses = Synapses(g0, g1, model = 'w:1', pre = 'v+=w') synapses.connect_random(g0, g1, sparseness = 0.2) synapses.w = np.arange(len(synapses)) # use a string io for temporary file # this is (almost) unnecessarily complicated, # it's just so that we don't create a file in the local directory f = cStringIO.StringIO() # save w and the connectivity w_before_save = synapses.w[:,:] synapses.save_connectivity(f) # SAVE # Now, recreate the synapses object synapses_after_save = Synapses(g0, g1, model = 'z:1', pre = 'v+=z') # same remark as above rf = cStringIO.StringIO(f.getvalue()) synapses_after_save.load_connectivity(rf) # LOAD w_after_save = synapses.w[:,:] assert (w_after_save == w_before_save).all() assert len(synapses) == len(synapses_after_save) if __name__ == '__main__': test_save_load_builtin() brian-1.4.3/brian/tests/testutils/test_sparse.py000066400000000000000000000012531266062647100220300ustar00rootroot00000000000000from brian.connections import SparseMatrix from numpy import zeros, array from nose.tools import * def test_sparse_matrix(): x = SparseMatrix((10, 10)) z = zeros((10, 10), dtype=int) x[0, 4] = 3 x[1, :] = 5 x[2, []] = [] x[3, [0]] = [6] x[4, [0, 1]] = [7, 8] x[5, 2:4] = [1, 2] x[6:8, 4:7] = 1 # same thing with z to compare z[0, 4] = 3 z[1, :] = 5 z[2, []] = [] z[3, [0]] = [6] z[4, [0, 1]] = [7, 8] z[5, 2:4] = [1, 2] z[6:8, 4:7] = 1 # check the values are correct y = array(x.todense(), dtype=int) assert (y==z).all() if __name__ == '__main__': test_sparse_matrix() brian-1.4.3/brian/tests/testutils/test_statistics.py000066400000000000000000000011121266062647100227170ustar00rootroot00000000000000from brian.tools.statistics import * from brian.units import * from brian.stdunits import * from numpy import * from numpy.random import * from nose.tools import * def test_group_correlations(): rate = 10.0 n = 100 poisson = cumsum(exponential(1 / rate, n)) spikes = [(0, t * second) for t in poisson] spikes.extend([(0, t * second + 1 * ms) for t in poisson]) spikes = sort_spikes(spikes) S, tauc = group_correlations(spikes, delta=3 * ms) assert abs(tauc[0] - .001) < .0005 if __name__ == '__main__': test_group_correlations() brian-1.4.3/brian/threshold.py000066400000000000000000000455451266062647100163020ustar00rootroot00000000000000# ---------------------------------------------------------------------------------- # Copyright ENS, INRIA, CNRS # Contributors: Romain Brette (brette@di.ens.fr) and Dan Goodman (goodman@di.ens.fr) # # Brian is a computer program whose purpose is to simulate models # of biological neural networks. # # This software is governed by the CeCILL license under French law and # abiding by the rules of distribution of free software. You can use, # modify and/ or redistribute the software under the terms of the CeCILL # license as circulated by CEA, CNRS and INRIA at the following URL # "http://www.cecill.info". # # As a counterpart to the access to the source code and rights to copy, # modify and redistribute granted by the license, users are provided only # with a limited warranty and the software's author, the holder of the # economic rights, and the successive licensors have only limited # liability. # # In this respect, the user's attention is drawn to the risks associated # with loading, using, modifying and/or developing or reproducing the # software by the user in light of its specific status of free software, # that may mean that it is complicated to manipulate, and that also # therefore means that it is reserved for developers and experienced # professionals having in-depth computer knowledge. Users are therefore # encouraged to load and test the software's suitability as regards their # requirements in conditions enabling the security of their systems and/or # data to be ensured and, more generally, to use and operate it in the # same conditions as regards security. # # The fact that you are presently reading this means that you have had # knowledge of the CeCILL license and that you accept its terms. # ---------------------------------------------------------------------------------- # ''' Threshold mechanisms ''' import re from random import sample # Python standard random module (sample is different) from numpy import clip, Inf from numpy.random import rand, randn try: import weave except ImportError: from scipy import weave from scipy import random from brian.clock import guess_clock from brian.globalprefs import get_global_preference from brian.inspection import namespace, get_identifiers from brian.log import log_warn from brian.units import check_units, second, msecond, mvolt from brian.utils.approximatecomparisons import is_approx_equal __all__ = ['Threshold', 'FunThreshold', 'VariableThreshold', 'NoThreshold', 'EmpiricalThreshold', 'SimpleFunThreshold', 'PoissonThreshold', 'HomogeneousPoissonThreshold', 'StringThreshold'] CThreshold = PythonThreshold = None def select_threshold(expr, eqs, level=0): ''' Automatically selects the appropriate Threshold object from a string. Matches the following patterns: var_name > or >= const : Threshold var_name > or >= var_name : VariableThreshold others : StringThreshold ''' global CThreshold, PythonThreshold use_codegen = (get_global_preference('usecodegen') and get_global_preference('usecodegenthreshold')) use_weave = (get_global_preference('useweave') and get_global_preference('usecodegenweave')) if use_codegen: if CThreshold is None: from brian.experimental.codegen.threshold import (CThreshold, PythonThreshold) if use_weave: log_warn('brian.threshold', 'Using codegen CThreshold') return CThreshold(expr, level=level + 1) else: log_warn('brian.threshold', 'Using codegen PythonThreshold') return PythonThreshold(expr, level=level + 1) # plan: # - see if it matches A > B or A >= B, if not select StringThreshold # - check if A, B both match diffeq variable names, and if so # select VariableThreshold # - check that A is a variable name, if not select StringThreshold # - extract all the identifiers from B, and if none of them are # callable, assume it is a constant, try to eval it and then use # Threshold. If not, or if eval fails, use StringThreshold. # This misses the case of e.g. V>10*mV*exp(1) because exp will be # callable, but in general a callable means that it could be # non-constant. expr = expr.strip() eqs.prepare() ns = namespace(expr, level=level + 1) s = re.search(r'^\s*(\w+)\s*>=?(.+)', expr) if not s: return StringThreshold(expr, level=level + 1) A = s.group(1) B = s.group(2).strip() if A not in eqs._diffeq_names: return StringThreshold(expr, level=level + 1) if B in eqs._diffeq_names: return VariableThreshold(B, A) try: vars = get_identifiers(B) except SyntaxError: return StringThreshold(expr, level=level + 1) all_vars = eqs._eq_names + eqs._diffeq_names + eqs._alias.keys() + ['t'] for v in vars: if v not in ns or v in all_vars or callable(ns[v]): return StringThreshold(expr, level=level + 1) try: val = eval(B, ns) except: return StringThreshold(expr, level=level + 1) return Threshold(val, A) class Threshold(object): ''' All neurons with a specified state variable above a fixed value fire a spike. **Initialised as:** :: Threshold([threshold=1*mV[,state=0]) with arguments: ``threshold`` The value above which a neuron will fire. ``state`` The state variable which is checked. **Compilation** Note that if the global variable ``useweave`` is set to ``True`` then this function will use a ``C++`` accelerated version which runs approximately 3x faster. ''' def __init__(self, threshold=1 * mvolt, state=0): self.threshold = threshold self.state = state self._useaccel = get_global_preference('useweave') self._cpp_compiler = get_global_preference('weavecompiler') self._extra_compile_args = ['-O3'] if self._cpp_compiler == 'gcc': self._extra_compile_args += get_global_preference('gcc_options') # ['-march=native', '-ffast-math'] def __call__(self, P): ''' Checks the threshold condition and returns spike times. P is the neuron group. Note the accelerated version runs 3x faster. ''' if self._useaccel: spikes = P._spikesarray V = P.state_(self.state) Vt = float(self.threshold) N = int(len(P)) code = """ int numspikes=0; for(int i=0;iVt) spikes(numspikes++) = i; return_val = numspikes; """ # WEAVE NOTE: set the environment variable USER if your username has a space # in it, say set USER=DanGoodman if your username is Dan Goodman, this is # because weave uses this to create file names, but doesn't correctly send these # values to the compiler, causing problems. numspikes = weave.inline(code, ['spikes', 'V', 'Vt', 'N'], compiler=self._cpp_compiler, type_converters=weave.converters.blitz, extra_compile_args=self._extra_compile_args) # WEAVE NOTE: setting verbose=True in the weave.inline function may help in # finding errors. return spikes[0:numspikes] else: return ((P.state_(self.state) > self.threshold).nonzero())[0] def __repr__(self): return '%s(threshold=%s, state=%s)' % (self.__class__.__name__, repr(self.threshold), repr(self.state)) def __str__(self): return ('Threshold mechanism with value=' + str(self.threshold) + ' acting on state ' + str(self.state)) class StringThreshold(Threshold): ''' A threshold specified by a string expression. Initialised with arguments: ``expr`` The expression used to test whether a neuron has fired a spike. Should be a single statement that returns a value. For example, ``'V>50*mV'`` or ``'V>Vt'``. ``level`` How many levels up in the calling sequence to look for names in the namespace. Usually 0 for user code. ''' def __init__(self, expr, level=0): self._namespace, unknowns = namespace(expr, level=level + 1, return_unknowns=True) self._vars = unknowns self._expr = expr self._code = compile(expr, "StringThreshold", "eval") class Replacer(object): def __init__(self, func, n): self.n = n self.func = func def __call__(self): return self.func(self.n) self._Replacer = Replacer def __call__(self, P): for var in self._vars: # couldn't we do this just once? self._namespace[var] = P.state(var) self._namespace['rand'] = self._Replacer(rand, len(P)) self._namespace['randn'] = self._Replacer(randn, len(P)) return eval(self._code, self._namespace).nonzero()[0] def __repr__(self): return '%s(%s)' % (self.__class__.__name__, repr(self._expr)) def __str__(self): return '%s using expression "%s"' % (self.__class__.__name__, str(self._expr)) class NoThreshold(Threshold): ''' No thresholding mechanism. **Initialised as:** :: NoThreshold() ''' def __init__(self): pass def __call__(self, P): return [] def __repr__(self): return "NoThreshold()" def __str__(self): return "No threshold" class FunThreshold(Threshold): ''' Threshold mechanism with a user-specified function. **Initialised as:** :: FunThreshold(thresholdfun) where ``thresholdfun`` is a function with one argument, the 2d state value array, where each row is an array of values for one state, of length N for N the number of neurons in the group. For efficiency, data are numpy arrays and there is no unit checking. Note: if you only need to consider one state variable, use the :class:`SimpleFunThreshold` object instead. ''' def __init__(self, thresholdfun): self.thresholdfun = thresholdfun # Threshold function def __call__(self, P): ''' Checks the threshold condition and returns spike times. P is the neuron group. ''' spikes = (self.thresholdfun(*P._S).nonzero())[0] return spikes def __repr__(self): return '%s(%r)' % (self.__class__.__name__, self.thresholdfun) def __str__(self): return 'Functional threshold mechanism' class SimpleFunThreshold(FunThreshold): ''' Threshold mechanism with a user-specified function. **Initialised as:** :: FunThreshold(thresholdfun[,state=0]) with arguments: ``thresholdfun`` A function with one argument, the array of values for the specified state variable. For efficiency, this is a numpy array, and there is no unit checking. ``state`` The name or number of the state variable to pass to the threshold function. **Sample usage:** :: FunThreshold(lambda V:V>=Vt,state='V') ''' def __init__(self, thresholdfun, state=0): self.thresholdfun = thresholdfun # Threshold function self.state = state def __call__(self, P): ''' Checks the threshold condition and returns spike times. P is the neuron group. ''' spikes = (self.thresholdfun(P.state_(self.state)).nonzero())[0] #P.LS[spikes]=P.clock.t # Time of last spike (this line should be general) return spikes class VariableThreshold(Threshold): ''' Threshold mechanism where one state variable is compared to another. **Initialised as:** :: VariableThreshold([threshold_state=1[,state=0]]) with arguments: ``threshold_state`` The state holding the lower bound for spiking. ``state`` The state that is checked. If ``x`` is the value of state variable ``threshold_state`` on neuron ``i`` and ``y`` is the value of state variable ``state`` on neuron ``i`` then neuron ``i`` will fire if ``y>x``. Typically, using this class is more time efficient than writing a custom thresholding operation. **Compilation** Note that if the global variable ``useweave`` is set to ``True`` then this function will use a ``C++`` accelerated version. ''' def __init__(self, threshold_state=1, state=0): self.threshold_state = threshold_state # State variable representing the threshold self.state = state self._useaccel = get_global_preference('useweave') self._cpp_compiler = get_global_preference('weavecompiler') self._extra_compile_args = ['-O3'] if self._cpp_compiler == 'gcc': self._extra_compile_args += get_global_preference('gcc_options') # ['-march=native', '-ffast-math'] def __call__(self, P): ''' Checks the threshold condition, resets and returns spike times. P is the neuron group. ''' if self._useaccel: spikes = P._spikesarray V = P.state_(self.state) Vt = P.state_(self.threshold_state) N = int(len(P)) code = """ int numspikes=0; for(int i=0;iVt(i)) spikes(numspikes++) = i; return_val = numspikes; """ numspikes = weave.inline(code, ['spikes', 'V', 'Vt', 'N'], \ compiler=self._cpp_compiler, type_converters=weave.converters.blitz, extra_compile_args=self._extra_compile_args) return spikes[0:numspikes] else: return ((P.state_(self.state) > P.state_(self.threshold_state)).nonzero())[0] def __repr__(self): return '%s(threshold_state=%s, state=%s)' % (self.__class__.__name__, repr(self.threshold_state), repr(self.state)) def __str__(self): return '%s comparing "%s" to the threshold "%s"' % (self.__class__.__name__, self.state, self.threshold_state) class EmpiricalThreshold(Threshold): ''' Empirical threshold, e.g. for Hodgkin-Huxley models. In empirical models such as the Hodgkin-Huxley method, after a spike neurons are not instantaneously reset, but reset themselves as part of the dynamical equations defining their behaviour. This class can be used to model that. It is a simple threshold mechanism that checks e.g. ``V>=Vt`` but it only does so for neurons that haven't recently fired (giving the dynamical equations time to reset the values naturally). It should be used in conjunction with the :class:`NoReset` object. **Initialised as:** :: EmpiricalThreshold([threshold=1*mV[,refractory=1*ms[,state=0[,clock]]]]) with arguments: ``threshold`` The lower bound for the state variable to induce a spike. ``refractory`` The time to wait after a spike before checking for spikes again. ``state`` The name or number of the state variable to check. ``clock`` If this object is being used for a :class:`NeuronGroup` which doesn't use the default clock, you need to specify its clock here. ''' @check_units(refractory=second) def __init__(self, threshold=1 * mvolt, refractory=1 * msecond, state=0, clock=None): self.threshold = threshold # Threshold value self.state = state clock = guess_clock(clock) self.refractory = int(refractory / clock.dt) # this assumes that if the state stays over the threshold, and say # refractory=5ms the user wants spiking at 0ms 5ms 10ms 15ms etc. if is_approx_equal(self.refractory * clock.dt, refractory) and self.refractory > 0: self.refractory -= 1 def __call__(self, P): ''' Checks the threshold condition, resets and returns spike times. P is the neuron group. ''' #spikes=where((P._S[0,:]>self.Vt) & ((P.LS self.threshold spikescond[P.LS[0:self.refractory]] = False return spikescond.nonzero()[0] #P.LS[spikes]=P.clock.t # Time of last spike (this line should be general) #return spikes class PoissonThreshold(Threshold): ''' Poisson threshold: a spike is produced with some probability S[0]*dt, or S[state]*dt. ''' # TODO: check the state has units in Hz def __init__(self, state=0): self.state = state def __call__(self, P): return (random.rand(len(P)) < P.state_(self.state)[:] * P.clock.dt).nonzero()[0] def __repr__(self): return '%s(state=%s)' % (self.__class__.__name__, repr(self.state)) def __str__(self): return '%s using state variable %s' % (self.__class__.__name__, str(self.state)) class HomogeneousPoissonThreshold(PoissonThreshold): ''' Poisson threshold for spike trains with identical rates. The underlying NeuronGroup has only one state variable. N.B.: "homogeneous" is meant in the spatial (not temporal) sense, the rate may change in time. ''' def __call__(self, P): # N.B.: is "float" necessary? # Other possibility to avoid sorting: use an exponential distribution n = random.poisson(float(len(P) * P.clock.dt * clip(P._S[self.state][0], 0, Inf))) # number of spikes if n > len(P): n = len(P) log_warn('brian.HomogeneousPoissonThreshold', 'HomogeneousPoissonThreshold cannot generate enough spikes.') spikes = sample(xrange(len(P)), n) spikes.sort() # necessary only for subgrouping return spikes brian-1.4.3/brian/timedarray.py000066400000000000000000000336051266062647100164410ustar00rootroot00000000000000from clock import * from network import * import neurongroup from units import second, check_units import numpy import warnings try: import pylab except: warnings.warn("Couldn't import pylab.") __all__ = ['TimedArray', 'TimedArraySetter', 'set_group_var_by_array'] class TimedArray(numpy.ndarray): ''' An array where each value has an associated time. Initialisation arguments: ``arr`` The values of the array. The first index is the time index. Any array shape works in principle, but only 1D/2D arrays are supported (other shapes may work, but may not). The idea is to, have the shapes (T,) or (T, N) for T the number of time steps and N the number of neurons. ``times`` A 1D array of times whose length should be the same as the first dimension of ``arr``. Usually it is preferable to specify a clock rather than an array of times, but this doesn't work in the case where the time intervals are not fixed. ``clock`` Specify the times corresponding to array values by a clock. The ``t`` attribute of the clock is the time of the first value in the array, and the time interval is the ``dt`` attribute of the clock. If neither ``times`` nor ``clock`` is specified, a clock will be guessed in the usual way (see :class:`Clock`). ``start, dt`` Rather than specifying a clock, you can specify the start time and time interval explicitly. Technically, this is useful because it doesn't create a :class:`Clock` object which can lead to ambiguity about which clock is the default. If dt is specified and start is not, start is assumed to be 0. Note that if the clock, or start time and dt, of the array should be the default clock values, then you should not specify clock, start or dt (see Technical notes below). Arbitrary slicing of the array is supported, but the clock will only be preserved where the intervals can be guaranteed to be fixed, that is except for the case where lists or numpy arrays are used on the time index. Timed arrays can be called as if they were a function of time if the array times are based on a clock (but not if the array times are arbitrary as the look up costs would be excessive). If ``x(t)`` is called where ``times[i]<=t 1: newdt = newtimes[1] - newtimes[0] newclock = Clock(t=newtimes[0] * second, dt=newdt * second) return TimedArray(x, clock=newclock) else: return TimedArray(x, self.times[item]) if isinstance(item, int): return TimedArray(x, self.times[item:item + 1]) if isinstance(item, tuple): item0 = item[0] times = self.times[item0] if isinstance(item0, slice) and self.clock is not None and hasattr(times, '__len__') and len(times) > 1: newdt = times[1] - times[0] newclock = Clock(t=times[0] * second, dt=newdt * second) return TimedArray(x, clock=newclock) if not isinstance(times, numpy.ndarray): times = numpy.array([times]) return TimedArray(x, times) times = self.times[item] if not isinstance(times, numpy.ndarray): times = numpy.array([times]) return TimedArray(x, times) def __getslice__(self, start, end): # Just use __getitem__ for this (it's been deprecated since Python 2.0 # but you need to implement it because the base class does) return self.__getitem__(slice(start, end)) def plot(self, *args, **kwds): if self.size > self.times.size and len(self.shape) == 2: for i in xrange(self.shape[1]): kwds['label'] = str(i) self[:, i].plot(*args, **kwds) else: pylab.plot(self.times, self, *args, **kwds) def __call__(self, t): if self.clock is None: raise ValueError('Can only call timed arrays if they are based on a clock.') else: if isinstance(t, (list, tuple)): t = numpy.array(t) if isinstance(t, neurongroup.TArray): # In this case, we know that t = ones(N)*t so we just use the first value t = t[0] elif isinstance(t, numpy.ndarray): if len(self.shape) > 2: raise ValueError('Calling TimedArray with array valued t only supported for 1D or 2D TimedArray.') if len(self.shape) == 2 and len(t) != self.shape[1]: raise ValueError('Calling TimedArray with array valued t on 2D TimedArray requires len(t)=arr.shape[1]') t = numpy.array(numpy.rint((t - self._t_init) / self._dt), dtype=int) t[t < 0] = 0 t[t >= len(self.times)] = len(self.times) - 1 if len(self.shape) == 1: return numpy.asarray(self)[t] return numpy.asarray(self)[t, numpy.arange(len(t))] t = float(t) ot = t t = int(numpy.rint((t - self._t_init) / self._dt)) if t < 0: t = 0 if t >= len(self.times): t = len(self.times) - 1 return numpy.asarray(self)[t] class TimedArraySetter(NetworkOperation): ''' Sets NeuronGroup values with a TimedArray. At the beginning of each update step, this object will set the values of a given state variable of a group with the value from the array corresponding to the current simulation time. Initialisation arguments: ``group`` The :class:`NeuronGroup` to which the variable belongs. ``var`` The name or index of the state variable in the group. ``arr`` The array of values used to set the variable in the group. Can be an array or a :class:`TimedArray`. If it is an array, you should specify the ``times`` or ``clock`` arguments, or leave them blank to use the default clock. ``times`` Times corresponding to the array values, see :class:`TimedArray` for more details. ``clock`` The clock for the :class:`NetworkOperation`. If none is specified, use the group's clock. If ``arr`` is not a :class:`TimedArray` then this clock will be used to initialise it too. ``start, dt`` Can specify these instead of a clock (see :class:`TimedArray` for details). ``when`` The standard :class:`NetworkOperation` ``when`` keyword, although note that the default value is 'start'. ''' @check_units(start=second, dt=second) def __init__(self, group, var, arr, times=None, clock=None, start=None, dt=None, when='start'): if clock is None: if isinstance(arr, TimedArray) and not arr.clock is None: self.clock = clock = arr.clock else: self.clock = clock = group.clock else: self.clock = clock self.when = when self.group = group self.var = var if not isinstance(arr, TimedArray): arr = TimedArray(arr, times=times, clock=clock, start=start, dt=dt) self.arr = arr self.reinit() def __call__(self): if self.arr.clock is None: # in this case, the time intervals need not be fixed so we # have to step through the array until we find the appropriate # one tcur = self.clock._t while True: if self._cur_i == len(self.arr.times) - 1: self.group.state_(self.var)[:] = self.arr[self._cur_i] return ti_next = self.arr.times[self._cur_i + 1] if ti_next > tcur: self.group.state_(self.var)[:] = self.arr[self._cur_i] return self._cur_i += 1 else: self.group.state_(self.var)[:] = self.arr(self.clock._t) def reinit(self): if self.arr.clock is None: self._cur_i = 0 @check_units(start=second, dt=second) def set_group_var_by_array(group, var, arr, times=None, clock=None, start=None, dt=None): ''' Sets NeuronGroup values with a TimedArray. Creates a :class:`TimedArraySetter`, see that class for details. ''' array_setter = TimedArraySetter(group, var, arr, times=times, clock=clock, start=start, dt=dt) group._owner.contained_objects.append(array_setter) brian-1.4.3/brian/tools/000077500000000000000000000000001266062647100150575ustar00rootroot00000000000000brian-1.4.3/brian/tools/__init__.py000066400000000000000000000002331266062647100171660ustar00rootroot00000000000000from io import * from tabulate import * from statistics import * from parameters import * from correlatedspikes import * from remotecontrol import * brian-1.4.3/brian/tools/autodiff.py000066400000000000000000000340441266062647100172370ustar00rootroot00000000000000''' Automatic differentiation. (C) R. Brette 2008 This file is released under the terms of the BSD licence -------------------------------------------------------- Method: forward accumulation by operator overloading. Use:: df=differentiate(f) # where f is a single-variable function y=differentiate(f,x0) # differentiate at x=x0 y=differentiate(f,x0,order=2) # second-order derivative at x=x0 y=differentiate(f,(a,b),order=(1,2)) # d2f/dxdy(a,b) y=differentiate(f,order=(1,2)) # d2f/dxdy a0,a1,a2=taylor_series(f,x0,order=2) # 2nd order Taylor series of f at x0 J=gradient(f,(a,b)) # gradient of f at (a,b) (returns a list) H=hessian(f,(a,b)) # hessian of f at (a,b) (returns a numpy array) TODO: * replace x by x0 * merge HigherDifferentiable in Differentiable? * simplify code * gradient, taylor_series, hessian: x0 should be optional * iadd, etc. (more operators) * NonDifferentiableException and maybe use this on conditions (when cmp=0) * Differential operators: Laplacian... * gradient, taylor_series, hessian: x0 should be optional * test with units, functions Rp->something, alternative differential operators, differential (f:Rp->Rn), complex numbers, arithmetic derivative * remove most dependency with numpy (except Hessian) * implicitly defined functions If several partial derivatives of the same function need to be calculated, it is faster to use the lower level object Differentiable. Example:: y=f(Differentiable('x',3),Differentiable('y',2)) will return a Differentiable object with y.val=f(3,2) and y.diff={'x': df/dx(3,2), 'y': df/dy(3,2)} ''' from numpy import exp, sin, cos, log, zeros # todo: look in frame instead? import types __all__ = ['differentiate', 'Differentiable', 'taylor_series', 'gradient', 'hessian'] def differentiate(f, x=None, order=1): ''' Calculate the derivative of f at point x. Higher-order derivatives are possible. Ex.: differentiate(lambda x:3*x*x+2,x=2,order=2) (Returns: 6.0) Partial derivatives: 1) Give a tuple with the order. Ex.: differentiate(lambda x,y:x*y+2*y+1,x=(1,2),order=(0,1)) (Returns: 3.0 (partial derivative d/dy) ) 2) Give a dictionnary with the order: Ex.: differentiate(lambda x,y:x*y+2*y+1,x=(1,2),order={'y':1}) ''' if x is None: return lambda x:differentiate(f, x, order) if (type(order) == types.ListType) or (type(order) == types.TupleType): # several variables # Build the list of arguments n = sum(order) # total order args = [HigherDifferentiable(i, x[i], n) for i in range(len(x))] y = f(*args) for i in xrange(len(x)): for _ in xrange(order[i]): if isinstance(y, Differentiable) and (i in y.diff): y = y.diff[i] else: # constant return 0. return y elif (type(order) == types.DictType): # named variables # Build the list of arguments n = sum(order.itervalues()) vars = list(f.func_code.co_varnames) args = [] args.extend(x) for name in order.iterkeys(): i = vars.index(name) args[i] = HigherDifferentiable(name, x[i], n) y = f(*args) for name, n in order.iteritems(): for j in range(n): if isinstance(y, Differentiable) and (name in y.diff): y = y.diff[name] else: # constant return 0. return y elif order == 0: return f(x) elif order == 1: y = f(Differentiable('x', x)) if isinstance(y, Differentiable) and ('x' in y.diff): return y.diff['x'] else: # constant return 0. else: return differentiate(lambda y:differentiate(f, y, order=order - 1), x) def taylor_series(f, x, order=1): ''' Returns the list of coefficients of the Taylor Series of f at x (scalar). ''' y = f(HigherDifferentiable('x', x, order)) series = [] n = 1. for i in range(order + 1): # Value val = y while isinstance(val, Differentiable): val = val.val series.append(val / n) n *= (i + 1) # Next order if isinstance(y, Differentiable) and ('x' in y.diff): y = y.diff['x'] else: y = 0. return series def gradient(f, x): ''' Gradient of f at x (= tuple). Result = list. ''' args = [Differentiable(i, val=x[i]) for i in range(len(x))] y = f(*args) if isinstance(y, Differentiable): result = [] for i in range(len(x)): if i in y.diff: result.append(y.diff[i]) else: result.append(0.) return result else: # constant return [0.] * len(x) def hessian(f, x): ''' Hessian of f at x (= tuple). Result = array. N.B.: not working with vectors. ''' args = [HigherDifferentiable(i, x[i], 2) for i in range(len(x))] y = f(*args) result = zeros((len(x), len(x))) if isinstance(y, Differentiable): # non-zero Hessian for i in range(len(x)): if i in y.diff and isinstance(y.diff[i], Differentiable): for j in range(len(x)): if j in y.diff[i].diff: result[i, j] = y.diff[i].diff[j] return result class Differentiable(object): ''' A differentiable variable. Implemented operations: +,-,*,[],len,exp,sin,cos,/,abs,sqrt,comparisons,** ''' def __init__(self, name=None, val=0.): ''' Initializes a variable and its derivative. If the name is None, then it is a constant. ''' self.val = val # value if name == None: # constant self.diff = {} # derivative else: self.diff = {name:1.} # or jacobian? (eye) def sqrt(self): return self ** .5 def __cmp__(self, x): if isinstance(x, Differentiable): return cmp(self.val, x.val) else: return cmp(self.val, x) def __abs__(self): # NOT WORKING WITH VECTORS if self.val == 0: # TODO: specific exception raise ZeroDivisionError, "x:abs(x) is not differentiable at x=0." elif self.val > 0: return self else: return - self def __add__(self, y): # VECTOR-READY if isinstance(y, Differentiable): zdict = {} zdict.update(self.diff) zdict.update(y.diff) for key in zdict.iterkeys(): if (key in self.diff) and (key in y.diff): zdict[key] = self.diff[key] + y.diff[key] z = Differentiable() z.val = self.val + y.val z.diff = zdict return z else: # Adding a constant z = Differentiable(val=self.val + y) z.diff = self.diff return z def __radd__(self, x): # VECTOR-READY if isinstance(x, Differentiable): zdict = {} zdict.update(self.diff) zdict.update(x.diff) for key in zdict.iterkeys(): if (key in self.diff) and (key in x.diff): zdict[key] = x.diff[key] + self.diff[key] z = Differentiable() z.val = x.val + self.val z.diff = zdict return z else: # Adding a constant z = Differentiable(val=x + self.val) z.diff = self.diff return z def __mul__(self, y): # !Not working with vectors! if isinstance(y, Differentiable): #TODO: with vectors zdict = {} zdict.update(self.diff) zdict.update(y.diff) for key in zdict.iterkeys(): if (key in self.diff) and (key in y.diff): zdict[key] = self.diff[key] * y.val + self.val * y.diff[key] elif (key in self.diff): zdict[key] = self.diff[key] * y.val else: zdict[key] = self.val * y.diff[key] z = Differentiable() z.val = self.val * y.val z.diff = zdict return z else: # Multiplying by a constant z = Differentiable(val=self.val * y) for key in self.diff: z.diff[key] = self.diff[key] * y return z def __rmul__(self, x): if isinstance(x, Differentiable): #TODO: with vectors zdict = {} zdict.update(self.diff) zdict.update(x.diff) for key in zdict.iterkeys(): if (key in self.diff) and (key in x.diff): zdict[key] = x.val * self.diff[key] + x.diff[key] * self.val elif (key in self.diff): zdict[key] = x.val * self.diff[key] else: zdict[key] = x.diff[key] * self.val z = Differentiable() z.val = x.val * self.val z.diff = zdict return z else: # Multiplying by a constant z = Differentiable(val=x * self.val) for key in self.diff: z.diff[key] = x * self.diff[key] return z def __div__(self, x): return self * (x ** -1) def __rdiv__(self, x): return x * (self ** -1) def __sub__(self, y): # VECTOR-READY if isinstance(y, Differentiable): zdict = {} zdict.update(self.diff) zdict.update(y.diff) for key in zdict.iterkeys(): if (key in self.diff) and (key in y.diff): zdict[key] = self.diff[key] - y.diff[key] z = Differentiable() z.val = self.val - y.val z.diff = zdict return z else: # Subtracting a constant z = Differentiable(val=self.val - y) z.diff = self.diff return z def __rsub__(self, x): # VECTOR-READY if isinstance(x, Differentiable): zdict = {} zdict.update(self.diff) zdict.update(x.diff) for key in zdict.iterkeys(): if (key in self.diff) and (key in x.diff): zdict[key] = x.diff[key] - self.diff[key] z = Differentiable() z.val = x.val - self.val z.diff = zdict return z else: # Subtracting a constant z = Differentiable(val=x - self.val) for key in self.diff: z.diff[key] = -self.diff[key] return z def __neg__(self): # VECTOR-READY z = Differentiable(val= -self.val) for key in self.diff: z.diff[key] = -self.diff[key] return z def __pow__(self, x): # NOT WORKING WITH VECTORS if isinstance(x, Differentiable): return exp(x * log(self)) elif x == 0: z = Differentiable(val=self.val ** x) for key in self.diff: z.diff[key] = 0 * self.diff[key] return z else: z = Differentiable(val=self.val ** x) for key in self.diff: z.diff[key] = x * self.val ** (x - 1) * self.diff[key] return z def __rpow__(self, x): # NOT WORKING WITH VECTORS return exp(self * log(x)) def __getitem__(self, i): z = Differentiable(val=self.val[i]) z.diff = {} for key in self.diff.iterkeys(): z.diff[key] = self.diff[key][i, :] return z def __len__(self): return len(self.val) def __str__(self): s = str(self.val) for key, value in self.diff.iteritems(): s += ' ; d/d' + key + '=' + str(value) return s def exp(self): # !! Not working for vectors !! zdict = {} zdict.update(self.diff) for key in zdict.iterkeys(): zdict[key] = exp(self.val) * self.diff[key] z = Differentiable(val=exp(self.val)) z.diff = zdict return z def cos(self): # !! Not working for vectors !! zdict = {} zdict.update(self.diff) for key in zdict.iterkeys(): zdict[key] = -sin(self.val) * self.diff[key] z = Differentiable(val=cos(self.val)) z.diff = zdict return z def sin(self): # !! Not working for vectors !! zdict = {} zdict.update(self.diff) for key in zdict.iterkeys(): zdict[key] = cos(self.val) * self.diff[key] z = Differentiable(val=sin(self.val)) z.diff = zdict return z def log(self): # !! Not working for vectors !! zdict = {} zdict.update(self.diff) for key in zdict.iterkeys(): zdict[key] = (1. / (self.val)) * self.diff[key] z = Differentiable(val=log(self.val)) z.diff = zdict return z class HigherDifferentiable(Differentiable): ''' A differentiable variable with order n. ''' def __init__(self, name=None, val=0., order=1): Differentiable.__init__(self, name, val) if order > 1: self.val = HigherDifferentiable(name, val, order - 1) if __name__ == '__main__': print "Derivative:" print "> differentiate(lambda x:3*x*x+2,x=2,order=2)" print differentiate(lambda x:3 * x * x + 2, x=2, order=2) print "> differentiate(lambda x:3*x*x+2,order=2)(2)" print differentiate(lambda x:3 * x * x + 2, order=2)(2) print "Partial derivative:" print "> differentiate(lambda x,y:x*y+2*y+1,x=(1,2),order=(0,1))" print differentiate(lambda x, y:x * y + 2 * y + 1, x=(1, 2), order=(0, 1)) brian-1.4.3/brian/tools/correlatedspikes.py000066400000000000000000000257511266062647100210060ustar00rootroot00000000000000# ---------------------------------------------------------------------------------- # Copyright ENS, INRIA, CNRS # Contributors: Romain Brette (brette@di.ens.fr) and Dan Goodman (goodman@di.ens.fr) # # Brian is a computer program whose purpose is to simulate models # of biological neural networks. # # This software is governed by the CeCILL license under French law and # abiding by the rules of distribution of free software. You can use, # modify and/ or redistribute the software under the terms of the CeCILL # license as circulated by CEA, CNRS and INRIA at the following URL # "http://www.cecill.info". # # As a counterpart to the access to the source code and rights to copy, # modify and redistribute granted by the license, users are provided only # with a limited warranty and the software's author, the holder of the # economic rights, and the successive licensors have only limited # liability. # # In this respect, the user's attention is drawn to the risks associated # with loading, using, modifying and/or developing or reproducing the # software by the user in light of its specific status of free software, # that may mean that it is complicated to manipulate, and that also # therefore means that it is reserved for developers and experienced # professionals having in-depth computer knowledge. Users are therefore # encouraged to load and test the software's suitability as regards their # requirements in conditions enabling the security of their systems and/or # data to be ensured and, more generally, to use and operate it in the # same conditions as regards security. # # The fact that you are presently reading this means that you have had # knowledge of the CeCILL license and that you accept its terms. # ---------------------------------------------------------------------------------- # ''' Generation of correlated spike trains. Based on the article: Brette, R. (2009). Generation of correlated spike trains. http://audition.ens.fr/brette/papers/Brette2008NC.html The models for correlated spike trains are from the paper but the implemented algorithms are simple ones. See brian.utils.statistics for correlograms. ''' from ..threshold import PoissonThreshold, HomogeneousPoissonThreshold from ..neurongroup import NeuronGroup from ..equations import Equations from scipy.special import erf from scipy.optimize import newton, fmin_tnc from scipy import * from ..units import check_units, hertz, second from ..utils.circular import SpikeContainer from numpy.random import poisson, binomial, rand, exponential from random import sample __all__ = ['rectified_gaussian', 'inv_rectified_gaussian', 'HomogeneousCorrelatedSpikeTrains', \ 'MixtureHomogeneousCorrelatedSpikeTrains', 'CorrelatedSpikeTrains', 'mixture_process', 'find_mixture'] """ Utility functions """ def rectified_gaussian(mu, sigma): ''' Calculates the mean and standard deviation for a rectified Gaussian distribution. mu, sigma: parameters of the original distribution Returns mur,sigmar: parameters of the rectified distribution ''' a = 1. + erf(mu / (sigma * (2 ** .5))); mur = (sigma / (2. * pi) ** .5) * exp(-0.5 * (mu / sigma) ** 2) + .5 * mu * a sigmar = ((mu - mur) * mur + .5 * sigma ** 2 * a) ** .5 return (mur, sigmar) def inv_rectified_gaussian(mur, sigmar): ''' Inverse of the function rectified_gaussian ''' if sigmar == 0 * sigmar: # for unit consistency return (mur, sigmar) x0 = mur / sigmar ratio = lambda u, v:u / v f = lambda x:ratio(*rectified_gaussian(x, 1.)) - x0 y = newton(f, x0 * 1.1) # Secant method sigma = mur / (exp(-0.5 * y ** 2) / ((2. * pi) ** .5) + .5 * y * (1. + erf(y * (2 ** (-.5))))) mu = y * sigma return (mu, sigma) """ Doubly stochastic processes (Cox processes). Good for long correlation time constants. """ class HomogeneousCorrelatedSpikeTrains(NeuronGroup): ''' Correlated spike trains with identical rates and homogeneous exponential correlations. Uses Cox processes (Ornstein-Uhlenbeck). ''' @check_units(r=hertz, tauc=second) def __init__(self, N, r, c, tauc, clock=None): ''' Initialization: r = rate (Hz) c = total correlation strength (in [0,1]) tauc = correlation time constant (ms) Cross-covariance functions are (c*r/tauc)*exp(-|s|/tauc) ''' self.N = N # Correction of mu and sigma sigmar = (c * r / (2. * tauc)) ** .5 mu, sigma = inv_rectified_gaussian(r, sigmar) eq = Equations('drate/dt=(mu-rate)/tauc + sigma*xi/tauc**.5 : Hz', mu=mu, tauc=tauc, sigma=sigma) NeuronGroup.__init__(self, 1, model=eq, threshold=HomogeneousPoissonThreshold(), clock=clock) self._use_next_allowed_spiketime_refractoriness = False self.rate = mu self.LS = SpikeContainer(1) # Spike storage def __len__(self): # We need to redefine this because it is not the size of the state matrix return self.N def __getslice__(self, i, j): Q = NeuronGroup.__getslice__(self, i, j) # Is this correct? Q.N = j - i return Q class MixtureHomogeneousCorrelatedSpikeTrains(NeuronGroup): def __init__(self, N, r, c, cl=None): """ Generates a pool of N homogeneous correlated spike trains with identical rates r and correlation c (0 <= c <= 1). """ NeuronGroup.__init__(self, N, model="v : 1") self.N = N self.r = r self.c = c if cl is None: cl = guessclock() self.clock = cl self.dt = cl.dt # called at each time step, returns the spiking neurons? def update(self): # Generates a source Poisson spike train with rate r/c if rand(1) <= self.r / self.c * self.dt: # If there is a source spike, it is copied to each target train with probability c spikes = nonzero(rand(self.N) <= self.c)[0] else: spikes = [] self.LS.push(spikes) def decompose_correlation_matrix(C, R): ''' Completes the diagonal of C and finds L such that C=LL^T. C is matrix of correlation coefficients with unspecified diagonal. R is the rate vector. C must be symmetric. N.B.: The diagonal of C is modified (with zeros). ''' # 0) Remove diagonal entries and calculate D (var_i(x) is should have magnitude r_i^2) D = R ** 2 C -= diag(diag(C)) # Completion # 1) Calculate D^{-1}C L = dot(diag(1. / D), C) # 2) Find the smallest eigenvalue eigenvals = linalg.eig(L)[0] alpha = -min(eigenvals[isreal(eigenvals)]) # 3) Complete the diagonal with alpha*ri^2 #alpha=alpha+.01; // avoids bad conditioning problems (uncomment if Cholesky fails) C += diag(alpha * D) # 4) Calculate a square root (Cholesky is unstable, use singular value decomposition) #return linalg.cholesky(C,lower=1) U, S, V = linalg.svd(C) return dot(dot(U, sqrt(diag(S))), V.T) class CorrelatedSpikeTrains(NeuronGroup): ''' Correlated spike trains with arbitrary rates and pair-wise exponential correlations. Uses Cox processes (Ornstein-Uhlenbeck). P.rate is the vector of (time-varying) rates. ''' @check_units(tauc=second) def __init__(self, rates, C, tauc, clock=None): ''' Initialization: rates = rates (Hz) C = correlation matrix tauc = correlation time constant (ms) Cross-covariance functions are C[i,j]*exp(-|s|/tauc) ''' eq = Equations(''' rate : Hz dy/dt = -y*(1./tauc)+xi/(.5*tauc)**.5 : 1 ''') NeuronGroup.__init__(self, len(rates), model=eq, threshold=PoissonThreshold(), \ clock=clock) self._R = array(rates) self._L = decompose_correlation_matrix(array(C), self._R) def update(self): # Calculate rates self.rate_ = self._R + dot(self._L, self.y_) NeuronGroup.update(self) row_vector = lambda V:V.reshape(1, len(V)) column_vector = lambda V:V.reshape(len(V), 1) def homogeneous_mixture(R, c): ''' Returns a mixture (nu,P) for a homogeneous synchronization structure: C(i,j)=2*c*(R(i)*R(j))/ ''' pass def find_mixture(R, C, iter=10000): ''' Finds a mixture matrix P and source rate vector nu from the correlation matrix C and the target rates R. Returns nu,P. Gradient descent. TODO: use Scipy optimization algorithms. ''' N = len(R) # Steps b = 0.1 / N a = b / N # Initial value: here such that F=0 P = eye(N) R = column_vector(R) nu = row_vector(R) for _ in xrange(iter): # Compute error Q = dot(P, diag(sqrt(nu.flatten()))) C2 = dot(Q, Q.T) E = linalg.norm(C - C2 - diag(diag(C - C2)), 'fro') F = sum(clip(dot(P, nu.T) - R, 0, Inf)) A = -(C - C2 - diag(diag(C - C2))) #print "E=",E,"F=",F,"Etot=",E*a+F*b # Gradient in E dPE = 4 * dot(dot(A, P), diag(nu.flatten())) dNuE = row_vector(2 * diag(dot(dot(P.T, A), P))) # Gradient in F HF = (dot(P, nu.T) - R) > 0 dNuF = dot(HF.T, P) dPF = dot(HF, nu) # One step of gradient descent P = P - a * dPE - b * dPF nu = nu - a * dNuE - b * dNuF # Clipping nu = clip(nu, 0, Inf) P = clip(P, 0, 1) # Now we complete nu = hstack((nu, (R - dot(P, nu.T)).T)) P = hstack((P, eye(N))) print E, F return nu, P def mixture_process(nu, P, tauc, t): ''' Generate correlated spike trains from a mixture process. nu = rates of source spike trains P = mixture matrix tauc = correlation time constant t = duration Returns a list of (neuron_number,spike_time) to be passed to SpikeGeneratorGroup. ''' n = array(poisson(nu * t)) # number of spikes for each source spike train if n.ndim == 0: n = array([n]) # Only non-zero entries: nonzero = n.nonzero()[0] n = n[nonzero] P = array(P.take(nonzero, axis=1)) nik = binomial(n, P) # number of spikes from k in i result = [] for k in xrange(P.shape[1]): spikes = rand(n[k]) * t for i in xrange(P.shape[0]): m = nik[i, k] if m > 0: if tauc > 0: selection = sample(spikes, m) + array(exponential(tauc, m)) else: selection = sample(spikes, m) result.extend(zip([i] * m, selection)) result = [(i,t*second) for i,t in result] return result if __name__ == '__main__': from time import time R = 2 + rand(5) C = rand(5, 5) C = .5 * (C + C.T) t1 = time() nu, P = find_mixture(R, C) t2 = time() print t2 - t1 brian-1.4.3/brian/tools/datamanager.py000066400000000000000000000214021266062647100176740ustar00rootroot00000000000000from uuid import uuid4 from getpass import getuser import os import shelve import platform from glob import glob from multiprocessing import Manager __all__ = ['DataManager'] class LockingSession(object): def __init__(self, dataman, session_filename): self.dataman = dataman self.session_filename = session_filename self.lock = Manager().Lock() def acquire(self): self.lock.acquire() self.session = DataManager.shelf(self.session_filename) def release(self): self.session.close() self.session = None self.lock.release() def __getitem__(self, item): self.acquire() ret = self.session[item] self.release() return ret def __setitem__(self, item, value): self.acquire() self.session[item] = value self.release() class DataManager(object): ''' DataManager is a simple class for managing data produced by multiple runs of a simulation carried out in separate processes or machines. Each process is assigned a unique ID and Python Shelf object to write its data to. Each shelf is a dictionary whose keys must be strings. The DataManager can collate information across multiple shelves using the get(key) method, which returns a dictionary with keys the unique session names, and values the value written in that session (typically only the values will be of interest). If each value is a tuple or list then you can use the get_merged(key) to get a concatenated list. If the data type is more complicated you can use the get(key) method and merge by hand. The idea is each process generates files with names that do not interfere with each other so that there are no file concurrency issues, and then in the data analysis phase, the data generated separately by each process is merged together. Methods: ``get(key)`` Return dictionary with keys the session names, and values the values stored in that session for the given key. ``get_merged(key)`` Return a single list of the merged lists or tuples if each value for every session is a list or tuple. ``get_matching(match)`` Returns a dictionary with keys the keys matching match and values get(key). If match is a string, a matching key has to start with that string. If match is a function, a key matches if match(key). ``get_merged_matching(match)`` Like get_merged(key) but across all keys that match. ``get_flat_matching(match)`` Returns a straight list of every value session[key] for all sessions and all keys matching match. ``iteritems()`` Returns all ``(key, value)`` pairs, for each Shelf file, as an iterator (useful for large files with too much data to be loaded into memory). ``itervalues()`` Return all values, for each Shelf file, as an iterator. ``items()``, ``values()`` As for ``iteritems`` and ``itervalues`` but returns a list rather than an iterator. ``itemcount()`` Returns the total number of items across all the Shelf files. ``keys()`` A list of all the keys across all sessions. ``session()`` Returns a randomly named session Shelf, multiple processes can write to these without worrying about concurrency issues. ``computer_session()`` Returns a consistently named Shelf specific to that user and computer, only one process can write to it without worrying about concurrency issues. ``locking_session()``, ``locking_computer_session()`` Returns a LockingSession object, a limited proxy to the underlying Shelf which acquires and releases a lock before and after every operation, making it safe for concurrent access. ``session_filenames()`` A list of all the shelf filenames for all sessions. ``make_unique_key()`` Generates a unique key for inserting an element into a session without overwriting data, uses uuid4. Attributes: ``basepath`` The base path for data files. ``computer_name`` A (hopefully) unique identifier for the user and computer, consists of the username and the computer network name. ``computer_session_filename`` The filename of the computer-specific session file. This file should only be accessed by one process at a time, there's no way to protect against concurrent write accesses causing it to be corrupted. ''' def __init__(self, name, datapath=''): # check if directory exists, and if not, make it basepath = os.path.join(datapath, name + '.data') if not os.path.exists(basepath): subpaths = (name + '.data').split('/') curpath = '' for path in subpaths: curpath += path if not os.path.exists(os.path.join(datapath, curpath)): os.mkdir(os.path.join(datapath, curpath)) curpath += '/' self.basepath = basepath self.computer_name = getuser() + '.' + platform.node() self.computer_session_filename = self.session_filename(self.computer_name) def session_name(self): return getuser() + '.' + str(uuid4()) def session_filename(self, session_name=None): if session_name is None: session_name = self.session_name() fname = os.path.normpath(os.path.join(self.basepath, session_name)) return fname @staticmethod def shelf(fname): return shelve.open(fname, protocol=2) def session(self, session_name=None): return self.shelf(self.session_filename(session_name)) def computer_session(self): return self.shelf(self.computer_session_filename) def locking_session(self, session_name=None): return LockingSession(self, self.session_filename(session_name)) def locking_computer_session(self): return LockingSession(self, self.computer_session_filename) def session_filenames(self): return glob(os.path.join(self.basepath, '*')) def get(self, key): allfiles = self.session_filenames() ret = {} for name in allfiles: path, file = os.path.split(name) shelf = shelve.open(name, protocol=2) if key in shelf: ret[file] = shelf[key] return ret def get_merged(self, key): allitems = self.get(key) ret = [] for _, val in allitems.iteritems(): if isinstance(val, (list, tuple)): ret.extend(val) else: raise TypeError('Can only get merged items of list or tuple type, use get() method and merge by hand.') return ret def get_matching_keys(self, match): allkeys = self.keys() matching_keys = [key for key in allkeys if (callable(match) and match(key)) or key.startswith(match)] return matching_keys def get_matching(self, match): ret = {} for key in self.get_matching_keys(match): ret[key] = self.get(key) return ret def get_merged_matching(self, match): ret = [] for key in self.get_matching_keys(match): ret.extend(self.get_merged(key)) return ret def get_flat_matching(self, match): allitems = self.get_matching(match) ret = [] for matching_key, matching_dict in allitems.iteritems(): ret.extend(matching_dict.values()) return ret def keys(self): allkeys = set([]) for name in self.session_filenames(): allkeys.update(set(shelve.open(name, protocol=2).keys())) return list(allkeys) def make_unique_key(self): return str(uuid4()) def iteritems(self): allfiles = self.session_filenames() for name in allfiles: shelf = shelve.open(name, protocol=2) for key, value in shelf.iteritems(): yield key, value def itervalues(self): for key, val in self.iteritems(): yield val def items(self): return list(self.iteritems()) def values(self): return list(self.itervalues()) def itemcount(self): return sum(len(shelve.open(name, protocol=2)) for name in self.session_filenames()) if __name__ == '__main__': d = DataManager('test/testing') #s = d.session() #s['a'] = [7] print d.get_merged('a') #d = DataManager('test') #print d.get_merged('b') #print d.keys() #print d['b'] #s = d.session() #s['b'] = 6 brian-1.4.3/brian/tools/information_theory.py000066400000000000000000000115711266062647100213550ustar00rootroot00000000000000''' Information theory measures. Adapted by R Brette from several papers (see in the code). Uses the ANN wrapper in scikits. ''' import scikits.ann as ann from scipy.special import gamma, psi from scipy.linalg import det, inv from scipy import * __all__ = ['nearest_distances', 'entropy', 'mutual_information', 'entropy_gaussian', 'mutual_information2'] def nearest_distances(X, k=1): ''' X = array(N,M) N = number of points M = number of dimensions returns the squared distance to the kth nearest neighbor for every point in X ''' ktree = ann.kdtree(X) _, d = ktree.knn(X, k + 1) # the first nearest neighbor is itself return d[:, -1] # returns the distance to the kth nearest neighbor def entropy_gaussian(C): ''' Entropy of a gaussian variable with covariance matrix C ''' if isscalar(C): # C is the variance return .5 * (1 + log(2 * pi)) + .5 * log(C) else: n = C.shape[0] # dimension return .5 * n * (1 + log(2 * pi)) + .5 * log(abs(det(C))) def entropy(X, k=1): ''' Returns the entropy of X, given as array X = array(n,dx) where n = number of samples dx = number of dimensions Optionally: k = number of nearest neighbors for density estimation ''' # Distance to kth nearest neighbor r = nearest_distances(X, k) # squared distances n, d = X.shape volume_unit_ball = (pi ** (.5 * d)) / gamma(.5 * d + 1) ''' F. Perez-Cruz, (2008). Estimation of Information Theoretic Measures for Continuous Random Variables. Advances in Neural Information Processing Systems 21 (NIPS). Vancouver (Canada), December. return .5*d*mean(log(r))+log(volume_unit_ball)+log(n-1)-log(k) ''' ''' Kozachenko, L. F. & Leonenko, N. N. 1987 Sample estimate of entropy of a random vector. Probl. Inf. Transm. 23, 95-101. See also: Evans, D. 2008 A computationally efficient estimator for mutual information, Proc. R. Soc. A 464 (2093), 1203-1215. and: Kraskov A, Stogbauer H, Grassberger P. (2004). Estimating mutual information. Phys Rev E 69(6 Pt 2):066138. ''' return .5 * d * mean(log(r)) + log(volume_unit_ball) + psi(n) - psi(k) def mutual_information(X, Y, k=1): ''' Returns the mutual information between X and Y. Each variable is a matrix X = array(n,dx) where n = number of samples dx,dy = number of dimensions Optionally, the following keyword argument can be specified: k = number of nearest neighbors for density estimation ''' return entropy(X) + entropy(Y) - entropy(hstack((X, Y)), k=k) def mutual_information2(X, p): ''' Mutual information of continuous variable X and discrete variable Y taking p values with equal probabilities. X is a matrix (n,d) such that X[(n/p)*i:(n/p)*(i+1),:] are the samples for the same value of Y. Maybe to a principal component analysis? (problem of highly correlated variables) Method: * Empirical average I= * p(X) and p(X|Y) are estimated with Gaussian assumptions ''' X = X / X.max() n, d = X.shape M = mean(X, axis=0).reshape((d, 1)) C = cov(X.T) C += C.max()*.001 * eye(d) # avoids degeneracy (hack!) invC = inv(C) m = int(n / p) # number of samples / value of Y I = 0. for i in range(p): Z = X[m * i:m * (i + 1), :] # same Y MZ = mean(Z, axis=0).reshape((d, 1)) CZ = cov(Z.T) CZ += CZ.max()*.001 * eye(d) # avoids degeneracy (hack!) #try: invCZ = inv(CZ) #except LinAlgError: # singular matrix! # #A=invC-invCZ Ii = 0. for j in range(m): x = X[m * i + j, :].reshape((d, 1)) Ii = dot(dot(x.T - M.T, invC), x - M) - dot(dot(x.T - MZ.T, invCZ), x - MZ) #Ii+=dot(dot(x.T,A),x) Ii = .5 * (log(det(C * invCZ)) + 1) + Ii / m I += Ii I = I / p return I[0, 0] if __name__ == '__main__': ''' Testing against correlated Gaussian variables (analytical results are known) ''' from scipy import * # Entropy of a 15-dimensional gaussian variable n = 10000 d = 15 P = randn(d, d) # change of variables C = dot(P, P.T) Y = randn(d, n) X = dot(P, Y) H = entropy_gaussian(C) Hest = entropy(X.T, k=5) print Hest, H # Mutual information between two correlated gaussian variables P = randn(2, 2) C = dot(P, P.T) U = randn(2, n) Z = dot(P, U).T X = Z[:, 0] X = X.reshape(len(X), 1) Y = Z[:, 1] Y = Y.reshape(len(Y), 1) # in bits print mutual_information(X, Y, k=5) / log(2.), \ (entropy_gaussian(C[0, 0]) + entropy_gaussian(C[1, 1]) - entropy_gaussian(C)) / log(2.) brian-1.4.3/brian/tools/io.py000066400000000000000000000136751266062647100160540ustar00rootroot00000000000000''' Input/output utility functions ''' import numpy as np import os from brian.stdunits import * from brian.units import * __all__ = ['read_neuron_dat', 'read_atf', 'load_aer'] # General readers def read_neuron_dat(name): ''' Reads a Neuron vector file (.dat). Returns vector of times, vector(s) of values ''' f = open(name) f.readline(), f.readline() # skip first two lines M = np.loadtxt(f) f.close() return M.T # return M[:, 0], M[:, 1] def read_atf(name): ''' Reads an Axon ATF file (.atf). Returns vector of times, vector of values ''' f = open(name) f.readline() n = int(f.readline().split()[0]) # skip first two lines for _ in range(n + 1): f.readline() M = np.loadtxt(f) f.close() return M[:, 0], M[:, 1] # Spikes saving (AER readers) def load_multiple_aer(filename, check_sorted = False, relative_time = False, directory = '.'): ''' Loads multiple AER files listed in the file. ''' f=open(filename,'rb') line = f.readline() res = [] line = line.strip('\n') while not line == '': res.append(load_aer(os.path.join(directory, line), check_sorted = check_sorted, reinit_time = relative_time)) line = f.readline() f.close() return res def load_aer(filename, check_sorted = False, reinit_time = False): ''' Loads Address Event Representation (AER) data files for use in Brian. Files contain spikes as a binary representation of an ``address`` (i.e. neuron identifier) and a timestamp. This function returns two arrays, an array of addresses (neuron indices) and an array of spike times (in second). Note: For index files (that point to multiple .(ae)dat files, typically aeidx files) it will return a list containing tuples (addr, time) as for single files. Usage: ids, times = load_aer('/path/to/file.aedat') Keyword Arguments: ``reinit_time`` If True, sets the first spike time to zero and all others relative to that one. ``check_sorted`` If True, checks if timestamps are sorted, and sorts them if necessary. Example use: To use the spikes recorded in the AER file ``filename`` in a Brian ``NeuronGroup``, one should do: addr, timestamp = load_AER(filename, reinit_time = True) G = AERSpikeGeneratorGroup((addr, timestamps)) An example script can be found in examples/misc/spikes_io.py ''' # This loading fun is inspired by the following Matlab script: # http://jaer.svn.sourceforge.net/viewvc/jaer/trunk/host/matlab/loadaerdat.m?revision=2001&content-type=text%2Fplain # Figure out extension, check filename, ... l = filename.split('.') ext = l[-1].strip('\n') filename = filename.strip('\n') directory = os.path.dirname(filename) if ext == 'aeidx': #AER data points to different AER files return load_multiple_AER(filename, check_sorted = check_sorted, relative_time = reinit_time, directory = directory) elif not (ext == 'dat' or ext == 'aedat'): raise ValueError('Wrong extension for AER data, should be dat, or aedat, it was '+ext) f=open(filename,'rb') # Load the encoding parameters # version of AER version=1 # default (if not found in the file) # value of dt dt = 1e-6 # default (if not found in the file) # skip header and look overrident values for dt/version line = f.readline() while len(line) == 0 or line[0] == '#': if line[:9] == "#!AER-DAT": version = int(float(line[9:-1])) if line[:21] == '# Timestamps tick is ': dt = eval(line[21:]) # print 'recognized dt = %.4f second' % dt line = f.readline() line += f.read() f.close() # Load the files if version==1: ''' Format is: sequence of (addr = 2 bytes,timestamp = 4 bytes) Number format is big endian ('>') ''' ## This commented paragraph is the non-vectorized version #nevents=len(line)/6 #for n in range(nevents): # events.append(unpack('>HI',line[n*6:(n+1)*6])) # address,timestamp x=np.fromstring(line, dtype=np.int16) # or uint16? x=x.reshape((len(x)/3,3)) addr=x[:,0].newbyteorder('>') timestamp=x[:,1:].copy() timestamp.dtype=int32 timestamp=timestamp.newbyteorder('>').flatten() else: # i.e. version==2 ''' Format is: sequence of (addr = 4 bytes,timestamp = 4 bytes) Number format is big endian ('>') ''' ## This commented paragraph is the non-vectorized version #nevents=len(line)/8 #for n in range(nevents): # events.append(unpack('>II',line[n*8:(n+1)*8])) # address,timestamp x = np.fromstring(line, dtype=np.int32).newbyteorder('>') addr = x[::2] if len(addr) == len(x[1::2]): timestamp = x[1::2] else: # alternative fallback: #timestamp = x[1::2] #print len(x) raise IOError("""Corrupted AER file, timestamps and addresses don't have the same lengths.""") # Sorts the events if necessary if check_sorted: if any(np.diff(timestamp)<0): # not sorted ind = np.argsort(timestamp) addr,timestamp = addr[ind],timestamp[ind] # Set first spike time to 0 if required if reinit_time: t0 = min(timestamp) timestamp -= t0 # Check for negative timestamps (i dont remember why?) if (timestamp<0).all(): raise ValueError("""AER file contains (only!) negative timestamps, \ consider using reinit_time = True""") return addr, timestamp * dt brian-1.4.3/brian/tools/parameters.py000066400000000000000000000203631266062647100176000ustar00rootroot00000000000000# ---------------------------------------------------------------------------------- # Copyright ENS, INRIA, CNRS # Contributors: Romain Brette (brette@di.ens.fr) and Dan Goodman (goodman@di.ens.fr) # # Brian is a computer program whose purpose is to simulate models # of biological neural networks. # # This software is governed by the CeCILL license under French law and # abiding by the rules of distribution of free software. You can use, # modify and/ or redistribute the software under the terms of the CeCILL # license as circulated by CEA, CNRS and INRIA at the following URL # "http://www.cecill.info". # # As a counterpart to the access to the source code and rights to copy, # modify and redistribute granted by the license, users are provided only # with a limited warranty and the software's author, the holder of the # economic rights, and the successive licensors have only limited # liability. # # In this respect, the user's attention is drawn to the risks associated # with loading, using, modifying and/or developing or reproducing the # software by the user in light of its specific status of free software, # that may mean that it is complicated to manipulate, and that also # therefore means that it is reserved for developers and experienced # professionals having in-depth computer knowledge. Users are therefore # encouraged to load and test the software's suitability as regards their # requirements in conditions enabling the security of their systems and/or # data to be ensured and, more generally, to use and operate it in the # same conditions as regards security. # # The fact that you are presently reading this means that you have had # knowledge of the CeCILL license and that you accept its terms. # ---------------------------------------------------------------------------------- # """ Classes and functions for storing and using parameters """ __all__ = ['attribdict', 'Parameters'] from itertools import chain from inspect import * import numpy class attribdict(dict): """ Dictionary that can be accessed via keys Note that attributes starting with _ won't be added to the dict but will instead be directly added to the object. Note that an attribdict cannot have new real attributes added to it unless they start with _, because they will be added to the dict instead. To add a real attribute, use object.__setattr__(obj,name,val). """ def __init__(self, **kwds): super(attribdict, self).__init__(**kwds) def __getattr__(self, name): try: return self[name] except KeyError: raise AttributeError def __setattr__(self, name, val): if name in dir(self) or (len(name) and name[0] == '_'): dict.__setattr__(self, name, val) return self[name] = val def __repr__(self): s = 'Attributes:' for k, v in self.iteritems(): s += '\n ' + k + ' = ' + str(v) return s class Parameters(attribdict): """ A storage class for keeping track of parameters Example usage:: p = Parameters( a = 5, b = 6, computed_parameters = ''' c = a + b ''') print p.c p.a = 1 print p.c The first ``print`` statement will give 11, the second gives 7. Details: Call as:: p = Parameters(...) Where the ``...`` consists of a list of keyword / value pairs (like a ``dict``). Keywords must not start with the underscore ``_`` character. Any keyword that starts with ``computed_`` should be a string of valid Python statements that compute new values based on the given ones. Whenever a non-computed value is changed, the computed parameters are recomputed, in alphabetical order of their keyword names (so ``computed_a`` is computed before ``computed_b`` for example). Non-computed values can be accessed and set via ``p.x``, ``p.x=1`` for example, whereas computed values can only be accessed and not set. New parameters can be added after the :class:`Parameters` object is created, including new ``computed_*`` parameters. You can 'derive' a new parameters object from a given one as follows:: p1 = Parameters(x=1) p2 = Parameters(y=2,**p1) print p2.x Note that changing the value of ``x`` in ``p2`` will not change the value of ``x`` in ``p1`` (this is a copy operation). """ def __init__(self, **kwds): super(Parameters, self).__init__(**kwds) self._recompute() def __getattr__(self, name): try: return self[name] except KeyError: try: return self._computed_values[name] except KeyError: raise AttributeError def __setattr__(self, name, val): if hasattr(self, '_computed_values') and name in self._computed_values: raise AttributeError('Cannot set computed value') attribdict.__setattr__(self, name, val) self._recompute() def _recompute(self): cv = dict(self) items = self.items() items.sort() for k, v in items: if k[:9] == 'computed_': v = '\n'.join([line.strip() for line in v.split('\n')]) exec v in numpy.__dict__, cv for k in cv.keys(): if k[:1] == '_': # this is used to get rid of things like __builtins__ etc. cv.pop(k) for k in self.iterkeys(): cv.pop(k) object.__setattr__(self, '_computed_values', cv) def ascode(self, name): """ Returns a string which can be executed which gives all the parameters name is the name of the Parameters variable in the local namespace: Usage: P = Parameters(x=1) exec P.ascode('P') print x """ s = '' allvals = dict(**self) allvals.update(self._computed_values) for k in allvals.iterkeys(): if k[:9] != 'computed_': s += k + '=' + name + '.' + k + '\n' return s def get_vars(self, *vars): ''' Returns a tuple of variables given their names vars can be a list of string names, or a single space separated string of names. ''' vars = [v.split(' ') for v in vars] return tuple(getattr(self, v) for v in chain(*vars)) def __repr__(self): s = 'Values:' for k, v in self.iteritems(): if k[:9] != 'computed_': s += '\n ' + k + ' = ' + str(v) if len(self._computed_values): s += '\n_computed values:' for k, v in self._computed_values.iteritems(): s += '\n ' + k + ' = ' + str(v) return s def __call__(self, **kwds): ''' Returns a copy with specified arguments overwritten Sample usage: default_p = Parameters(x=1,y=2) specific_p = default_p(x=3) ''' p = Parameters(**self) # pylint: disable-msg=W0621 for k, v in kwds.iteritems(): setattr(p, k, v) return p def __reduce__(self): return (_load_Parameters_from_pickle, (self.items(),)) def _load_Parameters_from_pickle(items): return Parameters(**dict(items)) if __name__ == "__main__": # turn off warning about attribute defined outside __init__ # pylint: disable-msg=W0201 p = Parameters( a=5, b=6, computed_p1=''' c = a + b ''', computed_p2=''' x = c**2 ''') q = Parameters( d=100, computed_q=''' e = a+d ''', **p ) print p.c p.a = 1 print p.c print p print print q q.a = -50 print print q print try: q.c = 5 except AttributeError, e: print e p.y = 6 p.computed_p3 = ''' w = a*b ''' print p p.a = 2 print p brian-1.4.3/brian/tools/particle_swarm.py000066400000000000000000000111741266062647100204510ustar00rootroot00000000000000from scipy import * from time import time, clock __all__ = ['particle_swarm'] def particle_swarm(X0, fun, iterations, pso_params, min_values=None, max_values=None, group_size=None, verbose=True, return_matrix=None): """ Computes the argument of fun which maximizes it using the Particle Swarm Optimization algorithm. INPUTS X0 is a N*M matrix N is the space dimension M is the number of particles If min_values is set, it is a N-long array such that X >= min_values. If max_values is set, it is a N-long array such that X <= max_values. fun(x0) is a 1*M row vector If group_size is set, it means that particles are grouped within groups of size group_size The objective function is assumed to make a different computation for each group. The PSO algorithm then maximizes f in each group independently. It returns a matrix of size N*M/group_size : each column is the result of the optimization for the corresponding group. OUTPUTS pso(x0, fun) returns argmax fun(x) """ (N, M) = X0.shape if group_size is None: group_size = M group_number = M / group_size if (min_values is None): min_values = -inf * ones(N) if (max_values is None): max_values = inf * ones(N) min_values = tile(min_values.reshape((-1, 1)), (1, M)) max_values = tile(max_values.reshape((-1, 1)), (1, M)) fitness_X = -inf * ones((1, M)) fitness_lbest = fitness_X fitness_gbest = -inf * ones(group_number) omega = pso_params[0] c1 = pso_params[1] c2 = pso_params[2] X0 = maximum(X0, min_values) X0 = minimum(X0, max_values) X = X0 V = zeros((N, M)) X_lbest = X X_gbest = X[:, arange(0, M, group_size)] # N*group_number matrix if return_matrix: fitness_X_matrix = zeros((M, iterations)) time0 = clock() for k in range(iterations): if verbose: print 'Step %d/%d' % (k + 1 , iterations) R1 = rand(N, M) R2 = rand(N, M) X_gbest2 = zeros((N, M)) for j in range(group_number): X_gbest2[:, j * group_size:(j + 1) * group_size] = tile(X_gbest[:, j].reshape(N, 1), (1, group_size)) V = omega * V + c1 * R1 * (X_lbest - X) + c2 * R2 * (X_gbest2 - X) X = X + V X = maximum(X, min_values) X = minimum(X, max_values) time1 = clock() fitness_X = fun(X) time2 = clock() if return_matrix: fitness_X_matrix[:, k] = fitness_X # Local update indices_lbest = nonzero(fitness_X > fitness_lbest)[1] if (len(indices_lbest) > 0): X_lbest[:, indices_lbest] = X[:, indices_lbest] fitness_lbest[:, indices_lbest] = fitness_X[:, indices_lbest] # Global update max_fitness_X = array([max(fitness_X[j * group_size:(j + 1) * group_size]) for j in range(group_number)]) for j in nonzero(max_fitness_X > fitness_gbest)[0]: # groups for which a global best has been reached at this iteration sub_fitness_X = fitness_X[j * group_size:(j + 1) * group_size] index_gbest = nonzero(sub_fitness_X == max_fitness_X[j])[0] if not(isscalar(index_gbest)): index_gbest = index_gbest[0] X_gbest[:, j] = X[:, j * group_size + index_gbest] fitness_gbest[j] = max_fitness_X[j] if verbose: print 'Evaluation time :', '%.2f s' % (time2 - time1) print 'Total elapsed time :', '%.2f s' % (clock() - time0) print 'Fitness: mean = %.3f, max = %.3f, std = %.3f' % (mean(fitness_X), max(fitness_X), std(fitness_X)) # print ' min :', '%.5f' % fitness_gbest.min() # print ' mean :', '%.5f' % fitness_gbest.mean() # print ' max :', '%.5f' % fitness_gbest.max() # print ' std :', '%.5f' % fitness_gbest.std() # for j in range(group_number): # print ' %d :' % (j+1), '%.5f' % fitness_gbest[j] print if return_matrix: return(X_gbest, fitness_gbest, clock() - time0, fitness_X_matrix) else: return (X_gbest, fitness_gbest, clock() - time0) if __name__ == "__main__": group_number = 5 group_size = 10 def fun(x): y = exp(-.7 * sum(x ** 2, axis=0)) return y result = optimize(X0=3 * rand(5, group_size * group_number), fun=fun, iterations=500, pso_params=[.8, 1.8, 1.8], group_size=group_size) # print '-------------------------------' # print '-------------------------------' # print 'Final value : %.10f' % result brian-1.4.3/brian/tools/remotecontrol.py000066400000000000000000000206731266062647100203350ustar00rootroot00000000000000''' Remote control of a Brian process, for example using an IPython shell The process running the simulation calls something like: server = RemoteControlServer() and the IPython shell calls: client = RemoteControlClient() The shell can now execute and evaluate in the server process via: spikes = client.evaluate('M.spikes') i, t = zip(*spikes) plot(t, i, '.') client.execute('stop()') ''' from ..network import NetworkOperation try: import multiprocessing from multiprocessing.connection import Listener, Client except ImportError: multiprocessing = None import select import inspect __all__ = ['RemoteControlServer', 'RemoteControlClient'] class RemoteControlServer(NetworkOperation): ''' Allows remote control (via IP) of a running Brian script Initialisation arguments: ``server`` The IP server details, a pair (host, port). If you want to allow control only on the one machine (for example, by an IPython shell), leave this as ``None`` (which defaults to host='localhost', port=2719). To allow remote control, use ('', portnumber). ``authkey`` The authentication key to allow access, change it from 'brian' if you are allowing access from outside (otherwise you allow others to run arbitrary code on your machine). ``clock`` The clock specifying how often to poll for incoming commands. ``global_ns``, ``local_ns``, ``level`` Namespaces in which incoming commands will be executed or evaluated, if you leave them blank it will be the local and global namespace of the frame from which this function was called (if level=1, or from a higher level if you specify a different level here). Once this object has been created, use a :class:`RemoteControlClient` to issue commands. **Example usage** Main simulation code includes a line like this:: server = RemoteControlServer() In an IPython shell you can do something like this:: client = RemoteControlClient() spikes = client.evaluate('M.spikes') i, t = zip(*spikes) plot(t, i, '.') client.execute('stop()') ''' def __init__(self, server=None, authkey='brian', clock=None, global_ns=None, local_ns=None, level=0): if multiprocessing is None: raise ImportError('Cannot import the required multiprocessing module.') NetworkOperation.__init__(self, lambda:None, clock=clock) if server is None: server = ('localhost', 2719) frame = inspect.stack()[level + 1][0] ns_global, ns_local = frame.f_globals, frame.f_locals if global_ns is None: global_ns = frame.f_globals if local_ns is None: local_ns = frame.f_locals self.local_ns = local_ns self.global_ns = global_ns self.listener = Listener(server, authkey=authkey) self.conn = None def __call__(self): if self.conn is None: # This is kind of a hack. The multiprocessing.Listener class doesn't # allow you to tell if an incoming connection has been requested # without accepting that connection, which means if nothing attempts # to connect it will wait forever for something to connect. What # we do here is check if there is any incoming data on the # underlying IP socket used internally by multiprocessing.Listener. socket = self.listener._listener._socket sel, _, _ = select.select([socket], [], [], 0) if len(sel): self.conn = self.listener.accept() if self.conn is None: return conn = self.conn global_ns = self.global_ns local_ns = self.local_ns paused = 1 while conn and paused != 0: if paused >= 0 and not conn.poll(): return try: job = conn.recv() except: self.conn = None break jobtype, jobargs = job if paused == 1: paused = 0 try: result = None if jobtype == 'exec': exec jobargs in global_ns, local_ns elif jobtype == 'eval': result = eval(jobargs, global_ns, local_ns) elif jobtype == 'setvar': varname, varval = jobargs local_ns[varname] = varval elif jobtype == 'pause': paused = -1 elif jobtype == 'go': paused = 0 except Exception, e: # if it raised an exception, we return that exception and the # client can then raise it. result = e conn.send(result) class RemoteControlClient(object): ''' Used to remotely control (via IP) a running Brian script Initialisation arguments: ``server`` The IP server details, a pair (host, port). If you want to allow control only on the one machine (for example, by an IPython shell), leave this as ``None`` (which defaults to host='localhost', port=2719). To allow remote control, use ('', portnumber). ``authkey`` The authentication key to allow access, change it from 'brian' if you are allowing access from outside (otherwise you allow others to run arbitrary code on your machine). Use a :class:`RemoteControlServer` on the simulation you want to control. Has the following methods: .. method:: execute(code) Executes the specified code in the server process. If it raises an exception, the server process will catch it and reraise it in the client process. .. method:: evaluate(code) Evaluate the code in the server process and return the result. If it raises an exception, the server process will catch it and reraise it in the client process. .. method:: set(name, value) Sets the variable ``name`` (a string) to the given value (can be an array, etc.). Note that the variable is set in the local namespace, not the global one, and so this cannot be used to modify global namespace variables. To do that, set a local namespace variable and then call :meth:`~RemoteControlClient.execute` with an instruction to change the global namespace variable. .. method:: pause() Temporarily stop the simulation in the server process, continue simulation with the :meth:'go' method. .. method:: go() Continue a simulation that was paused. .. method:: stop() Stop a simulation, equivalent to ``execute('stop()')``. **Example usage** Main simulation code includes a line like this:: server = RemoteControlServer() In an IPython shell you can do something like this:: client = RemoteControlClient() spikes = client.evaluate('M.spikes') i, t = zip(*spikes) plot(t, i, '.') client.execute('stop()') ''' def __init__(self, server=None, authkey='brian'): if multiprocessing is None: raise ImportError('Cannot import the required multiprocessing module.') if server is None: server = ('localhost', 2719) self.client = Client(server, authkey=authkey) def execute(self, code): self.client.send(('exec', code)) result = self.client.recv() if isinstance(result, Exception): raise result def evaluate(self, code): self.client.send(('eval', code)) result = self.client.recv() if isinstance(result, Exception): raise result return result def set(self, name, value): self.client.send(('setvar', (name, value))) result = self.client.recv() if isinstance(result, Exception): raise result def pause(self): self.client.send(('pause', '')) self.client.recv() def go(self): self.client.send(('go', '')) self.client.recv() def stop(self): self.execute('stop()') brian-1.4.3/brian/tools/statistics.py000066400000000000000000000265711266062647100176360ustar00rootroot00000000000000''' Spike statistics ---------------- In all functions below, spikes is a sorted list of spike times ''' from numpy import * from brian.units import check_units, second from brian.stdunits import ms, Hz from operator import itemgetter __all__ = ['firing_rate', 'CV', 'correlogram', 'autocorrelogram', 'CCF', 'ACF', 'CCVF', 'ACVF', 'group_correlations', 'sort_spikes', 'total_correlation', 'vector_strength', 'gamma_factor', 'get_gamma_factor_matrix', 'get_gamma_factor','spike_triggered_average'] # First-order statistics def firing_rate(spikes): ''' Rate of the spike train. ''' if len(spikes)<2: return NaN return (len(spikes) - 1) / (spikes[-1] - spikes[0]) def CV(spikes): ''' Coefficient of variation. ''' if spikes==[]: return NaN ISI = diff(spikes) # interspike intervals return std(ISI) / mean(ISI) # Second-order statistics def correlogram(T1, T2, width=20 * ms, bin=1 * ms, T=None): ''' Returns a cross-correlogram with lag in [-width,width] and given bin size. T is the total duration (optional) and should be greater than the duration of T1 and T2. The result is in Hz (rate of coincidences in each bin). N.B.: units are discarded. TODO: optimise? ''' if (T1==[]) or (T2==[]): # empty spike train return NaN # Remove units width = float(width) T1 = array(T1) T2 = array(T2) i = 0 j = 0 n = int(ceil(width / bin)) # Histogram length l = [] for t in T1: while i < len(T2) and T2[i] < t - width: # other possibility use searchsorted i += 1 while j < len(T2) and T2[j] < t + width: j += 1 l.extend(T2[i:j] - t) H, _ = histogram(l, bins=arange(2 * n + 1) * bin - n * bin) #, new = True) # Divide by time to get rate if T is None: T = max(T1[-1], T2[-1]) - min(T1[0], T2[0]) # Windowing function (triangle) W = zeros(2 * n) W[:n] = T - bin * arange(n - 1, -1, -1) W[n:] = T - bin * arange(n) return H / W def autocorrelogram(T0, width=20 * ms, bin=1 * ms, T=None): ''' Returns an autocorrelogram with lag in [-width,width] and given bin size. T is the total duration (optional) and should be greater than the duration of T1 and T2. The result is in Hz (rate of coincidences in each bin). N.B.: units are discarded. ''' return correlogram(T0, T0, width, bin, T) def CCF(T1, T2, width=20 * ms, bin=1 * ms, T=None): ''' Returns the cross-correlation function with lag in [-width,width] and given bin size. T is the total duration (optional). The result is in Hz**2: CCF(T1,T2)= N.B.: units are discarded. ''' return correlogram(T1, T2, width, bin, T) / bin def ACF(T0, width=20 * ms, bin=1 * ms, T=None): ''' Returns the autocorrelation function with lag in [-width,width] and given bin size. T is the total duration (optional). The result is in Hz**2: ACF(T0)= N.B.: units are discarded. ''' return CCF(T0, T0, width, bin, T) def CCVF(T1, T2, width=20 * ms, bin=1 * ms, T=None): ''' Returns the cross-covariance function with lag in [-width,width] and given bin size. T is the total duration (optional). The result is in Hz**2: CCVF(T1,T2)=- N.B.: units are discarded. ''' return CCF(T1, T2, width, bin, T) - firing_rate(T1) * firing_rate(T2) def ACVF(T0, width=20 * ms, bin=1 * ms, T=None): ''' Returns the autocovariance function with lag in [-width,width] and given bin size. T is the total duration (optional). The result is in Hz**2: ACVF(T0)=-**2 N.B.: units are discarded. ''' return CCVF(T0, T0, width, bin, T) def spike_triggered_average(spikes,stimulus,max_interval,dt,onset=None,display=False): ''' Spike triggered average reverse correlation. spikes is an array containing spike times stimulus is an array containing the stimulus max_interval (second) is the duration of the averaging window dt (second) is the sampling period onset (second) before which the spikes are discarded. Note: it will be at least as long as max_interval display (default=False) display the number of spikes processed out of the total number output the spike triggered average and the corresponding time axis ''' stimulus = stimulus.flatten() if onset < max_interval or onset == None: onset = max_interval nspikes = len(spikes) sta_length = int(max_interval/dt) spike_triggered_ensemble=zeros((nspikes,sta_length)) time_axis = linspace(0*ms,max_interval,sta_length) onset = float(onset) for ispike,spike in enumerate(spikes): if display==True: print 'sta: spike #',ispike,' out of :',nspikes if spike>onset: spike = int(spike/dt) print stimulus[spike-sta_length:spike].shape spike_triggered_ensemble[ispike,:] = stimulus[spike-sta_length:spike] ispike +=1 return sum(spike_triggered_ensemble,axis=0)[::-1]/(nspikes-1),time_axis def total_correlation(T1, T2, width=20 * ms, T=None): ''' Returns the total correlation coefficient with lag in [-width,width]. T is the total duration (optional). The result is a real (typically in [0,1]): total_correlation(T1,T2)=int(CCVF(T1,T2))/rate(T1) ''' if (T1==[]) or (T2==[]): # empty spike train return NaN # Remove units width = float(width) T1 = array(T1) T2 = array(T2) # Divide by time to get rate if T is None: T = max(T1[-1], T2[-1]) - min(T1[0], T2[0]) i = 0 j = 0 x = 0 for t in T1: while i < len(T2) and T2[i] < t - width: # other possibility use searchsorted i += 1 while j < len(T2) and T2[j] < t + width: j += 1 x += sum(1. / (T - abs(T2[i:j] - t))) # counts coincidences with windowing (probabilities) return float(x / firing_rate(T1)) - float(firing_rate(T2) * 2 * width) def sort_spikes(spikes): """ Sorts spikes stored in a (i,t) list by time. """ spikes = sorted(spikes, key=itemgetter(1)) return spikes def group_correlations(spikes, delta=None): """ Computes the pairwise correlation strength and timescale of the given pool of spike trains. spikes is a (i,t) list and must be sorted. delta is the length of the time window, 10*ms by default. """ aspikes = array(spikes) N = int(aspikes[:, 0].max()) + 1 # neuron count T = aspikes[:, 1].max() # total duration spikecount = zeros(N) tauc = zeros((N, N)) S = zeros((N, N)) if delta is None: delta = 10 * ms # size of the window windows = -2 * delta * ones(N) # windows[i] is the end of the window for neuron i = lastspike[i}+delta for i, t in spikes: sources = (t <= windows) # neurons such that (i,t) is a target spike for them if sum(sources) > 0: indices = nonzero(sources)[0] S[indices, i] += 1 delays = t - windows[indices] + delta # print i, t, indices, delays tauc[indices, i] += delays spikecount[i] += 1 windows[i] = t + delta # window update tauc /= S S = S / tile(spikecount.reshape((-1, 1)), (1, N)) # normalize S rates = spikecount / T S = S - tile(rates.reshape((1, -1)), (N, 1)) * delta S[isnan(S)] = 0.0 tauc[isnan(tauc)] = 0.0 return S, tauc # Phase-locking properties def vector_strength(spikes, period): ''' Returns the vector strength of the given train ''' return abs(mean(exp(array(spikes) * 1j * 2 * pi / period))) # Normalize the coincidence count of two spike trains (return the gamma factor) def get_gamma_factor(coincidence_count, model_length, target_length, target_rates, delta): NCoincAvg = 2 * delta * target_length * target_rates norm = .5 * (1 - 2 * delta * target_rates) gamma = (coincidence_count - NCoincAvg) / (norm * (target_length + model_length)) return gamma # Normalize the coincidence matrix between a set of trains (return the gamma factor matrix) def get_gamma_factor_matrix(coincidence_matrix, model_length, target_length, target_rates, delta): target_lengthMAT =tile(target_length,(len(model_length),1)) target_rateMAT =tile(target_rates,(len(model_length),1)) model_lengthMAT =tile(model_length.reshape(-1,1),(1,len(target_length))) NCoincAvg =2 * delta * target_lengthMAT * target_rateMAT norm =.5 * (1 - 2 * delta * target_rateMAT) # print target_rateMAT print coincidence_matrix #print NCoincAvg #print (norm * (target_lengthMAT + model_lengthMAT)) gamma = (coincidence_matrix - NCoincAvg) / (norm * (target_lengthMAT + model_lengthMAT)) gamma=triu(gamma,0)+triu(gamma,1).T return gamma # Gamma factor @check_units(delta=second) def gamma_factor(source, target, delta, normalize=True, dt=None): ''' Returns the gamma precision factor between source and target trains, with precision delta. source and target are lists of spike times. If normalize is True, the function returns the normalized gamma factor (less than 1.0), otherwise it returns the number of coincidences. dt is the precision of the trains, by default it is defaultclock.dt Reference: R. Jolivet et al., 'A benchmark test for a quantitative assessment of simple neuron models', Journal of Neuroscience Methods 169, no. 2 (2008): 417-424. ''' source = array(source) target = array(target) target_rate = firing_rate(target) * Hz if dt is None: delta_diff = delta else: source = array(rint(source / dt), dtype=int) target = array(rint(target / dt), dtype=int) delta_diff = int(rint(delta / dt)) source_length = len(source) target_length = len(target) if (target_length == 0 or source_length == 0): return 0 if (source_length > 1): bins = .5 * (source[1:] + source[:-1]) indices = digitize(target, bins) diff = abs(target - source[indices]) matched_spikes = (diff <= delta_diff) coincidences = sum(matched_spikes) else: indices = [amin(abs(source - target[i])) <= delta_diff for i in xrange(target_length)] coincidences = sum(indices) # Normalization of the coincidences count # NCoincAvg = 2 * delta * target_length * target_rate # norm = .5*(1 - 2 * target_rate * delta) # gamma = (coincidences - NCoincAvg)/(norm*(source_length + target_length)) # TODO: test this gamma = get_gamma_factor(coincidences, source_length, target_length, target_rate, delta) if normalize: return gamma else: return coincidences if __name__ == '__main__': from brian import * print vector_strength([1.1 * ms, 1 * ms, .9 * ms], 2 * ms) N = 100000 T1 = cumsum(rand(N) * 10 * ms) T2 = cumsum(rand(N) * 10 * ms) duration = T1[N / 2] # Cut so that both spike trains have the same duration T1 = T1[T1 < duration] T2 = T2[T2 < duration] print firing_rate(T1) C = CCVF(T1, T2, bin=1 * ms) print total_correlation(T1, T2) plot(C) show() brian-1.4.3/brian/tools/tabulate.py000066400000000000000000000120621266062647100172330ustar00rootroot00000000000000# ---------------------------------------------------------------------------------- # Copyright ENS, INRIA, CNRS # Contributors: Romain Brette (brette@di.ens.fr) and Dan Goodman (goodman@di.ens.fr) # # Brian is a computer program whose purpose is to simulate models # of biological neural networks. # # This software is governed by the CeCILL license under French law and # abiding by the rules of distribution of free software. You can use, # modify and/ or redistribute the software under the terms of the CeCILL # license as circulated by CEA, CNRS and INRIA at the following URL # "http://www.cecill.info". # # As a counterpart to the access to the source code and rights to copy, # modify and redistribute granted by the license, users are provided only # with a limited warranty and the software's author, the holder of the # economic rights, and the successive licensors have only limited # liability. # # In this respect, the user's attention is drawn to the risks associated # with loading, using, modifying and/or developing or reproducing the # software by the user in light of its specific status of free software, # that may mean that it is complicated to manipulate, and that also # therefore means that it is reserved for developers and experienced # professionals having in-depth computer knowledge. Users are therefore # encouraged to load and test the software's suitability as regards their # requirements in conditions enabling the security of their systems and/or # data to be ensured and, more generally, to use and operate it in the # same conditions as regards security. # # The fact that you are presently reading this means that you have had # knowledge of the CeCILL license and that you accept its terms. # ---------------------------------------------------------------------------------- # ''' Tabulation of numerical functions. ''' __all__ = ['Tabulate', 'TabulateInterp'] from brian.units import get_unit, Quantity, is_dimensionless from brian.unitsafefunctions import array, arange, zeros from numpy import NaN class Tabulate(object): ''' An object to tabulate a numerical function. Sample use:: g=Tabulate(f,0.,1.,1000) y=g(.5) v=g([.1,.3]) v=g(array([.1,.3])) Arguments of g must lie in [xmin,xmax). An IndexError is raised is arguments are above xmax, but not always when they are below xmin (it can give weird results). ''' def __init__(self, f, xmin, xmax, n): self.xmin = xmin self.xmax = xmax self.dx = (xmax - xmin) / float(n) self.invdx = 1 / self.dx self.unit = get_unit(f(xmin)) # Tabulation at midpoints x = xmin + (.5 + arange(n)) * self.dx try: self.f = f(x) except: # If it fails we try passing the values one by one self.f = zeros(n) * f(xmin) # for the unit for i in xrange(n): self.f[i] = f(x[i]) def __call__(self, x): try: # possible problem if x is an array and an array is wanted return self.f[array((array(x) - self.xmin) * self.invdx, dtype=int)] except IndexError: # out of bounds return NaN * self.unit def __repr__(self): return 'Tabulated function with ' + str(len(self.f)) + ' points' class TabulateInterp(object): ''' An object to tabulate a numerical function with linear interpolation. Sample use:: g=TabulateInterp(f,0.,1.,1000) y=g(.5) v=g([.1,.3]) v=g(array([.1,.3])) Arguments of g must lie in [xmin,xmax). An IndexError is raised is arguments are above xmax, but not always when they are below xmin (it can give weird results). ''' def __init__(self, f, xmin, xmax, n): self.xmin = xmin self.xmax = xmax self.dx = (xmax - xmin) / float(n - 1) self.invdx = 1 / self.dx # Not at midpoints here x = xmin + arange(n) * self.dx self.unit = get_unit(f(xmin)) try: self.f = f(x) except: # If it fails we try passing the values one by one self.f = zeros(n) * f(xmin) # for the unit for i in xrange(n): self.f[i] = f(x[i]) self.f = array(self.f) self.df = (self.f[range(1, n)] - self.f[range(n - 1)]) * float(self.invdx) def __call__(self, x): # the units of x is not checked y = array(x) - self.xmin ind = array(y * self.invdx, dtype=int) try: if is_dimensionless(x): # could be a problem if it is a Quantity with units=1 return self.f[ind] + self.df[ind] * (y - array(ind) * self.dx) else: return array(self.f[ind] + self.df[ind] * (y - array(ind) * self.dx)) * self.unit except IndexError: # out of bounds return NaN * self.unit def __repr__(self): return 'Tabulated function with ' + str(len(self.f)) + ' points (interpolated)' brian-1.4.3/brian/tools/taskfarm.py000066400000000000000000000333531266062647100172500ustar00rootroot00000000000000from datamanager import DataManager import multiprocessing from Queue import Empty as QueueEmpty import Tkinter from brian.utils.progressreporting import make_text_report import inspect import time import os from numpy import ndarray, zeros __all__ = ['run_tasks'] # This is the default task class used if the user provides only a function class FunctionTask(object): def __init__(self, func): self.func = func def __call__(self, *args, **kwds): # If the function has a 'report' argument, we pass it the reporter # function that will have been passed in kwds (see task_compute) fc = self.func.func_code if 'report' in fc.co_varnames[:fc.co_argcount] or fc.co_flags&8: return self.func(*args, **kwds) else: return self.func(*args) def run_tasks(dataman, task, items, gui=True, poolsize=0, initargs=None, initkwds=None, verbose=None, numitems=None): ''' Run a series of tasks using multiple CPUs on a single computer. Initialised with arguments: ``dataman`` The :class:`~brian.tools.datamanager.DataManager` object used to store the results in, see below. ``task`` The task function or class (see below). ``items`` A sequence (e.g. list or iterator) of arguments to be passed to the task. ``gui=True`` Whether or not to use a Tkinter based GUI to show progress and terminate the task run. ``poolsize=0`` The number of CPUs to use. If the value is 0, use all available CPUs, if it is -1 use all but one CPU, etc. ``initargs``, ``initkwds`` If ``task`` is a class, these are the initialisation arguments and keywords for the class. ``verbose=None`` Specify True or False to print out every progress message (defaults to False if the GUI is used, or True if not). ``numitems=None`` For iterables (rather than fixed length sequences), if you specify the number of items, an estimate of the time remaining will be given. The task (defined by a function or class, see below) will be called on each item in ``items``, and the results saved to ``dataman``. Results are stored in the format ``(key, val)`` where ``key`` is a unique but meaningless identifier. Results can be retrieved using ``dataman.values()`` or (for large data sets that should be iterated over) ``dataman.itervalues()``. The task can either be a function or a class. If it is a function, it will be called for each item in ``items``. If the items are tuples, the function will be called with those tuples as arguments (e.g. if the item is ``(1,2,3)`` the function will be called as ``task(1, 2, 3)``). If the task is a class, it can have an ``__init__`` method that is called once for each process (each CPU) at the beginning of the task run. If the ``__init__`` method has a ``process_number`` argument, it will be passed an integer value from 0 to ``numprocesses-1`` giving the number of the process (note, this is not the process ID). The class should define a ``__call__`` method that behaves the same as above for ``task`` being a function. In both cases (function or class), if the arguments include a keyword ``report`` then it will be passed a value that can be passed as the ``report`` keyword in Brian's :func:`run` function to give feedback on the simulation as it runs. A ``task`` function can also set ``self.taskname`` as a string that will be displayed on the GUI to give additional information. .. warning:: On Windows, make sure that :func:`run_tasks` is only called from within a block such as:: if __name__=='__main__': run_tasks(...) Otherwise, the program will go into a recursive loop. Note that this class only allows you to run tasks on a single computer, to distribute work over multiple computers, we suggest using `Playdoh `__. ''' # User can provide task as a class or a function, if its a function we # we use the default FunctionTask if not inspect.isclass(task): f = task initargs = (task,) task = FunctionTask else: f = task.__call__ fc = f.func_code if 'report' in fc.co_varnames[:fc.co_argcount] or fc.co_flags&8: will_report = True else: will_report = False if numitems is None and isinstance(items, (list, tuple, ndarray)): numitems = len(items) # This will be used to provide process safe access to the data manager # (so that multiple processes do not attempt to write to the session at # the same time) session = dataman.locking_computer_session() if poolsize<=0: numprocesses = poolsize+multiprocessing.cpu_count() elif poolsize>0: numprocesses = poolsize manager = multiprocessing.Manager() # We have to send the process number to the initializer via this silly # queue because of a limitation of multiprocessing process_number_queue = manager.Queue() for n in range(numprocesses): process_number_queue.put(n) # This will be used to send messages about the status of the run, i.e. # percentage complete message_queue = manager.Queue() if initargs is None: initargs = () if initkwds is None: initkwds = {} pool = multiprocessing.Pool(processes=numprocesses, initializer=pool_initializer, initargs=(process_number_queue, message_queue, dataman, session, task, initargs, initkwds)) results = pool.imap_unordered(task_compute, items) # We use this to map process IDs to task number, so that we can show the # information on the GUI in a consistent fashion pid_to_id = dict((pid, i) for i, pid in enumerate([p.pid for p in pool._pool])) start = time.time() stoprunningsim = [False] def terminate_sim(): # We acquire the datamanager session lock so that if a process is in the # middle of writing data, it won't be terminated until its finished, # meaning we can safely terminate the process without worrying about # data loss. session.acquire() pool.terminate() session.release() stoprunningsim[0] = True if gui: if verbose is None: verbose = False controller = GuiTaskController(numprocesses, terminate_sim, verbose=verbose, will_report=will_report) else: if verbose is None: verbose = True controller = TextTaskController(numprocesses, terminate_sim, verbose=verbose) for i in range(numprocesses): controller.update_process(i, 0, 0, 'No task information') i = 0 controller.update_overall(0, numitems) def empty_message_queue(): while not message_queue.empty(): try: pid, taskname, elapsed, complete = message_queue.get_nowait() controller.update_process(pid_to_id[pid], elapsed, complete, taskname) except QueueEmpty: break controller.update() while True: try: # This waits 0.1s for a new result, and otherwise raises a # TimeoutError that allows the GUI to update the percentage # complete nextresult = results.next(0.1) empty_message_queue() i = i+1 elapsed = time.time()-start complete = 0.0 controller.update_overall(i, numitems) except StopIteration: terminate_sim() print 'Finished.' break except (KeyboardInterrupt, SystemExit): terminate_sim() print 'Terminated task processes' raise except multiprocessing.TimeoutError: empty_message_queue() if stoprunningsim[0]: print 'Terminated task processes' break controller.destroy() # We store these values as global values, which are initialised by # pool_initializer on each process task_object = None task_dataman = None task_session = None task_message_queue = None def pool_initializer(process_number_queue, message_queue, dataman, session, task, initargs, initkwds): global task_object, task_dataman, task_session, task_message_queue n = process_number_queue.get() init_method = task.__init__ fc = init_method.func_code # Checks if there is a process_number argument explicitly given in the # __init__ method of the task class, the co_flags&8 checks i there is a # **kwds parameter in the definition if 'process_number' in fc.co_varnames[:fc.co_argcount] or fc.co_flags&8: initkwds['process_number'] = n task_object = task(*initargs, **initkwds) task_dataman = dataman task_session = session task_message_queue = message_queue def task_reporter(elapsed, complete): # If the task class defines a task name, we can display it with the # percentage complete if hasattr(task_object, 'taskname'): taskname = task_object.taskname else: taskname = 'No task information' # This queue is used by the main loop in run_tasks task_message_queue.put((os.getpid(), taskname, elapsed, complete)) def task_compute(args): if not isinstance(args, tuple): args = (args,) # We check if the task function has a report argument, and if it does we # pass it task_reporter so that it can integrate with the GUI kwds = {} fc = task_object.__call__.func_code if 'report' in fc.co_varnames[:fc.co_argcount] or fc.co_flags&8: kwds['report'] = task_reporter result = task_object(*args, **kwds) # Save the results, with a unique key, to the locking session of the dataman task_session[task_dataman.make_unique_key()] = result class TaskController(object): def __init__(self, processes, terminator, verbose=True): self.verbose = verbose self.completion = zeros(processes) self.numitems, self.numdone = None, 0 self.start_time = time.time() def update_process(self, i, elapsed, complete, msg): self.completion[i] = complete%1.0 if self.verbose: print 'Process '+str(i)+': '+make_text_report(elapsed, complete)+': '+msg _, msg = self.get_overall_completion() print msg def get_overall_completion(self): complete = 0.0 numitems, numdone = self.numitems, self.numdone elapsed = time.time()-self.start_time if numitems is not None: complete = (numdone+sum(self.completion))/numitems txt = 'Overall, '+str(numdone)+' done' if numitems is not None: txt += ' of '+str(numitems)+': '+make_text_report(elapsed, complete) return complete, txt def update_overall(self, numdone, numitems): self.numdone = numdone self.numitems = numitems def recompute_overall(self): pass def update(self): pass def destroy(self): pass class TextTaskController(TaskController): def update_overall(self, numdone, numitems): TaskController.update_overall(self, numdone, numitems) _, msg = self.get_overall_completion() print msg # task control GUI class GuiTaskController(Tkinter.Tk, TaskController): def __init__(self, processes, terminator, width=600, verbose=False, will_report=True): Tkinter.Tk.__init__(self, None) TaskController.__init__(self, processes, terminator, verbose=verbose) self.parent = None self.grid() button = Tkinter.Button(self, text='Terminate task', command=terminator) button.grid(column=0, row=0) self.pb_width = width self.progressbars = [] numbars = 1 self.will_report = will_report if will_report: numbars += processes for i in xrange(numbars): can = Tkinter.Canvas(self, width=width, height=30) can.grid(column=0, row=1+i) can.create_rectangle(0, 0, width, 30, fill='#aaaaaa') if i=y Use these functions for less precise tests (for example, if you have done some operations on two varaibles): -- is_approx_equal(x,y), for x=y -- is_approx_less_than_or_equal(x,y), for x<=y -- is_approx_greater_than_or_equal(x,y), for x>=y The underlying mechanism is that the more precise version tests for equality using machine epsilon precision, that is, x=y if abs(x-y)epsilon. The less precise mechanism simply uses 100*epsilon instead of epsilon. Use this function for testing if you want to specify an absolute tolerance: -- is_within_absolute_tolerance(x,y[,absolutetolerance]) The default tolerance is the sqrt of epsilon, or about 1e-8 for a 64 bit float Note also that you can use the numpy function: -- allclose(a, b, rtol = 1e-5, atol = 1e-8) Where rtol is the relative tolerance, and atol is the absolute tolerance which comes into play when the numbers are very close to zero. Warning: none of these functions can be guaranteed to work in the way you might expect them to. Errors can accumulate to the point where even 100*epsilon is an inappropriate test for approximate equality. """ import math # This finds the 'machine epsilon' for the current hardware float type, the # smallest value of epsilon so that 1+epsilon>1 epsilon = 1. while 1. + epsilon > 1.: epsilon /= 2 epsilon *= 2. # Result for 32 bit float should be: 1.1929093e-7 # For 64 bit float should be: 2.220446049250313e-16 # This value can be used for more approximate testing approxepsilon = epsilon * 10000 # This value is the default tolerance for medium sized numbers (used in the units class) defaultabsolutetolerance = math.sqrt(epsilon) # 1.4901161193847656e-008 on 64 bit system def is_equal(x, y): if x == y: return True return abs(x - y) < abs(x) * epsilon def is_less_than_or_equal(x, y): return x < y or is_equal(x, y) def is_greater_than_or_equal(x, y): return x > y or is_equal(x, y) def is_approx_equal(x, y): if x == y: return True return abs(x - y) < abs(x) * approxepsilon def is_approx_less_than_or_equal(x, y): return x < y or is_approx_equal(x, y) def is_approx_greater_than_or_equal(x, y): return x > y or is_approx_equal(x, y) def is_within_absolute_tolerance(x, y, absolutetolerance=defaultabsolutetolerance): return float(abs(x - y)) < absolutetolerance brian-1.4.3/brian/utils/ccircular/000077500000000000000000000000001266062647100170265ustar00rootroot00000000000000brian-1.4.3/brian/utils/ccircular/__init__.py000066400000000000000000000000021266062647100211270ustar00rootroot00000000000000 brian-1.4.3/brian/utils/ccircular/ccircular.h000066400000000000000000000024201266062647100211440ustar00rootroot00000000000000#ifndef _BRIAN_LIB_H #define _BRIAN_LIB_H #include #include #include #include #include using namespace std; #define neuron_value(group, neuron, state) group->S[neuron+state*(group->num_neurons)] #define BrianException std::runtime_error class CircularVector { private: inline int index(int i); inline int getitem(int i); public: long *X, cursor, n; long *retarray; CircularVector(int n); ~CircularVector(); void reinit(); void advance(int k); int __len__(); int __getitem__(int i); void __setitem__(int i, int x); void __getslice__(long **ret, int *ret_n, int i, int j); void get_conditional(long **ret, int *ret_n, int i, int j, int min, int max, int offset=0); void __setslice__(int i, int j, long *x, int n); string __repr__(); string __str__(); void expand(long n); }; class SpikeContainer { public: CircularVector *S; CircularVector *ind; int remaining_space; SpikeContainer(int m); ~SpikeContainer(); void reinit(); void push(long *x, int n); void lastspikes(long **ret, int *ret_n); void __getitem__(long **ret, int *ret_n, int i); void get_spikes(long **ret, int *ret_n, int delay, int origin, int N); void __getslice__(long **ret, int *ret_n, int i, int j); string __repr__(); string __str__(); }; #endif brian-1.4.3/brian/utils/ccircular/ccircular.i000066400000000000000000000015461266062647100211550ustar00rootroot00000000000000%module ccircular %include "exception.i" %include "std_string.i" %exception { try { $action } catch( std::runtime_error &e ) { PyErr_SetString(PyExc_RuntimeError, const_cast(e.what())); return NULL; } } %allowexception; %{ #define SWIG_FILE_WITH_INIT #include "ccircular.h" %} %include "numpy.i" %init %{ import_array(); %} %apply (double* INPLACE_ARRAY2, int DIM1, int DIM2) {(double *S, int n, int m)}; %apply (double* INPLACE_ARRAY2, int DIM1, int DIM2) {(double *M, int M_n, int M_m)}; %apply (long* INPLACE_ARRAY1, int DIM1) {(long *x, int n)}; %apply (long* INPLACE_ARRAY1, int DIM1) {(long *y, int n)}; %apply (double* INPLACE_ARRAY1, int DIM1) {(double *b, int b_n)}; %apply (double* ARGOUT_ARRAY1, int DIM1) {(double *S_out_flat, int nm)}; %apply (long** ARGOUTVIEW_ARRAY1, int* DIM1 ) {(long **ret, int *ret_n)}; %include "ccircular.h" brian-1.4.3/brian/utils/ccircular/ccircular.py000066400000000000000000000151421266062647100213520ustar00rootroot00000000000000# This file was automatically generated by SWIG (http://www.swig.org). # Version 2.0.4 # # Do not make changes to this file unless you know what you are doing--modify # the SWIG interface file instead. from sys import version_info if version_info >= (2,6,0): def swig_import_helper(): from os.path import dirname import imp fp = None try: fp, pathname, description = imp.find_module('_ccircular', [dirname(__file__)]) except ImportError: import _ccircular return _ccircular if fp is not None: try: _mod = imp.load_module('_ccircular', fp, pathname, description) finally: fp.close() return _mod _ccircular = swig_import_helper() del swig_import_helper else: import _ccircular del version_info try: _swig_property = property except NameError: pass # Python < 2.2 doesn't have 'property'. def _swig_setattr_nondynamic(self,class_type,name,value,static=1): if (name == "thisown"): return self.this.own(value) if (name == "this"): if type(value).__name__ == 'SwigPyObject': self.__dict__[name] = value return method = class_type.__swig_setmethods__.get(name,None) if method: return method(self,value) if (not static): self.__dict__[name] = value else: raise AttributeError("You cannot add attributes to %s" % self) def _swig_setattr(self,class_type,name,value): return _swig_setattr_nondynamic(self,class_type,name,value,0) def _swig_getattr(self,class_type,name): if (name == "thisown"): return self.this.own() method = class_type.__swig_getmethods__.get(name,None) if method: return method(self) raise AttributeError(name) def _swig_repr(self): try: strthis = "proxy of " + self.this.__repr__() except: strthis = "" return "<%s.%s; %s >" % (self.__class__.__module__, self.__class__.__name__, strthis,) try: _object = object _newclass = 1 except AttributeError: class _object : pass _newclass = 0 class CircularVector(_object): __swig_setmethods__ = {} __setattr__ = lambda self, name, value: _swig_setattr(self, CircularVector, name, value) __swig_getmethods__ = {} __getattr__ = lambda self, name: _swig_getattr(self, CircularVector, name) __swig_setmethods__["X"] = _ccircular.CircularVector_X_set __swig_getmethods__["X"] = _ccircular.CircularVector_X_get if _newclass:X = _swig_property(_ccircular.CircularVector_X_get, _ccircular.CircularVector_X_set) __swig_setmethods__["cursor"] = _ccircular.CircularVector_cursor_set __swig_getmethods__["cursor"] = _ccircular.CircularVector_cursor_get if _newclass:cursor = _swig_property(_ccircular.CircularVector_cursor_get, _ccircular.CircularVector_cursor_set) __swig_setmethods__["n"] = _ccircular.CircularVector_n_set __swig_getmethods__["n"] = _ccircular.CircularVector_n_get if _newclass:n = _swig_property(_ccircular.CircularVector_n_get, _ccircular.CircularVector_n_set) __swig_setmethods__["retarray"] = _ccircular.CircularVector_retarray_set __swig_getmethods__["retarray"] = _ccircular.CircularVector_retarray_get if _newclass:retarray = _swig_property(_ccircular.CircularVector_retarray_get, _ccircular.CircularVector_retarray_set) def __init__(self, *args): this = _ccircular.new_CircularVector(*args) try: self.this.append(this) except: self.this = this __swig_destroy__ = _ccircular.delete_CircularVector __del__ = lambda self : None; def reinit(self): return _ccircular.CircularVector_reinit(self) def advance(self, *args): return _ccircular.CircularVector_advance(self, *args) def __len__(self): return _ccircular.CircularVector___len__(self) def __getitem__(self, *args): return _ccircular.CircularVector___getitem__(self, *args) def __setitem__(self, *args): return _ccircular.CircularVector___setitem__(self, *args) def __getslice__(self, *args): return _ccircular.CircularVector___getslice__(self, *args) def get_conditional(self, *args): return _ccircular.CircularVector_get_conditional(self, *args) def __setslice__(self, *args): return _ccircular.CircularVector___setslice__(self, *args) def __repr__(self): return _ccircular.CircularVector___repr__(self) def __str__(self): return _ccircular.CircularVector___str__(self) def expand(self, *args): return _ccircular.CircularVector_expand(self, *args) CircularVector_swigregister = _ccircular.CircularVector_swigregister CircularVector_swigregister(CircularVector) class SpikeContainer(_object): __swig_setmethods__ = {} __setattr__ = lambda self, name, value: _swig_setattr(self, SpikeContainer, name, value) __swig_getmethods__ = {} __getattr__ = lambda self, name: _swig_getattr(self, SpikeContainer, name) __swig_setmethods__["S"] = _ccircular.SpikeContainer_S_set __swig_getmethods__["S"] = _ccircular.SpikeContainer_S_get if _newclass:S = _swig_property(_ccircular.SpikeContainer_S_get, _ccircular.SpikeContainer_S_set) __swig_setmethods__["ind"] = _ccircular.SpikeContainer_ind_set __swig_getmethods__["ind"] = _ccircular.SpikeContainer_ind_get if _newclass:ind = _swig_property(_ccircular.SpikeContainer_ind_get, _ccircular.SpikeContainer_ind_set) __swig_setmethods__["remaining_space"] = _ccircular.SpikeContainer_remaining_space_set __swig_getmethods__["remaining_space"] = _ccircular.SpikeContainer_remaining_space_get if _newclass:remaining_space = _swig_property(_ccircular.SpikeContainer_remaining_space_get, _ccircular.SpikeContainer_remaining_space_set) def __init__(self, *args): this = _ccircular.new_SpikeContainer(*args) try: self.this.append(this) except: self.this = this __swig_destroy__ = _ccircular.delete_SpikeContainer __del__ = lambda self : None; def reinit(self): return _ccircular.SpikeContainer_reinit(self) def push(self, *args): return _ccircular.SpikeContainer_push(self, *args) def lastspikes(self): return _ccircular.SpikeContainer_lastspikes(self) def __getitem__(self, *args): return _ccircular.SpikeContainer___getitem__(self, *args) def get_spikes(self, *args): return _ccircular.SpikeContainer_get_spikes(self, *args) def __getslice__(self, *args): return _ccircular.SpikeContainer___getslice__(self, *args) def __repr__(self): return _ccircular.SpikeContainer___repr__(self) def __str__(self): return _ccircular.SpikeContainer___str__(self) SpikeContainer_swigregister = _ccircular.SpikeContainer_swigregister SpikeContainer_swigregister(SpikeContainer) # This file is compatible with both classic and new-style classes. brian-1.4.3/brian/utils/ccircular/ccircular_wrap.cxx000066400000000000000000005465761266062647100226010ustar00rootroot00000000000000/* ---------------------------------------------------------------------------- * This file was automatically generated by SWIG (http://www.swig.org). * Version 2.0.4 * * This file is not intended to be easily readable and contains a number of * coding conventions designed to improve portability and efficiency. Do not make * changes to this file unless you know what you are doing--modify the SWIG * interface file instead. * ----------------------------------------------------------------------------- */ #define SWIGPYTHON #define SWIG_PYTHON_DIRECTOR_NO_VTABLE #ifdef __cplusplus /* SwigValueWrapper is described in swig.swg */ template class SwigValueWrapper { struct SwigMovePointer { T *ptr; SwigMovePointer(T *p) : ptr(p) { } ~SwigMovePointer() { delete ptr; } SwigMovePointer& operator=(SwigMovePointer& rhs) { T* oldptr = ptr; ptr = 0; delete oldptr; ptr = rhs.ptr; rhs.ptr = 0; return *this; } } pointer; SwigValueWrapper& operator=(const SwigValueWrapper& rhs); SwigValueWrapper(const SwigValueWrapper& rhs); public: SwigValueWrapper() : pointer(0) { } SwigValueWrapper& operator=(const T& t) { SwigMovePointer tmp(new T(t)); pointer = tmp; return *this; } operator T&() const { return *pointer.ptr; } T *operator&() { return pointer.ptr; } }; template T SwigValueInit() { return T(); } #endif /* ----------------------------------------------------------------------------- * This section contains generic SWIG labels for method/variable * declarations/attributes, and other compiler dependent labels. * ----------------------------------------------------------------------------- */ /* template workaround for compilers that cannot correctly implement the C++ standard */ #ifndef SWIGTEMPLATEDISAMBIGUATOR # if defined(__SUNPRO_CC) && (__SUNPRO_CC <= 0x560) # define SWIGTEMPLATEDISAMBIGUATOR template # elif defined(__HP_aCC) /* Needed even with `aCC -AA' when `aCC -V' reports HP ANSI C++ B3910B A.03.55 */ /* If we find a maximum version that requires this, the test would be __HP_aCC <= 35500 for A.03.55 */ # define SWIGTEMPLATEDISAMBIGUATOR template # else # define SWIGTEMPLATEDISAMBIGUATOR # endif #endif /* inline attribute */ #ifndef SWIGINLINE # if defined(__cplusplus) || (defined(__GNUC__) && !defined(__STRICT_ANSI__)) # define SWIGINLINE inline # else # define SWIGINLINE # endif #endif /* attribute recognised by some compilers to avoid 'unused' warnings */ #ifndef SWIGUNUSED # if defined(__GNUC__) # if !(defined(__cplusplus)) || (__GNUC__ > 3 || (__GNUC__ == 3 && __GNUC_MINOR__ >= 4)) # define SWIGUNUSED __attribute__ ((__unused__)) # else # define SWIGUNUSED # endif # elif defined(__ICC) # define SWIGUNUSED __attribute__ ((__unused__)) # else # define SWIGUNUSED # endif #endif #ifndef SWIG_MSC_UNSUPPRESS_4505 # if defined(_MSC_VER) # pragma warning(disable : 4505) /* unreferenced local function has been removed */ # endif #endif #ifndef SWIGUNUSEDPARM # ifdef __cplusplus # define SWIGUNUSEDPARM(p) # else # define SWIGUNUSEDPARM(p) p SWIGUNUSED # endif #endif /* internal SWIG method */ #ifndef SWIGINTERN # define SWIGINTERN static SWIGUNUSED #endif /* internal inline SWIG method */ #ifndef SWIGINTERNINLINE # define SWIGINTERNINLINE SWIGINTERN SWIGINLINE #endif /* exporting methods */ #if (__GNUC__ >= 4) || (__GNUC__ == 3 && __GNUC_MINOR__ >= 4) # ifndef GCC_HASCLASSVISIBILITY # define GCC_HASCLASSVISIBILITY # endif #endif #ifndef SWIGEXPORT # if defined(_WIN32) || defined(__WIN32__) || defined(__CYGWIN__) # if defined(STATIC_LINKED) # define SWIGEXPORT # else # define SWIGEXPORT __declspec(dllexport) # endif # else # if defined(__GNUC__) && defined(GCC_HASCLASSVISIBILITY) # define SWIGEXPORT __attribute__ ((visibility("default"))) # else # define SWIGEXPORT # endif # endif #endif /* calling conventions for Windows */ #ifndef SWIGSTDCALL # if defined(_WIN32) || defined(__WIN32__) || defined(__CYGWIN__) # define SWIGSTDCALL __stdcall # else # define SWIGSTDCALL # endif #endif /* Deal with Microsoft's attempt at deprecating C standard runtime functions */ #if !defined(SWIG_NO_CRT_SECURE_NO_DEPRECATE) && defined(_MSC_VER) && !defined(_CRT_SECURE_NO_DEPRECATE) # define _CRT_SECURE_NO_DEPRECATE #endif /* Deal with Microsoft's attempt at deprecating methods in the standard C++ library */ #if !defined(SWIG_NO_SCL_SECURE_NO_DEPRECATE) && defined(_MSC_VER) && !defined(_SCL_SECURE_NO_DEPRECATE) # define _SCL_SECURE_NO_DEPRECATE #endif /* Python.h has to appear first */ #include /* ----------------------------------------------------------------------------- * swigrun.swg * * This file contains generic C API SWIG runtime support for pointer * type checking. * ----------------------------------------------------------------------------- */ /* This should only be incremented when either the layout of swig_type_info changes, or for whatever reason, the runtime changes incompatibly */ #define SWIG_RUNTIME_VERSION "4" /* define SWIG_TYPE_TABLE_NAME as "SWIG_TYPE_TABLE" */ #ifdef SWIG_TYPE_TABLE # define SWIG_QUOTE_STRING(x) #x # define SWIG_EXPAND_AND_QUOTE_STRING(x) SWIG_QUOTE_STRING(x) # define SWIG_TYPE_TABLE_NAME SWIG_EXPAND_AND_QUOTE_STRING(SWIG_TYPE_TABLE) #else # define SWIG_TYPE_TABLE_NAME #endif /* You can use the SWIGRUNTIME and SWIGRUNTIMEINLINE macros for creating a static or dynamic library from the SWIG runtime code. In 99.9% of the cases, SWIG just needs to declare them as 'static'. But only do this if strictly necessary, ie, if you have problems with your compiler or suchlike. */ #ifndef SWIGRUNTIME # define SWIGRUNTIME SWIGINTERN #endif #ifndef SWIGRUNTIMEINLINE # define SWIGRUNTIMEINLINE SWIGRUNTIME SWIGINLINE #endif /* Generic buffer size */ #ifndef SWIG_BUFFER_SIZE # define SWIG_BUFFER_SIZE 1024 #endif /* Flags for pointer conversions */ #define SWIG_POINTER_DISOWN 0x1 #define SWIG_CAST_NEW_MEMORY 0x2 /* Flags for new pointer objects */ #define SWIG_POINTER_OWN 0x1 /* Flags/methods for returning states. The SWIG conversion methods, as ConvertPtr, return an integer that tells if the conversion was successful or not. And if not, an error code can be returned (see swigerrors.swg for the codes). Use the following macros/flags to set or process the returning states. In old versions of SWIG, code such as the following was usually written: if (SWIG_ConvertPtr(obj,vptr,ty.flags) != -1) { // success code } else { //fail code } Now you can be more explicit: int res = SWIG_ConvertPtr(obj,vptr,ty.flags); if (SWIG_IsOK(res)) { // success code } else { // fail code } which is the same really, but now you can also do Type *ptr; int res = SWIG_ConvertPtr(obj,(void **)(&ptr),ty.flags); if (SWIG_IsOK(res)) { // success code if (SWIG_IsNewObj(res) { ... delete *ptr; } else { ... } } else { // fail code } I.e., now SWIG_ConvertPtr can return new objects and you can identify the case and take care of the deallocation. Of course that also requires SWIG_ConvertPtr to return new result values, such as int SWIG_ConvertPtr(obj, ptr,...) { if () { if () { *ptr = ; return SWIG_NEWOBJ; } else { *ptr = ; return SWIG_OLDOBJ; } } else { return SWIG_BADOBJ; } } Of course, returning the plain '0(success)/-1(fail)' still works, but you can be more explicit by returning SWIG_BADOBJ, SWIG_ERROR or any of the SWIG errors code. Finally, if the SWIG_CASTRANK_MODE is enabled, the result code allows to return the 'cast rank', for example, if you have this int food(double) int fooi(int); and you call food(1) // cast rank '1' (1 -> 1.0) fooi(1) // cast rank '0' just use the SWIG_AddCast()/SWIG_CheckState() */ #define SWIG_OK (0) #define SWIG_ERROR (-1) #define SWIG_IsOK(r) (r >= 0) #define SWIG_ArgError(r) ((r != SWIG_ERROR) ? r : SWIG_TypeError) /* The CastRankLimit says how many bits are used for the cast rank */ #define SWIG_CASTRANKLIMIT (1 << 8) /* The NewMask denotes the object was created (using new/malloc) */ #define SWIG_NEWOBJMASK (SWIG_CASTRANKLIMIT << 1) /* The TmpMask is for in/out typemaps that use temporal objects */ #define SWIG_TMPOBJMASK (SWIG_NEWOBJMASK << 1) /* Simple returning values */ #define SWIG_BADOBJ (SWIG_ERROR) #define SWIG_OLDOBJ (SWIG_OK) #define SWIG_NEWOBJ (SWIG_OK | SWIG_NEWOBJMASK) #define SWIG_TMPOBJ (SWIG_OK | SWIG_TMPOBJMASK) /* Check, add and del mask methods */ #define SWIG_AddNewMask(r) (SWIG_IsOK(r) ? (r | SWIG_NEWOBJMASK) : r) #define SWIG_DelNewMask(r) (SWIG_IsOK(r) ? (r & ~SWIG_NEWOBJMASK) : r) #define SWIG_IsNewObj(r) (SWIG_IsOK(r) && (r & SWIG_NEWOBJMASK)) #define SWIG_AddTmpMask(r) (SWIG_IsOK(r) ? (r | SWIG_TMPOBJMASK) : r) #define SWIG_DelTmpMask(r) (SWIG_IsOK(r) ? (r & ~SWIG_TMPOBJMASK) : r) #define SWIG_IsTmpObj(r) (SWIG_IsOK(r) && (r & SWIG_TMPOBJMASK)) /* Cast-Rank Mode */ #if defined(SWIG_CASTRANK_MODE) # ifndef SWIG_TypeRank # define SWIG_TypeRank unsigned long # endif # ifndef SWIG_MAXCASTRANK /* Default cast allowed */ # define SWIG_MAXCASTRANK (2) # endif # define SWIG_CASTRANKMASK ((SWIG_CASTRANKLIMIT) -1) # define SWIG_CastRank(r) (r & SWIG_CASTRANKMASK) SWIGINTERNINLINE int SWIG_AddCast(int r) { return SWIG_IsOK(r) ? ((SWIG_CastRank(r) < SWIG_MAXCASTRANK) ? (r + 1) : SWIG_ERROR) : r; } SWIGINTERNINLINE int SWIG_CheckState(int r) { return SWIG_IsOK(r) ? SWIG_CastRank(r) + 1 : 0; } #else /* no cast-rank mode */ # define SWIG_AddCast # define SWIG_CheckState(r) (SWIG_IsOK(r) ? 1 : 0) #endif #include #ifdef __cplusplus extern "C" { #endif typedef void *(*swig_converter_func)(void *, int *); typedef struct swig_type_info *(*swig_dycast_func)(void **); /* Structure to store information on one type */ typedef struct swig_type_info { const char *name; /* mangled name of this type */ const char *str; /* human readable name of this type */ swig_dycast_func dcast; /* dynamic cast function down a hierarchy */ struct swig_cast_info *cast; /* linked list of types that can cast into this type */ void *clientdata; /* language specific type data */ int owndata; /* flag if the structure owns the clientdata */ } swig_type_info; /* Structure to store a type and conversion function used for casting */ typedef struct swig_cast_info { swig_type_info *type; /* pointer to type that is equivalent to this type */ swig_converter_func converter; /* function to cast the void pointers */ struct swig_cast_info *next; /* pointer to next cast in linked list */ struct swig_cast_info *prev; /* pointer to the previous cast */ } swig_cast_info; /* Structure used to store module information * Each module generates one structure like this, and the runtime collects * all of these structures and stores them in a circularly linked list.*/ typedef struct swig_module_info { swig_type_info **types; /* Array of pointers to swig_type_info structures that are in this module */ size_t size; /* Number of types in this module */ struct swig_module_info *next; /* Pointer to next element in circularly linked list */ swig_type_info **type_initial; /* Array of initially generated type structures */ swig_cast_info **cast_initial; /* Array of initially generated casting structures */ void *clientdata; /* Language specific module data */ } swig_module_info; /* Compare two type names skipping the space characters, therefore "char*" == "char *" and "Class" == "Class", etc. Return 0 when the two name types are equivalent, as in strncmp, but skipping ' '. */ SWIGRUNTIME int SWIG_TypeNameComp(const char *f1, const char *l1, const char *f2, const char *l2) { for (;(f1 != l1) && (f2 != l2); ++f1, ++f2) { while ((*f1 == ' ') && (f1 != l1)) ++f1; while ((*f2 == ' ') && (f2 != l2)) ++f2; if (*f1 != *f2) return (*f1 > *f2) ? 1 : -1; } return (int)((l1 - f1) - (l2 - f2)); } /* Check type equivalence in a name list like ||... Return 0 if not equal, 1 if equal */ SWIGRUNTIME int SWIG_TypeEquiv(const char *nb, const char *tb) { int equiv = 0; const char* te = tb + strlen(tb); const char* ne = nb; while (!equiv && *ne) { for (nb = ne; *ne; ++ne) { if (*ne == '|') break; } equiv = (SWIG_TypeNameComp(nb, ne, tb, te) == 0) ? 1 : 0; if (*ne) ++ne; } return equiv; } /* Check type equivalence in a name list like ||... Return 0 if equal, -1 if nb < tb, 1 if nb > tb */ SWIGRUNTIME int SWIG_TypeCompare(const char *nb, const char *tb) { int equiv = 0; const char* te = tb + strlen(tb); const char* ne = nb; while (!equiv && *ne) { for (nb = ne; *ne; ++ne) { if (*ne == '|') break; } equiv = (SWIG_TypeNameComp(nb, ne, tb, te) == 0) ? 1 : 0; if (*ne) ++ne; } return equiv; } /* Check the typename */ SWIGRUNTIME swig_cast_info * SWIG_TypeCheck(const char *c, swig_type_info *ty) { if (ty) { swig_cast_info *iter = ty->cast; while (iter) { if (strcmp(iter->type->name, c) == 0) { if (iter == ty->cast) return iter; /* Move iter to the top of the linked list */ iter->prev->next = iter->next; if (iter->next) iter->next->prev = iter->prev; iter->next = ty->cast; iter->prev = 0; if (ty->cast) ty->cast->prev = iter; ty->cast = iter; return iter; } iter = iter->next; } } return 0; } /* Identical to SWIG_TypeCheck, except strcmp is replaced with a pointer comparison */ SWIGRUNTIME swig_cast_info * SWIG_TypeCheckStruct(swig_type_info *from, swig_type_info *ty) { if (ty) { swig_cast_info *iter = ty->cast; while (iter) { if (iter->type == from) { if (iter == ty->cast) return iter; /* Move iter to the top of the linked list */ iter->prev->next = iter->next; if (iter->next) iter->next->prev = iter->prev; iter->next = ty->cast; iter->prev = 0; if (ty->cast) ty->cast->prev = iter; ty->cast = iter; return iter; } iter = iter->next; } } return 0; } /* Cast a pointer up an inheritance hierarchy */ SWIGRUNTIMEINLINE void * SWIG_TypeCast(swig_cast_info *ty, void *ptr, int *newmemory) { return ((!ty) || (!ty->converter)) ? ptr : (*ty->converter)(ptr, newmemory); } /* Dynamic pointer casting. Down an inheritance hierarchy */ SWIGRUNTIME swig_type_info * SWIG_TypeDynamicCast(swig_type_info *ty, void **ptr) { swig_type_info *lastty = ty; if (!ty || !ty->dcast) return ty; while (ty && (ty->dcast)) { ty = (*ty->dcast)(ptr); if (ty) lastty = ty; } return lastty; } /* Return the name associated with this type */ SWIGRUNTIMEINLINE const char * SWIG_TypeName(const swig_type_info *ty) { return ty->name; } /* Return the pretty name associated with this type, that is an unmangled type name in a form presentable to the user. */ SWIGRUNTIME const char * SWIG_TypePrettyName(const swig_type_info *type) { /* The "str" field contains the equivalent pretty names of the type, separated by vertical-bar characters. We choose to print the last name, as it is often (?) the most specific. */ if (!type) return NULL; if (type->str != NULL) { const char *last_name = type->str; const char *s; for (s = type->str; *s; s++) if (*s == '|') last_name = s+1; return last_name; } else return type->name; } /* Set the clientdata field for a type */ SWIGRUNTIME void SWIG_TypeClientData(swig_type_info *ti, void *clientdata) { swig_cast_info *cast = ti->cast; /* if (ti->clientdata == clientdata) return; */ ti->clientdata = clientdata; while (cast) { if (!cast->converter) { swig_type_info *tc = cast->type; if (!tc->clientdata) { SWIG_TypeClientData(tc, clientdata); } } cast = cast->next; } } SWIGRUNTIME void SWIG_TypeNewClientData(swig_type_info *ti, void *clientdata) { SWIG_TypeClientData(ti, clientdata); ti->owndata = 1; } /* Search for a swig_type_info structure only by mangled name Search is a O(log #types) We start searching at module start, and finish searching when start == end. Note: if start == end at the beginning of the function, we go all the way around the circular list. */ SWIGRUNTIME swig_type_info * SWIG_MangledTypeQueryModule(swig_module_info *start, swig_module_info *end, const char *name) { swig_module_info *iter = start; do { if (iter->size) { register size_t l = 0; register size_t r = iter->size - 1; do { /* since l+r >= 0, we can (>> 1) instead (/ 2) */ register size_t i = (l + r) >> 1; const char *iname = iter->types[i]->name; if (iname) { register int compare = strcmp(name, iname); if (compare == 0) { return iter->types[i]; } else if (compare < 0) { if (i) { r = i - 1; } else { break; } } else if (compare > 0) { l = i + 1; } } else { break; /* should never happen */ } } while (l <= r); } iter = iter->next; } while (iter != end); return 0; } /* Search for a swig_type_info structure for either a mangled name or a human readable name. It first searches the mangled names of the types, which is a O(log #types) If a type is not found it then searches the human readable names, which is O(#types). We start searching at module start, and finish searching when start == end. Note: if start == end at the beginning of the function, we go all the way around the circular list. */ SWIGRUNTIME swig_type_info * SWIG_TypeQueryModule(swig_module_info *start, swig_module_info *end, const char *name) { /* STEP 1: Search the name field using binary search */ swig_type_info *ret = SWIG_MangledTypeQueryModule(start, end, name); if (ret) { return ret; } else { /* STEP 2: If the type hasn't been found, do a complete search of the str field (the human readable name) */ swig_module_info *iter = start; do { register size_t i = 0; for (; i < iter->size; ++i) { if (iter->types[i]->str && (SWIG_TypeEquiv(iter->types[i]->str, name))) return iter->types[i]; } iter = iter->next; } while (iter != end); } /* neither found a match */ return 0; } /* Pack binary data into a string */ SWIGRUNTIME char * SWIG_PackData(char *c, void *ptr, size_t sz) { static const char hex[17] = "0123456789abcdef"; register const unsigned char *u = (unsigned char *) ptr; register const unsigned char *eu = u + sz; for (; u != eu; ++u) { register unsigned char uu = *u; *(c++) = hex[(uu & 0xf0) >> 4]; *(c++) = hex[uu & 0xf]; } return c; } /* Unpack binary data from a string */ SWIGRUNTIME const char * SWIG_UnpackData(const char *c, void *ptr, size_t sz) { register unsigned char *u = (unsigned char *) ptr; register const unsigned char *eu = u + sz; for (; u != eu; ++u) { register char d = *(c++); register unsigned char uu; if ((d >= '0') && (d <= '9')) uu = ((d - '0') << 4); else if ((d >= 'a') && (d <= 'f')) uu = ((d - ('a'-10)) << 4); else return (char *) 0; d = *(c++); if ((d >= '0') && (d <= '9')) uu |= (d - '0'); else if ((d >= 'a') && (d <= 'f')) uu |= (d - ('a'-10)); else return (char *) 0; *u = uu; } return c; } /* Pack 'void *' into a string buffer. */ SWIGRUNTIME char * SWIG_PackVoidPtr(char *buff, void *ptr, const char *name, size_t bsz) { char *r = buff; if ((2*sizeof(void *) + 2) > bsz) return 0; *(r++) = '_'; r = SWIG_PackData(r,&ptr,sizeof(void *)); if (strlen(name) + 1 > (bsz - (r - buff))) return 0; strcpy(r,name); return buff; } SWIGRUNTIME const char * SWIG_UnpackVoidPtr(const char *c, void **ptr, const char *name) { if (*c != '_') { if (strcmp(c,"NULL") == 0) { *ptr = (void *) 0; return name; } else { return 0; } } return SWIG_UnpackData(++c,ptr,sizeof(void *)); } SWIGRUNTIME char * SWIG_PackDataName(char *buff, void *ptr, size_t sz, const char *name, size_t bsz) { char *r = buff; size_t lname = (name ? strlen(name) : 0); if ((2*sz + 2 + lname) > bsz) return 0; *(r++) = '_'; r = SWIG_PackData(r,ptr,sz); if (lname) { strncpy(r,name,lname+1); } else { *r = 0; } return buff; } SWIGRUNTIME const char * SWIG_UnpackDataName(const char *c, void *ptr, size_t sz, const char *name) { if (*c != '_') { if (strcmp(c,"NULL") == 0) { memset(ptr,0,sz); return name; } else { return 0; } } return SWIG_UnpackData(++c,ptr,sz); } #ifdef __cplusplus } #endif /* Errors in SWIG */ #define SWIG_UnknownError -1 #define SWIG_IOError -2 #define SWIG_RuntimeError -3 #define SWIG_IndexError -4 #define SWIG_TypeError -5 #define SWIG_DivisionByZero -6 #define SWIG_OverflowError -7 #define SWIG_SyntaxError -8 #define SWIG_ValueError -9 #define SWIG_SystemError -10 #define SWIG_AttributeError -11 #define SWIG_MemoryError -12 #define SWIG_NullReferenceError -13 /* Compatibility macros for Python 3 */ #if PY_VERSION_HEX >= 0x03000000 #define PyClass_Check(obj) PyObject_IsInstance(obj, (PyObject *)&PyType_Type) #define PyInt_Check(x) PyLong_Check(x) #define PyInt_AsLong(x) PyLong_AsLong(x) #define PyInt_FromLong(x) PyLong_FromLong(x) #define PyString_Check(name) PyBytes_Check(name) #define PyString_FromString(x) PyUnicode_FromString(x) #define PyString_Format(fmt, args) PyUnicode_Format(fmt, args) #define PyString_AsString(str) PyBytes_AsString(str) #define PyString_Size(str) PyBytes_Size(str) #define PyString_InternFromString(key) PyUnicode_InternFromString(key) #define Py_TPFLAGS_HAVE_CLASS Py_TPFLAGS_BASETYPE #define PyString_AS_STRING(x) PyUnicode_AS_STRING(x) #define _PyLong_FromSsize_t(x) PyLong_FromSsize_t(x) #endif #ifndef Py_TYPE # define Py_TYPE(op) ((op)->ob_type) #endif /* SWIG APIs for compatibility of both Python 2 & 3 */ #if PY_VERSION_HEX >= 0x03000000 # define SWIG_Python_str_FromFormat PyUnicode_FromFormat #else # define SWIG_Python_str_FromFormat PyString_FromFormat #endif /* Warning: This function will allocate a new string in Python 3, * so please call SWIG_Python_str_DelForPy3(x) to free the space. */ SWIGINTERN char* SWIG_Python_str_AsChar(PyObject *str) { #if PY_VERSION_HEX >= 0x03000000 char *cstr; char *newstr; Py_ssize_t len; str = PyUnicode_AsUTF8String(str); PyBytes_AsStringAndSize(str, &cstr, &len); newstr = (char *) malloc(len+1); memcpy(newstr, cstr, len+1); Py_XDECREF(str); return newstr; #else return PyString_AsString(str); #endif } #if PY_VERSION_HEX >= 0x03000000 # define SWIG_Python_str_DelForPy3(x) free( (void*) (x) ) #else # define SWIG_Python_str_DelForPy3(x) #endif SWIGINTERN PyObject* SWIG_Python_str_FromChar(const char *c) { #if PY_VERSION_HEX >= 0x03000000 return PyUnicode_FromString(c); #else return PyString_FromString(c); #endif } /* Add PyOS_snprintf for old Pythons */ #if PY_VERSION_HEX < 0x02020000 # if defined(_MSC_VER) || defined(__BORLANDC__) || defined(_WATCOM) # define PyOS_snprintf _snprintf # else # define PyOS_snprintf snprintf # endif #endif /* A crude PyString_FromFormat implementation for old Pythons */ #if PY_VERSION_HEX < 0x02020000 #ifndef SWIG_PYBUFFER_SIZE # define SWIG_PYBUFFER_SIZE 1024 #endif static PyObject * PyString_FromFormat(const char *fmt, ...) { va_list ap; char buf[SWIG_PYBUFFER_SIZE * 2]; int res; va_start(ap, fmt); res = vsnprintf(buf, sizeof(buf), fmt, ap); va_end(ap); return (res < 0 || res >= (int)sizeof(buf)) ? 0 : PyString_FromString(buf); } #endif /* Add PyObject_Del for old Pythons */ #if PY_VERSION_HEX < 0x01060000 # define PyObject_Del(op) PyMem_DEL((op)) #endif #ifndef PyObject_DEL # define PyObject_DEL PyObject_Del #endif /* A crude PyExc_StopIteration exception for old Pythons */ #if PY_VERSION_HEX < 0x02020000 # ifndef PyExc_StopIteration # define PyExc_StopIteration PyExc_RuntimeError # endif # ifndef PyObject_GenericGetAttr # define PyObject_GenericGetAttr 0 # endif #endif /* Py_NotImplemented is defined in 2.1 and up. */ #if PY_VERSION_HEX < 0x02010000 # ifndef Py_NotImplemented # define Py_NotImplemented PyExc_RuntimeError # endif #endif /* A crude PyString_AsStringAndSize implementation for old Pythons */ #if PY_VERSION_HEX < 0x02010000 # ifndef PyString_AsStringAndSize # define PyString_AsStringAndSize(obj, s, len) {*s = PyString_AsString(obj); *len = *s ? strlen(*s) : 0;} # endif #endif /* PySequence_Size for old Pythons */ #if PY_VERSION_HEX < 0x02000000 # ifndef PySequence_Size # define PySequence_Size PySequence_Length # endif #endif /* PyBool_FromLong for old Pythons */ #if PY_VERSION_HEX < 0x02030000 static PyObject *PyBool_FromLong(long ok) { PyObject *result = ok ? Py_True : Py_False; Py_INCREF(result); return result; } #endif /* Py_ssize_t for old Pythons */ /* This code is as recommended by: */ /* http://www.python.org/dev/peps/pep-0353/#conversion-guidelines */ #if PY_VERSION_HEX < 0x02050000 && !defined(PY_SSIZE_T_MIN) typedef int Py_ssize_t; # define PY_SSIZE_T_MAX INT_MAX # define PY_SSIZE_T_MIN INT_MIN typedef inquiry lenfunc; typedef intargfunc ssizeargfunc; typedef intintargfunc ssizessizeargfunc; typedef intobjargproc ssizeobjargproc; typedef intintobjargproc ssizessizeobjargproc; typedef getreadbufferproc readbufferproc; typedef getwritebufferproc writebufferproc; typedef getsegcountproc segcountproc; typedef getcharbufferproc charbufferproc; static long PyNumber_AsSsize_t (PyObject *x, void *SWIGUNUSEDPARM(exc)) { long result = 0; PyObject *i = PyNumber_Int(x); if (i) { result = PyInt_AsLong(i); Py_DECREF(i); } return result; } #endif #if PY_VERSION_HEX < 0x02040000 #define Py_VISIT(op) \ do { \ if (op) { \ int vret = visit((op), arg); \ if (vret) \ return vret; \ } \ } while (0) #endif #if PY_VERSION_HEX < 0x02030000 typedef struct { PyTypeObject type; PyNumberMethods as_number; PyMappingMethods as_mapping; PySequenceMethods as_sequence; PyBufferProcs as_buffer; PyObject *name, *slots; } PyHeapTypeObject; #endif #if PY_VERSION_HEX < 0x02030000 typedef destructor freefunc; #endif #if ((PY_MAJOR_VERSION == 2 && PY_MINOR_VERSION > 6) || \ (PY_MAJOR_VERSION == 3 && PY_MINOR_VERSION > 0) || \ (PY_MAJOR_VERSION > 3)) # define SWIGPY_USE_CAPSULE # define SWIGPY_CAPSULE_NAME ((char*)"swig_runtime_data" SWIG_RUNTIME_VERSION ".type_pointer_capsule" SWIG_TYPE_TABLE_NAME) #endif #if PY_VERSION_HEX < 0x03020000 #define PyDescr_TYPE(x) (((PyDescrObject *)(x))->d_type) #define PyDescr_NAME(x) (((PyDescrObject *)(x))->d_name) #endif /* ----------------------------------------------------------------------------- * error manipulation * ----------------------------------------------------------------------------- */ SWIGRUNTIME PyObject* SWIG_Python_ErrorType(int code) { PyObject* type = 0; switch(code) { case SWIG_MemoryError: type = PyExc_MemoryError; break; case SWIG_IOError: type = PyExc_IOError; break; case SWIG_RuntimeError: type = PyExc_RuntimeError; break; case SWIG_IndexError: type = PyExc_IndexError; break; case SWIG_TypeError: type = PyExc_TypeError; break; case SWIG_DivisionByZero: type = PyExc_ZeroDivisionError; break; case SWIG_OverflowError: type = PyExc_OverflowError; break; case SWIG_SyntaxError: type = PyExc_SyntaxError; break; case SWIG_ValueError: type = PyExc_ValueError; break; case SWIG_SystemError: type = PyExc_SystemError; break; case SWIG_AttributeError: type = PyExc_AttributeError; break; default: type = PyExc_RuntimeError; } return type; } SWIGRUNTIME void SWIG_Python_AddErrorMsg(const char* mesg) { PyObject *type = 0; PyObject *value = 0; PyObject *traceback = 0; if (PyErr_Occurred()) PyErr_Fetch(&type, &value, &traceback); if (value) { char *tmp; PyObject *old_str = PyObject_Str(value); PyErr_Clear(); Py_XINCREF(type); PyErr_Format(type, "%s %s", tmp = SWIG_Python_str_AsChar(old_str), mesg); SWIG_Python_str_DelForPy3(tmp); Py_DECREF(old_str); Py_DECREF(value); } else { PyErr_SetString(PyExc_RuntimeError, mesg); } } #if defined(SWIG_PYTHON_NO_THREADS) # if defined(SWIG_PYTHON_THREADS) # undef SWIG_PYTHON_THREADS # endif #endif #if defined(SWIG_PYTHON_THREADS) /* Threading support is enabled */ # if !defined(SWIG_PYTHON_USE_GIL) && !defined(SWIG_PYTHON_NO_USE_GIL) # if (PY_VERSION_HEX >= 0x02030000) /* For 2.3 or later, use the PyGILState calls */ # define SWIG_PYTHON_USE_GIL # endif # endif # if defined(SWIG_PYTHON_USE_GIL) /* Use PyGILState threads calls */ # ifndef SWIG_PYTHON_INITIALIZE_THREADS # define SWIG_PYTHON_INITIALIZE_THREADS PyEval_InitThreads() # endif # ifdef __cplusplus /* C++ code */ class SWIG_Python_Thread_Block { bool status; PyGILState_STATE state; public: void end() { if (status) { PyGILState_Release(state); status = false;} } SWIG_Python_Thread_Block() : status(true), state(PyGILState_Ensure()) {} ~SWIG_Python_Thread_Block() { end(); } }; class SWIG_Python_Thread_Allow { bool status; PyThreadState *save; public: void end() { if (status) { PyEval_RestoreThread(save); status = false; }} SWIG_Python_Thread_Allow() : status(true), save(PyEval_SaveThread()) {} ~SWIG_Python_Thread_Allow() { end(); } }; # define SWIG_PYTHON_THREAD_BEGIN_BLOCK SWIG_Python_Thread_Block _swig_thread_block # define SWIG_PYTHON_THREAD_END_BLOCK _swig_thread_block.end() # define SWIG_PYTHON_THREAD_BEGIN_ALLOW SWIG_Python_Thread_Allow _swig_thread_allow # define SWIG_PYTHON_THREAD_END_ALLOW _swig_thread_allow.end() # else /* C code */ # define SWIG_PYTHON_THREAD_BEGIN_BLOCK PyGILState_STATE _swig_thread_block = PyGILState_Ensure() # define SWIG_PYTHON_THREAD_END_BLOCK PyGILState_Release(_swig_thread_block) # define SWIG_PYTHON_THREAD_BEGIN_ALLOW PyThreadState *_swig_thread_allow = PyEval_SaveThread() # define SWIG_PYTHON_THREAD_END_ALLOW PyEval_RestoreThread(_swig_thread_allow) # endif # else /* Old thread way, not implemented, user must provide it */ # if !defined(SWIG_PYTHON_INITIALIZE_THREADS) # define SWIG_PYTHON_INITIALIZE_THREADS # endif # if !defined(SWIG_PYTHON_THREAD_BEGIN_BLOCK) # define SWIG_PYTHON_THREAD_BEGIN_BLOCK # endif # if !defined(SWIG_PYTHON_THREAD_END_BLOCK) # define SWIG_PYTHON_THREAD_END_BLOCK # endif # if !defined(SWIG_PYTHON_THREAD_BEGIN_ALLOW) # define SWIG_PYTHON_THREAD_BEGIN_ALLOW # endif # if !defined(SWIG_PYTHON_THREAD_END_ALLOW) # define SWIG_PYTHON_THREAD_END_ALLOW # endif # endif #else /* No thread support */ # define SWIG_PYTHON_INITIALIZE_THREADS # define SWIG_PYTHON_THREAD_BEGIN_BLOCK # define SWIG_PYTHON_THREAD_END_BLOCK # define SWIG_PYTHON_THREAD_BEGIN_ALLOW # define SWIG_PYTHON_THREAD_END_ALLOW #endif /* ----------------------------------------------------------------------------- * Python API portion that goes into the runtime * ----------------------------------------------------------------------------- */ #ifdef __cplusplus extern "C" { #endif /* ----------------------------------------------------------------------------- * Constant declarations * ----------------------------------------------------------------------------- */ /* Constant Types */ #define SWIG_PY_POINTER 4 #define SWIG_PY_BINARY 5 /* Constant information structure */ typedef struct swig_const_info { int type; char *name; long lvalue; double dvalue; void *pvalue; swig_type_info **ptype; } swig_const_info; /* ----------------------------------------------------------------------------- * Wrapper of PyInstanceMethod_New() used in Python 3 * It is exported to the generated module, used for -fastproxy * ----------------------------------------------------------------------------- */ #if PY_VERSION_HEX >= 0x03000000 SWIGRUNTIME PyObject* SWIG_PyInstanceMethod_New(PyObject *SWIGUNUSEDPARM(self), PyObject *func) { return PyInstanceMethod_New(func); } #else SWIGRUNTIME PyObject* SWIG_PyInstanceMethod_New(PyObject *SWIGUNUSEDPARM(self), PyObject *SWIGUNUSEDPARM(func)) { return NULL; } #endif #ifdef __cplusplus } #endif /* ----------------------------------------------------------------------------- * pyrun.swg * * This file contains the runtime support for Python modules * and includes code for managing global variables and pointer * type checking. * * ----------------------------------------------------------------------------- */ /* Common SWIG API */ /* for raw pointers */ #define SWIG_Python_ConvertPtr(obj, pptr, type, flags) SWIG_Python_ConvertPtrAndOwn(obj, pptr, type, flags, 0) #define SWIG_ConvertPtr(obj, pptr, type, flags) SWIG_Python_ConvertPtr(obj, pptr, type, flags) #define SWIG_ConvertPtrAndOwn(obj,pptr,type,flags,own) SWIG_Python_ConvertPtrAndOwn(obj, pptr, type, flags, own) #ifdef SWIGPYTHON_BUILTIN #define SWIG_NewPointerObj(ptr, type, flags) SWIG_Python_NewPointerObj(self, ptr, type, flags) #else #define SWIG_NewPointerObj(ptr, type, flags) SWIG_Python_NewPointerObj(NULL, ptr, type, flags) #endif #define SWIG_InternalNewPointerObj(ptr, type, flags) SWIG_Python_NewPointerObj(NULL, ptr, type, flags) #define SWIG_CheckImplicit(ty) SWIG_Python_CheckImplicit(ty) #define SWIG_AcquirePtr(ptr, src) SWIG_Python_AcquirePtr(ptr, src) #define swig_owntype int /* for raw packed data */ #define SWIG_ConvertPacked(obj, ptr, sz, ty) SWIG_Python_ConvertPacked(obj, ptr, sz, ty) #define SWIG_NewPackedObj(ptr, sz, type) SWIG_Python_NewPackedObj(ptr, sz, type) /* for class or struct pointers */ #define SWIG_ConvertInstance(obj, pptr, type, flags) SWIG_ConvertPtr(obj, pptr, type, flags) #define SWIG_NewInstanceObj(ptr, type, flags) SWIG_NewPointerObj(ptr, type, flags) /* for C or C++ function pointers */ #define SWIG_ConvertFunctionPtr(obj, pptr, type) SWIG_Python_ConvertFunctionPtr(obj, pptr, type) #define SWIG_NewFunctionPtrObj(ptr, type) SWIG_Python_NewPointerObj(NULL, ptr, type, 0) /* for C++ member pointers, ie, member methods */ #define SWIG_ConvertMember(obj, ptr, sz, ty) SWIG_Python_ConvertPacked(obj, ptr, sz, ty) #define SWIG_NewMemberObj(ptr, sz, type) SWIG_Python_NewPackedObj(ptr, sz, type) /* Runtime API */ #define SWIG_GetModule(clientdata) SWIG_Python_GetModule() #define SWIG_SetModule(clientdata, pointer) SWIG_Python_SetModule(pointer) #define SWIG_NewClientData(obj) SwigPyClientData_New(obj) #define SWIG_SetErrorObj SWIG_Python_SetErrorObj #define SWIG_SetErrorMsg SWIG_Python_SetErrorMsg #define SWIG_ErrorType(code) SWIG_Python_ErrorType(code) #define SWIG_Error(code, msg) SWIG_Python_SetErrorMsg(SWIG_ErrorType(code), msg) #define SWIG_fail goto fail /* Runtime API implementation */ /* Error manipulation */ SWIGINTERN void SWIG_Python_SetErrorObj(PyObject *errtype, PyObject *obj) { SWIG_PYTHON_THREAD_BEGIN_BLOCK; PyErr_SetObject(errtype, obj); Py_DECREF(obj); SWIG_PYTHON_THREAD_END_BLOCK; } SWIGINTERN void SWIG_Python_SetErrorMsg(PyObject *errtype, const char *msg) { SWIG_PYTHON_THREAD_BEGIN_BLOCK; PyErr_SetString(errtype, (char *) msg); SWIG_PYTHON_THREAD_END_BLOCK; } #define SWIG_Python_Raise(obj, type, desc) SWIG_Python_SetErrorObj(SWIG_Python_ExceptionType(desc), obj) /* Set a constant value */ #if defined(SWIGPYTHON_BUILTIN) SWIGINTERN void SwigPyBuiltin_AddPublicSymbol(PyObject *seq, const char *key) { PyObject *s = PyString_InternFromString(key); PyList_Append(seq, s); Py_DECREF(s); } SWIGINTERN void SWIG_Python_SetConstant(PyObject *d, PyObject *public_interface, const char *name, PyObject *obj) { PyDict_SetItemString(d, (char *)name, obj); Py_DECREF(obj); if (public_interface) SwigPyBuiltin_AddPublicSymbol(public_interface, name); } #else SWIGINTERN void SWIG_Python_SetConstant(PyObject *d, const char *name, PyObject *obj) { PyDict_SetItemString(d, (char *)name, obj); Py_DECREF(obj); } #endif /* Append a value to the result obj */ SWIGINTERN PyObject* SWIG_Python_AppendOutput(PyObject* result, PyObject* obj) { #if !defined(SWIG_PYTHON_OUTPUT_TUPLE) if (!result) { result = obj; } else if (result == Py_None) { Py_DECREF(result); result = obj; } else { if (!PyList_Check(result)) { PyObject *o2 = result; result = PyList_New(1); PyList_SetItem(result, 0, o2); } PyList_Append(result,obj); Py_DECREF(obj); } return result; #else PyObject* o2; PyObject* o3; if (!result) { result = obj; } else if (result == Py_None) { Py_DECREF(result); result = obj; } else { if (!PyTuple_Check(result)) { o2 = result; result = PyTuple_New(1); PyTuple_SET_ITEM(result, 0, o2); } o3 = PyTuple_New(1); PyTuple_SET_ITEM(o3, 0, obj); o2 = result; result = PySequence_Concat(o2, o3); Py_DECREF(o2); Py_DECREF(o3); } return result; #endif } /* Unpack the argument tuple */ SWIGINTERN int SWIG_Python_UnpackTuple(PyObject *args, const char *name, Py_ssize_t min, Py_ssize_t max, PyObject **objs) { if (!args) { if (!min && !max) { return 1; } else { PyErr_Format(PyExc_TypeError, "%s expected %s%d arguments, got none", name, (min == max ? "" : "at least "), (int)min); return 0; } } if (!PyTuple_Check(args)) { if (min <= 1 && max >= 1) { register int i; objs[0] = args; for (i = 1; i < max; ++i) { objs[i] = 0; } return 2; } PyErr_SetString(PyExc_SystemError, "UnpackTuple() argument list is not a tuple"); return 0; } else { register Py_ssize_t l = PyTuple_GET_SIZE(args); if (l < min) { PyErr_Format(PyExc_TypeError, "%s expected %s%d arguments, got %d", name, (min == max ? "" : "at least "), (int)min, (int)l); return 0; } else if (l > max) { PyErr_Format(PyExc_TypeError, "%s expected %s%d arguments, got %d", name, (min == max ? "" : "at most "), (int)max, (int)l); return 0; } else { register int i; for (i = 0; i < l; ++i) { objs[i] = PyTuple_GET_ITEM(args, i); } for (; l < max; ++l) { objs[l] = 0; } return i + 1; } } } /* A functor is a function object with one single object argument */ #if PY_VERSION_HEX >= 0x02020000 #define SWIG_Python_CallFunctor(functor, obj) PyObject_CallFunctionObjArgs(functor, obj, NULL); #else #define SWIG_Python_CallFunctor(functor, obj) PyObject_CallFunction(functor, "O", obj); #endif /* Helper for static pointer initialization for both C and C++ code, for example static PyObject *SWIG_STATIC_POINTER(MyVar) = NewSomething(...); */ #ifdef __cplusplus #define SWIG_STATIC_POINTER(var) var #else #define SWIG_STATIC_POINTER(var) var = 0; if (!var) var #endif /* ----------------------------------------------------------------------------- * Pointer declarations * ----------------------------------------------------------------------------- */ /* Flags for new pointer objects */ #define SWIG_POINTER_NOSHADOW (SWIG_POINTER_OWN << 1) #define SWIG_POINTER_NEW (SWIG_POINTER_NOSHADOW | SWIG_POINTER_OWN) #define SWIG_POINTER_IMPLICIT_CONV (SWIG_POINTER_DISOWN << 1) #define SWIG_BUILTIN_TP_INIT (SWIG_POINTER_OWN << 2) #define SWIG_BUILTIN_INIT (SWIG_BUILTIN_TP_INIT | SWIG_POINTER_OWN) #ifdef __cplusplus extern "C" { #endif /* How to access Py_None */ #if defined(_WIN32) || defined(__WIN32__) || defined(__CYGWIN__) # ifndef SWIG_PYTHON_NO_BUILD_NONE # ifndef SWIG_PYTHON_BUILD_NONE # define SWIG_PYTHON_BUILD_NONE # endif # endif #endif #ifdef SWIG_PYTHON_BUILD_NONE # ifdef Py_None # undef Py_None # define Py_None SWIG_Py_None() # endif SWIGRUNTIMEINLINE PyObject * _SWIG_Py_None(void) { PyObject *none = Py_BuildValue((char*)""); Py_DECREF(none); return none; } SWIGRUNTIME PyObject * SWIG_Py_None(void) { static PyObject *SWIG_STATIC_POINTER(none) = _SWIG_Py_None(); return none; } #endif /* The python void return value */ SWIGRUNTIMEINLINE PyObject * SWIG_Py_Void(void) { PyObject *none = Py_None; Py_INCREF(none); return none; } /* SwigPyClientData */ typedef struct { PyObject *klass; PyObject *newraw; PyObject *newargs; PyObject *destroy; int delargs; int implicitconv; PyTypeObject *pytype; } SwigPyClientData; SWIGRUNTIMEINLINE int SWIG_Python_CheckImplicit(swig_type_info *ty) { SwigPyClientData *data = (SwigPyClientData *)ty->clientdata; return data ? data->implicitconv : 0; } SWIGRUNTIMEINLINE PyObject * SWIG_Python_ExceptionType(swig_type_info *desc) { SwigPyClientData *data = desc ? (SwigPyClientData *) desc->clientdata : 0; PyObject *klass = data ? data->klass : 0; return (klass ? klass : PyExc_RuntimeError); } SWIGRUNTIME SwigPyClientData * SwigPyClientData_New(PyObject* obj) { if (!obj) { return 0; } else { SwigPyClientData *data = (SwigPyClientData *)malloc(sizeof(SwigPyClientData)); /* the klass element */ data->klass = obj; Py_INCREF(data->klass); /* the newraw method and newargs arguments used to create a new raw instance */ if (PyClass_Check(obj)) { data->newraw = 0; data->newargs = obj; Py_INCREF(obj); } else { #if (PY_VERSION_HEX < 0x02020000) data->newraw = 0; #else data->newraw = PyObject_GetAttrString(data->klass, (char *)"__new__"); #endif if (data->newraw) { Py_INCREF(data->newraw); data->newargs = PyTuple_New(1); PyTuple_SetItem(data->newargs, 0, obj); } else { data->newargs = obj; } Py_INCREF(data->newargs); } /* the destroy method, aka as the C++ delete method */ data->destroy = PyObject_GetAttrString(data->klass, (char *)"__swig_destroy__"); if (PyErr_Occurred()) { PyErr_Clear(); data->destroy = 0; } if (data->destroy) { int flags; Py_INCREF(data->destroy); flags = PyCFunction_GET_FLAGS(data->destroy); #ifdef METH_O data->delargs = !(flags & (METH_O)); #else data->delargs = 0; #endif } else { data->delargs = 0; } data->implicitconv = 0; data->pytype = 0; return data; } } SWIGRUNTIME void SwigPyClientData_Del(SwigPyClientData *data) { Py_XDECREF(data->newraw); Py_XDECREF(data->newargs); Py_XDECREF(data->destroy); } /* =============== SwigPyObject =====================*/ typedef struct { PyObject_HEAD void *ptr; swig_type_info *ty; int own; PyObject *next; #ifdef SWIGPYTHON_BUILTIN PyObject *dict; #endif } SwigPyObject; SWIGRUNTIME PyObject * SwigPyObject_long(SwigPyObject *v) { return PyLong_FromVoidPtr(v->ptr); } SWIGRUNTIME PyObject * SwigPyObject_format(const char* fmt, SwigPyObject *v) { PyObject *res = NULL; PyObject *args = PyTuple_New(1); if (args) { if (PyTuple_SetItem(args, 0, SwigPyObject_long(v)) == 0) { PyObject *ofmt = SWIG_Python_str_FromChar(fmt); if (ofmt) { #if PY_VERSION_HEX >= 0x03000000 res = PyUnicode_Format(ofmt,args); #else res = PyString_Format(ofmt,args); #endif Py_DECREF(ofmt); } Py_DECREF(args); } } return res; } SWIGRUNTIME PyObject * SwigPyObject_oct(SwigPyObject *v) { return SwigPyObject_format("%o",v); } SWIGRUNTIME PyObject * SwigPyObject_hex(SwigPyObject *v) { return SwigPyObject_format("%x",v); } SWIGRUNTIME PyObject * #ifdef METH_NOARGS SwigPyObject_repr(SwigPyObject *v) #else SwigPyObject_repr(SwigPyObject *v, PyObject *args) #endif { const char *name = SWIG_TypePrettyName(v->ty); PyObject *repr = SWIG_Python_str_FromFormat("", name, (void *)v); if (v->next) { # ifdef METH_NOARGS PyObject *nrep = SwigPyObject_repr((SwigPyObject *)v->next); # else PyObject *nrep = SwigPyObject_repr((SwigPyObject *)v->next, args); # endif # if PY_VERSION_HEX >= 0x03000000 PyObject *joined = PyUnicode_Concat(repr, nrep); Py_DecRef(repr); Py_DecRef(nrep); repr = joined; # else PyString_ConcatAndDel(&repr,nrep); # endif } return repr; } SWIGRUNTIME int SwigPyObject_print(SwigPyObject *v, FILE *fp, int SWIGUNUSEDPARM(flags)) { char *str; #ifdef METH_NOARGS PyObject *repr = SwigPyObject_repr(v); #else PyObject *repr = SwigPyObject_repr(v, NULL); #endif if (repr) { str = SWIG_Python_str_AsChar(repr); fputs(str, fp); SWIG_Python_str_DelForPy3(str); Py_DECREF(repr); return 0; } else { return 1; } } SWIGRUNTIME PyObject * SwigPyObject_str(SwigPyObject *v) { char result[SWIG_BUFFER_SIZE]; return SWIG_PackVoidPtr(result, v->ptr, v->ty->name, sizeof(result)) ? SWIG_Python_str_FromChar(result) : 0; } SWIGRUNTIME int SwigPyObject_compare(SwigPyObject *v, SwigPyObject *w) { void *i = v->ptr; void *j = w->ptr; return (i < j) ? -1 : ((i > j) ? 1 : 0); } /* Added for Python 3.x, would it also be useful for Python 2.x? */ SWIGRUNTIME PyObject* SwigPyObject_richcompare(SwigPyObject *v, SwigPyObject *w, int op) { PyObject* res; if( op != Py_EQ && op != Py_NE ) { Py_INCREF(Py_NotImplemented); return Py_NotImplemented; } res = PyBool_FromLong( (SwigPyObject_compare(v, w)==0) == (op == Py_EQ) ? 1 : 0); return res; } SWIGRUNTIME PyTypeObject* SwigPyObject_TypeOnce(void); #ifdef SWIGPYTHON_BUILTIN static swig_type_info *SwigPyObject_stype = 0; SWIGRUNTIME PyTypeObject* SwigPyObject_type(void) { SwigPyClientData *cd; assert(SwigPyObject_stype); cd = (SwigPyClientData*) SwigPyObject_stype->clientdata; assert(cd); assert(cd->pytype); return cd->pytype; } #else SWIGRUNTIME PyTypeObject* SwigPyObject_type(void) { static PyTypeObject *SWIG_STATIC_POINTER(type) = SwigPyObject_TypeOnce(); return type; } #endif SWIGRUNTIMEINLINE int SwigPyObject_Check(PyObject *op) { #ifdef SWIGPYTHON_BUILTIN PyTypeObject *target_tp = SwigPyObject_type(); if (PyType_IsSubtype(op->ob_type, target_tp)) return 1; return (strcmp(op->ob_type->tp_name, "SwigPyObject") == 0); #else return (Py_TYPE(op) == SwigPyObject_type()) || (strcmp(Py_TYPE(op)->tp_name,"SwigPyObject") == 0); #endif } SWIGRUNTIME PyObject * SwigPyObject_New(void *ptr, swig_type_info *ty, int own); SWIGRUNTIME void SwigPyObject_dealloc(PyObject *v) { SwigPyObject *sobj = (SwigPyObject *) v; PyObject *next = sobj->next; if (sobj->own == SWIG_POINTER_OWN) { swig_type_info *ty = sobj->ty; SwigPyClientData *data = ty ? (SwigPyClientData *) ty->clientdata : 0; PyObject *destroy = data ? data->destroy : 0; if (destroy) { /* destroy is always a VARARGS method */ PyObject *res; if (data->delargs) { /* we need to create a temporary object to carry the destroy operation */ PyObject *tmp = SwigPyObject_New(sobj->ptr, ty, 0); res = SWIG_Python_CallFunctor(destroy, tmp); Py_DECREF(tmp); } else { PyCFunction meth = PyCFunction_GET_FUNCTION(destroy); PyObject *mself = PyCFunction_GET_SELF(destroy); res = ((*meth)(mself, v)); } Py_XDECREF(res); } #if !defined(SWIG_PYTHON_SILENT_MEMLEAK) else { const char *name = SWIG_TypePrettyName(ty); printf("swig/python detected a memory leak of type '%s', no destructor found.\n", (name ? name : "unknown")); } #endif } Py_XDECREF(next); PyObject_DEL(v); } SWIGRUNTIME PyObject* SwigPyObject_append(PyObject* v, PyObject* next) { SwigPyObject *sobj = (SwigPyObject *) v; #ifndef METH_O PyObject *tmp = 0; if (!PyArg_ParseTuple(next,(char *)"O:append", &tmp)) return NULL; next = tmp; #endif if (!SwigPyObject_Check(next)) { return NULL; } sobj->next = next; Py_INCREF(next); return SWIG_Py_Void(); } SWIGRUNTIME PyObject* #ifdef METH_NOARGS SwigPyObject_next(PyObject* v) #else SwigPyObject_next(PyObject* v, PyObject *SWIGUNUSEDPARM(args)) #endif { SwigPyObject *sobj = (SwigPyObject *) v; if (sobj->next) { Py_INCREF(sobj->next); return sobj->next; } else { return SWIG_Py_Void(); } } SWIGINTERN PyObject* #ifdef METH_NOARGS SwigPyObject_disown(PyObject *v) #else SwigPyObject_disown(PyObject* v, PyObject *SWIGUNUSEDPARM(args)) #endif { SwigPyObject *sobj = (SwigPyObject *)v; sobj->own = 0; return SWIG_Py_Void(); } SWIGINTERN PyObject* #ifdef METH_NOARGS SwigPyObject_acquire(PyObject *v) #else SwigPyObject_acquire(PyObject* v, PyObject *SWIGUNUSEDPARM(args)) #endif { SwigPyObject *sobj = (SwigPyObject *)v; sobj->own = SWIG_POINTER_OWN; return SWIG_Py_Void(); } SWIGINTERN PyObject* SwigPyObject_own(PyObject *v, PyObject *args) { PyObject *val = 0; #if (PY_VERSION_HEX < 0x02020000) if (!PyArg_ParseTuple(args,(char *)"|O:own",&val)) #else if (!PyArg_UnpackTuple(args, (char *)"own", 0, 1, &val)) #endif { return NULL; } else { SwigPyObject *sobj = (SwigPyObject *)v; PyObject *obj = PyBool_FromLong(sobj->own); if (val) { #ifdef METH_NOARGS if (PyObject_IsTrue(val)) { SwigPyObject_acquire(v); } else { SwigPyObject_disown(v); } #else if (PyObject_IsTrue(val)) { SwigPyObject_acquire(v,args); } else { SwigPyObject_disown(v,args); } #endif } return obj; } } #ifdef METH_O static PyMethodDef swigobject_methods[] = { {(char *)"disown", (PyCFunction)SwigPyObject_disown, METH_NOARGS, (char *)"releases ownership of the pointer"}, {(char *)"acquire", (PyCFunction)SwigPyObject_acquire, METH_NOARGS, (char *)"aquires ownership of the pointer"}, {(char *)"own", (PyCFunction)SwigPyObject_own, METH_VARARGS, (char *)"returns/sets ownership of the pointer"}, {(char *)"append", (PyCFunction)SwigPyObject_append, METH_O, (char *)"appends another 'this' object"}, {(char *)"next", (PyCFunction)SwigPyObject_next, METH_NOARGS, (char *)"returns the next 'this' object"}, {(char *)"__repr__",(PyCFunction)SwigPyObject_repr, METH_NOARGS, (char *)"returns object representation"}, {0, 0, 0, 0} }; #else static PyMethodDef swigobject_methods[] = { {(char *)"disown", (PyCFunction)SwigPyObject_disown, METH_VARARGS, (char *)"releases ownership of the pointer"}, {(char *)"acquire", (PyCFunction)SwigPyObject_acquire, METH_VARARGS, (char *)"aquires ownership of the pointer"}, {(char *)"own", (PyCFunction)SwigPyObject_own, METH_VARARGS, (char *)"returns/sets ownership of the pointer"}, {(char *)"append", (PyCFunction)SwigPyObject_append, METH_VARARGS, (char *)"appends another 'this' object"}, {(char *)"next", (PyCFunction)SwigPyObject_next, METH_VARARGS, (char *)"returns the next 'this' object"}, {(char *)"__repr__",(PyCFunction)SwigPyObject_repr, METH_VARARGS, (char *)"returns object representation"}, {0, 0, 0, 0} }; #endif #if PY_VERSION_HEX < 0x02020000 SWIGINTERN PyObject * SwigPyObject_getattr(SwigPyObject *sobj,char *name) { return Py_FindMethod(swigobject_methods, (PyObject *)sobj, name); } #endif SWIGRUNTIME PyTypeObject* SwigPyObject_TypeOnce(void) { static char swigobject_doc[] = "Swig object carries a C/C++ instance pointer"; static PyNumberMethods SwigPyObject_as_number = { (binaryfunc)0, /*nb_add*/ (binaryfunc)0, /*nb_subtract*/ (binaryfunc)0, /*nb_multiply*/ /* nb_divide removed in Python 3 */ #if PY_VERSION_HEX < 0x03000000 (binaryfunc)0, /*nb_divide*/ #endif (binaryfunc)0, /*nb_remainder*/ (binaryfunc)0, /*nb_divmod*/ (ternaryfunc)0,/*nb_power*/ (unaryfunc)0, /*nb_negative*/ (unaryfunc)0, /*nb_positive*/ (unaryfunc)0, /*nb_absolute*/ (inquiry)0, /*nb_nonzero*/ 0, /*nb_invert*/ 0, /*nb_lshift*/ 0, /*nb_rshift*/ 0, /*nb_and*/ 0, /*nb_xor*/ 0, /*nb_or*/ #if PY_VERSION_HEX < 0x03000000 0, /*nb_coerce*/ #endif (unaryfunc)SwigPyObject_long, /*nb_int*/ #if PY_VERSION_HEX < 0x03000000 (unaryfunc)SwigPyObject_long, /*nb_long*/ #else 0, /*nb_reserved*/ #endif (unaryfunc)0, /*nb_float*/ #if PY_VERSION_HEX < 0x03000000 (unaryfunc)SwigPyObject_oct, /*nb_oct*/ (unaryfunc)SwigPyObject_hex, /*nb_hex*/ #endif #if PY_VERSION_HEX >= 0x03000000 /* 3.0 */ 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 /* nb_inplace_add -> nb_index, nb_inplace_divide removed */ #elif PY_VERSION_HEX >= 0x02050000 /* 2.5.0 */ 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 /* nb_inplace_add -> nb_index */ #elif PY_VERSION_HEX >= 0x02020000 /* 2.2.0 */ 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 /* nb_inplace_add -> nb_inplace_true_divide */ #elif PY_VERSION_HEX >= 0x02000000 /* 2.0.0 */ 0,0,0,0,0,0,0,0,0,0,0 /* nb_inplace_add -> nb_inplace_or */ #endif }; static PyTypeObject swigpyobject_type; static int type_init = 0; if (!type_init) { const PyTypeObject tmp = { /* PyObject header changed in Python 3 */ #if PY_VERSION_HEX >= 0x03000000 PyVarObject_HEAD_INIT(NULL, 0) #else PyObject_HEAD_INIT(NULL) 0, /* ob_size */ #endif (char *)"SwigPyObject", /* tp_name */ sizeof(SwigPyObject), /* tp_basicsize */ 0, /* tp_itemsize */ (destructor)SwigPyObject_dealloc, /* tp_dealloc */ (printfunc)SwigPyObject_print, /* tp_print */ #if PY_VERSION_HEX < 0x02020000 (getattrfunc)SwigPyObject_getattr, /* tp_getattr */ #else (getattrfunc)0, /* tp_getattr */ #endif (setattrfunc)0, /* tp_setattr */ #if PY_VERSION_HEX >= 0x03000000 0, /* tp_reserved in 3.0.1, tp_compare in 3.0.0 but not used */ #else (cmpfunc)SwigPyObject_compare, /* tp_compare */ #endif (reprfunc)SwigPyObject_repr, /* tp_repr */ &SwigPyObject_as_number, /* tp_as_number */ 0, /* tp_as_sequence */ 0, /* tp_as_mapping */ (hashfunc)0, /* tp_hash */ (ternaryfunc)0, /* tp_call */ (reprfunc)SwigPyObject_str, /* tp_str */ PyObject_GenericGetAttr, /* tp_getattro */ 0, /* tp_setattro */ 0, /* tp_as_buffer */ Py_TPFLAGS_DEFAULT, /* tp_flags */ swigobject_doc, /* tp_doc */ 0, /* tp_traverse */ 0, /* tp_clear */ (richcmpfunc)SwigPyObject_richcompare,/* tp_richcompare */ 0, /* tp_weaklistoffset */ #if PY_VERSION_HEX >= 0x02020000 0, /* tp_iter */ 0, /* tp_iternext */ swigobject_methods, /* tp_methods */ 0, /* tp_members */ 0, /* tp_getset */ 0, /* tp_base */ 0, /* tp_dict */ 0, /* tp_descr_get */ 0, /* tp_descr_set */ 0, /* tp_dictoffset */ 0, /* tp_init */ 0, /* tp_alloc */ 0, /* tp_new */ 0, /* tp_free */ 0, /* tp_is_gc */ 0, /* tp_bases */ 0, /* tp_mro */ 0, /* tp_cache */ 0, /* tp_subclasses */ 0, /* tp_weaklist */ #endif #if PY_VERSION_HEX >= 0x02030000 0, /* tp_del */ #endif #if PY_VERSION_HEX >= 0x02060000 0, /* tp_version */ #endif #ifdef COUNT_ALLOCS 0,0,0,0 /* tp_alloc -> tp_next */ #endif }; swigpyobject_type = tmp; type_init = 1; #if PY_VERSION_HEX < 0x02020000 swigpyobject_type.ob_type = &PyType_Type; #else if (PyType_Ready(&swigpyobject_type) < 0) return NULL; #endif } return &swigpyobject_type; } SWIGRUNTIME PyObject * SwigPyObject_New(void *ptr, swig_type_info *ty, int own) { SwigPyObject *sobj = PyObject_NEW(SwigPyObject, SwigPyObject_type()); if (sobj) { sobj->ptr = ptr; sobj->ty = ty; sobj->own = own; sobj->next = 0; } return (PyObject *)sobj; } /* ----------------------------------------------------------------------------- * Implements a simple Swig Packed type, and use it instead of string * ----------------------------------------------------------------------------- */ typedef struct { PyObject_HEAD void *pack; swig_type_info *ty; size_t size; } SwigPyPacked; SWIGRUNTIME int SwigPyPacked_print(SwigPyPacked *v, FILE *fp, int SWIGUNUSEDPARM(flags)) { char result[SWIG_BUFFER_SIZE]; fputs("pack, v->size, 0, sizeof(result))) { fputs("at ", fp); fputs(result, fp); } fputs(v->ty->name,fp); fputs(">", fp); return 0; } SWIGRUNTIME PyObject * SwigPyPacked_repr(SwigPyPacked *v) { char result[SWIG_BUFFER_SIZE]; if (SWIG_PackDataName(result, v->pack, v->size, 0, sizeof(result))) { return SWIG_Python_str_FromFormat("", result, v->ty->name); } else { return SWIG_Python_str_FromFormat("", v->ty->name); } } SWIGRUNTIME PyObject * SwigPyPacked_str(SwigPyPacked *v) { char result[SWIG_BUFFER_SIZE]; if (SWIG_PackDataName(result, v->pack, v->size, 0, sizeof(result))){ return SWIG_Python_str_FromFormat("%s%s", result, v->ty->name); } else { return SWIG_Python_str_FromChar(v->ty->name); } } SWIGRUNTIME int SwigPyPacked_compare(SwigPyPacked *v, SwigPyPacked *w) { size_t i = v->size; size_t j = w->size; int s = (i < j) ? -1 : ((i > j) ? 1 : 0); return s ? s : strncmp((char *)v->pack, (char *)w->pack, 2*v->size); } SWIGRUNTIME PyTypeObject* SwigPyPacked_TypeOnce(void); SWIGRUNTIME PyTypeObject* SwigPyPacked_type(void) { static PyTypeObject *SWIG_STATIC_POINTER(type) = SwigPyPacked_TypeOnce(); return type; } SWIGRUNTIMEINLINE int SwigPyPacked_Check(PyObject *op) { return ((op)->ob_type == SwigPyPacked_TypeOnce()) || (strcmp((op)->ob_type->tp_name,"SwigPyPacked") == 0); } SWIGRUNTIME void SwigPyPacked_dealloc(PyObject *v) { if (SwigPyPacked_Check(v)) { SwigPyPacked *sobj = (SwigPyPacked *) v; free(sobj->pack); } PyObject_DEL(v); } SWIGRUNTIME PyTypeObject* SwigPyPacked_TypeOnce(void) { static char swigpacked_doc[] = "Swig object carries a C/C++ instance pointer"; static PyTypeObject swigpypacked_type; static int type_init = 0; if (!type_init) { const PyTypeObject tmp = { /* PyObject header changed in Python 3 */ #if PY_VERSION_HEX>=0x03000000 PyVarObject_HEAD_INIT(NULL, 0) #else PyObject_HEAD_INIT(NULL) 0, /* ob_size */ #endif (char *)"SwigPyPacked", /* tp_name */ sizeof(SwigPyPacked), /* tp_basicsize */ 0, /* tp_itemsize */ (destructor)SwigPyPacked_dealloc, /* tp_dealloc */ (printfunc)SwigPyPacked_print, /* tp_print */ (getattrfunc)0, /* tp_getattr */ (setattrfunc)0, /* tp_setattr */ #if PY_VERSION_HEX>=0x03000000 0, /* tp_reserved in 3.0.1 */ #else (cmpfunc)SwigPyPacked_compare, /* tp_compare */ #endif (reprfunc)SwigPyPacked_repr, /* tp_repr */ 0, /* tp_as_number */ 0, /* tp_as_sequence */ 0, /* tp_as_mapping */ (hashfunc)0, /* tp_hash */ (ternaryfunc)0, /* tp_call */ (reprfunc)SwigPyPacked_str, /* tp_str */ PyObject_GenericGetAttr, /* tp_getattro */ 0, /* tp_setattro */ 0, /* tp_as_buffer */ Py_TPFLAGS_DEFAULT, /* tp_flags */ swigpacked_doc, /* tp_doc */ 0, /* tp_traverse */ 0, /* tp_clear */ 0, /* tp_richcompare */ 0, /* tp_weaklistoffset */ #if PY_VERSION_HEX >= 0x02020000 0, /* tp_iter */ 0, /* tp_iternext */ 0, /* tp_methods */ 0, /* tp_members */ 0, /* tp_getset */ 0, /* tp_base */ 0, /* tp_dict */ 0, /* tp_descr_get */ 0, /* tp_descr_set */ 0, /* tp_dictoffset */ 0, /* tp_init */ 0, /* tp_alloc */ 0, /* tp_new */ 0, /* tp_free */ 0, /* tp_is_gc */ 0, /* tp_bases */ 0, /* tp_mro */ 0, /* tp_cache */ 0, /* tp_subclasses */ 0, /* tp_weaklist */ #endif #if PY_VERSION_HEX >= 0x02030000 0, /* tp_del */ #endif #if PY_VERSION_HEX >= 0x02060000 0, /* tp_version */ #endif #ifdef COUNT_ALLOCS 0,0,0,0 /* tp_alloc -> tp_next */ #endif }; swigpypacked_type = tmp; type_init = 1; #if PY_VERSION_HEX < 0x02020000 swigpypacked_type.ob_type = &PyType_Type; #else if (PyType_Ready(&swigpypacked_type) < 0) return NULL; #endif } return &swigpypacked_type; } SWIGRUNTIME PyObject * SwigPyPacked_New(void *ptr, size_t size, swig_type_info *ty) { SwigPyPacked *sobj = PyObject_NEW(SwigPyPacked, SwigPyPacked_type()); if (sobj) { void *pack = malloc(size); if (pack) { memcpy(pack, ptr, size); sobj->pack = pack; sobj->ty = ty; sobj->size = size; } else { PyObject_DEL((PyObject *) sobj); sobj = 0; } } return (PyObject *) sobj; } SWIGRUNTIME swig_type_info * SwigPyPacked_UnpackData(PyObject *obj, void *ptr, size_t size) { if (SwigPyPacked_Check(obj)) { SwigPyPacked *sobj = (SwigPyPacked *)obj; if (sobj->size != size) return 0; memcpy(ptr, sobj->pack, size); return sobj->ty; } else { return 0; } } /* ----------------------------------------------------------------------------- * pointers/data manipulation * ----------------------------------------------------------------------------- */ SWIGRUNTIMEINLINE PyObject * _SWIG_This(void) { return SWIG_Python_str_FromChar("this"); } static PyObject *swig_this = NULL; SWIGRUNTIME PyObject * SWIG_This(void) { if (swig_this == NULL) swig_this = _SWIG_This(); return swig_this; } /* #define SWIG_PYTHON_SLOW_GETSET_THIS */ /* TODO: I don't know how to implement the fast getset in Python 3 right now */ #if PY_VERSION_HEX>=0x03000000 #define SWIG_PYTHON_SLOW_GETSET_THIS #endif SWIGRUNTIME SwigPyObject * SWIG_Python_GetSwigThis(PyObject *pyobj) { PyObject *obj; if (SwigPyObject_Check(pyobj)) return (SwigPyObject *) pyobj; #ifdef SWIGPYTHON_BUILTIN (void)obj; # ifdef PyWeakref_CheckProxy if (PyWeakref_CheckProxy(pyobj)) { pyobj = PyWeakref_GET_OBJECT(pyobj); if (pyobj && SwigPyObject_Check(pyobj)) return (SwigPyObject*) pyobj; } # endif return NULL; #else obj = 0; #if (!defined(SWIG_PYTHON_SLOW_GETSET_THIS) && (PY_VERSION_HEX >= 0x02030000)) if (PyInstance_Check(pyobj)) { obj = _PyInstance_Lookup(pyobj, SWIG_This()); } else { PyObject **dictptr = _PyObject_GetDictPtr(pyobj); if (dictptr != NULL) { PyObject *dict = *dictptr; obj = dict ? PyDict_GetItem(dict, SWIG_This()) : 0; } else { #ifdef PyWeakref_CheckProxy if (PyWeakref_CheckProxy(pyobj)) { PyObject *wobj = PyWeakref_GET_OBJECT(pyobj); return wobj ? SWIG_Python_GetSwigThis(wobj) : 0; } #endif obj = PyObject_GetAttr(pyobj,SWIG_This()); if (obj) { Py_DECREF(obj); } else { if (PyErr_Occurred()) PyErr_Clear(); return 0; } } } #else obj = PyObject_GetAttr(pyobj,SWIG_This()); if (obj) { Py_DECREF(obj); } else { if (PyErr_Occurred()) PyErr_Clear(); return 0; } #endif if (obj && !SwigPyObject_Check(obj)) { /* a PyObject is called 'this', try to get the 'real this' SwigPyObject from it */ return SWIG_Python_GetSwigThis(obj); } return (SwigPyObject *)obj; #endif } /* Acquire a pointer value */ SWIGRUNTIME int SWIG_Python_AcquirePtr(PyObject *obj, int own) { if (own == SWIG_POINTER_OWN) { SwigPyObject *sobj = SWIG_Python_GetSwigThis(obj); if (sobj) { int oldown = sobj->own; sobj->own = own; return oldown; } } return 0; } /* Convert a pointer value */ SWIGRUNTIME int SWIG_Python_ConvertPtrAndOwn(PyObject *obj, void **ptr, swig_type_info *ty, int flags, int *own) { int res; SwigPyObject *sobj; if (!obj) return SWIG_ERROR; if (obj == Py_None) { if (ptr) *ptr = 0; return SWIG_OK; } res = SWIG_ERROR; sobj = SWIG_Python_GetSwigThis(obj); if (own) *own = 0; while (sobj) { void *vptr = sobj->ptr; if (ty) { swig_type_info *to = sobj->ty; if (to == ty) { /* no type cast needed */ if (ptr) *ptr = vptr; break; } else { swig_cast_info *tc = SWIG_TypeCheck(to->name,ty); if (!tc) { sobj = (SwigPyObject *)sobj->next; } else { if (ptr) { int newmemory = 0; *ptr = SWIG_TypeCast(tc,vptr,&newmemory); if (newmemory == SWIG_CAST_NEW_MEMORY) { assert(own); /* badly formed typemap which will lead to a memory leak - it must set and use own to delete *ptr */ if (own) *own = *own | SWIG_CAST_NEW_MEMORY; } } break; } } } else { if (ptr) *ptr = vptr; break; } } if (sobj) { if (own) *own = *own | sobj->own; if (flags & SWIG_POINTER_DISOWN) { sobj->own = 0; } res = SWIG_OK; } else { if (flags & SWIG_POINTER_IMPLICIT_CONV) { SwigPyClientData *data = ty ? (SwigPyClientData *) ty->clientdata : 0; if (data && !data->implicitconv) { PyObject *klass = data->klass; if (klass) { PyObject *impconv; data->implicitconv = 1; /* avoid recursion and call 'explicit' constructors*/ impconv = SWIG_Python_CallFunctor(klass, obj); data->implicitconv = 0; if (PyErr_Occurred()) { PyErr_Clear(); impconv = 0; } if (impconv) { SwigPyObject *iobj = SWIG_Python_GetSwigThis(impconv); if (iobj) { void *vptr; res = SWIG_Python_ConvertPtrAndOwn((PyObject*)iobj, &vptr, ty, 0, 0); if (SWIG_IsOK(res)) { if (ptr) { *ptr = vptr; /* transfer the ownership to 'ptr' */ iobj->own = 0; res = SWIG_AddCast(res); res = SWIG_AddNewMask(res); } else { res = SWIG_AddCast(res); } } } Py_DECREF(impconv); } } } } } return res; } /* Convert a function ptr value */ SWIGRUNTIME int SWIG_Python_ConvertFunctionPtr(PyObject *obj, void **ptr, swig_type_info *ty) { if (!PyCFunction_Check(obj)) { return SWIG_ConvertPtr(obj, ptr, ty, 0); } else { void *vptr = 0; /* here we get the method pointer for callbacks */ const char *doc = (((PyCFunctionObject *)obj) -> m_ml -> ml_doc); const char *desc = doc ? strstr(doc, "swig_ptr: ") : 0; if (desc) desc = ty ? SWIG_UnpackVoidPtr(desc + 10, &vptr, ty->name) : 0; if (!desc) return SWIG_ERROR; if (ty) { swig_cast_info *tc = SWIG_TypeCheck(desc,ty); if (tc) { int newmemory = 0; *ptr = SWIG_TypeCast(tc,vptr,&newmemory); assert(!newmemory); /* newmemory handling not yet implemented */ } else { return SWIG_ERROR; } } else { *ptr = vptr; } return SWIG_OK; } } /* Convert a packed value value */ SWIGRUNTIME int SWIG_Python_ConvertPacked(PyObject *obj, void *ptr, size_t sz, swig_type_info *ty) { swig_type_info *to = SwigPyPacked_UnpackData(obj, ptr, sz); if (!to) return SWIG_ERROR; if (ty) { if (to != ty) { /* check type cast? */ swig_cast_info *tc = SWIG_TypeCheck(to->name,ty); if (!tc) return SWIG_ERROR; } } return SWIG_OK; } /* ----------------------------------------------------------------------------- * Create a new pointer object * ----------------------------------------------------------------------------- */ /* Create a new instance object, without calling __init__, and set the 'this' attribute. */ SWIGRUNTIME PyObject* SWIG_Python_NewShadowInstance(SwigPyClientData *data, PyObject *swig_this) { #if (PY_VERSION_HEX >= 0x02020000) PyObject *inst = 0; PyObject *newraw = data->newraw; if (newraw) { inst = PyObject_Call(newraw, data->newargs, NULL); if (inst) { #if !defined(SWIG_PYTHON_SLOW_GETSET_THIS) PyObject **dictptr = _PyObject_GetDictPtr(inst); if (dictptr != NULL) { PyObject *dict = *dictptr; if (dict == NULL) { dict = PyDict_New(); *dictptr = dict; PyDict_SetItem(dict, SWIG_This(), swig_this); } } #else PyObject *key = SWIG_This(); PyObject_SetAttr(inst, key, swig_this); #endif } } else { #if PY_VERSION_HEX >= 0x03000000 inst = PyBaseObject_Type.tp_new((PyTypeObject*) data->newargs, Py_None, Py_None); PyObject_SetAttr(inst, SWIG_This(), swig_this); Py_TYPE(inst)->tp_flags &= ~Py_TPFLAGS_VALID_VERSION_TAG; #else PyObject *dict = PyDict_New(); PyDict_SetItem(dict, SWIG_This(), swig_this); inst = PyInstance_NewRaw(data->newargs, dict); Py_DECREF(dict); #endif } return inst; #else #if (PY_VERSION_HEX >= 0x02010000) PyObject *inst; PyObject *dict = PyDict_New(); PyDict_SetItem(dict, SWIG_This(), swig_this); inst = PyInstance_NewRaw(data->newargs, dict); Py_DECREF(dict); return (PyObject *) inst; #else PyInstanceObject *inst = PyObject_NEW(PyInstanceObject, &PyInstance_Type); if (inst == NULL) { return NULL; } inst->in_class = (PyClassObject *)data->newargs; Py_INCREF(inst->in_class); inst->in_dict = PyDict_New(); if (inst->in_dict == NULL) { Py_DECREF(inst); return NULL; } #ifdef Py_TPFLAGS_HAVE_WEAKREFS inst->in_weakreflist = NULL; #endif #ifdef Py_TPFLAGS_GC PyObject_GC_Init(inst); #endif PyDict_SetItem(inst->in_dict, SWIG_This(), swig_this); return (PyObject *) inst; #endif #endif } SWIGRUNTIME void SWIG_Python_SetSwigThis(PyObject *inst, PyObject *swig_this) { PyObject *dict; #if (PY_VERSION_HEX >= 0x02020000) && !defined(SWIG_PYTHON_SLOW_GETSET_THIS) PyObject **dictptr = _PyObject_GetDictPtr(inst); if (dictptr != NULL) { dict = *dictptr; if (dict == NULL) { dict = PyDict_New(); *dictptr = dict; } PyDict_SetItem(dict, SWIG_This(), swig_this); return; } #endif dict = PyObject_GetAttrString(inst, (char*)"__dict__"); PyDict_SetItem(dict, SWIG_This(), swig_this); Py_DECREF(dict); } SWIGINTERN PyObject * SWIG_Python_InitShadowInstance(PyObject *args) { PyObject *obj[2]; if (!SWIG_Python_UnpackTuple(args,(char*)"swiginit", 2, 2, obj)) { return NULL; } else { SwigPyObject *sthis = SWIG_Python_GetSwigThis(obj[0]); if (sthis) { SwigPyObject_append((PyObject*) sthis, obj[1]); } else { SWIG_Python_SetSwigThis(obj[0], obj[1]); } return SWIG_Py_Void(); } } /* Create a new pointer object */ SWIGRUNTIME PyObject * SWIG_Python_NewPointerObj(PyObject *self, void *ptr, swig_type_info *type, int flags) { SwigPyClientData *clientdata; PyObject * robj; int own; if (!ptr) return SWIG_Py_Void(); clientdata = type ? (SwigPyClientData *)(type->clientdata) : 0; own = (flags & SWIG_POINTER_OWN) ? SWIG_POINTER_OWN : 0; if (clientdata && clientdata->pytype) { SwigPyObject *newobj; if (flags & SWIG_BUILTIN_TP_INIT) { newobj = (SwigPyObject*) self; if (newobj->ptr) { PyObject *next_self = clientdata->pytype->tp_alloc(clientdata->pytype, 0); while (newobj->next) newobj = (SwigPyObject *) newobj->next; newobj->next = next_self; newobj = (SwigPyObject *)next_self; } } else { newobj = PyObject_New(SwigPyObject, clientdata->pytype); } if (newobj) { newobj->ptr = ptr; newobj->ty = type; newobj->own = own; newobj->next = 0; #ifdef SWIGPYTHON_BUILTIN newobj->dict = 0; #endif return (PyObject*) newobj; } return SWIG_Py_Void(); } assert(!(flags & SWIG_BUILTIN_TP_INIT)); robj = SwigPyObject_New(ptr, type, own); if (clientdata && !(flags & SWIG_POINTER_NOSHADOW)) { PyObject *inst = SWIG_Python_NewShadowInstance(clientdata, robj); if (inst) { Py_DECREF(robj); robj = inst; } } return robj; } /* Create a new packed object */ SWIGRUNTIMEINLINE PyObject * SWIG_Python_NewPackedObj(void *ptr, size_t sz, swig_type_info *type) { return ptr ? SwigPyPacked_New((void *) ptr, sz, type) : SWIG_Py_Void(); } /* -----------------------------------------------------------------------------* * Get type list * -----------------------------------------------------------------------------*/ #ifdef SWIG_LINK_RUNTIME void *SWIG_ReturnGlobalTypeList(void *); #endif SWIGRUNTIME swig_module_info * SWIG_Python_GetModule(void) { static void *type_pointer = (void *)0; /* first check if module already created */ if (!type_pointer) { #ifdef SWIG_LINK_RUNTIME type_pointer = SWIG_ReturnGlobalTypeList((void *)0); #else # ifdef SWIGPY_USE_CAPSULE type_pointer = PyCapsule_Import(SWIGPY_CAPSULE_NAME, 0); # else type_pointer = PyCObject_Import((char*)"swig_runtime_data" SWIG_RUNTIME_VERSION, (char*)"type_pointer" SWIG_TYPE_TABLE_NAME); # endif if (PyErr_Occurred()) { PyErr_Clear(); type_pointer = (void *)0; } #endif } return (swig_module_info *) type_pointer; } #if PY_MAJOR_VERSION < 2 /* PyModule_AddObject function was introduced in Python 2.0. The following function is copied out of Python/modsupport.c in python version 2.3.4 */ SWIGINTERN int PyModule_AddObject(PyObject *m, char *name, PyObject *o) { PyObject *dict; if (!PyModule_Check(m)) { PyErr_SetString(PyExc_TypeError, "PyModule_AddObject() needs module as first arg"); return SWIG_ERROR; } if (!o) { PyErr_SetString(PyExc_TypeError, "PyModule_AddObject() needs non-NULL value"); return SWIG_ERROR; } dict = PyModule_GetDict(m); if (dict == NULL) { /* Internal error -- modules must have a dict! */ PyErr_Format(PyExc_SystemError, "module '%s' has no __dict__", PyModule_GetName(m)); return SWIG_ERROR; } if (PyDict_SetItemString(dict, name, o)) return SWIG_ERROR; Py_DECREF(o); return SWIG_OK; } #endif SWIGRUNTIME void #ifdef SWIGPY_USE_CAPSULE SWIG_Python_DestroyModule(PyObject *obj) #else SWIG_Python_DestroyModule(void *vptr) #endif { #ifdef SWIGPY_USE_CAPSULE swig_module_info *swig_module = (swig_module_info *) PyCapsule_GetPointer(obj, SWIGPY_CAPSULE_NAME); #else swig_module_info *swig_module = (swig_module_info *) vptr; #endif swig_type_info **types = swig_module->types; size_t i; for (i =0; i < swig_module->size; ++i) { swig_type_info *ty = types[i]; if (ty->owndata) { SwigPyClientData *data = (SwigPyClientData *) ty->clientdata; if (data) SwigPyClientData_Del(data); } } Py_DECREF(SWIG_This()); swig_this = NULL; } SWIGRUNTIME void SWIG_Python_SetModule(swig_module_info *swig_module) { #if PY_VERSION_HEX >= 0x03000000 /* Add a dummy module object into sys.modules */ PyObject *module = PyImport_AddModule((char*)"swig_runtime_data" SWIG_RUNTIME_VERSION); #else static PyMethodDef swig_empty_runtime_method_table[] = { {NULL, NULL, 0, NULL} }; /* Sentinel */ PyObject *module = Py_InitModule((char*)"swig_runtime_data" SWIG_RUNTIME_VERSION, swig_empty_runtime_method_table); #endif #ifdef SWIGPY_USE_CAPSULE PyObject *pointer = PyCapsule_New((void *) swig_module, SWIGPY_CAPSULE_NAME, SWIG_Python_DestroyModule); if (pointer && module) { PyModule_AddObject(module, (char*)"type_pointer_capsule" SWIG_TYPE_TABLE_NAME, pointer); } else { Py_XDECREF(pointer); } #else PyObject *pointer = PyCObject_FromVoidPtr((void *) swig_module, SWIG_Python_DestroyModule); if (pointer && module) { PyModule_AddObject(module, (char*)"type_pointer" SWIG_TYPE_TABLE_NAME, pointer); } else { Py_XDECREF(pointer); } #endif } /* The python cached type query */ SWIGRUNTIME PyObject * SWIG_Python_TypeCache(void) { static PyObject *SWIG_STATIC_POINTER(cache) = PyDict_New(); return cache; } SWIGRUNTIME swig_type_info * SWIG_Python_TypeQuery(const char *type) { PyObject *cache = SWIG_Python_TypeCache(); PyObject *key = SWIG_Python_str_FromChar(type); PyObject *obj = PyDict_GetItem(cache, key); swig_type_info *descriptor; if (obj) { #ifdef SWIGPY_USE_CAPSULE descriptor = (swig_type_info *) PyCapsule_GetPointer(obj, NULL); #else descriptor = (swig_type_info *) PyCObject_AsVoidPtr(obj); #endif } else { swig_module_info *swig_module = SWIG_Python_GetModule(); descriptor = SWIG_TypeQueryModule(swig_module, swig_module, type); if (descriptor) { #ifdef SWIGPY_USE_CAPSULE obj = PyCapsule_New((void*) descriptor, NULL, NULL); #else obj = PyCObject_FromVoidPtr(descriptor, NULL); #endif PyDict_SetItem(cache, key, obj); Py_DECREF(obj); } } Py_DECREF(key); return descriptor; } /* For backward compatibility only */ #define SWIG_POINTER_EXCEPTION 0 #define SWIG_arg_fail(arg) SWIG_Python_ArgFail(arg) #define SWIG_MustGetPtr(p, type, argnum, flags) SWIG_Python_MustGetPtr(p, type, argnum, flags) SWIGRUNTIME int SWIG_Python_AddErrMesg(const char* mesg, int infront) { if (PyErr_Occurred()) { PyObject *type = 0; PyObject *value = 0; PyObject *traceback = 0; PyErr_Fetch(&type, &value, &traceback); if (value) { char *tmp; PyObject *old_str = PyObject_Str(value); Py_XINCREF(type); PyErr_Clear(); if (infront) { PyErr_Format(type, "%s %s", mesg, tmp = SWIG_Python_str_AsChar(old_str)); } else { PyErr_Format(type, "%s %s", tmp = SWIG_Python_str_AsChar(old_str), mesg); } SWIG_Python_str_DelForPy3(tmp); Py_DECREF(old_str); } return 1; } else { return 0; } } SWIGRUNTIME int SWIG_Python_ArgFail(int argnum) { if (PyErr_Occurred()) { /* add information about failing argument */ char mesg[256]; PyOS_snprintf(mesg, sizeof(mesg), "argument number %d:", argnum); return SWIG_Python_AddErrMesg(mesg, 1); } else { return 0; } } SWIGRUNTIMEINLINE const char * SwigPyObject_GetDesc(PyObject *self) { SwigPyObject *v = (SwigPyObject *)self; swig_type_info *ty = v ? v->ty : 0; return ty ? ty->str : (char*)""; } SWIGRUNTIME void SWIG_Python_TypeError(const char *type, PyObject *obj) { if (type) { #if defined(SWIG_COBJECT_TYPES) if (obj && SwigPyObject_Check(obj)) { const char *otype = (const char *) SwigPyObject_GetDesc(obj); if (otype) { PyErr_Format(PyExc_TypeError, "a '%s' is expected, 'SwigPyObject(%s)' is received", type, otype); return; } } else #endif { const char *otype = (obj ? obj->ob_type->tp_name : 0); if (otype) { PyObject *str = PyObject_Str(obj); const char *cstr = str ? SWIG_Python_str_AsChar(str) : 0; if (cstr) { PyErr_Format(PyExc_TypeError, "a '%s' is expected, '%s(%s)' is received", type, otype, cstr); SWIG_Python_str_DelForPy3(cstr); } else { PyErr_Format(PyExc_TypeError, "a '%s' is expected, '%s' is received", type, otype); } Py_XDECREF(str); return; } } PyErr_Format(PyExc_TypeError, "a '%s' is expected", type); } else { PyErr_Format(PyExc_TypeError, "unexpected type is received"); } } /* Convert a pointer value, signal an exception on a type mismatch */ SWIGRUNTIME void * SWIG_Python_MustGetPtr(PyObject *obj, swig_type_info *ty, int SWIGUNUSEDPARM(argnum), int flags) { void *result; if (SWIG_Python_ConvertPtr(obj, &result, ty, flags) == -1) { PyErr_Clear(); #if SWIG_POINTER_EXCEPTION if (flags) { SWIG_Python_TypeError(SWIG_TypePrettyName(ty), obj); SWIG_Python_ArgFail(argnum); } #endif } return result; } SWIGRUNTIME int SWIG_Python_NonDynamicSetAttr(PyObject *obj, PyObject *name, PyObject *value) { PyTypeObject *tp = obj->ob_type; PyObject *descr; PyObject *encoded_name; descrsetfunc f; int res; #ifdef Py_USING_UNICODE if (PyString_Check(name)) { name = PyUnicode_Decode(PyString_AsString(name), PyString_Size(name), NULL, NULL); if (!name) return -1; } else if (!PyUnicode_Check(name)) #else if (!PyString_Check(name)) #endif { PyErr_Format(PyExc_TypeError, "attribute name must be string, not '%.200s'", name->ob_type->tp_name); return -1; } else { Py_INCREF(name); } if (!tp->tp_dict) { if (PyType_Ready(tp) < 0) goto done; } res = -1; descr = _PyType_Lookup(tp, name); f = NULL; if (descr != NULL) f = descr->ob_type->tp_descr_set; if (!f) { if (PyString_Check(name)) { encoded_name = name; Py_INCREF(name); } else { encoded_name = PyUnicode_AsUTF8String(name); } PyErr_Format(PyExc_AttributeError, "'%.100s' object has no attribute '%.200s'", tp->tp_name, PyString_AsString(encoded_name)); Py_DECREF(encoded_name); } else { res = f(descr, obj, value); } done: Py_DECREF(name); return res; } #ifdef __cplusplus } #endif #define SWIG_exception_fail(code, msg) do { SWIG_Error(code, msg); SWIG_fail; } while(0) #define SWIG_contract_assert(expr, msg) if (!(expr)) { SWIG_Error(SWIG_RuntimeError, msg); SWIG_fail; } else #define SWIG_exception(code, msg) do { SWIG_Error(code, msg); SWIG_fail;; } while(0) /* -------- TYPES TABLE (BEGIN) -------- */ #define SWIGTYPE_p_CircularVector swig_types[0] #define SWIGTYPE_p_SpikeContainer swig_types[1] #define SWIGTYPE_p_char swig_types[2] #define SWIGTYPE_p_int swig_types[3] #define SWIGTYPE_p_long swig_types[4] #define SWIGTYPE_p_p_long swig_types[5] static swig_type_info *swig_types[7]; static swig_module_info swig_module = {swig_types, 6, 0, 0, 0, 0}; #define SWIG_TypeQuery(name) SWIG_TypeQueryModule(&swig_module, &swig_module, name) #define SWIG_MangledTypeQuery(name) SWIG_MangledTypeQueryModule(&swig_module, &swig_module, name) /* -------- TYPES TABLE (END) -------- */ #if (PY_VERSION_HEX <= 0x02000000) # if !defined(SWIG_PYTHON_CLASSIC) # error "This python version requires swig to be run with the '-classic' option" # endif #endif /*----------------------------------------------- @(target):= _ccircular.so ------------------------------------------------*/ #if PY_VERSION_HEX >= 0x03000000 # define SWIG_init PyInit__ccircular #else # define SWIG_init init_ccircular #endif #define SWIG_name "_ccircular" #define SWIGVERSION 0x020004 #define SWIG_VERSION SWIGVERSION #define SWIG_as_voidptr(a) const_cast< void * >(static_cast< const void * >(a)) #define SWIG_as_voidptrptr(a) ((void)SWIG_as_voidptr(*a),reinterpret_cast< void** >(a)) #include namespace swig { class SwigPtr_PyObject { protected: PyObject *_obj; public: SwigPtr_PyObject() :_obj(0) { } SwigPtr_PyObject(const SwigPtr_PyObject& item) : _obj(item._obj) { Py_XINCREF(_obj); } SwigPtr_PyObject(PyObject *obj, bool initial_ref = true) :_obj(obj) { if (initial_ref) { Py_XINCREF(_obj); } } SwigPtr_PyObject & operator=(const SwigPtr_PyObject& item) { Py_XINCREF(item._obj); Py_XDECREF(_obj); _obj = item._obj; return *this; } ~SwigPtr_PyObject() { Py_XDECREF(_obj); } operator PyObject *() const { return _obj; } PyObject *operator->() const { return _obj; } }; } namespace swig { struct SwigVar_PyObject : SwigPtr_PyObject { SwigVar_PyObject(PyObject* obj = 0) : SwigPtr_PyObject(obj, false) { } SwigVar_PyObject & operator = (PyObject* obj) { Py_XDECREF(_obj); _obj = obj; return *this; } }; } #include #define SWIG_FILE_WITH_INIT #include "ccircular.h" #ifndef SWIG_FILE_WITH_INIT # define NO_IMPORT_ARRAY #endif #include "stdio.h" #include SWIGINTERN int SWIG_AsVal_double (PyObject *obj, double *val) { int res = SWIG_TypeError; if (PyFloat_Check(obj)) { if (val) *val = PyFloat_AsDouble(obj); return SWIG_OK; } else if (PyInt_Check(obj)) { if (val) *val = PyInt_AsLong(obj); return SWIG_OK; } else if (PyLong_Check(obj)) { double v = PyLong_AsDouble(obj); if (!PyErr_Occurred()) { if (val) *val = v; return SWIG_OK; } else { PyErr_Clear(); } } #ifdef SWIG_PYTHON_CAST_MODE { int dispatch = 0; double d = PyFloat_AsDouble(obj); if (!PyErr_Occurred()) { if (val) *val = d; return SWIG_AddCast(SWIG_OK); } else { PyErr_Clear(); } if (!dispatch) { long v = PyLong_AsLong(obj); if (!PyErr_Occurred()) { if (val) *val = v; return SWIG_AddCast(SWIG_AddCast(SWIG_OK)); } else { PyErr_Clear(); } } } #endif return res; } #include #include SWIGINTERNINLINE int SWIG_CanCastAsInteger(double *d, double min, double max) { double x = *d; if ((min <= x && x <= max)) { double fx = floor(x); double cx = ceil(x); double rd = ((x - fx) < 0.5) ? fx : cx; /* simple rint */ if ((errno == EDOM) || (errno == ERANGE)) { errno = 0; } else { double summ, reps, diff; if (rd < x) { diff = x - rd; } else if (rd > x) { diff = rd - x; } else { return 1; } summ = rd + x; reps = diff/summ; if (reps < 8*DBL_EPSILON) { *d = rd; return 1; } } } return 0; } SWIGINTERN int SWIG_AsVal_long (PyObject *obj, long* val) { if (PyInt_Check(obj)) { if (val) *val = PyInt_AsLong(obj); return SWIG_OK; } else if (PyLong_Check(obj)) { long v = PyLong_AsLong(obj); if (!PyErr_Occurred()) { if (val) *val = v; return SWIG_OK; } else { PyErr_Clear(); } } #ifdef SWIG_PYTHON_CAST_MODE { int dispatch = 0; long v = PyInt_AsLong(obj); if (!PyErr_Occurred()) { if (val) *val = v; return SWIG_AddCast(SWIG_OK); } else { PyErr_Clear(); } if (!dispatch) { double d; int res = SWIG_AddCast(SWIG_AsVal_double (obj,&d)); if (SWIG_IsOK(res) && SWIG_CanCastAsInteger(&d, LONG_MIN, LONG_MAX)) { if (val) *val = (long)(d); return res; } } } #endif return SWIG_TypeError; } #define SWIG_From_long PyInt_FromLong #include #if !defined(SWIG_NO_LLONG_MAX) # if !defined(LLONG_MAX) && defined(__GNUC__) && defined (__LONG_LONG_MAX__) # define LLONG_MAX __LONG_LONG_MAX__ # define LLONG_MIN (-LLONG_MAX - 1LL) # define ULLONG_MAX (LLONG_MAX * 2ULL + 1ULL) # endif #endif SWIGINTERN int SWIG_AsVal_int (PyObject * obj, int *val) { long v; int res = SWIG_AsVal_long (obj, &v); if (SWIG_IsOK(res)) { if ((v < INT_MIN || v > INT_MAX)) { return SWIG_OverflowError; } else { if (val) *val = static_cast< int >(v); } } return res; } SWIGINTERNINLINE PyObject * SWIG_From_int (int value) { return SWIG_From_long (value); } /* Macros to extract array attributes. */ #define is_array(a) ((a) && PyArray_Check((PyArrayObject *)a)) #define array_type(a) (int)(PyArray_TYPE(a)) #define array_numdims(a) (((PyArrayObject *)a)->nd) #define array_dimensions(a) (((PyArrayObject *)a)->dimensions) #define array_size(a,i) (((PyArrayObject *)a)->dimensions[i]) #define array_data(a) (((PyArrayObject *)a)->data) #define array_is_contiguous(a) (PyArray_ISCONTIGUOUS(a)) #define array_is_native(a) (PyArray_ISNOTSWAPPED(a)) #define array_is_fortran(a) (PyArray_ISFORTRAN(a)) /* Given a PyObject, return a string describing its type. */ const char* pytype_string(PyObject* py_obj) { if (py_obj == NULL ) return "C NULL value"; if (py_obj == Py_None ) return "Python None" ; if (PyCallable_Check(py_obj)) return "callable" ; if (PyString_Check( py_obj)) return "string" ; if (PyInt_Check( py_obj)) return "int" ; if (PyFloat_Check( py_obj)) return "float" ; if (PyDict_Check( py_obj)) return "dict" ; if (PyList_Check( py_obj)) return "list" ; if (PyTuple_Check( py_obj)) return "tuple" ; if (PyFile_Check( py_obj)) return "file" ; if (PyModule_Check( py_obj)) return "module" ; if (PyInstance_Check(py_obj)) return "instance" ; return "unkown type"; } /* Given a NumPy typecode, return a string describing the type. */ const char* typecode_string(int typecode) { static const char* type_names[25] = {"bool", "byte", "unsigned byte", "short", "unsigned short", "int", "unsigned int", "long", "unsigned long", "long long", "unsigned long long", "float", "double", "long double", "complex float", "complex double", "complex long double", "object", "string", "unicode", "void", "ntypes", "notype", "char", "unknown"}; return typecode < 24 ? type_names[typecode] : type_names[24]; } /* Make sure input has correct numpy type. Allow character and byte * to match. Also allow int and long to match. This is deprecated. * You should use PyArray_EquivTypenums() instead. */ int type_match(int actual_type, int desired_type) { return PyArray_EquivTypenums(actual_type, desired_type); } /* Given a PyObject pointer, cast it to a PyArrayObject pointer if * legal. If not, set the python error string appropriately and * return NULL. */ PyArrayObject* obj_to_array_no_conversion(PyObject* input, int typecode) { PyArrayObject* ary = NULL; if (is_array(input) && (typecode == NPY_NOTYPE || PyArray_EquivTypenums(array_type(input), typecode))) { ary = (PyArrayObject*) input; } else if is_array(input) { const char* desired_type = typecode_string(typecode); const char* actual_type = typecode_string(array_type(input)); PyErr_Format(PyExc_TypeError, "Array of type '%s' required. Array of type '%s' given", desired_type, actual_type); ary = NULL; } else { const char * desired_type = typecode_string(typecode); const char * actual_type = pytype_string(input); PyErr_Format(PyExc_TypeError, "Array of type '%s' required. A '%s' was given", desired_type, actual_type); ary = NULL; } return ary; } /* Convert the given PyObject to a NumPy array with the given * typecode. On success, return a valid PyArrayObject* with the * correct type. On failure, the python error string will be set and * the routine returns NULL. */ PyArrayObject* obj_to_array_allow_conversion(PyObject* input, int typecode, int* is_new_object) { PyArrayObject* ary = NULL; PyObject* py_obj; if (is_array(input) && (typecode == NPY_NOTYPE || PyArray_EquivTypenums(array_type(input),typecode))) { ary = (PyArrayObject*) input; *is_new_object = 0; } else { py_obj = PyArray_FROMANY(input, typecode, 0, 0, NPY_DEFAULT); /* If NULL, PyArray_FromObject will have set python error value.*/ ary = (PyArrayObject*) py_obj; *is_new_object = 1; } return ary; } /* Given a PyArrayObject, check to see if it is contiguous. If so, * return the input pointer and flag it as not a new object. If it is * not contiguous, create a new PyArrayObject using the original data, * flag it as a new object and return the pointer. */ PyArrayObject* make_contiguous(PyArrayObject* ary, int* is_new_object, int min_dims, int max_dims) { PyArrayObject* result; if (array_is_contiguous(ary)) { result = ary; *is_new_object = 0; } else { result = (PyArrayObject*) PyArray_ContiguousFromObject((PyObject*)ary, array_type(ary), min_dims, max_dims); *is_new_object = 1; } return result; } /* Given a PyArrayObject, check to see if it is Fortran-contiguous. * If so, return the input pointer, but do not flag it as not a new * object. If it is not Fortran-contiguous, create a new * PyArrayObject using the original data, flag it as a new object * and return the pointer. */ PyArrayObject* make_fortran(PyArrayObject* ary, int* is_new_object, int min_dims, int max_dims) { PyArrayObject* result; if (array_is_fortran(ary)) { result = ary; *is_new_object = 0; } else { Py_INCREF(ary->descr); result = (PyArrayObject*) PyArray_FromArray(ary, ary->descr, NPY_FORTRAN); *is_new_object = 1; } return result; } /* Convert a given PyObject to a contiguous PyArrayObject of the * specified type. If the input object is not a contiguous * PyArrayObject, a new one will be created and the new object flag * will be set. */ PyArrayObject* obj_to_array_contiguous_allow_conversion(PyObject* input, int typecode, int* is_new_object) { int is_new1 = 0; int is_new2 = 0; PyArrayObject* ary2; PyArrayObject* ary1 = obj_to_array_allow_conversion(input, typecode, &is_new1); if (ary1) { ary2 = make_contiguous(ary1, &is_new2, 0, 0); if ( is_new1 && is_new2) { Py_DECREF(ary1); } ary1 = ary2; } *is_new_object = is_new1 || is_new2; return ary1; } /* Convert a given PyObject to a Fortran-ordered PyArrayObject of the * specified type. If the input object is not a Fortran-ordered * PyArrayObject, a new one will be created and the new object flag * will be set. */ PyArrayObject* obj_to_array_fortran_allow_conversion(PyObject* input, int typecode, int* is_new_object) { int is_new1 = 0; int is_new2 = 0; PyArrayObject* ary2; PyArrayObject* ary1 = obj_to_array_allow_conversion(input, typecode, &is_new1); if (ary1) { ary2 = make_fortran(ary1, &is_new2, 0, 0); if (is_new1 && is_new2) { Py_DECREF(ary1); } ary1 = ary2; } *is_new_object = is_new1 || is_new2; return ary1; } /* Test whether a python object is contiguous. If array is * contiguous, return 1. Otherwise, set the python error string and * return 0. */ int require_contiguous(PyArrayObject* ary) { int contiguous = 1; if (!array_is_contiguous(ary)) { PyErr_SetString(PyExc_TypeError, "Array must be contiguous. A non-contiguous array was given"); contiguous = 0; } return contiguous; } /* Require that a numpy array is not byte-swapped. If the array is * not byte-swapped, return 1. Otherwise, set the python error string * and return 0. */ int require_native(PyArrayObject* ary) { int native = 1; if (!array_is_native(ary)) { PyErr_SetString(PyExc_TypeError, "Array must have native byteorder. " "A byte-swapped array was given"); native = 0; } return native; } /* Require the given PyArrayObject to have a specified number of * dimensions. If the array has the specified number of dimensions, * return 1. Otherwise, set the python error string and return 0. */ int require_dimensions(PyArrayObject* ary, int exact_dimensions) { int success = 1; if (array_numdims(ary) != exact_dimensions) { PyErr_Format(PyExc_TypeError, "Array must have %d dimensions. Given array has %d dimensions", exact_dimensions, array_numdims(ary)); success = 0; } return success; } /* Require the given PyArrayObject to have one of a list of specified * number of dimensions. If the array has one of the specified number * of dimensions, return 1. Otherwise, set the python error string * and return 0. */ int require_dimensions_n(PyArrayObject* ary, int* exact_dimensions, int n) { int success = 0; int i; char dims_str[255] = ""; char s[255]; for (i = 0; i < n && !success; i++) { if (array_numdims(ary) == exact_dimensions[i]) { success = 1; } } if (!success) { for (i = 0; i < n-1; i++) { sprintf(s, "%d, ", exact_dimensions[i]); strcat(dims_str,s); } sprintf(s, " or %d", exact_dimensions[n-1]); strcat(dims_str,s); PyErr_Format(PyExc_TypeError, "Array must have %s dimensions. Given array has %d dimensions", dims_str, array_numdims(ary)); } return success; } /* Require the given PyArrayObject to have a specified shape. If the * array has the specified shape, return 1. Otherwise, set the python * error string and return 0. */ int require_size(PyArrayObject* ary, npy_intp* size, int n) { int i; int success = 1; int len; char desired_dims[255] = "["; char s[255]; char actual_dims[255] = "["; for(i=0; i < n;i++) { if (size[i] != -1 && size[i] != array_size(ary,i)) { success = 0; } } if (!success) { for (i = 0; i < n; i++) { if (size[i] == -1) { sprintf(s, "*,"); } else { sprintf(s, "%ld,", (long int)size[i]); } strcat(desired_dims,s); } len = strlen(desired_dims); desired_dims[len-1] = ']'; for (i = 0; i < n; i++) { sprintf(s, "%ld,", (long int)array_size(ary,i)); strcat(actual_dims,s); } len = strlen(actual_dims); actual_dims[len-1] = ']'; PyErr_Format(PyExc_TypeError, "Array must have shape of %s. Given array has shape of %s", desired_dims, actual_dims); } return success; } /* Require the given PyArrayObject to to be FORTRAN ordered. If the * the PyArrayObject is already FORTRAN ordered, do nothing. Else, * set the FORTRAN ordering flag and recompute the strides. */ int require_fortran(PyArrayObject* ary) { int success = 1; int nd = array_numdims(ary); int i; if (array_is_fortran(ary)) return success; /* Set the FORTRAN ordered flag */ ary->flags = NPY_FARRAY; /* Recompute the strides */ ary->strides[0] = ary->strides[nd-1]; for (i=1; i < nd; ++i) ary->strides[i] = ary->strides[i-1] * array_size(ary,i-1); return success; } SWIGINTERN swig_type_info* SWIG_pchar_descriptor(void) { static int init = 0; static swig_type_info* info = 0; if (!init) { info = SWIG_TypeQuery("_p_char"); init = 1; } return info; } SWIGINTERNINLINE PyObject * SWIG_FromCharPtrAndSize(const char* carray, size_t size) { if (carray) { if (size > INT_MAX) { swig_type_info* pchar_descriptor = SWIG_pchar_descriptor(); return pchar_descriptor ? SWIG_InternalNewPointerObj(const_cast< char * >(carray), pchar_descriptor, 0) : SWIG_Py_Void(); } else { #if PY_VERSION_HEX >= 0x03000000 return PyUnicode_FromStringAndSize(carray, static_cast< int >(size)); #else return PyString_FromStringAndSize(carray, static_cast< int >(size)); #endif } } else { return SWIG_Py_Void(); } } SWIGINTERNINLINE PyObject * SWIG_From_std_string (const std::string& s) { return SWIG_FromCharPtrAndSize(s.data(), s.size()); } #ifdef __cplusplus extern "C" { #endif SWIGINTERN PyObject *_wrap_CircularVector_X_set(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; CircularVector *arg1 = (CircularVector *) 0 ; long *arg2 = (long *) 0 ; void *argp1 = 0 ; int res1 = 0 ; void *argp2 = 0 ; int res2 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OO:CircularVector_X_set",&obj0,&obj1)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_CircularVector, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "CircularVector_X_set" "', argument " "1"" of type '" "CircularVector *""'"); } arg1 = reinterpret_cast< CircularVector * >(argp1); res2 = SWIG_ConvertPtr(obj1, &argp2,SWIGTYPE_p_long, SWIG_POINTER_DISOWN | 0 ); if (!SWIG_IsOK(res2)) { SWIG_exception_fail(SWIG_ArgError(res2), "in method '" "CircularVector_X_set" "', argument " "2"" of type '" "long *""'"); } arg2 = reinterpret_cast< long * >(argp2); { try { if (arg1) (arg1)->X = arg2; } catch( std::runtime_error &e ) { PyErr_SetString(PyExc_RuntimeError, const_cast(e.what())); return NULL; } } resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_CircularVector_X_get(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; CircularVector *arg1 = (CircularVector *) 0 ; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; long *result = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:CircularVector_X_get",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_CircularVector, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "CircularVector_X_get" "', argument " "1"" of type '" "CircularVector *""'"); } arg1 = reinterpret_cast< CircularVector * >(argp1); { try { result = (long *) ((arg1)->X); } catch( std::runtime_error &e ) { PyErr_SetString(PyExc_RuntimeError, const_cast(e.what())); return NULL; } } resultobj = SWIG_NewPointerObj(SWIG_as_voidptr(result), SWIGTYPE_p_long, 0 | 0 ); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_CircularVector_cursor_set(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; CircularVector *arg1 = (CircularVector *) 0 ; long arg2 ; void *argp1 = 0 ; int res1 = 0 ; long val2 ; int ecode2 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OO:CircularVector_cursor_set",&obj0,&obj1)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_CircularVector, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "CircularVector_cursor_set" "', argument " "1"" of type '" "CircularVector *""'"); } arg1 = reinterpret_cast< CircularVector * >(argp1); ecode2 = SWIG_AsVal_long(obj1, &val2); if (!SWIG_IsOK(ecode2)) { SWIG_exception_fail(SWIG_ArgError(ecode2), "in method '" "CircularVector_cursor_set" "', argument " "2"" of type '" "long""'"); } arg2 = static_cast< long >(val2); { try { if (arg1) (arg1)->cursor = arg2; } catch( std::runtime_error &e ) { PyErr_SetString(PyExc_RuntimeError, const_cast(e.what())); return NULL; } } resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_CircularVector_cursor_get(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; CircularVector *arg1 = (CircularVector *) 0 ; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; long result; if (!PyArg_ParseTuple(args,(char *)"O:CircularVector_cursor_get",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_CircularVector, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "CircularVector_cursor_get" "', argument " "1"" of type '" "CircularVector *""'"); } arg1 = reinterpret_cast< CircularVector * >(argp1); { try { result = (long) ((arg1)->cursor); } catch( std::runtime_error &e ) { PyErr_SetString(PyExc_RuntimeError, const_cast(e.what())); return NULL; } } resultobj = SWIG_From_long(static_cast< long >(result)); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_CircularVector_n_set(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; CircularVector *arg1 = (CircularVector *) 0 ; long arg2 ; void *argp1 = 0 ; int res1 = 0 ; long val2 ; int ecode2 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OO:CircularVector_n_set",&obj0,&obj1)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_CircularVector, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "CircularVector_n_set" "', argument " "1"" of type '" "CircularVector *""'"); } arg1 = reinterpret_cast< CircularVector * >(argp1); ecode2 = SWIG_AsVal_long(obj1, &val2); if (!SWIG_IsOK(ecode2)) { SWIG_exception_fail(SWIG_ArgError(ecode2), "in method '" "CircularVector_n_set" "', argument " "2"" of type '" "long""'"); } arg2 = static_cast< long >(val2); { try { if (arg1) (arg1)->n = arg2; } catch( std::runtime_error &e ) { PyErr_SetString(PyExc_RuntimeError, const_cast(e.what())); return NULL; } } resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_CircularVector_n_get(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; CircularVector *arg1 = (CircularVector *) 0 ; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; long result; if (!PyArg_ParseTuple(args,(char *)"O:CircularVector_n_get",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_CircularVector, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "CircularVector_n_get" "', argument " "1"" of type '" "CircularVector *""'"); } arg1 = reinterpret_cast< CircularVector * >(argp1); { try { result = (long) ((arg1)->n); } catch( std::runtime_error &e ) { PyErr_SetString(PyExc_RuntimeError, const_cast(e.what())); return NULL; } } resultobj = SWIG_From_long(static_cast< long >(result)); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_CircularVector_retarray_set(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; CircularVector *arg1 = (CircularVector *) 0 ; long *arg2 = (long *) 0 ; void *argp1 = 0 ; int res1 = 0 ; void *argp2 = 0 ; int res2 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OO:CircularVector_retarray_set",&obj0,&obj1)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_CircularVector, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "CircularVector_retarray_set" "', argument " "1"" of type '" "CircularVector *""'"); } arg1 = reinterpret_cast< CircularVector * >(argp1); res2 = SWIG_ConvertPtr(obj1, &argp2,SWIGTYPE_p_long, SWIG_POINTER_DISOWN | 0 ); if (!SWIG_IsOK(res2)) { SWIG_exception_fail(SWIG_ArgError(res2), "in method '" "CircularVector_retarray_set" "', argument " "2"" of type '" "long *""'"); } arg2 = reinterpret_cast< long * >(argp2); { try { if (arg1) (arg1)->retarray = arg2; } catch( std::runtime_error &e ) { PyErr_SetString(PyExc_RuntimeError, const_cast(e.what())); return NULL; } } resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_CircularVector_retarray_get(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; CircularVector *arg1 = (CircularVector *) 0 ; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; long *result = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:CircularVector_retarray_get",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_CircularVector, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "CircularVector_retarray_get" "', argument " "1"" of type '" "CircularVector *""'"); } arg1 = reinterpret_cast< CircularVector * >(argp1); { try { result = (long *) ((arg1)->retarray); } catch( std::runtime_error &e ) { PyErr_SetString(PyExc_RuntimeError, const_cast(e.what())); return NULL; } } resultobj = SWIG_NewPointerObj(SWIG_as_voidptr(result), SWIGTYPE_p_long, 0 | 0 ); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_new_CircularVector(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; int arg1 ; int val1 ; int ecode1 = 0 ; PyObject * obj0 = 0 ; CircularVector *result = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:new_CircularVector",&obj0)) SWIG_fail; ecode1 = SWIG_AsVal_int(obj0, &val1); if (!SWIG_IsOK(ecode1)) { SWIG_exception_fail(SWIG_ArgError(ecode1), "in method '" "new_CircularVector" "', argument " "1"" of type '" "int""'"); } arg1 = static_cast< int >(val1); { try { result = (CircularVector *)new CircularVector(arg1); } catch( std::runtime_error &e ) { PyErr_SetString(PyExc_RuntimeError, const_cast(e.what())); return NULL; } } resultobj = SWIG_NewPointerObj(SWIG_as_voidptr(result), SWIGTYPE_p_CircularVector, SWIG_POINTER_NEW | 0 ); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_delete_CircularVector(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; CircularVector *arg1 = (CircularVector *) 0 ; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:delete_CircularVector",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_CircularVector, SWIG_POINTER_DISOWN | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "delete_CircularVector" "', argument " "1"" of type '" "CircularVector *""'"); } arg1 = reinterpret_cast< CircularVector * >(argp1); { try { delete arg1; } catch( std::runtime_error &e ) { PyErr_SetString(PyExc_RuntimeError, const_cast(e.what())); return NULL; } } resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_CircularVector_reinit(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; CircularVector *arg1 = (CircularVector *) 0 ; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:CircularVector_reinit",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_CircularVector, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "CircularVector_reinit" "', argument " "1"" of type '" "CircularVector *""'"); } arg1 = reinterpret_cast< CircularVector * >(argp1); { try { (arg1)->reinit(); } catch( std::runtime_error &e ) { PyErr_SetString(PyExc_RuntimeError, const_cast(e.what())); return NULL; } } resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_CircularVector_advance(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; CircularVector *arg1 = (CircularVector *) 0 ; int arg2 ; void *argp1 = 0 ; int res1 = 0 ; int val2 ; int ecode2 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OO:CircularVector_advance",&obj0,&obj1)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_CircularVector, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "CircularVector_advance" "', argument " "1"" of type '" "CircularVector *""'"); } arg1 = reinterpret_cast< CircularVector * >(argp1); ecode2 = SWIG_AsVal_int(obj1, &val2); if (!SWIG_IsOK(ecode2)) { SWIG_exception_fail(SWIG_ArgError(ecode2), "in method '" "CircularVector_advance" "', argument " "2"" of type '" "int""'"); } arg2 = static_cast< int >(val2); { try { (arg1)->advance(arg2); } catch( std::runtime_error &e ) { PyErr_SetString(PyExc_RuntimeError, const_cast(e.what())); return NULL; } } resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_CircularVector___len__(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; CircularVector *arg1 = (CircularVector *) 0 ; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; int result; if (!PyArg_ParseTuple(args,(char *)"O:CircularVector___len__",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_CircularVector, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "CircularVector___len__" "', argument " "1"" of type '" "CircularVector *""'"); } arg1 = reinterpret_cast< CircularVector * >(argp1); { try { result = (int)(arg1)->__len__(); } catch( std::runtime_error &e ) { PyErr_SetString(PyExc_RuntimeError, const_cast(e.what())); return NULL; } } resultobj = SWIG_From_int(static_cast< int >(result)); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_CircularVector___getitem__(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; CircularVector *arg1 = (CircularVector *) 0 ; int arg2 ; void *argp1 = 0 ; int res1 = 0 ; int val2 ; int ecode2 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; int result; if (!PyArg_ParseTuple(args,(char *)"OO:CircularVector___getitem__",&obj0,&obj1)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_CircularVector, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "CircularVector___getitem__" "', argument " "1"" of type '" "CircularVector *""'"); } arg1 = reinterpret_cast< CircularVector * >(argp1); ecode2 = SWIG_AsVal_int(obj1, &val2); if (!SWIG_IsOK(ecode2)) { SWIG_exception_fail(SWIG_ArgError(ecode2), "in method '" "CircularVector___getitem__" "', argument " "2"" of type '" "int""'"); } arg2 = static_cast< int >(val2); { try { result = (int)(arg1)->__getitem__(arg2); } catch( std::runtime_error &e ) { PyErr_SetString(PyExc_RuntimeError, const_cast(e.what())); return NULL; } } resultobj = SWIG_From_int(static_cast< int >(result)); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_CircularVector___setitem__(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; CircularVector *arg1 = (CircularVector *) 0 ; int arg2 ; int arg3 ; void *argp1 = 0 ; int res1 = 0 ; int val2 ; int ecode2 = 0 ; int val3 ; int ecode3 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; PyObject * obj2 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OOO:CircularVector___setitem__",&obj0,&obj1,&obj2)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_CircularVector, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "CircularVector___setitem__" "', argument " "1"" of type '" "CircularVector *""'"); } arg1 = reinterpret_cast< CircularVector * >(argp1); ecode2 = SWIG_AsVal_int(obj1, &val2); if (!SWIG_IsOK(ecode2)) { SWIG_exception_fail(SWIG_ArgError(ecode2), "in method '" "CircularVector___setitem__" "', argument " "2"" of type '" "int""'"); } arg2 = static_cast< int >(val2); ecode3 = SWIG_AsVal_int(obj2, &val3); if (!SWIG_IsOK(ecode3)) { SWIG_exception_fail(SWIG_ArgError(ecode3), "in method '" "CircularVector___setitem__" "', argument " "3"" of type '" "int""'"); } arg3 = static_cast< int >(val3); { try { (arg1)->__setitem__(arg2,arg3); } catch( std::runtime_error &e ) { PyErr_SetString(PyExc_RuntimeError, const_cast(e.what())); return NULL; } } resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_CircularVector___getslice__(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; CircularVector *arg1 = (CircularVector *) 0 ; long **arg2 = (long **) 0 ; int *arg3 = (int *) 0 ; int arg4 ; int arg5 ; void *argp1 = 0 ; int res1 = 0 ; long *data_temp2 ; int dim_temp2 ; int val4 ; int ecode4 = 0 ; int val5 ; int ecode5 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; PyObject * obj2 = 0 ; { arg2 = &data_temp2; arg3 = &dim_temp2; } if (!PyArg_ParseTuple(args,(char *)"OOO:CircularVector___getslice__",&obj0,&obj1,&obj2)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_CircularVector, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "CircularVector___getslice__" "', argument " "1"" of type '" "CircularVector *""'"); } arg1 = reinterpret_cast< CircularVector * >(argp1); ecode4 = SWIG_AsVal_int(obj1, &val4); if (!SWIG_IsOK(ecode4)) { SWIG_exception_fail(SWIG_ArgError(ecode4), "in method '" "CircularVector___getslice__" "', argument " "4"" of type '" "int""'"); } arg4 = static_cast< int >(val4); ecode5 = SWIG_AsVal_int(obj2, &val5); if (!SWIG_IsOK(ecode5)) { SWIG_exception_fail(SWIG_ArgError(ecode5), "in method '" "CircularVector___getslice__" "', argument " "5"" of type '" "int""'"); } arg5 = static_cast< int >(val5); { try { (arg1)->__getslice__(arg2,arg3,arg4,arg5); } catch( std::runtime_error &e ) { PyErr_SetString(PyExc_RuntimeError, const_cast(e.what())); return NULL; } } resultobj = SWIG_Py_Void(); { npy_intp dims[1] = { *arg3 }; PyObject * array = PyArray_SimpleNewFromData(1, dims, NPY_LONG, (void*)(*arg2)); if (!array) SWIG_fail; resultobj = SWIG_Python_AppendOutput(resultobj,array); } return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_CircularVector_get_conditional__SWIG_0(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; CircularVector *arg1 = (CircularVector *) 0 ; long **arg2 = (long **) 0 ; int *arg3 = (int *) 0 ; int arg4 ; int arg5 ; int arg6 ; int arg7 ; int arg8 ; void *argp1 = 0 ; int res1 = 0 ; long *data_temp2 ; int dim_temp2 ; int val4 ; int ecode4 = 0 ; int val5 ; int ecode5 = 0 ; int val6 ; int ecode6 = 0 ; int val7 ; int ecode7 = 0 ; int val8 ; int ecode8 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; PyObject * obj2 = 0 ; PyObject * obj3 = 0 ; PyObject * obj4 = 0 ; PyObject * obj5 = 0 ; { arg2 = &data_temp2; arg3 = &dim_temp2; } if (!PyArg_ParseTuple(args,(char *)"OOOOOO:CircularVector_get_conditional",&obj0,&obj1,&obj2,&obj3,&obj4,&obj5)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_CircularVector, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "CircularVector_get_conditional" "', argument " "1"" of type '" "CircularVector *""'"); } arg1 = reinterpret_cast< CircularVector * >(argp1); ecode4 = SWIG_AsVal_int(obj1, &val4); if (!SWIG_IsOK(ecode4)) { SWIG_exception_fail(SWIG_ArgError(ecode4), "in method '" "CircularVector_get_conditional" "', argument " "4"" of type '" "int""'"); } arg4 = static_cast< int >(val4); ecode5 = SWIG_AsVal_int(obj2, &val5); if (!SWIG_IsOK(ecode5)) { SWIG_exception_fail(SWIG_ArgError(ecode5), "in method '" "CircularVector_get_conditional" "', argument " "5"" of type '" "int""'"); } arg5 = static_cast< int >(val5); ecode6 = SWIG_AsVal_int(obj3, &val6); if (!SWIG_IsOK(ecode6)) { SWIG_exception_fail(SWIG_ArgError(ecode6), "in method '" "CircularVector_get_conditional" "', argument " "6"" of type '" "int""'"); } arg6 = static_cast< int >(val6); ecode7 = SWIG_AsVal_int(obj4, &val7); if (!SWIG_IsOK(ecode7)) { SWIG_exception_fail(SWIG_ArgError(ecode7), "in method '" "CircularVector_get_conditional" "', argument " "7"" of type '" "int""'"); } arg7 = static_cast< int >(val7); ecode8 = SWIG_AsVal_int(obj5, &val8); if (!SWIG_IsOK(ecode8)) { SWIG_exception_fail(SWIG_ArgError(ecode8), "in method '" "CircularVector_get_conditional" "', argument " "8"" of type '" "int""'"); } arg8 = static_cast< int >(val8); { try { (arg1)->get_conditional(arg2,arg3,arg4,arg5,arg6,arg7,arg8); } catch( std::runtime_error &e ) { PyErr_SetString(PyExc_RuntimeError, const_cast(e.what())); return NULL; } } resultobj = SWIG_Py_Void(); { npy_intp dims[1] = { *arg3 }; PyObject * array = PyArray_SimpleNewFromData(1, dims, NPY_LONG, (void*)(*arg2)); if (!array) SWIG_fail; resultobj = SWIG_Python_AppendOutput(resultobj,array); } return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_CircularVector_get_conditional__SWIG_1(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; CircularVector *arg1 = (CircularVector *) 0 ; long **arg2 = (long **) 0 ; int *arg3 = (int *) 0 ; int arg4 ; int arg5 ; int arg6 ; int arg7 ; void *argp1 = 0 ; int res1 = 0 ; long *data_temp2 ; int dim_temp2 ; int val4 ; int ecode4 = 0 ; int val5 ; int ecode5 = 0 ; int val6 ; int ecode6 = 0 ; int val7 ; int ecode7 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; PyObject * obj2 = 0 ; PyObject * obj3 = 0 ; PyObject * obj4 = 0 ; { arg2 = &data_temp2; arg3 = &dim_temp2; } if (!PyArg_ParseTuple(args,(char *)"OOOOO:CircularVector_get_conditional",&obj0,&obj1,&obj2,&obj3,&obj4)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_CircularVector, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "CircularVector_get_conditional" "', argument " "1"" of type '" "CircularVector *""'"); } arg1 = reinterpret_cast< CircularVector * >(argp1); ecode4 = SWIG_AsVal_int(obj1, &val4); if (!SWIG_IsOK(ecode4)) { SWIG_exception_fail(SWIG_ArgError(ecode4), "in method '" "CircularVector_get_conditional" "', argument " "4"" of type '" "int""'"); } arg4 = static_cast< int >(val4); ecode5 = SWIG_AsVal_int(obj2, &val5); if (!SWIG_IsOK(ecode5)) { SWIG_exception_fail(SWIG_ArgError(ecode5), "in method '" "CircularVector_get_conditional" "', argument " "5"" of type '" "int""'"); } arg5 = static_cast< int >(val5); ecode6 = SWIG_AsVal_int(obj3, &val6); if (!SWIG_IsOK(ecode6)) { SWIG_exception_fail(SWIG_ArgError(ecode6), "in method '" "CircularVector_get_conditional" "', argument " "6"" of type '" "int""'"); } arg6 = static_cast< int >(val6); ecode7 = SWIG_AsVal_int(obj4, &val7); if (!SWIG_IsOK(ecode7)) { SWIG_exception_fail(SWIG_ArgError(ecode7), "in method '" "CircularVector_get_conditional" "', argument " "7"" of type '" "int""'"); } arg7 = static_cast< int >(val7); { try { (arg1)->get_conditional(arg2,arg3,arg4,arg5,arg6,arg7); } catch( std::runtime_error &e ) { PyErr_SetString(PyExc_RuntimeError, const_cast(e.what())); return NULL; } } resultobj = SWIG_Py_Void(); { npy_intp dims[1] = { *arg3 }; PyObject * array = PyArray_SimpleNewFromData(1, dims, NPY_LONG, (void*)(*arg2)); if (!array) SWIG_fail; resultobj = SWIG_Python_AppendOutput(resultobj,array); } return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_CircularVector_get_conditional(PyObject *self, PyObject *args) { int argc; PyObject *argv[7]; int ii; if (!PyTuple_Check(args)) SWIG_fail; argc = args ? (int)PyObject_Length(args) : 0; for (ii = 0; (ii < 6) && (ii < argc); ii++) { argv[ii] = PyTuple_GET_ITEM(args,ii); } if (argc == 5) { int _v; void *vptr = 0; int res = SWIG_ConvertPtr(argv[0], &vptr, SWIGTYPE_p_CircularVector, 0); _v = SWIG_CheckState(res); if (_v) { { int res = SWIG_AsVal_int(argv[1], NULL); _v = SWIG_CheckState(res); } if (_v) { { int res = SWIG_AsVal_int(argv[2], NULL); _v = SWIG_CheckState(res); } if (_v) { { int res = SWIG_AsVal_int(argv[3], NULL); _v = SWIG_CheckState(res); } if (_v) { { int res = SWIG_AsVal_int(argv[4], NULL); _v = SWIG_CheckState(res); } if (_v) { return _wrap_CircularVector_get_conditional__SWIG_1(self, args); } } } } } } if (argc == 6) { int _v; void *vptr = 0; int res = SWIG_ConvertPtr(argv[0], &vptr, SWIGTYPE_p_CircularVector, 0); _v = SWIG_CheckState(res); if (_v) { { int res = SWIG_AsVal_int(argv[1], NULL); _v = SWIG_CheckState(res); } if (_v) { { int res = SWIG_AsVal_int(argv[2], NULL); _v = SWIG_CheckState(res); } if (_v) { { int res = SWIG_AsVal_int(argv[3], NULL); _v = SWIG_CheckState(res); } if (_v) { { int res = SWIG_AsVal_int(argv[4], NULL); _v = SWIG_CheckState(res); } if (_v) { { int res = SWIG_AsVal_int(argv[5], NULL); _v = SWIG_CheckState(res); } if (_v) { return _wrap_CircularVector_get_conditional__SWIG_0(self, args); } } } } } } } fail: SWIG_SetErrorMsg(PyExc_NotImplementedError,"Wrong number or type of arguments for overloaded function 'CircularVector_get_conditional'.\n" " Possible C/C++ prototypes are:\n" " CircularVector::get_conditional(long **,int *,int,int,int,int,int)\n" " CircularVector::get_conditional(long **,int *,int,int,int,int)\n"); return 0; } SWIGINTERN PyObject *_wrap_CircularVector___setslice__(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; CircularVector *arg1 = (CircularVector *) 0 ; int arg2 ; int arg3 ; long *arg4 = (long *) 0 ; int arg5 ; void *argp1 = 0 ; int res1 = 0 ; int val2 ; int ecode2 = 0 ; int val3 ; int ecode3 = 0 ; PyArrayObject *array4 = NULL ; int i4 = 1 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; PyObject * obj2 = 0 ; PyObject * obj3 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OOOO:CircularVector___setslice__",&obj0,&obj1,&obj2,&obj3)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_CircularVector, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "CircularVector___setslice__" "', argument " "1"" of type '" "CircularVector *""'"); } arg1 = reinterpret_cast< CircularVector * >(argp1); ecode2 = SWIG_AsVal_int(obj1, &val2); if (!SWIG_IsOK(ecode2)) { SWIG_exception_fail(SWIG_ArgError(ecode2), "in method '" "CircularVector___setslice__" "', argument " "2"" of type '" "int""'"); } arg2 = static_cast< int >(val2); ecode3 = SWIG_AsVal_int(obj2, &val3); if (!SWIG_IsOK(ecode3)) { SWIG_exception_fail(SWIG_ArgError(ecode3), "in method '" "CircularVector___setslice__" "', argument " "3"" of type '" "int""'"); } arg3 = static_cast< int >(val3); { array4 = obj_to_array_no_conversion(obj3, NPY_LONG); if (!array4 || !require_dimensions(array4,1) || !require_contiguous(array4) || !require_native(array4)) SWIG_fail; arg4 = (long*) array_data(array4); arg5 = 1; for (i4=0; i4 < array_numdims(array4); ++i4) arg5 *= array_size(array4,i4); } { try { (arg1)->__setslice__(arg2,arg3,arg4,arg5); } catch( std::runtime_error &e ) { PyErr_SetString(PyExc_RuntimeError, const_cast(e.what())); return NULL; } } resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_CircularVector___repr__(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; CircularVector *arg1 = (CircularVector *) 0 ; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; std::string result; if (!PyArg_ParseTuple(args,(char *)"O:CircularVector___repr__",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_CircularVector, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "CircularVector___repr__" "', argument " "1"" of type '" "CircularVector *""'"); } arg1 = reinterpret_cast< CircularVector * >(argp1); { try { result = (arg1)->__repr__(); } catch( std::runtime_error &e ) { PyErr_SetString(PyExc_RuntimeError, const_cast(e.what())); return NULL; } } resultobj = SWIG_From_std_string(static_cast< std::string >(result)); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_CircularVector___str__(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; CircularVector *arg1 = (CircularVector *) 0 ; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; std::string result; if (!PyArg_ParseTuple(args,(char *)"O:CircularVector___str__",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_CircularVector, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "CircularVector___str__" "', argument " "1"" of type '" "CircularVector *""'"); } arg1 = reinterpret_cast< CircularVector * >(argp1); { try { result = (arg1)->__str__(); } catch( std::runtime_error &e ) { PyErr_SetString(PyExc_RuntimeError, const_cast(e.what())); return NULL; } } resultobj = SWIG_From_std_string(static_cast< std::string >(result)); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_CircularVector_expand(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; CircularVector *arg1 = (CircularVector *) 0 ; long arg2 ; void *argp1 = 0 ; int res1 = 0 ; long val2 ; int ecode2 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OO:CircularVector_expand",&obj0,&obj1)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_CircularVector, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "CircularVector_expand" "', argument " "1"" of type '" "CircularVector *""'"); } arg1 = reinterpret_cast< CircularVector * >(argp1); ecode2 = SWIG_AsVal_long(obj1, &val2); if (!SWIG_IsOK(ecode2)) { SWIG_exception_fail(SWIG_ArgError(ecode2), "in method '" "CircularVector_expand" "', argument " "2"" of type '" "long""'"); } arg2 = static_cast< long >(val2); { try { (arg1)->expand(arg2); } catch( std::runtime_error &e ) { PyErr_SetString(PyExc_RuntimeError, const_cast(e.what())); return NULL; } } resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *CircularVector_swigregister(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *obj; if (!PyArg_ParseTuple(args,(char*)"O:swigregister", &obj)) return NULL; SWIG_TypeNewClientData(SWIGTYPE_p_CircularVector, SWIG_NewClientData(obj)); return SWIG_Py_Void(); } SWIGINTERN PyObject *_wrap_SpikeContainer_S_set(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; SpikeContainer *arg1 = (SpikeContainer *) 0 ; CircularVector *arg2 = (CircularVector *) 0 ; void *argp1 = 0 ; int res1 = 0 ; void *argp2 = 0 ; int res2 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OO:SpikeContainer_S_set",&obj0,&obj1)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_SpikeContainer, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "SpikeContainer_S_set" "', argument " "1"" of type '" "SpikeContainer *""'"); } arg1 = reinterpret_cast< SpikeContainer * >(argp1); res2 = SWIG_ConvertPtr(obj1, &argp2,SWIGTYPE_p_CircularVector, SWIG_POINTER_DISOWN | 0 ); if (!SWIG_IsOK(res2)) { SWIG_exception_fail(SWIG_ArgError(res2), "in method '" "SpikeContainer_S_set" "', argument " "2"" of type '" "CircularVector *""'"); } arg2 = reinterpret_cast< CircularVector * >(argp2); { try { if (arg1) (arg1)->S = arg2; } catch( std::runtime_error &e ) { PyErr_SetString(PyExc_RuntimeError, const_cast(e.what())); return NULL; } } resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_SpikeContainer_S_get(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; SpikeContainer *arg1 = (SpikeContainer *) 0 ; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; CircularVector *result = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:SpikeContainer_S_get",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_SpikeContainer, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "SpikeContainer_S_get" "', argument " "1"" of type '" "SpikeContainer *""'"); } arg1 = reinterpret_cast< SpikeContainer * >(argp1); { try { result = (CircularVector *) ((arg1)->S); } catch( std::runtime_error &e ) { PyErr_SetString(PyExc_RuntimeError, const_cast(e.what())); return NULL; } } resultobj = SWIG_NewPointerObj(SWIG_as_voidptr(result), SWIGTYPE_p_CircularVector, 0 | 0 ); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_SpikeContainer_ind_set(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; SpikeContainer *arg1 = (SpikeContainer *) 0 ; CircularVector *arg2 = (CircularVector *) 0 ; void *argp1 = 0 ; int res1 = 0 ; void *argp2 = 0 ; int res2 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OO:SpikeContainer_ind_set",&obj0,&obj1)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_SpikeContainer, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "SpikeContainer_ind_set" "', argument " "1"" of type '" "SpikeContainer *""'"); } arg1 = reinterpret_cast< SpikeContainer * >(argp1); res2 = SWIG_ConvertPtr(obj1, &argp2,SWIGTYPE_p_CircularVector, SWIG_POINTER_DISOWN | 0 ); if (!SWIG_IsOK(res2)) { SWIG_exception_fail(SWIG_ArgError(res2), "in method '" "SpikeContainer_ind_set" "', argument " "2"" of type '" "CircularVector *""'"); } arg2 = reinterpret_cast< CircularVector * >(argp2); { try { if (arg1) (arg1)->ind = arg2; } catch( std::runtime_error &e ) { PyErr_SetString(PyExc_RuntimeError, const_cast(e.what())); return NULL; } } resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_SpikeContainer_ind_get(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; SpikeContainer *arg1 = (SpikeContainer *) 0 ; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; CircularVector *result = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:SpikeContainer_ind_get",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_SpikeContainer, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "SpikeContainer_ind_get" "', argument " "1"" of type '" "SpikeContainer *""'"); } arg1 = reinterpret_cast< SpikeContainer * >(argp1); { try { result = (CircularVector *) ((arg1)->ind); } catch( std::runtime_error &e ) { PyErr_SetString(PyExc_RuntimeError, const_cast(e.what())); return NULL; } } resultobj = SWIG_NewPointerObj(SWIG_as_voidptr(result), SWIGTYPE_p_CircularVector, 0 | 0 ); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_SpikeContainer_remaining_space_set(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; SpikeContainer *arg1 = (SpikeContainer *) 0 ; int arg2 ; void *argp1 = 0 ; int res1 = 0 ; int val2 ; int ecode2 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OO:SpikeContainer_remaining_space_set",&obj0,&obj1)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_SpikeContainer, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "SpikeContainer_remaining_space_set" "', argument " "1"" of type '" "SpikeContainer *""'"); } arg1 = reinterpret_cast< SpikeContainer * >(argp1); ecode2 = SWIG_AsVal_int(obj1, &val2); if (!SWIG_IsOK(ecode2)) { SWIG_exception_fail(SWIG_ArgError(ecode2), "in method '" "SpikeContainer_remaining_space_set" "', argument " "2"" of type '" "int""'"); } arg2 = static_cast< int >(val2); { try { if (arg1) (arg1)->remaining_space = arg2; } catch( std::runtime_error &e ) { PyErr_SetString(PyExc_RuntimeError, const_cast(e.what())); return NULL; } } resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_SpikeContainer_remaining_space_get(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; SpikeContainer *arg1 = (SpikeContainer *) 0 ; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; int result; if (!PyArg_ParseTuple(args,(char *)"O:SpikeContainer_remaining_space_get",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_SpikeContainer, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "SpikeContainer_remaining_space_get" "', argument " "1"" of type '" "SpikeContainer *""'"); } arg1 = reinterpret_cast< SpikeContainer * >(argp1); { try { result = (int) ((arg1)->remaining_space); } catch( std::runtime_error &e ) { PyErr_SetString(PyExc_RuntimeError, const_cast(e.what())); return NULL; } } resultobj = SWIG_From_int(static_cast< int >(result)); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_new_SpikeContainer(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; int arg1 ; int val1 ; int ecode1 = 0 ; PyObject * obj0 = 0 ; SpikeContainer *result = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:new_SpikeContainer",&obj0)) SWIG_fail; ecode1 = SWIG_AsVal_int(obj0, &val1); if (!SWIG_IsOK(ecode1)) { SWIG_exception_fail(SWIG_ArgError(ecode1), "in method '" "new_SpikeContainer" "', argument " "1"" of type '" "int""'"); } arg1 = static_cast< int >(val1); { try { result = (SpikeContainer *)new SpikeContainer(arg1); } catch( std::runtime_error &e ) { PyErr_SetString(PyExc_RuntimeError, const_cast(e.what())); return NULL; } } resultobj = SWIG_NewPointerObj(SWIG_as_voidptr(result), SWIGTYPE_p_SpikeContainer, SWIG_POINTER_NEW | 0 ); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_delete_SpikeContainer(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; SpikeContainer *arg1 = (SpikeContainer *) 0 ; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:delete_SpikeContainer",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_SpikeContainer, SWIG_POINTER_DISOWN | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "delete_SpikeContainer" "', argument " "1"" of type '" "SpikeContainer *""'"); } arg1 = reinterpret_cast< SpikeContainer * >(argp1); { try { delete arg1; } catch( std::runtime_error &e ) { PyErr_SetString(PyExc_RuntimeError, const_cast(e.what())); return NULL; } } resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_SpikeContainer_reinit(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; SpikeContainer *arg1 = (SpikeContainer *) 0 ; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:SpikeContainer_reinit",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_SpikeContainer, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "SpikeContainer_reinit" "', argument " "1"" of type '" "SpikeContainer *""'"); } arg1 = reinterpret_cast< SpikeContainer * >(argp1); { try { (arg1)->reinit(); } catch( std::runtime_error &e ) { PyErr_SetString(PyExc_RuntimeError, const_cast(e.what())); return NULL; } } resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_SpikeContainer_push(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; SpikeContainer *arg1 = (SpikeContainer *) 0 ; long *arg2 = (long *) 0 ; int arg3 ; void *argp1 = 0 ; int res1 = 0 ; PyArrayObject *array2 = NULL ; int i2 = 1 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OO:SpikeContainer_push",&obj0,&obj1)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_SpikeContainer, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "SpikeContainer_push" "', argument " "1"" of type '" "SpikeContainer *""'"); } arg1 = reinterpret_cast< SpikeContainer * >(argp1); { array2 = obj_to_array_no_conversion(obj1, NPY_LONG); if (!array2 || !require_dimensions(array2,1) || !require_contiguous(array2) || !require_native(array2)) SWIG_fail; arg2 = (long*) array_data(array2); arg3 = 1; for (i2=0; i2 < array_numdims(array2); ++i2) arg3 *= array_size(array2,i2); } { try { (arg1)->push(arg2,arg3); } catch( std::runtime_error &e ) { PyErr_SetString(PyExc_RuntimeError, const_cast(e.what())); return NULL; } } resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_SpikeContainer_lastspikes(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; SpikeContainer *arg1 = (SpikeContainer *) 0 ; long **arg2 = (long **) 0 ; int *arg3 = (int *) 0 ; void *argp1 = 0 ; int res1 = 0 ; long *data_temp2 ; int dim_temp2 ; PyObject * obj0 = 0 ; { arg2 = &data_temp2; arg3 = &dim_temp2; } if (!PyArg_ParseTuple(args,(char *)"O:SpikeContainer_lastspikes",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_SpikeContainer, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "SpikeContainer_lastspikes" "', argument " "1"" of type '" "SpikeContainer *""'"); } arg1 = reinterpret_cast< SpikeContainer * >(argp1); { try { (arg1)->lastspikes(arg2,arg3); } catch( std::runtime_error &e ) { PyErr_SetString(PyExc_RuntimeError, const_cast(e.what())); return NULL; } } resultobj = SWIG_Py_Void(); { npy_intp dims[1] = { *arg3 }; PyObject * array = PyArray_SimpleNewFromData(1, dims, NPY_LONG, (void*)(*arg2)); if (!array) SWIG_fail; resultobj = SWIG_Python_AppendOutput(resultobj,array); } return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_SpikeContainer___getitem__(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; SpikeContainer *arg1 = (SpikeContainer *) 0 ; long **arg2 = (long **) 0 ; int *arg3 = (int *) 0 ; int arg4 ; void *argp1 = 0 ; int res1 = 0 ; long *data_temp2 ; int dim_temp2 ; int val4 ; int ecode4 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; { arg2 = &data_temp2; arg3 = &dim_temp2; } if (!PyArg_ParseTuple(args,(char *)"OO:SpikeContainer___getitem__",&obj0,&obj1)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_SpikeContainer, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "SpikeContainer___getitem__" "', argument " "1"" of type '" "SpikeContainer *""'"); } arg1 = reinterpret_cast< SpikeContainer * >(argp1); ecode4 = SWIG_AsVal_int(obj1, &val4); if (!SWIG_IsOK(ecode4)) { SWIG_exception_fail(SWIG_ArgError(ecode4), "in method '" "SpikeContainer___getitem__" "', argument " "4"" of type '" "int""'"); } arg4 = static_cast< int >(val4); { try { (arg1)->__getitem__(arg2,arg3,arg4); } catch( std::runtime_error &e ) { PyErr_SetString(PyExc_RuntimeError, const_cast(e.what())); return NULL; } } resultobj = SWIG_Py_Void(); { npy_intp dims[1] = { *arg3 }; PyObject * array = PyArray_SimpleNewFromData(1, dims, NPY_LONG, (void*)(*arg2)); if (!array) SWIG_fail; resultobj = SWIG_Python_AppendOutput(resultobj,array); } return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_SpikeContainer_get_spikes(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; SpikeContainer *arg1 = (SpikeContainer *) 0 ; long **arg2 = (long **) 0 ; int *arg3 = (int *) 0 ; int arg4 ; int arg5 ; int arg6 ; void *argp1 = 0 ; int res1 = 0 ; long *data_temp2 ; int dim_temp2 ; int val4 ; int ecode4 = 0 ; int val5 ; int ecode5 = 0 ; int val6 ; int ecode6 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; PyObject * obj2 = 0 ; PyObject * obj3 = 0 ; { arg2 = &data_temp2; arg3 = &dim_temp2; } if (!PyArg_ParseTuple(args,(char *)"OOOO:SpikeContainer_get_spikes",&obj0,&obj1,&obj2,&obj3)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_SpikeContainer, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "SpikeContainer_get_spikes" "', argument " "1"" of type '" "SpikeContainer *""'"); } arg1 = reinterpret_cast< SpikeContainer * >(argp1); ecode4 = SWIG_AsVal_int(obj1, &val4); if (!SWIG_IsOK(ecode4)) { SWIG_exception_fail(SWIG_ArgError(ecode4), "in method '" "SpikeContainer_get_spikes" "', argument " "4"" of type '" "int""'"); } arg4 = static_cast< int >(val4); ecode5 = SWIG_AsVal_int(obj2, &val5); if (!SWIG_IsOK(ecode5)) { SWIG_exception_fail(SWIG_ArgError(ecode5), "in method '" "SpikeContainer_get_spikes" "', argument " "5"" of type '" "int""'"); } arg5 = static_cast< int >(val5); ecode6 = SWIG_AsVal_int(obj3, &val6); if (!SWIG_IsOK(ecode6)) { SWIG_exception_fail(SWIG_ArgError(ecode6), "in method '" "SpikeContainer_get_spikes" "', argument " "6"" of type '" "int""'"); } arg6 = static_cast< int >(val6); { try { (arg1)->get_spikes(arg2,arg3,arg4,arg5,arg6); } catch( std::runtime_error &e ) { PyErr_SetString(PyExc_RuntimeError, const_cast(e.what())); return NULL; } } resultobj = SWIG_Py_Void(); { npy_intp dims[1] = { *arg3 }; PyObject * array = PyArray_SimpleNewFromData(1, dims, NPY_LONG, (void*)(*arg2)); if (!array) SWIG_fail; resultobj = SWIG_Python_AppendOutput(resultobj,array); } return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_SpikeContainer___getslice__(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; SpikeContainer *arg1 = (SpikeContainer *) 0 ; long **arg2 = (long **) 0 ; int *arg3 = (int *) 0 ; int arg4 ; int arg5 ; void *argp1 = 0 ; int res1 = 0 ; long *data_temp2 ; int dim_temp2 ; int val4 ; int ecode4 = 0 ; int val5 ; int ecode5 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; PyObject * obj2 = 0 ; { arg2 = &data_temp2; arg3 = &dim_temp2; } if (!PyArg_ParseTuple(args,(char *)"OOO:SpikeContainer___getslice__",&obj0,&obj1,&obj2)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_SpikeContainer, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "SpikeContainer___getslice__" "', argument " "1"" of type '" "SpikeContainer *""'"); } arg1 = reinterpret_cast< SpikeContainer * >(argp1); ecode4 = SWIG_AsVal_int(obj1, &val4); if (!SWIG_IsOK(ecode4)) { SWIG_exception_fail(SWIG_ArgError(ecode4), "in method '" "SpikeContainer___getslice__" "', argument " "4"" of type '" "int""'"); } arg4 = static_cast< int >(val4); ecode5 = SWIG_AsVal_int(obj2, &val5); if (!SWIG_IsOK(ecode5)) { SWIG_exception_fail(SWIG_ArgError(ecode5), "in method '" "SpikeContainer___getslice__" "', argument " "5"" of type '" "int""'"); } arg5 = static_cast< int >(val5); { try { (arg1)->__getslice__(arg2,arg3,arg4,arg5); } catch( std::runtime_error &e ) { PyErr_SetString(PyExc_RuntimeError, const_cast(e.what())); return NULL; } } resultobj = SWIG_Py_Void(); { npy_intp dims[1] = { *arg3 }; PyObject * array = PyArray_SimpleNewFromData(1, dims, NPY_LONG, (void*)(*arg2)); if (!array) SWIG_fail; resultobj = SWIG_Python_AppendOutput(resultobj,array); } return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_SpikeContainer___repr__(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; SpikeContainer *arg1 = (SpikeContainer *) 0 ; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; std::string result; if (!PyArg_ParseTuple(args,(char *)"O:SpikeContainer___repr__",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_SpikeContainer, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "SpikeContainer___repr__" "', argument " "1"" of type '" "SpikeContainer *""'"); } arg1 = reinterpret_cast< SpikeContainer * >(argp1); { try { result = (arg1)->__repr__(); } catch( std::runtime_error &e ) { PyErr_SetString(PyExc_RuntimeError, const_cast(e.what())); return NULL; } } resultobj = SWIG_From_std_string(static_cast< std::string >(result)); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_SpikeContainer___str__(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; SpikeContainer *arg1 = (SpikeContainer *) 0 ; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; std::string result; if (!PyArg_ParseTuple(args,(char *)"O:SpikeContainer___str__",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_SpikeContainer, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "SpikeContainer___str__" "', argument " "1"" of type '" "SpikeContainer *""'"); } arg1 = reinterpret_cast< SpikeContainer * >(argp1); { try { result = (arg1)->__str__(); } catch( std::runtime_error &e ) { PyErr_SetString(PyExc_RuntimeError, const_cast(e.what())); return NULL; } } resultobj = SWIG_From_std_string(static_cast< std::string >(result)); return resultobj; fail: return NULL; } SWIGINTERN PyObject *SpikeContainer_swigregister(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *obj; if (!PyArg_ParseTuple(args,(char*)"O:swigregister", &obj)) return NULL; SWIG_TypeNewClientData(SWIGTYPE_p_SpikeContainer, SWIG_NewClientData(obj)); return SWIG_Py_Void(); } static PyMethodDef SwigMethods[] = { { (char *)"SWIG_PyInstanceMethod_New", (PyCFunction)SWIG_PyInstanceMethod_New, METH_O, NULL}, { (char *)"CircularVector_X_set", _wrap_CircularVector_X_set, METH_VARARGS, NULL}, { (char *)"CircularVector_X_get", _wrap_CircularVector_X_get, METH_VARARGS, NULL}, { (char *)"CircularVector_cursor_set", _wrap_CircularVector_cursor_set, METH_VARARGS, NULL}, { (char *)"CircularVector_cursor_get", _wrap_CircularVector_cursor_get, METH_VARARGS, NULL}, { (char *)"CircularVector_n_set", _wrap_CircularVector_n_set, METH_VARARGS, NULL}, { (char *)"CircularVector_n_get", _wrap_CircularVector_n_get, METH_VARARGS, NULL}, { (char *)"CircularVector_retarray_set", _wrap_CircularVector_retarray_set, METH_VARARGS, NULL}, { (char *)"CircularVector_retarray_get", _wrap_CircularVector_retarray_get, METH_VARARGS, NULL}, { (char *)"new_CircularVector", _wrap_new_CircularVector, METH_VARARGS, NULL}, { (char *)"delete_CircularVector", _wrap_delete_CircularVector, METH_VARARGS, NULL}, { (char *)"CircularVector_reinit", _wrap_CircularVector_reinit, METH_VARARGS, NULL}, { (char *)"CircularVector_advance", _wrap_CircularVector_advance, METH_VARARGS, NULL}, { (char *)"CircularVector___len__", _wrap_CircularVector___len__, METH_VARARGS, NULL}, { (char *)"CircularVector___getitem__", _wrap_CircularVector___getitem__, METH_VARARGS, NULL}, { (char *)"CircularVector___setitem__", _wrap_CircularVector___setitem__, METH_VARARGS, NULL}, { (char *)"CircularVector___getslice__", _wrap_CircularVector___getslice__, METH_VARARGS, NULL}, { (char *)"CircularVector_get_conditional", _wrap_CircularVector_get_conditional, METH_VARARGS, NULL}, { (char *)"CircularVector___setslice__", _wrap_CircularVector___setslice__, METH_VARARGS, NULL}, { (char *)"CircularVector___repr__", _wrap_CircularVector___repr__, METH_VARARGS, NULL}, { (char *)"CircularVector___str__", _wrap_CircularVector___str__, METH_VARARGS, NULL}, { (char *)"CircularVector_expand", _wrap_CircularVector_expand, METH_VARARGS, NULL}, { (char *)"CircularVector_swigregister", CircularVector_swigregister, METH_VARARGS, NULL}, { (char *)"SpikeContainer_S_set", _wrap_SpikeContainer_S_set, METH_VARARGS, NULL}, { (char *)"SpikeContainer_S_get", _wrap_SpikeContainer_S_get, METH_VARARGS, NULL}, { (char *)"SpikeContainer_ind_set", _wrap_SpikeContainer_ind_set, METH_VARARGS, NULL}, { (char *)"SpikeContainer_ind_get", _wrap_SpikeContainer_ind_get, METH_VARARGS, NULL}, { (char *)"SpikeContainer_remaining_space_set", _wrap_SpikeContainer_remaining_space_set, METH_VARARGS, NULL}, { (char *)"SpikeContainer_remaining_space_get", _wrap_SpikeContainer_remaining_space_get, METH_VARARGS, NULL}, { (char *)"new_SpikeContainer", _wrap_new_SpikeContainer, METH_VARARGS, NULL}, { (char *)"delete_SpikeContainer", _wrap_delete_SpikeContainer, METH_VARARGS, NULL}, { (char *)"SpikeContainer_reinit", _wrap_SpikeContainer_reinit, METH_VARARGS, NULL}, { (char *)"SpikeContainer_push", _wrap_SpikeContainer_push, METH_VARARGS, NULL}, { (char *)"SpikeContainer_lastspikes", _wrap_SpikeContainer_lastspikes, METH_VARARGS, NULL}, { (char *)"SpikeContainer___getitem__", _wrap_SpikeContainer___getitem__, METH_VARARGS, NULL}, { (char *)"SpikeContainer_get_spikes", _wrap_SpikeContainer_get_spikes, METH_VARARGS, NULL}, { (char *)"SpikeContainer___getslice__", _wrap_SpikeContainer___getslice__, METH_VARARGS, NULL}, { (char *)"SpikeContainer___repr__", _wrap_SpikeContainer___repr__, METH_VARARGS, NULL}, { (char *)"SpikeContainer___str__", _wrap_SpikeContainer___str__, METH_VARARGS, NULL}, { (char *)"SpikeContainer_swigregister", SpikeContainer_swigregister, METH_VARARGS, NULL}, { NULL, NULL, 0, NULL } }; /* -------- TYPE CONVERSION AND EQUIVALENCE RULES (BEGIN) -------- */ static swig_type_info _swigt__p_CircularVector = {"_p_CircularVector", "CircularVector *", 0, 0, (void*)0, 0}; static swig_type_info _swigt__p_SpikeContainer = {"_p_SpikeContainer", "SpikeContainer *", 0, 0, (void*)0, 0}; static swig_type_info _swigt__p_char = {"_p_char", "char *", 0, 0, (void*)0, 0}; static swig_type_info _swigt__p_int = {"_p_int", "int *", 0, 0, (void*)0, 0}; static swig_type_info _swigt__p_long = {"_p_long", "long *", 0, 0, (void*)0, 0}; static swig_type_info _swigt__p_p_long = {"_p_p_long", "long **", 0, 0, (void*)0, 0}; static swig_type_info *swig_type_initial[] = { &_swigt__p_CircularVector, &_swigt__p_SpikeContainer, &_swigt__p_char, &_swigt__p_int, &_swigt__p_long, &_swigt__p_p_long, }; static swig_cast_info _swigc__p_CircularVector[] = { {&_swigt__p_CircularVector, 0, 0, 0},{0, 0, 0, 0}}; static swig_cast_info _swigc__p_SpikeContainer[] = { {&_swigt__p_SpikeContainer, 0, 0, 0},{0, 0, 0, 0}}; static swig_cast_info _swigc__p_char[] = { {&_swigt__p_char, 0, 0, 0},{0, 0, 0, 0}}; static swig_cast_info _swigc__p_int[] = { {&_swigt__p_int, 0, 0, 0},{0, 0, 0, 0}}; static swig_cast_info _swigc__p_long[] = { {&_swigt__p_long, 0, 0, 0},{0, 0, 0, 0}}; static swig_cast_info _swigc__p_p_long[] = { {&_swigt__p_p_long, 0, 0, 0},{0, 0, 0, 0}}; static swig_cast_info *swig_cast_initial[] = { _swigc__p_CircularVector, _swigc__p_SpikeContainer, _swigc__p_char, _swigc__p_int, _swigc__p_long, _swigc__p_p_long, }; /* -------- TYPE CONVERSION AND EQUIVALENCE RULES (END) -------- */ static swig_const_info swig_const_table[] = { {0, 0, 0, 0.0, 0, 0}}; #ifdef __cplusplus } #endif /* ----------------------------------------------------------------------------- * Type initialization: * This problem is tough by the requirement that no dynamic * memory is used. Also, since swig_type_info structures store pointers to * swig_cast_info structures and swig_cast_info structures store pointers back * to swig_type_info structures, we need some lookup code at initialization. * The idea is that swig generates all the structures that are needed. * The runtime then collects these partially filled structures. * The SWIG_InitializeModule function takes these initial arrays out of * swig_module, and does all the lookup, filling in the swig_module.types * array with the correct data and linking the correct swig_cast_info * structures together. * * The generated swig_type_info structures are assigned staticly to an initial * array. We just loop through that array, and handle each type individually. * First we lookup if this type has been already loaded, and if so, use the * loaded structure instead of the generated one. Then we have to fill in the * cast linked list. The cast data is initially stored in something like a * two-dimensional array. Each row corresponds to a type (there are the same * number of rows as there are in the swig_type_initial array). Each entry in * a column is one of the swig_cast_info structures for that type. * The cast_initial array is actually an array of arrays, because each row has * a variable number of columns. So to actually build the cast linked list, * we find the array of casts associated with the type, and loop through it * adding the casts to the list. The one last trick we need to do is making * sure the type pointer in the swig_cast_info struct is correct. * * First off, we lookup the cast->type name to see if it is already loaded. * There are three cases to handle: * 1) If the cast->type has already been loaded AND the type we are adding * casting info to has not been loaded (it is in this module), THEN we * replace the cast->type pointer with the type pointer that has already * been loaded. * 2) If BOTH types (the one we are adding casting info to, and the * cast->type) are loaded, THEN the cast info has already been loaded by * the previous module so we just ignore it. * 3) Finally, if cast->type has not already been loaded, then we add that * swig_cast_info to the linked list (because the cast->type) pointer will * be correct. * ----------------------------------------------------------------------------- */ #ifdef __cplusplus extern "C" { #if 0 } /* c-mode */ #endif #endif #if 0 #define SWIGRUNTIME_DEBUG #endif SWIGRUNTIME void SWIG_InitializeModule(void *clientdata) { size_t i; swig_module_info *module_head, *iter; int found, init; clientdata = clientdata; /* check to see if the circular list has been setup, if not, set it up */ if (swig_module.next==0) { /* Initialize the swig_module */ swig_module.type_initial = swig_type_initial; swig_module.cast_initial = swig_cast_initial; swig_module.next = &swig_module; init = 1; } else { init = 0; } /* Try and load any already created modules */ module_head = SWIG_GetModule(clientdata); if (!module_head) { /* This is the first module loaded for this interpreter */ /* so set the swig module into the interpreter */ SWIG_SetModule(clientdata, &swig_module); module_head = &swig_module; } else { /* the interpreter has loaded a SWIG module, but has it loaded this one? */ found=0; iter=module_head; do { if (iter==&swig_module) { found=1; break; } iter=iter->next; } while (iter!= module_head); /* if the is found in the list, then all is done and we may leave */ if (found) return; /* otherwise we must add out module into the list */ swig_module.next = module_head->next; module_head->next = &swig_module; } /* When multiple interpeters are used, a module could have already been initialized in a different interpreter, but not yet have a pointer in this interpreter. In this case, we do not want to continue adding types... everything should be set up already */ if (init == 0) return; /* Now work on filling in swig_module.types */ #ifdef SWIGRUNTIME_DEBUG printf("SWIG_InitializeModule: size %d\n", swig_module.size); #endif for (i = 0; i < swig_module.size; ++i) { swig_type_info *type = 0; swig_type_info *ret; swig_cast_info *cast; #ifdef SWIGRUNTIME_DEBUG printf("SWIG_InitializeModule: type %d %s\n", i, swig_module.type_initial[i]->name); #endif /* if there is another module already loaded */ if (swig_module.next != &swig_module) { type = SWIG_MangledTypeQueryModule(swig_module.next, &swig_module, swig_module.type_initial[i]->name); } if (type) { /* Overwrite clientdata field */ #ifdef SWIGRUNTIME_DEBUG printf("SWIG_InitializeModule: found type %s\n", type->name); #endif if (swig_module.type_initial[i]->clientdata) { type->clientdata = swig_module.type_initial[i]->clientdata; #ifdef SWIGRUNTIME_DEBUG printf("SWIG_InitializeModule: found and overwrite type %s \n", type->name); #endif } } else { type = swig_module.type_initial[i]; } /* Insert casting types */ cast = swig_module.cast_initial[i]; while (cast->type) { /* Don't need to add information already in the list */ ret = 0; #ifdef SWIGRUNTIME_DEBUG printf("SWIG_InitializeModule: look cast %s\n", cast->type->name); #endif if (swig_module.next != &swig_module) { ret = SWIG_MangledTypeQueryModule(swig_module.next, &swig_module, cast->type->name); #ifdef SWIGRUNTIME_DEBUG if (ret) printf("SWIG_InitializeModule: found cast %s\n", ret->name); #endif } if (ret) { if (type == swig_module.type_initial[i]) { #ifdef SWIGRUNTIME_DEBUG printf("SWIG_InitializeModule: skip old type %s\n", ret->name); #endif cast->type = ret; ret = 0; } else { /* Check for casting already in the list */ swig_cast_info *ocast = SWIG_TypeCheck(ret->name, type); #ifdef SWIGRUNTIME_DEBUG if (ocast) printf("SWIG_InitializeModule: skip old cast %s\n", ret->name); #endif if (!ocast) ret = 0; } } if (!ret) { #ifdef SWIGRUNTIME_DEBUG printf("SWIG_InitializeModule: adding cast %s\n", cast->type->name); #endif if (type->cast) { type->cast->prev = cast; cast->next = type->cast; } type->cast = cast; } cast++; } /* Set entry in modules->types array equal to the type */ swig_module.types[i] = type; } swig_module.types[i] = 0; #ifdef SWIGRUNTIME_DEBUG printf("**** SWIG_InitializeModule: Cast List ******\n"); for (i = 0; i < swig_module.size; ++i) { int j = 0; swig_cast_info *cast = swig_module.cast_initial[i]; printf("SWIG_InitializeModule: type %d %s\n", i, swig_module.type_initial[i]->name); while (cast->type) { printf("SWIG_InitializeModule: cast type %s\n", cast->type->name); cast++; ++j; } printf("---- Total casts: %d\n",j); } printf("**** SWIG_InitializeModule: Cast List ******\n"); #endif } /* This function will propagate the clientdata field of type to * any new swig_type_info structures that have been added into the list * of equivalent types. It is like calling * SWIG_TypeClientData(type, clientdata) a second time. */ SWIGRUNTIME void SWIG_PropagateClientData(void) { size_t i; swig_cast_info *equiv; static int init_run = 0; if (init_run) return; init_run = 1; for (i = 0; i < swig_module.size; i++) { if (swig_module.types[i]->clientdata) { equiv = swig_module.types[i]->cast; while (equiv) { if (!equiv->converter) { if (equiv->type && !equiv->type->clientdata) SWIG_TypeClientData(equiv->type, swig_module.types[i]->clientdata); } equiv = equiv->next; } } } } #ifdef __cplusplus #if 0 { /* c-mode */ #endif } #endif #ifdef __cplusplus extern "C" { #endif /* Python-specific SWIG API */ #define SWIG_newvarlink() SWIG_Python_newvarlink() #define SWIG_addvarlink(p, name, get_attr, set_attr) SWIG_Python_addvarlink(p, name, get_attr, set_attr) #define SWIG_InstallConstants(d, constants) SWIG_Python_InstallConstants(d, constants) /* ----------------------------------------------------------------------------- * global variable support code. * ----------------------------------------------------------------------------- */ typedef struct swig_globalvar { char *name; /* Name of global variable */ PyObject *(*get_attr)(void); /* Return the current value */ int (*set_attr)(PyObject *); /* Set the value */ struct swig_globalvar *next; } swig_globalvar; typedef struct swig_varlinkobject { PyObject_HEAD swig_globalvar *vars; } swig_varlinkobject; SWIGINTERN PyObject * swig_varlink_repr(swig_varlinkobject *SWIGUNUSEDPARM(v)) { #if PY_VERSION_HEX >= 0x03000000 return PyUnicode_InternFromString(""); #else return PyString_FromString(""); #endif } SWIGINTERN PyObject * swig_varlink_str(swig_varlinkobject *v) { #if PY_VERSION_HEX >= 0x03000000 PyObject *str = PyUnicode_InternFromString("("); PyObject *tail; PyObject *joined; swig_globalvar *var; for (var = v->vars; var; var=var->next) { tail = PyUnicode_FromString(var->name); joined = PyUnicode_Concat(str, tail); Py_DecRef(str); Py_DecRef(tail); str = joined; if (var->next) { tail = PyUnicode_InternFromString(", "); joined = PyUnicode_Concat(str, tail); Py_DecRef(str); Py_DecRef(tail); str = joined; } } tail = PyUnicode_InternFromString(")"); joined = PyUnicode_Concat(str, tail); Py_DecRef(str); Py_DecRef(tail); str = joined; #else PyObject *str = PyString_FromString("("); swig_globalvar *var; for (var = v->vars; var; var=var->next) { PyString_ConcatAndDel(&str,PyString_FromString(var->name)); if (var->next) PyString_ConcatAndDel(&str,PyString_FromString(", ")); } PyString_ConcatAndDel(&str,PyString_FromString(")")); #endif return str; } SWIGINTERN int swig_varlink_print(swig_varlinkobject *v, FILE *fp, int SWIGUNUSEDPARM(flags)) { char *tmp; PyObject *str = swig_varlink_str(v); fprintf(fp,"Swig global variables "); fprintf(fp,"%s\n", tmp = SWIG_Python_str_AsChar(str)); SWIG_Python_str_DelForPy3(tmp); Py_DECREF(str); return 0; } SWIGINTERN void swig_varlink_dealloc(swig_varlinkobject *v) { swig_globalvar *var = v->vars; while (var) { swig_globalvar *n = var->next; free(var->name); free(var); var = n; } } SWIGINTERN PyObject * swig_varlink_getattr(swig_varlinkobject *v, char *n) { PyObject *res = NULL; swig_globalvar *var = v->vars; while (var) { if (strcmp(var->name,n) == 0) { res = (*var->get_attr)(); break; } var = var->next; } if (res == NULL && !PyErr_Occurred()) { PyErr_SetString(PyExc_NameError,"Unknown C global variable"); } return res; } SWIGINTERN int swig_varlink_setattr(swig_varlinkobject *v, char *n, PyObject *p) { int res = 1; swig_globalvar *var = v->vars; while (var) { if (strcmp(var->name,n) == 0) { res = (*var->set_attr)(p); break; } var = var->next; } if (res == 1 && !PyErr_Occurred()) { PyErr_SetString(PyExc_NameError,"Unknown C global variable"); } return res; } SWIGINTERN PyTypeObject* swig_varlink_type(void) { static char varlink__doc__[] = "Swig var link object"; static PyTypeObject varlink_type; static int type_init = 0; if (!type_init) { const PyTypeObject tmp = { /* PyObject header changed in Python 3 */ #if PY_VERSION_HEX >= 0x03000000 PyVarObject_HEAD_INIT(NULL, 0) #else PyObject_HEAD_INIT(NULL) 0, /* ob_size */ #endif (char *)"swigvarlink", /* tp_name */ sizeof(swig_varlinkobject), /* tp_basicsize */ 0, /* tp_itemsize */ (destructor) swig_varlink_dealloc, /* tp_dealloc */ (printfunc) swig_varlink_print, /* tp_print */ (getattrfunc) swig_varlink_getattr, /* tp_getattr */ (setattrfunc) swig_varlink_setattr, /* tp_setattr */ 0, /* tp_compare */ (reprfunc) swig_varlink_repr, /* tp_repr */ 0, /* tp_as_number */ 0, /* tp_as_sequence */ 0, /* tp_as_mapping */ 0, /* tp_hash */ 0, /* tp_call */ (reprfunc) swig_varlink_str, /* tp_str */ 0, /* tp_getattro */ 0, /* tp_setattro */ 0, /* tp_as_buffer */ 0, /* tp_flags */ varlink__doc__, /* tp_doc */ 0, /* tp_traverse */ 0, /* tp_clear */ 0, /* tp_richcompare */ 0, /* tp_weaklistoffset */ #if PY_VERSION_HEX >= 0x02020000 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, /* tp_iter -> tp_weaklist */ #endif #if PY_VERSION_HEX >= 0x02030000 0, /* tp_del */ #endif #if PY_VERSION_HEX >= 0x02060000 0, /* tp_version */ #endif #ifdef COUNT_ALLOCS 0,0,0,0 /* tp_alloc -> tp_next */ #endif }; varlink_type = tmp; type_init = 1; #if PY_VERSION_HEX < 0x02020000 varlink_type.ob_type = &PyType_Type; #else if (PyType_Ready(&varlink_type) < 0) return NULL; #endif } return &varlink_type; } /* Create a variable linking object for use later */ SWIGINTERN PyObject * SWIG_Python_newvarlink(void) { swig_varlinkobject *result = PyObject_NEW(swig_varlinkobject, swig_varlink_type()); if (result) { result->vars = 0; } return ((PyObject*) result); } SWIGINTERN void SWIG_Python_addvarlink(PyObject *p, char *name, PyObject *(*get_attr)(void), int (*set_attr)(PyObject *p)) { swig_varlinkobject *v = (swig_varlinkobject *) p; swig_globalvar *gv = (swig_globalvar *) malloc(sizeof(swig_globalvar)); if (gv) { size_t size = strlen(name)+1; gv->name = (char *)malloc(size); if (gv->name) { strncpy(gv->name,name,size); gv->get_attr = get_attr; gv->set_attr = set_attr; gv->next = v->vars; } } v->vars = gv; } SWIGINTERN PyObject * SWIG_globals(void) { static PyObject *_SWIG_globals = 0; if (!_SWIG_globals) _SWIG_globals = SWIG_newvarlink(); return _SWIG_globals; } /* ----------------------------------------------------------------------------- * constants/methods manipulation * ----------------------------------------------------------------------------- */ /* Install Constants */ SWIGINTERN void SWIG_Python_InstallConstants(PyObject *d, swig_const_info constants[]) { PyObject *obj = 0; size_t i; for (i = 0; constants[i].type; ++i) { switch(constants[i].type) { case SWIG_PY_POINTER: obj = SWIG_InternalNewPointerObj(constants[i].pvalue, *(constants[i]).ptype,0); break; case SWIG_PY_BINARY: obj = SWIG_NewPackedObj(constants[i].pvalue, constants[i].lvalue, *(constants[i].ptype)); break; default: obj = 0; break; } if (obj) { PyDict_SetItemString(d, constants[i].name, obj); Py_DECREF(obj); } } } /* -----------------------------------------------------------------------------*/ /* Fix SwigMethods to carry the callback ptrs when needed */ /* -----------------------------------------------------------------------------*/ SWIGINTERN void SWIG_Python_FixMethods(PyMethodDef *methods, swig_const_info *const_table, swig_type_info **types, swig_type_info **types_initial) { size_t i; for (i = 0; methods[i].ml_name; ++i) { const char *c = methods[i].ml_doc; if (c && (c = strstr(c, "swig_ptr: "))) { int j; swig_const_info *ci = 0; const char *name = c + 10; for (j = 0; const_table[j].type; ++j) { if (strncmp(const_table[j].name, name, strlen(const_table[j].name)) == 0) { ci = &(const_table[j]); break; } } if (ci) { void *ptr = (ci->type == SWIG_PY_POINTER) ? ci->pvalue : 0; if (ptr) { size_t shift = (ci->ptype) - types; swig_type_info *ty = types_initial[shift]; size_t ldoc = (c - methods[i].ml_doc); size_t lptr = strlen(ty->name)+2*sizeof(void*)+2; char *ndoc = (char*)malloc(ldoc + lptr + 10); if (ndoc) { char *buff = ndoc; strncpy(buff, methods[i].ml_doc, ldoc); buff += ldoc; strncpy(buff, "swig_ptr: ", 10); buff += 10; SWIG_PackVoidPtr(buff, ptr, ty->name, lptr); methods[i].ml_doc = ndoc; } } } } } } #ifdef __cplusplus } #endif /* -----------------------------------------------------------------------------* * Partial Init method * -----------------------------------------------------------------------------*/ #ifdef __cplusplus extern "C" #endif SWIGEXPORT #if PY_VERSION_HEX >= 0x03000000 PyObject* #else void #endif SWIG_init(void) { PyObject *m, *d, *md; #if PY_VERSION_HEX >= 0x03000000 static struct PyModuleDef SWIG_module = { # if PY_VERSION_HEX >= 0x03020000 PyModuleDef_HEAD_INIT, # else { PyObject_HEAD_INIT(NULL) NULL, /* m_init */ 0, /* m_index */ NULL, /* m_copy */ }, # endif (char *) SWIG_name, NULL, -1, SwigMethods, NULL, NULL, NULL, NULL }; #endif #if defined(SWIGPYTHON_BUILTIN) static SwigPyClientData SwigPyObject_clientdata = { 0, 0, 0, 0, 0, 0, 0 }; static PyGetSetDef this_getset_def = { (char *)"this", &SwigPyBuiltin_ThisClosure, NULL, NULL, NULL }; static SwigPyGetSet thisown_getset_closure = { (PyCFunction) SwigPyObject_own, (PyCFunction) SwigPyObject_own }; static PyGetSetDef thisown_getset_def = { (char *)"thisown", SwigPyBuiltin_GetterClosure, SwigPyBuiltin_SetterClosure, NULL, &thisown_getset_closure }; PyObject *metatype_args; PyTypeObject *builtin_pytype; int builtin_base_count; swig_type_info *builtin_basetype; PyObject *tuple; PyGetSetDescrObject *static_getset; PyTypeObject *metatype; SwigPyClientData *cd; PyObject *public_interface, *public_symbol; PyObject *this_descr; PyObject *thisown_descr; int i; (void)builtin_pytype; (void)builtin_base_count; (void)builtin_basetype; (void)tuple; (void)static_getset; /* metatype is used to implement static member variables. */ metatype_args = Py_BuildValue("(s(O){})", "SwigPyObjectType", &PyType_Type); assert(metatype_args); metatype = (PyTypeObject *) PyType_Type.tp_call((PyObject *) &PyType_Type, metatype_args, NULL); assert(metatype); Py_DECREF(metatype_args); metatype->tp_setattro = (setattrofunc) &SwigPyObjectType_setattro; assert(PyType_Ready(metatype) >= 0); #endif /* Fix SwigMethods to carry the callback ptrs when needed */ SWIG_Python_FixMethods(SwigMethods, swig_const_table, swig_types, swig_type_initial); #if PY_VERSION_HEX >= 0x03000000 m = PyModule_Create(&SWIG_module); #else m = Py_InitModule((char *) SWIG_name, SwigMethods); #endif md = d = PyModule_GetDict(m); SWIG_InitializeModule(0); #ifdef SWIGPYTHON_BUILTIN SwigPyObject_stype = SWIG_MangledTypeQuery("_p_SwigPyObject"); assert(SwigPyObject_stype); cd = (SwigPyClientData*) SwigPyObject_stype->clientdata; if (!cd) { SwigPyObject_stype->clientdata = &SwigPyObject_clientdata; SwigPyObject_clientdata.pytype = SwigPyObject_TypeOnce(); } else if (SwigPyObject_TypeOnce()->tp_basicsize != cd->pytype->tp_basicsize) { PyErr_SetString(PyExc_RuntimeError, "Import error: attempted to load two incompatible swig-generated modules."); # if PY_VERSION_HEX >= 0x03000000 return NULL; # else return; # endif } /* All objects have a 'this' attribute */ this_descr = PyDescr_NewGetSet(SwigPyObject_type(), &this_getset_def); (void)this_descr; /* All objects have a 'thisown' attribute */ thisown_descr = PyDescr_NewGetSet(SwigPyObject_type(), &thisown_getset_def); (void)thisown_descr; public_interface = PyList_New(0); public_symbol = 0; (void)public_symbol; PyDict_SetItemString(md, "__all__", public_interface); Py_DECREF(public_interface); for (i = 0; SwigMethods[i].ml_name != NULL; ++i) SwigPyBuiltin_AddPublicSymbol(public_interface, SwigMethods[i].ml_name); for (i = 0; swig_const_table[i].name != 0; ++i) SwigPyBuiltin_AddPublicSymbol(public_interface, swig_const_table[i].name); #endif SWIG_InstallConstants(d,swig_const_table); import_array(); #if PY_VERSION_HEX >= 0x03000000 return m; #else return; #endif } brian-1.4.3/brian/utils/ccircular/circular.cpp000066400000000000000000000146771266062647100213550ustar00rootroot00000000000000#include "ccircular.h" #include #include CircularVector::CircularVector(int n) { this->X = NULL; this->retarray = NULL; this->n = n; this->X = new long[n]; // we don't worry about memory errors for the moment... this->retarray = new long[n]; if(!this->X || !this->retarray){ if(this->X) { delete [] this->X; this->X = 0; } if(this->retarray) { delete [] this->retarray; this->retarray = 0; } throw BrianException("Not enough memory in creating CircularVector."); } this->reinit(); } CircularVector::~CircularVector() { if(this->X) delete [] this->X; if(this->retarray) delete [] this->retarray; this->X = NULL; this->retarray = NULL; } void CircularVector::expand(long n) { long orig_n = this->n; this->n += n; n = this->n; long *new_X = new long[n]; long *new_retarray = new long[n]; if(!new_X || !new_retarray){ if(new_X) delete [] new_X; if(new_retarray) delete [] new_retarray; throw BrianException("Not enough memory in expanding CircularVector."); } // newS.X[:S.n-S.cursor] = S.X[S.cursor:] memcpy((void *)new_X, (void *)(this->X+this->cursor), sizeof(long)*(orig_n-this->cursor)); // newS.X[S.n-S.cursor:S.n] = S.X[:S.cursor] memcpy((void *)(new_X+orig_n-this->cursor), (void *)(this->X), sizeof(long)*this->cursor); this->cursor = orig_n; delete [] this->X; this->X = new_X; delete [] this->retarray; this->retarray = new_retarray; } void CircularVector::reinit() { this->cursor = 0; for(int i=0;in;i++) this->X[i] = 0; } void CircularVector::advance(int k) { this->cursor = this->index(k); } int CircularVector::__len__() { return this->n; } inline int CircularVector::index(int i) { int j = (this->cursor+i)%this->n; if(j<0) j+=this->n; return j; } inline int CircularVector::getitem(int i) { return this->X[this->index(i)]; } int CircularVector::__getitem__(int i) { return this->getitem(i); } void CircularVector::__setitem__(int i, int x) { this->X[this->index(i)] = x; } void CircularVector::__getslice__(long **ret, int *ret_n, int i, int j) { int i0 = this->index(i); int j0 = this->index(j); int n = 0; for(int k=i0;k!=j0;k=(k+1)%this->n) this->retarray[n++] = this->X[k]; *ret = this->retarray; *ret_n = n; } void CircularVector::get_conditional(long **ret, int *ret_n, int i, int j, int min, int max, int offset) { int i0, j0; int lo, mid, hi; int ioff = this->index(i); int joff = this->index(j); int lensearch; if(joff>=ioff) lensearch = joff-ioff; else lensearch = this->n-(ioff-joff); // start with a binary search to the left, note that we use here the bisect_left // algorithm from the standard python library translated into C++ code, and // altered to work with a circular array with an offset. The lo and hi variables // vary from 0 to lensearch but the probe this->X[...] is offset by ioff, the // position in the X array of the i variable passed to the function. lo = 0; hi = lensearch; while(loX[(mid+ioff)%this->n]n; // then a binary search to the right //lo = 0; // we can use the lo output from the previous step hi = lensearch; while(loX[(mid+ioff)%this->n]n; // then fill in the return array int n = 0; for(int k=i0;k!=j0;k=(k+1)%this->n) this->retarray[n++] = this->X[k]-offset; *ret = this->retarray; *ret_n = n; } void CircularVector::__setslice__(int i, int j, long *x, int n) { if(j>i) { int i0 = this->index(i); int j0 = this->index(j); for(int k=i0,l=0;k!=j0 && ln,l++) this->X[k] = x[l]; } } string CircularVector::__repr__() { stringstream out; out << "CircularVector("; out << "cursor=" << this->cursor; out << ", X=["; for(int i=0;in;i++) { if(i) out << " "; out << this->X[i]; } out << "])"; return out.str(); } string CircularVector::__str__() { return this->__repr__(); } SpikeContainer::SpikeContainer(int m) { try{ this->S = NULL; this->ind = NULL; this->S = new CircularVector(2); this->remaining_space = 1; if(m<2) m=2; this->ind = new CircularVector(m+1); } catch(BrianException &e) { if(this->S){ delete this->S; this->S = 0; } if(this->ind){ delete this->ind; this->ind = 0; } throw; } } SpikeContainer::~SpikeContainer() { if(this->S) delete this->S; if(this->ind) delete this->ind; } void SpikeContainer::reinit() { this->S->reinit(); this->ind->reinit(); } void SpikeContainer::push(long *y, int n) { long freed_space = (this->ind->__getitem__(2)-this->ind->__getitem__(1))%this->S->n; if(freed_space<0) freed_space += this->S->n; this->remaining_space += freed_space; while(n>=this->remaining_space){ long orig_cursor = this->S->cursor; long orig_n = this->S->n; this->S->expand(this->S->n); // double size of S for(long i=0; iind->n; i++){ this->ind->X[i] = (this->ind->X[i]-orig_cursor)%orig_n; if(this->ind->X[i]<0) this->ind->X[i] += orig_n; if(this->ind->X[i]==0) this->ind->X[i] = orig_n; } this->remaining_space += orig_n; } this->S->__setslice__(0, n, y, n); this->S->advance(n); this->ind->advance(1); this->ind->__setitem__(0, this->S->cursor); this->remaining_space -= n; } void SpikeContainer::lastspikes(long **ret, int *ret_n) { this->S->__getslice__(ret, ret_n, this->ind->__getitem__(-1)-this->S->cursor, this->S->n); } void SpikeContainer::__getitem__(long **ret, int *ret_n, int i) { this->S->__getslice__(ret, ret_n, this->ind->__getitem__(-i-1)-this->S->cursor, this->ind->__getitem__(-i)-this->S->cursor+this->S->n); } void SpikeContainer::get_spikes(long **ret, int *ret_n, int delay, int origin, int N) { return this->S->get_conditional(ret, ret_n, this->ind->__getitem__(-delay-1)-this->S->cursor, this->ind->__getitem__(-delay)-this->S->cursor+this->S->n, origin, origin+N, origin); } void SpikeContainer::__getslice__(long **ret, int *ret_n, int i, int j) { return this->S->__getslice__(ret, ret_n, this->ind->__getitem__(-j)-this->S->cursor, this->ind->__getitem__(-i)-this->S->cursor+this->S->n); } string SpikeContainer::__repr__() { stringstream out; out << "SpikeContainer(" << endl; out << " S: "; out << this->S->__repr__() << endl; out << " ind: "; out << this->ind->__repr__(); out << ")"; return out.str(); } string SpikeContainer::__str__() { return this->__repr__(); } brian-1.4.3/brian/utils/ccircular/numpy.i000066400000000000000000001517001266062647100203540ustar00rootroot00000000000000/* -*- C -*- (not really, but good for syntax highlighting) */ #ifdef SWIGPYTHON %{ #ifndef SWIG_FILE_WITH_INIT # define NO_IMPORT_ARRAY #endif #include "stdio.h" #include %} /**********************************************************************/ /* The following code originally appeared in * enthought/kiva/agg/src/numeric.i written by Eric Jones. It was * translated from C++ to C by John Hunter. Bill Spotz has modified * it to fix some minor bugs, upgrade from Numeric to numpy (all * versions), add some comments and functionality, and convert from * direct code insertion to SWIG fragments. */ %fragment("NumPy_Macros", "header") { /* Macros to extract array attributes. */ %#define is_array(a) ((a) && PyArray_Check((PyArrayObject *)a)) %#define array_type(a) (int)(PyArray_TYPE(a)) %#define array_numdims(a) (((PyArrayObject *)a)->nd) %#define array_dimensions(a) (((PyArrayObject *)a)->dimensions) %#define array_size(a,i) (((PyArrayObject *)a)->dimensions[i]) %#define array_data(a) (((PyArrayObject *)a)->data) %#define array_is_contiguous(a) (PyArray_ISCONTIGUOUS(a)) %#define array_is_native(a) (PyArray_ISNOTSWAPPED(a)) %#define array_is_fortran(a) (PyArray_ISFORTRAN(a)) } /**********************************************************************/ %fragment("NumPy_Utilities", "header") { /* Given a PyObject, return a string describing its type. */ const char* pytype_string(PyObject* py_obj) { if (py_obj == NULL ) return "C NULL value"; if (py_obj == Py_None ) return "Python None" ; if (PyCallable_Check(py_obj)) return "callable" ; if (PyString_Check( py_obj)) return "string" ; if (PyInt_Check( py_obj)) return "int" ; if (PyFloat_Check( py_obj)) return "float" ; if (PyDict_Check( py_obj)) return "dict" ; if (PyList_Check( py_obj)) return "list" ; if (PyTuple_Check( py_obj)) return "tuple" ; if (PyFile_Check( py_obj)) return "file" ; if (PyModule_Check( py_obj)) return "module" ; if (PyInstance_Check(py_obj)) return "instance" ; return "unkown type"; } /* Given a NumPy typecode, return a string describing the type. */ const char* typecode_string(int typecode) { static const char* type_names[25] = {"bool", "byte", "unsigned byte", "short", "unsigned short", "int", "unsigned int", "long", "unsigned long", "long long", "unsigned long long", "float", "double", "long double", "complex float", "complex double", "complex long double", "object", "string", "unicode", "void", "ntypes", "notype", "char", "unknown"}; return typecode < 24 ? type_names[typecode] : type_names[24]; } /* Make sure input has correct numpy type. Allow character and byte * to match. Also allow int and long to match. This is deprecated. * You should use PyArray_EquivTypenums() instead. */ int type_match(int actual_type, int desired_type) { return PyArray_EquivTypenums(actual_type, desired_type); } } /**********************************************************************/ %fragment("NumPy_Object_to_Array", "header", fragment="NumPy_Backward_Compatibility", fragment="NumPy_Macros", fragment="NumPy_Utilities") { /* Given a PyObject pointer, cast it to a PyArrayObject pointer if * legal. If not, set the python error string appropriately and * return NULL. */ PyArrayObject* obj_to_array_no_conversion(PyObject* input, int typecode) { PyArrayObject* ary = NULL; if (is_array(input) && (typecode == NPY_NOTYPE || PyArray_EquivTypenums(array_type(input), typecode))) { ary = (PyArrayObject*) input; } else if is_array(input) { const char* desired_type = typecode_string(typecode); const char* actual_type = typecode_string(array_type(input)); PyErr_Format(PyExc_TypeError, "Array of type '%s' required. Array of type '%s' given", desired_type, actual_type); ary = NULL; } else { const char * desired_type = typecode_string(typecode); const char * actual_type = pytype_string(input); PyErr_Format(PyExc_TypeError, "Array of type '%s' required. A '%s' was given", desired_type, actual_type); ary = NULL; } return ary; } /* Convert the given PyObject to a NumPy array with the given * typecode. On success, return a valid PyArrayObject* with the * correct type. On failure, the python error string will be set and * the routine returns NULL. */ PyArrayObject* obj_to_array_allow_conversion(PyObject* input, int typecode, int* is_new_object) { PyArrayObject* ary = NULL; PyObject* py_obj; if (is_array(input) && (typecode == NPY_NOTYPE || PyArray_EquivTypenums(array_type(input),typecode))) { ary = (PyArrayObject*) input; *is_new_object = 0; } else { py_obj = PyArray_FROMANY(input, typecode, 0, 0, NPY_DEFAULT); /* If NULL, PyArray_FromObject will have set python error value.*/ ary = (PyArrayObject*) py_obj; *is_new_object = 1; } return ary; } /* Given a PyArrayObject, check to see if it is contiguous. If so, * return the input pointer and flag it as not a new object. If it is * not contiguous, create a new PyArrayObject using the original data, * flag it as a new object and return the pointer. */ PyArrayObject* make_contiguous(PyArrayObject* ary, int* is_new_object, int min_dims, int max_dims) { PyArrayObject* result; if (array_is_contiguous(ary)) { result = ary; *is_new_object = 0; } else { result = (PyArrayObject*) PyArray_ContiguousFromObject((PyObject*)ary, array_type(ary), min_dims, max_dims); *is_new_object = 1; } return result; } /* Given a PyArrayObject, check to see if it is Fortran-contiguous. * If so, return the input pointer, but do not flag it as not a new * object. If it is not Fortran-contiguous, create a new * PyArrayObject using the original data, flag it as a new object * and return the pointer. */ PyArrayObject* make_fortran(PyArrayObject* ary, int* is_new_object, int min_dims, int max_dims) { PyArrayObject* result; if (array_is_fortran(ary)) { result = ary; *is_new_object = 0; } else { Py_INCREF(ary->descr); result = (PyArrayObject*) PyArray_FromArray(ary, ary->descr, NPY_FORTRAN); *is_new_object = 1; } return result; } /* Convert a given PyObject to a contiguous PyArrayObject of the * specified type. If the input object is not a contiguous * PyArrayObject, a new one will be created and the new object flag * will be set. */ PyArrayObject* obj_to_array_contiguous_allow_conversion(PyObject* input, int typecode, int* is_new_object) { int is_new1 = 0; int is_new2 = 0; PyArrayObject* ary2; PyArrayObject* ary1 = obj_to_array_allow_conversion(input, typecode, &is_new1); if (ary1) { ary2 = make_contiguous(ary1, &is_new2, 0, 0); if ( is_new1 && is_new2) { Py_DECREF(ary1); } ary1 = ary2; } *is_new_object = is_new1 || is_new2; return ary1; } /* Convert a given PyObject to a Fortran-ordered PyArrayObject of the * specified type. If the input object is not a Fortran-ordered * PyArrayObject, a new one will be created and the new object flag * will be set. */ PyArrayObject* obj_to_array_fortran_allow_conversion(PyObject* input, int typecode, int* is_new_object) { int is_new1 = 0; int is_new2 = 0; PyArrayObject* ary2; PyArrayObject* ary1 = obj_to_array_allow_conversion(input, typecode, &is_new1); if (ary1) { ary2 = make_fortran(ary1, &is_new2, 0, 0); if (is_new1 && is_new2) { Py_DECREF(ary1); } ary1 = ary2; } *is_new_object = is_new1 || is_new2; return ary1; } } /* end fragment */ /**********************************************************************/ %fragment("NumPy_Array_Requirements", "header", fragment="NumPy_Backward_Compatibility", fragment="NumPy_Macros") { /* Test whether a python object is contiguous. If array is * contiguous, return 1. Otherwise, set the python error string and * return 0. */ int require_contiguous(PyArrayObject* ary) { int contiguous = 1; if (!array_is_contiguous(ary)) { PyErr_SetString(PyExc_TypeError, "Array must be contiguous. A non-contiguous array was given"); contiguous = 0; } return contiguous; } /* Require that a numpy array is not byte-swapped. If the array is * not byte-swapped, return 1. Otherwise, set the python error string * and return 0. */ int require_native(PyArrayObject* ary) { int native = 1; if (!array_is_native(ary)) { PyErr_SetString(PyExc_TypeError, "Array must have native byteorder. " "A byte-swapped array was given"); native = 0; } return native; } /* Require the given PyArrayObject to have a specified number of * dimensions. If the array has the specified number of dimensions, * return 1. Otherwise, set the python error string and return 0. */ int require_dimensions(PyArrayObject* ary, int exact_dimensions) { int success = 1; if (array_numdims(ary) != exact_dimensions) { PyErr_Format(PyExc_TypeError, "Array must have %d dimensions. Given array has %d dimensions", exact_dimensions, array_numdims(ary)); success = 0; } return success; } /* Require the given PyArrayObject to have one of a list of specified * number of dimensions. If the array has one of the specified number * of dimensions, return 1. Otherwise, set the python error string * and return 0. */ int require_dimensions_n(PyArrayObject* ary, int* exact_dimensions, int n) { int success = 0; int i; char dims_str[255] = ""; char s[255]; for (i = 0; i < n && !success; i++) { if (array_numdims(ary) == exact_dimensions[i]) { success = 1; } } if (!success) { for (i = 0; i < n-1; i++) { sprintf(s, "%d, ", exact_dimensions[i]); strcat(dims_str,s); } sprintf(s, " or %d", exact_dimensions[n-1]); strcat(dims_str,s); PyErr_Format(PyExc_TypeError, "Array must have %s dimensions. Given array has %d dimensions", dims_str, array_numdims(ary)); } return success; } /* Require the given PyArrayObject to have a specified shape. If the * array has the specified shape, return 1. Otherwise, set the python * error string and return 0. */ int require_size(PyArrayObject* ary, npy_intp* size, int n) { int i; int success = 1; int len; char desired_dims[255] = "["; char s[255]; char actual_dims[255] = "["; for(i=0; i < n;i++) { if (size[i] != -1 && size[i] != array_size(ary,i)) { success = 0; } } if (!success) { for (i = 0; i < n; i++) { if (size[i] == -1) { sprintf(s, "*,"); } else { sprintf(s, "%ld,", (long int)size[i]); } strcat(desired_dims,s); } len = strlen(desired_dims); desired_dims[len-1] = ']'; for (i = 0; i < n; i++) { sprintf(s, "%ld,", (long int)array_size(ary,i)); strcat(actual_dims,s); } len = strlen(actual_dims); actual_dims[len-1] = ']'; PyErr_Format(PyExc_TypeError, "Array must have shape of %s. Given array has shape of %s", desired_dims, actual_dims); } return success; } /* Require the given PyArrayObject to to be FORTRAN ordered. If the * the PyArrayObject is already FORTRAN ordered, do nothing. Else, * set the FORTRAN ordering flag and recompute the strides. */ int require_fortran(PyArrayObject* ary) { int success = 1; int nd = array_numdims(ary); int i; if (array_is_fortran(ary)) return success; /* Set the FORTRAN ordered flag */ ary->flags = NPY_FARRAY; /* Recompute the strides */ ary->strides[0] = ary->strides[nd-1]; for (i=1; i < nd; ++i) ary->strides[i] = ary->strides[i-1] * array_size(ary,i-1); return success; } } /* Combine all NumPy fragments into one for convenience */ %fragment("NumPy_Fragments", "header", fragment="NumPy_Backward_Compatibility", fragment="NumPy_Macros", fragment="NumPy_Utilities", fragment="NumPy_Object_to_Array", fragment="NumPy_Array_Requirements") { } /* End John Hunter translation (with modifications by Bill Spotz) */ /* %numpy_typemaps() macro * * This macro defines a family of 41 typemaps that allow C arguments * of the form * * (DATA_TYPE IN_ARRAY1[ANY]) * (DATA_TYPE* IN_ARRAY1, DIM_TYPE DIM1) * (DIM_TYPE DIM1, DATA_TYPE* IN_ARRAY1) * * (DATA_TYPE IN_ARRAY2[ANY][ANY]) * (DATA_TYPE* IN_ARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2) * (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* IN_ARRAY2) * (DATA_TYPE* IN_FARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2) * (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* IN_FARRAY2) * * (DATA_TYPE IN_ARRAY3[ANY][ANY][ANY]) * (DATA_TYPE* IN_ARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3) * (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DATA_TYPE* IN_ARRAY3) * (DATA_TYPE* IN_FARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3) * (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DATA_TYPE* IN_FARRAY3) * * (DATA_TYPE INPLACE_ARRAY1[ANY]) * (DATA_TYPE* INPLACE_ARRAY1, DIM_TYPE DIM1) * (DIM_TYPE DIM1, DATA_TYPE* INPLACE_ARRAY1) * * (DATA_TYPE INPLACE_ARRAY2[ANY][ANY]) * (DATA_TYPE* INPLACE_ARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2) * (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* INPLACE_ARRAY2) * (DATA_TYPE* INPLACE_FARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2) * (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* INPLACE_FARRAY2) * * (DATA_TYPE INPLACE_ARRAY3[ANY][ANY][ANY]) * (DATA_TYPE* INPLACE_ARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3) * (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DATA_TYPE* INPLACE_ARRAY3) * (DATA_TYPE* INPLACE_FARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3) * (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DATA_TYPE* INPLACE_FARRAY3) * * (DATA_TYPE ARGOUT_ARRAY1[ANY]) * (DATA_TYPE* ARGOUT_ARRAY1, DIM_TYPE DIM1) * (DIM_TYPE DIM1, DATA_TYPE* ARGOUT_ARRAY1) * * (DATA_TYPE ARGOUT_ARRAY2[ANY][ANY]) * * (DATA_TYPE ARGOUT_ARRAY3[ANY][ANY][ANY]) * * (DATA_TYPE** ARGOUTVIEW_ARRAY1, DIM_TYPE* DIM1) * (DIM_TYPE* DIM1, DATA_TYPE** ARGOUTVIEW_ARRAY1) * * (DATA_TYPE** ARGOUTVIEW_ARRAY2, DIM_TYPE* DIM1, DIM_TYPE* DIM2) * (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DATA_TYPE** ARGOUTVIEW_ARRAY2) * (DATA_TYPE** ARGOUTVIEW_FARRAY2, DIM_TYPE* DIM1, DIM_TYPE* DIM2) * (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DATA_TYPE** ARGOUTVIEW_FARRAY2) * * (DATA_TYPE** ARGOUTVIEW_ARRAY3, DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3) * (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DATA_TYPE** ARGOUTVIEW_ARRAY3) * (DATA_TYPE** ARGOUTVIEW_FARRAY3, DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3) * (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DATA_TYPE** ARGOUTVIEW_FARRAY3) * * where "DATA_TYPE" is any type supported by the NumPy module, and * "DIM_TYPE" is any int-like type suitable for specifying dimensions. * The difference between "ARRAY" typemaps and "FARRAY" typemaps is * that the "FARRAY" typemaps expect FORTRAN ordering of * multidimensional arrays. In python, the dimensions will not need * to be specified (except for the "DATA_TYPE* ARGOUT_ARRAY1" * typemaps). The IN_ARRAYs can be a numpy array or any sequence that * can be converted to a numpy array of the specified type. The * INPLACE_ARRAYs must be numpy arrays of the appropriate type. The * ARGOUT_ARRAYs will be returned as new numpy arrays of the * appropriate type. * * These typemaps can be applied to existing functions using the * %apply directive. For example: * * %apply (double* IN_ARRAY1, int DIM1) {(double* series, int length)}; * double prod(double* series, int length); * * %apply (int DIM1, int DIM2, double* INPLACE_ARRAY2) * {(int rows, int cols, double* matrix )}; * void floor(int rows, int cols, double* matrix, double f); * * %apply (double IN_ARRAY3[ANY][ANY][ANY]) * {(double tensor[2][2][2] )}; * %apply (double ARGOUT_ARRAY3[ANY][ANY][ANY]) * {(double low[2][2][2] )}; * %apply (double ARGOUT_ARRAY3[ANY][ANY][ANY]) * {(double upp[2][2][2] )}; * void luSplit(double tensor[2][2][2], * double low[2][2][2], * double upp[2][2][2] ); * * or directly with * * double prod(double* IN_ARRAY1, int DIM1); * * void floor(int DIM1, int DIM2, double* INPLACE_ARRAY2, double f); * * void luSplit(double IN_ARRAY3[ANY][ANY][ANY], * double ARGOUT_ARRAY3[ANY][ANY][ANY], * double ARGOUT_ARRAY3[ANY][ANY][ANY]); */ %define %numpy_typemaps(DATA_TYPE, DATA_TYPECODE, DIM_TYPE) /************************/ /* Input Array Typemaps */ /************************/ /* Typemap suite for (DATA_TYPE IN_ARRAY1[ANY]) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DATA_TYPE IN_ARRAY1[ANY]) { $1 = is_array($input) || PySequence_Check($input); } %typemap(in, fragment="NumPy_Fragments") (DATA_TYPE IN_ARRAY1[ANY]) (PyArrayObject* array=NULL, int is_new_object=0) { npy_intp size[1] = { $1_dim0 }; array = obj_to_array_contiguous_allow_conversion($input, DATA_TYPECODE, &is_new_object); if (!array || !require_dimensions(array, 1) || !require_size(array, size, 1)) SWIG_fail; $1 = ($1_ltype) array_data(array); } %typemap(freearg) (DATA_TYPE IN_ARRAY1[ANY]) { if (is_new_object$argnum && array$argnum) { Py_DECREF(array$argnum); } } /* Typemap suite for (DATA_TYPE* IN_ARRAY1, DIM_TYPE DIM1) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DATA_TYPE* IN_ARRAY1, DIM_TYPE DIM1) { $1 = is_array($input) || PySequence_Check($input); } %typemap(in, fragment="NumPy_Fragments") (DATA_TYPE* IN_ARRAY1, DIM_TYPE DIM1) (PyArrayObject* array=NULL, int is_new_object=0) { npy_intp size[1] = { -1 }; array = obj_to_array_contiguous_allow_conversion($input, DATA_TYPECODE, &is_new_object); if (!array || !require_dimensions(array, 1) || !require_size(array, size, 1)) SWIG_fail; $1 = (DATA_TYPE*) array_data(array); $2 = (DIM_TYPE) array_size(array,0); } %typemap(freearg) (DATA_TYPE* IN_ARRAY1, DIM_TYPE DIM1) { if (is_new_object$argnum && array$argnum) { Py_DECREF(array$argnum); } } /* Typemap suite for (DIM_TYPE DIM1, DATA_TYPE* IN_ARRAY1) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DIM_TYPE DIM1, DATA_TYPE* IN_ARRAY1) { $1 = is_array($input) || PySequence_Check($input); } %typemap(in, fragment="NumPy_Fragments") (DIM_TYPE DIM1, DATA_TYPE* IN_ARRAY1) (PyArrayObject* array=NULL, int is_new_object=0) { npy_intp size[1] = {-1}; array = obj_to_array_contiguous_allow_conversion($input, DATA_TYPECODE, &is_new_object); if (!array || !require_dimensions(array, 1) || !require_size(array, size, 1)) SWIG_fail; $1 = (DIM_TYPE) array_size(array,0); $2 = (DATA_TYPE*) array_data(array); } %typemap(freearg) (DIM_TYPE DIM1, DATA_TYPE* IN_ARRAY1) { if (is_new_object$argnum && array$argnum) { Py_DECREF(array$argnum); } } /* Typemap suite for (DATA_TYPE IN_ARRAY2[ANY][ANY]) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DATA_TYPE IN_ARRAY2[ANY][ANY]) { $1 = is_array($input) || PySequence_Check($input); } %typemap(in, fragment="NumPy_Fragments") (DATA_TYPE IN_ARRAY2[ANY][ANY]) (PyArrayObject* array=NULL, int is_new_object=0) { npy_intp size[2] = { $1_dim0, $1_dim1 }; array = obj_to_array_contiguous_allow_conversion($input, DATA_TYPECODE, &is_new_object); if (!array || !require_dimensions(array, 2) || !require_size(array, size, 2)) SWIG_fail; $1 = ($1_ltype) array_data(array); } %typemap(freearg) (DATA_TYPE IN_ARRAY2[ANY][ANY]) { if (is_new_object$argnum && array$argnum) { Py_DECREF(array$argnum); } } /* Typemap suite for (DATA_TYPE* IN_ARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DATA_TYPE* IN_ARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2) { $1 = is_array($input) || PySequence_Check($input); } %typemap(in, fragment="NumPy_Fragments") (DATA_TYPE* IN_ARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2) (PyArrayObject* array=NULL, int is_new_object=0) { npy_intp size[2] = { -1, -1 }; array = obj_to_array_contiguous_allow_conversion($input, DATA_TYPECODE, &is_new_object); if (!array || !require_dimensions(array, 2) || !require_size(array, size, 2)) SWIG_fail; $1 = (DATA_TYPE*) array_data(array); $2 = (DIM_TYPE) array_size(array,0); $3 = (DIM_TYPE) array_size(array,1); } %typemap(freearg) (DATA_TYPE* IN_ARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2) { if (is_new_object$argnum && array$argnum) { Py_DECREF(array$argnum); } } /* Typemap suite for (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* IN_ARRAY2) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* IN_ARRAY2) { $1 = is_array($input) || PySequence_Check($input); } %typemap(in, fragment="NumPy_Fragments") (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* IN_ARRAY2) (PyArrayObject* array=NULL, int is_new_object=0) { npy_intp size[2] = { -1, -1 }; array = obj_to_array_contiguous_allow_conversion($input, DATA_TYPECODE, &is_new_object); if (!array || !require_dimensions(array, 2) || !require_size(array, size, 2)) SWIG_fail; $1 = (DIM_TYPE) array_size(array,0); $2 = (DIM_TYPE) array_size(array,1); $3 = (DATA_TYPE*) array_data(array); } %typemap(freearg) (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* IN_ARRAY2) { if (is_new_object$argnum && array$argnum) { Py_DECREF(array$argnum); } } /* Typemap suite for (DATA_TYPE* IN_FARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DATA_TYPE* IN_FARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2) { $1 = is_array($input) || PySequence_Check($input); } %typemap(in, fragment="NumPy_Fragments") (DATA_TYPE* IN_FARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2) (PyArrayObject* array=NULL, int is_new_object=0) { npy_intp size[2] = { -1, -1 }; array = obj_to_array_fortran_allow_conversion($input, DATA_TYPECODE, &is_new_object); if (!array || !require_dimensions(array, 2) || !require_size(array, size, 2) || !require_fortran(array)) SWIG_fail; $1 = (DATA_TYPE*) array_data(array); $2 = (DIM_TYPE) array_size(array,0); $3 = (DIM_TYPE) array_size(array,1); } %typemap(freearg) (DATA_TYPE* IN_FARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2) { if (is_new_object$argnum && array$argnum) { Py_DECREF(array$argnum); } } /* Typemap suite for (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* IN_FARRAY2) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* IN_FARRAY2) { $1 = is_array($input) || PySequence_Check($input); } %typemap(in, fragment="NumPy_Fragments") (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* IN_FARRAY2) (PyArrayObject* array=NULL, int is_new_object=0) { npy_intp size[2] = { -1, -1 }; array = obj_to_array_contiguous_allow_conversion($input, DATA_TYPECODE, &is_new_object); if (!array || !require_dimensions(array, 2) || !require_size(array, size, 2) || !require_fortran(array)) SWIG_fail; $1 = (DIM_TYPE) array_size(array,0); $2 = (DIM_TYPE) array_size(array,1); $3 = (DATA_TYPE*) array_data(array); } %typemap(freearg) (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* IN_FARRAY2) { if (is_new_object$argnum && array$argnum) { Py_DECREF(array$argnum); } } /* Typemap suite for (DATA_TYPE IN_ARRAY3[ANY][ANY][ANY]) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DATA_TYPE IN_ARRAY3[ANY][ANY][ANY]) { $1 = is_array($input) || PySequence_Check($input); } %typemap(in, fragment="NumPy_Fragments") (DATA_TYPE IN_ARRAY3[ANY][ANY][ANY]) (PyArrayObject* array=NULL, int is_new_object=0) { npy_intp size[3] = { $1_dim0, $1_dim1, $1_dim2 }; array = obj_to_array_contiguous_allow_conversion($input, DATA_TYPECODE, &is_new_object); if (!array || !require_dimensions(array, 3) || !require_size(array, size, 3)) SWIG_fail; $1 = ($1_ltype) array_data(array); } %typemap(freearg) (DATA_TYPE IN_ARRAY3[ANY][ANY][ANY]) { if (is_new_object$argnum && array$argnum) { Py_DECREF(array$argnum); } } /* Typemap suite for (DATA_TYPE* IN_ARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, * DIM_TYPE DIM3) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DATA_TYPE* IN_ARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3) { $1 = is_array($input) || PySequence_Check($input); } %typemap(in, fragment="NumPy_Fragments") (DATA_TYPE* IN_ARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3) (PyArrayObject* array=NULL, int is_new_object=0) { npy_intp size[3] = { -1, -1, -1 }; array = obj_to_array_contiguous_allow_conversion($input, DATA_TYPECODE, &is_new_object); if (!array || !require_dimensions(array, 3) || !require_size(array, size, 3)) SWIG_fail; $1 = (DATA_TYPE*) array_data(array); $2 = (DIM_TYPE) array_size(array,0); $3 = (DIM_TYPE) array_size(array,1); $4 = (DIM_TYPE) array_size(array,2); } %typemap(freearg) (DATA_TYPE* IN_ARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3) { if (is_new_object$argnum && array$argnum) { Py_DECREF(array$argnum); } } /* Typemap suite for (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, * DATA_TYPE* IN_ARRAY3) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DATA_TYPE* IN_ARRAY3) { $1 = is_array($input) || PySequence_Check($input); } %typemap(in, fragment="NumPy_Fragments") (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DATA_TYPE* IN_ARRAY3) (PyArrayObject* array=NULL, int is_new_object=0) { npy_intp size[3] = { -1, -1, -1 }; array = obj_to_array_contiguous_allow_conversion($input, DATA_TYPECODE, &is_new_object); if (!array || !require_dimensions(array, 3) || !require_size(array, size, 3)) SWIG_fail; $1 = (DIM_TYPE) array_size(array,0); $2 = (DIM_TYPE) array_size(array,1); $3 = (DIM_TYPE) array_size(array,2); $4 = (DATA_TYPE*) array_data(array); } %typemap(freearg) (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DATA_TYPE* IN_ARRAY3) { if (is_new_object$argnum && array$argnum) { Py_DECREF(array$argnum); } } /* Typemap suite for (DATA_TYPE* IN_FARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, * DIM_TYPE DIM3) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DATA_TYPE* IN_FARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3) { $1 = is_array($input) || PySequence_Check($input); } %typemap(in, fragment="NumPy_Fragments") (DATA_TYPE* IN_FARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3) (PyArrayObject* array=NULL, int is_new_object=0) { npy_intp size[3] = { -1, -1, -1 }; array = obj_to_array_fortran_allow_conversion($input, DATA_TYPECODE, &is_new_object); if (!array || !require_dimensions(array, 3) || !require_size(array, size, 3) | !require_fortran(array)) SWIG_fail; $1 = (DATA_TYPE*) array_data(array); $2 = (DIM_TYPE) array_size(array,0); $3 = (DIM_TYPE) array_size(array,1); $4 = (DIM_TYPE) array_size(array,2); } %typemap(freearg) (DATA_TYPE* IN_FARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3) { if (is_new_object$argnum && array$argnum) { Py_DECREF(array$argnum); } } /* Typemap suite for (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, * DATA_TYPE* IN_FARRAY3) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DATA_TYPE* IN_FARRAY3) { $1 = is_array($input) || PySequence_Check($input); } %typemap(in, fragment="NumPy_Fragments") (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DATA_TYPE* IN_FARRAY3) (PyArrayObject* array=NULL, int is_new_object=0) { npy_intp size[3] = { -1, -1, -1 }; array = obj_to_array_contiguous_allow_conversion($input, DATA_TYPECODE, &is_new_object); if (!array || !require_dimensions(array, 3) || !require_size(array, size, 3) || !require_fortran(array)) SWIG_fail; $1 = (DIM_TYPE) array_size(array,0); $2 = (DIM_TYPE) array_size(array,1); $3 = (DIM_TYPE) array_size(array,2); $4 = (DATA_TYPE*) array_data(array); } %typemap(freearg) (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DATA_TYPE* IN_FARRAY3) { if (is_new_object$argnum && array$argnum) { Py_DECREF(array$argnum); } } /***************************/ /* In-Place Array Typemaps */ /***************************/ /* Typemap suite for (DATA_TYPE INPLACE_ARRAY1[ANY]) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DATA_TYPE INPLACE_ARRAY1[ANY]) { $1 = is_array($input) && PyArray_EquivTypenums(array_type($input), DATA_TYPECODE); } %typemap(in, fragment="NumPy_Fragments") (DATA_TYPE INPLACE_ARRAY1[ANY]) (PyArrayObject* array=NULL) { npy_intp size[1] = { $1_dim0 }; array = obj_to_array_no_conversion($input, DATA_TYPECODE); if (!array || !require_dimensions(array,1) || !require_size(array, size, 1) || !require_contiguous(array) || !require_native(array)) SWIG_fail; $1 = ($1_ltype) array_data(array); } /* Typemap suite for (DATA_TYPE* INPLACE_ARRAY1, DIM_TYPE DIM1) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DATA_TYPE* INPLACE_ARRAY1, DIM_TYPE DIM1) { $1 = is_array($input) && PyArray_EquivTypenums(array_type($input), DATA_TYPECODE); } %typemap(in, fragment="NumPy_Fragments") (DATA_TYPE* INPLACE_ARRAY1, DIM_TYPE DIM1) (PyArrayObject* array=NULL, int i=1) { array = obj_to_array_no_conversion($input, DATA_TYPECODE); if (!array || !require_dimensions(array,1) || !require_contiguous(array) || !require_native(array)) SWIG_fail; $1 = (DATA_TYPE*) array_data(array); $2 = 1; for (i=0; i < array_numdims(array); ++i) $2 *= array_size(array,i); } /* Typemap suite for (DIM_TYPE DIM1, DATA_TYPE* INPLACE_ARRAY1) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DIM_TYPE DIM1, DATA_TYPE* INPLACE_ARRAY1) { $1 = is_array($input) && PyArray_EquivTypenums(array_type($input), DATA_TYPECODE); } %typemap(in, fragment="NumPy_Fragments") (DIM_TYPE DIM1, DATA_TYPE* INPLACE_ARRAY1) (PyArrayObject* array=NULL, int i=0) { array = obj_to_array_no_conversion($input, DATA_TYPECODE); if (!array || !require_dimensions(array,1) || !require_contiguous(array) || !require_native(array)) SWIG_fail; $1 = 1; for (i=0; i < array_numdims(array); ++i) $1 *= array_size(array,i); $2 = (DATA_TYPE*) array_data(array); } /* Typemap suite for (DATA_TYPE INPLACE_ARRAY2[ANY][ANY]) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DATA_TYPE INPLACE_ARRAY2[ANY][ANY]) { $1 = is_array($input) && PyArray_EquivTypenums(array_type($input), DATA_TYPECODE); } %typemap(in, fragment="NumPy_Fragments") (DATA_TYPE INPLACE_ARRAY2[ANY][ANY]) (PyArrayObject* array=NULL) { npy_intp size[2] = { $1_dim0, $1_dim1 }; array = obj_to_array_no_conversion($input, DATA_TYPECODE); if (!array || !require_dimensions(array,2) || !require_size(array, size, 2) || !require_contiguous(array) || !require_native(array)) SWIG_fail; $1 = ($1_ltype) array_data(array); } /* Typemap suite for (DATA_TYPE* INPLACE_ARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DATA_TYPE* INPLACE_ARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2) { $1 = is_array($input) && PyArray_EquivTypenums(array_type($input), DATA_TYPECODE); } %typemap(in, fragment="NumPy_Fragments") (DATA_TYPE* INPLACE_ARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2) (PyArrayObject* array=NULL) { array = obj_to_array_no_conversion($input, DATA_TYPECODE); if (!array || !require_dimensions(array,2) || !require_contiguous(array) || !require_native(array)) SWIG_fail; $1 = (DATA_TYPE*) array_data(array); $2 = (DIM_TYPE) array_size(array,0); $3 = (DIM_TYPE) array_size(array,1); } /* Typemap suite for (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* INPLACE_ARRAY2) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* INPLACE_ARRAY2) { $1 = is_array($input) && PyArray_EquivTypenums(array_type($input), DATA_TYPECODE); } %typemap(in, fragment="NumPy_Fragments") (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* INPLACE_ARRAY2) (PyArrayObject* array=NULL) { array = obj_to_array_no_conversion($input, DATA_TYPECODE); if (!array || !require_dimensions(array,2) || !require_contiguous(array) || !require_native(array)) SWIG_fail; $1 = (DIM_TYPE) array_size(array,0); $2 = (DIM_TYPE) array_size(array,1); $3 = (DATA_TYPE*) array_data(array); } /* Typemap suite for (DATA_TYPE* INPLACE_FARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DATA_TYPE* INPLACE_FARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2) { $1 = is_array($input) && PyArray_EquivTypenums(array_type($input), DATA_TYPECODE); } %typemap(in, fragment="NumPy_Fragments") (DATA_TYPE* INPLACE_FARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2) (PyArrayObject* array=NULL) { array = obj_to_array_no_conversion($input, DATA_TYPECODE); if (!array || !require_dimensions(array,2) || !require_contiguous(array) || !require_native(array) || !require_fortran(array)) SWIG_fail; $1 = (DATA_TYPE*) array_data(array); $2 = (DIM_TYPE) array_size(array,0); $3 = (DIM_TYPE) array_size(array,1); } /* Typemap suite for (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* INPLACE_FARRAY2) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* INPLACE_FARRAY2) { $1 = is_array($input) && PyArray_EquivTypenums(array_type($input), DATA_TYPECODE); } %typemap(in, fragment="NumPy_Fragments") (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* INPLACE_FARRAY2) (PyArrayObject* array=NULL) { array = obj_to_array_no_conversion($input, DATA_TYPECODE); if (!array || !require_dimensions(array,2) || !require_contiguous(array) || !require_native(array) || !require_fortran(array)) SWIG_fail; $1 = (DIM_TYPE) array_size(array,0); $2 = (DIM_TYPE) array_size(array,1); $3 = (DATA_TYPE*) array_data(array); } /* Typemap suite for (DATA_TYPE INPLACE_ARRAY3[ANY][ANY][ANY]) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DATA_TYPE INPLACE_ARRAY3[ANY][ANY][ANY]) { $1 = is_array($input) && PyArray_EquivTypenums(array_type($input), DATA_TYPECODE); } %typemap(in, fragment="NumPy_Fragments") (DATA_TYPE INPLACE_ARRAY3[ANY][ANY][ANY]) (PyArrayObject* array=NULL) { npy_intp size[3] = { $1_dim0, $1_dim1, $1_dim2 }; array = obj_to_array_no_conversion($input, DATA_TYPECODE); if (!array || !require_dimensions(array,3) || !require_size(array, size, 3) || !require_contiguous(array) || !require_native(array)) SWIG_fail; $1 = ($1_ltype) array_data(array); } /* Typemap suite for (DATA_TYPE* INPLACE_ARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, * DIM_TYPE DIM3) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DATA_TYPE* INPLACE_ARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3) { $1 = is_array($input) && PyArray_EquivTypenums(array_type($input), DATA_TYPECODE); } %typemap(in, fragment="NumPy_Fragments") (DATA_TYPE* INPLACE_ARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3) (PyArrayObject* array=NULL) { array = obj_to_array_no_conversion($input, DATA_TYPECODE); if (!array || !require_dimensions(array,3) || !require_contiguous(array) || !require_native(array)) SWIG_fail; $1 = (DATA_TYPE*) array_data(array); $2 = (DIM_TYPE) array_size(array,0); $3 = (DIM_TYPE) array_size(array,1); $4 = (DIM_TYPE) array_size(array,2); } /* Typemap suite for (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, * DATA_TYPE* INPLACE_ARRAY3) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DATA_TYPE* INPLACE_ARRAY3) { $1 = is_array($input) && PyArray_EquivTypenums(array_type($input), DATA_TYPECODE); } %typemap(in, fragment="NumPy_Fragments") (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DATA_TYPE* INPLACE_ARRAY3) (PyArrayObject* array=NULL) { array = obj_to_array_no_conversion($input, DATA_TYPECODE); if (!array || !require_dimensions(array,3) || !require_contiguous(array) || !require_native(array)) SWIG_fail; $1 = (DIM_TYPE) array_size(array,0); $2 = (DIM_TYPE) array_size(array,1); $3 = (DIM_TYPE) array_size(array,2); $4 = (DATA_TYPE*) array_data(array); } /* Typemap suite for (DATA_TYPE* INPLACE_FARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, * DIM_TYPE DIM3) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DATA_TYPE* INPLACE_FARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3) { $1 = is_array($input) && PyArray_EquivTypenums(array_type($input), DATA_TYPECODE); } %typemap(in, fragment="NumPy_Fragments") (DATA_TYPE* INPLACE_FARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3) (PyArrayObject* array=NULL) { array = obj_to_array_no_conversion($input, DATA_TYPECODE); if (!array || !require_dimensions(array,3) || !require_contiguous(array) || !require_native(array) || !require_fortran(array)) SWIG_fail; $1 = (DATA_TYPE*) array_data(array); $2 = (DIM_TYPE) array_size(array,0); $3 = (DIM_TYPE) array_size(array,1); $4 = (DIM_TYPE) array_size(array,2); } /* Typemap suite for (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, * DATA_TYPE* INPLACE_FARRAY3) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DATA_TYPE* INPLACE_FARRAY3) { $1 = is_array($input) && PyArray_EquivTypenums(array_type($input), DATA_TYPECODE); } %typemap(in, fragment="NumPy_Fragments") (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DATA_TYPE* INPLACE_FARRAY3) (PyArrayObject* array=NULL) { array = obj_to_array_no_conversion($input, DATA_TYPECODE); if (!array || !require_dimensions(array,3) || !require_contiguous(array) || !require_native(array) || !require_fortran(array)) SWIG_fail; $1 = (DIM_TYPE) array_size(array,0); $2 = (DIM_TYPE) array_size(array,1); $3 = (DIM_TYPE) array_size(array,2); $4 = (DATA_TYPE*) array_data(array); } /*************************/ /* Argout Array Typemaps */ /*************************/ /* Typemap suite for (DATA_TYPE ARGOUT_ARRAY1[ANY]) */ %typemap(in,numinputs=0, fragment="NumPy_Backward_Compatibility,NumPy_Macros") (DATA_TYPE ARGOUT_ARRAY1[ANY]) (PyObject * array = NULL) { npy_intp dims[1] = { $1_dim0 }; array = PyArray_SimpleNew(1, dims, DATA_TYPECODE); if (!array) SWIG_fail; $1 = ($1_ltype) array_data(array); } %typemap(argout) (DATA_TYPE ARGOUT_ARRAY1[ANY]) { $result = SWIG_Python_AppendOutput($result,array$argnum); } /* Typemap suite for (DATA_TYPE* ARGOUT_ARRAY1, DIM_TYPE DIM1) */ %typemap(in,numinputs=1, fragment="NumPy_Fragments") (DATA_TYPE* ARGOUT_ARRAY1, DIM_TYPE DIM1) (PyObject * array = NULL) { npy_intp dims[1]; if (!PyInt_Check($input)) { const char* typestring = pytype_string($input); PyErr_Format(PyExc_TypeError, "Int dimension expected. '%s' given.", typestring); SWIG_fail; } $2 = (DIM_TYPE) PyInt_AsLong($input); dims[0] = (npy_intp) $2; array = PyArray_SimpleNew(1, dims, DATA_TYPECODE); if (!array) SWIG_fail; $1 = (DATA_TYPE*) array_data(array); } %typemap(argout) (DATA_TYPE* ARGOUT_ARRAY1, DIM_TYPE DIM1) { $result = SWIG_Python_AppendOutput($result,array$argnum); } /* Typemap suite for (DIM_TYPE DIM1, DATA_TYPE* ARGOUT_ARRAY1) */ %typemap(in,numinputs=1, fragment="NumPy_Fragments") (DIM_TYPE DIM1, DATA_TYPE* ARGOUT_ARRAY1) (PyObject * array = NULL) { npy_intp dims[1]; if (!PyInt_Check($input)) { const char* typestring = pytype_string($input); PyErr_Format(PyExc_TypeError, "Int dimension expected. '%s' given.", typestring); SWIG_fail; } $1 = (DIM_TYPE) PyInt_AsLong($input); dims[0] = (npy_intp) $1; array = PyArray_SimpleNew(1, dims, DATA_TYPECODE); if (!array) SWIG_fail; $2 = (DATA_TYPE*) array_data(array); } %typemap(argout) (DIM_TYPE DIM1, DATA_TYPE* ARGOUT_ARRAY1) { $result = SWIG_Python_AppendOutput($result,array$argnum); } /* Typemap suite for (DATA_TYPE ARGOUT_ARRAY2[ANY][ANY]) */ %typemap(in,numinputs=0, fragment="NumPy_Backward_Compatibility,NumPy_Macros") (DATA_TYPE ARGOUT_ARRAY2[ANY][ANY]) (PyObject * array = NULL) { npy_intp dims[2] = { $1_dim0, $1_dim1 }; array = PyArray_SimpleNew(2, dims, DATA_TYPECODE); if (!array) SWIG_fail; $1 = ($1_ltype) array_data(array); } %typemap(argout) (DATA_TYPE ARGOUT_ARRAY2[ANY][ANY]) { $result = SWIG_Python_AppendOutput($result,array$argnum); } /* Typemap suite for (DATA_TYPE ARGOUT_ARRAY3[ANY][ANY][ANY]) */ %typemap(in,numinputs=0, fragment="NumPy_Backward_Compatibility,NumPy_Macros") (DATA_TYPE ARGOUT_ARRAY3[ANY][ANY][ANY]) (PyObject * array = NULL) { npy_intp dims[3] = { $1_dim0, $1_dim1, $1_dim2 }; array = PyArray_SimpleNew(3, dims, DATA_TYPECODE); if (!array) SWIG_fail; $1 = ($1_ltype) array_data(array); } %typemap(argout) (DATA_TYPE ARGOUT_ARRAY3[ANY][ANY][ANY]) { $result = SWIG_Python_AppendOutput($result,array$argnum); } /*****************************/ /* Argoutview Array Typemaps */ /*****************************/ /* Typemap suite for (DATA_TYPE** ARGOUTVIEW_ARRAY1, DIM_TYPE* DIM1) */ %typemap(in,numinputs=0) (DATA_TYPE** ARGOUTVIEW_ARRAY1, DIM_TYPE* DIM1 ) (DATA_TYPE* data_temp , DIM_TYPE dim_temp) { $1 = &data_temp; $2 = &dim_temp; } %typemap(argout, fragment="NumPy_Backward_Compatibility") (DATA_TYPE** ARGOUTVIEW_ARRAY1, DIM_TYPE* DIM1) { npy_intp dims[1] = { *$2 }; PyObject * array = PyArray_SimpleNewFromData(1, dims, DATA_TYPECODE, (void*)(*$1)); if (!array) SWIG_fail; $result = SWIG_Python_AppendOutput($result,array); } /* Typemap suite for (DIM_TYPE* DIM1, DATA_TYPE** ARGOUTVIEW_ARRAY1) */ %typemap(in,numinputs=0) (DIM_TYPE* DIM1 , DATA_TYPE** ARGOUTVIEW_ARRAY1) (DIM_TYPE dim_temp, DATA_TYPE* data_temp ) { $1 = &dim_temp; $2 = &data_temp; } %typemap(argout, fragment="NumPy_Backward_Compatibility") (DIM_TYPE* DIM1, DATA_TYPE** ARGOUTVIEW_ARRAY1) { npy_intp dims[1] = { *$1 }; PyObject * array = PyArray_SimpleNewFromData(1, dims, DATA_TYPECODE, (void*)(*$2)); if (!array) SWIG_fail; $result = SWIG_Python_AppendOutput($result,array); } /* Typemap suite for (DATA_TYPE** ARGOUTVIEW_ARRAY2, DIM_TYPE* DIM1, DIM_TYPE* DIM2) */ %typemap(in,numinputs=0) (DATA_TYPE** ARGOUTVIEW_ARRAY2, DIM_TYPE* DIM1 , DIM_TYPE* DIM2 ) (DATA_TYPE* data_temp , DIM_TYPE dim1_temp, DIM_TYPE dim2_temp) { $1 = &data_temp; $2 = &dim1_temp; $3 = &dim2_temp; } %typemap(argout, fragment="NumPy_Backward_Compatibility") (DATA_TYPE** ARGOUTVIEW_ARRAY2, DIM_TYPE* DIM1, DIM_TYPE* DIM2) { npy_intp dims[2] = { *$2, *$3 }; PyObject * array = PyArray_SimpleNewFromData(2, dims, DATA_TYPECODE, (void*)(*$1)); if (!array) SWIG_fail; $result = SWIG_Python_AppendOutput($result,array); } /* Typemap suite for (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DATA_TYPE** ARGOUTVIEW_ARRAY2) */ %typemap(in,numinputs=0) (DIM_TYPE* DIM1 , DIM_TYPE* DIM2 , DATA_TYPE** ARGOUTVIEW_ARRAY2) (DIM_TYPE dim1_temp, DIM_TYPE dim2_temp, DATA_TYPE* data_temp ) { $1 = &dim1_temp; $2 = &dim2_temp; $3 = &data_temp; } %typemap(argout, fragment="NumPy_Backward_Compatibility") (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DATA_TYPE** ARGOUTVIEW_ARRAY2) { npy_intp dims[2] = { *$1, *$2 }; PyObject * array = PyArray_SimpleNewFromData(2, dims, DATA_TYPECODE, (void*)(*$3)); if (!array) SWIG_fail; $result = SWIG_Python_AppendOutput($result,array); } /* Typemap suite for (DATA_TYPE** ARGOUTVIEW_FARRAY2, DIM_TYPE* DIM1, DIM_TYPE* DIM2) */ %typemap(in,numinputs=0) (DATA_TYPE** ARGOUTVIEW_FARRAY2, DIM_TYPE* DIM1 , DIM_TYPE* DIM2 ) (DATA_TYPE* data_temp , DIM_TYPE dim1_temp, DIM_TYPE dim2_temp) { $1 = &data_temp; $2 = &dim1_temp; $3 = &dim2_temp; } %typemap(argout, fragment="NumPy_Backward_Compatibility,NumPy_Array_Requirements") (DATA_TYPE** ARGOUTVIEW_FARRAY2, DIM_TYPE* DIM1, DIM_TYPE* DIM2) { npy_intp dims[2] = { *$2, *$3 }; PyObject * obj = PyArray_SimpleNewFromData(2, dims, DATA_TYPECODE, (void*)(*$1)); PyArrayObject * array = (PyArrayObject*) obj; if (!array || !require_fortran(array)) SWIG_fail; $result = SWIG_Python_AppendOutput($result,obj); } /* Typemap suite for (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DATA_TYPE** ARGOUTVIEW_FARRAY2) */ %typemap(in,numinputs=0) (DIM_TYPE* DIM1 , DIM_TYPE* DIM2 , DATA_TYPE** ARGOUTVIEW_FARRAY2) (DIM_TYPE dim1_temp, DIM_TYPE dim2_temp, DATA_TYPE* data_temp ) { $1 = &dim1_temp; $2 = &dim2_temp; $3 = &data_temp; } %typemap(argout, fragment="NumPy_Backward_Compatibility,NumPy_Array_Requirements") (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DATA_TYPE** ARGOUTVIEW_FARRAY2) { npy_intp dims[2] = { *$1, *$2 }; PyObject * obj = PyArray_SimpleNewFromData(2, dims, DATA_TYPECODE, (void*)(*$3)); PyArrayObject * array = (PyArrayObject*) obj; if (!array || !require_fortran(array)) SWIG_fail; $result = SWIG_Python_AppendOutput($result,obj); } /* Typemap suite for (DATA_TYPE** ARGOUTVIEW_ARRAY3, DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3) */ %typemap(in,numinputs=0) (DATA_TYPE** ARGOUTVIEW_ARRAY3, DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3) (DATA_TYPE* data_temp, DIM_TYPE dim1_temp, DIM_TYPE dim2_temp, DIM_TYPE dim3_temp) { $1 = &data_temp; $2 = &dim1_temp; $3 = &dim2_temp; $4 = &dim3_temp; } %typemap(argout, fragment="NumPy_Backward_Compatibility") (DATA_TYPE** ARGOUTVIEW_ARRAY3, DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3) { npy_intp dims[3] = { *$2, *$3, *$4 }; PyObject * array = PyArray_SimpleNewFromData(3, dims, DATA_TYPECODE, (void*)(*$1)); if (!array) SWIG_fail; $result = SWIG_Python_AppendOutput($result,array); } /* Typemap suite for (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DATA_TYPE** ARGOUTVIEW_ARRAY3) */ %typemap(in,numinputs=0) (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DATA_TYPE** ARGOUTVIEW_ARRAY3) (DIM_TYPE dim1_temp, DIM_TYPE dim2_temp, DIM_TYPE dim3_temp, DATA_TYPE* data_temp) { $1 = &dim1_temp; $2 = &dim2_temp; $3 = &dim3_temp; $4 = &data_temp; } %typemap(argout, fragment="NumPy_Backward_Compatibility") (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DATA_TYPE** ARGOUTVIEW_ARRAY3) { npy_intp dims[3] = { *$1, *$2, *$3 }; PyObject * array = PyArray_SimpleNewFromData(3, dims, DATA_TYPECODE, (void*)(*$3)); if (!array) SWIG_fail; $result = SWIG_Python_AppendOutput($result,array); } /* Typemap suite for (DATA_TYPE** ARGOUTVIEW_FARRAY3, DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3) */ %typemap(in,numinputs=0) (DATA_TYPE** ARGOUTVIEW_FARRAY3, DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3) (DATA_TYPE* data_temp, DIM_TYPE dim1_temp, DIM_TYPE dim2_temp, DIM_TYPE dim3_temp) { $1 = &data_temp; $2 = &dim1_temp; $3 = &dim2_temp; $4 = &dim3_temp; } %typemap(argout, fragment="NumPy_Backward_Compatibility,NumPy_Array_Requirements") (DATA_TYPE** ARGOUTVIEW_FARRAY3, DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3) { npy_intp dims[3] = { *$2, *$3, *$4 }; PyObject * obj = PyArray_SimpleNewFromData(3, dims, DATA_TYPECODE, (void*)(*$1)); PyArrayObject * array = (PyArrayObject*) obj; if (!array || require_fortran(array)) SWIG_fail; $result = SWIG_Python_AppendOutput($result,obj); } /* Typemap suite for (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DATA_TYPE** ARGOUTVIEW_FARRAY3) */ %typemap(in,numinputs=0) (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DATA_TYPE** ARGOUTVIEW_FARRAY3) (DIM_TYPE dim1_temp, DIM_TYPE dim2_temp, DIM_TYPE dim3_temp, DATA_TYPE* data_temp) { $1 = &dim1_temp; $2 = &dim2_temp; $3 = &dim3_temp; $4 = &data_temp; } %typemap(argout, fragment="NumPy_Backward_Compatibility,NumPy_Array_Requirements") (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DATA_TYPE** ARGOUTVIEW_FARRAY3) { npy_intp dims[3] = { *$1, *$2, *$3 }; PyObject * obj = PyArray_SimpleNewFromData(3, dims, DATA_TYPECODE, (void*)(*$3)); PyArrayObject * array = (PyArrayObject*) obj; if (!array || require_fortran(array)) SWIG_fail; $result = SWIG_Python_AppendOutput($result,obj); } %enddef /* %numpy_typemaps() macro */ /* *************************************************************** */ /* Concrete instances of the %numpy_typemaps() macro: Each invocation * below applies all of the typemaps above to the specified data type. */ %numpy_typemaps(signed char , NPY_BYTE , int) %numpy_typemaps(unsigned char , NPY_UBYTE , int) %numpy_typemaps(short , NPY_SHORT , int) %numpy_typemaps(unsigned short , NPY_USHORT , int) %numpy_typemaps(int , NPY_INT , int) %numpy_typemaps(unsigned int , NPY_UINT , int) %numpy_typemaps(long , NPY_LONG , int) %numpy_typemaps(unsigned long , NPY_ULONG , int) %numpy_typemaps(long long , NPY_LONGLONG , int) %numpy_typemaps(unsigned long long, NPY_ULONGLONG, int) %numpy_typemaps(float , NPY_FLOAT , int) %numpy_typemaps(double , NPY_DOUBLE , int) /* *************************************************************** * The follow macro expansion does not work, because C++ bool is 4 * bytes and NPY_BOOL is 1 byte * * %numpy_typemaps(bool, NPY_BOOL, int) */ /* *************************************************************** * On my Mac, I get the following warning for this macro expansion: * 'swig/python detected a memory leak of type 'long double *', no destructor found.' * * %numpy_typemaps(long double, NPY_LONGDOUBLE, int) */ /* *************************************************************** * Swig complains about a syntax error for the following macro * expansions: * * %numpy_typemaps(complex float, NPY_CFLOAT , int) * * %numpy_typemaps(complex double, NPY_CDOUBLE, int) * * %numpy_typemaps(complex long double, NPY_CLONGDOUBLE, int) */ #endif /* SWIGPYTHON */ brian-1.4.3/brian/utils/ccircular/setup.py000066400000000000000000000015321266062647100205410ustar00rootroot00000000000000#!/usr/bin/env python """ setup.py file for C++ version of circular spike container """ from distutils.core import setup, Extension import os, numpy numpy_base_dir = os.path.split(numpy.__file__)[0] numpy_include_dir = os.path.join(numpy_base_dir, 'core/include') # 'c:\\python25\\lib\\site-packages\\numpy\\core\\include' ccircular_module = Extension('_ccircular', sources=['ccircular_wrap.cxx', 'circular.cpp', ], include_dirs=[numpy_include_dir], ) setup (name='ccircular', version='0.1', author="Dan Goodman", description="""C++ version of circular spike container""", ext_modules=[ccircular_module], py_modules=["ccircular"], ) brian-1.4.3/brian/utils/ccircular/swigcompile.bat000066400000000000000000000001131266062647100220330ustar00rootroot00000000000000@echo off swig -python -c++ ccircular.i setup.py build_ext --inplace %* brian-1.4.3/brian/utils/circular.py000066400000000000000000000260651266062647100172460ustar00rootroot00000000000000# ---------------------------------------------------------------------------------- # Copyright ENS, INRIA, CNRS # Contributors: Romain Brette (brette@di.ens.fr) and Dan Goodman (goodman@di.ens.fr) # # Brian is a computer program whose purpose is to simulate models # of biological neural networks. # # This software is governed by the CeCILL license under French law and # abiding by the rules of distribution of free software. You can use, # modify and/ or redistribute the software under the terms of the CeCILL # license as circulated by CEA, CNRS and INRIA at the following URL # "http://www.cecill.info". # # As a counterpart to the access to the source code and rights to copy, # modify and redistribute granted by the license, users are provided only # with a limited warranty and the software's author, the holder of the # economic rights, and the successive licensors have only limited # liability. # # In this respect, the user's attention is drawn to the risks associated # with loading, using, modifying and/or developing or reproducing the # software by the user in light of its specific status of free software, # that may mean that it is complicated to manipulate, and that also # therefore means that it is reserved for developers and experienced # professionals having in-depth computer knowledge. Users are therefore # encouraged to load and test the software's suitability as regards their # requirements in conditions enabling the security of their systems and/or # data to be ensured and, more generally, to use and operate it in the # same conditions as regards security. # # The fact that you are presently reading this means that you have had # knowledge of the CeCILL license and that you accept its terms. # ---------------------------------------------------------------------------------- # ''' Circular arrays Ideas for speed improvements: use put, putmask and take with mode='wrap' and out=... ''' from numpy import * try: import weave except ImportError: from scipy import weave import bisect import os import warnings from ..globalprefs import get_global_preference __all__ = ['CircularVector', 'SpikeContainer'] class CircularVector(object): ''' A vector with circular structure. Variables: * X = the data (array of size n) * cursor = current position in the array (where the 0 index is) ''' def __init__(self, n, dtype=float, useweave=False, compiler=None): # pylint: disable-msg=W0621 ''' n is the size of the vector. ''' self.X = zeros(n, dtype=dtype) self.dtype = dtype self.cursor = 0 self.n = n self._useweave = useweave if useweave: self._optimisedreturnarray = zeros(n, dtype=dtype) self._cpp_compiler = compiler self._extra_compile_args = ['-O3'] if self._cpp_compiler == 'gcc': self._extra_compile_args += get_global_preference('gcc_options') # ['-march=native', '-ffast-math'] else: self._cpp_compiler = '' def reinit(self): self.X[:] = zeros(self.n, self.dtype) self.cursor = 0 def advance(self, k): self.cursor = (self.cursor + k) % self.n def __len__(self): return self.n def __getitem__(self, i): ''' V[i] ''' return self.X[(self.cursor + i) % self.n] def __setitem__(self, i, x): ''' V[i]=x ''' self.X[(self.cursor + i) % self.n] = x def __getslice__(self, i, j): n = self.n i0 = (self.cursor + i) % n # pylint: disable-msg=W0621 j0 = (self.cursor + j) % n # pylint: disable-msg=W0621 if j0 >= i0: return self.X[i0:j0] else: #return self.X[range(i0,n)+range(0,j0)] return concatenate((self.X[i0:], self.X[0:j0])) # this version is MUCH faster def get_conditional(self, i, j, min, max, offset=0): """ Returns only those vectors with values between min and max This rather specialised usage of a circular vector is for the benefit of the SpikeContainer class get_spikes method, which is in turn used by the NeuronGroup.get_spikes method. It returns v-offset for those elements v in self[i:j] such that min=min && Xk i: n = self.n i0 = (self.cursor + i) % n # pylint: disable-msg=W0621 j0 = (self.cursor + j) % n if j0 > i0: self.X[i0:j0] = W elif isinstance(W, ndarray): self.X[i0:] = W[0:n - i0] self.X[0:j0] = W[n - i0:n - i0 + j0] def __repr__(self): return repr(hstack((self[0:self.n - 1], self[self.n - 1:self.n]))) def __print__(self): return (hstack((self[0:self.n - 1], self[self.n - 1:self.n]))).__print__() class SpikeContainer(object): ''' An object that stores previous spikes. S[0] is an array of the last spikes (neuron indexes). S[1] is an array with the spikes at time t-dt, etc. S[0:50] contains all spikes in last 50 bins. ''' def __init__(self, m, useweave=False, compiler=None): ''' n = maximum number of spikes stored (not used anymore) m = maximum number of bins stored ''' if m < 2: m = 2 self.S = CircularVector(2, dtype=int, useweave=useweave, compiler=compiler) self.ind = CircularVector(m + 1, dtype=int, useweave=useweave, compiler=compiler) # indexes of bins self.remaining_space = 1 self._useweave = useweave self.m = m def reinit(self): self.S.reinit() self.ind.reinit() def push(self, spikes): ''' Stores spikes in the array at time dt. ''' ns = len(spikes) self.remaining_space += (self.ind[2] - self.ind[1]) % self.S.n while ns >= self.remaining_space: # double size of array S = self.S newS = CircularVector(2 * S.n, dtype=int, useweave=S._useweave, compiler=S._cpp_compiler) newS.X[:S.n - S.cursor] = S.X[S.cursor:] newS.X[S.n - S.cursor:S.n] = S.X[:S.cursor] newS.cursor = S.n self.S = newS self.ind.X = (self.ind.X - S.cursor) % S.n self.ind.X[self.ind.X == 0] = S.n self.remaining_space += S.n self.S[0:ns] = spikes self.S.advance(ns) self.ind.advance(1) self.ind[0] = self.S.cursor self.remaining_space -= ns def lastspikes(self): ''' Returns S[0]. ''' return self.S[self.ind[-1] - self.S.cursor:self.S.n] def __getitem__(self, i): ''' S[i]: returns the spikes at time t-i*dt. ''' # NB: this could be optimized return self.S[self.ind[-i - 1] - self.S.cursor:self.ind[-i] - self.S.cursor + self.S.n] # optimised version of the above, but the speed improvement is not very much, might be # better to just wait and write a fully C/C++ version of the whole library def get_spikes(self, delay, origin, N): """ Returns those spikes in self[delay] between origin and origin+N """ return self.S.get_conditional(self.ind[-delay - 1] - self.S.cursor, \ self.ind[-delay] - self.S.cursor + self.S.n, \ origin, origin + N, origin) def __getslice__(self, i, j): return self.S[self.ind[-j] - self.S.cursor:self.ind[-i] - self.S.cursor + self.S.n] def __repr__(self): return "Spike container." def __print__(self): return self.__repr__() def __reduce__(self): return (unpickle_SpikeContainer, (self.m, tuple(self[i].copy() for i in xrange(self.m)))) try: import ccircular.ccircular as _ccircular class SpikeContainer(_ccircular.SpikeContainer): def __init__(self, m, useweave=False, compiler=None): _ccircular.SpikeContainer.__init__(self, m) self.m = m def __reduce__(self): return (unpickle_SpikeContainer, (self.m, tuple(self[i].copy() for i in xrange(self.m)))) #warnings.warn('Using C++ SpikeContainer') except ImportError: pass def unpickle_SpikeContainer(m, allspikes): newsc = SpikeContainer(m) for spikes in allspikes[::-1]: newsc.push(spikes) return newsc # I am not sure that class below is useful! class ModInt(object): ''' A number in Z/nZ, i.e., modulo n. Variables: * x = number modulo n * n Implemented: additions and subtraction. N.B.: not a very useful class. ''' def __init__(self, x, n): self.x = x self.n = n def __add__(self, m): if isinstance(m, ModInt): assert self.n == m.n return ModInt((self.x + m.x) % self.n, self.n) else: return ModInt((self.x + m) % self.n, self.n) def __radd__(self, m): if isinstance(m, ModInt): assert self.n == m.n return ModInt((self.x + m.x) % self.n, self.n) else: return ModInt((self.x + m) % self.n, self.n) def __sub__(self, m): if isinstance(m, ModInt): assert self.n == m.n return ModInt((self.x - m.x) % self.n, self.n) else: return ModInt((self.x - m) % self.n, self.n) def __repr__(self): return str(self.x) + ' [' + str(self.n) + ']' def __print__(self): return self.__repr__() brian-1.4.3/brian/utils/documentation.py000066400000000000000000000106711266062647100203070ustar00rootroot00000000000000# ---------------------------------------------------------------------------------- # Copyright ENS, INRIA, CNRS # Contributors: Romain Brette (brette@di.ens.fr) and Dan Goodman (goodman@di.ens.fr) # # Brian is a computer program whose purpose is to simulate models # of biological neural networks. # # This software is governed by the CeCILL license under French law and # abiding by the rules of distribution of free software. You can use, # modify and/ or redistribute the software under the terms of the CeCILL # license as circulated by CEA, CNRS and INRIA at the following URL # "http://www.cecill.info". # # As a counterpart to the access to the source code and rights to copy, # modify and redistribute granted by the license, users are provided only # with a limited warranty and the software's author, the holder of the # economic rights, and the successive licensors have only limited # liability. # # In this respect, the user's attention is drawn to the risks associated # with loading, using, modifying and/or developing or reproducing the # software by the user in light of its specific status of free software, # that may mean that it is complicated to manipulate, and that also # therefore means that it is reserved for developers and experienced # professionals having in-depth computer knowledge. Users are therefore # encouraged to load and test the software's suitability as regards their # requirements in conditions enabling the security of their systems and/or # data to be ensured and, more generally, to use and operate it in the # same conditions as regards security. # # The fact that you are presently reading this means that you have had # knowledge of the CeCILL license and that you accept its terms. # ---------------------------------------------------------------------------------- # """ Various utilities for documentation """ def indent_string(s, numtabs=1, spacespertab=4, split=False): """ Indents a given string or list of lines split=True returns the output as a list of lines """ indent = ' ' * (numtabs * spacespertab) if isinstance(s, str): indentedstring = indent + s.replace('\n', '\n' + indent) else: indentedstring = '' for l in s: indentedstring += indent + l + '\n' indentedstring = indentedstring.rstrip() + '\n' if split: return indentedstring.split('\n') return indentedstring def flattened_docstring(docstr, numtabs=0, spacespertab=4, split=False): """ Returns a docstring with the indentation removed according to the Python standard split=True returns the output as a list of lines Changing numtabs adds a custom indentation afterwards """ if isinstance(docstr, str): lines = docstr.split('\n') else: lines = docstr if len(lines) < 2: # nothing to do return docstr flattenedstring = '' # Interpret multiline strings according to the Python docstring standard indentlevel = min(# the smallest number of whitespace characters in the lines of the description map(# the number of whitespaces at the beginning of each string in the lines of the description lambda l:len(l) - len(l.lstrip()), # the number of whitespaces at the beginning of the string filter(# only those lines with some text on are counted lambda l:len(l.strip()) , lines[1:] # ignore the first line ))) if lines[0].strip(): # treat first line differently (probably has nothing on it) flattenedstring += lines[0] + '\n' for l in lines[1:-1]: flattenedstring += l[indentlevel:] + '\n' if lines[-1].strip(): # treat last line differently (probably has nothing on it) flattenedstring += lines[-1][indentlevel:] + '\n' return indent_string(flattenedstring, numtabs=numtabs, spacespertab=spacespertab, split=split) def rest_section(name, level='-', split=False): """ Returns a restructuredtext section heading Looks like: name ---- """ s = name + '\n' + level * len(name) + '\n\n' if split: return s.split('\n') return s if __name__ == '__main__': print rest_section('Test'), print flattened_docstring(flattened_docstring.__doc__), brian-1.4.3/brian/utils/dynamicarray.py000066400000000000000000000233631266062647100201230ustar00rootroot00000000000000from numpy import * import gc __all__ = ['DynamicArray', 'DynamicArray1D'] def getslices(shape): return tuple(slice(0, x) for x in shape) class DynamicArray(object): ''' An N-dimensional dynamic array class The array can be resized in any dimension, and the class will handle allocating a new block of data and copying when necessary. .. warning:: The data will NOT be contiguous for >1D arrays. To ensure this, you will either need to use 1D arrays, or to copy the data, or use the shrink method with the current size (although note that in both cases you negate the memory and efficiency benefits of the dynamic array). Initialisation arguments: ``shape``, ``dtype`` The shape and dtype of the array to initialise, as in Numpy. For 1D arrays, shape can be a single int, for ND arrays it should be a tuple. ``factor`` The resizing factor (see notes below). Larger values tend to lead to more wasted memory, but more computationally efficient code. ``use_numpy_resize``, ``refcheck`` Normally, when you resize the array it creates a new array and copies the data. Sometimes, it is possible to resize an array without a copy, and if this option is set it will attempt to do this. However, this can cause memory problems if you are not careful so the option is off by default. You need to ensure that you do not create slices of the array so that no references to the memory exist other than the main array object. If you are sure you know what you're doing, you can switch this reference check off. Note that resizing in this way is only done if you resize in the first dimension. The array is initialised with zeros. The data is stored in the attribute ``data`` which is a Numpy array. **Methods** .. automethod:: resize .. automethod:: shrink Some numpy methods are implemented and can work directly on the array object, including ``len(arr)``, ``arr[...]`` and ``arr[...]=...``. In other cases, use the ``data`` attribute. **Usage example** :: x = DynamicArray((2, 3), dtype=int) x[:] = 1 x.resize((3, 3)) x[:] += 1 x.resize((3, 4)) x[:] += 1 x.resize((4, 4)) x[:] += 1 x.data[:] = x.data**2 print x.data This should give the output:: [[16 16 16 4] [16 16 16 4] [ 9 9 9 4] [ 1 1 1 1]] **Notes** The dynamic array returns a ``data`` attribute which is a view on the larger ``_data`` attribute. When a resize operation is performed, and a specific dimension is enlarged beyond the size in the ``_data`` attribute, the size is increased to the larger of ``cursize*factor`` and ``newsize``. This ensures that the amortized cost of increasing the size of the array is O(1). ''' def __init__(self, shape, dtype=float, factor=2, use_numpy_resize=False, refcheck=True): if isinstance(shape, int): shape = (shape,) self._data = zeros(shape, dtype=dtype) self.data = self._data self.dtype = dtype self.shape = self._data.shape self.factor = factor self.use_numpy_resize = use_numpy_resize self.refcheck = refcheck def resize(self, newshape): ''' Resizes the data to the new shape, which can be a different size to the current data, but should have the same rank, i.e. same number of dimensions. ''' if isscalar(newshape): newshape = (newshape,) datashapearr = array(self._data.shape) shapearr = array(self.shape) newshapearr = array(newshape) if (shapearr==newshapearr).all(): return resizedimensions = newshapearr>datashapearr if resizedimensions.any(): # resize of the data is needed minnewshapearr = datashapearr.copy() dimstoinc = minnewshapearr[resizedimensions] incdims = array(dimstoinc*self.factor, dtype=int) newdims = maximum(incdims, dimstoinc+1) minnewshapearr[resizedimensions] = newdims newshapearr = maximum(newshapearr, minnewshapearr) do_resize = False if self.use_numpy_resize and self._data.flags['C_CONTIGUOUS']: if sum(resizedimensions)==resizedimensions[0]: do_resize = True if do_resize: self.data = None self._data.resize(tuple(newshapearr), refcheck=self.refcheck) else: newdata = zeros(tuple(newshapearr), dtype=self.dtype) slices = getslices(self._data.shape) newdata[slices] = self._data self._data = newdata self.data = self._data[getslices(newshape)] self.shape = self.data.shape def shrink(self, newshape): ''' Reduces the data to the given shape, which should be smaller than the current shape. :meth:`resize` can also be used with smaller values, but it will not shrink the allocated memory, whereas :meth:`shrink` will reallocate the memory. This method should only be used infrequently, as if it is used frequently it will negate the computational efficiency benefits of the DynamicArray. ''' if isinstance(newshape, int): newshape = (newshape,) shapearr = array(self.shape) newshapearr = array(newshape) if (newshapearr<=shapearr).all(): newdata = zeros(newshapearr, dtype=self.dtype) newdata[:] = self._data[getslices(newshapearr)] self._data = newdata self.shape = tuple(newshapearr) self.data = self._data def __getitem__(self, item): return self.data.__getitem__(item) def __getslice__(self, start, end): return self.data.__getslice__(start, end) def __setitem__(self, item, val): self.data.__setitem__(item, val) def __setslice__(self, start, end, val): self.data.__setslice__(start, end, val) def __len__(self): return len(self.data) def __str__(self): return self.data.__str__() def __repr__(self): return self.data.__repr__() class DynamicArray1D(DynamicArray): ''' Version of :class:`DynamicArray` with specialised ``resize`` method designed to be more efficient. ''' def resize(self, newshape): shape, = self.shape # we work with int shapes only if newshape<=shape: return datashape, = self._data.shape if newshape>datashape: newdatashape = max(newshape, int(shape*self.factor)+1) if self.use_numpy_resize and self._data.flags['C_CONTIGUOUS']: self.data = None self._data.resize(newdatashape, refcheck=self.refcheck) else: newdata = zeros(newdatashape, dtype=self.dtype) newdata[:shape] = self.data self._data = newdata self.data = self._data[:newshape] self.shape = (newshape,) if __name__=='__main__': if 1: x = DynamicArray1D(2, use_numpy_resize=True) x[0] = 1 x[1] = 2 print x x.resize(3) print x, x._data x.resize(4) print x, x._data if 0: x = DynamicArray((2, 2), use_numpy_resize=True) x[0, 0] = 0 x[0, 1] = 1 x[1, 0] = 2 x[1, 1] = 3 print x x.resize((3, 2)) print x if 0: import time, gc # speed comparison between numpy resize and not numpy resize max_size = 400*1024*1024/8 # 1GB array repeats = 5 factor = 1.1 collect = False def dotiming(**kwds): tottime = 0 for _ in xrange(repeats): sz = 1 x = DynamicArray(sz, dtype=float, factor=factor, **kwds) start = time.time() while sz 1: return exp(x) if out is None: y = empty(x.shape) _fastexp(x, y) return y _fastexp(x, out) return out brian-1.4.3/brian/utils/fastexp/fastexp.cpp000066400000000000000000000001561266062647100207110ustar00rootroot00000000000000#include "fexp.h" void fastexp(double *x, int n, double *y, int m) { for(;n;n--) *y++ = fexp(*x++); } brian-1.4.3/brian/utils/fastexp/fastexp.h000066400000000000000000000000631266062647100203530ustar00rootroot00000000000000void fastexp(double *x, int n, double *y, int m); brian-1.4.3/brian/utils/fastexp/fastexp.i000066400000000000000000000004401266062647100203530ustar00rootroot00000000000000%module fastexp %{ #define SWIG_FILE_WITH_INIT #include "fastexp.h" %} %include "numpy.i" %init %{ import_array(); %} %apply (double* INPLACE_ARRAY1, int DIM1) {(double *x, int n)}; %apply (double* INPLACE_ARRAY1, int DIM1) {(double *y, int m)}; %include "fastexp.h" brian-1.4.3/brian/utils/fastexp/fastexp.py000066400000000000000000000042751266062647100205650ustar00rootroot00000000000000# This file was automatically generated by SWIG (http://www.swig.org). # Version 2.0.4 # # Do not make changes to this file unless you know what you are doing--modify # the SWIG interface file instead. from sys import version_info if version_info >= (2,6,0): def swig_import_helper(): from os.path import dirname import imp fp = None try: fp, pathname, description = imp.find_module('_fastexp', [dirname(__file__)]) except ImportError: import _fastexp return _fastexp if fp is not None: try: _mod = imp.load_module('_fastexp', fp, pathname, description) finally: fp.close() return _mod _fastexp = swig_import_helper() del swig_import_helper else: import _fastexp del version_info try: _swig_property = property except NameError: pass # Python < 2.2 doesn't have 'property'. def _swig_setattr_nondynamic(self,class_type,name,value,static=1): if (name == "thisown"): return self.this.own(value) if (name == "this"): if type(value).__name__ == 'SwigPyObject': self.__dict__[name] = value return method = class_type.__swig_setmethods__.get(name,None) if method: return method(self,value) if (not static): self.__dict__[name] = value else: raise AttributeError("You cannot add attributes to %s" % self) def _swig_setattr(self,class_type,name,value): return _swig_setattr_nondynamic(self,class_type,name,value,0) def _swig_getattr(self,class_type,name): if (name == "thisown"): return self.this.own() method = class_type.__swig_getmethods__.get(name,None) if method: return method(self) raise AttributeError(name) def _swig_repr(self): try: strthis = "proxy of " + self.this.__repr__() except: strthis = "" return "<%s.%s; %s >" % (self.__class__.__module__, self.__class__.__name__, strthis,) try: _object = object _newclass = 1 except AttributeError: class _object : pass _newclass = 0 def fastexp(*args): return _fastexp.fastexp(*args) fastexp = _fastexp.fastexp # This file is compatible with both classic and new-style classes. brian-1.4.3/brian/utils/fastexp/fastexp_wrap.cxx000066400000000000000000003622331266062647100217710ustar00rootroot00000000000000/* ---------------------------------------------------------------------------- * This file was automatically generated by SWIG (http://www.swig.org). * Version 2.0.4 * * This file is not intended to be easily readable and contains a number of * coding conventions designed to improve portability and efficiency. Do not make * changes to this file unless you know what you are doing--modify the SWIG * interface file instead. * ----------------------------------------------------------------------------- */ #define SWIGPYTHON #define SWIG_PYTHON_DIRECTOR_NO_VTABLE #ifdef __cplusplus /* SwigValueWrapper is described in swig.swg */ template class SwigValueWrapper { struct SwigMovePointer { T *ptr; SwigMovePointer(T *p) : ptr(p) { } ~SwigMovePointer() { delete ptr; } SwigMovePointer& operator=(SwigMovePointer& rhs) { T* oldptr = ptr; ptr = 0; delete oldptr; ptr = rhs.ptr; rhs.ptr = 0; return *this; } } pointer; SwigValueWrapper& operator=(const SwigValueWrapper& rhs); SwigValueWrapper(const SwigValueWrapper& rhs); public: SwigValueWrapper() : pointer(0) { } SwigValueWrapper& operator=(const T& t) { SwigMovePointer tmp(new T(t)); pointer = tmp; return *this; } operator T&() const { return *pointer.ptr; } T *operator&() { return pointer.ptr; } }; template T SwigValueInit() { return T(); } #endif /* ----------------------------------------------------------------------------- * This section contains generic SWIG labels for method/variable * declarations/attributes, and other compiler dependent labels. * ----------------------------------------------------------------------------- */ /* template workaround for compilers that cannot correctly implement the C++ standard */ #ifndef SWIGTEMPLATEDISAMBIGUATOR # if defined(__SUNPRO_CC) && (__SUNPRO_CC <= 0x560) # define SWIGTEMPLATEDISAMBIGUATOR template # elif defined(__HP_aCC) /* Needed even with `aCC -AA' when `aCC -V' reports HP ANSI C++ B3910B A.03.55 */ /* If we find a maximum version that requires this, the test would be __HP_aCC <= 35500 for A.03.55 */ # define SWIGTEMPLATEDISAMBIGUATOR template # else # define SWIGTEMPLATEDISAMBIGUATOR # endif #endif /* inline attribute */ #ifndef SWIGINLINE # if defined(__cplusplus) || (defined(__GNUC__) && !defined(__STRICT_ANSI__)) # define SWIGINLINE inline # else # define SWIGINLINE # endif #endif /* attribute recognised by some compilers to avoid 'unused' warnings */ #ifndef SWIGUNUSED # if defined(__GNUC__) # if !(defined(__cplusplus)) || (__GNUC__ > 3 || (__GNUC__ == 3 && __GNUC_MINOR__ >= 4)) # define SWIGUNUSED __attribute__ ((__unused__)) # else # define SWIGUNUSED # endif # elif defined(__ICC) # define SWIGUNUSED __attribute__ ((__unused__)) # else # define SWIGUNUSED # endif #endif #ifndef SWIG_MSC_UNSUPPRESS_4505 # if defined(_MSC_VER) # pragma warning(disable : 4505) /* unreferenced local function has been removed */ # endif #endif #ifndef SWIGUNUSEDPARM # ifdef __cplusplus # define SWIGUNUSEDPARM(p) # else # define SWIGUNUSEDPARM(p) p SWIGUNUSED # endif #endif /* internal SWIG method */ #ifndef SWIGINTERN # define SWIGINTERN static SWIGUNUSED #endif /* internal inline SWIG method */ #ifndef SWIGINTERNINLINE # define SWIGINTERNINLINE SWIGINTERN SWIGINLINE #endif /* exporting methods */ #if (__GNUC__ >= 4) || (__GNUC__ == 3 && __GNUC_MINOR__ >= 4) # ifndef GCC_HASCLASSVISIBILITY # define GCC_HASCLASSVISIBILITY # endif #endif #ifndef SWIGEXPORT # if defined(_WIN32) || defined(__WIN32__) || defined(__CYGWIN__) # if defined(STATIC_LINKED) # define SWIGEXPORT # else # define SWIGEXPORT __declspec(dllexport) # endif # else # if defined(__GNUC__) && defined(GCC_HASCLASSVISIBILITY) # define SWIGEXPORT __attribute__ ((visibility("default"))) # else # define SWIGEXPORT # endif # endif #endif /* calling conventions for Windows */ #ifndef SWIGSTDCALL # if defined(_WIN32) || defined(__WIN32__) || defined(__CYGWIN__) # define SWIGSTDCALL __stdcall # else # define SWIGSTDCALL # endif #endif /* Deal with Microsoft's attempt at deprecating C standard runtime functions */ #if !defined(SWIG_NO_CRT_SECURE_NO_DEPRECATE) && defined(_MSC_VER) && !defined(_CRT_SECURE_NO_DEPRECATE) # define _CRT_SECURE_NO_DEPRECATE #endif /* Deal with Microsoft's attempt at deprecating methods in the standard C++ library */ #if !defined(SWIG_NO_SCL_SECURE_NO_DEPRECATE) && defined(_MSC_VER) && !defined(_SCL_SECURE_NO_DEPRECATE) # define _SCL_SECURE_NO_DEPRECATE #endif /* Python.h has to appear first */ #include /* ----------------------------------------------------------------------------- * swigrun.swg * * This file contains generic C API SWIG runtime support for pointer * type checking. * ----------------------------------------------------------------------------- */ /* This should only be incremented when either the layout of swig_type_info changes, or for whatever reason, the runtime changes incompatibly */ #define SWIG_RUNTIME_VERSION "4" /* define SWIG_TYPE_TABLE_NAME as "SWIG_TYPE_TABLE" */ #ifdef SWIG_TYPE_TABLE # define SWIG_QUOTE_STRING(x) #x # define SWIG_EXPAND_AND_QUOTE_STRING(x) SWIG_QUOTE_STRING(x) # define SWIG_TYPE_TABLE_NAME SWIG_EXPAND_AND_QUOTE_STRING(SWIG_TYPE_TABLE) #else # define SWIG_TYPE_TABLE_NAME #endif /* You can use the SWIGRUNTIME and SWIGRUNTIMEINLINE macros for creating a static or dynamic library from the SWIG runtime code. In 99.9% of the cases, SWIG just needs to declare them as 'static'. But only do this if strictly necessary, ie, if you have problems with your compiler or suchlike. */ #ifndef SWIGRUNTIME # define SWIGRUNTIME SWIGINTERN #endif #ifndef SWIGRUNTIMEINLINE # define SWIGRUNTIMEINLINE SWIGRUNTIME SWIGINLINE #endif /* Generic buffer size */ #ifndef SWIG_BUFFER_SIZE # define SWIG_BUFFER_SIZE 1024 #endif /* Flags for pointer conversions */ #define SWIG_POINTER_DISOWN 0x1 #define SWIG_CAST_NEW_MEMORY 0x2 /* Flags for new pointer objects */ #define SWIG_POINTER_OWN 0x1 /* Flags/methods for returning states. The SWIG conversion methods, as ConvertPtr, return an integer that tells if the conversion was successful or not. And if not, an error code can be returned (see swigerrors.swg for the codes). Use the following macros/flags to set or process the returning states. In old versions of SWIG, code such as the following was usually written: if (SWIG_ConvertPtr(obj,vptr,ty.flags) != -1) { // success code } else { //fail code } Now you can be more explicit: int res = SWIG_ConvertPtr(obj,vptr,ty.flags); if (SWIG_IsOK(res)) { // success code } else { // fail code } which is the same really, but now you can also do Type *ptr; int res = SWIG_ConvertPtr(obj,(void **)(&ptr),ty.flags); if (SWIG_IsOK(res)) { // success code if (SWIG_IsNewObj(res) { ... delete *ptr; } else { ... } } else { // fail code } I.e., now SWIG_ConvertPtr can return new objects and you can identify the case and take care of the deallocation. Of course that also requires SWIG_ConvertPtr to return new result values, such as int SWIG_ConvertPtr(obj, ptr,...) { if () { if () { *ptr = ; return SWIG_NEWOBJ; } else { *ptr = ; return SWIG_OLDOBJ; } } else { return SWIG_BADOBJ; } } Of course, returning the plain '0(success)/-1(fail)' still works, but you can be more explicit by returning SWIG_BADOBJ, SWIG_ERROR or any of the SWIG errors code. Finally, if the SWIG_CASTRANK_MODE is enabled, the result code allows to return the 'cast rank', for example, if you have this int food(double) int fooi(int); and you call food(1) // cast rank '1' (1 -> 1.0) fooi(1) // cast rank '0' just use the SWIG_AddCast()/SWIG_CheckState() */ #define SWIG_OK (0) #define SWIG_ERROR (-1) #define SWIG_IsOK(r) (r >= 0) #define SWIG_ArgError(r) ((r != SWIG_ERROR) ? r : SWIG_TypeError) /* The CastRankLimit says how many bits are used for the cast rank */ #define SWIG_CASTRANKLIMIT (1 << 8) /* The NewMask denotes the object was created (using new/malloc) */ #define SWIG_NEWOBJMASK (SWIG_CASTRANKLIMIT << 1) /* The TmpMask is for in/out typemaps that use temporal objects */ #define SWIG_TMPOBJMASK (SWIG_NEWOBJMASK << 1) /* Simple returning values */ #define SWIG_BADOBJ (SWIG_ERROR) #define SWIG_OLDOBJ (SWIG_OK) #define SWIG_NEWOBJ (SWIG_OK | SWIG_NEWOBJMASK) #define SWIG_TMPOBJ (SWIG_OK | SWIG_TMPOBJMASK) /* Check, add and del mask methods */ #define SWIG_AddNewMask(r) (SWIG_IsOK(r) ? (r | SWIG_NEWOBJMASK) : r) #define SWIG_DelNewMask(r) (SWIG_IsOK(r) ? (r & ~SWIG_NEWOBJMASK) : r) #define SWIG_IsNewObj(r) (SWIG_IsOK(r) && (r & SWIG_NEWOBJMASK)) #define SWIG_AddTmpMask(r) (SWIG_IsOK(r) ? (r | SWIG_TMPOBJMASK) : r) #define SWIG_DelTmpMask(r) (SWIG_IsOK(r) ? (r & ~SWIG_TMPOBJMASK) : r) #define SWIG_IsTmpObj(r) (SWIG_IsOK(r) && (r & SWIG_TMPOBJMASK)) /* Cast-Rank Mode */ #if defined(SWIG_CASTRANK_MODE) # ifndef SWIG_TypeRank # define SWIG_TypeRank unsigned long # endif # ifndef SWIG_MAXCASTRANK /* Default cast allowed */ # define SWIG_MAXCASTRANK (2) # endif # define SWIG_CASTRANKMASK ((SWIG_CASTRANKLIMIT) -1) # define SWIG_CastRank(r) (r & SWIG_CASTRANKMASK) SWIGINTERNINLINE int SWIG_AddCast(int r) { return SWIG_IsOK(r) ? ((SWIG_CastRank(r) < SWIG_MAXCASTRANK) ? (r + 1) : SWIG_ERROR) : r; } SWIGINTERNINLINE int SWIG_CheckState(int r) { return SWIG_IsOK(r) ? SWIG_CastRank(r) + 1 : 0; } #else /* no cast-rank mode */ # define SWIG_AddCast # define SWIG_CheckState(r) (SWIG_IsOK(r) ? 1 : 0) #endif #include #ifdef __cplusplus extern "C" { #endif typedef void *(*swig_converter_func)(void *, int *); typedef struct swig_type_info *(*swig_dycast_func)(void **); /* Structure to store information on one type */ typedef struct swig_type_info { const char *name; /* mangled name of this type */ const char *str; /* human readable name of this type */ swig_dycast_func dcast; /* dynamic cast function down a hierarchy */ struct swig_cast_info *cast; /* linked list of types that can cast into this type */ void *clientdata; /* language specific type data */ int owndata; /* flag if the structure owns the clientdata */ } swig_type_info; /* Structure to store a type and conversion function used for casting */ typedef struct swig_cast_info { swig_type_info *type; /* pointer to type that is equivalent to this type */ swig_converter_func converter; /* function to cast the void pointers */ struct swig_cast_info *next; /* pointer to next cast in linked list */ struct swig_cast_info *prev; /* pointer to the previous cast */ } swig_cast_info; /* Structure used to store module information * Each module generates one structure like this, and the runtime collects * all of these structures and stores them in a circularly linked list.*/ typedef struct swig_module_info { swig_type_info **types; /* Array of pointers to swig_type_info structures that are in this module */ size_t size; /* Number of types in this module */ struct swig_module_info *next; /* Pointer to next element in circularly linked list */ swig_type_info **type_initial; /* Array of initially generated type structures */ swig_cast_info **cast_initial; /* Array of initially generated casting structures */ void *clientdata; /* Language specific module data */ } swig_module_info; /* Compare two type names skipping the space characters, therefore "char*" == "char *" and "Class" == "Class", etc. Return 0 when the two name types are equivalent, as in strncmp, but skipping ' '. */ SWIGRUNTIME int SWIG_TypeNameComp(const char *f1, const char *l1, const char *f2, const char *l2) { for (;(f1 != l1) && (f2 != l2); ++f1, ++f2) { while ((*f1 == ' ') && (f1 != l1)) ++f1; while ((*f2 == ' ') && (f2 != l2)) ++f2; if (*f1 != *f2) return (*f1 > *f2) ? 1 : -1; } return (int)((l1 - f1) - (l2 - f2)); } /* Check type equivalence in a name list like ||... Return 0 if not equal, 1 if equal */ SWIGRUNTIME int SWIG_TypeEquiv(const char *nb, const char *tb) { int equiv = 0; const char* te = tb + strlen(tb); const char* ne = nb; while (!equiv && *ne) { for (nb = ne; *ne; ++ne) { if (*ne == '|') break; } equiv = (SWIG_TypeNameComp(nb, ne, tb, te) == 0) ? 1 : 0; if (*ne) ++ne; } return equiv; } /* Check type equivalence in a name list like ||... Return 0 if equal, -1 if nb < tb, 1 if nb > tb */ SWIGRUNTIME int SWIG_TypeCompare(const char *nb, const char *tb) { int equiv = 0; const char* te = tb + strlen(tb); const char* ne = nb; while (!equiv && *ne) { for (nb = ne; *ne; ++ne) { if (*ne == '|') break; } equiv = (SWIG_TypeNameComp(nb, ne, tb, te) == 0) ? 1 : 0; if (*ne) ++ne; } return equiv; } /* Check the typename */ SWIGRUNTIME swig_cast_info * SWIG_TypeCheck(const char *c, swig_type_info *ty) { if (ty) { swig_cast_info *iter = ty->cast; while (iter) { if (strcmp(iter->type->name, c) == 0) { if (iter == ty->cast) return iter; /* Move iter to the top of the linked list */ iter->prev->next = iter->next; if (iter->next) iter->next->prev = iter->prev; iter->next = ty->cast; iter->prev = 0; if (ty->cast) ty->cast->prev = iter; ty->cast = iter; return iter; } iter = iter->next; } } return 0; } /* Identical to SWIG_TypeCheck, except strcmp is replaced with a pointer comparison */ SWIGRUNTIME swig_cast_info * SWIG_TypeCheckStruct(swig_type_info *from, swig_type_info *ty) { if (ty) { swig_cast_info *iter = ty->cast; while (iter) { if (iter->type == from) { if (iter == ty->cast) return iter; /* Move iter to the top of the linked list */ iter->prev->next = iter->next; if (iter->next) iter->next->prev = iter->prev; iter->next = ty->cast; iter->prev = 0; if (ty->cast) ty->cast->prev = iter; ty->cast = iter; return iter; } iter = iter->next; } } return 0; } /* Cast a pointer up an inheritance hierarchy */ SWIGRUNTIMEINLINE void * SWIG_TypeCast(swig_cast_info *ty, void *ptr, int *newmemory) { return ((!ty) || (!ty->converter)) ? ptr : (*ty->converter)(ptr, newmemory); } /* Dynamic pointer casting. Down an inheritance hierarchy */ SWIGRUNTIME swig_type_info * SWIG_TypeDynamicCast(swig_type_info *ty, void **ptr) { swig_type_info *lastty = ty; if (!ty || !ty->dcast) return ty; while (ty && (ty->dcast)) { ty = (*ty->dcast)(ptr); if (ty) lastty = ty; } return lastty; } /* Return the name associated with this type */ SWIGRUNTIMEINLINE const char * SWIG_TypeName(const swig_type_info *ty) { return ty->name; } /* Return the pretty name associated with this type, that is an unmangled type name in a form presentable to the user. */ SWIGRUNTIME const char * SWIG_TypePrettyName(const swig_type_info *type) { /* The "str" field contains the equivalent pretty names of the type, separated by vertical-bar characters. We choose to print the last name, as it is often (?) the most specific. */ if (!type) return NULL; if (type->str != NULL) { const char *last_name = type->str; const char *s; for (s = type->str; *s; s++) if (*s == '|') last_name = s+1; return last_name; } else return type->name; } /* Set the clientdata field for a type */ SWIGRUNTIME void SWIG_TypeClientData(swig_type_info *ti, void *clientdata) { swig_cast_info *cast = ti->cast; /* if (ti->clientdata == clientdata) return; */ ti->clientdata = clientdata; while (cast) { if (!cast->converter) { swig_type_info *tc = cast->type; if (!tc->clientdata) { SWIG_TypeClientData(tc, clientdata); } } cast = cast->next; } } SWIGRUNTIME void SWIG_TypeNewClientData(swig_type_info *ti, void *clientdata) { SWIG_TypeClientData(ti, clientdata); ti->owndata = 1; } /* Search for a swig_type_info structure only by mangled name Search is a O(log #types) We start searching at module start, and finish searching when start == end. Note: if start == end at the beginning of the function, we go all the way around the circular list. */ SWIGRUNTIME swig_type_info * SWIG_MangledTypeQueryModule(swig_module_info *start, swig_module_info *end, const char *name) { swig_module_info *iter = start; do { if (iter->size) { register size_t l = 0; register size_t r = iter->size - 1; do { /* since l+r >= 0, we can (>> 1) instead (/ 2) */ register size_t i = (l + r) >> 1; const char *iname = iter->types[i]->name; if (iname) { register int compare = strcmp(name, iname); if (compare == 0) { return iter->types[i]; } else if (compare < 0) { if (i) { r = i - 1; } else { break; } } else if (compare > 0) { l = i + 1; } } else { break; /* should never happen */ } } while (l <= r); } iter = iter->next; } while (iter != end); return 0; } /* Search for a swig_type_info structure for either a mangled name or a human readable name. It first searches the mangled names of the types, which is a O(log #types) If a type is not found it then searches the human readable names, which is O(#types). We start searching at module start, and finish searching when start == end. Note: if start == end at the beginning of the function, we go all the way around the circular list. */ SWIGRUNTIME swig_type_info * SWIG_TypeQueryModule(swig_module_info *start, swig_module_info *end, const char *name) { /* STEP 1: Search the name field using binary search */ swig_type_info *ret = SWIG_MangledTypeQueryModule(start, end, name); if (ret) { return ret; } else { /* STEP 2: If the type hasn't been found, do a complete search of the str field (the human readable name) */ swig_module_info *iter = start; do { register size_t i = 0; for (; i < iter->size; ++i) { if (iter->types[i]->str && (SWIG_TypeEquiv(iter->types[i]->str, name))) return iter->types[i]; } iter = iter->next; } while (iter != end); } /* neither found a match */ return 0; } /* Pack binary data into a string */ SWIGRUNTIME char * SWIG_PackData(char *c, void *ptr, size_t sz) { static const char hex[17] = "0123456789abcdef"; register const unsigned char *u = (unsigned char *) ptr; register const unsigned char *eu = u + sz; for (; u != eu; ++u) { register unsigned char uu = *u; *(c++) = hex[(uu & 0xf0) >> 4]; *(c++) = hex[uu & 0xf]; } return c; } /* Unpack binary data from a string */ SWIGRUNTIME const char * SWIG_UnpackData(const char *c, void *ptr, size_t sz) { register unsigned char *u = (unsigned char *) ptr; register const unsigned char *eu = u + sz; for (; u != eu; ++u) { register char d = *(c++); register unsigned char uu; if ((d >= '0') && (d <= '9')) uu = ((d - '0') << 4); else if ((d >= 'a') && (d <= 'f')) uu = ((d - ('a'-10)) << 4); else return (char *) 0; d = *(c++); if ((d >= '0') && (d <= '9')) uu |= (d - '0'); else if ((d >= 'a') && (d <= 'f')) uu |= (d - ('a'-10)); else return (char *) 0; *u = uu; } return c; } /* Pack 'void *' into a string buffer. */ SWIGRUNTIME char * SWIG_PackVoidPtr(char *buff, void *ptr, const char *name, size_t bsz) { char *r = buff; if ((2*sizeof(void *) + 2) > bsz) return 0; *(r++) = '_'; r = SWIG_PackData(r,&ptr,sizeof(void *)); if (strlen(name) + 1 > (bsz - (r - buff))) return 0; strcpy(r,name); return buff; } SWIGRUNTIME const char * SWIG_UnpackVoidPtr(const char *c, void **ptr, const char *name) { if (*c != '_') { if (strcmp(c,"NULL") == 0) { *ptr = (void *) 0; return name; } else { return 0; } } return SWIG_UnpackData(++c,ptr,sizeof(void *)); } SWIGRUNTIME char * SWIG_PackDataName(char *buff, void *ptr, size_t sz, const char *name, size_t bsz) { char *r = buff; size_t lname = (name ? strlen(name) : 0); if ((2*sz + 2 + lname) > bsz) return 0; *(r++) = '_'; r = SWIG_PackData(r,ptr,sz); if (lname) { strncpy(r,name,lname+1); } else { *r = 0; } return buff; } SWIGRUNTIME const char * SWIG_UnpackDataName(const char *c, void *ptr, size_t sz, const char *name) { if (*c != '_') { if (strcmp(c,"NULL") == 0) { memset(ptr,0,sz); return name; } else { return 0; } } return SWIG_UnpackData(++c,ptr,sz); } #ifdef __cplusplus } #endif /* Errors in SWIG */ #define SWIG_UnknownError -1 #define SWIG_IOError -2 #define SWIG_RuntimeError -3 #define SWIG_IndexError -4 #define SWIG_TypeError -5 #define SWIG_DivisionByZero -6 #define SWIG_OverflowError -7 #define SWIG_SyntaxError -8 #define SWIG_ValueError -9 #define SWIG_SystemError -10 #define SWIG_AttributeError -11 #define SWIG_MemoryError -12 #define SWIG_NullReferenceError -13 /* Compatibility macros for Python 3 */ #if PY_VERSION_HEX >= 0x03000000 #define PyClass_Check(obj) PyObject_IsInstance(obj, (PyObject *)&PyType_Type) #define PyInt_Check(x) PyLong_Check(x) #define PyInt_AsLong(x) PyLong_AsLong(x) #define PyInt_FromLong(x) PyLong_FromLong(x) #define PyString_Check(name) PyBytes_Check(name) #define PyString_FromString(x) PyUnicode_FromString(x) #define PyString_Format(fmt, args) PyUnicode_Format(fmt, args) #define PyString_AsString(str) PyBytes_AsString(str) #define PyString_Size(str) PyBytes_Size(str) #define PyString_InternFromString(key) PyUnicode_InternFromString(key) #define Py_TPFLAGS_HAVE_CLASS Py_TPFLAGS_BASETYPE #define PyString_AS_STRING(x) PyUnicode_AS_STRING(x) #define _PyLong_FromSsize_t(x) PyLong_FromSsize_t(x) #endif #ifndef Py_TYPE # define Py_TYPE(op) ((op)->ob_type) #endif /* SWIG APIs for compatibility of both Python 2 & 3 */ #if PY_VERSION_HEX >= 0x03000000 # define SWIG_Python_str_FromFormat PyUnicode_FromFormat #else # define SWIG_Python_str_FromFormat PyString_FromFormat #endif /* Warning: This function will allocate a new string in Python 3, * so please call SWIG_Python_str_DelForPy3(x) to free the space. */ SWIGINTERN char* SWIG_Python_str_AsChar(PyObject *str) { #if PY_VERSION_HEX >= 0x03000000 char *cstr; char *newstr; Py_ssize_t len; str = PyUnicode_AsUTF8String(str); PyBytes_AsStringAndSize(str, &cstr, &len); newstr = (char *) malloc(len+1); memcpy(newstr, cstr, len+1); Py_XDECREF(str); return newstr; #else return PyString_AsString(str); #endif } #if PY_VERSION_HEX >= 0x03000000 # define SWIG_Python_str_DelForPy3(x) free( (void*) (x) ) #else # define SWIG_Python_str_DelForPy3(x) #endif SWIGINTERN PyObject* SWIG_Python_str_FromChar(const char *c) { #if PY_VERSION_HEX >= 0x03000000 return PyUnicode_FromString(c); #else return PyString_FromString(c); #endif } /* Add PyOS_snprintf for old Pythons */ #if PY_VERSION_HEX < 0x02020000 # if defined(_MSC_VER) || defined(__BORLANDC__) || defined(_WATCOM) # define PyOS_snprintf _snprintf # else # define PyOS_snprintf snprintf # endif #endif /* A crude PyString_FromFormat implementation for old Pythons */ #if PY_VERSION_HEX < 0x02020000 #ifndef SWIG_PYBUFFER_SIZE # define SWIG_PYBUFFER_SIZE 1024 #endif static PyObject * PyString_FromFormat(const char *fmt, ...) { va_list ap; char buf[SWIG_PYBUFFER_SIZE * 2]; int res; va_start(ap, fmt); res = vsnprintf(buf, sizeof(buf), fmt, ap); va_end(ap); return (res < 0 || res >= (int)sizeof(buf)) ? 0 : PyString_FromString(buf); } #endif /* Add PyObject_Del for old Pythons */ #if PY_VERSION_HEX < 0x01060000 # define PyObject_Del(op) PyMem_DEL((op)) #endif #ifndef PyObject_DEL # define PyObject_DEL PyObject_Del #endif /* A crude PyExc_StopIteration exception for old Pythons */ #if PY_VERSION_HEX < 0x02020000 # ifndef PyExc_StopIteration # define PyExc_StopIteration PyExc_RuntimeError # endif # ifndef PyObject_GenericGetAttr # define PyObject_GenericGetAttr 0 # endif #endif /* Py_NotImplemented is defined in 2.1 and up. */ #if PY_VERSION_HEX < 0x02010000 # ifndef Py_NotImplemented # define Py_NotImplemented PyExc_RuntimeError # endif #endif /* A crude PyString_AsStringAndSize implementation for old Pythons */ #if PY_VERSION_HEX < 0x02010000 # ifndef PyString_AsStringAndSize # define PyString_AsStringAndSize(obj, s, len) {*s = PyString_AsString(obj); *len = *s ? strlen(*s) : 0;} # endif #endif /* PySequence_Size for old Pythons */ #if PY_VERSION_HEX < 0x02000000 # ifndef PySequence_Size # define PySequence_Size PySequence_Length # endif #endif /* PyBool_FromLong for old Pythons */ #if PY_VERSION_HEX < 0x02030000 static PyObject *PyBool_FromLong(long ok) { PyObject *result = ok ? Py_True : Py_False; Py_INCREF(result); return result; } #endif /* Py_ssize_t for old Pythons */ /* This code is as recommended by: */ /* http://www.python.org/dev/peps/pep-0353/#conversion-guidelines */ #if PY_VERSION_HEX < 0x02050000 && !defined(PY_SSIZE_T_MIN) typedef int Py_ssize_t; # define PY_SSIZE_T_MAX INT_MAX # define PY_SSIZE_T_MIN INT_MIN typedef inquiry lenfunc; typedef intargfunc ssizeargfunc; typedef intintargfunc ssizessizeargfunc; typedef intobjargproc ssizeobjargproc; typedef intintobjargproc ssizessizeobjargproc; typedef getreadbufferproc readbufferproc; typedef getwritebufferproc writebufferproc; typedef getsegcountproc segcountproc; typedef getcharbufferproc charbufferproc; static long PyNumber_AsSsize_t (PyObject *x, void *SWIGUNUSEDPARM(exc)) { long result = 0; PyObject *i = PyNumber_Int(x); if (i) { result = PyInt_AsLong(i); Py_DECREF(i); } return result; } #endif #if PY_VERSION_HEX < 0x02040000 #define Py_VISIT(op) \ do { \ if (op) { \ int vret = visit((op), arg); \ if (vret) \ return vret; \ } \ } while (0) #endif #if PY_VERSION_HEX < 0x02030000 typedef struct { PyTypeObject type; PyNumberMethods as_number; PyMappingMethods as_mapping; PySequenceMethods as_sequence; PyBufferProcs as_buffer; PyObject *name, *slots; } PyHeapTypeObject; #endif #if PY_VERSION_HEX < 0x02030000 typedef destructor freefunc; #endif #if ((PY_MAJOR_VERSION == 2 && PY_MINOR_VERSION > 6) || \ (PY_MAJOR_VERSION == 3 && PY_MINOR_VERSION > 0) || \ (PY_MAJOR_VERSION > 3)) # define SWIGPY_USE_CAPSULE # define SWIGPY_CAPSULE_NAME ((char*)"swig_runtime_data" SWIG_RUNTIME_VERSION ".type_pointer_capsule" SWIG_TYPE_TABLE_NAME) #endif #if PY_VERSION_HEX < 0x03020000 #define PyDescr_TYPE(x) (((PyDescrObject *)(x))->d_type) #define PyDescr_NAME(x) (((PyDescrObject *)(x))->d_name) #endif /* ----------------------------------------------------------------------------- * error manipulation * ----------------------------------------------------------------------------- */ SWIGRUNTIME PyObject* SWIG_Python_ErrorType(int code) { PyObject* type = 0; switch(code) { case SWIG_MemoryError: type = PyExc_MemoryError; break; case SWIG_IOError: type = PyExc_IOError; break; case SWIG_RuntimeError: type = PyExc_RuntimeError; break; case SWIG_IndexError: type = PyExc_IndexError; break; case SWIG_TypeError: type = PyExc_TypeError; break; case SWIG_DivisionByZero: type = PyExc_ZeroDivisionError; break; case SWIG_OverflowError: type = PyExc_OverflowError; break; case SWIG_SyntaxError: type = PyExc_SyntaxError; break; case SWIG_ValueError: type = PyExc_ValueError; break; case SWIG_SystemError: type = PyExc_SystemError; break; case SWIG_AttributeError: type = PyExc_AttributeError; break; default: type = PyExc_RuntimeError; } return type; } SWIGRUNTIME void SWIG_Python_AddErrorMsg(const char* mesg) { PyObject *type = 0; PyObject *value = 0; PyObject *traceback = 0; if (PyErr_Occurred()) PyErr_Fetch(&type, &value, &traceback); if (value) { char *tmp; PyObject *old_str = PyObject_Str(value); PyErr_Clear(); Py_XINCREF(type); PyErr_Format(type, "%s %s", tmp = SWIG_Python_str_AsChar(old_str), mesg); SWIG_Python_str_DelForPy3(tmp); Py_DECREF(old_str); Py_DECREF(value); } else { PyErr_SetString(PyExc_RuntimeError, mesg); } } #if defined(SWIG_PYTHON_NO_THREADS) # if defined(SWIG_PYTHON_THREADS) # undef SWIG_PYTHON_THREADS # endif #endif #if defined(SWIG_PYTHON_THREADS) /* Threading support is enabled */ # if !defined(SWIG_PYTHON_USE_GIL) && !defined(SWIG_PYTHON_NO_USE_GIL) # if (PY_VERSION_HEX >= 0x02030000) /* For 2.3 or later, use the PyGILState calls */ # define SWIG_PYTHON_USE_GIL # endif # endif # if defined(SWIG_PYTHON_USE_GIL) /* Use PyGILState threads calls */ # ifndef SWIG_PYTHON_INITIALIZE_THREADS # define SWIG_PYTHON_INITIALIZE_THREADS PyEval_InitThreads() # endif # ifdef __cplusplus /* C++ code */ class SWIG_Python_Thread_Block { bool status; PyGILState_STATE state; public: void end() { if (status) { PyGILState_Release(state); status = false;} } SWIG_Python_Thread_Block() : status(true), state(PyGILState_Ensure()) {} ~SWIG_Python_Thread_Block() { end(); } }; class SWIG_Python_Thread_Allow { bool status; PyThreadState *save; public: void end() { if (status) { PyEval_RestoreThread(save); status = false; }} SWIG_Python_Thread_Allow() : status(true), save(PyEval_SaveThread()) {} ~SWIG_Python_Thread_Allow() { end(); } }; # define SWIG_PYTHON_THREAD_BEGIN_BLOCK SWIG_Python_Thread_Block _swig_thread_block # define SWIG_PYTHON_THREAD_END_BLOCK _swig_thread_block.end() # define SWIG_PYTHON_THREAD_BEGIN_ALLOW SWIG_Python_Thread_Allow _swig_thread_allow # define SWIG_PYTHON_THREAD_END_ALLOW _swig_thread_allow.end() # else /* C code */ # define SWIG_PYTHON_THREAD_BEGIN_BLOCK PyGILState_STATE _swig_thread_block = PyGILState_Ensure() # define SWIG_PYTHON_THREAD_END_BLOCK PyGILState_Release(_swig_thread_block) # define SWIG_PYTHON_THREAD_BEGIN_ALLOW PyThreadState *_swig_thread_allow = PyEval_SaveThread() # define SWIG_PYTHON_THREAD_END_ALLOW PyEval_RestoreThread(_swig_thread_allow) # endif # else /* Old thread way, not implemented, user must provide it */ # if !defined(SWIG_PYTHON_INITIALIZE_THREADS) # define SWIG_PYTHON_INITIALIZE_THREADS # endif # if !defined(SWIG_PYTHON_THREAD_BEGIN_BLOCK) # define SWIG_PYTHON_THREAD_BEGIN_BLOCK # endif # if !defined(SWIG_PYTHON_THREAD_END_BLOCK) # define SWIG_PYTHON_THREAD_END_BLOCK # endif # if !defined(SWIG_PYTHON_THREAD_BEGIN_ALLOW) # define SWIG_PYTHON_THREAD_BEGIN_ALLOW # endif # if !defined(SWIG_PYTHON_THREAD_END_ALLOW) # define SWIG_PYTHON_THREAD_END_ALLOW # endif # endif #else /* No thread support */ # define SWIG_PYTHON_INITIALIZE_THREADS # define SWIG_PYTHON_THREAD_BEGIN_BLOCK # define SWIG_PYTHON_THREAD_END_BLOCK # define SWIG_PYTHON_THREAD_BEGIN_ALLOW # define SWIG_PYTHON_THREAD_END_ALLOW #endif /* ----------------------------------------------------------------------------- * Python API portion that goes into the runtime * ----------------------------------------------------------------------------- */ #ifdef __cplusplus extern "C" { #endif /* ----------------------------------------------------------------------------- * Constant declarations * ----------------------------------------------------------------------------- */ /* Constant Types */ #define SWIG_PY_POINTER 4 #define SWIG_PY_BINARY 5 /* Constant information structure */ typedef struct swig_const_info { int type; char *name; long lvalue; double dvalue; void *pvalue; swig_type_info **ptype; } swig_const_info; /* ----------------------------------------------------------------------------- * Wrapper of PyInstanceMethod_New() used in Python 3 * It is exported to the generated module, used for -fastproxy * ----------------------------------------------------------------------------- */ #if PY_VERSION_HEX >= 0x03000000 SWIGRUNTIME PyObject* SWIG_PyInstanceMethod_New(PyObject *SWIGUNUSEDPARM(self), PyObject *func) { return PyInstanceMethod_New(func); } #else SWIGRUNTIME PyObject* SWIG_PyInstanceMethod_New(PyObject *SWIGUNUSEDPARM(self), PyObject *SWIGUNUSEDPARM(func)) { return NULL; } #endif #ifdef __cplusplus } #endif /* ----------------------------------------------------------------------------- * pyrun.swg * * This file contains the runtime support for Python modules * and includes code for managing global variables and pointer * type checking. * * ----------------------------------------------------------------------------- */ /* Common SWIG API */ /* for raw pointers */ #define SWIG_Python_ConvertPtr(obj, pptr, type, flags) SWIG_Python_ConvertPtrAndOwn(obj, pptr, type, flags, 0) #define SWIG_ConvertPtr(obj, pptr, type, flags) SWIG_Python_ConvertPtr(obj, pptr, type, flags) #define SWIG_ConvertPtrAndOwn(obj,pptr,type,flags,own) SWIG_Python_ConvertPtrAndOwn(obj, pptr, type, flags, own) #ifdef SWIGPYTHON_BUILTIN #define SWIG_NewPointerObj(ptr, type, flags) SWIG_Python_NewPointerObj(self, ptr, type, flags) #else #define SWIG_NewPointerObj(ptr, type, flags) SWIG_Python_NewPointerObj(NULL, ptr, type, flags) #endif #define SWIG_InternalNewPointerObj(ptr, type, flags) SWIG_Python_NewPointerObj(NULL, ptr, type, flags) #define SWIG_CheckImplicit(ty) SWIG_Python_CheckImplicit(ty) #define SWIG_AcquirePtr(ptr, src) SWIG_Python_AcquirePtr(ptr, src) #define swig_owntype int /* for raw packed data */ #define SWIG_ConvertPacked(obj, ptr, sz, ty) SWIG_Python_ConvertPacked(obj, ptr, sz, ty) #define SWIG_NewPackedObj(ptr, sz, type) SWIG_Python_NewPackedObj(ptr, sz, type) /* for class or struct pointers */ #define SWIG_ConvertInstance(obj, pptr, type, flags) SWIG_ConvertPtr(obj, pptr, type, flags) #define SWIG_NewInstanceObj(ptr, type, flags) SWIG_NewPointerObj(ptr, type, flags) /* for C or C++ function pointers */ #define SWIG_ConvertFunctionPtr(obj, pptr, type) SWIG_Python_ConvertFunctionPtr(obj, pptr, type) #define SWIG_NewFunctionPtrObj(ptr, type) SWIG_Python_NewPointerObj(NULL, ptr, type, 0) /* for C++ member pointers, ie, member methods */ #define SWIG_ConvertMember(obj, ptr, sz, ty) SWIG_Python_ConvertPacked(obj, ptr, sz, ty) #define SWIG_NewMemberObj(ptr, sz, type) SWIG_Python_NewPackedObj(ptr, sz, type) /* Runtime API */ #define SWIG_GetModule(clientdata) SWIG_Python_GetModule() #define SWIG_SetModule(clientdata, pointer) SWIG_Python_SetModule(pointer) #define SWIG_NewClientData(obj) SwigPyClientData_New(obj) #define SWIG_SetErrorObj SWIG_Python_SetErrorObj #define SWIG_SetErrorMsg SWIG_Python_SetErrorMsg #define SWIG_ErrorType(code) SWIG_Python_ErrorType(code) #define SWIG_Error(code, msg) SWIG_Python_SetErrorMsg(SWIG_ErrorType(code), msg) #define SWIG_fail goto fail /* Runtime API implementation */ /* Error manipulation */ SWIGINTERN void SWIG_Python_SetErrorObj(PyObject *errtype, PyObject *obj) { SWIG_PYTHON_THREAD_BEGIN_BLOCK; PyErr_SetObject(errtype, obj); Py_DECREF(obj); SWIG_PYTHON_THREAD_END_BLOCK; } SWIGINTERN void SWIG_Python_SetErrorMsg(PyObject *errtype, const char *msg) { SWIG_PYTHON_THREAD_BEGIN_BLOCK; PyErr_SetString(errtype, (char *) msg); SWIG_PYTHON_THREAD_END_BLOCK; } #define SWIG_Python_Raise(obj, type, desc) SWIG_Python_SetErrorObj(SWIG_Python_ExceptionType(desc), obj) /* Set a constant value */ #if defined(SWIGPYTHON_BUILTIN) SWIGINTERN void SwigPyBuiltin_AddPublicSymbol(PyObject *seq, const char *key) { PyObject *s = PyString_InternFromString(key); PyList_Append(seq, s); Py_DECREF(s); } SWIGINTERN void SWIG_Python_SetConstant(PyObject *d, PyObject *public_interface, const char *name, PyObject *obj) { PyDict_SetItemString(d, (char *)name, obj); Py_DECREF(obj); if (public_interface) SwigPyBuiltin_AddPublicSymbol(public_interface, name); } #else SWIGINTERN void SWIG_Python_SetConstant(PyObject *d, const char *name, PyObject *obj) { PyDict_SetItemString(d, (char *)name, obj); Py_DECREF(obj); } #endif /* Append a value to the result obj */ SWIGINTERN PyObject* SWIG_Python_AppendOutput(PyObject* result, PyObject* obj) { #if !defined(SWIG_PYTHON_OUTPUT_TUPLE) if (!result) { result = obj; } else if (result == Py_None) { Py_DECREF(result); result = obj; } else { if (!PyList_Check(result)) { PyObject *o2 = result; result = PyList_New(1); PyList_SetItem(result, 0, o2); } PyList_Append(result,obj); Py_DECREF(obj); } return result; #else PyObject* o2; PyObject* o3; if (!result) { result = obj; } else if (result == Py_None) { Py_DECREF(result); result = obj; } else { if (!PyTuple_Check(result)) { o2 = result; result = PyTuple_New(1); PyTuple_SET_ITEM(result, 0, o2); } o3 = PyTuple_New(1); PyTuple_SET_ITEM(o3, 0, obj); o2 = result; result = PySequence_Concat(o2, o3); Py_DECREF(o2); Py_DECREF(o3); } return result; #endif } /* Unpack the argument tuple */ SWIGINTERN int SWIG_Python_UnpackTuple(PyObject *args, const char *name, Py_ssize_t min, Py_ssize_t max, PyObject **objs) { if (!args) { if (!min && !max) { return 1; } else { PyErr_Format(PyExc_TypeError, "%s expected %s%d arguments, got none", name, (min == max ? "" : "at least "), (int)min); return 0; } } if (!PyTuple_Check(args)) { if (min <= 1 && max >= 1) { register int i; objs[0] = args; for (i = 1; i < max; ++i) { objs[i] = 0; } return 2; } PyErr_SetString(PyExc_SystemError, "UnpackTuple() argument list is not a tuple"); return 0; } else { register Py_ssize_t l = PyTuple_GET_SIZE(args); if (l < min) { PyErr_Format(PyExc_TypeError, "%s expected %s%d arguments, got %d", name, (min == max ? "" : "at least "), (int)min, (int)l); return 0; } else if (l > max) { PyErr_Format(PyExc_TypeError, "%s expected %s%d arguments, got %d", name, (min == max ? "" : "at most "), (int)max, (int)l); return 0; } else { register int i; for (i = 0; i < l; ++i) { objs[i] = PyTuple_GET_ITEM(args, i); } for (; l < max; ++l) { objs[l] = 0; } return i + 1; } } } /* A functor is a function object with one single object argument */ #if PY_VERSION_HEX >= 0x02020000 #define SWIG_Python_CallFunctor(functor, obj) PyObject_CallFunctionObjArgs(functor, obj, NULL); #else #define SWIG_Python_CallFunctor(functor, obj) PyObject_CallFunction(functor, "O", obj); #endif /* Helper for static pointer initialization for both C and C++ code, for example static PyObject *SWIG_STATIC_POINTER(MyVar) = NewSomething(...); */ #ifdef __cplusplus #define SWIG_STATIC_POINTER(var) var #else #define SWIG_STATIC_POINTER(var) var = 0; if (!var) var #endif /* ----------------------------------------------------------------------------- * Pointer declarations * ----------------------------------------------------------------------------- */ /* Flags for new pointer objects */ #define SWIG_POINTER_NOSHADOW (SWIG_POINTER_OWN << 1) #define SWIG_POINTER_NEW (SWIG_POINTER_NOSHADOW | SWIG_POINTER_OWN) #define SWIG_POINTER_IMPLICIT_CONV (SWIG_POINTER_DISOWN << 1) #define SWIG_BUILTIN_TP_INIT (SWIG_POINTER_OWN << 2) #define SWIG_BUILTIN_INIT (SWIG_BUILTIN_TP_INIT | SWIG_POINTER_OWN) #ifdef __cplusplus extern "C" { #endif /* How to access Py_None */ #if defined(_WIN32) || defined(__WIN32__) || defined(__CYGWIN__) # ifndef SWIG_PYTHON_NO_BUILD_NONE # ifndef SWIG_PYTHON_BUILD_NONE # define SWIG_PYTHON_BUILD_NONE # endif # endif #endif #ifdef SWIG_PYTHON_BUILD_NONE # ifdef Py_None # undef Py_None # define Py_None SWIG_Py_None() # endif SWIGRUNTIMEINLINE PyObject * _SWIG_Py_None(void) { PyObject *none = Py_BuildValue((char*)""); Py_DECREF(none); return none; } SWIGRUNTIME PyObject * SWIG_Py_None(void) { static PyObject *SWIG_STATIC_POINTER(none) = _SWIG_Py_None(); return none; } #endif /* The python void return value */ SWIGRUNTIMEINLINE PyObject * SWIG_Py_Void(void) { PyObject *none = Py_None; Py_INCREF(none); return none; } /* SwigPyClientData */ typedef struct { PyObject *klass; PyObject *newraw; PyObject *newargs; PyObject *destroy; int delargs; int implicitconv; PyTypeObject *pytype; } SwigPyClientData; SWIGRUNTIMEINLINE int SWIG_Python_CheckImplicit(swig_type_info *ty) { SwigPyClientData *data = (SwigPyClientData *)ty->clientdata; return data ? data->implicitconv : 0; } SWIGRUNTIMEINLINE PyObject * SWIG_Python_ExceptionType(swig_type_info *desc) { SwigPyClientData *data = desc ? (SwigPyClientData *) desc->clientdata : 0; PyObject *klass = data ? data->klass : 0; return (klass ? klass : PyExc_RuntimeError); } SWIGRUNTIME SwigPyClientData * SwigPyClientData_New(PyObject* obj) { if (!obj) { return 0; } else { SwigPyClientData *data = (SwigPyClientData *)malloc(sizeof(SwigPyClientData)); /* the klass element */ data->klass = obj; Py_INCREF(data->klass); /* the newraw method and newargs arguments used to create a new raw instance */ if (PyClass_Check(obj)) { data->newraw = 0; data->newargs = obj; Py_INCREF(obj); } else { #if (PY_VERSION_HEX < 0x02020000) data->newraw = 0; #else data->newraw = PyObject_GetAttrString(data->klass, (char *)"__new__"); #endif if (data->newraw) { Py_INCREF(data->newraw); data->newargs = PyTuple_New(1); PyTuple_SetItem(data->newargs, 0, obj); } else { data->newargs = obj; } Py_INCREF(data->newargs); } /* the destroy method, aka as the C++ delete method */ data->destroy = PyObject_GetAttrString(data->klass, (char *)"__swig_destroy__"); if (PyErr_Occurred()) { PyErr_Clear(); data->destroy = 0; } if (data->destroy) { int flags; Py_INCREF(data->destroy); flags = PyCFunction_GET_FLAGS(data->destroy); #ifdef METH_O data->delargs = !(flags & (METH_O)); #else data->delargs = 0; #endif } else { data->delargs = 0; } data->implicitconv = 0; data->pytype = 0; return data; } } SWIGRUNTIME void SwigPyClientData_Del(SwigPyClientData *data) { Py_XDECREF(data->newraw); Py_XDECREF(data->newargs); Py_XDECREF(data->destroy); } /* =============== SwigPyObject =====================*/ typedef struct { PyObject_HEAD void *ptr; swig_type_info *ty; int own; PyObject *next; #ifdef SWIGPYTHON_BUILTIN PyObject *dict; #endif } SwigPyObject; SWIGRUNTIME PyObject * SwigPyObject_long(SwigPyObject *v) { return PyLong_FromVoidPtr(v->ptr); } SWIGRUNTIME PyObject * SwigPyObject_format(const char* fmt, SwigPyObject *v) { PyObject *res = NULL; PyObject *args = PyTuple_New(1); if (args) { if (PyTuple_SetItem(args, 0, SwigPyObject_long(v)) == 0) { PyObject *ofmt = SWIG_Python_str_FromChar(fmt); if (ofmt) { #if PY_VERSION_HEX >= 0x03000000 res = PyUnicode_Format(ofmt,args); #else res = PyString_Format(ofmt,args); #endif Py_DECREF(ofmt); } Py_DECREF(args); } } return res; } SWIGRUNTIME PyObject * SwigPyObject_oct(SwigPyObject *v) { return SwigPyObject_format("%o",v); } SWIGRUNTIME PyObject * SwigPyObject_hex(SwigPyObject *v) { return SwigPyObject_format("%x",v); } SWIGRUNTIME PyObject * #ifdef METH_NOARGS SwigPyObject_repr(SwigPyObject *v) #else SwigPyObject_repr(SwigPyObject *v, PyObject *args) #endif { const char *name = SWIG_TypePrettyName(v->ty); PyObject *repr = SWIG_Python_str_FromFormat("", name, (void *)v); if (v->next) { # ifdef METH_NOARGS PyObject *nrep = SwigPyObject_repr((SwigPyObject *)v->next); # else PyObject *nrep = SwigPyObject_repr((SwigPyObject *)v->next, args); # endif # if PY_VERSION_HEX >= 0x03000000 PyObject *joined = PyUnicode_Concat(repr, nrep); Py_DecRef(repr); Py_DecRef(nrep); repr = joined; # else PyString_ConcatAndDel(&repr,nrep); # endif } return repr; } SWIGRUNTIME int SwigPyObject_print(SwigPyObject *v, FILE *fp, int SWIGUNUSEDPARM(flags)) { char *str; #ifdef METH_NOARGS PyObject *repr = SwigPyObject_repr(v); #else PyObject *repr = SwigPyObject_repr(v, NULL); #endif if (repr) { str = SWIG_Python_str_AsChar(repr); fputs(str, fp); SWIG_Python_str_DelForPy3(str); Py_DECREF(repr); return 0; } else { return 1; } } SWIGRUNTIME PyObject * SwigPyObject_str(SwigPyObject *v) { char result[SWIG_BUFFER_SIZE]; return SWIG_PackVoidPtr(result, v->ptr, v->ty->name, sizeof(result)) ? SWIG_Python_str_FromChar(result) : 0; } SWIGRUNTIME int SwigPyObject_compare(SwigPyObject *v, SwigPyObject *w) { void *i = v->ptr; void *j = w->ptr; return (i < j) ? -1 : ((i > j) ? 1 : 0); } /* Added for Python 3.x, would it also be useful for Python 2.x? */ SWIGRUNTIME PyObject* SwigPyObject_richcompare(SwigPyObject *v, SwigPyObject *w, int op) { PyObject* res; if( op != Py_EQ && op != Py_NE ) { Py_INCREF(Py_NotImplemented); return Py_NotImplemented; } res = PyBool_FromLong( (SwigPyObject_compare(v, w)==0) == (op == Py_EQ) ? 1 : 0); return res; } SWIGRUNTIME PyTypeObject* SwigPyObject_TypeOnce(void); #ifdef SWIGPYTHON_BUILTIN static swig_type_info *SwigPyObject_stype = 0; SWIGRUNTIME PyTypeObject* SwigPyObject_type(void) { SwigPyClientData *cd; assert(SwigPyObject_stype); cd = (SwigPyClientData*) SwigPyObject_stype->clientdata; assert(cd); assert(cd->pytype); return cd->pytype; } #else SWIGRUNTIME PyTypeObject* SwigPyObject_type(void) { static PyTypeObject *SWIG_STATIC_POINTER(type) = SwigPyObject_TypeOnce(); return type; } #endif SWIGRUNTIMEINLINE int SwigPyObject_Check(PyObject *op) { #ifdef SWIGPYTHON_BUILTIN PyTypeObject *target_tp = SwigPyObject_type(); if (PyType_IsSubtype(op->ob_type, target_tp)) return 1; return (strcmp(op->ob_type->tp_name, "SwigPyObject") == 0); #else return (Py_TYPE(op) == SwigPyObject_type()) || (strcmp(Py_TYPE(op)->tp_name,"SwigPyObject") == 0); #endif } SWIGRUNTIME PyObject * SwigPyObject_New(void *ptr, swig_type_info *ty, int own); SWIGRUNTIME void SwigPyObject_dealloc(PyObject *v) { SwigPyObject *sobj = (SwigPyObject *) v; PyObject *next = sobj->next; if (sobj->own == SWIG_POINTER_OWN) { swig_type_info *ty = sobj->ty; SwigPyClientData *data = ty ? (SwigPyClientData *) ty->clientdata : 0; PyObject *destroy = data ? data->destroy : 0; if (destroy) { /* destroy is always a VARARGS method */ PyObject *res; if (data->delargs) { /* we need to create a temporary object to carry the destroy operation */ PyObject *tmp = SwigPyObject_New(sobj->ptr, ty, 0); res = SWIG_Python_CallFunctor(destroy, tmp); Py_DECREF(tmp); } else { PyCFunction meth = PyCFunction_GET_FUNCTION(destroy); PyObject *mself = PyCFunction_GET_SELF(destroy); res = ((*meth)(mself, v)); } Py_XDECREF(res); } #if !defined(SWIG_PYTHON_SILENT_MEMLEAK) else { const char *name = SWIG_TypePrettyName(ty); printf("swig/python detected a memory leak of type '%s', no destructor found.\n", (name ? name : "unknown")); } #endif } Py_XDECREF(next); PyObject_DEL(v); } SWIGRUNTIME PyObject* SwigPyObject_append(PyObject* v, PyObject* next) { SwigPyObject *sobj = (SwigPyObject *) v; #ifndef METH_O PyObject *tmp = 0; if (!PyArg_ParseTuple(next,(char *)"O:append", &tmp)) return NULL; next = tmp; #endif if (!SwigPyObject_Check(next)) { return NULL; } sobj->next = next; Py_INCREF(next); return SWIG_Py_Void(); } SWIGRUNTIME PyObject* #ifdef METH_NOARGS SwigPyObject_next(PyObject* v) #else SwigPyObject_next(PyObject* v, PyObject *SWIGUNUSEDPARM(args)) #endif { SwigPyObject *sobj = (SwigPyObject *) v; if (sobj->next) { Py_INCREF(sobj->next); return sobj->next; } else { return SWIG_Py_Void(); } } SWIGINTERN PyObject* #ifdef METH_NOARGS SwigPyObject_disown(PyObject *v) #else SwigPyObject_disown(PyObject* v, PyObject *SWIGUNUSEDPARM(args)) #endif { SwigPyObject *sobj = (SwigPyObject *)v; sobj->own = 0; return SWIG_Py_Void(); } SWIGINTERN PyObject* #ifdef METH_NOARGS SwigPyObject_acquire(PyObject *v) #else SwigPyObject_acquire(PyObject* v, PyObject *SWIGUNUSEDPARM(args)) #endif { SwigPyObject *sobj = (SwigPyObject *)v; sobj->own = SWIG_POINTER_OWN; return SWIG_Py_Void(); } SWIGINTERN PyObject* SwigPyObject_own(PyObject *v, PyObject *args) { PyObject *val = 0; #if (PY_VERSION_HEX < 0x02020000) if (!PyArg_ParseTuple(args,(char *)"|O:own",&val)) #else if (!PyArg_UnpackTuple(args, (char *)"own", 0, 1, &val)) #endif { return NULL; } else { SwigPyObject *sobj = (SwigPyObject *)v; PyObject *obj = PyBool_FromLong(sobj->own); if (val) { #ifdef METH_NOARGS if (PyObject_IsTrue(val)) { SwigPyObject_acquire(v); } else { SwigPyObject_disown(v); } #else if (PyObject_IsTrue(val)) { SwigPyObject_acquire(v,args); } else { SwigPyObject_disown(v,args); } #endif } return obj; } } #ifdef METH_O static PyMethodDef swigobject_methods[] = { {(char *)"disown", (PyCFunction)SwigPyObject_disown, METH_NOARGS, (char *)"releases ownership of the pointer"}, {(char *)"acquire", (PyCFunction)SwigPyObject_acquire, METH_NOARGS, (char *)"aquires ownership of the pointer"}, {(char *)"own", (PyCFunction)SwigPyObject_own, METH_VARARGS, (char *)"returns/sets ownership of the pointer"}, {(char *)"append", (PyCFunction)SwigPyObject_append, METH_O, (char *)"appends another 'this' object"}, {(char *)"next", (PyCFunction)SwigPyObject_next, METH_NOARGS, (char *)"returns the next 'this' object"}, {(char *)"__repr__",(PyCFunction)SwigPyObject_repr, METH_NOARGS, (char *)"returns object representation"}, {0, 0, 0, 0} }; #else static PyMethodDef swigobject_methods[] = { {(char *)"disown", (PyCFunction)SwigPyObject_disown, METH_VARARGS, (char *)"releases ownership of the pointer"}, {(char *)"acquire", (PyCFunction)SwigPyObject_acquire, METH_VARARGS, (char *)"aquires ownership of the pointer"}, {(char *)"own", (PyCFunction)SwigPyObject_own, METH_VARARGS, (char *)"returns/sets ownership of the pointer"}, {(char *)"append", (PyCFunction)SwigPyObject_append, METH_VARARGS, (char *)"appends another 'this' object"}, {(char *)"next", (PyCFunction)SwigPyObject_next, METH_VARARGS, (char *)"returns the next 'this' object"}, {(char *)"__repr__",(PyCFunction)SwigPyObject_repr, METH_VARARGS, (char *)"returns object representation"}, {0, 0, 0, 0} }; #endif #if PY_VERSION_HEX < 0x02020000 SWIGINTERN PyObject * SwigPyObject_getattr(SwigPyObject *sobj,char *name) { return Py_FindMethod(swigobject_methods, (PyObject *)sobj, name); } #endif SWIGRUNTIME PyTypeObject* SwigPyObject_TypeOnce(void) { static char swigobject_doc[] = "Swig object carries a C/C++ instance pointer"; static PyNumberMethods SwigPyObject_as_number = { (binaryfunc)0, /*nb_add*/ (binaryfunc)0, /*nb_subtract*/ (binaryfunc)0, /*nb_multiply*/ /* nb_divide removed in Python 3 */ #if PY_VERSION_HEX < 0x03000000 (binaryfunc)0, /*nb_divide*/ #endif (binaryfunc)0, /*nb_remainder*/ (binaryfunc)0, /*nb_divmod*/ (ternaryfunc)0,/*nb_power*/ (unaryfunc)0, /*nb_negative*/ (unaryfunc)0, /*nb_positive*/ (unaryfunc)0, /*nb_absolute*/ (inquiry)0, /*nb_nonzero*/ 0, /*nb_invert*/ 0, /*nb_lshift*/ 0, /*nb_rshift*/ 0, /*nb_and*/ 0, /*nb_xor*/ 0, /*nb_or*/ #if PY_VERSION_HEX < 0x03000000 0, /*nb_coerce*/ #endif (unaryfunc)SwigPyObject_long, /*nb_int*/ #if PY_VERSION_HEX < 0x03000000 (unaryfunc)SwigPyObject_long, /*nb_long*/ #else 0, /*nb_reserved*/ #endif (unaryfunc)0, /*nb_float*/ #if PY_VERSION_HEX < 0x03000000 (unaryfunc)SwigPyObject_oct, /*nb_oct*/ (unaryfunc)SwigPyObject_hex, /*nb_hex*/ #endif #if PY_VERSION_HEX >= 0x03000000 /* 3.0 */ 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 /* nb_inplace_add -> nb_index, nb_inplace_divide removed */ #elif PY_VERSION_HEX >= 0x02050000 /* 2.5.0 */ 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 /* nb_inplace_add -> nb_index */ #elif PY_VERSION_HEX >= 0x02020000 /* 2.2.0 */ 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 /* nb_inplace_add -> nb_inplace_true_divide */ #elif PY_VERSION_HEX >= 0x02000000 /* 2.0.0 */ 0,0,0,0,0,0,0,0,0,0,0 /* nb_inplace_add -> nb_inplace_or */ #endif }; static PyTypeObject swigpyobject_type; static int type_init = 0; if (!type_init) { const PyTypeObject tmp = { /* PyObject header changed in Python 3 */ #if PY_VERSION_HEX >= 0x03000000 PyVarObject_HEAD_INIT(NULL, 0) #else PyObject_HEAD_INIT(NULL) 0, /* ob_size */ #endif (char *)"SwigPyObject", /* tp_name */ sizeof(SwigPyObject), /* tp_basicsize */ 0, /* tp_itemsize */ (destructor)SwigPyObject_dealloc, /* tp_dealloc */ (printfunc)SwigPyObject_print, /* tp_print */ #if PY_VERSION_HEX < 0x02020000 (getattrfunc)SwigPyObject_getattr, /* tp_getattr */ #else (getattrfunc)0, /* tp_getattr */ #endif (setattrfunc)0, /* tp_setattr */ #if PY_VERSION_HEX >= 0x03000000 0, /* tp_reserved in 3.0.1, tp_compare in 3.0.0 but not used */ #else (cmpfunc)SwigPyObject_compare, /* tp_compare */ #endif (reprfunc)SwigPyObject_repr, /* tp_repr */ &SwigPyObject_as_number, /* tp_as_number */ 0, /* tp_as_sequence */ 0, /* tp_as_mapping */ (hashfunc)0, /* tp_hash */ (ternaryfunc)0, /* tp_call */ (reprfunc)SwigPyObject_str, /* tp_str */ PyObject_GenericGetAttr, /* tp_getattro */ 0, /* tp_setattro */ 0, /* tp_as_buffer */ Py_TPFLAGS_DEFAULT, /* tp_flags */ swigobject_doc, /* tp_doc */ 0, /* tp_traverse */ 0, /* tp_clear */ (richcmpfunc)SwigPyObject_richcompare,/* tp_richcompare */ 0, /* tp_weaklistoffset */ #if PY_VERSION_HEX >= 0x02020000 0, /* tp_iter */ 0, /* tp_iternext */ swigobject_methods, /* tp_methods */ 0, /* tp_members */ 0, /* tp_getset */ 0, /* tp_base */ 0, /* tp_dict */ 0, /* tp_descr_get */ 0, /* tp_descr_set */ 0, /* tp_dictoffset */ 0, /* tp_init */ 0, /* tp_alloc */ 0, /* tp_new */ 0, /* tp_free */ 0, /* tp_is_gc */ 0, /* tp_bases */ 0, /* tp_mro */ 0, /* tp_cache */ 0, /* tp_subclasses */ 0, /* tp_weaklist */ #endif #if PY_VERSION_HEX >= 0x02030000 0, /* tp_del */ #endif #if PY_VERSION_HEX >= 0x02060000 0, /* tp_version */ #endif #ifdef COUNT_ALLOCS 0,0,0,0 /* tp_alloc -> tp_next */ #endif }; swigpyobject_type = tmp; type_init = 1; #if PY_VERSION_HEX < 0x02020000 swigpyobject_type.ob_type = &PyType_Type; #else if (PyType_Ready(&swigpyobject_type) < 0) return NULL; #endif } return &swigpyobject_type; } SWIGRUNTIME PyObject * SwigPyObject_New(void *ptr, swig_type_info *ty, int own) { SwigPyObject *sobj = PyObject_NEW(SwigPyObject, SwigPyObject_type()); if (sobj) { sobj->ptr = ptr; sobj->ty = ty; sobj->own = own; sobj->next = 0; } return (PyObject *)sobj; } /* ----------------------------------------------------------------------------- * Implements a simple Swig Packed type, and use it instead of string * ----------------------------------------------------------------------------- */ typedef struct { PyObject_HEAD void *pack; swig_type_info *ty; size_t size; } SwigPyPacked; SWIGRUNTIME int SwigPyPacked_print(SwigPyPacked *v, FILE *fp, int SWIGUNUSEDPARM(flags)) { char result[SWIG_BUFFER_SIZE]; fputs("pack, v->size, 0, sizeof(result))) { fputs("at ", fp); fputs(result, fp); } fputs(v->ty->name,fp); fputs(">", fp); return 0; } SWIGRUNTIME PyObject * SwigPyPacked_repr(SwigPyPacked *v) { char result[SWIG_BUFFER_SIZE]; if (SWIG_PackDataName(result, v->pack, v->size, 0, sizeof(result))) { return SWIG_Python_str_FromFormat("", result, v->ty->name); } else { return SWIG_Python_str_FromFormat("", v->ty->name); } } SWIGRUNTIME PyObject * SwigPyPacked_str(SwigPyPacked *v) { char result[SWIG_BUFFER_SIZE]; if (SWIG_PackDataName(result, v->pack, v->size, 0, sizeof(result))){ return SWIG_Python_str_FromFormat("%s%s", result, v->ty->name); } else { return SWIG_Python_str_FromChar(v->ty->name); } } SWIGRUNTIME int SwigPyPacked_compare(SwigPyPacked *v, SwigPyPacked *w) { size_t i = v->size; size_t j = w->size; int s = (i < j) ? -1 : ((i > j) ? 1 : 0); return s ? s : strncmp((char *)v->pack, (char *)w->pack, 2*v->size); } SWIGRUNTIME PyTypeObject* SwigPyPacked_TypeOnce(void); SWIGRUNTIME PyTypeObject* SwigPyPacked_type(void) { static PyTypeObject *SWIG_STATIC_POINTER(type) = SwigPyPacked_TypeOnce(); return type; } SWIGRUNTIMEINLINE int SwigPyPacked_Check(PyObject *op) { return ((op)->ob_type == SwigPyPacked_TypeOnce()) || (strcmp((op)->ob_type->tp_name,"SwigPyPacked") == 0); } SWIGRUNTIME void SwigPyPacked_dealloc(PyObject *v) { if (SwigPyPacked_Check(v)) { SwigPyPacked *sobj = (SwigPyPacked *) v; free(sobj->pack); } PyObject_DEL(v); } SWIGRUNTIME PyTypeObject* SwigPyPacked_TypeOnce(void) { static char swigpacked_doc[] = "Swig object carries a C/C++ instance pointer"; static PyTypeObject swigpypacked_type; static int type_init = 0; if (!type_init) { const PyTypeObject tmp = { /* PyObject header changed in Python 3 */ #if PY_VERSION_HEX>=0x03000000 PyVarObject_HEAD_INIT(NULL, 0) #else PyObject_HEAD_INIT(NULL) 0, /* ob_size */ #endif (char *)"SwigPyPacked", /* tp_name */ sizeof(SwigPyPacked), /* tp_basicsize */ 0, /* tp_itemsize */ (destructor)SwigPyPacked_dealloc, /* tp_dealloc */ (printfunc)SwigPyPacked_print, /* tp_print */ (getattrfunc)0, /* tp_getattr */ (setattrfunc)0, /* tp_setattr */ #if PY_VERSION_HEX>=0x03000000 0, /* tp_reserved in 3.0.1 */ #else (cmpfunc)SwigPyPacked_compare, /* tp_compare */ #endif (reprfunc)SwigPyPacked_repr, /* tp_repr */ 0, /* tp_as_number */ 0, /* tp_as_sequence */ 0, /* tp_as_mapping */ (hashfunc)0, /* tp_hash */ (ternaryfunc)0, /* tp_call */ (reprfunc)SwigPyPacked_str, /* tp_str */ PyObject_GenericGetAttr, /* tp_getattro */ 0, /* tp_setattro */ 0, /* tp_as_buffer */ Py_TPFLAGS_DEFAULT, /* tp_flags */ swigpacked_doc, /* tp_doc */ 0, /* tp_traverse */ 0, /* tp_clear */ 0, /* tp_richcompare */ 0, /* tp_weaklistoffset */ #if PY_VERSION_HEX >= 0x02020000 0, /* tp_iter */ 0, /* tp_iternext */ 0, /* tp_methods */ 0, /* tp_members */ 0, /* tp_getset */ 0, /* tp_base */ 0, /* tp_dict */ 0, /* tp_descr_get */ 0, /* tp_descr_set */ 0, /* tp_dictoffset */ 0, /* tp_init */ 0, /* tp_alloc */ 0, /* tp_new */ 0, /* tp_free */ 0, /* tp_is_gc */ 0, /* tp_bases */ 0, /* tp_mro */ 0, /* tp_cache */ 0, /* tp_subclasses */ 0, /* tp_weaklist */ #endif #if PY_VERSION_HEX >= 0x02030000 0, /* tp_del */ #endif #if PY_VERSION_HEX >= 0x02060000 0, /* tp_version */ #endif #ifdef COUNT_ALLOCS 0,0,0,0 /* tp_alloc -> tp_next */ #endif }; swigpypacked_type = tmp; type_init = 1; #if PY_VERSION_HEX < 0x02020000 swigpypacked_type.ob_type = &PyType_Type; #else if (PyType_Ready(&swigpypacked_type) < 0) return NULL; #endif } return &swigpypacked_type; } SWIGRUNTIME PyObject * SwigPyPacked_New(void *ptr, size_t size, swig_type_info *ty) { SwigPyPacked *sobj = PyObject_NEW(SwigPyPacked, SwigPyPacked_type()); if (sobj) { void *pack = malloc(size); if (pack) { memcpy(pack, ptr, size); sobj->pack = pack; sobj->ty = ty; sobj->size = size; } else { PyObject_DEL((PyObject *) sobj); sobj = 0; } } return (PyObject *) sobj; } SWIGRUNTIME swig_type_info * SwigPyPacked_UnpackData(PyObject *obj, void *ptr, size_t size) { if (SwigPyPacked_Check(obj)) { SwigPyPacked *sobj = (SwigPyPacked *)obj; if (sobj->size != size) return 0; memcpy(ptr, sobj->pack, size); return sobj->ty; } else { return 0; } } /* ----------------------------------------------------------------------------- * pointers/data manipulation * ----------------------------------------------------------------------------- */ SWIGRUNTIMEINLINE PyObject * _SWIG_This(void) { return SWIG_Python_str_FromChar("this"); } static PyObject *swig_this = NULL; SWIGRUNTIME PyObject * SWIG_This(void) { if (swig_this == NULL) swig_this = _SWIG_This(); return swig_this; } /* #define SWIG_PYTHON_SLOW_GETSET_THIS */ /* TODO: I don't know how to implement the fast getset in Python 3 right now */ #if PY_VERSION_HEX>=0x03000000 #define SWIG_PYTHON_SLOW_GETSET_THIS #endif SWIGRUNTIME SwigPyObject * SWIG_Python_GetSwigThis(PyObject *pyobj) { PyObject *obj; if (SwigPyObject_Check(pyobj)) return (SwigPyObject *) pyobj; #ifdef SWIGPYTHON_BUILTIN (void)obj; # ifdef PyWeakref_CheckProxy if (PyWeakref_CheckProxy(pyobj)) { pyobj = PyWeakref_GET_OBJECT(pyobj); if (pyobj && SwigPyObject_Check(pyobj)) return (SwigPyObject*) pyobj; } # endif return NULL; #else obj = 0; #if (!defined(SWIG_PYTHON_SLOW_GETSET_THIS) && (PY_VERSION_HEX >= 0x02030000)) if (PyInstance_Check(pyobj)) { obj = _PyInstance_Lookup(pyobj, SWIG_This()); } else { PyObject **dictptr = _PyObject_GetDictPtr(pyobj); if (dictptr != NULL) { PyObject *dict = *dictptr; obj = dict ? PyDict_GetItem(dict, SWIG_This()) : 0; } else { #ifdef PyWeakref_CheckProxy if (PyWeakref_CheckProxy(pyobj)) { PyObject *wobj = PyWeakref_GET_OBJECT(pyobj); return wobj ? SWIG_Python_GetSwigThis(wobj) : 0; } #endif obj = PyObject_GetAttr(pyobj,SWIG_This()); if (obj) { Py_DECREF(obj); } else { if (PyErr_Occurred()) PyErr_Clear(); return 0; } } } #else obj = PyObject_GetAttr(pyobj,SWIG_This()); if (obj) { Py_DECREF(obj); } else { if (PyErr_Occurred()) PyErr_Clear(); return 0; } #endif if (obj && !SwigPyObject_Check(obj)) { /* a PyObject is called 'this', try to get the 'real this' SwigPyObject from it */ return SWIG_Python_GetSwigThis(obj); } return (SwigPyObject *)obj; #endif } /* Acquire a pointer value */ SWIGRUNTIME int SWIG_Python_AcquirePtr(PyObject *obj, int own) { if (own == SWIG_POINTER_OWN) { SwigPyObject *sobj = SWIG_Python_GetSwigThis(obj); if (sobj) { int oldown = sobj->own; sobj->own = own; return oldown; } } return 0; } /* Convert a pointer value */ SWIGRUNTIME int SWIG_Python_ConvertPtrAndOwn(PyObject *obj, void **ptr, swig_type_info *ty, int flags, int *own) { int res; SwigPyObject *sobj; if (!obj) return SWIG_ERROR; if (obj == Py_None) { if (ptr) *ptr = 0; return SWIG_OK; } res = SWIG_ERROR; sobj = SWIG_Python_GetSwigThis(obj); if (own) *own = 0; while (sobj) { void *vptr = sobj->ptr; if (ty) { swig_type_info *to = sobj->ty; if (to == ty) { /* no type cast needed */ if (ptr) *ptr = vptr; break; } else { swig_cast_info *tc = SWIG_TypeCheck(to->name,ty); if (!tc) { sobj = (SwigPyObject *)sobj->next; } else { if (ptr) { int newmemory = 0; *ptr = SWIG_TypeCast(tc,vptr,&newmemory); if (newmemory == SWIG_CAST_NEW_MEMORY) { assert(own); /* badly formed typemap which will lead to a memory leak - it must set and use own to delete *ptr */ if (own) *own = *own | SWIG_CAST_NEW_MEMORY; } } break; } } } else { if (ptr) *ptr = vptr; break; } } if (sobj) { if (own) *own = *own | sobj->own; if (flags & SWIG_POINTER_DISOWN) { sobj->own = 0; } res = SWIG_OK; } else { if (flags & SWIG_POINTER_IMPLICIT_CONV) { SwigPyClientData *data = ty ? (SwigPyClientData *) ty->clientdata : 0; if (data && !data->implicitconv) { PyObject *klass = data->klass; if (klass) { PyObject *impconv; data->implicitconv = 1; /* avoid recursion and call 'explicit' constructors*/ impconv = SWIG_Python_CallFunctor(klass, obj); data->implicitconv = 0; if (PyErr_Occurred()) { PyErr_Clear(); impconv = 0; } if (impconv) { SwigPyObject *iobj = SWIG_Python_GetSwigThis(impconv); if (iobj) { void *vptr; res = SWIG_Python_ConvertPtrAndOwn((PyObject*)iobj, &vptr, ty, 0, 0); if (SWIG_IsOK(res)) { if (ptr) { *ptr = vptr; /* transfer the ownership to 'ptr' */ iobj->own = 0; res = SWIG_AddCast(res); res = SWIG_AddNewMask(res); } else { res = SWIG_AddCast(res); } } } Py_DECREF(impconv); } } } } } return res; } /* Convert a function ptr value */ SWIGRUNTIME int SWIG_Python_ConvertFunctionPtr(PyObject *obj, void **ptr, swig_type_info *ty) { if (!PyCFunction_Check(obj)) { return SWIG_ConvertPtr(obj, ptr, ty, 0); } else { void *vptr = 0; /* here we get the method pointer for callbacks */ const char *doc = (((PyCFunctionObject *)obj) -> m_ml -> ml_doc); const char *desc = doc ? strstr(doc, "swig_ptr: ") : 0; if (desc) desc = ty ? SWIG_UnpackVoidPtr(desc + 10, &vptr, ty->name) : 0; if (!desc) return SWIG_ERROR; if (ty) { swig_cast_info *tc = SWIG_TypeCheck(desc,ty); if (tc) { int newmemory = 0; *ptr = SWIG_TypeCast(tc,vptr,&newmemory); assert(!newmemory); /* newmemory handling not yet implemented */ } else { return SWIG_ERROR; } } else { *ptr = vptr; } return SWIG_OK; } } /* Convert a packed value value */ SWIGRUNTIME int SWIG_Python_ConvertPacked(PyObject *obj, void *ptr, size_t sz, swig_type_info *ty) { swig_type_info *to = SwigPyPacked_UnpackData(obj, ptr, sz); if (!to) return SWIG_ERROR; if (ty) { if (to != ty) { /* check type cast? */ swig_cast_info *tc = SWIG_TypeCheck(to->name,ty); if (!tc) return SWIG_ERROR; } } return SWIG_OK; } /* ----------------------------------------------------------------------------- * Create a new pointer object * ----------------------------------------------------------------------------- */ /* Create a new instance object, without calling __init__, and set the 'this' attribute. */ SWIGRUNTIME PyObject* SWIG_Python_NewShadowInstance(SwigPyClientData *data, PyObject *swig_this) { #if (PY_VERSION_HEX >= 0x02020000) PyObject *inst = 0; PyObject *newraw = data->newraw; if (newraw) { inst = PyObject_Call(newraw, data->newargs, NULL); if (inst) { #if !defined(SWIG_PYTHON_SLOW_GETSET_THIS) PyObject **dictptr = _PyObject_GetDictPtr(inst); if (dictptr != NULL) { PyObject *dict = *dictptr; if (dict == NULL) { dict = PyDict_New(); *dictptr = dict; PyDict_SetItem(dict, SWIG_This(), swig_this); } } #else PyObject *key = SWIG_This(); PyObject_SetAttr(inst, key, swig_this); #endif } } else { #if PY_VERSION_HEX >= 0x03000000 inst = PyBaseObject_Type.tp_new((PyTypeObject*) data->newargs, Py_None, Py_None); PyObject_SetAttr(inst, SWIG_This(), swig_this); Py_TYPE(inst)->tp_flags &= ~Py_TPFLAGS_VALID_VERSION_TAG; #else PyObject *dict = PyDict_New(); PyDict_SetItem(dict, SWIG_This(), swig_this); inst = PyInstance_NewRaw(data->newargs, dict); Py_DECREF(dict); #endif } return inst; #else #if (PY_VERSION_HEX >= 0x02010000) PyObject *inst; PyObject *dict = PyDict_New(); PyDict_SetItem(dict, SWIG_This(), swig_this); inst = PyInstance_NewRaw(data->newargs, dict); Py_DECREF(dict); return (PyObject *) inst; #else PyInstanceObject *inst = PyObject_NEW(PyInstanceObject, &PyInstance_Type); if (inst == NULL) { return NULL; } inst->in_class = (PyClassObject *)data->newargs; Py_INCREF(inst->in_class); inst->in_dict = PyDict_New(); if (inst->in_dict == NULL) { Py_DECREF(inst); return NULL; } #ifdef Py_TPFLAGS_HAVE_WEAKREFS inst->in_weakreflist = NULL; #endif #ifdef Py_TPFLAGS_GC PyObject_GC_Init(inst); #endif PyDict_SetItem(inst->in_dict, SWIG_This(), swig_this); return (PyObject *) inst; #endif #endif } SWIGRUNTIME void SWIG_Python_SetSwigThis(PyObject *inst, PyObject *swig_this) { PyObject *dict; #if (PY_VERSION_HEX >= 0x02020000) && !defined(SWIG_PYTHON_SLOW_GETSET_THIS) PyObject **dictptr = _PyObject_GetDictPtr(inst); if (dictptr != NULL) { dict = *dictptr; if (dict == NULL) { dict = PyDict_New(); *dictptr = dict; } PyDict_SetItem(dict, SWIG_This(), swig_this); return; } #endif dict = PyObject_GetAttrString(inst, (char*)"__dict__"); PyDict_SetItem(dict, SWIG_This(), swig_this); Py_DECREF(dict); } SWIGINTERN PyObject * SWIG_Python_InitShadowInstance(PyObject *args) { PyObject *obj[2]; if (!SWIG_Python_UnpackTuple(args,(char*)"swiginit", 2, 2, obj)) { return NULL; } else { SwigPyObject *sthis = SWIG_Python_GetSwigThis(obj[0]); if (sthis) { SwigPyObject_append((PyObject*) sthis, obj[1]); } else { SWIG_Python_SetSwigThis(obj[0], obj[1]); } return SWIG_Py_Void(); } } /* Create a new pointer object */ SWIGRUNTIME PyObject * SWIG_Python_NewPointerObj(PyObject *self, void *ptr, swig_type_info *type, int flags) { SwigPyClientData *clientdata; PyObject * robj; int own; if (!ptr) return SWIG_Py_Void(); clientdata = type ? (SwigPyClientData *)(type->clientdata) : 0; own = (flags & SWIG_POINTER_OWN) ? SWIG_POINTER_OWN : 0; if (clientdata && clientdata->pytype) { SwigPyObject *newobj; if (flags & SWIG_BUILTIN_TP_INIT) { newobj = (SwigPyObject*) self; if (newobj->ptr) { PyObject *next_self = clientdata->pytype->tp_alloc(clientdata->pytype, 0); while (newobj->next) newobj = (SwigPyObject *) newobj->next; newobj->next = next_self; newobj = (SwigPyObject *)next_self; } } else { newobj = PyObject_New(SwigPyObject, clientdata->pytype); } if (newobj) { newobj->ptr = ptr; newobj->ty = type; newobj->own = own; newobj->next = 0; #ifdef SWIGPYTHON_BUILTIN newobj->dict = 0; #endif return (PyObject*) newobj; } return SWIG_Py_Void(); } assert(!(flags & SWIG_BUILTIN_TP_INIT)); robj = SwigPyObject_New(ptr, type, own); if (clientdata && !(flags & SWIG_POINTER_NOSHADOW)) { PyObject *inst = SWIG_Python_NewShadowInstance(clientdata, robj); if (inst) { Py_DECREF(robj); robj = inst; } } return robj; } /* Create a new packed object */ SWIGRUNTIMEINLINE PyObject * SWIG_Python_NewPackedObj(void *ptr, size_t sz, swig_type_info *type) { return ptr ? SwigPyPacked_New((void *) ptr, sz, type) : SWIG_Py_Void(); } /* -----------------------------------------------------------------------------* * Get type list * -----------------------------------------------------------------------------*/ #ifdef SWIG_LINK_RUNTIME void *SWIG_ReturnGlobalTypeList(void *); #endif SWIGRUNTIME swig_module_info * SWIG_Python_GetModule(void) { static void *type_pointer = (void *)0; /* first check if module already created */ if (!type_pointer) { #ifdef SWIG_LINK_RUNTIME type_pointer = SWIG_ReturnGlobalTypeList((void *)0); #else # ifdef SWIGPY_USE_CAPSULE type_pointer = PyCapsule_Import(SWIGPY_CAPSULE_NAME, 0); # else type_pointer = PyCObject_Import((char*)"swig_runtime_data" SWIG_RUNTIME_VERSION, (char*)"type_pointer" SWIG_TYPE_TABLE_NAME); # endif if (PyErr_Occurred()) { PyErr_Clear(); type_pointer = (void *)0; } #endif } return (swig_module_info *) type_pointer; } #if PY_MAJOR_VERSION < 2 /* PyModule_AddObject function was introduced in Python 2.0. The following function is copied out of Python/modsupport.c in python version 2.3.4 */ SWIGINTERN int PyModule_AddObject(PyObject *m, char *name, PyObject *o) { PyObject *dict; if (!PyModule_Check(m)) { PyErr_SetString(PyExc_TypeError, "PyModule_AddObject() needs module as first arg"); return SWIG_ERROR; } if (!o) { PyErr_SetString(PyExc_TypeError, "PyModule_AddObject() needs non-NULL value"); return SWIG_ERROR; } dict = PyModule_GetDict(m); if (dict == NULL) { /* Internal error -- modules must have a dict! */ PyErr_Format(PyExc_SystemError, "module '%s' has no __dict__", PyModule_GetName(m)); return SWIG_ERROR; } if (PyDict_SetItemString(dict, name, o)) return SWIG_ERROR; Py_DECREF(o); return SWIG_OK; } #endif SWIGRUNTIME void #ifdef SWIGPY_USE_CAPSULE SWIG_Python_DestroyModule(PyObject *obj) #else SWIG_Python_DestroyModule(void *vptr) #endif { #ifdef SWIGPY_USE_CAPSULE swig_module_info *swig_module = (swig_module_info *) PyCapsule_GetPointer(obj, SWIGPY_CAPSULE_NAME); #else swig_module_info *swig_module = (swig_module_info *) vptr; #endif swig_type_info **types = swig_module->types; size_t i; for (i =0; i < swig_module->size; ++i) { swig_type_info *ty = types[i]; if (ty->owndata) { SwigPyClientData *data = (SwigPyClientData *) ty->clientdata; if (data) SwigPyClientData_Del(data); } } Py_DECREF(SWIG_This()); swig_this = NULL; } SWIGRUNTIME void SWIG_Python_SetModule(swig_module_info *swig_module) { #if PY_VERSION_HEX >= 0x03000000 /* Add a dummy module object into sys.modules */ PyObject *module = PyImport_AddModule((char*)"swig_runtime_data" SWIG_RUNTIME_VERSION); #else static PyMethodDef swig_empty_runtime_method_table[] = { {NULL, NULL, 0, NULL} }; /* Sentinel */ PyObject *module = Py_InitModule((char*)"swig_runtime_data" SWIG_RUNTIME_VERSION, swig_empty_runtime_method_table); #endif #ifdef SWIGPY_USE_CAPSULE PyObject *pointer = PyCapsule_New((void *) swig_module, SWIGPY_CAPSULE_NAME, SWIG_Python_DestroyModule); if (pointer && module) { PyModule_AddObject(module, (char*)"type_pointer_capsule" SWIG_TYPE_TABLE_NAME, pointer); } else { Py_XDECREF(pointer); } #else PyObject *pointer = PyCObject_FromVoidPtr((void *) swig_module, SWIG_Python_DestroyModule); if (pointer && module) { PyModule_AddObject(module, (char*)"type_pointer" SWIG_TYPE_TABLE_NAME, pointer); } else { Py_XDECREF(pointer); } #endif } /* The python cached type query */ SWIGRUNTIME PyObject * SWIG_Python_TypeCache(void) { static PyObject *SWIG_STATIC_POINTER(cache) = PyDict_New(); return cache; } SWIGRUNTIME swig_type_info * SWIG_Python_TypeQuery(const char *type) { PyObject *cache = SWIG_Python_TypeCache(); PyObject *key = SWIG_Python_str_FromChar(type); PyObject *obj = PyDict_GetItem(cache, key); swig_type_info *descriptor; if (obj) { #ifdef SWIGPY_USE_CAPSULE descriptor = (swig_type_info *) PyCapsule_GetPointer(obj, NULL); #else descriptor = (swig_type_info *) PyCObject_AsVoidPtr(obj); #endif } else { swig_module_info *swig_module = SWIG_Python_GetModule(); descriptor = SWIG_TypeQueryModule(swig_module, swig_module, type); if (descriptor) { #ifdef SWIGPY_USE_CAPSULE obj = PyCapsule_New((void*) descriptor, NULL, NULL); #else obj = PyCObject_FromVoidPtr(descriptor, NULL); #endif PyDict_SetItem(cache, key, obj); Py_DECREF(obj); } } Py_DECREF(key); return descriptor; } /* For backward compatibility only */ #define SWIG_POINTER_EXCEPTION 0 #define SWIG_arg_fail(arg) SWIG_Python_ArgFail(arg) #define SWIG_MustGetPtr(p, type, argnum, flags) SWIG_Python_MustGetPtr(p, type, argnum, flags) SWIGRUNTIME int SWIG_Python_AddErrMesg(const char* mesg, int infront) { if (PyErr_Occurred()) { PyObject *type = 0; PyObject *value = 0; PyObject *traceback = 0; PyErr_Fetch(&type, &value, &traceback); if (value) { char *tmp; PyObject *old_str = PyObject_Str(value); Py_XINCREF(type); PyErr_Clear(); if (infront) { PyErr_Format(type, "%s %s", mesg, tmp = SWIG_Python_str_AsChar(old_str)); } else { PyErr_Format(type, "%s %s", tmp = SWIG_Python_str_AsChar(old_str), mesg); } SWIG_Python_str_DelForPy3(tmp); Py_DECREF(old_str); } return 1; } else { return 0; } } SWIGRUNTIME int SWIG_Python_ArgFail(int argnum) { if (PyErr_Occurred()) { /* add information about failing argument */ char mesg[256]; PyOS_snprintf(mesg, sizeof(mesg), "argument number %d:", argnum); return SWIG_Python_AddErrMesg(mesg, 1); } else { return 0; } } SWIGRUNTIMEINLINE const char * SwigPyObject_GetDesc(PyObject *self) { SwigPyObject *v = (SwigPyObject *)self; swig_type_info *ty = v ? v->ty : 0; return ty ? ty->str : (char*)""; } SWIGRUNTIME void SWIG_Python_TypeError(const char *type, PyObject *obj) { if (type) { #if defined(SWIG_COBJECT_TYPES) if (obj && SwigPyObject_Check(obj)) { const char *otype = (const char *) SwigPyObject_GetDesc(obj); if (otype) { PyErr_Format(PyExc_TypeError, "a '%s' is expected, 'SwigPyObject(%s)' is received", type, otype); return; } } else #endif { const char *otype = (obj ? obj->ob_type->tp_name : 0); if (otype) { PyObject *str = PyObject_Str(obj); const char *cstr = str ? SWIG_Python_str_AsChar(str) : 0; if (cstr) { PyErr_Format(PyExc_TypeError, "a '%s' is expected, '%s(%s)' is received", type, otype, cstr); SWIG_Python_str_DelForPy3(cstr); } else { PyErr_Format(PyExc_TypeError, "a '%s' is expected, '%s' is received", type, otype); } Py_XDECREF(str); return; } } PyErr_Format(PyExc_TypeError, "a '%s' is expected", type); } else { PyErr_Format(PyExc_TypeError, "unexpected type is received"); } } /* Convert a pointer value, signal an exception on a type mismatch */ SWIGRUNTIME void * SWIG_Python_MustGetPtr(PyObject *obj, swig_type_info *ty, int SWIGUNUSEDPARM(argnum), int flags) { void *result; if (SWIG_Python_ConvertPtr(obj, &result, ty, flags) == -1) { PyErr_Clear(); #if SWIG_POINTER_EXCEPTION if (flags) { SWIG_Python_TypeError(SWIG_TypePrettyName(ty), obj); SWIG_Python_ArgFail(argnum); } #endif } return result; } SWIGRUNTIME int SWIG_Python_NonDynamicSetAttr(PyObject *obj, PyObject *name, PyObject *value) { PyTypeObject *tp = obj->ob_type; PyObject *descr; PyObject *encoded_name; descrsetfunc f; int res; #ifdef Py_USING_UNICODE if (PyString_Check(name)) { name = PyUnicode_Decode(PyString_AsString(name), PyString_Size(name), NULL, NULL); if (!name) return -1; } else if (!PyUnicode_Check(name)) #else if (!PyString_Check(name)) #endif { PyErr_Format(PyExc_TypeError, "attribute name must be string, not '%.200s'", name->ob_type->tp_name); return -1; } else { Py_INCREF(name); } if (!tp->tp_dict) { if (PyType_Ready(tp) < 0) goto done; } res = -1; descr = _PyType_Lookup(tp, name); f = NULL; if (descr != NULL) f = descr->ob_type->tp_descr_set; if (!f) { if (PyString_Check(name)) { encoded_name = name; Py_INCREF(name); } else { encoded_name = PyUnicode_AsUTF8String(name); } PyErr_Format(PyExc_AttributeError, "'%.100s' object has no attribute '%.200s'", tp->tp_name, PyString_AsString(encoded_name)); Py_DECREF(encoded_name); } else { res = f(descr, obj, value); } done: Py_DECREF(name); return res; } #ifdef __cplusplus } #endif #define SWIG_exception_fail(code, msg) do { SWIG_Error(code, msg); SWIG_fail; } while(0) #define SWIG_contract_assert(expr, msg) if (!(expr)) { SWIG_Error(SWIG_RuntimeError, msg); SWIG_fail; } else /* -------- TYPES TABLE (BEGIN) -------- */ #define SWIGTYPE_p_char swig_types[0] static swig_type_info *swig_types[2]; static swig_module_info swig_module = {swig_types, 1, 0, 0, 0, 0}; #define SWIG_TypeQuery(name) SWIG_TypeQueryModule(&swig_module, &swig_module, name) #define SWIG_MangledTypeQuery(name) SWIG_MangledTypeQueryModule(&swig_module, &swig_module, name) /* -------- TYPES TABLE (END) -------- */ #if (PY_VERSION_HEX <= 0x02000000) # if !defined(SWIG_PYTHON_CLASSIC) # error "This python version requires swig to be run with the '-classic' option" # endif #endif /*----------------------------------------------- @(target):= _fastexp.so ------------------------------------------------*/ #if PY_VERSION_HEX >= 0x03000000 # define SWIG_init PyInit__fastexp #else # define SWIG_init init_fastexp #endif #define SWIG_name "_fastexp" #define SWIGVERSION 0x020004 #define SWIG_VERSION SWIGVERSION #define SWIG_as_voidptr(a) const_cast< void * >(static_cast< const void * >(a)) #define SWIG_as_voidptrptr(a) ((void)SWIG_as_voidptr(*a),reinterpret_cast< void** >(a)) #include namespace swig { class SwigPtr_PyObject { protected: PyObject *_obj; public: SwigPtr_PyObject() :_obj(0) { } SwigPtr_PyObject(const SwigPtr_PyObject& item) : _obj(item._obj) { Py_XINCREF(_obj); } SwigPtr_PyObject(PyObject *obj, bool initial_ref = true) :_obj(obj) { if (initial_ref) { Py_XINCREF(_obj); } } SwigPtr_PyObject & operator=(const SwigPtr_PyObject& item) { Py_XINCREF(item._obj); Py_XDECREF(_obj); _obj = item._obj; return *this; } ~SwigPtr_PyObject() { Py_XDECREF(_obj); } operator PyObject *() const { return _obj; } PyObject *operator->() const { return _obj; } }; } namespace swig { struct SwigVar_PyObject : SwigPtr_PyObject { SwigVar_PyObject(PyObject* obj = 0) : SwigPtr_PyObject(obj, false) { } SwigVar_PyObject & operator = (PyObject* obj) { Py_XDECREF(_obj); _obj = obj; return *this; } }; } #define SWIG_FILE_WITH_INIT #include "fastexp.h" #ifndef SWIG_FILE_WITH_INIT # define NO_IMPORT_ARRAY #endif #include "stdio.h" #include /* Macros to extract array attributes. */ #define is_array(a) ((a) && PyArray_Check((PyArrayObject *)a)) #define array_type(a) (int)(PyArray_TYPE(a)) #define array_numdims(a) (((PyArrayObject *)a)->nd) #define array_dimensions(a) (((PyArrayObject *)a)->dimensions) #define array_size(a,i) (((PyArrayObject *)a)->dimensions[i]) #define array_data(a) (((PyArrayObject *)a)->data) #define array_is_contiguous(a) (PyArray_ISCONTIGUOUS(a)) #define array_is_native(a) (PyArray_ISNOTSWAPPED(a)) #define array_is_fortran(a) (PyArray_ISFORTRAN(a)) /* Given a PyObject, return a string describing its type. */ const char* pytype_string(PyObject* py_obj) { if (py_obj == NULL ) return "C NULL value"; if (py_obj == Py_None ) return "Python None" ; if (PyCallable_Check(py_obj)) return "callable" ; if (PyString_Check( py_obj)) return "string" ; if (PyInt_Check( py_obj)) return "int" ; if (PyFloat_Check( py_obj)) return "float" ; if (PyDict_Check( py_obj)) return "dict" ; if (PyList_Check( py_obj)) return "list" ; if (PyTuple_Check( py_obj)) return "tuple" ; if (PyFile_Check( py_obj)) return "file" ; if (PyModule_Check( py_obj)) return "module" ; if (PyInstance_Check(py_obj)) return "instance" ; return "unkown type"; } /* Given a NumPy typecode, return a string describing the type. */ const char* typecode_string(int typecode) { static const char* type_names[25] = {"bool", "byte", "unsigned byte", "short", "unsigned short", "int", "unsigned int", "long", "unsigned long", "long long", "unsigned long long", "float", "double", "long double", "complex float", "complex double", "complex long double", "object", "string", "unicode", "void", "ntypes", "notype", "char", "unknown"}; return typecode < 24 ? type_names[typecode] : type_names[24]; } /* Make sure input has correct numpy type. Allow character and byte * to match. Also allow int and long to match. This is deprecated. * You should use PyArray_EquivTypenums() instead. */ int type_match(int actual_type, int desired_type) { return PyArray_EquivTypenums(actual_type, desired_type); } /* Given a PyObject pointer, cast it to a PyArrayObject pointer if * legal. If not, set the python error string appropriately and * return NULL. */ PyArrayObject* obj_to_array_no_conversion(PyObject* input, int typecode) { PyArrayObject* ary = NULL; if (is_array(input) && (typecode == NPY_NOTYPE || PyArray_EquivTypenums(array_type(input), typecode))) { ary = (PyArrayObject*) input; } else if is_array(input) { const char* desired_type = typecode_string(typecode); const char* actual_type = typecode_string(array_type(input)); PyErr_Format(PyExc_TypeError, "Array of type '%s' required. Array of type '%s' given", desired_type, actual_type); ary = NULL; } else { const char * desired_type = typecode_string(typecode); const char * actual_type = pytype_string(input); PyErr_Format(PyExc_TypeError, "Array of type '%s' required. A '%s' was given", desired_type, actual_type); ary = NULL; } return ary; } /* Convert the given PyObject to a NumPy array with the given * typecode. On success, return a valid PyArrayObject* with the * correct type. On failure, the python error string will be set and * the routine returns NULL. */ PyArrayObject* obj_to_array_allow_conversion(PyObject* input, int typecode, int* is_new_object) { PyArrayObject* ary = NULL; PyObject* py_obj; if (is_array(input) && (typecode == NPY_NOTYPE || PyArray_EquivTypenums(array_type(input),typecode))) { ary = (PyArrayObject*) input; *is_new_object = 0; } else { py_obj = PyArray_FROMANY(input, typecode, 0, 0, NPY_DEFAULT); /* If NULL, PyArray_FromObject will have set python error value.*/ ary = (PyArrayObject*) py_obj; *is_new_object = 1; } return ary; } /* Given a PyArrayObject, check to see if it is contiguous. If so, * return the input pointer and flag it as not a new object. If it is * not contiguous, create a new PyArrayObject using the original data, * flag it as a new object and return the pointer. */ PyArrayObject* make_contiguous(PyArrayObject* ary, int* is_new_object, int min_dims, int max_dims) { PyArrayObject* result; if (array_is_contiguous(ary)) { result = ary; *is_new_object = 0; } else { result = (PyArrayObject*) PyArray_ContiguousFromObject((PyObject*)ary, array_type(ary), min_dims, max_dims); *is_new_object = 1; } return result; } /* Given a PyArrayObject, check to see if it is Fortran-contiguous. * If so, return the input pointer, but do not flag it as not a new * object. If it is not Fortran-contiguous, create a new * PyArrayObject using the original data, flag it as a new object * and return the pointer. */ PyArrayObject* make_fortran(PyArrayObject* ary, int* is_new_object, int min_dims, int max_dims) { PyArrayObject* result; if (array_is_fortran(ary)) { result = ary; *is_new_object = 0; } else { Py_INCREF(ary->descr); result = (PyArrayObject*) PyArray_FromArray(ary, ary->descr, NPY_FORTRAN); *is_new_object = 1; } return result; } /* Convert a given PyObject to a contiguous PyArrayObject of the * specified type. If the input object is not a contiguous * PyArrayObject, a new one will be created and the new object flag * will be set. */ PyArrayObject* obj_to_array_contiguous_allow_conversion(PyObject* input, int typecode, int* is_new_object) { int is_new1 = 0; int is_new2 = 0; PyArrayObject* ary2; PyArrayObject* ary1 = obj_to_array_allow_conversion(input, typecode, &is_new1); if (ary1) { ary2 = make_contiguous(ary1, &is_new2, 0, 0); if ( is_new1 && is_new2) { Py_DECREF(ary1); } ary1 = ary2; } *is_new_object = is_new1 || is_new2; return ary1; } /* Convert a given PyObject to a Fortran-ordered PyArrayObject of the * specified type. If the input object is not a Fortran-ordered * PyArrayObject, a new one will be created and the new object flag * will be set. */ PyArrayObject* obj_to_array_fortran_allow_conversion(PyObject* input, int typecode, int* is_new_object) { int is_new1 = 0; int is_new2 = 0; PyArrayObject* ary2; PyArrayObject* ary1 = obj_to_array_allow_conversion(input, typecode, &is_new1); if (ary1) { ary2 = make_fortran(ary1, &is_new2, 0, 0); if (is_new1 && is_new2) { Py_DECREF(ary1); } ary1 = ary2; } *is_new_object = is_new1 || is_new2; return ary1; } /* Test whether a python object is contiguous. If array is * contiguous, return 1. Otherwise, set the python error string and * return 0. */ int require_contiguous(PyArrayObject* ary) { int contiguous = 1; if (!array_is_contiguous(ary)) { PyErr_SetString(PyExc_TypeError, "Array must be contiguous. A non-contiguous array was given"); contiguous = 0; } return contiguous; } /* Require that a numpy array is not byte-swapped. If the array is * not byte-swapped, return 1. Otherwise, set the python error string * and return 0. */ int require_native(PyArrayObject* ary) { int native = 1; if (!array_is_native(ary)) { PyErr_SetString(PyExc_TypeError, "Array must have native byteorder. " "A byte-swapped array was given"); native = 0; } return native; } /* Require the given PyArrayObject to have a specified number of * dimensions. If the array has the specified number of dimensions, * return 1. Otherwise, set the python error string and return 0. */ int require_dimensions(PyArrayObject* ary, int exact_dimensions) { int success = 1; if (array_numdims(ary) != exact_dimensions) { PyErr_Format(PyExc_TypeError, "Array must have %d dimensions. Given array has %d dimensions", exact_dimensions, array_numdims(ary)); success = 0; } return success; } /* Require the given PyArrayObject to have one of a list of specified * number of dimensions. If the array has one of the specified number * of dimensions, return 1. Otherwise, set the python error string * and return 0. */ int require_dimensions_n(PyArrayObject* ary, int* exact_dimensions, int n) { int success = 0; int i; char dims_str[255] = ""; char s[255]; for (i = 0; i < n && !success; i++) { if (array_numdims(ary) == exact_dimensions[i]) { success = 1; } } if (!success) { for (i = 0; i < n-1; i++) { sprintf(s, "%d, ", exact_dimensions[i]); strcat(dims_str,s); } sprintf(s, " or %d", exact_dimensions[n-1]); strcat(dims_str,s); PyErr_Format(PyExc_TypeError, "Array must have %s dimensions. Given array has %d dimensions", dims_str, array_numdims(ary)); } return success; } /* Require the given PyArrayObject to have a specified shape. If the * array has the specified shape, return 1. Otherwise, set the python * error string and return 0. */ int require_size(PyArrayObject* ary, npy_intp* size, int n) { int i; int success = 1; int len; char desired_dims[255] = "["; char s[255]; char actual_dims[255] = "["; for(i=0; i < n;i++) { if (size[i] != -1 && size[i] != array_size(ary,i)) { success = 0; } } if (!success) { for (i = 0; i < n; i++) { if (size[i] == -1) { sprintf(s, "*,"); } else { sprintf(s, "%ld,", (long int)size[i]); } strcat(desired_dims,s); } len = strlen(desired_dims); desired_dims[len-1] = ']'; for (i = 0; i < n; i++) { sprintf(s, "%ld,", (long int)array_size(ary,i)); strcat(actual_dims,s); } len = strlen(actual_dims); actual_dims[len-1] = ']'; PyErr_Format(PyExc_TypeError, "Array must have shape of %s. Given array has shape of %s", desired_dims, actual_dims); } return success; } /* Require the given PyArrayObject to to be FORTRAN ordered. If the * the PyArrayObject is already FORTRAN ordered, do nothing. Else, * set the FORTRAN ordering flag and recompute the strides. */ int require_fortran(PyArrayObject* ary) { int success = 1; int nd = array_numdims(ary); int i; if (array_is_fortran(ary)) return success; /* Set the FORTRAN ordered flag */ ary->flags = NPY_FARRAY; /* Recompute the strides */ ary->strides[0] = ary->strides[nd-1]; for (i=1; i < nd; ++i) ary->strides[i] = ary->strides[i-1] * array_size(ary,i-1); return success; } #ifdef __cplusplus extern "C" { #endif SWIGINTERN PyObject *_wrap_fastexp(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; double *arg1 = (double *) 0 ; int arg2 ; double *arg3 = (double *) 0 ; int arg4 ; PyArrayObject *array1 = NULL ; int i1 = 1 ; PyArrayObject *array3 = NULL ; int i3 = 1 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OO:fastexp",&obj0,&obj1)) SWIG_fail; { array1 = obj_to_array_no_conversion(obj0, NPY_DOUBLE); if (!array1 || !require_dimensions(array1,1) || !require_contiguous(array1) || !require_native(array1)) SWIG_fail; arg1 = (double*) array_data(array1); arg2 = 1; for (i1=0; i1 < array_numdims(array1); ++i1) arg2 *= array_size(array1,i1); } { array3 = obj_to_array_no_conversion(obj1, NPY_DOUBLE); if (!array3 || !require_dimensions(array3,1) || !require_contiguous(array3) || !require_native(array3)) SWIG_fail; arg3 = (double*) array_data(array3); arg4 = 1; for (i3=0; i3 < array_numdims(array3); ++i3) arg4 *= array_size(array3,i3); } fastexp(arg1,arg2,arg3,arg4); resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } static PyMethodDef SwigMethods[] = { { (char *)"SWIG_PyInstanceMethod_New", (PyCFunction)SWIG_PyInstanceMethod_New, METH_O, NULL}, { (char *)"fastexp", _wrap_fastexp, METH_VARARGS, NULL}, { NULL, NULL, 0, NULL } }; /* -------- TYPE CONVERSION AND EQUIVALENCE RULES (BEGIN) -------- */ static swig_type_info _swigt__p_char = {"_p_char", "char *", 0, 0, (void*)0, 0}; static swig_type_info *swig_type_initial[] = { &_swigt__p_char, }; static swig_cast_info _swigc__p_char[] = { {&_swigt__p_char, 0, 0, 0},{0, 0, 0, 0}}; static swig_cast_info *swig_cast_initial[] = { _swigc__p_char, }; /* -------- TYPE CONVERSION AND EQUIVALENCE RULES (END) -------- */ static swig_const_info swig_const_table[] = { {0, 0, 0, 0.0, 0, 0}}; #ifdef __cplusplus } #endif /* ----------------------------------------------------------------------------- * Type initialization: * This problem is tough by the requirement that no dynamic * memory is used. Also, since swig_type_info structures store pointers to * swig_cast_info structures and swig_cast_info structures store pointers back * to swig_type_info structures, we need some lookup code at initialization. * The idea is that swig generates all the structures that are needed. * The runtime then collects these partially filled structures. * The SWIG_InitializeModule function takes these initial arrays out of * swig_module, and does all the lookup, filling in the swig_module.types * array with the correct data and linking the correct swig_cast_info * structures together. * * The generated swig_type_info structures are assigned staticly to an initial * array. We just loop through that array, and handle each type individually. * First we lookup if this type has been already loaded, and if so, use the * loaded structure instead of the generated one. Then we have to fill in the * cast linked list. The cast data is initially stored in something like a * two-dimensional array. Each row corresponds to a type (there are the same * number of rows as there are in the swig_type_initial array). Each entry in * a column is one of the swig_cast_info structures for that type. * The cast_initial array is actually an array of arrays, because each row has * a variable number of columns. So to actually build the cast linked list, * we find the array of casts associated with the type, and loop through it * adding the casts to the list. The one last trick we need to do is making * sure the type pointer in the swig_cast_info struct is correct. * * First off, we lookup the cast->type name to see if it is already loaded. * There are three cases to handle: * 1) If the cast->type has already been loaded AND the type we are adding * casting info to has not been loaded (it is in this module), THEN we * replace the cast->type pointer with the type pointer that has already * been loaded. * 2) If BOTH types (the one we are adding casting info to, and the * cast->type) are loaded, THEN the cast info has already been loaded by * the previous module so we just ignore it. * 3) Finally, if cast->type has not already been loaded, then we add that * swig_cast_info to the linked list (because the cast->type) pointer will * be correct. * ----------------------------------------------------------------------------- */ #ifdef __cplusplus extern "C" { #if 0 } /* c-mode */ #endif #endif #if 0 #define SWIGRUNTIME_DEBUG #endif SWIGRUNTIME void SWIG_InitializeModule(void *clientdata) { size_t i; swig_module_info *module_head, *iter; int found, init; clientdata = clientdata; /* check to see if the circular list has been setup, if not, set it up */ if (swig_module.next==0) { /* Initialize the swig_module */ swig_module.type_initial = swig_type_initial; swig_module.cast_initial = swig_cast_initial; swig_module.next = &swig_module; init = 1; } else { init = 0; } /* Try and load any already created modules */ module_head = SWIG_GetModule(clientdata); if (!module_head) { /* This is the first module loaded for this interpreter */ /* so set the swig module into the interpreter */ SWIG_SetModule(clientdata, &swig_module); module_head = &swig_module; } else { /* the interpreter has loaded a SWIG module, but has it loaded this one? */ found=0; iter=module_head; do { if (iter==&swig_module) { found=1; break; } iter=iter->next; } while (iter!= module_head); /* if the is found in the list, then all is done and we may leave */ if (found) return; /* otherwise we must add out module into the list */ swig_module.next = module_head->next; module_head->next = &swig_module; } /* When multiple interpeters are used, a module could have already been initialized in a different interpreter, but not yet have a pointer in this interpreter. In this case, we do not want to continue adding types... everything should be set up already */ if (init == 0) return; /* Now work on filling in swig_module.types */ #ifdef SWIGRUNTIME_DEBUG printf("SWIG_InitializeModule: size %d\n", swig_module.size); #endif for (i = 0; i < swig_module.size; ++i) { swig_type_info *type = 0; swig_type_info *ret; swig_cast_info *cast; #ifdef SWIGRUNTIME_DEBUG printf("SWIG_InitializeModule: type %d %s\n", i, swig_module.type_initial[i]->name); #endif /* if there is another module already loaded */ if (swig_module.next != &swig_module) { type = SWIG_MangledTypeQueryModule(swig_module.next, &swig_module, swig_module.type_initial[i]->name); } if (type) { /* Overwrite clientdata field */ #ifdef SWIGRUNTIME_DEBUG printf("SWIG_InitializeModule: found type %s\n", type->name); #endif if (swig_module.type_initial[i]->clientdata) { type->clientdata = swig_module.type_initial[i]->clientdata; #ifdef SWIGRUNTIME_DEBUG printf("SWIG_InitializeModule: found and overwrite type %s \n", type->name); #endif } } else { type = swig_module.type_initial[i]; } /* Insert casting types */ cast = swig_module.cast_initial[i]; while (cast->type) { /* Don't need to add information already in the list */ ret = 0; #ifdef SWIGRUNTIME_DEBUG printf("SWIG_InitializeModule: look cast %s\n", cast->type->name); #endif if (swig_module.next != &swig_module) { ret = SWIG_MangledTypeQueryModule(swig_module.next, &swig_module, cast->type->name); #ifdef SWIGRUNTIME_DEBUG if (ret) printf("SWIG_InitializeModule: found cast %s\n", ret->name); #endif } if (ret) { if (type == swig_module.type_initial[i]) { #ifdef SWIGRUNTIME_DEBUG printf("SWIG_InitializeModule: skip old type %s\n", ret->name); #endif cast->type = ret; ret = 0; } else { /* Check for casting already in the list */ swig_cast_info *ocast = SWIG_TypeCheck(ret->name, type); #ifdef SWIGRUNTIME_DEBUG if (ocast) printf("SWIG_InitializeModule: skip old cast %s\n", ret->name); #endif if (!ocast) ret = 0; } } if (!ret) { #ifdef SWIGRUNTIME_DEBUG printf("SWIG_InitializeModule: adding cast %s\n", cast->type->name); #endif if (type->cast) { type->cast->prev = cast; cast->next = type->cast; } type->cast = cast; } cast++; } /* Set entry in modules->types array equal to the type */ swig_module.types[i] = type; } swig_module.types[i] = 0; #ifdef SWIGRUNTIME_DEBUG printf("**** SWIG_InitializeModule: Cast List ******\n"); for (i = 0; i < swig_module.size; ++i) { int j = 0; swig_cast_info *cast = swig_module.cast_initial[i]; printf("SWIG_InitializeModule: type %d %s\n", i, swig_module.type_initial[i]->name); while (cast->type) { printf("SWIG_InitializeModule: cast type %s\n", cast->type->name); cast++; ++j; } printf("---- Total casts: %d\n",j); } printf("**** SWIG_InitializeModule: Cast List ******\n"); #endif } /* This function will propagate the clientdata field of type to * any new swig_type_info structures that have been added into the list * of equivalent types. It is like calling * SWIG_TypeClientData(type, clientdata) a second time. */ SWIGRUNTIME void SWIG_PropagateClientData(void) { size_t i; swig_cast_info *equiv; static int init_run = 0; if (init_run) return; init_run = 1; for (i = 0; i < swig_module.size; i++) { if (swig_module.types[i]->clientdata) { equiv = swig_module.types[i]->cast; while (equiv) { if (!equiv->converter) { if (equiv->type && !equiv->type->clientdata) SWIG_TypeClientData(equiv->type, swig_module.types[i]->clientdata); } equiv = equiv->next; } } } } #ifdef __cplusplus #if 0 { /* c-mode */ #endif } #endif #ifdef __cplusplus extern "C" { #endif /* Python-specific SWIG API */ #define SWIG_newvarlink() SWIG_Python_newvarlink() #define SWIG_addvarlink(p, name, get_attr, set_attr) SWIG_Python_addvarlink(p, name, get_attr, set_attr) #define SWIG_InstallConstants(d, constants) SWIG_Python_InstallConstants(d, constants) /* ----------------------------------------------------------------------------- * global variable support code. * ----------------------------------------------------------------------------- */ typedef struct swig_globalvar { char *name; /* Name of global variable */ PyObject *(*get_attr)(void); /* Return the current value */ int (*set_attr)(PyObject *); /* Set the value */ struct swig_globalvar *next; } swig_globalvar; typedef struct swig_varlinkobject { PyObject_HEAD swig_globalvar *vars; } swig_varlinkobject; SWIGINTERN PyObject * swig_varlink_repr(swig_varlinkobject *SWIGUNUSEDPARM(v)) { #if PY_VERSION_HEX >= 0x03000000 return PyUnicode_InternFromString(""); #else return PyString_FromString(""); #endif } SWIGINTERN PyObject * swig_varlink_str(swig_varlinkobject *v) { #if PY_VERSION_HEX >= 0x03000000 PyObject *str = PyUnicode_InternFromString("("); PyObject *tail; PyObject *joined; swig_globalvar *var; for (var = v->vars; var; var=var->next) { tail = PyUnicode_FromString(var->name); joined = PyUnicode_Concat(str, tail); Py_DecRef(str); Py_DecRef(tail); str = joined; if (var->next) { tail = PyUnicode_InternFromString(", "); joined = PyUnicode_Concat(str, tail); Py_DecRef(str); Py_DecRef(tail); str = joined; } } tail = PyUnicode_InternFromString(")"); joined = PyUnicode_Concat(str, tail); Py_DecRef(str); Py_DecRef(tail); str = joined; #else PyObject *str = PyString_FromString("("); swig_globalvar *var; for (var = v->vars; var; var=var->next) { PyString_ConcatAndDel(&str,PyString_FromString(var->name)); if (var->next) PyString_ConcatAndDel(&str,PyString_FromString(", ")); } PyString_ConcatAndDel(&str,PyString_FromString(")")); #endif return str; } SWIGINTERN int swig_varlink_print(swig_varlinkobject *v, FILE *fp, int SWIGUNUSEDPARM(flags)) { char *tmp; PyObject *str = swig_varlink_str(v); fprintf(fp,"Swig global variables "); fprintf(fp,"%s\n", tmp = SWIG_Python_str_AsChar(str)); SWIG_Python_str_DelForPy3(tmp); Py_DECREF(str); return 0; } SWIGINTERN void swig_varlink_dealloc(swig_varlinkobject *v) { swig_globalvar *var = v->vars; while (var) { swig_globalvar *n = var->next; free(var->name); free(var); var = n; } } SWIGINTERN PyObject * swig_varlink_getattr(swig_varlinkobject *v, char *n) { PyObject *res = NULL; swig_globalvar *var = v->vars; while (var) { if (strcmp(var->name,n) == 0) { res = (*var->get_attr)(); break; } var = var->next; } if (res == NULL && !PyErr_Occurred()) { PyErr_SetString(PyExc_NameError,"Unknown C global variable"); } return res; } SWIGINTERN int swig_varlink_setattr(swig_varlinkobject *v, char *n, PyObject *p) { int res = 1; swig_globalvar *var = v->vars; while (var) { if (strcmp(var->name,n) == 0) { res = (*var->set_attr)(p); break; } var = var->next; } if (res == 1 && !PyErr_Occurred()) { PyErr_SetString(PyExc_NameError,"Unknown C global variable"); } return res; } SWIGINTERN PyTypeObject* swig_varlink_type(void) { static char varlink__doc__[] = "Swig var link object"; static PyTypeObject varlink_type; static int type_init = 0; if (!type_init) { const PyTypeObject tmp = { /* PyObject header changed in Python 3 */ #if PY_VERSION_HEX >= 0x03000000 PyVarObject_HEAD_INIT(NULL, 0) #else PyObject_HEAD_INIT(NULL) 0, /* ob_size */ #endif (char *)"swigvarlink", /* tp_name */ sizeof(swig_varlinkobject), /* tp_basicsize */ 0, /* tp_itemsize */ (destructor) swig_varlink_dealloc, /* tp_dealloc */ (printfunc) swig_varlink_print, /* tp_print */ (getattrfunc) swig_varlink_getattr, /* tp_getattr */ (setattrfunc) swig_varlink_setattr, /* tp_setattr */ 0, /* tp_compare */ (reprfunc) swig_varlink_repr, /* tp_repr */ 0, /* tp_as_number */ 0, /* tp_as_sequence */ 0, /* tp_as_mapping */ 0, /* tp_hash */ 0, /* tp_call */ (reprfunc) swig_varlink_str, /* tp_str */ 0, /* tp_getattro */ 0, /* tp_setattro */ 0, /* tp_as_buffer */ 0, /* tp_flags */ varlink__doc__, /* tp_doc */ 0, /* tp_traverse */ 0, /* tp_clear */ 0, /* tp_richcompare */ 0, /* tp_weaklistoffset */ #if PY_VERSION_HEX >= 0x02020000 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, /* tp_iter -> tp_weaklist */ #endif #if PY_VERSION_HEX >= 0x02030000 0, /* tp_del */ #endif #if PY_VERSION_HEX >= 0x02060000 0, /* tp_version */ #endif #ifdef COUNT_ALLOCS 0,0,0,0 /* tp_alloc -> tp_next */ #endif }; varlink_type = tmp; type_init = 1; #if PY_VERSION_HEX < 0x02020000 varlink_type.ob_type = &PyType_Type; #else if (PyType_Ready(&varlink_type) < 0) return NULL; #endif } return &varlink_type; } /* Create a variable linking object for use later */ SWIGINTERN PyObject * SWIG_Python_newvarlink(void) { swig_varlinkobject *result = PyObject_NEW(swig_varlinkobject, swig_varlink_type()); if (result) { result->vars = 0; } return ((PyObject*) result); } SWIGINTERN void SWIG_Python_addvarlink(PyObject *p, char *name, PyObject *(*get_attr)(void), int (*set_attr)(PyObject *p)) { swig_varlinkobject *v = (swig_varlinkobject *) p; swig_globalvar *gv = (swig_globalvar *) malloc(sizeof(swig_globalvar)); if (gv) { size_t size = strlen(name)+1; gv->name = (char *)malloc(size); if (gv->name) { strncpy(gv->name,name,size); gv->get_attr = get_attr; gv->set_attr = set_attr; gv->next = v->vars; } } v->vars = gv; } SWIGINTERN PyObject * SWIG_globals(void) { static PyObject *_SWIG_globals = 0; if (!_SWIG_globals) _SWIG_globals = SWIG_newvarlink(); return _SWIG_globals; } /* ----------------------------------------------------------------------------- * constants/methods manipulation * ----------------------------------------------------------------------------- */ /* Install Constants */ SWIGINTERN void SWIG_Python_InstallConstants(PyObject *d, swig_const_info constants[]) { PyObject *obj = 0; size_t i; for (i = 0; constants[i].type; ++i) { switch(constants[i].type) { case SWIG_PY_POINTER: obj = SWIG_InternalNewPointerObj(constants[i].pvalue, *(constants[i]).ptype,0); break; case SWIG_PY_BINARY: obj = SWIG_NewPackedObj(constants[i].pvalue, constants[i].lvalue, *(constants[i].ptype)); break; default: obj = 0; break; } if (obj) { PyDict_SetItemString(d, constants[i].name, obj); Py_DECREF(obj); } } } /* -----------------------------------------------------------------------------*/ /* Fix SwigMethods to carry the callback ptrs when needed */ /* -----------------------------------------------------------------------------*/ SWIGINTERN void SWIG_Python_FixMethods(PyMethodDef *methods, swig_const_info *const_table, swig_type_info **types, swig_type_info **types_initial) { size_t i; for (i = 0; methods[i].ml_name; ++i) { const char *c = methods[i].ml_doc; if (c && (c = strstr(c, "swig_ptr: "))) { int j; swig_const_info *ci = 0; const char *name = c + 10; for (j = 0; const_table[j].type; ++j) { if (strncmp(const_table[j].name, name, strlen(const_table[j].name)) == 0) { ci = &(const_table[j]); break; } } if (ci) { void *ptr = (ci->type == SWIG_PY_POINTER) ? ci->pvalue : 0; if (ptr) { size_t shift = (ci->ptype) - types; swig_type_info *ty = types_initial[shift]; size_t ldoc = (c - methods[i].ml_doc); size_t lptr = strlen(ty->name)+2*sizeof(void*)+2; char *ndoc = (char*)malloc(ldoc + lptr + 10); if (ndoc) { char *buff = ndoc; strncpy(buff, methods[i].ml_doc, ldoc); buff += ldoc; strncpy(buff, "swig_ptr: ", 10); buff += 10; SWIG_PackVoidPtr(buff, ptr, ty->name, lptr); methods[i].ml_doc = ndoc; } } } } } } #ifdef __cplusplus } #endif /* -----------------------------------------------------------------------------* * Partial Init method * -----------------------------------------------------------------------------*/ #ifdef __cplusplus extern "C" #endif SWIGEXPORT #if PY_VERSION_HEX >= 0x03000000 PyObject* #else void #endif SWIG_init(void) { PyObject *m, *d, *md; #if PY_VERSION_HEX >= 0x03000000 static struct PyModuleDef SWIG_module = { # if PY_VERSION_HEX >= 0x03020000 PyModuleDef_HEAD_INIT, # else { PyObject_HEAD_INIT(NULL) NULL, /* m_init */ 0, /* m_index */ NULL, /* m_copy */ }, # endif (char *) SWIG_name, NULL, -1, SwigMethods, NULL, NULL, NULL, NULL }; #endif #if defined(SWIGPYTHON_BUILTIN) static SwigPyClientData SwigPyObject_clientdata = { 0, 0, 0, 0, 0, 0, 0 }; static PyGetSetDef this_getset_def = { (char *)"this", &SwigPyBuiltin_ThisClosure, NULL, NULL, NULL }; static SwigPyGetSet thisown_getset_closure = { (PyCFunction) SwigPyObject_own, (PyCFunction) SwigPyObject_own }; static PyGetSetDef thisown_getset_def = { (char *)"thisown", SwigPyBuiltin_GetterClosure, SwigPyBuiltin_SetterClosure, NULL, &thisown_getset_closure }; PyObject *metatype_args; PyTypeObject *builtin_pytype; int builtin_base_count; swig_type_info *builtin_basetype; PyObject *tuple; PyGetSetDescrObject *static_getset; PyTypeObject *metatype; SwigPyClientData *cd; PyObject *public_interface, *public_symbol; PyObject *this_descr; PyObject *thisown_descr; int i; (void)builtin_pytype; (void)builtin_base_count; (void)builtin_basetype; (void)tuple; (void)static_getset; /* metatype is used to implement static member variables. */ metatype_args = Py_BuildValue("(s(O){})", "SwigPyObjectType", &PyType_Type); assert(metatype_args); metatype = (PyTypeObject *) PyType_Type.tp_call((PyObject *) &PyType_Type, metatype_args, NULL); assert(metatype); Py_DECREF(metatype_args); metatype->tp_setattro = (setattrofunc) &SwigPyObjectType_setattro; assert(PyType_Ready(metatype) >= 0); #endif /* Fix SwigMethods to carry the callback ptrs when needed */ SWIG_Python_FixMethods(SwigMethods, swig_const_table, swig_types, swig_type_initial); #if PY_VERSION_HEX >= 0x03000000 m = PyModule_Create(&SWIG_module); #else m = Py_InitModule((char *) SWIG_name, SwigMethods); #endif md = d = PyModule_GetDict(m); SWIG_InitializeModule(0); #ifdef SWIGPYTHON_BUILTIN SwigPyObject_stype = SWIG_MangledTypeQuery("_p_SwigPyObject"); assert(SwigPyObject_stype); cd = (SwigPyClientData*) SwigPyObject_stype->clientdata; if (!cd) { SwigPyObject_stype->clientdata = &SwigPyObject_clientdata; SwigPyObject_clientdata.pytype = SwigPyObject_TypeOnce(); } else if (SwigPyObject_TypeOnce()->tp_basicsize != cd->pytype->tp_basicsize) { PyErr_SetString(PyExc_RuntimeError, "Import error: attempted to load two incompatible swig-generated modules."); # if PY_VERSION_HEX >= 0x03000000 return NULL; # else return; # endif } /* All objects have a 'this' attribute */ this_descr = PyDescr_NewGetSet(SwigPyObject_type(), &this_getset_def); (void)this_descr; /* All objects have a 'thisown' attribute */ thisown_descr = PyDescr_NewGetSet(SwigPyObject_type(), &thisown_getset_def); (void)thisown_descr; public_interface = PyList_New(0); public_symbol = 0; (void)public_symbol; PyDict_SetItemString(md, "__all__", public_interface); Py_DECREF(public_interface); for (i = 0; SwigMethods[i].ml_name != NULL; ++i) SwigPyBuiltin_AddPublicSymbol(public_interface, SwigMethods[i].ml_name); for (i = 0; swig_const_table[i].name != 0; ++i) SwigPyBuiltin_AddPublicSymbol(public_interface, swig_const_table[i].name); #endif SWIG_InstallConstants(d,swig_const_table); import_array(); #if PY_VERSION_HEX >= 0x03000000 return m; #else return; #endif } brian-1.4.3/brian/utils/fastexp/fexp.c000066400000000000000000000047531266062647100176500ustar00rootroot00000000000000/* -------------------------------------------------------------- Fastest exponent function with 11-bit precision. Home page: www.imach.uran.ru/exp Copyright 2001-2002 by Dr. Raul N.Shakirov, IMach of RAS(UB), Phillip S. Pang, Ph.D. Biochemistry and Molecular Biophysics. Columbia University. NYC. All Rights Reserved. Permission has been granted to copy, distribute and modify software in any context without fee, including a commercial application, provided that the aforesaid copyright statement is present here as well as exhaustive description of changes. THE SOFTWARE IS DISTRIBUTED "AS IS". NO WARRANTY OF ANY KIND IS EXPRESSED OR IMPLIED. YOU USE AT YOUR OWN RISK. THE AUTHOR WILL NOT BE LIABLE FOR DATA LOSS, DAMAGES, LOSS OF PROFITS OR ANY OTHER KIND OF LOSS WHILE USING OR MISUSING THIS SOFTWARE. Method: A Fast, Compact Approximation of the Exponential Function Technical Report IDSIA-07-98 to appear in Neural Computation 11(4) Nicol N. Schraudolph ftp://ftp.idsia.ch/pub/nic/exp.ps.gz -------------------------------------------------------------- */ #include "fexp.h" /* Header file for fexp() function */ #ifdef __cplusplus extern "C" { #endif/*__cplusplus*/ /* -------------------------------------------------------------- Structure and constants for fexp() function and macros. -------------------------------------------------------------- */ union eco _eco; const double _eco_m = (1048576L/0.693147180559945309417232121458177); const double _eco_a = (1072693248L - 60801L); /* -------------------------------------------------------------- Name: fexp Purpose: 11-bit precision exponent for Intel x86. Usage: fexp (arg) Domain: Same as for standard exp() function (approximately -709 <= arg <= 709). Result: Approximate exp of arg, if within domain, otherwise undefined. -------------------------------------------------------------- */ inline double fexp (double arg) { #ifdef _MSC_VER #pragma warning(push) #pragma warning(disable: 4410) __asm fld _eco_m __asm fmul arg __asm fadd _eco_a __asm fistp _eco.n.j return _eco.d; #pragma warning(pop) #else /*_MSC_VER*/ return RFEXP (arg); #endif/*_MSC_VER*/ } #ifdef __cplusplus } #endif/*__cplusplus*/ brian-1.4.3/brian/utils/fastexp/fexp.h000066400000000000000000000105371266062647100176520ustar00rootroot00000000000000/* -------------------------------------------------------------- Header file for exponent function with 11-bit precision. Home page: www.imach.uran.ru/exp Copyright 2001-2002 by Dr. Raul N.Shakirov, IMach of RAS(UB), Phillip S. Pang, Ph.D. Biochemistry and Molecular Biophysics. Columbia University. NYC. All Rights Reserved. Permission has been granted to copy, distribute and modify software in any context without fee, including a commercial application, provided that the aforesaid copyright statement is present here as well as exhaustive description of changes. THE SOFTWARE IS DISTRIBUTED "AS IS". NO WARRANTY OF ANY KIND IS EXPRESSED OR IMPLIED. YOU USE AT YOUR OWN RISK. THE AUTHOR WILL NOT BE LIABLE FOR DATA LOSS, DAMAGES, LOSS OF PROFITS OR ANY OTHER KIND OF LOSS WHILE USING OR MISUSING THIS SOFTWARE. Method: A Fast, Compact Approximation of the Exponential Function Technical Report IDSIA-07-98 to appear in Neural Computation 11(4) Nicol N. Schraudolph ftp://ftp.idsia.ch/pub/nic/exp.ps.gz -------------------------------------------------------------- */ #ifndef FEXP_H #define FEXP_H #ifdef __cplusplus extern "C" { #endif/*__cplusplus*/ /* -------------------------------------------------------------- Structure and constants for fexp() function and macros. -------------------------------------------------------------- */ union eco { double d; struct {long i, j;} n; }; extern union eco _eco; extern const double _eco_m; extern const double _eco_a; /* -------------------------------------------------------------- Name: fexp Purpose: 11-bit precision exponent for Intel x86. Usage: fexp (arg) Domain: Same as for standard exp() function (approximately -709 <= arg <= 709). Result: Approximate exp of arg, if within domain, otherwise undefined. -------------------------------------------------------------- */ extern double fexp (double arg); /* -------------------------------------------------------------- Name: RFEXP Purpose: ANSI compatible 11-bit precision exponent for machines, which support IEEE-754 and store least significant bit of integers first. Usage: RFEXP (arg) Domain: Same as for standard exp() function (approximately -709 <= arg <= 709). Result: Approximate exp of arg if within domain, otherwise undefined. -------------------------------------------------------------- */ #define RFEXP(v) (_eco.n.j = (long)(_eco_m * (v) + _eco_a), _eco.d) /* -------------------------------------------------------------- Name: LFEXP Purpose: ANSI compatible 11-bit precision exponent for machines, which support IEEE-754 and store the highest significant bit of integers first. Usage: LFEXP (arg) Domain: Same as for standard exp() function (approximately -709 <= arg <= 709). Result: Approximate exp of arg if within domain, otherwise undefined. -------------------------------------------------------------- */ #define LFEXP(v) (_eco.n.i = (long)(_eco_m * (v) + _eco_a), _eco.d) /* -------------------------------------------------------------- Name: USE_RFEXP Purpose: Check if machine supports IEEE-754 and stores the least significant bit of integers first. Usage: USE_RFEXP() Result: Non-zero value if yes, 0 if no. -------------------------------------------------------------- */ #define USE_RFEXP (eco.d = 1.0, (eco.n.j - 1072693248L || eco.n.i == 0)) /* -------------------------------------------------------------- Name: USE_LFEXP Purpose: Check if machine supports IEEE-754 and stores the highest significant bit of integers first. Usage: USE_LFEXP() Result: Non-zero value if yes, 0 if no. -------------------------------------------------------------- */ #define USE_LFEXP (eco.d = 1.0, (eco.n.i == 1072693248L || eco.n.j == 0)) #ifdef __cplusplus } #endif/*__cplusplus*/ #endif/*FEXP_H*/ brian-1.4.3/brian/utils/fastexp/numpy.i000066400000000000000000001517001266062647100200570ustar00rootroot00000000000000/* -*- C -*- (not really, but good for syntax highlighting) */ #ifdef SWIGPYTHON %{ #ifndef SWIG_FILE_WITH_INIT # define NO_IMPORT_ARRAY #endif #include "stdio.h" #include %} /**********************************************************************/ /* The following code originally appeared in * enthought/kiva/agg/src/numeric.i written by Eric Jones. It was * translated from C++ to C by John Hunter. Bill Spotz has modified * it to fix some minor bugs, upgrade from Numeric to numpy (all * versions), add some comments and functionality, and convert from * direct code insertion to SWIG fragments. */ %fragment("NumPy_Macros", "header") { /* Macros to extract array attributes. */ %#define is_array(a) ((a) && PyArray_Check((PyArrayObject *)a)) %#define array_type(a) (int)(PyArray_TYPE(a)) %#define array_numdims(a) (((PyArrayObject *)a)->nd) %#define array_dimensions(a) (((PyArrayObject *)a)->dimensions) %#define array_size(a,i) (((PyArrayObject *)a)->dimensions[i]) %#define array_data(a) (((PyArrayObject *)a)->data) %#define array_is_contiguous(a) (PyArray_ISCONTIGUOUS(a)) %#define array_is_native(a) (PyArray_ISNOTSWAPPED(a)) %#define array_is_fortran(a) (PyArray_ISFORTRAN(a)) } /**********************************************************************/ %fragment("NumPy_Utilities", "header") { /* Given a PyObject, return a string describing its type. */ const char* pytype_string(PyObject* py_obj) { if (py_obj == NULL ) return "C NULL value"; if (py_obj == Py_None ) return "Python None" ; if (PyCallable_Check(py_obj)) return "callable" ; if (PyString_Check( py_obj)) return "string" ; if (PyInt_Check( py_obj)) return "int" ; if (PyFloat_Check( py_obj)) return "float" ; if (PyDict_Check( py_obj)) return "dict" ; if (PyList_Check( py_obj)) return "list" ; if (PyTuple_Check( py_obj)) return "tuple" ; if (PyFile_Check( py_obj)) return "file" ; if (PyModule_Check( py_obj)) return "module" ; if (PyInstance_Check(py_obj)) return "instance" ; return "unkown type"; } /* Given a NumPy typecode, return a string describing the type. */ const char* typecode_string(int typecode) { static const char* type_names[25] = {"bool", "byte", "unsigned byte", "short", "unsigned short", "int", "unsigned int", "long", "unsigned long", "long long", "unsigned long long", "float", "double", "long double", "complex float", "complex double", "complex long double", "object", "string", "unicode", "void", "ntypes", "notype", "char", "unknown"}; return typecode < 24 ? type_names[typecode] : type_names[24]; } /* Make sure input has correct numpy type. Allow character and byte * to match. Also allow int and long to match. This is deprecated. * You should use PyArray_EquivTypenums() instead. */ int type_match(int actual_type, int desired_type) { return PyArray_EquivTypenums(actual_type, desired_type); } } /**********************************************************************/ %fragment("NumPy_Object_to_Array", "header", fragment="NumPy_Backward_Compatibility", fragment="NumPy_Macros", fragment="NumPy_Utilities") { /* Given a PyObject pointer, cast it to a PyArrayObject pointer if * legal. If not, set the python error string appropriately and * return NULL. */ PyArrayObject* obj_to_array_no_conversion(PyObject* input, int typecode) { PyArrayObject* ary = NULL; if (is_array(input) && (typecode == NPY_NOTYPE || PyArray_EquivTypenums(array_type(input), typecode))) { ary = (PyArrayObject*) input; } else if is_array(input) { const char* desired_type = typecode_string(typecode); const char* actual_type = typecode_string(array_type(input)); PyErr_Format(PyExc_TypeError, "Array of type '%s' required. Array of type '%s' given", desired_type, actual_type); ary = NULL; } else { const char * desired_type = typecode_string(typecode); const char * actual_type = pytype_string(input); PyErr_Format(PyExc_TypeError, "Array of type '%s' required. A '%s' was given", desired_type, actual_type); ary = NULL; } return ary; } /* Convert the given PyObject to a NumPy array with the given * typecode. On success, return a valid PyArrayObject* with the * correct type. On failure, the python error string will be set and * the routine returns NULL. */ PyArrayObject* obj_to_array_allow_conversion(PyObject* input, int typecode, int* is_new_object) { PyArrayObject* ary = NULL; PyObject* py_obj; if (is_array(input) && (typecode == NPY_NOTYPE || PyArray_EquivTypenums(array_type(input),typecode))) { ary = (PyArrayObject*) input; *is_new_object = 0; } else { py_obj = PyArray_FROMANY(input, typecode, 0, 0, NPY_DEFAULT); /* If NULL, PyArray_FromObject will have set python error value.*/ ary = (PyArrayObject*) py_obj; *is_new_object = 1; } return ary; } /* Given a PyArrayObject, check to see if it is contiguous. If so, * return the input pointer and flag it as not a new object. If it is * not contiguous, create a new PyArrayObject using the original data, * flag it as a new object and return the pointer. */ PyArrayObject* make_contiguous(PyArrayObject* ary, int* is_new_object, int min_dims, int max_dims) { PyArrayObject* result; if (array_is_contiguous(ary)) { result = ary; *is_new_object = 0; } else { result = (PyArrayObject*) PyArray_ContiguousFromObject((PyObject*)ary, array_type(ary), min_dims, max_dims); *is_new_object = 1; } return result; } /* Given a PyArrayObject, check to see if it is Fortran-contiguous. * If so, return the input pointer, but do not flag it as not a new * object. If it is not Fortran-contiguous, create a new * PyArrayObject using the original data, flag it as a new object * and return the pointer. */ PyArrayObject* make_fortran(PyArrayObject* ary, int* is_new_object, int min_dims, int max_dims) { PyArrayObject* result; if (array_is_fortran(ary)) { result = ary; *is_new_object = 0; } else { Py_INCREF(ary->descr); result = (PyArrayObject*) PyArray_FromArray(ary, ary->descr, NPY_FORTRAN); *is_new_object = 1; } return result; } /* Convert a given PyObject to a contiguous PyArrayObject of the * specified type. If the input object is not a contiguous * PyArrayObject, a new one will be created and the new object flag * will be set. */ PyArrayObject* obj_to_array_contiguous_allow_conversion(PyObject* input, int typecode, int* is_new_object) { int is_new1 = 0; int is_new2 = 0; PyArrayObject* ary2; PyArrayObject* ary1 = obj_to_array_allow_conversion(input, typecode, &is_new1); if (ary1) { ary2 = make_contiguous(ary1, &is_new2, 0, 0); if ( is_new1 && is_new2) { Py_DECREF(ary1); } ary1 = ary2; } *is_new_object = is_new1 || is_new2; return ary1; } /* Convert a given PyObject to a Fortran-ordered PyArrayObject of the * specified type. If the input object is not a Fortran-ordered * PyArrayObject, a new one will be created and the new object flag * will be set. */ PyArrayObject* obj_to_array_fortran_allow_conversion(PyObject* input, int typecode, int* is_new_object) { int is_new1 = 0; int is_new2 = 0; PyArrayObject* ary2; PyArrayObject* ary1 = obj_to_array_allow_conversion(input, typecode, &is_new1); if (ary1) { ary2 = make_fortran(ary1, &is_new2, 0, 0); if (is_new1 && is_new2) { Py_DECREF(ary1); } ary1 = ary2; } *is_new_object = is_new1 || is_new2; return ary1; } } /* end fragment */ /**********************************************************************/ %fragment("NumPy_Array_Requirements", "header", fragment="NumPy_Backward_Compatibility", fragment="NumPy_Macros") { /* Test whether a python object is contiguous. If array is * contiguous, return 1. Otherwise, set the python error string and * return 0. */ int require_contiguous(PyArrayObject* ary) { int contiguous = 1; if (!array_is_contiguous(ary)) { PyErr_SetString(PyExc_TypeError, "Array must be contiguous. A non-contiguous array was given"); contiguous = 0; } return contiguous; } /* Require that a numpy array is not byte-swapped. If the array is * not byte-swapped, return 1. Otherwise, set the python error string * and return 0. */ int require_native(PyArrayObject* ary) { int native = 1; if (!array_is_native(ary)) { PyErr_SetString(PyExc_TypeError, "Array must have native byteorder. " "A byte-swapped array was given"); native = 0; } return native; } /* Require the given PyArrayObject to have a specified number of * dimensions. If the array has the specified number of dimensions, * return 1. Otherwise, set the python error string and return 0. */ int require_dimensions(PyArrayObject* ary, int exact_dimensions) { int success = 1; if (array_numdims(ary) != exact_dimensions) { PyErr_Format(PyExc_TypeError, "Array must have %d dimensions. Given array has %d dimensions", exact_dimensions, array_numdims(ary)); success = 0; } return success; } /* Require the given PyArrayObject to have one of a list of specified * number of dimensions. If the array has one of the specified number * of dimensions, return 1. Otherwise, set the python error string * and return 0. */ int require_dimensions_n(PyArrayObject* ary, int* exact_dimensions, int n) { int success = 0; int i; char dims_str[255] = ""; char s[255]; for (i = 0; i < n && !success; i++) { if (array_numdims(ary) == exact_dimensions[i]) { success = 1; } } if (!success) { for (i = 0; i < n-1; i++) { sprintf(s, "%d, ", exact_dimensions[i]); strcat(dims_str,s); } sprintf(s, " or %d", exact_dimensions[n-1]); strcat(dims_str,s); PyErr_Format(PyExc_TypeError, "Array must have %s dimensions. Given array has %d dimensions", dims_str, array_numdims(ary)); } return success; } /* Require the given PyArrayObject to have a specified shape. If the * array has the specified shape, return 1. Otherwise, set the python * error string and return 0. */ int require_size(PyArrayObject* ary, npy_intp* size, int n) { int i; int success = 1; int len; char desired_dims[255] = "["; char s[255]; char actual_dims[255] = "["; for(i=0; i < n;i++) { if (size[i] != -1 && size[i] != array_size(ary,i)) { success = 0; } } if (!success) { for (i = 0; i < n; i++) { if (size[i] == -1) { sprintf(s, "*,"); } else { sprintf(s, "%ld,", (long int)size[i]); } strcat(desired_dims,s); } len = strlen(desired_dims); desired_dims[len-1] = ']'; for (i = 0; i < n; i++) { sprintf(s, "%ld,", (long int)array_size(ary,i)); strcat(actual_dims,s); } len = strlen(actual_dims); actual_dims[len-1] = ']'; PyErr_Format(PyExc_TypeError, "Array must have shape of %s. Given array has shape of %s", desired_dims, actual_dims); } return success; } /* Require the given PyArrayObject to to be FORTRAN ordered. If the * the PyArrayObject is already FORTRAN ordered, do nothing. Else, * set the FORTRAN ordering flag and recompute the strides. */ int require_fortran(PyArrayObject* ary) { int success = 1; int nd = array_numdims(ary); int i; if (array_is_fortran(ary)) return success; /* Set the FORTRAN ordered flag */ ary->flags = NPY_FARRAY; /* Recompute the strides */ ary->strides[0] = ary->strides[nd-1]; for (i=1; i < nd; ++i) ary->strides[i] = ary->strides[i-1] * array_size(ary,i-1); return success; } } /* Combine all NumPy fragments into one for convenience */ %fragment("NumPy_Fragments", "header", fragment="NumPy_Backward_Compatibility", fragment="NumPy_Macros", fragment="NumPy_Utilities", fragment="NumPy_Object_to_Array", fragment="NumPy_Array_Requirements") { } /* End John Hunter translation (with modifications by Bill Spotz) */ /* %numpy_typemaps() macro * * This macro defines a family of 41 typemaps that allow C arguments * of the form * * (DATA_TYPE IN_ARRAY1[ANY]) * (DATA_TYPE* IN_ARRAY1, DIM_TYPE DIM1) * (DIM_TYPE DIM1, DATA_TYPE* IN_ARRAY1) * * (DATA_TYPE IN_ARRAY2[ANY][ANY]) * (DATA_TYPE* IN_ARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2) * (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* IN_ARRAY2) * (DATA_TYPE* IN_FARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2) * (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* IN_FARRAY2) * * (DATA_TYPE IN_ARRAY3[ANY][ANY][ANY]) * (DATA_TYPE* IN_ARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3) * (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DATA_TYPE* IN_ARRAY3) * (DATA_TYPE* IN_FARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3) * (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DATA_TYPE* IN_FARRAY3) * * (DATA_TYPE INPLACE_ARRAY1[ANY]) * (DATA_TYPE* INPLACE_ARRAY1, DIM_TYPE DIM1) * (DIM_TYPE DIM1, DATA_TYPE* INPLACE_ARRAY1) * * (DATA_TYPE INPLACE_ARRAY2[ANY][ANY]) * (DATA_TYPE* INPLACE_ARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2) * (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* INPLACE_ARRAY2) * (DATA_TYPE* INPLACE_FARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2) * (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* INPLACE_FARRAY2) * * (DATA_TYPE INPLACE_ARRAY3[ANY][ANY][ANY]) * (DATA_TYPE* INPLACE_ARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3) * (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DATA_TYPE* INPLACE_ARRAY3) * (DATA_TYPE* INPLACE_FARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3) * (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DATA_TYPE* INPLACE_FARRAY3) * * (DATA_TYPE ARGOUT_ARRAY1[ANY]) * (DATA_TYPE* ARGOUT_ARRAY1, DIM_TYPE DIM1) * (DIM_TYPE DIM1, DATA_TYPE* ARGOUT_ARRAY1) * * (DATA_TYPE ARGOUT_ARRAY2[ANY][ANY]) * * (DATA_TYPE ARGOUT_ARRAY3[ANY][ANY][ANY]) * * (DATA_TYPE** ARGOUTVIEW_ARRAY1, DIM_TYPE* DIM1) * (DIM_TYPE* DIM1, DATA_TYPE** ARGOUTVIEW_ARRAY1) * * (DATA_TYPE** ARGOUTVIEW_ARRAY2, DIM_TYPE* DIM1, DIM_TYPE* DIM2) * (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DATA_TYPE** ARGOUTVIEW_ARRAY2) * (DATA_TYPE** ARGOUTVIEW_FARRAY2, DIM_TYPE* DIM1, DIM_TYPE* DIM2) * (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DATA_TYPE** ARGOUTVIEW_FARRAY2) * * (DATA_TYPE** ARGOUTVIEW_ARRAY3, DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3) * (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DATA_TYPE** ARGOUTVIEW_ARRAY3) * (DATA_TYPE** ARGOUTVIEW_FARRAY3, DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3) * (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DATA_TYPE** ARGOUTVIEW_FARRAY3) * * where "DATA_TYPE" is any type supported by the NumPy module, and * "DIM_TYPE" is any int-like type suitable for specifying dimensions. * The difference between "ARRAY" typemaps and "FARRAY" typemaps is * that the "FARRAY" typemaps expect FORTRAN ordering of * multidimensional arrays. In python, the dimensions will not need * to be specified (except for the "DATA_TYPE* ARGOUT_ARRAY1" * typemaps). The IN_ARRAYs can be a numpy array or any sequence that * can be converted to a numpy array of the specified type. The * INPLACE_ARRAYs must be numpy arrays of the appropriate type. The * ARGOUT_ARRAYs will be returned as new numpy arrays of the * appropriate type. * * These typemaps can be applied to existing functions using the * %apply directive. For example: * * %apply (double* IN_ARRAY1, int DIM1) {(double* series, int length)}; * double prod(double* series, int length); * * %apply (int DIM1, int DIM2, double* INPLACE_ARRAY2) * {(int rows, int cols, double* matrix )}; * void floor(int rows, int cols, double* matrix, double f); * * %apply (double IN_ARRAY3[ANY][ANY][ANY]) * {(double tensor[2][2][2] )}; * %apply (double ARGOUT_ARRAY3[ANY][ANY][ANY]) * {(double low[2][2][2] )}; * %apply (double ARGOUT_ARRAY3[ANY][ANY][ANY]) * {(double upp[2][2][2] )}; * void luSplit(double tensor[2][2][2], * double low[2][2][2], * double upp[2][2][2] ); * * or directly with * * double prod(double* IN_ARRAY1, int DIM1); * * void floor(int DIM1, int DIM2, double* INPLACE_ARRAY2, double f); * * void luSplit(double IN_ARRAY3[ANY][ANY][ANY], * double ARGOUT_ARRAY3[ANY][ANY][ANY], * double ARGOUT_ARRAY3[ANY][ANY][ANY]); */ %define %numpy_typemaps(DATA_TYPE, DATA_TYPECODE, DIM_TYPE) /************************/ /* Input Array Typemaps */ /************************/ /* Typemap suite for (DATA_TYPE IN_ARRAY1[ANY]) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DATA_TYPE IN_ARRAY1[ANY]) { $1 = is_array($input) || PySequence_Check($input); } %typemap(in, fragment="NumPy_Fragments") (DATA_TYPE IN_ARRAY1[ANY]) (PyArrayObject* array=NULL, int is_new_object=0) { npy_intp size[1] = { $1_dim0 }; array = obj_to_array_contiguous_allow_conversion($input, DATA_TYPECODE, &is_new_object); if (!array || !require_dimensions(array, 1) || !require_size(array, size, 1)) SWIG_fail; $1 = ($1_ltype) array_data(array); } %typemap(freearg) (DATA_TYPE IN_ARRAY1[ANY]) { if (is_new_object$argnum && array$argnum) { Py_DECREF(array$argnum); } } /* Typemap suite for (DATA_TYPE* IN_ARRAY1, DIM_TYPE DIM1) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DATA_TYPE* IN_ARRAY1, DIM_TYPE DIM1) { $1 = is_array($input) || PySequence_Check($input); } %typemap(in, fragment="NumPy_Fragments") (DATA_TYPE* IN_ARRAY1, DIM_TYPE DIM1) (PyArrayObject* array=NULL, int is_new_object=0) { npy_intp size[1] = { -1 }; array = obj_to_array_contiguous_allow_conversion($input, DATA_TYPECODE, &is_new_object); if (!array || !require_dimensions(array, 1) || !require_size(array, size, 1)) SWIG_fail; $1 = (DATA_TYPE*) array_data(array); $2 = (DIM_TYPE) array_size(array,0); } %typemap(freearg) (DATA_TYPE* IN_ARRAY1, DIM_TYPE DIM1) { if (is_new_object$argnum && array$argnum) { Py_DECREF(array$argnum); } } /* Typemap suite for (DIM_TYPE DIM1, DATA_TYPE* IN_ARRAY1) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DIM_TYPE DIM1, DATA_TYPE* IN_ARRAY1) { $1 = is_array($input) || PySequence_Check($input); } %typemap(in, fragment="NumPy_Fragments") (DIM_TYPE DIM1, DATA_TYPE* IN_ARRAY1) (PyArrayObject* array=NULL, int is_new_object=0) { npy_intp size[1] = {-1}; array = obj_to_array_contiguous_allow_conversion($input, DATA_TYPECODE, &is_new_object); if (!array || !require_dimensions(array, 1) || !require_size(array, size, 1)) SWIG_fail; $1 = (DIM_TYPE) array_size(array,0); $2 = (DATA_TYPE*) array_data(array); } %typemap(freearg) (DIM_TYPE DIM1, DATA_TYPE* IN_ARRAY1) { if (is_new_object$argnum && array$argnum) { Py_DECREF(array$argnum); } } /* Typemap suite for (DATA_TYPE IN_ARRAY2[ANY][ANY]) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DATA_TYPE IN_ARRAY2[ANY][ANY]) { $1 = is_array($input) || PySequence_Check($input); } %typemap(in, fragment="NumPy_Fragments") (DATA_TYPE IN_ARRAY2[ANY][ANY]) (PyArrayObject* array=NULL, int is_new_object=0) { npy_intp size[2] = { $1_dim0, $1_dim1 }; array = obj_to_array_contiguous_allow_conversion($input, DATA_TYPECODE, &is_new_object); if (!array || !require_dimensions(array, 2) || !require_size(array, size, 2)) SWIG_fail; $1 = ($1_ltype) array_data(array); } %typemap(freearg) (DATA_TYPE IN_ARRAY2[ANY][ANY]) { if (is_new_object$argnum && array$argnum) { Py_DECREF(array$argnum); } } /* Typemap suite for (DATA_TYPE* IN_ARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DATA_TYPE* IN_ARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2) { $1 = is_array($input) || PySequence_Check($input); } %typemap(in, fragment="NumPy_Fragments") (DATA_TYPE* IN_ARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2) (PyArrayObject* array=NULL, int is_new_object=0) { npy_intp size[2] = { -1, -1 }; array = obj_to_array_contiguous_allow_conversion($input, DATA_TYPECODE, &is_new_object); if (!array || !require_dimensions(array, 2) || !require_size(array, size, 2)) SWIG_fail; $1 = (DATA_TYPE*) array_data(array); $2 = (DIM_TYPE) array_size(array,0); $3 = (DIM_TYPE) array_size(array,1); } %typemap(freearg) (DATA_TYPE* IN_ARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2) { if (is_new_object$argnum && array$argnum) { Py_DECREF(array$argnum); } } /* Typemap suite for (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* IN_ARRAY2) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* IN_ARRAY2) { $1 = is_array($input) || PySequence_Check($input); } %typemap(in, fragment="NumPy_Fragments") (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* IN_ARRAY2) (PyArrayObject* array=NULL, int is_new_object=0) { npy_intp size[2] = { -1, -1 }; array = obj_to_array_contiguous_allow_conversion($input, DATA_TYPECODE, &is_new_object); if (!array || !require_dimensions(array, 2) || !require_size(array, size, 2)) SWIG_fail; $1 = (DIM_TYPE) array_size(array,0); $2 = (DIM_TYPE) array_size(array,1); $3 = (DATA_TYPE*) array_data(array); } %typemap(freearg) (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* IN_ARRAY2) { if (is_new_object$argnum && array$argnum) { Py_DECREF(array$argnum); } } /* Typemap suite for (DATA_TYPE* IN_FARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DATA_TYPE* IN_FARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2) { $1 = is_array($input) || PySequence_Check($input); } %typemap(in, fragment="NumPy_Fragments") (DATA_TYPE* IN_FARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2) (PyArrayObject* array=NULL, int is_new_object=0) { npy_intp size[2] = { -1, -1 }; array = obj_to_array_fortran_allow_conversion($input, DATA_TYPECODE, &is_new_object); if (!array || !require_dimensions(array, 2) || !require_size(array, size, 2) || !require_fortran(array)) SWIG_fail; $1 = (DATA_TYPE*) array_data(array); $2 = (DIM_TYPE) array_size(array,0); $3 = (DIM_TYPE) array_size(array,1); } %typemap(freearg) (DATA_TYPE* IN_FARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2) { if (is_new_object$argnum && array$argnum) { Py_DECREF(array$argnum); } } /* Typemap suite for (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* IN_FARRAY2) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* IN_FARRAY2) { $1 = is_array($input) || PySequence_Check($input); } %typemap(in, fragment="NumPy_Fragments") (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* IN_FARRAY2) (PyArrayObject* array=NULL, int is_new_object=0) { npy_intp size[2] = { -1, -1 }; array = obj_to_array_contiguous_allow_conversion($input, DATA_TYPECODE, &is_new_object); if (!array || !require_dimensions(array, 2) || !require_size(array, size, 2) || !require_fortran(array)) SWIG_fail; $1 = (DIM_TYPE) array_size(array,0); $2 = (DIM_TYPE) array_size(array,1); $3 = (DATA_TYPE*) array_data(array); } %typemap(freearg) (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* IN_FARRAY2) { if (is_new_object$argnum && array$argnum) { Py_DECREF(array$argnum); } } /* Typemap suite for (DATA_TYPE IN_ARRAY3[ANY][ANY][ANY]) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DATA_TYPE IN_ARRAY3[ANY][ANY][ANY]) { $1 = is_array($input) || PySequence_Check($input); } %typemap(in, fragment="NumPy_Fragments") (DATA_TYPE IN_ARRAY3[ANY][ANY][ANY]) (PyArrayObject* array=NULL, int is_new_object=0) { npy_intp size[3] = { $1_dim0, $1_dim1, $1_dim2 }; array = obj_to_array_contiguous_allow_conversion($input, DATA_TYPECODE, &is_new_object); if (!array || !require_dimensions(array, 3) || !require_size(array, size, 3)) SWIG_fail; $1 = ($1_ltype) array_data(array); } %typemap(freearg) (DATA_TYPE IN_ARRAY3[ANY][ANY][ANY]) { if (is_new_object$argnum && array$argnum) { Py_DECREF(array$argnum); } } /* Typemap suite for (DATA_TYPE* IN_ARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, * DIM_TYPE DIM3) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DATA_TYPE* IN_ARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3) { $1 = is_array($input) || PySequence_Check($input); } %typemap(in, fragment="NumPy_Fragments") (DATA_TYPE* IN_ARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3) (PyArrayObject* array=NULL, int is_new_object=0) { npy_intp size[3] = { -1, -1, -1 }; array = obj_to_array_contiguous_allow_conversion($input, DATA_TYPECODE, &is_new_object); if (!array || !require_dimensions(array, 3) || !require_size(array, size, 3)) SWIG_fail; $1 = (DATA_TYPE*) array_data(array); $2 = (DIM_TYPE) array_size(array,0); $3 = (DIM_TYPE) array_size(array,1); $4 = (DIM_TYPE) array_size(array,2); } %typemap(freearg) (DATA_TYPE* IN_ARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3) { if (is_new_object$argnum && array$argnum) { Py_DECREF(array$argnum); } } /* Typemap suite for (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, * DATA_TYPE* IN_ARRAY3) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DATA_TYPE* IN_ARRAY3) { $1 = is_array($input) || PySequence_Check($input); } %typemap(in, fragment="NumPy_Fragments") (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DATA_TYPE* IN_ARRAY3) (PyArrayObject* array=NULL, int is_new_object=0) { npy_intp size[3] = { -1, -1, -1 }; array = obj_to_array_contiguous_allow_conversion($input, DATA_TYPECODE, &is_new_object); if (!array || !require_dimensions(array, 3) || !require_size(array, size, 3)) SWIG_fail; $1 = (DIM_TYPE) array_size(array,0); $2 = (DIM_TYPE) array_size(array,1); $3 = (DIM_TYPE) array_size(array,2); $4 = (DATA_TYPE*) array_data(array); } %typemap(freearg) (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DATA_TYPE* IN_ARRAY3) { if (is_new_object$argnum && array$argnum) { Py_DECREF(array$argnum); } } /* Typemap suite for (DATA_TYPE* IN_FARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, * DIM_TYPE DIM3) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DATA_TYPE* IN_FARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3) { $1 = is_array($input) || PySequence_Check($input); } %typemap(in, fragment="NumPy_Fragments") (DATA_TYPE* IN_FARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3) (PyArrayObject* array=NULL, int is_new_object=0) { npy_intp size[3] = { -1, -1, -1 }; array = obj_to_array_fortran_allow_conversion($input, DATA_TYPECODE, &is_new_object); if (!array || !require_dimensions(array, 3) || !require_size(array, size, 3) | !require_fortran(array)) SWIG_fail; $1 = (DATA_TYPE*) array_data(array); $2 = (DIM_TYPE) array_size(array,0); $3 = (DIM_TYPE) array_size(array,1); $4 = (DIM_TYPE) array_size(array,2); } %typemap(freearg) (DATA_TYPE* IN_FARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3) { if (is_new_object$argnum && array$argnum) { Py_DECREF(array$argnum); } } /* Typemap suite for (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, * DATA_TYPE* IN_FARRAY3) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DATA_TYPE* IN_FARRAY3) { $1 = is_array($input) || PySequence_Check($input); } %typemap(in, fragment="NumPy_Fragments") (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DATA_TYPE* IN_FARRAY3) (PyArrayObject* array=NULL, int is_new_object=0) { npy_intp size[3] = { -1, -1, -1 }; array = obj_to_array_contiguous_allow_conversion($input, DATA_TYPECODE, &is_new_object); if (!array || !require_dimensions(array, 3) || !require_size(array, size, 3) || !require_fortran(array)) SWIG_fail; $1 = (DIM_TYPE) array_size(array,0); $2 = (DIM_TYPE) array_size(array,1); $3 = (DIM_TYPE) array_size(array,2); $4 = (DATA_TYPE*) array_data(array); } %typemap(freearg) (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DATA_TYPE* IN_FARRAY3) { if (is_new_object$argnum && array$argnum) { Py_DECREF(array$argnum); } } /***************************/ /* In-Place Array Typemaps */ /***************************/ /* Typemap suite for (DATA_TYPE INPLACE_ARRAY1[ANY]) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DATA_TYPE INPLACE_ARRAY1[ANY]) { $1 = is_array($input) && PyArray_EquivTypenums(array_type($input), DATA_TYPECODE); } %typemap(in, fragment="NumPy_Fragments") (DATA_TYPE INPLACE_ARRAY1[ANY]) (PyArrayObject* array=NULL) { npy_intp size[1] = { $1_dim0 }; array = obj_to_array_no_conversion($input, DATA_TYPECODE); if (!array || !require_dimensions(array,1) || !require_size(array, size, 1) || !require_contiguous(array) || !require_native(array)) SWIG_fail; $1 = ($1_ltype) array_data(array); } /* Typemap suite for (DATA_TYPE* INPLACE_ARRAY1, DIM_TYPE DIM1) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DATA_TYPE* INPLACE_ARRAY1, DIM_TYPE DIM1) { $1 = is_array($input) && PyArray_EquivTypenums(array_type($input), DATA_TYPECODE); } %typemap(in, fragment="NumPy_Fragments") (DATA_TYPE* INPLACE_ARRAY1, DIM_TYPE DIM1) (PyArrayObject* array=NULL, int i=1) { array = obj_to_array_no_conversion($input, DATA_TYPECODE); if (!array || !require_dimensions(array,1) || !require_contiguous(array) || !require_native(array)) SWIG_fail; $1 = (DATA_TYPE*) array_data(array); $2 = 1; for (i=0; i < array_numdims(array); ++i) $2 *= array_size(array,i); } /* Typemap suite for (DIM_TYPE DIM1, DATA_TYPE* INPLACE_ARRAY1) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DIM_TYPE DIM1, DATA_TYPE* INPLACE_ARRAY1) { $1 = is_array($input) && PyArray_EquivTypenums(array_type($input), DATA_TYPECODE); } %typemap(in, fragment="NumPy_Fragments") (DIM_TYPE DIM1, DATA_TYPE* INPLACE_ARRAY1) (PyArrayObject* array=NULL, int i=0) { array = obj_to_array_no_conversion($input, DATA_TYPECODE); if (!array || !require_dimensions(array,1) || !require_contiguous(array) || !require_native(array)) SWIG_fail; $1 = 1; for (i=0; i < array_numdims(array); ++i) $1 *= array_size(array,i); $2 = (DATA_TYPE*) array_data(array); } /* Typemap suite for (DATA_TYPE INPLACE_ARRAY2[ANY][ANY]) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DATA_TYPE INPLACE_ARRAY2[ANY][ANY]) { $1 = is_array($input) && PyArray_EquivTypenums(array_type($input), DATA_TYPECODE); } %typemap(in, fragment="NumPy_Fragments") (DATA_TYPE INPLACE_ARRAY2[ANY][ANY]) (PyArrayObject* array=NULL) { npy_intp size[2] = { $1_dim0, $1_dim1 }; array = obj_to_array_no_conversion($input, DATA_TYPECODE); if (!array || !require_dimensions(array,2) || !require_size(array, size, 2) || !require_contiguous(array) || !require_native(array)) SWIG_fail; $1 = ($1_ltype) array_data(array); } /* Typemap suite for (DATA_TYPE* INPLACE_ARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DATA_TYPE* INPLACE_ARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2) { $1 = is_array($input) && PyArray_EquivTypenums(array_type($input), DATA_TYPECODE); } %typemap(in, fragment="NumPy_Fragments") (DATA_TYPE* INPLACE_ARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2) (PyArrayObject* array=NULL) { array = obj_to_array_no_conversion($input, DATA_TYPECODE); if (!array || !require_dimensions(array,2) || !require_contiguous(array) || !require_native(array)) SWIG_fail; $1 = (DATA_TYPE*) array_data(array); $2 = (DIM_TYPE) array_size(array,0); $3 = (DIM_TYPE) array_size(array,1); } /* Typemap suite for (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* INPLACE_ARRAY2) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* INPLACE_ARRAY2) { $1 = is_array($input) && PyArray_EquivTypenums(array_type($input), DATA_TYPECODE); } %typemap(in, fragment="NumPy_Fragments") (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* INPLACE_ARRAY2) (PyArrayObject* array=NULL) { array = obj_to_array_no_conversion($input, DATA_TYPECODE); if (!array || !require_dimensions(array,2) || !require_contiguous(array) || !require_native(array)) SWIG_fail; $1 = (DIM_TYPE) array_size(array,0); $2 = (DIM_TYPE) array_size(array,1); $3 = (DATA_TYPE*) array_data(array); } /* Typemap suite for (DATA_TYPE* INPLACE_FARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DATA_TYPE* INPLACE_FARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2) { $1 = is_array($input) && PyArray_EquivTypenums(array_type($input), DATA_TYPECODE); } %typemap(in, fragment="NumPy_Fragments") (DATA_TYPE* INPLACE_FARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2) (PyArrayObject* array=NULL) { array = obj_to_array_no_conversion($input, DATA_TYPECODE); if (!array || !require_dimensions(array,2) || !require_contiguous(array) || !require_native(array) || !require_fortran(array)) SWIG_fail; $1 = (DATA_TYPE*) array_data(array); $2 = (DIM_TYPE) array_size(array,0); $3 = (DIM_TYPE) array_size(array,1); } /* Typemap suite for (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* INPLACE_FARRAY2) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* INPLACE_FARRAY2) { $1 = is_array($input) && PyArray_EquivTypenums(array_type($input), DATA_TYPECODE); } %typemap(in, fragment="NumPy_Fragments") (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* INPLACE_FARRAY2) (PyArrayObject* array=NULL) { array = obj_to_array_no_conversion($input, DATA_TYPECODE); if (!array || !require_dimensions(array,2) || !require_contiguous(array) || !require_native(array) || !require_fortran(array)) SWIG_fail; $1 = (DIM_TYPE) array_size(array,0); $2 = (DIM_TYPE) array_size(array,1); $3 = (DATA_TYPE*) array_data(array); } /* Typemap suite for (DATA_TYPE INPLACE_ARRAY3[ANY][ANY][ANY]) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DATA_TYPE INPLACE_ARRAY3[ANY][ANY][ANY]) { $1 = is_array($input) && PyArray_EquivTypenums(array_type($input), DATA_TYPECODE); } %typemap(in, fragment="NumPy_Fragments") (DATA_TYPE INPLACE_ARRAY3[ANY][ANY][ANY]) (PyArrayObject* array=NULL) { npy_intp size[3] = { $1_dim0, $1_dim1, $1_dim2 }; array = obj_to_array_no_conversion($input, DATA_TYPECODE); if (!array || !require_dimensions(array,3) || !require_size(array, size, 3) || !require_contiguous(array) || !require_native(array)) SWIG_fail; $1 = ($1_ltype) array_data(array); } /* Typemap suite for (DATA_TYPE* INPLACE_ARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, * DIM_TYPE DIM3) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DATA_TYPE* INPLACE_ARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3) { $1 = is_array($input) && PyArray_EquivTypenums(array_type($input), DATA_TYPECODE); } %typemap(in, fragment="NumPy_Fragments") (DATA_TYPE* INPLACE_ARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3) (PyArrayObject* array=NULL) { array = obj_to_array_no_conversion($input, DATA_TYPECODE); if (!array || !require_dimensions(array,3) || !require_contiguous(array) || !require_native(array)) SWIG_fail; $1 = (DATA_TYPE*) array_data(array); $2 = (DIM_TYPE) array_size(array,0); $3 = (DIM_TYPE) array_size(array,1); $4 = (DIM_TYPE) array_size(array,2); } /* Typemap suite for (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, * DATA_TYPE* INPLACE_ARRAY3) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DATA_TYPE* INPLACE_ARRAY3) { $1 = is_array($input) && PyArray_EquivTypenums(array_type($input), DATA_TYPECODE); } %typemap(in, fragment="NumPy_Fragments") (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DATA_TYPE* INPLACE_ARRAY3) (PyArrayObject* array=NULL) { array = obj_to_array_no_conversion($input, DATA_TYPECODE); if (!array || !require_dimensions(array,3) || !require_contiguous(array) || !require_native(array)) SWIG_fail; $1 = (DIM_TYPE) array_size(array,0); $2 = (DIM_TYPE) array_size(array,1); $3 = (DIM_TYPE) array_size(array,2); $4 = (DATA_TYPE*) array_data(array); } /* Typemap suite for (DATA_TYPE* INPLACE_FARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, * DIM_TYPE DIM3) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DATA_TYPE* INPLACE_FARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3) { $1 = is_array($input) && PyArray_EquivTypenums(array_type($input), DATA_TYPECODE); } %typemap(in, fragment="NumPy_Fragments") (DATA_TYPE* INPLACE_FARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3) (PyArrayObject* array=NULL) { array = obj_to_array_no_conversion($input, DATA_TYPECODE); if (!array || !require_dimensions(array,3) || !require_contiguous(array) || !require_native(array) || !require_fortran(array)) SWIG_fail; $1 = (DATA_TYPE*) array_data(array); $2 = (DIM_TYPE) array_size(array,0); $3 = (DIM_TYPE) array_size(array,1); $4 = (DIM_TYPE) array_size(array,2); } /* Typemap suite for (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, * DATA_TYPE* INPLACE_FARRAY3) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DATA_TYPE* INPLACE_FARRAY3) { $1 = is_array($input) && PyArray_EquivTypenums(array_type($input), DATA_TYPECODE); } %typemap(in, fragment="NumPy_Fragments") (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DATA_TYPE* INPLACE_FARRAY3) (PyArrayObject* array=NULL) { array = obj_to_array_no_conversion($input, DATA_TYPECODE); if (!array || !require_dimensions(array,3) || !require_contiguous(array) || !require_native(array) || !require_fortran(array)) SWIG_fail; $1 = (DIM_TYPE) array_size(array,0); $2 = (DIM_TYPE) array_size(array,1); $3 = (DIM_TYPE) array_size(array,2); $4 = (DATA_TYPE*) array_data(array); } /*************************/ /* Argout Array Typemaps */ /*************************/ /* Typemap suite for (DATA_TYPE ARGOUT_ARRAY1[ANY]) */ %typemap(in,numinputs=0, fragment="NumPy_Backward_Compatibility,NumPy_Macros") (DATA_TYPE ARGOUT_ARRAY1[ANY]) (PyObject * array = NULL) { npy_intp dims[1] = { $1_dim0 }; array = PyArray_SimpleNew(1, dims, DATA_TYPECODE); if (!array) SWIG_fail; $1 = ($1_ltype) array_data(array); } %typemap(argout) (DATA_TYPE ARGOUT_ARRAY1[ANY]) { $result = SWIG_Python_AppendOutput($result,array$argnum); } /* Typemap suite for (DATA_TYPE* ARGOUT_ARRAY1, DIM_TYPE DIM1) */ %typemap(in,numinputs=1, fragment="NumPy_Fragments") (DATA_TYPE* ARGOUT_ARRAY1, DIM_TYPE DIM1) (PyObject * array = NULL) { npy_intp dims[1]; if (!PyInt_Check($input)) { const char* typestring = pytype_string($input); PyErr_Format(PyExc_TypeError, "Int dimension expected. '%s' given.", typestring); SWIG_fail; } $2 = (DIM_TYPE) PyInt_AsLong($input); dims[0] = (npy_intp) $2; array = PyArray_SimpleNew(1, dims, DATA_TYPECODE); if (!array) SWIG_fail; $1 = (DATA_TYPE*) array_data(array); } %typemap(argout) (DATA_TYPE* ARGOUT_ARRAY1, DIM_TYPE DIM1) { $result = SWIG_Python_AppendOutput($result,array$argnum); } /* Typemap suite for (DIM_TYPE DIM1, DATA_TYPE* ARGOUT_ARRAY1) */ %typemap(in,numinputs=1, fragment="NumPy_Fragments") (DIM_TYPE DIM1, DATA_TYPE* ARGOUT_ARRAY1) (PyObject * array = NULL) { npy_intp dims[1]; if (!PyInt_Check($input)) { const char* typestring = pytype_string($input); PyErr_Format(PyExc_TypeError, "Int dimension expected. '%s' given.", typestring); SWIG_fail; } $1 = (DIM_TYPE) PyInt_AsLong($input); dims[0] = (npy_intp) $1; array = PyArray_SimpleNew(1, dims, DATA_TYPECODE); if (!array) SWIG_fail; $2 = (DATA_TYPE*) array_data(array); } %typemap(argout) (DIM_TYPE DIM1, DATA_TYPE* ARGOUT_ARRAY1) { $result = SWIG_Python_AppendOutput($result,array$argnum); } /* Typemap suite for (DATA_TYPE ARGOUT_ARRAY2[ANY][ANY]) */ %typemap(in,numinputs=0, fragment="NumPy_Backward_Compatibility,NumPy_Macros") (DATA_TYPE ARGOUT_ARRAY2[ANY][ANY]) (PyObject * array = NULL) { npy_intp dims[2] = { $1_dim0, $1_dim1 }; array = PyArray_SimpleNew(2, dims, DATA_TYPECODE); if (!array) SWIG_fail; $1 = ($1_ltype) array_data(array); } %typemap(argout) (DATA_TYPE ARGOUT_ARRAY2[ANY][ANY]) { $result = SWIG_Python_AppendOutput($result,array$argnum); } /* Typemap suite for (DATA_TYPE ARGOUT_ARRAY3[ANY][ANY][ANY]) */ %typemap(in,numinputs=0, fragment="NumPy_Backward_Compatibility,NumPy_Macros") (DATA_TYPE ARGOUT_ARRAY3[ANY][ANY][ANY]) (PyObject * array = NULL) { npy_intp dims[3] = { $1_dim0, $1_dim1, $1_dim2 }; array = PyArray_SimpleNew(3, dims, DATA_TYPECODE); if (!array) SWIG_fail; $1 = ($1_ltype) array_data(array); } %typemap(argout) (DATA_TYPE ARGOUT_ARRAY3[ANY][ANY][ANY]) { $result = SWIG_Python_AppendOutput($result,array$argnum); } /*****************************/ /* Argoutview Array Typemaps */ /*****************************/ /* Typemap suite for (DATA_TYPE** ARGOUTVIEW_ARRAY1, DIM_TYPE* DIM1) */ %typemap(in,numinputs=0) (DATA_TYPE** ARGOUTVIEW_ARRAY1, DIM_TYPE* DIM1 ) (DATA_TYPE* data_temp , DIM_TYPE dim_temp) { $1 = &data_temp; $2 = &dim_temp; } %typemap(argout, fragment="NumPy_Backward_Compatibility") (DATA_TYPE** ARGOUTVIEW_ARRAY1, DIM_TYPE* DIM1) { npy_intp dims[1] = { *$2 }; PyObject * array = PyArray_SimpleNewFromData(1, dims, DATA_TYPECODE, (void*)(*$1)); if (!array) SWIG_fail; $result = SWIG_Python_AppendOutput($result,array); } /* Typemap suite for (DIM_TYPE* DIM1, DATA_TYPE** ARGOUTVIEW_ARRAY1) */ %typemap(in,numinputs=0) (DIM_TYPE* DIM1 , DATA_TYPE** ARGOUTVIEW_ARRAY1) (DIM_TYPE dim_temp, DATA_TYPE* data_temp ) { $1 = &dim_temp; $2 = &data_temp; } %typemap(argout, fragment="NumPy_Backward_Compatibility") (DIM_TYPE* DIM1, DATA_TYPE** ARGOUTVIEW_ARRAY1) { npy_intp dims[1] = { *$1 }; PyObject * array = PyArray_SimpleNewFromData(1, dims, DATA_TYPECODE, (void*)(*$2)); if (!array) SWIG_fail; $result = SWIG_Python_AppendOutput($result,array); } /* Typemap suite for (DATA_TYPE** ARGOUTVIEW_ARRAY2, DIM_TYPE* DIM1, DIM_TYPE* DIM2) */ %typemap(in,numinputs=0) (DATA_TYPE** ARGOUTVIEW_ARRAY2, DIM_TYPE* DIM1 , DIM_TYPE* DIM2 ) (DATA_TYPE* data_temp , DIM_TYPE dim1_temp, DIM_TYPE dim2_temp) { $1 = &data_temp; $2 = &dim1_temp; $3 = &dim2_temp; } %typemap(argout, fragment="NumPy_Backward_Compatibility") (DATA_TYPE** ARGOUTVIEW_ARRAY2, DIM_TYPE* DIM1, DIM_TYPE* DIM2) { npy_intp dims[2] = { *$2, *$3 }; PyObject * array = PyArray_SimpleNewFromData(2, dims, DATA_TYPECODE, (void*)(*$1)); if (!array) SWIG_fail; $result = SWIG_Python_AppendOutput($result,array); } /* Typemap suite for (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DATA_TYPE** ARGOUTVIEW_ARRAY2) */ %typemap(in,numinputs=0) (DIM_TYPE* DIM1 , DIM_TYPE* DIM2 , DATA_TYPE** ARGOUTVIEW_ARRAY2) (DIM_TYPE dim1_temp, DIM_TYPE dim2_temp, DATA_TYPE* data_temp ) { $1 = &dim1_temp; $2 = &dim2_temp; $3 = &data_temp; } %typemap(argout, fragment="NumPy_Backward_Compatibility") (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DATA_TYPE** ARGOUTVIEW_ARRAY2) { npy_intp dims[2] = { *$1, *$2 }; PyObject * array = PyArray_SimpleNewFromData(2, dims, DATA_TYPECODE, (void*)(*$3)); if (!array) SWIG_fail; $result = SWIG_Python_AppendOutput($result,array); } /* Typemap suite for (DATA_TYPE** ARGOUTVIEW_FARRAY2, DIM_TYPE* DIM1, DIM_TYPE* DIM2) */ %typemap(in,numinputs=0) (DATA_TYPE** ARGOUTVIEW_FARRAY2, DIM_TYPE* DIM1 , DIM_TYPE* DIM2 ) (DATA_TYPE* data_temp , DIM_TYPE dim1_temp, DIM_TYPE dim2_temp) { $1 = &data_temp; $2 = &dim1_temp; $3 = &dim2_temp; } %typemap(argout, fragment="NumPy_Backward_Compatibility,NumPy_Array_Requirements") (DATA_TYPE** ARGOUTVIEW_FARRAY2, DIM_TYPE* DIM1, DIM_TYPE* DIM2) { npy_intp dims[2] = { *$2, *$3 }; PyObject * obj = PyArray_SimpleNewFromData(2, dims, DATA_TYPECODE, (void*)(*$1)); PyArrayObject * array = (PyArrayObject*) obj; if (!array || !require_fortran(array)) SWIG_fail; $result = SWIG_Python_AppendOutput($result,obj); } /* Typemap suite for (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DATA_TYPE** ARGOUTVIEW_FARRAY2) */ %typemap(in,numinputs=0) (DIM_TYPE* DIM1 , DIM_TYPE* DIM2 , DATA_TYPE** ARGOUTVIEW_FARRAY2) (DIM_TYPE dim1_temp, DIM_TYPE dim2_temp, DATA_TYPE* data_temp ) { $1 = &dim1_temp; $2 = &dim2_temp; $3 = &data_temp; } %typemap(argout, fragment="NumPy_Backward_Compatibility,NumPy_Array_Requirements") (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DATA_TYPE** ARGOUTVIEW_FARRAY2) { npy_intp dims[2] = { *$1, *$2 }; PyObject * obj = PyArray_SimpleNewFromData(2, dims, DATA_TYPECODE, (void*)(*$3)); PyArrayObject * array = (PyArrayObject*) obj; if (!array || !require_fortran(array)) SWIG_fail; $result = SWIG_Python_AppendOutput($result,obj); } /* Typemap suite for (DATA_TYPE** ARGOUTVIEW_ARRAY3, DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3) */ %typemap(in,numinputs=0) (DATA_TYPE** ARGOUTVIEW_ARRAY3, DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3) (DATA_TYPE* data_temp, DIM_TYPE dim1_temp, DIM_TYPE dim2_temp, DIM_TYPE dim3_temp) { $1 = &data_temp; $2 = &dim1_temp; $3 = &dim2_temp; $4 = &dim3_temp; } %typemap(argout, fragment="NumPy_Backward_Compatibility") (DATA_TYPE** ARGOUTVIEW_ARRAY3, DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3) { npy_intp dims[3] = { *$2, *$3, *$4 }; PyObject * array = PyArray_SimpleNewFromData(3, dims, DATA_TYPECODE, (void*)(*$1)); if (!array) SWIG_fail; $result = SWIG_Python_AppendOutput($result,array); } /* Typemap suite for (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DATA_TYPE** ARGOUTVIEW_ARRAY3) */ %typemap(in,numinputs=0) (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DATA_TYPE** ARGOUTVIEW_ARRAY3) (DIM_TYPE dim1_temp, DIM_TYPE dim2_temp, DIM_TYPE dim3_temp, DATA_TYPE* data_temp) { $1 = &dim1_temp; $2 = &dim2_temp; $3 = &dim3_temp; $4 = &data_temp; } %typemap(argout, fragment="NumPy_Backward_Compatibility") (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DATA_TYPE** ARGOUTVIEW_ARRAY3) { npy_intp dims[3] = { *$1, *$2, *$3 }; PyObject * array = PyArray_SimpleNewFromData(3, dims, DATA_TYPECODE, (void*)(*$3)); if (!array) SWIG_fail; $result = SWIG_Python_AppendOutput($result,array); } /* Typemap suite for (DATA_TYPE** ARGOUTVIEW_FARRAY3, DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3) */ %typemap(in,numinputs=0) (DATA_TYPE** ARGOUTVIEW_FARRAY3, DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3) (DATA_TYPE* data_temp, DIM_TYPE dim1_temp, DIM_TYPE dim2_temp, DIM_TYPE dim3_temp) { $1 = &data_temp; $2 = &dim1_temp; $3 = &dim2_temp; $4 = &dim3_temp; } %typemap(argout, fragment="NumPy_Backward_Compatibility,NumPy_Array_Requirements") (DATA_TYPE** ARGOUTVIEW_FARRAY3, DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3) { npy_intp dims[3] = { *$2, *$3, *$4 }; PyObject * obj = PyArray_SimpleNewFromData(3, dims, DATA_TYPECODE, (void*)(*$1)); PyArrayObject * array = (PyArrayObject*) obj; if (!array || require_fortran(array)) SWIG_fail; $result = SWIG_Python_AppendOutput($result,obj); } /* Typemap suite for (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DATA_TYPE** ARGOUTVIEW_FARRAY3) */ %typemap(in,numinputs=0) (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DATA_TYPE** ARGOUTVIEW_FARRAY3) (DIM_TYPE dim1_temp, DIM_TYPE dim2_temp, DIM_TYPE dim3_temp, DATA_TYPE* data_temp) { $1 = &dim1_temp; $2 = &dim2_temp; $3 = &dim3_temp; $4 = &data_temp; } %typemap(argout, fragment="NumPy_Backward_Compatibility,NumPy_Array_Requirements") (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DATA_TYPE** ARGOUTVIEW_FARRAY3) { npy_intp dims[3] = { *$1, *$2, *$3 }; PyObject * obj = PyArray_SimpleNewFromData(3, dims, DATA_TYPECODE, (void*)(*$3)); PyArrayObject * array = (PyArrayObject*) obj; if (!array || require_fortran(array)) SWIG_fail; $result = SWIG_Python_AppendOutput($result,obj); } %enddef /* %numpy_typemaps() macro */ /* *************************************************************** */ /* Concrete instances of the %numpy_typemaps() macro: Each invocation * below applies all of the typemaps above to the specified data type. */ %numpy_typemaps(signed char , NPY_BYTE , int) %numpy_typemaps(unsigned char , NPY_UBYTE , int) %numpy_typemaps(short , NPY_SHORT , int) %numpy_typemaps(unsigned short , NPY_USHORT , int) %numpy_typemaps(int , NPY_INT , int) %numpy_typemaps(unsigned int , NPY_UINT , int) %numpy_typemaps(long , NPY_LONG , int) %numpy_typemaps(unsigned long , NPY_ULONG , int) %numpy_typemaps(long long , NPY_LONGLONG , int) %numpy_typemaps(unsigned long long, NPY_ULONGLONG, int) %numpy_typemaps(float , NPY_FLOAT , int) %numpy_typemaps(double , NPY_DOUBLE , int) /* *************************************************************** * The follow macro expansion does not work, because C++ bool is 4 * bytes and NPY_BOOL is 1 byte * * %numpy_typemaps(bool, NPY_BOOL, int) */ /* *************************************************************** * On my Mac, I get the following warning for this macro expansion: * 'swig/python detected a memory leak of type 'long double *', no destructor found.' * * %numpy_typemaps(long double, NPY_LONGDOUBLE, int) */ /* *************************************************************** * Swig complains about a syntax error for the following macro * expansions: * * %numpy_typemaps(complex float, NPY_CFLOAT , int) * * %numpy_typemaps(complex double, NPY_CDOUBLE, int) * * %numpy_typemaps(complex long double, NPY_CLONGDOUBLE, int) */ #endif /* SWIGPYTHON */ brian-1.4.3/brian/utils/fastexp/setup.py000066400000000000000000000014531266062647100202460ustar00rootroot00000000000000#!/usr/bin/env python """ setup.py file for SWIG example """ from distutils.core import setup, Extension import os, numpy numpy_base_dir = os.path.split(numpy.__file__)[0] numpy_include_dir = os.path.join(numpy_base_dir, 'core/include') fastexp_module = Extension('_fastexp', sources=['fastexp_wrap.cxx', 'fastexp.cpp', 'fexp.c'], include_dirs=[numpy_include_dir], extra_compile_args=['-O3'] ) setup (name='fastexp', version='0.1', author="Dan Goodman", description="""Simple swig example from docs""", ext_modules=[fastexp_module], py_modules=["fastexp"], ) brian-1.4.3/brian/utils/fastexp/swigcompile.bat000066400000000000000000000001211266062647100215350ustar00rootroot00000000000000@echo off swig -python -c++ fastexp.i setup.py build_ext --inplace -c mingw32 brian-1.4.3/brian/utils/fastexp/testfastexp.py000066400000000000000000000005461266062647100214620ustar00rootroot00000000000000import time from numpy import * from numpy.random import * from fastexp import * x = array([1, 2, 3], dtype=float) y = exp(x) z = zeros(len(x)) fastexp(x, z) print y print z x = randn(10000000) y = empty(x.shape) t = time.time() z = exp(x) print "exp", time.time() - t t = time.time() fastexp(x, y) print "fastexp", time.time() - t brian-1.4.3/brian/utils/fastexp/testfastexp2.py000066400000000000000000000001711266062647100215360ustar00rootroot00000000000000from numpy import * from numpy.random import * from __init__ import * x = randn(3) print exp(x) print fastexp(x) brian-1.4.3/brian/utils/progressreporting.py000066400000000000000000000173551266062647100212420ustar00rootroot00000000000000import sys, time __all__ = ['ProgressReporter'] def time_rep(t): ''' Textual representation of time t given in seconds ''' t = int(t) if t < 60: return str(t) + 's' secs = t % 60 mins = t // 60 if mins < 60: return str(mins) + 'm ' + str(secs) + 's' mins = mins % 60 hours = t // (60 * 60) if hours < 24: return str(hours) + 'h ' + str(mins) + 'm ' + str(secs) + 's' hours = hours % 24 days = t // (60 * 60 * 24) return str(days) + 'd ' + str(hours) + 'h ' + str(mins) + 'm ' + str(secs) + 's' def make_text_report(elapsed, complete): s = str(int(100 * complete)) + '% complete, ' s += time_rep(elapsed) + ' elapsed' if complete > .001: remtime = elapsed / complete - elapsed s += ', approximately ' + time_rep(remtime) + ' remaining.' else: s += '.' return s def build_text_reporter(output_stream): def text_report(elapsed, complete): s = make_text_report(elapsed, complete) + '\n' output_stream.write(s) output_stream.flush() return text_report class ProgressReporter(object): ''' Standard text and graphical progress reports Initialised with arguments: ``report`` Can be one of the following strings: ``'print'``, ``'text'``, ``'stdout'`` Reports progress to standard console. ``'stderr'`` Reports progress to error console. ``'graphical'``, ``'tkinter'`` A simple graphical progress bar using Tkinter. Alternatively, it can be any output stream in which case text reports will be sent to it, or a custom callback function ``report(elapsed, complete)`` taking arguments ``elapsed`` the amount of time that has passed and ``complete`` the fraction of the computation finished. ``period`` How often reports should be generated in seconds. ``first_report`` The time of the first report (nothing will be done before this amount of time has elapsed). Methods: .. method:: start() Call at the beginning of a task to start timing it. .. method:: finish() Call at the end of a task to finish timing it. Note that with the Tkinter class, if you do not call this it will stop the Python script from finishing, stopping memory from being freed up. .. method:: update(complete) Call with the fraction of the task (or subtask if ``subtask()`` has been called) completed, between 0 and 1. .. method:: subtask(complete, tasksize) After calling ``subtask(complete, tasksize)``, subsequent calls to update will report progress between a fraction ``complete`` and ``complete+tasksize`` of the total task. ``complete`` represents the amount of the total task completed at the beginning of the task, and ``tasksize`` the size of the subtask as a proportion of the whole task. .. method:: equal_subtask(tasknum, numtasks) If a task can be divided into ``numtasks`` equally sized subtasks, you can use this method instead of ``subtask``, where ``tasknum`` is the number of the subtask about to start. Note that in Python 2.6+, this can be used as a context manager, and it will automatically call the :meth:`start` and :meth:`finish` methods at the beginning and end, e.g.:: with ProgressReporter(period=0.1) as progress: for i in xrange(10): time.sleep(1) progress.update((i+1.0)/10) ''' def __init__(self, report='stderr', period=10.0, first_report=-1.0): self.period = float(period) #self.report = get_reporter(report) self.report = None self.report_option = report self.first_report = first_report self.first_time = 0 self.start() # just in case the user forgets to call start() def start(self): self.start_time = time.time() self.next_report_time = self.start_time + self.period self.first_time = self.start_time + self.first_report self.subtask_complete = 0.0 self.subtask_size = 1.0 def finish(self): self.update(1) def subtask(self, complete, tasksize): self.subtask_complete = complete self.subtask_size = tasksize def equal_subtask(self, tasknum, numtasks): self.subtask(float(tasknum) / float(numtasks), 1. / numtasks) def update(self, complete): cur_time = time.time() totalcomplete = self.subtask_complete + complete * self.subtask_size if cur_time > self.first_time or complete==1: if self.report is None: self.report = get_reporter(self.report_option) if cur_time > self.next_report_time or totalcomplete == 1.0 or totalcomplete == 1: self.next_report_time = cur_time + self.period elapsed = time.time() - self.start_time self.report(elapsed, totalcomplete) def __enter__(self): self.start() return self def __exit__(self, type, value, traceback): self.finish() def get_reporter(report): if report == 'print' or report == 'text' or report == 'stdout': report = build_text_reporter(sys.stdout) elif report == 'stderr': report = build_text_reporter(sys.stderr) elif hasattr(report, 'write') and hasattr(report, 'flush'): report = build_text_reporter(report) elif report == 'graphical' or report == 'tkinter': import Tkinter class ProgressBar(object): ''' Adapted from: http://code.activestate.com/recipes/492230/ ''' # Create Progress Bar def __init__(self, width, height): self.__root = Tkinter.Tk() self.__root.resizable(False, False) self.__root.title('Progress Bar') self.__canvas = Tkinter.Canvas(self.__root, width=width, height=height) self.__canvas.grid() self.__width = width self.__height = height # Open Progress Bar def open(self): self.__root.deiconify() # Close Progress Bar def close(self): self.__root.withdraw() # Update Progress Bar def update(self, ratio, newtitle=None): self.__canvas.delete(Tkinter.ALL) self.__canvas.create_rectangle(0, 0, self.__width * ratio, \ self.__height, fill='blue') if newtitle is not None: self.__root.title(newtitle) self.__root.update() pb = ProgressBar(500, 20) pb.closed = False def report(elapsed, complete): try: if complete == 1.0 and pb.closed == False: pb.close() pb.closed = True else: pb.update(complete, make_text_report(elapsed, complete)) except Tkinter.TclError: # exception handling in the case that the user shuts the window pass return report if __name__ == '__main__': import time report = ProgressReporter('stderr', 0.1, 2.0) report.start() for i in xrange(30): print 'iteration', i time.sleep(0.1) report.update(i/30.0) report.finish() brian-1.4.3/brian/utils/separate_equations.py000066400000000000000000000117031266062647100213270ustar00rootroot00000000000000''' Code to separate Equations into independent subsets. This can be used in STDP to allow for nonlinear equations to be given. Also, a modified version of this code could probably be used to define independent equations for use in compound solvers. For example, you could split into separate independent sets and have one solver for each set. In some circumstances, this might be more efficient (e.g. a linear and nonlinear component). It may need to be modified so that the dependencies are directional, i.e. X depending on Y doesn't mean Y depends on X (which you want in the case of the STDP separation), that way you could have a linear solver for a subset and a nonlinear solver that depends on some of the linear variables. In this case you would probably look for minimal subgraphs which are allowed to have incoming edges but no outgoing edges, construct solvers for them, and then construct a compound solver for the rest. ''' if __name__ == '__main__': from brian.equations import Equations from brian.inspection import get_identifiers else: from ..equations import Equations from ..inspection import get_identifiers from collections import defaultdict from copy import copy __all__ = ['separate_equations'] def next_independent_subgraph(G): ''' Returns an independent subgraph of G and removes that subgraph from G. Here G is a graph represented as a dictionary, so that G[node] is a set of all the neighbours of node. The caller is responsible for constructing a well formed G which has all the appropriate nodes, and where edges in the graph are both ways so that b in G[a] <=> a in G[b]. ''' # We start from just one (randomly selected) node and iteratively add # elements to the subgraph from the set of neighbours of the subgraph # until there are no more neighbours to be added, and then we return. # This algorithm may not be efficient but we're likely only dealing with # fairly small graphs anyway. nodes = dict([G.popitem()]) found_new = True while found_new: found_new = False for node, targets in nodes.items(): for target in targets: if target in G: # if target not in G, we assume it has already been added nodes[target] = G.pop(target) found_new = True else: # this should be true if the caller has constructed G correctly assert target in nodes return nodes def separate_equations(eqs, additional_dependencies=[]): eqs.prepare() all_vars = eqs._string.keys() # Construct a list of dependency sets, where each variable in each # dependency set induces a dependency on each other variable in each # dependency set depsets = [] for var in all_vars: ids = set(get_identifiers(eqs._string[var])) ids = ids.intersection(all_vars) ids.add(var) depsets.append(ids) for expr in additional_dependencies: ids = set(get_identifiers(expr)) ids = ids.intersection(all_vars) depsets.append(ids) # Construct a graph deps which indicates what variable depends on which # other variables (or is depended on by other variables). deps = defaultdict(set) for ids in depsets: for id1 in ids: for id2 in ids: deps[id1].add(id2) # Extract all the independent subgraphs ind_graphs = [] while len(deps): ind_graphs.append(set(next_independent_subgraph(deps).keys())) if len(ind_graphs) == 1: return [eqs] # Finally, we construct an Equations object for each of the subgraphs ind_eqs = [] for G in ind_graphs: neweqs = Equations() for var in G: if var in eqs._eq_names: neweqs.add_eq(var, eqs._string[var], eqs._units[var], local_namespace=eqs._namespace[var]) elif var in eqs._diffeq_names: nonzero = var in eqs._diffeq_names_nonzero neweqs.add_diffeq(var, eqs._string[var], eqs._units[var], local_namespace=eqs._namespace[var], nonzero=nonzero) elif var in eqs._alias.keys(): neweqs.add_alias(var, eqs._string[var].strip()) else: assert False ind_eqs.append(neweqs) return ind_eqs if __name__ == '__main__': from brian import * # T = second # eqs = Equations(''' # da/dt = y/T : 1 # db/dt = x/T : 1 # c : 1 # x = c # y = x*x : 1 # dd/dt = e/T : 1 # de/dt = d/T : 1 # df/dt = e/T : 1 # ''') tau_pre = tau_post = 10 * ms eqs = Equations(""" dA_pre/dt = -A_pre/tau_pre : 1 dA_post/dt = -A_post/tau_post : 1 """) eqs_sep = separate_equations(eqs) for e in eqs_sep: print '******************' print e brian-1.4.3/brian/utils/sparse_patch/000077500000000000000000000000001266062647100175335ustar00rootroot00000000000000brian-1.4.3/brian/utils/sparse_patch/0_6_0.py000066400000000000000000000047671266062647100207260ustar00rootroot00000000000000from scipy import sparse import numpy from itertools import izip from operator import isSequenceType import bisect from numpy import ndarray import itertools class lil_matrix(sparse.lil_matrix): # Unfortunately we still need to implement this because although scipy 0.7.0 # now supports X[a:b,c:d] for sparse X it is unbelievably slow (shabby code # on their part). def __setitem__(self, index, W): """ Speed-up if x is a sparse matrix. TODO: checks (first remove the data). TODO: once we've got this working in all cases, should we submit to scipy? """ try: i, j = index except (ValueError, TypeError): raise IndexError, "invalid index" if isinstance(i, slice) and isinstance(j, slice) and\ (i.step is None) and (j.step is None) and\ (isinstance(W, sparse.lil_matrix) or isinstance(W, numpy.ndarray)): rows = self.rows[i] datas = self.data[i] j0 = j.start if isinstance(W, sparse.lil_matrix): for row, data, rowW, dataW in izip(rows, datas, W.rows, W.data): jj = bisect.bisect(row, j0) # Find the insertion point row[jj:jj] = [j0 + k for k in rowW] data[jj:jj] = dataW elif isinstance(W, ndarray): nq = W.shape[1] for row, data, rowW in izip(rows, datas, W): jj = bisect.bisect(row, j0) # Find the insertion point row[jj:jj] = range(j0, j0 + nq) data[jj:jj] = rowW elif isinstance(i, int) and isinstance(j, (list, tuple, numpy.ndarray)): row = dict(izip(self.rows[i], self.data[i])) try: row.update(dict(izip(j, W))) except TypeError: row.update(dict(izip(j, itertools.repeat(W)))) items = row.items() items.sort() row, data = izip(*items) self.rows[i] = list(row) self.data[i] = list(data) elif isinstance(i, slice) and isinstance(j, int) and isSequenceType(W): # This corrects a bug in scipy sparse matrix as of version 0.7.0, but # it is not efficient! for w, k in izip(W, xrange(*i.indices(self.shape[0]))): sparse.lil_matrix.__setitem__(self, (k, j), w) else: sparse.lil_matrix.__setitem__(self, index, W) brian-1.4.3/brian/utils/sparse_patch/0_7_0.py000066400000000000000000000041721266062647100207150ustar00rootroot00000000000000from scipy import sparse import numpy from itertools import izip from operator import isSequenceType import bisect from numpy import ndarray import itertools class lil_matrix(sparse.lil_matrix): # Unfortunately we still need to implement this because although scipy 0.7.0 # now supports X[a:b,c:d] for sparse X it is unbelievably slow (shabby code # on their part). def __setitem__(self, index, W): """ Speed-up if x is a sparse matrix. TODO: checks (first remove the data). TODO: once we've got this working in all cases, should we submit to scipy? """ try: i, j = index except (ValueError, TypeError): raise IndexError, "invalid index" if isinstance(i, slice) and isinstance(j, slice) and\ (i.step is None) and (j.step is None) and\ (isinstance(W, sparse.lil_matrix) or isinstance(W, numpy.ndarray)): rows = self.rows[i] datas = self.data[i] j0 = j.start if isinstance(W, sparse.lil_matrix): for row, data, rowW, dataW in izip(rows, datas, W.rows, W.data): jj = bisect.bisect(row, j0) # Find the insertion point row[jj:jj] = [j0 + k for k in rowW] data[jj:jj] = dataW elif isinstance(W, ndarray): nq = W.shape[1] for row, data, rowW in izip(rows, datas, W): jj = bisect.bisect(row, j0) # Find the insertion point row[jj:jj] = range(j0, j0 + nq) data[jj:jj] = rowW elif isinstance(i, slice) and isinstance(j, int) and isSequenceType(W): # This corrects a bug in scipy sparse matrix as of version 0.7.0, but # it is not efficient! for w, k in izip(W, xrange(*i.indices(self.shape[0]))): sparse.lil_matrix.__setitem__(self, (k, j), w) elif isinstance(i, int) and isSequenceType(j) and len(j) == 0: return else: sparse.lil_matrix.__setitem__(self, index, W) brian-1.4.3/brian/utils/sparse_patch/0_7_1.py000066400000000000000000000061231266062647100207140ustar00rootroot00000000000000from scipy import sparse import numpy import itertools from itertools import izip, repeat from operator import isSequenceType, isNumberType import bisect from numpy import ndarray class lil_matrix(sparse.lil_matrix): # Unfortunately we still need to implement this because although scipy 0.7.0 # now supports X[a:b,c:d] for sparse X it is unbelievably slow (shabby code # on their part). def __setitem__(self, index, W): """ Speed-up if x is a sparse matrix. TODO: checks (first remove the data). TODO: once we've got this working in all cases, should we submit to scipy? """ try: i, j = index except (ValueError, TypeError): raise IndexError, "invalid index" if isinstance(i, slice) and isinstance(j, slice) and\ (i.step is None) and (j.step is None) and\ (isinstance(W, sparse.lil_matrix) or isinstance(W, numpy.ndarray)): rows = self.rows[i] datas = self.data[i] j0 = j.start if isinstance(W, sparse.lil_matrix): for row, data, rowW, dataW in izip(rows, datas, W.rows, W.data): jj = bisect.bisect(row, j0) # Find the insertion point row[jj:jj] = [j0 + k for k in rowW] data[jj:jj] = dataW elif isinstance(W, ndarray): nq = W.shape[1] for row, data, rowW in izip(rows, datas, W): jj = bisect.bisect(row, j0) # Find the insertion point row[jj:jj] = range(j0, j0 + nq) data[jj:jj] = rowW elif isinstance(i, int) and isinstance(j, (list, tuple, numpy.ndarray)): if len(j) == 0: return row = dict(izip(self.rows[i], self.data[i])) try: row.update(dict(izip(j, W))) except TypeError: row.update(dict(izip(j, itertools.repeat(W)))) items = row.items() items.sort() row, data = izip(*items) self.rows[i] = list(row) self.data[i] = list(data) elif isinstance(i, slice) and isinstance(j, int) and isSequenceType(W): # This corrects a bug in scipy sparse matrix as of version 0.7.0, but # it is not efficient! for w, k in izip(W, xrange(*i.indices(self.shape[0]))): sparse.lil_matrix.__setitem__(self, (k, j), w) elif isinstance(i, int) and isinstance(j, slice) and (isNumberType(W) and not isSequenceType(W)): # this fixes a bug in scipy 0.7.1 sparse.lil_matrix.__setitem__(self, index, [W] * len(xrange(*j.indices(self.shape[1])))) elif isinstance(i, slice) and isinstance(j, slice) and isNumberType(W): n = len(xrange(*i.indices(self.shape[0]))) m = len(xrange(*j.indices(self.shape[1]))) sparse.lil_matrix.__setitem__(self, index, W * numpy.ones((n, m))) else: sparse.lil_matrix.__setitem__(self, index, W) brian-1.4.3/brian/utils/sparse_patch/__init__.py000066400000000000000000000023271266062647100216500ustar00rootroot00000000000000''' This package maintains multiple patches for scipy.sparse.lil_matrix for the various different versions of scipy. It chooses dynamically at run time which version to use. ''' import scipy, warnings, os, imp import scipy.sparse __all__ = ['lil_matrix', 'spmatrix'] default = '0_7_1' table = { '0.12.0': '0_7_1', '0.11.0': '0_7_1', '0.10.1': '0_7_1', '0.10.0': '0_7_1', '0.9.0': '0_7_1', '0.8.0': '0_7_1', '0.7.2': '0_7_1', '0.7.1': '0_7_1', '0.7.1rc3': '0_7_1', '0.7.1rc2': '0_7_1', '0.7.1rc1': '0_7_1', '0.7.0': '0_7_0', '0.7.0rc2': '0_7_0', '0.7.0b1': '0_7_0', '0.6.0': '0_6_0', } if scipy.__version__ in table: modulename = table[scipy.__version__] else: for i in xrange(1, len(scipy.__version__)): v = scipy.__version__[:-i] if v in table: modulename = table[v] break else: modulename = default module = __import__(modulename, globals(), locals(), [], -1) lil_matrix = module.lil_matrix spmatrix = scipy.sparse.spmatrix if __name__ == '__main__': x = lil_matrix((5, 5)) x[2, 3] = 5 print x.todense() brian-1.4.3/brian_no_units.py000066400000000000000000000001641266062647100162100ustar00rootroot00000000000000from brian_unit_prefs import turn_off_units import warnings turn_off_units() warnings.warn("Turning off units") brian-1.4.3/brian_no_units_no_warnings.py000066400000000000000000000001111266062647100206040ustar00rootroot00000000000000from brian_unit_prefs import turn_off_units turn_off_units(warn=False) brian-1.4.3/brian_setup_info.py000066400000000000000000000032441266062647100165270ustar00rootroot00000000000000version = '1.4.3' # the create_extras.py script will automatically generate an extras files # containing the following files extras_folders = ['tutorials/tutorial1_basic_concepts/*.py', 'tutorials/tutorial1_basic_concepts/*.txt', 'tutorials/tutorial2_connections/*.py', 'tutorials/tutorial2_connections/*.txt', 'examples/*.py', 'examples/*.txt', 'examples/audition/*.py', 'examples/electrophysiology/*.py', 'examples/electrophysiology/*.npy', 'examples/frompapers/*.py', 'examples/frompapers/computing with neural synchrony/*.txt', 'examples/frompapers/computing with neural synchrony/olfaction/*.py', 'examples/frompapers/computing with neural synchrony/duration selectivity/*.py', 'examples/frompapers/computing with neural synchrony/hearing/*.py', 'examples/frompapers/computing with neural synchrony/coincidence detection and synchrony/*.py', 'examples/hears/*.py', 'examples/hears/tan_carney_2003/*.py', 'examples/interface/*.*', 'examples/misc/*.py', 'examples/modelfitting/*.py', 'examples/modelfitting/*.txt', 'examples/multiprocessing/*.py', 'examples/plasticity/*.py', 'examples/synapses/*.py', 'docs/*.*', 'docs/_images/*.jpg', 'docs/_sources/*.*', 'docs/_static/*.*' ] brian-1.4.3/brian_unit_prefs.py000066400000000000000000000012131266062647100165240ustar00rootroot00000000000000"""This module is used by the package Brian for turning units on and off. For the user, you can ignore this unless you want to turn units off, in which case you simply put at the top of your program (before importing anything from brian): from brian_unit_prefs import turn_off_units turn_off_units() Or to turn units off and not have a warning printed: from brian_unit_prefs import turn_off_units turn_off_units(warn=False) """ class _unit_prefs(): pass bup = _unit_prefs() bup.use_units = True bup.warn_about_units = True def turn_off_units(warn=True): bup.use_units = False bup.warn_about_units = warn brian-1.4.3/dev/000077500000000000000000000000001266062647100134025ustar00rootroot00000000000000brian-1.4.3/dev/BEPs/000077500000000000000000000000001266062647100141735ustar00rootroot00000000000000brian-1.4.3/dev/BEPs/BEP-0000066400000000000000000000005241266062647100146620ustar00rootroot00000000000000BEP-0 Brian Enhanced Proposals (BEPs) propose new features with implementation specifications. See http://www.python.org/dev/peps/ and particularly http://www.python.org/dev/peps/pep-0001/ for explanations and format guidelines for enhancement proposals. Closed BEPs go to the closed folder and can serve for the documentation. brian-1.4.3/dev/BEPs/BEP-10-Error handling000066400000000000000000000053541266062647100176050ustar00rootroot00000000000000BEP-10: Error handling Abstract: Errors raised by Brian scripts are often hard to understand because the error is typically raised in a subroutine of a Brian module that the user doesn't know about. The idea would be to have clearer error messages and error catching mechanisms that would relate the error and its context to the Brian script. I am just listing a few ideas here. Defining a new exception BrianError and subclasses ================================================== For example, we could imagine that each major class would have its exception type, and the exception would be called with the instance of the class (self) and other runtime information. A separate module could gather a number of functions to make the message nicer and to do some introspection into the traceback, the frames and the user script (using the get_source function from the inspect module). These could be methods of a base Exception class BrianError. [Dan adds: agreed, seems like a good idea.] Nested exceptions ================= In many cases, the function that raises the exception does not know the context, such as the user-defined differential equations, etc. One could imagine to use a specific exception to pass information to the calling function, e.g.: def a_specific_method(self): ... raise SpecificException(self,other_information) def global_function(): try: eqs.a_specific_method() except SpecificException as e: useful_info=e.other_information raise WhatEverError("I got one!",useful_info) # or WhatEverError(e,...) [Dan adds: we had this in the past, or something very similar, and it didn't work very well, it basically made it more difficult to understand what was going on. We should have something like this, but only in those cases where an explicit error catching mechanism fails I think. Rather than improve the error reporting (which is what this tries to do), it would be much better to improve Brian's code so that it understands errors and deals with them more appropriately. At the moment we basically just hope there won't be any... Romain: yes I didn't mean something like the bug handler, "global-function" here does not embed the simulation, it could just be any method. There is at least one example currently where the units mismatch error is caught by the Equations object which changes it into a message about the differential equations being non-homogeneous] Real cases ========== We should list here a number of real cases that we would like to handle. 1. times = property(fget=lambda self:QuantityArray(self._times)) AttributeError: 'StateMonitor' object has no attribute '_times' Most likely: the statemonitor was initialised without setting the record keyword. brian-1.4.3/dev/BEPs/BEP-13-The Brian console000066400000000000000000000032531266062647100201250ustar00rootroot00000000000000BEP-13: The Brian console Abstract: The Brian console allows access to objects from a running Brian script, such as groups, connections and monitors. Two independent applications can be made: 1) a text console, possibly based on an existing interpreter (e.g. ipython); 2) a graphical interface, which would also allow online visualisation. The Brian console could also be used to control the simulation. Communication with the Brian script =================================== The communication server in the Brian script could be implemented simply as a network operation (called, say, every second), which would have access to a running Network object. The client would be an independent application, which would send Python commands and obtain information about objects in the Brian script, while the simulation is still running. Commands (start/stop/save/resume) could also be sent. Graphical interface =================== The server sends the list of objects in the Network to the client. That list is then displayed (possibly with names, if they exist). The user can pick variables to monitor them. Online imaging could even be done using monitors (e.g. a monitor is added to the network and reset every second). Online visualisation ==================== * Oscilloscope: traces of a variable are monitored. * Imaging: variables or spikes are displayed in real time with color coding. * Connectivity: fibers with varying thickness (3D?) represent the synaptic weights. Dendrites and/or axon from a specific neuron can be selected. Implementation ============== We could use for example Pyro for the communications. The GUI could use wxpython. brian-1.4.3/dev/BEPs/BEP-15-Compartmental models.txt000066400000000000000000000327471266062647100216520ustar00rootroot00000000000000BEP-15: Compartmental models Abstract: I describe a way to implement compartmental models in Brian. To model a neuron with a morphology, the standard approach is to divide the morphology in many isopotential compartments, with currents flowing through the membrane or through the two sides of the compartment. In the limit of infinitely many compartments, we obtain the cable equation. If there any many compartments with the same equations (but possibly different parameter values), then the same trick that applies for neuron groups applies for groups of compartments. The main difference is that there is only 1 output spike. There are several possible designs: * New NeuronGroup, indexes correspond to input synapses. In that case, each row of the state matrix would be a (spatially distributed) variable, while columns would correspond to different compartments. A function would map location ("LLR.15*um") to compartment index. * A NeuronGroup with a new StateUpdater and relevant methods. Maybe N should be 1. Description of a compartmental model ==================================== I feel that the notion of "compartment" should be mostly hidden because it is in fact an implementation detail much as the choice of the timestep. Most models can be described by: * currents that are spatially distributed (membrane currents) * point currents (synapses and soma) * morphology * a few global parameters (axial resistance, etc) * a threshold condition (and probably no reset), which is really a spike detection mechanism The Connection object needs to address variables by their spatial location, in a single string (although this might actually work with an object, with minor modifications to Connection and a few other places). It involves changing the method get_var_index(state) in NeuronGroup, where state would be for example "LLR.15*um.v" (branch left twice, branch right, advance 15 �m) or "LLR.end.v". In the same way, state monitors need to address by spatial location for distributed variables and also by name for point processes. Another possibility for names could be "v@LLR.15*um" or "v@mysynapse". Those names are translated into indexes. How to describe the spatial models? One possibility is to have a special term in the equation which would correspond to the longitudinal current. This way we keep the spirit of Brian. For example: eqs=''' dv/dt=(gl*(El-v)+Icable)/C : volt ''' The term Icable would then be replaced by the adequate value by the updater. This is very easy to implement internally, Icable just needs to be another variable. Alternatively, one could simply define the membrane current: eqs=''' Im=gl*(El-v) : amp/cm**2 ''' This second option is better, I think. The NeuronGroup is then initialized with: * spatial model equations (above) * morphology (we need a new module here, maybe using the experimental one); the morphology is a binary tree, except perhaps at the soma (e.g. 1 axon + 2 dendritic trees) * global parameters * point currents (possibly later), which could actually be non-spiking NeuronGroups. * maybe somatic equations, possibly as point currents * optionally, threshold condition One point we may want to think about is calcium dynamics: we should check whether it can be incorporated in the same framework (Ca dynamics follows a sort of cable equation). Data structure ============== I can imagine that there are at least two matrices: * One for spatially distributed variables, each row of the state matrix would be a (spatially distributed) variable, while columns would correspond to different compartments. A function would map location ("LLR.15*um") to compartment index. This would be the standard state matrix. * One or several for point processes, each row is a variable, columns index processes. Each such block could actually be handled as a NeuronGroup (with spike input, possibly a Connection structure). One also needs to find a simple way to set the distributed parameters, which could be the following: neuron["LLR.15*um":"LLR.30*um"] would create a subgroup that corresponds to a branch of the neuron. Internally, it is essentially a view on the relevant compartments. Then one could write things like branch=neuron["LLR.15*um":"LLR.30*um"] branch.gl=2*branch.gK where the latter is a vector operation. Morphological parameters would be available as variables (e.g. branch.diameter, branch.surface). The location of the compartment in the branch should also be indicated (branch.x), but we have to decide whether it should be the relative or absolute location. It could also be interesting if the branch was iterable: for compartment in branch: compartment.gl=2*compartment.gK # this is in surfacic units Point processes =============== Synapses and electrodes are point processes, that is, not spatially distributed. It makes sense to group synapses of the same type into a NeuronGroup. Thus, the neuron stores a list of NeuronGroups for point processes, together with the indexes of compartments where these point processes are inserted. The syntax could be as follows: electrode="Iinj : amp" neuron.dendrites.LR[5*um].insert(model=electrode,current="Iinj") But this implies that the method checks whether there is already a group of processes with the same model. Maybe dictionaries of models as strings and as Equation objects. The problem is that we won't be able to use NeuronGroups at construction time, which will cause some problems. Alternatively, we could have a state matrix implemented as a dynamic array, or have it predeclared: P=neuron.point_process(model=synapse,current="Iinj",N=10) neuron.dendrites.LR[5*um].insert(P) Internally, the model could be a Current (it could be passed as a string or Current object). New attributes are created: neuron.Iinj is a vector with the value of Iinj for all point processes. This is done by overriding the getattr/setattr methods and directing them to the neuron groups that store the point processes. Monitoring ========== Special monitors need to be added to monitor distributed variables. Without modification, the following should work, through the subgrouping mechanism: M=StateMonitor(neuron.axon[50*um:100*um],'v',record=True) It could be nice to have this syntax: M=StateMonitor(neuron.axon,'v',record=[50*um:100*um]) and access recorded values in a similar way, e.g. M[60*um]. State update ============ 1) Update point processes, as NeuronGroups 2) Update the spatial equation (e.g. using Hines method) 3) Add point currents The state update for the cable equation can be done with Hines method: Int J Biomed Comput. 1984 Jan-Feb;15(1):69-76. Efficient computation of branched nerve equations. Hines M. Available on Neuron's website: http://www.neuron.yale.edu/neuron/nrnpubs Another method here: http://www.jstor.org/stable/pdfplus/2157690.pdf The Backward Euler Method for Numerical Solution of the Hodgkin-Huxley Equations of Nerve Conduction. Mascagni M. 1990 And: Digital computer solutions for excitation and propagation of the nerve impulse. Cooley & Dodge 1966 In Hines method, values of ionic currents are calculated at midpoints of time steps as a function of V(t), and the values of the conductances are considered as constant within a timestep for the integration of the cable equation. The cable equation uses a simple implicit scheme followed by an explicit step. As I understand it, the update of Im (IHH in the paper) and the integration of the cable equation are completely independent. Therefore, we can have separate stateupdaters which are called in the following order: 1) Update the spatial equation with Hines method. 2) Update the membrane current with a standard vectorized updater (2nd order?). 3) Update the point processes with a standard vectorized updater. (actually I think 2 should be before 1, but it won't make a difference) Mascagni's method is backward Euler, with the advantage that convergence is guaranteed. The algorithm is as follows: 1) Solve the discrete system for V(t+dt), where the other variables are constant and assumed to be known at time t+dt (first iteration: use value at t). This is sparse linear system (could be computed with scipy.sparse.linalg?). 2) Solve for the other variables at t+dt using a backward Euler step with the values of V from step 1. In principle this is a simple 1D linear system (assuming conditional linearity for these variables). 3) Repeat until convergence (how many times?). In comparison, Hines method uses a single iteration, which is solving a sparse linear system as well (in fact the same one, but at time t+dt/2). 1) Calculate other variables at t+dt/2, by an Euler step from time t-dt/2, that uses the values at time t for V, and estimated at time t for the other variables using the average between t-dt/2 and t+dt/2 (this makes it implicit). 2) Solve for V(t+dt/2) then do a forward step to t+dt. This looks to me like one step of the backward Euler method (except it's 2 then 1). There is pseudo-code in Hines paper. Both methods rely on the conditional linearity of the equations, therefore it cannot work for all types of models (e.g. exponential model). To deal with branches, there are two options: 1) Use Hines method. 2) Use domain decomposition. This means using the linearity of the problem (it is linear once the other variables considered fixed, by conditional linearity) to solve the equation separately on each branch and then combine them linearly. The big advantage is that this way we can still use the solve_banded method of scipy. Neuron groups ============= How to describe neuron groups with morphologies? Two options: * neurons have the same morphology * neurons have different morphologies, but the same models It seems to me that option 2 is possible. However the threshold mechanism must be changed. It might also be possible to have gap junctions with this scheme. Building the morphology ======================= See experimental.morphology2. This should be a separate class, created before the neuron is initialised. mymorph=Morphology('pyramidalL5.swc') mymorph=Morphology() # Creates a soma (1 compartment) or Soma() mymorph.dendrites=Cylinder(l=100*um,d=2*um,n=20) # Inserts cylinder at soma, 20 compartments, subtree name "dendrites" mymorph.dendrites.diameter=linspace(1*um,2*um,len(mymorph.dendrites)) for compartment in mymorph.dendrites: # maybe not very useful compartment.diameter=... mymorph.dendrites.L=Cylinder(l=50*um,d=1*um,n=10) # Creates a child branch mymorph.dendrites.R=Cylinder(l=50*um,d=1*um,n=10) # Creates another child branch mymorph.dendrites.LL=Cylinder(l=50*um,d=1*um,n=10) # A short hand for L.L Data structure -------------- A neuron morphology is a tree of branches, and each branch is a list of segments. The root is the soma. Each segment has a diameter and length (except perhaps the soma). The children can be named, but there are two special names "L" and "R" (left/right), which allows concise access using binary strings of the form LLR (left left right). Additional information (which is in swc files) could be stored, e.g. 3D coordinates. The data structure is a tree of branches. Each branch has the following attributes: * diameter, length, area, x, y, z for all compartments (vectors) * number of compartments (could be implicit in vector length) * A dictionary of children, and possibly the parent. Subgrouping ----------- Ex: subtree=mymorph.dendrites.LLR mymorph.dendrites returns a subtree, mymorph.dendrites.LLR too but it's a shorthand for L.L.R. Internally, it does mymorph.dendrites.L.LR (recursive algorithm). subtree=mymorph.dendrites[1131] If it's not a binary tree, then we need to access children with numbers, this can be done with setitem. Insertion --------- Ex: mymorph.dendrites.L=Cylinder(l=50*um,d=1*um,n=10) # or Branch? Ex2: mymorph.dendrites.LLR=Cylinder(l=50*um,d=1*um,n=10) using setattr. The instruction replaces or creates the subtree (child) with the given morphology. LLR is shorthand for L.L.R (recursive algorithm as for subgrouping). Examples ======== mymorph=Morphology('pyramidalL5.swc') eqs=''' # The same equations for the whole neuron, but possibly different parameter values Im=gl*(El-v)+gNa*m**3*h*(ENa-v) : amp/cm**2 # distributed transmembrane current gNa : siemens/cm**2 # spatially distributed conductance dm/dt=(minf-m)/tauinf : 1 # etc ''' neuron=CompartmentalNeuron(model=eqs,morphology=mymorph,threshold="axon[50*um].v>0*mV",reset=None,refractory=4*ms, cm=0.9*uF/cm**2,Ri=150*ohm/cm) # or SpatialNeuron # Note: the threshold and reset have to be handled differently, as if N=1 initial_segment=neuron["axon.0*um":"axon.100*um"] # initial_segment=neuron.axon[0*um:100*um] initial_segment.gNa=linspace(0*nS/cm**2,3000*nS/cm**2,len(initial_segment)) initial_segment.cm=1*uF/cm**2 # We change the default specific capacitance (useful e.g. if myelinated) # Point processes exc_synapse=""" Ie=ge*(Ee-v) : amp dge/dt=-ge : siemens """ neuron.dendrites.LLR[10*um].insert_current(model=exc_synapse,current="Ie") # neuron.dendrites is a subgroup; also neuron.dendrites.LLR (or perhaps .L.L.R ?) # other method names: insert, point_process electrode="Iinj : amp" neuron.dendrites.LR[5*um].insert_current(model=electrode,current="Iinj") M=StateMonitor(neuron.axon,'v',record=[50*um:100*um]) # maybe a special monitor? SpatialStateMonitor # record can be True (entire tree), 50*um, [50*um:100*um] or [:] (entire branch) @network_operation def inject_random_current(): neuron.dendrites.LR[5*um].Iinj=rand()*nA brian-1.4.3/dev/BEPs/BEP-18-New refractoriness mechanism.txt000066400000000000000000000300621266062647100232570ustar00rootroot00000000000000BEP-18: New refractoriness mechanism Introduction ------------ It seems to me that the current mechanisms for resets and refractoriness is a bit ad-hoc - and only really works nicely for the simple case of a fixed refractory period for all neurons, with relatively simple (not custom) resets. Background ---------- The original mechanism worked by replacing the Reset class with a Refractoriness class that instead of using the list of neurons that had just spiked, uses the list of neurons that have spiked in the last T time steps. This has two problems, first of all it was still just a reset, so because thresholding and propagation happen before resetting, strong inputs could cause a spike during the refractory period (and this did happen). We fixed this by adding an additional mechanism that, after thresholding, checks if a neuron is in its refractory period, and removes it from the list of spikes if it is. This solves the problem, but is a little ad hoc. The second problem with the original mechanism is that it doesn't work well for custom resets. This meant we had to write several additional custom refractoriness classes. These are SimpleCustomRefractoriness and CustomRefractoriness. The first of these takes a Python function, a refractory period, and a state variable as arguments. When the neuron spikes, it saves the value of the specified state variable after the Python function is called, and then holds it at that value for the refractory period. The second, CustomRefractoriness, takes two functions, a reset function and a refractory function, and a refractory period. Initially the reset function is called, and then for the duration of the refractory period, the refractory function is called on the list of indices of neurons that are in their refractory states. This system is now reasonably general, but it misses several features. First of all, it doesn't play nicely with things like string resets because it requires you specify a Python function. Secondly, there was no support for variable refractoriness until recently, and there's only a rather simple form of support for it even now (only with a simple reset). Proposal -------- First of all, we do away with having refractoriness be a special case of resetting. We'll now have separate reset and refractoriness classes that function in different ways. What happens is that each time step, the reset class is called on the list of neurons that have just spiked. If the refractoriness class has a 'start' method (for example), it also gets called with this list. Finally, the NeuronGroup object handles variable refractoriness, and has a new method get_refractory_indices() as well as the older get_spikes(), and the refractoriness classes will use this method. The most important refractoriness class, possibly the base class for the others, would just hold a set of variables at their values after the reset, so you might specify reset='V=Vr; Vt+=deltaVt' and refractory=Clamp('V', 5*ms). (See below for some ideas on syntax.) In the scheme I'm proposing then, the absolute refractoriness is handled in a special case way by NeuronGroup itself, and the clamping of variables is handled separately by refractoriness classes. Another possibility would be to have the absolute refractoriness handled by the refractoriness class, we could have a base class that just handles this, as well as base class that handles clamping, and these could be combined into a class that derives from both of them to get both behaviours (for example). That's conceptually nice from a programming point of view, but maybe not so nice for the user? Variable refractoriness ----------------------- Another matter that requires some thought is how variable refractoriness should work. At the moment, in the simplest case of a simple reset, you can have fixed refractoriness, a fixed refractoriness array, or specify a variable of the equations that gives the refractory time. In this latter case, you also have to provide a max_refractory argument (but this wouldn't be necessary if we used the mechanism I described above because you don't need to remember spike times). Technical note: the way NeuronGroup handles refractoriness at the moment is to store an array _next_allowed_spiketime of length the number of neurons in the group, which tells it the next time that the neurons are allowed to fire a spike. After a spike, this array is incremented for each spiking neuron by its refractory period. To get the list of indices of neurons that are currently refractory then you just have to do _next_allowed_spiketime>t. Syntax ------ There's also the syntax to be considered, and it's a slightly tricky problem. At the moment, the user writes things like reset=-60*mV and refractory=5*ms, and that works OK, but if they want more complicated things they have to specify a refractoriness class for the reset. Here's a possibility. We always use variable refractoriness, and we introduce a forced state variable 'refractory' to give the refractory periods. If the user specifies refractory in their equations, then we do nothing, otherwise we add it to their equations as a parameter. So if they wanted, they could have a differential equation governing refractoriness. Simple cases: reset=value (implied variable V, v, Vm, vm) refractory=value or array clamps the implied variable to its value after reset reset='var=val' as above, but use the reset state variable instead of implied var reset=string, function, class refractory=value or array clamps the implied variable V, v, Vm, vm at given period In more complex cases, you need to specify two bits of information for the refractoriness, the variables/code for the refractoriness, and the refractory period. In fact, the latter is sort of optional, because you can specify the periods afterwards by changing the 'refractory' state variable. I would suggest we introduce a new keyword for this case. The 'refractory' keyword has always referred to a length of time in the past, and I think logically it's nice to keep it that way rather than have it mean several different things. The new keyword could be something like 'refractoriness' or 'refractorycode' or 'refractorytype'. Depending on its type, it would mean one of: state variable or tuple of state variables: clamp string, function, class: custom code Some examples with this syntax: G = NeuronGroup(N, eqs, reset=Vr, refractory=5*ms) G = NeuronGroup(N, eqs, reset=Vr, refractory=5*ms*rand(N)) G = NeuronGroup(N, eqs, reset='x=x0', refractory=5*ms) G = NeuronGroup(N, eqs, reset='V=Vr; Vt+=deltaVt', refractory=5*ms) G = NeuronGroup(N, eqs, reset='x=x0; y+=deltay', refractory=5*ms, refractoriness='x') G = NeuronGroup(N, eqs, reset='x=x0; y+=deltay', refractory=5*ms, refractoriness='x=x0') The 'refractory' keyword would be strictly optional, as you could always do something like this afterwards anyway: G.refractory = 5*ms G.refractory = 5*ms*rand(N) Well, that's one proposal for the syntax, I can think of several others. For example, you could keep only a 'refractory' keyword, but allow tuples like refractory=('x', 5*ms) to mean clamp variable 'x' for 5*ms, or refractory=('x', 'y', 5*ms*rand(N)) to mean clamp variables 'x' and 'y' for 5*ms*rand(N), or refractory=('x=x0', 5*ms). My feeling is that this probably ends up being more confusing in the long run though. Romain's proposal ----------------- [from briansupport] Maybe one way to see the problem is that we are trying to specify spiking models (=hybrid systems) with two states, active and refractory. Each of these two states may be defined by a separate set of differential equations, and transitions are defined by conditions (=threshold) and discrete operations (=reset). In other words, refractoriness could be defined in exactly the same way as a standard model, with equations, threshold and reset. We could provide a special variable, "lastspike" or something like that, which would store the time of the last spike (i.e., last transition active->refractory). Here is how Dan's examples could be done in this way (keyword names are not great): > G = NeuronGroup(N, eqs, reset=Vr, refractory=5*ms) G=NeuronGroup(N, eqs, reset=Vr, refractory_eqs='dv/dt=0*volt/second : volt', refractory_threshold='t>lastspike+5*ms') or without clamping v: G=NeuronGroup(N, eqs, reset=Vr, threshold='(v>Vt) & (t>lastspike+5*ms)') > G = NeuronGroup(N, eqs, reset=Vr, refractory=5*ms*rand(N)) G=NeuronGroup(N, eqs, reset='v=Vr;refractory=5*ms*rand(N)', refractory_eqs='dv/dt=0*volt/second : volt', refractory_threshold='t>lastspike+refractory') and eqs contain the line "refractory : second". > G = NeuronGroup(N, eqs, reset='x=x0', refractory=5*ms) > G = NeuronGroup(N, eqs, reset='V=Vr; Vt+=deltaVt', refractory=5*ms) > G = NeuronGroup(N, eqs, reset='x=x0; y+=deltay', refractory=5*ms, > refractoriness='x') > G = NeuronGroup(N, eqs, reset='x=x0; y+=deltay', refractory=5*ms, > refractoriness='x=x0') All these can be done exactly in the same way, e.g.: G=NeuronGroup(N, eqs, reset='x=x0; y+=deltay', refractory_eqs='dx/dt=0*volt/second : volt', refractory_threshold='t>lastspike+5*ms') Keywords are not great (maybe refractory_end instead of refractory_threshold? and perhaps simply "refractory" instead of "refractory_eqs"?). The variables for the refractory state should be the same as for the active state, since it's the same neuron. So I suggest that 1) all variables must be defined in the model equations (eqs) (possibly as parameters), 2) by default, unspecified variables in the refractory equations have the same dynamics as in the active state. For the implementation: * At construction time, we make a stateupdater, a threshold and a reset for the refractory state, almost exactly as for the active state. These must modified so that "lastspike" contains the time of the last spike time. * We add an extra boolean array in each group, which says which neurons are active or refractory (.refractory?). * At run time, we apply each state updater, threshold and reset to their corresponding subgroup. It's almost as if we had 2 groups instead of 1, except the partition is dynamic. It is not very different from Dan's proposition, but I think it's conceptually simpler and therefore might be easier to use/understand. It's also slightly more general as you can have differential equations during the refractory state. I think having code called during refractoriness is not very elegant, because it means artifically introducing clock-based events. Syntax ------ There are two states, active and refractory. So we need: * equations for active state (A) * equations for refractory state (R) (unspecified = same as active) * transition A->R (threshold and reset) * transition R->A (threshold and reset) Spikes are produced at the A->R transition. In the namespace, one variable stores the time of the last spike (lastspike?). I'm not sure the two sets of equations should be in the same string, because they need to be turned into Equation objects. Or, it should be possible to pass the two sets separately (possibly as Equation objects). Example: ''' active: dv/dt=-v/tau : volt refractory: dv/dt=0 # should the units be specified? (I'd say yes to keep the same syntax) ''' or: model='...', refractory='...' How to set the transitions? We already have the reset and threshold keywords for the A->R transition. We probably don't really need a reset for the R->A transition (right?). We need a keyword for the transition from refractory to active (threshold only). Suggestions: * to_active * refractory_threshold * unrefractory * activate * refractory_end * refractory_until My favorite is the last one for the moment, but I'd like something better (shorter). -------------- Examples -------- Equations: ''' dv/dt=-v/tau : volt (active) ''' A->R: threshold="v>vt" reset="v=0" R->A: refractory_until="t>last_spike+5*ms" ? refractory=5*ms ? reactivate="" ? Implementation: * store inactive variables * full state update * copy inactive variables back brian-1.4.3/dev/BEPs/BEP-19-Plotting000066400000000000000000000023031266062647100165470ustar00rootroot00000000000000BEP-19: Plotting Abstract: It would be nice to have a few functions to create publication-ready figures. It seems to me that we currently duplicate a lot of code every time we need to create our figures. Some of this is Brian-specific, but most is probably not. Style options ------------- * colors (BW default) * figure size: possibly choices for different publications (with choice of 1 or 2 columns) * fonts: could also be defined from publication styles * point size (line thickness) and symbol size (cross, dot, etc) General plotting functions -------------------------- * add letters to panels (A, B, C...) * axis scales with horizontal/vertical segments * insets (e.g. zooms) * ticks (not too many etc) Brian-specific plotting functions --------------------------------- * Add spikes to traces (uses a spike list and a state monitor) * Remove reset lines from traces Saving and restoring -------------------- What would be nice is an elegant way to save a figure, or the data produced by a script, and then make the figure in a separate script, with an easy way to quickly redo a figure with a different format or new data. Perhaps simply saving a dictionary of numpy arrays. brian-1.4.3/dev/BEPs/BEP-20-AER interface.txt000066400000000000000000000222411266062647100201100ustar00rootroot00000000000000BEP-20: AER interface Abstract: Address Event Representation is a format used to communicate to and from neuromorphic chips with timed events (spikes). The goal is to be able to listen to AER events and send AER events. The idea is to interface with the JAVA program jAER, which can communicate with a chip: http://sourceforge.net/projects/jaer/ There are two different issues: * communicating with the program (jAERviewer) to send/receive AER events * running Brian in realtime and feed/send AER events See dev/ideas/AER for scripts. Listening to jAERviewer ======================= First steps with jAERviewer: * Run jAERviewer * Click on File > Remote > enable unicast... * Check that the value of timestampMultiplier has a dot and not a comma (this is a bug due to French localisation) * You can change the UDP port as you like (make sure your firewall allows communications through it). * You may also change the buffer size (size of packets). * We probably want to check useLocalTimestampsEnabled, it seems to output timestamps in microseconds (but there are some weird problems, sign problems, maybe it's little endian in this case). * Drag and drop or open a file with AER events Now jAERviewer plays the events and send them as packets through the UDP port. Packets are in the following format: Format: int32 sequenceNumber int32 address0 int32 timestamp0 int32 address1 int32 timestamp2 etc with java's big-endian format. Note that this can be changed when you set the UDP connection (for example, sequenceNumber is optional; you can also have little-endian instead, or change the type of timestamps). * sequenceNumber is the number of the packet. Normally, it should just increase by 1 after every packet. So the use of this is probably to check that you haven't missed a packet. Then follows a list of events in the form (address, timestamp) * address is a chip-specific way to identify the event (e.g. pixel index). * timestamp is the time of the event, in unknown units (but ints). Looks like microseconds or even smaller units. For the 128x128 retina chip, the 32 bit address is formatted as follows: 1 bit polarity (ON/OFF event) 7 bits x 7 bits y There's a function extract_retina_event in AERlisten.py to extract the information, but we should try to vectorise this. Using the socket module, it's very simple to listen to these events. There's still a little bit of work to make it more efficient. TODO: * check the units of timestamp (and that local option) * perhaps write other functions for different chips, we probably just need to adapt Matlab functions: http://jaer.svn.sourceforge.net/viewvc/jaer/trunk/host/matlab/ See this for the silicon cochlea: http://jaer.svn.sourceforge.net/viewvc/jaer/trunk/host/matlab/cochlea/extractCochleaEventsFromAddr.m?revision=429&content-type=text%2Fplain Sending events to jAERviewer ============================ I haven't looked at this yet, but it seems fairly straightforward. In principle: * Enable it on the viewer: File>Remote>Open unicast... * Send UDP packets in the same format. Controlling chips through jAERviewer ==================================== It seems that we can communicate through UDP to send chip commands to jAERviewer. More precisely, to set biases, which are the parameters on the chip: http://sourceforge.net/apps/trac/jaer/wiki/Interfacing%20to%20jAER Reading/writing AER .dat files ============================== Read: http://jaer.svn.sourceforge.net/viewvc/jaer/trunk/host/matlab/loadaerdat.m?revision=2001&content-type=text%2Fplain Save: http://jaer.svn.sourceforge.net/viewvc/jaer/trunk/host/matlab/saveaerdat.m?revision=2001&content-type=text%2Fplain If we don't want real-time, this should the easiest way: just load a previously recorded AER file and use Brian to play it as an input (SpikeGeneratorGroup). An example is given in loadAER.py (seems to work). Feeding Brian with AER events ============================= One simple possibility is to do it offline: record an AER file with jAERviewer, then load them in Brian into a SpikeGeneratorGroup. This seems relatively straightforward, except perhaps SpikeGeneratorGroup might be a little slow (see below). Real-time Brian --------------- If we want to do it online, then Brian has to run in real time. So first of all, the timestep must be large enough so that it's possible. Here's the idea that is implemented in brianRT.py and in experimental/neuromorphic/realtime.py. To ensure reliable processing, Brian must have a processing latency. That is, all AER events are postponed by a fixed latency. During the simulation, we measure the real time (time()) and try to synchronize the Brian clock more or less with it by inserting pauses (sleep()). More precisely, if we don't want to miss events, then Brian must always be ahead of real time by no more than the latency (otherwise events could be scheduled at a time that has already passed). Here's a simple algorithm to do this: * Have a network_operation running every T. * The operation checks if briantime>realtime, in which case it sleeps to catch up with real time. If Brian runs faster than real time, this ensures that Brian is always less than T ahead of real time. In practice, with overheads and temporary slow downs of the simulation, it will be a little bit more. Thus events should be postponed by a bit more than T. Note that only one clock may be synchronized with real time. This algorithm could be implemented has an object in Brian. For example, as an option in run(). Or as a special Clock, or a dedicated function or object (RealtimeController or something like that). I think that since only one clock can be synchronized to real time, it would make sense to have it as an option to run, e.g.: run(realtime=myclock) # e.g. defaultclock This would just add an object to the network. There is an important parameter to set, which is T. But since the synchronizing operation is a small overhead, it could simply be set by default to a fixed value, perhaps a few ms. A global preference could be used to set it. Then the only possibly remaining parameter is the starting point of the clock. In principle, when the simulation runs, the real time clock should be offset so that it matches the simulation clock. Should there be a warning or an error if Brian cannot run as fast as real time? Another thing is we might to lock Brian to the speed of AER events rather than to real time, in case it's not the same. Feeding events -------------- Look at monitor_AER_events for an example. I think we first need to use a non-blocking socket to listen to events (otherwise we need a thread): mysocket.setblocking(0). Listening can be implemented as a network_operation. The simplest way to feed events is to define a SpikeGeneratorGroup and add events to the spike list, e.g.: S.spiketimes+=event_list (assuming the events are sorted by time, and they should be) We also need to synchronize the starting time of events with the real time clock. In addition, there's the problem of repetition in the viewer, where time goes back to the beginning. Thus we should check whether time decreases, in which case we should re-synchronize. Efficiency ---------- Currently, SpikeGeneratorGroup loops over events, and this is probably not very efficient if we have many events. There is also the issue of units (we currently need to multiply all timestamps by second). Maybe a digitized version would be better. It would basically work as the LS structure (SpikeContainer object). In fact, I think since it inherits from NeuronGroup, we just need to redefine init() and push_spikes(), and the rest comes from NeuronGroup. (Actually it could be the default behavior when it is initialized with spike times rather than a generator). Another (perhaps simpler) option is to allow the next spike iterator in SpikeGeneratorGroup to return vectors of neurons (for the same timing) (actually this might already work). In fact, this numpy command should be used: u, indices = unique(timestamp, return_index=True) Then the idea would be to use a SpikeContainer which would not necessarily store all timesteps but only non-empty ones (there would also be circular vector storing the timestamps). Current state ============= * Loading AER files seems fine now. * SpikeGeneratorGroup.gather() is too slow, and running a gathered group is too slow. The problem is that the design of SpikeGeneratorThreshold.__call__() is rather bad. It uses a where() function on a N-sized array with ones at positions of spikes. Very inefficient. * SpikeQueue now replaces SpikeGeneratorGroup. Running is faster, but construction is slower, and overall it is not faster (why?). * monitor_AER_events listens to spikes through UDP. TODO ==== * Write SpikeQueue.push_spiketimes * Make SpikeQueue faster for construction * Make a cleaner version of SpikeQueue. This could rely on a new SpikeContainer with future events (in addition to past events, or independently) (FutureSpikeContainer). * We have some problems with timestamps through UDP which are sometimes <0. Why? * monitor_AER_events: feed the SpikeQueue with it. Can we do it in real time?brian-1.4.3/dev/BEPs/BEP-22-GPU-Brian-Hears000066400000000000000000000206341266062647100174740ustar00rootroot00000000000000BEP-22: Brian Hears on GPU Abstract: Brian Hears should be updated to run on the GPU without transferring data to/from the CPU frequently to remove the data transfer bottleneck. Issues ====== * Final operation should be seemless, the user shouldn't need to have to do anything except install PyCUDA. * GPU operations should be extensible, i.e. the user can provide GPU code to avoid reintroducing a CPU<->GPU bottleneck * Output data should be allowed to connect straightforwardly with Brian, this could be just via GPUNeuronGroup possibly. However, that means implementing reset/threshold on GPU. Avoiding the bottleneck ----------------------- The simplest way to avoid the GPU/CPU bottleneck would be to allow data to stay in the global memory of the GPU. This could be achieved by adding a keyword argument to buffer_fetch, say allow_gpu. This would make everything transparent to implement and allow for mixed GPU/CPU operation and backwards compatibility. However, in chains of filterbanks it may not be efficient, because it involves a read/write to global memory for each filterbank in the chain, and this gives us a global memory bottleneck. A more complicated solution would be to allow the automatic merging of compatible filterbanks to avoid read/writes to global memory. There are several possible ways to do this, each involving a merged kernel. One possibility is to work sample by sample, that is we write a per-sample operation and combine the loops together. So, for example if we combined say, a LinearFilterbank with a FunctionFilterbank to half-wave rectify and compress we might have something like this for the LinearFilterbank: __global__ void filt(SCALAR *_b, SCALAR *_a, SCALAR *_x, SCALAR *_zi, SCALAR *_y, int numsamples) { int j = blockIdx.x * blockDim.x + threadIdx.x; if(j>=n) return; for(int s=0; s0)*x; x = fpow(x, 1.0/3.0); So, the sample-by-sample approach would be to have a template: __global__ void filt(int numsamples, SCALAR *x, %(ARGUMENT_DECLARATIONS) ) { int j = blockIdx.x * blockDim.x + threadIdx.x; if(j>=n) return; %(FILTER_INIT_CODE) for(int s=0; s0)*x; x = fpow(x, 1.0/3.0); ''') For maximum flexibility, all approaches could be implemented, i.e. combining kernels either sample-by-sample or in terms of buffers; and allowing buffer_fetch to return either CPU or GPU data (if a switch is provided). The more efficient methods would be preferred if they existed, but otherwise not. Another major issue is how to handle filterbanks which change the number of channels, and ones which have multiple inputs/outputs. In these cases, kernel combining may be impossible, and we may have to use writes to global memory. However, optimisations that use kernel combination may be possible in some cases. I imagine what we will need to do is have some sort of graph analyser that generates a graph of the filters and selects the approach according to the topology. Issues: * Providing code that is not sample-by-sample but works on buffers * Handling filters with different min/max buffer sizes * Using shared memory for code that works on buffers? How to do it? * Syntax for users to provide code * Method for passing arguments to the filt function, e.g. specifying a namespace along with the code. * FFT based filtering on the GPU? * How to handle filters which change the number of channels on the GPU? e.g. Interleave, etc. * How to handle filters with multiple inputs/outputs * How to handle feedback Code generation --------------- Mostly, complicated code generation issues can be ignored for Brian Hears, however there are a few cases where it might be relevant. Filter code may require access to variables, and the way to do this is probably to provide a namespace for the code. This is the approach I'm planning for the code generation generally. So it might be worth outlining the approach here briefly: The idea is to have a Code object, or something like it, which has two major properties, a code string, or compiled code object, and a namespace. The code can be in several different languages (Python, CPU C, GPU C, etc.). In Python, it is simply a case of doing: exec code_str in namespace In C on CPU it would be: weave.inline(code_str, local_dict=namespace) And on GPU it would be more complicated because you would need to parse the namespace into an argument list and then pass this to the kernel function, etc. Code objects may also be combined, for example you might want to do a bit of initialisation in Python before running the main code in C, so the Code object should have some way of allowing combinations, shared namespaces, etc. That's a brief outline of how I see the code generation stuff working, and it might be good to use this for Brian Hears. Maybe just implementing a design for the approach above would be a good start, without worrying about the more complicated issues of generating the code strings themselves. The other code generation issue is the generation of code from user specified functions. One way to do this is to inspect the function source code, which might be possible in some cases. For lambda functions, the approach I mentioned before of using sympy Symbol objects might be possible. In other cases, we might just have to force the user to specify the code explicitly. Plan ==== This is a first draft of the implementation plan: (1) Write a very simple code generation module as described above, that only handles the framework, i.e. the Code object. I'll do this (Dan). (2) Design a framework that is able to handle all the problems described above (e.g. different numbers of channels, multiple inputs/outputs). This framework should allow for the possibility of optimisations, but doesn't necessarily have to include an implementation. At this stage, the design should be not too detailed. (3) Go through all of the filterbanks that we know about, and all the examples we have and see if the framework could handle them. If not, redesign and iterate (2-3). (4) Implement the design, starting with the simplest cases such as linear filterbank chains and then moving on to the more complicated cases. (5) Performance testing. (6) Implement the code generation stuff for users to write extended filters, or filters with feedback. (7) More performance testing. Separate to this, we need to implement GPU reset/threshold, and this can be folded into GPUNeuronGroup when it is done. Framework ========= Very sketchy ideas: * Use Code object as in code generation * Generate a filterbank graph and analyser * Add a gpu=True/False flag to buffer_fetch * Use per-sample and per-buffer kernel combination via templates brian-1.4.3/dev/BEPs/BEP-23-Event-driven code generation.txt000066400000000000000000000043131266062647100231400ustar00rootroot00000000000000BEP-23-Event-driven code generation.txt Abstract: For the Synapses class (see BEP-21), we need to be able to turn differential equations into event-driven update code. This BEP describes how it can be done. Note: this is partially done in Synapses Example ======= This is the Synapses code for STP: S=Synapses(source,target, model='''dx/dt=(1-x)/taud : 1 du/dt=(U-u)/tauf : 1 w : 1''', pre='''v=v+w*u*x x=x*(1-u) u=u+U*(1-u) ''') # STP We want to turn it into this: S=Synapses(source,target, model='''x : 1 u : 1 w : 1''', pre='''u=U+(u-U)*exp((t-lastspike)/tauf) x=1+(x-1)*exp((t-lastspike)/taud) v=v+w*u*x x*=(1-u) u+=U*(1-u) ''') # Event-driven STP Ideas ===== I can see two ways to do this: 1) Use Sympy do symbolically solve the equations at construction time, and rewrite the pre/post code. 2) Calculate the matrix form of the equations: dX/dt=A*X+B, and then use the matrix exponential at update time. The second idea is simpler because we already have all the code to do it, it's essentially the same as the LinearStateUpdater for NeuronGroup. But in the long run, the first idea might make things simpler (plus we may be able to treat more cases), and directly usable for C/GPU code generation. Subtleties ========== There are several subtleties: * Sometimes we need to keep to continuous updates. This is the case if there is a lumped variable (e.g. gtot=sum of g for all synapses). * If only part of the equations can be turned into event-driven code, then we need to make sure that the other variables are independent, otherwise we will have to keep continuous updates for all dependent variables. Current implementation ====================== This is currently implemented in Synapses, for 1D linear differential equations. We use the flag "(event-driven)" in the definition string, so it is explicit. What is missing is a more general mechanism for higher dimensions. brian-1.4.3/dev/BEPs/BEP-24-Dana-and-Brian.txt000066400000000000000000000006721266062647100202240ustar00rootroot00000000000000BEP-24: Dana and Brian Abstract: Random ideas ============ * See if we integrate it with the new Synapses object * Other option: derive from Connection and overload propagate() * Group -> NeuronGroup * in Group: N=(50,100)-> N =50*100 and store the shape of the group somewhere * Construction of sparse connection matrices with kernels * Discrete time updates: find a syntax (V(n+1)=f(V(n),...)); update with Euler and dt=1. brian-1.4.3/dev/BEPs/BEP-3-Quality tools000066400000000000000000000016521266062647100174370ustar00rootroot00000000000000BEP-3: Quality tools Abstract: We should set up a series of quality tools (scripts) to ensure the quality of the code, such as style, test and documentation coverage, efficiency tests (benchmarks). This would be very useful if we want to welcome new developers. Available tools --------------- * pylint, pychecker, pymetrics, pyflakes * In Eclipse: run as code coverage + code coverage results view * In Eclipse: pylint (in Pydev preferences). I could not install it yet... Tests ----- [DONE] * Have an additional separate module with user-defined tests? (or more global tests) * Handling bugs: write failing tests. When the bug is solved, the test passes and can be added to the test suite. Style ----- * Style checker (e.g. from brian import * and check all globals) Miscellaneous ideas ------------------- [DONE] * Write a guide for developers * Automatically find obsolete functions/classes (unused ones) brian-1.4.3/dev/BEPs/BEP-4-Simplifying Brian000066400000000000000000000024161266062647100201740ustar00rootroot00000000000000BEP-4: Simplifying Brian Abstract: This BEP proposes to simplify Brian's code by moving some classes to an "obsolete" folder. In order to make the code more readable and also to simplify the syntax of Brian, I propose a number of simplifications. Removed classes should be put in an "obsolete" folder so as to keep backward compatibility while simplifying the code. A "deprecated" warning could be raised when those classes are used (see http://seewhatever.de/blog/?p=161). First, the standard structure of a model is equations + threshold + reset, where the threshold is a Python expression evaluating to a boolean and reset a series of Python statements. Threshold --------- StringThreshold should be the standard mechanism. Obsolete classes: * NoThreshold * FunThreshold * SimpleFunThreshold * VariableThreshold PoissonThreshold and EmpiricalThreshold could also be replaced by or supplemented with versions with expressions, where PoissonThreshold expressions should return a vector of 0..1 values. Reset ----- StringReset should be the standard mechanism. Obsolete classes: * VariableReset * FunReset * NoReset Model ----- Is the Model class really useful? (neuronmodel module) quantityarray ------------- We could move it the obsolete folder.brian-1.4.3/dev/BEPs/BEP-5-Integral to differential000066400000000000000000000050531266062647100214540ustar00rootroot00000000000000BEP-5: Integral to differential Abstract: This BEP proposes a function to find the linear differential system associated to a given impulse response (e.g. PSPs, PSCs, STDP rules...). Experimental module: experimental/integrodiff Example: g(t)=t*exp(-t/tau) should be converted to: dg/dt=(x-g)/tau dx/dt=-x/tau with appropriate scaling, and the initial condition x=1,g=0 at time t=0. Idea: use the automatic differentiation to calculate d^ng/dt^n and calculate the rank of a set of vectors g^{(n)}(t_i) to find the dimension. Then use e.g. least square minimization to find the matrix. Issue: this does not work if there are free variables (e.g. tau). Implementation proposal: ------------------------ eqs=equations("g(t)=t*exp(-t/tau)") # returns Equations or string object # I couldn't find a proper name for the function! The differential system contains variables g and g_in and is such that the solution with initial condition g_in=1 and x=0 for all other variables is such that g(t) matches the given expression. How to do it: * A duration T is passed (typically a few times tau) * re matching to extract g, t (time variable) and the RHS expression * conversion to a function of t * nth-derivatives of g are calculated with automatic differentiation * N points are randomly drawn in [0,T] and N vectors are calculated, each vector holds the value of g^{(i)}(t_j). * The rank is calculated as follows: calculate the best LSQ approximation of g{(n)} as a function of g^i (i pow(x,y)). There are some issues here, for example the optimal Python update at the end is:: P._S += dt*P._dS and not:: V += dt*V__tmp W += dt*W__tmp In addition, the expression rewriting is not straightforward to do (but can work in many cases probably). Issues ------ In the above we've only considered the Euler update, but different solvers (in particular the implicit solver) may have more complicated schemes which might involve pre and post processing of the data, and we may also want to think about mixed exact linear state updaters with inexact nonlinear state updaters, etc. So can this all be specified in one scheme? Need to do a survey of the existing state updaters to determine this. In addition, there are also string thresholds and resets, various custom operations, etc. Ideally the code generators should be able to handle as much of this as possible in the easiest way possible. There should probably also be a fallback mechanism, whereby people can provide their own translated code if they wish, which can be used alongside other generated code. This would be useful for example if there were more or less optimal ways to do things in different languages or if our automatic translators failed or weren't defined for a particular case (e.g. custom operation). Proposal ======== A scheme should be defined for specifying per-neuron updates using expressions and potentially intermediate variables. For example using the template matching from the Python string module a specification of the Euler update might be:: intermediate: $vartype ${var}__tmp = $var_expr update: $var += ${var}__tmp This is only a suggestion of a scheme, the precise scheme used should be based on a survey of the current solvers, string thresholds/resets and so on. In addition, some sort of scheme for dealing with more complicated solvers? Or just special casing these? What about mixed linear/nonlinear for example? Some other possible schemes for Euler update:: @foreachneuron @foreachvar: $all_vars @intermediate: ${var}__tmp $var_expr @foreachvar: $all_vars $var += ${var}__tmp This latter is uglier, but maybe makes it more flexible and allows for better optimisation for target languages. It may also be possible to combine this more easily with things like mixed linear and nonlinear state updates. One might imagine for example something like this:: @exactlinear: $linear_vars @$nonlinear_scheme: $nonlinear_vars Is this level of complexity justified though?brian-1.4.3/dev/BEPs/closed/000077500000000000000000000000001266062647100154445ustar00rootroot00000000000000brian-1.4.3/dev/BEPs/closed/BEP-1-STP000066400000000000000000000041731266062647100166040ustar00rootroot00000000000000BEP-1: Short term plasticity Abstract: Implementation of short term plasticity following the Tsodyks-Markram model. The model: presynaptic spikes produce instantaneous changes in synaptic variables, which also follow differential equations. The synaptic weight is modulated by one of those variables, in a multiplicative way. References ========== Mongillo et al (2005). Synaptic Theory of Working Memory, Science. dx/dt=(1-x)/taud (depression) du/dt=(U-u)/tauf (facilitation) spike: u->u+U*(1-u); x->x*(1-u) u*x is the modulation factor (in 0..1), weights are modulated by the value of u*x before updating. Markram et al (1998). Differential signaling via the same axon of neocortical pyramidal neurons, PNAS. [same model] Tsodyks & Markram (1997). The neural code between neocortical pyramidal neurons depends on neurotransmitter release probability, PNAS. Senn et al (2001). An Algorithm for Modifying Neurotransmitter Release Probability Based on Pre- and Postsynaptic Spike Timing, Neural Comp. Implementation ============== A number of variables are involved in the model. Since they are only modified by presynaptic spikes, a group of the size of the presynaptic group P should be created, with the appropriate dynamics. This group G receives spikes from P through a SpikeMonitor, which modify its variables in an event-driven way. Spikes are directly forwarded to the postsynaptic group Q, with a modulation. That connection should replace the original connection between P and Q. This can be done simply by replacing the source variable of the Connection. To deal with delays correctly, the delays from P->Q should be transferred to P->G and removed from P->Q. The value of the modulation variable is only required at presynaptic spike times. It implies that the integration of the equations can be event-driven, which would minimize the simulation cost. Interface ========= A similar interface as for STDP should be chosen, e.g.: stp=STP(C,parameters) where C is the Connection. One could pass the equations as a string as for STDP, but are there any other models of synaptic dynamics being used? brian-1.4.3/dev/BEPs/closed/BEP-11-Screensaver computing000066400000000000000000000124471266062647100224700ustar00rootroot00000000000000BEP-11: Screensaver computing Abstract: At a given time in a lab, many (most?) computers are not used. This huge unused computing power could be used for intensive Brian simulations. A simple way to do this is to write a screensaver program that would process a queue of Python jobs. **** THIS IS NOW MOSTLY DONE WITH PLAYDOH **** ********************************************************* It looks like Condor does most things listed here!! So what we should do maybe is: 1) something to automatize/simplify making Condor configuration files 2) something to gather Condor results 3) something to manage jobs (i.e., system calls to the the many Condor executables to look at the job queue etc), a GUI could be cool. ********************************************************* Existing projects: ================== * parallelpython * A Windows screensaver in Python :http://homepage.hispeed.ch/py430/python/ * Lots of full-featured grid computing systems that look really complicated * An undocumented job queue system called "jobba.TheHutt()": http://code.google.com/p/jobba/ * Peer-to-peer in Python: http://cs.berry.edu/~nhamid/p2p/framework-python.html http://cs.berry.edu/~nhamid/p2p/filer-python.html * Grid computing: http://en.wikipedia.org/wiki/Job_Submission_Description_Language http://en.wikipedia.org/wiki/Globus_Toolkit * Condor: I previously couldn't install it, but I just did! It looks like we might use it for this project. How about other O/S? (non Windows) Specificities of the project ============================ We consider a number of machines with Brian installed. There are two types of machines: * Ones that want some work to be done. These won't do the job of someone else, they prefer doing their job first. Let's call them "masters". * Ones that have nothing to do. Let's call them "slaves". Besides, most machines will be probably be used by non-Brian users, which means that we don't want to be updating those machines all the time with a new screensaver every time we change a feature. There should also perhaps be a Brian update mechanism. Thus, slaves do the jobs given by masters, and masters do their own jobs. A slave runs several scripts: * one script that gets jobs from masters and send the results back. * one script executed on each processor, given by the slave script. A master runs: * one script that gets sends to slaves and get the results back. * one script executed on each processor, given by the master script. The screensaver should immediately end all running scripts when it is stopped by the user. Time should be equally shared between masters. Simplest way to do it: a slave selects a random master every time it wants to get a script (not a random script, since there would be a bias for masters with many scripts). Security ======== The Brian screensaver executes Python scripts, which can do about anything bad. Maybe there should be some way to avoid accidents? (or worse!) Communication of scripts ======================== With parallelpython, there are many constraints. Maybe the simplest way is to communicate an entire script. Since there can be user-defined modules or even data, an entire folder could be communicated through an archive file. Slave-specific instructions could be given in a configuration file, e.g.: http://docs.python.org/library/configparser.html That file could be included in the archive. The main brian script would load that file which could contain parameters or any information. The slave does not even need to know about this configuration file: it directly executes the main script. What needs to be specified is * the name of the main script to execute * the data to send back * and maybe, the dependencies (Brian version and other modules) Here is an idea: * the main script is called "main.py" in the root directory, or "main_something.py". * the "results" folder must be sent back at the end of the script. It is up to the Brian script to put whatever it wants in that folder. Advantage: this scheme preserves maximum flexibility from the slave side, so that all the updating will be essentially for the master machines. From the master point of view, adding a job to the queue means communicating: * the folder with the script or the files (with possibly exclusions, etc) * a configuration file(s) or any picklable object. If it is a file, it is put in the archive sent to the slaves. If it is a picklable object (e.g. dictionary), it is passed directly to the slave and will be an argument to the executed script (sys.argv). * possibly, the dependencies. Non-Brian-friendly slaves ========================= Some slaves may not have Brian installed, or not the correct dependencies, etc. There is still hope: py2exe could be used to compile the main script to an exe file. All the rest would be unchanged. The screensaver could also be an executable program (compiled Python). Py2exe tutorial: http://www.py2exe.org/index.cgi/Tutorial From the slave point of view, a job is simply an archive file with a file to execute at the root, and a "results" folder to send back at the end. Notes ===== Since this does not seem to depend on Brian, it could be an independent effort (a Python job queueing mechanism based on a screensaver). It could use parallelpython for the communications. brian-1.4.3/dev/BEPs/closed/BEP-12-Scheduled events.txt000066400000000000000000000131321266062647100222160ustar00rootroot00000000000000BEP-12: Scheduled events Abstract: Objects should be able to schedule simple future events in large numbers. For example, heterogeneous axonal delays could be implemented in this way by allowing a connection to schedule 'add' events in the future. It could also be used for combining STDP with heterogeneous delays, and several other things. It can be done in particular cases with pure Python, but there are probably significant speed advantages to using C code here. Note: this is somewhat implemented in SpikeQueue Scheduled events ================ Consider the following scheme for a heterogeneous axonal delay connection. Each time a spike arrives at neuron i, the set of target neurons j with corresponding delays d_ij are found. For each target neuron the event V_j+=W_ij is scheduled at time t+d_ij. This mechanism can be made generally useful for other things by allowing more complicated events than just +=. The problem is speed and vectorisation. Computations that involve O(number of synaptic events) Python overheads are too slow. However, at least in specific cases it is possible to reduce the Python overheads to O(number of spikes) as in the case of connections at the moment. The trick is to use a dynamic cylindrical array implementing an event queue. Consider the event x+=y for some x, y. At each time t, there should be a linear array I of indices and then we simply evaluate the code X[I]+=Y[I] for X, Y the arrays where the x, y are found. In fact, this doesn't work exactly as stated if I has repeats (see the section below). The cylindrical array is a 2D array with two indices, a time index and an event number index. The time index is circular. Each time the event queue at a particular time is evaluted, the event queue at that time is emptied and can be reused later on (this is done by simply setting the number of events in that queue to zero, where the number of events is a circular array). Pushing events on to the queue also needs to be vectorised. This problem can also be solved using pure Python in a generic way, although a C version is likely much more efficient (see below). Issues ------ *How fast could the pure Python version be?* If the pure Python code was significantly slower and Brian came to rely on this scheduled event stuff it would necessitate a move to built distributions or requiring users to have a compiler. This may not be as bad as it sounds because a built distribution could be produced for Windows and maybe even Mac users, and for Linux most users will have a compiler installed on their system already. *Can it be made generic?* A difficulty is that it seems hard to make this scheme generic so that users can write their own events which would be necessary for STDP for example. The example of implementing x+=y below illustrates the difficulties. Generation of C code gives one approach, but ideally we don't want to rely on users having to have a C compiler on their system. Technical details and sample implementation ------------------------------------------- To evaluate x+=y in a vectorised way, we would like to write X[I]+=Y[I], but unfortunately this doesn't work with numpy if there are repeats in I. There is a way around this:: J = unique(I) K = digitize(I, J)-1 b = bincount(K) X[J] += b*Y[J] Unfortunately, this is slower than it needs to be because unique(I) involves a sort operation and is therefore O(n log(n)) rather than just O(n), for n=len(I). It is also not generic because it relies on the properties of addition (repeated addition -> multiplication). It could be made generic for any numpy ufunc by using the reduceat method of ufuncs however. The equivalent C code for the above is just:: for(int i=0; i2.3).nonzero()[0] # operation on GPU, returns a CPU integer array Existing packages ================= * PyCuda: http://mathema.tician.de/software/pycuda and PyOpenCL: http://pypi.python.org/pypi/pyopencl/ There is a GPUArray which does what we want, but there is probably no lazy evaluation (so it is probably slow). * GpuPy: http://www.tricity.wsu.edu/~bobl/personal/mypubs/2009_gpupy_toms.pdf It is based on OpenGL/CG and uses lazy evaluation. It is very close to what we want. Unfortunately there is no public release, and the first author has no email address, the second author does not reply. * Cuda-ndarray: http://code.google.com/p/cuda-ndarray/ It uses CUDA and is based on Theano, which does lazy evaluation. It is currently undocumented and there is no Windows build, but the project looks cool. * Gpulib: http://gpulib.txcorp.com/ does not seem to maintain the Python interface. * Matlab toolbox: http://www.accelereyes.com/ Low-level implementation ======================== CUDA / OpenCL: OpenCL probably has more future than CUDA and it is more universal. Functionality ============= * element-wise operations on vectors (arithmetical, exp/log, exponentiation) * same but on views (x[2:7]) * assignment (x[:]=2*y) * boolean operations on vectors (x>2.5) and the nonzero() method * multiplying a N*M matrix by an M*M matrix, where N is large and M is small (but this could be done with vector operations). * random number generation (gpurand(N)) The syntax must be the same as with normal arrays. brian-1.4.3/dev/BEPs/closed/BEP-16-Model fitting toolbox.txt000066400000000000000000000032171266062647100231740ustar00rootroot00000000000000BEP-16: Model fitting toolbox Abstract: This toolbox allows to fit a neuron model to data : give a data set (a set of experimentally-recorded spike trains from a single neuron), give a neuron model with free parameters, and the toolbox finds the best fitting model parameters. Code Example ============ from brian.library.modelfitting import * data = [(i,t),...,(j,s)] # experimental spike trains with N neurons params, value=modelfitting(model="dv/dt=-v/tau : volt", reset=0*mV, threshold=10*mV, data=data, input=I, # a vector or a matrix or a list of vectors tau=(0*ms,5*ms,10*ms,30*ms), # (min,init_min,init_max,max) C=(0*pF,1*pF,2*pF,inf), verbose=False # display information ) Notes ===== - If an initial value is set ('init'), the fitting procedure will start from initial parameters values sampled from a normal distribution centered on that value. The standard deviation may be specified, for example : init = init * (1+sigma*randn(N)), with a fixed default sigma (.3 or ?). - If an initial interval is set ('init_min' and 'init_max'), the fitting procedure will start from initial parameters values sampled from an uniform distribution within that interval. An user-defined fitness function may be defined with an optional argument to the constructor. The fitting procedure returns : - 'best_parameters' contains the best parameters found as a Parameters object (Parameters(tau=array([.012, .009, ..., .015]),...) - 'best_value' contains the best fitness value (the gamma factor by default) brian-1.4.3/dev/BEPs/closed/BEP-17-Vectorised STDP000066400000000000000000000027011266062647100210620ustar00rootroot00000000000000BEP-17: Vectorised STDP Abstract: Synaptic plasticity and learning act on a slow timescale. In many cases, the network is presented with many stimuli (e.g. sounds) sequentially. I propose to present the stimuli in parallel, and either have a shared connection matrix or regularly collect the weight changes. * This BEP is considered obsolete * (I don't think that it's a good idea any more, the learning timescale might be faster than we previously thought) One simple idea --------------- * Create a big group. * Divide in subgroups P[i]. * Create connections between subgroups, but change C.W to refer to those of layer 0 (subgroup P[0]). It should be done after the connection is compressed. * Define STDP in each layer. Here we probably only save computation time on the state update. A better way would be to have a single Connection and STDP object rather than one per layer, but the difficulty is to have a shared connection matrix. I think it could work, except when two different layers want to change the same synaptic weight at the same time, which would happen any time two corresponding neurons fire simultaneously in different layers. Maybe this case could be treated explicitly since it should not happen so often. In summary, what is needed is essentially a new connection matrix structure which implements duplicates over layers. For example, W[i].data would return W0[i].repeat(M) and W[i].target would be precomputed. brian-1.4.3/dev/BEPs/closed/BEP-2-STDP000066400000000000000000000150741266062647100167130ustar00rootroot00000000000000BEP-2: Spike-timing-dependent plasticity Interface ========= How to specify STDP: * presynaptic group * postsynaptic group * connection matrix * STDP rule One possibility is to pass a Connection object, which holds the first 3 elements. Alternatively, keyword arguments could be passed when initialising the Connection object. Or a Connection method could be used: myconnection.set_STDP(...). How to specify the STDP rule ---------------------------- * Differential equations ^^^^^^^^^^^^^^^^^^^^^^^^ This is the most general way of specifying the STDP rule. One can define directly the synaptic model, e.g.: pre: dA_pre/dt=-A_pre/tau_pre : 1 post: dA_post/dt=-A_post/tau_post : 1 spike_pre: A_pre+=dA_pre w=clip(w+A_post,0,gmax) # or w+=A_post and use automatic clipping spike_post: A_post+=dA_post w=clip(w+A_pre,0,gmax) How to pass this model? Possibly like NeuronGroup (advantage: same mechanism): STDP(eqs_pre='dA_pre/...',spikes_pre='A_pre...',eqs_post='dA_post/...',spikes_post='A_post...') and w is a special identifier meaning synaptic weight. Note that it is necessary to define pre and post-synaptic variables, in order to interpret the expressions in spike_pre and spike_post. But these could in principle be guessed. Presynaptic (postsynaptic) variables are those variables modified by presynaptic (postsynaptic) spikes. All identifiers in the "spike_pre" statement that are in left-hand sides are modified. This is not trivial to do but possible. Syntax: stdp=STDP(C,eqs=''' dA_pre/... dA_post/...''', # equations string (with units etc) pre='A_pre...', # python statements post='A_post...'[, bounds=(0,gmax)]) # clipping For the future? eqs=""" pre: dA_pre/dt=-A_pre/tau_pre : 1 spike: A_pre+=dA_pre w=clip(w+A_post,0,gmax) # or w+=A_post and use automatic clipping post: dA_post/dt=-A_post/tau_post : 1 spike: A_post+=dA_post w=clip(w+A_pre,0,gmax) """ myconnection=Connection(...,STDP=eqs) Implementation: [DONE]* get identifiers from pre and post code [DONE]* find which ones are modified (e.g. regular expression matching; careful with comments) [DONE]* separate differential equations in pre/post [DONE]* check pre/post consistency * create virtual groups (inherit NeuronGroup; Group?), pre and post [DONE]* pre/post code: add [spikes,:] etc [DONE]* bounds: add one line to pre/post code (clip(w,min,max,w)) * event-driven code; do some speed tests * create forward and backward Connection objects; propagate does pre or post code and event-driven updates * Presynaptic and postsynaptic groups are created, with the corresponding variables (A_pre, A_post) and differential equations. * Operation associated to presynaptic spikes: A_pre[spikes]+=dA_pre # insertion of [spikes] after presynaptic variables w[spikes,:]=clip(w[spikes,:]+A_post,0,gmax) # insertion of [spikes,:] after w and w is the connection matrix. * Same for postsynaptic spikes except [:,spikes] is inserted. To implement the last 2 operations, there should be a forward and backward propagate function in Connection, that would be called by the network object. Notes: * In some rules (last developments by W. Gerstner and M. van Rossum), the differential system can depend on the membrane potential (or another variable) of the postsynaptic neuron (not of the presynaptic one). This could be included in the specification above, but only for the postsynaptic side (I don't think it is possible to vectorise if it is on the presynaptic side). We should probably check that in those papers. * Phenomenological ^^^^^^^^^^^^^^^^^^ The user must provide: * f(t>0) and f(t<0) (weight modification function) * consider all or nearest spikes, for both presynaptic and postsynaptic spikes * weight update rule, for t>0 and t<0. E.g.: w+=dw or w+=(1-w)*dw This is less general than the specification based on differential systems (e.g. triplet rules are not possible). Example: set_STDP(f_pre="Apre*exp(-t/tau_pre)", f_post="Apost*exp(-t/tau_post)", pre='all',post='nearest', update_pre='(1-w)*dw',update_post='dw') with defaults pre='all', post='all', update_pre='dw', update_post='dw'. [the _pre and _post suffixes are not good; should causal/anticausal or something like that; or maybe _prepost and _postpre, or pos/neg]. Alternatively, bounds="soft"/"hard"/"semisoft". Bounds should also be specified, if necessary. The functions (f_pre, f_post) would be turned into differential systems (see experimental/integrodiff). Interaction "all" converts to Apre[spikes]+=1, interaction "nearest" converts to Apre[spikes]=1. * Standard predefined types ^^^^^^^^^^^^^^^^^^^^^^^^^^^ Typically, most people use exponential STDP, with various types of interactions (all-to-all/nearest neighbour, additive/multiplicative etc). Essentially, this only applies to the definition of differential equations, i.e., of the weight change function (f_pre and f_post). Example: myconnection.set_exponentialSTDP(Apre=,Apost=,tau_pre=,tau_post=) # and all other keyword arguments as above (all/nearest/) Event-driven implementation =========================== In an event-driven implementation, pre and postsynaptic variables are only updated at spike times. In the example above: A_pre[spikes]=A_pre[spikes]*exp(-(t-lastt[spikes])/tau_pre), where lastt is a vector of last update times. And: lastt[spikes]=t In that case, 2 possibilities: * specific neuron groups with no update method, but instead another method for specific updates. One idea: def update(self,index=None), where index is the list of neurons to update. * no groups, the vectors A_pre and A_post are stored and managed by the Connection object or a STDP object. That does not seem very general, however. * no groups, the vectors A_pre and A_post are stored in the pre and postsynaptic groups, but updated somewhere else (at spike times). The last possibility looks better. How to calculate exact solutions of linear differential systems: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ An idea could be to use some matric decomposition: http://en.wikipedia.org/wiki/Matrix_decomposition such as diagonalization (but that is not always possible) or (most general) the Jordan decomposition. Alternatively, one could use expm(At), where t has to be calculated for each updated neuron index (too slow?). The scipy.linalg function expm is written in Pure Python using the Padé approximation (rational approximation), so it might be possible to vectorise it. brian-1.4.3/dev/BEPs/closed/BEP-21-Synaptic dynamics000066400000000000000000000503141266062647100216000ustar00rootroot00000000000000BEP-21-Synaptic dynamics Abstract: Currently, synaptic dynamics are only handled at the pre or post-synaptic neuron, rather than at the local synapse. For example, for a given postsynaptic neuron, there is a single equation for all synapses with the same dynamics (e.g. AMPA excitatory current) and all presynaptic spikes impact the same variable. In STDP, synaptic variables are considered as either presynaptic or postsynaptic, not specific of each synapse. This design implies strong constraint on synaptic models (kinetics and plasticity). This BEP aims at overcoming these limitations. Note: this is now the Synapses class Missing features and a few ideas ================================ * Nonlinear synapses (e.g. NMDA). These require one conductance variable per synapse, and separate dynamics. The idea is to define differential equations on synaptic weights. We could use a fake NeuronGroup as a proxy to synaptic weights + state updater. We could use a slower clock to speed up the simulation. Issues: 1) several synaptic variables, 2) heterogeneous delays, 3) routing spikes (now presynaptic neurons act on different variables). A priori, it is not possible to do event-driven dynamics, because of the interaction of the conductances with the membrane equation. But maybe there is a trick to be found. * Probabilistic synapses. The main difficulty here is to integrate it with STP and STDP. * Multiple synapses (with different delays) between the same pre/post neurons This is almost done, except we need to check if it works with STDP. We also need to think of a nice syntax. * STDP: access to postsynaptic variables This is probably not a major problem (and can be done with the current version, although not in an elegant way), unless it is coupled with local synaptic variables. We have one example (Fusi's STDP rule) from people in the mailing list. * STDP: rules with local synaptic variables Here we need event-driven dynamics. But one issue is dealing with heterogeneous delays. We should start with an existing example (STDP1/2). Then the most interesting rule is Graupner/Brunel's. * Neuromodulation. There is a question on this in the Brian support list. This is also something that NEST supports. There are two issues here: 1) continuous weight dynamics, which should be computed with event-driven updates, 2) modulation by an external signal, which is a trace of spikes from other neurons. At this point, I'm not familiar enough with this to address point 2. * Heterosynaptic modifications (see eg. Kempter et al. PNAS 2001). Synaptic modifications may not be local, for example may affect all synapses from the same presynaptic neuron. The main technical difficulty, I think, is dealing with heterogeneous delays, which might require event queues. Multiple synapses ----------------- Having multiple synapses (for a given pre/post pair) is entirely possible with the current sparse connection structure (although STDP should be checked). What is really needed is a nice syntax to create these synapses and access the weights. Nonlinear synapses ------------------ The classic example is NMDA synapses. Reference: Probabilistic decision making by slow reverberation in cortical circuits. XJ Wang, Neuron 2002. Model: dg/dt=-a*g+b*x*(1-g) dx/dt=-c*x and spikes act on x (addition). The conductance then enters the membrane equation. The problem here is that it is a nonlinear system (product of x and g), and therefore we cannot simply simulate the total conductance, as we do with linear synapses ("lumped variable"). Each synapse must be individually simulated. This implies a huge computational cost (proportional to number of synapses/dt for each second). It cannot be event-driven because the value of the total conductance is needed at each time step. There is an example in the support list, where people used a NetworkOperation to do it. Tricks ^^^^^^ Example x(t) should be close to 0 most of the time, because 1/c represents the rise time, which is short. When x=0, the equations are linear (linear decay of g), and therefore we only need to simulate the lumped variable gtot(t) corresponding to the sum of all conductances for which x=0. This could however be quite painful to code. Another approach is to use an approximation of NMDA dynamics: dg/dt=-a*g+b*x dx/dt=-c*x and x->x+(1-g)*w This makes sense because g(t) changes slowly relatively to x(t). If we do that, the equations are linear and we only need to simulate the lumped variables. However, we still need individual synaptic values for g (not lumped) at spike time. Here we could use event-driven updates of g - this is possible because the equations are linear. The cost is now proportional to the number of transmitted spikes. (N.B.: actually if we can do that this could make a short paper). Again, we need 1) local synaptic variables, 2) event-driven updates. In addition, we need to be able to modulate synaptic weights with synaptic variables (perhaps implement a synaptic reset: "x=x+(1-g)*w"). Proposition: a new Connection class =================================== I propose a new Connection class, let's call it Synapses for now. It follows a similar syntax as STDP, but defines everything synaptic. I propose that this should be the class used for code generation (GPU/C), because it is a single class for everything synaptic. Examples -------- S=Synapses(source,target,model="w : 1",pre="v+=w") # standard synapse S=Synapses(source,target, model='''dg/dt=-a*g+b*x*(1-g) : 1 dx/dt=-c*x : 1 w : 1 # synaptic weight ''', pre='x+=w') # NMDA synapses S=Synapses(source,target, model='''dA_pre/dt=-A_pre/tau_pre : 1 dA_post/dt=-A_post/tau_post : 1 w : 1 # synaptic weight ''', pre='''x+=w A_pre+=dA_pre;w+=A_post''' post='''A_post+=dA_post;w+=A_pre''') # STDP S=Synapses(source,target, model='''dA_pre/dt=-A_pre/tau_pre : 1 dA_post/dt=-A_post/tau_post : 1 w : 1 # synaptic weight ''', pre='''v+=w A_pre+=dA_pre;w=clip(w+A_post,0,inf)''' post='''A_post+=dA_post;w=clip(w+A_pre,0,inf)''') # STDP with clipping S=Synapses(source,target, model='''dx/dt=(1-x)/taud : 1 du/dt=(U-u)/tauf : 1 w : 1''', pre='''v=v+w*u*x x=x*(1-u) u=u+U*(1-u) ''') # STP S=Synapses(source,target, model='''x : 1 u : 1 w : 1''', pre='''u=U+(u-U)*exp((t-lastspike)/tauf) x=1+(x-1)*exp((t-lastspike)/taud) v=v+w*u*x x*=(1-u) u+=U*(1-u) ''') # Event-driven STP S=Synapses(source,target,model="""w : 1 p : 1 # transmission probability""", pre="v+=w*(rand()pre spikes, we also add a concatenated array of columns (for each column, we have the array of presynaptic neurons, and we concatenate everything), with two additional arrays with starting point and length of the columns. Memory consumption for N neurons, K synapses (total, not per neuron): * K*(1 + number of synaptic variables + 2(delays, possibly 1) + 1 (lastspike)) * Nsource*2 * K+Ntarget*2 (reverse access) If we assume 8 bytes for double, 4 bytes for indexes (but could be 2 in many cases), 2 bytes for delays (in timesteps), 4 bytes for lastspike (in timesteps): * K*(4 + 8*number of synaptic variables + 2(1)*2 + 4) * Nsource*2*4 * K*4+Ntarget*2*4 So in total, including reverse access (only if there is a post code): K*8*(n+2)+(Nsource+Ntarget)*8 For example, this is about 40 bytes per synapse for an STDP synapse with pre and post delays. For a static synapse, this would be 14 bytes. This does not include spike queues (to hold spike events), which should take about K*4 bytes (this assumes that one neuron cannot spike twice given the maximum delay). The memory consumption does not seem unreasonable (apparently slightly lower than NEST). Equations --------- Equations defined in the model should work as in NeuronGroup, except on synaptic variables. Pre/post code should work as in STDP. A few specificities: * a non-defined variable is first assumed to be postsynaptic (otherwise global). One potential issue here is if we want to consider (dendritic) delays for postsynaptic variables (soma->synapse and converse). * t holds the current time * lastspike holds the time of last update of the synapse * rand() is redefined or rewritten for vectorisation (this allows probabilistic synapses). We may want to have other random distributions such as binomial. * We may also want a way to access presynaptic variables, for example to code gap junctions. One possibility is the explicit suffix _pre (e.g. v_pre). One subtlety for the Python code (which is not an issue for GPU/C) is that when executing something like 'v+=w', this will be interpreted as 'v[targets[synapses]]+=w[synapses]', but then there might be repeted indexes in targets[synapses]. This does not work with Numpy. There are two options to solve this problem: 1) v (in fact all postsynaptic variables) is an instance of a new class where self addition and multiplication are overloaded (and possibly direct assignment is forbidden). We then use a trick (see posts on the development list) using sorting to implement it. This is not great because we can't implement everything with this. 2) we separate 'synapses' in groups of synapse indexes with no repeated postsynaptic neurons, then we execute the code sequentially for each group. The idea is that in most practical cases, there should be no repetition. This would not be so for very dense networks with cortex-like connectivity (10,000 synapses per neuron), but there we will probably need to use code generation anyway. Option 2 looks efficient enough. Event-driven updates -------------------- Event-driven updates can be specified by the user. But it would be nice to be able to write the differential equations, and let Brian calculate the event-driven operations. This seems possible with linear equations, either with sympy or by calculating the matrix exponential. Lumped variables ---------------- For nonlinear synapses (and also gap junctions), we need to be able to define a "lumped variable", generally the total conductance, that is updated at every timestep as the sum of all presynaptic values for each neuron (is there a fast way do it in scipy.sparse?). There are several possibilities for the syntax. We could use something like linked_var, for example: P.g=lumped_var(synapses.g) where P is the NeuronGroup, and g must have been defined as a parameter in P. Can we come up with a better syntax? I don't think it could be in the model definition of the neuron group, because the Synapses object is usually not defined at that time. Construction and access ----------------------- We need some nice methods to build the connection structure and access synaptic variables. Currently, we can do things like: * S[2,5]=1*nS * S.connect_random(group1[0:20],group2[10:40],sparseness=0.02,weight=5*nS) * S.connect_full(group1,group2,weight=lambda i,j:(1+cos(i-j))*2*nS) and delays can also be set. But now we may have more synaptic variables. One difficulty is that they have a sparse structure, but with the same non-zero elements. For direct access, I suggest to use a similar syntax as for NeuronGroups: * S.w[2,5]=1*nS * S.w[1,:]=2*nS The first instruction sets w=1*nS for synapse 2->5. The second instruction may be ambiguous: should all elements be created, or should only non-zero elements be assigned? If we have several synaptic variables, creating elements by these instructions, or having different semantics depending on the variable, is dangerous. Therefore the semantics is that assignment never creates synapses, and we use a special syntax to create synapses: S[2,3:5]=True # or an array This creates synapses 2->3 and 2->4. We could also have several synapses per pre/post pair: S[2,3:5]=(2,3) # 2 synapses from 2 to 3, 3 from 2 to 4 Then access is with a third coordinate: S.w[2,3,1] # this is the 2nd synapse on 2->3 And we can write: S.w[2,3]=(1*nS,2*nS) I propose to allow this syntax: S.w[group1,group2]="(1+cos(i-j))*2*nS" The advantage this syntax is that it can be used for code generation (e.g. on GPU). Here we need two special names for pre and post indexes. These could be i/j or pre/post. This also works to create connections: S[group1,group2]='rand()<0.2' which creates a sparse connection structure. One problem with this is that it won't be efficient, as it requires O(N*N) operations, rather then the number of synapses. There are two (possibly complementary) options to solve this. 1) We still have connect_random, connect_one_to_one, etc. But I suggest that at least we replace lambda functions with strings. Also the weight keyword should be replaced by synaptic variable names. 2) We use sparse boolean matrices (or integer matrices) on right hand sides: S[P,Q]=one_to_one(P,Q) that is, the right hand side is a sparse boolean matrix (or possibly int matrix). These boolean matrices could be also used for monitoring. We could be inspired by Mikael Djurfeldt's "connection set algebra". Synapse monitors ---------------- We use a StateMonitor. For example: M = StateMonitor(S,'w',record=[(2,3),(7,1),(3,3)]) The main issue is to set record indexes. I suggest we use exactly the same syntax as for access/construction above. This way, we can simply use a method in the synapse object which turns (2,3) etc. into synapse indexes. It makes coding the new monitor for Synapses trivial. Spike queues ------------ In the Synapses class, pre/post operations must be applied at the right times, not in advance. Therefore, in a given time step, with heterogeneous delays, a Synapses object must receive spike events as a list of synapse indexes. This is done by the spike queue class, which stores future synaptic events produced by a given presynaptic neuron group. The structure is a 2D array, where row is the time bin and column is the position in that bin (each row is a stack) (or the converse). The array is circular in the time dimension. There is a 1D array giving the position of the last added event in each time bin. Main methods: * Output the current events: we simply get the first (or last) row (up to the last added event), so this is fast. We then shift the cursor of the circular array by one row. * Feed events. Each presynaptic neuron has a corresponding array of target synapses et corresponding delays. We must push each target synapse (index) on top of the row corresponding to the delay. If all synapses have different delays, this is relatively easy to vectorize. It is a bit more difficult if there are synapses with the same delays. For a given presynaptic neuron, each synaptic delay corresponds to coordinates (i,j) in the circular array of stacks, where i is the delay (stack index) and j is index relative to the top of the stack (0=top, 1=1 above top). The absolute location in the structure is then calculated as n[i]+j, where n[i] is the location of the top of stack i. The only difficulty is to calculate j, and in Python this requires sorting (see development mailing list). It can be preprocessed if event feeding involves a loop over presynaptic spikes (if it's vectorized then it's not possible anymore). In this case it takes K*4 bytes. * Resizing. When a stack is full (row size exceeded), we double (or multiply by 1.5 or other) the size of the 2D array (number of columns). I note that if the 2D array is implemented in the other direction (columns instead of rows), this only means resizing the array without having to copy anything. On the other hand, it makes getting the current events a bit slower (because not continuous). The whole class should also have a C version, which would be much faster. Memory consumption: this is essentially the 2D circular array, which is dynamic. Each entry is 4B (synapse index). The total size should be the maximum number of synaptic events within the time span of the queue (max delay - min delay). Within that time span, each neuron can spike no more than p=(max delay - min delay)/refractory period, and therefore a given synapse index cannot be present more than p times in the array. Thus, memory consumption is always smaller than K*4*p. This does not seem very large (compare with K*40 for an STDP synapse). Advantages with this class: * It concentrates all the hard to vectorize/time consuming part of the simulation. Therefore if we have a fast code for this, it may make simulations faster. * The LS object is no longer useful. We only need to store the spikes at the current time step, and the time of the last spikes for each neuron (for event-driven updates). * No more difference between homogeneous and heterogeneous delays. When delays are homogeneous, the code is (maybe) automatically faster because we have contiguous indexes in the 2D array. * We can vectorize synaptic operations over synapses, not just over postsynaptic neurons, and therefore we can expect a speed gain. * Could be good for GPU/C code generation. * We can now use NEST's trick: if all delays and refractory periods are greater than a period delta, then we only need to transfer spikes to synapses every delta rather than every timestep. This should provide speed-ups for GPU and code generation, perhaps also cluster simulations if we want to do that. Tasks ===== A number of things can be done independently. High priority: * Write a few examples * Sparse matrix structure with multiple values. [V] : Sort of done, by the state vector, even though it may not be completely efficient * Separation of an array of synapse index into a list of arrays with no repeated postsynaptic neurons (synapse_separation.py) [V] : Done * Spike queue, Python version (spikequeue.py, see also scheduled_events.py). [V] : Done * Main Synapse code (could be adapted from NeuronGroup, Connection and STDP). * Access and construction. [V] : Done Low priority: * Possibly, array class with overloaded self addition/multiplication * Turning differential equations into event-driven code. * Lumped variable code. * Synaptic monitoring (should be trivial after access/construction is done). * GPU code * Spike queue, C version. brian-1.4.3/dev/BEPs/closed/BEP-6-Initialising connections000066400000000000000000000023561266062647100230720ustar00rootroot00000000000000BEP-6: Initialising connections Abstract: Currently, one must initialise a connection in two steps, first construct the Connection object, then define the actual connections. In most typical cases, the connections follow simple schemes that can be described in one line. It would be more intuitive to do it in a single instruction. STATUS: DONE (docs to write) Examples: C = Connection(G, H, 'V', random_connectivity(p=0.1, weight=1*mV)) or C = Connection(G, H, 'V', weight=1*mV, p=0.1) I would favor the latter syntax, which I find more straightforward. The type of weight keyword tells what initialisation function should be called: * single value: connect_full (dense connection with identical weights) * function: connect_full (functional meaning) * matrix: connect (can be dense or sparse) If the keyword "p" is specified, then the connect_random method is called. Maybe it is not explicit enough though? proba? probability? sparseness? I would go for sparseness, which is the most explicit. In that case it could be added as a keyword to connect_random (alias for p). This does not cover all the cases, but it is still possible to use the connect methods in specific cases, while keeping an intuitive and efficient syntax. brian-1.4.3/dev/BEPs/closed/BEP-7-DelayConnection.txt000066400000000000000000000100311266062647100220260ustar00rootroot00000000000000BEP-7: DelayConnection Abstract: Connections with heterogeneous delays Introduction ============ Want a Connection object that can handle neurons with delays that vary either per neuron or per synapse. Issues ====== Functionality ------------- There is a difference between presynaptic and postsynaptic delays. Do we want to include both? How important is that compared to implementation decisions (see below)? Syntax ------ Instantiation ~~~~~~~~~~~~~ The current syntax for the instantiation of the DelayConnection class is the same as for the Connection class with one extra keyword delay_dimension=1 or delay_dimension=2 (for per-neuron or per-synapse delays). This keyword could easily be renamed, e.g.: * synaptic_delays=True would correspond to delay_dimension=2 and would be the default, and synaptic_delays=False would correspond to delay_dimension=1. * neuronal_delays=False would correspond to delay_dimension=2 and would be the default, and neurons_delays=True for dim 1. A related issue would be whether or not we want the user to have to use DelayConnection or whether we could just add keywords to Connection, and that if those keywords were present the Connection class would just make itself into a DelayConnection class. Example: C=Connection(P,Q,'ge',delay=True) Referring to delays ~~~~~~~~~~~~~~~~~~~ More problematic is what should be done about defining connections. The current implementation stores two matrices, one for the delays and one for the weights. The matrices have the same type. These are currently accessed through a ``delays`` property, i.e.:: C.delays[i,j] = ... There are some technical issues about how you make sure that the weight matrix and the delay matrix have exactly the same structure, see below. Initialising delays ~~~~~~~~~~~~~~~~~~~ At the moment, there are no helper functions like ``connect_random`` and ``connect_full`` that do anything for delays. You have to just initialise these by hand. Some proposals: * Implement a ``initialise_random_delay`` function and/or a ``initialise_delay`` function that takes a function ``f(i,j)`` as an argument. This would then be evaluated for every nonzero entry in the weight matrix (so that initialisation of the weight matrix would have to come before initialisation of the delay matrix). * ``set_delays``? * Override the ``connect_random`` functions to include extra keywords like ``delays=...``. For example, you could have ``delays=(min,max)``, ``delays=func``, etc. Implementation ============== Propagation ~~~~~~~~~~~ The current implementation of DelayConnection is postsynaptic. An array is stored which is NxM where N is the number of target neurons, and M is the number of time steps for the maximum delay, i.e. maxdelay/dt. This array is circular in time and has a current index pointer. When a spike arrives it is propagated to the appropriate row of the array, and when the current index pointer reaches a row the cumulative effects of that row are propagated to the target neurons. This implementation is reasonably efficient and can work with both per-neuron and per-synapse delays easily, and can handle dense and sparse matrices easily. Delays are stored either as a vector or a second array of the same type and shape as the weight matrix. Delays are stored as floats and converted to ints on the fly (so that delays can be easily changed at runtime). Keeping delays and weights in sync ---------------------------------- For sparse matrices they should have the same nonzero entries and this should be enforced somehow. The current implementation at compression time first compresses the weight matrix, duplicates that structure and copies the delay values from the construction matrix to the weight matrix. If a delay value is missing from the construction matrix it is set to 0, if a weight value corresponding to an entry in the sparse matrix is missing, it is ignored. After compression, it is at the moment up to the user to keep these in sync.brian-1.4.3/dev/__init__.py000066400000000000000000000000021266062647100155030ustar00rootroot00000000000000 brian-1.4.3/dev/benchmarking/000077500000000000000000000000001266062647100160325ustar00rootroot00000000000000brian-1.4.3/dev/benchmarking/PyNN_benchmark.py000066400000000000000000000033141266062647100212430ustar00rootroot00000000000000# coding: latin-1 """ Benchmark adapted from a PyNN benchmark. """ from brian import * import time #set_global_preferences(useweave=False, usecodegen=False, usenewpropagate=False) start_time = time.time() taum = 20 * ms taue = 5 * ms taui = 10 * ms Vt = -50 * mV Vr = -60 * mV El = -49 * mV eqs = Equations(''' dv/dt = (ge+gi-(v-El))/taum : volt dge/dt = -ge/taue : volt dgi/dt = -gi/taui : volt ''') P = NeuronGroup(10000, model=eqs, threshold=Vt, reset=Vr, refractory=5 * ms) P.v = Vr P.ge = 0 * mV P.gi = 0 * mV Pe = P.subgroup(8000) Pi = P.subgroup(2000) we = (60 * 0.27 / 10) * mV # excitatory synaptic weight (voltage) wi = (-20 * 4.5 / 10) * mV # inhibitory synaptic weight Ce = Connection(Pe, P, 'ge', weight=we, sparseness=0.02, #delay=lambda:0.2*ms,max_delay=5*ms) delay=0.2*ms) Ci = Connection(Pi, P, 'gi', weight=wi, sparseness=0.02, #delay=lambda:0.2*ms,max_delay=5*ms) delay=0.2*ms) P.v = Vr + rand(len(P)) * (Vt - Vr) # Record the number of spikes Me = PopulationSpikeCounter(Pe) Mi = PopulationSpikeCounter(Pi) # A population rate monitor #M = PopulationRateMonitor(P) M=StateMonitor(P,True) print "Network construction time:", time.time() - start_time, "seconds" print len(P), "neurons in the network" print "Simulation running..." start_time = time.time() run(1 * msecond) print "Preparation time:", time.time()-start_time start_time = time.time() run(2 * second) duration = time.time() - start_time print "Simulation time:", duration, "seconds" print Me.nspikes, "excitatory spikes" print Mi.nspikes, "inhibitory spikes" #plot(M.times / ms, M.smooth_rate(2 * ms, 'gaussian')) #show() brian-1.4.3/dev/benchmarking/coba/000077500000000000000000000000001266062647100167365ustar00rootroot00000000000000brian-1.4.3/dev/benchmarking/coba/COBAEuler.m000066400000000000000000000036771266062647100206320ustar00rootroot00000000000000% // COBA % // % // This Scilab code is an implementation of a benchmark described % // in the following review paper: % // % // Simulation of networks of spiking neurons: A review of tools and strategies (2006). % // Brette, Rudolph, Carnevale, Hines, Beeman, Bower, Diesmann, Goodman, Harris, Zirpe, % // Natschläger, Pecevski, Ermentrout, Djurfeldt, Lansner, Rochel, Vibert, Alvarez, Muller, % // Davison, El Boustani and Destexhe. % // Journal of Computational Neuroscience % // % // Benchmark 1: random network of integrate-and-fire neurons with exponential synaptic conductances % // % // Clock-driven implementation with Euler integration % // % // --------------------- % // % // Romain Brette (June 2006) % // brette@di.ens.fr % // % // 8s % // Parameters Ne=3200; Ni=800; N=Ne+Ni; conProba=0.02; taum=20; taue=5; taui=10; Vt = 10; Vr = 0; dt=0.1; we=6/10; wi=67/10; duration=1000; refrac=5; Ee=0+60; Ei=-80+60; % State variables (membrane potential, excitatory current, inhibitory current) S=zeros(N,3); S(:,1)=randn(N,1)*5-5; S(:,2)=randn(N,1)*1.5+4; S(:,3)=randn(N,1)*12+20; % Connectivity matrix: 2% connectivity with unitary weights % This is a sparse matrix (otherwise it is too big and slow) W=sprand(N,N,conProba)>0; % Last spike times - for refractory period LS=zeros(N,1)-1000; % Simulation disp('Starting simulation...') t1=clock; t=0; allspikes=[]; while tt-refrac),1)=Vr; spikes=find(S(1:Ne,1)>Vt); S(:,2)=S(:,2)+we*(sum(W(:,spikes),2)); spikes=find(S(Ne+1:end,1)>Vt); % Actually there is a mistake: use spikes+Ne S(:,3)=S(:,3)+wi*(sum(W(:,spikes),2)); spikes=find(S(:,1)>Vt); LS(spikes)=t; S(spikes,1)=Vr; allspikes=[allspikes;spikes,ones(size(spikes))*t]; t=t+dt; end disp(etime(clock,t1)) brian-1.4.3/dev/benchmarking/coba/COBAEuler.sce000066400000000000000000000064501266062647100211400ustar00rootroot00000000000000// COBAEuler // // This Scilab code is an implementation of a benchmark described // in the following review paper: // // Simulation of networks of spiking neurons: A review of tools and strategies (2006). // Brette, Rudolph, Carnevale, Hines, Beeman, Bower, Diesmann, Goodman, Harris, Zirpe, // Natschläger, Pecevski, Ermentrout, Djurfeldt, Lansner, Rochel, Vibert, Alvarez, Muller, // Davison, El Boustani and Destexhe. // Journal of Computational Neuroscience // // Benchmark 1: random network of integrate-and-fire neurons with exponential synaptic conductances // // Clock-driven implementation with Euler integration // (no spike time interpolation) // // The simulation takes about 100s on my PC // // --------------------- // // Romain Brette (June 2006) // brette@di.ens.fr // // Parameters Ne=3200; // Number of excitatory neurons Ni=800; // Number of inhibitory neurons N=Ne+Ni; // Total number of neurons conProba=0.02; // Connection probability taum=20; // Membrane time constant (ms) taue=5; // Excitatory synaptic time constant taui=10; // Inhibitory synaptic time constant Vt = 10; // threshold, relative to rest (mV) Vr = 0; // reset (mV) Ee = 0+60; // excitatory reversal potential (relative to rest) Ei = -80+60; // inhibitory reversal potential dt=0.1; // time step we=6/10; // excitatory synaptic weight (relative to gl) wi=67/10; // inhibitory synaptic weight duration=1000; // duration of the simulation (ms) refrac=5; // refractory period stimulation=-1; // duration of the initial stimulation (<0: no stimulation) // State variables (membrane potential, excitatory current, inhibitory current) S=zeros(N,3); if stimulation<0 // No stimulation: the network must already be in an irregular state S(:,1)=grand(N,1,'nor',-5,5); S(:,2)=grand(N,1,'nor',4,1.5); S(:,3)=grand(N,1,'nor',20,12); end // Connectivity matrix: 2% connectivity with unitary weights // This is a sparse matrix (otherwise it is too big and slow) W=bool2s(sprand(N,N,conProba,'uniform')>0); // Last spike times - for refractory period LS=zeros(N,1)-1000; // Simulation printf("Starting simulation...\n"); timer(); t=0; allspikes=[]; // Contains spikes (neuron,time) while tt-refrac),1)=Vr; // PROPAGATION OF SPIKES // Excitatory neurons spikes=find(S(1:Ne,1)>Vt); // List of neurons that meet threshold condition S(:,2)=S(:,2)+we*(sum(W(:,spikes),'c')); // Update the state of targets // Inhibitory neurons spikes=find(S(Ne+1:$,1)>Vt); S(:,3)=S(:,3)+wi*(sum(W(:,spikes),'c')); // Reset neurons after spiking spikes=find(S(:,1)>Vt); LS(spikes)=t; // Time of last spike S(spikes,1)=Vr; // Reset membrane potential // Store spike times allspikes=[allspikes;spikes',ones(spikes')*t]; // Each row is (neuron number,spike time) t=t+dt; end // Display the computation time timer() // Display the spikes //clf; // Clear the display //plot(allspikes(:,2),allspikes(:,1),'.'); brian-1.4.3/dev/benchmarking/constant_estimation/000077500000000000000000000000001266062647100221175ustar00rootroot00000000000000brian-1.4.3/dev/benchmarking/constant_estimation/estimate_constants.py000066400000000000000000000221361266062647100264040ustar00rootroot00000000000000''' Still to do: * Estimate with and without compiling turned on * Investigate and estimate the cost of cache misses in the spiking part ''' from brian import * from run_opts import * import time import scipy import numpy def network_and_lazy_group(N=1): G = [NeuronGroup(1, model='dV/dt=-V/(10*ms):volt') for _ in range(N)] net = Network(G) net.run(0 * ms) #print len(net._update_schedule[id(defaultclock)]) reinit_default_clock() t = time.time() net.run(duration) return time.time() - t def network_lazy_group_and_zero_connections(N=1): G = NeuronGroup(1, model='dV/dt=-V/(10*ms):volt') C = [ Connection(G, G) for _ in range(N) ] net = Network(G, C) # problem with MultiConnection, this reproduces part of the activity of # network.prepare so that MultiConnection isn't used net.set_clock() net.connections = C for c in C: c.compress() net._build_update_schedule() net.prepared = True net.run(0 * ms) #print len(net._update_schedule[id(defaultclock)]) reinit_default_clock() t = time.time() net.run(duration) return time.time() - t def linear_network(N=100, vars=None): if vars is None: vars = 3 eqs = ''' dV/dt = (ge+gi-(V+49*mV))/(20*ms) : volt dge/dt = -ge/(5*ms) : volt dgi/dt = -gi/(10*ms) : volt ''' else: eqs = '' for i in range(vars): # upper triangular differential equation eqs += 'dV' + str(i) + '/dt=-(' + '+'.join('V' + str(j) for j in range(i + 1)) + ')/(10*ms):volt\n' G = NeuronGroup(N, model=eqs) net = Network(G) net.run(0 * ms) reinit_default_clock() t = time.time() net.run(duration) return time.time() - t def linear_network_cachemiss(N=100, vars=None): if vars is None: vars = 3 eqs = ''' dV/dt = (ge+gi-(V+49*mV))/(20*ms) : volt dge/dt = -ge/(5*ms) : volt dgi/dt = -gi/(10*ms) : volt ''' else: eqs = '' for i in range(vars): # upper triangular differential equation eqs += 'dV' + str(i) + '/dt=-(' + '+'.join('V' + str(j) for j in range(i + 1)) + ')/(10*ms):volt\n' G = NeuronGroup(N, model=eqs) x = numpy.ones(cachemiss_array_size) @network_operation def hitcache(): x[:] = 1. net = Network(G, hitcache) net.run(0 * ms) reinit_default_clock() t = time.time() net.run(duration) return time.time() - t def spiking_network(rate, synapses_per_neuron=1): # rate is per neuron firing rate eqs = 'dV/dt=-V/(1*Msecond):volt' rate = int(N_spiking_network * rate * defaultclock.dt) G = NeuronGroup(N_spiking_network, model=eqs, threshold=10 * mV, reset=1 * volt) H = NeuronGroup(synapses_per_neuron, model='V:volt') G.V[:rate] = 1 * volt G.V[rate:] = 0 * volt C = Connection(G, H, 'V') C.connect_full(G, H, weight=1 * nvolt) net = Network(G, H, C) net.run(0 * ms) reinit_default_clock() t = time.time() net.run(duration) return time.time() - t def threshold(N=100, abovethresh=50): G = NeuronGroup(N, model='V:volt', threshold=1 * mV) G.V[:abovethresh] = 2 * mV net = Network(G) net.run(0 * ms) reinit_default_clock() t = time.time() net.run(duration) return time.time() - t def threshold_cachemiss(N=100, abovethresh=50): G = NeuronGroup(N, model='V:volt', threshold=1 * mV) G.V[:abovethresh] = 2 * mV x = numpy.ones(cachemiss_array_size) @network_operation def hitcache(): x[:] = 1. net = Network(G, hitcache) net.run(0 * ms) reinit_default_clock() t = time.time() net.run(duration) return time.time() - t def do_time(f, *params): return (f(*params) * second) / (duration / defaultclock.dt) time_network_and_lazy_group = [ do_time(network_and_lazy_group, i) for i in range(1, max_groups + 1) ] slope, intercept, r, tt, stderr = scipy.stats.linregress(range(1, max_groups + 1), time_network_and_lazy_group) overhead_network = intercept * second overhead_neurongroup = slope * second print 'Network update overhead per dt =', overhead_network print 'NeuronGroup overhead per dt (approx, with 1 linear neuron) =', overhead_neurongroup subplot(331) plot(range(1, max_groups + 1), time_network_and_lazy_group) plot(range(0, max_groups + 1), [intercept + i * slope for i in range(0, max_groups + 1)]) title('Groups overhead') time_network_lazy_group_and_zero_connections = [ do_time(network_lazy_group_and_zero_connections, i) for i in range(max_connections + 1) ] slope, intercept, r, tt, stderr = scipy.stats.linregress(range(max_connections + 1), time_network_lazy_group_and_zero_connections) overhead_connection = slope * second print 'Connection overhead per dt =', overhead_connection print 'Sanity check, these should be roughly equal:', intercept * second, ',', overhead_network + overhead_neurongroup subplot(332) plot(range(max_connections + 1), time_network_lazy_group_and_zero_connections) plot(range(max_connections + 1), [intercept + i * slope for i in range(max_connections + 1)]) title('Connections overhead') time_linear_network = [ do_time(linear_network, N) for N in N_linear_network ] slope, intercept, r, tt, stderr = scipy.stats.linregress(N_linear_network, time_linear_network) perneuron = slope * second print 'Sanity check, these should be roughly equal:', intercept * second, ',', overhead_network + overhead_neurongroup print 'Time per linear neuron with numstates=3 per dt =', perneuron subplot(333) plot(N_linear_network, time_linear_network) plot([0] + N_linear_network, [intercept + i * slope for i in [0] + N_linear_network]) title('Linear 3 states') xlabel('N') time_linear_network_cachemiss = [ do_time(linear_network_cachemiss, N) for N in N_linear_network ] slope, intercept, r, tt, stderr = scipy.stats.linregress(N_linear_network, time_linear_network_cachemiss) perneuron_cachemiss = slope * second print 'Time per linear neuron with numstates=3 per dt, cachemiss:', perneuron_cachemiss subplot(334) plot(N_linear_network, time_linear_network_cachemiss) plot([0] + N_linear_network, [intercept + i * slope for i in [0] + N_linear_network]) title('Linear 3 states cachemiss') xlabel('N') #time_multivar_linear_network = [ do_time(linear_network, N_multivar_linear_network, M) for M in vars_linear_network ] #fitcoeffs = polyfit(vars_linear_network, time_multivar_linear_network, 2) #qfitcoeffs = array(fitcoeffs)*second #perneuron_variableconst = slope*second #print 'Time per linear neuron as function of numstates=M:', qfitcoeffs[1]/N_multivar_linear_network, 'M +', qfitcoeffs[0]/N_multivar_linear_network, 'M^2' #subplot(334) #plot(vars_linear_network, time_multivar_linear_network) #plot([0]+vars_linear_network, [fitcoeffs[2]+i*fitcoeffs[1]+i*i*fitcoeffs[0] for i in [0]+vars_linear_network]) #title('Linear in numstates, numneurons='+str(N_multivar_linear_network)) time_spiking_network = [ do_time(spiking_network, rate, fixed_syn_spiking_network) for rate in rates_spiking_network ] slope, intercept, r, tt, stderr = scipy.stats.linregress(rates_spiking_network, time_spiking_network) perhertz = slope * second perspikeandsyn = perhertz / (1.0 * Hz * fixed_syn_spiking_network * N_spiking_network * defaultclock.dt) subplot(335) plot(rates_spiking_network, time_spiking_network, '.') plot([0, max(rates_spiking_network)], [intercept + float(i) * slope for i in [0, max(rates_spiking_network)]]) title('Spiking') xlabel('rate') time_spiking_network_syn = [ do_time(spiking_network, rate_syn, syn) for syn in syn_spiking_network ] slope, intercept, r, tt, stderr = scipy.stats.linregress(syn_spiking_network, time_spiking_network_syn) persyn = slope * second / (rate_syn * N_spiking_network * defaultclock.dt) perspike = (perspikeandsyn - persyn) * fixed_syn_spiking_network print 'Time per synapse:', persyn print 'Time per spike:', perspike subplot(336) plot(syn_spiking_network, time_spiking_network_syn, '.') plot([0, max(syn_spiking_network)], [intercept + float(i) * slope for i in [0, max(syn_spiking_network)]]) title('Spiking') xlabel('synapses per neuron') time_threshold = [ do_time(threshold, N) for N in N_threshold ] slope, intercept, r, tt, stderr = scipy.stats.linregress(N_threshold, time_threshold) threshold_perneuron = slope * second print 'Thresholding operation per neuron:', threshold_perneuron subplot(338) plot(N_threshold, time_threshold, '.') plot([0, max(N_threshold)], [intercept + float(i) * slope for i in [0, max(N_threshold)]]) title('Thresholding') time_threshold_cachemiss = [ do_time(threshold_cachemiss, N) for N in N_threshold ] slope, intercept, r, tt, stderr = scipy.stats.linregress(N_threshold, 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vî"“¯~¨¥g±^3–|UM¾*v¾Š¨ïŒUoù*BÆâ÷ 0¦&}üø1:Ë—/¦¦¦èéé‰ñññùÞÖ_’eYåhhh¨üÝÕÕUuÞ•+WbýúõÑØØ«V­ŠãÇÇôôô/×{ûömìÝ»7–-[ÍÍÍÑßß_šærÍ¿[___Õ¬ÿ~üg¸-ÒÜ?þ›»iÓ¦Xºti”J¥hkk‹íÛ·ÇØØØœ÷Y+³ÏfõêÕ¿ÜóˆbÍ~òäÉØ°aC´´´Ä‚ ¢££#âÉ“'sÞc-ÌñéÓ§8|øp¬\¹2-ZkÖ¬‰S§NÍyµ2÷O7nŒÎÎÎ?=§h³ŽŽFooo´´´Tîùððp|ýúuN{¬•¹‹J¾ú—Zyë5cÉWÕ䫹í±æþ©ž3V=æ«‹ù—EüÉáP7¼ô€”’€œÂ€”’€œÂ€”’€œÂ€”’€œÂ€”’€œÂ€”’€œÂ€”’€œÂ€”’€œÂ€”’€œÂ€”’€œÂ€”’€œÂ€”’€œÂ€”’€œÂ€”’€œÂ€”’€œÂ€”’€œÂ€”’€œÂ€”’€œÂ€”’€œÂ€”’€œÂ€”’€œÂ€”’€œÂ€”’€œÂ€”’€Ü`)b¬ñ™ IEND®B`‚brian-1.4.3/dev/benchmarking/constant_estimation/predictions.py000066400000000000000000000044261266062647100250220ustar00rootroot00000000000000from brian import * import time N = 100 N_set = [int(N) for N in randint(100, 8000, size=100)] #set_global_preferences(useweave=True) us = usecond ns = nsecond def get_predicted_time(N, r): theta = 0.02 * N T = 1 * second dt = 0.1 * ms if get_global_preference('useweave'): pass else: overhead_fudge = 80 * us wN = 6.4 * us wC = 16.4 * us wG = 22.3 * us aG = 45 * ns aG_cachemiss = 138 * ns athr = 35.1 * ns athr_cachemiss = 90 * ns asp = 11 * us asyn = 59.3 * ns p_lower = (T / dt) * (overhead_fudge + wN + 2 * wC + wG + N * (aG + athr)) + T * r * N * (asp + asyn * theta) p_middle = (T / dt) * (overhead_fudge + wN + 2 * wC + wG + N * (aG_cachemiss + athr)) + T * r * N * (asp + asyn * theta) p_upper = (T / dt) * (overhead_fudge + wN + 2 * wC + wG + N * (aG_cachemiss + athr_cachemiss)) + T * r * N * (asp + asyn * theta) return (p_lower, p_middle, p_upper) def get_exp(N): eqs = ''' dv/dt = (ge+gi-(v+49*mV))/(20*ms) : volt dge/dt = -ge/(5*ms) : volt dgi/dt = -gi/(10*ms) : volt ''' Ne = int(N * 0.8) Ni = N - Ne P = NeuronGroup(N, model=eqs, threshold= -50 * mV, reset= -60 * mV) P.v = -60 * mV + 10 * mV * rand(len(P)) Pe = P.subgroup(Ne) Pi = P.subgroup(Ni) Ce = Connection(Pe, P, 'ge') Ci = Connection(Pi, P, 'gi') Ce.connect_random(Pe, P, 0.02, weight=1.62 * mV) Ci.connect_random(Pi, P, 0.02, weight= -9 * mV) M = PopulationSpikeCounter(P) net = Network(P, Ce, Ci, M) net.run(100 * ms) M.reinit() t = time.time() net.run(1 * second) t = time.time() - t return (float(M.nspikes) / (N * 1. * second), t * second) #r, t = get_exp(N) #print 'Rate:', r #print 'Actual time:', t #print 'Predicted time:', get_predicted_time(N, r) rates, t = zip(*[get_exp(N) for N in N_set]) p, pcm_middle, pcm_max = zip(*[get_predicted_time(N, r) for N, r in zip(N_set, rates)]) subplot(121) plot(N_set, t, '.') plot(N_set, p, '.') plot(N_set, pcm_middle, '.') plot(N_set, pcm_max, '.') legend(('Actual', 'No cache miss', 'Cache miss on group', 'All cache misses'), 'upper left') subplot(122) plot(N_set, rates, '.') show() brian-1.4.3/dev/benchmarking/constant_estimation/run_opts.py000066400000000000000000000010721266062647100243420ustar00rootroot00000000000000from brian import * duration = 1 * second defaultclock.dt = 0.1 * ms max_groups = 5 max_connections = 5 N_linear_network = [100, 200, 300, 1000, 2000, 4000] cachemiss_array_size = 300000 # should take up 2MB N_multivar_linear_network = 1000 vars_linear_network = range(1, 6) N_spiking_network = 1000 rates_spiking_network = [x * Hz for x in randint(20, 300, size=10)] fixed_syn_spiking_network = 500 syn_spiking_network = list(randint(100, 1000, size=10))#+list(randint(1000,5000,size=10)) rate_syn = 50 * Hz N_threshold = randint(100, 1000, size=20) brian-1.4.3/dev/benchmarking/constant_estimation/temp.py000066400000000000000000000014511266062647100234370ustar00rootroot00000000000000from brian import * import time set_global_preferences(useweave=True) N = 4000 duration = 5 * second eqs = ''' dv/dt = (ge+gi-(v+49*mV))/(20*ms) : volt dge/dt = -ge/(5*ms) : volt dgi/dt = -gi/(10*ms) : volt ''' Ne = int(N * 0.8) Ni = N - Ne P = NeuronGroup(N, model=eqs, threshold= -48 * mV)#,reset=-60*mV) P.v = -60 * mV + 10 * mV * rand(len(P)) Pe = P.subgroup(Ne) Pi = P.subgroup(Ni) Ce = Connection(Pe, P, 'ge') Ci = Connection(Pi, P, 'gi') Ce.connect_random(Pe, P, 0.02, weight=1.62 * mV) Ci.connect_random(Pi, P, 0.02, weight= -9 * mV) M = PopulationSpikeCounter(P) #net = MagicNetwork(verbose=False) net = Network(P, Ce, Ci) net.run(100 * ms) t = time.time() net.run(duration) print time.time() - t print M.nspikes raw_input('Press enter') brian-1.4.3/dev/benchmarking/cuba/000077500000000000000000000000001266062647100167445ustar00rootroot00000000000000brian-1.4.3/dev/benchmarking/cuba/CUBA.cpp000066400000000000000000000170461266062647100201720ustar00rootroot00000000000000/* This is an implementation of a benchmark described in the following review paper: Simulation of networks of spiking neurons: A review of tools and strategies (2006). Brette, Rudolph, Carnevale, Hines, Beeman, Bower, Diesmann, Goodman, Harris, Zirpe, Natschl�ger, Pecevski, Ermentrout, Djurfeldt, Lansner, Rochel, Vibert, Alvarez, Muller, Davison, El Boustani and Destexhe. Journal of Computational Neuroscience Benchmark 2: random network of integrate-and-fire neurons with exponential synaptic currents Clock-driven implementation with exact subthreshold integration (but spike times are aligned to the grid) R. Brette and Dan Goodman - May 2008 */ #include #include #include #include #include #include using namespace std; #define taum 20. #define taue 5. #define taui 10. #define Vt -50. #define Vr -60. #define El -49. #define Nvals 1000, 2000, 4000, 8000, 16000, 32000 #define N_once 32000 #define N_varywe 4000 #define wevals 1.62, 2.4, 2.9, 3.2 #define Ne_prop 0.8 double we = (60*0.27/10.); #define wi (-20*4.5/10.) #define DURATION 2500. #define dt .1 #define refrac 5. #define REPEATS 10 #define BEST 7 const double Amatvals[3][3] = { { 0.99501248, 0., 0. }, { 0.00493794, 0.98019867, 0. }, { 0.00496265, 0., 0.99004983 } }; const double Cvecvals[3] = { -2.44388520e-01, 0., 0. }; double Amat[3][3]; double Cvec[3]; bool use_connections=true; bool vary_weights=false; bool run_once=false; void (*the_state_updater)(double &v, double &ge, double &gi); void stateupdater_euler(double &v, double &ge, double &gi) {// optimised this should come to 6 adds and 3 multiplies v+=(dt/taum)*(ge+gi-v+El); // optimised this would be v+=(dt/taum)*(ge+gi-v+El) where dt/taum is a const, so 1 mul and 4 adds ge+=(-dt/taue)*ge; // optimised this is 1 mul and 1 add gi+=(-dt/taui)*gi; // optimised this is 1 mul and 1 add } void stateupdater_matrix(double &v, double &ge, double &gi) {// this comes to 9 muls, 9 adds and 3 copies static double vnew, genew, ginew; vnew = v*Amat[0][0]+ge*Amat[1][0]+gi*Amat[2][0]+Cvec[0]; genew = v*Amat[0][1]+ge*Amat[1][1]+gi*Amat[2][1]+Cvec[1]; ginew = v*Amat[0][2]+ge*Amat[1][2]+gi*Amat[2][2]+Cvec[2]; v = vnew; ge = genew; gi = ginew; } void stateupdater_matrix_efficient(double &v, double &ge, double &gi) { // Note: 0s should be optimised away, and nonzeros should be replaced // with their constant values. Also note that v depends on initial // values of v, ge, gi but ge and gi only depend on ge, gi respectively // so we can do this calculation in this order without storing temporary // variables v = v*Amatvals[0][0]+ge*Amatvals[1][0]+gi*Amatvals[2][0]+Cvecvals[0]; ge = v*Amatvals[0][1]+ge*Amatvals[1][1]+gi*Amatvals[2][1]+Cvecvals[1]; gi = v*Amatvals[0][2]+ge*Amatvals[1][2]+gi*Amatvals[2][2]+Cvecvals[2]; } pair cuba(int N) { int Ne = (int)((double)N*Ne_prop); int Ni = N-Ne; // State variables double v[N],ge[N],gi[N]; for(int i=0;iVt) { nspikes++; if(use_connections) { for(int j=0;j((t2-t1)*1./CLOCKS_PER_SEC, nspikes); } bool compare_cubarun(pair c1, pair c2) { return c1.first average_cuba(int N, int repeats, int best) { list< pair > cubaruns; for(int i=0;i >::iterator i=cubaruns.begin();i!=cubaruns.end() && j(t/(double)best,ns/best); } void runs(vector N, int repeats, int best) { cout << "cpp_cuba = []" << endl; for(int i=0;i tn = average_cuba(N[i], repeats, best); cout << "cpp_cuba.append((" << N[i] << ", " << tn.first << ", " << tn.second << ", cur_result))" << endl; } } void runs_varywe(vector wev, int repeats, int best) { cout << "cpp_cuba = []" << endl; for(int i=0;i tn = average_cuba(N_varywe, repeats, best); cout << "# we = " << we << endl; cout << "cpp_cuba.append((" << N_varywe << ", " << tn.first << ", " << tn.second << ", cur_result))" << endl; } } int main(int argc, char *argv[]) { the_state_updater = stateupdater_euler; if(argc>1) { for(int i=1;i N(Nints, Nints + sizeof(Nints) / sizeof(int) ); double _we[] = {wevals}; vector wev(_we, _we + sizeof(_we) / sizeof(double)); if(run_once) { pair p = cuba(N_once); cout << "time_taken = " << p.first << endl; cout << "num_spikes = " << p.second << endl; } else { if(vary_weights) runs_varywe(wev, REPEATS, BEST); else runs(N, REPEATS, BEST); } return 0; } brian-1.4.3/dev/benchmarking/cuba/CUBA.sce000066400000000000000000000057571266062647100201700ustar00rootroot00000000000000// CUBA // // This Scilab code is an implementation of a benchmark described // in the following review paper: // // Simulation of networks of spiking neurons: A review of tools and strategies (2006). // Brette, Rudolph, Carnevale, Hines, Beeman, Bower, Diesmann, Goodman, Harris, Zirpe, // Natschläger, Pecevski, Ermentrout, Djurfeldt, Lansner, Rochel, Vibert, Alvarez, Muller, // Davison, El Boustani and Destexhe. // Journal of Computational Neuroscience // // Benchmark 2: random network of integrate-and-fire neurons with exponential synaptic currents // // Clock-driven implementation with exact subthreshold integration // (but spike times are aligned to the grid) // // The simulation takes about 40s on my PC // // --------------------- // // Romain Brette (June 2006) // brette@di.ens.fr // // Parameters Ne=3200; // Number of excitatory neurons Ni=800; // Number of inhibitory neurons N=Ne+Ni; // Total number of neurons conProba=0.02; // Connection probability taum=20; // Membrane time constant (ms) taue=5; // Excitatory synaptic time constant taui=10; // Inhibitory synaptic time constant Vt = -50+49; // threshold, relative to rest (mV) Vr = -60+49; // reset (mV) dt=0.1; // time step we=60*0.27/10; // excitatory synaptic weight (voltage) wi=-20*4.5/10; // inhibitory synaptic weight duration=1000; // duration of the simulation (ms) refrac=5; // refractory period // Update matrix A=[exp(-dt/taum),0,0;... we*taue/(taum-taue)*(exp(-dt/taum)-exp(-dt/taue)),exp(-dt/taue),0;... wi*taui/(taum-taui)*(exp(-dt/taum)-exp(-dt/taui)),0,exp(-dt/taui)]; // State variables (membrane potential, excitatory current, inhibitory current) S=zeros(N,3); S(:,1)=grand(N,1,'unf',Vr,Vt); // Potential: uniform between reset and threshold // Connectivity matrix: 2% connectivity with unitary weights // This is a sparse matrix (otherwise it is too big and slow) W=bool2s(sprand(N,N,conProba,'uniform')>0); // Last spike times - for refractory period LS=zeros(N,1)-1000; // Simulation printf("Starting simulation...\n"); timer(); t=0; allspikes=[]; // Contains spikes (neuron,time) while tt-refrac),1)=Vr; // PROPAGATION OF SPIKES // Excitatory neurons spikes=find(S(1:Ne,1)>Vt); // List of neurons that meet threshold condition S(:,2)=S(:,2)+(sum(W(:,spikes),'c')); // Update the state of targets // Inhibitory neurons spikes=find(S(Ne+1:$,1)>Vt); S(:,3)=S(:,3)+(sum(W(:,spikes),'c')); // Reset neurons after spiking spikes=find(S(:,1)>Vt); LS(spikes)=t; // Time of last spike S(spikes,1)=Vr; // Reset membrane potential // Store spike times allspikes=[allspikes;spikes',ones(spikes')*t]; // Each row is (neuron number,spike time) t=t+dt; // Advance simulation time end // Display the computation time timer() // Display the spikes clf; // Clear the display plot(allspikes(:,2),allspikes(:,1),'.'); brian-1.4.3/dev/benchmarking/cuba/analyse_results.py000066400000000000000000000124131266062647100225340ustar00rootroot00000000000000from numpy import * from pylab import * import pickle import os import itertools import cuba_runopts basedir = 'data' def get_packaged_results(packagename, var, desc): if basedir: packagename = basedir + '/' + packagename exec open(packagename + '.py').read() res = locals()[var] return (res, desc) def get_brian(ext, desc, runtype=''): if ext: ext = '_' + ext if runtype: runtype = '_' + runtype pre = '' if basedir: pre = basedir + '/' inputfile = open(pre + 'brian_cuba' + runtype + '_results' + ext + '.pkl', 'rb') brian_cuba = pickle.load(inputfile) inputfile.close() return (brian_cuba, desc) def plotresults(res, desc): N, t, spikes, results = zip(*res) R = array(results) tstd = std(R, axis=1)[:, 0] errorbar(N, t, yerr=tstd, label=desc) def plotcomparativeresults(base, res, desc): Nb, tb, spikesb, resultsb = zip(*base) N, t, spikes, results = zip(*res) tb = array(tb)[:len(N)] plot(N, array(t) / tb, '-o', label=desc) def plotrates(res, desc): N, t, spikes, results = zip(*res) R = array(results) tstd = std(R, axis=1)[:, 1] errorbar(N, spikes, yerr=tstd, label=desc) def plotvaryconresults(res, desc): N, t, spikes, results = zip(*res) results = array(results) results[:, :, 1] /= N[0] * cuba_runopts.duration spikes = array(spikes) / (N[0] * cuba_runopts.duration) for k, n in zip(results, N): R = array(k) plot(R[:, 1], R[:, 0], '.', label=desc) R = array(results) ststd = std(R, axis=1) sstd = ststd[:, 1] tstd = ststd[:, 0] errorbar(spikes, t, tstd, sstd, label=desc) def plotvaryweresults(res, desc): N, t, spikes, results = zip(*res) results = list(itertools.chain(*results)) results = array(results) results[:, 1] /= N[0] * cuba_runopts.duration plot(results[:, 1], results[:, 0], '.', label=desc) def do_analysis(all_results, showrates=True): cpp_cuba = all_results[0][0] cpp_cuba_desc = all_results[0][1] N, t, spikes, results = zip(*cpp_cuba) Nmax = max(N) Nmin = min(N) d1 = 1 if showrates: d2 = 3 else: d2 = 2 figure() subplot(d1, d2, 1) for r, desc in all_results: plotresults(r, desc) #legend([desc for r,desc in all_results]) #legend(loc='upper left') axis(ymin=0, xmin=Nmin, xmax=Nmax) xlabel('N') ylabel('Time (s)') subplot(d1, d2, 2) for r, desc in all_results: plotcomparativeresults(cpp_cuba, r, desc) #legend([desc for r,desc in all_results]) #legend(loc='upper left') legend() axis(ymin=0, xmin=Nmin, xmax=Nmax) xlabel('N') ylabel('Time relative to ' + cpp_cuba_desc + ' (s)') if showrates: subplot(d1, d2, 3) for r, desc in all_results: plotrates(r, desc) axis(ymin=0, xmin=Nmin, xmax=Nmax) xlabel('N') ylabel('Num. spikes') # if showrates: # subplot(d1,d2,4) # else: # subplot(d1,d2,3) # figlegend() # show() def do_analysis_varywe(all_results): figure() for res, desc in all_results: plotvaryweresults(res, desc) xlabel('Firing rate per neuron (Hz)') ylabel('Time (s)') legend(loc='upper left') # do_analysis(all_results) def cd(s): global basedir basedir = s if __name__ == '__main__': all_results = [] all_results.append(get_packaged_results('cpp_cuba_results_matrix', 'cpp_cuba', 'C++ (gen. matrix)')) all_results.append(get_packaged_results('cpp_cuba_results', 'cpp_cuba', 'C++ (Euler)')) all_results.append(get_packaged_results('cpp_cuba_results_matrix_efficient', 'cpp_cuba', 'C++ (fix. matrix)')) all_results.append(get_packaged_results('matlab_cuba_results', 'matlab_cuba', 'Matlab')) all_results.append(get_brian('compile_nounits', 'Brian compiled, no units')) do_analysis(all_results, showrates=False) all_results = [] all_results.append(get_packaged_results('cpp_cuba_nospiking_results_matrix', 'cpp_cuba', 'C++ (gen. matrix)')) all_results.append(get_packaged_results('cpp_cuba_nospiking_results', 'cpp_cuba', 'C++ (Euler)')) all_results.append(get_packaged_results('matlab_cuba_nospiking_results', 'matlab_cuba', 'Matlab')) all_results.append(get_brian('compile_nounits', 'Brian compiled, no units', 'nospiking')) do_analysis(all_results, showrates=False) # all_results = [] # all_results.append(get_packaged_results('cpp_cuba_varywe_results_matrix','cpp_cuba','C++ (gen. matrix)')) # all_results.append(get_packaged_results('cpp_cuba_varywe_results','cpp_cuba','C++ (Euler)')) # all_results.append(get_packaged_results('matlab_cuba_varywe_results','matlab_cuba','Matlab')) # all_results.append(get_brian('compile_nounits_1000','Brian','varywe')) # do_analysis_varywe(all_results) all_results = [] all_results.append(get_packaged_results('cpp_cuba_varywe_results_matrix_4000', 'cpp_cuba', 'C++ (gen. matrix)')) all_results.append(get_packaged_results('matlab_cuba_varywe_results_4000', 'matlab_cuba', 'Matlab')) all_results.append(get_brian('compile_nounits_4000', 'Brian', 'varywe')) do_analysis_varywe(all_results) show() brian-1.4.3/dev/benchmarking/cuba/callgraphs/000077500000000000000000000000001266062647100210645ustar00rootroot00000000000000brian-1.4.3/dev/benchmarking/cuba/callgraphs/cuba-bigcallgraph-Connection.__init__.png000066400000000000000000001332771266062647100307710ustar00rootroot00000000000000‰PNG  IHDR|=Ѿ ÝbKGDÿÿÿ ½§“tIMEÙ 2"^cγ 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Ÿk{+?5Vl¼šyäöÏÕ)L­ç¦õØjŽ¡¦fëµVà°eYŠÿ—ݯ¶N®~LOÍeY’cŒšëˆúáßQu¯<†\ ŽNx;¿ùµ hª›bÀ(Ö€L¼ßïêŸãlÛFÕ ÏËV³õ|íÚsõÚ,îbÏ£SNv3ĺJݵ ¬¦êÄBƒÔz&­Cíö¹íŽv{Ô¬Ùq¤›$·ÖK륱k®ƒ.8ßﺮëÝ“€ÙÔt)Ô„"¹:WuBç›ï óÍÇßF§0; else W1 = conmat(N/2,conProba); W2 = conmat(N/2,conProba); W3 = conmat(N/2,conProba); W4 = conmat(N/2,conProba); W = [W1 W2 ; W3 W4]; end brian-1.4.3/dev/benchmarking/cuba/cuba.asv000066400000000000000000000040211266062647100203660ustar00rootroot00000000000000function [ttaken, nspikes] = cuba(N) % // CUBA % // % // This Matlab code is an implementation of a benchmark described % // in the following review paper: % // % // Simulation of networks of spiking neurons: A review of tools and strategies (2006). % // Brette, Rudolph, Carnevale, Hines, Beeman, Bower, Diesmann, Goodman, Harris, Zirpe, % // Natschläger, Pecevski, Ermentrout, Djurfeldt, Lansner, Rochel, Vibert, Alvarez, Muller, % // Davison, El Boustani and Destexhe. % // Journal of Computational Neuroscience % // % // Benchmark 2: random network of integrate-and-fire neurons with exponential synaptic currents % // % // Clock-driven implementation with exact subthreshold integration % // (but spike times are aligned to the grid) % // % // --------------------- % // % // Romain Brette (June 2006) and Dan Goodman (May 2008) % // brette@di.ens.fr % // % % // Parameters Ne=floor(N*0.8); Ni=N-Ne; conProba=0.02; taum=20; taue=5; taui=10; Vt = -50+49; Vr = -60+49; dt=0.1; we=60*0.27/10; wi=-20*4.5/10; duration=2500; refrac=5; % Update matrix A=[exp(-dt/taum),0,0;... we*taue/(taum-taue)*(exp(-dt/taum)-exp(-dt/taue)),exp(-dt/taue),0;... wi*taui/(taum-taui)*(exp(-dt/taum)-exp(-dt/taui)),0,exp(-dt/taui)]; % State variables (membrane potential, excitatory current, inhibitory current) S=zeros(N,3); S(:,1)=rand(N,1)*(Vt-Vr)+Vr; % Potential: uniform between reset and threshold % Connectivity matrix: 2% connectivity with unitary weights % This is a sparse matrix (otherwise it is too big and slow) W=conmat(N,conProba); % Last spike times - for refractory period LS=zeros(N,1)-1000; % Simulation t1=clock; t=0; allspikes=[]; ns=0; while tt-refrac,1)=Vr; spikesarr = S(:,1)>Vt; spikes = spikesarr(1:Ne); S(:,2)=S(:,2)+(sum(W(:,spikes),2)); spikes = spikesarr(Ne+1:end); S(:,3)=S(:,3)+(sum(W(:,spikes),2)); LS(spikesarr)=t; S(spikesarr,1)=Vr; ns=ns+sum(spikesarr); t=t+dt; end nspikes = ns; ttaken = etime(clock,t1); brian-1.4.3/dev/benchmarking/cuba/cuba.m000066400000000000000000000044431266062647100200410ustar00rootroot00000000000000function [ttaken, nspikes] = cuba(N, use_connections, we) % // CUBA % // % // This Matlab code is an implementation of a benchmark described % // in the following review paper: % // % // Simulation of networks of spiking neurons: A review of tools and strategies (2006). % // Brette, Rudolph, Carnevale, Hines, Beeman, Bower, Diesmann, Goodman, Harris, Zirpe, % // Natschläger, Pecevski, Ermentrout, Djurfeldt, Lansner, Rochel, Vibert, Alvarez, Muller, % // Davison, El Boustani and Destexhe. % // Journal of Computational Neuroscience % // % // Benchmark 2: random network of integrate-and-fire neurons with exponential synaptic currents % // % // Clock-driven implementation with exact subthreshold integration % // (but spike times are aligned to the grid) % // % // --------------------- % // % // Romain Brette (June 2006) and Dan Goodman (May 2008) % // brette@di.ens.fr % // % % // Parameters Ne=floor(N*0.8); Ni=N-Ne; conProba=80.0/N; taum=20; taue=5; taui=10; Vt = -50+49; Vr = -60+49; dt=0.1; if nargin<3, we=60*0.27/10; end wi=-20*4.5/10; duration=2500; refrac=5; % Update matrix A=[exp(-dt/taum),0,0;... we*taue/(taum-taue)*(exp(-dt/taum)-exp(-dt/taue)),exp(-dt/taue),0;... wi*taui/(taum-taui)*(exp(-dt/taum)-exp(-dt/taui)),0,exp(-dt/taui)]; %B=[exp(-dt/taum),0,0;... % taue/(taum-taue)*(exp(-dt/taum)-exp(-dt/taue)),exp(-dt/taue),0;... % taui/(taum-taui)*(exp(-dt/taum)-exp(-dt/taui)),0,exp(-dt/taui)] % State variables (membrane potential, excitatory current, inhibitory current) S=zeros(N,3); S(:,1)=rand(N,1)*(Vt-Vr)+Vr; % Potential: uniform between reset and threshold % Connectivity matrix: 80 synapses with unitary weights % This is a sparse matrix (otherwise it is too big and slow) if use_connections W=conmat(N,conProba); end % Last spike times - for refractory period LS=zeros(N,1)-1000; % Simulation t1=clock; t=0; allspikes=[]; ns=0; while tt-refrac,1)=Vr; spikesarr = S(:,1)>Vt; if use_connections spikes = spikesarr(1:Ne); S(:,2)=S(:,2)+(sum(W(:,spikes),2)); spikes = spikesarr(Ne+1:end); S(:,3)=S(:,3)+(sum(W(:,spikes),2)); end LS(spikesarr)=t; S(spikesarr,1)=Vr; ns=ns+sum(spikesarr); t=t+dt; end nspikes = ns; ttaken = etime(clock,t1); brian-1.4.3/dev/benchmarking/cuba/cuba.py000066400000000000000000000065121266062647100202340ustar00rootroot00000000000000from brian import * import time import pickle from operator import itemgetter from cuba_runopts import * def cubanetwork(N, Nsyn=80., we=1.62 * mV): eqs = ''' dv/dt = (ge+gi-(v+49*mV))/(20*ms) : volt dge/dt = -ge/(5*ms) : volt dgi/dt = -gi/(10*ms) : volt ''' Ne = int(N * 0.8) Ni = N - Ne P = NeuronGroup(N, model=eqs, threshold= -50 * mV, reset= -60 * mV, refractory=5 * ms) P.v = -60 * mV + 10 * mV * rand(len(P)) Pe = P.subgroup(Ne) Pi = P.subgroup(Ni) if cuba_opts['connections']: Ce = Connection(Pe, P, 'ge') Ci = Connection(Pi, P, 'gi') cp = Nsyn / N Ce.connect_random(Pe, P, cp, weight=we) Ci.connect_random(Pi, P, cp, weight= -9 * mV) # from brian.experimental.stdp_sparse import SparseSTDPConnectionMatrix # Ce.W = SparseSTDPConnectionMatrix(Ce.W) # Ci.W = SparseSTDPConnectionMatrix(Ci.W) else: Ce = None Ci = None M = PopulationSpikeCounter(P) if cuba_opts['connections']: net = Network(P, Ce, Ci, M) else: net = Network(P, M) net.run(0 * ms) net.Nsyn = Nsyn net.we = we return P, Pe, Pi, Ce, Ci, M, net def cuba(P, Pe, Pi, Ce, Ci, M, net): reinit_default_clock() P.v = -60 * mV + 10 * mV * rand(len(P)) if cuba_opts['connections']: Ce.__init__(Pe, P, 'ge') Ci.__init__(Pi, P, 'gi') cp = net.Nsyn / len(P) Ce.connect_random(Pe, P, cp, weight=net.we) Ci.connect_random(Pi, P, cp, weight= -9 * mV) Ce.W.freeze() Ci.W.freeze() M.reinit() tstart = time.time() net.run(duration) tend = time.time() print '.', return tend - tstart, M.nspikes def cuba_average(N, repeats, best, Nsyn=80., we=1.62 * mV): cubadata = cubanetwork(N, Nsyn, we) cubaruns = [cuba(*cubadata) for i in range(repeats)] print print 'Finished run', N, '-', Nsyn, '-', we cubaruns.sort(key=itemgetter(0)) cubaruns = cubaruns[:best] t, ns = zip(*cubaruns) t = array(t) ns = array(ns) return N, mean(t), mean(ns), cubaruns def cuba_runs(Nvals, repeats, best): return [cuba_average(N, repeats, best) for N in Nvals] def cuba_runs_varyconnectivity(N, Nsynvals, repeats, best): return [cuba_average(N, repeats, best, Nsyn) for Nsyn in Nsynvals] def cuba_runs_varywe(N, wevals, repeats, best): return [cuba_average(N, repeats, best, we=we) for we in wevals] def do_runs(fname='data/brian_cuba_results.pkl'): ca = cuba_runs(Nvals, repeats, best) output = open(fname, 'wb') pickle.dump(ca, output, -1) output.close() return ca def do_runs_varyconnectivity(fname='data/brian_cuba_varycon_results.pkl'): ca = cuba_runs_varyconnectivity(N_varycon, Nsynvals, repeats, best) output = open(fname, 'wb') pickle.dump(ca, output, -1) output.close() return ca def do_runs_varywe(fname='data/brian_cuba_varywe_results.pkl'): ca = cuba_runs_varywe(N_varywe, wevals, repeats, best) output = open(fname, 'wb') pickle.dump(ca, output, -1) output.close() return ca if __name__ == '__main__': #print do_runs() #print do_runs_varyconnectivity() X = cubanetwork(16000, we=9 * mV) print 'Built X' print cuba(*X) brian-1.4.3/dev/benchmarking/cuba/cuba_average.asv000066400000000000000000000003511266062647100220620ustar00rootroot00000000000000function [avt, avn, runs] = cuba_average(N, repeats, best) runs = []; for i=1:repeats [t, n] = cuba(N); runs = [runs ; [t n]]; end [sruns, i] = sort(runs); runs = runs(i(:,1),:); runs = runs(1:best,:); = mean(runs);brian-1.4.3/dev/benchmarking/cuba/cuba_average.m000066400000000000000000000006131266062647100215260ustar00rootroot00000000000000function [avt, avn, runs] = cuba_average(N, repeats, best, use_connections, we) runs = []; for i=1:repeats if nargin<5 [t, n] = cuba(N, use_connections); else [t, n] = cuba(N, use_connections, we); end runs = [runs ; [t n]]; i end [sruns, i] = sort(runs); runs = runs(i(:,1),:); runs = runs(1:best,:); m = mean(runs,1); avt = m(1); avn = m(2);brian-1.4.3/dev/benchmarking/cuba/cuba_big_callgraph.py000066400000000000000000000120551266062647100230710ustar00rootroot00000000000000from brian import * import pycallgraph dorun = True def ff(call_stack, module_name, class_name, func_name, full_name): if func_name == 'cuba': return True if not 'brian' in module_name: return False if func_name == '__len__': return False if 'units' in module_name: return False return True def ff_nomagicstateupdater(call_stack, module_name, class_name, func_name, full_name): if func_name == 'cuba': return True if not 'brian' in module_name: return False if func_name == '__len__': return False if 'units' in module_name: return False if func_name == 'magic_state_updater' or func_name == 'prepare': return True if 'magic' in module_name: return False for n in call_stack + [full_name]: if 'magic_state_updater' in n: return False if 'prepare' in n: return False return True def ff_magicstateupdater(call_stack, module_name, class_name, func_name, full_name): if func_name == 'cuba': return True if not 'brian' in module_name: return False if func_name == '__len__': return False if 'units' in module_name: return False if func_name == 'prepare': return True for n in call_stack + [full_name]: if 'prepare' in n: return False for n in call_stack + [full_name]: if 'magic_state_updater' in n: return True return False def ff_prepare(call_stack, module_name, class_name, func_name, full_name): if func_name == 'cuba': return True if not 'brian' in module_name: return False if func_name == '__len__': return False if 'units' in module_name: return False for n in call_stack + [full_name]: if 'prepare' in n: return True return False def ff_noprepare(call_stack, module_name, class_name, func_name, full_name): if func_name == 'cuba': return True if not 'brian' in module_name: return False if func_name == '__len__': return False if 'units' in module_name: return False if func_name == 'prepare': return True for n in call_stack + [full_name]: if 'prepare' in n: return False return True def cuba(): N = 4000 Ne = int(N * 0.8) Ni = N - Ne p = 80. / N eqs = ''' dv/dt = (ge+gi-(v+49*mV))/(20*ms) : volt dge/dt = -ge/(5*ms) : volt dgi/dt = -gi/(10*ms) : volt ''' pycallgraph.start_trace(filter_func=ff_nomagicstateupdater) P = NeuronGroup(N, eqs, threshold= -50 * mV, reset= -60 * mV) pycallgraph.stop_trace() pycallgraph.make_dot_graph('callgraphs/cuba-bigcallgraph-NeuronGroup.__init__.no_magic_state_updater.png') P.v = -60 * mV + 10 * mV * rand(len(P)) Pe = P.subgroup(Ne) Pi = P.subgroup(Ni) pycallgraph.start_trace(filter_func=ff) Ce = Connection(Pe, P, 'ge', weight=1.62 * mV, sparseness=p) pycallgraph.stop_trace() pycallgraph.make_dot_graph('callgraphs/cuba-bigcallgraph-Connection.__init__.png') Ci = Connection(Pi, P, 'gi', weight= -9 * mV, sparseness=p) pycallgraph.start_trace(filter_func=ff) M = SpikeMonitor(P) trace = StateMonitor(P, 'v', record=0) pycallgraph.stop_trace() pycallgraph.make_dot_graph('callgraphs/cuba-bigcallgraph-Monitors.__init__.png') pycallgraph.start_trace(filter_func=ff_prepare) run(.1 * ms) pycallgraph.stop_trace() pycallgraph.make_dot_graph('callgraphs/cuba-bigcallgraph-run-prepare.png') cuba() def cuba(): N = 4000 Ne = int(N * 0.8) Ni = N - Ne p = 80. / N eqs = ''' dv/dt = (ge+gi-(v+49*mV))/(20*ms) : volt dge/dt = -ge/(5*ms) : volt dgi/dt = -gi/(10*ms) : volt ''' pycallgraph.start_trace(filter_func=ff_magicstateupdater) P = NeuronGroup(N, eqs, threshold= -50 * mV, reset= -60 * mV) pycallgraph.stop_trace() pycallgraph.make_dot_graph('callgraphs/cuba-bigcallgraph-NeuronGroup.__init__.magic_state_updater.png') P.v = -60 * mV + 10 * mV * rand(len(P)) Pe = P.subgroup(Ne) Pi = P.subgroup(Ni) Ce = Connection(Pe, P, 'ge', weight=1.62 * mV, sparseness=p) Ci = Connection(Pi, P, 'gi', weight= -9 * mV, sparseness=p) M = SpikeMonitor(P) trace = StateMonitor(P, 'v', record=0) pycallgraph.start_trace(filter_func=ff_noprepare) net = MagicNetwork() pycallgraph.stop_trace() pycallgraph.make_dot_graph('callgraphs/cuba-bigcallgraph-run-noprepare-netinit.png') pycallgraph.start_trace(filter_func=ff_noprepare) net.run(.1 * second) pycallgraph.stop_trace() pycallgraph.make_dot_graph('callgraphs/cuba-bigcallgraph-run-noprepare-nonetinit.png') cuba() def cuba(): eqs = ''' dv/dt = (ge+gi-(v+49*mV))/(20*ms) : volt dge/dt = -ge/(5*ms) : volt dgi/dt = -gi/(10*ms) : volt ''' eqs = Equations(eqs) pycallgraph.start_trace(filter_func=ff) eqs.prepare() pycallgraph.stop_trace() pycallgraph.make_dot_graph('callgraphs/cuba-bigcallgraph-Equations.prepare.png') cuba() brian-1.4.3/dev/benchmarking/cuba/cuba_brian.py000066400000000000000000000016371266062647100214120ustar00rootroot00000000000000''' Very short example program. ''' #import brian_no_units from brian import * from time import time N = 10000 # number of neurons Ne = int(N * 0.8) # excitatory neurons Ni = N - Ne # inhibitory neurons p = 80. / N duration = 1000 * ms eqs = ''' dv/dt = (ge+gi-(v+49*mV))/(20*ms) : volt dge/dt = -ge/(5*ms) : volt dgi/dt = -gi/(10*ms) : volt ''' P = NeuronGroup(N, model=eqs, threshold= -50 * mV, reset= -60 * mV) P.v = -60 * mV + 10 * mV * rand(len(P)) Pe = P.subgroup(Ne) Pi = P.subgroup(Ni) Ce = Connection(Pe, P, 'ge', weight=1.62 * mV, sparseness=p) Ci = Connection(Pi, P, 'gi', weight= -9 * mV, sparseness=p) M = SpikeMonitor(P) trace = StateMonitor(P, 'v', record=0) t1 = time() run(1 * second) t2 = time() print "Simulated in", t2 - t1, "s" print len(M.spikes), "spikes" subplot(211) raster_plot(M) subplot(212) plot(trace.times / ms, trace[0] / mV) show() brian-1.4.3/dev/benchmarking/cuba/cuba_callgraph.py000066400000000000000000000013201266062647100222410ustar00rootroot00000000000000from brian import * set_global_preferences(useweave=True) import cuba_runopts cuba_runopts.duration = 100 * ms import pycallgraph from cuba import * cg_func = 'Connection.do_propagate' def ff(pat): def f(call_stack, module_name, class_name, func_name, full_name): if not 'brian' in module_name: return False for n in call_stack + [full_name]: if pat in n: return True return False return f def cuba(P, Pe, Pi, Ce, Ci, M, net): net.run(duration) c = cubanetwork(4000) pycallgraph.start_trace(filter_func=ff(cg_func)) cuba(*c) pycallgraph.stop_trace() pycallgraph.make_dot_graph('callgraphs/cuba-callgraph-' + cg_func + '.png') brian-1.4.3/dev/benchmarking/cuba/cuba_compile_nounits.py000066400000000000000000000003021266062647100235120ustar00rootroot00000000000000import brian_no_units from brian import * set_global_preferences(useweave=True) import cuba cuba.do_runs('data/brian_cuba_results_compile_nounits.pkl') #print cuba.cuba_average(32000,1,1) brian-1.4.3/dev/benchmarking/cuba/cuba_do_runs.asv000066400000000000000000000010471266062647100221240ustar00rootroot00000000000000function cuba_do_runs(Nvals,repeats,best,use_connections,vary_we) if use_connections fname = 'data/matlab_cuba_results.py'; else fname = 'data/matlab_cuba_nospiking_results.py'; end fid=fopen(fname,'w'); fprintf(fid,'matlab_cuba = []\n'); for N=Nvals fprintf(fid,'cur_result = []\n'); [t,n,r] = cuba_average(N,repeats,best, use_connections); for s=r' fprintf(fid,'cur_result.append((%g,%g))\n',s(1),s(2)); end fprintf(fid,'matlab_cuba.append((%d,%g,%g,cur_result))\n',N,t,n); N end fclose(fid);brian-1.4.3/dev/benchmarking/cuba/cuba_do_runs.m000066400000000000000000000017671266062647100216000ustar00rootroot00000000000000function cuba_do_runs(Nvals,repeats,best,use_connections,vary_we,wevals) fname = 'data/matlab_cuba_results.py'; if not(use_connections) fname = 'data/matlab_cuba_nospiking_results.py'; end if vary_we fname = 'data/matlab_cuba_varywe_results.py'; end fid=fopen(fname,'w'); fprintf(fid,'matlab_cuba = []\n'); if vary_we for we=wevals fprintf(fid,'cur_result = []\n'); [t,n,r] = cuba_average(Nvals,repeats,best, use_connections, we); for s=r' fprintf(fid,'cur_result.append((%g,%g))\n',s(1),s(2)); end fprintf(fid,'matlab_cuba.append((%d,%g,%g,cur_result))\n',Nvals,t,n); we end else for N=Nvals fprintf(fid,'cur_result = []\n'); [t,n,r] = cuba_average(N,repeats,best, use_connections); for s=r' fprintf(fid,'cur_result.append((%g,%g))\n',s(1),s(2)); end fprintf(fid,'matlab_cuba.append((%d,%g,%g,cur_result))\n',N,t,n); N end end fclose(fid);brian-1.4.3/dev/benchmarking/cuba/cuba_nospikes.py000066400000000000000000000003531266062647100221440ustar00rootroot00000000000000import brian_no_units from brian import * set_global_preferences(useweave=True) import cuba_runopts cuba_runopts.cuba_opts['connections'] = False import cuba cuba.do_runs('data/brian_cuba_nospiking_results_compile_nounits.pkl') brian-1.4.3/dev/benchmarking/cuba/cuba_once.py000066400000000000000000000023131266062647100212330ustar00rootroot00000000000000import brian_no_units from brian import * import time #set_global_preferences(useweave=False) print 'Compilation on', get_global_preference('useweave') duration = 2.5 * second Nsyn = 80. we = 9 * mV N = 16000 eqs = ''' dv/dt = (ge+gi-(v+49*mV))/(20*ms) : volt dge/dt = -ge/(5*ms) : volt dgi/dt = -gi/(10*ms) : volt ''' Ne = int(N * 0.8) Ni = N - Ne P = NeuronGroup(N, model=eqs, threshold= -50 * mV, reset= -60 * mV, refractory=5 * ms) P.v = -60 * mV + 10 * mV * rand(len(P)) Pe = P.subgroup(Ne) Pi = P.subgroup(Ni) Ce = Connection(Pe, P, 'ge') Ci = Connection(Pi, P, 'gi') cp = Nsyn / N Ce.connect_random(Pe, P, cp, weight=we) Ci.connect_random(Pi, P, cp, weight= -9 * mV) M = PopulationSpikeCounter(P) P.v = -60 * mV + 10 * mV * rand(len(P)) #Ce.W.freeze() #Ci.W.freeze() run(0 * ms) def f(): tstart = time.time() run(duration) tend = time.time() print 'Time taken', tend - tstart print 'Num spikes', M.nspikes f() #import cProfile as profile #import pstats #profile.run('f()','cuba_once.prof') #stats = pstats.Stats('cuba_once.prof') ##stats.strip_dirs() #stats.sort_stats('cumulative', 'calls') #stats.print_stats(50) brian-1.4.3/dev/benchmarking/cuba/cuba_purepython.py000066400000000000000000000073711266062647100225350ustar00rootroot00000000000000''' A pure Python version of the CUBA example, that reproduces basic Brian principles. Fixed overhead is about 0.35 s. Real time simulation for N=1300. ''' from pylab import * import bisect from time import time from random import sample from scipy import random as scirandom """ Parameters """ N = 1300 # number of neurons Ne = int(N * 0.8) # excitatory neurons Ni = N - Ne # inhibitory neurons mV = ms = 1e-3 # units dt = 0.1 * ms # timestep taum = 20 * ms # membrane time constant taue = 5 * ms taui = 10 * ms p = 80.0 / N # connection probability (80 synapses per neuron) Vt = -1 * mV # threshold = -50+49 Vr = -11 * mV # reset = -60+49 we = 60 * 0.27 / 10 # excitatory weight wi = -20 * 4.5 / 10 # inhibitory weight duration = 1000 * ms """ Equations --------- eqs=''' dv/dt = (ge+gi-(v+49*mV))/(20*ms) : volt dge/dt = -ge/(5*ms) : volt dgi/dt = -gi/(10*ms) : volt ''' This is a linear system, so each update corresponds to multiplying the state matrix by a (3,3) 'update matrix' """ # Update matrix A = array([[exp(-dt / taum), 0, 0], [taue / (taum - taue) * (exp(-dt / taum) - exp(-dt / taue)), exp(-dt / taue), 0], [taui / (taum - taui) * (exp(-dt / taum) - exp(-dt / taui)), 0, exp(-dt / taui)]]).T """ State variables --------------- P=NeuronGroup(4000,model=eqs, threshold=-50*mV,reset=-60*mV) """ S = zeros((3, N)) """ Initialisation -------------- P.v=-60*mV+10*mV*rand(len(P)) """ S[0, :] = rand(N) * (Vt - Vr) + Vr # Potential: uniform between reset and threshold """ Connectivity matrices --------------------- Pe=P.subgroup(3200) # excitatory group Pi=P.subgroup(800) # inhibitory group Ce=Connection(Pe,P,'ge',weight=1.62*mV,sparseness=p) Ci=Connection(Pi,P,'gi',weight=-9*mV,sparseness=p) """ We_target = [] We_weight = [] for _ in range(Ne): k = scirandom.binomial(N, p, 1)[0] target = sample(xrange(N), k) target.sort() We_target.append(array(target)) We_weight.append(array([1.62 * mV] * k)) Wi_target = [] Wi_weight = [] for _ in range(Ni): k = scirandom.binomial(N, p, 1)[0] target = sample(xrange(N), k) target.sort() Wi_target.append(array(target)) Wi_weight.append(array([-9 * mV] * k)) """ Spike monitor ------------- M=SpikeMonitor(P) will contain a list of (i,t), where neuron i spiked at time t. """ spike_monitor = [] # Empty list of spikes """ State monitor ------------- trace=StateMonitor(P,'v',record=0) # record only neuron 0 """ trace = [] # Will contain v(t) for each t (for neuron 0) """ Simulation ---------- run(duration) """ t1 = time() t = 0 * ms while t < duration: # STATE UPDATES S[:] = dot(A, S) # Threshold all_spikes = (S[0, :] > Vt).nonzero()[0] # List of neurons that meet threshold condition # PROPAGATION OF SPIKES # Excitatory neurons spikes = all_spikes[0:bisect.bisect_left(all_spikes, Ne)] for i in spikes: S[1, We_target[i]] += We_weight[i] # Inhibitory neurons spikes = all_spikes[bisect.bisect_left(all_spikes, Ne):] - Ne for i in spikes: S[2, Wi_target[i]] += Wi_weight[i] # Reset neurons after spiking S[0, all_spikes] = Vr # Reset membrane potential # Spike monitor spike_monitor += [(i, t) for i in all_spikes] # State monitor trace.append(S[0, 0]) t += dt t2 = time() print "Simulated in", t2 - t1, "s" print len(spike_monitor), "spikes" """ Plot ---- subplot(211) raster_plot(M) subplot(212) plot(trace.times/ms,trace[0]/mV) show() """ subplot(211) i, t = zip(*spike_monitor) plot(i, t, '.') subplot(212) plot(arange(len(trace)) * dt / ms, array(trace) / mV) show() brian-1.4.3/dev/benchmarking/cuba/cuba_runopts.py000066400000000000000000000005731266062647100220270ustar00rootroot00000000000000from brian import * # long run vals Nvals = [1000, 2000, 4000, 8000, 16000, 32000] N_varycon = 1000 N_varywe = 4000 Nsynvals = [80., 200., 500., 1000.] wevals = [1.62 * mV, 3 * mV, 6 * mV, 9 * mV] duration = 2.5 * second repeats = 10 best = 7 cuba_opts = {'connections':True} # debug run vals #Nvals = [1000, 2000] #duration = 2.5*second #repeats = 3 #best = 2 brian-1.4.3/dev/benchmarking/cuba/cuba_varyconnectivity_compile_nounits.py000066400000000000000000000002661266062647100272230ustar00rootroot00000000000000import brian_no_units from brian import * set_global_preferences(useweave=True) import cuba cuba.do_runs_varyconnectivity('data/brian_cuba_varycon_results_compile_nounits.pkl') brian-1.4.3/dev/benchmarking/cuba/cuba_varywe_compile_nounits.py000066400000000000000000000003441266062647100251150ustar00rootroot00000000000000import brian_no_units from brian import * set_global_preferences(useweave=True) import cuba from cuba_runopts import N_varywe cuba.do_runs_varywe('data/brian_cuba_varywe_results_compile_nounits_' + str(N_varywe) + '.pkl') brian-1.4.3/dev/benchmarking/cuba/data/000077500000000000000000000000001266062647100176555ustar00rootroot00000000000000brian-1.4.3/dev/benchmarking/cuba/data/brian_cuba_nospiking_results_compile_nounits.pkl000066400000000000000000000021521266062647100316030ustar00rootroot00000000000000€]q((Mècnumpy.core.multiarray scalar qcnumpy dtype qUf8qKK‡qRq(KUG@+€JŸ¾†q?G@+—PJj¾†q@G@+¸QèJŽ¾†qAG@,ÀJr¾†qBetqC(M€>hhU·mÛþN>@qD†qERqFhhU’$I’þù&AqG†qHRqI]qJ(G@=xdJê| †qKG@=˜`J} †qLG@=˜dJÿ| †qMG@>gï J } †qNG@>|(ôJ} †qOG@>‡ïœJ} †qPG@>‡ï Jå| †qQetqR(M}hhU’$IJqR@qS†qTRqUhhU·mÛ¶òù6AqV†qWRqX]qY(G@RiûèJóù†qZG@RpJ)ú†q[G@RpõÃJéù†q\G@Rq%Jäù†q]G@Rs Jõù†q^G@Rt1J#ú†q_G@RvJ¢ù†q`etqae.brian-1.4.3/dev/benchmarking/cuba/data/brian_cuba_results.pkl000066400000000000000000000016371266062647100242420ustar00rootroot00000000000000€]q((Mècnumpy.core.multiarray scalar qcnumpy dtype qUf8qKK‡qRq(KUG@hhU’$I”OàK@qD†qERqFhhUÛ¶mÛÖ^ø@qG†qHRqI]qJ(G@KJ¡k†qKG@K„”J-w†qLG@KÀJϘ†qMG@KÐJR‹†qNG@KðJŽ‹†qOG@L" JJ3††qPG@Lì0JΆqQetqRe.brian-1.4.3/dev/benchmarking/cuba/data/brian_cuba_results_allopts.pkl000066400000000000000000000015401266062647100257710ustar00rootroot00000000000000€]q((Mècnumpy.core.multiarray scalar qcnumpy dtype qUf8qKK‡qRq(KUG@:¬0K†q?G@@HbK†q@G@@‚ JK†qAG@@ˆbK†qBetqC(M€>hhUI’$¸ÁI@qD†qERqFhhUqG†qHRqI]qJ(G@I)ûèK†qKG@I©ûæK†qLG@I¹ûèK†qMG@IÞzK†qNG@Iô”K†qOG@IõãTK†qPG@IöK†qQetqRe.brian-1.4.3/dev/benchmarking/cuba/data/brian_cuba_results_compile_nounits.pkl000066400000000000000000000021161266062647100275220ustar00rootroot00000000000000€]q((Mècnumpy.core.multiarray scalar qcnumpy dtype qUf8qKK‡qRq(KUG@-÷ÎØJ·¿†q?G@.JC¶†q@G@.J¯·†qAG@.1(JX¶†qBetqC(M€>hhU’$I‚J`Û†q[G@Q[ JÀÀ†q\G@QfJl͆q]G@Q¨JÒ®†q^G@QÌýôJÇÁ†q_G@RiûèJdˆq`etqae.brian-1.4.3/dev/benchmarking/cuba/data/brian_cuba_varycon_results_compile_nounits.pkl000066400000000000000000000013421266062647100312630ustar00rootroot00000000000000€]q((Mècnumpy.core.multiarray scalar qcnumpy dtype qUf8qKK‡qRq(KUG@!@M§ †q?G@!wÎØMÛ †q@G@!xQèM> †qAG@!ˆ1(MˆqBetqCe.brian-1.4.3/dev/benchmarking/cuba/data/brian_cuba_varywe_results_compile_nounits.pkl000066400000000000000000000013421266062647100311170ustar00rootroot00000000000000€]q((Mècnumpy.core.multiarray scalar qcnumpy dtype qUf8qKK‡qRq(KUG@ G®Myl†q?G@ wÎØMp†q@G@ €M‰o†qAG@ bPMÿm†qBetqCe.brian-1.4.3/dev/benchmarking/cuba/data/brian_cuba_varywe_results_compile_nounits_1000.pkl000066400000000000000000000013421266062647100315570ustar00rootroot00000000000000€]q((Mècnumpy.core.multiarray scalar qcnumpy dtype qUf8qKK‡qRq(KUG@Oß0Mk†q?G@Oß@M']†q@G@`AMp[†qAG@o°MLg†qBetqCe.brian-1.4.3/dev/benchmarking/cuba/data/brian_cuba_varywe_results_compile_nounits_4000.pkl000066400000000000000000000014141266062647100315620ustar00rootroot00000000000000€]q((M cnumpy.core.multiarray scalar qcnumpy dtype qUf8qKK‡qRq(KUG@D¢ HJÅz†q?G@E ûæJNˆ†q@G@E÷ïžJ~†qAG@R±ûçJ¿c†qBetqCe.brian-1.4.3/dev/benchmarking/cuba/data/cpp_cuba_nospiking_results.py000066400000000000000000000035431266062647100256520ustar00rootroot00000000000000# no spiking cpp_cuba = [] cur_result = [] cur_result.append((0.625, 47119)) cur_result.append((0.625, 47127)) cur_result.append((0.625, 47120)) cur_result.append((0.625, 47128)) cur_result.append((0.625, 47144)) cur_result.append((0.625, 47118)) cur_result.append((0.625, 47124)) cpp_cuba.append((1000, 0.625, 47125, cur_result)) cur_result = [] cur_result.append((1.25, 94243)) cur_result.append((1.25, 94234)) cur_result.append((1.25, 94231)) cur_result.append((1.25, 94241)) cur_result.append((1.25, 94262)) cur_result.append((1.265, 94254)) cur_result.append((1.265, 94240)) cpp_cuba.append((2000, 1.25429, 94243, cur_result)) cur_result = [] cur_result.append((2.5, 188481)) cur_result.append((2.5, 188441)) cur_result.append((2.5, 188469)) cur_result.append((2.5, 188500)) cur_result.append((2.515, 188517)) cur_result.append((2.515, 188449)) cur_result.append((2.516, 188492)) cpp_cuba.append((4000, 2.50657, 188478, cur_result)) cur_result = [] cur_result.append((5.015, 376942)) cur_result.append((5.015, 376926)) cur_result.append((5.016, 376912)) cur_result.append((5.016, 376986)) cur_result.append((5.016, 376924)) cur_result.append((5.016, 376915)) cur_result.append((5.031, 376889)) cpp_cuba.append((8000, 5.01786, 376927, cur_result)) cur_result = [] cur_result.append((10, 753830)) cur_result.append((10, 753920)) cur_result.append((10.015, 753800)) cur_result.append((10.015, 753880)) cur_result.append((10.015, 753832)) cur_result.append((10.016, 753864)) cur_result.append((10.016, 753908)) cpp_cuba.append((16000, 10.011, 753862, cur_result)) cur_result = [] cur_result.append((19.985, 1507664)) cur_result.append((19.985, 1507734)) cur_result.append((19.985, 1507621)) cur_result.append((19.999, 1507708)) cur_result.append((20, 1507772)) cur_result.append((20.015, 1507724)) cur_result.append((20.015, 1507749)) cpp_cuba.append((32000, 19.9977, 1507710, cur_result)) brian-1.4.3/dev/benchmarking/cuba/data/cpp_cuba_nospiking_results_matrix.py000066400000000000000000000036111266062647100272320ustar00rootroot00000000000000# no spiking # state updater matrix cpp_cuba = [] cur_result = [] cur_result.append((0.546, 47081)) cur_result.append((0.546, 47065)) cur_result.append((0.547, 47081)) cur_result.append((0.547, 47069)) cur_result.append((0.547, 47070)) cur_result.append((0.547, 47063)) cur_result.append((0.562, 47068)) cpp_cuba.append((1000, 0.548857, 47071, cur_result)) cur_result = [] cur_result.append((1.109, 94155)) cur_result.append((1.109, 94133)) cur_result.append((1.11, 94146)) cur_result.append((1.11, 94127)) cur_result.append((1.125, 94123)) cur_result.append((1.125, 94128)) cur_result.append((1.125, 94159)) cpp_cuba.append((2000, 1.11614, 94138, cur_result)) cur_result = [] cur_result.append((2.218, 188277)) cur_result.append((2.234, 188288)) cur_result.append((2.234, 188255)) cur_result.append((2.234, 188288)) cur_result.append((2.234, 188281)) cur_result.append((2.235, 188278)) cur_result.append((2.235, 188293)) cpp_cuba.append((4000, 2.232, 188280, cur_result)) cur_result = [] cur_result.append((4.454, 376575)) cur_result.append((4.468, 376564)) cur_result.append((4.468, 376558)) cur_result.append((4.469, 376555)) cur_result.append((4.469, 376577)) cur_result.append((4.469, 376572)) cur_result.append((4.469, 376519)) cpp_cuba.append((8000, 4.46657, 376560, cur_result)) cur_result = [] cur_result.append((8.922, 753101)) cur_result.append((8.937, 753093)) cur_result.append((8.937, 753125)) cur_result.append((8.938, 753133)) cur_result.append((8.938, 753075)) cur_result.append((8.953, 753130)) cur_result.append((8.953, 753072)) cpp_cuba.append((16000, 8.93971, 753104, cur_result)) cur_result = [] cur_result.append((17.86, 1506337)) cur_result.append((17.875, 1506294)) cur_result.append((17.89, 1506298)) cur_result.append((17.89, 1506186)) cur_result.append((17.891, 1506187)) cur_result.append((17.891, 1506249)) cur_result.append((17.906, 1506222)) cpp_cuba.append((32000, 17.8861, 1506253, cur_result)) brian-1.4.3/dev/benchmarking/cuba/data/cpp_cuba_results.py000066400000000000000000000035371266062647100235740ustar00rootroot00000000000000cpp_cuba = [] cur_result = [] cur_result.append((1.218, 13078)) cur_result.append((1.218, 14821)) cur_result.append((1.219, 13974)) cur_result.append((1.219, 13459)) cur_result.append((1.219, 12956)) cur_result.append((1.234, 12610)) cur_result.append((1.234, 13118)) cpp_cuba.append((1000, 1.223, 13430, cur_result)) cur_result = [] cur_result.append((2.453, 27186)) cur_result.append((2.453, 26968)) cur_result.append((2.453, 30628)) cur_result.append((2.453, 26832)) cur_result.append((2.453, 27972)) cur_result.append((2.453, 26748)) cur_result.append((2.454, 28212)) cpp_cuba.append((2000, 2.45314, 27792, cur_result)) cur_result = [] cur_result.append((4.891, 50997)) cur_result.append((4.907, 58062)) cur_result.append((4.907, 53690)) cur_result.append((4.922, 58489)) cur_result.append((4.922, 55833)) cur_result.append((4.922, 52216)) cur_result.append((4.922, 56328)) cpp_cuba.append((4000, 4.91329, 55087, cur_result)) cur_result = [] cur_result.append((9.812, 113406)) cur_result.append((9.813, 114724)) cur_result.append((9.828, 111036)) cur_result.append((9.828, 117508)) cur_result.append((9.828, 116590)) cur_result.append((9.828, 111868)) cur_result.append((9.828, 110196)) cpp_cuba.append((8000, 9.82357, 113618, cur_result)) cur_result = [] cur_result.append((19.641, 226950)) cur_result.append((19.641, 222833)) cur_result.append((19.657, 225588)) cur_result.append((19.672, 227887)) cur_result.append((19.672, 227507)) cur_result.append((19.672, 228602)) cur_result.append((19.687, 227276)) cpp_cuba.append((16000, 19.6631, 226663, cur_result)) cur_result = [] cur_result.append((39.297, 461256)) cur_result.append((39.329, 437449)) cur_result.append((39.406, 455786)) cur_result.append((39.437, 453378)) cur_result.append((39.469, 440361)) cur_result.append((39.515, 460886)) cur_result.append((39.515, 436913)) cpp_cuba.append((32000, 39.424, 449432, cur_result)) brian-1.4.3/dev/benchmarking/cuba/data/cpp_cuba_results_matrix.py000066400000000000000000000035541266062647100251570ustar00rootroot00000000000000# state updater matrix cpp_cuba = [] cur_result = [] cur_result.append((0.515, 13164)) cur_result.append((0.516, 14274)) cur_result.append((0.531, 13321)) cur_result.append((0.531, 15238)) cur_result.append((0.531, 14255)) cur_result.append((0.531, 15068)) cur_result.append((0.531, 14269)) cpp_cuba.append((1000, 0.526571, 14227, cur_result)) cur_result = [] cur_result.append((1.062, 27479)) cur_result.append((1.062, 29666)) cur_result.append((1.062, 28035)) cur_result.append((1.062, 26050)) cur_result.append((1.062, 27550)) cur_result.append((1.062, 27269)) cur_result.append((1.062, 26390)) cpp_cuba.append((2000, 1.062, 27491, cur_result)) cur_result = [] cur_result.append((2.125, 57944)) cur_result.append((2.125, 54110)) cur_result.append((2.14, 55195)) cur_result.append((2.14, 56090)) cur_result.append((2.141, 59532)) cur_result.append((2.141, 54270)) cur_result.append((2.141, 54660)) cpp_cuba.append((4000, 2.13614, 55971, cur_result)) cur_result = [] cur_result.append((4.281, 115071)) cur_result.append((4.296, 114289)) cur_result.append((4.297, 111168)) cur_result.append((4.297, 110257)) cur_result.append((4.297, 110280)) cur_result.append((4.297, 109854)) cur_result.append((4.297, 108158)) cpp_cuba.append((8000, 4.29457, 111296, cur_result)) cur_result = [] cur_result.append((8.609, 227239)) cur_result.append((8.609, 226572)) cur_result.append((8.609, 227187)) cur_result.append((8.61, 221087)) cur_result.append((8.61, 218919)) cur_result.append((8.625, 223704)) cur_result.append((8.625, 230842)) cpp_cuba.append((16000, 8.61386, 225078, cur_result)) cur_result = [] cur_result.append((17.25, 435872)) cur_result.append((17.281, 445224)) cur_result.append((17.281, 443977)) cur_result.append((17.297, 440817)) cur_result.append((17.313, 457360)) cur_result.append((17.328, 445058)) cur_result.append((17.328, 436900)) cpp_cuba.append((32000, 17.2969, 443601, cur_result)) brian-1.4.3/dev/benchmarking/cuba/data/cpp_cuba_results_matrix_efficient.py000066400000000000000000000035441266062647100271720ustar00rootroot00000000000000# state updater efficient matrix cpp_cuba = [] cur_result = [] cur_result.append((0.484, 12630)) cur_result.append((0.484, 12971)) cur_result.append((0.485, 12067)) cur_result.append((0.485, 11022)) cur_result.append((0.5, 12467)) cur_result.append((0.5, 14010)) cur_result.append((0.5, 14782)) cpp_cuba.append((1000, 0.491143, 12849, cur_result)) cur_result = [] cur_result.append((1, 28892)) cur_result.append((1, 28788)) cur_result.append((1, 26385)) cur_result.append((1, 28657)) cur_result.append((1.015, 27457)) cur_result.append((1.015, 28723)) cur_result.append((1.016, 28258)) cpp_cuba.append((2000, 1.00657, 28165, cur_result)) cur_result = [] cur_result.append((2.015, 56620)) cur_result.append((2.015, 59338)) cur_result.append((2.015, 56192)) cur_result.append((2.016, 54668)) cur_result.append((2.016, 54382)) cur_result.append((2.031, 55674)) cur_result.append((2.031, 53102)) cpp_cuba.append((4000, 2.01986, 55710, cur_result)) cur_result = [] cur_result.append((4.078, 111468)) cur_result.append((4.078, 106301)) cur_result.append((4.094, 109214)) cur_result.append((4.109, 113523)) cur_result.append((4.109, 110819)) cur_result.append((4.11, 113900)) cur_result.append((4.11, 111796)) cpp_cuba.append((8000, 4.09829, 111003, cur_result)) cur_result = [] cur_result.append((8.188, 221741)) cur_result.append((8.188, 223291)) cur_result.append((8.203, 222971)) cur_result.append((8.203, 222729)) cur_result.append((8.203, 223177)) cur_result.append((8.203, 224072)) cur_result.append((8.203, 226810)) cpp_cuba.append((16000, 8.19871, 223541, cur_result)) cur_result = [] cur_result.append((16.375, 446596)) cur_result.append((16.391, 447211)) cur_result.append((16.391, 465655)) cur_result.append((16.406, 446498)) cur_result.append((16.406, 453930)) cur_result.append((16.422, 444214)) cur_result.append((16.437, 458857)) cpp_cuba.append((32000, 16.404, 451851, cur_result)) brian-1.4.3/dev/benchmarking/cuba/data/cpp_cuba_varywe_results.py000066400000000000000000000024421266062647100251630ustar00rootroot00000000000000# vary weights # state updater matrix cpp_cuba = [] cur_result = [] cur_result.append((0.718, 13579)) cur_result.append((0.719, 15140)) cur_result.append((0.719, 13051)) cur_result.append((0.734, 13342)) cur_result.append((0.734, 14620)) cur_result.append((0.735, 13061)) cur_result.append((0.735, 12719)) # we = 1.62 cpp_cuba.append((1000, 0.727714, 13644, cur_result)) cur_result = [] cur_result.append((0.734, 21315)) cur_result.append((0.734, 19045)) cur_result.append((0.734, 18084)) cur_result.append((0.734, 16624)) cur_result.append((0.735, 20383)) cur_result.append((0.735, 20307)) cur_result.append((0.735, 16269)) # we = 2.4 cpp_cuba.append((1000, 0.734429, 18861, cur_result)) cur_result = [] cur_result.append((0.735, 19993)) cur_result.append((0.735, 25975)) cur_result.append((0.75, 23947)) cur_result.append((0.75, 24037)) cur_result.append((0.75, 27073)) cur_result.append((0.75, 27273)) cur_result.append((0.75, 23499)) # we = 2.9 cpp_cuba.append((1000, 0.745714, 24542, cur_result)) cur_result = [] cur_result.append((0.734, 24987)) cur_result.append((0.734, 25586)) cur_result.append((0.734, 21295)) cur_result.append((0.735, 26025)) cur_result.append((0.75, 23231)) cur_result.append((0.75, 27206)) cur_result.append((0.75, 28540)) # we = 3.2 cpp_cuba.append((1000, 0.741, 25267, cur_result)) brian-1.4.3/dev/benchmarking/cuba/data/cpp_cuba_varywe_results_matrix.py000066400000000000000000000024451266062647100265520ustar00rootroot00000000000000# vary weights # state updater matrix cpp_cuba = [] cur_result = [] cur_result.append((0.719, 11483)) cur_result.append((0.719, 12639)) cur_result.append((0.719, 13725)) cur_result.append((0.734, 14989)) cur_result.append((0.734, 11584)) cur_result.append((0.735, 14212)) cur_result.append((0.735, 12722)) # we = 1.62 cpp_cuba.append((1000, 0.727857, 13050, cur_result)) cur_result = [] cur_result.append((0.718, 14607)) cur_result.append((0.734, 19707)) cur_result.append((0.734, 18944)) cur_result.append((0.734, 19395)) cur_result.append((0.735, 16693)) cur_result.append((0.735, 15263)) cur_result.append((0.735, 15468)) # we = 2.4 cpp_cuba.append((1000, 0.732143, 17153, cur_result)) cur_result = [] cur_result.append((0.734, 23597)) cur_result.append((0.734, 21946)) cur_result.append((0.734, 24026)) cur_result.append((0.735, 23047)) cur_result.append((0.735, 25863)) cur_result.append((0.75, 24000)) cur_result.append((0.75, 25434)) # we = 2.9 cpp_cuba.append((1000, 0.738857, 23987, cur_result)) cur_result = [] cur_result.append((0.734, 20868)) cur_result.append((0.75, 27938)) cur_result.append((0.75, 29506)) cur_result.append((0.75, 21027)) cur_result.append((0.75, 27691)) cur_result.append((0.75, 28883)) cur_result.append((0.75, 28942)) # we = 3.2 cpp_cuba.append((1000, 0.747714, 26407, cur_result)) brian-1.4.3/dev/benchmarking/cuba/data/cpp_cuba_varywe_results_matrix_4000.py000066400000000000000000000024611266062647100272130ustar00rootroot00000000000000# vary weights # state updater matrix cpp_cuba = [] cur_result = [] cur_result.append((3.015, 56409)) cur_result.append((3.031, 57891)) cur_result.append((3.032, 55646)) cur_result.append((3.046, 55931)) cur_result.append((3.047, 56880)) cur_result.append((3.047, 58541)) cur_result.append((3.047, 55876)) # we = 1.62 cpp_cuba.append((4000, 3.03786, 56739, cur_result)) cur_result = [] cur_result.append((3.015, 75145)) cur_result.append((3.047, 77944)) cur_result.append((3.078, 84491)) cur_result.append((3.078, 73464)) cur_result.append((3.078, 74534)) cur_result.append((3.093, 75515)) cur_result.append((3.094, 79403)) # we = 2.4 cpp_cuba.append((4000, 3.069, 77213, cur_result)) cur_result = [] cur_result.append((3.063, 100789)) cur_result.append((3.078, 87112)) cur_result.append((3.079, 103179)) cur_result.append((3.109, 98336)) cur_result.append((3.11, 109254)) cur_result.append((3.125, 98400)) cur_result.append((3.156, 103958)) # we = 2.9 cpp_cuba.append((4000, 3.10286, 100146, cur_result)) cur_result = [] cur_result.append((3.094, 98138)) cur_result.append((3.14, 115752)) cur_result.append((3.141, 108803)) cur_result.append((3.141, 114630)) cur_result.append((3.141, 118636)) cur_result.append((3.156, 101919)) cur_result.append((3.156, 113417)) # we = 3.2 cpp_cuba.append((4000, 3.13843, 110185, cur_result)) brian-1.4.3/dev/benchmarking/cuba/data/cpp_noopt_cuba_results.py000066400000000000000000000035311266062647100250050ustar00rootroot00000000000000cpp_cuba = [] cur_result = [] cur_result.append((1.203, 27348)) cur_result.append((1.218, 27742)) cur_result.append((1.218, 24746)) cur_result.append((1.218, 27684)) cur_result.append((1.218, 26823)) cur_result.append((1.219, 26142)) cur_result.append((1.219, 27053)) cpp_cuba.append((1000, 1.21614, 26791, cur_result)) cur_result = [] cur_result.append((2.422, 43313)) cur_result.append((2.437, 41318)) cur_result.append((2.453, 40244)) cur_result.append((2.453, 42605)) cur_result.append((2.453, 41910)) cur_result.append((2.453, 38561)) cur_result.append((2.453, 41332)) cpp_cuba.append((2000, 2.44629, 41326, cur_result)) cur_result = [] cur_result.append((4.922, 57502)) cur_result.append((4.922, 56403)) cur_result.append((4.922, 60314)) cur_result.append((4.922, 61694)) cur_result.append((4.937, 60793)) cur_result.append((4.938, 57913)) cur_result.append((4.938, 57660)) cpp_cuba.append((4000, 4.92871, 58897, cur_result)) cur_result = [] cur_result.append((9.922, 76993)) cur_result.append((9.922, 78815)) cur_result.append((9.922, 83013)) cur_result.append((9.937, 79851)) cur_result.append((9.937, 78007)) cur_result.append((9.937, 84676)) cur_result.append((9.937, 79907)) cpp_cuba.append((8000, 9.93057, 80180, cur_result)) cur_result = [] cur_result.append((19.843, 106264)) cur_result.append((19.859, 108549)) cur_result.append((19.875, 109513)) cur_result.append((19.89, 108580)) cur_result.append((19.891, 109881)) cur_result.append((19.891, 105568)) cur_result.append((19.891, 108225)) cpp_cuba.append((16000, 19.8771, 108082, cur_result)) cur_result = [] cur_result.append((40.047, 140000)) cur_result.append((40.063, 144434)) cur_result.append((40.078, 145460)) cur_result.append((40.078, 146447)) cur_result.append((40.078, 145407)) cur_result.append((40.079, 144632)) cur_result.append((40.093, 145885)) cpp_cuba.append((32000, 40.0737, 144609, cur_result)) brian-1.4.3/dev/benchmarking/cuba/data/cuba_allopts.py000066400000000000000000000047421266062647100227060ustar00rootroot00000000000000import brian_no_units from brian import * import time import pickle from operator import itemgetter from cuba_runopts import * set_global_preferences(useweave=True) def cubanetwork(N): Vr = -49 * mV invtauv = 1 / (20 * ms) invtauge = 1 / (5 * ms) invtaugi = 1 / (10 * ms) eqs = ''' dv/dt = (ge+gi-(v-Vr))*invtauv : volt dge/dt = -ge*invtauge : volt dgi/dt = -gi*invtaugi : volt ''' Ne = int(N * 0.8) Ni = N - Ne P = NeuronGroup(N, model=eqs, threshold= -50 * mV, reset= -60 * mV, compile=True, freeze=True) P.v = -60 * mV + 10 * mV * rand(len(P)) Pe = P.subgroup(Ne) Pi = P.subgroup(Ni) Ce = Connection(Pe, P, 'ge') Ci = Connection(Pi, P, 'gi') Ce.connect_random(Pe, P, 0.02, weight=1.62 * mV) Ci.connect_random(Pi, P, 0.02, weight= -9 * mV) net = Network(P, Ce, Ci) net.run(0 * ms) return P, Pe, Pi, Ce, Ci, net def cuba(P, Pe, Pi, Ce, Ci, net): reinit_default_clock() P.v = -60 * mV + 10 * mV * rand(len(P)) Ce.__init__(Pe, P, 'ge') Ci.__init__(Pi, P, 'gi') Ce.connect_random(Pe, P, 0.02, weight=1.62 * mV) Ci.connect_random(Pi, P, 0.02, weight= -9 * mV) tstart = time.time() net.run(duration) tend = time.time() return tend - tstart, 0 def cuba_average(N, repeats, best): cubadata = cubanetwork(N) cubaruns = [cuba(*cubadata) for i in range(repeats)] print 'Finished run', N cubaruns.sort(key=itemgetter(0)) cubaruns = cubaruns[:best] t, ns = zip(*cubaruns) t = array(t) ns = array(ns) return N, mean(t), mean(ns), cubaruns def cuba_runs(Nvals, repeats, best): return [cuba_average(N, repeats, best) for N in Nvals] def do_runs(fname='brian_cuba_results_allopts.pkl'): ca = cuba_runs(Nvals, repeats, best) output = open(fname, 'wb') pickle.dump(ca, output, -1) output.close() return ca if __name__ == '__main__': print do_runs() # cubadata = cubanetwork(32000) # print cuba(*cubadata) # def f(): # cubadata = cubanetwork(4000) # print cuba(*cubadata) # f() # import cProfile as profile # profile.run('f()') # import hotshot, hotshot.stats # prof = hotshot.Profile('cuba_allopts.prof') # prof.runcall(f) # stats = hotshot.stats.load("cuba_allopts.prof") # stats.strip_dirs() # stats.sort_stats('cumulative','time','calls') # stats.print_stats() brian-1.4.3/dev/benchmarking/cuba/data/matlab_cuba_nospiking_results.py000066400000000000000000000036571266062647100263360ustar00rootroot00000000000000matlab_cuba = [] cur_result = [] cur_result.append((2.125, 47077)) cur_result.append((2.14, 47077)) cur_result.append((2.14, 47086)) cur_result.append((2.14, 47078)) cur_result.append((2.14, 47073)) cur_result.append((2.141, 47077)) cur_result.append((2.141, 47065)) matlab_cuba.append((1000, 2.13814, 47076.1, cur_result)) cur_result = [] cur_result.append((4.109, 94146)) cur_result.append((4.109, 94134)) cur_result.append((4.109, 94136)) cur_result.append((4.109, 94142)) cur_result.append((4.109, 94139)) cur_result.append((4.11, 94140)) cur_result.append((4.11, 94150)) matlab_cuba.append((2000, 4.10929, 94141, cur_result)) cur_result = [] cur_result.append((8.031, 188295)) cur_result.append((8.031, 188291)) cur_result.append((8.031, 188255)) cur_result.append((8.031, 188259)) cur_result.append((8.032, 188287)) cur_result.append((8.032, 188287)) cur_result.append((8.032, 188277)) matlab_cuba.append((4000, 8.03143, 188279, cur_result)) cur_result = [] cur_result.append((15.937, 376549)) cur_result.append((15.937, 376541)) cur_result.append((15.938, 376496)) cur_result.append((15.938, 376582)) cur_result.append((15.938, 376564)) cur_result.append((15.938, 376578)) cur_result.append((15.953, 376596)) matlab_cuba.append((8000, 15.9399, 376558, cur_result)) cur_result = [] cur_result.append((32.016, 753127)) cur_result.append((32.203, 753118)) cur_result.append((32.234, 753119)) cur_result.append((32.235, 753116)) cur_result.append((32.235, 753170)) cur_result.append((32.25, 753125)) cur_result.append((32.265, 753121)) matlab_cuba.append((16000, 32.2054, 753128, cur_result)) cur_result = [] cur_result.append((72.687, 1.50619e+006)) cur_result.append((72.704, 1.50632e+006)) cur_result.append((72.765, 1.50623e+006)) cur_result.append((72.765, 1.5062e+006)) cur_result.append((72.782, 1.50625e+006)) cur_result.append((72.796, 1.50621e+006)) cur_result.append((72.797, 1.5062e+006)) matlab_cuba.append((32000, 72.7566, 1.50623e+006, cur_result)) brian-1.4.3/dev/benchmarking/cuba/data/matlab_cuba_results.py000066400000000000000000000036071266062647100242500ustar00rootroot00000000000000matlab_cuba = [] cur_result = [] cur_result.append((3.218, 12575)) cur_result.append((3.219, 12523)) cur_result.append((3.219, 12556)) cur_result.append((3.234, 13424)) cur_result.append((3.234, 12273)) cur_result.append((3.234, 13285)) cur_result.append((3.234, 12778)) matlab_cuba.append((1000, 3.22743, 12773.4, cur_result)) cur_result = [] cur_result.append((5.562, 25040)) cur_result.append((5.563, 26868)) cur_result.append((5.563, 25649)) cur_result.append((5.578, 28446)) cur_result.append((5.578, 26913)) cur_result.append((5.578, 28090)) cur_result.append((5.593, 29634)) matlab_cuba.append((2000, 5.57357, 27234.3, cur_result)) cur_result = [] cur_result.append((10.235, 56650)) cur_result.append((10.281, 50746)) cur_result.append((10.312, 53043)) cur_result.append((10.313, 53601)) cur_result.append((10.313, 53706)) cur_result.append((10.344, 56080)) cur_result.append((10.344, 53358)) matlab_cuba.append((4000, 10.306, 53883.4, cur_result)) cur_result = [] cur_result.append((19.375, 111075)) cur_result.append((19.579, 113625)) cur_result.append((19.703, 107771)) cur_result.append((19.813, 110982)) cur_result.append((19.875, 109932)) cur_result.append((20.219, 112037)) cur_result.append((20.422, 108886)) matlab_cuba.append((8000, 19.8551, 110615, cur_result)) cur_result = [] cur_result.append((38.219, 222401)) cur_result.append((38.234, 219630)) cur_result.append((38.25, 222039)) cur_result.append((38.25, 222061)) cur_result.append((38.266, 222187)) cur_result.append((38.266, 219204)) cur_result.append((38.281, 219177)) matlab_cuba.append((16000, 38.2523, 220957, cur_result)) cur_result = [] cur_result.append((85.531, 443881)) cur_result.append((85.562, 440329)) cur_result.append((85.578, 439382)) cur_result.append((85.765, 442683)) cur_result.append((85.86, 443141)) cur_result.append((85.875, 445420)) cur_result.append((85.891, 440660)) matlab_cuba.append((32000, 85.7231, 442214, cur_result)) brian-1.4.3/dev/benchmarking/cuba/data/matlab_cuba_varywe_results.py000066400000000000000000000023531266062647100256420ustar00rootroot00000000000000matlab_cuba = [] cur_result = [] cur_result.append((4.532, 12789)) cur_result.append((4.547, 13436)) cur_result.append((4.562, 14370)) cur_result.append((4.562, 13462)) cur_result.append((4.563, 14667)) cur_result.append((4.563, 12155)) cur_result.append((4.579, 13731)) matlab_cuba.append((1000, 4.55829, 13515.7, cur_result)) cur_result = [] cur_result.append((4.609, 18324)) cur_result.append((4.625, 18587)) cur_result.append((4.64, 18563)) cur_result.append((4.641, 17520)) cur_result.append((4.641, 18806)) cur_result.append((4.641, 17696)) cur_result.append((4.656, 18252)) matlab_cuba.append((1000, 4.63614, 18249.7, cur_result)) cur_result = [] cur_result.append((4.609, 20156)) cur_result.append((4.625, 19513)) cur_result.append((4.672, 21783)) cur_result.append((4.672, 21492)) cur_result.append((4.687, 21224)) cur_result.append((4.687, 23118)) cur_result.append((4.687, 24035)) matlab_cuba.append((1000, 4.66271, 21617.3, cur_result)) cur_result = [] cur_result.append((4.609, 18235)) cur_result.append((4.609, 22806)) cur_result.append((4.64, 22725)) cur_result.append((4.657, 26908)) cur_result.append((4.687, 25605)) cur_result.append((4.687, 25845)) cur_result.append((4.688, 30786)) matlab_cuba.append((1000, 4.65386, 24701.4, cur_result)) brian-1.4.3/dev/benchmarking/cuba/data/matlab_cuba_varywe_results_4000.py000066400000000000000000000024071266062647100263050ustar00rootroot00000000000000matlab_cuba = [] cur_result = [] cur_result.append((14.812, 49881)) cur_result.append((14.828, 54379)) cur_result.append((14.86, 55478)) cur_result.append((14.922, 59780)) cur_result.append((14.953, 60881)) cur_result.append((15.031, 54989)) cur_result.append((15.203, 54739)) matlab_cuba.append((4000, 14.9441, 55732.4, cur_result)) cur_result = [] cur_result.append((15.062, 72498)) cur_result.append((15.062, 76315)) cur_result.append((15.078, 69005)) cur_result.append((15.093, 71171)) cur_result.append((15.109, 77631)) cur_result.append((15.125, 76631)) cur_result.append((15.172, 75326)) matlab_cuba.append((4000, 15.1001, 74082.4, cur_result)) cur_result = [] cur_result.append((15.156, 98313)) cur_result.append((15.203, 89282)) cur_result.append((15.25, 99827)) cur_result.append((15.281, 93991)) cur_result.append((15.328, 96570)) cur_result.append((15.422, 93928)) cur_result.append((15.547, 88829)) matlab_cuba.append((4000, 15.3124, 94391.4, cur_result)) cur_result = [] cur_result.append((15.062, 100487)) cur_result.append((15.109, 98760)) cur_result.append((15.11, 102197)) cur_result.append((15.14, 100652)) cur_result.append((15.157, 101606)) cur_result.append((15.25, 112632)) cur_result.append((15.25, 102386)) matlab_cuba.append((4000, 15.154, 102674, cur_result)) brian-1.4.3/dev/benchmarking/cuba/data_dan_laptop/000077500000000000000000000000001266062647100220565ustar00rootroot00000000000000brian-1.4.3/dev/benchmarking/cuba/data_dan_laptop/brian_cuba_nospiking_results_compile_nounits.pkl000066400000000000000000000016731266062647100340130ustar00rootroot00000000000000€]q((Mècnumpy.core.multiarray scalar qcnumpy dtype qUf8qKK‡qRq(KUG@9[çpJg¾†q?G@9d”J¡¾†q@G@9oß<J¾†qAG@9p ÄJ…¾†qBetqC(M€>hhUnÛ¶{KŠH@qD†qERqFhhUnÛ¶móù&AqG†qHRqI]qJ(G@Gb JJ} †qKG@H‹çlJ} †qLG@H±ë„Jä| †qMG@Hº¬J!} †qNG@HÈbJù| †qOG@HÑë†JÚ| †qPG@HÔ”Jç| †qQetqRe.brian-1.4.3/dev/benchmarking/cuba/data_dan_laptop/brian_cuba_results.pkl000066400000000000000000000016371266062647100264430ustar00rootroot00000000000000€]q((Mècnumpy.core.multiarray scalar qcnumpy dtype qUf8qKK‡qRq(KUG@hhU’$I”OàK@qD†qERqFhhUÛ¶mÛÖ^ø@qG†qHRqI]qJ(G@KJ¡k†qKG@K„”J-w†qLG@KÀJϘ†qMG@KÐJR‹†qNG@KðJŽ‹†qOG@L" JJ3††qPG@Lì0JΆqQetqRe.brian-1.4.3/dev/benchmarking/cuba/data_dan_laptop/brian_cuba_results_allopts.pkl000066400000000000000000000015401266062647100301720ustar00rootroot00000000000000€]q((Mècnumpy.core.multiarray scalar qcnumpy dtype qUf8qKK‡qRq(KUG@:¬0K†q?G@@HbK†q@G@@‚ JK†qAG@@ˆbK†qBetqC(M€>hhUI’$¸ÁI@qD†qERqFhhUqG†qHRqI]qJ(G@I)ûèK†qKG@I©ûæK†qLG@I¹ûèK†qMG@IÞzK†qNG@Iô”K†qOG@IõãTK†qPG@IöK†qQetqRe.brian-1.4.3/dev/benchmarking/cuba/data_dan_laptop/brian_cuba_results_compile_nounits.pkl000066400000000000000000000016371266062647100317320ustar00rootroot00000000000000€]q((Mècnumpy.core.multiarray scalar qcnumpy dtype qUf8qKK‡qRq(KUG@:¼(ôJxã†q?G@Aó¶J.·†q@G@A7ïžJAÀ†qAG@AL0J½Ì†qBetqC(M€>hhUI’$cG”H@qD†qERqFhhUnÛ¶m3æ AqG†qHRqI]qJ(G@H6J$†qKG@H|0J‚†qLG@H€JU’†qMG@H–J’¶†qNG@H©ûèJ¥†qOG@HÉûèJ…¨†qPG@HÑë„JÓ †qQetqRe.brian-1.4.3/dev/benchmarking/cuba/data_dan_laptop/brian_cuba_varycon_results_compile_nounits.pkl000066400000000000000000000013421266062647100334640ustar00rootroot00000000000000€]q((Mècnumpy.core.multiarray scalar qcnumpy dtype qUf8qKK‡qRq(KUG@!@M§ †q?G@!wÎØMÛ †q@G@!xQèM> †qAG@!ˆ1(MˆqBetqCe.brian-1.4.3/dev/benchmarking/cuba/data_dan_laptop/brian_cuba_varywe_results_compile_nounits.pkl000066400000000000000000000013421266062647100333200ustar00rootroot00000000000000€]q((Mècnumpy.core.multiarray scalar qcnumpy dtype qUf8qKK‡qRq(KUG@ G®Myl†q?G@ wÎØMp†q@G@ €M‰o†qAG@ bPMÿm†qBetqCe.brian-1.4.3/dev/benchmarking/cuba/data_dan_laptop/brian_cuba_varywe_results_compile_nounits_1000.pkl000066400000000000000000000013421266062647100337600ustar00rootroot00000000000000€]q((Mècnumpy.core.multiarray scalar qcnumpy dtype qUf8qKK‡qRq(KUG@Oß0Mk†q?G@Oß@M']†q@G@`AMp[†qAG@o°MLg†qBetqCe.brian-1.4.3/dev/benchmarking/cuba/data_dan_laptop/brian_cuba_varywe_results_compile_nounits_4000.pkl000066400000000000000000000014141266062647100337630ustar00rootroot00000000000000€]q((M cnumpy.core.multiarray scalar qcnumpy dtype qUf8qKK‡qRq(KUG@D¢ HJÅz†q?G@E ûæJNˆ†q@G@E÷ïžJ~†qAG@R±ûçJ¿c†qBetqCe.brian-1.4.3/dev/benchmarking/cuba/data_dan_laptop/cpp_cuba_nospiking_results.py000066400000000000000000000030441266062647100300470ustar00rootroot00000000000000cpp_cuba = [] cur_result = [] cur_result.append((0.906, 47138)) cur_result.append((0.907, 47137)) cur_result.append((0.907, 47126)) cur_result.append((0.907, 47135)) cur_result.append((0.921, 47133)) cur_result.append((0.921, 47129)) cur_result.append((0.921, 47111)) cpp_cuba.append((1000, 0.912857, 47129, cur_result)) cur_result = [] cur_result.append((1.843, 94260)) cur_result.append((1.843, 94267)) cur_result.append((1.843, 94246)) cur_result.append((1.844, 94240)) cur_result.append((1.844, 94250)) cur_result.append((1.844, 94265)) cur_result.append((1.844, 94252)) cpp_cuba.append((2000, 1.84357, 94254, cur_result)) cur_result = [] cur_result.append((3.687, 188519)) cur_result.append((3.687, 188517)) cur_result.append((3.687, 188511)) cur_result.append((3.688, 188519)) cur_result.append((3.688, 188468)) cur_result.append((3.688, 188495)) cur_result.append((3.688, 188503)) cpp_cuba.append((4000, 3.68757, 188504, cur_result)) cur_result = [] cur_result.append((7.39, 376966)) cur_result.append((7.39, 377018)) cur_result.append((7.391, 377029)) cur_result.append((7.391, 376968)) cur_result.append((7.391, 376997)) cur_result.append((7.406, 377027)) cur_result.append((7.406, 377013)) cpp_cuba.append((8000, 7.395, 377002, cur_result)) cur_result = [] cur_result.append((14.75, 754034)) cur_result.append((14.765, 754002)) cur_result.append((14.766, 754002)) cur_result.append((14.797, 754044)) cur_result.append((14.797, 754038)) cur_result.append((14.797, 754027)) cur_result.append((14.812, 753952)) cpp_cuba.append((16000, 14.7834, 754014, cur_result)) brian-1.4.3/dev/benchmarking/cuba/data_dan_laptop/cpp_cuba_nospiking_results_matrix.py000066400000000000000000000031101266062647100314250ustar00rootroot00000000000000# state updater matrix # no spiking cpp_cuba = [] cur_result = [] cur_result.append((0.719, 47065)) cur_result.append((0.735, 47066)) cur_result.append((0.75, 47070)) cur_result.append((0.75, 47075)) cur_result.append((0.75, 47080)) cur_result.append((0.75, 47067)) cur_result.append((0.75, 47076)) cpp_cuba.append((1000, 0.743429, 47071, cur_result)) cur_result = [] cur_result.append((1.531, 94149)) cur_result.append((1.547, 94143)) cur_result.append((1.547, 94138)) cur_result.append((1.547, 94157)) cur_result.append((1.547, 94141)) cur_result.append((1.547, 94151)) cur_result.append((1.547, 94148)) cpp_cuba.append((2000, 1.54471, 94146, cur_result)) cur_result = [] cur_result.append((3.078, 188271)) cur_result.append((3.093, 188268)) cur_result.append((3.093, 188279)) cur_result.append((3.094, 188298)) cur_result.append((3.094, 188296)) cur_result.append((3.094, 188285)) cur_result.append((3.109, 188287)) cpp_cuba.append((4000, 3.09357, 188283, cur_result)) cur_result = [] cur_result.append((6.156, 376601)) cur_result.append((6.156, 376555)) cur_result.append((6.172, 376560)) cur_result.append((6.172, 376554)) cur_result.append((6.187, 376613)) cur_result.append((6.188, 376557)) cur_result.append((6.203, 376575)) cpp_cuba.append((8000, 6.17629, 376573, cur_result)) cur_result = [] cur_result.append((12.359, 753106)) cur_result.append((12.359, 753105)) cur_result.append((12.375, 753120)) cur_result.append((12.391, 753084)) cur_result.append((12.391, 753129)) cur_result.append((12.406, 753169)) cur_result.append((12.437, 753041)) cpp_cuba.append((16000, 12.3883, 753107, cur_result)) brian-1.4.3/dev/benchmarking/cuba/data_dan_laptop/cpp_cuba_results.py000066400000000000000000000030431266062647100257650ustar00rootroot00000000000000cpp_cuba = [] cur_result = [] cur_result.append((1.547, 12492)) cur_result.append((1.562, 13270)) cur_result.append((1.563, 12612)) cur_result.append((1.563, 14608)) cur_result.append((1.563, 13557)) cur_result.append((1.578, 13830)) cur_result.append((1.578, 14528)) cpp_cuba.append((1000, 1.56486, 13556, cur_result)) cur_result = [] cur_result.append((3.156, 27996)) cur_result.append((3.171, 27565)) cur_result.append((3.172, 27530)) cur_result.append((3.172, 28385)) cur_result.append((3.172, 26215)) cur_result.append((3.187, 28784)) cur_result.append((3.203, 29023)) cpp_cuba.append((2000, 3.17614, 27928, cur_result)) cur_result = [] cur_result.append((6.313, 51521)) cur_result.append((6.328, 56365)) cur_result.append((6.328, 53449)) cur_result.append((6.328, 55684)) cur_result.append((6.328, 54904)) cur_result.append((6.344, 52057)) cur_result.append((6.359, 57125)) cpp_cuba.append((4000, 6.33257, 54443, cur_result)) cur_result = [] cur_result.append((12.672, 107986)) cur_result.append((12.672, 113659)) cur_result.append((12.672, 111962)) cur_result.append((12.703, 110528)) cur_result.append((12.719, 117892)) cur_result.append((12.719, 112842)) cur_result.append((12.719, 115592)) cpp_cuba.append((8000, 12.6966, 112923, cur_result)) cur_result = [] cur_result.append((25.39, 217478)) cur_result.append((25.406, 224203)) cur_result.append((25.438, 223854)) cur_result.append((25.438, 225404)) cur_result.append((25.5, 232267)) cur_result.append((25.515, 228547)) cur_result.append((25.547, 236801)) cpp_cuba.append((16000, 25.462, 226936, cur_result)) brian-1.4.3/dev/benchmarking/cuba/data_dan_laptop/cpp_cuba_results_matrix.py000066400000000000000000000030541266062647100273530ustar00rootroot00000000000000# state updater matrix cpp_cuba = [] cur_result = [] cur_result.append((0.718, 11849)) cur_result.append((0.719, 14806)) cur_result.append((0.719, 13174)) cur_result.append((0.734, 14615)) cur_result.append((0.734, 15207)) cur_result.append((0.734, 16170)) cur_result.append((0.734, 11345)) cpp_cuba.append((1000, 0.727429, 13880, cur_result)) cur_result = [] cur_result.append((1.485, 25208)) cur_result.append((1.5, 26391)) cur_result.append((1.5, 28480)) cur_result.append((1.5, 25441)) cur_result.append((1.5, 26547)) cur_result.append((1.5, 28714)) cur_result.append((1.5, 27736)) cpp_cuba.append((2000, 1.49786, 26931, cur_result)) cur_result = [] cur_result.append((3.015, 58415)) cur_result.append((3.015, 56405)) cur_result.append((3.015, 56021)) cur_result.append((3.015, 54404)) cur_result.append((3.016, 58215)) cur_result.append((3.016, 56354)) cur_result.append((3.032, 57278)) cpp_cuba.append((4000, 3.01771, 56727, cur_result)) cur_result = [] cur_result.append((6.078, 111766)) cur_result.append((6.078, 113716)) cur_result.append((6.094, 108011)) cur_result.append((6.094, 110692)) cur_result.append((6.094, 117476)) cur_result.append((6.094, 110049)) cur_result.append((6.109, 113526)) cpp_cuba.append((8000, 6.09157, 112176, cur_result)) cur_result = [] cur_result.append((12.187, 220381)) cur_result.append((12.203, 219933)) cur_result.append((12.203, 216318)) cur_result.append((12.218, 222389)) cur_result.append((12.219, 216335)) cur_result.append((12.234, 222409)) cur_result.append((12.235, 222137)) cpp_cuba.append((16000, 12.2141, 219986, cur_result)) brian-1.4.3/dev/benchmarking/cuba/data_dan_laptop/cpp_cuba_results_matrix_efficient.py000066400000000000000000000030721266062647100313670ustar00rootroot00000000000000# state updater efficient matrix cpp_cuba = [] cur_result = [] cur_result.append((0.734, 13873)) cur_result.append((0.734, 12728)) cur_result.append((0.734, 16332)) cur_result.append((0.735, 14791)) cur_result.append((0.75, 14317)) cur_result.append((0.75, 13968)) cur_result.append((0.75, 13461)) cpp_cuba.append((1000, 0.741, 14210, cur_result)) cur_result = [] cur_result.append((1.531, 29507)) cur_result.append((1.531, 29427)) cur_result.append((1.531, 27380)) cur_result.append((1.531, 26954)) cur_result.append((1.532, 28112)) cur_result.append((1.532, 26619)) cur_result.append((1.546, 31221)) cpp_cuba.append((2000, 1.53343, 28460, cur_result)) cur_result = [] cur_result.append((3.078, 54078)) cur_result.append((3.078, 58927)) cur_result.append((3.078, 53072)) cur_result.append((3.078, 53584)) cur_result.append((3.093, 55317)) cur_result.append((3.094, 58091)) cur_result.append((3.094, 55884)) cpp_cuba.append((4000, 3.08471, 55564, cur_result)) cur_result = [] cur_result.append((6.156, 104794)) cur_result.append((6.156, 111930)) cur_result.append((6.172, 107288)) cur_result.append((6.172, 115206)) cur_result.append((6.172, 115852)) cur_result.append((6.188, 107904)) cur_result.append((6.188, 115616)) cpp_cuba.append((8000, 6.172, 111227, cur_result)) cur_result = [] cur_result.append((12.375, 226296)) cur_result.append((12.375, 218678)) cur_result.append((12.375, 221668)) cur_result.append((12.375, 224721)) cur_result.append((12.391, 223906)) cur_result.append((12.406, 230418)) cur_result.append((12.406, 229200)) cpp_cuba.append((16000, 12.3861, 224983, cur_result)) brian-1.4.3/dev/benchmarking/cuba/data_dan_laptop/cpp_cuba_varywe_results.py000066400000000000000000000024421266062647100273640ustar00rootroot00000000000000# vary weights # state updater matrix cpp_cuba = [] cur_result = [] cur_result.append((0.718, 13579)) cur_result.append((0.719, 15140)) cur_result.append((0.719, 13051)) cur_result.append((0.734, 13342)) cur_result.append((0.734, 14620)) cur_result.append((0.735, 13061)) cur_result.append((0.735, 12719)) # we = 1.62 cpp_cuba.append((1000, 0.727714, 13644, cur_result)) cur_result = [] cur_result.append((0.734, 21315)) cur_result.append((0.734, 19045)) cur_result.append((0.734, 18084)) cur_result.append((0.734, 16624)) cur_result.append((0.735, 20383)) cur_result.append((0.735, 20307)) cur_result.append((0.735, 16269)) # we = 2.4 cpp_cuba.append((1000, 0.734429, 18861, cur_result)) cur_result = [] cur_result.append((0.735, 19993)) cur_result.append((0.735, 25975)) cur_result.append((0.75, 23947)) cur_result.append((0.75, 24037)) cur_result.append((0.75, 27073)) cur_result.append((0.75, 27273)) cur_result.append((0.75, 23499)) # we = 2.9 cpp_cuba.append((1000, 0.745714, 24542, cur_result)) cur_result = [] cur_result.append((0.734, 24987)) cur_result.append((0.734, 25586)) cur_result.append((0.734, 21295)) cur_result.append((0.735, 26025)) cur_result.append((0.75, 23231)) cur_result.append((0.75, 27206)) cur_result.append((0.75, 28540)) # we = 3.2 cpp_cuba.append((1000, 0.741, 25267, cur_result)) brian-1.4.3/dev/benchmarking/cuba/data_dan_laptop/cpp_cuba_varywe_results_matrix.py000066400000000000000000000024451266062647100307530ustar00rootroot00000000000000# vary weights # state updater matrix cpp_cuba = [] cur_result = [] cur_result.append((0.719, 11483)) cur_result.append((0.719, 12639)) cur_result.append((0.719, 13725)) cur_result.append((0.734, 14989)) cur_result.append((0.734, 11584)) cur_result.append((0.735, 14212)) cur_result.append((0.735, 12722)) # we = 1.62 cpp_cuba.append((1000, 0.727857, 13050, cur_result)) cur_result = [] cur_result.append((0.718, 14607)) cur_result.append((0.734, 19707)) cur_result.append((0.734, 18944)) cur_result.append((0.734, 19395)) cur_result.append((0.735, 16693)) cur_result.append((0.735, 15263)) cur_result.append((0.735, 15468)) # we = 2.4 cpp_cuba.append((1000, 0.732143, 17153, cur_result)) cur_result = [] cur_result.append((0.734, 23597)) cur_result.append((0.734, 21946)) cur_result.append((0.734, 24026)) cur_result.append((0.735, 23047)) cur_result.append((0.735, 25863)) cur_result.append((0.75, 24000)) cur_result.append((0.75, 25434)) # we = 2.9 cpp_cuba.append((1000, 0.738857, 23987, cur_result)) cur_result = [] cur_result.append((0.734, 20868)) cur_result.append((0.75, 27938)) cur_result.append((0.75, 29506)) cur_result.append((0.75, 21027)) cur_result.append((0.75, 27691)) cur_result.append((0.75, 28883)) cur_result.append((0.75, 28942)) # we = 3.2 cpp_cuba.append((1000, 0.747714, 26407, cur_result)) brian-1.4.3/dev/benchmarking/cuba/data_dan_laptop/cpp_cuba_varywe_results_matrix_4000.py000066400000000000000000000024611266062647100314140ustar00rootroot00000000000000# vary weights # state updater matrix cpp_cuba = [] cur_result = [] cur_result.append((3.015, 56409)) cur_result.append((3.031, 57891)) cur_result.append((3.032, 55646)) cur_result.append((3.046, 55931)) cur_result.append((3.047, 56880)) cur_result.append((3.047, 58541)) cur_result.append((3.047, 55876)) # we = 1.62 cpp_cuba.append((4000, 3.03786, 56739, cur_result)) cur_result = [] cur_result.append((3.015, 75145)) cur_result.append((3.047, 77944)) cur_result.append((3.078, 84491)) cur_result.append((3.078, 73464)) cur_result.append((3.078, 74534)) cur_result.append((3.093, 75515)) cur_result.append((3.094, 79403)) # we = 2.4 cpp_cuba.append((4000, 3.069, 77213, cur_result)) cur_result = [] cur_result.append((3.063, 100789)) cur_result.append((3.078, 87112)) cur_result.append((3.079, 103179)) cur_result.append((3.109, 98336)) cur_result.append((3.11, 109254)) cur_result.append((3.125, 98400)) cur_result.append((3.156, 103958)) # we = 2.9 cpp_cuba.append((4000, 3.10286, 100146, cur_result)) cur_result = [] cur_result.append((3.094, 98138)) cur_result.append((3.14, 115752)) cur_result.append((3.141, 108803)) cur_result.append((3.141, 114630)) cur_result.append((3.141, 118636)) cur_result.append((3.156, 101919)) cur_result.append((3.156, 113417)) # we = 3.2 cpp_cuba.append((4000, 3.13843, 110185, cur_result)) brian-1.4.3/dev/benchmarking/cuba/data_dan_laptop/cpp_noopt_cuba_results.py000066400000000000000000000035311266062647100272060ustar00rootroot00000000000000cpp_cuba = [] cur_result = [] cur_result.append((1.203, 27348)) cur_result.append((1.218, 27742)) cur_result.append((1.218, 24746)) cur_result.append((1.218, 27684)) cur_result.append((1.218, 26823)) cur_result.append((1.219, 26142)) cur_result.append((1.219, 27053)) cpp_cuba.append((1000, 1.21614, 26791, cur_result)) cur_result = [] cur_result.append((2.422, 43313)) cur_result.append((2.437, 41318)) cur_result.append((2.453, 40244)) cur_result.append((2.453, 42605)) cur_result.append((2.453, 41910)) cur_result.append((2.453, 38561)) cur_result.append((2.453, 41332)) cpp_cuba.append((2000, 2.44629, 41326, cur_result)) cur_result = [] cur_result.append((4.922, 57502)) cur_result.append((4.922, 56403)) cur_result.append((4.922, 60314)) cur_result.append((4.922, 61694)) cur_result.append((4.937, 60793)) cur_result.append((4.938, 57913)) cur_result.append((4.938, 57660)) cpp_cuba.append((4000, 4.92871, 58897, cur_result)) cur_result = [] cur_result.append((9.922, 76993)) cur_result.append((9.922, 78815)) cur_result.append((9.922, 83013)) cur_result.append((9.937, 79851)) cur_result.append((9.937, 78007)) cur_result.append((9.937, 84676)) cur_result.append((9.937, 79907)) cpp_cuba.append((8000, 9.93057, 80180, cur_result)) cur_result = [] cur_result.append((19.843, 106264)) cur_result.append((19.859, 108549)) cur_result.append((19.875, 109513)) cur_result.append((19.89, 108580)) cur_result.append((19.891, 109881)) cur_result.append((19.891, 105568)) cur_result.append((19.891, 108225)) cpp_cuba.append((16000, 19.8771, 108082, cur_result)) cur_result = [] cur_result.append((40.047, 140000)) cur_result.append((40.063, 144434)) cur_result.append((40.078, 145460)) cur_result.append((40.078, 146447)) cur_result.append((40.078, 145407)) cur_result.append((40.079, 144632)) cur_result.append((40.093, 145885)) cpp_cuba.append((32000, 40.0737, 144609, cur_result)) brian-1.4.3/dev/benchmarking/cuba/data_dan_laptop/cuba_allopts.py000066400000000000000000000047421266062647100251070ustar00rootroot00000000000000import brian_no_units from brian import * import time import pickle from operator import itemgetter from cuba_runopts import * set_global_preferences(useweave=True) def cubanetwork(N): Vr = -49 * mV invtauv = 1 / (20 * ms) invtauge = 1 / (5 * ms) invtaugi = 1 / (10 * ms) eqs = ''' dv/dt = (ge+gi-(v-Vr))*invtauv : volt dge/dt = -ge*invtauge : volt dgi/dt = -gi*invtaugi : volt ''' Ne = int(N * 0.8) Ni = N - Ne P = NeuronGroup(N, model=eqs, threshold= -50 * mV, reset= -60 * mV, compile=True, freeze=True) P.v = -60 * mV + 10 * mV * rand(len(P)) Pe = P.subgroup(Ne) Pi = P.subgroup(Ni) Ce = Connection(Pe, P, 'ge') Ci = Connection(Pi, P, 'gi') Ce.connect_random(Pe, P, 0.02, weight=1.62 * mV) Ci.connect_random(Pi, P, 0.02, weight= -9 * mV) net = Network(P, Ce, Ci) net.run(0 * ms) return P, Pe, Pi, Ce, Ci, net def cuba(P, Pe, Pi, Ce, Ci, net): reinit_default_clock() P.v = -60 * mV + 10 * mV * rand(len(P)) Ce.__init__(Pe, P, 'ge') Ci.__init__(Pi, P, 'gi') Ce.connect_random(Pe, P, 0.02, weight=1.62 * mV) Ci.connect_random(Pi, P, 0.02, weight= -9 * mV) tstart = time.time() net.run(duration) tend = time.time() return tend - tstart, 0 def cuba_average(N, repeats, best): cubadata = cubanetwork(N) cubaruns = [cuba(*cubadata) for i in range(repeats)] print 'Finished run', N cubaruns.sort(key=itemgetter(0)) cubaruns = cubaruns[:best] t, ns = zip(*cubaruns) t = array(t) ns = array(ns) return N, mean(t), mean(ns), cubaruns def cuba_runs(Nvals, repeats, best): return [cuba_average(N, repeats, best) for N in Nvals] def do_runs(fname='brian_cuba_results_allopts.pkl'): ca = cuba_runs(Nvals, repeats, best) output = open(fname, 'wb') pickle.dump(ca, output, -1) output.close() return ca if __name__ == '__main__': print do_runs() # cubadata = cubanetwork(32000) # print cuba(*cubadata) # def f(): # cubadata = cubanetwork(4000) # print cuba(*cubadata) # f() # import cProfile as profile # profile.run('f()') # import hotshot, hotshot.stats # prof = hotshot.Profile('cuba_allopts.prof') # prof.runcall(f) # stats = hotshot.stats.load("cuba_allopts.prof") # stats.strip_dirs() # stats.sort_stats('cumulative','time','calls') # stats.print_stats() brian-1.4.3/dev/benchmarking/cuba/data_dan_laptop/matlab_cuba_nospiking_results.py000066400000000000000000000031061266062647100305240ustar00rootroot00000000000000matlab_cuba = [] cur_result = [] cur_result.append((2.75, 47074)) cur_result.append((2.765, 47061)) cur_result.append((2.781, 47083)) cur_result.append((2.781, 47068)) cur_result.append((2.782, 47080)) cur_result.append((2.782, 47075)) cur_result.append((2.797, 47080)) matlab_cuba.append((1000, 2.77686, 47074.4, cur_result)) cur_result = [] cur_result.append((5.297, 94137)) cur_result.append((5.312, 94119)) cur_result.append((5.313, 94148)) cur_result.append((5.313, 94146)) cur_result.append((5.313, 94144)) cur_result.append((5.328, 94150)) cur_result.append((5.329, 94142)) matlab_cuba.append((2000, 5.315, 94140.9, cur_result)) cur_result = [] cur_result.append((10.406, 188324)) cur_result.append((10.406, 188290)) cur_result.append((10.422, 188281)) cur_result.append((10.437, 188257)) cur_result.append((10.453, 188282)) cur_result.append((10.453, 188230)) cur_result.append((10.484, 188296)) matlab_cuba.append((4000, 10.4373, 188280, cur_result)) cur_result = [] cur_result.append((20.672, 376555)) cur_result.append((20.687, 376529)) cur_result.append((20.703, 376513)) cur_result.append((20.703, 376524)) cur_result.append((20.719, 376510)) cur_result.append((20.75, 376620)) cur_result.append((20.782, 376573)) matlab_cuba.append((8000, 20.7166, 376546, cur_result)) cur_result = [] cur_result.append((41.484, 753131)) cur_result.append((41.485, 753095)) cur_result.append((41.5, 753083)) cur_result.append((41.859, 753130)) cur_result.append((41.875, 753095)) cur_result.append((41.953, 753148)) cur_result.append((41.953, 753062)) matlab_cuba.append((16000, 41.7299, 753106, cur_result)) brian-1.4.3/dev/benchmarking/cuba/data_dan_laptop/matlab_cuba_results.py000066400000000000000000000030541266062647100264450ustar00rootroot00000000000000matlab_cuba = [] cur_result = [] cur_result.append((4.532, 13002)) cur_result.append((4.719, 13546)) cur_result.append((4.734, 14188)) cur_result.append((4.766, 14416)) cur_result.append((5.016, 13016)) cur_result.append((5.109, 11786)) cur_result.append((5.125, 13286)) matlab_cuba.append((1000, 4.85729, 13320, cur_result)) cur_result = [] cur_result.append((8.968, 27211)) cur_result.append((8.984, 27025)) cur_result.append((9, 28237)) cur_result.append((9, 28377)) cur_result.append((9, 27837)) cur_result.append((9, 29419)) cur_result.append((9.015, 25807)) matlab_cuba.append((2000, 8.99529, 27701.9, cur_result)) cur_result = [] cur_result.append((18.844, 57430)) cur_result.append((19.313, 53993)) cur_result.append((24.719, 53769)) cur_result.append((25, 53294)) cur_result.append((25.328, 54465)) cur_result.append((25.359, 53925)) cur_result.append((25.375, 55653)) matlab_cuba.append((4000, 23.4197, 54647, cur_result)) cur_result = [] cur_result.append((40.891, 111331)) cur_result.append((40.953, 110601)) cur_result.append((41.047, 110262)) cur_result.append((41.094, 108372)) cur_result.append((41.156, 111738)) cur_result.append((41.172, 110305)) cur_result.append((41.203, 112663)) matlab_cuba.append((8000, 41.0737, 110753, cur_result)) cur_result = [] cur_result.append((82.266, 212895)) cur_result.append((87.875, 217234)) cur_result.append((91.469, 213008)) cur_result.append((92.11, 214208)) cur_result.append((94.89, 224568)) cur_result.append((98.078, 229190)) cur_result.append((98.563, 221218)) matlab_cuba.append((16000, 92.1787, 218903, cur_result)) brian-1.4.3/dev/benchmarking/cuba/data_dan_laptop/matlab_cuba_varywe_results.py000066400000000000000000000023531266062647100300430ustar00rootroot00000000000000matlab_cuba = [] cur_result = [] cur_result.append((4.532, 12789)) cur_result.append((4.547, 13436)) cur_result.append((4.562, 14370)) cur_result.append((4.562, 13462)) cur_result.append((4.563, 14667)) cur_result.append((4.563, 12155)) cur_result.append((4.579, 13731)) matlab_cuba.append((1000, 4.55829, 13515.7, cur_result)) cur_result = [] cur_result.append((4.609, 18324)) cur_result.append((4.625, 18587)) cur_result.append((4.64, 18563)) cur_result.append((4.641, 17520)) cur_result.append((4.641, 18806)) cur_result.append((4.641, 17696)) cur_result.append((4.656, 18252)) matlab_cuba.append((1000, 4.63614, 18249.7, cur_result)) cur_result = [] cur_result.append((4.609, 20156)) cur_result.append((4.625, 19513)) cur_result.append((4.672, 21783)) cur_result.append((4.672, 21492)) cur_result.append((4.687, 21224)) cur_result.append((4.687, 23118)) cur_result.append((4.687, 24035)) matlab_cuba.append((1000, 4.66271, 21617.3, cur_result)) cur_result = [] cur_result.append((4.609, 18235)) cur_result.append((4.609, 22806)) cur_result.append((4.64, 22725)) cur_result.append((4.657, 26908)) cur_result.append((4.687, 25605)) cur_result.append((4.687, 25845)) cur_result.append((4.688, 30786)) matlab_cuba.append((1000, 4.65386, 24701.4, cur_result)) brian-1.4.3/dev/benchmarking/cuba/data_dan_laptop/matlab_cuba_varywe_results_4000.py000066400000000000000000000024071266062647100305060ustar00rootroot00000000000000matlab_cuba = [] cur_result = [] cur_result.append((14.812, 49881)) cur_result.append((14.828, 54379)) cur_result.append((14.86, 55478)) cur_result.append((14.922, 59780)) cur_result.append((14.953, 60881)) cur_result.append((15.031, 54989)) cur_result.append((15.203, 54739)) matlab_cuba.append((4000, 14.9441, 55732.4, cur_result)) cur_result = [] cur_result.append((15.062, 72498)) cur_result.append((15.062, 76315)) cur_result.append((15.078, 69005)) cur_result.append((15.093, 71171)) cur_result.append((15.109, 77631)) cur_result.append((15.125, 76631)) cur_result.append((15.172, 75326)) matlab_cuba.append((4000, 15.1001, 74082.4, cur_result)) cur_result = [] cur_result.append((15.156, 98313)) cur_result.append((15.203, 89282)) cur_result.append((15.25, 99827)) cur_result.append((15.281, 93991)) cur_result.append((15.328, 96570)) cur_result.append((15.422, 93928)) cur_result.append((15.547, 88829)) matlab_cuba.append((4000, 15.3124, 94391.4, cur_result)) cur_result = [] cur_result.append((15.062, 100487)) cur_result.append((15.109, 98760)) cur_result.append((15.11, 102197)) cur_result.append((15.14, 100652)) cur_result.append((15.157, 101606)) cur_result.append((15.25, 112632)) cur_result.append((15.25, 102386)) matlab_cuba.append((4000, 15.154, 102674, cur_result)) brian-1.4.3/dev/benchmarking/cuba/runbrianbenchmarks.bat000066400000000000000000000000551266062647100233120ustar00rootroot00000000000000@echo off cuba_compile_nounits.py cuba.py brian-1.4.3/dev/benchmarking/large_network_benchmark.py000066400000000000000000000040021266062647100232550ustar00rootroot00000000000000""" Benchmark of a large network matching the specifications in Morrison et al. (2005). (Network from Brunel 2000). Without synapses: 122s With synapses: 220s NEST: 1600-6400s depending on the machine Interesting point: spike propagations are approximately as expensive as state updates although the number of synapses is huge. This is in fact not so surprising: 250 000 spike propagations / second 100 000 state updates / second (and each state update involves random number generation) This suggests that spike propagation is generally not a big issue in practice. Brian's simulation time might be an understimated because of caching and/or memory access issues. """ from brian import * from time import time dt = defaultclock.dt N = 100000 # neurons p = 10000 # synapses (that's a billion synapses) F = 2.5 * Hz # firing rate duration = 1 * second taum = 20 * ms Vt = -50 * mV Vr = -60 * mV El = -49 * mV sigma = 1 * mV eqs = """ dv/dt = -(v-El)/taum + sigma/taum**.5*xi: volt """ p_active = F * dt Nactive = int(N * p_active) # number of active neurons every time step active_ones = arange(N) < Nactive P = NeuronGroup(N, model=eqs, threshold="rand(N)=next_change: # new input input.rate=input_rate(all_inputs,randint(Nin)) next_change=exponential()*stimulus_period rate = PopulationRateMonitor(neurons) start_time = time() run(30 * second, report='text') print "Simulation time:", time() - start_time figure() subplot(311) plot(rate.times / second, rate.smooth_rate(100 * ms)) subplot(312) hist(synapses_ff.W.alldata / gmax, 20) figure() imshow(synapses_ff.W.todense()/gmax) colorbar() #figure() #imshow(synapses_rec.W.todense()/gmax) #colorbar() show() brian-1.4.3/dev/debugging/add_remove_from_network.py000066400000000000000000000003711266062647100226110ustar00rootroot00000000000000from brian import * G = NeuronGroup(1, 'dv/dt=1/second:1') H = NeuronGroup(1, 'dv/dt=1/second:1') net = Network(G) net.run(100*ms) print G.v, H.v net.add(H) net.run(100*ms) print G.v, H.v net.remove(G) net.run(500*ms) print G.v, H.v brian-1.4.3/dev/debugging/adding_equations.py000066400000000000000000000007771266062647100212400ustar00rootroot00000000000000from brian import * bof = 3.0 tau = 10 * ms eq_base = Equations(''' w = V*bof : 1 ''') eq_extra_1 = Equations(''' dV/dt = (w-V)/tau : 1 ''') eq_extra_2 = Equations(''' dV/dt = (2*w-V)/tau : 1 ''') eq1 = eq_extra_1 + eq_base eq2 = eq_extra_2 + eq_base print eq1._namespace['w'].keys() eq1.prepare() print eq1._namespace['w'].keys() eq2.prepare() #exit() G1 = NeuronGroup(1, eq1) G2 = NeuronGroup(1, eq2) run(1 * ms) print G1.V print G1.w print G2.V print G2.w brian-1.4.3/dev/debugging/bertrand_codegen_bugs.py000066400000000000000000000006721266062647100222210ustar00rootroot00000000000000from brian import * nbr_neurons=2 gl=2*nS*linspace(0, 1, nbr_neurons) El=-65*mV*ones(nbr_neurons) #C=12*pF*ones(nbr_neurons) ## that was changed from previous example C=12*pF eqs_leak=""" ileak = gl*(El-v) : amp """ eqs=""" dv/dt=(ileak)/C : volt """ eqs+=eqs_leak neuron=NeuronGroup(nbr_neurons,model=eqs, compile=True)#method='Euler') M = StateMonitor(neuron, 'v', record=True) run(100*ms,report='text') M.plot() show() brian-1.4.3/dev/debugging/c_stdp_bug.py000066400000000000000000000036441266062647100200270ustar00rootroot00000000000000''' There's a bug in CSTDP so that if you have more target neurons than sources, it will segfault, suggesting that one of the values passed to the C code is wrong. ''' from brian import * set_global_preferences(useweave=True, usecstdp=True) log_level_debug() Nin = 100 Nout = 200 duration = 40 * second Fmax = 50 * Hz tau = 10 * ms taue = 2 * ms taui = 5 * ms sigma = 0.#0.4 # Note that factor (tau/taue) makes integral(v(t)) the same when the connection # acts on ge as if it acted directly on v. eqs = Equations(''' #dv/dt = -v/tau + sigma*xi/(2*tau)**.5 : 1 dv/dt = (-v+(tau/taue)*ge-(tau/taui)*gi)/tau + sigma*xi/(2*tau)**.5 : 1 dge/dt = -ge/taue : 1 dgi/dt = -gi/taui : 1 excitatory = ge inhibitory = gi ''') reset = 0 threshold = 1 refractory = 0 * ms taup = 5 * ms taud = 5 * ms Ap = .1 Ad = -Ap * taup / taud * 1.2 wmax_ff = 0.1 wmax_rec = wmax_ff wmax_inh = wmax_rec width = 0.2 recwidth = 0.2 Gin = PoissonGroup(Nin) Gout = NeuronGroup(Nout, eqs, reset=reset, threshold=threshold, refractory=refractory) ff = Connection(Gin, Gout, 'excitatory', structure='dense') for i in xrange(Nin): ff[i, :] = (rand(Nout) > .5) * wmax_ff rec = Connection(Gout, Gout, 'excitatory') for i in xrange(Nout): d = abs(float(i) / Nout - linspace(0, 1, Nout)) d[d > .5] = 1. - d[d > .5] dsquared = d ** 2 prob = exp(-dsquared / (2 * recwidth ** 2)) prob[i] = -1 inds = (rand(Nout) < prob).nonzero()[0] w = rand(len(inds)) * wmax_rec rec[i, inds] = w inh = Connection(Gout, Gout, 'inhibitory', sparseness=1, weight=wmax_inh) stdp_ff = ExponentialSTDP(ff, taup, taud, Ap, Ad, wmax=wmax_ff) #stdp_rec = ExponentialSTDP(rec, taup, taud, Ap, Ad, wmax=wmax_rec) run(0 * ms) @network_operation(clock=EventClock(dt=20 * ms)) def stimulation(): Gin.rate = Fmax * exp(-(linspace(0, 1, Nin) - rand())**2 / (2 * width ** 2)) run(1*second, report='stderr') brian-1.4.3/dev/debugging/clearall.py000066400000000000000000000002401266062647100174620ustar00rootroot00000000000000from brian import * rem = [] def f(): G = NeuronGroup(1, 'V:1') rem.append(G) f() clear(True, all=True) #clear(True) print rem[0]._S brian-1.4.3/dev/debugging/clocks.py000066400000000000000000000003541266062647100171670ustar00rootroot00000000000000from brian import * c = Clock(dt=1*ms, t=0.5*ms) @network_operation(clock=c) def f(): print c.t if c.t>5*ms: c.t -= 0.01*ms run(10*ms) net = MagicNetwork() net.prepare() print net._update_schedule brian-1.4.3/dev/debugging/clocks_with_order.py000066400000000000000000000005741266062647100214210ustar00rootroot00000000000000from brian import * c1 = Clock(dt=2*ms, order=1) c2 = Clock(dt=3*ms, order=2) c3 = Clock(dt=5*ms, order=0) @network_operation(clock=c1) def f1(): print '1:', c1.t @network_operation(clock=c2) def f2(): print '2:', c2.t @network_operation(clock=c3) def f3(): print '3:', c3.t net = MagicNetwork() net.run(20*ms) print c1.t, c2.t, c3.t brian-1.4.3/dev/debugging/codegen_with_array_variables.py000066400000000000000000000004771266062647100236040ustar00rootroot00000000000000from brian import * #set_global_preferences(usecodegen=False) N = 5 freq = linspace(0, 1, 5) eqs = ''' dV/dt = cos(freq*V)/(1*second) : 1 #dV/dt = freq*(1-V)/(1*second) : 1 #freq : 1 ''' G = NeuronGroup(N, eqs) #G.freq = freq M = StateMonitor(G, 'V', record=True) run(1*second) M.plot() show()brian-1.4.3/dev/debugging/conditionalequations.py000066400000000000000000000003311266062647100221400ustar00rootroot00000000000000from brian import * tau=10*ms x1=-1*volt x2=0*volt model='dv/dt=((v>0)*x1+(v<=0)*x2)/tau:volt' G=NeuronGroup(1,model,method='Euler') S=StateMonitor(G,'v',record=0) G.v=1*volt run(50*ms) S.plot() show() brian-1.4.3/dev/debugging/current.txt000066400000000000000000007741221266062647100175750ustar00rootroot000000000000003.016568799999999800e-10 2.394286400000000100e-10 2.561820399999999800e-10 3.129022499999999700e-10 3.263210700000000300e-10 2.819560099999999900e-10 2.898018499999999900e-10 3.902305799999999500e-10 3.435866999999999800e-10 2.800511399999999900e-10 2.264853100000000000e-10 1.700641899999999800e-10 2.708672299999999800e-10 3.316012600000000000e-10 2.627519500000000000e-10 2.174713400000000000e-10 1.930140199999999800e-10 1.492345599999999800e-10 1.073529900000000000e-10 1.341937200000000100e-10 1.022353999999999900e-10 2.092781999999999900e-10 2.666377699999999600e-10 3.213366899999999900e-10 2.682081299999999900e-10 3.203768000000000000e-10 2.744351300000000000e-10 2.189016000000000100e-10 2.512772600000000300e-10 2.535695799999999700e-10 2.763241899999999800e-10 2.874125100000000100e-10 2.428849899999999900e-10 1.845221600000000100e-10 2.832252500000000000e-10 2.476725999999999800e-10 2.039482499999999900e-10 2.602072200000000000e-10 2.162888900000000000e-10 3.250853299999999900e-10 3.317072999999999500e-10 4.403020000000000400e-10 3.697261499999999900e-10 4.356578500000000100e-10 4.418407299999999900e-10 3.992835399999999900e-10 3.597969400000000100e-10 3.783488599999999800e-10 5.015378299999999600e-10 5.373890999999999600e-10 4.984227399999999200e-10 5.994653799999999900e-10 5.585758200000000400e-10 5.190513200000000700e-10 4.605281499999999900e-10 5.073135700000000600e-10 4.551891200000000200e-10 3.942453499999999700e-10 3.558469400000000300e-10 3.844010899999999800e-10 4.507337499999999600e-10 4.454921299999999500e-10 4.356806499999999900e-10 3.839236499999999800e-10 3.411034500000000000e-10 2.836185400000000100e-10 2.280389800000000000e-10 3.399055500000000000e-10 3.830644899999999600e-10 4.834636700000000200e-10 4.140728299999999900e-10 5.069358200000000000e-10 4.448021899999999900e-10 4.883313399999999800e-10 5.835043899999999100e-10 5.697561500000000200e-10 4.951372400000000200e-10 4.164636799999999800e-10 5.358900599999999500e-10 6.285282899999999300e-10 6.643443599999999900e-10 6.179968900000000400e-10 6.526690499999999900e-10 5.939190700000000500e-10 5.388203000000000000e-10 4.906802200000000500e-10 4.353442399999999800e-10 4.351768299999999800e-10 4.678073299999999600e-10 5.162146900000000300e-10 5.056607700000000100e-10 5.398739799999999600e-10 5.800481899999999800e-10 6.759469299999999000e-10 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1.651055000000000000e-10 2.738960300000000000e-10 3.204315299999999900e-10 3.189614399999999600e-10 3.706060400000000100e-10 3.215630700000000000e-10 3.543227700000000100e-10 3.012145200000000100e-10 3.707235300000000000e-10 3.744977200000000300e-10 3.429783600000000200e-10 3.539699899999999700e-10 3.028747800000000000e-10 3.439421300000000300e-10 2.895630600000000100e-10 2.457579099999999800e-10 2.002317799999999800e-10 3.152508499999999800e-10 2.690769700000000100e-10 2.222397800000000000e-10 1.827045100000000100e-10 1.854404100000000000e-10 1.428855299999999800e-10 1.453090800000000000e-10 2.686153600000000100e-10 2.207917099999999900e-10 2.371530300000000100e-10 3.057325299999999900e-10 2.569570299999999600e-10 2.156855399999999900e-10 2.741785599999999700e-10 2.204762200000000100e-10 2.310251800000000100e-10 1.788240900000000000e-10 1.335544300000000000e-10 8.530189099999999200e-11 3.617491699999999800e-11 1.630061099999999900e-11 -1.342324699999999900e-11 4.369246099999999900e-11 6.886736899999999100e-11 2.520922900000000000e-11 2.861836100000000100e-11 1.334393900000000000e-10 9.605369599999999500e-11 1.587826200000000100e-10 1.703208500000000000e-10 1.158051800000000000e-10 6.711576999999999800e-11 9.054243300000000700e-11 3.709237000000000300e-11 -6.765967399999999800e-12 -4.848470300000000400e-11 -3.426839899999999900e-12 -5.324983399999999800e-11 -7.707228999999998900e-11 -3.833658599999999400e-11 -7.545095999999999400e-11 -1.148758900000000000e-10 -1.462567899999999900e-10 -1.863107400000000100e-10 -2.049931900000000000e-10 -2.310559800000000100e-10 -2.510902399999999900e-10 -2.745379899999999500e-10 -2.062557200000000000e-10 -1.823159999999999900e-10 -1.500851599999999900e-10 -1.741963500000000000e-10 -1.446523599999999800e-10 -1.757818100000000100e-10 -2.186611399999999800e-10 -1.433780400000000100e-10 -1.689503499999999800e-10 -1.595037599999999900e-10 -1.913691399999999800e-10 -2.378196500000000000e-10 -2.595779299999999800e-10 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3.245814299999999900e-10 4.060672299999999900e-10 4.842719900000000000e-10 4.156155399999999900e-10 4.401087800000000400e-10 4.918898599999999400e-10 4.401003100000000000e-10 4.602526199999999700e-10 5.042483800000000100e-10 brian-1.4.3/dev/debugging/delayconnectionsubgroups.py000066400000000000000000000004021266062647100230330ustar00rootroot00000000000000from brian import * G = NeuronGroup(4, 'V:1') Ga = G.subgroup(2) Gb = G.subgroup(2) C = Connection(G, G, 'V', delay=True, structure='dense') C.connect_full(Ga, Gb, weight=1, delay=lambda:rand()) print C.W.todense() print C.delayvec.todense() brian-1.4.3/dev/debugging/densedelayconnection.py000066400000000000000000000011061266062647100221020ustar00rootroot00000000000000from brian import * N = 100 H = SpikeGeneratorGroup(1, [(0, 0 * ms)]) G = NeuronGroup(N, 'V:1', threshold=1, reset=0) C = Connection(H, G, structure='dense', delay=True, weight=2.0, max_delay=10 * ms) C.delay[0, :] = linspace(0 * ms, 10 * ms, N) sp = SpikeMonitor(G) Mv = StateMonitor(G, 'V', record=True) #run(5*ms) # #imshow(C._delayedreaction, origin='lower left', interpolation='nearest') #axhline(C._cur_delay_ind) #show() #exit() run(15 * ms) subplot(211) raster_plot(sp) subplot(212) for i in range(N): plot(Mv.times, Mv[i] + i) show() brian-1.4.3/dev/debugging/electrophysiology/000077500000000000000000000000001266062647100211215ustar00rootroot00000000000000brian-1.4.3/dev/debugging/electrophysiology/AECVC.py000066400000000000000000000021451266062647100223160ustar00rootroot00000000000000# AEC experiment (voltage-clamp) from brian import * from brian.library.electrophysiology import * myclock = Clock(dt=.1 * ms) clock_rec = Clock(dt=.1 * ms) #log_level_debug() taum = 20 * ms gl = 20 * nS Cm = taum * gl Re = 50 * Mohm Ce = 0.1 * ms / Re eqs = Equations(''' dvm/dt=(-gl*vm+i_inj)/Cm : volt I:amp ''') eqs += electrode(.6 * Re, Ce) eqs += current_clamp(vm='v_el', i_inj='i_cmd', i_cmd='I', Re=.4 * Re, Ce=Ce) setup = NeuronGroup(1, model=eqs, clock=myclock) board = VC_AEC(setup, 'v_rec', 'I', gain=300 * nS, gain2=20 * nS / ms, clock=clock_rec) recording = StateMonitor(board, 'record', record=True, clock=myclock) soma = StateMonitor(setup, 'vm', record=True, clock=myclock) run(50 * ms) board.start_injection() run(1 * second) board.stop_injection() run(100 * ms) board.estimate() print 'Re=', sum(board.Ke) * ohm board.switch_on() run(50 * ms) board.command = 10 * mV run(200 * ms) board.command = 0 * mV run(150 * ms) board.switch_off() figure() plot(recording.times / nA, recording[0] / nA, 'b') figure() plot(soma.times / ms, soma[0] / mV, 'r') show() brian-1.4.3/dev/debugging/electrophysiology/Ke.txt000066400000000000000000000025201266062647100222200ustar00rootroot00000000000000-1.982334203505550900e+003 -6.198917250053097600e+003 2.648314605844980500e+007 1.166471160594967600e+007 5.760574073130378500e+006 2.856765817452879200e+006 1.421645029603264100e+006 7.207487882138713000e+005 3.735314034737859000e+005 2.028883665537894400e+005 1.140859890486854100e+005 6.630039520707573700e+004 4.010300701581689600e+004 2.740134684803946600e+004 2.444371037920689600e+004 1.513474446474516500e+004 1.389981188299627100e+004 2.557690355058220300e+004 3.027886061871182400e+004 3.222463518091461800e+004 3.080736618732185500e+004 2.778413373482660600e+004 2.370824125265530500e+004 1.038297330679921500e+004 9.042826539895160700e+003 1.729893228383380800e+004 1.529957159491182600e+004 1.440370145648309200e+004 1.521353349018186800e+004 1.144925845707853100e+004 7.858340348761871600e+003 6.377648618922094100e+003 1.538332266865180500e+004 1.297750091112523300e+004 7.390998236499162900e+003 6.603692990890031100e+003 6.145135540403273800e+003 -1.551828781630210200e+003 -3.283983102487342400e+003 -1.021217673562974100e+003 1.578035000971006400e+003 5.726796301459863600e+003 2.489188085987803300e+003 -4.606385247765188100e+003 -1.960191077823775200e+003 6.266355990816735400e+003 7.812428681748972800e+003 -7.747321383017035700e+002 -9.057271727212739300e+003 -9.144431552774140100e+003 brian-1.4.3/dev/debugging/electrophysiology/amp_ex.py000066400000000000000000000023261266062647100227470ustar00rootroot00000000000000# Test of amplifier module # NOT WORKING from brian import * from brian.library.electrophysiology import * model_clock = Clock(dt=.1 * ms) sampling_clock = Clock(dt=.1 * ms) Cm = 200 * pF Re = 50 * Mohm Ce = 0.5 * ms / Re R = 10 * ms / Cm freq = 100 * Hz eqs = Equations(''' dv/dt=ie/Cm-v/(10*ms) : volt I:amp u=v ''') eqs += electrode([.6 * Re, .4 * Re], [Ce, Ce], 'vr', 'v', 'ie', 'ic') eqs += current_clamp('vr', 'ic', 'v_bridge', 'I', bridge=0 * ohm, capa_comp=.95 * Ce) neuron = NeuronGroup(1, model=eqs, clock=model_clock) ampli = AcquisitionBoard(neuron, 'v_bridge', 'I', sampling_clock) mon = StateMonitor(neuron, 'v', record=0, clock=sampling_clock) mon_vr = StateMonitor(ampli, 'record', record=0, clock=sampling_clock) mon_I = StateMonitor(ampli, 'command', record=0, clock=sampling_clock) @network_operation(clock=sampling_clock, when='middle') def command(): #neuron.E=sin(sampling_clock.t*2*pi*freq)*10*mV ampli.command = rand()*1 * nA - .5 * nA run(1000 * ms) v = mon_vr[0] / mV i = mon_I[0] / nA K = full_kernel(v, i, 200) Ke = electrode_kernel_soma(K, 50) #plot(Ke/Mohm) print sum(Ke) #plot(mon.times/ms,mon[0]/mV,'b') #plot(mon.times/ms,mon_vr[0]/mV,'g') #show() brian-1.4.3/dev/debugging/electrophysiology/iterativeVC.py000066400000000000000000000025561266062647100237300ustar00rootroot00000000000000# Iterative voltage-clamp # R. Brette 2008 from brian import * #from brian.library.amplifier import * #from brian.library.electrodes import * C = 200 * pF tau = 20 * ms gl = C / tau R = 1. / gl E = 20 * mV # holding potential g = 150 * nS # initial gain eqs = ''' dv/dt=(-gl*v+I)/C : volt I : amp ''' neuron = NeuronGroup(1, model=eqs) mon = StateMonitor(neuron, 'v', record=0) j = 0 current = zeros(500) * amp @network_operation def inject(): global j if j < 500: neuron.I = current[j] j = j + 1 else: neuron.I = 0 * nA dt = defaultclock.dt err = 1e15 * mV ** 2 prev_err = err maxcur = 2 * (C / dt) * E run(10 * ms) #E=E*(1-exp(-linspace(0*ms,50*ms,500)/(1*ms))) for i in range(100): j = 0 run(50 * ms) v = mon[0][-500:] v += rand(len(v)) * 2 * mV err = sum(E - v) # clamp error print sqrt(mean((E - v) ** 2)) / float(mV) #print err #current=g*(E-v) #g=1.2*g #current+=g*(E-v) # Park's algorithm prev = zeros(len(current)) prev[1:] = (E - v)[0:-1] #current+=C/dt*(E-v-exp(-dt/tau)*prev) # Kawato et al current += g * (E - v - .99 * prev) current = clip(current, -maxcur, maxcur) # use best combination of last 2? + new independent component #g=2.*g run(100 * ms) # rest plot(mon.times / ms, mon[0] / mV) show() brian-1.4.3/dev/debugging/equations_test.py000066400000000000000000000001401266062647100207510ustar00rootroot00000000000000from brian import * tau = 1 * ms eqs = Equations('dx/dt=-x/tau : 1', x='y') eqs.prepare() brian-1.4.3/dev/debugging/fractionaldelays.py000066400000000000000000000006311266062647100212330ustar00rootroot00000000000000from brian import * from brian.hears import * sound = whitenoise(10*ms) delays = linspace(-1*ms, 1*ms, 1000) fd = FractionalDelay(sound, delays) fd2 = FractionalDelay(sound, array(delays*sound.samplerate, dtype=int)/sound.samplerate) I = fd.process() I2 = fd2.process() imshow(I, interpolation='nearest', aspect='auto') figure() imshow(I2, interpolation='nearest', aspect='auto') show() brian-1.4.3/dev/debugging/gpu_filtering_linux.py000066400000000000000000000006761266062647100217750ustar00rootroot00000000000000from brian import * from brian.hears import* from brian.hears import filtering #filtering.use_gpu = False samplerate = 44.1*kHz defaultclock.dt = 1/samplerate cfmin, cfmax, cfN = 100*Hz, 2*kHz, 5 cf = erbspace(cfmin, cfmax, cfN) sound = Sound(array([1]), rate=samplerate).extend(50*ms) fb = GammatoneFilterbank(samplerate, cf) G = FilterbankGroup(fb, sound) M = StateMonitor(G, 'output', record=True) run(sound.duration) M.plot() show() brian-1.4.3/dev/debugging/gpu_on_linux.py000066400000000000000000000034741266062647100204250ustar00rootroot00000000000000from brian.hears import * import brian.hears.filtering as filtering from numpy import ones, int32 import multiprocessing import pycuda #import pycuda.autoinit as autoinit import pycuda.driver as drv import pycuda.gpuarray as gpuarray from pycuda.compiler import SourceModule import os, sys import atexit #def set_gpu_device(n): # """ # This function makes pycuda use GPU number n in the system. # """ # try: # pycuda.context.detach() # except: # pass # pycuda.context = drv.Device(n).make_context() # #def close_cuda(): # """ # Closes the current context. MUST be called at the end of the script. # """ # if pycuda.context is not None: # try: # pycuda.context.pop() # pycuda.context = None # except: # pass # #atexit.register(close_cuda) def doit(x): # drv.init() # print id(pycuda.context) filtering.set_gpu_device(x) # print 'a' # pycuda.context = drv.Device(0).make_context() # print 'b' sys.stdin = file(os.devnull) sys.stdout = file(os.devnull) code = ''' __global__ void test(double *x, int n) { int i = blockIdx.x * blockDim.x + threadIdx.x; if(i>=n) return; x[i] *= 2.0; } ''' mod = SourceModule(code) f = mod.get_function('test') x = gpuarray.to_gpu(ones(100)) f(x, int32(100), block=(100, 1, 1)) y = x.get() filtering.close_cuda() return y if __name__ == '__main__': # pycuda.context=None #import sys # print 'A' # set_gpu_device(0) # print 'B' # filtering.set_gpu_device(0) # print 'C' # filtering.set_gpu_device(0) # print 'D' #drv.init() #drv.init() #doit(0) #print id(pycuda.context) #close_cuda() pool = multiprocessing.Pool(2) result = pool.map(doit, [0,0]) print result brian-1.4.3/dev/debugging/hears/000077500000000000000000000000001266062647100164375ustar00rootroot00000000000000brian-1.4.3/dev/debugging/hears/approx_filtering_model.py000066400000000000000000000072431266062647100235530ustar00rootroot00000000000000from brian import * set_global_preferences(usenewbrianhears=True) from brian.hears import * from plot_count import ircam_plot_count import time import pickle hrtfdb = IRCAM_LISTEN(r'D:\HRTF\IRCAM') subject = 1002 hrtfset = hrtfdb.load_subject(subject) index = randint(hrtfset.num_indices) cfmin, cfmax, cfN = 150*Hz, 5*kHz, 40 sound = Sound.whitenoise(500*ms) hrtf = hrtfset.hrtf[index] cf = erbspace(cfmin, cfmax, cfN) num_indices = hrtfset.num_indices try: all_itds, all_ilds = pickle.load(open('itd_ild_data.pkl', 'r')) print 'Loaded pregenerated ITD/ILD data' except IOError: print 'Performing ITD/ILD analysis (may take several minutes)' start = time.time() all_itds = [] all_ilds = [] for j in xrange(num_indices): hrirset = Sound(hrtfset.hrtf[j].fir.T, samplerate=hrtfset.samplerate) fb = GammatoneFilterbank(Repeat(hrirset, cfN), hstack((cf, cf))) fb.buffer_init() filtered_hrirset = fb.buffer_fetch(0, hrtfset.num_samples) itds = [] ilds = [] for i in xrange(cfN): left = filtered_hrirset[:, i] right = filtered_hrirset[:, i+cfN] Lf = fft(hstack((left, zeros(len(left))))) Rf = fft(hstack((right[::-1], zeros(len(right))))) C = ifft(Lf*Rf).real i = argmax(C)+1-len(left) itds.append(i/hrtfset.samplerate) ilds.append(sqrt(amax(C)/sum(right**2))) itds = array(itds) ilds = array(ilds) all_itds.append(itds) all_ilds.append(ilds) print 'Time taken:', time.time()-start pickle.dump((all_itds, all_ilds), open('itd_ild_data.pkl', 'w')) print 'File saved as itd_ild_data.pkl' d = array([all_itds[j][i] for i in xrange(cfN) for j in xrange(num_indices)]) g = array([all_ilds[j][i] for i in xrange(cfN) for j in xrange(num_indices)]) gains = hstack((1/g, g)) gains_dB = 20*log10(gains) abs_gains_dB = abs(gains_dB) r = -abs_gains_dB[:len(gains)/2] r = hstack((r, r)) gains_dB += r gains = 10**(gains_dB/20) gains = reshape(gains, (1, len(gains))) delays_L = where(d>=0, zeros(len(d)), -d) delays_R = where(d>=0, d, zeros(len(d))) delay_max = max(amax(delays_L), amax(delays_R))*second # The following actually defines the filters and neurons hrtf_fb = hrtf.filterbank(sound) gfb = GammatoneFilterbank( RestructureFilterbank(hrtf_fb, cfN), hstack((cf, cf))) gains_fb = FunctionFilterbank( RestructureFilterbank(gfb, num_indices), lambda x:x*gains, ) cochlea = FunctionFilterbank(gains_fb, lambda x:15*clip(x, 0, Inf)**(1.0/3.0)) eqs = ''' dV/dt = (I-V)/(1*ms)+0.1*xi/(0.5*ms)**.5 : 1 I : 1 ''' G = FilterbankGroup(cochlea, 'I', eqs, reset=0, threshold=1, refractory=5*ms) cd = NeuronGroup(num_indices*cfN, eqs, reset=0, threshold=1, clock=G.clock) C = Connection(G, cd, 'V', delay=True, max_delay=delay_max) for i in xrange(num_indices*cfN): C[i, i] = 0.5 C[i+num_indices*cfN, i] = 0.5 C.delay[i, i] = delays_L[i] C.delay[i+cfN*num_indices, i] = delays_R[i] M = SpikeMonitor(G, record=sound.duration<50*ms) Mcd = SpikeMonitor(cd, record=sound.duration<50*ms) counter = SpikeCounter(cd) start = time.time() run(sound.duration, report='stderr') print 'Time taken:', time.time()-start count = counter.count print 'Input spike rate:', float(M.nspikes)/(len(G)*G.clock.t) print 'Coincidence spike rate:', float(Mcd.nspikes)/(len(cd)*G.clock.t) if sound.duration<50*ms: subplot(221) raster_plot(M) subplot(223) raster_plot(Mcd) subplot(122) ircam_plot_count(hrtfset, count, index=index) show() brian-1.4.3/dev/debugging/hears/filtering_test.py000066400000000000000000000026751266062647100220450ustar00rootroot00000000000000from time import time #http://amtoolbox.sourceforge.net/doc/filters/gammatone.php from brian import * set_global_preferences(useweave=True) from scipy.io import loadmat,savemat from brian.hears import * #from zilany import * simulation_duration = 1*ms set_default_samplerate(100*kHz) #sound = whitenoise(simulation_duration) file="/home/bertrand/Data/MatlabProg/brian_hears/ZilanyCarney-JASAcode-2009/sound.mat" X=loadmat(file,struct_as_record=False) sound = Sound(X['sound'].flatten()) sound.samplerate = 100*kHz #sound = sound.atlevel(10*dB) # level in rms dB SPL #X={} #X['sound'] = sound.__array__() #savemat('/home/bertrand/Data/MatlabProg/brian_hears/ZilanyCarney-JASAcode-2009/sound.mat',X) #sound = Sound(randn(1000)) #plot(sound) #show() #sound.samplerate = 100*kHz cf = array([100*Hz,100*Hz,100*Hz,100*Hz])#erbspace(100*Hz, 1000*Hz, 50) # centre frequencies cf = erbspace(100*Hz, 1000*Hz, 500) # centre frequencies param_drnl = {} #param_drnl['lp_nl_cutoff_m'] = 1.1 zilany_filter=ZILANY(sound, cf,32) #zilany_filter=DRNL(sound, cf) t1=time() drnl = zilany_filter.process() print time()-t1 #drnl =zilany_filter.rsigma X={} X['out_BM'] = drnl[:] #X['out_BM'] = zilany_filter.rsigma savemat('/home/bertrand/Data/MatlabProg/brian_hears/ZilanyCarney-JASAcode-2009/out_BM.mat',X) #figure() subplot(211) ##print drnl[:]R plot(drnl[:]) #imshow(flipud(drnl.T), aspect='auto') subplot(212) #print sound plot(sound) #imshow(flipud(dcgc.T), aspect='auto') show() brian-1.4.3/dev/debugging/hears/filtering_test2.py000077500000000000000000000026411266062647100221230ustar00rootroot00000000000000from time import time #http://amtoolbox.sourceforge.net/doc/filters/gammatone.php from brian import * set_global_preferences(useweave=True) from scipy.io import loadmat,savemat from brian.hears import * #from zilany import * simulation_duration = 100*ms set_default_samplerate(50*kHz) sound = whitenoise(simulation_duration) #file="/home/bertrand/Data/MatlabProg/brian_hears/Carney/sound.mat" #X=loadmat(file,struct_as_record=False) #sound = Sound(X['sound'].flatten()) sound.samplerate = 50*kHz sound = sound.atlevel(120*dB) # level in rms dB SPL X={} X['sound'] = sound.__array__() savemat('/home/bertrand/Data/MatlabProg/brian_hears/Carney/sound.mat',X) #sound = Sound(randn(1000)) #plot(sound) #show() #sound.samplerate = 100*kHz cf = array([1000*Hz])#erbspace(100*Hz, 1000*Hz, 50) # centre frequencies #cf = erbspace(100*Hz, 1000*Hz, 500) # centre frequencies param_drnl = {} #param_drnl['lp_nl_cutoff_m'] = 1.1 zilany_filter=TAN(sound, cf,1) #zilany_filter=DRNL(sound, cf) t1=time() drnl = zilany_filter.process() print time()-t1 print drnl.shape #drnl =zilany_filter.control_cont #drnl =zilany_filter.signal_cont X={} X['out_BM'] = drnl[:] #X['out_BM'] = zilany_filter.param savemat('/home/bertrand/Data/MatlabProg/brian_hears/Carney/out_BM.mat',X) #figure() subplot(211) ##print drnl[:]R plot(drnl[:]) #imshow(flipud(drnl.T), aspect='auto') subplot(212) #print sound plot(sound) #imshow(flipud(dcgc.T), aspect='auto') show() brian-1.4.3/dev/debugging/hears/ideal_filtering_model.py000066400000000000000000000036371266062647100233230ustar00rootroot00000000000000# TODO: add gains to this model, to make it equivalent to original model from brian import * set_global_preferences(usenewbrianhears=True) from brian.hears import * from plot_count import ircam_plot_count import time hrtfdb = IRCAM_LISTEN(r'D:\HRTF\IRCAM') subject = 1002 hrtfset = hrtfdb.load_subject(subject) index = randint(hrtfset.num_indices) cfmin, cfmax, cfN = 150*Hz, 5*kHz, 40 sound = Sound.whitenoise(500*ms) hrtf = hrtfset.hrtf[index] cf = erbspace(cfmin, cfmax, cfN) num_indices = hrtfset.num_indices hrtf_fb = hrtf.filterbank(sound) hrtfset_fb = hrtfset.filterbank( RestructureFilterbank(hrtf_fb, indexmapping=repeat([1, 0], hrtfset.num_indices)), minimum_buffer_size=1024) gfb = GammatoneFilterbank( RestructureFilterbank(hrtfset_fb, cfN), tile(cf, hrtfset_fb.nchannels)) cochlea = FunctionFilterbank(gfb, lambda x:15*clip(x, 0, Inf)**(1.0/3.0)) eqs = ''' dV/dt = (I-V)/(1*ms)+0.1*xi/(0.5*ms)**.5 : 1 I : 1 ''' G = FilterbankGroup(cochlea, 'I', eqs, reset=0, threshold=1, refractory=5*ms) cd = NeuronGroup(num_indices*cfN, eqs, reset=0, threshold=1, clock=G.clock) C = Connection(G, cd, 'V') for i in xrange(num_indices*cfN): C[i, i] = 0.5 C[i+num_indices*cfN, i] = 0.5 M = SpikeMonitor(G, record=sound.duration<50*ms) Mcd = SpikeMonitor(cd, record=sound.duration<50*ms) counter = SpikeCounter(cd) start = time.time() run(sound.duration, report='stderr') print 'Time taken:', time.time()-start count = counter.count count.shape = (num_indices, cfN) count = count.T count = reshape(count, (count.size,)) print 'Input spike rate:', float(M.nspikes)/(len(G)*G.clock.t) print 'Coincidence spike rate:', float(Mcd.nspikes)/(len(cd)*G.clock.t) if sound.duration<50*ms: subplot(221) raster_plot(M) subplot(223) raster_plot(Mcd) subplot(122) ircam_plot_count(hrtfset, count, index=index) show() brian-1.4.3/dev/debugging/hears/lfb_speed_test.py000066400000000000000000000014231266062647100217730ustar00rootroot00000000000000from brian import * set_global_preferences(usenewbrianhears=True, #useweave=False, ) from brian.hears import * from scipy.signal import lfilter import time N = 1000 duration = 10240 bufsize = 1024 x = Sound(randn(duration, 1), samplerate=44100*Hz) fb = GammatoneFilterbank(x, erbspace(20*Hz, 20*kHz, N)) if 0: # Doing it with lfilter channel by channel b, a = fb.filt_b, fb.filt_a n, m, p = b.shape start = time.time() for i in xrange(n): for k in xrange(p): lfilter(b[i,:,k], a[i,:,k], x) print time.time()-start fb.buffer_init() start = time.time() for i in xrange(0, duration, bufsize): fb.buffer_fetch(i, i+bufsize) end = time.time() print end-start brian-1.4.3/dev/debugging/hears/plot_count.py000066400000000000000000000033771266062647100212110ustar00rootroot00000000000000from brian import * def ircam_plot_count(hrtfset, count, index=None, showbest=True, absolute=False, vmin=None, vmax=None, I=None, ms=20, mew=2, indexcol='k', bestcol='w'): if I is None: I = arange(len(count)) count = array(count, dtype=float) num_indices = hrtfset.num_indices count.shape = (count.size/num_indices, num_indices) count = sum(count, axis=0) img = zeros((10, 24)) for i, c in enumerate(count): if i in I: elev = hrtfset.coordinates['elev'][i] azim = hrtfset.coordinates['azim'][i] if elev<60: w = 1 elif elev==60: w = 2 elif elev==75: w = 4 elif elev==90: w = 24 azim = -180 if azim>=180: azim -= 360 x = int((azim+180)/15) y = int((elev+45)/15) img[y, x:x+w] = c if absolute: imshow(img, origin='lower left', interpolation='nearest', extent=(-180-7.5, 180-7.5, -45-7.5, 90+7.5), vmin=vmin, vmax=vmax) axis('tight') else: imshow(img, origin='lower left', interpolation='nearest', extent=(-180-7.5, 180-7.5, -45-7.5, 90+7.5)) axis('tight') if index is not None: azim = hrtfset.coordinates['azim'][index] elev = hrtfset.coordinates['elev'][index] if azim>=180: azim -= 360 plot([azim], [elev], '+', ms=ms, mew=mew, color=indexcol) if showbest: i = argmax(count) azim = hrtfset.coordinates['azim'][i] elev = hrtfset.coordinates['elev'][i] if azim>=180: azim -= 360 plot([azim], [elev], 'x', ms=ms, mew=mew, color=bestcol) return img brian-1.4.3/dev/debugging/hears/testing.py000066400000000000000000000020041266062647100204620ustar00rootroot00000000000000from brian import * set_global_preferences(usenewbrianhears=True) from brian.hears import * #x = Sound(randn(15,2), samplerate=100*Hz) # #print x #print x.buffer_fetch(0, 10) #print x.buffer_fetch(5, 15) #print x.buffer_fetch(10, 20) #y = Sound(0.1*randn(200,2), samplerate=100*Hz) # #x = x+y #x = x.shift(500*ms) #x = Sound.sequence((x, x)) #print x.intensity('peak') #x = x.repeat(2) #x.copy_from(y) x = Sound.tone(500*Hz, 500*ms) y = Sound.tone(5000*Hz, 500*ms) x, y = y, x #z = Sound(array([x.flatten(), y.flatten()]).T, samplerate=x.samplerate) z = Sound((x, y), samplerate=x.samplerate) z.play(True, True) x=z #x = Sound([lambda t:sin(2*pi*t*50), # lambda t:cos(2*pi*t*100)], samplerate=10000*Hz, duration=1*second) #x = Sound(randn(100,2), samplerate=100*Hz).ramped(duration=200*ms) #x = Sound.tone(500*Hz, 500*ms) #for i in xrange(x.nchannels): # plot(x.times, x.channel(i)) #show() #x = Sound(randn(44100,2), samplerate=44100*Hz) #x.left.spectrogram() #show()brian-1.4.3/dev/debugging/hears/testing10.py000066400000000000000000000006331266062647100206310ustar00rootroot00000000000000from brian import * set_global_preferences(usenewbrianhears=True) from brian.hears import * #x = Sound((whitenoise(100*ms), whitenoise(100*ms))) #x[:50*ms] = 0.1*Sound((whitenoise(50*ms), whitenoise(50*ms))) #x[:50*ms, 0] = 0.1*whitenoise(50*ms) x = whitenoise(100*ms)*Sound(linspace(0, 1, int(100*ms*(44.1*kHz))))#[:100*ms] x = x[::-1] #x[:50*ms] = 0.1*whitenoise(50*ms) plot(x[:,0]) show() brian-1.4.3/dev/debugging/hears/testing11.py000066400000000000000000000004421266062647100206300ustar00rootroot00000000000000from brian import * set_global_preferences(usenewbrianhears=True) from brian.hears import * x = Sound(randn(15, 1), samplerate=100*Hz) fb = DoNothingFilterbank(x) fb.maximum_buffer_size = 5 y = fb.buffer_fetch(0, 15) print amax(abs(y-asarray(x))) #plot(y) #plot(x) #show() brian-1.4.3/dev/debugging/hears/testing12.py000066400000000000000000000036421266062647100206360ustar00rootroot00000000000000from brian import * set_global_preferences(usenewbrianhears=True) from brian.hears import * method = 'function' #x = whitenoise(1*second) x = sequence((whitenoise(250*ms), 2.0*whitenoise(750*ms))) # This class implements the gain (see Filterbank for details) class GainFilterbank(Filterbank): def __init__(self, source, gain=1.0): Filterbank.__init__(self, source) self.gain = gain def buffer_apply(self, input): return self.gain*input if method=='class': # This is the class for the updater object class GainController(object): def __init__(self, target, target_rms, time_constant): self.target_rms = target_rms self.time_constant = time_constant self.target = target def reinit(self): self.sumsquare = 0 self.numsamples = 0 def __call__(self, input): T = input.shape[0]/self.target.samplerate self.sumsquare += sum(input**2) self.numsamples += input.size rms = sqrt(self.sumsquare/self.numsamples) g = self.target.gain g_tgt = self.target_rms/rms tau = self.time_constant self.target.gain = g_tgt+exp(-T/tau)*(g-g_tgt) updater = GainController(fb, 0.2, 50*ms) else: target_rms = 0.2 tau = 50*ms sumsquare = 0 numsamples = 0 def updater(input): global sumsquare, numsamples T = input.shape[0]/fb.samplerate sumsquare += sum(input**2) numsamples += input.size rms = sqrt(sumsquare/numsamples) g = fb.gain g_tgt = target_rms/rms fb.gain = g_tgt+exp(-T/tau)*(g-g_tgt) fb = GainFilterbank(x) control = ControlFilterbank(fb, x, fb, updater, 10*ms) #x[:] *= 10.0 y = control.fetch(x.duration) subplot(211) plot(x) plot(y) subplot(212) plot(y/x) show() brian-1.4.3/dev/debugging/hears/testing13.py000066400000000000000000000005571266062647100206410ustar00rootroot00000000000000from brian import * set_global_preferences(usenewbrianhears=True) from brian.hears import * x = whitenoise(1*second) print x.level x.level = 10*dB x.level += 5*dB print x.level x = Sound((whitenoise(1*second), whitenoise(1*second))) print x.level x.level = (30, 5) x = x.atlevel((20, 18)) x = x*gain(5*dB) print x.level plot(x) show() brian-1.4.3/dev/debugging/hears/testing14.py000066400000000000000000000002741266062647100206360ustar00rootroot00000000000000from brian import * set_global_preferences(usenewbrianhears=True) from brian.hears import * print dB, dB.__class__ x = 3*dB print x, x.__class__ print -x print abs(x) print x+1 brian-1.4.3/dev/debugging/hears/testing15.py000066400000000000000000000026051266062647100206370ustar00rootroot00000000000000from brian import * set_global_preferences(usenewbrianhears=True) from brian.hears import * x1 = Sound((whitenoise(100*ms), whitenoise(100*ms))) x1.level = (0*dB, 10*dB) x2 = Sound((whitenoise(100*ms), whitenoise(100*ms))) x2.level = (20*dB, 30*dB) x3 = Sound((whitenoise(100*ms), whitenoise(100*ms))) x3.level = (40*dB, 50*dB) x4 = Sound((whitenoise(100*ms), whitenoise(100*ms))) x4.level = (60*dB, 70*dB) x5 = Sound((whitenoise(100*ms), whitenoise(100*ms))) x5.level = (80*dB, 90*dB) fb1 = RestructureFilterbank((x1, x2), numrepeat=3, type='serial') fb2 = RestructureFilterbank((x3, x4), type='interleave', numtile=3) fb3 = RestructureFilterbank((fb1, fb2), type='interleave') rep = Repeat(x1, 3) fb4 = Interleave(Join(rep, Repeat(x2, 3)), Tile(Interleave(x3, x4), 3)) print fb1.indexmapping print fb2.indexmapping print fb3.indexmapping print fb4.indexmapping names = ['x1', 'x2', 'x3', 'x4', 'x5', 'fb1', 'fb2', 'fb3', 'fb4'] objids = [eval('id('+name+')') for name in names] namefromid = dict(zip(objids, names)) for s in fb3.source: print namefromid[id(s)] print '---' for s in fb4.source: print namefromid[id(s)] y = Sound(fb3.buffer_fetch(0, len(x1))) print y.level rep.source = x5 fb4.buffer_init() print Sound(fb4.buffer_fetch(0, len(x1))).level for s in fb4.source: print namefromid[id(s)] brian-1.4.3/dev/debugging/hears/testing16.py000066400000000000000000000003121266062647100206310ustar00rootroot00000000000000from brian import * set_global_preferences(usenewbrianhears=True) from brian.hears import * s1 = whitenoise(100*ms) s2 = tone(1*kHz, 100*ms) play([s1, s2], s1, s2, normalise=True, sleep=True) brian-1.4.3/dev/debugging/hears/testing17.py000066400000000000000000000006401266062647100206360ustar00rootroot00000000000000from brian import * set_global_preferences(usenewbrianhears=True) from brian.hears import * hrtfdb = IRCAM_LISTEN(r'F:\HRTF\IRCAM', compensated=True) hrtfset = hrtfdb.load_subject(1002) print amin(hrtfset.coordinates['elev']) print len(hrtfset) hrtfset = hrtfset.subset(lambda elev:abs(elev)<=15) print len(hrtfset) hrtf = hrtfset(azim=30, elev=15) plot(hrtf.left) plot(hrtf.right) show() brian-1.4.3/dev/debugging/hears/testing18.py000066400000000000000000000015471266062647100206460ustar00rootroot00000000000000from brian import * set_global_preferences(usenewbrianhears=True) from brian.hears import * # Load database hrtfdb = IRCAM_LISTEN(r'D:\HRTF\IRCAM') hrtfset = hrtfdb.load_subject(1002) # Select only the horizontal plane hrtfset = hrtfset.subset(lambda elev: elev==0) # Set up a filterbank sound = whitenoise(10*ms) fb = hrtfset.filterbank(sound) # Extract the filtered response and plot img = fb.buffer_fetch(0, len(sound)).T img_left = img[:img.shape[0]/2, :] img_right = img[img.shape[0]/2:, :] subplot(121) imshow(img_left, origin='lower left', aspect='auto', extent=(0, sound.duration/ms, 0, 360)) xlabel('Time (ms)') ylabel('Azimuth') title('Left ear') subplot(122) imshow(img_right, origin='lower left', aspect='auto', extent=(0, sound.duration/ms, 0, 360)) xlabel('Time (ms)') ylabel('Azimuth') title('Right ear') show() brian-1.4.3/dev/debugging/hears/testing19.py000066400000000000000000000022011266062647100206330ustar00rootroot00000000000000from brian import * set_global_preferences(usenewbrianhears=True) from brian.hears import * import sys, warnings hrtfdb = IRCAM_LISTEN(r'D:\HRTF\IRCAM') hrtfset = hrtfdb.load_subject(1002) hrtf = hrtfset(azim=30, elev=15) def grpdelay(b, a, nfft=512, whole=False, samplerate=44.1*kHz): if not whole: nfft *= 2 w = arange(nfft, dtype=float)/nfft*samplerate oa = len(a)-1 # order of a(z) oc = oa+len(b)-1 # order of b(z) c = convolve(b, a[::-1]) # c(z) = b(z)*a(1/z)*z^-oa cr = c*arange(oc+1, dtype=float) num = fft(cr, nfft) den = fft(c, nfft) minmag = 10*sys.float_info.epsilon polebins, = (abs(den) 0: return self else: return - self def __add__(self, y): # VECTOR-READY if isinstance(y, Differentiable): zdict = {} zdict.update(self.diff) zdict.update(y.diff) for key in zdict.iterkeys(): if (key in self.diff) and (key in y.diff): zdict[key] = self.diff[key] + y.diff[key] z = Differentiable() z.val = self.val + y.val z.diff = zdict return z else: # Adding a constant z = Differentiable(val=self.val + y) z.diff = self.diff return z def __radd__(self, x): # VECTOR-READY if isinstance(x, Differentiable): zdict = {} zdict.update(self.diff) zdict.update(x.diff) for key in zdict.iterkeys(): if (key in self.diff) and (key in x.diff): zdict[key] = x.diff[key] + self.diff[key] z = Differentiable() z.val = x.val + self.val z.diff = zdict return z else: # Adding a constant z = Differentiable(val=x + self.val) z.diff = self.diff return z def __mul__(self, y): # !Not working with vectors! if isinstance(y, Differentiable): #TODO: with vectors zdict = {} zdict.update(self.diff) zdict.update(y.diff) for key in zdict.iterkeys(): if (key in self.diff) and (key in y.diff): zdict[key] = self.diff[key] * y.val + self.val * y.diff[key] elif (key in self.diff): zdict[key] = self.diff[key] * y.val else: zdict[key] = self.val * y.diff[key] z = Differentiable() z.val = self.val * y.val z.diff = zdict return z else: # Multiplying by a constant z = Differentiable(val=self.val * y) for key in self.diff: z.diff[key] = self.diff[key] * y return z def __rmul__(self, x): if isinstance(x, Differentiable): #TODO: with vectors zdict = {} zdict.update(self.diff) zdict.update(x.diff) for key in zdict.iterkeys(): if (key in self.diff) and (key in x.diff): zdict[key] = x.val * self.diff[key] + x.diff[key] * self.val elif (key in self.diff): zdict[key] = x.val * self.diff[key] else: zdict[key] = x.diff[key] * self.val z = Differentiable() z.val = x.val * self.val z.diff = zdict return z else: # Multiplying by a constant z = Differentiable(val=x * self.val) for key in self.diff: z.diff[key] = x * self.diff[key] return z def __div__(self, x): return self * (x ** -1) def __rdiv__(self, x): return x * (self ** -1) def __sub__(self, y): # VECTOR-READY if isinstance(y, Differentiable): zdict = {} zdict.update(self.diff) zdict.update(y.diff) for key in zdict.iterkeys(): if (key in self.diff) and (key in y.diff): zdict[key] = self.diff[key] - y.diff[key] z = Differentiable() z.val = self.val - y.val z.diff = zdict return z else: # Subtracting a constant z = Differentiable(val=self.val - y) z.diff = self.diff return z def __rsub__(self, x): # VECTOR-READY if isinstance(x, Differentiable): zdict = {} zdict.update(self.diff) zdict.update(x.diff) for key in zdict.iterkeys(): if (key in self.diff) and (key in x.diff): zdict[key] = x.diff[key] - self.diff[key] z = Differentiable() z.val = x.val - self.val z.diff = zdict return z else: # Subtracting a constant z = Differentiable(val=x - self.val) for key in self.diff: z.diff[key] = -self.diff[key] return z def __neg__(self): # VECTOR-READY z = Differentiable(val= -self.val) for key in self.diff: z.diff[key] = -self.diff[key] return z def __pow__(self, x): # NOT WORKING WITH VECTORS if isinstance(x, Differentiable): return exp(x * log(self)) elif x == 0: z = Differentiable(val=self.val ** x) for key in self.diff: z.diff[key] = 0 * self.diff[key] return z else: z = Differentiable(val=self.val ** x) for key in self.diff: z.diff[key] = x * self.val ** (x - 1) * self.diff[key] return z def __rpow__(self, x): # NOT WORKING WITH VECTORS return exp(self * log(x)) def __getitem__(self, i): z = Differentiable(val=self.val[i]) z.diff = {} for key in self.diff.iterkeys(): z.diff[key] = self.diff[key][i, :] return z def __len__(self): return len(self.val) def __str__(self): s = str(self.val) for key, value in self.diff.iteritems(): s += ' ; d/d' + key + '=' + str(value) return s def exp(self): # !! Not working for vectors !! zdict = {} zdict.update(self.diff) for key in zdict.iterkeys(): zdict[key] = exp(self.val) * self.diff[key] z = Differentiable(val=exp(self.val)) z.diff = zdict return z def cos(self): # !! Not working for vectors !! zdict = {} zdict.update(self.diff) for key in zdict.iterkeys(): zdict[key] = -sin(self.val) * self.diff[key] z = Differentiable(val=cos(self.val)) z.diff = zdict return z def sin(self): # !! Not working for vectors !! zdict = {} zdict.update(self.diff) for key in zdict.iterkeys(): zdict[key] = cos(self.val) * self.diff[key] z = Differentiable(val=sin(self.val)) z.diff = zdict return z def log(self): # !! Not working for vectors !! zdict = {} zdict.update(self.diff) for key in zdict.iterkeys(): zdict[key] = (1. / (self.val)) * self.diff[key] z = Differentiable(val=log(self.val)) z.diff = zdict return z class HigherDifferentiable(Differentiable): ''' A differentiable variable with order n. ''' def __init__(self, name=None, val=0., order=1): Differentiable.__init__(self, name, val) if order > 1: self.val = HigherDifferentiable(name, val, order - 1) #Utility function def eye_lil_matrix(n): ''' Returns the identity matrix of size n as a lil_matrix (sparse matrix). ''' M = sparse.lil_matrix((n, n)) M.setdiag([1.] * n) return M brian-1.4.3/dev/debugging/old/barbieri_brunel2007.py000066400000000000000000000023171266062647100221270ustar00rootroot00000000000000''' Adapted from Irregular persistent activity induced by synaptic excitatory feedback F Barbieri, N Brunel - Frontiers in Computational Neuroscience, 2007 R Brette 2009 ''' from brian import * theta = 20 * mV Vr = 15 * mV tau = 5 * ms tau_rp = 2 * ms N = 800 nu_sp = 3 * Hz mu_ext = 10 * mV # (?) sigma_ext = 8 * mV tau_r = .05 * ms tau_rn = 2 * ms tau_d = 5 * ms tau_dn = 100 * ms gamma = .9 u = 0.5 tau_rec = 160 * ms lambd = 0.1 D = 1 * ms # delay (?) J = 35 * mV # (?) #defaultclock.dt=.001*ms eqs = ''' dv/dt=(-v+I_rec+mu_ext*tau**.5*xi)/tau : volt I_rec=s+z : volt ds/dt=(x-s)/tau_d : volt dx/dt=-x/tau_r : volt dz/dt=(h-z)/tau_dn : volt dh/dt=-h/tau_rn : volt ''' neurons = NeuronGroup(N, model=eqs, threshold=theta, reset=Vr, refractory=tau_rp) synapses_ampa = Connection(neurons, neurons, 'x', weight=(1 - gamma) * tau / (tau_r * N) * J) synapses_nmda = Connection(neurons, neurons, 'h', weight=gamma * tau / (tau_rn * N) * J) stp1 = STP(synapses_ampa, taud=tau_rec, tauf=.1 * ms, U=u) stp2 = STP(synapses_nmda, taud=tau_rec, tauf=.1 * ms, U=u) spikes = SpikeMonitor(neurons) run(1 * second) #run(500*ms) raster_plot(spikes) print spikes.nspikes * 1. / N show() brian-1.4.3/dev/debugging/old/brunel2001.py000066400000000000000000000053551266062647100202670ustar00rootroot00000000000000# From Ariel Zylberberg from brian import * vl = -70 * mvolt vth = -50 * mvolt vreset = -55 * mvolt tauAMPA = 2 * ms tauNMDA = 100 * ms tauGABA = 10 * ms a = 0.062 * mvolt ** -1 b = 3.57 * 1 Mg = 1 ve = 0 * mvolt vi = -70 * mvolt eqs = ''' dv/dt = (-gl*(v-vl)-Isyn)*(1./cm) : volt Isyn = IAMPA+INMDA+IGABA : amp IAMPA = (gextAMPA*sextAMPA+grecAMPA*srecAMPA)*(v-ve) : amp INMDA = gNMDA*sNMDA*(v-ve)/(1+Mg*exp(-a*v)/b) : amp IGABA = gGABA*sGABA*(v-vi) : amp dsextAMPA/dt = -sextAMPA/tauAMPA : 1 dsrecAMPA/dt = -srecAMPA/tauAMPA : 1 dsNMDA/dt = -sNMDA/tauNMDA : 1 dsGABA/dt = -sGABA/tauGABA : 1 dss/dt = (0.63-ss)/tauNMDA : 1 cm : nfarad gl : nsiemens gextAMPA : nsiemens grecAMPA : nsiemens gNMDA : nsiemens gGABA : nsiemens ''' N = 1000 Ne = 800 Ni = 200 Ge = NeuronGroup(Ne, model=eqs, threshold=vth, reset=vreset, refractory=2 * msecond) Gi = NeuronGroup(Ni, model=eqs, threshold=vth, reset=vreset, refractory=1 * msecond) Ge.cm = 0.5 * nfarad Ge.gl = 25 * nsiemens Ge.gextAMPA = 2.08 * nsiemens Ge.grecAMPA = 0.104 * nsiemens Ge.gNMDA = 0.327 * nsiemens Ge.gGABA = 1.25 * nsiemens Gi.cm = 0.2 * nfarad Gi.gl = 20 * nsiemens Gi.gextAMPA = 1.62 * nsiemens Gi.grecAMPA = 0.081 * nsiemens Gi.gNMDA = 0.258 * nsiemens Gi.gGABA = 0.973 * nsiemens #init Ge.ss = 0 Ge.sNMDA = 0 Ge.sGABA = 0 Ge.sextAMPA = 0 Ge.srecAMPA = 0 Ge.v = -60 * mV Gi.ss = 0 Gi.sNMDA = 0 Gi.sGABA = 0 Gi.sextAMPA = 0 Gi.srecAMPA = 0 Gi.v = -60 * mV #recurrent connections def reset_fun(P, spikes): P.v_[spikes] = vreset P.ss_[spikes] = 0 C = [] init_list = [(Ge, Ge, 'srecAMPA', None), (Ge, Gi, 'srecAMPA', None), (Ge, Ge, 'sNMDA', 'ss')] for source, target, variable, modulation in init_list: C.append(Connection(source, target, variable, modulation=modulation)) C[-1].connect_full(source, target, weight=1) C1 = Connection(Ge, Ge, 'srecAMPA') C1.connect_full(Ge, Ge, weight=1) C2 = Connection(Ge, Gi, 'srecAMPA') C2.connect_full(Ge, Gi, weight=1) C3 = Connection(Ge, Ge, 'sNMDA', modulation='ss') C3.connect_full(Ge, Ge, weight=1) C4 = Connection(Ge, Gi, 'sNMDA', modulation='ss') C4.connect_full(Ge, Gi, weight=1) C5 = Connection(Gi, Ge, 'sGABA') C5.connect_full(Gi, Ge, weight=1) C6 = Connection(Gi, Gi, 'sGABA') C6.connect_full(Gi, Gi, weight=1) #external connections Pe = PoissonGroup(Ne, 2400 * Hz) Ce = Connection(Pe, Ge, 'sextAMPA') Ce.connect_one_to_one(Pe, Ge, weight=1) Pi = PoissonGroup(Ni, 2400 * Hz) Ci = Connection(Pi, Gi, 'sextAMPA') Ci.connect_one_to_one(Pi, Gi, weight=1) #record Me = PopulationRateMonitor(Ge, bin=25 * ms) Mi = PopulationRateMonitor(Gi, bin=25 * ms) #run run(10.0 * second) #plot plot(Me.times / ms, Me.rate / Hz) plot(Mi.times / ms, Mi.rate / Hz) show() brian-1.4.3/dev/debugging/old/gdelays.py000066400000000000000000000020441266062647100201150ustar00rootroot00000000000000# Delays arising from interaction of synaptic conductances # The idea: changing ge changes the delay # Second idea: synaptic depression/facilitation changes the delay too from brian import * taum = 20 * ms Ee = 70 * mV # relative to rest; Ei = 0 * mV # shunting inhibition taue = 5 * ms taui = 10 * ms N0 = 10 N = N0 * N0 eqs = ''' dv/dt=(-v+ge*(Ee-v)+gi*(Ei-v))/taum : volt dge/dt=-ge/taue : 1 # relative conductance dgi/dt=-gi/taui : 1 # relative conductance ''' neurons = NeuronGroup(N, model=eqs) ge_range = linspace(0, 5, N0) gi_range = linspace(0, 5, N0) neurons.ge = array(ge_range * ones((N0, N0))).reshape(N) neurons.gi = array(gi_range * ones((N0, N0))).T.reshape(N) mon = StateMonitor(neurons, 'v', record=True) # that's heavy! run(100 * ms) m = zeros(N) for i in range(N): m[i] = mon.times[argmax(mon[i])] / ms figure() hist(m, 10) figure() pcolor(array(m).reshape(N0, N0)) #contour(array(m).reshape(N0,N0)) xlabel('ge') ylabel('gi') figure() plot(mon.times / ms, mon[2 * N0 + 2] / mV) show() brian-1.4.3/dev/debugging/old/generation_of_correlated_spike_trains.py000066400000000000000000000075071266062647100262740ustar00rootroot00000000000000''' Brette, R. (2008). Generation of correlated spike trains. Response of a leaky integrate-and-fire model to correlated inputs. Comparison with Ruben's equations. Fig 8 in the paper. ''' from brian import * from time import time # Firing rate of an integrate-and-fire model with white noise input current fWNIF = lambda x: exp(x ** 2) * (1. + special.erf(x)) def rateIFWhiteNoise(refrac, tau, theta_eff, H_eff): a, _ = integrate.quad(fWNIF, H_eff, theta_eff) return 1. / (refrac + a * tau * sqrt(pi)) # Two independently correlated populations (no correlation between the two) # ** Doubly stochastic processes ** Ne = 800 Ni = 200 tauc = 1 * ms Je = 0.04 Ji = -0.16 nu = 10. * Hz duration = 20 * second c = 0.001 # 0.02 taum = 10. * ms refrac = 2. * ms # Scaling scaling = 20. nu = nu * scaling Je = Je / sqrt(scaling) Ji = Ji / sqrt(scaling) ''' ********** Simulation ********** ''' name = 'mixtureB' if name == 'Cox': # Cox processes input_exc = HomogeneousCorrelatedSpikeTrains(Ne, nu, c, tauc) input_inh = HomogeneousCorrelatedSpikeTrains(Ni, nu, c, tauc) elif name == 'mixtureA': # Mixture processes Te = mixture_process(nu=array([nu / c]), P=ones((Ne, 1)) * c, tauc=tauc, t=duration) Ti = mixture_process(nu=array([nu / c]), P=ones((Ni, 1)) * c, tauc=tauc, t=duration) input_exc = SpikeGeneratorGroup(Ne, Te) input_inh = SpikeGeneratorGroup(Ni, Ti) elif name == 'mixtureB': # Mixture processes Pe = eye(Ne, Ne + 1) Pe[:, Ne] = 1 Pi = eye(Ni, Ni + 1) Pi[:, Ni] = 1 Te = mixture_process(nu=array([c * nu] * Ne + [(1 - c) * nu]), P=Pe, tauc=tauc, t=duration) Ti = mixture_process(nu=array([c * nu] * Ni + [(1 - c) * nu]), P=Pi, tauc=tauc, t=duration) input_exc = SpikeGeneratorGroup(Ne, Te) input_inh = SpikeGeneratorGroup(Ni, Ti) neuron = NeuronGroup(1, model='dv/dt=-v/taum : 1', reset=0, threshold=1, refractory=refrac) exc_synapses = Connection(input_exc, neuron, 'v') exc_synapses.connect_full(input_exc, neuron, Je) inh_synapses = Connection(input_inh, neuron, 'v') inh_synapses.connect_full(input_inh, neuron, Ji) counter = SpikeCounter(neuron) t1 = time() run(duration) t2 = time() print "Simulated in", t2 - t1, "s" print "Rate:", counter[0] / duration ''' *********************** Theoretical predictions *********************** ''' # Ruben's formulas F = c + 1. # Fano factor rho = c / F # Spike count correlation sigmaw2 = Je * Je * Ne * nu + Ji * Ji * Ni * nu sigma2 = Je * Je * nu * Ne * (F - 1. + (Ne - 1.) * F * rho) + Ji * Ji * nu * Ni * (F - 1. + (Ni - 1.) * F * rho) alpha = sigma2 / sigmaw2 # correlation magnitude mu = Je * Ne * nu + Ji * Ni * nu sigmaeff = sqrt(sigmaw2 + sigma2) sigmaw = sqrt(sigmaw2) thetahat = (1. - mu * taum) / (sigmaw * sqrt(taum)) Hhat = -mu * taum / (sigmaw * sqrt(taum)) print "Fano factor =", F print "Spike count correlation (rho) =", rho print "Correlation magnitude (alpha) =", alpha print "Mean input =", mu print "Input variance =", sigma2 nueff = rateIFWhiteNoise(refrac, taum, (1. - mu * taum) / (sigmaeff * sqrt(taum)), -mu * taum / (sigmaeff * sqrt(taum))) nu0 = rateIFWhiteNoise(refrac, taum, thetahat, Hhat) print "Theoretical rate for uncorrelated inputs =", nu0 print "Theoretical rate for instantaneous correlations =", nueff # coefficient for Ruben's long tauc formula C = alpha * (taum * nu0) ** 2 * (taum * nu0 * (fWNIF(thetahat) - fWNIF(Hhat)) ** 2 / (1. - nu0 * refrac) - \ (thetahat * fWNIF(thetahat) - Hhat * fWNIF(Hhat)) / sqrt(2)) print "C =", C shortprediction = nueff - alpha * nu0 * nu0 * sqrt(.5 * pi * taum * tauc) * fWNIF((1. - mu * taum) / (sigmaw * sqrt(taum))) longprediction = nu0 + C / tauc print "Predicted rate for short tauc:", shortprediction print "Predicted rate for long tauc:", longprediction brian-1.4.3/dev/debugging/old/hansel_et_al_1998.py000066400000000000000000000025021266062647100215740ustar00rootroot00000000000000''' Hansel et al (1998) On Numerical Simulations of Integrate-and-Fire Neural Networks. Neural Computation The authors show that a very small time step is necessary to obtain some synchronization results. The model is a network of coupled oscillators (all-to-all). ''' from brian import * #defaultclock.dt=.01*ms N = 128 duration1 = 2 * second duration2 = 2 * second Vl = -60 * mV theta = -40 * mV tau = 10 * ms tau1 = 3 * ms tau2 = 1 * ms I0 = 21 * mV # I couldn't find the value in the text! # there is also an error in the text: replace tau*I0 by I0 w = .7 * mV # slightly below critical value eqs = ''' dV/dt=(Vl-V+I+I0)/tau : volt dI/dt=(x-I)/tau1 : volt dx/dt=-x/tau2 : volt ''' T = -tau * log((I0 - theta) / (I0 - Vl)) P = NeuronGroup(N, model=eqs, threshold=theta, reset=Vl) C = Connection(P, P, 'x', weight=w) c = 1. # in 0..1 P.V = I0 + (Vl - I0) * exp(-c * arange(N) / N * T / tau) run(duration1) M = PopulationRateMonitor(P) # average spiking activity S = StateMonitor(P, 'V') # to measure the individual variances of V mv = [] @network_operation def monitor_averageV(): mv.append(mean(P.V)) run(duration2) deltaN = var(mv) delta = mean(S.var) sigma = deltaN / delta # Measure of synchrony print sigma # Visual check plot(M.times / ms, M.smooth_rate(1 * ms)) show() brian-1.4.3/dev/debugging/old/scorpion_simple.py000066400000000000000000000024141266062647100216730ustar00rootroot00000000000000''' Adapted (and simplified) from Theory of Arachnid Prey Localization W. Sturzl, R. Kempter, and J. L. van Hemmen PRL 2000 The membrane potential is normalized (i.e. 0 = rest, 1 = threshold). ''' from brian import * # Parameters radius = 2.5 * cm wave_speed = 50 * meter / second prey_angle = 0 legs_angle = pi / 8 + pi / 4 * arange(8) wave = lambda t:.2 * sin(2 * pi * 300 * Hz * t) * cos(2 * pi * 25 * Hz * t) # Leg mechanical receptors tau = 1 * ms sigma = .01 eqs_legs = """ dv/dt=(1+wave(t-d)-v)/tau+sigma*(2./tau)**.5*xi:1 d : second # wave delay """ legs = NeuronGroup(8, model=eqs_legs, threshold=1, reset=0, refractory=1 * ms) legs.d = radius / wave_speed * (1 - cos(prey_angle - legs_angle)) # Command neurons eqs_neuron = ''' dv/dt=(x-v)/tau : 1 dx/dt=-x/tau : 1 # PSPs are alpha functions ''' neurons = NeuronGroup(8, model=eqs_neuron, threshold=1, reset=0) synapses_ex = IdentityConnection(legs, neurons, 'x', weight=4) synapses_inh = Connection(legs, neurons, 'x', delay=.7 * ms) for i in range(8): synapses_inh[i, (4 + i - 1) % 8] = -1 synapses_inh[i, (4 + i) % 8] = -1 synapses_inh[i, (4 + i + 1) % 8] = -1 spikes = SpikeCounter(neurons) run(300 * ms) polar(legs_angle, spikes.count) print spikes.count show() brian-1.4.3/dev/debugging/pickle_spikecontainer.py000066400000000000000000000015121266062647100222530ustar00rootroot00000000000000from brian import * from brian.utils.circular import SpikeContainer import pickle def unpickle_SpikeContainer(m, allspikes): newsc = SpikeContainer(m) for spikes in allspikes[::-1]: newsc.push(spikes) return newsc #class SpikeContainer(SpikeContainer): # def __init__(self, m, useweave=False, compiler=None): # super(SpikeContainer, self).__init__(m) # self.m = m # def __reduce__(self): # return (unpickle_SpikeContainer, (self.m, tuple(self[i].copy() for i in xrange(self.m)))) sc = SpikeContainer(10) # push some spikes into sc for i in xrange(100): sc.push(randint(1000, size=randint(5))) print sc.lastspikes() print sc[0], sc[1], sc[2], sc[9] s = pickle.dumps(sc) sc = pickle.loads(s) print sc.lastspikes() print sc[0], sc[1], sc[2], sc[9] brian-1.4.3/dev/debugging/pycuda_exp_crash.py000066400000000000000000000005721266062647100212340ustar00rootroot00000000000000from numpy import zeros import numpy from pycuda import autoinit from pycuda import gpuarray from pycuda import compiler x = gpuarray.to_gpu(zeros(10, numpy.float32)) src = ''' __global__ void f(float *x) { x[0] = exp(x[0]); } ''' gpu_mod = compiler.SourceModule(src) gpu_func = gpu_mod.get_function("f") gpu_func(x, block=(1, 1, 1), grid=(1, 1)) print x.get() brian-1.4.3/dev/debugging/randommatrix.py000066400000000000000000000011031266062647100204070ustar00rootroot00000000000000from brian import * P = NeuronGroup(100, model='dv/dt=0*Hz:1') Q = NeuronGroup(200, model='dv/dt=0*Hz:1') C = Connection(P, Q, 'v') C.connect_random(P, Q, sparseness=lambda i, j:exp(-.05 * abs(i - j)), weight=lambda i, j:abs(cos(.1 * (i - j)))) C2 = Connection(P, Q, 'v') C2.connect_random(P, Q, sparseness=lambda i, j:exp(-.05 * abs(i - j)), weight=lambda: rand()) print C.W.nnz, C2.W.nnz figure() subplot(211) imshow(C.W.todense(), interpolation='nearest', origin='lower') subplot(212) imshow(C2.W.todense(), interpolation='nearest', origin='lower') show() brian-1.4.3/dev/debugging/refractoriness_in_modelfitting.py000066400000000000000000000027461266062647100242040ustar00rootroot00000000000000from brian import * from brian.library.modelfitting import * if __name__ == '__main__': duration = 1*second model = Equations(''' dV/dt=(R*I-V)/tau : 1 I : 1 R : 1 tau : second ''') input = randn(int(duration/defaultclock.dt))*2e-10+4e-10 def getspikes(): G = NeuronGroup(1, model, reset=0, threshold=1, refractory=5*ms) G.R = 3e9 G.tau = 20*ms M = SpikeMonitor(G) G.I = TimedArray(input) run(duration) i, t = zip(*M.spikes) clear(True) reinit_default_clock() return t spikes0 = getspikes() spikes = [] for i in xrange(1): spikes.extend([(i, spike + 5*i*ms) for spike in spikes0]) results = modelfitting( model = model, reset = 0, threshold = 1, data = spikes, input = input, dt = .1*ms, popsize = 150000, maxiter = 50, gpu = 1, algorithm = CMAES, delta = 1*ms, R = [1.0e9, 9.0e9], tau = [10*ms, 40*ms], delays = [-20*ms, 20*ms], refractory=[0*ms, 0*ms, 10*ms, 10*ms]) print_table(results) brian-1.4.3/dev/debugging/reusing_equations.py000066400000000000000000000003241266062647100214520ustar00rootroot00000000000000from brian import * tau = 10 * ms bof = 3.0 eqs = Equations(''' dx/dt = -x/tau : 1 V = x*bof : 1 ''') G = NeuronGroup(1, eqs) G2 = NeuronGroup(1, eqs) G.x = 1 G2.x = 2 print G.V print G2.V brian-1.4.3/dev/debugging/sepeq_aliases.py000066400000000000000000000006311266062647100205250ustar00rootroot00000000000000from brian import * from brian.utils.separate_equations import * eqs = Equations(''' dx/dt = -x/second : 1 dy/dt = -y/second : 1 z = x-y : 1 du/dt = -u/second : 1 dv/dt = -v/second : 1 w = u-v : 1 ''') print separate_equations(eqs) eqs = Equations(''' dx/dt = -x/second : 1 y = x*x : 1 z = x du/dt = -u/second : 1 #v = z+u : 1 ''') print separate_equations(eqs) brian-1.4.3/dev/debugging/shinomoto.py000066400000000000000000000025651266062647100177360ustar00rootroot00000000000000''' Test of Shinomoto's automatic bin size selection Probably not working yet! ''' from brian import * import time start_time = time.time() taum = 20 * ms taue = 5 * ms taui = 10 * ms Vt = -50 * mV Vr = -60 * mV El = -49 * mV eqs = Equations(''' dv/dt = (ge+gi-(v-El))/taum : volt dge/dt = -ge/taue : volt dgi/dt = -gi/taui : volt ''') P = NeuronGroup(4000, model=eqs, threshold=Vt, reset=Vr, refractory=5 * ms) P.v = Vr P.ge = 0 * mV P.gi = 0 * mV Pe = P.subgroup(3200) Pi = P.subgroup(800) we = (60 * 0.27 / 10) * mV # excitatory synaptic weight (voltage) wi = (-20 * 4.5 / 10) * mV # inhibitory synaptic weight Ce = Connection(Pe, P, 'ge', weight=we, sparseness=0.02) Ci = Connection(Pi, P, 'gi', weight=wi, sparseness=0.02) P.v = Vr + rand(len(P)) * (Vt - Vr) # Record the number of spikes Me = PopulationSpikeCounter(Pe) Mi = PopulationSpikeCounter(Pi) # A population rate monitor M = PopulationRateMonitor(P[:200]) print "Network construction time:", time.time() - start_time, "seconds" print len(P), "neurons in the network" print "Simulation running..." run(1 * msecond) start_time = time.time() run(1 * second) duration = time.time() - start_time print "Simulation time:", duration, "seconds" print Me.nspikes, "excitatory spikes" print Mi.nspikes, "inhibitory spikes" plot(M.times / ms, M.smooth_rate(filter='flat')) show() brian-1.4.3/dev/debugging/spikes.txt000066400000000000000000000007561266062647100174040ustar00rootroot000000000000005.890000000000128500e-02 1.259000000000014600e-01 2.564999999999990600e-01 2.784000000000013100e-01 3.195000000000014500e-01 3.524999999999991500e-01 3.848999999999982400e-01 4.194999999999993200e-01 4.557999999999999800e-01 4.826000000000014700e-01 5.149000000000008000e-01 5.381999999999997900e-01 5.676999999999985400e-01 6.749000000000009400e-01 7.154999999999986900e-01 7.504999999999988300e-01 8.883000000000009800e-01 9.280000000000008200e-01 9.682999999999992700e-01 brian-1.4.3/dev/debugging/stdp_aliases.py000066400000000000000000000005311266062647100203610ustar00rootroot00000000000000from brian import * set_global_preferences(usecstdp=False) G = NeuronGroup(10, 'V:1') C = Connection(G, G, 'V') eqs_stdp = Equations(''' dx/dt = -x/second : 1 y = x*x : 1 du/dt = -u/second : 1 v = u*u : 1 ''') stdp = STDP(C, eqs=eqs_stdp, pre='x+=1; w+=v', post='u+=1; w+=y', wmax=10) brian-1.4.3/dev/debugging/stdp_delays_order_of_operations_bug.py000066400000000000000000000011061266062647100251770ustar00rootroot00000000000000from brian import * #set_global_preferences(usecstdp=False) #log_level_debug() G = NeuronGroup(1, 'V:1', reset=0, threshold=1) C = Connection(G, G, 'V', delay=True, max_delay=5*ms ) eqs_stdp = """ dA_pre/dt = -A_pre/(10*ms) : 1 dA_post/dt = -A_post/(10*ms) : 1 """ stdp = STDP(C, eqs=eqs_stdp, pre=''' A_pre += 1 w += A_pre*A_post A_pre += 1 ''', post=''' A_post += 1 w += A_pre ''', wmax=1) G.V = 2 run(1*ms) brian-1.4.3/dev/debugging/stdp_dependencies.py000066400000000000000000000005761266062647100213770ustar00rootroot00000000000000from brian import * #set_global_preferences(usecstdp=False) G = NeuronGroup(10, 'V:1') C = Connection(G, G, 'V') eqs_stdp = Equations(''' dx/dt = -x/second : 1 dy/dt = -y/second : 1 du/dt = -u/second : 1 dv/dt = -v/second : 1 ''') stdp = STDP(C, eqs=eqs_stdp, pre='x+=1; y+=1; w+=u+v', post='u+=1; v+=1; w+=x+y', wmax=10) brian-1.4.3/dev/debugging/test_nogroup.py000066400000000000000000000001221266062647100204320ustar00rootroot00000000000000from brian import * @network_operation def f(): print "yo" run(1*ms) brian-1.4.3/dev/debugging/troubleshooting/000077500000000000000000000000001266062647100205645ustar00rootroot00000000000000brian-1.4.3/dev/debugging/troubleshooting/2008/000077500000000000000000000000001266062647100211555ustar00rootroot00000000000000brian-1.4.3/dev/debugging/troubleshooting/2008/customrefractoriness.py000066400000000000000000000117721266062647100260230ustar00rootroot00000000000000from brian import * __all__ = ['SimpleCustomRefractoriness', 'CustomRefractoriness'] class SimpleCustomRefractoriness(Refractoriness): ''' Holds the state variable at the custom reset value for a fixed time after a spike. **Initialised as:** :: SimpleCustomRefractoriness(resetfunc[,period=5*ms[,state=0]]) with arguments: ``resetfun`` The custom reset function ``resetfun(P, spikes)`` for ``P`` a :class:`NeuronGroup` and ``spikes`` a list of neurons that fired spikes. ``period`` The length of time to hold at the reset value. ``state`` The name or number of the state variable to reset and hold, it is your responsibility to check that this corresponds to the custom reset function. The assumption is that ``resetfun(P, spikes)`` will reset the state variable ``state`` on the group ``P`` for the spikes with indices ``spikes``. The values assigned by the custom reset function are stored by this object, and they are clamped at these values for ``period``. This object does not introduce refractoriness for more than the one specified variable ``state`` or for spike indices other than those in the variable ``spikes`` passed to the custom reset function. ''' @check_units(period=second) def __init__(self, resetfun, period=5 * msecond, state=0): self.period = period self.resetfun = resetfun self.state = state self._periods = {} # a dictionary mapping group IDs to periods self.statevectors = {} self.lastresetvalues = {} def __call__(self, P): ''' Clamps state variable at reset value. ''' # if we haven't computed the integer period for this group yet. # do so now if id(P) in self._periods: period = self._periods[id(P)] else: period = int(self.period / P.clock.dt) + 1 self._periods[id(P)] = period V = self.statevectors.get(id(P), None) if V is None: V = P.state_(self.state) self.statevectors[id(P)] = V LRV = self.lastresetvalues.get(id(P), None) if LRV is None: LRV = zeros(len(V)) self.lastresetvalues[id(P)] = LRV lastspikes = P.LS.lastspikes() self.resetfun(P, lastspikes) # call custom reset function LRV[lastspikes] = V[lastspikes] # store a copy of the custom resetted values clampedindices = P.LS[0:period] V[clampedindices] = LRV[clampedindices] # clamp at custom resetted values def __repr__(self): return 'Custom refractory period, ' + str(self.period) class CustomRefractoriness(Refractoriness): ''' Holds the state variable at the custom reset value for a fixed time after a spike. **Initialised as:** :: CustomRefractoriness(resetfunc[,period=5*ms[,refracfunc=resetfunc]]) with arguments: ``resetfunc`` The custom reset function ``resetfunc(P, spikes)`` for ``P`` a :class:`NeuronGroup` and ``spikes`` a list of neurons that fired spikes. ``refracfunc`` The custom refractoriness function ``refracfunc(P, indices)`` for ``P`` a :class:`NeuronGroup` and ``indices`` a list of neurons that are in their refractory periods. In some cases, you can choose not to specify this, and it will use the reset function. ``period`` The length of time to hold at the reset value. ''' @check_units(period=second) def __init__(self, resetfun, period=5 * msecond, refracfunc=None): self.period = period self.resetfun = resetfun if refracfunc is None: refracfunc = resetfun self.refracfunc = refracfunc self._periods = {} # a dictionary mapping group IDs to periods def __call__(self, P): ''' Clamps state variable at reset value. ''' # if we haven't computed the integer period for this group yet. # do so now if id(P) in self._periods: period = self._periods[id(P)] else: period = int(self.period / P.clock.dt) + 1 self._periods[id(P)] = period lastspikes = P.LS.lastspikes() self.resetfun(P, lastspikes) # call custom reset function clampedindices = P.LS[0:period] self.refracfunc(P, clampedindices) def __repr__(self): return 'Custom refractory period, ' + str(self.period) if __name__ == '__main__': def f(P, spikes): P.V[spikes] = rand(len(spikes)) * 0.5 R = SimpleCustomRefractoriness(f) G = NeuronGroup(5, model=''' dV/dt = -(V-1.1)/(5*ms) : 1 ''', reset=R, threshold=1) M = StateMonitor(G, 'V', record=True) run(1 * second) for i in range(5): plot(M.times, M[i]) show() brian-1.4.3/dev/debugging/troubleshooting/2008/francis_connection_from_brian_support_13_aug_2008.py000066400000000000000000000013111266062647100331710ustar00rootroot00000000000000from brian import * eqs = ''' dv/dt = (ge+gi-(v+49*mV))/(20*ms) : volt dge/dt = -ge/(5*ms) : volt dgi/dt = -gi/(10*ms) : volt ''' P = NeuronGroup(40, model=eqs, threshold= -50 * mV, reset= -60 * mV) P.v = -60 * mV + 10 * mV * rand(len(P)) Pe = P.subgroup(32) Pi = P.subgroup(8) Ce = Connection(Pe, P, 'ge') Ci = Connection(Pi, P, 'gi') Ce.connect_random(Pe, P, 1.0, weight=1.62 * mV) Ci.connect_random(Pi, P, 0.02, weight= -9 * mV) M = SpikeMonitor(P) run(1 * ms) # Here do the compress and count then number of connections Ce.compress() countCe = 0 for i in range(0, 32): for j in range(0, 41): if abs(Ce[i, j]) > 1e-10: countCe += 1 print Ce.W print countCe brian-1.4.3/dev/debugging/troubleshooting/2008/jonathan_18_aug_2008.py000066400000000000000000000026001266062647100251440ustar00rootroot00000000000000from brian import * from customrefractoriness import * #----Simulation parameters cl = Clock(dt=0.1 * ms) period = 5 * ms #period of time over which the slope of depolarization is computed n = int(period / cl.dt) N = 1 k = 16 * mV / ms #----Fixed parameters ENa = 60 * mV El = -70 * mV tau = 5 * ms tauh = 5 * ms #----Input parameters mu = 5 * mV sigma = 2 * 15 * mV tauc = 10 * ms #----Na channels parameters va = -28.69 * mV ka = 6.77 * mV vi = -71.87 * mV ki = 7.65 * mV r = 10.8 eqs = Equations(""" # Sodium channel model Pa=1/(1+exp(-(v-va)/ka)) : 1 Pi=1/(1+exp(-(v-vi)/ki)) : 1 # Membrane equation dv/dt=(-r*Pa*h*(v-ENa)-(v-El)+I)/tau : volt dh/dt=((1-Pi)-h)/tauh : 1 # Input dI/dt=(mu-I)/tauc+sigma*(.5*tauc)**-.5*xi : volt """) def myreset(P, spikes): P.v[spikes] = El P.h[spikes] = 1 #neuron=NeuronGroup(N,model=eqs,threshold=0*mV,reset=myreset,refractory=50*ms) neuron = NeuronGroup(N, model=eqs, threshold=0 * mV, reset=CustomRefractoriness(myreset, 50 * ms)) #----Initial conditions neuron.v = El neuron.h = 1 #----Record variables Mv = StateMonitor(neuron, 'v', record=0) Mh = StateMonitor(neuron, 'h', record=0) Msp = SpikeMonitor(neuron) run(150 * ms) #--Fig2:Parameters time course--------------- figure() subplot(211) plot(Mv.times / ms, Mv[0] / mV) subplot(212) plot(Mh.times / ms, Mh[0]) show() brian-1.4.3/dev/debugging/troubleshooting/2008/jonathan_19_aug_2008.py000066400000000000000000000051311266062647100251470ustar00rootroot00000000000000from brian import * from customrefractoriness import * from scipy.optimize import fmin from brian.tools.autodiff import * #----Fixed parameters ENa = 60 * mV El = -70 * mV tau = 5 * ms tauh = 5 * ms #----Input parameters mu = 0 * mV sigma = 2 * 15 * mV tauc = 10 * ms k = 1 / 3 * sigma / ms #empirical threshold criterion #----Na channels parameters #--load the data #Va,Ka,Vi,Ki,R=load('Nav1_6.txt').T#I don't have this file Va, Ka, Vi, Ki, R = (zeros(10), zeros(10), zeros(10), zeros(10), zeros(10)) N = len(Va) eqs = Equations(""" # Sodium channel model Pa=1/(1+exp(-(v-va)/ka)) : 1 Pi=1/(1+exp(-(v-vi)/ki)) : 1 # Membrane equation dv/dt=(-r*Pa*h*(v-ENa)-(v-El)+I)/tau : volt dh/dt=((1-Pi)-h)/tauh : 1 # Input dI/dt=(mu-I)/tauc+sigma*(.5*tauc)**-.5*xi : volt # Sodium channel model Parameters va : mV ka : mV vi : mV ki : mV r : 1 """) eqs.prepare() #--Function for computing the EIF and threshold dynamics parameters def threshold_parameters(f, h_inf, rest=El): ''' Calculates the threshold and the slope factor from the equation tau*dv/dt=f(v) ''' vt = fmin(f, rest, disp=False)[0] # Threshold (simplex algorithm) vt = vt * volt deltat = 1 / (tau * differentiate(f, vt, order=2)) # Slope factor a = -differentiate(h_inf, vt, order=1) * deltat / h_inf(vt) b = -deltat * math.log(h_inf(vt)) + (1 - a) * vt return (vt, deltat, a, b) #--Predictions for each Na channel model vt = [[] for i in range(len(Va))] deltat = [[] for i in range(len(Va))] a = [[] for i in range(len(Va))] b = [[] for i in range(len(Va))] pente = [[] for i in range(len(Va))] ordonnee = [[] for i in range(len(Va))] figure() for j in range(len(Va)): print "Sodium channel model", j + 1 print "----------------------" va = Va[j] * mV ka = Ka[j] * mV vi = Vi[j] * mV ki = Ki[j] * mV r = R[j] print "Va =", va, "mV ; ka =", ka, "mV ;", "Vi =", vi, "mV ; ki =", ki, "mV" #--Boltzmann functions Pa = lambda v: eqs.apply('Pa', {'v':v, 'va':va, 'ka':ka}) # activation h_inf = lambda v: 1 - eqs.apply('Pi', {'v':v, 'vi':vi, 'ki':ki}) # inactivation #--The EIF model # F=lambda v: eqs.apply('v',{'Pa':Pa(v),'r':r,'h':1,'I':mu,'v':v}) # TODO: Romain, this doesn't work and probably should, see note in Equations.apply() F = lambda v: eqs.apply('v', {'r':r, 'h':1, 'I':mu, 'v':v, 'va':va, 'ka':ka}) v = linspace(-100 * mV, 0 * mV, 200) plot(v / mV, Pa(v)) plot(v / mV, F(v) * ms / mV) show() brian-1.4.3/dev/debugging/troubleshooting/2008/julio_monteiro_20_aug_2008.py000066400000000000000000000032631266062647100263770ustar00rootroot00000000000000#!/usr/bin/env python from brian import * from brian.library.IF import * from general_stdp import * from heterog_delay import * N = 3 T = 500 * ms max_delay = 20 * ms vm0 = -65 * mV w0 = 0.2 * vm0 / ms Vt = -30 * mV input_freq = 20 * Hz input_curr = 20 * mV #STDP gmax = 0.010 tau_pre = 20 * ms tau_post = 20 * ms dA_pre = gmax * .005 dA_post = -dA_pre * 1.05 eqs = Izhikevich(a=0.02 / ms, b=0.2 / ms) reset = AdaptiveReset(Vr= -75 * mV, b=8.0 * nA) model = Model(model=eqs, threshold=Vt, reset=reset, max_delay=max_delay) G = N * model G.vm = vm0 G.w = w0 def nextspike(): interval = 1 / input_freq nexttime = interval while True: yield (0, nexttime) nexttime += interval input = SpikeGeneratorGroup(1, nextspike()) Ic = Connection(input, G) C1 = Connection(G, G, structure="dense", delay=1 * ms) C2 = Connection(G, G, structure="dense", delay=2 * ms) Ic[0, 0] = input_curr Ic[0, 1] = input_curr #for i in range(len(G)): # C1[i]=[0.0]*len(G) # C2[i]=[0.0]*len(G) C1[0, 2] = 0.009 C2[1, 2] = 0.009 stdp1 = SongAbbottSTDP(C1, gmax=gmax, tau_pre=tau_pre, tau_post=tau_post, dA_pre=dA_pre, dA_post=dA_post) stdp2 = SongAbbottSTDP(C2, gmax=gmax, tau_pre=tau_pre, tau_post=tau_post, dA_pre=dA_pre, dA_post=dA_post) SI = SpikeMonitor(input) S = SpikeMonitor(G) tr_a = StateMonitor(G, 'vm', record=[2]) tr_T = StateMonitor(G, 'w', record=[2]) print "Before" print C1.W print C2.W run(T) subplot(211) raster_plot(SI, S) subplot(223) plot(tr_a.times / ms, tr_a[2]) subplot(224) plot(tr_T.times / ms, tr_T[2]) print "After" print C1.W print C2.W show() brian-1.4.3/dev/debugging/troubleshooting/2008/julio_monteiro_21_aug_2008_izhikevich_spnet.py000066400000000000000000000114021266062647100320200ustar00rootroot00000000000000#!/usr/bin/env python #import brian_no_units from brian import * from brian.library.IF import * from brian.experimental.general_stdp import * from brian.experimental.heterog_delay import * from brian.experimental.stdp_sparse import * from brian.connection import DenseConnectionMatrix import numpy import time #set_global_preferences(useweave=True) defaultclock.dt = 0.5 * ms class VariableIzhikevichReset(object): def __call__(self, P): spikes = P.LS.lastspikes() P.vm_[spikes] = Vr P.w_[spikes] += P.d_[spikes] T = 10000 * ms #Network parameters N = 1000 # total number of neurons Ne = 800 # number of excitatory neurons Ni = 200 # number of inhibitory neurons Nc = 100 # number of synapses per neuron vm0 = -65 * mV # the initial membrane current w0 = 0.2 * vm0 / ms # the initial recovery varible Vr = -65 * mV Vt = 30 * mV # "firing" threshold min_delay = 1 * ms max_delay = 20 * ms # maximum axonal delay i_delay = 1 * ms # the inhibitory delay wE0 = 6 * mV #initial positive synaptic weight wI0 = -5 * mV #initial negative synaptic weight # I think these are wrong, the units should be mV #wE0= 6*nA #initial positive synaptic weight #wI0= -5*nA #initial negative synaptic weight #STDP gmax = 10 * mV#0.010 # maximum synaptic weight tau_pre = 20 * ms tau_post = 20 * ms dA_pre = gmax * .005 dA_post = -dA_pre * 1.05 eqs = Izhikevich(a='a', b=0.2 / ms) + ''' a : Hz d : volt/second ''' model = Model(model=eqs, threshold=Vt, reset=VariableIzhikevichReset()) #dt bit needed for versions of Brian which aren't recent G = NeuronGroup(N, model, max_delay=i_delay + defaultclock.dt, compile=True, freeze=True) Ge = G.subgroup(Ne) Gi = G.subgroup(Ni) Ge.vm = vm0 Ge.w = w0 Ge.a = 0.02 / ms Ge.d = 8.0 * mV / ms Gi.vm = vm0 Gi.w = w0 Gi.a = 0.1 / ms Gi.d = 2.0 * mV / ms start_time = time.time() Ce = HeterogeneousDelayConnection(Ge, G, max_delay=max_delay, delays_per_synapse=True) #Cei = HeterogeneousDelayConnection(Ge, Gi, max_delay=max_delay, delays_per_synapse=True) Cie = Connection(Gi, Ge, delay=1 * ms) # Add Nc connections to each neuron in the network #Ce.connect_random(Ge, Ge, p=float(Nc)/N, weight=wE0) #Ce.connect_random(Ge, Gi, p=float(Nc)/N, weight=wI0)#really want this to be wI0? Ce.connect_random(Ge, G, p=float(Nc) / N, weight=wE0) for i in range(len(Ge)): Ce[i, i] = 0 Ce.delayvec = DenseConnectionMatrix((len(Ge), len(G))) # faster in the current implementaton I think # This way is slow, but if we're making delayvec a sparse matrix we have to do it like this #for i in xrange(len(Ge)): # for j in xrange(len(G)): # if abs(Ce[i,j])>1e-30: # Ce.delayvec[i,j]=rand()*(max_delay-min_delay)+min_delay Ce.delayvec[:] = random((len(Ge), len(G))) * (max_delay - min_delay) + min_delay # Use this if you need to be sure they are integer multiples of 1ms, but not necessary I think #Ce.delayvec[:] = ((random((len(Ge), len(G)))*max_delay/ms).astype(int)).astype(float)*ms # An alternative idea: SparseSTDPConnectionMatrix faster for delayvec too? Answer: not noticeably #Ce.delayvec = SparseSTDPConnectionMatrix(Ce.W) #Ce.delayvec[:] = rand(len(Ce.delayvec[:]))*(max_delay-min_delay)+min_delay #Ce.delayvec.ndim = 2 Cie.connect_random(Gi, Ge, p=float(Nc) / N, weight=wI0) # if you turn STDP off it takes 20 times longer! The SparseSTDPConnectionMatrix is MORE efficient than # what was there before!! STDPe = SongAbbottSTDP(Ce, gmax=gmax, tau_pre=tau_pre, tau_post=tau_post, dA_pre=dA_pre, dA_post=dA_post) Ce.W = SparseSTDPConnectionMatrix(Ce.W) print "Network build time:", time.time() - start_time, "seconds" thalamic_alternate = True Gvm_ = G.vm_ thaldelta = float(20 * mV) @network_operation(when='start') def thalamic_input(): global thalamic_alternate # at each cycle, some random neuron gets a 20*mV boost if thalamic_alternate: n = int(N * random()) Gvm_[n] += thaldelta#float(20*mV) thalamic_alternate = False else: thalamic_alternate = True S = SpikeMonitor(G) net = MagicNetwork() #net.update_schedule_groups_resets_connections() def do_run(T=T, doplot=True): start_time = time.time() net.run(T) print "Excecution time:", time.time() - start_time, "seconds" if doplot: raster_plot(S) show() do_run() #import cProfile #cProfile.run('do_run(1000*ms,False)') #import pycallgraph #cg_func = 'Network.update' #def ff(pat): # def f(call_stack, module_name, class_name, func_name, full_name): # if not 'brian' in module_name: return False # for n in call_stack+[full_name]: # if pat in n: # return True # return False # return f #pycallgraph.start_trace(filter_func=ff(cg_func)) #do_run(1*second,False) #pycallgraph.stop_trace() #pycallgraph.make_dot_graph('callgraph-'+cg_func+'.png') brian-1.4.3/dev/debugging/troubleshooting/2008/julio_monteiro_25_aug_2008_izhikevich_spnet.py000066400000000000000000000131731266062647100320330ustar00rootroot00000000000000#!/usr/bin/env python #import brian_no_units from brian import * from brian.library.IF import * from brian.experimental.general_stdp import * from brian.experimental.heterog_delay import * from brian.experimental.stdp_sparse import * from brian.connection import DenseConnectionMatrix import numpy import time, os import pickle set_global_preferences(useweave=True) defaultclock.dt = 0.5 * ms class VariableIzhikevichReset(object): def __call__(self, P): spikes = P.LS.lastspikes() P.vm_[spikes] = Vr P.w_[spikes] += P.d_[spikes] T = 100 * ms #Network parameters N = 1000 # total number of neurons Ne = 800 # number of excitatory neurons Ni = 200 # number of inhibitory neurons Nc = 100 # number of synapses per neuron vm0 = -65 * mV # the initial membrane current w0 = 0.2 * vm0 / ms # the initial recovery varible Vr = -65 * mV Vt = 30 * mV # "firing" threshold min_delay = 1 * ms max_delay = 20 * ms # maximum axonal delay i_delay = 1 * ms # the inhibitory delay wE0 = 6 * mV #initial positive synaptic weight wI0 = -5 * mV #initial negative synaptic weight # I think these are wrong, the units should be mV #wE0= 6*nA #initial positive synaptic weight #wI0= -5*nA #initial negative synaptic weight #STDP gmax = 10 * mV#0.010 # maximum synaptic weight tau_pre = 20 * ms tau_post = 20 * ms dA_pre = gmax * .005 dA_post = -dA_pre * 1.05 if os.path.exists('izhikevich.pkl'): print "Loading saved network from file..." inputfile = open('izhikevich.pkl', 'rb') objs = pickle.load(inputfile) inputfile.close() clk, model, G, Ce, Cie, S, net = objs else: eqs = Izhikevich(a='a', b=0.2 / ms) + ''' a : Hz d : volt/second ''' model = Model(model=eqs, threshold=Vt, reset=VariableIzhikevichReset()) #dt bit needed for versions of Brian which aren't recent G = NeuronGroup(N, model, max_delay=i_delay + defaultclock.dt, compile=True, freeze=True) Ge = G.subgroup(Ne) Gi = G.subgroup(Ni) Ge.vm = vm0 Ge.w = w0 Ge.a = 0.02 / ms Ge.d = 8.0 * mV / ms Gi.vm = vm0 Gi.w = w0 Gi.a = 0.1 / ms Gi.d = 2.0 * mV / ms start_time = time.time() Ce = HeterogeneousDelayConnection(Ge, G, max_delay=max_delay, delays_per_synapse=True) #Cei = HeterogeneousDelayConnection(Ge, Gi, max_delay=max_delay, delays_per_synapse=True) Cie = Connection(Gi, Ge, delay=1 * ms) # Add Nc connections to each neuron in the network #Ce.connect_random(Ge, Ge, p=float(Nc)/N, weight=wE0) #Ce.connect_random(Ge, Gi, p=float(Nc)/N, weight=wI0)#really want this to be wI0? Ce.connect_random(Ge, G, p=float(Nc) / N, weight=wE0) for i in range(len(Ge)): Ce[i, i] = 0 Ce.delayvec = DenseConnectionMatrix((len(Ge), len(G))) # faster in the current implementaton I think # This way is slow, but if we're making delayvec a sparse matrix we have to do it like this #for i in xrange(len(Ge)): # for j in xrange(len(G)): # if abs(Ce[i,j])>1e-30: # Ce.delayvec[i,j]=rand()*(max_delay-min_delay)+min_delay Ce.delayvec[:] = random((len(Ge), len(G))) * (max_delay - min_delay) + min_delay # Use this if you need to be sure they are integer multiples of 1ms, but not necessary I think #Ce.delayvec[:] = ((random((len(Ge), len(G)))*max_delay/ms).astype(int)).astype(float)*ms # An alternative idea: SparseSTDPConnectionMatrix faster for delayvec too? Answer: not noticeably #Ce.delayvec = SparseSTDPConnectionMatrix(Ce.W) #Ce.delayvec[:] = rand(len(Ce.delayvec[:]))*(max_delay-min_delay)+min_delay #Ce.delayvec.ndim = 2 Cie.connect_random(Gi, Ge, p=float(Nc) / N, weight=wI0) # if you turn STDP off it takes 20 times longer! The SparseSTDPConnectionMatrix is MORE efficient than # what was there before!! STDPe = SongAbbottSTDP(Ce, gmax=gmax, tau_pre=tau_pre, tau_post=tau_post, dA_pre=dA_pre, dA_post=dA_post) Ce.W = SparseSTDPConnectionMatrix(Ce.W) print "Network build time:", time.time() - start_time, "seconds" thalamic_alternate = True Gvm_ = G.vm_ thaldelta = float(20 * mV) S = SpikeMonitor(G) @network_operation(when='start') def thalamic_input(): global thalamic_alternate # at each cycle, some random neuron gets a 20*mV boost if thalamic_alternate: n = int(N * random()) Gvm_[n] += thaldelta#float(20*mV) thalamic_alternate = False else: thalamic_alternate = True net = MagicNetwork() #net.update_schedule_groups_resets_connections() def do_run(T=T, doplot=True): start_time = time.time() print "Running the Izhikevich spnet simulation for %s:" % display_in_unit(T, second) net.run(T) print "Excecution time:", time.time() - start_time, "seconds" if doplot: raster_plot(S) show() do_run() # save the network to a file clk = defaultclock obj = (clk, model, G, Ce, Cie, S, net) output = open('izhikevich.pkl', 'wb') pickle.dump(obj, output, -1) output.close() #import cProfile #cProfile.run('do_run(1000*ms,False)') #import pycallgraph #cg_func = 'Network.update' #def ff(pat): # def f(call_stack, module_name, class_name, func_name, full_name): # if not 'brian' in module_name: return False # for n in call_stack+[full_name]: # if pat in n: # return True # return False # return f #pycallgraph.start_trace(filter_func=ff(cg_func)) #do_run(1*second,False) #pycallgraph.stop_trace() #pycallgraph.make_dot_graph('callgraph-'+cg_func+'.png') brian-1.4.3/dev/debugging/troubleshooting/2008/julio_monteiro_9_sep_2008.py000066400000000000000000000014671266062647100263450ustar00rootroot00000000000000from brian import * class InputGenerator(object): """ A generator class to be passed to MultipleSpikeGeneratorGroup """ def __init__(self, phase=0 * ms, period=1 * ms): self.phase = phase self.period = period def __call__(self): t = self.phase while(True): yield t t += self.period ig1 = InputGenerator(3 * ms, 10 * ms) ig2 = InputGenerator(5 * ms, 10 * ms) G = MultipleSpikeGeneratorGroup([ig1, ig2]) print G._threshold.spiketimes print G._threshold.spiketimeiter M = SpikeMonitor(G) net = Network(G, M) net.run(20 * ms) print M.spikes net.reinit() # after reinit I don't see any firings print defaultclock.t print G._threshold.spiketimes print G._threshold.spiketimeiter ig1.phase = 5 * ms ig2.phase = 5 * ms net.run(20 * ms) print M.spikes brian-1.4.3/dev/debugging/troubleshooting/2008/pierre_yger_delay_problem.py000066400000000000000000000032711266062647100267440ustar00rootroot00000000000000# SOLUTION 2: DERIVED CLASS from brian import * from brian.directcontrol import MultipleSpikeGeneratorThreshold class MultipleSpikeGeneratorGroupWithDelays(MultipleSpikeGeneratorGroup): def __init__(self, spiketimes, clock=None, max_delay=0 * ms): clock = guess_clock(clock) thresh = MultipleSpikeGeneratorThreshold(spiketimes) NeuronGroup.__init__(self, len(spiketimes), model=LazyStateUpdater(), threshold=thresh, clock=clock, max_delay=max_delay) tau = 10 * ms sigma = 5 * mV eqs = 'dv/dt = -v/tau : volt' pop = NeuronGroup(1, model=eqs, threshold=10 * mV, reset=0 * mV, refractory=5 * ms) input = MultipleSpikeGeneratorGroupWithDelays([[0.1 * second, 0.2 * second, 0.3 * second, 0.4 * second, 0.5 * second]], max_delay=3 * ms) pop.v = -60 * mV C = Connection(input, pop, 'v', delay=2 * ms) C.connect_one_to_one(input, pop, weight=15 * mV) Minp = SpikeMonitor(input) M = SpikeMonitor(pop, True) run(1 * second) print Minp.spikes print M.spikes # SOLUTION 1: INTERMEDIATE GROUP # #from brian import * #tau=10*ms #sigma=5*mV #eqs='dv/dt = -v/tau : volt' #pop=NeuronGroup(1,model=eqs,threshold=10*mV,reset=0*mV,refractory=5*ms) #inputgen=MultipleSpikeGeneratorGroup([[0.1*second,0.2*second,0.3*second,0.4*second,0.5*second]]) #input=NeuronGroup(len(inputgen),model='v:1',threshold=0.5,reset=0.,max_delay=3*ms) #C_inputgen_input=IdentityConnection(inputgen,input) #pop.v=-60*mV #C=Connection(input,pop,'v',delay=2*ms-defaultclock.dt) #C.connect_one_to_one(input, pop, weight=15*mV) #Minpgen = SpikeMonitor(inputgen) #Minp = SpikeMonitor(input) #M=SpikeMonitor(pop,True) #run(1*second) #print Minpgen.spikes #print Minp.spikes #print M.spikes brian-1.4.3/dev/debugging/troubleshooting/2008/refractory_before_threshold.py000066400000000000000000000027001266062647100273040ustar00rootroot00000000000000from brian import * import numpy __all__ = ['RefractoryBeforeThresholdNeuronGroup', 'NeuronGroup'] class RefractoryBeforeThresholdNeuronGroup(NeuronGroup): ''' NeuronGroup that performs refractoriness before thresholding If you have neurons that receiving very strong inputs enough to drive them from reset to threshold in a single time step dt then you can use this temporary hacked version of NeuronGroup to prevent spiking during the refractory period. Performance may not be great, and possibly the refractory period will be what you have given + dt. This should be fixed in a later release of Brian. ''' def update(self): self._state_updater(self) # update the variables if isinstance(self._resetfun, Refractoriness): self._resetfun(self) spikes = self._threshold(self) # get spikes if not isinstance(spikes, numpy.ndarray): spikes = array(spikes, dtype=int) self.LS.push(spikes) # Store spikes NeuronGroup = RefractoryBeforeThresholdNeuronGroup if __name__ == '__main__': #from brian import NeuronGroup G = NeuronGroup(1, 'V:1', threshold=1, reset=0, refractory=5 * ms) @network_operation(when='before_groups') def f(): G.V = 2 M = StateMonitor(G, 'V', record=True, when='before_resets') sp = SpikeMonitor(G) run(40 * ms) print sp.spikes plot(M.times, M[0]) show() brian-1.4.3/dev/debugging/troubleshooting/2008/tim_masquelier_custom_reset_aug_08.py000066400000000000000000000010251266062647100305040ustar00rootroot00000000000000from brian import * def custom_reset(P, spikes): P.V_[spikes] = 0 # plus do other stuff here... gen = MultipleSpikeGeneratorGroup([[10 * ms, 20 * ms, 30 * ms]]) inp = NeuronGroup(1, model='V:1', threshold=0.5, reset=custom_reset) outp = NeuronGroup(1, model='V:1', threshold=0.5, reset=0.0) C_gen_inp = IdentityConnection(gen, inp) C = IdentityConnection(inp, outp) Mgen = SpikeMonitor(gen) Minp = SpikeMonitor(inp) Moutp = SpikeMonitor(outp) run(100 * ms) for M in [Mgen, Minp, Moutp]: print M.spikes brian-1.4.3/dev/debugging/troubleshooting/2009/000077500000000000000000000000001266062647100211565ustar00rootroot00000000000000brian-1.4.3/dev/debugging/troubleshooting/2009/anthony_sherbondy_23_dec_2009.py000066400000000000000000000027771266062647100270730ustar00rootroot00000000000000from brian import * import sys log_level_warn() if 0: class Clock(Clock): def tick(self): print 'Ticking:', id(self), self super(Clock, self).tick() def repfunc(layer1): def rep(spikes): print 'rep:', id(layer1), layer1.clock.t, spikes return rep for ii in range(0, 5): # Setup a new clock sim_clk = Clock(dt=1 * ms) if 0: print 'This sim clock:', id(sim_clk) # Setup up a particular sequence of spikes spiketimes = [(0, 1 * ms), (0, 2 * ms), (0, 3 * ms), (0, 4 * ms)] layer1 = SpikeGeneratorGroup(2, spiketimes, clock=sim_clk) if 0: print 'SGG clock:', id(layer1.clock) # Setup output recorder spikes1 = SpikeMonitor(layer1) if 0: spikes2 = SpikeMonitor(layer1, function=repfunc(layer1)) if 0: @network_operation(clock=sim_clk) def rep2(): print 'NAS:', layer1._next_allowed_spiketime net = MagicNetwork() net.prepare() if 1: print 'Groups:', map(id, net.groups) print 'Connections:', map(id, net.connections) print 'Update schedule:' for clk, v in net._update_schedule.items(): print ' Clock:', id(clk) print ' Schedule:', v sys.stdout.flush() if 0: if hasattr(net, 'clocks'): print 'Clocks =', map(id, net.clocks) net.run(5 * ms) print 'Counted', spikes1.nspikes, 'spikes.' #forget(layer1) #clear() brian-1.4.3/dev/debugging/troubleshooting/2009/david_10_october_2009.py000066400000000000000000000130761266062647100253150ustar00rootroot00000000000000""" A model of working memory from Wang (2002) implemented in Brian """ import time time_start = time.time() debugg = True # set True for debugging (checks the units) if not debugg: import brian_no_units from brian import * # === Define parameters # size of the network excit_n = 1600 inhib_n = 400 # simulation-related parameters simtime = 1000.0 * ms # total simulation time [ms] dt = 0.02 * ms # simulation step length [ms] # === Parameters determining model dynamics ===== # pyramidal cells Cm_e = 0.5 * nF # [nF] total capacitance gl_e = 25.0 * nS # [ns] total leak conductance El_e = -70.0 * mV # [mV] leak reversal potential Vth_e = -50.0 * mV # [mV] threshold potential Vr_e = -55.0 * mV # [mV] resting potential tr_e = 2.0 * ms # [ms] refractory time # interneuron cells Cm_i = 0.2 * nF # [nF] total capacitance gl_i = 20.0 * nS # [ns] total leak conductance El_i = -70.0 * mV # [mV] leak reversal potential Vth_i = -50.0 * mV # [mV] threshold potential Vr_i = -55.0 * mV # [mV] resting potential tr_i = 1.0 * ms # [ms] refractory time #structure N1 = 240 N2 = 2 * N1 f = 0.15 wp = 1.7 wm = 1.0 - f * (wp - 1.0) / (1.0 - f) def structure(i, j): if(i == j): return 0 if (i < N1): if (j < N1): return wp elif (j < N2): return wm else: return 1.0 elif (i < N2): if (j < N1): return wm elif (j < N2): return wp else: return 1.0 else: if (j < N2): return wm else: return 1.0 # external input fext = 2400.0 * Hz # [Hz] external input frequency (poisson train) def stimulus(t): if (t < 1 * second): return fext elif (t < 1.5 * second): return 2440.0 * Hz else: return fext # AMPA receptor (APMAR) Vs_ampa = 0.0 * mV # [mV] synaptic reversial potential t_ampa = 2.0 * ms # [ms] exponential decay time constant g_ext_e = 2.1 * nS # [nS] maximum conductance from external to pyramidal cells g_ext_i = 1.62 * nS # [nS] maximum conductance from external to interneuron cells g_ampa_i = 0.04 * nS g_ampa_e = 0.05 * nS # GABA receptor (GABAR) Vs_gaba = -70.0 * mV # [mV] synaptic reversial potential t_gaba = 5.0 * ms # [ms] exponential decay time constant g_gaba_e = 1.3 * nS g_gaba_i = 1.0 * nS # NMDA receptor (NMDAR) Vs_nmda = 0.0 * mV # [mV] synaptic reversial potential ts_nmda = 100.0 * ms # [ms] decay time of NMDA currents tx_nmda = 2.0 * ms # [ms] controls the rise time of NMDAR channels alfa_nmda = 0.5 * kHz # [kHz] controls the saturation properties of NMDAR channels g_nmda_e = 0.165 * nS g_nmda_i = 0.13 * nS ################################# eqs_e = ''' dv/dt = (-gl_e*(v-El_e)-g_ext_e*(v-Vs_ampa)*s_ext-g_gaba_e*(v-Vs_gaba)*s_gaba-g_ampa_e*(v-Vs_ampa)*s_ampa-s_tot*g_nmda_e*(v-Vs_nmda)/(1+exp(-0.062*v/(1*mV))/3.57))/Cm_e: volt ds_ext/dt = -s_ext/(t_ampa) : 1 ds_gaba/dt = -s_gaba/(t_gaba) : 1 ds_ampa/dt = -s_ampa/(t_ampa) : 1 ds_nmda/dt = -s_nmda/(ts_nmda)+alfa_nmda*x*(1-s_nmda) : 1 dx/dt = -x/(tx_nmda) : 1 s_tot : 1 ''' eqs_i = ''' dv/dt = (-gl_i*(v-El_i)-g_ext_i*(v-Vs_ampa)*s_ext-g_gaba_i*(v-Vs_gaba)*s_gaba-g_ampa_i*(v-Vs_ampa)*s_ampa-s_tot*g_nmda_i*(v-Vs_nmda)/(1+exp(-0.062*v/(1*mV))/3.57))/Cm_i: volt ds_ext/dt = -s_ext/(t_ampa) : 1 ds_gaba/dt = -s_gaba/(t_gaba) : 1 ds_ampa/dt = -s_ampa/(t_ampa) : 1 s_tot : 1 ''' simulation_clock = Clock(dt=dt) # Setting up the populations print "Setting up the populations ..." Pe = NeuronGroup(excit_n, eqs_e, threshold=Vth_e, reset=Vr_e, refractory=tr_e, order=2) Pe.v = El_e Pe.s_ext = 0 Pe.s_ampa = 0 Pe.s_gaba = 0 Pe.s_nmda = 0 Pe.x = 0 Pi = NeuronGroup(inhib_n, eqs_i, threshold=Vth_i, reset=Vr_i, refractory=tr_i, order=2) Pi.v = El_i Pi.s_ext = 0 Pi.s_ampa = 0 Pi.s_gaba = 0 #3 excitatory subgroups: 1 & 2 are selective to motion, ns not Pe_1 = Pe.subgroup(N1) Pe_2 = Pe.subgroup(N1) Pe_ns = Pe.subgroup(excit_n - 2 * N1) PG1 = PoissonGroup(N1, lambda t:stimulus(t)) PG2 = PoissonGroup(N1, lambda t:stimulus(t)) PGns = PoissonGroup(excit_n - 2 * N1, fext) PGi = PoissonGroup(inhib_n, fext) # Creating connections print "Creating static conections ..." selfnmda = IdentityConnection(Pe, Pe, 'x', weight=1.0, delay=0.5 * ms) Cp1 = IdentityConnection(PG1, Pe_1, 's_ext', weight=1.0) Cp2 = IdentityConnection(PG2, Pe_2, 's_ext', weight=1.0) Cpns = IdentityConnection(PGns, Pe_ns, 's_ext', weight=1.0) Cpi = IdentityConnection(PGi, Pi, 's_ext', weight=1.0) Cie = Connection(Pi, Pe, 's_gaba', weight=1.0, delay=0.5 * ms) Cii = Connection(Pi, Pi, 's_gaba', weight=1.0, delay=0.5 * ms) Cei = Connection(Pe, Pi, 's_ampa', weight=1.0, delay=0.5 * ms) Cee = Connection(Pe, Pe, 's_ampa', weight=structure, delay=0.5 * ms) for i in range(len(Pi)): Cii[i, i] = 0 Wee = zeros((excit_n, excit_n)) for i in range(len(Pe)): for j in range(len(Pe)): Wee[i, j] = structure(i, j) Wei = ones((excit_n, inhib_n)) Wei = transpose(Wei) @network_operation(simulation_clock, when='start') def f(simulation_clock): s_NMDA_full = transpose(array([Pe.s_nmda])) # get the s_NMDA values of the excitatory neurons Pe.s_tot = transpose(dot(Wee, s_NMDA_full)) # Calculate the new s_tot values based on the weights for the excitatory neurons Pi.s_tot = transpose(dot(Wei, s_NMDA_full)) # Calculate the new s_tot values based on the weights for the inhibitory neurons # initiating monitors M = SpikeMonitor(Pe) #N = SpikeMonitor(Pi) print "Running ..." run(simtime, report='text') print "Simulation finished." raster_plot(M) #raster_plot(N) show() brian-1.4.3/dev/debugging/troubleshooting/2009/james_28_july_2009.py000066400000000000000000000012171266062647100246560ustar00rootroot00000000000000from brian import * from brian.library.synapses import * taue = 5 * ms gl = 20 * nS Ee = 0 * mV El = -60 * mV Vt = -50 * mV Vr = -60 * mV eqs = MembraneEquation(C=200 * pF) eqs += Current('I=gl*(El-vm):amp') eqs += alpha_conductance(input='ge', E=Ee, tau=taue) input = PoissonGroup(2, rates=50 * Hz) G = NeuronGroup(N=1, model=eqs, threshold=Vt, reset=Vr) C = Connection(input, G, 'ge') C[0, 0] = 10 * nS C[1, 0] = 5 * nS Mvm = StateMonitor(G, 'vm', record=True) Mge = StateMonitor(G, 'ge_current', record=True) run(100 * ms) figure() subplot(211) plot(Mvm.times, Mvm[0]) subplot(212) plot(Mge.times, Mge[0]) show() brian-1.4.3/dev/debugging/troubleshooting/2009/james_h_1_september_2009.py000066400000000000000000000017251266062647100261030ustar00rootroot00000000000000from brian import * gmax = 1.5 tau_pre = 20 * ms tau_post = tau_pre dA_pre = .001 dA_post = -dA_pre * tau_pre / tau_post * 1.05 input = PoissonGroup(2, 50 * Hz) output = NeuronGroup(2, 'V:1', reset=0, threshold=0.5) relay = IdentityConnection(input, output, delay=1 * ms) C = Connection(input, output) C[0, 0] = C[1, 1] = 1e-100 eqs_stdp = ''' dA_pre/dt = -A_pre/tau_pre : 1 dA_post/dt = -A_post/tau_post : 1 ''' dA_post *= gmax dA_pre *= gmax class Heap(object): pass h = Heap() h.dA_post = dA_post h.dA_pre = dA_pre stdp = STDP(C , eqs=eqs_stdp, pre='A_pre+=h.dA_pre; w+=A_post', post='A_post+=h.dA_post; w+=A_pre', wmax=gmax) Mi = SpikeMonitor(input) Mo = SpikeMonitor(output) w0 = [] w1 = [] @network_operation(clock=EventClock(dt=1 * ms)) def recweights(): w0.append(C[0, 0]) w1.append(C[1, 1]) run(1 * second) h.dA_post *= 5 h.dA_pre *= 5 run(1 * second) plot(w0) plot(w1) show() brian-1.4.3/dev/debugging/troubleshooting/2009/james_may_25_2009.py000066400000000000000000000013171266062647100244570ustar00rootroot00000000000000from brian import * from brian.library.IF import * inputGroup = PoissonGroup(500, 54) Mall = SpikeMonitor(inputGroup) run(5000 * ms) # XXX eqs = Izhikevich(a=0.02 / ms, b=0.2 / ms) Ginput = SpikeGeneratorGroup(500, Mall.spikes) Goutput = NeuronGroup(1, model=eqs, threshold=30 * mV, reset=AdaptiveReset(Vr= -65 * mV, b=0.0805 * nA)) Mvolt = StateMonitor(Goutput, 'vm', record=0) C = Connection(Ginput, Goutput, state='vm') C.connect_full(weight=0.475 * mV) Ginput.v = -65 * mV Goutput.v = -65 * mV stdp = ExponentialSTDP(C, 33 * ms, 16.8 * ms, 0.01, -0.01, wmax=0.01) reinit_default_clock() run(5000 * ms) plot(Mvolt.times / (1 * msecond), Mvolt[0] / (1 * mvolt)) show() brian-1.4.3/dev/debugging/troubleshooting/2009/julio_monteiro_26_may_2009.py000066400000000000000000000041601266062647100264160ustar00rootroot00000000000000#!/usr/bin/env python from brian import * from brian.library.IF import * T = 3000 * ms Ni = 200 Ne = 800 N = Ni + Ne conn_prob = 0.1 exc_weigth0 = 6 * mV inh_weigth0 = -5 * mV max_delay = 20 * ms vm0 = -65 * mV w0 = 0.2 * vm0 / ms Vt = -30 * mV input_freq = 20 * Hz input_curr = 20 * mV eqs_e = Izhikevich(a=0.02 / ms, b=0.2 / ms) rst_e = AdaptiveReset(Vr= -75 * mV, b=8.0 * nA) Me = Model(model=eqs_e, threshold=Vt, reset=rst_e, max_delay=max_delay) eqs_i = Izhikevich(a=0.1 / ms, b=0.2 / ms) rst_i = AdaptiveReset(Vr= -75 * mV, b=2.0 * nA) Mi = Model(model=eqs_i, threshold=Vt, reset=rst_i, max_delay=max_delay) Gi = Ni * Mi Gi.vm = vm0 Gi.w = w0 Ge = Ni * Mi Ge.vm = vm0 Ge.w = w0 def nextspike(): interval = 1 / input_freq nexttime = interval while True: yield (0, nexttime) nexttime += interval # random thalamic input input = SpikeGeneratorGroup(1, nextspike()) Ice = Connection(input, Ge) Ici = Connection(input, Gi) # uniform distribution of exc. synaptic delays Cee = DelayConnection(Ge, Ge, max_delay=20 * ms) Cee.connect_random(Ge, Ge, conn_prob / 2, exc_weigth0, delay=(0 * ms, 20 * ms)) Cei = DelayConnection(Ge, Gi, max_delay=20 * ms) Cei.connect_random(Ge, Gi, conn_prob / 2, exc_weigth0, delay=(0 * ms, 20 * ms)) Cie = DelayConnection(Gi, Ge, max_delay=1 * ms) # all inhibitory delays are 1 ms Cie.connect_random(Gi, Ge, conn_prob, inh_weigth0, delay=1 * ms) #STDP gmax = 0.010 tau_pre = 20 * ms tau_post = 20 * ms dA_pre = gmax * .005 dA_post = -dA_pre * 1.05 eqs_stdp = """ dA_pre/dt = -A_pre/tau_pre : 1 dA_post/dt = -A_post/tau_post : 1 """ stdp_ee = STDP(Cee, eqs=eqs_stdp, pre='''A_pre+=dA_pre; w+=A_post''', post='''A_post+=dA_post; w+=A_pre''', wmax=gmax) stdp_ei = STDP(Cei, eqs=eqs_stdp, pre='''A_pre+=dA_pre; w+=A_post''', post='''A_post+=dA_post; w+=A_pre''', wmax=gmax) stdp_ie = STDP(Cie, eqs=eqs_stdp, pre='''A_pre+=dA_pre; w+=A_post''', post='''A_post+=dA_post; w+=A_pre''', wmax=gmax) Se = SpikeMonitor(Ge) Si = SpikeMonitor(Gi) run(T) raster_plot(Se, Si) show() brian-1.4.3/dev/debugging/troubleshooting/2009/kerry.py000066400000000000000000000013471266062647100226710ustar00rootroot00000000000000import numpy, scipy from numpy import * from brian import * #physical characteristics Cap = 2 * uF / (cm * cm) GNa = 0.02 * siemens / (cm * cm) GK = 0.02 * siemens / (cm * cm) GLeak = 0.002 * siemens / (cm * cm) ELeak = -70 * mV EK = -100 * mV ENa = 50 * mV #ML characteristics v1 = -11.2 * mV v2 = 18 * mV v3 = -20 * mV v4 = 10 * mV phiScalingFactor = 0.15 * mV / mV TauCofactor = 2 * ms eqs = Equations(''' dvm/dt=(-GNa*minf*(vm-ENa)-GK*w*(vm-EK)-GLeak(vm-ELeak))/200*pF:mV minf=0.5*(1+tanh((vm-v1)/v2)):1 winf=0.5*(1+tanh((vm-v3)/v4)):1 TauW=TauCofactor/cosh((vm-v3)/(2*v4)):ms dw/dt=phiScalingFactor*(winf-w)/TauW:kHz''') #eqs.compile_functions(True) neuron = NeuronGroup(1, eqs, freeze=True, method='Euler') brian-1.4.3/dev/debugging/troubleshooting/2009/mark_orphanides_20_june_2009.py000066400000000000000000000010551266062647100266730ustar00rootroot00000000000000from brian import * #G = NeuronGroup(1, 'v:1\nw:1', threshold='(v>1)&(w>1)') #M = SpikeMonitor(G) #G.v = 2 #run(1*ms) #print M.spikes #G.w = 2 #run(1*ms) #print M.spikes eqs = ''' dV/dt = dV_dt: 1 dV_dt = V/(3*ms) : 1/second thresh = (V>1)&(dV_dt>1/second) : 1 ''' G = NeuronGroup(1, eqs, threshold=EmpiricalThreshold(0, 1 * ms, 'thresh')) G.V = 0.1 M = MultiStateMonitor(G, ('V', 'dV_dt', 'thresh'), record=True) run(10 * ms) subplot(311) M['V'].plot() subplot(312) M['dV_dt'].plot() subplot(313) M['thresh'].plot() show() brian-1.4.3/dev/debugging/troubleshooting/2009/mark_orphanides_23_june_2009.py000066400000000000000000000025541266062647100267030ustar00rootroot00000000000000import brian_no_units from brian import * reinit() #RS a = 0.02 b = 0.2 c = -65 d = 8 #--------------------------------------------------------------------------------- defaultclock.dt = 0.001 n = 2 #number of neurons in the network r = 20 #strength of external input eqs = Equations(''' dv/dt= 0.04*v**2 + 5*v + 140 - u + I: 1 du/dt=a*(b*v-u):1 I:1 ''') PG = PoissonGroup(n, rates=2 * Hz) G = NeuronGroup (n, model=eqs, reset="v=c;u=u+d", threshold='v>=30') @network_operation(when='after_groups') def Icurrent(): G.I = 10 C2 = IdentityConnection(PG, G, 'I', weight=r) G.v = -65 G.u = b * G.v G.I = 10 Sp = SpikeMonitor(G) M = MultiStateMonitor(G, record=True) net = Network(Icurrent, PG, G, C2, Sp, M) net.run(140) figure(1) raster_plot(Sp) figure(2) subplot(4, 1, 1) plot(M['v'].times, M['v'][0]) ylabel('v (dimensionless)') subplot(4, 1, 2) plot(M['I'].times, M['I'][0]) ylabel('I (dimensionless)') subplot(4, 1, 3) plot(M['u'].times, M['u'][0]) xlabel('Time/seconds') ylabel('u (dimensionless)') figure(3) subplot(4, 1, 1) plot(M['v'].times, M['v'][1]) ylabel('v (dimensionless)') subplot(4, 1, 2) plot(M['I'].times, M['I'][1]) ylabel('I (dimensionless)') subplot(4, 1, 3) plot(M['u'].times, M['u'][1]) xlabel('Time/seconds') ylabel('u (dimensionless)') show() brian-1.4.3/dev/debugging/troubleshooting/2009/michael_charry_27_aug_2009.py000066400000000000000000000003111266062647100263130ustar00rootroot00000000000000from brian import * from brian.library.ionic_currents import * eqs = MembraneEquation(C=0.2 * nF) eqs += leak_current(gl=10 * nS, El= -70 * mV) eqs += K_current_HH(20 * nS, -80 * mV) print eqs brian-1.4.3/dev/debugging/troubleshooting/2009/nemo_136_1_dec_2009.py000066400000000000000000000027301266062647100245660ustar00rootroot00000000000000from brian import * N = 200 defaultclock.dt = 0.5 * ms izh_eqs = ''' dv/dt=0.04/ms/mV*v**2+5/ms*v+140*mV/ms-u+ge-gi+i:volt du/dt=a*(b*v-u):volt/second a:1/second b:1/second dge/dt=-ge/ms:volt/second dgi/dt=-gi/ms:volt/second i:volt/second c:volt d:volt ''' reset_eqs = ''' v=c;u=u+d ''' group = NeuronGroup(200, model=izh_eqs, threshold=30 * volt, reset=reset_eqs) sub1 = group[:100] sub2 = group[100:] for i in range(len(sub1)): sub1[i].a = 0.02 / ms sub1[i].b = 0.2 / ms sub1[i].c = -65.0 * mV sub1[i].d = 8.0 * mV for i in range(len(sub2)): sub2[i].a = 0.02 / ms sub2[i].b = 0.2 / ms sub2[i].c = -65.0 * mV sub2[i].d = 8.0 * mV #direct links linksexcit = Connection(sub1, sub2, 'ge', structure='dense') #CASE 1 linksexcit.connect_full(sub1, sub2, weight=lambda i, j:rand()*10.0 * mV / ms) #linksexcit.connect_full(sub1,sub2,weight=lambda i,j:rand(array(j).size)*10.0*mV/ms) #CASE 2 #create custom links #for i in range(len(sub1)): # for j in range(len(sub2)): # linksexcit[i,j]=rand()*10*mV/ms clock_i = Clock(dt=1 * ms) @network_operation def myoperation(clock_i): sub1.i = [10 * randn()*mV / ms for i in range(len(sub1))] group.v = -65 * mV group.u = group.v * group.b spkm = SpikeMonitor(group) spkc1 = SpikeCounter(sub1) spkc2 = SpikeCounter(sub1) run(200 * ms) print spkc1.nspikes print spkc2.nspikes raster_plot() figure() imshow(linksexcit.W.todense()) colorbar() show() brian-1.4.3/dev/debugging/troubleshooting/2009/noelia.py000066400000000000000000000020171266062647100227770ustar00rootroot00000000000000from pylab import * from scipy import integrate # parameters area = 0.0002 Cm = (1) * area El = -63.8 EK = -90 ENa = 55 gl = (0.1025) * area g_na = (35) * area g_kd = (9) * area Iapp = 0.001 def dV_dt(V, t=0): alpham = 0.1 * (V[0] + 35.) / (1. - exp(-(V[0] + 35.) / 10.)) betam = 4. * exp(-(V[0] + 60.) / 18.) alphah = 0.07 * exp(-(V[0] + 58.) / 20.) betah = 1. / (1. + exp(-(V[0] + 28.) / 10.)) alphan = 0.05 * (V[0] + 34) / (1. - exp(-(V[0] + 34) / 10)) betan = 0.625 * exp(-(V[0] + 44) / 80) minf = alpham / (alpham + betam) return array([(gl * (El - V[0]) - g_na * (minf) ** 3 * V[1] * (V[0] - ENa) - g_kd * (V [2] ** 4) * (V[0] - EK) + Iapp) / Cm, 5 * (alphah * (1 - V[1]) - betah * V[1]), alphan * (1 - V[2]) - betan * V[2] ]) V0 = ([-65, 0, 0]) t = linspace(0, 100, 100000) print " running..." V, infodict = integrate.odeint(dV_dt, V0, t, full_output=True) infodict['message'] plot(t, V[:, 0], 'r-') show() brian-1.4.3/dev/debugging/troubleshooting/2009/noelia2.py000066400000000000000000000030471266062647100230650ustar00rootroot00000000000000from brian import * from brian.units import check_units, siemens, volt from brian.membrane_equations import Current def leak_current_int(gl, El, current_name=None): return Current('I=gl*(El-vm) : amp', gl=gl, El=El, I=current_name) def K_current_int(gmax, EK, current_name=None): return Current(''' I=gmax*n**4*(EK-vm) : amp dn/dt=alphan*(1-n)-betan*n : 1 alphan=.05*(vm+34.*mV)/(1-exp(-(vm+34.*mV)/(10.*mV)))/mV/ms : Hz betan=.625*exp(-(vm+44.*mV)/(80.*mV))/ms : Hz ''', gmax=gmax, EK=EK, I=current_name) def Na_current_int(gmax, ENa, current_name=None): return Current(''' I=gmax/((1+betam/alpham)**3)*h*(ENa-vm) : amp dh/dt=alphah*(1-h)-betah*h : 1 alpham=.5*(vm+35.*mV)/(1-exp(-(vm+35.*mV)/(10.*mV)))/mV/ms : Hz betam=20.*exp(-(vm+60.*mV)/(18.*mV))/ms : Hz alphah=.35*exp(-(vm+58.*mV)/(20.*mV))/ms : Hz betah=5./(1.+exp(-(vm+28.*mV)/(10.*mV)))/ms : Hz ''', gmax=gmax, ENa=ENa, I=current_name) c = Clock(dt=.01 * ms) # more precise El = -65 * mV EK = -90 * mV ENa = 55 * mV eqs = MembraneEquation(0.01 * uF) + leak_current_int(.003 * msiemens, El, current_name='Il') eqs += K_current_int(0.36 * msiemens, EK, current_name='IK') eqs += Na_current_int(1.20 * msiemens, ENa) eqs += Current(I='Iapp:amp') neuron = NeuronGroup(1, eqs) neuron.h = 1. neuron.n = .1 neuron.vm = -64 * mV trace = StateMonitor(neuron, 'vm', record=True) run(100 * ms) neuron.Iapp = 0. run(100 * ms) print trace[0] / mV plot(trace.times / ms, trace[0] / mV) show() brian-1.4.3/dev/debugging/troubleshooting/2009/tom_sharp_8_july_2009.py000066400000000000000000000006521266062647100254730ustar00rootroot00000000000000from brian import * N = 10 burst_clock = EventClock(dt=100 * ms) def burst_generator(): start = burst_clock.t times = [(i, start + rand()*10 * ms) for i in range(N)] times.sort(key=lambda (i, t):t) return times @network_operation(clock=burst_clock) def next_burst(): G.reinit() G = SpikeGeneratorGroup(N, burst_generator) M = SpikeMonitor(G) run(500 * ms) raster_plot(M) show() brian-1.4.3/dev/debugging/troubleshooting/2010/000077500000000000000000000000001266062647100211465ustar00rootroot00000000000000brian-1.4.3/dev/debugging/troubleshooting/2010/adrien_wohrer_10_oct_2010.py000066400000000000000000000005511266062647100261600ustar00rootroot00000000000000from brian import * dt = 0.1*ms N = 500 G = NeuronGroup(1, 'V:1') G.V = TimedArray(linspace(0, 1, N), dt=dt) M = StateMonitor(G, 'V', record=True) net = MagicNetwork() net.prepare() try: print net.clocks except: print net.clock, net.clock.dt net.run(N*dt) V = M[0] subplot(211) plot(V) subplot(212) plot(diff(V)) show() brian-1.4.3/dev/debugging/troubleshooting/2010/antoine_bergel_04_11_2010.py000066400000000000000000000004731266062647100257470ustar00rootroot00000000000000''' Bug with insert_spikes, filed by Antoine Bergel on 4 Nov 2010 ''' from brian import * G=NeuronGroup(1,model='dv/dt=100*Hz:1',threshold=1,reset=0) M = SpikeMonitor(G) Mv = StateMonitor(G, 'v', record=True) run(100*ms) print Mv.values.shape Mv.insert_spikes(M,5) plot(Mv.times/ms, Mv[0]) show() brian-1.4.3/dev/debugging/troubleshooting/2010/ben_30_june_2010.py000066400000000000000000000005271266062647100242550ustar00rootroot00000000000000from brian import * from scipy import sparse P=NeuronGroup(1000,model='v:volt') C=Connection(P,P,sparseness=0.1,weight=1*mV) C2=Connection(P,P[0]) for i in range(len(P)): #print C[i,:].sum() # but sum(C[i,:]) does not work C2[i,0]=C[i,:].sum() W=sparse.lil_matrix((5,5)) print W[1,:].sum() # same problem for sum(W[1,:]) brian-1.4.3/dev/debugging/troubleshooting/2010/francis_05_oct_2010.py000066400000000000000000000032521266062647100247620ustar00rootroot00000000000000# Neural Cortical Model of a single neuron with poisson input from brian import * from brian.library.synapses import * import time import random defaultclock.dt=0.01*ms vr = -90*mvolt # reset/resting membrane potential vt = -55*mvolt # threshold ref = 1*msecond # refractory period tauge = 0.25*msecond # excitatory conductance time constant El = -70*mvolt # resting potential Ee = 0*mvolt # reversal potential of excitatory ionic channels gl = 10.*nsiemens # leak conductance #compute the somatic and dendritic capacitance for each neuron type Rmse = 600*Mohm taue = 10*msecond # somatic excitatory membrane time constant Ces = taue/Rmse # excitatory cell somatic capacitance #Poisson input value Pinput = 150.5*nS # Highly exaggerated! Pfreq = 50*kHz # Number of neurons N = 1 ###### Neuron model exc_soma = MembraneEquation(C=Ces) + Current('I=gl*(El-vm):amp') exc_soma += alpha_conductance(input='ge' ,E=Ee, tau=tauge) print exc_soma # Create Excitatory Neural Sheet NG = NeuronGroup(N, model=exc_soma, threshold='vm>vt',reset=vr,refractory=ref) ###### Make the Connections #Poisson Input PG = PoissonGroup(N, N*[Pfreq]) P = Connection(PG, NG, 'ge') P.connect_one_to_one(PG, NG, weight=Pinput) SpP = SpikeMonitor(PG) SpNG = SpikeMonitor(NG) M=StateMonitor(NG,'vm',record=True) #status print 'Running...' run(1*second) ###### analysis output print SpP.nspikes, '\tpoisson spikes' print SpNG.nspikes, '\ttotal spikes' subplot(221) raster_plot(SpP) ylabel('Poisson Neuron Id') subplot(223) raster_plot(SpNG) ylabel('Neuron Id') subplot(222) plot(M.times/ms,M[0]/mV) show() brian-1.4.3/dev/debugging/troubleshooting/2010/laure_27_jan_2010.py000066400000000000000000000023321266062647100244320ustar00rootroot00000000000000# -*- coding:utf-8 -*- from brian import * from brian.library.IF import * import numpy # Parameters area = 20000 * umetre ** 2 Cm = (20 * ufarad * cm ** -2) * area # Time constants taue = 5 * ms taui = 10 * ms #Maximal conductances gK = (8.0 * usiemens * cm ** -2) * area gCa = (4.4 * usiemens * cm ** -2) * area #type II gL = (2.0 * usiemens * cm ** -2) * area # Reversal potentials EK = -80 * mV ECa = -60 * mV EL = 120 * mV Ee = 1 * mV#0*mV Ei = -80 * mV v1 = -1.2 * mV v2 = 18 * mV v3 = 2 * mV v4 = 30 * mV we = 30 * nS ##6# excitatory synaptic weight (voltage) wi = 67 * nS # inhibitory synaptic weight phi = 0, 04 * 1 # that's the typo: should be 0.04*1 Vr = -70 * mV threshold = 48 * mV#-30 # Model ##Morris-Lecar## eqsRS = Equations(''' dvm/dt=(gL*(EL-vm)+ gK*w/mV*(EK-vm)+ gCa*minf*(ECa-vm) +(ge*(Ee-vm)+gi*(Ei-vm)))/Cm + I:volt dw/dt= phi*(winf*mV-w)/tauw: volt minf = 0.5*(1+exp(2*((vm-v1)/v2))-1)/(exp(2*((vm-v1)/v2))+1):1 winf = 0.5*(1+exp(2*((vm-v3)/v4))-1)/(exp(2*((vm-v3)/v4))+1):1 tauw = 1*ms/((exp((vm-v3)/v4)+exp(-(vm-v3)/v4))/2):second dge/dt = -ge*(1./taue): siemens dgi/dt = -gi*(1./taui): siemens I: volt/second ''') group = NeuronGroup(2, model=eqsRS, method='Euler') brian-1.4.3/dev/debugging/troubleshooting/2010/mike_14_jan_2010.py000066400000000000000000000036231266062647100242470ustar00rootroot00000000000000from brian import * from matplotlib import rc rc('font', **{'family':'sans-serif', 'sans-serif':['Helvetica']}) rc('text', usetex=True) #Parameters from Kuznetsov et al. (2006) Table 1 Mg = 1.4 * mmole F = 96485.3999 * coulomb / mole E_Ca = 100 * mV g_L = 0.05 * msiemens / cm2 g_maxCa = 0.2 * msiemens / cm2 g_maxK = 0.4 * msiemens / cm2 g_maxKCa = 0.3 * msiemens / cm2 g_AMPA = 0.07 * msiemens / cm2 #From Figure 5 Kuznetsov et al. (2006) g_maxNMDA = 1 * msiemens / cm2 #From Figure 5 Kuznetsov et al. (2006) E_k = -90 * mV E_L = -50 * mV E_AMPA = 0 * mV E_NMDA = 0 * mV p_Ca = 2500 * um / second beta = 0.05 # ratio of free to total calcium z = 2 # Valence of Calcium Ion C = 1 * uF / cm2 k = 250 * nmole / cm2 r = 1 * um eqs = Equations(''' dV/dt = (g_Ca*(E_Ca-V) + g_K *(E_k-V) + g_KCa*(E_k-V) + g_L*(E_L-V) + g_NMDA*(E_NMDA-V)+g_AMPA*(E_AMPA-V)+ I)/C: volt dCa/dt = (4*beta)*(g_Ca/(z*F)*(E_Ca-V) - p_Ca*Ca/r) : nmole/cm2 g_KCa = g_maxKCa * Ca**4/(Ca**4 + k**4) : msiemens/cm2 alpha_Ca = -0.0032*(V+50*mV)/((exp(-(V+50*mV)/(5*mV))-1)*mV) : 1 beta_Ca = 0.05*exp(-(V+55*mV)/(40*mV)) : 1 g_Ca = g_maxCa*(alpha_Ca/(alpha_Ca + beta_Ca))**4 : usiemens/cm2 g_NMDA = g_maxNMDA/(1+Mg/(10*mmole)*exp(-V/(12.5*mV))) : usiemens/cm2 g_K = g_maxK/(1+exp(-(V+10*mV)/(7*mV))) : usiemens/cm2 I : uA/cm2 ''') test = NeuronGroup(1, model=eqs, threshold= -40 * mV, reset= -53 * mV) trace = StateMonitor(test, 'V', record=True) stimulus = StateMonitor(test, 'I', record=True) test.V = -50 * mV run(1000 * msecond) test.I = 1 * uA / cm ** 2 run(500 * msecond) test.I = 0 * uA / cm ** 2 run(500 * msecond) figure() title('Simulation') subplot(211) plot(trace.times / ms, trace[0] / mV) ylabel('Voltage (mV)') subplot(212) plot(stimulus.times / ms, stimulus[0] / uA * cm2) ylabel(r'\Current Density ($\frac{\mu A}{cm^{2}}$)') xlabel('Time (ms)') show() brian-1.4.3/dev/debugging/troubleshooting/2010/mike_22_apr_2010.py000066400000000000000000000057331266062647100242640ustar00rootroot00000000000000from brian import * from brian.library.random_processes import * import multiprocessing # Parameters C = 281 * pF gL = 30 * nS taum = C / gL EL = -70.6 * mV VT = -50.4 * mV DeltaT = 2 * mV Vcut = VT + 5 * DeltaT taue = 2.728 * ms taui = 10.49 * ms Ee = 0 * mV Ei = -75 * mV eGain = 1 iGain = 2 # Pick an electrophysiological behaviour tauw, a, b, Vr = 144 * ms, 4 * nS, 0.0805 * nA, -70.6 * mV # Regular spiking (as inthe paper) #tauw,a,b,Vr=20*ms,4*nS,0.5*nA,VT+5*mV # Bursting #tauw,a,b,Vr=144*ms,2*C/(144*ms),0*nA,-70.6*mV # Fast spiking eqs = """ dvm/dt=(gL*(EL-vm)+gL*DeltaT*exp((vm-VT)/DeltaT)+I-w+eGain*ge*(Ee-vm)+iGain*gi*(Ei-vm))/C : volt dw/dt=(a*(vm-EL)-w)/tauw : amp I : amp """ eqs += OrnsteinUhlenbeck('ge', mu=2. * gL / 3, sigma=2. * gL / 3, tau=taue) eqs += OrnsteinUhlenbeck('gi', mu=4. * gL / 3, sigma=4. * gL / 3, tau=taui) """Trace is a non-dimensionalized array of voltages a StateMonitor recorded. Spikes is the accompanying SpikeMonitor passed to ease finding spikes in trace. Cutoff is the voltage at which to truncate each spike.""" def formatTraces(trace, spikes, cutoff, dt): answer = trace for _, t in spikes.spikes:answer[int(t / defaultclock.dt)] = 20 * mV return answer # This is the function that we want to compute for various different parameters def singleNeuronTrial(duration): # These two lines reset the clock to 0 and clear any remaining data so that # memory use doesn't build up over multiple runs. reinit_default_clock() clear(True) neuron = NeuronGroup(1, model=eqs, threshold=Vcut, reset="vm=Vr;w+=b", freeze=True) neuron.vm = EL trace = StateMonitor(neuron, 'vm', record=True) cutoff = 20 * mV spikes = SpikeMonitor(neuron) excitation = StateMonitor(neuron, 'ge', record=True) inhibition = StateMonitor(neuron, 'gi', record=True) run(duration * ms) formattedTrace = format(trace[0], spikes, cutoff, defaultclock.dt) return formattedTrace, trace.times / ms, excitation, inhibition def visualize(results): #Compute Averages traces = mean(results[0]) times = results[1] excitations = mean(results[2]) exTimes = results[2].times inhibitions = mean(results[3]) inTimes = results[3].times figure() subplot(211) title('Average AdExp Basal Activity over 100 2s Trial') ylabel('Voltage (mV)') plot(times / ms, traces / mV) subplot(212) plot(exTimes / ms, (excitations * (Ee - traces)) / nA) plot(Intimes / ms, (inhibitions * (Ei - traces)) / nA, 'r') title('Background Activity') ylabel('Current (nA)') xlabel('Time (ms)') legend(('Excitation', 'Inihibition')) if __name__ == '__main__': pool = multiprocessing.Pool() # uses num_cpu processes by default runNumber = 10 duration = 20 args = [duration] * runNumber #results = pool.map(singleNeuronTrial, args) results = map(singleNeuronTrial, args) visualize(results) show() brian-1.4.3/dev/debugging/troubleshooting/2010/sam_adams_16_02_2010.py000066400000000000000000000102301266062647100247120ustar00rootroot00000000000000from brian import * from brian.library.random_processes import * # General variables N = 100 RN_idx = 10 # the reinforced neuron connP = 0.02 # Connection probability for reduced noise connections connP_EE_scale = 1.0 # Scaling factor for EE connection connP_IE_scale = 1.2 # Scaling factor for IE connection # Parameters for conductance based Leaky Integrate and Fire Neuron Cm = 0.3 * nfarad # Membrane capacitance Rm = 100 * Mohm # Membrane resistance # Time constants taus = 5 * msecond # Excitatory/Inhibitory synapse time constant # Potential settings Ee = 0 * mV # Excitatory synapse reversal potential Ei = -75 * mV # Inhibitory synapse reversal potential Vthresh = -59 * mV # Threshold Potential Vresting = -70 * mV # Resting Potential (V(0)) Vreset = -70 * mvolt # Reset potential # Delays Trefrac = 5 * msecond # Refractory period Tdelay = 1 * msecond # Synaptic time delay # STDP variables tau_pre = 30 * msecond tau_post = 30 * msecond Ap = 0.01 Am = -Ap * tau_pre / tau_post * 1.05 # approx -0.01 # RM-STDP specific variables TauElig = 0.4 * second # Tau for eligibility trace ArPos = 1.379 # Alpha plus for reward kernel ArNeg = 0.27 # Alpha neg for reward kernel TauRPos = 0.2 * second # Tau plus for reward kernel TauRNeg = 1.0 * second # Tau neg for reward kernel TDelay = 0.2 * second # Reward delay last_rn_spike = 0.0 * second # Time of previous reinforced neuron spike current_rn_spike = 0.0 * second # Time of most recent reinforced neuron spike current_reward = 0.0 # Global variable for reward signal # some arrays for plotting an eligibility trace f_times = [] f_values = [] # O-U Noise equations eqs_Enoise = OrnsteinUhlenbeck('Enoise', mu=0.012 * usiemens, sigma=0.003 * usiemens, tau=2.7 * ms) eqs_Inoise = OrnsteinUhlenbeck('Inoise', mu=0.057 * usiemens, sigma=0.0066 * usiemens, tau=10.5 * ms) # Equations for eligibility trace # f is the eligibility kernel function # c is the eligibility trace which is # (STDP wt change * f) elig_eqs = Equations(''' last_time:second tc_elig = (t - last_time) *(1.0/(TauElig + TDelay)):1 f = 0.5 * tc_elig * exp(1.0 - tc_elig):1 c:1 ''') # Equations eqs_full = Equations(''' dV/dt = (V - Vresting)*(1./(Cm*Rm)) + (ge*(V - Ee))*(1./Cm) + (gi*(V - Ei))*(1./Cm) - (0.2*Enoise*(V - Ee))*(1./Cm) - (0.2*Inoise*(V - Ei))*(1./Cm): volt dge/dt = -ge*(1./taus) : siemens dgi/dt = -gi*(1./taus) : siemens ''') eqs_main = eqs_full + eqs_Enoise + eqs_Inoise + elig_eqs # Main neuron groups Pe = NeuronGroup(N / 2, model=eqs_main, threshold=Vthresh, reset=Vreset, refractory=Trefrac, implicit=True) Pe.V = Vresting Pe.ge = 0 * mV Pe.gi = 0 * mV Pe.last_time = 0.0 * second Pi = NeuronGroup(N / 2, model=eqs_main, threshold=Vthresh, reset=Vreset, refractory=Trefrac, implicit=True) Pi.V = Vresting Pi.ge = 0 * mV Pi.gi = 0 * mV Pi.last_time = 0.0 * second # Synapse weight equation parameters Wscale = 0.8 WExcScale = 1.0 WInhScale = 1.4 taum = Rm * Cm we = ((Vthresh - Vresting) * WExcScale * Wscale) / ((Ee - Vresting) * Rm * taus / (taum - taus) * (((taus / taum) ** (taus / (taum - taus))) - ((taus / taum) ** (taum / (taum - taus))))) wi = ((Vthresh - Vresting) * WInhScale * Wscale) / ((Vresting - Ei) * Rm * taus / (taum - taus) * (((taus / taum) ** (taus / (taum - taus))) - ((taus / taum) ** (taum / (taum - taus))))) # Maximum weight allowable during STDP is set to 2 * initial excitatory weight wMax = we * 2.0 # Make a neuron subgroup which is the reinforced neuron RN = Pe[RN_idx] Cee = Connection(Pe, Pe, 'ge', weight=we, sparseness=connP * connP_EE_scale, delay=Tdelay) Cie = Connection(Pe, Pe, 'gi', weight=wi, sparseness=connP * connP_IE_scale, delay=Tdelay) # Custom network operations to collect the eligibility # trace for reinforced neuron for plottingNameError: global name 'TauElig' is not defined @network_operation def save_elig(): f_times.append(defaultclock.t) f_values.append(Pe.f[RN_idx]) M = SpikeMonitor(Pe) run(200 * msecond) raster_plot(M) # Create plot of elig values figure() plot(elig_times, elig, 'k') xlabel("Time/ms") ylabel("Elig") suptitle('Eligibility Trace', fontsize=12) brian-1.4.3/dev/debugging/troubleshooting/2010/sam_v_adams_1_feb_2010.py000066400000000000000000000055021266062647100254720ustar00rootroot00000000000000''' Note: this doesn't work because you have dw_ij/dt = c_ij(t)d(t) c_ij(t) = sum over all pairs s, f_c(t)W(s) where f_c(s) is an alpha function, and W(s) is the STDP learning function. The problem is that not only is there a differential equation for w, but there is also a differential equation for c_ij. We could solve this by having a new system for STDP which allows you to specify differential equations at a synaptic level. Would even that be enough? It would be relatively easy at least to adapt STDP so that it could do: 1. differential equations and spike time code at the separable neuronal level 2. differential equations and spike time code at the synaptic level For example, code like: dw/dt = -w/tau could be turned into Python code like: w__tmp = -w/tau w += dt*w__tmp where here the symbol 'w' would refer to the flattened array of all synapse weights. Or it could be exactly solved with code like (for suitable consts): w[:] = const0*w+const1 More synaptic variables could be introduced with extra copies of the array. This would all work fine for sparse and dense matrices, and may be more difficult for dynamic matrices. These would have to be linked so that shape changes in one are mirrored in shape changes in the other. Perhaps it is worth having a class that ensures several dynamic ConnectionMatrix objects keep the same underlying structure of nonzero entries? It would be relatively easy to achieve, it would just be a proxy to the other matrices. This all works fine for zero delays, how about for heterogeneous delays? In terms of memory use, it's fine to store a copy of the recent values of the per-neuron variables, but unrealistic to store a copy of the recent values of per-synapse variables in all but a very few cases. ''' from brian import * sim_clock = Clock(dt=.1 * ms) weight_update_clock = Clock(dt=10 * ms) N = 100 tau_m = 10 * ms G = NeuronGroup(N, 'dV/dt=-V/tau_m:1', reset=0, threshold=1, clock=sim_clock) C = Connection(G, G, 'V', delay=True) C_trace = Connection(G, G, 'V', delay=True) forget(C_trace) for i in range(N): C_trace[i, :] = C[i, :] C_trace.delay[i, :] = C.delay[i, :] C_trace.compress() C_trace.W.alldata[:] = 0 @network_operation(clock=weight_update_clock) def weight_update(): C.W.alldata += weight_update_clock.dt * C_trace.W.alldata stdp = STDP(C_trace, ''' dA_pre/dt = (A_pre_aux-A_pre)/tau_pre : 1 dA_pre_aux/dt = -A_pre_aux/tau_pre_aux : 1 dA_post/dt = (A_post_aux-A_post)/tau_post : 1 dA_post_aux/dt = -A_post_aux/tau_post_aux : 1 ''', pre=''' A_pre_aux += delta_A_pre_aux w += A_post ''', post=''' A_post_aux += delta_A_post_aux w += A_pre ''', wmax=wmax) brian-1.4.3/dev/debugging/troubleshooting/2010/sam_v_adams_25_jan_2010.py000066400000000000000000000056121266062647100255760ustar00rootroot00000000000000# Reward - based STDP based on code from nemo136 # posted on Brian Support Google Group from brian import * #--------------------------------------------------- # Variable declarations #--------------------------------------------------- # Model variables and equations dt = 1 * ms tau = 10 * ms Vr = -70 * mV Vl = -55 * mV lif_eqs = ''' dVm/dt=(Vl-Vm)/tau : volt K : volt ''' # Basic STDP variables tau_pre = 10 * ms tau_post = 10 * ms gmax = 1 dA_pre = .001 dA_post = -dA_pre * tau_pre / tau_post * 2.5 #------------------------------------------------------- # Set up neuron groups #------------------------------------------------------- # Input group of Poisson neurons groupi = PoissonGroup(100, rates=linspace(0.1 * Hz, 10 * Hz, 100)) # Main neuron population, split into two subgroups groupa = NeuronGroup(100, model=lif_eqs, threshold= -55 * mV, reset=Vr, max_delay=6 * ms,) suba = groupa[0:50] subb = groupa[50:100] #------------------------------------------------------- # Create connections #------------------------------------------------------- # Connect input to group a - this inputs to Voltage in main eqs input_signal = Connection(groupi, suba, 'Vm', weight=0.2 * mV, sparseness=0.6, delay=4 * ms) # Connect group a to b - this inputs to Voltage in main eqs synapses = Connection(suba, subb, 'Vm', weight=0.1 * mV, sparseness=0.7, delay=3 * ms, structure='sparse', column_access=True) # Make trace connection which is a copy of synapses connection trace = Connection(suba, subb, 'K', weight=synapses.W) trace.compress() T_matrix = trace.W #------------------------------------------------------- # Set up STDP on trace connection to collect the weight # changes rather than apply them immediately #------------------------------------------------------- eqs_stdp = ''' dA_pre/dt=-A_pre/tau_pre : 1 dA_post/dt=-A_post/tau_post : 1 ''' stdp = STDP(trace, eqs=eqs_stdp, pre='A_pre+=dA_pre;w+=A_post', post='A_post+=dA_post;w+=A_pre', wmax=gmax) def update_trace(spikes): if len(spikes) > 0: # Apply reward to STDP weights which have been # collected in trace for j in spikes: T_matrix[:, j] *= 0.05 # for i in xrange(len(trace.W.get_col(0))): # for j in spikes: # if not isinstance(T_matrix[i,j],int): # T_matrix[i,j] *= 0.05 #--------------------------------------------------------- # Create monitors #--------------------------------------------------------- # Custom SpikeMonitor for applying reward Sa1 = SpikeMonitor(suba, function=update_trace) # Set clock defaultclock.dt = 1.0 * ms # Initialise the Voltage to resting voltage, Vr groupa.Vm = Vr # Run run(500 * msecond) brian-1.4.3/dev/debugging/troubleshooting/2010/sam_v_adams_3_april_2010.py000066400000000000000000000054721266062647100260550ustar00rootroot00000000000000''' Simple pickling example with a small network. This script looks for a previous data file and if it finds one reads in saved network parameters and connection weights. Network parameters and connection weights are saved at the end of a run. ''' from brian import * import pickle # Look for an import file - if not there start # a new empty one try: imported_data = pickle.load(open('./saved.pickle', 'rb')) defaultclock.t = imported_data['t'] print 'Starting from saved progress at time', defaultclock.t except IOError: print 'No data to import, starting new run' imported_data = {} # function which saves the network and connection # data using pickle. def save_progress(): s = str(int((defaultclock.t + .5 * ms) / second)) imported_data['P._S'] = P._S imported_data['t'] = defaultclock.t exc_inds = [Ce.W[i, :].ind for i in range(Ne)] exc_weights = [asarray(Ce.W[i, :]) for i in range(Ne)] inh_inds = [Ci.W[i, :].ind for i in range(Ni)] inh_weights = [asarray(Ci.W[i, :]) for i in range(Ni)] imported_data['exc_inds'] = exc_inds imported_data['exc_weights'] = exc_weights imported_data['inh_inds'] = inh_inds imported_data['inh_weights'] = inh_weights pickle.dump(imported_data, open('./saved.pickle', 'wb'), -1) print 'Saved progress up to time', s, 'second' # test network function using a different clock # from default @network_operation(clock=EventClock(t=defaultclock.t, dt=1000 * msecond)) def test_func(): print "Executing test_func at ", defaultclock.t # Network parameters Ne = 200 Ni = 50 # Model equations eqs = ''' dv/dt = (ge+gi-(v+49*mV))/(20*ms) : volt dge/dt = -ge/(5*ms) : volt dgi/dt = -gi/(10*ms) : volt ''' # Create Neuron group P = NeuronGroup(Ne + Ni, model=eqs, threshold= -50 * mV, reset= -60 * mV) P.v = -60 * mV Pe = P.subgroup(Ne) Pi = P.subgroup(Ni) # If initialising from previous data then read in Neuron # group data if imported_data: P._S[:] = imported_data['P._S'] # Create connections Ce = Connection(Pe, P, 'ge') Ci = Connection(Pi, P, 'gi') # If initialising from previous data then read in Connection # data, otherwise connect randomly if imported_data: exc_inds = imported_data['exc_inds'] exc_weights = imported_data['exc_weights'] inh_inds = imported_data['inh_inds'] inh_weights = imported_data['inh_weights'] for i in xrange(Ne): Ce[i, exc_inds[i]] = exc_weights[i] for i in xrange(Ni): Ci[i, inh_inds[i]] = inh_weights[i] else: Ce.connect_random(Pe, P, 0.02, weight=1.62 * mV) Ci.connect_random(Pi, P, 0.02, weight= -9 * mV) # Do the run M = SpikeMonitor(P) run(4000 * ms) # Save progress after run save_progress() # show what we've computed so far... raster_plot(M) show() brian-1.4.3/dev/debugging/troubleshooting/2010/toomas_kirt_9_nov_2010.py000066400000000000000000000017311266062647100256310ustar00rootroot00000000000000from brian import * import gc piecesize = 1000 def break_up_connection(source, target, var, weight, sparseness, piecesize=piecesize): N = len(source) pieces = range(0, N, piecesize)+[N] conns = [] for start, end in zip(pieces[:-1], pieces[1:]): C = Connection(source[start:end], target, var, weight=weight, sparseness=sparseness, column_access=False) C.compress() gc.collect() conns.append(C) magic_register(*conns) return conns eqs = ''' dv/dt = (ge+gi-(v+49*mV))/(20*ms) : volt dge/dt = -ge/(5*ms) : volt dgi/dt = -gi/(10*ms) : volt ''' P = NeuronGroup(11000, eqs, threshold=-50*mV, reset=-60*mV) P.v = -60*mV Pe = P.subgroup(8500) Pi = P.subgroup(2500) Ce = break_up_connection(Pe, P, 'ge', weight=1.62*mV, sparseness=0.2) Ci = break_up_connection(Pi, P, 'gi', weight=-9*mV, sparseness=0.2) M = SpikeMonitor(P) run(1*second) raster_plot(M) show() brian-1.4.3/dev/debugging/troubleshooting/2010/triplet_rule_STDP_10_aug_2010.py000066400000000000000000000041111266062647100266570ustar00rootroot00000000000000# # Triplet rule STDP # # UNDER DEVELOPMENT - TESTING THE TRIPLET RULE IMPLEMENTATION # # from brian import * rate = 0.050 *kHz # Spike frequency delta= 1 / rate # Interspike interval N = 60 # Number of spikes t0 = 0 *ms # Time of the first spike t1 = t0 + N * delta # Time of the last spike tau = 1*ms tau_plus = 16.8 *ms tau_x = 101 *ms tau_minus = 33.7 *ms tau_y = 125 *ms A2_minus = 7e-3 A3_minus = 2.3e-4 A2_plus = 5e-10 A3_plus = 6.2e-3 presyn_train = linspace(t0, t1, N, endpoint= False) postsyn_train = presyn_train + 10*ms input1 = SpikeGeneratorGroup(1, [(0, t * second) for t in presyn_train]) input2 = SpikeGeneratorGroup(1, [(0, t * second) for t in postsyn_train]) eqs = """ dv/dt = 0 * mV/ms : mV dw/dt = 0 /ms:1 """ def myresetfunc(P, spikes): P.v[spikes] = H SCR = SimpleCustomRefractoriness(myresetfunc, 0.01 *ms, state='v') P = NeuronGroup(2, model=eqs, threshold="v > 5*mV", reset=0*mV) P1 = P.subgroup(1) P2 = P.subgroup(1) C1 = Connection(input1, P1, 'v') C1[0,0] = 10*mV C2 = Connection(input2, P2, 'v') C2[0,0] = 10*mV C = Connection(P1, P2, 'w', weight=0) eqs_stdp=Equations(''' dr1/dt = -r1/tau_plus : 1 dr2/dt = -r2/tau_x : 1 #www = r1 - r2 : 1 do1/dt = -o1/tau_minus : 1 do2/dt = -o2/tau_y : 1 #zzz = o1 - o2 : 1 ''') #Triplet rule: fails to run stdp=STDP(C,eqs=eqs_stdp,pre='r1+=1;w-=o1*(A2_minus+A3_minus*r2);r2+=1', post='o1+=1;w+=r1*(A2_plus+A3_plus*o2);o2+=1',wmax=100)#stdp=STDP(C,eqs=eqs_stdp,pre='r1+=1;w-=o1*(A2_minus+A3_minus*r2);r2+=1', post='o1+=1;w+=(A2_plus+A3_plus*o2);o2+=1',wmax=100) #Modification: no mixing of pre and post terms on the weight update #stdp=STDP(C,eqs=eqs_stdp,pre='r1+=1;w-=o1*(A2_minus+A3_minus);r2+=1', post='o1+=1;w+=r1*(A2_plus+A3_plus);o2+=1',wmax=100)#stdp=STDP(C,eqs=eqs_stdp,pre='r1+=1;w-=o1*(A2_minus+A3_minus*r2);r2+=1', post='o1+=1;w+=(A2_plus+A3_plus*o2);o2+=1',wmax=100) P.v = 0 *mV M = StateMonitor(P, 'v', record=True) run(600 *ms) plot(M.times / ms, M[0] / mV, M.times / ms, M[1] / mV) show() brian-1.4.3/dev/debugging/troubleshooting/2011/000077500000000000000000000000001266062647100211475ustar00rootroot00000000000000brian-1.4.3/dev/debugging/troubleshooting/2011/delta_ij.mat000066400000000000000000000371241266062647100234340ustar00rootroot00000000000000MATLAB 5.0 MAT-file, Platform: MACI64, Created on: Wed Nov 2 11:20:56 2011 IMÌ=xœ”Y 8ÔÝ÷»É`˜ÅÌfA„’å•Jœ,%¤M%{…(ÉeK²„”´Ñ‚$$J*í‘l‘xE–TŠ$$½)^‰¿ÿóÿ™ûÞïïÿó<ópÏ=÷̹÷~ιçÞ‘Âápw}q8Ñé¿â¸ÿùûßùO[yúã6ýøOÛÍÝ;p«‹×Nüôÿ‘¾8ÂÌ›Sìþ$ÂäÉ(ÓýD˜‘[KÓ’¦$yí ñž¨=ñDx¨ï˜#ô™†‡®Û=àðúƒ¾tÖž}/¸‰£íf=ñ\&ÈêFÑ_9 ïå*úˆUËÂC‡#^i’¶Sk‚“‡Éœ*Øæ"R[’åÑyò×ý‚¿·½T„…*½çvÑñ þ])JGÃÚÚ«›<½ó={ÖÉñõûg9£†õ[‘o&µÅêu€„‘C7&†QXåü}ÊDtÆý­=;é ˜dWnÜ‚ü=zçsîÊAì÷y\VõYNãÉ°„]}•À5x´›ì†çÉe¿)­~Šôžö};öŒ±W,Rô6žA£úJT¨ý±¨û¾¤ *Ÿ„±`4¡A¡ˆB–± lÍ’ã}Ç9`»®Ìªþ*j¹á3Âdyv-»ªüåù®CŒA £ÍëÇ?áæn´Ò”CóµÓ¸ý…«öWÖ¨0ùÚqÜ´ô€éV´ž©Ñ/Ô:…M—-JrRBÝËÒIˆW‡}£=¾­âk7»ž“û|d.¯?YnŽœ±®"8&|zÇMAû¸ÿŒÿ™ùê¼öðT†t‹.?J>£Aª‰slß)į‹€¢‹í½9à¶hw©ÇM2ܵ­xµ§LŸHM‹tbBçheûvT‹”_ &q*ÄNª 3!LxÝZ÷{\ˆ¢LL§û—%îs)x…â’ºÑì³S‹×~¼¡±0i\¨ÓâÚ—»øQSEŒ¯_ôçW÷Wk•xí…Çöo%£<À³ûý!3, »O™’EÑÿÝ ï\¤cäü°°%i„‹cô}ã6ý©ì( é»s:,$ t,ÄŠ ™ág ¹½"<}£2'¿ã2À.QÒ<%!qRKß”NóÆ4Ç4ŠÃeÏò©‚û¶¹¿V^O ×xÖî”k4È^«â  ƒxtÞ8³!wŠˆggPy¡Ç“+'­1ÃaôÌ».z¸È^½äÛõêâÀ&ñØÙ%¾¢fv÷F±ãþ-|if }EsøŽïWK·ZMæõ“ëY¢xRváÝEâµíöp'>¢üS¿nFÇîïŠdé.¨¡Ayâ—ˆ}^Tà^®—°/GüÐl¹[ko.ã‡ò;Õýùç ËUç”y+Ãød±“4Wz?ÚŽŠÉ››åÀ¤ÿvˆD…ÀÃQž$._;üðÖÉv¥ó”?#J˜¹ž¿è"}úVb òwÒ½ã©W):ÏXú~ôÆëmaA@†„†Ã*6äõnf· CXìr‹Ó$(KØ6¿{˜ sÄ-ÚWƒâp/'Ü[CF°óo\õäʆl˜p°¶t Gë­¶eÙæ}Äë’íq¯–&ÊCžz‚}x$æøÐJöpᓽ¼RH"ÆžYw¯oVÀØŸ GË=·¼Â‚¿¶,Ž DÓ!Ö'Ûåº>hÊ­ÝGö±¥Í$Pû; w¦– ;p75ϲ ­ðíÙؾ¹Ú€6Nfp ‡=~N‚¶§Ò;®P@qgÕŽ_…³óìÿ ¦áû ¸· XûÚ\Åd—Y—1áŠâ>êƒßÂê12ìÛ¦uÅHÍ7âñÏÃ>a à¦lÀ5W„ä½…M‰²`cåOÙŠ]·ªÛ‰¥–?0¼9¼KØ ÜúéÐþSGn£Êß}‚!2¬g-qB~¾È¥«:ÌLD­,%ÀúÎbÏõjòöØèÞ ªFÏ6aç¿„% #Q2ë*á#å'#%|f¯Oz‚nô>iáÏ×<ï8?Gæ Òí<ÉÙïç<ÿ^ÿñX&Ù©wûº3Yvk ÜFû¡~^bþÒDe¾v;æ¦ Q@œªgˆ3KÙ“¦ PäßNu>ÂàhÕXº lÞí*6UÄÎgÃРIÈä7>:lTãµ7õ¤‡æ«`ÆmsˆÌ‹ìW‚E¸%ê*yyªÉN­³…ÒÇ,ªÈìëÇ›œ)°ì=„ ­©ýÞxxhbÏé™æa×ñyùudpT,ö"O0á®ÆÁŽ‚èü×»ûq«`5ŠÑ L[².<Ú2½ìoÒ!ÐÛ½zþyoôµtHC¡¯?§ÕýÚÒôyÀ vdаùê{@ï”ÓZlütœ1b½2ûù5‘½]×¢{¦æˆBÝj1H½Û Ö|‰ R§r–›¦#¾:·Ì?Íõç”ñ§Øy“—žÑHŽ Ã‡GK¶äLÒanöÁÒ4þ(±ÜõÆó@/üi_Œ®¶Îq{3ÌÅØQd*Ô§ëÂW±ôÕñt´Þ1kåKæÚ¡|øn°;ã”5pˆ—w({[éøŸ6(_æÉÎs°Áæ¹# µ&_(¢‹o0Fy;ý˜_tHÿßÏ  D@‹ò–‰GϦç(ÐVþbÛ% wëhd®AëMË¿5ß Å™±jE¯9‚{×Þ—Šò²€ížê½s@Y,-©g <ß‹ä¦( ?‹=qÎõÍ^Øu×ÝR a¢‰¡,XÀkÜ–0Ó1ˆž§XOp¢ÀÑ¥qK—¸Q@ÇRîê©yˆT¹gƒÃCÅ}‰âOsøßkª¬‚)Z•2ðr<†ý9Ík$¨CÈë7ö]G½;ÒyóyÄÛ1µpÆ> 4ß9‚‹'ÛƒPuºûµœ&aLoÇdÙv ÄåÖO왃ê³À ½O5tßVL|BmõÇæϯîÚwNÇsyò#ÇkÕ¤½‘®fñÆ\ø/ÿÿ”Yy<Ô[Ø—ìcc6cfÌ !d- =iD*K …’h±Sו$K …Ô‘JY’$Š’$¥«B’l¹B’µûzÿàôé÷Χû~ÿ›çœóüÎyöç™ÂÆ{µS$ðuu/‹m#AVâÓÖŒ­¢Àó èÙ­&©bóôµ]~Úþ¬ùß,Ëkºµ©2кã½äT -ÁÔUSÃÇ£|ñó†®÷=ž¬¦ˆø‹>[Ù–‰!V¦®…ÿ{;ˆ^oׯÓäâìª#“>Høí¹9è‰7u©¸‰C¶d€a1™ú¥vD}úüy%†l‹…Îîï"aíJz÷dw :i©%,p·Zß3d°76Ö¾K㣞ŒÕt*¿ßb|.?¿Ÿî °áñ0²3¹nÕpJVO.KåHËѹ ÉÿáħƂ••,Ìþ9œ|ý¾rÅ*8Ÿ0\öÝX³¯ˆsFõÒ>Éÿ,Ÿó4Fyq•,Í<·3·Q!hjÅ IÖÞî NCM C×÷(¥,çÃÒ[mNmÚÂ/ ÚB5'›ï¢ 䡱Ñn(!Gozè/Àœ#¬ÒÉ×ÑFú©8dÁBòT6Ð?“Çñ¾4¿V$'õGzªŸ ß¹ô5ü_ßZôé‹ ÉÒM_MP|ÄÇxÿøþɇèÅK;_«€¹Ç¯h{Z|þ¢!|‹§ÖUS@5•ˆ'ˆ° sSðûúýÈßj9!.ôÙ¿Ñ£ÂÑogm·j/äÊϨ®€ Ø:ОÏ겟 4Î÷™ïÉAöÍÑÜnˆô{Ô±Yä)½§*Žwaî"&¸Wùn<ÂïÓ¥’Jfˆ¿Æƒ[ÃâØ|9ƒ wõ[ xÌzf¢æ¸¿fë´Á\Bx+ªOùê—nÝ:ŠïFÕþ6º§yã÷ÅÛä ÷\åßÕˆ^øqpG¼&ÒwÛ•GEq·àû^öí²I8º|¢ÿÝ6_ç- ›6æ§`è½™Na·ðd }Ùk2yšcŠÏHsÁÆ÷:»§õÇ„0tšð¢}ÌÙ:gh@ù1Ÿ6ÿü ‰ã6k±~0s°DW‹ä÷r¦ÐqÐÃÉ%ÆÆÊÂd?oíà#îúøè¤ ýñ'ؼ;!fý×<[$7=¯’‘`±È§ÕÊ·oW¹úp‰èo¿l˜ª„ô3­äö—"Ÿgó›GTú*t߬Î#îM(Þ¾°};S?†äeÿ8¹Ó¤”®dÒÞø“@ƒÔX€Ÿõ—ñ…fÆRDp”•ÍõÚF”ÜÃF/4%€°L+[ç¹4Œîêù¶ië§B©&¾Å½RP ç¾ìÜ^tîù§‡Ä0ûËH¢Ç Öp—OúªÂSKVcÏýŠÌ«‹R† ?ò·\³7®”¼iDÇ "¤MH w¥C‚Íc F¿·—_x7w$sÛ_¤O'DŹʃ~Ôc›ÕÅlˆÿãÓȱÍTVl摃ü.w•—Êß©Å¡ ʲÐó6WðëlWw}#ì%¼F†ÉÍó­.µ/»Éœÿ=£]ñ÷úÓLðÐTá0ä9 Õ5ЌꙔªW¬Aº 8?2- ,Dò¬~ʖجËRy<¶£øHܼÏÚDåE±ýmQÊ9¨ÞçÓˆ¤øü†—•VÐÅP=Ír{uìc—${XËšúü^ŽU6Ö©ä_kƒÝÔJ‚Íñ¿íÎá@ã&ÎjÉ2ñùõ<õ7BïŸCͶÒKûÎÁ•V÷ð²3Š?)I%Ds´‘ÔV„Q¡ |LYÔ‡æÐh}$ú„ #…Û2zоä®/Ûû')ÀÉpyÖƒâ÷ô Ý3//…ž·.9ª8€éBÄG9Pô6lÕÿïq¡ª8ÿøel]‘:)pÞG[øØÝÍç° g2 ö²6¾Îa`FœçR*6íM ½«ç^·]®všýÇÀûççÑæ÷%<Ù¥ƒí#OŠÞ¡úc¿cc¦³JE}‡,¿ú4o® ˜q؉—.PÀæL¦'éõl½˜;Í7~Q¬š]nkErn©-¾¾³»ÝxíãçuFvhÿ¹çàF€¾‹ÍtïH¸sÌ :´Gny¹<Õ嬤ëæ× môI¢H .y¦ëúÙËÀè7²»X w=ñ.Ä?h4ÃÆSn0ºwípc78ÏV’³³kü ¾ŠAÊyJªÒÝÙø°Û%¥"„ãÞÎ×H)¸´òö´‚56ÿµ(¸¥kjKB+ÏõŒc0ëôPoõÃ~Hþíå%3ëþe‚ž|ìÖ 2ò'õ€‹×4ëI°L¬*U&…ôê´–Ògüo¿†×Jéá vf½|kzòÑg’ F÷Dœ ¾K®}ê¹Ê½ÿ3ÿγ)t)XÛ㧼°utB÷µû„ƒØ÷Ù,ý–´Ò åÛ×£ÒMüÙPþ:[j§1¶_5ŠÖ–Þ·ŸøÐ/m eaàÅ뉮²@]·ña”éïOÓßÕW|ÐT›ßŸU°- 2èð¾j“V€êëÖ54ÝŠ“ƒ/Þß5P4EÌô0¨@ýÌ$„Ÿ–P†™n_Ýæ Üû¾ aïó6^eCà[ïÕÝà*ÿOâßØùÉÄX>×Ù4KjìÃöMaÏ\… åÁ\ï CPQ|y6W“V)sýnÃuNø> ħPð$³Žéɪ²&jéÿ1÷šíÄhtõlý!ÓU$Á€¯›—Îú-¯šÅÇRP¦íÓY*H‡Àÿ?ÎËëÉÖ0¥Bu{趣ÈOsŒ=«÷|I@ße w«o {ªt˜Æ_ Èz[n¿ù™Jt&ó•Š4ÿP”ûn†âB¹µŠÓ ®÷õͳڹ´{=ШÕऽÝã¬ìýNS9®û‡^€Üf/T—^U:t2í2÷~*øQt~†-¾yÛ|¸Û! 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-c/tau_c : 1 # your postsynaptic calcium variable v : volt # a copy of the postsynaptic v ''' stdp=STDP(synapses, eqs=eqs_stdp,pre="w+=w0*(c>1)*(v>5*mV);x",post="c+=1;v", wmax=10*mV) stdp.post_group.v=linked_var(P,'v') run(1000*ms) brian-1.4.3/dev/debugging/troubleshooting/2011/hannah_deming_21_july_2011.py000066400000000000000000000045371266062647100263220ustar00rootroot00000000000000from brian import * from time import * #Parameters taum = 10*msecond taue = 5*msecond vt_input = -54*mvolt vt_PC= -54*mvolt vr = -60*mvolt E1 = -60*mvolt start_weight=0.6 tau_pre=20*ms tau_post=tau_pre gmax= 1 dA_pre=.01 dA_post=-dA_pre*tau_pre/tau_post*1.05 vr_input=vr # right? #Signal1 + singal2 transformed eqs_source1 = ''' Y1 = .5*sin(5*t/ms)*volt + .5*sin(7*t/ms)*volt : volt dV/dt = -V/ms : 1 ''' #Signal1 + singal2 transformed eqs_source2 = ''' Y2 = .5*sin(5*t/ms)*volt - .5*sin(7*t/ms)*volt : volt dV/dt = -V/ms : 1 ''' #Rule for neuron encoding a source eqs_inputs=''' dV/dt= (-(V-E1)+ s)/taum : volt s : volt ''' #Rule for neuron extracting the principal component eqs_PC1_LIF=''' dV/dt = (-(V-E1)+ge)/taum : volt dge/dt = -ge/taue : volt ''' #Creating neurons source1=NeuronGroup(1, model=eqs_source1) #'neuron' representing source1 source2=NeuronGroup(1, model=eqs_source2) #'neuron' representing source2 inputs=NeuronGroup(2, model=eqs_inputs, threshold=vt_input,reset=vr_input) #2 neurons, 1 for each source inputs.V=vr input1 = inputs.subgroup(1) #neuron converting source1 into a spike train input2 = inputs.subgroup(1) #neuron converting souce2 into a spike train PC=NeuronGroup(1, model=eqs_PC1_LIF, threshold=vt_PC, reset=vr) #neuron for extracting the first principal component PC.V=vr #linking analogue value of source to a LIF neuron's voltage input1.s=linked_var(source1, 'Y1') input2.s=linked_var(source2, 'Y2') #Excitatory connection between the inputs and the principal component # extracting neuron, starting weight is set in the parameter section Cmain = Connection(inputs, PC, 'ge', weight=start_weight*volt) #Adding STDP to connection between inputs and principal component neuron eqs_stdp=''' dA_pre/dt = -A_pre/tau_pre:1 dA_post/dt = -A_post/tau_post:1 ''' dA_post*=gmax dA_pre*=gmax #post code adapted to have synaptic scaling (sum of squaes of weights = 1) stdp_with_scaling= STDP(Cmain, eqs_stdp, pre='A_pre+=dA_pre;w+=A_post', post='A_post+=dA_post;w+=A_pre; w=w/(sum(w**2)**(.5))', wmax=gmax) #stdp_w/out_scaling= STDP(Cmain, eqs_stdp, pre='A_pre+=dA_pre;w #+=A_post',post='A_post+=dA_post;w+=A_pre', wmax=gmax) run(100*msecond) plot(Cmain.W.todense(), '.') print sum(Cmain.W.todense()**2) xlim((-.5, 1.5)) show() brian-1.4.3/dev/debugging/troubleshooting/2011/jiannis_6_oct_2011.py000066400000000000000000000010601266062647100247060ustar00rootroot00000000000000from brian import * from numpy import random from scipy import sparse,linalg N_PY = 100 alpha_x = 1.0 t_final = 0.01*second sim_clock = Clock(dt=0.05*ms) PY = NeuronGroup(N=N_PY, model='V:1',clock=sim_clock, #threshold=EmpiricalThreshold(state='Vs',threshold=10,refractory=3*ms,clock=sim_clock), threshold=10, order=2,freeze=True) CPP = zeros((N_PY,N_PY)) CPP[20,50] = 1. CPP = sparse.lil_matrix(CPP) C = Connection(PY,PY,'V',weight=alpha_x*CPP) run(t_final) brian-1.4.3/dev/debugging/troubleshooting/2011/laure_11_may_2011.py000066400000000000000000000012261266062647100244440ustar00rootroot00000000000000from brian import * from time import time N = 1 #number of neurons # Parameters C=281*pF gL=30*nS taum=C/gL EL=-70.6*mV VT=-50.4*mV DeltaT=2*mV Vcut=VT+5*DeltaT b=0*nA a=3*gL tauw=2*taum Vr=-70.6*mV #For the sinusoidal stim current ampl = 1.0*nA#*nS*mV f=10/second#*Hz eqs=""" dvm/dt=(gL*(EL-vm)+gL*DeltaT*exp((vm-VT)/DeltaT)+I-w + ampl*sin(2*pi*f*t))/C : volt dw/dt=(a*(vm-EL)-w)/tauw : amp I : amp """ neuron=NeuronGroup(N,model=eqs,threshold=Vcut,reset="vm=Vr;w+=b",freeze=True) neuron.vm=EL trace=StateMonitor(neuron,'vm',record=0) spikes=SpikeMonitor(neuron) run(100*ms) plot(trace.times/ms,trace[0]/mV) show() brian-1.4.3/dev/debugging/troubleshooting/2011/masquelier_30_june_2011.py000066400000000000000000000023261266062647100256610ustar00rootroot00000000000000from brian import * from scipy.sparse import csr_matrix import time N = 1000 p = 0.1 repeats = 1000 G = NeuronGroup(N, 'V:1') H = NeuronGroup(N, 'V:1') Cs = Connection(G, H, 'V', sparseness=p, weight=1.0, structure='sparse') Cd = Connection(G, H, 'V', sparseness=p, weight=1.0, structure='dense') Cs.compress() Ws = Cs.W Wd = Cd.W VG = asarray(G.V) VH = asarray(H.V) rowj, rowdata = Ws.rowj, Ws.rowdata def f(): VG[:] = 0 VG[:] = 0 for i in arange(0, N): # i is the source VG[rowj[i]] += VH[i]*rowdata[i] start = time.time() for _ in xrange(repeats): f() print 'Time using loop:', time.time()-start def f(): VG[:] = 0 VG[:] = 0 for i in xrange(N): # i is the source VG[rowj[i]] += VH[i]*rowdata[i] start = time.time() for _ in xrange(repeats): f() print 'Time using optimised loop:', time.time()-start def f(): VG[:] = dot(VH, Wd) start = time.time() for _ in xrange(repeats): f() print 'Time using dense:', time.time()-start Wss = csr_matrix((Ws.alldata, Ws.allj, Ws.rowind), (N, N)) def f(): VG[:] = Wss*VH start = time.time() for _ in xrange(repeats): f() print 'Time using scipy sparse:', time.time()-start brian-1.4.3/dev/debugging/troubleshooting/2011/michel_machin_7_november_2011.py000066400000000000000000000117021266062647100270700ustar00rootroot00000000000000def importSpkTrain(Nout,file_name="embeddedInputs.mat",var="embeddedInputs"): global InhNeur, ExNeur, SpkTrainEx, SpkTrainInh cellImport = scipy.io.loadmat(file_name, matlab_compatible=True) inputSpkTimes = cellImport[var] allSpkTrain=[] allSpkTrainImport=[] for j in range(Nout): spkTimeTemp=[] for ind1 in range(len(inputSpkTimes[j,inputInstance-1])): spkTimeTemp.append(Quantity(inputSpkTimes[j,inputInstance-1][ind1][0])*ms) allSpkTrain.append(spkTimeTemp) shuffleNeur=random.permutation(Nout) # implement 12.5 or 25 % of inhibition #InhNeur = shuffleNeur[:Nout/4] InhNeur = shuffleNeur[:Nout/16] # 16 #ExNeur = shuffleNeur[Nout/4:len(shuffleNeur)] ExNeur = shuffleNeur[Nout/16:len(shuffleNeur)] #16 SpkTrainEx=[] for indEx in range(len(ExNeur)): SpkTrainEx.append(allSpkTrain[ExNeur[indEx]]) SpkTrainInh=[] for indInh in range(len(InhNeur)): SpkTrainInh.append(allSpkTrain[InhNeur[indInh]]) return InhNeur, ExNeur, SpkTrainEx, SpkTrainInh def Wdefinition(Nout,ExNeur,InhNeur): global W_ex, W_inh W=ones((Nout,Nout)) W_ex=W[:len(ExNeur),:] W_inh=W[len(ExNeur):,:] return W_ex, W_inh def importDelays(file_name2="delta_ij.mat",var2="delta_ij"): global delta_ij d = scipy.io.loadmat(file_name2, matlab_compatible=True, mdict=None) delta_ij=d[var2] return delta_ij #------------------------------------------------------------------------------- from brian import * from numpy import * from scipy import * import scipy.io from scipy.stats import poisson from numpy.random import * simclock = Clock(dt=0.001 *ms) monclock = Clock(dt=0.1 *ms) patch_size = 8 # numPix=patch_size*patch_size Nout = numPix inputInstance = 3 # C_m = 0.5 * nF tau_m = 20 * ms Vt = -50 * mV # spike threshold | 1 * mV Vr = -60 * mV # resting potential and reset value | 0 * mV stdVr = 3 # std of Vr E_L = -70 * mV # resting potential | 0 * mV gL = 100 # leak conductance 100 * nS E_ex = 0 * mV # excitatory reverse potential #g_ex = 25 * nS tau_ex = 1 * ms # 5 * ms E_inh = -70 * mV # inhibitory reverse potential #g_inh = 20 * nS tau_inh = 1 * ms # 10 * mS ref = 3 * ms # neurons' refractory period # COBA model eqs = Equations(''' dV/dt = (-gL*(V-E_L)-g_ex*(V-E_ex)-g_inh*(V-E_inh))*(1./C_m) : volt dg_ex/dt = -g_ex/tau_ex : nS dg_inh/dt = -g_inh/tau_inh : nS ''') #eqs = Equations(''' # dV/dt = (-gL*(V-E_L)-g_ex*(V-E_ex)-g_inh*(V-E_inh))*(1./tau_m) : volt # dg_ex/dt = -g_ex/tau_ex : 1 # dg_inh/dt = -g_inh/tau_inh : 1 #''') SYN_PROB=0.5 #0.2 def f(spikes): #print spikes.shape if (spikes.shape[0])>0: prob=scipy.stats.bernoulli.rvs(SYN_PROB,size=spikes.shape[0]) spikes*=prob import random from numpy import * importSpkTrain(Nout) Input_ex = MultipleSpikeGeneratorGroup(SpkTrainEx, clock=simclock) Input_inh = MultipleSpikeGeneratorGroup(SpkTrainInh, clock=simclock) OutLayer = NeuronGroup(N=Nout, model=eqs, threshold=Vt, reset=Vr, refractory=ref, clock=simclock) #, freeze=False, implicit=True) Wdefinition(Nout,ExNeur,InhNeur) importDelays() C_ex = Connection(Input_ex,OutLayer,'g_ex',delay=True,max_delay=delta_ij.max()*ms) C_inh = Connection(Input_inh,OutLayer,'g_inh',delay=True,max_delay=delta_ij.max()*ms) for ej,ei in ((ej,ei) for ej in range(len(ExNeur)) for ei in range(Nout)): C_ex.delay[ej,ei] = Quantity(delta_ij[ExNeur[ej],inputInstance-1])*ms C_ex.connect(Input_ex,OutLayer,W_ex) for ij,ii in ((ij,ii) for ij in range(len(InhNeur)) for ii in range(Nout)): C_inh.delay[ij,ii] = Quantity(delta_ij[InhNeur[ij],inputInstance-1])*ms C_inh.connect(Input_inh,OutLayer,W_inh) SMonitor = [] SMonitor.append(SpikeMonitor(OutLayer, function=f)) OutLayer.V = Vr+(Vt-Vr)*rand(len(OutLayer)) # uniform random initialization of the membrane voltage V of the Output layer between Vr and Vt #OutLayer.V = random.normal(Vr,stdVr,len(OutLayer)) # Monitors --------------------------------------------------------------------- I_ex = SpikeMonitor(Input_ex) I_inh = SpikeMonitor(Input_inh) M = SpikeMonitor(OutLayer,record=True) outVolt = StateMonitor(OutLayer,'V', record=True, clock=simclock) outGex = StateMonitor(OutLayer,'g_ex', record=True, clock=monclock) outGinh = StateMonitor(OutLayer,'g_inh', record=True, clock=monclock) # run and plot ----------------------------------------------------------------- run(0.05 * second) figure() raster_plot(M) show() xlabel('time (ms)') ylabel('neuron number') show() #raster_plot(I_ex) #raster_plot(I_inh) #show() # figure() plot(outVolt.times / ms, outVolt[0] / mV) show() figure() plot(outGex.times, outGex[0]) plot(outGinh.times, outGinh[0]) show() brian-1.4.3/dev/debugging/troubleshooting/2011/mitch_july_20_2011.py000066400000000000000000000016571266062647100246450ustar00rootroot00000000000000''' Example with short term plasticity model Neurons with regular inputs and depressing synapses ''' from brian import * from brian.experimental.connectionmonitor import * import pygame tau_e=3*ms taum=10*ms A_SE=250*pA Rm=100*Mohm N=10 eqs=''' dx/dt=rate : 1 rate : Hz ''' input=NeuronGroup(N,model=eqs,threshold=1.,reset=0) input.rate=linspace(5*Hz,30*Hz,N) eqs_neuron=''' dv/dt=(Rm*i-v)/taum:volt di/dt=-i/tau_e:amp ''' neuron=NeuronGroup(N,model=eqs_neuron) C=Connection(input,neuron,'i') C.connect_one_to_one(weight=A_SE) stp=STP(C,taud=1*ms,tauf=100*ms,U=.1) # facilitation #stp=STP(C,taud=100*ms,tauf=10*ms,U=.6) # depression trace=StateMonitor(neuron,'v',record=[0,N-1]) M=ConnectionMonitor(C, display='pygame', size=(640, 640)) run(1000*ms) pygame.quit() subplot(211) plot(trace.times/ms,trace[0]/mV) title('Vm') subplot(212) plot(trace.times/ms,trace[N-1]/mV) title('Vm') show() brian-1.4.3/dev/debugging/troubleshooting/2011/toomas_kirt_memtest.py000066400000000000000000000022351266062647100256140ustar00rootroot00000000000000from brian import * import gc piecesize = 200 def break_up_connection(source, target, var, weight, sparseness, piecesize=piecesize): N = len(source) pieces = range(0, N, piecesize)+[N] conns = [] for start, end in zip(pieces[:-1], pieces[1:]): C = Connection(source[start:end], target, var, weight=weight, sparseness=sparseness, column_access=True) # C.compress() gc.collect() conns.append(C) # magic_register(*conns) return conns eqs = ''' dv/dt = (ge+gi-(v+49*mV))/(20*ms) : volt dge/dt = -ge/(5*ms) : volt dgi/dt = -gi/(10*ms) : volt ''' P = NeuronGroup(1000, eqs, threshold=-50*mV, reset=-60*mV) P.v = -60*mV Pe = P.subgroup(800) Pi = P.subgroup(200) Ce = break_up_connection(Pe, P, 'ge', weight=1.62*mV, sparseness=0.2) Ci = break_up_connection(Pi, P, 'gi', weight=-9*mV, sparseness=0.2) # Plasticity gmax = 0.01 tau_pre = 20 * ms tau_post = tau_pre dA_pre = .01 dA_post = -dA_pre * tau_pre / tau_post * 2.5 stdp = ExponentialSTDP(Ce[0], tau_pre, tau_post, dA_pre, dA_post, wmax=gmax, update='mixed') M = SpikeMonitor(P) run(1*second,report='text') raster_plot(M) show() brian-1.4.3/dev/debugging/troubleshooting/2012/000077500000000000000000000000001266062647100211505ustar00rootroot00000000000000brian-1.4.3/dev/debugging/troubleshooting/2012/benvarkey_31_07_2012.py000066400000000000000000000021351266062647100247660ustar00rootroot00000000000000import brian_no_units import brian from pylab import * eq1=brian.Equations('g=gmax*c0 :volt') #eq1+=brian.Equations('g_ch : 1') #uncomment this to make it work eq1+=brian.Equations('dc0/dt=-1 :volt') eq1+=brian.Equations('gmax :1') g1=brian.NeuronGroup(1, model=eq1) eq2=brian.Equations('dv/dt=-v+gch*(erev-v): volt') eq2+=brian.Equations('erev :1') eq2+=brian.Equations('gch :1') g2=brian.NeuronGroup(1, model=eq2) #g1.g_ch = brian.linked_var(g1, 'g') #uncomment this to make it work g2.gch = brian.linked_var(g1, 'g') #comment this to make it work #g2.gch = brian.linked_var(g1, 'g_ch') #uncomment this to make it work s1=brian.StateMonitor(g1, 'g', record=0) s2=brian.StateMonitor(g1, 'c0', record=0) s3=brian.StateMonitor(g2, 'v', record=0) s4=brian.StateMonitor(g2, 'gch', record=0) s5=brian.StateMonitor(g1, 'gmax', record=0) #initialize g1.g=0.0 #commenting this ALONE will make it work #g1.g_ch=0.0 #uncomment this to make it work g1.gmax=2.0 g2.erev=2.0 n=brian.Network() n.add(g1) n.add(g2) n.add((s1, s2, s3, s4, s5)) n.run(1) plot(s4.times, s4[0]) show()brian-1.4.3/dev/debugging/troubleshooting/2012/ehsan_27_jan_2012.py000066400000000000000000000007011266062647100244220ustar00rootroot00000000000000from brian import * from brian.experimental.synapses import * Vth, Vr, El = 0 * mV, -60*mV, -70*mV eqs = Equations(''' dVm/dt = (El - Vm + Pse*(Vm-0*mV) - Psi*(Vm+80*mV))/(20*msecond) :volt Pse :1 Psi :1 ''') Excit = NeuronGroup(1, eqs, threshold = Vth, reset=Vr) Inhib = NeuronGroup(1, eqs, threshold = Vth, reset=Vr) SynapsE2I=Synapses(Excit, Inhib, model='''dPse/dt = -Pse/(5*msecond) : 1 w : 1 ''') Inhib.Pse=SynapsE2I.Pse brian-1.4.3/dev/debugging/troubleshooting/2012/k_mergen_19_july_2012_nonlinear_synapses.py000066400000000000000000000043631266062647100313310ustar00rootroot00000000000000""" NMDA synapses """ from brian import NeuronGroup, StateMonitor, set_global_preferences, run, Clock from brian.stdunits import ms import time from brian.experimental.synapses import * from brian import log_level_debug log_level_debug() set_global_preferences(useweave=True,usecodegen=True,usecodegenweave=True,usenewpropagate=True,usecstdp=True) from matplotlib.pyplot import plot, show, subplot params = {} params["t_Nr"] = 2*ms params["t_Nf"] = 80*ms params["t_AMPA"] = 5*ms simclock = Clock(dt=0.01*ms) input=NeuronGroup(2,model='dv/dt=1/(10*ms):1', threshold=1, reset=0,clock=simclock) neurons = NeuronGroup(1, model="""dv/dt=(NMDAo+AMPAo-v)/(10*ms) : 1 NMDAo : 1 AMPAo : 1""", freeze = True,clock=simclock) ampadyn = ''' dAMPAoS/dt = -AMPAoS/t_AMPA : 1 AMPAi = AMPAoS AMPAo = AMPAoS / (t_AMPA /msecond) : 1 ''' nmdadyn = ''' dNMDAoS/dt = (1/t_Nr)*(Nnor*NMDAi-NMDAoS) : 1 dNMDAi/dt = -(1/t_Nf)*NMDAi : 1 Nnor = (t_Nf/t_Nr)**((t_Nr)/(t_Nf - t_Nr)) : 1 Nscal = (t_Nf/msecond)**(t_Nf/(t_Nf - t_Nr))/(t_Nr/msecond)**(t_Nr/(t_Nf - t_Nr)) : 1 NMDAo = NMDAoS / Nscal : 1 w : 1 # synaptic weight ''' s_eq = SynapticEquations(nmdadyn+ampadyn,**params) S=Synapses(input,neurons, model=s_eq, pre='NMDAi+=w\nAMPAi+=w',post='',clock=simclock) # NMDA synapses neurons.NMDAo=S.NMDAo neurons.AMPAo=S.AMPAo S[:,:]=True S.w=[0.01,0.01] S.delay=[0.003,0.005] input.v=[0.,0.5] M=StateMonitor(S,'NMDAo',record=True,clock=simclock) M0 = StateMonitor(S,'NMDAi',record=True,clock=simclock) M2=StateMonitor(S,'AMPAo',record=True,clock=simclock) Mn=StateMonitor(neurons,'v',record=True,clock=simclock) run(100*ms) subplot(411) plot(M0.times/ms,M0[0]) plot(M0.times/ms,M0[1]) subplot(412) plot(M.times/ms,M[0]) plot(M.times/ms,M[1]) subplot(413) plot(M2.times/ms,M2[0]) plot(M2.times/ms,M2[1]) subplot(414) plot(Mn.times/ms,Mn[0]) show() brian-1.4.3/dev/debugging/troubleshooting/2012/miha_pelko_9_5_2012.py000066400000000000000000000007101266062647100247500ustar00rootroot00000000000000from brian import * from time import time N = 100 M = 100 duration = 10*second st = [[t*second for t in sorted(rand(M)*duration)] for _ in xrange(N)] G = MultipleSpikeGeneratorGroup(st) #G = SpikeGeneratorGroup(N, [(i, t) for i in xrange(len(st)) for t in st[i]]) start = time() run(1*ms) construct = time()-start start = time() run(duration-1*ms, report='stderr') print 'Run time', time()-start print 'Construction time', construct brian-1.4.3/dev/debugging/troubleshooting/2012/o_mackwood_17_09_2012.py000066400000000000000000000101751266062647100251330ustar00rootroot00000000000000import brian_no_units from brian.globalprefs import set_global_preferences set_global_preferences(useweave=True, usecodegen=True, usecodegenweave=True, usecodegenstateupdate = True, usenewpropagate = True, usecstdp = True, gcc_options = ['-O3', '-ffast-math','-march=core2'] ) def run_stdp(NE,NI,v_init,C_e,C_ii,C_ie,mon_bin,dt): from brian.neurongroup import NeuronGroup from brian.monitor import PopulationRateMonitor from brian.stdunits import mV, ms, nS, pF, pA, Hz from brian.units import second from brian.equations import Equations from brian.network import Network from brian.connections import Connection from brian.stdp import STDP from brian.clock import Clock runtime = 10*second eta = 1e-2 # Learning rate tau_stdp = 20*ms # STDP time constant alpha = 3*Hz*tau_stdp*2 # Target rate parameter gmax = 100 # Maximum inhibitory weight eqs_neurons=''' dv/dt=(-gl*(v-el)-(g_ampa*w*v+g_gaba*(v-er)*w)+bgcurrent)/memc : volt dg_ampa/dt = -g_ampa/tau_ampa : 1 dg_gaba/dt = -g_gaba/tau_gaba : 1 ''' namespace = {'tau_ampa':5.0*ms,'tau_gaba':10.0*ms, 'bgcurrent':200*pA,'memc':200.0*pF, 'el':-60*mV,'w':1.*nS,'gl':10.0*nS,'er':-80*mV} eqs_neurons = Equations(eqs_neurons, ** namespace) clock = Clock(dt) neurons=NeuronGroup(NE+NI,model=eqs_neurons,clock=clock, threshold=-50.*mV,reset=-60*mV,refractory=5*ms) neurons.v = v_init Pe=neurons.subgroup(NE) Pi=neurons.subgroup(NI) rme = PopulationRateMonitor(Pe,mon_bin) rmi = PopulationRateMonitor(Pi,mon_bin) con_e = Connection(Pe,neurons,'g_ampa') con_ie = Connection(Pi,Pe,'g_gaba') con_ii = Connection(Pi,Pi,'g_gaba') con_e.connect_from_sparse(C_e, column_access=True) con_ie.connect_from_sparse(C_ie, column_access=True) con_ii.connect_from_sparse(C_ii, column_access=True) eqs_istdp = ''' dA_pre/dt=-A_pre/tau_stdp : 1 dA_post/dt=-A_post/tau_stdp : 1 ''' stdp_params = {'tau_stdp':tau_stdp, 'eta':eta, 'alpha':alpha} eqs_istdp = Equations(eqs_istdp, **stdp_params) stdp_ie = STDP(con_ie, eqs=eqs_istdp, pre='''A_pre+=1. w+=(A_post-alpha)*eta''', post='''A_post+=1. w+=A_pre*eta''', wmax=gmax) net = Network(neurons, con_e, con_ie, con_ii, stdp_ie, rme, rmi) net.run(runtime,report='text') return (rme.times,rme.rate), (rmi.times,rmi.rate) if __name__ == '__main__': import matplotlib.pyplot as plt from brian.network import clear from brian.connections.construction import random_matrix from brian.stdunits import mV,ms import numpy as np NE = 8000 # Number of excitatory cells NI = NE/4 # Number of inhibitory cells epsilon = 0.15 # Sparseness of synaptic connections mon_bin = 1*ms dt = 0.1*ms C_e = random_matrix(NE, NE+NI,epsilon, value=0.3) C_ii = random_matrix(NI, NI, epsilon, value=3.0) C_ie = random_matrix(NI, NE, epsilon, value=2.0) v_init = -80*np.random.rand(NE+NI)*mV rme1, rmi1 = run_stdp(NE,NI,v_init,C_e,C_ii,C_ie,mon_bin,dt) clear() rme2, rmi2 = run_stdp(NE,NI,v_init,C_e,C_ii,C_ie,mon_bin,dt) filter_width = 200*ms width_dt = int(filter_width / mon_bin) # width in number of bins window = np.exp(-np.arange(-2 * width_dt, 2 * width_dt + 1) ** 2 * 1. / (2 * (width_dt) ** 2)) fig = plt.figure() ax_inh = fig.add_subplot(2,1,1) ax_exc = fig.add_subplot(2,1,2) for rme,rmi,run_str in [(rme1,rmi1,'First'),(rme2,rmi2,'Second')]: ax_exc.plot(rme[0],np.convolve(rme[1], window * 1. / np.sum(window), mode='same'), label='Exc - '+run_str+' run') ax_inh.plot(rmi[0],np.convolve(rmi[1], window * 1. / np.sum(window), mode='same'), label='Inh - '+run_str+' run') ax_exc.legend() ax_inh.legend() plt.show() brian-1.4.3/dev/debugging/units_bug_with_sound.py000066400000000000000000000003111266062647100221440ustar00rootroot00000000000000#import brian_no_units from brian import * from brian.hears import * print second.__class__ snd = silence(1 * second) snd[0*second:0.5*second] = 0 snd[0:100] = 0 snd[0*second:0.5*volt] = 0 brian-1.4.3/dev/debugging/variable_refractoriness.py000066400000000000000000000011601266062647100226030ustar00rootroot00000000000000from brian import * eqs = ''' dV/dt = (0.5-V)/(10*ms) : 1 refrac : second ''' #G = NeuronGroup(10, eqs, reset=0, threshold=1, refractory=0.5*ms) #G = NeuronGroup(10, eqs, reset=0, threshold=1, refractory=linspace(0*ms, 5*ms, 10, endpoint=False)) G = NeuronGroup(10, eqs, reset=0, threshold=1, refractory='refrac', max_refractory=10 * ms) G.refrac = linspace(0 * ms, 5 * ms, 10, endpoint=False) G.V = 2 M = StateMonitor(G, 'V', record=True) run(1 * ms) #print G._next_allowed_spiketime run(9 * ms) G.V = 2 #G.refrac = linspace(2*ms, 0*ms, 10, endpoint=False) run(5 * ms) M.plot() show() brian-1.4.3/dev/debugging/xi_in_equation.py000066400000000000000000000010621266062647100207210ustar00rootroot00000000000000from brian import * set_global_preferences(useweave=False, usecodegen=False) tau=1*ms sigmax=1*mV sigmay=2*mV # These equations don't work #eqs=""" #dx/dt=-x/tau+sigmax*u/tau**.5 : volt #dy/dt=-y/tau+sigmay*u/tau**.5 : volt #u=xi : second**(-.5) #""" # Workaround eqs=""" v = x+sigmax*u : volt w = y+sigmay*u : volt dx/dt=-x/tau : volt dy/dt=-y/tau : volt du/dt=xi/tau**.5 : 1 """ neuron=NeuronGroup(1,model=eqs) #M=MultiStateMonitor(neuron, record=True) M=MultiStateMonitor(neuron, ['v', 'w'], record=True) run(10*ms) M.plot() show() brian-1.4.3/dev/ideas/000077500000000000000000000000001266062647100144675ustar00rootroot00000000000000brian-1.4.3/dev/ideas/AER/000077500000000000000000000000001266062647100150765ustar00rootroot00000000000000brian-1.4.3/dev/ideas/AER/AERlisten.py000066400000000000000000000024771266062647100173100ustar00rootroot00000000000000""" Listens to AER packets through UDP. Format: int32 sequenceNumber int32 address0 int32 timestamp0 int32 address1 int32 timestamp2 etc """ from scipy import * import socket from struct import * from time import time from brian.experimental.neuromorphic import * UDP_IP="localhost" UDP_PORT=8991 sock = socket.socket( socket.AF_INET, # Internet socket.SOCK_DGRAM ) # UDP sock.bind( (UDP_IP,UDP_PORT) ) first_time=True t0=1e12 while time()-t0<5.: # run for 5 s data, _ = sock.recvfrom( 63000 ) # buffer size packet_number=unpack('>I',data[:4]) # can we vectorize? # events are (address, timestamp) #events=[unpack('>II',data[(8*n+4):(8*n+12)]) for n in range(len(data)/8-1)] events=fromstring(data[4:],int32).newbyteorder('>') # something's wrong with the times if (len(events)>0): addr=events[::2] timestamp=events[1::2] if first_time: # Synchronize event timestamps and clock first_time=False t0=time() #ts0=events[0][1] ts0=timestamp[0] #ts=events[0][1]-ts0 ts=timestamp[0]-ts0 # Show first timestamp, clock time, first event print ts*1e-6,time()-t0,extract_DVS_event(addr[0])#,timestamp[0] #events[0][0]) sock.close() brian-1.4.3/dev/ideas/AER/brianRT.py000066400000000000000000000016761266062647100170230ustar00rootroot00000000000000''' Realtime Brian Pauses are inserted so that Brian runs always between real time and about 50 ms ahead of it (probably a bit more). Principle: every 50 ms, we check whether Brian is ahead by more than 100 ms, and if so we insert a sleep() to synchronize. NB: of course Brian needs to be faster than real time! (so use a large timestep) Other features of this real time stuff: * set the offset of the real time clock (reference time) ''' from brian import * from time import time,sleep defaultclock.dt=0.5*ms G=NeuronGroup(1000,model='dv/dt=-v/(10*ms):1') first_time=True @network_operation(EventClock(dt=50*ms)) def catch_up(cl): global start_time,first_time # First time: synchronize Brian and real time if first_time: start_time=time() first_time=False real_time=time()-start_time print cl.t,real_time if cl._t>real_time: sleep(cl._t-real_time) run(10*second) brian-1.4.3/dev/ideas/AER/loadAER.py000066400000000000000000000022021266062647100167130ustar00rootroot00000000000000""" Loads an AER .dat file and plays it in Brian This works (more or less). The main bottleneck is creating the SpikeGeneratorGroup, specifically the gather() method. """ from brian import * from brian.experimental.neuromorphic import * from time import time #defaultclock.dt=1*ms path=r'C:\Users\Romain\Desktop\jaerSampleData\DVS128' filename=r'\Tmpdiff128-2006-02-03T14-39-45-0800-0 tobi eye.dat' addr,timestamp=load_AER(path+filename) print len(addr),"events" #print "Timestamps sorted:",all(diff(timestamp)>=0) # Check if sorted x,y,pol=extract_DVS_event(addr) spiketimes=array((y,timestamp*1e-6)).T t0=time() #P=SpikeGeneratorGroup(128,spiketimes,gather=False) # True: 3.66 (3.55 for the bottleneck) + 1.45 # False: 0.04 + 0.75 (Running is also faster!!) P=SpikeQueue(128,timestamp*1e-6,y,check_sorted=False) # 0.88 + .47 (Running is faster, but construction is slower) t00=time() print "Group creation:",t00-t0 M=SpikeMonitor(P) t0=time() run(1*second) t00=time() print "Running time:",t00-t0 # Checking spikes=array(zip(*M.spikes)[1]) print timestamp[:10],spikes[:10]*1e6 raster_plot(M) show() brian-1.4.3/dev/ideas/AER/monitor_AER_events.py000066400000000000000000000040221266062647100212100ustar00rootroot00000000000000""" Monitor AER events with real-time Brian. I think it works (although not sure) but it's really too slow. It needs to be vectorised. Problems with negative timestamps """ #import brian_no_units from brian import * import socket from struct import * from time import time,sleep from brian.experimental.neuromorphic import * # Open the UDP socket UDP_IP="localhost" UDP_PORT=8991 sock = socket.socket( socket.AF_INET, # Internet socket.SOCK_DGRAM ) # UDP sock.bind( (UDP_IP,UDP_PORT) ) sock.setblocking(0) # non-blocking defaultclock.dt=1*ms # so that it's fast enough R=RealtimeController(dt=100*ms) #retina=SpikeGeneratorGroup(128,[]) allspikes=[] # Listen to AER first_event=True offset=0. latency=200*ms @network_operation(EventClock(dt=20*ms)) def listen(): global first_event,offset,allspikes try: while True: data= sock.recvfrom( 63000 )[0] #packet_number=unpack('>I',data[:4]) # not very useful #events=[unpack('>II',data[(8*n+4):(8*n+12)]) for n in range(len(data)/8-1)] events=fromstring(data[4:],int32).newbyteorder('>') if (len(events)>0): events=events[:32] # a small selection addr=events[::2] timestamp=events[1::2] if first_event: # Synchronize event timestamps and clock first_event=False t0=time()-start_time ts0=timestamp[0]*1e-6 offset=t0-ts0 _,addr,_=extract_DVS_event(addr) #spiketimes=array((addr,timestamp*1e-6+offset+float(latency))).T #spiketimes=retina.gather(spiketimes,defaultclock.dt) allspikes+=zip(addr,timestamp*1e-6+offset+float(latency)) except: # no more events (can we do this in a cleaner way?) pass #M=SpikeMonitor(retina) start_time=time() run(5*second) sock.close() i,t=zip(*allspikes) plot(t,i,'.') #raster_plot(M) show() brian-1.4.3/dev/ideas/__init__.py000066400000000000000000000000021266062647100165700ustar00rootroot00000000000000 brian-1.4.3/dev/ideas/alt_heterog_delays.py000066400000000000000000000016121266062647100206770ustar00rootroot00000000000000from brian import * class FutureSpikeContainer(object): def __init__(self, nsteps): self.nsteps = nsteps self.maxspikes = 2 self.ind = zeros((2, nsteps), dtype=int) self.nspikes = zeros(nsteps, dtype=int) self.cursor = 0 def insert(self, spikes, delays): t = (self.timecursor + delays) % self.nsteps I = argsort(t) t = t[I] spikes = spikes[I] # TODO: finish this - use scheduled_events.py which basically does the # same thing # assume for the moment that no overflows happen def getspikes(self): spikes = self.ind[0:self.nspikes[self.cursor], self.cursor] self.nspikes[timecursor] = 0 self.timecursor += 1 if self.timecursor == self.nsteps: self.timecursor = 0 return spikes if __name__ == '__pass__': pass brian-1.4.3/dev/ideas/autoparallelise.py000066400000000000000000000047431266062647100202370ustar00rootroot00000000000000''' Ideas for automating the work of parallelising a network * Given a network object net, work through each of the objects adapting them all for the expansion of some parameter. This is nice because it doesn't require the user to write any new code just add in an extra auto-parallelisation line. The difficult thing is that it would require adapting the objects so that as well as creating them they also store a copy of their parameters in such a way that they can be recreated (e.g. extending a neurongroup from N=1 to N=1000). This is tricky at the moment. * Given a function that defines a simulation to be run, rewrite the code automatically. One nice way to do this is to replace each NeuronGroup object in the global namespace of the function with a new type of NeuronGroup object that automatically adapts it to the parameter set given (multiplies N by number of different parameters, etc.). This is also nice since it means the user just has to wrap their code in a function. The difficult thing here is that referring to the objects defined by the function is tricky - how should it be done? ''' from brian import * import inspect from copy import copy def printargs(*args, **kwds): print args print kwds class AutoParallelNetwork(Network): def __init__(self, makenet, params): self.apn_params = params self.apn_makenet = makenet Network.__init__(self) makenet_globals = copy(makenet.func_globals) makenet_globals['NeuronGroup'] = printargs newfuncspace = {} exec inspect.getsource(makenet) in makenet_globals, newfuncspace exec makenet.func_name + '()' in makenet_globals, newfuncspace def makenet(): global M Vr = -70 * mV Vt = -55 * mV El = -54 * mV eqs = ''' dV/dt = -(V-El)/tau : volt tau : second ''' model = Model(equations=eqs, threshold=Vt, reset=Vr) G = NeuronGroup(1, model) # spikes = linspace(10*ms,100*ms,25) # input = MultipleSpikeGeneratorGroup([spikes]) # C = Connection(input, G) # C[0,0] = 5*mV M = StateMonitor(G, 'V', record=True) # G.V = Vr # G.tau = 10*ms # net = MagicNetwork(verbose=False) # return net makenet() print 'made net' anet = AutoParallelNetwork(makenet, [ ('G', 'tau', [5 * ms, 10 * ms, 15 * ms]) ] ) #net = makenet() #net.run(100*ms) # #plot(M.times/ms,M[0]/mV) #show() brian-1.4.3/dev/ideas/better_sympy_ccode.py000066400000000000000000000014611266062647100207260ustar00rootroot00000000000000from sympy.printing.ccode import CCodePrinter from sympy.printing.str import StrPrinter from sympy.printing.precedence import precedence, PRECEDENCE from sympy.core.basic import S def boolean_printer(origop, newop, start=''): def f(self, expr): PREC = precedence(expr) return start + newop.join(self.parenthesize(a, PREC) for a in expr.args) f.__name__ = '_print_' + origop return f class NewCCodePrinter(CCodePrinter): _print_And = boolean_printer('And', '&&') _print_Or = boolean_printer('Or', '||') _print_Not = boolean_printer('Not', '', '!') def newccode(expr): return NewCCodePrinter().doprint(expr) if __name__ == '__main__': from sympy import * x = Symbol('x') y = Symbol('y') z = ~(x + y) print newccode(z) brian-1.4.3/dev/ideas/cache_propagate.py000066400000000000000000000076131266062647100201550ustar00rootroot00000000000000''' Seems to make it go slower rather than faster... ''' from brian import * import numpy from scipy import weave neuron_column_width = 512 # various values, none of them make it faster class CacheConnection(Connection): def propagate(self, spikes): if not self.iscompressed: self.compress() if len(spikes): if not isinstance(spikes, numpy.ndarray): spikes = array(spikes, dtype=int) sv = self.target._S[self.nstate] rows = self.W.get_rows(spikes) nspikes = len(spikes) N = len(sv) code = """ using namespace std; //vector< blitz::Array > rowset; vector< double* > rowset; rowset.reserve(nspikes); for(int j=0;j row = convert_to_blitz(_row,"row"); //rowset[j] = row; rowset[j] = (double*)_row->data; Py_DECREF(_rowsj); } for(int start=0; startN) end=N; for(int j=0;j row = convert_to_blitz(_row,"row");*/ //blitz::Array row = rowset[j]; double *row = rowset[j]; for(int k=start;k']) if __name__=='__main__': log_level_debug() import time #set_global_preferences(usenewpropagate=False) N = 10000 tau = 10*ms duration = 100*ms p = 0.1 eqs = ''' dv/dt = -v/(10*ms) : 1 ''' G = NeuronGroup(N, eqs, reset=-2, threshold=-1) G.v = rand(N)-2 C = Connection(G, G, 'v', weight=1e-10, sparseness=p, structure='dense') #C = CacheConnection(G, G, 'v', weight=1e-10, sparseness=p, structure='dense') M = SpikeMonitor(G, record=False) run(1*ms) forget(C) start = time.time() run(duration) end = time.time() t_noconnection = end-start recall(C) start = time.time() run(duration) end = time.time() t_both = end-start t_connection = t_both - t_noconnection print 'Propagation time:', t_connection print 'Average neuron firing rate:', M.nspikes/duration/N print 'Average spikes per time step:', M.nspikes/(duration/defaultclock.dt) print 'Average synapses per time step:', M.nspikes*(N*p)/(duration/defaultclock.dt) # raster_plot(M) # show() brian-1.4.3/dev/ideas/cluster/000077500000000000000000000000001266062647100161505ustar00rootroot00000000000000brian-1.4.3/dev/ideas/cluster/cluster.py000066400000000000000000000165431266062647100202140ustar00rootroot00000000000000''' Distributed simulations on clusters, using MPI. Groups hosted on other machines are mapped to virtual groups. Connection objects do not change (simply, source or target may be virtual). The ClientNetwork object runs the network normally, except it sends and receives spikes from times to times to other machines. The ServerNetwork object creates ClientNetwork objects and distributes them over the machines. It does not need supervise the simulation. How about creation? (in particular, connection objects should be created on the target machine) It should be possible to create the objects on the target machines, e.g.: * create the group/connection on the server * communicate the object to the client * convert to virtual group on the server How about monitoring? Simply communicate the monitors to the machine hosting the relevant neuron group. How about plasticity? Objects on a node: * (real) groups * virtual groups * connections to groups only (from groups and virtual groups) A time step involves: * normal calls to real groups (update, threshold, reset) * broadcast of spikes from all groups; can be done in the threshold operation * usual connection propagate Essentially, what needs to be done is: * removing update/reset from virtual groups * having a special threshold in virtual group which simply does a broadcast * adding the broadcast to real groups Note that this mechanism can handle homogeneous delays in a simple way (the LS structure pushes the spikes as usual). Thus, the main difficulty is setting up the simulation. There can also difficulties with monitors and network operations. How to distribute the groups? 1) Simplest possibility: use preexisting groups, do not split them. Then simply divide them equally among processors. 2) Divide every group equally in the number of processors. 3) Divide the set of groups between processors, then divide each group. Strategy 2 has an advantage: NeuronGroups and Connections can be divided at initialisation time. Then the connect functions of Connection could be overriden so that the matrices are only created on the clients (faster and much better for memory). It could be done on the same way for neuron groups, but it is probably sufficient to do the construction on the server and do the transfer when the network run is called. However with network operations, state transfers should be possible at run time. One simple possibility would be something like: from brian import * from brian.cluster import * and the latter statement would redefine NeuronGroup, Connection, etc. Something like: NeuronGroup = ClusterNeuronGroup Connection = ClusterConnection The same script could be run on all processors, that could make things much simpler. For example, consider 2 processors and the statement P=NeuronGroup(100,model=...) creates a ClusterNeuronGroup on each processor, in which either P[:50] or P[50:] is real while the rest is virtual (it may not be necessary). Then P.v=rand(100) would only assign the relevant part of the vector. It seems to be wasting a lot of time since the same operation is repeated on all processors, but they are done in parallel so it is not less efficient than doing everything on a server and transferring the data (in fact it is more efficient since we avoid the transfers). Connections would work in the same way, with minor code changes. Monitors could also work similarly, which would work as if each group P were replaced by a subgroup. The only asymmetry would be that processor 0 would gather all information from monitors at the end, and possibly some analysis, plotting, etc. So there should probably be some specific code for the server. That makes it less interesting to use the same script for all nodes... Also, the statement from brian.cluster import * could be avoided by placing a test in brian.__init__ that checks the number of processor (i.e., whether the script has been run with mpiexec). Alternatively, one could use a special Python script to run a script on a cluster (which would call mpiexec). ServerGroup: * stores all state variables normally * transfers only when network is running (e.g. use a flag when running) * transfers are triggered by reads/writes at run time (gather operation?); more specifically: + write operation triggers a flag for transfer at end of time step + read operation triggers an immediate transfer if flag is up and pushes flag down (flag up at end of time step). * updates, threshold, resets only concern the beginning of the group (non virtual) A NeuronGroup could be turned into a ServerGroup when the first run function is called. This way all subclasses of NeuronGroup could be used. The ServerGroup holds a reference to the real NeuronGroup and inserts the required operations. This way, monitors can stay on the server side, essentially unchanged. The problem with this strategy is that plasticity is implemented with monitors. In fact on the client side, one could use the same class as ServerGroup, except the indexes of the real subgroup are different. Reads/writes would be forbidden in virtual groups (not a problem if monitors stay on the server side). What might be necessary for transparent use is that all objects (groups, connections) should have a subgrouping mechanism. Yet another strategy based on option 1), i.e., do not subdivide groups: * When a NeuronGroup is created, it is decided internally whether it should be on the main server or on a client (dynamic load balancing based only on the number of neurons). The corresponding groups are sent to the clients. * When a Connection is created, it already knows where the groups are, so it is simple to handle. * That technique duplicates all spike monitors, but maybe we can change that behaviour. ''' import pypar from brian.neurongroup import * from brian.network import * from brian.globalprefs import set_global_preferences from cluster_client import run_client class VirtualGroup(NeuronGroup): ''' A group that is hosted on another machine. A number of methods will raise an exception (e.g. accessing the state variables). ''' def __init__(self, group, machine): ''' Initializes the virtual group: group: real group machine: machine id ''' # How do we deal with clocks? self._S0 = group._S0 self.staticvars = group.staticvars self.var_index = group.var_index self._max_delay = group._max_delay self.LS = group.LS self._owner = machine self._length = len(group) self._numstates = group.num_states() # Update and reset are disabled def update(self): pass def reset(self): pass def __len__(self): return self._length def num_states(self): return self._numstates def __repr__(self): return 'Virtual group of ' + str(len(self)) + ' neurons' class ServerNetwork(Network): ''' Network class for running a simulation over a cluster. The server manages the clients. ''' pass # Identification myid = pypar.rank() # id of this process nproc = pypar.size() # number of processors if myid > 0: # client import sys run_client() sys.exit(0) # Server #Network=ServerNetwork set_global_preferences(cluster_server=True) brian-1.4.3/dev/ideas/cluster/cluster_client.py000066400000000000000000000010211266062647100215330ustar00rootroot00000000000000''' A client script to be run for cluster simulations. ''' from brian import * import pypar __all__ = ['run_client'] class ClientNetwork(Network): ''' Network class for running a simulation over a cluster, client side. ''' pass def run_client(): ''' Runs ''' # Identification myid = pypar.rank() # id of this process nproc = pypar.size() # number of processors print "I am client", myid pypar.finalize() if __name__ == '__main__': run_client() brian-1.4.3/dev/ideas/cluster/mpi_notes.txt000066400000000000000000000100371266062647100207070ustar00rootroot00000000000000Working in: ~/src/brian-svn-trunk/dev/ideas/ See also comments at top of: ~/src/brian-svn-trunk/dev/ideas/cluster/cluster.py Working on 1st example: copy of examples/misc/expIF_network.py SVN r2146 to: ~/src/brian-svn-trunk/dev/ideas/cluster/expIF_network_mpi.py it would be useful to try both uniform and non-uniform delays for this example. Strategy Proposal: vvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvv # Please ammend as we iterate on this NeuronGroup -> DistributedNeuronGroup size passed by user is treated as global_size, local_ids = scatter(global_ids, rank) ... which should be treatable using concepts of NeuronSubGroup ... i.e. DistributedNeuronGroup.local_group returns a subgroup which is the local neurons, it would also be used extensivly internally. -> DistributedNeuronGroup implicit try of import mpi4py, and getting of MPI.COMM_WORLD.size to determine distribution layout. if size=1, warn the user. Connection calls would need to do runtime exchange of source,target,delay triplets, i.e. local network determines targets in the global_group for its local_group, and then an MPI.allgather exchange is performed. Once delays have been gathered, connection determines its min_delay and informs its source DistributedNeuronGroup. Since all DistributedNeuronGroup instances on all MPI processes should be included in same connection objects, the min_delays of all DistributedNeuronGroups would be thus synchronized. The DistributedNeuronGroup should have an external clock with ticks of min_delay/2, and an internal resolution dt. The Network loop lets each DIstNeuronGroup update in jumps of min_delay/2. At the start of each update(), the DistributedNeuronGroup issues an collection of MPI_Isend/Irecv pairs between each MPI_rank pair with spikes of the last update() as the message. Prior to this send, it should block on the "request" objects of the Isend/Irecv messages of the previous timestep. In this way, messaging is asynchronous, and arrives by time=min_delay. This has the limitation that itmight not scale with the # of DistributedNeuronGroups per node ... but let's wait and see. The min_delay/2 approach could also be applied to GPUs but even internal to the kernel ... which can result in a significant impovement in compute density per neuron parameter fetch. How are spike routing and delays handled? connections/delayconnection.py would need to be augmented with a min_delay ... def do_propagate(self): self.propagate(self.source.get_spikes(0)) ---> here 0->min_delay. connections/connection.py treat delay as min_delay. DistributedNeuronGroup -> should be informed also about min_delay from the Connection object. The loop in Network.run() -> self.update() self.clock.tick() here the clock.tick should be configured to tick @ min_delay/2 ... Connection -> docstring """ ``propagate(spikes)`` Action to take when source neurons with indices in ``spikes`` fired. ``do_propagate()`` The method called by the :class:`Network` ``update()`` step, typically just propagates the spikes obtained by calling the ``get_spikes`` method of the ``source`` :class:`NeuronGroup`. """ *** def do_propagate(self): self.propagate(self.source.get_spikes(self.delay)) *** So how is the delay used? ~/src/brian-svn-trunk/brian/neurongroup.py NeuronGroup def get_spikes(self, delay=0): ''' Returns indexes of neurons that spiked at time t-delay*dt. ''' -> return self.LS.get_spikes(delay, self._origin, len(self)) OK, so delays are fixed for a Connection???? What is NeuronGroup.LS ? self.LS = SpikeContainer(self._max_delay, useweave=get_global_preference('useweave'), compiler=get_global_preference('weavecompiler')) # Spike storage There's also connections/delayconnection.py would need to be augmented with a min_delay ... def do_propagate(self): self.propagate(self.source.get_spikes(0)) ---> here 0->min_delay. ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ brian-1.4.3/dev/ideas/codegen_rewrite/000077500000000000000000000000001266062647100176345ustar00rootroot00000000000000brian-1.4.3/dev/ideas/codegen_rewrite/old/000077500000000000000000000000001266062647100204125ustar00rootroot00000000000000brian-1.4.3/dev/ideas/codegen_rewrite/old/codegen_with_python_syntax.py000066400000000000000000000046601266062647100264400ustar00rootroot00000000000000import re, inspect # We just use this class to identify certain variables class Symbol(str): pass # This class is basically just a mutable string class OutputCode(object): def __init__(self): self.code = '' def __iadd__(self, code): self.code = self.code + code return self # Iterating over instances of this class generates code # for iterating over a C++ array, it yields a single # Symbol object, the variable name of the value in the # array class Array(object): def __init__(self, code, name, dtype='double'): self.name = name self.dtype = dtype self.code = code def __iter__(self): def f(): self.code += 'for(int {name}_index=0; {name}_index<{name}_len; {name}_index++){{\n'.format(name=self.name) self.code += '{dtype} &{name} = {name}_array[{name}_index];\n'.format(dtype=self.dtype, name=self.name) yield Symbol(self.name) self.code += '}\n' return f() # Instances of this class generate C++ code from Python syntax # code snippets, replacing variable names that are a Symbol in the # namespace with the value of that Symbol. class Evaluator(object): def __init__(self, code): self.code = code def __call__(self, code): # The set of variables in the code snippet vars = re.findall(r'\b(\w+)\b', code) # Extract any names from the namespace of the calling frame frame = inspect.stack()[1][0] globals, locals = frame.f_globals, frame.f_locals values = {} for var in vars: if var in locals: values[var] = locals[var] elif var in globals: values[var] = globals[var] # Replace any variables whose values are Symbols with their values for var, value in values.iteritems(): if isinstance(value, Symbol): code = re.sub(r'\b{var}\b'.format(var=var), str(value), code) # Turn Python snippets into C++ (just a simplified version for now) code = code.replace(';', '\n') lines = [line.strip() for line in code.split('\n')] code = ''.join(line + ';\n' for line in lines) self.code += code if __name__ == '__main__': code = OutputCode() evaluate = Evaluator(code) X = Array(code, 'values') for x in X: evaluate('x += 5; x *= 2') print code.code brian-1.4.3/dev/ideas/codegen_rewrite/old/codeobject.py000066400000000000000000000065571266062647100231020ustar00rootroot00000000000000""" The basic Code object """ from brian import * from brian.globalprefs import get_global_preference from scipy import weave __all__ = ['Code', 'PythonCode', 'CCode', 'GPUCode', ] class Code(object): ''' The basic Code object used for all Python/C/GPU code generation. The Code object has the following attributes: ``code_str`` A representation of the code in string form ``namespace`` A dictionary of name/value pairs in which the code will be executed ``code_compiled`` An optional value (can be None) consisting of some representation of the compiled form of the code ``pre_code``, ``post_code`` Two optional Code objects which can be in the same or different languages, and can share partially or wholly the namespace. They are called (respectively) before or after the current code object is executed. Each language (e.g. PythonCode) extends some or all of the methods:: ``__init__(...)`` Unsurprisingly used for initialising the object, should call Code.__init__ with all of its arguments. ``compile()`` Compiles the code, if necessary. If not necessary, set the ``code_compiled`` value to any dummy value other than None. ``run()`` Runs the compiled code in the namespace. It will usually not be necessary to override the call mechanism: ``__call__(**kwds)`` Calls ``pre_code(**kwds)``, updates the namespace with ``kwds``, executes the code (calls ``self.run()``) and then calls ``post_code(**kwds)``. ''' def __init__(self, code_str, namespace, pre_code=None, post_code=None): self.code_str = code_str self.namespace = namespace self.code_compiled = None self.pre_code = pre_code self.post_code = post_code def compile(self): pass def run(self): pass def __call__(self, **kwds): if self.pre_code is not None: self.pre_code(**kwds) self.namespace.update(kwds) if self.code_compiled is None: self.compile() self.run() if self.post_code is not None: self.post_code(**kwds) class PythonCode(Code): def compile(self): self.code_compiled = compile(self.code_str, 'PythonCode()', 'exec') def run(self): exec self.code_compiled in globals(), self.namespace class CCode(Code): def __init__(self, code_str, namespace, pre_code=None, post_code=None): Code.__init__(self, code_str, namespace, pre_code=pre_code, post_code=post_code) self._weave_compiler = get_global_preference('weavecompiler') self._extra_compile_args = ['-O3'] if self._weave_compiler == 'gcc': self._extra_compile_args += get_global_preference('gcc_options') self.code_compiled = 0 def run(self): weave.inline(self.code_str, self.namespace.keys(), local_dict = self.namespace, #support_code=c_support_code, compiler=self._weave_compiler, extra_compile_args=self._extra_compile_args) class GPUCode(Code): pass if __name__=='__main__': passbrian-1.4.3/dev/ideas/codegen_rewrite/old/connection.py000066400000000000000000000137411266062647100231310ustar00rootroot00000000000000''' SIMPLEST WAY: - Use something like previous codegen propagation stuff to generate basic template, which will resolve _synapse_index and _target_index (and also _source_index). - Use current code generation stuff once that has been generated. BETTER WAY: - Allow Symbols to introduce loops in resolving dependencies, and make _synapse_index and _target_index into Symbols. - Requires a change of syntax to Symbol, may make it more complicated - On the other hand, the new propagation code is ugly, and needs to be adapted to work for Python as well With better way, transformations would be something like (for C) 1. Starting point V += w; Deps: V, w 2. Resolve V double &V = _arr_V[_target_index]; V += w; Deps: w, _target_index (_arr_V added to namespace) 3. Resolve w double &w = _alldata_w[_synapse_index]; double &V = _arr_V[_target_index]; V += w; Deps: _target_index, _synapse_index (_alldata_w added to namespace) 4. Resolve _target_index int target_index = _allj_w[_synapse_index]; double &w = _alldata_w[_synapse_index]; double &V = _arr_V[_target_index]; V += w; Deps: _synapse_index (_allj_w added to namespace) 5. Resolve _synapse_index for(int _synapse_index=_rowind_w[_source_index]; _synapse_index<_rowind_w[_source_index+1]; _synapse_index++) { int target_index = w_allj[_synapse_index]; double &w = _alldata_w[_synapse_index]; double &V = _arr_V[_target_index]; V += w; } Deps: _source_index (_rowind_w added to namespace) 6. Resolve _source_index int _source_index = _spikes[_spike_index]; for(int _synapse_index=_rowind_w[_source_index]; _synapse_index<_rowind_w[_source_index+1]; _synapse_index++) { int target_index = w_allj[_synapse_index]; double &w = _alldata_w[_synapse_index]; double &V = _arr_V[_target_index]; V += w; } Deps: _spike_index (_spikes added to namespace) 7. Resolve _spike_index for(int _spike_index=0; _spike_index<_spikes_len; _spike_index++) { int _source_index = _spikes[_spike_index]; for(int _synapse_index=_rowind_w[_source_index]; _synapse_index<_rowind_w[_source_index+1]; _synapse_index++) { int target_index = w_allj[_synapse_index]; double &w = _alldata_w[_synapse_index]; double &V = _arr_V[_target_index]; V += w; } } Deps: none (_spikes_len added to namespace) Same thing in Python: 1. Starting point V += w; Deps: V, w 2. Resolve V V[_target_index] += w Deps: w, _target_index 3. Resolve w w = _alldata_w[_synapse_index] V[_target_index] += w Deps: _target_index, _synapse_index (_alldata_w added to namespace) 4. Resolve _target_index _target_index = _allj_w[_synapse_index] w = _alldata_w[_synapse_index] V[_target_index] += w Deps: _synapse_index (_allj_w added to namespace) 5. Resolve _synapse_index _synapse_index = slice(_rowind_w[_source_index], _rowind_w[_source_index+1]) _target_index = _allj_w[_synapse_index] w = _alldata_w[_synapse_index] V[_target_index] += w Deps: _source_idnex (_rowind_w added to namespace) 6. Resolve _source_index for _source_index in _spikes: _synapse_index = slice(_rowind_w[_source_index], _rowind_w[_source_index+1]) _target_index = _allj_w[_synapse_index] w = _alldata_w[_synapse_index] V[_target_index] += w Deps: none (_spikes added to namespace) Dependencies: _synapse_index -> _rowind_w, _source_index _target_index -> _allj_w, _synapse_index w -> _synapse_index V -> _target_index Python: for _source_index in _spikes: _synapse_index = slice(_rowind_w[_source_index], _rowind_w[_source_index+1]) _target_index = _allj_w[_synapse_index] w = _alldata_w[_synapse_index] V[_target_index] += w C: for(int _spike_index=0; _spike_index<_spikes_len; _spike_index++) { int _source_index = _spikes[_spike_index]; for(int _row_index=_rowind_w[_source_index]; _row_index<_rowind_w[_source_index+1]; _row_index++) { int target_index = w_allj[_row_index]; double &V = _arr_V[_target_index]; double &w = _alldata_w[_synapse_index]; V += w; } } ''' from brian import * from symbols import * class SparseConnectionMatrixValueSymbol(Symbol): def __init__(self, matrix, name, language): self.matrix = matrix Symbol.__init__(self, name, language) def update_namespace(self, namespace): namespace['_alldata_'+self.name] = self.matrix.alldata @property def load(self): if self.language.name=='python': template = '{name} = _alldata_{name}[_synapse_index]' elif self.language.name=='c': template = 'double &{name} = _alldata_{name}[_synapse_index];' return template.format(name=self.name) @property def write(self): if self.language.name=='python': template = '{name} = _alldata_{name}[_synapse_index]' return template.format(name=self.name) elif self.language.name=='c': return self.name @property def depends(self): return ['_synapse_index'] class CodeGenConnection(Connection): def propagate(self, spikes): Connection.propagate(self, spikes) def make_connection_code_block(group, eqs, reset, language): pass brian-1.4.3/dev/ideas/codegen_rewrite/old/equations.py000066400000000000000000000013431266062647100227750ustar00rootroot00000000000000from brian import * from brian.optimiser import freeze __all__ = ['freeze_with_equations', 'frozen_equations', ] def freeze_with_equations(inputcode, eqs, ns): inputcode = inputcode.strip() all_variables = eqs._eq_names+eqs._diffeq_names+eqs._alias.keys()+['t'] inputcode = freeze(inputcode, all_variables, ns) return inputcode def frozen_equations(eqs): frozen_eqs = {} eqs.prepare() all_variables = eqs._eq_names + eqs._diffeq_names + eqs._alias.keys() + ['t'] for var in eqs._diffeq_names: namespace = eqs._namespace[var] var_expr = freeze(eqs._string[var], all_variables, namespace) frozen_eqs[var] = var_expr return frozen_eqs brian-1.4.3/dev/ideas/codegen_rewrite/old/expressions.py000066400000000000000000000071521266062647100233530ustar00rootroot00000000000000from brian import * from brian.inspection import get_identifiers from brian.optimiser import symbolic_eval from sympy.printing.ccode import CCodePrinter import sympy from formatting import * import re __all__ = ['Expression', 'Statement', 'statements_from_codestring', ] class Expression(object): def __init__(self, expr): self.expr = expr self.sympy_expr = symbolic_eval(self.expr) def __str__(self): return self.expr def substitute_symbols(self, expr, symbols): substitutions = dict((name, sym.read) for name, sym in symbols.iteritems()) return word_substitute(expr, substitutions) def convert_to(self, language, symbols={}): if language.name=='python': return self.substitute_symbols(self.expr, symbols) elif language.name=='c': return self.substitute_symbols( CCodePrinter().doprint(self.sympy_expr), symbols) class Statement(object): def __init__(self, var, op, expr, boolean=False): self.var = var.strip() self.op = op.strip() if isinstance(expr, str): expr = Expression(expr) self.expr = expr self.boolean = boolean def __str__(self): return self.var+' '+self.op+' '+str(self.expr) def convert_to(self, language, symbols={}): if self.var in symbols: sym = symbols[self.var] if self.op==':=': initial = sym.define+' = ' else: initial = sym.write+' '+self.op+' ' else: if self.op==':=': if language.name=='python': initial = self.var+' = ' elif language.name=='c': if self.boolean: initial = 'bool '+self.var+' = ' else: initial = language.scalar+' '+self.var+' = ' else: initial = self.var+' '+self.op+' ' statementstr = initial+self.expr.convert_to(language, symbols) if language.name=='c': statementstr += ';' return statementstr def statements_from_codestring(code, eqs=None, infer_definitions=False): lines = [line.strip() for line in code.split('\n') if line.strip()] statements = [] if eqs is None: defined = set() else: defined = set(eqs._diffeq_names) for line in lines: m = re.search(r'[^><=]=', line) if not m: raise ValueError("Could not extract statement from line: "+line) start, end = m.start(), m.end() op = line[start:end].strip() var = line[:start].strip() expr = line[end:].strip() # var should be a single word if len(re.findall(r'\b\w+\b', var))!=1: raise ValueError("LHS in statement must be single variable name, line: "+line) if op=='=' and infer_definitions and var not in defined: op = ':=' defined.add(var) stmt = Statement(var, op, expr) statements.append(stmt) return statements if __name__=='__main__': tau = 10*ms Vt0 = 1.0 taut = 100*ms eqs = Equations(''' dV/dt = (-V+I)/tau : 1 dI/dt = -I/tau : 1 dVt/dt = (Vt0-Vt)/taut : 1 ''') reset = ''' Vt += 0.5 V = 0 I = 0 W = 5 W = 2 ''' statements = statements_from_codestring(reset, eqs, infer_definitions=True) for stmt in statements: print stmt brian-1.4.3/dev/ideas/codegen_rewrite/old/formatting.py000066400000000000000000000012541266062647100231400ustar00rootroot00000000000000from string import Formatter import re __all__ = ['CodeFormatter', 'word_substitute'] class CodeFormatter(Formatter): def __init__(self, namespace=None): Formatter.__init__(self) if namespace is None: namespace = {} self.namespace = namespace def get_value(self, first, args, kwargs): try: return Formatter.get_value(self, first, args, kwargs) except KeyError: return eval(first, self.namespace) def word_substitute(expr, substitutions): for var, replace_var in substitutions.iteritems(): expr = re.sub(r'\b' + var + r'\b', str(replace_var), expr) return expr brian-1.4.3/dev/ideas/codegen_rewrite/old/generation.py000066400000000000000000000052121266062647100231170ustar00rootroot00000000000000from brian import * from expressions import * from formatter import * from codeobject import * from brian.utils.documentation import flattened_docstring, indent_string __all__ = ['CodeBlock', ] class CodeBlock(object): def __init__(self, resolved, content, tabs=0): self.resolved = resolved if isinstance(content, str) and '\n' in content: content = [flattened_docstring(content)] self.content = content self.tabs = tabs def __str__(self): s = '' for c in self.content: if isinstance(c, Statement): c = indent_string(str(c), self.tabs) elif isinstance(c, str): c = indent_string(c, self.tabs) else: c = str(c)+'\n' s = s+c return s def indented(self, tabs=1): return CodeBlock(self.resolved, self.content, self.tabs+tabs) def generate_codestr(self, language, symbols, symbols_to_load=None): codestr = '' # first we generate code to load whichever variables # can be resolved (all dependencies met) if symbols_to_load is None: symbols_to_load = symbols new_symbols_to_load = {} for name, sym in symbols_to_load.items(): deps = sym.depends if set(deps).issubset(set(self.resolved)): codestr += indent_string(sym.load, self.tabs) else: new_symbols_to_load[name] = sym # now we generate the content (recursively) for item in self.content: itemstr = None if isinstance(item, str): itemstr = indent_string(item, self.tabs) if isinstance(item, Statement): itemstr = item.convert_to(language, symbols) itemstr = indent_string(itemstr, self.tabs) if isinstance(item, CodeBlock): itemstr = item.generate_codestr(language, symbols, new_symbols_to_load) if itemstr is None: raise TypeError("Unknown code block item type") codestr = codestr+itemstr return codestr def generate(self, language, symbols): codestr = self.generate_codestr(language, symbols) namespace = {} for name, sym in symbols.iteritems(): sym.update_namespace(namespace) if language.name=='python': code = PythonCode(codestr, namespace) elif language.name=='c': code = CCode(codestr, namespace) return code brian-1.4.3/dev/ideas/codegen_rewrite/old/integration.py000066400000000000000000000045611266062647100233150ustar00rootroot00000000000000from brian import * from expressions import * from formatter import * from equations import * __all__ = ['EquationsContainer', 'make_integration_step', 'euler', 'rk2', 'exp_euler', ] class EquationsContainer(object): def __init__(self, eqs): frozen = self.frozen = frozen_equations(eqs) self.all = [(var, frozen[var]) for var in eqs._diffeq_names] self.nonzero = [(var, frozen[var]) for var in eqs._diffeq_names_nonzero] self.names = eqs._diffeq_names self.names_nonzero = eqs._diffeq_names_nonzero def __iter__(self): return iter(self.all) def make_integration_step(method, eqs): eqs_container = EquationsContainer(eqs) statements = list(method(eqs_container)) return statements def euler(eqs): for var, expr in eqs.nonzero: yield Statement('_temp_'+var, ':=', expr) for var, expr in eqs.nonzero: yield Statement(var, '+=', '_temp_'+var+'*dt') def rk2(eqs): for var, expr in eqs: yield Statement('_buf_'+var, ':=', expr) yield Statement('_half_'+var, ':=', '.5*dt*_buf_'+var) yield Statement('_half_'+var, '+=', var) for var, expr in eqs.nonzero: half_subst = dict((var, '_half_'+var) for var in eqs.names) expr = word_substitute(expr, half_subst) yield Statement('_buf_'+var, '=', expr) yield Statement(var, '+=', 'dt*_buf_'+var) def exp_euler(eqs): for var, expr in eqs.nonzero: expr_B = word_substitute(expr, {var:0}) expr_A = word_substitute(expr, {var:1}) var_B = '_B_'+var var_A = '_A_'+var yield Statement(var_B, ':=', expr_B) yield Statement(var_A, ':=', expr_A) yield Statement(var_A, '-=', var_B) yield Statement(var_B, '/=', var_A) yield Statement(var_A, '*=', 'dt') for var, expr in eqs.nonzero: yield Statement(var, '+=', '_B_'+var) yield Statement(var, '*=', 'exp(_A_{var})'.format(var=var)) yield Statement(var, '-=', '_B_'+var) if __name__=='__main__': tau = 10*ms Vt0 = 1.0 taut = 100*ms eqs = Equations(''' dV/dt = (-V+I)/tau : 1 dI/dt = -I/tau : 1 dVt/dt = (Vt0-Vt)/taut : 1 ''') eqs.prepare() for stmt in make_integration_step(exp_euler, eqs): print str(stmt) brian-1.4.3/dev/ideas/codegen_rewrite/old/languages.py000066400000000000000000000007231266062647100227340ustar00rootroot00000000000000from brian import * __all__ = ['Language', 'PythonLanguage', 'CLanguage', ] class Language(object): def __init__(self, name): self.name = name.strip().lower() class PythonLanguage(Language): def __init__(self): Language.__init__(self, 'python') class CLanguage(Language): def __init__(self, scalar='double'): Language.__init__(self, 'c') self.scalar = scalar brian-1.4.3/dev/ideas/codegen_rewrite/old/propagation_specification_ideas.py000066400000000000000000000136221266062647100273600ustar00rootroot00000000000000from brian.inspection import namespace import re class Symbol(str): pass class OutputCode(object): def __init__(self, source=None): self.code = '' if source is None: source = self self.source = source self.curtarget = self def newtarget(self): self.curtarget = OutputCode(self.curtarget) return self.curtarget def close(self): self.source.code = self.source.code + self.curtarget.code self.curtarget = self.curtarget.source def add(self, line): self.curtarget.code = self.curtarget.code + line def __str__(self): s = '' tabs = 0 for line in self.code.split('\n'): if '}' in line and '{' not in line: tabs -= 4 s += ' ' * tabs + line + '\n' if '{' in line and '}' not in line: tabs += 4 return s __repr__ = __str__ class Maker(object): def __init__(self, cls, outputcode): self.outputcode = outputcode self.cls = cls self.instance = 0 def __iter__(self): self.instance += 1 return self.cls(self.outputcode.newtarget(), self.instance) class Spikes(object): def __init__(self, targetcode, instance=0): self.instance = instance self.targetcode = targetcode self.targetcode.add('for(int spike_index=0; spike_index0: return ([i-1], weight*ones(1)) else: return ([i+1], weight*ones(1)) C = Connection(G1, G2, structure=UserComputedSparseConnectionMatrix, row_func=f) Initialisation arguments: ``dims`` The pair ``(N,M)`` specifying the dimensions of the matrix. ``row_func`` The function ``f(i)`` which for a row ``i`` returns a pair ``(indices, values))`` consisting of a list or array ``indices`` with the indices of the nonzero elements of the row, and an array of the same length ``values`` giving the weight matrix for those indices. Note that you are responsible for making sure the function returns consistent results (so random functions should be initialised with a seed based on the row ``i``). **Limitations** This type of connection matrix cannot be changed during a run, and cannot be used with methods like :class:`Connection.connect_random`. **Efficiency considerations** This connection matrix is for sparse connectivity, if the connectivity is dense you might get better performance with :class:`UserComputedConnectionMatrix`. ''' def __init__(self, dims, row_func): self.sourcelen, self.targetlen = dims self.row_func = row_func self.cur_row = numpy.zeros(dims[1]) def add_row(self, i, X): indices, values = self.row_func(i) X[indices] += values def add_scaled_row(self, i, X, factor): # modulation may not work? need factor[self.rows[i]] here? is factor a number or an array? X[indices] += factor * values def get_row(self, i): indices, values = self.row_func(i) self.cur_row[:] = 0.0 self.cur_row[indices] = values return self.cur_row def __getitem__(self, item): if isinstance(item, int): return self.get_row(item) if isinstance(item, tuple): if len(item) == 2: item_i, item_j = item if isinstance(item_i, int) and isinstance(item_j, slice): if is_colon_slice(item_j): return self.get_row(item_i) raise ValueError('Only "i,:" indexing supported.') if __name__ == '__main__': eqs = ''' dv/dt = (ge+gi-(v+49*mV))/(20*ms) : volt dge/dt = -ge/(5*ms) : volt dgi/dt = -gi/(10*ms) : volt ''' N = 4000 #N = 20000 #N = 100000 #N = 1000000 duration = 1000 * ms usecc = True usesparsecc = True Ne = int(0.8 * N) Ni = N - Ne prob = 80. / N P = NeuronGroup(N, model=eqs, threshold= -50 * mV, reset= -60 * mV) P.v = -60 * mV + 10 * mV * rand(len(P)) Pe = P.subgroup(Ne) Pi = P.subgroup(Ni) if usecc: if usesparsecc: connmat = UserComputedSparseConnectionMatrix rfunc = random_sparse_row_func else: connmat = UserComputedConnectionMatrix rfunc = random_row_func Ce = Connection(Pe, P, 'ge', structure=connmat, row_func=rfunc(N, p=prob, weight=1.62 * mV)) Ci = Connection(Pi, P, 'gi', structure=connmat, row_func=rfunc(N, p=prob, weight= -9 * mV)) else: Ce = Connection(Pe, P, 'ge') Ci = Connection(Pi, P, 'gi') Ce.connect_random(Pe, P, prob, weight=1.62 * mV) Ci.connect_random(Pi, P, prob, weight= -9 * mV) if N > 5000: M = PopulationSpikeCounter(P) else: M = SpikeMonitor(P) if N > 100000: @network_operation() def f(t): print 'Current time', t import time t = time.time() run(duration) if usecc: if usesparsecc: print 'Using sparse computed connection' else: print 'Using computed connection' else: print 'Using standard connection' print "Time taken to run simulation:", time.time() - t print "Number of spikes:", M.nspikes if N <= 5000: raster_plot(M) show() # Statistics, sample runs on my laptop # #N=4000, T=1s # #Using standard connection #Time taken to run simulation: 5.76499986649 #Number of spikes: 24860 # #Using computed connection #Time taken to run simulation: 12.0629999638 #Number of spikes: 24761 # #Using sparse computed connection #Time taken to run simulation: 11.3589999676 #Number of spikes: 23421 # #N=20000, T=1s # #Using standard connection #Time taken to run simulation: 23.8900001049 #Number of spikes: 122241 #RAM usage: 94MB # #Using computed connection #Time taken to run simulation: 74.75 #Number of spikes: 121995 #RAM usage: 39MB # #Using sparse computed connection #Time taken to run simulation: 49.3440001011 #Number of spikes: 121841 #RAM usage: 39MB # #N=100000, T=10ms # #Using standard connection #Time taken to run simulation: 8.17100000381 #Number of spikes: 10971 #RAM usage: 330MB # #Using computed connection #Time taken to run simulation: 13.390999794 #Number of spikes: 11299 #RAM usage: 45MB # #Using sparse computed connection #Time taken to run simulation: 4.98399996758 #Number of spikes: 11035 #RAM usage: 45MB # #N=1m, T=1ms # #Using computed connection #Time taken to run simulation: 137.796999931 #Number of spikes: 6219 #RAM usage: 95MB # #Using sparse computed connection #Time taken to run simulation: 4.04700016975 #Number of spikes: 6181 #RAM usage: 120MB brian-1.4.3/dev/ideas/computedconnection_commented.py000066400000000000000000000136551266062647100230060ustar00rootroot00000000000000# -*- coding:utf-8 -*- #class UserComputedConnectionMatrix(ConnectionMatrix): # ''' # A computed connection matrix defined by a user-specified function # # Normally this matrix will be initialised by passing the class # object to the :class:`Connection` object. In the initialisation # of the :class:`Connection` specify ``structure=UserComputedConnectionMatrix`` # and add the keyword ``row_func=...``, e.g.:: # # def f(i): # return max_weight*ones(N)/(1+(arange(N)-i)**2) # C = Connection(G1, G2, structure=UserComputedConnectionMatrix, row_func=f) # # Initialisation arguments: # # ``dims`` # The pair ``(N,M)`` specifying the dimensions of the matrix. # ``row_func`` # The function ``f(i)`` which returns an array of length ``M``, # the weight matrix for row ``i``. Note that you are responsible # for making sure the function returns consistent results (so # random functions should be initialised with a seed based on # the row ``i``). # # **Limitations** # # This type of connection matrix cannot be changed during a run, and # cannot be used with methods like :class:`Connection.connect_random`. # # **Efficiency considerations** # # This connection matrix is for dense connectivity, if the connectivity # is sparse you might get better performance with :class:`UserComputedSparseConnectionMatrix`. # ''' # def __init__(self, dims, row_func): # self.sourcelen, self.targetlen = dims # self.row_func = row_func # # def get_row(self, i): # return self.row_func(i) # # def __getitem__(self, item): # if isinstance(item,int): # return self.get_row(item) # if isinstance(item,tuple): # if len(item)==2: # item_i, item_j = item # if isinstance(item_i, int) and isinstance(item_j, slice): # if is_colon_slice(item_j): # return self.get_row(item_i) # raise ValueError('Only "i,:" indexing supported.') # #def random_sparse_row_func(N, p, weight=1., initseed=None): # ''' # Returns a random connectivity ``row_func`` for use with :class:`UserComputedSparseConnectionMatrix` # # Gives equivalent output to the :meth:`Connection.connect_random` method. # # Arguments: # # ``N`` # The number of target neurons. # ``p`` # The probability of a synapse. # ``weight`` # The connection weight (must be a single value). # ``initseed`` # The initial seed value (for reproducible results). # ''' # if initseed is None: # initseed = pyrandom.randint(100000,1000000) # replace this # myrange = numpy.arange(N, dtype=int) # def row_func(i): # pyrandom.seed(initseed+int(i)) # scirandom.seed(initseed+int(i)) # k = scirandom.binomial(N, p, 1)[0] # return (pyrandom.sample(myrange,k), weight) # return row_func # #class UserComputedSparseConnectionMatrix(ConnectionMatrix): # ''' # A computed sparse connection matrix defined by a user-specified function # # Normally this matrix will be initialised by passing the class # object to the :class:`Connection` object. In the initialisation # of the :class:`Connection` specify ``structure=UserComputedSparseConnectionMatrix`` # and add the keyword ``row_func=...``, e.g.:: # # def f(i): # if 00: # return ([i-1], weight*ones(1)) # else: # return ([i+1], weight*ones(1)) # C = Connection(G1, G2, structure=UserComputedSparseConnectionMatrix, row_func=f) # # Initialisation arguments: # # ``dims`` # The pair ``(N,M)`` specifying the dimensions of the matrix. # ``row_func`` # The function ``f(i)`` which for a row ``i`` returns a pair ``(indices, values))`` # consisting of a list or array ``indices`` with the indices of the # nonzero elements of the row, and an array of the same length ``values`` # giving the weight matrix for those indices. Note that you are responsible # for making sure the function returns consistent results (so # random functions should be initialised with a seed based on # the row ``i``). # # **Limitations** # # This type of connection matrix cannot be changed during a run, and # cannot be used with methods like :class:`Connection.connect_random`. # # **Efficiency considerations** # # This connection matrix is for sparse connectivity, if the connectivity # is dense you might get better performance with :class:`UserComputedConnectionMatrix`. # ''' # def __init__(self, dims, row_func): # self.sourcelen, self.targetlen = dims # self.row_func = row_func # self.cur_row = numpy.zeros(dims[1]) # # def add_row(self,i,X): # indices, values = self.row_func(i) # X[indices]+=values # # def add_scaled_row(self,i,X,factor): # # modulation may not work? need factor[self.rows[i]] here? is factor a number or an array? # X[indices]+=factor*values # # def get_row(self, i): # indices, values = self.row_func(i) # self.cur_row[:] = 0.0 # self.cur_row[indices] = values # return self.cur_row # # def __getitem__(self, item): # if isinstance(item,int): # return self.get_row(item) # if isinstance(item,tuple): # if len(item)==2: # item_i, item_j = item # if isinstance(item_i, int) and isinstance(item_j, slice): # if is_colon_slice(item_j): # return self.get_row(item_i) # raise ValueError('Only "i,:" indexing supported.') brian-1.4.3/dev/ideas/computedconstantrandomconnection.py000066400000000000000000000055461266062647100237260ustar00rootroot00000000000000import random as pyrandom from brian import * from scipy import random as scirandom from brian.connection import ConnectionMatrix class ConstantComputedRandomConnectionMatrix(ConnectionMatrix): def __init__(self, dims, val): self.sourcelen, self.targetlen = dims self.initseed = pyrandom.randint(100000, 1000000) # replace this self.val = val def get_random_indices(self, i): pyrandom.seed(self.initseed + int(i)) scirandom.seed(self.initseed + int(i)) k = scirandom.binomial(self.m, self.p, 1)[0] return self.offset + array(pyrandom.sample(xrange(self.m), k), dtype=int) def add_row(self, i, X): X[self.get_random_indices(i)] += self.val def add_scaled_row(self, i, X, factor): # modulation may not work? need factor[self.rows[i]] here? is factor a number or an array? X[self.get_random_indices(i)] += factor * self.val def random_matrix(self, i_start, i_end, m, offset, p): self.m = m self.offset = offset self.p = p class ConstantComputedRandomConnection(Connection): def __init__(self, source, target, state=0, delay=0 * msecond, modulation=None, weight=1., p=0.1): Connection.__init__(self, source, target, state=state, delay=delay, modulation=modulation) self.W = ConstantComputedRandomConnectionMatrix((len(source), len(target)), weight) self.weight = weight self.connect_random(source, target, p) def connect_random(self, P, Q, p): weight = self.weight try: weight + Q._S0[self.nstate] except DimensionMismatchError, inst: raise DimensionMismatchError("Incorrects unit for the synaptic weights.", *inst._dims) i_start = P._origin - self.source._origin i_end = i_start + len(P) offset = Q._origin - self.target._origin m = len(Q) self.W.random_matrix(i_start, i_end, m, offset, p) eqs = ''' dv/dt = (ge+gi-(v+49*mV))/(20*ms) : volt dge/dt = -ge/(5*ms) : volt dgi/dt = -gi/(10*ms) : volt ''' N = 1000000#it works! duration = 10 * ms usecc = True Ne = int(0.8 * N) Ni = N - Ne P = NeuronGroup(N, model=eqs, threshold= -50 * mV, reset= -60 * mV) P.v = -60 * mV + 10 * mV * rand(len(P)) Pe = P.subgroup(Ne) Pi = P.subgroup(Ni) if usecc: Ce = ConstantComputedRandomConnection(Pe, P, 'ge', weight=1.62 * mV, p=0.02) Ci = ConstantComputedRandomConnection(Pi, P, 'gi', weight= -9 * mV, p=0.02) else: Ce = Connection(Pe, P, 'ge') Ci = Connection(Pi, P, 'gi') Ce.connect_random(Pe, P, 0.02, weight=1.62 * mV) Ci.connect_random(Pi, P, 0.02, weight= -9 * mV) #M=SpikeMonitor(P) M = PopulationSpikeCounter(P) import time t = time.time() run(duration) print time.time() - t #raster_plot(M) #show() print M.nspikes brian-1.4.3/dev/ideas/condor/000077500000000000000000000000001266062647100157535ustar00rootroot00000000000000brian-1.4.3/dev/ideas/condor/brianjob.py000066400000000000000000000003051266062647100201110ustar00rootroot00000000000000''' This a job to be executed by Condor ''' from brian import * #import sys #arg=sys.argv[1] from time import time print "Hi everyone!" print "Here is a voltage:", randint(100) * mV brian-1.4.3/dev/ideas/condor/brianjob.sub000066400000000000000000000007161266062647100202600ustar00rootroot00000000000000# A simple Condor job Executable = C:\Python25\python.exe Universe = vanilla Arguments = D:\EclipseWorkspace\Brian\dev\ideas\condor\brianjob.py Output = out.$(Process) Error = err.$(Process) Log =log.$(Process) initialdir = D:\EclipseWorkspace\Brian\dev\ideas\condor #should_transfer_files = YES #when_to_transfer_output = ON_EXIT #transfer_input_files = file1,file2 run_as_owner=True #load_profile = True environment = "MPLCONFIGDIR=." Queue 3 brian-1.4.3/dev/ideas/condor/condor_installation000066400000000000000000000023241266062647100217440ustar00rootroot00000000000000[Windows instruction] Personal Condor (jobs run on local machine only): ================================================= What worked for me: * Path: C:\condor\bin * What seems to be important in the config file: the hostname. Give it at installation time. * Do not specify the administrator email (I had a bug with condor_mail.exe never stopping! I don't know why) * Have the latest Java JRE installed * To debug, install Condor to accept all jobs immediately * Choose to create a new pool Apparently, Condor jobs are run with a special run account that doesn't have many rights. Follow the instructions there: http://www.cs.wisc.edu/condor/manual/v7.2/6_2Microsoft_Windows.html Set the local config file as they say, customize "Administrator" and don't forget to set the password. Then all Condor submission files need to have the following line: run_as_owner=True (otherwise weave raises an error) Second problem, Matplotlib cannot run: RuntimeError: Failed to create ~/.matplotlib; consider setting MPLCONFIGDIR to a writable directory for matplotlib configuration data Solution: add the following line to your Condor submission files: environment = "MPLCONFIGDIR=." or choose any other directory. brian-1.4.3/dev/ideas/condor/condortool.py000066400000000000000000000002251266062647100205060ustar00rootroot00000000000000''' A tool to submit jobs to Condor ''' import os condor_path = "C:\\condor\\bin\\" os.system(condor_path + " condor_submit brianjob.sub") brian-1.4.3/dev/ideas/constantconnection.py000066400000000000000000000051341266062647100207550ustar00rootroot00000000000000from brian import * from random import sample from scipy import random from brian.connection import ConnectionMatrix class ConstantSparseConnectionMatrix(ConnectionMatrix): def __init__(self, dims, val): self.sourcelen, self.targetlen = dims self.rows = [ array([], dtype=int) for _ in xrange(self.sourcelen) ] self.val = val def add_row(self, i, X): X[self.rows[i]] += self.val def add_scaled_row(self, i, X, factor): # modulation may not work? need factor[self.rows[i]] here? is factor a number or an array? X[self.rows[i]] += factor * self.val def random_matrix(self, i_start, i_end, m, offset, p): for i in xrange(i_start, i_end): k = random.binomial(m, p, 1)[0] r = offset + array(sample(xrange(m), k), dtype=int) self.rows[i] = r class ConstantConnection(Connection): def __init__(self, source, target, state=0, delay=0 * msecond, modulation=None, weight=1.): Connection.__init__(self, source, target, state=state, delay=delay, modulation=modulation) self.W = ConstantSparseConnectionMatrix((len(source), len(target)), weight) self.weight = weight def connect_random(self, P, Q, p): weight = self.weight try: weight + Q._S0[self.nstate] except DimensionMismatchError, inst: raise DimensionMismatchError("Incorrects unit for the synaptic weights.", *inst._dims) i_start = P._origin - self.source._origin i_end = i_start + len(P) offset = Q._origin - self.target._origin m = len(Q) self.W.random_matrix(i_start, i_end, m, offset, p) eqs = ''' dv/dt = (ge+gi-(v+49*mV))/(20*ms) : volt dge/dt = -ge/(5*ms) : volt dgi/dt = -gi/(10*ms) : volt ''' N = 64000 duration = 500 * ms usecc = True Ne = int(0.8 * N) Ni = N - Ne P = NeuronGroup(N, model=eqs, threshold= -50 * mV, reset= -60 * mV) P.v = -60 * mV + 10 * mV * rand(len(P)) Pe = P.subgroup(Ne) Pi = P.subgroup(Ni) if usecc: Ce = ConstantConnection(Pe, P, 'ge', weight=1.62 * mV) Ci = ConstantConnection(Pi, P, 'gi', weight= -9 * mV) Ce.connect_random(Pe, P, 0.02) Ci.connect_random(Pi, P, 0.02) else: Ce = Connection(Pe, P, 'ge') Ci = Connection(Pi, P, 'gi') Ce.connect_random(Pe, P, 0.02, weight=1.62 * mV) Ci.connect_random(Pi, P, 0.02, weight= -9 * mV) #M=SpikeMonitor(P) M = PopulationSpikeCounter(P) import time t = time.time() run(duration) print time.time() - t #raster_plot(M) #show() print M.nspikes brian-1.4.3/dev/ideas/coo_connection.py000066400000000000000000000013751266062647100200460ustar00rootroot00000000000000from brian import * from scipy.sparse import coo_matrix import gc import random import numpy from scipy import weave from time import time if __name__=='__main__': usenew = False n = 100000 m = 4000000 seed(12321312) random.seed(340832) G = PoissonGroup(n, rates=50*Hz) H = NeuronGroup(n, 'v:1') i = randint(n, size=m) j = randint(n, size=m) w = rand(m) x = coo_matrix((w, (i, j)), shape=(n, n)) #y = x.tocsr() #y = x.tocsr() start = time() if usenew: C = Connection(G, H, 'v') C.connect_from_sparse(x) print 'New time:', else: C = Connection(G, H, 'v', weight=x.tolil()) C.compress() print 'Old time:', print time()-start raw_input() brian-1.4.3/dev/ideas/cppgen/000077500000000000000000000000001266062647100157435ustar00rootroot00000000000000brian-1.4.3/dev/ideas/cppgen/brianlib.h000066400000000000000000000112621266062647100177000ustar00rootroot00000000000000#ifndef _BRIAN_LIB_H #define _BRIAN_LIB_H #include #include #include using namespace std; #define neuron_value(group, neuron, state) (group)->S[((neuron)+(group)->origin)+(state)*((group)->S_m)] //#define neuron_value(group, neuron, state) (group)->S[((neuron))+(state)*((group)->num_neurons)] #define matrix_value(matrix, i, j, n, m) (matrix)[(i)*(m)+(j)] class StateUpdater; class Threshold; class ResetBase; class SpikeContainer; class CircularVector; class Connection; class NeuronGroup { public: double *S; int num_vars, num_neurons, S_m; StateUpdater *su; Threshold *thr; ResetBase *resetobj; SpikeContainer *LS; int *spikesarray; NeuronGroup *owner; int origin; NeuronGroup(double *S, int n, int m, StateUpdater *su, Threshold *thr, ResetBase *reset, int ls_n, int ls_m); NeuronGroup(NeuronGroup *parent, int origin, int length); ~NeuronGroup(); void get_spikes(int **ret, int *ret_n, int delay=0); void update(); void reset(); }; class StateUpdater { public: virtual void __call__(NeuronGroup *group) {}; // SWIG can't handle pure virtual functions }; class LinearStateUpdater : public StateUpdater { public: double *M; int M_n, M_m; double *b; int b_n; LinearStateUpdater(double *M, int M_n, int M_m, double *b, int b_n) : M(M), M_n(M_n), M_m(M_m), b(b), b_n(b_n) {} virtual void __call__(NeuronGroup *group); // like in Python string __repr__(); }; class Threshold { public: double value; int state; Threshold(int state, double value) : value(value), state(state) {} void __call__(int **ret, int *ret_n, NeuronGroup *group); }; class ResetBase { public: virtual void __call__(NeuronGroup *group) {}; }; class Reset : public ResetBase { public: double value; int state; Reset(int state, double value) : value(value), state(state) {} virtual void __call__(NeuronGroup *group); // like in Python }; // Note that this Refractoriness has to be initialised with an integer period rather // than a float one. TODO: change this? class Refractoriness : public Reset { public: int period; Refractoriness(int state, double value, int period) : Reset(state, value), period(period) {} virtual void __call__(NeuronGroup *group); // like in Python }; class NetworkOperation { public: virtual void __call__() {} }; class StateMonitor : public NetworkOperation { public: NeuronGroup *group; int state; vector< vector > values; StateMonitor(NeuronGroup *group, int state); // Putting this virtual destructor in causes a memory leak, need to work out why //virtual ~StateMonitor() {}; virtual void __call__(); vector __getitem__(int i); }; class Network { public: list groups; list operations; list connections; void add(NeuronGroup *group); void add(NetworkOperation *op); void add(Connection *conn); void update(); void run(int timesteps); }; class CircularVector { private: inline int index(int i); inline int getitem(int i); public: int *X, cursor, n; int *retarray; CircularVector(int n); ~CircularVector(); void reinit(); void advance(int k); int __len__(); int __getitem__(int i); void __setitem__(int i, int x); void __getslice__(int **ret, int *ret_n, int i, int j); void get_conditional(int **ret, int *ret_n, int i, int j, int min, int max, int offset=0); void __setslice__(int i, int j, int *x, int n); string __repr__(); string __str__(); }; class SpikeContainer { public: CircularVector *S, *ind; SpikeContainer(int n, int m); ~SpikeContainer(); void reinit(); void push(int *x, int n); void lastspikes(int **ret, int *ret_n); void __getitem__(int **ret, int *ret_n, int i); void get_spikes(int **ret, int *ret_n, int delay, int origin, int N); void __getslice__(int **ret, int *ret_n, int i, int j); string __repr__(); string __str__(); }; class ConnectionMatrix { public: virtual void add_row(int i, double *b, int b_n) {}; virtual void add_rows(int *x, int n, double *b, int b_n); }; class DenseConnectionMatrix : public ConnectionMatrix { public: double *W; int n, m; DenseConnectionMatrix(double *S, int n, int m) : W(S), n(n), m(m) {}; virtual void add_row(int i, double *b, int b_n); }; class Connection { public: NeuronGroup *source, *target; ConnectionMatrix *connmat; int state, delay; Connection(NeuronGroup *source, NeuronGroup *target, ConnectionMatrix *connmat, int state=0, int delay=0) : source(source), target(target), connmat(connmat), state(state), delay(delay) {} virtual void propagate(int *x, int n); virtual void do_propagate(); }; #endif brian-1.4.3/dev/ideas/cppgen/brianlib.i000066400000000000000000000015101266062647100176740ustar00rootroot00000000000000%module brianlib %include "std_string.i" %include "std_vector.i" %include "std_list.i" %{ #define SWIG_FILE_WITH_INIT #include "brianlib.h" %} namespace std { %template(SpikeList) list; %template(VectorDouble) vector; } %include "numpy.i" %init %{ import_array(); %} %apply (double* INPLACE_ARRAY2, int DIM1, int DIM2) {(double *S, int n, int m)}; %apply (double* INPLACE_ARRAY2, int DIM1, int DIM2) {(double *M, int M_n, int M_m)}; %apply (int* INPLACE_ARRAY1, int DIM1) {(int *x, int n)}; %apply (int* INPLACE_ARRAY1, int DIM1) {(int *y, int n)}; %apply (double* INPLACE_ARRAY1, int DIM1) {(double *b, int b_n)}; %apply (double* ARGOUT_ARRAY1, int DIM1) {(double *S_out_flat, int nm)}; %apply (int** ARGOUTVIEW_ARRAY1, int* DIM1 ) {(int **ret, int *ret_n)}; %include "brianlib.h" brian-1.4.3/dev/ideas/cppgen/brianlib.py000066400000000000000000000771651266062647100201170ustar00rootroot00000000000000# This file was automatically generated by SWIG (http://www.swig.org). # Version 1.3.33 # # Don't modify this file, modify the SWIG interface instead. # This file is compatible with both classic and new-style classes. import _brianlib import new new_instancemethod = new.instancemethod try: _swig_property = property except NameError: pass # Python < 2.2 doesn't have 'property'. def _swig_setattr_nondynamic(self, class_type, name, value, static=1): if (name == "thisown"): return self.this.own(value) if (name == "this"): if type(value).__name__ == 'PySwigObject': self.__dict__[name] = value return method = class_type.__swig_setmethods__.get(name, None) if method: return method(self, value) if (not static) or hasattr(self, name): self.__dict__[name] = value else: raise AttributeError("You cannot add attributes to %s" % self) def _swig_setattr(self, class_type, name, value): return _swig_setattr_nondynamic(self, class_type, name, value, 0) def _swig_getattr(self, class_type, name): if (name == "thisown"): return self.this.own() method = class_type.__swig_getmethods__.get(name, None) if method: return method(self) raise AttributeError, name def _swig_repr(self): try: strthis = "proxy of " + self.this.__repr__() except: strthis = "" return "<%s.%s; %s >" % (self.__class__.__module__, self.__class__.__name__, strthis,) import types try: _object = types.ObjectType _newclass = 1 except AttributeError: class _object : pass _newclass = 0 del types class PySwigIterator(_object): __swig_setmethods__ = {} __setattr__ = lambda self, name, value: _swig_setattr(self, PySwigIterator, name, value) __swig_getmethods__ = {} __getattr__ = lambda self, name: _swig_getattr(self, PySwigIterator, name) def __init__(self): raise AttributeError, "No constructor defined" __repr__ = _swig_repr __swig_destroy__ = _brianlib.delete_PySwigIterator __del__ = lambda self : None; def value(*args): return _brianlib.PySwigIterator_value(*args) def incr(*args): return _brianlib.PySwigIterator_incr(*args) def decr(*args): return _brianlib.PySwigIterator_decr(*args) def distance(*args): return _brianlib.PySwigIterator_distance(*args) def equal(*args): return _brianlib.PySwigIterator_equal(*args) def copy(*args): return _brianlib.PySwigIterator_copy(*args) def next(*args): return _brianlib.PySwigIterator_next(*args) def previous(*args): return _brianlib.PySwigIterator_previous(*args) def advance(*args): return _brianlib.PySwigIterator_advance(*args) def __eq__(*args): return _brianlib.PySwigIterator___eq__(*args) def __ne__(*args): return _brianlib.PySwigIterator___ne__(*args) def __iadd__(*args): return _brianlib.PySwigIterator___iadd__(*args) def __isub__(*args): return _brianlib.PySwigIterator___isub__(*args) def __add__(*args): return _brianlib.PySwigIterator___add__(*args) def __sub__(*args): return _brianlib.PySwigIterator___sub__(*args) def __iter__(self): return self PySwigIterator_swigregister = _brianlib.PySwigIterator_swigregister PySwigIterator_swigregister(PySwigIterator) class SpikeList(_object): __swig_setmethods__ = {} __setattr__ = lambda self, name, value: _swig_setattr(self, SpikeList, name, value) __swig_getmethods__ = {} __getattr__ = lambda self, name: _swig_getattr(self, SpikeList, name) __repr__ = _swig_repr def iterator(*args): return _brianlib.SpikeList_iterator(*args) def __iter__(self): return self.iterator() def __nonzero__(*args): return _brianlib.SpikeList___nonzero__(*args) def __len__(*args): return _brianlib.SpikeList___len__(*args) def pop(*args): return _brianlib.SpikeList_pop(*args) def __getslice__(*args): return _brianlib.SpikeList___getslice__(*args) def __setslice__(*args): return _brianlib.SpikeList___setslice__(*args) def __delslice__(*args): return _brianlib.SpikeList___delslice__(*args) def __delitem__(*args): return _brianlib.SpikeList___delitem__(*args) def __getitem__(*args): return _brianlib.SpikeList___getitem__(*args) def __setitem__(*args): return _brianlib.SpikeList___setitem__(*args) def append(*args): return _brianlib.SpikeList_append(*args) def empty(*args): return _brianlib.SpikeList_empty(*args) def size(*args): return _brianlib.SpikeList_size(*args) def clear(*args): return _brianlib.SpikeList_clear(*args) def swap(*args): return _brianlib.SpikeList_swap(*args) def get_allocator(*args): return _brianlib.SpikeList_get_allocator(*args) def begin(*args): return _brianlib.SpikeList_begin(*args) def end(*args): return _brianlib.SpikeList_end(*args) def rbegin(*args): return _brianlib.SpikeList_rbegin(*args) def rend(*args): return _brianlib.SpikeList_rend(*args) def pop_back(*args): return _brianlib.SpikeList_pop_back(*args) def erase(*args): return _brianlib.SpikeList_erase(*args) def __init__(self, *args): this = _brianlib.new_SpikeList(*args) try: self.this.append(this) except: self.this = this def push_back(*args): return _brianlib.SpikeList_push_back(*args) def front(*args): return _brianlib.SpikeList_front(*args) def back(*args): return _brianlib.SpikeList_back(*args) def assign(*args): return _brianlib.SpikeList_assign(*args) def resize(*args): return _brianlib.SpikeList_resize(*args) def insert(*args): return _brianlib.SpikeList_insert(*args) def pop_front(*args): return _brianlib.SpikeList_pop_front(*args) def push_front(*args): return _brianlib.SpikeList_push_front(*args) def reverse(*args): return _brianlib.SpikeList_reverse(*args) __swig_destroy__ = _brianlib.delete_SpikeList __del__ = lambda self : None; SpikeList_swigregister = _brianlib.SpikeList_swigregister SpikeList_swigregister(SpikeList) class VectorDouble(_object): __swig_setmethods__ = {} __setattr__ = lambda self, name, value: _swig_setattr(self, VectorDouble, name, value) __swig_getmethods__ = {} __getattr__ = lambda self, name: _swig_getattr(self, VectorDouble, name) __repr__ = _swig_repr def iterator(*args): return _brianlib.VectorDouble_iterator(*args) def __iter__(self): return self.iterator() def __nonzero__(*args): return _brianlib.VectorDouble___nonzero__(*args) def __len__(*args): return _brianlib.VectorDouble___len__(*args) def pop(*args): return _brianlib.VectorDouble_pop(*args) def __getslice__(*args): return _brianlib.VectorDouble___getslice__(*args) def __setslice__(*args): return _brianlib.VectorDouble___setslice__(*args) def __delslice__(*args): return _brianlib.VectorDouble___delslice__(*args) def __delitem__(*args): return _brianlib.VectorDouble___delitem__(*args) def __getitem__(*args): return _brianlib.VectorDouble___getitem__(*args) def __setitem__(*args): return _brianlib.VectorDouble___setitem__(*args) def append(*args): return _brianlib.VectorDouble_append(*args) def empty(*args): return _brianlib.VectorDouble_empty(*args) def size(*args): return _brianlib.VectorDouble_size(*args) def clear(*args): return _brianlib.VectorDouble_clear(*args) def swap(*args): return _brianlib.VectorDouble_swap(*args) def get_allocator(*args): return _brianlib.VectorDouble_get_allocator(*args) def begin(*args): return _brianlib.VectorDouble_begin(*args) def end(*args): return _brianlib.VectorDouble_end(*args) def rbegin(*args): return _brianlib.VectorDouble_rbegin(*args) def rend(*args): return _brianlib.VectorDouble_rend(*args) def pop_back(*args): return _brianlib.VectorDouble_pop_back(*args) def erase(*args): return _brianlib.VectorDouble_erase(*args) def __init__(self, *args): this = _brianlib.new_VectorDouble(*args) try: self.this.append(this) except: self.this = this def push_back(*args): return _brianlib.VectorDouble_push_back(*args) def front(*args): return _brianlib.VectorDouble_front(*args) def back(*args): return _brianlib.VectorDouble_back(*args) def assign(*args): return _brianlib.VectorDouble_assign(*args) def resize(*args): return _brianlib.VectorDouble_resize(*args) def insert(*args): return _brianlib.VectorDouble_insert(*args) def reserve(*args): return _brianlib.VectorDouble_reserve(*args) def capacity(*args): return _brianlib.VectorDouble_capacity(*args) __swig_destroy__ = _brianlib.delete_VectorDouble __del__ = lambda self : None; VectorDouble_swigregister = _brianlib.VectorDouble_swigregister VectorDouble_swigregister(VectorDouble) class NeuronGroup(_object): __swig_setmethods__ = {} __setattr__ = lambda self, name, value: _swig_setattr(self, NeuronGroup, name, value) __swig_getmethods__ = {} __getattr__ = lambda self, name: _swig_getattr(self, NeuronGroup, name) __repr__ = _swig_repr __swig_setmethods__["S"] = _brianlib.NeuronGroup_S_set __swig_getmethods__["S"] = _brianlib.NeuronGroup_S_get if _newclass:S = _swig_property(_brianlib.NeuronGroup_S_get, _brianlib.NeuronGroup_S_set) __swig_setmethods__["num_vars"] = _brianlib.NeuronGroup_num_vars_set __swig_getmethods__["num_vars"] = _brianlib.NeuronGroup_num_vars_get if _newclass:num_vars = _swig_property(_brianlib.NeuronGroup_num_vars_get, _brianlib.NeuronGroup_num_vars_set) __swig_setmethods__["num_neurons"] = _brianlib.NeuronGroup_num_neurons_set __swig_getmethods__["num_neurons"] = _brianlib.NeuronGroup_num_neurons_get if _newclass:num_neurons = _swig_property(_brianlib.NeuronGroup_num_neurons_get, _brianlib.NeuronGroup_num_neurons_set) __swig_setmethods__["S_m"] = _brianlib.NeuronGroup_S_m_set __swig_getmethods__["S_m"] = _brianlib.NeuronGroup_S_m_get if _newclass:S_m = _swig_property(_brianlib.NeuronGroup_S_m_get, _brianlib.NeuronGroup_S_m_set) __swig_setmethods__["su"] = _brianlib.NeuronGroup_su_set __swig_getmethods__["su"] = _brianlib.NeuronGroup_su_get if _newclass:su = _swig_property(_brianlib.NeuronGroup_su_get, _brianlib.NeuronGroup_su_set) __swig_setmethods__["thr"] = _brianlib.NeuronGroup_thr_set __swig_getmethods__["thr"] = _brianlib.NeuronGroup_thr_get if _newclass:thr = _swig_property(_brianlib.NeuronGroup_thr_get, _brianlib.NeuronGroup_thr_set) __swig_setmethods__["resetobj"] = _brianlib.NeuronGroup_resetobj_set __swig_getmethods__["resetobj"] = _brianlib.NeuronGroup_resetobj_get if _newclass:resetobj = _swig_property(_brianlib.NeuronGroup_resetobj_get, _brianlib.NeuronGroup_resetobj_set) __swig_setmethods__["LS"] = _brianlib.NeuronGroup_LS_set __swig_getmethods__["LS"] = _brianlib.NeuronGroup_LS_get if _newclass:LS = _swig_property(_brianlib.NeuronGroup_LS_get, _brianlib.NeuronGroup_LS_set) __swig_setmethods__["spikesarray"] = _brianlib.NeuronGroup_spikesarray_set __swig_getmethods__["spikesarray"] = _brianlib.NeuronGroup_spikesarray_get if _newclass:spikesarray = _swig_property(_brianlib.NeuronGroup_spikesarray_get, _brianlib.NeuronGroup_spikesarray_set) __swig_setmethods__["owner"] = _brianlib.NeuronGroup_owner_set __swig_getmethods__["owner"] = _brianlib.NeuronGroup_owner_get if _newclass:owner = _swig_property(_brianlib.NeuronGroup_owner_get, _brianlib.NeuronGroup_owner_set) __swig_setmethods__["origin"] = _brianlib.NeuronGroup_origin_set __swig_getmethods__["origin"] = _brianlib.NeuronGroup_origin_get if _newclass:origin = _swig_property(_brianlib.NeuronGroup_origin_get, _brianlib.NeuronGroup_origin_set) def __init__(self, *args): this = _brianlib.new_NeuronGroup(*args) try: self.this.append(this) except: self.this = this __swig_destroy__ = _brianlib.delete_NeuronGroup __del__ = lambda self : None; def get_spikes(*args): return _brianlib.NeuronGroup_get_spikes(*args) def update(*args): return _brianlib.NeuronGroup_update(*args) def reset(*args): return _brianlib.NeuronGroup_reset(*args) NeuronGroup_swigregister = _brianlib.NeuronGroup_swigregister NeuronGroup_swigregister(NeuronGroup) class StateUpdater(_object): __swig_setmethods__ = {} __setattr__ = lambda self, name, value: _swig_setattr(self, StateUpdater, name, value) __swig_getmethods__ = {} __getattr__ = lambda self, name: _swig_getattr(self, StateUpdater, name) __repr__ = _swig_repr def __call__(*args): return _brianlib.StateUpdater___call__(*args) def __init__(self, *args): this = _brianlib.new_StateUpdater(*args) try: self.this.append(this) except: self.this = this __swig_destroy__ = _brianlib.delete_StateUpdater __del__ = lambda self : None; StateUpdater_swigregister = _brianlib.StateUpdater_swigregister StateUpdater_swigregister(StateUpdater) class LinearStateUpdater(StateUpdater): __swig_setmethods__ = {} for _s in [StateUpdater]: __swig_setmethods__.update(getattr(_s, '__swig_setmethods__', {})) __setattr__ = lambda self, name, value: _swig_setattr(self, LinearStateUpdater, name, value) __swig_getmethods__ = {} for _s in [StateUpdater]: __swig_getmethods__.update(getattr(_s, '__swig_getmethods__', {})) __getattr__ = lambda self, name: _swig_getattr(self, LinearStateUpdater, name) __swig_setmethods__["M"] = _brianlib.LinearStateUpdater_M_set __swig_getmethods__["M"] = _brianlib.LinearStateUpdater_M_get if _newclass:M = _swig_property(_brianlib.LinearStateUpdater_M_get, _brianlib.LinearStateUpdater_M_set) __swig_setmethods__["M_n"] = _brianlib.LinearStateUpdater_M_n_set __swig_getmethods__["M_n"] = _brianlib.LinearStateUpdater_M_n_get if _newclass:M_n = _swig_property(_brianlib.LinearStateUpdater_M_n_get, _brianlib.LinearStateUpdater_M_n_set) __swig_setmethods__["M_m"] = _brianlib.LinearStateUpdater_M_m_set __swig_getmethods__["M_m"] = _brianlib.LinearStateUpdater_M_m_get if _newclass:M_m = _swig_property(_brianlib.LinearStateUpdater_M_m_get, _brianlib.LinearStateUpdater_M_m_set) __swig_setmethods__["b"] = _brianlib.LinearStateUpdater_b_set __swig_getmethods__["b"] = _brianlib.LinearStateUpdater_b_get if _newclass:b = _swig_property(_brianlib.LinearStateUpdater_b_get, _brianlib.LinearStateUpdater_b_set) __swig_setmethods__["b_n"] = _brianlib.LinearStateUpdater_b_n_set __swig_getmethods__["b_n"] = _brianlib.LinearStateUpdater_b_n_get if _newclass:b_n = _swig_property(_brianlib.LinearStateUpdater_b_n_get, _brianlib.LinearStateUpdater_b_n_set) def __init__(self, *args): this = _brianlib.new_LinearStateUpdater(*args) try: self.this.append(this) except: self.this = this def __call__(*args): return _brianlib.LinearStateUpdater___call__(*args) def __repr__(*args): return _brianlib.LinearStateUpdater___repr__(*args) __swig_destroy__ = _brianlib.delete_LinearStateUpdater __del__ = lambda self : None; LinearStateUpdater_swigregister = _brianlib.LinearStateUpdater_swigregister LinearStateUpdater_swigregister(LinearStateUpdater) class Threshold(_object): __swig_setmethods__ = {} __setattr__ = lambda self, name, value: _swig_setattr(self, Threshold, name, value) __swig_getmethods__ = {} __getattr__ = lambda self, name: _swig_getattr(self, Threshold, name) __repr__ = _swig_repr __swig_setmethods__["value"] = _brianlib.Threshold_value_set __swig_getmethods__["value"] = _brianlib.Threshold_value_get if _newclass:value = _swig_property(_brianlib.Threshold_value_get, _brianlib.Threshold_value_set) __swig_setmethods__["state"] = _brianlib.Threshold_state_set __swig_getmethods__["state"] = _brianlib.Threshold_state_get if _newclass:state = _swig_property(_brianlib.Threshold_state_get, _brianlib.Threshold_state_set) def __init__(self, *args): this = _brianlib.new_Threshold(*args) try: self.this.append(this) except: self.this = this def __call__(*args): return _brianlib.Threshold___call__(*args) __swig_destroy__ = _brianlib.delete_Threshold __del__ = lambda self : None; Threshold_swigregister = _brianlib.Threshold_swigregister Threshold_swigregister(Threshold) class ResetBase(_object): __swig_setmethods__ = {} __setattr__ = lambda self, name, value: _swig_setattr(self, ResetBase, name, value) __swig_getmethods__ = {} __getattr__ = lambda self, name: _swig_getattr(self, ResetBase, name) __repr__ = _swig_repr def __call__(*args): return _brianlib.ResetBase___call__(*args) def __init__(self, *args): this = _brianlib.new_ResetBase(*args) try: self.this.append(this) except: self.this = this __swig_destroy__ = _brianlib.delete_ResetBase __del__ = lambda self : None; ResetBase_swigregister = _brianlib.ResetBase_swigregister ResetBase_swigregister(ResetBase) class Reset(ResetBase): __swig_setmethods__ = {} for _s in [ResetBase]: __swig_setmethods__.update(getattr(_s, '__swig_setmethods__', {})) __setattr__ = lambda self, name, value: _swig_setattr(self, Reset, name, value) __swig_getmethods__ = {} for _s in [ResetBase]: __swig_getmethods__.update(getattr(_s, '__swig_getmethods__', {})) __getattr__ = lambda self, name: _swig_getattr(self, Reset, name) __repr__ = _swig_repr __swig_setmethods__["value"] = _brianlib.Reset_value_set __swig_getmethods__["value"] = _brianlib.Reset_value_get if _newclass:value = _swig_property(_brianlib.Reset_value_get, _brianlib.Reset_value_set) __swig_setmethods__["state"] = _brianlib.Reset_state_set __swig_getmethods__["state"] = _brianlib.Reset_state_get if _newclass:state = _swig_property(_brianlib.Reset_state_get, _brianlib.Reset_state_set) def __init__(self, *args): this = _brianlib.new_Reset(*args) try: self.this.append(this) except: self.this = this def __call__(*args): return _brianlib.Reset___call__(*args) __swig_destroy__ = _brianlib.delete_Reset __del__ = lambda self : None; Reset_swigregister = _brianlib.Reset_swigregister Reset_swigregister(Reset) class Refractoriness(Reset): __swig_setmethods__ = {} for _s in [Reset]: __swig_setmethods__.update(getattr(_s, '__swig_setmethods__', {})) __setattr__ = lambda self, name, value: _swig_setattr(self, Refractoriness, name, value) __swig_getmethods__ = {} for _s in [Reset]: __swig_getmethods__.update(getattr(_s, '__swig_getmethods__', {})) __getattr__ = lambda self, name: _swig_getattr(self, Refractoriness, name) __repr__ = _swig_repr __swig_setmethods__["period"] = _brianlib.Refractoriness_period_set __swig_getmethods__["period"] = _brianlib.Refractoriness_period_get if _newclass:period = _swig_property(_brianlib.Refractoriness_period_get, _brianlib.Refractoriness_period_set) def __init__(self, *args): this = _brianlib.new_Refractoriness(*args) try: self.this.append(this) except: self.this = this def __call__(*args): return _brianlib.Refractoriness___call__(*args) __swig_destroy__ = _brianlib.delete_Refractoriness __del__ = lambda self : None; Refractoriness_swigregister = _brianlib.Refractoriness_swigregister Refractoriness_swigregister(Refractoriness) class NetworkOperation(_object): __swig_setmethods__ = {} __setattr__ = lambda self, name, value: _swig_setattr(self, NetworkOperation, name, value) __swig_getmethods__ = {} __getattr__ = lambda self, name: _swig_getattr(self, NetworkOperation, name) __repr__ = _swig_repr def __call__(*args): return _brianlib.NetworkOperation___call__(*args) def __init__(self, *args): this = _brianlib.new_NetworkOperation(*args) try: self.this.append(this) except: self.this = this __swig_destroy__ = _brianlib.delete_NetworkOperation __del__ = lambda self : None; NetworkOperation_swigregister = _brianlib.NetworkOperation_swigregister NetworkOperation_swigregister(NetworkOperation) class StateMonitor(NetworkOperation): __swig_setmethods__ = {} for _s in [NetworkOperation]: __swig_setmethods__.update(getattr(_s, '__swig_setmethods__', {})) __setattr__ = lambda self, name, value: _swig_setattr(self, StateMonitor, name, value) __swig_getmethods__ = {} for _s in [NetworkOperation]: __swig_getmethods__.update(getattr(_s, '__swig_getmethods__', {})) __getattr__ = lambda self, name: _swig_getattr(self, StateMonitor, name) __repr__ = _swig_repr __swig_setmethods__["group"] = _brianlib.StateMonitor_group_set __swig_getmethods__["group"] = _brianlib.StateMonitor_group_get if _newclass:group = _swig_property(_brianlib.StateMonitor_group_get, _brianlib.StateMonitor_group_set) __swig_setmethods__["state"] = _brianlib.StateMonitor_state_set __swig_getmethods__["state"] = _brianlib.StateMonitor_state_get if _newclass:state = _swig_property(_brianlib.StateMonitor_state_get, _brianlib.StateMonitor_state_set) __swig_setmethods__["values"] = _brianlib.StateMonitor_values_set __swig_getmethods__["values"] = _brianlib.StateMonitor_values_get if _newclass:values = _swig_property(_brianlib.StateMonitor_values_get, _brianlib.StateMonitor_values_set) def __init__(self, *args): this = _brianlib.new_StateMonitor(*args) try: self.this.append(this) except: self.this = this def __call__(*args): return _brianlib.StateMonitor___call__(*args) def __getitem__(*args): return _brianlib.StateMonitor___getitem__(*args) __swig_destroy__ = _brianlib.delete_StateMonitor __del__ = lambda self : None; StateMonitor_swigregister = _brianlib.StateMonitor_swigregister StateMonitor_swigregister(StateMonitor) class Network(_object): __swig_setmethods__ = {} __setattr__ = lambda self, name, value: _swig_setattr(self, Network, name, value) __swig_getmethods__ = {} __getattr__ = lambda self, name: _swig_getattr(self, Network, name) __repr__ = _swig_repr __swig_setmethods__["groups"] = _brianlib.Network_groups_set __swig_getmethods__["groups"] = _brianlib.Network_groups_get if _newclass:groups = _swig_property(_brianlib.Network_groups_get, _brianlib.Network_groups_set) __swig_setmethods__["operations"] = _brianlib.Network_operations_set __swig_getmethods__["operations"] = _brianlib.Network_operations_get if _newclass:operations = _swig_property(_brianlib.Network_operations_get, _brianlib.Network_operations_set) __swig_setmethods__["connections"] = _brianlib.Network_connections_set __swig_getmethods__["connections"] = _brianlib.Network_connections_get if _newclass:connections = _swig_property(_brianlib.Network_connections_get, _brianlib.Network_connections_set) def add(*args): return _brianlib.Network_add(*args) def update(*args): return _brianlib.Network_update(*args) def run(*args): return _brianlib.Network_run(*args) def __init__(self, *args): this = _brianlib.new_Network(*args) try: self.this.append(this) except: self.this = this __swig_destroy__ = _brianlib.delete_Network __del__ = lambda self : None; Network_swigregister = _brianlib.Network_swigregister Network_swigregister(Network) class CircularVector(_object): __swig_setmethods__ = {} __setattr__ = lambda self, name, value: _swig_setattr(self, CircularVector, name, value) __swig_getmethods__ = {} __getattr__ = lambda self, name: _swig_getattr(self, CircularVector, name) __swig_setmethods__["X"] = _brianlib.CircularVector_X_set __swig_getmethods__["X"] = _brianlib.CircularVector_X_get if _newclass:X = _swig_property(_brianlib.CircularVector_X_get, _brianlib.CircularVector_X_set) __swig_setmethods__["cursor"] = _brianlib.CircularVector_cursor_set __swig_getmethods__["cursor"] = _brianlib.CircularVector_cursor_get if _newclass:cursor = _swig_property(_brianlib.CircularVector_cursor_get, _brianlib.CircularVector_cursor_set) __swig_setmethods__["n"] = _brianlib.CircularVector_n_set __swig_getmethods__["n"] = _brianlib.CircularVector_n_get if _newclass:n = _swig_property(_brianlib.CircularVector_n_get, _brianlib.CircularVector_n_set) __swig_setmethods__["retarray"] = _brianlib.CircularVector_retarray_set __swig_getmethods__["retarray"] = _brianlib.CircularVector_retarray_get if _newclass:retarray = _swig_property(_brianlib.CircularVector_retarray_get, _brianlib.CircularVector_retarray_set) def __init__(self, *args): this = _brianlib.new_CircularVector(*args) try: self.this.append(this) except: self.this = this __swig_destroy__ = _brianlib.delete_CircularVector __del__ = lambda self : None; def reinit(*args): return _brianlib.CircularVector_reinit(*args) def advance(*args): return _brianlib.CircularVector_advance(*args) def __len__(*args): return _brianlib.CircularVector___len__(*args) def __getitem__(*args): return _brianlib.CircularVector___getitem__(*args) def __setitem__(*args): return _brianlib.CircularVector___setitem__(*args) def __getslice__(*args): return _brianlib.CircularVector___getslice__(*args) def get_conditional(*args): return _brianlib.CircularVector_get_conditional(*args) def __setslice__(*args): return _brianlib.CircularVector___setslice__(*args) def __repr__(*args): return _brianlib.CircularVector___repr__(*args) def __str__(*args): return _brianlib.CircularVector___str__(*args) CircularVector_swigregister = _brianlib.CircularVector_swigregister CircularVector_swigregister(CircularVector) class SpikeContainer(_object): __swig_setmethods__ = {} __setattr__ = lambda self, name, value: _swig_setattr(self, SpikeContainer, name, value) __swig_getmethods__ = {} __getattr__ = lambda self, name: _swig_getattr(self, SpikeContainer, name) __swig_setmethods__["S"] = _brianlib.SpikeContainer_S_set __swig_getmethods__["S"] = _brianlib.SpikeContainer_S_get if _newclass:S = _swig_property(_brianlib.SpikeContainer_S_get, _brianlib.SpikeContainer_S_set) __swig_setmethods__["ind"] = _brianlib.SpikeContainer_ind_set __swig_getmethods__["ind"] = _brianlib.SpikeContainer_ind_get if _newclass:ind = _swig_property(_brianlib.SpikeContainer_ind_get, _brianlib.SpikeContainer_ind_set) def __init__(self, *args): this = _brianlib.new_SpikeContainer(*args) try: self.this.append(this) except: self.this = this __swig_destroy__ = _brianlib.delete_SpikeContainer __del__ = lambda self : None; def reinit(*args): return _brianlib.SpikeContainer_reinit(*args) def push(*args): return _brianlib.SpikeContainer_push(*args) def lastspikes(*args): return _brianlib.SpikeContainer_lastspikes(*args) def __getitem__(*args): return _brianlib.SpikeContainer___getitem__(*args) def get_spikes(*args): return _brianlib.SpikeContainer_get_spikes(*args) def __getslice__(*args): return _brianlib.SpikeContainer___getslice__(*args) def __repr__(*args): return _brianlib.SpikeContainer___repr__(*args) def __str__(*args): return _brianlib.SpikeContainer___str__(*args) SpikeContainer_swigregister = _brianlib.SpikeContainer_swigregister SpikeContainer_swigregister(SpikeContainer) class ConnectionMatrix(_object): __swig_setmethods__ = {} __setattr__ = lambda self, name, value: _swig_setattr(self, ConnectionMatrix, name, value) __swig_getmethods__ = {} __getattr__ = lambda self, name: _swig_getattr(self, ConnectionMatrix, name) __repr__ = _swig_repr def add_row(*args): return _brianlib.ConnectionMatrix_add_row(*args) def add_rows(*args): return _brianlib.ConnectionMatrix_add_rows(*args) def __init__(self, *args): this = _brianlib.new_ConnectionMatrix(*args) try: self.this.append(this) except: self.this = this __swig_destroy__ = _brianlib.delete_ConnectionMatrix __del__ = lambda self : None; ConnectionMatrix_swigregister = _brianlib.ConnectionMatrix_swigregister ConnectionMatrix_swigregister(ConnectionMatrix) class DenseConnectionMatrix(ConnectionMatrix): __swig_setmethods__ = {} for _s in [ConnectionMatrix]: __swig_setmethods__.update(getattr(_s, '__swig_setmethods__', {})) __setattr__ = lambda self, name, value: _swig_setattr(self, DenseConnectionMatrix, name, value) __swig_getmethods__ = {} for _s in [ConnectionMatrix]: __swig_getmethods__.update(getattr(_s, '__swig_getmethods__', {})) __getattr__ = lambda self, name: _swig_getattr(self, DenseConnectionMatrix, name) __repr__ = _swig_repr __swig_setmethods__["W"] = _brianlib.DenseConnectionMatrix_W_set __swig_getmethods__["W"] = _brianlib.DenseConnectionMatrix_W_get if _newclass:W = _swig_property(_brianlib.DenseConnectionMatrix_W_get, _brianlib.DenseConnectionMatrix_W_set) __swig_setmethods__["n"] = _brianlib.DenseConnectionMatrix_n_set __swig_getmethods__["n"] = _brianlib.DenseConnectionMatrix_n_get if _newclass:n = _swig_property(_brianlib.DenseConnectionMatrix_n_get, _brianlib.DenseConnectionMatrix_n_set) __swig_setmethods__["m"] = _brianlib.DenseConnectionMatrix_m_set __swig_getmethods__["m"] = _brianlib.DenseConnectionMatrix_m_get if _newclass:m = _swig_property(_brianlib.DenseConnectionMatrix_m_get, _brianlib.DenseConnectionMatrix_m_set) def __init__(self, *args): this = _brianlib.new_DenseConnectionMatrix(*args) try: self.this.append(this) except: self.this = this def add_row(*args): return _brianlib.DenseConnectionMatrix_add_row(*args) __swig_destroy__ = _brianlib.delete_DenseConnectionMatrix __del__ = lambda self : None; DenseConnectionMatrix_swigregister = _brianlib.DenseConnectionMatrix_swigregister DenseConnectionMatrix_swigregister(DenseConnectionMatrix) class Connection(_object): __swig_setmethods__ = {} __setattr__ = lambda self, name, value: _swig_setattr(self, Connection, name, value) __swig_getmethods__ = {} __getattr__ = lambda self, name: _swig_getattr(self, Connection, name) __repr__ = _swig_repr __swig_setmethods__["source"] = _brianlib.Connection_source_set __swig_getmethods__["source"] = _brianlib.Connection_source_get if _newclass:source = _swig_property(_brianlib.Connection_source_get, _brianlib.Connection_source_set) __swig_setmethods__["target"] = _brianlib.Connection_target_set __swig_getmethods__["target"] = _brianlib.Connection_target_get if _newclass:target = _swig_property(_brianlib.Connection_target_get, _brianlib.Connection_target_set) __swig_setmethods__["connmat"] = _brianlib.Connection_connmat_set __swig_getmethods__["connmat"] = _brianlib.Connection_connmat_get if _newclass:connmat = _swig_property(_brianlib.Connection_connmat_get, _brianlib.Connection_connmat_set) __swig_setmethods__["state"] = _brianlib.Connection_state_set __swig_getmethods__["state"] = _brianlib.Connection_state_get if _newclass:state = _swig_property(_brianlib.Connection_state_get, _brianlib.Connection_state_set) __swig_setmethods__["delay"] = _brianlib.Connection_delay_set __swig_getmethods__["delay"] = _brianlib.Connection_delay_get if _newclass:delay = _swig_property(_brianlib.Connection_delay_get, _brianlib.Connection_delay_set) def __init__(self, *args): this = _brianlib.new_Connection(*args) try: self.this.append(this) except: self.this = this def propagate(*args): return _brianlib.Connection_propagate(*args) def do_propagate(*args): return _brianlib.Connection_do_propagate(*args) __swig_destroy__ = _brianlib.delete_Connection __del__ = lambda self : None; Connection_swigregister = _brianlib.Connection_swigregister Connection_swigregister(Connection) brian-1.4.3/dev/ideas/cppgen/brianlib_wrap.cxx000066400000000000000000020033101266062647100213010ustar00rootroot00000000000000/* ---------------------------------------------------------------------------- * This file was automatically generated by SWIG (http://www.swig.org). * Version 1.3.33 * * This file is not intended to be easily readable and contains a number of * coding conventions designed to improve portability and efficiency. Do not make * changes to this file unless you know what you are doing--modify the SWIG * interface file instead. * ----------------------------------------------------------------------------- */ #define SWIGPYTHON #define SWIG_PYTHON_DIRECTOR_NO_VTABLE #ifdef __cplusplus template class SwigValueWrapper { T *tt; public: SwigValueWrapper() : tt(0) { } SwigValueWrapper(const SwigValueWrapper& rhs) : tt(new T(*rhs.tt)) { } SwigValueWrapper(const T& t) : tt(new T(t)) { } ~SwigValueWrapper() { delete tt; } SwigValueWrapper& operator=(const T& t) { delete tt; tt = new T(t); return *this; } operator T&() const { return *tt; } T *operator&() { return tt; } private: SwigValueWrapper& operator=(const SwigValueWrapper& rhs); }; template T SwigValueInit() { return T(); } #endif /* ----------------------------------------------------------------------------- * This section contains generic SWIG labels for method/variable * declarations/attributes, and other compiler dependent labels. * ----------------------------------------------------------------------------- */ /* template workaround for compilers that cannot correctly implement the C++ standard */ #ifndef SWIGTEMPLATEDISAMBIGUATOR # if defined(__SUNPRO_CC) && (__SUNPRO_CC <= 0x560) # define SWIGTEMPLATEDISAMBIGUATOR template # elif defined(__HP_aCC) /* Needed even with `aCC -AA' when `aCC -V' reports HP ANSI C++ B3910B A.03.55 */ /* If we find a maximum version that requires this, the test would be __HP_aCC <= 35500 for A.03.55 */ # define SWIGTEMPLATEDISAMBIGUATOR template # else # define SWIGTEMPLATEDISAMBIGUATOR # endif #endif /* inline attribute */ #ifndef SWIGINLINE # if defined(__cplusplus) || (defined(__GNUC__) && !defined(__STRICT_ANSI__)) # define SWIGINLINE inline # else # define SWIGINLINE # endif #endif /* attribute recognised by some compilers to avoid 'unused' warnings */ #ifndef SWIGUNUSED # if defined(__GNUC__) # if !(defined(__cplusplus)) || (__GNUC__ > 3 || (__GNUC__ == 3 && __GNUC_MINOR__ >= 4)) # define SWIGUNUSED __attribute__ ((__unused__)) # else # define SWIGUNUSED # endif # elif defined(__ICC) # define SWIGUNUSED __attribute__ ((__unused__)) # else # define SWIGUNUSED # endif #endif #ifndef SWIGUNUSEDPARM # ifdef __cplusplus # define SWIGUNUSEDPARM(p) # else # define SWIGUNUSEDPARM(p) p SWIGUNUSED # endif #endif /* internal SWIG method */ #ifndef SWIGINTERN # define SWIGINTERN static SWIGUNUSED #endif /* internal inline SWIG method */ #ifndef SWIGINTERNINLINE # define SWIGINTERNINLINE SWIGINTERN SWIGINLINE #endif /* exporting methods */ #if (__GNUC__ >= 4) || (__GNUC__ == 3 && __GNUC_MINOR__ >= 4) # ifndef GCC_HASCLASSVISIBILITY # define GCC_HASCLASSVISIBILITY # endif #endif #ifndef SWIGEXPORT # if defined(_WIN32) || defined(__WIN32__) || defined(__CYGWIN__) # if defined(STATIC_LINKED) # define SWIGEXPORT # else # define SWIGEXPORT __declspec(dllexport) # endif # else # if defined(__GNUC__) && defined(GCC_HASCLASSVISIBILITY) # define SWIGEXPORT __attribute__ ((visibility("default"))) # else # define SWIGEXPORT # endif # endif #endif /* calling conventions for Windows */ #ifndef SWIGSTDCALL # if defined(_WIN32) || defined(__WIN32__) || defined(__CYGWIN__) # define SWIGSTDCALL __stdcall # else # define SWIGSTDCALL # endif #endif /* Deal with Microsoft's attempt at deprecating C standard runtime functions */ #if !defined(SWIG_NO_CRT_SECURE_NO_DEPRECATE) && defined(_MSC_VER) && !defined(_CRT_SECURE_NO_DEPRECATE) # define _CRT_SECURE_NO_DEPRECATE #endif /* Deal with Microsoft's attempt at deprecating methods in the standard C++ library */ #if !defined(SWIG_NO_SCL_SECURE_NO_DEPRECATE) && defined(_MSC_VER) && !defined(_SCL_SECURE_NO_DEPRECATE) # define _SCL_SECURE_NO_DEPRECATE #endif /* Python.h has to appear first */ #include /* ----------------------------------------------------------------------------- * swigrun.swg * * This file contains generic CAPI SWIG runtime support for pointer * type checking. * ----------------------------------------------------------------------------- */ /* This should only be incremented when either the layout of swig_type_info changes, or for whatever reason, the runtime changes incompatibly */ #define SWIG_RUNTIME_VERSION "3" /* define SWIG_TYPE_TABLE_NAME as "SWIG_TYPE_TABLE" */ #ifdef SWIG_TYPE_TABLE # define SWIG_QUOTE_STRING(x) #x # define SWIG_EXPAND_AND_QUOTE_STRING(x) SWIG_QUOTE_STRING(x) # define SWIG_TYPE_TABLE_NAME SWIG_EXPAND_AND_QUOTE_STRING(SWIG_TYPE_TABLE) #else # define SWIG_TYPE_TABLE_NAME #endif /* You can use the SWIGRUNTIME and SWIGRUNTIMEINLINE macros for creating a static or dynamic library from the swig runtime code. In 99.9% of the cases, swig just needs to declare them as 'static'. But only do this if is strictly necessary, ie, if you have problems with your compiler or so. */ #ifndef SWIGRUNTIME # define SWIGRUNTIME SWIGINTERN #endif #ifndef SWIGRUNTIMEINLINE # define SWIGRUNTIMEINLINE SWIGRUNTIME SWIGINLINE #endif /* Generic buffer size */ #ifndef SWIG_BUFFER_SIZE # define SWIG_BUFFER_SIZE 1024 #endif /* Flags for pointer conversions */ #define SWIG_POINTER_DISOWN 0x1 /* Flags for new pointer objects */ #define SWIG_POINTER_OWN 0x1 /* Flags/methods for returning states. The swig conversion methods, as ConvertPtr, return and integer that tells if the conversion was successful or not. And if not, an error code can be returned (see swigerrors.swg for the codes). Use the following macros/flags to set or process the returning states. In old swig versions, you usually write code as: if (SWIG_ConvertPtr(obj,vptr,ty.flags) != -1) { // success code } else { //fail code } Now you can be more explicit as: int res = SWIG_ConvertPtr(obj,vptr,ty.flags); if (SWIG_IsOK(res)) { // success code } else { // fail code } that seems to be the same, but now you can also do Type *ptr; int res = SWIG_ConvertPtr(obj,(void **)(&ptr),ty.flags); if (SWIG_IsOK(res)) { // success code if (SWIG_IsNewObj(res) { ... delete *ptr; } else { ... } } else { // fail code } I.e., now SWIG_ConvertPtr can return new objects and you can identify the case and take care of the deallocation. Of course that requires also to SWIG_ConvertPtr to return new result values, as int SWIG_ConvertPtr(obj, ptr,...) { if () { if () { *ptr = ; return SWIG_NEWOBJ; } else { *ptr = ; return SWIG_OLDOBJ; } } else { return SWIG_BADOBJ; } } Of course, returning the plain '0(success)/-1(fail)' still works, but you can be more explicit by returning SWIG_BADOBJ, SWIG_ERROR or any of the swig errors code. Finally, if the SWIG_CASTRANK_MODE is enabled, the result code allows to return the 'cast rank', for example, if you have this int food(double) int fooi(int); and you call food(1) // cast rank '1' (1 -> 1.0) fooi(1) // cast rank '0' just use the SWIG_AddCast()/SWIG_CheckState() */ #define SWIG_OK (0) #define SWIG_ERROR (-1) #define SWIG_IsOK(r) (r >= 0) #define SWIG_ArgError(r) ((r != SWIG_ERROR) ? r : SWIG_TypeError) /* The CastRankLimit says how many bits are used for the cast rank */ #define SWIG_CASTRANKLIMIT (1 << 8) /* The NewMask denotes the object was created (using new/malloc) */ #define SWIG_NEWOBJMASK (SWIG_CASTRANKLIMIT << 1) /* The TmpMask is for in/out typemaps that use temporal objects */ #define SWIG_TMPOBJMASK (SWIG_NEWOBJMASK << 1) /* Simple returning values */ #define SWIG_BADOBJ (SWIG_ERROR) #define SWIG_OLDOBJ (SWIG_OK) #define SWIG_NEWOBJ (SWIG_OK | SWIG_NEWOBJMASK) #define SWIG_TMPOBJ (SWIG_OK | SWIG_TMPOBJMASK) /* Check, add and del mask methods */ #define SWIG_AddNewMask(r) (SWIG_IsOK(r) ? (r | SWIG_NEWOBJMASK) : r) #define SWIG_DelNewMask(r) (SWIG_IsOK(r) ? (r & ~SWIG_NEWOBJMASK) : r) #define SWIG_IsNewObj(r) (SWIG_IsOK(r) && (r & SWIG_NEWOBJMASK)) #define SWIG_AddTmpMask(r) (SWIG_IsOK(r) ? (r | SWIG_TMPOBJMASK) : r) #define SWIG_DelTmpMask(r) (SWIG_IsOK(r) ? (r & ~SWIG_TMPOBJMASK) : r) #define SWIG_IsTmpObj(r) (SWIG_IsOK(r) && (r & SWIG_TMPOBJMASK)) /* Cast-Rank Mode */ #if defined(SWIG_CASTRANK_MODE) # ifndef SWIG_TypeRank # define SWIG_TypeRank unsigned long # endif # ifndef SWIG_MAXCASTRANK /* Default cast allowed */ # define SWIG_MAXCASTRANK (2) # endif # define SWIG_CASTRANKMASK ((SWIG_CASTRANKLIMIT) -1) # define SWIG_CastRank(r) (r & SWIG_CASTRANKMASK) SWIGINTERNINLINE int SWIG_AddCast(int r) { return SWIG_IsOK(r) ? ((SWIG_CastRank(r) < SWIG_MAXCASTRANK) ? (r + 1) : SWIG_ERROR) : r; } SWIGINTERNINLINE int SWIG_CheckState(int r) { return SWIG_IsOK(r) ? SWIG_CastRank(r) + 1 : 0; } #else /* no cast-rank mode */ # define SWIG_AddCast # define SWIG_CheckState(r) (SWIG_IsOK(r) ? 1 : 0) #endif #include #ifdef __cplusplus extern "C" { #endif typedef void *(*swig_converter_func)(void *); typedef struct swig_type_info *(*swig_dycast_func)(void **); /* Structure to store inforomation on one type */ typedef struct swig_type_info { const char *name; /* mangled name of this type */ const char *str; /* human readable name of this type */ swig_dycast_func dcast; /* dynamic cast function down a hierarchy */ struct swig_cast_info *cast; /* linked list of types that can cast into this type */ void *clientdata; /* language specific type data */ int owndata; /* flag if the structure owns the clientdata */ } swig_type_info; /* Structure to store a type and conversion function used for casting */ typedef struct swig_cast_info { swig_type_info *type; /* pointer to type that is equivalent to this type */ swig_converter_func converter; /* function to cast the void pointers */ struct swig_cast_info *next; /* pointer to next cast in linked list */ struct swig_cast_info *prev; /* pointer to the previous cast */ } swig_cast_info; /* Structure used to store module information * Each module generates one structure like this, and the runtime collects * all of these structures and stores them in a circularly linked list.*/ typedef struct swig_module_info { swig_type_info **types; /* Array of pointers to swig_type_info structures that are in this module */ size_t size; /* Number of types in this module */ struct swig_module_info *next; /* Pointer to next element in circularly linked list */ swig_type_info **type_initial; /* Array of initially generated type structures */ swig_cast_info **cast_initial; /* Array of initially generated casting structures */ void *clientdata; /* Language specific module data */ } swig_module_info; /* Compare two type names skipping the space characters, therefore "char*" == "char *" and "Class" == "Class", etc. Return 0 when the two name types are equivalent, as in strncmp, but skipping ' '. */ SWIGRUNTIME int SWIG_TypeNameComp(const char *f1, const char *l1, const char *f2, const char *l2) { for (;(f1 != l1) && (f2 != l2); ++f1, ++f2) { while ((*f1 == ' ') && (f1 != l1)) ++f1; while ((*f2 == ' ') && (f2 != l2)) ++f2; if (*f1 != *f2) return (*f1 > *f2) ? 1 : -1; } return (int)((l1 - f1) - (l2 - f2)); } /* Check type equivalence in a name list like ||... Return 0 if not equal, 1 if equal */ SWIGRUNTIME int SWIG_TypeEquiv(const char *nb, const char *tb) { int equiv = 0; const char* te = tb + strlen(tb); const char* ne = nb; while (!equiv && *ne) { for (nb = ne; *ne; ++ne) { if (*ne == '|') break; } equiv = (SWIG_TypeNameComp(nb, ne, tb, te) == 0) ? 1 : 0; if (*ne) ++ne; } return equiv; } /* Check type equivalence in a name list like ||... Return 0 if equal, -1 if nb < tb, 1 if nb > tb */ SWIGRUNTIME int SWIG_TypeCompare(const char *nb, const char *tb) { int equiv = 0; const char* te = tb + strlen(tb); const char* ne = nb; while (!equiv && *ne) { for (nb = ne; *ne; ++ne) { if (*ne == '|') break; } equiv = (SWIG_TypeNameComp(nb, ne, tb, te) == 0) ? 1 : 0; if (*ne) ++ne; } return equiv; } /* think of this as a c++ template<> or a scheme macro */ #define SWIG_TypeCheck_Template(comparison, ty) \ if (ty) { \ swig_cast_info *iter = ty->cast; \ while (iter) { \ if (comparison) { \ if (iter == ty->cast) return iter; \ /* Move iter to the top of the linked list */ \ iter->prev->next = iter->next; \ if (iter->next) \ iter->next->prev = iter->prev; \ iter->next = ty->cast; \ iter->prev = 0; \ if (ty->cast) ty->cast->prev = iter; \ ty->cast = iter; \ return iter; \ } \ iter = iter->next; \ } \ } \ return 0 /* Check the typename */ SWIGRUNTIME swig_cast_info * SWIG_TypeCheck(const char *c, swig_type_info *ty) { SWIG_TypeCheck_Template(strcmp(iter->type->name, c) == 0, ty); } /* Same as previous function, except strcmp is replaced with a pointer comparison */ SWIGRUNTIME swig_cast_info * SWIG_TypeCheckStruct(swig_type_info *from, swig_type_info *into) { SWIG_TypeCheck_Template(iter->type == from, into); } /* Cast a pointer up an inheritance hierarchy */ SWIGRUNTIMEINLINE void * SWIG_TypeCast(swig_cast_info *ty, void *ptr) { return ((!ty) || (!ty->converter)) ? ptr : (*ty->converter)(ptr); } /* Dynamic pointer casting. Down an inheritance hierarchy */ SWIGRUNTIME swig_type_info * SWIG_TypeDynamicCast(swig_type_info *ty, void **ptr) { swig_type_info *lastty = ty; if (!ty || !ty->dcast) return ty; while (ty && (ty->dcast)) { ty = (*ty->dcast)(ptr); if (ty) lastty = ty; } return lastty; } /* Return the name associated with this type */ SWIGRUNTIMEINLINE const char * SWIG_TypeName(const swig_type_info *ty) { return ty->name; } /* Return the pretty name associated with this type, that is an unmangled type name in a form presentable to the user. */ SWIGRUNTIME const char * SWIG_TypePrettyName(const swig_type_info *type) { /* The "str" field contains the equivalent pretty names of the type, separated by vertical-bar characters. We choose to print the last name, as it is often (?) the most specific. */ if (!type) return NULL; if (type->str != NULL) { const char *last_name = type->str; const char *s; for (s = type->str; *s; s++) if (*s == '|') last_name = s+1; return last_name; } else return type->name; } /* Set the clientdata field for a type */ SWIGRUNTIME void SWIG_TypeClientData(swig_type_info *ti, void *clientdata) { swig_cast_info *cast = ti->cast; /* if (ti->clientdata == clientdata) return; */ ti->clientdata = clientdata; while (cast) { if (!cast->converter) { swig_type_info *tc = cast->type; if (!tc->clientdata) { SWIG_TypeClientData(tc, clientdata); } } cast = cast->next; } } SWIGRUNTIME void SWIG_TypeNewClientData(swig_type_info *ti, void *clientdata) { SWIG_TypeClientData(ti, clientdata); ti->owndata = 1; } /* Search for a swig_type_info structure only by mangled name Search is a O(log #types) We start searching at module start, and finish searching when start == end. Note: if start == end at the beginning of the function, we go all the way around the circular list. */ SWIGRUNTIME swig_type_info * SWIG_MangledTypeQueryModule(swig_module_info *start, swig_module_info *end, const char *name) { swig_module_info *iter = start; do { if (iter->size) { register size_t l = 0; register size_t r = iter->size - 1; do { /* since l+r >= 0, we can (>> 1) instead (/ 2) */ register size_t i = (l + r) >> 1; const char *iname = iter->types[i]->name; if (iname) { register int compare = strcmp(name, iname); if (compare == 0) { return iter->types[i]; } else if (compare < 0) { if (i) { r = i - 1; } else { break; } } else if (compare > 0) { l = i + 1; } } else { break; /* should never happen */ } } while (l <= r); } iter = iter->next; } while (iter != end); return 0; } /* Search for a swig_type_info structure for either a mangled name or a human readable name. It first searches the mangled names of the types, which is a O(log #types) If a type is not found it then searches the human readable names, which is O(#types). We start searching at module start, and finish searching when start == end. Note: if start == end at the beginning of the function, we go all the way around the circular list. */ SWIGRUNTIME swig_type_info * SWIG_TypeQueryModule(swig_module_info *start, swig_module_info *end, const char *name) { /* STEP 1: Search the name field using binary search */ swig_type_info *ret = SWIG_MangledTypeQueryModule(start, end, name); if (ret) { return ret; } else { /* STEP 2: If the type hasn't been found, do a complete search of the str field (the human readable name) */ swig_module_info *iter = start; do { register size_t i = 0; for (; i < iter->size; ++i) { if (iter->types[i]->str && (SWIG_TypeEquiv(iter->types[i]->str, name))) return iter->types[i]; } iter = iter->next; } while (iter != end); } /* neither found a match */ return 0; } /* Pack binary data into a string */ SWIGRUNTIME char * SWIG_PackData(char *c, void *ptr, size_t sz) { static const char hex[17] = "0123456789abcdef"; register const unsigned char *u = (unsigned char *) ptr; register const unsigned char *eu = u + sz; for (; u != eu; ++u) { register unsigned char uu = *u; *(c++) = hex[(uu & 0xf0) >> 4]; *(c++) = hex[uu & 0xf]; } return c; } /* Unpack binary data from a string */ SWIGRUNTIME const char * SWIG_UnpackData(const char *c, void *ptr, size_t sz) { register unsigned char *u = (unsigned char *) ptr; register const unsigned char *eu = u + sz; for (; u != eu; ++u) { register char d = *(c++); register unsigned char uu; if ((d >= '0') && (d <= '9')) uu = ((d - '0') << 4); else if ((d >= 'a') && (d <= 'f')) uu = ((d - ('a'-10)) << 4); else return (char *) 0; d = *(c++); if ((d >= '0') && (d <= '9')) uu |= (d - '0'); else if ((d >= 'a') && (d <= 'f')) uu |= (d - ('a'-10)); else return (char *) 0; *u = uu; } return c; } /* Pack 'void *' into a string buffer. */ SWIGRUNTIME char * SWIG_PackVoidPtr(char *buff, void *ptr, const char *name, size_t bsz) { char *r = buff; if ((2*sizeof(void *) + 2) > bsz) return 0; *(r++) = '_'; r = SWIG_PackData(r,&ptr,sizeof(void *)); if (strlen(name) + 1 > (bsz - (r - buff))) return 0; strcpy(r,name); return buff; } SWIGRUNTIME const char * SWIG_UnpackVoidPtr(const char *c, void **ptr, const char *name) { if (*c != '_') { if (strcmp(c,"NULL") == 0) { *ptr = (void *) 0; return name; } else { return 0; } } return SWIG_UnpackData(++c,ptr,sizeof(void *)); } SWIGRUNTIME char * SWIG_PackDataName(char *buff, void *ptr, size_t sz, const char *name, size_t bsz) { char *r = buff; size_t lname = (name ? strlen(name) : 0); if ((2*sz + 2 + lname) > bsz) return 0; *(r++) = '_'; r = SWIG_PackData(r,ptr,sz); if (lname) { strncpy(r,name,lname+1); } else { *r = 0; } return buff; } SWIGRUNTIME const char * SWIG_UnpackDataName(const char *c, void *ptr, size_t sz, const char *name) { if (*c != '_') { if (strcmp(c,"NULL") == 0) { memset(ptr,0,sz); return name; } else { return 0; } } return SWIG_UnpackData(++c,ptr,sz); } #ifdef __cplusplus } #endif /* Errors in SWIG */ #define SWIG_UnknownError -1 #define SWIG_IOError -2 #define SWIG_RuntimeError -3 #define SWIG_IndexError -4 #define SWIG_TypeError -5 #define SWIG_DivisionByZero -6 #define SWIG_OverflowError -7 #define SWIG_SyntaxError -8 #define SWIG_ValueError -9 #define SWIG_SystemError -10 #define SWIG_AttributeError -11 #define SWIG_MemoryError -12 #define SWIG_NullReferenceError -13 /* Add PyOS_snprintf for old Pythons */ #if PY_VERSION_HEX < 0x02020000 # if defined(_MSC_VER) || defined(__BORLANDC__) || defined(_WATCOM) # define PyOS_snprintf _snprintf # else # define PyOS_snprintf snprintf # endif #endif /* A crude PyString_FromFormat implementation for old Pythons */ #if PY_VERSION_HEX < 0x02020000 #ifndef SWIG_PYBUFFER_SIZE # define SWIG_PYBUFFER_SIZE 1024 #endif static PyObject * PyString_FromFormat(const char *fmt, ...) { va_list ap; char buf[SWIG_PYBUFFER_SIZE * 2]; int res; va_start(ap, fmt); res = vsnprintf(buf, sizeof(buf), fmt, ap); va_end(ap); return (res < 0 || res >= (int)sizeof(buf)) ? 0 : PyString_FromString(buf); } #endif /* Add PyObject_Del for old Pythons */ #if PY_VERSION_HEX < 0x01060000 # define PyObject_Del(op) PyMem_DEL((op)) #endif #ifndef PyObject_DEL # define PyObject_DEL PyObject_Del #endif /* A crude PyExc_StopIteration exception for old Pythons */ #if PY_VERSION_HEX < 0x02020000 # ifndef PyExc_StopIteration # define PyExc_StopIteration PyExc_RuntimeError # endif # ifndef PyObject_GenericGetAttr # define PyObject_GenericGetAttr 0 # endif #endif /* Py_NotImplemented is defined in 2.1 and up. */ #if PY_VERSION_HEX < 0x02010000 # ifndef Py_NotImplemented # define Py_NotImplemented PyExc_RuntimeError # endif #endif /* A crude PyString_AsStringAndSize implementation for old Pythons */ #if PY_VERSION_HEX < 0x02010000 # ifndef PyString_AsStringAndSize # define PyString_AsStringAndSize(obj, s, len) {*s = PyString_AsString(obj); *len = *s ? strlen(*s) : 0;} # endif #endif /* PySequence_Size for old Pythons */ #if PY_VERSION_HEX < 0x02000000 # ifndef PySequence_Size # define PySequence_Size PySequence_Length # endif #endif /* PyBool_FromLong for old Pythons */ #if PY_VERSION_HEX < 0x02030000 static PyObject *PyBool_FromLong(long ok) { PyObject *result = ok ? Py_True : Py_False; Py_INCREF(result); return result; } #endif /* Py_ssize_t for old Pythons */ /* This code is as recommended by: */ /* http://www.python.org/dev/peps/pep-0353/#conversion-guidelines */ #if PY_VERSION_HEX < 0x02050000 && !defined(PY_SSIZE_T_MIN) typedef int Py_ssize_t; # define PY_SSIZE_T_MAX INT_MAX # define PY_SSIZE_T_MIN INT_MIN #endif /* ----------------------------------------------------------------------------- * error manipulation * ----------------------------------------------------------------------------- */ SWIGRUNTIME PyObject* SWIG_Python_ErrorType(int code) { PyObject* type = 0; switch(code) { case SWIG_MemoryError: type = PyExc_MemoryError; break; case SWIG_IOError: type = PyExc_IOError; break; case SWIG_RuntimeError: type = PyExc_RuntimeError; break; case SWIG_IndexError: type = PyExc_IndexError; break; case SWIG_TypeError: type = PyExc_TypeError; break; case SWIG_DivisionByZero: type = PyExc_ZeroDivisionError; break; case SWIG_OverflowError: type = PyExc_OverflowError; break; case SWIG_SyntaxError: type = PyExc_SyntaxError; break; case SWIG_ValueError: type = PyExc_ValueError; break; case SWIG_SystemError: type = PyExc_SystemError; break; case SWIG_AttributeError: type = PyExc_AttributeError; break; default: type = PyExc_RuntimeError; } return type; } SWIGRUNTIME void SWIG_Python_AddErrorMsg(const char* mesg) { PyObject *type = 0; PyObject *value = 0; PyObject *traceback = 0; if (PyErr_Occurred()) PyErr_Fetch(&type, &value, &traceback); if (value) { PyObject *old_str = PyObject_Str(value); PyErr_Clear(); Py_XINCREF(type); PyErr_Format(type, "%s %s", PyString_AsString(old_str), mesg); Py_DECREF(old_str); Py_DECREF(value); } else { PyErr_Format(PyExc_RuntimeError, mesg); } } #if defined(SWIG_PYTHON_NO_THREADS) # if defined(SWIG_PYTHON_THREADS) # undef SWIG_PYTHON_THREADS # endif #endif #if defined(SWIG_PYTHON_THREADS) /* Threading support is enabled */ # if !defined(SWIG_PYTHON_USE_GIL) && !defined(SWIG_PYTHON_NO_USE_GIL) # if (PY_VERSION_HEX >= 0x02030000) /* For 2.3 or later, use the PyGILState calls */ # define SWIG_PYTHON_USE_GIL # endif # endif # if defined(SWIG_PYTHON_USE_GIL) /* Use PyGILState threads calls */ # ifndef SWIG_PYTHON_INITIALIZE_THREADS # define SWIG_PYTHON_INITIALIZE_THREADS PyEval_InitThreads() # endif # ifdef __cplusplus /* C++ code */ class SWIG_Python_Thread_Block { bool status; PyGILState_STATE state; public: void end() { if (status) { PyGILState_Release(state); status = false;} } SWIG_Python_Thread_Block() : status(true), state(PyGILState_Ensure()) {} ~SWIG_Python_Thread_Block() { end(); } }; class SWIG_Python_Thread_Allow { bool status; PyThreadState *save; public: void end() { if (status) { PyEval_RestoreThread(save); status = false; }} SWIG_Python_Thread_Allow() : status(true), save(PyEval_SaveThread()) {} ~SWIG_Python_Thread_Allow() { end(); } }; # define SWIG_PYTHON_THREAD_BEGIN_BLOCK SWIG_Python_Thread_Block _swig_thread_block # define SWIG_PYTHON_THREAD_END_BLOCK _swig_thread_block.end() # define SWIG_PYTHON_THREAD_BEGIN_ALLOW SWIG_Python_Thread_Allow _swig_thread_allow # define SWIG_PYTHON_THREAD_END_ALLOW _swig_thread_allow.end() # else /* C code */ # define SWIG_PYTHON_THREAD_BEGIN_BLOCK PyGILState_STATE _swig_thread_block = PyGILState_Ensure() # define SWIG_PYTHON_THREAD_END_BLOCK PyGILState_Release(_swig_thread_block) # define SWIG_PYTHON_THREAD_BEGIN_ALLOW PyThreadState *_swig_thread_allow = PyEval_SaveThread() # define SWIG_PYTHON_THREAD_END_ALLOW PyEval_RestoreThread(_swig_thread_allow) # endif # else /* Old thread way, not implemented, user must provide it */ # if !defined(SWIG_PYTHON_INITIALIZE_THREADS) # define SWIG_PYTHON_INITIALIZE_THREADS # endif # if !defined(SWIG_PYTHON_THREAD_BEGIN_BLOCK) # define SWIG_PYTHON_THREAD_BEGIN_BLOCK # endif # if !defined(SWIG_PYTHON_THREAD_END_BLOCK) # define SWIG_PYTHON_THREAD_END_BLOCK # endif # if !defined(SWIG_PYTHON_THREAD_BEGIN_ALLOW) # define SWIG_PYTHON_THREAD_BEGIN_ALLOW # endif # if !defined(SWIG_PYTHON_THREAD_END_ALLOW) # define SWIG_PYTHON_THREAD_END_ALLOW # endif # endif #else /* No thread support */ # define SWIG_PYTHON_INITIALIZE_THREADS # define SWIG_PYTHON_THREAD_BEGIN_BLOCK # define SWIG_PYTHON_THREAD_END_BLOCK # define SWIG_PYTHON_THREAD_BEGIN_ALLOW # define SWIG_PYTHON_THREAD_END_ALLOW #endif /* ----------------------------------------------------------------------------- * Python API portion that goes into the runtime * ----------------------------------------------------------------------------- */ #ifdef __cplusplus extern "C" { #if 0 } /* cc-mode */ #endif #endif /* ----------------------------------------------------------------------------- * Constant declarations * ----------------------------------------------------------------------------- */ /* Constant Types */ #define SWIG_PY_POINTER 4 #define SWIG_PY_BINARY 5 /* Constant information structure */ typedef struct swig_const_info { int type; char *name; long lvalue; double dvalue; void *pvalue; swig_type_info **ptype; } swig_const_info; #ifdef __cplusplus #if 0 { /* cc-mode */ #endif } #endif /* ----------------------------------------------------------------------------- * See the LICENSE file for information on copyright, usage and redistribution * of SWIG, and the README file for authors - http://www.swig.org/release.html. * * pyrun.swg * * This file contains the runtime support for Python modules * and includes code for managing global variables and pointer * type checking. * * ----------------------------------------------------------------------------- */ /* Common SWIG API */ /* for raw pointers */ #define SWIG_Python_ConvertPtr(obj, pptr, type, flags) SWIG_Python_ConvertPtrAndOwn(obj, pptr, type, flags, 0) #define SWIG_ConvertPtr(obj, pptr, type, flags) SWIG_Python_ConvertPtr(obj, pptr, type, flags) #define SWIG_ConvertPtrAndOwn(obj,pptr,type,flags,own) SWIG_Python_ConvertPtrAndOwn(obj, pptr, type, flags, own) #define SWIG_NewPointerObj(ptr, type, flags) SWIG_Python_NewPointerObj(ptr, type, flags) #define SWIG_CheckImplicit(ty) SWIG_Python_CheckImplicit(ty) #define SWIG_AcquirePtr(ptr, src) SWIG_Python_AcquirePtr(ptr, src) #define swig_owntype int /* for raw packed data */ #define SWIG_ConvertPacked(obj, ptr, sz, ty) SWIG_Python_ConvertPacked(obj, ptr, sz, ty) #define SWIG_NewPackedObj(ptr, sz, type) SWIG_Python_NewPackedObj(ptr, sz, type) /* for class or struct pointers */ #define SWIG_ConvertInstance(obj, pptr, type, flags) SWIG_ConvertPtr(obj, pptr, type, flags) #define SWIG_NewInstanceObj(ptr, type, flags) SWIG_NewPointerObj(ptr, type, flags) /* for C or C++ function pointers */ #define SWIG_ConvertFunctionPtr(obj, pptr, type) SWIG_Python_ConvertFunctionPtr(obj, pptr, type) #define SWIG_NewFunctionPtrObj(ptr, type) SWIG_Python_NewPointerObj(ptr, type, 0) /* for C++ member pointers, ie, member methods */ #define SWIG_ConvertMember(obj, ptr, sz, ty) SWIG_Python_ConvertPacked(obj, ptr, sz, ty) #define SWIG_NewMemberObj(ptr, sz, type) SWIG_Python_NewPackedObj(ptr, sz, type) /* Runtime API */ #define SWIG_GetModule(clientdata) SWIG_Python_GetModule() #define SWIG_SetModule(clientdata, pointer) SWIG_Python_SetModule(pointer) #define SWIG_NewClientData(obj) PySwigClientData_New(obj) #define SWIG_SetErrorObj SWIG_Python_SetErrorObj #define SWIG_SetErrorMsg SWIG_Python_SetErrorMsg #define SWIG_ErrorType(code) SWIG_Python_ErrorType(code) #define SWIG_Error(code, msg) SWIG_Python_SetErrorMsg(SWIG_ErrorType(code), msg) #define SWIG_fail goto fail /* Runtime API implementation */ /* Error manipulation */ SWIGINTERN void SWIG_Python_SetErrorObj(PyObject *errtype, PyObject *obj) { SWIG_PYTHON_THREAD_BEGIN_BLOCK; PyErr_SetObject(errtype, obj); Py_DECREF(obj); SWIG_PYTHON_THREAD_END_BLOCK; } SWIGINTERN void SWIG_Python_SetErrorMsg(PyObject *errtype, const char *msg) { SWIG_PYTHON_THREAD_BEGIN_BLOCK; PyErr_SetString(errtype, (char *) msg); SWIG_PYTHON_THREAD_END_BLOCK; } #define SWIG_Python_Raise(obj, type, desc) SWIG_Python_SetErrorObj(SWIG_Python_ExceptionType(desc), obj) /* Set a constant value */ SWIGINTERN void SWIG_Python_SetConstant(PyObject *d, const char *name, PyObject *obj) { PyDict_SetItemString(d, (char*) name, obj); Py_DECREF(obj); } /* Append a value to the result obj */ SWIGINTERN PyObject* SWIG_Python_AppendOutput(PyObject* result, PyObject* obj) { #if !defined(SWIG_PYTHON_OUTPUT_TUPLE) if (!result) { result = obj; } else if (result == Py_None) { Py_DECREF(result); result = obj; } else { if (!PyList_Check(result)) { PyObject *o2 = result; result = PyList_New(1); PyList_SetItem(result, 0, o2); } PyList_Append(result,obj); Py_DECREF(obj); } return result; #else PyObject* o2; PyObject* o3; if (!result) { result = obj; } else if (result == Py_None) { Py_DECREF(result); result = obj; } else { if (!PyTuple_Check(result)) { o2 = result; result = PyTuple_New(1); PyTuple_SET_ITEM(result, 0, o2); } o3 = PyTuple_New(1); PyTuple_SET_ITEM(o3, 0, obj); o2 = result; result = PySequence_Concat(o2, o3); Py_DECREF(o2); Py_DECREF(o3); } return result; #endif } /* Unpack the argument tuple */ SWIGINTERN int SWIG_Python_UnpackTuple(PyObject *args, const char *name, Py_ssize_t min, Py_ssize_t max, PyObject **objs) { if (!args) { if (!min && !max) { return 1; } else { PyErr_Format(PyExc_TypeError, "%s expected %s%d arguments, got none", name, (min == max ? "" : "at least "), (int)min); return 0; } } if (!PyTuple_Check(args)) { PyErr_SetString(PyExc_SystemError, "UnpackTuple() argument list is not a tuple"); return 0; } else { register Py_ssize_t l = PyTuple_GET_SIZE(args); if (l < min) { PyErr_Format(PyExc_TypeError, "%s expected %s%d arguments, got %d", name, (min == max ? "" : "at least "), (int)min, (int)l); return 0; } else if (l > max) { PyErr_Format(PyExc_TypeError, "%s expected %s%d arguments, got %d", name, (min == max ? "" : "at most "), (int)max, (int)l); return 0; } else { register int i; for (i = 0; i < l; ++i) { objs[i] = PyTuple_GET_ITEM(args, i); } for (; l < max; ++l) { objs[l] = 0; } return i + 1; } } } /* A functor is a function object with one single object argument */ #if PY_VERSION_HEX >= 0x02020000 #define SWIG_Python_CallFunctor(functor, obj) PyObject_CallFunctionObjArgs(functor, obj, NULL); #else #define SWIG_Python_CallFunctor(functor, obj) PyObject_CallFunction(functor, "O", obj); #endif /* Helper for static pointer initialization for both C and C++ code, for example static PyObject *SWIG_STATIC_POINTER(MyVar) = NewSomething(...); */ #ifdef __cplusplus #define SWIG_STATIC_POINTER(var) var #else #define SWIG_STATIC_POINTER(var) var = 0; if (!var) var #endif /* ----------------------------------------------------------------------------- * Pointer declarations * ----------------------------------------------------------------------------- */ /* Flags for new pointer objects */ #define SWIG_POINTER_NOSHADOW (SWIG_POINTER_OWN << 1) #define SWIG_POINTER_NEW (SWIG_POINTER_NOSHADOW | SWIG_POINTER_OWN) #define SWIG_POINTER_IMPLICIT_CONV (SWIG_POINTER_DISOWN << 1) #ifdef __cplusplus extern "C" { #if 0 } /* cc-mode */ #endif #endif /* How to access Py_None */ #if defined(_WIN32) || defined(__WIN32__) || defined(__CYGWIN__) # ifndef SWIG_PYTHON_NO_BUILD_NONE # ifndef SWIG_PYTHON_BUILD_NONE # define SWIG_PYTHON_BUILD_NONE # endif # endif #endif #ifdef SWIG_PYTHON_BUILD_NONE # ifdef Py_None # undef Py_None # define Py_None SWIG_Py_None() # endif SWIGRUNTIMEINLINE PyObject * _SWIG_Py_None(void) { PyObject *none = Py_BuildValue((char*)""); Py_DECREF(none); return none; } SWIGRUNTIME PyObject * SWIG_Py_None(void) { static PyObject *SWIG_STATIC_POINTER(none) = _SWIG_Py_None(); return none; } #endif /* The python void return value */ SWIGRUNTIMEINLINE PyObject * SWIG_Py_Void(void) { PyObject *none = Py_None; Py_INCREF(none); return none; } /* PySwigClientData */ typedef struct { PyObject *klass; PyObject *newraw; PyObject *newargs; PyObject *destroy; int delargs; int implicitconv; } PySwigClientData; SWIGRUNTIMEINLINE int SWIG_Python_CheckImplicit(swig_type_info *ty) { PySwigClientData *data = (PySwigClientData *)ty->clientdata; return data ? data->implicitconv : 0; } SWIGRUNTIMEINLINE PyObject * SWIG_Python_ExceptionType(swig_type_info *desc) { PySwigClientData *data = desc ? (PySwigClientData *) desc->clientdata : 0; PyObject *klass = data ? data->klass : 0; return (klass ? klass : PyExc_RuntimeError); } SWIGRUNTIME PySwigClientData * PySwigClientData_New(PyObject* obj) { if (!obj) { return 0; } else { PySwigClientData *data = (PySwigClientData *)malloc(sizeof(PySwigClientData)); /* the klass element */ data->klass = obj; Py_INCREF(data->klass); /* the newraw method and newargs arguments used to create a new raw instance */ if (PyClass_Check(obj)) { data->newraw = 0; data->newargs = obj; Py_INCREF(obj); } else { #if (PY_VERSION_HEX < 0x02020000) data->newraw = 0; #else data->newraw = PyObject_GetAttrString(data->klass, (char *)"__new__"); #endif if (data->newraw) { Py_INCREF(data->newraw); data->newargs = PyTuple_New(1); PyTuple_SetItem(data->newargs, 0, obj); } else { data->newargs = obj; } Py_INCREF(data->newargs); } /* the destroy method, aka as the C++ delete method */ data->destroy = PyObject_GetAttrString(data->klass, (char *)"__swig_destroy__"); if (PyErr_Occurred()) { PyErr_Clear(); data->destroy = 0; } if (data->destroy) { int flags; Py_INCREF(data->destroy); flags = PyCFunction_GET_FLAGS(data->destroy); #ifdef METH_O data->delargs = !(flags & (METH_O)); #else data->delargs = 0; #endif } else { data->delargs = 0; } data->implicitconv = 0; return data; } } SWIGRUNTIME void PySwigClientData_Del(PySwigClientData* data) { Py_XDECREF(data->newraw); Py_XDECREF(data->newargs); Py_XDECREF(data->destroy); } /* =============== PySwigObject =====================*/ typedef struct { PyObject_HEAD void *ptr; swig_type_info *ty; int own; PyObject *next; } PySwigObject; SWIGRUNTIME PyObject * PySwigObject_long(PySwigObject *v) { return PyLong_FromVoidPtr(v->ptr); } SWIGRUNTIME PyObject * PySwigObject_format(const char* fmt, PySwigObject *v) { PyObject *res = NULL; PyObject *args = PyTuple_New(1); if (args) { if (PyTuple_SetItem(args, 0, PySwigObject_long(v)) == 0) { PyObject *ofmt = PyString_FromString(fmt); if (ofmt) { res = PyString_Format(ofmt,args); Py_DECREF(ofmt); } Py_DECREF(args); } } return res; } SWIGRUNTIME PyObject * PySwigObject_oct(PySwigObject *v) { return PySwigObject_format("%o",v); } SWIGRUNTIME PyObject * PySwigObject_hex(PySwigObject *v) { return PySwigObject_format("%x",v); } SWIGRUNTIME PyObject * #ifdef METH_NOARGS PySwigObject_repr(PySwigObject *v) #else PySwigObject_repr(PySwigObject *v, PyObject *args) #endif { const char *name = SWIG_TypePrettyName(v->ty); PyObject *hex = PySwigObject_hex(v); PyObject *repr = PyString_FromFormat("", name, PyString_AsString(hex)); Py_DECREF(hex); if (v->next) { #ifdef METH_NOARGS PyObject *nrep = PySwigObject_repr((PySwigObject *)v->next); #else PyObject *nrep = PySwigObject_repr((PySwigObject *)v->next, args); #endif PyString_ConcatAndDel(&repr,nrep); } return repr; } SWIGRUNTIME int PySwigObject_print(PySwigObject *v, FILE *fp, int SWIGUNUSEDPARM(flags)) { #ifdef METH_NOARGS PyObject *repr = PySwigObject_repr(v); #else PyObject *repr = PySwigObject_repr(v, NULL); #endif if (repr) { fputs(PyString_AsString(repr), fp); Py_DECREF(repr); return 0; } else { return 1; } } SWIGRUNTIME PyObject * PySwigObject_str(PySwigObject *v) { char result[SWIG_BUFFER_SIZE]; return SWIG_PackVoidPtr(result, v->ptr, v->ty->name, sizeof(result)) ? PyString_FromString(result) : 0; } SWIGRUNTIME int PySwigObject_compare(PySwigObject *v, PySwigObject *w) { void *i = v->ptr; void *j = w->ptr; return (i < j) ? -1 : ((i > j) ? 1 : 0); } SWIGRUNTIME PyTypeObject* _PySwigObject_type(void); SWIGRUNTIME PyTypeObject* PySwigObject_type(void) { static PyTypeObject *SWIG_STATIC_POINTER(type) = _PySwigObject_type(); return type; } SWIGRUNTIMEINLINE int PySwigObject_Check(PyObject *op) { return ((op)->ob_type == PySwigObject_type()) || (strcmp((op)->ob_type->tp_name,"PySwigObject") == 0); } SWIGRUNTIME PyObject * PySwigObject_New(void *ptr, swig_type_info *ty, int own); SWIGRUNTIME void PySwigObject_dealloc(PyObject *v) { PySwigObject *sobj = (PySwigObject *) v; PyObject *next = sobj->next; if (sobj->own) { swig_type_info *ty = sobj->ty; PySwigClientData *data = ty ? (PySwigClientData *) ty->clientdata : 0; PyObject *destroy = data ? data->destroy : 0; if (destroy) { /* destroy is always a VARARGS method */ PyObject *res; if (data->delargs) { /* we need to create a temporal object to carry the destroy operation */ PyObject *tmp = PySwigObject_New(sobj->ptr, ty, 0); res = SWIG_Python_CallFunctor(destroy, tmp); Py_DECREF(tmp); } else { PyCFunction meth = PyCFunction_GET_FUNCTION(destroy); PyObject *mself = PyCFunction_GET_SELF(destroy); res = ((*meth)(mself, v)); } Py_XDECREF(res); } else { const char *name = SWIG_TypePrettyName(ty); #if !defined(SWIG_PYTHON_SILENT_MEMLEAK) printf("swig/python detected a memory leak of type '%s', no destructor found.\n", name); #endif } } Py_XDECREF(next); PyObject_DEL(v); } SWIGRUNTIME PyObject* PySwigObject_append(PyObject* v, PyObject* next) { PySwigObject *sobj = (PySwigObject *) v; #ifndef METH_O PyObject *tmp = 0; if (!PyArg_ParseTuple(next,(char *)"O:append", &tmp)) return NULL; next = tmp; #endif if (!PySwigObject_Check(next)) { return NULL; } sobj->next = next; Py_INCREF(next); return SWIG_Py_Void(); } SWIGRUNTIME PyObject* #ifdef METH_NOARGS PySwigObject_next(PyObject* v) #else PySwigObject_next(PyObject* v, PyObject *SWIGUNUSEDPARM(args)) #endif { PySwigObject *sobj = (PySwigObject *) v; if (sobj->next) { Py_INCREF(sobj->next); return sobj->next; } else { return SWIG_Py_Void(); } } SWIGINTERN PyObject* #ifdef METH_NOARGS PySwigObject_disown(PyObject *v) #else PySwigObject_disown(PyObject* v, PyObject *SWIGUNUSEDPARM(args)) #endif { PySwigObject *sobj = (PySwigObject *)v; sobj->own = 0; return SWIG_Py_Void(); } SWIGINTERN PyObject* #ifdef METH_NOARGS PySwigObject_acquire(PyObject *v) #else PySwigObject_acquire(PyObject* v, PyObject *SWIGUNUSEDPARM(args)) #endif { PySwigObject *sobj = (PySwigObject *)v; sobj->own = SWIG_POINTER_OWN; return SWIG_Py_Void(); } SWIGINTERN PyObject* PySwigObject_own(PyObject *v, PyObject *args) { PyObject *val = 0; #if (PY_VERSION_HEX < 0x02020000) if (!PyArg_ParseTuple(args,(char *)"|O:own",&val)) #else if (!PyArg_UnpackTuple(args, (char *)"own", 0, 1, &val)) #endif { return NULL; } else { PySwigObject *sobj = (PySwigObject *)v; PyObject *obj = PyBool_FromLong(sobj->own); if (val) { #ifdef METH_NOARGS if (PyObject_IsTrue(val)) { PySwigObject_acquire(v); } else { PySwigObject_disown(v); } #else if (PyObject_IsTrue(val)) { PySwigObject_acquire(v,args); } else { PySwigObject_disown(v,args); } #endif } return obj; } } #ifdef METH_O static PyMethodDef swigobject_methods[] = { {(char *)"disown", (PyCFunction)PySwigObject_disown, METH_NOARGS, (char *)"releases ownership of the pointer"}, {(char *)"acquire", (PyCFunction)PySwigObject_acquire, METH_NOARGS, (char *)"aquires ownership of the pointer"}, {(char *)"own", (PyCFunction)PySwigObject_own, METH_VARARGS, (char *)"returns/sets ownership of the pointer"}, {(char *)"append", (PyCFunction)PySwigObject_append, METH_O, (char *)"appends another 'this' object"}, {(char *)"next", (PyCFunction)PySwigObject_next, METH_NOARGS, (char *)"returns the next 'this' object"}, {(char *)"__repr__",(PyCFunction)PySwigObject_repr, METH_NOARGS, (char *)"returns object representation"}, {0, 0, 0, 0} }; #else static PyMethodDef swigobject_methods[] = { {(char *)"disown", (PyCFunction)PySwigObject_disown, METH_VARARGS, (char *)"releases ownership of the pointer"}, {(char *)"acquire", (PyCFunction)PySwigObject_acquire, METH_VARARGS, (char *)"aquires ownership of the pointer"}, {(char *)"own", (PyCFunction)PySwigObject_own, METH_VARARGS, (char *)"returns/sets ownership of the pointer"}, {(char *)"append", (PyCFunction)PySwigObject_append, METH_VARARGS, (char *)"appends another 'this' object"}, {(char *)"next", (PyCFunction)PySwigObject_next, METH_VARARGS, (char *)"returns the next 'this' object"}, {(char *)"__repr__",(PyCFunction)PySwigObject_repr, METH_VARARGS, (char *)"returns object representation"}, {0, 0, 0, 0} }; #endif #if PY_VERSION_HEX < 0x02020000 SWIGINTERN PyObject * PySwigObject_getattr(PySwigObject *sobj,char *name) { return Py_FindMethod(swigobject_methods, (PyObject *)sobj, name); } #endif SWIGRUNTIME PyTypeObject* _PySwigObject_type(void) { static char swigobject_doc[] = "Swig object carries a C/C++ instance pointer"; static PyNumberMethods PySwigObject_as_number = { (binaryfunc)0, /*nb_add*/ (binaryfunc)0, /*nb_subtract*/ (binaryfunc)0, /*nb_multiply*/ (binaryfunc)0, /*nb_divide*/ (binaryfunc)0, /*nb_remainder*/ (binaryfunc)0, /*nb_divmod*/ (ternaryfunc)0,/*nb_power*/ (unaryfunc)0, /*nb_negative*/ (unaryfunc)0, /*nb_positive*/ (unaryfunc)0, /*nb_absolute*/ (inquiry)0, /*nb_nonzero*/ 0, /*nb_invert*/ 0, /*nb_lshift*/ 0, /*nb_rshift*/ 0, /*nb_and*/ 0, /*nb_xor*/ 0, /*nb_or*/ (coercion)0, /*nb_coerce*/ (unaryfunc)PySwigObject_long, /*nb_int*/ (unaryfunc)PySwigObject_long, /*nb_long*/ (unaryfunc)0, /*nb_float*/ (unaryfunc)PySwigObject_oct, /*nb_oct*/ (unaryfunc)PySwigObject_hex, /*nb_hex*/ #if PY_VERSION_HEX >= 0x02050000 /* 2.5.0 */ 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 /* nb_inplace_add -> nb_index */ #elif PY_VERSION_HEX >= 0x02020000 /* 2.2.0 */ 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 /* nb_inplace_add -> nb_inplace_true_divide */ #elif PY_VERSION_HEX >= 0x02000000 /* 2.0.0 */ 0,0,0,0,0,0,0,0,0,0,0 /* nb_inplace_add -> nb_inplace_or */ #endif }; static PyTypeObject pyswigobject_type; static int type_init = 0; if (!type_init) { const PyTypeObject tmp = { PyObject_HEAD_INIT(NULL) 0, /* ob_size */ (char *)"PySwigObject", /* tp_name */ sizeof(PySwigObject), /* tp_basicsize */ 0, /* tp_itemsize */ (destructor)PySwigObject_dealloc, /* tp_dealloc */ (printfunc)PySwigObject_print, /* tp_print */ #if PY_VERSION_HEX < 0x02020000 (getattrfunc)PySwigObject_getattr, /* tp_getattr */ #else (getattrfunc)0, /* tp_getattr */ #endif (setattrfunc)0, /* tp_setattr */ (cmpfunc)PySwigObject_compare, /* tp_compare */ (reprfunc)PySwigObject_repr, /* tp_repr */ &PySwigObject_as_number, /* tp_as_number */ 0, /* tp_as_sequence */ 0, /* tp_as_mapping */ (hashfunc)0, /* tp_hash */ (ternaryfunc)0, /* tp_call */ (reprfunc)PySwigObject_str, /* tp_str */ PyObject_GenericGetAttr, /* tp_getattro */ 0, /* tp_setattro */ 0, /* tp_as_buffer */ Py_TPFLAGS_DEFAULT, /* tp_flags */ swigobject_doc, /* tp_doc */ 0, /* tp_traverse */ 0, /* tp_clear */ 0, /* tp_richcompare */ 0, /* tp_weaklistoffset */ #if PY_VERSION_HEX >= 0x02020000 0, /* tp_iter */ 0, /* tp_iternext */ swigobject_methods, /* tp_methods */ 0, /* tp_members */ 0, /* tp_getset */ 0, /* tp_base */ 0, /* tp_dict */ 0, /* tp_descr_get */ 0, /* tp_descr_set */ 0, /* tp_dictoffset */ 0, /* tp_init */ 0, /* tp_alloc */ 0, /* tp_new */ 0, /* tp_free */ 0, /* tp_is_gc */ 0, /* tp_bases */ 0, /* tp_mro */ 0, /* tp_cache */ 0, /* tp_subclasses */ 0, /* tp_weaklist */ #endif #if PY_VERSION_HEX >= 0x02030000 0, /* tp_del */ #endif #ifdef COUNT_ALLOCS 0,0,0,0 /* tp_alloc -> tp_next */ #endif }; pyswigobject_type = tmp; pyswigobject_type.ob_type = &PyType_Type; type_init = 1; } return &pyswigobject_type; } SWIGRUNTIME PyObject * PySwigObject_New(void *ptr, swig_type_info *ty, int own) { PySwigObject *sobj = PyObject_NEW(PySwigObject, PySwigObject_type()); if (sobj) { sobj->ptr = ptr; sobj->ty = ty; sobj->own = own; sobj->next = 0; } return (PyObject *)sobj; } /* ----------------------------------------------------------------------------- * Implements a simple Swig Packed type, and use it instead of string * ----------------------------------------------------------------------------- */ typedef struct { PyObject_HEAD void *pack; swig_type_info *ty; size_t size; } PySwigPacked; SWIGRUNTIME int PySwigPacked_print(PySwigPacked *v, FILE *fp, int SWIGUNUSEDPARM(flags)) { char result[SWIG_BUFFER_SIZE]; fputs("pack, v->size, 0, sizeof(result))) { fputs("at ", fp); fputs(result, fp); } fputs(v->ty->name,fp); fputs(">", fp); return 0; } SWIGRUNTIME PyObject * PySwigPacked_repr(PySwigPacked *v) { char result[SWIG_BUFFER_SIZE]; if (SWIG_PackDataName(result, v->pack, v->size, 0, sizeof(result))) { return PyString_FromFormat("", result, v->ty->name); } else { return PyString_FromFormat("", v->ty->name); } } SWIGRUNTIME PyObject * PySwigPacked_str(PySwigPacked *v) { char result[SWIG_BUFFER_SIZE]; if (SWIG_PackDataName(result, v->pack, v->size, 0, sizeof(result))){ return PyString_FromFormat("%s%s", result, v->ty->name); } else { return PyString_FromString(v->ty->name); } } SWIGRUNTIME int PySwigPacked_compare(PySwigPacked *v, PySwigPacked *w) { size_t i = v->size; size_t j = w->size; int s = (i < j) ? -1 : ((i > j) ? 1 : 0); return s ? s : strncmp((char *)v->pack, (char *)w->pack, 2*v->size); } SWIGRUNTIME PyTypeObject* _PySwigPacked_type(void); SWIGRUNTIME PyTypeObject* PySwigPacked_type(void) { static PyTypeObject *SWIG_STATIC_POINTER(type) = _PySwigPacked_type(); return type; } SWIGRUNTIMEINLINE int PySwigPacked_Check(PyObject *op) { return ((op)->ob_type == _PySwigPacked_type()) || (strcmp((op)->ob_type->tp_name,"PySwigPacked") == 0); } SWIGRUNTIME void PySwigPacked_dealloc(PyObject *v) { if (PySwigPacked_Check(v)) { PySwigPacked *sobj = (PySwigPacked *) v; free(sobj->pack); } PyObject_DEL(v); } SWIGRUNTIME PyTypeObject* _PySwigPacked_type(void) { static char swigpacked_doc[] = "Swig object carries a C/C++ instance pointer"; static PyTypeObject pyswigpacked_type; static int type_init = 0; if (!type_init) { const PyTypeObject tmp = { PyObject_HEAD_INIT(NULL) 0, /* ob_size */ (char *)"PySwigPacked", /* tp_name */ sizeof(PySwigPacked), /* tp_basicsize */ 0, /* tp_itemsize */ (destructor)PySwigPacked_dealloc, /* tp_dealloc */ (printfunc)PySwigPacked_print, /* tp_print */ (getattrfunc)0, /* tp_getattr */ (setattrfunc)0, /* tp_setattr */ (cmpfunc)PySwigPacked_compare, /* tp_compare */ (reprfunc)PySwigPacked_repr, /* tp_repr */ 0, /* tp_as_number */ 0, /* tp_as_sequence */ 0, /* tp_as_mapping */ (hashfunc)0, /* tp_hash */ (ternaryfunc)0, /* tp_call */ (reprfunc)PySwigPacked_str, /* tp_str */ PyObject_GenericGetAttr, /* tp_getattro */ 0, /* tp_setattro */ 0, /* tp_as_buffer */ Py_TPFLAGS_DEFAULT, /* tp_flags */ swigpacked_doc, /* tp_doc */ 0, /* tp_traverse */ 0, /* tp_clear */ 0, /* tp_richcompare */ 0, /* tp_weaklistoffset */ #if PY_VERSION_HEX >= 0x02020000 0, /* tp_iter */ 0, /* tp_iternext */ 0, /* tp_methods */ 0, /* tp_members */ 0, /* tp_getset */ 0, /* tp_base */ 0, /* tp_dict */ 0, /* tp_descr_get */ 0, /* tp_descr_set */ 0, /* tp_dictoffset */ 0, /* tp_init */ 0, /* tp_alloc */ 0, /* tp_new */ 0, /* tp_free */ 0, /* tp_is_gc */ 0, /* tp_bases */ 0, /* tp_mro */ 0, /* tp_cache */ 0, /* tp_subclasses */ 0, /* tp_weaklist */ #endif #if PY_VERSION_HEX >= 0x02030000 0, /* tp_del */ #endif #ifdef COUNT_ALLOCS 0,0,0,0 /* tp_alloc -> tp_next */ #endif }; pyswigpacked_type = tmp; pyswigpacked_type.ob_type = &PyType_Type; type_init = 1; } return &pyswigpacked_type; } SWIGRUNTIME PyObject * PySwigPacked_New(void *ptr, size_t size, swig_type_info *ty) { PySwigPacked *sobj = PyObject_NEW(PySwigPacked, PySwigPacked_type()); if (sobj) { void *pack = malloc(size); if (pack) { memcpy(pack, ptr, size); sobj->pack = pack; sobj->ty = ty; sobj->size = size; } else { PyObject_DEL((PyObject *) sobj); sobj = 0; } } return (PyObject *) sobj; } SWIGRUNTIME swig_type_info * PySwigPacked_UnpackData(PyObject *obj, void *ptr, size_t size) { if (PySwigPacked_Check(obj)) { PySwigPacked *sobj = (PySwigPacked *)obj; if (sobj->size != size) return 0; memcpy(ptr, sobj->pack, size); return sobj->ty; } else { return 0; } } /* ----------------------------------------------------------------------------- * pointers/data manipulation * ----------------------------------------------------------------------------- */ SWIGRUNTIMEINLINE PyObject * _SWIG_This(void) { return PyString_FromString("this"); } SWIGRUNTIME PyObject * SWIG_This(void) { static PyObject *SWIG_STATIC_POINTER(swig_this) = _SWIG_This(); return swig_this; } /* #define SWIG_PYTHON_SLOW_GETSET_THIS */ SWIGRUNTIME PySwigObject * SWIG_Python_GetSwigThis(PyObject *pyobj) { if (PySwigObject_Check(pyobj)) { return (PySwigObject *) pyobj; } else { PyObject *obj = 0; #if (!defined(SWIG_PYTHON_SLOW_GETSET_THIS) && (PY_VERSION_HEX >= 0x02030000)) if (PyInstance_Check(pyobj)) { obj = _PyInstance_Lookup(pyobj, SWIG_This()); } else { PyObject **dictptr = _PyObject_GetDictPtr(pyobj); if (dictptr != NULL) { PyObject *dict = *dictptr; obj = dict ? PyDict_GetItem(dict, SWIG_This()) : 0; } else { #ifdef PyWeakref_CheckProxy if (PyWeakref_CheckProxy(pyobj)) { PyObject *wobj = PyWeakref_GET_OBJECT(pyobj); return wobj ? SWIG_Python_GetSwigThis(wobj) : 0; } #endif obj = PyObject_GetAttr(pyobj,SWIG_This()); if (obj) { Py_DECREF(obj); } else { if (PyErr_Occurred()) PyErr_Clear(); return 0; } } } #else obj = PyObject_GetAttr(pyobj,SWIG_This()); if (obj) { Py_DECREF(obj); } else { if (PyErr_Occurred()) PyErr_Clear(); return 0; } #endif if (obj && !PySwigObject_Check(obj)) { /* a PyObject is called 'this', try to get the 'real this' PySwigObject from it */ return SWIG_Python_GetSwigThis(obj); } return (PySwigObject *)obj; } } /* Acquire a pointer value */ SWIGRUNTIME int SWIG_Python_AcquirePtr(PyObject *obj, int own) { if (own) { PySwigObject *sobj = SWIG_Python_GetSwigThis(obj); if (sobj) { int oldown = sobj->own; sobj->own = own; return oldown; } } return 0; } /* Convert a pointer value */ SWIGRUNTIME int SWIG_Python_ConvertPtrAndOwn(PyObject *obj, void **ptr, swig_type_info *ty, int flags, int *own) { if (!obj) return SWIG_ERROR; if (obj == Py_None) { if (ptr) *ptr = 0; return SWIG_OK; } else { PySwigObject *sobj = SWIG_Python_GetSwigThis(obj); while (sobj) { void *vptr = sobj->ptr; if (ty) { swig_type_info *to = sobj->ty; if (to == ty) { /* no type cast needed */ if (ptr) *ptr = vptr; break; } else { swig_cast_info *tc = SWIG_TypeCheck(to->name,ty); if (!tc) { sobj = (PySwigObject *)sobj->next; } else { if (ptr) *ptr = SWIG_TypeCast(tc,vptr); break; } } } else { if (ptr) *ptr = vptr; break; } } if (sobj) { if (own) *own = sobj->own; if (flags & SWIG_POINTER_DISOWN) { sobj->own = 0; } return SWIG_OK; } else { int res = SWIG_ERROR; if (flags & SWIG_POINTER_IMPLICIT_CONV) { PySwigClientData *data = ty ? (PySwigClientData *) ty->clientdata : 0; if (data && !data->implicitconv) { PyObject *klass = data->klass; if (klass) { PyObject *impconv; data->implicitconv = 1; /* avoid recursion and call 'explicit' constructors*/ impconv = SWIG_Python_CallFunctor(klass, obj); data->implicitconv = 0; if (PyErr_Occurred()) { PyErr_Clear(); impconv = 0; } if (impconv) { PySwigObject *iobj = SWIG_Python_GetSwigThis(impconv); if (iobj) { void *vptr; res = SWIG_Python_ConvertPtrAndOwn((PyObject*)iobj, &vptr, ty, 0, 0); if (SWIG_IsOK(res)) { if (ptr) { *ptr = vptr; /* transfer the ownership to 'ptr' */ iobj->own = 0; res = SWIG_AddCast(res); res = SWIG_AddNewMask(res); } else { res = SWIG_AddCast(res); } } } Py_DECREF(impconv); } } } } return res; } } } /* Convert a function ptr value */ SWIGRUNTIME int SWIG_Python_ConvertFunctionPtr(PyObject *obj, void **ptr, swig_type_info *ty) { if (!PyCFunction_Check(obj)) { return SWIG_ConvertPtr(obj, ptr, ty, 0); } else { void *vptr = 0; /* here we get the method pointer for callbacks */ const char *doc = (((PyCFunctionObject *)obj) -> m_ml -> ml_doc); const char *desc = doc ? strstr(doc, "swig_ptr: ") : 0; if (desc) { desc = ty ? SWIG_UnpackVoidPtr(desc + 10, &vptr, ty->name) : 0; if (!desc) return SWIG_ERROR; } if (ty) { swig_cast_info *tc = SWIG_TypeCheck(desc,ty); if (!tc) return SWIG_ERROR; *ptr = SWIG_TypeCast(tc,vptr); } else { *ptr = vptr; } return SWIG_OK; } } /* Convert a packed value value */ SWIGRUNTIME int SWIG_Python_ConvertPacked(PyObject *obj, void *ptr, size_t sz, swig_type_info *ty) { swig_type_info *to = PySwigPacked_UnpackData(obj, ptr, sz); if (!to) return SWIG_ERROR; if (ty) { if (to != ty) { /* check type cast? */ swig_cast_info *tc = SWIG_TypeCheck(to->name,ty); if (!tc) return SWIG_ERROR; } } return SWIG_OK; } /* ----------------------------------------------------------------------------- * Create a new pointer object * ----------------------------------------------------------------------------- */ /* Create a new instance object, whitout calling __init__, and set the 'this' attribute. */ SWIGRUNTIME PyObject* SWIG_Python_NewShadowInstance(PySwigClientData *data, PyObject *swig_this) { #if (PY_VERSION_HEX >= 0x02020000) PyObject *inst = 0; PyObject *newraw = data->newraw; if (newraw) { inst = PyObject_Call(newraw, data->newargs, NULL); if (inst) { #if !defined(SWIG_PYTHON_SLOW_GETSET_THIS) PyObject **dictptr = _PyObject_GetDictPtr(inst); if (dictptr != NULL) { PyObject *dict = *dictptr; if (dict == NULL) { dict = PyDict_New(); *dictptr = dict; PyDict_SetItem(dict, SWIG_This(), swig_this); } } #else PyObject *key = SWIG_This(); PyObject_SetAttr(inst, key, swig_this); #endif } } else { PyObject *dict = PyDict_New(); PyDict_SetItem(dict, SWIG_This(), swig_this); inst = PyInstance_NewRaw(data->newargs, dict); Py_DECREF(dict); } return inst; #else #if (PY_VERSION_HEX >= 0x02010000) PyObject *inst; PyObject *dict = PyDict_New(); PyDict_SetItem(dict, SWIG_This(), swig_this); inst = PyInstance_NewRaw(data->newargs, dict); Py_DECREF(dict); return (PyObject *) inst; #else PyInstanceObject *inst = PyObject_NEW(PyInstanceObject, &PyInstance_Type); if (inst == NULL) { return NULL; } inst->in_class = (PyClassObject *)data->newargs; Py_INCREF(inst->in_class); inst->in_dict = PyDict_New(); if (inst->in_dict == NULL) { Py_DECREF(inst); return NULL; } #ifdef Py_TPFLAGS_HAVE_WEAKREFS inst->in_weakreflist = NULL; #endif #ifdef Py_TPFLAGS_GC PyObject_GC_Init(inst); #endif PyDict_SetItem(inst->in_dict, SWIG_This(), swig_this); return (PyObject *) inst; #endif #endif } SWIGRUNTIME void SWIG_Python_SetSwigThis(PyObject *inst, PyObject *swig_this) { PyObject *dict; #if (PY_VERSION_HEX >= 0x02020000) && !defined(SWIG_PYTHON_SLOW_GETSET_THIS) PyObject **dictptr = _PyObject_GetDictPtr(inst); if (dictptr != NULL) { dict = *dictptr; if (dict == NULL) { dict = PyDict_New(); *dictptr = dict; } PyDict_SetItem(dict, SWIG_This(), swig_this); return; } #endif dict = PyObject_GetAttrString(inst, (char*)"__dict__"); PyDict_SetItem(dict, SWIG_This(), swig_this); Py_DECREF(dict); } SWIGINTERN PyObject * SWIG_Python_InitShadowInstance(PyObject *args) { PyObject *obj[2]; if (!SWIG_Python_UnpackTuple(args,(char*)"swiginit", 2, 2, obj)) { return NULL; } else { PySwigObject *sthis = SWIG_Python_GetSwigThis(obj[0]); if (sthis) { PySwigObject_append((PyObject*) sthis, obj[1]); } else { SWIG_Python_SetSwigThis(obj[0], obj[1]); } return SWIG_Py_Void(); } } /* Create a new pointer object */ SWIGRUNTIME PyObject * SWIG_Python_NewPointerObj(void *ptr, swig_type_info *type, int flags) { if (!ptr) { return SWIG_Py_Void(); } else { int own = (flags & SWIG_POINTER_OWN) ? SWIG_POINTER_OWN : 0; PyObject *robj = PySwigObject_New(ptr, type, own); PySwigClientData *clientdata = type ? (PySwigClientData *)(type->clientdata) : 0; if (clientdata && !(flags & SWIG_POINTER_NOSHADOW)) { PyObject *inst = SWIG_Python_NewShadowInstance(clientdata, robj); if (inst) { Py_DECREF(robj); robj = inst; } } return robj; } } /* Create a new packed object */ SWIGRUNTIMEINLINE PyObject * SWIG_Python_NewPackedObj(void *ptr, size_t sz, swig_type_info *type) { return ptr ? PySwigPacked_New((void *) ptr, sz, type) : SWIG_Py_Void(); } /* -----------------------------------------------------------------------------* * Get type list * -----------------------------------------------------------------------------*/ #ifdef SWIG_LINK_RUNTIME void *SWIG_ReturnGlobalTypeList(void *); #endif SWIGRUNTIME swig_module_info * SWIG_Python_GetModule(void) { static void *type_pointer = (void *)0; /* first check if module already created */ if (!type_pointer) { #ifdef SWIG_LINK_RUNTIME type_pointer = SWIG_ReturnGlobalTypeList((void *)0); #else type_pointer = PyCObject_Import((char*)"swig_runtime_data" SWIG_RUNTIME_VERSION, (char*)"type_pointer" SWIG_TYPE_TABLE_NAME); if (PyErr_Occurred()) { PyErr_Clear(); type_pointer = (void *)0; } #endif } return (swig_module_info *) type_pointer; } #if PY_MAJOR_VERSION < 2 /* PyModule_AddObject function was introduced in Python 2.0. The following function is copied out of Python/modsupport.c in python version 2.3.4 */ SWIGINTERN int PyModule_AddObject(PyObject *m, char *name, PyObject *o) { PyObject *dict; if (!PyModule_Check(m)) { PyErr_SetString(PyExc_TypeError, "PyModule_AddObject() needs module as first arg"); return SWIG_ERROR; } if (!o) { PyErr_SetString(PyExc_TypeError, "PyModule_AddObject() needs non-NULL value"); return SWIG_ERROR; } dict = PyModule_GetDict(m); if (dict == NULL) { /* Internal error -- modules must have a dict! */ PyErr_Format(PyExc_SystemError, "module '%s' has no __dict__", PyModule_GetName(m)); return SWIG_ERROR; } if (PyDict_SetItemString(dict, name, o)) return SWIG_ERROR; Py_DECREF(o); return SWIG_OK; } #endif SWIGRUNTIME void SWIG_Python_DestroyModule(void *vptr) { swig_module_info *swig_module = (swig_module_info *) vptr; swig_type_info **types = swig_module->types; size_t i; for (i =0; i < swig_module->size; ++i) { swig_type_info *ty = types[i]; if (ty->owndata) { PySwigClientData *data = (PySwigClientData *) ty->clientdata; if (data) PySwigClientData_Del(data); } } Py_DECREF(SWIG_This()); } SWIGRUNTIME void SWIG_Python_SetModule(swig_module_info *swig_module) { static PyMethodDef swig_empty_runtime_method_table[] = { {NULL, NULL, 0, NULL} };/* Sentinel */ PyObject *module = Py_InitModule((char*)"swig_runtime_data" SWIG_RUNTIME_VERSION, swig_empty_runtime_method_table); PyObject *pointer = PyCObject_FromVoidPtr((void *) swig_module, SWIG_Python_DestroyModule); if (pointer && module) { PyModule_AddObject(module, (char*)"type_pointer" SWIG_TYPE_TABLE_NAME, pointer); } else { Py_XDECREF(pointer); } } /* The python cached type query */ SWIGRUNTIME PyObject * SWIG_Python_TypeCache(void) { static PyObject *SWIG_STATIC_POINTER(cache) = PyDict_New(); return cache; } SWIGRUNTIME swig_type_info * SWIG_Python_TypeQuery(const char *type) { PyObject *cache = SWIG_Python_TypeCache(); PyObject *key = PyString_FromString(type); PyObject *obj = PyDict_GetItem(cache, key); swig_type_info *descriptor; if (obj) { descriptor = (swig_type_info *) PyCObject_AsVoidPtr(obj); } else { swig_module_info *swig_module = SWIG_Python_GetModule(); descriptor = SWIG_TypeQueryModule(swig_module, swig_module, type); if (descriptor) { obj = PyCObject_FromVoidPtr(descriptor, NULL); PyDict_SetItem(cache, key, obj); Py_DECREF(obj); } } Py_DECREF(key); return descriptor; } /* For backward compatibility only */ #define SWIG_POINTER_EXCEPTION 0 #define SWIG_arg_fail(arg) SWIG_Python_ArgFail(arg) #define SWIG_MustGetPtr(p, type, argnum, flags) SWIG_Python_MustGetPtr(p, type, argnum, flags) SWIGRUNTIME int SWIG_Python_AddErrMesg(const char* mesg, int infront) { if (PyErr_Occurred()) { PyObject *type = 0; PyObject *value = 0; PyObject *traceback = 0; PyErr_Fetch(&type, &value, &traceback); if (value) { PyObject *old_str = PyObject_Str(value); Py_XINCREF(type); PyErr_Clear(); if (infront) { PyErr_Format(type, "%s %s", mesg, PyString_AsString(old_str)); } else { PyErr_Format(type, "%s %s", PyString_AsString(old_str), mesg); } Py_DECREF(old_str); } return 1; } else { return 0; } } SWIGRUNTIME int SWIG_Python_ArgFail(int argnum) { if (PyErr_Occurred()) { /* add information about failing argument */ char mesg[256]; PyOS_snprintf(mesg, sizeof(mesg), "argument number %d:", argnum); return SWIG_Python_AddErrMesg(mesg, 1); } else { return 0; } } SWIGRUNTIMEINLINE const char * PySwigObject_GetDesc(PyObject *self) { PySwigObject *v = (PySwigObject *)self; swig_type_info *ty = v ? v->ty : 0; return ty ? ty->str : (char*)""; } SWIGRUNTIME void SWIG_Python_TypeError(const char *type, PyObject *obj) { if (type) { #if defined(SWIG_COBJECT_TYPES) if (obj && PySwigObject_Check(obj)) { const char *otype = (const char *) PySwigObject_GetDesc(obj); if (otype) { PyErr_Format(PyExc_TypeError, "a '%s' is expected, 'PySwigObject(%s)' is received", type, otype); return; } } else #endif { const char *otype = (obj ? obj->ob_type->tp_name : 0); if (otype) { PyObject *str = PyObject_Str(obj); const char *cstr = str ? PyString_AsString(str) : 0; if (cstr) { PyErr_Format(PyExc_TypeError, "a '%s' is expected, '%s(%s)' is received", type, otype, cstr); } else { PyErr_Format(PyExc_TypeError, "a '%s' is expected, '%s' is received", type, otype); } Py_XDECREF(str); return; } } PyErr_Format(PyExc_TypeError, "a '%s' is expected", type); } else { PyErr_Format(PyExc_TypeError, "unexpected type is received"); } } /* Convert a pointer value, signal an exception on a type mismatch */ SWIGRUNTIME void * SWIG_Python_MustGetPtr(PyObject *obj, swig_type_info *ty, int argnum, int flags) { void *result; if (SWIG_Python_ConvertPtr(obj, &result, ty, flags) == -1) { PyErr_Clear(); if (flags & SWIG_POINTER_EXCEPTION) { SWIG_Python_TypeError(SWIG_TypePrettyName(ty), obj); SWIG_Python_ArgFail(argnum); } } return result; } #ifdef __cplusplus #if 0 { /* cc-mode */ #endif } #endif #define SWIG_exception_fail(code, msg) do { SWIG_Error(code, msg); SWIG_fail; } while(0) #define SWIG_contract_assert(expr, msg) if (!(expr)) { SWIG_Error(SWIG_RuntimeError, msg); SWIG_fail; } else #define SWIG_exception(code, msg) do { SWIG_Error(code, msg); SWIG_fail;; } while(0) /* -------- TYPES TABLE (BEGIN) -------- */ #define SWIGTYPE_p_CircularVector swig_types[0] #define SWIGTYPE_p_Connection swig_types[1] #define SWIGTYPE_p_ConnectionMatrix swig_types[2] #define SWIGTYPE_p_DenseConnectionMatrix swig_types[3] #define SWIGTYPE_p_LinearStateUpdater swig_types[4] #define SWIGTYPE_p_Network swig_types[5] #define SWIGTYPE_p_NetworkOperation swig_types[6] #define SWIGTYPE_p_NeuronGroup swig_types[7] #define SWIGTYPE_p_Refractoriness swig_types[8] #define SWIGTYPE_p_Reset swig_types[9] #define SWIGTYPE_p_ResetBase swig_types[10] #define SWIGTYPE_p_SpikeContainer swig_types[11] #define SWIGTYPE_p_StateMonitor swig_types[12] #define SWIGTYPE_p_StateUpdater swig_types[13] #define SWIGTYPE_p_Threshold swig_types[14] #define SWIGTYPE_p_allocator_type swig_types[15] #define SWIGTYPE_p_char swig_types[16] #define SWIGTYPE_p_difference_type swig_types[17] #define SWIGTYPE_p_double swig_types[18] #define SWIGTYPE_p_int swig_types[19] #define SWIGTYPE_p_p_PyObject swig_types[20] #define SWIGTYPE_p_p_int swig_types[21] #define SWIGTYPE_p_size_type swig_types[22] #define SWIGTYPE_p_std__invalid_argument swig_types[23] #define SWIGTYPE_p_std__listTConnection_p_std__allocatorTConnection_p_t_t swig_types[24] #define SWIGTYPE_p_std__listTNetworkOperation_p_std__allocatorTNetworkOperation_p_t_t swig_types[25] #define SWIGTYPE_p_std__listTNeuronGroup_p_std__allocatorTNeuronGroup_p_t_t swig_types[26] #define SWIGTYPE_p_std__listTint_std__allocatorTint_t_t swig_types[27] #define SWIGTYPE_p_std__listTint_std__allocatorTint_t_t__allocator_type swig_types[28] #define SWIGTYPE_p_std__vectorTdouble_std__allocatorTdouble_t_t swig_types[29] #define SWIGTYPE_p_std__vectorTdouble_std__allocatorTdouble_t_t__allocator_type swig_types[30] #define SWIGTYPE_p_std__vectorTstd__vectorTdouble_std__allocatorTdouble_t_t_std__allocatorTstd__vectorTdouble_std__allocatorTdouble_t_t_t_t swig_types[31] #define SWIGTYPE_p_swig__PySwigIterator swig_types[32] #define SWIGTYPE_p_value_type swig_types[33] static swig_type_info *swig_types[35]; static swig_module_info swig_module = {swig_types, 34, 0, 0, 0, 0}; #define SWIG_TypeQuery(name) SWIG_TypeQueryModule(&swig_module, &swig_module, name) #define SWIG_MangledTypeQuery(name) SWIG_MangledTypeQueryModule(&swig_module, &swig_module, name) /* -------- TYPES TABLE (END) -------- */ #if (PY_VERSION_HEX <= 0x02000000) # if !defined(SWIG_PYTHON_CLASSIC) # error "This python version requires swig to be run with the '-classic' option" # endif #endif /*----------------------------------------------- @(target):= _brianlib.so ------------------------------------------------*/ #define SWIG_init init_brianlib #define SWIG_name "_brianlib" #define SWIGVERSION 0x010333 #define SWIG_VERSION SWIGVERSION #define SWIG_as_voidptr(a) const_cast< void * >(static_cast< const void * >(a)) #define SWIG_as_voidptrptr(a) ((void)SWIG_as_voidptr(*a),reinterpret_cast< void** >(a)) #include namespace swig { class PyObject_ptr { protected: PyObject *_obj; public: PyObject_ptr() :_obj(0) { } PyObject_ptr(const PyObject_ptr& item) : _obj(item._obj) { Py_XINCREF(_obj); } PyObject_ptr(PyObject *obj, bool initial_ref = true) :_obj(obj) { if (initial_ref) Py_XINCREF(_obj); } PyObject_ptr & operator=(const PyObject_ptr& item) { Py_XINCREF(item._obj); Py_XDECREF(_obj); _obj = item._obj; return *this; } ~PyObject_ptr() { Py_XDECREF(_obj); } operator PyObject *() const { return _obj; } PyObject *operator->() const { return _obj; } }; } namespace swig { struct PyObject_var : PyObject_ptr { PyObject_var(PyObject* obj = 0) : PyObject_ptr(obj, false) { } PyObject_var & operator = (PyObject* obj) { Py_XDECREF(_obj); _obj = obj; return *this; } }; } #include #include #include #if defined(__SUNPRO_CC) && defined(_RWSTD_VER) # define SWIG_STD_NOASSIGN_STL # define SWIG_STD_NOINSERT_TEMPLATE_STL # define SWIG_STD_NOITERATOR_TRAITS_STL #endif #if defined(__GNUC__) # if __GNUC__ == 2 && __GNUC_MINOR <= 96 # define SWIG_STD_NOMODERN_STL # endif #endif #include #include namespace swig { struct stop_iteration { }; struct PySwigIterator { private: PyObject_ptr _seq; protected: PySwigIterator(PyObject *seq) : _seq(seq) { } public: virtual ~PySwigIterator() {} // Access iterator method, required by Python virtual PyObject *value() const = 0; // Forward iterator method, required by Python virtual PySwigIterator *incr(size_t n = 1) = 0; // Backward iterator method, very common in C++, but not required in Python virtual PySwigIterator *decr(size_t n = 1) { throw stop_iteration(); } // Random access iterator methods, but not required in Python virtual ptrdiff_t distance(const PySwigIterator &x) const { throw std::invalid_argument("operation not supported"); } virtual bool equal (const PySwigIterator &x) const { throw std::invalid_argument("operation not supported"); } // C++ common/needed methods virtual PySwigIterator *copy() const = 0; PyObject *next() { PyObject *obj = value(); incr(); return obj; } PyObject *previous() { decr(); return value(); } PySwigIterator *advance(ptrdiff_t n) { return (n > 0) ? incr(n) : decr(-n); } bool operator == (const PySwigIterator& x) const { return equal(x); } bool operator != (const PySwigIterator& x) const { return ! operator==(x); } PySwigIterator& operator += (ptrdiff_t n) { return *advance(n); } PySwigIterator& operator -= (ptrdiff_t n) { return *advance(-n); } PySwigIterator* operator + (ptrdiff_t n) const { return copy()->advance(n); } PySwigIterator* operator - (ptrdiff_t n) const { return copy()->advance(-n); } ptrdiff_t operator - (const PySwigIterator& x) const { return x.distance(*this); } static swig_type_info* descriptor() { static int init = 0; static swig_type_info* desc = 0; if (!init) { desc = SWIG_TypeQuery("swig::PySwigIterator *"); init = 1; } return desc; } }; } SWIGINTERN int SWIG_AsVal_double (PyObject *obj, double *val) { int res = SWIG_TypeError; if (PyFloat_Check(obj)) { if (val) *val = PyFloat_AsDouble(obj); return SWIG_OK; } else if (PyInt_Check(obj)) { if (val) *val = PyInt_AsLong(obj); return SWIG_OK; } else if (PyLong_Check(obj)) { double v = PyLong_AsDouble(obj); if (!PyErr_Occurred()) { if (val) *val = v; return SWIG_OK; } else { PyErr_Clear(); } } #ifdef SWIG_PYTHON_CAST_MODE { int dispatch = 0; double d = PyFloat_AsDouble(obj); if (!PyErr_Occurred()) { if (val) *val = d; return SWIG_AddCast(SWIG_OK); } else { PyErr_Clear(); } if (!dispatch) { long v = PyLong_AsLong(obj); if (!PyErr_Occurred()) { if (val) *val = v; return SWIG_AddCast(SWIG_AddCast(SWIG_OK)); } else { PyErr_Clear(); } } } #endif return res; } #include #include SWIGINTERNINLINE int SWIG_CanCastAsInteger(double *d, double min, double max) { double x = *d; if ((min <= x && x <= max)) { double fx = floor(x); double cx = ceil(x); double rd = ((x - fx) < 0.5) ? fx : cx; /* simple rint */ if ((errno == EDOM) || (errno == ERANGE)) { errno = 0; } else { double summ, reps, diff; if (rd < x) { diff = x - rd; } else if (rd > x) { diff = rd - x; } else { return 1; } summ = rd + x; reps = diff/summ; if (reps < 8*DBL_EPSILON) { *d = rd; return 1; } } } return 0; } SWIGINTERN int SWIG_AsVal_unsigned_SS_long (PyObject *obj, unsigned long *val) { if (PyInt_Check(obj)) { long v = PyInt_AsLong(obj); if (v >= 0) { if (val) *val = v; return SWIG_OK; } else { return SWIG_OverflowError; } } else if (PyLong_Check(obj)) { unsigned long v = PyLong_AsUnsignedLong(obj); if (!PyErr_Occurred()) { if (val) *val = v; return SWIG_OK; } else { PyErr_Clear(); } } #ifdef SWIG_PYTHON_CAST_MODE { int dispatch = 0; unsigned long v = PyLong_AsUnsignedLong(obj); if (!PyErr_Occurred()) { if (val) *val = v; return SWIG_AddCast(SWIG_OK); } else { PyErr_Clear(); } if (!dispatch) { double d; int res = SWIG_AddCast(SWIG_AsVal_double (obj,&d)); if (SWIG_IsOK(res) && SWIG_CanCastAsInteger(&d, 0, ULONG_MAX)) { if (val) *val = (unsigned long)(d); return res; } } } #endif return SWIG_TypeError; } SWIGINTERNINLINE int SWIG_AsVal_size_t (PyObject * obj, size_t *val) { unsigned long v; int res = SWIG_AsVal_unsigned_SS_long (obj, val ? &v : 0); if (SWIG_IsOK(res) && val) *val = static_cast< size_t >(v); return res; } #define SWIG_From_long PyInt_FromLong SWIGINTERNINLINE PyObject * SWIG_From_ptrdiff_t (ptrdiff_t value) { return SWIG_From_long (static_cast< long >(value)); } SWIGINTERNINLINE PyObject* SWIG_From_bool (bool value) { return PyBool_FromLong(value ? 1 : 0); } SWIGINTERN int SWIG_AsVal_long (PyObject *obj, long* val) { if (PyInt_Check(obj)) { if (val) *val = PyInt_AsLong(obj); return SWIG_OK; } else if (PyLong_Check(obj)) { long v = PyLong_AsLong(obj); if (!PyErr_Occurred()) { if (val) *val = v; return SWIG_OK; } else { PyErr_Clear(); } } #ifdef SWIG_PYTHON_CAST_MODE { int dispatch = 0; long v = PyInt_AsLong(obj); if (!PyErr_Occurred()) { if (val) *val = v; return SWIG_AddCast(SWIG_OK); } else { PyErr_Clear(); } if (!dispatch) { double d; int res = SWIG_AddCast(SWIG_AsVal_double (obj,&d)); if (SWIG_IsOK(res) && SWIG_CanCastAsInteger(&d, LONG_MIN, LONG_MAX)) { if (val) *val = (long)(d); return res; } } } #endif return SWIG_TypeError; } SWIGINTERNINLINE int SWIG_AsVal_ptrdiff_t (PyObject * obj, ptrdiff_t *val) { long v; int res = SWIG_AsVal_long (obj, val ? &v : 0); if (SWIG_IsOK(res) && val) *val = static_cast< ptrdiff_t >(v); return res; } #include #include #include #include #define SWIG_FILE_WITH_INIT #include "brianlib.h" namespace swig { template struct noconst_traits { typedef Type noconst_type; }; template struct noconst_traits { typedef Type noconst_type; }; /* type categories */ struct pointer_category { }; struct value_category { }; /* General traits that provides type_name and type_info */ template struct traits { }; template inline const char* type_name() { return traits::noconst_type >::type_name(); } template struct traits_info { static swig_type_info *type_query(std::string name) { name += " *"; return SWIG_TypeQuery(name.c_str()); } static swig_type_info *type_info() { static swig_type_info *info = type_query(type_name()); return info; } }; template inline swig_type_info *type_info() { return traits_info::type_info(); } /* Partial specialization for pointers */ template struct traits { typedef pointer_category category; static std::string make_ptr_name(const char* name) { std::string ptrname = name; ptrname += " *"; return ptrname; } static const char* type_name() { static std::string name = make_ptr_name(swig::type_name()); return name.c_str(); } }; template struct traits_as { }; template struct traits_check { }; } namespace swig { /* Traits that provides the from method */ template struct traits_from_ptr { static PyObject *from(Type *val, int owner = 0) { return SWIG_NewPointerObj(val, type_info(), owner); } }; template struct traits_from { static PyObject *from(const Type& val) { return traits_from_ptr::from(new Type(val), 1); } }; template struct traits_from { static PyObject *from(Type* val) { return traits_from_ptr::from(val, 0); } }; template struct traits_from { static PyObject *from(const Type* val) { return traits_from_ptr::from(const_cast(val), 0); } }; template inline PyObject *from(const Type& val) { return traits_from::from(val); } template inline PyObject *from_ptr(Type* val, int owner) { return traits_from_ptr::from(val, owner); } /* Traits that provides the asval/as/check method */ template struct traits_asptr { static int asptr(PyObject *obj, Type **val) { Type *p; int res = (SWIG_ConvertPtr(obj, (void**)&p, type_info(), 0) == SWIG_OK) ? SWIG_OLDOBJ : 0; if (SWIG_IsOK(res)) { if (val) *val = p; } return res; } }; template inline int asptr(PyObject *obj, Type **vptr) { return traits_asptr::asptr(obj, vptr); } template struct traits_asval { static int asval(PyObject *obj, Type *val) { if (val) { Type *p = 0; int res = traits_asptr::asptr(obj, &p); if (!SWIG_IsOK(res)) return res; if (p) { typedef typename noconst_traits::noconst_type noconst_type; *(const_cast(val)) = *p; if (SWIG_IsNewObj(res)){ delete p; res = SWIG_DelNewMask(res); } return res; } else { return SWIG_ERROR; } } else { return traits_asptr::asptr(obj, (Type **)(0)); } } }; template struct traits_asval { static int asval(PyObject *obj, Type **val) { if (val) { typedef typename noconst_traits::noconst_type noconst_type; noconst_type *p = 0; int res = traits_asptr::asptr(obj, &p); if (SWIG_IsOK(res)) { *(const_cast(val)) = p; } return res; } else { return traits_asptr::asptr(obj, (Type **)(0)); } } }; template inline int asval(PyObject *obj, Type *val) { return traits_asval::asval(obj, val); } template struct traits_as { static Type as(PyObject *obj, bool throw_error) { Type v; int res = asval(obj, &v); if (!obj || !SWIG_IsOK(res)) { if (!PyErr_Occurred()) { SWIG_Error(SWIG_TypeError, swig::type_name()); } if (throw_error) throw std::invalid_argument("bad type"); } return v; } }; template struct traits_as { static Type as(PyObject *obj, bool throw_error) { Type *v = 0; int res = (obj ? traits_asptr::asptr(obj, &v) : SWIG_ERROR); if (SWIG_IsOK(res) && v) { if (SWIG_IsNewObj(res)) { Type r(*v); delete v; return r; } else { return *v; } } else { // Uninitialized return value, no Type() constructor required. static Type *v_def = (Type*) malloc(sizeof(Type)); if (!PyErr_Occurred()) { SWIG_Error(SWIG_TypeError, swig::type_name()); } if (throw_error) throw std::invalid_argument("bad type"); memset(v_def,0,sizeof(Type)); return *v_def; } } }; template struct traits_as { static Type* as(PyObject *obj, bool throw_error) { Type *v = 0; int res = (obj ? traits_asptr::asptr(obj, &v) : SWIG_ERROR); if (SWIG_IsOK(res)) { return v; } else { if (!PyErr_Occurred()) { SWIG_Error(SWIG_TypeError, swig::type_name()); } if (throw_error) throw std::invalid_argument("bad type"); return 0; } } }; template inline Type as(PyObject *obj, bool te = false) { return traits_as::category>::as(obj, te); } template struct traits_check { static bool check(PyObject *obj) { int res = obj ? asval(obj, (Type *)(0)) : SWIG_ERROR; return SWIG_IsOK(res) ? true : false; } }; template struct traits_check { static bool check(PyObject *obj) { int res = obj ? asptr(obj, (Type **)(0)) : SWIG_ERROR; return SWIG_IsOK(res) ? true : false; } }; template inline bool check(PyObject *obj) { return traits_check::category>::check(obj); } } #include namespace std { template <> struct less : public binary_function { bool operator()(PyObject * v, PyObject *w) const { bool res; SWIG_PYTHON_THREAD_BEGIN_BLOCK; res = PyObject_Compare(v, w) < 0; SWIG_PYTHON_THREAD_END_BLOCK; return res; } }; template <> struct less : public binary_function { bool operator()(const swig::PyObject_ptr& v, const swig::PyObject_ptr& w) const { return std::less()(v, w); } }; template <> struct less : public binary_function { bool operator()(const swig::PyObject_var& v, const swig::PyObject_var& w) const { return std::less()(v, w); } }; } namespace swig { template <> struct traits { typedef value_category category; static const char* type_name() { return "PyObject *"; } }; template <> struct traits_asval { typedef PyObject * value_type; static int asval(PyObject *obj, value_type *val) { if (val) *val = obj; return SWIG_OK; } }; template <> struct traits_check { static bool check(PyObject *) { return true; } }; template <> struct traits_from { typedef PyObject * value_type; static PyObject *from(const value_type& val) { Py_XINCREF(val); return val; } }; } namespace swig { inline size_t check_index(ptrdiff_t i, size_t size, bool insert = false) { if ( i < 0 ) { if ((size_t) (-i) <= size) return (size_t) (i + size); } else if ( (size_t) i < size ) { return (size_t) i; } else if (insert && ((size_t) i == size)) { return size; } throw std::out_of_range("index out of range"); } inline size_t slice_index(ptrdiff_t i, size_t size) { if ( i < 0 ) { if ((size_t) (-i) <= size) { return (size_t) (i + size); } else { throw std::out_of_range("index out of range"); } } else { return ( (size_t) i < size ) ? ((size_t) i) : size; } } template inline typename Sequence::iterator getpos(Sequence* self, Difference i) { typename Sequence::iterator pos = self->begin(); std::advance(pos, check_index(i,self->size())); return pos; } template inline typename Sequence::const_iterator cgetpos(const Sequence* self, Difference i) { typename Sequence::const_iterator pos = self->begin(); std::advance(pos, check_index(i,self->size())); return pos; } template inline Sequence* getslice(const Sequence* self, Difference i, Difference j) { typename Sequence::size_type size = self->size(); typename Sequence::size_type ii = swig::check_index(i, size); typename Sequence::size_type jj = swig::slice_index(j, size); if (jj > ii) { typename Sequence::const_iterator vb = self->begin(); typename Sequence::const_iterator ve = self->begin(); std::advance(vb,ii); std::advance(ve,jj); return new Sequence(vb, ve); } else { return new Sequence(); } } template inline void setslice(Sequence* self, Difference i, Difference j, const InputSeq& v) { typename Sequence::size_type size = self->size(); typename Sequence::size_type ii = swig::check_index(i, size, true); typename Sequence::size_type jj = swig::slice_index(j, size); if (jj < ii) jj = ii; size_t ssize = jj - ii; if (ssize <= v.size()) { typename Sequence::iterator sb = self->begin(); typename InputSeq::const_iterator vmid = v.begin(); std::advance(sb,ii); std::advance(vmid, jj - ii); self->insert(std::copy(v.begin(), vmid, sb), vmid, v.end()); } else { typename Sequence::iterator sb = self->begin(); typename Sequence::iterator se = self->begin(); std::advance(sb,ii); std::advance(se,jj); self->erase(sb,se); self->insert(sb, v.begin(), v.end()); } } template inline void delslice(Sequence* self, Difference i, Difference j) { typename Sequence::size_type size = self->size(); typename Sequence::size_type ii = swig::check_index(i, size, true); typename Sequence::size_type jj = swig::slice_index(j, size); if (jj > ii) { typename Sequence::iterator sb = self->begin(); typename Sequence::iterator se = self->begin(); std::advance(sb,ii); std::advance(se,jj); self->erase(sb,se); } } } #if !defined(SWIG_STD_NOITERATOR_TRAITS_STL) #include #else namespace std { template struct iterator_traits { typedef ptrdiff_t difference_type; typedef typename Iterator::value_type value_type; }; #if defined(__SUNPRO_CC) && defined(_RWSTD_VER) template struct iterator_traits<__reverse_bi_iterator > { typedef Distance difference_type; typedef T value_type; }; #endif template struct iterator_traits { typedef T value_type; typedef ptrdiff_t difference_type; }; template inline typename iterator_traits<_InputIterator>::difference_type distance(_InputIterator __first, _InputIterator __last) { typename iterator_traits<_InputIterator>::difference_type __n = 0; while (__first != __last) { ++__first; ++__n; } return __n; } } #endif namespace swig { template class PySwigIterator_T : public PySwigIterator { public: typedef OutIterator out_iterator; typedef typename std::iterator_traits::value_type value_type; typedef PySwigIterator_T self_type; PySwigIterator_T(out_iterator curr, PyObject *seq) : PySwigIterator(seq), current(curr) { } const out_iterator& get_current() const { return current; } bool equal (const PySwigIterator &iter) const { const self_type *iters = dynamic_cast(&iter); if (iters) { return (current == iters->get_current()); } else { throw std::invalid_argument("bad iterator type"); } } ptrdiff_t distance(const PySwigIterator &iter) const { const self_type *iters = dynamic_cast(&iter); if (iters) { return std::distance(current, iters->get_current()); } else { throw std::invalid_argument("bad iterator type"); } } protected: out_iterator current; }; template struct from_oper { typedef const ValueType& argument_type; typedef PyObject *result_type; result_type operator()(argument_type v) const { return swig::from(v); } }; template::value_type, typename FromOper = from_oper > class PySwigIteratorOpen_T : public PySwigIterator_T { public: FromOper from; typedef OutIterator out_iterator; typedef ValueType value_type; typedef PySwigIterator_T base; typedef PySwigIteratorOpen_T self_type; PySwigIteratorOpen_T(out_iterator curr, PyObject *seq) : PySwigIterator_T(curr, seq) { } PyObject *value() const { return from(static_cast(*(base::current))); } PySwigIterator *copy() const { return new self_type(*this); } PySwigIterator *incr(size_t n = 1) { while (n--) { ++base::current; } return this; } PySwigIterator *decr(size_t n = 1) { while (n--) { --base::current; } return this; } }; template::value_type, typename FromOper = from_oper > class PySwigIteratorClosed_T : public PySwigIterator_T { public: FromOper from; typedef OutIterator out_iterator; typedef ValueType value_type; typedef PySwigIterator_T base; typedef PySwigIteratorClosed_T self_type; PySwigIteratorClosed_T(out_iterator curr, out_iterator first, out_iterator last, PyObject *seq) : PySwigIterator_T(curr, seq), begin(first), end(last) { } PyObject *value() const { if (base::current == end) { throw stop_iteration(); } else { return from(static_cast(*(base::current))); } } PySwigIterator *copy() const { return new self_type(*this); } PySwigIterator *incr(size_t n = 1) { while (n--) { if (base::current == end) { throw stop_iteration(); } else { ++base::current; } } return this; } PySwigIterator *decr(size_t n = 1) { while (n--) { if (base::current == begin) { throw stop_iteration(); } else { --base::current; } } return this; } private: out_iterator begin; out_iterator end; }; template inline PySwigIterator* make_output_iterator(const OutIter& current, const OutIter& begin,const OutIter& end, PyObject *seq = 0) { return new PySwigIteratorClosed_T(current, begin, end, seq); } template inline PySwigIterator* make_output_iterator(const OutIter& current, PyObject *seq = 0) { return new PySwigIteratorOpen_T(current, seq); } } namespace swig { template struct PySequence_Ref { PySequence_Ref(PyObject* seq, int index) : _seq(seq), _index(index) { } operator T () const { swig::PyObject_var item = PySequence_GetItem(_seq, _index); try { return swig::as(item, true); } catch (std::exception& e) { char msg[1024]; sprintf(msg, "in sequence element %d ", _index); if (!PyErr_Occurred()) { SWIG_Error(SWIG_TypeError, swig::type_name()); } SWIG_Python_AddErrorMsg(msg); SWIG_Python_AddErrorMsg(e.what()); throw; } } PySequence_Ref& operator=(const T& v) { PySequence_SetItem(_seq, _index, swig::from(v)); return *this; } private: PyObject* _seq; int _index; }; template struct PySequence_ArrowProxy { PySequence_ArrowProxy(const T& x): m_value(x) {} const T* operator->() const { return &m_value; } operator const T*() const { return &m_value; } T m_value; }; template struct PySequence_InputIterator { typedef PySequence_InputIterator self; typedef std::random_access_iterator_tag iterator_category; typedef Reference reference; typedef T value_type; typedef T* pointer; typedef int difference_type; PySequence_InputIterator() { } PySequence_InputIterator(PyObject* seq, int index) : _seq(seq), _index(index) { } reference operator*() const { return reference(_seq, _index); } PySequence_ArrowProxy operator->() const { return PySequence_ArrowProxy(operator*()); } bool operator==(const self& ri) const { return (_index == ri._index) && (_seq == ri._seq); } bool operator!=(const self& ri) const { return !(operator==(ri)); } self& operator ++ () { ++_index; return *this; } self& operator -- () { --_index; return *this; } self& operator += (difference_type n) { _index += n; return *this; } self operator +(difference_type n) const { return self(_seq, _index + n); } self& operator -= (difference_type n) { _index -= n; return *this; } self operator -(difference_type n) const { return self(_seq, _index - n); } difference_type operator - (const self& ri) const { return _index - ri._index; } bool operator < (const self& ri) const { return _index < ri._index; } reference operator[](difference_type n) const { return reference(_seq, _index + n); } private: PyObject* _seq; difference_type _index; }; template struct PySequence_Cont { typedef PySequence_Ref reference; typedef const PySequence_Ref const_reference; typedef T value_type; typedef T* pointer; typedef int difference_type; typedef int size_type; typedef const pointer const_pointer; typedef PySequence_InputIterator iterator; typedef PySequence_InputIterator const_iterator; PySequence_Cont(PyObject* seq) : _seq(0) { if (!PySequence_Check(seq)) { throw std::invalid_argument("a sequence is expected"); } _seq = seq; Py_INCREF(_seq); } ~PySequence_Cont() { if (_seq) Py_DECREF(_seq); } size_type size() const { return static_cast(PySequence_Size(_seq)); } bool empty() const { return size() == 0; } iterator begin() { return iterator(_seq, 0); } const_iterator begin() const { return const_iterator(_seq, 0); } iterator end() { return iterator(_seq, size()); } const_iterator end() const { return const_iterator(_seq, size()); } reference operator[](difference_type n) { return reference(_seq, n); } const_reference operator[](difference_type n) const { return const_reference(_seq, n); } bool check(bool set_err = true) const { int s = size(); for (int i = 0; i < s; ++i) { swig::PyObject_var item = PySequence_GetItem(_seq, i); if (!swig::check(item)) { if (set_err) { char msg[1024]; sprintf(msg, "in sequence element %d", i); SWIG_Error(SWIG_RuntimeError, msg); } return false; } } return true; } private: PyObject* _seq; }; } #include #if !defined(SWIG_NO_LLONG_MAX) # if !defined(LLONG_MAX) && defined(__GNUC__) && defined (__LONG_LONG_MAX__) # define LLONG_MAX __LONG_LONG_MAX__ # define LLONG_MIN (-LLONG_MAX - 1LL) # define ULLONG_MAX (LLONG_MAX * 2ULL + 1ULL) # endif #endif SWIGINTERN int SWIG_AsVal_int (PyObject * obj, int *val) { long v; int res = SWIG_AsVal_long (obj, &v); if (SWIG_IsOK(res)) { if ((v < INT_MIN || v > INT_MAX)) { return SWIG_OverflowError; } else { if (val) *val = static_cast< int >(v); } } return res; } SWIGINTERNINLINE PyObject * SWIG_From_int (int value) { return SWIG_From_long (value); } namespace swig { template <> struct traits { typedef value_category category; static const char* type_name() { return"int"; } }; template <> struct traits_asval { typedef int value_type; static int asval(PyObject *obj, value_type *val) { return SWIG_AsVal_int (obj, val); } }; template <> struct traits_from { typedef int value_type; static PyObject *from(const value_type& val) { return SWIG_From_int (val); } }; } namespace swig { template inline void assign(const PySeq& pyseq, Seq* seq) { #ifdef SWIG_STD_NOASSIGN_STL typedef typename PySeq::value_type value_type; typename PySeq::const_iterator it = pyseq.begin(); for (;it != pyseq.end(); ++it) { seq->insert(seq->end(),(value_type)(*it)); } #else seq->assign(pyseq.begin(), pyseq.end()); #endif } template struct traits_asptr_stdseq { typedef Seq sequence; typedef T value_type; static int asptr(PyObject *obj, sequence **seq) { if (PySequence_Check(obj)) { try { PySequence_Cont pyseq(obj); if (seq) { sequence *pseq = new sequence(); assign(pyseq, pseq); *seq = pseq; return SWIG_NEWOBJ; } else { return pyseq.check() ? SWIG_OK : SWIG_ERROR; } } catch (std::exception& e) { if (seq) { if (!PyErr_Occurred()) { PyErr_SetString(PyExc_TypeError, e.what()); } } return SWIG_ERROR; } } else { sequence *p; if (SWIG_ConvertPtr(obj,(void**)&p, swig::type_info(),0) == SWIG_OK) { if (seq) *seq = p; return SWIG_OLDOBJ; } } return SWIG_ERROR; } }; template struct traits_from_stdseq { typedef Seq sequence; typedef T value_type; typedef typename Seq::size_type size_type; typedef typename sequence::const_iterator const_iterator; static PyObject *from(const sequence& seq) { size_type size = seq.size(); if (size <= (size_type)INT_MAX) { PyObject *obj = PyTuple_New((int)size); int i = 0; for (const_iterator it = seq.begin(); it != seq.end(); ++it, ++i) { PyTuple_SetItem(obj,i,swig::from(*it)); } return obj; } else { PyErr_SetString(PyExc_OverflowError,"sequence size not valid in python"); return NULL; } } }; } namespace swig { template struct traits_asptr > { static int asptr(PyObject *obj, std::list **lis) { return traits_asptr_stdseq >::asptr(obj, lis); } }; template struct traits_from > { static PyObject *from(const std::list & vec) { return traits_from_stdseq >::from(vec); } }; } namespace swig { template <> struct traits > > { typedef pointer_category category; static const char* type_name() { return "std::list<" "int" ", " "std::allocator" " >"; } }; } SWIGINTERN swig::PySwigIterator *std_list_Sl_int_Sg__iterator(std::list *self,PyObject **PYTHON_SELF){ return swig::make_output_iterator(self->begin(), self->begin(), self->end(), *PYTHON_SELF); } SWIGINTERN bool std_list_Sl_int_Sg____nonzero__(std::list const *self){ return !(self->empty()); } SWIGINTERN std::list::size_type std_list_Sl_int_Sg____len__(std::list const *self){ return self->size(); } SWIGINTERNINLINE PyObject* SWIG_From_unsigned_SS_long (unsigned long value) { return (value > LONG_MAX) ? PyLong_FromUnsignedLong(value) : PyInt_FromLong(static_cast< long >(value)); } SWIGINTERNINLINE PyObject * SWIG_From_size_t (size_t value) { return SWIG_From_unsigned_SS_long (static_cast< unsigned long >(value)); } SWIGINTERN std::list::value_type std_list_Sl_int_Sg__pop(std::list *self){ if (self->size() == 0) throw std::out_of_range("pop from empty container"); std::list >::value_type x = self->back(); self->pop_back(); return x; } SWIGINTERN std::list > *std_list_Sl_int_Sg____getslice__(std::list *self,std::list::difference_type i,std::list::difference_type j){ return swig::getslice(self, i, j); } SWIGINTERN void std_list_Sl_int_Sg____setslice__(std::list *self,std::list::difference_type i,std::list::difference_type j,std::list > const &v){ swig::setslice(self, i, j, v); } SWIGINTERN void std_list_Sl_int_Sg____delslice__(std::list *self,std::list::difference_type i,std::list::difference_type j){ swig::delslice(self, i, j); } SWIGINTERN void std_list_Sl_int_Sg____delitem__(std::list *self,std::list::difference_type i){ self->erase(swig::getpos(self,i)); } SWIGINTERN std::list::value_type const &std_list_Sl_int_Sg____getitem__(std::list const *self,std::list::difference_type i){ return *(swig::cgetpos(self, i)); } SWIGINTERN void std_list_Sl_int_Sg____setitem__(std::list *self,std::list::difference_type i,std::list::value_type const &x){ *(swig::getpos(self,i)) = x; } SWIGINTERN void std_list_Sl_int_Sg__append(std::list *self,std::list::value_type const &x){ self->push_back(x); } #define SWIG_From_double PyFloat_FromDouble namespace swig { template <> struct traits { typedef value_category category; static const char* type_name() { return"double"; } }; template <> struct traits_asval { typedef double value_type; static int asval(PyObject *obj, value_type *val) { return SWIG_AsVal_double (obj, val); } }; template <> struct traits_from { typedef double value_type; static PyObject *from(const value_type& val) { return SWIG_From_double (val); } }; } namespace swig { template struct traits_asptr > { static int asptr(PyObject *obj, std::vector **vec) { return traits_asptr_stdseq >::asptr(obj, vec); } }; template struct traits_from > { static PyObject *from(const std::vector& vec) { return traits_from_stdseq >::from(vec); } }; } namespace swig { template <> struct traits > > { typedef pointer_category category; static const char* type_name() { return "std::vector<" "double" "," "std::allocator" " >"; } }; } SWIGINTERN swig::PySwigIterator *std_vector_Sl_double_Sg__iterator(std::vector *self,PyObject **PYTHON_SELF){ return swig::make_output_iterator(self->begin(), self->begin(), self->end(), *PYTHON_SELF); } SWIGINTERN bool std_vector_Sl_double_Sg____nonzero__(std::vector const *self){ return !(self->empty()); } SWIGINTERN std::vector::size_type std_vector_Sl_double_Sg____len__(std::vector const *self){ return self->size(); } SWIGINTERN std::vector::value_type std_vector_Sl_double_Sg__pop(std::vector *self){ if (self->size() == 0) throw std::out_of_range("pop from empty container"); std::vector >::value_type x = self->back(); self->pop_back(); return x; } SWIGINTERN std::vector > *std_vector_Sl_double_Sg____getslice__(std::vector *self,std::vector::difference_type i,std::vector::difference_type j){ return swig::getslice(self, i, j); } SWIGINTERN void std_vector_Sl_double_Sg____setslice__(std::vector *self,std::vector::difference_type i,std::vector::difference_type j,std::vector > const &v){ swig::setslice(self, i, j, v); } SWIGINTERN void std_vector_Sl_double_Sg____delslice__(std::vector *self,std::vector::difference_type i,std::vector::difference_type j){ swig::delslice(self, i, j); } SWIGINTERN void std_vector_Sl_double_Sg____delitem__(std::vector *self,std::vector::difference_type i){ self->erase(swig::getpos(self,i)); } SWIGINTERN std::vector::value_type const &std_vector_Sl_double_Sg____getitem__(std::vector const *self,std::vector::difference_type i){ return *(swig::cgetpos(self, i)); } SWIGINTERN void std_vector_Sl_double_Sg____setitem__(std::vector *self,std::vector::difference_type i,std::vector::value_type const &x){ *(swig::getpos(self,i)) = x; } SWIGINTERN void std_vector_Sl_double_Sg__append(std::vector *self,std::vector::value_type const &x){ self->push_back(x); } #ifndef SWIG_FILE_WITH_INIT # define NO_IMPORT_ARRAY #endif #include "stdio.h" #include /* Support older NumPy data type names */ #if NDARRAY_VERSION < 0x01000000 #define NPY_BOOL PyArray_BOOL #define NPY_BYTE PyArray_BYTE #define NPY_UBYTE PyArray_UBYTE #define NPY_SHORT PyArray_SHORT #define NPY_USHORT PyArray_USHORT #define NPY_INT PyArray_INT #define NPY_UINT PyArray_UINT #define NPY_LONG PyArray_LONG #define NPY_ULONG PyArray_ULONG #define NPY_LONGLONG PyArray_LONGLONG #define NPY_ULONGLONG PyArray_ULONGLONG #define NPY_FLOAT PyArray_FLOAT #define NPY_DOUBLE PyArray_DOUBLE #define NPY_LONGDOUBLE PyArray_LONGDOUBLE #define NPY_CFLOAT PyArray_CFLOAT #define NPY_CDOUBLE PyArray_CDOUBLE #define NPY_CLONGDOUBLE PyArray_CLONGDOUBLE #define NPY_OBJECT PyArray_OBJECT #define NPY_STRING PyArray_STRING #define NPY_UNICODE PyArray_UNICODE #define NPY_VOID PyArray_VOID #define NPY_NTYPES PyArray_NTYPES #define NPY_NOTYPE PyArray_NOTYPE #define NPY_CHAR PyArray_CHAR #define NPY_USERDEF PyArray_USERDEF #define npy_intp intp #define NPY_MAX_BYTE MAX_BYTE #define NPY_MIN_BYTE MIN_BYTE #define NPY_MAX_UBYTE MAX_UBYTE #define NPY_MAX_SHORT MAX_SHORT #define NPY_MIN_SHORT MIN_SHORT #define NPY_MAX_USHORT MAX_USHORT #define NPY_MAX_INT MAX_INT #define NPY_MIN_INT MIN_INT #define NPY_MAX_UINT MAX_UINT #define NPY_MAX_LONG MAX_LONG #define NPY_MIN_LONG MIN_LONG #define NPY_MAX_ULONG MAX_ULONG #define NPY_MAX_LONGLONG MAX_LONGLONG #define NPY_MIN_LONGLONG MIN_LONGLONG #define NPY_MAX_ULONGLONG MAX_ULONGLONG #define NPY_MAX_INTP MAX_INTP #define NPY_MIN_INTP MIN_INTP #define NPY_FARRAY FARRAY #define NPY_F_CONTIGUOUS F_CONTIGUOUS #endif /* Macros to extract array attributes. */ #define is_array(a) ((a) && PyArray_Check((PyArrayObject *)a)) #define array_type(a) (int)(PyArray_TYPE(a)) #define array_numdims(a) (((PyArrayObject *)a)->nd) #define array_dimensions(a) (((PyArrayObject *)a)->dimensions) #define array_size(a,i) (((PyArrayObject *)a)->dimensions[i]) #define array_data(a) (((PyArrayObject *)a)->data) #define array_is_contiguous(a) (PyArray_ISCONTIGUOUS(a)) #define array_is_native(a) (PyArray_ISNOTSWAPPED(a)) #define array_is_fortran(a) (PyArray_ISFORTRAN(a)) /* Given a PyObject, return a string describing its type. */ char* pytype_string(PyObject* py_obj) { if (py_obj == NULL ) return "C NULL value"; if (py_obj == Py_None ) return "Python None" ; if (PyCallable_Check(py_obj)) return "callable" ; if (PyString_Check( py_obj)) return "string" ; if (PyInt_Check( py_obj)) return "int" ; if (PyFloat_Check( py_obj)) return "float" ; if (PyDict_Check( py_obj)) return "dict" ; if (PyList_Check( py_obj)) return "list" ; if (PyTuple_Check( py_obj)) return "tuple" ; if (PyFile_Check( py_obj)) return "file" ; if (PyModule_Check( py_obj)) return "module" ; if (PyInstance_Check(py_obj)) return "instance" ; return "unkown type"; } /* Given a NumPy typecode, return a string describing the type. */ char* typecode_string(int typecode) { static char* type_names[25] = {"bool", "byte", "unsigned byte", "short", "unsigned short", "int", "unsigned int", "long", "unsigned long", "long long", "unsigned long long", "float", "double", "long double", "complex float", "complex double", "complex long double", "object", "string", "unicode", "void", "ntypes", "notype", "char", "unknown"}; return typecode < 24 ? type_names[typecode] : type_names[24]; } /* Make sure input has correct numpy type. Allow character and byte * to match. Also allow int and long to match. This is deprecated. * You should use PyArray_EquivTypenums() instead. */ int type_match(int actual_type, int desired_type) { return PyArray_EquivTypenums(actual_type, desired_type); } /* Given a PyObject pointer, cast it to a PyArrayObject pointer if * legal. If not, set the python error string appropriately and * return NULL. */ PyArrayObject* obj_to_array_no_conversion(PyObject* input, int typecode) { PyArrayObject* ary = NULL; if (is_array(input) && (typecode == NPY_NOTYPE || PyArray_EquivTypenums(array_type(input), typecode))) { ary = (PyArrayObject*) input; } else if is_array(input) { char* desired_type = typecode_string(typecode); char* actual_type = typecode_string(array_type(input)); PyErr_Format(PyExc_TypeError, "Array of type '%s' required. Array of type '%s' given", desired_type, actual_type); ary = NULL; } else { char * desired_type = typecode_string(typecode); char * actual_type = pytype_string(input); PyErr_Format(PyExc_TypeError, "Array of type '%s' required. A '%s' was given", desired_type, actual_type); ary = NULL; } return ary; } /* Convert the given PyObject to a NumPy array with the given * typecode. On success, return a valid PyArrayObject* with the * correct type. On failure, the python error string will be set and * the routine returns NULL. */ PyArrayObject* obj_to_array_allow_conversion(PyObject* input, int typecode, int* is_new_object) { PyArrayObject* ary = NULL; PyObject* py_obj; if (is_array(input) && (typecode == NPY_NOTYPE || PyArray_EquivTypenums(array_type(input),typecode))) { ary = (PyArrayObject*) input; *is_new_object = 0; } else { py_obj = PyArray_FromObject(input, typecode, 0, 0); /* If NULL, PyArray_FromObject will have set python error value.*/ ary = (PyArrayObject*) py_obj; *is_new_object = 1; } return ary; } /* Given a PyArrayObject, check to see if it is contiguous. If so, * return the input pointer and flag it as not a new object. If it is * not contiguous, create a new PyArrayObject using the original data, * flag it as a new object and return the pointer. */ PyArrayObject* make_contiguous(PyArrayObject* ary, int* is_new_object, int min_dims, int max_dims) { PyArrayObject* result; if (array_is_contiguous(ary)) { result = ary; *is_new_object = 0; } else { result = (PyArrayObject*) PyArray_ContiguousFromObject((PyObject*)ary, array_type(ary), min_dims, max_dims); *is_new_object = 1; } return result; } /* Convert a given PyObject to a contiguous PyArrayObject of the * specified type. If the input object is not a contiguous * PyArrayObject, a new one will be created and the new object flag * will be set. */ PyArrayObject* obj_to_array_contiguous_allow_conversion(PyObject* input, int typecode, int* is_new_object) { int is_new1 = 0; int is_new2 = 0; PyArrayObject* ary2; PyArrayObject* ary1 = obj_to_array_allow_conversion(input, typecode, &is_new1); if (ary1) { ary2 = make_contiguous(ary1, &is_new2, 0, 0); if ( is_new1 && is_new2) { Py_DECREF(ary1); } ary1 = ary2; } *is_new_object = is_new1 || is_new2; return ary1; } /* Test whether a python object is contiguous. If array is * contiguous, return 1. Otherwise, set the python error string and * return 0. */ int require_contiguous(PyArrayObject* ary) { int contiguous = 1; if (!array_is_contiguous(ary)) { PyErr_SetString(PyExc_TypeError, "Array must be contiguous. A non-contiguous array was given"); contiguous = 0; } return contiguous; } /* Require that a numpy array is not byte-swapped. If the array is * not byte-swapped, return 1. Otherwise, set the python error string * and return 0. */ int require_native(PyArrayObject* ary) { int native = 1; if (!array_is_native(ary)) { PyErr_SetString(PyExc_TypeError, "Array must have native byteorder. " "A byte-swapped array was given"); native = 0; } return native; } /* Require the given PyArrayObject to have a specified number of * dimensions. If the array has the specified number of dimensions, * return 1. Otherwise, set the python error string and return 0. */ int require_dimensions(PyArrayObject* ary, int exact_dimensions) { int success = 1; if (array_numdims(ary) != exact_dimensions) { PyErr_Format(PyExc_TypeError, "Array must have %d dimensions. Given array has %d dimensions", exact_dimensions, array_numdims(ary)); success = 0; } return success; } /* Require the given PyArrayObject to have one of a list of specified * number of dimensions. If the array has one of the specified number * of dimensions, return 1. Otherwise, set the python error string * and return 0. */ int require_dimensions_n(PyArrayObject* ary, int* exact_dimensions, int n) { int success = 0; int i; char dims_str[255] = ""; char s[255]; for (i = 0; i < n && !success; i++) { if (array_numdims(ary) == exact_dimensions[i]) { success = 1; } } if (!success) { for (i = 0; i < n-1; i++) { sprintf(s, "%d, ", exact_dimensions[i]); strcat(dims_str,s); } sprintf(s, " or %d", exact_dimensions[n-1]); strcat(dims_str,s); PyErr_Format(PyExc_TypeError, "Array must have %s dimensions. Given array has %d dimensions", dims_str, array_numdims(ary)); } return success; } /* Require the given PyArrayObject to have a specified shape. If the * array has the specified shape, return 1. Otherwise, set the python * error string and return 0. */ int require_size(PyArrayObject* ary, npy_intp* size, int n) { int i; int success = 1; int len; char desired_dims[255] = "["; char s[255]; char actual_dims[255] = "["; for(i=0; i < n;i++) { if (size[i] != -1 && size[i] != array_size(ary,i)) { success = 0; } } if (!success) { for (i = 0; i < n; i++) { if (size[i] == -1) { sprintf(s, "*,"); } else { sprintf(s, "%ld,", (long int)size[i]); } strcat(desired_dims,s); } len = strlen(desired_dims); desired_dims[len-1] = ']'; for (i = 0; i < n; i++) { sprintf(s, "%ld,", (long int)array_size(ary,i)); strcat(actual_dims,s); } len = strlen(actual_dims); actual_dims[len-1] = ']'; PyErr_Format(PyExc_TypeError, "Array must have shape of %s. Given array has shape of %s", desired_dims, actual_dims); } return success; } /* Require the given PyArrayObject to to be FORTRAN ordered. If the * the PyArrayObject is already FORTRAN ordered, do nothing. Else, * set the FORTRAN ordering flag and recompute the strides. */ int require_fortran(PyArrayObject* ary) { int success = 1; int nd = array_numdims(ary); int i; if (array_is_fortran(ary)) return success; /* Set the FORTRAN ordered flag */ ary->flags = NPY_FARRAY; /* Recompute the strides */ ary->strides[0] = ary->strides[nd-1]; for (i=1; i < nd; ++i) ary->strides[i] = ary->strides[i-1] * array_size(ary,i-1); return success; } SWIGINTERN swig_type_info* SWIG_pchar_descriptor(void) { static int init = 0; static swig_type_info* info = 0; if (!init) { info = SWIG_TypeQuery("_p_char"); init = 1; } return info; } SWIGINTERNINLINE PyObject * SWIG_FromCharPtrAndSize(const char* carray, size_t size) { if (carray) { if (size > INT_MAX) { swig_type_info* pchar_descriptor = SWIG_pchar_descriptor(); return pchar_descriptor ? SWIG_NewPointerObj(const_cast< char * >(carray), pchar_descriptor, 0) : SWIG_Py_Void(); } else { return PyString_FromStringAndSize(carray, static_cast< int >(size)); } } else { return SWIG_Py_Void(); } } SWIGINTERNINLINE PyObject * SWIG_From_std_string (const std::string& s) { if (s.size()) { return SWIG_FromCharPtrAndSize(s.data(), s.size()); } else { return SWIG_FromCharPtrAndSize(s.c_str(), 0); } } #ifdef __cplusplus extern "C" { #endif SWIGINTERN PyObject *_wrap_delete_PySwigIterator(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; swig::PySwigIterator *arg1 = (swig::PySwigIterator *) 0 ; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:delete_PySwigIterator",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_swig__PySwigIterator, SWIG_POINTER_DISOWN | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "delete_PySwigIterator" "', argument " "1"" of type '" "swig::PySwigIterator *""'"); } arg1 = reinterpret_cast< swig::PySwigIterator * >(argp1); delete arg1; resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_PySwigIterator_value(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; swig::PySwigIterator *arg1 = (swig::PySwigIterator *) 0 ; PyObject *result = 0 ; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:PySwigIterator_value",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_swig__PySwigIterator, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "PySwigIterator_value" "', argument " "1"" of type '" "swig::PySwigIterator const *""'"); } arg1 = reinterpret_cast< swig::PySwigIterator * >(argp1); try { result = (PyObject *)((swig::PySwigIterator const *)arg1)->value(); } catch(swig::stop_iteration &_e) { { (void)_e; SWIG_SetErrorObj(PyExc_StopIteration, SWIG_Py_Void()); SWIG_fail; } } resultobj = result; return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_PySwigIterator_incr__SWIG_0(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; swig::PySwigIterator *arg1 = (swig::PySwigIterator *) 0 ; size_t arg2 ; swig::PySwigIterator *result = 0 ; void *argp1 = 0 ; int res1 = 0 ; size_t val2 ; int ecode2 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OO:PySwigIterator_incr",&obj0,&obj1)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_swig__PySwigIterator, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "PySwigIterator_incr" "', argument " "1"" of type '" "swig::PySwigIterator *""'"); } arg1 = reinterpret_cast< swig::PySwigIterator * >(argp1); ecode2 = SWIG_AsVal_size_t(obj1, &val2); if (!SWIG_IsOK(ecode2)) { SWIG_exception_fail(SWIG_ArgError(ecode2), "in method '" "PySwigIterator_incr" "', argument " "2"" of type '" "size_t""'"); } arg2 = static_cast< size_t >(val2); try { result = (swig::PySwigIterator *)(arg1)->incr(arg2); } catch(swig::stop_iteration &_e) { { (void)_e; SWIG_SetErrorObj(PyExc_StopIteration, SWIG_Py_Void()); SWIG_fail; } } resultobj = SWIG_NewPointerObj(SWIG_as_voidptr(result), SWIGTYPE_p_swig__PySwigIterator, 0 | 0 ); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_PySwigIterator_incr__SWIG_1(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; swig::PySwigIterator *arg1 = (swig::PySwigIterator *) 0 ; swig::PySwigIterator *result = 0 ; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:PySwigIterator_incr",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_swig__PySwigIterator, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "PySwigIterator_incr" "', argument " "1"" of type '" "swig::PySwigIterator *""'"); } arg1 = reinterpret_cast< swig::PySwigIterator * >(argp1); try { result = (swig::PySwigIterator *)(arg1)->incr(); } catch(swig::stop_iteration &_e) { { (void)_e; SWIG_SetErrorObj(PyExc_StopIteration, SWIG_Py_Void()); SWIG_fail; } } resultobj = SWIG_NewPointerObj(SWIG_as_voidptr(result), SWIGTYPE_p_swig__PySwigIterator, 0 | 0 ); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_PySwigIterator_incr(PyObject *self, PyObject *args) { int argc; PyObject *argv[3]; int ii; if (!PyTuple_Check(args)) SWIG_fail; argc = (int)PyObject_Length(args); for (ii = 0; (ii < argc) && (ii < 2); ii++) { argv[ii] = PyTuple_GET_ITEM(args,ii); } if (argc == 1) { int _v; void *vptr = 0; int res = SWIG_ConvertPtr(argv[0], &vptr, SWIGTYPE_p_swig__PySwigIterator, 0); _v = SWIG_CheckState(res); if (_v) { return _wrap_PySwigIterator_incr__SWIG_1(self, args); } } if (argc == 2) { int _v; void *vptr = 0; int res = SWIG_ConvertPtr(argv[0], &vptr, SWIGTYPE_p_swig__PySwigIterator, 0); _v = SWIG_CheckState(res); if (_v) { { int res = SWIG_AsVal_size_t(argv[1], NULL); _v = SWIG_CheckState(res); } if (_v) { return _wrap_PySwigIterator_incr__SWIG_0(self, args); } } } fail: SWIG_SetErrorMsg(PyExc_NotImplementedError,"Wrong number of arguments for overloaded function 'PySwigIterator_incr'.\n Possible C/C++ prototypes are:\n"" incr(swig::PySwigIterator *,size_t)\n"" incr(swig::PySwigIterator *)\n"); return NULL; } SWIGINTERN PyObject *_wrap_PySwigIterator_decr__SWIG_0(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; swig::PySwigIterator *arg1 = (swig::PySwigIterator *) 0 ; size_t arg2 ; swig::PySwigIterator *result = 0 ; void *argp1 = 0 ; int res1 = 0 ; size_t val2 ; int ecode2 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OO:PySwigIterator_decr",&obj0,&obj1)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_swig__PySwigIterator, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "PySwigIterator_decr" "', argument " "1"" of type '" "swig::PySwigIterator *""'"); } arg1 = reinterpret_cast< swig::PySwigIterator * >(argp1); ecode2 = SWIG_AsVal_size_t(obj1, &val2); if (!SWIG_IsOK(ecode2)) { SWIG_exception_fail(SWIG_ArgError(ecode2), "in method '" "PySwigIterator_decr" "', argument " "2"" of type '" "size_t""'"); } arg2 = static_cast< size_t >(val2); try { result = (swig::PySwigIterator *)(arg1)->decr(arg2); } catch(swig::stop_iteration &_e) { { (void)_e; SWIG_SetErrorObj(PyExc_StopIteration, SWIG_Py_Void()); SWIG_fail; } } resultobj = SWIG_NewPointerObj(SWIG_as_voidptr(result), SWIGTYPE_p_swig__PySwigIterator, 0 | 0 ); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_PySwigIterator_decr__SWIG_1(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; swig::PySwigIterator *arg1 = (swig::PySwigIterator *) 0 ; swig::PySwigIterator *result = 0 ; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:PySwigIterator_decr",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_swig__PySwigIterator, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "PySwigIterator_decr" "', argument " "1"" of type '" "swig::PySwigIterator *""'"); } arg1 = reinterpret_cast< swig::PySwigIterator * >(argp1); try { result = (swig::PySwigIterator *)(arg1)->decr(); } catch(swig::stop_iteration &_e) { { (void)_e; SWIG_SetErrorObj(PyExc_StopIteration, SWIG_Py_Void()); SWIG_fail; } } resultobj = SWIG_NewPointerObj(SWIG_as_voidptr(result), SWIGTYPE_p_swig__PySwigIterator, 0 | 0 ); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_PySwigIterator_decr(PyObject *self, PyObject *args) { int argc; PyObject *argv[3]; int ii; if (!PyTuple_Check(args)) SWIG_fail; argc = (int)PyObject_Length(args); for (ii = 0; (ii < argc) && (ii < 2); ii++) { argv[ii] = PyTuple_GET_ITEM(args,ii); } if (argc == 1) { int _v; void *vptr = 0; int res = SWIG_ConvertPtr(argv[0], &vptr, SWIGTYPE_p_swig__PySwigIterator, 0); _v = SWIG_CheckState(res); if (_v) { return _wrap_PySwigIterator_decr__SWIG_1(self, args); } } if (argc == 2) { int _v; void *vptr = 0; int res = SWIG_ConvertPtr(argv[0], &vptr, SWIGTYPE_p_swig__PySwigIterator, 0); _v = SWIG_CheckState(res); if (_v) { { int res = SWIG_AsVal_size_t(argv[1], NULL); _v = SWIG_CheckState(res); } if (_v) { return _wrap_PySwigIterator_decr__SWIG_0(self, args); } } } fail: SWIG_SetErrorMsg(PyExc_NotImplementedError,"Wrong number of arguments for overloaded function 'PySwigIterator_decr'.\n Possible C/C++ prototypes are:\n"" decr(swig::PySwigIterator *,size_t)\n"" decr(swig::PySwigIterator *)\n"); return NULL; } SWIGINTERN PyObject *_wrap_PySwigIterator_distance(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; swig::PySwigIterator *arg1 = (swig::PySwigIterator *) 0 ; swig::PySwigIterator *arg2 = 0 ; ptrdiff_t result; void *argp1 = 0 ; int res1 = 0 ; void *argp2 = 0 ; int res2 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OO:PySwigIterator_distance",&obj0,&obj1)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_swig__PySwigIterator, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "PySwigIterator_distance" "', argument " "1"" of type '" "swig::PySwigIterator const *""'"); } arg1 = reinterpret_cast< swig::PySwigIterator * >(argp1); res2 = SWIG_ConvertPtr(obj1, &argp2, SWIGTYPE_p_swig__PySwigIterator, 0 | 0); if (!SWIG_IsOK(res2)) { SWIG_exception_fail(SWIG_ArgError(res2), "in method '" "PySwigIterator_distance" "', argument " "2"" of type '" "swig::PySwigIterator const &""'"); } if (!argp2) { SWIG_exception_fail(SWIG_ValueError, "invalid null reference " "in method '" "PySwigIterator_distance" "', argument " "2"" of type '" "swig::PySwigIterator const &""'"); } arg2 = reinterpret_cast< swig::PySwigIterator * >(argp2); try { result = ((swig::PySwigIterator const *)arg1)->distance((swig::PySwigIterator const &)*arg2); } catch(std::invalid_argument &_e) { SWIG_Python_Raise(SWIG_NewPointerObj((new std::invalid_argument(static_cast< const std::invalid_argument& >(_e))),SWIGTYPE_p_std__invalid_argument,SWIG_POINTER_OWN), "std::invalid_argument", SWIGTYPE_p_std__invalid_argument); SWIG_fail; } resultobj = SWIG_From_ptrdiff_t(static_cast< ptrdiff_t >(result)); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_PySwigIterator_equal(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; swig::PySwigIterator *arg1 = (swig::PySwigIterator *) 0 ; swig::PySwigIterator *arg2 = 0 ; bool result; void *argp1 = 0 ; int res1 = 0 ; void *argp2 = 0 ; int res2 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OO:PySwigIterator_equal",&obj0,&obj1)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_swig__PySwigIterator, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "PySwigIterator_equal" "', argument " "1"" of type '" "swig::PySwigIterator const *""'"); } arg1 = reinterpret_cast< swig::PySwigIterator * >(argp1); res2 = SWIG_ConvertPtr(obj1, &argp2, SWIGTYPE_p_swig__PySwigIterator, 0 | 0); if (!SWIG_IsOK(res2)) { SWIG_exception_fail(SWIG_ArgError(res2), "in method '" "PySwigIterator_equal" "', argument " "2"" of type '" "swig::PySwigIterator const &""'"); } if (!argp2) { SWIG_exception_fail(SWIG_ValueError, "invalid null reference " "in method '" "PySwigIterator_equal" "', argument " "2"" of type '" "swig::PySwigIterator const &""'"); } arg2 = reinterpret_cast< swig::PySwigIterator * >(argp2); try { result = (bool)((swig::PySwigIterator const *)arg1)->equal((swig::PySwigIterator const &)*arg2); } catch(std::invalid_argument &_e) { SWIG_Python_Raise(SWIG_NewPointerObj((new std::invalid_argument(static_cast< const std::invalid_argument& >(_e))),SWIGTYPE_p_std__invalid_argument,SWIG_POINTER_OWN), "std::invalid_argument", SWIGTYPE_p_std__invalid_argument); SWIG_fail; } resultobj = SWIG_From_bool(static_cast< bool >(result)); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_PySwigIterator_copy(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; swig::PySwigIterator *arg1 = (swig::PySwigIterator *) 0 ; swig::PySwigIterator *result = 0 ; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:PySwigIterator_copy",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_swig__PySwigIterator, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "PySwigIterator_copy" "', argument " "1"" of type '" "swig::PySwigIterator const *""'"); } arg1 = reinterpret_cast< swig::PySwigIterator * >(argp1); result = (swig::PySwigIterator *)((swig::PySwigIterator const *)arg1)->copy(); resultobj = SWIG_NewPointerObj(SWIG_as_voidptr(result), SWIGTYPE_p_swig__PySwigIterator, SWIG_POINTER_OWN | 0 ); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_PySwigIterator_next(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; swig::PySwigIterator *arg1 = (swig::PySwigIterator *) 0 ; PyObject *result = 0 ; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:PySwigIterator_next",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_swig__PySwigIterator, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "PySwigIterator_next" "', argument " "1"" of type '" "swig::PySwigIterator *""'"); } arg1 = reinterpret_cast< swig::PySwigIterator * >(argp1); try { result = (PyObject *)(arg1)->next(); } catch(swig::stop_iteration &_e) { { (void)_e; SWIG_SetErrorObj(PyExc_StopIteration, SWIG_Py_Void()); SWIG_fail; } } resultobj = result; return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_PySwigIterator_previous(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; swig::PySwigIterator *arg1 = (swig::PySwigIterator *) 0 ; PyObject *result = 0 ; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:PySwigIterator_previous",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_swig__PySwigIterator, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "PySwigIterator_previous" "', argument " "1"" of type '" "swig::PySwigIterator *""'"); } arg1 = reinterpret_cast< swig::PySwigIterator * >(argp1); try { result = (PyObject *)(arg1)->previous(); } catch(swig::stop_iteration &_e) { { (void)_e; SWIG_SetErrorObj(PyExc_StopIteration, SWIG_Py_Void()); SWIG_fail; } } resultobj = result; return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_PySwigIterator_advance(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; swig::PySwigIterator *arg1 = (swig::PySwigIterator *) 0 ; ptrdiff_t arg2 ; swig::PySwigIterator *result = 0 ; void *argp1 = 0 ; int res1 = 0 ; ptrdiff_t val2 ; int ecode2 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OO:PySwigIterator_advance",&obj0,&obj1)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_swig__PySwigIterator, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "PySwigIterator_advance" "', argument " "1"" of type '" "swig::PySwigIterator *""'"); } arg1 = reinterpret_cast< swig::PySwigIterator * >(argp1); ecode2 = SWIG_AsVal_ptrdiff_t(obj1, &val2); if (!SWIG_IsOK(ecode2)) { SWIG_exception_fail(SWIG_ArgError(ecode2), "in method '" "PySwigIterator_advance" "', argument " "2"" of type '" "ptrdiff_t""'"); } arg2 = static_cast< ptrdiff_t >(val2); try { result = (swig::PySwigIterator *)(arg1)->advance(arg2); } catch(swig::stop_iteration &_e) { { (void)_e; SWIG_SetErrorObj(PyExc_StopIteration, SWIG_Py_Void()); SWIG_fail; } } resultobj = SWIG_NewPointerObj(SWIG_as_voidptr(result), SWIGTYPE_p_swig__PySwigIterator, 0 | 0 ); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_PySwigIterator___eq__(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; swig::PySwigIterator *arg1 = (swig::PySwigIterator *) 0 ; swig::PySwigIterator *arg2 = 0 ; bool result; void *argp1 = 0 ; int res1 = 0 ; void *argp2 = 0 ; int res2 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OO:PySwigIterator___eq__",&obj0,&obj1)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_swig__PySwigIterator, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "PySwigIterator___eq__" "', argument " "1"" of type '" "swig::PySwigIterator const *""'"); } arg1 = reinterpret_cast< swig::PySwigIterator * >(argp1); res2 = SWIG_ConvertPtr(obj1, &argp2, SWIGTYPE_p_swig__PySwigIterator, 0 | 0); if (!SWIG_IsOK(res2)) { SWIG_exception_fail(SWIG_ArgError(res2), "in method '" "PySwigIterator___eq__" "', argument " "2"" of type '" "swig::PySwigIterator const &""'"); } if (!argp2) { SWIG_exception_fail(SWIG_ValueError, "invalid null reference " "in method '" "PySwigIterator___eq__" "', argument " "2"" of type '" "swig::PySwigIterator const &""'"); } arg2 = reinterpret_cast< swig::PySwigIterator * >(argp2); result = (bool)((swig::PySwigIterator const *)arg1)->operator ==((swig::PySwigIterator const &)*arg2); resultobj = SWIG_From_bool(static_cast< bool >(result)); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_PySwigIterator___ne__(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; swig::PySwigIterator *arg1 = (swig::PySwigIterator *) 0 ; swig::PySwigIterator *arg2 = 0 ; bool result; void *argp1 = 0 ; int res1 = 0 ; void *argp2 = 0 ; int res2 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OO:PySwigIterator___ne__",&obj0,&obj1)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_swig__PySwigIterator, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "PySwigIterator___ne__" "', argument " "1"" of type '" "swig::PySwigIterator const *""'"); } arg1 = reinterpret_cast< swig::PySwigIterator * >(argp1); res2 = SWIG_ConvertPtr(obj1, &argp2, SWIGTYPE_p_swig__PySwigIterator, 0 | 0); if (!SWIG_IsOK(res2)) { SWIG_exception_fail(SWIG_ArgError(res2), "in method '" "PySwigIterator___ne__" "', argument " "2"" of type '" "swig::PySwigIterator const &""'"); } if (!argp2) { SWIG_exception_fail(SWIG_ValueError, "invalid null reference " "in method '" "PySwigIterator___ne__" "', argument " "2"" of type '" "swig::PySwigIterator const &""'"); } arg2 = reinterpret_cast< swig::PySwigIterator * >(argp2); result = (bool)((swig::PySwigIterator const *)arg1)->operator !=((swig::PySwigIterator const &)*arg2); resultobj = SWIG_From_bool(static_cast< bool >(result)); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_PySwigIterator___iadd__(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; swig::PySwigIterator *arg1 = (swig::PySwigIterator *) 0 ; ptrdiff_t arg2 ; swig::PySwigIterator *result = 0 ; void *argp1 = 0 ; int res1 = 0 ; ptrdiff_t val2 ; int ecode2 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OO:PySwigIterator___iadd__",&obj0,&obj1)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_swig__PySwigIterator, SWIG_POINTER_DISOWN | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "PySwigIterator___iadd__" "', argument " "1"" of type '" "swig::PySwigIterator *""'"); } arg1 = reinterpret_cast< swig::PySwigIterator * >(argp1); ecode2 = SWIG_AsVal_ptrdiff_t(obj1, &val2); if (!SWIG_IsOK(ecode2)) { SWIG_exception_fail(SWIG_ArgError(ecode2), "in method '" "PySwigIterator___iadd__" "', argument " "2"" of type '" "ptrdiff_t""'"); } arg2 = static_cast< ptrdiff_t >(val2); try { { swig::PySwigIterator &_result_ref = (arg1)->operator +=(arg2); result = (swig::PySwigIterator *) &_result_ref; } } catch(swig::stop_iteration &_e) { { (void)_e; SWIG_SetErrorObj(PyExc_StopIteration, SWIG_Py_Void()); SWIG_fail; } } resultobj = SWIG_NewPointerObj(SWIG_as_voidptr(result), SWIGTYPE_p_swig__PySwigIterator, SWIG_POINTER_OWN | 0 ); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_PySwigIterator___isub__(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; swig::PySwigIterator *arg1 = (swig::PySwigIterator *) 0 ; ptrdiff_t arg2 ; swig::PySwigIterator *result = 0 ; void *argp1 = 0 ; int res1 = 0 ; ptrdiff_t val2 ; int ecode2 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OO:PySwigIterator___isub__",&obj0,&obj1)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_swig__PySwigIterator, SWIG_POINTER_DISOWN | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "PySwigIterator___isub__" "', argument " "1"" of type '" "swig::PySwigIterator *""'"); } arg1 = reinterpret_cast< swig::PySwigIterator * >(argp1); ecode2 = SWIG_AsVal_ptrdiff_t(obj1, &val2); if (!SWIG_IsOK(ecode2)) { SWIG_exception_fail(SWIG_ArgError(ecode2), "in method '" "PySwigIterator___isub__" "', argument " "2"" of type '" "ptrdiff_t""'"); } arg2 = static_cast< ptrdiff_t >(val2); try { { swig::PySwigIterator &_result_ref = (arg1)->operator -=(arg2); result = (swig::PySwigIterator *) &_result_ref; } } catch(swig::stop_iteration &_e) { { (void)_e; SWIG_SetErrorObj(PyExc_StopIteration, SWIG_Py_Void()); SWIG_fail; } } resultobj = SWIG_NewPointerObj(SWIG_as_voidptr(result), SWIGTYPE_p_swig__PySwigIterator, SWIG_POINTER_OWN | 0 ); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_PySwigIterator___add__(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; swig::PySwigIterator *arg1 = (swig::PySwigIterator *) 0 ; ptrdiff_t arg2 ; swig::PySwigIterator *result = 0 ; void *argp1 = 0 ; int res1 = 0 ; ptrdiff_t val2 ; int ecode2 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OO:PySwigIterator___add__",&obj0,&obj1)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_swig__PySwigIterator, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "PySwigIterator___add__" "', argument " "1"" of type '" "swig::PySwigIterator const *""'"); } arg1 = reinterpret_cast< swig::PySwigIterator * >(argp1); ecode2 = SWIG_AsVal_ptrdiff_t(obj1, &val2); if (!SWIG_IsOK(ecode2)) { SWIG_exception_fail(SWIG_ArgError(ecode2), "in method '" "PySwigIterator___add__" "', argument " "2"" of type '" "ptrdiff_t""'"); } arg2 = static_cast< ptrdiff_t >(val2); try { result = (swig::PySwigIterator *)((swig::PySwigIterator const *)arg1)->operator +(arg2); } catch(swig::stop_iteration &_e) { { (void)_e; SWIG_SetErrorObj(PyExc_StopIteration, SWIG_Py_Void()); SWIG_fail; } } resultobj = SWIG_NewPointerObj(SWIG_as_voidptr(result), SWIGTYPE_p_swig__PySwigIterator, SWIG_POINTER_OWN | 0 ); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_PySwigIterator___sub____SWIG_0(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; swig::PySwigIterator *arg1 = (swig::PySwigIterator *) 0 ; ptrdiff_t arg2 ; swig::PySwigIterator *result = 0 ; void *argp1 = 0 ; int res1 = 0 ; ptrdiff_t val2 ; int ecode2 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OO:PySwigIterator___sub__",&obj0,&obj1)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_swig__PySwigIterator, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "PySwigIterator___sub__" "', argument " "1"" of type '" "swig::PySwigIterator const *""'"); } arg1 = reinterpret_cast< swig::PySwigIterator * >(argp1); ecode2 = SWIG_AsVal_ptrdiff_t(obj1, &val2); if (!SWIG_IsOK(ecode2)) { SWIG_exception_fail(SWIG_ArgError(ecode2), "in method '" "PySwigIterator___sub__" "', argument " "2"" of type '" "ptrdiff_t""'"); } arg2 = static_cast< ptrdiff_t >(val2); try { result = (swig::PySwigIterator *)((swig::PySwigIterator const *)arg1)->operator -(arg2); } catch(swig::stop_iteration &_e) { { (void)_e; SWIG_SetErrorObj(PyExc_StopIteration, SWIG_Py_Void()); SWIG_fail; } } resultobj = SWIG_NewPointerObj(SWIG_as_voidptr(result), SWIGTYPE_p_swig__PySwigIterator, SWIG_POINTER_OWN | 0 ); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_PySwigIterator___sub____SWIG_1(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; swig::PySwigIterator *arg1 = (swig::PySwigIterator *) 0 ; swig::PySwigIterator *arg2 = 0 ; ptrdiff_t result; void *argp1 = 0 ; int res1 = 0 ; void *argp2 = 0 ; int res2 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OO:PySwigIterator___sub__",&obj0,&obj1)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_swig__PySwigIterator, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "PySwigIterator___sub__" "', argument " "1"" of type '" "swig::PySwigIterator const *""'"); } arg1 = reinterpret_cast< swig::PySwigIterator * >(argp1); res2 = SWIG_ConvertPtr(obj1, &argp2, SWIGTYPE_p_swig__PySwigIterator, 0 | 0); if (!SWIG_IsOK(res2)) { SWIG_exception_fail(SWIG_ArgError(res2), "in method '" "PySwigIterator___sub__" "', argument " "2"" of type '" "swig::PySwigIterator const &""'"); } if (!argp2) { SWIG_exception_fail(SWIG_ValueError, "invalid null reference " "in method '" "PySwigIterator___sub__" "', argument " "2"" of type '" "swig::PySwigIterator const &""'"); } arg2 = reinterpret_cast< swig::PySwigIterator * >(argp2); result = ((swig::PySwigIterator const *)arg1)->operator -((swig::PySwigIterator const &)*arg2); resultobj = SWIG_From_ptrdiff_t(static_cast< ptrdiff_t >(result)); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_PySwigIterator___sub__(PyObject *self, PyObject *args) { int argc; PyObject *argv[3]; int ii; if (!PyTuple_Check(args)) SWIG_fail; argc = (int)PyObject_Length(args); for (ii = 0; (ii < argc) && (ii < 2); ii++) { argv[ii] = PyTuple_GET_ITEM(args,ii); } if (argc == 2) { int _v; void *vptr = 0; int res = SWIG_ConvertPtr(argv[0], &vptr, SWIGTYPE_p_swig__PySwigIterator, 0); _v = SWIG_CheckState(res); if (_v) { int res = SWIG_ConvertPtr(argv[1], 0, SWIGTYPE_p_swig__PySwigIterator, 0); _v = SWIG_CheckState(res); if (_v) { return _wrap_PySwigIterator___sub____SWIG_1(self, args); } } } if (argc == 2) { int _v; void *vptr = 0; int res = SWIG_ConvertPtr(argv[0], &vptr, SWIGTYPE_p_swig__PySwigIterator, 0); _v = SWIG_CheckState(res); if (_v) { { int res = SWIG_AsVal_ptrdiff_t(argv[1], NULL); _v = SWIG_CheckState(res); } if (_v) { return _wrap_PySwigIterator___sub____SWIG_0(self, args); } } } fail: Py_INCREF(Py_NotImplemented); return Py_NotImplemented; } SWIGINTERN PyObject *PySwigIterator_swigregister(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *obj; if (!PyArg_ParseTuple(args,(char*)"O|swigregister", &obj)) return NULL; SWIG_TypeNewClientData(SWIGTYPE_p_swig__PySwigIterator, SWIG_NewClientData(obj)); return SWIG_Py_Void(); } SWIGINTERN PyObject *_wrap_SpikeList_iterator(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; std::list *arg1 = (std::list *) 0 ; PyObject **arg2 = (PyObject **) 0 ; swig::PySwigIterator *result = 0 ; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; arg2 = &obj0; if (!PyArg_ParseTuple(args,(char *)"O:SpikeList_iterator",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_std__listTint_std__allocatorTint_t_t, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "SpikeList_iterator" "', argument " "1"" of type '" "std::list *""'"); } arg1 = reinterpret_cast< std::list * >(argp1); result = (swig::PySwigIterator *)std_list_Sl_int_Sg__iterator(arg1,arg2); resultobj = SWIG_NewPointerObj(SWIG_as_voidptr(result), SWIGTYPE_p_swig__PySwigIterator, SWIG_POINTER_OWN | 0 ); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_SpikeList___nonzero__(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; std::list *arg1 = (std::list *) 0 ; bool result; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:SpikeList___nonzero__",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_std__listTint_std__allocatorTint_t_t, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "SpikeList___nonzero__" "', argument " "1"" of type '" "std::list const *""'"); } arg1 = reinterpret_cast< std::list * >(argp1); result = (bool)std_list_Sl_int_Sg____nonzero__((std::list const *)arg1); resultobj = SWIG_From_bool(static_cast< bool >(result)); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_SpikeList___len__(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; std::list *arg1 = (std::list *) 0 ; std::list::size_type result; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:SpikeList___len__",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_std__listTint_std__allocatorTint_t_t, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "SpikeList___len__" "', argument " "1"" of type '" "std::list const *""'"); } arg1 = reinterpret_cast< std::list * >(argp1); result = std_list_Sl_int_Sg____len__((std::list const *)arg1); resultobj = SWIG_From_size_t(static_cast< size_t >(result)); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_SpikeList_pop(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; std::list *arg1 = (std::list *) 0 ; std::list::value_type result; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:SpikeList_pop",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_std__listTint_std__allocatorTint_t_t, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "SpikeList_pop" "', argument " "1"" of type '" "std::list *""'"); } arg1 = reinterpret_cast< std::list * >(argp1); try { result = (std::list::value_type)std_list_Sl_int_Sg__pop(arg1); } catch(std::out_of_range &_e) { SWIG_exception_fail(SWIG_IndexError, (&_e)->what()); } resultobj = SWIG_From_int(static_cast< int >(result)); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_SpikeList___getslice__(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; std::list *arg1 = (std::list *) 0 ; std::list::difference_type arg2 ; std::list::difference_type arg3 ; std::list > *result = 0 ; void *argp1 = 0 ; int res1 = 0 ; ptrdiff_t val2 ; int ecode2 = 0 ; ptrdiff_t val3 ; int ecode3 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; PyObject * obj2 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OOO:SpikeList___getslice__",&obj0,&obj1,&obj2)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_std__listTint_std__allocatorTint_t_t, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "SpikeList___getslice__" "', argument " "1"" of type '" "std::list *""'"); } arg1 = reinterpret_cast< std::list * >(argp1); ecode2 = SWIG_AsVal_ptrdiff_t(obj1, &val2); if (!SWIG_IsOK(ecode2)) { SWIG_exception_fail(SWIG_ArgError(ecode2), "in method '" "SpikeList___getslice__" "', argument " "2"" of type '" "std::list::difference_type""'"); } arg2 = static_cast< std::list::difference_type >(val2); ecode3 = SWIG_AsVal_ptrdiff_t(obj2, &val3); if (!SWIG_IsOK(ecode3)) { SWIG_exception_fail(SWIG_ArgError(ecode3), "in method '" "SpikeList___getslice__" "', argument " "3"" of type '" "std::list::difference_type""'"); } arg3 = static_cast< std::list::difference_type >(val3); try { result = (std::list > *)std_list_Sl_int_Sg____getslice__(arg1,arg2,arg3); } catch(std::out_of_range &_e) { SWIG_exception_fail(SWIG_IndexError, (&_e)->what()); } resultobj = SWIG_NewPointerObj(SWIG_as_voidptr(result), SWIGTYPE_p_std__listTint_std__allocatorTint_t_t, SWIG_POINTER_OWN | 0 ); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_SpikeList___setslice__(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; std::list *arg1 = (std::list *) 0 ; std::list::difference_type arg2 ; std::list::difference_type arg3 ; std::list > *arg4 = 0 ; void *argp1 = 0 ; int res1 = 0 ; ptrdiff_t val2 ; int ecode2 = 0 ; ptrdiff_t val3 ; int ecode3 = 0 ; int res4 = SWIG_OLDOBJ ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; PyObject * obj2 = 0 ; PyObject * obj3 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OOOO:SpikeList___setslice__",&obj0,&obj1,&obj2,&obj3)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_std__listTint_std__allocatorTint_t_t, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "SpikeList___setslice__" "', argument " "1"" of type '" "std::list *""'"); } arg1 = reinterpret_cast< std::list * >(argp1); ecode2 = SWIG_AsVal_ptrdiff_t(obj1, &val2); if (!SWIG_IsOK(ecode2)) { SWIG_exception_fail(SWIG_ArgError(ecode2), "in method '" "SpikeList___setslice__" "', argument " "2"" of type '" "std::list::difference_type""'"); } arg2 = static_cast< std::list::difference_type >(val2); ecode3 = SWIG_AsVal_ptrdiff_t(obj2, &val3); if (!SWIG_IsOK(ecode3)) { SWIG_exception_fail(SWIG_ArgError(ecode3), "in method '" "SpikeList___setslice__" "', argument " "3"" of type '" "std::list::difference_type""'"); } arg3 = static_cast< std::list::difference_type >(val3); { std::list > *ptr = (std::list > *)0; res4 = swig::asptr(obj3, &ptr); if (!SWIG_IsOK(res4)) { SWIG_exception_fail(SWIG_ArgError(res4), "in method '" "SpikeList___setslice__" "', argument " "4"" of type '" "std::list > const &""'"); } if (!ptr) { SWIG_exception_fail(SWIG_ValueError, "invalid null reference " "in method '" "SpikeList___setslice__" "', argument " "4"" of type '" "std::list > const &""'"); } arg4 = ptr; } try { std_list_Sl_int_Sg____setslice__(arg1,arg2,arg3,(std::list > const &)*arg4); } catch(std::out_of_range &_e) { SWIG_exception_fail(SWIG_IndexError, (&_e)->what()); } catch(std::invalid_argument &_e) { SWIG_exception_fail(SWIG_ValueError, (&_e)->what()); } resultobj = SWIG_Py_Void(); if (SWIG_IsNewObj(res4)) delete arg4; return resultobj; fail: if (SWIG_IsNewObj(res4)) delete arg4; return NULL; } SWIGINTERN PyObject *_wrap_SpikeList___delslice__(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; std::list *arg1 = (std::list *) 0 ; std::list::difference_type arg2 ; std::list::difference_type arg3 ; void *argp1 = 0 ; int res1 = 0 ; ptrdiff_t val2 ; int ecode2 = 0 ; ptrdiff_t val3 ; int ecode3 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; PyObject * obj2 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OOO:SpikeList___delslice__",&obj0,&obj1,&obj2)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_std__listTint_std__allocatorTint_t_t, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "SpikeList___delslice__" "', argument " "1"" of type '" "std::list *""'"); } arg1 = reinterpret_cast< std::list * >(argp1); ecode2 = SWIG_AsVal_ptrdiff_t(obj1, &val2); if (!SWIG_IsOK(ecode2)) { SWIG_exception_fail(SWIG_ArgError(ecode2), "in method '" "SpikeList___delslice__" "', argument " "2"" of type '" "std::list::difference_type""'"); } arg2 = static_cast< std::list::difference_type >(val2); ecode3 = SWIG_AsVal_ptrdiff_t(obj2, &val3); if (!SWIG_IsOK(ecode3)) { SWIG_exception_fail(SWIG_ArgError(ecode3), "in method '" "SpikeList___delslice__" "', argument " "3"" of type '" "std::list::difference_type""'"); } arg3 = static_cast< std::list::difference_type >(val3); try { std_list_Sl_int_Sg____delslice__(arg1,arg2,arg3); } catch(std::out_of_range &_e) { SWIG_exception_fail(SWIG_IndexError, (&_e)->what()); } resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_SpikeList___delitem__(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; std::list *arg1 = (std::list *) 0 ; std::list::difference_type arg2 ; void *argp1 = 0 ; int res1 = 0 ; ptrdiff_t val2 ; int ecode2 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OO:SpikeList___delitem__",&obj0,&obj1)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_std__listTint_std__allocatorTint_t_t, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "SpikeList___delitem__" "', argument " "1"" of type '" "std::list *""'"); } arg1 = reinterpret_cast< std::list * >(argp1); ecode2 = SWIG_AsVal_ptrdiff_t(obj1, &val2); if (!SWIG_IsOK(ecode2)) { SWIG_exception_fail(SWIG_ArgError(ecode2), "in method '" "SpikeList___delitem__" "', argument " "2"" of type '" "std::list::difference_type""'"); } arg2 = static_cast< std::list::difference_type >(val2); try { std_list_Sl_int_Sg____delitem__(arg1,arg2); } catch(std::out_of_range &_e) { SWIG_exception_fail(SWIG_IndexError, (&_e)->what()); } resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_SpikeList___getitem__(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; std::list *arg1 = (std::list *) 0 ; std::list::difference_type arg2 ; std::list::value_type *result = 0 ; void *argp1 = 0 ; int res1 = 0 ; ptrdiff_t val2 ; int ecode2 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OO:SpikeList___getitem__",&obj0,&obj1)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_std__listTint_std__allocatorTint_t_t, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "SpikeList___getitem__" "', argument " "1"" of type '" "std::list const *""'"); } arg1 = reinterpret_cast< std::list * >(argp1); ecode2 = SWIG_AsVal_ptrdiff_t(obj1, &val2); if (!SWIG_IsOK(ecode2)) { SWIG_exception_fail(SWIG_ArgError(ecode2), "in method '" "SpikeList___getitem__" "', argument " "2"" of type '" "std::list::difference_type""'"); } arg2 = static_cast< std::list::difference_type >(val2); try { { std::list::value_type const &_result_ref = std_list_Sl_int_Sg____getitem__((std::list const *)arg1,arg2); result = (std::list::value_type *) &_result_ref; } } catch(std::out_of_range &_e) { SWIG_exception_fail(SWIG_IndexError, (&_e)->what()); } resultobj = SWIG_From_int(static_cast< int >(*result)); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_SpikeList___setitem__(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; std::list *arg1 = (std::list *) 0 ; std::list::difference_type arg2 ; std::list::value_type *arg3 = 0 ; void *argp1 = 0 ; int res1 = 0 ; ptrdiff_t val2 ; int ecode2 = 0 ; std::list::value_type temp3 ; int val3 ; int ecode3 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; PyObject * obj2 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OOO:SpikeList___setitem__",&obj0,&obj1,&obj2)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_std__listTint_std__allocatorTint_t_t, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "SpikeList___setitem__" "', argument " "1"" of type '" "std::list *""'"); } arg1 = reinterpret_cast< std::list * >(argp1); ecode2 = SWIG_AsVal_ptrdiff_t(obj1, &val2); if (!SWIG_IsOK(ecode2)) { SWIG_exception_fail(SWIG_ArgError(ecode2), "in method '" "SpikeList___setitem__" "', argument " "2"" of type '" "std::list::difference_type""'"); } arg2 = static_cast< std::list::difference_type >(val2); ecode3 = SWIG_AsVal_int(obj2, &val3); if (!SWIG_IsOK(ecode3)) { SWIG_exception_fail(SWIG_ArgError(ecode3), "in method '" "SpikeList___setitem__" "', argument " "3"" of type '" "std::list::value_type""'"); } temp3 = static_cast< std::list::value_type >(val3); arg3 = &temp3; try { std_list_Sl_int_Sg____setitem__(arg1,arg2,(int const &)*arg3); } catch(std::out_of_range &_e) { SWIG_exception_fail(SWIG_IndexError, (&_e)->what()); } resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_SpikeList_append(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; std::list *arg1 = (std::list *) 0 ; std::list::value_type *arg2 = 0 ; void *argp1 = 0 ; int res1 = 0 ; std::list::value_type temp2 ; int val2 ; int ecode2 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OO:SpikeList_append",&obj0,&obj1)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_std__listTint_std__allocatorTint_t_t, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "SpikeList_append" "', argument " "1"" of type '" "std::list *""'"); } arg1 = reinterpret_cast< std::list * >(argp1); ecode2 = SWIG_AsVal_int(obj1, &val2); if (!SWIG_IsOK(ecode2)) { SWIG_exception_fail(SWIG_ArgError(ecode2), "in method '" "SpikeList_append" "', argument " "2"" of type '" "std::list::value_type""'"); } temp2 = static_cast< std::list::value_type >(val2); arg2 = &temp2; std_list_Sl_int_Sg__append(arg1,(int const &)*arg2); resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_new_SpikeList__SWIG_0(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; std::list *result = 0 ; if (!PyArg_ParseTuple(args,(char *)":new_SpikeList")) SWIG_fail; result = (std::list *)new std::list(); resultobj = SWIG_NewPointerObj(SWIG_as_voidptr(result), SWIGTYPE_p_std__listTint_std__allocatorTint_t_t, SWIG_POINTER_NEW | 0 ); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_new_SpikeList__SWIG_1(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; std::list *arg1 = 0 ; std::list *result = 0 ; int res1 = SWIG_OLDOBJ ; PyObject * obj0 = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:new_SpikeList",&obj0)) SWIG_fail; { std::list > *ptr = (std::list > *)0; res1 = swig::asptr(obj0, &ptr); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "new_SpikeList" "', argument " "1"" of type '" "std::list const &""'"); } if (!ptr) { SWIG_exception_fail(SWIG_ValueError, "invalid null reference " "in method '" "new_SpikeList" "', argument " "1"" of type '" "std::list const &""'"); } arg1 = ptr; } result = (std::list *)new std::list((std::list const &)*arg1); resultobj = SWIG_NewPointerObj(SWIG_as_voidptr(result), SWIGTYPE_p_std__listTint_std__allocatorTint_t_t, SWIG_POINTER_NEW | 0 ); if (SWIG_IsNewObj(res1)) delete arg1; return resultobj; fail: if (SWIG_IsNewObj(res1)) delete arg1; return NULL; } SWIGINTERN PyObject *_wrap_SpikeList_empty(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; std::list *arg1 = (std::list *) 0 ; bool result; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:SpikeList_empty",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_std__listTint_std__allocatorTint_t_t, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "SpikeList_empty" "', argument " "1"" of type '" "std::list const *""'"); } arg1 = reinterpret_cast< std::list * >(argp1); result = (bool)((std::list const *)arg1)->empty(); resultobj = SWIG_From_bool(static_cast< bool >(result)); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_SpikeList_size(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; std::list *arg1 = (std::list *) 0 ; std::list::size_type result; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:SpikeList_size",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_std__listTint_std__allocatorTint_t_t, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "SpikeList_size" "', argument " "1"" of type '" "std::list const *""'"); } arg1 = reinterpret_cast< std::list * >(argp1); result = ((std::list const *)arg1)->size(); resultobj = SWIG_From_size_t(static_cast< size_t >(result)); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_SpikeList_clear(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; std::list *arg1 = (std::list *) 0 ; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:SpikeList_clear",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_std__listTint_std__allocatorTint_t_t, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "SpikeList_clear" "', argument " "1"" of type '" "std::list *""'"); } arg1 = reinterpret_cast< std::list * >(argp1); (arg1)->clear(); resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_SpikeList_swap(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; std::list *arg1 = (std::list *) 0 ; std::list *arg2 = 0 ; void *argp1 = 0 ; int res1 = 0 ; void *argp2 = 0 ; int res2 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OO:SpikeList_swap",&obj0,&obj1)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_std__listTint_std__allocatorTint_t_t, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "SpikeList_swap" "', argument " "1"" of type '" "std::list *""'"); } arg1 = reinterpret_cast< std::list * >(argp1); res2 = SWIG_ConvertPtr(obj1, &argp2, SWIGTYPE_p_std__listTint_std__allocatorTint_t_t, 0 ); if (!SWIG_IsOK(res2)) { SWIG_exception_fail(SWIG_ArgError(res2), "in method '" "SpikeList_swap" "', argument " "2"" of type '" "std::list &""'"); } if (!argp2) { SWIG_exception_fail(SWIG_ValueError, "invalid null reference " "in method '" "SpikeList_swap" "', argument " "2"" of type '" "std::list &""'"); } arg2 = reinterpret_cast< std::list * >(argp2); (arg1)->swap(*arg2); resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_SpikeList_get_allocator(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; std::list *arg1 = (std::list *) 0 ; SwigValueWrapper< std::allocator > result; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:SpikeList_get_allocator",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_std__listTint_std__allocatorTint_t_t, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "SpikeList_get_allocator" "', argument " "1"" of type '" "std::list const *""'"); } arg1 = reinterpret_cast< std::list * >(argp1); result = ((std::list const *)arg1)->get_allocator(); resultobj = SWIG_NewPointerObj((new std::list::allocator_type(static_cast< const std::list::allocator_type& >(result))), SWIGTYPE_p_std__listTint_std__allocatorTint_t_t__allocator_type, SWIG_POINTER_OWN | 0 ); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_SpikeList_begin__SWIG_0(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; std::list *arg1 = (std::list *) 0 ; std::list::iterator result; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:SpikeList_begin",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_std__listTint_std__allocatorTint_t_t, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "SpikeList_begin" "', argument " "1"" of type '" "std::list *""'"); } arg1 = reinterpret_cast< std::list * >(argp1); result = (arg1)->begin(); resultobj = SWIG_NewPointerObj(swig::make_output_iterator(static_cast< const std::list::iterator & >(result)), swig::PySwigIterator::descriptor(),SWIG_POINTER_OWN); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_SpikeList_begin__SWIG_1(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; std::list *arg1 = (std::list *) 0 ; std::list::const_iterator result; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:SpikeList_begin",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_std__listTint_std__allocatorTint_t_t, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "SpikeList_begin" "', argument " "1"" of type '" "std::list const *""'"); } arg1 = reinterpret_cast< std::list * >(argp1); result = ((std::list const *)arg1)->begin(); resultobj = SWIG_NewPointerObj(swig::make_output_iterator(static_cast< const std::list::const_iterator & >(result)), swig::PySwigIterator::descriptor(),SWIG_POINTER_OWN); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_SpikeList_begin(PyObject *self, PyObject *args) { int argc; PyObject *argv[2]; int ii; if (!PyTuple_Check(args)) SWIG_fail; argc = (int)PyObject_Length(args); for (ii = 0; (ii < argc) && (ii < 1); ii++) { argv[ii] = PyTuple_GET_ITEM(args,ii); } if (argc == 1) { int _v; int res = swig::asptr(argv[0], (std::list >**)(0)); _v = SWIG_CheckState(res); if (_v) { return _wrap_SpikeList_begin__SWIG_0(self, args); } } if (argc == 1) { int _v; int res = swig::asptr(argv[0], (std::list >**)(0)); _v = SWIG_CheckState(res); if (_v) { return _wrap_SpikeList_begin__SWIG_1(self, args); } } fail: SWIG_SetErrorMsg(PyExc_NotImplementedError,"Wrong number of arguments for overloaded function 'SpikeList_begin'.\n Possible C/C++ prototypes are:\n"" begin(std::list *)\n"" begin(std::list const *)\n"); return NULL; } SWIGINTERN PyObject *_wrap_SpikeList_end__SWIG_0(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; std::list *arg1 = (std::list *) 0 ; std::list::iterator result; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:SpikeList_end",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_std__listTint_std__allocatorTint_t_t, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "SpikeList_end" "', argument " "1"" of type '" "std::list *""'"); } arg1 = reinterpret_cast< std::list * >(argp1); result = (arg1)->end(); resultobj = SWIG_NewPointerObj(swig::make_output_iterator(static_cast< const std::list::iterator & >(result)), swig::PySwigIterator::descriptor(),SWIG_POINTER_OWN); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_SpikeList_end__SWIG_1(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; std::list *arg1 = (std::list *) 0 ; std::list::const_iterator result; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:SpikeList_end",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_std__listTint_std__allocatorTint_t_t, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "SpikeList_end" "', argument " "1"" of type '" "std::list const *""'"); } arg1 = reinterpret_cast< std::list * >(argp1); result = ((std::list const *)arg1)->end(); resultobj = SWIG_NewPointerObj(swig::make_output_iterator(static_cast< const std::list::const_iterator & >(result)), swig::PySwigIterator::descriptor(),SWIG_POINTER_OWN); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_SpikeList_end(PyObject *self, PyObject *args) { int argc; PyObject *argv[2]; int ii; if (!PyTuple_Check(args)) SWIG_fail; argc = (int)PyObject_Length(args); for (ii = 0; (ii < argc) && (ii < 1); ii++) { argv[ii] = PyTuple_GET_ITEM(args,ii); } if (argc == 1) { int _v; int res = swig::asptr(argv[0], (std::list >**)(0)); _v = SWIG_CheckState(res); if (_v) { return _wrap_SpikeList_end__SWIG_0(self, args); } } if (argc == 1) { int _v; int res = swig::asptr(argv[0], (std::list >**)(0)); _v = SWIG_CheckState(res); if (_v) { return _wrap_SpikeList_end__SWIG_1(self, args); } } fail: SWIG_SetErrorMsg(PyExc_NotImplementedError,"Wrong number of arguments for overloaded function 'SpikeList_end'.\n Possible C/C++ prototypes are:\n"" end(std::list *)\n"" end(std::list const *)\n"); return NULL; } SWIGINTERN PyObject *_wrap_SpikeList_rbegin__SWIG_0(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; std::list *arg1 = (std::list *) 0 ; std::list::reverse_iterator result; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:SpikeList_rbegin",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_std__listTint_std__allocatorTint_t_t, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "SpikeList_rbegin" "', argument " "1"" of type '" "std::list *""'"); } arg1 = reinterpret_cast< std::list * >(argp1); result = (arg1)->rbegin(); resultobj = SWIG_NewPointerObj(swig::make_output_iterator(static_cast< const std::list::reverse_iterator & >(result)), swig::PySwigIterator::descriptor(),SWIG_POINTER_OWN); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_SpikeList_rbegin__SWIG_1(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; std::list *arg1 = (std::list *) 0 ; std::list::const_reverse_iterator result; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:SpikeList_rbegin",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_std__listTint_std__allocatorTint_t_t, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "SpikeList_rbegin" "', argument " "1"" of type '" "std::list const *""'"); } arg1 = reinterpret_cast< std::list * >(argp1); result = ((std::list const *)arg1)->rbegin(); resultobj = SWIG_NewPointerObj(swig::make_output_iterator(static_cast< const std::list::const_reverse_iterator & >(result)), swig::PySwigIterator::descriptor(),SWIG_POINTER_OWN); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_SpikeList_rbegin(PyObject *self, PyObject *args) { int argc; PyObject *argv[2]; int ii; if (!PyTuple_Check(args)) SWIG_fail; argc = (int)PyObject_Length(args); for (ii = 0; (ii < argc) && (ii < 1); ii++) { argv[ii] = PyTuple_GET_ITEM(args,ii); } if (argc == 1) { int _v; int res = swig::asptr(argv[0], (std::list >**)(0)); _v = SWIG_CheckState(res); if (_v) { return _wrap_SpikeList_rbegin__SWIG_0(self, args); } } if (argc == 1) { int _v; int res = swig::asptr(argv[0], (std::list >**)(0)); _v = SWIG_CheckState(res); if (_v) { return _wrap_SpikeList_rbegin__SWIG_1(self, args); } } fail: SWIG_SetErrorMsg(PyExc_NotImplementedError,"Wrong number of arguments for overloaded function 'SpikeList_rbegin'.\n Possible C/C++ prototypes are:\n"" rbegin(std::list *)\n"" rbegin(std::list const *)\n"); return NULL; } SWIGINTERN PyObject *_wrap_SpikeList_rend__SWIG_0(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; std::list *arg1 = (std::list *) 0 ; std::list::reverse_iterator result; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:SpikeList_rend",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_std__listTint_std__allocatorTint_t_t, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "SpikeList_rend" "', argument " "1"" of type '" "std::list *""'"); } arg1 = reinterpret_cast< std::list * >(argp1); result = (arg1)->rend(); resultobj = SWIG_NewPointerObj(swig::make_output_iterator(static_cast< const std::list::reverse_iterator & >(result)), swig::PySwigIterator::descriptor(),SWIG_POINTER_OWN); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_SpikeList_rend__SWIG_1(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; std::list *arg1 = (std::list *) 0 ; std::list::const_reverse_iterator result; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:SpikeList_rend",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_std__listTint_std__allocatorTint_t_t, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "SpikeList_rend" "', argument " "1"" of type '" "std::list const *""'"); } arg1 = reinterpret_cast< std::list * >(argp1); result = ((std::list const *)arg1)->rend(); resultobj = SWIG_NewPointerObj(swig::make_output_iterator(static_cast< const std::list::const_reverse_iterator & >(result)), swig::PySwigIterator::descriptor(),SWIG_POINTER_OWN); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_SpikeList_rend(PyObject *self, PyObject *args) { int argc; PyObject *argv[2]; int ii; if (!PyTuple_Check(args)) SWIG_fail; argc = (int)PyObject_Length(args); for (ii = 0; (ii < argc) && (ii < 1); ii++) { argv[ii] = PyTuple_GET_ITEM(args,ii); } if (argc == 1) { int _v; int res = swig::asptr(argv[0], (std::list >**)(0)); _v = SWIG_CheckState(res); if (_v) { return _wrap_SpikeList_rend__SWIG_0(self, args); } } if (argc == 1) { int _v; int res = swig::asptr(argv[0], (std::list >**)(0)); _v = SWIG_CheckState(res); if (_v) { return _wrap_SpikeList_rend__SWIG_1(self, args); } } fail: SWIG_SetErrorMsg(PyExc_NotImplementedError,"Wrong number of arguments for overloaded function 'SpikeList_rend'.\n Possible C/C++ prototypes are:\n"" rend(std::list *)\n"" rend(std::list const *)\n"); return NULL; } SWIGINTERN PyObject *_wrap_new_SpikeList__SWIG_2(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; std::list::size_type arg1 ; std::list *result = 0 ; size_t val1 ; int ecode1 = 0 ; PyObject * obj0 = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:new_SpikeList",&obj0)) SWIG_fail; ecode1 = SWIG_AsVal_size_t(obj0, &val1); if (!SWIG_IsOK(ecode1)) { SWIG_exception_fail(SWIG_ArgError(ecode1), "in method '" "new_SpikeList" "', argument " "1"" of type '" "std::list::size_type""'"); } arg1 = static_cast< std::list::size_type >(val1); result = (std::list *)new std::list(arg1); resultobj = SWIG_NewPointerObj(SWIG_as_voidptr(result), SWIGTYPE_p_std__listTint_std__allocatorTint_t_t, SWIG_POINTER_NEW | 0 ); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_SpikeList_pop_back(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; std::list *arg1 = (std::list *) 0 ; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:SpikeList_pop_back",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_std__listTint_std__allocatorTint_t_t, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "SpikeList_pop_back" "', argument " "1"" of type '" "std::list *""'"); } arg1 = reinterpret_cast< std::list * >(argp1); (arg1)->pop_back(); resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_SpikeList_resize__SWIG_0(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; std::list *arg1 = (std::list *) 0 ; std::list::size_type arg2 ; void *argp1 = 0 ; int res1 = 0 ; size_t val2 ; int ecode2 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OO:SpikeList_resize",&obj0,&obj1)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_std__listTint_std__allocatorTint_t_t, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "SpikeList_resize" "', argument " "1"" of type '" "std::list *""'"); } arg1 = reinterpret_cast< std::list * >(argp1); ecode2 = SWIG_AsVal_size_t(obj1, &val2); if (!SWIG_IsOK(ecode2)) { SWIG_exception_fail(SWIG_ArgError(ecode2), "in method '" "SpikeList_resize" "', argument " "2"" of type '" "std::list::size_type""'"); } arg2 = static_cast< std::list::size_type >(val2); (arg1)->resize(arg2); resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_SpikeList_erase__SWIG_0(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; std::list *arg1 = (std::list *) 0 ; std::list::iterator arg2 ; std::list::iterator result; void *argp1 = 0 ; int res1 = 0 ; swig::PySwigIterator *iter2 = 0 ; int res2 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OO:SpikeList_erase",&obj0,&obj1)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_std__listTint_std__allocatorTint_t_t, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "SpikeList_erase" "', argument " "1"" of type '" "std::list *""'"); } arg1 = reinterpret_cast< std::list * >(argp1); res2 = SWIG_ConvertPtr(obj1, SWIG_as_voidptrptr(&iter2), swig::PySwigIterator::descriptor(), 0); if (!SWIG_IsOK(res2) || !iter2) { SWIG_exception_fail(SWIG_ArgError(SWIG_TypeError), "in method '" "SpikeList_erase" "', argument " "2"" of type '" "std::list::iterator""'"); } else { swig::PySwigIterator_T::iterator > *iter_t = dynamic_cast::iterator > *>(iter2); if (iter_t) { arg2 = iter_t->get_current(); } else { SWIG_exception_fail(SWIG_ArgError(SWIG_TypeError), "in method '" "SpikeList_erase" "', argument " "2"" of type '" "std::list::iterator""'"); } } result = (arg1)->erase(arg2); resultobj = SWIG_NewPointerObj(swig::make_output_iterator(static_cast< const std::list::iterator & >(result)), swig::PySwigIterator::descriptor(),SWIG_POINTER_OWN); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_SpikeList_erase__SWIG_1(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; std::list *arg1 = (std::list *) 0 ; std::list::iterator arg2 ; std::list::iterator arg3 ; std::list::iterator result; void *argp1 = 0 ; int res1 = 0 ; swig::PySwigIterator *iter2 = 0 ; int res2 ; swig::PySwigIterator *iter3 = 0 ; int res3 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; PyObject * obj2 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OOO:SpikeList_erase",&obj0,&obj1,&obj2)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_std__listTint_std__allocatorTint_t_t, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "SpikeList_erase" "', argument " "1"" of type '" "std::list *""'"); } arg1 = reinterpret_cast< std::list * >(argp1); res2 = SWIG_ConvertPtr(obj1, SWIG_as_voidptrptr(&iter2), swig::PySwigIterator::descriptor(), 0); if (!SWIG_IsOK(res2) || !iter2) { SWIG_exception_fail(SWIG_ArgError(SWIG_TypeError), "in method '" "SpikeList_erase" "', argument " "2"" of type '" "std::list::iterator""'"); } else { swig::PySwigIterator_T::iterator > *iter_t = dynamic_cast::iterator > *>(iter2); if (iter_t) { arg2 = iter_t->get_current(); } else { SWIG_exception_fail(SWIG_ArgError(SWIG_TypeError), "in method '" "SpikeList_erase" "', argument " "2"" of type '" "std::list::iterator""'"); } } res3 = SWIG_ConvertPtr(obj2, SWIG_as_voidptrptr(&iter3), swig::PySwigIterator::descriptor(), 0); if (!SWIG_IsOK(res3) || !iter3) { SWIG_exception_fail(SWIG_ArgError(SWIG_TypeError), "in method '" "SpikeList_erase" "', argument " "3"" of type '" "std::list::iterator""'"); } else { swig::PySwigIterator_T::iterator > *iter_t = dynamic_cast::iterator > *>(iter3); if (iter_t) { arg3 = iter_t->get_current(); } else { SWIG_exception_fail(SWIG_ArgError(SWIG_TypeError), "in method '" "SpikeList_erase" "', argument " "3"" of type '" "std::list::iterator""'"); } } result = (arg1)->erase(arg2,arg3); resultobj = SWIG_NewPointerObj(swig::make_output_iterator(static_cast< const std::list::iterator & >(result)), swig::PySwigIterator::descriptor(),SWIG_POINTER_OWN); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_SpikeList_erase(PyObject *self, PyObject *args) { int argc; PyObject *argv[4]; int ii; if (!PyTuple_Check(args)) SWIG_fail; argc = (int)PyObject_Length(args); for (ii = 0; (ii < argc) && (ii < 3); ii++) { argv[ii] = PyTuple_GET_ITEM(args,ii); } if (argc == 2) { int _v; int res = swig::asptr(argv[0], (std::list >**)(0)); _v = SWIG_CheckState(res); if (_v) { swig::PySwigIterator *iter = 0; int res = SWIG_ConvertPtr(argv[1], SWIG_as_voidptrptr(&iter), swig::PySwigIterator::descriptor(), 0); _v = (SWIG_IsOK(res) && iter && (dynamic_cast::iterator > *>(iter) != 0)); if (_v) { return _wrap_SpikeList_erase__SWIG_0(self, args); } } } if (argc == 3) { int _v; int res = swig::asptr(argv[0], (std::list >**)(0)); _v = SWIG_CheckState(res); if (_v) { swig::PySwigIterator *iter = 0; int res = SWIG_ConvertPtr(argv[1], SWIG_as_voidptrptr(&iter), swig::PySwigIterator::descriptor(), 0); _v = (SWIG_IsOK(res) && iter && (dynamic_cast::iterator > *>(iter) != 0)); if (_v) { swig::PySwigIterator *iter = 0; int res = SWIG_ConvertPtr(argv[2], SWIG_as_voidptrptr(&iter), swig::PySwigIterator::descriptor(), 0); _v = (SWIG_IsOK(res) && iter && (dynamic_cast::iterator > *>(iter) != 0)); if (_v) { return _wrap_SpikeList_erase__SWIG_1(self, args); } } } } fail: SWIG_SetErrorMsg(PyExc_NotImplementedError,"Wrong number of arguments for overloaded function 'SpikeList_erase'.\n Possible C/C++ prototypes are:\n"" erase(std::list *,std::list::iterator)\n"" erase(std::list *,std::list::iterator,std::list::iterator)\n"); return NULL; } SWIGINTERN PyObject *_wrap_new_SpikeList__SWIG_3(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; std::list::size_type arg1 ; std::list::value_type *arg2 = 0 ; std::list *result = 0 ; size_t val1 ; int ecode1 = 0 ; std::list::value_type temp2 ; int val2 ; int ecode2 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OO:new_SpikeList",&obj0,&obj1)) SWIG_fail; ecode1 = SWIG_AsVal_size_t(obj0, &val1); if (!SWIG_IsOK(ecode1)) { SWIG_exception_fail(SWIG_ArgError(ecode1), "in method '" "new_SpikeList" "', argument " "1"" of type '" "std::list::size_type""'"); } arg1 = static_cast< std::list::size_type >(val1); ecode2 = SWIG_AsVal_int(obj1, &val2); if (!SWIG_IsOK(ecode2)) { SWIG_exception_fail(SWIG_ArgError(ecode2), "in method '" "new_SpikeList" "', argument " "2"" of type '" "std::list::value_type""'"); } temp2 = static_cast< std::list::value_type >(val2); arg2 = &temp2; result = (std::list *)new std::list(arg1,(std::list::value_type const &)*arg2); resultobj = SWIG_NewPointerObj(SWIG_as_voidptr(result), SWIGTYPE_p_std__listTint_std__allocatorTint_t_t, SWIG_POINTER_NEW | 0 ); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_new_SpikeList(PyObject *self, PyObject *args) { int argc; PyObject *argv[3]; int ii; if (!PyTuple_Check(args)) SWIG_fail; argc = (int)PyObject_Length(args); for (ii = 0; (ii < argc) && (ii < 2); ii++) { argv[ii] = PyTuple_GET_ITEM(args,ii); } if (argc == 0) { return _wrap_new_SpikeList__SWIG_0(self, args); } if (argc == 1) { int _v; { int res = SWIG_AsVal_size_t(argv[0], NULL); _v = SWIG_CheckState(res); } if (_v) { return _wrap_new_SpikeList__SWIG_2(self, args); } } if (argc == 1) { int _v; int res = swig::asptr(argv[0], (std::list >**)(0)); _v = SWIG_CheckState(res); if (_v) { return _wrap_new_SpikeList__SWIG_1(self, args); } } if (argc == 2) { int _v; { int res = SWIG_AsVal_size_t(argv[0], NULL); _v = SWIG_CheckState(res); } if (_v) { { int res = SWIG_AsVal_int(argv[1], NULL); _v = SWIG_CheckState(res); } if (_v) { return _wrap_new_SpikeList__SWIG_3(self, args); } } } fail: SWIG_SetErrorMsg(PyExc_NotImplementedError,"Wrong number of arguments for overloaded function 'new_SpikeList'.\n Possible C/C++ prototypes are:\n"" std::list<(int)>()\n"" std::list<(int)>(std::list const &)\n"" std::list<(int)>(std::list::size_type)\n"" std::list<(int)>(std::list::size_type,std::list::value_type const &)\n"); return NULL; } SWIGINTERN PyObject *_wrap_SpikeList_push_back(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; std::list *arg1 = (std::list *) 0 ; std::list::value_type *arg2 = 0 ; void *argp1 = 0 ; int res1 = 0 ; std::list::value_type temp2 ; int val2 ; int ecode2 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OO:SpikeList_push_back",&obj0,&obj1)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_std__listTint_std__allocatorTint_t_t, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "SpikeList_push_back" "', argument " "1"" of type '" "std::list *""'"); } arg1 = reinterpret_cast< std::list * >(argp1); ecode2 = SWIG_AsVal_int(obj1, &val2); if (!SWIG_IsOK(ecode2)) { SWIG_exception_fail(SWIG_ArgError(ecode2), "in method '" "SpikeList_push_back" "', argument " "2"" of type '" "std::list::value_type""'"); } temp2 = static_cast< std::list::value_type >(val2); arg2 = &temp2; (arg1)->push_back((std::list::value_type const &)*arg2); resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_SpikeList_front(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; std::list *arg1 = (std::list *) 0 ; std::list::value_type *result = 0 ; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:SpikeList_front",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_std__listTint_std__allocatorTint_t_t, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "SpikeList_front" "', argument " "1"" of type '" "std::list const *""'"); } arg1 = reinterpret_cast< std::list * >(argp1); { std::list::value_type const &_result_ref = ((std::list const *)arg1)->front(); result = (std::list::value_type *) &_result_ref; } resultobj = SWIG_From_int(static_cast< int >(*result)); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_SpikeList_back(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; std::list *arg1 = (std::list *) 0 ; std::list::value_type *result = 0 ; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:SpikeList_back",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_std__listTint_std__allocatorTint_t_t, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "SpikeList_back" "', argument " "1"" of type '" "std::list const *""'"); } arg1 = reinterpret_cast< std::list * >(argp1); { std::list::value_type const &_result_ref = ((std::list const *)arg1)->back(); result = (std::list::value_type *) &_result_ref; } resultobj = SWIG_From_int(static_cast< int >(*result)); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_SpikeList_assign(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; std::list *arg1 = (std::list *) 0 ; std::list::size_type arg2 ; std::list::value_type *arg3 = 0 ; void *argp1 = 0 ; int res1 = 0 ; size_t val2 ; int ecode2 = 0 ; std::list::value_type temp3 ; int val3 ; int ecode3 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; PyObject * obj2 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OOO:SpikeList_assign",&obj0,&obj1,&obj2)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_std__listTint_std__allocatorTint_t_t, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "SpikeList_assign" "', argument " "1"" of type '" "std::list *""'"); } arg1 = reinterpret_cast< std::list * >(argp1); ecode2 = SWIG_AsVal_size_t(obj1, &val2); if (!SWIG_IsOK(ecode2)) { SWIG_exception_fail(SWIG_ArgError(ecode2), "in method '" "SpikeList_assign" "', argument " "2"" of type '" "std::list::size_type""'"); } arg2 = static_cast< std::list::size_type >(val2); ecode3 = SWIG_AsVal_int(obj2, &val3); if (!SWIG_IsOK(ecode3)) { SWIG_exception_fail(SWIG_ArgError(ecode3), "in method '" "SpikeList_assign" "', argument " "3"" of type '" "std::list::value_type""'"); } temp3 = static_cast< std::list::value_type >(val3); arg3 = &temp3; (arg1)->assign(arg2,(std::list::value_type const &)*arg3); resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_SpikeList_resize__SWIG_1(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; std::list *arg1 = (std::list *) 0 ; std::list::size_type arg2 ; std::list::value_type *arg3 = 0 ; void *argp1 = 0 ; int res1 = 0 ; size_t val2 ; int ecode2 = 0 ; std::list::value_type temp3 ; int val3 ; int ecode3 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; PyObject * obj2 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OOO:SpikeList_resize",&obj0,&obj1,&obj2)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_std__listTint_std__allocatorTint_t_t, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "SpikeList_resize" "', argument " "1"" of type '" "std::list *""'"); } arg1 = reinterpret_cast< std::list * >(argp1); ecode2 = SWIG_AsVal_size_t(obj1, &val2); if (!SWIG_IsOK(ecode2)) { SWIG_exception_fail(SWIG_ArgError(ecode2), "in method '" "SpikeList_resize" "', argument " "2"" of type '" "std::list::size_type""'"); } arg2 = static_cast< std::list::size_type >(val2); ecode3 = SWIG_AsVal_int(obj2, &val3); if (!SWIG_IsOK(ecode3)) { SWIG_exception_fail(SWIG_ArgError(ecode3), "in method '" "SpikeList_resize" "', argument " "3"" of type '" "std::list::value_type""'"); } temp3 = static_cast< std::list::value_type >(val3); arg3 = &temp3; (arg1)->resize(arg2,(std::list::value_type const &)*arg3); resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_SpikeList_resize(PyObject *self, PyObject *args) { int argc; PyObject *argv[4]; int ii; if (!PyTuple_Check(args)) SWIG_fail; argc = (int)PyObject_Length(args); for (ii = 0; (ii < argc) && (ii < 3); ii++) { argv[ii] = PyTuple_GET_ITEM(args,ii); } if (argc == 2) { int _v; int res = swig::asptr(argv[0], (std::list >**)(0)); _v = SWIG_CheckState(res); if (_v) { { int res = SWIG_AsVal_size_t(argv[1], NULL); _v = SWIG_CheckState(res); } if (_v) { return _wrap_SpikeList_resize__SWIG_0(self, args); } } } if (argc == 3) { int _v; int res = swig::asptr(argv[0], (std::list >**)(0)); _v = SWIG_CheckState(res); if (_v) { { int res = SWIG_AsVal_size_t(argv[1], NULL); _v = SWIG_CheckState(res); } if (_v) { { int res = SWIG_AsVal_int(argv[2], NULL); _v = SWIG_CheckState(res); } if (_v) { return _wrap_SpikeList_resize__SWIG_1(self, args); } } } } fail: SWIG_SetErrorMsg(PyExc_NotImplementedError,"Wrong number of arguments for overloaded function 'SpikeList_resize'.\n Possible C/C++ prototypes are:\n"" resize(std::list *,std::list::size_type)\n"" resize(std::list *,std::list::size_type,std::list::value_type const &)\n"); return NULL; } SWIGINTERN PyObject *_wrap_SpikeList_insert__SWIG_0(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; std::list *arg1 = (std::list *) 0 ; std::list::iterator arg2 ; std::list::value_type *arg3 = 0 ; std::list::iterator result; void *argp1 = 0 ; int res1 = 0 ; swig::PySwigIterator *iter2 = 0 ; int res2 ; std::list::value_type temp3 ; int val3 ; int ecode3 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; PyObject * obj2 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OOO:SpikeList_insert",&obj0,&obj1,&obj2)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_std__listTint_std__allocatorTint_t_t, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "SpikeList_insert" "', argument " "1"" of type '" "std::list *""'"); } arg1 = reinterpret_cast< std::list * >(argp1); res2 = SWIG_ConvertPtr(obj1, SWIG_as_voidptrptr(&iter2), swig::PySwigIterator::descriptor(), 0); if (!SWIG_IsOK(res2) || !iter2) { SWIG_exception_fail(SWIG_ArgError(SWIG_TypeError), "in method '" "SpikeList_insert" "', argument " "2"" of type '" "std::list::iterator""'"); } else { swig::PySwigIterator_T::iterator > *iter_t = dynamic_cast::iterator > *>(iter2); if (iter_t) { arg2 = iter_t->get_current(); } else { SWIG_exception_fail(SWIG_ArgError(SWIG_TypeError), "in method '" "SpikeList_insert" "', argument " "2"" of type '" "std::list::iterator""'"); } } ecode3 = SWIG_AsVal_int(obj2, &val3); if (!SWIG_IsOK(ecode3)) { SWIG_exception_fail(SWIG_ArgError(ecode3), "in method '" "SpikeList_insert" "', argument " "3"" of type '" "std::list::value_type""'"); } temp3 = static_cast< std::list::value_type >(val3); arg3 = &temp3; result = (arg1)->insert(arg2,(std::list::value_type const &)*arg3); resultobj = SWIG_NewPointerObj(swig::make_output_iterator(static_cast< const std::list::iterator & >(result)), swig::PySwigIterator::descriptor(),SWIG_POINTER_OWN); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_SpikeList_insert__SWIG_1(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; std::list *arg1 = (std::list *) 0 ; std::list::iterator arg2 ; std::list::size_type arg3 ; std::list::value_type *arg4 = 0 ; void *argp1 = 0 ; int res1 = 0 ; swig::PySwigIterator *iter2 = 0 ; int res2 ; size_t val3 ; int ecode3 = 0 ; std::list::value_type temp4 ; int val4 ; int ecode4 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; PyObject * obj2 = 0 ; PyObject * obj3 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OOOO:SpikeList_insert",&obj0,&obj1,&obj2,&obj3)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_std__listTint_std__allocatorTint_t_t, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "SpikeList_insert" "', argument " "1"" of type '" "std::list *""'"); } arg1 = reinterpret_cast< std::list * >(argp1); res2 = SWIG_ConvertPtr(obj1, SWIG_as_voidptrptr(&iter2), swig::PySwigIterator::descriptor(), 0); if (!SWIG_IsOK(res2) || !iter2) { SWIG_exception_fail(SWIG_ArgError(SWIG_TypeError), "in method '" "SpikeList_insert" "', argument " "2"" of type '" "std::list::iterator""'"); } else { swig::PySwigIterator_T::iterator > *iter_t = dynamic_cast::iterator > *>(iter2); if (iter_t) { arg2 = iter_t->get_current(); } else { SWIG_exception_fail(SWIG_ArgError(SWIG_TypeError), "in method '" "SpikeList_insert" "', argument " "2"" of type '" "std::list::iterator""'"); } } ecode3 = SWIG_AsVal_size_t(obj2, &val3); if (!SWIG_IsOK(ecode3)) { SWIG_exception_fail(SWIG_ArgError(ecode3), "in method '" "SpikeList_insert" "', argument " "3"" of type '" "std::list::size_type""'"); } arg3 = static_cast< std::list::size_type >(val3); ecode4 = SWIG_AsVal_int(obj3, &val4); if (!SWIG_IsOK(ecode4)) { SWIG_exception_fail(SWIG_ArgError(ecode4), "in method '" "SpikeList_insert" "', argument " "4"" of type '" "std::list::value_type""'"); } temp4 = static_cast< std::list::value_type >(val4); arg4 = &temp4; (arg1)->insert(arg2,arg3,(std::list::value_type const &)*arg4); resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_SpikeList_insert(PyObject *self, PyObject *args) { int argc; PyObject *argv[5]; int ii; if (!PyTuple_Check(args)) SWIG_fail; argc = (int)PyObject_Length(args); for (ii = 0; (ii < argc) && (ii < 4); ii++) { argv[ii] = PyTuple_GET_ITEM(args,ii); } if (argc == 3) { int _v; int res = swig::asptr(argv[0], (std::list >**)(0)); _v = SWIG_CheckState(res); if (_v) { swig::PySwigIterator *iter = 0; int res = SWIG_ConvertPtr(argv[1], SWIG_as_voidptrptr(&iter), swig::PySwigIterator::descriptor(), 0); _v = (SWIG_IsOK(res) && iter && (dynamic_cast::iterator > *>(iter) != 0)); if (_v) { { int res = SWIG_AsVal_int(argv[2], NULL); _v = SWIG_CheckState(res); } if (_v) { return _wrap_SpikeList_insert__SWIG_0(self, args); } } } } if (argc == 4) { int _v; int res = swig::asptr(argv[0], (std::list >**)(0)); _v = SWIG_CheckState(res); if (_v) { swig::PySwigIterator *iter = 0; int res = SWIG_ConvertPtr(argv[1], SWIG_as_voidptrptr(&iter), swig::PySwigIterator::descriptor(), 0); _v = (SWIG_IsOK(res) && iter && (dynamic_cast::iterator > *>(iter) != 0)); if (_v) { { int res = SWIG_AsVal_size_t(argv[2], NULL); _v = SWIG_CheckState(res); } if (_v) { { int res = SWIG_AsVal_int(argv[3], NULL); _v = SWIG_CheckState(res); } if (_v) { return _wrap_SpikeList_insert__SWIG_1(self, args); } } } } } fail: SWIG_SetErrorMsg(PyExc_NotImplementedError,"Wrong number of arguments for overloaded function 'SpikeList_insert'.\n Possible C/C++ prototypes are:\n"" insert(std::list *,std::list::iterator,std::list::value_type const &)\n"" insert(std::list *,std::list::iterator,std::list::size_type,std::list::value_type const &)\n"); return NULL; } SWIGINTERN PyObject *_wrap_SpikeList_pop_front(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; std::list *arg1 = (std::list *) 0 ; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:SpikeList_pop_front",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_std__listTint_std__allocatorTint_t_t, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "SpikeList_pop_front" "', argument " "1"" of type '" "std::list *""'"); } arg1 = reinterpret_cast< std::list * >(argp1); (arg1)->pop_front(); resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_SpikeList_push_front(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; std::list *arg1 = (std::list *) 0 ; std::list::value_type *arg2 = 0 ; void *argp1 = 0 ; int res1 = 0 ; std::list::value_type temp2 ; int val2 ; int ecode2 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OO:SpikeList_push_front",&obj0,&obj1)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_std__listTint_std__allocatorTint_t_t, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "SpikeList_push_front" "', argument " "1"" of type '" "std::list *""'"); } arg1 = reinterpret_cast< std::list * >(argp1); ecode2 = SWIG_AsVal_int(obj1, &val2); if (!SWIG_IsOK(ecode2)) { SWIG_exception_fail(SWIG_ArgError(ecode2), "in method '" "SpikeList_push_front" "', argument " "2"" of type '" "std::list::value_type""'"); } temp2 = static_cast< std::list::value_type >(val2); arg2 = &temp2; (arg1)->push_front((std::list::value_type const &)*arg2); resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_SpikeList_reverse(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; std::list *arg1 = (std::list *) 0 ; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:SpikeList_reverse",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_std__listTint_std__allocatorTint_t_t, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "SpikeList_reverse" "', argument " "1"" of type '" "std::list *""'"); } arg1 = reinterpret_cast< std::list * >(argp1); (arg1)->reverse(); resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_delete_SpikeList(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; std::list *arg1 = (std::list *) 0 ; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:delete_SpikeList",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_std__listTint_std__allocatorTint_t_t, SWIG_POINTER_DISOWN | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "delete_SpikeList" "', argument " "1"" of type '" "std::list *""'"); } arg1 = reinterpret_cast< std::list * >(argp1); delete arg1; resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *SpikeList_swigregister(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *obj; if (!PyArg_ParseTuple(args,(char*)"O|swigregister", &obj)) return NULL; SWIG_TypeNewClientData(SWIGTYPE_p_std__listTint_std__allocatorTint_t_t, SWIG_NewClientData(obj)); return SWIG_Py_Void(); } SWIGINTERN PyObject *_wrap_VectorDouble_iterator(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; std::vector *arg1 = (std::vector *) 0 ; PyObject **arg2 = (PyObject **) 0 ; swig::PySwigIterator *result = 0 ; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; arg2 = &obj0; if (!PyArg_ParseTuple(args,(char *)"O:VectorDouble_iterator",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_std__vectorTdouble_std__allocatorTdouble_t_t, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "VectorDouble_iterator" "', argument " "1"" of type '" "std::vector *""'"); } arg1 = reinterpret_cast< std::vector * >(argp1); result = (swig::PySwigIterator *)std_vector_Sl_double_Sg__iterator(arg1,arg2); resultobj = SWIG_NewPointerObj(SWIG_as_voidptr(result), SWIGTYPE_p_swig__PySwigIterator, SWIG_POINTER_OWN | 0 ); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_VectorDouble___nonzero__(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; std::vector *arg1 = (std::vector *) 0 ; bool result; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:VectorDouble___nonzero__",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_std__vectorTdouble_std__allocatorTdouble_t_t, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "VectorDouble___nonzero__" "', argument " "1"" of type '" "std::vector const *""'"); } arg1 = reinterpret_cast< std::vector * >(argp1); result = (bool)std_vector_Sl_double_Sg____nonzero__((std::vector const *)arg1); resultobj = SWIG_From_bool(static_cast< bool >(result)); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_VectorDouble___len__(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; std::vector *arg1 = (std::vector *) 0 ; std::vector::size_type result; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:VectorDouble___len__",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_std__vectorTdouble_std__allocatorTdouble_t_t, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "VectorDouble___len__" "', argument " "1"" of type '" "std::vector const *""'"); } arg1 = reinterpret_cast< std::vector * >(argp1); result = std_vector_Sl_double_Sg____len__((std::vector const *)arg1); resultobj = SWIG_From_size_t(static_cast< size_t >(result)); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_VectorDouble_pop(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; std::vector *arg1 = (std::vector *) 0 ; std::vector::value_type result; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:VectorDouble_pop",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_std__vectorTdouble_std__allocatorTdouble_t_t, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "VectorDouble_pop" "', argument " "1"" of type '" "std::vector *""'"); } arg1 = reinterpret_cast< std::vector * >(argp1); try { result = (std::vector::value_type)std_vector_Sl_double_Sg__pop(arg1); } catch(std::out_of_range &_e) { SWIG_exception_fail(SWIG_IndexError, (&_e)->what()); } resultobj = SWIG_From_double(static_cast< double >(result)); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_VectorDouble___getslice__(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; std::vector *arg1 = (std::vector *) 0 ; std::vector::difference_type arg2 ; std::vector::difference_type arg3 ; std::vector > *result = 0 ; void *argp1 = 0 ; int res1 = 0 ; ptrdiff_t val2 ; int ecode2 = 0 ; ptrdiff_t val3 ; int ecode3 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; PyObject * obj2 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OOO:VectorDouble___getslice__",&obj0,&obj1,&obj2)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_std__vectorTdouble_std__allocatorTdouble_t_t, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "VectorDouble___getslice__" "', argument " "1"" of type '" "std::vector *""'"); } arg1 = reinterpret_cast< std::vector * >(argp1); ecode2 = SWIG_AsVal_ptrdiff_t(obj1, &val2); if (!SWIG_IsOK(ecode2)) { SWIG_exception_fail(SWIG_ArgError(ecode2), "in method '" "VectorDouble___getslice__" "', argument " "2"" of type '" "std::vector::difference_type""'"); } arg2 = static_cast< std::vector::difference_type >(val2); ecode3 = SWIG_AsVal_ptrdiff_t(obj2, &val3); if (!SWIG_IsOK(ecode3)) { SWIG_exception_fail(SWIG_ArgError(ecode3), "in method '" "VectorDouble___getslice__" "', argument " "3"" of type '" "std::vector::difference_type""'"); } arg3 = static_cast< std::vector::difference_type >(val3); try { result = (std::vector > *)std_vector_Sl_double_Sg____getslice__(arg1,arg2,arg3); } catch(std::out_of_range &_e) { SWIG_exception_fail(SWIG_IndexError, (&_e)->what()); } resultobj = SWIG_NewPointerObj(SWIG_as_voidptr(result), SWIGTYPE_p_std__vectorTdouble_std__allocatorTdouble_t_t, SWIG_POINTER_OWN | 0 ); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_VectorDouble___setslice__(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; std::vector *arg1 = (std::vector *) 0 ; std::vector::difference_type arg2 ; std::vector::difference_type arg3 ; std::vector > *arg4 = 0 ; void *argp1 = 0 ; int res1 = 0 ; ptrdiff_t val2 ; int ecode2 = 0 ; ptrdiff_t val3 ; int ecode3 = 0 ; int res4 = SWIG_OLDOBJ ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; PyObject * obj2 = 0 ; PyObject * obj3 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OOOO:VectorDouble___setslice__",&obj0,&obj1,&obj2,&obj3)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_std__vectorTdouble_std__allocatorTdouble_t_t, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "VectorDouble___setslice__" "', argument " "1"" of type '" "std::vector *""'"); } arg1 = reinterpret_cast< std::vector * >(argp1); ecode2 = SWIG_AsVal_ptrdiff_t(obj1, &val2); if (!SWIG_IsOK(ecode2)) { SWIG_exception_fail(SWIG_ArgError(ecode2), "in method '" "VectorDouble___setslice__" "', argument " "2"" of type '" "std::vector::difference_type""'"); } arg2 = static_cast< std::vector::difference_type >(val2); ecode3 = SWIG_AsVal_ptrdiff_t(obj2, &val3); if (!SWIG_IsOK(ecode3)) { SWIG_exception_fail(SWIG_ArgError(ecode3), "in method '" "VectorDouble___setslice__" "', argument " "3"" of type '" "std::vector::difference_type""'"); } arg3 = static_cast< std::vector::difference_type >(val3); { std::vector > *ptr = (std::vector > *)0; res4 = swig::asptr(obj3, &ptr); if (!SWIG_IsOK(res4)) { SWIG_exception_fail(SWIG_ArgError(res4), "in method '" "VectorDouble___setslice__" "', argument " "4"" of type '" "std::vector > const &""'"); } if (!ptr) { SWIG_exception_fail(SWIG_ValueError, "invalid null reference " "in method '" "VectorDouble___setslice__" "', argument " "4"" of type '" "std::vector > const &""'"); } arg4 = ptr; } try { std_vector_Sl_double_Sg____setslice__(arg1,arg2,arg3,(std::vector > const &)*arg4); } catch(std::out_of_range &_e) { SWIG_exception_fail(SWIG_IndexError, (&_e)->what()); } catch(std::invalid_argument &_e) { SWIG_exception_fail(SWIG_ValueError, (&_e)->what()); } resultobj = SWIG_Py_Void(); if (SWIG_IsNewObj(res4)) delete arg4; return resultobj; fail: if (SWIG_IsNewObj(res4)) delete arg4; return NULL; } SWIGINTERN PyObject *_wrap_VectorDouble___delslice__(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; std::vector *arg1 = (std::vector *) 0 ; std::vector::difference_type arg2 ; std::vector::difference_type arg3 ; void *argp1 = 0 ; int res1 = 0 ; ptrdiff_t val2 ; int ecode2 = 0 ; ptrdiff_t val3 ; int ecode3 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; PyObject * obj2 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OOO:VectorDouble___delslice__",&obj0,&obj1,&obj2)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_std__vectorTdouble_std__allocatorTdouble_t_t, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "VectorDouble___delslice__" "', argument " "1"" of type '" "std::vector *""'"); } arg1 = reinterpret_cast< std::vector * >(argp1); ecode2 = SWIG_AsVal_ptrdiff_t(obj1, &val2); if (!SWIG_IsOK(ecode2)) { SWIG_exception_fail(SWIG_ArgError(ecode2), "in method '" "VectorDouble___delslice__" "', argument " "2"" of type '" "std::vector::difference_type""'"); } arg2 = static_cast< std::vector::difference_type >(val2); ecode3 = SWIG_AsVal_ptrdiff_t(obj2, &val3); if (!SWIG_IsOK(ecode3)) { SWIG_exception_fail(SWIG_ArgError(ecode3), "in method '" "VectorDouble___delslice__" "', argument " "3"" of type '" "std::vector::difference_type""'"); } arg3 = static_cast< std::vector::difference_type >(val3); try { std_vector_Sl_double_Sg____delslice__(arg1,arg2,arg3); } catch(std::out_of_range &_e) { SWIG_exception_fail(SWIG_IndexError, (&_e)->what()); } resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_VectorDouble___delitem__(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; std::vector *arg1 = (std::vector *) 0 ; std::vector::difference_type arg2 ; void *argp1 = 0 ; int res1 = 0 ; ptrdiff_t val2 ; int ecode2 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OO:VectorDouble___delitem__",&obj0,&obj1)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_std__vectorTdouble_std__allocatorTdouble_t_t, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "VectorDouble___delitem__" "', argument " "1"" of type '" "std::vector *""'"); } arg1 = reinterpret_cast< std::vector * >(argp1); ecode2 = SWIG_AsVal_ptrdiff_t(obj1, &val2); if (!SWIG_IsOK(ecode2)) { SWIG_exception_fail(SWIG_ArgError(ecode2), "in method '" "VectorDouble___delitem__" "', argument " "2"" of type '" "std::vector::difference_type""'"); } arg2 = static_cast< std::vector::difference_type >(val2); try { std_vector_Sl_double_Sg____delitem__(arg1,arg2); } catch(std::out_of_range &_e) { SWIG_exception_fail(SWIG_IndexError, (&_e)->what()); } resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_VectorDouble___getitem__(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; std::vector *arg1 = (std::vector *) 0 ; std::vector::difference_type arg2 ; std::vector::value_type *result = 0 ; void *argp1 = 0 ; int res1 = 0 ; ptrdiff_t val2 ; int ecode2 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OO:VectorDouble___getitem__",&obj0,&obj1)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_std__vectorTdouble_std__allocatorTdouble_t_t, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "VectorDouble___getitem__" "', argument " "1"" of type '" "std::vector const *""'"); } arg1 = reinterpret_cast< std::vector * >(argp1); ecode2 = SWIG_AsVal_ptrdiff_t(obj1, &val2); if (!SWIG_IsOK(ecode2)) { SWIG_exception_fail(SWIG_ArgError(ecode2), "in method '" "VectorDouble___getitem__" "', argument " "2"" of type '" "std::vector::difference_type""'"); } arg2 = static_cast< std::vector::difference_type >(val2); try { { std::vector::value_type const &_result_ref = std_vector_Sl_double_Sg____getitem__((std::vector const *)arg1,arg2); result = (std::vector::value_type *) &_result_ref; } } catch(std::out_of_range &_e) { SWIG_exception_fail(SWIG_IndexError, (&_e)->what()); } resultobj = SWIG_From_double(static_cast< double >(*result)); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_VectorDouble___setitem__(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; std::vector *arg1 = (std::vector *) 0 ; std::vector::difference_type arg2 ; std::vector::value_type *arg3 = 0 ; void *argp1 = 0 ; int res1 = 0 ; ptrdiff_t val2 ; int ecode2 = 0 ; std::vector::value_type temp3 ; double val3 ; int ecode3 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; PyObject * obj2 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OOO:VectorDouble___setitem__",&obj0,&obj1,&obj2)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_std__vectorTdouble_std__allocatorTdouble_t_t, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "VectorDouble___setitem__" "', argument " "1"" of type '" "std::vector *""'"); } arg1 = reinterpret_cast< std::vector * >(argp1); ecode2 = SWIG_AsVal_ptrdiff_t(obj1, &val2); if (!SWIG_IsOK(ecode2)) { SWIG_exception_fail(SWIG_ArgError(ecode2), "in method '" "VectorDouble___setitem__" "', argument " "2"" of type '" "std::vector::difference_type""'"); } arg2 = static_cast< std::vector::difference_type >(val2); ecode3 = SWIG_AsVal_double(obj2, &val3); if (!SWIG_IsOK(ecode3)) { SWIG_exception_fail(SWIG_ArgError(ecode3), "in method '" "VectorDouble___setitem__" "', argument " "3"" of type '" "std::vector::value_type""'"); } temp3 = static_cast< std::vector::value_type >(val3); arg3 = &temp3; try { std_vector_Sl_double_Sg____setitem__(arg1,arg2,(double const &)*arg3); } catch(std::out_of_range &_e) { SWIG_exception_fail(SWIG_IndexError, (&_e)->what()); } resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_VectorDouble_append(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; std::vector *arg1 = (std::vector *) 0 ; std::vector::value_type *arg2 = 0 ; void *argp1 = 0 ; int res1 = 0 ; std::vector::value_type temp2 ; double val2 ; int ecode2 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OO:VectorDouble_append",&obj0,&obj1)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_std__vectorTdouble_std__allocatorTdouble_t_t, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "VectorDouble_append" "', argument " "1"" of type '" "std::vector *""'"); } arg1 = reinterpret_cast< std::vector * >(argp1); ecode2 = SWIG_AsVal_double(obj1, &val2); if (!SWIG_IsOK(ecode2)) { SWIG_exception_fail(SWIG_ArgError(ecode2), "in method '" "VectorDouble_append" "', argument " "2"" of type '" "std::vector::value_type""'"); } temp2 = static_cast< std::vector::value_type >(val2); arg2 = &temp2; std_vector_Sl_double_Sg__append(arg1,(double const &)*arg2); resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_new_VectorDouble__SWIG_0(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; std::vector *result = 0 ; if (!PyArg_ParseTuple(args,(char *)":new_VectorDouble")) SWIG_fail; result = (std::vector *)new std::vector(); resultobj = SWIG_NewPointerObj(SWIG_as_voidptr(result), SWIGTYPE_p_std__vectorTdouble_std__allocatorTdouble_t_t, SWIG_POINTER_NEW | 0 ); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_new_VectorDouble__SWIG_1(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; std::vector *arg1 = 0 ; std::vector *result = 0 ; int res1 = SWIG_OLDOBJ ; PyObject * obj0 = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:new_VectorDouble",&obj0)) SWIG_fail; { std::vector > *ptr = (std::vector > *)0; res1 = swig::asptr(obj0, &ptr); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "new_VectorDouble" "', argument " "1"" of type '" "std::vector const &""'"); } if (!ptr) { SWIG_exception_fail(SWIG_ValueError, "invalid null reference " "in method '" "new_VectorDouble" "', argument " "1"" of type '" "std::vector const &""'"); } arg1 = ptr; } result = (std::vector *)new std::vector((std::vector const &)*arg1); resultobj = SWIG_NewPointerObj(SWIG_as_voidptr(result), SWIGTYPE_p_std__vectorTdouble_std__allocatorTdouble_t_t, SWIG_POINTER_NEW | 0 ); if (SWIG_IsNewObj(res1)) delete arg1; return resultobj; fail: if (SWIG_IsNewObj(res1)) delete arg1; return NULL; } SWIGINTERN PyObject *_wrap_VectorDouble_empty(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; std::vector *arg1 = (std::vector *) 0 ; bool result; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:VectorDouble_empty",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_std__vectorTdouble_std__allocatorTdouble_t_t, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "VectorDouble_empty" "', argument " "1"" of type '" "std::vector const *""'"); } arg1 = reinterpret_cast< std::vector * >(argp1); result = (bool)((std::vector const *)arg1)->empty(); resultobj = SWIG_From_bool(static_cast< bool >(result)); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_VectorDouble_size(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; std::vector *arg1 = (std::vector *) 0 ; std::vector::size_type result; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:VectorDouble_size",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_std__vectorTdouble_std__allocatorTdouble_t_t, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "VectorDouble_size" "', argument " "1"" of type '" "std::vector const *""'"); } arg1 = reinterpret_cast< std::vector * >(argp1); result = ((std::vector const *)arg1)->size(); resultobj = SWIG_From_size_t(static_cast< size_t >(result)); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_VectorDouble_clear(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; std::vector *arg1 = (std::vector *) 0 ; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:VectorDouble_clear",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_std__vectorTdouble_std__allocatorTdouble_t_t, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "VectorDouble_clear" "', argument " "1"" of type '" "std::vector *""'"); } arg1 = reinterpret_cast< std::vector * >(argp1); (arg1)->clear(); resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_VectorDouble_swap(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; std::vector *arg1 = (std::vector *) 0 ; std::vector *arg2 = 0 ; void *argp1 = 0 ; int res1 = 0 ; void *argp2 = 0 ; int res2 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OO:VectorDouble_swap",&obj0,&obj1)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_std__vectorTdouble_std__allocatorTdouble_t_t, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "VectorDouble_swap" "', argument " "1"" of type '" "std::vector *""'"); } arg1 = reinterpret_cast< std::vector * >(argp1); res2 = SWIG_ConvertPtr(obj1, &argp2, SWIGTYPE_p_std__vectorTdouble_std__allocatorTdouble_t_t, 0 ); if (!SWIG_IsOK(res2)) { SWIG_exception_fail(SWIG_ArgError(res2), "in method '" "VectorDouble_swap" "', argument " "2"" of type '" "std::vector &""'"); } if (!argp2) { SWIG_exception_fail(SWIG_ValueError, "invalid null reference " "in method '" "VectorDouble_swap" "', argument " "2"" of type '" "std::vector &""'"); } arg2 = reinterpret_cast< std::vector * >(argp2); (arg1)->swap(*arg2); resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_VectorDouble_get_allocator(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; std::vector *arg1 = (std::vector *) 0 ; SwigValueWrapper< std::allocator > result; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:VectorDouble_get_allocator",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_std__vectorTdouble_std__allocatorTdouble_t_t, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "VectorDouble_get_allocator" "', argument " "1"" of type '" "std::vector const *""'"); } arg1 = reinterpret_cast< std::vector * >(argp1); result = ((std::vector const *)arg1)->get_allocator(); resultobj = SWIG_NewPointerObj((new std::vector::allocator_type(static_cast< const std::vector::allocator_type& >(result))), SWIGTYPE_p_std__vectorTdouble_std__allocatorTdouble_t_t__allocator_type, SWIG_POINTER_OWN | 0 ); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_VectorDouble_begin__SWIG_0(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; std::vector *arg1 = (std::vector *) 0 ; std::vector::iterator result; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:VectorDouble_begin",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_std__vectorTdouble_std__allocatorTdouble_t_t, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "VectorDouble_begin" "', argument " "1"" of type '" "std::vector *""'"); } arg1 = reinterpret_cast< std::vector * >(argp1); result = (arg1)->begin(); resultobj = SWIG_NewPointerObj(swig::make_output_iterator(static_cast< const std::vector::iterator & >(result)), swig::PySwigIterator::descriptor(),SWIG_POINTER_OWN); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_VectorDouble_begin__SWIG_1(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; std::vector *arg1 = (std::vector *) 0 ; std::vector::const_iterator result; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:VectorDouble_begin",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_std__vectorTdouble_std__allocatorTdouble_t_t, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "VectorDouble_begin" "', argument " "1"" of type '" "std::vector const *""'"); } arg1 = reinterpret_cast< std::vector * >(argp1); result = ((std::vector const *)arg1)->begin(); resultobj = SWIG_NewPointerObj(swig::make_output_iterator(static_cast< const std::vector::const_iterator & >(result)), swig::PySwigIterator::descriptor(),SWIG_POINTER_OWN); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_VectorDouble_begin(PyObject *self, PyObject *args) { int argc; PyObject *argv[2]; int ii; if (!PyTuple_Check(args)) SWIG_fail; argc = (int)PyObject_Length(args); for (ii = 0; (ii < argc) && (ii < 1); ii++) { argv[ii] = PyTuple_GET_ITEM(args,ii); } if (argc == 1) { int _v; int res = swig::asptr(argv[0], (std::vector >**)(0)); _v = SWIG_CheckState(res); if (_v) { return _wrap_VectorDouble_begin__SWIG_0(self, args); } } if (argc == 1) { int _v; int res = swig::asptr(argv[0], (std::vector >**)(0)); _v = SWIG_CheckState(res); if (_v) { return _wrap_VectorDouble_begin__SWIG_1(self, args); } } fail: SWIG_SetErrorMsg(PyExc_NotImplementedError,"Wrong number of arguments for overloaded function 'VectorDouble_begin'.\n Possible C/C++ prototypes are:\n"" begin(std::vector *)\n"" begin(std::vector const *)\n"); return NULL; } SWIGINTERN PyObject *_wrap_VectorDouble_end__SWIG_0(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; std::vector *arg1 = (std::vector *) 0 ; std::vector::iterator result; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:VectorDouble_end",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_std__vectorTdouble_std__allocatorTdouble_t_t, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "VectorDouble_end" "', argument " "1"" of type '" "std::vector *""'"); } arg1 = reinterpret_cast< std::vector * >(argp1); result = (arg1)->end(); resultobj = SWIG_NewPointerObj(swig::make_output_iterator(static_cast< const std::vector::iterator & >(result)), swig::PySwigIterator::descriptor(),SWIG_POINTER_OWN); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_VectorDouble_end__SWIG_1(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; std::vector *arg1 = (std::vector *) 0 ; std::vector::const_iterator result; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:VectorDouble_end",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_std__vectorTdouble_std__allocatorTdouble_t_t, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "VectorDouble_end" "', argument " "1"" of type '" "std::vector const *""'"); } arg1 = reinterpret_cast< std::vector * >(argp1); result = ((std::vector const *)arg1)->end(); resultobj = SWIG_NewPointerObj(swig::make_output_iterator(static_cast< const std::vector::const_iterator & >(result)), swig::PySwigIterator::descriptor(),SWIG_POINTER_OWN); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_VectorDouble_end(PyObject *self, PyObject *args) { int argc; PyObject *argv[2]; int ii; if (!PyTuple_Check(args)) SWIG_fail; argc = (int)PyObject_Length(args); for (ii = 0; (ii < argc) && (ii < 1); ii++) { argv[ii] = PyTuple_GET_ITEM(args,ii); } if (argc == 1) { int _v; int res = swig::asptr(argv[0], (std::vector >**)(0)); _v = SWIG_CheckState(res); if (_v) { return _wrap_VectorDouble_end__SWIG_0(self, args); } } if (argc == 1) { int _v; int res = swig::asptr(argv[0], (std::vector >**)(0)); _v = SWIG_CheckState(res); if (_v) { return _wrap_VectorDouble_end__SWIG_1(self, args); } } fail: SWIG_SetErrorMsg(PyExc_NotImplementedError,"Wrong number of arguments for overloaded function 'VectorDouble_end'.\n Possible C/C++ prototypes are:\n"" end(std::vector *)\n"" end(std::vector const *)\n"); return NULL; } SWIGINTERN PyObject *_wrap_VectorDouble_rbegin__SWIG_0(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; std::vector *arg1 = (std::vector *) 0 ; std::vector::reverse_iterator result; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:VectorDouble_rbegin",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_std__vectorTdouble_std__allocatorTdouble_t_t, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "VectorDouble_rbegin" "', argument " "1"" of type '" "std::vector *""'"); } arg1 = reinterpret_cast< std::vector * >(argp1); result = (arg1)->rbegin(); resultobj = SWIG_NewPointerObj(swig::make_output_iterator(static_cast< const std::vector::reverse_iterator & >(result)), swig::PySwigIterator::descriptor(),SWIG_POINTER_OWN); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_VectorDouble_rbegin__SWIG_1(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; std::vector *arg1 = (std::vector *) 0 ; std::vector::const_reverse_iterator result; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:VectorDouble_rbegin",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_std__vectorTdouble_std__allocatorTdouble_t_t, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "VectorDouble_rbegin" "', argument " "1"" of type '" "std::vector const *""'"); } arg1 = reinterpret_cast< std::vector * >(argp1); result = ((std::vector const *)arg1)->rbegin(); resultobj = SWIG_NewPointerObj(swig::make_output_iterator(static_cast< const std::vector::const_reverse_iterator & >(result)), swig::PySwigIterator::descriptor(),SWIG_POINTER_OWN); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_VectorDouble_rbegin(PyObject *self, PyObject *args) { int argc; PyObject *argv[2]; int ii; if (!PyTuple_Check(args)) SWIG_fail; argc = (int)PyObject_Length(args); for (ii = 0; (ii < argc) && (ii < 1); ii++) { argv[ii] = PyTuple_GET_ITEM(args,ii); } if (argc == 1) { int _v; int res = swig::asptr(argv[0], (std::vector >**)(0)); _v = SWIG_CheckState(res); if (_v) { return _wrap_VectorDouble_rbegin__SWIG_0(self, args); } } if (argc == 1) { int _v; int res = swig::asptr(argv[0], (std::vector >**)(0)); _v = SWIG_CheckState(res); if (_v) { return _wrap_VectorDouble_rbegin__SWIG_1(self, args); } } fail: SWIG_SetErrorMsg(PyExc_NotImplementedError,"Wrong number of arguments for overloaded function 'VectorDouble_rbegin'.\n Possible C/C++ prototypes are:\n"" rbegin(std::vector *)\n"" rbegin(std::vector const *)\n"); return NULL; } SWIGINTERN PyObject *_wrap_VectorDouble_rend__SWIG_0(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; std::vector *arg1 = (std::vector *) 0 ; std::vector::reverse_iterator result; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:VectorDouble_rend",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_std__vectorTdouble_std__allocatorTdouble_t_t, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "VectorDouble_rend" "', argument " "1"" of type '" "std::vector *""'"); } arg1 = reinterpret_cast< std::vector * >(argp1); result = (arg1)->rend(); resultobj = SWIG_NewPointerObj(swig::make_output_iterator(static_cast< const std::vector::reverse_iterator & >(result)), swig::PySwigIterator::descriptor(),SWIG_POINTER_OWN); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_VectorDouble_rend__SWIG_1(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; std::vector *arg1 = (std::vector *) 0 ; std::vector::const_reverse_iterator result; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:VectorDouble_rend",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_std__vectorTdouble_std__allocatorTdouble_t_t, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "VectorDouble_rend" "', argument " "1"" of type '" "std::vector const *""'"); } arg1 = reinterpret_cast< std::vector * >(argp1); result = ((std::vector const *)arg1)->rend(); resultobj = SWIG_NewPointerObj(swig::make_output_iterator(static_cast< const std::vector::const_reverse_iterator & >(result)), swig::PySwigIterator::descriptor(),SWIG_POINTER_OWN); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_VectorDouble_rend(PyObject *self, PyObject *args) { int argc; PyObject *argv[2]; int ii; if (!PyTuple_Check(args)) SWIG_fail; argc = (int)PyObject_Length(args); for (ii = 0; (ii < argc) && (ii < 1); ii++) { argv[ii] = PyTuple_GET_ITEM(args,ii); } if (argc == 1) { int _v; int res = swig::asptr(argv[0], (std::vector >**)(0)); _v = SWIG_CheckState(res); if (_v) { return _wrap_VectorDouble_rend__SWIG_0(self, args); } } if (argc == 1) { int _v; int res = swig::asptr(argv[0], (std::vector >**)(0)); _v = SWIG_CheckState(res); if (_v) { return _wrap_VectorDouble_rend__SWIG_1(self, args); } } fail: SWIG_SetErrorMsg(PyExc_NotImplementedError,"Wrong number of arguments for overloaded function 'VectorDouble_rend'.\n Possible C/C++ prototypes are:\n"" rend(std::vector *)\n"" rend(std::vector const *)\n"); return NULL; } SWIGINTERN PyObject *_wrap_new_VectorDouble__SWIG_2(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; std::vector::size_type arg1 ; std::vector *result = 0 ; size_t val1 ; int ecode1 = 0 ; PyObject * obj0 = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:new_VectorDouble",&obj0)) SWIG_fail; ecode1 = SWIG_AsVal_size_t(obj0, &val1); if (!SWIG_IsOK(ecode1)) { SWIG_exception_fail(SWIG_ArgError(ecode1), "in method '" "new_VectorDouble" "', argument " "1"" of type '" "std::vector::size_type""'"); } arg1 = static_cast< std::vector::size_type >(val1); result = (std::vector *)new std::vector(arg1); resultobj = SWIG_NewPointerObj(SWIG_as_voidptr(result), SWIGTYPE_p_std__vectorTdouble_std__allocatorTdouble_t_t, SWIG_POINTER_NEW | 0 ); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_VectorDouble_pop_back(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; std::vector *arg1 = (std::vector *) 0 ; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:VectorDouble_pop_back",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_std__vectorTdouble_std__allocatorTdouble_t_t, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "VectorDouble_pop_back" "', argument " "1"" of type '" "std::vector *""'"); } arg1 = reinterpret_cast< std::vector * >(argp1); (arg1)->pop_back(); resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_VectorDouble_resize__SWIG_0(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; std::vector *arg1 = (std::vector *) 0 ; std::vector::size_type arg2 ; void *argp1 = 0 ; int res1 = 0 ; size_t val2 ; int ecode2 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OO:VectorDouble_resize",&obj0,&obj1)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_std__vectorTdouble_std__allocatorTdouble_t_t, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "VectorDouble_resize" "', argument " "1"" of type '" "std::vector *""'"); } arg1 = reinterpret_cast< std::vector * >(argp1); ecode2 = SWIG_AsVal_size_t(obj1, &val2); if (!SWIG_IsOK(ecode2)) { SWIG_exception_fail(SWIG_ArgError(ecode2), "in method '" "VectorDouble_resize" "', argument " "2"" of type '" "std::vector::size_type""'"); } arg2 = static_cast< std::vector::size_type >(val2); (arg1)->resize(arg2); resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_VectorDouble_erase__SWIG_0(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; std::vector *arg1 = (std::vector *) 0 ; std::vector::iterator arg2 ; std::vector::iterator result; void *argp1 = 0 ; int res1 = 0 ; swig::PySwigIterator *iter2 = 0 ; int res2 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OO:VectorDouble_erase",&obj0,&obj1)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_std__vectorTdouble_std__allocatorTdouble_t_t, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "VectorDouble_erase" "', argument " "1"" of type '" "std::vector *""'"); } arg1 = reinterpret_cast< std::vector * >(argp1); res2 = SWIG_ConvertPtr(obj1, SWIG_as_voidptrptr(&iter2), swig::PySwigIterator::descriptor(), 0); if (!SWIG_IsOK(res2) || !iter2) { SWIG_exception_fail(SWIG_ArgError(SWIG_TypeError), "in method '" "VectorDouble_erase" "', argument " "2"" of type '" "std::vector::iterator""'"); } else { swig::PySwigIterator_T::iterator > *iter_t = dynamic_cast::iterator > *>(iter2); if (iter_t) { arg2 = iter_t->get_current(); } else { SWIG_exception_fail(SWIG_ArgError(SWIG_TypeError), "in method '" "VectorDouble_erase" "', argument " "2"" of type '" "std::vector::iterator""'"); } } result = (arg1)->erase(arg2); resultobj = SWIG_NewPointerObj(swig::make_output_iterator(static_cast< const std::vector::iterator & >(result)), swig::PySwigIterator::descriptor(),SWIG_POINTER_OWN); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_VectorDouble_erase__SWIG_1(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; std::vector *arg1 = (std::vector *) 0 ; std::vector::iterator arg2 ; std::vector::iterator arg3 ; std::vector::iterator result; void *argp1 = 0 ; int res1 = 0 ; swig::PySwigIterator *iter2 = 0 ; int res2 ; swig::PySwigIterator *iter3 = 0 ; int res3 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; PyObject * obj2 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OOO:VectorDouble_erase",&obj0,&obj1,&obj2)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_std__vectorTdouble_std__allocatorTdouble_t_t, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "VectorDouble_erase" "', argument " "1"" of type '" "std::vector *""'"); } arg1 = reinterpret_cast< std::vector * >(argp1); res2 = SWIG_ConvertPtr(obj1, SWIG_as_voidptrptr(&iter2), swig::PySwigIterator::descriptor(), 0); if (!SWIG_IsOK(res2) || !iter2) { SWIG_exception_fail(SWIG_ArgError(SWIG_TypeError), "in method '" "VectorDouble_erase" "', argument " "2"" of type '" "std::vector::iterator""'"); } else { swig::PySwigIterator_T::iterator > *iter_t = dynamic_cast::iterator > *>(iter2); if (iter_t) { arg2 = iter_t->get_current(); } else { SWIG_exception_fail(SWIG_ArgError(SWIG_TypeError), "in method '" "VectorDouble_erase" "', argument " "2"" of type '" "std::vector::iterator""'"); } } res3 = SWIG_ConvertPtr(obj2, SWIG_as_voidptrptr(&iter3), swig::PySwigIterator::descriptor(), 0); if (!SWIG_IsOK(res3) || !iter3) { SWIG_exception_fail(SWIG_ArgError(SWIG_TypeError), "in method '" "VectorDouble_erase" "', argument " "3"" of type '" "std::vector::iterator""'"); } else { swig::PySwigIterator_T::iterator > *iter_t = dynamic_cast::iterator > *>(iter3); if (iter_t) { arg3 = iter_t->get_current(); } else { SWIG_exception_fail(SWIG_ArgError(SWIG_TypeError), "in method '" "VectorDouble_erase" "', argument " "3"" of type '" "std::vector::iterator""'"); } } result = (arg1)->erase(arg2,arg3); resultobj = SWIG_NewPointerObj(swig::make_output_iterator(static_cast< const std::vector::iterator & >(result)), swig::PySwigIterator::descriptor(),SWIG_POINTER_OWN); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_VectorDouble_erase(PyObject *self, PyObject *args) { int argc; PyObject *argv[4]; int ii; if (!PyTuple_Check(args)) SWIG_fail; argc = (int)PyObject_Length(args); for (ii = 0; (ii < argc) && (ii < 3); ii++) { argv[ii] = PyTuple_GET_ITEM(args,ii); } if (argc == 2) { int _v; int res = swig::asptr(argv[0], (std::vector >**)(0)); _v = SWIG_CheckState(res); if (_v) { swig::PySwigIterator *iter = 0; int res = SWIG_ConvertPtr(argv[1], SWIG_as_voidptrptr(&iter), swig::PySwigIterator::descriptor(), 0); _v = (SWIG_IsOK(res) && iter && (dynamic_cast::iterator > *>(iter) != 0)); if (_v) { return _wrap_VectorDouble_erase__SWIG_0(self, args); } } } if (argc == 3) { int _v; int res = swig::asptr(argv[0], (std::vector >**)(0)); _v = SWIG_CheckState(res); if (_v) { swig::PySwigIterator *iter = 0; int res = SWIG_ConvertPtr(argv[1], SWIG_as_voidptrptr(&iter), swig::PySwigIterator::descriptor(), 0); _v = (SWIG_IsOK(res) && iter && (dynamic_cast::iterator > *>(iter) != 0)); if (_v) { swig::PySwigIterator *iter = 0; int res = SWIG_ConvertPtr(argv[2], SWIG_as_voidptrptr(&iter), swig::PySwigIterator::descriptor(), 0); _v = (SWIG_IsOK(res) && iter && (dynamic_cast::iterator > *>(iter) != 0)); if (_v) { return _wrap_VectorDouble_erase__SWIG_1(self, args); } } } } fail: SWIG_SetErrorMsg(PyExc_NotImplementedError,"Wrong number of arguments for overloaded function 'VectorDouble_erase'.\n Possible C/C++ prototypes are:\n"" erase(std::vector *,std::vector::iterator)\n"" erase(std::vector *,std::vector::iterator,std::vector::iterator)\n"); return NULL; } SWIGINTERN PyObject *_wrap_new_VectorDouble__SWIG_3(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; std::vector::size_type arg1 ; std::vector::value_type *arg2 = 0 ; std::vector *result = 0 ; size_t val1 ; int ecode1 = 0 ; std::vector::value_type temp2 ; double val2 ; int ecode2 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OO:new_VectorDouble",&obj0,&obj1)) SWIG_fail; ecode1 = SWIG_AsVal_size_t(obj0, &val1); if (!SWIG_IsOK(ecode1)) { SWIG_exception_fail(SWIG_ArgError(ecode1), "in method '" "new_VectorDouble" "', argument " "1"" of type '" "std::vector::size_type""'"); } arg1 = static_cast< std::vector::size_type >(val1); ecode2 = SWIG_AsVal_double(obj1, &val2); if (!SWIG_IsOK(ecode2)) { SWIG_exception_fail(SWIG_ArgError(ecode2), "in method '" "new_VectorDouble" "', argument " "2"" of type '" "std::vector::value_type""'"); } temp2 = static_cast< std::vector::value_type >(val2); arg2 = &temp2; result = (std::vector *)new std::vector(arg1,(std::vector::value_type const &)*arg2); resultobj = SWIG_NewPointerObj(SWIG_as_voidptr(result), SWIGTYPE_p_std__vectorTdouble_std__allocatorTdouble_t_t, SWIG_POINTER_NEW | 0 ); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_new_VectorDouble(PyObject *self, PyObject *args) { int argc; PyObject *argv[3]; int ii; if (!PyTuple_Check(args)) SWIG_fail; argc = (int)PyObject_Length(args); for (ii = 0; (ii < argc) && (ii < 2); ii++) { argv[ii] = PyTuple_GET_ITEM(args,ii); } if (argc == 0) { return _wrap_new_VectorDouble__SWIG_0(self, args); } if (argc == 1) { int _v; { int res = SWIG_AsVal_size_t(argv[0], NULL); _v = SWIG_CheckState(res); } if (_v) { return _wrap_new_VectorDouble__SWIG_2(self, args); } } if (argc == 1) { int _v; int res = swig::asptr(argv[0], (std::vector >**)(0)); _v = SWIG_CheckState(res); if (_v) { return _wrap_new_VectorDouble__SWIG_1(self, args); } } if (argc == 2) { int _v; { int res = SWIG_AsVal_size_t(argv[0], NULL); _v = SWIG_CheckState(res); } if (_v) { { int res = SWIG_AsVal_double(argv[1], NULL); _v = SWIG_CheckState(res); } if (_v) { return _wrap_new_VectorDouble__SWIG_3(self, args); } } } fail: SWIG_SetErrorMsg(PyExc_NotImplementedError,"Wrong number of arguments for overloaded function 'new_VectorDouble'.\n Possible C/C++ prototypes are:\n"" std::vector<(double)>()\n"" std::vector<(double)>(std::vector const &)\n"" std::vector<(double)>(std::vector::size_type)\n"" std::vector<(double)>(std::vector::size_type,std::vector::value_type const &)\n"); return NULL; } SWIGINTERN PyObject *_wrap_VectorDouble_push_back(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; std::vector *arg1 = (std::vector *) 0 ; std::vector::value_type *arg2 = 0 ; void *argp1 = 0 ; int res1 = 0 ; std::vector::value_type temp2 ; double val2 ; int ecode2 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OO:VectorDouble_push_back",&obj0,&obj1)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_std__vectorTdouble_std__allocatorTdouble_t_t, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "VectorDouble_push_back" "', argument " "1"" of type '" "std::vector *""'"); } arg1 = reinterpret_cast< std::vector * >(argp1); ecode2 = SWIG_AsVal_double(obj1, &val2); if (!SWIG_IsOK(ecode2)) { SWIG_exception_fail(SWIG_ArgError(ecode2), "in method '" "VectorDouble_push_back" "', argument " "2"" of type '" "std::vector::value_type""'"); } temp2 = static_cast< std::vector::value_type >(val2); arg2 = &temp2; (arg1)->push_back((std::vector::value_type const &)*arg2); resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_VectorDouble_front(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; std::vector *arg1 = (std::vector *) 0 ; std::vector::value_type *result = 0 ; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:VectorDouble_front",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_std__vectorTdouble_std__allocatorTdouble_t_t, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "VectorDouble_front" "', argument " "1"" of type '" "std::vector const *""'"); } arg1 = reinterpret_cast< std::vector * >(argp1); { std::vector::value_type const &_result_ref = ((std::vector const *)arg1)->front(); result = (std::vector::value_type *) &_result_ref; } resultobj = SWIG_From_double(static_cast< double >(*result)); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_VectorDouble_back(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; std::vector *arg1 = (std::vector *) 0 ; std::vector::value_type *result = 0 ; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:VectorDouble_back",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_std__vectorTdouble_std__allocatorTdouble_t_t, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "VectorDouble_back" "', argument " "1"" of type '" "std::vector const *""'"); } arg1 = reinterpret_cast< std::vector * >(argp1); { std::vector::value_type const &_result_ref = ((std::vector const *)arg1)->back(); result = (std::vector::value_type *) &_result_ref; } resultobj = SWIG_From_double(static_cast< double >(*result)); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_VectorDouble_assign(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; std::vector *arg1 = (std::vector *) 0 ; std::vector::size_type arg2 ; std::vector::value_type *arg3 = 0 ; void *argp1 = 0 ; int res1 = 0 ; size_t val2 ; int ecode2 = 0 ; std::vector::value_type temp3 ; double val3 ; int ecode3 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; PyObject * obj2 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OOO:VectorDouble_assign",&obj0,&obj1,&obj2)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_std__vectorTdouble_std__allocatorTdouble_t_t, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "VectorDouble_assign" "', argument " "1"" of type '" "std::vector *""'"); } arg1 = reinterpret_cast< std::vector * >(argp1); ecode2 = SWIG_AsVal_size_t(obj1, &val2); if (!SWIG_IsOK(ecode2)) { SWIG_exception_fail(SWIG_ArgError(ecode2), "in method '" "VectorDouble_assign" "', argument " "2"" of type '" "std::vector::size_type""'"); } arg2 = static_cast< std::vector::size_type >(val2); ecode3 = SWIG_AsVal_double(obj2, &val3); if (!SWIG_IsOK(ecode3)) { SWIG_exception_fail(SWIG_ArgError(ecode3), "in method '" "VectorDouble_assign" "', argument " "3"" of type '" "std::vector::value_type""'"); } temp3 = static_cast< std::vector::value_type >(val3); arg3 = &temp3; (arg1)->assign(arg2,(std::vector::value_type const &)*arg3); resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_VectorDouble_resize__SWIG_1(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; std::vector *arg1 = (std::vector *) 0 ; std::vector::size_type arg2 ; std::vector::value_type *arg3 = 0 ; void *argp1 = 0 ; int res1 = 0 ; size_t val2 ; int ecode2 = 0 ; std::vector::value_type temp3 ; double val3 ; int ecode3 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; PyObject * obj2 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OOO:VectorDouble_resize",&obj0,&obj1,&obj2)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_std__vectorTdouble_std__allocatorTdouble_t_t, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "VectorDouble_resize" "', argument " "1"" of type '" "std::vector *""'"); } arg1 = reinterpret_cast< std::vector * >(argp1); ecode2 = SWIG_AsVal_size_t(obj1, &val2); if (!SWIG_IsOK(ecode2)) { SWIG_exception_fail(SWIG_ArgError(ecode2), "in method '" "VectorDouble_resize" "', argument " "2"" of type '" "std::vector::size_type""'"); } arg2 = static_cast< std::vector::size_type >(val2); ecode3 = SWIG_AsVal_double(obj2, &val3); if (!SWIG_IsOK(ecode3)) { SWIG_exception_fail(SWIG_ArgError(ecode3), "in method '" "VectorDouble_resize" "', argument " "3"" of type '" "std::vector::value_type""'"); } temp3 = static_cast< std::vector::value_type >(val3); arg3 = &temp3; (arg1)->resize(arg2,(std::vector::value_type const &)*arg3); resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_VectorDouble_resize(PyObject *self, PyObject *args) { int argc; PyObject *argv[4]; int ii; if (!PyTuple_Check(args)) SWIG_fail; argc = (int)PyObject_Length(args); for (ii = 0; (ii < argc) && (ii < 3); ii++) { argv[ii] = PyTuple_GET_ITEM(args,ii); } if (argc == 2) { int _v; int res = swig::asptr(argv[0], (std::vector >**)(0)); _v = SWIG_CheckState(res); if (_v) { { int res = SWIG_AsVal_size_t(argv[1], NULL); _v = SWIG_CheckState(res); } if (_v) { return _wrap_VectorDouble_resize__SWIG_0(self, args); } } } if (argc == 3) { int _v; int res = swig::asptr(argv[0], (std::vector >**)(0)); _v = SWIG_CheckState(res); if (_v) { { int res = SWIG_AsVal_size_t(argv[1], NULL); _v = SWIG_CheckState(res); } if (_v) { { int res = SWIG_AsVal_double(argv[2], NULL); _v = SWIG_CheckState(res); } if (_v) { return _wrap_VectorDouble_resize__SWIG_1(self, args); } } } } fail: SWIG_SetErrorMsg(PyExc_NotImplementedError,"Wrong number of arguments for overloaded function 'VectorDouble_resize'.\n Possible C/C++ prototypes are:\n"" resize(std::vector *,std::vector::size_type)\n"" resize(std::vector *,std::vector::size_type,std::vector::value_type const &)\n"); return NULL; } SWIGINTERN PyObject *_wrap_VectorDouble_insert__SWIG_0(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; std::vector *arg1 = (std::vector *) 0 ; std::vector::iterator arg2 ; std::vector::value_type *arg3 = 0 ; std::vector::iterator result; void *argp1 = 0 ; int res1 = 0 ; swig::PySwigIterator *iter2 = 0 ; int res2 ; std::vector::value_type temp3 ; double val3 ; int ecode3 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; PyObject * obj2 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OOO:VectorDouble_insert",&obj0,&obj1,&obj2)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_std__vectorTdouble_std__allocatorTdouble_t_t, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "VectorDouble_insert" "', argument " "1"" of type '" "std::vector *""'"); } arg1 = reinterpret_cast< std::vector * >(argp1); res2 = SWIG_ConvertPtr(obj1, SWIG_as_voidptrptr(&iter2), swig::PySwigIterator::descriptor(), 0); if (!SWIG_IsOK(res2) || !iter2) { SWIG_exception_fail(SWIG_ArgError(SWIG_TypeError), "in method '" "VectorDouble_insert" "', argument " "2"" of type '" "std::vector::iterator""'"); } else { swig::PySwigIterator_T::iterator > *iter_t = dynamic_cast::iterator > *>(iter2); if (iter_t) { arg2 = iter_t->get_current(); } else { SWIG_exception_fail(SWIG_ArgError(SWIG_TypeError), "in method '" "VectorDouble_insert" "', argument " "2"" of type '" "std::vector::iterator""'"); } } ecode3 = SWIG_AsVal_double(obj2, &val3); if (!SWIG_IsOK(ecode3)) { SWIG_exception_fail(SWIG_ArgError(ecode3), "in method '" "VectorDouble_insert" "', argument " "3"" of type '" "std::vector::value_type""'"); } temp3 = static_cast< std::vector::value_type >(val3); arg3 = &temp3; result = (arg1)->insert(arg2,(std::vector::value_type const &)*arg3); resultobj = SWIG_NewPointerObj(swig::make_output_iterator(static_cast< const std::vector::iterator & >(result)), swig::PySwigIterator::descriptor(),SWIG_POINTER_OWN); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_VectorDouble_insert__SWIG_1(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; std::vector *arg1 = (std::vector *) 0 ; std::vector::iterator arg2 ; std::vector::size_type arg3 ; std::vector::value_type *arg4 = 0 ; void *argp1 = 0 ; int res1 = 0 ; swig::PySwigIterator *iter2 = 0 ; int res2 ; size_t val3 ; int ecode3 = 0 ; std::vector::value_type temp4 ; double val4 ; int ecode4 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; PyObject * obj2 = 0 ; PyObject * obj3 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OOOO:VectorDouble_insert",&obj0,&obj1,&obj2,&obj3)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_std__vectorTdouble_std__allocatorTdouble_t_t, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "VectorDouble_insert" "', argument " "1"" of type '" "std::vector *""'"); } arg1 = reinterpret_cast< std::vector * >(argp1); res2 = SWIG_ConvertPtr(obj1, SWIG_as_voidptrptr(&iter2), swig::PySwigIterator::descriptor(), 0); if (!SWIG_IsOK(res2) || !iter2) { SWIG_exception_fail(SWIG_ArgError(SWIG_TypeError), "in method '" "VectorDouble_insert" "', argument " "2"" of type '" "std::vector::iterator""'"); } else { swig::PySwigIterator_T::iterator > *iter_t = dynamic_cast::iterator > *>(iter2); if (iter_t) { arg2 = iter_t->get_current(); } else { SWIG_exception_fail(SWIG_ArgError(SWIG_TypeError), "in method '" "VectorDouble_insert" "', argument " "2"" of type '" "std::vector::iterator""'"); } } ecode3 = SWIG_AsVal_size_t(obj2, &val3); if (!SWIG_IsOK(ecode3)) { SWIG_exception_fail(SWIG_ArgError(ecode3), "in method '" "VectorDouble_insert" "', argument " "3"" of type '" "std::vector::size_type""'"); } arg3 = static_cast< std::vector::size_type >(val3); ecode4 = SWIG_AsVal_double(obj3, &val4); if (!SWIG_IsOK(ecode4)) { SWIG_exception_fail(SWIG_ArgError(ecode4), "in method '" "VectorDouble_insert" "', argument " "4"" of type '" "std::vector::value_type""'"); } temp4 = static_cast< std::vector::value_type >(val4); arg4 = &temp4; (arg1)->insert(arg2,arg3,(std::vector::value_type const &)*arg4); resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_VectorDouble_insert(PyObject *self, PyObject *args) { int argc; PyObject *argv[5]; int ii; if (!PyTuple_Check(args)) SWIG_fail; argc = (int)PyObject_Length(args); for (ii = 0; (ii < argc) && (ii < 4); ii++) { argv[ii] = PyTuple_GET_ITEM(args,ii); } if (argc == 3) { int _v; int res = swig::asptr(argv[0], (std::vector >**)(0)); _v = SWIG_CheckState(res); if (_v) { swig::PySwigIterator *iter = 0; int res = SWIG_ConvertPtr(argv[1], SWIG_as_voidptrptr(&iter), swig::PySwigIterator::descriptor(), 0); _v = (SWIG_IsOK(res) && iter && (dynamic_cast::iterator > *>(iter) != 0)); if (_v) { { int res = SWIG_AsVal_double(argv[2], NULL); _v = SWIG_CheckState(res); } if (_v) { return _wrap_VectorDouble_insert__SWIG_0(self, args); } } } } if (argc == 4) { int _v; int res = swig::asptr(argv[0], (std::vector >**)(0)); _v = SWIG_CheckState(res); if (_v) { swig::PySwigIterator *iter = 0; int res = SWIG_ConvertPtr(argv[1], SWIG_as_voidptrptr(&iter), swig::PySwigIterator::descriptor(), 0); _v = (SWIG_IsOK(res) && iter && (dynamic_cast::iterator > *>(iter) != 0)); if (_v) { { int res = SWIG_AsVal_size_t(argv[2], NULL); _v = SWIG_CheckState(res); } if (_v) { { int res = SWIG_AsVal_double(argv[3], NULL); _v = SWIG_CheckState(res); } if (_v) { return _wrap_VectorDouble_insert__SWIG_1(self, args); } } } } } fail: SWIG_SetErrorMsg(PyExc_NotImplementedError,"Wrong number of arguments for overloaded function 'VectorDouble_insert'.\n Possible C/C++ prototypes are:\n"" insert(std::vector *,std::vector::iterator,std::vector::value_type const &)\n"" insert(std::vector *,std::vector::iterator,std::vector::size_type,std::vector::value_type const &)\n"); return NULL; } SWIGINTERN PyObject *_wrap_VectorDouble_reserve(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; std::vector *arg1 = (std::vector *) 0 ; std::vector::size_type arg2 ; void *argp1 = 0 ; int res1 = 0 ; size_t val2 ; int ecode2 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OO:VectorDouble_reserve",&obj0,&obj1)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_std__vectorTdouble_std__allocatorTdouble_t_t, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "VectorDouble_reserve" "', argument " "1"" of type '" "std::vector *""'"); } arg1 = reinterpret_cast< std::vector * >(argp1); ecode2 = SWIG_AsVal_size_t(obj1, &val2); if (!SWIG_IsOK(ecode2)) { SWIG_exception_fail(SWIG_ArgError(ecode2), "in method '" "VectorDouble_reserve" "', argument " "2"" of type '" "std::vector::size_type""'"); } arg2 = static_cast< std::vector::size_type >(val2); (arg1)->reserve(arg2); resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_VectorDouble_capacity(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; std::vector *arg1 = (std::vector *) 0 ; std::vector::size_type result; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:VectorDouble_capacity",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_std__vectorTdouble_std__allocatorTdouble_t_t, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "VectorDouble_capacity" "', argument " "1"" of type '" "std::vector const *""'"); } arg1 = reinterpret_cast< std::vector * >(argp1); result = ((std::vector const *)arg1)->capacity(); resultobj = SWIG_From_size_t(static_cast< size_t >(result)); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_delete_VectorDouble(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; std::vector *arg1 = (std::vector *) 0 ; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:delete_VectorDouble",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_std__vectorTdouble_std__allocatorTdouble_t_t, SWIG_POINTER_DISOWN | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "delete_VectorDouble" "', argument " "1"" of type '" "std::vector *""'"); } arg1 = reinterpret_cast< std::vector * >(argp1); delete arg1; resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *VectorDouble_swigregister(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *obj; if (!PyArg_ParseTuple(args,(char*)"O|swigregister", &obj)) return NULL; SWIG_TypeNewClientData(SWIGTYPE_p_std__vectorTdouble_std__allocatorTdouble_t_t, SWIG_NewClientData(obj)); return SWIG_Py_Void(); } SWIGINTERN PyObject *_wrap_NeuronGroup_S_set(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; NeuronGroup *arg1 = (NeuronGroup *) 0 ; double *arg2 = (double *) 0 ; void *argp1 = 0 ; int res1 = 0 ; void *argp2 = 0 ; int res2 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OO:NeuronGroup_S_set",&obj0,&obj1)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_NeuronGroup, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "NeuronGroup_S_set" "', argument " "1"" of type '" "NeuronGroup *""'"); } arg1 = reinterpret_cast< NeuronGroup * >(argp1); res2 = SWIG_ConvertPtr(obj1, &argp2,SWIGTYPE_p_double, SWIG_POINTER_DISOWN | 0 ); if (!SWIG_IsOK(res2)) { SWIG_exception_fail(SWIG_ArgError(res2), "in method '" "NeuronGroup_S_set" "', argument " "2"" of type '" "double *""'"); } arg2 = reinterpret_cast< double * >(argp2); if (arg1) (arg1)->S = arg2; resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_NeuronGroup_S_get(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; NeuronGroup *arg1 = (NeuronGroup *) 0 ; double *result = 0 ; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:NeuronGroup_S_get",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_NeuronGroup, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "NeuronGroup_S_get" "', argument " "1"" of type '" "NeuronGroup *""'"); } arg1 = reinterpret_cast< NeuronGroup * >(argp1); result = (double *) ((arg1)->S); resultobj = SWIG_NewPointerObj(SWIG_as_voidptr(result), SWIGTYPE_p_double, 0 | 0 ); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_NeuronGroup_num_vars_set(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; NeuronGroup *arg1 = (NeuronGroup *) 0 ; int arg2 ; void *argp1 = 0 ; int res1 = 0 ; int val2 ; int ecode2 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OO:NeuronGroup_num_vars_set",&obj0,&obj1)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_NeuronGroup, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "NeuronGroup_num_vars_set" "', argument " "1"" of type '" "NeuronGroup *""'"); } arg1 = reinterpret_cast< NeuronGroup * >(argp1); ecode2 = SWIG_AsVal_int(obj1, &val2); if (!SWIG_IsOK(ecode2)) { SWIG_exception_fail(SWIG_ArgError(ecode2), "in method '" "NeuronGroup_num_vars_set" "', argument " "2"" of type '" "int""'"); } arg2 = static_cast< int >(val2); if (arg1) (arg1)->num_vars = arg2; resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_NeuronGroup_num_vars_get(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; NeuronGroup *arg1 = (NeuronGroup *) 0 ; int result; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:NeuronGroup_num_vars_get",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_NeuronGroup, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "NeuronGroup_num_vars_get" "', argument " "1"" of type '" "NeuronGroup *""'"); } arg1 = reinterpret_cast< NeuronGroup * >(argp1); result = (int) ((arg1)->num_vars); resultobj = SWIG_From_int(static_cast< int >(result)); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_NeuronGroup_num_neurons_set(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; NeuronGroup *arg1 = (NeuronGroup *) 0 ; int arg2 ; void *argp1 = 0 ; int res1 = 0 ; int val2 ; int ecode2 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OO:NeuronGroup_num_neurons_set",&obj0,&obj1)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_NeuronGroup, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "NeuronGroup_num_neurons_set" "', argument " "1"" of type '" "NeuronGroup *""'"); } arg1 = reinterpret_cast< NeuronGroup * >(argp1); ecode2 = SWIG_AsVal_int(obj1, &val2); if (!SWIG_IsOK(ecode2)) { SWIG_exception_fail(SWIG_ArgError(ecode2), "in method '" "NeuronGroup_num_neurons_set" "', argument " "2"" of type '" "int""'"); } arg2 = static_cast< int >(val2); if (arg1) (arg1)->num_neurons = arg2; resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_NeuronGroup_num_neurons_get(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; NeuronGroup *arg1 = (NeuronGroup *) 0 ; int result; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:NeuronGroup_num_neurons_get",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_NeuronGroup, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "NeuronGroup_num_neurons_get" "', argument " "1"" of type '" "NeuronGroup *""'"); } arg1 = reinterpret_cast< NeuronGroup * >(argp1); result = (int) ((arg1)->num_neurons); resultobj = SWIG_From_int(static_cast< int >(result)); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_NeuronGroup_S_m_set(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; NeuronGroup *arg1 = (NeuronGroup *) 0 ; int arg2 ; void *argp1 = 0 ; int res1 = 0 ; int val2 ; int ecode2 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OO:NeuronGroup_S_m_set",&obj0,&obj1)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_NeuronGroup, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "NeuronGroup_S_m_set" "', argument " "1"" of type '" "NeuronGroup *""'"); } arg1 = reinterpret_cast< NeuronGroup * >(argp1); ecode2 = SWIG_AsVal_int(obj1, &val2); if (!SWIG_IsOK(ecode2)) { SWIG_exception_fail(SWIG_ArgError(ecode2), "in method '" "NeuronGroup_S_m_set" "', argument " "2"" of type '" "int""'"); } arg2 = static_cast< int >(val2); if (arg1) (arg1)->S_m = arg2; resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_NeuronGroup_S_m_get(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; NeuronGroup *arg1 = (NeuronGroup *) 0 ; int result; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:NeuronGroup_S_m_get",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_NeuronGroup, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "NeuronGroup_S_m_get" "', argument " "1"" of type '" "NeuronGroup *""'"); } arg1 = reinterpret_cast< NeuronGroup * >(argp1); result = (int) ((arg1)->S_m); resultobj = SWIG_From_int(static_cast< int >(result)); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_NeuronGroup_su_set(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; NeuronGroup *arg1 = (NeuronGroup *) 0 ; StateUpdater *arg2 = (StateUpdater *) 0 ; void *argp1 = 0 ; int res1 = 0 ; void *argp2 = 0 ; int res2 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OO:NeuronGroup_su_set",&obj0,&obj1)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_NeuronGroup, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "NeuronGroup_su_set" "', argument " "1"" of type '" "NeuronGroup *""'"); } arg1 = reinterpret_cast< NeuronGroup * >(argp1); res2 = SWIG_ConvertPtr(obj1, &argp2,SWIGTYPE_p_StateUpdater, SWIG_POINTER_DISOWN | 0 ); if (!SWIG_IsOK(res2)) { SWIG_exception_fail(SWIG_ArgError(res2), "in method '" "NeuronGroup_su_set" "', argument " "2"" of type '" "StateUpdater *""'"); } arg2 = reinterpret_cast< StateUpdater * >(argp2); if (arg1) (arg1)->su = arg2; resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_NeuronGroup_su_get(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; NeuronGroup *arg1 = (NeuronGroup *) 0 ; StateUpdater *result = 0 ; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:NeuronGroup_su_get",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_NeuronGroup, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "NeuronGroup_su_get" "', argument " "1"" of type '" "NeuronGroup *""'"); } arg1 = reinterpret_cast< NeuronGroup * >(argp1); result = (StateUpdater *) ((arg1)->su); resultobj = SWIG_NewPointerObj(SWIG_as_voidptr(result), SWIGTYPE_p_StateUpdater, 0 | 0 ); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_NeuronGroup_thr_set(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; NeuronGroup *arg1 = (NeuronGroup *) 0 ; Threshold *arg2 = (Threshold *) 0 ; void *argp1 = 0 ; int res1 = 0 ; void *argp2 = 0 ; int res2 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OO:NeuronGroup_thr_set",&obj0,&obj1)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_NeuronGroup, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "NeuronGroup_thr_set" "', argument " "1"" of type '" "NeuronGroup *""'"); } arg1 = reinterpret_cast< NeuronGroup * >(argp1); res2 = SWIG_ConvertPtr(obj1, &argp2,SWIGTYPE_p_Threshold, SWIG_POINTER_DISOWN | 0 ); if (!SWIG_IsOK(res2)) { SWIG_exception_fail(SWIG_ArgError(res2), "in method '" "NeuronGroup_thr_set" "', argument " "2"" of type '" "Threshold *""'"); } arg2 = reinterpret_cast< Threshold * >(argp2); if (arg1) (arg1)->thr = arg2; resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_NeuronGroup_thr_get(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; NeuronGroup *arg1 = (NeuronGroup *) 0 ; Threshold *result = 0 ; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:NeuronGroup_thr_get",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_NeuronGroup, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "NeuronGroup_thr_get" "', argument " "1"" of type '" "NeuronGroup *""'"); } arg1 = reinterpret_cast< NeuronGroup * >(argp1); result = (Threshold *) ((arg1)->thr); resultobj = SWIG_NewPointerObj(SWIG_as_voidptr(result), SWIGTYPE_p_Threshold, 0 | 0 ); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_NeuronGroup_resetobj_set(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; NeuronGroup *arg1 = (NeuronGroup *) 0 ; ResetBase *arg2 = (ResetBase *) 0 ; void *argp1 = 0 ; int res1 = 0 ; void *argp2 = 0 ; int res2 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OO:NeuronGroup_resetobj_set",&obj0,&obj1)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_NeuronGroup, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "NeuronGroup_resetobj_set" "', argument " "1"" of type '" "NeuronGroup *""'"); } arg1 = reinterpret_cast< NeuronGroup * >(argp1); res2 = SWIG_ConvertPtr(obj1, &argp2,SWIGTYPE_p_ResetBase, SWIG_POINTER_DISOWN | 0 ); if (!SWIG_IsOK(res2)) { SWIG_exception_fail(SWIG_ArgError(res2), "in method '" "NeuronGroup_resetobj_set" "', argument " "2"" of type '" "ResetBase *""'"); } arg2 = reinterpret_cast< ResetBase * >(argp2); if (arg1) (arg1)->resetobj = arg2; resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_NeuronGroup_resetobj_get(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; NeuronGroup *arg1 = (NeuronGroup *) 0 ; ResetBase *result = 0 ; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:NeuronGroup_resetobj_get",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_NeuronGroup, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "NeuronGroup_resetobj_get" "', argument " "1"" of type '" "NeuronGroup *""'"); } arg1 = reinterpret_cast< NeuronGroup * >(argp1); result = (ResetBase *) ((arg1)->resetobj); resultobj = SWIG_NewPointerObj(SWIG_as_voidptr(result), SWIGTYPE_p_ResetBase, 0 | 0 ); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_NeuronGroup_LS_set(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; NeuronGroup *arg1 = (NeuronGroup *) 0 ; SpikeContainer *arg2 = (SpikeContainer *) 0 ; void *argp1 = 0 ; int res1 = 0 ; void *argp2 = 0 ; int res2 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OO:NeuronGroup_LS_set",&obj0,&obj1)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_NeuronGroup, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "NeuronGroup_LS_set" "', argument " "1"" of type '" "NeuronGroup *""'"); } arg1 = reinterpret_cast< NeuronGroup * >(argp1); res2 = SWIG_ConvertPtr(obj1, &argp2,SWIGTYPE_p_SpikeContainer, SWIG_POINTER_DISOWN | 0 ); if (!SWIG_IsOK(res2)) { SWIG_exception_fail(SWIG_ArgError(res2), "in method '" "NeuronGroup_LS_set" "', argument " "2"" of type '" "SpikeContainer *""'"); } arg2 = reinterpret_cast< SpikeContainer * >(argp2); if (arg1) (arg1)->LS = arg2; resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_NeuronGroup_LS_get(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; NeuronGroup *arg1 = (NeuronGroup *) 0 ; SpikeContainer *result = 0 ; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:NeuronGroup_LS_get",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_NeuronGroup, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "NeuronGroup_LS_get" "', argument " "1"" of type '" "NeuronGroup *""'"); } arg1 = reinterpret_cast< NeuronGroup * >(argp1); result = (SpikeContainer *) ((arg1)->LS); resultobj = SWIG_NewPointerObj(SWIG_as_voidptr(result), SWIGTYPE_p_SpikeContainer, 0 | 0 ); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_NeuronGroup_spikesarray_set(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; NeuronGroup *arg1 = (NeuronGroup *) 0 ; int *arg2 = (int *) 0 ; void *argp1 = 0 ; int res1 = 0 ; void *argp2 = 0 ; int res2 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OO:NeuronGroup_spikesarray_set",&obj0,&obj1)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_NeuronGroup, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "NeuronGroup_spikesarray_set" "', argument " "1"" of type '" "NeuronGroup *""'"); } arg1 = reinterpret_cast< NeuronGroup * >(argp1); res2 = SWIG_ConvertPtr(obj1, &argp2,SWIGTYPE_p_int, SWIG_POINTER_DISOWN | 0 ); if (!SWIG_IsOK(res2)) { SWIG_exception_fail(SWIG_ArgError(res2), "in method '" "NeuronGroup_spikesarray_set" "', argument " "2"" of type '" "int *""'"); } arg2 = reinterpret_cast< int * >(argp2); if (arg1) (arg1)->spikesarray = arg2; resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_NeuronGroup_spikesarray_get(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; NeuronGroup *arg1 = (NeuronGroup *) 0 ; int *result = 0 ; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:NeuronGroup_spikesarray_get",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_NeuronGroup, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "NeuronGroup_spikesarray_get" "', argument " "1"" of type '" "NeuronGroup *""'"); } arg1 = reinterpret_cast< NeuronGroup * >(argp1); result = (int *) ((arg1)->spikesarray); resultobj = SWIG_NewPointerObj(SWIG_as_voidptr(result), SWIGTYPE_p_int, 0 | 0 ); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_NeuronGroup_owner_set(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; NeuronGroup *arg1 = (NeuronGroup *) 0 ; NeuronGroup *arg2 = (NeuronGroup *) 0 ; void *argp1 = 0 ; int res1 = 0 ; void *argp2 = 0 ; int res2 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OO:NeuronGroup_owner_set",&obj0,&obj1)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_NeuronGroup, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "NeuronGroup_owner_set" "', argument " "1"" of type '" "NeuronGroup *""'"); } arg1 = reinterpret_cast< NeuronGroup * >(argp1); res2 = SWIG_ConvertPtr(obj1, &argp2,SWIGTYPE_p_NeuronGroup, SWIG_POINTER_DISOWN | 0 ); if (!SWIG_IsOK(res2)) { SWIG_exception_fail(SWIG_ArgError(res2), "in method '" "NeuronGroup_owner_set" "', argument " "2"" of type '" "NeuronGroup *""'"); } arg2 = reinterpret_cast< NeuronGroup * >(argp2); if (arg1) (arg1)->owner = arg2; resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_NeuronGroup_owner_get(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; NeuronGroup *arg1 = (NeuronGroup *) 0 ; NeuronGroup *result = 0 ; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:NeuronGroup_owner_get",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_NeuronGroup, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "NeuronGroup_owner_get" "', argument " "1"" of type '" "NeuronGroup *""'"); } arg1 = reinterpret_cast< NeuronGroup * >(argp1); result = (NeuronGroup *) ((arg1)->owner); resultobj = SWIG_NewPointerObj(SWIG_as_voidptr(result), SWIGTYPE_p_NeuronGroup, 0 | 0 ); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_NeuronGroup_origin_set(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; NeuronGroup *arg1 = (NeuronGroup *) 0 ; int arg2 ; void *argp1 = 0 ; int res1 = 0 ; int val2 ; int ecode2 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OO:NeuronGroup_origin_set",&obj0,&obj1)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_NeuronGroup, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "NeuronGroup_origin_set" "', argument " "1"" of type '" "NeuronGroup *""'"); } arg1 = reinterpret_cast< NeuronGroup * >(argp1); ecode2 = SWIG_AsVal_int(obj1, &val2); if (!SWIG_IsOK(ecode2)) { SWIG_exception_fail(SWIG_ArgError(ecode2), "in method '" "NeuronGroup_origin_set" "', argument " "2"" of type '" "int""'"); } arg2 = static_cast< int >(val2); if (arg1) (arg1)->origin = arg2; resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_NeuronGroup_origin_get(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; NeuronGroup *arg1 = (NeuronGroup *) 0 ; int result; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:NeuronGroup_origin_get",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_NeuronGroup, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "NeuronGroup_origin_get" "', argument " "1"" of type '" "NeuronGroup *""'"); } arg1 = reinterpret_cast< NeuronGroup * >(argp1); result = (int) ((arg1)->origin); resultobj = SWIG_From_int(static_cast< int >(result)); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_new_NeuronGroup__SWIG_0(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; double *arg1 = (double *) 0 ; int arg2 ; int arg3 ; StateUpdater *arg4 = (StateUpdater *) 0 ; Threshold *arg5 = (Threshold *) 0 ; ResetBase *arg6 = (ResetBase *) 0 ; int arg7 ; int arg8 ; NeuronGroup *result = 0 ; PyArrayObject *array1 = NULL ; void *argp4 = 0 ; int res4 = 0 ; void *argp5 = 0 ; int res5 = 0 ; void *argp6 = 0 ; int res6 = 0 ; int val7 ; int ecode7 = 0 ; int val8 ; int ecode8 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; PyObject * obj2 = 0 ; PyObject * obj3 = 0 ; PyObject * obj4 = 0 ; PyObject * obj5 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OOOOOO:new_NeuronGroup",&obj0,&obj1,&obj2,&obj3,&obj4,&obj5)) SWIG_fail; { array1 = obj_to_array_no_conversion(obj0, NPY_DOUBLE); if (!array1 || !require_dimensions(array1,2) || !require_contiguous(array1) || !require_native(array1)) SWIG_fail; arg1 = (double*) array_data(array1); arg2 = (int) array_size(array1,0); arg3 = (int) array_size(array1,1); } res4 = SWIG_ConvertPtr(obj1, &argp4,SWIGTYPE_p_StateUpdater, 0 | 0 ); if (!SWIG_IsOK(res4)) { SWIG_exception_fail(SWIG_ArgError(res4), "in method '" "new_NeuronGroup" "', argument " "4"" of type '" "StateUpdater *""'"); } arg4 = reinterpret_cast< StateUpdater * >(argp4); res5 = SWIG_ConvertPtr(obj2, &argp5,SWIGTYPE_p_Threshold, 0 | 0 ); if (!SWIG_IsOK(res5)) { SWIG_exception_fail(SWIG_ArgError(res5), "in method '" "new_NeuronGroup" "', argument " "5"" of type '" "Threshold *""'"); } arg5 = reinterpret_cast< Threshold * >(argp5); res6 = SWIG_ConvertPtr(obj3, &argp6,SWIGTYPE_p_ResetBase, 0 | 0 ); if (!SWIG_IsOK(res6)) { SWIG_exception_fail(SWIG_ArgError(res6), "in method '" "new_NeuronGroup" "', argument " "6"" of type '" "ResetBase *""'"); } arg6 = reinterpret_cast< ResetBase * >(argp6); ecode7 = SWIG_AsVal_int(obj4, &val7); if (!SWIG_IsOK(ecode7)) { SWIG_exception_fail(SWIG_ArgError(ecode7), "in method '" "new_NeuronGroup" "', argument " "7"" of type '" "int""'"); } arg7 = static_cast< int >(val7); ecode8 = SWIG_AsVal_int(obj5, &val8); if (!SWIG_IsOK(ecode8)) { SWIG_exception_fail(SWIG_ArgError(ecode8), "in method '" "new_NeuronGroup" "', argument " "8"" of type '" "int""'"); } arg8 = static_cast< int >(val8); result = (NeuronGroup *)new NeuronGroup(arg1,arg2,arg3,arg4,arg5,arg6,arg7,arg8); resultobj = SWIG_NewPointerObj(SWIG_as_voidptr(result), SWIGTYPE_p_NeuronGroup, SWIG_POINTER_NEW | 0 ); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_new_NeuronGroup__SWIG_1(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; NeuronGroup *arg1 = (NeuronGroup *) 0 ; int arg2 ; int arg3 ; NeuronGroup *result = 0 ; void *argp1 = 0 ; int res1 = 0 ; int val2 ; int ecode2 = 0 ; int val3 ; int ecode3 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; PyObject * obj2 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OOO:new_NeuronGroup",&obj0,&obj1,&obj2)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_NeuronGroup, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "new_NeuronGroup" "', argument " "1"" of type '" "NeuronGroup *""'"); } arg1 = reinterpret_cast< NeuronGroup * >(argp1); ecode2 = SWIG_AsVal_int(obj1, &val2); if (!SWIG_IsOK(ecode2)) { SWIG_exception_fail(SWIG_ArgError(ecode2), "in method '" "new_NeuronGroup" "', argument " "2"" of type '" "int""'"); } arg2 = static_cast< int >(val2); ecode3 = SWIG_AsVal_int(obj2, &val3); if (!SWIG_IsOK(ecode3)) { SWIG_exception_fail(SWIG_ArgError(ecode3), "in method '" "new_NeuronGroup" "', argument " "3"" of type '" "int""'"); } arg3 = static_cast< int >(val3); result = (NeuronGroup *)new NeuronGroup(arg1,arg2,arg3); resultobj = SWIG_NewPointerObj(SWIG_as_voidptr(result), SWIGTYPE_p_NeuronGroup, SWIG_POINTER_NEW | 0 ); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_new_NeuronGroup(PyObject *self, PyObject *args) { int argc; PyObject *argv[7]; int ii; if (!PyTuple_Check(args)) SWIG_fail; argc = (int)PyObject_Length(args); for (ii = 0; (ii < argc) && (ii < 6); ii++) { argv[ii] = PyTuple_GET_ITEM(args,ii); } if (argc == 3) { int _v; void *vptr = 0; int res = SWIG_ConvertPtr(argv[0], &vptr, SWIGTYPE_p_NeuronGroup, 0); _v = SWIG_CheckState(res); if (_v) { { int res = SWIG_AsVal_int(argv[1], NULL); _v = SWIG_CheckState(res); } if (_v) { { int res = SWIG_AsVal_int(argv[2], NULL); _v = SWIG_CheckState(res); } if (_v) { return _wrap_new_NeuronGroup__SWIG_1(self, args); } } } } if (argc == 6) { int _v; { _v = is_array(argv[0]) && PyArray_EquivTypenums(array_type(argv[0]), NPY_DOUBLE); } if (_v) { void *vptr = 0; int res = SWIG_ConvertPtr(argv[1], &vptr, SWIGTYPE_p_StateUpdater, 0); _v = SWIG_CheckState(res); if (_v) { void *vptr = 0; int res = SWIG_ConvertPtr(argv[2], &vptr, SWIGTYPE_p_Threshold, 0); _v = SWIG_CheckState(res); if (_v) { void *vptr = 0; int res = SWIG_ConvertPtr(argv[3], &vptr, SWIGTYPE_p_ResetBase, 0); _v = SWIG_CheckState(res); if (_v) { { int res = SWIG_AsVal_int(argv[4], NULL); _v = SWIG_CheckState(res); } if (_v) { { int res = SWIG_AsVal_int(argv[5], NULL); _v = SWIG_CheckState(res); } if (_v) { return _wrap_new_NeuronGroup__SWIG_0(self, args); } } } } } } } fail: SWIG_SetErrorMsg(PyExc_NotImplementedError,"Wrong number of arguments for overloaded function 'new_NeuronGroup'.\n Possible C/C++ prototypes are:\n"" NeuronGroup(double *,int,int,StateUpdater *,Threshold *,ResetBase *,int,int)\n"" NeuronGroup(NeuronGroup *,int,int)\n"); return NULL; } SWIGINTERN PyObject *_wrap_delete_NeuronGroup(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; NeuronGroup *arg1 = (NeuronGroup *) 0 ; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:delete_NeuronGroup",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_NeuronGroup, SWIG_POINTER_DISOWN | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "delete_NeuronGroup" "', argument " "1"" of type '" "NeuronGroup *""'"); } arg1 = reinterpret_cast< NeuronGroup * >(argp1); delete arg1; resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_NeuronGroup_get_spikes__SWIG_0(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; NeuronGroup *arg1 = (NeuronGroup *) 0 ; int **arg2 = (int **) 0 ; int *arg3 = (int *) 0 ; int arg4 ; void *argp1 = 0 ; int res1 = 0 ; int *data_temp2 ; int dim_temp2 ; int val4 ; int ecode4 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; { arg2 = &data_temp2; arg3 = &dim_temp2; } if (!PyArg_ParseTuple(args,(char *)"OO:NeuronGroup_get_spikes",&obj0,&obj1)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_NeuronGroup, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "NeuronGroup_get_spikes" "', argument " "1"" of type '" "NeuronGroup *""'"); } arg1 = reinterpret_cast< NeuronGroup * >(argp1); ecode4 = SWIG_AsVal_int(obj1, &val4); if (!SWIG_IsOK(ecode4)) { SWIG_exception_fail(SWIG_ArgError(ecode4), "in method '" "NeuronGroup_get_spikes" "', argument " "4"" of type '" "int""'"); } arg4 = static_cast< int >(val4); (arg1)->get_spikes(arg2,arg3,arg4); resultobj = SWIG_Py_Void(); { npy_intp dims[1] = { *arg3 }; PyObject * array = PyArray_SimpleNewFromData(1, dims, NPY_INT, (void*)(*arg2)); if (!array) SWIG_fail; resultobj = SWIG_Python_AppendOutput(resultobj,array); } return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_NeuronGroup_get_spikes__SWIG_1(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; NeuronGroup *arg1 = (NeuronGroup *) 0 ; int **arg2 = (int **) 0 ; int *arg3 = (int *) 0 ; void *argp1 = 0 ; int res1 = 0 ; int *data_temp2 ; int dim_temp2 ; PyObject * obj0 = 0 ; { arg2 = &data_temp2; arg3 = &dim_temp2; } if (!PyArg_ParseTuple(args,(char *)"O:NeuronGroup_get_spikes",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_NeuronGroup, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "NeuronGroup_get_spikes" "', argument " "1"" of type '" "NeuronGroup *""'"); } arg1 = reinterpret_cast< NeuronGroup * >(argp1); (arg1)->get_spikes(arg2,arg3); resultobj = SWIG_Py_Void(); { npy_intp dims[1] = { *arg3 }; PyObject * array = PyArray_SimpleNewFromData(1, dims, NPY_INT, (void*)(*arg2)); if (!array) SWIG_fail; resultobj = SWIG_Python_AppendOutput(resultobj,array); } return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_NeuronGroup_get_spikes(PyObject *self, PyObject *args) { int argc; PyObject *argv[3]; int ii; if (!PyTuple_Check(args)) SWIG_fail; argc = (int)PyObject_Length(args); for (ii = 0; (ii < argc) && (ii < 2); ii++) { argv[ii] = PyTuple_GET_ITEM(args,ii); } if (argc == 1) { int _v; void *vptr = 0; int res = SWIG_ConvertPtr(argv[0], &vptr, SWIGTYPE_p_NeuronGroup, 0); _v = SWIG_CheckState(res); if (_v) { return _wrap_NeuronGroup_get_spikes__SWIG_1(self, args); } } if (argc == 2) { int _v; void *vptr = 0; int res = SWIG_ConvertPtr(argv[0], &vptr, SWIGTYPE_p_NeuronGroup, 0); _v = SWIG_CheckState(res); if (_v) { { int res = SWIG_AsVal_int(argv[1], NULL); _v = SWIG_CheckState(res); } if (_v) { return _wrap_NeuronGroup_get_spikes__SWIG_0(self, args); } } } fail: SWIG_SetErrorMsg(PyExc_NotImplementedError,"Wrong number of arguments for overloaded function 'NeuronGroup_get_spikes'.\n Possible C/C++ prototypes are:\n"" get_spikes(NeuronGroup *,int **,int *,int)\n"" get_spikes(NeuronGroup *,int **,int *)\n"); return NULL; } SWIGINTERN PyObject *_wrap_NeuronGroup_update(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; NeuronGroup *arg1 = (NeuronGroup *) 0 ; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:NeuronGroup_update",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_NeuronGroup, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "NeuronGroup_update" "', argument " "1"" of type '" "NeuronGroup *""'"); } arg1 = reinterpret_cast< NeuronGroup * >(argp1); (arg1)->update(); resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_NeuronGroup_reset(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; NeuronGroup *arg1 = (NeuronGroup *) 0 ; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:NeuronGroup_reset",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_NeuronGroup, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "NeuronGroup_reset" "', argument " "1"" of type '" "NeuronGroup *""'"); } arg1 = reinterpret_cast< NeuronGroup * >(argp1); (arg1)->reset(); resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *NeuronGroup_swigregister(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *obj; if (!PyArg_ParseTuple(args,(char*)"O|swigregister", &obj)) return NULL; SWIG_TypeNewClientData(SWIGTYPE_p_NeuronGroup, SWIG_NewClientData(obj)); return SWIG_Py_Void(); } SWIGINTERN PyObject *_wrap_StateUpdater___call__(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; StateUpdater *arg1 = (StateUpdater *) 0 ; NeuronGroup *arg2 = (NeuronGroup *) 0 ; void *argp1 = 0 ; int res1 = 0 ; void *argp2 = 0 ; int res2 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OO:StateUpdater___call__",&obj0,&obj1)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_StateUpdater, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "StateUpdater___call__" "', argument " "1"" of type '" "StateUpdater *""'"); } arg1 = reinterpret_cast< StateUpdater * >(argp1); res2 = SWIG_ConvertPtr(obj1, &argp2,SWIGTYPE_p_NeuronGroup, 0 | 0 ); if (!SWIG_IsOK(res2)) { SWIG_exception_fail(SWIG_ArgError(res2), "in method '" "StateUpdater___call__" "', argument " "2"" of type '" "NeuronGroup *""'"); } arg2 = reinterpret_cast< NeuronGroup * >(argp2); (arg1)->__call__(arg2); resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_new_StateUpdater(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; StateUpdater *result = 0 ; if (!PyArg_ParseTuple(args,(char *)":new_StateUpdater")) SWIG_fail; result = (StateUpdater *)new StateUpdater(); resultobj = SWIG_NewPointerObj(SWIG_as_voidptr(result), SWIGTYPE_p_StateUpdater, SWIG_POINTER_NEW | 0 ); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_delete_StateUpdater(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; StateUpdater *arg1 = (StateUpdater *) 0 ; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:delete_StateUpdater",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_StateUpdater, SWIG_POINTER_DISOWN | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "delete_StateUpdater" "', argument " "1"" of type '" "StateUpdater *""'"); } arg1 = reinterpret_cast< StateUpdater * >(argp1); delete arg1; resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *StateUpdater_swigregister(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *obj; if (!PyArg_ParseTuple(args,(char*)"O|swigregister", &obj)) return NULL; SWIG_TypeNewClientData(SWIGTYPE_p_StateUpdater, SWIG_NewClientData(obj)); return SWIG_Py_Void(); } SWIGINTERN PyObject *_wrap_LinearStateUpdater_M_set(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; LinearStateUpdater *arg1 = (LinearStateUpdater *) 0 ; double *arg2 = (double *) 0 ; void *argp1 = 0 ; int res1 = 0 ; void *argp2 = 0 ; int res2 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OO:LinearStateUpdater_M_set",&obj0,&obj1)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_LinearStateUpdater, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "LinearStateUpdater_M_set" "', argument " "1"" of type '" "LinearStateUpdater *""'"); } arg1 = reinterpret_cast< LinearStateUpdater * >(argp1); res2 = SWIG_ConvertPtr(obj1, &argp2,SWIGTYPE_p_double, SWIG_POINTER_DISOWN | 0 ); if (!SWIG_IsOK(res2)) { SWIG_exception_fail(SWIG_ArgError(res2), "in method '" "LinearStateUpdater_M_set" "', argument " "2"" of type '" "double *""'"); } arg2 = reinterpret_cast< double * >(argp2); if (arg1) (arg1)->M = arg2; resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_LinearStateUpdater_M_get(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; LinearStateUpdater *arg1 = (LinearStateUpdater *) 0 ; double *result = 0 ; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:LinearStateUpdater_M_get",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_LinearStateUpdater, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "LinearStateUpdater_M_get" "', argument " "1"" of type '" "LinearStateUpdater *""'"); } arg1 = reinterpret_cast< LinearStateUpdater * >(argp1); result = (double *) ((arg1)->M); resultobj = SWIG_NewPointerObj(SWIG_as_voidptr(result), SWIGTYPE_p_double, 0 | 0 ); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_LinearStateUpdater_M_n_set(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; LinearStateUpdater *arg1 = (LinearStateUpdater *) 0 ; int arg2 ; void *argp1 = 0 ; int res1 = 0 ; int val2 ; int ecode2 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OO:LinearStateUpdater_M_n_set",&obj0,&obj1)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_LinearStateUpdater, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "LinearStateUpdater_M_n_set" "', argument " "1"" of type '" "LinearStateUpdater *""'"); } arg1 = reinterpret_cast< LinearStateUpdater * >(argp1); ecode2 = SWIG_AsVal_int(obj1, &val2); if (!SWIG_IsOK(ecode2)) { SWIG_exception_fail(SWIG_ArgError(ecode2), "in method '" "LinearStateUpdater_M_n_set" "', argument " "2"" of type '" "int""'"); } arg2 = static_cast< int >(val2); if (arg1) (arg1)->M_n = arg2; resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_LinearStateUpdater_M_n_get(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; LinearStateUpdater *arg1 = (LinearStateUpdater *) 0 ; int result; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:LinearStateUpdater_M_n_get",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_LinearStateUpdater, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "LinearStateUpdater_M_n_get" "', argument " "1"" of type '" "LinearStateUpdater *""'"); } arg1 = reinterpret_cast< LinearStateUpdater * >(argp1); result = (int) ((arg1)->M_n); resultobj = SWIG_From_int(static_cast< int >(result)); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_LinearStateUpdater_M_m_set(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; LinearStateUpdater *arg1 = (LinearStateUpdater *) 0 ; int arg2 ; void *argp1 = 0 ; int res1 = 0 ; int val2 ; int ecode2 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OO:LinearStateUpdater_M_m_set",&obj0,&obj1)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_LinearStateUpdater, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "LinearStateUpdater_M_m_set" "', argument " "1"" of type '" "LinearStateUpdater *""'"); } arg1 = reinterpret_cast< LinearStateUpdater * >(argp1); ecode2 = SWIG_AsVal_int(obj1, &val2); if (!SWIG_IsOK(ecode2)) { SWIG_exception_fail(SWIG_ArgError(ecode2), "in method '" "LinearStateUpdater_M_m_set" "', argument " "2"" of type '" "int""'"); } arg2 = static_cast< int >(val2); if (arg1) (arg1)->M_m = arg2; resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_LinearStateUpdater_M_m_get(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; LinearStateUpdater *arg1 = (LinearStateUpdater *) 0 ; int result; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:LinearStateUpdater_M_m_get",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_LinearStateUpdater, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "LinearStateUpdater_M_m_get" "', argument " "1"" of type '" "LinearStateUpdater *""'"); } arg1 = reinterpret_cast< LinearStateUpdater * >(argp1); result = (int) ((arg1)->M_m); resultobj = SWIG_From_int(static_cast< int >(result)); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_LinearStateUpdater_b_set(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; LinearStateUpdater *arg1 = (LinearStateUpdater *) 0 ; double *arg2 = (double *) 0 ; void *argp1 = 0 ; int res1 = 0 ; void *argp2 = 0 ; int res2 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OO:LinearStateUpdater_b_set",&obj0,&obj1)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_LinearStateUpdater, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "LinearStateUpdater_b_set" "', argument " "1"" of type '" "LinearStateUpdater *""'"); } arg1 = reinterpret_cast< LinearStateUpdater * >(argp1); res2 = SWIG_ConvertPtr(obj1, &argp2,SWIGTYPE_p_double, SWIG_POINTER_DISOWN | 0 ); if (!SWIG_IsOK(res2)) { SWIG_exception_fail(SWIG_ArgError(res2), "in method '" "LinearStateUpdater_b_set" "', argument " "2"" of type '" "double *""'"); } arg2 = reinterpret_cast< double * >(argp2); if (arg1) (arg1)->b = arg2; resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_LinearStateUpdater_b_get(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; LinearStateUpdater *arg1 = (LinearStateUpdater *) 0 ; double *result = 0 ; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:LinearStateUpdater_b_get",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_LinearStateUpdater, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "LinearStateUpdater_b_get" "', argument " "1"" of type '" "LinearStateUpdater *""'"); } arg1 = reinterpret_cast< LinearStateUpdater * >(argp1); result = (double *) ((arg1)->b); resultobj = SWIG_NewPointerObj(SWIG_as_voidptr(result), SWIGTYPE_p_double, 0 | 0 ); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_LinearStateUpdater_b_n_set(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; LinearStateUpdater *arg1 = (LinearStateUpdater *) 0 ; int arg2 ; void *argp1 = 0 ; int res1 = 0 ; int val2 ; int ecode2 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OO:LinearStateUpdater_b_n_set",&obj0,&obj1)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_LinearStateUpdater, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "LinearStateUpdater_b_n_set" "', argument " "1"" of type '" "LinearStateUpdater *""'"); } arg1 = reinterpret_cast< LinearStateUpdater * >(argp1); ecode2 = SWIG_AsVal_int(obj1, &val2); if (!SWIG_IsOK(ecode2)) { SWIG_exception_fail(SWIG_ArgError(ecode2), "in method '" "LinearStateUpdater_b_n_set" "', argument " "2"" of type '" "int""'"); } arg2 = static_cast< int >(val2); if (arg1) (arg1)->b_n = arg2; resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_LinearStateUpdater_b_n_get(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; LinearStateUpdater *arg1 = (LinearStateUpdater *) 0 ; int result; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:LinearStateUpdater_b_n_get",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_LinearStateUpdater, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "LinearStateUpdater_b_n_get" "', argument " "1"" of type '" "LinearStateUpdater *""'"); } arg1 = reinterpret_cast< LinearStateUpdater * >(argp1); result = (int) ((arg1)->b_n); resultobj = SWIG_From_int(static_cast< int >(result)); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_new_LinearStateUpdater(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; double *arg1 = (double *) 0 ; int arg2 ; int arg3 ; double *arg4 = (double *) 0 ; int arg5 ; LinearStateUpdater *result = 0 ; PyArrayObject *array1 = NULL ; PyArrayObject *array4 = NULL ; int i4 = 1 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OO:new_LinearStateUpdater",&obj0,&obj1)) SWIG_fail; { array1 = obj_to_array_no_conversion(obj0, NPY_DOUBLE); if (!array1 || !require_dimensions(array1,2) || !require_contiguous(array1) || !require_native(array1)) SWIG_fail; arg1 = (double*) array_data(array1); arg2 = (int) array_size(array1,0); arg3 = (int) array_size(array1,1); } { array4 = obj_to_array_no_conversion(obj1, NPY_DOUBLE); if (!array4 || !require_dimensions(array4,1) || !require_contiguous(array4) || !require_native(array4)) SWIG_fail; arg4 = (double*) array_data(array4); arg5 = 1; for (i4=0; i4 < array_numdims(array4); ++i4) arg5 *= array_size(array4,i4); } result = (LinearStateUpdater *)new LinearStateUpdater(arg1,arg2,arg3,arg4,arg5); resultobj = SWIG_NewPointerObj(SWIG_as_voidptr(result), SWIGTYPE_p_LinearStateUpdater, SWIG_POINTER_NEW | 0 ); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_LinearStateUpdater___call__(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; LinearStateUpdater *arg1 = (LinearStateUpdater *) 0 ; NeuronGroup *arg2 = (NeuronGroup *) 0 ; void *argp1 = 0 ; int res1 = 0 ; void *argp2 = 0 ; int res2 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OO:LinearStateUpdater___call__",&obj0,&obj1)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_LinearStateUpdater, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "LinearStateUpdater___call__" "', argument " "1"" of type '" "LinearStateUpdater *""'"); } arg1 = reinterpret_cast< LinearStateUpdater * >(argp1); res2 = SWIG_ConvertPtr(obj1, &argp2,SWIGTYPE_p_NeuronGroup, 0 | 0 ); if (!SWIG_IsOK(res2)) { SWIG_exception_fail(SWIG_ArgError(res2), "in method '" "LinearStateUpdater___call__" "', argument " "2"" of type '" "NeuronGroup *""'"); } arg2 = reinterpret_cast< NeuronGroup * >(argp2); (arg1)->__call__(arg2); resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_LinearStateUpdater___repr__(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; LinearStateUpdater *arg1 = (LinearStateUpdater *) 0 ; std::string result; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:LinearStateUpdater___repr__",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_LinearStateUpdater, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "LinearStateUpdater___repr__" "', argument " "1"" of type '" "LinearStateUpdater *""'"); } arg1 = reinterpret_cast< LinearStateUpdater * >(argp1); result = (arg1)->__repr__(); resultobj = SWIG_From_std_string(static_cast< std::string >(result)); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_delete_LinearStateUpdater(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; LinearStateUpdater *arg1 = (LinearStateUpdater *) 0 ; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:delete_LinearStateUpdater",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_LinearStateUpdater, SWIG_POINTER_DISOWN | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "delete_LinearStateUpdater" "', argument " "1"" of type '" "LinearStateUpdater *""'"); } arg1 = reinterpret_cast< LinearStateUpdater * >(argp1); delete arg1; resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *LinearStateUpdater_swigregister(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *obj; if (!PyArg_ParseTuple(args,(char*)"O|swigregister", &obj)) return NULL; SWIG_TypeNewClientData(SWIGTYPE_p_LinearStateUpdater, SWIG_NewClientData(obj)); return SWIG_Py_Void(); } SWIGINTERN PyObject *_wrap_Threshold_value_set(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; Threshold *arg1 = (Threshold *) 0 ; double arg2 ; void *argp1 = 0 ; int res1 = 0 ; double val2 ; int ecode2 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OO:Threshold_value_set",&obj0,&obj1)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_Threshold, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "Threshold_value_set" "', argument " "1"" of type '" "Threshold *""'"); } arg1 = reinterpret_cast< Threshold * >(argp1); ecode2 = SWIG_AsVal_double(obj1, &val2); if (!SWIG_IsOK(ecode2)) { SWIG_exception_fail(SWIG_ArgError(ecode2), "in method '" "Threshold_value_set" "', argument " "2"" of type '" "double""'"); } arg2 = static_cast< double >(val2); if (arg1) (arg1)->value = arg2; resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_Threshold_value_get(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; Threshold *arg1 = (Threshold *) 0 ; double result; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:Threshold_value_get",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_Threshold, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "Threshold_value_get" "', argument " "1"" of type '" "Threshold *""'"); } arg1 = reinterpret_cast< Threshold * >(argp1); result = (double) ((arg1)->value); resultobj = SWIG_From_double(static_cast< double >(result)); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_Threshold_state_set(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; Threshold *arg1 = (Threshold *) 0 ; int arg2 ; void *argp1 = 0 ; int res1 = 0 ; int val2 ; int ecode2 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OO:Threshold_state_set",&obj0,&obj1)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_Threshold, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "Threshold_state_set" "', argument " "1"" of type '" "Threshold *""'"); } arg1 = reinterpret_cast< Threshold * >(argp1); ecode2 = SWIG_AsVal_int(obj1, &val2); if (!SWIG_IsOK(ecode2)) { SWIG_exception_fail(SWIG_ArgError(ecode2), "in method '" "Threshold_state_set" "', argument " "2"" of type '" "int""'"); } arg2 = static_cast< int >(val2); if (arg1) (arg1)->state = arg2; resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_Threshold_state_get(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; Threshold *arg1 = (Threshold *) 0 ; int result; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:Threshold_state_get",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_Threshold, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "Threshold_state_get" "', argument " "1"" of type '" "Threshold *""'"); } arg1 = reinterpret_cast< Threshold * >(argp1); result = (int) ((arg1)->state); resultobj = SWIG_From_int(static_cast< int >(result)); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_new_Threshold(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; int arg1 ; double arg2 ; Threshold *result = 0 ; int val1 ; int ecode1 = 0 ; double val2 ; int ecode2 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OO:new_Threshold",&obj0,&obj1)) SWIG_fail; ecode1 = SWIG_AsVal_int(obj0, &val1); if (!SWIG_IsOK(ecode1)) { SWIG_exception_fail(SWIG_ArgError(ecode1), "in method '" "new_Threshold" "', argument " "1"" of type '" "int""'"); } arg1 = static_cast< int >(val1); ecode2 = SWIG_AsVal_double(obj1, &val2); if (!SWIG_IsOK(ecode2)) { SWIG_exception_fail(SWIG_ArgError(ecode2), "in method '" "new_Threshold" "', argument " "2"" of type '" "double""'"); } arg2 = static_cast< double >(val2); result = (Threshold *)new Threshold(arg1,arg2); resultobj = SWIG_NewPointerObj(SWIG_as_voidptr(result), SWIGTYPE_p_Threshold, SWIG_POINTER_NEW | 0 ); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_Threshold___call__(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; Threshold *arg1 = (Threshold *) 0 ; int **arg2 = (int **) 0 ; int *arg3 = (int *) 0 ; NeuronGroup *arg4 = (NeuronGroup *) 0 ; void *argp1 = 0 ; int res1 = 0 ; int *data_temp2 ; int dim_temp2 ; void *argp4 = 0 ; int res4 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; { arg2 = &data_temp2; arg3 = &dim_temp2; } if (!PyArg_ParseTuple(args,(char *)"OO:Threshold___call__",&obj0,&obj1)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_Threshold, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "Threshold___call__" "', argument " "1"" of type '" "Threshold *""'"); } arg1 = reinterpret_cast< Threshold * >(argp1); res4 = SWIG_ConvertPtr(obj1, &argp4,SWIGTYPE_p_NeuronGroup, 0 | 0 ); if (!SWIG_IsOK(res4)) { SWIG_exception_fail(SWIG_ArgError(res4), "in method '" "Threshold___call__" "', argument " "4"" of type '" "NeuronGroup *""'"); } arg4 = reinterpret_cast< NeuronGroup * >(argp4); (arg1)->__call__(arg2,arg3,arg4); resultobj = SWIG_Py_Void(); { npy_intp dims[1] = { *arg3 }; PyObject * array = PyArray_SimpleNewFromData(1, dims, NPY_INT, (void*)(*arg2)); if (!array) SWIG_fail; resultobj = SWIG_Python_AppendOutput(resultobj,array); } return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_delete_Threshold(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; Threshold *arg1 = (Threshold *) 0 ; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:delete_Threshold",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_Threshold, SWIG_POINTER_DISOWN | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "delete_Threshold" "', argument " "1"" of type '" "Threshold *""'"); } arg1 = reinterpret_cast< Threshold * >(argp1); delete arg1; resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *Threshold_swigregister(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *obj; if (!PyArg_ParseTuple(args,(char*)"O|swigregister", &obj)) return NULL; SWIG_TypeNewClientData(SWIGTYPE_p_Threshold, SWIG_NewClientData(obj)); return SWIG_Py_Void(); } SWIGINTERN PyObject *_wrap_ResetBase___call__(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; ResetBase *arg1 = (ResetBase *) 0 ; NeuronGroup *arg2 = (NeuronGroup *) 0 ; void *argp1 = 0 ; int res1 = 0 ; void *argp2 = 0 ; int res2 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OO:ResetBase___call__",&obj0,&obj1)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_ResetBase, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "ResetBase___call__" "', argument " "1"" of type '" "ResetBase *""'"); } arg1 = reinterpret_cast< ResetBase * >(argp1); res2 = SWIG_ConvertPtr(obj1, &argp2,SWIGTYPE_p_NeuronGroup, 0 | 0 ); if (!SWIG_IsOK(res2)) { SWIG_exception_fail(SWIG_ArgError(res2), "in method '" "ResetBase___call__" "', argument " "2"" of type '" "NeuronGroup *""'"); } arg2 = reinterpret_cast< NeuronGroup * >(argp2); (arg1)->__call__(arg2); resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_new_ResetBase(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; ResetBase *result = 0 ; if (!PyArg_ParseTuple(args,(char *)":new_ResetBase")) SWIG_fail; result = (ResetBase *)new ResetBase(); resultobj = SWIG_NewPointerObj(SWIG_as_voidptr(result), SWIGTYPE_p_ResetBase, SWIG_POINTER_NEW | 0 ); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_delete_ResetBase(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; ResetBase *arg1 = (ResetBase *) 0 ; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:delete_ResetBase",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_ResetBase, SWIG_POINTER_DISOWN | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "delete_ResetBase" "', argument " "1"" of type '" "ResetBase *""'"); } arg1 = reinterpret_cast< ResetBase * >(argp1); delete arg1; resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *ResetBase_swigregister(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *obj; if (!PyArg_ParseTuple(args,(char*)"O|swigregister", &obj)) return NULL; SWIG_TypeNewClientData(SWIGTYPE_p_ResetBase, SWIG_NewClientData(obj)); return SWIG_Py_Void(); } SWIGINTERN PyObject *_wrap_Reset_value_set(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; Reset *arg1 = (Reset *) 0 ; double arg2 ; void *argp1 = 0 ; int res1 = 0 ; double val2 ; int ecode2 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OO:Reset_value_set",&obj0,&obj1)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_Reset, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "Reset_value_set" "', argument " "1"" of type '" "Reset *""'"); } arg1 = reinterpret_cast< Reset * >(argp1); ecode2 = SWIG_AsVal_double(obj1, &val2); if (!SWIG_IsOK(ecode2)) { SWIG_exception_fail(SWIG_ArgError(ecode2), "in method '" "Reset_value_set" "', argument " "2"" of type '" "double""'"); } arg2 = static_cast< double >(val2); if (arg1) (arg1)->value = arg2; resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_Reset_value_get(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; Reset *arg1 = (Reset *) 0 ; double result; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:Reset_value_get",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_Reset, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "Reset_value_get" "', argument " "1"" of type '" "Reset *""'"); } arg1 = reinterpret_cast< Reset * >(argp1); result = (double) ((arg1)->value); resultobj = SWIG_From_double(static_cast< double >(result)); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_Reset_state_set(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; Reset *arg1 = (Reset *) 0 ; int arg2 ; void *argp1 = 0 ; int res1 = 0 ; int val2 ; int ecode2 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OO:Reset_state_set",&obj0,&obj1)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_Reset, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "Reset_state_set" "', argument " "1"" of type '" "Reset *""'"); } arg1 = reinterpret_cast< Reset * >(argp1); ecode2 = SWIG_AsVal_int(obj1, &val2); if (!SWIG_IsOK(ecode2)) { SWIG_exception_fail(SWIG_ArgError(ecode2), "in method '" "Reset_state_set" "', argument " "2"" of type '" "int""'"); } arg2 = static_cast< int >(val2); if (arg1) (arg1)->state = arg2; resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_Reset_state_get(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; Reset *arg1 = (Reset *) 0 ; int result; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:Reset_state_get",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_Reset, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "Reset_state_get" "', argument " "1"" of type '" "Reset *""'"); } arg1 = reinterpret_cast< Reset * >(argp1); result = (int) ((arg1)->state); resultobj = SWIG_From_int(static_cast< int >(result)); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_new_Reset(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; int arg1 ; double arg2 ; Reset *result = 0 ; int val1 ; int ecode1 = 0 ; double val2 ; int ecode2 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OO:new_Reset",&obj0,&obj1)) SWIG_fail; ecode1 = SWIG_AsVal_int(obj0, &val1); if (!SWIG_IsOK(ecode1)) { SWIG_exception_fail(SWIG_ArgError(ecode1), "in method '" "new_Reset" "', argument " "1"" of type '" "int""'"); } arg1 = static_cast< int >(val1); ecode2 = SWIG_AsVal_double(obj1, &val2); if (!SWIG_IsOK(ecode2)) { SWIG_exception_fail(SWIG_ArgError(ecode2), "in method '" "new_Reset" "', argument " "2"" of type '" "double""'"); } arg2 = static_cast< double >(val2); result = (Reset *)new Reset(arg1,arg2); resultobj = SWIG_NewPointerObj(SWIG_as_voidptr(result), SWIGTYPE_p_Reset, SWIG_POINTER_NEW | 0 ); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_Reset___call__(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; Reset *arg1 = (Reset *) 0 ; NeuronGroup *arg2 = (NeuronGroup *) 0 ; void *argp1 = 0 ; int res1 = 0 ; void *argp2 = 0 ; int res2 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OO:Reset___call__",&obj0,&obj1)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_Reset, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "Reset___call__" "', argument " "1"" of type '" "Reset *""'"); } arg1 = reinterpret_cast< Reset * >(argp1); res2 = SWIG_ConvertPtr(obj1, &argp2,SWIGTYPE_p_NeuronGroup, 0 | 0 ); if (!SWIG_IsOK(res2)) { SWIG_exception_fail(SWIG_ArgError(res2), "in method '" "Reset___call__" "', argument " "2"" of type '" "NeuronGroup *""'"); } arg2 = reinterpret_cast< NeuronGroup * >(argp2); (arg1)->__call__(arg2); resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_delete_Reset(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; Reset *arg1 = (Reset *) 0 ; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:delete_Reset",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_Reset, SWIG_POINTER_DISOWN | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "delete_Reset" "', argument " "1"" of type '" "Reset *""'"); } arg1 = reinterpret_cast< Reset * >(argp1); delete arg1; resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *Reset_swigregister(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *obj; if (!PyArg_ParseTuple(args,(char*)"O|swigregister", &obj)) return NULL; SWIG_TypeNewClientData(SWIGTYPE_p_Reset, SWIG_NewClientData(obj)); return SWIG_Py_Void(); } SWIGINTERN PyObject *_wrap_Refractoriness_period_set(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; Refractoriness *arg1 = (Refractoriness *) 0 ; int arg2 ; void *argp1 = 0 ; int res1 = 0 ; int val2 ; int ecode2 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OO:Refractoriness_period_set",&obj0,&obj1)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_Refractoriness, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "Refractoriness_period_set" "', argument " "1"" of type '" "Refractoriness *""'"); } arg1 = reinterpret_cast< Refractoriness * >(argp1); ecode2 = SWIG_AsVal_int(obj1, &val2); if (!SWIG_IsOK(ecode2)) { SWIG_exception_fail(SWIG_ArgError(ecode2), "in method '" "Refractoriness_period_set" "', argument " "2"" of type '" "int""'"); } arg2 = static_cast< int >(val2); if (arg1) (arg1)->period = arg2; resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_Refractoriness_period_get(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; Refractoriness *arg1 = (Refractoriness *) 0 ; int result; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:Refractoriness_period_get",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_Refractoriness, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "Refractoriness_period_get" "', argument " "1"" of type '" "Refractoriness *""'"); } arg1 = reinterpret_cast< Refractoriness * >(argp1); result = (int) ((arg1)->period); resultobj = SWIG_From_int(static_cast< int >(result)); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_new_Refractoriness(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; int arg1 ; double arg2 ; int arg3 ; Refractoriness *result = 0 ; int val1 ; int ecode1 = 0 ; double val2 ; int ecode2 = 0 ; int val3 ; int ecode3 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; PyObject * obj2 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OOO:new_Refractoriness",&obj0,&obj1,&obj2)) SWIG_fail; ecode1 = SWIG_AsVal_int(obj0, &val1); if (!SWIG_IsOK(ecode1)) { SWIG_exception_fail(SWIG_ArgError(ecode1), "in method '" "new_Refractoriness" "', argument " "1"" of type '" "int""'"); } arg1 = static_cast< int >(val1); ecode2 = SWIG_AsVal_double(obj1, &val2); if (!SWIG_IsOK(ecode2)) { SWIG_exception_fail(SWIG_ArgError(ecode2), "in method '" "new_Refractoriness" "', argument " "2"" of type '" "double""'"); } arg2 = static_cast< double >(val2); ecode3 = SWIG_AsVal_int(obj2, &val3); if (!SWIG_IsOK(ecode3)) { SWIG_exception_fail(SWIG_ArgError(ecode3), "in method '" "new_Refractoriness" "', argument " "3"" of type '" "int""'"); } arg3 = static_cast< int >(val3); result = (Refractoriness *)new Refractoriness(arg1,arg2,arg3); resultobj = SWIG_NewPointerObj(SWIG_as_voidptr(result), SWIGTYPE_p_Refractoriness, SWIG_POINTER_NEW | 0 ); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_Refractoriness___call__(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; Refractoriness *arg1 = (Refractoriness *) 0 ; NeuronGroup *arg2 = (NeuronGroup *) 0 ; void *argp1 = 0 ; int res1 = 0 ; void *argp2 = 0 ; int res2 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OO:Refractoriness___call__",&obj0,&obj1)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_Refractoriness, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "Refractoriness___call__" "', argument " "1"" of type '" "Refractoriness *""'"); } arg1 = reinterpret_cast< Refractoriness * >(argp1); res2 = SWIG_ConvertPtr(obj1, &argp2,SWIGTYPE_p_NeuronGroup, 0 | 0 ); if (!SWIG_IsOK(res2)) { SWIG_exception_fail(SWIG_ArgError(res2), "in method '" "Refractoriness___call__" "', argument " "2"" of type '" "NeuronGroup *""'"); } arg2 = reinterpret_cast< NeuronGroup * >(argp2); (arg1)->__call__(arg2); resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_delete_Refractoriness(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; Refractoriness *arg1 = (Refractoriness *) 0 ; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:delete_Refractoriness",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_Refractoriness, SWIG_POINTER_DISOWN | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "delete_Refractoriness" "', argument " "1"" of type '" "Refractoriness *""'"); } arg1 = reinterpret_cast< Refractoriness * >(argp1); delete arg1; resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *Refractoriness_swigregister(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *obj; if (!PyArg_ParseTuple(args,(char*)"O|swigregister", &obj)) return NULL; SWIG_TypeNewClientData(SWIGTYPE_p_Refractoriness, SWIG_NewClientData(obj)); return SWIG_Py_Void(); } SWIGINTERN PyObject *_wrap_NetworkOperation___call__(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; NetworkOperation *arg1 = (NetworkOperation *) 0 ; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:NetworkOperation___call__",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_NetworkOperation, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "NetworkOperation___call__" "', argument " "1"" of type '" "NetworkOperation *""'"); } arg1 = reinterpret_cast< NetworkOperation * >(argp1); (arg1)->__call__(); resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_new_NetworkOperation(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; NetworkOperation *result = 0 ; if (!PyArg_ParseTuple(args,(char *)":new_NetworkOperation")) SWIG_fail; result = (NetworkOperation *)new NetworkOperation(); resultobj = SWIG_NewPointerObj(SWIG_as_voidptr(result), SWIGTYPE_p_NetworkOperation, SWIG_POINTER_NEW | 0 ); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_delete_NetworkOperation(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; NetworkOperation *arg1 = (NetworkOperation *) 0 ; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:delete_NetworkOperation",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_NetworkOperation, SWIG_POINTER_DISOWN | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "delete_NetworkOperation" "', argument " "1"" of type '" "NetworkOperation *""'"); } arg1 = reinterpret_cast< NetworkOperation * >(argp1); delete arg1; resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *NetworkOperation_swigregister(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *obj; if (!PyArg_ParseTuple(args,(char*)"O|swigregister", &obj)) return NULL; SWIG_TypeNewClientData(SWIGTYPE_p_NetworkOperation, SWIG_NewClientData(obj)); return SWIG_Py_Void(); } SWIGINTERN PyObject *_wrap_StateMonitor_group_set(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; StateMonitor *arg1 = (StateMonitor *) 0 ; NeuronGroup *arg2 = (NeuronGroup *) 0 ; void *argp1 = 0 ; int res1 = 0 ; void *argp2 = 0 ; int res2 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OO:StateMonitor_group_set",&obj0,&obj1)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_StateMonitor, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "StateMonitor_group_set" "', argument " "1"" of type '" "StateMonitor *""'"); } arg1 = reinterpret_cast< StateMonitor * >(argp1); res2 = SWIG_ConvertPtr(obj1, &argp2,SWIGTYPE_p_NeuronGroup, SWIG_POINTER_DISOWN | 0 ); if (!SWIG_IsOK(res2)) { SWIG_exception_fail(SWIG_ArgError(res2), "in method '" "StateMonitor_group_set" "', argument " "2"" of type '" "NeuronGroup *""'"); } arg2 = reinterpret_cast< NeuronGroup * >(argp2); if (arg1) (arg1)->group = arg2; resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_StateMonitor_group_get(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; StateMonitor *arg1 = (StateMonitor *) 0 ; NeuronGroup *result = 0 ; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:StateMonitor_group_get",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_StateMonitor, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "StateMonitor_group_get" "', argument " "1"" of type '" "StateMonitor *""'"); } arg1 = reinterpret_cast< StateMonitor * >(argp1); result = (NeuronGroup *) ((arg1)->group); resultobj = SWIG_NewPointerObj(SWIG_as_voidptr(result), SWIGTYPE_p_NeuronGroup, 0 | 0 ); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_StateMonitor_state_set(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; StateMonitor *arg1 = (StateMonitor *) 0 ; int arg2 ; void *argp1 = 0 ; int res1 = 0 ; int val2 ; int ecode2 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OO:StateMonitor_state_set",&obj0,&obj1)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_StateMonitor, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "StateMonitor_state_set" "', argument " "1"" of type '" "StateMonitor *""'"); } arg1 = reinterpret_cast< StateMonitor * >(argp1); ecode2 = SWIG_AsVal_int(obj1, &val2); if (!SWIG_IsOK(ecode2)) { SWIG_exception_fail(SWIG_ArgError(ecode2), "in method '" "StateMonitor_state_set" "', argument " "2"" of type '" "int""'"); } arg2 = static_cast< int >(val2); if (arg1) (arg1)->state = arg2; resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_StateMonitor_state_get(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; StateMonitor *arg1 = (StateMonitor *) 0 ; int result; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:StateMonitor_state_get",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_StateMonitor, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "StateMonitor_state_get" "', argument " "1"" of type '" "StateMonitor *""'"); } arg1 = reinterpret_cast< StateMonitor * >(argp1); result = (int) ((arg1)->state); resultobj = SWIG_From_int(static_cast< int >(result)); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_StateMonitor_values_set(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; StateMonitor *arg1 = (StateMonitor *) 0 ; std::vector > *arg2 = (std::vector > *) 0 ; void *argp1 = 0 ; int res1 = 0 ; void *argp2 = 0 ; int res2 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OO:StateMonitor_values_set",&obj0,&obj1)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_StateMonitor, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "StateMonitor_values_set" "', argument " "1"" of type '" "StateMonitor *""'"); } arg1 = reinterpret_cast< StateMonitor * >(argp1); res2 = SWIG_ConvertPtr(obj1, &argp2,SWIGTYPE_p_std__vectorTstd__vectorTdouble_std__allocatorTdouble_t_t_std__allocatorTstd__vectorTdouble_std__allocatorTdouble_t_t_t_t, 0 | 0 ); if (!SWIG_IsOK(res2)) { SWIG_exception_fail(SWIG_ArgError(res2), "in method '" "StateMonitor_values_set" "', argument " "2"" of type '" "std::vector > *""'"); } arg2 = reinterpret_cast< std::vector > * >(argp2); if (arg1) (arg1)->values = *arg2; resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_StateMonitor_values_get(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; StateMonitor *arg1 = (StateMonitor *) 0 ; std::vector > *result = 0 ; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:StateMonitor_values_get",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_StateMonitor, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "StateMonitor_values_get" "', argument " "1"" of type '" "StateMonitor *""'"); } arg1 = reinterpret_cast< StateMonitor * >(argp1); result = (std::vector > *)& ((arg1)->values); resultobj = SWIG_NewPointerObj(SWIG_as_voidptr(result), SWIGTYPE_p_std__vectorTstd__vectorTdouble_std__allocatorTdouble_t_t_std__allocatorTstd__vectorTdouble_std__allocatorTdouble_t_t_t_t, 0 | 0 ); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_new_StateMonitor(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; NeuronGroup *arg1 = (NeuronGroup *) 0 ; int arg2 ; StateMonitor *result = 0 ; void *argp1 = 0 ; int res1 = 0 ; int val2 ; int ecode2 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OO:new_StateMonitor",&obj0,&obj1)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_NeuronGroup, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "new_StateMonitor" "', argument " "1"" of type '" "NeuronGroup *""'"); } arg1 = reinterpret_cast< NeuronGroup * >(argp1); ecode2 = SWIG_AsVal_int(obj1, &val2); if (!SWIG_IsOK(ecode2)) { SWIG_exception_fail(SWIG_ArgError(ecode2), "in method '" "new_StateMonitor" "', argument " "2"" of type '" "int""'"); } arg2 = static_cast< int >(val2); result = (StateMonitor *)new StateMonitor(arg1,arg2); resultobj = SWIG_NewPointerObj(SWIG_as_voidptr(result), SWIGTYPE_p_StateMonitor, SWIG_POINTER_NEW | 0 ); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_StateMonitor___call__(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; StateMonitor *arg1 = (StateMonitor *) 0 ; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:StateMonitor___call__",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_StateMonitor, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "StateMonitor___call__" "', argument " "1"" of type '" "StateMonitor *""'"); } arg1 = reinterpret_cast< StateMonitor * >(argp1); (arg1)->__call__(); resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_StateMonitor___getitem__(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; StateMonitor *arg1 = (StateMonitor *) 0 ; int arg2 ; std::vector result; void *argp1 = 0 ; int res1 = 0 ; int val2 ; int ecode2 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OO:StateMonitor___getitem__",&obj0,&obj1)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_StateMonitor, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "StateMonitor___getitem__" "', argument " "1"" of type '" "StateMonitor *""'"); } arg1 = reinterpret_cast< StateMonitor * >(argp1); ecode2 = SWIG_AsVal_int(obj1, &val2); if (!SWIG_IsOK(ecode2)) { SWIG_exception_fail(SWIG_ArgError(ecode2), "in method '" "StateMonitor___getitem__" "', argument " "2"" of type '" "int""'"); } arg2 = static_cast< int >(val2); result = (arg1)->__getitem__(arg2); resultobj = swig::from(static_cast< std::vector > >(result)); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_delete_StateMonitor(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; StateMonitor *arg1 = (StateMonitor *) 0 ; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:delete_StateMonitor",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_StateMonitor, SWIG_POINTER_DISOWN | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "delete_StateMonitor" "', argument " "1"" of type '" "StateMonitor *""'"); } arg1 = reinterpret_cast< StateMonitor * >(argp1); delete arg1; resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *StateMonitor_swigregister(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *obj; if (!PyArg_ParseTuple(args,(char*)"O|swigregister", &obj)) return NULL; SWIG_TypeNewClientData(SWIGTYPE_p_StateMonitor, SWIG_NewClientData(obj)); return SWIG_Py_Void(); } SWIGINTERN PyObject *_wrap_Network_groups_set(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; Network *arg1 = (Network *) 0 ; std::list *arg2 = (std::list *) 0 ; void *argp1 = 0 ; int res1 = 0 ; void *argp2 = 0 ; int res2 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OO:Network_groups_set",&obj0,&obj1)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_Network, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "Network_groups_set" "', argument " "1"" of type '" "Network *""'"); } arg1 = reinterpret_cast< Network * >(argp1); res2 = SWIG_ConvertPtr(obj1, &argp2,SWIGTYPE_p_std__listTNeuronGroup_p_std__allocatorTNeuronGroup_p_t_t, 0 | 0 ); if (!SWIG_IsOK(res2)) { SWIG_exception_fail(SWIG_ArgError(res2), "in method '" "Network_groups_set" "', argument " "2"" of type '" "std::list *""'"); } arg2 = reinterpret_cast< std::list * >(argp2); if (arg1) (arg1)->groups = *arg2; resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_Network_groups_get(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; Network *arg1 = (Network *) 0 ; std::list *result = 0 ; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:Network_groups_get",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_Network, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "Network_groups_get" "', argument " "1"" of type '" "Network *""'"); } arg1 = reinterpret_cast< Network * >(argp1); result = (std::list *)& ((arg1)->groups); resultobj = SWIG_NewPointerObj(SWIG_as_voidptr(result), SWIGTYPE_p_std__listTNeuronGroup_p_std__allocatorTNeuronGroup_p_t_t, 0 | 0 ); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_Network_operations_set(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; Network *arg1 = (Network *) 0 ; std::list *arg2 = (std::list *) 0 ; void *argp1 = 0 ; int res1 = 0 ; void *argp2 = 0 ; int res2 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OO:Network_operations_set",&obj0,&obj1)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_Network, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "Network_operations_set" "', argument " "1"" of type '" "Network *""'"); } arg1 = reinterpret_cast< Network * >(argp1); res2 = SWIG_ConvertPtr(obj1, &argp2,SWIGTYPE_p_std__listTNetworkOperation_p_std__allocatorTNetworkOperation_p_t_t, 0 | 0 ); if (!SWIG_IsOK(res2)) { SWIG_exception_fail(SWIG_ArgError(res2), "in method '" "Network_operations_set" "', argument " "2"" of type '" "std::list *""'"); } arg2 = reinterpret_cast< std::list * >(argp2); if (arg1) (arg1)->operations = *arg2; resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_Network_operations_get(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; Network *arg1 = (Network *) 0 ; std::list *result = 0 ; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:Network_operations_get",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_Network, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "Network_operations_get" "', argument " "1"" of type '" "Network *""'"); } arg1 = reinterpret_cast< Network * >(argp1); result = (std::list *)& ((arg1)->operations); resultobj = SWIG_NewPointerObj(SWIG_as_voidptr(result), SWIGTYPE_p_std__listTNetworkOperation_p_std__allocatorTNetworkOperation_p_t_t, 0 | 0 ); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_Network_connections_set(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; Network *arg1 = (Network *) 0 ; std::list *arg2 = (std::list *) 0 ; void *argp1 = 0 ; int res1 = 0 ; void *argp2 = 0 ; int res2 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OO:Network_connections_set",&obj0,&obj1)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_Network, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "Network_connections_set" "', argument " "1"" of type '" "Network *""'"); } arg1 = reinterpret_cast< Network * >(argp1); res2 = SWIG_ConvertPtr(obj1, &argp2,SWIGTYPE_p_std__listTConnection_p_std__allocatorTConnection_p_t_t, 0 | 0 ); if (!SWIG_IsOK(res2)) { SWIG_exception_fail(SWIG_ArgError(res2), "in method '" "Network_connections_set" "', argument " "2"" of type '" "std::list *""'"); } arg2 = reinterpret_cast< std::list * >(argp2); if (arg1) (arg1)->connections = *arg2; resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_Network_connections_get(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; Network *arg1 = (Network *) 0 ; std::list *result = 0 ; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:Network_connections_get",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_Network, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "Network_connections_get" "', argument " "1"" of type '" "Network *""'"); } arg1 = reinterpret_cast< Network * >(argp1); result = (std::list *)& ((arg1)->connections); resultobj = SWIG_NewPointerObj(SWIG_as_voidptr(result), SWIGTYPE_p_std__listTConnection_p_std__allocatorTConnection_p_t_t, 0 | 0 ); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_Network_add__SWIG_0(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; Network *arg1 = (Network *) 0 ; NeuronGroup *arg2 = (NeuronGroup *) 0 ; void *argp1 = 0 ; int res1 = 0 ; void *argp2 = 0 ; int res2 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OO:Network_add",&obj0,&obj1)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_Network, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "Network_add" "', argument " "1"" of type '" "Network *""'"); } arg1 = reinterpret_cast< Network * >(argp1); res2 = SWIG_ConvertPtr(obj1, &argp2,SWIGTYPE_p_NeuronGroup, 0 | 0 ); if (!SWIG_IsOK(res2)) { SWIG_exception_fail(SWIG_ArgError(res2), "in method '" "Network_add" "', argument " "2"" of type '" "NeuronGroup *""'"); } arg2 = reinterpret_cast< NeuronGroup * >(argp2); (arg1)->add(arg2); resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_Network_add__SWIG_1(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; Network *arg1 = (Network *) 0 ; NetworkOperation *arg2 = (NetworkOperation *) 0 ; void *argp1 = 0 ; int res1 = 0 ; void *argp2 = 0 ; int res2 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OO:Network_add",&obj0,&obj1)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_Network, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "Network_add" "', argument " "1"" of type '" "Network *""'"); } arg1 = reinterpret_cast< Network * >(argp1); res2 = SWIG_ConvertPtr(obj1, &argp2,SWIGTYPE_p_NetworkOperation, 0 | 0 ); if (!SWIG_IsOK(res2)) { SWIG_exception_fail(SWIG_ArgError(res2), "in method '" "Network_add" "', argument " "2"" of type '" "NetworkOperation *""'"); } arg2 = reinterpret_cast< NetworkOperation * >(argp2); (arg1)->add(arg2); resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_Network_add__SWIG_2(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; Network *arg1 = (Network *) 0 ; Connection *arg2 = (Connection *) 0 ; void *argp1 = 0 ; int res1 = 0 ; void *argp2 = 0 ; int res2 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OO:Network_add",&obj0,&obj1)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_Network, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "Network_add" "', argument " "1"" of type '" "Network *""'"); } arg1 = reinterpret_cast< Network * >(argp1); res2 = SWIG_ConvertPtr(obj1, &argp2,SWIGTYPE_p_Connection, 0 | 0 ); if (!SWIG_IsOK(res2)) { SWIG_exception_fail(SWIG_ArgError(res2), "in method '" "Network_add" "', argument " "2"" of type '" "Connection *""'"); } arg2 = reinterpret_cast< Connection * >(argp2); (arg1)->add(arg2); resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_Network_add(PyObject *self, PyObject *args) { int argc; PyObject *argv[3]; int ii; if (!PyTuple_Check(args)) SWIG_fail; argc = (int)PyObject_Length(args); for (ii = 0; (ii < argc) && (ii < 2); ii++) { argv[ii] = PyTuple_GET_ITEM(args,ii); } if (argc == 2) { int _v; void *vptr = 0; int res = SWIG_ConvertPtr(argv[0], &vptr, SWIGTYPE_p_Network, 0); _v = SWIG_CheckState(res); if (_v) { void *vptr = 0; int res = SWIG_ConvertPtr(argv[1], &vptr, SWIGTYPE_p_NeuronGroup, 0); _v = SWIG_CheckState(res); if (_v) { return _wrap_Network_add__SWIG_0(self, args); } } } if (argc == 2) { int _v; void *vptr = 0; int res = SWIG_ConvertPtr(argv[0], &vptr, SWIGTYPE_p_Network, 0); _v = SWIG_CheckState(res); if (_v) { void *vptr = 0; int res = SWIG_ConvertPtr(argv[1], &vptr, SWIGTYPE_p_NetworkOperation, 0); _v = SWIG_CheckState(res); if (_v) { return _wrap_Network_add__SWIG_1(self, args); } } } if (argc == 2) { int _v; void *vptr = 0; int res = SWIG_ConvertPtr(argv[0], &vptr, SWIGTYPE_p_Network, 0); _v = SWIG_CheckState(res); if (_v) { void *vptr = 0; int res = SWIG_ConvertPtr(argv[1], &vptr, SWIGTYPE_p_Connection, 0); _v = SWIG_CheckState(res); if (_v) { return _wrap_Network_add__SWIG_2(self, args); } } } fail: SWIG_SetErrorMsg(PyExc_NotImplementedError,"Wrong number of arguments for overloaded function 'Network_add'.\n Possible C/C++ prototypes are:\n"" add(Network *,NeuronGroup *)\n"" add(Network *,NetworkOperation *)\n"" add(Network *,Connection *)\n"); return NULL; } SWIGINTERN PyObject *_wrap_Network_update(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; Network *arg1 = (Network *) 0 ; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:Network_update",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_Network, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "Network_update" "', argument " "1"" of type '" "Network *""'"); } arg1 = reinterpret_cast< Network * >(argp1); (arg1)->update(); resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_Network_run(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; Network *arg1 = (Network *) 0 ; int arg2 ; void *argp1 = 0 ; int res1 = 0 ; int val2 ; int ecode2 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OO:Network_run",&obj0,&obj1)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_Network, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "Network_run" "', argument " "1"" of type '" "Network *""'"); } arg1 = reinterpret_cast< Network * >(argp1); ecode2 = SWIG_AsVal_int(obj1, &val2); if (!SWIG_IsOK(ecode2)) { SWIG_exception_fail(SWIG_ArgError(ecode2), "in method '" "Network_run" "', argument " "2"" of type '" "int""'"); } arg2 = static_cast< int >(val2); (arg1)->run(arg2); resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_new_Network(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; Network *result = 0 ; if (!PyArg_ParseTuple(args,(char *)":new_Network")) SWIG_fail; result = (Network *)new Network(); resultobj = SWIG_NewPointerObj(SWIG_as_voidptr(result), SWIGTYPE_p_Network, SWIG_POINTER_NEW | 0 ); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_delete_Network(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; Network *arg1 = (Network *) 0 ; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:delete_Network",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_Network, SWIG_POINTER_DISOWN | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "delete_Network" "', argument " "1"" of type '" "Network *""'"); } arg1 = reinterpret_cast< Network * >(argp1); delete arg1; resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *Network_swigregister(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *obj; if (!PyArg_ParseTuple(args,(char*)"O|swigregister", &obj)) return NULL; SWIG_TypeNewClientData(SWIGTYPE_p_Network, SWIG_NewClientData(obj)); return SWIG_Py_Void(); } SWIGINTERN PyObject *_wrap_CircularVector_X_set(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; CircularVector *arg1 = (CircularVector *) 0 ; int *arg2 = (int *) 0 ; void *argp1 = 0 ; int res1 = 0 ; void *argp2 = 0 ; int res2 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OO:CircularVector_X_set",&obj0,&obj1)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_CircularVector, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "CircularVector_X_set" "', argument " "1"" of type '" "CircularVector *""'"); } arg1 = reinterpret_cast< CircularVector * >(argp1); res2 = SWIG_ConvertPtr(obj1, &argp2,SWIGTYPE_p_int, SWIG_POINTER_DISOWN | 0 ); if (!SWIG_IsOK(res2)) { SWIG_exception_fail(SWIG_ArgError(res2), "in method '" "CircularVector_X_set" "', argument " "2"" of type '" "int *""'"); } arg2 = reinterpret_cast< int * >(argp2); if (arg1) (arg1)->X = arg2; resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_CircularVector_X_get(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; CircularVector *arg1 = (CircularVector *) 0 ; int *result = 0 ; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:CircularVector_X_get",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_CircularVector, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "CircularVector_X_get" "', argument " "1"" of type '" "CircularVector *""'"); } arg1 = reinterpret_cast< CircularVector * >(argp1); result = (int *) ((arg1)->X); resultobj = SWIG_NewPointerObj(SWIG_as_voidptr(result), SWIGTYPE_p_int, 0 | 0 ); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_CircularVector_cursor_set(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; CircularVector *arg1 = (CircularVector *) 0 ; int arg2 ; void *argp1 = 0 ; int res1 = 0 ; int val2 ; int ecode2 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OO:CircularVector_cursor_set",&obj0,&obj1)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_CircularVector, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "CircularVector_cursor_set" "', argument " "1"" of type '" "CircularVector *""'"); } arg1 = reinterpret_cast< CircularVector * >(argp1); ecode2 = SWIG_AsVal_int(obj1, &val2); if (!SWIG_IsOK(ecode2)) { SWIG_exception_fail(SWIG_ArgError(ecode2), "in method '" "CircularVector_cursor_set" "', argument " "2"" of type '" "int""'"); } arg2 = static_cast< int >(val2); if (arg1) (arg1)->cursor = arg2; resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_CircularVector_cursor_get(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; CircularVector *arg1 = (CircularVector *) 0 ; int result; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:CircularVector_cursor_get",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_CircularVector, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "CircularVector_cursor_get" "', argument " "1"" of type '" "CircularVector *""'"); } arg1 = reinterpret_cast< CircularVector * >(argp1); result = (int) ((arg1)->cursor); resultobj = SWIG_From_int(static_cast< int >(result)); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_CircularVector_n_set(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; CircularVector *arg1 = (CircularVector *) 0 ; int arg2 ; void *argp1 = 0 ; int res1 = 0 ; int val2 ; int ecode2 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OO:CircularVector_n_set",&obj0,&obj1)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_CircularVector, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "CircularVector_n_set" "', argument " "1"" of type '" "CircularVector *""'"); } arg1 = reinterpret_cast< CircularVector * >(argp1); ecode2 = SWIG_AsVal_int(obj1, &val2); if (!SWIG_IsOK(ecode2)) { SWIG_exception_fail(SWIG_ArgError(ecode2), "in method '" "CircularVector_n_set" "', argument " "2"" of type '" "int""'"); } arg2 = static_cast< int >(val2); if (arg1) (arg1)->n = arg2; resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_CircularVector_n_get(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; CircularVector *arg1 = (CircularVector *) 0 ; int result; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:CircularVector_n_get",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_CircularVector, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "CircularVector_n_get" "', argument " "1"" of type '" "CircularVector *""'"); } arg1 = reinterpret_cast< CircularVector * >(argp1); result = (int) ((arg1)->n); resultobj = SWIG_From_int(static_cast< int >(result)); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_CircularVector_retarray_set(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; CircularVector *arg1 = (CircularVector *) 0 ; int *arg2 = (int *) 0 ; void *argp1 = 0 ; int res1 = 0 ; void *argp2 = 0 ; int res2 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OO:CircularVector_retarray_set",&obj0,&obj1)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_CircularVector, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "CircularVector_retarray_set" "', argument " "1"" of type '" "CircularVector *""'"); } arg1 = reinterpret_cast< CircularVector * >(argp1); res2 = SWIG_ConvertPtr(obj1, &argp2,SWIGTYPE_p_int, SWIG_POINTER_DISOWN | 0 ); if (!SWIG_IsOK(res2)) { SWIG_exception_fail(SWIG_ArgError(res2), "in method '" "CircularVector_retarray_set" "', argument " "2"" of type '" "int *""'"); } arg2 = reinterpret_cast< int * >(argp2); if (arg1) (arg1)->retarray = arg2; resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_CircularVector_retarray_get(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; CircularVector *arg1 = (CircularVector *) 0 ; int *result = 0 ; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:CircularVector_retarray_get",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_CircularVector, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "CircularVector_retarray_get" "', argument " "1"" of type '" "CircularVector *""'"); } arg1 = reinterpret_cast< CircularVector * >(argp1); result = (int *) ((arg1)->retarray); resultobj = SWIG_NewPointerObj(SWIG_as_voidptr(result), SWIGTYPE_p_int, 0 | 0 ); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_new_CircularVector(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; int arg1 ; CircularVector *result = 0 ; int val1 ; int ecode1 = 0 ; PyObject * obj0 = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:new_CircularVector",&obj0)) SWIG_fail; ecode1 = SWIG_AsVal_int(obj0, &val1); if (!SWIG_IsOK(ecode1)) { SWIG_exception_fail(SWIG_ArgError(ecode1), "in method '" "new_CircularVector" "', argument " "1"" of type '" "int""'"); } arg1 = static_cast< int >(val1); result = (CircularVector *)new CircularVector(arg1); resultobj = SWIG_NewPointerObj(SWIG_as_voidptr(result), SWIGTYPE_p_CircularVector, SWIG_POINTER_NEW | 0 ); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_delete_CircularVector(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; CircularVector *arg1 = (CircularVector *) 0 ; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:delete_CircularVector",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_CircularVector, SWIG_POINTER_DISOWN | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "delete_CircularVector" "', argument " "1"" of type '" "CircularVector *""'"); } arg1 = reinterpret_cast< CircularVector * >(argp1); delete arg1; resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_CircularVector_reinit(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; CircularVector *arg1 = (CircularVector *) 0 ; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:CircularVector_reinit",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_CircularVector, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "CircularVector_reinit" "', argument " "1"" of type '" "CircularVector *""'"); } arg1 = reinterpret_cast< CircularVector * >(argp1); (arg1)->reinit(); resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_CircularVector_advance(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; CircularVector *arg1 = (CircularVector *) 0 ; int arg2 ; void *argp1 = 0 ; int res1 = 0 ; int val2 ; int ecode2 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OO:CircularVector_advance",&obj0,&obj1)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_CircularVector, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "CircularVector_advance" "', argument " "1"" of type '" "CircularVector *""'"); } arg1 = reinterpret_cast< CircularVector * >(argp1); ecode2 = SWIG_AsVal_int(obj1, &val2); if (!SWIG_IsOK(ecode2)) { SWIG_exception_fail(SWIG_ArgError(ecode2), "in method '" "CircularVector_advance" "', argument " "2"" of type '" "int""'"); } arg2 = static_cast< int >(val2); (arg1)->advance(arg2); resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_CircularVector___len__(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; CircularVector *arg1 = (CircularVector *) 0 ; int result; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:CircularVector___len__",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_CircularVector, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "CircularVector___len__" "', argument " "1"" of type '" "CircularVector *""'"); } arg1 = reinterpret_cast< CircularVector * >(argp1); result = (int)(arg1)->__len__(); resultobj = SWIG_From_int(static_cast< int >(result)); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_CircularVector___getitem__(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; CircularVector *arg1 = (CircularVector *) 0 ; int arg2 ; int result; void *argp1 = 0 ; int res1 = 0 ; int val2 ; int ecode2 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OO:CircularVector___getitem__",&obj0,&obj1)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_CircularVector, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "CircularVector___getitem__" "', argument " "1"" of type '" "CircularVector *""'"); } arg1 = reinterpret_cast< CircularVector * >(argp1); ecode2 = SWIG_AsVal_int(obj1, &val2); if (!SWIG_IsOK(ecode2)) { SWIG_exception_fail(SWIG_ArgError(ecode2), "in method '" "CircularVector___getitem__" "', argument " "2"" of type '" "int""'"); } arg2 = static_cast< int >(val2); result = (int)(arg1)->__getitem__(arg2); resultobj = SWIG_From_int(static_cast< int >(result)); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_CircularVector___setitem__(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; CircularVector *arg1 = (CircularVector *) 0 ; int arg2 ; int arg3 ; void *argp1 = 0 ; int res1 = 0 ; int val2 ; int ecode2 = 0 ; int val3 ; int ecode3 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; PyObject * obj2 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OOO:CircularVector___setitem__",&obj0,&obj1,&obj2)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_CircularVector, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "CircularVector___setitem__" "', argument " "1"" of type '" "CircularVector *""'"); } arg1 = reinterpret_cast< CircularVector * >(argp1); ecode2 = SWIG_AsVal_int(obj1, &val2); if (!SWIG_IsOK(ecode2)) { SWIG_exception_fail(SWIG_ArgError(ecode2), "in method '" "CircularVector___setitem__" "', argument " "2"" of type '" "int""'"); } arg2 = static_cast< int >(val2); ecode3 = SWIG_AsVal_int(obj2, &val3); if (!SWIG_IsOK(ecode3)) { SWIG_exception_fail(SWIG_ArgError(ecode3), "in method '" "CircularVector___setitem__" "', argument " "3"" of type '" "int""'"); } arg3 = static_cast< int >(val3); (arg1)->__setitem__(arg2,arg3); resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_CircularVector___getslice__(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; CircularVector *arg1 = (CircularVector *) 0 ; int **arg2 = (int **) 0 ; int *arg3 = (int *) 0 ; int arg4 ; int arg5 ; void *argp1 = 0 ; int res1 = 0 ; int *data_temp2 ; int dim_temp2 ; int val4 ; int ecode4 = 0 ; int val5 ; int ecode5 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; PyObject * obj2 = 0 ; { arg2 = &data_temp2; arg3 = &dim_temp2; } if (!PyArg_ParseTuple(args,(char *)"OOO:CircularVector___getslice__",&obj0,&obj1,&obj2)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_CircularVector, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "CircularVector___getslice__" "', argument " "1"" of type '" "CircularVector *""'"); } arg1 = reinterpret_cast< CircularVector * >(argp1); ecode4 = SWIG_AsVal_int(obj1, &val4); if (!SWIG_IsOK(ecode4)) { SWIG_exception_fail(SWIG_ArgError(ecode4), "in method '" "CircularVector___getslice__" "', argument " "4"" of type '" "int""'"); } arg4 = static_cast< int >(val4); ecode5 = SWIG_AsVal_int(obj2, &val5); if (!SWIG_IsOK(ecode5)) { SWIG_exception_fail(SWIG_ArgError(ecode5), "in method '" "CircularVector___getslice__" "', argument " "5"" of type '" "int""'"); } arg5 = static_cast< int >(val5); (arg1)->__getslice__(arg2,arg3,arg4,arg5); resultobj = SWIG_Py_Void(); { npy_intp dims[1] = { *arg3 }; PyObject * array = PyArray_SimpleNewFromData(1, dims, NPY_INT, (void*)(*arg2)); if (!array) SWIG_fail; resultobj = SWIG_Python_AppendOutput(resultobj,array); } return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_CircularVector_get_conditional__SWIG_0(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; CircularVector *arg1 = (CircularVector *) 0 ; int **arg2 = (int **) 0 ; int *arg3 = (int *) 0 ; int arg4 ; int arg5 ; int arg6 ; int arg7 ; int arg8 ; void *argp1 = 0 ; int res1 = 0 ; int *data_temp2 ; int dim_temp2 ; int val4 ; int ecode4 = 0 ; int val5 ; int ecode5 = 0 ; int val6 ; int ecode6 = 0 ; int val7 ; int ecode7 = 0 ; int val8 ; int ecode8 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; PyObject * obj2 = 0 ; PyObject * obj3 = 0 ; PyObject * obj4 = 0 ; PyObject * obj5 = 0 ; { arg2 = &data_temp2; arg3 = &dim_temp2; } if (!PyArg_ParseTuple(args,(char *)"OOOOOO:CircularVector_get_conditional",&obj0,&obj1,&obj2,&obj3,&obj4,&obj5)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_CircularVector, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "CircularVector_get_conditional" "', argument " "1"" of type '" "CircularVector *""'"); } arg1 = reinterpret_cast< CircularVector * >(argp1); ecode4 = SWIG_AsVal_int(obj1, &val4); if (!SWIG_IsOK(ecode4)) { SWIG_exception_fail(SWIG_ArgError(ecode4), "in method '" "CircularVector_get_conditional" "', argument " "4"" of type '" "int""'"); } arg4 = static_cast< int >(val4); ecode5 = SWIG_AsVal_int(obj2, &val5); if (!SWIG_IsOK(ecode5)) { SWIG_exception_fail(SWIG_ArgError(ecode5), "in method '" "CircularVector_get_conditional" "', argument " "5"" of type '" "int""'"); } arg5 = static_cast< int >(val5); ecode6 = SWIG_AsVal_int(obj3, &val6); if (!SWIG_IsOK(ecode6)) { SWIG_exception_fail(SWIG_ArgError(ecode6), "in method '" "CircularVector_get_conditional" "', argument " "6"" of type '" "int""'"); } arg6 = static_cast< int >(val6); ecode7 = SWIG_AsVal_int(obj4, &val7); if (!SWIG_IsOK(ecode7)) { SWIG_exception_fail(SWIG_ArgError(ecode7), "in method '" "CircularVector_get_conditional" "', argument " "7"" of type '" "int""'"); } arg7 = static_cast< int >(val7); ecode8 = SWIG_AsVal_int(obj5, &val8); if (!SWIG_IsOK(ecode8)) { SWIG_exception_fail(SWIG_ArgError(ecode8), "in method '" "CircularVector_get_conditional" "', argument " "8"" of type '" "int""'"); } arg8 = static_cast< int >(val8); (arg1)->get_conditional(arg2,arg3,arg4,arg5,arg6,arg7,arg8); resultobj = SWIG_Py_Void(); { npy_intp dims[1] = { *arg3 }; PyObject * array = PyArray_SimpleNewFromData(1, dims, NPY_INT, (void*)(*arg2)); if (!array) SWIG_fail; resultobj = SWIG_Python_AppendOutput(resultobj,array); } return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_CircularVector_get_conditional__SWIG_1(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; CircularVector *arg1 = (CircularVector *) 0 ; int **arg2 = (int **) 0 ; int *arg3 = (int *) 0 ; int arg4 ; int arg5 ; int arg6 ; int arg7 ; void *argp1 = 0 ; int res1 = 0 ; int *data_temp2 ; int dim_temp2 ; int val4 ; int ecode4 = 0 ; int val5 ; int ecode5 = 0 ; int val6 ; int ecode6 = 0 ; int val7 ; int ecode7 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; PyObject * obj2 = 0 ; PyObject * obj3 = 0 ; PyObject * obj4 = 0 ; { arg2 = &data_temp2; arg3 = &dim_temp2; } if (!PyArg_ParseTuple(args,(char *)"OOOOO:CircularVector_get_conditional",&obj0,&obj1,&obj2,&obj3,&obj4)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_CircularVector, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "CircularVector_get_conditional" "', argument " "1"" of type '" "CircularVector *""'"); } arg1 = reinterpret_cast< CircularVector * >(argp1); ecode4 = SWIG_AsVal_int(obj1, &val4); if (!SWIG_IsOK(ecode4)) { SWIG_exception_fail(SWIG_ArgError(ecode4), "in method '" "CircularVector_get_conditional" "', argument " "4"" of type '" "int""'"); } arg4 = static_cast< int >(val4); ecode5 = SWIG_AsVal_int(obj2, &val5); if (!SWIG_IsOK(ecode5)) { SWIG_exception_fail(SWIG_ArgError(ecode5), "in method '" "CircularVector_get_conditional" "', argument " "5"" of type '" "int""'"); } arg5 = static_cast< int >(val5); ecode6 = SWIG_AsVal_int(obj3, &val6); if (!SWIG_IsOK(ecode6)) { SWIG_exception_fail(SWIG_ArgError(ecode6), "in method '" "CircularVector_get_conditional" "', argument " "6"" of type '" "int""'"); } arg6 = static_cast< int >(val6); ecode7 = SWIG_AsVal_int(obj4, &val7); if (!SWIG_IsOK(ecode7)) { SWIG_exception_fail(SWIG_ArgError(ecode7), "in method '" "CircularVector_get_conditional" "', argument " "7"" of type '" "int""'"); } arg7 = static_cast< int >(val7); (arg1)->get_conditional(arg2,arg3,arg4,arg5,arg6,arg7); resultobj = SWIG_Py_Void(); { npy_intp dims[1] = { *arg3 }; PyObject * array = PyArray_SimpleNewFromData(1, dims, NPY_INT, (void*)(*arg2)); if (!array) SWIG_fail; resultobj = SWIG_Python_AppendOutput(resultobj,array); } return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_CircularVector_get_conditional(PyObject *self, PyObject *args) { int argc; PyObject *argv[7]; int ii; if (!PyTuple_Check(args)) SWIG_fail; argc = (int)PyObject_Length(args); for (ii = 0; (ii < argc) && (ii < 6); ii++) { argv[ii] = PyTuple_GET_ITEM(args,ii); } if (argc == 5) { int _v; void *vptr = 0; int res = SWIG_ConvertPtr(argv[0], &vptr, SWIGTYPE_p_CircularVector, 0); _v = SWIG_CheckState(res); if (_v) { { int res = SWIG_AsVal_int(argv[1], NULL); _v = SWIG_CheckState(res); } if (_v) { { int res = SWIG_AsVal_int(argv[2], NULL); _v = SWIG_CheckState(res); } if (_v) { { int res = SWIG_AsVal_int(argv[3], NULL); _v = SWIG_CheckState(res); } if (_v) { { int res = SWIG_AsVal_int(argv[4], NULL); _v = SWIG_CheckState(res); } if (_v) { return _wrap_CircularVector_get_conditional__SWIG_1(self, args); } } } } } } if (argc == 6) { int _v; void *vptr = 0; int res = SWIG_ConvertPtr(argv[0], &vptr, SWIGTYPE_p_CircularVector, 0); _v = SWIG_CheckState(res); if (_v) { { int res = SWIG_AsVal_int(argv[1], NULL); _v = SWIG_CheckState(res); } if (_v) { { int res = SWIG_AsVal_int(argv[2], NULL); _v = SWIG_CheckState(res); } if (_v) { { int res = SWIG_AsVal_int(argv[3], NULL); _v = SWIG_CheckState(res); } if (_v) { { int res = SWIG_AsVal_int(argv[4], NULL); _v = SWIG_CheckState(res); } if (_v) { { int res = SWIG_AsVal_int(argv[5], NULL); _v = SWIG_CheckState(res); } if (_v) { return _wrap_CircularVector_get_conditional__SWIG_0(self, args); } } } } } } } fail: SWIG_SetErrorMsg(PyExc_NotImplementedError,"Wrong number of arguments for overloaded function 'CircularVector_get_conditional'.\n Possible C/C++ prototypes are:\n"" get_conditional(CircularVector *,int **,int *,int,int,int,int,int)\n"" get_conditional(CircularVector *,int **,int *,int,int,int,int)\n"); return NULL; } SWIGINTERN PyObject *_wrap_CircularVector___setslice__(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; CircularVector *arg1 = (CircularVector *) 0 ; int arg2 ; int arg3 ; int *arg4 = (int *) 0 ; int arg5 ; void *argp1 = 0 ; int res1 = 0 ; int val2 ; int ecode2 = 0 ; int val3 ; int ecode3 = 0 ; PyArrayObject *array4 = NULL ; int i4 = 1 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; PyObject * obj2 = 0 ; PyObject * obj3 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OOOO:CircularVector___setslice__",&obj0,&obj1,&obj2,&obj3)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_CircularVector, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "CircularVector___setslice__" "', argument " "1"" of type '" "CircularVector *""'"); } arg1 = reinterpret_cast< CircularVector * >(argp1); ecode2 = SWIG_AsVal_int(obj1, &val2); if (!SWIG_IsOK(ecode2)) { SWIG_exception_fail(SWIG_ArgError(ecode2), "in method '" "CircularVector___setslice__" "', argument " "2"" of type '" "int""'"); } arg2 = static_cast< int >(val2); ecode3 = SWIG_AsVal_int(obj2, &val3); if (!SWIG_IsOK(ecode3)) { SWIG_exception_fail(SWIG_ArgError(ecode3), "in method '" "CircularVector___setslice__" "', argument " "3"" of type '" "int""'"); } arg3 = static_cast< int >(val3); { array4 = obj_to_array_no_conversion(obj3, NPY_INT); if (!array4 || !require_dimensions(array4,1) || !require_contiguous(array4) || !require_native(array4)) SWIG_fail; arg4 = (int*) array_data(array4); arg5 = 1; for (i4=0; i4 < array_numdims(array4); ++i4) arg5 *= array_size(array4,i4); } (arg1)->__setslice__(arg2,arg3,arg4,arg5); resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_CircularVector___repr__(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; CircularVector *arg1 = (CircularVector *) 0 ; std::string result; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:CircularVector___repr__",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_CircularVector, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "CircularVector___repr__" "', argument " "1"" of type '" "CircularVector *""'"); } arg1 = reinterpret_cast< CircularVector * >(argp1); result = (arg1)->__repr__(); resultobj = SWIG_From_std_string(static_cast< std::string >(result)); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_CircularVector___str__(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; CircularVector *arg1 = (CircularVector *) 0 ; std::string result; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:CircularVector___str__",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_CircularVector, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "CircularVector___str__" "', argument " "1"" of type '" "CircularVector *""'"); } arg1 = reinterpret_cast< CircularVector * >(argp1); result = (arg1)->__str__(); resultobj = SWIG_From_std_string(static_cast< std::string >(result)); return resultobj; fail: return NULL; } SWIGINTERN PyObject *CircularVector_swigregister(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *obj; if (!PyArg_ParseTuple(args,(char*)"O|swigregister", &obj)) return NULL; SWIG_TypeNewClientData(SWIGTYPE_p_CircularVector, SWIG_NewClientData(obj)); return SWIG_Py_Void(); } SWIGINTERN PyObject *_wrap_SpikeContainer_S_set(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; SpikeContainer *arg1 = (SpikeContainer *) 0 ; CircularVector *arg2 = (CircularVector *) 0 ; void *argp1 = 0 ; int res1 = 0 ; void *argp2 = 0 ; int res2 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OO:SpikeContainer_S_set",&obj0,&obj1)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_SpikeContainer, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "SpikeContainer_S_set" "', argument " "1"" of type '" "SpikeContainer *""'"); } arg1 = reinterpret_cast< SpikeContainer * >(argp1); res2 = SWIG_ConvertPtr(obj1, &argp2,SWIGTYPE_p_CircularVector, SWIG_POINTER_DISOWN | 0 ); if (!SWIG_IsOK(res2)) { SWIG_exception_fail(SWIG_ArgError(res2), "in method '" "SpikeContainer_S_set" "', argument " "2"" of type '" "CircularVector *""'"); } arg2 = reinterpret_cast< CircularVector * >(argp2); if (arg1) (arg1)->S = arg2; resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_SpikeContainer_S_get(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; SpikeContainer *arg1 = (SpikeContainer *) 0 ; CircularVector *result = 0 ; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:SpikeContainer_S_get",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_SpikeContainer, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "SpikeContainer_S_get" "', argument " "1"" of type '" "SpikeContainer *""'"); } arg1 = reinterpret_cast< SpikeContainer * >(argp1); result = (CircularVector *) ((arg1)->S); resultobj = SWIG_NewPointerObj(SWIG_as_voidptr(result), SWIGTYPE_p_CircularVector, 0 | 0 ); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_SpikeContainer_ind_set(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; SpikeContainer *arg1 = (SpikeContainer *) 0 ; CircularVector *arg2 = (CircularVector *) 0 ; void *argp1 = 0 ; int res1 = 0 ; void *argp2 = 0 ; int res2 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OO:SpikeContainer_ind_set",&obj0,&obj1)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_SpikeContainer, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "SpikeContainer_ind_set" "', argument " "1"" of type '" "SpikeContainer *""'"); } arg1 = reinterpret_cast< SpikeContainer * >(argp1); res2 = SWIG_ConvertPtr(obj1, &argp2,SWIGTYPE_p_CircularVector, SWIG_POINTER_DISOWN | 0 ); if (!SWIG_IsOK(res2)) { SWIG_exception_fail(SWIG_ArgError(res2), "in method '" "SpikeContainer_ind_set" "', argument " "2"" of type '" "CircularVector *""'"); } arg2 = reinterpret_cast< CircularVector * >(argp2); if (arg1) (arg1)->ind = arg2; resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_SpikeContainer_ind_get(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; SpikeContainer *arg1 = (SpikeContainer *) 0 ; CircularVector *result = 0 ; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:SpikeContainer_ind_get",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_SpikeContainer, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "SpikeContainer_ind_get" "', argument " "1"" of type '" "SpikeContainer *""'"); } arg1 = reinterpret_cast< SpikeContainer * >(argp1); result = (CircularVector *) ((arg1)->ind); resultobj = SWIG_NewPointerObj(SWIG_as_voidptr(result), SWIGTYPE_p_CircularVector, 0 | 0 ); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_new_SpikeContainer(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; int arg1 ; int arg2 ; SpikeContainer *result = 0 ; int val1 ; int ecode1 = 0 ; int val2 ; int ecode2 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OO:new_SpikeContainer",&obj0,&obj1)) SWIG_fail; ecode1 = SWIG_AsVal_int(obj0, &val1); if (!SWIG_IsOK(ecode1)) { SWIG_exception_fail(SWIG_ArgError(ecode1), "in method '" "new_SpikeContainer" "', argument " "1"" of type '" "int""'"); } arg1 = static_cast< int >(val1); ecode2 = SWIG_AsVal_int(obj1, &val2); if (!SWIG_IsOK(ecode2)) { SWIG_exception_fail(SWIG_ArgError(ecode2), "in method '" "new_SpikeContainer" "', argument " "2"" of type '" "int""'"); } arg2 = static_cast< int >(val2); result = (SpikeContainer *)new SpikeContainer(arg1,arg2); resultobj = SWIG_NewPointerObj(SWIG_as_voidptr(result), SWIGTYPE_p_SpikeContainer, SWIG_POINTER_NEW | 0 ); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_delete_SpikeContainer(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; SpikeContainer *arg1 = (SpikeContainer *) 0 ; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:delete_SpikeContainer",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_SpikeContainer, SWIG_POINTER_DISOWN | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "delete_SpikeContainer" "', argument " "1"" of type '" "SpikeContainer *""'"); } arg1 = reinterpret_cast< SpikeContainer * >(argp1); delete arg1; resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_SpikeContainer_reinit(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; SpikeContainer *arg1 = (SpikeContainer *) 0 ; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:SpikeContainer_reinit",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_SpikeContainer, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "SpikeContainer_reinit" "', argument " "1"" of type '" "SpikeContainer *""'"); } arg1 = reinterpret_cast< SpikeContainer * >(argp1); (arg1)->reinit(); resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_SpikeContainer_push(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; SpikeContainer *arg1 = (SpikeContainer *) 0 ; int *arg2 = (int *) 0 ; int arg3 ; void *argp1 = 0 ; int res1 = 0 ; PyArrayObject *array2 = NULL ; int i2 = 1 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OO:SpikeContainer_push",&obj0,&obj1)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_SpikeContainer, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "SpikeContainer_push" "', argument " "1"" of type '" "SpikeContainer *""'"); } arg1 = reinterpret_cast< SpikeContainer * >(argp1); { array2 = obj_to_array_no_conversion(obj1, NPY_INT); if (!array2 || !require_dimensions(array2,1) || !require_contiguous(array2) || !require_native(array2)) SWIG_fail; arg2 = (int*) array_data(array2); arg3 = 1; for (i2=0; i2 < array_numdims(array2); ++i2) arg3 *= array_size(array2,i2); } (arg1)->push(arg2,arg3); resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_SpikeContainer_lastspikes(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; SpikeContainer *arg1 = (SpikeContainer *) 0 ; int **arg2 = (int **) 0 ; int *arg3 = (int *) 0 ; void *argp1 = 0 ; int res1 = 0 ; int *data_temp2 ; int dim_temp2 ; PyObject * obj0 = 0 ; { arg2 = &data_temp2; arg3 = &dim_temp2; } if (!PyArg_ParseTuple(args,(char *)"O:SpikeContainer_lastspikes",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_SpikeContainer, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "SpikeContainer_lastspikes" "', argument " "1"" of type '" "SpikeContainer *""'"); } arg1 = reinterpret_cast< SpikeContainer * >(argp1); (arg1)->lastspikes(arg2,arg3); resultobj = SWIG_Py_Void(); { npy_intp dims[1] = { *arg3 }; PyObject * array = PyArray_SimpleNewFromData(1, dims, NPY_INT, (void*)(*arg2)); if (!array) SWIG_fail; resultobj = SWIG_Python_AppendOutput(resultobj,array); } return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_SpikeContainer___getitem__(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; SpikeContainer *arg1 = (SpikeContainer *) 0 ; int **arg2 = (int **) 0 ; int *arg3 = (int *) 0 ; int arg4 ; void *argp1 = 0 ; int res1 = 0 ; int *data_temp2 ; int dim_temp2 ; int val4 ; int ecode4 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; { arg2 = &data_temp2; arg3 = &dim_temp2; } if (!PyArg_ParseTuple(args,(char *)"OO:SpikeContainer___getitem__",&obj0,&obj1)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_SpikeContainer, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "SpikeContainer___getitem__" "', argument " "1"" of type '" "SpikeContainer *""'"); } arg1 = reinterpret_cast< SpikeContainer * >(argp1); ecode4 = SWIG_AsVal_int(obj1, &val4); if (!SWIG_IsOK(ecode4)) { SWIG_exception_fail(SWIG_ArgError(ecode4), "in method '" "SpikeContainer___getitem__" "', argument " "4"" of type '" "int""'"); } arg4 = static_cast< int >(val4); (arg1)->__getitem__(arg2,arg3,arg4); resultobj = SWIG_Py_Void(); { npy_intp dims[1] = { *arg3 }; PyObject * array = PyArray_SimpleNewFromData(1, dims, NPY_INT, (void*)(*arg2)); if (!array) SWIG_fail; resultobj = SWIG_Python_AppendOutput(resultobj,array); } return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_SpikeContainer_get_spikes(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; SpikeContainer *arg1 = (SpikeContainer *) 0 ; int **arg2 = (int **) 0 ; int *arg3 = (int *) 0 ; int arg4 ; int arg5 ; int arg6 ; void *argp1 = 0 ; int res1 = 0 ; int *data_temp2 ; int dim_temp2 ; int val4 ; int ecode4 = 0 ; int val5 ; int ecode5 = 0 ; int val6 ; int ecode6 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; PyObject * obj2 = 0 ; PyObject * obj3 = 0 ; { arg2 = &data_temp2; arg3 = &dim_temp2; } if (!PyArg_ParseTuple(args,(char *)"OOOO:SpikeContainer_get_spikes",&obj0,&obj1,&obj2,&obj3)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_SpikeContainer, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "SpikeContainer_get_spikes" "', argument " "1"" of type '" "SpikeContainer *""'"); } arg1 = reinterpret_cast< SpikeContainer * >(argp1); ecode4 = SWIG_AsVal_int(obj1, &val4); if (!SWIG_IsOK(ecode4)) { SWIG_exception_fail(SWIG_ArgError(ecode4), "in method '" "SpikeContainer_get_spikes" "', argument " "4"" of type '" "int""'"); } arg4 = static_cast< int >(val4); ecode5 = SWIG_AsVal_int(obj2, &val5); if (!SWIG_IsOK(ecode5)) { SWIG_exception_fail(SWIG_ArgError(ecode5), "in method '" "SpikeContainer_get_spikes" "', argument " "5"" of type '" "int""'"); } arg5 = static_cast< int >(val5); ecode6 = SWIG_AsVal_int(obj3, &val6); if (!SWIG_IsOK(ecode6)) { SWIG_exception_fail(SWIG_ArgError(ecode6), "in method '" "SpikeContainer_get_spikes" "', argument " "6"" of type '" "int""'"); } arg6 = static_cast< int >(val6); (arg1)->get_spikes(arg2,arg3,arg4,arg5,arg6); resultobj = SWIG_Py_Void(); { npy_intp dims[1] = { *arg3 }; PyObject * array = PyArray_SimpleNewFromData(1, dims, NPY_INT, (void*)(*arg2)); if (!array) SWIG_fail; resultobj = SWIG_Python_AppendOutput(resultobj,array); } return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_SpikeContainer___getslice__(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; SpikeContainer *arg1 = (SpikeContainer *) 0 ; int **arg2 = (int **) 0 ; int *arg3 = (int *) 0 ; int arg4 ; int arg5 ; void *argp1 = 0 ; int res1 = 0 ; int *data_temp2 ; int dim_temp2 ; int val4 ; int ecode4 = 0 ; int val5 ; int ecode5 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; PyObject * obj2 = 0 ; { arg2 = &data_temp2; arg3 = &dim_temp2; } if (!PyArg_ParseTuple(args,(char *)"OOO:SpikeContainer___getslice__",&obj0,&obj1,&obj2)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_SpikeContainer, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "SpikeContainer___getslice__" "', argument " "1"" of type '" "SpikeContainer *""'"); } arg1 = reinterpret_cast< SpikeContainer * >(argp1); ecode4 = SWIG_AsVal_int(obj1, &val4); if (!SWIG_IsOK(ecode4)) { SWIG_exception_fail(SWIG_ArgError(ecode4), "in method '" "SpikeContainer___getslice__" "', argument " "4"" of type '" "int""'"); } arg4 = static_cast< int >(val4); ecode5 = SWIG_AsVal_int(obj2, &val5); if (!SWIG_IsOK(ecode5)) { SWIG_exception_fail(SWIG_ArgError(ecode5), "in method '" "SpikeContainer___getslice__" "', argument " "5"" of type '" "int""'"); } arg5 = static_cast< int >(val5); (arg1)->__getslice__(arg2,arg3,arg4,arg5); resultobj = SWIG_Py_Void(); { npy_intp dims[1] = { *arg3 }; PyObject * array = PyArray_SimpleNewFromData(1, dims, NPY_INT, (void*)(*arg2)); if (!array) SWIG_fail; resultobj = SWIG_Python_AppendOutput(resultobj,array); } return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_SpikeContainer___repr__(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; SpikeContainer *arg1 = (SpikeContainer *) 0 ; std::string result; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:SpikeContainer___repr__",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_SpikeContainer, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "SpikeContainer___repr__" "', argument " "1"" of type '" "SpikeContainer *""'"); } arg1 = reinterpret_cast< SpikeContainer * >(argp1); result = (arg1)->__repr__(); resultobj = SWIG_From_std_string(static_cast< std::string >(result)); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_SpikeContainer___str__(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; SpikeContainer *arg1 = (SpikeContainer *) 0 ; std::string result; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:SpikeContainer___str__",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_SpikeContainer, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "SpikeContainer___str__" "', argument " "1"" of type '" "SpikeContainer *""'"); } arg1 = reinterpret_cast< SpikeContainer * >(argp1); result = (arg1)->__str__(); resultobj = SWIG_From_std_string(static_cast< std::string >(result)); return resultobj; fail: return NULL; } SWIGINTERN PyObject *SpikeContainer_swigregister(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *obj; if (!PyArg_ParseTuple(args,(char*)"O|swigregister", &obj)) return NULL; SWIG_TypeNewClientData(SWIGTYPE_p_SpikeContainer, SWIG_NewClientData(obj)); return SWIG_Py_Void(); } SWIGINTERN PyObject *_wrap_ConnectionMatrix_add_row(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; ConnectionMatrix *arg1 = (ConnectionMatrix *) 0 ; int arg2 ; double *arg3 = (double *) 0 ; int arg4 ; void *argp1 = 0 ; int res1 = 0 ; int val2 ; int ecode2 = 0 ; PyArrayObject *array3 = NULL ; int i3 = 1 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; PyObject * obj2 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OOO:ConnectionMatrix_add_row",&obj0,&obj1,&obj2)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_ConnectionMatrix, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "ConnectionMatrix_add_row" "', argument " "1"" of type '" "ConnectionMatrix *""'"); } arg1 = reinterpret_cast< ConnectionMatrix * >(argp1); ecode2 = SWIG_AsVal_int(obj1, &val2); if (!SWIG_IsOK(ecode2)) { SWIG_exception_fail(SWIG_ArgError(ecode2), "in method '" "ConnectionMatrix_add_row" "', argument " "2"" of type '" "int""'"); } arg2 = static_cast< int >(val2); { array3 = obj_to_array_no_conversion(obj2, NPY_DOUBLE); if (!array3 || !require_dimensions(array3,1) || !require_contiguous(array3) || !require_native(array3)) SWIG_fail; arg3 = (double*) array_data(array3); arg4 = 1; for (i3=0; i3 < array_numdims(array3); ++i3) arg4 *= array_size(array3,i3); } (arg1)->add_row(arg2,arg3,arg4); resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_ConnectionMatrix_add_rows(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; ConnectionMatrix *arg1 = (ConnectionMatrix *) 0 ; int *arg2 = (int *) 0 ; int arg3 ; double *arg4 = (double *) 0 ; int arg5 ; void *argp1 = 0 ; int res1 = 0 ; PyArrayObject *array2 = NULL ; int i2 = 1 ; PyArrayObject *array4 = NULL ; int i4 = 1 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; PyObject * obj2 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OOO:ConnectionMatrix_add_rows",&obj0,&obj1,&obj2)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_ConnectionMatrix, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "ConnectionMatrix_add_rows" "', argument " "1"" of type '" "ConnectionMatrix *""'"); } arg1 = reinterpret_cast< ConnectionMatrix * >(argp1); { array2 = obj_to_array_no_conversion(obj1, NPY_INT); if (!array2 || !require_dimensions(array2,1) || !require_contiguous(array2) || !require_native(array2)) SWIG_fail; arg2 = (int*) array_data(array2); arg3 = 1; for (i2=0; i2 < array_numdims(array2); ++i2) arg3 *= array_size(array2,i2); } { array4 = obj_to_array_no_conversion(obj2, NPY_DOUBLE); if (!array4 || !require_dimensions(array4,1) || !require_contiguous(array4) || !require_native(array4)) SWIG_fail; arg4 = (double*) array_data(array4); arg5 = 1; for (i4=0; i4 < array_numdims(array4); ++i4) arg5 *= array_size(array4,i4); } (arg1)->add_rows(arg2,arg3,arg4,arg5); resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_new_ConnectionMatrix(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; ConnectionMatrix *result = 0 ; if (!PyArg_ParseTuple(args,(char *)":new_ConnectionMatrix")) SWIG_fail; result = (ConnectionMatrix *)new ConnectionMatrix(); resultobj = SWIG_NewPointerObj(SWIG_as_voidptr(result), SWIGTYPE_p_ConnectionMatrix, SWIG_POINTER_NEW | 0 ); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_delete_ConnectionMatrix(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; ConnectionMatrix *arg1 = (ConnectionMatrix *) 0 ; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:delete_ConnectionMatrix",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_ConnectionMatrix, SWIG_POINTER_DISOWN | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "delete_ConnectionMatrix" "', argument " "1"" of type '" "ConnectionMatrix *""'"); } arg1 = reinterpret_cast< ConnectionMatrix * >(argp1); delete arg1; resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *ConnectionMatrix_swigregister(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *obj; if (!PyArg_ParseTuple(args,(char*)"O|swigregister", &obj)) return NULL; SWIG_TypeNewClientData(SWIGTYPE_p_ConnectionMatrix, SWIG_NewClientData(obj)); return SWIG_Py_Void(); } SWIGINTERN PyObject *_wrap_DenseConnectionMatrix_W_set(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; DenseConnectionMatrix *arg1 = (DenseConnectionMatrix *) 0 ; double *arg2 = (double *) 0 ; void *argp1 = 0 ; int res1 = 0 ; void *argp2 = 0 ; int res2 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OO:DenseConnectionMatrix_W_set",&obj0,&obj1)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_DenseConnectionMatrix, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "DenseConnectionMatrix_W_set" "', argument " "1"" of type '" "DenseConnectionMatrix *""'"); } arg1 = reinterpret_cast< DenseConnectionMatrix * >(argp1); res2 = SWIG_ConvertPtr(obj1, &argp2,SWIGTYPE_p_double, SWIG_POINTER_DISOWN | 0 ); if (!SWIG_IsOK(res2)) { SWIG_exception_fail(SWIG_ArgError(res2), "in method '" "DenseConnectionMatrix_W_set" "', argument " "2"" of type '" "double *""'"); } arg2 = reinterpret_cast< double * >(argp2); if (arg1) (arg1)->W = arg2; resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_DenseConnectionMatrix_W_get(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; DenseConnectionMatrix *arg1 = (DenseConnectionMatrix *) 0 ; double *result = 0 ; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:DenseConnectionMatrix_W_get",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_DenseConnectionMatrix, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "DenseConnectionMatrix_W_get" "', argument " "1"" of type '" "DenseConnectionMatrix *""'"); } arg1 = reinterpret_cast< DenseConnectionMatrix * >(argp1); result = (double *) ((arg1)->W); resultobj = SWIG_NewPointerObj(SWIG_as_voidptr(result), SWIGTYPE_p_double, 0 | 0 ); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_DenseConnectionMatrix_n_set(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; DenseConnectionMatrix *arg1 = (DenseConnectionMatrix *) 0 ; int arg2 ; void *argp1 = 0 ; int res1 = 0 ; int val2 ; int ecode2 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OO:DenseConnectionMatrix_n_set",&obj0,&obj1)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_DenseConnectionMatrix, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "DenseConnectionMatrix_n_set" "', argument " "1"" of type '" "DenseConnectionMatrix *""'"); } arg1 = reinterpret_cast< DenseConnectionMatrix * >(argp1); ecode2 = SWIG_AsVal_int(obj1, &val2); if (!SWIG_IsOK(ecode2)) { SWIG_exception_fail(SWIG_ArgError(ecode2), "in method '" "DenseConnectionMatrix_n_set" "', argument " "2"" of type '" "int""'"); } arg2 = static_cast< int >(val2); if (arg1) (arg1)->n = arg2; resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_DenseConnectionMatrix_n_get(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; DenseConnectionMatrix *arg1 = (DenseConnectionMatrix *) 0 ; int result; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:DenseConnectionMatrix_n_get",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_DenseConnectionMatrix, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "DenseConnectionMatrix_n_get" "', argument " "1"" of type '" "DenseConnectionMatrix *""'"); } arg1 = reinterpret_cast< DenseConnectionMatrix * >(argp1); result = (int) ((arg1)->n); resultobj = SWIG_From_int(static_cast< int >(result)); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_DenseConnectionMatrix_m_set(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; DenseConnectionMatrix *arg1 = (DenseConnectionMatrix *) 0 ; int arg2 ; void *argp1 = 0 ; int res1 = 0 ; int val2 ; int ecode2 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OO:DenseConnectionMatrix_m_set",&obj0,&obj1)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_DenseConnectionMatrix, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "DenseConnectionMatrix_m_set" "', argument " "1"" of type '" "DenseConnectionMatrix *""'"); } arg1 = reinterpret_cast< DenseConnectionMatrix * >(argp1); ecode2 = SWIG_AsVal_int(obj1, &val2); if (!SWIG_IsOK(ecode2)) { SWIG_exception_fail(SWIG_ArgError(ecode2), "in method '" "DenseConnectionMatrix_m_set" "', argument " "2"" of type '" "int""'"); } arg2 = static_cast< int >(val2); if (arg1) (arg1)->m = arg2; resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_DenseConnectionMatrix_m_get(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; DenseConnectionMatrix *arg1 = (DenseConnectionMatrix *) 0 ; int result; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:DenseConnectionMatrix_m_get",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_DenseConnectionMatrix, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "DenseConnectionMatrix_m_get" "', argument " "1"" of type '" "DenseConnectionMatrix *""'"); } arg1 = reinterpret_cast< DenseConnectionMatrix * >(argp1); result = (int) ((arg1)->m); resultobj = SWIG_From_int(static_cast< int >(result)); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_new_DenseConnectionMatrix(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; double *arg1 = (double *) 0 ; int arg2 ; int arg3 ; DenseConnectionMatrix *result = 0 ; PyArrayObject *array1 = NULL ; PyObject * obj0 = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:new_DenseConnectionMatrix",&obj0)) SWIG_fail; { array1 = obj_to_array_no_conversion(obj0, NPY_DOUBLE); if (!array1 || !require_dimensions(array1,2) || !require_contiguous(array1) || !require_native(array1)) SWIG_fail; arg1 = (double*) array_data(array1); arg2 = (int) array_size(array1,0); arg3 = (int) array_size(array1,1); } result = (DenseConnectionMatrix *)new DenseConnectionMatrix(arg1,arg2,arg3); resultobj = SWIG_NewPointerObj(SWIG_as_voidptr(result), SWIGTYPE_p_DenseConnectionMatrix, SWIG_POINTER_NEW | 0 ); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_DenseConnectionMatrix_add_row(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; DenseConnectionMatrix *arg1 = (DenseConnectionMatrix *) 0 ; int arg2 ; double *arg3 = (double *) 0 ; int arg4 ; void *argp1 = 0 ; int res1 = 0 ; int val2 ; int ecode2 = 0 ; PyArrayObject *array3 = NULL ; int i3 = 1 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; PyObject * obj2 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OOO:DenseConnectionMatrix_add_row",&obj0,&obj1,&obj2)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_DenseConnectionMatrix, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "DenseConnectionMatrix_add_row" "', argument " "1"" of type '" "DenseConnectionMatrix *""'"); } arg1 = reinterpret_cast< DenseConnectionMatrix * >(argp1); ecode2 = SWIG_AsVal_int(obj1, &val2); if (!SWIG_IsOK(ecode2)) { SWIG_exception_fail(SWIG_ArgError(ecode2), "in method '" "DenseConnectionMatrix_add_row" "', argument " "2"" of type '" "int""'"); } arg2 = static_cast< int >(val2); { array3 = obj_to_array_no_conversion(obj2, NPY_DOUBLE); if (!array3 || !require_dimensions(array3,1) || !require_contiguous(array3) || !require_native(array3)) SWIG_fail; arg3 = (double*) array_data(array3); arg4 = 1; for (i3=0; i3 < array_numdims(array3); ++i3) arg4 *= array_size(array3,i3); } (arg1)->add_row(arg2,arg3,arg4); resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_delete_DenseConnectionMatrix(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; DenseConnectionMatrix *arg1 = (DenseConnectionMatrix *) 0 ; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:delete_DenseConnectionMatrix",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_DenseConnectionMatrix, SWIG_POINTER_DISOWN | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "delete_DenseConnectionMatrix" "', argument " "1"" of type '" "DenseConnectionMatrix *""'"); } arg1 = reinterpret_cast< DenseConnectionMatrix * >(argp1); delete arg1; resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *DenseConnectionMatrix_swigregister(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *obj; if (!PyArg_ParseTuple(args,(char*)"O|swigregister", &obj)) return NULL; SWIG_TypeNewClientData(SWIGTYPE_p_DenseConnectionMatrix, SWIG_NewClientData(obj)); return SWIG_Py_Void(); } SWIGINTERN PyObject *_wrap_Connection_source_set(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; Connection *arg1 = (Connection *) 0 ; NeuronGroup *arg2 = (NeuronGroup *) 0 ; void *argp1 = 0 ; int res1 = 0 ; void *argp2 = 0 ; int res2 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OO:Connection_source_set",&obj0,&obj1)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_Connection, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "Connection_source_set" "', argument " "1"" of type '" "Connection *""'"); } arg1 = reinterpret_cast< Connection * >(argp1); res2 = SWIG_ConvertPtr(obj1, &argp2,SWIGTYPE_p_NeuronGroup, SWIG_POINTER_DISOWN | 0 ); if (!SWIG_IsOK(res2)) { SWIG_exception_fail(SWIG_ArgError(res2), "in method '" "Connection_source_set" "', argument " "2"" of type '" "NeuronGroup *""'"); } arg2 = reinterpret_cast< NeuronGroup * >(argp2); if (arg1) (arg1)->source = arg2; resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_Connection_source_get(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; Connection *arg1 = (Connection *) 0 ; NeuronGroup *result = 0 ; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:Connection_source_get",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_Connection, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "Connection_source_get" "', argument " "1"" of type '" "Connection *""'"); } arg1 = reinterpret_cast< Connection * >(argp1); result = (NeuronGroup *) ((arg1)->source); resultobj = SWIG_NewPointerObj(SWIG_as_voidptr(result), SWIGTYPE_p_NeuronGroup, 0 | 0 ); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_Connection_target_set(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; Connection *arg1 = (Connection *) 0 ; NeuronGroup *arg2 = (NeuronGroup *) 0 ; void *argp1 = 0 ; int res1 = 0 ; void *argp2 = 0 ; int res2 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OO:Connection_target_set",&obj0,&obj1)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_Connection, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "Connection_target_set" "', argument " "1"" of type '" "Connection *""'"); } arg1 = reinterpret_cast< Connection * >(argp1); res2 = SWIG_ConvertPtr(obj1, &argp2,SWIGTYPE_p_NeuronGroup, SWIG_POINTER_DISOWN | 0 ); if (!SWIG_IsOK(res2)) { SWIG_exception_fail(SWIG_ArgError(res2), "in method '" "Connection_target_set" "', argument " "2"" of type '" "NeuronGroup *""'"); } arg2 = reinterpret_cast< NeuronGroup * >(argp2); if (arg1) (arg1)->target = arg2; resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_Connection_target_get(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; Connection *arg1 = (Connection *) 0 ; NeuronGroup *result = 0 ; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:Connection_target_get",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_Connection, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "Connection_target_get" "', argument " "1"" of type '" "Connection *""'"); } arg1 = reinterpret_cast< Connection * >(argp1); result = (NeuronGroup *) ((arg1)->target); resultobj = SWIG_NewPointerObj(SWIG_as_voidptr(result), SWIGTYPE_p_NeuronGroup, 0 | 0 ); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_Connection_connmat_set(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; Connection *arg1 = (Connection *) 0 ; ConnectionMatrix *arg2 = (ConnectionMatrix *) 0 ; void *argp1 = 0 ; int res1 = 0 ; void *argp2 = 0 ; int res2 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OO:Connection_connmat_set",&obj0,&obj1)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_Connection, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "Connection_connmat_set" "', argument " "1"" of type '" "Connection *""'"); } arg1 = reinterpret_cast< Connection * >(argp1); res2 = SWIG_ConvertPtr(obj1, &argp2,SWIGTYPE_p_ConnectionMatrix, SWIG_POINTER_DISOWN | 0 ); if (!SWIG_IsOK(res2)) { SWIG_exception_fail(SWIG_ArgError(res2), "in method '" "Connection_connmat_set" "', argument " "2"" of type '" "ConnectionMatrix *""'"); } arg2 = reinterpret_cast< ConnectionMatrix * >(argp2); if (arg1) (arg1)->connmat = arg2; resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_Connection_connmat_get(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; Connection *arg1 = (Connection *) 0 ; ConnectionMatrix *result = 0 ; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:Connection_connmat_get",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_Connection, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "Connection_connmat_get" "', argument " "1"" of type '" "Connection *""'"); } arg1 = reinterpret_cast< Connection * >(argp1); result = (ConnectionMatrix *) ((arg1)->connmat); resultobj = SWIG_NewPointerObj(SWIG_as_voidptr(result), SWIGTYPE_p_ConnectionMatrix, 0 | 0 ); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_Connection_state_set(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; Connection *arg1 = (Connection *) 0 ; int arg2 ; void *argp1 = 0 ; int res1 = 0 ; int val2 ; int ecode2 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OO:Connection_state_set",&obj0,&obj1)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_Connection, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "Connection_state_set" "', argument " "1"" of type '" "Connection *""'"); } arg1 = reinterpret_cast< Connection * >(argp1); ecode2 = SWIG_AsVal_int(obj1, &val2); if (!SWIG_IsOK(ecode2)) { SWIG_exception_fail(SWIG_ArgError(ecode2), "in method '" "Connection_state_set" "', argument " "2"" of type '" "int""'"); } arg2 = static_cast< int >(val2); if (arg1) (arg1)->state = arg2; resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_Connection_state_get(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; Connection *arg1 = (Connection *) 0 ; int result; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:Connection_state_get",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_Connection, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "Connection_state_get" "', argument " "1"" of type '" "Connection *""'"); } arg1 = reinterpret_cast< Connection * >(argp1); result = (int) ((arg1)->state); resultobj = SWIG_From_int(static_cast< int >(result)); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_Connection_delay_set(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; Connection *arg1 = (Connection *) 0 ; int arg2 ; void *argp1 = 0 ; int res1 = 0 ; int val2 ; int ecode2 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OO:Connection_delay_set",&obj0,&obj1)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_Connection, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "Connection_delay_set" "', argument " "1"" of type '" "Connection *""'"); } arg1 = reinterpret_cast< Connection * >(argp1); ecode2 = SWIG_AsVal_int(obj1, &val2); if (!SWIG_IsOK(ecode2)) { SWIG_exception_fail(SWIG_ArgError(ecode2), "in method '" "Connection_delay_set" "', argument " "2"" of type '" "int""'"); } arg2 = static_cast< int >(val2); if (arg1) (arg1)->delay = arg2; resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_Connection_delay_get(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; Connection *arg1 = (Connection *) 0 ; int result; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:Connection_delay_get",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_Connection, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "Connection_delay_get" "', argument " "1"" of type '" "Connection *""'"); } arg1 = reinterpret_cast< Connection * >(argp1); result = (int) ((arg1)->delay); resultobj = SWIG_From_int(static_cast< int >(result)); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_new_Connection__SWIG_0(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; NeuronGroup *arg1 = (NeuronGroup *) 0 ; NeuronGroup *arg2 = (NeuronGroup *) 0 ; ConnectionMatrix *arg3 = (ConnectionMatrix *) 0 ; int arg4 ; int arg5 ; Connection *result = 0 ; void *argp1 = 0 ; int res1 = 0 ; void *argp2 = 0 ; int res2 = 0 ; void *argp3 = 0 ; int res3 = 0 ; int val4 ; int ecode4 = 0 ; int val5 ; int ecode5 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; PyObject * obj2 = 0 ; PyObject * obj3 = 0 ; PyObject * obj4 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OOOOO:new_Connection",&obj0,&obj1,&obj2,&obj3,&obj4)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_NeuronGroup, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "new_Connection" "', argument " "1"" of type '" "NeuronGroup *""'"); } arg1 = reinterpret_cast< NeuronGroup * >(argp1); res2 = SWIG_ConvertPtr(obj1, &argp2,SWIGTYPE_p_NeuronGroup, 0 | 0 ); if (!SWIG_IsOK(res2)) { SWIG_exception_fail(SWIG_ArgError(res2), "in method '" "new_Connection" "', argument " "2"" of type '" "NeuronGroup *""'"); } arg2 = reinterpret_cast< NeuronGroup * >(argp2); res3 = SWIG_ConvertPtr(obj2, &argp3,SWIGTYPE_p_ConnectionMatrix, 0 | 0 ); if (!SWIG_IsOK(res3)) { SWIG_exception_fail(SWIG_ArgError(res3), "in method '" "new_Connection" "', argument " "3"" of type '" "ConnectionMatrix *""'"); } arg3 = reinterpret_cast< ConnectionMatrix * >(argp3); ecode4 = SWIG_AsVal_int(obj3, &val4); if (!SWIG_IsOK(ecode4)) { SWIG_exception_fail(SWIG_ArgError(ecode4), "in method '" "new_Connection" "', argument " "4"" of type '" "int""'"); } arg4 = static_cast< int >(val4); ecode5 = SWIG_AsVal_int(obj4, &val5); if (!SWIG_IsOK(ecode5)) { SWIG_exception_fail(SWIG_ArgError(ecode5), "in method '" "new_Connection" "', argument " "5"" of type '" "int""'"); } arg5 = static_cast< int >(val5); result = (Connection *)new Connection(arg1,arg2,arg3,arg4,arg5); resultobj = SWIG_NewPointerObj(SWIG_as_voidptr(result), SWIGTYPE_p_Connection, SWIG_POINTER_NEW | 0 ); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_new_Connection__SWIG_1(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; NeuronGroup *arg1 = (NeuronGroup *) 0 ; NeuronGroup *arg2 = (NeuronGroup *) 0 ; ConnectionMatrix *arg3 = (ConnectionMatrix *) 0 ; int arg4 ; Connection *result = 0 ; void *argp1 = 0 ; int res1 = 0 ; void *argp2 = 0 ; int res2 = 0 ; void *argp3 = 0 ; int res3 = 0 ; int val4 ; int ecode4 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; PyObject * obj2 = 0 ; PyObject * obj3 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OOOO:new_Connection",&obj0,&obj1,&obj2,&obj3)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_NeuronGroup, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "new_Connection" "', argument " "1"" of type '" "NeuronGroup *""'"); } arg1 = reinterpret_cast< NeuronGroup * >(argp1); res2 = SWIG_ConvertPtr(obj1, &argp2,SWIGTYPE_p_NeuronGroup, 0 | 0 ); if (!SWIG_IsOK(res2)) { SWIG_exception_fail(SWIG_ArgError(res2), "in method '" "new_Connection" "', argument " "2"" of type '" "NeuronGroup *""'"); } arg2 = reinterpret_cast< NeuronGroup * >(argp2); res3 = SWIG_ConvertPtr(obj2, &argp3,SWIGTYPE_p_ConnectionMatrix, 0 | 0 ); if (!SWIG_IsOK(res3)) { SWIG_exception_fail(SWIG_ArgError(res3), "in method '" "new_Connection" "', argument " "3"" of type '" "ConnectionMatrix *""'"); } arg3 = reinterpret_cast< ConnectionMatrix * >(argp3); ecode4 = SWIG_AsVal_int(obj3, &val4); if (!SWIG_IsOK(ecode4)) { SWIG_exception_fail(SWIG_ArgError(ecode4), "in method '" "new_Connection" "', argument " "4"" of type '" "int""'"); } arg4 = static_cast< int >(val4); result = (Connection *)new Connection(arg1,arg2,arg3,arg4); resultobj = SWIG_NewPointerObj(SWIG_as_voidptr(result), SWIGTYPE_p_Connection, SWIG_POINTER_NEW | 0 ); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_new_Connection__SWIG_2(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; NeuronGroup *arg1 = (NeuronGroup *) 0 ; NeuronGroup *arg2 = (NeuronGroup *) 0 ; ConnectionMatrix *arg3 = (ConnectionMatrix *) 0 ; Connection *result = 0 ; void *argp1 = 0 ; int res1 = 0 ; void *argp2 = 0 ; int res2 = 0 ; void *argp3 = 0 ; int res3 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; PyObject * obj2 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OOO:new_Connection",&obj0,&obj1,&obj2)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_NeuronGroup, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "new_Connection" "', argument " "1"" of type '" "NeuronGroup *""'"); } arg1 = reinterpret_cast< NeuronGroup * >(argp1); res2 = SWIG_ConvertPtr(obj1, &argp2,SWIGTYPE_p_NeuronGroup, 0 | 0 ); if (!SWIG_IsOK(res2)) { SWIG_exception_fail(SWIG_ArgError(res2), "in method '" "new_Connection" "', argument " "2"" of type '" "NeuronGroup *""'"); } arg2 = reinterpret_cast< NeuronGroup * >(argp2); res3 = SWIG_ConvertPtr(obj2, &argp3,SWIGTYPE_p_ConnectionMatrix, 0 | 0 ); if (!SWIG_IsOK(res3)) { SWIG_exception_fail(SWIG_ArgError(res3), "in method '" "new_Connection" "', argument " "3"" of type '" "ConnectionMatrix *""'"); } arg3 = reinterpret_cast< ConnectionMatrix * >(argp3); result = (Connection *)new Connection(arg1,arg2,arg3); resultobj = SWIG_NewPointerObj(SWIG_as_voidptr(result), SWIGTYPE_p_Connection, SWIG_POINTER_NEW | 0 ); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_new_Connection(PyObject *self, PyObject *args) { int argc; PyObject *argv[6]; int ii; if (!PyTuple_Check(args)) SWIG_fail; argc = (int)PyObject_Length(args); for (ii = 0; (ii < argc) && (ii < 5); ii++) { argv[ii] = PyTuple_GET_ITEM(args,ii); } if (argc == 3) { int _v; void *vptr = 0; int res = SWIG_ConvertPtr(argv[0], &vptr, SWIGTYPE_p_NeuronGroup, 0); _v = SWIG_CheckState(res); if (_v) { void *vptr = 0; int res = SWIG_ConvertPtr(argv[1], &vptr, SWIGTYPE_p_NeuronGroup, 0); _v = SWIG_CheckState(res); if (_v) { void *vptr = 0; int res = SWIG_ConvertPtr(argv[2], &vptr, SWIGTYPE_p_ConnectionMatrix, 0); _v = SWIG_CheckState(res); if (_v) { return _wrap_new_Connection__SWIG_2(self, args); } } } } if (argc == 4) { int _v; void *vptr = 0; int res = SWIG_ConvertPtr(argv[0], &vptr, SWIGTYPE_p_NeuronGroup, 0); _v = SWIG_CheckState(res); if (_v) { void *vptr = 0; int res = SWIG_ConvertPtr(argv[1], &vptr, SWIGTYPE_p_NeuronGroup, 0); _v = SWIG_CheckState(res); if (_v) { void *vptr = 0; int res = SWIG_ConvertPtr(argv[2], &vptr, SWIGTYPE_p_ConnectionMatrix, 0); _v = SWIG_CheckState(res); if (_v) { { int res = SWIG_AsVal_int(argv[3], NULL); _v = SWIG_CheckState(res); } if (_v) { return _wrap_new_Connection__SWIG_1(self, args); } } } } } if (argc == 5) { int _v; void *vptr = 0; int res = SWIG_ConvertPtr(argv[0], &vptr, SWIGTYPE_p_NeuronGroup, 0); _v = SWIG_CheckState(res); if (_v) { void *vptr = 0; int res = SWIG_ConvertPtr(argv[1], &vptr, SWIGTYPE_p_NeuronGroup, 0); _v = SWIG_CheckState(res); if (_v) { void *vptr = 0; int res = SWIG_ConvertPtr(argv[2], &vptr, SWIGTYPE_p_ConnectionMatrix, 0); _v = SWIG_CheckState(res); if (_v) { { int res = SWIG_AsVal_int(argv[3], NULL); _v = SWIG_CheckState(res); } if (_v) { { int res = SWIG_AsVal_int(argv[4], NULL); _v = SWIG_CheckState(res); } if (_v) { return _wrap_new_Connection__SWIG_0(self, args); } } } } } } fail: SWIG_SetErrorMsg(PyExc_NotImplementedError,"Wrong number of arguments for overloaded function 'new_Connection'.\n Possible C/C++ prototypes are:\n"" Connection(NeuronGroup *,NeuronGroup *,ConnectionMatrix *,int,int)\n"" Connection(NeuronGroup *,NeuronGroup *,ConnectionMatrix *,int)\n"" Connection(NeuronGroup *,NeuronGroup *,ConnectionMatrix *)\n"); return NULL; } SWIGINTERN PyObject *_wrap_Connection_propagate(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; Connection *arg1 = (Connection *) 0 ; int *arg2 = (int *) 0 ; int arg3 ; void *argp1 = 0 ; int res1 = 0 ; PyArrayObject *array2 = NULL ; int i2 = 1 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OO:Connection_propagate",&obj0,&obj1)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_Connection, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "Connection_propagate" "', argument " "1"" of type '" "Connection *""'"); } arg1 = reinterpret_cast< Connection * >(argp1); { array2 = obj_to_array_no_conversion(obj1, NPY_INT); if (!array2 || !require_dimensions(array2,1) || !require_contiguous(array2) || !require_native(array2)) SWIG_fail; arg2 = (int*) array_data(array2); arg3 = 1; for (i2=0; i2 < array_numdims(array2); ++i2) arg3 *= array_size(array2,i2); } (arg1)->propagate(arg2,arg3); resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_Connection_do_propagate(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; Connection *arg1 = (Connection *) 0 ; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:Connection_do_propagate",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_Connection, 0 | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "Connection_do_propagate" "', argument " "1"" of type '" "Connection *""'"); } arg1 = reinterpret_cast< Connection * >(argp1); (arg1)->do_propagate(); resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_delete_Connection(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; Connection *arg1 = (Connection *) 0 ; void *argp1 = 0 ; int res1 = 0 ; PyObject * obj0 = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:delete_Connection",&obj0)) SWIG_fail; res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_Connection, SWIG_POINTER_DISOWN | 0 ); if (!SWIG_IsOK(res1)) { SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "delete_Connection" "', argument " "1"" of type '" "Connection *""'"); } arg1 = reinterpret_cast< Connection * >(argp1); delete arg1; resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } SWIGINTERN PyObject *Connection_swigregister(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *obj; if (!PyArg_ParseTuple(args,(char*)"O|swigregister", &obj)) return NULL; SWIG_TypeNewClientData(SWIGTYPE_p_Connection, SWIG_NewClientData(obj)); return SWIG_Py_Void(); } static PyMethodDef SwigMethods[] = { { (char *)"delete_PySwigIterator", _wrap_delete_PySwigIterator, METH_VARARGS, NULL}, { (char *)"PySwigIterator_value", _wrap_PySwigIterator_value, METH_VARARGS, NULL}, { (char *)"PySwigIterator_incr", _wrap_PySwigIterator_incr, METH_VARARGS, NULL}, { (char *)"PySwigIterator_decr", _wrap_PySwigIterator_decr, METH_VARARGS, NULL}, { (char *)"PySwigIterator_distance", _wrap_PySwigIterator_distance, METH_VARARGS, NULL}, { (char *)"PySwigIterator_equal", _wrap_PySwigIterator_equal, METH_VARARGS, NULL}, { (char *)"PySwigIterator_copy", _wrap_PySwigIterator_copy, METH_VARARGS, NULL}, { (char *)"PySwigIterator_next", _wrap_PySwigIterator_next, METH_VARARGS, NULL}, { (char *)"PySwigIterator_previous", _wrap_PySwigIterator_previous, METH_VARARGS, NULL}, { (char *)"PySwigIterator_advance", _wrap_PySwigIterator_advance, METH_VARARGS, NULL}, { (char *)"PySwigIterator___eq__", _wrap_PySwigIterator___eq__, METH_VARARGS, NULL}, { (char *)"PySwigIterator___ne__", _wrap_PySwigIterator___ne__, METH_VARARGS, NULL}, { (char *)"PySwigIterator___iadd__", _wrap_PySwigIterator___iadd__, METH_VARARGS, NULL}, { (char *)"PySwigIterator___isub__", _wrap_PySwigIterator___isub__, METH_VARARGS, NULL}, { (char *)"PySwigIterator___add__", _wrap_PySwigIterator___add__, METH_VARARGS, NULL}, { (char *)"PySwigIterator___sub__", _wrap_PySwigIterator___sub__, METH_VARARGS, NULL}, { (char *)"PySwigIterator_swigregister", PySwigIterator_swigregister, METH_VARARGS, NULL}, { (char *)"SpikeList_iterator", _wrap_SpikeList_iterator, METH_VARARGS, NULL}, { (char *)"SpikeList___nonzero__", _wrap_SpikeList___nonzero__, METH_VARARGS, NULL}, { (char *)"SpikeList___len__", _wrap_SpikeList___len__, METH_VARARGS, NULL}, { (char *)"SpikeList_pop", _wrap_SpikeList_pop, METH_VARARGS, NULL}, { (char *)"SpikeList___getslice__", _wrap_SpikeList___getslice__, METH_VARARGS, NULL}, { (char *)"SpikeList___setslice__", _wrap_SpikeList___setslice__, METH_VARARGS, NULL}, { (char *)"SpikeList___delslice__", _wrap_SpikeList___delslice__, METH_VARARGS, NULL}, { (char *)"SpikeList___delitem__", _wrap_SpikeList___delitem__, METH_VARARGS, NULL}, { (char *)"SpikeList___getitem__", _wrap_SpikeList___getitem__, METH_VARARGS, NULL}, { (char *)"SpikeList___setitem__", _wrap_SpikeList___setitem__, METH_VARARGS, NULL}, { (char *)"SpikeList_append", _wrap_SpikeList_append, METH_VARARGS, NULL}, { (char *)"SpikeList_empty", _wrap_SpikeList_empty, METH_VARARGS, NULL}, { (char *)"SpikeList_size", _wrap_SpikeList_size, METH_VARARGS, NULL}, { (char *)"SpikeList_clear", _wrap_SpikeList_clear, METH_VARARGS, NULL}, { (char *)"SpikeList_swap", _wrap_SpikeList_swap, METH_VARARGS, NULL}, { (char *)"SpikeList_get_allocator", _wrap_SpikeList_get_allocator, METH_VARARGS, NULL}, { (char *)"SpikeList_begin", _wrap_SpikeList_begin, METH_VARARGS, NULL}, { (char *)"SpikeList_end", _wrap_SpikeList_end, METH_VARARGS, NULL}, { (char *)"SpikeList_rbegin", _wrap_SpikeList_rbegin, METH_VARARGS, NULL}, { (char *)"SpikeList_rend", _wrap_SpikeList_rend, METH_VARARGS, NULL}, { (char *)"SpikeList_pop_back", _wrap_SpikeList_pop_back, METH_VARARGS, NULL}, { (char *)"SpikeList_erase", _wrap_SpikeList_erase, METH_VARARGS, NULL}, { (char *)"new_SpikeList", _wrap_new_SpikeList, METH_VARARGS, NULL}, { (char *)"SpikeList_push_back", _wrap_SpikeList_push_back, METH_VARARGS, NULL}, { (char *)"SpikeList_front", _wrap_SpikeList_front, METH_VARARGS, NULL}, { (char *)"SpikeList_back", _wrap_SpikeList_back, METH_VARARGS, NULL}, { (char *)"SpikeList_assign", _wrap_SpikeList_assign, METH_VARARGS, NULL}, { (char *)"SpikeList_resize", _wrap_SpikeList_resize, METH_VARARGS, NULL}, { (char *)"SpikeList_insert", _wrap_SpikeList_insert, METH_VARARGS, NULL}, { (char *)"SpikeList_pop_front", _wrap_SpikeList_pop_front, METH_VARARGS, NULL}, { (char *)"SpikeList_push_front", _wrap_SpikeList_push_front, METH_VARARGS, NULL}, { (char *)"SpikeList_reverse", _wrap_SpikeList_reverse, METH_VARARGS, NULL}, { (char *)"delete_SpikeList", _wrap_delete_SpikeList, METH_VARARGS, NULL}, { (char *)"SpikeList_swigregister", SpikeList_swigregister, METH_VARARGS, NULL}, { (char *)"VectorDouble_iterator", _wrap_VectorDouble_iterator, METH_VARARGS, NULL}, { (char *)"VectorDouble___nonzero__", _wrap_VectorDouble___nonzero__, METH_VARARGS, NULL}, { (char *)"VectorDouble___len__", _wrap_VectorDouble___len__, METH_VARARGS, NULL}, { (char *)"VectorDouble_pop", _wrap_VectorDouble_pop, METH_VARARGS, NULL}, { (char *)"VectorDouble___getslice__", _wrap_VectorDouble___getslice__, METH_VARARGS, NULL}, { (char *)"VectorDouble___setslice__", _wrap_VectorDouble___setslice__, METH_VARARGS, NULL}, { (char *)"VectorDouble___delslice__", _wrap_VectorDouble___delslice__, METH_VARARGS, NULL}, { (char *)"VectorDouble___delitem__", _wrap_VectorDouble___delitem__, METH_VARARGS, NULL}, { (char *)"VectorDouble___getitem__", _wrap_VectorDouble___getitem__, METH_VARARGS, NULL}, { (char *)"VectorDouble___setitem__", _wrap_VectorDouble___setitem__, METH_VARARGS, NULL}, { (char *)"VectorDouble_append", _wrap_VectorDouble_append, METH_VARARGS, NULL}, { (char *)"VectorDouble_empty", _wrap_VectorDouble_empty, METH_VARARGS, NULL}, { (char *)"VectorDouble_size", _wrap_VectorDouble_size, METH_VARARGS, NULL}, { (char *)"VectorDouble_clear", _wrap_VectorDouble_clear, METH_VARARGS, NULL}, { (char *)"VectorDouble_swap", _wrap_VectorDouble_swap, METH_VARARGS, NULL}, { (char *)"VectorDouble_get_allocator", _wrap_VectorDouble_get_allocator, METH_VARARGS, NULL}, { (char *)"VectorDouble_begin", _wrap_VectorDouble_begin, METH_VARARGS, NULL}, { (char *)"VectorDouble_end", _wrap_VectorDouble_end, METH_VARARGS, NULL}, { (char *)"VectorDouble_rbegin", _wrap_VectorDouble_rbegin, METH_VARARGS, NULL}, { (char *)"VectorDouble_rend", _wrap_VectorDouble_rend, METH_VARARGS, NULL}, { (char *)"VectorDouble_pop_back", _wrap_VectorDouble_pop_back, METH_VARARGS, NULL}, { (char *)"VectorDouble_erase", _wrap_VectorDouble_erase, METH_VARARGS, NULL}, { (char *)"new_VectorDouble", _wrap_new_VectorDouble, METH_VARARGS, NULL}, { (char *)"VectorDouble_push_back", _wrap_VectorDouble_push_back, METH_VARARGS, NULL}, { (char *)"VectorDouble_front", _wrap_VectorDouble_front, METH_VARARGS, NULL}, { (char *)"VectorDouble_back", _wrap_VectorDouble_back, METH_VARARGS, NULL}, { (char *)"VectorDouble_assign", _wrap_VectorDouble_assign, METH_VARARGS, NULL}, { (char *)"VectorDouble_resize", _wrap_VectorDouble_resize, METH_VARARGS, NULL}, { (char *)"VectorDouble_insert", _wrap_VectorDouble_insert, METH_VARARGS, NULL}, { (char *)"VectorDouble_reserve", _wrap_VectorDouble_reserve, METH_VARARGS, NULL}, { (char *)"VectorDouble_capacity", _wrap_VectorDouble_capacity, METH_VARARGS, NULL}, { (char *)"delete_VectorDouble", _wrap_delete_VectorDouble, METH_VARARGS, NULL}, { (char *)"VectorDouble_swigregister", VectorDouble_swigregister, METH_VARARGS, NULL}, { (char *)"NeuronGroup_S_set", _wrap_NeuronGroup_S_set, METH_VARARGS, NULL}, { (char *)"NeuronGroup_S_get", _wrap_NeuronGroup_S_get, METH_VARARGS, NULL}, { (char *)"NeuronGroup_num_vars_set", _wrap_NeuronGroup_num_vars_set, METH_VARARGS, NULL}, { (char *)"NeuronGroup_num_vars_get", _wrap_NeuronGroup_num_vars_get, METH_VARARGS, NULL}, { (char *)"NeuronGroup_num_neurons_set", _wrap_NeuronGroup_num_neurons_set, METH_VARARGS, NULL}, { (char *)"NeuronGroup_num_neurons_get", _wrap_NeuronGroup_num_neurons_get, METH_VARARGS, NULL}, { (char *)"NeuronGroup_S_m_set", _wrap_NeuronGroup_S_m_set, METH_VARARGS, NULL}, { (char *)"NeuronGroup_S_m_get", _wrap_NeuronGroup_S_m_get, METH_VARARGS, NULL}, { (char *)"NeuronGroup_su_set", _wrap_NeuronGroup_su_set, METH_VARARGS, NULL}, { (char *)"NeuronGroup_su_get", _wrap_NeuronGroup_su_get, METH_VARARGS, NULL}, { (char *)"NeuronGroup_thr_set", _wrap_NeuronGroup_thr_set, METH_VARARGS, NULL}, { (char *)"NeuronGroup_thr_get", _wrap_NeuronGroup_thr_get, METH_VARARGS, NULL}, { (char *)"NeuronGroup_resetobj_set", _wrap_NeuronGroup_resetobj_set, METH_VARARGS, NULL}, { (char *)"NeuronGroup_resetobj_get", _wrap_NeuronGroup_resetobj_get, METH_VARARGS, NULL}, { (char *)"NeuronGroup_LS_set", _wrap_NeuronGroup_LS_set, METH_VARARGS, NULL}, { (char *)"NeuronGroup_LS_get", _wrap_NeuronGroup_LS_get, METH_VARARGS, NULL}, { (char *)"NeuronGroup_spikesarray_set", _wrap_NeuronGroup_spikesarray_set, METH_VARARGS, NULL}, { (char *)"NeuronGroup_spikesarray_get", _wrap_NeuronGroup_spikesarray_get, METH_VARARGS, NULL}, { (char *)"NeuronGroup_owner_set", _wrap_NeuronGroup_owner_set, METH_VARARGS, NULL}, { (char *)"NeuronGroup_owner_get", _wrap_NeuronGroup_owner_get, METH_VARARGS, NULL}, { (char *)"NeuronGroup_origin_set", _wrap_NeuronGroup_origin_set, METH_VARARGS, NULL}, { (char *)"NeuronGroup_origin_get", _wrap_NeuronGroup_origin_get, METH_VARARGS, NULL}, { (char *)"new_NeuronGroup", _wrap_new_NeuronGroup, METH_VARARGS, NULL}, { (char *)"delete_NeuronGroup", _wrap_delete_NeuronGroup, METH_VARARGS, NULL}, { (char *)"NeuronGroup_get_spikes", _wrap_NeuronGroup_get_spikes, METH_VARARGS, NULL}, { (char *)"NeuronGroup_update", _wrap_NeuronGroup_update, METH_VARARGS, NULL}, { (char *)"NeuronGroup_reset", _wrap_NeuronGroup_reset, METH_VARARGS, NULL}, { (char *)"NeuronGroup_swigregister", NeuronGroup_swigregister, METH_VARARGS, NULL}, { (char *)"StateUpdater___call__", _wrap_StateUpdater___call__, METH_VARARGS, NULL}, { (char *)"new_StateUpdater", _wrap_new_StateUpdater, METH_VARARGS, NULL}, { (char *)"delete_StateUpdater", _wrap_delete_StateUpdater, METH_VARARGS, NULL}, { (char *)"StateUpdater_swigregister", StateUpdater_swigregister, METH_VARARGS, NULL}, { (char *)"LinearStateUpdater_M_set", _wrap_LinearStateUpdater_M_set, METH_VARARGS, NULL}, { (char *)"LinearStateUpdater_M_get", _wrap_LinearStateUpdater_M_get, METH_VARARGS, NULL}, { (char *)"LinearStateUpdater_M_n_set", _wrap_LinearStateUpdater_M_n_set, METH_VARARGS, NULL}, { (char *)"LinearStateUpdater_M_n_get", _wrap_LinearStateUpdater_M_n_get, METH_VARARGS, NULL}, { (char *)"LinearStateUpdater_M_m_set", _wrap_LinearStateUpdater_M_m_set, METH_VARARGS, NULL}, { (char *)"LinearStateUpdater_M_m_get", _wrap_LinearStateUpdater_M_m_get, METH_VARARGS, NULL}, { (char *)"LinearStateUpdater_b_set", _wrap_LinearStateUpdater_b_set, METH_VARARGS, NULL}, { (char *)"LinearStateUpdater_b_get", _wrap_LinearStateUpdater_b_get, METH_VARARGS, NULL}, { (char *)"LinearStateUpdater_b_n_set", _wrap_LinearStateUpdater_b_n_set, METH_VARARGS, NULL}, { (char *)"LinearStateUpdater_b_n_get", _wrap_LinearStateUpdater_b_n_get, METH_VARARGS, NULL}, { (char *)"new_LinearStateUpdater", _wrap_new_LinearStateUpdater, METH_VARARGS, NULL}, { (char *)"LinearStateUpdater___call__", _wrap_LinearStateUpdater___call__, METH_VARARGS, NULL}, { (char *)"LinearStateUpdater___repr__", _wrap_LinearStateUpdater___repr__, METH_VARARGS, NULL}, { (char *)"delete_LinearStateUpdater", _wrap_delete_LinearStateUpdater, METH_VARARGS, NULL}, { (char *)"LinearStateUpdater_swigregister", LinearStateUpdater_swigregister, METH_VARARGS, NULL}, { (char *)"Threshold_value_set", _wrap_Threshold_value_set, METH_VARARGS, NULL}, { (char *)"Threshold_value_get", _wrap_Threshold_value_get, METH_VARARGS, NULL}, { (char *)"Threshold_state_set", _wrap_Threshold_state_set, METH_VARARGS, NULL}, { (char *)"Threshold_state_get", _wrap_Threshold_state_get, METH_VARARGS, NULL}, { (char *)"new_Threshold", _wrap_new_Threshold, METH_VARARGS, NULL}, { (char *)"Threshold___call__", _wrap_Threshold___call__, METH_VARARGS, NULL}, { (char *)"delete_Threshold", _wrap_delete_Threshold, METH_VARARGS, NULL}, { (char *)"Threshold_swigregister", Threshold_swigregister, METH_VARARGS, NULL}, { (char *)"ResetBase___call__", _wrap_ResetBase___call__, METH_VARARGS, NULL}, { (char *)"new_ResetBase", _wrap_new_ResetBase, METH_VARARGS, NULL}, { (char *)"delete_ResetBase", _wrap_delete_ResetBase, METH_VARARGS, NULL}, { (char *)"ResetBase_swigregister", ResetBase_swigregister, METH_VARARGS, NULL}, { (char *)"Reset_value_set", _wrap_Reset_value_set, METH_VARARGS, NULL}, { (char *)"Reset_value_get", _wrap_Reset_value_get, METH_VARARGS, NULL}, { (char *)"Reset_state_set", _wrap_Reset_state_set, METH_VARARGS, NULL}, { (char *)"Reset_state_get", _wrap_Reset_state_get, METH_VARARGS, NULL}, { (char *)"new_Reset", _wrap_new_Reset, METH_VARARGS, NULL}, { (char *)"Reset___call__", _wrap_Reset___call__, METH_VARARGS, NULL}, { (char *)"delete_Reset", _wrap_delete_Reset, METH_VARARGS, NULL}, { (char *)"Reset_swigregister", Reset_swigregister, METH_VARARGS, NULL}, { (char *)"Refractoriness_period_set", _wrap_Refractoriness_period_set, METH_VARARGS, NULL}, { (char *)"Refractoriness_period_get", _wrap_Refractoriness_period_get, METH_VARARGS, NULL}, { (char *)"new_Refractoriness", _wrap_new_Refractoriness, METH_VARARGS, NULL}, { (char *)"Refractoriness___call__", _wrap_Refractoriness___call__, METH_VARARGS, NULL}, { (char *)"delete_Refractoriness", _wrap_delete_Refractoriness, METH_VARARGS, NULL}, { (char *)"Refractoriness_swigregister", Refractoriness_swigregister, METH_VARARGS, NULL}, { (char *)"NetworkOperation___call__", _wrap_NetworkOperation___call__, METH_VARARGS, NULL}, { (char *)"new_NetworkOperation", _wrap_new_NetworkOperation, METH_VARARGS, NULL}, { (char *)"delete_NetworkOperation", _wrap_delete_NetworkOperation, METH_VARARGS, NULL}, { (char *)"NetworkOperation_swigregister", NetworkOperation_swigregister, METH_VARARGS, NULL}, { (char *)"StateMonitor_group_set", _wrap_StateMonitor_group_set, METH_VARARGS, NULL}, { (char *)"StateMonitor_group_get", _wrap_StateMonitor_group_get, METH_VARARGS, NULL}, { (char *)"StateMonitor_state_set", _wrap_StateMonitor_state_set, METH_VARARGS, NULL}, { (char *)"StateMonitor_state_get", _wrap_StateMonitor_state_get, METH_VARARGS, NULL}, { (char *)"StateMonitor_values_set", _wrap_StateMonitor_values_set, METH_VARARGS, NULL}, { (char *)"StateMonitor_values_get", _wrap_StateMonitor_values_get, METH_VARARGS, NULL}, { (char *)"new_StateMonitor", _wrap_new_StateMonitor, METH_VARARGS, NULL}, { (char *)"StateMonitor___call__", _wrap_StateMonitor___call__, METH_VARARGS, NULL}, { (char *)"StateMonitor___getitem__", _wrap_StateMonitor___getitem__, METH_VARARGS, NULL}, { (char *)"delete_StateMonitor", _wrap_delete_StateMonitor, METH_VARARGS, NULL}, { (char *)"StateMonitor_swigregister", StateMonitor_swigregister, METH_VARARGS, NULL}, { (char *)"Network_groups_set", _wrap_Network_groups_set, METH_VARARGS, NULL}, { (char *)"Network_groups_get", _wrap_Network_groups_get, METH_VARARGS, NULL}, { (char *)"Network_operations_set", _wrap_Network_operations_set, METH_VARARGS, NULL}, { (char *)"Network_operations_get", _wrap_Network_operations_get, METH_VARARGS, NULL}, { (char *)"Network_connections_set", _wrap_Network_connections_set, METH_VARARGS, NULL}, { (char *)"Network_connections_get", _wrap_Network_connections_get, METH_VARARGS, NULL}, { (char *)"Network_add", _wrap_Network_add, METH_VARARGS, NULL}, { (char *)"Network_update", _wrap_Network_update, METH_VARARGS, NULL}, { (char *)"Network_run", _wrap_Network_run, METH_VARARGS, NULL}, { (char *)"new_Network", _wrap_new_Network, METH_VARARGS, NULL}, { (char *)"delete_Network", _wrap_delete_Network, METH_VARARGS, NULL}, { (char *)"Network_swigregister", Network_swigregister, METH_VARARGS, NULL}, { (char *)"CircularVector_X_set", _wrap_CircularVector_X_set, METH_VARARGS, NULL}, { (char *)"CircularVector_X_get", _wrap_CircularVector_X_get, METH_VARARGS, NULL}, { (char *)"CircularVector_cursor_set", _wrap_CircularVector_cursor_set, METH_VARARGS, NULL}, { (char *)"CircularVector_cursor_get", _wrap_CircularVector_cursor_get, METH_VARARGS, NULL}, { (char *)"CircularVector_n_set", _wrap_CircularVector_n_set, METH_VARARGS, NULL}, { (char *)"CircularVector_n_get", _wrap_CircularVector_n_get, METH_VARARGS, NULL}, { (char *)"CircularVector_retarray_set", _wrap_CircularVector_retarray_set, METH_VARARGS, NULL}, { (char *)"CircularVector_retarray_get", _wrap_CircularVector_retarray_get, METH_VARARGS, NULL}, { (char *)"new_CircularVector", _wrap_new_CircularVector, METH_VARARGS, NULL}, { (char *)"delete_CircularVector", _wrap_delete_CircularVector, METH_VARARGS, NULL}, { (char *)"CircularVector_reinit", _wrap_CircularVector_reinit, METH_VARARGS, NULL}, { (char *)"CircularVector_advance", _wrap_CircularVector_advance, METH_VARARGS, NULL}, { (char *)"CircularVector___len__", _wrap_CircularVector___len__, METH_VARARGS, NULL}, { (char *)"CircularVector___getitem__", _wrap_CircularVector___getitem__, METH_VARARGS, NULL}, { (char *)"CircularVector___setitem__", _wrap_CircularVector___setitem__, METH_VARARGS, NULL}, { (char *)"CircularVector___getslice__", _wrap_CircularVector___getslice__, METH_VARARGS, NULL}, { (char *)"CircularVector_get_conditional", _wrap_CircularVector_get_conditional, METH_VARARGS, NULL}, { (char *)"CircularVector___setslice__", _wrap_CircularVector___setslice__, METH_VARARGS, NULL}, { (char *)"CircularVector___repr__", _wrap_CircularVector___repr__, METH_VARARGS, NULL}, { (char *)"CircularVector___str__", _wrap_CircularVector___str__, METH_VARARGS, NULL}, { (char *)"CircularVector_swigregister", CircularVector_swigregister, METH_VARARGS, NULL}, { (char *)"SpikeContainer_S_set", _wrap_SpikeContainer_S_set, METH_VARARGS, NULL}, { (char *)"SpikeContainer_S_get", _wrap_SpikeContainer_S_get, METH_VARARGS, NULL}, { (char *)"SpikeContainer_ind_set", _wrap_SpikeContainer_ind_set, METH_VARARGS, NULL}, { (char *)"SpikeContainer_ind_get", _wrap_SpikeContainer_ind_get, METH_VARARGS, NULL}, { (char *)"new_SpikeContainer", _wrap_new_SpikeContainer, METH_VARARGS, NULL}, { (char *)"delete_SpikeContainer", _wrap_delete_SpikeContainer, METH_VARARGS, NULL}, { (char *)"SpikeContainer_reinit", _wrap_SpikeContainer_reinit, METH_VARARGS, NULL}, { (char *)"SpikeContainer_push", _wrap_SpikeContainer_push, METH_VARARGS, NULL}, { (char *)"SpikeContainer_lastspikes", _wrap_SpikeContainer_lastspikes, METH_VARARGS, NULL}, { (char *)"SpikeContainer___getitem__", _wrap_SpikeContainer___getitem__, METH_VARARGS, NULL}, { (char *)"SpikeContainer_get_spikes", _wrap_SpikeContainer_get_spikes, METH_VARARGS, NULL}, { (char *)"SpikeContainer___getslice__", _wrap_SpikeContainer___getslice__, METH_VARARGS, NULL}, { (char *)"SpikeContainer___repr__", _wrap_SpikeContainer___repr__, METH_VARARGS, NULL}, { (char *)"SpikeContainer___str__", _wrap_SpikeContainer___str__, METH_VARARGS, NULL}, { (char *)"SpikeContainer_swigregister", SpikeContainer_swigregister, METH_VARARGS, NULL}, { (char *)"ConnectionMatrix_add_row", _wrap_ConnectionMatrix_add_row, METH_VARARGS, NULL}, { (char *)"ConnectionMatrix_add_rows", _wrap_ConnectionMatrix_add_rows, METH_VARARGS, NULL}, { (char *)"new_ConnectionMatrix", _wrap_new_ConnectionMatrix, METH_VARARGS, NULL}, { (char *)"delete_ConnectionMatrix", _wrap_delete_ConnectionMatrix, METH_VARARGS, NULL}, { (char *)"ConnectionMatrix_swigregister", ConnectionMatrix_swigregister, METH_VARARGS, NULL}, { (char *)"DenseConnectionMatrix_W_set", _wrap_DenseConnectionMatrix_W_set, METH_VARARGS, NULL}, { (char *)"DenseConnectionMatrix_W_get", _wrap_DenseConnectionMatrix_W_get, METH_VARARGS, NULL}, { (char *)"DenseConnectionMatrix_n_set", _wrap_DenseConnectionMatrix_n_set, METH_VARARGS, NULL}, { (char *)"DenseConnectionMatrix_n_get", _wrap_DenseConnectionMatrix_n_get, METH_VARARGS, NULL}, { (char *)"DenseConnectionMatrix_m_set", _wrap_DenseConnectionMatrix_m_set, METH_VARARGS, NULL}, { (char *)"DenseConnectionMatrix_m_get", _wrap_DenseConnectionMatrix_m_get, METH_VARARGS, NULL}, { (char *)"new_DenseConnectionMatrix", _wrap_new_DenseConnectionMatrix, METH_VARARGS, NULL}, { (char *)"DenseConnectionMatrix_add_row", _wrap_DenseConnectionMatrix_add_row, METH_VARARGS, NULL}, { (char *)"delete_DenseConnectionMatrix", _wrap_delete_DenseConnectionMatrix, METH_VARARGS, NULL}, { (char *)"DenseConnectionMatrix_swigregister", DenseConnectionMatrix_swigregister, METH_VARARGS, NULL}, { (char *)"Connection_source_set", _wrap_Connection_source_set, METH_VARARGS, NULL}, { (char *)"Connection_source_get", _wrap_Connection_source_get, METH_VARARGS, NULL}, { (char *)"Connection_target_set", _wrap_Connection_target_set, METH_VARARGS, NULL}, { (char *)"Connection_target_get", _wrap_Connection_target_get, METH_VARARGS, NULL}, { (char *)"Connection_connmat_set", _wrap_Connection_connmat_set, METH_VARARGS, NULL}, { (char *)"Connection_connmat_get", _wrap_Connection_connmat_get, METH_VARARGS, NULL}, { (char *)"Connection_state_set", _wrap_Connection_state_set, METH_VARARGS, NULL}, { (char *)"Connection_state_get", _wrap_Connection_state_get, METH_VARARGS, NULL}, { (char *)"Connection_delay_set", _wrap_Connection_delay_set, METH_VARARGS, NULL}, { (char *)"Connection_delay_get", _wrap_Connection_delay_get, METH_VARARGS, NULL}, { (char *)"new_Connection", _wrap_new_Connection, METH_VARARGS, NULL}, { (char *)"Connection_propagate", _wrap_Connection_propagate, METH_VARARGS, NULL}, { (char *)"Connection_do_propagate", _wrap_Connection_do_propagate, METH_VARARGS, NULL}, { (char *)"delete_Connection", _wrap_delete_Connection, METH_VARARGS, NULL}, { (char *)"Connection_swigregister", Connection_swigregister, METH_VARARGS, NULL}, { NULL, NULL, 0, NULL } }; /* -------- TYPE CONVERSION AND EQUIVALENCE RULES (BEGIN) -------- */ static void *_p_StateMonitorTo_p_NetworkOperation(void *x) { return (void *)((NetworkOperation *) ((StateMonitor *) x)); } static void *_p_DenseConnectionMatrixTo_p_ConnectionMatrix(void *x) { return (void *)((ConnectionMatrix *) ((DenseConnectionMatrix *) x)); } static void *_p_ResetTo_p_ResetBase(void *x) { return (void *)((ResetBase *) ((Reset *) x)); } static void *_p_RefractorinessTo_p_ResetBase(void *x) { return (void *)((ResetBase *) (Reset *) ((Refractoriness *) x)); } static void *_p_LinearStateUpdaterTo_p_StateUpdater(void *x) { return (void *)((StateUpdater *) ((LinearStateUpdater *) x)); } static void *_p_RefractorinessTo_p_Reset(void *x) { return (void *)((Reset *) ((Refractoriness *) x)); } static swig_type_info _swigt__p_CircularVector = {"_p_CircularVector", "CircularVector *", 0, 0, (void*)0, 0}; static swig_type_info _swigt__p_Connection = {"_p_Connection", "Connection *", 0, 0, (void*)0, 0}; static swig_type_info _swigt__p_ConnectionMatrix = {"_p_ConnectionMatrix", "ConnectionMatrix *", 0, 0, (void*)0, 0}; static swig_type_info _swigt__p_DenseConnectionMatrix = {"_p_DenseConnectionMatrix", "DenseConnectionMatrix *", 0, 0, (void*)0, 0}; static swig_type_info _swigt__p_LinearStateUpdater = {"_p_LinearStateUpdater", "LinearStateUpdater *", 0, 0, (void*)0, 0}; static swig_type_info _swigt__p_Network = {"_p_Network", "Network *", 0, 0, (void*)0, 0}; static swig_type_info _swigt__p_NetworkOperation = {"_p_NetworkOperation", "NetworkOperation *", 0, 0, (void*)0, 0}; static swig_type_info _swigt__p_NeuronGroup = {"_p_NeuronGroup", "NeuronGroup *", 0, 0, (void*)0, 0}; static swig_type_info _swigt__p_Refractoriness = {"_p_Refractoriness", "Refractoriness *", 0, 0, (void*)0, 0}; static swig_type_info _swigt__p_Reset = {"_p_Reset", "Reset *", 0, 0, (void*)0, 0}; static swig_type_info _swigt__p_ResetBase = {"_p_ResetBase", "ResetBase *", 0, 0, (void*)0, 0}; static swig_type_info _swigt__p_SpikeContainer = {"_p_SpikeContainer", "SpikeContainer *", 0, 0, (void*)0, 0}; static swig_type_info _swigt__p_StateMonitor = {"_p_StateMonitor", "StateMonitor *", 0, 0, (void*)0, 0}; static swig_type_info _swigt__p_StateUpdater = {"_p_StateUpdater", "StateUpdater *", 0, 0, (void*)0, 0}; static swig_type_info _swigt__p_Threshold = {"_p_Threshold", "Threshold *", 0, 0, (void*)0, 0}; static swig_type_info _swigt__p_allocator_type = {"_p_allocator_type", "allocator_type *", 0, 0, (void*)0, 0}; static swig_type_info _swigt__p_char = {"_p_char", "char *", 0, 0, (void*)0, 0}; static swig_type_info _swigt__p_difference_type = {"_p_difference_type", "difference_type *", 0, 0, (void*)0, 0}; static swig_type_info _swigt__p_double = {"_p_double", "double *", 0, 0, (void*)0, 0}; static swig_type_info _swigt__p_int = {"_p_int", "int *", 0, 0, (void*)0, 0}; static swig_type_info _swigt__p_p_PyObject = {"_p_p_PyObject", "PyObject **", 0, 0, (void*)0, 0}; static swig_type_info _swigt__p_p_int = {"_p_p_int", "int **", 0, 0, (void*)0, 0}; static swig_type_info _swigt__p_size_type = {"_p_size_type", "size_type *", 0, 0, (void*)0, 0}; static swig_type_info _swigt__p_std__invalid_argument = {"_p_std__invalid_argument", "std::invalid_argument *", 0, 0, (void*)0, 0}; static swig_type_info _swigt__p_std__listTConnection_p_std__allocatorTConnection_p_t_t = {"_p_std__listTConnection_p_std__allocatorTConnection_p_t_t", "std::list > *|std::list *", 0, 0, (void*)0, 0}; static swig_type_info _swigt__p_std__listTNetworkOperation_p_std__allocatorTNetworkOperation_p_t_t = {"_p_std__listTNetworkOperation_p_std__allocatorTNetworkOperation_p_t_t", "std::list > *|std::list *", 0, 0, (void*)0, 0}; static swig_type_info _swigt__p_std__listTNeuronGroup_p_std__allocatorTNeuronGroup_p_t_t = {"_p_std__listTNeuronGroup_p_std__allocatorTNeuronGroup_p_t_t", "std::list > *|std::list *", 0, 0, (void*)0, 0}; static swig_type_info _swigt__p_std__listTint_std__allocatorTint_t_t = {"_p_std__listTint_std__allocatorTint_t_t", "std::list > *|std::list *", 0, 0, (void*)0, 0}; static swig_type_info _swigt__p_std__listTint_std__allocatorTint_t_t__allocator_type = {"_p_std__listTint_std__allocatorTint_t_t__allocator_type", "std::list::allocator_type *|std::allocator *", 0, 0, (void*)0, 0}; static swig_type_info _swigt__p_std__vectorTdouble_std__allocatorTdouble_t_t = {"_p_std__vectorTdouble_std__allocatorTdouble_t_t", "std::vector > *|std::vector *", 0, 0, (void*)0, 0}; static swig_type_info _swigt__p_std__vectorTdouble_std__allocatorTdouble_t_t__allocator_type = {"_p_std__vectorTdouble_std__allocatorTdouble_t_t__allocator_type", "std::vector::allocator_type *|std::allocator *", 0, 0, (void*)0, 0}; static swig_type_info _swigt__p_std__vectorTstd__vectorTdouble_std__allocatorTdouble_t_t_std__allocatorTstd__vectorTdouble_std__allocatorTdouble_t_t_t_t = {"_p_std__vectorTstd__vectorTdouble_std__allocatorTdouble_t_t_std__allocatorTstd__vectorTdouble_std__allocatorTdouble_t_t_t_t", "std::vector > *|std::vector >,std::allocator > > > *", 0, 0, (void*)0, 0}; static swig_type_info _swigt__p_swig__PySwigIterator = {"_p_swig__PySwigIterator", "swig::PySwigIterator *", 0, 0, (void*)0, 0}; static swig_type_info _swigt__p_value_type = {"_p_value_type", "value_type *", 0, 0, (void*)0, 0}; static swig_type_info *swig_type_initial[] = { &_swigt__p_CircularVector, &_swigt__p_Connection, &_swigt__p_ConnectionMatrix, &_swigt__p_DenseConnectionMatrix, &_swigt__p_LinearStateUpdater, &_swigt__p_Network, &_swigt__p_NetworkOperation, &_swigt__p_NeuronGroup, &_swigt__p_Refractoriness, &_swigt__p_Reset, &_swigt__p_ResetBase, &_swigt__p_SpikeContainer, &_swigt__p_StateMonitor, &_swigt__p_StateUpdater, &_swigt__p_Threshold, &_swigt__p_allocator_type, &_swigt__p_char, &_swigt__p_difference_type, &_swigt__p_double, &_swigt__p_int, &_swigt__p_p_PyObject, &_swigt__p_p_int, &_swigt__p_size_type, &_swigt__p_std__invalid_argument, &_swigt__p_std__listTConnection_p_std__allocatorTConnection_p_t_t, &_swigt__p_std__listTNetworkOperation_p_std__allocatorTNetworkOperation_p_t_t, &_swigt__p_std__listTNeuronGroup_p_std__allocatorTNeuronGroup_p_t_t, &_swigt__p_std__listTint_std__allocatorTint_t_t, &_swigt__p_std__listTint_std__allocatorTint_t_t__allocator_type, &_swigt__p_std__vectorTdouble_std__allocatorTdouble_t_t, &_swigt__p_std__vectorTdouble_std__allocatorTdouble_t_t__allocator_type, &_swigt__p_std__vectorTstd__vectorTdouble_std__allocatorTdouble_t_t_std__allocatorTstd__vectorTdouble_std__allocatorTdouble_t_t_t_t, &_swigt__p_swig__PySwigIterator, &_swigt__p_value_type, }; static swig_cast_info _swigc__p_CircularVector[] = { {&_swigt__p_CircularVector, 0, 0, 0},{0, 0, 0, 0}}; static swig_cast_info _swigc__p_Connection[] = { {&_swigt__p_Connection, 0, 0, 0},{0, 0, 0, 0}}; static swig_cast_info _swigc__p_ConnectionMatrix[] = { {&_swigt__p_ConnectionMatrix, 0, 0, 0}, {&_swigt__p_DenseConnectionMatrix, _p_DenseConnectionMatrixTo_p_ConnectionMatrix, 0, 0},{0, 0, 0, 0}}; static swig_cast_info _swigc__p_DenseConnectionMatrix[] = { {&_swigt__p_DenseConnectionMatrix, 0, 0, 0},{0, 0, 0, 0}}; static swig_cast_info _swigc__p_LinearStateUpdater[] = { {&_swigt__p_LinearStateUpdater, 0, 0, 0},{0, 0, 0, 0}}; static swig_cast_info _swigc__p_Network[] = { {&_swigt__p_Network, 0, 0, 0},{0, 0, 0, 0}}; static swig_cast_info _swigc__p_NetworkOperation[] = { {&_swigt__p_StateMonitor, _p_StateMonitorTo_p_NetworkOperation, 0, 0}, {&_swigt__p_NetworkOperation, 0, 0, 0},{0, 0, 0, 0}}; static swig_cast_info _swigc__p_NeuronGroup[] = { {&_swigt__p_NeuronGroup, 0, 0, 0},{0, 0, 0, 0}}; static swig_cast_info _swigc__p_Refractoriness[] = { {&_swigt__p_Refractoriness, 0, 0, 0},{0, 0, 0, 0}}; static swig_cast_info _swigc__p_Reset[] = { {&_swigt__p_Reset, 0, 0, 0}, {&_swigt__p_Refractoriness, _p_RefractorinessTo_p_Reset, 0, 0},{0, 0, 0, 0}}; static swig_cast_info _swigc__p_ResetBase[] = { {&_swigt__p_ResetBase, 0, 0, 0}, {&_swigt__p_Reset, _p_ResetTo_p_ResetBase, 0, 0}, {&_swigt__p_Refractoriness, _p_RefractorinessTo_p_ResetBase, 0, 0},{0, 0, 0, 0}}; static swig_cast_info _swigc__p_SpikeContainer[] = { {&_swigt__p_SpikeContainer, 0, 0, 0},{0, 0, 0, 0}}; static swig_cast_info _swigc__p_StateMonitor[] = { {&_swigt__p_StateMonitor, 0, 0, 0},{0, 0, 0, 0}}; static swig_cast_info _swigc__p_StateUpdater[] = { {&_swigt__p_StateUpdater, 0, 0, 0}, {&_swigt__p_LinearStateUpdater, _p_LinearStateUpdaterTo_p_StateUpdater, 0, 0},{0, 0, 0, 0}}; static swig_cast_info _swigc__p_Threshold[] = { {&_swigt__p_Threshold, 0, 0, 0},{0, 0, 0, 0}}; static swig_cast_info _swigc__p_allocator_type[] = { {&_swigt__p_allocator_type, 0, 0, 0},{0, 0, 0, 0}}; static swig_cast_info _swigc__p_char[] = { {&_swigt__p_char, 0, 0, 0},{0, 0, 0, 0}}; static swig_cast_info _swigc__p_difference_type[] = { {&_swigt__p_difference_type, 0, 0, 0},{0, 0, 0, 0}}; static swig_cast_info _swigc__p_double[] = { {&_swigt__p_double, 0, 0, 0},{0, 0, 0, 0}}; static swig_cast_info _swigc__p_int[] = { {&_swigt__p_int, 0, 0, 0},{0, 0, 0, 0}}; static swig_cast_info _swigc__p_p_PyObject[] = { {&_swigt__p_p_PyObject, 0, 0, 0},{0, 0, 0, 0}}; static swig_cast_info _swigc__p_p_int[] = { {&_swigt__p_p_int, 0, 0, 0},{0, 0, 0, 0}}; static swig_cast_info _swigc__p_size_type[] = { {&_swigt__p_size_type, 0, 0, 0},{0, 0, 0, 0}}; static swig_cast_info _swigc__p_std__invalid_argument[] = { {&_swigt__p_std__invalid_argument, 0, 0, 0},{0, 0, 0, 0}}; static swig_cast_info _swigc__p_std__listTConnection_p_std__allocatorTConnection_p_t_t[] = { {&_swigt__p_std__listTConnection_p_std__allocatorTConnection_p_t_t, 0, 0, 0},{0, 0, 0, 0}}; static swig_cast_info _swigc__p_std__listTNetworkOperation_p_std__allocatorTNetworkOperation_p_t_t[] = { {&_swigt__p_std__listTNetworkOperation_p_std__allocatorTNetworkOperation_p_t_t, 0, 0, 0},{0, 0, 0, 0}}; static swig_cast_info _swigc__p_std__listTNeuronGroup_p_std__allocatorTNeuronGroup_p_t_t[] = { {&_swigt__p_std__listTNeuronGroup_p_std__allocatorTNeuronGroup_p_t_t, 0, 0, 0},{0, 0, 0, 0}}; static swig_cast_info _swigc__p_std__listTint_std__allocatorTint_t_t[] = { {&_swigt__p_std__listTint_std__allocatorTint_t_t, 0, 0, 0},{0, 0, 0, 0}}; static swig_cast_info _swigc__p_std__listTint_std__allocatorTint_t_t__allocator_type[] = { {&_swigt__p_std__listTint_std__allocatorTint_t_t__allocator_type, 0, 0, 0},{0, 0, 0, 0}}; static swig_cast_info _swigc__p_std__vectorTdouble_std__allocatorTdouble_t_t[] = { {&_swigt__p_std__vectorTdouble_std__allocatorTdouble_t_t, 0, 0, 0},{0, 0, 0, 0}}; static swig_cast_info _swigc__p_std__vectorTdouble_std__allocatorTdouble_t_t__allocator_type[] = { {&_swigt__p_std__vectorTdouble_std__allocatorTdouble_t_t__allocator_type, 0, 0, 0},{0, 0, 0, 0}}; static swig_cast_info _swigc__p_std__vectorTstd__vectorTdouble_std__allocatorTdouble_t_t_std__allocatorTstd__vectorTdouble_std__allocatorTdouble_t_t_t_t[] = { {&_swigt__p_std__vectorTstd__vectorTdouble_std__allocatorTdouble_t_t_std__allocatorTstd__vectorTdouble_std__allocatorTdouble_t_t_t_t, 0, 0, 0},{0, 0, 0, 0}}; static swig_cast_info _swigc__p_swig__PySwigIterator[] = { {&_swigt__p_swig__PySwigIterator, 0, 0, 0},{0, 0, 0, 0}}; static swig_cast_info _swigc__p_value_type[] = { {&_swigt__p_value_type, 0, 0, 0},{0, 0, 0, 0}}; static swig_cast_info *swig_cast_initial[] = { _swigc__p_CircularVector, _swigc__p_Connection, _swigc__p_ConnectionMatrix, _swigc__p_DenseConnectionMatrix, _swigc__p_LinearStateUpdater, _swigc__p_Network, _swigc__p_NetworkOperation, _swigc__p_NeuronGroup, _swigc__p_Refractoriness, _swigc__p_Reset, _swigc__p_ResetBase, _swigc__p_SpikeContainer, _swigc__p_StateMonitor, _swigc__p_StateUpdater, _swigc__p_Threshold, _swigc__p_allocator_type, _swigc__p_char, _swigc__p_difference_type, _swigc__p_double, _swigc__p_int, _swigc__p_p_PyObject, _swigc__p_p_int, _swigc__p_size_type, _swigc__p_std__invalid_argument, _swigc__p_std__listTConnection_p_std__allocatorTConnection_p_t_t, _swigc__p_std__listTNetworkOperation_p_std__allocatorTNetworkOperation_p_t_t, _swigc__p_std__listTNeuronGroup_p_std__allocatorTNeuronGroup_p_t_t, _swigc__p_std__listTint_std__allocatorTint_t_t, _swigc__p_std__listTint_std__allocatorTint_t_t__allocator_type, _swigc__p_std__vectorTdouble_std__allocatorTdouble_t_t, _swigc__p_std__vectorTdouble_std__allocatorTdouble_t_t__allocator_type, _swigc__p_std__vectorTstd__vectorTdouble_std__allocatorTdouble_t_t_std__allocatorTstd__vectorTdouble_std__allocatorTdouble_t_t_t_t, _swigc__p_swig__PySwigIterator, _swigc__p_value_type, }; /* -------- TYPE CONVERSION AND EQUIVALENCE RULES (END) -------- */ static swig_const_info swig_const_table[] = { {0, 0, 0, 0.0, 0, 0}}; #ifdef __cplusplus } #endif /* ----------------------------------------------------------------------------- * Type initialization: * This problem is tough by the requirement that no dynamic * memory is used. Also, since swig_type_info structures store pointers to * swig_cast_info structures and swig_cast_info structures store pointers back * to swig_type_info structures, we need some lookup code at initialization. * The idea is that swig generates all the structures that are needed. * The runtime then collects these partially filled structures. * The SWIG_InitializeModule function takes these initial arrays out of * swig_module, and does all the lookup, filling in the swig_module.types * array with the correct data and linking the correct swig_cast_info * structures together. * * The generated swig_type_info structures are assigned staticly to an initial * array. We just loop through that array, and handle each type individually. * First we lookup if this type has been already loaded, and if so, use the * loaded structure instead of the generated one. Then we have to fill in the * cast linked list. The cast data is initially stored in something like a * two-dimensional array. Each row corresponds to a type (there are the same * number of rows as there are in the swig_type_initial array). Each entry in * a column is one of the swig_cast_info structures for that type. * The cast_initial array is actually an array of arrays, because each row has * a variable number of columns. So to actually build the cast linked list, * we find the array of casts associated with the type, and loop through it * adding the casts to the list. The one last trick we need to do is making * sure the type pointer in the swig_cast_info struct is correct. * * First off, we lookup the cast->type name to see if it is already loaded. * There are three cases to handle: * 1) If the cast->type has already been loaded AND the type we are adding * casting info to has not been loaded (it is in this module), THEN we * replace the cast->type pointer with the type pointer that has already * been loaded. * 2) If BOTH types (the one we are adding casting info to, and the * cast->type) are loaded, THEN the cast info has already been loaded by * the previous module so we just ignore it. * 3) Finally, if cast->type has not already been loaded, then we add that * swig_cast_info to the linked list (because the cast->type) pointer will * be correct. * ----------------------------------------------------------------------------- */ #ifdef __cplusplus extern "C" { #if 0 } /* c-mode */ #endif #endif #if 0 #define SWIGRUNTIME_DEBUG #endif SWIGRUNTIME void SWIG_InitializeModule(void *clientdata) { size_t i; swig_module_info *module_head, *iter; int found; clientdata = clientdata; /* check to see if the circular list has been setup, if not, set it up */ if (swig_module.next==0) { /* Initialize the swig_module */ swig_module.type_initial = swig_type_initial; swig_module.cast_initial = swig_cast_initial; swig_module.next = &swig_module; } /* Try and load any already created modules */ module_head = SWIG_GetModule(clientdata); if (!module_head) { /* This is the first module loaded for this interpreter */ /* so set the swig module into the interpreter */ SWIG_SetModule(clientdata, &swig_module); module_head = &swig_module; } else { /* the interpreter has loaded a SWIG module, but has it loaded this one? */ found=0; iter=module_head; do { if (iter==&swig_module) { found=1; break; } iter=iter->next; } while (iter!= module_head); /* if the is found in the list, then all is done and we may leave */ if (found) return; /* otherwise we must add out module into the list */ swig_module.next = module_head->next; module_head->next = &swig_module; } /* Now work on filling in swig_module.types */ #ifdef SWIGRUNTIME_DEBUG printf("SWIG_InitializeModule: size %d\n", swig_module.size); #endif for (i = 0; i < swig_module.size; ++i) { swig_type_info *type = 0; swig_type_info *ret; swig_cast_info *cast; #ifdef SWIGRUNTIME_DEBUG printf("SWIG_InitializeModule: type %d %s\n", i, swig_module.type_initial[i]->name); #endif /* if there is another module already loaded */ if (swig_module.next != &swig_module) { type = SWIG_MangledTypeQueryModule(swig_module.next, &swig_module, swig_module.type_initial[i]->name); } if (type) { /* Overwrite clientdata field */ #ifdef SWIGRUNTIME_DEBUG printf("SWIG_InitializeModule: found type %s\n", type->name); #endif if (swig_module.type_initial[i]->clientdata) { type->clientdata = swig_module.type_initial[i]->clientdata; #ifdef SWIGRUNTIME_DEBUG printf("SWIG_InitializeModule: found and overwrite type %s \n", type->name); #endif } } else { type = swig_module.type_initial[i]; } /* Insert casting types */ cast = swig_module.cast_initial[i]; while (cast->type) { /* Don't need to add information already in the list */ ret = 0; #ifdef SWIGRUNTIME_DEBUG printf("SWIG_InitializeModule: look cast %s\n", cast->type->name); #endif if (swig_module.next != &swig_module) { ret = SWIG_MangledTypeQueryModule(swig_module.next, &swig_module, cast->type->name); #ifdef SWIGRUNTIME_DEBUG if (ret) printf("SWIG_InitializeModule: found cast %s\n", ret->name); #endif } if (ret) { if (type == swig_module.type_initial[i]) { #ifdef SWIGRUNTIME_DEBUG printf("SWIG_InitializeModule: skip old type %s\n", ret->name); #endif cast->type = ret; ret = 0; } else { /* Check for casting already in the list */ swig_cast_info *ocast = SWIG_TypeCheck(ret->name, type); #ifdef SWIGRUNTIME_DEBUG if (ocast) printf("SWIG_InitializeModule: skip old cast %s\n", ret->name); #endif if (!ocast) ret = 0; } } if (!ret) { #ifdef SWIGRUNTIME_DEBUG printf("SWIG_InitializeModule: adding cast %s\n", cast->type->name); #endif if (type->cast) { type->cast->prev = cast; cast->next = type->cast; } type->cast = cast; } cast++; } /* Set entry in modules->types array equal to the type */ swig_module.types[i] = type; } swig_module.types[i] = 0; #ifdef SWIGRUNTIME_DEBUG printf("**** SWIG_InitializeModule: Cast List ******\n"); for (i = 0; i < swig_module.size; ++i) { int j = 0; swig_cast_info *cast = swig_module.cast_initial[i]; printf("SWIG_InitializeModule: type %d %s\n", i, swig_module.type_initial[i]->name); while (cast->type) { printf("SWIG_InitializeModule: cast type %s\n", cast->type->name); cast++; ++j; } printf("---- Total casts: %d\n",j); } printf("**** SWIG_InitializeModule: Cast List ******\n"); #endif } /* This function will propagate the clientdata field of type to * any new swig_type_info structures that have been added into the list * of equivalent types. It is like calling * SWIG_TypeClientData(type, clientdata) a second time. */ SWIGRUNTIME void SWIG_PropagateClientData(void) { size_t i; swig_cast_info *equiv; static int init_run = 0; if (init_run) return; init_run = 1; for (i = 0; i < swig_module.size; i++) { if (swig_module.types[i]->clientdata) { equiv = swig_module.types[i]->cast; while (equiv) { if (!equiv->converter) { if (equiv->type && !equiv->type->clientdata) SWIG_TypeClientData(equiv->type, swig_module.types[i]->clientdata); } equiv = equiv->next; } } } } #ifdef __cplusplus #if 0 { /* c-mode */ #endif } #endif #ifdef __cplusplus extern "C" { #endif /* Python-specific SWIG API */ #define SWIG_newvarlink() SWIG_Python_newvarlink() #define SWIG_addvarlink(p, name, get_attr, set_attr) SWIG_Python_addvarlink(p, name, get_attr, set_attr) #define SWIG_InstallConstants(d, constants) SWIG_Python_InstallConstants(d, constants) /* ----------------------------------------------------------------------------- * global variable support code. * ----------------------------------------------------------------------------- */ typedef struct swig_globalvar { char *name; /* Name of global variable */ PyObject *(*get_attr)(void); /* Return the current value */ int (*set_attr)(PyObject *); /* Set the value */ struct swig_globalvar *next; } swig_globalvar; typedef struct swig_varlinkobject { PyObject_HEAD swig_globalvar *vars; } swig_varlinkobject; SWIGINTERN PyObject * swig_varlink_repr(swig_varlinkobject *SWIGUNUSEDPARM(v)) { return PyString_FromString(""); } SWIGINTERN PyObject * swig_varlink_str(swig_varlinkobject *v) { PyObject *str = PyString_FromString("("); swig_globalvar *var; for (var = v->vars; var; var=var->next) { PyString_ConcatAndDel(&str,PyString_FromString(var->name)); if (var->next) PyString_ConcatAndDel(&str,PyString_FromString(", ")); } PyString_ConcatAndDel(&str,PyString_FromString(")")); return str; } SWIGINTERN int swig_varlink_print(swig_varlinkobject *v, FILE *fp, int SWIGUNUSEDPARM(flags)) { PyObject *str = swig_varlink_str(v); fprintf(fp,"Swig global variables "); fprintf(fp,"%s\n", PyString_AsString(str)); Py_DECREF(str); return 0; } SWIGINTERN void swig_varlink_dealloc(swig_varlinkobject *v) { swig_globalvar *var = v->vars; while (var) { swig_globalvar *n = var->next; free(var->name); free(var); var = n; } } SWIGINTERN PyObject * swig_varlink_getattr(swig_varlinkobject *v, char *n) { PyObject *res = NULL; swig_globalvar *var = v->vars; while (var) { if (strcmp(var->name,n) == 0) { res = (*var->get_attr)(); break; } var = var->next; } if (res == NULL && !PyErr_Occurred()) { PyErr_SetString(PyExc_NameError,"Unknown C global variable"); } return res; } SWIGINTERN int swig_varlink_setattr(swig_varlinkobject *v, char *n, PyObject *p) { int res = 1; swig_globalvar *var = v->vars; while (var) { if (strcmp(var->name,n) == 0) { res = (*var->set_attr)(p); break; } var = var->next; } if (res == 1 && !PyErr_Occurred()) { PyErr_SetString(PyExc_NameError,"Unknown C global variable"); } return res; } SWIGINTERN PyTypeObject* swig_varlink_type(void) { static char varlink__doc__[] = "Swig var link object"; static PyTypeObject varlink_type; static int type_init = 0; if (!type_init) { const PyTypeObject tmp = { PyObject_HEAD_INIT(NULL) 0, /* Number of items in variable part (ob_size) */ (char *)"swigvarlink", /* Type name (tp_name) */ sizeof(swig_varlinkobject), /* Basic size (tp_basicsize) */ 0, /* Itemsize (tp_itemsize) */ (destructor) swig_varlink_dealloc, /* Deallocator (tp_dealloc) */ (printfunc) swig_varlink_print, /* Print (tp_print) */ (getattrfunc) swig_varlink_getattr, /* get attr (tp_getattr) */ (setattrfunc) swig_varlink_setattr, /* Set attr (tp_setattr) */ 0, /* tp_compare */ (reprfunc) swig_varlink_repr, /* tp_repr */ 0, /* tp_as_number */ 0, /* tp_as_sequence */ 0, /* tp_as_mapping */ 0, /* tp_hash */ 0, /* tp_call */ (reprfunc)swig_varlink_str, /* tp_str */ 0, /* tp_getattro */ 0, /* tp_setattro */ 0, /* tp_as_buffer */ 0, /* tp_flags */ varlink__doc__, /* tp_doc */ 0, /* tp_traverse */ 0, /* tp_clear */ 0, /* tp_richcompare */ 0, /* tp_weaklistoffset */ #if PY_VERSION_HEX >= 0x02020000 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, /* tp_iter -> tp_weaklist */ #endif #if PY_VERSION_HEX >= 0x02030000 0, /* tp_del */ #endif #ifdef COUNT_ALLOCS 0,0,0,0 /* tp_alloc -> tp_next */ #endif }; varlink_type = tmp; varlink_type.ob_type = &PyType_Type; type_init = 1; } return &varlink_type; } /* Create a variable linking object for use later */ SWIGINTERN PyObject * SWIG_Python_newvarlink(void) { swig_varlinkobject *result = PyObject_NEW(swig_varlinkobject, swig_varlink_type()); if (result) { result->vars = 0; } return ((PyObject*) result); } SWIGINTERN void SWIG_Python_addvarlink(PyObject *p, char *name, PyObject *(*get_attr)(void), int (*set_attr)(PyObject *p)) { swig_varlinkobject *v = (swig_varlinkobject *) p; swig_globalvar *gv = (swig_globalvar *) malloc(sizeof(swig_globalvar)); if (gv) { size_t size = strlen(name)+1; gv->name = (char *)malloc(size); if (gv->name) { strncpy(gv->name,name,size); gv->get_attr = get_attr; gv->set_attr = set_attr; gv->next = v->vars; } } v->vars = gv; } SWIGINTERN PyObject * SWIG_globals(void) { static PyObject *_SWIG_globals = 0; if (!_SWIG_globals) _SWIG_globals = SWIG_newvarlink(); return _SWIG_globals; } /* ----------------------------------------------------------------------------- * constants/methods manipulation * ----------------------------------------------------------------------------- */ /* Install Constants */ SWIGINTERN void SWIG_Python_InstallConstants(PyObject *d, swig_const_info constants[]) { PyObject *obj = 0; size_t i; for (i = 0; constants[i].type; ++i) { switch(constants[i].type) { case SWIG_PY_POINTER: obj = SWIG_NewPointerObj(constants[i].pvalue, *(constants[i]).ptype,0); break; case SWIG_PY_BINARY: obj = SWIG_NewPackedObj(constants[i].pvalue, constants[i].lvalue, *(constants[i].ptype)); break; default: obj = 0; break; } if (obj) { PyDict_SetItemString(d, constants[i].name, obj); Py_DECREF(obj); } } } /* -----------------------------------------------------------------------------*/ /* Fix SwigMethods to carry the callback ptrs when needed */ /* -----------------------------------------------------------------------------*/ SWIGINTERN void SWIG_Python_FixMethods(PyMethodDef *methods, swig_const_info *const_table, swig_type_info **types, swig_type_info **types_initial) { size_t i; for (i = 0; methods[i].ml_name; ++i) { const char *c = methods[i].ml_doc; if (c && (c = strstr(c, "swig_ptr: "))) { int j; swig_const_info *ci = 0; const char *name = c + 10; for (j = 0; const_table[j].type; ++j) { if (strncmp(const_table[j].name, name, strlen(const_table[j].name)) == 0) { ci = &(const_table[j]); break; } } if (ci) { size_t shift = (ci->ptype) - types; swig_type_info *ty = types_initial[shift]; size_t ldoc = (c - methods[i].ml_doc); size_t lptr = strlen(ty->name)+2*sizeof(void*)+2; char *ndoc = (char*)malloc(ldoc + lptr + 10); if (ndoc) { char *buff = ndoc; void *ptr = (ci->type == SWIG_PY_POINTER) ? ci->pvalue : 0; if (ptr) { strncpy(buff, methods[i].ml_doc, ldoc); buff += ldoc; strncpy(buff, "swig_ptr: ", 10); buff += 10; SWIG_PackVoidPtr(buff, ptr, ty->name, lptr); methods[i].ml_doc = ndoc; } } } } } } #ifdef __cplusplus } #endif /* -----------------------------------------------------------------------------* * Partial Init method * -----------------------------------------------------------------------------*/ #ifdef __cplusplus extern "C" #endif SWIGEXPORT void SWIG_init(void) { PyObject *m, *d; /* Fix SwigMethods to carry the callback ptrs when needed */ SWIG_Python_FixMethods(SwigMethods, swig_const_table, swig_types, swig_type_initial); m = Py_InitModule((char *) SWIG_name, SwigMethods); d = PyModule_GetDict(m); SWIG_InitializeModule(0); SWIG_InstallConstants(d,swig_const_table); import_array(); } brian-1.4.3/dev/ideas/cppgen/circular.cpp000066400000000000000000000123531266062647100202570ustar00rootroot00000000000000#include "brianlib.h" #include CircularVector::CircularVector(int n) { this->n = n; this->X = new int[n]; // we don't worry about memory errors for the moment... this->retarray = new int[n]; this->reinit(); } CircularVector::~CircularVector() { if(this->X) delete [] this->X; if(this->retarray) delete [] this->retarray; this->X = NULL; this->retarray = NULL; } void CircularVector::reinit() { this->cursor = 0; for(int i=0;in;i++) this->X[i] = 0; } void CircularVector::advance(int k) { this->cursor = this->index(k); } int CircularVector::__len__() { return this->n; } inline int CircularVector::index(int i) { int j = (this->cursor+i)%this->n; if(j<0) j+=this->n; return j; } inline int CircularVector::getitem(int i) { return this->X[this->index(i)]; } int CircularVector::__getitem__(int i) { return this->getitem(i); } void CircularVector::__setitem__(int i, int x) { this->X[this->index(i)] = x; } void CircularVector::__getslice__(int **ret, int *ret_n, int i, int j) { int i0 = this->index(i); int j0 = this->index(j); int n = 0; for(int k=i0;k!=j0;k=(k+1)%this->n) this->retarray[n++] = this->X[k]; *ret = this->retarray; *ret_n = n; } // This can potentially be sped up substantially using a bisection algorithm /*void CircularVector::get_conditional(int **ret, int *ret_n, int i, int j, int min, int max, int offset) { int i0 = this->index(i); int j0 = this->index(j); int n = 0; for(int k=i0;k!=j0;k=(k+1)%this->n) { int Xk = this->X[k]; if(Xk>=min && Xkretarray[n++] = Xk-offset; } *ret = this->retarray; *ret_n = n; }*/ void CircularVector::get_conditional(int **ret, int *ret_n, int i, int j, int min, int max, int offset) { int i0, j0; int lo, mid, hi; int ioff = this->index(i); int joff = this->index(j); int lensearch; if(joff>=ioff) lensearch = joff-ioff; else lensearch = this->n-(ioff-joff); // start with a binary search to the left, note that we use here the bisect_left // algorithm from the standard python library translated into C++ code, and // altered to work with a circular array with an offset. The lo and hi variables // vary from 0 to lensearch but the probe this->X[...] is offset by ioff, the // position in the X array of the i variable passed to the function. lo = 0; hi = lensearch; while(loX[(mid+ioff)%this->n]n; // then a binary search to the right //lo = 0; // we can use the lo output from the previous step hi = lensearch; while(loX[(mid+ioff)%this->n]n; // then fill in the return array int n = 0; for(int k=i0;k!=j0;k=(k+1)%this->n) this->retarray[n++] = this->X[k]-offset; *ret = this->retarray; *ret_n = n; } void CircularVector::__setslice__(int i, int j, int *x, int n) { if(j>i) { int i0 = this->index(i); int j0 = this->index(j); for(int k=i0,l=0;k!=j0 && ln,l++) this->X[k] = x[l]; } } string CircularVector::__repr__() { stringstream out; out << "CircularVector("; out << "cursor=" << this->cursor; out << ", X=["; for(int i=0;in;i++) { if(i) out << " "; out << this->X[i]; } out << "])"; return out.str(); } string CircularVector::__str__() { return this->__repr__(); } SpikeContainer::SpikeContainer(int n, int m) { this->S = new CircularVector(n+1); this->ind = new CircularVector(m+1); } SpikeContainer::~SpikeContainer() { if(this->S) delete this->S; if(this->ind) delete this->ind; } void SpikeContainer::reinit() { this->S->reinit(); this->ind->reinit(); } void SpikeContainer::push(int *y, int n) { this->S->__setslice__(0, n, y, n); this->S->advance(n); this->ind->advance(1); this->ind->__setitem__(0, this->S->cursor); } void SpikeContainer::lastspikes(int **ret, int *ret_n) { this->S->__getslice__(ret, ret_n, this->ind->__getitem__(-1)-this->S->cursor, this->S->n); } void SpikeContainer::__getitem__(int **ret, int *ret_n, int i) { this->S->__getslice__(ret, ret_n, this->ind->__getitem__(-i-1)-this->S->cursor, this->ind->__getitem__(-i)-this->S->cursor+this->S->n); } void SpikeContainer::get_spikes(int **ret, int *ret_n, int delay, int origin, int N) { return this->S->get_conditional(ret, ret_n, this->ind->__getitem__(-delay-1)-this->S->cursor, this->ind->__getitem__(-delay)-this->S->cursor+this->S->n, origin, origin+N, origin); } void SpikeContainer::__getslice__(int **ret, int *ret_n, int i, int j) { return this->S->__getslice__(ret, ret_n, this->ind->__getitem__(-j)-this->S->cursor, this->ind->__getitem__(-i)-this->S->cursor+this->S->n); } string SpikeContainer::__repr__() { stringstream out; out << "SpikeContainer(" << endl; out << " S: "; out << this->S->__repr__() << endl; out << " ind: "; out << this->ind->__repr__(); out << ")"; return out.str(); } string SpikeContainer::__str__() { return this->__repr__(); } brian-1.4.3/dev/ideas/cppgen/connection.cpp000066400000000000000000000011601266062647100206040ustar00rootroot00000000000000#include "brianlib.h" void ConnectionMatrix::add_rows(int *x, int n, double *b, int b_n) { for(int i=0;iadd_row(x[i], b, b_n); } void DenseConnectionMatrix::add_row(int i, double *b, int b_n) { for(int j=0;jW, i, j, this->n, this->m); } void Connection::do_propagate() { int *x, n; this->source->get_spikes(&x, &n, this->delay); this->propagate(x, n); } void Connection::propagate(int *x, int n) { this->connmat->add_rows(x, n, &(neuron_value(this->target, this->target->origin, this->state)), this->target->num_neurons); } brian-1.4.3/dev/ideas/cppgen/ideas_for_automatic_cpp_generation.txt000066400000000000000000000020411266062647100255570ustar00rootroot00000000000000Ideas for automatic C++ code generation --------------------------------------- - To start with, you still initialise the code with Python, but if certain conditions are met then the run statement can be converted into pure C++ code (if all of the operations in the schedule have C++ versions). Could start by implementing: - LinearStateUpdater - Threshold, produces spike list - Reset and Refractoriness, takes argument spike list - Propagate - SpikeMonitor/StateMonitor Ideas for framework: - generate large inline C++ file and run it with weave.inline, have a set of standard classes which can be included. I think you can specify 'support' header files in weave, but I'm not sure if it can dynamically use code that is already compiled. - use SWIG, have a standard data structure for Network and just pass data to it. This is nice because code only has to be compiled once, and for Windows distributions the user wouldn't even have to do that. I would tend towards the SWIG option to start with.brian-1.4.3/dev/ideas/cppgen/monitor.cpp000066400000000000000000000007121266062647100201360ustar00rootroot00000000000000#include "brianlib.h" StateMonitor::StateMonitor(NeuronGroup *group, int state) { this->group = group; this->state = state; for(int i=0;inum_neurons;i++) this->values.push_back(vector()); } void StateMonitor::__call__() { for(int i=0;inum_neurons;i++) this->values[i].push_back(neuron_value(this->group, i, this->state)); } vector StateMonitor::__getitem__(int i) { return this->values[i]; } brian-1.4.3/dev/ideas/cppgen/network.cpp000066400000000000000000000016041266062647100201410ustar00rootroot00000000000000#include "brianlib.h" void Network::add(NeuronGroup *group) { this->groups.push_back(group); } void Network::add(NetworkOperation *op) { this->operations.push_back(op); } void Network::add(Connection *conn) { this->connections.push_back(conn); } void Network::update() { // Groups for(list::iterator i=this->groups.begin();i!=this->groups.end();i++) (*i)->update(); // Connections for(list::iterator i=this->connections.begin();i!=this->connections.end();i++) (*i)->do_propagate(); // Resets for(list::iterator i=this->groups.begin();i!=this->groups.end();i++) (*i)->reset(); // End operations for(list::iterator i=this->operations.begin();i!=this->operations.end();i++) (*i)->__call__(); } void Network::run(int timesteps) { for(int i=0;iupdate(); } brian-1.4.3/dev/ideas/cppgen/neurongroup.cpp000066400000000000000000000033251266062647100210350ustar00rootroot00000000000000#include "brianlib.h" NeuronGroup::NeuronGroup(double *S, int n, int m, StateUpdater *su, Threshold *thr, ResetBase *reset, int ls_n, int ls_m) { this->S = S; this->num_vars = n; this->num_neurons = m; this->S_m = m; this->su = su; this->thr = thr; this->resetobj = reset; this->LS = new SpikeContainer(ls_n, ls_m); this->spikesarray = new int [this->num_neurons]; this->owner = this; this->origin = 0; } NeuronGroup::NeuronGroup(NeuronGroup *parent, int origin, int length) { this->owner = parent; this->origin = origin; this->S = parent->S; this->num_vars = parent->num_vars; this->num_neurons = length; this->S_m = parent->S_m; this->su = NULL; this->thr = NULL; this->resetobj = NULL; this->LS = parent->LS; this->spikesarray = parent->spikesarray; } NeuronGroup::~NeuronGroup() { if(this->owner==this) { if(this->LS) delete this->LS; if(this->spikesarray) delete [] this->spikesarray; this->LS = NULL; this->spikesarray = NULL; } } /* void NeuronGroup::get_S_flat(double *S_out_flat, int nm) { for(int i=0;iS[i]; } */ void NeuronGroup::update() { if(this->owner==this) { if(this->su) this->su->__call__(this); if(this->thr) { int *ret; int ret_n; //SpikeList spikes = this->thr->__call__(this); this->thr->__call__(&ret, &ret_n, this); if(this->LS) //this->LS->push(spikes); this->LS->push(ret, ret_n); } } } void NeuronGroup::get_spikes(int **ret, int *ret_n, int delay) { this->LS->get_spikes(ret, ret_n, delay, this->origin, this->num_neurons); } void NeuronGroup::reset() { if(this->resetobj) this->resetobj->__call__(this); } brian-1.4.3/dev/ideas/cppgen/numpy.i000066400000000000000000001554401266062647100172760ustar00rootroot00000000000000/* -*- C -*- (not really, but good for syntax highlighting) */ #ifdef SWIGPYTHON %{ #ifndef SWIG_FILE_WITH_INIT # define NO_IMPORT_ARRAY #endif #include "stdio.h" #include %} /**********************************************************************/ %fragment("NumPy_Backward_Compatibility", "header") { /* Support older NumPy data type names */ %#if NDARRAY_VERSION < 0x01000000 %#define NPY_BOOL PyArray_BOOL %#define NPY_BYTE PyArray_BYTE %#define NPY_UBYTE PyArray_UBYTE %#define NPY_SHORT PyArray_SHORT %#define NPY_USHORT PyArray_USHORT %#define NPY_INT PyArray_INT %#define NPY_UINT PyArray_UINT %#define NPY_LONG PyArray_LONG %#define NPY_ULONG PyArray_ULONG %#define NPY_LONGLONG PyArray_LONGLONG %#define NPY_ULONGLONG PyArray_ULONGLONG %#define NPY_FLOAT PyArray_FLOAT %#define NPY_DOUBLE PyArray_DOUBLE %#define NPY_LONGDOUBLE PyArray_LONGDOUBLE %#define NPY_CFLOAT PyArray_CFLOAT %#define NPY_CDOUBLE PyArray_CDOUBLE %#define NPY_CLONGDOUBLE PyArray_CLONGDOUBLE %#define NPY_OBJECT PyArray_OBJECT %#define NPY_STRING PyArray_STRING %#define NPY_UNICODE PyArray_UNICODE %#define NPY_VOID PyArray_VOID %#define NPY_NTYPES PyArray_NTYPES %#define NPY_NOTYPE PyArray_NOTYPE %#define NPY_CHAR PyArray_CHAR %#define NPY_USERDEF PyArray_USERDEF %#define npy_intp intp %#define NPY_MAX_BYTE MAX_BYTE %#define NPY_MIN_BYTE MIN_BYTE %#define NPY_MAX_UBYTE MAX_UBYTE %#define NPY_MAX_SHORT MAX_SHORT %#define NPY_MIN_SHORT MIN_SHORT %#define NPY_MAX_USHORT MAX_USHORT %#define NPY_MAX_INT MAX_INT %#define NPY_MIN_INT MIN_INT %#define NPY_MAX_UINT MAX_UINT %#define NPY_MAX_LONG MAX_LONG %#define NPY_MIN_LONG MIN_LONG %#define NPY_MAX_ULONG MAX_ULONG %#define NPY_MAX_LONGLONG MAX_LONGLONG %#define NPY_MIN_LONGLONG MIN_LONGLONG %#define NPY_MAX_ULONGLONG MAX_ULONGLONG %#define NPY_MAX_INTP MAX_INTP %#define NPY_MIN_INTP MIN_INTP %#define NPY_FARRAY FARRAY %#define NPY_F_CONTIGUOUS F_CONTIGUOUS %#endif } /**********************************************************************/ /* The following code originally appeared in * enthought/kiva/agg/src/numeric.i written by Eric Jones. It was * translated from C++ to C by John Hunter. Bill Spotz has modified * it to fix some minor bugs, upgrade from Numeric to numpy (all * versions), add some comments and functionality, and convert from * direct code insertion to SWIG fragments. */ %fragment("NumPy_Macros", "header") { /* Macros to extract array attributes. */ %#define is_array(a) ((a) && PyArray_Check((PyArrayObject *)a)) %#define array_type(a) (int)(PyArray_TYPE(a)) %#define array_numdims(a) (((PyArrayObject *)a)->nd) %#define array_dimensions(a) (((PyArrayObject *)a)->dimensions) %#define array_size(a,i) (((PyArrayObject *)a)->dimensions[i]) %#define array_data(a) (((PyArrayObject *)a)->data) %#define array_is_contiguous(a) (PyArray_ISCONTIGUOUS(a)) %#define array_is_native(a) (PyArray_ISNOTSWAPPED(a)) %#define array_is_fortran(a) (PyArray_ISFORTRAN(a)) } /**********************************************************************/ %fragment("NumPy_Utilities", "header") { /* Given a PyObject, return a string describing its type. */ char* pytype_string(PyObject* py_obj) { if (py_obj == NULL ) return "C NULL value"; if (py_obj == Py_None ) return "Python None" ; if (PyCallable_Check(py_obj)) return "callable" ; if (PyString_Check( py_obj)) return "string" ; if (PyInt_Check( py_obj)) return "int" ; if (PyFloat_Check( py_obj)) return "float" ; if (PyDict_Check( py_obj)) return "dict" ; if (PyList_Check( py_obj)) return "list" ; if (PyTuple_Check( py_obj)) return "tuple" ; if (PyFile_Check( py_obj)) return "file" ; if (PyModule_Check( py_obj)) return "module" ; if (PyInstance_Check(py_obj)) return "instance" ; return "unkown type"; } /* Given a NumPy typecode, return a string describing the type. */ char* typecode_string(int typecode) { static char* type_names[25] = {"bool", "byte", "unsigned byte", "short", "unsigned short", "int", "unsigned int", "long", "unsigned long", "long long", "unsigned long long", "float", "double", "long double", "complex float", "complex double", "complex long double", "object", "string", "unicode", "void", "ntypes", "notype", "char", "unknown"}; return typecode < 24 ? type_names[typecode] : type_names[24]; } /* Make sure input has correct numpy type. Allow character and byte * to match. Also allow int and long to match. This is deprecated. * You should use PyArray_EquivTypenums() instead. */ int type_match(int actual_type, int desired_type) { return PyArray_EquivTypenums(actual_type, desired_type); } } /**********************************************************************/ %fragment("NumPy_Object_to_Array", "header", fragment="NumPy_Backward_Compatibility", fragment="NumPy_Macros", fragment="NumPy_Utilities") { /* Given a PyObject pointer, cast it to a PyArrayObject pointer if * legal. If not, set the python error string appropriately and * return NULL. */ PyArrayObject* obj_to_array_no_conversion(PyObject* input, int typecode) { PyArrayObject* ary = NULL; if (is_array(input) && (typecode == NPY_NOTYPE || PyArray_EquivTypenums(array_type(input), typecode))) { ary = (PyArrayObject*) input; } else if is_array(input) { char* desired_type = typecode_string(typecode); char* actual_type = typecode_string(array_type(input)); PyErr_Format(PyExc_TypeError, "Array of type '%s' required. Array of type '%s' given", desired_type, actual_type); ary = NULL; } else { char * desired_type = typecode_string(typecode); char * actual_type = pytype_string(input); PyErr_Format(PyExc_TypeError, "Array of type '%s' required. A '%s' was given", desired_type, actual_type); ary = NULL; } return ary; } /* Convert the given PyObject to a NumPy array with the given * typecode. On success, return a valid PyArrayObject* with the * correct type. On failure, the python error string will be set and * the routine returns NULL. */ PyArrayObject* obj_to_array_allow_conversion(PyObject* input, int typecode, int* is_new_object) { PyArrayObject* ary = NULL; PyObject* py_obj; if (is_array(input) && (typecode == NPY_NOTYPE || PyArray_EquivTypenums(array_type(input),typecode))) { ary = (PyArrayObject*) input; *is_new_object = 0; } else { py_obj = PyArray_FromObject(input, typecode, 0, 0); /* If NULL, PyArray_FromObject will have set python error value.*/ ary = (PyArrayObject*) py_obj; *is_new_object = 1; } return ary; } /* Given a PyArrayObject, check to see if it is contiguous. If so, * return the input pointer and flag it as not a new object. If it is * not contiguous, create a new PyArrayObject using the original data, * flag it as a new object and return the pointer. */ PyArrayObject* make_contiguous(PyArrayObject* ary, int* is_new_object, int min_dims, int max_dims) { PyArrayObject* result; if (array_is_contiguous(ary)) { result = ary; *is_new_object = 0; } else { result = (PyArrayObject*) PyArray_ContiguousFromObject((PyObject*)ary, array_type(ary), min_dims, max_dims); *is_new_object = 1; } return result; } /* Convert a given PyObject to a contiguous PyArrayObject of the * specified type. If the input object is not a contiguous * PyArrayObject, a new one will be created and the new object flag * will be set. */ PyArrayObject* obj_to_array_contiguous_allow_conversion(PyObject* input, int typecode, int* is_new_object) { int is_new1 = 0; int is_new2 = 0; PyArrayObject* ary2; PyArrayObject* ary1 = obj_to_array_allow_conversion(input, typecode, &is_new1); if (ary1) { ary2 = make_contiguous(ary1, &is_new2, 0, 0); if ( is_new1 && is_new2) { Py_DECREF(ary1); } ary1 = ary2; } *is_new_object = is_new1 || is_new2; return ary1; } } /**********************************************************************/ %fragment("NumPy_Array_Requirements", "header", fragment="NumPy_Backward_Compatibility", fragment="NumPy_Macros") { /* Test whether a python object is contiguous. If array is * contiguous, return 1. Otherwise, set the python error string and * return 0. */ int require_contiguous(PyArrayObject* ary) { int contiguous = 1; if (!array_is_contiguous(ary)) { PyErr_SetString(PyExc_TypeError, "Array must be contiguous. A non-contiguous array was given"); contiguous = 0; } return contiguous; } /* Require that a numpy array is not byte-swapped. If the array is * not byte-swapped, return 1. Otherwise, set the python error string * and return 0. */ int require_native(PyArrayObject* ary) { int native = 1; if (!array_is_native(ary)) { PyErr_SetString(PyExc_TypeError, "Array must have native byteorder. " "A byte-swapped array was given"); native = 0; } return native; } /* Require the given PyArrayObject to have a specified number of * dimensions. If the array has the specified number of dimensions, * return 1. Otherwise, set the python error string and return 0. */ int require_dimensions(PyArrayObject* ary, int exact_dimensions) { int success = 1; if (array_numdims(ary) != exact_dimensions) { PyErr_Format(PyExc_TypeError, "Array must have %d dimensions. Given array has %d dimensions", exact_dimensions, array_numdims(ary)); success = 0; } return success; } /* Require the given PyArrayObject to have one of a list of specified * number of dimensions. If the array has one of the specified number * of dimensions, return 1. Otherwise, set the python error string * and return 0. */ int require_dimensions_n(PyArrayObject* ary, int* exact_dimensions, int n) { int success = 0; int i; char dims_str[255] = ""; char s[255]; for (i = 0; i < n && !success; i++) { if (array_numdims(ary) == exact_dimensions[i]) { success = 1; } } if (!success) { for (i = 0; i < n-1; i++) { sprintf(s, "%d, ", exact_dimensions[i]); strcat(dims_str,s); } sprintf(s, " or %d", exact_dimensions[n-1]); strcat(dims_str,s); PyErr_Format(PyExc_TypeError, "Array must have %s dimensions. Given array has %d dimensions", dims_str, array_numdims(ary)); } return success; } /* Require the given PyArrayObject to have a specified shape. If the * array has the specified shape, return 1. Otherwise, set the python * error string and return 0. */ int require_size(PyArrayObject* ary, npy_intp* size, int n) { int i; int success = 1; int len; char desired_dims[255] = "["; char s[255]; char actual_dims[255] = "["; for(i=0; i < n;i++) { if (size[i] != -1 && size[i] != array_size(ary,i)) { success = 0; } } if (!success) { for (i = 0; i < n; i++) { if (size[i] == -1) { sprintf(s, "*,"); } else { sprintf(s, "%ld,", (long int)size[i]); } strcat(desired_dims,s); } len = strlen(desired_dims); desired_dims[len-1] = ']'; for (i = 0; i < n; i++) { sprintf(s, "%ld,", (long int)array_size(ary,i)); strcat(actual_dims,s); } len = strlen(actual_dims); actual_dims[len-1] = ']'; PyErr_Format(PyExc_TypeError, "Array must have shape of %s. Given array has shape of %s", desired_dims, actual_dims); } return success; } /* Require the given PyArrayObject to to be FORTRAN ordered. If the * the PyArrayObject is already FORTRAN ordered, do nothing. Else, * set the FORTRAN ordering flag and recompute the strides. */ int require_fortran(PyArrayObject* ary) { int success = 1; int nd = array_numdims(ary); int i; if (array_is_fortran(ary)) return success; /* Set the FORTRAN ordered flag */ ary->flags = NPY_FARRAY; /* Recompute the strides */ ary->strides[0] = ary->strides[nd-1]; for (i=1; i < nd; ++i) ary->strides[i] = ary->strides[i-1] * array_size(ary,i-1); return success; } } /* Combine all NumPy fragments into one for convenience */ %fragment("NumPy_Fragments", "header", fragment="NumPy_Backward_Compatibility", fragment="NumPy_Macros", fragment="NumPy_Utilities", fragment="NumPy_Object_to_Array", fragment="NumPy_Array_Requirements") { } /* End John Hunter translation (with modifications by Bill Spotz) */ /* %numpy_typemaps() macro * * This macro defines a family of 41 typemaps that allow C arguments * of the form * * (DATA_TYPE IN_ARRAY1[ANY]) * (DATA_TYPE* IN_ARRAY1, DIM_TYPE DIM1) * (DIM_TYPE DIM1, DATA_TYPE* IN_ARRAY1) * * (DATA_TYPE IN_ARRAY2[ANY][ANY]) * (DATA_TYPE* IN_ARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2) * (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* IN_ARRAY2) * (DATA_TYPE* IN_FARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2) * (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* IN_FARRAY2) * * (DATA_TYPE IN_ARRAY3[ANY][ANY][ANY]) * (DATA_TYPE* IN_ARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3) * (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DATA_TYPE* IN_ARRAY3) * (DATA_TYPE* IN_FARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3) * (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DATA_TYPE* IN_FARRAY3) * * (DATA_TYPE INPLACE_ARRAY1[ANY]) * (DATA_TYPE* INPLACE_ARRAY1, DIM_TYPE DIM1) * (DIM_TYPE DIM1, DATA_TYPE* INPLACE_ARRAY1) * * (DATA_TYPE INPLACE_ARRAY2[ANY][ANY]) * (DATA_TYPE* INPLACE_ARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2) * (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* INPLACE_ARRAY2) * (DATA_TYPE* INPLACE_FARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2) * (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* INPLACE_FARRAY2) * * (DATA_TYPE INPLACE_ARRAY3[ANY][ANY][ANY]) * (DATA_TYPE* INPLACE_ARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3) * (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DATA_TYPE* INPLACE_ARRAY3) * (DATA_TYPE* INPLACE_FARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3) * (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DATA_TYPE* INPLACE_FARRAY3) * * (DATA_TYPE ARGOUT_ARRAY1[ANY]) * (DATA_TYPE* ARGOUT_ARRAY1, DIM_TYPE DIM1) * (DIM_TYPE DIM1, DATA_TYPE* ARGOUT_ARRAY1) * * (DATA_TYPE ARGOUT_ARRAY2[ANY][ANY]) * * (DATA_TYPE ARGOUT_ARRAY3[ANY][ANY][ANY]) * * (DATA_TYPE** ARGOUTVIEW_ARRAY1, DIM_TYPE* DIM1) * (DIM_TYPE* DIM1, DATA_TYPE** ARGOUTVIEW_ARRAY1) * * (DATA_TYPE** ARGOUTVIEW_ARRAY2, DIM_TYPE* DIM1, DIM_TYPE* DIM2) * (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DATA_TYPE** ARGOUTVIEW_ARRAY2) * (DATA_TYPE** ARGOUTVIEW_FARRAY2, DIM_TYPE* DIM1, DIM_TYPE* DIM2) * (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DATA_TYPE** ARGOUTVIEW_FARRAY2) * * (DATA_TYPE** ARGOUTVIEW_ARRAY3, DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3) * (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DATA_TYPE** ARGOUTVIEW_ARRAY3) * (DATA_TYPE** ARGOUTVIEW_FARRAY3, DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3) * (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DATA_TYPE** ARGOUTVIEW_FARRAY3) * * where "DATA_TYPE" is any type supported by the NumPy module, and * "DIM_TYPE" is any int-like type suitable for specifying dimensions. * The difference between "ARRAY" typemaps and "FARRAY" typemaps is * that the "FARRAY" typemaps expect FORTRAN ordering of * multidimensional arrays. In python, the dimensions will not need * to be specified (except for the "DATA_TYPE* ARGOUT_ARRAY1" * typemaps). The IN_ARRAYs can be a numpy array or any sequence that * can be converted to a numpy array of the specified type. The * INPLACE_ARRAYs must be numpy arrays of the appropriate type. The * ARGOUT_ARRAYs will be returned as new numpy arrays of the * appropriate type. * * These typemaps can be applied to existing functions using the * %apply directive. For example: * * %apply (double* IN_ARRAY1, int DIM1) {(double* series, int length)}; * double prod(double* series, int length); * * %apply (int DIM1, int DIM2, double* INPLACE_ARRAY2) * {(int rows, int cols, double* matrix )}; * void floor(int rows, int cols, double* matrix, double f); * * %apply (double IN_ARRAY3[ANY][ANY][ANY]) * {(double tensor[2][2][2] )}; * %apply (double ARGOUT_ARRAY3[ANY][ANY][ANY]) * {(double low[2][2][2] )}; * %apply (double ARGOUT_ARRAY3[ANY][ANY][ANY]) * {(double upp[2][2][2] )}; * void luSplit(double tensor[2][2][2], * double low[2][2][2], * double upp[2][2][2] ); * * or directly with * * double prod(double* IN_ARRAY1, int DIM1); * * void floor(int DIM1, int DIM2, double* INPLACE_ARRAY2, double f); * * void luSplit(double IN_ARRAY3[ANY][ANY][ANY], * double ARGOUT_ARRAY3[ANY][ANY][ANY], * double ARGOUT_ARRAY3[ANY][ANY][ANY]); */ %define %numpy_typemaps(DATA_TYPE, DATA_TYPECODE, DIM_TYPE) /************************/ /* Input Array Typemaps */ /************************/ /* Typemap suite for (DATA_TYPE IN_ARRAY1[ANY]) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DATA_TYPE IN_ARRAY1[ANY]) { $1 = is_array($input) || PySequence_Check($input); } %typemap(in, fragment="NumPy_Fragments") (DATA_TYPE IN_ARRAY1[ANY]) (PyArrayObject* array=NULL, int is_new_object=0) { npy_intp size[1] = { $1_dim0 }; array = obj_to_array_contiguous_allow_conversion($input, DATA_TYPECODE, &is_new_object); if (!array || !require_dimensions(array, 1) || !require_size(array, size, 1)) SWIG_fail; $1 = ($1_ltype) array_data(array); } %typemap(freearg) (DATA_TYPE IN_ARRAY1[ANY]) { if (is_new_object$argnum && array$argnum) { Py_DECREF(array$argnum); } } /* Typemap suite for (DATA_TYPE* IN_ARRAY1, DIM_TYPE DIM1) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DATA_TYPE* IN_ARRAY1, DIM_TYPE DIM1) { $1 = is_array($input) || PySequence_Check($input); } %typemap(in, fragment="NumPy_Fragments") (DATA_TYPE* IN_ARRAY1, DIM_TYPE DIM1) (PyArrayObject* array=NULL, int is_new_object=0) { npy_intp size[1] = { -1 }; array = obj_to_array_contiguous_allow_conversion($input, DATA_TYPECODE, &is_new_object); if (!array || !require_dimensions(array, 1) || !require_size(array, size, 1)) SWIG_fail; $1 = (DATA_TYPE*) array_data(array); $2 = (DIM_TYPE) array_size(array,0); } %typemap(freearg) (DATA_TYPE* IN_ARRAY1, DIM_TYPE DIM1) { if (is_new_object$argnum && array$argnum) { Py_DECREF(array$argnum); } } /* Typemap suite for (DIM_TYPE DIM1, DATA_TYPE* IN_ARRAY1) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DIM_TYPE DIM1, DATA_TYPE* IN_ARRAY1) { $1 = is_array($input) || PySequence_Check($input); } %typemap(in, fragment="NumPy_Fragments") (DIM_TYPE DIM1, DATA_TYPE* IN_ARRAY1) (PyArrayObject* array=NULL, int is_new_object=0) { npy_intp size[1] = {-1}; array = obj_to_array_contiguous_allow_conversion($input, DATA_TYPECODE, &is_new_object); if (!array || !require_dimensions(array, 1) || !require_size(array, size, 1)) SWIG_fail; $1 = (DIM_TYPE) array_size(array,0); $2 = (DATA_TYPE*) array_data(array); } %typemap(freearg) (DIM_TYPE DIM1, DATA_TYPE* IN_ARRAY1) { if (is_new_object$argnum && array$argnum) { Py_DECREF(array$argnum); } } /* Typemap suite for (DATA_TYPE IN_ARRAY2[ANY][ANY]) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DATA_TYPE IN_ARRAY2[ANY][ANY]) { $1 = is_array($input) || PySequence_Check($input); } %typemap(in, fragment="NumPy_Fragments") (DATA_TYPE IN_ARRAY2[ANY][ANY]) (PyArrayObject* array=NULL, int is_new_object=0) { npy_intp size[2] = { $1_dim0, $1_dim1 }; array = obj_to_array_contiguous_allow_conversion($input, DATA_TYPECODE, &is_new_object); if (!array || !require_dimensions(array, 2) || !require_size(array, size, 2)) SWIG_fail; $1 = ($1_ltype) array_data(array); } %typemap(freearg) (DATA_TYPE IN_ARRAY2[ANY][ANY]) { if (is_new_object$argnum && array$argnum) { Py_DECREF(array$argnum); } } /* Typemap suite for (DATA_TYPE* IN_ARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DATA_TYPE* IN_ARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2) { $1 = is_array($input) || PySequence_Check($input); } %typemap(in, fragment="NumPy_Fragments") (DATA_TYPE* IN_ARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2) (PyArrayObject* array=NULL, int is_new_object=0) { npy_intp size[2] = { -1, -1 }; array = obj_to_array_contiguous_allow_conversion($input, DATA_TYPECODE, &is_new_object); if (!array || !require_dimensions(array, 2) || !require_size(array, size, 2)) SWIG_fail; $1 = (DATA_TYPE*) array_data(array); $2 = (DIM_TYPE) array_size(array,0); $3 = (DIM_TYPE) array_size(array,1); } %typemap(freearg) (DATA_TYPE* IN_ARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2) { if (is_new_object$argnum && array$argnum) { Py_DECREF(array$argnum); } } /* Typemap suite for (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* IN_ARRAY2) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* IN_ARRAY2) { $1 = is_array($input) || PySequence_Check($input); } %typemap(in, fragment="NumPy_Fragments") (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* IN_ARRAY2) (PyArrayObject* array=NULL, int is_new_object=0) { npy_intp size[2] = { -1, -1 }; array = obj_to_array_contiguous_allow_conversion($input, DATA_TYPECODE, &is_new_object); if (!array || !require_dimensions(array, 2) || !require_size(array, size, 2)) SWIG_fail; $1 = (DIM_TYPE) array_size(array,0); $2 = (DIM_TYPE) array_size(array,1); $3 = (DATA_TYPE*) array_data(array); } %typemap(freearg) (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* IN_ARRAY2) { if (is_new_object$argnum && array$argnum) { Py_DECREF(array$argnum); } } /* Typemap suite for (DATA_TYPE* IN_FARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DATA_TYPE* IN_FARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2) { $1 = is_array($input) || PySequence_Check($input); } %typemap(in, fragment="NumPy_Fragments") (DATA_TYPE* IN_FARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2) (PyArrayObject* array=NULL, int is_new_object=0) { npy_intp size[2] = { -1, -1 }; array = obj_to_array_contiguous_allow_conversion($input, DATA_TYPECODE, &is_new_object); if (!array || !require_dimensions(array, 2) || !require_size(array, size, 2) || !require_fortran(array)) SWIG_fail; $1 = (DATA_TYPE*) array_data(array); $2 = (DIM_TYPE) array_size(array,0); $3 = (DIM_TYPE) array_size(array,1); } %typemap(freearg) (DATA_TYPE* IN_FARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2) { if (is_new_object$argnum && array$argnum) { Py_DECREF(array$argnum); } } /* Typemap suite for (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* IN_FARRAY2) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* IN_FARRAY2) { $1 = is_array($input) || PySequence_Check($input); } %typemap(in, fragment="NumPy_Fragments") (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* IN_FARRAY2) (PyArrayObject* array=NULL, int is_new_object=0) { npy_intp size[2] = { -1, -1 }; array = obj_to_array_contiguous_allow_conversion($input, DATA_TYPECODE, &is_new_object); if (!array || !require_dimensions(array, 2) || !require_size(array, size, 2) || !require_fortran(array)) SWIG_fail; $1 = (DIM_TYPE) array_size(array,0); $2 = (DIM_TYPE) array_size(array,1); $3 = (DATA_TYPE*) array_data(array); } %typemap(freearg) (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* IN_FARRAY2) { if (is_new_object$argnum && array$argnum) { Py_DECREF(array$argnum); } } /* Typemap suite for (DATA_TYPE IN_ARRAY3[ANY][ANY][ANY]) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DATA_TYPE IN_ARRAY3[ANY][ANY][ANY]) { $1 = is_array($input) || PySequence_Check($input); } %typemap(in, fragment="NumPy_Fragments") (DATA_TYPE IN_ARRAY3[ANY][ANY][ANY]) (PyArrayObject* array=NULL, int is_new_object=0) { npy_intp size[3] = { $1_dim0, $1_dim1, $1_dim2 }; array = obj_to_array_contiguous_allow_conversion($input, DATA_TYPECODE, &is_new_object); if (!array || !require_dimensions(array, 3) || !require_size(array, size, 3)) SWIG_fail; $1 = ($1_ltype) array_data(array); } %typemap(freearg) (DATA_TYPE IN_ARRAY3[ANY][ANY][ANY]) { if (is_new_object$argnum && array$argnum) { Py_DECREF(array$argnum); } } /* Typemap suite for (DATA_TYPE* IN_ARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, * DIM_TYPE DIM3) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DATA_TYPE* IN_ARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3) { $1 = is_array($input) || PySequence_Check($input); } %typemap(in, fragment="NumPy_Fragments") (DATA_TYPE* IN_ARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3) (PyArrayObject* array=NULL, int is_new_object=0) { npy_intp size[3] = { -1, -1, -1 }; array = obj_to_array_contiguous_allow_conversion($input, DATA_TYPECODE, &is_new_object); if (!array || !require_dimensions(array, 3) || !require_size(array, size, 3)) SWIG_fail; $1 = (DATA_TYPE*) array_data(array); $2 = (DIM_TYPE) array_size(array,0); $3 = (DIM_TYPE) array_size(array,1); $4 = (DIM_TYPE) array_size(array,2); } %typemap(freearg) (DATA_TYPE* IN_ARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3) { if (is_new_object$argnum && array$argnum) { Py_DECREF(array$argnum); } } /* Typemap suite for (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, * DATA_TYPE* IN_ARRAY3) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DATA_TYPE* IN_ARRAY3) { $1 = is_array($input) || PySequence_Check($input); } %typemap(in, fragment="NumPy_Fragments") (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DATA_TYPE* IN_ARRAY3) (PyArrayObject* array=NULL, int is_new_object=0) { npy_intp size[3] = { -1, -1, -1 }; array = obj_to_array_contiguous_allow_conversion($input, DATA_TYPECODE, &is_new_object); if (!array || !require_dimensions(array, 3) || !require_size(array, size, 3)) SWIG_fail; $1 = (DIM_TYPE) array_size(array,0); $2 = (DIM_TYPE) array_size(array,1); $3 = (DIM_TYPE) array_size(array,2); $4 = (DATA_TYPE*) array_data(array); } %typemap(freearg) (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DATA_TYPE* IN_ARRAY3) { if (is_new_object$argnum && array$argnum) { Py_DECREF(array$argnum); } } /* Typemap suite for (DATA_TYPE* IN_FARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, * DIM_TYPE DIM3) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DATA_TYPE* IN_FARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3) { $1 = is_array($input) || PySequence_Check($input); } %typemap(in, fragment="NumPy_Fragments") (DATA_TYPE* IN_FARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3) (PyArrayObject* array=NULL, int is_new_object=0) { npy_intp size[3] = { -1, -1, -1 }; array = obj_to_array_contiguous_allow_conversion($input, DATA_TYPECODE, &is_new_object); if (!array || !require_dimensions(array, 3) || !require_size(array, size, 3) | !require_fortran(array)) SWIG_fail; $1 = (DATA_TYPE*) array_data(array); $2 = (DIM_TYPE) array_size(array,0); $3 = (DIM_TYPE) array_size(array,1); $4 = (DIM_TYPE) array_size(array,2); } %typemap(freearg) (DATA_TYPE* IN_FARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3) { if (is_new_object$argnum && array$argnum) { Py_DECREF(array$argnum); } } /* Typemap suite for (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, * DATA_TYPE* IN_FARRAY3) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DATA_TYPE* IN_FARRAY3) { $1 = is_array($input) || PySequence_Check($input); } %typemap(in, fragment="NumPy_Fragments") (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DATA_TYPE* IN_FARRAY3) (PyArrayObject* array=NULL, int is_new_object=0) { npy_intp size[3] = { -1, -1, -1 }; array = obj_to_array_contiguous_allow_conversion($input, DATA_TYPECODE, &is_new_object); if (!array || !require_dimensions(array, 3) || !require_size(array, size, 3) || !require_fortran(array)) SWIG_fail; $1 = (DIM_TYPE) array_size(array,0); $2 = (DIM_TYPE) array_size(array,1); $3 = (DIM_TYPE) array_size(array,2); $4 = (DATA_TYPE*) array_data(array); } %typemap(freearg) (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DATA_TYPE* IN_FARRAY3) { if (is_new_object$argnum && array$argnum) { Py_DECREF(array$argnum); } } /***************************/ /* In-Place Array Typemaps */ /***************************/ /* Typemap suite for (DATA_TYPE INPLACE_ARRAY1[ANY]) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DATA_TYPE INPLACE_ARRAY1[ANY]) { $1 = is_array($input) && PyArray_EquivTypenums(array_type($input), DATA_TYPECODE); } %typemap(in, fragment="NumPy_Fragments") (DATA_TYPE INPLACE_ARRAY1[ANY]) (PyArrayObject* array=NULL) { npy_intp size[1] = { $1_dim0 }; array = obj_to_array_no_conversion($input, DATA_TYPECODE); if (!array || !require_dimensions(array,1) || !require_size(array, size, 1) || !require_contiguous(array) || !require_native(array)) SWIG_fail; $1 = ($1_ltype) array_data(array); } /* Typemap suite for (DATA_TYPE* INPLACE_ARRAY1, DIM_TYPE DIM1) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DATA_TYPE* INPLACE_ARRAY1, DIM_TYPE DIM1) { $1 = is_array($input) && PyArray_EquivTypenums(array_type($input), DATA_TYPECODE); } %typemap(in, fragment="NumPy_Fragments") (DATA_TYPE* INPLACE_ARRAY1, DIM_TYPE DIM1) (PyArrayObject* array=NULL, int i=1) { array = obj_to_array_no_conversion($input, DATA_TYPECODE); if (!array || !require_dimensions(array,1) || !require_contiguous(array) || !require_native(array)) SWIG_fail; $1 = (DATA_TYPE*) array_data(array); $2 = 1; for (i=0; i < array_numdims(array); ++i) $2 *= array_size(array,i); } /* Typemap suite for (DIM_TYPE DIM1, DATA_TYPE* INPLACE_ARRAY1) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DIM_TYPE DIM1, DATA_TYPE* INPLACE_ARRAY1) { $1 = is_array($input) && PyArray_EquivTypenums(array_type($input), DATA_TYPECODE); } %typemap(in, fragment="NumPy_Fragments") (DIM_TYPE DIM1, DATA_TYPE* INPLACE_ARRAY1) (PyArrayObject* array=NULL, int i=0) { array = obj_to_array_no_conversion($input, DATA_TYPECODE); if (!array || !require_dimensions(array,1) || !require_contiguous(array) || !require_native(array)) SWIG_fail; $1 = 1; for (i=0; i < array_numdims(array); ++i) $1 *= array_size(array,i); $2 = (DATA_TYPE*) array_data(array); } /* Typemap suite for (DATA_TYPE INPLACE_ARRAY2[ANY][ANY]) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DATA_TYPE INPLACE_ARRAY2[ANY][ANY]) { $1 = is_array($input) && PyArray_EquivTypenums(array_type($input), DATA_TYPECODE); } %typemap(in, fragment="NumPy_Fragments") (DATA_TYPE INPLACE_ARRAY2[ANY][ANY]) (PyArrayObject* array=NULL) { npy_intp size[2] = { $1_dim0, $1_dim1 }; array = obj_to_array_no_conversion($input, DATA_TYPECODE); if (!array || !require_dimensions(array,2) || !require_size(array, size, 2) || !require_contiguous(array) || !require_native(array)) SWIG_fail; $1 = ($1_ltype) array_data(array); } /* Typemap suite for (DATA_TYPE* INPLACE_ARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DATA_TYPE* INPLACE_ARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2) { $1 = is_array($input) && PyArray_EquivTypenums(array_type($input), DATA_TYPECODE); } %typemap(in, fragment="NumPy_Fragments") (DATA_TYPE* INPLACE_ARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2) (PyArrayObject* array=NULL) { array = obj_to_array_no_conversion($input, DATA_TYPECODE); if (!array || !require_dimensions(array,2) || !require_contiguous(array) || !require_native(array)) SWIG_fail; $1 = (DATA_TYPE*) array_data(array); $2 = (DIM_TYPE) array_size(array,0); $3 = (DIM_TYPE) array_size(array,1); } /* Typemap suite for (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* INPLACE_ARRAY2) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* INPLACE_ARRAY2) { $1 = is_array($input) && PyArray_EquivTypenums(array_type($input), DATA_TYPECODE); } %typemap(in, fragment="NumPy_Fragments") (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* INPLACE_ARRAY2) (PyArrayObject* array=NULL) { array = obj_to_array_no_conversion($input, DATA_TYPECODE); if (!array || !require_dimensions(array,2) || !require_contiguous(array) || !require_native(array)) SWIG_fail; $1 = (DIM_TYPE) array_size(array,0); $2 = (DIM_TYPE) array_size(array,1); $3 = (DATA_TYPE*) array_data(array); } /* Typemap suite for (DATA_TYPE* INPLACE_FARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DATA_TYPE* INPLACE_FARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2) { $1 = is_array($input) && PyArray_EquivTypenums(array_type($input), DATA_TYPECODE); } %typemap(in, fragment="NumPy_Fragments") (DATA_TYPE* INPLACE_FARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2) (PyArrayObject* array=NULL) { array = obj_to_array_no_conversion($input, DATA_TYPECODE); if (!array || !require_dimensions(array,2) || !require_contiguous(array) || !require_native(array) || !require_fortran(array)) SWIG_fail; $1 = (DATA_TYPE*) array_data(array); $2 = (DIM_TYPE) array_size(array,0); $3 = (DIM_TYPE) array_size(array,1); } /* Typemap suite for (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* INPLACE_FARRAY2) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* INPLACE_FARRAY2) { $1 = is_array($input) && PyArray_EquivTypenums(array_type($input), DATA_TYPECODE); } %typemap(in, fragment="NumPy_Fragments") (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* INPLACE_FARRAY2) (PyArrayObject* array=NULL) { array = obj_to_array_no_conversion($input, DATA_TYPECODE); if (!array || !require_dimensions(array,2) || !require_contiguous(array) || !require_native(array) || !require_fortran(array)) SWIG_fail; $1 = (DIM_TYPE) array_size(array,0); $2 = (DIM_TYPE) array_size(array,1); $3 = (DATA_TYPE*) array_data(array); } /* Typemap suite for (DATA_TYPE INPLACE_ARRAY3[ANY][ANY][ANY]) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DATA_TYPE INPLACE_ARRAY3[ANY][ANY][ANY]) { $1 = is_array($input) && PyArray_EquivTypenums(array_type($input), DATA_TYPECODE); } %typemap(in, fragment="NumPy_Fragments") (DATA_TYPE INPLACE_ARRAY3[ANY][ANY][ANY]) (PyArrayObject* array=NULL) { npy_intp size[3] = { $1_dim0, $1_dim1, $1_dim2 }; array = obj_to_array_no_conversion($input, DATA_TYPECODE); if (!array || !require_dimensions(array,3) || !require_size(array, size, 3) || !require_contiguous(array) || !require_native(array)) SWIG_fail; $1 = ($1_ltype) array_data(array); } /* Typemap suite for (DATA_TYPE* INPLACE_ARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, * DIM_TYPE DIM3) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DATA_TYPE* INPLACE_ARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3) { $1 = is_array($input) && PyArray_EquivTypenums(array_type($input), DATA_TYPECODE); } %typemap(in, fragment="NumPy_Fragments") (DATA_TYPE* INPLACE_ARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3) (PyArrayObject* array=NULL) { array = obj_to_array_no_conversion($input, DATA_TYPECODE); if (!array || !require_dimensions(array,3) || !require_contiguous(array) || !require_native(array)) SWIG_fail; $1 = (DATA_TYPE*) array_data(array); $2 = (DIM_TYPE) array_size(array,0); $3 = (DIM_TYPE) array_size(array,1); $4 = (DIM_TYPE) array_size(array,2); } /* Typemap suite for (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, * DATA_TYPE* INPLACE_ARRAY3) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DATA_TYPE* INPLACE_ARRAY3) { $1 = is_array($input) && PyArray_EquivTypenums(array_type($input), DATA_TYPECODE); } %typemap(in, fragment="NumPy_Fragments") (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DATA_TYPE* INPLACE_ARRAY3) (PyArrayObject* array=NULL) { array = obj_to_array_no_conversion($input, DATA_TYPECODE); if (!array || !require_dimensions(array,3) || !require_contiguous(array) || !require_native(array)) SWIG_fail; $1 = (DIM_TYPE) array_size(array,0); $2 = (DIM_TYPE) array_size(array,1); $3 = (DIM_TYPE) array_size(array,2); $4 = (DATA_TYPE*) array_data(array); } /* Typemap suite for (DATA_TYPE* INPLACE_FARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, * DIM_TYPE DIM3) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DATA_TYPE* INPLACE_FARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3) { $1 = is_array($input) && PyArray_EquivTypenums(array_type($input), DATA_TYPECODE); } %typemap(in, fragment="NumPy_Fragments") (DATA_TYPE* INPLACE_FARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3) (PyArrayObject* array=NULL) { array = obj_to_array_no_conversion($input, DATA_TYPECODE); if (!array || !require_dimensions(array,3) || !require_contiguous(array) || !require_native(array) || !require_fortran(array)) SWIG_fail; $1 = (DATA_TYPE*) array_data(array); $2 = (DIM_TYPE) array_size(array,0); $3 = (DIM_TYPE) array_size(array,1); $4 = (DIM_TYPE) array_size(array,2); } /* Typemap suite for (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, * DATA_TYPE* INPLACE_FARRAY3) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DATA_TYPE* INPLACE_FARRAY3) { $1 = is_array($input) && PyArray_EquivTypenums(array_type($input), DATA_TYPECODE); } %typemap(in, fragment="NumPy_Fragments") (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DATA_TYPE* INPLACE_FARRAY3) (PyArrayObject* array=NULL) { array = obj_to_array_no_conversion($input, DATA_TYPECODE); if (!array || !require_dimensions(array,3) || !require_contiguous(array) || !require_native(array) || !require_fortran(array)) SWIG_fail; $1 = (DIM_TYPE) array_size(array,0); $2 = (DIM_TYPE) array_size(array,1); $3 = (DIM_TYPE) array_size(array,2); $4 = (DATA_TYPE*) array_data(array); } /*************************/ /* Argout Array Typemaps */ /*************************/ /* Typemap suite for (DATA_TYPE ARGOUT_ARRAY1[ANY]) */ %typemap(in,numinputs=0, fragment="NumPy_Backward_Compatibility,NumPy_Macros") (DATA_TYPE ARGOUT_ARRAY1[ANY]) (PyObject * array = NULL) { npy_intp dims[1] = { $1_dim0 }; array = PyArray_SimpleNew(1, dims, DATA_TYPECODE); if (!array) SWIG_fail; $1 = ($1_ltype) array_data(array); } %typemap(argout) (DATA_TYPE ARGOUT_ARRAY1[ANY]) { $result = SWIG_Python_AppendOutput($result,array$argnum); } /* Typemap suite for (DATA_TYPE* ARGOUT_ARRAY1, DIM_TYPE DIM1) */ %typemap(in,numinputs=1, fragment="NumPy_Fragments") (DATA_TYPE* ARGOUT_ARRAY1, DIM_TYPE DIM1) (PyObject * array = NULL) { npy_intp dims[1]; if (!PyInt_Check($input)) { char* typestring = pytype_string($input); PyErr_Format(PyExc_TypeError, "Int dimension expected. '%s' given.", typestring); SWIG_fail; } $2 = (DIM_TYPE) PyInt_AsLong($input); dims[0] = (npy_intp) $2; array = PyArray_SimpleNew(1, dims, DATA_TYPECODE); if (!array) SWIG_fail; $1 = (DATA_TYPE*) array_data(array); } %typemap(argout) (DATA_TYPE* ARGOUT_ARRAY1, DIM_TYPE DIM1) { $result = SWIG_Python_AppendOutput($result,array$argnum); } /* Typemap suite for (DIM_TYPE DIM1, DATA_TYPE* ARGOUT_ARRAY1) */ %typemap(in,numinputs=1, fragment="NumPy_Fragments") (DIM_TYPE DIM1, DATA_TYPE* ARGOUT_ARRAY1) (PyObject * array = NULL) { npy_intp dims[1]; if (!PyInt_Check($input)) { char* typestring = pytype_string($input); PyErr_Format(PyExc_TypeError, "Int dimension expected. '%s' given.", typestring); SWIG_fail; } $1 = (DIM_TYPE) PyInt_AsLong($input); dims[0] = (npy_intp) $1; array = PyArray_SimpleNew(1, dims, DATA_TYPECODE); if (!array) SWIG_fail; $2 = (DATA_TYPE*) array_data(array); } %typemap(argout) (DIM_TYPE DIM1, DATA_TYPE* ARGOUT_ARRAY1) { $result = SWIG_Python_AppendOutput($result,array$argnum); } /* Typemap suite for (DATA_TYPE ARGOUT_ARRAY2[ANY][ANY]) */ %typemap(in,numinputs=0, fragment="NumPy_Backward_Compatibility,NumPy_Macros") (DATA_TYPE ARGOUT_ARRAY2[ANY][ANY]) (PyObject * array = NULL) { npy_intp dims[2] = { $1_dim0, $1_dim1 }; array = PyArray_SimpleNew(2, dims, DATA_TYPECODE); if (!array) SWIG_fail; $1 = ($1_ltype) array_data(array); } %typemap(argout) (DATA_TYPE ARGOUT_ARRAY2[ANY][ANY]) { $result = SWIG_Python_AppendOutput($result,array$argnum); } /* Typemap suite for (DATA_TYPE ARGOUT_ARRAY3[ANY][ANY][ANY]) */ %typemap(in,numinputs=0, fragment="NumPy_Backward_Compatibility,NumPy_Macros") (DATA_TYPE ARGOUT_ARRAY3[ANY][ANY][ANY]) (PyObject * array = NULL) { npy_intp dims[3] = { $1_dim0, $1_dim1, $1_dim2 }; array = PyArray_SimpleNew(3, dims, DATA_TYPECODE); if (!array) SWIG_fail; $1 = ($1_ltype) array_data(array); } %typemap(argout) (DATA_TYPE ARGOUT_ARRAY3[ANY][ANY][ANY]) { $result = SWIG_Python_AppendOutput($result,array$argnum); } /*****************************/ /* Argoutview Array Typemaps */ /*****************************/ /* Typemap suite for (DATA_TYPE** ARGOUTVIEW_ARRAY1, DIM_TYPE* DIM1) */ %typemap(in,numinputs=0) (DATA_TYPE** ARGOUTVIEW_ARRAY1, DIM_TYPE* DIM1 ) (DATA_TYPE* data_temp , DIM_TYPE dim_temp) { $1 = &data_temp; $2 = &dim_temp; } %typemap(argout, fragment="NumPy_Backward_Compatibility") (DATA_TYPE** ARGOUTVIEW_ARRAY1, DIM_TYPE* DIM1) { npy_intp dims[1] = { *$2 }; PyObject * array = PyArray_SimpleNewFromData(1, dims, DATA_TYPECODE, (void*)(*$1)); if (!array) SWIG_fail; $result = SWIG_Python_AppendOutput($result,array); } /* Typemap suite for (DIM_TYPE* DIM1, DATA_TYPE** ARGOUTVIEW_ARRAY1) */ %typemap(in,numinputs=0) (DIM_TYPE* DIM1 , DATA_TYPE** ARGOUTVIEW_ARRAY1) (DIM_TYPE dim_temp, DATA_TYPE* data_temp ) { $1 = &dim_temp; $2 = &data_temp; } %typemap(argout, fragment="NumPy_Backward_Compatibility") (DIM_TYPE* DIM1, DATA_TYPE** ARGOUTVIEW_ARRAY1) { npy_intp dims[1] = { *$1 }; PyObject * array = PyArray_SimpleNewFromData(1, dims, DATA_TYPECODE, (void*)(*$2)); if (!array) SWIG_fail; $result = SWIG_Python_AppendOutput($result,array); } /* Typemap suite for (DATA_TYPE** ARGOUTVIEW_ARRAY2, DIM_TYPE* DIM1, DIM_TYPE* DIM2) */ %typemap(in,numinputs=0) (DATA_TYPE** ARGOUTVIEW_ARRAY2, DIM_TYPE* DIM1 , DIM_TYPE* DIM2 ) (DATA_TYPE* data_temp , DIM_TYPE dim1_temp, DIM_TYPE dim2_temp) { $1 = &data_temp; $2 = &dim1_temp; $3 = &dim2_temp; } %typemap(argout, fragment="NumPy_Backward_Compatibility") (DATA_TYPE** ARGOUTVIEW_ARRAY2, DIM_TYPE* DIM1, DIM_TYPE* DIM2) { npy_intp dims[2] = { *$2, *$3 }; PyObject * array = PyArray_SimpleNewFromData(2, dims, DATA_TYPECODE, (void*)(*$1)); if (!array) SWIG_fail; $result = SWIG_Python_AppendOutput($result,array); } /* Typemap suite for (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DATA_TYPE** ARGOUTVIEW_ARRAY2) */ %typemap(in,numinputs=0) (DIM_TYPE* DIM1 , DIM_TYPE* DIM2 , DATA_TYPE** ARGOUTVIEW_ARRAY2) (DIM_TYPE dim1_temp, DIM_TYPE dim2_temp, DATA_TYPE* data_temp ) { $1 = &dim1_temp; $2 = &dim2_temp; $3 = &data_temp; } %typemap(argout, fragment="NumPy_Backward_Compatibility") (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DATA_TYPE** ARGOUTVIEW_ARRAY2) { npy_intp dims[2] = { *$1, *$2 }; PyObject * array = PyArray_SimpleNewFromData(2, dims, DATA_TYPECODE, (void*)(*$3)); if (!array) SWIG_fail; $result = SWIG_Python_AppendOutput($result,array); } /* Typemap suite for (DATA_TYPE** ARGOUTVIEW_FARRAY2, DIM_TYPE* DIM1, DIM_TYPE* DIM2) */ %typemap(in,numinputs=0) (DATA_TYPE** ARGOUTVIEW_FARRAY2, DIM_TYPE* DIM1 , DIM_TYPE* DIM2 ) (DATA_TYPE* data_temp , DIM_TYPE dim1_temp, DIM_TYPE dim2_temp) { $1 = &data_temp; $2 = &dim1_temp; $3 = &dim2_temp; } %typemap(argout, fragment="NumPy_Backward_Compatibility,NumPy_Array_Requirements") (DATA_TYPE** ARGOUTVIEW_FARRAY2, DIM_TYPE* DIM1, DIM_TYPE* DIM2) { npy_intp dims[2] = { *$2, *$3 }; PyObject * obj = PyArray_SimpleNewFromData(2, dims, DATA_TYPECODE, (void*)(*$1)); PyArrayObject * array = (PyArrayObject*) obj; if (!array || !require_fortran(array)) SWIG_fail; $result = SWIG_Python_AppendOutput($result,obj); } /* Typemap suite for (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DATA_TYPE** ARGOUTVIEW_FARRAY2) */ %typemap(in,numinputs=0) (DIM_TYPE* DIM1 , DIM_TYPE* DIM2 , DATA_TYPE** ARGOUTVIEW_FARRAY2) (DIM_TYPE dim1_temp, DIM_TYPE dim2_temp, DATA_TYPE* data_temp ) { $1 = &dim1_temp; $2 = &dim2_temp; $3 = &data_temp; } %typemap(argout, fragment="NumPy_Backward_Compatibility,NumPy_Array_Requirements") (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DATA_TYPE** ARGOUTVIEW_FARRAY2) { npy_intp dims[2] = { *$1, *$2 }; PyObject * obj = PyArray_SimpleNewFromData(2, dims, DATA_TYPECODE, (void*)(*$3)); PyArrayObject * array = (PyArrayObject*) obj; if (!array || !require_fortran(array)) SWIG_fail; $result = SWIG_Python_AppendOutput($result,obj); } /* Typemap suite for (DATA_TYPE** ARGOUTVIEW_ARRAY3, DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3) */ %typemap(in,numinputs=0) (DATA_TYPE** ARGOUTVIEW_ARRAY3, DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3) (DATA_TYPE* data_temp, DIM_TYPE dim1_temp, DIM_TYPE dim2_temp, DIM_TYPE dim3_temp) { $1 = &data_temp; $2 = &dim1_temp; $3 = &dim2_temp; $4 = &dim3_temp; } %typemap(argout, fragment="NumPy_Backward_Compatibility") (DATA_TYPE** ARGOUTVIEW_ARRAY3, DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3) { npy_intp dims[3] = { *$2, *$3, *$4 }; PyObject * array = PyArray_SimpleNewFromData(3, dims, DATA_TYPECODE, (void*)(*$1)); if (!array) SWIG_fail; $result = SWIG_Python_AppendOutput($result,array); } /* Typemap suite for (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DATA_TYPE** ARGOUTVIEW_ARRAY3) */ %typemap(in,numinputs=0) (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DATA_TYPE** ARGOUTVIEW_ARRAY3) (DIM_TYPE dim1_temp, DIM_TYPE dim2_temp, DIM_TYPE dim3_temp, DATA_TYPE* data_temp) { $1 = &dim1_temp; $2 = &dim2_temp; $3 = &dim3_temp; $4 = &data_temp; } %typemap(argout, fragment="NumPy_Backward_Compatibility") (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DATA_TYPE** ARGOUTVIEW_ARRAY3) { npy_intp dims[3] = { *$1, *$2, *$3 }; PyObject * array = PyArray_SimpleNewFromData(3, dims, DATA_TYPECODE, (void*)(*$3)); if (!array) SWIG_fail; $result = SWIG_Python_AppendOutput($result,array); } /* Typemap suite for (DATA_TYPE** ARGOUTVIEW_FARRAY3, DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3) */ %typemap(in,numinputs=0) (DATA_TYPE** ARGOUTVIEW_FARRAY3, DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3) (DATA_TYPE* data_temp, DIM_TYPE dim1_temp, DIM_TYPE dim2_temp, DIM_TYPE dim3_temp) { $1 = &data_temp; $2 = &dim1_temp; $3 = &dim2_temp; $4 = &dim3_temp; } %typemap(argout, fragment="NumPy_Backward_Compatibility,NumPy_Array_Requirements") (DATA_TYPE** ARGOUTVIEW_FARRAY3, DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3) { npy_intp dims[3] = { *$2, *$3, *$4 }; PyObject * obj = PyArray_SimpleNewFromData(3, dims, DATA_TYPECODE, (void*)(*$1)); PyArrayObject * array = (PyArrayObject*) obj; if (!array || require_fortran(array)) SWIG_fail; $result = SWIG_Python_AppendOutput($result,obj); } /* Typemap suite for (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DATA_TYPE** ARGOUTVIEW_FARRAY3) */ %typemap(in,numinputs=0) (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DATA_TYPE** ARGOUTVIEW_FARRAY3) (DIM_TYPE dim1_temp, DIM_TYPE dim2_temp, DIM_TYPE dim3_temp, DATA_TYPE* data_temp) { $1 = &dim1_temp; $2 = &dim2_temp; $3 = &dim3_temp; $4 = &data_temp; } %typemap(argout, fragment="NumPy_Backward_Compatibility,NumPy_Array_Requirements") (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DATA_TYPE** ARGOUTVIEW_FARRAY3) { npy_intp dims[3] = { *$1, *$2, *$3 }; PyObject * obj = PyArray_SimpleNewFromData(3, dims, DATA_TYPECODE, (void*)(*$3)); PyArrayObject * array = (PyArrayObject*) obj; if (!array || require_fortran(array)) SWIG_fail; $result = SWIG_Python_AppendOutput($result,obj); } %enddef /* %numpy_typemaps() macro */ /* *************************************************************** */ /* Concrete instances of the %numpy_typemaps() macro: Each invocation * below applies all of the typemaps above to the specified data type. */ %numpy_typemaps(signed char , NPY_BYTE , int) %numpy_typemaps(unsigned char , NPY_UBYTE , int) %numpy_typemaps(short , NPY_SHORT , int) %numpy_typemaps(unsigned short , NPY_USHORT , int) %numpy_typemaps(int , NPY_INT , int) %numpy_typemaps(unsigned int , NPY_UINT , int) %numpy_typemaps(long , NPY_LONG , int) %numpy_typemaps(unsigned long , NPY_ULONG , int) %numpy_typemaps(long long , NPY_LONGLONG , int) %numpy_typemaps(unsigned long long, NPY_ULONGLONG, int) %numpy_typemaps(float , NPY_FLOAT , int) %numpy_typemaps(double , NPY_DOUBLE , int) /* *************************************************************** * The follow macro expansion does not work, because C++ bool is 4 * bytes and NPY_BOOL is 1 byte * * %numpy_typemaps(bool, NPY_BOOL, int) */ /* *************************************************************** * On my Mac, I get the following warning for this macro expansion: * 'swig/python detected a memory leak of type 'long double *', no destructor found.' * * %numpy_typemaps(long double, NPY_LONGDOUBLE, int) */ /* *************************************************************** * Swig complains about a syntax error for the following macro * expansions: * * %numpy_typemaps(complex float, NPY_CFLOAT , int) * * %numpy_typemaps(complex double, NPY_CDOUBLE, int) * * %numpy_typemaps(complex long double, NPY_CLONGDOUBLE, int) */ #endif /* SWIGPYTHON */ brian-1.4.3/dev/ideas/cppgen/old/000077500000000000000000000000001266062647100165215ustar00rootroot00000000000000brian-1.4.3/dev/ideas/cppgen/old/brianlib.cpp000066400000000000000000000016011266062647100210050ustar00rootroot00000000000000#include "brianlib.h" #include using namespace std; LabelledArrays::LabelledArrays(string a, vector b) { this->a = a; this->b = b; } string LabelledArrays::get_msg() { double x = 0.0; for(vector::iterator i=this->b.begin();i!=this->b.end();i++) x += *i; string s; stringstream out; out << "sum of array labelled " << this->a << " is " << x; s = out.str(); return s; } LabelledArrays2::LabelledArrays2(string a, double *b, int n) { this->a = a; this->b = b; this->n = n; } string LabelledArrays2::get_msg() { double x = 0.0; for(int i=0;in;i++) x += this->b[i]; string s; stringstream out; out << "array = "; for(int i=0;in;i++) out << this->b[i] << " "; out << endl; out << "sum of array labelled " << this->a << " of length " << this->n << " is " << x; s = out.str(); return s; } brian-1.4.3/dev/ideas/cppgen/old/brianlib.h000066400000000000000000000006131266062647100204540ustar00rootroot00000000000000#ifndef _BRIAN_LIB_H #define _BRIAN_LIB_H #include #include using namespace std; class LabelledArrays { string a; vector b; public: LabelledArrays(string a, vector b); string get_msg(); }; class LabelledArrays2 { string a; double *b; int n; public: LabelledArrays2(string a, double *b, int n); string get_msg(); }; #endif brian-1.4.3/dev/ideas/cppgen/old/brianlib.i000066400000000000000000000007511266062647100204600ustar00rootroot00000000000000%module brianlib %include "std_string.i" %include "std_vector.i" %{ #define SWIG_FILE_WITH_INIT #include "brianlib.h" %} %include "numpy.i" %init %{ import_array(); %} // Use this technique to create a vector argument namespace std { %template(DoubleVector) vector; } // use this technique to pass numpy arrays to a function with the // given arguments %apply (double* INPLACE_ARRAY1, int DIM1) {(double *b, int n)}; %include "brianlib.h" brian-1.4.3/dev/ideas/cppgen/old/setup.py000066400000000000000000000011101266062647100202240ustar00rootroot00000000000000#!/usr/bin/env python """ setup.py file for SWIG example """ from distutils.core import setup, Extension brianlib_module = Extension('_brianlib', sources=['brianlib_wrap.cxx', 'brianlib.cpp'], include_dirs=['c:\\python25\\lib\\site-packages\\numpy\\core\\include'] ) setup (name='brianlib', version='0.1', author="Dan Goodman", description="""Simple swig example from docs""", ext_modules=[brianlib_module], py_modules=["brianlib"], ) brian-1.4.3/dev/ideas/cppgen/old/test_brianlib.py000066400000000000000000000003071266062647100217140ustar00rootroot00000000000000from brianlib import * from numpy import * #x = LabelledArrays('blah', DoubleVector([3.2, 1.5, 6.4])) #print x.get_msg() x = LabelledArrays2('blah', array([3.2, 1.5, 6.4])) print x.get_msg() brian-1.4.3/dev/ideas/cppgen/reset.cpp000066400000000000000000000021421266062647100175700ustar00rootroot00000000000000#include "brianlib.h" /* void Reset::__call__(NeuronGroup *group) { //SpikeList &spikelist = group->last_spikes; SpikeList spikelist = group->LS->lastspikes(); for(SpikeList::iterator i=spikelist.begin();i!=spikelist.end();i++) //group->S[this->state+group->num_vars*(*i)] = this->value; neuron_value(group, *i, this->state) = this->value; // V[P.LS.lastspikes()] = self.resetvalue } */ void Reset::__call__(NeuronGroup *group) { int *ret; int ret_n; group->LS->lastspikes(&ret, &ret_n); for(int i=0;istate) = this->value; } /* void Refractoriness::__call__(NeuronGroup *group) { SpikeList spikelist = group->LS->__getslice__(0, period); for(SpikeList::iterator i=spikelist.begin();i!=spikelist.end();i++) neuron_value(group, *i, this->state) = this->value; // V[P.LS[0:period]] = self.resetvalue } */ void Refractoriness::__call__(NeuronGroup *group) { int *ret; int ret_n; group->LS->__getslice__(&ret, &ret_n, 0, this->period); for(int i=0;istate) = this->value; }brian-1.4.3/dev/ideas/cppgen/setup.py000066400000000000000000000022421266062647100174550ustar00rootroot00000000000000#!/usr/bin/env python """ setup.py file for SWIG example """ from distutils.core import setup, Extension import os, numpy numpy_base_dir = os.path.split(numpy.__file__)[0] numpy_include_dir = os.path.join(numpy_base_dir, 'core/include') brianlib_module = Extension('_brianlib', sources=['brianlib_wrap.cxx', 'circular.cpp', 'monitor.cpp', 'neurongroup.cpp', 'network.cpp', 'reset.cpp', 'stateupdater.cpp', 'threshold.cpp', 'connection.cpp', ], include_dirs=[numpy_include_dir], extra_compile_args=['-O3'] ) setup (name='brianlib', version='0.1', author="Dan Goodman", description="""Simple swig example from docs""", ext_modules=[brianlib_module], py_modules=["brianlib"], ) brian-1.4.3/dev/ideas/cppgen/stateupdater.cpp000066400000000000000000000023131266062647100211530ustar00rootroot00000000000000#include "brianlib.h" #include void LinearStateUpdater::__call__(NeuronGroup *group) { // n = len(P) // m = len(self) // S = P._S // A = self.A // c = self._C int m = this->M_n; int n = group->num_neurons; double *A = this->M; double *c = this->b; double x[m]; //for(int i=0;iM_n; double *A = this->M; double *c = this->b; stringstream out; out << "LinearStateUpdater" << endl; out << "A = " << endl; for(int i=0;inum_neurons;i++) //if(group->S[this->state+group->num_vars*i]>this->value) if(neuron_value(group, i, this->state)>this->value) spikelist.push_back(i); return spikelist; } */ void Threshold::__call__(int **ret, int *ret_n, NeuronGroup *group) { int n = 0; for(int i=0;inum_neurons;i++) if(neuron_value(group, i, this->state)>this->value) group->spikesarray[n++] = i; *ret = group->spikesarray; *ret_n = n; } brian-1.4.3/dev/ideas/cuda/000077500000000000000000000000001266062647100154035ustar00rootroot00000000000000brian-1.4.3/dev/ideas/cuda/algorithmic_issues.txt000066400000000000000000000076651266062647100220570ustar00rootroot00000000000000Algorithmic issues ================== NeuronGroup ----------- The main issue is compaction. Synapses -------- The main issue is probably coalescence. Consider two connected groups of neurons P and Q, with p synapses per neuron on average. I also consider the most interesting case, when there is STDP and heterogeneous delays. Forward propagation ^^^^^^^^^^^^^^^^^^^ Let us first consider forward propagation with various conduction delays. When a spike is produced by presynaptic neuron i, the spike reaches p synaptic targets, at different times. These spikes will then affect synaptic variables, and/or postsynaptic variables. We consider that these variables are instantly modified (i.e., no buffering). In particular, synaptic weight is modified by each incoming spike. At any given time step, we have to consider a set of incoming spikes from different neurons. Assuming that different neurons fire independently, variables corresponding to different presynaptic neurons must be considered as non contiguous (in the sense that contiguity can only occur by chance). Therefore, it is enough to examine the case of spikes coming from a single presynaptic neuron. Synapses activated with different delays will not be processed at the same time, and therefore can also be considered as non-contiguous (in the sense that they will always imply multiple memory accesses). Therefore, synapses that may be considered as contiguous are only those with the same presynaptic neuron and delay. In summary, optimizing global memory accesses means optimizing the contiguity of synaptic variables corresponding the same presynaptic neuron and delay, i.e., minimizing the number of memory blocks occupied by a given presynaptic neuron and delay. Backward propagation ^^^^^^^^^^^^^^^^^^^^ In the most general setting, the same reasoning applies to backward propagation. This means simultaneously: * minimizing the number of memory blocks occupied by a given presynaptic neuron and delay. * minimizing the number of memory blocks occupied by a given postsynaptic neuron and backward delay. However, in most practical cases, the backward delay is zero or constant. This means that the second constraint becomes: * minimizing the number of memory blocks occupied by a given postsynaptic neuron. Swapping ^^^^^^^^ What happens if synapse indexes i,j are swapped? I note Sf(i) the forward set of i (set of all synapses that are simultaneously modified in the forward direction) and Sb(i) the backward set. I note B(i) the memory block of i (which is a set of 32 indexes). * A change in cost only occurs if B(i) and B(j) are different. * A negative change in cost only occurs if B(i) or B(j) is represented only once in one of Sf(i), Sb(i), Sf(j), Sb(j). (That is, the intersection of B(i) and Sf(i) is just one element). * The net change is: * -1 access in each set of (Sf(i),Sb(i)) where B(i) is represented only once * -1 access in each set of (Sf(j),Sb(j)) where B(j) is represented only once * +1 access in each set of (Sf(j),Sb(j)) where B(i) is not represented * +1 access in each set of (Sf(i),Sb(i)) where B(j) is not represented This means that the net change for i<->j is c(i)+c(j)+c(i->j)+c(j->i) (c for cost). One idea would be to pick a random synapse, and pick the best synapse to swap with. Here is a simple algorithm to start with: 1) Calculate c(i) for all i. 2) Pick two random synapse i and j among the ones with c(i)=c(j)=-2. Swap them. 3) Update c(i) and c(j). Buffering ^^^^^^^^^ Another option is to apply synaptic changes only after some time, so as to access more memory at the same time. This is not possible for all cases. In particular, propagation of spikes (v+=w) has to be instantaneous. However, in this case we could imagine optimisations, e.g. reading w at the time when spikes are produced rather than received (in this case we must minimize the number of memory blocks occupied by a given presynaptic neuron). brian-1.4.3/dev/ideas/cuda/compaction_and_cumsum_notes.txt000066400000000000000000000017401266062647100237250ustar00rootroot00000000000000Some bookmarks: http://courses.ece.illinois.edu/ece498/al/textbook/Chapter5-CudaPerformance.pdf http://www.google.fr/search?q=gpu+gather+scatter&btnG=Search&hl=en&client=firefox-a&rls=org.mozilla%3Aen-GB%3Aofficial&sa=2 http://delivery.acm.org/10.1145/1570000/1564500/a1_4-cederman.pdf?key1=1564500&key2=3041514521&coll=ACM&dl=ACM&CFID=15151515&CFTOKEN=6184618 http://www.control.isy.liu.se/~fredrik/reports/07eusipcoGPU.pdf http://http.developer.nvidia.com/GPUGems2/gpugems2_chapter36.html http://gpgpu.org/tag/parallel-algorithms http://mgarland.org/files/papers/nvr-2008-003.pdf http://graphics.idav.ucdavis.edu/publications/print_pub?pub_id=894 http://gpgpu.org/developer/cudpp http://en.wikipedia.org/wiki/Prefix_sum http://developer.download.nvidia.com/compute/cuda/sdk/website/projects/scan/doc/scan.pdf http://graphics.idav.ucdavis.edu/publications/print_pub?pub_id=915 http://www.cse.chalmers.se/~uffe/streamcompaction.pdf http://www.cse.chalmers.se/~billeter/pub/pp/brian-1.4.3/dev/ideas/cuda/compaction_with_pycuda.py000066400000000000000000000072651266062647100225230ustar00rootroot00000000000000from brian import * from brian.experimental.codegen2 import * import pycuda from pycuda import gpuarray from pycuda import compiler from pycuda import scan from pycuda import driver if __name__=='__main__': N = 10000 p = 100*Hz*defaultclock.dt#0.001 index_dtype = int64 index_typestr = 'int64_t' x = p-rand(N) truexc, = (x>0).nonzero() gx = gpuarray.to_gpu(x) #print 'numspiked =', len(truexc) threshold_code = ''' __global__ void threshold(double *x, INT *spiked, INT N) { const int thread = threadIdx.x+blockIdx.x*blockDim.x; if(thread>=N) return; spiked[thread] = (INT)(x[thread]>0.0); } '''.replace('INT', index_typestr) threshold_module = compiler.SourceModule(threshold_code) threshold_func = threshold_module.get_function('threshold') spiked = array(hstack((x>0, False)), dtype=index_dtype) gspiked = gpuarray.zeros(N+1, dtype=index_dtype) block, grid = compute_block_grid(1024, N) threshold_func(gx, gspiked, index_dtype(N), block=block, grid=grid) if not equal(spiked-gspiked.get(), 0).all(): print 'gspiked incorrect!' print spiked print gspiked exit() threshold_func(gx, gspiked, index_dtype(N), block=block, grid=grid) scancomp = GPUScanCompactor(N, index_dtype=index_dtype) xc = scancomp(gspiked) correct = (len(xc)==len(truexc) and equal(xc, truexc).all()) print 'GPUScanCompactor correct', correct threshold_func(gx, gspiked, index_dtype(N), block=block, grid=grid) atomcomp = GPUAtomicCompactor(N, index_dtype=index_dtype) xc = atomcomp(gspiked) correct = (len(xc)==len(truexc) and equal(xc, truexc).all()) print 'GPUAtomicCompactor correct', correct #exit() repeats = 1000 import time start = time.time() for i in xrange(repeats): threshold_func(gx, gspiked, index_dtype(N), block=block, grid=grid) end = time.time() threshold_time = end-start start = time.time() for i in xrange(repeats): threshold_func(gx, gspiked, index_dtype(N), block=block, grid=grid) scancomp.get_gpu(gspiked) end = time.time() gpu_only_time = end-start start = time.time() for i in xrange(repeats): threshold_func(gx, gspiked, index_dtype(N), block=block, grid=grid) scancomp(gspiked) end = time.time() comp_time = end-start start = time.time() for i in xrange(repeats): threshold_func(gx, gspiked, index_dtype(N), block=block, grid=grid) spiked = gspiked.get()[:-1] spiked.nonzero() end = time.time() cpu_time = end-start start = time.time() for i in xrange(repeats): threshold_func(gx, gspiked, index_dtype(N), block=block, grid=grid) atomcomp(gspiked) end = time.time() atom_time = end-start uatomcomp = GPUAtomicCompactor(N, sorted=False, index_dtype=index_dtype) start = time.time() for i in xrange(repeats): threshold_func(gx, gspiked, index_dtype(N), block=block, grid=grid) atomcomp(gspiked) end = time.time() atom_unsorted_time = end-start print print 'PROFILING N =', N, 'p =', p print print 'Time using CPU after download:', (cpu_time-threshold_time)/repeats*second print 'Time with scan compactor (GPU only):', (gpu_only_time-threshold_time)/repeats*second print 'Time with scan compactor (inc download):', (comp_time-threshold_time)/repeats*second print 'Time with atomic compactor (inc CPU sort):', (atom_time-threshold_time)/repeats*second print 'Time with atomic compactor (unsorted):', (atom_unsorted_time-threshold_time)/repeats*second brian-1.4.3/dev/ideas/cuda/cuba_example_nemo_net.py000066400000000000000000000030021266062647100222610ustar00rootroot00000000000000use_nemo = True do_callgraph = False plot_output = True from brian import * set_global_preferences(useweave=True, usecodegen=True, usecodegenweave=True, usecodegenstateupdate=True, usecodegenthreshold=True, usenewpropagate=True, usecstdp=True) if use_nemo: from brian.experimental.cuda.briantonemo import * import time eqs = ''' dv/dt = (ge-gi-(v+49*mV))/(20*ms) : volt dge/dt = -ge/(5*ms) : volt dgi/dt = -gi/(10*ms) : volt ''' P = NeuronGroup(4000, model=eqs, threshold=-50*mV, reset=-60*mV) P.v = -60 * mV + 10 * mV * rand(len(P)) Pe = P.subgroup(3200) Pi = P.subgroup(800) Ce = Connection(Pe, P, 'ge', weight=1.62*mV, sparseness=0.02, delay=(0*ms, 0*ms), #structure='dense', ) Ci = Connection(Pi, P, 'gi', weight=9*mV, sparseness=0.02, delay=(0*ms, 0*ms), #structure='dense', ) M = SpikeMonitor(P) net = MagicNetwork() start = time.time() net.prepare() net.run(1*ms) print 'Preparation time:', time.time()-start if do_callgraph: import pycallgraph def ff(call_stack, module_name, class_name, func_name, full_name): if not 'brian' in module_name: return False return True pycallgraph.start_trace(filter_func=ff) start = time.time() net.run(1 * second) print 'Run time:', time.time()-start if do_callgraph: pycallgraph.stop_trace() pycallgraph.make_dot_graph('callgraph.png') if plot_output: raster_plot(M) show() brian-1.4.3/dev/ideas/cuda/gpu_week.txt000066400000000000000000000027411266062647100177560ustar00rootroot00000000000000GPU week - Debriefing ===================== What we have now: * a NemoConnection that can run a (non plastic) DelayConnection on Nemo (except modulation). * a synfire chain example with heterogeneous delays for tests. What we would like to have: * the Synapses class on GPU This requires 1) backward spike delivery, 2) execution of user code on multiple synaptic variables. * a complete NeuronGroup on GPU (with threshold) For this we can reuse some of Nemo's code. * Automatic synapse indexing: see the corresponding document. This would be needed to optimize global memory transfers for both the forward and backward spike propagation (for STDP) > Andrius/Cyrille (if it's worth it perhaps we could think of a GPU algorithm?) * How to directly construct networks on GPU (could be important in some cases) NeuronGroup on GPU ------------------ * Threshold/compaction * New refractory system * Stochastic equations * Poisson groups Synapses -------- * Finish the Synapses class (Python) > Romain * Running multiple connections: run multiple Nemo networks and share neuron variables * Backward propagation * Code generation, use Pycuda to run kernels (for pre/post code in Synapses) * Optimal synapse indexing for forward and backpropagation of spikes > Andrius/Cyrille Other things ------------ * Brian Hears * Check other algorithms in the literature (especially for STDP) (e.g. Jayram's) * We have Issam's code the di svn (Brian/Issam_BrianToCuda) brian-1.4.3/dev/ideas/cuda/installing_cuda_on_windows.txt000066400000000000000000000102761266062647100235600ustar00rootroot00000000000000INSTALLING CUDA ON WINDOWS ========================== * Install all CUDA tools from NVIDIA, at least 2.0 * Install MS Visual Studio 2003 (= VC71) [* Install MS Visual Studio Express 2008] (optional) * Install Boost C++ from Boost Pro Free binary installer, include Python binaries for VC71. * Download PyCuda * Create a file siteconf.py in the pycuda folder: BOOST_INC_DIR = [r'C:\Program Files\boost\boost_1_37'] BOOST_LIB_DIR = [r'C:\Program Files\boost\boost_1_37\lib'] BOOST_PYTHON_LIBNAME = ['boost_python-vc71-mt-1_37'] CUDA_ROOT = r'C:\CUDA' CUDADRV_LIB_DIR = [r'C:\CUDA\lib'] CUDADRV_LIBNAME = ['cuda'] CXXFLAGS = [] LDFLAGS = [] * Check the directories above with your configuration. * Copy the Boost file boost_python-vc71-mt-1_37.dll to the Windows folder. * Run the Visual Studio 2003 command prompt, go to the pycuda folder and run setup.py install. * Add the following directories to your Path environment variable: C:\Program Files\Microsoft Visual Studio .NET 2003\Common7\IDE C:\Program Files\Microsoft Visual Studio .NET 2003\VC7\BIN [optionnally, replace by the MS Visual Studio Express 2008 directories] Installing PyCUDA on Windows update ----------------------------------- I just installed PyCUDA 0.93 and it required some significant modifications of the above: * Install CUDA 2.3 * Install MSVS 2003 * Install Boost latest version (I used 1.37 not the latest) * Download PyCuda * Compile Boost with MSVS: - Run MSVS command prompt and go to boost directory - Build Boost for Python: + bjam toolset=msvs-7.1 -–with-python -–build-type=complete stage + (wait a long time) - Build Boost threads: + bjam toolset=msvs-7.1 -–with-thread -–build-type=complete stage - Build Boost date_time (no idea why): + bjam toolset=msvs-7.1 -–with-date_time -–build-type=complete stage - Copy BoostDir/stage/lib/*.dll to C:\Windows * Compile PyCuda with MSVS: - Run MSVS command prompt and go to PyCuda download directory - Configure: + configure.py –boost-inc-dir=c:\boost_1_37_0 –boost-lib-dir=c:\boost_1_37_0\stage\lib –boost-python-libname=boost_python-vc71-mt-1_37 –boost-thread-libname=boost_thread-vc71-mt-1_37 –cuda-root=c:\cuda - Build and install: + setup.py build + setup.py install * Make sure you have a HOME environment variable set ALTERNATIVE INSTALLATION ------------------------ This installation uses cygwin for compiling pypar, as explained here: http://boodebr.org/main/python/build-windows-extensions MS Visual Studio is used for compiling cuda files. * Install all CUDA tools from NVIDIA, at least 2.0 * Install MS Visual Studio Express 2008 (or other MS compiler) * Install Boost C++ from Boost Pro Free binary installer, include Python binaries for ???. [maybe a cygwin version?] * Create a file c:\python25\lib\distutils\distutils.cfg: [build] compiler = mingw32 * Download PyCuda * Create a file siteconf.py in the pycuda folder: BOOST_INC_DIR = [r'C:\Program Files\boost\boost_1_37'] BOOST_LIB_DIR = [r'C:\Program Files\boost\boost_1_37\lib'] BOOST_PYTHON_LIBNAME = ['boost_python-vc71-mt-1_37'] CUDA_ROOT = r'C:\CUDA' CUDADRV_LIB_DIR = [r'C:\CUDA\lib'] CUDADRV_LIBNAME = ['cuda'] CXXFLAGS = [] LDFLAGS = [] * Check the directories above with your configuration. * Copy the Boost file boost_python-vc71-mt-1_37.dll to the Windows folder. * Go to the pycuda folder and run setup.py install. * Add the following directories to your Path environment variable: C:\Program Files\Microsoft Visual Studio 9.0\Common7\IDE C:\Program Files\Microsoft Visual Studio 9.0\VC\bin [check with your configuration] INSTALLING PYCUDA ON WINDOWS 7 64 bits -------------------------------------- * Install 64 bits versions of all programs (remove all previous 32 bits first), following instructions there: http://wiki.tiker.net/PyCuda/Installation/Windows#Windows_7_64-bit_with_Visual_Studio_Professional_2008_.28Strictly_Binary_Versions.29 * There will probably be missing DLLs. When you run into an error, find the dependencies using Dependency Walker. Search the files on your drive and copy them in C:\Windows\SysWOW64. brian-1.4.3/dev/ideas/cuda/modelfitting/000077500000000000000000000000001266062647100200705ustar00rootroot00000000000000brian-1.4.3/dev/ideas/cuda/modelfitting/current.txt000066400000000000000000010200761266062647100223210ustar00rootroot000000000000004.300138999999999500e-010 4.892829899999999700e-010 4.599434999999999400e-010 4.037063600000000100e-010 4.472828500000000100e-010 4.083343499999999700e-010 3.469549500000000000e-010 3.070368599999999800e-010 2.692203399999999900e-010 2.715904300000000400e-010 3.278977200000000000e-010 2.840735100000000000e-010 2.467967399999999700e-010 3.090051999999999700e-010 2.639109900000000400e-010 2.277441300000000000e-010 1.885118600000000200e-010 1.569777800000000000e-010 1.309194900000000000e-010 9.457099900000000400e-011 1.123802299999999900e-010 7.090257999999999900e-011 1.416034100000000000e-010 1.109640700000000100e-010 8.315773799999998700e-011 5.678972600000000200e-011 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1.642386200000000000e-010 1.516876599999999900e-010 1.397939900000000100e-010 1.239329799999999900e-010 1.133858900000000000e-010 1.034020000000000000e-010 1.509951000000000100e-010 1.318686600000000100e-010 1.207560500000000100e-010 1.103098899999999900e-010 1.003764599999999900e-010 9.097436700000000400e-011 7.453361900000000200e-011 6.619576299999999200e-011 5.361086099999999600e-011 4.617509700000000300e-011 3.927802799999999800e-011 2.530249499999999800e-011 1.443549299999999800e-011 8.637982600000000600e-012 3.290776500000000200e-012 -1.773289200000000000e-012 -6.537787899999999300e-012 5.748858700000000700e-012 6.771956400000000100e-013 -4.019994399999999400e-012 -9.074643399999999300e-012 -1.330241099999999900e-011 brian-1.4.3/dev/ideas/cuda/modelfitting/example.py000066400000000000000000000152611266062647100221020ustar00rootroot00000000000000import pycuda.autoinit as autoinit import pycuda.driver as drv import pycuda from pycuda.gpuarray import GPUArray from pycuda import gpuarray import bisect import numpy, pylab, time from matplotlib.cm import jet try: from pycuda.compiler import SourceModule except ImportError: from pycuda.driver import SourceModule N = 10000 blocksize = 256 # too many registers for 512 doplot = True kernel_run_length = None # set to None to make it the maximum N = int(numpy.ceil(1. * N / blocksize) * blocksize) if drv.get_version() == (2, 0, 0): # cuda version precision = 'float' elif drv.get_version() > (2, 0, 0): precision = 'double' else: raise Exception, "CUDA 2.0 required" if precision == 'double': mydtype = numpy.float64 else: mydtype = numpy.float32 block = (blocksize, 1, 1) grid = (N / blocksize, 1) mod = SourceModule(""" __global__ void runsim( int Tstart, int Tend, // Start, end time as integer (t=T*dt) SCALAR *V_arr, // State variables for each neuron SCALAR *tau_arr, SCALAR *R_arr, SCALAR *I_arr, // Input current int *spikecount, // Number of spikes produced by each neuron int *num_coincidences, // Count of coincidences for each neuron int *spiketimes, // Array of all spike times as integers int *spiketime_indices // Pointer into above array for each neuron ) { const int neuron_index = blockIdx.x * blockDim.x + threadIdx.x; // Load variables at start SCALAR V = V_arr[neuron_index]; SCALAR tau = tau_arr[neuron_index]; SCALAR R = R_arr[neuron_index]; int spiketime_index = spiketime_indices[neuron_index]; int last_spike_time = spiketimes[spiketime_index]; int next_spike_time = spiketimes[spiketime_index+1]; int ncoinc = num_coincidences[neuron_index]; int nspikes = spikecount[neuron_index]; for(int T=Tstart; T1.; // Vt=1 nspikes += has_spiked; // Reset //if(has_spiked) V = V*!has_spiked; // Vr=0 // Coincidence counter ncoinc += has_spiked && (((last_spike_time+_delta_)>=T) || ((next_spike_time-_delta_)<=T)); if(T==next_spike_time){ // divergence for each input spike spiketime_index++; last_spike_time = next_spike_time; next_spike_time = spiketimes[spiketime_index+1]; } } // Store variables at end V_arr[neuron_index] = V; spiketime_indices[neuron_index] = spiketime_index; num_coincidences[neuron_index] = ncoinc; spikecount[neuron_index] = nspikes; } """.replace('SCALAR', precision).replace('_dt_', '0.0001').replace('_delta_', '40')) runsim = mod.get_function("runsim") # Read data from files I = numpy.loadtxt('current_long.txt') duration = len(I) if kernel_run_length is None: kernel_run_length = duration spiketimes = numpy.array((numpy.loadtxt('spikes_long.txt') - 28.) / 0.0001, dtype=int) # we add a -10000 at the beginning as there is no previous spike at the beginning, # and one at the end because the kernel moves to the next spike by checking for the # existence of the current spike spiketimes = numpy.hstack((-10000, spiketimes, -10000)) V = gpuarray.to_gpu(numpy.zeros(N, dtype=mydtype)) R = gpuarray.to_gpu(numpy.array(numpy.random.rand(N) * 2e9 + 1e9, dtype=mydtype)) tau = gpuarray.to_gpu(numpy.array(numpy.random.rand(N) * 0.050 + 0.001, dtype=mydtype)) I = gpuarray.to_gpu(numpy.array(I, dtype=mydtype)) num_coincidences = gpuarray.to_gpu(numpy.zeros(N, dtype=int)) spiketime_indices = gpuarray.to_gpu(numpy.zeros(N, dtype=int)) spiketimes = gpuarray.to_gpu(numpy.array(spiketimes, dtype=int)) spikecount = gpuarray.to_gpu(numpy.zeros(N, dtype=int)) #runsim.prepare(('i', 'i', 'i', 'i'), block) #stateupdate_args = (grid, int(V.gpudata), int(ge.gpudata), int(gi.gpudata)) #stateupdate.prepared_call(*stateupdate_args) start_time = time.time() for i in xrange(duration / kernel_run_length): runsim( numpy.int32(i * kernel_run_length), numpy.int32((i + 1) * kernel_run_length), V, tau, R, I, spikecount, num_coincidences, spiketimes, spiketime_indices, block=block, grid=grid ) autoinit.context.synchronize() running_time = time.time() - start_time print 'N:', N, 'blocksize:', blocksize print 'Duration:', duration, 'steps =', duration * 0.0001, 's' print 'Kernel run length:', kernel_run_length, 'steps =', kernel_run_length * 0.0001, 's' print 'Total running time:', running_time if doplot: spikecount = spikecount.get() num_coincidences = num_coincidences.get() R = R.get() tau = tau.get() print 'Spike count varies between', spikecount.min(), 'and', spikecount.max() print 'Num coincidences varies between', num_coincidences.min(), 'and', num_coincidences.max() pylab.subplot(221) pylab.scatter(R, tau, color=jet(spikecount / float(spikecount.max()))) pylab.xlabel('R') pylab.ylabel('tau') pylab.title('Spike count, max = ' + str(spikecount.max())) pylab.axis('tight') pylab.subplot(222) pylab.scatter(R, tau, color=jet(num_coincidences / float(num_coincidences.max()))) pylab.xlabel('R') pylab.ylabel('tau') pylab.title('Num coincidences, max = ' + str(num_coincidences.max())) pylab.axis('tight') spikecount -= num_coincidences num_coincidences -= spikecount num_coincidences[num_coincidences < 0] = 0 maxcoinc = num_coincidences.max() num_coincidences = (1. * num_coincidences) / maxcoinc pylab.subplot(212) pylab.scatter(R, tau, color=jet(num_coincidences)) pylab.xlabel('R') pylab.ylabel('tau') pylab.title('Hot = ' + str(maxcoinc) + ' excess coincidences, cool = 0 or less') pylab.axis('tight') pylab.show() brian-1.4.3/dev/ideas/cuda/modelfitting/spikes.txt000066400000000000000000000006601266062647100221310ustar00rootroot000000000000003.207229999999999800e+001 3.211220000000000100e+001 3.214699999999999800e+001 3.220870000000000000e+001 3.225300000000000000e+001 3.230650000000000000e+001 3.238580000000000300e+001 3.245309999999999900e+001 3.249799999999999800e+001 3.258820000000000100e+001 3.263230000000000100e+001 3.270700000000000100e+001 3.278560000000000200e+001 3.285009999999999800e+001 3.289690000000000200e+001 3.295550000000000100e+001 brian-1.4.3/dev/ideas/cuda/nemo_integration/000077500000000000000000000000001266062647100207445ustar00rootroot00000000000000brian-1.4.3/dev/ideas/cuda/nemo_integration/nemo_integration.txt000066400000000000000000000074371266062647100250610ustar00rootroot00000000000000Integration of NEMO and Brian ============================= Meeting notes, 9 Jan 2012 ------------------------- How Nemo works: Partition ^^^^^^^^^ Neurons are partitioned into groups, one group per streaming multiprocessor (SM). Scatter ^^^^^^^ On each SM, there is a local queue for outgoing spikes. Processing is local but the queue is stored in global memory. The local queue is very close to Brian's SpikeQueue, with a few differences. Each column corresponds to a time bin in the future, and is a stack of events. Each event is a couple (i,d) meaning: neuron i spiked with delay d (in time bins). It is inserted in the time bin in which the event will occur on the postsynaptic side. The delay is only used to address the correct set of synapses (all those synapses that have presynaptic neuron i and delay d). To insert events, each neuron has a list of possible outgoing delays, stored with a bit array (64 bits for 64 possible delays). In the SpikeQueue, only a synapse number is inserted. This means that each event in the local queue of Nemo corresponds to a set of synapses (all those synapses that have presynaptic neuron i and delay d). In a second phase, events for the current time bin are communicated between SMs. Then each SM gets a list of indexes of synapses that receive spikes. The synapse structure (weights) is arranged so that synapses with the same (i,d) (synapses that have presynaptic neuron i and delay d) are contiguous in memory. Together, the local and global phase (without the gather operation below) corresponds to the functionality of the SpikeQueue. But, for Brian I think there is no need for a separate SpikeQueue object since it will always be attached to a Synapse object. Therefore it seems possible to merge it with the gather operation. Gather ^^^^^^ Once we have activated synapses for all neurons in a SM, Nemo calculates the sum of weights for these synapses for each postsynaptic neuron. This is the output of the gather operation, that is passed to the state update operation. This corresponds to one issue (which is problematic in Python) that we discussed for the Synapse class. Update ^^^^^^ The update is almost the same as in Brian. The main difference is that in Nemo, propagation of spikes does *not* modify target state variables. It only gathers the total modification of the target variable (assuming linearity), and passes this number to each number. There is a possibility for two possible target states, excitatory and inhibitory. The state updater in Nemo then retrieves these numbers and adds them to the corresponding target state. In Brian, we would rather have the gather operation directly modify the target state variable. Threshold ^^^^^^^^^ In Nemo, the threshold condition is stored as a boolean. From this, a compact list of indexes is computed using atomic operations on each SM. Then a complete list is obtained from these lists. Only the SM-specific compact lists are required for the scatter operation. For Brian, we would then only need the global compaction for monitoring spikes, but not the propagation in principle. STDP ^^^^ Currently, this is done using pre-post STDP functions, but we would need trace-based models. General stuff ------------- * Local memory is 64KB, while global memory is slow. Theoretical maximum bandwidth for global memory is 192 GB/s (but in practice it's only a fraction of this). To maximize it, one must used coalesced accesses. * Nemo's scatter/gather algorithms are good for a large number of synapses. This suggests to use different algorithms depending on the connectivity structure. If we use a single Synapses object, then we could have an option to choose the algorithm (e.g. GPU='Nemo'). * We need to be able to deal with multiple neuron groups and connections. brian-1.4.3/dev/ideas/cuda/nemo_integration/sfc_example_nemo.py000066400000000000000000000042571266062647100246320ustar00rootroot00000000000000from brian import * from brian.experimental.cuda.briantonemo import * defaultclock.dt = 1*ms layerfanout = 3 # each layer connects to this many other layers layerdepth = 2 # length of chain of layers numperlayer = 100 # number of neurons in each layer tau = 10*ms Vr = 0 Vt = 1 Vrest0 = 2 # resting potential of first layer (greater than Vt to force spikes) delaymin, delaymax = 5*ms, 15*ms weight = 0.02 layerp = 1.0 # probability of connection from layer to layer refractory = 1*second eqs = ''' dV/dt = -(V-Vrest)/tau : 1 Vrest : 1 ''' # Graph structure of the connections, each layer connects to three other layers # and we store the summed delays between layers so that we can plot it layers = {} layerdelay = {0:0*second} nextlayer = 1 curlayers = [0] for i in xrange(layerdepth): nextcurlayers = [] for layer in curlayers: for j in xrange(layerfanout): delay = rand()*(delaymax-delaymin)+delaymin layers[layer, nextlayer] = delay nextcurlayers.append(nextlayer) layerdelay[nextlayer] = delay+layerdelay[layer] nextlayer += 1 curlayers = nextcurlayers # Geometric series! numlayers = (layerfanout**(layerdepth+1)-1)/(layerfanout-1) N = numlayers*numperlayer G = NeuronGroup(N, eqs, reset=Vr, threshold=Vt, refractory=refractory) G.V = rand(N)*(Vt-Vr)+Vr G.Vrest = 0 # Initialise layer 0 to fire spikes G.Vrest[0:numperlayer] = Vrest0 if 1: C = NemoConnection(G, G, 'V', delay=True, max_delay=delaymax) else: C = Connection(G, G, 'V', delay=True, max_delay=delaymax) for (i, j), d in layers.items(): # connect neurons in layer i to layer j with delay d C.connect_random(G[i*numperlayer:(i+1)*numperlayer], G[j*numperlayer:(j+1)*numperlayer], p=layerp, delay=d, weight=weight) M = SpikeMonitor(G) run(.1*second) print 'total spikes:', M.nspikes if 1: raster_plot(M) for i, d in layerdelay.items(): fill([d/ms, (d+tau)/ms, (d+tau)/ms, d/ms], [i*numperlayer, i*numperlayer, (i+1)*numperlayer, (i+1)*numperlayer], color=(0.5, 0.5, 0.5)) show() brian-1.4.3/dev/ideas/cuda/nemo_integration/sfc_example_nemo_net.py000066400000000000000000000042571266062647100255000ustar00rootroot00000000000000from brian import * from brian.experimental.cuda.briantonemo import * defaultclock.dt = 1*ms layerfanout = 3 # each layer connects to this many other layers layerdepth = 2 # length of chain of layers numperlayer = 100 # number of neurons in each layer tau = 10*ms Vr = 0 Vt = 1 Vrest0 = 2 # resting potential of first layer (greater than Vt to force spikes) delaymin, delaymax = 5*ms, 15*ms weight = 0.02 layerp = 1.0 # probability of connection from layer to layer refractory = 1*second eqs = ''' dV/dt = -(V-Vrest)/tau : 1 Vrest : 1 ''' # Graph structure of the connections, each layer connects to three other layers # and we store the summed delays between layers so that we can plot it layers = {} layerdelay = {0:0*second} nextlayer = 1 curlayers = [0] for i in xrange(layerdepth): nextcurlayers = [] for layer in curlayers: for j in xrange(layerfanout): delay = rand()*(delaymax-delaymin)+delaymin layers[layer, nextlayer] = delay nextcurlayers.append(nextlayer) layerdelay[nextlayer] = delay+layerdelay[layer] nextlayer += 1 curlayers = nextcurlayers # Geometric series! numlayers = (layerfanout**(layerdepth+1)-1)/(layerfanout-1) N = numlayers*numperlayer G = [NeuronGroup(numperlayer, eqs, reset=Vr, threshold=Vt, refractory=refractory) for _ in xrange(numlayers)] for g in G: g.V = rand(numperlayer)*(Vt-Vr)+Vr g.Vrest = 0 # Initialise layer 0 to fire spikes G[0].Vrest = Vrest0 C = {} for (i, j), d in layers.items(): # connect neurons in layer i to layer j with delay d C[i, j] = Connection(G[i], G[j], 'V', delay=(d, d), max_delay=delaymax, sparseness=layerp, weight=weight, structure='sparse') M = [SpikeMonitor(g) for g in G] if 1: net = NemoNetwork(G, C.values(), M) else: net = Network(G, C.values(), M) net.run(.1*second) print 'total spikes:', sum(m.nspikes for m in M) if 1: raster_plot(*M) for i, d in layerdelay.items(): fill([d/ms, (d+tau)/ms, (d+tau)/ms, d/ms], [i, i, i+1, i+1], color=(0.5, 0.5, 0.5)) show() brian-1.4.3/dev/ideas/cuda/nemo_integration/topographic_map.py000066400000000000000000000027621266062647100245010ustar00rootroot00000000000000''' Topographic map - an example of complicated connections. Two layers of neurons. The first layer is connected randomly to the second one in a topographical way. The second layer has random lateral connections. ''' from brian import * from numpy.random import seed seed(324328740) from random import seed seed(352347323) from brian.experimental.cuda.briantonemo import * N = 100 tau = 10 * ms tau_e = 2 * ms # AMPA synapse eqs = ''' dv/dt=(I-v)/tau : volt dI/dt=-I/tau_e : volt ''' rates = zeros(N) * Hz rates[N / 2 - 10:N / 2 + 10] = ones(20) * 30 * Hz layer1 = PoissonGroup(N, rates=rates) layer2 = NeuronGroup(N, model=eqs, threshold=10 * mV, reset=0 * mV) topomap = lambda i, j:exp(-abs(i - j) * .1) * 3 * mV feedforward = Connection(layer1, layer2, sparseness=.5, weight=topomap) #feedforward[2,3]=1*mV lateralmap = lambda i, j:exp(-abs(i - j) * .05) * 0.5 * mV recurrent = Connection(layer2, layer2, sparseness=.5, weight=lateralmap) spikes = SpikeMonitor(layer2) M = StateMonitor(layer2, 'v', record=[50,51,52]) net = MagicNetwork() net.run(.1 * second) # #print len(feedforward.W.alldata), len(recurrent.W.alldata) #print feedforward.W[50,:].ind #if hasattr(net, 'nemo_sim'): # def print_tgts(n): # L = net.nemo_net.get_synapses_from(n) # L = [net.nemo_net.get_synapse_target(l) for l in L] # print L # print_tgts(50) # print_tgts(150) # print_tgts(250) subplot(211) raster_plot(spikes) subplot(212) M.plot() show() brian-1.4.3/dev/ideas/cuda/notes.txt000066400000000000000000000006721266062647100173010ustar00rootroot00000000000000Notes about Brian on GPU ======================== Thresholding and compaction --------------------------- This can be quite slow. To make it faster, one option if there is a refractory period of at least R time bins and there is a minimum synaptic transmission delay of at least R is to calculate spiketimes every R time bins, and feed them to the Synapses object. This means that when a spike is produced, its timing is stored. brian-1.4.3/dev/ideas/cuda/old/000077500000000000000000000000001266062647100161615ustar00rootroot00000000000000brian-1.4.3/dev/ideas/cuda/old/benchmark_memcpy.py000066400000000000000000000014741266062647100220450ustar00rootroot00000000000000from numpy import * import pycuda import pycuda.autoinit as autoinit import pycuda.driver as drv from pycuda import gpuarray import time N = 100000000 steps = 10 use_pagelocked = False do_get = True do_set = False getset_factor = float(int(do_get) + int(do_set)) a_cpu = zeros(N) a_gpu = pycuda.gpuarray.to_gpu(a_cpu) start = time.time() for _ in xrange(steps): if do_get: a_gpu.set(a_cpu) if do_set: a_gpu.get(a_cpu) duration = time.time() - start print 'Array size', N print 'Pagelocked', use_pagelocked print 'Number of steps', steps print 'Download', do_get, 'upload', do_set print 'Total memory copied (GB)', N * 8. * steps * getset_factor / 1024. ** 3 print 'Time taken (sec)', duration print 'Transfer rate (GB/sec)', N * 8. * steps * getset_factor / 1024. ** 3 / duration brian-1.4.3/dev/ideas/cuda/old/benchmark_stateupdate.py000066400000000000000000000062521266062647100230750ustar00rootroot00000000000000import pycuda.autoinit as autoinit import pycuda.driver as drv from pycuda.gpuarray import GPUArray from pycuda import gpuarray import bisect import numpy, pylab, time N = 1000000 blocksize = 512 duration = 10000 N = int(numpy.ceil(1. * N / blocksize) * blocksize) if drv.get_version() == (2, 0, 0): # cuda version precision = 'float' elif drv.get_version() > (2, 0, 0): precision = 'double' else: raise Exception, "CUDA 2.0 required" if precision == 'double': mydtype = numpy.float64 else: mydtype = numpy.float32 block = (blocksize, 1, 1) grid = (N / blocksize, 1) mod = drv.SourceModule(""" /*__global__ void stateupdate(SCALAR *V_arr, SCALAR *ge_arr, SCALAR *gi_arr) { int i = blockIdx.x * blockDim.x + threadIdx.x; SCALAR V = V_arr[i]; SCALAR ge = ge_arr[i]; SCALAR gi = gi_arr[i]; // SCALAR V__tmp = (ge+gi-(V+0.049))/0.02; // SCALAR ge__tmp = -ge/0.005; // SCALAR gi__tmp = -gi/0.01; SCALAR V__tmp = (ge+gi-(V+0.049))*50; SCALAR ge__tmp = -ge*200; SCALAR gi__tmp = -gi*100; V_arr[i] = V+0.0001*V__tmp; ge_arr[i] = ge+0.0001*ge__tmp; gi_arr[i] = gi+0.0001*gi__tmp; }*/ __global__ void stateupdate(SCALAR *V_arr, SCALAR *ge_arr, SCALAR *gi_arr) { int i = blockIdx.x * blockDim.x + threadIdx.x; SCALAR V = V_arr[i]; SCALAR ge = ge_arr[i]; SCALAR gi = gi_arr[i]; V_arr[i] = V+1; ge_arr[i] = ge+1; gi_arr[i] = gi+1; } __global__ void stateupdate_noglobal(SCALAR *V_arr, SCALAR *ge_arr, SCALAR *gi_arr) { int i = blockIdx.x * blockDim.x + threadIdx.x; SCALAR V = 0.;//V_arr[i]; SCALAR ge = 0.;//ge_arr[i]; SCALAR gi = 0.;//gi_arr[i]; SCALAR V__tmp = (ge+gi-(V+0.049))/0.02; SCALAR ge__tmp = -ge/0.005; SCALAR gi__tmp = -gi/0.01; V = V+0.0001*V__tmp; ge = ge+0.0001*ge__tmp; gi = gi+0.0001*gi__tmp; } """.replace('SCALAR', precision)) stateupdate = mod.get_function("stateupdate") stateupdate_noglobal = mod.get_function("stateupdate_noglobal") V = gpuarray.to_gpu(numpy.array(numpy.random.rand(N) * 0.01 - 0.06, dtype=mydtype)) ge = gpuarray.to_gpu(numpy.zeros(N, dtype=mydtype)) gi = gpuarray.to_gpu(numpy.zeros(N, dtype=mydtype)) stateupdate.prepare(('i', 'i', 'i'), block) stateupdate_args = (grid, int(V.gpudata), int(ge.gpudata), int(gi.gpudata)) stateupdate.prepared_call(*stateupdate_args) #stateupdate_noglobal.prepared_call(*stateupdate_args) def run_sim(stateupdate=stateupdate): for t in xrange(duration): stateupdate.launch_grid(*grid) start = time.time() run_sim() duration_stateupdate = time.time() - start start = time.time() run_sim(stateupdate_noglobal) duration_stateupdate_noglobal = time.time() - start data_copied_gb = N * mydtype().nbytes * 3 * duration * 2. / 1024. ** 3 transfer_rate_gb_sec = data_copied_gb / (duration_stateupdate - duration_stateupdate_noglobal) print 'N:', N, 'blocksize:', blocksize, 'numsteps:', duration print 'GPU time:', duration_stateupdate print 'GPU time (no global memory access):', duration_stateupdate_noglobal print 'Data transferred (GB):', data_copied_gb print 'Data transfer rate (GB/sec):', transfer_rate_gb_sec brian-1.4.3/dev/ideas/cuda/old/benchmark_threshold.py000066400000000000000000000207751266062647100225540ustar00rootroot00000000000000# NON-ATOMIC ALGORITHM STILL DOESN'T WORK IF N>16K, # ALMOST CERTAINLY TO DO WITH SHARED MEMORY SIZE import pycuda.autoinit as autoinit import pycuda.driver as drv from pycuda.gpuarray import GPUArray from pycuda import gpuarray import bisect import numpy, pylab, time, random from scipy import weave N = 512 * 512 nspike = int(0.1 * N) blocksize = 512 duration = 10000 use_atomic_threshold = False N = int(numpy.ceil(1. * N / blocksize) * blocksize) if drv.get_version() == (2, 0, 0): # cuda version precision = 'float' elif drv.get_version() > (2, 0, 0): precision = 'double' else: raise Exception, "CUDA 2.0 required" if precision == 'double': mydtype = numpy.float64 else: mydtype = numpy.float32 block = (blocksize, 1, 1) Ngrid = N / blocksize grid = (Ngrid, 1) grid1 = (1, 1) mod = drv.SourceModule(""" __global__ void stateupdate(SCALAR *V_arr, SCALAR *ge_arr, SCALAR *gi_arr) { int i = blockIdx.x * blockDim.x + threadIdx.x; SCALAR V = V_arr[i]; SCALAR ge = ge_arr[i]; SCALAR gi = gi_arr[i]; SCALAR V__tmp = (ge+gi-(V+0.049))/0.02; SCALAR ge__tmp = -ge/0.005; SCALAR gi__tmp = -gi/0.01; V_arr[i] = V+0.0001*V__tmp; ge_arr[i] = ge+0.0001*ge__tmp; gi_arr[i] = gi+0.0001*gi__tmp; } __global__ void threshold(SCALAR *V, int *spikes, bool *spiked, unsigned int *global_j, int N) { int i = blockIdx.x * blockDim.x + threadIdx.x; bool this_spiked = V[i]>-0.05; spiked[i] = this_spiked; if(this_spiked) { spikes[atomicInc(global_j, N)] = i; } } /////// TODO: next three functions are untested, but should be a non-blocking threshold function if N<=512*512 __global__ void threshold_blocksumcount(SCALAR *V, unsigned int *blocksumcount) { __shared__ unsigned short int partialsum[BLOCKSIZE]; int i = blockIdx.x * blockDim.x + threadIdx.x; int t = threadIdx.x; int b = blockIdx.x; bool this_spiked = V[i]>-0.05; partialsum[t] = (unsigned short int)this_spiked; for(unsigned int stride=blockDim.x/2; stride>=1; stride/=2) { __syncthreads(); if(t=stride) partialsum[t] += partialsum[t-stride]; } cumblocksumcount[t+1] = partialsum[t]; } __global__ void threshold_compact(SCALAR *V, int *spikes, unsigned int *blocksumcount, unsigned int *cumblocksumcount) { __shared__ unsigned short int partialsum[BLOCKSIZE]; int i = blockIdx.x * blockDim.x + threadIdx.x; int t = threadIdx.x; int b = blockIdx.x; // only compact those blocks with some spikes in (many will not in general, and this introduces no divergence) if(blocksumcount[b]>0) { bool this_spiked = V[i]>-0.05; partialsum[t] = (unsigned short int)this_spiked; for(unsigned int stride=1; stride=stride) partialsum[t] += partialsum[t-stride]; } __syncthreads(); if(this_spiked) spikes[(unsigned int)partialsum[t]+cumblocksumcount[b]-1] = i; //spikes[i] = partialsum[t]+cumblocksumcount[b]; } } """.replace('SCALAR', precision).replace('BLOCKSIZE', str(blocksize))) stateupdate = mod.get_function("stateupdate") threshold = mod.get_function("threshold") threshold_blocksumcount = mod.get_function("threshold_blocksumcount") threshold_cumsum = mod.get_function("threshold_cumsum") threshold_compact = mod.get_function("threshold_compact") V_cpu = numpy.zeros(N, dtype=mydtype) V_cpu[:nspike] = -0.04 # above threshold V_cpu[nspike:] = -0.06 # below threshold random.shuffle(V_cpu) V = gpuarray.to_gpu(V_cpu) ge = gpuarray.to_gpu(numpy.zeros(N, dtype=mydtype)) gi = gpuarray.to_gpu(numpy.zeros(N, dtype=mydtype)) gpu_spikes = drv.mem_alloc(4 * N) gpu_spiked = gpuarray.to_gpu(numpy.zeros(N, dtype=bool)) gpu_spike_index = drv.mem_alloc(4) spikes = master_spikes = numpy.zeros(N, dtype=int) spike_index = numpy.zeros(1, dtype=numpy.uint32) gpu_blocksumcount = gpuarray.to_gpu(numpy.zeros(Ngrid, dtype=numpy.uint32)) gpu_cumblocksumcount = gpuarray.to_gpu(numpy.zeros(Ngrid + 1, dtype=numpy.uint32)) drv.memcpy_htod(gpu_spikes, master_spikes) drv.memcpy_htod(gpu_spike_index, spike_index) stateupdate.prepare(('i', 'i', 'i'), block) threshold.prepare(('i', 'i', 'i', 'i', numpy.int32), block) threshold_blocksumcount.prepare(('i', 'i'), block) threshold_cumsum.prepare(('i', 'i'), block) threshold_compact.prepare(('i', 'i', 'i', 'i'), block) stateupdate_args = (grid, int(V.gpudata), int(ge.gpudata), int(gi.gpudata)) threshold_args = (grid, int(V.gpudata), int(gpu_spikes), int(gpu_spiked.gpudata), int(gpu_spike_index), numpy.int32(N)) threshold_blocksumcount_args = (grid, int(V.gpudata), int(gpu_blocksumcount.gpudata)) threshold_cumsum_args = (grid1, int(gpu_blocksumcount.gpudata), int(gpu_cumblocksumcount.gpudata)) threshold_compact_args = (grid, int(V.gpudata), int(gpu_spikes), int(gpu_blocksumcount.gpudata), int(gpu_cumblocksumcount.gpudata)) stateupdate.prepared_call(*stateupdate_args) threshold.prepared_call(*threshold_args) if 0: #print 'before blocksumcount' threshold_blocksumcount.prepared_call(*threshold_blocksumcount_args) autoinit.context.synchronize() #print gpu_blocksumcount.get() #print sum(V_cpu[:512]>-0.05), sum(V_cpu[512:1024]>-0.05) # these two should be the same... #print numpy.cumsum(gpu_blocksumcount.get()) #print 'before cumsum' threshold_cumsum.prepared_call(*threshold_cumsum_args) autoinit.context.synchronize() #print gpu_cumblocksumcount.get() A1 = gpu_cumblocksumcount.get()[1:] A2 = numpy.cumsum(gpu_blocksumcount.get()) print 'Passed first cumsum test:', (A1 == A2).all() #print 'before compact' threshold_compact.prepared_call(*threshold_compact_args) autoinit.context.synchronize() drv.memcpy_dtoh(spikes, gpu_spikes) #pylab.plot(spikes) #pylab.plot(numpy.cumsum(numpy.array(V_cpu>-0.05, dtype=int))) #pylab.show() # #exit() #print spikes[:102] #print gpu_cumblocksumcount.get()[-1] #print sum(V_cpu>-0.05) S1 = spikes[:gpu_cumblocksumcount.get()[-1]] S2 = (V_cpu > -0.05).nonzero()[0] S1.sort() S2.sort() #print S1 #print S2 print 'Passed spikes test:', (S1 == S2).all() # TODO: not quite there yet... exit() else: threshold_blocksumcount.prepared_call(*threshold_blocksumcount_args) autoinit.context.synchronize() threshold_cumsum.prepared_call(*threshold_cumsum_args) autoinit.context.synchronize() threshold_compact.prepared_call(*threshold_compact_args) autoinit.context.synchronize() def run_sim(): if use_atomic_threshold: for t in xrange(duration): spike_index[0] = 0 drv.memcpy_htod(gpu_spike_index, spike_index) threshold.launch_grid(*grid) else: for t in xrange(duration): threshold_blocksumcount.launch_grid(*grid) autoinit.context.synchronize() threshold_cumsum.launch_grid(*grid) autoinit.context.synchronize() threshold_compact.launch_grid(*grid) autoinit.context.synchronize() def run_sim_cpu(): for t in xrange(duration): Vt = -0.05 code = """ int numspikes=0; for(int i=0;iVt) spikes(numspikes++) = i; return_val = numspikes; """ numspikes = weave.inline(code, ['spikes', 'V_cpu', 'Vt', 'N'], compiler='gcc', type_converters=weave.converters.blitz) start = time.time() run_sim() timetaken_gpu = time.time() - start start = time.time() run_sim_cpu() timetaken_cpu = time.time() - start print 'N:', N, 'nspike', nspike, 'blocksize:', blocksize, 'numsteps:', duration print 'Atomic threshold algorithm:', use_atomic_threshold print 'GPU time:', timetaken_gpu print 'CPU time:', timetaken_cpu brian-1.4.3/dev/ideas/cuda/old/briancuba.py000066400000000000000000000025131266062647100204620ustar00rootroot00000000000000import brian_no_units from brian import * import time #set_global_preferences(useweave=False) print 'Compilation on', get_global_preference('useweave') duration = 1 * second Nsyn = 80. we = 1.62 * mV N = 512 * 16 structure = 'dense' eqs = ''' dv/dt = (ge+gi-(v+49*mV))/(20*ms) : volt dge/dt = -ge/(5*ms) : volt dgi/dt = -gi/(10*ms) : volt ''' Ne = int(N * 0.8) Ni = N - Ne P = NeuronGroup(N, model=eqs, threshold= -50 * mV, reset= -60 * mV, method='Euler', compile=True, freeze=True)#, refractory=5*ms) P.v = -60 * mV + 10 * mV * rand(len(P)) Pe = P.subgroup(Ne) Pi = P.subgroup(Ni) Ce = Connection(Pe, P, 'ge', structure=structure) Ci = Connection(Pi, P, 'gi', structure=structure) cp = Nsyn / N Ce.connect_random(Pe, P, cp, weight=we) Ci.connect_random(Pi, P, cp, weight= -9 * mV) M = PopulationSpikeCounter(P) P.v = -60 * mV + 10 * mV * rand(len(P)) #Ce.W.freeze() #Ci.W.freeze() run(0 * ms) def f(): tstart = time.time() run(duration) tend = time.time() print 'N:', N print 'CPU time:', tend - tstart print 'Num spikes', M.nspikes f() #import cProfile as profile #import pstats #profile.run('f()','cuba_once.prof') #stats = pstats.Stats('cuba_once.prof') ##stats.strip_dirs() #stats.sort_stats('cumulative', 'calls') #stats.print_stats(50) brian-1.4.3/dev/ideas/cuda/old/cuba_romainsparse.py000066400000000000000000000062541266062647100222370ustar00rootroot00000000000000import pycuda.autoinit as autoinit import pycuda.driver as drv from pycuda.compiler import SourceModule from pycuda.gpuarray import GPUArray from pycuda import gpuarray import bisect import numpy, pylab, time from itertools import repeat import cProfile as profile import pstats import brian N = 10000 blocksize = 512 N = int(numpy.ceil(1. * N / blocksize) * blocksize) duration = 10000 record = False doprofile = False sparseness = 80. / N if drv.get_version() == (2, 0, 0): # cuda version precision = 'float' elif drv.get_version() > (2, 0, 0): precision = 'double' else: raise Exception, "CUDA 2.0 required" if precision == 'double': mydtype = numpy.float64 else: mydtype = numpy.float32 block = (blocksize, 1, 1) grid = (N / blocksize, 1) Ne = int(N * 0.8) Ni = N - Ne shared_memory_size = 2 ** 14 # 16k matrix_split_size = blocksize * (shared_memory_size // mydtype().nbytes // blocksize) print 'Matrix split size:', matrix_split_size mod = SourceModule(""" __global__ void stateupdate_and_threshold(SCALAR *V_arr, SCALAR *ge_arr, SCALAR *gi_arr, int *spikes, bool *spiked, unsigned int *global_j, int N) { int i = blockIdx.x * blockDim.x + threadIdx.x; SCALAR V = V_arr[i]; SCALAR ge = ge_arr[i]; SCALAR gi = gi_arr[i]; SCALAR V__tmp = (ge+gi-(V+0.049))/0.02; SCALAR ge__tmp = -ge/0.005; SCALAR gi__tmp = -gi/0.01; V += 0.0001*V__tmp; ge += 0.0001*ge__tmp; gi += 0.0001*gi__tmp; V_arr[i] = V; ge_arr[i] = ge; gi_arr[i] = gi; bool this_spiked = V>-0.05; spiked[i] = this_spiked; if(this_spiked) spikes[atomicInc(global_j, N)] = i; } __global__ void reset(SCALAR *V, bool *spiked) { int i = blockIdx.x * blockDim.x + threadIdx.x; bool has_spiked = spiked[i]; //V[i] = (V[i]*!has_spiked)+(-0.06)*has_spiked; // i.e. V[i]=-0.06 if spiked[i] - but this operation requires two global mem accesses if(has_spiked) V[i] = -0.06; } """.replace('SCALAR', precision)) stateupdate_and_threshold = mod.get_function("stateupdate_and_threshold") reset = mod.get_function("reset") V = gpuarray.to_gpu(numpy.array(numpy.random.rand(N) * 0.01 - 0.06, dtype=mydtype)) ge = gpuarray.to_gpu(numpy.zeros(N, dtype=mydtype)) gi = gpuarray.to_gpu(numpy.zeros(N, dtype=mydtype)) we = 0.00162 wi = -0.009 P = brian.NeuronGroup(N, model='V:1') Pe = P.subgroup(Ne) Pi = P.subgroup(Ni) Ce = brian.Connection(Pe, P, sparseness=sparseness, weight=we).W.connection_matrix() Ci = brian.Connection(Pi, P, sparseness=sparseness, weight=wi).W.connection_matrix() Ce.alldata = numpy.array(Ce.alldata, dtype=mydtype) Ci.alldata = numpy.array(Ci.alldata, dtype=mydtype) Ce_alldata = drv.mem_alloc(Ce.alldata.nbytes) drv.memcpy_htod(Ce_alldata, Ce.alldata) Ce_allj = drv.mem_alloc(Ce.allj.nbytes) drv.memcpy_htod(Ce_allj, Ce.allj) Ce_rowind = drv.mem_alloc(Ce.rowind.nbytes) drv.memcpy_htod(Ce_rowind, Ce.rowind) Ci_alldata = drv.mem_alloc(Ci.alldata.nbytes) drv.memcpy_htod(Ci_alldata, Ci.alldata) Ci_allj = drv.mem_alloc(Ci.allj.nbytes) drv.memcpy_htod(Ci_allj, Ci.allj) Ci_rowind = drv.mem_alloc(Ci.rowind.nbytes) drv.memcpy_htod(Ci_rowind, Ci.rowind) brian-1.4.3/dev/ideas/cuda/old/cuda-algorithms.tex000066400000000000000000000347301266062647100217750ustar00rootroot00000000000000\documentclass[a4paper]{article} \usepackage{amsmath} \usepackage[dvips]{graphicx} \usepackage{psfrag} \usepackage{listings} \usepackage{listings-options} \usepackage[authoryear]{natbib} \usepackage{url} \addtolength{\marginparwidth}{0.5cm} \addtolength{\hoffset}{-2cm} \addtolength{\textwidth}{3cm} \addtolength{\voffset}{-2cm} \addtolength{\textheight}{4cm} \setlength{\parindent}{0pt} \setlength{\parskip}{1.3ex} \newcommand{\tododiagram}[2]{\begin{figure}[ht]\begin{center}\framebox[0.5\textwidth]{\textbf{Diagram to be made}}\end{center}\caption{#2}\label{#1}\end{figure}} \newcommand{\todo}[1]{\marginpar{\scriptsize #1}} \newcommand{\code}[1]{\lstinline£#1£} \newcommand{\ud}{\mathrm{d}} \newcommand{\dt}{\ud t} \newcommand{\remark}[1]{\textbf{[#1]}} \newcommand{\mV}{\mbox{ mV}} \newcommand{\Hz}{\mbox{ Hz}} \newcommand{\GHz}{\mbox{ GHz}} \newcommand{\GB}{\mbox{ GB}} \newcommand{\ms}{\mbox{ ms}} \newcommand{\maybe}[1]{{\color{green}#1}\todo{Keep the bit in green?}} \newcommand{\question}[1]{\textbf{Question: #1}} \title{CUDA algorithms for Brian} \date{} \begin{document} \maketitle We use C++ on the GPU via the PyCuda library (\url{http://mathema.tician.de/software/pycuda}) for Python (which handles passing the data to nvcc, the Nvidia compiler, and so on). \section{Algorithmic overview} The basic algorithm for Brian simulations is: \begin{enumerate} \item Update state variables, i.e. integration time step $\dt$ (\textbf{State updates}) \item Compute list of neurons which spiked, i.e. those with $V>V_t$ (\textbf{Thresholding}) \item Propagate spikes according to values in weight matrix for those neurons which have spiked, i.e. for all j, $V_j \leftarrow V_j + \sum_{i} W_{ij}$ where the sum is over all neurons $i$ that spiked (\textbf{Propagation}) \item Reset neurons which spiked $V\leftarrow V_r$ (\textbf{Reset}) \end{enumerate} \section{Data storage} For a group of $N$ neurons we store a state matrix $S$ which is $M\times N$ where $M$ is the number of variables for each neuron and $N$ is the number of neurons (usually $N$ large and $M$ small). On the GPU, we access this data by writing \lstinline$S[i+j*N]$ where i is the neuron index from 0 to $N-1$ and j is the state variable index. At the moment, we don't pay any attention to aligning the data nicely (and presumably this would speed it up). \question{Can this be improved? For example, would it be better to store each variable as its own array rather than having a 2D array?} Dense connection matrices are stored as a 2D array, and sparse connection matrices are explained in the section below. \section{State updates} For linear systems of differential equations, we will use the CUDA BLAS library to do matrix multiplication (i.e. \code{cublasDgemm}). For nonlinear systems, we generate C++ code from the differential equations. For example, the following equations: \begin{lstlisting} ''' dV/dt = W*W/(100*ms) : 1 dW/dt = -V/(100*ms) : 1 ''' \end{lstlisting} automatically generate the following C++ code for the GPU (using Euler integration): \begin{lstlisting}[language=C++] __global__ void stateupdate(int N, double t, double *S) { int i = blockIdx.x * blockDim.x + threadIdx.x; if(i>=N) return; double &V = S[i+0*N]; double &W = S[i+1*N]; double V__tmp = W*W/(100*0.001); double W__tmp = -V/(100*0.001); V += 0.0001*V__tmp; W += 0.0001*W__tmp; } \end{lstlisting} \section{Threshold and Reset} The idea of the thresholding operation is that the condition $V>V_t$ should only hold on any given time step for a very small number of neurons, so we generate a thread for each neuron and have a conditionally executed write to global memory if the thresholding operation is passed. My hope is that although the atomicInc operation and the write to global memory (and the branching) are problematic for the GPU, the fact that they are rare events means that overall this is efficient. After the thresholding operation is run, the first global\_j elements of the array spikes are copied from the GPU to the CPU for the propagation operation which is managed spike by spike by the CPU (see next section). \begin{lstlisting}[language=C++] __global__ void threshold(SCALAR *V, int *spikes, bool *spiked, unsigned int *global_j, int N) { int i = blockIdx.x * blockDim.x + threadIdx.x; if(i>=N) return; bool this_spiked = V[i]>-0.05; spiked[i] = this_spiked; if(this_spiked) { unsigned int j = atomicInc(global_j, N); spikes[j] = i; } } \end{lstlisting} \question{Is there a faster way to do the thresholding operation? Are my assumptions about the atomicInc operation etc. correct?} The reset operation is much simpler and just uses the previously stored boolean value to conditionally reset (but without branching). \begin{lstlisting}[language=C++] __global__ void reset(int N, SCALAR *V, bool *spiked) { int i = blockIdx.x * blockDim.x + threadIdx.x; if(i>=N) return; V[i] = (V[i]*!spiked[i])+(-0.06)*spiked[i]; // i.e. V[i]=-0.06 if spiked[i] } \end{lstlisting} \question{Would it be more efficient to do this another way? e.g. via indirection over the spikes array we computed earlier? My impression is that if memory access is patterned things tend to be faster than if you access memory via indirection on the GPU, but maybe because the number of neurons that spiked is so small, this would be quicker (probably it would actually).} \section{Propagation} \subsection{Dense matrix} For dense matrices, we give each target neuron a thread, and in each thread loop over the set of spikes. This involves a conditional (in the for loop), but each thread will in the end execute the same thread so if I understand the way the GPU works correctly, this means there is no performance penalty (that's only incurred when the branching goes two different ways in different threads)? \begin{lstlisting}[language=C++] __global__ void propagate(int *spikes, int numspikes, float *v, float *W, int N) { int i = blockIdx.x * blockDim.x + threadIdx.x; for(int j=0; j=rowind[spike+1]-startindex) return; i += startindex; V[allj[i]] += alldata[i]; } \end{lstlisting} \question{Are there better sparse matrix structures for doing this operation on the GPU? What would the algorithm be like?} See also the section below on spike management. \section{Other stuff} \subsection{Spike management} In Brian generally, we don't always have a simple relationship that spike propagation is based on the list of neurons that just fired a spike, because for example there are refractory periods, so we might have to fetch spike indices from an earlier time period. On the CPU, we do this by keeping a circular array of spike indices but I guess this is probably not a good structure for the GPU. \question{Could a circular array structure be efficiently implemented on a GPU?} Also, sometimes we do spike propagation to or from a subset of a group of neurons (e.g. from neurons 0-100 of a group of 1000, to neurons 100-200). This means we have to handle offsets. At the moment, we're thinking that it's probably easiest to just do all this stuff on the CPU rather than on the GPU. Here is a simple example where we have an excitatory and inhibitory subgroup of neurons. First of all, the set of neurons that spiked is downloaded from the GPU, then it is sorted and divided into the group of excitatory and inhibitory neurons by bisection. Then we loop separately over the two subdivided sets of spikes. \begin{lstlisting} drv.memcpy_dtoh(spike_index, gpu_spike_index) numspikes = spike_index[0] spikes = master_spikes[:numspikes] drv.memcpy_dtoh(spikes, gpu_spikes) spikes.sort() numspikes_exc = bisect.bisect_left(spikes, Ne) numspikes_inh = numspikes - numspikes_exc for i in spikes[:numspikes_exc]: ncols = len(Ce.rowj[i]) if ncols%512==0: spikegrid = (ncols/512, 1) else: spikegrid = (ncols/512+1, 1) propagate_spike.prepared_call(spikegrid, numpy.int32(i), *propagate_spike_end_args_exc) for i in spikes[numspikes_exc:]-Ne: ncols = len(Ci.rowj[i]) if ncols%512==0: spikegrid = (ncols/512, 1) else: spikegrid = (ncols/512+1, 1) propagate_spike.prepared_call(spikegrid, numpy.int32(i), *propagate_spike_end_args_inh) \end{lstlisting} \question{Any way that any of this stuff could be implemented on the GPU?} \subsection{Filtering sounds} We're also interested in doing some simulation of the auditory system, and the first stage of this involves taking a sound and breaking it down into a large (around 3000) number of channels, using a different filter for each channel. We potentially want to run this for long periods of time, so we can't use standard implementations of filtering that filter the whole sound in one go because of memory constraints. So instead, we want to do all 3000 filters in parallel, but only for one time step $\dt$ (where $\dt=1/f_s$ and $f_s$ is the sample rate). Without going in to too many details, the code below was automatically generated for a cascade of four fourth order filters whose coefficients are stored in the arrays \code{a} and \code{b}, and which have persistent memory stored in the variable \code{zi}. The input comes from \code{x} and the output goes to \code{y}. The variables \code{a}, \code{b} are 3D arrays of size $(n,m,p)$ where $n$ is the number of channels, $m$ is the order of each filter, and $p$ is the number of filters in a chain. The variable \code{zi} has size $(n,m-1,p)$. \begin{lstlisting}[language=C++] #define x(i) _x[i] #define y(i) _y[i] #define a(i,j,k) _a[(i)+(j)*10000+(k)*10000*3] #define b(i,j,k) _b[(i)+(j)*10000+(k)*10000*3] #define zi(i,j,k) _zi[(i)+(j)*10000+(k)*10000*(3-1)] __global__ void filt(double *_b, double *_a, double *_x, double *_zi, double *_y) { int j = blockIdx.x * blockDim.x + threadIdx.x; if(j>=10000) return; y(j) = b(j,0,0)*x(j) + zi(j,0,0); zi(j,0,0) = b(j,0+1,0)*x(j) + zi(j,0+1,0) - a(j,0+1,0)*y(j); zi(j,3-2,0) = b(j,3-1,0)*x(j) - a(j,3-1,0)*y(j); x(j) = y(j); y(j) = b(j,0,1)*x(j) + zi(j,0,1); zi(j,0,1) = b(j,0+1,1)*x(j) + zi(j,0+1,1) - a(j,0+1,1)*y(j); zi(j,3-2,1) = b(j,3-1,1)*x(j) - a(j,3-1,1)*y(j); x(j) = y(j); y(j) = b(j,0,2)*x(j) + zi(j,0,2); zi(j,0,2) = b(j,0+1,2)*x(j) + zi(j,0+1,2) - a(j,0+1,2)*y(j); zi(j,3-2,2) = b(j,3-1,2)*x(j) - a(j,3-1,2)*y(j); x(j) = y(j); y(j) = b(j,0,3)*x(j) + zi(j,0,3); zi(j,0,3) = b(j,0+1,3)*x(j) + zi(j,0+1,3) - a(j,0+1,3)*y(j); zi(j,3-2,3) = b(j,3-1,3)*x(j) - a(j,3-1,3)*y(j); x(j) = y(j); y(j) = b(j,0,4)*x(j) + zi(j,0,4); zi(j,0,4) = b(j,0+1,4)*x(j) + zi(j,0+1,4) - a(j,0+1,4)*y(j); zi(j,3-2,4) = b(j,3-1,4)*x(j) - a(j,3-1,4)*y(j); x(j) = y(j); y(j) = b(j,0,5)*x(j) + zi(j,0,5); zi(j,0,5) = b(j,0+1,5)*x(j) + zi(j,0+1,5) - a(j,0+1,5)*y(j); zi(j,3-2,5) = b(j,3-1,5)*x(j) - a(j,3-1,5)*y(j); x(j) = y(j); y(j) = b(j,0,6)*x(j) + zi(j,0,6); zi(j,0,6) = b(j,0+1,6)*x(j) + zi(j,0+1,6) - a(j,0+1,6)*y(j); zi(j,3-2,6) = b(j,3-1,6)*x(j) - a(j,3-1,6)*y(j); x(j) = y(j); y(j) = b(j,0,7)*x(j) + zi(j,0,7); zi(j,0,7) = b(j,0+1,7)*x(j) + zi(j,0+1,7) - a(j,0+1,7)*y(j); zi(j,3-2,7) = b(j,3-1,7)*x(j) - a(j,3-1,7)*y(j); x(j) = y(j); y(j) = b(j,0,8)*x(j) + zi(j,0,8); zi(j,0,8) = b(j,0+1,8)*x(j) + zi(j,0+1,8) - a(j,0+1,8)*y(j); zi(j,3-2,8) = b(j,3-1,8)*x(j) - a(j,3-1,8)*y(j); x(j) = y(j); y(j) = b(j,0,9)*x(j) + zi(j,0,9); zi(j,0,9) = b(j,0+1,9)*x(j) + zi(j,0+1,9) - a(j,0+1,9)*y(j); zi(j,3-2,9) = b(j,3-1,9)*x(j) - a(j,3-1,9)*y(j); } \end{lstlisting} \question{The code above is based on naively using the formula defining a digital filter (from the Matlab documentation to be precise), unrolling all the loops and parallelising it on the GPU. Do you know any GPU filtering algorithms? Is there a better way to do what I'm doing above?} \end{document}brian-1.4.3/dev/ideas/cuda/old/gpuplan.txt000066400000000000000000000022611266062647100203710ustar00rootroot00000000000000Plan for Brian on GPU ===================== * PyCuda installation (Windows): produce a binary? From the CUBA example --------------------- P=NeuronGroup(N,model=eqs, threshold=-50*mV,reset=-60*mV) * Create state matrix and LS structure on GPU. * Create GPU update code from equations. * Create GPU threshold code. * Create GPU reset code. P.v=-60*mV+10*mV*rand(len(P)) * P.v is a view on the state matrix, with a special cached structure. Uploading is only necessary before code is executed on the GPU. Downloading is only necessary if CPU is not in sync with GPU. Pe=P.subgroup(Ne) Pi=P.subgroup(Ni) * Should work correctly if views are implemented on cached arrays. Ce=Connection(Pe,P,'ge',weight=1.62*mV,sparseness=p) Ci=Connection(Pi,P,'gi',weight=-9*mV,sparseness=p) * Create weight matrices on GPU (sparse array structure). * Create GPU propagate code. M=SpikeMonitor(P) * Should work correctly (runs on CPU). It could be useful if data were not transferred at every timestep, using some sort of cache mechanism. trace=StateMonitor(P,'v',record=0) * Should work correctly (runs on CPU). run(1*second) brian-1.4.3/dev/ideas/cuda/old/listings-options.sty000066400000000000000000000011051266062647100222440ustar00rootroot00000000000000% % settings for the lstlistings package (see docu in listings.pdf) % =============================================================== \RequirePackage{listings} \RequirePackage{color} \RequirePackage{textcomp} \definecolor{green}{rgb}{0,0.5,0} \lstset{language=Python} \lstloadlanguages{Python} \lstset{ basicstyle=\ttfamily, keywordstyle=\color{blue}, identifierstyle=\bfseries, stringstyle=\color{green}, morestring=[s]{'''}{'''}, morestring=[s]{"""}{"""}, upquote=true, showstringspaces=false, columns=fixed, basewidth={0.5em,0.4em} }brian-1.4.3/dev/ideas/cuda/old/pycudacuba.py000066400000000000000000000137611266062647100206630ustar00rootroot00000000000000import pycuda.autoinit as autoinit import pycuda.driver as drv from pycuda.gpuarray import GPUArray from pycuda import gpuarray import bisect import numpy, pylab, time from itertools import repeat import cProfile as profile import pstats N = 512 * 3200 blocksize = 512 duration = 10000 record = False doprofile = False if drv.get_version() == (2, 0, 0): # cuda version precision = 'float' elif drv.get_version() > (2, 0, 0): precision = 'double' else: raise Exception, "CUDA 2.0 required" if precision == 'double': mydtype = numpy.float64 else: mydtype = numpy.float32 block = (blocksize, 1, 1) grid = (N / blocksize, 1) Ne = int(N * 0.8) Ni = N - Ne mod = drv.SourceModule(""" __global__ void stateupdate(SCALAR *V_arr, SCALAR *ge_arr, SCALAR *gi_arr) { int i = blockIdx.x * blockDim.x + threadIdx.x; SCALAR V = V_arr[i]; SCALAR ge = ge_arr[i]; SCALAR gi = gi_arr[i]; SCALAR V__tmp = (ge+gi-(V+0.049))/0.02; SCALAR ge__tmp = -ge/0.005; SCALAR gi__tmp = -gi/0.01; V_arr[i] = V+0.0001*V__tmp; ge_arr[i] = ge+0.0001*ge__tmp; gi_arr[i] = gi+0.0001*gi__tmp; } __global__ void threshold(SCALAR *V, int *spikes, bool *spiked, unsigned int *global_j, int N) { int i = blockIdx.x * blockDim.x + threadIdx.x; bool this_spiked = V[i]>-0.05; spiked[i] = this_spiked; if(this_spiked) // && i-0.05; spiked[i] = this_spiked; if(this_spiked) // && i-0.05) { spiked[i] = true; spikes[j++] = i; } else { spiked[i] = false; } } *global_j = j; } __global__ void propagate(int *spikes, int numspikes, SCALAR *v, SCALAR W, int N, int offset) { int i = blockIdx.x * blockDim.x + threadIdx.x; for(int j=0; j (2, 0, 0): precision = 'double' else: raise Exception, "CUDA 2.0 required" if precision == 'double': mydtype = numpy.float64 else: mydtype = numpy.float32 block = (blocksize, 1, 1) grid = (N / blocksize, 1) Ne = int(N * 0.8) Ni = N - Ne mod = drv.SourceModule(""" __global__ void stateupdate(SCALAR *V, SCALAR *ge, SCALAR *gi) { int i = blockIdx.x * blockDim.x + threadIdx.x; SCALAR V__tmp = (ge[i]+gi[i]-(V[i]+0.049))/0.02; SCALAR ge__tmp = -ge[i]/0.005; SCALAR gi__tmp = -gi[i]/0.01; V[i] += 0.0001*V__tmp; ge[i] += 0.0001*ge__tmp; gi[i] += 0.0001*gi__tmp; } __global__ void threshold(SCALAR *V, int *spikes, bool *spiked, unsigned int *global_j, int N) { int i = blockIdx.x * blockDim.x + threadIdx.x; bool this_spiked = V[i]>-0.05; spiked[i] = this_spiked; if(this_spiked) // && i-0.05) { spiked[i] = true; spikes[j++] = i; } else { spiked[i] = false; } } *global_j = j; } __global__ void propagate(int *spikes, int numspikes, SCALAR *v, SCALAR *W, int N) { int i = blockIdx.x * blockDim.x + threadIdx.x; for(int j=0; j=rowind[spike+1]-startindex) return; i += startindex; V[allj[i]] += alldata[i]; } __global__ void reset(SCALAR *V, bool *spiked) { int i = blockIdx.x * blockDim.x + threadIdx.x; V[i] = (V[i]*!spiked[i])+(-0.06)*spiked[i]; // i.e. V[i]=-0.06 if spiked[i] } """.replace('SCALAR', precision)) stateupdate = mod.get_function("stateupdate") threshold = mod.get_function("threshold") single_thread_threshold = mod.get_function("single_thread_threshold") propagate = mod.get_function("propagate") propagate_spike = mod.get_function("propagate_spike") reset = mod.get_function("reset") V = gpuarray.to_gpu(numpy.array(numpy.random.rand(N) * 0.01 - 0.06, dtype=mydtype)) ge = gpuarray.to_gpu(numpy.zeros(N, dtype=mydtype)) gi = gpuarray.to_gpu(numpy.zeros(N, dtype=mydtype)) we = 0.00162 wi = -0.009 P = brian.NeuronGroup(N, model='V:1') Pe = P.subgroup(Ne) Pi = P.subgroup(Ni) Ce = brian.Connection(Pe, P, sparseness=sparseness, weight=we).W.connection_matrix() Ci = brian.Connection(Pi, P, sparseness=sparseness, weight=wi).W.connection_matrix() Ce.alldata = numpy.array(Ce.alldata, dtype=mydtype) Ci.alldata = numpy.array(Ci.alldata, dtype=mydtype) Ce_alldata = drv.mem_alloc(Ce.alldata.nbytes) drv.memcpy_htod(Ce_alldata, Ce.alldata) Ce_allj = drv.mem_alloc(Ce.allj.nbytes) drv.memcpy_htod(Ce_allj, Ce.allj) Ce_rowind = drv.mem_alloc(Ce.rowind.nbytes) drv.memcpy_htod(Ce_rowind, Ce.rowind) Ci_alldata = drv.mem_alloc(Ci.alldata.nbytes) drv.memcpy_htod(Ci_alldata, Ci.alldata) Ci_allj = drv.mem_alloc(Ci.allj.nbytes) drv.memcpy_htod(Ci_allj, Ci.allj) Ci_rowind = drv.mem_alloc(Ci.rowind.nbytes) drv.memcpy_htod(Ci_rowind, Ci.rowind) gpu_spikes = drv.mem_alloc(4 * N) gpu_spikes_exc = drv.mem_alloc(4 * N) gpu_spikes_inh = drv.mem_alloc(4 * N) gpu_spiked = gpuarray.to_gpu(numpy.zeros(N, dtype=bool)) gpu_spike_index = drv.mem_alloc(4) master_spikes = numpy.zeros(N, dtype=int) spike_index = numpy.zeros(1, dtype=numpy.uint32) drv.memcpy_htod(gpu_spikes, master_spikes) drv.memcpy_htod(gpu_spike_index, spike_index) recspikes = [] recv0 = numpy.zeros(duration, dtype=mydtype) totalnspikes = 0 stateupdate.prepare(('i', 'i', 'i'), block) threshold.prepare(('i', 'i', 'i', 'i', numpy.int32), block) single_thread_threshold.prepare(('i', 'i', 'i', 'i', numpy.int32), (1, 1, 1)) propagate.prepare(('i', numpy.int32, 'i', 'i', numpy.int32), block) propagate_spike.prepare((numpy.int32, 'i', 'i', 'i', 'i'), block) reset.prepare(('i', 'i'), block) stateupdate_args = (grid, int(V.gpudata), int(ge.gpudata), int(gi.gpudata)) threshold_args = (grid, int(V.gpudata), int(gpu_spikes), int(gpu_spiked.gpudata), int(gpu_spike_index), numpy.int32(N)) single_thread_threshold_args = ((1, 1), int(V.gpudata), int(gpu_spikes), int(gpu_spiked.gpudata), int(gpu_spike_index), numpy.int32(N)) reset_args = (grid, int(V.gpudata), int(gpu_spiked.gpudata)) propagate_spike_end_args_exc = (int(Ce_alldata), int(Ce_allj), int(Ce_rowind), int(ge.gpudata)) propagate_spike_end_args_inh = (int(Ci_alldata), int(Ci_allj), int(Ci_rowind), int(gi.gpudata)) def run_sim(): global totalnspikes, recspikes, recv0 for t in xrange(duration): #stateupdate(V, ge, gi, block=block, grid=grid) if t == 0: stateupdate.prepared_call(*stateupdate_args) else: stateupdate.launch_grid(*grid) spike_index[0] = 0 drv.memcpy_htod(gpu_spike_index, spike_index) #threshold(V, gpu_spikes, gpu_spiked, gpu_spike_index, numpy.int32(N), block=block, grid=grid) if t == 0: threshold.prepared_call(*threshold_args) else: threshold.launch_grid(*grid) # MUCH! slower #single_thread_threshold.prepared_call(*single_thread_threshold_args) drv.memcpy_dtoh(spike_index, gpu_spike_index) numspikes = spike_index[0] spikes = master_spikes[:numspikes] drv.memcpy_dtoh(spikes, gpu_spikes) spikes.sort() numspikes_exc = bisect.bisect_left(spikes, Ne) numspikes_inh = numspikes - numspikes_exc #drv.memcpy_htod(gpu_spikes_exc, spikes[:numspikes_exc]) #drv.memcpy_htod(gpu_spikes_inh, spikes[numspikes_exc:]-Ne) #propagate(gpu_spikes_exc, numpy.int32(numspikes_exc), ge, CeW, numpy.int32(N), block=block, grid=grid) #propagate.prepared_call(grid, int(gpu_spikes_exc), numpy.int32(numspikes_exc), int(ge.gpudata), int(CeW.gpudata), numpy.int32(N)) #propagate(gpu_spikes_inh, numpy.int32(numspikes_inh), gi, CiW, numpy.int32(N), block=block, grid=grid) #propagate.prepared_call(grid, int(gpu_spikes_inh), numpy.int32(numspikes_inh), int(gi.gpudata), int(CiW.gpudata), numpy.int32(N)) for i in spikes[:numspikes_exc]: ncols = len(Ce.rowj[i]) if ncols % 512 == 0: spikegrid = (ncols / 512, 1) else: spikegrid = (ncols / 512 + 1, 1) propagate_spike.prepared_call(spikegrid, numpy.int32(i), *propagate_spike_end_args_exc) for i in spikes[numspikes_exc:] - Ne: ncols = len(Ci.rowj[i]) if ncols % 512 == 0: spikegrid = (ncols / 512, 1) else: spikegrid = (ncols / 512 + 1, 1) propagate_spike.prepared_call(spikegrid, numpy.int32(i), *propagate_spike_end_args_inh) #reset(V, gpu_spiked, block=block, grid=grid) if t == 0: reset.prepared_call(*reset_args) else: reset.launch_grid(*grid) if record: recspikes += zip(spikes, repeat(t)) drv.memcpy_dtoh(recv0[t:t + 1], V.gpudata) totalnspikes += numspikes if doprofile: start = time.time() profile.run('run_sim()', 'cudacuba.prof') end = time.time() stats = pstats.Stats('cudacuba.prof') #stats.strip_dirs() stats.sort_stats('cumulative', 'calls') stats.print_stats(50) else: start = time.time() run_sim() end = time.time() print 'GPU time:', end - start print 'Number of spikes:', totalnspikes if record: i, t = zip(*recspikes) pylab.subplot(121) pylab.plot(t, i, '.') pylab.subplot(122) pylab.plot(recv0) pylab.show() brian-1.4.3/dev/ideas/cuda/old/test_chagpp.py000066400000000000000000000054501266062647100210400ustar00rootroot00000000000000''' Attempting to use chag:pp CUDA library with pycuda or with weave. This doesn't work. Message on PyCUDA list follows: On Dienstag 29 September 2009, Dan Goodman wrote: > > Hi all, > > > > Has anyone tried using pycuda with a library of GPU code such as CUDPP > > [1] or chag:pp [2]? How do you use these in pycuda? Is it just a matter > > of passing some header and lib file directories to nvcc via the > > options=... keyword for SourceModule? SourceModule won't work because it's meant only for device-side code, and most of these libraries expose host-side interfaces. If the library is written with the driver API in mind, it's as simple as wrapping it into Python using, e.g. Boost.Python, cython, etc. If it is using the CUDA "runtime" API, it's not quite as simple, because then Nvidia's idiotic driver/runtime dichotomy kicks in and prevents direct communication. Luckily, however, I learned at the Nvidia conference last week that a) they're aware that this is causing problems, and b) this issue will go away in CUDA 3.0, which will be released along with their new hardware. HTH, Andreas ''' import pycuda.autoinit as autoinit import pycuda.driver as drv from pycuda.gpuarray import GPUArray from pycuda import gpuarray import bisect import numpy, pylab, time, random from scipy import weave N = 1024 mod = drv.SourceModule(''' #include __global__ void test() { int i = blockIdx.x * blockDim.x + threadIdx.x; i = i+1; } ''', options=[r'-I"C:\Documents and Settings\goodman.CELERI\Bureau\source-20090929"'], no_extern_c=True) #x = numpy.random.randn(N) #xgpu = gpuarray.to_gpu(x) #ygpu = gpuarray.to_gpu(numpy.zeros(x.shape)) #vec = gpuarray.to_gpu(numpy.zeros(1, dtype=int)) #z = x[x>=0.0] # #xgpu_start = int(xgpu.gpudata) #xgpu_end = xgpu_start+N*8 #ygpu_start = int(ygpu.gpudata) #vec_start = int(vec.gpudata) # #src = ''' #struct Predicate #{ # __device__ bool operator() (double value) const # { # return value>0.0; # } #} # #pp::compact( # (double *)xgpu_start, /* Input start pointer */ # (double *)xgpu_end, /* Input end pointer */ # (double *)ygpu_start, /* Output start pointer */ # vec_start, /* Storage for valid element count */ # Predicate() /* Predicate */ # ); #''' # #weave.inline(src, ['xgpu_start', 'xgpu_end', 'ygpu_start', 'vec_start'], # compiler='gcc',#msvc works too # headers=[ # ''], # include_dirs=['C:\\CUDA\\include', # r'C:\Documents and Settings\goodman.CELERI\Bureau\source-20090929'], # libraries=[], # library_dirs=['C:\\CUDA\\lib'], # ) brian-1.4.3/dev/ideas/cuda/old/testcuda.py000066400000000000000000000102141266062647100203450ustar00rootroot00000000000000''' The code below uses the GPU to compute a matrix-matrix product. Notes: - The C code is adapted from the simpleCUBLAS sample in the CUDA SDK - Matrices have to have Fortran ordering [is this equivalent to transposing? if so this is not a problem because BLAS includes the option to operate on transposed matrices] - Timing comparisons are for the time required to initialise CUDA, upload, perform the operation, and then download again, so for small times the CPU performs better. On mine at n=1000 it's about the same, but for N=4000 the GPU is 10x faster. - I'm not sure how you're supposed to indicate failure and return in a Weave inline function, so that's just ignored for the moment, if one of the functions returns an error the program will probably just crash. ''' from numpy import * from scipy import weave from scipy import randn import time n = 4000 x = array(randn(n, n), order='F') y = array(randn(n, n), order='F') z = array(zeros((n, n)), order='F') code = ''' cublasStatus status; double *d_x = 0; double *d_y = 0; double *d_z = 0; double alpha = 1.0; double beta = 0.0; int n2 = n*n; //fprintf (stderr, "fprintf working\\n"); status = cublasInit(); if (status != CUBLAS_STATUS_SUCCESS) { fprintf (stderr, "!!!! CUBLAS initialization error\\n"); //return EXIT_FAILURE; } //////////////////// Alloc device mem /////////////////////////// status = cublasAlloc(n2, sizeof(d_x[0]), (void**)&d_x); if (status != CUBLAS_STATUS_SUCCESS) { fprintf (stderr, "!!!! device memory allocation error (x)]\\n"); //return EXIT_FAILURE; } status = cublasAlloc(n2, sizeof(d_y[0]), (void**)&d_y); if (status != CUBLAS_STATUS_SUCCESS) { fprintf (stderr, "!!!! device memory allocation error (y)]\\n"); //return EXIT_FAILURE; } status = cublasAlloc(n2, sizeof(d_z[0]), (void**)&d_z); if (status != CUBLAS_STATUS_SUCCESS) { fprintf (stderr, "!!!! device memory allocation error (z)]\\n"); //return EXIT_FAILURE; } //////////////////// Upload /////////////////////////// status = cublasSetVector(n2, sizeof(x[0]), x, 1, d_x, 1); if (status != CUBLAS_STATUS_SUCCESS) { fprintf (stderr, "!!!! device access error (write x)\\n"); //return EXIT_FAILURE; } status = cublasSetVector(n2, sizeof(y[0]), y, 1, d_y, 1); if (status != CUBLAS_STATUS_SUCCESS) { fprintf (stderr, "!!!! device access error (write y)\\n"); //return EXIT_FAILURE; } //////////////////// Multiply /////////////////////////// cublasDgemm('n', 'n', n, n, n, alpha, d_x, n, d_y, n, beta, d_z, n); status = cublasGetError(); if (status != CUBLAS_STATUS_SUCCESS) { fprintf (stderr, "!!!! kernel execution error.\\n"); //return EXIT_FAILURE; } //////////////////// Download /////////////////////////// status = cublasGetVector(n2, sizeof(z[0]), d_z, 1, z, 1); if (status != CUBLAS_STATUS_SUCCESS) { fprintf (stderr, "!!!! device access error (read z)\\n"); //return EXIT_FAILURE; } //////////////////// Free memory /////////////////////////// status = cublasFree(d_x); if (status != CUBLAS_STATUS_SUCCESS) { fprintf (stderr, "!!!! memory free error (x)\\n"); //return EXIT_FAILURE; } status = cublasFree(d_y); if (status != CUBLAS_STATUS_SUCCESS) { fprintf (stderr, "!!!! memory free error (y)\\n"); //return EXIT_FAILURE; } status = cublasFree(d_z); if (status != CUBLAS_STATUS_SUCCESS) { fprintf (stderr, "!!!! memory free error (z)\\n"); //return EXIT_FAILURE; } /* Shutdown */ status = cublasShutdown(); if (status != CUBLAS_STATUS_SUCCESS) { fprintf (stderr, "!!!! shutdown error \\n"); //return EXIT_FAILURE; } ''' start = time.time() weave.inline(code, ['x', 'y', 'z', 'n'], compiler='gcc', #msvc works too headers=['"cublas.h"'], include_dirs=['C:\\CUDA\\include'], libraries=['cublas'], library_dirs=['C:\\CUDA\\lib'], ) end = time.time() print 'GPU time:', end - start start = time.time() w = dot(x, y) end = time.time() print 'CPU time:', end - start print 'Max abs difference in values:', amax(abs(w - z)) print 'OK' brian-1.4.3/dev/ideas/cuda/old/testpycuda.py000066400000000000000000000040401266062647100207160ustar00rootroot00000000000000import pycuda.autoinit as autoinit import pycuda.compiler as drv # .driver before pycuda 0.93 from pycuda.gpuarray import GPUArray from pycuda import gpuarray import numpy, pylab, time N = 10000 duration = int(0.1 * 10000) record = False showfinal = True doget = True dosync = False block = (512, 1, 1) grid = (int(N / 512) + 1, 1) #Device attributes #MAX_THREADS_PER_BLOCK : 512 #MAX_BLOCK_DIM_X : 512 #MAX_BLOCK_DIM_Y : 512 #MAX_BLOCK_DIM_Z : 64 #MAX_GRID_DIM_X : 65535 #MAX_GRID_DIM_Y : 65535 #MAX_GRID_DIM_Z : 1 #MAX_SHARED_MEMORY_PER_BLOCK : 16384 #TOTAL_CONSTANT_MEMORY : 65536 #WARP_SIZE : 32 #MAX_PITCH : 262144 #MAX_REGISTERS_PER_BLOCK : 16384 #CLOCK_RATE : 1296000 #TEXTURE_ALIGNMENT : 256 #GPU_OVERLAP : 1 #MULTIPROCESSOR_COUNT : 30 mod = drv.SourceModule(""" __global__ void stateupdate(double *v, double *w) { //const int i = threadIdx.x; int i = blockIdx.x * blockDim.x + threadIdx.x; double v__tmp = w[i]*w[i]/(100*0.001); double w__tmp = -v[i]/(100*0.001); v[i] += 0.0001*v__tmp; w[i] += 0.0001*w__tmp; } """) stateupdate = mod.get_function("stateupdate") v = gpuarray.to_gpu(numpy.ones(N)) w = gpuarray.to_gpu(numpy.zeros(N)) #ov = numpy.zeros(N) ov = drv.pagelocked_zeros((N,), dtype=float) recv = [] start = time.time() for _ in xrange(duration): stateupdate(v, w, block=block, grid=grid) if record: recv.append(v.get()) elif doget: v.get(ov) elif dosync: autoinit.context.synchronize() print 'GPU code:', time.time() - start if record: x = numpy.array(recv) pylab.plot(x[:, 0]) if showfinal: pylab.figure() pylab.plot(v.get()) if record or showfinal: pylab.show() ''' Some results: dV/dt = W*W/(100*ms) : 1 dW/dt = -V/(100*ms) : 1 10k neurons for 1000 time steps Python: 0.5s C++: 0.4s GPUgetpagelocked: 0.1s GPUsync: 0.047s 100k neurons Python: 9.3s C++: 2.9s GPUgetpagelocked: 0.45s GPUsync: 0.16s 1M neurons Python: 111s C++: 27.6s GPUgetpagelocked: 4.1s GPUsync: 1.2s ''' brian-1.4.3/dev/ideas/cuda/old/testpycuda_propagate.py000066400000000000000000000032611266062647100227640ustar00rootroot00000000000000import pycuda.autoinit as autoinit import pycuda.driver as drv from pycuda.gpuarray import GPUArray from pycuda import gpuarray import numpy, pylab, time N = 512 * 8 spikes = [i for i in range(N) if numpy.random.rand() < 0.1] blocksize = 512 block = (blocksize, 1, 1) grid = (N / blocksize, 1) repeats = 1000 print 'N:', N print 'numspikes:', len(spikes), 'proportion', float(len(spikes)) / N print 'repeats:', repeats print 'block:', block, 'grid:', grid mod = drv.SourceModule(""" __global__ void propagate(int *spikes, int numspikes, float *v, float *W, int N) { int i = blockIdx.x * blockDim.x + threadIdx.x; for(int j=0; jx0 && i x0)[0] print'CPU time with numpy:', time.time() - start print all(Jret == Jcpu) print len(Jret), float(len(Jret)) / N brian-1.4.3/dev/ideas/cuda/optimal_synapse_indexing.txt000066400000000000000000000054361266062647100232500ustar00rootroot00000000000000Optimal partitioning of synapses ================================ How to optimize the indexing of synapses such as to minimize the number of block accesses in both the forward and backward direction? Each block contains 32 synapses. When a spike is produced, it will reach synapses at various delays. Operations are executed on the synapses after these delays. Therefore there will be simultaneous operations for all synapses that have the same presynaptic neuron and presynaptic delay. The same holds for the backward propagation problem. Mathematically, we have two partitions of the synapse indexes. To each index corresponds a block. We want to renumber the indexes (permutation) so as to minimize the number of blocks in each set of both partitions. Alternative view (Marcel's idea) -------------------------------- The blocks are a partition of the set of synapses in fixed size groups (32 elements). We define a graph by putting an edge between any two synapses that are simultaneously accessed, either in the forward or backward direction. We then look for the partition that minimizes the number of graph cuts. This is a classical graph theory problem: http://www.sandia.gov/~bahendr/partitioning.html http://en.wikipedia.org/wiki/Graph_partition There is Python package, PyMetis: http://mathema.tician.de/software/pymetis This is based on Metis, which uses these algorithms: [3] G. Karypis and V. Kumar. Multilevel k-way partitioning scheme for irregular graphs. Journal of Parallel and Distributed Computing, 48(1):96–129, 1998. [4] G. Karypis and V. Kumar. A fast and high quality multilevel scheme for partitioning irregular graphs. SIAM Journal on Scientific Computing, 20(1):359–392, 1999. Another python package for graph partitioning: http://networkx.lanl.gov/ Maybe somebody wrote GPU algorithms too? Maths ----- A cut in the partition corresponds to two synapses belonging to different blocks that are accessed simultaneously, either in the forward or backward way. Consider a forward group of n synapses separated into M blocks (a set of simultaneously accessed synapses). The associated cost is M block accesses. To each synapse in a block of p synapses corresponds n-p cuts. Therefore the total number of cuts is: n^2-sum(p_i^2), where p_i is the number of synapses in block i (with n=sum(p_i)). This is equal to sum(p_i*p_j, i!=j) (for what it's worth). Therefore, in the graph cut view, we maximize: sum(p_i^2). If we assume that all blocks have roughly the same size (which is likely to be false), this means p_i=n/M, so sum(p_i^2)=n^2/M. In this case, the graph cut algorithm maximizes: sum(n_k^2/M_k), where M_k is the number of blocks in each group. 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Questions: - if several events in sequence have the same target, how do we do accumulate these? this is essentially the problem of vectorising Synapses in Python, so maybe we can use the same solution? i.e. vectorise over indices without repeats, and loop over repeats. Benefits: - in principle, everything can be coalesced Costs: - we need to efficiently sort the synaptic operations, which is O(numevents log(numevents)), but there exist efficient sorting algorithms and libraries to do thisbrian-1.4.3/dev/ideas/cuda/propagation/profile_all_gpu.py000066400000000000000000000257521266062647100234560ustar00rootroot00000000000000from brian import * from brian.experimental.codegen2 import * import numpy import time import numpy.random as nrandom import random as prandom from mpl_toolkits.mplot3d import Axes3D #log_level_info() ##### PROFILING CODE def profile(gpu_module, Nsource, Ntarget, Nstate, complexity, sparseness, rate, parameters={}, target_time=1.0, scalar='double', dothreshreset=True, doconn=True, ): clear(True, True) reinit_default_clock() use_gpu = bool(gpu_module) if use_gpu: ns = {} exec 'from %s import GPUConnection'%gpu_module in ns GPUConnection = ns['GPUConnection'] Conn = GPUConnection language = GPULanguage(force_sync=False, scalar=scalar, ) else: Conn = Connection language = CLanguage() parameters = {} nrandom.seed(3212212) prandom.seed(3432334) expr = 'X' for i in xrange(complexity): expr = 'sin({expr}+X)'.format(expr=expr) eqs = ''' dv/dt = {rate}/second : 1 '''.format(rate=float(rate)) for i in xrange(Nstate): eqs += 'dX/dt = {expr}/(10*ms) : 1\n'.format(expr=expr).replace('X', 'x'+str(i)) #source = PoissonGroup(Nsource, rate) reset = 'v = 0' threshold = 'v>1' if dothreshreset: source = NeuronGroup(Nsource, eqs, reset=reset, threshold=threshold ) else: source = NeuronGroup(Nsource, eqs) source.v = rand(Nsource) source._state_updater = CodeGenStateUpdater(source, euler, language, clock=source.clock) if dothreshreset: source._threshold = CodeGenThreshold(source, threshold, language) source._resetfun = CodeGenReset(source, reset, language) target = NeuronGroup(Ntarget, 'dV/dt=0/second:1') target._state_updater = CodeGenStateUpdater(target, euler, language, clock=target.clock) if doconn: C = Conn(source, target, 'V', weight=1, sparseness=sparseness, **parameters) run(10*ms) @network_operation def check_done(): if time.time()-start>target_time: stop() start = time.time() run(1e10*second) # if use_gpu: # language.gpu_man.copy_to_host(True) end = time.time() elapsed = defaultclock.t realtime_ratio = (end-start)/float(elapsed) summary = '' if use_gpu: summary += 'Using GPU (algorithm %s)\n'%gpu_module for key, value in parameters.items(): summary += ' '+key+': '+str(value)+'\n' else: summary += 'Using CPU\n' summary += 'Nsource={Nsource}, Ntarget={Ntarget}, sparseness={sparseness}, rate={rate}\n'.format( Nsource=Nsource, Ntarget=Ntarget, sparseness=sparseness, rate=rate) summary += 'Ratio compared to realtime: %.3f'%realtime_ratio return realtime_ratio, summary if __name__=='__main__': if 1: log_level_info() ratio, summary = profile( '', # 'vectorise_over_postsynaptic_offset', # 'double_vectorise_over_spsyn_targetidx_blocked', #scalar='float', dothreshreset=True, doconn=True, Nsource=10000, Ntarget=10000, sparseness=0.1, rate=10*Hz, Nstate=3, complexity=2, parameters=dict(), ) print summary if 0: show_3d_plots = True show_individual_2d_plots = True # only used if 3D plots not used repeats = 1 bases = { 'C++':('b', dict(gpu_module='')), 'CUDA/VPO':('g', dict(gpu_module='vectorise_over_postsynaptic_offset', parameters=dict())), 'CUDA/VSS':('r', dict(gpu_module='vectorise_over_spiking_synapses', parameters=dict())), # 'CUDA/VSS/NA':((0.5,0,0), dict(gpu_module='vectorise_over_spiking_synapses', # parameters=dict(use_atomic=False))), 'CUDA/DVPOT':('y', dict(gpu_module='double_vectorise_over_postsynoff_targetidx_blocked', parameters=dict())), # 'CUDA/DVPOT/MW':((0.5,0.5,0), dict(gpu_module='double_vectorise_over_postsynoff_targetidx_blocked', # parameters=dict(masked_write=False))), 'CUDA/DVSST':('m', dict(gpu_module='double_vectorise_over_spsyn_targetidx_blocked', parameters=dict())), 'CUDA/DVSST/MW':((0.5,0,0.5), dict(gpu_module='double_vectorise_over_spsyn_targetidx_blocked', parameters=dict(masked_write=False))), } plot_params = [ #dict(Nsource=100, Ntarget=100000, sparseness=0.1, # spikesperdt=[1, 5, 10, 50, 100], # ), #dict(Nsource=100, Ntarget=100000, spikesperdt=5, # sparseness=[0.001, 0.01, 0.1, 0.2], # ), dict(Nsource=100, Ntarget=100000, # low detail # spikesperdt=[1, 10, 50, 100], # sparseness=[0.001, 0.01, 0.1, 0.3], # medium detail # spikesperdt=[1, 3, 5, 10, 25, 50], # sparseness=[0.001, 0.005, 0.01, 0.05, 0.1], # high detail spikesperdt=range(1,10)+range(10, 101, 10), sparseness=exp(linspace(log(0.001), log(0.3), 20)), ), ] start_time = time.time() for pp in plot_params: basekwds = pp varying = {} unvarying = [] images = {} for k, v in pp.items(): if isinstance(v, (list, tuple, ndarray)): varying[k] = v else: unvarying.append(k+'='+str(v)) unvarying = ', '.join(unvarying) if len(varying)>2 or len(varying)==0: print 'Can only vary one or two parameters at a time.' exit() if len(varying)==1: (varykey, varyval), = varying.items() figure() title(unvarying) elif len(varying)==2: (varykey1, varyval1), (varykey2, varyval2) = varying.items() if show_3d_plots: f = figure() title(unvarying) ax = f.add_subplot(111, projection='3d') for ibase, (name, (col, kwds)) in enumerate(bases.items()): kwds.update(basekwds) if len(varying)==1: T = [] for v in varyval: kwds[varykey] = v allt = [] for _ in xrange(repeats): t, summary = profile_gpu_connection(**kwds) print summary allt.append(t) t = mean(allt) T.append(t) plot(varyval, T, '-o', label=name, color=col) elif len(varying)==2: allT = [] for v1 in varyval1: T = [] for v2 in varyval2: kwds[varykey1] = v1 kwds[varykey2] = v2 allt = [] for _ in xrange(repeats): t, summary = profile_gpu_connection(**kwds) print summary allt.append(t) t = mean(allt) # t = (-1)**ibase*((v1/amax(varyval1))**2+(v2/amax(varyval2))**2)+ibase T.append(t) allT.append(T) I = array(allT).T images[name] = I if show_3d_plots: V1, V2 = meshgrid(arange(len(varyval1)), arange(len(varyval2))) ax.plot_surface(V1, V2, I, color=col, label=name, alpha=0.7, rstride=1, cstride=1, shade=False) elif show_individual_2d_plots: figure() title(unvarying+'\n'+name) #imshow(I, origin='lower left', aspect='auto', interpolation='nearest') pcolor(I) # works better over ssh colorbar() idx = array(xticks()[0], dtype=int) idx = unique(idx[idx=0] xticks(idx+0.5, array(varyval1)[idx]) idx = array(yticks()[0], dtype=int) idx = unique(idx[idx=0] yticks(idx+0.5, array(varyval2)[idx]) xlabel(varykey1) ylabel(varykey2) if len(varying)==1: xlabel(varykey) ylabel('Ratio compared to realtime') legend() elif len(varying)==2: if show_3d_plots: xlabel(varykey1) ylabel(varykey2) idx = array(ax.get_xticks(), dtype=int) idx = unique(idx[idx=0] xticks(idx+0.5, array(varyval1)[idx]) idx = array(yticks()[0], dtype=int) idx = unique(idx[idx=0] yticks(idx+0.5, array(varyval2)[idx]) xlabel(varykey1) ylabel(varykey2) print 'Finished, took %ds'%int(time.time()-start_time) show() brian-1.4.3/dev/ideas/cuda/propagation/profile_connection.py000066400000000000000000000412511266062647100241620ustar00rootroot00000000000000from brian import * from brian.experimental.codegen2 import * import numpy import time import numpy.random as nrandom import random as prandom from mpl_toolkits.mplot3d import Axes3D import joblib from matplotlib import cm, rcParams #log_level_info() ##### PROFILING CODE cache_mem = joblib.Memory(cachedir='/home/dan/programming/joblib_cache', verbose=0) @cache_mem.cache def profile_gpu_connection(gpu_module, Nsource, Ntarget, sparseness, spikesperdt, parameters={}, target_time=1.0, arbitrary=None): clear(True, True) reinit_default_clock() use_gpu = bool(gpu_module) if use_gpu: ns = {} exec 'from %s import GPUConnection'%gpu_module in ns GPUConnection = ns['GPUConnection'] Conn = GPUConnection language = GPULanguage(force_sync=False) else: Conn = Connection language = CLanguage() parameters = {} nrandom.seed(3212212) prandom.seed(3432334) rate = spikesperdt/(Nsource*defaultclock.dt) source = PoissonGroup(Nsource, rate) target = NeuronGroup(Ntarget, 'dv/dt=0/second:1') target._state_updater = CodeGenStateUpdater(target, euler, language, clock=target.clock) C = Conn(source, target, 'v', weight=1, sparseness=sparseness, **parameters) run(10*ms) @network_operation def check_done(): if time.time()-start>target_time: stop() start = time.time() run(1e10*second) if use_gpu: language.gpu_man.copy_to_host(True) end = time.time() elapsed = defaultclock.t realtime_ratio = (end-start)/float(elapsed) summary = '' if use_gpu: summary += 'Using GPU (algorithm %s)\n'%gpu_module for key, value in parameters.items(): summary += ' '+key+': '+str(value)+'\n' else: summary += 'Using CPU\n' summary += 'Nsource={Nsource}, Ntarget={Ntarget}, sparseness={sparseness}, spikesperdt={spikesperdt}\n'.format( Nsource=Nsource, Ntarget=Ntarget, sparseness=sparseness, spikesperdt=spikesperdt) summary += 'Ratio compared to realtime: %.3f'%realtime_ratio return realtime_ratio, summary if __name__=='__main__': if 0: ratio, summary = profile_gpu_connection( 'vectorise_over_postsynaptic_offset', True, Nsource=4000, Ntarget=4000, sparseness=0.02, spikesperdt=2, parameters=dict( use_atomic=False, ), ) print summary if 1: show_3d_plots = False show_individual_2d_plots = False # only used if 3D plots not used repeats = 1 # bases = { ## 'C++':('b', dict(gpu_module='')), # 'CUDA/VPO':('g', dict(gpu_module='vectorise_over_postsynaptic_offset', # parameters=dict())), ## 'CUDA/VPO/F':((0.5, 1.0, 0.5), ## dict(gpu_module='vectorise_over_postsynaptic_offset', ## parameters=dict(use_float=True))), # 'CUDA/VSS':('r', dict(gpu_module='vectorise_over_spiking_synapses', # parameters=dict())), ## 'CUDA/VSS/F':((1.0, 0.5, 0.5), ## dict(gpu_module='vectorise_over_spiking_synapses', ## parameters=dict(use_float=True))), ## 'CUDA/VSS/NA':((0.5,0,0), dict(gpu_module='vectorise_over_spiking_synapses', ## parameters=dict(use_atomic=False))), # 'CUDA/DVPOT':('y', dict(gpu_module='double_vectorise_over_postsynoff_targetidx_blocked', # parameters=dict())), ## 'CUDA/DVPOT/F':((1,1,.5), ## dict(gpu_module='double_vectorise_over_postsynoff_targetidx_blocked', ## parameters=dict(use_float=True))), # 'CUDA/DVPOT/MW':((0.5,0.5,0), dict(gpu_module='double_vectorise_over_postsynoff_targetidx_blocked', # parameters=dict(masked_write=False))), ## 'CUDA/DVPOT/MW':((0.75,0.75,0.5), ## dict(gpu_module='double_vectorise_over_postsynoff_targetidx_blocked', ## parameters=dict(masked_write=False, ## use_float=True))), # 'CUDA/DVSST':('m', dict(gpu_module='double_vectorise_over_spsyn_targetidx_blocked', # parameters=dict())), ## 'CUDA/DVSST/F':((1,0.5,1), ## dict(gpu_module='double_vectorise_over_spsyn_targetidx_blocked', ## parameters=dict(use_float=True))), # 'CUDA/DVSST/MW':((0.5,0,0.5), # dict(gpu_module='double_vectorise_over_spsyn_targetidx_blocked', # parameters=dict(masked_write=False))), ## 'CUDA/DVSST/MW/F':((0.75,0.5,0.75), ## dict(gpu_module='double_vectorise_over_spsyn_targetidx_blocked', ## parameters=dict(masked_write=False, ## use_float=True))), # } bases = { 'CUDA/VPO':('g', dict(gpu_module='vectorise_over_postsynaptic_offset', parameters=dict())), 'CUDA/VSS':('r', dict(gpu_module='vectorise_over_spiking_synapses', parameters=dict())), 'CUDA/DVPOT':('y', dict(gpu_module='double_vectorise_over_postsynoff_targetidx_blocked', parameters=dict())), # 'CUDA/DVPOT/MW':((0.5,0.5,0), dict(gpu_module='double_vectorise_over_postsynoff_targetidx_blocked', # parameters=dict(masked_write=False))), 'CUDA/DVSST':('m', dict(gpu_module='double_vectorise_over_spsyn_targetidx_blocked', parameters=dict())), # 'CUDA/DVSST/MW':((0.5,0,0.5), # dict(gpu_module='double_vectorise_over_spsyn_targetidx_blocked', # parameters=dict(masked_write=False))), } repeats = 10 target_time = 1.0 # repeats = 1 # target_time = 0.1 plot_params = [ dict(Nsource=100, Ntarget=100000, # low detail # spikesperdt=[1, 10, 50, 100], # sparseness=[0.001, 0.01, 0.1, 0.3], # medium detail # spikesperdt=[1, 3, 5, 10, 25, 50], # sparseness=[0.001, 0.005, 0.01, 0.05, 0.1], # high detail # spikesperdt=range(1,10)+range(10, 101, 10), # sparseness=exp(linspace(log(0.001), log(0.3), 20)), # low detail, large range # spikesperdt=[1, 10, 50, 100], # sparseness=[0.05, 0.1, 0.3, 0.4, 0.5], # medium detail, large range # spikesperdt=range(2, 10, 2)+range(10, 101, 20), # sparseness=exp(linspace(log(0.001), log(0.8), 11)), # sparseness=hstack((exp(linspace(log(0.001), log(0.1), 6)), # linspace(0.1, 0.8, 5) # )), # medium detail linear sparseness, large range # spikesperdt=range(1, 10, 2)+range(10, 101, 20), # sparseness=linspace(0.001, 0.5, 10), # high detail, large range spikesperdt=range(1, 10)+range(10, 101, 10), sparseness=exp(linspace(log(0.001), log(0.8), 19)), ), ] labels = {'spikesperdt': 'Spikes per timestep', 'sparseness': 'Sparseness (log scale)', } start_time = time.time() for pp in plot_params: basekwds = pp varying = {} unvarying = [] images = {} for k, v in pp.items(): if isinstance(v, (list, tuple, ndarray)): varying[k] = v else: unvarying.append(k+'='+str(v)) unvarying = ', '.join(unvarying) if len(varying)>2 or len(varying)==0: print 'Can only vary one or two parameters at a time.' exit() if len(varying)==1: (varykey, varyval), = varying.items() figure() title(unvarying) elif len(varying)==2: (varykey1, varyval1), (varykey2, varyval2) = varying.items() if show_3d_plots: f = figure() title(unvarying) ax = f.add_subplot(111, projection='3d') for ibase, (name, (col, kwds)) in enumerate(bases.items()): kwds.update(basekwds) if len(varying)==1: T = [] for v in varyval: kwds[varykey] = v allt = [] for rep in xrange(repeats): kwds['arbitrary'] = rep kwds['target_time'] = target_time t, summary = profile_gpu_connection(**kwds) print summary allt.append(t) t = mean(allt) T.append(t) plot(varyval, T, '-o', label=name, color=col) elif len(varying)==2: allT = [] for v1 in varyval1: T = [] for v2 in varyval2: kwds[varykey1] = v1 kwds[varykey2] = v2 allt = [] for rep in xrange(repeats): kwds['arbitrary'] = rep kwds['target_time'] = target_time t, summary = profile_gpu_connection(**kwds) print summary allt.append(t) t = mean(allt) # t = (-1)**ibase*((v1/amax(varyval1))**2+(v2/amax(varyval2))**2)+ibase T.append(t) allT.append(T) I = array(allT).T images[name] = I if show_3d_plots: V1, V2 = meshgrid(arange(len(varyval1)), arange(len(varyval2))) ax.plot_surface(V1, V2, I, color=col, label=name, alpha=0.7, rstride=1, cstride=1, shade=False) elif show_individual_2d_plots: figure() title(unvarying+'\n'+name) #imshow(I, origin='lower left', aspect='auto', interpolation='nearest') pcolor(I) # works better over ssh colorbar() idx = array(xticks()[0], dtype=int) idx = unique(idx[idx=0] xticks(idx+0.5, array(varyval1)[idx]) idx = array(yticks()[0], dtype=int) idx = unique(idx[idx=0] yticks(idx+0.5, array(varyval2)[idx]) xlabel(labels.get(varykey1, varykey1)) ylabel(labels.get(varykey2, varykey2)) if len(varying)==1: xlabel(labels.get(varykey, varykey)) ylabel('Ratio compared to realtime') legend() elif len(varying)==2: if show_3d_plots: xlabel(labels.get(varykey1, varykey1)) ylabel(labels.get(varykey2, varykey2)) idx = array(ax.get_xticks(), dtype=int) idx = unique(idx[idx=0] idx_x = idx xticks(idx+0.5, array(varyval1)[idx]) idx = array(yticks()[0], dtype=int) idx = hstack((idx, len(varyval2)-1)) idx = unique(idx[idx=0] #yticks(idx+0.5, array(varyval2)[idx]) yticks(idx+0.5, ['%.3f'%y for y in array(varyval2)[idx]]) xlabel(labels.get(varykey1, varykey1)) ylabel(labels.get(varykey2, varykey2)) axis('tight') gca().set_aspect('equal') ratios_w2b = zeros((len(varyval2), len(varyval1))) ratios_s2f = zeros((len(varyval2), len(varyval1))) for i in xrange(len(varyval1)): for j in xrange(len(varyval2)): best = 1e10 worst = 0 c = 'k' allI = [] for name, I in images.items(): allI.append(I[j, i]) best = amin(allI) worst = amax(allI) ratios_w2b[j, i] = worst/best allI.sort() ratios_s2f[j, i] = allI[1]/allI[0] subplot(132) text(-0.15, 1.1, 'B', size=14, transform=gca().transAxes) title('Ratio worst:best') # title('Ratio second best:best') imshow(ratios_w2b, origin='lower left', aspect='auto', interpolation='nearest', # vmin=1, vmax=amax(ratios_w2b), extent=(0, len(varyval1), 0, len(varyval2))) axis('tight') colorbar() xticks(idx_x+0.5, array(varyval1)[idx_x]) yticks(idx+0.5, ['' for _ in idx])#array(varyval2)[idx]) xlabel(labels.get(varykey1, varykey1)) #ylabel(labels.get(varykey2, varykey2)) gca().set_aspect('equal') subplot(133) text(-0.15, 1.1, 'C', size=14, transform=gca().transAxes) title('Ratio second best:best') imshow(ratios_s2f, origin='lower left', aspect='auto', interpolation='nearest', #vmin=1, vmax=amax(ratios_w2b), extent=(0, len(varyval1), 0, len(varyval2))) axis('tight') colorbar() xticks(idx_x+0.5, array(varyval1)[idx_x]) yticks(idx+0.5, ['' for _ in idx])#array(varyval2)[idx]) xlabel(labels.get(varykey1, varykey1)) #ylabel(labels.get(varykey2, varykey2)) gca().set_aspect('equal') print 'Finished, took %ds'%int(time.time()-start_time) show() brian-1.4.3/dev/ideas/cuda/propagation/test_connection.py000066400000000000000000000043541266062647100235040ustar00rootroot00000000000000from brian import * from brian.experimental.codegen2 import * import numpy import time import numpy.random as nrandom import random as prandom try: import pycuda except ImportError: pycuda = None log_level_info() ##### TESTING PARAMETERS #from vectorise_over_postsynaptic_offset import * #from vectorise_over_spiking_synapses import * #from double_vectorise_over_postsynoff_targetidx_blocked import * from double_vectorise_over_spsyn_targetidx_blocked import * use_gpu = True parameters = dict(use_atomic=True) do_plot = False scalar = 'float' #scalar = 'double' ##### PROFILING CODE if use_gpu: Conn = GPUConnection language = GPULanguage(force_sync=do_plot, scalar=scalar) parameters['use_float'] = scalar=='float' else: Conn = Connection language = CLanguage() parameters = {} eqs = ''' dv/dt = (ge+gi-(v+49*mV))/(20*ms) : volt dge/dt = -ge/(5*ms) : volt dgi/dt = -gi/(10*ms) : volt ''' threshold = 'v > -50*mV' reset = ''' v = -60*mV ''' nrandom.seed(213213) prandom.seed(343831) P = NeuronGroup(4000, eqs, threshold=threshold, reset=reset) P.v = -60 * mV + 10 * mV * rand(len(P)) Pe = P.subgroup(3200) Pi = P.subgroup(800) P._state_updater = CodeGenStateUpdater(P, euler, language, clock=P.clock) P._threshold = CodeGenThreshold(P, threshold, language) P._resetfun = CodeGenReset(P, reset, language) Ce = Conn(Pe, P, 'ge', weight=1.62*mV, sparseness=0.02, **parameters) Ci = Conn(Pi, P, 'gi', weight=-9*mV, sparseness=0.02, **parameters) if do_plot: M = SpikeMonitor(P) Mv = StateMonitor(P, 'ge', record=arange(100)) else: M = SpikeCounter(P) if use_gpu: language.gpu_man.copy_to_device(True) run(10*ms) #exit() start = time.time() run(0.1*second-10*ms, report='stderr') end = time.time() if use_gpu: print 'Using GPU' for k, v in parameters.items(): print ' '+k+':', v else: print 'Using CPU' correct_nspikes = 2443 # copied from CPU run print 'Time:', end-start print 'Num spikes:', M.nspikes, 'should be', correct_nspikes if M.nspikes==correct_nspikes: print 'Success!' else: print 'FAILED!' if do_plot: subplot(211) raster_plot(M) subplot(212) Mv.plot() show() brian-1.4.3/dev/ideas/cuda/propagation/vectorise_over_postsynaptic_offset.py000066400000000000000000000174331266062647100275340ustar00rootroot00000000000000''' Notes: Based on Issam's forDan/178_end.cu. The idea is that for each spiking neuron i, it is connected to tgt[off[i]+k] for 0<=k=numspikes) return; const int spike = spikes[spikes_index]; if(synaptic_offset 0).nonzero()[0] y = drv.mem_alloc(100 * 8) drv.memcpy_htod(y, x) out = drv.mem_alloc(100 * 8) count = drv.mem_alloc(4) #y = to_gpu(x) #out = to_gpu(zeros(len(x))) #count = to_gpu(array([0], dtype=int)) find_positive(int(y), int(y) + len(x) * 8, int(out), int(count)) #find_positive(int(y.gpudata), int(y.gpudata)+len(x)*8, # int(out.gpudata), int(count.gpudata)) z = zeros(100) drv.memcpy_dtoh(z, out) c = array([0], dtype=int) drv.memcpy_dtoh(c, count) z = z[:c[0]] #z = out.get()[:count.get()[0]] print z - x[I] brian-1.4.3/dev/ideas/cuda/test_chag_pp/testchagpp.cu000066400000000000000000000012251266062647100225360ustar00rootroot00000000000000#include extern "C" { #include "testchagpp.h" namespace pp = chag::pp; struct Predicate { __device__ bool operator() (double value) const { return value>0.0; } }; void find_positive( int x_gpu_start, int x_gpu_end, int y_gpu_start, int count_start ) { pp::compact( (double *)x_gpu_start, /* Input start pointer */ (double *)x_gpu_end, /* Input end pointer */ (double *)y_gpu_start, /* Output start pointer */ (size_t *)count_start, /* Storage for valid element count */ Predicate() /* Predicate */ ); } }brian-1.4.3/dev/ideas/cuda/test_chag_pp/testchagpp.h000066400000000000000000000002711266062647100223560ustar00rootroot00000000000000#ifndef _TEST_CHAG_PP #define _TEST_CHAG_PP __declspec(dllexport) void find_positive( int x_gpu_start, int x_gpu_end, int y_gpu_start, int count_start ); #endif brian-1.4.3/dev/ideas/cuda/test_chag_pp/testchagpp.i000066400000000000000000000001571266062647100223620ustar00rootroot00000000000000%module testchagpp %{ #define SWIG_FILE_WITH_INIT #include "testchagpp.h" %} %include "testchagpp.h" brian-1.4.3/dev/ideas/cuda/test_chag_pp/testchagpp.py000066400000000000000000000032361266062647100225630ustar00rootroot00000000000000# This file was automatically generated by SWIG (http://www.swig.org). # Version 1.3.36 # # Don't modify this file, modify the SWIG interface instead. # This file is compatible with both classic and new-style classes. import _testchagpp import new new_instancemethod = new.instancemethod try: _swig_property = property except NameError: pass # Python < 2.2 doesn't have 'property'. def _swig_setattr_nondynamic(self, class_type, name, value, static=1): if (name == "thisown"): return self.this.own(value) if (name == "this"): if type(value).__name__ == 'PySwigObject': self.__dict__[name] = value return method = class_type.__swig_setmethods__.get(name, None) if method: return method(self, value) if (not static) or hasattr(self, name): self.__dict__[name] = value else: raise AttributeError("You cannot add attributes to %s" % self) def _swig_setattr(self, class_type, name, value): return _swig_setattr_nondynamic(self, class_type, name, value, 0) def _swig_getattr(self, class_type, name): if (name == "thisown"): return self.this.own() method = class_type.__swig_getmethods__.get(name, None) if method: return method(self) raise AttributeError, name def _swig_repr(self): try: strthis = "proxy of " + self.this.__repr__() except: strthis = "" return "<%s.%s; %s >" % (self.__class__.__module__, self.__class__.__name__, strthis,) import types try: _object = types.ObjectType _newclass = 1 except AttributeError: class _object : pass _newclass = 0 del types find_positive = _testchagpp.find_positive brian-1.4.3/dev/ideas/cuda/test_chag_pp/testchagpp_wrap.cxx000066400000000000000000003034211266062647100237650ustar00rootroot00000000000000/* ---------------------------------------------------------------------------- * This file was automatically generated by SWIG (http://www.swig.org). * Version 1.3.36 * * This file is not intended to be easily readable and contains a number of * coding conventions designed to improve portability and efficiency. Do not make * changes to this file unless you know what you are doing--modify the SWIG * interface file instead. * ----------------------------------------------------------------------------- */ #define SWIGPYTHON #define SWIG_PYTHON_DIRECTOR_NO_VTABLE #ifdef __cplusplus template class SwigValueWrapper { T *tt; public: SwigValueWrapper() : tt(0) { } SwigValueWrapper(const SwigValueWrapper& rhs) : tt(new T(*rhs.tt)) { } SwigValueWrapper(const T& t) : tt(new T(t)) { } ~SwigValueWrapper() { delete tt; } SwigValueWrapper& operator=(const T& t) { delete tt; tt = new T(t); return *this; } operator T&() const { return *tt; } T *operator&() { return tt; } private: SwigValueWrapper& operator=(const SwigValueWrapper& rhs); }; template T SwigValueInit() { return T(); } #endif /* ----------------------------------------------------------------------------- * This section contains generic SWIG labels for method/variable * declarations/attributes, and other compiler dependent labels. * ----------------------------------------------------------------------------- */ /* template workaround for compilers that cannot correctly implement the C++ standard */ #ifndef SWIGTEMPLATEDISAMBIGUATOR # if defined(__SUNPRO_CC) && (__SUNPRO_CC <= 0x560) # define SWIGTEMPLATEDISAMBIGUATOR template # elif defined(__HP_aCC) /* Needed even with `aCC -AA' when `aCC -V' reports HP ANSI C++ B3910B A.03.55 */ /* If we find a maximum version that requires this, the test would be __HP_aCC <= 35500 for A.03.55 */ # define SWIGTEMPLATEDISAMBIGUATOR template # else # define SWIGTEMPLATEDISAMBIGUATOR # endif #endif /* inline attribute */ #ifndef SWIGINLINE # if defined(__cplusplus) || (defined(__GNUC__) && !defined(__STRICT_ANSI__)) # define SWIGINLINE inline # else # define SWIGINLINE # endif #endif /* attribute recognised by some compilers to avoid 'unused' warnings */ #ifndef SWIGUNUSED # if defined(__GNUC__) # if !(defined(__cplusplus)) || (__GNUC__ > 3 || (__GNUC__ == 3 && __GNUC_MINOR__ >= 4)) # define SWIGUNUSED __attribute__ ((__unused__)) # else # define SWIGUNUSED # endif # elif defined(__ICC) # define SWIGUNUSED __attribute__ ((__unused__)) # else # define SWIGUNUSED # endif #endif #ifndef SWIG_MSC_UNSUPPRESS_4505 # if defined(_MSC_VER) # pragma warning(disable : 4505) /* unreferenced local function has been removed */ # endif #endif #ifndef SWIGUNUSEDPARM # ifdef __cplusplus # define SWIGUNUSEDPARM(p) # else # define SWIGUNUSEDPARM(p) p SWIGUNUSED # endif #endif /* internal SWIG method */ #ifndef SWIGINTERN # define SWIGINTERN static SWIGUNUSED #endif /* internal inline SWIG method */ #ifndef SWIGINTERNINLINE # define SWIGINTERNINLINE SWIGINTERN SWIGINLINE #endif /* exporting methods */ #if (__GNUC__ >= 4) || (__GNUC__ == 3 && __GNUC_MINOR__ >= 4) # ifndef GCC_HASCLASSVISIBILITY # define GCC_HASCLASSVISIBILITY # endif #endif #ifndef SWIGEXPORT # if defined(_WIN32) || defined(__WIN32__) || defined(__CYGWIN__) # if defined(STATIC_LINKED) # define SWIGEXPORT # else # define SWIGEXPORT __declspec(dllexport) # endif # else # if defined(__GNUC__) && defined(GCC_HASCLASSVISIBILITY) # define SWIGEXPORT __attribute__ ((visibility("default"))) # else # define SWIGEXPORT # endif # endif #endif /* calling conventions for Windows */ #ifndef SWIGSTDCALL # if defined(_WIN32) || defined(__WIN32__) || defined(__CYGWIN__) # define SWIGSTDCALL __stdcall # else # define SWIGSTDCALL # endif #endif /* Deal with Microsoft's attempt at deprecating C standard runtime functions */ #if !defined(SWIG_NO_CRT_SECURE_NO_DEPRECATE) && defined(_MSC_VER) && !defined(_CRT_SECURE_NO_DEPRECATE) # define _CRT_SECURE_NO_DEPRECATE #endif /* Deal with Microsoft's attempt at deprecating methods in the standard C++ library */ #if !defined(SWIG_NO_SCL_SECURE_NO_DEPRECATE) && defined(_MSC_VER) && !defined(_SCL_SECURE_NO_DEPRECATE) # define _SCL_SECURE_NO_DEPRECATE #endif /* Python.h has to appear first */ #include /* ----------------------------------------------------------------------------- * swigrun.swg * * This file contains generic CAPI SWIG runtime support for pointer * type checking. * ----------------------------------------------------------------------------- */ /* This should only be incremented when either the layout of swig_type_info changes, or for whatever reason, the runtime changes incompatibly */ #define SWIG_RUNTIME_VERSION "4" /* define SWIG_TYPE_TABLE_NAME as "SWIG_TYPE_TABLE" */ #ifdef SWIG_TYPE_TABLE # define SWIG_QUOTE_STRING(x) #x # define SWIG_EXPAND_AND_QUOTE_STRING(x) SWIG_QUOTE_STRING(x) # define SWIG_TYPE_TABLE_NAME SWIG_EXPAND_AND_QUOTE_STRING(SWIG_TYPE_TABLE) #else # define SWIG_TYPE_TABLE_NAME #endif /* You can use the SWIGRUNTIME and SWIGRUNTIMEINLINE macros for creating a static or dynamic library from the swig runtime code. In 99.9% of the cases, swig just needs to declare them as 'static'. But only do this if is strictly necessary, ie, if you have problems with your compiler or so. */ #ifndef SWIGRUNTIME # define SWIGRUNTIME SWIGINTERN #endif #ifndef SWIGRUNTIMEINLINE # define SWIGRUNTIMEINLINE SWIGRUNTIME SWIGINLINE #endif /* Generic buffer size */ #ifndef SWIG_BUFFER_SIZE # define SWIG_BUFFER_SIZE 1024 #endif /* Flags for pointer conversions */ #define SWIG_POINTER_DISOWN 0x1 #define SWIG_CAST_NEW_MEMORY 0x2 /* Flags for new pointer objects */ #define SWIG_POINTER_OWN 0x1 /* Flags/methods for returning states. The swig conversion methods, as ConvertPtr, return and integer that tells if the conversion was successful or not. And if not, an error code can be returned (see swigerrors.swg for the codes). Use the following macros/flags to set or process the returning states. In old swig versions, you usually write code as: if (SWIG_ConvertPtr(obj,vptr,ty.flags) != -1) { // success code } else { //fail code } Now you can be more explicit as: int res = SWIG_ConvertPtr(obj,vptr,ty.flags); if (SWIG_IsOK(res)) { // success code } else { // fail code } that seems to be the same, but now you can also do Type *ptr; int res = SWIG_ConvertPtr(obj,(void **)(&ptr),ty.flags); if (SWIG_IsOK(res)) { // success code if (SWIG_IsNewObj(res) { ... delete *ptr; } else { ... } } else { // fail code } I.e., now SWIG_ConvertPtr can return new objects and you can identify the case and take care of the deallocation. Of course that requires also to SWIG_ConvertPtr to return new result values, as int SWIG_ConvertPtr(obj, ptr,...) { if () { if () { *ptr = ; return SWIG_NEWOBJ; } else { *ptr = ; return SWIG_OLDOBJ; } } else { return SWIG_BADOBJ; } } Of course, returning the plain '0(success)/-1(fail)' still works, but you can be more explicit by returning SWIG_BADOBJ, SWIG_ERROR or any of the swig errors code. Finally, if the SWIG_CASTRANK_MODE is enabled, the result code allows to return the 'cast rank', for example, if you have this int food(double) int fooi(int); and you call food(1) // cast rank '1' (1 -> 1.0) fooi(1) // cast rank '0' just use the SWIG_AddCast()/SWIG_CheckState() */ #define SWIG_OK (0) #define SWIG_ERROR (-1) #define SWIG_IsOK(r) (r >= 0) #define SWIG_ArgError(r) ((r != SWIG_ERROR) ? r : SWIG_TypeError) /* The CastRankLimit says how many bits are used for the cast rank */ #define SWIG_CASTRANKLIMIT (1 << 8) /* The NewMask denotes the object was created (using new/malloc) */ #define SWIG_NEWOBJMASK (SWIG_CASTRANKLIMIT << 1) /* The TmpMask is for in/out typemaps that use temporal objects */ #define SWIG_TMPOBJMASK (SWIG_NEWOBJMASK << 1) /* Simple returning values */ #define SWIG_BADOBJ (SWIG_ERROR) #define SWIG_OLDOBJ (SWIG_OK) #define SWIG_NEWOBJ (SWIG_OK | SWIG_NEWOBJMASK) #define SWIG_TMPOBJ (SWIG_OK | SWIG_TMPOBJMASK) /* Check, add and del mask methods */ #define SWIG_AddNewMask(r) (SWIG_IsOK(r) ? (r | SWIG_NEWOBJMASK) : r) #define SWIG_DelNewMask(r) (SWIG_IsOK(r) ? (r & ~SWIG_NEWOBJMASK) : r) #define SWIG_IsNewObj(r) (SWIG_IsOK(r) && (r & SWIG_NEWOBJMASK)) #define SWIG_AddTmpMask(r) (SWIG_IsOK(r) ? (r | SWIG_TMPOBJMASK) : r) #define SWIG_DelTmpMask(r) (SWIG_IsOK(r) ? (r & ~SWIG_TMPOBJMASK) : r) #define SWIG_IsTmpObj(r) (SWIG_IsOK(r) && (r & SWIG_TMPOBJMASK)) /* Cast-Rank Mode */ #if defined(SWIG_CASTRANK_MODE) # ifndef SWIG_TypeRank # define SWIG_TypeRank unsigned long # endif # ifndef SWIG_MAXCASTRANK /* Default cast allowed */ # define SWIG_MAXCASTRANK (2) # endif # define SWIG_CASTRANKMASK ((SWIG_CASTRANKLIMIT) -1) # define SWIG_CastRank(r) (r & SWIG_CASTRANKMASK) SWIGINTERNINLINE int SWIG_AddCast(int r) { return SWIG_IsOK(r) ? ((SWIG_CastRank(r) < SWIG_MAXCASTRANK) ? (r + 1) : SWIG_ERROR) : r; } SWIGINTERNINLINE int SWIG_CheckState(int r) { return SWIG_IsOK(r) ? SWIG_CastRank(r) + 1 : 0; } #else /* no cast-rank mode */ # define SWIG_AddCast # define SWIG_CheckState(r) (SWIG_IsOK(r) ? 1 : 0) #endif #include #ifdef __cplusplus extern "C" { #endif typedef void *(*swig_converter_func)(void *, int *); typedef struct swig_type_info *(*swig_dycast_func)(void **); /* Structure to store information on one type */ typedef struct swig_type_info { const char *name; /* mangled name of this type */ const char *str; /* human readable name of this type */ swig_dycast_func dcast; /* dynamic cast function down a hierarchy */ struct swig_cast_info *cast; /* linked list of types that can cast into this type */ void *clientdata; /* language specific type data */ int owndata; /* flag if the structure owns the clientdata */ } swig_type_info; /* Structure to store a type and conversion function used for casting */ typedef struct swig_cast_info { swig_type_info *type; /* pointer to type that is equivalent to this type */ swig_converter_func converter; /* function to cast the void pointers */ struct swig_cast_info *next; /* pointer to next cast in linked list */ struct swig_cast_info *prev; /* pointer to the previous cast */ } swig_cast_info; /* Structure used to store module information * Each module generates one structure like this, and the runtime collects * all of these structures and stores them in a circularly linked list.*/ typedef struct swig_module_info { swig_type_info **types; /* Array of pointers to swig_type_info structures that are in this module */ size_t size; /* Number of types in this module */ struct swig_module_info *next; /* Pointer to next element in circularly linked list */ swig_type_info **type_initial; /* Array of initially generated type structures */ swig_cast_info **cast_initial; /* Array of initially generated casting structures */ void *clientdata; /* Language specific module data */ } swig_module_info; /* Compare two type names skipping the space characters, therefore "char*" == "char *" and "Class" == "Class", etc. Return 0 when the two name types are equivalent, as in strncmp, but skipping ' '. */ SWIGRUNTIME int SWIG_TypeNameComp(const char *f1, const char *l1, const char *f2, const char *l2) { for (;(f1 != l1) && (f2 != l2); ++f1, ++f2) { while ((*f1 == ' ') && (f1 != l1)) ++f1; while ((*f2 == ' ') && (f2 != l2)) ++f2; if (*f1 != *f2) return (*f1 > *f2) ? 1 : -1; } return (int)((l1 - f1) - (l2 - f2)); } /* Check type equivalence in a name list like ||... Return 0 if not equal, 1 if equal */ SWIGRUNTIME int SWIG_TypeEquiv(const char *nb, const char *tb) { int equiv = 0; const char* te = tb + strlen(tb); const char* ne = nb; while (!equiv && *ne) { for (nb = ne; *ne; ++ne) { if (*ne == '|') break; } equiv = (SWIG_TypeNameComp(nb, ne, tb, te) == 0) ? 1 : 0; if (*ne) ++ne; } return equiv; } /* Check type equivalence in a name list like ||... Return 0 if equal, -1 if nb < tb, 1 if nb > tb */ SWIGRUNTIME int SWIG_TypeCompare(const char *nb, const char *tb) { int equiv = 0; const char* te = tb + strlen(tb); const char* ne = nb; while (!equiv && *ne) { for (nb = ne; *ne; ++ne) { if (*ne == '|') break; } equiv = (SWIG_TypeNameComp(nb, ne, tb, te) == 0) ? 1 : 0; if (*ne) ++ne; } return equiv; } /* think of this as a c++ template<> or a scheme macro */ #define SWIG_TypeCheck_Template(comparison, ty) \ if (ty) { \ swig_cast_info *iter = ty->cast; \ while (iter) { \ if (comparison) { \ if (iter == ty->cast) return iter; \ /* Move iter to the top of the linked list */ \ iter->prev->next = iter->next; \ if (iter->next) \ iter->next->prev = iter->prev; \ iter->next = ty->cast; \ iter->prev = 0; \ if (ty->cast) ty->cast->prev = iter; \ ty->cast = iter; \ return iter; \ } \ iter = iter->next; \ } \ } \ return 0 /* Check the typename */ SWIGRUNTIME swig_cast_info * SWIG_TypeCheck(const char *c, swig_type_info *ty) { SWIG_TypeCheck_Template(strcmp(iter->type->name, c) == 0, ty); } /* Same as previous function, except strcmp is replaced with a pointer comparison */ SWIGRUNTIME swig_cast_info * SWIG_TypeCheckStruct(swig_type_info *from, swig_type_info *into) { SWIG_TypeCheck_Template(iter->type == from, into); } /* Cast a pointer up an inheritance hierarchy */ SWIGRUNTIMEINLINE void * SWIG_TypeCast(swig_cast_info *ty, void *ptr, int *newmemory) { return ((!ty) || (!ty->converter)) ? ptr : (*ty->converter)(ptr, newmemory); } /* Dynamic pointer casting. Down an inheritance hierarchy */ SWIGRUNTIME swig_type_info * SWIG_TypeDynamicCast(swig_type_info *ty, void **ptr) { swig_type_info *lastty = ty; if (!ty || !ty->dcast) return ty; while (ty && (ty->dcast)) { ty = (*ty->dcast)(ptr); if (ty) lastty = ty; } return lastty; } /* Return the name associated with this type */ SWIGRUNTIMEINLINE const char * SWIG_TypeName(const swig_type_info *ty) { return ty->name; } /* Return the pretty name associated with this type, that is an unmangled type name in a form presentable to the user. */ SWIGRUNTIME const char * SWIG_TypePrettyName(const swig_type_info *type) { /* The "str" field contains the equivalent pretty names of the type, separated by vertical-bar characters. We choose to print the last name, as it is often (?) the most specific. */ if (!type) return NULL; if (type->str != NULL) { const char *last_name = type->str; const char *s; for (s = type->str; *s; s++) if (*s == '|') last_name = s+1; return last_name; } else return type->name; } /* Set the clientdata field for a type */ SWIGRUNTIME void SWIG_TypeClientData(swig_type_info *ti, void *clientdata) { swig_cast_info *cast = ti->cast; /* if (ti->clientdata == clientdata) return; */ ti->clientdata = clientdata; while (cast) { if (!cast->converter) { swig_type_info *tc = cast->type; if (!tc->clientdata) { SWIG_TypeClientData(tc, clientdata); } } cast = cast->next; } } SWIGRUNTIME void SWIG_TypeNewClientData(swig_type_info *ti, void *clientdata) { SWIG_TypeClientData(ti, clientdata); ti->owndata = 1; } /* Search for a swig_type_info structure only by mangled name Search is a O(log #types) We start searching at module start, and finish searching when start == end. Note: if start == end at the beginning of the function, we go all the way around the circular list. */ SWIGRUNTIME swig_type_info * SWIG_MangledTypeQueryModule(swig_module_info *start, swig_module_info *end, const char *name) { swig_module_info *iter = start; do { if (iter->size) { register size_t l = 0; register size_t r = iter->size - 1; do { /* since l+r >= 0, we can (>> 1) instead (/ 2) */ register size_t i = (l + r) >> 1; const char *iname = iter->types[i]->name; if (iname) { register int compare = strcmp(name, iname); if (compare == 0) { return iter->types[i]; } else if (compare < 0) { if (i) { r = i - 1; } else { break; } } else if (compare > 0) { l = i + 1; } } else { break; /* should never happen */ } } while (l <= r); } iter = iter->next; } while (iter != end); return 0; } /* Search for a swig_type_info structure for either a mangled name or a human readable name. It first searches the mangled names of the types, which is a O(log #types) If a type is not found it then searches the human readable names, which is O(#types). We start searching at module start, and finish searching when start == end. Note: if start == end at the beginning of the function, we go all the way around the circular list. */ SWIGRUNTIME swig_type_info * SWIG_TypeQueryModule(swig_module_info *start, swig_module_info *end, const char *name) { /* STEP 1: Search the name field using binary search */ swig_type_info *ret = SWIG_MangledTypeQueryModule(start, end, name); if (ret) { return ret; } else { /* STEP 2: If the type hasn't been found, do a complete search of the str field (the human readable name) */ swig_module_info *iter = start; do { register size_t i = 0; for (; i < iter->size; ++i) { if (iter->types[i]->str && (SWIG_TypeEquiv(iter->types[i]->str, name))) return iter->types[i]; } iter = iter->next; } while (iter != end); } /* neither found a match */ return 0; } /* Pack binary data into a string */ SWIGRUNTIME char * SWIG_PackData(char *c, void *ptr, size_t sz) { static const char hex[17] = "0123456789abcdef"; register const unsigned char *u = (unsigned char *) ptr; register const unsigned char *eu = u + sz; for (; u != eu; ++u) { register unsigned char uu = *u; *(c++) = hex[(uu & 0xf0) >> 4]; *(c++) = hex[uu & 0xf]; } return c; } /* Unpack binary data from a string */ SWIGRUNTIME const char * SWIG_UnpackData(const char *c, void *ptr, size_t sz) { register unsigned char *u = (unsigned char *) ptr; register const unsigned char *eu = u + sz; for (; u != eu; ++u) { register char d = *(c++); register unsigned char uu; if ((d >= '0') && (d <= '9')) uu = ((d - '0') << 4); else if ((d >= 'a') && (d <= 'f')) uu = ((d - ('a'-10)) << 4); else return (char *) 0; d = *(c++); if ((d >= '0') && (d <= '9')) uu |= (d - '0'); else if ((d >= 'a') && (d <= 'f')) uu |= (d - ('a'-10)); else return (char *) 0; *u = uu; } return c; } /* Pack 'void *' into a string buffer. */ SWIGRUNTIME char * SWIG_PackVoidPtr(char *buff, void *ptr, const char *name, size_t bsz) { char *r = buff; if ((2*sizeof(void *) + 2) > bsz) return 0; *(r++) = '_'; r = SWIG_PackData(r,&ptr,sizeof(void *)); if (strlen(name) + 1 > (bsz - (r - buff))) return 0; strcpy(r,name); return buff; } SWIGRUNTIME const char * SWIG_UnpackVoidPtr(const char *c, void **ptr, const char *name) { if (*c != '_') { if (strcmp(c,"NULL") == 0) { *ptr = (void *) 0; return name; } else { return 0; } } return SWIG_UnpackData(++c,ptr,sizeof(void *)); } SWIGRUNTIME char * SWIG_PackDataName(char *buff, void *ptr, size_t sz, const char *name, size_t bsz) { char *r = buff; size_t lname = (name ? strlen(name) : 0); if ((2*sz + 2 + lname) > bsz) return 0; *(r++) = '_'; r = SWIG_PackData(r,ptr,sz); if (lname) { strncpy(r,name,lname+1); } else { *r = 0; } return buff; } SWIGRUNTIME const char * SWIG_UnpackDataName(const char *c, void *ptr, size_t sz, const char *name) { if (*c != '_') { if (strcmp(c,"NULL") == 0) { memset(ptr,0,sz); return name; } else { return 0; } } return SWIG_UnpackData(++c,ptr,sz); } #ifdef __cplusplus } #endif /* Errors in SWIG */ #define SWIG_UnknownError -1 #define SWIG_IOError -2 #define SWIG_RuntimeError -3 #define SWIG_IndexError -4 #define SWIG_TypeError -5 #define SWIG_DivisionByZero -6 #define SWIG_OverflowError -7 #define SWIG_SyntaxError -8 #define SWIG_ValueError -9 #define SWIG_SystemError -10 #define SWIG_AttributeError -11 #define SWIG_MemoryError -12 #define SWIG_NullReferenceError -13 /* Add PyOS_snprintf for old Pythons */ #if PY_VERSION_HEX < 0x02020000 # if defined(_MSC_VER) || defined(__BORLANDC__) || defined(_WATCOM) # define PyOS_snprintf _snprintf # else # define PyOS_snprintf snprintf # endif #endif /* A crude PyString_FromFormat implementation for old Pythons */ #if PY_VERSION_HEX < 0x02020000 #ifndef SWIG_PYBUFFER_SIZE # define SWIG_PYBUFFER_SIZE 1024 #endif static PyObject * PyString_FromFormat(const char *fmt, ...) { va_list ap; char buf[SWIG_PYBUFFER_SIZE * 2]; int res; va_start(ap, fmt); res = vsnprintf(buf, sizeof(buf), fmt, ap); va_end(ap); return (res < 0 || res >= (int)sizeof(buf)) ? 0 : PyString_FromString(buf); } #endif /* Add PyObject_Del for old Pythons */ #if PY_VERSION_HEX < 0x01060000 # define PyObject_Del(op) PyMem_DEL((op)) #endif #ifndef PyObject_DEL # define PyObject_DEL PyObject_Del #endif /* A crude PyExc_StopIteration exception for old Pythons */ #if PY_VERSION_HEX < 0x02020000 # ifndef PyExc_StopIteration # define PyExc_StopIteration PyExc_RuntimeError # endif # ifndef PyObject_GenericGetAttr # define PyObject_GenericGetAttr 0 # endif #endif /* Py_NotImplemented is defined in 2.1 and up. */ #if PY_VERSION_HEX < 0x02010000 # ifndef Py_NotImplemented # define Py_NotImplemented PyExc_RuntimeError # endif #endif /* A crude PyString_AsStringAndSize implementation for old Pythons */ #if PY_VERSION_HEX < 0x02010000 # ifndef PyString_AsStringAndSize # define PyString_AsStringAndSize(obj, s, len) {*s = PyString_AsString(obj); *len = *s ? strlen(*s) : 0;} # endif #endif /* PySequence_Size for old Pythons */ #if PY_VERSION_HEX < 0x02000000 # ifndef PySequence_Size # define PySequence_Size PySequence_Length # endif #endif /* PyBool_FromLong for old Pythons */ #if PY_VERSION_HEX < 0x02030000 static PyObject *PyBool_FromLong(long ok) { PyObject *result = ok ? Py_True : Py_False; Py_INCREF(result); return result; } #endif /* Py_ssize_t for old Pythons */ /* This code is as recommended by: */ /* http://www.python.org/dev/peps/pep-0353/#conversion-guidelines */ #if PY_VERSION_HEX < 0x02050000 && !defined(PY_SSIZE_T_MIN) typedef int Py_ssize_t; # define PY_SSIZE_T_MAX INT_MAX # define PY_SSIZE_T_MIN INT_MIN #endif /* ----------------------------------------------------------------------------- * error manipulation * ----------------------------------------------------------------------------- */ SWIGRUNTIME PyObject* SWIG_Python_ErrorType(int code) { PyObject* type = 0; switch(code) { case SWIG_MemoryError: type = PyExc_MemoryError; break; case SWIG_IOError: type = PyExc_IOError; break; case SWIG_RuntimeError: type = PyExc_RuntimeError; break; case SWIG_IndexError: type = PyExc_IndexError; break; case SWIG_TypeError: type = PyExc_TypeError; break; case SWIG_DivisionByZero: type = PyExc_ZeroDivisionError; break; case SWIG_OverflowError: type = PyExc_OverflowError; break; case SWIG_SyntaxError: type = PyExc_SyntaxError; break; case SWIG_ValueError: type = PyExc_ValueError; break; case SWIG_SystemError: type = PyExc_SystemError; break; case SWIG_AttributeError: type = PyExc_AttributeError; break; default: type = PyExc_RuntimeError; } return type; } SWIGRUNTIME void SWIG_Python_AddErrorMsg(const char* mesg) { PyObject *type = 0; PyObject *value = 0; PyObject *traceback = 0; if (PyErr_Occurred()) PyErr_Fetch(&type, &value, &traceback); if (value) { PyObject *old_str = PyObject_Str(value); PyErr_Clear(); Py_XINCREF(type); PyErr_Format(type, "%s %s", PyString_AsString(old_str), mesg); Py_DECREF(old_str); Py_DECREF(value); } else { PyErr_SetString(PyExc_RuntimeError, mesg); } } #if defined(SWIG_PYTHON_NO_THREADS) # if defined(SWIG_PYTHON_THREADS) # undef SWIG_PYTHON_THREADS # endif #endif #if defined(SWIG_PYTHON_THREADS) /* Threading support is enabled */ # if !defined(SWIG_PYTHON_USE_GIL) && !defined(SWIG_PYTHON_NO_USE_GIL) # if (PY_VERSION_HEX >= 0x02030000) /* For 2.3 or later, use the PyGILState calls */ # define SWIG_PYTHON_USE_GIL # endif # endif # if defined(SWIG_PYTHON_USE_GIL) /* Use PyGILState threads calls */ # ifndef SWIG_PYTHON_INITIALIZE_THREADS # define SWIG_PYTHON_INITIALIZE_THREADS PyEval_InitThreads() # endif # ifdef __cplusplus /* C++ code */ class SWIG_Python_Thread_Block { bool status; PyGILState_STATE state; public: void end() { if (status) { PyGILState_Release(state); status = false;} } SWIG_Python_Thread_Block() : status(true), state(PyGILState_Ensure()) {} ~SWIG_Python_Thread_Block() { end(); } }; class SWIG_Python_Thread_Allow { bool status; PyThreadState *save; public: void end() { if (status) { PyEval_RestoreThread(save); status = false; }} SWIG_Python_Thread_Allow() : status(true), save(PyEval_SaveThread()) {} ~SWIG_Python_Thread_Allow() { end(); } }; # define SWIG_PYTHON_THREAD_BEGIN_BLOCK SWIG_Python_Thread_Block _swig_thread_block # define SWIG_PYTHON_THREAD_END_BLOCK _swig_thread_block.end() # define SWIG_PYTHON_THREAD_BEGIN_ALLOW SWIG_Python_Thread_Allow _swig_thread_allow # define SWIG_PYTHON_THREAD_END_ALLOW _swig_thread_allow.end() # else /* C code */ # define SWIG_PYTHON_THREAD_BEGIN_BLOCK PyGILState_STATE _swig_thread_block = PyGILState_Ensure() # define SWIG_PYTHON_THREAD_END_BLOCK PyGILState_Release(_swig_thread_block) # define SWIG_PYTHON_THREAD_BEGIN_ALLOW PyThreadState *_swig_thread_allow = PyEval_SaveThread() # define SWIG_PYTHON_THREAD_END_ALLOW PyEval_RestoreThread(_swig_thread_allow) # endif # else /* Old thread way, not implemented, user must provide it */ # if !defined(SWIG_PYTHON_INITIALIZE_THREADS) # define SWIG_PYTHON_INITIALIZE_THREADS # endif # if !defined(SWIG_PYTHON_THREAD_BEGIN_BLOCK) # define SWIG_PYTHON_THREAD_BEGIN_BLOCK # endif # if !defined(SWIG_PYTHON_THREAD_END_BLOCK) # define SWIG_PYTHON_THREAD_END_BLOCK # endif # if !defined(SWIG_PYTHON_THREAD_BEGIN_ALLOW) # define SWIG_PYTHON_THREAD_BEGIN_ALLOW # endif # if !defined(SWIG_PYTHON_THREAD_END_ALLOW) # define SWIG_PYTHON_THREAD_END_ALLOW # endif # endif #else /* No thread support */ # define SWIG_PYTHON_INITIALIZE_THREADS # define SWIG_PYTHON_THREAD_BEGIN_BLOCK # define SWIG_PYTHON_THREAD_END_BLOCK # define SWIG_PYTHON_THREAD_BEGIN_ALLOW # define SWIG_PYTHON_THREAD_END_ALLOW #endif /* ----------------------------------------------------------------------------- * Python API portion that goes into the runtime * ----------------------------------------------------------------------------- */ #ifdef __cplusplus extern "C" { #if 0 } /* cc-mode */ #endif #endif /* ----------------------------------------------------------------------------- * Constant declarations * ----------------------------------------------------------------------------- */ /* Constant Types */ #define SWIG_PY_POINTER 4 #define SWIG_PY_BINARY 5 /* Constant information structure */ typedef struct swig_const_info { int type; char *name; long lvalue; double dvalue; void *pvalue; swig_type_info **ptype; } swig_const_info; #ifdef __cplusplus #if 0 { /* cc-mode */ #endif } #endif /* ----------------------------------------------------------------------------- * See the LICENSE file for information on copyright, usage and redistribution * of SWIG, and the README file for authors - http://www.swig.org/release.html. * * pyrun.swg * * This file contains the runtime support for Python modules * and includes code for managing global variables and pointer * type checking. * * ----------------------------------------------------------------------------- */ /* Common SWIG API */ /* for raw pointers */ #define SWIG_Python_ConvertPtr(obj, pptr, type, flags) SWIG_Python_ConvertPtrAndOwn(obj, pptr, type, flags, 0) #define SWIG_ConvertPtr(obj, pptr, type, flags) SWIG_Python_ConvertPtr(obj, pptr, type, flags) #define SWIG_ConvertPtrAndOwn(obj,pptr,type,flags,own) SWIG_Python_ConvertPtrAndOwn(obj, pptr, type, flags, own) #define SWIG_NewPointerObj(ptr, type, flags) SWIG_Python_NewPointerObj(ptr, type, flags) #define SWIG_CheckImplicit(ty) SWIG_Python_CheckImplicit(ty) #define SWIG_AcquirePtr(ptr, src) SWIG_Python_AcquirePtr(ptr, src) #define swig_owntype int /* for raw packed data */ #define SWIG_ConvertPacked(obj, ptr, sz, ty) SWIG_Python_ConvertPacked(obj, ptr, sz, ty) #define SWIG_NewPackedObj(ptr, sz, type) SWIG_Python_NewPackedObj(ptr, sz, type) /* for class or struct pointers */ #define SWIG_ConvertInstance(obj, pptr, type, flags) SWIG_ConvertPtr(obj, pptr, type, flags) #define SWIG_NewInstanceObj(ptr, type, flags) SWIG_NewPointerObj(ptr, type, flags) /* for C or C++ function pointers */ #define SWIG_ConvertFunctionPtr(obj, pptr, type) SWIG_Python_ConvertFunctionPtr(obj, pptr, type) #define SWIG_NewFunctionPtrObj(ptr, type) SWIG_Python_NewPointerObj(ptr, type, 0) /* for C++ member pointers, ie, member methods */ #define SWIG_ConvertMember(obj, ptr, sz, ty) SWIG_Python_ConvertPacked(obj, ptr, sz, ty) #define SWIG_NewMemberObj(ptr, sz, type) SWIG_Python_NewPackedObj(ptr, sz, type) /* Runtime API */ #define SWIG_GetModule(clientdata) SWIG_Python_GetModule() #define SWIG_SetModule(clientdata, pointer) SWIG_Python_SetModule(pointer) #define SWIG_NewClientData(obj) PySwigClientData_New(obj) #define SWIG_SetErrorObj SWIG_Python_SetErrorObj #define SWIG_SetErrorMsg SWIG_Python_SetErrorMsg #define SWIG_ErrorType(code) SWIG_Python_ErrorType(code) #define SWIG_Error(code, msg) SWIG_Python_SetErrorMsg(SWIG_ErrorType(code), msg) #define SWIG_fail goto fail /* Runtime API implementation */ /* Error manipulation */ SWIGINTERN void SWIG_Python_SetErrorObj(PyObject *errtype, PyObject *obj) { SWIG_PYTHON_THREAD_BEGIN_BLOCK; PyErr_SetObject(errtype, obj); Py_DECREF(obj); SWIG_PYTHON_THREAD_END_BLOCK; } SWIGINTERN void SWIG_Python_SetErrorMsg(PyObject *errtype, const char *msg) { SWIG_PYTHON_THREAD_BEGIN_BLOCK; PyErr_SetString(errtype, (char *) msg); SWIG_PYTHON_THREAD_END_BLOCK; } #define SWIG_Python_Raise(obj, type, desc) SWIG_Python_SetErrorObj(SWIG_Python_ExceptionType(desc), obj) /* Set a constant value */ SWIGINTERN void SWIG_Python_SetConstant(PyObject *d, const char *name, PyObject *obj) { PyDict_SetItemString(d, (char*) name, obj); Py_DECREF(obj); } /* Append a value to the result obj */ SWIGINTERN PyObject* SWIG_Python_AppendOutput(PyObject* result, PyObject* obj) { #if !defined(SWIG_PYTHON_OUTPUT_TUPLE) if (!result) { result = obj; } else if (result == Py_None) { Py_DECREF(result); result = obj; } else { if (!PyList_Check(result)) { PyObject *o2 = result; result = PyList_New(1); PyList_SetItem(result, 0, o2); } PyList_Append(result,obj); Py_DECREF(obj); } return result; #else PyObject* o2; PyObject* o3; if (!result) { result = obj; } else if (result == Py_None) { Py_DECREF(result); result = obj; } else { if (!PyTuple_Check(result)) { o2 = result; result = PyTuple_New(1); PyTuple_SET_ITEM(result, 0, o2); } o3 = PyTuple_New(1); PyTuple_SET_ITEM(o3, 0, obj); o2 = result; result = PySequence_Concat(o2, o3); Py_DECREF(o2); Py_DECREF(o3); } return result; #endif } /* Unpack the argument tuple */ SWIGINTERN int SWIG_Python_UnpackTuple(PyObject *args, const char *name, Py_ssize_t min, Py_ssize_t max, PyObject **objs) { if (!args) { if (!min && !max) { return 1; } else { PyErr_Format(PyExc_TypeError, "%s expected %s%d arguments, got none", name, (min == max ? "" : "at least "), (int)min); return 0; } } if (!PyTuple_Check(args)) { PyErr_SetString(PyExc_SystemError, "UnpackTuple() argument list is not a tuple"); return 0; } else { register Py_ssize_t l = PyTuple_GET_SIZE(args); if (l < min) { PyErr_Format(PyExc_TypeError, "%s expected %s%d arguments, got %d", name, (min == max ? "" : "at least "), (int)min, (int)l); return 0; } else if (l > max) { PyErr_Format(PyExc_TypeError, "%s expected %s%d arguments, got %d", name, (min == max ? "" : "at most "), (int)max, (int)l); return 0; } else { register int i; for (i = 0; i < l; ++i) { objs[i] = PyTuple_GET_ITEM(args, i); } for (; l < max; ++l) { objs[l] = 0; } return i + 1; } } } /* A functor is a function object with one single object argument */ #if PY_VERSION_HEX >= 0x02020000 #define SWIG_Python_CallFunctor(functor, obj) PyObject_CallFunctionObjArgs(functor, obj, NULL); #else #define SWIG_Python_CallFunctor(functor, obj) PyObject_CallFunction(functor, "O", obj); #endif /* Helper for static pointer initialization for both C and C++ code, for example static PyObject *SWIG_STATIC_POINTER(MyVar) = NewSomething(...); */ #ifdef __cplusplus #define SWIG_STATIC_POINTER(var) var #else #define SWIG_STATIC_POINTER(var) var = 0; if (!var) var #endif /* ----------------------------------------------------------------------------- * Pointer declarations * ----------------------------------------------------------------------------- */ /* Flags for new pointer objects */ #define SWIG_POINTER_NOSHADOW (SWIG_POINTER_OWN << 1) #define SWIG_POINTER_NEW (SWIG_POINTER_NOSHADOW | SWIG_POINTER_OWN) #define SWIG_POINTER_IMPLICIT_CONV (SWIG_POINTER_DISOWN << 1) #ifdef __cplusplus extern "C" { #if 0 } /* cc-mode */ #endif #endif /* How to access Py_None */ #if defined(_WIN32) || defined(__WIN32__) || defined(__CYGWIN__) # ifndef SWIG_PYTHON_NO_BUILD_NONE # ifndef SWIG_PYTHON_BUILD_NONE # define SWIG_PYTHON_BUILD_NONE # endif # endif #endif #ifdef SWIG_PYTHON_BUILD_NONE # ifdef Py_None # undef Py_None # define Py_None SWIG_Py_None() # endif SWIGRUNTIMEINLINE PyObject * _SWIG_Py_None(void) { PyObject *none = Py_BuildValue((char*)""); Py_DECREF(none); return none; } SWIGRUNTIME PyObject * SWIG_Py_None(void) { static PyObject *SWIG_STATIC_POINTER(none) = _SWIG_Py_None(); return none; } #endif /* The python void return value */ SWIGRUNTIMEINLINE PyObject * SWIG_Py_Void(void) { PyObject *none = Py_None; Py_INCREF(none); return none; } /* PySwigClientData */ typedef struct { PyObject *klass; PyObject *newraw; PyObject *newargs; PyObject *destroy; int delargs; int implicitconv; } PySwigClientData; SWIGRUNTIMEINLINE int SWIG_Python_CheckImplicit(swig_type_info *ty) { PySwigClientData *data = (PySwigClientData *)ty->clientdata; return data ? data->implicitconv : 0; } SWIGRUNTIMEINLINE PyObject * SWIG_Python_ExceptionType(swig_type_info *desc) { PySwigClientData *data = desc ? (PySwigClientData *) desc->clientdata : 0; PyObject *klass = data ? data->klass : 0; return (klass ? klass : PyExc_RuntimeError); } SWIGRUNTIME PySwigClientData * PySwigClientData_New(PyObject* obj) { if (!obj) { return 0; } else { PySwigClientData *data = (PySwigClientData *)malloc(sizeof(PySwigClientData)); /* the klass element */ data->klass = obj; Py_INCREF(data->klass); /* the newraw method and newargs arguments used to create a new raw instance */ if (PyClass_Check(obj)) { data->newraw = 0; data->newargs = obj; Py_INCREF(obj); } else { #if (PY_VERSION_HEX < 0x02020000) data->newraw = 0; #else data->newraw = PyObject_GetAttrString(data->klass, (char *)"__new__"); #endif if (data->newraw) { Py_INCREF(data->newraw); data->newargs = PyTuple_New(1); PyTuple_SetItem(data->newargs, 0, obj); } else { data->newargs = obj; } Py_INCREF(data->newargs); } /* the destroy method, aka as the C++ delete method */ data->destroy = PyObject_GetAttrString(data->klass, (char *)"__swig_destroy__"); if (PyErr_Occurred()) { PyErr_Clear(); data->destroy = 0; } if (data->destroy) { int flags; Py_INCREF(data->destroy); flags = PyCFunction_GET_FLAGS(data->destroy); #ifdef METH_O data->delargs = !(flags & (METH_O)); #else data->delargs = 0; #endif } else { data->delargs = 0; } data->implicitconv = 0; return data; } } SWIGRUNTIME void PySwigClientData_Del(PySwigClientData* data) { Py_XDECREF(data->newraw); Py_XDECREF(data->newargs); Py_XDECREF(data->destroy); } /* =============== PySwigObject =====================*/ typedef struct { PyObject_HEAD void *ptr; swig_type_info *ty; int own; PyObject *next; } PySwigObject; SWIGRUNTIME PyObject * PySwigObject_long(PySwigObject *v) { return PyLong_FromVoidPtr(v->ptr); } SWIGRUNTIME PyObject * PySwigObject_format(const char* fmt, PySwigObject *v) { PyObject *res = NULL; PyObject *args = PyTuple_New(1); if (args) { if (PyTuple_SetItem(args, 0, PySwigObject_long(v)) == 0) { PyObject *ofmt = PyString_FromString(fmt); if (ofmt) { res = PyString_Format(ofmt,args); Py_DECREF(ofmt); } Py_DECREF(args); } } return res; } SWIGRUNTIME PyObject * PySwigObject_oct(PySwigObject *v) { return PySwigObject_format("%o",v); } SWIGRUNTIME PyObject * PySwigObject_hex(PySwigObject *v) { return PySwigObject_format("%x",v); } SWIGRUNTIME PyObject * #ifdef METH_NOARGS PySwigObject_repr(PySwigObject *v) #else PySwigObject_repr(PySwigObject *v, PyObject *args) #endif { const char *name = SWIG_TypePrettyName(v->ty); PyObject *hex = PySwigObject_hex(v); PyObject *repr = PyString_FromFormat("", name, PyString_AsString(hex)); Py_DECREF(hex); if (v->next) { #ifdef METH_NOARGS PyObject *nrep = PySwigObject_repr((PySwigObject *)v->next); #else PyObject *nrep = PySwigObject_repr((PySwigObject *)v->next, args); #endif PyString_ConcatAndDel(&repr,nrep); } return repr; } SWIGRUNTIME int PySwigObject_print(PySwigObject *v, FILE *fp, int SWIGUNUSEDPARM(flags)) { #ifdef METH_NOARGS PyObject *repr = PySwigObject_repr(v); #else PyObject *repr = PySwigObject_repr(v, NULL); #endif if (repr) { fputs(PyString_AsString(repr), fp); Py_DECREF(repr); return 0; } else { return 1; } } SWIGRUNTIME PyObject * PySwigObject_str(PySwigObject *v) { char result[SWIG_BUFFER_SIZE]; return SWIG_PackVoidPtr(result, v->ptr, v->ty->name, sizeof(result)) ? PyString_FromString(result) : 0; } SWIGRUNTIME int PySwigObject_compare(PySwigObject *v, PySwigObject *w) { void *i = v->ptr; void *j = w->ptr; return (i < j) ? -1 : ((i > j) ? 1 : 0); } SWIGRUNTIME PyTypeObject* _PySwigObject_type(void); SWIGRUNTIME PyTypeObject* PySwigObject_type(void) { static PyTypeObject *SWIG_STATIC_POINTER(type) = _PySwigObject_type(); return type; } SWIGRUNTIMEINLINE int PySwigObject_Check(PyObject *op) { return ((op)->ob_type == PySwigObject_type()) || (strcmp((op)->ob_type->tp_name,"PySwigObject") == 0); } SWIGRUNTIME PyObject * PySwigObject_New(void *ptr, swig_type_info *ty, int own); SWIGRUNTIME void PySwigObject_dealloc(PyObject *v) { PySwigObject *sobj = (PySwigObject *) v; PyObject *next = sobj->next; if (sobj->own == SWIG_POINTER_OWN) { swig_type_info *ty = sobj->ty; PySwigClientData *data = ty ? (PySwigClientData *) ty->clientdata : 0; PyObject *destroy = data ? data->destroy : 0; if (destroy) { /* destroy is always a VARARGS method */ PyObject *res; if (data->delargs) { /* we need to create a temporal object to carry the destroy operation */ PyObject *tmp = PySwigObject_New(sobj->ptr, ty, 0); res = SWIG_Python_CallFunctor(destroy, tmp); Py_DECREF(tmp); } else { PyCFunction meth = PyCFunction_GET_FUNCTION(destroy); PyObject *mself = PyCFunction_GET_SELF(destroy); res = ((*meth)(mself, v)); } Py_XDECREF(res); } #if !defined(SWIG_PYTHON_SILENT_MEMLEAK) else { const char *name = SWIG_TypePrettyName(ty); printf("swig/python detected a memory leak of type '%s', no destructor found.\n", (name ? name : "unknown")); } #endif } Py_XDECREF(next); PyObject_DEL(v); } SWIGRUNTIME PyObject* PySwigObject_append(PyObject* v, PyObject* next) { PySwigObject *sobj = (PySwigObject *) v; #ifndef METH_O PyObject *tmp = 0; if (!PyArg_ParseTuple(next,(char *)"O:append", &tmp)) return NULL; next = tmp; #endif if (!PySwigObject_Check(next)) { return NULL; } sobj->next = next; Py_INCREF(next); return SWIG_Py_Void(); } SWIGRUNTIME PyObject* #ifdef METH_NOARGS PySwigObject_next(PyObject* v) #else PySwigObject_next(PyObject* v, PyObject *SWIGUNUSEDPARM(args)) #endif { PySwigObject *sobj = (PySwigObject *) v; if (sobj->next) { Py_INCREF(sobj->next); return sobj->next; } else { return SWIG_Py_Void(); } } SWIGINTERN PyObject* #ifdef METH_NOARGS PySwigObject_disown(PyObject *v) #else PySwigObject_disown(PyObject* v, PyObject *SWIGUNUSEDPARM(args)) #endif { PySwigObject *sobj = (PySwigObject *)v; sobj->own = 0; return SWIG_Py_Void(); } SWIGINTERN PyObject* #ifdef METH_NOARGS PySwigObject_acquire(PyObject *v) #else PySwigObject_acquire(PyObject* v, PyObject *SWIGUNUSEDPARM(args)) #endif { PySwigObject *sobj = (PySwigObject *)v; sobj->own = SWIG_POINTER_OWN; return SWIG_Py_Void(); } SWIGINTERN PyObject* PySwigObject_own(PyObject *v, PyObject *args) { PyObject *val = 0; #if (PY_VERSION_HEX < 0x02020000) if (!PyArg_ParseTuple(args,(char *)"|O:own",&val)) #else if (!PyArg_UnpackTuple(args, (char *)"own", 0, 1, &val)) #endif { return NULL; } else { PySwigObject *sobj = (PySwigObject *)v; PyObject *obj = PyBool_FromLong(sobj->own); if (val) { #ifdef METH_NOARGS if (PyObject_IsTrue(val)) { PySwigObject_acquire(v); } else { PySwigObject_disown(v); } #else if (PyObject_IsTrue(val)) { PySwigObject_acquire(v,args); } else { PySwigObject_disown(v,args); } #endif } return obj; } } #ifdef METH_O static PyMethodDef swigobject_methods[] = { {(char *)"disown", (PyCFunction)PySwigObject_disown, METH_NOARGS, (char *)"releases ownership of the pointer"}, {(char *)"acquire", (PyCFunction)PySwigObject_acquire, METH_NOARGS, (char *)"aquires ownership of the pointer"}, {(char *)"own", (PyCFunction)PySwigObject_own, METH_VARARGS, (char *)"returns/sets ownership of the pointer"}, {(char *)"append", (PyCFunction)PySwigObject_append, METH_O, (char *)"appends another 'this' object"}, {(char *)"next", (PyCFunction)PySwigObject_next, METH_NOARGS, (char *)"returns the next 'this' object"}, {(char *)"__repr__",(PyCFunction)PySwigObject_repr, METH_NOARGS, (char *)"returns object representation"}, {0, 0, 0, 0} }; #else static PyMethodDef swigobject_methods[] = { {(char *)"disown", (PyCFunction)PySwigObject_disown, METH_VARARGS, (char *)"releases ownership of the pointer"}, {(char *)"acquire", (PyCFunction)PySwigObject_acquire, METH_VARARGS, (char *)"aquires ownership of the pointer"}, {(char *)"own", (PyCFunction)PySwigObject_own, METH_VARARGS, (char *)"returns/sets ownership of the pointer"}, {(char *)"append", (PyCFunction)PySwigObject_append, METH_VARARGS, (char *)"appends another 'this' object"}, {(char *)"next", (PyCFunction)PySwigObject_next, METH_VARARGS, (char *)"returns the next 'this' object"}, {(char *)"__repr__",(PyCFunction)PySwigObject_repr, METH_VARARGS, (char *)"returns object representation"}, {0, 0, 0, 0} }; #endif #if PY_VERSION_HEX < 0x02020000 SWIGINTERN PyObject * PySwigObject_getattr(PySwigObject *sobj,char *name) { return Py_FindMethod(swigobject_methods, (PyObject *)sobj, name); } #endif SWIGRUNTIME PyTypeObject* _PySwigObject_type(void) { static char swigobject_doc[] = "Swig object carries a C/C++ instance pointer"; static PyNumberMethods PySwigObject_as_number = { (binaryfunc)0, /*nb_add*/ (binaryfunc)0, /*nb_subtract*/ (binaryfunc)0, /*nb_multiply*/ (binaryfunc)0, /*nb_divide*/ (binaryfunc)0, /*nb_remainder*/ (binaryfunc)0, /*nb_divmod*/ (ternaryfunc)0,/*nb_power*/ (unaryfunc)0, /*nb_negative*/ (unaryfunc)0, /*nb_positive*/ (unaryfunc)0, /*nb_absolute*/ (inquiry)0, /*nb_nonzero*/ 0, /*nb_invert*/ 0, /*nb_lshift*/ 0, /*nb_rshift*/ 0, /*nb_and*/ 0, /*nb_xor*/ 0, /*nb_or*/ (coercion)0, /*nb_coerce*/ (unaryfunc)PySwigObject_long, /*nb_int*/ (unaryfunc)PySwigObject_long, /*nb_long*/ (unaryfunc)0, /*nb_float*/ (unaryfunc)PySwigObject_oct, /*nb_oct*/ (unaryfunc)PySwigObject_hex, /*nb_hex*/ #if PY_VERSION_HEX >= 0x02050000 /* 2.5.0 */ 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 /* nb_inplace_add -> nb_index */ #elif PY_VERSION_HEX >= 0x02020000 /* 2.2.0 */ 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 /* nb_inplace_add -> nb_inplace_true_divide */ #elif PY_VERSION_HEX >= 0x02000000 /* 2.0.0 */ 0,0,0,0,0,0,0,0,0,0,0 /* nb_inplace_add -> nb_inplace_or */ #endif }; static PyTypeObject pyswigobject_type; static int type_init = 0; if (!type_init) { const PyTypeObject tmp = { PyObject_HEAD_INIT(NULL) 0, /* ob_size */ (char *)"PySwigObject", /* tp_name */ sizeof(PySwigObject), /* tp_basicsize */ 0, /* tp_itemsize */ (destructor)PySwigObject_dealloc, /* tp_dealloc */ (printfunc)PySwigObject_print, /* tp_print */ #if PY_VERSION_HEX < 0x02020000 (getattrfunc)PySwigObject_getattr, /* tp_getattr */ #else (getattrfunc)0, /* tp_getattr */ #endif (setattrfunc)0, /* tp_setattr */ (cmpfunc)PySwigObject_compare, /* tp_compare */ (reprfunc)PySwigObject_repr, /* tp_repr */ &PySwigObject_as_number, /* tp_as_number */ 0, /* tp_as_sequence */ 0, /* tp_as_mapping */ (hashfunc)0, /* tp_hash */ (ternaryfunc)0, /* tp_call */ (reprfunc)PySwigObject_str, /* tp_str */ PyObject_GenericGetAttr, /* tp_getattro */ 0, /* tp_setattro */ 0, /* tp_as_buffer */ Py_TPFLAGS_DEFAULT, /* tp_flags */ swigobject_doc, /* tp_doc */ 0, /* tp_traverse */ 0, /* tp_clear */ 0, /* tp_richcompare */ 0, /* tp_weaklistoffset */ #if PY_VERSION_HEX >= 0x02020000 0, /* tp_iter */ 0, /* tp_iternext */ swigobject_methods, /* tp_methods */ 0, /* tp_members */ 0, /* tp_getset */ 0, /* tp_base */ 0, /* tp_dict */ 0, /* tp_descr_get */ 0, /* tp_descr_set */ 0, /* tp_dictoffset */ 0, /* tp_init */ 0, /* tp_alloc */ 0, /* tp_new */ 0, /* tp_free */ 0, /* tp_is_gc */ 0, /* tp_bases */ 0, /* tp_mro */ 0, /* tp_cache */ 0, /* tp_subclasses */ 0, /* tp_weaklist */ #endif #if PY_VERSION_HEX >= 0x02030000 0, /* tp_del */ #endif #ifdef COUNT_ALLOCS 0,0,0,0 /* tp_alloc -> tp_next */ #endif }; pyswigobject_type = tmp; pyswigobject_type.ob_type = &PyType_Type; type_init = 1; } return &pyswigobject_type; } SWIGRUNTIME PyObject * PySwigObject_New(void *ptr, swig_type_info *ty, int own) { PySwigObject *sobj = PyObject_NEW(PySwigObject, PySwigObject_type()); if (sobj) { sobj->ptr = ptr; sobj->ty = ty; sobj->own = own; sobj->next = 0; } return (PyObject *)sobj; } /* ----------------------------------------------------------------------------- * Implements a simple Swig Packed type, and use it instead of string * ----------------------------------------------------------------------------- */ typedef struct { PyObject_HEAD void *pack; swig_type_info *ty; size_t size; } PySwigPacked; SWIGRUNTIME int PySwigPacked_print(PySwigPacked *v, FILE *fp, int SWIGUNUSEDPARM(flags)) { char result[SWIG_BUFFER_SIZE]; fputs("pack, v->size, 0, sizeof(result))) { fputs("at ", fp); fputs(result, fp); } fputs(v->ty->name,fp); fputs(">", fp); return 0; } SWIGRUNTIME PyObject * PySwigPacked_repr(PySwigPacked *v) { char result[SWIG_BUFFER_SIZE]; if (SWIG_PackDataName(result, v->pack, v->size, 0, sizeof(result))) { return PyString_FromFormat("", result, v->ty->name); } else { return PyString_FromFormat("", v->ty->name); } } SWIGRUNTIME PyObject * PySwigPacked_str(PySwigPacked *v) { char result[SWIG_BUFFER_SIZE]; if (SWIG_PackDataName(result, v->pack, v->size, 0, sizeof(result))){ return PyString_FromFormat("%s%s", result, v->ty->name); } else { return PyString_FromString(v->ty->name); } } SWIGRUNTIME int PySwigPacked_compare(PySwigPacked *v, PySwigPacked *w) { size_t i = v->size; size_t j = w->size; int s = (i < j) ? -1 : ((i > j) ? 1 : 0); return s ? s : strncmp((char *)v->pack, (char *)w->pack, 2*v->size); } SWIGRUNTIME PyTypeObject* _PySwigPacked_type(void); SWIGRUNTIME PyTypeObject* PySwigPacked_type(void) { static PyTypeObject *SWIG_STATIC_POINTER(type) = _PySwigPacked_type(); return type; } SWIGRUNTIMEINLINE int PySwigPacked_Check(PyObject *op) { return ((op)->ob_type == _PySwigPacked_type()) || (strcmp((op)->ob_type->tp_name,"PySwigPacked") == 0); } SWIGRUNTIME void PySwigPacked_dealloc(PyObject *v) { if (PySwigPacked_Check(v)) { PySwigPacked *sobj = (PySwigPacked *) v; free(sobj->pack); } PyObject_DEL(v); } SWIGRUNTIME PyTypeObject* _PySwigPacked_type(void) { static char swigpacked_doc[] = "Swig object carries a C/C++ instance pointer"; static PyTypeObject pyswigpacked_type; static int type_init = 0; if (!type_init) { const PyTypeObject tmp = { PyObject_HEAD_INIT(NULL) 0, /* ob_size */ (char *)"PySwigPacked", /* tp_name */ sizeof(PySwigPacked), /* tp_basicsize */ 0, /* tp_itemsize */ (destructor)PySwigPacked_dealloc, /* tp_dealloc */ (printfunc)PySwigPacked_print, /* tp_print */ (getattrfunc)0, /* tp_getattr */ (setattrfunc)0, /* tp_setattr */ (cmpfunc)PySwigPacked_compare, /* tp_compare */ (reprfunc)PySwigPacked_repr, /* tp_repr */ 0, /* tp_as_number */ 0, /* tp_as_sequence */ 0, /* tp_as_mapping */ (hashfunc)0, /* tp_hash */ (ternaryfunc)0, /* tp_call */ (reprfunc)PySwigPacked_str, /* tp_str */ PyObject_GenericGetAttr, /* tp_getattro */ 0, /* tp_setattro */ 0, /* tp_as_buffer */ Py_TPFLAGS_DEFAULT, /* tp_flags */ swigpacked_doc, /* tp_doc */ 0, /* tp_traverse */ 0, /* tp_clear */ 0, /* tp_richcompare */ 0, /* tp_weaklistoffset */ #if PY_VERSION_HEX >= 0x02020000 0, /* tp_iter */ 0, /* tp_iternext */ 0, /* tp_methods */ 0, /* tp_members */ 0, /* tp_getset */ 0, /* tp_base */ 0, /* tp_dict */ 0, /* tp_descr_get */ 0, /* tp_descr_set */ 0, /* tp_dictoffset */ 0, /* tp_init */ 0, /* tp_alloc */ 0, /* tp_new */ 0, /* tp_free */ 0, /* tp_is_gc */ 0, /* tp_bases */ 0, /* tp_mro */ 0, /* tp_cache */ 0, /* tp_subclasses */ 0, /* tp_weaklist */ #endif #if PY_VERSION_HEX >= 0x02030000 0, /* tp_del */ #endif #ifdef COUNT_ALLOCS 0,0,0,0 /* tp_alloc -> tp_next */ #endif }; pyswigpacked_type = tmp; pyswigpacked_type.ob_type = &PyType_Type; type_init = 1; } return &pyswigpacked_type; } SWIGRUNTIME PyObject * PySwigPacked_New(void *ptr, size_t size, swig_type_info *ty) { PySwigPacked *sobj = PyObject_NEW(PySwigPacked, PySwigPacked_type()); if (sobj) { void *pack = malloc(size); if (pack) { memcpy(pack, ptr, size); sobj->pack = pack; sobj->ty = ty; sobj->size = size; } else { PyObject_DEL((PyObject *) sobj); sobj = 0; } } return (PyObject *) sobj; } SWIGRUNTIME swig_type_info * PySwigPacked_UnpackData(PyObject *obj, void *ptr, size_t size) { if (PySwigPacked_Check(obj)) { PySwigPacked *sobj = (PySwigPacked *)obj; if (sobj->size != size) return 0; memcpy(ptr, sobj->pack, size); return sobj->ty; } else { return 0; } } /* ----------------------------------------------------------------------------- * pointers/data manipulation * ----------------------------------------------------------------------------- */ SWIGRUNTIMEINLINE PyObject * _SWIG_This(void) { return PyString_FromString("this"); } SWIGRUNTIME PyObject * SWIG_This(void) { static PyObject *SWIG_STATIC_POINTER(swig_this) = _SWIG_This(); return swig_this; } /* #define SWIG_PYTHON_SLOW_GETSET_THIS */ SWIGRUNTIME PySwigObject * SWIG_Python_GetSwigThis(PyObject *pyobj) { if (PySwigObject_Check(pyobj)) { return (PySwigObject *) pyobj; } else { PyObject *obj = 0; #if (!defined(SWIG_PYTHON_SLOW_GETSET_THIS) && (PY_VERSION_HEX >= 0x02030000)) if (PyInstance_Check(pyobj)) { obj = _PyInstance_Lookup(pyobj, SWIG_This()); } else { PyObject **dictptr = _PyObject_GetDictPtr(pyobj); if (dictptr != NULL) { PyObject *dict = *dictptr; obj = dict ? PyDict_GetItem(dict, SWIG_This()) : 0; } else { #ifdef PyWeakref_CheckProxy if (PyWeakref_CheckProxy(pyobj)) { PyObject *wobj = PyWeakref_GET_OBJECT(pyobj); return wobj ? SWIG_Python_GetSwigThis(wobj) : 0; } #endif obj = PyObject_GetAttr(pyobj,SWIG_This()); if (obj) { Py_DECREF(obj); } else { if (PyErr_Occurred()) PyErr_Clear(); return 0; } } } #else obj = PyObject_GetAttr(pyobj,SWIG_This()); if (obj) { Py_DECREF(obj); } else { if (PyErr_Occurred()) PyErr_Clear(); return 0; } #endif if (obj && !PySwigObject_Check(obj)) { /* a PyObject is called 'this', try to get the 'real this' PySwigObject from it */ return SWIG_Python_GetSwigThis(obj); } return (PySwigObject *)obj; } } /* Acquire a pointer value */ SWIGRUNTIME int SWIG_Python_AcquirePtr(PyObject *obj, int own) { if (own == SWIG_POINTER_OWN) { PySwigObject *sobj = SWIG_Python_GetSwigThis(obj); if (sobj) { int oldown = sobj->own; sobj->own = own; return oldown; } } return 0; } /* Convert a pointer value */ SWIGRUNTIME int SWIG_Python_ConvertPtrAndOwn(PyObject *obj, void **ptr, swig_type_info *ty, int flags, int *own) { if (!obj) return SWIG_ERROR; if (obj == Py_None) { if (ptr) *ptr = 0; return SWIG_OK; } else { PySwigObject *sobj = SWIG_Python_GetSwigThis(obj); if (own) *own = 0; while (sobj) { void *vptr = sobj->ptr; if (ty) { swig_type_info *to = sobj->ty; if (to == ty) { /* no type cast needed */ if (ptr) *ptr = vptr; break; } else { swig_cast_info *tc = SWIG_TypeCheck(to->name,ty); if (!tc) { sobj = (PySwigObject *)sobj->next; } else { if (ptr) { int newmemory = 0; *ptr = SWIG_TypeCast(tc,vptr,&newmemory); if (newmemory == SWIG_CAST_NEW_MEMORY) { assert(own); if (own) *own = *own | SWIG_CAST_NEW_MEMORY; } } break; } } } else { if (ptr) *ptr = vptr; break; } } if (sobj) { if (own) *own = *own | sobj->own; if (flags & SWIG_POINTER_DISOWN) { sobj->own = 0; } return SWIG_OK; } else { int res = SWIG_ERROR; if (flags & SWIG_POINTER_IMPLICIT_CONV) { PySwigClientData *data = ty ? (PySwigClientData *) ty->clientdata : 0; if (data && !data->implicitconv) { PyObject *klass = data->klass; if (klass) { PyObject *impconv; data->implicitconv = 1; /* avoid recursion and call 'explicit' constructors*/ impconv = SWIG_Python_CallFunctor(klass, obj); data->implicitconv = 0; if (PyErr_Occurred()) { PyErr_Clear(); impconv = 0; } if (impconv) { PySwigObject *iobj = SWIG_Python_GetSwigThis(impconv); if (iobj) { void *vptr; res = SWIG_Python_ConvertPtrAndOwn((PyObject*)iobj, &vptr, ty, 0, 0); if (SWIG_IsOK(res)) { if (ptr) { *ptr = vptr; /* transfer the ownership to 'ptr' */ iobj->own = 0; res = SWIG_AddCast(res); res = SWIG_AddNewMask(res); } else { res = SWIG_AddCast(res); } } } Py_DECREF(impconv); } } } } return res; } } } /* Convert a function ptr value */ SWIGRUNTIME int SWIG_Python_ConvertFunctionPtr(PyObject *obj, void **ptr, swig_type_info *ty) { if (!PyCFunction_Check(obj)) { return SWIG_ConvertPtr(obj, ptr, ty, 0); } else { void *vptr = 0; /* here we get the method pointer for callbacks */ const char *doc = (((PyCFunctionObject *)obj) -> m_ml -> ml_doc); const char *desc = doc ? strstr(doc, "swig_ptr: ") : 0; if (desc) { desc = ty ? SWIG_UnpackVoidPtr(desc + 10, &vptr, ty->name) : 0; if (!desc) return SWIG_ERROR; } if (ty) { swig_cast_info *tc = SWIG_TypeCheck(desc,ty); if (tc) { int newmemory = 0; *ptr = SWIG_TypeCast(tc,vptr,&newmemory); assert(!newmemory); /* newmemory handling not yet implemented */ } else { return SWIG_ERROR; } } else { *ptr = vptr; } return SWIG_OK; } } /* Convert a packed value value */ SWIGRUNTIME int SWIG_Python_ConvertPacked(PyObject *obj, void *ptr, size_t sz, swig_type_info *ty) { swig_type_info *to = PySwigPacked_UnpackData(obj, ptr, sz); if (!to) return SWIG_ERROR; if (ty) { if (to != ty) { /* check type cast? */ swig_cast_info *tc = SWIG_TypeCheck(to->name,ty); if (!tc) return SWIG_ERROR; } } return SWIG_OK; } /* ----------------------------------------------------------------------------- * Create a new pointer object * ----------------------------------------------------------------------------- */ /* Create a new instance object, whitout calling __init__, and set the 'this' attribute. */ SWIGRUNTIME PyObject* SWIG_Python_NewShadowInstance(PySwigClientData *data, PyObject *swig_this) { #if (PY_VERSION_HEX >= 0x02020000) PyObject *inst = 0; PyObject *newraw = data->newraw; if (newraw) { inst = PyObject_Call(newraw, data->newargs, NULL); if (inst) { #if !defined(SWIG_PYTHON_SLOW_GETSET_THIS) PyObject **dictptr = _PyObject_GetDictPtr(inst); if (dictptr != NULL) { PyObject *dict = *dictptr; if (dict == NULL) { dict = PyDict_New(); *dictptr = dict; PyDict_SetItem(dict, SWIG_This(), swig_this); } } #else PyObject *key = SWIG_This(); PyObject_SetAttr(inst, key, swig_this); #endif } } else { PyObject *dict = PyDict_New(); PyDict_SetItem(dict, SWIG_This(), swig_this); inst = PyInstance_NewRaw(data->newargs, dict); Py_DECREF(dict); } return inst; #else #if (PY_VERSION_HEX >= 0x02010000) PyObject *inst; PyObject *dict = PyDict_New(); PyDict_SetItem(dict, SWIG_This(), swig_this); inst = PyInstance_NewRaw(data->newargs, dict); Py_DECREF(dict); return (PyObject *) inst; #else PyInstanceObject *inst = PyObject_NEW(PyInstanceObject, &PyInstance_Type); if (inst == NULL) { return NULL; } inst->in_class = (PyClassObject *)data->newargs; Py_INCREF(inst->in_class); inst->in_dict = PyDict_New(); if (inst->in_dict == NULL) { Py_DECREF(inst); return NULL; } #ifdef Py_TPFLAGS_HAVE_WEAKREFS inst->in_weakreflist = NULL; #endif #ifdef Py_TPFLAGS_GC PyObject_GC_Init(inst); #endif PyDict_SetItem(inst->in_dict, SWIG_This(), swig_this); return (PyObject *) inst; #endif #endif } SWIGRUNTIME void SWIG_Python_SetSwigThis(PyObject *inst, PyObject *swig_this) { PyObject *dict; #if (PY_VERSION_HEX >= 0x02020000) && !defined(SWIG_PYTHON_SLOW_GETSET_THIS) PyObject **dictptr = _PyObject_GetDictPtr(inst); if (dictptr != NULL) { dict = *dictptr; if (dict == NULL) { dict = PyDict_New(); *dictptr = dict; } PyDict_SetItem(dict, SWIG_This(), swig_this); return; } #endif dict = PyObject_GetAttrString(inst, (char*)"__dict__"); PyDict_SetItem(dict, SWIG_This(), swig_this); Py_DECREF(dict); } SWIGINTERN PyObject * SWIG_Python_InitShadowInstance(PyObject *args) { PyObject *obj[2]; if (!SWIG_Python_UnpackTuple(args,(char*)"swiginit", 2, 2, obj)) { return NULL; } else { PySwigObject *sthis = SWIG_Python_GetSwigThis(obj[0]); if (sthis) { PySwigObject_append((PyObject*) sthis, obj[1]); } else { SWIG_Python_SetSwigThis(obj[0], obj[1]); } return SWIG_Py_Void(); } } /* Create a new pointer object */ SWIGRUNTIME PyObject * SWIG_Python_NewPointerObj(void *ptr, swig_type_info *type, int flags) { if (!ptr) { return SWIG_Py_Void(); } else { int own = (flags & SWIG_POINTER_OWN) ? SWIG_POINTER_OWN : 0; PyObject *robj = PySwigObject_New(ptr, type, own); PySwigClientData *clientdata = type ? (PySwigClientData *)(type->clientdata) : 0; if (clientdata && !(flags & SWIG_POINTER_NOSHADOW)) { PyObject *inst = SWIG_Python_NewShadowInstance(clientdata, robj); if (inst) { Py_DECREF(robj); robj = inst; } } return robj; } } /* Create a new packed object */ SWIGRUNTIMEINLINE PyObject * SWIG_Python_NewPackedObj(void *ptr, size_t sz, swig_type_info *type) { return ptr ? PySwigPacked_New((void *) ptr, sz, type) : SWIG_Py_Void(); } /* -----------------------------------------------------------------------------* * Get type list * -----------------------------------------------------------------------------*/ #ifdef SWIG_LINK_RUNTIME void *SWIG_ReturnGlobalTypeList(void *); #endif SWIGRUNTIME swig_module_info * SWIG_Python_GetModule(void) { static void *type_pointer = (void *)0; /* first check if module already created */ if (!type_pointer) { #ifdef SWIG_LINK_RUNTIME type_pointer = SWIG_ReturnGlobalTypeList((void *)0); #else type_pointer = PyCObject_Import((char*)"swig_runtime_data" SWIG_RUNTIME_VERSION, (char*)"type_pointer" SWIG_TYPE_TABLE_NAME); if (PyErr_Occurred()) { PyErr_Clear(); type_pointer = (void *)0; } #endif } return (swig_module_info *) type_pointer; } #if PY_MAJOR_VERSION < 2 /* PyModule_AddObject function was introduced in Python 2.0. The following function is copied out of Python/modsupport.c in python version 2.3.4 */ SWIGINTERN int PyModule_AddObject(PyObject *m, char *name, PyObject *o) { PyObject *dict; if (!PyModule_Check(m)) { PyErr_SetString(PyExc_TypeError, "PyModule_AddObject() needs module as first arg"); return SWIG_ERROR; } if (!o) { PyErr_SetString(PyExc_TypeError, "PyModule_AddObject() needs non-NULL value"); return SWIG_ERROR; } dict = PyModule_GetDict(m); if (dict == NULL) { /* Internal error -- modules must have a dict! */ PyErr_Format(PyExc_SystemError, "module '%s' has no __dict__", PyModule_GetName(m)); return SWIG_ERROR; } if (PyDict_SetItemString(dict, name, o)) return SWIG_ERROR; Py_DECREF(o); return SWIG_OK; } #endif SWIGRUNTIME void SWIG_Python_DestroyModule(void *vptr) { swig_module_info *swig_module = (swig_module_info *) vptr; swig_type_info **types = swig_module->types; size_t i; for (i =0; i < swig_module->size; ++i) { swig_type_info *ty = types[i]; if (ty->owndata) { PySwigClientData *data = (PySwigClientData *) ty->clientdata; if (data) PySwigClientData_Del(data); } } Py_DECREF(SWIG_This()); } SWIGRUNTIME void SWIG_Python_SetModule(swig_module_info *swig_module) { static PyMethodDef swig_empty_runtime_method_table[] = { {NULL, NULL, 0, NULL} };/* Sentinel */ PyObject *module = Py_InitModule((char*)"swig_runtime_data" SWIG_RUNTIME_VERSION, swig_empty_runtime_method_table); PyObject *pointer = PyCObject_FromVoidPtr((void *) swig_module, SWIG_Python_DestroyModule); if (pointer && module) { PyModule_AddObject(module, (char*)"type_pointer" SWIG_TYPE_TABLE_NAME, pointer); } else { Py_XDECREF(pointer); } } /* The python cached type query */ SWIGRUNTIME PyObject * SWIG_Python_TypeCache(void) { static PyObject *SWIG_STATIC_POINTER(cache) = PyDict_New(); return cache; } SWIGRUNTIME swig_type_info * SWIG_Python_TypeQuery(const char *type) { PyObject *cache = SWIG_Python_TypeCache(); PyObject *key = PyString_FromString(type); PyObject *obj = PyDict_GetItem(cache, key); swig_type_info *descriptor; if (obj) { descriptor = (swig_type_info *) PyCObject_AsVoidPtr(obj); } else { swig_module_info *swig_module = SWIG_Python_GetModule(); descriptor = SWIG_TypeQueryModule(swig_module, swig_module, type); if (descriptor) { obj = PyCObject_FromVoidPtr(descriptor, NULL); PyDict_SetItem(cache, key, obj); Py_DECREF(obj); } } Py_DECREF(key); return descriptor; } /* For backward compatibility only */ #define SWIG_POINTER_EXCEPTION 0 #define SWIG_arg_fail(arg) SWIG_Python_ArgFail(arg) #define SWIG_MustGetPtr(p, type, argnum, flags) SWIG_Python_MustGetPtr(p, type, argnum, flags) SWIGRUNTIME int SWIG_Python_AddErrMesg(const char* mesg, int infront) { if (PyErr_Occurred()) { PyObject *type = 0; PyObject *value = 0; PyObject *traceback = 0; PyErr_Fetch(&type, &value, &traceback); if (value) { PyObject *old_str = PyObject_Str(value); Py_XINCREF(type); PyErr_Clear(); if (infront) { PyErr_Format(type, "%s %s", mesg, PyString_AsString(old_str)); } else { PyErr_Format(type, "%s %s", PyString_AsString(old_str), mesg); } Py_DECREF(old_str); } return 1; } else { return 0; } } SWIGRUNTIME int SWIG_Python_ArgFail(int argnum) { if (PyErr_Occurred()) { /* add information about failing argument */ char mesg[256]; PyOS_snprintf(mesg, sizeof(mesg), "argument number %d:", argnum); return SWIG_Python_AddErrMesg(mesg, 1); } else { return 0; } } SWIGRUNTIMEINLINE const char * PySwigObject_GetDesc(PyObject *self) { PySwigObject *v = (PySwigObject *)self; swig_type_info *ty = v ? v->ty : 0; return ty ? ty->str : (char*)""; } SWIGRUNTIME void SWIG_Python_TypeError(const char *type, PyObject *obj) { if (type) { #if defined(SWIG_COBJECT_TYPES) if (obj && PySwigObject_Check(obj)) { const char *otype = (const char *) PySwigObject_GetDesc(obj); if (otype) { PyErr_Format(PyExc_TypeError, "a '%s' is expected, 'PySwigObject(%s)' is received", type, otype); return; } } else #endif { const char *otype = (obj ? obj->ob_type->tp_name : 0); if (otype) { PyObject *str = PyObject_Str(obj); const char *cstr = str ? PyString_AsString(str) : 0; if (cstr) { PyErr_Format(PyExc_TypeError, "a '%s' is expected, '%s(%s)' is received", type, otype, cstr); } else { PyErr_Format(PyExc_TypeError, "a '%s' is expected, '%s' is received", type, otype); } Py_XDECREF(str); return; } } PyErr_Format(PyExc_TypeError, "a '%s' is expected", type); } else { PyErr_Format(PyExc_TypeError, "unexpected type is received"); } } /* Convert a pointer value, signal an exception on a type mismatch */ SWIGRUNTIME void * SWIG_Python_MustGetPtr(PyObject *obj, swig_type_info *ty, int argnum, int flags) { void *result; if (SWIG_Python_ConvertPtr(obj, &result, ty, flags) == -1) { PyErr_Clear(); if (flags & SWIG_POINTER_EXCEPTION) { SWIG_Python_TypeError(SWIG_TypePrettyName(ty), obj); SWIG_Python_ArgFail(argnum); } } return result; } #ifdef __cplusplus #if 0 { /* cc-mode */ #endif } #endif #define SWIG_exception_fail(code, msg) do { SWIG_Error(code, msg); SWIG_fail; } while(0) #define SWIG_contract_assert(expr, msg) if (!(expr)) { SWIG_Error(SWIG_RuntimeError, msg); SWIG_fail; } else /* -------- TYPES TABLE (BEGIN) -------- */ #define SWIGTYPE_p_char swig_types[0] static swig_type_info *swig_types[2]; static swig_module_info swig_module = {swig_types, 1, 0, 0, 0, 0}; #define SWIG_TypeQuery(name) SWIG_TypeQueryModule(&swig_module, &swig_module, name) #define SWIG_MangledTypeQuery(name) SWIG_MangledTypeQueryModule(&swig_module, &swig_module, name) /* -------- TYPES TABLE (END) -------- */ #if (PY_VERSION_HEX <= 0x02000000) # if !defined(SWIG_PYTHON_CLASSIC) # error "This python version requires swig to be run with the '-classic' option" # endif #endif /*----------------------------------------------- @(target):= _testchagpp.so ------------------------------------------------*/ #define SWIG_init init_testchagpp #define SWIG_name "_testchagpp" #define SWIGVERSION 0x010336 #define SWIG_VERSION SWIGVERSION #define SWIG_as_voidptr(a) const_cast< void * >(static_cast< const void * >(a)) #define SWIG_as_voidptrptr(a) ((void)SWIG_as_voidptr(*a),reinterpret_cast< void** >(a)) #include namespace swig { class PyObject_ptr { protected: PyObject *_obj; public: PyObject_ptr() :_obj(0) { } PyObject_ptr(const PyObject_ptr& item) : _obj(item._obj) { Py_XINCREF(_obj); } PyObject_ptr(PyObject *obj, bool initial_ref = true) :_obj(obj) { if (initial_ref) { Py_XINCREF(_obj); } } PyObject_ptr & operator=(const PyObject_ptr& item) { Py_XINCREF(item._obj); Py_XDECREF(_obj); _obj = item._obj; return *this; } ~PyObject_ptr() { Py_XDECREF(_obj); } operator PyObject *() const { return _obj; } PyObject *operator->() const { return _obj; } }; } namespace swig { struct PyObject_var : PyObject_ptr { PyObject_var(PyObject* obj = 0) : PyObject_ptr(obj, false) { } PyObject_var & operator = (PyObject* obj) { Py_XDECREF(_obj); _obj = obj; return *this; } }; } #define SWIG_FILE_WITH_INIT #include "testchagpp.h" #include #if !defined(SWIG_NO_LLONG_MAX) # if !defined(LLONG_MAX) && defined(__GNUC__) && defined (__LONG_LONG_MAX__) # define LLONG_MAX __LONG_LONG_MAX__ # define LLONG_MIN (-LLONG_MAX - 1LL) # define ULLONG_MAX (LLONG_MAX * 2ULL + 1ULL) # endif #endif SWIGINTERN int SWIG_AsVal_double (PyObject *obj, double *val) { int res = SWIG_TypeError; if (PyFloat_Check(obj)) { if (val) *val = PyFloat_AsDouble(obj); return SWIG_OK; } else if (PyInt_Check(obj)) { if (val) *val = PyInt_AsLong(obj); return SWIG_OK; } else if (PyLong_Check(obj)) { double v = PyLong_AsDouble(obj); if (!PyErr_Occurred()) { if (val) *val = v; return SWIG_OK; } else { PyErr_Clear(); } } #ifdef SWIG_PYTHON_CAST_MODE { int dispatch = 0; double d = PyFloat_AsDouble(obj); if (!PyErr_Occurred()) { if (val) *val = d; return SWIG_AddCast(SWIG_OK); } else { PyErr_Clear(); } if (!dispatch) { long v = PyLong_AsLong(obj); if (!PyErr_Occurred()) { if (val) *val = v; return SWIG_AddCast(SWIG_AddCast(SWIG_OK)); } else { PyErr_Clear(); } } } #endif return res; } #include #include SWIGINTERNINLINE int SWIG_CanCastAsInteger(double *d, double min, double max) { double x = *d; if ((min <= x && x <= max)) { double fx = floor(x); double cx = ceil(x); double rd = ((x - fx) < 0.5) ? fx : cx; /* simple rint */ if ((errno == EDOM) || (errno == ERANGE)) { errno = 0; } else { double summ, reps, diff; if (rd < x) { diff = x - rd; } else if (rd > x) { diff = rd - x; } else { return 1; } summ = rd + x; reps = diff/summ; if (reps < 8*DBL_EPSILON) { *d = rd; return 1; } } } return 0; } SWIGINTERN int SWIG_AsVal_long (PyObject *obj, long* val) { if (PyInt_Check(obj)) { if (val) *val = PyInt_AsLong(obj); return SWIG_OK; } else if (PyLong_Check(obj)) { long v = PyLong_AsLong(obj); if (!PyErr_Occurred()) { if (val) *val = v; return SWIG_OK; } else { PyErr_Clear(); } } #ifdef SWIG_PYTHON_CAST_MODE { int dispatch = 0; long v = PyInt_AsLong(obj); if (!PyErr_Occurred()) { if (val) *val = v; return SWIG_AddCast(SWIG_OK); } else { PyErr_Clear(); } if (!dispatch) { double d; int res = SWIG_AddCast(SWIG_AsVal_double (obj,&d)); if (SWIG_IsOK(res) && SWIG_CanCastAsInteger(&d, LONG_MIN, LONG_MAX)) { if (val) *val = (long)(d); return res; } } } #endif return SWIG_TypeError; } SWIGINTERN int SWIG_AsVal_int (PyObject * obj, int *val) { long v; int res = SWIG_AsVal_long (obj, &v); if (SWIG_IsOK(res)) { if ((v < INT_MIN || v > INT_MAX)) { return SWIG_OverflowError; } else { if (val) *val = static_cast< int >(v); } } return res; } #ifdef __cplusplus extern "C" { #endif SWIGINTERN PyObject *_wrap_find_positive(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; int arg1 ; int arg2 ; int arg3 ; int arg4 ; int val1 ; int ecode1 = 0 ; int val2 ; int ecode2 = 0 ; int val3 ; int ecode3 = 0 ; int val4 ; int ecode4 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; PyObject * obj2 = 0 ; PyObject * obj3 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OOOO:find_positive",&obj0,&obj1,&obj2,&obj3)) SWIG_fail; ecode1 = SWIG_AsVal_int(obj0, &val1); if (!SWIG_IsOK(ecode1)) { SWIG_exception_fail(SWIG_ArgError(ecode1), "in method '" "find_positive" "', argument " "1"" of type '" "int""'"); } arg1 = static_cast< int >(val1); ecode2 = SWIG_AsVal_int(obj1, &val2); if (!SWIG_IsOK(ecode2)) { SWIG_exception_fail(SWIG_ArgError(ecode2), "in method '" "find_positive" "', argument " "2"" of type '" "int""'"); } arg2 = static_cast< int >(val2); ecode3 = SWIG_AsVal_int(obj2, &val3); if (!SWIG_IsOK(ecode3)) { SWIG_exception_fail(SWIG_ArgError(ecode3), "in method '" "find_positive" "', argument " "3"" of type '" "int""'"); } arg3 = static_cast< int >(val3); ecode4 = SWIG_AsVal_int(obj3, &val4); if (!SWIG_IsOK(ecode4)) { SWIG_exception_fail(SWIG_ArgError(ecode4), "in method '" "find_positive" "', argument " "4"" of type '" "int""'"); } arg4 = static_cast< int >(val4); find_positive(arg1,arg2,arg3,arg4); resultobj = SWIG_Py_Void(); return resultobj; fail: return NULL; } static PyMethodDef SwigMethods[] = { { (char *)"find_positive", _wrap_find_positive, METH_VARARGS, NULL}, { NULL, NULL, 0, NULL } }; /* -------- TYPE CONVERSION AND EQUIVALENCE RULES (BEGIN) -------- */ static swig_type_info _swigt__p_char = {"_p_char", "char *", 0, 0, (void*)0, 0}; static swig_type_info *swig_type_initial[] = { &_swigt__p_char, }; static swig_cast_info _swigc__p_char[] = { {&_swigt__p_char, 0, 0, 0},{0, 0, 0, 0}}; static swig_cast_info *swig_cast_initial[] = { _swigc__p_char, }; /* -------- TYPE CONVERSION AND EQUIVALENCE RULES (END) -------- */ static swig_const_info swig_const_table[] = { {0, 0, 0, 0.0, 0, 0}}; #ifdef __cplusplus } #endif /* ----------------------------------------------------------------------------- * Type initialization: * This problem is tough by the requirement that no dynamic * memory is used. Also, since swig_type_info structures store pointers to * swig_cast_info structures and swig_cast_info structures store pointers back * to swig_type_info structures, we need some lookup code at initialization. * The idea is that swig generates all the structures that are needed. * The runtime then collects these partially filled structures. * The SWIG_InitializeModule function takes these initial arrays out of * swig_module, and does all the lookup, filling in the swig_module.types * array with the correct data and linking the correct swig_cast_info * structures together. * * The generated swig_type_info structures are assigned staticly to an initial * array. We just loop through that array, and handle each type individually. * First we lookup if this type has been already loaded, and if so, use the * loaded structure instead of the generated one. Then we have to fill in the * cast linked list. The cast data is initially stored in something like a * two-dimensional array. Each row corresponds to a type (there are the same * number of rows as there are in the swig_type_initial array). Each entry in * a column is one of the swig_cast_info structures for that type. * The cast_initial array is actually an array of arrays, because each row has * a variable number of columns. So to actually build the cast linked list, * we find the array of casts associated with the type, and loop through it * adding the casts to the list. The one last trick we need to do is making * sure the type pointer in the swig_cast_info struct is correct. * * First off, we lookup the cast->type name to see if it is already loaded. * There are three cases to handle: * 1) If the cast->type has already been loaded AND the type we are adding * casting info to has not been loaded (it is in this module), THEN we * replace the cast->type pointer with the type pointer that has already * been loaded. * 2) If BOTH types (the one we are adding casting info to, and the * cast->type) are loaded, THEN the cast info has already been loaded by * the previous module so we just ignore it. * 3) Finally, if cast->type has not already been loaded, then we add that * swig_cast_info to the linked list (because the cast->type) pointer will * be correct. * ----------------------------------------------------------------------------- */ #ifdef __cplusplus extern "C" { #if 0 } /* c-mode */ #endif #endif #if 0 #define SWIGRUNTIME_DEBUG #endif SWIGRUNTIME void SWIG_InitializeModule(void *clientdata) { size_t i; swig_module_info *module_head, *iter; int found, init; clientdata = clientdata; /* check to see if the circular list has been setup, if not, set it up */ if (swig_module.next==0) { /* Initialize the swig_module */ swig_module.type_initial = swig_type_initial; swig_module.cast_initial = swig_cast_initial; swig_module.next = &swig_module; init = 1; } else { init = 0; } /* Try and load any already created modules */ module_head = SWIG_GetModule(clientdata); if (!module_head) { /* This is the first module loaded for this interpreter */ /* so set the swig module into the interpreter */ SWIG_SetModule(clientdata, &swig_module); module_head = &swig_module; } else { /* the interpreter has loaded a SWIG module, but has it loaded this one? */ found=0; iter=module_head; do { if (iter==&swig_module) { found=1; break; } iter=iter->next; } while (iter!= module_head); /* if the is found in the list, then all is done and we may leave */ if (found) return; /* otherwise we must add out module into the list */ swig_module.next = module_head->next; module_head->next = &swig_module; } /* When multiple interpeters are used, a module could have already been initialized in a different interpreter, but not yet have a pointer in this interpreter. In this case, we do not want to continue adding types... everything should be set up already */ if (init == 0) return; /* Now work on filling in swig_module.types */ #ifdef SWIGRUNTIME_DEBUG printf("SWIG_InitializeModule: size %d\n", swig_module.size); #endif for (i = 0; i < swig_module.size; ++i) { swig_type_info *type = 0; swig_type_info *ret; swig_cast_info *cast; #ifdef SWIGRUNTIME_DEBUG printf("SWIG_InitializeModule: type %d %s\n", i, swig_module.type_initial[i]->name); #endif /* if there is another module already loaded */ if (swig_module.next != &swig_module) { type = SWIG_MangledTypeQueryModule(swig_module.next, &swig_module, swig_module.type_initial[i]->name); } if (type) { /* Overwrite clientdata field */ #ifdef SWIGRUNTIME_DEBUG printf("SWIG_InitializeModule: found type %s\n", type->name); #endif if (swig_module.type_initial[i]->clientdata) { type->clientdata = swig_module.type_initial[i]->clientdata; #ifdef SWIGRUNTIME_DEBUG printf("SWIG_InitializeModule: found and overwrite type %s \n", type->name); #endif } } else { type = swig_module.type_initial[i]; } /* Insert casting types */ cast = swig_module.cast_initial[i]; while (cast->type) { /* Don't need to add information already in the list */ ret = 0; #ifdef SWIGRUNTIME_DEBUG printf("SWIG_InitializeModule: look cast %s\n", cast->type->name); #endif if (swig_module.next != &swig_module) { ret = SWIG_MangledTypeQueryModule(swig_module.next, &swig_module, cast->type->name); #ifdef SWIGRUNTIME_DEBUG if (ret) printf("SWIG_InitializeModule: found cast %s\n", ret->name); #endif } if (ret) { if (type == swig_module.type_initial[i]) { #ifdef SWIGRUNTIME_DEBUG printf("SWIG_InitializeModule: skip old type %s\n", ret->name); #endif cast->type = ret; ret = 0; } else { /* Check for casting already in the list */ swig_cast_info *ocast = SWIG_TypeCheck(ret->name, type); #ifdef SWIGRUNTIME_DEBUG if (ocast) printf("SWIG_InitializeModule: skip old cast %s\n", ret->name); #endif if (!ocast) ret = 0; } } if (!ret) { #ifdef SWIGRUNTIME_DEBUG printf("SWIG_InitializeModule: adding cast %s\n", cast->type->name); #endif if (type->cast) { type->cast->prev = cast; cast->next = type->cast; } type->cast = cast; } cast++; } /* Set entry in modules->types array equal to the type */ swig_module.types[i] = type; } swig_module.types[i] = 0; #ifdef SWIGRUNTIME_DEBUG printf("**** SWIG_InitializeModule: Cast List ******\n"); for (i = 0; i < swig_module.size; ++i) { int j = 0; swig_cast_info *cast = swig_module.cast_initial[i]; printf("SWIG_InitializeModule: type %d %s\n", i, swig_module.type_initial[i]->name); while (cast->type) { printf("SWIG_InitializeModule: cast type %s\n", cast->type->name); cast++; ++j; } printf("---- Total casts: %d\n",j); } printf("**** SWIG_InitializeModule: Cast List ******\n"); #endif } /* This function will propagate the clientdata field of type to * any new swig_type_info structures that have been added into the list * of equivalent types. It is like calling * SWIG_TypeClientData(type, clientdata) a second time. */ SWIGRUNTIME void SWIG_PropagateClientData(void) { size_t i; swig_cast_info *equiv; static int init_run = 0; if (init_run) return; init_run = 1; for (i = 0; i < swig_module.size; i++) { if (swig_module.types[i]->clientdata) { equiv = swig_module.types[i]->cast; while (equiv) { if (!equiv->converter) { if (equiv->type && !equiv->type->clientdata) SWIG_TypeClientData(equiv->type, swig_module.types[i]->clientdata); } equiv = equiv->next; } } } } #ifdef __cplusplus #if 0 { /* c-mode */ #endif } #endif #ifdef __cplusplus extern "C" { #endif /* Python-specific SWIG API */ #define SWIG_newvarlink() SWIG_Python_newvarlink() #define SWIG_addvarlink(p, name, get_attr, set_attr) SWIG_Python_addvarlink(p, name, get_attr, set_attr) #define SWIG_InstallConstants(d, constants) SWIG_Python_InstallConstants(d, constants) /* ----------------------------------------------------------------------------- * global variable support code. * ----------------------------------------------------------------------------- */ typedef struct swig_globalvar { char *name; /* Name of global variable */ PyObject *(*get_attr)(void); /* Return the current value */ int (*set_attr)(PyObject *); /* Set the value */ struct swig_globalvar *next; } swig_globalvar; typedef struct swig_varlinkobject { PyObject_HEAD swig_globalvar *vars; } swig_varlinkobject; SWIGINTERN PyObject * swig_varlink_repr(swig_varlinkobject *SWIGUNUSEDPARM(v)) { return PyString_FromString(""); } SWIGINTERN PyObject * swig_varlink_str(swig_varlinkobject *v) { PyObject *str = PyString_FromString("("); swig_globalvar *var; for (var = v->vars; var; var=var->next) { PyString_ConcatAndDel(&str,PyString_FromString(var->name)); if (var->next) PyString_ConcatAndDel(&str,PyString_FromString(", ")); } PyString_ConcatAndDel(&str,PyString_FromString(")")); return str; } SWIGINTERN int swig_varlink_print(swig_varlinkobject *v, FILE *fp, int SWIGUNUSEDPARM(flags)) { PyObject *str = swig_varlink_str(v); fprintf(fp,"Swig global variables "); fprintf(fp,"%s\n", PyString_AsString(str)); Py_DECREF(str); return 0; } SWIGINTERN void swig_varlink_dealloc(swig_varlinkobject *v) { swig_globalvar *var = v->vars; while (var) { swig_globalvar *n = var->next; free(var->name); free(var); var = n; } } SWIGINTERN PyObject * swig_varlink_getattr(swig_varlinkobject *v, char *n) { PyObject *res = NULL; swig_globalvar *var = v->vars; while (var) { if (strcmp(var->name,n) == 0) { res = (*var->get_attr)(); break; } var = var->next; } if (res == NULL && !PyErr_Occurred()) { PyErr_SetString(PyExc_NameError,"Unknown C global variable"); } return res; } SWIGINTERN int swig_varlink_setattr(swig_varlinkobject *v, char *n, PyObject *p) { int res = 1; swig_globalvar *var = v->vars; while (var) { if (strcmp(var->name,n) == 0) { res = (*var->set_attr)(p); break; } var = var->next; } if (res == 1 && !PyErr_Occurred()) { PyErr_SetString(PyExc_NameError,"Unknown C global variable"); } return res; } SWIGINTERN PyTypeObject* swig_varlink_type(void) { static char varlink__doc__[] = "Swig var link object"; static PyTypeObject varlink_type; static int type_init = 0; if (!type_init) { const PyTypeObject tmp = { PyObject_HEAD_INIT(NULL) 0, /* Number of items in variable part (ob_size) */ (char *)"swigvarlink", /* Type name (tp_name) */ sizeof(swig_varlinkobject), /* Basic size (tp_basicsize) */ 0, /* Itemsize (tp_itemsize) */ (destructor) swig_varlink_dealloc, /* Deallocator (tp_dealloc) */ (printfunc) swig_varlink_print, /* Print (tp_print) */ (getattrfunc) swig_varlink_getattr, /* get attr (tp_getattr) */ (setattrfunc) swig_varlink_setattr, /* Set attr (tp_setattr) */ 0, /* tp_compare */ (reprfunc) swig_varlink_repr, /* tp_repr */ 0, /* tp_as_number */ 0, /* tp_as_sequence */ 0, /* tp_as_mapping */ 0, /* tp_hash */ 0, /* tp_call */ (reprfunc)swig_varlink_str, /* tp_str */ 0, /* tp_getattro */ 0, /* tp_setattro */ 0, /* tp_as_buffer */ 0, /* tp_flags */ varlink__doc__, /* tp_doc */ 0, /* tp_traverse */ 0, /* tp_clear */ 0, /* tp_richcompare */ 0, /* tp_weaklistoffset */ #if PY_VERSION_HEX >= 0x02020000 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, /* tp_iter -> tp_weaklist */ #endif #if PY_VERSION_HEX >= 0x02030000 0, /* tp_del */ #endif #ifdef COUNT_ALLOCS 0,0,0,0 /* tp_alloc -> tp_next */ #endif }; varlink_type = tmp; varlink_type.ob_type = &PyType_Type; type_init = 1; } return &varlink_type; } /* Create a variable linking object for use later */ SWIGINTERN PyObject * SWIG_Python_newvarlink(void) { swig_varlinkobject *result = PyObject_NEW(swig_varlinkobject, swig_varlink_type()); if (result) { result->vars = 0; } return ((PyObject*) result); } SWIGINTERN void SWIG_Python_addvarlink(PyObject *p, char *name, PyObject *(*get_attr)(void), int (*set_attr)(PyObject *p)) { swig_varlinkobject *v = (swig_varlinkobject *) p; swig_globalvar *gv = (swig_globalvar *) malloc(sizeof(swig_globalvar)); if (gv) { size_t size = strlen(name)+1; gv->name = (char *)malloc(size); if (gv->name) { strncpy(gv->name,name,size); gv->get_attr = get_attr; gv->set_attr = set_attr; gv->next = v->vars; } } v->vars = gv; } SWIGINTERN PyObject * SWIG_globals(void) { static PyObject *_SWIG_globals = 0; if (!_SWIG_globals) _SWIG_globals = SWIG_newvarlink(); return _SWIG_globals; } /* ----------------------------------------------------------------------------- * constants/methods manipulation * ----------------------------------------------------------------------------- */ /* Install Constants */ SWIGINTERN void SWIG_Python_InstallConstants(PyObject *d, swig_const_info constants[]) { PyObject *obj = 0; size_t i; for (i = 0; constants[i].type; ++i) { switch(constants[i].type) { case SWIG_PY_POINTER: obj = SWIG_NewPointerObj(constants[i].pvalue, *(constants[i]).ptype,0); break; case SWIG_PY_BINARY: obj = SWIG_NewPackedObj(constants[i].pvalue, constants[i].lvalue, *(constants[i].ptype)); break; default: obj = 0; break; } if (obj) { PyDict_SetItemString(d, constants[i].name, obj); Py_DECREF(obj); } } } /* -----------------------------------------------------------------------------*/ /* Fix SwigMethods to carry the callback ptrs when needed */ /* -----------------------------------------------------------------------------*/ SWIGINTERN void SWIG_Python_FixMethods(PyMethodDef *methods, swig_const_info *const_table, swig_type_info **types, swig_type_info **types_initial) { size_t i; for (i = 0; methods[i].ml_name; ++i) { const char *c = methods[i].ml_doc; if (c && (c = strstr(c, "swig_ptr: "))) { int j; swig_const_info *ci = 0; const char *name = c + 10; for (j = 0; const_table[j].type; ++j) { if (strncmp(const_table[j].name, name, strlen(const_table[j].name)) == 0) { ci = &(const_table[j]); break; } } if (ci) { size_t shift = (ci->ptype) - types; swig_type_info *ty = types_initial[shift]; size_t ldoc = (c - methods[i].ml_doc); size_t lptr = strlen(ty->name)+2*sizeof(void*)+2; char *ndoc = (char*)malloc(ldoc + lptr + 10); if (ndoc) { char *buff = ndoc; void *ptr = (ci->type == SWIG_PY_POINTER) ? ci->pvalue : 0; if (ptr) { strncpy(buff, methods[i].ml_doc, ldoc); buff += ldoc; strncpy(buff, "swig_ptr: ", 10); buff += 10; SWIG_PackVoidPtr(buff, ptr, ty->name, lptr); methods[i].ml_doc = ndoc; } } } } } } #ifdef __cplusplus } #endif /* -----------------------------------------------------------------------------* * Partial Init method * -----------------------------------------------------------------------------*/ #ifdef __cplusplus extern "C" #endif SWIGEXPORT void SWIG_init(void) { PyObject *m, *d; /* Fix SwigMethods to carry the callback ptrs when needed */ SWIG_Python_FixMethods(SwigMethods, swig_const_table, swig_types, swig_type_initial); m = Py_InitModule((char *) SWIG_name, SwigMethods); d = PyModule_GetDict(m); SWIG_InitializeModule(0); SWIG_InstallConstants(d,swig_const_table); } brian-1.4.3/dev/ideas/cuda/test_chag_pp/testdll.cpp000066400000000000000000000002301266062647100222150ustar00rootroot00000000000000extern "C" { __declspec(dllexport) double dosum(double *x, int n) { double ret=0.0; for(int i=0;i 1e-10: t = defaultclock._t G_ed.V_[0] = G_ed.V_[0] * exp((G_ed.last_event_time - t) / 0.5) + G_ed.exc_[0] G_ed.exc_[0] = 0.0 G_ed.last_event_time = t ####### Run and plot run(1 * second) plot(M.times, M[0]) plot(M_ed.times, M_ed[0]) show() brian-1.4.3/dev/ideas/gpulib/000077500000000000000000000000001266062647100157515ustar00rootroot00000000000000brian-1.4.3/dev/ideas/gpulib/gpulib_test.py000066400000000000000000000000211266062647100206350ustar00rootroot00000000000000import gpulib brian-1.4.3/dev/ideas/gpulib/originalcoba.py000066400000000000000000000042451266062647100207610ustar00rootroot00000000000000# coding: latin-1 """ This is an implementation of a benchmark described in the following review paper: Simulation of networks of spiking neurons: A review of tools and strategies (2006). Brette, Rudolph, Carnevale, Hines, Beeman, Bower, Diesmann, Goodman, Harris, Zirpe, Natschläger, Pecevski, Ermentrout, Djurfeldt, Lansner, Rochel, Vibert, Alvarez, Muller, Davison, El Boustani and Destexhe. Journal of Computational Neuroscience Benchmark 1: random network of integrate-and-fire neurons with exponential synaptic conductances Clock-driven implementation with Euler integration (no spike time interpolation) R. Brette - Dec 2007 About 5.5s. With compilation: 4s """ #import brian_no_units from brian import * import time #set_global_preferences(useweave=True) # Time constants taum = 20 * msecond taue = 5 * msecond taui = 10 * msecond # Reversal potentials Ee = (0. + 60.) * mvolt Ei = (-80. + 60.) * mvolt start_time = time.time() eqs = Equations(''' dv/dt = (-v+ge*(Ee-v)+gi*(Ei-v))*(1./taum) : volt dge/dt = -ge*(1./taue) : 1 dgi/dt = -gi*(1./taui) : 1 ''') # NB 1: conductances are in units of the leak conductance # NB 2: multiplication is faster than division P = NeuronGroup(4000, model=eqs, threshold=10 * mvolt, \ reset=0 * mvolt, refractory=5 * msecond, order=1, compile=True) #STOPPPPPP Pe = P.subgroup(3200) Pi = P.subgroup(800) Ce = Connection(Pe, P, 'ge') Ci = Connection(Pi, P, 'gi') we = 6. / 10. # excitatory synaptic weight (voltage) wi = 67. / 10. # inhibitory synaptic weight Ce.connect_random(Pe, P, 0.02, weight=we) Ci.connect_random(Pi, P, 0.02, weight=wi) # Initialization P.v = (randn(len(P)) * 5 - 5) * mvolt P.ge = randn(len(P)) * 1.5 + 4 P.gi = randn(len(P)) * 12 + 20 # Record the number of spikes Me = PopulationSpikeCounter(Pe) Mi = PopulationSpikeCounter(Pi) print "Network construction time:", time.time() - start_time, "seconds" print "Simulation running..." start_time = time.time() run(1 * second) duration = time.time() - start_time print "Simulation time:", duration, "seconds" print Me.nspikes, "excitatory spikes" print Mi.nspikes, "inhibitory spikes" brian-1.4.3/dev/ideas/gpulib/originalcuba.py000066400000000000000000000015061266062647100207640ustar00rootroot00000000000000''' Very short example program. ''' from brian import * import time Nplot = 4 N = 4000 Ne = int(0.8 * N) Ni = N - Ne T = 200 * ms eqs = ''' dv/dt = (ge+gi-(v+49*mV))/(20*ms) : volt dge/dt = -ge/(5*ms) : volt dgi/dt = -gi/(10*ms) : volt ''' P = NeuronGroup(N, model=eqs, threshold= -50 * mV, reset= -60 * mV) P.v = -60 * mV + 10 * mV * rand(len(P)) Pe = P.subgroup(Ne) Pi = P.subgroup(Ni) Ce = Connection(Pe, P, 'ge') Ci = Connection(Pi, P, 'gi') Ce.connect_random(Pe, P, 0.02, weight=1.62 * mV) Ci.connect_random(Pi, P, 0.02, weight= -9 * mV) M = SpikeMonitor(P) MV = StateMonitor(P, 'v', record=range(Nplot)) start = time.time() run(T) print 'Time taken', time.time() - start print 'Spikes', M.nspikes raster_plot(M) figure() for i in range(Nplot): plot(MV[i]) show() brian-1.4.3/dev/ideas/gpulib/simplifiedcoba.py000066400000000000000000000055671266062647100213120ustar00rootroot00000000000000import numpy import time import random from itertools import repeat # units second = 1. ms = 0.001 mV = 0.001 # Parameters N = 4000 Ne = int(N * 0.8) Ni = N - Ne Nplot = 4 dt = 0.1 * ms T = 1 * second numsteps = int(T / dt) p = 0.02 Vr = 0 * mV Vt = 10 * mV we = 6. / 10. # excitatory synaptic weight (voltage) wi = 67. / 10. # inhibitory synaptic weight refrac = 5 * ms # State variable S=[v;ge;gi] and variable used in Euler step # dS=[v';ge';gi'] used in the main loop below S = numpy.zeros((3, N)) dS = numpy.zeros((3, N)) v, ge, gi = S v__tmp, ge__tmp, gi__tmp = dS # Last spike times, stores the most recent time that a neuron # has spiked, which is used for refractory periods LS = numpy.zeros(N) LS[:] = -2 * refrac # Initialisation of state variables S[0, :] = (numpy.random.randn(N) * 5 - 5) * mV S[1, :] = numpy.random.randn(N) * 1.5 + 4 S[2, :] = numpy.random.randn(N) * 12 + 20 # Weight matrix # Generate random connectivity matrix (note: no weights) W = [] for _ in xrange(N): k = numpy.random.binomial(N, p, 1)[0] a = random.sample(xrange(N), k) a.sort() a = numpy.array(a) W.append(a) Vrec = [[] for _ in range(Nplot)] spikesrec = [] t = 0. Nspikes = 0 start = time.time() for _ in xrange(numsteps): # EULER UPDATE CODE, this is the update code generated by Brian for the equations: #dv/dt = (-v+ge*(Ee-v)+gi*(Ei-v))*(1./taum) : volt #dge/dt = -ge*(1./taue) : 1 #dgi/dt = -gi*(1./taui) : 1 v__tmp[:] = (-v + ge * (0.06 - v) + gi * (-0.02 - v)) * (1. / 0.02) ge__tmp[:] = -ge * (1. / 0.005) gi__tmp[:] = -gi * (1. / 0.01) S += dt * dS # spike propagation, essentially the same as CUBA except I've used the names v,ge,gi # here instead of S[0,:] etc. spikes = (v > Vt).nonzero()[0] for i in spikes: if i < Ne: ge[W[i]] += we else: gi[W[i]] += wi # refractory period, if the last spike of neuron i was within refrac of the current time, # fix the value to Vr - note that this is how Brian used to do this, but now we have a # slightly different mechanism, but this is very close. LS[spikes] = t v[LS > t - refrac] = Vr # Monitoring, actually takes about 1/3 of the time of the simulation when I ran it, # for speed comparisons this bit should be commented out Nspikes += len(spikes) spikesrec += zip(spikes, repeat(t)) for i in range(Nplot): Vrec[i].append(S[0, i]) t += dt print 'Time taken', time.time() - start print 'Spikes', Nspikes try: import pylab if len(spikesrec): sn, st = zip(*spikesrec) pylab.subplot(121) pylab.plot(st, sn, '.') pylab.subplot(122) for i in range(Nplot): pylab.plot(Vrec[i]) pylab.show() except ImportError: print 'Cannot plot, no pylab' brian-1.4.3/dev/ideas/gpulib/simplifiedcuba.py000066400000000000000000000033061266062647100213050ustar00rootroot00000000000000import numpy import time import random from itertools import repeat # units second = 1. ms = 0.001 mV = 0.001 # parameters N = 4000 Ne = int(N * 0.8) Ni = N - Ne Nplot = 4 dt = 0.1 * ms T = 200 * ms numsteps = int(T / dt) Vr = -60 * mV Vt = -50 * mV p = 0.02 we = 1.62 * mV wi = -9 * mV # Matrices for state update, S(t+dt)=A*S+_C A = numpy.array([[ 0.99501248, 0.00493794, 0.00496265], [ 0. , 0.98019867, 0. ], [ 0. , 0. , 0.99004983]]) _C = numpy.array([[ -2.44388520e-04], [ -8.58745657e-21], [ 6.90431479e-20]]) # Initialise state matrix and assign uniform random membrane potentials S = numpy.zeros((3, N)) S[0, :] = [random.uniform(Vr, Vt) for _ in xrange(N)] # Generate random connectivity matrix (note: no weights) W = [] for _ in xrange(N): k = numpy.random.binomial(N, p, 1)[0] a = random.sample(xrange(N), k) a.sort() a = numpy.array(a) W.append(a) Vrec = [[] for _ in range(Nplot)] spikesrec = [] t = 0. Nspikes = 0 start = time.time() for _ in xrange(numsteps): S[:] = numpy.dot(A, S) + _C spikes = (S[0, :] > Vt).nonzero()[0] for i in spikes: if i < Ne: S[1, W[i]] += we else: S[2, W[i]] += wi S[0, spikes] = Vr Nspikes += len(spikes) spikesrec += zip(spikes, repeat(t)) t += dt for i in range(Nplot): Vrec[i].append(S[0, i]) print 'Time taken', time.time() - start print 'Spikes', Nspikes try: import pylab sn, st = zip(*spikesrec) pylab.plot(st, sn, '.') pylab.figure() for i in range(Nplot): pylab.plot(Vrec[i]) pylab.show() except ImportError: print 'Cannot plot, no pylab' brian-1.4.3/dev/ideas/gpulib/simplifiedcuba_onlystateupdate.py000066400000000000000000000025711266062647100246150ustar00rootroot00000000000000''' Very short example program. ''' from numpy import * #from pylab import * import time import random second = 1. ms = 0.001 mV = 0.001 N = 4000 dt = 0.1 * ms T = 100 * ms numsteps = int(T / dt) Vr = -60 * mV Vt = -50 * mV S = zeros((3, N)) A = array([[ 0.99501248, 0.00493794, 0.00496265], [ 0. , 0.98019867, 0. ], [ 0. , 0. , 0.99004983]]) _C = array([[ -2.44388520e-04], [ -8.58745657e-21], [ 6.90431479e-20]]) S[0, :] = [random.uniform(Vr, Vt) for _ in xrange(N)] l = [[] for _ in range(N)] t = 0 start = time.time() Nspike = 0 for _ in xrange(numsteps): S[:] = dot(A, S) + _C I = where(S[0, :] > Vt)[0] Nspike += len(I) S[0, I] = Vr t += dt # for i in range(N): # l[i].append(S[0,i]) print time.time() - start print Nspike #for i in range(N): # plot(l[i]) #show() #from brian import * # #eqs=''' #dv/dt = (ge+gi-(v+49*mV))/(20*ms) : volt #dge/dt = -ge/(5*ms) : volt #dgi/dt = -gi/(10*ms) : volt #''' # #P=NeuronGroup(4000,model=eqs, # threshold=-50*mV,reset=-60*mV) #P.v=-60*mV+10*mV*rand(len(P)) #Pe=P.subgroup(3200) #Pi=P.subgroup(800) # #Ce=Connection(Pe,P,'ge') #Ci=Connection(Pi,P,'gi') #Ce.connect_random(Pe, P, 0.02,weight=1.62*mV) #Ci.connect_random(Pi, P, 0.02,weight=-9*mV) # #M=SpikeMonitor(P) # #run(1*second) #raster_plot(M) #show() brian-1.4.3/dev/ideas/group_delay.py000066400000000000000000000155461266062647100173660ustar00rootroot00000000000000# -*- coding:utf-8 -*- """ Group delay of filters. This gives really noisy outputs. I added a Gaussian filter. I have a doubt: is group delay the same as narrow band ITD? No! It's the characteristic delay! Actually, group delay computes the characteristic delay, not the ITD. * I think the ITD is the delay with phase consistent with IPD that is closest to the group delay. * [WRONG REMARKS BELOW!] A simple observation: * characteristic delay is just the mean ITD in a frequency band * characteristic phase is? ITD = -dphi/(2*pi*df) (=CD) (more precisely, group delay) IPD = IPD(f)-ITD*2*pi*df CP = IPD(f) - dphi/df * f (phase at 0 freq) = IPD(f) + CD *2*pi*f Other idea: * linear regression of IPD vs frequency in S1, with frequency windows in log or ERB space (e.g. 1/3 octave) Trick: nonlinear least square regression on vector (sin(phi),cos(phi)) (same as regression in complex plane) (could done recursively) * could be weighted by power at that frequency (or thresholded, e.g., only include 50% best frequencies) """ from brian import * from brian.hears.newversion import * import os from scipy.signal import freqz def group_delay(f): ''' Group delay of a filter. Group delay is -d{angle(w)}/dw. Smith algorithm. ''' # First compute the FFT x=array(f).flatten() #x+=max(x)*(1e-20*rand(len(x))-.5*1e-20) # avoid divisions by 0 dt=1./f.samplerate freq=fftfreq(len(x),dt)[1:len(x)/2] a=fft(x*arange(len(x))) b=fft(x) grpd=dt*(a/b).real[1:len(x)/2] # group delay # Smoothing width_dt=20 filter='flat' window = {'gaussian': exp(-arange(-2 * width_dt, 2 * width_dt + 1) ** 2 * 1. / (2 * (width_dt) ** 2)), 'flat': ones(width_dt)}[filter] grpd=convolve(grpd, window * 1. / sum(window), mode='same') # Selection #ind=(freq>200) & (freq<3000) #freq=freq[ind] #grpd=grpd[ind] return freq,grpd def ITD(fL,fR): ''' ITD vs. frequency using group delays Doesn't work at all! ''' dt=1/fL.samplerate fL=array(fL).flatten() fR=array(fR).flatten() n=len(fL) b=fft(fR)/fft(fL) a=fft(arange(n)*ifft(b)) freq=fftfreq(n,dt) return freq[:n/2],dt*(a/b).real[:n/2] # only positive frequencies def IPD_raw(fL,fR): dt=1/fL.samplerate fL=array(fL).flatten() fR=array(fR).flatten() n=len(fL) xR,xL=fft(fR)[1:n/2],fft(fL)[1:n/2] x=xR/xL freq=fftfreq(n,dt)[1:n/2] return freq,-angle(x) # is it? def IPD(fL,fR,threshold=0.1): ''' IPD vs. frequency (by unwrapping). Maybe: we should look at the power in these frequencies and threshold. There's a magnitude threshold. An ITD-based unwrapping could be chosen, i.e. minimize difference with ITD expectation. ''' dt=1/fL.samplerate fL=array(fL).flatten() fR=array(fR).flatten() n=len(fL) xR,xL=fft(fR)[1:n/2],fft(fL)[1:n/2] x=xR/xL freq=fftfreq(n,dt)[1:n/2] ind=(abs(xR)>threshold) & (abs(xL)>threshold) unwrapped=unwrap(angle(x[ind])) y=zeros(n/2-1) y[ind]=unwrapped # Fill blanks (assumes linear phase + constant ITD on boundaries) ind=find(ind) breaks=find(diff(ind)>1) for i in range(len(breaks)): start=ind[breaks[i]]+1 end=ind[breaks[i]+1] # not included y[start:end]=linspace(y[start-1],y[end],end-start) if len(ind)>0: if ind[0]>0: y[:ind[0]]=freq[:ind[0]]*y[ind[0]]/freq[ind[0]] if ind[-1]pi]-=2*pi ipd[(ipd-grpd_phase)<-pi]+=2*pi itd=grpd+(ipd-grpd_phase)/(2*pi*freq) return itd choice='IRCAM' hrtf_locations = [ r'C:\HRTF\\' + choice, r'D:\HRTF\\' + choice, r'/home/bertrand/Data/Measurements/HRTF/' + choice, r'C:\Documents and Settings\dan\My Documents\Programming\\' + choice ] found = 0 for path in hrtf_locations: if os.path.exists(path): found = 1 break if found == 0: raise IOError('Cannot find IRCAM HRTF location, add to ircam_locations') ircam = IRCAM_LISTEN(path) h = ircam.load_subject(1002) h = h.subset(lambda azim,elev:azim==120 and elev==30) #freq,xL=group_delay(h.hrtf[0].left) #_,xR=group_delay(h.hrtf[0].right) #itd=(xR-xL) left=h.hrtf[0].left.flatten() right=h.hrtf[0].right.flatten() freq,itd=ITD3(left,right,threshold=0.05) itd2=group_delay(right)[1]-group_delay(left)[1] itd4=ITD4(left,right) _,ipd=IPD_raw(left,right)#,threshold=0.0) #ind=(freq>200) & (freq<3000) #freq=freq[ind] #itd=itd[ind] #subplot(211) #plot(freq,itd*1e6) subplot(312) ind=(freq<5000) & (freq>180) #plot(freq[ind],itd[ind]*1e6,'r') # CD plot(freq[ind],itd2[ind]*1e6,'r') #plot(freq[ind],itd4[ind]*1e6,'r') plot(freq[ind],unwrap(ipd[ind])/(2*pi*freq[ind])*1e6,'b') # ITD #plot(freq[ind],(group_delay(right)[1][ind]-group_delay(left)[1][ind])*1e6,'k') #ylim(0,1500) subplot(311) plot(freq[ind],ipd[ind]) # IPD subplot(313) CP = IPD_raw(left,right)[1] + itd *2*pi*freq # sure about this?? hist((((CP[ind]+pi) % (2*pi))-pi)/(2*pi)) #plot(freq[ind],(((CP[ind]+pi) % (2*pi))-pi)/(2*pi)) # CP #ylim(-0.5,0.5) #plot(smooth(smooth(smooth(smooth(freq)))),smooth(smooth(smooth(smooth(ITD))))) #plot(h.hrtf[0].left) #plot(h.hrtf[0].right) show() brian-1.4.3/dev/ideas/how_stdp_with_delays_works.py000066400000000000000000000033771266062647100225230ustar00rootroot00000000000000from brian import * N = 100 wmax = 0.1 tau_pre = 10 * ms tau_post = 10 * ms delta_A_pre = 0.1 * wmax delta_A_post = -1.2 * delta_A_pre max_delay = 5 * ms G = NeuronGroup(N, 'dV/dt=(1.1-V)/(10*ms):1', reset=0, threshold=1) G.V = rand(N) C = Connection(G, G, 'V', sparseness=0.1, weight=lambda i, j:rand()*wmax, delay=(0 * ms, max_delay)) ### STDP G_pre = NeuronGroup(N, 'dA_pre/dt=-A_pre/tau_pre:1') G_post = NeuronGroup(N, 'dA_post/dt=-A_post/tau_post:1') A_pre_recent = RecentStateMonitor(G_pre, 'A_pre', duration=max_delay) A_post_recent = RecentStateMonitor(G_post, 'A_post', duration=max_delay) def update_on_pre_spikes_immediate(spikes): if len(spikes): G_pre.A_pre[spikes] += delta_A_pre def update_on_pre_spikes_delayed(spikes): if len(spikes): times_seq = C.delayvec.get_rows(spikes) times_seq = [max_delay - times for times in times_seq] A_post_delayed_seq = A_post_recent.get_past_values_sequence(times_seq) for i, A_post_delayed in zip(spikes, A_post_delayed_seq): C.W[i, :] = clip(C.W[i, :] + A_post_delayed, 0, wmax) def update_on_post_spikes(spikes): if len(spikes): G_post.A_post[spikes] += delta_A_post times_seq = C.delayvec.get_cols(spikes) A_pre_delayed_seq = A_pre_recent.get_past_values_sequence(times_seq) for i, A_pre_delayed in zip(spikes, A_pre_delayed_seq): C.W[:, i] = clip(C.W[:, i] + A_pre_delayed, 0, wmax) M_pre_immediate = SpikeMonitor(G, function=update_on_pre_spikes_immediate) M_pre_delayed = SpikeMonitor(G, function=update_on_pre_spikes_delayed, delay=max_delay) M_post = SpikeMonitor(G, function=update_on_post_spikes) M = SpikeMonitor(G) run(1 * second) raster_plot(M) show() brian-1.4.3/dev/ideas/how_stdp_works.py000066400000000000000000000017451266062647100201240ustar00rootroot00000000000000from brian import * N = 100 wmax = 0.1 tau_pre = 10 * ms tau_post = 10 * ms delta_A_pre = 0.1 * wmax delta_A_post = -1.2 * delta_A_pre G = NeuronGroup(N, 'dV/dt=(1.1-V)/(10*ms):1', reset=0, threshold=1) G.V = rand(N) C = Connection(G, G, 'V', sparseness=0.1, weight=lambda i, j:rand()*wmax) ### STDP G_pre = NeuronGroup(N, 'dA_pre/dt=-A_pre/tau_pre:1') G_post = NeuronGroup(N, 'dA_post/dt=-A_post/tau_post:1') def update_on_pre_spikes(spikes): if len(spikes): G_pre.A_pre[spikes] += delta_A_pre for i in spikes: C.W[i, :] = clip(C.W[i, :] + G_post.A_post, 0, wmax) def update_on_post_spikes(spikes): if len(spikes): G_post.A_post[spikes] += delta_A_post for i in spikes: C.W[:, i] = clip(C.W[:, i] + G_pre.A_pre, 0, wmax) M_pre = SpikeMonitor(G, function=update_on_pre_spikes) M_post = SpikeMonitor(G, function=update_on_post_spikes) M = SpikeMonitor(G) run(1 * second) raster_plot(M) show() brian-1.4.3/dev/ideas/linearstateupdater/000077500000000000000000000000001266062647100203675ustar00rootroot00000000000000brian-1.4.3/dev/ideas/linearstateupdater/linearstateupdater.py000066400000000000000000000116641266062647100246510ustar00rootroot00000000000000from brian import * from scipy.linalg import expm def trapezoidal_integration(f, a, b, n): """ Computes numerically the integral of the function f between a and b with a step dt, using the trapezoidal method. f(t) must accept vectors of t, and returns a D*n matrix, D is the dimension of the output space of f, n is the length of t. """ I = f(a) + f(b) dt = (b - a) * 1.0 / n t = a + dt * arange(1, n) I += 2 * sum(f(t), axis=0) I *= dt / 2 return I def simpson_integration(f, a, b, n): """ Computes numerically the integral of the function f between a and b with a step dt, using the Simpson method. f(t) must accept vectors of t, and returns a D*n matrix, D is the dimension of the output space of f, n is the length of t. """ n = 2 * int(n / 2) I = f(a) + f(b) dt = (b - a) * 1.0 / n te = a + dt * arange(2, n, 2) to = a + dt * arange(1, n, 2) I += 2 * sum(f(te), axis=0) I += 4 * sum(f(to), axis=0) I *= dt / 3 return I def test_integration(): a = randn(1) b = randn(1) c = randn(1) A = randn(1) B = A + randn(1) ** 2 f = lambda x:a * exp(x) + b * x + c Itrap = trapezoidal_integration(f, A, B, 100) Isimp = simpson_integration(f, A, B, 100) Itrue = a * (exp(B) - exp(A)) + b * (B ** 2 / 2 - A ** 2 / 2) + c * (B - A) print abs(Itrap - Itrue), abs(Isimp - Itrue) def compute_exact1(X0, b, t): a = array([[X0[0] + b[1], b[0] - X0[1]], [X0[1] - b[0], b[1] + X0[0]]]) c = array([[cos(t)], [sin(t)]]) b2 = array([[-b[1]], [b[0]]]) X = dot(a, c) + b2 return X.transpose()[0] def compute_exact2(X0, b, t): X = array([(X0 + b) * exp(t) - b]) return X def compute_euler(X0, A, b, t, n): dt = t / n X = zeros((len(X0), n + 1)) X[:, 0] = X0 for i in range(n): X[:, i + 1] = X[:, i] + dt * (dot(A, X[:, i]) + b) return X def compute_Ab(A, b, dt, nint): eAt = expm(A * dt) def f(u): if isscalar(u): return dot(expm(-A * u), b) else: return array([dot(expm(-A * v), b) for v in u]) # numerical integration #b2 = dot(eAt, trapezoidal_integration(f, 0, dt, nint)) # exact calculation when A invertible b2 = dot(dot(inv(A), eAt - eye(len(A))), b) return eAt, b2 def compute_exactstep(X0, A, b, t, n, nint): dt = t / n X = zeros((len(X0), n + 1)) X[:, 0] = X0 # eAt = expm(A*dt) # def f(u): # if isscalar(u): # return dot(expm(-A*u),b) # else: # return array([dot(expm(-A*v),b) for v in u]) # eAtint = dot(eAt, trapezoidal_integration(f, 0, dt, nint)) eAt, eAtint = compute_Ab(A, b, dt, nint) for i in range(n): X[:, i + 1] = dot(eAt, X[:, i]) + eAtint return X def test_integral(): a = 1000#randn(1) b = 1000#randn(1) dt = .0001 nint = 10 def f(u): if isscalar(u): return exp(-a * u) * b return reshape(exp(-a * u) * b, (len(u), 1)) I = simpson_integration(f, 0, dt, nint) print "actual integration error ", abs(I[0] - b / a * (1 - exp(-a * dt))) print "upper bound integration error ", dt ** 5 / (180 * nint ** (4)) * exp(abs(a) * dt) * abs(b) * a ** 4 def test_sim(): """ Simulation of the system X' = AX+b, X(0) = X0, between 0 and T, with different integration schemes. """ A = array([[0, -1], [1, 0]]) D = len(A) b = 10 * randn(D) X0 = 10 * randn(D) T = 1.0 dt = .0001 n = 10000 nint = 100 X_exact = zeros((D, n + 1)) for i in xrange(0, n + 1): X_exact[:, i] = compute_exact1(X0, b, i * dt) print "exact", X_exact[:, -1] X_euler = compute_euler(X0, A, b, T, n) print "euler error: ", max(abs(X_euler[:,-1]-X_exact[:,-1])) X_exactstep = compute_exactstep(X0, A, b, T, n, nint) print "exact steps error:", max(abs(X_exactstep[:, -1] - X_exact[:, -1])) def precision(dt, n, D, a, b): return dt ** 5 / (180 * n ** 4) * sqrt(D) * a ** 4 * exp(2 * a * dt) * b def test_precision(): dt = 1e-4 n = 100 D = 10 a = b = 1000 print precision(dt, n, D, a, b) def is_equal(x, y, epsilon): return (abs(x - y) < abs(x) * epsilon).all() if __name__ == '__main__': test_sim() else: epsilon = 1. while 1. + epsilon > 1.: epsilon /= 2 epsilon *= 2000. A = array([[0, -1], [1, 0]]) D = len(A) b = 10 * randn(D) dt = .0001 nint = 16 btilde2 = compute_Ab(A, b, dt, nint)[1] for i in xrange(12): nint *= 2 btilde = compute_Ab(A, b, dt, nint)[1] print nint, max(abs(btilde - btilde2)), max(abs(btilde)) * epsilon if max(abs(btilde - btilde2)) <= max(abs(btilde)) * epsilon: # or it's getting worse break btilde2 = btilde brian-1.4.3/dev/ideas/linearstateupdater/updater.tex000066400000000000000000000076761266062647100225750ustar00rootroot00000000000000\documentclass[a4paper]{article} \usepackage{amsmath} \title{Linear state updater} \author{} \newcommand{\dt}{\mathrm{d}t} \newcommand{\dx}{\mathrm{d}x} \newcommand{\normeuc}[1]{\left\| #1 \right\|_2} \newcommand{\normF}[1]{\left\| #1 \right\|_F} \newcommand{\norminf}[1]{\left\| #1 \right\|_\infty} \newcommand{\eps}{\varepsilon} \begin{document} \maketitle We give a method for integrating numerically a linear differential system with arbitrary precision. Let $\dot X = AX+b$ be a system of linear differential equations, with $A$ an $D \times D$ matrix and $b$ an $D$-long vector. We want to integrate the system over $[0,\dt]$ with $X(0) = X_0$ and precision $\eps$, that is, such that $\norminf{\widetilde X(\dt)-X(\dt)} \leq \eps$ where $X(t)$ is the exact solution, and $\widetilde X(t)$ is the numerical solution. $\dt$ is supposed to be small, typically $\dt = 10^{-4}$. We now find the matrix $\widetilde A$ and the vector $\widetilde b$ such that : \begin{equation} X(\dt) = \widetilde A X_0 + \widetilde b \end{equation} We get: \begin{align} \widetilde A &= \exp(A \,\dt)\\ \widetilde b &= \exp(A \,\dt) \cdot \int_0^{\dt} \exp(-A u) \cdot b \, \mathrm{d}u \end{align} We can calculate $\exp(A \, \dt)$ with high accuracy in the scipy library. The integral is more difficult to calculate with high precision. We propose to use the Simpson integration scheme with $n$ steps within $[0,\dt]$. The Simpson method consists in using the following expression for calculating the integral of a function $f$ between $a$ and $b$ with $n$ subintervals ($n$ is even): \begin{equation} \int_a^b f(x) \, \dx\approx \left(\frac{h}{3}\right) \bigg[f(a)+2\sum_{j=1}^{n/2-1}f(a+2jh)+ 4\sum_{j=1}^{n/2}f(a+(2j-1)h)+f(b) \bigg] \end{equation} \noindent where $h = (b-a)/n$. Here, we use the Simpson method with the vector-valued function $f(x) = \exp(-A x) \cdot b$. This requires to calculate the matrix $\exp(-A x)$ for $n$ values of $x$ between $0$ and $\dt$. The question is now: how to chose $n$ such that the precision of $\widetilde X(\dt)$ is $\eps$ ? We now answer to this question. In general, the Simpson method yields an absolute error with respect to the exact integral given by: \begin{equation} \left| \int_a^b f(x) \, \dx - I_{\textrm{simpson}} \right| \leq \frac{(b-a)^5}{180 n^4} \, \max_{[a,b]} |f^{(4)}(x)| \end{equation} Here, we have: \begin{equation} \frac{\mathrm{d}}{\dt} \exp(-A u) \mathrm{d} u = -A \exp(-A u) \end{equation} \noindent hence: \begin{equation} \frac{\mathrm{d}^4}{\dt^4} \exp(-A u) \mathrm{d} u = A^4 \exp(-A u) \end{equation} Therefore, we find that: \begin{align} \normeuc{\widetilde X(\dt) - X(\dt)} &= \normeuc{\widetilde b-b}\\ &\leq \normeuc{\exp(A \, \dt)} \cdot \normeuc{I_{\textrm{simpson}} - \int_0^{\dt} \exp(-A u) \mathrm{d}u}\\ &\leq \normeuc{\exp(A \, \dt)} \cdot \frac{\dt^5}{180n^4} \cdot \sqrt{D} \cdot \max_{[0,\dt]} \norminf{A^4 \cdot \exp(-A u) \cdot b}\\ &\leq \normeuc{\exp(A \, \dt)} \cdot \frac{\dt^5}{180n^4} \cdot \sqrt{D} \cdot \normeuc{A}^4 \cdot \normeuc{b} \cdot e^{\normeuc{A}\dt}\\ &\leq \frac{\dt^5}{180n^4} \cdot \sqrt{D} \normeuc{A}^4 e^{2\normeuc{A}\dt} \normeuc{b} \end{align} Finally, we find that: \begin{equation} \norminf{\widetilde X(\dt) - X(\dt)} \leq \frac{\dt^5}{180n^4} \sqrt{D} \normF{A}^4 e^{2\normF{A}\dt} \normeuc{b} \end{equation} \noindent where $\normF{A}$ is the Frobenius norm of $A$ (the euclidean norm of $A$ seen as a $D^2$-long vector), which is simpler to compute than $\normeuc{A}$. In order to have $\norminf{\widetilde X(\dt) - X(\dt)} \leq \eps$, one must choose $n$ such that: \begin{equation} n \geq \left(\frac{\dt^5}{180 \eps} \sqrt{D} \normeuc{A}^4 e^{2\normeuc{A}\dt} \normeuc{b}\right)^{1/4} \end{equation} With this formula, we find that $n = 100$ is sufficient to achieve machine precision for $\widetilde b$ with $\dt = 10^{-4}$, $D = 10$, and $\normF{A}, \normeuc{b} \leq 10^3$. \end{document}brian-1.4.3/dev/ideas/modelfitting/000077500000000000000000000000001266062647100171545ustar00rootroot00000000000000brian-1.4.3/dev/ideas/modelfitting/criteria.py000077500000000000000000000522271266062647100213430ustar00rootroot00000000000000from brian import Equations, NeuronGroup, Clock, CoincidenceCounter, Network, zeros, array, \ ones, kron, ms, second, concatenate, hstack, sort, nonzero, diff, TimedArray, \ reshape, sum, log, Monitor, NetworkOperation, defaultclock, linspace, vstack, \ arange, sort_spikes, rint, SpikeMonitor, Connection, int32, double from brian.tools.statistics import firing_rate, get_gamma_factor try: import pycuda import pycuda.driver as drv import pycuda from pycuda import gpuarray can_use_gpu = True except ImportError: can_use_gpu = False from brian.experimental.codegen.integration_schemes import * import sys, cPickle class Criterion(Monitor, NetworkOperation): """ Abstract class from which modelfitting criterions should derive. Derived classes should implement the following methods: ``initialize(self, **params)`` Called once before the simulation. ```params`` is a dictionary with criterion-specific parameters. ``timestep_call(self)`` Called at every timestep. ``spike_call(self, neurons)`` Called at every spike, with the spiking neurons as argument. ``get_values(self)`` Called at the end, returns the criterion values. You have access to the following methods: ``self.get_value(self, varname)`` Returns the value of the specified variable for all neurons (vector). You have access to the following attributes: ``self.step`` The time step (integer) ``self.group`` The NeuronGroup ``self.traces=None`` Target traces. A 2-dimensional K*T array where K is the number of targets, and T the total number of timesteps. It is still a 2-dimensional array when K=1. It is None if not specified. ``self.spikes=None`` A list of target spike trains : [(i,t)..] where i is the target index and t the spike time. ``self.N`` The number of neurons in the NeuronGroup ``self.K=1`` The number of targets. ``self.duration`` The total duration of the simulation, in seconds. ``self.total_steps`` The total number of time steps. ``self.dt`` The timestep duration, in seconds. ``self.delays=zeros(self.n)`` The delays for every neuron, in seconds. The delay is relative to the target. ``self.onset=0*ms`` The onset, in seconds. The first timesteps, before onset, should be discarded in the criterion. ``self.intdelays=0`` The delays, but in number of timesteps (``int(delays/dt)``). NOTE: traces and spikes have all been sliced before being passed to the criterion object """ def __init__(self, group, traces=None, spikes=None, targets_count=1, duration=None, onset=0*ms, spikes_inline=None, spikes_offset=None, traces_inline=None, traces_offset=None, delays=None, when='end', **params): NetworkOperation.__init__(self, None, clock=group.clock, when=when) self.group = group # needed by SpikeMonitor self.source = group self.source.set_max_delay(0) self.delay = 0 self.N = len(group) # number of neurons self.K = targets_count # number of targets self.dt = self.clock.dt if traces is not None: self.traces = array(traces) # KxT array if self.traces.ndim == 1: self.traces = self.traces.reshape((1,-1)) assert targets_count==self.traces.shape[0] self.spikes = spikes # get the duration from the traces if duration is not specified in the constructor if duration is None: duration = self.traces.shape[1] # total number of steps self.duration = duration self.total_steps = int(duration/self.dt) if delays is None: delays = zeros(self.n) self.delays = delays self.onset = int(onset/self.dt) self.intdelays = array(self.delays/self.clock.dt, dtype=int) self.mindelay = min(delays) self.maxdelay = max(delays) # the following data is sliced self.spikes_inline = spikes_inline self.spikes_offset = spikes_offset self.traces_inline = traces_inline self.traces_offset = traces_offset if self.spikes is not None: # target spike count and rates self.target_spikes_count = self.get_spikes_count(self.spikes) self.target_spikes_rate = self.get_spikes_rate(self.spikes) self.initialize(**params) def step(self): """ Return the current time step """ return int(round(self.clock.t/self.dt)) def get_spikes_count(self, spikes): count = zeros(self.K) for (i,t) in spikes: count[i] += 1 return count def get_spikes_rate(self, spikes): count = self.get_spikes_count(spikes) return count*1.0/self.duration def get_gpu_code(self): # TO IMPLEMENT """ Returns CUDA code snippets to put at various places in the CUDA code template. It must return a dictionary with the following items: %CRITERION_DECLARATION%: kernel declaration code %CRITERION_INIT%: initialization code %CRITERION_TIMESTEP%: main code, called at every time step %CRITERION_END%: finalization code """ log_warn("GPU code not implemented by the derived criterion class") def initialize_cuda_variables(self): # TO IMPLEMENT """ Initialize criterion-specific CUDA variables here. """ pass def get_kernel_arguments(self): # TO IMPLEMENT """ Return a list of objects to pass to the CUDA kernel. """ pass def initialize(self): # TO IMPLEMENT """ Override this method to initialize the criterion before the simulation """ pass def timestep_call(self): # TO IMPLEMENT """ Override this method to do something at every time step """ pass def spike_call(self, neurons): # TO IMPLEMENT """ Override this method to do something at every time spike. neurons contains the list of neurons that just spiked """ pass def get_value(self, varname): return self.group.state_(varname) def __call__(self): self.timestep_call() neurons = self.group.get_spikes() self.spike_call(neurons) # def propagate(self, neurons): # self.spike_call(neurons) def get_values(self): # TO IMPLEMENT """ Override this method to return the criterion values at the end. It must return one (or several, as a tuple) additive values, i.e., values corresponding to slices. The method normalize can normalize combined values. """ pass additive_values = property(get_values) def normalize(self, values): # TO IMPLEMENT """ Values contains combined criterion values. It is either a vector of values (as many values as neurons), or a tuple with different vector of as many values as neurons. """ pass class CriterionStruct(object): type = None # 'trace', 'spike' or 'both' def get_name(self): return self.__class__.__name__ name = property(get_name) class LpErrorCriterion(Criterion): type = 'traces' def initialize(self, p=2, varname='v'): """ Called at the beginning of every iteration. The keyword arguments here are specified in modelfitting.initialize_criterion(). """ self.p = double(p) self.varname = varname self._error = zeros((self.K, self.N)) def timestep_call(self): v = self.get_value(self.varname) t = self.step()+1 if t= self.duration: return d = self.intdelays indices = (t-d>=0)&(t-d0: if t >= self.onset: self.spike_count[spiking_neurons] += 1 T_spiking = array(rint((t + self.delays[spiking_neurons]) / dt), dtype=int) remaining_neurons = spiking_neurons remaining_T_spiking = T_spiking while True: remaining_indices, = (remaining_T_spiking > self.next_spike_time[remaining_neurons]).nonzero() if len(remaining_indices): indices = remaining_neurons[remaining_indices] self.spiketime_index[indices] += 1 self.last_spike_time[indices] = self.next_spike_time[indices] self.next_spike_time[indices] = array(rint(self.spikes_inline[self.spiketime_index[indices] + 1] / dt), dtype=int) if self.algorithm == 'exclusive': self.last_spike_allowed[indices] = self.next_spike_allowed[indices] self.next_spike_allowed[indices] = True remaining_neurons = remaining_neurons[remaining_indices] remaining_T_spiking = remaining_T_spiking[remaining_indices] else: break # Updates coincidences count near_last_spike = self.last_spike_time[spiking_neurons] + self.delta >= T_spiking near_next_spike = self.next_spike_time[spiking_neurons] - self.delta <= T_spiking last_spike_allowed = self.last_spike_allowed[spiking_neurons] next_spike_allowed = self.next_spike_allowed[spiking_neurons] I = (near_last_spike & last_spike_allowed) | (near_next_spike & next_spike_allowed) if t >= self.onset: self.coincidences[spiking_neurons[I]] += 1 if self.algorithm == 'exclusive': near_both_allowed = (near_last_spike & last_spike_allowed) & (near_next_spike & next_spike_allowed) self.last_spike_allowed[spiking_neurons] = last_spike_allowed & -near_last_spike self.next_spike_allowed[spiking_neurons] = (next_spike_allowed & -near_next_spike) | near_both_allowed def get_cuda_code(self): code = {} # DECLARATION code['%CRITERION_DECLARE%'] = """ int *spikecount, // Number of spikes produced by each neuron int *num_coincidences, // Count of coincidences for each neuron """ if self.algorithm == 'exclusive': code['%CRITERION_DECLARE%'] += """ bool *last_spike_allowed_arr, bool *next_spike_allowed_arr, """ # INITIALIZATION code['%CRITERION_INIT%'] = """ int ncoinc = num_coincidences[neuron_index]; int nspikes = spikecount[neuron_index]; int last_spike_time = spiketimes[spiketime_index]; int next_spike_time = spiketimes[spiketime_index+1]; """ if self.algorithm == 'exclusive': code['%CRITERION_INIT%'] += """ bool last_spike_allowed = last_spike_allowed_arr[neuron_index]; bool next_spike_allowed = next_spike_allowed_arr[neuron_index]; """ # TIMESTEP code['%CRITERION_TIMESTEP%'] = """ const int Tspike = T+spikedelay; """ if self.algorithm == 'inclusive': code['%CRITERION_TIMESTEP%'] += """ ncoinc += has_spiked && (((last_spike_time+%d)>=Tspike) || ((next_spike_time-%d)<=Tspike)); """ % (self.delta, self.delta) if self.algorithm == 'exclusive': code['%CRITERION_TIMESTEP%'] += """ bool near_last_spike = last_spike_time+%d>=Tspike; bool near_next_spike = next_spike_time-%d<=Tspike; near_last_spike = near_last_spike && has_spiked; near_next_spike = near_next_spike && has_spiked; ncoinc += (near_last_spike&&last_spike_allowed) || (near_next_spike&&next_spike_allowed); bool near_both_allowed = (near_last_spike&&last_spike_allowed) && (near_next_spike&&next_spike_allowed); last_spike_allowed = last_spike_allowed && !near_last_spike; next_spike_allowed = (next_spike_allowed && !near_next_spike) || near_both_allowed; """ % (self.delta, self.delta) code['%CRITERION_TIMESTEP%'] += """ nspikes += has_spiked*(T>=onset); if(Tspike>=next_spike_time){ spiketime_index++; last_spike_time = next_spike_time; next_spike_time = spiketimes[spiketime_index+1]; """ if self.algorithm == 'exclusive': code['%CRITERION_TIMESTEP%'] += """ last_spike_allowed = next_spike_allowed; next_spike_allowed = true; """ code['%CRITERION_TIMESTEP%'] += """ } """ # FINALIZATION code['%CRITERION_END%'] = """ num_coincidences[neuron_index] = ncoinc; spikecount[neuron_index] = nspikes; """ if self.algorithm == 'exclusive': code['%CRITERION_END%'] += """ last_spike_allowed_arr[neuron_index] = last_spike_allowed; next_spike_allowed_arr[neuron_index] = next_spike_allowed; """ return code def initialize_cuda_variables(self): """ Initialize GPU variables to pass to the kernel """ self.spike_count_gpu = gpuarray.to_gpu(zeros(self.N, dtype=int32)) self.coincidences_gpu = gpuarray.to_gpu(zeros(self.N, dtype=int32)) if self.algorithm == 'exclusive': self.last_spike_allowed_arr = gpuarray.to_gpu(zeros(self.N, dtype=bool)) self.next_spike_allowed_arr = gpuarray.to_gpu(ones(self.N, dtype=bool)) def get_kernel_arguments(self): """ Return a list of objects to pass to the CUDA kernel. """ args = [self.spike_count_gpu, self.coincidences_gpu] if self.algorithm == 'exclusive': args += [self.last_spike_allowed_arr, self.next_spike_allowed_arr] return args def update_gpu_values(self): """ Call gpuarray.get() on final values, so that get_values() returns updated values. """ self.coincidences = self.coincidences_gpu.get() self.spike_count = self.spike_count_gpu.get() def get_values(self): return (self.coincidences, self.spike_count) def normalize(self, values): coincidence_count = values[0] spike_count = values[1] delta = self.delta*self.dt gamma = get_gamma_factor(coincidence_count, spike_count, self.target_spikes_count, self.target_spikes_rate, delta) return gamma class GammaFactor(CriterionStruct): def __init__(self, delta = 4*ms, coincidence_count_algorithm = 'exclusive'): self.type = 'spikes' self.delta = delta self.coincidence_count_algorithm = coincidence_count_algorithm class VanRossumCriterion(Criterion): type = 'spikes' def initialize(self, p=2, varname='v'): # TODO pass def timestep_call(self): # TODO pass def get_values(self): # TODO pass def normalize(self, error): # TODO pass class VanRossum(CriterionStruct): # TODO def __init__(self, tau): self.type = 'spikes' self.tau = tau 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4.729548800000000300e-10 4.982496199999999700e-10 4.412199100000000200e-10 5.362828199999999800e-10 4.814211299999999400e-10 5.794692099999999600e-10 6.264166699999999500e-10 6.037387799999999700e-10 5.407925300000000500e-10 5.472408099999999500e-10 5.612849999999999900e-10 5.617628199999999800e-10 4.955062399999999800e-10 4.784148599999999400e-10 4.214541300000000200e-10 5.191141699999999400e-10 5.095931899999999400e-10 5.047502700000000000e-10 4.458296799999999700e-10 3.819177900000000400e-10 3.739174799999999800e-10 3.180505800000000000e-10 3.761014299999999800e-10 3.665818400000000200e-10 3.860499099999999900e-10 3.243378100000000000e-10 3.187359900000000000e-10 4.309115800000000200e-10 4.194402499999999800e-10 4.254606200000000200e-10 4.075628799999999900e-10 3.395045700000000100e-10 4.537708300000000000e-10 4.644728900000000000e-10 4.058343200000000000e-10 4.125242200000000200e-10 3.740500299999999900e-10 3.133049700000000100e-10 2.575790899999999800e-10 3.571953300000000000e-10 4.407579299999999700e-10 4.359071499999999700e-10 5.215503399999999900e-10 4.558650999999999900e-10 5.130713199999999500e-10 6.082056400000000300e-10 6.288755000000000200e-10 7.164000999999999600e-10 7.392945099999999700e-10 8.114228200000000300e-10 7.199507899999999800e-10 8.209818400000000200e-10 8.887737100000000100e-10 8.632740800000000400e-10 9.365571000000000300e-10 9.421967499999999100e-10 9.683270899999999700e-10 8.872164000000000500e-10 9.231305200000000400e-10 9.223840000000000100e-10 8.306044000000000600e-10 7.593908799999999900e-10 6.967717899999999700e-10 6.221731099999999300e-10 6.991201199999999800e-10 6.224023099999999900e-10 6.432714400000000000e-10 5.938163500000000000e-10 5.060354500000000400e-10 5.241412400000000600e-10 5.600175500000000400e-10 5.903996899999999100e-10 5.028370100000000100e-10 5.287648299999999500e-10 4.627107700000000200e-10 4.888777800000000100e-10 5.356427299999999800e-10 4.550973399999999500e-10 4.982501199999999300e-10 5.232967999999999500e-10 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2.602819299999999700e-10 2.633338400000000000e-10 3.003998800000000000e-10 3.078338699999999800e-10 4.127528200000000000e-10 4.197536299999999600e-10 4.456154500000000100e-10 3.760712399999999700e-10 3.803942599999999700e-10 4.316598799999999700e-10 4.334251100000000000e-10 4.632300100000000100e-10 5.361532299999999700e-10 4.701766699999999800e-10 4.176239299999999800e-10 3.556272599999999900e-10 2.752185099999999900e-10 2.105907900000000000e-10 1.606286500000000000e-10 1.578928300000000000e-10 9.819172699999999400e-11 1.514767799999999800e-10 1.010327199999999900e-10 2.059522900000000100e-10 1.363347199999999900e-10 1.508183100000000000e-10 1.886088999999999900e-10 1.332334299999999900e-10 2.332400700000000200e-10 2.380362399999999900e-10 2.728694200000000100e-10 3.785114099999999900e-10 3.287163400000000100e-10 2.896417700000000200e-10 3.898441300000000100e-10 3.885025600000000300e-10 4.902150899999999300e-10 5.016400499999999500e-10 4.313740100000000100e-10 5.256549700000000500e-10 4.785736499999999600e-10 4.011064900000000000e-10 4.857524900000000300e-10 5.334288500000000300e-10 4.636956000000000100e-10 4.034288199999999900e-10 3.958219300000000000e-10 4.285352300000000000e-10 4.361703600000000100e-10 4.739732200000000400e-10 4.013191699999999900e-10 4.100621800000000200e-10 3.453037499999999900e-10 4.046781500000000200e-10 3.433623000000000000e-10 3.060072099999999900e-10 2.377778999999999800e-10 1.948293500000000100e-10 2.262816200000000000e-10 2.345915200000000100e-10 1.739439999999999800e-10 1.166736100000000000e-10 2.374029700000000200e-10 3.602376899999999900e-10 3.554667000000000000e-10 2.903620599999999900e-10 2.159098199999999800e-10 1.635850100000000000e-10 2.131553199999999800e-10 2.196112500000000200e-10 3.011219100000000200e-10 2.528633900000000000e-10 2.473124799999999800e-10 2.056481099999999900e-10 2.191149699999999900e-10 2.225988199999999800e-10 2.231938199999999900e-10 2.758033899999999700e-10 2.981017000000000100e-10 2.836908900000000100e-10 3.835489899999999800e-10 3.236733399999999700e-10 2.561863900000000200e-10 1.971172000000000000e-10 2.795437800000000300e-10 2.218721899999999900e-10 2.665234500000000400e-10 2.582379900000000200e-10 3.032180799999999900e-10 2.371846399999999900e-10 2.885860999999999800e-10 3.985401800000000300e-10 3.229447399999999900e-10 3.417928800000000200e-10 2.788022700000000400e-10 2.134916599999999800e-10 1.516403599999999800e-10 1.048615800000000100e-10 1.625399700000000100e-10 1.316803999999999800e-10 1.481564099999999900e-10 1.988875999999999800e-10 1.545090299999999900e-10 1.601422699999999800e-10 2.147493900000000000e-10 1.530985199999999900e-10 1.064110300000000000e-10 6.750152199999999900e-11 5.877699900000000900e-11 1.188495300000000100e-10 5.457518600000000000e-11 4.254190899999999700e-13 6.954148299999999600e-11 1.290142800000000200e-10 1.331174799999999900e-10 1.848553400000000200e-10 2.062908799999999800e-10 1.744066399999999900e-10 2.256577100000000100e-10 3.355543000000000200e-10 4.115106400000000200e-10 3.637543299999999600e-10 3.619634199999999900e-10 3.497932900000000100e-10 3.746998399999999700e-10 3.118141499999999600e-10 2.496726400000000000e-10 2.762628900000000400e-10 2.054431700000000000e-10 3.045408100000000000e-10 2.489507700000000200e-10 2.771442000000000300e-10 2.691914699999999600e-10 3.682459699999999800e-10 4.183412500000000100e-10 4.601104900000000400e-10 4.463862300000000000e-10 3.835960000000000200e-10 3.245814299999999900e-10 4.060672299999999900e-10 4.842719900000000000e-10 4.156155399999999900e-10 4.401087800000000400e-10 4.918898599999999400e-10 4.401003100000000000e-10 4.602526199999999700e-10 5.042483800000000100e-10 brian-1.4.3/dev/ideas/modelfitting/gpu_modelfitting.py000077500000000000000000000514631266062647100231020ustar00rootroot00000000000000from brian import Equations, NeuronGroup, Clock, CoincidenceCounter, Network, zeros, array, \ ones, kron, ms, second, concatenate, hstack, sort, nonzero, diff, TimedArray, \ reshape, sum, log, Monitor, NetworkOperation, defaultclock, linspace, vstack, \ arange, sort_spikes, rint, SpikeMonitor, Connection, Threshold, Reset, \ int32, double, VariableReset, StringReset, VariableThreshold, StringThreshold from brian.tools.statistics import firing_rate, get_gamma_factor from playdoh import * import brian.optimiser as optimiser import pycuda.driver as drv import pycuda from pycuda.gpuarray import GPUArray from pycuda import gpuarray try: from pycuda.compiler import SourceModule except ImportError: from pycuda.driver import SourceModule from numpy import * from brian.experimental.codegen.integration_schemes import * from brian.experimental.codegen.codegen_gpu import * import re __all__ = ['GPUModelFitting', 'euler_scheme', 'rk2_scheme', 'exp_euler_scheme' ] if drv.get_version() == (2, 0, 0): # cuda version default_precision = 'float' elif drv.get_version() > (2, 0, 0): default_precision = 'double' else: raise Exception, "CUDA 2.0 required" class ModelfittingGPUCodeGenerator(GPUCodeGenerator): def generate(self, eqs, scheme): vartype = self.vartype() code = '' for line in self.scheme(eqs, scheme).split('\n'): line = line.strip() if line: code += ' ' + line + '\n' return code def get_cuda_template(): return """ __global__ void runsim( // ITERATIONS int Tstart, int Tend, // Start, end time as integer (t=T*dt) int duration, // Total duration as integer // STATE VARIABLES %SCALAR% *state_vars, // State variables are offset from this // INPUT PARAMETERS double *I_arr, // Input current int *I_arr_offset, // Input current offset (for separate input // currents for each neuron) // DELAYS PARAMETERS int *spikedelay_arr, // Integer delay for each spike // REFRACTORY PARAMETERS int *refractory_arr, // Integer refractory times int *next_allowed_spiketime_arr, // Integer time of the next allowed spike (for refractoriness) // CRITERION SPECIFIC PARAMETERS %CRITERION_DECLARE% // DATA DECLARE %DATA_DECLARE% // STATEMONITOR DECLARE %STATEMONITOR_DECLARE% // MISC PARAMETERS int onset // Time onset (only count spikes from here onwards) ) { // NEURON INDEX const int neuron_index = blockIdx.x * blockDim.x + threadIdx.x; if(neuron_index>=%NUM_NEURONS%) return; // EXTRACT STATE VARIABLES %EXTRACT_STATE_VARIABLES% // LOAD VARIABLES %LOAD_VARIABLES% // DATA INIT %DATA_INIT% // STATEMONITOR INIT %STATEMONITOR_INIT% // CRITERION INITIALIZATION %CRITERION_INIT% // INPUT INITIALIZATION int I_offset = I_arr_offset[neuron_index]; // DELAYS INITIALIZATION int spikedelay = spikedelay_arr[neuron_index]; // REFRACTORY INITIALIZATION const int refractory = refractory_arr[neuron_index]; int next_allowed_spiketime = next_allowed_spiketime_arr[neuron_index]; for(int T=Tstart; T' + str(float(threshold.threshold)) elif isinstance(threshold, VariableThreshold): state = threshold.state if isinstance(state, int): state = eqs._diffeq_names[state] threshold = state + '>' + threshold.threshold_state elif isinstance(threshold, StringThreshold): namespace = threshold._namespace expr = threshold._expr all_variables = eqs._eq_names + eqs._diffeq_names + eqs._alias.keys() + ['t'] expr = optimiser.freeze(expr, all_variables, namespace) threshold = expr else: raise ValueError('Threshold must be constant, VariableThreshold or StringThreshold.') # Substitute threshold src = src.replace('%THRESHOLD%', threshold) return src def generate_reset_code(self, src): eqs = self.eqs reset = self.G._resetfun if reset.__class__ is Reset: state = reset.state if isinstance(state, int): state = eqs._diffeq_names[state] reset = state + ' = ' + str(float(reset.resetvalue)) elif isinstance(reset, VariableReset): state = reset.state if isinstance(state, int): state = eqs._diffeq_names[state] reset = state + ' = ' + reset.resetvaluestate elif isinstance(reset, StringReset): namespace = reset._namespace expr = reset._expr all_variables = eqs._eq_names + eqs._diffeq_names + eqs._alias.keys() + ['t'] expr = optimiser.freeze(expr, all_variables, namespace) reset = expr # self.reset = reset # Substitute reset reset = '\n '.join(line.strip() + ';' for line in reset.split('\n') if line.strip()) src = src.replace('%RESET%', reset) return src def generate_data_code(self, src): # Substitute spikes/traces declare if self.criterion.type == 'spikes': data_declare = """ int *spiketimes, // Array of all spike times as integers (begin and // end each train with large negative value) int *spiketime_indices, // Pointer into above array for each neuron """ if self.criterion.type == 'traces': data_declare = """ double *traces_arr, int *traces_arr_offset, """ src = src.replace('%DATA_DECLARE%', data_declare) # Substitute spikes/traces init if self.criterion.type == 'spikes': data_init = """ int spiketime_index = spiketime_indices[neuron_index]; """ if self.criterion.type == 'traces': data_init = """ int trace_offset = traces_arr_offset[neuron_index]; double trace_value = 0.0; int Tdelay = 0; """ src = src.replace('%DATA_INIT%', data_init) # Substitute spikes/traces update if self.criterion.type == 'spikes': data_update = """ """ if self.criterion.type == 'traces': data_update = """ Tdelay = T+spikedelay; if ((Tdelay>=0)&(Tdelay=2.2 pass self.block = (blocksize, 1, 1) self.grid = (int(ceil(float(self.N) / blocksize)), 1) self.kernel_func_kwds = {'block':self.block, 'grid':self.grid} mydtype = self.mydtype N = self.N eqs = self.eqs statevars_arr = gpuarray.to_gpu(array(self.G._S.flatten(), dtype=mydtype)) self.I = gpuarray.to_gpu(array(I, dtype=mydtype)) self.statevars_arr = statevars_arr self.I_offset = gpuarray.to_gpu(array(I_offset, dtype=int32)) # SPIKES if self.criterion.type == 'spikes': self.initialize_spikes(spiketimes, spiketimes_offset) # TRACES if self.criterion.type == 'traces': self.initialize_traces(traces, traces_offset) self.criterion.initialize_cuda_variables() self.initialize_delays(spikedelays) self.initialize_refractory(refractory) if self.statemonitor_var is not None: self.initialize_statemonitor() self.initialize_kernel_arguments() def launch(self, duration, stepsize=1 * second): if stepsize is None: self.kernel_func(int32(0), int32(duration / self.dt), *self.kernel_func_args, **self.kernel_func_kwds) pycuda.context.synchronize() else: stepsize = int(stepsize / self.dt) duration = int(duration / self.dt) for Tstart in xrange(0, duration, stepsize): Tend = Tstart + min(stepsize, duration - Tstart) self.kernel_func(int32(Tstart), int32(Tend), int32(duration), *self.kernel_func_args, **self.kernel_func_kwds) pycuda.context.synchronize() def get_statemonitor_values(self): values = self.statemonitor_values.get() values = values.reshape((self.N, -1)) return values brian-1.4.3/dev/ideas/modelfitting/modelfitting.py000077500000000000000000000466561266062647100222370ustar00rootroot00000000000000from brian import Equations, NeuronGroup, Clock, CoincidenceCounter, Network, zeros, array, \ ones, kron, ms, second, concatenate, hstack, sort, nonzero, diff, TimedArray, \ reshape, sum, log, Monitor, NetworkOperation, defaultclock, linspace, vstack, \ arange, sort_spikes, rint, SpikeMonitor, Connection from brian.tools.statistics import firing_rate, get_gamma_factor try: from playdoh import * except Exception, e: print e raise ImportError("Playdoh must be installed (https://code.google.com/p/playdoh/)") try: import pycuda from gpu_modelfitting import GPUModelFitting can_use_gpu = True except ImportError: can_use_gpu = False from brian.experimental.codegen.integration_schemes import * from criteria import * from simulator import * import sys, cPickle __all__ = ['modelfitting', 'print_table', 'PSO', 'GA','CMAES', 'MAXCPU', 'MAXGPU', 'GammaFactor', 'LpError', 'simulate', 'debug_level', 'info_level', 'warning_level', 'open_server'] class ModelFitting(Fitness): def initialize(self, **kwds): # Gets the key,value pairs in shared_data for key, val in self.shared_data.iteritems(): setattr(self, key, val) # Gets the key,value pairs in **kwds for key, val in kwds.iteritems(): setattr(self, key, val) self.model = cPickle.loads(self.model) if type(self.model) is str: self.model = Equations(self.model) self.simulator = Simulator(self.model, self.reset, self.threshold, inputs = self.inputs, input_var = self.input_var, dt = self.dt, refractory = self.refractory, max_refractory = self.max_refractory, spikes = self.spikes, traces = self.traces, groups = self.groups, slices = self.slices, overlap = self.overlap, onset = self.onset, neurons = self.nodesize, initial_values = self.initial_values, unit_type = self.unit_type, stepsize = self.stepsize, precision = self.precision, criterion = self.criterion, method = self.method ) def evaluate(self, **param_values): """ Use fitparams['delays'] to take delays into account Use fitparams['refractory'] to take refractory into account """ values = self.simulator.run(**param_values) return values def modelfitting(model=None, reset=None, threshold=None, refractory=0*ms, data=None, input_var='I', input=None, dt=None, popsize=1000, maxiter=10, slices=1, overlap=0*second, initial_values=None, stepsize=100 * ms, unit_type=None, total_units=None, cpu=None, gpu=None, precision='double', # set to 'float' or 'double' to specify single or double precision on the GPU machines=[], allocation=None, returninfo=False, scaling=None, algorithm=CMAES, async = None, criterion=None, optparams={}, method='Euler', **params): """ Model fitting function. Fits a spiking neuron model to electrophysiological data (injected current and spikes). See also the section :ref:`model-fitting-library` in the user manual. **Arguments** ``model`` An :class:`~brian.Equations` object containing the equations defining the model. ``reset`` A reset value for the membrane potential, or a string containing the reset equations. ``threshold`` A threshold value for the membrane potential, or a string containing the threshold equations. ``refractory`` The refractory period in second. If it's a single value, the same refractory will be used in all the simulations. If it's a list or a tuple, the fitting will also optimize the refractory period (see ``**params`` below). Warning: when using a refractory period, you can't use a custom reset, only a fixed one. ``data`` A list of spike times, or a list of several spike trains as a list of pairs (index, spike time) if the fit must be performed in parallel over several target spike trains. In this case, the modelfitting function returns as many parameters sets as target spike trains. ``input_var='I'`` The variable name used in the equations for the input current. ``input`` A vector of values containing the time-varying signal the neuron responds to (generally an injected current). ``dt`` The time step of the input (the inverse of the sampling frequency). ``**params`` The list of parameters to fit the model with. Each parameter must be set as follows: ``param_name=[bound_min, min, max, bound_max]`` where ``bound_min`` and ``bound_max`` are the boundaries, and ``min`` and ``max`` specify the interval from which the parameter values are uniformly sampled at the beginning of the optimization algorithm. If not using boundaries, set ``param_name=[min, max]``. Also, you can add a fit parameter which is a spike delay for all spikes : add the special parameter ``delays`` in ``**params``, for example ``modelfitting(..., delays=[-10*ms, 10*ms])``. You can also add fit the refractory period by specifying ``modelfitting(..., refractory=[-10*ms, 10*ms])``. ``popsize`` Size of the population (number of particles) per target train used by the optimization algorithm. ``maxiter`` Number of iterations in the optimization algorithm. ``optparams`` Optimization algorithm parameters. It is a dictionary: keys are parameter names, values are parameter values or lists of parameters (one value per group). This argument is specific to the optimization algorithm used. See :class:`PSO`, :class:`GA`, :class:`CMAES`. ``delta=4*ms`` The precision factor delta (a scalar value in second). ``slices=1`` The number of time slices to use. ``overlap=0*ms`` When using several time slices, the overlap between consecutive slices, in seconds. ``initial_values`` A dictionary containing the initial values for the state variables. ``cpu`` The number of CPUs to use in parallel. It is set to the number of CPUs in the machine by default. ``gpu`` The number of GPUs to use in parallel. It is set to the number of GPUs in the machine by default. ``precision`` GPU only: a string set to either ``float`` or ``double`` to specify whether to use single or double precision on the GPU. If it is not specified, it will use the best precision available. ``returninfo=False`` Boolean indicating whether the modelfitting function should return technical information about the optimization. ``scaling=None`` Specify the scaling used for the parameters during the optimization. It can be ``None`` or ``'mapminmax'``. It is ``None`` by default (no scaling), and ``mapminmax`` by default for the CMAES algorithm. ``algorithm=CMAES`` The optimization algorithm. It can be :class:`PSO`, :class:`GA` or :class:`CMAES`. ``optparams={}`` Optimization parameters. See ``method='Euler'`` Integration scheme used on the CPU and GPU: ``'Euler'`` (default), ``RK``, or ``exponential_Euler``. See also :ref:`numerical-integration`. ``machines=[]`` A list of machine names to use in parallel. See :ref:`modelfitting-clusters`. **Return values** Return an :class:`OptimizationResult` object with the following attributes: ``best_pos`` Minimizing position found by the algorithm. For array-like fitness functions, it is a single vector if there is one group, or a list of vectors. For keyword-like fitness functions, it is a dictionary where keys are parameter names and values are numeric values. If there are several groups, it is a list of dictionaries. ``best_fit`` The value of the fitness function for the best positions. It is a single value if there is one group, or it is a list if there are several groups. ``info`` A dictionary containing various information about the optimization. Also, the following syntax is possible with an ``OptimizationResult`` instance ``or``. The ``key`` is either an optimizing parameter name for keyword-like fitness functions, or a dimension index for array-like fitness functions. ``or[key]`` it is the best ``key`` parameter found (single value), or the list of the best parameters ``key`` found for all groups. ``or[i]`` where ``i`` is a group index. This object has attributes ``best_pos``, ``best_fit``, ``info`` but only for group ``i``. ``or[i][key]`` where ``i`` is a group index, is the same as ``or[i].best_pos[key]``. For more details on the gamma factor, see `Jolivet et al. 2008, "A benchmark test for a quantitative assessment of simple neuron models", J. Neurosci. Methods `__ (available in PDF `here `__). """ for param in params.keys(): if (param not in model._diffeq_names) and (param != 'delays'): raise Exception("Parameter %s must be defined as a parameter in the model" % param) if criterion is None: criterion = GammaFactor() data = array(data) if criterion.type == 'spikes': # Make sure that 'data' is a N*2-array if data.ndim == 1: data = concatenate((zeros((len(data), 1)), data.reshape((-1, 1))), axis=1) spikes = data traces = None groups = int(array(data)[:, 0].max() + 1) # number of target trains elif criterion.type == 'trace': if data.ndim == 1: data = data.reshape((1,-1)) spikes = None traces = data groups = data.shape[0] elif criterion.type == 'both': # TODO log_warn("Not implemented yet") pass inputs = input if inputs.ndim==1: inputs = inputs.reshape((1,-1)) # dt must be set if dt is None: raise Exception('dt (sampling frequency of the input) must be set') # default overlap when no time slicing if slices == 1: overlap = 0*ms # default allocation if cpu is None and gpu is None and unit_type is None: if CANUSEGPU: unit_type = 'GPU' else: unit_type = 'CPU' # check numerical integration method if (gpu>0 or unit_type == 'GPU') and method not in ['Euler', 'RK', 'exponential_Euler']: raise Exception("The method can only be 'Euler', 'RK', or 'exponential_Euler' when using the GPU") if method not in ['Euler', 'RK', 'exponential_Euler', 'linear', 'nonlinear']: raise Exception("The method can only be 'Euler', 'RK', 'exponential_Euler', 'linear', or 'nonlinear'") if (algorithm == CMAES) & (scaling is None): scaling = 'mapminmax' # determines whether optimization over refractoriness or not if type(refractory) is tuple or type(refractory) is list: params['refractory'] = refractory max_refractory = refractory[-1] else: max_refractory = None # duration = len(input) * dt # duration of the input # keyword arguments for Modelfitting initialize kwds = dict( model=cPickle.dumps(model), threshold=threshold, reset=reset, refractory=refractory, max_refractory=max_refractory, input_var=input_var, dt=dt, criterion=criterion, slices=slices, overlap=overlap, returninfo=returninfo, precision=precision, stepsize=stepsize, method=method, onset=overlap) shared_data = dict(inputs=inputs, traces=traces, spikes=spikes, initial_values=initial_values) if async: r = maximize_async( ModelFitting, shared_data=shared_data, kwds = kwds, groups=groups, popsize=popsize, maxiter=maxiter, optparams=optparams, unit_type = unit_type, total_units = total_units, machines=machines, allocation=allocation, cpu=cpu, gpu=gpu, returninfo=returninfo, codedependencies=[], algorithm=algorithm, scaling=scaling, **params) else: r = maximize( ModelFitting, shared_data=shared_data, kwds = kwds, groups=groups, popsize=popsize, maxiter=maxiter, optparams=optparams, unit_type = unit_type, total_units = total_units, machines=machines, allocation=allocation, cpu=cpu, gpu=gpu, returninfo=returninfo, codedependencies=[], algorithm=algorithm, scaling=scaling, **params) # r is (results, fitinfo) or (results) return r #def get_spikes(model=None, reset=None, threshold=None, # input=None, input_var='I', dt=None, # **params): # """ # Retrieves the spike times corresponding to the best parameters found by # the modelfitting function. # # **Arguments** # # ``model``, ``reset``, ``threshold``, ``input``, ``input_var``, ``dt`` # Same parameters as for the ``modelfitting`` function. # # ``**params`` # The best parameters returned by the ``modelfitting`` function. # # **Returns** # # ``spiketimes`` # The spike times of the model with the given input and parameters. # """ # duration = len(input) * dt # ngroups = len(params[params.keys()[0]]) # # group = NeuronGroup(N=ngroups, model=model, reset=reset, threshold=threshold, # clock=Clock(dt=dt)) # group.set_var_by_array(input_var, TimedArray(input, clock=group.clock)) # for param, values in params.iteritems(): # if (param == 'delays') | (param == 'fitness'): # continue # group.state(param)[:] = values # # M = SpikeMonitor(group) # net = Network(group, M) # net.run(duration) # reinit_default_clock() # return M.spikes # #def predict(model=None, reset=None, threshold=None, # data=None, delta=4 * ms, # input=None, input_var='I', dt=None, # **params): # """ # Predicts the gamma factor of a fitted model with respect to the data with # a different input current. # # **Arguments** # # ``model``, ``reset``, ``threshold``, ``input_var``, ``dt`` # Same parameters as for the ``modelfitting`` function. # # ``input`` # The input current, that can be different from the current used for the fitting # procedure. # # ``data`` # The experimental spike times to compute the gamma factor against. They have # been obtained with the current ``input``. # # ``**params`` # The best parameters returned by the ``modelfitting`` function. # # **Returns** # # ``gamma`` # The gamma factor of the model spike trains against the data. # If there were several groups in the fitting procedure, it is a vector # containing the gamma factor for each group. # """ # spikes = get_spikes(model=model, reset=reset, threshold=threshold, # input=input, input_var=input_var, dt=dt, # **params) # # ngroups = len(params[params.keys()[0]]) # gamma = zeros(ngroups) # for i in xrange(ngroups): # spk = [t for j, t in spikes if j == i] # gamma[i] = gamma_factor(spk, data, delta, normalize=True, dt=dt) # if len(gamma) == 1: # return gamma[0] # else: # return gamma if __name__ == '__main__': from brian import loadtxt, ms, savetxt, loadtxt, Equations, NeuronGroup, run, SpikeMonitor,\ StateMonitor, Network from pylab import * def generate_data(): g = NeuronGroup(1, model=equations, reset=0, threshold=1) g.I = TimedArray(input, dt=.1*ms) g.tau = 25*ms g.R = 3e9 SpM = SpikeMonitor(g) StM = StateMonitor(g, 'V', record=True) net = Network(g, SpM, StM) net.run(1*second) return StM.values[0], SpM.spikes equations = Equations(''' dV/dt=(R*I-V)/tau : 1 I : 1 R : 1 tau : second ''') input = loadtxt('current.txt') # ARTIFICIAL DATA: R=3e9, tau=25*ms # spikes = loadtxt('spikes.txt') # real data # trace, spikes = generate_data() # savetxt('trace_artificial.txt', trace) # savetxt('spikes_artificial.txt', spikes) trace = loadtxt('trace_artificial.txt') spikes= loadtxt('spikes_artificial.txt') # GAMMA FACTOR criterion = GammaFactor(delta=2*ms) data = spikes # data[:,1] += 50*ms # LP ERROR criterion = LpError(p=2, varname='V') data = trace results = modelfitting( model = equations, reset = 0, threshold = 1, data = data, input = input, gpu = 1, dt = .1*ms, popsize = 1000, maxiter = 1, criterion = criterion, R = [1.0e9, 9.0e9], tau = [10*ms, 40*ms], ) print_table(results) brian-1.4.3/dev/ideas/modelfitting/note.txt000077500000000000000000000002541266062647100206660ustar00rootroot00000000000000 Bertrand: maybe the monitor like CoincidenceCounter should take the same input format as modelffiting. It would be much easier if someone wants to use those monitor alone.brian-1.4.3/dev/ideas/modelfitting/simulator.py000077500000000000000000000504301266062647100215520ustar00rootroot00000000000000from brian import Equations, NeuronGroup, Clock, CoincidenceCounter, Network, zeros, array, \ ones, kron, ms, second, concatenate, hstack, sort, nonzero, diff, TimedArray, \ reshape, sum, log, Monitor, NetworkOperation, defaultclock, linspace, vstack, \ arange, sort_spikes, rint, SpikeMonitor, Connection, StateMonitor from brian.tools.statistics import firing_rate, get_gamma_factor try: from playdoh import * except Exception, e: print e raise ImportError("Playdoh must be installed (https://code.google.com/p/playdoh/)") try: import pycuda from gpu_modelfitting import GPUModelFitting can_use_gpu = True except ImportError: can_use_gpu = False from brian.experimental.codegen.integration_schemes import * from criteria import * import sys, cPickle class DataTransformer(object): """ Transform spike, input and trace data from user-friendly data structures, like 2 dimensional arrays or lists of spikes, into algorithm- and GPU-friendly structures, i.e. inline vectors (1 dimensional) easily parallelizable. """ def __init__(self, neurons, input, spikes = None, traces = None, dt = defaultclock.dt, slices = 1, overlap = 0*ms, groups = 1): self.neurons = neurons # number of particles on the node self.input = input # a IxT array self.spikes = spikes # a list of spikes [(i,t)...] self.traces = traces # a KxT array self.slices = slices self.overlap = overlap self.groups = groups self.dt = dt # ensure 2 dimensions if self.input.ndim == 1: self.input = self.input.reshape((1,-1)) if self.traces is not None: if self.traces.ndim == 1: self.traces = self.traces.reshape((1,-1)) self.inputs_count = self.input.shape[0] self.T = self.input.shape[1] # number of steps self.duration = self.T*self.dt self.subpopsize = self.neurons/self.groups # number of neurons per group: nodesize/groups self.input = self.input[:,0:self.slices * (self.T / self.slices)] # makes sure that len(input) is a multiple of slices self.sliced_steps = self.T / self.slices # timesteps per slice self.overlap_steps = int(self.overlap / self.dt) # timesteps during the overlap self.total_steps = self.sliced_steps + self.overlap_steps # total number of timesteps self.sliced_duration = self.overlap + self.duration / self.slices # duration of the vectorized simulation self.N = self.neurons * self.slices # TOTAL number of neurons on this node self.input = hstack((zeros((self.inputs_count, self.overlap_steps)), self.input)) # add zeros at the beginning because there is no overlap from the previous slice def slice_spikes(self, spikes): # from standard structure to standard structure sliced_spikes = [] slice_length = self.sliced_steps*self.dt for (i,t) in spikes: slice = int(t/slice_length) newt = self.overlap + (t % slice_length)*second newi = i + self.groups*slice # discard unreachable spikes if newi >= (self.slices*self.groups): continue sliced_spikes.append((newi, newt)) sliced_spikes = sort_spikes(sliced_spikes) return sliced_spikes def slice_traces(self, traces): # from standard structure to standard structure k = traces.shape[0] sliced_traces = zeros((k*self.slices, self.total_steps)) for slice in xrange(self.slices): i0 = slice*k i1 = (slice+1)*k j0 = slice*self.sliced_steps j1 = (slice+1)*self.sliced_steps sliced_traces[i0:i1,self.overlap_steps:] = traces[:,j0:j1] if slice>0: sliced_traces[i0:i1,:self.overlap_steps] = traces[:,j0-self.overlap_steps:j0] return sliced_traces def transform_spikes(self, spikes): # from standard structure to inline structure i, t = zip(*spikes) i = array(i) t = array(t) alls = [] n = 0 pointers = [] model_target = [] for j in xrange(self.groups): s = sort(t[i == j]) s = hstack((-1 * second, s, self.duration + 1 * second)) model_target.extend([j] * self.subpopsize) alls.append(s) pointers.append(n) n += len(s) pointers = array(pointers, dtype=int) model_target = array(hstack(model_target), dtype=int) spikes_inline = hstack(alls) spikes_offset = pointers[model_target] return spikes_inline, spikes_offset def transform_traces(self, traces): # from standard structure to inline structure K, T = traces.shape traces_inline = traces.flatten() traces_offset = array(kron(arange(K), T*ones(self.subpopsize)), dtype=int) return traces_inline, traces_offset class Simulator(object): def __init__(self, model, reset, threshold, inputs, input_var = 'I', dt = defaultclock.dt, refractory = 0*ms, max_refractory = None, spikes = None, traces = None, groups = 1, slices = 1, overlap = 0*second, onset = 0*second, neurons = 1000, # = nodesize = number of neurons on this node = total number of neurons/slices initial_values = None, unit_type = 'CPU', stepsize = 100*ms, precision = 'double', criterion = None, statemonitor_var=None, method = 'Euler' ): self.model = model self.reset = reset self.threshold = threshold self.inputs = inputs self.input_var = input_var self.dt = dt self.refractory = refractory self.max_refractory = max_refractory self.spikes = spikes self.traces = traces self.initial_values = initial_values self.groups = groups self.slices = slices self.overlap = overlap self.onset = onset self.neurons = neurons self.unit_type = unit_type self.statemonitor_var = statemonitor_var self.stepsize = stepsize self.precision = precision self.criterion = criterion self.method = method self.use_gpu = self.unit_type=='GPU' if self.statemonitor_var is not None: self.statemonitor_values = zeros(self.neurons) self.initialize_neurongroup() self.transform_data() self.inject_input() self.initialize_criterion(delays=zeros(self.neurons)) if self.use_gpu: self.initialize_gpu() def initialize_neurongroup(self): # Add 'refractory' parameter on the CPU only if not self.use_gpu: if self.max_refractory is not None: refractory = 'refractory' self.model.add_param('refractory', second) else: refractory = self.refractory else: if self.max_refractory is not None: refractory = 0*ms else: refractory = self.refractory # Must recompile the Equations : the functions are not transfered after pickling/unpickling self.model.compile_functions() self.group = NeuronGroup(self.neurons, model=self.model, reset=self.reset, threshold=self.threshold, refractory=refractory, max_refractory = self.max_refractory, method = self.method, clock=Clock(dt=self.dt)) if self.initial_values is not None: for param, value in self.initial_values.iteritems(): self.group.state(param)[:] = value def initialize_gpu(self): # Select integration scheme according to method if self.method == 'Euler': scheme = euler_scheme elif self.method == 'RK': scheme = rk2_scheme elif self.method == 'exponential_Euler': scheme = exp_euler_scheme else: raise Exception("The numerical integration method is not valid") self.mf = GPUModelFitting(self.group, self.model, self.criterion_object, self.input_var, self.onset, statemonitor_var = self.statemonitor_var, duration = self.sliced_duration, precision=self.precision, scheme=scheme) def transform_data(self): self.transformer = DataTransformer(self.neurons, self.inputs, spikes = self.spikes, traces = self.traces, dt = self.dt, slices = self.slices, overlap = self.overlap, groups = self.groups) self.total_steps = self.transformer.total_steps self.sliced_duration = self.transformer.sliced_duration self.sliced_inputs = self.transformer.slice_traces(self.inputs) self.inputs_inline, self.inputs_offset = self.transformer.transform_traces(self.sliced_inputs) if self.traces is not None: self.sliced_traces = self.transformer.slice_traces(self.traces) self.traces_inline, self.traces_offset = self.transformer.transform_traces(self.sliced_traces) else: self.sliced_traces, self.traces_inline, self.traces_offset = None, None, None if self.spikes is not None: self.sliced_spikes = self.transformer.slice_spikes(self.spikes) self.spikes_inline, self.spikes_offset = self.transformer.transform_spikes(self.sliced_spikes) else: self.sliced_spikes, self.spikes_inline, self.spikes_offset = None, None, None def inject_input(self): # Injects current in consecutive subgroups, where I_offset have the same value # on successive intervals I_offset = self.inputs_offset k = -1 for i in hstack((nonzero(diff(I_offset))[0], len(I_offset) - 1)): I_offset_subgroup_value = I_offset[i] I_offset_subgroup_length = i - k sliced_subgroup = self.group.subgroup(I_offset_subgroup_length) input_sliced_values = self.inputs_inline[I_offset_subgroup_value:I_offset_subgroup_value + self.total_steps] sliced_subgroup.set_var_by_array(self.input_var, TimedArray(input_sliced_values, clock=self.group.clock)) k = i def initialize_criterion(self, **criterion_params): # general criterion parameters params = dict(group=self.group, traces=self.sliced_traces, spikes=self.sliced_spikes, targets_count=self.groups*self.slices, duration=self.sliced_duration, onset=self.onset, spikes_inline=self.spikes_inline, spikes_offset=self.spikes_offset, traces_inline=self.traces_inline, traces_offset=self.traces_offset) for key,val in criterion_params.iteritems(): params[key] = val criterion_name = self.criterion.__class__.__name__ # criterion-specific parameters if criterion_name == 'GammaFactor': params['delta'] = self.criterion.delta params['coincidence_count_algorithm'] = self.criterion.coincidence_count_algorithm self.criterion_object = GammaFactorCriterion(**params) if criterion_name == 'LpError': params['p'] = self.criterion.p params['varname'] = self.criterion.varname self.criterion_object = LpErrorCriterion(**params) def update_neurongroup(self, **param_values): """ Inject fitting parameters into the NeuronGroup """ # Sets the parameter values in the NeuronGroup object self.group.reinit() for param, value in param_values.iteritems(): self.group.state(param)[:] = kron(value, ones(self.slices)) # kron param_values if slicing # Reinitializes the model variables if self.initial_values is not None: for param, value in self.initial_values.iteritems(): self.group.state(param)[:] = value def combine_sliced_values(self, values): if type(values) is tuple: combined_values = tuple([sum(reshape(v, (self.slices, -1)), axis=0) for v in values]) else: combined_values = sum(reshape(values, (self.slices, -1)), axis=0) return combined_values def run(self, **param_values): delays = param_values.pop('delays', zeros(self.neurons)) refractory = param_values.pop('refractory', zeros(self.neurons)) self.update_neurongroup(**param_values) # repeat spike delays and refractory to take slices into account delays = kron(delays, ones(self.slices)) refractory = kron(refractory, ones(self.slices)) # TODO: add here parameters to criterion_params if a criterion must use some parameters criterion_params = dict(delays=delays) self.update_neurongroup(**param_values) self.initialize_criterion(**criterion_params) if self.use_gpu: # Reinitializes the simulation object self.mf.reinit_vars(self.criterion_object, self.inputs_inline, self.inputs_offset, self.spikes_inline, self.spikes_offset, self.traces_inline, self.traces_offset, delays, refractory ) # LAUNCHES the simulation on the GPU self.mf.launch(self.sliced_duration, self.stepsize) # Synchronize the GPU values with a call to gpuarray.get() self.criterion_object.update_gpu_values() else: # set the refractory period if self.max_refractory is not None: self.group.refractory = refractory # Launch the simulation on the CPU self.group.clock.reinit() net = Network(self.group, self.criterion_object) if self.statemonitor_var is not None: self.statemonitor = StateMonitor(self.group, self.statemonitor_var, record=True) net.add(self.statemonitor) net.run(self.sliced_duration) sliced_values = self.criterion_object.get_values() combined_values = self.combine_sliced_values(sliced_values) values = self.criterion_object.normalize(combined_values) return values def get_statemonitor_values(self): if not self.use_gpu: return self.statemonitor.values else: return self.mf.get_statemonitor_values() def simulate( model, reset = None, threshold = None, input = None, input_var = 'I', dt = defaultclock.dt, refractory = 0*ms, max_refractory = None, data = None, groups = 1, slices = 1, overlap = 0*second, neurons = 1, initial_values = None, use_gpu = False, stepsize = 100*ms, precision = 'double', criterion = None, record = None, method = 'Euler', **params): unit_type = 'CPU' if use_gpu: unit_type = 'GPU' for param in params.keys(): if (param not in model._diffeq_names) and (param != 'delays'): raise Exception("Parameter %s must be defined as a parameter in the model" % param) if criterion is None: criterion = GammaFactor() data = array(data) if criterion.type == 'spikes': # Make sure that 'data' is a N*2-array if data.ndim == 1: data = concatenate((zeros((len(data), 1)), data.reshape((-1, 1))), axis=1) spikes = data traces = None groups = int(array(data)[:, 0].max() + 1) # number of target trains elif criterion.type == 'trace': if data.ndim == 1: data = data.reshape((1,-1)) spikes = None traces = data groups = data.shape[0] elif criterion.type == 'both': # TODO log_warn("Not implemented yet") pass inputs = input if inputs.ndim==1: inputs = inputs.reshape((1,-1)) # dt must be set if dt is None: raise Exception('dt (sampling frequency of the input) must be set') # default overlap when no time slicing if slices == 1: overlap = 0*ms # check numerical integration method if use_gpu and method not in ['Euler', 'RK', 'exponential_Euler']: raise Exception("The method can only be 'Euler', 'RK', or 'exponential_Euler' when using the GPU") if method not in ['Euler', 'RK', 'exponential_Euler', 'linear', 'nonlinear']: raise Exception("The method can only be 'Euler', 'RK', 'exponential_Euler', 'linear', or 'nonlinear'") # determines whether optimization over refractoriness or not if type(refractory) is tuple or type(refractory) is list: params['refractory'] = refractory max_refractory = refractory[-1] else: max_refractory = None # Initialize GPU if use_gpu: set_gpu_device(0) simulator = Simulator(model, reset, threshold, inputs, input_var = input_var, dt = dt, refractory = refractory, max_refractory = max_refractory, spikes = spikes, traces = traces, groups = groups, slices = slices, overlap = overlap, onset = overlap, neurons = neurons, initial_values = initial_values, unit_type = unit_type, stepsize = stepsize, precision = precision, criterion = criterion, statemonitor_var = record, method = method) criterion_values = simulator.run(**params) if record is not None: record_values = simulator.get_statemonitor_values() return criterion_values, record_values else: return criterion_values if __name__ == '__main__': from brian import loadtxt, ms, savetxt, loadtxt, Equations, NeuronGroup, run, SpikeMonitor,\ StateMonitor, Network from pylab import * equations = Equations(''' dV/dt=(R*I-V)/tau : 1 I : 1 R : 1 tau : second ''') input = loadtxt('current.txt') trace = loadtxt('trace_artificial.txt') spikes= loadtxt('spikes_artificial.txt') # GAMMA FACTOR criterion = GammaFactor(delta=2*ms) data = spikes # data[:,1] += 50*ms # LP ERROR criterion = LpError(p=2, varname='V') data = trace # SIMULATE, EVALUATE CRITERION AND RECORD TRACES ON GPU neurons = 5 R = [3e9]*neurons tau = linspace(25*ms, 30*ms, neurons) criterion_values, record_values = simulate(model = equations, reset = 0, threshold = 1, data = data, input = input, use_gpu = True, dt = .1*ms, criterion = criterion, record = 'V', neurons = neurons, R = R, tau = tau, ) print criterion_values plot(trace) plot(record_values.transpose()) show() brian-1.4.3/dev/ideas/modelfitting/spikes.txt000066400000000000000000000007561266062647100212230ustar00rootroot000000000000005.890000000000128500e-02 1.259000000000014600e-01 2.564999999999990600e-01 2.784000000000013100e-01 3.195000000000014500e-01 3.524999999999991500e-01 3.848999999999982400e-01 4.194999999999993200e-01 4.557999999999999800e-01 4.826000000000014700e-01 5.149000000000008000e-01 5.381999999999997900e-01 5.676999999999985400e-01 6.749000000000009400e-01 7.154999999999986900e-01 7.504999999999988300e-01 8.883000000000009800e-01 9.280000000000008200e-01 9.682999999999992700e-01 brian-1.4.3/dev/ideas/modelfitting/spikes_artificial.txt000066400000000000000000000025411266062647100234040ustar00rootroot000000000000000.000000000000000000e+00 5.590000000000000500e-02 0.000000000000000000e+00 9.720000000000000900e-02 0.000000000000000000e+00 1.345000000000000100e-01 0.000000000000000000e+00 1.642000000000000100e-01 0.000000000000000000e+00 2.505999999999999900e-01 0.000000000000000000e+00 2.683000000000000400e-01 0.000000000000000000e+00 2.878000000000000000e-01 0.000000000000000000e+00 3.208000000000000300e-01 0.000000000000000000e+00 3.485000000000000300e-01 0.000000000000000000e+00 3.770000000000000000e-01 0.000000000000000000e+00 4.020000000000000200e-01 0.000000000000000000e+00 4.245000000000000400e-01 0.000000000000000000e+00 4.537000000000000500e-01 0.000000000000000000e+00 4.778000000000000000e-01 0.000000000000000000e+00 5.072999999999999700e-01 0.000000000000000000e+00 5.302999999999999900e-01 0.000000000000000000e+00 5.552000000000000300e-01 0.000000000000000000e+00 5.781000000000000600e-01 0.000000000000000000e+00 6.658000000000000600e-01 0.000000000000000000e+00 6.960000000000000600e-01 0.000000000000000000e+00 7.225000000000000300e-01 0.000000000000000000e+00 7.480999999999999900e-01 0.000000000000000000e+00 8.059000000000000600e-01 0.000000000000000000e+00 8.827000000000000400e-01 0.000000000000000000e+00 9.097000000000000600e-01 0.000000000000000000e+00 9.365000000000000000e-01 0.000000000000000000e+00 9.663000000000000500e-01 brian-1.4.3/dev/ideas/modelfitting/trace_artificial.txt000066400000000000000000007736401266062647100232230ustar00rootroot000000000000000.000000000000000000e+00 3.619882559999999500e-03 6.478546709759999000e-03 9.526817002920958500e-03 1.324353673490927400e-02 1.710641542796963900e-02 2.042146188625776000e-02 2.381739823871273000e-02 2.840489560575788100e-02 3.241431642333485200e-02 3.564527283764151000e-02 3.822051546629094700e-02 4.010840368442578100e-02 4.319837682968807900e-02 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brian-1.4.3/dev/ideas/morphology/000077500000000000000000000000001266062647100166665ustar00rootroot00000000000000brian-1.4.3/dev/ideas/morphology/Remy/000077500000000000000000000000001266062647100176025ustar00rootroot00000000000000brian-1.4.3/dev/ideas/morphology/Remy/cable_equation_dirac.py000066400000000000000000000071341266062647100242760ustar00rootroot00000000000000if __name__ == '__main__': ''' Simulation of a cable equation Seems to work (matches examples/misc/cable). TODO: * Add active currents (state updater) * Check algorithm (e.g. w/ Hines) * Add point processes * Add branches ''' from brian import * from morphology import * from spatialneuron_monoprocess import * from time import time ''' The simulation is in two stages: 1) Solve the discrete system for V(t+dt), where the other variables are constant and assumed to be known at time t+dt (first iteration: use value at t). This is a sparse linear system (could be computed with scipy.sparse.linalg?). 2) Calculate Im at t+dt using a backward Euler step with the values of V from step 1. That is, do one step of implicit Euler. And possibly repeat (until convergence) (how many times?). The discretized longitudinal current is: a/(2*R)*(V[i+1]-2*V[i]+V[i-1])/dx**2 Artificial gridpoints are used so that: (V[1]-V[-1])/(2*dx) = dV/dt(0) # =0 in general (V[N+1]-V[N-1])/(2*dx) = dV/dt(L) # =0 in general Consider also the capacitive current (and leak?). Actually in Mascagni it's more complicated, because we use the full membrane equation (conductances in front V in particular). Therefore perhaps we would need sympy to reorganize the equation first as dV/dt=aV+b (see the Synapses class). x=scipy.linalg.solve_banded((lower,upper),ab,b) lower = number of lower diagonals upper = number of upper diagonals ab = array(l+u+1,M) each row is one diagonal a[i,j]=ab[u+i-j,j] ''' defaultclock.dt=0.1*ms dt = defaultclock.dt length=4000*um diameter=1*um n=300 Ri=100 * ohm * cm Cm=1 * uF / cm ** 2 refractory=4 * ms morpho = Soma(diameter=1 * um) morpho.L = Cylinder(length=length/3, diameter=diameter, n=n/3, type='axon') morpho.LL = Cylinder(length=length/3, diameter=diameter, n=n/3, type='axon') morpho.LLL = Cylinder(length=length/3, diameter=diameter, n=n/3, type='axon') environment ='''El = 0 * mV gl = 0.02 * msiemens / cm ** 2 ''' exec(environment) # Typical equations eqs=''' # The same equations for the whole neuron, but possibly different parameter values Im=gl0*(El-v)+I : amp/cm**2 # distributed transmembrane current I : amp/cm**2 # applied current gl0:siemens/cm**2 ''' neuron = SpatialNeuron(morphology=morpho, model=eqs, refractory=refractory, Cm=Cm, Ri=Ri,diffeq_nonzero=False,implicit=False) neuron.v=El neuron.I=0*amp/cm**2 neuron.gl= 0.02 * msiemens / cm ** 2 I0 = 50 * nA/neuron.area[0] neuron.I[1]= I0 M=StateMonitor(neuron,'v',record=True) taum = neuron.Cm[0]/gl * second**4 * (volt/ohm)**2 / (metre**4 * kilogram) rm = taum/(Cm * pi * diameter) ra = (4 * Ri)/(pi * diameter**2) la = sqrt(rm/ra) dx = length / n T = 3*taum print 'taum=',taum print "lambda = ",la print "temps de simulation : ",T/taum," taum" start = time() run(defaultclock.dt) neuron.I = 0*amp/cm**2 neuron.changed = True run(T-defaultclock.dt) end = time() print end - start," s" max = zeros(n) for i in range(n): for t in range(len(M.times)): if M[i][t] > M[i][max[i]]: max[i] = t i = arange(n) p1, = plot(i*dx/mm,max *ms,'r') #t* en fonction de x p2, = plot(i*dx/mm, taum / 4 * (sqrt(1+4*(i*dx)**2/la**2)-1) * ms / dt,'g') # t* pour un cable infini xlabel('position (mm)') ylabel('temps pour lequel le potentiel est maximal') legend([p1,p2],["simulation","theorie"], loc='upper left') #plot(i*dx/mm, (max - taum / 4 * (sqrt(1+4*(i*dx)**2/la**2)-1) / dt)/max) #for i in range(n-10): #plot(M.times/ms,M[35+(i/10)*3]/mV) show() brian-1.4.3/dev/ideas/morphology/Remy/cable_equation_rall.py000066400000000000000000000111261266062647100241420ustar00rootroot00000000000000if __name__ == '__main__': ''' Simulation of a cable equation Seems to work (matches examples/misc/cable). TODO: * Add active currents (state updater) * Check algorithm (e.g. w/ Hines) * Add point processes * Add branches ''' from brian import * from morphology import * from spatialneuron_monoprocess import * from time import time ''' The simulation is in two stages: 1) Solve the discrete system for V(t+dt), where the other variables are constant and assumed to be known at time t+dt (first iteration: use value at t). This is a sparse linear system (could be computed with scipy.sparse.linalg?). 2) Calculate Im at t+dt using a backward Euler step with the values of V from step 1. That is, do one step of implicit Euler. And possibly repeat (until convergence) (how many times?). The discretized longitudinal current is: a/(2*R)*(V[i+1]-2*V[i]+V[i-1])/dx**2 Artificial gridpoints are used so that: (V[1]-V[-1])/(2*dx) = dV/dt(0) # =0 in general (V[N+1]-V[N-1])/(2*dx) = dV/dt(L) # =0 in general Consider also the capacitive current (and leak?). Actually in Mascagni it's more complicated, because we use the full membrane equation (conductances in front V in particular). Therefore perhaps we would need sympy to reorganize the equation first as dV/dt=aV+b (see the Synapses class). x=scipy.linalg.solve_banded((lower,upper),ab,b) lower = number of lower diagonals upper = number of upper diagonals ab = array(l+u+1,M) each row is one diagonal a[i,j]=ab[u+i-j,j] ''' defaultclock.dt=1*ms n0 = 300 n1 = 200 n2 = 700 n=n0+n1+n2 L0 = n0*2*um L1 = n1*2*um L2 = n2*2*um d0 = 1*um d1 = 0.8*um d2 = 1.3*um Ri=100 * ohm * cm Cm=1 * uF / cm ** 2 refractory=4 * ms dx0 = L0 / n0 morpho = Cylinder(length=dx0, diameter=d0, n=1, type='axon') morpho.L = Cylinder(length=L0, diameter=d0, n=n0, type='axon') morpho.LL = Cylinder(length=L1, diameter=d1, n=n1, type='axon') morpho.LR = Cylinder(length=L2, diameter=d2, n=n2, type='axon') environment ='''El = 0 * mV gl = 0.02 * msiemens / cm ** 2 ''' exec(environment) # Typical equations eqs=''' # The same equations for the whole neuron, but possibly different parameter values Im=gl0*(El-v)+I : amp/cm**2 # distributed transmembrane current I : amp/cm**2 # applied current gl0:siemens/cm**2 ''' neuron = SpatialNeuron(morphology=morpho, model=eqs, refractory=refractory, Cm=Cm, Ri=Ri,implicit=False,diffeq_nonzero=False) neuron.v=El neuron.gl0 = 0.02 * msiemens / cm ** 2 # neuron.I=0*amp/cm**2 # I0 = .05 * nA/neuron.area[1] # neuron.I[1]= I0 M=StateMonitor(neuron,'v',record=True) taum = neuron.Cm[0]/gl * second**4 * (volt/ohm)**2 / (metre**4 * kilogram) rm0 = taum/(Cm * pi * d0) ra0 = (4 * Ri)/(pi * d0**2) la0 = sqrt(rm0/ra0) dx0 = L0 / n0 CL0 = cosh(L0 / la0) SL0 = sinh(L0 / la0) R_infinity_0 = sqrt(rm0 * ra0) rm1 = taum/(Cm * pi * d1) ra1 = (4 * Ri)/(pi * d1**2) la1 = sqrt(rm1/ra1) dx1 = L1 / n1 CL1 = cosh(L1 / la1) SL1 = sinh(L1 / la1) R_infinity_1 = sqrt(rm1 * ra1) R_in_1 = R_infinity_1 * CL1/SL1 rm2 = taum/(Cm * pi * d2) ra2 = (4 * Ri)/(pi * d2**2) la2 = sqrt(rm2/ra2) dx2 = L2 / n2 CL2 = cosh(L2 / la2) SL2 = sinh(L2 / la2) R_infinity_2 = sqrt(rm2 * ra2) R_in_2 = R_infinity_2 * CL2/SL2 R_L_0 = (R_in_2 * R_in_1)/(R_in_2 + R_in_1) R_in_0 = R_infinity_0 * (R_L_0 + R_infinity_0 * SL0/CL0)/(R_infinity_0 + R_L_0 * SL0/CL0) # V0_th = R_in_0 * I0 * neuron.area[1] V0_th = 100 * mV neuron.v[0] = V0_th neuron.bc[0] = 0 T = 30*taum print 'taum=',taum print "temps de simulation : ",T/taum," taum" start = time() run(T) end = time() print end - start," s" testTime = (T-1*ms)/defaultclock.dt i = arange(n+1) i0 = arange(n0+1) i1 = arange(n0,n0+n1+1) i2 = arange(n0+n1,n0+n1+n2+1) photo = zeros(n+1) for k in xrange(n+1): photo[k] = M[k][testTime] p1, = plot (i ,photo/mV,'r') p2, = plot(i0,V0_th * (R_L_0 * cosh((L0-i0*dx0)/la0) + R_infinity_0 * sinh((L0-i0*dx0)/la0))/(R_L_0 * CL0 + R_infinity_0 * SL0) / mV,'g') V1 = V0_th * (R_L_0 * cosh((L0-n0*dx0)/la0) + R_infinity_0 * sinh((L0-n0*dx0)/la0))/(R_L_0 * CL0 + R_infinity_0 * SL0) p3, = plot(i1, V1 / CL1 * cosh((L1 - (i1 - n0) * dx1) / la1 ) / mV,'g') p4, = plot(i2, V1 / CL2 * cosh((L2 - (i2 - (n0+n1)) * dx2) / la2 ) / mV,'g') xlabel('position (compartiment)') ylabel('potentiel (mV)') legend([p1,p2],["simulation","theorie"], loc='upper right') """ for i in range(n): #plot(M.times/ms,M[35+(i/10)*3]/mV) """ show() brian-1.4.3/dev/ideas/morphology/Remy/cable_equation_static.py000066400000000000000000000115061266062647100245010ustar00rootroot00000000000000if __name__ == '__main__': ''' Simulation of a cable equation Seems to work (matches examples/misc/cable). TODO: * Add active currents (state updater) * Check algorithm (e.g. w/ Hines) * Add point processes * Add branches ''' from brian import * from morphology import * from spatialneuron_monoprocess import * from time import time ''' The simulation is in two stages: 1) Solve the discrete system for V(t+dt), where the other variables are constant and assumed to be known at time t+dt (first iteration: use value at t). This is a sparse linear system (could be computed with scipy.sparse.linalg?). 2) Calculate Im at t+dt using a backward Euler step with the values of V from step 1. That is, do one step of implicit Euler. And possibly repeat (until convergence) (how many times?). The discretized longitudinal current is: a/(2*R)*(V[i+1]-2*V[i]+V[i-1])/dx**2 Artificial gridpoints are used so that: (V[1]-V[-1])/(2*dx) = dV/dt(0) # =0 in general (V[N+1]-V[N-1])/(2*dx) = dV/dt(L) # =0 in general Consider also the capacitive current (and leak?). Actually in Mascagni it's more complicated, because we use the full membrane equation (conductances in front V in particular). Therefore perhaps we would need sympy to reorganize the equation first as dV/dt=aV+b (see the Synapses class). x=scipy.linalg.solve_banded((lower,upper),ab,b) lower = number of lower diagonals upper = number of upper diagonals ab = array(l+u+1,M) each row is one diagonal a[i,j]=ab[u+i-j,j] ''' defaultclock.dt=0.1*ms length=3000*um diameter=1*um n = 100 br = 0 #(+1) Ri=100 * ohm * cm Cm=1 * uF / cm ** 2 refractory=4 * ms length = (length/(br+1))* (br+1) n = (n/(br+1)) * (br+1) dx = length / n morpho = Cylinder(length=dx, diameter=diameter, n=1, type='axon') morpho.L = Cylinder(length=length/(br+1), diameter=diameter, n=n/(br+1), type='axon') if br>0: morpho.LL = Cylinder(length=length/(br+1), diameter=diameter, n=n/(br+1), type='axon') if br>1: morpho.LLL = Cylinder(length=length/(br+1), diameter=diameter, n=n/(br+1), type='axon') if br>2: morpho.LLLL = Cylinder(length=length/(br+1), diameter=diameter, n=n/(br+1), type='axon') if br>3: morpho.LLLLL = Cylinder(length=length/(br+1), diameter=diameter, n=n/(br+1), type='axon') if br>4: morpho.LLLLLL = Cylinder(length=length/(br+1), diameter=diameter, n=n/(br+1), type='axon') if br>5: morpho.LLLLLLL = Cylinder(length=length/(br+1), diameter=diameter, n=n/(br+1), type='axon') if br>6: morpho.LLLLLLLL = Cylinder(length=length/(br+1), diameter=diameter, n=n/(br+1), type='axon') if br>7: morpho.LLLLLLLLL = Cylinder(length=length/(br+1), diameter=diameter, n=n/(br+1), type='axon') if br>8: morpho.LLLLLLLLLL = Cylinder(length=length/(br+1), diameter=diameter, n=n/(br+1), type='axon') environment ='''El = 0 * mV gl = 0.02 * msiemens / cm ** 2 ''' exec(environment) # Typical equations eqs=''' # The same equations for the whole neuron, but possibly different parameter values Im=gl*(El-v)+I : amp/cm**2 # distributed transmembrane current I : amp/cm**2 # applied current ''' neuron = SpatialNeuron(morphology=morpho, model=eqs, refractory=refractory, Cm=Cm, Ri=Ri,diffeq_nonzero=False) neuron.v=El neuron.I=0*amp/cm**2 neuron.bc[0] = 0 # M=StateMonitor(neuron,'v',record=True) taum = neuron.Cm[0]/gl * second**4 * (volt/ohm)**2 / (metre**4 * kilogram) rm = taum/(Cm * pi * diameter) ra = (4 * Ri)/(pi * diameter**2) la = sqrt(rm/ra) CL = cosh(length / la) SL = sinh(length / la) R_infinity = sqrt(rm * ra) R_sealed = R_infinity * CL/SL # V0_lim_sealed = R_sealed * I0 * neuron.area[1] R_killed = R_infinity * SL/CL # V0_lim_killed = R_killed * I0 * neuron.area[1] V0_lim_sealed = 100 * mV neuron.v[0] = V0_lim_sealed T = 20*taum print 'taum=',taum print "lambda = ",la print "temps de simulation : ",T/taum," taum" print "longueur : ",length/la," lambda" start = time() run(T) end = time() print end - start," s" #testTime = (T-1*ms)/defaultclock.dt i = arange(n+1) photo = zeros(n+1) diff = 0 k_max = 0 for k in xrange(n+1): # photo[k] = M[k][testTime] photo[k] = neuron.v[k] if k>0 : if diff < abs(photo[k]/mV * metre**2 * kilogram /(second**3 * volt/ohm)- V0_lim_sealed / CL * cosh((length - k * dx) / la ) / mV) : diff = abs(photo[k]/mV * metre**2 * kilogram /(second**3 * volt/ohm)- V0_lim_sealed / CL * cosh((length - k * dx) / la ) / mV) k_max = k print diff print k_max plot (i * dx/mm,photo/mV,'r') plot(i*dx / mm,V0_lim_sealed / CL * cosh((length - i * dx) / la ) / mV,'g') xlabel('position (mm)') ylabel('potentiel (mV)') # plot(i*dx / mm,V0_lim_killed / SL * sinh((length - i * dx) / la ) / mV,'b') # for i in range(n): # plot(M.times/ms,M[i]/mV) show() brian-1.4.3/dev/ideas/morphology/Remy/gpustateupdater.py000066400000000000000000000477461266062647100234170ustar00rootroot00000000000000import pycuda.driver as cuda import pycuda.autoinit from pycuda.compiler import SourceModule from brian.stateupdater import NonlinearStateUpdater from numpy import zeros, ones import numpy import brian.optimiser as optimiser from brian.inspection import get_identifiers from time import time class GPUExponentialEulerStateUpdater(NonlinearStateUpdater): def __init__(self, eqs, clock=None, freeze=False): ''' Initialize a nonlinear model with dynamics dX/dt = f(X). ''' # TODO: global pref? self.eqs = eqs self.optimized = compile self.changed = True self.prepared=False self.firstTime = True self.index_to_varname = [] self.varname_to_index = {} self.index_nonzero = [] if freeze: self.eqs.compile_functions(freeze=freeze) self._frozen=freeze self.compile_functions_C(eqs) mod = SourceModule(""" __global__ void integrate(double *A,double *B,double *S_in,double *S_out,double dt) { int idx = threadIdx.x + blockIdx.x * blockDim.x; double a = A[idx]; double b = B[idx]; S_out[idx]= - b + (S_in[idx] + b) * (1.+a*dt*(1.+.5*a*dt*(1.+(1./3.)*a*dt))); } __global__ void copyVar(double *S_new, double *old, int var_index, int var_len) { int idx = var_index + blockIdx.x * var_len; S_new[idx]= old[idx]; } __global__ void getV(double *S_out, double *v, int var_index, int var_len) { int idx = var_index + blockIdx.x * var_len; S_out[idx] = v[blockIdx.x]; } __global__ void find_variation(double *S_in,double *S_out,double variation,double dt) { int idx = threadIdx.x + blockIdx.x * blockDim.x; double delta = fabs(S_out[idx] - S_in[idx])/dt; double onepercent = 0.01 * fabs(S_in[idx]); if(delta > onepercent){ variation = 1.1; } } """) var_len = len(dict.fromkeys(self.eqs._diffeq_names))+1 self.func = mod.get_function("integrate") self.func.prepare(["P","P","P","P","d"],block=(var_len,1,1)) self.copyVar = mod.get_function("copyVar") self.copyVar.prepare(["P","P","i","i"],block=(1,1,1)) self.getV = mod.get_function("getV")#idea : if neuron did not change : use this to update S_out and then copy S_out to S_in self.getV.prepare(["P","P","i","i"],block=(1,1,1)) self.find_var = mod.get_function("find_variation") self.find_var.prepare(["P","P",'d',"d"],block=(var_len,1,1)) def prepare(self, P): n = len(P.state_(self.eqs._diffeq_names_nonzero[0])) var_len = len(dict.fromkeys(self.eqs._diffeq_names))+1 # +1 needed to store t for index,varname in enumerate(self.eqs._diffeq_names): self.index_to_varname.append(varname) self.varname_to_index[varname]= index if varname in self.eqs._diffeq_names_nonzero : self.index_nonzero.append(index) self.S_in = cuda.pagelocked_zeros((n,var_len),numpy.float64) self.S_out = cuda.pagelocked_zeros((n,var_len),numpy.float64) nbytes = n * var_len * numpy.dtype(numpy.float64).itemsize self.S_in_gpu = cuda.mem_alloc(nbytes) self.S_out_gpu = cuda.mem_alloc(nbytes) Z = zeros((n,var_len)) self.A_gpu = cuda.mem_alloc(nbytes) cuda.memcpy_htod(self.A_gpu, Z) self.B_gpu = cuda.mem_alloc(nbytes) cuda.memcpy_htod(self.B_gpu, Z) self.S_temp_gpu = cuda.mem_alloc(nbytes) modFun={} self.applyFun = {} for x in self.index_nonzero: s = self.eqs._function_C_String[self.index_to_varname[x]] args_fun =[] for i in xrange(var_len): args_fun.append("S_temp["+str(i)+" + blockIdx.x * var_len]") modFun[x] = SourceModule(""" __device__ double f"""+ s +""" __global__ void applyFun(double *A,double *B,double *S_in,double *S_temp, int x, int var_len) { int idx = x + blockIdx.x * var_len; S_temp[idx] = 0; B[idx] = f("""+",".join(args_fun)+"""); S_temp[idx] = 1; A[idx] = f("""+",".join(args_fun)+""") - B[idx]; B[idx] /= A[idx]; S_temp[idx] = S_in[idx]; } """) self.applyFun[x] = modFun[x].get_function("applyFun") self.applyFun[x].prepare(['P','P','P','P','i','i'],block=(1,1,1)) self.calc_dict = {} self.already_calc = {} def __call__(self, P): if self.prepared==False: self.prepare(P) self.prepared = True ''' Updates the state variables. Careful here: always use the slice operation for affectations. P is the neuron group. ''' var_len = len(dict.fromkeys(self.eqs._diffeq_names))+1 # +1 needed to store t if self.changed : if not self.firstTime: cuda.memcpy_dtoh(self.S_out, self.S_out_gpu) for var in self.eqs._diffeq_names_nonzero: Temp = P.state_(var) Temp[:] = self.S_out[:,self.varname_to_index[var]] else : self.firstTime = False for var in self.eqs._diffeq_names: self.S_in[:,self.varname_to_index[var]] = P.state_(var) if self._frozen: self.S_in[:,var_len-1]=P.clock._t # without units else: self.S_in[:,var_len-1] = P.clock.t #time cuda.memcpy_htod(self.S_in_gpu, self.S_in) cuda.memcpy_dtod(self.S_out_gpu,self.S_in_gpu, self.S_in.size * self.S_in.dtype.itemsize) cuda.memcpy_dtod(self.S_temp_gpu,self.S_in_gpu, self.S_in.size * self.S_in.dtype.itemsize) else : cuda.memcpy_dtod(self.S_in_gpu,self.S_out_gpu, self.S_in.size * self.S_in.dtype.itemsize) cuda.memcpy_dtod(self.S_temp_gpu,self.S_out_gpu, self.S_in.size * self.S_in.dtype.itemsize) self.gpu_exponential_euler(P.clock._dt) self.changed = False def updateV(self,v_gpu): n = len(self.S_in) var_len = len(self.S_in[0]) var_lenBis = numpy.int32(var_len) vx = numpy.int32(self.varname_to_index['v']) self.getV.prepared_call((n,1),self.S_out_gpu,v_gpu,vx,var_lenBis) def gpu_exponential_euler(self,dt): n = len(self.S_in) var_len = len(self.S_in[0]) var_lenBis = numpy.int32(var_len) for x in self.index_nonzero: x = numpy.int32(x) self.applyFun[x].prepared_call((n,1),self.A_gpu, self.B_gpu, self.S_in_gpu, self.S_temp_gpu, x, var_lenBis) dt = numpy.float64(dt) self.func.prepared_call((n,1),self.A_gpu,self.B_gpu,self.S_in_gpu,self.S_out_gpu,dt) def calc(self,var,res_gpu): n = len(self.S_in) var_len = len(self.S_in[0]) if var not in self.already_calc: vars_strings = [] for var_aux in self.eqs._diffeq_names: vars_strings.append("double "+ var_aux) vars_strings.append("double t") expr = self.eqs._string[var] namespace = self.eqs._namespace[var] # name space of the function all_variables = self.eqs._eq_names + self.eqs._diffeq_names + self.eqs._alias.keys() + ['t'] expr = optimiser.freeze(expr, all_variables, namespace) s = "(" + ",".join(vars_strings) + ") { double result = " + expr + "; return result; }" for var_aux in self.eqs._diffeq_names: #this is ugly. really ugly. s = s.replace(var_aux+'**2',var_aux+'*'+var_aux) s = s.replace(var_aux+'**3',var_aux+'*'+var_aux+'*'+var_aux) s = s.replace(var_aux+'**4',var_aux+'*'+var_aux+'*'+var_aux+'*'+var_aux) s = s.replace(var_aux+'**5',var_aux+'*'+var_aux+'*'+var_aux+'*'+var_aux+'*'+var_aux) args_fun =[] for i in xrange(var_len): args_fun.append("S_out["+str(i)+" + blockIdx.x * var_len]") mod = SourceModule(""" __device__ double f"""+ s +""" __global__ void calc(double *res,double *S_out, int var_len) { int idx = blockIdx.x; res[idx] = f("""+",".join(args_fun)+"""); } """) self.calc_dict[var] = mod.get_function("calc") self.calc_dict[var].prepare(['P','P','i'],block=(1,1,1)) self.already_calc[var] = True self.calc_dict[var].prepared_call((n,1),res_gpu,self.S_out_gpu,numpy.int32(var_len)) def compile_functions_C(self, eqs, freeze=False): """ Compile all functions defined as strings. If freeze is True, all external parameters and units are replaced by their value. ALL FUNCTIONS MUST HAVE STRINGS. """ all_variables = eqs._eq_names + eqs._diffeq_names + eqs._alias.keys() + ['t'] # Check if freezable freeze = freeze and all([optimiser.freeze(expr, all_variables, eqs._namespace[name])\ for name, expr in eqs._string.iteritems()]) eqs._frozen = freeze vars_strings = [] for var in eqs._diffeq_names: vars_strings.append("double "+ var) vars_strings.append("double t") # Compile strings to functions for name, expr in eqs._string.iteritems(): namespace = eqs._namespace[name] # name space of the function expr = optimiser.freeze(expr, all_variables, namespace) s = "(" + ",".join(vars_strings) + ") { double result = " + expr + "; return result; }" eqs._function_C_String[name] = s class GPUExponentialEulerStateUpdater_float(NonlinearStateUpdater): def __init__(self, eqs, clock=None, freeze=False): ''' Initialize a nonlinear model with dynamics dX/dt = f(X). ''' # TODO: global pref? self.eqs = eqs self.optimized = compile self.changed = True self.prepared=False self.firstTime = True self.index_to_varname = [] self.varname_to_index = {} self.index_nonzero = [] if freeze: self.eqs.compile_functions(freeze=freeze) self._frozen=freeze self.compile_functions_C(eqs) mod = SourceModule(""" __global__ void integrate(float *A,float *B,float *S_in,float *S_out,float dt) { int idx = threadIdx.x + blockIdx.x * blockDim.x; float a = A[idx]; float b = B[idx]; S_out[idx]= - b + (S_in[idx] + b) * (1.+a*dt*(1.+.5*a*dt*(1.+(1./3.)*a*dt))); } __global__ void copyVar(float *S_new, float *old, int var_index, int var_len) { int idx = var_index + blockIdx.x * var_len; S_new[idx]= old[idx]; } __global__ void getV(float *S_out, float *v, int var_index, int var_len) { int idx = var_index + blockIdx.x * var_len; S_out[idx] = v[blockIdx.x]; } __global__ void find_variation(float *S_in,float *S_out,float variation,float dt) { int idx = threadIdx.x + blockIdx.x * blockDim.x; float delta = fabs(S_out[idx] - S_in[idx])/dt; float onepercent = 0.01 * fabs(S_in[idx]); if(delta > onepercent){ variation = 1.1; } } """) var_len = len(dict.fromkeys(self.eqs._diffeq_names))+1 self.func = mod.get_function("integrate") self.func.prepare(["P","P","P","P","f"],block=(var_len,1,1)) self.copyVar = mod.get_function("copyVar") self.copyVar.prepare(["P","P","i","i"],block=(1,1,1)) self.getV = mod.get_function("getV")#idea : if neuron did not change : use this to update S_out and then copy S_out to S_in self.getV.prepare(["P","P","i","i"],block=(1,1,1)) self.find_var = mod.get_function("find_variation") self.find_var.prepare(["P","P",'f',"f"],block=(var_len,1,1)) def prepare(self, P): n = len(P.state_(self.eqs._diffeq_names_nonzero[0])) var_len = len(dict.fromkeys(self.eqs._diffeq_names))+1 # +1 needed to store t for index,varname in enumerate(self.eqs._diffeq_names): self.index_to_varname.append(varname) self.varname_to_index[varname]= index if varname in self.eqs._diffeq_names_nonzero : self.index_nonzero.append(index) self.S_in = cuda.pagelocked_zeros((n,var_len),numpy.float32) self.S_out = cuda.pagelocked_zeros((n,var_len),numpy.float32) nbytes = n * var_len * numpy.dtype(numpy.float32).itemsize self.S_in_gpu = cuda.mem_alloc(nbytes) self.S_out_gpu = cuda.mem_alloc(nbytes) Z = zeros((n,var_len),numpy.float32) self.A_gpu = cuda.mem_alloc(nbytes) cuda.memcpy_htod(self.A_gpu, Z) self.B_gpu = cuda.mem_alloc(nbytes) cuda.memcpy_htod(self.B_gpu, Z) self.S_temp_gpu = cuda.mem_alloc(nbytes) modFun={} self.applyFun = {} for x in self.index_nonzero: s = self.eqs._function_C_String[self.index_to_varname[x]] args_fun =[] for i in xrange(var_len): args_fun.append("S_temp["+str(i)+" + blockIdx.x * var_len]") modFun[x] = SourceModule(""" __device__ double f"""+ s +""" __global__ void applyFun(float *A,float *B,float *S_in,float *S_temp, int x, int var_len) { int idx = x + blockIdx.x * var_len; S_temp[idx] = 0; B[idx] = f("""+",".join(args_fun)+"""); S_temp[idx] = 1; A[idx] = f("""+",".join(args_fun)+""") - B[idx]; B[idx] /= A[idx]; S_temp[idx] = S_in[idx]; } """) self.applyFun[x] = modFun[x].get_function("applyFun") self.applyFun[x].prepare(['P','P','P','P','i','i'],block=(1,1,1)) self.calc_dict = {} self.already_calc = {} def __call__(self, P): if self.prepared==False: self.prepare(P) self.prepared = True ''' Updates the state variables. Careful here: always use the slice operation for affectations. P is the neuron group. ''' var_len = len(dict.fromkeys(self.eqs._diffeq_names))+1 # +1 needed to store t if self.changed : if not self.firstTime: cuda.memcpy_dtoh(self.S_out, self.S_out_gpu) for var in self.eqs._diffeq_names_nonzero: Temp = P.state_(var) Temp[:] = self.S_out[:,self.varname_to_index[var]] else : self.firstTime = False for var in self.eqs._diffeq_names: self.S_in[:,self.varname_to_index[var]] = P.state_(var).astype(numpy.float32) if self._frozen: self.S_in[:,var_len-1]= numpy.float32(P.clock._t) # without units else: self.S_in[:,var_len-1] = numpy.float32(P.clock.t) #time cuda.memcpy_htod(self.S_in_gpu, self.S_in) cuda.memcpy_dtod(self.S_out_gpu,self.S_in_gpu, self.S_in.size * self.S_in.dtype.itemsize) cuda.memcpy_dtod(self.S_temp_gpu,self.S_in_gpu, self.S_in.size * self.S_in.dtype.itemsize) else : cuda.memcpy_dtod(self.S_in_gpu,self.S_out_gpu, self.S_in.size * self.S_in.dtype.itemsize) cuda.memcpy_dtod(self.S_temp_gpu,self.S_out_gpu, self.S_in.size * self.S_in.dtype.itemsize) self.gpu_exponential_euler(P.clock._dt) self.changed = False def updateV(self,v_gpu): n = len(self.S_in) var_len = len(self.S_in[0]) var_lenBis = numpy.int32(var_len) vx = numpy.int32(self.varname_to_index['v']) self.getV.prepared_call((n,1),self.S_out_gpu,v_gpu,vx,var_lenBis) def gpu_exponential_euler(self,dt): n = len(self.S_in) var_len = len(self.S_in[0]) var_lenBis = numpy.int32(var_len) dt = numpy.float32(dt) for x in self.index_nonzero: x = numpy.int32(x) self.applyFun[x].prepared_call((n,1),self.A_gpu, self.B_gpu, self.S_in_gpu, self.S_temp_gpu, x, var_lenBis) self.func.prepared_call((n,1),self.A_gpu,self.B_gpu,self.S_in_gpu,self.S_out_gpu,dt) def calc(self,var,res_gpu): n = len(self.S_in) var_len = len(self.S_in[0]) if var not in self.already_calc: vars_strings = [] for var_aux in self.eqs._diffeq_names: vars_strings.append("float "+ var_aux) vars_strings.append("float t") expr = self.eqs._string[var] namespace = self.eqs._namespace[var] # name space of the function all_variables = self.eqs._eq_names + self.eqs._diffeq_names + self.eqs._alias.keys() + ['t'] expr = optimiser.freeze(expr, all_variables, namespace) s = "(" + ",".join(vars_strings) + ") { float result = " + expr + "; return result; }" for var_aux in self.eqs._diffeq_names: #this is ugly. really ugly. s = s.replace(var_aux+'**2',var_aux+'*'+var_aux) s = s.replace(var_aux+'**3',var_aux+'*'+var_aux+'*'+var_aux) s = s.replace(var_aux+'**4',var_aux+'*'+var_aux+'*'+var_aux+'*'+var_aux) s = s.replace(var_aux+'**5',var_aux+'*'+var_aux+'*'+var_aux+'*'+var_aux+'*'+var_aux) args_fun =[] for i in xrange(var_len): args_fun.append("S_out["+str(i)+" + blockIdx.x * var_len]") mod = SourceModule(""" __device__ float f"""+ s +""" __global__ void calc(float *res,float *S_out, int var_len) { int idx = blockIdx.x; res[idx] = f("""+",".join(args_fun)+"""); } """) self.calc_dict[var] = mod.get_function("calc") self.calc_dict[var].prepare(['P','P','i'],block=(1,1,1)) self.already_calc[var] = True self.calc_dict[var].prepared_call((n,1),res_gpu,self.S_out_gpu,numpy.int32(var_len)) def compile_functions_C(self, eqs, freeze=False): """ Compile all functions defined as strings. If freeze is True, all external parameters and units are replaced by their value. ALL FUNCTIONS MUST HAVE STRINGS. """ all_variables = eqs._eq_names + eqs._diffeq_names + eqs._alias.keys() + ['t'] # Check if freezable freeze = freeze and all([optimiser.freeze(expr, all_variables, eqs._namespace[name])\ for name, expr in eqs._string.iteritems()]) eqs._frozen = freeze vars_strings = [] for var in eqs._diffeq_names: vars_strings.append("float "+ var) vars_strings.append("float t") # Compile strings to functions for name, expr in eqs._string.iteritems(): namespace = eqs._namespace[name] # name space of the function expr = optimiser.freeze(expr, all_variables, namespace) s = "(" + ",".join(vars_strings) + ") {float result = " + expr + "; return result; }" eqs._function_C_String[name] = sbrian-1.4.3/dev/ideas/morphology/Remy/hodgkin_huxley_1952_2.py000066400000000000000000000116641266062647100241060ustar00rootroot00000000000000''' Hodgkin-Huxley equations (1952) ''' if __name__ == '__main__': import brian_no_units from brian import * from morphology import * from spatialneuron import * #from spatialneuron_monoprocess import * #from spatialneuron_float import * from time import time from numpy import zeros defaultclock.dt=0.1*ms length = 10*cm diameter=2*238*um compartments=1024 morpho = Soma(diameter=1 * um) morpho.L = Cylinder(length=length, diameter=diameter, n=compartments, type='axon') """ morpho.L = Cylinder(length=length/5, diameter=diameter, n=compartments/5, type='axon') morpho.LL = Cylinder(length=length/5, diameter=diameter, n=compartments/5, type='axon') morpho.LLL = Cylinder(length=length/5, diameter=diameter, n=compartments/5, type='axon') morpho.LLLL = Cylinder(length=length/5, diameter=diameter, n=compartments/5, type='axon') morpho.LLLLL = Cylinder(length=length/5, diameter=diameter, n=compartments/5, type='axon') #""" #morpho = Morphology('mp_ma_40984_gc2.CNG.swc') #morpho = Morphology('oi24rpy1.CNG.swc') El = 10.613* mV ENa = 115*mV EK = -12 * mV gl = 0.3 * msiemens / cm ** 2 gNa = 120 * msiemens / cm ** 2 gK = 36 * msiemens / cm ** 2 """ El = 0 * mV gl = 0.02 * msiemens / cm ** 2 """ # Typical equations eqs=''' # The same equations for the whole neuron, but possibly different parameter values Im=gl*(El-v)+gNa*m**3*h*(ENa-v)+gK*n**4*(EK-v)+I : amp/cm**2 # distributed transmembrane current I:amp/cm**2 # applied current dm/dt=alpham*(1-m)-betam*m : 1 dn/dt=alphan*(1-n)-betan*n : 1 dh/dt=alphah*(1-h)-betah*h : 1 alpham=(0.1/mV)*(-v+25*mV)/(exp((-v+25*mV)/(10*mV))-1)/ms : Hz betam=4.*exp(-v/(18*mV))/ms : Hz alphah=0.07*exp(-v/(20*mV))/ms : Hz betah=1./(exp((-v+30*mV)/(10*mV))+1)/ms : Hz alphan=(0.01/mV)*(-v+10*mV)/(exp((-v+10*mV)/(10*mV))-1)/ms : Hz betan=0.125*exp(-v/(80*mV))/ms : Hz ''' """ eqs=''' # The same equations for the whole neuron, but possibly different parameter values Im=gl*(El-v)+I : amp/cm**2 # distributed transmembrane current I : amp/cm**2 # applied current ''' """ neuron = SpatialNeuron(morphology=morpho, model=eqs, Cm=1 * uF / cm ** 2, Ri=35.4 * ohm * cm) #neuron = SpatialNeuron(morphology=morpho, model=eqs, Cm=1 * uF / cm ** 2, Ri=35.4 * ohm * cm,diffeq_nonzero=False) neuron.v=0*mV neuron.h=1 neuron.m=0 neuron.n=0 neuron.I=0*amp/cm**2 M=StateMonitor(neuron,'v',record=True) #print 'taum=',neuron.Cm[0]/gl * second**4 * (volt/ohm)**2 / (metre**4 * kilogram) insert_point = 1 start = time() """ run(30*ms) neuron.I[insert_point] = .07 * uA/neuron.area[insert_point] neuron.changed = True run(0.04*ms) neuron.I=0*amp/cm**2 neuron.changed = True run(0.01*ms) morpho.plot(simple=True,values=neuron.v,max_val=50*mV,min_val=-10*mV) run(0.1*ms) morpho.plot(simple=True,values=neuron.v,max_val=50*mV,min_val=-10*mV) run(0.3*ms) morpho.plot(simple=True,values=neuron.v,max_val=50*mV,min_val=-10*mV) run(0.5*ms) morpho.plot(simple=True,values=neuron.v,max_val=50*mV,min_val=-10*mV) run(0.5*ms) morpho.plot(simple=True,values=neuron.v,max_val=50*mV,min_val=-10*mV) run(1*ms) morpho.plot(simple=True,values=neuron.v,max_val=50*mV,min_val=-10*mV) run(0.3*ms) morpho.plot(simple=True,values=neuron.v,max_val=50*mV,min_val=-10*mV) run(0.3*ms) morpho.plot(simple=True,values=neuron.v,max_val=50*mV,min_val=-10*mV) #run(1*ms) #morpho.plot(simple=True,values=neuron.v,max_val=50*mV,min_val=-10*mV) #run(0.3*ms) #morpho.plot(simple=True,values=neuron.v,max_val=50*mV,min_val=-10*mV) #run(5*ms) #morpho.plot(simple=True,values=neuron.v,max_val=50*mV,min_val=-10*mV) #""" """ run(0.01*ms) #""" #""" run(30*ms) neuron.I[insert_point] = .7 * uA/neuron.area[insert_point] neuron.changed = True run(3*ms) neuron.I[insert_point] = 0 *amp/cm**2 neuron.changed = True run(50*ms) #""" """ run(0.01*ms) start = time() run(50*ms) #""" end = time() print end - start," s" #""" p = [] for i in range(compartments/50): pp, = plot(M.times/ms,M[i*50+1]/mV) p.append(pp) xlabel('temps (ms)') ylabel('potentiel (mV)') legend(p,["V au compartiment 1","V au compartiment 51","V au compartiment 101","V au compartiment 151","V au compartiment 201"]) #""" """ p1, = plot(M.times/ms,neuron._state_updater.timeUpdater[1:],'r') p2, = plot(M.times/ms,neuron._state_updater.timeDevice[1:],'b') p3, = plot(M.times/ms,neuron._state_updater.timeDeviceU[1:],'b--') p4, = plot(M.times/ms,neuron._state_updater.timeDeviceT[1:],'b-.') p5, = plot(M.times/ms,neuron._state_updater.timeHost[1:],'g') p6, = plot(M.times/ms,neuron._state_updater.timeSolveHost[1:],'g--') p7, = plot(M.times/ms,neuron._state_updater.timeFillHost[1:],'g-.') p8, = plot(M.times/ms,neuron._state_updater.timeFin[1:],'g:') legend([p1,p2,p3,p4,p5,p6,p7,p8],["Courant","Branches","DU","DT","Points de jonction","SH","FH","Fin"], loc='upper left') #""" show()brian-1.4.3/dev/ideas/morphology/Remy/morphology.py000066400000000000000000000453541266062647100223660ustar00rootroot00000000000000''' Neuronal morphology module for Brian. TODO: * Maybe str: should give a text representation of the tree * set_length etc.: should be recursive * check_consistency: check that lengths, area and coordinates are consistent (and fix) * rescale: change number of compartments ''' from brian.group import Group from scipy import rand from numpy import * from brian.units import meter from brian.stdunits import um import warnings from pylab import figure import copy try: from mpl_toolkits.mplot3d import Axes3D except: warnings.warn('Pylab 0.99.1 is required for 3D plots') __all__ = ['Morphology', 'Cylinder', 'Soma'] class Morphology(object): ''' Neuronal morphology (=tree of branches). ''' def __init__(self, filename=None, n=None): self.children = [] self._namedkid = {} self.iscompressed = False if filename is not None: self.loadswc(filename) elif n is not None: # Creates a branch with n compartments # The problem here is that these parameters should have some self-consistency self.x, self.y, self.z, self.diameter, self.length, self.area = [zeros(n) for _ in range(6)] def set_length(self): ''' Sets the length of compartments according to their coordinates ''' x = hstack((0 * um, self.x)) y = hstack((0 * um, self.y)) z = hstack((0 * um, self.z)) self.length = sum((x[1:] - x[:-1]) ** 2 + (y[1:] - y[:-1]) ** 2 + (z[1:] - z[:-1]) ** 2) ** .5 def set_area(self): ''' Sets the area of compartments according to diameter and length (assuming cylinders) ''' self.area = pi * self.diameter * self.length def set_coordinates(self): ''' Sets the coordinates of compartments according to their lengths (taking a random direction) ''' l = cumsum(self.length) theta = rand()*2 * pi phi = rand()*2 * pi self.x = l * sin(theta) * cos(phi) self.y = l * sin(theta) * sin(phi) self.z = l * cos(theta) def loadswc(self, filename): ''' Reads a SWC file containing a neuronal morphology. Large database at http://neuromorpho.org/neuroMorpho Information below from http://www.mssm.edu/cnic/swc.html SWC File Format The format of an SWC file is fairly simple. It is a text file consisting of a header with various fields beginning with a # character, and a series of three dimensional points containing an index, radius, type, and connectivity information. The lines in the text file representing points have the following layout. n T x y z R P n is an integer label that identifies the current point and increments by one from one line to the next. T is an integer representing the type of neuronal segment, such as soma, axon, apical dendrite, etc. The standard accepted integer values are given below. * 0 = undefined * 1 = soma * 2 = axon * 3 = dendrite * 4 = apical dendrite * 5 = fork point * 6 = end point * 7 = custom x, y, z gives the cartesian coordinates of each node. R is the radius at that node. P indicates the parent (the integer label) of the current point or -1 to indicate an origin (soma). ''' # 1) Create the list of segments, each segment has a list of children lines = open(filename).read().splitlines() segment = [] # list of segments types = ['undefined', 'soma', 'axon', 'dendrite', 'apical', 'fork', 'end', 'custom'] previousn = -1 for line in lines: if line[0] != '#': # comment numbers = line.split() n = int(numbers[0]) - 1 T = types[int(numbers[1])] x = float(numbers[2]) * um y = float(numbers[3]) * um z = float(numbers[4]) * um R = float(numbers[5]) * um P = int(numbers[6]) - 1 # 0-based indexing if (n != previousn + 1): raise ValueError, "Bad format in file " + filename seg = dict(x=x, y=y, z=z, T=T, diameter=2 * R, parent=P, children=[]) location = (x, y, z) if T == 'soma': seg['area'] = 4 * pi * R ** 2 seg['length'] = 0 * um else: # dendrite locationP = (segment[P]['x'], segment[P]['y'], segment[P]['z']) seg['length'] = (sum((array(location) - array(locationP)) ** 2)) ** .5 * meter seg['area'] = seg['length'] * 2 * pi * R if P >= 0: segment[P]['children'].append(n) segment.append(seg) previousn = n # We assume that the first segment is the root self.create_from_segments(segment) def create_from_segments(self, segment, origin=0): """ Recursively create the morphology from a list of segments. Each segment has attributes: x,y,z,diameter,area,length (vectors) and children (list). It also creates a dictionary of names (_namedkid). """ n = origin if segment[origin]['T'] != 'soma': # if it's a soma, only one compartment while (len(segment[n]['children']) == 1) and (segment[n]['T'] != 'soma'): # Go to the end of the branch n += 1 # End of branch branch = segment[origin:n + 1] # Set attributes self.diameter, self.length, self.area, self.x, self.y, self.z = \ zip(*[(seg['diameter'], seg['length'], seg['area'], seg['x'], seg['y'], seg['z']) for seg in branch]) self.diameter, self.length, self.area, self.x, self.y, self.z = array(self.diameter), array(self.length), \ array(self.area), array(self.x), array(self.y), array(self.z) self.type = segment[n]['T'] # normally same type for all compartments in the branch # Create children (list) self.children = [Morphology().create_from_segments(segment, origin=c) for c in segment[n]['children']] # Create dictionary of names (enumerates children from number 1) for i, child in enumerate(self.children): self._namedkid[str(i + 1)] = child # Name the child if possible if child.type in ['soma', 'axon', 'dendrite']: if child.type in self._namedkid: self._namedkid[child.type] = None # two children with the same name: erase (see next block) else: self._namedkid[child.type] = child # Erase useless names for k in self._namedkid.keys(): if self._namedkid[k] is None: del self._namedkid[k] # If two kids, name them L (left) and R (right) if len(self.children) == 2: self._namedkid['L'] = self._namedkid['1'] self._namedkid['R'] = self._namedkid['2'] return self def branch(self): ''' Returns the current branch without the children. ''' morpho = copy.copy(self) morpho.children = [] morpho._namedkid = {} return morpho def __getitem__(self, x): """ Returns the subtree named x. Ex.: neuron['axon'] or neuron['11213'] neuron[10*um:20*um] returns the subbranch from 10 um to 20 um. neuron[10*um] returns one compartment. neuron[5] returns compartment number 5. TODO: neuron[:] returns the full branch. """ if type(x) == type(0): # int: returns one compartment morpho = self.branch() i = x j = i + 1 morpho.diameter = morpho.diameter[i:j] morpho.length = morpho.length[i:j] morpho.area = morpho.area[i:j] morpho.x = morpho.x[i:j] morpho.y = morpho.y[i:j] morpho.z = morpho.z[i:j] if hasattr(morpho, '_origin'): morpho._origin += i return morpho elif isinstance(x, slice): # neuron[10*um:20*um] morpho = self.branch() start, stop = x.start, x.stop l = cumsum(morpho.length) # coordinate on the branch i = searchsorted(l, start) j = searchsorted(l, stop) morpho.diameter = morpho.diameter[i:j] morpho.length = morpho.length[i:j] morpho.area = morpho.area[i:j] morpho.x = morpho.x[i:j] morpho.y = morpho.y[i:j] morpho.z = morpho.z[i:j] if hasattr(morpho, '_origin'): morpho._origin += i return morpho elif isinstance(x, float): # neuron[10*um] morpho = self.branch() l = cumsum(morpho.length) # coordinate on the branch i = searchsorted(l, x) j = i + 1 morpho.diameter = morpho.diameter[i:j] morpho.length = morpho.length[i:j] morpho.area = morpho.area[i:j] morpho.x = morpho.x[i:j] morpho.y = morpho.y[i:j] morpho.z = morpho.z[i:j] if hasattr(morpho, '_origin'): morpho._origin += i return morpho else: x = str(x) # convert int to string if (len(x) > 1) and all([c in 'LR123456789' for c in x]): # binary string of the form LLLRLR or 1213 (or mixed) return self._namedkid[x[0]][x[1:]] elif x in self._namedkid: return self._namedkid[x] else: raise AttributeError, "The subtree " + x + " does not exist" def __setitem__(self, x, kid): """ Inserts the subtree and name it x. Ex.: neuron['axon'] or neuron['11213'] If the tree already exists with another name, then it creates a synonym for this tree. The coordinates of the subtree are relative before function call, and are absolute after function call. """ x = str(x) # convert int to string if (len(x) > 1) and all([c in 'LR123456789' for c in x]): # binary string of the form LLLRLR or 1213 (or mixed) self._namedkid[x[0]][x[1:]] = kid elif x in self._namedkid: raise AttributeError, "The subtree " + x + " already exists" else: # Update coordinates kid.x += self.x[-1] kid.y += self.y[-1] kid.z += self.z[-1] if kid not in self.children: self.children.append(kid) self._namedkid[str(len(self.children))] = kid # numbered child self._namedkid[x] = kid def __delitem__(self, x): """ Removes the subtree x. """ x = str(x) # convert int to string if (len(x) > 1) and all([c in 'LR123456789' for c in x]): # binary string of the form LLLRLR or 1213 (or mixed) del self._namedkid[x[0]][x[1:]] elif x in self._namedkid: child = self._namedkid[x] # Delete from name dictionary for name, kid in self._namedkid.items(): if kid is child: del self._namedkid[name] # Delete from list of children for i, kid in enumerate(self.children): if kid is child: del self.children[i] else: raise AttributeError, "The subtree " + x + " does not exist" def __getattr__(self, x): """ Returns the subtree named x. Ex.: axon=neuron.axon """ return self[x] def __setattr__(self, x, kid): """ Attach a subtree and named it x. If the subtree is None then the subtree x is deleted. Ex.: neuron.axon=Soma(diameter=10*um) Ex.: neuron.axon=None """ if isinstance(kid, Morphology): if kid is None: del self[x] else: self[x] = kid else: # If it is not a subtree, then it's a normal class attribute object.__setattr__(self, x, kid) def __len__(self): """ Returns the total number of compartments. """ return len(self.x) + sum(len(child) for child in self.children) def compress(self, diameter=None, length=None, area=None, x=None, y=None, z=None, origin=0): """ Compresses the tree by changing the compartment vectors to views on a matrix (or vectors). The morphology cannot be changed anymore but all other functions should work normally. origin : offset in the base matrix """ self._origin = origin n = len(self.x) # Update values of vectors diameter[:n] = self.diameter length[:n] = self.length area[:n] = self.area x[:n] = self.x y[:n] = self.y z[:n] = self.z # Attributes are now views on these vectors self.diameter = diameter[:n] self.length = length[:n] self.area = area[:n] self.x = x[:n] self.y = y[:n] self.z = z[:n] for kid in self.children: kid.compress(diameter=diameter[n:], length=length[n:], area=area[n:], x=x[n:], y=y[n:], z=z[n:], origin=origin + n) n += len(kid) self.iscompressed = True def plot(self, axes=None, simple=True, origin=None, values=None, max_val=None, min_val=None, offset=0): """ Plots the morphology in 3D. Units are um. axes : the figure axes (new figure if not given) simple : if True, the diameter of branches is ignored values : an array of scalars. adjust color according to the relative values. ignored if simple is True. offset : used to pick the right index in values """ if values != None: maximum = max(values) minimum = min(values) Range = maximum - minimum if axes is None: # new figure fig = figure() axes = Axes3D(fig) x, y, z, d = self.x / um, self.y / um, self.z / um, self.diameter / um if origin is not None: x0, y0, z0 = origin x = hstack((x0, x)) y = hstack((y0, y)) z = hstack((z0, z)) if len(x) == 1: # root with a single compartment: probably just the soma axes.plot(x, y, z, "r.", linewidth=d[0]) else: if simple : if values == None : axes.plot(x, y, z, 'k') else : # Remy's code: color plot (imaging) mean_value = 0 for n in range(1, len(x)): mean_value += values[offset+n-1] mean_value /= len(x) if max_val==None: axes.plot(x, y, z, color=((mean_value-minimum)/Range,0,(maximum-mean_value)/Range)) else: val = min(mean_value,max_val) val = max(val,min_val) Range = max_val - min_val axes.plot(x, y, z, color=((val-min_val)/Range,0,(max_val-val)/Range)) else: # linewidth reflects compartment diameter for n in range(1, len(x)): if values==None : axes.plot([x[n - 1], x[n]], [y[n - 1], y[n]], [z[n - 1], z[n]], 'k', linewidth=d[n - 1]) #axes.plot(x, y, z, 'k') else : if max_val==None: axes.plot([x[n - 1], x[n]], [y[n - 1], y[n]], [z[n - 1], z[n]], color=((values[offset+n-1]-minimum)/Range,0,(maximum-values[offset+n-1])/Range), linewidth=d[n - 1]) else: val = min(values[offset+n-1],max_val) val = max(val,min_val) Range = max_val - min_val axes.plot([x[n - 1], x[n]], [y[n - 1], y[n]], [z[n - 1], z[n]], color=((val-min_val)/Range,0,(max_val-val)/Range), linewidth=d[n - 1]) #axes.plot(x, y, z, color=((values[offset+n-1]-minimum)/Range,0,(maximum-values[offset+n-1])/Range)) for c in self.children: c.plot(origin=(x[-1], y[-1], z[-1]), axes=axes, simple=simple,values=values, max_val=max_val, min_val=min_val,offset = offset + len(x)-1) class Cylinder(Morphology): """ A cylinder. """ def __init__(self, length=None, diameter=None, n=1, type=None, x=None, y=None, z=None): """ Creates a cylinder. n: number of compartments. type : 'soma', 'axon' or 'dendrite' x,y,z : end point (relative to origin of cylinder) length is optional (and ignored) if x,y,z is specified If x,y,z unspecified: random direction """ Morphology.__init__(self) if x is None: theta = rand()*2 * pi phi = rand()*2 * pi x = length * sin(theta) * cos(phi) y = length * sin(theta) * sin(phi) z = length * cos(theta) else: length = (sum(array((x, y, z)) ** 2)) ** .5 scale = arange(1, n + 1) * 1. / n self.x, self.y, self.z = x * scale, y * scale, z * scale self.length = ones(n) * length / n self.diameter = ones(n) * diameter self.area = ones(n) * pi * diameter * length / n self.type = type class Soma(Morphology): # or Sphere? """ A spherical soma. """ def __init__(self, diameter=None): Morphology.__init__(self) self.diameter = ones(1) * diameter self.area = ones(1) * pi * diameter ** 2 self.length = zeros(1) self.x, self.y, self.z = zeros(1), zeros(1), zeros(1) self.type = 'soma' if __name__ == '__main__': from pylab import show morpho=Morphology('D:\Python27\Lib\site-packages\mp_ma_40984_gc2.CNG.swc') # retinal ganglion cell #morpho = Morphology('oi24rpy1.CNG.swc') # visual L3 pyramidal cell print len(morpho), "compartments" morpho.axon = None morpho.plot() #morpho=Cylinder(length=10*um,diameter=1*um,n=10) #morpho.plot(simple=True) morpho = Soma(diameter=10 * um) morpho.dendrite = Cylinder(length=3 * um, diameter=1 * um, n=10) morpho.dendrite.L = Cylinder(length=5 * um, diameter=1 * um, n=10) morpho.dendrite.R = Cylinder(length=7 * um, diameter=1 * um, n=10) morpho.dendrite.LL = Cylinder(length=3 * um, diameter=1 * um, n=10) morpho.axon = Morphology(n=5) morpho.axon.diameter = ones(5) * 1 * um morpho.axon.length = [1 * um, 2 * um, 1 * um, 3 * um, 1 * um] morpho.axon.set_coordinates() morpho.axon.set_area() morpho.plot(simple=True) show() brian-1.4.3/dev/ideas/morphology/Remy/scalableTridiagonalSolver.py000066400000000000000000000133341266062647100252770ustar00rootroot00000000000000import pycuda.driver as cuda import pycuda.autoinit from pycuda.compiler import SourceModule def solve_tridiag(ld,d,ud,b,x,k,n): #will be destroyed in process : d, b. mod = SourceModule(""" __global__ void pThomas(double *ld, double *d, double *ud, double *b, double *x, int gap, int n_eq) { /** * n_eq - number of equations * start - first equation * gap - gap between two equations * ld - sub-diagonal (means it is the diagonal below the main diagonal) -- indexed from 1..n-1 * d - the main diagonal * ud - sup-diagonal (means it is the diagonal above the main diagonal) -- indexed from 0..n-2 * b - right part * x - the answer */ int start = threadIdx.x * gap; int nn = start + n_eq * gap; for (int i = start + gap; i < nn; i += gap) { i = start + j * gap; double m = ld[i]/d[i-gap]; d[i] = d[i] - m * ud[i - gap]; b[i] = b[i] - m*b[i-gap]; } x[nn-gap] = b[nn-gap]/d[nn-gap]; for (int i = start + (n_eq - 2)*gap; i >= start; i -= gap) x[i] = (b[i] - ud[i] * x[i+gap]) / d[i]; } __global__ void tiledPCR(double *ld, double *d, double *ud, double *b, double *x, int k, int totalSize) { int tile = pow(2,k); __shared__ double window_ld[1024]; // size : (toute la shared = 64k) / ((taille d'un element = 8) * (3 diag + un second membre)) __shared__ double window_d[1024]; // size doit etre multiple de 4; division par 2 pour ? __shared__ double window_ud[1024]; __shared__ double window_b[1024]; double k1; double k2; int gap; int ofst_read; int pow_i; int ofst_write; int offset = 0; int idx = threadIdx.x; //threadIdx.x va de 0 a 255 //initialyze window window_ld[idx-128] = ld[idx]; window_ld[idx+128] = ld[idx+256]; window_d[idx-128] = d[idx]; window_d[idx+128] = d[idx+256]; window_ud[idx-128] = ud[idx]; window_ud[idx+128] = ud[idx+256]; window_b[idx-128] = b[idx]; window_b[idx+128] = b[idx+256]; int pow_i_0 = 2; int ofst_write_0 = 0; for(int i=2;i<(k+1);i++) { pow_i_0 *= 2; ofst_write_0 += pow_i_0; } //---------------------------cas offset=0----------------------------- //---------------------------FIN cas offset=0----------------------------- //----------------------------cas general----------------------------- while(offset < totalSize - tile) // ? { gap = 1; pow_i = pow_i_0; ofst_write = ofst_write_0; ofst_read = 0; for(int iter=0;iter<(k-1);iter++) //k=8; 256 threads { k1 = window_ld[ofst_read+idx]/window_d[ofst_read+idx-gap]; k2 = window_ud[ofst_read+idx]/window_d[ofst_read+idx+gap]; window_ld[ofst_write+idx] = - window_ld[ofst_read+idx-gap]*k1; window_d[ofst_write+idx] = window_d[ofst_read+idx] - window_ud[ofst_read+idx-gap]*k1 - window_ld[ofst_read+idx+gap] * k2; window_ud[ofst_write+idx] = - window_ud[ofst_read+idx+gap]*k2; window_b[ofst_write+idx] = window_b[ofst_read+idx] - window_b[ofst_read+idx-gap]*k1 - window_b[ofst_read+idx+gap] * k2; ofst_write = ofst_write - pow_i; pow_i /= 2; ofst_read = ofst_write + pow_i; gap *= 2; __syncthreads(); } //iter = k-1 : gap *= 2; k1 = window_ld[idx]/window_d[idx-gap]; k2 = window_ud[idx]/window_d[idx+gap]; ld[idx-256+offset] = - window_ld[idx-gap]*k1; d[idx-256] = window_d[idx] - window_ud[idx-gap]*k1 - window_ld[idx+gap] * k2; ud[idx-256] = - window_ud[idx+gap]*k2; b[idx-256] = window_b[idx] - window_b[idx-gap]*k1 - window_b[idx+gap] * k2; __syncthreads(); //on fait quoi avec window ? on stocke les systemes resultats ou ? //decaler window. ne pas le faire dans tous les threads ! for(int i = 0; i<768; i++) { window_ld[i] = window_ld[i+256]; window_d[i] = window_d[i+256]; window_ud[i] = window_ud[i+256]; window_b[i] = window_b[i+256]; } for(int i = 768; i<1024; i++) { window_ld[i] = ld[i+offset]; //offset ? } offset += 256; } //----------------------------FIN cas general----------------------------- //----------------------------cas offset = max---------------------------- //---------------------FIN cas offset = max----------------------- } """) pThomas = mod.get_function("pThomas") pThomas.prepare(['P','P','P','P','P','i','i'],block=(2**k,1,1)) pThomas.prepared_call(ld,d,ud,b,x,2**k,n/(2**k)) brian-1.4.3/dev/ideas/morphology/Remy/spatialneuron.py000066400000000000000000001040311266062647100230370ustar00rootroot00000000000000''' Compartmental neurons what the state_updater does every step : -update the currents -solve the cable equation on the tree *find the solution space on every branch (solve 3 tridiagonal systems) *find the voltages of the branch points (a small linear system) *determine V with these two informations TODO : *remove the neuron.changed attribute. Do some automatic test. *remove the diffeq_nonzero argument. test it instead. *test if the state_updater has a "calc" method to update variables like gtot on the gpu. *solve PV =B on the gpu. *at the end of a time step, import some variables on the cpu. use the StateMonitor. *change the tridiagonal solver (useful for neurons with many compartments, >1000). see A Scalable Tridiagonal Solver for GPUs, Kim et al,2011. explanation on the "type" of a branch point : 1 : normal point in the tree. not a leaf. 2 : a sealed end. 0 : this point has a fixed potential. can be a killed end if V(t=0)=0. This is an int an is used as such in equations : some values are doubled for a sealed end. ex : Aplus becomes 2*Aplus. so : Aplus <- pointType*Aplus. ''' import pycuda.driver as cuda import pycuda.autoinit from pycuda.compiler import SourceModule from morphology import Morphology from brian.stdunits import * from brian.units import * from brian.reset import NoReset from brian.stateupdater import StateUpdater from gpustateupdater import * from spatialstateupdater_linear import * from brian.inspection import * from brian.optimiser import * from itertools import count from brian.neurongroup import NeuronGroup from numpy import zeros, ones, isscalar from numpy.linalg import solve from brian.clock import guess_clock from brian.equations import Equations import functools import warnings from scipy import weave from time import time try: import sympy use_sympy = True except: warnings.warn('sympy not installed: some features in SpatialNeuron will not be available') use_sympy = False __all__ = ['SpatialNeuron', 'CompartmentalNeuron'] class SpatialNeuron(NeuronGroup): """ Compartmental model with morphology. """ def __init__(self, morphology=None, model=None, threshold=None, reset=NoReset(), refractory=0 * ms, level=0, clock=None, unit_checking=True, compile=False, freeze=False, implicit=True, Cm=0.9 * uF / cm ** 2, Ri=150 * ohm * cm, bc_type = 2, diffeq_nonzero=True): clock = guess_clock(clock) N = len(morphology) # number of compartments if isinstance(model, str): model = Equations(model, level=level + 1) model += Equations(''' v:volt # membrane potential ''') # Process model equations (Im) to extract total conductance and the remaining current if use_sympy: try: membrane_eq=model._string['Im'] # the membrane equation except: raise TypeError,"The transmembrane current Im must be defined" # Check conditional linearity ids=get_identifiers(membrane_eq) _namespace=dict.fromkeys(ids,1.) # there is a possibility of problems here (division by zero) _namespace['v']=AffineFunction() eval(membrane_eq,model._namespace['v'],_namespace) try: eval(membrane_eq,model._namespace['v'],_namespace) except: # not linear raise TypeError,"The membrane current must be linear with respect to v" # Extracts the total conductance from Im, and the remaining current z=symbolic_eval(membrane_eq) symbol_v=sympy.Symbol('v') b=z.subs(symbol_v,0) a=-sympy.simplify(z.subs(symbol_v,1)-b) gtot_str="_gtot="+str(a)+": siemens/cm**2" I0_str="_I0="+str(b)+": amp/cm**2" model+=Equations(gtot_str+"\n"+I0_str,level=level+1) # better: explicit insertion with namespace of v else: raise TypeError,"The Sympy package must be installed for SpatialNeuron" # Equations for morphology (isn't it a duplicate??) eqs_morphology = Equations(""" diameter : um length : um x : um y : um z : um area : um**2 """) full_model = model + eqs_morphology NeuronGroup.__init__(self, N, model=full_model, threshold=threshold, reset=reset, refractory=refractory, level=level + 1, clock=clock, unit_checking=unit_checking, implicit=implicit) self.model_with_diffeq_nonzero = diffeq_nonzero if self.model_with_diffeq_nonzero : self._state_updater = SpatialStateUpdater(self, clock) else : self._state_updater = LinearSpatialStateUpdater(self, clock) self.Cm = ones(len(self))*Cm self.Ri = Ri self.bc_type = bc_type self.bc = ones(len(self)) self.changed = True # Insert morphology self.morphology = morphology self.morphology.compress(diameter=self.diameter, length=self.length, x=self.x, y=self.y, z=self.z, area=self.area) def subgroup(self, N): # Subgrouping cannot be done in this way raise NotImplementedError def __getitem__(self, x): ''' Subgrouping mechanism. self['axon'] returns the subtree named "axon". TODO: self[:] returns the full branch. ''' morpho = self.morphology[x] N = self[morpho._origin:morpho._origin + len(morpho)] N.morphology = morpho return N def __getattr__(self, x): if (x != 'morphology') and ((x in self.morphology._namedkid) or all([c in 'LR123456789' for c in x])): # subtree return self[x] else: return NeuronGroup.__getattr__(self, x) class SpatialStateUpdater(StateUpdater): """ State updater for compartmental models, using a GPU """ def __init__(self, neuron, clock=None): self.eqs = neuron._eqs self.neuron = neuron self._isprepared = False self._state_updater = GPUExponentialEulerStateUpdater(neuron._eqs,clock=neuron.clock) self.first_test_gtot=True self.callcount=0 def prepare_branch(self, morphology, mid_diameter,ante=0): ''' 1) fill neuron.branches and neuron.index with information about the morphology of the neuron 2) change some wrong values in Aplus and Aminus. Indeed these were correct only if the neuron is unbranched (linear cable). Knowledge of the morphology gives correct values 3) fill neuron.bc (boundary conditions) ''' branch = morphology.branch() i=branch._origin j= i + len(branch) - 2 endpoint = j + 1 self.neuron.index[i:endpoint+1] = self.neuron.BPcount children_number = 0 #connections between branches for x in (morphology.children):#parent of segment n isnt always n-1 at branch points. We need to change Aplus and Aminus gc = 2 * msiemens/cm**2 startpoint = x._origin mid_diameter[startpoint] = .5*(self.neuron.diameter[endpoint]+self.neuron.diameter[startpoint]) self.Aminus[startpoint]=mid_diameter[startpoint]**2/(4*self.neuron.diameter[startpoint]*self.neuron.length[startpoint]**2*self.neuron.Ri) if endpoint>0: self.Aplus[startpoint]=mid_diameter[startpoint]**2/(4*self.neuron.diameter[endpoint]*self.neuron.length[endpoint]**2*self.neuron.Ri) else : self.Aplus[startpoint]=gc self.Aminus[startpoint]=gc children_number+=1 #boundary conditions pointType = self.neuron.bc[endpoint] hasChild = (children_number>0) if (not hasChild) and (pointType == 1): self.neuron.bc[endpoint] = self.neuron.bc_type #extract informations about the branches index_ante = self.neuron.index[ante] bp = endpoint index = self.neuron.BPcount self.i_list.append(i) self.j_list.append(j) self.bp_list.append(bp) self.pointType_list.append(max(1,pointType)) self.pointTypeAnte_list.append(max(1,self.neuron.bc[ante])) self.temp[index] = index_ante self.id.append(index) if (j-i+2>1): self.test_list.append(1) else : self.test_list.append(0) for x in xrange(j-i+2): self.ante_list.append(ante) self.post_list.append(bp) self.ante_list_idx.append(index_ante) self.post_list_idx.append(index) if index_ante == 0 and index != 0: self.ind0.append(index) if pointType==0 : self.ind_bctype_0.append(bp) if self.new_tridiag: self.i_list_bis.append(i) ii = i else: ii = self.i_list[-1] if j-ii+1>2: self.j_list_bis.append(j) self.new_tridiag = True else : self.new_tridiag = False #initialize the parts of the linear systems that will not change if (j-i+2)>1: #j-i+2 = len(branch) #initialize ab self.ab0[i:j] = self.Aplus[i+1:j+1] self.ab2[i+1:j+1] = self.Aminus[i+1:j+1] #initialize bL VL0 = 1 * volt self.bL[i] = (- VL0 * self.Aminus[i]) #initialize bR VR0 = 1 * volt self.bR[j] = (- VR0 * self.Aplus[j+1]) self.neuron.BPcount += 1 for x in (morphology.children): self.prepare_branch(x,mid_diameter,endpoint) def prepare(self): ''' From Hines 1984 paper, discrete formula is: A_plus*V(i+1)-(A_plus+A_minus)*V(i)+A_minus*V(i-1)=Cm/dt*(V(i,t+dt)-V(i,t))+gtot(i)*V(i)-I0(i) A_plus: i->i+1 A_minus: i->i-1 This gives the following tridiagonal system: A_plus*V(i+1)-(Cm/dt+gtot(i)+A_plus+A_minus)*V(i)+A_minus*V(i-1)=-Cm/dt*V(i,t)-I0(i) Boundaries, one simple possibility (sealed ends): -(Cm/dt+gtot(n)+A_minus)*V(n)+A_minus*V(n-1)=-Cm/dt*V(n,t)-I0(n) A_plus*V(1)-(Cm/dt+gtot(0)+A_plus)*V(0)=-Cm/dt*V(0,t)-I0(0) ''' mid_diameter = zeros(len(self.neuron)) # mid(i) : (i-1) <-> i mid_diameter[1:] = .5*(self.neuron.diameter[:-1]+self.neuron.diameter[1:]) self.Aplus = zeros(len(self.neuron)) # A+ i -> j = Aplus(j) self.Aminus = zeros(len(self.neuron)) # A- i <- j = Aminus(j) self.Aplus[1]= mid_diameter[1]**2/(4*self.neuron.diameter[1]*self.neuron.length[1]**2*self.neuron.Ri) self.Aplus[2:]=mid_diameter[2:]**2/(4*self.neuron.diameter[1:-1]*self.neuron.length[1:-1]**2*self.neuron.Ri) self.Aminus[1:]=mid_diameter[1:]**2/(4*self.neuron.diameter[1:]*self.neuron.length[1:]**2*self.neuron.Ri) self.neuron.index = zeros(len(self.neuron),int) # gives the index of the branch containing the current compartment self.neuron.BPcount = 0 # number of branch points (or branches). = len(self.neuron.branches) self.bL = zeros((len(self.neuron)),numpy.float64) self.bR = zeros((len(self.neuron)),numpy.float64) self.ab0 = zeros(len(self.neuron)) self.ab1 = zeros((len(self.neuron)),numpy.float64) self.ab2 = zeros(len(self.neuron)) self.ab1_base = zeros(len(self.neuron)) self.gtot = zeros(len(self.neuron)) self.I0 = zeros(len(self.neuron)) #some lists of indexes describing the neuron. self.i_list = []#the indexes of the first points of the branches in the neuron. len = neuron.BPcount self.j_list = [] #the indexes of the last points of the branches in the neuron. len = neuron.BPcount self.i_list_bis = [] self.j_list_bis = [] self.new_tridiag = True self.bp_list = [] #the indexes of the branch points in the neuron. len = neuron.BPcount self.pointType_list = []#boundary condition on bp. len = neuron.BPcount self.pointTypeAnte_list = [] #boundary condition on ante. len = neuron.BPcount self.index_ante_list1 = []#index of the parent branch of the current branch. index is in [0,neuron.BPcount] self.index_ante_list2 = [] self.ante_list = [] #the indexes in the neuron of the branch points connected to i, for every compartment. len = len(self.neuron) self.post_list = [] #for every compartment, contains the index of the branch point. len = len(self.neuron) self.ante_list_idx = []#index in the branches ( in [0,neuron.BPcount]) self.post_list_idx = [] self.id = [] #list of every integer in [0,neuron.BPcount]. used in step to change some values in a matrix self.test_list = []#for each branch : 1 if the branch has more than 3 compartments, else 0 self.temp = zeros(len(self.neuron)) #used to construct index_ante_list1, 2. self.ind0 = [] #indexes (in [0,neuron.BPcount]) of the branches connected to compartment 0 self.ind_bctype_0 = [] #indexes of the branch point with boundary condition 0 (constant V) # prepare_branch : fill neuron.index, neuron.branches, changes Aplus & Aminus self.prepare_branch(self.neuron.morphology, mid_diameter,0) #two arrays used in finalize to know the branch points before and after a compartment : self.ante_arr = numpy.array(self.ante_list_idx) self.post_arr = numpy.array(self.post_list_idx) self.index_ante_list1, self.ind1 = numpy.unique(self.temp,return_index=True) self.ind1 = numpy.sort(self.ind1) self.index_ante_list1 = self.temp[self.ind1] self.index_ante_list1 = list(self.index_ante_list1) self.ind2 = [] for x in xrange(self.neuron.BPcount): self.ind2.append(x) self.ind2 = numpy.delete(self.ind2,self.ind1,None) self.ind2 = numpy.setdiff1d(self.ind2, self.ind0, assume_unique=True) self.index_ante_list2 = self.temp[self.ind2] self.index_ante_list2 = list(self.index_ante_list2) self.index_ante_list = [] for idx in xrange(self.neuron.BPcount): self.index_ante_list.append(self.temp[idx]) #in case of a sealed end, Aminus and Aplus are doubled : self.Aminus_bp = self.Aminus[self.bp_list] self.Aminus_bp [:] *= self.pointType_list[:] self.Aplus_i = self.Aplus[self.i_list] self.Aplus_i[:] *= self.pointTypeAnte_list[:] # linear system P V = B used to deal with the voltage at branch points and take boundary conditions into account. self.P = zeros((self.neuron.BPcount,self.neuron.BPcount)) self.B = zeros(self.neuron.BPcount) self.solution_bp = zeros(self.neuron.BPcount) #--------------------------------------------------------GPU------------------------ n = len(self.neuron) mod = SourceModule(""" __global__ void updateAB_gtot(double *ab1, double *ab1_base, double *gtot) { int idx = threadIdx.x + blockIdx.x * blockDim.x; ab1[idx] = ab1_base[idx] - gtot[idx]; ab1[idx+gridDim.x] = ab1_base[idx] - gtot[idx]; ab1[idx+2*gridDim.x] = ab1_base[idx] - gtot[idx]; } __global__ void updateBD(double *bd, double *Cm, double dt,double *v, double *I0) { int idx = threadIdx.x + blockIdx.x * blockDim.x; bd[idx] = - Cm[idx] / dt * v[idx] - I0[idx]; } __global__ void finalizeFun(double *v, double *v_bp, int *ante,int *post, double *b, int m) { int idx = threadIdx.x + blockIdx.x * blockDim.x; int idx_a = ante[idx]; int idx_p = post[idx]; v[idx] = b[idx + m] * v_bp[idx_a] + b[idx + 2*m] * v_bp[idx_p] + b[idx]; // vL, vR, d } __global__ void finalizeFunBis(double *v, double *v_bp, int *GPU_data_int) { int idx = threadIdx.x + blockIdx.x * blockDim.x; int bp = GPU_data_int[4 * idx + 3]; v[bp] = v_bp[idx]; } __global__ void initPB(double *P, double *B, int BPcount) { int idx = threadIdx.x + blockIdx.x * blockDim.x; int idy = threadIdx.x + blockIdx.y * blockDim.y; P[idx + idy * BPcount] = 0.0; B[idx] = 0.0; } //GPU_data_int is : ante_list, i_list, j_list, bp_list //GPU_data_double is : test_list, Aplus, Aminus __global__ void fillPB(double *P, double *B, double *b, double *Cm_l, double *gtot_l, int *GPU_data_int, double *GPU_data_double, double *I0_l, double *v_l, int BPcount, double dt, int m) { int idx = threadIdx.x + blockIdx.x * blockDim.x; int idx_ante = GPU_data_int[4 * idx]; int i = GPU_data_int[4 * idx + 1]; int j = GPU_data_int[4 * idx + 2]; int bp = GPU_data_int[4 * idx + 3]; double test = GPU_data_double[3 * idx]; double Aplus = GPU_data_double[3 * idx + 1]; double Aminus = GPU_data_double[3 * idx + 2]; double Cm = Cm_l[bp]; double gtot = gtot_l[bp]; double I0 = I0_l[bp]; double v_bp = v_l[bp]; double vLright = b[j + m] * test; double vRleft = b[i + 2*m] * test; double vRright = b[j + 2*m] * test; double dright = b[j] * test; B[idx] += -Cm/dt * v_bp - I0 -Aminus * dright; P[idx * BPcount + idx] += -Cm/dt - gtot + Aminus * (vRright - 1.0); P[idx * BPcount + idx_ante] += Aminus * vLright; P[idx_ante * BPcount + idx] += Aplus * vRleft; } __global__ void fillPB_bis(double *P, double *B, double *b, int *GPU_data_int, double *GPU_data_double, int BPcount, int *indices, int m) { int idx_temp = threadIdx.x + blockIdx.x * blockDim.x; int idx = indices[idx_temp]; int idx_ante = GPU_data_int[4 * idx]; int i = GPU_data_int[4 * idx + 1]; int test = GPU_data_double[3 * idx]; double Aplus = GPU_data_double[3 * idx + 1]; double vLleft = b[i + m] * test; double dleft = b[i] * test; B[idx_ante] += - Aplus * dleft; P[idx_ante * (BPcount + 1)] += Aplus * (vLleft - 1.0); } __global__ void badFillPB_0(double *P, double *B, double *b, int *GPU_data_int, double *GPU_data_double, int *indices, double *Cm_l, double *gtot_l, double *I0_l, double *v_l, int len_indices, int m, double dt) { double Cm = Cm_l[0]; double gtot = gtot_l[0]; double I0 = I0_l[0]; double v_0 = v_l[0]; B[0] = - Cm/dt * v_0 - I0; P[0] = - Cm/dt - gtot; int idx; int i; int test; double Aplus; double vLleft; double dleft; for (int idx_temp=0;idx_temp vd, vL, vR cuda.memcpy_htod(self.ab0_gpu, ab0) cuda.memcpy_htod(self.ab2_gpu, ab2) cuda.memcpy_htod(self.bL_gpu, self.bL) cuda.memcpy_htod(self.bR_gpu, self.bR) self.ante_gpu = cuda.mem_alloc(self.ante_arr.size * self.ante_arr.dtype.itemsize) self.post_gpu = cuda.mem_alloc(self.ante_arr.size * self.ante_arr.dtype.itemsize) self.v_old_gpu = cuda.mem_alloc(self.neuron.v.size * dtype.itemsize) cuda.memcpy_htod(self.ante_gpu,self.ante_arr) cuda.memcpy_htod(self.post_gpu,self.post_arr) self.v_branchpoints = zeros(self.neuron.BPcount) self.v_bp_gpu = cuda.mem_alloc(self.v_branchpoints.size * dtype.itemsize) def step(self, neuron): """ solve the cable equation on the tree *find the solution space on every branch (solve 3 tridiagonal systems) *find the voltages of the branch points (a small linear system) *determine V with these two informations """ #------------------------------------solve tridiagonal systems on the branchs------------------------- #update some variables and matrices self.calc("_gtot",self.gtot_gpu) self.calc("_I0",self.I0_gpu) if self.neuron.changed : self.update_ab_base_gpu() self.neuron.changed = False self.update_ab_gtot_gpu() self.update_bd_gpu() # finds the three solutions that we need to have V. this function solves ab X = b for b = bd,bL,bR. self.calculate_vd_vL_vR_gpu() #-----------fill P and B, matrix and right hand used to find the voltage at the branch points----------------- # 1) initPB reset P and B. every value is 0. # 2) fillPB calculates the coefficients of P and B where there is no lock issue. # 3) fillPB_bis does the same on two exclusive lists of indexes in P and B, ind1 and ind2 to avoid lock issues # 4) badFillPB_0 ajusts the value in the particular case of 0. this one is not parallel but not big. # 5) resetPB_type0 ajusts P and B in case of a branchpoint of type 0 (constant V) self.initPB.prepared_call((neuron.BPcount, neuron.BPcount),self.P_gpu, self.B_gpu, numpy.int32(neuron.BPcount)) self.fillPB.prepared_call((neuron.BPcount,1),self.P_gpu, self.B_gpu, self.b_gpu, self.Cm_gpu, self.gtot_gpu, self.GPU_data_int, self.GPU_data_double, self.I0_gpu, self.v_gpu, numpy.int32(neuron.BPcount), self.neuron.clock.dt, numpy.int32(len(neuron))) self.fillPB_bis.prepared_call((len(self.ind1),1),self.P_gpu, self.B_gpu, self.b_gpu, self.GPU_data_int, self.GPU_data_double, numpy.int32(neuron.BPcount), self.ind1_gpu, numpy.int32(len(neuron))) if len(self.ind2)>0: self.fillPB_bis.prepared_call((len(self.ind2),1),self.P_gpu, self.B_gpu, self.b_gpu, self.GPU_data_int, self.GPU_data_double, numpy.int32(neuron.BPcount), self.ind2_gpu, numpy.int32(len(neuron))) self.badFillPB_0.prepared_call((1,1),self.P_gpu, self.B_gpu, self.b_gpu, self.GPU_data_int, self.GPU_data_double, self.ind0_gpu, self.Cm_gpu, self.gtot_gpu, self.I0_gpu, self.v_gpu, numpy.int32(len(self.ind0)), numpy.int32(len(neuron)),neuron.clock.dt)#this is not parallel if len(self.ind_bctype_0): self.resetPB_type0.prepared_call((len(self.ind_bctype_0), neuron.BPcount),self.P_gpu, self.B_gpu, self.v_gpu, self.ind_bctype_0_gpu, numpy.int32(neuron.BPcount)) #------------------------------------------------------solve PV=B----------------------------------- cuda.memcpy_dtoh(self.P,self.P_gpu) #copy P and B from the GPU to solve PV=B on the CPU cuda.memcpy_dtoh(self.B,self.B_gpu) self.solution_bp = solve(self.P,self.B) #solve PV=B. better : do it on the GPU, use a sparse solver. cuda.memcpy_htod(self.v_bp_gpu, self.solution_bp) #copy the results to the GPU #-------------------------------------------------------update v------------------------------------- self.finalize_v_gpu() # V(x) = V(left) * vL(x) + V(right) * vR(x) + Vd(x) cuda.memcpy_dtoh(self.neuron.v,self.v_gpu) #bad : should copy requested variables def solve_branchpoints(self): #project : solve PV=B on the GPU, using a sparse matrix format and an interative solver. not used. BPcount = self.neuron.BPcount code =""" // where to perform the computation typedef cusp::device_memory MemorySpace; // which floating point type to use typedef double ValueType; // create an empty sparse matrix structure (coo format) cusp::coo_matrix P; // allocate storage for solution (x) and right hand side (b) cusp::array1d x(P.num_rows, 0); cusp::array1d B(P.num_rows, 1); //TODO fill P and B // set stopping criteria: // iteration_limit = 100 // relative_tolerance = 1e-3 cusp::default_monitor monitor(B, 100, 1e-3); // set preconditioner (identity) cusp::identity_operator M(P.num_rows, P.num_rows); // solve the linear system P * x = B with the BiConjugate Gradient Stabilized method cusp::krylov::bicgstab(P, x, B, monitor, M); """ support_code='' includepath = ["/usr/local/cuda/include/"] libpath = ['/usr/local/cuda/lib/','/usr/lib/'] weave.inline(code,['BPcount'],support_code=support_code,headers = ["","","","",""], include_dirs=includepath, library_dirs=libpath, libraries=["cusp"], runtime_library_dirs=libpath, compiler="gcc") def calc(self,var,res_gpu): #compute the value of neuron.var and store it in res_gpu. the _state_updater must have a "calc" method. self._state_updater.calc(var,res_gpu) """ #if the _state_updater doesn't have a 'calc' method cuda.memcpy_htod(res_gpu,self.neuron.var) """ def update_ab_base_gpu(self): #part of ab that doesn't change if there is no prompt from the operator. self.ab1_base[:-1] = (- self.neuron.Cm[:-1] / self.neuron.clock.dt * second - self.Aminus[:-1] - self.Aplus[1:]) self.ab1_base[-1] = (- self.neuron.Cm[-1] / self.neuron.clock.dt * second - self.Aminus[-1]) cuda.memcpy_htod(self.ab1_base_gpu,self.ab1_base) def update_ab_gtot_gpu(self): #this is called every step. changing part of ab. n = len(self.neuron) self.updateAB_gtot.prepared_call((n,1), self.ab1_gpu, self.ab1_base_gpu, self.gtot_gpu) def update_bd_gpu(self): n = len(self.neuron) dt = self.neuron.clock.dt self.updateBD.prepared_call((n,1), self.b_gpu, self.Cm_gpu, dt, self.v_gpu, self.I0_gpu) def calculate_vd_vL_vR_gpu(self): # b_gpu contains bd,X,X. update it to be bd,bL,bR then solve a tridiagonal system. # b_gpu now contains vd,vL,vR m = len(self.neuron) ab0_gpu_ptr = self.ab0_gpu_ptr ab1_gpu_ptr = self.ab1_gpu_ptr ab2_gpu_ptr = self.ab2_gpu_ptr bL_gpu_ptr = self.bL_gpu_ptr bR_gpu_ptr = self.bR_gpu_ptr b_gpu_ptr = self.b_gpu_ptr code =""" cusparseHandle_t handle = 0; cusparseCreate(&handle); double *bL_gpu = (double *)bL_gpu_ptr; double *bR_gpu = (double *)bR_gpu_ptr; double *b_gpu = (double *)b_gpu_ptr; double *ab0_gpu = (double *)ab0_gpu_ptr; double *ab1_gpu = (double *)ab1_gpu_ptr; double *ab2_gpu = (double *)ab2_gpu_ptr; //bd is up to date in b_gpu cudaMemcpy(b_gpu + m, bL_gpu, m*sizeof(double), cudaMemcpyDeviceToDevice); cudaMemcpy(b_gpu + 2*m, bR_gpu, m*sizeof(double), cudaMemcpyDeviceToDevice); cusparseDgtsvStridedBatch(handle, m, ab2_gpu, ab1_gpu, ab0_gpu, b_gpu, 3, m); //now b_gpu contains vd, vL, vR cudaDeviceSynchronize(); cusparseDestroy(handle); """ support_code='extern "C" cusparseStatus_t cusparseDgtsvStridedBatch(cusparseHandle_t handle, int m,const double *dl, const double *d,const double *du, double *x,int batchCount, int batchStride);' includepath = ["/usr/local/cuda/include/"] libpath = ['/usr/local/cuda/lib/','/usr/lib/'] weave.inline(code,['ab0_gpu_ptr','ab1_gpu_ptr','ab2_gpu_ptr','bL_gpu_ptr','bR_gpu_ptr','m','b_gpu_ptr'],support_code=support_code,headers = ["","",""], include_dirs=includepath, library_dirs=libpath, libraries=["cusparse"], runtime_library_dirs=libpath, compiler="gcc") def calculate_vd_vL_vR_gpu_branches(self): # an attempt to cut the tridiagonal system in many parts. This is slow and useless. m = len(self.neuron) systemsCount = len(self.i_list_bis) i_list = numpy.array(self.i_list_bis) j_list = numpy.array(self.j_list_bis) ab0_gpu_ptr = self.ab0_gpu_ptr ab1_gpu_ptr = self.ab1_gpu_ptr ab2_gpu_ptr = self.ab2_gpu_ptr bL_gpu_ptr = self.bL_gpu_ptr bR_gpu_ptr = self.bR_gpu_ptr b_gpu_ptr = self.b_gpu_ptr code =""" cusparseHandle_t handle = 0; cusparseCreate(&handle); double *bL_gpu = (double *)bL_gpu_ptr; double *bR_gpu = (double *)bR_gpu_ptr; double *b_gpu = (double *)b_gpu_ptr; double *ab0_gpu = (double *)ab0_gpu_ptr; double *ab1_gpu = (double *)ab1_gpu_ptr; double *ab2_gpu = (double *)ab2_gpu_ptr; //bd is up to date in b_gpu cudaMemcpy(b_gpu + m, bL_gpu, m*sizeof(double), cudaMemcpyDeviceToDevice); cudaMemcpy(b_gpu + 2*m, bR_gpu, m*sizeof(double), cudaMemcpyDeviceToDevice); int i; int j; int n; for(int idx=0;idx","",""], include_dirs=includepath, library_dirs=libpath, libraries=["cusparse"], runtime_library_dirs=libpath, compiler="gcc") def finalize_v_gpu(self): # V(x) = V(left) * vL(x) + V(right) * vR(x) + Vd(x) n = len(self.neuron) self.finalizeFun.prepared_call((n,1), self.v_gpu, self.v_bp_gpu, self.ante_gpu, self.post_gpu, self.b_gpu, numpy.int32(len(self.neuron))) self.finalizeFunBis.prepared_call((self.neuron.BPcount,1), self.v_gpu, self.v_bp_gpu, self.GPU_data_int) self._state_updater.updateV(self.v_gpu) def __call__(self, neuron): ''' Updates the state variables. ''' if not self._isprepared: self.prepare() self._isprepared=True print "state updater prepared" self.callcount+=1 print self.callcount if neuron.changed: self._state_updater.changed = True self._state_updater(neuron) #update the currents self.step(neuron) #integrate the cable equation def __len__(self): ''' Number of state variables ''' return len(self.eqs) CompartmentalNeuron = SpatialNeuronbrian-1.4.3/dev/ideas/morphology/Remy/spatialneuron_monoprocess.py000066400000000000000000000407661266062647100255040ustar00rootroot00000000000000''' Compartmental neurons ''' from morphology import Morphology from brian.stdunits import * from brian.units import * from brian.reset import NoReset from brian.stateupdater import StateUpdater #from gpustateupdater import * from brian.inspection import * from brian.optimiser import * from itertools import count from brian.neurongroup import NeuronGroup from scipy.linalg import solve_banded from numpy import zeros, ones, isscalar, diag_indices from numpy.linalg import solve from brian.clock import guess_clock from brian.equations import Equations import functools import warnings from math import ceil, log from scipy import weave import numpy from time import time import trace try: import sympy use_sympy = True except: warnings.warn('sympy not installed: some features in SpatialNeuron will not be available') use_sympy = False __all__ = ['SpatialNeuron', 'CompartmentalNeuron'] class SpatialNeuron(NeuronGroup): """ Compartmental model with morphology. """ def __init__(self, morphology=None, model=None, threshold=None, reset=NoReset(), refractory=0 * ms, level=0, clock=None, unit_checking=True, compile=False, freeze=False, implicit=True, Cm=0.9 * uF / cm ** 2, Ri=150 * ohm * cm, bc_type = 2, diffeq_nonzero=True): clock = guess_clock(clock) N = len(morphology) # number of compartments if isinstance(model, str): model = Equations(model, level=level + 1) model += Equations(''' v:volt # membrane potential ''') # Process model equations (Im) to extract total conductance and the remaining current if use_sympy: try: membrane_eq=model._string['Im'] # the membrane equation except: raise TypeError,"The transmembrane current Im must be defined" # Check conditional linearity ids=get_identifiers(membrane_eq) _namespace=dict.fromkeys(ids,1.) # there is a possibility of problems here (division by zero) _namespace['v']=AffineFunction() eval(membrane_eq,model._namespace['v'],_namespace) try: eval(membrane_eq,model._namespace['v'],_namespace) except: # not linear raise TypeError,"The membrane current must be linear with respect to v" # Extracts the total conductance from Im, and the remaining current z=symbolic_eval(membrane_eq) symbol_v=sympy.Symbol('v') b=z.subs(symbol_v,0) a=-sympy.simplify(z.subs(symbol_v,1)-b) gtot_str="_gtot="+str(a)+": siemens/cm**2" I0_str="_I0="+str(b)+": amp/cm**2" model+=Equations(gtot_str+"\n"+I0_str,level=level+1) # better: explicit insertion with namespace of v else: raise TypeError,"The Sympy package must be installed for SpatialNeuron" # Equations for morphology (isn't it a duplicate??) eqs_morphology = Equations(""" diameter : um length : um x : um y : um z : um area : um**2 """) full_model = model + eqs_morphology NeuronGroup.__init__(self, N, model=full_model, threshold=threshold, reset=reset, refractory=refractory, level=level + 1, clock=clock, unit_checking=unit_checking, implicit=implicit) self.model_with_diffeq_nonzero = diffeq_nonzero self._state_updater = SpatialStateUpdater(self, clock) self.Cm = ones(len(self))*Cm self.Ri = Ri self.bc_type = bc_type #default boundary condition on leaves self.bc = ones(len(self)) # boundary conditions on branch points self.changed = True # Insert morphology self.morphology = morphology self.morphology.compress(diameter=self.diameter, length=self.length, x=self.x, y=self.y, z=self.z, area=self.area) def subgroup(self, N): # Subgrouping cannot be done in this way raise NotImplementedError def __getitem__(self, x): ''' Subgrouping mechanism. self['axon'] returns the subtree named "axon". TODO: self[:] returns the full branch. ''' morpho = self.morphology[x] N = self[morpho._origin:morpho._origin + len(morpho)] N.morphology = morpho return N def __getattr__(self, x): if (x != 'morphology') and ((x in self.morphology._namedkid) or all([c in 'LR123456789' for c in x])): # subtree return self[x] else: return NeuronGroup.__getattr__(self, x) class SpatialStateUpdater(StateUpdater): """ State updater for compartmental models. """ def __init__(self, neuron, clock=None): self.eqs = neuron._eqs self.neuron = neuron self._isprepared = False self._state_updater=neuron._state_updater # to update the currents self.first_test_gtot=True self.callcount=0 def prepare_branch(self, morphology, mid_diameter,ante=0): ''' 1) fill neuron.branches and neuron.index with information about the morphology of the neuron 2) change some wrong values in Aplus and Aminus. Indeed these were correct only if the neuron is a linear cable. Knowledge of the morphology gives correct values. 3) fill neuron.bc (boundary conditions) ''' branch = morphology.branch() i=branch._origin j= i + len(branch) - 2 endpoint = j + 1 self.neuron.index[i:endpoint+1] = self.neuron.BPcount children_number = 0 #connections between branches for x in (morphology.children):#parent of segment n isn't always n-1 at branch points. We need to change Aplus and Aminus gc = 2 * msiemens/cm**2 startpoint = x._origin mid_diameter[startpoint] = .5*(self.neuron.diameter[endpoint]+self.neuron.diameter[startpoint]) self.Aminus[startpoint]=mid_diameter[startpoint]**2/(4*self.neuron.diameter[startpoint]*self.neuron.length[startpoint]**2*self.neuron.Ri) if endpoint>0: self.Aplus[startpoint]=mid_diameter[startpoint]**2/(4*self.neuron.diameter[endpoint]*self.neuron.length[endpoint]**2*self.neuron.Ri) else : self.Aplus[startpoint]=gc self.Aminus[startpoint]=gc children_number+=1 #boundary conditions pointType = self.neuron.bc[endpoint] hasChild = (children_number>0) if (not hasChild) and (pointType == 1): #if the branch point is a leaf of the tree : apply default boundary condition self.neuron.bc[endpoint] = self.neuron.bc_type #extract informations about the branches index_ante = self.neuron.index[ante] bp = endpoint index = self.neuron.BPcount self.i_list.append(i) self.j_list.append(j) self.bp_list.append(bp) self.pointType_list.append(max(1,pointType)) self.pointTypeAnte_list.append(max(1,self.neuron.bc[ante])) self.temp[index] = index_ante self.id.append(index) self.test_list.append((j-i+2)>1) for x in xrange(j-i+2): self.ante_list.append(ante) self.post_list.append(bp) if index_ante == 0: self.ind0.append(index) if pointType==0 : self.ind_bctype_0.append(bp) #initialize the parts of the linear systems that will not change if (j-i+2)>1: #j-i+2 = len(branch) #initialize ab self.ab[0,i:j]= self.Aplus[i:j] self.ab[2,i:j]= self.Aminus[i+1:j+1] #initialize bL VL0 = 1 * volt self.bL[i] = (- VL0 * self.Aminus[i]) #initialize bR VR0 = 1 * volt self.bR[j] = (- VR0 * self.Aplus[j+1]) self.neuron.BPcount += 1 for x in (morphology.children): self.prepare_branch(x,mid_diameter,endpoint) def prepare(self): ''' From Hines 1984 paper, discrete formula is: A_plus*V(i+1)-(A_plus+A_minus)*V(i)+A_minus*V(i-1)=Cm/dt*(V(i,t+dt)-V(i,t))+gtot(i)*V(i)-I0(i) A_plus: i->i+1 A_minus: i->i-1 This gives the following tridiagonal system: A_plus*V(i+1)-(Cm/dt+gtot(i)+A_plus+A_minus)*V(i)+A_minus*V(i-1)=-Cm/dt*V(i,t)-I0(i) ''' mid_diameter = zeros(len(self.neuron)) # mid(i) : (i-1) <-> i mid_diameter[1:] = .5*(self.neuron.diameter[:-1]+self.neuron.diameter[1:]) self.Aplus = zeros(len(self.neuron)) # A+ i -> j = Aplus(j) self.Aminus = zeros(len(self.neuron)) # A- i <- j = Aminus(j) self.Aplus[1]= mid_diameter[1]**2/(4*self.neuron.diameter[1]*self.neuron.length[1]**2*self.neuron.Ri) self.Aplus[2:]=mid_diameter[2:]**2/(4*self.neuron.diameter[1:-1]*self.neuron.length[1:-1]**2*self.neuron.Ri) self.Aminus[1:]=mid_diameter[1:]**2/(4*self.neuron.diameter[1:]*self.neuron.length[1:]**2*self.neuron.Ri) self.neuron.index = zeros(len(self.neuron),int) # gives the index of the branch containing the current compartment self.neuron.BPcount = 0 # number of branch points (or branches). = len(self.neuron.branches) #the three solutions for V on a branch self.vL = zeros((len(self.neuron)),numpy.float64) self.vR = zeros((len(self.neuron)),numpy.float64) self.d = zeros((len(self.neuron)),numpy.float64) #matrix and right hand in the tridiagonal systems that we solve to find vL, vR and d. self.bL = zeros((len(self.neuron)),numpy.float64) self.bR = zeros((len(self.neuron)),numpy.float64) self.bd = zeros((len(self.neuron)),numpy.float64) self.ab = zeros((3,len(self.neuron))) self.ab1_base = zeros(len(self.neuron)) self.gtot = zeros(len(self.neuron)) self.I0 = zeros(len(self.neuron)) self.i_list = [] #the indexes of the first points of the branches in the neuron. len = neuron.BPcount self.j_list = [] #the indexes of the last points of the branches in the neuron. len = neuron.BPcount self.bp_list = [] #the indexes of the branch points in the neuron. len = neuron.BPcount self.pointType_list = [] #boundary condition on bp. len = neuron.BPcount self.pointTypeAnte_list = [] #boundary condition on ante. len = neuron.BPcount self.index_ante_list1 = [] #index of the parent branch of the current branch. index is in [0,neuron.BPcount] self.index_ante_list2 = [] self.ante_list = [] #the indexes in the neuron of the branch points connected to i, for every compartment. len = len(self.neuron) self.post_list = [] #for every compartment, contains the index of the branch point. len = len(self.neuron) self.test_list = [] #for each branch : 1 if the branch has more than 3 compartments, else 0 self.id = [] #list of every integer in [0,neuron.BPcount]. used in step to change some values in a matrix self.temp = zeros(len(self.neuron)) #used to construct index_ante_list0, 1, 2. self.ind0 = [] #indexes (in [0,neuron.BPcount]) of the branches connected to compartment 0 self.ind_bctype_0 = [] #indexes of the branch point with boundary condition 0 (constant V) # prepare_branch : fill the lists, changes Aplus & Aminus self.prepare_branch(self.neuron.morphology, mid_diameter,0) self.index_ante_list1, self.ind1 = numpy.unique(numpy.array(self.temp,int),return_index=True) self.ind1 = numpy.sort(self.ind1) self.index_ante_list1 = self.temp[self.ind1] self.index_ante_list1 = list(self.index_ante_list1) self.ind2 = [] for x in xrange(self.neuron.BPcount): self.ind2.append(x) self.ind2 = numpy.delete(self.ind2,self.ind1,None) self.ind2 = numpy.setdiff1d(self.ind2, self.ind0, assume_unique=True) self.index_ante_list2 = self.temp[self.ind2] self.index_ante_list2 = list(self.index_ante_list2) self.index_ante_list = [] for idx in xrange(self.neuron.BPcount): self.index_ante_list.append(self.temp[idx]) # linear system P V = B used to deal with the voltage at branch points and take boundary conditions into account. self.P = zeros((self.neuron.BPcount,self.neuron.BPcount)) self.B = zeros(self.neuron.BPcount) self.solution_bp = zeros(self.neuron.BPcount) #in case of a sealed end, Aminus and Aplus are doubled : self.Aminus_bp = self.Aminus[self.bp_list] self.Aminus_bp [:] *= self.pointType_list[:] self.Aplus_i = self.Aplus[self.i_list] self.Aplus_i[:] *= self.pointTypeAnte_list[:] def step(self, neuron): if self.first_test_gtot and isscalar(neuron._gtot): self.first_test_gtot=False neuron._gtot = ones(len(neuron)) * neuron._gtot self.gtot = neuron._gtot #this compute the value of neuron._gtot. #if we call neuron._gtot[1] and then neuron._gtot[2] it does 2 computations #here we call it only one time on the whole array. this is much faster self.I0 = neuron._I0 #------------------------------------solve tridiagonal systems on the branchs------------------------- #ab is the matrix in the tridiagonal systems describing the branches. #bd is a right hand in one of these tridiagonal systems. if self.neuron.changed : # neuron.changed = True <=> there was a new input somewhere. example : the user does neuron.I[x] = y self.update_ab_base() self.update_ab_gtot() self.update_bd() self.calculate_vd_vL_vR() self.neuron.changed = False #-----------fill P and B, matrix and right hand used to find the voltage at the branch points----------------- self.P[:,:] = 0 self.B[:] = 0 Cm = neuron.Cm[self.bp_list] dt = neuron.clock.dt gtot = self.gtot[self.bp_list] I0 = self.I0[self.bp_list] v_bp = neuron.v[self.bp_list] vLleft = self.vL[self.i_list] vLright = self.vL[self.j_list] vRleft = self.vR[self.i_list] vRright = self.vR[self.j_list] dleft = self.d[self.i_list] dright = self.d[self.j_list] vLleft[:] *= self.test_list[:] #if a branch has less than 3 compartments, this equals 0. #thus we can do the same work on every branch point. vLright[:] *= self.test_list[:] vRleft[:] *= self.test_list[:] vRright[:] *= self.test_list[:] dleft[:] *= self.test_list[:] dright[:] *= self.test_list[:] self.B[self.index_ante_list1] += - self.Aplus_i[self.ind1[:]] * dleft[self.ind1[:]] self.B[self.index_ante_list2] += - self.Aplus_i[self.ind2[:]] * dleft[self.ind2[:]] self.B[0] += sum(- self.Aplus_i[self.ind0[:]] * dleft[self.ind0[:]]) self.P[(self.index_ante_list1,self.index_ante_list1)] += self.Aplus_i[self.ind1[:]] * (vLleft[self.ind1[:]] - 1) self.P[(self.index_ante_list2,self.index_ante_list2)] += self.Aplus_i[self.ind2[:]] * (vLleft[self.ind2[:]] - 1) self.P[0,0] += sum(self.Aplus_i[self.ind0[:]] * (vLleft[self.ind0[:]] - 1)) di = diag_indices(neuron.BPcount) self.B[:] += - Cm[:]/dt * second * v_bp[:] - I0[:] - self.Aminus_bp[:] * dright[:] self.P[di] += - Cm[:]/dt * second - gtot[:] + self.Aminus_bp[:] * (vRright[:] - 1) self.P[(self.id,self.index_ante_list)] += self.Aminus_bp[:] *vLright[:] self.P[(self.index_ante_list,self.id)] += self.Aplus_i[:] *vRleft[:] self.P[self.ind_bctype_0,:] = 0 self.P[(self.ind_bctype_0,self.ind_bctype_0)] = 1 self.B[self.ind_bctype_0] = neuron.v[self.ind_bctype_0] #------------------------------------------------------solve PV=B----------------------------------- self.solution_bp = solve(self.P,self.B) neuron.v[self.bp_list] = self.solution_bp[:] #-------------------------------------------------------update v------------------------------------- self.finalize_v_global() def update_ab_base(self): #part of ab that doesn't change if there is no prompt from the operator. self.ab1_base[:-1] = (- self.neuron.Cm[:-1] / self.neuron.clock.dt * second - self.Aminus[:-1] - self.Aplus[1:]) self.ab1_base[-1] = (- self.neuron.Cm[-1] / self.neuron.clock.dt * second - self.Aminus[-1]) def update_ab_gtot(self): #this is called every step. changing part of ab. self.ab[1,:] = self.ab1_base[:] - self.neuron._gtot[:] def update_bd(self): #bd is a right hand side in a tridiagonal system self.bd[:] = -self.neuron.Cm[:] / self.neuron.clock.dt * self.neuron.v[:] - self.neuron._I0[:] def calculate_vd_vL_vR(self): for index in xrange(self.neuron.BPcount) : if self.test_list[index] : i = self.i_list[index] j = self.j_list[index] self.vL[i:j+1] = solve_banded((1,1),self.ab[:,i:j+1],self.bL[i:j+1],overwrite_ab=False,overwrite_b=False) self.vR[i:j+1] = solve_banded((1,1),self.ab[:,i:j+1],self.bR[i:j+1],overwrite_ab=False,overwrite_b=False) self.d[i:j+1] = solve_banded((1,1),self.ab[:,i:j+1],self.bd[i:j+1],overwrite_ab=False,overwrite_b=False) def finalize_v_global(self): #V(x) = V(left) * vL(x) + V(right) * vR(x) + d(x) self.neuron.v[:] = self.vL[:] * self.neuron.v[self.ante_list[:]] + self.vR[:] * self.neuron.v[self.post_list[:]] + self.d[:] self.neuron.v[self.bp_list] = self.solution_bp[:] def __call__(self, neuron): ''' Updates the state variables. ''' if not self._isprepared: self.prepare() self._isprepared=True print "state updater prepared" self.callcount+=1 print self.callcount #Update I,V if neuron.changed : self._state_updater.changed = True self._state_updater(neuron) #update the currents self.step(neuron) #update V def __len__(self): ''' Number of state variables ''' return len(self.eqs) CompartmentalNeuron = SpatialNeuron brian-1.4.3/dev/ideas/morphology/Remy/spatialstateupdater_linear.py000066400000000000000000000743611266062647100256040ustar00rootroot00000000000000import pycuda.driver as cuda import pycuda.autoinit from pycuda.compiler import SourceModule from morphology import Morphology from brian.stdunits import * from brian.units import * from brian.reset import NoReset from brian.stateupdater import StateUpdater from gpustateupdater import * from brian.inspection import * from brian.optimiser import * from itertools import count from brian.neurongroup import NeuronGroup from scipy.linalg import solve_banded from numpy import zeros, ones, isscalar, diag_indices from numpy.linalg import solve from brian.clock import guess_clock from brian.equations import Equations import functools import warnings from math import ceil, log from scipy import weave from time import time import trace try: import sympy use_sympy = True except: warnings.warn('sympy not installed: some features in SpatialNeuron will not be available') use_sympy = False __all__ = ['LinearSpatialStateUpdater'] class LinearSpatialStateUpdater(StateUpdater): """ State updater for compartmental models. """ def __init__(self, neuron, clock=None): self.eqs = neuron._eqs self.neuron = neuron self._isprepared = False self.first_step = True self._state_updater=neuron._state_updater # to update the currents self.first_test_gtot=True self.callcount=0 def prepare_branch(self, morphology, mid_diameter,ante=0): ''' 1) fill neuron.branches and neuron.index with information about the morphology of the neuron 2) change some wrong values in Aplus and Aminus. Indeed these were correct only if the neuron is linear. Knowledge of the morphology gives correct values 3) fill neuron.bc (boundary conditions) ''' branch = morphology.branch() i=branch._origin j= i + len(branch) - 2 endpoint = j + 1 self.neuron.index[i:endpoint+1] = self.neuron.BPcount children_number = 0 for x in (morphology.children):#parent of segment n isnt always n-1 at branch points. We need to change Aplus and Aminus #gc = 2 * msiemens/cm**2 startpoint = x._origin mid_diameter[startpoint] = .5*(self.neuron.diameter[endpoint]+self.neuron.diameter[startpoint]) self.Aminus[startpoint]=mid_diameter[startpoint]**2/(4*self.neuron.diameter[startpoint]*self.neuron.length[startpoint]**2*self.neuron.Ri) if endpoint>0: self.Aplus[startpoint]=mid_diameter[startpoint]**2/(4*self.neuron.diameter[endpoint]*self.neuron.length[endpoint]**2*self.neuron.Ri) #else : #self.Aplus[startpoint]=gc #self.Aminus[startpoint]=gc children_number+=1 pointType = self.neuron.bc[endpoint] hasChild = (children_number>0) if (not hasChild) and (pointType == 1): self.neuron.bc[endpoint] = self.neuron.bc_type pointType = self.neuron.bc[endpoint] index_ante = self.neuron.index[ante] self.neuron.branches.append((i,j,endpoint,ante,index_ante,pointType)) self.neuron.BPcount += 1 if (j-i+2)>1: #j-i+2 = len(branch) self.neuron.long_branches_count +=1 #initialize ab self.ab[0,i:j]= self.Aplus[i+1:j+1] self.ab0[i:j] = self.Aplus[i+1:j+1] self.ab[2,i+1:j+1]= self.Aminus[i+1:j+1] self.ab2[i+1:j+1] = self.Aminus[i+1:j+1] #initialize bL VL0 = 1 * volt self.bL[i] = (- VL0 * self.Aminus[i]) #initialize bR VR0 = 1 * volt self.bR[j] = (- VR0 * self.Aplus[j+1]) for x in (morphology.children): self.prepare_branch(x,mid_diameter,endpoint) def prepare(self): ''' From Hines 1984 paper, discrete formula is: A_plus*V(i+1)-(A_plus+A_minus)*V(i)+A_minus*V(i-1)=Cm/dt*(V(i,t+dt)-V(i,t))+gtot(i)*V(i)-I0(i) A_plus: i->i+1 A_minus: i->i-1 This gives the following tridiagonal system: A_plus*V(i+1)-(Cm/dt+gtot(i)+A_plus+A_minus)*V(i)+A_minus*V(i-1)=-Cm/dt*V(i,t)-I0(i) Boundaries, one simple possibility (sealed ends): -(Cm/dt+gtot(n)+A_minus)*V(n)+A_minus*V(n-1)=-Cm/dt*V(n,t)-I0(n) A_plus*V(1)-(Cm/dt+gtot(0)+A_plus)*V(0)=-Cm/dt*V(0,t)-I0(0) ''' mid_diameter = zeros(len(self.neuron)) # mid(i) : (i-1) <-> i mid_diameter[1:] = .5*(self.neuron.diameter[:-1]+self.neuron.diameter[1:]) self.Aplus = zeros(len(self.neuron)) # A+ i -> j = Aplus(j) self.Aminus = zeros(len(self.neuron)) # A- i <- j = Aminus(j) self.Aplus[1]= mid_diameter[1]**2/(4*self.neuron.diameter[1]*self.neuron.length[1]**2*self.neuron.Ri) self.Aplus[2:]=mid_diameter[2:]**2/(4*self.neuron.diameter[1:-1]*self.neuron.length[1:-1]**2*self.neuron.Ri) self.Aminus[1:]=mid_diameter[1:]**2/(4*self.neuron.diameter[1:]*self.neuron.length[1:]**2*self.neuron.Ri) self.neuron.index = zeros(len(self.neuron),int) # gives the index of the branch containing the current compartment self.neuron.branches = [] # (i,j,bp,ante,ante_index,pointType) # i is the first compartment # bp is the last, a branch point # j is the end of the "inner branch". j = bp-1 # ante is the branch point to which i is connected self.neuron.BPcount = 0 # number of branch points (or branches). = len(self.neuron.branches) self.neuron.long_branches_count = 0 # number of branches with len(branch) > 1 #self.vL = cuda.pagelocked_zeros((len(self.neuron)),numpy.float64) #self.vR = cuda.pagelocked_zeros((len(self.neuron)),numpy.float64) #self.d = cuda.pagelocked_zeros((len(self.neuron)),numpy.float64) self.bL = cuda.pagelocked_zeros((len(self.neuron)),numpy.float64) self.bR = cuda.pagelocked_zeros((len(self.neuron)),numpy.float64) #self.bd = cuda.pagelocked_zeros((len(self.neuron)),numpy.float64) self.ab = zeros((3,len(self.neuron))) self.ab0 = zeros(len(self.neuron)) self.ab1 = cuda.pagelocked_zeros((len(self.neuron)),numpy.float64) self.ab2 = zeros(len(self.neuron)) self.ab1_base = zeros(len(self.neuron)) #self.res = cuda.pagelocked_zeros((3 * len(self.neuron)),numpy.float64) self.mTrunc = 0 # used to truncate vL and vR self.delta_list = zeros(len(self.neuron)) #used to find mTrunc # prepare_branch : fill neuron.index, neuron.branches, changes Aplus & Aminus self.prepare_branch(self.neuron.morphology, mid_diameter,0) # linear system P V = B used to deal with the voltage at branch points and take boundary conditions into account. self.P = zeros((self.neuron.BPcount,self.neuron.BPcount)) self.B = zeros(self.neuron.BPcount) self.solution_bp = zeros(self.neuron.BPcount) self.gtot = zeros(len(self.neuron)) self.I0 = zeros(len(self.neuron)) self.i_list = [] self.j_list = [] self.i_list_bis = [] self.j_list_bis = [] new_tridiag = True self.bp_list = [] self.pointType_list = [] self.pointTypeAnte_list = [] self.index_ante_list0 = [] self.index_ante_list1 = [] self.index_ante_list2 = [] self.ante_list = [] self.post_list = [] self.ante_list_idx = [] self.post_list_idx = [] self.id = [] self.test_list = [] temp = zeros(self.neuron.BPcount) self.ind0 = [] self.ind_bctype_0 = [] for index,(i,j,bp,ante,index_ante,pointType) in enumerate(self.neuron.branches) : self.i_list.append(i) self.j_list.append(j) if new_tridiag: self.i_list_bis.append(i) ii = i else: ii = self.i_list[-1] if j-ii+1>2: self.j_list_bis.append(j) new_tridiag = True else : new_tridiag = False self.bp_list.append(bp) self.pointType_list.append(max(1,pointType)) self.pointTypeAnte_list.append(max(1,self.neuron.bc[ante])) temp[index] = index_ante self.id.append(index) if (j-i+2>1): self.test_list.append(1) else : self.test_list.append(0) for x in xrange(j-i+2): self.ante_list.append(ante) self.post_list.append(bp) self.ante_list_idx.append(index_ante) self.post_list_idx.append(index) if index_ante == 0 and index != 0: self.ind0.append(index) if pointType==0 : self.ind_bctype_0.append(bp) self.ante_arr = numpy.array(self.ante_list_idx) self.post_arr = numpy.array(self.post_list_idx) self.index_ante_list1, self.ind1 = numpy.unique(temp,return_index=True) self.ind1 = numpy.sort(self.ind1) self.index_ante_list1 = temp[self.ind1] self.index_ante_list1 = list(self.index_ante_list1) self.ind2 = [] for x in xrange(self.neuron.BPcount): self.ind2.append(x) self.ind2 = numpy.delete(self.ind2,self.ind1,None) self.ind2 = numpy.setdiff1d(self.ind2, self.ind0, assume_unique=True) self.index_ante_list2 = temp[self.ind2] self.index_ante_list2 = list(self.index_ante_list2) self.index_ante_list = list(temp) self.Aminus_bp = self.Aminus[self.bp_list] self.Aminus_bp [:] *= self.pointType_list[:] self.Aplus_i = self.Aplus[self.i_list] self.Aplus_i[:] *= self.pointTypeAnte_list[:] #--------------------------------------------------------GPU------------------------ n = len(self.neuron) mod = SourceModule(""" __global__ void updateAB_gtot(double *ab1, double *ab1_base, double *gtot) { int idx = threadIdx.x + blockIdx.x * blockDim.x; ab1[idx] = ab1_base[idx] - gtot[idx]; ab1[idx+gridDim.x] = ab1_base[idx] - gtot[idx]; ab1[idx+2*gridDim.x] = ab1_base[idx] - gtot[idx]; } __global__ void updateBD(double *bd, double *Cm, double dt,double *v, double *I0) { int idx = threadIdx.x + blockIdx.x * blockDim.x; bd[idx] = - Cm[idx] / dt * v[idx] - I0[idx]; } __global__ void finalizeFun(double *v, double *v_bp, int *ante,int *post, double *b, int m) { int idx = threadIdx.x + blockIdx.x * blockDim.x; int idx_a = ante[idx]; int idx_p = post[idx]; v[idx] = b[idx + m] * v_bp[idx_a] + b[idx + 2*m] * v_bp[idx_p] + b[idx]; // vL, vR, d } __global__ void finalizeFunBis(double *v, double *v_bp, int *GPU_data_int) { int idx = threadIdx.x + blockIdx.x * blockDim.x; int bp = GPU_data_int[4 * idx + 3]; v[bp] = v_bp[idx]; } __global__ void initPB(double *P, double *B, int BPcount) { int idx = threadIdx.x + blockIdx.x * blockDim.x; int idy = threadIdx.x + blockIdx.y * blockDim.y; P[idx + idy * BPcount] = 0.0; B[idx] = 0.0; } //GPU_data_int is : ante_list, i_list, j_list, bp_list //GPU_data_double is : test_list, Aplus, Aminus __global__ void fillPB(double *P, double *B, double *b, double *Cm_l, double *gtot_l, int *GPU_data_int, double *GPU_data_double, double *I0_l, double *v_l, int BPcount, double dt, int m) { int idx = threadIdx.x + blockIdx.x * blockDim.x; int idx_ante = GPU_data_int[4 * idx]; int i = GPU_data_int[4 * idx + 1]; int j = GPU_data_int[4 * idx + 2]; int bp = GPU_data_int[4 * idx + 3]; double test = GPU_data_double[3 * idx]; double Aplus = GPU_data_double[3 * idx + 1]; double Aminus = GPU_data_double[3 * idx + 2]; double Cm = Cm_l[bp]; double gtot = gtot_l[bp]; double I0 = I0_l[bp]; double v_bp = v_l[bp]; double vLright = b[j + m] * test; double vRleft = b[i + 2*m] * test; double vRright = b[j + 2*m] * test; double dright = b[j] * test; B[idx] += -Cm/dt * v_bp - I0 -Aminus * dright; P[idx * BPcount + idx] += -Cm/dt - gtot + Aminus * (vRright - 1.0); P[idx * BPcount + idx_ante] += Aminus * vLright; P[idx_ante * BPcount + idx] += Aplus * vRleft; } __global__ void fillPB_bis(double *P, double *B, double *b, int *GPU_data_int, double *GPU_data_double, int BPcount, int *indices, int m) { int idx_temp = threadIdx.x + blockIdx.x * blockDim.x; int idx = indices[idx_temp]; int idx_ante = GPU_data_int[4 * idx]; int i = GPU_data_int[4 * idx + 1]; int test = GPU_data_double[3 * idx]; double Aplus = GPU_data_double[3 * idx + 1]; double vLleft = b[i + m] * test; double dleft = b[i] * test; B[idx_ante] += - Aplus * dleft; P[idx_ante * (BPcount + 1)] += Aplus * (vLleft - 1.0); } __global__ void badFillPB_0(double *P, double *B, double *b, int *GPU_data_int, double *GPU_data_double, int *indices, double *Cm_l, double *gtot_l, double *I0_l, double *v_l, int len_indices, int m, double dt) { double Cm = Cm_l[0]; double gtot = gtot_l[0]; double I0 = I0_l[0]; double v_0 = v_l[0]; B[0] = - Cm/dt * v_0 - I0; P[0] = - Cm/dt - gtot; int idx; int i; int test; double Aplus; double vLleft; double dleft; for (int idx_temp=0;idx_temp vd + vL + vR cuda.memcpy_htod(self.ab0_gpu, ab0) cuda.memcpy_htod(self.ab2_gpu, ab2) cuda.memcpy_htod(self.bL_gpu, self.bL) cuda.memcpy_htod(self.bR_gpu, self.bR) self.ante_gpu = cuda.mem_alloc(self.ante_arr.size * self.ante_arr.dtype.itemsize) self.post_gpu = cuda.mem_alloc(self.ante_arr.size * self.ante_arr.dtype.itemsize) self.v_old_gpu = cuda.mem_alloc(self.neuron.v.size * dtype.itemsize) cuda.memcpy_htod(self.ante_gpu,self.ante_arr) cuda.memcpy_htod(self.post_gpu,self.post_arr) #---------------------------------------------------------------------------------- self.v_branchpoints = zeros(self.neuron.BPcount) self.v_bp_gpu = cuda.mem_alloc(self.v_branchpoints.size * dtype.itemsize) self.timeDevice = [0] self.timeDeviceU = [0] self.timeDeviceT = [0] self.timeHost = [0] self.timeUpdater = [0] self.timeSolveHost = [0] self.timeFillHost = [0] self.timeFin = [0] def step(self, neuron): startDevice = time() start = time() if self.first_test_gtot: self.first_test_gtot=False neuron._gtot = ones(len(neuron)) * neuron._gtot if self.first_step : self.gtot = neuron._gtot cuda.memcpy_htod(self.gtot_gpu,self.gtot) self.update_ab_base_gpu() self.update_ab_gtot_gpu() if self.neuron.changed : self.I0 = neuron._I0 cuda.memcpy_htod(self.I0_gpu,self.I0) self.update_bd_gpu() end = time() self.timeDeviceU.append(self.timeDeviceU[-1] + end - start) start = time() if self.first_step : self.calculate_vd_vL_vR_gpu() else: self.calculate_vd_gpu() end = time() self.timeDeviceT.append(self.timeDeviceT[-1] + end - start) endDevice = time() self.timeDevice.append(self.timeDevice[-1] + endDevice-startDevice) startHost = time() #-----------------------------------------------------fill P and B---------------------------------- # 1) initPB reset P and B. every value is 0. # 2) fillPB calculates the coefficients of P and B where there is no lock issue. # 3) fillPB_bis does the same on two exclusive lists of indexes in P and B, ind1 and ind2 to avoid lock issues # 3) badFillPB_0 ajusts the value in the particular case of 0. this one is not parallel but not big. start = time() self.initPB.prepared_call((neuron.BPcount, neuron.BPcount),self.P_gpu, self.B_gpu, numpy.int32(neuron.BPcount)) self.fillPB.prepared_call((neuron.BPcount,1),self.P_gpu, self.B_gpu, self.b_gpu, self.Cm_gpu, self.gtot_gpu, self.GPU_data_int, self.GPU_data_double, self.I0_gpu, self.v_gpu, numpy.int32(neuron.BPcount), self.neuron.clock.dt, numpy.int32(len(neuron))) self.fillPB_bis.prepared_call((len(self.ind1),1),self.P_gpu, self.B_gpu, self.b_gpu, self.GPU_data_int, self.GPU_data_double, numpy.int32(neuron.BPcount), self.ind1_gpu, numpy.int32(len(neuron))) if len(self.ind2)>0: self.fillPB_bis.prepared_call((len(self.ind2),1),self.P_gpu, self.B_gpu, self.b_gpu, self.GPU_data_int, self.GPU_data_double, numpy.int32(neuron.BPcount), self.ind2_gpu, numpy.int32(len(neuron))) self.badFillPB_0.prepared_call((1,1),self.P_gpu, self.B_gpu, self.b_gpu, self.GPU_data_int, self.GPU_data_double, self.ind0_gpu, self.Cm_gpu, self.gtot_gpu, self.I0_gpu, self.v_gpu, numpy.int32(len(self.ind0)), numpy.int32(len(neuron)),neuron.clock.dt)#this is not parallel if len(self.ind_bctype_0)>0: self.resetPB_type0.prepared_call((len(self.ind_bctype_0), neuron.BPcount), self.P_gpu, self.B_gpu, self.v_gpu, self.ind_bctype_0_gpu, numpy.int32(neuron.BPcount)) end = time() self.timeFillHost.append(self.timeFillHost[-1] + end - start) #------------------------------------------------------solve PV=B----------------------------------- cuda.memcpy_dtoh(self.P,self.P_gpu) cuda.memcpy_dtoh(self.B,self.B_gpu) start = time() self.solution_bp = solve(self.P,self.B) # better : do it on the GPU, use a sparse solver. end = time() self.timeSolveHost.append(self.timeSolveHost[-1] + end - start) cuda.memcpy_htod(self.v_bp_gpu, self.solution_bp) #-------------------------------------------------------update v------------------------------------- start = time() self.finalize_v_gpu() end = time() self.timeFin.append(self.timeFin[-1] + end - start) cuda.memcpy_dtoh(self.neuron.v,self.v_gpu) endHost = time() self.timeHost.append(self.timeHost[-1] + endHost - startHost) self.first_step = False def solve_branchpoints(self): BPcount = self.neuron.BPcount code =""" // where to perform the computation typedef cusp::device_memory MemorySpace; // which floating point type to use typedef double ValueType; // create an empty sparse matrix structure (coo format) cusp::coo_matrix P; // allocate storage for solution (x) and right hand side (b) cusp::array1d x(P.num_rows, 0); cusp::array1d B(P.num_rows, 1); // fill P and B // set stopping criteria: // iteration_limit = 100 // relative_tolerance = 1e-3 cusp::default_monitor monitor(B, 100, 1e-3); // set preconditioner (identity) cusp::identity_operator M(P.num_rows, P.num_rows); // solve the linear system P * x = B with the BiConjugate Gradient Stabilized method cusp::krylov::bicgstab(P, x, B, monitor, M); """ support_code='' includepath = ["/usr/local/cuda/include/"] libpath = ['/usr/local/cuda/lib/','/usr/lib/'] weave.inline(code,['BPcount'],support_code=support_code,headers = ["","","","",""], include_dirs=includepath, library_dirs=libpath, libraries=["cusp"], runtime_library_dirs=libpath, compiler="gcc") def calc(self,var,res_gpu): #compute the value of neuron.var and store it in res_gpu self._state_updater.calc(var,res_gpu) def update_ab_base_gpu(self): self.ab1_base[:-1] = (- self.neuron.Cm[:-1] / self.neuron.clock.dt * second - self.Aminus[:-1] - self.Aplus[1:]) self.ab1_base[-1] = (- self.neuron.Cm[-1] / self.neuron.clock.dt * second - self.Aminus[-1]) cuda.memcpy_htod(self.ab1_base_gpu,self.ab1_base) def update_ab_gtot_gpu(self): n = len(self.neuron) self.updateAB_gtot.prepared_call((n,1), self.ab1_gpu, self.ab1_base_gpu, self.gtot_gpu) def update_bd_gpu(self): n = len(self.neuron) dt = self.neuron.clock.dt self.updateBD.prepared_call((n,1), self.b_gpu, self.Cm_gpu, dt, self.v_gpu, self.I0_gpu) def calculate_vd_vL_vR_gpu(self): m = len(self.neuron) ab0_gpu_ptr = self.ab0_gpu_ptr ab1_gpu_ptr = self.ab1_gpu_ptr ab2_gpu_ptr = self.ab2_gpu_ptr bL_gpu_ptr = self.bL_gpu_ptr bR_gpu_ptr = self.bR_gpu_ptr b_gpu_ptr = self.b_gpu_ptr code =""" cusparseHandle_t handle = 0; cusparseCreate(&handle); double *bL_gpu = (double *)bL_gpu_ptr; double *bR_gpu = (double *)bR_gpu_ptr; double *b_gpu = (double *)b_gpu_ptr; double *ab0_gpu = (double *)ab0_gpu_ptr; double *ab1_gpu = (double *)ab1_gpu_ptr; double *ab2_gpu = (double *)ab2_gpu_ptr; //bd is up to date in b_gpu cudaMemcpy(b_gpu + m, bL_gpu, m*sizeof(double), cudaMemcpyDeviceToDevice); cudaMemcpy(b_gpu + 2*m, bR_gpu, m*sizeof(double), cudaMemcpyDeviceToDevice); cusparseDgtsvStridedBatch(handle, m, ab2_gpu, ab1_gpu, ab0_gpu, b_gpu, 3, m); //now b_gpu contains vd, vL, vR cudaDeviceSynchronize(); cusparseDestroy(handle); """ support_code='extern "C" cusparseStatus_t cusparseDgtsvStridedBatch(cusparseHandle_t handle, int m,const double *dl, const double *d,const double *du, double *x,int batchCount, int batchStride);' includepath = ["/usr/local/cuda/include/"] libpath = ['/usr/local/cuda/lib/','/usr/lib/'] weave.inline(code,['ab0_gpu_ptr','ab1_gpu_ptr','ab2_gpu_ptr','bL_gpu_ptr','bR_gpu_ptr','m','b_gpu_ptr'],support_code=support_code,headers = ["","",""], include_dirs=includepath, library_dirs=libpath, libraries=["cusparse"], runtime_library_dirs=libpath, compiler="gcc") def calculate_vd_gpu(self): m = len(self.neuron) ab0_gpu_ptr = self.ab0_gpu_ptr ab1_gpu_ptr = self.ab1_gpu_ptr ab2_gpu_ptr = self.ab2_gpu_ptr b_gpu_ptr = self.b_gpu_ptr code =""" cusparseHandle_t handle = 0; cusparseCreate(&handle); double *b_gpu = (double *)b_gpu_ptr; double *ab0_gpu = (double *)ab0_gpu_ptr; double *ab1_gpu = (double *)ab1_gpu_ptr; double *ab2_gpu = (double *)ab2_gpu_ptr; //bd is up to date in b_gpu cusparseDgtsvStridedBatch(handle, m, ab2_gpu, ab1_gpu, ab0_gpu, b_gpu, 1, m); //now b_gpu contains vd, vL, vR cudaDeviceSynchronize(); cusparseDestroy(handle); """ support_code='extern "C" cusparseStatus_t cusparseDgtsvStridedBatch(cusparseHandle_t handle, int m,const double *dl, const double *d,const double *du, double *x,int batchCount, int batchStride);' includepath = ["/usr/local/cuda/include/"] libpath = ['/usr/local/cuda/lib/','/usr/lib/'] weave.inline(code,['ab0_gpu_ptr','ab1_gpu_ptr','ab2_gpu_ptr','m','b_gpu_ptr'],support_code=support_code,headers = ["","",""], include_dirs=includepath, library_dirs=libpath, libraries=["cusparse"], runtime_library_dirs=libpath, compiler="gcc") def calculate_vd_vL_vR_gpu_branches(self): m = len(self.neuron) systemsCount = len(self.i_list_bis) i_list = numpy.array(self.i_list_bis) j_list = numpy.array(self.j_list_bis) ab0_gpu_ptr = self.ab0_gpu_ptr ab1_gpu_ptr = self.ab1_gpu_ptr ab2_gpu_ptr = self.ab2_gpu_ptr bL_gpu_ptr = self.bL_gpu_ptr bR_gpu_ptr = self.bR_gpu_ptr b_gpu_ptr = self.b_gpu_ptr code =""" cusparseHandle_t handle = 0; cusparseCreate(&handle); double *bL_gpu = (double *)bL_gpu_ptr; double *bR_gpu = (double *)bR_gpu_ptr; double *b_gpu = (double *)b_gpu_ptr; double *ab0_gpu = (double *)ab0_gpu_ptr; double *ab1_gpu = (double *)ab1_gpu_ptr; double *ab2_gpu = (double *)ab2_gpu_ptr; //bd is up to date in b_gpu cudaMemcpy(b_gpu + m, bL_gpu, m*sizeof(double), cudaMemcpyDeviceToDevice); cudaMemcpy(b_gpu + 2*m, bR_gpu, m*sizeof(double), cudaMemcpyDeviceToDevice); int i; int j; int n; for(int idx=0;idx","",""], include_dirs=includepath, library_dirs=libpath, libraries=["cusparse"], runtime_library_dirs=libpath, compiler="gcc") def finalize_v_gpu(self): n = len(self.neuron) self.finalizeFun.prepared_call((n,1), self.v_gpu, self.v_bp_gpu, self.ante_gpu, self.post_gpu, self.b_gpu, numpy.int32(len(self.neuron))) self.finalizeFunBis.prepared_call((self.neuron.BPcount,1), self.v_gpu, self.v_bp_gpu, self.GPU_data_int) def had_variation(self): return self._state_updater.had_variation(self.neuron.clock.dt); def __call__(self, neuron): ''' Updates the state variables. ''' dt_max = 0.1 * ms dt_min = 0.01 * ms if not self._isprepared: self.prepare() self._isprepared=True print "state updater prepared" self.callcount+=1 print self.callcount #Update I,V startUpdater = time() if neuron.changed: self._state_updater.changed = True self._state_updater(neuron) endUpdater = time() self.timeUpdater.append(self.timeUpdater[-1] + endUpdater-startUpdater) self.step(neuron) total_time = self.timeUpdater[-1]+self.timeDevice[-1]+self.timeHost[-1] print " tot:",total_time," U:",self.timeUpdater[-1]," D:",self.timeDevice[-1]," DU:",self.timeDeviceU[-1]," DT:",self.timeDeviceT[-1]-self.timeDeviceT[1]," H:",self.timeHost[-1]," SH:",self.timeSolveHost[-1]," FH:",self.timeFillHost[-1]," Fin:",self.timeFin[-1] def __len__(self): ''' Number of state variables ''' return len(self.eqs) brian-1.4.3/dev/ideas/morphology/TODO.txt000066400000000000000000000012651266062647100202000ustar00rootroot00000000000000TODO ---- * Cm should be (optionally) a distributed variable (for myeline). It could be done simply by a check that Cm is in the equations (if Cm=None, then we use the distributed variable). * Write examples, including point processes * Think about point processes. Use the Synapses object? * Domain decomposition? * Morphology: adding morphologies (at least cylinders)? * Find out and specify the underlying electrical circuit (compartments/half-compartments?) * Check with what Neuron does * One thing is missing the Morphology: the position of the current branch in the big matrix (Note: all other variables, length etc, are views on the big matrix, for the current branch).brian-1.4.3/dev/ideas/morphology/algorithms.txt000066400000000000000000000141001266062647100215740ustar00rootroot00000000000000Algorithms for simulating branched cable equations -------------------------------------------------- The state update for the cable equation can be done with Hines method: Int J Biomed Comput. 1984 Jan-Feb;15(1):69-76. Efficient computation of branched nerve equations. Hines M. Available on Neuron's website: http://www.neuron.yale.edu/neuron/nrnpubs Another method here: http://www.jstor.org/stable/pdfplus/2157690.pdf The Backward Euler Method for Numerical Solution of the Hodgkin-Huxley Equations of Nerve Conduction. Mascagni M. 1990 And: Digital computer solutions for excitation and propagation of the nerve impulse. Cooley & Dodge 1966 In Hines method, values of ionic currents are calculated at midpoints of time steps as a function of V(t), and the values of the conductances are considered as constant within a timestep for the integration of the cable equation. The cable equation uses a simple implicit scheme followed by an explicit step. As I understand it, the update of Im (IHH in the paper) and the integration of the cable equation are completely independent. Therefore, we can have separate stateupdaters which are called in the following order: 1) Update the spatial equation with Hines method. 2) Update the membrane current with a standard vectorized updater (2nd order?). 3) Update the point processes with a standard vectorized updater. (actually I think 2 should be before 1, but it won't make a difference) Mascagni's method is backward Euler, with the advantage that convergence is guaranteed. The algorithm is as follows: 1) Solve the discrete system for V(t+dt), where the other variables are constant and assumed to be known at time t+dt (first iteration: use value at t). This is sparse linear system (could be computed with scipy.sparse.linalg?). 2) Solve for the other variables at t+dt using a backward Euler step with the values of V from step 1. In principle this is a simple 1D linear system (assuming conditional linearity for these variables). 3) Repeat until convergence (how many times?). In comparison, Hines method uses a single iteration, which is solving a sparse linear system as well (in fact the same one, but at time t+dt/2). 1) Calculate other variables at t+dt/2, by an Euler step from time t-dt/2, that uses the values at time t for V, and estimated at time t for the other variables using the average between t-dt/2 and t+dt/2 (this makes it implicit). 2) Solve for V(t+dt/2) then do a forward step to t+dt. This looks to me like one step of the backward Euler method (except it's 2 then 1). There is pseudo-code in Hines paper. Both methods rely on the conditional linearity of the equations, therefore it cannot work for all types of models (e.g. exponential model). To deal with branches, there are two options: 1) Use Hines method. 2) Use domain decomposition. This means using the linearity of the problem (it is linear once the other variables considered fixed, by conditional linearity) to solve the equation separately on each branch and then combine them linearly. The big advantage is that this way we can still use the solve_banded method of scipy. ---- The simulation is in two stages: 1) Solve the discrete system for V(t+dt), where the other variables are constant and assumed to be known at time t+dt (first iteration: use value at t). This is a sparse linear system (could be computed with scipy.sparse.linalg?). 2) Calculate Im at t+dt using a backward Euler step with the values of V from step 1. That is, do one step of implicit Euler. And possibly repeat (until convergence) (how many times?). The discretized longitudinal current is: a/(2*R)*(V[i+1]-2*V[i]+V[i-1])/dx**2 Artificial gridpoints are used so that: (V[1]-V[-1])/(2*dx) = dV/dt(0) # =0 in general (V[N+1]-V[N-1])/(2*dx) = dV/dt(L) # =0 in general Consider also the capacitive current (and leak?). Actually in Mascagni it's more complicated, because we use the full membrane equation (conductances in front V in particular). Therefore perhaps we would need sympy to reorganize the equation first as dV/dt=aV+b (see the Synapses class). x=scipy.linalg.solve_banded((lower,upper),ab,b) lower = number of lower diagonals upper = number of upper diagonals ab = array(l+u+1,M) each row is one diagonal a[i,j]=ab[u+i-j,j] -------------------- Domain decomposition -------------------- Consider two connected cables, with the junction point at distance L. The linear equations (that is, assuming fixed variables other than v) are: L_A v^A = I^A L_B v^B = I^B for the two cables A and B. Continuity requirements are: * v^A(L)=v^B(0) # potential * d/dx v^A(L)=d/dx v^B(0) # current There are homogeneous boundardy conditions on the two ends (zero current). Consider the first equation, with boundary condition at 0 and free (unknown) voltage at L. Then the set of solutions is a linear one-dimensional manifold: v^A = k*v_0^A + v_*^A where v_0^A is a solution to the homogeneous equation, and v_*^A is a particular solution. For example, choose: * v_0(L)=1 and v_*^A(L)=0 Then v^A(L)=k. The same can be done for cable B. The two continuity requirements mean that: * the constants (k) are identical * they are determined by a linear equation expressing the continuity of current at L: k (d/dx v_0^A - d/dx v_0^B) = d/dx v_*^B - d/dx v_*^A Therefore, an algorithm is: * Solve 2 tridiagonal systems per branch. * Solve the unknowns by a linear system with the dimension of the order of the number of branches * Do linear combinations. The first and third steps can be done in parallel. When the branches connect on both ends, we need two unknown constants per branch. This is solved as follows: * Particular solution v_*(0)=v_*(L)=0 * Homogeneous solution v_0(0)=1 and v_0(L)=0 * Homogeneous solution v_1(0)=0 and v_0(L)=1 * Combination: v = v_*(0) + v(0)v_0 + v(L)v_L (v(0) and v(L) are the two unknowns) Alternatively, boundary conditions could be expressed as currents rather than potentials. When there are several branches, we simply change to current continuity condition to Kirchoff's law. brian-1.4.3/dev/ideas/morphology/cable_equation.py000066400000000000000000000042471266062647100222220ustar00rootroot00000000000000''' Simulation of a cable equation Seems to work (matches examples/misc/cable). TODO: * Add active currents (state updater) * Check algorithm (e.g. w/ Hines) * Add point processes * Add branches ''' from brian import * from morphology import * from spatialneuron import * ''' The simulation is in two stages: 1) Solve the discrete system for V(t+dt), where the other variables are constant and assumed to be known at time t+dt (first iteration: use value at t). This is a sparse linear system (could be computed with scipy.sparse.linalg?). 2) Calculate Im at t+dt using a backward Euler step with the values of V from step 1. That is, do one step of implicit Euler. And possibly repeat (until convergence) (how many times?). The discretized longitudinal current is: a/(2*R)*(V[i+1]-2*V[i]+V[i-1])/dx**2 Artificial gridpoints are used so that: (V[1]-V[-1])/(2*dx) = dV/dt(0) # =0 in general (V[N+1]-V[N-1])/(2*dx) = dV/dt(L) # =0 in general Consider also the capacitive current (and leak?). Actually in Mascagni it's more complicated, because we use the full membrane equation (conductances in front V in particular). Therefore perhaps we would need sympy to reorganize the equation first as dV/dt=aV+b (see the Synapses class). x=scipy.linalg.solve_banded((lower,upper),ab,b) lower = number of lower diagonals upper = number of upper diagonals ab = array(l+u+1,M) each row is one diagonal a[i,j]=ab[u+i-j,j] ''' defaultclock.dt=0.1*ms morpho=Cylinder(length=1000*um, diameter=1*um, n=100, type='axon') El = 0 * mV gl = 0.02 * msiemens / cm ** 2 # Typical equations eqs=''' # The same equations for the whole neuron, but possibly different parameter values Im=gl*(El-v)+I : amp/cm**2 # distributed transmembrane current I : amp/cm**2 # applied current ''' neuron = SpatialNeuron(morphology=morpho, model=eqs, refractory=4 * ms, Cm=1 * uF / cm ** 2, Ri=100 * ohm * cm) neuron.v=El neuron.I=0*amp/cm**2 neuron.I[50]=.05 * nA/neuron.area[0] M=StateMonitor(neuron,'v',record=True) print 'taum=',neuron.Cm/gl run(1*ms) neuron.I=0 run(200*ms) for i in range(10): plot(M.times/ms,M[35+i*3]/mV) show() brian-1.4.3/dev/ideas/morphology/cable_equation2.py000066400000000000000000000017551266062647100223050ustar00rootroot00000000000000""" Cable equation with Na channel. (Segment with current injection at one end). """ from brian import * from morphology import * from spatialneuron import * defaultclock.dt=0.1*ms morpho=Cylinder(length=1000*um, diameter=1*um, n=100, type='axon') El = -70 * mV ENa = 50*mV gl = 0.02 * msiemens / cm ** 2 gNa = 100*gl # Typical equations eqs=''' # The same equations for the whole neuron, but possibly different parameter values Im=gl*(El-v)+gNa*m**3*(ENa-v)+I : amp/cm**2 # distributed transmembrane current I:amp/cm**2 # applied current dm/dt=(minf-m)/(0.3*ms) : 1 minf=1./(1+exp(-(v+30*mV)/(6*mV))) : 1 ''' neuron = SpatialNeuron(morphology=morpho, model=eqs, refractory=4 * ms, Cm=1 * uF / cm ** 2, Ri=100 * ohm * cm) neuron.v=El neuron.I=0*amp/cm**2 neuron.I[0]=.03 * nA/neuron.area[0] # current injection at one end M=StateMonitor(neuron,'v',record=True) print 'taum=',neuron.Cm/gl run(200*ms) for i in range(10): plot(M.times/ms,M[i*10]/mV) show() brian-1.4.3/dev/ideas/morphology/compare_with_neuron/000077500000000000000000000000001266062647100227355ustar00rootroot00000000000000brian-1.4.3/dev/ideas/morphology/compare_with_neuron/ball_and_stick.py000066400000000000000000000031121266062647100262350ustar00rootroot00000000000000""" The ball-and-stick model * There is a 20% difference in access resistance, compared to Neuron, for soma+axon, if it is a single branch * Cylinder works perfectly (passive). * Tapered cylinder works almost perfectly, but not exactly. """ from brian import * from brian.experimental.morphology import * defaultclock.dt=0.1*ms # Morphology morpho=Soma(30*um) morpho.axon=Cylinder(diameter=1*um,length=300*um,n=100) ''' diam=linspace(10*um,3*um,1000) morpho=Morphology(n=500) morpho.length[:]=0.3*um morpho.diameter[:]=diam[:500] # tapered axon morpho.set_area() morpho.set_coordinates() morpho2=Morphology(n=500) morpho2.length[:]=0.3*um morpho2.diameter[:]=diam[500:] # tapered axon morpho2.set_area() morpho2.set_coordinates() morpho.L=morpho2 ''' #morpho=Cylinder(diameter=30*um,length=30*um,n=1) #morpho.axon=Cylinder(diameter=1*um,length=300*um,n=100) #morpho=Cylinder(diameter=1*um,length=300*um,n=100) # Passive channels gL=1e-4*siemens/cm**2 EL=-70*mV eqs=''' Im=gL*(EL-v)+I : amp/cm**2 I : amp/cm**2 ''' neuron = SpatialNeuron(morphology=morpho, model=eqs, Cm=1 * uF / cm ** 2, Ri=100 * ohm * cm) neuron.v=-65*mV neuron.I=0*amp/cm**2 # Monitors mon=StateMonitor(neuron,'v',record=[0,50]) run(1*ms) neuron.I[0]=0.2*nA/neuron.area[0] run(50*ms) neuron.I=0*amp run(495*ms,report='text') # Load Neuron data file=r'D:\My Dropbox\LocalEclipseWorkspace\Neuron\example.dat' x,y,z=read_neuron_dat(file) subplot(211) plot(x,y,'b') plot(mon.times/ms,mon[0]/mV,'r') subplot(212) plot(x,z,'b') plot(mon.times/ms,mon[50]/mV,'r') show() brian-1.4.3/dev/ideas/morphology/compare_with_neuron/bipolar_cell.py000066400000000000000000000021661266062647100257430ustar00rootroot00000000000000''' A pseudo MSO neuron, with two dendrites and one axon (fake geometry). ''' from brian import * from brian.experimental.morphology import * defaultclock.dt=0.1*ms # Morphology morpho=Soma(30*um) morpho.axon=Cylinder(diameter=1*um,length=300*um,n=100) morpho.L=Cylinder(diameter=1*um,length=100*um,n=50) morpho.R=Cylinder(diameter=1*um,length=150*um,n=50) # Passive channels gL=1e-4*siemens/cm**2 EL=-70*mV eqs=''' Im=gL*(EL-v)+I : amp/cm**2 I : amp/cm**2 ''' neuron = SpatialNeuron(morphology=morpho, model=eqs, Cm=1 * uF / cm ** 2, Ri=100 * ohm * cm) neuron.v=EL neuron.I=0*amp/cm**2 # Monitors mon_soma=StateMonitor(neuron,'v',record=[0]) mon_L=StateMonitor(neuron.L,'v',record=True) mon_R=StateMonitor(neuron.R,'v',record=25) run(1*ms) neuron.L.I[25]=0.2*nA/neuron.L.area[25] # injecting in the left dendrite run(5*ms) neuron.I=0*amp run(50*ms,report='text') subplot(211) plot(mon_L.times/ms,mon_soma[0]/mV,'k') plot(mon_L.times/ms,mon_L[25]/mV,'r') plot(mon_L.times/ms,mon_R[25]/mV,'b') subplot(212) for i in [0,5,10,15,20,25,30,35,40,45]: plot(mon_L.times/ms,mon_L[i]/mV) show() brian-1.4.3/dev/ideas/morphology/compare_with_neuron/cable_equation_dirac.py000066400000000000000000000037561266062647100274370ustar00rootroot00000000000000# It works! from brian import * from brian.experimental.morphology import * from time import time defaultclock.dt=0.1*ms dt = defaultclock.dt length=4000*um diameter=1*um n=300 Ri=100 * ohm * cm Cm=1 * uF / cm ** 2 refractory=4 * ms morpho = Soma(diameter=1 * um) morpho.length=0.1*um morpho.L = Cylinder(length=length/3, diameter=diameter, n=n/3, type='axon') morpho.LL = Cylinder(length=length/3, diameter=diameter, n=n/3, type='axon') morpho.LLL = Cylinder(length=length/3, diameter=diameter, n=n/3, type='axon') El = 0 * mV gl = 0.02 * msiemens / cm ** 2 # Typical equations eqs=''' # The same equations for the whole neuron, but possibly different parameter values Im=gl*(El-v)+I : amp/cm**2 # distributed transmembrane current I : amp/cm**2 # applied current ''' neuron = SpatialNeuron(morphology=morpho, model=eqs, refractory=refractory, Cm=Cm, Ri=Ri) neuron.v=El neuron.I=0*amp/cm**2 I0 = 50 * nA/neuron.area[0] neuron.I[1]= I0 M=StateMonitor(neuron,'v',record=True) taum = neuron.Cm[0]/gl * second**4 * (volt/ohm)**2 / (metre**4 * kilogram) rm = taum/(Cm * pi * diameter) ra = (4 * Ri)/(pi * diameter**2) la = sqrt(rm/ra) dx = length / n T = 3*taum print 'taum=',taum print "lambda = ",la print "temps de simulation : ",T/taum," taum" start = time() run(defaultclock.dt) neuron.I = 0*amp/cm**2 neuron.changed = True run(T-defaultclock.dt) end = time() print end - start," s" max = zeros(n) for i in range(n): for t in range(len(M.times)): if M[i][t] > M[i][max[i]]: max[i] = t i = arange(n) p1, = plot(i*dx/mm,max *ms,'r') #t* en fonction de x p2, = plot(i*dx/mm, taum / 4 * (sqrt(1+4*(i*dx)**2/la**2)-1) * ms / dt,'g') # t* pour un cable infini xlabel('position (mm)') ylabel('temps pour lequel le potentiel est maximal') legend([p1,p2],["simulation","theorie"], loc='upper left') #plot(i*dx/mm, (max - taum / 4 * (sqrt(1+4*(i*dx)**2/la**2)-1) / dt)/max) #for i in range(n-10): #plot(M.times/ms,M[35+(i/10)*3]/mV) show() brian-1.4.3/dev/ideas/morphology/compare_with_neuron/hodgkin_huxley_1952.py000066400000000000000000000034101266062647100270060ustar00rootroot00000000000000''' Hodgkin-Huxley equations (1952) #2 ''' from brian import * from brian.experimental.morphology import * defaultclock.dt=0.01*ms morpho=Soma(3000*um) morpho.axon=Cylinder(length=10*cm, diameter=2*238*um, n=1000, type='axon') morpho.dendrite=Cylinder(length=5*cm, diameter=100*um, n=1000, type='axon') vshift=-65*mV El = 10.613* mV +vshift ENa = 115*mV +vshift EK = -12 * mV +vshift gl = 0.3 * msiemens / cm ** 2 # Typical equations eqs=''' # The same equations for the whole neuron, but possibly different parameter values Im=gl*(El-v)+gNa*m**3*h*(ENa-v)+gK*n**4*(EK-v)+I : amp/cm**2 # distributed transmembrane current I:amp/cm**2 # applied current dm/dt=alpham*(1-m)-betam*m : 1 dn/dt=alphan*(1-n)-betan*n : 1 dh/dt=alphah*(1-h)-betah*h : 1 v0=v-vshift : volt alpham=(0.1/mV)*(-v0+25*mV)/(exp((-v0+25*mV)/(10*mV))-1)/ms : Hz betam=4.*exp(-v0/(18*mV))/ms : Hz alphah=0.07*exp(-v0/(20*mV))/ms : Hz betah=1./(exp((-v0+30*mV)/(10*mV))+1)/ms : Hz alphan=(0.01/mV)*(-v0+10*mV)/(exp((-v0+10*mV)/(10*mV))-1)/ms : Hz betan=0.125*exp(-v0/(80*mV))/ms : Hz gNa : siemens/cm**2 gK : siemens/cm**2 ''' neuron = SpatialNeuron(morphology=morpho, model=eqs, Cm=1 * uF / cm ** 2, Ri=35.4 * ohm * cm) neuron.v=0*mV+vshift neuron.h=1 neuron.m=0 neuron.n=.5 neuron.gNa[:] = 120 * msiemens / cm ** 2 neuron.gK[:] = 36 * msiemens / cm ** 2 neuron.I=0*amp/cm**2 M=StateMonitor(neuron,'v',record=True) run(50*ms) neuron.I[0]=1 * uA/neuron.area[0] # current injection at one end run(3*ms) neuron.I=0*amp/cm**2 run(50*ms) # Load Neuron data file=r'D:\My Dropbox\LocalEclipseWorkspace\Neuron\hh2.dat' x,y,z=read_neuron_dat(file) subplot(211) plot(x,y,'b') plot(M.times/ms,M[0]/mV,'r') subplot(212) plot(x,z,'b') plot(M.times/ms,M[500]/mV,'r') show() brian-1.4.3/dev/ideas/morphology/compare_with_neuron/hodgkin_huxley_1952_2.py000066400000000000000000000032261266062647100272340ustar00rootroot00000000000000''' Hodgkin-Huxley equations (1952) Conduction velocity is about 12.5 m/s (is it right?) ''' from brian import * from brian.experimental.morphology import * defaultclock.dt=0.01*ms morpho=Cylinder(length=10*cm, diameter=2*238*um, n=1000, type='axon') vshift=-65*mV El = 10.613* mV +vshift ENa = 115*mV +vshift EK = -12 * mV +vshift gl = 0.3 * msiemens / cm ** 2 gNa = 120 * msiemens / cm ** 2 gK = 36 * msiemens / cm ** 2 # Typical equations eqs=''' # The same equations for the whole neuron, but possibly different parameter values Im=gl*(El-v)+gNa*m**3*h*(ENa-v)+gK*n**4*(EK-v)+I : amp/cm**2 # distributed transmembrane current I:amp/cm**2 # applied current dm/dt=alpham*(1-m)-betam*m : 1 dn/dt=alphan*(1-n)-betan*n : 1 dh/dt=alphah*(1-h)-betah*h : 1 v0=v-vshift : volt alpham=(0.1/mV)*(-v0+25*mV)/(exp((-v0+25*mV)/(10*mV))-1)/ms : Hz betam=4.*exp(-v0/(18*mV))/ms : Hz alphah=0.07*exp(-v0/(20*mV))/ms : Hz betah=1./(exp((-v0+30*mV)/(10*mV))+1)/ms : Hz alphan=(0.01/mV)*(-v0+10*mV)/(exp((-v0+10*mV)/(10*mV))-1)/ms : Hz betan=0.125*exp(-v0/(80*mV))/ms : Hz ''' neuron = SpatialNeuron(morphology=morpho, model=eqs, Cm=1 * uF / cm ** 2, Ri=35.4 * ohm * cm) neuron.v=0*mV+vshift neuron.h=1 neuron.m=0 neuron.n=.5 neuron.I=0*amp/cm**2 M=StateMonitor(neuron,'v',record=True) run(50*ms) neuron.I[0]=1 * uA/neuron.area[0] # current injection at one end run(3*ms) neuron.I=0*amp/cm**2 run(50*ms) # Load Neuron data file=r'D:\My Dropbox\LocalEclipseWorkspace\Neuron\hh.dat' x,y,z=read_neuron_dat(file) subplot(211) plot(x,y,'b') plot(M.times/ms,M[0]/mV,'r') subplot(212) plot(x,z,'b') plot(M.times/ms,M[500]/mV,'r') show() brian-1.4.3/dev/ideas/morphology/compare_with_neuron/infinite_cable.py000066400000000000000000000013201266062647100262360ustar00rootroot00000000000000''' An infinite cable. ''' from brian import * from brian.experimental.morphology import * defaultclock.dt=0.1*ms # Morphology morpho=Cylinder(diameter=1*um,length=3*mm,n=500) # Passive channels gL=1e-4*siemens/cm**2 EL=-70*mV eqs=''' Im=gL*(EL-v)+I : amp/cm**2 I : amp/cm**2 ''' neuron = SpatialNeuron(morphology=morpho, model=eqs, Cm=1 * uF / cm ** 2, Ri=100 * ohm * cm) neuron.v=EL neuron.I=0*amp/cm**2 # Monitors mon=StateMonitor(neuron,'v',record=True) run(1*ms) neuron.I[len(neuron)/2]=0.2*nA/neuron.area[len(neuron)/2] # injecting in the middle run(1*ms) neuron.I=0*amp run(50*ms,report='text') for i in range(0,len(neuron)/2,20): plot(mon.times/ms,mon[i]/mV) show() brian-1.4.3/dev/ideas/morphology/compare_with_neuron/mso_cell.py000066400000000000000000000021241266062647100251030ustar00rootroot00000000000000''' A pseudo MSO neuron, with two dendrites and one axon (fake geometry). ''' from brian import * from brian.experimental.morphology import * defaultclock.dt=0.1*ms # Morphology morpho=Soma(30*um) morpho.axon=Cylinder(diameter=1*um,length=300*um,n=100) morpho.L=Cylinder(diameter=3*um,length=100*um,n=50) morpho.R=Cylinder(diameter=3*um,length=150*um,n=50) # Passive channels gL=1e-4*siemens/cm**2 EL=-70*mV eqs=''' Im=gL*(EL-v)+I : amp/cm**2 I : amp/cm**2 ''' neuron = SpatialNeuron(morphology=morpho, model=eqs, Cm=1 * uF / cm ** 2, Ri=100 * ohm * cm) neuron.v=-65*mV neuron.I=0*amp/cm**2 neuron.gL=1e-4*siemens/cm**2 # Monitors mon=StateMonitor(neuron,'v',record=[0,50]) run(1*ms) neuron.I[0]=0.2*nA/neuron.area[0] #neuron.changed=True run(50*ms) neuron.I=0*amp #neuron.changed=True run(495*ms,report='text') # Load Neuron data file=r'D:\My Dropbox\LocalEclipseWorkspace\Neuron\mso_cell.dat' x,y,z=read_neuron_dat(file) subplot(211) plot(x,y,'b') plot(mon.times/ms,mon[0]/mV,'r') subplot(212) plot(x,z,'b') plot(mon.times/ms,mon[50]/mV,'r') show() brian-1.4.3/dev/ideas/morphology/compare_with_neuron/spike_initiation.py000066400000000000000000000023571266062647100266600ustar00rootroot00000000000000''' Ball and stick with Na and K channels ''' from brian import * from brian.experimental.morphology import * defaultclock.dt=0.025*ms # Morphology morpho=Soma(30*um) morpho.axon=Cylinder(diameter=1*um,length=300*um,n=100) # Channels gL=1e-4*siemens/cm**2 EL=-70*mV ENa = 50*mV ka = 6*mV ki=6*mV va = -30*mV vi = -50*mV EK=-90*mV vk = -20*mV kk = 8*mV eqs=''' Im=gL*(EL-v)+I+gNa*m*h*(ENa-v)+gK*n*(EK-v) : amp/cm**2 dm/dt=(minf-m)/(0.3*ms) : 1 # simplified Na channel dh/dt=(hinf-h)/(3*ms) : 1 # inactivation dn/dt=(ninf-n)/(5*ms) : 1 # K+ minf=1/(1+exp((va-v)/ka)) : 1 hinf=1/(1+exp((v-vi)/ki)) : 1 ninf=1/(1+exp((vk-v)/kk)) : 1 I : amp/cm**2 gNa : siemens/cm**2 gK : siemens/cm**2 ''' neuron = SpatialNeuron(morphology=morpho, model=eqs, Cm=1 * uF / cm ** 2, Ri=100 * ohm * cm) neuron.v=-65*mV neuron.I=0*amp/cm**2 neuron.axon.gNa[10:20]=700*gL neuron.axon.gK[10:20]=700*gL # Monitors mon=StateMonitor(neuron,'v',record=True) run(1*ms) neuron.I[0]=0.15*nA/neuron.area[0] run(50*ms) neuron.I=0*amp run(95*ms,report='text') plot(mon.times/ms,mon[0]/mV,'r') plot(mon.times/ms,mon[20]/mV,'g') plot(mon.times/ms,mon[40]/mV,'b') plot(mon.times/ms,mon[60]/mV,'k') plot(mon.times/ms,mon[80]/mV,'y') show() brian-1.4.3/dev/ideas/morphology/dodge_cooley_1973.py000066400000000000000000000050221266062647100223560ustar00rootroot00000000000000""" Dodge and Cooley 1973. Model of a motoneuron. Doesn't seem to work: no propagation of APs. """ from brian import * from morphology import * from spatialneuron import * #from spatialneuron_remy import * # does not give the same results! defaultclock.dt=0.01*ms morpho=Morphology(n=47) # Dendrites morpho.length[:30]=4500*um/30 morpho.diameter[:30]=30*2*um # Soma morpho.length[30:36]=300*um/6 morpho.diameter[30:36]=30*2*um # Initial segment morpho.length[36:41]=100*um/5 morpho.diameter[36:41]=5*2*um # Myelin morpho.length[41:46]=400*um/5 morpho.diameter[41:46]=8*2*um # Node morpho.length[46]=75*um morpho.diameter[46]=10*2*um morpho.set_area() morpho.set_coordinates() El = 0 * mV ENa = 115*mV EK = -5 * mV # Typical equations eqs=''' # The same equations for the whole neuron, but possibly different parameter values Im=gL*(El-v)+gNa*m**3*h*(ENa-v)+gK*n**4*(EK-v)+I : amp/cm**2 # distributed transmembrane current I:amp/cm**2 # applied current gNa:siemens/cm**2 gK:siemens/cm**2 gL:siemens/cm**2 Cm:farad/cm**2 dm/dt=alpham*(1-m)-betam*m : 1 dn/dt=alphan*(1-n)-betan*n : 1 dh/dt=alphah*(1-h)-betah*h : 1 alpham=(0.4/mV)*(25*mV-v)/(exp((25*mV-v)/(5*mV))-1)/ms : Hz betam=(0.4/mV)*(v-55*mV)/(exp((v-55*mV)/(5*mV))-1)/ms : Hz alphah=0.28*exp((10*mV-v)/(20*mV))/ms : Hz betah=4/(exp((40*mV-v)/(10*mV))+1)/ms : Hz alphan=(0.2/mV)*(20*mV-v)/(exp((20*mV-v)/(10*mV))-1)/ms : Hz betan=0.25*exp((10*mV-v)/(80*mV))/ms : Hz ''' neuron = SpatialNeuron(morphology=morpho, model=eqs, Cm=1 * uF / cm ** 2, Ri=100 * ohm * cm) neuron.v=El neuron.h=1 neuron.m=0 neuron.n=0 neuron.I=0*amp/cm**2 M=StateMonitor(neuron,'v',record=True) neuron.gL[0:36] = 0.167 * msiemens / cm ** 2 neuron.gNa[0:30] = 0* msiemens / cm ** 2 neuron.gK[0:30] = 0* msiemens / cm ** 2 neuron.gNa[30:36] = 70* msiemens / cm ** 2 neuron.gK[30:36] = 17.5* msiemens / cm ** 2 neuron.gL[36:41] = 1 * msiemens / cm ** 2 neuron.gNa[36:41] = 600* msiemens / cm ** 2 neuron.gK[36:41] = 100* msiemens / cm ** 2 neuron.gL[41:46] = 0.05 * msiemens / cm ** 2 neuron.Cm[41:46] = 0.05 * uF / cm ** 2 # myelin neuron.gNa[41:46] = 0* msiemens / cm ** 2 neuron.gK[41:46] = 0* msiemens / cm ** 2 neuron.gL[46] = 3 * msiemens / cm ** 2 neuron.gNa[46] = 600* msiemens / cm ** 2 neuron.gK[46] = 100* msiemens / cm ** 2 run(50*ms) neuron.I[35]=0.5 * uA/neuron.area[30] # current injection at the soma run(0.5*ms) neuron.I[35]=0 * nA/neuron.area[30] run(50*ms,report='text') for i in [33,38,43,46]: plot(M.times/ms,M[i]/mV) show() brian-1.4.3/dev/ideas/morphology/examples/000077500000000000000000000000001266062647100205045ustar00rootroot00000000000000brian-1.4.3/dev/ideas/morphology/examples/cable_equation_static.py000066400000000000000000000062121266062647100254010ustar00rootroot00000000000000''' Simulation of a cable equation ''' from brian import * from brian.experimental.morphology import * from time import time defaultclock.dt=0.1*ms length=3000*um diameter=1*um n = 100 br = 0 #(+1) Ri=100 * ohm * cm Cm=1 * uF / cm ** 2 refractory=4 * ms length = (length/(br+1))* (br+1) n = (n/(br+1)) * (br+1) dx = length / n morpho = Cylinder(length=dx, diameter=diameter, n=1, type='axon') morpho.L = Cylinder(length=length/(br+1), diameter=diameter, n=n/(br+1), type='axon') if br>0: morpho.LL = Cylinder(length=length/(br+1), diameter=diameter, n=n/(br+1), type='axon') if br>1: morpho.LLL = Cylinder(length=length/(br+1), diameter=diameter, n=n/(br+1), type='axon') if br>2: morpho.LLLL = Cylinder(length=length/(br+1), diameter=diameter, n=n/(br+1), type='axon') if br>3: morpho.LLLLL = Cylinder(length=length/(br+1), diameter=diameter, n=n/(br+1), type='axon') if br>4: morpho.LLLLLL = Cylinder(length=length/(br+1), diameter=diameter, n=n/(br+1), type='axon') if br>5: morpho.LLLLLLL = Cylinder(length=length/(br+1), diameter=diameter, n=n/(br+1), type='axon') if br>6: morpho.LLLLLLLL = Cylinder(length=length/(br+1), diameter=diameter, n=n/(br+1), type='axon') if br>7: morpho.LLLLLLLLL = Cylinder(length=length/(br+1), diameter=diameter, n=n/(br+1), type='axon') if br>8: morpho.LLLLLLLLLL = Cylinder(length=length/(br+1), diameter=diameter, n=n/(br+1), type='axon') El = 0 * mV gl = 0.02 * msiemens / cm ** 2 # Typical equations eqs=''' Im=gl*(El-v)+I : amp/cm**2 # distributed transmembrane current ''' neuron = SpatialNeuron(morphology=morpho, model=eqs, refractory=refractory, Cm=Cm, Ri=Ri) neuron.v=El neuron.I=0*amp/cm**2 # M=StateMonitor(neuron,'v',record=True) taum = neuron.Cm[0]/gl * second**4 * (volt/ohm)**2 / (metre**4 * kilogram) rm = taum/(Cm * pi * diameter) ra = (4 * Ri)/(pi * diameter**2) la = sqrt(rm/ra) CL = cosh(length / la) SL = sinh(length / la) R_infinity = sqrt(rm * ra) R_sealed = R_infinity * CL/SL # V0_lim_sealed = R_sealed * I0 * neuron.area[1] R_killed = R_infinity * SL/CL # V0_lim_killed = R_killed * I0 * neuron.area[1] V0_lim_sealed = 100 * mV neuron.v[0] = V0_lim_sealed T = 20*taum print 'taum=',taum print "lambda = ",la print "temps de simulation : ",T/taum," taum" print "longueur : ",length/la," lambda" start = time() run(T) end = time() print end - start," s" #testTime = (T-1*ms)/defaultclock.dt i = arange(n+1) photo = zeros(n+1) diff = 0 k_max = 0 for k in xrange(n+1): # photo[k] = M[k][testTime] photo[k] = neuron.v[k] if k>0 : if diff < abs(photo[k]/mV * metre**2 * kilogram /(second**3 * volt/ohm)- V0_lim_sealed / CL * cosh((length - k * dx) / la ) / mV) : diff = abs(photo[k]/mV * metre**2 * kilogram /(second**3 * volt/ohm)- V0_lim_sealed / CL * cosh((length - k * dx) / la ) / mV) k_max = k print diff print k_max plot (i * dx/mm,photo/mV,'r') plot(i*dx / mm,V0_lim_sealed / CL * cosh((length - i * dx) / la ) / mV,'g') xlabel('position (mm)') ylabel('potentiel (mV)') # plot(i*dx / mm,V0_lim_killed / SL * sinh((length - i * dx) / la ) / mV,'b') # for i in range(n): # plot(M.times/ms,M[i]/mV) show() brian-1.4.3/dev/ideas/morphology/examples/dodge_cooley_1973.py000066400000000000000000000047031266062647100242010ustar00rootroot00000000000000""" Dodge and Cooley 1973. Model of a motoneuron. Doesn't seem to work: no propagation of APs. """ from brian import * from brian.experimental.morphology import * defaultclock.dt=0.01*ms morpho=Morphology(n=47) # Dendrites morpho.length[:30]=4500*um/30 morpho.diameter[:30]=30*2*um # Soma morpho.length[30:36]=300*um/6 morpho.diameter[30:36]=30*2*um # Initial segment morpho.length[36:41]=100*um/5 morpho.diameter[36:41]=5*2*um # Myelin morpho.length[41:46]=400*um/5 morpho.diameter[41:46]=8*2*um # Node morpho.length[46]=75*um morpho.diameter[46]=10*2*um morpho.set_area() morpho.set_coordinates() El = 0 * mV ENa = 115*mV EK = -5 * mV # Typical equations eqs=''' # The same equations for the whole neuron, but possibly different parameter values Im=gL*(El-v)+gNa*m**3*h*(ENa-v)+gK*n**4*(EK-v)+I : amp/cm**2 # distributed transmembrane current I:amp/cm**2 # applied current gNa:siemens/cm**2 gK:siemens/cm**2 gL:siemens/cm**2 Cm:farad/cm**2 dm/dt=alpham*(1-m)-betam*m : 1 dn/dt=alphan*(1-n)-betan*n : 1 dh/dt=alphah*(1-h)-betah*h : 1 alpham=(0.4/mV)*(25*mV-v)/(exp((25*mV-v)/(5*mV))-1)/ms : Hz betam=(0.4/mV)*(v-55*mV)/(exp((v-55*mV)/(5*mV))-1)/ms : Hz alphah=0.28*exp((10*mV-v)/(20*mV))/ms : Hz betah=4/(exp((40*mV-v)/(10*mV))+1)/ms : Hz alphan=(0.2/mV)*(20*mV-v)/(exp((20*mV-v)/(10*mV))-1)/ms : Hz betan=0.25*exp((10*mV-v)/(80*mV))/ms : Hz ''' neuron = SpatialNeuron(morphology=morpho, model=eqs, Cm=1 * uF / cm ** 2, Ri=100 * ohm * cm) neuron.v=El neuron.h=1 neuron.m=0 neuron.n=0 neuron.I=0*amp/cm**2 M=StateMonitor(neuron,'v',record=True) neuron.gL[0:36] = 0.167 * msiemens / cm ** 2 neuron.gNa[0:30] = 0* msiemens / cm ** 2 neuron.gK[0:30] = 0* msiemens / cm ** 2 neuron.gNa[30:36] = 70* msiemens / cm ** 2 neuron.gK[30:36] = 17.5* msiemens / cm ** 2 neuron.gL[36:41] = 1 * msiemens / cm ** 2 neuron.gNa[36:41] = 600* msiemens / cm ** 2 neuron.gK[36:41] = 100* msiemens / cm ** 2 neuron.gL[41:46] = 0.05 * msiemens / cm ** 2 neuron.Cm[41:46] = 0.05 * uF / cm ** 2 # myelin neuron.gNa[41:46] = 0* msiemens / cm ** 2 neuron.gK[41:46] = 0* msiemens / cm ** 2 neuron.gL[46] = 3 * msiemens / cm ** 2 neuron.gNa[46] = 600* msiemens / cm ** 2 neuron.gK[46] = 100* msiemens / cm ** 2 run(50*ms) neuron.I[35]=0.5 * uA/neuron.area[30] # current injection at the soma run(0.5*ms) neuron.I[35]=0 * nA/neuron.area[30] run(50*ms,report='text') for i in [33,38,43,46]: plot(M.times/ms,M[i]/mV) show() brian-1.4.3/dev/ideas/morphology/examples/hodgkin_huxley_1952.py000066400000000000000000000026311266062647100245610ustar00rootroot00000000000000''' Hodgkin-Huxley equations (1952) Conduction velocity is about 12.5 m/s (is it right?) ''' from brian import * from brian.experimental.morphology import * defaultclock.dt=0.1*ms morpho=Cylinder(length=10*cm, diameter=2*238*um, n=1000, type='axon') El = 10.613* mV ENa = 115*mV EK = -12 * mV gl = 0.3 * msiemens / cm ** 2 gNa = 120 * msiemens / cm ** 2 gK = 36 * msiemens / cm ** 2 # Typical equations eqs=''' # The same equations for the whole neuron, but possibly different parameter values Im=gl*(El-v)+gNa*m**3*h*(ENa-v)+gK*n**4*(EK-v)+I : amp/cm**2 # distributed transmembrane current I:amp/cm**2 # applied current dm/dt=alpham*(1-m)-betam*m : 1 dn/dt=alphan*(1-n)-betan*n : 1 dh/dt=alphah*(1-h)-betah*h : 1 alpham=(0.1/mV)*(-v+25*mV)/(exp((-v+25*mV)/(10*mV))-1)/ms : Hz betam=4.*exp(-v/(18*mV))/ms : Hz alphah=0.07*exp(-v/(20*mV))/ms : Hz betah=1./(exp((-v+30*mV)/(10*mV))+1)/ms : Hz alphan=(0.01/mV)*(-v+10*mV)/(exp((-v+10*mV)/(10*mV))-1)/ms : Hz betan=0.125*exp(-v/(80*mV))/ms : Hz ''' neuron = SpatialNeuron(morphology=morpho, model=eqs, Cm=1 * uF / cm ** 2, Ri=35.4 * ohm * cm) neuron.v=0*mV neuron.h=1 neuron.m=0 neuron.n=.5 neuron.I=0*amp/cm**2 M=StateMonitor(neuron,'v',record=True) run(50*ms) neuron.I[0]=1 * uA/neuron.area[0] # current injection at one end run(3*ms) neuron.I=0*amp/cm**2 run(50*ms) for i in range(10): plot(M.times/ms,M[i*10]/mV) show() brian-1.4.3/dev/ideas/morphology/hodgkin_huxley_1952.py000066400000000000000000000026441266062647100227470ustar00rootroot00000000000000''' Hodgkin-Huxley equations (1952) Conduction velocity is about 12.5 m/s (is it right?) ''' from brian import * from brian.experimental.morphology import * defaultclock.dt=0.01*ms morpho=Cylinder(length=10*cm, diameter=2*238*um, n=1000, type='axon') -65*mV El = 10.613* mV ENa = 115*mV EK = -12 * mV gl = 0.3 * msiemens / cm ** 2 gNa = 120 * msiemens / cm ** 2 gK = 36 * msiemens / cm ** 2 # Typical equations eqs=''' # The same equations for the whole neuron, but possibly different parameter values Im=gl*(El-v)+gNa*m**3*h*(ENa-v)+gK*n**4*(EK-v)+I : amp/cm**2 # distributed transmembrane current I:amp/cm**2 # applied current dm/dt=alpham*(1-m)-betam*m : 1 dn/dt=alphan*(1-n)-betan*n : 1 dh/dt=alphah*(1-h)-betah*h : 1 alpham=(0.1/mV)*(-v+25*mV)/(exp((-v+25*mV)/(10*mV))-1)/ms : Hz betam=4.*exp(-v/(18*mV))/ms : Hz alphah=0.07*exp(-v/(20*mV))/ms : Hz betah=1./(exp((-v+30*mV)/(10*mV))+1)/ms : Hz alphan=(0.01/mV)*(-v+10*mV)/(exp((-v+10*mV)/(10*mV))-1)/ms : Hz betan=0.125*exp(-v/(80*mV))/ms : Hz ''' neuron = SpatialNeuron(morphology=morpho, model=eqs, Cm=1 * uF / cm ** 2, Ri=35.4 * ohm * cm) neuron.v=0*mV neuron.h=1 neuron.m=0 neuron.n=.5 neuron.I=0*amp/cm**2 M=StateMonitor(neuron,'v',record=True) run(50*ms) neuron.I[0]=1 * uA/neuron.area[0] # current injection at one end run(3*ms) neuron.I=0*amp/cm**2 run(50*ms) for i in range(10): plot(M.times/ms,M[i*10]/mV) show() brian-1.4.3/dev/ideas/morphology/morphology.py000066400000000000000000000417231266062647100214460ustar00rootroot00000000000000''' Neuronal morphology module for Brian. TODO: * Maybe str: should give a text representation of the tree * set_length etc.: should be recursive * check_consistency: check that lengths, area and coordinates are consistent (and fix) * rescale: change number of compartments ''' from brian.group import Group from scipy import rand from numpy import * from brian.units import meter from brian.stdunits import um import warnings from pylab import figure import copy try: from mpl_toolkits.mplot3d import Axes3D except: warnings.warn('Pylab 0.99.1 is required for 3D plots') __all__ = ['Morphology', 'Cylinder', 'Soma'] class Morphology(object): ''' Neuronal morphology (=tree of branches). ''' def __init__(self, filename=None, n=None): self.children = [] self._namedkid = {} self.iscompressed = False if filename is not None: self.loadswc(filename) elif n is not None: # Creates a branch with n compartments # The problem here is that these parameters should have some self-consistency self.x, self.y, self.z, self.diameter, self.length, self.area = [zeros(n) for _ in range(6)] def set_length(self): ''' Sets the length of compartments according to their coordinates ''' x = hstack((0 * um, self.x)) y = hstack((0 * um, self.y)) z = hstack((0 * um, self.z)) self.length = sum((x[1:] - x[:-1]) ** 2 + (y[1:] - y[:-1]) ** 2 + (z[1:] - z[:-1]) ** 2) ** .5 def set_area(self): ''' Sets the area of compartments according to diameter and length (assuming cylinders) ''' self.area = pi * self.diameter * self.length def set_coordinates(self): ''' Sets the coordinates of compartments according to their lengths (taking a random direction) ''' l = cumsum(self.length) theta = rand()*2 * pi phi = rand()*2 * pi self.x = l * sin(theta) * cos(phi) self.y = l * sin(theta) * sin(phi) self.z = l * cos(theta) def loadswc(self, filename): ''' Reads a SWC file containing a neuronal morphology. Large database at http://neuromorpho.org/neuroMorpho Information below from http://www.mssm.edu/cnic/swc.html SWC File Format The format of an SWC file is fairly simple. It is a text file consisting of a header with various fields beginning with a # character, and a series of three dimensional points containing an index, radius, type, and connectivity information. The lines in the text file representing points have the following layout. n T x y z R P n is an integer label that identifies the current point and increments by one from one line to the next. T is an integer representing the type of neuronal segment, such as soma, axon, apical dendrite, etc. The standard accepted integer values are given below. * 0 = undefined * 1 = soma * 2 = axon * 3 = dendrite * 4 = apical dendrite * 5 = fork point * 6 = end point * 7 = custom x, y, z gives the cartesian coordinates of each node. R is the radius at that node. P indicates the parent (the integer label) of the current point or -1 to indicate an origin (soma). ''' # 1) Create the list of segments, each segment has a list of children lines = open(filename).read().splitlines() segment = [] # list of segments types = ['undefined', 'soma', 'axon', 'dendrite', 'apical', 'fork', 'end', 'custom'] previousn = -1 for line in lines: if line[0] != '#': # comment numbers = line.split() n = int(numbers[0]) - 1 T = types[int(numbers[1])] x = float(numbers[2]) * um y = float(numbers[3]) * um z = float(numbers[4]) * um R = float(numbers[5]) * um P = int(numbers[6]) - 1 # 0-based indexing if (n != previousn + 1): raise ValueError, "Bad format in file " + filename seg = dict(x=x, y=y, z=z, T=T, diameter=2 * R, parent=P, children=[]) location = (x, y, z) if T == 'soma': seg['area'] = 4 * pi * R ** 2 seg['length'] = 0 * um else: # dendrite locationP = (segment[P]['x'], segment[P]['y'], segment[P]['z']) seg['length'] = (sum((array(location) - array(locationP)) ** 2)) ** .5 * meter seg['area'] = seg['length'] * 2 * pi * R if P >= 0: segment[P]['children'].append(n) segment.append(seg) previousn = n # We assume that the first segment is the root self.create_from_segments(segment) def create_from_segments(self, segment, origin=0): """ Recursively create the morphology from a list of segments. Each segment has attributes: x,y,z,diameter,area,length (vectors) and children (list). It also creates a dictionary of names (_namedkid). """ n = origin if segment[origin]['T'] != 'soma': # if it's a soma, only one compartment while (len(segment[n]['children']) == 1) and (segment[n]['T'] != 'soma'): # Go to the end of the branch n += 1 # End of branch branch = segment[origin:n + 1] # Set attributes self.diameter, self.length, self.area, self.x, self.y, self.z = \ zip(*[(seg['diameter'], seg['length'], seg['area'], seg['x'], seg['y'], seg['z']) for seg in branch]) self.diameter, self.length, self.area, self.x, self.y, self.z = array(self.diameter), array(self.length), \ array(self.area), array(self.x), array(self.y), array(self.z) self.type = segment[n]['T'] # normally same type for all compartments in the branch # Create children (list) self.children = [Morphology().create_from_segments(segment, origin=c) for c in segment[n]['children']] # Create dictionary of names (enumerates children from number 1) for i, child in enumerate(self.children): self._namedkid[str(i + 1)] = child # Name the child if possible if child.type in ['soma', 'axon', 'dendrite']: if child.type in self._namedkid: self._namedkid[child.type] = None # two children with the same name: erase (see next block) else: self._namedkid[child.type] = child # Erase useless names for k in self._namedkid.keys(): if self._namedkid[k] is None: del self._namedkid[k] # If two kids, name them L (left) and R (right) if len(self.children) == 2: self._namedkid['L'] = self._namedkid['1'] self._namedkid['R'] = self._namedkid['2'] return self def branch(self): ''' Returns the current branch without the children. ''' morpho = copy.copy(self) morpho.children = [] morpho._namedkid = {} return morpho def __getitem__(self, x): """ Returns the subtree named x. Ex.: neuron['axon'] or neuron['11213'] neuron[10*um:20*um] returns the subbranch from 10 um to 20 um. neuron[10*um] returns one compartment. neuron[5] returns compartment number 5. TODO: neuron[:] returns the full branch. """ if type(x) == type(0): # int: returns one compartment morpho = self.branch() i = x j = i + 1 morpho.diameter = morpho.diameter[i:j] morpho.length = morpho.length[i:j] morpho.area = morpho.area[i:j] morpho.x = morpho.x[i:j] morpho.y = morpho.y[i:j] morpho.z = morpho.z[i:j] if hasattr(morpho, '_origin'): morpho._origin += i return morpho elif isinstance(x, slice): # neuron[10*um:20*um] morpho = self.branch() start, stop = x.start, x.stop l = cumsum(morpho.length) # coordinate on the branch i = searchsorted(l, start) j = searchsorted(l, stop) morpho.diameter = morpho.diameter[i:j] morpho.length = morpho.length[i:j] morpho.area = morpho.area[i:j] morpho.x = morpho.x[i:j] morpho.y = morpho.y[i:j] morpho.z = morpho.z[i:j] if hasattr(morpho, '_origin'): morpho._origin += i return morpho elif isinstance(x, float): # neuron[10*um] morpho = self.branch() l = cumsum(morpho.length) # coordinate on the branch i = searchsorted(l, x) j = i + 1 morpho.diameter = morpho.diameter[i:j] morpho.length = morpho.length[i:j] morpho.area = morpho.area[i:j] morpho.x = morpho.x[i:j] morpho.y = morpho.y[i:j] morpho.z = morpho.z[i:j] if hasattr(morpho, '_origin'): morpho._origin += i return morpho else: x = str(x) # convert int to string if (len(x) > 1) and all([c in 'LR123456789' for c in x]): # binary string of the form LLLRLR or 1213 (or mixed) return self._namedkid[x[0]][x[1:]] elif x in self._namedkid: return self._namedkid[x] else: raise AttributeError, "The subtree " + x + " does not exist" def __setitem__(self, x, kid): """ Inserts the subtree and name it x. Ex.: neuron['axon'] or neuron['11213'] If the tree already exists with another name, then it creates a synonym for this tree. The coordinates of the subtree are relative before function call, and are absolute after function call. """ x = str(x) # convert int to string if (len(x) > 1) and all([c in 'LR123456789' for c in x]): # binary string of the form LLLRLR or 1213 (or mixed) self._namedkid[x[0]][x[1:]] = kid elif x in self._namedkid: raise AttributeError, "The subtree " + x + " already exists" else: # Update coordinates kid.x += self.x[-1] kid.y += self.y[-1] kid.z += self.z[-1] if kid not in self.children: self.children.append(kid) self._namedkid[str(len(self.children))] = kid # numbered child self._namedkid[x] = kid def __delitem__(self, x): """ Removes the subtree x. """ x = str(x) # convert int to string if (len(x) > 1) and all([c in 'LR123456789' for c in x]): # binary string of the form LLLRLR or 1213 (or mixed) del self._namedkid[x[0]][x[1:]] elif x in self._namedkid: child = self._namedkid[x] # Delete from name dictionary for name, kid in self._namedkid.items(): if kid is child: del self._namedkid[name] # Delete from list of children for i, kid in enumerate(self.children): if kid is child: del self.children[i] else: raise AttributeError, "The subtree " + x + " does not exist" def __getattr__(self, x): """ Returns the subtree named x. Ex.: axon=neuron.axon """ return self[x] def __setattr__(self, x, kid): """ Attach a subtree and named it x. If the subtree is None then the subtree x is deleted. Ex.: neuron.axon=Soma(diameter=10*um) Ex.: neuron.axon=None """ if isinstance(kid, Morphology): if kid is None: del self[x] else: self[x] = kid else: # If it is not a subtree, then it's a normal class attribute object.__setattr__(self, x, kid) def __len__(self): """ Returns the total number of compartments. """ return len(self.x) + sum(len(child) for child in self.children) def compress(self, diameter=None, length=None, area=None, x=None, y=None, z=None, origin=0): """ Compresses the tree by changing the compartment vectors to views on a matrix (or vectors). The morphology cannot be changed anymore but all other functions should work normally. origin : offset in the base matrix """ self._origin = origin n = len(self.x) # Update values of vectors diameter[:n] = self.diameter length[:n] = self.length area[:n] = self.area x[:n] = self.x y[:n] = self.y z[:n] = self.z # Attributes are now views on these vectors self.diameter = diameter[:n] self.length = length[:n] self.area = area[:n] self.x = x[:n] self.y = y[:n] self.z = z[:n] for kid in self.children: kid.compress(diameter=diameter[n:], length=length[n:], area=area[n:], x=x[n:], y=y[n:], z=z[n:], origin=origin+n) n += len(kid) self.iscompressed = True def plot(self, axes=None, simple=True, origin=None): """ Plots the morphology in 3D. Units are um. axes : the figure axes (new figure if not given) simple : if True, the diameter of branches is ignored """ if axes is None: # new figure fig = figure() axes = Axes3D(fig) x, y, z, d = self.x / um, self.y / um, self.z / um, self.diameter / um if origin is not None: x0, y0, z0 = origin x = hstack((x0, x)) y = hstack((y0, y)) z = hstack((z0, z)) if len(x) == 1: # root with a single compartment: probably just the soma axes.plot(x, y, z, "r.", linewidth=d[0]) else: if simple: axes.plot(x, y, z, "k") else: # linewidth reflects compartment diameter for n in range(1, len(x)): axes.plot([x[n - 1], x[n]], [y[n - 1], y[n]], [z[n - 1], z[n]], 'k', linewidth=d[n - 1]) for c in self.children: c.plot(origin=(x[-1], y[-1], z[-1]), axes=axes, simple=simple) class Cylinder(Morphology): """ A cylinder. """ def __init__(self, length=None, diameter=None, n=1, type=None, x=None, y=None, z=None): """ Creates a cylinder. n: number of compartments. type : 'soma', 'axon' or 'dendrite' x,y,z : end point (relative to origin of cylinder) length is optional (and ignored) if x,y,z is specified If x,y,z unspecified: random direction """ Morphology.__init__(self) if x is None: theta = rand()*2 * pi phi = rand()*2 * pi x = length * sin(theta) * cos(phi) y = length * sin(theta) * sin(phi) z = length * cos(theta) else: length = (sum(array((x, y, z)) ** 2)) ** .5 scale = arange(1, n + 1) * 1. / n self.x, self.y, self.z = x * scale, y * scale, z * scale self.length = ones(n) * length / n self.diameter = ones(n) * diameter self.area = ones(n) * pi * diameter * length / n self.type = type class Soma(Morphology): # or Sphere? """ A spherical soma. """ def __init__(self, diameter=None): Morphology.__init__(self) self.diameter = ones(1) * diameter self.area = ones(1) * pi * diameter ** 2 self.length = zeros(1) self.x, self.y, self.z = zeros(1), zeros(1), zeros(1) self.type = 'soma' if __name__ == '__main__': from pylab import show #morpho=Morphology('mp_ma_40984_gc2.CNG.swc') # retinal ganglion cell morpho = Morphology('oi24rpy1.CNG.swc') # visual L3 pyramidal cell print len(morpho), "compartments" morpho.axon = None morpho.plot() #morpho=Cylinder(length=10*um,diameter=1*um,n=10) #morpho.plot(simple=True) morpho = Soma(diameter=10 * um) morpho.dendrite = Cylinder(length=3 * um, diameter=1 * um, n=10) morpho.dendrite.L = Cylinder(length=5 * um, diameter=1 * um, n=10) morpho.dendrite.R = Cylinder(length=7 * um, diameter=1 * um, n=10) morpho.dendrite.LL = Cylinder(length=3 * um, diameter=1 * um, n=10) morpho.axon = Morphology(n=5) morpho.axon.diameter = ones(5) * 1 * um morpho.axon.length = [1 * um, 2 * um, 1 * um, 3 * um, 1 * um] morpho.axon.set_coordinates() morpho.axon.set_area() morpho.plot(simple=True) show() brian-1.4.3/dev/ideas/morphology/morphology_old.py000066400000000000000000000317671266062647100223130ustar00rootroot00000000000000''' Neuronal morphology module for Brian. Old version - maybe a few functions could still be useful (e.g. simplify) Typical values for intrinsic parameters Cm =0.7 - 1 uF/(cm**2) # specific membrane capacitance Ri = 70 - 200 ohm*cm # intracellular resistivity Rm = variable, choose taum=Rm*Cm first # specific membrane resistance (ohm*cm**2) TODO: * discretise * problem with simplify and location * check if surfacic when applying on a branch ''' from brian.units import meter, ohm from brian.stdunits import um, cm, ms, uF, mV from numpy import sqrt, array, pi, mean from brian.compartments import * from brian.membrane_equations import * from brian import sum def space_constant(d, Rm, Ri): return (d * Rm / Ri) ** .5 class Morphology(object): ''' Morphology class ---------------- ''' def __init__(self, name=None, Cm=1 * uF / (cm ** 2)): ''' Cm is the specific membrane capacitance ''' self._segments = [] if name is not None: self.load(name, Cm) def load(self, name, Cm=1 * uF / (cm ** 2)): ''' Reads a SWC file containing a neuronal morphology and returns a morphology object Cm is the specific membrane capacitance. segments is a dictionary of segments, each segment is a dictionary with keys: * parent: index of the parent segment * type: type of segment in 'undefined','soma','dendrite','axon','apical','fork','end','custom' * length * area * radius * location: (x,y,z) coordinates Large database at http://neuromorpho.org/neuroMorpho Information below from http://www.mssm.edu/cnic/swc.html SWC File Format The format of an SWC file is fairly simple. It is a text file consisting of a header with various fields beginning with a # character, and a series of three dimensional points containing an index, radius, type, and connectivity information. The lines in the text file representing points have the following layout. n T x y z R P n is an integer label that identifies the current point and increments by one from one line to the next. T is an integer representing the type of neuronal segment, such as soma, axon, apical dendrite, etc. The standard accepted integer values are given below. * 0 = undefined * 1 = soma * 2 = axon * 3 = dendrite * 4 = apical dendrite * 5 = fork point * 6 = end point * 7 = custom x, y, z gives the cartesian coordinates of each node. R is the radius at that node. P indicates the parent (the integer label) of the current point or -1 to indicate an origin (soma). ''' # First create the list of segments lines = open(name).read().splitlines() segments = [] branching_points = [] types = ['undefined', 'soma', 'axon', 'dendrite', 'apical', 'fork', 'end', 'custom'] for line in lines: if line[0] != '#': # comment numbers = line.split() n = int(numbers[0]) - 1 T = types[int(numbers[1])] x = float(numbers[2]) * um y = float(numbers[3]) * um z = float(numbers[4]) * um R = float(numbers[5]) * um P = int(numbers[6]) - 1 # 0-based indexing segment = dict(n=n, type=T, location=(x, y, z), radius=R, parent=P) if T == 'soma': segment['area'] = 4 * pi * segment['radius'] ** 2 else: # dendrite segment['length'] = (sum((array(segment['location']) - array(segments[P]['location'])) ** 2)) ** .5 * meter segment['area'] = segment['length'] * 2 * pi * segment['radius'] if (P != n - 1) and (P > -2): # P is a branching point branching_points.append(P) segments.append(segment) # Create branches # branches = list of dict(begin,end,segments) where segments are segment indexes branches = [] branch = dict(start=0, segments=[], children=0) for segment in segments: n = segment['n'] if segment['n'] in branching_points: # end of branch branch['segments'].append(n) branch['end'] = n branches.append(branch) branch = dict(start=n + 1, segments=[], children=0) elif segment['parent'] != n - 1: # new branch branch['end'] = n - 1 branches.append(branch) branch = dict(start=n, segments=[n], children=0) else: branch['segments'].append(n) # Last branch branch['end'] = n branches.append(branch) # Make segment dictionary self._segments = dict() for segment in segments: self._segments[segment['n']] = segment # Name branches and segments # The soma is 'soma' self._branches = dict() for branch in branches: #if branch['type'] parent = self._segments[branch['start']]['parent'] if parent in self._segments: b = [b for b in branches if parent in b['segments']][0] # parent branch if b['name'] == 'soma': branch['name'] = str(b['children']) else: branch['name'] = b['name'] + str(b['children']) b['children'] += 1 else: branch['name'] = 'soma' self._branches[branch['name']] = branch self.build_equations(Cm) def info(self): ''' Prints information about the morphology ''' print len([s for n, s in self._segments.iteritems() if s['n'] == n]), 'segments' print 'Branches:' for name, branch in self._branches.iteritems(): area = sum([self._segments[s]['area'] for s in branch['segments']]) * meter ** 2 length = sum([self._segments[s]['length'] for s in branch['segments']\ if 'length' in self._segments[s]]) * meter print name, ':', len(branch['segments']), 'segments ; area =', area, '; length =', length def build_equations(self, Cm=1 * uF / (cm ** 2)): ''' Builds a dictionary of equations. Cm is the specific capacitance. ''' self._eqs = dict() for n, segment in self._segments.iteritems(): if segment['n'] == n: # otherwise it is a copy self._eqs[n] = MembraneEquation(C=Cm * segment['area']) self._eqs[n].area = segment['area'] def __getitem__(self, x): ''' Returns a branch or a compartment (equations). ''' if type(x) == type((0, 0)): # tuple: compartment branch, location = x return self.compartment(branch, location) else: # single variable: branch return self.branch(x) def index(self, branch, location): ''' Returns the index of the segment at given location on given branch ''' branch = str(branch) branch = self._branches[branch] if type(location) == type(0): # index return branch['start'] + location else: # position x = 0 * meter oldx = x for s in branch['segments']: x += self._segments[s]['length'] if x > location: # The closer one if x - location < location - oldx: return s else: return s - 1 oldx = x raise IndexError, 'Location not found' def radius(self, branch, location=None): ''' Returns the radius of a compartment or branch ''' if location is None: # branch: return mean radius return mean([self.radius(branch, n) for n in self._branches[branch]['segments']]) * meter else: seg = self._segments[self.index(branch, location)] return seg['radius'] def diameter(self, branch, location=None): ''' Returns the diameter of a compartment or branch ''' return 2 * self.radius(branch, location) def length(self, branch, location=None): ''' Returns the length of a compartment or branch. ''' if location is None: # branch l = 0 * meter for n in self._branches[branch]['segments']: l += self.length(branch, n) return l else: return self._segments[self.index(branch, location)]['length'] def area(self, branch, location=None): ''' Returns the area of a compartment or branch. ''' if location is None: # branch a = 0 * meter ** 2 for n in self._branches[branch]['segments']: a += self.area(branch, n) return a else: seg = self._segments[self.index(branch, location)] if seg['type'] == 'soma': return 4 * pi * self.radius(branch, location) ** 2 else: return self.length(branch, location) * 2 * pi * self.radius(branch, location) def branch(self, branch, children=False): ''' Returns a branch. children = True: children are also returned (subtree) ''' branch = str(branch) if children: l = [] for br in self._branches.iterkeys(): if (branch == 'soma') or br.startswith(branch): l.extend(self.branch(br)) return l else: return [self._eqs[n] for n in self._branches[branch]['segments']] def subtree(self, branch): return self.branch(branch, children=True) def compartment(self, branch, location): ''' Returns the segment at given location on given branch ''' return self._eqs[self.index(branch, location)] def equations(self, Ri=100 * ohm * cm): ''' Returns an Equations object for the whole morphology. Ri is the intracellular resistivity. Connects all segments together. ''' eqs = Compartments(self._eqs) for n, s in self._segments.iteritems(): if s['n'] == n: # otherwise it is a copy n0 = s['parent'] if n0 in self._segments: s0 = self._segments[n0] if s0['type'] == 'soma': l = .5 * s['length'] else: l = .5 * (s['length'] + s0['length']) # connection length Ra = Ri * l / (pi * .5 * (s0['radius'] ** 2 + s['radius'] ** 2)) eqs.connect(n0, n, Ra) return eqs def simplify(self): ''' Replaces branches by equivalent cylinders. Preserves the total area and electrotonic length. ''' for branch in self._branches.itervalues(): if branch['name'] != 'soma': segs = [self._segments[n] for n in branch['segments']] a = sum([seg['area'] for seg in segs]) l = sum([seg['length'] / sqrt(seg['radius']) for seg in segs]) r = (a / (2 * pi * l)) ** (2. / 3.) l0 = (r ** .5) * l segs[0]['radius'] = r * meter #segs[0]['length']=l0 # length is the length of connecting segment segs[0]['area'] = a * meter ** 2 self._segments[branch['end']] = segs[0] branch['segments'] = branch['segments'][0:1] # Destroy other segments for seg in segs[1:-1]: del self._segments[seg['n']] def insert_current(self, current, branch, location=None): if location is None: # on branch if current._prefix == '__current_': # point current raise ValueError, "The current should be surfacic" branch = morpho.branch(branch) for c in branch: c += current else: c = morpho.compartment(branch, location) c += current if __name__ == '__main__': morpho = Morphology('mp_ma_40984_gc2.CNG.swc') # retinal ganglion cell morpho.info() #print morpho.branch(100) #morpho.simplify() #morpho.info() morpho.insert_current(Current('I=g*(E-vm) : amp/(cm**2)', surfacic=True, g=1 * uF / (cm ** 2) / (20 * ms), E= -75 * mV), 101, 10 * um) print morpho[101, 10 * um] model = morpho.equations(Ri=70 * ohm * cm) brian-1.4.3/dev/ideas/morphology/mp_ma_40984_gc2.CNG.swc000066400000000000000000000267671266062647100224300ustar00rootroot00000000000000# Original file mp.ma.40984.gc2.asc.swc edited by Duncan Donohue using SWCfix version 1.21 on 8/31/05. # Errors and fixes documented in mp.ma.40984.gc2.asc.swc.err. See SWCfix1.21.doc for more information. # # Amaral to SWC conversion from L-Measure. R. Scorcioni: rscorcio@gmu.edu # Original fileName:C:\Documents and Settings\Admin\Desktop\Miller-Eutectic\mp.ma.40984.gc2.asc # # ORIGINAL_SOURCE # CREATURE # REGION # FIELD/LAYER # TYPE # CONTRIBUTOR # REFERENCE # RAW # EXTRAS # SOMA_AREA # SHRINKAGE_CORRECTION # VERSION_NUMBER # VERSION_DATE # ********************************************* # SCALE 1.0 1.0 1.0 1 1 0.2917 0.04167 -0.1458 12.030 -1 2 3 12. 6.5 1. 0.850 1 3 3 15. 9. 1.5 0.75 2 4 3 18.5 10. 2.5 0.65 3 5 3 17. 8. 3. 0.15 4 6 3 18. 7. 2.5 0.15 5 7 3 16. 4. 8. 0.15 6 8 3 14. 0.5 8. 0.15 7 9 3 7. -11.5 9. 0.09 8 10 3 1.5 -19. 8. 0.09 9 11 3 -3.5 -22. 9. 0.09 10 12 3 -9.5 -24.5 9. 0.09 11 13 3 -17.5 -24. 9. 0.09 12 14 3 -21.5 -23. 10.5 0.09 13 15 3 -25. -22.5 10.5 0.09 14 16 3 21.5 10. 2. 0.45 4 17 3 24.5 11.5 2.5 0.4 16 18 3 27. 10.5 4. 0.3 17 19 3 28.5 10. 6. 0.2 18 20 3 29.5 11. 7. 0.2 19 21 3 31. 9.5 8.5 0.25 20 22 3 36. 11.5 9. 0.2 21 23 3 37.5 9. 10.5 0.2 22 24 3 38.5 4. 10. 0.25 23 25 3 42. 3.5 11. 0.2 24 26 3 44. 0.5 10. 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152. -89. 11. 0.09 338 340 3 154.5 -94. 12.5 0.09 339 341 3 47. -36.5 8.5 0.15 307 342 3 52.5 -36. 10.5 0.09 341 343 3 55. -36. 9.5 0.09 342 344 3 59.5 -36. 9.5 0.09 343 345 3 61. -39. 9.5 0.09 344 346 3 63. -41. 9.5 0.09 345 347 3 65. -45.5 9.5 0.09 346 348 3 68.5 -46.5 8.5 0.049 347 349 3 70. -49. 8.5 0.049 348 350 3 68.5 -52. 10. 0.049 349 351 3 68.5 -55.5 8.5 0.049 350 352 3 71.5 -61.5 10. 0.049 351 353 3 76.5 -62.5 9. 0.049 352 brian-1.4.3/dev/ideas/morphology/oi24rpy1.CNG.swc000066400000000000000000023030641266062647100214130ustar00rootroot00000000000000# Original file oi24rpy1.asc.swc edited by Duncan Donohue using StdSwc version 1.21 on 8/22/05. # Irregularities and fixes documented in oi24rpy1.asc.swc.std. See StdSwc1.21.doc for more information. # # Neurolucida to SWC conversion from L-Measure. R. Scorcioni: rscorcio@gmu.edu # Original fileName:C:\Documents and Settings\Admin\Desktop\eysel\oi24rpy1.asc # # ORIGINAL_SOURCE # CREATURE # REGION # FIELD/LAYER # TYPE # CONTRIBUTOR # REFERENCE # RAW # EXTRAS # SOMA_AREA # SHRINKAGE_CORRECTION # VERSION_NUMBER # VERSION_DATE # ********************************************* # SCALE 1.0 1.0 1.0 1 1 -21910.8 -7388.66 216.957 10.485 -1 2 4 -21911.8 -7390.21 232.92 3.22 1 3 4 -21912.1 -7388.21 236.59 2.725 2 4 4 -21912.8 -7385.5 239.71 2.355 3 5 4 -21913 -7384.13 247.16 1.86 4 6 4 -21913.1 -7383.92 247.21 1.86 5 7 4 -21913.7 -7382.31 250 1.86 6 8 4 -21913.7 -7382.43 250.69 1.86 7 9 4 -21914.4 -7380.22 248.55 1.86 8 10 4 -21914.6 -7378.42 248.87 1.86 9 11 4 -21918.9 -7372.62 250.23 1.985 10 12 4 -21919.6 -7371.91 250.41 1.985 11 13 4 -21920.5 -7369.79 251.08 1.985 12 14 4 -21920.5 -7369.83 251.31 1.985 13 15 4 -21920.4 -7369.68 253.82 1.985 14 16 4 -21921 -7368.54 260.46 1.985 15 17 4 -21921 -7368.59 260.76 1.985 16 18 4 -21921.9 -7366.92 260.97 1.985 17 19 4 -21921.7 -7366.84 259.37 1.985 18 20 4 -21921.4 -7367.07 259.3 1.735 19 21 4 -21921 -7369.1 258.93 1.61 20 22 4 -21921.1 -7368.88 258.98 1.61 21 23 4 -21920.6 -7369.83 258.77 1.61 22 24 4 -21920.1 -7371 259.89 1.61 23 25 4 -21919.3 -7371.39 261.27 1.485 24 26 4 -21918.9 -7371.87 261.15 1.365 25 27 4 -21918.3 -7372.27 261.03 1.24 26 28 4 -21917.3 -7372.9 260.83 1.24 27 29 4 -21916.8 -7374.15 260.58 1.24 28 30 4 -21917 -7374.91 260.46 1.115 29 31 4 -21916.7 -7376.02 260.87 0.865 30 32 4 -21916.4 -7378.19 263.6 0.865 31 33 4 -21916.5 -7379.25 263.43 0.745 32 34 4 -21916.8 -7380.85 263.19 0.745 33 35 4 -21916.3 -7383.44 262.73 0.745 34 36 4 -21915.4 -7385.42 262.31 0.745 35 37 4 -21914.5 -7387.1 261.95 0.745 36 38 4 -21914 -7387.84 263.33 0.745 37 39 4 -21913.5 -7389.03 263.08 0.745 38 40 4 -21912.5 -7389.92 262.84 0.62 39 41 4 -21913.1 -7391.34 262.66 0.62 40 42 4 -21912.6 -7392.06 262.5 0.62 41 43 4 -21912.3 -7392.02 262.48 0.62 42 44 4 -21911.5 -7393.15 262.22 0.62 43 45 4 -21911.5 -7393.41 262.18 0.62 44 46 4 -21911.9 -7394.8 264.68 0.62 45 47 4 -21909.4 -7394.81 264.44 0.495 46 48 4 -21907.4 -7396.44 267.61 0.495 47 49 4 -21906.2 -7398.4 267.71 0.495 48 50 4 -21904.5 -7399.26 269.57 0.495 49 51 4 -21904.3 -7400.4 270.01 0.495 50 52 4 -21902.7 -7401.73 272.87 0.495 51 53 4 -21900.8 -7403.4 273.41 0.495 52 54 4 -21899 -7403.27 275.98 0.495 53 55 4 -21899.3 -7403.29 276 0.495 54 56 4 -21898.3 -7403.89 275.93 0.495 55 57 4 -21897.3 -7404.45 276.79 0.495 56 58 4 -21897.2 -7404.57 277.49 0.495 57 59 4 -21895.6 -7405.77 281.39 0.495 58 60 4 -21895.5 -7405.82 281.68 0.495 59 61 4 -21894.5 -7408.02 287.01 0.495 60 62 4 -21894.4 -7408.14 287.76 0.495 61 63 4 -21892.8 -7409.86 291.47 0.495 62 64 4 -21891 -7410.34 294.48 0.495 63 65 4 -21889.4 -7412.21 297.29 0.495 64 66 4 -21889.4 -7412.23 297.4 0.495 65 67 4 -21888.2 -7413.25 299.56 0.495 66 68 4 -21887.9 -7413.17 299.55 0.495 67 69 4 -21886.1 -7414.67 300.58 0.495 68 70 4 -21884.5 -7416.79 304.78 0.495 69 71 4 -21883.6 -7418.6 304.97 0.495 70 72 4 -21883.3 -7418.69 305.63 0.495 71 73 4 -21881.9 -7419.84 306.01 0.495 72 74 4 -21881.6 -7419.78 306 0.495 73 75 4 -21880.3 -7421.21 307.73 0.37 74 76 4 -21878 -7422.02 307.37 0.37 75 77 4 -21878 -7422.27 307.33 0.37 76 78 4 -21876.8 -7423.72 308.7 0.37 77 79 4 -21876.7 -7423.75 308.91 0.37 78 80 4 -21874.9 -7424.23 308.89 0.37 79 81 4 -21874.8 -7424.27 309.15 0.37 80 82 4 -21872.6 -7425.56 313.41 0.37 81 83 4 -21871.7 -7427.78 312.96 0.37 82 84 4 -21870.4 -7428.68 312.69 0.37 83 85 4 -21869.5 -7431.23 319.88 0.37 84 86 4 -21868.2 -7433.64 323.9 0.37 85 87 4 -21867.7 -7433.56 323.86 0.37 86 88 4 -21866.4 -7434.96 324.84 0.37 87 89 4 -21866.9 -7436.07 324.7 0.37 88 90 4 -21865.8 -7438.34 330.44 0.37 89 91 4 -21864.3 -7438.36 330.36 0.37 90 92 4 -21862.9 -7440.24 334.22 0.37 91 93 4 -21860.7 -7443.62 345.87 0.37 92 94 4 -21859.8 -7443.4 347.22 0.37 93 95 4 -21858.4 -7444.14 348.2 0.37 94 96 4 -21858.3 -7444.26 348.93 0.37 95 97 4 -21856.1 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1540 1542 4 -21911.2 -7394.52 503.23 0.745 1541 1543 4 -21910.4 -7393.69 510.14 0.62 1542 1544 4 -21909.3 -7393.29 517.11 0.62 1543 1545 4 -21907.5 -7394.03 524.88 0.62 1544 1546 4 -21907.6 -7393.77 524.93 0.62 1545 1547 4 -21904.5 -7394.32 536.37 0.495 1546 1548 4 -21900.4 -7394.56 551.91 0.495 1547 1549 4 -21896.2 -7394.52 560.43 0.495 1548 1550 4 -21933.6 -7369.99 343.15 0.495 1339 1551 4 -21935.6 -7367.54 344.64 0.495 1550 1552 4 -21937.7 -7365.89 348.93 0.495 1551 1553 4 -21939 -7363.74 352.75 0.495 1552 1554 4 -21940 -7362.08 355.01 0.37 1553 1555 4 -21940.3 -7362.14 355.15 0.37 1554 1556 4 -21941.5 -7359.51 360.77 0.37 1555 1557 4 -21944.2 -7357.46 365.45 0.37 1556 1558 4 -21946.5 -7355.43 370.5 0.37 1557 1559 4 -21948.4 -7354.95 370.75 0.37 1558 1560 4 -21949.6 -7354.37 375.71 0.37 1559 1561 4 -21950.4 -7353.17 375.98 0.37 1560 1562 4 -21950.3 -7351.71 377.27 0.37 1561 1563 4 -21943.2 -7349.39 375.98 0.37 1562 1564 4 -21944.4 -7346.86 378.97 0.37 1563 1565 4 -21944.3 -7346.99 379.73 0.37 1564 1566 4 -21945.2 -7344.59 383.62 0.37 1565 1567 4 -21945.3 -7344.32 383.67 0.37 1566 1568 4 -21946.3 -7341.44 386.32 0.37 1567 1569 4 -21947.4 -7339.42 389.11 0.37 1568 1570 4 -21949.1 -7336.55 391.12 0.37 1569 1571 4 -21949.1 -7336.35 391.16 0.37 1570 1572 4 -21951.8 -7333.91 397.42 0.25 1571 1573 4 -21951.9 -7333.64 397.47 0.25 1572 1574 4 -21953.8 -7331.74 401.46 0.25 1573 1575 4 -21955.2 -7329.69 405.42 0.25 1574 1576 4 -21955.8 -7327.97 405.76 0.25 1575 1577 4 -21955.8 -7327.74 405.8 0.25 1576 1578 4 -21958.3 -7323.21 414.31 0.25 1577 1579 4 -21959.8 -7320.14 424.53 0.25 1578 1580 4 -21959.7 -7320.33 425.67 0.25 1579 1581 2 -21902.8 -7385.45 200.3 1.115 1 1582 2 -21901.9 -7385.82 200.16 0.99 1581 1583 2 -21901.2 -7386.83 199.91 0.865 1582 1584 2 -21900.7 -7386.9 194.75 0.62 1583 1585 2 -21899.8 -7387.57 191.26 0.495 1584 1586 2 -21900 -7387.6 191.28 0.495 1585 1587 2 -21899.2 -7388.85 188.26 0.495 1586 1588 2 -21900.3 -7389.4 184.31 0.495 1587 1589 2 -21899.7 -7391.42 183.93 0.495 1588 1590 2 -21899.8 -7391.56 178.43 0.495 1589 1591 2 -21895.5 -7390.62 178.19 0.495 1590 1592 2 -21894.9 -7391.04 178.06 0.495 1591 1593 2 -21894.3 -7392.2 175.95 0.495 1592 1594 2 -21894.2 -7392.74 171.79 0.495 1593 1595 2 -21894.3 -7392.58 170.86 0.495 1594 1596 2 -21893.8 -7393.42 169.72 0.495 1595 1597 2 -21893.4 -7393.85 168.13 0.495 1596 1598 2 -21893.5 -7393.65 166.94 0.495 1597 1599 2 -21892.7 -7394.39 164.42 0.495 1598 1600 2 -21892 -7394.96 163.44 0.495 1599 1601 2 -21892.1 -7394.92 163.22 0.495 1600 1602 2 -21891.5 -7395.32 162.92 0.495 1601 1603 2 -21891.5 -7395.05 162.97 0.495 1602 1604 2 -21890.5 -7394.73 157.58 0.495 1603 1605 2 -21892.5 -7392.47 153.72 0.495 1604 1606 2 -21892.6 -7392.24 153.77 0.495 1605 1607 2 -21894.3 -7391.1 152.14 0.495 1606 1608 2 -21894.5 -7390.64 150.85 0.495 1607 1609 2 -21894.5 -7389.12 149.86 0.495 1608 1610 2 -21894.2 -7389.04 149.85 0.495 1609 1611 2 -21893.3 -7387.57 147.11 0.495 1610 1612 2 -21893.1 -7387.27 147.01 0.495 1611 1613 2 -21892 -7386.48 146.73 0.495 1612 1614 2 -21892.2 -7386.19 146.6 0.495 1613 1615 2 -21890.7 -7384.82 146.27 0.495 1614 1616 2 -21890.2 -7384.86 145.39 0.495 1615 1617 2 -21889.3 -7384.72 145.27 0.495 1616 1618 2 -21889.1 -7384.15 145.34 0.495 1617 1619 2 -21888.9 -7384.08 145.33 0.495 1618 1620 2 -21889 -7381.29 138.72 0.495 1619 1621 2 -21887.4 -7380.45 138.52 0.495 1620 1622 2 -21887.6 -7380.24 137.23 0.495 1621 1623 2 -21886.5 -7378.7 134.18 0.495 1622 1624 2 -21886.6 -7378.56 133.34 0.495 1623 1625 2 -21885.8 -7378.04 131.09 0.495 1624 1626 2 -21885.8 -7378 130.84 0.495 1625 1627 2 -21885.5 -7378.1 127.35 0.495 1626 1628 2 -21885 -7378.59 124.27 0.495 1627 1629 2 -21885 -7378.52 123.84 0.495 1628 1630 2 -21884.7 -7379.46 122.23 0.495 1629 1631 2 -21884.5 -7378.99 121.28 0.495 1630 1632 2 -21883.8 -7379.08 119.34 0.495 1631 1633 2 -21883.6 -7378.74 119.38 0.495 1632 1634 2 -21883.2 -7379.06 115.26 0.495 1633 1635 2 -21882.4 -7378.54 111.96 0.495 1634 1636 2 -21882.6 -7378.08 110.61 0.495 1635 1637 2 -21882 -7378.53 112.42 0.495 1636 1638 2 -21881.8 -7377.38 107.36 0.495 1637 1639 2 -21881.8 -7377.1 107.41 0.495 1638 1640 2 -21880 -7378.54 104.92 0.495 1639 1641 2 -21879.7 -7378.28 104.94 0.495 1640 1642 2 -21877.3 -7378.83 104.62 0.495 1641 1643 2 -21876.1 -7379.25 100.06 0.495 1642 1644 2 -21876.3 -7379.24 100.09 0.495 1643 1645 2 -21873.8 -7378.19 97.86 0.495 1644 1646 2 -21872.4 -7378.57 92.25 0.495 1645 1647 2 -21872.8 -7378.24 91.53 0.495 1646 1648 2 -21872.5 -7375.5 85.58 0.495 1647 1649 2 -21871.8 -7374.61 85.65 0.37 1648 1650 2 -21871.8 -7374.33 85.71 0.37 1649 1651 2 -21871.1 -7372.29 85.62 0.37 1650 1652 2 -21870.4 -7371.15 85.75 0.37 1651 1653 2 -21871.2 -7369.18 86.15 0.37 1652 1654 2 -21872.3 -7367.48 86.53 0.37 1653 1655 2 -21872.3 -7365.66 86.84 0.37 1654 1656 2 -21872.5 -7363.77 88.38 0.37 1655 1657 2 -21873.1 -7363.06 89.88 0.37 1656 1658 2 -21873.6 -7361.82 90.13 0.37 1657 1659 2 -21875.4 -7361.06 90.42 0.37 1658 1660 2 -21875.4 -7360.76 90.47 0.37 1659 1661 2 -21876.7 -7359.85 94.79 0.37 1660 1662 2 -21878 -7358.95 94.95 0.37 1661 1663 2 -21878 -7358.93 94.78 0.37 1662 1664 2 -21880.5 -7357.66 94.57 0.37 1663 1665 2 -21880.6 -7357.49 93.52 0.37 1664 1666 2 -21882.5 -7356.82 91.07 0.37 1665 1667 2 -21884.3 -7358.03 91.68 0.37 1666 1668 2 -21886.6 -7358.36 91.84 0.37 1667 1669 2 -21887.7 -7358.27 91.96 0.37 1668 1670 2 -21888.1 -7357.26 92.17 0.37 1669 1671 2 -21890.1 -7357.35 92.34 0.37 1670 1672 2 -21892 -7356.97 90.39 0.37 1671 1673 2 -21894 -7356.8 90.6 0.37 1672 1674 2 -21894 -7356.54 90.65 0.37 1673 1675 2 -21895.5 -7355.99 90.89 0.37 1674 1676 2 -21897.2 -7356.66 90.1 0.37 1675 1677 2 -21899.4 -7357.25 90.2 0.37 1676 1678 2 -21902.6 -7355.84 91.8 0.37 1677 1679 2 -21904 -7355.83 91.93 0.37 1678 1680 2 -21907 -7355.26 92.31 0.37 1679 1681 2 -21907.8 -7355.37 92.36 0.37 1680 1682 2 -21911.2 -7355.11 92.73 0.37 1681 1683 2 -21911.4 -7354.08 92.92 0.37 1682 1684 2 -21912.8 -7354.06 93.05 0.37 1683 1685 2 -21914.2 -7354.28 93.14 0.37 1684 1686 2 -21916.6 -7353.66 93.47 0.37 1685 1687 2 -21916.8 -7353.65 93.5 0.37 1686 1688 2 -21919 -7353 92.59 0.37 1687 1689 2 -21919 -7352.76 92.63 0.37 1688 1690 2 -21921.4 -7352.03 92.98 0.37 1689 1691 2 -21921.6 -7352.14 92.98 0.37 1690 1692 2 -21925.6 -7352.22 93.34 0.37 1691 1693 2 -21926 -7352.02 93.4 0.37 1692 1694 2 -21927.9 -7352.04 93.58 0.37 1693 1695 2 -21928.2 -7351.86 93.64 0.37 1694 1696 2 -21929.7 -7350.79 93.96 0.37 1695 1697 2 -21930.4 -7350.4 94.09 0.37 1696 1698 2 -21932.5 -7349.38 94.46 0.37 1697 1699 2 -21936.6 -7348.46 94.99 0.37 1698 1700 2 -21939.1 -7348.03 93.46 0.37 1699 1701 2 -21943 -7346.8 94.02 0.37 1700 1702 2 -21946.3 -7346.54 91.32 0.37 1701 1703 2 -21950.1 -7345.87 91.79 0.37 1702 1704 2 -21952.9 -7343.92 92.37 0.37 1703 1705 2 -21952.9 -7343.64 92.42 0.37 1704 1706 2 -21955.8 -7342 92.95 0.37 1705 1707 2 -21956 -7342.03 92.98 0.37 1706 1708 2 -21958.5 -7341.53 90.97 0.25 1707 1709 2 -21960 -7341.25 91.15 0.25 1708 1710 2 -21960.3 -7341.01 91.23 0.25 1709 1711 2 -21962 -7341.29 91.34 0.25 1710 1712 2 -21962.3 -7340.81 91.45 0.25 1711 1713 2 -21964.6 -7340.6 91.7 0.25 1712 1714 2 -21965 -7339.93 91.84 0.25 1713 1715 2 -21968.2 -7339.14 92.28 0.25 1714 1716 2 -21968.6 -7338.91 92.35 0.25 1715 1717 2 -21970.3 -7338.68 92.55 0.25 1716 1718 2 -21970.6 -7338.46 92.61 0.25 1717 1719 2 -21971.3 -7339.09 94.02 0.25 1718 1720 2 -21972.3 -7337.92 94.31 0.25 1719 1721 2 -21973.2 -7337.78 94.42 0.25 1720 1722 2 -21975.3 -7337.31 93.08 0.25 1721 1723 2 -21975.7 -7336.89 93.18 0.25 1722 1724 2 -21975.8 -7335.86 93.36 0.25 1723 1725 2 -21976.1 -7335.62 93.44 0.25 1724 1726 2 -21977.8 -7335.63 93.59 0.25 1725 1727 2 -21980 -7335.52 94.13 0.25 1726 1728 2 -21982.1 -7335.36 94.35 0.25 1727 1729 2 -21983.3 -7334.97 94.53 0.25 1728 1730 2 -21983.4 -7334.72 94.58 0.25 1729 1731 2 -21984.5 -7332.9 93.84 0.25 1730 1732 2 -21986.1 -7333.7 93.85 0.25 1731 1733 2 -21988.2 -7332.95 94.18 0.25 1732 1734 2 -21990.2 -7331.53 93.95 0.25 1733 1735 2 -21992.6 -7330.61 94.33 0.25 1734 1736 2 -21997.1 -7328.71 95.06 0.25 1735 1737 2 -21997.4 -7328.76 95.08 0.25 1736 1738 2 -21999.7 -7328.11 95.41 0.25 1737 1739 2 -21999.8 -7327.58 95.51 0.25 1738 1740 2 -22001 -7327.27 95.67 0.25 1739 1741 2 -22001.3 -7327.05 95.73 0.25 1740 1742 2 -22004 -7326.11 96.14 0.25 1741 1743 2 -22007.4 -7325.45 94.02 0.25 1742 1744 2 -22010.1 -7324.33 94.46 0.25 1743 1745 2 -22010.9 -7323.94 96.08 0.25 1744 1746 2 -22012.9 -7323.71 96.31 0.25 1745 1747 2 -22015.2 -7323.54 96.56 0.25 1746 1748 2 -22015.2 -7323.33 96.59 0.25 1747 1749 2 -22017 -7321.88 94.74 0.25 1748 1750 2 -22017 -7322.18 94.69 0.25 1749 1751 2 -22018.1 -7322.31 94.77 0.25 1750 1752 2 -22018.3 -7322.34 94.8 0.25 1751 1753 2 -22019.5 -7322.29 94.91 0.25 1752 1754 2 -22020.1 -7322.11 95 0.25 1753 1755 2 -22021.5 -7322.34 95.09 0.25 1754 1756 2 -22023.5 -7322.15 95.31 0.25 1755 1757 2 -22025.7 -7320.97 94.42 0.25 1756 1758 2 -22026.7 -7320.03 94.67 0.25 1757 1759 2 -22027.2 -7319.07 94.87 0.25 1758 1760 2 -22028.4 -7318.22 95.13 0.25 1759 1761 2 -22028.5 -7317.97 95.17 0.25 1760 1762 2 -22030.6 -7318.57 95.28 0.25 1761 1763 2 -22030.9 -7318.62 95.3 0.25 1762 1764 2 -22031 -7317.86 95.43 0.25 1763 1765 2 -22033.1 -7317.14 95.75 0.25 1764 1766 2 -22033.2 -7316.33 95.89 0.25 1765 1767 2 -22033.6 -7316.16 95.95 0.25 1766 1768 2 -22033.6 -7315.62 96.05 0.25 1767 1769 2 -22034 -7315.13 96.16 0.25 1768 1770 2 -22034.9 -7314.73 96.31 0.25 1769 1771 2 -22035.5 -7315.22 98.2 0.25 1770 1772 2 -22037 -7314.91 98.4 0.25 1771 1773 2 -22037.2 -7314.96 98.41 0.25 1772 1774 2 -22042.3 -7312.73 96.75 0.125 1773 1775 2 -21904.7 -7356.81 85.01 0.25 1677 1776 2 -21904.7 -7356.71 84.36 0.25 1775 1777 2 -21908.1 -7357.01 84.7 0.25 1776 1778 2 -21910.9 -7356.87 81.57 0.25 1777 1779 2 -21913 -7358.66 79.16 0.25 1778 1780 2 -21913 -7358.6 78.79 0.25 1779 1781 2 -21915.7 -7360.1 77.34 0.25 1780 1782 2 -21918.3 -7360.86 74.63 0.25 1781 1783 2 -21920.7 -7362.03 74.66 0.25 1782 1784 2 -21922.6 -7362.36 74.79 0.25 1783 1785 2 -21960.7 -7355.05 55.17 0.25 1784 1786 2 -21962.8 -7356.2 55.18 0.25 1785 1787 2 -21966.3 -7357.66 54.43 0.25 1786 1788 2 -21969 -7358.85 54.48 0.25 1787 1789 2 -21969.8 -7358.96 54.54 0.25 1788 1790 2 -21973.1 -7359.53 54.75 0.25 1789 1791 2 -21974 -7359.18 54.89 0.25 1790 1792 2 -21974.6 -7359 54.98 0.25 1791 1793 2 -21974.9 -7358.79 55.04 0.25 1792 1794 2 -21976.7 -7358.03 55.34 0.25 1793 1795 2 -21977.7 -7357.11 55.58 0.25 1794 1796 2 -21978 -7356.9 55.65 0.25 1795 1797 2 -21981 -7355.82 56.11 0.25 1796 1798 2 -21981.4 -7355.32 56.22 0.25 1797 1799 2 -21981.8 -7354.93 56.32 0.25 1798 1800 2 -21981.8 -7354.37 56.42 0.25 1799 1801 2 -21982.1 -7354.15 56.49 0.25 1800 1802 2 -21985.1 -7353.3 52.31 0.25 1801 1803 2 -21985.4 -7353.32 52.31 0.25 1802 1804 2 -21987.1 -7353.55 52.17 0.25 1803 1805 2 -21987.4 -7353.48 51.69 0.25 1804 1806 2 -21988.4 -7353.57 49.68 0.25 1805 1807 2 -21988.9 -7353.69 49.71 0.25 1806 1808 2 -21990.1 -7353.55 49.84 0.25 1807 1809 2 -21991.2 -7353.77 49.9 0.25 1808 1810 2 -21997.8 -7349.59 51.21 0.37 1809 1811 2 -21998 -7349.93 51.18 0.37 1810 1812 2 -21998.7 -7349.24 51.36 0.37 1811 1813 2 -22000.8 -7348.59 51.66 0.37 1812 1814 2 -22002.3 -7347.86 51.93 0.37 1813 1815 2 -22004.5 -7347.96 49.08 0.37 1814 1816 2 -22006.7 -7348.11 49.19 0.37 1815 1817 2 -22008.8 -7348.97 48.82 0.37 1816 1818 2 -22008.8 -7348.94 48.63 0.37 1817 1819 2 -22010.7 -7349.96 47.71 0.37 1818 1820 2 -22012.6 -7350.54 47.78 0.37 1819 1821 2 -22012.6 -7350.31 47.83 0.37 1820 1822 2 -22013.2 -7349.85 47.96 0.37 1821 1823 2 -22014.1 -7350.46 45.92 0.37 1822 1824 2 -22015.5 -7350.76 46 0.37 1823 1825 2 -22016.3 -7351.14 46.01 0.37 1824 1826 2 -22016.6 -7350.96 46.07 0.37 1825 1827 2 -22016.9 -7350.74 46.13 0.37 1826 1828 2 -22017 -7350.49 46.18 0.37 1827 1829 2 -22019 -7350.35 44.98 0.37 1828 1830 2 -22020.8 -7350.44 45.13 0.37 1829 1831 2 -22022.2 -7350.16 45.31 0.37 1830 1832 2 -22022.6 -7349.74 45.42 0.37 1831 1833 2 -22023.6 -7350.44 45.4 0.37 1832 1834 2 -22025.2 -7350.04 42.99 0.37 1833 1835 2 -22026.5 -7350.54 43.03 0.37 1834 1836 2 -22027.1 -7350.62 43.07 0.37 1835 1837 2 -22027.1 -7350.93 43.02 0.37 1836 1838 2 -22028.4 -7351.16 43.11 0.37 1837 1839 2 -22028.7 -7351.46 43.08 0.37 1838 1840 2 -22032.2 -7351.81 39.89 0.37 1839 1841 2 -22053.3 -7350.96 37.18 0.37 1840 1842 2 -22055 -7351.06 37.32 0.37 1841 1843 2 -22057.4 -7350.43 37.65 0.37 1842 1844 2 -22058.2 -7350.56 37.7 0.37 1843 1845 2 -22059.1 -7350.22 37.84 0.37 1844 1846 2 -22059.4 -7350.26 37.86 0.37 1845 1847 2 -22060.4 -7350.47 37.93 0.37 1846 1848 2 -22060.4 -7350.73 37.88 0.37 1847 1849 2 -22064.1 -7350.72 34.28 0.37 1848 1850 2 -22065.7 -7351.06 34.37 0.37 1849 1851 2 -22067.1 -7351.04 34.51 0.37 1850 1852 2 -22068 -7350.69 34.65 0.37 1851 1853 2 -22068.4 -7350.24 34.76 0.37 1852 1854 2 -22069.2 -7350.64 34.77 0.37 1853 1855 2 -22069.5 -7350.71 34.79 0.37 1854 1856 2 -22070.6 -7350.63 34.9 0.37 1855 1857 2 -22071.2 -7350.26 35.02 0.37 1856 1858 2 -22076.2 -7349.34 35.64 0.37 1857 1859 2 -22078.4 -7348.93 32.83 0.37 1858 1860 2 -22079.8 -7347.65 33.17 0.37 1859 1861 2 -22081 -7348.64 33.12 0.37 1860 1862 2 -22081.3 -7348.49 33.18 0.37 1861 1863 2 -22083.1 -7348.41 32.61 0.37 1862 1864 2 -22083 -7348.62 32.57 0.37 1863 1865 2 -22085.2 -7349.03 32.71 0.37 1864 1866 2 -22087 -7349.13 32.85 0.37 1865 1867 2 -22089.2 -7349 33.09 0.37 1866 1868 2 -22090.1 -7349.29 30.73 0.37 1867 1869 2 -22091.2 -7348.18 31.01 0.37 1868 1870 2 -22092.5 -7348.69 31.05 0.37 1869 1871 2 -22092.8 -7348.73 31.07 0.37 1870 1872 2 -22095.5 -7349.53 31.19 0.37 1871 1873 2 -22095.7 -7349.59 31.2 0.37 1872 1874 2 -22097.5 -7349.07 28.31 0.37 1873 1875 2 -22098.3 -7349.22 28.36 0.37 1874 1876 2 -22098.8 -7349.32 28.4 0.37 1875 1877 2 -22099.6 -7349.72 28.4 0.37 1876 1878 2 -22100.2 -7349.36 28.52 0.37 1877 1879 2 -22100.5 -7349.14 28.59 0.37 1878 1880 2 -22101.2 -7348.99 28.67 0.37 1879 1881 2 -22104.6 -7350.16 28.8 0.37 1880 1882 2 -22106.1 -7349.94 28.96 0.37 1881 1883 2 -22106.8 -7349.24 29.14 0.37 1882 1884 2 -22107.9 -7348.28 27.18 0.37 1883 1885 2 -22108.4 -7347.07 27.43 0.37 1884 1886 2 -22110 -7346.06 23.36 0.37 1885 1887 2 -22110 -7346.32 23.31 0.37 1886 1888 2 -22111.2 -7346.48 21.75 0.37 1887 1889 2 -22111.1 -7346.76 21.69 0.37 1888 1890 2 -22112.2 -7347.25 21.71 0.37 1889 1891 2 -22112.4 -7347.55 21.68 0.37 1890 1892 2 -22114 -7348.09 21.74 0.37 1891 1893 2 -22115.5 -7349.44 21.66 0.37 1892 1894 2 -22116.2 -7350.1 21.61 0.37 1893 1895 2 -22118 -7350.37 19.83 0.37 1894 1896 2 -22119.3 -7350.64 19.91 0.37 1895 1897 2 -22121.7 -7350.42 19.38 0.37 1896 1898 2 -22123.7 -7350.28 19.59 0.37 1897 1899 2 -22125.2 -7349.73 19.7 0.37 1898 1900 2 -22126.3 -7348.34 20.03 0.37 1899 1901 2 -22127.3 -7347.71 18.33 0.37 1900 1902 2 -22129 -7347.76 18.48 0.37 1901 1903 2 -22129.6 -7347.84 18.52 0.37 1902 1904 2 -22130.9 -7347.93 16.23 0.37 1903 1905 2 -22132 -7347.64 16.39 0.37 1904 1906 2 -22132.3 -7347.67 16.4 0.37 1905 1907 2 -22133.3 -7347.31 16.56 0.37 1906 1908 2 -22133.5 -7347.38 16.57 0.37 1907 1909 2 -22135.5 -7347.74 16.69 0.37 1908 1910 2 -22135.7 -7347.82 16.7 0.37 1909 1911 2 -22137.5 -7347.08 17 0.37 1910 1912 2 -22137.6 -7346.84 17.04 0.37 1911 1913 2 -22138.2 -7346.68 17.12 0.37 1912 1914 2 -22138.5 -7346.72 17.14 0.37 1913 1915 2 -22139 -7346.59 17.22 0.37 1914 1916 2 -22139.3 -7346.4 17.28 0.37 1915 1917 2 -22141.7 -7346.73 15.52 0.37 1916 1918 2 -22142 -7346.8 15.54 0.37 1917 1919 2 -22142.7 -7346.42 14.44 0.37 1918 1920 2 -22144.3 -7346.14 12.8 0.37 1919 1921 2 -22144.2 -7346.33 12.31 0.37 1920 1922 2 -22145 -7346.69 11.96 0.37 1921 1923 2 -22145.3 -7346.74 11.98 0.37 1922 1924 2 -22146.8 -7346.47 12.16 0.37 1923 1925 2 -22148.9 -7346.72 9.78 0.25 1924 1926 2 -22150.5 -7346.76 9.93 0.25 1925 1927 2 -22151.4 -7346.92 9.98 0.25 1926 1928 2 -22153.5 -7346.02 10.33 0.25 1927 1929 2 -22153.5 -7346.28 10.28 0.25 1928 1930 2 -22156.5 -7345.81 9.46 0.25 1929 1931 2 -22165.2 -7346.55 4.94 0.25 1930 1932 2 -22165.5 -7346.57 4.67 0.25 1931 1933 2 -22166.9 -7346.54 4.8 0.25 1932 1934 2 -22168.2 -7347.15 5.12 0.25 1933 1935 2 -22168.1 -7347.4 5.07 0.25 1934 1936 2 -22169 -7347.7 3.03 0.25 1935 1937 2 -22170.9 -7348.61 3.05 0.25 1936 1938 2 -22172.5 -7348.89 3.16 0.25 1937 1939 2 -22174.7 -7348.24 1.87 0.25 1938 1940 2 -22177 -7348.11 2.11 0.25 1939 1941 2 -22177.6 -7347.73 2.23 0.25 1940 1942 2 -22178.7 -7347.95 2.29 0.25 1941 1943 2 -22179.5 -7346.78 2.56 0.25 1942 1944 2 -22179.8 -7346.34 2.67 0.25 1943 1945 2 -22181.9 -7345.63 2.98 0.25 1944 1946 2 -22183.1 -7344.56 1.88 0.25 1945 1947 2 -22183.3 -7344.62 1.89 0.25 1946 1948 2 -22184.5 -7342.98 2.27 0.25 1947 1949 2 -22184.4 -7343.24 2.22 0.25 1948 1950 2 -22187.2 -7343 -0.3 0.25 1949 1951 2 -22187.2 -7343.27 -0.35 0.25 1950 1952 2 -22190.3 -7343.59 -1.52 0.25 1951 1953 2 -22190.5 -7343.65 -1.51 0.25 1952 1954 2 -22192.4 -7344.74 -1.51 0.25 1953 1955 2 -22193.8 -7344.06 -3.25 0.25 1954 1956 2 -22194.4 -7343.89 -3.16 0.25 1955 1957 2 -22196.1 -7343.91 -3.01 0.25 1956 1958 2 -22196.9 -7344.32 -3.12 0.25 1957 1959 2 -22197.7 -7343.85 -4.87 0.25 1958 1960 2 -22198.1 -7344.69 -4.97 0.25 1959 1961 2 -22198.7 -7344.53 -4.89 0.25 1960 1962 2 -22198.9 -7344.59 -4.88 0.25 1961 1963 2 -22199.5 -7344.36 -4.78 0.25 1962 1964 2 -22199.6 -7344.12 -4.74 0.25 1963 1965 2 -22201.3 -7343.02 -6.42 0.25 1964 1966 2 -22201.6 -7342.7 -5.49 0.25 1965 1967 2 -22203.8 -7343.04 -5.33 0.25 1966 1968 2 -22204.2 -7342.86 -5.27 0.25 1967 1969 2 -22206.6 -7343.78 -5.2 0.25 1968 1970 2 -22209.1 -7344.45 -5.08 0.25 1969 1971 2 -22209.3 -7344.51 -5.06 0.25 1970 1972 2 -22211.6 -7344.6 -4.87 0.25 1971 1973 2 -22214.2 -7344.49 -4.6 0.25 1972 1974 2 -22214.4 -7344.57 -4.59 0.25 1973 1975 2 -22216.7 -7344.39 -4.35 0.25 1974 1976 2 -22218.2 -7343.83 -4.11 0.25 1975 1977 2 -22219.8 -7344.65 -4.1 0.25 1976 1978 2 -22221.9 -7344.9 -5.8 0.25 1977 1979 2 -22223.9 -7346.18 -7.95 0.25 1978 1980 2 -22224.6 -7346.6 -7.85 0.25 1979 1981 2 -22225.8 -7346.81 -7.78 0.25 1980 1982 2 -22226 -7346.59 -7.71 0.25 1981 1983 2 -22226.4 -7345.85 -7.55 0.25 1982 1984 2 -22226.7 -7345.87 -7.53 0.25 1983 1985 2 -22228.4 -7345.85 -10.61 0.25 1984 1986 2 -22230.4 -7345.95 -10.44 0.25 1985 1987 2 -22230.7 -7345.46 -10.33 0.25 1986 1988 2 -22231 -7345.53 -10.31 0.25 1987 1989 2 -22232.1 -7345.42 -10.19 0.25 1988 1990 2 -22232.5 -7344.95 -10.07 0.25 1989 1991 2 -22232.8 -7345 -10.06 0.25 1990 1992 2 -22233.1 -7344.8 -10 0.25 1991 1993 2 -22233.7 -7344.32 -9.86 0.25 1992 1994 2 -22234 -7344.13 -9.8 0.25 1993 1995 2 -22235.4 -7344.37 -9.7 0.25 1994 1996 2 -22235.7 -7344.43 -9.69 0.25 1995 1997 2 -22236.8 -7344.32 -9.56 0.25 1996 1998 2 -22237.4 -7344.18 -9.48 0.25 1997 1999 2 -22238 -7344.03 -9.4 0.25 1998 2000 2 -22238.4 -7343.82 -9.34 0.25 1999 2001 2 -22239 -7343.65 -9.25 0.25 2000 2002 2 -22239.8 -7343.79 -9.2 0.25 2001 2003 2 -22240.4 -7343.59 -9.11 0.25 2002 2004 2 -22241 -7342.93 -8.94 0.25 2003 2005 2 -22241.1 -7342.66 -8.89 0.25 2004 2006 2 -22242.4 -7343.4 -8.89 0.25 2005 2007 2 -22243.8 -7344.39 -10.74 0.25 2006 2008 2 -22243.7 -7344.63 -10.78 0.25 2007 2009 2 -22245.9 -7345.25 -10.68 0.25 2008 2010 2 -22247.4 -7345.01 -10.5 0.25 2009 2011 2 -22247.7 -7344.76 -10.43 0.25 2010 2012 2 -22248 -7344.53 -10.37 0.25 2011 2013 2 -22248.5 -7343.56 -10.16 0.25 2012 2014 2 -22249.1 -7343.38 -10.08 0.25 2013 2015 2 -22249.7 -7342.94 -9.95 0.25 2014 2016 2 -22250.9 -7342.64 -9.79 0.25 2015 2017 2 -22251.5 -7342.49 -9.71 0.25 2016 2018 2 -22251.5 -7342.22 -9.66 0.25 2017 2019 2 -22251.8 -7342.03 -9.6 0.25 2018 2020 2 -22252.2 -7341.77 -9.52 0.25 2019 2021 2 -22278.5 -7349.83 -25.11 0.25 2020 2022 2 -22285.3 -7351.06 -27.24 0.25 2021 2023 2 -22286.1 -7351.19 -27.18 0.25 2022 2024 2 -22287.4 -7351.95 -27.19 0.25 2023 2025 2 -22287.7 -7351.99 -27.17 0.25 2024 2026 2 -22289.5 -7351.78 -28.36 0.25 2025 2027 2 -22291 -7351.22 -28.14 0.25 2026 2028 2 -22291.3 -7351.26 -28.12 0.25 2027 2029 2 -22292.5 -7351.43 -28.04 0.25 2028 2030 2 -22292.4 -7351.68 -28.09 0.25 2029 2031 2 -22294.4 -7352.15 -28.83 0.25 2030 2032 2 -22294.7 -7352.22 -28.75 0.25 2031 2033 2 -22296.2 -7351.91 -28.56 0.25 2032 2034 2 -22296.2 -7351.65 -28.52 0.25 2033 2035 2 -22297.2 -7352.07 -28.49 0.25 2034 2036 2 -22297.5 -7352.39 -28.52 0.25 2035 2037 2 -22298.5 -7352.61 -28.45 0.25 2036 2038 2 -22301.2 -7352.11 -31.65 0.25 2037 2039 2 -22301.1 -7352.4 -31.7 0.25 2038 2040 2 -22302.1 -7353.09 -31.72 0.25 2039 2041 2 -22302.9 -7353.98 -31.8 0.25 2040 2042 2 -22303.2 -7354.05 -31.78 0.25 2041 2043 2 -22304 -7354.2 -31.73 0.25 2042 2044 2 -22304.8 -7354.32 -31.67 0.25 2043 2045 2 -22305.6 -7354.47 -31.62 0.25 2044 2046 2 -22305.6 -7354.2 -31.57 0.25 2045 2047 2 -22307 -7355.24 -31.62 0.25 2046 2048 2 -22308.8 -7354.96 -33.14 0.25 2047 2049 2 -22309.3 -7355.06 -33.1 0.25 2048 2050 2 -22310.2 -7355.17 -33.04 0.25 2049 2051 2 -22310.7 -7355.54 -33.05 0.25 2050 2052 2 -22312.6 -7356.37 -33.01 0.25 2051 2053 2 -22312.9 -7356.42 -32.99 0.25 2052 2054 2 -22313.4 -7356.23 -32.91 0.25 2053 2055 2 -22313.5 -7356.02 -32.87 0.25 2054 2056 2 -22315.5 -7356.68 -35.27 0.25 2055 2057 2 -22316.9 -7357.19 -35.28 0.25 2056 2058 2 -22316.9 -7357.17 -35.44 0.25 2057 2059 2 -22319.2 -7357.83 -36.7 0.25 2058 2060 2 -22319.5 -7357.56 -37.09 0.25 2059 2061 2 -22321.3 -7357.65 -37.86 0.25 2060 2062 2 -22322.4 -7357.87 -37.79 0.25 2061 2063 2 -22324.1 -7357.86 -37.63 0.25 2062 2064 2 -22324.4 -7357.91 -37.61 0.25 2063 2065 2 -22326.5 -7357.95 -39.06 0.25 2064 2066 2 -22326.5 -7357.93 -39.21 0.25 2065 2067 2 -22334 -7359.18 -43.43 0.25 2066 2068 2 -22335.7 -7359.49 -44.5 0.25 2067 2069 2 -22335.8 -7359.4 -45.07 0.25 2068 2070 2 -22337.2 -7359.97 -46.18 0.25 2069 2071 2 -22338 -7360.45 -47.37 0.25 2070 2072 2 -22338.5 -7361.21 -48.03 0.25 2071 2073 2 -22338.7 -7361.04 -49.02 0.25 2072 2074 2 -22339.7 -7361.5 -49 0.25 2073 2075 2 -22339.8 -7361.37 -49.8 0.25 2074 2076 2 -22341.5 -7361.13 -49.6 0.25 2075 2077 2 -22341.6 -7361.07 -50.01 0.25 2076 2078 2 -22344.2 -7362.02 -50.12 0.25 2077 2079 2 -22346.8 -7362.57 -50.78 0.25 2078 2080 2 -22346.7 -7362.81 -50.83 0.25 2079 2081 2 -22349.2 -7362.88 -52.38 0.25 2080 2082 2 -22349.9 -7363.53 -52.41 0.25 2081 2083 2 -22350.2 -7363.58 -52.4 0.25 2082 2084 2 -22350.9 -7364.21 -52.43 0.25 2083 2085 2 -22351.5 -7364.07 -52.35 0.25 2084 2086 2 -22352.4 -7362.98 -54.54 0.25 2085 2087 2 -22352.7 -7363.01 -54.52 0.25 2086 2088 2 -22354.4 -7363.07 -54.39 0.25 2087 2089 2 -22356 -7362.74 -56.11 0.25 2088 2090 2 -22357.9 -7363.33 -55.87 0.25 2089 2091 2 -22358.2 -7363.6 -55.89 0.25 2090 2092 2 -22361.1 -7364.7 -57.16 0.125 2091 2093 2 -22361.9 -7364.83 -57.1 0.125 2092 2094 2 -22363.5 -7364.41 -59.04 0.125 2093 2095 2 -22364.6 -7365.08 -59.05 0.125 2094 2096 2 -22364.9 -7365.07 -59.02 0.125 2095 2097 2 -22365.8 -7365 -60.24 0.125 2096 2098 2 -22366.9 -7365.15 -60.16 0.125 2097 2099 2 -22367.2 -7365.22 -60.14 0.125 2098 2100 2 -22368.1 -7364.81 -59.99 0.125 2099 2101 2 -22369.2 -7365.58 -60.72 0.125 2100 2102 2 -22370.3 -7365.19 -60.54 0.125 2101 2103 2 -22371.4 -7365.63 -60.52 0.125 2102 2104 2 -22371.7 -7365.65 -60.49 0.125 2103 2105 2 -22372.5 -7365.76 -60.43 0.125 2104 2106 2 -22372.9 -7365.58 -60.37 0.125 2105 2107 2 -22374.6 -7365.57 -61.3 0.125 2106 2108 2 -22375.1 -7366.09 -61.93 0.125 2107 2109 2 -22375.3 -7366.91 -62.04 0.125 2108 2110 2 -22376.6 -7367.2 -64.1 0.125 2109 2111 2 -22377.5 -7367.71 -66.3 0.125 2110 2112 2 -22378.3 -7368.36 -66.34 0.125 2111 2113 2 -22379.4 -7369.01 -66.42 0.125 2112 2114 2 -22379.7 -7369.68 -67.55 0.125 2113 2115 2 -22380 -7369.71 -67.76 0.125 2114 2116 2 -22381.7 -7369.92 -69.1 0.125 2115 2117 2 -22382.8 -7370.56 -69.11 0.125 2116 2118 2 -22384.2 -7370.24 -68.92 0.125 2117 2119 2 -22384.5 -7370.31 -68.91 0.125 2118 2120 2 -22385.3 -7370.4 -68.84 0.125 2119 2121 2 -22387.6 -7370.29 -72.33 0.125 2120 2122 2 -22387.8 -7370.32 -72.31 0.125 2121 2123 2 -22388.8 -7369.99 -73.32 0.125 2122 2124 2 -22389.1 -7370.02 -73.3 0.125 2123 2125 2 -22390.6 -7369.38 -73.51 0.125 2124 2126 2 -22390.9 -7369.4 -73.49 0.125 2125 2127 2 -22392.9 -7368.8 -73.56 0.125 2126 2128 2 -22395.4 -7369.14 -73.38 0.125 2127 2129 2 -22397.9 -7370.55 -77.83 0.125 2128 2130 2 -22398.1 -7370.85 -77.86 0.125 2129 2131 2 -22399.8 -7372.9 -78.04 0.125 2130 2132 2 -22402.4 -7372.63 -81.18 0.125 2131 2133 2 -22440.9 -7376.04 -98.4 0.125 2132 2134 2 -22443.3 -7376.62 -98.27 0.125 2133 2135 2 -22444.2 -7376.74 -98.21 0.125 2134 2136 2 -22446.9 -7375.49 -97.75 0.125 2135 2137 2 -22447.2 -7375 -97.64 0.125 2136 2138 2 -22447.6 -7374.26 -97.48 0.125 2137 2139 2 -22447.9 -7374.04 -97.42 0.125 2138 2140 2 -22450.2 -7373.82 -97.17 0.125 2139 2141 2 -22450.4 -7374.09 -97.19 0.125 2140 2142 2 -22451.4 -7375.27 -97.29 0.125 2141 2143 2 -22451.6 -7375.57 -97.32 0.125 2142 2144 2 -22451.8 -7376.11 -97.39 0.125 2143 2145 2 -22453.4 -7377.37 -97.45 0.125 2144 2146 2 -22454.8 -7377.75 -97.79 0.125 2145 2147 2 -22456.8 -7378.28 -99.32 0.125 2146 2148 2 -22457.1 -7378.77 -99.71 0.125 2147 2149 2 -22456.9 -7380.24 -100.59 0.125 2148 2150 2 -22457 -7380.41 -101.33 0.125 2149 2151 2 -22456.9 -7381.26 -102.19 0.125 2150 2152 2 -22457.5 -7382.5 -108 0.125 2151 2153 2 -22457.2 -7383.08 -109.28 0.125 2152 2154 2 -22457.5 -7383.08 -109.26 0.125 2153 2155 2 -22458.7 -7383.96 -111.52 0.125 2154 2156 2 -22459.9 -7383.93 -113.84 0.125 2155 2157 2 -22459.9 -7384.2 -113.88 0.125 2156 2158 2 -22460.6 -7385.45 -113.35 0.125 2157 2159 2 -22466.4 -7381.97 -112.23 0.125 2158 2160 2 -22468.7 -7382.23 -111.45 0.125 2159 2161 2 -22470.5 -7382.45 -113.21 0.125 2160 2162 2 -22470.4 -7382.45 -113.22 0.125 2161 2163 2 -22471.9 -7382.46 -113.09 0.125 2162 2164 2 -22472.8 -7382.34 -112.99 0.125 2163 2165 2 -22473.8 -7382.77 -112.99 0.125 2164 2166 2 -22474.7 -7382.35 -116.02 0.125 2165 2167 2 -22475 -7382.19 -115.96 0.125 2166 2168 2 -22476.9 -7382.8 -115.88 0.125 2167 2169 2 -22478.1 -7382.72 -115.76 0.125 2168 2170 2 -22478.6 -7381.21 -115.46 0.125 2169 2171 2 -22479.6 -7382.35 -116.24 0.125 2170 2172 2 -22481.5 -7383.16 -117.84 0.125 2171 2173 2 -22481.6 -7383.09 -118.27 0.125 2172 2174 2 -22483.1 -7381.95 -120.1 0.125 2173 2175 2 -22483.8 -7382.88 -120.28 0.125 2174 2176 2 -22483.8 -7382.86 -120.41 0.125 2175 2177 2 -22484.9 -7382.45 -123.68 0.125 2176 2178 2 -22485.5 -7382.54 -123.64 0.125 2177 2179 2 -22486.6 -7382.75 -123.57 0.125 2178 2180 2 -22486.9 -7382.28 -123.46 0.125 2179 2181 2 -22488.8 -7381.88 -128.87 0.125 2180 2182 2 -22515 -7388.77 -154.34 0.125 2181 2183 2 -22518.5 -7389.2 -156.99 0.125 2182 2184 2 -22518.8 -7389.24 -156.97 0.125 2183 2185 2 -22523.3 -7389.58 -160.89 0.125 2184 2186 2 -22524.5 -7389.24 -160.72 0.125 2185 2187 2 -22525.9 -7388.5 -161.36 0.125 2186 2188 2 -22527.3 -7388.43 -161.65 0.125 2187 2189 2 -22528 -7388.2 -163.35 0.125 2188 2190 2 -22530.6 -7388.64 -165.29 0.125 2189 2191 2 -22531.3 -7388.61 -165.75 0.125 2190 2192 2 -22532.9 -7389.52 -162.55 0.125 2191 2193 2 -22533.5 -7389.64 -162.25 0.125 2192 2194 2 -22535.6 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2261 2263 2 -22606.9 -7427.66 -232.85 0.125 2262 2264 2 -22609.3 -7427.91 -236.09 0.125 2263 2265 2 -22609.2 -7428.15 -236.22 0.125 2264 2266 2 -22611.5 -7428.35 -237.92 0.125 2265 2267 2 -22614.1 -7429.5 -238.14 0.125 2266 2268 2 -22614.2 -7429.45 -238.45 0.125 2267 2269 2 -22615.3 -7429.07 -242.02 0.125 2268 2270 2 -22615.6 -7429.11 -242 0.125 2269 2271 2 -22617.5 -7429.32 -242.03 0.125 2270 2272 2 -22617.6 -7429.24 -242.52 0.125 2271 2273 2 -22620.2 -7428.63 -244.49 0.125 2272 2274 2 -22622.4 -7429.15 -246.01 0.125 2273 2275 2 -22622.7 -7428.97 -246.01 0.125 2274 2276 2 -22624 -7428.49 -245.84 0.125 2275 2277 2 -22624.6 -7428.72 -248.41 0.125 2276 2278 2 -22629.8 -7437.44 -249.37 0.125 2277 2279 2 -22630.9 -7437.39 -249.26 0.125 2278 2280 2 -22631.2 -7437.44 -249.24 0.125 2279 2281 2 -22632.8 -7437.98 -249.18 0.125 2280 2282 2 -22634.5 -7438.48 -250.98 0.125 2281 2283 2 -22637.3 -7438.77 -253.78 0.125 2282 2284 2 -22638.1 -7438.68 -253.69 0.125 2283 2285 2 -22640.2 -7439.8 -253.68 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0.125 2307 2309 2 -22668.4 -7445.53 -282.86 0.125 2308 2310 2 -22669.4 -7445.45 -285.7 0.125 2309 2311 2 -22669.5 -7445.24 -285.65 0.125 2310 2312 2 -22671.2 -7444.7 -288.72 0.125 2311 2313 2 -22672.9 -7445.41 -289.45 0.125 2312 2314 2 -22676.8 -7445.27 -296.96 0.125 2313 2315 2 -22680.4 -7447.21 -297.38 0.125 2314 2316 2 -22681.6 -7447.01 -298.05 0.125 2315 2317 2 -22684.1 -7446.33 -302.68 0.125 2316 2318 2 -22684.6 -7446.43 -302.65 0.125 2317 2319 2 -22685.3 -7446.11 -303.52 0.125 2318 2320 2 -22685.8 -7446.5 -303.53 0.125 2319 2321 2 -22686.8 -7446.96 -303.51 0.125 2320 2322 2 -22687.1 -7447.01 -303.49 0.125 2321 2323 2 -22687.8 -7446.35 -303.36 0.125 2322 2324 2 -22687.8 -7446.33 -303.51 0.125 2323 2325 2 -22688.9 -7445.84 -305.58 0.125 2324 2326 2 -22689.2 -7445.87 -305.56 0.125 2325 2327 2 -22690.8 -7446.46 -305.51 0.125 2326 2328 2 -22692.3 -7447.5 -305.54 0.125 2327 2329 2 -22693.9 -7448.06 -310.07 0.125 2328 2330 2 -22696.5 -7448.8 -313.08 0.125 2329 2331 2 -22697.8 -7449.87 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2469 2 -22869.3 -7441.23 -455.17 0.125 2468 2470 2 -22870.9 -7441.36 -457.67 0.125 2469 2471 2 -22871.7 -7441.49 -457.61 0.125 2470 2472 2 -22873.1 -7441.71 -459.22 0.125 2471 2473 2 -22874.3 -7441.71 -459.15 0.125 2472 2474 2 -22874.3 -7441.68 -459.34 0.125 2473 2475 2 -22876.3 -7442.02 -461.09 0.125 2474 2476 2 -22876.3 -7442.25 -461.14 0.125 2475 2477 2 -22876.7 -7442.25 -463.21 0.125 2476 2478 2 -22877.5 -7441.04 -462.93 0.125 2477 2479 2 -22878.7 -7441.18 -464.96 0.125 2478 2480 2 -22879.8 -7441.37 -464.89 0.125 2479 2481 2 -22880.1 -7441.17 -464.83 0.125 2480 2482 2 -22881.6 -7440.41 -467.53 0.125 2481 2483 2 -22882.8 -7440.09 -467.54 0.125 2482 2484 2 -22884.1 -7440.13 -470.04 0.125 2483 2485 2 -22887.9 -7440.06 -471.33 0.125 2484 2486 2 -22890.4 -7439.7 -474.43 0.125 2485 2487 2 -22890.7 -7439.78 -474.42 0.125 2486 2488 2 -22892.1 -7440.08 -475.63 0.125 2487 2489 2 -22892.4 -7440.1 -475.61 0.125 2488 2490 2 -22893.7 -7440.04 -477.43 0.125 2489 2491 2 -22894 -7439.81 -477.37 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144.66 0.37 2951 2953 2 -21995.1 -7284.19 144.82 0.37 2952 2954 2 -21997.5 -7282.41 144.79 0.37 2953 2955 2 -22017.3 -7262.5 160.43 0.37 2954 2956 2 -22019 -7260.95 160.84 0.37 2955 2957 2 -22020.1 -7258.86 163.26 0.37 2956 2958 2 -22021.6 -7257.91 164.15 0.37 2957 2959 2 -22022.1 -7256.42 164.45 0.37 2958 2960 2 -22024.2 -7256.02 164.75 0.37 2959 2961 2 -22024.5 -7255.82 164.82 0.37 2960 2962 2 -22025.3 -7255.1 166.26 0.37 2961 2963 2 -22025.6 -7254.39 168.14 0.37 2962 2964 2 -22026.2 -7252.75 168.68 0.37 2963 2965 2 -22027.5 -7251.65 169.03 0.37 2964 2966 2 -22029.4 -7250.42 169.5 0.37 2965 2967 2 -22030 -7248.68 169.85 0.37 2966 2968 2 -22030.7 -7247.51 170.12 0.37 2967 2969 2 -22032.8 -7247.33 170.35 0.37 2968 2970 2 -22033.6 -7247.21 170.45 0.37 2969 2971 2 -22033.9 -7247.26 170.46 0.37 2970 2972 2 -22034.8 -7246.86 170.62 0.37 2971 2973 2 -22034.8 -7246.59 170.67 0.37 2972 2974 2 -22035.8 -7246.05 170.87 0.37 2973 2975 2 -22035.8 -7245.76 170.92 0.37 2974 2976 2 -22036.6 -7245.4 172.55 0.37 2975 2977 2 -22036.7 -7245.15 172.6 0.37 2976 2978 2 -22037.7 -7244.29 172.84 0.37 2977 2979 2 -22037.8 -7243.5 172.98 0.37 2978 2980 2 -22037.9 -7242.72 173.12 0.37 2979 2981 2 -22038.2 -7242.49 173.19 0.37 2980 2982 2 -22038.3 -7241.94 173.29 0.37 2981 2983 2 -22039.6 -7241.63 176.04 0.37 2982 2984 2 -22040.2 -7241.23 176.17 0.37 2983 2985 2 -22041.6 -7240.4 176.43 0.37 2984 2986 2 -22041.7 -7239.58 176.57 0.37 2985 2987 2 -22041.7 -7239.36 176.68 0.37 2986 2988 2 -22043.5 -7238.73 177.55 0.37 2987 2989 2 -22044.2 -7237.56 177.84 0.37 2988 2990 2 -22044.5 -7237.08 177.95 0.37 2989 2991 2 -22045.7 -7235.48 178.51 0.37 2990 2992 2 -22045.7 -7235.5 178.62 0.37 2991 2993 2 -22047.3 -7235.28 180.33 0.37 2992 2994 2 -22048.9 -7233.73 180.74 0.37 2993 2995 2 -22049.2 -7232.39 180.67 0.37 2994 2996 2 -22049.9 -7231.47 180.89 0.37 2995 2997 2 -22050.2 -7231.23 180.96 0.37 2996 2998 2 -22051.3 -7230.41 181.19 0.37 2997 2999 2 -22051.3 -7230.12 181.24 0.37 2998 3000 2 -22052.5 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3118 3120 2 -22168.6 -7129.93 295.41 0.125 3119 3121 2 -22171.2 -7127.93 295.98 0.125 3120 3122 2 -22172.4 -7125.72 298.12 0.125 3121 3123 2 -22174.7 -7125.29 300.21 0.125 3122 3124 2 -22175.6 -7124.13 300.5 0.125 3123 3125 2 -22176 -7123.89 300.57 0.125 3124 3126 2 -22178 -7121.82 304.27 0.125 3125 3127 2 -22181 -7118.73 307.72 0.125 3126 3128 2 -22182.8 -7118.2 308.08 0.125 3127 3129 2 -22183.9 -7118.12 308.64 0.125 3128 3130 2 -22184.1 -7118.27 309.18 0.125 3129 3131 2 -22185.1 -7117.99 311.47 0.125 3130 3132 2 -22185.2 -7117.19 311.61 0.125 3131 3133 2 -22185.6 -7116.74 311.72 0.125 3132 3134 2 -22185.7 -7116.19 311.82 0.125 3133 3135 2 -22186 -7116 311.88 0.125 3134 3136 2 -22186.8 -7115.34 313.82 0.125 3135 3137 2 -22188.3 -7114.75 314.06 0.125 3136 3138 2 -22188.5 -7113.18 314.37 0.125 3137 3139 2 -22189.7 -7112.56 314.59 0.125 3138 3140 2 -22190 -7112.62 314.61 0.125 3139 3141 2 -22191.7 -7111.79 314.91 0.125 3140 3142 2 -22193.5 -7112.18 317.14 0.125 3141 3143 2 -22193.5 -7112.26 317.62 0.125 3142 3144 2 -22196.5 -7112.78 318.41 0.125 3143 3145 2 -22199.6 -7111.97 320.93 0.125 3144 3146 2 -22199.6 -7111.7 320.98 0.125 3145 3147 2 -22200.5 -7109.72 321.46 0.125 3146 3148 2 -22200.5 -7109.17 321.56 0.125 3147 3149 2 -22201.3 -7107.3 322.39 0.125 3148 3150 2 -22201.6 -7107.1 322.69 0.125 3149 3151 2 -22201.5 -7104.96 323.31 0.125 3150 3152 2 -22201.8 -7105.02 323.33 0.125 3151 3153 2 -22203.5 -7098.54 324.56 0.125 3152 3154 2 -22205.2 -7097.04 324.96 0.125 3153 3155 2 -22206.4 -7097 325.08 0.125 3154 3156 2 -22207.8 -7096.78 325.65 0.125 3155 3157 2 -22207.8 -7096.57 325.69 0.125 3156 3158 2 -22208.8 -7095.67 325.94 0.125 3157 3159 2 -22209 -7094.91 326.08 0.125 3158 3160 2 -22210.2 -7093.69 328.6 0.125 3159 3161 2 -22210.4 -7092.64 328.79 0.125 3160 3162 2 -22210.8 -7091.07 331.67 0.125 3161 3163 2 -22210.8 -7091.11 331.9 0.125 3162 3164 2 -22211.9 -7089.05 334.53 0.125 3163 3165 2 -22212.6 -7088.39 334.72 0.125 3164 3166 2 -22212.5 -7088.48 335.24 0.125 3165 3167 2 -22213.9 -7087.92 338.24 0.125 3166 3168 2 -22215 -7086.8 336.99 0.125 3167 3169 2 -22216.2 -7086.76 337.1 0.125 3168 3170 2 -22217.7 -7086.51 337.28 0.125 3169 3171 2 -22218 -7086.08 337.48 0.125 3170 3172 2 -22219.8 -7084.53 337.75 0.125 3171 3173 2 -22220.2 -7083.27 339.59 0.125 3172 3174 2 -22221.4 -7082.7 341.02 0.125 3173 3175 2 -22221.2 -7082.67 342.62 0.125 3174 3176 2 -22222 -7081.54 343.22 0.125 3175 3177 2 -22222.2 -7079 344.16 0.125 3176 3178 2 -22222.8 -7077.95 346.79 0.125 3177 3179 2 -22222.8 -7077.69 346.84 0.125 3178 3180 2 -22223.7 -7077.49 347.78 0.125 3179 3181 2 -22224 -7077.52 347.8 0.125 3180 3182 2 -22226.6 -7076.7 348.11 0.125 3181 3183 2 -22227.8 -7076.92 348.18 0.125 3182 3184 2 -22228 -7077.19 348.16 0.125 3183 3185 2 -22230.9 -7079.34 353.66 0.125 3184 3186 2 -22233.2 -7082.49 358.17 0.125 3185 3187 2 -22233.5 -7082.52 358.19 0.125 3186 3188 2 -22235.2 -7085.95 365.73 0.125 3187 3189 2 -22235.5 -7086.01 365.77 0.125 3188 3190 2 -22236.6 -7086.61 368.07 0.125 3189 3191 2 -22237.6 -7086.88 370.33 0.125 3190 3192 2 -22237.8 -7087.85 378.32 0.125 3191 3193 2 -22238.6 -7087.22 381.51 0.125 3192 3194 2 -22238.5 -7087.26 381.76 0.125 3193 3195 2 -22239 -7085.8 384.08 0.125 3194 3196 2 -22238.9 -7085.86 385.76 0.125 3195 3197 2 -22224.7 -7075.84 349.8 0.125 3181 3198 2 -22226 -7074.61 350.71 0.125 3197 3199 2 -22226.7 -7074.2 350.78 0.125 3198 3200 2 -22226.7 -7073.96 350.82 0.125 3199 3201 2 -22227.3 -7073.73 352.28 0.125 3200 3202 2 -22227.4 -7073.8 354.07 0.125 3201 3203 2 -22228.3 -7072.37 354.38 0.125 3202 3204 2 -22229.9 -7071.11 354.75 0.125 3203 3205 2 -22230.6 -7070.48 354.92 0.125 3204 3206 2 -22231 -7070.24 354.99 0.125 3205 3207 2 -22232.2 -7069.45 355.24 0.125 3206 3208 2 -22232.6 -7068.97 355.36 0.125 3207 3209 2 -22233.2 -7068.6 355.48 0.125 3208 3210 2 -22233.3 -7068.06 355.58 0.125 3209 3211 2 -22233.7 -7067.51 360.97 0.125 3210 3212 2 -22234 -7067.03 361.27 0.125 3211 3213 2 -22234.5 -7066.31 361.43 0.125 3212 3214 2 -22235.2 -7065.69 361.67 0.125 3213 3215 2 -22236.4 -7065.12 361.88 0.125 3214 3216 2 -22236.3 -7065.17 362.15 0.125 3215 3217 2 -22237.3 -7063.81 365.75 0.125 3216 3218 2 -22237.3 -7063.52 365.8 0.125 3217 3219 2 -22238 -7062.86 365.97 0.125 3218 3220 2 -22238.3 -7062.7 366.05 0.125 3219 3221 2 -22240 -7062.42 367.26 0.125 3220 3222 2 -22240 -7062.26 368.11 0.125 3221 3223 2 -22240.5 -7061.09 368.58 0.125 3222 3224 2 -22240.3 -7060.5 368.66 0.125 3223 3225 2 -22240.5 -7058.3 368.53 0.125 3224 3226 2 -22240.5 -7058.1 368.57 0.125 3225 3227 2 -22241.8 -7057.26 368.83 0.125 3226 3228 2 -22242.2 -7056.77 368.94 0.125 3227 3229 2 -22243.4 -7055.71 370.75 0.125 3228 3230 2 -22244.3 -7055.44 371.36 0.125 3229 3231 2 -22244.7 -7054.97 371.47 0.125 3230 3232 2 -22244.7 -7054.72 371.52 0.125 3231 3233 2 -22246.4 -7053.17 376.46 0.125 3232 3234 2 -22246.8 -7052.97 376.52 0.125 3233 3235 2 -22247.5 -7052.11 376.73 0.125 3234 3236 2 -22247.5 -7051.81 376.79 0.125 3235 3237 2 -22249.8 -7050.72 377.18 0.125 3236 3238 2 -22249.8 -7050.4 377.23 0.125 3237 3239 2 -22251.6 -7049.92 377.26 0.125 3238 3240 2 -22254.4 -7048.19 380.17 0.125 3239 3241 2 -22255.7 -7046.65 380.55 0.125 3240 3242 2 -22255.8 -7046.41 380.76 0.125 3241 3243 2 -22256.2 -7046.27 384.17 0.125 3242 3244 2 -22256.1 -7046.51 385.58 0.125 3243 3245 2 -22257.3 -7045.56 386.62 0.125 3244 3246 2 -22257.3 -7045.43 387.44 0.125 3245 3247 2 -22259.3 -7044.93 388.75 0.125 3246 3248 2 -22259.5 -7044.16 388.9 0.125 3247 3249 2 -22260.5 -7043.27 389.27 0.125 3248 3250 2 -22260.8 -7042.87 389.53 0.125 3249 3251 2 -22261.5 -7042.52 389.83 0.125 3250 3252 2 -22264 -7041.77 392.19 0.125 3251 3253 2 -22264.3 -7041.73 391.93 0.125 3252 3254 2 -22265.8 -7041.77 392.12 0.125 3253 3255 2 -22265.8 -7041.26 392.21 0.125 3254 3256 2 -22266.2 -7041.09 392.27 0.125 3255 3257 2 -22267.3 -7040.86 393.08 0.125 3256 3258 2 -22267.5 -7040.94 394.76 0.125 3257 3259 2 -22267.5 -7040.71 394.8 0.125 3258 3260 2 -22267.8 -7040.67 394.33 0.125 3259 3261 2 -22267.6 -7041.08 396.83 0.125 3260 3262 2 -22268 -7040.66 397.24 0.125 3261 3263 2 -22268.3 -7040.02 397.82 0.125 3262 3264 2 -22268.4 -7039.79 397.86 0.125 3263 3265 2 -22268.4 -7039.36 398.5 0.125 3264 3266 2 -22269.3 -7037.09 400.27 0.125 3265 3267 2 -22271.2 -7036.03 401.74 0.125 3266 3268 2 -22273.5 -7033.67 399.74 0.125 3267 3269 2 -22276.8 -7032.27 402.15 0.125 3268 3270 2 -22278.6 -7031.81 406.98 0.125 3269 3271 2 -22279.8 -7031.03 407.38 0.125 3270 3272 2 -22279.9 -7030.74 407.43 0.125 3271 3273 2 -22281.4 -7029.25 411.02 0.125 3272 3274 2 -22282.3 -7028.63 411.26 0.125 3273 3275 2 -22284.7 -7028.12 414.94 0.125 3274 3276 2 -22285.6 -7027.47 416.8 0.125 3275 3277 2 -22286.8 -7025.29 417.28 0.125 3276 3278 2 -22287.5 -7022.76 417.84 0.125 3277 3279 2 -22288.7 -7021.05 418.23 0.125 3278 3280 2 -22288.8 -7020.54 418.32 0.125 3279 3281 2 -22290.7 -7018.76 420.49 0.125 3280 3282 2 -22292.8 -7017.57 421.33 0.125 3281 3283 2 -22292.8 -7017.45 422.03 0.125 3282 3284 2 -22295.2 -7016.55 424.12 0.125 3283 3285 2 -22295.2 -7016.29 424.27 0.125 3284 3286 2 -22296.4 -7015.98 424.44 0.125 3285 3287 2 -22298.9 -7014.47 426.56 0.125 3286 3288 2 -22299.9 -7011.75 427.11 0.125 3287 3289 2 -22300 -7011.23 427.2 0.125 3288 3290 2 -22300.9 -7009.51 427.57 0.125 3289 3291 2 -22300.9 -7009.23 427.62 0.125 3290 3292 2 -22301.8 -7008.23 430.5 0.125 3291 3293 2 -22301.8 -7007.98 430.54 0.125 3292 3294 2 -22303.3 -7007.41 430.78 0.125 3293 3295 2 -22304.8 -7007.15 430.96 0.125 3294 3296 2 -22305.8 -7006.92 430.58 0.125 3295 3297 2 -22308 -7007.79 430.02 0.125 3296 3298 2 -22310.2 -7009.08 427.8 0.125 3297 3299 2 -22312.5 -7010.85 427.59 0.125 3298 3300 2 -22312.7 -7010.93 427.6 0.125 3299 3301 2 -22316.5 -7011.66 422.31 0.125 3300 3302 2 -22316.7 -7011.7 422.33 0.125 3301 3303 2 -22318.6 -7012.82 422.26 0.125 3302 3304 2 -22321.5 -7013.86 419.09 0.125 3303 3305 2 -22323.7 -7015.02 419.1 0.125 3304 3306 2 -22323.7 -7014.99 418.95 0.125 3305 3307 2 -22325.2 -7014.09 418.5 0.125 3306 3308 2 -22327.3 -7014.87 416.3 0.125 3307 3309 2 -22332.3 -7017.85 413.95 0.125 3308 3310 2 -22335 -7020.26 413.91 0.125 3309 3311 2 -22338.4 -7020.72 413.02 0.125 3310 3312 2 -22339.5 -7020.92 413.09 0.125 3311 3313 2 -22342.1 -7021.76 410.58 0.125 3312 3314 2 -22343.4 -7022.31 410.61 0.125 3313 3315 2 -22345 -7023.14 410.62 0.125 3314 3316 2 -22345.6 -7024.29 410.48 0.125 3315 3317 2 -22346.9 -7024.82 410.52 0.125 3316 3318 2 -22348.7 -7025.8 410.82 0.125 3317 3319 2 -22351.3 -7026.21 409.03 0.125 3318 3320 2 -22352.9 -7027.42 408.02 0.125 3319 3321 2 -22356 -7030.86 407.09 0.125 3320 3322 2 -22357.9 -7033.21 406.28 0.125 3321 3323 2 -22359.1 -7035.63 405.99 0.125 3322 3324 2 -22360.5 -7037.74 405.9 0.125 3323 3325 2 -22361.5 -7038.67 403.93 0.125 3324 3326 2 -22362.8 -7040.51 402.29 0.125 3325 3327 2 -22363.9 -7042.1 402.13 0.125 3326 3328 2 -22365.6 -7043.2 402.11 0.125 3327 3329 2 -22367.7 -7044.26 402.82 0.125 3328 3330 2 -22307 -7003.86 433.71 0.125 3296 3331 2 -22307 -7003.58 433.76 0.125 3330 3332 2 -22307.6 -6998 434.77 0.125 3331 3333 2 -22309.7 -6996.19 439.35 0.125 3332 3334 2 -22310 -6996.18 439.38 0.125 3333 3335 2 -22311.7 -6995.96 439.58 0.125 3334 3336 2 -22314.3 -6994.28 440.1 0.125 3335 3337 2 -22316 -6992.94 440.48 0.125 3336 3338 2 -22315.2 -6992.53 440.47 0.125 3337 3339 2 -22315.7 -6991.29 440.72 0.125 3338 3340 2 -22316.4 -6988.73 441.21 0.125 3339 3341 2 -22317.4 -6988.08 441.41 0.125 3340 3342 2 -22318.9 -6985.55 438.51 0.125 3341 3343 2 -22320.2 -6983.18 437.64 0.125 3342 3344 2 -22323.1 -6981.6 436.99 0.125 3343 3345 2 -22323.4 -6981.61 437.02 0.125 3344 3346 2 -22325.9 -6978.05 435 0.125 3345 3347 2 -22326.2 -6978.37 434.97 0.125 3346 3348 2 -22327.2 -6977.47 435.21 0.125 3347 3349 2 -22327.5 -6977.54 435.23 0.125 3348 3350 2 -22327.4 -6975.61 435.55 0.125 3349 3351 2 -22329.2 -6974.08 435.96 0.125 3350 3352 2 -22330.7 -6973.54 436.19 0.125 3351 3353 2 -22330.8 -6972.75 436.34 0.125 3352 3354 2 -22331 -6971.96 436.48 0.125 3353 3355 2 -22331 -6971.71 436.52 0.125 3354 3356 2 -22332.7 -6969.27 435.21 0.125 3355 3357 2 -22333 -6968.77 435.32 0.125 3356 3358 2 -22333.1 -6968.52 435.37 0.125 3357 3359 2 -22334.7 -6966.77 433.5 0.125 3358 3360 2 -22335 -6966.56 433.56 0.125 3359 3361 2 -22335.9 -6966.21 433.71 0.125 3360 3362 2 -22336 -6965.96 433.75 0.125 3361 3363 2 -22336.3 -6964.07 434.09 0.125 3362 3364 2 -22336.6 -6963.64 434.19 0.125 3363 3365 2 -22338.2 -6963.05 434.44 0.125 3364 3366 2 -22339.1 -6961.72 436.77 0.125 3365 3367 2 -22339.3 -6960.38 437.02 0.125 3366 3368 2 -22339.4 -6960.13 437.06 0.125 3367 3369 2 -22339.8 -6959.4 437.22 0.125 3368 3370 2 -22340.8 -6958.49 437.47 0.125 3369 3371 2 -22342.5 -6957.18 437.84 0.125 3370 3372 2 -22344.6 -6955.94 439.73 0.125 3371 3373 2 -22344.6 -6955.69 439.78 0.125 3372 3374 2 -22345.1 -6954.41 444.71 0.125 3373 3375 2 -22347.2 -6953.85 450.61 0.125 3374 3376 2 -22349.9 -6953.65 454.61 0.125 3375 3377 2 -22348.9 -6952.87 457.06 0.125 3376 3378 2 -22348.9 -6952.69 462.47 0.125 3377 3379 2 -22349.9 -6949.76 468.22 0.125 3378 3380 2 -22346.5 -6954.07 438.36 0.125 3373 3381 2 -22348 -6953.81 438.54 0.125 3380 3382 2 -22348.2 -6953.86 438.55 0.125 3381 3383 2 -22348.7 -6953.11 438.66 0.125 3382 3384 2 -22352.5 -6949.73 438.21 0.125 3383 3385 2 -22352.7 -6949.79 438.23 0.125 3384 3386 2 -22354.4 -6948.74 438.55 0.125 3385 3387 2 -22355.6 -6948.14 438.77 0.125 3386 3388 2 -22355.7 -6947.36 438.91 0.125 3387 3389 2 -22358.9 -6945.8 439.46 0.125 3388 3390 2 -22358.9 -6945.48 439.52 0.125 3389 3391 2 -22359.6 -6945.08 439.65 0.125 3390 3392 2 -22361.3 -6942.13 438.77 0.125 3391 3393 2 -22361.3 -6941.87 438.81 0.125 3392 3394 2 -22361.6 -6940.58 439.05 0.125 3393 3395 2 -22361.8 -6940.4 439.11 0.125 3394 3396 2 -22362.9 -6939.47 439.36 0.125 3395 3397 2 -22363.2 -6939.48 439.38 0.125 3396 3398 2 -22363.5 -6939.05 439.49 0.125 3397 3399 2 -22363.6 -6938.79 439.53 0.125 3398 3400 2 -22365.7 -6936.44 440.02 0.125 3399 3401 2 -22366 -6936.5 440.04 0.125 3400 3402 2 -22365.9 -6935.11 440.21 0.125 3401 3403 2 -22366 -6934.85 440.26 0.125 3402 3404 2 -22369.1 -6931.99 439.67 0.125 3403 3405 2 -22369.4 -6932.04 439.68 0.125 3404 3406 2 -22370.7 -6931.14 439.96 0.125 3405 3407 2 -22371.6 -6930.41 441.26 0.125 3406 3408 2 -22373.2 -6929.28 442.61 0.125 3407 3409 2 -22373.2 -6929.01 442.65 0.125 3408 3410 2 -22375.4 -6926.17 443.33 0.125 3409 3411 2 -22376.2 -6924.51 444.23 0.125 3410 3412 2 -22376.2 -6924.6 444.81 0.125 3411 3413 2 -22378.6 -6922.32 445.16 0.125 3412 3414 2 -22380 -6920.67 445.57 0.125 3413 3415 2 -22380.8 -6919.46 445.84 0.125 3414 3416 2 -22382.7 -6917.59 447.78 0.125 3415 3417 2 -22383.8 -6916.47 448.04 0.125 3416 3418 2 -22383.9 -6915.93 448.14 0.125 3417 3419 2 -22384.2 -6915.72 448.2 0.125 3418 3420 2 -22386.3 -6913.69 448.73 0.125 3419 3421 2 -22388.6 -6911.94 449.24 0.125 3420 3422 2 -22391 -6911.01 454.06 0.125 3421 3423 2 -22392.7 -6909.94 454.44 0.125 3422 3424 2 -22392.6 -6910.02 454.94 0.125 3423 3425 2 -22393.3 -6908.76 456.53 0.125 3424 3426 2 -22393.5 -6908.84 456.62 0.125 3425 3427 2 -22396 -6906.71 459.62 0.125 3426 3428 2 -22398 -6904.22 464.08 0.125 3427 3429 2 -22397.9 -6904.26 464.36 0.125 3428 3430 2 -22401.4 -6899.44 465.01 0.125 3429 3431 2 -22406.8 -6892.78 470.64 0.125 3430 3432 2 -22401.2 -6904.61 466.37 0.125 3429 3433 2 -22402.7 -6904.64 466.5 0.125 3432 3434 2 -22404.1 -6902.96 466.91 0.125 3433 3435 2 -22407.1 -6902.97 470.07 0.125 3434 3436 2 -22407 -6903.04 470.48 0.125 3435 3437 2 -22410 -6901.86 472.15 0.125 3436 3438 2 -22411 -6901.37 476.35 0.125 3437 3439 2 -22413 -6899.9 476.78 0.125 3438 3440 2 -22413.3 -6899.42 476.89 0.125 3439 3441 2 -22413.4 -6898.54 478.14 0.125 3440 3442 2 -22413.7 -6898.57 478.16 0.125 3441 3443 2 -22414.1 -6897.1 480.49 0.125 3442 3444 2 -22414.2 -6896.56 480.59 0.125 3443 3445 2 -22415.2 -6894.07 481.18 0.125 3444 3446 2 -22415.2 -6893.85 481.22 0.125 3445 3447 2 -22416 -6892.19 482.36 0.125 3446 3448 2 -22417.9 -6890.55 485.39 0.125 3447 3449 2 -22418.8 -6890.18 485.54 0.125 3448 3450 2 -22418.9 -6889.67 485.63 0.125 3449 3451 2 -22420.5 -6887.34 488.11 0.125 3450 3452 2 -22420.5 -6887.5 489.11 0.125 3451 3453 2 -22421.9 -6886.44 487.83 0.125 3452 3454 2 -22422.6 -6883.87 492.92 0.125 3453 3455 2 -22417.6 -6884.59 492.34 0.125 3454 3456 2 -22418.6 -6882.85 496.67 0.125 3455 3457 2 -22419.6 -6879.46 500.99 0.125 3456 3458 2 -22135.1 -7160.19 250.3 0.125 3090 3459 2 -22136.3 -7159.28 252.03 0.125 3458 3460 2 -22138.2 -7157.34 249.03 0.25 3459 3461 2 -22139.7 -7156.51 249.26 0.25 3460 3462 2 -22142.2 -7154.69 249.53 0.25 3461 3463 2 -22145.6 -7153.97 247.66 0.25 3462 3464 2 -22147.1 -7152.91 247.97 0.25 3463 3465 2 -22149.8 -7150.62 247.13 0.25 3464 3466 2 -22150 -7150.6 247.16 0.25 3465 3467 2 -22152.5 -7149.39 247.59 0.25 3466 3468 2 -22154.1 -7147.73 248.02 0.25 3467 3469 2 -22155.4 -7146.86 248.28 0.25 3468 3470 2 -22157.2 -7145.04 248.89 0.25 3469 3471 2 -22159.7 -7144.41 248 0.25 3470 3472 2 -22161.5 -7143.83 248.26 0.25 3471 3473 2 -22163.9 -7142.75 247.97 0.25 3472 3474 2 -22165.6 -7141.1 248.21 0.25 3473 3475 2 -22165.7 -7140.03 248.35 0.25 3474 3476 2 -22165.8 -7139.74 248.4 0.25 3475 3477 2 -22167 -7139.09 248.41 0.25 3476 3478 2 -22167 -7139.08 248.3 0.25 3477 3479 2 -22168.5 -7138.46 248.39 0.25 3478 3480 2 -22170.2 -7137.13 247.25 0.25 3479 3481 2 -22172 -7136.32 247.55 0.25 3480 3482 2 -22172 -7136.05 247.6 0.25 3481 3483 2 -22172.4 -7135.08 247.8 0.25 3482 3484 2 -22165.3 -7136.36 246.92 0.37 3483 3485 2 -22168.8 -7135.27 240.62 0.37 3484 3486 2 -22169.5 -7133.83 240.93 0.37 3485 3487 2 -22170.5 -7131.33 241.44 0.37 3486 3488 2 -22172.3 -7129.41 237.94 0.37 3487 3489 2 -22172.7 -7128.42 238.14 0.37 3488 3490 2 -22175 -7126.94 236.91 0.37 3489 3491 2 -22176.8 -7124.79 236.89 0.37 3490 3492 2 -22178.1 -7123.02 234.91 0.37 3491 3493 2 -22180.1 -7123.13 235.08 0.37 3492 3494 2 -22182.2 -7122.44 235.39 0.37 3493 3495 2 -22185 -7120.73 232.28 0.37 3494 3496 2 -22186.6 -7118.04 231 0.37 3495 3497 2 -22187.7 -7117.1 230.69 0.37 3496 3498 2 -22189.7 -7115.38 225.56 0.37 3497 3499 2 -22190.7 -7114.49 225.54 0.37 3498 3500 2 -22191.5 -7113.29 225.81 0.37 3499 3501 2 -22193.3 -7111.52 224.85 0.37 3500 3502 2 -22195.6 -7109.77 224.99 0.37 3501 3503 2 -22197.6 -7108 220.81 0.37 3502 3504 2 -22199.1 -7105.64 221.33 0.37 3503 3505 2 -22200.7 -7104.55 221.48 0.37 3504 3506 2 -22206.5 -7100.54 213.36 0.37 3505 3507 2 -22209.8 -7099.01 210.98 0.37 3506 3508 2 -22210.3 -7097.5 211.27 0.37 3507 3509 2 -22213.3 -7095.11 211.7 0.37 3508 3510 2 -22216 -7092.83 207.19 0.37 3509 3511 2 -22218.9 -7092.23 207.34 0.37 3510 3512 2 -22221.8 -7089.18 201.15 0.25 3511 3513 2 -22224.8 -7087.24 199.87 0.25 3512 3514 2 -22226.5 -7086 198.57 0.25 3513 3515 2 -22228.7 -7085.3 197.33 0.25 3514 3516 2 -22230.7 -7082.14 192.94 0.25 3515 3517 2 -22232 -7079.65 191.7 0.25 3516 3518 2 -22234.4 -7077.49 188.01 0.25 3517 3519 2 -22235.6 -7075.65 187.17 0.25 3518 3520 2 -22236.7 -7075.09 184.71 0.25 3519 3521 2 -22238.7 -7074.15 185.05 0.125 3520 3522 2 -22243 -7068.55 180.69 0.125 3521 3523 2 -22245.9 -7063.5 175.92 0.125 3522 3524 2 -22248.3 -7059.32 167.21 0.125 3523 3525 2 -22248.8 -7057.5 167.35 0.125 3524 3526 2 -22252 -7054.46 161.28 0.125 3525 3527 2 -22253.2 -7054.53 160.7 0.125 3526 3528 2 -22253.8 -7052.46 160.73 0.125 3527 3529 2 -22253.8 -7050.78 160.49 0.125 3528 3530 2 -22256.3 -7048.33 158.51 0.125 3529 3531 2 -22257.9 -7046.32 156.69 0.125 3530 3532 2 -22259.4 -7044.73 155.58 0.125 3531 3533 2 -22261.3 -7042.28 154.03 0.125 3532 3534 2 -22263.6 -7040.42 153.79 0.125 3533 3535 2 -22265.8 -7037.74 149.16 0.125 3534 3536 2 -22266.6 -7037.6 149.25 0.125 3535 3537 2 -22267.4 -7036.15 149.41 0.125 3536 3538 2 -22268.5 -7033.52 149.07 0.125 3537 3539 2 -22271.9 -7032.09 142.98 0.125 3538 3540 2 -22273.1 -7030.53 142.22 0.125 3539 3541 2 -22273.8 -7028.79 141.11 0.125 3540 3542 2 -22275.7 -7026.06 136.49 0.125 3541 3543 2 -22276.1 -7024.81 136.74 0.125 3542 3544 2 -22277.2 -7022.88 137.16 0.125 3543 3545 2 -22280 -7018.77 131.75 0.125 3544 3546 2 -22282.7 -7016.66 131.24 0.125 3545 3547 2 -22290.3 -7019.22 131.53 0.125 3546 3548 2 -22292 -7017.44 131.99 0.125 3547 3549 2 -22293.2 -7015.57 132.41 0.125 3548 3550 2 -22295.3 -7012.03 132.06 0.125 3549 3551 2 -22297.2 -7011.08 132.32 0.125 3550 3552 2 -22298.2 -7009.53 130.4 0.125 3551 3553 2 -22299 -7008.12 130.68 0.125 3552 3554 2 -22300.8 -7007.4 130.95 0.125 3553 3555 2 -22303.2 -7005.75 125.57 0.125 3554 3556 2 -22304.5 -7004.95 125.82 0.125 3555 3557 2 -22306.8 -7002.9 121.31 0.125 3556 3558 2 -22307.6 -7001.7 121.58 0.125 3557 3559 2 -22309.1 -7000.96 119.05 0.125 3558 3560 2 -22309.1 -6999.15 119.35 0.125 3559 3561 2 -22309.3 -6997.86 119.59 0.125 3560 3562 2 -22311.4 -6995.6 120.16 0.125 3561 3563 2 -22313.9 -6993.65 118.83 0.125 3562 3564 2 -22314.9 -6991.78 116.68 0.125 3563 3565 2 -22314.8 -6990.07 114.88 0.125 3564 3566 2 -22316.4 -6988.26 113.82 0.125 3565 3567 2 -22317.6 -6986.75 111.88 0.125 3566 3568 2 -22318.5 -6986.41 112.02 0.125 3567 3569 2 -22318.7 -6985.11 112.25 0.125 3568 3570 2 -22320.1 -6983.38 108.64 0.125 3569 3571 2 -22320.4 -6982.09 108.87 0.125 3570 3572 2 -22321.3 -6981.07 106.97 0.125 3571 3573 2 -22321.2 -6979.92 106.88 0.125 3572 3574 2 -22322.9 -6979.35 104.82 0.125 3573 3575 2 -22323.3 -6978.65 104.97 0.125 3574 3576 2 -22321.8 -6973.33 102.61 0.125 3575 3577 2 -22323.7 -6972.94 101.68 0.125 3576 3578 2 -22324.9 -6970.96 101.68 0.125 3577 3579 2 -22329.5 -6962.81 100.13 0.125 3578 3580 2 -22334.1 -6956.31 100.39 0.125 3579 3581 2 -22336.8 -6953.9 101.04 0.125 3580 3582 2 -22338 -6952.04 101.46 0.125 3581 3583 2 -22338.4 -6948.15 102.14 0.125 3582 3584 2 -22340.5 -6945.93 102.7 0.125 3583 3585 2 -22342.2 -6944.09 103.17 0.125 3584 3586 2 -22342.7 -6943.15 103.37 0.125 3585 3587 2 -22344.1 -6939.86 103.34 0.125 3586 3588 2 -22346.1 -6937.11 102.71 0.125 3587 3589 2 -22350.3 -6933.67 100.51 0.125 3588 3590 2 -22353.2 -6931.84 101.09 0.125 3589 3591 2 -22357.2 -6927.74 100.22 0.125 3590 3592 2 -22358.7 -6925.63 100.71 0.125 3591 3593 2 -22360.9 -6921.3 101.64 0.125 3592 3594 2 -22362.7 -6918.75 102.23 0.125 3593 3595 2 -22365.8 -6915.61 98.46 0.125 3594 3596 2 -22368.2 -6911.61 99.35 0.125 3595 3597 2 -22371.9 -6907.72 98.42 0.125 3596 3598 2 -22373.2 -6905.34 98.93 0.125 3597 3599 2 -22376.6 -6900.03 97.88 0.125 3598 3600 2 -22378.6 -6895.94 98.76 0.125 3599 3601 2 -22379.4 -6895.01 98.98 0.125 3600 3602 2 -22380.9 -6891.77 98.9 0.125 3601 3603 2 -22380.7 -6887.9 99.52 0.125 3602 3604 2 -22379.6 -6883.58 96.17 0.125 3603 3605 2 -22379.9 -6881.7 96.39 0.125 3604 3606 2 -22379 -6879.93 96.44 0.125 3605 3607 2 -22378.2 -6877.94 96.7 0.125 3606 3608 2 -22378.6 -6875.15 97.2 0.125 3607 3609 2 -22379.4 -6871.18 96.23 0.125 3608 3610 2 -22379.3 -6867.07 94.62 0.125 3609 3611 2 -22379 -6865.42 94.86 0.125 3610 3612 2 -22379.7 -6864.19 95.13 0.125 3611 3613 2 -22381.4 -6861.05 91.42 0.125 3612 3614 2 -22382.3 -6859.92 91.7 0.125 3613 3615 2 -22382.3 -6858.28 91.97 0.125 3614 3616 2 -22381.8 -6857.71 92.02 0.125 3615 3617 2 -22381.9 -6855.82 92.33 0.125 3616 3618 2 -22381 -6853.86 92.58 0.125 3617 3619 2 -22379.5 -6850.07 89.6 0.125 3618 3620 2 -22378.5 -6847.51 89.93 0.125 3619 3621 2 -22377.2 -6844.59 87.06 0.125 3620 3622 2 -22376.3 -6842.09 84.6 0.125 3621 3623 2 -22375.8 -6840.39 84.83 0.125 3622 3624 2 -22375.9 -6839.12 85.06 0.125 3623 3625 2 -22376.5 -6838.4 82.21 0.125 3624 3626 2 -21870.3 -7372.4 83.95 0.37 1650 3627 2 -21870.5 -7370.37 84.31 0.37 3626 3628 2 -21870.3 -7369.13 86.56 0.37 3627 3629 2 -21870.7 -7366.77 86.99 0.37 3628 3630 2 -21870.7 -7366.55 87.03 0.37 3629 3631 2 -21870.1 -7364.8 87.27 0.37 3630 3632 2 -21869.8 -7364.57 87.28 0.37 3631 3633 2 -21869.3 -7362.61 87.55 0.37 3632 3634 2 -21869.1 -7362.03 87.62 0.37 3633 3635 2 -21869.9 -7360.58 87.94 0.37 3634 3636 2 -21870 -7359.53 88.13 0.37 3635 3637 2 -21869.6 -7358.68 88.23 0.37 3636 3638 2 -21869.8 -7357.64 88.42 0.37 3637 3639 2 -21870.1 -7356 92.69 0.37 3638 3640 2 -21871.1 -7354.64 93.27 0.37 3639 3641 2 -21871.1 -7354.42 93.57 0.37 3640 3642 2 -21870.9 -7352.09 94.47 0.37 3641 3643 2 -21870.9 -7351.82 94.52 0.37 3642 3644 2 -21871.4 -7350.14 95.13 0.37 3643 3645 2 -21871.5 -7349.64 95.49 0.37 3644 3646 2 -21872.3 -7347.51 96.6 0.37 3645 3647 2 -21872.7 -7346.54 96.8 0.37 3646 3648 2 -21873.6 -7343.74 98.6 0.37 3647 3649 2 -21874.7 -7342.07 98.98 0.37 3648 3650 2 -21875 -7341.83 99.05 0.37 3649 3651 2 -21876.3 -7339.67 99.59 0.37 3650 3652 2 -21876.3 -7339.4 99.64 0.37 3651 3653 2 -21878.1 -7337.91 101.89 0.37 3652 3654 2 -21880.2 -7335.36 102.64 0.37 3653 3655 2 -21880.1 -7335.11 102.67 0.37 3654 3656 2 -21881.5 -7331.71 104.02 0.37 3655 3657 2 -21881.3 -7331.82 106.17 0.37 3656 3658 2 -21881.2 -7329.52 105.63 0.37 3657 3659 2 -21881.2 -7329.26 105.68 0.37 3658 3660 2 -21881 -7328.38 106.87 0.37 3659 3661 2 -21881 -7327.86 106.97 0.37 3660 3662 2 -21880 -7325.79 110.02 0.37 3661 3663 2 -21879.9 -7325.45 110.06 0.37 3662 3664 2 -21878.3 -7324.68 110.01 0.37 3663 3665 2 -21878.2 -7323.35 110.21 0.37 3664 3666 2 -21878 -7322.83 110.28 0.37 3665 3667 2 -21876.5 -7321.18 110.42 0.37 3666 3668 2 -21876.6 -7321.01 110.45 0.37 3667 3669 2 -21876.9 -7318.92 110.82 0.37 3668 3670 2 -21877.5 -7316.63 111.26 0.37 3669 3671 2 -21879.9 -7315.47 111.68 0.37 3670 3672 2 -21881.3 -7314.1 112.03 0.37 3671 3673 2 -21882.5 -7311.62 112.56 0.37 3672 3674 2 -21882.6 -7311.13 112.65 0.37 3673 3675 2 -21883.4 -7310.38 115.45 0.37 3674 3676 2 -21883.7 -7308.07 115.86 0.37 3675 3677 2 -21884 -7305.52 118.23 0.25 3676 3678 2 -21884 -7305.26 118.28 0.25 3677 3679 2 -21884.4 -7302.68 118.75 0.25 3678 3680 2 -21884.5 -7302.43 118.79 0.25 3679 3681 2 -21885.1 -7300.13 119.23 0.25 3680 3682 2 -21885.4 -7299.67 119.34 0.25 3681 3683 2 -21886.4 -7298.75 119.58 0.25 3682 3684 2 -21887.2 -7297.3 119.9 0.25 3683 3685 2 -21887.5 -7296.87 120 0.25 3684 3686 2 -21886.9 -7293.24 120.54 0.37 3685 3687 2 -21887.3 -7290.92 120.96 0.37 3686 3688 2 -21889.4 -7286.12 119.38 0.37 3687 3689 2 -21889.3 -7286.2 119.88 0.37 3688 3690 2 -21890.2 -7284.11 121.01 0.37 3689 3691 2 -21892.2 -7278.85 126.04 0.37 3690 3692 2 -21892.3 -7275.45 126.6 0.37 3691 3693 2 -21892.4 -7271.26 127.49 0.37 3692 3694 2 -21894.1 -7269.72 127.91 0.37 3693 3695 2 -21895.9 -7267.82 126.23 0.37 3694 3696 2 -21897.4 -7265.97 126.67 0.37 3695 3697 2 -21897.6 -7266.03 126.68 0.37 3696 3698 2 -21898.3 -7264.41 129.13 0.37 3697 3699 2 -21898.6 -7261.02 129.71 0.37 3698 3700 2 -21898.8 -7259.98 129.91 0.37 3699 3701 2 -21899.8 -7257.51 130.41 0.37 3700 3702 2 -21899.8 -7257.27 130.45 0.37 3701 3703 2 -21900.9 -7256.79 135.71 0.37 3702 3704 2 -21901 -7256.53 135.76 0.37 3703 3705 2 -21902.8 -7252.9 136.53 0.37 3704 3706 2 -21902.6 -7248.22 139.16 0.37 3705 3707 2 -21903.2 -7244.85 139.77 0.37 3706 3708 2 -21902.7 -7243.19 142.81 0.25 3707 3709 2 -21903.7 -7241.76 143.14 0.25 3708 3710 2 -21903.7 -7241.56 143.17 0.25 3709 3711 2 -21904.1 -7239.48 143.55 0.25 3710 3712 2 -21904 -7238.45 143.71 0.25 3711 3713 2 -21905.1 -7237.69 141.7 0.25 3712 3714 2 -21905.4 -7237.5 141.76 0.25 3713 3715 2 -21905.9 -7236.24 142.02 0.25 3714 3716 2 -21906 -7235.98 142.06 0.25 3715 3717 2 -21906.2 -7234.95 142.25 0.25 3716 3718 2 -21906.8 -7234.03 143.98 0.25 3717 3719 2 -21908.3 -7233.24 144.26 0.25 3718 3720 2 -21908.4 -7232.98 144.3 0.25 3719 3721 2 -21909 -7232.58 144.43 0.25 3720 3722 2 -21909.9 -7228.94 144.78 0.25 3721 3723 2 -21910.4 -7227.48 145.07 0.25 3722 3724 2 -21910.5 -7227.24 145.11 0.25 3723 3725 2 -21911 -7224.17 145.67 0.25 3724 3726 2 -21911.3 -7220.5 146.31 0.25 3725 3727 2 -21910.2 -7216.34 145.04 0.25 3726 3728 2 -21910 -7211.8 145.78 0.25 3727 3729 2 -21910 -7211.53 145.82 0.25 3728 3730 2 -21910.5 -7208.53 143.61 0.25 3729 3731 2 -21910.6 -7207.74 143.75 0.25 3730 3732 2 -21910.5 -7204.11 146.32 0.25 3731 3733 2 -21910.6 -7203.85 146.4 0.25 3732 3734 2 -21910.8 -7202.61 146.63 0.25 3733 3735 2 -21911.8 -7200.14 147.12 0.25 3734 3736 2 -21911.6 -7199.56 147.2 0.25 3735 3737 2 -21911.7 -7199.31 147.25 0.25 3736 3738 2 -21910.7 -7196.71 149.65 0.25 3737 3739 2 -21911.3 -7196.31 149.78 0.25 3738 3740 2 -21911.4 -7196.05 149.83 0.25 3739 3741 2 -21911.7 -7194.29 150.14 0.25 3740 3742 2 -21911.5 -7193.98 150.17 0.25 3741 3743 2 -21912 -7192.75 150.42 0.25 3742 3744 2 -21911.9 -7192.49 150.47 0.25 3743 3745 2 -21911.4 -7191.59 153.31 0.25 3744 3746 2 -21911.3 -7191.73 154.15 0.25 3745 3747 2 -21911.4 -7188.24 154.52 0.25 3746 3748 2 -21911.4 -7187.99 154.56 0.25 3747 3749 2 -21912.7 -7186.94 154.86 0.25 3748 3750 2 -21913.5 -7183.42 153 0.25 3749 3751 2 -21913.5 -7183.19 153.04 0.25 3750 3752 2 -21914.1 -7182.49 153.22 0.25 3751 3753 2 -21913.9 -7182.22 153.25 0.25 3752 3754 2 -21914 -7181.42 153.39 0.25 3753 3755 2 -21914.1 -7181.17 153.44 0.25 3754 3756 2 -21914.5 -7180.46 153.59 0.25 3755 3757 2 -21914.4 -7179.39 153.76 0.25 3756 3758 2 -21914.5 -7179.11 153.81 0.25 3757 3759 2 -21914.2 -7178.83 153.84 0.25 3758 3760 2 -21914.6 -7175.97 155.77 0.25 3759 3761 2 -21914 -7173.96 157.09 0.25 3760 3762 2 -21913.3 -7172.31 158.88 0.25 3761 3763 2 -21913.4 -7172 158.94 0.25 3762 3764 2 -21913.7 -7166.89 156.01 0.25 3763 3765 2 -21913.4 -7166.59 156.03 0.25 3764 3766 2 -21913.8 -7160.68 162.15 0.25 3765 3767 2 -21915 -7156.83 163.68 0.25 3766 3768 2 -21915.9 -7154.64 164.13 0.25 3767 3769 2 -21916.2 -7152.6 164.5 0.25 3768 3770 2 -21916.3 -7152.36 164.55 0.25 3769 3771 2 -21915.8 -7150.34 164.3 0.25 3770 3772 2 -21915.7 -7149.61 163.52 0.25 3771 3773 2 -21915.5 -7149.27 163.55 0.25 3772 3774 2 -21916.7 -7145.57 164.28 0.25 3773 3775 2 -21915.6 -7141.85 159.85 0.25 3774 3776 2 -21915.9 -7141.92 159.86 0.25 3775 3777 2 -21915.8 -7140.32 160.12 0.25 3776 3778 2 -21915.9 -7140.05 160.17 0.25 3777 3779 2 -21916.7 -7136.98 160.26 0.25 3778 3780 2 -21916.8 -7136.69 160.11 0.25 3779 3781 2 -21917.7 -7134.24 160.6 0.25 3780 3782 2 -21917.8 -7134 160.65 0.25 3781 3783 2 -21916.8 -7131.43 160.98 0.25 3782 3784 2 -21916.6 -7130.85 161.06 0.25 3783 3785 2 -21917.5 -7129.15 161.3 0.25 3784 3786 2 -21917.5 -7128.88 161.35 0.25 3785 3787 2 -21917.4 -7126.94 164.25 0.25 3786 3788 2 -21917.5 -7125.91 164.44 0.25 3787 3789 2 -21917.2 -7124.77 164.59 0.25 3788 3790 2 -21917 -7124.51 164.61 0.25 3789 3791 2 -21916.6 -7123.15 164.81 0.25 3790 3792 2 -21916.3 -7123.07 164.79 0.25 3791 3793 2 -21916.4 -7121.45 167.83 0.25 3792 3794 2 -21916.3 -7120.65 167.95 0.25 3793 3795 2 -21916.4 -7119.87 168.09 0.25 3794 3796 2 -21916.5 -7119.13 168.22 0.25 3795 3797 2 -21916.6 -7118.85 168.27 0.25 3796 3798 2 -21917 -7115.4 166.77 0.25 3797 3799 2 -21917.7 -7113.19 167.58 0.25 3798 3800 2 -21917.9 -7111.91 167.81 0.25 3799 3801 2 -21917.9 -7111.66 167.86 0.25 3800 3802 2 -21917.9 -7110.07 168.12 0.25 3801 3803 2 -21917.9 -7109.83 168.16 0.25 3802 3804 2 -21917.7 -7107.21 170.48 0.25 3803 3805 2 -21918.2 -7105.75 170.78 0.25 3804 3806 2 -21917 -7104.73 171.02 0.25 3805 3807 2 -21916.3 -7103.34 171.19 0.25 3806 3808 2 -21916.1 -7102.77 171.26 0.25 3807 3809 2 -21915.2 -7101.24 172.72 0.25 3808 3810 2 -21915.2 -7100.96 172.78 0.25 3809 3811 2 -21916.1 -7099.12 173.68 0.25 3810 3812 2 -21916.1 -7096.88 176.2 0.25 3811 3813 2 -21916.1 -7096.6 176.25 0.25 3812 3814 2 -21916.5 -7095.36 176.66 0.25 3813 3815 2 -21917.1 -7093.62 177 0.25 3814 3816 2 -21917.2 -7093.38 177.05 0.25 3815 3817 2 -21917.3 -7091.98 177.95 0.25 3816 3818 2 -21917.7 -7090.93 179.33 0.25 3817 3819 2 -21918.1 -7088.65 180.07 0.25 3818 3820 2 -21918.9 -7086.93 181.75 0.25 3819 3821 2 -21918.9 -7086.41 181.84 0.25 3820 3822 2 -21920.8 -7084.57 184.89 0.25 3821 3823 2 -21920.8 -7084.59 185.04 0.25 3822 3824 2 -21922.5 -7082.51 185.55 0.25 3823 3825 2 -21922.5 -7082.27 185.59 0.25 3824 3826 2 -21923 -7080.57 189.22 0.25 3825 3827 2 -21923 -7080.27 189.27 0.25 3826 3828 2 -21922.4 -7078.87 189.44 0.25 3827 3829 2 -21922 -7078 189.55 0.25 3828 3830 2 -21921.5 -7075.79 189.87 0.25 3829 3831 2 -21921.2 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256.77 0.125 4205 4207 2 -22017.6 -6619.72 257.36 0.125 4206 4208 2 -22017.7 -6618.96 257.5 0.125 4207 4209 2 -22018.1 -6618.23 257.65 0.125 4208 4210 2 -22018.5 -6617.25 257.86 0.125 4209 4211 2 -22019 -6616.02 258.11 0.125 4210 4212 2 -22019.1 -6615.53 258.19 0.125 4211 4213 2 -22019.9 -6611.92 255.74 0.125 4212 4214 2 -22020 -6611.68 255.79 0.125 4213 4215 2 -22020.2 -6610.14 256.08 0.125 4214 4216 2 -22020.3 -6609.39 256.22 0.125 4215 4217 2 -22020.8 -6607.9 256.56 0.125 4216 4218 2 -22020.8 -6606.62 256.81 0.125 4217 4219 2 -22022.6 -6601.94 255.09 0.125 4218 4220 2 -22022.7 -6600.91 255.28 0.125 4219 4221 2 -22021.5 -6596.48 255.95 0.125 4220 4222 2 -22021.7 -6595.46 256.13 0.125 4221 4223 2 -22022.4 -6592.75 256.65 0.125 4222 4224 2 -22022.8 -6590.16 257.12 0.125 4223 4225 2 -22023.6 -6586.19 252.53 0.125 4224 4226 2 -22023.7 -6580.13 253.55 0.125 4225 4227 2 -22024 -6578.9 252.42 0.125 4226 4228 2 -22024 -6575.76 252.94 0.125 4227 4229 2 -22024.5 -6572.64 253.5 0.125 4228 4230 2 -22024.6 -6572.13 253.59 0.125 4229 4231 2 -22024.3 -6571.83 253.62 0.125 4230 4232 2 -22024.6 -6570.58 253.85 0.125 4231 4233 2 -22024.6 -6570.34 253.89 0.125 4232 4234 2 -22025.5 -6567.32 253.14 0.125 4233 4235 2 -22027.9 -6565.3 251.55 0.125 4234 4236 2 -22028.2 -6564.37 253.66 0.125 4235 4237 2 -22028.5 -6564.4 253.68 0.125 4236 4238 2 -22030.2 -6562.6 253.88 0.125 4237 4239 2 -22030.3 -6562.53 253.49 0.125 4238 4240 2 -22030.4 -6560.1 253.86 0.125 4239 4241 2 -22030.5 -6559.9 253.9 0.125 4240 4242 2 -22030.6 -6555.44 251.49 0.125 4241 4243 2 -22032.5 -6553.08 250.41 0.125 4242 4244 2 -22032.5 -6552.84 250.45 0.125 4243 4245 2 -22032.7 -6551.29 250.73 0.125 4244 4246 2 -22032.9 -6550.52 250.87 0.125 4245 4247 2 -22033 -6547.86 251.33 0.125 4246 4248 2 -22032.9 -6547.12 251.44 0.125 4247 4249 2 -22033.1 -6545.8 251.67 0.125 4248 4250 2 -22033.9 -6543.3 250.48 0.125 4249 4251 2 -22033.9 -6543.03 250.53 0.125 4250 4252 2 -22034.5 -6541.31 250.87 0.125 4251 4253 2 -22034.7 -6540.52 251.01 0.125 4252 4254 2 -22035 -6538.75 251.33 0.125 4253 4255 2 -22034.7 -6538.42 251.36 0.125 4254 4256 2 -22036 -6534.33 251.71 0.125 4255 4257 2 -22035.7 -6532.71 251.95 0.125 4256 4258 2 -22035.7 -6532.46 252 0.125 4257 4259 2 -22035.4 -6531.34 252.15 0.125 4258 4260 2 -22035.4 -6531.09 252.19 0.125 4259 4261 2 -22031.8 -6527.77 252.14 0.125 4260 4262 2 -22028.1 -6526.83 258.2 0.125 4261 4263 2 -22027.3 -6524.56 264.02 0.125 4262 4264 2 -22026.7 -6522.85 264.25 0.125 4263 4265 2 -22027 -6522.68 264.49 0.125 4264 4266 2 -21870.6 -7377.64 83.98 0.745 1647 4267 2 -21869.3 -7378.44 80.14 0.745 4266 4268 2 -21867.3 -7378.45 75.84 0.745 4267 4269 2 -21867.5 -7378.47 75.86 0.745 4268 4270 2 -21865.7 -7378.47 69.65 0.745 4269 4271 2 -21864.5 -7379.02 65.03 0.745 4270 4272 2 -21863.8 -7379.23 62.09 0.745 4271 4273 2 -21862.3 -7379.23 58.69 0.745 4272 4274 2 -21860.3 -7378.64 55.54 0.745 4273 4275 2 -21859.7 -7378.45 48.98 0.745 4274 4276 2 -21859.7 -7378.37 48.46 0.745 4275 4277 2 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4325 2 -21847.8 -7323.75 179.32 0.125 4324 4326 2 -21847.9 -7322.95 183.92 0.125 4325 4327 2 -21848.3 -7321.04 184.27 0.25 4326 4328 2 -21848.7 -7319.51 184.57 0.25 4327 4329 2 -21854.3 -7322.44 184.61 0.25 4328 4330 2 -21853.1 -7321.87 194.65 0.25 4329 4331 2 -21852.5 -7321.29 196.46 0.25 4330 4332 2 -21852.2 -7320.84 201.71 0.25 4331 4333 2 -21852.7 -7320.02 204.38 0.25 4332 4334 2 -21852.5 -7318.45 209.6 0.25 4333 4335 2 -21852.5 -7318.41 214.21 0.25 4334 4336 2 -21852.5 -7318.31 215.13 0.25 4335 4337 2 -21851.8 -7318.29 216.87 0.25 4336 4338 2 -21850.9 -7317.78 217.69 0.25 4337 4339 2 -21850.2 -7317.42 220.7 0.25 4338 4340 2 -21850.1 -7317.48 221.04 0.25 4339 4341 2 -21850.4 -7316.42 224.27 0.25 4340 4342 2 -21850.5 -7315.99 227.72 0.25 4341 4343 2 -21850.5 -7315.73 227.77 0.25 4342 4344 2 -21849.7 -7316.03 229.98 0.25 4343 4345 2 -21849.8 -7315.79 230.03 0.25 4344 4346 2 -21850.4 -7315.62 233.35 0.25 4345 4347 2 -21850.4 -7315.32 233.39 0.25 4346 4348 2 -21850.7 -7313.78 236.69 0.25 4347 4349 2 -21851.4 -7314.05 242.53 0.25 4348 4350 2 -21852.8 -7314.14 246.51 0.25 4349 4351 2 -21851.7 -7312.9 249.39 0.25 4350 4352 2 -21851.5 -7312.71 256.27 0.25 4351 4353 2 -21853.6 -7304.2 259.49 0.125 4352 4354 2 -21853.7 -7303.19 259.99 0.125 4353 4355 2 -21853.7 -7303.22 260.15 0.125 4354 4356 2 -21852.9 -7302.35 266.63 0.125 4355 4357 2 -21853 -7302.09 266.68 0.125 4356 4358 2 -21853.2 -7301.78 269.16 0.125 4357 4359 2 -21853.2 -7301.59 269.53 0.125 4358 4360 2 -21853.5 -7300.66 276.2 0.125 4359 4361 2 -21853.5 -7298.72 279.18 0.125 4360 4362 2 -21853.7 -7298.75 279.2 0.125 4361 4363 2 -21854.9 -7298.19 279.41 0.125 4362 4364 2 -21855 -7297.99 279.45 0.125 4363 4365 2 -21855.7 -7297.06 284.43 0.125 4364 4366 2 -21855.7 -7297.32 284.39 0.125 4365 4367 2 -21855.7 -7297.08 288.86 0.125 4366 4368 2 -21855.6 -7296.76 291.91 0.125 4367 4369 2 -21855.6 -7296.52 291.96 0.125 4368 4370 2 -21856.6 -7296.87 298.99 0.125 4369 4371 2 -21856.7 -7296.12 299.13 0.125 4370 4372 2 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4417 4419 2 -21908.8 -7287.73 422.43 0.125 4418 4420 2 -21908.9 -7285.39 422.83 0.125 4419 4421 2 -21908.2 -7283.77 426.86 0.125 4420 4422 2 -21908.1 -7283.87 427.48 0.125 4421 4423 2 -21907.1 -7277.89 427.53 0.125 4422 4424 2 -21905.3 -7275.12 429.43 0.125 4423 4425 2 -21903.7 -7272.95 429.64 0.125 4424 4426 2 -21903.5 -7272.59 429.68 0.125 4425 4427 2 -21902.5 -7270 430.02 0.125 4426 4428 2 -21902.4 -7269.17 430.15 0.125 4427 4429 2 -21902.8 -7267.39 430.48 0.125 4428 4430 2 -21902.6 -7267.11 430.51 0.125 4429 4431 2 -21900.9 -7258.69 436.35 0.125 4430 4432 2 -21897.6 -7250.46 448.71 0.125 4431 4433 2 -21898.5 -7247.36 450.34 0.125 4432 4434 2 -21898.5 -7246 450.57 0.125 4433 4435 2 -21898 -7241.4 455.91 0.125 4434 4436 2 -21898.3 -7241.47 455.92 0.125 4435 4437 2 -21899.4 -7239.69 457.01 0.125 4436 4438 2 -21900.4 -7234.61 464.24 0.125 4437 4439 2 -21905 -7234.28 464.73 0.125 4438 4440 2 -21905.5 -7231.12 465.3 0.125 4439 4441 2 -21905.5 -7231.33 465.27 0.125 4440 4442 2 -21904.4 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4466 2 -21842.8 -7319.64 153.86 0.125 4465 4467 2 -21842.8 -7319.16 158.77 0.125 4466 4468 2 -21842.8 -7319.18 158.88 0.125 4467 4469 2 -21842.8 -7317.35 159.2 0.125 4468 4470 2 -21842.6 -7316.09 162.85 0.125 4469 4471 2 -21842.3 -7314.51 163.51 0.125 4470 4472 2 -21842.2 -7314.31 163.98 0.125 4471 4473 2 -21842 -7313.07 167.79 0.125 4472 4474 2 -21841.3 -7311.4 169.56 0.125 4473 4475 2 -21842.1 -7310.56 171.81 0.125 4474 4476 2 -21842.7 -7308.81 172.16 0.125 4475 4477 2 -21841.8 -7308.25 174.19 0.125 4476 4478 2 -21840.9 -7306.93 175.13 0.125 4477 4479 2 -21841.3 -7305.53 176.24 0.125 4478 4480 2 -21840.6 -7303.87 178.13 0.125 4479 4481 2 -21840.6 -7303.02 180.77 0.125 4480 4482 2 -21840.5 -7303.21 181.98 0.125 4481 4483 2 -21840.7 -7301.95 182.61 0.125 4482 4484 2 -21840.2 -7301.73 186.42 0.125 4483 4485 2 -21840 -7299.59 186.75 0.125 4484 4486 2 -21839.8 -7297.18 187.14 0.125 4485 4487 2 -21846.2 -7300.04 187.25 0.125 4486 4488 2 -21845.2 -7299.22 191.06 0.125 4487 4489 2 -21844.8 -7297.07 191.38 0.125 4488 4490 2 -21843.8 -7296.45 194.74 0.125 4489 4491 2 -21843.6 -7296.4 194.72 0.125 4490 4492 2 -21842.8 -7294.54 197.04 0.125 4491 4493 2 -21841.5 -7291.65 200.1 0.125 4492 4494 2 -21840.4 -7289.98 203.88 0.125 4493 4495 2 -21839 -7285.84 207.37 0.125 4494 4496 2 -21837.9 -7283.5 211.94 0.125 4495 4497 2 -21837.8 -7282.56 214.51 0.125 4496 4498 2 -21837.7 -7279.92 217.93 0.125 4497 4499 2 -21837.2 -7278.78 218.08 0.125 4498 4500 2 -21837 -7277.61 219.19 0.125 4499 4501 2 -21838.1 -7275.14 221.25 0.125 4500 4502 2 -21840.3 -7274.23 224.04 0.125 4501 4503 2 -21842.1 -7273.56 228.43 0.125 4502 4504 2 -21844.8 -7272.67 228.97 0.125 4503 4505 2 -21847.7 -7272.2 231.82 0.125 4504 4506 2 -21849.6 -7270.9 232.57 0.08 4505 4507 2 -21851.6 -7270.66 236.12 0.08 4506 4508 2 -21852.2 -7271.63 239.25 0.08 4507 4509 2 -21853.1 -7272.25 243.05 0.125 4508 4510 2 -21854.6 -7272.45 243.15 0.125 4509 4511 2 -21856.3 -7272.15 243.37 0.125 4510 4512 2 -21856.1 -7271.47 247.4 0.125 4511 4513 2 -21855.1 -7270.27 247.42 0.125 4512 4514 2 -21854.2 -7269.23 248.48 0.08 4513 4515 2 -21854 -7268.43 248.59 0.08 4514 4516 2 -21854 -7268.65 248.55 0.08 4515 4517 2 -21853.6 -7266.21 250.44 0.08 4516 4518 2 -21853.2 -7264.82 250.63 0.08 4517 4519 2 -21853 -7262.75 254.24 0.08 4518 4520 2 -21853.2 -7261.15 254.51 0.08 4519 4521 2 -21853.2 -7260.9 254.55 0.08 4520 4522 2 -21853.8 -7260.17 259.32 0.08 4521 4523 2 -21854.7 -7259.81 259.47 0.08 4522 4524 2 -21855.1 -7259.34 259.58 0.08 4523 4525 2 -21855.7 -7258.88 259.72 0.08 4524 4526 2 -21856.4 -7257.88 259.94 0.08 4525 4527 2 -21857 -7257.46 260.07 0.08 4526 4528 2 -21857.4 -7256.73 260.22 0.08 4527 4529 2 -21859.4 -7255.05 263.22 0.08 4528 4530 2 -21860.3 -7252.53 263.72 0.08 4529 4531 2 -21860.3 -7252.25 263.77 0.08 4530 4532 2 -21861.3 -7244.18 263.37 0.08 4531 4533 2 -21861.9 -7242.7 263.67 0.08 4532 4534 2 -21862 -7240.33 264.07 0.08 4533 4535 2 -21862.3 -7238.34 266.57 0.08 4534 4536 2 -21863.8 -7234.94 262.89 0.08 4535 4537 2 -21863.8 -7234.66 262.94 0.08 4536 4538 2 -21864.2 -7234.23 263.04 0.08 4537 4539 2 -21864.2 -7233.96 263.09 0.08 4538 4540 2 -21864.7 -7233.13 271.5 0.08 4539 4541 2 -21865 -7233.17 271.45 0.26 4540 4542 2 -21865 -7231.8 271.67 0.135 4541 4543 2 -21866.1 -7230.45 271.99 0.135 4542 4544 2 -21866.1 -7230.2 272.04 0.135 4543 4545 2 -21866.4 -7228.66 272.32 0.08 4544 4546 2 -21866.4 -7228.37 272.37 0.08 4545 4547 2 -21867.1 -7227.51 274.28 0.08 4546 4548 2 -21867.8 -7225.22 279 0.08 4547 4549 2 -21868.5 -7224.3 279.47 0.08 4548 4550 2 -21869.8 -7218.98 285.07 0.08 4549 4551 2 -21869.9 -7218.75 285.11 0.08 4550 4552 2 -21872.9 -7211.57 290.93 0.08 4551 4553 2 -21874.6 -7208.62 296.96 0.08 4552 4554 2 -21874.5 -7208.69 297.39 0.08 4553 4555 2 -21876.3 -7206.9 297.85 0.08 4554 4556 2 -21876.3 -7206.63 297.9 0.08 4555 4557 2 -21879.8 -7201.59 304.33 0.08 4556 4558 2 -21879.8 -7201.35 304.37 0.08 4557 4559 2 -21881.7 -7197.12 310.8 0.08 4558 4560 2 -21883.9 -7194.58 313.46 0.08 4559 4561 2 -21884.3 -7193.25 320.64 0.08 4560 4562 2 -21884.6 -7193.27 320.66 0.08 4561 4563 2 -21881.6 -7186.79 339.25 0.08 4562 4564 2 -21881.8 -7186.86 339.26 0.08 4563 4565 2 -21881.4 -7186.33 339.8 0.08 4564 4566 2 -21887 -7189.95 319.35 0.08 4562 4567 2 -21887.6 -7188.68 319.61 0.08 4566 4568 2 -21887.6 -7188.42 319.65 0.08 4567 4569 2 -21888.2 -7188.81 326.97 0.08 4568 4570 2 -21888.2 -7189.05 326.92 0.08 4569 4571 2 -21888.8 -7188.66 327.05 0.08 4570 4572 2 -21854.5 -7377.37 51.88 0.25 4278 4573 2 -21851.3 -7376.8 54.53 0.25 4572 4574 2 -21848.8 -7376.48 54.73 0.25 4573 4575 2 -21848.5 -7376.43 54.71 0.25 4574 4576 2 -21844.5 -7376.1 56.33 0.25 4575 4577 2 -21841.9 -7375.68 57.73 0.25 4576 4578 2 -21837.9 -7374.95 59.74 0.25 4577 4579 2 -21837.3 -7374.82 59.71 0.25 4578 4580 2 -21833.6 -7375.14 59.94 0.25 4579 4581 2 -21833 -7374.73 59.96 0.25 4580 4582 2 -21832.8 -7374.72 59.93 0.25 4581 4583 2 -21829.2 -7371.87 60.73 0.25 4582 4584 2 -21825.9 -7369.96 63.3 0.25 4583 4585 2 -21826 -7369.72 63.34 0.25 4584 4586 2 -21824.1 -7368.6 63.35 0.25 4585 4587 2 -21823.4 -7368.14 63.35 0.25 4586 4588 2 -21823.1 -7367.89 63.38 0.25 4587 4589 2 -21820.7 -7366.99 63.3 0.25 4588 4590 2 -21820.5 -7366.94 63.28 0.25 4589 4591 2 -21818.7 -7366.92 63.12 0.25 4590 4592 2 -21818 -7366.26 63.16 0.25 4591 4593 2 -21816.7 -7366.42 65.15 0.25 4592 4594 2 -21814.9 -7366.66 64.99 0.25 4593 4595 2 -21812 -7366.48 70.8 0.25 4594 4596 2 -21812.1 -7366.22 70.88 0.25 4595 4597 2 -21809.6 -7365.62 71.04 0.25 4596 4598 2 -21809.3 -7365.36 71.29 0.25 4597 4599 2 -21806.6 -7364.46 71.73 0.25 4598 4600 2 -21806.1 -7364.36 71.7 0.25 4599 4601 2 -21801.5 -7363.19 73.27 0.25 4600 4602 2 -21795.3 -7362.33 79.77 0.25 4601 4603 2 -21795.1 -7362.02 79.8 0.25 4602 4604 2 -21793 -7356.71 80.96 0.25 4603 4605 2 -21789.3 -7350.21 88.87 0.25 4604 4606 2 -21787.3 -7349.8 89.72 0.25 4605 4607 2 -21786.8 -7349.49 89.72 0.25 4606 4608 2 -21785.5 -7350.06 91.07 0.25 4607 4609 2 -21784.6 -7349.92 91.02 0.25 4608 4610 2 -21782.2 -7350.69 91.42 0.25 4609 4611 2 -21781.9 -7350.7 91.39 0.25 4610 4612 2 -21780.2 -7350.67 91.37 0.25 4611 4613 2 -21779.7 -7350.59 91.33 0.25 4612 4614 2 -21778.1 -7349.32 91.61 0.25 4613 4615 2 -21777.7 -7348.73 91.67 0.25 4614 4616 2 -21777.1 -7348.65 91.63 0.25 4615 4617 2 -21773.8 -7347.21 92.43 0.25 4616 4618 2 -21773 -7346.82 92.42 0.25 4617 4619 2 -21770.8 -7345.16 91.35 0.25 4618 4620 2 -21769.9 -7345.35 91.23 0.25 4619 4621 2 -21768.3 -7345.02 91.13 0.25 4620 4622 2 -21767.8 -7344.69 91.14 0.25 4621 4623 2 -21767.8 -7344.41 91.19 0.25 4622 4624 2 -21766.6 -7343.43 91.24 0.25 4623 4625 2 -21766.3 -7343.13 91.26 0.25 4624 4626 2 -21764.1 -7342.74 91.12 0.25 4625 4627 2 -21763 -7342.58 91.04 0.25 4626 4628 2 -21763.1 -7342.3 91.09 0.25 4627 4629 2 -21761.4 -7342.29 90.94 0.25 4628 4630 2 -21761.1 -7342.22 90.92 0.25 4629 4631 2 -21758.9 -7342.26 94.32 0.25 4630 4632 2 -21758.8 -7341.78 93.11 0.25 4631 4633 2 -21757.5 -7340.79 93.31 0.25 4632 4634 2 -21756.9 -7340.67 94.58 0.25 4633 4635 2 -21756.6 -7340.34 94.61 0.25 4634 4636 2 -21750.8 -7339.42 98.85 0.25 4635 4637 2 -21750.6 -7338.87 98.93 0.25 4636 4638 2 -21750.3 -7338.57 98.96 0.25 4637 4639 2 -21748.1 -7337.79 99.66 0.25 4638 4640 2 -21747.8 -7337.8 99.63 0.25 4639 4641 2 -21744.5 -7336.3 101.93 0.25 4640 4642 2 -21744.3 -7335.74 102.01 0.25 4641 4643 2 -21740.6 -7332.98 100.2 0.25 4642 4644 2 -21738.4 -7331.8 100.19 0.37 4643 4645 2 -21737 -7329.72 100.41 0.37 4644 4646 2 -21736.8 -7329.4 100.44 0.37 4645 4647 2 -21735 -7328.28 100.45 0.37 4646 4648 2 -21734.1 -7327.09 100.57 0.37 4647 4649 2 -21732.4 -7326.76 100.46 0.37 4648 4650 2 -21731.1 -7326.3 100.42 0.37 4649 4651 2 -21730.9 -7325.94 100.45 0.37 4650 4652 2 -21730.5 -7325.1 100.56 0.37 4651 4653 2 -21730.3 -7324.53 100.63 0.37 4652 4654 2 -21730 -7323.98 100.7 0.37 4653 4655 2 -21729.3 -7323.59 100.7 0.37 4654 4656 2 -21727 -7321.89 100.76 0.37 4655 4657 2 -21726.3 -7321.73 100.72 0.37 4656 4658 2 -21723.7 -7320.93 98.79 0.37 4657 4659 2 -21721 -7319.68 98.75 0.37 4658 4660 2 -21720.7 -7319.61 98.74 0.37 4659 4661 2 -21719 -7318.54 98.75 0.37 4660 4662 2 -21718.4 -7318.42 98.72 0.37 4661 4663 2 -21716.6 -7317.31 98.73 0.37 4662 4664 2 -21716.7 -7317.08 98.78 0.37 4663 4665 2 -21715.2 -7315.75 98.85 0.37 4664 4666 2 -21714.4 -7315.6 98.81 0.37 4665 4667 2 -21712.3 -7316 98.55 0.37 4666 4668 2 -21711.8 -7315.93 98.51 0.37 4667 4669 2 -21710.5 -7315.19 98.51 0.37 4668 4670 2 -21710 -7315.06 98.48 0.37 4669 4671 2 -21710 -7314.78 98.53 0.37 4670 4672 2 -21707.8 -7313.63 100.1 0.37 4671 4673 2 -21707.5 -7313.58 100.09 0.37 4672 4674 2 -21706.8 -7312.94 100.13 0.37 4673 4675 2 -21706.6 -7312.9 100.11 0.37 4674 4676 2 -21704 -7312.96 99.85 0.37 4675 4677 2 -21703.7 -7313.14 99.79 0.37 4676 4678 2 -21703.4 -7313.13 99.77 0.37 4677 4679 2 -21702.2 -7313.71 99.56 0.37 4678 4680 2 -21701.1 -7313.49 99.5 0.37 4679 4681 2 -21697.7 -7312.86 102.02 0.37 4680 4682 2 -21696.1 -7310.93 100.4 0.37 4681 4683 2 -21693.8 -7309.47 100.42 0.37 4682 4684 2 -21693.3 -7309.36 100.39 0.37 4683 4685 2 -21691 -7309.23 100.2 0.37 4684 4686 2 -21690.7 -7309.19 100.18 0.37 4685 4687 2 -21687.9 -7308.34 102.54 0.37 4686 4688 2 -21687.2 -7306.3 102.8 0.37 4687 4689 2 -21686.1 -7305.61 102.82 0.37 4688 4690 2 -21684.2 -7303.67 102.96 0.37 4689 4691 2 -21683.1 -7303.22 102.93 0.37 4690 4692 2 -21679.2 -7301.5 102.85 0.37 4691 4693 2 -21678.3 -7301.33 102.8 0.37 4692 4694 2 -21673.5 -7300.01 102.92 0.37 4693 4695 2 -21671.1 -7299.73 105.05 0.25 4694 4696 2 -21669.1 -7298.3 105.09 0.25 4695 4697 2 -21666 -7296.69 105.07 0.25 4696 4698 2 -21662.2 -7294.97 106.54 0.25 4697 4699 2 -21659.4 -7293.67 106.49 0.25 4698 4700 2 -21657.8 -7292.64 106.52 0.25 4699 4701 2 -21657.7 -7291.55 106.69 0.25 4700 4702 2 -21655.2 -7290.59 109.58 0.25 4701 4703 2 -21655.2 -7290.33 109.63 0.25 4702 4704 2 -21652.9 -7288.84 109.66 0.25 4703 4705 2 -21652.3 -7287.41 109.85 0.25 4704 4706 2 -21651 -7286.86 109.81 0.25 4705 4707 2 -21650.4 -7286.82 109.76 0.25 4706 4708 2 -21648.1 -7286.95 109.53 0.25 4707 4709 2 -21647.6 -7286.61 109.54 0.25 4708 4710 2 -21645.2 -7285.95 109.85 0.25 4709 4711 2 -21644.9 -7285.63 109.93 0.25 4710 4712 2 -21644 -7285.53 111.64 0.25 4711 4713 2 -21643.2 -7285.4 111.59 0.25 4712 4714 2 -21641.2 -7285.56 111.37 0.25 4713 4715 2 -21641.2 -7285.29 111.42 0.25 4714 4716 2 -21640.5 -7284.39 111.5 0.25 4715 4717 2 -21640 -7284.04 111.51 0.25 4716 4718 2 -21638.6 -7284.28 111.34 0.25 4717 4719 2 -21638.3 -7283.99 111.36 0.25 4718 4720 2 -21636.8 -7282.69 111.44 0.25 4719 4721 2 -21635.8 -7282.23 111.42 0.25 4720 4722 2 -21635.6 -7281.9 111.45 0.25 4721 4723 2 -21635.4 -7281.62 111.48 0.25 4722 4724 2 -21633.7 -7281.3 111.38 0.25 4723 4725 2 -21633.5 -7280.73 111.45 0.25 4724 4726 2 -21632.5 -7279.82 111.51 0.25 4725 4727 2 -21632 -7279.45 111.61 0.25 4726 4728 2 -21630.9 -7278.92 112.57 0.25 4727 4729 2 -21630.2 -7279.2 113.4 0.25 4728 4730 2 -21629.4 -7279.07 113.35 0.25 4729 4731 2 -21629.2 -7278.76 113.51 0.25 4730 4732 2 -21627.8 -7278.05 113.55 0.25 4731 4733 2 -21627.4 -7277.7 113.57 0.25 4732 4734 2 -21626.6 -7277.55 113.52 0.25 4733 4735 2 -21626 -7277.47 113.48 0.25 4734 4736 2 -21625.8 -7277.36 113.47 0.25 4735 4737 2 -21624.7 -7276.69 113.49 0.25 4736 4738 2 -21623.9 -7276.54 113.43 0.25 4737 4739 2 -21622.5 -7276.58 113.37 0.25 4738 4740 2 -21621.4 -7276.16 113.64 0.25 4739 4741 2 -21621.1 -7276.1 113.62 0.25 4740 4742 2 -21620.1 -7275.22 114.12 0.25 4741 4743 2 -21619 -7274.84 114.44 0.25 4742 4744 2 -21615.8 -7273.8 114.6 0.25 4743 4745 2 -21614.7 -7273.33 114.62 0.25 4744 4746 2 -21613.3 -7273.35 114.51 0.25 4745 4747 2 -21613.1 -7273.03 114.58 0.25 4746 4748 2 -21611.3 -7271.85 114.23 0.25 4747 4749 2 -21609.1 -7271.97 113.99 0.25 4748 4750 2 -21608 -7271.52 113.97 0.25 4749 4751 2 -21607.8 -7271.5 113.95 0.25 4750 4752 2 -21606.8 -7270.28 114.06 0.25 4751 4753 2 -21606 -7270.1 114.02 0.25 4752 4754 2 -21603.8 -7269.98 113.83 0.25 4753 4755 2 -21603.5 -7269.92 113.81 0.25 4754 4756 2 -21594.2 -7266.8 112.7 0.25 4755 4757 2 -21591.1 -7265.98 113.94 0.25 4756 4758 2 -21590.9 -7265.94 113.92 0.25 4757 4759 2 -21589 -7265.31 113.85 0.25 4758 4760 2 -21588.4 -7265.24 113.81 0.25 4759 4761 2 -21588.5 -7264.99 113.85 0.25 4760 4762 2 -21586.2 -7265.11 113.62 0.25 4761 4763 2 -21583.3 -7265.02 114.08 0.25 4762 4764 2 -21582.8 -7264.92 114.05 0.25 4763 4765 2 -21580.9 -7264.32 113.97 0.25 4764 4766 2 -21579.8 -7263.85 113.94 0.25 4765 4767 2 -21579.6 -7263.79 113.93 0.25 4766 4768 2 -21578 -7262.76 113.96 0.25 4767 4769 2 -21577 -7262 113.99 0.25 4768 4770 2 -21576.8 -7261.99 113.97 0.25 4769 4771 2 -21575.8 -7261.53 113.95 0.25 4770 4772 2 -21574.7 -7260.81 113.97 0.25 4771 4773 2 -21572.8 -7261.06 117.04 0.25 4772 4774 2 -21572.5 -7261.02 117.02 0.25 4773 4775 2 -21570 -7260.84 116.81 0.25 4774 4776 2 -21569.8 -7260.26 116.89 0.25 4775 4777 2 -21566.5 -7259.59 117.64 0.25 4776 4778 2 -21564.3 -7259.18 117.5 0.25 4777 4779 2 -21563.8 -7258.85 117.51 0.25 4778 4780 2 -21562.4 -7258.61 117.42 0.25 4779 4781 2 -21561 -7258.35 117.34 0.25 4780 4782 2 -21559.3 -7257.27 117.35 0.25 4781 4783 2 -21557.6 -7256.91 117.26 0.25 4782 4784 2 -21555.3 -7257.36 116.97 0.25 4783 4785 2 -21553.6 -7257.27 116.82 0.25 4784 4786 2 -21549.5 -7255.63 114.38 0.25 4785 4787 2 -21546.8 -7254.31 115.57 0.25 4786 4788 2 -21545.9 -7254.93 115.38 0.25 4787 4789 2 -21544.6 -7254.16 115.38 0.25 4788 4790 2 -21544.3 -7253.86 115.41 0.25 4789 4791 2 -21544.1 -7253.82 115.4 0.25 4790 4792 2 -21541.8 -7252.95 117.53 0.25 4791 4793 2 -21541.4 -7252.65 117.53 0.25 4792 4794 2 -21538.1 -7253.12 117.15 0.25 4793 4795 2 -21537 -7252.94 117.08 0.25 4794 4796 2 -21534.8 -7252.53 116.94 0.25 4795 4797 2 -21534.1 -7252.13 116.94 0.25 4796 4798 2 -21531.6 -7251.42 116.82 0.25 4797 4799 2 -21530.6 -7250.98 116.8 0.25 4798 4800 2 -21530.3 -7250.94 116.78 0.25 4799 4801 2 -21526.2 -7250.46 116.47 0.25 4800 4802 2 -21524.5 -7250.16 116.37 0.25 4801 4803 2 -21520.4 -7249.69 116.06 0.25 4802 4804 2 -21519.5 -7249.8 115.96 0.25 4803 4805 2 -21516.5 -7248.66 118.5 0.25 4804 4806 2 -21511.8 -7246.41 117.89 0.125 4805 4807 2 -21508.6 -7245.08 117.81 0.125 4806 4808 2 -21508.4 -7244.76 117.84 0.125 4807 4809 2 -21503.9 -7242.89 117.73 0.125 4808 4810 2 -21503.7 -7242.62 117.75 0.125 4809 4811 2 -21503 -7240.1 118.1 0.125 4810 4812 2 -21501.7 -7237.24 120.09 0.125 4811 4813 2 -21500.6 -7236.54 120.11 0.125 4812 4814 2 -21500.1 -7236.17 120.13 0.125 4813 4815 2 -21499.5 -7236.09 120.09 0.125 4814 4816 2 -21496.6 -7235.05 119.98 0.125 4815 4817 2 -21495.3 -7234.79 119.9 0.125 4816 4818 2 -21495.2 -7235.09 119.85 0.125 4817 4819 2 -21492.1 -7235.24 119.53 0.125 4818 4820 2 -21491.5 -7235.16 119.49 0.125 4819 4821 2 -21487.9 -7235.32 119.12 0.125 4820 4822 2 -21487.6 -7235.26 119.1 0.125 4821 4823 2 -21485.9 -7235.46 118.91 0.125 4822 4824 2 -21483.3 -7235.2 119.61 0.125 4823 4825 2 -21481.3 -7235.07 119.44 0.125 4824 4826 2 -21480.1 -7233.85 119.54 0.125 4825 4827 2 -21479.9 -7233.79 119.52 0.125 4826 4828 2 -21477.2 -7232.77 119.44 0.125 4827 4829 2 -21476.6 -7232.66 119.41 0.125 4828 4830 2 -21473.6 -7232.18 119.21 0.125 4829 4831 2 -21473.4 -7231.86 119.24 0.125 4830 4832 2 -21471 -7231.55 122.72 0.125 4831 4833 2 -21470.2 -7231.4 122.66 0.125 4832 4834 2 -21468.1 -7231.56 122.45 0.125 4833 4835 2 -21467 -7231.36 122.38 0.125 4834 4836 2 -21464.9 -7231 122.23 0.125 4835 4837 2 -21464.6 -7230.66 122.27 0.125 4836 4838 2 -21463.1 -7229.39 122.34 0.125 4837 4839 2 -21462.7 -7229.01 122.36 0.125 4838 4840 2 -21461.6 -7228.58 122.33 0.125 4839 4841 2 -21460.5 -7228.08 122.31 0.125 4840 4842 2 -21456.4 -7225.76 122.3 0.125 4841 4843 2 -21456.1 -7225.7 122.29 0.125 4842 4844 2 -21453.4 -7225.87 124.03 0.125 4843 4845 2 -21452.6 -7225.72 123.98 0.125 4844 4846 2 -21448.9 -7224.77 123.79 0.125 4845 4847 2 -21448.8 -7224.49 123.83 0.125 4846 4848 2 -21447.9 -7223.78 123.69 0.125 4847 4849 2 -21447.6 -7223.7 123.67 0.125 4848 4850 2 -21445.4 -7223.56 123.49 0.125 4849 4851 2 -21445.1 -7223.52 123.47 0.125 4850 4852 2 -21443.5 -7222.99 123.41 0.125 4851 4853 2 -21443 -7222.88 123.37 0.125 4852 4854 2 -21442.2 -7222.49 123.36 0.125 4853 4855 2 -21441 -7222.28 123.29 0.125 4854 4856 2 -21440.2 -7222.15 123.24 0.125 4855 4857 2 -21439 -7221.42 123.24 0.125 4856 4858 2 -21438.2 -7221 123.24 0.125 4857 4859 2 -21437 -7219.98 123.29 0.125 4858 4860 2 -21436.7 -7219.66 123.32 0.125 4859 4861 2 -21436.4 -7219.6 123.31 0.125 4860 4862 2 -21432.7 -7218.99 126.24 0.125 4861 4863 2 -21432.9 -7219.04 126.25 0.125 4862 4864 2 -21430.5 -7217.37 128.35 0.125 4863 4865 2 -21430.8 -7217.44 128.27 0.125 4864 4866 2 -21428.2 -7216.52 131.57 0.125 4865 4867 2 -21425.5 -7215.76 131.57 0.125 4866 4868 2 -21425.4 -7215.85 132.12 0.125 4867 4869 2 -21422.7 -7215.94 134.93 0.125 4868 4870 2 -21419.3 -7214.51 134.85 0.125 4869 4871 2 -21418.9 -7213.62 134.96 0.125 4870 4872 2 -21417.8 -7213.2 134.93 0.125 4871 4873 2 -21417.5 -7213.12 134.91 0.125 4872 4874 2 -21415 -7211.91 134.87 0.125 4873 4875 2 -21414.4 -7211.51 134.89 0.125 4874 4876 2 -21412.9 -7210.47 134.92 0.125 4875 4877 2 -21412.1 -7210.35 134.86 0.125 4876 4878 2 -21410.7 -7209.23 137.7 0.125 4877 4879 2 -21410.2 -7208.9 137.7 0.125 4878 4880 2 -21408.9 -7208.13 137.71 0.125 4879 4881 2 -21407.8 -7207.93 137.64 0.125 4880 4882 2 -21406.5 -7207.49 137.59 0.125 4881 4883 2 -21406.2 -7207.15 137.62 0.125 4882 4884 2 -21405.3 -7206.19 137.69 0.125 4883 4885 2 -21404.8 -7205.83 137.7 0.125 4884 4886 2 -21402.5 -7204.76 139.79 0.125 4885 4887 2 -21401.2 -7204.53 139.7 0.125 4886 4888 2 -21400.1 -7203.83 139.72 0.125 4887 4889 2 -21399 -7202.22 139.88 0.125 4888 4890 2 -21397 -7201.54 142.15 0.125 4889 4891 2 -21395.5 -7200.74 142.15 0.125 4890 4892 2 -21394.7 -7200.35 142.19 0.125 4891 4893 2 -21394.4 -7200.35 142.17 0.125 4892 4894 2 -21393 -7199.65 144.26 0.125 4893 4895 2 -21392.7 -7199.33 144.29 0.125 4894 4896 2 -21391.4 -7198.15 146.74 0.125 4895 4897 2 -21391 -7198.09 146.72 0.125 4896 4898 2 -21387.7 -7196.59 145.86 0.125 4897 4899 2 -21387.6 -7196.74 146.77 0.125 4898 4900 2 -21385.5 -7195.24 147.69 0.125 4899 4901 2 -21383 -7194.22 150.61 0.125 4900 4902 2 -21383 -7194.42 150.58 0.125 4901 4903 2 -21379.3 -7192.72 150.28 0.125 4902 4904 2 -21379 -7192.68 150.26 0.125 4903 4905 2 -21377.3 -7191.54 150.28 0.125 4904 4906 2 -21376.3 -7190.85 150.3 0.125 4905 4907 2 -21371.9 -7190.13 154.7 0.125 4906 4908 2 -21371.5 -7190.05 154.62 0.125 4907 4909 2 -21370 -7189.46 154.52 0.125 4908 4910 2 -21369.8 -7189.23 154.61 0.125 4909 4911 2 -21366.4 -7188.17 156.77 0.125 4910 4912 2 -21365.3 -7188 156.8 0.125 4911 4913 2 -21362.8 -7187.37 157.14 0.125 4912 4914 2 -21362.5 -7187.08 157.16 0.125 4913 4915 2 -21359.1 -7186.64 159.24 0.125 4914 4916 2 -21358 -7186.19 159.22 0.125 4915 4917 2 -21354.9 -7184.56 159.21 0.125 4916 4918 2 -21354 -7183.83 159.24 0.125 4917 4919 2 -21350.4 -7182.52 159.67 0.125 4918 4920 2 -21345.5 -7181.39 160.88 0.125 4919 4921 2 -21342.6 -7181.09 161.94 0.125 4920 4922 2 -21340.7 -7180.32 157.87 0.125 4921 4923 2 -21337.7 -7179.79 157.67 0.125 4922 4924 2 -21334.5 -7177.72 159.63 0.125 4923 4925 2 -21341.2 -7183.7 159.27 0.125 4924 4926 2 -21338 -7181.91 159.26 0.125 4925 4927 2 -21332.6 -7179.55 159.15 0.125 4926 4928 2 -21331.9 -7179.2 159.14 0.125 4927 4929 2 -21330.3 -7177.91 159.21 0.125 4928 4930 2 -21329.8 -7177.59 159.21 0.125 4929 4931 2 -21327.7 -7176.19 159.25 0.125 4930 4932 2 -21326.6 -7175.84 159.2 0.125 4931 4933 2 -21326 -7175.69 159.17 0.125 4932 4934 2 -21324.8 -7174.71 159.22 0.125 4933 4935 2 -21323.8 -7173.56 159.31 0.125 4934 4936 2 -21322.8 -7172.37 159.42 0.125 4935 4937 2 -21321.8 -7171.43 159.48 0.125 4936 4938 2 -21319.1 -7170.34 159.8 0.125 4937 4939 2 -21318.6 -7169.75 159.85 0.125 4938 4940 2 -21316.7 -7168.94 159.81 0.125 4939 4941 2 -21315.8 -7168.82 159.75 0.125 4940 4942 2 -21314 -7167.54 159.79 0.125 4941 4943 2 -21313.8 -7167.2 159.82 0.125 4942 4944 2 -21309.8 -7166.15 161.2 0.125 4943 4945 2 -21305.8 -7163.85 163.34 0.125 4944 4946 2 -21305.5 -7163.78 163.32 0.125 4945 4947 2 -21304.3 -7163.64 163.24 0.125 4946 4948 2 -21303.8 -7163.54 163.2 0.125 4947 4949 2 -21303 -7163.69 163.1 0.125 4948 4950 2 -21302.1 -7163.57 163.04 0.125 4949 4951 2 -21296.2 -7161.86 165.06 0.125 4950 4952 2 -21295.2 -7161.42 165.03 0.125 4951 4953 2 -21293.2 -7161.14 164.89 0.125 4952 4954 2 -21291.8 -7160.96 164.79 0.125 4953 4955 2 -21289.5 -7161.01 165.32 0.125 4954 4956 2 -21289.1 -7161.19 166.48 0.125 4955 4957 2 -21286.5 -7161.11 166.25 0.125 4956 4958 2 -21285.1 -7160.89 166.16 0.125 4957 4959 2 -21282.8 -7159.51 166.17 0.125 4958 4960 2 -21281.7 -7159.09 166.14 0.125 4959 4961 2 -21280.6 -7158.89 166.07 0.125 4960 4962 2 -21280.1 -7158.58 166.07 0.125 4961 4963 2 -21276 -7159.02 169.84 0.125 4962 4964 2 -21274.4 -7158.51 169.77 0.125 4963 4965 2 -21271 -7158.31 169.49 0.125 4964 4966 2 -21270.5 -7157.99 169.49 0.125 4965 4967 2 -21267.8 -7157.38 168.06 0.125 4966 4968 2 -21267.3 -7157.06 168.06 0.125 4967 4969 2 -21266.8 -7156.68 168.08 0.125 4968 4970 2 -21264.1 -7155.28 168.06 0.125 4969 4971 2 -21263.6 -7154.95 168.07 0.125 4970 4972 2 -21263.4 -7154.64 168.1 0.125 4971 4973 2 -21261.2 -7154.02 172.14 0.125 4972 4974 2 -21260.1 -7153.59 172.11 0.125 4973 4975 2 -21258 -7152.7 172.06 0.125 4974 4976 2 -21257.4 -7152.15 172.1 0.125 4975 4977 2 -21255.9 -7150.61 172.21 0.125 4976 4978 2 -21255.4 -7150.27 172.22 0.125 4977 4979 2 -21255.2 -7149.98 172.25 0.125 4978 4980 2 -21253.1 -7149.55 174.5 0.125 4979 4981 2 -21252.5 -7149.22 174.5 0.125 4980 4982 2 -21252 -7148.91 174.52 0.125 4981 4983 2 -21251 -7148.45 174.5 0.125 4982 4984 2 -21249.5 -7148.3 174.39 0.125 4983 4985 2 -21248.7 -7148.18 174.33 0.125 4984 4986 2 -21248.2 -7147.85 174.34 0.125 4985 4987 2 -21247.9 -7147.58 174.36 0.125 4986 4988 2 -21245.8 -7146.35 178.24 0.125 4987 4989 2 -21245.1 -7145.7 178.28 0.125 4988 4990 2 -21244.8 -7145.66 178.26 0.125 4989 4991 2 -21242.9 -7142.98 178.52 0.125 4990 4992 2 -21242 -7141.25 178.72 0.125 4991 4993 2 -21237.3 -7137.86 185.74 0.125 4992 4994 2 -21237.6 -7137.9 185.76 0.125 4993 4995 2 -21235.7 -7135.05 186.45 0.125 4994 4996 2 -21235.5 -7134.74 186.5 0.125 4995 4997 2 -21233.1 -7132.48 192.33 0.125 4996 4998 2 -21232.9 -7131.92 192.4 0.125 4997 4999 2 -21231.6 -7130.72 192.48 0.125 4998 5000 2 -21231.4 -7130.11 192.56 0.125 4999 5001 2 -21231.1 -7130.11 192.53 0.125 5000 5002 2 -21228 -7125.29 193.85 0.125 5001 5003 2 -21227.8 -7124.71 193.92 0.125 5002 5004 2 -21226 -7122.39 195.9 0.125 5003 5005 2 -21225.5 -7122.07 195.91 0.125 5004 5006 2 -21225.2 -7121.76 195.93 0.125 5005 5007 2 -21223.1 -7120.16 196 0.125 5006 5008 2 -21223 -7119.59 196.08 0.125 5007 5009 2 -21222.7 -7119.57 196.06 0.125 5008 5010 2 -21222.3 -7117.93 196.3 0.125 5009 5011 2 -21221.8 -7117.03 196.41 0.125 5010 5012 2 -21221.9 -7116.81 196.45 0.125 5011 5013 2 -21220 -7116.22 199.1 0.125 5012 5014 2 -21219.5 -7115.95 199.1 0.125 5013 5015 2 -21219.5 -7115.66 199.15 0.125 5014 5016 2 -21217.2 -7113.87 200.05 0.125 5015 5017 2 -21216.1 -7113.03 200.88 0.125 5016 5018 2 -21213.4 -7111.61 200.86 0.125 5017 5019 2 -21212.5 -7110.65 200.93 0.125 5018 5020 2 -21211.9 -7110.07 200.98 0.125 5019 5021 2 -21206.7 -7102.7 201.73 0.125 5020 5022 2 -21206.7 -7102.45 201.77 0.125 5021 5023 2 -21202.8 -7097.06 203.19 0.125 5022 5024 2 -21202.8 -7096.77 203.24 0.125 5023 5025 2 -21200.7 -7095.42 203.27 0.125 5024 5026 2 -21200.5 -7095.12 203.3 0.125 5025 5027 2 -21198.1 -7094.62 205.17 0.125 5026 5028 2 -21198.3 -7093.58 205.36 0.125 5027 5029 2 -21197 -7092.1 205.49 0.125 5028 5030 2 -21196.8 -7091.82 205.51 0.125 5029 5031 2 -21196.3 -7091.18 205.57 0.125 5030 5032 2 -21196 -7091.15 205.55 0.125 5031 5033 2 -21192.5 -7089.67 210.49 0.125 5032 5034 2 -21192.2 -7089.4 210.51 0.125 5033 5035 2 -21192.3 -7089.12 210.56 0.125 5034 5036 2 -21187.2 -7085.56 212.55 0.125 5035 5037 2 -21186.9 -7085.23 212.58 0.125 5036 5038 2 -21186.1 -7084.85 212.57 0.125 5037 5039 2 -21185.6 -7084.3 212.61 0.125 5038 5040 2 -21183.4 -7082.11 215.9 0.125 5039 5041 2 -21182.9 -7082.04 215.86 0.125 5040 5042 2 -21181.3 -7082.63 215.61 0.125 5041 5043 2 -21180.8 -7082.3 215.62 0.125 5042 5044 2 -21178.2 -7080.87 215.61 0.125 5043 5045 2 -21177.6 -7080.53 215.62 0.125 5044 5046 2 -21176.3 -7080.32 215.52 0.125 5045 5047 2 -21175.5 -7080.21 215.46 0.125 5046 5048 2 -21175.1 -7078.61 215.87 0.125 5047 5049 2 -21174.8 -7078.32 215.89 0.125 5048 5050 2 -21173.3 -7077.02 215.96 0.125 5049 5051 2 -21172.8 -7076.68 215.98 0.125 5050 5052 2 -21170 -7074.94 218.7 0.125 5051 5053 2 -21169.5 -7074.06 218.8 0.125 5052 5054 2 -21169.2 -7074.03 218.78 0.125 5053 5055 2 -21168.5 -7073.4 218.82 0.125 5054 5056 2 -21168 -7072.81 218.86 0.125 5055 5057 2 -21165.1 -7072 221.15 0.125 5056 5058 2 -21163.8 -7070.73 221.24 0.125 5057 5059 2 -21163.6 -7070.46 221.26 0.125 5058 5060 2 -21163.5 -7068.87 221.52 0.125 5059 5061 2 -21163 -7068.55 221.52 0.125 5060 5062 2 -21162.7 -7068.27 221.54 0.125 5061 5063 2 -21161.7 -7067.58 221.56 0.125 5062 5064 2 -21160.9 -7066.87 221.6 0.125 5063 5065 2 -21160 -7065.56 225.35 0.125 5064 5066 2 -21159.4 -7065.23 225.36 0.125 5065 5067 2 -21158.4 -7064.3 225.42 0.125 5066 5068 2 -21157.4 -7063.63 225.43 0.125 5067 5069 2 -21156.6 -7063.25 225.42 0.125 5068 5070 2 -21155.1 -7061.29 226.45 0.125 5069 5071 2 -21155.1 -7061.05 226.54 0.125 5070 5072 2 -21153.9 -7060.34 227.93 0.125 5071 5073 2 -21153.6 -7060.19 228.89 0.125 5072 5074 2 -21151.4 -7059.8 232.79 0.125 5073 5075 2 -21150 -7056.03 228.06 0.125 5074 5076 2 -21148 -7055.23 229.41 0.125 5075 5077 2 -21147.6 -7054.11 229.55 0.125 5076 5078 2 -21145 -7051.86 229.49 0.125 5077 5079 2 -21143.2 -7050.52 230.78 0.125 5078 5080 2 -21142.6 -7050.44 230.74 0.125 5079 5081 2 -21141.2 -7050.49 230.6 0.125 5080 5082 2 -21140.6 -7050.41 230.56 0.125 5081 5083 2 -21140.7 -7050.15 230.6 0.125 5082 5084 2 -21139.3 -7049.7 230.55 0.125 5083 5085 2 -21138.3 -7049 230.57 0.125 5084 5086 2 -21137.2 -7048.58 230.54 0.125 5085 5087 2 -21136.4 -7047.7 230.61 0.125 5086 5088 2 -21133.2 -7044.63 229.53 0.125 5087 5089 2 -21133 -7044.07 229.6 0.125 5088 5090 2 -21131.5 -7042.33 229.75 0.125 5089 5091 2 -21131.1 -7041.43 229.86 0.125 5090 5092 2 -21130.8 -7041.15 229.88 0.125 5091 5093 2 -21129.4 -7039.59 230 0.125 5092 5094 2 -21128.3 -7038.92 230.01 0.125 5093 5095 2 -21126 -7037.94 232.1 0.125 5094 5096 2 -21125.8 -7037.61 232.14 0.125 5095 5097 2 -21123.3 -7037.05 232 0.125 5096 5098 2 -21122.5 -7036.64 231.99 0.125 5097 5099 2 -21121.5 -7035.67 232.05 0.125 5098 5100 2 -21120.4 -7035.02 232.06 0.125 5099 5101 2 -21119.7 -7033.71 232.84 0.125 5100 5102 2 -21120 -7033.76 232.86 0.125 5101 5103 2 -21117.7 -7031.59 233 0.125 5102 5104 2 -21122.7 -7026.19 234.36 0.125 5103 5105 2 -21121.2 -7023.5 234.67 0.125 5104 5106 2 -21118.9 -7020.65 234.92 0.125 5105 5107 2 -21117.1 -7018.21 235.16 0.125 5106 5108 2 -21116.8 -7017.42 232.77 0.125 5107 5109 2 -21113.9 -7015.53 234.42 0.125 5108 5110 2 -21114 -7015.31 234.46 0.125 5109 5111 2 -21112.6 -7013.41 234.65 0.125 5110 5112 2 -21112.1 -7012.74 234.71 0.125 5111 5113 2 -21111.2 -7011.78 234.79 0.125 5112 5114 2 -21111 -7011.23 234.87 0.125 5113 5115 2 -21109.1 -7010.06 236.31 0.125 5114 5116 2 -21108.8 -7009.97 236.29 0.125 5115 5117 2 -21107.2 -7009.54 236.7 0.125 5116 5118 2 -21106.5 -7008.58 236.79 0.125 5117 5119 2 -21105.9 -7007.34 236.93 0.125 5118 5120 2 -21105.6 -7007.32 236.91 0.125 5119 5121 2 -21103.8 -7006.16 236.94 0.125 5120 5122 2 -21101.4 -7003.86 237.09 0.125 5121 5123 2 -21100.9 -7003.49 237.11 0.125 5122 5124 2 -21097.6 -7001.96 239.66 0.125 5123 5125 2 -21095.5 -7001.03 239.62 0.125 5124 5126 2 -21095.2 -7000.95 239.61 0.125 5125 5127 2 -21094.3 -7000 239.68 0.125 5126 5128 2 -21094.1 -6999.42 239.75 0.125 5127 5129 2 -21093.9 -6999.11 239.79 0.125 5128 5130 2 -21093.1 -6998.68 239.78 0.125 5129 5131 2 -21092 -6998.2 239.77 0.125 5130 5132 2 -21089.3 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0.125 5540 5542 2 -21735.2 -7286.62 147.43 0.125 5541 5543 2 -21735.3 -7286.1 147.53 0.125 5542 5544 2 -21735.4 -7285.07 147.74 0.125 5543 5545 2 -21734.3 -7283.31 152.39 0.125 5544 5546 2 -21734.2 -7282.21 152.57 0.125 5545 5547 2 -21734 -7280.76 156.73 0.125 5546 5548 2 -21734.9 -7280.11 156.92 0.125 5547 5549 2 -21735 -7279.36 157.06 0.125 5548 5550 2 -21734.3 -7279.35 159.21 0.125 5549 5551 2 -21734.3 -7277.75 159.48 0.125 5550 5552 2 -21734.5 -7276.78 159.66 0.125 5551 5553 2 -21732.8 -7275.98 165.87 0.125 5552 5554 2 -21732.7 -7273.06 166.34 0.125 5553 5555 2 -21732 -7272.4 166.38 0.125 5554 5556 2 -21731.7 -7272.31 166.37 0.125 5555 5557 2 -21731.2 -7271.08 173.2 0.125 5556 5558 2 -21730.9 -7268.86 174.74 0.125 5557 5559 2 -21730.9 -7268.63 174.78 0.125 5558 5560 2 -21728.3 -7265.82 179.89 0.125 5559 5561 2 -21739.3 -7268.49 180.48 0.25 5560 5562 2 -21740 -7266.79 182.57 0.25 5561 5563 2 -21740.3 -7266.06 184.45 0.25 5562 5564 2 -21741.1 -7263.85 188.34 0.25 5563 5565 2 -21741.4 -7262.41 190.76 0.25 5564 5566 2 -21741.9 -7260.75 191.99 0.25 5565 5567 2 -21741.6 -7261.23 194.88 0.25 5566 5568 2 -21742.7 -7259.3 195.3 0.25 5567 5569 2 -21742.7 -7259.03 195.37 0.25 5568 5570 2 -21743 -7257.06 199.53 0.25 5569 5571 2 -21743.6 -7256.43 199.69 0.25 5570 5572 2 -21743.8 -7255.1 199.93 0.25 5571 5573 2 -21744.1 -7253.74 202.74 0.25 5572 5574 2 -21744.1 -7254 202.7 0.25 5573 5575 2 -21744.8 -7252.41 204.16 0.25 5574 5576 2 -21744.6 -7250.98 208.5 0.25 5575 5577 2 -21744.4 -7248.3 208.93 0.25 5576 5578 2 -21744.4 -7248.06 208.97 0.25 5577 5579 2 -21743.5 -7246.8 211.84 0.25 5578 5580 2 -21743.4 -7245.97 211.96 0.25 5579 5581 2 -21743.1 -7245.94 211.94 0.25 5580 5582 2 -21741.7 -7243.45 217.4 0.25 5581 5583 2 -21740.6 -7241.6 219.81 0.25 5582 5584 2 -21739.4 -7239.86 223.24 0.25 5583 5585 2 -21740.6 -7238.41 222.01 0.25 5584 5586 2 -21739.9 -7236.34 227.68 0.25 5585 5587 2 -21740 -7233.4 228.18 0.25 5586 5588 2 -21739.8 -7232.34 228.34 0.25 5587 5589 2 -21739.9 -7232.08 228.39 0.25 5588 5590 2 -21739.9 -7232.15 231.95 0.25 5589 5591 2 -21740.4 -7231.98 238.06 0.25 5590 5592 2 -21740.4 -7231.71 238.11 0.25 5591 5593 2 -21740.5 -7230.95 238.29 0.25 5592 5594 2 -21740.4 -7230.8 238.96 0.25 5593 5595 2 -21740.3 -7228.13 234.88 0.25 5594 5596 2 -21749.6 -7354.29 72.38 0.37 5491 5597 2 -21747.2 -7353.08 72.36 0.37 5596 5598 2 -21746.1 -7353.17 72.23 0.37 5597 5599 2 -21745.7 -7353.35 72.18 0.37 5598 5600 2 -21745.4 -7354.11 72.02 0.37 5599 5601 2 -21745.1 -7354.1 71.99 0.37 5600 5602 2 -21741.1 -7354.45 74.35 0.37 5601 5603 2 -21740.9 -7354.18 74.37 0.37 5602 5604 2 -21738.2 -7353.66 74.2 0.37 5603 5605 2 -21737.6 -7353.36 74.2 0.37 5604 5606 2 -21735.1 -7352.09 76.94 0.37 5605 5607 2 -21734.8 -7351.92 77.7 0.37 5606 5608 2 -21731.9 -7351.49 79.28 0.37 5607 5609 2 -21731.3 -7351.59 80.42 0.37 5608 5610 2 -21730.2 -7351.44 80.49 0.37 5609 5611 2 -21729.8 -7351.68 80.42 0.37 5610 5612 2 -21729 -7351.27 80.42 0.37 5611 5613 2 -21728.8 -7350.94 80.45 0.37 5612 5614 2 -21728 -7350.85 80.38 0.37 5613 5615 2 -21727.4 -7350.74 80.35 0.37 5614 5616 2 -21726.8 -7350.65 80.31 0.37 5615 5617 2 -21725.4 -7350.63 81.36 0.37 5616 5618 2 -21724.5 -7350.45 81.31 0.37 5617 5619 2 -21723.6 -7349.51 81.38 0.37 5618 5620 2 -21723 -7349.42 81.34 0.37 5619 5621 2 -21720.9 -7349.42 83.17 0.37 5620 5622 2 -21719.9 -7350.33 82.94 0.37 5621 5623 2 -21716.4 -7350.12 84.6 0.37 5622 5624 2 -21713.5 -7349.54 86.84 0.37 5623 5625 2 -21712.8 -7348.68 87.23 0.37 5624 5626 2 -21712 -7348.55 87.23 0.37 5625 5627 2 -21711.7 -7348.51 87.27 0.37 5626 5628 2 -21709.1 -7347.86 88.78 0.37 5627 5629 2 -21709.1 -7347.63 88.91 0.37 5628 5630 2 -21708.3 -7347.36 89.76 0.37 5629 5631 2 -21708 -7347.07 89.78 0.37 5630 5632 2 -21705.7 -7346.83 90.47 0.37 5631 5633 2 -21705.5 -7346.39 91.09 0.37 5632 5634 2 -21704.4 -7345.98 91.39 0.37 5633 5635 2 -21703.5 -7345.86 91.65 0.37 5634 5636 2 -21702 -7346.26 91.77 0.37 5635 5637 2 -21701.8 -7346.03 92.1 0.37 5636 5638 2 -21700.5 -7345.25 92.1 0.37 5637 5639 2 -21699.3 -7345.4 92.56 0.37 5638 5640 2 -21699 -7345.35 92.54 0.37 5639 5641 2 -21698.4 -7345.36 93.07 0.37 5640 5642 2 -21697.8 -7345.27 93.04 0.37 5641 5643 2 -21696.4 -7344.96 93.96 0.37 5642 5644 2 -21695.9 -7344.65 93.96 0.37 5643 5645 2 -21693.4 -7343.8 94.6 0.37 5644 5646 2 -21693.2 -7343.58 94.61 0.37 5645 5647 2 -21691.2 -7342.78 95.3 0.37 5646 5648 2 -21691 -7342.74 95.28 0.37 5647 5649 2 -21688.5 -7342.08 95.16 0.37 5648 5650 2 -21688.3 -7342.06 95.14 0.37 5649 5651 2 -21687.5 -7341.16 95.22 0.37 5650 5652 2 -21687.3 -7340.86 95.25 0.37 5651 5653 2 -21685 -7340.62 95.88 0.37 5652 5654 2 -21682 -7338.7 95.91 0.37 5653 5655 2 -21678.2 -7337.79 95.71 0.37 5654 5656 2 -21675.7 -7338.03 96.32 0.37 5655 5657 2 -21674 -7337.45 96.27 0.37 5656 5658 2 -21673.4 -7337.55 96.19 0.37 5657 5659 2 -21672.6 -7337.73 96.08 0.37 5658 5660 2 -21670 -7337.78 95.84 0.37 5659 5661 2 -21669.8 -7337.2 95.92 0.37 5660 5662 2 -21668 -7336.37 95.88 0.37 5661 5663 2 -21666 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0.37 5686 5688 2 -21604.9 -7329.4 108.05 0.37 5687 5689 2 -21603.8 -7329.44 107.95 0.37 5688 5690 2 -21603.2 -7329.36 107.97 0.37 5689 5691 2 -21602.4 -7329.23 107.99 0.37 5690 5692 2 -21601.5 -7328.89 108.31 0.37 5691 5693 2 -21600.9 -7329.03 109.77 0.37 5692 5694 2 -21600.1 -7328.63 109.76 0.37 5693 5695 2 -21599.2 -7328.79 109.9 0.37 5694 5696 2 -21598.3 -7328.89 109.81 0.37 5695 5697 2 -21597.1 -7328.98 109.82 0.37 5696 5698 2 -21596.4 -7328.84 109.77 0.37 5697 5699 2 -21595 -7328.4 110.27 0.37 5698 5700 2 -21593.1 -7328.91 111.8 0.25 5699 5701 2 -21591.3 -7329.44 111.55 0.25 5700 5702 2 -21590.8 -7329.33 111.51 0.25 5701 5703 2 -21590 -7328.89 111.51 0.25 5702 5704 2 -21589.4 -7328.8 111.48 0.25 5703 5705 2 -21589.1 -7329 111.42 0.25 5704 5706 2 -21587.4 -7329.27 111.21 0.25 5705 5707 2 -21586.6 -7329.1 111.16 0.25 5706 5708 2 -21586.3 -7328.74 111.19 0.25 5707 5709 2 -21584.8 -7328.25 111.13 0.25 5708 5710 2 -21584.1 -7328.41 111.05 0.25 5709 5711 2 -21583.3 -7328.49 110.95 0.25 5710 5712 2 -21583 -7328.18 110.98 0.25 5711 5713 2 -21582 -7327.99 110.91 0.25 5712 5714 2 -21580.6 -7327.76 110.82 0.25 5713 5715 2 -21580 -7327.68 110.79 0.25 5714 5716 2 -21578.3 -7326.8 113.6 0.25 5715 5717 2 -21578.2 -7325.95 113.73 0.25 5716 5718 2 -21578.2 -7325.71 113.77 0.25 5717 5719 2 -21577 -7324.96 113.78 0.25 5718 5720 2 -21576.7 -7324.88 113.76 0.25 5719 5721 2 -21574.6 -7323.51 113.8 0.25 5720 5722 2 -21574.4 -7323.2 113.83 0.25 5721 5723 2 -21572.8 -7321.24 113.32 0.25 5722 5724 2 -21573.1 -7318.17 112.68 0.25 5723 5725 2 -21572.8 -7316.75 112.89 0.25 5724 5726 2 -21572.8 -7316.51 112.92 0.25 5725 5727 2 -21572 -7316.1 112.92 0.25 5726 5728 2 -21508.2 -7300.79 129.05 0.25 5727 5729 2 -21508.2 -7300.53 129.09 0.25 5728 5730 2 -21506.3 -7300.14 128.78 0.25 5729 5731 2 -21505.8 -7299.79 128.79 0.25 5730 5732 2 -21504 -7300.27 128.55 0.25 5731 5733 2 -21503.8 -7300.2 128.53 0.25 5732 5734 2 -21502.7 -7299.48 128.55 0.25 5733 5735 2 -21502.2 -7299.39 128.52 0.25 5734 5736 2 -21502.2 -7299.66 128.47 0.25 5735 5737 2 -21500.7 -7299.69 128.33 0.25 5736 5738 2 -21499.7 -7299.5 128.27 0.25 5737 5739 2 -21499.1 -7299.43 128.22 0.25 5738 5740 2 -21498.6 -7298.99 128.25 0.25 5739 5741 2 -21498.3 -7298.96 128.23 0.25 5740 5742 2 -21497.5 -7299.13 128.12 0.25 5741 5743 2 -21496.6 -7298.95 128.07 0.25 5742 5744 2 -21495.5 -7299.03 127.95 0.25 5743 5745 2 -21495.2 -7298.93 127.85 0.25 5744 5746 2 -21494.7 -7299.15 130.92 0.25 5745 5747 2 -21494.3 -7299.22 132.95 0.25 5746 5748 2 -21494.1 -7298.88 132.98 0.25 5747 5749 2 -21493.6 -7298.24 133.05 0.25 5748 5750 2 -21493.1 -7297.9 133.05 0.25 5749 5751 2 -21492.8 -7297.8 132.83 0.25 5750 5752 2 -21492.3 -7297.73 132.63 0.25 5751 5753 2 -21491.7 -7297.62 132.59 0.25 5752 5754 2 -21490.9 -7297.47 132.54 0.25 5753 5755 2 -21489.6 -7296.98 132.56 0.25 5754 5756 2 -21489.1 -7296.58 132.57 0.25 5755 5757 2 -21486.3 -7294.53 132.66 0.25 5756 5758 2 -21485.8 -7294.43 132.63 0.25 5757 5759 2 -21484.7 -7294.24 132.55 0.25 5758 5760 2 -21483 -7293.95 132.45 0.25 5759 5761 2 -21482.8 -7294.14 132.39 0.25 5760 5762 2 -21480.6 -7293.29 132.33 0.25 5761 5763 2 -21479.3 -7292.73 132.3 0.25 5762 5764 2 -21477.1 -7292.52 136.04 0.25 5763 5765 2 -21476.9 -7292.19 136.07 0.25 5764 5766 2 -21476.6 -7292.15 136.05 0.25 5765 5767 2 -21475.5 -7291.73 136.13 0.25 5766 5768 2 -21474.7 -7291.61 136.28 0.25 5767 5769 2 -21473.5 -7290.19 136.99 0.25 5768 5770 2 -21473.3 -7289.86 137.02 0.25 5769 5771 2 -21472 -7289.59 139.63 0.25 5770 5772 2 -21393.8 -7270.14 138.63 0.25 5771 5773 2 -21391.7 -7268.95 135.64 0.25 5772 5774 2 -21390.6 -7268.73 135.58 0.25 5773 5775 2 -21390.3 -7268.68 135.56 0.25 5774 5776 2 -21389.4 -7268.8 135.45 0.25 5775 5777 2 -21388.3 -7268.61 135.38 0.25 5776 5778 2 -21387.2 -7268.94 135.22 0.25 5777 5779 2 -21386.9 -7268.88 135.21 0.25 5778 5780 2 -21385.1 -7268.84 135.05 0.25 5779 5781 2 -21384.6 -7268.77 135.01 0.25 5780 5782 2 -21383 -7268.19 134.96 0.25 5781 5783 2 -21381.9 -7268.26 134.84 0.25 5782 5784 2 -21381.7 -7267.91 134.88 0.25 5783 5785 2 -21381.1 -7267.85 134.83 0.25 5784 5786 2 -21380.8 -7267.81 134.81 0.25 5785 5787 2 -21380 -7267.68 134.76 0.25 5786 5788 2 -21379.1 -7267.8 134.66 0.25 5787 5789 2 -21378.3 -7267.66 134.61 0.25 5788 5790 2 -21377.5 -7267.18 134.61 0.25 5789 5791 2 -21375.5 -7266.84 135.9 0.25 5790 5792 2 -21374.4 -7266.65 135.83 0.25 5791 5793 2 -21374.2 -7266.35 135.86 0.25 5792 5794 2 -21373.4 -7265.97 135.85 0.25 5793 5795 2 -21372.6 -7265.79 135.8 0.25 5794 5796 2 -21371.4 -7266.35 135.6 0.25 5795 5797 2 -21371.1 -7266.32 135.57 0.25 5796 5798 2 -21370.1 -7265.87 135.55 0.25 5797 5799 2 -21369.2 -7265.45 135.54 0.25 5798 5800 2 -21365.6 -7265.49 138.78 0.25 5799 5801 2 -21365.1 -7265.35 138.75 0.25 5800 5802 2 -21362.3 -7265.12 138.53 0.25 5801 5803 2 -21361.8 -7264.99 138.5 0.25 5802 5804 2 -21361.8 -7264.73 138.54 0.25 5803 5805 2 -21356.3 -7263.75 138.04 0.25 5804 5806 2 -21355.8 -7263.63 138.01 0.25 5805 5807 2 -21354.4 -7263.47 137.91 0.25 5806 5808 2 -21353.6 -7263.27 137.87 0.25 5807 5809 2 -21352.7 -7263.41 137.76 0.25 5808 5810 2 -21352.3 -7263.04 137.78 0.25 5809 5811 2 -21352 -7262.98 137.76 0.25 5810 5812 2 -21350 -7262.9 137.59 0.25 5811 5813 2 -21349.2 -7263 137.49 0.25 5812 5814 2 -21348.9 -7262.94 137.48 0.25 5813 5815 2 -21347.7 -7263.28 137.31 0.25 5814 5816 2 -21347.3 -7264.1 136.2 0.25 5815 5817 2 -21347.2 -7265.13 136.01 0.25 5816 5818 2 -21346.2 -7267.68 135.81 0.25 5817 5819 2 -21345.4 -7268.82 135.55 0.25 5818 5820 2 -21346.1 -7268.79 128.81 0.25 5819 5821 2 -21345.4 -7270.61 127.28 0.25 5820 5822 2 -21345.3 -7270.87 127.23 0.25 5821 5823 2 -21343.5 -7271.57 126.95 0.25 5822 5824 2 -21342.5 -7270.35 127.06 0.25 5823 5825 2 -21342.7 -7268.82 124.53 0.25 5824 5826 2 -21341.4 -7268.31 124.49 0.25 5825 5827 2 -21340.3 -7269.72 124.15 0.25 5826 5828 2 -21340.3 -7269.93 124.12 0.25 5827 5829 2 -21344.5 -7263.18 138.19 0.25 5815 5830 2 -21342.2 -7262.69 136.12 0.25 5829 5831 2 -21341.4 -7262.55 136.06 0.25 5830 5832 2 -21332.4 -7260.45 135.82 0.25 5831 5833 2 -21331.9 -7260.11 135.83 0.25 5832 5834 2 -21331.6 -7260.04 135.81 0.25 5833 5835 2 -21329.3 -7259.1 135.75 0.25 5834 5836 2 -21326.3 -7258.31 135.66 0.25 5835 5837 2 -21325.8 -7257.68 135.72 0.25 5836 5838 2 -21325.1 -7257.76 136.83 0.25 5837 5839 2 -21322.5 -7257.81 137.75 0.25 5838 5840 2 -21321.1 -7257.7 138.53 0.25 5839 5841 2 -21319.1 -7257.87 138.32 0.25 5840 5842 2 -21318.3 -7257.72 138.26 0.25 5841 5843 2 -21316 -7257.35 138.12 0.25 5842 5844 2 -21313.9 -7256.92 137.98 0.25 5843 5845 2 -21308.4 -7255.54 138.37 0.25 5844 5846 2 -21307.6 -7255.41 138.31 0.25 5845 5847 2 -21305.1 -7255.05 138.55 0.25 5846 5848 2 -21304.8 -7255.2 138.5 0.25 5847 5849 2 -21304.5 -7255.45 138.43 0.25 5848 5850 2 -21302.1 -7256.08 138.1 0.25 5849 5851 2 -21301.8 -7255.8 138.12 0.25 5850 5852 2 -21299.8 -7254.87 138.12 0.25 5851 5853 2 -21298.4 -7254.62 138.03 0.25 5852 5854 2 -21296.5 -7254.05 137.98 0.25 5853 5855 2 -21294.3 -7253.38 138.07 0.25 5854 5856 2 -21294.3 -7253.66 138.02 0.25 5855 5857 2 -21292.7 -7252.73 138.51 0.25 5856 5858 2 -21292.8 -7252.44 138.69 0.25 5857 5859 2 -21291.9 -7252.28 139.94 0.25 5858 5860 2 -21291 -7252.41 139.94 0.25 5859 5861 2 -21289.8 -7252.45 139.83 0.25 5860 5862 2 -21289.3 -7252.37 139.79 0.25 5861 5863 2 -21289 -7252.08 139.82 0.25 5862 5864 2 -21288.8 -7252.03 139.8 0.25 5863 5865 2 -21287.8 -7251.58 139.78 0.25 5864 5866 2 -21287.5 -7251.49 139.77 0.25 5865 5867 2 -21286.7 -7251.1 139.76 0.25 5866 5868 2 -21285.7 -7250.39 139.78 0.25 5867 5869 2 -21285.1 -7250.23 139.76 0.25 5868 5870 2 -21282.8 -7250.39 139.52 0.25 5869 5871 2 -21281.8 -7250.19 139.45 0.25 5870 5872 2 -21281.4 -7250.44 139.38 0.25 5871 5873 2 -21279.8 -7249.85 139.32 0.25 5872 5874 2 -21278.8 -7249.41 139.3 0.25 5873 5875 2 -21278.5 -7249.12 139.33 0.25 5874 5876 2 -21277.9 -7248.22 139.41 0.25 5875 5877 2 -21277.3 -7248.06 139.39 0.25 5876 5878 2 -21277.1 -7247.84 139.4 0.25 5877 5879 2 -21275.2 -7247.64 141.77 0.25 5878 5880 2 -21274.7 -7247.81 141.69 0.25 5879 5881 2 -21272.4 -7247.68 141.51 0.25 5880 5882 2 -21272.1 -7247.64 141.49 0.25 5881 5883 2 -21270.9 -7246.59 141.54 0.25 5882 5884 2 -21270.7 -7246.34 141.56 0.25 5883 5885 2 -21269.9 -7245.9 141.57 0.25 5884 5886 2 -21269.4 -7245.57 141.57 0.25 5885 5887 2 -21266.6 -7245.34 141.35 0.25 5886 5888 2 -21265 -7244.77 141.29 0.25 5887 5889 2 -21264.7 -7244.72 141.27 0.25 5888 5890 2 -21260.8 -7243.76 142.69 0.25 5889 5891 2 -21260 -7243.88 142.59 0.25 5890 5892 2 -21258.6 -7243.63 142.5 0.25 5891 5893 2 -21258.3 -7243.61 142.48 0.25 5892 5894 2 -21257.2 -7243.65 142.36 0.25 5893 5895 2 -21256.3 -7243.76 142.27 0.25 5894 5896 2 -21259.2 -7247.37 141.93 0.125 5895 5897 2 -21256.6 -7247.32 141.7 0.125 5896 5898 2 -21254.7 -7247.05 141.57 0.125 5897 5899 2 -21254.1 -7246.71 141.57 0.125 5898 5900 2 -21251.8 -7246.67 141.37 0.125 5899 5901 2 -21251.6 -7246.64 141.35 0.125 5900 5902 2 -21249.3 -7246.83 141.1 0.125 5901 5903 2 -21248.8 -7246.77 141.06 0.125 5902 5904 2 -21247.8 -7247.72 140.81 0.125 5903 5905 2 -21247.2 -7247.6 140.78 0.125 5904 5906 2 -21246.1 -7247.17 140.75 0.125 5905 5907 2 -21245.6 -7246.87 140.75 0.125 5906 5908 2 -21245.1 -7246.79 140.71 0.125 5907 5909 2 -21244.2 -7246.68 140.65 0.125 5908 5910 2 -21243.7 -7246.32 140.66 0.125 5909 5911 2 -21242.3 -7246.41 140.51 0.125 5910 5912 2 -21242 -7246.36 140.49 0.125 5911 5913 2 -21241.3 -7246.1 142.66 0.125 5912 5914 2 -21241.4 -7245.77 142.28 0.125 5913 5915 2 -21239.9 -7246.37 142.05 0.125 5914 5916 2 -21238.2 -7246.14 141.93 0.125 5915 5917 2 -21238 -7245.88 141.94 0.125 5916 5918 2 -21236.9 -7244.92 142.01 0.125 5917 5919 2 -21237 -7244.63 142.06 0.125 5918 5920 2 -21234.8 -7244.11 141.94 0.125 5919 5921 2 -21233.2 -7243.31 141.93 0.125 5920 5922 2 -21233 -7243.02 141.95 0.125 5921 5923 2 -21232.7 -7242.93 141.94 0.125 5922 5924 2 -21230 -7242.06 141.83 0.125 5923 5925 2 -21227.3 -7241.45 140.49 0.125 5924 5926 2 -21227.3 -7241.36 139.96 0.125 5925 5927 2 -21225.3 -7241.4 139.77 0.125 5926 5928 2 -21224.5 -7241.27 139.71 0.125 5927 5929 2 -21222.5 -7241.04 139.89 0.125 5928 5930 2 -21222.5 -7241.15 140.53 0.125 5929 5931 2 -21220.8 -7240.65 140.53 0.125 5930 5932 2 -21220.6 -7240.61 140.62 0.125 5931 5933 2 -21218.9 -7240.15 140.54 0.125 5932 5934 2 -21218.3 -7240.09 140.5 0.125 5933 5935 2 -21217.8 -7239.76 140.5 0.125 5934 5936 2 -21216.7 -7239.85 140.38 0.125 5935 5937 2 -21216.2 -7239.5 140.39 0.125 5936 5938 2 -21215.9 -7239.46 140.37 0.125 5937 5939 2 -21214.8 -7238.79 140.39 0.125 5938 5940 2 -21214.6 -7238.28 140.45 0.125 5939 5941 2 -21214.3 -7238.18 140.44 0.125 5940 5942 2 -21213.6 -7237.31 140.51 0.125 5941 5943 2 -21213.1 -7237.01 140.51 0.125 5942 5944 2 -21212.8 -7236.92 140.51 0.125 5943 5945 2 -21212 -7236.79 140.45 0.125 5944 5946 2 -21211.1 -7236.72 140.38 0.125 5945 5947 2 -21210.3 -7236.57 140.33 0.125 5946 5948 2 -21210 -7236.55 140.3 0.125 5947 5949 2 -21208.3 -7236.31 140.19 0.125 5948 5950 2 -21208.1 -7236 140.22 0.125 5949 5951 2 -21206.5 -7235.55 140.14 0.125 5950 5952 2 -21205.9 -7235.15 140.15 0.125 5951 5953 2 -21205.6 -7235.16 140.12 0.125 5952 5954 2 -21205.2 -7234.83 140.13 0.125 5953 5955 2 -21204.3 -7234.68 140.08 0.125 5954 5956 2 -21203.2 -7234.56 139.99 0.125 5955 5957 2 -21202.6 -7234.72 139.91 0.125 5956 5958 2 -21202.3 -7234.67 139.89 0.125 5957 5959 2 -21198.3 -7234.82 140.33 0.125 5958 5960 2 -21197 -7235.66 140.08 0.125 5959 5961 2 -21196.7 -7236.16 139.96 0.125 5960 5962 2 -21196.5 -7236.16 139.94 0.125 5961 5963 2 -21195.8 -7236.3 139.86 0.125 5962 5964 2 -21194.7 -7236.14 139.78 0.125 5963 5965 2 -21193.1 -7235.67 139.7 0.125 5964 5966 2 -21192.3 -7235.29 139.7 0.125 5965 5967 2 -21191.3 -7234.62 139.71 0.125 5966 5968 2 -21190.5 -7234.23 139.7 0.125 5967 5969 2 -21189.3 -7233.7 140.74 0.125 5968 5970 2 -21187.5 -7232.68 140.74 0.125 5969 5971 2 -21186.8 -7230.95 140.96 0.125 5970 5972 2 -21185.7 -7230.6 140.92 0.125 5971 5973 2 -21185.5 -7230.29 140.94 0.125 5972 5974 2 -21184.9 -7229.91 140.96 0.125 5973 5975 2 -21183.9 -7229.24 140.97 0.125 5974 5976 2 -21182.9 -7228.37 141.02 0.125 5975 5977 2 -21181.6 -7227.61 141.02 0.125 5976 5978 2 -21181 -7227.52 140.99 0.125 5977 5979 2 -21179.9 -7226.88 140.99 0.125 5978 5980 2 -21179 -7225.89 141.06 0.125 5979 5981 2 -21178.7 -7225.91 141.03 0.125 5980 5982 2 -21176.7 -7223.48 141.25 0.125 5981 5983 2 -21174.2 -7223.01 145.04 0.125 5982 5984 2 -21173.3 -7224.13 147.28 0.125 5983 5985 2 -21172.8 -7224.32 147.2 0.125 5984 5986 2 -21172.7 -7224.57 147.15 0.125 5985 5987 2 -21171.6 -7224.82 146.02 0.125 5986 5988 2 -21171.3 -7225.01 145.96 0.125 5987 5989 2 -21167.6 -7225.04 145.61 0.125 5988 5990 2 -21166.2 -7224.84 145.52 0.125 5989 5991 2 -21164.6 -7224.32 145.45 0.125 5990 5992 2 -21164 -7224.02 145.45 0.125 5991 5993 2 -21161.9 -7223.2 145.38 0.125 5992 5994 2 -21160.8 -7223.04 145.3 0.125 5993 5995 2 -21159.3 -7223.07 145.16 0.125 5994 5996 2 -21158.5 -7223.2 145.06 0.125 5995 5997 2 -21157.4 -7223.33 144.94 0.125 5996 5998 2 -21156.8 -7223.23 144.9 0.125 5997 5999 2 -21155.1 -7223.3 144.73 0.125 5998 6000 2 -21154 -7223.1 144.66 0.125 5999 6001 2 -21153.4 -7223.08 144.61 0.125 6000 6002 2 -21152.3 -7222.89 144.54 0.125 6001 6003 2 -21150.9 -7223.22 144.34 0.125 6002 6004 2 -21150 -7223.35 144.24 0.125 6003 6005 2 -21148.7 -7224.51 143.93 0.125 6004 6006 2 -21148.1 -7224.65 143.85 0.125 6005 6007 2 -21147.6 -7224.6 143.81 0.125 6006 6008 2 -21146.7 -7224.5 143.74 0.125 6007 6009 2 -21146.2 -7224.43 143.7 0.125 6008 6010 2 -21144.7 -7223.92 145.06 0.125 6009 6011 2 -21142.3 -7222.94 145.87 0.125 6010 6012 2 -21142 -7222.92 145.85 0.125 6011 6013 2 -21140 -7222.64 145.71 0.125 6012 6014 2 -21139.7 -7222.58 145.69 0.125 6013 6015 2 -21138.4 -7221.74 146.39 0.125 6014 6016 2 -21138.1 -7221.43 146.42 0.125 6015 6017 2 -21136.5 -7220.91 146.17 0.125 6016 6018 2 -21136.5 -7220.87 145.96 0.125 6017 6019 2 -21135.6 -7221.29 145.8 0.125 6018 6020 2 -21133.4 -7220.78 146.15 0.125 6019 6021 2 -21133.2 -7221.08 147.9 0.125 6020 6022 2 -21131.5 -7220.72 148.73 0.125 6021 6023 2 -21130.4 -7221.09 148.56 0.125 6022 6024 2 -21129.3 -7220.65 148.53 0.125 6023 6025 2 -21128.4 -7220.38 149.02 0.125 6024 6026 2 -21128 -7220.39 149.43 0.125 6025 6027 2 -21126.3 -7220.79 149.74 0.125 6026 6028 2 -21125.9 -7220.83 150.28 0.125 6027 6029 2 -21123.6 -7221.41 150.74 0.125 6028 6030 2 -21123.3 -7221.64 150.67 0.125 6029 6031 2 -21121.4 -7222.71 150.33 0.125 6030 6032 2 -21121.2 -7222.6 149.71 0.125 6031 6033 2 -21119.1 -7222.1 148.47 0.125 6032 6034 2 -21118.8 -7221.77 148.46 0.125 6033 6035 2 -21117.7 -7221.85 148.17 0.125 6034 6036 2 -21116.6 -7221.68 147.9 0.125 6035 6037 2 -21116.6 -7221.95 147.85 0.125 6036 6038 2 -21114.5 -7222.58 146.75 0.125 6037 6039 2 -21113.5 -7222.35 146.2 0.125 6038 6040 2 -21111.5 -7221.04 144.89 0.125 6039 6041 2 -21078.3 -7216.84 141.95 0.125 6040 6042 2 -21074.8 -7216.07 141.65 0.125 6041 6043 2 -21074.4 -7216.38 142.08 0.125 6042 6044 2 -21071.9 -7216.27 141.91 0.125 6043 6045 2 -21071.8 -7216.56 141.86 0.125 6044 6046 2 -21070.7 -7216.64 141.74 0.125 6045 6047 2 -21069.3 -7216.17 141.69 0.125 6046 6048 2 -21068.8 -7216.1 141.65 0.125 6047 6049 2 -21068.5 -7216.34 141.58 0.125 6048 6050 2 -21068 -7216.01 141.59 0.125 6049 6051 2 -21067.7 -7215.72 141.61 0.125 6050 6052 2 -21067.5 -7215.42 141.64 0.125 6051 6053 2 -21066.3 -7215.26 141.56 0.125 6052 6054 2 -21065.8 -7215.17 141.52 0.125 6053 6055 2 -21064.9 -7215.33 141.42 0.125 6054 6056 2 -21064.6 -7215.3 141.4 0.125 6055 6057 2 -21063.6 -7214.58 141.42 0.125 6056 6058 2 -21063.4 -7214.27 141.45 0.125 6057 6059 2 -21062.2 -7214.67 141.27 0.125 6058 6060 2 -21061.9 -7214.64 141.25 0.125 6059 6061 2 -21061.4 -7214.03 141.3 0.125 6060 6062 2 -21060.1 -7213.34 141.29 0.125 6061 6063 2 -21059.2 -7213.19 141.24 0.125 6062 6064 2 -21058.7 -7213.16 141.19 0.125 6063 6065 2 -21058.2 -7212.83 141.2 0.125 6064 6066 2 -21057.1 -7212.42 141.17 0.125 6065 6067 2 -21056.2 -7212.29 141.11 0.125 6066 6068 2 -21054 -7211.85 141.91 0.125 6067 6069 2 -21053 -7211.95 143.28 0.125 6068 6070 2 -21052.1 -7212.09 143.19 0.125 6069 6071 2 -21051.6 -7212.03 143.14 0.125 6070 6072 2 -21050.7 -7211.88 143.09 0.125 6071 6073 2 -21049.1 -7211.44 143.02 0.125 6072 6074 2 -21048.5 -7211.37 142.98 0.125 6073 6075 2 -21047.7 -7210.95 142.97 0.125 6074 6076 2 -21046.9 -7210.58 142.99 0.125 6075 6077 2 -21045.2 -7210.88 142.78 0.125 6076 6078 2 -21044.5 -7209.71 142.91 0.125 6077 6079 2 -21044 -7209.38 142.91 0.125 6078 6080 2 -21042.1 -7210.44 142.56 0.125 6079 6081 2 -21039.9 -7210.16 142.41 0.125 6080 6082 2 -21039.3 -7209.8 142.41 0.125 6081 6083 2 -21037.1 -7209.77 142.21 0.125 6082 6084 2 -21036 -7209.85 142.09 0.125 6083 6085 2 -21034.5 -7210.21 141.89 0.125 6084 6086 2 -21033.1 -7210.93 139.67 0.125 6085 6087 2 -21030.8 -7211.62 138.74 0.125 6086 6088 2 -21029.7 -7211.46 138.66 0.125 6087 6089 2 -21029.2 -7211.39 138.62 0.125 6088 6090 2 -21027.8 -7211.17 138.53 0.125 6089 6091 2 -21023 -7210.75 137.8 0.125 6090 6092 2 -21021.9 -7210.57 137.79 0.125 6091 6093 2 -21019.7 -7210.28 137.63 0.125 6092 6094 2 -21018.3 -7209.86 137.71 0.125 6093 6095 2 -21017.8 -7209.75 137.68 0.125 6094 6096 2 -20988.9 -7218.75 130.67 0.25 6095 6097 2 -20987.6 -7220.41 130.27 0.25 6096 6098 2 -20986 -7220.72 130.08 0.25 6097 6099 2 -20983.7 -7221.48 129.73 0.25 6098 6100 2 -20982.9 -7221.36 129.67 0.25 6099 6101 2 -20980.5 -7221.35 131.47 0.25 6100 6102 2 -20979.9 -7221.26 131.43 0.25 6101 6103 2 -20978.9 -7220.31 131.28 0.25 6102 6104 2 -20977.3 -7220.23 130.57 0.25 6103 6105 2 -20977 -7220.22 130.54 0.25 6104 6106 2 -20974.4 -7220.67 133.56 0.25 6105 6107 2 -20974.2 -7220.62 133.36 0.25 6106 6108 2 -20971.3 -7220.8 133.3 0.25 6107 6109 2 -20971 -7220.75 133.28 0.25 6108 6110 2 -20968.4 -7220.86 134.15 0.25 6109 6111 2 -20967.8 -7220.78 134.22 0.25 6110 6112 2 -20965.8 -7221.35 134.08 0.25 6111 6113 2 -20965.2 -7221.22 134.11 0.25 6112 6114 2 -20965.2 -7221.52 134.05 0.25 6113 6115 2 -20963.6 -7222.61 133.82 0.25 6114 6116 2 -20962.8 -7222.5 133.74 0.25 6115 6117 2 -20962.5 -7222.21 133.76 0.25 6116 6118 2 -20962.3 -7222.05 133.03 0.25 6117 6119 2 -20962.1 -7221.75 133.06 0.25 6118 6120 2 -20962.1 -7221.25 130.14 0.25 6119 6121 2 -20959 -7221.77 132.32 0.25 6120 6122 2 -20958.4 -7221.44 132.32 0.25 6121 6123 2 -20956.2 -7221.12 132.16 0.25 6122 6124 2 -20955.2 -7220.71 132.13 0.25 6123 6125 2 -20954.6 -7220.38 132.14 0.25 6124 6126 2 -20954.1 -7220.29 132.1 0.25 6125 6127 2 -20952.1 -7220.5 131.51 0.25 6126 6128 2 -20950.6 -7220.81 131.32 0.25 6127 6129 2 -20950.4 -7220.47 131.35 0.25 6128 6130 2 -20948.8 -7219.54 131.36 0.25 6129 6131 2 -20948.5 -7219.18 131.39 0.25 6130 6132 2 -20948.4 -7218.63 131.47 0.25 6131 6133 2 -20947.5 -7218.52 131.41 0.25 6132 6134 2 -20946.1 -7218.35 131.3 0.25 6133 6135 2 -20945 -7218.21 131.22 0.25 6134 6136 2 -20944 -7217.76 131.2 0.25 6135 6137 2 -20943.4 -7217.69 131.16 0.25 6136 6138 2 -20941.6 -7218.93 130.12 0.25 6137 6139 2 -20941.3 -7218.84 130.11 0.25 6138 6140 2 -20940.9 -7219.37 131.79 0.25 6139 6141 2 -20940.3 -7219.33 131.74 0.25 6140 6142 2 -20938.8 -7220.29 133.63 0.25 6141 6143 2 -20938.6 -7220.25 133.61 0.25 6142 6144 2 -20937.8 -7219.9 133.83 0.25 6143 6145 2 -20937.5 -7219.85 133.81 0.25 6144 6146 2 -20936.7 -7219.73 133.76 0.25 6145 6147 2 -20936.3 -7219.75 133.72 0.25 6146 6148 2 -20934.8 -7220.06 135.08 0.25 6147 6149 2 -20934.5 -7219.98 135.07 0.25 6148 6150 2 -20933.6 -7220.44 135.08 0.25 6149 6151 2 -20932.7 -7220.59 134.97 0.25 6150 6152 2 -20931.6 -7220.16 134.99 0.25 6151 6153 2 -20931.1 -7220.08 134.96 0.25 6152 6154 2 -20929.9 -7220.18 134.84 0.25 6153 6155 2 -20929.4 -7220.11 134.9 0.25 6154 6156 2 -20927.7 -7220.16 134.74 0.25 6155 6157 2 -20927.3 -7220.34 134.67 0.25 6156 6158 2 -20927.1 -7220.33 134.65 0.25 6157 6159 2 -20926 -7219.91 134.62 0.25 6158 6160 2 -20924.9 -7219.77 134.54 0.25 6159 6161 2 -20923.9 -7218.55 134.65 0.25 6160 6162 2 -20923.7 -7218.22 134.68 0.25 6161 6163 2 -20922.1 -7218.62 136.39 0.25 6162 6164 2 -20921.8 -7218.59 136.37 0.25 6163 6165 2 -20919.6 -7218.86 134.98 0.25 6164 6166 2 -20919.3 -7218.85 134.96 0.25 6165 6167 2 -20917.6 -7219.12 134.75 0.25 6166 6168 2 -20917 -7219.08 134.7 0.25 6167 6169 2 -20915.3 -7219.09 134.54 0.25 6168 6170 2 -20915 -7219.05 134.52 0.25 6169 6171 2 -20914.5 -7218.47 134.57 0.25 6170 6172 2 -20913.7 -7218.06 134.56 0.25 6171 6173 2 -20912.9 -7217.39 134.6 0.25 6172 6174 2 -20911.8 -7217.27 134.52 0.125 6173 6175 2 -20911 -7217.44 134.41 0.125 6174 6176 2 -20910.7 -7217.39 134.39 0.125 6175 6177 2 -20909.2 -7218.23 134.11 0.125 6176 6178 2 -20907.9 -7218.51 134.79 0.125 6177 6179 2 -20907.3 -7218.44 135.12 0.125 6178 6180 2 -20907.3 -7218.47 135.3 0.125 6179 6181 2 -20907 -7218.42 135.28 0.125 6180 6182 2 -20906.7 -7218.11 136.78 0.125 6181 6183 2 -20906.4 -7218.14 136.74 0.125 6182 6184 2 -20905 -7217.92 136.83 0.125 6183 6185 2 -20904.8 -7217.6 136.87 0.125 6184 6186 2 -20904.3 -7217.56 136.82 0.125 6185 6187 2 -20903.7 -7217.72 136.74 0.125 6186 6188 2 -20903.1 -7217.62 136.7 0.125 6187 6189 2 -20902.8 -7217.59 136.68 0.125 6188 6190 2 -20902.4 -7218.34 136.52 0.125 6189 6191 2 -20901.9 -7218.26 136.48 0.125 6190 6192 2 -20901.3 -7218.19 136.44 0.125 6191 6193 2 -20901 -7218.12 136.43 0.125 6192 6194 2 -20900.2 -7218.26 136.32 0.125 6193 6195 2 -20899.3 -7218.19 136.27 0.125 6194 6196 2 -20898.8 -7218.07 136.24 0.125 6195 6197 2 -20898.5 -7217.79 136.26 0.125 6196 6198 2 -20898.3 -7217.74 136.24 0.125 6197 6199 2 -20898 -7217.74 136.21 0.125 6198 6200 2 -20897.4 -7218.16 136.09 0.125 6199 6201 2 -20896.8 -7217.84 136.09 0.125 6200 6202 2 -20895.2 -7217.35 136.02 0.125 6201 6203 2 -20894 -7217.21 135.93 0.125 6202 6204 2 -20894 -7217.41 135.9 0.125 6203 6205 2 -20893.1 -7217.86 135.74 0.125 6204 6206 2 -20892.8 -7218.05 135.68 0.125 6205 6207 2 -20892.5 -7218.02 135.66 0.125 6206 6208 2 -20892.4 -7218.2 136.75 0.125 6207 6209 2 -20892.1 -7218.49 137.02 0.125 6208 6210 2 -20891.8 -7218.45 137 0.125 6209 6211 2 -20890.4 -7218.22 136.9 0.125 6210 6212 2 -20890.1 -7218.2 136.88 0.125 6211 6213 2 -20888.9 -7219.08 136.62 0.125 6212 6214 2 -20888.6 -7218.79 136.64 0.125 6213 6215 2 -20887.4 -7218.36 136.59 0.125 6214 6216 2 -20886.8 -7218 136.6 0.125 6215 6217 2 -20886.8 -7217.48 136.69 0.125 6216 6218 2 -20886.3 -7217.43 136.65 0.125 6217 6219 2 -20886 -7217.39 136.63 0.125 6218 6220 2 -20884.8 -7217.07 137.4 0.125 6219 6221 2 -20884.3 -7216.84 138.13 0.125 6220 6222 2 -20883.2 -7216.44 138.13 0.125 6221 6223 2 -20882.7 -7216.13 138.13 0.125 6222 6224 2 -20882.7 -7215.86 138.18 0.125 6223 6225 2 -20879.1 -7216.28 140.17 0.125 6224 6226 2 -20879 -7216.66 140.74 0.125 6225 6227 2 -20875.9 -7216.15 141.58 0.125 6226 6228 2 -20875.6 -7216.11 141.56 0.125 6227 6229 2 -20870.3 -7215.19 141.81 0.125 6228 6230 2 -20870 -7214.9 141.83 0.125 6229 6231 2 -20864.5 -7214.37 144.42 0.125 6230 6232 2 -20864.2 -7214.34 144.4 0.125 6231 6233 2 -20860.8 -7214.63 144.03 0.125 6232 6234 2 -20859.7 -7214.52 143.95 0.125 6233 6235 2 -20856.9 -7214.39 143.7 0.125 6234 6236 2 -20856.7 -7214.09 143.73 0.125 6235 6237 2 -20855.2 -7213.88 143.63 0.125 6236 6238 2 -20852.2 -7212.9 143.39 0.125 6237 6239 2 -20852 -7212.87 143.37 0.125 6238 6240 2 -20848.2 -7212.9 143.01 0.125 6239 6241 2 -20847.4 -7213.05 142.91 0.125 6240 6242 2 -20846 -7212.81 142.82 0.125 6241 6243 2 -20846 -7212.56 142.86 0.125 6242 6244 2 -20843.2 -7211.41 144.45 0.125 6243 6245 2 -20842.7 -7211.04 144.46 0.125 6244 6246 2 -20841.5 -7211.12 145.51 0.125 6245 6247 2 -20841 -7210.75 145.52 0.125 6246 6248 2 -20841 -7210.51 145.56 0.125 6247 6249 2 -20839.3 -7210.14 146.09 0.125 6248 6250 2 -20838.5 -7210 146.03 0.125 6249 6251 2 -20835.4 -7210.61 143.95 0.125 6250 6252 2 -20834.8 -7210.51 143.91 0.125 6251 6253 2 -20830.1 -7209.89 143.57 0.125 6252 6254 2 -20829.8 -7209.84 143.55 0.125 6253 6255 2 -20826.9 -7208.87 143.44 0.125 6254 6256 2 -20826.6 -7208.33 143.51 0.125 6255 6257 2 -20826.1 -7207.98 143.51 0.125 6256 6258 2 -20823.2 -7206.77 143.44 0.125 6257 6259 2 -20823 -7205.97 143.55 0.125 6258 6260 2 -20816.6 -7205.15 146.55 0.125 6259 6261 2 -20816.3 -7205.11 146.53 0.125 6260 6262 2 -20812.2 -7204.02 146.33 0.125 6261 6263 2 -20811.7 -7203.66 146.34 0.125 6262 6264 2 -20807.2 -7203.88 149.21 0.125 6263 6265 2 -20806 -7201.85 149.44 0.125 6264 6266 2 -20803.8 -7200.19 152.42 0.125 6265 6267 2 -20801 -7199.79 152.22 0.125 6266 6268 2 -20800.7 -7199.5 152.24 0.125 6267 6269 2 -20799.1 -7198.99 152.18 0.125 6268 6270 2 -20793.8 -7197.41 153.03 0.125 6269 6271 2 -20788.8 -7197.33 156.23 0.125 6270 6272 2 -20784.8 -7195.88 155.63 0.125 6271 6273 2 -20782.2 -7195.45 156.46 0.125 6272 6274 2 -20781.9 -7195.41 156.44 0.125 6273 6275 2 -20780 -7194.3 156.45 0.125 6274 6276 2 -20778.1 -7193.12 157.03 0.125 6275 6277 2 -20776.5 -7192.87 156.91 0.125 6276 6278 2 -20776 -7193.07 156.83 0.125 6277 6279 2 -20772.4 -7193.91 156.36 0.125 6278 6280 2 -20771.8 -7193.84 156.32 0.125 6279 6281 2 -20771.6 -7194.1 156.25 0.125 6280 6282 2 -20768.1 -7194.64 155.83 0.125 6281 6283 2 -20767.8 -7194.31 155.86 0.125 6282 6284 2 -20767.6 -7194.31 155.84 0.125 6283 6285 2 -20765.9 -7193.56 155.81 0.125 6284 6286 2 -20765.7 -7193.27 155.84 0.125 6285 6287 2 -20765.4 -7193.19 155.82 0.125 6286 6288 2 -20764.4 -7192.49 155.84 0.125 6287 6289 2 -20763.5 -7192.4 155.78 0.125 6288 6290 2 -20761.3 -7192.13 155.61 0.125 6289 6291 2 -20760 -7191.89 155.53 0.125 6290 6292 2 -20756.5 -7191.68 155.24 0.125 6291 6293 2 -20756.6 -7191.45 155.28 0.125 6292 6294 2 -20754.2 -7190.35 155.57 0.125 6293 6295 2 -20752.9 -7190.51 157.43 0.125 6294 6296 2 -20752.5 -7190.5 157.4 0.125 6295 6297 2 -20749.8 -7189.99 157.94 0.125 6296 6298 2 -20749.8 -7189.69 157.99 0.125 6297 6299 2 -20748 -7188.62 157.99 0.125 6298 6300 2 -20747.4 -7188.29 158 0.125 6299 6301 2 -20747.1 -7188.24 157.98 0.125 6300 6302 2 -20745.6 -7187 158.05 0.125 6301 6303 2 -20745.1 -7186.41 158.1 0.125 6302 6304 2 -20744.1 -7185.45 158.16 0.125 6303 6305 2 -20743.9 -7185.17 158.18 0.125 6304 6306 2 -20742.1 -7184.12 156.51 0.125 6305 6307 2 -20741.1 -7183.41 156.53 0.125 6306 6308 2 -20740.8 -7183.37 156.51 0.125 6307 6309 2 -20740.2 -7181.71 156.73 0.125 6308 6310 2 -20739.9 -7181.41 156.75 0.125 6309 6311 2 -20738.4 -7180.14 156.82 0.125 6310 6312 2 -20738.2 -7179.3 156.94 0.125 6311 6313 2 -20732.9 -7175.22 156.01 0.125 6312 6314 2 -20732.9 -7174.92 156.06 0.125 6313 6315 2 -20730.7 -7174.61 155.9 0.125 6314 6316 2 -20730.1 -7174.51 155.87 0.125 6315 6317 2 -20726.3 -7173.47 155.68 0.125 6316 6318 2 -20724.8 -7172.04 154.75 0.125 6317 6319 2 -20724.8 -7172.11 155.14 0.125 6318 6320 2 -20723.8 -7170.62 155.3 0.125 6319 6321 2 -20720 -7169.68 155.43 0.125 6320 6322 2 -20719.7 -7169.66 155.59 0.125 6321 6323 2 -20713.9 -7166.26 156.09 0.125 6322 6324 2 -20713.9 -7166.01 156.13 0.125 6323 6325 2 -20709.3 -7165.52 156.62 0.125 6324 6326 2 -20707.5 -7164.31 157.33 0.125 6325 6327 2 -20705.1 -7162.95 157.81 0.125 6326 6328 2 -20704.6 -7162.67 157.86 0.125 6327 6329 2 -20701.9 -7161.94 160.2 0.125 6328 6330 2 -20701.8 -7162.23 160.15 0.125 6329 6331 2 -20699.9 -7161.94 160.01 0.125 6330 6332 2 -20699.1 -7161.8 159.96 0.125 6331 6333 2 -20694.1 -7160.56 159.7 0.125 6332 6334 2 -20689 -7157.3 158.48 0.125 6333 6335 2 -20686.1 -7157.3 159.28 0.125 6334 6336 2 -20684.1 -7157.27 159.1 0.125 6335 6337 2 -20683.3 -7157.13 159.04 0.125 6336 6338 2 -20682.2 -7156.27 159.09 0.125 6337 6339 2 -20681.8 -7155.69 159.14 0.125 6338 6340 2 -20681.8 -7155.38 159.19 0.125 6339 6341 2 -20679.5 -7152.17 161.14 0.125 6340 6342 2 -20676.2 -7149.61 164.43 0.125 6341 6343 2 -20675.6 -7149.57 164.43 0.125 6342 6344 2 -20673.7 -7149.26 164.39 0.125 6343 6345 2 -20673.4 -7149.26 164.41 0.125 6344 6346 2 -20670.7 -7148.64 164.3 0.125 6345 6347 2 -20670.1 -7148.55 164.3 0.125 6346 6348 2 -20665.7 -7147.66 164.12 0.125 6347 6349 2 -20664.9 -7147.33 164.15 0.125 6348 6350 2 -20663.3 -7146.31 164.21 0.125 6349 6351 2 -20661.9 -7146.11 164.15 0.125 6350 6352 2 -20661.6 -7146.31 164.09 0.125 6351 6353 2 -20655.1 -7143.67 164.23 0.125 6352 6354 2 -20648.8 -7142.33 167.61 0.125 6353 6355 2 -20649.2 -7136.31 168.65 0.125 6354 6356 2 -20639.9 -7133.74 170.14 0.125 6355 6357 2 -20635.9 -7132.83 171.91 0.125 6356 6358 2 -20632.7 -7131.74 172.53 0.125 6357 6359 2 -20632.5 -7131.7 172.51 0.125 6358 6360 2 -20626.7 -7129.41 174.78 0.125 6359 6361 2 -20621.4 -7128.39 176.03 0.125 6360 6362 2 -20621.4 -7128.14 176.07 0.125 6361 6363 2 -20618.7 -7126.3 176.12 0.125 6362 6364 2 -20618.5 -7125.97 176.18 0.125 6363 6365 2 -20618.2 -7125.66 176.2 0.125 6364 6366 2 -20615.7 -7125 176.34 0.125 6365 6367 2 -20615.8 -7124.7 176.4 0.125 6366 6368 2 -20613.8 -7124.64 176.22 0.125 6367 6369 2 -20613.6 -7124.23 176.27 0.125 6368 6370 2 -20612 -7123.17 176.17 0.125 6369 6371 2 -20611.6 -7122.79 176.19 0.125 6370 6372 2 -20611.1 -7122.41 176.21 0.125 6371 6373 2 -20609.2 -7121.77 176.14 0.125 6372 6374 2 -20609 -7121.47 176.16 0.125 6373 6375 2 -20607.9 -7121.04 176.14 0.125 6374 6376 2 -20607.4 -7120.88 176.11 0.125 6375 6377 2 -20603 -7117.4 179.61 0.125 6376 6378 2 -20601.2 -7117.91 179.36 0.125 6377 6379 2 -20600.9 -7117.57 179.39 0.125 6378 6380 2 -20599.7 -7116.55 179.45 0.125 6379 6381 2 -20599.4 -7116.51 179.43 0.125 6380 6382 2 -20598.9 -7116.14 179.44 0.125 6381 6383 2 -20598.5 -7115.77 179.46 0.125 6382 6384 2 -20598.2 -7115.71 179.44 0.125 6383 6385 2 -20597.9 -7115.66 179.42 0.125 6384 6386 2 -20595.4 -7114.95 179.52 0.125 6385 6387 2 -20593.5 -7114.39 180 0.125 6386 6388 2 -20593.2 -7114.36 179.98 0.125 6387 6389 2 -20591.8 -7113.97 180.72 0.125 6388 6390 2 -20591.5 -7113.71 181.26 0.125 6389 6391 2 -20589.3 -7113.33 181.12 0.125 6390 6392 2 -20586.9 -7111.54 180.08 0.125 6391 6393 2 -20586.7 -7111.22 180.11 0.125 6392 6394 2 -20585 -7111.19 179.96 0.125 6393 6395 2 -20584.5 -7111.06 179.93 0.125 6394 6396 2 -20583.2 -7111.63 179.72 0.125 6395 6397 2 -20582.6 -7111.53 179.68 0.125 6396 6398 2 -20581.8 -7111.37 179.63 0.125 6397 6399 2 -20581.3 -7110.78 179.69 0.125 6398 6400 2 -20580.6 -7110.06 179.74 0.125 6399 6401 2 -20580.4 -7109.73 179.77 0.125 6400 6402 2 -20578.5 -7109.38 179.65 0.125 6401 6403 2 -20577.9 -7109.26 179.62 0.125 6402 6404 2 -20578 -7109 179.67 0.125 6403 6405 2 -20575.7 -7108.18 181.99 0.125 6404 6406 2 -20574 -7107.87 181.89 0.125 6405 6407 2 -20572.6 -7107.87 181.76 0.125 6406 6408 2 -20571.8 -7107.72 181.7 0.125 6407 6409 2 -20570.4 -7107.14 181.68 0.125 6408 6410 2 -20569.6 -7106.96 181.63 0.125 6409 6411 2 -20569.7 -7106.72 181.67 0.125 6410 6412 2 -20566.5 -7105.85 181.55 0.125 6411 6413 2 -20566.2 -7105.78 181.53 0.125 6412 6414 2 -20564.7 -7105.81 181.39 0.125 6413 6415 2 -20564.4 -7105.71 181.38 0.125 6414 6416 2 -20563.3 -7105.52 181.31 0.125 6415 6417 2 -20562.3 -7105.31 181.25 0.125 6416 6418 2 -20562 -7104.99 181.28 0.125 6417 6419 2 -20558.7 -7104.74 184.84 0.125 6418 6420 2 -20558.5 -7104.46 184.86 0.125 6419 6421 2 -20557.4 -7104.19 184.8 0.125 6420 6422 2 -20557.4 -7103.93 184.85 0.125 6421 6423 2 -20555.3 -7102.57 185.34 0.125 6422 6424 2 -20554.3 -7102.11 185.32 0.125 6423 6425 2 -20553.5 -7101.93 185.27 0.125 6424 6426 2 -20552.1 -7101.67 185.19 0.125 6425 6427 2 -20551 -7101.46 185.12 0.125 6426 6428 2 -20550.3 -7100.81 185.15 0.125 6427 6429 2 -20549.8 -7100.17 185.22 0.125 6428 6430 2 -20549.7 -7099.35 185.35 0.125 6429 6431 2 -20549 -7098.39 185.44 0.125 6430 6432 2 -20547.7 -7097.62 185.45 0.125 6431 6433 2 -20545.8 -7097.78 188.13 0.125 6432 6434 2 -20545.8 -7097.83 188.43 0.125 6433 6435 2 -20543 -7097.42 188.87 0.125 6434 6436 2 -20538.2 -7096.19 191.85 0.125 6435 6437 2 -20534.5 -7095.2 193.05 0.125 6436 6438 2 -20533.7 -7093.97 193.17 0.125 6437 6439 2 -20531.8 -7093.35 193.09 0.125 6438 6440 2 -20531.1 -7092.39 193.18 0.125 6439 6441 2 -20526.8 -7091.79 195.71 0.125 6440 6442 2 -20525.7 -7091.36 195.69 0.125 6441 6443 2 -20518.2 -7090.66 198.2 0.125 6442 6444 2 -20517.7 -7090.32 198.2 0.125 6443 6445 2 -20517.4 -7090.28 198.19 0.125 6444 6446 2 -20516.1 -7089.73 198.15 0.125 6445 6447 2 -20515.9 -7089.19 198.22 0.125 6446 6448 2 -20515.2 -7088.48 198.27 0.125 6447 6449 2 -20514.7 -7088.1 198.29 0.125 6448 6450 2 -20514.5 -7088.1 198.27 0.125 6449 6451 2 -20512.5 -7087.42 198.2 0.125 6450 6452 2 -20512.3 -7087.11 198.23 0.125 6451 6453 2 -20511 -7084.98 198.46 0.125 6452 6454 2 -20510.8 -7084.71 198.49 0.125 6453 6455 2 -20510.5 -7084.66 198.47 0.125 6454 6456 2 -20509.1 -7084.4 198.38 0.125 6455 6457 2 -20508.9 -7084.06 198.42 0.125 6456 6458 2 -20504.9 -7082.38 199.17 0.125 6457 6459 2 -20491.9 -7078.32 203.8 0.125 6458 6460 2 -20489.6 -7077.11 203.78 0.125 6459 6461 2 -20480.8 -7074.79 204.3 0.125 6460 6462 2 -20480 -7074.35 204.3 0.125 6461 6463 2 -20479.8 -7074.04 204.33 0.125 6462 6464 2 -20473.5 -7071.98 207.04 0.125 6463 6465 2 -20468.6 -7069.47 205.61 0.125 6464 6466 2 -20468.3 -7069.39 205.6 0.125 6465 6467 2 -20467.5 -7068.17 205.72 0.125 6466 6468 2 -20467.2 -7067.87 205.74 0.125 6467 6469 2 -20465 -7067.43 205.61 0.125 6468 6470 2 -20464.7 -7067.37 205.59 0.125 6469 6471 2 -20463.1 -7067.01 205.37 0.125 6470 6472 2 -21138.1 -7221.17 151.06 0.125 6016 6473 2 -21136.3 -7221.14 157.04 0.125 6472 6474 2 -21134.1 -7221.5 163.48 0.125 6473 6475 2 -21134.3 -7222.56 170.82 0.125 6474 6476 2 -21134.1 -7222.85 172.53 0.125 6475 6477 2 -21134.5 -7222.33 175.76 0.125 6476 6478 2 -21134.5 -7222.08 175.81 0.125 6477 6479 2 -21131.5 -7221.01 176.37 0.125 6478 6480 2 -21131.5 -7220.74 176.41 0.125 6479 6481 2 -21130.8 -7220.32 176.41 0.125 6480 6482 2 -21130.5 -7220.03 176.44 0.125 6481 6483 2 -21130 -7219.45 176.49 0.125 6482 6484 2 -21129.8 -7219.16 176.51 0.125 6483 6485 2 -21129 -7218.79 176.5 0.125 6484 6486 2 -21128.8 -7217.71 176.66 0.125 6485 6487 2 -21128.9 -7216.89 176.8 0.125 6486 6488 2 -21127.7 -7215.69 176.89 0.125 6487 6489 2 -21126.4 -7214.15 177.03 0.125 6488 6490 2 -21126.2 -7213.9 177.05 0.125 6489 6491 2 -21124.6 -7213.13 177.02 0.125 6490 6492 2 -21124.1 -7212.52 177.07 0.125 6491 6493 2 -21134 -7221.04 181.14 0.125 6478 6494 2 -21132.5 -7220.76 186.61 0.125 6493 6495 2 -21132.5 -7220.71 188.04 0.125 6494 6496 2 -21132.3 -7220.94 189.43 0.125 6495 6497 2 -21130 -7220.77 194.71 0.125 6496 6498 2 -21129.7 -7220.72 194.69 0.125 6497 6499 2 -21128.3 -7221.49 199.87 0.125 6498 6500 2 -21128 -7221.67 201.01 0.125 6499 6501 2 -21126.3 -7221.51 204.67 0.125 6500 6502 2 -21187.9 -7237.25 141.75 0.125 5968 6503 2 -21186.4 -7237.85 141.51 0.125 6502 6504 2 -21182.9 -7239.62 143.37 0.125 6503 6505 2 -21181.3 -7240.18 144.69 0.125 6504 6506 2 -21180.2 -7240.03 144.61 0.125 6505 6507 2 -21179.1 -7240.52 146.29 0.125 6506 6508 2 -21179 -7240.7 147.39 0.125 6507 6509 2 -21178.1 -7242.05 149.56 0.125 6508 6510 2 -21177.9 -7242.98 150.59 0.125 6509 6511 2 -21169.1 -7259.06 158.25 0.125 6510 6512 2 -21166.6 -7261.12 157.68 0.125 6511 6513 2 -21166.5 -7261.61 157.59 0.125 6512 6514 2 -21166.2 -7261.83 157.52 0.125 6513 6515 2 -21164.2 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0.125 6584 6586 2 -21111 -7298.34 198.25 0.125 6585 6587 2 -21111 -7298.62 198.23 0.125 6586 6588 2 -21110.5 -7299.89 198.35 0.125 6587 6589 2 -21110.1 -7300.08 198.28 0.125 6588 6590 2 -21109.4 -7300.82 198.41 0.125 6589 6591 2 -21109.1 -7300.79 198.39 0.125 6590 6592 2 -21108.2 -7300.86 198.46 0.125 6591 6593 2 -21108.2 -7301.4 198.36 0.125 6592 6594 2 -21107.5 -7302.12 198.45 0.125 6593 6595 2 -21107.2 -7302.08 198.59 0.125 6594 6596 2 -21104.8 -7303.02 201.81 0.125 6595 6597 2 -21104.4 -7303.23 201.74 0.125 6596 6598 2 -21103.5 -7303.58 201.6 0.125 6597 6599 2 -21101.2 -7305.33 205.92 0.125 6598 6600 2 -21099.9 -7305.82 206.65 0.125 6599 6601 2 -21097.7 -7308.01 207.58 0.125 6600 6602 2 -21096.5 -7309.22 209.53 0.125 6601 6603 2 -21096 -7309.65 210.5 0.125 6602 6604 2 -21094.8 -7310.2 210.42 0.125 6603 6605 2 -21093.7 -7310.22 210.31 0.125 6604 6606 2 -21092.4 -7311.05 210.05 0.125 6605 6607 2 -21091.7 -7311.21 209.98 0.125 6606 6608 2 -21090.5 -7311.76 209.78 0.125 6607 6609 2 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222.07 0.125 6631 6633 2 -21070.8 -7322.5 222.63 0.125 6632 6634 2 -21070.1 -7322.65 222.54 0.125 6633 6635 2 -21069 -7322.71 222.42 0.125 6634 6636 2 -21068.4 -7322.6 222.39 0.125 6635 6637 2 -21067.6 -7322.68 222.3 0.125 6636 6638 2 -21066.9 -7323.13 222.16 0.125 6637 6639 2 -21065.8 -7324.23 221.88 0.125 6638 6640 2 -21065.6 -7324.17 221.86 0.125 6639 6641 2 -21061.8 -7326.65 224.45 0.125 6640 6642 2 -21061.5 -7326.59 224.29 0.125 6641 6643 2 -21060.5 -7326.06 223.97 0.125 6642 6644 2 -21059.7 -7325.9 223.92 0.125 6643 6645 2 -21058.8 -7326.02 223.82 0.125 6644 6646 2 -21056.3 -7327.31 227.37 0.125 6645 6647 2 -21055.2 -7327.33 227.26 0.125 6646 6648 2 -21054.9 -7327.27 227.24 0.125 6647 6649 2 -21054.2 -7328.22 227.02 0.125 6648 6650 2 -21053.5 -7328.85 226.85 0.125 6649 6651 2 -21053.4 -7329.12 226.8 0.125 6650 6652 2 -21052.4 -7329.96 226.56 0.125 6651 6653 2 -21052.2 -7329.9 226.55 0.125 6652 6654 2 -21049.7 -7330.22 232.08 0.125 6653 6655 2 -21048.8 -7330.33 231.98 0.125 6654 6656 2 -21047.6 -7331.12 231.74 0.125 6655 6657 2 -21047.2 -7331.35 231.67 0.125 6656 6658 2 -21046 -7331.89 231.46 0.125 6657 6659 2 -21045.2 -7331.45 231.46 0.125 6658 6660 2 -21040.8 -7333.84 235.37 0.125 6659 6661 2 -21040 -7333.69 235.32 0.125 6660 6662 2 -21038 -7334.15 238.1 0.125 6661 6663 2 -21037.5 -7333.99 238.08 0.125 6662 6664 2 -21036.8 -7334.41 237.95 0.125 6663 6665 2 -21035.1 -7335.36 238.98 0.125 6664 6666 2 -21034.8 -7335.33 238.96 0.125 6665 6667 2 -21033.6 -7335.44 234.92 0.125 6666 6668 2 -21028.5 -7339.42 240.77 0.125 6667 6669 2 -21020.1 -7341.93 243.38 0.125 6668 6670 2 -21020 -7348.27 242.32 0.125 6669 6671 2 -21018.7 -7348.86 242.1 0.125 6670 6672 2 -21016.1 -7349.44 244.71 0.125 6671 6673 2 -21013.5 -7349.7 248.73 0.125 6672 6674 2 -21013.3 -7349.66 248.75 0.125 6673 6675 2 -21009.4 -7350.75 247.43 0.125 6674 6676 2 -21001.7 -7353.27 251.66 0.125 6675 6677 2 -20990.1 -7355.59 253.3 0.125 6676 6678 2 -20990.1 -7355.69 253.9 0.125 6677 6679 2 -20986.1 -7357.15 258 0.125 6678 6680 2 -20985.5 -7357.34 258.03 0.125 6679 6681 2 -20983.7 -7355.56 258.15 0.125 6680 6682 2 -20984 -7355.59 258.17 0.125 6681 6683 2 -21061.2 -7327.21 223.78 0.125 6640 6684 2 -21061.1 -7327.3 224.31 0.125 6683 6685 2 -21060 -7328.58 224.68 0.125 6684 6686 2 -21059.9 -7328.69 225.33 0.125 6685 6687 2 -21057.3 -7328.85 225.92 0.125 6686 6688 2 -21057.2 -7329.1 225.88 0.125 6687 6689 2 -21055.8 -7330.69 225.48 0.125 6688 6690 2 -21055.4 -7331.13 225.37 0.125 6689 6691 2 -21055.3 -7331.92 225.23 0.125 6690 6692 2 -21053.8 -7332.73 228.25 0.125 6691 6693 2 -21053.6 -7333.44 229.25 0.125 6692 6694 2 -21052.5 -7335.06 231.45 0.125 6693 6695 2 -21052.7 -7335.59 231.38 0.125 6694 6696 2 -21052.7 -7336.93 232.81 0.125 6695 6697 2 -21052.4 -7338.14 233.86 0.125 6696 6698 2 -21052.2 -7337.88 233.88 0.125 6697 6699 2 -21051.8 -7338.08 233.82 0.125 6698 6700 2 -21051.1 -7339.07 233.89 0.125 6699 6701 2 -21050.9 -7339.31 235.29 0.125 6700 6702 2 -21049.4 -7341.16 235.46 0.125 6701 6703 2 -21049.3 -7341.93 235.32 0.125 6702 6704 2 -21048.9 -7342.14 235.26 0.125 6703 6705 2 -21048.2 -7342.59 235.12 0.125 6704 6706 2 -21047.3 -7343.34 234.08 0.125 6705 6707 2 -21047.4 -7343.26 233.56 0.125 6706 6708 2 -21047.4 -7344.85 231.7 0.125 6707 6709 2 -21047.5 -7344.75 231.14 0.125 6708 6710 2 -21046.1 -7346.89 227.93 0.125 6709 6711 2 -21045.9 -7349.62 223.7 0.125 6710 6712 2 -21045.8 -7349.9 223.65 0.125 6711 6713 2 -21045.1 -7352.45 223.16 0.125 6712 6714 2 -21044.7 -7353.14 223.01 0.125 6713 6715 2 -21043.4 -7353.66 222.76 0.125 6714 6716 2 -21043.1 -7355.65 221.94 0.125 6715 6717 2 -21043.1 -7355.5 221.07 0.125 6716 6718 2 -21042.7 -7356.83 219.64 0.125 6717 6719 2 -21042.8 -7356.69 218.85 0.125 6718 6720 2 -21042.6 -7357.23 220.55 0.125 6719 6721 2 -21042.2 -7357.7 220.44 0.125 6720 6722 2 -21041.1 -7360.38 219.89 0.125 6721 6723 2 -21040.7 -7362.98 219.42 0.125 6722 6724 2 -21040 -7365.24 218.98 0.125 6723 6725 2 -21039.8 -7366.03 218.84 0.125 6724 6726 2 -21039.2 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161.21 0.25 7516 7518 2 -21720.1 -7383.24 161.13 0.25 7517 7519 2 -21719.8 -7383.19 161.11 0.25 7518 7520 2 -21717.9 -7384.69 160.69 0.25 7519 7521 2 -21717 -7384.8 160.59 0.25 7520 7522 2 -21714.7 -7385.18 160.31 0.25 7521 7523 2 -21714.4 -7385.13 160.29 0.25 7522 7524 2 -21712.6 -7385.86 160 0.25 7523 7525 2 -21711.7 -7386.48 159.81 0.25 7524 7526 2 -21709.5 -7387.67 159.41 0.25 7525 7527 2 -21709.2 -7387.87 159.35 0.25 7526 7528 2 -21706.9 -7389.58 158.85 0.25 7527 7529 2 -21706.6 -7389.8 158.78 0.25 7528 7530 2 -21706.7 -7390.88 158.61 0.25 7529 7531 2 -21706.6 -7391.44 158.56 0.25 7530 7532 2 -21712 -7395.88 158.33 0.37 7531 7533 2 -21711 -7397.83 160.92 0.37 7532 7534 2 -21710.9 -7397.98 161.78 0.37 7533 7535 2 -21710.4 -7399.21 162.86 0.37 7534 7536 2 -21709.2 -7401.61 163.67 0.37 7535 7537 2 -21708.9 -7403.15 163.39 0.37 7536 7538 2 -21707.9 -7404.37 163.13 0.37 7537 7539 2 -21707.9 -7404.47 163.72 0.37 7538 7540 2 -21707.9 -7406.61 163.61 0.25 7539 7541 2 -21707.8 -7406.66 163.89 0.25 7540 7542 2 -21707.8 -7408.41 164.39 0.25 7541 7543 2 -21706.8 -7409.81 164.16 0.25 7542 7544 2 -21706.6 -7411.91 163.79 0.25 7543 7545 2 -21706.4 -7413.84 165.52 0.25 7544 7546 2 -21706.5 -7415.47 165.26 0.25 7545 7547 2 -21705.6 -7416.34 165.04 0.25 7546 7548 2 -21705 -7417.2 167.21 0.25 7547 7549 2 -21705.3 -7419.13 166.93 0.25 7548 7550 2 -21704.9 -7420.39 166.67 0.25 7549 7551 2 -21704.4 -7422.15 166.34 0.25 7550 7552 2 -21704 -7422.4 166.26 0.25 7551 7553 2 -21702.9 -7422.81 166.09 0.25 7552 7554 2 -21702.6 -7422.76 166.07 0.25 7553 7555 2 -21701.6 -7424.81 167.9 0.25 7554 7556 2 -21701 -7426.9 167.5 0.25 7555 7557 2 -21701 -7426.93 167.69 0.25 7556 7558 2 -21701.4 -7428.61 168.06 0.25 7557 7559 2 -21701.3 -7428.72 168.75 0.25 7558 7560 2 -21701.3 -7430.45 168.87 0.25 7559 7561 2 -21701.3 -7430.52 169.27 0.25 7560 7562 2 -21701.6 -7431.71 166.74 0.25 7561 7563 2 -21702.2 -7432.59 165.2 0.25 7562 7564 2 -21702 -7433.89 164.97 0.25 7563 7565 2 -21702.1 -7435.49 164.71 0.25 7564 7566 2 -21702.9 -7436.16 164.67 0.25 7565 7567 2 -21702.5 -7436.34 164.62 0.25 7566 7568 2 -21702.2 -7436.58 164.55 0.25 7567 7569 2 -21701.9 -7436.82 164.48 0.25 7568 7570 2 -21701.2 -7437.74 164.26 0.25 7569 7571 2 -21700.9 -7437.71 164.24 0.25 7570 7572 2 -21701.7 -7438.87 164.12 0.25 7571 7573 2 -21701.8 -7439.43 164.04 0.25 7572 7574 2 -21701.2 -7439.65 163.95 0.25 7573 7575 2 -21701 -7439.84 163.89 0.25 7574 7576 2 -21700.1 -7440.01 163.78 0.25 7575 7577 2 -21699.7 -7440.48 163.67 0.25 7576 7578 2 -21699.3 -7441.75 163.41 0.25 7577 7579 2 -21699 -7441.99 163.34 0.25 7578 7580 2 -21699 -7444.02 164.36 0.25 7579 7581 2 -21698.1 -7445.25 165.51 0.25 7580 7582 2 -21698.1 -7445.32 165.93 0.25 7581 7583 2 -21697.4 -7445.64 166.63 0.25 7582 7584 2 -21697.1 -7445.54 166.62 0.25 7583 7585 2 -21696.7 -7446.59 166.41 0.25 7584 7586 2 -21697.5 -7447.65 165.72 0.25 7585 7587 2 -21696.5 -7448.29 165.86 0.25 7586 7588 2 -21695.8 -7448.88 166.15 0.25 7587 7589 2 -21694.2 -7450.16 166.88 0.25 7588 7590 2 -21693.7 -7451.43 166.77 0.25 7589 7591 2 -21692.3 -7452.72 168.85 0.25 7590 7592 2 -21692.4 -7453.51 168.73 0.25 7591 7593 2 -21691.3 -7454.12 168.52 0.25 7592 7594 2 -21690.8 -7454.83 168.01 0.25 7593 7595 2 -21690.8 -7454.87 168.23 0.25 7594 7596 2 -21690.5 -7455.62 168.07 0.25 7595 7597 2 -21689.4 -7456.15 170.29 0.25 7596 7598 2 -21689.2 -7457.62 171.02 0.25 7597 7599 2 -21688.3 -7457.5 170.95 0.25 7598 7600 2 -21687.4 -7457.62 170.86 0.25 7599 7601 2 -21687 -7458.92 170.6 0.25 7600 7602 2 -21686.9 -7459.11 170.56 0.25 7601 7603 2 -21687.1 -7460.5 170.35 0.25 7602 7604 2 -21685.4 -7462.36 174.32 0.25 7603 7605 2 -21684.8 -7462.27 174.29 0.25 7604 7606 2 -21684.5 -7462.47 174.22 0.25 7605 7607 2 -21683.8 -7463.44 174 0.25 7606 7608 2 -21683 -7465.22 173.69 0.25 7607 7609 2 -21682.9 -7465.73 173.67 0.25 7608 7610 2 -21682.5 -7468.36 174.79 0.25 7609 7611 2 -21682.2 -7468.26 174.77 0.25 7610 7612 2 -21681.3 -7468.69 174.61 0.25 7611 7613 2 -21681.4 -7468.43 174.66 0.25 7612 7614 2 -21680.5 -7468.06 174.64 0.25 7613 7615 2 -21680.3 -7468.01 174.63 0.25 7614 7616 2 -21679.3 -7468.39 175.8 0.25 7615 7617 2 -21679.2 -7469.87 175.15 0.25 7616 7618 2 -21677.5 -7470.48 176.55 0.25 7617 7619 2 -21675.8 -7472.88 176.16 0.25 7618 7620 2 -21675.1 -7474.1 175.89 0.25 7619 7621 2 -21675 -7474.35 175.84 0.25 7620 7622 2 -21674.9 -7475.17 175.69 0.25 7621 7623 2 -21674.7 -7475.11 175.68 0.25 7622 7624 2 -21673.7 -7475.66 176.52 0.25 7623 7625 2 -21672.5 -7475.81 176.39 0.25 7624 7626 2 -21671.4 -7475.92 176.27 0.25 7625 7627 2 -21670.6 -7477.75 174.91 0.25 7626 7628 2 -21669.9 -7478.95 174.65 0.25 7627 7629 2 -21668.6 -7479.5 176.91 0.25 7628 7630 2 -21667.9 -7479.92 176.78 0.25 7629 7631 2 -21667.6 -7480.62 176.63 0.25 7630 7632 2 -21666.9 -7481.03 177.52 0.25 7631 7633 2 -21667 -7483.48 179.11 0.25 7632 7634 2 -21666.3 -7483.93 179.02 0.25 7633 7635 2 -21665.4 -7483.98 176.84 0.25 7634 7636 2 -21665 -7483.65 175.45 0.25 7635 7637 2 -21663.5 -7483.73 175.3 0.25 7636 7638 2 -21663.2 -7483.67 175.28 0.25 7637 7639 2 -21661.7 -7484.52 174.99 0.25 7638 7640 2 -21661.2 -7486.33 174.64 0.25 7639 7641 2 -21660.9 -7489.16 174.14 0.25 7640 7642 2 -21660.1 -7489.61 175.53 0.25 7641 7643 2 -21659 -7489.76 175.59 0.25 7642 7644 2 -21658.9 -7490.03 175.54 0.25 7643 7645 2 -21658.4 -7490.93 176.56 0.25 7644 7646 2 -21658.6 -7491.18 177.91 0.25 7645 7647 2 -21659.2 -7491.84 179.69 0.25 7646 7648 2 -21659.4 -7492.71 179.61 0.25 7647 7649 2 -21659.2 -7493.56 180.07 0.25 7648 7650 2 -21658.9 -7493.79 180.01 0.25 7649 7651 2 -21657.4 -7494.63 179.72 0.25 7650 7652 2 -21657 -7495.14 179.61 0.25 7651 7653 2 -21656.6 -7496.13 179.41 0.25 7652 7654 2 -21656.6 -7496.4 179.36 0.25 7653 7655 2 -21656.1 -7498.19 182.28 0.25 7654 7656 2 -21656.1 -7498.22 182.44 0.25 7655 7657 2 -21655.8 -7500.57 182.02 0.25 7656 7658 2 -21655.6 -7500.54 182 0.25 7657 7659 2 -21654.1 -7501.37 181.72 0.25 7658 7660 2 -21652.9 -7501.47 181.6 0.25 7659 7661 2 -21652.1 -7503.24 181.23 0.25 7660 7662 2 -21650.6 -7503.79 181 0.25 7661 7663 2 -21650.5 -7504.06 180.95 0.25 7662 7664 2 -21650.9 -7505.98 180.67 0.25 7663 7665 2 -21650.9 -7506.25 180.62 0.25 7664 7666 2 -21649.3 -7507.16 180.79 0.25 7665 7667 2 -21649 -7507.13 180.77 0.25 7666 7668 2 -21648.7 -7507.6 180.76 0.25 7667 7669 2 -21648 -7508.83 180.48 0.25 7668 7670 2 -21647.7 -7508.8 180.47 0.25 7669 7671 2 -21646.5 -7509.14 180.29 0.25 7670 7672 2 -21646.6 -7510.17 181.47 0.25 7671 7673 2 -21645.5 -7511.17 181.47 0.25 7672 7674 2 -21644.8 -7512.65 181.15 0.25 7673 7675 2 -21644.8 -7512.9 181.11 0.25 7674 7676 2 -21644.1 -7514.94 182.39 0.25 7675 7677 2 -21644 -7515.73 182.25 0.25 7676 7678 2 -21642.5 -7518.07 182.63 0.25 7677 7679 2 -21642.3 -7518.02 182.62 0.25 7678 7680 2 -21639.8 -7518.49 183.93 0.25 7679 7681 2 -21639.3 -7518.88 181.63 0.25 7680 7682 2 -21639.2 -7519.08 182.88 0.25 7681 7683 2 -21638.5 -7520.09 182.89 0.25 7682 7684 2 -21638.3 -7521.9 182.57 0.25 7683 7685 2 -21638 -7521.82 182.55 0.25 7684 7686 2 -21636.5 -7522.7 182.27 0.25 7685 7687 2 -21636.5 -7522.93 182.23 0.25 7686 7688 2 -21635.5 -7523.87 181.98 0.25 7687 7689 2 -21635.2 -7524.1 181.92 0.25 7688 7690 2 -21634.5 -7525.05 181.7 0.25 7689 7691 2 -21634.2 -7525.27 181.63 0.25 7690 7692 2 -21633.1 -7528.09 183.1 0.25 7691 7693 2 -21633.2 -7528.28 180.92 0.25 7692 7694 2 -21633 -7529.6 180.68 0.25 7693 7695 2 -21632.8 -7529.58 180.66 0.25 7694 7696 2 -21632.4 -7530.31 180.51 0.25 7695 7697 2 -21632.1 -7530.5 180.3 0.25 7696 7698 2 -21631.6 -7532.03 179.86 0.25 7697 7699 2 -21631.6 -7532.25 179.59 0.25 7698 7700 2 -21631.2 -7533.27 176.35 0.25 7699 7701 2 -21630.8 -7533.41 176.3 0.25 7700 7702 2 -21630.8 -7533.7 176.25 0.25 7701 7703 2 -21630.1 -7534.4 176.07 0.25 7702 7704 2 -21629.8 -7534.89 175.96 0.25 7703 7705 2 -21629.6 -7536.71 175.64 0.25 7704 7706 2 -21629.6 -7536.97 175.59 0.25 7705 7707 2 -21627.1 -7537.98 175.2 0.25 7706 7708 2 -21626.5 -7538.17 175.11 0.25 7707 7709 2 -21626 -7538.33 175.03 0.25 7708 7710 2 -21625 -7539.25 174.79 0.25 7709 7711 2 -21624.7 -7539.24 174.76 0.25 7710 7712 2 -21623.4 -7540.7 174.96 0.25 7711 7713 2 -21622.5 -7541.29 174.25 0.25 7712 7714 2 -21622.5 -7541.53 174.21 0.25 7713 7715 2 -21622 -7542.58 174.32 0.25 7714 7716 2 -21621.9 -7543.05 175.34 0.25 7715 7717 2 -21620.4 -7545.32 175.28 0.25 7716 7718 2 -21620.1 -7545.57 175.21 0.25 7717 7719 2 -21619.3 -7545.39 175.16 0.25 7718 7720 2 -21619 -7545.11 175.19 0.25 7719 7721 2 -21619 -7545.8 174.72 0.25 7720 7722 2 -21618.9 -7546.09 174.67 0.25 7721 7723 2 -21618.8 -7547.45 173.37 0.25 7722 7724 2 -21618.8 -7547.72 173.32 0.25 7723 7725 2 -21618.5 -7549.29 173.04 0.25 7724 7726 2 -21618.6 -7549.54 173 0.25 7725 7727 2 -21617.9 -7550.52 172.77 0.25 7726 7728 2 -21617.6 -7550.74 172.71 0.25 7727 7729 2 -21616.2 -7552.91 172.21 0.25 7728 7730 2 -21615.2 -7554.01 171.38 0.25 7729 7731 2 -21614.9 -7554.01 171.35 0.25 7730 7732 2 -21614.5 -7554.97 171.15 0.25 7731 7733 2 -21614.2 -7555.2 171.08 0.25 7732 7734 2 -21613.1 -7555.32 170.96 0.25 7733 7735 2 -21612.5 -7555.5 170.87 0.25 7734 7736 2 -21612.1 -7556.51 170.67 0.25 7735 7737 2 -21612.3 -7557.34 170.54 0.25 7736 7738 2 -21612.5 -7557.7 171.19 0.25 7737 7739 2 -21612.1 -7558.51 171.03 0.25 7738 7740 2 -21612 -7558.71 170.99 0.25 7739 7741 2 -21611.9 -7559.28 170.88 0.25 7740 7742 2 -21609.9 -7562.09 174.73 0.25 7741 7743 2 -21609.6 -7562.08 174.7 0.25 7742 7744 2 -21608.9 -7562.61 174.99 0.25 7743 7745 2 -21608.6 -7562.54 174.98 0.25 7744 7746 2 -21608.3 -7563.35 175.03 0.25 7745 7747 2 -21608 -7563.32 175.01 0.25 7746 7748 2 -21607.3 -7563.36 172.83 0.25 7747 7749 2 -21607.2 -7563.84 172.75 0.25 7748 7750 2 -21606.9 -7563.82 172.73 0.25 7749 7751 2 -21606.3 -7564.25 172.6 0.25 7750 7752 2 -21606.3 -7564.53 172.56 0.25 7751 7753 2 -21605.5 -7565.85 172.91 0.25 7752 7754 2 -21605.2 -7566.06 172.85 0.25 7753 7755 2 -21604.5 -7566.57 172.81 0.25 7754 7756 2 -21604.2 -7567.06 172.75 0.25 7755 7757 2 -21603.9 -7567.01 172.73 0.25 7756 7758 2 -21603.2 -7567.71 172.55 0.25 7757 7759 2 -21602.9 -7567.92 172.57 0.25 7758 7760 2 -21601.5 -7568.29 172.44 0.25 7759 7761 2 -21600.9 -7568.2 172.4 0.25 7760 7762 2 -21600.1 -7571.87 176.82 0.25 7761 7763 2 -21598.1 -7571.98 180.4 0.25 7762 7764 2 -21596.7 -7572.23 179.55 0.25 7763 7765 2 -21596.4 -7572.16 179.54 0.25 7764 7766 2 -21594.6 -7572.51 179.62 0.25 7765 7767 2 -21594.3 -7572.7 179.56 0.25 7766 7768 2 -21593.6 -7575.14 181.6 0.25 7767 7769 2 -21593.6 -7574.89 181.61 0.25 7768 7770 2 -21592.5 -7574.21 178.52 0.25 7769 7771 2 -21591.8 -7577.48 182.2 0.25 7770 7772 2 -21591.5 -7577.73 182.13 0.25 7771 7773 2 -21590.9 -7579.43 182.82 0.25 7772 7774 2 -21589.8 -7580.58 184.32 0.25 7773 7775 2 -21589.5 -7580.82 184.25 0.25 7774 7776 2 -21588.3 -7581.18 184.08 0.25 7775 7777 2 -21588 -7581.4 184.01 0.25 7776 7778 2 -21587.7 -7581.66 183.94 0.25 7777 7779 2 -21586.8 -7583.64 183.53 0.25 7778 7780 2 -21588.9 -7570.71 185.87 0.125 7779 7781 2 -21587.5 -7572.34 185.46 0.125 7780 7782 2 -21586.2 -7574.25 185.4 0.125 7781 7783 2 -21586.2 -7574.51 185.35 0.125 7782 7784 2 -21585.5 -7575.17 185.18 0.125 7783 7785 2 -21584 -7575.65 184.57 0.125 7784 7786 2 -21583.3 -7576.24 184.01 0.125 7785 7787 2 -21583.3 -7576.45 183.97 0.125 7786 7788 2 -21583.2 -7578.6 183.61 0.125 7787 7789 2 -21583 -7579.69 183.83 0.125 7788 7790 2 -21582.5 -7580.92 183.57 0.125 7789 7791 2 -21582.5 -7580.95 183.73 0.125 7790 7792 2 -21581.4 -7582.9 184.83 0.125 7791 7793 2 -21581.3 -7584.68 185.93 0.125 7792 7794 2 -21580.3 -7585.55 185.69 0.125 7793 7795 2 -21579.2 -7586.64 185.41 0.125 7794 7796 2 -21579.2 -7586.93 185.36 0.125 7795 7797 2 -21578.6 -7588.42 185.06 0.125 7796 7798 2 -21577.5 -7588.46 184.94 0.125 7797 7799 2 -21577.3 -7589.17 186.12 0.125 7798 7800 2 -21576.8 -7589.87 185.96 0.125 7799 7801 2 -21576.9 -7590.97 185.79 0.125 7800 7802 2 -21575.9 -7591.86 185.54 0.125 7801 7803 2 -21574.2 -7594.06 185.79 0.125 7802 7804 2 -21573 -7596.73 185.23 0.125 7803 7805 2 -21572.5 -7598.61 186.93 0.125 7804 7806 2 -21572 -7599.86 186.72 0.125 7805 7807 2 -21571.8 -7600.33 186.63 0.125 7806 7808 2 -21571.5 -7600.57 186.56 0.125 7807 7809 2 -21571.7 -7601.11 186.49 0.125 7808 7810 2 -21571.5 -7602.41 186.25 0.125 7809 7811 2 -21570.3 -7602.49 186.13 0.125 7810 7812 2 -21569.1 -7603.01 185.93 0.125 7811 7813 2 -21568.8 -7603.22 185.86 0.125 7812 7814 2 -21568.4 -7604.82 187.49 0.125 7813 7815 2 -21568.3 -7605.07 187.44 0.125 7814 7816 2 -21568.1 -7606.07 187.26 0.125 7815 7817 2 -21567.8 -7606.29 187.19 0.125 7816 7818 2 -21567.5 -7606.24 187.17 0.125 7817 7819 2 -21566.3 -7606.78 186.97 0.125 7818 7820 2 -21566 -7608.04 186.76 0.125 7819 7821 2 -21566 -7608.3 186.71 0.125 7820 7822 2 -21566 -7609.39 187.74 0.125 7821 7823 2 -21565.5 -7611.3 185.55 0.125 7822 7824 2 -21565.4 -7611.57 185.5 0.125 7823 7825 2 -21565 -7613.18 187.35 0.125 7824 7826 2 -21564.3 -7614.96 188.49 0.125 7825 7827 2 -21563.9 -7615.39 188.39 0.125 7826 7828 2 -21563.6 -7615.95 187.26 0.125 7827 7829 2 -21563.3 -7616.14 187.2 0.125 7828 7830 2 -21561.9 -7617.7 186.78 0.125 7829 7831 2 -21561.8 -7617.98 186.73 0.125 7830 7832 2 -21561.5 -7619.5 186.45 0.125 7831 7833 2 -21561.2 -7619.98 186.34 0.125 7832 7834 2 -21561.1 -7620.23 186.29 0.125 7833 7835 2 -21560.4 -7621.47 189.32 0.125 7834 7836 2 -21560.4 -7621.69 189.14 0.125 7835 7837 2 -21559.7 -7623.72 188.75 0.125 7836 7838 2 -21559.6 -7624.21 188.64 0.125 7837 7839 2 -21559 -7624.66 188.51 0.125 7838 7840 2 -21558.7 -7624.86 188.45 0.125 7839 7841 2 -21557.8 -7625.82 190.13 0.125 7840 7842 2 -21557.1 -7626.35 190.8 0.125 7841 7843 2 -21557 -7626.88 190.7 0.125 7842 7844 2 -21556.4 -7628.35 190.4 0.125 7843 7845 2 -21556.2 -7629.36 190.22 0.125 7844 7846 2 -21556 -7630.41 190.02 0.125 7845 7847 2 -21555.6 -7631.14 189.87 0.125 7846 7848 2 -21554.2 -7633.42 191.93 0.125 7847 7849 2 -21554.4 -7635.11 191.67 0.125 7848 7850 2 -21554.5 -7636.41 191.49 0.125 7849 7851 2 -21554.4 -7636.68 191.44 0.125 7850 7852 2 -21553.1 -7639.11 191.15 0.125 7851 7853 2 -21553 -7639.63 191.05 0.125 7852 7854 2 -21552.5 -7640.64 190.84 0.125 7853 7855 2 -21552.5 -7640.88 190.8 0.125 7854 7856 2 -21551.7 -7643.62 196.14 0.125 7855 7857 2 -21551.2 -7644.56 195.94 0.125 7856 7858 2 -21550.9 -7644.76 195.88 0.125 7857 7859 2 -21549.3 -7645.55 195.6 0.125 7858 7860 2 -21549 -7645.47 195.58 0.125 7859 7861 2 -21549 -7647.01 196.46 0.125 7860 7862 2 -21548.6 -7647.44 196.36 0.125 7861 7863 2 -21547.8 -7648.38 196.17 0.125 7862 7864 2 -21547.8 -7648.65 196.12 0.125 7863 7865 2 -21547.4 -7649.35 196.02 0.125 7864 7866 2 -21546.6 -7650.47 199.73 0.125 7865 7867 2 -21546.6 -7650.73 199.68 0.125 7866 7868 2 -21545 -7653.06 199.15 0.125 7867 7869 2 -21544.7 -7653.29 199.08 0.125 7868 7870 2 -21544.6 -7654.01 198.95 0.125 7869 7871 2 -21544.9 -7655.39 198.75 0.125 7870 7872 2 -21545.3 -7657.41 198.51 0.125 7871 7873 2 -21545.5 -7659.83 198.13 0.125 7872 7874 2 -21545.1 -7660.54 197.97 0.125 7873 7875 2 -21544.8 -7660.49 197.95 0.125 7874 7876 2 -21543.5 -7661.31 197.7 0.125 7875 7877 2 -21542.3 -7663.45 197.23 0.125 7876 7878 2 -21542 -7664.73 196.99 0.125 7877 7879 2 -21541.2 -7667.78 196.41 0.125 7878 7880 2 -21540.8 -7667.97 196.35 0.125 7879 7881 2 -21540.8 -7668.22 196.3 0.125 7880 7882 2 -21540.3 -7668.95 196.14 0.125 7881 7883 2 -21540.3 -7669.2 196.09 0.125 7882 7884 2 -21540.5 -7670.07 195.96 0.125 7883 7885 2 -21540.4 -7670.28 195.92 0.125 7884 7886 2 -21539.7 -7670.97 195.75 0.125 7885 7887 2 -21539.2 -7672.39 195.46 0.125 7886 7888 2 -21538.9 -7672.61 195.39 0.125 7887 7889 2 -21538.2 -7673.33 195.21 0.125 7888 7890 2 -21537.8 -7673.73 195.11 0.125 7889 7891 2 -21537.3 -7674.71 194.9 0.125 7890 7892 2 -21537 -7676.16 195.49 0.125 7891 7893 2 -21536.3 -7676.83 195.31 0.125 7892 7894 2 -21535.3 -7677.26 195.63 0.125 7893 7895 2 -21534 -7677.64 197.72 0.125 7894 7896 2 -21533.4 -7677.82 197.64 0.125 7895 7897 2 -21532.3 -7677.79 197.54 0.125 7896 7898 2 -21532.2 -7678.35 197.43 0.125 7897 7899 2 -21531.4 -7679.51 197.17 0.125 7898 7900 2 -21531.1 -7679.5 197.15 0.125 7899 7901 2 -21530.5 -7679.9 197.02 0.125 7900 7902 2 -21530.2 -7679.81 197.01 0.125 7901 7903 2 -21529.3 -7680.94 197.99 0.125 7902 7904 2 -21529 -7682.55 197.9 0.125 7903 7905 2 -21528.5 -7683.61 198.49 0.125 7904 7906 2 -21528 -7684.85 198.24 0.125 7905 7907 2 -21527.5 -7685.85 198.03 0.125 7906 7908 2 -21527.2 -7686.3 197.93 0.125 7907 7909 2 -21525.8 -7685.87 197.06 0.125 7908 7910 2 -21524.5 -7687.79 198.37 0.125 7909 7911 2 -21524 -7688.24 196.59 0.125 7910 7912 2 -21523.6 -7690 196.27 0.125 7911 7913 2 -21523.1 -7692.59 195.8 0.125 7912 7914 2 -21522 -7694.5 198.68 0.125 7913 7915 2 -21522 -7694.45 198.41 0.125 7914 7916 2 -21521.3 -7696.82 198.45 0.125 7915 7917 2 -21521 -7697.01 198.39 0.125 7916 7918 2 -21519.2 -7699.86 199.23 0.125 7917 7919 2 -21518 -7701.61 200.93 0.125 7918 7920 2 -21516.3 -7702.95 200.91 0.125 7919 7921 2 -21516.2 -7702.99 201.15 0.125 7920 7922 2 -21515.6 -7704.78 201.08 0.125 7921 7923 2 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214.96 0.125 7945 7947 2 -21503.8 -7731.67 214.8 0.125 7946 7948 2 -21503.5 -7731.91 214.74 0.125 7947 7949 2 -21503 -7732.63 214.57 0.125 7948 7950 2 -21502.3 -7733.54 214.36 0.125 7949 7951 2 -21500.7 -7734.84 217.09 0.125 7950 7952 2 -21500.7 -7735.06 217.05 0.125 7951 7953 2 -21499.9 -7736.04 217.09 0.125 7952 7954 2 -21499.6 -7736.26 217.02 0.125 7953 7955 2 -21497.3 -7739.02 218.11 0.125 7954 7956 2 -21495.7 -7740.8 218.87 0.125 7955 7957 2 -21495.7 -7740.87 219.29 0.125 7956 7958 2 -21494.3 -7743.26 218.85 0.125 7957 7959 2 -21494.1 -7745.27 216.43 0.125 7958 7960 2 -21492.5 -7746.48 215.72 0.125 7959 7961 2 -21491.7 -7747.39 215.31 0.125 7960 7962 2 -21491.8 -7747.24 214.4 0.125 7961 7963 2 -21490.9 -7747.77 213.7 0.125 7962 7964 2 -21490.2 -7750.03 213.22 0.125 7963 7965 2 -21489.6 -7750.3 212.45 0.125 7964 7966 2 -21489 -7750.78 211.09 0.125 7965 7967 2 -21488.8 -7750.94 211.04 0.125 7966 7968 2 -21488 -7751.87 210.82 0.125 7967 7969 2 -21487.4 -7752.14 209.78 0.125 7968 7970 2 -21487.2 -7752.3 209.47 0.125 7969 7971 2 -21487.1 -7752.56 209.43 0.125 7970 7972 2 -21486.3 -7753.98 208.92 0.125 7971 7973 2 -21485.9 -7754.41 208.81 0.125 7972 7974 2 -21484.9 -7755.29 208.56 0.125 7973 7975 2 -21484 -7755.67 208.41 0.125 7974 7976 2 -21483.4 -7755.52 208.39 0.125 7975 7977 2 -21482.5 -7756.11 208.18 0.125 7976 7978 2 -21482.2 -7756.33 208.11 0.125 7977 7979 2 -21481.8 -7756.54 208.02 0.125 7978 7980 2 -21481.5 -7756.97 207.64 0.125 7979 7981 2 -21481.1 -7757.16 207.57 0.125 7980 7982 2 -21480.9 -7757.19 206.55 0.125 7981 7983 2 -21481 -7757.53 205.21 0.125 7982 7984 2 -21480 -7759.18 204.84 0.125 7983 7985 2 -21479.9 -7759.94 204.7 0.125 7984 7986 2 -21479.3 -7761.69 204.3 0.125 7985 7987 2 -21479.4 -7761.62 203.88 0.125 7986 7988 2 -21478.4 -7762.11 204.46 0.125 7987 7989 2 -21478.2 -7762.86 204.32 0.125 7988 7990 2 -21477.9 -7763.08 204.25 0.125 7989 7991 2 -21477.9 -7763.35 204.21 0.125 7990 7992 2 -21477.8 -7763.58 204.16 0.125 7991 7993 2 -21476.9 -7765.52 203.75 0.125 7992 7994 2 -21476.5 -7765.98 203.5 0.125 7993 7995 2 -21476.3 -7765.94 203.49 0.125 7994 7996 2 -21475.6 -7766.35 203.36 0.125 7995 7997 2 -21475 -7766.5 203.28 0.125 7996 7998 2 -21474.5 -7766.4 203.24 0.125 7997 7999 2 -21473.9 -7766.49 203.17 0.125 7998 8000 2 -21473 -7766.6 203.07 0.125 7999 8001 2 -21472.7 -7766.54 203.05 0.125 8000 8002 2 -21472 -7767.19 202.88 0.125 8001 8003 2 -21471.4 -7768.22 204.53 0.125 8002 8004 2 -21471.1 -7770.03 204.2 0.125 8003 8005 2 -21470.5 -7771.53 203.89 0.125 8004 8006 2 -21469 -7771.44 201.94 0.125 8005 8007 2 -21469 -7771.37 201.54 0.125 8006 8008 2 -21468.7 -7773.11 201.11 0.125 8007 8009 2 -21467.1 -7773.83 200.59 0.125 8008 8010 2 -21465.8 -7774.9 200.29 0.125 8009 8011 2 -21464.8 -7776.15 199.12 0.125 8010 8012 2 -21463 -7779.13 196.18 0.125 8011 8013 2 -21462.4 -7780.85 195.84 0.125 8012 8014 2 -21461.6 -7783.38 195.33 0.125 8013 8015 2 -21460.5 -7784.75 194.7 0.125 8014 8016 2 -21460.3 -7784.37 194.38 0.125 8015 8017 2 -21457.8 -7787.26 193.24 0.125 8016 8018 2 -21457.8 -7787.24 193.08 0.125 8017 8019 2 -21456.5 -7788.45 192.16 0.125 8018 8020 2 -21455.7 -7790.1 190.13 0.125 8019 8021 2 -21455.5 -7790.06 190.12 0.125 8020 8022 2 -21455.2 -7793.18 189.56 0.125 8021 8023 2 -21454.4 -7793.88 189.51 0.125 8022 8024 2 -21454.5 -7794.88 185.83 0.125 8023 8025 2 -21454.2 -7795.92 182.69 0.125 8024 8026 2 -21453 -7797.31 178.29 0.125 8025 8027 2 -21452.5 -7797.2 178.15 0.125 8026 8028 2 -21452.4 -7797.43 178.1 0.125 8027 8029 2 -21451.1 -7798.5 177.8 0.125 8028 8030 2 -21450.7 -7798.99 177.69 0.125 8029 8031 2 -21450.4 -7799.49 177.57 0.125 8030 8032 2 -21448.9 -7802.92 176.86 0.125 8031 8033 2 -21448 -7804.38 176.77 0.125 8032 8034 2 -21446.4 -7805.33 176.44 0.125 8033 8035 2 -21446 -7805.96 175.4 0.125 8034 8036 2 -21445.9 -7807.49 173.43 0.125 8035 8037 2 -21445.9 -7807.38 172.76 0.125 8036 8038 2 -21445.4 -7808.08 171.23 0.125 8037 8039 2 -21443.3 -7810.02 170.67 0.125 8038 8040 2 -21441.7 -7812.16 170.17 0.125 8039 8041 2 -21441.1 -7812.98 168.1 0.125 8040 8042 2 -21440.1 -7814.13 167.7 0.125 8041 8043 2 -21439.7 -7814.56 167.59 0.125 8042 8044 2 -21438.6 -7816.05 167.83 0.125 8043 8045 2 -21437.2 -7817.49 166.3 0.125 8044 8046 2 -21437.3 -7817.41 165.82 0.125 8045 8047 2 -21436.2 -7819.34 162.44 0.125 8046 8048 2 -21435.9 -7819.5 162.27 0.125 8047 8049 2 -21435.6 -7819.72 162.2 0.125 8048 8050 2 -21434 -7822.06 161.67 0.125 8049 8051 2 -21433.7 -7822.63 160.61 0.125 8050 8052 2 -21433.3 -7824.42 160.28 0.125 8051 8053 2 -21432.8 -7826.17 159.94 0.125 8052 8054 2 -21432.6 -7827.45 158.09 0.125 8053 8055 2 -21432.6 -7827.51 158.48 0.125 8054 8056 2 -21431.3 -7829.68 158.01 0.125 8055 8057 2 -21430.6 -7831.18 155.22 0.125 8056 8058 2 -21430.1 -7834.31 154.65 0.125 8057 8059 2 -21428.6 -7836.14 154.17 0.125 8058 8060 2 -21428.5 -7836.38 154.13 0.125 8059 8061 2 -21427.2 -7838.78 153.61 0.125 8060 8062 2 -21426.1 -7840.23 153.59 0.125 8061 8063 2 -21426.1 -7840.22 153.49 0.125 8062 8064 2 -21425.8 -7842.11 152.15 0.125 8063 8065 2 -21426.1 -7842.16 152.17 0.125 8064 8066 2 -21425.2 -7843.85 151.8 0.125 8065 8067 2 -21425.2 -7844.08 151.76 0.125 8066 8068 2 -21424.6 -7845.57 151.46 0.125 8067 8069 2 -21424.6 -7845.84 151.42 0.125 8068 8070 2 -21423.7 -7846.22 151.27 0.125 8069 8071 2 -21423.5 -7846.72 151.17 0.125 8070 8072 2 -21423 -7847.98 150.92 0.125 8071 8073 2 -21423 -7848.18 150.88 0.125 8072 8074 2 -21422.7 -7849.39 147.22 0.125 8073 8075 2 -21422.6 -7849.66 147.17 0.125 8074 8076 2 -21422.2 -7850.64 146.96 0.125 8075 8077 2 -21422.1 -7850.86 146.92 0.125 8076 8078 2 -21421.6 -7852.09 146.67 0.125 8077 8079 2 -21421.6 -7852.28 146.11 0.125 8078 8080 2 -21421.7 -7852.64 143.43 0.125 8079 8081 2 -21421.7 -7852.9 143.38 0.125 8080 8082 2 -21420.9 -7853.54 143.15 0.125 8081 8083 2 -21420.9 -7854.07 143.06 0.125 8082 8084 2 -21420.5 -7854.27 142.99 0.125 8083 8085 2 -21420.2 -7854.72 142.88 0.125 8084 8086 2 -21419.6 -7854.89 142.8 0.125 8085 8087 2 -21419.5 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0.125 8109 8111 2 -21403.4 -7873.8 132.35 0.125 8110 8112 2 -21403.2 -7874.33 132.24 0.125 8111 8113 2 -21402.7 -7875.79 131.95 0.125 8112 8114 2 -21401.5 -7877.73 131.52 0.125 8113 8115 2 -21400.8 -7878.41 131.34 0.125 8114 8116 2 -21400.2 -7878.78 131.22 0.125 8115 8117 2 -21400.1 -7879.31 131.12 0.125 8116 8118 2 -21399.7 -7879.74 131.02 0.125 8117 8119 2 -21399.6 -7880.03 130.96 0.125 8118 8120 2 -21399.3 -7880.19 130.91 0.125 8119 8121 2 -21398.2 -7882.44 127.48 0.125 8120 8122 2 -21397.7 -7882.96 129.58 0.125 8121 8123 2 -21397.6 -7883.21 129.53 0.125 8122 8124 2 -21395.6 -7885.19 129.01 0.125 8123 8125 2 -21395.3 -7885.37 128.95 0.125 8124 8126 2 -21394.6 -7885.8 128.82 0.125 8125 8127 2 -21392.4 -7888.86 128.1 0.125 8126 8128 2 -21390.7 -7890.37 127.69 0.125 8127 8129 2 -21371.9 -7915.01 111.88 0.125 8128 8130 2 -21370.8 -7917.77 111.39 0.125 8129 8131 2 -21370.9 -7919.05 111.18 0.125 8130 8132 2 -21370.3 -7920.41 111.8 0.125 8131 8133 2 -21369.5 -7921.38 111.57 0.125 8132 8134 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8873 2 -22010.6 -8024.82 45.05 0.25 8872 8874 2 -22011.6 -8025.46 41.74 0.25 8873 8875 2 -22010.8 -8025.53 36.72 0.25 8874 8876 2 -22010.1 -8026.98 30.34 0.25 8875 8877 2 -22008.2 -8028.44 28.26 0.25 8876 8878 2 -22006.6 -8029.78 28.09 0.25 8877 8879 2 -22006.2 -8030.41 22.92 0.25 8878 8880 2 -22006.8 -8027.7 17.88 0.25 8879 8881 2 -22007.8 -8026.71 17.65 0.25 8880 8882 2 -22010 -8025.53 13.49 0.25 8881 8883 2 -22011.3 -8025.26 10.98 0.25 8882 8884 2 -22012.7 -8026.03 9.16 0.25 8883 8885 2 -22015 -8026.12 4.47 0.25 8884 8886 2 -22014.2 -8027.12 4.22 0.25 8885 8887 2 -22014.9 -8027.06 1.95 0.25 8886 8888 2 -22016.2 -8026.76 0.5 0.25 8887 8889 2 -22018.8 -8026.33 -4.04 0.25 8888 8890 2 -22019 -8027.98 -5.54 0.25 8889 8891 2 -22020.2 -8028.9 -8.97 0.25 8890 8892 2 -22021.1 -8029.37 -11.74 0.25 8891 8893 2 -22022.7 -8029.97 -15.83 0.25 8892 8894 2 -22024.1 -8031.56 -18.78 0.25 8893 8895 2 -22024.3 -8033.73 -19.14 0.25 8894 8896 2 -22024.2 -8034.71 -19.51 0.25 8895 8897 2 -22024.1 -8035.59 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66.98 0.125 9223 9225 2 -21995.8 -7981.33 70.49 0.125 9224 9226 2 -21995.2 -7982.82 70.19 0.125 9225 9227 2 -21995 -7985.47 76.18 0.125 9226 9228 2 -21994 -7988.38 82.63 0.125 9227 9229 2 -21997.3 -7987.65 84.02 0.125 9228 9230 2 -21997.1 -7989.2 83.75 0.125 9229 9231 2 -21996.6 -7990.49 83.61 0.125 9230 9232 2 -21996.5 -7992.78 84.48 0.125 9231 9233 2 -21996.2 -7995.35 88.36 0.125 9232 9234 2 -21996 -7997.15 87.81 0.125 9233 9235 2 -21996.7 -8001.86 92.28 0.125 9234 9236 2 -21997.4 -8004.48 94.33 0.125 9235 9237 2 -21998.3 -8006.86 96.32 0.125 9236 9238 2 -21998.8 -8008.66 98.47 0.125 9237 9239 2 -21999.4 -8008.68 93.68 0.125 9238 9240 2 -21999 -8010.67 95.79 0.125 9239 9241 2 -21999.5 -8012.71 96.14 0.125 9240 9242 2 -22000.5 -8013.44 96.57 0.125 9241 9243 2 -22000.2 -8015.42 100.2 0.125 9242 9244 2 -22001.7 -8016.36 102.93 0.125 9243 9245 2 -22003.3 -8016.59 103.05 0.125 9244 9246 2 -22005.3 -8016.86 103.19 0.125 9245 9247 2 -22007.7 -8016.87 104.48 0.125 9246 9248 2 -22010.5 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0.37 9867 9869 3 -21955.8 -7378.16 166.07 0.37 9868 9870 3 -21957.9 -7377.5 166.32 0.37 9869 9871 3 -21959.5 -7376.81 165.57 0.37 9870 9872 3 -21960.2 -7376.14 165.75 0.37 9871 9873 3 -21962.2 -7375.96 165.97 0.37 9872 9874 3 -21966.1 -7373.74 160.55 0.37 9873 9875 3 -21969.4 -7373.79 160.83 0.37 9874 9876 3 -21972.2 -7372.49 161.31 0.37 9875 9877 3 -21974.4 -7371.22 161.7 0.37 9876 9878 3 -21976.3 -7369.57 158.21 0.37 9877 9879 3 -21976.6 -7369.64 158.22 0.37 9878 9880 3 -21978.2 -7368.93 157.14 0.37 9879 9881 3 -21979.5 -7368.57 155.64 0.37 9880 9882 3 -21980.3 -7367.15 155.95 0.37 9881 9883 3 -21981.7 -7367.13 156.09 0.37 9882 9884 3 -21983.3 -7366.32 156.19 0.37 9883 9885 3 -21984.6 -7365.27 155.26 0.37 9884 9886 3 -21984.8 -7364.27 155.44 0.37 9885 9887 3 -21987.5 -7362.1 151.58 0.37 9886 9888 3 -21990.3 -7360.41 147.1 0.37 9887 9889 3 -21990.2 -7360.62 147.06 0.37 9888 9890 3 -21991 -7361.03 147.06 0.37 9889 9891 3 -21993.5 -7361.37 143.49 0.37 9890 9892 3 -21994.9 -7361.14 143.66 0.37 9891 9893 3 -21994.9 -7361.41 143.61 0.37 9892 9894 3 -21996.8 -7361.06 141.7 0.37 9893 9895 3 -21998.1 -7361.27 141.77 0.37 9894 9896 3 -21998.5 -7361.34 141.79 0.37 9895 9897 3 -22000 -7360.71 141.23 0.37 9896 9898 3 -22001.1 -7360.6 141.35 0.37 9897 9899 3 -22001.7 -7360.66 141.38 0.37 9898 9900 3 -22003.7 -7359.88 139.63 0.37 9899 9901 3 -22004.6 -7359.59 139.74 0.37 9900 9902 3 -22006.3 -7358.08 137.75 0.37 9901 9903 3 -22007.7 -7358.34 137.84 0.37 9902 9904 3 -22008 -7358.4 137.85 0.37 9903 9905 3 -22008.6 -7358.26 137.92 0.37 9904 9906 3 -22009.3 -7357.22 137.54 0.37 9905 9907 3 -22011.7 -7356.37 133.72 0.37 9906 9908 3 -22012.6 -7356.01 133.86 0.37 9907 9909 3 -22013.8 -7355.44 134.07 0.37 9908 9910 3 -22014.9 -7355.07 132.39 0.37 9909 9911 3 -22016.3 -7355.29 132.37 0.37 9910 9912 3 -22017.1 -7355.12 132.22 0.37 9911 9913 3 -22017.2 -7354.86 132.27 0.37 9912 9914 3 -22017.6 -7354.1 132.43 0.37 9913 9915 3 -22017.7 -7353.91 132.47 0.37 9914 9916 3 -22018.1 -7352.95 132.67 0.37 9915 9917 3 -22018.1 -7352.67 132.72 0.37 9916 9918 3 -22019.5 -7352.84 132.63 0.37 9917 9919 3 -22020.8 -7352.3 132.84 0.37 9918 9920 3 -22022.7 -7350.84 133.26 0.37 9919 9921 3 -22022.6 -7348.69 132.15 0.125 9920 9922 3 -22022.2 -7347.3 132.35 0.125 9921 9923 3 -22022.3 -7347.02 132.4 0.125 9922 9924 3 -22022 -7345.43 132.64 0.125 9923 9925 3 -22022 -7345.17 132.69 0.125 9924 9926 3 -22021.5 -7343.43 132.64 0.125 9925 9927 3 -22021.2 -7342.1 132.83 0.125 9926 9928 3 -22021.4 -7340.84 133.06 0.125 9927 9929 3 -22021.7 -7340.54 133.13 0.125 9928 9930 3 -22022.4 -7339.89 133.3 0.125 9929 9931 3 -21927 -7393.03 202.75 0.865 9575 9932 3 -21928.2 -7393.8 199.97 0.865 9931 9933 3 -21928.3 -7393.58 198.63 0.865 9932 9934 3 -21929.7 -7395.3 195.02 0.865 9933 9935 3 -21929.8 -7395.28 194.95 0.865 9934 9936 3 -21932 -7397.47 194.76 0.865 9935 9937 3 -21933.8 -7399.34 193.24 0.865 9936 9938 3 -21935.7 -7400.31 192.87 0.865 9937 9939 3 -21936.1 -7400.19 191.97 0.865 9938 9940 3 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141.17 0.37 10010 10012 3 -22031.6 -7378.99 141.35 0.37 10011 10013 3 -22031.6 -7379.27 141.3 0.37 10012 10014 3 -22032.9 -7379.52 141.34 0.37 10013 10015 3 -22034.1 -7379.43 141.42 0.37 10014 10016 3 -22035 -7378.83 141.61 0.37 10015 10017 3 -22037.6 -7378.07 139.77 0.37 10016 10018 3 -22038.6 -7376.95 140.05 0.37 10017 10019 3 -22038.9 -7377.01 140.06 0.37 10018 10020 3 -22040.5 -7375.99 140.38 0.37 10019 10021 3 -22040.5 -7376.22 140.34 0.37 10020 10022 3 -22043.1 -7375.49 140.9 0.25 10021 10023 3 -22043.4 -7375.5 140.93 0.25 10022 10024 3 -22046.2 -7375.44 141.2 0.25 10023 10025 3 -22046.5 -7375.23 141.26 0.25 10024 10026 3 -22049.5 -7376.06 141.57 0.25 10025 10027 3 -22049.5 -7375.79 141.62 0.25 10026 10028 3 -22054.6 -7375.76 141.21 0.25 10027 10029 3 -22054.6 -7375.78 141.35 0.25 10028 10030 3 -22055.9 -7376.41 142.21 0.25 10029 10031 3 -22058.1 -7376.53 142.4 0.25 10030 10032 3 -22059.2 -7376.83 142.96 0.125 10031 10033 3 -21941.3 -7388.02 179.77 0.37 9953 10034 3 -21943.2 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146.6 0.37 10147 10149 3 -21965.3 -7502.1 145.85 0.37 10148 10150 3 -21965.3 -7504.24 144.29 0.37 10149 10151 3 -21965.2 -7504.76 144.2 0.37 10150 10152 3 -21964.6 -7507.14 142.63 0.37 10151 10153 3 -21964.7 -7507.05 142.09 0.37 10152 10154 3 -21964.5 -7508.93 143.86 0.37 10153 10155 3 -21964.9 -7508.71 142.39 0.37 10154 10156 3 -21964.6 -7510.75 141.98 0.37 10155 10157 3 -21965 -7511.88 141.83 0.25 10156 10158 3 -21966.1 -7513.37 141.69 0.25 10157 10159 3 -21966.3 -7513.73 141.65 0.25 10158 10160 3 -21966.5 -7514.54 141.53 0.25 10159 10161 3 -21966.8 -7514.32 141.6 0.37 10160 10162 3 -21967 -7516.22 141.31 0.37 10161 10163 3 -21967.5 -7516.64 137.22 0.37 10162 10164 3 -21967.7 -7517.18 137.15 0.37 10163 10165 3 -21968.1 -7518.09 137.04 0.25 10164 10166 3 -21968.1 -7519.64 136.78 0.25 10165 10167 3 -21967.9 -7520.93 136.55 0.37 10166 10168 3 -21968.1 -7522 134.87 0.37 10167 10169 3 -21968.8 -7523.13 134.43 0.37 10168 10170 3 -21969 -7523.95 134.15 0.25 10169 10171 3 -21969 -7523.86 133.6 0.25 10170 10172 3 -21969.7 -7525.39 132.62 0.25 10171 10173 3 -21970.1 -7526.58 131.3 0.25 10172 10174 3 -21971.2 -7437.49 179.1 0.62 10094 10175 3 -21972.6 -7437.15 179.29 0.495 10174 10176 3 -21974.4 -7437.72 177.81 0.37 10175 10177 3 -21976.1 -7439 179.31 0.37 10176 10178 3 -21976.3 -7439.79 179.2 0.37 10177 10179 3 -21976.4 -7440.88 179.03 0.37 10178 10180 3 -21977.2 -7441.78 178.95 0.37 10179 10181 3 -21977.9 -7444.48 178.58 0.37 10180 10182 3 -21977.9 -7444.79 178.52 0.37 10181 10183 3 -21978 -7446.11 178.31 0.37 10182 10184 3 -21979.1 -7448.38 178.04 0.37 10183 10185 3 -21980.2 -7449.35 177.98 0.37 10184 10186 3 -21981.7 -7450.77 177.38 0.37 10185 10187 3 -21982.3 -7452.44 176.85 0.37 10186 10188 3 -21982.5 -7453.23 176.74 0.37 10187 10189 3 -21983.3 -7454.09 176.29 0.25 10188 10190 3 -21983.3 -7454.33 176.24 0.37 10189 10191 3 -21983.7 -7455.22 176.14 0.37 10190 10192 3 -21984.4 -7456.85 175.68 0.25 10191 10193 3 -21985.1 -7458.26 175.49 0.25 10192 10194 3 -21985.1 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3 -22032.8 -7455.34 211.06 0.25 10239 10241 3 -22037.5 -7455.43 209.93 0.25 10240 10242 3 -21921.7 -7383.42 213.66 1.24 1 10243 3 -21923.2 -7382.39 209.65 1.24 10242 10244 3 -21924.1 -7381.74 209.85 1.24 10243 10245 3 -21925.6 -7380.85 210.14 1.24 10244 10246 3 -21926.9 -7379.96 210.4 1.24 10245 10247 3 -21928.8 -7378.34 207.58 0.99 10246 10248 3 -21930.9 -7377.29 207.91 0.99 10247 10249 3 -21932.4 -7375.64 205.16 1.24 10248 10250 3 -21934 -7374.55 205.45 1.115 10249 10251 3 -21935.5 -7373.21 202.78 0.745 10250 10252 3 -21936.7 -7372.54 203 0.495 10251 10253 3 -21938.6 -7372 201.76 0.62 10252 10254 3 -21939.5 -7371.59 201.91 1.115 10253 10255 3 -21939.5 -7371.88 201.86 1.115 10254 10256 3 -21940.8 -7372.35 201.91 1.115 10255 10257 3 -21942 -7371.97 202.08 0.865 10256 10258 3 -21943.4 -7370.76 200.38 0.745 10257 10259 3 -21944.8 -7370.95 200.48 0.745 10258 10260 3 -21946.8 -7370.94 200.66 0.62 10259 10261 3 -21948.3 -7370.37 200.9 0.62 10260 10262 3 -21950.1 -7369.7 197.33 0.62 10261 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132.48 0.37 10284 10286 3 -21971.9 -7371.04 132.03 0.37 10285 10287 3 -21972.8 -7369.79 130.31 0.37 10286 10288 3 -21974.7 -7368.19 122.62 0.25 10287 10289 3 -21976 -7366.19 113.44 0.25 10288 10290 3 -21976 -7366.76 119.81 0.25 10289 10291 3 -21974.5 -7365.27 119.92 0.495 10290 10292 3 -21974.8 -7362.64 114.04 0.495 10291 10293 3 -21974.6 -7362.33 114.07 0.495 10292 10294 3 -21973.6 -7359.71 109.52 0.37 10293 10295 3 -21972.6 -7360.64 109.27 0.37 10294 10296 3 -21972.3 -7358 102.4 0.37 10295 10297 3 -21971 -7356.06 97.33 0.37 10296 10298 3 -21970.5 -7354.05 97.36 0.37 10297 10299 3 -21970.7 -7352.83 97.96 0.125 10298 10300 3 -21952.5 -7368.46 197.76 0.62 10263 10301 3 -21954.2 -7368.42 197.93 0.62 10300 10302 3 -21956.2 -7368.43 198.11 0.62 10301 10303 3 -21957.5 -7367.27 198.42 0.62 10302 10304 3 -21957.5 -7367.01 198.47 0.62 10303 10305 3 -21958.7 -7366.29 194.9 0.62 10304 10306 3 -21959.3 -7367.69 196.16 0.495 10305 10307 3 -21960.6 -7368.11 196.22 0.495 10306 10308 3 -21961.9 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10331 3 -21981.9 -7376.27 148.63 0.25 10330 10332 3 -21981.8 -7376.51 148.58 0.25 10331 10333 3 -21983.1 -7377.19 148.15 0.25 10332 10334 3 -21984.6 -7377.83 145.87 0.25 10333 10335 3 -21985 -7378.95 145.61 0.25 10334 10336 3 -21986.8 -7379.68 145.09 0.25 10335 10337 3 -21988.2 -7379 142.85 0.25 10336 10338 3 -21990.1 -7378.81 140.32 0.25 10337 10339 3 -21991.8 -7379.96 137.5 0.25 10338 10340 3 -21993 -7381.56 136.39 0.25 10339 10341 3 -21993 -7381.52 136.15 0.25 10340 10342 3 -21994.7 -7382.21 135.12 0.25 10341 10343 3 -21995.7 -7382.21 132.88 0.25 10342 10344 3 -21995.8 -7382.03 131.81 0.25 10343 10345 3 -21997.7 -7382.79 129.67 0.25 10344 10346 3 -21999.2 -7383.45 127.58 0.25 10345 10347 3 -22000.2 -7384.72 130.65 0.25 10346 10348 3 -22000.3 -7384.59 129.82 0.25 10347 10349 3 -22000.1 -7386.16 128.06 0.25 10348 10350 3 -21999.8 -7387.92 127.74 0.25 10349 10351 3 -22001.9 -7381.27 117.38 0.25 10348 10352 3 -22002.7 -7380.12 117.64 0.25 10351 10353 3 -22003 -7380.16 117.66 0.25 10352 10354 3 -21966.1 -7369.48 192.55 0.62 10310 10355 3 -21968.1 -7369.54 192.73 0.495 10354 10356 3 -21969.8 -7369.24 192.94 0.495 10355 10357 3 -21971.6 -7368.7 193.2 0.495 10356 10358 3 -21972.8 -7368.64 193.31 0.495 10357 10359 3 -21973 -7368.61 193.35 0.495 10358 10360 3 -21975 -7368.91 193.48 0.495 10359 10361 3 -21975.6 -7368.1 190.18 0.495 10360 10362 3 -21976.9 -7368.85 190.18 0.37 10361 10363 3 -21977.3 -7369.14 188.54 0.37 10362 10364 3 -21975.1 -7369.05 188.35 0.37 10363 10365 3 -21977.5 -7368.88 186.63 0.37 10364 10366 3 -21977.6 -7368.8 186.17 0.37 10365 10367 3 -21977.4 -7367.18 184.9 0.37 10366 10368 3 -21978.4 -7366.74 182.89 0.37 10367 10369 3 -21978.5 -7366.66 182.36 0.37 10368 10370 3 -21978.8 -7367.48 179.28 0.37 10369 10371 3 -21980.9 -7367.29 177.78 0.37 10370 10372 3 -21981 -7367.13 176.83 0.37 10371 10373 3 -21982.5 -7367.43 175.63 0.37 10372 10374 3 -21982.7 -7367.5 175.64 0.37 10373 10375 3 -21984.5 -7366.58 173.5 0.37 10374 10376 3 -21986.2 -7366.63 173.65 0.37 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148.91 0.37 10398 10400 3 -22012.5 -7356.46 148.93 0.37 10399 10401 3 -22013.7 -7356.16 149.1 0.37 10400 10402 3 -22014.1 -7355.42 149.26 0.37 10401 10403 3 -22014.1 -7355.16 149.3 0.37 10402 10404 3 -22015.4 -7354.31 146.19 0.37 10403 10405 3 -22015.3 -7354.52 146.15 0.37 10404 10406 3 -22016.5 -7353.97 146.35 0.37 10405 10407 3 -22018.8 -7353.85 146.59 0.37 10406 10408 3 -22019.5 -7354.53 146.54 0.37 10407 10409 3 -22020.6 -7353.22 143.07 0.37 10408 10410 3 -22022 -7352.92 143.26 0.37 10409 10411 3 -22023.3 -7352.57 136.94 0.37 10410 10412 3 -22023.6 -7352.65 136.96 0.37 10411 10413 3 -22024.9 -7352.86 134.16 0.37 10412 10414 3 -22026.2 -7352.44 129.25 0.37 10413 10415 3 -22026.2 -7352.41 129.1 0.37 10414 10416 3 -22022.3 -7352.32 131.79 0.37 10415 10417 3 -22022.4 -7352.3 131.71 0.37 10416 10418 3 -22021.4 -7350.79 131.66 0.37 10417 10419 3 -22019.1 -7350.01 126.05 0.37 10418 10420 3 -22018.7 -7350.18 122.85 0.37 10419 10421 3 -21977.4 -7368.37 191.9 0.495 10361 10422 3 -21979.2 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3 -22049.2 -7370.77 187.51 0.37 10467 10469 3 -22051.3 -7372.7 185.75 0.25 10468 10470 3 -22052.7 -7374.3 185.62 0.25 10469 10471 3 -22056.3 -7377.22 183.38 0.25 10470 10472 3 -22057 -7377.88 183.33 0.25 10471 10473 3 -22057.3 -7378.12 183.32 0.25 10472 10474 3 -22057.4 -7379.47 183.1 0.25 10473 10475 3 -22059 -7379.69 178.35 0.25 10474 10476 3 -22061.1 -7380.84 178.36 0.25 10475 10477 3 -22061.1 -7380.76 177.86 0.25 10476 10478 3 -22064 -7380.94 173.24 0.25 10477 10479 3 -22067.8 -7381.66 173.48 0.25 10478 10480 3 -22072 -7383.21 173.62 0.25 10479 10481 3 -22074.4 -7384.43 173.64 0.25 10480 10482 3 -21960.3 -7366.73 199.27 0.495 10305 10483 3 -21962.1 -7366.37 199.49 0.495 10482 10484 3 -21962.9 -7364.75 198.7 0.495 10483 10485 3 -21963.6 -7364.05 198.88 0.495 10484 10486 3 -21964.5 -7363.13 199.12 0.495 10485 10487 3 -21964.6 -7362.92 199.16 0.495 10486 10488 3 -21966.5 -7363.13 199.31 0.495 10487 10489 3 -21968.4 -7361.92 198.77 0.37 10488 10490 3 -21968.5 -7361.18 198.9 0.37 10489 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0.37 10512 10514 3 -21975.8 -7343.71 202.99 0.37 10513 10515 3 -21976.2 -7342.69 203.14 0.37 10514 10516 3 -21978.5 -7341.02 206.06 0.37 10515 10517 3 -21978.9 -7340.32 206.21 0.37 10516 10518 3 -21980.2 -7339.42 206.48 0.37 10517 10519 3 -21980.8 -7338.98 206.61 0.37 10518 10520 3 -21980.8 -7338.73 206.65 0.37 10519 10521 3 -21981.2 -7338.01 206.81 0.37 10520 10522 3 -21983 -7336.92 207.16 0.37 10521 10523 3 -21984.6 -7337.66 207.18 0.37 10522 10524 3 -21984.7 -7337.41 207.23 0.37 10523 10525 3 -21985 -7337.21 207.29 0.37 10524 10526 3 -21985 -7336.95 207.34 0.37 10525 10527 3 -21985.8 -7335.97 209.25 0.37 10526 10528 3 -21986 -7334.97 209.43 0.37 10527 10529 3 -21987.8 -7333.93 209.78 0.37 10528 10530 3 -21989 -7333.81 209.9 0.37 10529 10531 3 -21990.3 -7332.81 209.43 0.37 10530 10532 3 -21991.5 -7331.9 209.7 0.37 10531 10533 3 -21994.3 -7330.44 210.2 0.37 10532 10534 3 -21996 -7329.9 210.46 0.37 10533 10535 3 -21998 -7328.98 212.89 0.37 10534 10536 3 -22000 -7328.94 213.08 0.37 10535 10537 3 -22000.2 -7328.71 213.15 0.37 10536 10538 3 -22002.2 -7328.48 213.37 0.37 10537 10539 3 -22003.2 -7327.42 214.38 0.495 10538 10540 3 -22005.3 -7326.42 214.75 0.37 10539 10541 3 -22006.6 -7325.5 215.02 0.37 10540 10542 3 -22008.3 -7325.18 215.23 0.37 10541 10543 3 -22009.1 -7323.74 218.81 0.37 10542 10544 3 -22010.4 -7323.28 221.48 0.37 10543 10545 3 -22013.2 -7322.14 220.61 0.37 10544 10546 3 -22014.2 -7321.21 220.86 0.37 10545 10547 3 -22014.5 -7320.98 220.92 0.37 10546 10548 3 -22014.7 -7319.45 221.2 0.37 10547 10549 3 -22015.2 -7317.67 221.54 0.37 10548 10550 3 -22015.5 -7317.7 221.56 0.37 10549 10551 3 -22017.2 -7316.87 223.23 0.37 10550 10552 3 -22018.2 -7315.95 223.48 0.37 10551 10553 3 -22019.4 -7314.77 223.79 0.37 10552 10554 3 -22020.5 -7313.07 224.17 0.37 10553 10555 3 -22021.7 -7312.18 224.43 0.37 10554 10556 3 -22023.3 -7311.73 226.85 0.37 10555 10557 3 -22024.7 -7310.33 227.21 0.37 10556 10558 3 -22026.5 -7308.48 229.34 0.37 10557 10559 3 -22028.2 -7307.76 230.18 0.37 10558 10560 3 -22028.5 -7307.79 230.2 0.37 10559 10561 3 -22030.5 -7305.03 231.49 0.37 10560 10562 3 -22032 -7304.2 231.76 0.37 10561 10563 3 -22034.2 -7302.85 232.19 0.37 10562 10564 3 -22034.2 -7300.74 232.54 0.37 10563 10565 3 -22034.2 -7300.49 232.58 0.37 10564 10566 3 -22036 -7299.66 232.88 0.37 10565 10567 3 -22037.6 -7298.22 232.75 0.37 10566 10568 3 -22038.9 -7296.85 233.1 0.37 10567 10569 3 -22039 -7296.56 233.15 0.37 10568 10570 3 -22039.4 -7295.04 233.45 0.25 10569 10571 3 -22040.4 -7294.13 233.69 0.25 10570 10572 3 -22040.7 -7293.91 233.76 0.25 10571 10573 3 -22041.3 -7294.05 237.21 0.37 10572 10574 3 -22041.6 -7294.1 237.23 0.37 10573 10575 3 -22042.2 -7293.66 237.36 0.37 10574 10576 3 -22043.2 -7292.48 237.65 0.25 10575 10577 3 -22043.2 -7292.19 237.7 0.25 10576 10578 3 -22043.9 -7290.99 237.97 0.37 10577 10579 3 -22045 -7289.54 238.3 0.25 10578 10580 3 -22045.3 -7289.56 238.32 0.25 10579 10581 3 -22045.7 -7289.92 243.13 0.37 10580 10582 3 -22046.9 -7289.82 243.26 0.37 10581 10583 3 -22048.4 -7289.02 243.53 0.37 10582 10584 3 -22048.7 -7288.75 243.6 0.37 10583 10585 3 -22050 -7288.69 245.45 0.37 10584 10586 3 -22052.3 -7288.09 246.58 0.125 10585 10587 3 -22052.2 -7288.15 246.99 0.125 10586 10588 3 -22055.5 -7286.96 251.63 0.125 10587 10589 3 -21972.6 -7352.61 197.08 0.37 10500 10590 3 -21973.4 -7350.85 197.26 0.37 10589 10591 3 -21974 -7348.45 192.56 0.37 10590 10592 3 -21969.2 -7347.82 192.22 0.37 10591 10593 3 -21969.3 -7347 192.36 0.37 10592 10594 3 -21970.2 -7345.4 192.71 0.37 10593 10595 3 -21970.2 -7345.1 192.76 0.37 10594 10596 3 -21971.8 -7343.47 189.84 0.37 10595 10597 3 -21973.1 -7342.92 190.05 0.25 10596 10598 3 -21973.4 -7341.12 190.38 0.37 10597 10599 3 -21975.5 -7339.59 187.65 0.37 10598 10600 3 -21975.9 -7339.18 187.75 0.37 10599 10601 3 -21975.7 -7338.61 187.83 0.37 10600 10602 3 -21975.7 -7338.37 187.87 0.37 10601 10603 3 -21976.1 -7337.36 188.08 0.37 10602 10604 3 -21977.3 -7337.05 188.24 0.37 10603 10605 3 -21978 -7335.71 186.2 0.37 10604 10606 3 -21977.8 -7335.19 186.26 0.37 10605 10607 3 -21978.5 -7333.94 186.54 0.37 10606 10608 3 -21978.6 -7333.72 186.58 0.37 10607 10609 3 -21979 -7333.52 186.65 0.37 10608 10610 3 -21979.8 -7332.75 184.71 0.37 10609 10611 3 -21980 -7332.56 184.76 0.37 10610 10612 3 -21980.2 -7332.28 184.82 0.37 10611 10613 3 -21981.7 -7331.31 184.26 0.37 10612 10614 3 -21981.8 -7331.17 183.42 0.37 10613 10615 3 -21982.5 -7330.57 183.58 0.37 10614 10616 3 -21983 -7329.59 183.79 0.37 10615 10617 3 -21983 -7329.31 183.84 0.37 10616 10618 3 -21982.4 -7327.07 182.37 0.37 10617 10619 3 -21982.7 -7326.59 182.48 0.37 10618 10620 3 -21982.3 -7325.73 182.58 0.37 10619 10621 3 -21982.1 -7325.4 182.62 0.37 10620 10622 3 -21981.2 -7323.03 180.79 0.37 10621 10623 3 -21981.2 -7322.79 180.84 0.37 10622 10624 3 -21981.5 -7321.4 180.27 0.37 10623 10625 3 -21981.6 -7321.01 179.7 0.37 10624 10626 3 -21981.5 -7320.21 179.73 0.37 10625 10627 3 -21981.2 -7319.9 179.76 0.37 10626 10628 3 -21980.8 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153.53 0.37 10764 10766 3 -21876.5 -7257.07 150.8 0.37 10765 10767 3 -21876.1 -7256.22 150.96 0.37 10766 10768 3 -21874.1 -7256.4 150.77 0.37 10767 10769 3 -21873.9 -7256.36 150.77 0.37 10768 10770 3 -21872.9 -7254.47 148.61 0.25 10769 10771 3 -21870.8 -7253.28 148.61 0.25 10770 10772 3 -21865.5 -7250.49 142.71 0.25 10771 10773 3 -21865 -7250.54 141.71 0.37 10772 10774 3 -21909.1 -7312.57 171.07 0.37 10714 10775 3 -21909.3 -7312.58 171.09 0.37 10774 10776 3 -21909.9 -7310.83 168.26 0.37 10775 10777 3 -21910 -7310.08 168.39 0.37 10776 10778 3 -21910.9 -7309.18 165.61 0.37 10777 10779 3 -21911.2 -7307.91 164.3 0.37 10778 10780 3 -21912.4 -7307.56 164.46 0.37 10779 10781 3 -21912.4 -7307.33 164.51 0.37 10780 10782 3 -21911.9 -7305.92 163.08 0.37 10781 10783 3 -21912.3 -7303.9 157.67 0.37 10782 10784 3 -21913.6 -7303.7 156.76 0.37 10783 10785 3 -21913.5 -7302.46 156.09 0.37 10784 10786 3 -21914 -7300.07 151.59 0.37 10785 10787 3 -21913.7 -7300.04 151.57 0.37 10786 10788 3 -21913.4 -7298.88 154.2 0.37 10787 10789 3 -21912.8 -7297.13 151.28 0.37 10788 10790 3 -21912.9 -7296.89 151.32 0.37 10789 10791 3 -21913.7 -7296.76 151.42 0.37 10790 10792 3 -21915.1 -7295.42 151.77 0.37 10791 10793 3 -21915.2 -7295.09 151.4 0.37 10792 10794 3 -21915.4 -7293.38 152.37 0.37 10793 10795 3 -21915 -7292.57 152.66 0.37 10794 10796 3 -21914.7 -7290.09 146.62 0.25 10795 10797 3 -21914.8 -7289.13 147.08 0.25 10796 10798 3 -21915.1 -7288.71 147.65 0.37 10797 10799 3 -21915.2 -7288.45 147.71 0.37 10798 10800 3 -21915 -7286.38 142.44 0.37 10799 10801 3 -21915 -7285.11 142.65 0.37 10800 10802 3 -21914.7 -7284.75 142.69 0.37 10801 10803 3 -21914.7 -7283.03 139 0.25 10802 10804 3 -21914.8 -7282.75 139.05 0.25 10803 10805 3 -21914.8 -7282.51 139.09 0.25 10804 10806 3 -21914 -7280.89 138.35 0.37 10805 10807 3 -21914.4 -7279.98 139.09 0.37 10806 10808 3 -21914.4 -7279.73 139.14 0.37 10807 10809 3 -21915 -7276.27 136.14 0.37 10808 10810 3 -21916 -7275.17 136.41 0.37 10809 10811 3 -21916.8 -7275.29 136.47 0.37 10810 10812 3 -21916.8 -7275.01 136.52 0.37 10811 10813 3 -21917.3 -7274.29 136.68 0.37 10812 10814 3 -21917.3 -7274.01 136.73 0.37 10813 10815 3 -21918.4 -7273.16 135.17 0.37 10814 10816 3 -21918.4 -7273.42 135.12 0.37 10815 10817 3 -21919.6 -7272.6 135.38 0.37 10816 10818 3 -21920.3 -7271.91 135.56 0.37 10817 10819 3 -21920.7 -7271.73 135.62 0.37 10818 10820 3 -21922.2 -7270.22 132.06 0.37 10819 10821 3 -21923.3 -7269.69 134.03 0.25 10820 10822 3 -21923.3 -7269.73 134.28 0.25 10821 10823 3 -21925.1 -7267.93 128.52 0.25 10822 10824 3 -21927.3 -7268.84 128.57 0.25 10823 10825 3 -21929.2 -7269.16 128.7 0.25 10824 10826 3 -21930.5 -7267.73 130.18 0.25 10825 10827 3 -21932.5 -7267.26 125.57 0.25 10826 10828 3 -21934.3 -7266.77 125.52 0.25 10827 10829 3 -21934.4 -7266.54 125.57 0.25 10828 10830 3 -21936.1 -7265.13 125.45 0.25 10829 10831 3 -21903.8 -7333.31 173.46 0.37 10699 10832 3 -21905.1 -7332.5 169.32 0.37 10831 10833 3 -21907.1 -7331.77 166.52 0.37 10832 10834 3 -21907.4 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3 -21928.1 -7324.18 133.73 0.37 10856 10858 3 -21928.2 -7323.94 133.78 0.37 10857 10859 3 -21928 -7323.11 133.9 0.37 10858 10860 3 -21928.7 -7320.85 129.87 0.37 10859 10861 3 -21929.8 -7320.51 127.02 0.37 10860 10862 3 -21929.8 -7320.26 127.07 0.37 10861 10863 3 -21931.1 -7319.41 127.1 0.37 10862 10864 3 -21932.4 -7318.32 127.28 0.37 10863 10865 3 -21933.2 -7317.52 126.9 0.37 10864 10866 3 -21935 -7313.21 126.91 0.25 10865 10867 3 -21936.1 -7311.46 123.77 0.25 10866 10868 3 -21936.2 -7311.22 123.82 0.25 10867 10869 3 -21937.5 -7311.43 123.91 0.25 10868 10870 3 -21937.8 -7311.45 123.93 0.25 10869 10871 3 -21938.5 -7310.74 124.11 0.25 10870 10872 3 -21938.5 -7310.25 124.2 0.25 10871 10873 3 -21939.4 -7308.31 120.21 0.25 10872 10874 3 -21939.2 -7308.59 121.92 0.25 10873 10875 3 -21938.7 -7308.28 121.92 0.25 10874 10876 3 -21938.5 -7307.96 121.95 0.25 10875 10877 3 -21938.3 -7307.58 121.72 0.25 10876 10878 3 -21938 -7307.25 121.75 0.25 10877 10879 3 -21937.7 -7306.27 120.71 0.25 10878 10880 3 -21937.6 -7305.73 119.19 0.25 10879 10881 3 -21937.5 -7304.69 118.28 0.25 10880 10882 3 -21937.4 -7303.86 118.4 0.25 10881 10883 3 -21937.4 -7302.82 117.07 0.25 10882 10884 3 -21937.2 -7302.24 117.14 0.25 10883 10885 3 -21936.6 -7300.7 120.96 0.25 10884 10886 3 -21936.9 -7300.46 121.04 0.25 10885 10887 3 -21936.7 -7299.91 121.11 0.495 10886 10888 3 -21906.1 -7374.04 208.82 1.24 10666 10889 3 -21904.9 -7372.03 209.04 1.24 10888 10890 3 -21903.8 -7370.52 212.07 1.24 10889 10891 3 -21903.9 -7369.47 212.26 1.24 10890 10892 3 -21903.6 -7367.32 212.58 1.115 10891 10893 3 -21903.5 -7365.96 212.8 1.24 10892 10894 3 -21902.9 -7363.55 213.15 1.24 10893 10895 3 -21902.2 -7362.91 213.18 1.24 10894 10896 3 -21902 -7361.78 213.35 1.24 10895 10897 3 -21902 -7361.54 213.4 1.24 10896 10898 3 -21901.8 -7360.98 213.47 1.24 10897 10899 3 -21901.5 -7359.07 213.76 1.115 10898 10900 3 -21901.5 -7358.81 213.8 1.115 10899 10901 3 -21901.8 -7356.78 214.16 1.115 10900 10902 3 -21901.3 -7355.43 211.75 1.115 10901 10903 3 -21901.2 -7354.07 211.96 1.115 10902 10904 3 -21900.8 -7352.94 212.11 1.115 10903 10905 3 -21900.8 -7352.66 212.16 1.115 10904 10906 3 -21900.5 -7350.84 212.43 0.99 10905 10907 3 -21900.9 -7349.8 212.64 0.99 10906 10908 3 -21900.2 -7348.13 212.85 0.99 10907 10909 3 -21899 -7346.9 212.94 0.99 10908 10910 3 -21898.6 -7345.79 213.09 1.115 10909 10911 3 -21898.4 -7343.17 216.09 1.24 10910 10912 3 -21897.9 -7340.02 212.57 1.24 10911 10913 3 -21896.2 -7339.51 213.92 0.62 10912 10914 3 -21894.5 -7338.29 211.24 0.495 10913 10915 3 -21893.5 -7337.61 211.26 0.62 10914 10916 3 -21892.9 -7335.94 211.47 0.62 10915 10917 3 -21891.3 -7335.2 211.44 0.62 10916 10918 3 -21890.2 -7334.65 210.89 0.62 10917 10919 3 -21888.7 -7332.89 211.14 0.495 10918 10920 3 -21888.3 -7330.96 209.73 0.495 10919 10921 3 -21887.6 -7329.79 209.86 0.495 10920 10922 3 -21887.6 -7329.55 209.9 0.495 10921 10923 3 -21886.7 -7328.07 210.05 0.495 10922 10924 3 -21886.3 -7326.65 205.26 0.37 10923 10925 3 -21885.2 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0.25 11084 11086 3 -21874.3 -7328.55 213.85 0.37 11026 11087 3 -21874.3 -7328.28 213.9 0.37 11086 11088 3 -21873.4 -7327.4 213.95 0.37 11087 11089 3 -21872.3 -7326.44 214.02 0.37 11088 11090 3 -21871.2 -7325.08 216.4 0.37 11089 11091 3 -21870.8 -7324.25 216.5 0.37 11090 11092 3 -21870.8 -7323.97 216.54 0.37 11091 11093 3 -21870.3 -7323.37 216.74 0.37 11092 11094 3 -21869.4 -7321.64 216.94 0.37 11093 11095 3 -21868.8 -7319.48 217.25 0.37 11094 11096 3 -21868.2 -7317.8 217.46 0.37 11095 11097 3 -21867.5 -7315.86 217.73 0.37 11096 11098 3 -21866.6 -7314.39 217.88 0.37 11097 11099 3 -21865 -7313.38 222.28 0.37 11098 11100 3 -21863.8 -7312.14 222.36 0.37 11099 11101 3 -21862.4 -7311.68 222.31 0.37 11100 11102 3 -21862.2 -7311.12 222.39 0.37 11101 11103 3 -21861.7 -7310.77 222.39 0.37 11102 11104 3 -21859.8 -7310.3 225.46 0.37 11103 11105 3 -21859.4 -7309.12 225.62 0.37 11104 11106 3 -21859.4 -7308.89 225.66 0.37 11105 11107 3 -21857.8 -7308.4 225.6 0.37 11106 11108 3 -21857.6 -7308.09 225.65 0.37 11107 11109 3 -21855.7 -7307.33 225.6 0.37 11108 11110 3 -21855.7 -7307.06 225.65 0.37 11109 11111 3 -21854.2 -7306.46 227.81 0.25 11110 11112 3 -21853.6 -7304.51 228.08 0.25 11111 11113 3 -21853.1 -7303.65 228.17 0.25 11112 11114 3 -21853.1 -7303.34 228.23 0.25 11113 11115 3 -21851.6 -7303.12 230.86 0.25 11114 11116 3 -21851.1 -7301.98 231.01 0.25 11115 11117 3 -21850.6 -7301.62 231.02 0.37 11116 11118 3 -21849.8 -7301.22 231.01 0.37 11117 11119 3 -21849.1 -7300.39 231.08 0.25 11118 11120 3 -21849.1 -7300.12 231.13 0.25 11119 11121 3 -21848.4 -7299.72 231.12 0.37 11120 11122 3 -21848.1 -7299.16 231.2 0.37 11121 11123 3 -21846.5 -7298.72 231.11 0.25 11122 11124 3 -21845.9 -7298.34 231.12 0.25 11123 11125 3 -21845.4 -7298 231.13 0.495 11124 11126 3 -21844.6 -7297.58 231.13 0.495 11125 11127 3 -21843.7 -7296.42 231.23 0.25 11126 11128 3 -21842.3 -7295.68 231.23 0.25 11127 11129 3 -21840.5 -7295.6 233.33 0.37 11128 11130 3 -21839.5 -7294.67 233.39 0.25 11129 11131 3 -21837.8 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-21841.6 -7262.64 252.96 0.25 11542 11544 3 -21841.6 -7262.4 253 0.25 11543 11545 3 -21839.5 -7259.58 252.65 0.25 11544 11546 3 -21838.9 -7259.27 252.65 0.25 11545 11547 3 -21838.7 -7258.93 252.69 0.25 11546 11548 3 -21838.1 -7257.26 252.9 0.25 11547 11549 3 -21837.8 -7256.71 252.97 0.25 11548 11550 3 -21837.3 -7256.09 253.03 0.25 11549 11551 3 -21837.1 -7255.57 253.1 0.25 11550 11552 3 -21837 -7255.01 253.17 0.25 11551 11553 3 -21836.3 -7253.29 253.4 0.25 11552 11554 3 -21836 -7253.04 253.41 0.25 11553 11555 3 -21835.8 -7252.67 253.45 0.25 11554 11556 3 -21833.7 -7251.12 258.14 0.25 11555 11557 3 -21833.5 -7250.9 257.17 0.25 11556 11558 3 -21831.3 -7249.37 259.4 0.25 11557 11559 3 -21831 -7249.09 259.43 0.25 11558 11560 3 -21830.5 -7248.22 261.07 0.25 11559 11561 3 -21830.3 -7247.4 261.63 0.25 11560 11562 3 -21828.1 -7244.91 260.89 0.25 11561 11563 3 -21826.2 -7242.45 260.97 0.25 11562 11564 3 -21826.5 -7242.01 261.07 0.25 11563 11565 3 -21826.5 -7241.7 261.12 0.25 11564 11566 3 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3 -21897.5 -7395.97 206.15 1.61 11588 11590 3 -21895.8 -7397.28 199.43 1.985 11589 11591 3 -21894.4 -7397.32 199.2 1.985 11590 11592 3 -21894.7 -7397.62 194.88 1.735 11591 11593 3 -21895.1 -7398.91 194.04 1.24 11592 11594 3 -21894.3 -7400.4 193.72 0.99 11593 11595 3 -21894.8 -7401.01 190.73 0.865 11594 11596 3 -21894.2 -7402.31 192.34 0.62 11595 11597 3 -21893.7 -7403.13 189.44 0.495 11596 11598 3 -21893.9 -7404.29 188.23 0.495 11597 11599 3 -21893.6 -7405.13 187.12 0.495 11598 11600 3 -21893.5 -7405.97 185.71 0.62 11599 11601 3 -21893.8 -7405.98 185.74 0.62 11600 11602 3 -21894 -7406.96 181.74 0.495 11601 11603 3 -21894.2 -7407.24 181.72 0.495 11602 11604 3 -21894.2 -7408.37 180.54 0.495 11603 11605 3 -21893.4 -7409.89 180.22 0.495 11604 11606 3 -21893.4 -7411.14 178.05 0.495 11605 11607 3 -21893.2 -7412.3 174.08 0.495 11606 11608 3 -21893.1 -7413.37 173.89 0.495 11607 11609 3 -21889.1 -7413.55 175.81 0.495 11608 11610 3 -21889 -7413.89 171.84 0.495 11609 11611 3 -21887.9 -7415.27 171.51 0.495 11610 11612 3 -21887.9 -7416.88 171.25 0.495 11611 11613 3 -21887.6 -7417.26 166.42 0.495 11612 11614 3 -21886.9 -7418.67 165.62 0.495 11613 11615 3 -21887.2 -7418.67 165.65 0.495 11614 11616 3 -21886.6 -7420.14 165.35 0.495 11615 11617 3 -21886 -7421.07 158.94 0.495 11616 11618 3 -21885.4 -7422.18 156.48 0.495 11617 11619 3 -21884 -7423.17 157.29 0.37 11618 11620 3 -21883 -7424.32 157.01 0.37 11619 11621 3 -21882.2 -7425.28 156.78 0.37 11620 11622 3 -21881.1 -7426.88 156.41 0.37 11621 11623 3 -21880.1 -7427.74 156.17 0.37 11622 11624 3 -21879.9 -7429.02 155.95 0.37 11623 11625 3 -21879.5 -7429.79 155.78 0.37 11624 11626 3 -21878.6 -7430.1 155.64 0.37 11625 11627 3 -21878.1 -7431.57 155.3 0.37 11626 11628 3 -21877.6 -7432.85 155.04 0.37 11627 11629 3 -21876.8 -7434.51 154.69 0.37 11628 11630 3 -21876.3 -7435.7 154.14 0.37 11629 11631 3 -21875.7 -7435.85 154.06 0.37 11630 11632 3 -21875.7 -7436.13 154.01 0.37 11631 11633 3 -21875.3 -7436.59 153.9 0.37 11632 11634 3 -21875 -7436.57 153.88 0.37 11633 11635 3 -21874 -7437.66 153.63 0.37 11634 11636 3 -21872.9 -7439 152.96 0.37 11635 11637 3 -21872.4 -7440.01 149.95 0.37 11636 11638 3 -21871.6 -7441.42 149.65 0.37 11637 11639 3 -21871.5 -7442.43 149.47 0.37 11638 11640 3 -21870.9 -7443.95 149.16 0.37 11639 11641 3 -21870.3 -7444.12 149.08 0.37 11640 11642 3 -21869.7 -7444.47 148.96 0.37 11641 11643 3 -21869.2 -7445.21 145.68 0.37 11642 11644 3 -21868.7 -7446.4 145.43 0.37 11643 11645 3 -21868.1 -7446.6 145.34 0.37 11644 11646 3 -21867.3 -7447.54 145.12 0.37 11645 11647 3 -21866.5 -7449.21 144.77 0.37 11646 11648 3 -21866.3 -7449.66 142.96 0.37 11647 11649 3 -21865.3 -7450.51 142.72 0.37 11648 11650 3 -21864.2 -7450.6 142.6 0.37 11649 11651 3 -21863.8 -7451.55 142.4 0.37 11650 11652 3 -21862.9 -7452.18 140.66 0.37 11651 11653 3 -21862.4 -7453.14 140.46 0.37 11652 11654 3 -21861.6 -7454.07 137.07 0.37 11653 11655 3 -21861 -7454.52 136.94 0.37 11654 11656 3 -21861 -7454.72 136.9 0.37 11655 11657 3 -21861 -7456.01 136.69 0.37 11656 11658 3 -21859.5 -7456.3 136.3 0.37 11657 11659 3 -21858.7 -7457.41 134.35 0.37 11658 11660 3 -21858.9 -7459.55 134.02 0.37 11659 11661 3 -21858.1 -7460.57 129.91 0.37 11660 11662 3 -21857.2 -7462.79 129.45 0.37 11661 11663 3 -21856 -7464.95 124.42 0.37 11662 11664 3 -21854.9 -7468.11 123.79 0.37 11663 11665 3 -21854 -7469.71 119.5 0.37 11664 11666 3 -21852.6 -7471.04 119.16 0.37 11665 11667 3 -21851.9 -7472.05 114.83 0.37 11666 11668 3 -21850.8 -7473.44 114.5 0.25 11667 11669 3 -21849.9 -7474.03 114.32 0.25 11668 11670 3 -21848.7 -7475.68 113.93 0.37 11669 11671 3 -21848.3 -7476.39 113.78 0.37 11670 11672 3 -21847.6 -7477.85 113.47 0.25 11671 11673 3 -21847.1 -7478.82 113.26 0.25 11672 11674 3 -21846.7 -7479.79 113.04 0.37 11673 11675 3 -21846 -7480.19 112.91 0.37 11674 11676 3 -21844.1 -7481.43 112.53 0.37 11675 11677 3 -21844.4 -7482.67 110.22 0.37 11676 11678 3 -21843.8 -7483.75 106.11 0.37 11677 11679 3 -21843.2 -7484.44 105.94 0.37 11678 11680 3 -21842.9 -7484.56 101.12 0.25 11679 11681 3 -21842.3 -7484.77 101.03 0.25 11680 11682 3 -21841.8 -7485.71 100.83 0.37 11681 11683 3 -21840.9 -7486.34 100.64 0.37 11682 11684 3 -21840 -7486.99 100.45 0.25 11683 11685 3 -21839.4 -7487.43 100.32 0.25 11684 11686 3 -21838.3 -7488.27 100.08 0.37 11685 11687 3 -21837.9 -7488.45 96.8 0.25 11686 11688 3 -21836.8 -7488.71 96.64 0.25 11687 11689 3 -21835.4 -7490.35 96.24 0.37 11688 11690 3 -21834.4 -7491.15 95.51 0.37 11689 11691 3 -21833.4 -7491.26 92.67 0.25 11690 11692 3 -21832.3 -7492.91 92.3 0.25 11691 11693 3 -21829 -7496.95 89.16 0.25 11692 11694 3 -21827.9 -7497.84 88.91 0.25 11693 11695 3 -21884 -7424.46 153.69 0.495 11618 11696 3 -21883.7 -7425.7 150.52 0.495 11695 11697 3 -21883.2 -7427.22 148.93 0.495 11696 11698 3 -21882.7 -7429.25 148.54 0.495 11697 11699 3 -21882.9 -7430.62 145.5 0.495 11698 11700 3 -21883 -7431.88 144.83 0.495 11699 11701 3 -21882.2 -7432.25 141.89 0.495 11700 11702 3 -21882 -7434.12 141.55 0.495 11701 11703 3 -21881.8 -7435.03 136.3 0.495 11702 11704 3 -21881.5 -7437.06 135.83 0.495 11703 11705 3 -21881.6 -7437.79 132.41 0.495 11704 11706 3 -21881.2 -7440.25 131.13 0.495 11705 11707 3 -21881.5 -7441.41 128.21 0.495 11706 11708 3 -21881.2 -7442.9 127.94 0.495 11707 11709 3 -21881 -7444.18 127.7 0.495 11708 11710 3 -21881 -7444.83 120.63 0.495 11709 11711 3 -21880.7 -7446.85 120.19 0.495 11710 11712 3 -21880.8 -7446.83 120.07 0.495 11711 11713 3 -21880.9 -7449.03 115.46 0.495 11712 11714 3 -21880.7 -7450.32 115.23 0.495 11713 11715 3 -21880.4 -7452.53 109.97 0.495 11714 11716 3 -21880.1 -7454.36 109.64 0.495 11715 11717 3 -21880.3 -7455.92 105.97 0.495 11716 11718 3 -21880.3 -7457.14 103.87 0.37 11717 11719 3 -21880.9 -7457.81 101.15 0.37 11718 11720 3 -21880.9 -7457.98 100.34 0.37 11719 11721 3 -21881 -7459.23 100.14 0.37 11720 11722 3 -21880.8 -7459.84 100.03 0.37 11721 11723 3 -21880.1 -7461.2 99.73 0.37 11722 11724 3 -21879.8 -7463.78 96.17 0.37 11723 11725 3 -21879.8 -7466.32 93.51 0.37 11724 11726 3 -21879.3 -7468.33 93.12 0.37 11725 11727 3 -21879.5 -7468.35 93.14 0.37 11726 11728 3 -21879 -7470.45 90.03 0.37 11727 11729 3 -21879 -7470.66 89.99 0.37 11728 11730 3 -21879.5 -7471.94 90.32 0.25 11729 11731 3 -21880 -7472.54 88.57 0.25 11730 11732 3 -21880.6 -7473.61 86.33 0.25 11731 11733 3 -21881.3 -7474.88 85.28 0.25 11732 11734 3 -21881.4 -7474.77 84.67 0.25 11733 11735 3 -21881.9 -7475.75 82.16 0.25 11734 11736 3 -21882 -7477.25 81.32 0.25 11735 11737 3 -21882.2 -7477.32 81.33 0.25 11736 11738 3 -21882.9 -7477.83 76.01 0.25 11737 11739 3 -21882.6 -7477.75 76 0.25 11738 11740 3 -21882.5 -7477.93 72.51 0.25 11739 11741 3 -21882 -7477.96 67.07 0.25 11740 11742 3 -21880.6 -7480.65 65.01 0.25 11741 11743 3 -21879.7 -7482.08 61.54 0.25 11742 11744 3 -21879.4 -7482.55 61.43 0.25 11743 11745 3 -21879.3 -7482.85 61.37 0.25 11744 11746 3 -21879.5 -7484.48 60.02 0.25 11745 11747 3 -21879.6 -7484.23 58.5 0.25 11746 11748 3 -21880.1 -7485.79 56.33 0.25 11747 11749 3 -21880.2 -7485.59 55.13 0.25 11748 11750 3 -21880.9 -7488.1 49.94 0.25 11749 11751 3 -21881 -7491.27 49.43 0.25 11750 11752 3 -21892.2 -7397.93 196.67 1.61 11592 11753 3 -21890.7 -7398.52 196.44 1.61 11752 11754 3 -21889.6 -7398.59 196.32 1.485 11753 11755 3 -21888.7 -7399.04 196.16 1.24 11754 11756 3 -21887.3 -7399.49 195.11 1.24 11755 11757 3 -21887.3 -7399.24 195.16 1.24 11756 11758 3 -21886.5 -7400.68 194.84 0.62 11757 11759 3 -21885.3 -7401.88 194.53 0.62 11758 11760 3 -21884.7 -7402.7 192.01 0.495 11759 11761 3 -21885.1 -7402.46 192.08 0.495 11760 11762 3 -21884.5 -7403.69 190.02 0.495 11761 11763 3 -21884.5 -7403.39 190.07 0.495 11762 11764 3 -21883.8 -7404.4 189.84 0.495 11763 11765 3 -21883.7 -7405.68 189.61 0.495 11764 11766 3 -21883.6 -7406.08 188.88 0.495 11765 11767 3 -21882.9 -7407.01 188.52 0.495 11766 11768 3 -21882.7 -7408.13 187.19 0.495 11767 11769 3 -21882.7 -7408.03 186.6 0.495 11768 11770 3 -21880.8 -7409.32 185.89 0.495 11769 11771 3 -21880.9 -7409.22 185.32 0.495 11770 11772 3 -21879.8 -7409.42 184.24 0.495 11771 11773 3 -21878.6 -7408.51 183.29 0.495 11772 11774 3 -21877.8 -7408.17 183.27 0.495 11773 11775 3 -21876 -7407.91 183.15 0.495 11774 11776 3 -21875 -7407.47 183.13 0.495 11775 11777 3 -21873.8 -7407.35 183.04 0.495 11776 11778 3 -21873.6 -7407.09 183.06 0.495 11777 11779 3 -21872 -7406.95 182.09 0.495 11778 11780 3 -21870.7 -7407 180.29 0.495 11779 11781 3 -21869.8 -7406.95 177.67 0.495 11780 11782 3 -21869.8 -7406.84 176.96 0.495 11781 11783 3 -21869.1 -7407.26 173.79 0.495 11782 11784 3 -21869.1 -7407.52 173.74 0.495 11783 11785 3 -21867.2 -7409.14 173.29 0.495 11784 11786 3 -21865.7 -7409.04 170.8 0.495 11785 11787 3 -21866 -7409.14 170.82 0.495 11786 11788 3 -21860.7 -7409.17 170.31 0.495 11787 11789 3 -21860 -7409.6 170.18 0.495 11788 11790 3 -21858.8 -7410.24 166.34 0.37 11789 11791 3 -21858.2 -7411.76 166.03 0.37 11790 11792 3 -21857.3 -7412.61 165.39 0.37 11791 11793 3 -21855.9 -7413.44 162.31 0.37 11792 11794 3 -21855.4 -7415.16 161.91 0.37 11793 11795 3 -21854.3 -7415.92 159.45 0.37 11794 11796 3 -21853.6 -7416.78 157.5 0.37 11795 11797 3 -21853.7 -7416.54 156.06 0.37 11796 11798 3 -21853.7 -7417.47 154.15 0.37 11797 11799 3 -21852.8 -7418.01 154.44 0.37 11798 11800 3 -21851.6 -7420.16 156.99 0.25 11799 11801 3 -21851.3 -7420.38 156.93 0.37 11800 11802 3 -21850.3 -7421.76 156.58 0.37 11801 11803 3 -21849.3 -7422.45 155.39 0.37 11802 11804 3 -21848.6 -7422.93 147.98 0.37 11803 11805 3 -21848.6 -7422.68 148.03 0.37 11804 11806 3 -21847.2 -7424.22 147.49 0.37 11805 11807 3 -21847.4 -7423.84 146.93 0.37 11806 11808 3 -21846.1 -7424.77 145.38 0.37 11807 11809 3 -21843.8 -7426.14 143.36 0.37 11808 11810 3 -21843 -7427.82 142.76 0.37 11809 11811 3 -21842 -7429.32 139.99 0.37 11810 11812 3 -21840.6 -7430.72 136.92 0.37 11811 11813 3 -21839.3 -7431.74 136.54 0.37 11812 11814 3 -21838.2 -7431.54 130.43 0.37 11813 11815 3 -21837 -7432.55 129.5 0.37 11814 11816 3 -21835.6 -7433.37 126.28 0.37 11815 11817 3 -21834.2 -7434.55 123.42 0.37 11816 11818 3 -21832.8 -7435.71 119.12 0.37 11817 11819 3 -21831.9 -7435.85 116.5 0.37 11818 11820 3 -21830.5 -7437.8 114.88 0.37 11819 11821 3 -21829.2 -7439.52 112.19 0.37 11820 11822 3 -21827.8 -7440.81 108.67 0.37 11821 11823 3 -21827.2 -7441.95 104.77 0.37 11822 11824 3 -21825.4 -7443.26 107.83 0.37 11823 11825 3 -21824.8 -7443.71 103.14 0.25 11824 11826 3 -21824.1 -7444.33 102.97 0.25 11825 11827 3 -21823.2 -7444.55 99.01 0.25 11826 11828 3 -21823.2 -7444.49 98.62 0.25 11827 11829 3 -21822.8 -7445.34 94.59 0.25 11828 11830 3 -21820.3 -7446.49 98.82 0.25 11829 11831 3 -21819.6 -7446.99 94.9 0.25 11830 11832 3 -21818.2 -7448.85 94.37 0.25 11831 11833 3 -21817.1 -7449.85 90.36 0.25 11832 11834 3 -21815 -7452.5 88.84 0.25 11833 11835 3 -21814 -7454.27 86.37 0.25 11834 11836 3 -21813.3 -7456.81 85.88 0.25 11835 11837 3 -21812.6 -7457.73 85.66 0.25 11836 11838 3 -21810.1 -7459.04 85.67 0.125 11837 11839 3 -21809.5 -7460.14 83.18 0.125 11838 11840 3 -21808.8 -7461.44 81.96 0.125 11839 11841 3 -21807.9 -7461.98 78.04 0.125 11840 11842 3 -21807.8 -7462.24 78 0.125 11841 11843 3 -21806.7 -7464.33 76.95 0.125 11842 11844 3 -21805.9 -7465.42 74.65 0.125 11843 11845 3 -21804.4 -7466.85 74.76 0.125 11844 11846 3 -21803.3 -7467.03 70.68 0.125 11845 11847 3 -21803 -7467.25 70.62 0.125 11846 11848 3 -21802.7 -7467.21 70.59 0.125 11847 11849 3 -21800.2 -7469.1 69.78 0.125 11848 11850 3 -21799.9 -7469.06 69.77 0.125 11849 11851 3 -21798.7 -7469.69 69.55 0.125 11850 11852 3 -21798 -7469.84 69.46 0.125 11851 11853 3 -21796.8 -7470.87 68.8 0.125 11852 11854 3 -21796.3 -7470.82 68.76 0.125 11853 11855 3 -21795.2 -7470.87 66.94 0.125 11854 11856 3 -21794.9 -7470.81 66.92 0.125 11855 11857 3 -21794.1 -7471.44 64.95 0.125 11856 11858 3 -21850.8 -7417.48 153.77 0.37 11798 11859 3 -21849 -7416.81 148.74 0.37 11858 11860 3 -21846.6 -7416.85 142.41 0.37 11859 11861 3 -21843.7 -7417.29 136.68 0.37 11860 11862 3 -21842.4 -7416.46 131.72 0.37 11861 11863 3 -21840.4 -7417.4 131.37 0.37 11862 11864 3 -21840.1 -7417.32 131.36 0.37 11863 11865 3 -21837 -7417.32 126.67 0.37 11864 11866 3 -21836.6 -7418.31 126.47 0.37 11865 11867 3 -21835.8 -7418.3 119.69 0.37 11866 11868 3 -21835 -7419.04 117.06 0.37 11867 11869 3 -21834.2 -7419.46 111.11 0.37 11868 11870 3 -21833.6 -7420.03 105.65 0.37 11869 11871 3 -21833.7 -7419.77 105.7 0.37 11870 11872 3 -21832.8 -7421.68 105.3 0.37 11871 11873 3 -21831.6 -7421.24 102.29 0.25 11872 11874 3 -21831.1 -7421.94 99.28 0.25 11873 11875 3 -21830.2 -7422.61 94.66 0.25 11874 11876 3 -21829.6 -7421.27 89.21 0.25 11875 11877 3 -21828.3 -7420.5 89.22 0.25 11876 11878 3 -21826.8 -7421.49 90.14 0.125 11877 11879 3 -21826.8 -7421.23 90.18 0.125 11878 11880 3 -21824.6 -7419.96 83.38 0.125 11879 11881 3 -21822.9 -7418.71 82.35 0.125 11880 11882 3 -21822.7 -7417.91 82.47 0.125 11881 11883 3 -21823.2 -7415.28 77.96 0.125 11882 11884 3 -21824.8 -7414.51 72.56 0.125 11883 11885 3 -21824.8 -7414.25 72.61 0.125 11884 11886 3 -21826.6 -7412.7 67.09 0.125 11885 11887 3 -21826.8 -7411.4 67.32 0.125 11886 11888 3 -21826.6 -7410.86 67.39 0.125 11887 11889 3 -21828 -7408.78 63.58 0.125 11888 11890 3 -21828 -7408.76 63.43 0.125 11889 11891 3 -21828.1 -7408.08 64.08 0.125 11890 11892 3 -21884.8 -7399.42 198.13 1.115 11757 11893 3 -21882.8 -7398.88 198.03 1.115 11892 11894 3 -21883.1 -7398.96 198.05 1.115 11893 11895 3 -21881.4 -7398.92 197.9 1.115 11894 11896 3 -21880 -7399.28 197.7 1.115 11895 11897 3 -21877.4 -7398.93 197.52 1.115 11896 11898 3 -21875.9 -7399.77 197.24 1.115 11897 11899 3 -21874.1 -7400.01 195.11 1.115 11898 11900 3 -21872.8 -7400.61 194.9 1.115 11899 11901 3 -21871.4 -7401.24 194.66 1.115 11900 11902 3 -21870 -7403.44 197.64 1.115 11901 11903 3 -21870 -7403.21 197.68 1.115 11902 11904 3 -21869.2 -7403.6 197.54 1.115 11903 11905 3 -21869.3 -7404.72 197.36 1.115 11904 11906 3 -21869.2 -7404.96 197.32 1.115 11905 11907 3 -21868.7 -7406.72 196.98 0.99 11906 11908 3 -21868.3 -7408.24 196.68 0.865 11907 11909 3 -21867.2 -7410.31 195.33 0.865 11908 11910 3 -21867.3 -7411.68 195.12 0.865 11909 11911 3 -21866.3 -7413.36 194.74 0.865 11910 11912 3 -21866.2 -7413.66 194.69 0.865 11911 11913 3 -21864.9 -7415.03 194.28 0.745 11912 11914 3 -21864.2 -7416.08 197.52 0.745 11913 11915 3 -21864.1 -7417.07 197.35 0.745 11914 11916 3 -21863.9 -7418.11 193.9 0.745 11915 11917 3 -21864.2 -7418.13 193.92 0.745 11916 11918 3 -21863.6 -7418.85 193.75 0.745 11917 11919 3 -21862.8 -7420.1 193.47 0.62 11918 11920 3 -21862 -7421.58 193.15 0.62 11919 11921 3 -21861.6 -7422.55 192.95 0.62 11920 11922 3 -21860.8 -7423.24 192.76 0.62 11921 11923 3 -21860.2 -7423.38 192.68 0.495 11922 11924 3 -21860.1 -7424.3 191.75 0.495 11923 11925 3 -21859.7 -7426 193.94 0.495 11924 11926 3 -21859.7 -7425.84 189.74 0.495 11925 11927 3 -21858.7 -7427.42 193.23 0.495 11926 11928 3 -21858.6 -7427.65 193.18 0.495 11927 11929 3 -21858.3 -7428.92 191.3 0.495 11928 11930 3 -21858.6 -7428.96 191.32 0.495 11929 11931 3 -21858.2 -7430.12 190.33 0.495 11930 11932 3 -21858 -7431.89 189.99 0.495 11931 11933 3 -21857.7 -7432.34 189.49 0.495 11932 11934 3 -21857.4 -7432.59 189.41 0.495 11933 11935 3 -21856.7 -7433.27 189.24 0.495 11934 11936 3 -21856.4 -7434.01 189.09 0.495 11935 11937 3 -21857.4 -7435.28 187.75 0.495 11936 11938 3 -21856.8 -7436.81 189.31 0.495 11937 11939 3 -21856.8 -7437.07 189.26 0.495 11938 11940 3 -21856.5 -7437.54 189.15 0.495 11939 11941 3 -21855.8 -7439.49 189.72 0.495 11940 11942 3 -21855.3 -7441.27 189.38 0.495 11941 11943 3 -21855.2 -7443.77 191.46 0.495 11942 11944 3 -21855 -7444.59 191.31 0.495 11943 11945 3 -21854.6 -7445.54 191.11 0.495 11944 11946 3 -21854.8 -7446.36 190.99 0.495 11945 11947 3 -21854.8 -7446.67 190.94 0.495 11946 11948 3 -21854.9 -7447.5 190.82 0.37 11947 11949 3 -21854.6 -7447.95 190.71 0.37 11948 11950 3 -21854.5 -7448.21 190.66 0.37 11949 11951 3 -21854.2 -7449.33 189.52 0.37 11950 11952 3 -21853.7 -7450.61 189.26 0.37 11951 11953 3 -21853.3 -7452.73 190.74 0.37 11952 11954 3 -21852.6 -7454.47 190.38 0.37 11953 11955 3 -21851.5 -7456.66 189.93 0.37 11954 11956 3 -21851.2 -7458.71 189.59 0.37 11955 11957 3 -21849.7 -7458.83 189.47 0.37 11956 11958 3 -21848.8 -7459.24 189.32 0.37 11957 11959 3 -21847.9 -7459.95 189.12 0.37 11958 11960 3 -21848.2 -7459.95 189.15 0.37 11959 11961 3 -21847.8 -7461.25 188.89 0.62 11960 11962 3 -21846.9 -7461.35 188.79 0.37 11961 11963 3 -21846 -7461.55 188.9 0.37 11962 11964 3 -21845.4 -7462.02 188.77 0.37 11963 11965 3 -21845.4 -7461.72 188.82 0.37 11964 11966 3 -21844.1 -7463.11 188.47 0.37 11965 11967 3 -21843.5 -7463.31 188.38 0.37 11966 11968 3 -21843.6 -7464.88 188.13 0.37 11967 11969 3 -21843.3 -7465.4 188.01 0.37 11968 11970 3 -21842.8 -7466.15 187.85 0.37 11969 11971 3 -21842.2 -7466.63 187.71 0.37 11970 11972 3 -21841.3 -7466.51 189.6 0.37 11971 11973 3 -21840.7 -7466.66 189.52 0.37 11972 11974 3 -21840.2 -7468.22 189.22 0.37 11973 11975 3 -21839.5 -7468.65 189.08 0.37 11974 11976 3 -21838.9 -7468.81 189 0.37 11975 11977 3 -21837.8 -7469.43 188.79 0.37 11976 11978 3 -21837.7 -7469.76 188.73 0.37 11977 11979 3 -21837.2 -7470.21 187.21 0.37 11978 11980 3 -21836.6 -7470.61 187.08 0.37 11979 11981 3 -21836.2 -7471.34 186.93 0.37 11980 11982 3 -21835.7 -7473.17 186.58 0.37 11981 11983 3 -21834.8 -7473.59 186.42 0.37 11982 11984 3 -21834 -7474.41 188.54 0.37 11983 11985 3 -21833.8 -7475.42 188.35 0.37 11984 11986 3 -21833.6 -7477 188.08 0.37 11985 11987 3 -21833.3 -7477.97 187.58 0.37 11986 11988 3 -21832.5 -7479.17 187.31 0.37 11987 11989 3 -21832.8 -7479.2 187.33 0.37 11988 11990 3 -21832 -7479.34 187.23 0.37 11989 11991 3 -21831.3 -7479.8 187.09 0.37 11990 11992 3 -21830.5 -7479.92 186.99 0.37 11991 11993 3 -21829.2 -7480.3 186.81 0.37 11992 11994 3 -21829.2 -7480.56 186.77 0.37 11993 11995 3 -21829 -7481.85 186.54 0.37 11994 11996 3 -21828.8 -7482.08 186.48 0.37 11995 11997 3 -21828.4 -7482.85 186.31 0.37 11996 11998 3 -21828.4 -7483.08 186.27 0.37 11997 11999 3 -21828.2 -7484.15 186.08 0.37 11998 12000 3 -21828.2 -7484.38 186.04 0.37 11999 12001 3 -21828 -7485.67 185.81 0.37 12000 12002 3 -21827.2 -7487.39 186.65 0.37 12001 12003 3 -21826.7 -7488.88 186.36 0.37 12002 12004 3 -21827 -7488.89 186.38 0.37 12003 12005 3 -21825.7 -7489.79 186.12 0.37 12004 12006 3 -21824.5 -7490.43 185.9 0.37 12005 12007 3 -21824.6 -7491.81 185.68 0.37 12006 12008 3 -21824.5 -7493.07 185.47 0.25 12007 12009 3 -21823.6 -7492.98 185.4 0.495 12008 12010 3 -21823.1 -7492.9 185.37 0.495 12009 12011 3 -21822.8 -7493.12 185.3 0.495 12010 12012 3 -21821.8 -7493.77 185.1 0.25 12011 12013 3 -21822.1 -7493.82 185.12 0.25 12012 12014 3 -21821.4 -7495.01 184.86 0.37 12013 12015 3 -21821.3 -7495.8 184.72 0.37 12014 12016 3 -21821.6 -7495.83 184.74 0.37 12015 12017 3 -21820.4 -7496.99 185.86 0.37 12016 12018 3 -21819.8 -7497.99 185.63 0.37 12017 12019 3 -21819.8 -7497.7 185.69 0.37 12018 12020 3 -21819.2 -7498.19 185.55 0.125 12019 12021 3 -21818.6 -7498.63 185.42 0.125 12020 12022 3 -21818.8 -7498.63 185.44 0.125 12021 12023 3 -21818.2 -7498.83 185.35 0.37 12022 12024 3 -21817.6 -7499.53 185.18 0.37 12023 12025 3 -21817.5 -7500.29 185.04 0.37 12024 12026 3 -21817.5 -7500.08 185.08 0.37 12025 12027 3 -21816.8 -7501.21 184.56 0.125 12026 12028 3 -21816.6 -7501.52 183.43 0.37 12027 12029 3 -21816.5 -7501.61 183.95 0.37 12028 12030 3 -21816.1 -7503.15 183.65 0.37 12029 12031 3 -21815.9 -7504.17 183.47 0.125 12030 12032 3 -21815.5 -7505.15 183.27 0.125 12031 12033 3 -21815.2 -7505.46 183.18 0.125 12032 12034 3 -21815.2 -7505.14 183.24 0.125 12033 12035 3 -21814.8 -7506.67 182.94 0.495 12034 12036 3 -21815.1 -7506.71 182.96 0.495 12035 12037 3 -21815.2 -7507.79 182.8 0.495 12036 12038 3 -21814.3 -7509.66 183.31 0.25 12037 12039 3 -21813.6 -7510.63 183.09 0.25 12038 12040 3 -21813 -7511.35 182.91 0.495 12039 12041 3 -21812.6 -7511.83 182.8 0.495 12040 12042 3 -21811.9 -7512.8 182.57 0.25 12041 12043 3 -21811.6 -7513.28 182.46 0.25 12042 12044 3 -21810.8 -7514.78 182.14 0.495 12043 12045 3 -21810.5 -7515.63 181.48 0.125 12044 12046 3 -21810.7 -7515.72 181.49 0.125 12045 12047 3 -21810.9 -7516.53 181.38 0.125 12046 12048 3 -21811.1 -7517.35 181.39 0.37 12047 12049 3 -21810.9 -7518.89 181.12 0.125 12048 12050 3 -21811.3 -7519.78 181.01 0.125 12049 12051 3 -21811.1 -7519.73 180.99 0.495 12050 12052 3 -21811.1 -7519.97 180.95 0.495 12051 12053 3 -21810.9 -7521.08 180.76 0.25 12052 12054 3 -21810.9 -7521.31 180.71 0.25 12053 12055 3 -21811.1 -7521.73 181.35 0.25 12054 12056 3 -21811.3 -7524.15 180.98 0.25 12055 12057 3 -21811.3 -7526.53 180.58 0.25 12056 12058 3 -21811.1 -7528.1 180.3 0.25 12057 12059 3 -21851.5 -7460.51 190.08 0.495 11956 12060 3 -21851.5 -7461.94 190.35 0.495 12059 12061 3 -21851.2 -7461.9 190.33 0.495 12060 12062 3 -21851.1 -7462.67 190.19 0.495 12061 12063 3 -21850.8 -7463.2 190.08 0.37 12062 12064 3 -21850.4 -7464.44 189.83 0.37 12063 12065 3 -21850.1 -7465.84 190.49 0.37 12064 12066 3 -21850.3 -7466.96 190.33 0.37 12065 12067 3 -21849.6 -7467.68 190.14 0.37 12066 12068 3 -21849.5 -7468.85 192.41 0.37 12067 12069 3 -21850 -7469.72 192.32 0.37 12068 12070 3 -21850.3 -7471.54 193.1 0.37 12069 12071 3 -21850.5 -7472.61 192.94 0.37 12070 12072 3 -21850.7 -7474.09 194.96 0.37 12071 12073 3 -21850.9 -7474.62 194.89 0.37 12072 12074 3 -21850.8 -7474.92 194.83 0.37 12073 12075 3 -21850.1 -7475.78 195.68 0.37 12074 12076 3 -21850.2 -7476.78 196.54 0.25 12075 12077 3 -21850.3 -7477.49 197.41 0.25 12076 12078 3 -21850.2 -7477.65 198.39 0.25 12077 12079 3 -21850.6 -7477.71 196.8 0.495 12078 12080 3 -21850.3 -7478.87 199.14 0.495 12079 12081 3 -21851 -7479.57 199.3 0.25 12080 12082 3 -21851.5 -7479.86 199.3 0.495 12081 12083 3 -21851.1 -7481.39 201.81 0.25 12082 12084 3 -21850.8 -7482.13 201.65 0.25 12083 12085 3 -21850.7 -7483.15 201.48 0.495 12084 12086 3 -21849.8 -7484.14 203.11 0.125 12085 12087 3 -21849.2 -7484.35 203.02 0.125 12086 12088 3 -21849.2 -7484.64 202.97 0.125 12087 12089 3 -21848.9 -7484.85 202.91 0.37 12088 12090 3 -21849.8 -7485.62 203.6 0.125 12089 12091 3 -21850 -7486.44 203.53 0.125 12090 12092 3 -21849.6 -7487.45 207.88 0.37 12091 12093 3 -21850.4 -7488.62 209.54 0.37 12092 12094 3 -21850.3 -7489.93 209.31 0.125 12093 12095 3 -21850.1 -7490.99 209.12 0.37 12094 12096 3 -21849.5 -7492.7 214.53 0.125 12095 12097 3 -21849.4 -7493.74 214.35 0.37 12096 12098 3 -21850 -7495.13 215.56 0.37 12097 12099 3 -21850.8 -7495.23 215.62 0.37 12098 12100 3 -21851.7 -7496.07 213.59 0.125 12099 12101 3 -21851.9 -7496.36 213.56 0.125 12100 12102 3 -21852.3 -7497.41 214.62 0.37 12101 12103 3 -21854 -7499.5 214.41 0.37 12102 12104 3 -21854.6 -7501.01 219.42 0.125 12103 12105 3 -21854.8 -7501.87 219.29 0.125 12104 12106 3 -21854.6 -7503.42 219.02 0.125 12105 12107 3 -21854.7 -7504.72 218.81 0.125 12106 12108 3 -21854.2 -7505.55 219.23 0.125 12107 12109 3 -21854.4 -7506.63 219.07 0.37 12108 12110 3 -21854.8 -7507.75 218.93 0.37 12109 12111 3 -21854.8 -7436.4 185.45 0.37 11936 12112 3 -21853.5 -7437.85 185.09 0.37 12111 12113 3 -21853.2 -7438.61 184.93 0.37 12112 12114 3 -21849.7 -7441.33 179.91 0.37 12113 12115 3 -21847.3 -7444.77 173.57 0.37 12114 12116 3 -21845.9 -7445.7 173.28 0.37 12115 12117 3 -21845.6 -7445.37 173.32 0.37 12116 12118 3 -21843.7 -7446.97 172.87 0.37 12117 12119 3 -21843.5 -7446.92 172.85 0.37 12118 12120 3 -21841.9 -7447.33 170.11 0.37 12119 12121 3 -21840.7 -7448.1 168.9 0.37 12120 12122 3 -21838.4 -7449.39 170.48 0.37 12121 12123 3 -21837.4 -7450.59 170.18 0.37 12122 12124 3 -21836.6 -7450.78 170.07 0.125 12123 12125 3 -21835.8 -7450.86 169.98 0.125 12124 12126 3 -21834.8 -7451.02 169.87 0.37 12125 12127 3 -21834.1 -7452.53 169.55 0.37 12126 12128 3 -21834.1 -7452.24 169.6 0.37 12127 12129 3 -21833.7 -7453.26 169.39 0.125 12128 12130 3 -21833.1 -7453.42 169.31 0.125 12129 12131 3 -21832.9 -7453.19 168.18 0.37 12130 12132 3 -21832.3 -7453.93 168 0.37 12131 12133 3 -21832.2 -7454.7 167.86 0.125 12132 12134 3 -21831.8 -7455.68 167.66 0.37 12133 12135 3 -21830.2 -7456.55 167.37 0.125 12134 12136 3 -21829.7 -7456.53 167.7 0.125 12135 12137 3 -21829 -7457.25 167.51 0.37 12136 12138 3 -21829 -7457.49 167.47 0.37 12137 12139 3 -21828.8 -7458.1 164.91 0.125 12138 12140 3 -21828.5 -7458.34 164.85 0.37 12139 12141 3 -21827.6 -7458.49 164.74 0.37 12140 12142 3 -21826.9 -7459.12 164.57 0.125 12141 12143 3 -21826.8 -7459.28 165.57 0.125 12142 12144 3 -21826 -7459.75 165.41 0.125 12143 12145 3 -21825.2 -7460.43 162.04 0.37 12144 12146 3 -21824 -7461.34 161.78 0.125 12145 12147 3 -21824.3 -7461.39 161.79 0.37 12146 12148 3 -21823 -7462.22 161.43 0.37 12147 12149 3 -21822.6 -7463.2 161.23 0.37 12148 12150 3 -21821.7 -7464.14 160.99 0.25 12149 12151 3 -21820.9 -7465.33 160.72 0.25 12150 12152 3 -21819.3 -7466.04 158.11 0.25 12151 12153 3 -21818.6 -7466.7 159.03 0.25 12152 12154 3 -21817.2 -7466.49 158.93 0.25 12153 12155 3 -21811.7 -7465.6 158.31 0.125 12154 12156 3 -21809.3 -7466.67 157.33 0.37 12155 12157 3 -21808.8 -7466.3 157.34 0.37 12156 12158 3 -21807.4 -7466.93 154.67 0.125 12157 12159 3 -21806.8 -7467.37 154.54 0.125 12158 12160 3 -21806.5 -7467.55 154.48 0.125 12159 12161 3 -21806.1 -7467.76 154.41 0.125 12160 12162 3 -21804.6 -7469.44 156.18 0.37 12161 12163 3 -21804 -7469.36 156.14 0.37 12162 12164 3 -21802.4 -7469.31 155.99 0.125 12163 12165 3 -21801.7 -7469.77 155.86 0.125 12164 12166 3 -21801.4 -7470.2 155.75 0.25 12165 12167 3 -21800.8 -7471.68 155.46 0.37 12166 12168 3 -21800.9 -7471.42 155.5 0.37 12167 12169 3 -21800.6 -7473.2 155.18 0.25 12168 12170 3 -21799.9 -7474.57 152.87 0.25 12169 12171 3 -21799.6 -7476.14 152.59 0.25 12170 12172 3 -21799.2 -7477.65 150.82 0.37 12171 12173 3 -21799.1 -7478.17 150.72 0.25 12172 12174 3 -21798.5 -7478.8 150.56 0.25 12173 12175 3 -21797.5 -7479.69 150.32 0.37 12174 12176 3 -21797.7 -7479.72 150.34 0.37 12175 12177 3 -21796.5 -7480.33 150.12 0.25 12176 12178 3 -21795.2 -7481.42 149.82 0.37 12177 12179 3 -21795.3 -7481.15 149.87 0.37 12178 12180 3 -21794.2 -7482.34 150.16 0.37 12179 12181 3 -21793.6 -7484.15 149.81 0.37 12180 12182 3 -21793.9 -7484.16 149.83 0.37 12181 12183 3 -21794.3 -7485.02 149.72 0.125 12182 12184 3 -21794.6 -7486.42 149.53 0.125 12183 12185 3 -21794.2 -7487.13 149.37 0.25 12184 12186 3 -21795.2 -7487.78 149.36 0.25 12185 12187 3 -21795.4 -7488.36 149.28 0.37 12186 12188 3 -21796.2 -7489.82 150.6 0.25 12187 12189 3 -21796.6 -7490.46 150.53 0.495 12188 12190 3 -21797.8 -7491.71 150.44 0.37 12189 12191 3 -21798.8 -7492.96 150.32 0.125 12190 12192 3 -21799.9 -7493.13 150.4 0.125 12191 12193 3 -21800.1 -7493.47 150.36 0.125 12192 12194 3 -21801.6 -7494.05 152.29 0.25 12193 12195 3 -21803.6 -7493.34 152.6 0.25 12194 12196 3 -21803.9 -7493.41 152.61 0.37 12195 12197 3 -21803.3 -7495.4 152.18 0.125 12194 12198 3 -21804.1 -7495.8 152.19 0.37 12197 12199 3 -21857.6 -7426.2 187.15 0.495 11926 12200 3 -21856.2 -7426.81 185.58 0.37 12199 12201 3 -21853.7 -7427.15 183.11 0.37 12200 12202 3 -21852 -7427.89 182.08 0.495 12201 12203 3 -21850.3 -7428.32 181.21 0.495 12202 12204 3 -21848.8 -7428.64 181.02 0.495 12203 12205 3 -21846.9 -7428.89 180.8 0.495 12204 12206 3 -21845.6 -7429.71 180.54 0.495 12205 12207 3 -21844.9 -7430.2 180.4 0.495 12206 12208 3 -21844.7 -7430.17 180.38 0.495 12207 12209 3 -21843.8 -7430.56 180.23 0.37 12208 12210 3 -21842.8 -7430.7 180.12 0.37 12209 12211 3 -21842.3 -7431.15 179.93 0.37 12210 12212 3 -21841.6 -7431.83 179.75 0.37 12211 12213 3 -21841 -7432.55 179.57 0.25 12212 12214 3 -21840.8 -7432.77 177.83 0.495 12213 12215 3 -21839.9 -7432.91 177.73 0.495 12214 12216 3 -21838.7 -7434.25 180.25 0.37 12215 12217 3 -21837.8 -7434.97 180.04 0.37 12216 12218 3 -21836.8 -7435.61 179.85 0.37 12217 12219 3 -21836 -7435.82 178.46 0.37 12218 12220 3 -21835.4 -7436.25 178.33 0.37 12219 12221 3 -21834.7 -7437.77 178.23 0.37 12220 12222 3 -21834 -7438.72 178.01 0.37 12221 12223 3 -21833 -7439.42 177.8 0.25 12222 12224 3 -21832.7 -7439.91 177.69 0.37 12223 12225 3 -21831.4 -7441.02 177.35 0.37 12224 12226 3 -21830.8 -7441.19 177.27 0.37 12225 12227 3 -21829.3 -7441.64 177.59 0.37 12226 12228 3 -21828.1 -7442.26 177.38 0.37 12227 12229 3 -21827.2 -7442.91 177.18 0.37 12228 12230 3 -21825.5 -7444.29 178.38 0.37 12229 12231 3 -21824.3 -7445.13 178.07 0.37 12230 12232 3 -21823.8 -7446.09 174.57 0.25 12231 12233 3 -21823.2 -7446.55 174.44 0.25 12232 12234 3 -21821.9 -7447.17 174.22 0.25 12233 12235 3 -21820.5 -7447.69 173.79 0.25 12234 12236 3 -21820.2 -7448.48 172.04 0.25 12235 12237 3 -21819.3 -7449.15 171.84 0.25 12236 12238 3 -21818.4 -7449.31 171.73 0.25 12237 12239 3 -21817.6 -7449.9 171.03 0.37 12238 12240 3 -21816.3 -7450.48 170.82 0.37 12239 12241 3 -21815.1 -7451.11 170.72 0.25 12240 12242 3 -21814.5 -7451.59 170.6 0.25 12241 12243 3 -21813.3 -7452.24 170.38 0.37 12242 12244 3 -21812.1 -7452.05 170.31 0.37 12243 12245 3 -21811.5 -7451.72 167.39 0.37 12244 12246 3 -21810.9 -7452.45 167.2 0.37 12245 12247 3 -21810 -7452.83 167.06 0.37 12246 12248 3 -21809.3 -7453.26 166.93 0.25 12247 12249 3 -21808.8 -7453.18 166.89 0.25 12248 12250 3 -21808.2 -7453.65 166.75 0.25 12249 12251 3 -21807.5 -7454.32 166.58 0.37 12250 12252 3 -21805.8 -7454.64 166.36 0.37 12251 12253 3 -21804 -7454.91 166.16 0.37 12252 12254 3 -21803.4 -7455.33 162.81 0.37 12253 12255 3 -21802.6 -7456.6 162.53 0.25 12254 12256 3 -21802.1 -7456.74 162.45 0.37 12255 12257 3 -21801.5 -7456.87 162.38 0.37 12256 12258 3 -21801.4 -7457.14 162.33 0.37 12257 12259 3 -21800.2 -7458.06 162.06 0.37 12258 12260 3 -21799.9 -7458.32 161.99 0.495 12259 12261 3 -21799.3 -7458.74 161.86 0.495 12260 12262 3 -21797.5 -7459.4 160.8 0.37 12261 12263 3 -21796.2 -7461.1 160.4 0.37 12262 12264 3 -21795.5 -7462.3 160.13 0.37 12263 12265 3 -21795.7 -7462.35 160.14 0.37 12264 12266 3 -21794.5 -7462.73 160.35 0.125 12265 12267 3 -21793.9 -7463.5 160.17 0.37 12266 12268 3 -21793.2 -7464.18 159.99 0.37 12267 12269 3 -21792.3 -7465.13 159.74 0.125 12268 12270 3 -21790.8 -7464.91 159.65 0.125 12269 12271 3 -21790.5 -7465.39 159.54 0.37 12270 12272 3 -21788.3 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197.75 0.37 12499 12501 3 -21851.9 -7387.57 197.8 0.37 12500 12502 3 -21850.6 -7384.81 198.48 0.37 12501 12503 3 -21849.4 -7382.88 199.25 0.37 12502 12504 3 -21847.6 -7381.07 202.76 0.37 12503 12505 3 -21846.6 -7379.44 204.9 0.37 12504 12506 3 -21846.5 -7379.46 205.05 0.37 12505 12507 3 -21844.7 -7378.53 208.68 0.37 12506 12508 3 -21844 -7377.38 208.81 0.37 12507 12509 3 -21842.5 -7375.61 210.92 0.37 12508 12510 3 -21841.3 -7375.2 210.88 0.37 12509 12511 3 -21840.5 -7374.94 214.5 0.37 12510 12512 3 -21839.1 -7374.04 221.5 0.37 12511 12513 3 -21838.6 -7375.18 229.68 0.37 12512 12514 3 -21838.6 -7374.93 229.72 0.37 12513 12515 3 -21837.8 -7376.56 238.05 0.37 12514 12516 3 -21837.7 -7376.51 239.24 0.37 12515 12517 3 -21837.5 -7377.15 241.58 0.37 12516 12518 3 -21837.8 -7377.21 241.6 0.37 12517 12519 3 -21839.2 -7367.93 244.31 0.495 12518 12520 3 -21837 -7367.42 248.05 0.495 12519 12521 3 -21834.7 -7366.66 250.8 0.495 12520 12522 3 -21834.7 -7366.78 251.49 0.495 12521 12523 3 -21832.7 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-7479.91 176.83 0.495 13204 13206 3 -21902.1 -7480.61 176.65 0.495 13205 13207 3 -21901.4 -7481.6 176.42 0.495 13206 13208 3 -21900.4 -7482 176.27 0.37 13207 13209 3 -21900.5 -7482.28 176.22 0.37 13208 13210 3 -21899.6 -7482.44 176.12 0.37 13209 13211 3 -21898.9 -7483.63 175.85 0.37 13210 13212 3 -21898 -7484.02 175.7 0.37 13211 13213 3 -21897 -7484.94 175.46 0.37 13212 13214 3 -21895.2 -7485.5 175.2 0.37 13213 13215 3 -21895 -7485.44 175.18 0.37 13214 13216 3 -21894.8 -7486.48 175 0.37 13215 13217 3 -21894.1 -7487.73 174.67 0.37 13216 13218 3 -21893.2 -7487.88 174.57 0.37 13217 13219 3 -21892.6 -7488.58 174.39 0.37 13218 13220 3 -21891.9 -7489.53 174.17 0.37 13219 13221 3 -21891.5 -7490.26 174.01 0.37 13220 13222 3 -21891 -7492.03 173.67 0.37 13221 13223 3 -21890.2 -7492.66 171.48 0.37 13222 13224 3 -21889.3 -7492.82 171.37 0.37 13223 13225 3 -21888 -7493.4 169.36 0.37 13224 13226 3 -21888.2 -7493.4 169.38 0.37 13225 13227 3 -21887.6 -7494.37 169.16 0.37 13226 13228 3 -21886.6 -7495.3 168.91 0.37 13227 13229 3 -21885.7 -7495.91 168.35 0.37 13228 13230 3 -21885.7 -7496.18 168.31 0.37 13229 13231 3 -21885.1 -7496.07 168.27 0.37 13230 13232 3 -21884.5 -7496.3 168.18 0.37 13231 13233 3 -21883.8 -7496.56 165.73 0.37 13232 13234 3 -21883.7 -7497.33 165.59 0.37 13233 13235 3 -21882.8 -7498.26 165.35 0.37 13234 13236 3 -21882.7 -7498.51 165.3 0.37 13235 13237 3 -21882.1 -7499 165.16 0.37 13236 13238 3 -21881.5 -7499.89 163.01 0.37 13237 13239 3 -21881 -7500.04 162.93 0.37 13238 13240 3 -21879.6 -7499.85 162.83 0.37 13239 13241 3 -21879.3 -7499.8 162.81 0.37 13240 13242 3 -21879 -7500.04 162.74 0.37 13241 13243 3 -21878.4 -7502.08 162.35 0.37 13242 13244 3 -21878 -7502.76 163.47 0.37 13243 13245 3 -21876.4 -7504.11 163.09 0.37 13244 13246 3 -21875.4 -7505.04 162.85 0.37 13245 13247 3 -21875.6 -7505.35 162.82 0.37 13246 13248 3 -21875 -7506.04 162.65 0.37 13247 13249 3 -21873.8 -7506.46 162.47 0.37 13248 13250 3 -21873.5 -7506.93 162.36 0.37 13249 13251 3 -21873.4 -7507.73 162.22 0.37 13250 13252 3 -21873.3 -7507.93 162.18 0.37 13251 13253 3 -21873.1 -7510.29 161.76 0.37 13252 13254 3 -21873 -7510.56 161.71 0.37 13253 13255 3 -21872.2 -7511.77 158.45 0.37 13254 13256 3 -21871.7 -7513.54 158.11 0.37 13255 13257 3 -21871 -7514.52 157.88 0.37 13256 13258 3 -21870.2 -7515.2 156.05 0.37 13257 13259 3 -21870.2 -7515.45 156.01 0.37 13258 13260 3 -21869.5 -7516.13 155.83 0.37 13259 13261 3 -21868.8 -7517.34 155.57 0.37 13260 13262 3 -21868.2 -7517.53 155.48 0.37 13261 13263 3 -21868.2 -7517.8 155.43 0.37 13262 13264 3 -21867.8 -7518.81 155.23 0.37 13263 13265 3 -21867 -7520.02 154.96 0.25 13264 13266 3 -21867.3 -7520.33 154.93 0.25 13265 13267 3 -21866.9 -7521.33 154.73 0.25 13266 13268 3 -21866.3 -7521.77 154.6 0.25 13267 13269 3 -21866 -7522.92 155.1 0.25 13268 13270 3 -21865.6 -7524.18 154.84 0.25 13269 13271 3 -21864 -7525.59 154.46 0.25 13270 13272 3 -21864.8 -7526.2 154.44 0.25 13271 13273 3 -21864.4 -7526.93 154.28 0.25 13272 13274 3 -21864.3 -7527.95 154.1 0.25 13273 13275 3 -21864.3 -7530.05 153.75 0.25 13274 13276 3 -21863.6 -7531.35 153.47 0.25 13275 13277 3 -21861.7 -7532.65 153.08 0.25 13276 13278 3 -21862 -7534.19 153.79 0.25 13277 13279 3 -21861.8 -7535.73 153.51 0.25 13278 13280 3 -21861.5 -7536.33 152.64 0.25 13279 13281 3 -21904.7 -7479.84 173.3 0.495 13204 13282 3 -21903.7 -7481.01 173.01 0.495 13281 13283 3 -21903.1 -7482.4 170.28 0.495 13282 13284 3 -21903.4 -7482.4 170.31 0.495 13283 13285 3 -21903 -7483.63 169.98 0.495 13284 13286 3 -21902.5 -7485.15 169.43 0.495 13285 13287 3 -21902.5 -7484.89 169.48 0.495 13286 13288 3 -21901.7 -7485.69 166.89 0.495 13287 13289 3 -21902 -7485.74 166.91 0.495 13288 13290 3 -21901.3 -7486.04 164.99 0.495 13289 13291 3 -21900.6 -7487.57 163.31 0.495 13290 13292 3 -21899.7 -7488.02 163.15 0.495 13291 13293 3 -21898.9 -7488.66 161.23 0.495 13292 13294 3 -21894.2 -7488.74 160.78 0.495 13293 13295 3 -21894.2 -7488.49 160.82 0.495 13294 13296 3 -21893.7 -7489.88 161.44 0.37 13295 13297 3 -21893.3 -7491.05 159.43 0.37 13296 13298 3 -21892.8 -7491.18 159.35 0.37 13297 13299 3 -21892 -7492.01 155.38 0.37 13298 13300 3 -21892.3 -7492.09 155.39 0.37 13299 13301 3 -21891.4 -7492.8 154.02 0.37 13300 13302 3 -21889.6 -7494.77 154.69 0.37 13301 13303 3 -21889.3 -7495.95 151.14 0.37 13302 13304 3 -21888.6 -7497.18 149.57 0.37 13303 13305 3 -21888.7 -7497.1 149.07 0.37 13304 13306 3 -21888.2 -7498.04 148.73 0.37 13305 13307 3 -21887.7 -7499.32 148.47 0.37 13306 13308 3 -21886.7 -7500.65 148.15 0.37 13307 13309 3 -21887 -7500.8 144.15 0.25 13308 13310 3 -21886.2 -7501.45 143.98 0.37 13309 13311 3 -21884.9 -7502.82 143.63 0.25 13310 13312 3 -21884.8 -7503.09 143.58 0.25 13311 13313 3 -21884.7 -7503.28 140.27 0.25 13312 13314 3 -21883.8 -7503.66 140.13 0.495 13313 13315 3 -21883.3 -7503.81 140.05 0.495 13314 13316 3 -21882.1 -7505.19 139.72 0.25 13315 13317 3 -21881.1 -7504.89 138.95 0.25 13316 13318 3 -21879.5 -7505.88 138.64 0.37 13317 13319 3 -21878.2 -7505.64 138.55 0.25 13318 13320 3 -21878.2 -7505.42 138.59 0.25 13319 13321 3 -21877 -7505.98 138.39 0.25 13320 13322 3 -21875 -7506.16 138.17 0.37 13321 13323 3 -21874.4 -7506.59 138.04 0.37 13322 13324 3 -21873.5 -7507.83 135.29 0.37 13323 13325 3 -21872.6 -7509.52 134.93 0.37 13324 13326 3 -21871.8 -7510.73 134.65 0.25 13325 13327 3 -21870.9 -7511.36 134.46 0.25 13326 13328 3 -21870.1 -7511.64 132.61 0.37 13327 13329 3 -21868.6 -7512.33 131.69 0.25 13328 13330 3 -21867.8 -7512.9 129.54 0.37 13329 13331 3 -21867.8 -7513.11 129.51 0.37 13330 13332 3 -21866.8 -7513.76 129.31 0.37 13331 13333 3 -21865.9 -7513.69 126.8 0.25 13332 13334 3 -21864 -7514.75 126.45 0.25 13333 13335 3 -21924.4 -7454.09 186.36 0.62 13177 13336 3 -21924.2 -7455.63 182.74 0.62 13335 13337 3 -21924.2 -7455.57 182.37 0.62 13336 13338 3 -21924.5 -7457.46 179.2 0.62 13337 13339 3 -21924.2 -7458.88 181 0.495 13338 13340 3 -21923.8 -7459.6 180.85 0.495 13339 13341 3 -21923.6 -7460.93 180.62 0.495 13340 13342 3 -21924.4 -7461.89 179.11 0.495 13341 13343 3 -21924 -7462.82 178.92 0.62 13342 13344 3 -21924.2 -7464.29 177.74 0.62 13343 13345 3 -21924.1 -7465.31 177.56 0.62 13344 13346 3 -21923.1 -7466.56 177.45 0.62 13345 13347 3 -21922.7 -7467.82 177.19 0.62 13346 13348 3 -21922.5 -7468.79 177.02 0.62 13347 13349 3 -21922.3 -7470.63 176.69 0.495 13348 13350 3 -21922 -7471.72 173.59 0.495 13349 13351 3 -21922 -7472.49 173.46 0.62 13350 13352 3 -21922.1 -7473.57 173.29 0.62 13351 13353 3 -21921.7 -7474.32 173.13 0.495 13352 13354 3 -21921 -7475.57 172.86 0.495 13353 13355 3 -21921 -7476.75 170.62 0.495 13354 13356 3 -21921.7 -7478.1 169.97 0.62 13355 13357 3 -21921.8 -7479.96 169.7 0.62 13356 13358 3 -21921.9 -7481.03 169.54 0.62 13357 13359 3 -21922 -7482.41 169.32 0.495 13358 13360 3 -21921.8 -7483.74 169.18 0.495 13359 13361 3 -21922 -7484.93 168.13 0.495 13360 13362 3 -21922.8 -7485.32 168.14 0.495 13361 13363 3 -21922.4 -7486.33 168.22 0.495 13362 13364 3 -21922.3 -7487.11 168.08 0.495 13363 13365 3 -21922.4 -7488.54 168.23 0.495 13364 13366 3 -21922.5 -7489.58 168.07 0.495 13365 13367 3 -21921.9 -7490.71 167.13 0.495 13366 13368 3 -21921.9 -7492.29 166.87 0.495 13367 13369 3 -21921.8 -7493.87 166.57 0.37 13368 13370 3 -21920.8 -7494.69 165.86 0.37 13369 13371 3 -21920.7 -7495.48 165.72 0.37 13370 13372 3 -21919.7 -7496.41 165.47 0.37 13371 13373 3 -21919.1 -7497.38 165.25 0.37 13372 13374 3 -21918.1 -7498.5 164.54 0.37 13373 13375 3 -21917.2 -7499.17 164.34 0.37 13374 13376 3 -21917 -7500.73 164.07 0.37 13375 13377 3 -21916.9 -7501.23 163.97 0.37 13376 13378 3 -21916.9 -7501.5 163.93 0.37 13377 13379 3 -21916 -7502.17 163.73 0.37 13378 13380 3 -21915.5 -7503.12 163.53 0.37 13379 13381 3 -21916.1 -7504.02 161.94 0.37 13380 13382 3 -21915.7 -7505.28 161.69 0.37 13381 13383 3 -21915.3 -7507.67 161.46 0.37 13382 13384 3 -21910.3 -7506.92 161.11 0.37 13383 13385 3 -21909.5 -7508.22 161.41 0.37 13384 13386 3 -21908.9 -7509.72 161.11 0.37 13385 13387 3 -21908.7 -7511.01 160.88 0.37 13386 13388 3 -21908.8 -7511.8 160.76 0.37 13387 13389 3 -21909.2 -7512.95 160.6 0.37 13388 13390 3 -21909.1 -7513.69 160.47 0.37 13389 13391 3 -21908.5 -7515.49 160.25 0.37 13390 13392 3 -21908.2 -7517.24 159.92 0.37 13391 13393 3 -21908.2 -7517.52 159.87 0.37 13392 13394 3 -21908.1 -7519.36 159.56 0.37 13393 13395 3 -21908.2 -7520.93 159.3 0.37 13394 13396 3 -21908.1 -7521.18 159.26 0.37 13395 13397 3 -21907.9 -7522.72 158.98 0.37 13396 13398 3 -21907.8 -7523 158.93 0.37 13397 13399 3 -21907.3 -7524.93 158.11 0.37 13398 13400 3 -21907.7 -7527.38 157.74 0.37 13399 13401 3 -21908.4 -7529.87 157.4 0.37 13400 13402 3 -21909.5 -7531.62 157.21 0.25 13401 13403 3 -21910 -7532.51 157.11 0.37 13402 13404 3 -21909.9 -7534.36 156.8 0.37 13403 13405 3 -21910.4 -7535 155.44 0.25 13404 13406 3 -21909.9 -7536.48 155.15 0.25 13405 13407 3 -21909.7 -7538 154.87 0.37 13406 13408 3 -21909.5 -7539.04 154.68 0.37 13407 13409 3 -21908.8 -7540.63 151.99 0.25 13408 13410 3 -21908.6 -7542.15 151.71 0.25 13409 13411 3 -21908.3 -7543.44 151.47 0.37 13410 13412 3 -21908.4 -7543.18 151.52 0.37 13411 13413 3 -21908.6 -7545.57 151.14 0.37 13412 13414 3 -21908.6 -7545.32 151.19 0.37 13413 13415 3 -21907.3 -7547.76 146.4 0.25 13414 13416 3 -21907.1 -7549.05 146.15 0.37 13415 13417 3 -21906.9 -7550.54 145.88 0.37 13416 13418 3 -21906.8 -7552.5 145.98 0.37 13417 13419 3 -21907 -7554.37 145.69 0.25 13418 13420 3 -21907 -7554.59 145.66 0.25 13419 13421 3 -21906.9 -7555.41 145.51 0.25 13420 13422 3 -21906.5 -7555.88 145.39 0.495 13421 13423 3 -21906.9 -7556.99 145.25 0.495 13422 13424 3 -21907.6 -7558.17 145.11 0.495 13423 13425 3 -21907.6 -7559.75 144.85 0.37 13424 13426 3 -21907.6 -7561.31 144.59 0.37 13425 13427 3 -21907.4 -7562.66 144.35 0.37 13426 13428 3 -21907.9 -7564.1 141.61 0.37 13427 13429 3 -21909.1 -7565.16 141.55 0.37 13428 13430 3 -21909.3 -7566.69 139.68 0.25 13429 13431 3 -21909.5 -7568.58 139.39 0.37 13430 13432 3 -21909.8 -7569.98 139.19 0.25 13431 13433 3 -21910.5 -7571.1 139.07 0.37 13432 13434 3 -21910.5 -7572.73 138.8 0.37 13433 13435 3 -21910.3 -7573.75 138.61 0.37 13434 13436 3 -21910.8 -7575.6 134.93 0.25 13435 13437 3 -21910.7 -7575.74 135.79 0.25 13436 13438 3 -21910.8 -7576.61 135.93 0.25 13437 13439 3 -21912 -7578.38 134.19 0.25 13438 13440 3 -21911.5 -7580.12 133.85 0.25 13439 13441 3 -21911.4 -7580.66 133.75 0.25 13440 13442 3 -21911.2 -7583.47 133.27 0.25 13441 13443 3 -21911.1 -7583.73 133.22 0.25 13442 13444 3 -21910.2 -7589.38 132.2 0.25 13443 13445 3 -21910.5 -7589.43 132.21 0.25 13444 13446 3 -21911.3 -7591.88 129.95 0.25 13445 13447 3 -21912.1 -7594.09 129.66 0.25 13446 13448 3 -21912.3 -7596.31 129.31 0.25 13447 13449 3 -21912.3 -7598.46 126.51 0.25 13448 13450 3 -21912.3 -7600.3 126.2 0.25 13449 13451 3 -21912.5 -7601.14 126.12 0.37 13450 13452 3 -21912.4 -7603.03 125.81 0.25 13451 13453 3 -21912.5 -7604.59 125.55 0.37 13452 13454 3 -21912.2 -7606.11 125.28 0.25 13453 13455 3 -21912.5 -7606.17 125.29 0.25 13454 13456 3 -21912 -7607.13 125.09 0.37 13455 13457 3 -21912.1 -7608.74 124.83 0.25 13456 13458 3 -21911.8 -7610.24 124.55 0.37 13457 13459 3 -21911.8 -7611.82 124.29 0.25 13458 13460 3 -21911.7 -7613.94 123.93 0.37 13459 13461 3 -21910.9 -7619.5 120.92 0.125 13460 brian-1.4.3/dev/ideas/morphology/spatialneuron.py000066400000000000000000000207741266062647100221360ustar00rootroot00000000000000''' Compartmental neurons See BEP-15 TODO: * Threshold and reset are special (not as normal NeuronGroup because only 1 spike) * Hines method * Point processes * StateMonitor * neuron.plot('gl') * Iteration (over the branch or the entire tree?) ''' from morphology import * from brian.stdunits import * from brian.units import * from brian.reset import NoReset from brian.stateupdater import StateUpdater from brian.equations import Equations from brian.inspection import * from brian.optimiser import * from brian.group import Group from brian.clock import guess_clock from itertools import count from brian.neurongroup import NeuronGroup from scipy.linalg import solve_banded from numpy import zeros try: import sympy use_sympy = True except: warnings.warn('sympy not installed: some features in SpatialNeuron will not be available') use_sympy = False __all__ = ['SpatialNeuron', 'CompartmentalNeuron'] class SpatialNeuron(NeuronGroup): """ Compartmental model with morphology. """ def __init__(self, morphology=None, model=None, threshold=None, reset=NoReset(), refractory=0 * ms, level=0, clock=None, unit_checking=True, compile=False, freeze=False, Cm=0.9 * uF / cm ** 2, Ri=150 * ohm * cm): clock = guess_clock(clock) N = len(morphology) # number of compartments if isinstance(model, str): model = Equations(model, level=level + 1) model += Equations(''' v:volt # membrane potential ''') # Process model equations (Im) to extract total conductance and the remaining current if use_sympy: try: membrane_eq=model._string['Im'] # the membrane equation except: raise TypeError,"The transmembrane current Im must be defined" # Check conditional linearity ids=get_identifiers(membrane_eq) _namespace=dict.fromkeys(ids,1.) # there is a possibility of problems here (division by zero) _namespace['v']=AffineFunction() eval(membrane_eq,model._namespace['v'],_namespace) try: eval(membrane_eq,model._namespace['v'],_namespace) except: # not linear raise TypeError,"The membrane current must be linear with respect to v" # Extracts the total conductance from Im, and the remaining current z=symbolic_eval(membrane_eq) symbol_v=sympy.Symbol('v') b=z.subs(symbol_v,0) a=-sympy.simplify(z.subs(symbol_v,1)-b) gtot_str="_gtot="+str(a)+": siemens/cm**2" I0_str="_I0="+str(b)+": amp/cm**2" model+=Equations(gtot_str+"\n"+I0_str,level=level+1) # better: explicit insertion with namespace of v else: raise TypeError,"The Sympy package must be installed for SpatialNeuron" # Equations for morphology (isn't it a duplicate??) eqs_morphology = Equations(""" diameter : um length : um x : um y : um z : um area : um**2 """) full_model = model + eqs_morphology # Create the state updater NeuronGroup.__init__(self, N, model=full_model, threshold=threshold, reset=reset, refractory=refractory, level=level + 1, clock=clock, unit_checking=unit_checking, implicit=True) #Group.__init__(self, full_model, N, unit_checking=unit_checking, level=level+1) #self._eqs = model #var_names = full_model._diffeq_names self.Cm = Cm # could be a vector? self.Ri = Ri self._state_updater = SpatialStateUpdater(self, clock) #S0 = {} # Fill missing units #for key, value in full_model._units.iteritems(): # if not key in S0: # S0[key] = 0 * value #self._S0 = [0] * len(var_names) #for var, i in zip(var_names, count()): # self._S0[i] = S0[var] # Insert morphology self.morphology = morphology self.morphology.compress(diameter=self.diameter, length=self.length, x=self.x, y=self.y, z=self.z, area=self.area) def subgroup(self, N): # Subgrouping cannot be done in this way raise NotImplementedError def __getitem__(self, x): ''' Subgrouping mechanism. self['axon'] returns the subtree named "axon". TODO: self[:] returns the full branch. ''' morpho = self.morphology[x] N = self[morpho._origin:morpho._origin + len(morpho)] N.morphology = morpho return N def __getattr__(self, x): if (x != 'morphology') and ((x in self.morphology._namedkid) or all([c in 'LR123456789' for c in x])): # subtree return self[x] else: return NeuronGroup.__getattr__(self, x) class SpatialStateUpdater(StateUpdater): """ State updater for compartmental models. For the moment I assume there is a single branch (=axon). """ def __init__(self, neuron, clock=None): self.eqs = neuron._eqs self.neuron = neuron self._isprepared = False self._state_updater=neuron._state_updater # to update the currents def prepare(self): ''' From Hines 1984 paper, discrete formula is: A_plus*V(i+1)-(A_plus+A_minus)*V(i)+A_minus*V(i-1)=Cm/dt*(V(i,t+dt)-V(i,t))+gtot(i)*V(i)-I0(i) A_plus: i->i+1 A_minus: i->i-1 This gives the following tridiagonal system: A_plus*V(i+1)-(Cm/dt+gtot(i)+A_plus+A_minus)*V(i)+A_minus*V(i-1)=-Cm/dt*V(i,t)-I0(i) Boundaries, one simple possibility (sealed ends): -(Cm/dt+gtot(n)+A_minus)*V(n)+A_minus*V(n-1)=-Cm/dt*V(n,t)-I0(n) A_plus*V(1)-(Cm/dt+gtot(0)+A_plus)*V(0)=-Cm/dt*V(0,t)-I0(0) For the domain decomposition idea, most can be precalculated, and only the diagonal elements (gtot(i)) and the RHS will change. ''' mid_diameter=.5*(self.neuron.diameter[:-1]+self.neuron.diameter[1:]) # i -> i+1 self.Aplus=mid_diameter**2/(4*self.neuron.diameter[:-1]*self.neuron.length[:-1]**2*self.neuron.Ri) self.Aminus=mid_diameter**2/(4*self.neuron.diameter[1:]*self.neuron.length[1:]**2*self.neuron.Ri) def __call__(self, neuron): ''' Updates the state variables. ''' if not self._isprepared: self.prepare() self._isprepared=True # Update the membrane currents (should it be after (no real difference though)) self._state_updater(neuron) ''' x=solve_banded((lower,upper),ab,b) lower = number of lower diagonals = 1 upper = number of upper diagonals = 1 ab = array(l+u+1,M) each row is one diagonal a[i,j]=ab[u+i-j,j] ''' b=-neuron.Cm/neuron.clock.dt*neuron.v-neuron._I0 ab = zeros((3,len(neuron))) # part of it could be precomputed # a[i,j]=ab[1+i-j,j] # ab[1,:]=main diagonal # ab[0,1:]=upper diagonal # ab[2,:-1]=lower diagonal ab[0,1:]=self.Aplus ab[2,:-1]=self.Aminus ab[1,:]=-neuron.Cm/neuron.clock.dt-neuron._gtot ab[1,1:]-=self.Aminus ab[1,:-1]-=self.Aplus neuron.v=solve_banded((1,1),ab,b,overwrite_ab=True,overwrite_b=True) def __len__(self): ''' Number of state variables ''' return len(self.eqs) CompartmentalNeuron = SpatialNeuron if __name__ == '__main__': from brian import * morpho = Morphology('oi24rpy1.CNG.swc') # visual L3 pyramidal cell print len(morpho), "compartments" El = -70 * mV ENa = 50 * mV eqs = ''' # The same equations for the whole neuron, but possibly different parameter values Im=gl*(El-v)+gNa*m**3*(ENa-v) : amp/cm**2 # distributed transmembrane current gl : siemens/cm**2 # spatially distributed conductance gNa : siemens/cm**2 dm/dt=(minf-m)/(0.3*ms) : 1 minf=1./(1+exp(-(v+30*mV)/(6*mV))) : 1 ''' neuron = SpatialNeuron(morphology=morpho, threshold="axon[50*um].v>0*mV", model=eqs, refractory=4 * ms, Cm=0.9 * uF / cm ** 2, Ri=150 * ohm * cm) neuron.axon[0 * um:50 * um].gl = 1e-3 * siemens / cm ** 2 print sum(neuron.axon.gl) print neuron.axon[40 * um].gl #branch=neuron.axon[0*um:50*um] neuron.morphology.plot() show() brian-1.4.3/dev/ideas/morphology/spatialneuron_remy.py000066400000000000000000000407511266062647100231670ustar00rootroot00000000000000''' Compartmental neurons ''' from brian.experimental.morphology import Morphology from brian.stdunits import * from brian.units import * from brian.reset import NoReset from brian.stateupdater import StateUpdater from brian.inspection import * from brian.optimiser import * from itertools import count from brian.neurongroup import NeuronGroup from scipy.linalg import solve_banded from numpy import zeros, ones, isscalar, diag_indices from numpy.linalg import solve from brian.clock import guess_clock from brian.equations import Equations import functools import warnings from math import ceil, log from scipy import weave from time import time import trace import numpy try: import sympy use_sympy = True except: warnings.warn('sympy not installed: some features in SpatialNeuron will not be available') use_sympy = False __all__ = ['SpatialNeuron', 'CompartmentalNeuron'] class SpatialNeuron(NeuronGroup): """ Compartmental model with morphology. """ def __init__(self, morphology=None, model=None, threshold=None, reset=NoReset(), refractory=0 * ms, level=0, clock=None, unit_checking=True, compile=False, freeze=False, implicit=True, Cm=0.9 * uF / cm ** 2, Ri=150 * ohm * cm, bc_type = 2, diffeq_nonzero=True): clock = guess_clock(clock) N = len(morphology) # number of compartments if isinstance(model, str): model = Equations(model, level=level + 1) model += Equations(''' v:volt # membrane potential ''') # Process model equations (Im) to extract total conductance and the remaining current if use_sympy: try: membrane_eq=model._string['Im'] # the membrane equation except: raise TypeError,"The transmembrane current Im must be defined" # Check conditional linearity ids=get_identifiers(membrane_eq) _namespace=dict.fromkeys(ids,1.) # there is a possibility of problems here (division by zero) _namespace['v']=AffineFunction() eval(membrane_eq,model._namespace['v'],_namespace) try: eval(membrane_eq,model._namespace['v'],_namespace) except: # not linear raise TypeError,"The membrane current must be linear with respect to v" # Extracts the total conductance from Im, and the remaining current z=symbolic_eval(membrane_eq) symbol_v=sympy.Symbol('v') b=z.subs(symbol_v,0) a=-sympy.simplify(z.subs(symbol_v,1)-b) gtot_str="_gtot="+str(a)+": siemens/cm**2" I0_str="_I0="+str(b)+": amp/cm**2" model+=Equations(gtot_str+"\n"+I0_str,level=level+1) # better: explicit insertion with namespace of v else: raise TypeError,"The Sympy package must be installed for SpatialNeuron" # Equations for morphology (isn't it a duplicate??) eqs_morphology = Equations(""" diameter : um length : um x : um y : um z : um area : um**2 """) full_model = model + eqs_morphology NeuronGroup.__init__(self, N, model=full_model, threshold=threshold, reset=reset, refractory=refractory, level=level + 1, clock=clock, unit_checking=unit_checking, implicit=implicit) self.model_with_diffeq_nonzero = diffeq_nonzero self._state_updater = SpatialStateUpdater(self, clock) self.Cm = ones(len(self))*Cm self.Ri = Ri self.bc_type = bc_type #default boundary condition on leaves self.bc = ones(len(self)) # boundary conditions on branch points self.changed = True # Insert morphology self.morphology = morphology self.morphology.compress(diameter=self.diameter, length=self.length, x=self.x, y=self.y, z=self.z, area=self.area) def subgroup(self, N): # Subgrouping cannot be done in this way raise NotImplementedError def __getitem__(self, x): ''' Subgrouping mechanism. self['axon'] returns the subtree named "axon". TODO: self[:] returns the full branch. ''' morpho = self.morphology[x] N = self[morpho._origin:morpho._origin + len(morpho)] N.morphology = morpho return N def __getattr__(self, x): if (x != 'morphology') and ((x in self.morphology._namedkid) or all([c in 'LR123456789' for c in x])): # subtree return self[x] else: return NeuronGroup.__getattr__(self, x) class SpatialStateUpdater(StateUpdater): """ State updater for compartmental models. """ def __init__(self, neuron, clock=None): self.eqs = neuron._eqs self.neuron = neuron self._isprepared = False self._state_updater=neuron._state_updater # to update the currents self.first_test_gtot=True self.callcount=0 def prepare_branch(self, morphology, mid_diameter,ante=0): ''' 1) fill neuron.branches and neuron.index with information about the morphology of the neuron 2) change some wrong values in Aplus and Aminus. Indeed these were correct only if the neuron is a linear cable. Knowledge of the morphology gives correct values. 3) fill neuron.bc (boundary conditions) ''' branch = morphology.branch() i=branch._origin j= i + len(branch) - 2 endpoint = j + 1 self.neuron.index[i:endpoint+1] = self.neuron.BPcount children_number = 0 #connections between branches for x in (morphology.children):#parent of segment n isn't always n-1 at branch points. We need to change Aplus and Aminus gc = 2 * msiemens/cm**2 startpoint = x._origin mid_diameter[startpoint] = .5*(self.neuron.diameter[endpoint]+self.neuron.diameter[startpoint]) self.Aminus[startpoint]=mid_diameter[startpoint]**2/(4*self.neuron.diameter[startpoint]*self.neuron.length[startpoint]**2*self.neuron.Ri) if endpoint>0: self.Aplus[startpoint]=mid_diameter[startpoint]**2/(4*self.neuron.diameter[endpoint]*self.neuron.length[endpoint]**2*self.neuron.Ri) else : self.Aplus[startpoint]=gc self.Aminus[startpoint]=gc children_number+=1 #boundary conditions pointType = self.neuron.bc[endpoint] hasChild = (children_number>0) if (not hasChild) and (pointType == 1): #if the branch point is a leaf of the tree : apply default boundary condition self.neuron.bc[endpoint] = self.neuron.bc_type #extract informations about the branches index_ante = self.neuron.index[ante] bp = endpoint index = self.neuron.BPcount self.i_list.append(i) self.j_list.append(j) self.bp_list.append(bp) self.pointType_list.append(max(1,pointType)) self.pointTypeAnte_list.append(max(1,self.neuron.bc[ante])) self.temp[index] = index_ante self.id.append(index) self.test_list.append((j-i+2)>1) for x in xrange(j-i+2): self.ante_list.append(ante) self.post_list.append(bp) if index_ante == 0: self.ind0.append(index) if pointType==0 : self.ind_bctype_0.append(bp) #initialize the parts of the linear systems that will not change if (j-i+2)>1: #j-i+2 = len(branch) #initialize ab self.ab[0,i:j]= self.Aplus[i:j] self.ab[2,i:j]= self.Aminus[i+1:j+1] #initialize bL VL0 = 1 * volt self.bL[i] = (- VL0 * self.Aminus[i]) #initialize bR VR0 = 1 * volt self.bR[j] = (- VR0 * self.Aplus[j+1]) self.neuron.BPcount += 1 for x in (morphology.children): self.prepare_branch(x,mid_diameter,endpoint) def prepare(self): ''' From Hines 1984 paper, discrete formula is: A_plus*V(i+1)-(A_plus+A_minus)*V(i)+A_minus*V(i-1)=Cm/dt*(V(i,t+dt)-V(i,t))+gtot(i)*V(i)-I0(i) A_plus: i->i+1 A_minus: i->i-1 This gives the following tridiagonal system: A_plus*V(i+1)-(Cm/dt+gtot(i)+A_plus+A_minus)*V(i)+A_minus*V(i-1)=-Cm/dt*V(i,t)-I0(i) ''' mid_diameter = zeros(len(self.neuron)) # mid(i) : (i-1) <-> i mid_diameter[1:] = .5*(self.neuron.diameter[:-1]+self.neuron.diameter[1:]) self.Aplus = zeros(len(self.neuron)) # A+ i -> j = Aplus(j) self.Aminus = zeros(len(self.neuron)) # A- i <- j = Aminus(j) self.Aplus[1]= mid_diameter[1]**2/(4*self.neuron.diameter[1]*self.neuron.length[1]**2*self.neuron.Ri) self.Aplus[2:]=mid_diameter[2:]**2/(4*self.neuron.diameter[1:-1]*self.neuron.length[1:-1]**2*self.neuron.Ri) self.Aminus[1:]=mid_diameter[1:]**2/(4*self.neuron.diameter[1:]*self.neuron.length[1:]**2*self.neuron.Ri) self.neuron.index = zeros(len(self.neuron),int) # gives the index of the branch containing the current compartment self.neuron.BPcount = 0 # number of branch points (or branches). = len(self.neuron.branches) #the three solutions for V on a branch self.vL = zeros((len(self.neuron)),numpy.float64) self.vR = zeros((len(self.neuron)),numpy.float64) self.d = zeros((len(self.neuron)),numpy.float64) #matrix and right hand in the tridiagonal systems that we solve to find vL, vR and d. self.bL = zeros((len(self.neuron)),numpy.float64) self.bR = zeros((len(self.neuron)),numpy.float64) self.bd = zeros((len(self.neuron)),numpy.float64) self.ab = zeros((3,len(self.neuron))) self.ab1_base = zeros(len(self.neuron)) self.gtot = zeros(len(self.neuron)) self.I0 = zeros(len(self.neuron)) self.i_list = [] #the indexes of the first points of the branches in the neuron. len = neuron.BPcount self.j_list = [] #the indexes of the last points of the branches in the neuron. len = neuron.BPcount self.bp_list = [] #the indexes of the branch points in the neuron. len = neuron.BPcount self.pointType_list = [] #boundary condition on bp. len = neuron.BPcount self.pointTypeAnte_list = [] #boundary condition on ante. len = neuron.BPcount self.index_ante_list1 = [] #index of the parent branch of the current branch. index is in [0,neuron.BPcount] self.index_ante_list2 = [] self.ante_list = [] #the indexes in the neuron of the branch points connected to i, for every compartment. len = len(self.neuron) self.post_list = [] #for every compartment, contains the index of the branch point. len = len(self.neuron) self.test_list = [] #for each branch : 1 if the branch has more than 3 compartments, else 0 self.id = [] #list of every integer in [0,neuron.BPcount]. used in step to change some values in a matrix self.temp = zeros(len(self.neuron)) #used to construct index_ante_list0, 1, 2. self.ind0 = [] #indexes (in [0,neuron.BPcount]) of the branches connected to compartment 0 self.ind_bctype_0 = [] #indexes of the branch point with boundary condition 0 (constant V) # prepare_branch : fill the lists, changes Aplus & Aminus self.prepare_branch(self.neuron.morphology, mid_diameter,0) self.index_ante_list1, self.ind1 = numpy.unique(numpy.array(self.temp,int),return_index=True) self.ind1 = numpy.sort(self.ind1) self.index_ante_list1 = self.temp[self.ind1] self.index_ante_list1 = list(self.index_ante_list1) self.ind2 = [] for x in xrange(self.neuron.BPcount): self.ind2.append(x) self.ind2 = numpy.delete(self.ind2,self.ind1,None) self.ind2 = numpy.setdiff1d(self.ind2, self.ind0, assume_unique=True) self.index_ante_list2 = self.temp[self.ind2] self.index_ante_list2 = list(self.index_ante_list2) self.index_ante_list = [] for idx in xrange(self.neuron.BPcount): self.index_ante_list.append(self.temp[idx]) # linear system P V = B used to deal with the voltage at branch points and take boundary conditions into account. self.P = zeros((self.neuron.BPcount,self.neuron.BPcount)) self.B = zeros(self.neuron.BPcount) self.solution_bp = zeros(self.neuron.BPcount) #in case of a sealed end, Aminus and Aplus are doubled : self.Aminus_bp = self.Aminus[self.bp_list] self.Aminus_bp [:] *= self.pointType_list[:] self.Aplus_i = self.Aplus[self.i_list] self.Aplus_i[:] *= self.pointTypeAnte_list[:] def step(self, neuron): if self.first_test_gtot and isscalar(neuron._gtot): self.first_test_gtot=False neuron._gtot = ones(len(neuron)) * neuron._gtot self.gtot = neuron._gtot #this compute the value of neuron._gtot. #if we call neuron._gtot[1] and then neuron._gtot[2] it does 2 computations #here we call it only one time on the whole array. this is much faster self.I0 = neuron._I0 #------------------------------------solve tridiagonal systems on the branchs------------------------- #ab is the matrix in the tridiagonal systems describing the branches. #bd is a right hand in one of these tridiagonal systems. if self.neuron.changed : # neuron.changed = True <=> there was a new input somewhere. example : the user does neuron.I[x] = y self.update_ab_base() self.update_ab_gtot() self.update_bd() self.calculate_vd_vL_vR() self.neuron.changed = False #-----------fill P and B, matrix and right hand used to find the voltage at the branch points----------------- self.P[:,:] = 0 self.B[:] = 0 Cm = neuron.Cm[self.bp_list] dt = neuron.clock.dt gtot = self.gtot[self.bp_list] I0 = self.I0[self.bp_list] v_bp = neuron.v[self.bp_list] vLleft = self.vL[self.i_list] vLright = self.vL[self.j_list] vRleft = self.vR[self.i_list] vRright = self.vR[self.j_list] dleft = self.d[self.i_list] dright = self.d[self.j_list] vLleft[:] *= self.test_list[:] #if a branch has less than 3 compartments, this equals 0. #thus we can do the same work on every branch point. vLright[:] *= self.test_list[:] vRleft[:] *= self.test_list[:] vRright[:] *= self.test_list[:] dleft[:] *= self.test_list[:] dright[:] *= self.test_list[:] self.B[self.index_ante_list1] += - self.Aplus_i[self.ind1[:]] * dleft[self.ind1[:]] self.B[self.index_ante_list2] += - self.Aplus_i[self.ind2[:]] * dleft[self.ind2[:]] self.B[0] += sum(- self.Aplus_i[self.ind0[:]] * dleft[self.ind0[:]]) self.P[(self.index_ante_list1,self.index_ante_list1)] += self.Aplus_i[self.ind1[:]] * (vLleft[self.ind1[:]] - 1) self.P[(self.index_ante_list2,self.index_ante_list2)] += self.Aplus_i[self.ind2[:]] * (vLleft[self.ind2[:]] - 1) self.P[0,0] += sum(self.Aplus_i[self.ind0[:]] * (vLleft[self.ind0[:]] - 1)) di = diag_indices(neuron.BPcount) self.B[:] += - Cm[:]/dt * second * v_bp[:] - I0[:] - self.Aminus_bp[:] * dright[:] self.P[di] += - Cm[:]/dt * second - gtot[:] + self.Aminus_bp[:] * (vRright[:] - 1) self.P[(self.id,self.index_ante_list)] += self.Aminus_bp[:] *vLright[:] self.P[(self.index_ante_list,self.id)] += self.Aplus_i[:] *vRleft[:] self.P[self.ind_bctype_0,:] = 0 self.P[(self.ind_bctype_0,self.ind_bctype_0)] = 1 self.B[self.ind_bctype_0] = neuron.v[self.ind_bctype_0] #------------------------------------------------------solve PV=B----------------------------------- self.solution_bp = solve(self.P,self.B) neuron.v[self.bp_list] = self.solution_bp[:] #-------------------------------------------------------update v------------------------------------- self.finalize_v_global() def update_ab_base(self): #part of ab that doesn't change if there is no prompt from the operator. self.ab1_base[:-1] = (- self.neuron.Cm[:-1] / self.neuron.clock.dt * second - self.Aminus[:-1] - self.Aplus[1:]) self.ab1_base[-1] = (- self.neuron.Cm[-1] / self.neuron.clock.dt * second - self.Aminus[-1]) def update_ab_gtot(self): #this is called every step. changing part of ab. self.ab[1,:] = self.ab1_base[:] - self.neuron._gtot[:] def update_bd(self): #bd is a right hand side in a tridiagonal system self.bd[:] = -self.neuron.Cm[:] / self.neuron.clock.dt * self.neuron.v[:] - self.neuron._I0[:] def calculate_vd_vL_vR(self): for index in xrange(self.neuron.BPcount) : if self.test_list[index] : i = self.i_list[index] j = self.j_list[index] self.vL[i:j+1] = solve_banded((1,1),self.ab[:,i:j+1],self.bL[i:j+1],overwrite_ab=False,overwrite_b=False) self.vR[i:j+1] = solve_banded((1,1),self.ab[:,i:j+1],self.bR[i:j+1],overwrite_ab=False,overwrite_b=False) self.d[i:j+1] = solve_banded((1,1),self.ab[:,i:j+1],self.bd[i:j+1],overwrite_ab=False,overwrite_b=False) def finalize_v_global(self): #V(x) = V(left) * vL(x) + V(right) * vR(x) + d(x) self.neuron.v[:] = self.vL[:] * self.neuron.v[self.ante_list[:]] + self.vR[:] * self.neuron.v[self.post_list[:]] + self.d[:] self.neuron.v[self.bp_list] = self.solution_bp[:] def __call__(self, neuron): ''' Updates the state variables. ''' if not self._isprepared: self.prepare() self._isprepared=True print "state updater prepared" self.callcount+=1 print self.callcount #Update I,V if neuron.changed : self._state_updater.changed = True self._state_updater(neuron) #update the currents self.step(neuron) #update V def __len__(self): ''' Number of state variables ''' return len(self.eqs) CompartmentalNeuron = SpatialNeuron brian-1.4.3/dev/ideas/multicore/000077500000000000000000000000001266062647100164725ustar00rootroot00000000000000brian-1.4.3/dev/ideas/multicore/benchmark.py000066400000000000000000000004441266062647100210000ustar00rootroot00000000000000''' Testing multiprocessing fro Brian ''' from brian import * from multiprocessing import Pool from time import time A = rand(3, 3) X1 = rand(3, 1000) X2 = rand(3, 1000) def f(X): return dot(A, X) p = Pool(2) t1 = time() p.map(f, [X1, X2]) t2 = time() print t2 - t1 brian-1.4.3/dev/ideas/multiple_events/000077500000000000000000000000001266062647100177065ustar00rootroot00000000000000brian-1.4.3/dev/ideas/multiple_events/multiple_events.py000066400000000000000000000101461266062647100235010ustar00rootroot00000000000000""" Here I try do define multiple events on a NeuronGroup (not just spikes). How to make this in a nice way? 1) Insert it in NeuronGroup. We would pass a list of threshold/reset. It is handled as currently. 2) Have multiple states, as we thought for refractoriness. But instead of having one system of equations for each state, we just have a separate threshold and reset. 3a) Insert it in NeuronGroup, but have a new integer variable, "state". Each state corresponds to a specific additional event type. Additional threshods/ resets are called only when the neuron is in state 0, 1, etc. Ex: threshold=("v>1","v>0.8","v<0.8") # on state: all, 0, 1 reset=("v=0","state=1","state=0") b) This could also be implicit, that is, we assume that event triggering is prevented as long as the condition still holds true. This would give simply: threshold=("v>1","v>0.8") reset="v=0" and the state would be automatically dealt with, possibly via a new threshold class, e.g. PositiveCrossing("v>1"). 4) Have a new class attached to a group, e.g.: ParasiteGroup(P,threshold,reset) Threshold for that class could, by default, be a positive crossing. This could create a subgroup of P. I prefer 3 & 4. Option 4 is simpler, but perhaps might integrate less nicely with STDP (but not sure). But it also works nicely with SpikeMonitor. Here option 4 is implemented. """ from brian import * from brian.utils.circular import * import copy class PositiveCrossing(Threshold): ''' The event (spike) is triggered only the first time the threshold condition is satisfied. That is, after an event is triggered, further events are prevented until the condition is false again. **Initialised as:** :: PositiveCrossing(threshold) where ``threshold`` is another threshold. Warning: an instance can be used with only one group. ''' # Not the most efficient way to do it, but it works! def __init__(self,threshold): self._threshold=threshold self._firsttime=True def __call__(self, P): if self._firsttime: self._firsttime=False self._available=ones(len(P),dtype=bool_) all_spikes=self._threshold(P) spikes=all_spikes[self._available[all_spikes]] self._available[:]=True self._available[all_spikes]=False return spikes def __repr__(self): return "Positive crossing threshold" def ParasiteGroup(P, threshold, reset=NoReset()): ''' This is a NeuronGroup that tracks events on P, that is, that produces a spike when the threshold condition is true and was false at the previous time step (positive crossing). Optionally, a reset operation can be applied (maybe we could remove this). Events are stored for as long as spikes in P (could be an option). ''' # This would be probably nicer in a proper subclass # or WatcherGroup? Q=P[:] # New subgroup Q._owner=Q # This separates it from its parents (otherwise it is not run) Q.set_instance_id(level=1) # This makes available to magic tools Q._state_updater=LazyStateUpdater() # Does not update the state matrix Q._threshold=PositiveCrossing(StringThreshold(threshold, level=1)) # Defines a new threshold Q._resetfun=reset # No reset Q.LS = SpikeContainer(Q._max_delay, useweave=get_global_preference('useweave'), compiler=get_global_preference('weavecompiler')) # Spike storage return Q if __name__=='__main__': tau=10*ms P=NeuronGroup(10,model='dv/dt=(1.1-v)/tau:1',threshold=1,reset=0) P2=ParasiteGroup(P,"v>0.6") P3=ParasiteGroup(P,"v>0.8") M=StateMonitor(P,'v',record=0) S=SpikeMonitor(P) S2=SpikeMonitor(P2) S3=SpikeMonitor(P3) run(100*ms) subplot(411) plot(M.times/ms,M[0]) subplot(412) raster_plot(S) xlim(0,100) subplot(413) raster_plot(S2) xlim(0,100) subplot(414) raster_plot(S3) xlim(0,100) show() brian-1.4.3/dev/ideas/multiprocessing/000077500000000000000000000000001266062647100177165ustar00rootroot00000000000000brian-1.4.3/dev/ideas/multiprocessing/cluster_manager.py000066400000000000000000000007031266062647100234430ustar00rootroot00000000000000from numpy import * from multiprocessing.connection import Client N = 10000 numprocesses = 6 complexity = 10 address = ('localhost', 2718) conn = Client(address, authkey='secret password') print 'Connection acquired' x = ones(N) conn.send(x) print 'Shared data sent' results = [] for a in xrange(numprocesses): conn.send((a, complexity)) results.append(conn.recv()) print results conn.send(None) conn.close() brian-1.4.3/dev/ideas/multiprocessing/cluster_worker.py000066400000000000000000000011341266062647100233410ustar00rootroot00000000000000from numpy import * from multiprocessing.connection import Listener from array import array def f(x, a, n): s = 0 for _ in xrange(n): s += sum(x) * a return s address = ('localhost', 2718) # family is deduced to be 'AF_INET' listener = Listener(address, authkey='secret password') print 'Listener created' conn = listener.accept() print 'connection accepted from', listener.last_accepted x = conn.recv() while True: item = conn.recv() if item is None: break a, n = item conn.send(f(x, a, n)) listener.close() conn.close() brian-1.4.3/dev/ideas/multiprocessing/clustertools_example_manager.py000066400000000000000000000014051266062647100262370ustar00rootroot00000000000000from numpy import * from brian.library.modelfitting.clustertools import * class work_class(object): def __init__(self, shared_data, use_gpu): self.x = shared_data['x'] def process(self, job): return sum(self.x) * job if __name__ == '__main__': shared_data = {'x':ones(9000)} manager = ClusterManager(work_class, shared_data, machines=['Cyrille-Ulm'], named_pipe=True, # gpu_policy='require_all', # own_max_gpu=0, # own_max_cpu=2, ) results = manager.process_jobs(arange(manager.total_processes) + 1) manager.finished() print results brian-1.4.3/dev/ideas/multiprocessing/clustertools_example_worker.py000066400000000000000000000003531266062647100261370ustar00rootroot00000000000000from clustertools_example_manager import * if __name__ == '__main__': cluster_worker_script(work_class, max_gpu=1, max_cpu=3, named_pipe=True, ) brian-1.4.3/dev/ideas/multiprocessing/handythread.py000066400000000000000000000041021266062647100225600ustar00rootroot00000000000000import sys import time import threading from itertools import izip, count __all__ = ['foreach', 'parallel_map'] def foreach(f, l, threads=3, return_=False): """ Apply f to each element of l, in parallel """ if threads > 1: iteratorlock = threading.Lock() exceptions = [] if return_: n = 0 d = {} i = izip(count(), l.__iter__()) else: i = l.__iter__() def runall(): while True: iteratorlock.acquire() try: try: if exceptions: return v = i.next() finally: iteratorlock.release() except StopIteration: return try: if return_: n, x = v d[n] = f(x) else: f(v) except: e = sys.exc_info() iteratorlock.acquire() try: exceptions.append(e) finally: iteratorlock.release() threadlist = [threading.Thread(target=runall) for j in xrange(threads)] for t in threadlist: t.start() for t in threadlist: t.join() if exceptions: a, b, c = exceptions[0] raise a, b, c if return_: r = d.items() r.sort() return [v for (n, v) in r] else: if return_: return [f(v) for v in l] else: for v in l: f(v) return def parallel_map(f, l, threads=3): return foreach(f, l, threads=threads, return_=True) if __name__ == '__main__': def f(x): print x time.sleep(0.5) foreach(f, range(10)) def g(x): time.sleep(0.5) print x raise ValueError, x time.sleep(0.5) foreach(g, range(10)) brian-1.4.3/dev/ideas/multiprocessing/job_server_processes_pipes.py000066400000000000000000000037751266062647100257320ustar00rootroot00000000000000from numpy import * import multiprocessing import time from multiprocessing import sharedctypes from numpy import ctypeslib import ctypes def f(x, a, n): s = 0 for _ in xrange(n): s += sum(x) * a return s def process_listener(x, conn): x = ctypeslib.as_array(x) while True: conn.poll() a, n = conn.recv() conn.send(f(x, a, n)) if __name__ == '__main__': N = 10000000 numprocesses = 6 complexity = 10 print 'Array size:', N print 'Num processors:', numprocesses print 'Task complexity:', complexity print x = ones(N) x_sct = sharedctypes.Array(ctypes.c_double, x, lock=False) A = 1 + arange(numprocesses) pipes = [multiprocessing.Pipe() for _ in xrange(numprocesses)] server_conns, client_conns = zip(*pipes) processes = [multiprocessing.Process( target=process_listener, args=(x_sct, conn) ) for conn in client_conns] for p in processes: p.start() start = time.time() for i, a in enumerate(A): server_conns[i].send((a, complexity)) for i in xrange(numprocesses): server_conns[i].poll() server_conns[i].recv() end = time.time() t_multiprocessing_1 = end - start start = time.time() for i, a in enumerate(A): server_conns[i].send((a, complexity)) for i in xrange(numprocesses): server_conns[i].poll() server_conns[i].recv() end = time.time() t_multiprocessing_2 = end - start for p in processes: p.terminate() start = time.time() for a in A: f(x, a, complexity) end = time.time() t_single = end - start print 'Single:', t_single print 'Multiprocessing 1:', t_multiprocessing_1 print 'Multiprocessing 2:', t_multiprocessing_2 print 'S/M1:', t_single / t_multiprocessing_1 print 'S/M2:', t_single / t_multiprocessing_2 brian-1.4.3/dev/ideas/multiprocessing/job_server_processes_queues.py000066400000000000000000000040211266062647100261020ustar00rootroot00000000000000from numpy import * import multiprocessing import time from multiprocessing import sharedctypes from numpy import ctypeslib import ctypes def f(x, a, n): s = 0 for _ in xrange(n): s += sum(x) * a return s def process_listener(x, task_queue, done_queue): x = ctypeslib.as_array(x) for task in iter(task_queue.get, 'STOP'): a, n = task done_queue.put(f(x, a, n)) if __name__ == '__main__': N = 10000000 numprocesses = 6 complexity = 10 print 'Array size:', N print 'Num processors:', numprocesses print 'Task complexity:', complexity print x = ones(N) x_sct = sharedctypes.Array(ctypes.c_double, x, lock=False) A = 1 + arange(numprocesses) task_queues = [multiprocessing.Queue() for _ in xrange(numprocesses)] done_queues = [multiprocessing.Queue() for _ in xrange(numprocesses)] processes = [multiprocessing.Process( target=process_listener, args=(x_sct, tasks, done) ) for tasks, done in zip(task_queues, done_queues)] for p in processes: p.start() start = time.time() for i, a in enumerate(A): task_queues[i].put((a, complexity)) for i in xrange(numprocesses): done_queues[i].get() end = time.time() t_multiprocessing_1 = end - start start = time.time() for i, a in enumerate(A): task_queues[i].put((a, complexity)) for i in xrange(numprocesses): done_queues[i].get() end = time.time() t_multiprocessing_2 = end - start for p in processes: p.terminate() start = time.time() for a in A: f(x, a, complexity) end = time.time() t_single = end - start print 'Single:', t_single print 'Multiprocessing 1:', t_multiprocessing_1 print 'Multiprocessing 2:', t_multiprocessing_2 print 'S/M1:', t_single / t_multiprocessing_1 print 'S/M2:', t_single / t_multiprocessing_2 brian-1.4.3/dev/ideas/multiprocessing/multicore.py000066400000000000000000000015631266062647100223000ustar00rootroot00000000000000""" Brian on multiple processors """ from brian import * from multiprocessing import Queue, Process, Pool from time import time eqs = ''' dv/dt = (ge+gi-(v+49*mV))/(20*ms) : volt dge/dt = -ge/(5*ms) : volt dgi/dt = -gi/(10*ms) : volt ''' P = NeuronGroup(4000, model=eqs, threshold= -50 * mV, reset= -60 * mV) P.v = -60 * mV + 10 * mV * rand(len(P)) Pe = P.subgroup(3200) Pi = P.subgroup(800) Ce = Connection(Pe, P, 'ge') Ci = Connection(Pi, P, 'gi') Ce.connect_random(Pe, P, 0.02, weight=1.62 * mV) Ci.connect_random(Pi, P, 0.02, weight= -9 * mV) M = SpikeMonitor(P) duration = 1 * second t = 0 * second p = Pool(2) t1 = time() while t < duration: #p=Process(NeuronGroup.update,[Pe,Pi]) #P.reset() Ce.do_propagate() Ci.do_propagate() M() t += .1 * ms t2 = time() print t2 - t1 raster_plot(M) show() brian-1.4.3/dev/ideas/multiprocessing/multicore_cstateupdater.py000066400000000000000000000103021266062647100252170ustar00rootroot00000000000000from brian import * from brian.experimental.codegen.codegen_c import * from brian.experimental.codegen.integration_schemes import * import time from scipy import weave import numpy, scipy from brian.experimental.codegen.c_support_code import * from handythread import * import re class MulticoreCStateUpdater(StateUpdater): def __init__(self, eqs, scheme, clock=None, freeze=False, threads=1): self.threads = threads self.clock = guess_clock(clock) self.code_c = CCodeGenerator().generate(eqs, scheme) self.code_c = "Py_BEGIN_ALLOW_THREADS\n" + self.code_c + "Py_END_ALLOW_THREADS\n" code = '' for line in self.code_c.split('\n'): if line.startswith('double *') and line.endswith('num_neurons;'): line = line[:-1] + '+offset;' if line == 'for(int _i=0;_i 1: P._state_updater = MulticoreLinearStateUpdater(P._state_updater, P, ncpu=ncpu) run(1 * msecond) start = time.time() run(duration) end = time.time() print 'N:', N print 'duration:', duration print 'ncpu:', ncpu print 'Time taken:', end - start del P._state_updater if monitor: subplot(211) raster_plot(M) subplot(212) Mv.plot() show() brian-1.4.3/dev/ideas/multiprocessing/multiprocess_and_gpu.py000066400000000000000000000056511266062647100245250ustar00rootroot00000000000000''' Sample code showing running multiple processes including multiple GPUs See Brian/examples/multiprocessing/* for detailed examples with comments. Notes for Cyrille: This file works whether or not a GPU is present. If there is a GPU then it will launch num_gpus processes, otherwise it will launch num_cpus processes. Things to know: * Launch one process for each CPU/GPU * Have a function f and a set of arguments x * Each value f(x) is computed in a separate process * The arguments x need to be picklable. Most Brian objects are not, so you'll need to pass parameters etc. - enough parameters that you can reconstruct the ModelFitting objects in each separate process * If using the GPU, you need to pass a copy of the process number to each process so that it knows which GPU to use ''' import multiprocessing try: import pycuda.autoinit as autoinit import pycuda.driver as drv def set_gpu_device(n): ''' This function makes pycuda use GPU number n in the system. ''' global _gpu_context # we make this a global variable so that a # reference is kept to it and the context is not # deallocated autoinit.context.pop() # delete the old context before making a new one _gpu_context = drv.Device(n).make_context() use_gpu = True except ImportError: use_gpu = False # Note that func actually takes only one argument, that argument is a tuple # of arguments (which is why there are double parentheses). The reason is that # pool.map only works for functions of one argument. Note also that the arguments # have to be picklable and not all Brian objects are, however all numpy objects # should be. def func((process_n, x)): # if we are using the GPU then there should be one process for each # GPU, and process_n should use GPU number process_n. if use_gpu: set_gpu_device(process_n) # just a trivial computation to give the idea return x ** 2 if __name__ == '__main__': # If we have GPUs we use them, otherwise we use CPUs if use_gpu: numprocesses = drv.Device.count() # number of GPUs present in the system print 'Using %d GPUs' % numprocesses else: numprocesses = multiprocessing.cpu_count() # number of CPUs in the system print 'Using %d CPUs' % numprocesses pool = multiprocessing.Pool(processes=numprocesses) # args consists of pairs (process_n, x) where process_n is the number of the # process, which is used to initialiase the correct GPU if one is present, # and x is the argument to the function (see notes in module docstring # about this) args = zip(range(numprocesses), range(2, numprocesses + 2)) results = pool.map(func, args) # launches multiple processes for (pid, arg), res in zip(args, results): print 'Process', pid, 'argument', arg, 'result', res brian-1.4.3/dev/ideas/multiprocessing/multiprocessing_shared_mem.py000066400000000000000000000044121266062647100257040ustar00rootroot00000000000000from numpy import * import multiprocessing import time from multiprocessing import sharedctypes from numpy import ctypeslib import ctypes def f(x, a, n): s = 0 for _ in xrange(n): s += sum(a * x) return s def test_with_copy_func((x, a, n)): return f(x, a, n) def test_with_copy(N, M, complexity): x = ones(N) A = 1 + arange(M) pool = multiprocessing.Pool() args = [(x, a, complexity) for a in A] start = time.time() results = pool.map(test_with_copy_func, args) end = time.time() t_multiprocessing = end - start results = [] start = time.time() for x, a, complexity in args: results.append(test_with_copy_func((x, a, complexity))) end = time.time() t_single = end - start print 'Test with copy' print '--------------' print 'Single:', t_single print 'Multiprocessing:', t_multiprocessing print 'S/M:', t_single / t_multiprocessing def test_with_shared_func(x, a, complexity): x = ctypeslib.as_array(x) return f(x, a, complexity) def test_with_shared(N, M, complexity): x = ones(N) x_sct = sharedctypes.Array(ctypes.c_double, x, lock=False) A = 1 + arange(M) #pool = multiprocessing.Pool() start = time.time() processes = [multiprocessing.Process(target=test_with_shared_func, args=(x_sct, a, complexity)) for a in A] for p in processes: p.start() for p in processes: p.join() end = time.time() t_multiprocessing = end - start results = [] args = [(x, a, complexity) for a in A] start = time.time() for x, a, complexity in args: results.append(test_with_shared_func(x, a, complexity)) end = time.time() t_single = end - start print 'Test with shared' print '----------------' print 'Single:', t_single print 'Multiprocessing:', t_multiprocessing print 'S/M:', t_single / t_multiprocessing if __name__ == '__main__': N = 10000000 M = 8 complexity = 1 print 'Array size:', N print 'Num processors:', M print 'Task complexity:', complexity print test_with_shared(N, M, complexity) print test_with_copy(N, M, complexity) brian-1.4.3/dev/ideas/multisynapses.py000066400000000000000000000011061266062647100177570ustar00rootroot00000000000000# -*- coding:utf-8 -*- """ Multiple synapses for each axon """ from brian import * input=SpikeGeneratorGroup(2,[(0,10*ms),(1,50*ms)]) neurons=NeuronGroup(3,model='dv/dt=-v/(10*ms):1') synapses=Connection(input,neurons,'v',delay=True,max_delay=10*ms) M=StateMonitor(neurons,'v',record=True) #synapses[0,0]=1 #synapses.delay[0,0]=2*ms synapses.W.rows[0]=[1,1] synapses.W.data[0]=[1,.5] synapses.delayvec.rows[0]=[1,1] synapses.delayvec.data[0]=[2*ms,5*ms] run(80*ms) plot(M.times/ms,M[0],'r') plot(M.times/ms,M[1],'b') plot(M.times/ms,M[2],'k') show() brian-1.4.3/dev/ideas/neuronal_music.py000066400000000000000000000014771266062647100200750ustar00rootroot00000000000000# -*- coding:utf-8 -*- """ Sounds generated by neurons Doesn't really work! """ from brian import * from brian.hears import * import time eqs = ''' dv/dt = (ge+gi-(v+49*mV))/(20*ms) : volt dge/dt = -ge/(5*ms) : volt dgi/dt = -gi/(10*ms) : volt ''' P = NeuronGroup(4000, model=eqs, threshold= -50 * mV, reset= -60 * mV) P.v = -60 * mV + 10 * mV * rand(len(P)) Pe = P.subgroup(3200) Pi = P.subgroup(800) Ce = Connection(Pe, P, 'ge', weight=1.62 * mV, sparseness=0.02) Ci = Connection(Pi, P, 'gi', weight= -9 * mV, sparseness=0.02) def play(spikes): if len(spikes) > 0: click(0.1 * ms, clip(len(spikes) * .1, 0, 1)).extend(0.1 * ms).play() #time.sleep(1*ms) M = SpikeMonitor(P, function=play) #click(1*ms,.1).play() #whitenoise(10*ms).play() run(1 * second) brian-1.4.3/dev/ideas/new_raster_plot.py000066400000000000000000000007111266062647100202470ustar00rootroot00000000000000''' A different way of plotting raster plots ''' from brian import * tau = 5 * ms N = 10 f = 50 * Hz a = .2 eqs = ''' dv/dt=(v0-v+a*(sin(2*pi*f*t)+sin(4*pi*f*t)))/tau : 1 v0 : 1 ''' neurons = NeuronGroup(N, model=eqs, threshold=1, reset=0) neurons.v0 = 1.37 neurons.v = rand(N) run(1 * second) S = SpikeMonitor(neurons) run(100 * ms) #raster_plot(S) for i, t in S.spikes: plot([t, t], [i, i + .9], 'b', linewidth=2) show() brian-1.4.3/dev/ideas/new_refractoriness.py000066400000000000000000000230731266062647100207500ustar00rootroot00000000000000""" Idea: Refractoriness should be controlled by allowing user provided code for any of the following events: * Start of refractoriness (equivalent to reset in the old scheme) * During refractoriness (equivalent to refractoriness in the old scheme) * End of refractoriness (didn't exist in the old scheme) Code can set any of the following variables: * is_refractory: directly control whether or not the neuron is in a refractory period. If it is set False during refractoriness it will automatically trigger the end of refractoriness event. * refractory_until, refractory_for: control the duration of the refractoriness either by setting the end time, or the duration In addition, it is backwards compatible in that you can specify an array of refractory times, or a state variable which can be used for variable refractoriness, or of course just a single value. Users provide code either as Python functions or strings for each of the three events, but there are some default or built-in types of refractoriness you can use, notably the reset and clamp. The reset will allow you to just specify a reset function (like V=Vr) and will set the refractory_for based on either the constant, array or state variable. The clamp automatically records the post-reset values of one or more variables and holds them there for the period of the refractoriness. Internally, it is implemented using the existing scheme of having an internal NeuronGroup variable _next_allowed_spiketime which gives the time when the neuron is next allowed to spike. Each time step, the following sequence of operations is carried out: * spikes = threshold() * Filter spikes based on t>_next_allowed_spiketime * Trigger during_refractoriness events based on _next_allowed_spiketime>=t * Trigger end_refractoriness events based on _next_allowed_spiketime_next_allowed_spiketime is O(num neurons) but I don't see any way to avoid this if we want to allow variable refractoriness, and in any case the state update and threshold are already O(num neurons). We could have optimisations in the case that we know that user code doesn't use the variable refractoriness features. TODO: * Nice syntax for users * Allow strings for code + Python and C versions preferably """ from brian import * import numpy class TriggeringArray(numpy.ndarray): def __new__(subtype, arr, trigger): # All numpy.ndarray subclasses need something like this, see # http://www.scipy.org/Subclasses return numpy.array(arr, copy=False).view(subtype) def __init__(self, arr, trigger): self.trigger = trigger self.triggered = False def __getitem__(self, item): return asarray(numpy.ndarray.__getitem__(self, item)) def __setitem__(self, item, value): self.trigger(item, value) self.triggered = True numpy.ndarray.__setitem__(self, item, value) OrigNeuronGroup = NeuronGroup class NeuronGroup(OrigNeuronGroup): def __init__(self, *args, **kwds): refractoriness = kwds.pop('refractoriness') OrigNeuronGroup.__init__(self, *args, **kwds) self._refractoriness = refractoriness self.is_refractory = TriggeringArray(zeros(len(self), dtype=bool), self._is_refractory_trigger) self.refractory_until = TriggeringArray(zeros(len(self)), self._refractory_until_trigger) self.refractory_for = TriggeringArray(zeros(len(self)), self._refractory_for_trigger) def _is_refractory_trigger(self, spikes, values): if isinstance(values, numpy.ndarray): self._next_allowed_spiketime[spikes[values!=0]] = 1e300 # i.e. refractory for ever self._next_allowed_spiketime[spikes[values==0]] = self.clock._t else: if values: self._next_allowed_spiketime[spikes] = 1e300 else: self._next_allowed_spiketime[spikes] = self.clock._t def _refractory_for_trigger(self, spikes, values): self._next_allowed_spiketime[spikes] = self.clock._t+values def _refractory_until_trigger(self, spikes, values): self._next_allowed_spiketime[spikes] = values def update(self): self._state_updater(self) # update the variables if self._spiking: refrac = self._refractoriness spikes = self._threshold(self) # get spikes if not isinstance(spikes, numpy.ndarray): spikes = array(spikes, dtype=int) # Filter refractory spikes spikes = spikes[self._next_allowed_spiketime[spikes]<=self.clock._t] # Trigger during_refractory based on currently refractory indices current_refrac_time = self._next_allowed_spiketime-self.clock._t currently_refractory, = (current_refrac_time>=0).nonzero() if refrac.during is not None: refrac.during(self, currently_refractory) # might need to recompute this if is_refractory or others have # been used. if self.is_refractory.triggered or self.refractory_until.triggered or self.refractory_for.triggered: self.is_refractory.triggered = False self.refractory_until.triggered = False self.refractory_for.triggered = False current_refrac_time[currently_refractory] = self._next_allowed_spiketime[currently_refractory]-self.clock._t # Trigger end_refractory based on stop_refractory stop_refractory = currently_refractory[current_refrac_time[currently_refractory]1*ms def end(G, indices): G.w[indices] = 0 ref = Refractoriness(start, during, end) G = NeuronGroup(3, eqs, threshold=1, refractoriness=ref, refractory='refractory', max_refractory=5*ms # dummy value ) G._variable_refractory_time = True G._refractory_variable = 'refractory' G._refractory_array = None G.refractory = [2*ms, 3*ms, 5*ms] M = MultiStateMonitor(G, record=True) Msp = SpikeMonitor(G) run(40*ms) for k in Msp.spikes: print k for i, var in enumerate(M.vars): subplot(len(M.vars), 1, i+1) M[var].plot() title(var) show() brian-1.4.3/dev/ideas/newneurongroup/000077500000000000000000000000001266062647100175645ustar00rootroot00000000000000brian-1.4.3/dev/ideas/newneurongroup/newneurongroup.py000066400000000000000000000072321266062647100232370ustar00rootroot00000000000000""" NewNeuronGroup: Has multiple states Each state has: Name/ID Set of equations Set of events Each event is defined by: Condition Response Equations are combined, state updates are performed by code generation, linear part of differential equations are grouped together where possible so that dot operation can be performed on a contiguous slice, with preference to 'main' state. New equation syntax: statename: ... eqs statename: ... eqs First statename is left out, and is called 'main' with id 0. Equations follow the original Brian form, but you can add additional tags after each equation to add meta-info: (const) Variable is constant, can use this info in numerical integration solvers Event syntax: condition is straight code response is a sequence of statements Special events: spike condition is threshold response is 'event->reset' reset no condition response is given by user 'reset' followed by 'event->main' Have additional special names: state -> statename: changes the neuron state event -> eventname: immediately calls the response of a given event Refractoriness: Standard form handled like so: spike event: event->refractory_reset state->refractory refractory_reset event: refractory_until = t+refractory event->reset reset event: provided by user end_refractory event: condition: t>=refractory_until response: state->main More complicated forms can be implemented Additional names: refractory : second refractory_until : second Higher order differential equations: Can be converted to systems of first order differential equations What should the syntax be? Easy bit is: 3 * d2x/dt2 + 2 * d2y/dt2 = 6*x 4 * d2x/dt2 + 1 * d2y/dt2 = 5*y But how do you specify units and other properties? x in volt, y is const, etc.? But this introduces a different syntax to the old one. But it can be backwards compatible, e.g. tau dV/dt = Vr-V V, Vr in volt Vr is const Other ideas for syntax, not to use strings so heavily, e.g.: eqs = Equations( # first equations is for state 'main' ''' dV/dt = -(Vr-V)/tau dVt/dt = -(Vt0-Vt)/taut ''', # keywords give state names, here 'refractory' refractory=''' dVt/dt = -(Vt0-Vt)/taut ''', parameters=['Vr', 'Vt0'], units={'V':volt, 'Vt0':volt}, constants=['Vr', 'Vt0'], ) This could be backwards compatible by keeping the ':unit' syntax and the parameter syntax. Another question is how to share equations between states, e.g. you might want an adaptation variable to continue evolving during a refractory period even though the membrane potential stops evolving. You can copy the differential equation, but it violates the principle of not duplicating code/definitions (because they might go out of sync). - One possibility would be to have a set of shared equations that are always active, but I'm not sure there is a good syntax for this and it's not so general anyway + one syntax would be '(shared)' after the definition - You could have a syntax like 'x from main' in the definition of a state, to copy the equation of x from state main. """ brian-1.4.3/dev/ideas/notes.txt000066400000000000000000000033671266062647100163710ustar00rootroot00000000000000Connection matrix: ------------------ * dense * sparse * calculated (e.g. with random seeds) * probabilistic (release probabilities) Should have operations for STDP / short-term plasticity Operations: ** Construction ** * W[i,j]=... (setitem) * W[i,j] (getitem) (view?) * W[s1,s2]=... where s1,s2=slices (-> submatrix; connect function) * freeze() (e.g. lil_matrix to faster version) -> used when the structure (graph) will not change anymore (use instead of compress()) ** Propagation ** * X+=W[i,:] (propagation), where X is dense e.g. W.addrow(i,X) * Short-term plasticity (modulation): X+=W[i,:]*scalar ** Plasticity ** Careful: dendritic or axonal delay? * W[i,:]+=scalar or vector * W[i,:]*=idem * idem for columns maybe: W.set_row(i,x), set_column(j,x), add_row, etc. Do we need views? Event-driven algorithms ----------------------- With homogeneous synaptic delays, event-driven algorithms should be possible (otherwise it would be too slow because non-vectorisable). The idea is that each tick of the clock is an emitted spike (next neuron to spike). Algorithm for one spike: * Find which neuron is spiking next (top of priority queue) * Synchronous update of all target neurons (possibly delayed) * Updates of spike timings of target neurons in priority queue The last bit only needs to be done in C (e.g. inline function for a start). It is even possible that spike timing updates are rare enough to be done in Python. Is it possible to mix clock-driven and event-driven networks? (maybe) The data structure should be very different. What is needed for a neuron group: * Advance to a given time * Spike timing calculation * Reset function Possibly, these could replace model, threshold and reset objects? brian-1.4.3/dev/ideas/old/000077500000000000000000000000001266062647100152455ustar00rootroot00000000000000brian-1.4.3/dev/ideas/old/alternative_qarray.txt000066400000000000000000000043011266062647100217010ustar00rootroot00000000000000Alternative Quantity and QuantityArray design notes =================================================== For consideration for Brian releases after 1.0. The current Quantity and qarray design works as follows: The class Dimension is implemented entirely in Python and defines a tuple of indices of each of the 7 basic SI dimensions. The class Quantity is derived from numpy's float and adds a Dimension object. The class qarray is derived from numpy's ndarray and adds a unit array on top, which can be either homogeneous or not (corresponding to classes homog_unitarray or unitarray). These classes are derived from ndarray. Both store a copy of the units as Quantity objects with float value 1.0. This system has multiple levels of indirection, and the result is slow code. A qarray based on homogeneous units performs worse than 10 times slower than a straight numpy array, whereas one with nonhomogeneous units performs several thousand times slower (i.e. unusable in practice for anything other than tiny arrays). Alternative design strategy --------------------------- This design strategy could be implemented in Python or C code, the latter would be considerably faster. Dimension class could be a numpy array. Could define a DimensionArray class, which could potentially be implemented directly as a numpy array with one extra dimension (so that the DimensionArray of a class with shape S would be an array with shape S+(7,). There would also be either a HomogeneousDimensionArray class which would essentially just store one dimension and keep track of an appropriate shape, or the alternative qarray class could keep track of this (maybe this is actually better?). Finally, an alternative QuantityArray class would be based on a DimensionArray and an underlying ndarray. This would save many levels of indirection and redundant processing, and because everything would be based on numpy arrays, it should run much faster. Other considerations -------------------- There is also the possibility of mixed homogeneous/non-homogeneous units. For example, one column may have all units the same, whereas another will have varying units. This ought to be possible to implement.brian-1.4.3/dev/ideas/old/exp_with_diffeq.py000066400000000000000000000023771266062647100207750ustar00rootroot00000000000000from brian import * if False: tau = 1 * second eqs = ''' dy/dt = v/tau : 1 dv/dt = -y/tau : 1 dx/dt = exp(y)/tau : 1 dw/dt = z/tau : 1 # w should be the same as x dz/dt = v*z/tau : 1 ''' G = NeuronGroup(1, eqs) M = MultiStateMonitor(G, record=True, when='start') G.y = 0 G.v = 1 G.x = 0 G.w = G.x G.z = exp(G.y) run(1 * second) M.plot() legend() show() if True: tau = 1 * second eqs = ''' dy/dt=exp(y)/tau:1 dx/dt=u/tau:1 du/dt=u*u/tau:1 ''' G = NeuronGroup(1, eqs) M = MultiStateMonitor(G, record=True, when='start') G.y = 1 G.x = G.y G.u = exp(G.y) run(exp(-1) * 0.99 * second) z = -log(-M.times + exp(-1)) subplot(211) plot(M.times, M['x', 0], label='dx/dt=u, du/dt=u*u') plot(M.times, M['y', 0], label='dy/dt=exp(y)') plot(M.times, z, label='analytic soln') # analytic solution legend(loc='upper left') subplot(212) plot(M.times, abs(M['x', 0] - z), label='abs err in x') plot(M.times, abs(M['y', 0] - z), label='abs err in y') legend(loc='upper left') print 'max err in x', max(abs(M['x', 0] - z)) print 'max err in y', max(abs(M['y', 0] - z)) show() brian-1.4.3/dev/ideas/picloud/000077500000000000000000000000001266062647100161265ustar00rootroot00000000000000brian-1.4.3/dev/ideas/picloud/briantest.py000066400000000000000000000026551266062647100205030ustar00rootroot00000000000000import cloud import time from brian import * from multimap import * def stdp_example(N=1000): taum = 10 * ms tau_pre = 20 * ms tau_post = tau_pre Ee = 0 * mV vt = -54 * mV vr = -60 * mV El = -74 * mV taue = 5 * ms F = 15 * Hz gmax = .01 dA_pre = .01 dA_post = -dA_pre * tau_pre / tau_post * 1.05 eqs_neurons = ''' dv/dt=(ge*(Ee-vr)+El-v)/taum : volt # the synaptic current is linearized dge/dt=-ge/taue : 1 ''' input = PoissonGroup(N, rates=F) neurons = NeuronGroup(1, model=eqs_neurons, threshold=vt, reset=vr) synapses = Connection(input, neurons, 'ge', weight=rand(len(input), len(neurons)) * gmax) neurons.v = vr #stdp=ExponentialSTDP(synapses,tau_pre,tau_post,dA_pre,dA_post,wmax=gmax) ## Explicit STDP rule eqs_stdp = ''' dA_pre/dt=-A_pre/tau_pre : 1 dA_post/dt=-A_post/tau_post : 1 ''' dA_post *= gmax dA_pre *= gmax stdp = STDP(synapses, eqs=eqs_stdp, pre='A_pre+=dA_pre;w+=A_post', post='A_post+=dA_post;w+=A_pre', wmax=gmax) rate = PopulationRateMonitor(neurons) run(30 * second, report='text') return mean(rate.rate) ilist = [1000 for _ in xrange(max_parallel)] jids = multimap(stdp_example, ilist) t1 = time.clock() results = retrieve(jids) t = time.clock() - t1 print "All jobs done in %.1f seconds, results :" % t print results brian-1.4.3/dev/ideas/picloud/multimap.py000066400000000000000000000042751266062647100203400ustar00rootroot00000000000000import cloud from keys import * from numpy import array, cumsum, nonzero naccounts = len(api_keys) max_parallel = sum(array(parallelism[:naccounts])) print "Current parallelism level : %d" % max_parallel __all__ = ['multimap', 'retrieve', 'status', 'naccounts', 'max_parallel'] def multimap(fun, args, naccounts=None): if naccounts is None: naccounts = len(api_keys) max_parallel = sum(array(parallelism[:naccounts])) if len(args) <= max_parallel: naccounts = nonzero(cumsum(parallelism) - len(args) >= 0)[0][0] + 1 size = parallelism[:naccounts] else: size = [len(args) / naccounts for _ in xrange(naccounts)] if naccounts > 1: size[-1] = len(args) - sum(array(size[:-1])) else: size[0] = len(args) # jids[i] contains the job indices for account i jids = [None for _ in xrange(naccounts)] # Launches the jobs k = 0 for i in xrange(naccounts): api_key = api_keys[i] api_secretkey = api_secretkeys[i] n = size[i] args_tmp = args[k:k + n] if len(args_tmp) > 0: print "Launching %d jobs with account %d..." % (len(args_tmp), i + 1) cloud.setkey(api_key=api_key, api_secretkey=api_secretkey) jids[i] = cloud.map(fun, args_tmp, _high_cpu=True) print " Jobs:", jids[i] k += n return jids def retrieve(jids): naccounts = len(jids) results = [] # Retrieves the results for i in xrange(naccounts): api_key = api_keys[i] api_secretkey = api_secretkeys[i] cloud.setkey(api_key=api_key, api_secretkey=api_secretkey) print "Retrieving results for account %d..." % (i + 1) results.extend(cloud.result(jids[i])) return results def status(jids): naccounts = len(jids) status = [] # Retrieves the results for i in xrange(naccounts): api_key = api_keys[i] api_secretkey = api_secretkeys[i] cloud.setkey(api_key=api_key, api_secretkey=api_secretkey) print "Retrieving status for account %d..." % (i + 1) status.extend(cloud.status(jids[i])) return status brian-1.4.3/dev/ideas/picloud/notes.txt000066400000000000000000000033711266062647100200230ustar00rootroot00000000000000Using Brian with Picloud ======================== Install the Picloud client -------------------------- * Download the following file : https://media.picloud.com/downloads/cloud/1.9/cloud-1.9-py2.6.egg * Open a console in the same folder where the file was downloaded, and run the command : easy_install cloud-1.9-py2.6.egg Test Script ----------- * Run the script 'test.py' and wait a few seconds for the jobs to be finished ! Test Brian Script ----------------- * Run the script 'briantest.py' and wait about 3 minutes for the 32 jobs to be finished ! Using Picloud ------------- * Copy 'multimap.py' and 'keys.py' into your working directory. * To use Picloud by calling a single function with multiple arguments in parallel, first write your function with a single argument (you can have global variables defined in the script that are used in the function) : from multimap import * def fun(x): # do something return y Your function can do network simulations with Brian, but you can't use any plotting functions. * Define the list of arguments : args = [arg1, arg2, arg3, ...] * Launch the jobs in the cloud with the 'multimap' function : jids = multimap(fun, args) * After a few seconds, jids contains the list of the job indices (one job = one argument). The machines in the cloud are now running your functions, so this may take a while. You can shutdown your computer, the jobs will still be running. * To retrieve the results, call the 'retrieve' function : results = retrieve(jids) This line will complete only when all jobs have completed. Finally, 'results' is the list of the results for all the arguments. brian-1.4.3/dev/ideas/picloud/test.py000066400000000000000000000007771266062647100174720ustar00rootroot00000000000000from multimap import * import time def fun(x): return x ** 2 # max_parallel is defined in multimap.py and contains the maximum number of jobs that can # run simultaneously. args = range(max_parallel) # Launch the jobs on the cloud and returns the job ids used to retrieve the results later. jids = multimap(fun, args) # Prints the status of the jobs after 10 seconds time.sleep(10) print status(jids) # Retrieves the results (BLOCKING call) results = retrieve(jids) print results brian-1.4.3/dev/ideas/playdoh/000077500000000000000000000000001266062647100161275ustar00rootroot00000000000000brian-1.4.3/dev/ideas/playdoh/distfun_brianbenchmark.py000066400000000000000000000036151266062647100232100ustar00rootroot00000000000000from brian import NeuronGroup, SpikeCounter, Network, ms, second, isscalar def fun(sigma, args): """ This function computes the mean firing rate of a LIF neuron with white noise input current (OU process with threshold). """ if not isscalar(sigma): raise Exception('sigma must be a scalar') N = args['N'] tau = args['tau'] model = args['model'] reset = args['reset'] threshold = args['threshold'] duration = args['duration'] G = NeuronGroup(N, model=model, reset=reset, threshold=threshold) M = SpikeCounter(G) net = Network(G, M) net.run(duration) r = M.nspikes * 1.0 / N return r if __name__ == '__main__': from playdoh import * import numpy as np import time N = 2000 tau = 10 * ms model = '''dV/dt=-V/tau+sigma*(2/tau)**.5*xi : 1''' reset = 0 threshold = 1 duration = 1 * second sigmas = list(np.linspace(.3, .6, 8)) t = time.clock() for s in sigmas: r = fun(s, dict(N=N, tau=tau, model=model, reset=reset, threshold=threshold, duration=duration)) print "Mean firing rate with sigma = %.2f : %.2f Hz" % (s, r) print "Serial time = %.3f s" % (time.clock() - t) print dfun = distribute(fun, dict(N=N, tau=tau, model=model, reset=reset, threshold=threshold, duration=duration)) t = time.clock() results = dfun(sigmas) t = time.clock() - t for s, r in zip(sigmas, results): print "Mean firing rate with sigma = %.2f : %.2f Hz" % (s, r) print "Parallel time = %.3f s" % t brian-1.4.3/dev/ideas/playdoh/distfun_brianexample.py000066400000000000000000000037571266062647100227200ustar00rootroot00000000000000from brian import NeuronGroup, SpikeCounter, Network, ms, second, isscalar """ This script shows how to distribute a Brian script without effort. Define a function as follows : def fun(x, args): # ... return r This function defines a NeuronGroup and does some operations before returning a result r. 'x' is a parameter, and you want to calculate r for different values of x. Also, there are several static parameters in args that do not change. If you call r = fun(x, dict(arg1=val1, ...)) then your function should return the right result. If you want to evaluate fun with several values of x, then you would do : xlist = ... rlist = zeros(len(xlist)) for i in xrange(len(xlist)): rlist[i] = fun(xlist[i], dict(arg1=val1, ...)) This code snippet can be accelerated if you have multiple CPUs by doing this : from playdoh import * dfun = distribute(fun, dict(arg1=val1, ...)) xlist = ... rlist = dfun(xlist) That's it! """ def fun(sigma, args): """ This function computes the mean firing rate of a LIF neuron with white noise input current (OU process with threshold). """ if not isscalar(sigma): raise Exception('sigma must be a scalar') N = args['N'] tau = args['tau'] model = args['model'] reset = args['reset'] threshold = args['threshold'] duration = args['duration'] G = NeuronGroup(N, model=model, reset=reset, threshold=threshold) M = SpikeCounter(G) net = Network(G, M) net.run(duration) r = M.nspikes * 1.0 / N return r if __name__ == '__main__': from playdoh import * N = 2000 tau = 10 * ms model = '''dV/dt=-V/tau+sigma*(2/tau)**.5*xi : 1''' reset = 0 threshold = 1 duration = 1 * second sigmas = [.3, .4, .5, .6] dfun = distribute(fun, dict(N=N, tau=tau, duration=duration, model=model, reset=reset, threshold=threshold)) rates = dfun(sigmas) print rates brian-1.4.3/dev/ideas/probabilistic_synapses.py000066400000000000000000000030061266062647100216130ustar00rootroot00000000000000""" Stochastic synaptic transmission. In this example, two Poisson neurons project to N=20 neurons. For the first input, the transmission probability increases (0 to 1) from neuron 0 to neuron 19. For the second input, the transmission probability decreases (1 to 0) from neuron 0 to neuron 19. The membrane potential of all 20 neurons are shown. """ from brian import * # This is dense, but we could do a sparse one class ProbabilisticConstructionMatrix(DenseConstructionMatrix): def __init__(self,shape,**kwds): DenseConstructionMatrix.__init__(self,shape,**kwds) self.w0=1. # constant synaptic weight def connection_matrix(self): return ProbabilisticConnectionMatrix(self) class ProbabilisticConnectionMatrix(DenseConnectionMatrix): def __init__(self, val, **kwds): DenseConnectionMatrix.__init__(self, val, **kwds) self.w0=val.w0 def get_rows(self, rows): return [(rand(len(self.rows[i])) #endif supp_code = ''' class Work { public: double *x; int istart, iend, N; Work(double *x, int istart, int iend, int N) : x(x), istart(istart), iend(iend), N(N) {}; }; void do_work(void* workobj) { Work *w = (Work*)workobj; double *x = w->x; int istart = w->istart; int iend = w->iend; int N = w->N; for(int i=istart; i'], extra_compile_args=['-pthread'], ) end = time.time() print end-start plot(x) show() brian-1.4.3/dev/ideas/pthreads/win32/000077500000000000000000000000001266062647100172435ustar00rootroot00000000000000brian-1.4.3/dev/ideas/pthreads/win32/pthread.c000066400000000000000000001614221266062647100210440ustar00rootroot00000000000000 pthread.c - numexpr - Project Hosting on Google Code
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/*
 * Code for simulating pthreads API on Windows.  This is Git-specific,
 * but it is enough for Numexpr needs too.
 *
 * Copyright (C) 2009 Andrzej K. Haczewski <ahaczewski@gmail.com>
 *
 * DISCLAIMER: The implementation is Git-specific, it is subset of original
 * Pthreads API, without lots of other features that Git doesn't use.
 * Git also makes sure that the passed arguments are valid, so there's
 * no need for double-checking.
 */

#include "pthread.h"

#include <errno.h>
#include <limits.h>


void die(const char *err, ...)
{
	printf("%s", err);
	exit(-1);
}

static unsigned __stdcall win32_start_routine(void *arg)
{
	pthread_t *thread = arg;
	thread->arg = thread->start_routine(thread->arg);
	return 0;
}

int pthread_create(pthread_t *thread, const void *unused,
		   void *(*start_routine)(void*), void *arg)
{
	thread->arg = arg;
	thread->start_routine = start_routine;
	thread->handle = (HANDLE)
		_beginthreadex(NULL, 0, win32_start_routine, thread, 0, NULL);

	if (!thread->handle)
		return errno;
	else
		return 0;
}

int win32_pthread_join(pthread_t *thread, void **value_ptr)
{
	DWORD result = WaitForSingleObject(thread->handle, INFINITE);
	switch (result) {
		case WAIT_OBJECT_0:
			if (value_ptr)
				*value_ptr = thread->arg;
			return 0;
		case WAIT_ABANDONED:
			return EINVAL;
		default:
			return GetLastError();
	}
}

int pthread_cond_init(pthread_cond_t *cond, const void *unused)
{
	cond->waiters = 0;
	cond->was_broadcast = 0;
	InitializeCriticalSection(&cond->waiters_lock);

	cond->sema = CreateSemaphore(NULL, 0, LONG_MAX, NULL);
	if (!cond->sema)
		die("CreateSemaphore() failed");

	cond->continue_broadcast = CreateEvent(NULL,	/* security */
				FALSE,			/* auto-reset */
				FALSE,			/* not signaled */
				NULL);			/* name */
	if (!cond->continue_broadcast)
		die("CreateEvent() failed");

	return 0;
}

int pthread_cond_destroy(pthread_cond_t *cond)
{
	CloseHandle(cond->sema);
	CloseHandle(cond->continue_broadcast);
	DeleteCriticalSection(&cond->waiters_lock);
	return 0;
}

int pthread_cond_wait(pthread_cond_t *cond, CRITICAL_SECTION *mutex)
{
	int last_waiter;

	EnterCriticalSection(&cond->waiters_lock);
	cond->waiters++;
	LeaveCriticalSection(&cond->waiters_lock);

	/*
	 * Unlock external mutex and wait for signal.
	 * NOTE: we've held mutex locked long enough to increment
	 * waiters count above, so there's no problem with
	 * leaving mutex unlocked before we wait on semaphore.
	 */
	LeaveCriticalSection(mutex);

	/* let's wait - ignore return value */
	WaitForSingleObject(cond->sema, INFINITE);

	/*
	 * Decrease waiters count. If we are the last waiter, then we must
	 * notify the broadcasting thread that it can continue.
	 * But if we continued due to cond_signal, we do not have to do that
	 * because the signaling thread knows that only one waiter continued.
	 */
	EnterCriticalSection(&cond->waiters_lock);
	cond->waiters--;
	last_waiter = cond->was_broadcast && cond->waiters == 0;
	LeaveCriticalSection(&cond->waiters_lock);

	if (last_waiter) {
		/*
		 * cond_broadcast was issued while mutex was held. This means
		 * that all other waiters have continued, but are contending
		 * for the mutex at the end of this function because the
		 * broadcasting thread did not leave cond_broadcast, yet.
		 * (This is so that it can be sure that each waiter has
		 * consumed exactly one slice of the semaphor.)
		 * The last waiter must tell the broadcasting thread that it
		 * can go on.
		 */
		SetEvent(cond->continue_broadcast);
		/*
		 * Now we go on to contend with all other waiters for
		 * the mutex. Auf in den Kampf!
		 */
	}
	/* lock external mutex again */
	EnterCriticalSection(mutex);

	return 0;
}

/*
 * IMPORTANT: This implementation requires that pthread_cond_signal
 * is called while the mutex is held that is used in the corresponding
 * pthread_cond_wait calls!
 */
int pthread_cond_signal(pthread_cond_t *cond)
{
	int have_waiters;

	EnterCriticalSection(&cond->waiters_lock);
	have_waiters = cond->waiters > 0;
	LeaveCriticalSection(&cond->waiters_lock);

	/*
	 * Signal only when there are waiters
	 */
	if (have_waiters)
		return ReleaseSemaphore(cond->sema, 1, NULL) ?
			0 : GetLastError();
	else
		return 0;
}

/*
 * DOUBLY IMPORTANT: This implementation requires that pthread_cond_broadcast
 * is called while the mutex is held that is used in the corresponding
 * pthread_cond_wait calls!
 */
int pthread_cond_broadcast(pthread_cond_t *cond)
{
	EnterCriticalSection(&cond->waiters_lock);

	if ((cond->was_broadcast = cond->waiters > 0)) {
		/* wake up all waiters */
		ReleaseSemaphore(cond->sema, cond->waiters, NULL);
		LeaveCriticalSection(&cond->waiters_lock);
		/*
		 * At this point all waiters continue. Each one takes its
		 * slice of the semaphor. Now it's our turn to wait: Since
		 * the external mutex is held, no thread can leave cond_wait,
		 * yet. For this reason, we can be sure that no thread gets
		 * a chance to eat *more* than one slice. OTOH, it means
		 * that the last waiter must send us a wake-up.
		 */
		WaitForSingleObject(cond->continue_broadcast, INFINITE);
		/*
		 * Since the external mutex is held, no thread can enter
		 * cond_wait, and, hence, it is safe to reset this flag
		 * without cond->waiters_lock held.
		 */
		cond->was_broadcast = 0;
	} else {
		LeaveCriticalSection(&cond->waiters_lock);
	}
	return 0;
}

Change log

r204 by faltet on Jul 30 (5 days ago)   Diff 
Merged branch multithreaded into trunk.
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Older revisions

r195 by faltet on Jul 29 (6 days ago)   Diff 
Small modifications needed to compile
with MSVC 2008.
r194 by faltet on Jul 29 (6 days ago)   Diff 
Attempt to avoid the use of the
pthreads library on Win32.  Using the
pthreads API emulation for Win that
uses Git.
All revisions of this file

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brian-1.4.3/dev/ideas/pthreads/win32/pthread.h000066400000000000000000001111641266062647100210470ustar00rootroot00000000000000 pthread.h - numexpr - Project Hosting on Google Code
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/*
 * Header used to adapt pthread-based POSIX code to Windows API threads.
 *
 * Copyright (C) 2009 Andrzej K. Haczewski <ahaczewski@gmail.com>
 */

#ifndef PTHREAD_H
#define PTHREAD_H

#ifndef WIN32_LEAN_AND_MEAN
#define WIN32_LEAN_AND_MEAN
#endif

#include <windows.h>

/*
 * Defines that adapt Windows API threads to pthreads API
 */
#define pthread_mutex_t CRITICAL_SECTION

#define pthread_mutex_init(a,b) InitializeCriticalSection((a))
#define pthread_mutex_destroy(a) DeleteCriticalSection((a))
#define pthread_mutex_lock EnterCriticalSection
#define pthread_mutex_unlock LeaveCriticalSection

/*
 * Implement simple condition variable for Windows threads, based on ACE
 * implementation.
 *
 * See original implementation: http://bit.ly/1vkDjo
 * ACE homepage: http://www.cse.wustl.edu/~schmidt/ACE.html
 * See also: http://www.cse.wustl.edu/~schmidt/win32-cv-1.html
 */
typedef struct {
	LONG waiters;
	int was_broadcast;
	CRITICAL_SECTION waiters_lock;
	HANDLE sema;
	HANDLE continue_broadcast;
} pthread_cond_t;

extern int pthread_cond_init(pthread_cond_t *cond, const void *unused);
extern int pthread_cond_destroy(pthread_cond_t *cond);
extern int pthread_cond_wait(pthread_cond_t *cond, CRITICAL_SECTION *mutex);
extern int pthread_cond_signal(pthread_cond_t *cond);
extern int pthread_cond_broadcast(pthread_cond_t *cond);

/*
 * Simple thread creation implementation using pthread API
 */
typedef struct {
	HANDLE handle;
	void *(*start_routine)(void*);
	void *arg;
} pthread_t;

extern int pthread_create(pthread_t *thread, const void *unused,
			  void *(*start_routine)(void*), void *arg);

/*
 * To avoid the need of copying a struct, we use small macro wrapper to pass
 * pointer to win32_pthread_join instead.
 */
#define pthread_join(a, b) win32_pthread_join(&(a), (b))

extern int win32_pthread_join(pthread_t *thread, void **value_ptr);

#endif /* PTHREAD_H */

Change log

r204 by faltet on Jul 30 (5 days ago)   Diff 
Merged branch multithreaded into trunk.
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r194 by faltet on Jul 29 (6 days ago)   Diff 
Attempt to avoid the use of the
pthreads library on Win32.  Using the
pthreads API emulation for Win that
uses Git.
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brian-1.4.3/dev/ideas/pylab_interface.py000066400000000000000000000005731266062647100201750ustar00rootroot00000000000000from brian import * from matplotlib.widgets import Button fig = figure() plot([0, 0], [1, 1]) def onbutton(e): x, y = e.xdata, e.ydata print x, y def nextbutton(e): print 'hello' fig.canvas.mpl_connect('button_press_event', onbutton) axnext = axes([0.81, 0.05, 0.1, 0.075]) bnext = Button(axnext, 'Next') bnext.on_clicked(nextbutton) show() brian-1.4.3/dev/ideas/pylab_wx.py000066400000000000000000000051011266062647100166630ustar00rootroot00000000000000# Check autoglad (gtk) import wxversion wxversion.ensureMinimal('2.8') from numpy import arange, sin, pi import matplotlib # uncomment the following to use wx rather than wxagg #matplotlib.use('WX') #from matplotlib.backends.backend_wx import FigureCanvasWx as FigureCanvas # comment out the following to use wx rather than wxagg matplotlib.use('WXAgg') from matplotlib.backends.backend_wxagg import FigureCanvasWxAgg as FigureCanvas from matplotlib.backends.backend_wx import NavigationToolbar2Wx from matplotlib.figure import Figure import wx class CanvasFrame(wx.Frame): def __init__(self): wx.Frame.__init__(self, None, -1, 'CanvasFrame', size=(550, 350)) self.SetBackgroundColour(wx.NamedColor("WHITE")) self.figure = Figure() self.axes = self.figure.add_subplot(111) t = arange(0.0, 3.0, 0.01) s = sin(2 * pi * t) self.axes.plot(t, s) self.canvas = FigureCanvas(self, -1, self.figure) self.sizer = wx.BoxSizer(wx.VERTICAL) self.sizer.Add(self.canvas, 1, wx.LEFT | wx.TOP | wx.GROW) self.SetSizer(self.sizer) self.Fit() self.add_toolbar() # comment this out for no toolbar def add_toolbar(self): self.toolbar = NavigationToolbar2Wx(self.canvas) self.toolbar.Realize() if wx.Platform == '__WXMAC__': # Mac platform (OSX 10.3, MacPython) does not seem to cope with # having a toolbar in a sizer. This work-around gets the buttons # back, but at the expense of having the toolbar at the top self.SetToolBar(self.toolbar) else: # On Windows platform, default window size is incorrect, so set # toolbar width to figure width. tw, th = self.toolbar.GetSizeTuple() fw, fh = self.canvas.GetSizeTuple() # By adding toolbar in sizer, we are able to put it at the bottom # of the frame - so appearance is closer to GTK version. # As noted above, doesn't work for Mac. self.toolbar.SetSize(wx.Size(fw, th)) self.sizer.Add(self.toolbar, 0, wx.LEFT | wx.EXPAND) # update the axes menu on the toolbar self.toolbar.update() def OnPaint(self, event): self.canvas.draw() class App(wx.App): def OnInit(self): 'Create the main window and insert the custom frame' frame = CanvasFrame() frame.Show(True) return True app = App(0) app.MainLoop() brian-1.4.3/dev/ideas/pypar/000077500000000000000000000000001266062647100156225ustar00rootroot00000000000000brian-1.4.3/dev/ideas/pypar/cluster_test.py000066400000000000000000000026331266062647100207200ustar00rootroot00000000000000''' Test of the cluster module ''' from brian import * import time from brian.experimental.cluster import * print "I am the server" start_time = time.time() taum = 20 * ms taue = 5 * ms taui = 10 * ms Vt = -50 * mV Vr = -60 * mV El = -49 * mV eqs = Equations(''' dv/dt = (ge+gi-(v-El))/taum : volt dge/dt = -ge/taue : volt dgi/dt = -gi/taui : volt ''') P = NeuronGroup(4000, model=eqs, threshold=Vt, reset=Vr, refractory=5 * ms) P.v = Vr P.ge = 0 * mV P.gi = 0 * mV Pe = P.subgroup(3200) Pi = P.subgroup(800) we = (60 * 0.27 / 10) * mV # excitatory synaptic weight (voltage) wi = (-20 * 4.5 / 10) * mV # inhibitory synaptic weight Ce = Connection(Pe, P, 'ge', weight=we, sparseness=0.02) Ci = Connection(Pi, P, 'gi', weight=wi, sparseness=0.02) P.v = Vr + rand(len(P)) * (Vt - Vr) # Record the number of spikes Me = PopulationSpikeCounter(Pe) Mi = PopulationSpikeCounter(Pi) # A population rate monitor M = PopulationRateMonitor(P) print "Network construction time:", time.time() - start_time, "seconds" print len(P), "neurons in the network" print "Simulation running..." start_time = time.time() run(1 * second) duration = time.time() - start_time print "Simulation time:", duration, "seconds" print Me.nspikes, "excitatory spikes" print Mi.nspikes, "inhibitory spikes" #plot(M.times/ms,M.smooth_rate(2*ms,'gaussian')) #show() pypar.finalize() # how to avoid that? brian-1.4.3/dev/ideas/pypar/installing_pypar.txt000066400000000000000000000021051266062647100217400ustar00rootroot00000000000000INSTALLING PYPAR ON WINDOWS =========================== * Download and install MPICH2 (binary installer for Windows); you need to give a password (same as when logging?) * Add C:\Program Files\MPICH2\bin to your Path environment variable. * Your Windows user account needs to be password protected (if not, go to the control panel / user accounts and set a password). * Run wmpiregister and register your user name and password. * Download pypar. * Open setup.py and replace the command get_mpi_flags by the following: def get_mpi_flags(): return {'inc_dirs': ['C:\Program Files\MPICH2\include'],'lib_dirs': ['C:\Program Files\MPICH2\lib'], 'libs':['mpi'], 'def_macros': [], 'undef_macros': []} and check the directories. * setup.py install (possibly with MSVC 2003 but maybe not necessarily) * To run a python script on two processors, use: mpiexec -n 2 python myscript.py Alternatively, use the mpiexec wrapper (in Programs) and write e.g. python C:\Python25\Lib\site-packages\pypar\test_init.py and select the number of processors. The job can be saved. brian-1.4.3/dev/ideas/pypar/parallel_cuba.py000066400000000000000000000063531266062647100207710ustar00rootroot00000000000000''' Proof of concept - CUBA on a cluster with MPI Slower than on a single processor but it works! ''' from brian import * import pypar import time class ReceiverThreshold(Threshold): ''' Receives spikes from other processor ''' def __init__(self, owner): self.owner = owner # processor owning the group def __call__(self, P): nspikes = array([0]) pypar.broadcast(nspikes, self.owner) nspikes = nspikes[0] spikes = zeros(nspikes, dtype=int) if nspikes > 0: pypar.broadcast(spikes, self.owner) return spikes class SenderThreshold(Threshold): ''' Sends spikes to other processors ''' def __init__(self, owner, threshold): self.owner = owner # processor owning the group self._threshold = threshold def __call__(self, P): spikes = array(self._threshold(P), dtype=int) nspikes = array([len(spikes)]) pypar.broadcast(nspikes, self.owner) nspikes = nspikes[0] if nspikes > 0: pypar.broadcast(spikes, self.owner) return spikes # Identification myid = pypar.rank() # id of this process nproc = pypar.size() # number of processors node = pypar.get_processor_name() print "I am processor %d of %d on node %s" % (myid, nproc, node) if nproc != 2: raise Exception, "This example only works with 2 processors" N = 32000 Ne = int(N * .8) Ni = int(N * .2) sparseness = 80. / N taum = 20 * ms taue = 5 * ms taui = 10 * ms Vt = -50 * mV Vr = -60 * mV El = -49 * mV we = (60 * 0.27 / 10) * mV # excitatory synaptic weight (voltage) wi = (-20 * 4.5 / 10) * mV # inhibitory synaptic weight eqs = Equations(''' dv/dt = (ge+gi-(v-El))/taum : volt dge/dt = -ge/taue : volt dgi/dt = -gi/taui : volt ''') if myid == 0: # excitatory group Pe = NeuronGroup(Ne, model=eqs, threshold=Vt, reset=Vr, refractory=5 * ms) Pe._threshold = SenderThreshold(0, Pe._threshold) Pi = NeuronGroup(Ni, model=eqs, threshold=ReceiverThreshold(1)) Pi._state_updater = LazyStateUpdater() Pe.v = Vr + rand(len(Pe)) * (Vt - Vr) Cee = Connection(Pe, Pe, 'ge', weight=we, sparseness=sparseness) Cie = Connection(Pi, Pe, 'gi', weight=wi, sparseness=sparseness) else: # inhibitory group Pi = NeuronGroup(Ni, model=eqs, threshold=Vt, reset=Vr, refractory=5 * ms) Pi._threshold = SenderThreshold(1, Pi._threshold) Pe = NeuronGroup(Ne, model=eqs, threshold=ReceiverThreshold(0)) Pe._state_updater = LazyStateUpdater() Pi.v = Vr + rand(len(Pi)) * (Vt - Vr) Cei = Connection(Pe, Pi, 'ge', weight=we, sparseness=sparseness) Cii = Connection(Pi, Pi, 'gi', weight=wi, sparseness=sparseness) # Record the number of spikes Me = PopulationSpikeCounter(Pe) Mi = PopulationSpikeCounter(Pi) # A population rate monitor #M = PopulationRateMonitor(P) print len(Pe) + len(Pi), "neurons in the network" print "Simulation running..." start_time = time.time() run(1 * second) duration = time.time() - start_time print "Simulation time:", duration, "seconds" print Me.nspikes, "excitatory spikes" print Mi.nspikes, "inhibitory spikes" #plot(M.times/ms,M.smooth_rate(2*ms,'gaussian')) #show() pypar.finalize() brian-1.4.3/dev/ideas/pypar/pypar_test.py000066400000000000000000000035121266062647100203670ustar00rootroot00000000000000''' Spike propagation with pypar. I am not sure how broadcasting works. Maybe it is not the better strategy. Other option: send spikes in ring (n turns to transfer all spikes). Documentation: http://datamining.anu.edu.au/~ole/pypar/DOC ''' from brian import * import pypar # Identification myid = pypar.rank() # id of this process nproc = pypar.size() # number of processors node = pypar.get_processor_name() print "I am processor %d of %d on node %s" % (myid, nproc, node) # Generation of spikes myspikes = randint(100, size=randint(10)) print "I am sending", myspikes # Broadcasting spikes = [[] for _ in range(nproc)] spikes[myid] = myspikes nspikes = array([0]) for i in range(nproc): if i == myid: # that's me! # Create spikes nspikes[0] = len(myspikes) pypar.broadcast(nspikes, i) if i != myid: # This would be the virtual group spikes[i] = zeros(nspikes, dtype=int) pypar.broadcast(spikes[i], i) print "I received", spikes pypar.finalize() #if myid == 0: # x=2*mV # pypar.send(x,1) # y=pypar.receive(1) # print "I got a big number:",y # v=zeros(10) # pypar.receive(1,buffer=v) # faster with arrays, does not copy the data # print "and now random numbers!" # print v # print "I am sending spikes!" # nspikes=array([3]) # pypar.broadcast(nspikes,0) # spikes=randint(100,size=nspikes) # pypar.broadcast(spikes,0) #else: # x=pypar.receive(0) # print "I received a message:",x # print "and I am doubling it" # pypar.send(2*x,0) # print "I send an array now" # pypar.send(randn(10),0,use_buffer=True) # nspikes=array([0]) # pypar.broadcast(nspikes,0) # spikes=zeros(nspikes,dtype=int) # pypar.broadcast(spikes,0) # print "Broadcast result:" # print spikes #pypar.finalize() brian-1.4.3/dev/ideas/pypar/testjob.txt000066400000000000000000000002371266062647100200370ustar00rootroot00000000000000exe python D:\EclipseWorkspace\Brian\dev\ideas\pypar\pypar_test.py n 2 wdir hosts cfg mpich1 env map channel default extra window no log no brian-1.4.3/dev/ideas/pyro/000077500000000000000000000000001266062647100154605ustar00rootroot00000000000000brian-1.4.3/dev/ideas/pyro/brianconsole.py000066400000000000000000000000021266062647100205000ustar00rootroot00000000000000 brian-1.4.3/dev/ideas/pyro/pyroCUBA.py000066400000000000000000000017311266062647100174600ustar00rootroot00000000000000''' Very short example program. ''' from brian import * import Pyro.core from Pyro.errors import PyroError class BrianServer(Pyro.core.ObjBase): def __init__(self): Pyro.core.ObjBase.__init__(self) Pyro.core.initServer() daemon = Pyro.core.Daemon() print print 'The Pyro Daemon is running on ', daemon.hostname + ':' + str(daemon.port) print '(you may need this info for the client to connect to)' print eqs = ''' dv/dt = (ge+gi-(v+49*mV))/(20*ms) : volt dge/dt = -ge/(5*ms) : volt dgi/dt = -gi/(10*ms) : volt ''' P = NeuronGroup(4000, model=eqs, threshold= -50 * mV, reset= -60 * mV) P.v = -60 * mV + 10 * mV * rand(len(P)) Pe = P.subgroup(3200) Pi = P.subgroup(800) Ce = Connection(Pe, P, 'ge', weight=1.62 * mV, sparseness=0.02) Ci = Connection(Pi, P, 'gi', weight= -9 * mV, sparseness=0.02) M = SpikeMonitor(P) # Pyro loop @network_operation def server(): pass run(1 * second) raster_plot(M) show() brian-1.4.3/dev/ideas/random_event_clock.py000066400000000000000000000010141266062647100206710ustar00rootroot00000000000000from brian import * class RandomEventClock(EventClock): def __init__(self, t=0 * ms, dt=1 * second, randomfunc=None): EventClock.__init__(self, t=t, dt=dt) if randomfunc is None: randomfunc = rand self.randomfunc = randomfunc def tick(self): self._t += self.randomfunc()*self._dt G = NeuronGroup(1, 'V:1') M = StateMonitor(G, 'V', record=True) @network_operation(clock=RandomEventClock()) def f(): G.V += 1 run(5 * second) M.plot() show() brian-1.4.3/dev/ideas/rate_based_game_of_life.py000066400000000000000000000016031266062647100216060ustar00rootroot00000000000000''' This demonstrates the 'Game of Life' example from DANA, integrated into Brian's framework. ''' from brian import * from scipy.signal import convolve2d from pylab import gray width = 50 N = width*width steps = 64 duration = steps*defaultclock.dt kernel = array([[1,1,1], [1,0,1], [1,1,1]]) G = NeuronGroup(N, 'alive:1') G.alive = randint(2, size=N) alive = reshape(G.alive, (width, width)) plotshape = int(ceil(sqrt(steps))) plotnum = 1 @network_operation def update(): global plotnum neighbs = convolve2d(alive, kernel, mode='same') alive[:] = maximum(0, 1.0-(neighbs<1.5)-(neighbs>3.5)-(neighbs<2.5)*(1-alive)) if plotnum<=plotshape*plotshape: subplot(plotshape, plotshape, plotnum) imshow(alive.copy(), interpolation='nearest') gray() plotnum += 1 run(10*ms) show() brian-1.4.3/dev/ideas/screensaver.py000066400000000000000000000025551266062647100173700ustar00rootroot00000000000000''' Brian screensaver Uses pyscr (google "Python screensaver") ''' import pyscr class MySaver(pyscr.Screensaver): #set up timer for tick() calls TIMEBASE = 1. # called every second def configure(self): #called to open the screensaver configuration #maybe implement here something with venster or win32all from ctypes import windll windll.user32.MessageBoxA(0, "This is the configuration of the Brian screensaver.", "Brian Screensaver Configuration", 0, 0, 0) def initialize(self): #called once when the screensaver is started pass def finalize(self): #called when the screensaver terminates pass def tick(self): #called when the timer tick occurs, set up with startTimer from above self.dc.beginDrawing() self.dc.setColor(0xffffffL) self.dc.setTextColor(0xff0000L) self.dc.setBgColor(0x000000L) self.dc.setFont("arial") #~ self.dc.setBgTransparent(True) self.dc.drawText((100, 100), "Brian is thinking...") w, h = self.dc.getSize() self.dc.drawRect((0, 0), (w - 1, h - 1)) self.dc.fillEllipse((50, 50), (60, 60)) self.dc.endDrawing() #standard 'main' detection and startof screensaver if __name__ == '__main__': pyscr.main() brian-1.4.3/dev/ideas/shell/000077500000000000000000000000001266062647100155765ustar00rootroot00000000000000brian-1.4.3/dev/ideas/shell/brian_console.py000066400000000000000000000020071266062647100207640ustar00rootroot00000000000000from brian import * from brian.library.ionic_currents import * from IPython.Shell import IPShellEmbed ipshell = IPShellEmbed() N = 100 duration = 1 * second El = 10.6 * mV EK = -12 * mV ENa = 120 * mV eqs = MembraneEquation(1 * uF) + leak_current(.3 * msiemens, El) eqs += K_current_HH(36 * msiemens, EK) + Na_current_HH(120 * msiemens, ENa) eqs += Current('I:amp') neuron = NeuronGroup(N, eqs, implicit=True, freeze=True) trace = RecentStateMonitor(neuron, 'vm', record=[0, 1, 2], duration=50 * ms) neuron.I = 10 * uA * rand(N) ion() trace.plot(refresh=1 * ms) net = MagicNetwork() while defaultclock.t < duration: if hasattr(net, 'stopped') and net.stopped: break try: print 'Starting at time', defaultclock.t net.run(duration - defaultclock.t) except KeyboardInterrupt: print 'Interrupted at time', defaultclock.t ipshell(local_ns=locals(), global_ns=globals()) print 'Finished at time', defaultclock.t ioff() clf() trace.plot() show() brian-1.4.3/dev/ideas/shell/embedded_ipython_console.py000066400000000000000000000273251266062647100232060ustar00rootroot00000000000000""" This is a modified version of source code from the Accerciser project (http://live.gnome.org/accerciser). Backend to the console plugin. @author: Eitan Isaacson @organization: IBM Corporation @copyright: Copyright (c) 2007 IBM Corporation @license: BSD All rights reserved. This program and the accompanying materials are made available under the terms of the BSD which accompanies this distribution, and is available at U{http://www.opensource.org/licenses/bsd-license.php} """ import re import sys import os from StringIO import StringIO import Tkinter import IPython class IterableIPShell: def __init__(self, argv=None, user_ns=None, user_global_ns=None, cin=None, cout=None, cerr=None, input_func=None): if input_func: IPython.iplib.raw_input_original = input_func if cin: IPython.Shell.Term.cin = cin if cout: IPython.Shell.Term.cout = cout if cerr: IPython.Shell.Term.cerr = cerr if argv is None: argv = [] # This is to get rid of the blockage that occurs during # IPython.Shell.InteractiveShell.user_setup() IPython.iplib.raw_input = lambda x: None self.term = IPython.genutils.IOTerm(cin=cin, cout=cout, cerr=cerr) os.environ['TERM'] = 'dumb' excepthook = sys.excepthook self.IP = IPython.Shell.make_IPython(argv, user_ns=user_ns, user_global_ns=user_global_ns, embedded=True, shell_class=IPython.Shell.InteractiveShell) self.IP.system = lambda cmd: self.shell(self.IP.var_expand(cmd), header='IPython system call: ', verbose=self.IP.rc.system_verbose) sys.excepthook = excepthook self.iter_more = 0 self.history_level = 0 self.complete_sep = re.compile('[\s\{\}\[\]\(\)]') def execute(self): self.history_level = 0 orig_stdout = sys.stdout sys.stdout = IPython.Shell.Term.cout try: line = self.IP.raw_input(None, self.iter_more) if self.IP.autoindent: self.IP.readline_startup_hook(None) except KeyboardInterrupt: self.IP.write('\nKeyboardInterrupt\n') self.IP.resetbuffer() # keep cache in sync with the prompt counter: self.IP.outputcache.prompt_count -= 1 if self.IP.autoindent: self.IP.indent_current_nsp = 0 self.iter_more = 0 except: self.IP.showtraceback() else: self.iter_more = self.IP.push(line) if (self.IP.SyntaxTB.last_syntax_error and self.IP.rc.autoedit_syntax): self.IP.edit_syntax_error() if self.iter_more: self.prompt = str(self.IP.outputcache.prompt2).strip() if self.IP.autoindent: self.IP.readline_startup_hook(self.IP.pre_readline) else: self.prompt = str(self.IP.outputcache.prompt1).strip() sys.stdout = orig_stdout def historyBack(self): self.history_level -= 1 return self._getHistory() def historyForward(self): self.history_level += 1 return self._getHistory() def _getHistory(self): try: rv = self.IP.user_ns['In'][self.history_level].strip('\n') except IndexError: self.history_level = 0 rv = '' return rv def updateNamespace(self, ns_dict): self.IP.user_ns.update(ns_dict) def complete(self, line): split_line = self.complete_sep.split(line) possibilities = self.IP.complete(split_line[-1]) if possibilities: common_prefix = reduce(self._commonPrefix, possibilities) completed = line[:-len(split_line[-1])] + common_prefix else: completed = line return completed, possibilities def _commonPrefix(self, str1, str2): for i in range(len(str1)): if not str2.startswith(str1[:i + 1]): return str1[:i] return str1 def shell(self, cmd, verbose=0, debug=0, header=''): stat = 0 if verbose or debug: print header + cmd # flush stdout so we don't mangle python's buffering if not debug: input, output = os.popen4(cmd) print output.read() output.close() input.close() ansi_colors = {'0;30': 'Black', '0;31': 'Red', '0;32': 'Green', '0;33': 'Brown', '0;34': 'Blue', '0;35': 'Purple', '0;36': 'Cyan', '0;37': 'LightGray', '1;30': 'DarkGray', '1;31': 'DarkRed', '1;32': 'SeaGreen', '1;33': 'Yellow', '1;34': 'LightBlue', '1;35': 'MediumPurple', '1;36': 'LightCyan', '1;37': 'White'} class TkConsoleView(Tkinter.Text): def __init__(self, root): Tkinter.Text.__init__(self, root) # As the stdout,stderr etc. get fiddled about with we need to put any # debug output into a file self.debug = 0 if self.debug: self.o = open('debug.out', 'w') # Keeps track of where the insert cursor should be on the entry line self.mark = 'scroll_mark' self.mark_set(self.mark, Tkinter.END) self.mark_gravity(self.mark, Tkinter.RIGHT) # Set the tags for colouring the text for code in ansi_colors: self.tag_config(code, foreground=ansi_colors[code]) self.tag_config('notouch') # Tag for indicating what areas of the widget aren't editable # colour_pat matches the colour tags and places these in a group # match character with hex value 01 (start of heading?) zero or more times, followed by # the hex character 1b (escape) then "[" and group ...things.. followed by m (?) and then # hex character 02 (start of text) zero or more times self.color_pat = re.compile('\x01?\x1b\[(.*?)m\x02?') self.line_start = 'line_start' # Tracks start of user input on the line (excluding prompt) self.mark_set(self.line_start, Tkinter.INSERT) self.mark_gravity(self.line_start, Tkinter.LEFT) self._setBindings() def write(self, text, editable=False): segments = self.color_pat.split(text) # First is blank line segment = segments.pop(0) # Keep track of where we started entering text so we can set as non-editable self.start_mark = 'start_mark' self.mark_set(self.start_mark, Tkinter.INSERT) self.mark_gravity(self.start_mark, Tkinter.LEFT) self.insert(Tkinter.END, segment) if segments: # Just return the colour tags ansi_tags = self.color_pat.findall(text) for tag in ansi_tags: i = segments.index(tag) self.insert(Tkinter.END, segments[i + 1], tag) segments.pop(i) if not editable: if self.debug: print "adding notouch between %s : %s" % (self.index(self.start_mark), \ self.index(Tkinter.INSERT)) self.tag_add('notouch', self.start_mark, "%s-1c" % Tkinter.INSERT) self.mark_unset(self.start_mark) #jmht self.scroll_mark_onscreen(self.mark) def showBanner(self, banner): """Print the supplied banner on starting the shell""" self.write(banner) def showPrompt(self, prompt): self.write(prompt) self.mark_set(self.line_start, Tkinter.INSERT) self.see(Tkinter.INSERT) #Make sure we can always see the prompt def changeLine(self, text): self.delete(self.line_start, "%s lineend" % self.line_start) self.write(text, True) def getCurrentLine(self): rv = self.get(self.line_start, Tkinter.END) if self.debug: print >> self.o, "getCurrentline: %s" % rv print >> self.o, "INSERT: %s" % Tkinter.END print >> self.o, "END: %s" % Tkinter.INSERT print >> self.o, "line_start: %s" % self.index(self.line_start) return rv def showReturned(self, text): self.tag_add('notouch', self.line_start, "%s lineend" % self.line_start) self.write('\n' + text) if text: self.write('\n') self.showPrompt(self.prompt) #self.mark_set(self.line_start,Tkinter.END) #jmht don't need this as showprompt sets mark def _setBindings(self): """ Bind the keys we require. REM: if a bound function returns "break" then no other bindings are called If it returns None, then the other default bindings are called. """ self.bind("", self.processKeyPress) self.bind("", self.processEnterPress) self.bind("", self.processUpPress) self.bind("", self.processDownPress) self.bind("", self.processTabPress) self.bind("", self.processBackSpacePress) def isEditable(self): """ Scan the notouch tag range in pairs and see if the INSERT index falls between any of them. """ ranges = self.tag_ranges('notouch') first = None for idx in ranges: if not first: first = idx continue else: if self.debug: print "Comparing %s between %s : %s " % (self.index(Tkinter.INSERT), first, idx) if self.compare(Tkinter.INSERT, '>=', first) and \ self.compare(Tkinter.INSERT, '<=', idx): return False first = None return True def processKeyPress(self, event): if self.debug: print >> self.o, "processKeyPress got key: %s" % event.char print >> self.o, "processKeyPress INSERT: %s" % self.index(Tkinter.INSERT) print >> self.o, "processKeyPress END: %s" % self.index(Tkinter.END) if not self.isEditable(): # Move cursor mark to start of line self.mark_set(Tkinter.INSERT, self.mark) # Make sure line_start follows inserted text self.mark_set(self.mark, "%s+1c" % Tkinter.INSERT) def processBackSpacePress(self, event): if not self.isEditable(): return "break" def processEnterPress(self, event): self._processLine() return "break" # Need break to stop the other bindings being called def processUpPress(self, event): self.changeLine(self.historyBack()) return "break" def processDownPress(self, event): self.changeLine(self.historyForward()) return "break" def processTabPress(self, event): if not self.getCurrentLine().strip(): return completed, possibilities = self.complete(self.getCurrentLine()) if len(possibilities) > 1: slice = self.getCurrentLine() self.write('\n') for symbol in possibilities: self.write(symbol + '\n') self.showPrompt(self.prompt) self.changeLine(completed or slice) return "break" class IPythonView(TkConsoleView, IterableIPShell): def __init__(self, root, banner=None): TkConsoleView.__init__(self, root) self.cout = StringIO() IterableIPShell.__init__(self, cout=self.cout, cerr=self.cout, input_func=self.raw_input) if banner: self.showBanner(banner) self.execute() self.cout.truncate(0) self.showPrompt(self.prompt) self.interrupt = False def raw_input(self, prompt=''): if self.interrupt: self.interrupt = False raise KeyboardInterrupt return self.getCurrentLine() def _processLine(self): self.history_pos = 0 self.execute() rv = self.cout.getvalue() if self.debug: print >> self.o, "_processLine got rv: %s" % rv if rv: rv = rv.strip('\n') self.showReturned(rv) self.cout.truncate(0) if __name__ == "__main__": root = Tkinter.Tk() s = IPythonView(root) s.pack() root.mainloop() brian-1.4.3/dev/ideas/shell/remotecontrol_client.py000066400000000000000000000007201266062647100224010ustar00rootroot00000000000000from brian import * import time client = RemoteControlClient() def compute_rate(spikes): i, t = zip(*spikes) i = array(i) t = array(t) tmin = amin(t) tmax = amax(t) return len(i) / (tmax - tmin) spikes = client.evaluate('M.spikes') print 'Rate:', compute_rate(spikes) client.execute('Ce.W.alldata[:]*=2') time.sleep(5) spikes = client.evaluate('M.spikes') print 'Rate:', compute_rate(spikes) client.execute('stop()') brian-1.4.3/dev/ideas/shell/remotecontrol_server.py000066400000000000000000000012271266062647100224340ustar00rootroot00000000000000from brian import * eqs = ''' dv/dt = (ge+gi-(v+49*mV))/(20*ms) : volt dge/dt = -ge/(5*ms) : volt dgi/dt = -gi/(10*ms) : volt ''' P = NeuronGroup(4000, model=eqs, threshold= -50 * mV, reset= -60 * mV) P.v = -60 * mV + 10 * mV * rand(len(P)) Pe = P.subgroup(3200) Pi = P.subgroup(800) Ce = Connection(Pe, P, 'ge', weight=1.62 * mV, sparseness=0.02) Ci = Connection(Pi, P, 'gi', weight= -9 * mV, sparseness=0.02) M = SpikeMonitor(P) @network_operation(clock=EventClock(dt=1 * second)) def clearspikes(): M.reinit() print 'Simulated', defaultclock.t srv = RemoteControlServer() run(1e10 * second) print 'Finished!' brian-1.4.3/dev/ideas/shell/run_server.py000066400000000000000000000027041266062647100203450ustar00rootroot00000000000000from brian import * from multiprocessing.connection import Listener, Client import select server = ('localhost', 2719) server_authkey = 'brian' eqs = ''' dv/dt = (ge+gi-(v+49*mV))/(20*ms) : volt dge/dt = -ge/(5*ms) : volt dgi/dt = -gi/(10*ms) : volt ''' P = NeuronGroup(4000, model=eqs, threshold= -50 * mV, reset= -60 * mV) P.v = -60 * mV + 10 * mV * rand(len(P)) Pe = P.subgroup(3200) Pi = P.subgroup(800) Ce = Connection(Pe, P, 'ge', weight=1.62 * mV, sparseness=0.02) Ci = Connection(Pi, P, 'gi', weight= -9 * mV, sparseness=0.02) M = SpikeMonitor(P) @network_operation(clock=EventClock(dt=1 * second)) def clearspikes(): M.reinit() print 'Simulated', defaultclock.t listener = Listener(server, authkey=server_authkey) #conn = listener.accept() conn = None global_ns = globals() local_ns = locals() @network_operation def server_check(): global conn if conn is None: socket = listener._listener._socket sel, _, _ = select.select([socket], [], [], 0) if len(sel): conn = listener.accept() if conn is None: return if not conn.poll(): return job = conn.recv() jobtype, jobargs = job if jobtype == 'exec': exec jobargs in global_ns, local_ns result = None elif jobtype == 'eval': result = eval(jobargs, global_ns, local_ns) conn.send(result) run(1e10 * second) print 'Finished!' brian-1.4.3/dev/ideas/sse.py000066400000000000000000000036341266062647100156410ustar00rootroot00000000000000''' Testing whether or not GCC generates code using SSE extensions, and if not, can we make it so that it does. ''' from brian import * from scipy import weave import time dtype = float32 repeats = 100 N = 1000000 complexity = 10 x = zeros(N, dtype=dtype) y = ones(N, dtype=dtype) if dtype is float32: supp = ''' typedef float scalar; typedef float vec __attribute__ ((vector_size (16))); ''' else: supp = ''' typedef double scalar; typedef double vec __attribute__ ((vector_size (16))); ''' code = ''' for(int j=0;j0 """ # The model has hard bounds wmin and wmax # NB: maybe we should have a rectify function (=clip(x,0,inf)) N = NeuronGroup(2, 'dv/dt=1*Hz:1') C = Connection(N, N, 'v') mystdp = STDP(C, eqs=eqs_stdp, pre=pre, post=post, wmax=2 * mV) mystdp.post_group.v = linked_var(N, 'v') run(1 * ms) brian-1.4.3/dev/ideas/swig_with_templates/000077500000000000000000000000001266062647100205515ustar00rootroot00000000000000brian-1.4.3/dev/ideas/swig_with_templates/setup.py000066400000000000000000000012301266062647100222570ustar00rootroot00000000000000#!/usr/bin/env python """ setup.py file for C++ template container """ from distutils.core import setup, Extension import os templatecontainer_module = Extension('_templatecontainer', sources=['templatecontainer_wrap.cxx', 'templatecontainer.cpp', ], ) setup (name='templatecontainer', version='0.1', author="Dan Goodman", description="""C++ version of template container""", ext_modules=[templatecontainer_module], py_modules=["templatecontainer"], ) brian-1.4.3/dev/ideas/swig_with_templates/swigcompile.sh000066400000000000000000000001121266062647100234210ustar00rootroot00000000000000swig -python -c++ templatecontainer.i python setup.py build_ext --inplace brian-1.4.3/dev/ideas/swig_with_templates/templatecontainer.cpp000066400000000000000000000000401266062647100247650ustar00rootroot00000000000000#include "templatecontainer.h" brian-1.4.3/dev/ideas/swig_with_templates/templatecontainer.h000066400000000000000000000002701266062647100244370ustar00rootroot00000000000000#ifndef _BRIAN_TEMPLATECONTAINER_H #define _BRIAN_TEMPLATECONTAINER_H template class TemplateContainer { public: Type x; TemplateContainer(Type x) : x(x) {} }; #endif brian-1.4.3/dev/ideas/swig_with_templates/templatecontainer.i000066400000000000000000000003631266062647100244430ustar00rootroot00000000000000%module templatecontainer %{ #define SWIG_FILE_WITH_INIT #include "templatecontainer.h" %} %include "templatecontainer.h" %template(TemplateContainerInt) TemplateContainer; %template(TemplateContainerDouble) TemplateContainer; brian-1.4.3/dev/ideas/swig_with_templates/templatecontainer.py000066400000000000000000000103001266062647100246330ustar00rootroot00000000000000# This file was automatically generated by SWIG (http://www.swig.org). # Version 1.3.40 # # Do not make changes to this file unless you know what you are doing--modify # the SWIG interface file instead. # This file is compatible with both classic and new-style classes. from sys import version_info if version_info >= (2,6,0): def swig_import_helper(): from os.path import dirname import imp fp = None try: fp, pathname, description = imp.find_module('_templatecontainer', [dirname(__file__)]) except ImportError: import _templatecontainer return _templatecontainer if fp is not None: try: _mod = imp.load_module('_templatecontainer', fp, pathname, description) finally: fp.close() return _mod _templatecontainer = swig_import_helper() del swig_import_helper else: import _templatecontainer del version_info try: _swig_property = property except NameError: pass # Python < 2.2 doesn't have 'property'. def _swig_setattr_nondynamic(self,class_type,name,value,static=1): if (name == "thisown"): return self.this.own(value) if (name == "this"): if type(value).__name__ == 'SwigPyObject': self.__dict__[name] = value return method = class_type.__swig_setmethods__.get(name,None) if method: return method(self,value) if (not static) or hasattr(self,name): self.__dict__[name] = value else: raise AttributeError("You cannot add attributes to %s" % self) def _swig_setattr(self,class_type,name,value): return _swig_setattr_nondynamic(self,class_type,name,value,0) def _swig_getattr(self,class_type,name): if (name == "thisown"): return self.this.own() method = class_type.__swig_getmethods__.get(name,None) if method: return method(self) raise AttributeError(name) def _swig_repr(self): try: strthis = "proxy of " + self.this.__repr__() except: strthis = "" return "<%s.%s; %s >" % (self.__class__.__module__, self.__class__.__name__, strthis,) try: _object = object _newclass = 1 except AttributeError: class _object : pass _newclass = 0 class TemplateContainerInt(_object): __swig_setmethods__ = {} __setattr__ = lambda self, name, value: _swig_setattr(self, TemplateContainerInt, name, value) __swig_getmethods__ = {} __getattr__ = lambda self, name: _swig_getattr(self, TemplateContainerInt, name) __repr__ = _swig_repr __swig_setmethods__["x"] = _templatecontainer.TemplateContainerInt_x_set __swig_getmethods__["x"] = _templatecontainer.TemplateContainerInt_x_get if _newclass:x = _swig_property(_templatecontainer.TemplateContainerInt_x_get, _templatecontainer.TemplateContainerInt_x_set) def __init__(self, *args): this = _templatecontainer.new_TemplateContainerInt(*args) try: self.this.append(this) except: self.this = this __swig_destroy__ = _templatecontainer.delete_TemplateContainerInt __del__ = lambda self : None; TemplateContainerInt_swigregister = _templatecontainer.TemplateContainerInt_swigregister TemplateContainerInt_swigregister(TemplateContainerInt) class TemplateContainerDouble(_object): __swig_setmethods__ = {} __setattr__ = lambda self, name, value: _swig_setattr(self, TemplateContainerDouble, name, value) __swig_getmethods__ = {} __getattr__ = lambda self, name: _swig_getattr(self, TemplateContainerDouble, name) __repr__ = _swig_repr __swig_setmethods__["x"] = _templatecontainer.TemplateContainerDouble_x_set __swig_getmethods__["x"] = _templatecontainer.TemplateContainerDouble_x_get if _newclass:x = _swig_property(_templatecontainer.TemplateContainerDouble_x_get, _templatecontainer.TemplateContainerDouble_x_set) def __init__(self, *args): this = _templatecontainer.new_TemplateContainerDouble(*args) try: self.this.append(this) except: self.this = this __swig_destroy__ = _templatecontainer.delete_TemplateContainerDouble __del__ = lambda self : None; TemplateContainerDouble_swigregister = _templatecontainer.TemplateContainerDouble_swigregister TemplateContainerDouble_swigregister(TemplateContainerDouble) brian-1.4.3/dev/ideas/swig_with_templates/test.py000066400000000000000000000003331266062647100221010ustar00rootroot00000000000000from templatecontainer import TemplateContainerInt, TemplateContainerDouble t = TemplateContainerInt(5) print t.x t.x = 7 print t.x s = TemplateContainerDouble(3.4) print s.x u = TemplateContainerDouble(3) print u.x brian-1.4.3/dev/ideas/synapses/000077500000000000000000000000001266062647100163345ustar00rootroot00000000000000brian-1.4.3/dev/ideas/synapses/batch_apply.py000066400000000000000000000200711266062647100211740ustar00rootroot00000000000000from numpy import * __all__ = ['apply_batched'] def apply_batched_orig(op, inds, debug=False): ''' Applies op(tgt, src) in batches, where tgt will have each of the values in inds precisely once, and src will cover each integer from 0 to len(inds)-1 precisely once. For each call op(tgt, src) tgt and src are arrays of indices where tgt=inds[src], and it is guaranteed that there are no repeated values in tgt. ''' u, i = unique(inds, return_index=True) if debug: print inds, u, i op(u, i) if len(u)-1).any(): u, i = unique(inds, return_index=True) if debug: print inds, u, i op(u[1:], i[1:]) inds[i] = -1 def apply_batched_orig_improved(op, inds, debug=False): ''' Applies op(tgt, src) in batches, where tgt will have each of the values in inds precisely once, and src will cover each integer from 0 to len(inds)-1 precisely once. For each call op(tgt, src) tgt and src are arrays of indices where tgt=inds[src], and it is guaranteed that there are no repeated values in tgt. ''' u, i = unique(inds, return_index=True) if debug: print inds, u, i op(u, i) if len(u) 25e6 synapses 1054 Synapses: 1 variable: 611 MB -> 24B/syn 3 variables: 985 MB 3 vars + pre/post: 1037 MB -> 41B/syn Connection (sparse but no delay): 1151 MB -> 46B/syn DelayConnection (1 ms max delay) 1164 MB DelayConnection + STDP (1 ms max delay) 1155 MB """ from brian import * from time import time import sys, gc from brian.experimental.synapses import * #from guppy import hpy #h = hpy() N=3000 #print h.heap() def countbytes(obj, name='', found=None, printsizes=True): if found is None: found = set() if id(obj) in found: return 0 found.add(id(obj)) totalbytes = sys.getsizeof(obj) if hasattr(obj, 'nbytes'): if hasattr(obj, 'base') and isinstance(obj.base, ndarray): return countbytes(obj.base, name=name+'.base', found=found) nb = obj.nbytes if isinstance(nb, int): totalbytes += nb if hasattr(obj, '__dict__'): for k, v in obj.__dict__.iteritems(): if not isinstance(v, NeuronGroup): nb = countbytes(v, name=name+'.'+k, found=found) totalbytes += nb if isinstance(obj, list): if len(obj): if isinstance(obj[0], (int, float)): # assume it's homogeneous first = countbytes(obj[0], name=name+'[0]', found=found) sz = len(obj)*first totalbytes += sz else: for i, v in enumerate(obj): totalbytes += countbytes(v, name=name+'['+str(i)+']', found=found, printsizes=False) if isinstance(obj, ndarray) and obj.dtype==object: for v in obj: totalbytes += countbytes(v, name=name+'[something]', found=found) if printsizes and totalbytes>1024*1024: print 'Size of', name, '=', totalbytes/1024**2, 'MB' return totalbytes #1740 -> 574 P=NeuronGroup(N,model="v:1") k=1 if k==0: S=Synapses(P,model='''w:1 Apre:1 Apost:1''',pre='v+=w',post='w+=Apre') S[:,:]=True nb = countbytes(S, 'S') print 'S uses', nb/1024**2, 'MB (%d)'%nb elif k==1: C=Connection(P,P,'v',delay=True,max_delay=1*ms) C.connect_full(P,P,weight=1.,delay=0.1*ms) print 'Before compression' print nb = countbytes(C, 'C') print print 'Before compression C uses', nb/1024**2, 'MB (%d)'%nb print C.W.data[0].__class__, C.W.data[0][0].__class__ print C.W.rows[0].__class__, C.W.rows[0][0].__class__ gc.collect() _=raw_input("Press enter") C.compress() # print C.W.__class__ print print 'After compression' print nb = countbytes(C, 'C') print print 'After compression C uses', nb/1024**2, 'MB (%d)'%nb print 'Expected size of C', 2*16*N*N/1024**2, 'MB' elif k==2: C=Connection(P,P,'v',delay=True,max_delay=1*ms) C.connect_full(P,P,weight=1.) S=ExponentialSTDP(C, 10*ms, 10*ms, 1., 1.,wmax=1.) gc.collect() _=raw_input("Press enter") #print h.heap() brian-1.4.3/dev/ideas/synapses/old examples/000077500000000000000000000000001266062647100207115ustar00rootroot00000000000000brian-1.4.3/dev/ideas/synapses/old examples/construction.py000066400000000000000000000010731266062647100240160ustar00rootroot00000000000000''' In this script I test the synapses initialization/construction of Synapses ''' from brian import * import dev.ideas.synapses.synapses as syn reload(syn) log_level_debug() gin = NeuronGroup(10, model = 'dv/dt = -v/(5*ms) : 1') gout = NeuronGroup(20, model = 'dv/dt = -v/(5*ms) : 1') synapses = syn.Synapses(gin, gout, model = '''w : 1; z : 1''', pre = 'v += w + rand()') synapses[0,:] = 2 print synapses synapses.w[:,:] = 2.1 synapses.z[1,:] = 4 print synapses.w print synapses.z synapses.z[0, :, :] = arange(len(synapses)*2, dtype = np.float32) print synapses.z brian-1.4.3/dev/ideas/synapses/old examples/model_synapse.py000066400000000000000000000017121266062647100241260ustar00rootroot00000000000000''' In this script I test the basic Synapses behavior: synapses = syn.Synapses(gin, gout, model = 'w : 1', pre = 'v += w') ''' from brian import * import dev.ideas.synapses.synapses as syn reload(syn) log_level_debug() reinit_default_clock() if True: spikes = [(0, 10*ms), (1, 10*ms), (1, 20*ms), (2, 30*ms)] gin = SpikeGeneratorGroup(3, spikes) gout = NeuronGroup(2, model = 'dv/dt = -v/(5*ms) : 1') eqs = ''' w : 1 u : 1 z = u + v : 1 dx/dt = -x/(10*ms) : 1 ''' pre = ''' v += w ; x += w ''' # init synapses synapses = syn.Synapses(gin, gout, model = eqs, pre = pre) # set synapses and weights synapses[:, 0] = True synapses[1, 1] = True synapses.w[:, 0] = 1 synapses.w[1, 1] = 3 # monitor and run Mv = StateMonitor(gout, 'v', record = True) net = Network(gin, gout, synapses, Mv) net.run(100*ms) for i in range(len(gout)): plot(Mv.times/ms, Mv[i]) show() brian-1.4.3/dev/ideas/synapses/old examples/simple_synapse.py000066400000000000000000000044501266062647100243210ustar00rootroot00000000000000''' In this script I test the basic Synapses behavior: synapses = syn.Synapses(gin, gout, model = 'w : 1', pre = 'v += w') ''' from brian import * import dev.ideas.synapses.synapses as syn reload(syn) log_level_debug() reinit_default_clock() if False: # SIMPLE CASE (no delay) spikes = [(0, 10*ms), (1, 10*ms), (1, 20*ms), (2, 30*ms)] gin = SpikeGeneratorGroup(3, spikes) gout = NeuronGroup(2, model = 'dv/dt = -v/(5*ms) : 1') # init synapses synapses = syn.Synapses(gin, gout, model = '''w : 1; z : 1''', pre = 'v += w') # set synapses and weights synapses[:, 0] = True synapses[1, 1] = True synapses.w[:, 0] = 1 synapses.w[1, 1] = 3 # monitor and run Mv = StateMonitor(gout, 'v', record = True) net = Network(gin, gout, synapses, Mv) net.run(100*ms) for i in range(len(gout)): plot(Mv.times/ms, Mv[i]) show() if False: # DELAYS spikes = [(0, 10*ms)] gin = SpikeGeneratorGroup(3, spikes) gout = NeuronGroup(2, model = 'dv/dt = -v/(5*ms) : 1') # init synapses synapses = syn.Synapses(gin, gout, model = '''w : 1; z : 1''', pre = 'v += w', max_delay = 3*ms) # set synapses and weights synapses[0, 0] = 10 synapses.w[0, 0] = 1 synapses.delay[:, 0] = 5*ms synapses.delay[:, 0, 5:] = 2*ms print synapses.delay # monitor and run Mv = StateMonitor(gout, 'v', record = True) net = Network(gin, gout, synapses, Mv) net.run(100*ms) for i in range(len(gout)): plot(Mv.times/ms, Mv[i]) show() if True: # Testing resizing of the spikequeue spikes = [(0, 10*ms)] spikes += [ (i, 30*ms) for i in range(1,10)] gin = SpikeGeneratorGroup(11, spikes) gout = NeuronGroup(2, model = 'dv/dt = -v/(5*ms) : 1') # init synapses synapses = syn.Synapses(gin, gout, model = '''w : 1; z : 1''', pre = 'v += w', max_delay = 3*ms) # set synapses and weights synapses[:, 0] = 10 synapses.w[:, 0] = 1 synapses.delay[:, 0] = 0*ms synapses.delay[:, 0, 5:] = 2*ms print synapses.delay # monitor and run Mv = StateMonitor(gout, 'v', record = True) net = Network(gin, gout, synapses, Mv) net.run(100*ms) for i in range(len(gout)): plot(Mv.times/ms, Mv[i]) show() brian-1.4.3/dev/ideas/synapses/old examples/sparse_synapses.py000066400000000000000000000012561266062647100245110ustar00rootroot00000000000000''' Testing the ability to construct synapses with strings! ''' from brian import * import dev.ideas.synapses.synapses as syn reload(syn) log_level_debug() gin = NeuronGroup(1000, model = 'dv/dt = -v/(5*ms) : 1') gout = NeuronGroup(1000, model = 'dv/dt = -v/(5*ms) : 1') # init synapses print 'SPARSE SYNAPSES' synapses = syn.Synapses(gin, gout, model = '''w : 1; z : 1''', pre = 'v += w') synapses[:,:] = '''rand() < .03''' print 'proportion of created synapses: ', float(len(synapses)) / (len(gin) * len(gout)) del synapses synapses = syn.Synapses(gin, gout, model = '''w : 1; z : 1''', pre = 'v += w') print 'MULTIPLE SYNAPSES' synapses[:,:] = ''' (i == j)''' # this is slow brian-1.4.3/dev/ideas/synapses/old examples/state_vectors_simple.py000066400000000000000000000007771266062647100255340ustar00rootroot00000000000000import dev.ideas.synapses.statevectors as sv import numpy as np reload(sv) v = sv.ConstructionSparseStateVector(4, (np.int8, np.int8, np.float64, np.float64)) nvalues = 52 values = [] values.append(np.arange(nvalues, dtype = np.int8)) values.append(np.arange(nvalues, dtype = np.int8)[::-1]) values.append(np.array(np.random.rand(nvalues), dtype = np.float64)) values.append(np.array(np.random.rand(nvalues), dtype = np.float64)) v.append(values) v.append(values) v.append(values) vcomp = v.compress() brian-1.4.3/dev/ideas/synapses/old/000077500000000000000000000000001266062647100171125ustar00rootroot00000000000000brian-1.4.3/dev/ideas/synapses/old/scheduled_events.py000066400000000000000000000254501266062647100230160ustar00rootroot00000000000000''' Simple implementation of an event scheduling scheme for Brian. The idea is that a spike schedules a future event V[i] += val for i an index and val a float value. Below this idea is used to implement dendritic delays in an alternative way to DelayConnection. You could implement axonal delays by using V[i] += W[j] for i and j both indices (here j would be a pointer to an appropriate entry in a weight matrix W, and i an appropriate neuron state variable index). ''' from brian import * from scipy import weave import numpy from itertools import izip N = 10 max_events_per_timestep = 100000 G = NeuronGroup(1, 'V:1', reset=0, threshold=0.5) H = NeuronGroup(N, 'V:1', reset=0, threshold=0.5) class ScheduledEventDelayConnection(DelayConnection): def __init__(self, *args, **kwds): DelayConnection.__init__(self, *args, **kwds) self.scheduled_events_ptr = numpy.zeros((self._max_delay, max_events_per_timestep), dtype=int) self.scheduled_events_val = numpy.zeros((self._max_delay, max_events_per_timestep), dtype=float) self.num_scheduled_events = numpy.zeros(self._max_delay, dtype=int) self.scheduled_events_index = 0 @network_operation(when='after_connections') # for the moment, don't worry about clock details def process_scheduled_events(): num_scheduled_events = int(self.num_scheduled_events[self.scheduled_events_index]) if num_scheduled_events: ptr = self.scheduled_events_ptr[self.scheduled_events_index, :num_scheduled_events] val = self.scheduled_events_val[self.scheduled_events_index, :num_scheduled_events] sv = self.target._S[self.nstate] #sv[ptr] += val I = argsort(ptr) ptrs = ptr[I] vals = val[I] J = hstack((True, (ptrs[1:]!=ptrs[:-1]))).nonzero()[0] sv[ptrs[J]] += add.reduceat(vals, J) # with a bit of work, this can be made to work for any ufunc using # the .reduceat method of ufuncs after sorting ptr, etc. Still doesn't work # in the general case of any Python expression though. self.num_scheduled_events[self.scheduled_events_index] = 0 self.scheduled_events_index = (self.scheduled_events_index + 1) % self._max_delay self.contained_objects.append(process_scheduled_events) self.constant_delays = True # set to True to assume that delays will not change over time self.constant_weights = True self.need_to_precompute_sorted_delays = self.constant_delays for f in self.contained_objects: if f.__name__=='delayed_propagate': self.contained_objects.remove(f) break def propagate(self, spikes): if not self.iscompressed: self.compress() if self.need_to_precompute_sorted_delays: self.sort_indices = [] self.int_delay = [] self.target_indices = [] self.sorted_Wrow = [] self.Jp = [] self.event_index_offset = [] self.added_events = [] for i in xrange(len(self.source)): dvecrow = self.delayvec[i, :] int_delay = array(self._invtargetdt * dvecrow, dtype=int) sort_indices = argsort(int_delay) int_delay = int_delay[sort_indices] J = int_delay[1:] != int_delay[:-1] K = int_delay[1:] == int_delay[:-1] A = hstack((0, cumsum(array(J, dtype=int)))) B = hstack((0, cumsum(array(K, dtype=int)))) BJ = hstack((0, B[J])) event_index_offset = B - BJ[A] Jp = hstack((J, True)) added_events = event_index_offset[Jp] + 1 self.sort_indices.append(sort_indices) self.int_delay.append(int_delay) self.event_index_offset.append(event_index_offset) self.Jp.append(Jp) self.target_indices.append(dvecrow.ind[sort_indices]) self.added_events.append(added_events) if self.constant_weights: self.sorted_Wrow.append(self.W[i, :][sort_indices]) self.need_to_precompute_sorted_delays = False if len(spikes): if False: # C code version nspikes = len(spikes) W = asarray(self.W) n, m = W.shape numevents = self.num_scheduled_events ptr, val = self.scheduled_events_ptr, self.scheduled_events_val index = self.scheduled_events_index idt = self._invtargetdt md = self._max_delay dvec = asarray(self.delayvec) sv = self.target._S[self.nstate] code = ''' for(int i=0; i delay synapse -> presynaptic i but for effiency, we need the mappings: presynaptic i -> synapse presynaptic i -> delay We will still need the mapping: synapse -> presynaptic i but not: synapse -> delay Actually, does this need to be a Brian object? It could directly be called by Synapses. """ from brian import * import time INITIAL_MAXSPIKESPER_DT = 1 # This is a 2D circular array, but also a SpikeMonitor class SpikeQueue(SpikeMonitor): ''' * Initialization * Initialized with a source NeuronGroup, a Synapses object (from which it fetches the delays), a maximum delay Arguments ``source'' self explanatory ``synapses'' self explanatory Keywords ``max_delay'' in seconds ``maxevents'' Maximum initial number of events in each timestep. Notice that the structure will grow dynamically of there are more events than that, so you shouldn't bother. * Circular 2D array structure * A spike queue is implemented as a circular 2D array. * At the beginning or end of each timestep: queue.next() * To get all spikes: events=queue.peek() It returns the indexes of all synapses receiving an event. * When a presynaptic spike is emitted, the following is executed: queue.insert(delay,offset,target) where delay is the array of synaptic delays of targets in timesteps, offset is the array of offsets within each timestep, target is the array of synapse indexes of targets. The offset is used to solve the problem of multiple synapses with the same delay. For example, if there are two target synapses 7 and 9 with delay 2 timesteps: queue.insert([2,2],[0,1],[7,9]) Thus, offsets are determined by delays. They could be either precalculated (faster), or determined at run time (saves memory). Note that if they are determined at run time, then it may be possible to also vectorize over presynaptic spikes. * SpikeMonitor structure * It automatically updates the underlying structure by instantiating the propagate() method of the SpikeMonitor Ideas: ------ * remove the max_delay keyword and have the structure created with another method (at run time) ''' def __init__(self, source, synapses, max_delay = 0, maxevents = INITIAL_MAXSPIKESPER_DT, precompute_offsets = False): # SpikeMonitor structure self.source = source #NeuronGroup self.synapses = synapses #Synapses self.max_delay = max_delay nsteps = int(np.floor((max_delay)/(self.source.clock.dt)))+1 # number of time steps, maximum number of spikes per time step self.X = zeros((nsteps, maxevents), dtype = int) # target synapses self.X_flat = self.X.reshape(nsteps*maxevents,) self.currenttime = 0 self.n = zeros(nsteps, dtype = int) # number of events in each time step self._all_offsets = None self.precompute_offsets = precompute_offsets super(SpikeQueue, self).__init__(source, record = False) ################################ SPIKE QUEUE DATASTRUCTURE ###################### def next(self): # Advance by one timestep self.n[self.currenttime]=0 # erase self.currenttime=(self.currenttime+1) % len(self.n) def peek(self): # Events in the current timestep return self.X[self.currenttime,:self.n[self.currenttime]] def compute_all_offsets(self, all_delays): t0 = time.time() self._all_offsets = self.offsets(all_delays) log_debug('spikequeue.offsets', 'Offsets computed in '+str(time.time()-t0)) def offsets(self, delay): # Calculates offsets corresponding to a delay array # Maybe it could be precalculated? (an int16 array?) I = argsort(delay) xs = delay[I] J = xs[1:]!=xs[:-1] #K = xs[1:]==xs[:-1] A = hstack((0, cumsum(J))) #B = hstack((0, cumsum(K))) B = hstack((0, cumsum(-J))) BJ = hstack((0, B[J])) ei = B-BJ[A] ofs = zeros_like(delay) ofs[I] = ei return ofs def insert(self, delay, offset, target): # Vectorized insertion of spike events # delay = delay in timestepp # offset = offset within timestep # target = target synaptic index timesteps = (self.currenttime + delay) % len(self.n) # Compute new stack sizes: old_nevents = self.n[timesteps].copy() # because we need this for the final assignment, but we need to precompute the new one to check for overflow self.n[timesteps] += offset+1 # that's a trick (to update stack size), plus we pre-compute it to check for overflow m = max(self.n[timesteps]) # If overflow, then at least one self.n is bigger than the size if (m >= self.X.shape[1]): self.resize(m) self.X_flat[(self.currenttime*self.X.shape[1]+offset+\ old_nevents)\ % len(self.X)]=target def resize(self, maxevents): ''' Resizes the underlying data structure (number of columns = spikes per dt). max events will be rounded to the closest power of 2. ''' # old and new sizes old_maxevents = self.X.shape[1] new_maxevents = 2**ceil(log2(maxevents)) # new array newX = zeros((self.X.shape[0], new_maxevents), dtype = self.X.dtype) newX[:, :old_maxevents] = self.X[:, :old_maxevents] # copy old data self.X = newX self.X_flat = self.X.reshape(self.X.shape[0]*new_maxevents,) log_debug('spikequeue', 'Resizing SpikeQueue') def propagate(self, spikes): if len(spikes): # synapse identification, # this seems ok in terms of speed even though I dont like the for loop. # any idea? see test_fastsynapseidentification.py synapses = self.synapses.pre2synapses(spikes) if len(synapses): # delay getting: delay = self.synapses.delay[synapses] if self.precompute_offsets: if self._all_offsets == None: self.compute_all_offsets(self.synapses._statevector.delay) self.insert(delay, self._all_offsets[synapses], synapses) else: offsets = self.offsets(delay) self.insert(delay, offsets, synapses) ######################################## UTILS def plot(self, display = True): for i in range(self.X.shape[0]): idx = (i + self.currenttime ) % self.X.shape[0] data = self.X[idx, :self.n[idx]] plot(idx * ones(len(data)), data, '.') if display: show() ''' NOTE: the test code below is probably not working anymore since I changed the way it is created The connection has arrays of synaptic variables (same as state matrix of neuron groups). Two synaptic variables are the index of the postsynaptic neuron and of the presynaptic neuron (i,j). (int32 or int16). In addition, the connection must have, for each presynaptic neuron: * list of target synapses (int32) * corresponding delays in timesteps (int16) * corresponding offsets (int16 is probably sufficient, or less) These types (int32 etc) could be determined at construction time, or at the time of conversion construction->connection (run time). Same thing for postsynaptic neuron (for STDP) This could also be determined at run time (depending on whether post=None or not) Total memory: * number of synapses * 12 * 2 (if bidirectional) + synaptic variables (weights) ''' # We need to write some speed tests if __name__=='__main__': queue=SpikeQueue(5,30) Nsynapses=4000*80 # in the CUBA example nspikes=160 delays=randint(160,size=nspikes) # average number of spikes per dt in CUBA targets=randint(Nsynapses,size=nspikes) #print queue.offsets(delays) t1=time() for _ in range(10000): # 10000 timesteps per second d=queue.offsets(delays) queue.insert(delays,d,targets) queue.next() events=queue.peek() queue.plot() t2=time() print t2-t1 brian-1.4.3/dev/ideas/synapses/old/statevectors.py000066400000000000000000000264671266062647100222310ustar00rootroot00000000000000''' This class defines data structures for the Synapses object. ** ConstructionSparseStateVector The main one is the ConstructionSparseStateVector, look into it's doc for details! TODO: - Finish to write the SparseStateVector (not dynamic), and use it accordingly in the Synapses code - More thorough test of set/get - think about the issue discussed with Romain: hashtable for lookup of values? keep sorted spikes? ** ParameterVector (worst name in the world) It is only returned by the getattr method of the Synapse object and is used for the fancy slicing (see doc) TODO: - Check the behavior for 3-D slicing ''' import numpy as np INITIAL_LEN = 2 # the initial length of the ConstructionSparseStateVector ########################## UTILS ########################## def dtype2str(dtype): ''' Converts a numpy.dtype object into a string for indexing in SparseStateVector's value dictionary ''' return str(dtype)[13:-2] def ensure_iterable(x): ''' Makes sure the value x is iterable, if it's not (a float/int, etc...) turn it into an array ''' if not np.iterable(x): return np.array([x]) else: return x def slice2range(sl, n): ''' Given a slice object and a length, returns the array of indices concerned by the slice. Useful in all the getitem methods! ''' tmp = np.arange(n, dtype = np.int32) return ensure_iterable(tmp[sl]) ########################## STATE VECTORS ########################## class SparseStateVector(object): ''' CURRENTLY NOT USED Compressed (i.e not dynamic) state vectors array Basically should act as a labeled, static ndarray with possibly different dtypes for different rows. ''' def __init__(self, data, dtypes, row_indices, nvalues, labels = []): self.dtypes = dtypes self.nrows = len(dtypes) self.nvalues = nvalues if labels: if not len(labels) == self.nrows: raise ValueError('Wrong number of labels') self.labels = labels self.row_indices = row_indices self._values_dict = data def __getitem__(self, key): ## Before going further I have to know exactly how i'll be using those if not isinstance(key, tuple) and len(key) == 2: raise IndexError('Wrong index') else: rows = slice2range(key[0], self.shape[0]) cols = slice2range(key[1], self.shape[1]) new_dtypes = [typ for i,typ in enumerate(self.dtypes) if i in rows] for typ in cur_dtypes: pass @property def shape(self): return (self.nrows, self.nvalues) class ConstructionSparseStateVector(object): ''' This class is used to construct the state vector structure underlying the synapses object. It is basically a 2-D array with possibly different types per row. The rows in this object are also labeled, and can be retrieved and set via v.fieldname. More importantly, it is dynamic, and doubles its length every time a value is entered that reaches the maximum size. ** Initialization ** V = ConstructionSparseStateVector(nrows, dtype = int, labels = None) ``nrows'' is the number of rows (fields) of the data structure ``dtype'' is either a single, or a tuple of numpy dtypes that correspond to the wanted dtypes for each row ``labels'' when set, one can get each row by accessing the object's attribute of the same name Example: ConstructionSparseStateVector(3, dtype = (int32, float32, int32), labels = ['pre', 'weight', 'post']) ** Dynamic Structure ** One can add new values (columns) on the fly to the data structure by append them, for example is V is the structure in the above example, one can add 2 values as follows: values = [np.ones(2, dtype = int32), np.randn(2, dtype = float32), np.zeros(2, dtype = int32)] V.append(values) This will add two 1s in the 'pre' field, two random numbers in the 'weight' field etc... ** Accessing the fields ** The recommended way of doing so is to label the field and use its label as an attribute. In the above example, we'd use V.pre To get the ``pre'' field. ** To Static ** ..automethod:: compress TODO: Use that! ** Internals! ** ``_values_dict'' Internally the arrays of the different dtypes are stored into a dictionary, and values of the same type in the same array. The function dtype2str converts the dtype object into a string (int32 -> 'int32') and the int32 values of the structure are accessed internally by self._values_dict['int32'] ``nvalues'' Because of the dynamic structure, the arrays in the dictionary can be bigger than there is data in them, hence we keep the number of relevant values. ``row_indices'' row_indices[i] keeps track of the row index of the row i in it's dedicated array. to get row i we thus do tmp = self._values_dict[dtype2str(self.dtype[i])] return tmp[row_indices[i],:] I think that's about it! ''' def __init__(self, nrows, dtype = int, labels = None): self.nrows = nrows # check dtype: if not isinstance(dtype, tuple): dtype = (dtype,)*nrows if not len(dtype) == nrows: raise ValueError('Wrong number of dtypes') self.dtypes = dtype # possibly row labels if labels: if not len(labels) == nrows: raise ValueError('Wrong number of labels') self.labels = labels self._var_positions = {} # take same dtypes and gather them nrows_per_typ, self.unique_typ = [], [] self.row_indices = np.zeros(len(self.dtypes), dtype = int) # will hold the number of the row in the dedicated dtype array for i,typ in enumerate(self.dtypes): if not typ in self.unique_typ: self.unique_typ.append(typ) nrows_per_typ.append(0) idtyp = self.unique_typ.index(typ) self.row_indices[i] = nrows_per_typ[idtyp] if labels: self._var_positions[labels[i]] = (dtype2str(typ), nrows_per_typ[idtyp]) nrows_per_typ[idtyp] += 1 self._values_dict = {} # now construct the state nd arrays for i,typ in enumerate(self.unique_typ): self._values_dict[dtype2str(typ)] = np.zeros((nrows_per_typ[i], INITIAL_LEN), dtype = typ) self.nvalues = 0 # initially no values instantiated self.datastruct_size = INITIAL_LEN # datastructure size is the size (nrows) of the arrays def append(self, values): ''' Values must be a 2-D array containing the pre/post neuron numbers to be added. ''' # where to insert the values initial_index = self.nvalues final_index = initial_index + values.shape[1] # if the new size is bigger that the datastruct size, resize the arrays if final_index > self.datastruct_size: newsize = 2**np.ceil(np.log2(final_index)) for typ in self.unique_typ: newarray = np.zeros((self._values_dict[dtype2str(typ)].shape[0], newsize), dtype = typ) newarray[:, :self.nvalues] = self._values_dict[dtype2str(typ)][:, :self.nvalues] self._values_dict[dtype2str(typ)] = newarray self.datastruct_size = newsize # appending the pre/post neuron numbers pre_typ, pre_row = self._var_positions['_pre'] post_typ, post_row = self._var_positions['_post'] self._values_dict[pre_typ][pre_row, initial_index:final_index] = values[0,:] self._values_dict[post_typ][post_row, initial_index:final_index] = values[1,:] self.nvalues = final_index return initial_index def compress(self): compressed_values_dict = {} for i, typ in enumerate(self.dtypes): compressed_values_dict[dtype2str(typ)] = self._values_dict[dtype2str(typ)][:, :self.nvalues] return SparseStateVector(compressed_values_dict, self.dtypes, self.row_indices, self.nvalues, labels = self.labels) def __getattr__(self, name, default = ''): if name == '_allstates': return self._values_dict['float32'] if name in self.labels: pre_typ, pre_row = self._var_positions[name] # where to look return self._values_dict[pre_typ][pre_row, :self.nvalues] try: self.__dict__[name] except KeyError: raise AttributeError('State vector object doesn\'t have a '+name+' attribute') def __getitem__(self, name): if name in self.labels: pre_typ, pre_row = self._var_positions[name] # where to look return self._values_dict[pre_typ][pre_row, :self.nvalues] else: raise KeyError('Item '+name+' not in statevector') class ParameterVector(object): ''' Helpful for the setattr of Synapses, indeed we want synapses.w[slice_pre, slice_post] = 1 to mean for all the presyn neurons of slice_pre for which there is a synapse, resp, postsyn, set the weight to 1. hence synapses.w must be of a special class whose __setitem__ method is overriden, otherwise the returned value of synapses.w isn't the right shape etc... Also we want: synapses.w[slice_pre, slice_post, :5] = 2 to mean the 5 first synapses between slice_pre and slice_post neurons = 2 KWD ARGS ``delay_dt'' is here because in the case of delays, then the value (float with unit) must be converted into timesteps (int), hence to do this this object has to know about the dt. ''' def __init__(self, data, synapses, delay_dt = None): self.data = data self.groups_shape = (len(synapses.source), len(synapses.target)) self.synapses = synapses self.delay_dt = delay_dt def __setitem__(self, key, value): if isinstance(key, tuple): pre_synapses = self.synapses.pre2synapses(slice2range(key[0] , self.groups_shape[0])) post_synapses = self.synapses.post2synapses(slice2range(key[0] , self.groups_shape[0])) indices = np.intersect1d(pre_synapses, post_synapses) # Shite! now I do I do that??! if len(key) > 2: indices = indices[slice2range(key[2], len(indices))] if self.delay_dt: value = np.array(np.round(value/self.delay_dt), dtype = self.data.dtype) self.data[indices] = value else: raise IndexError def __getitem__(self, key): return self.data.__getitem__(key) def __repr__(self): return self.data.__repr__() def __str__(self): return self.data.__repr__() brian-1.4.3/dev/ideas/synapses/old/synapse_separation.py000066400000000000000000000050231266062647100233730ustar00rootroot00000000000000""" Separation of an array of synapses into a list of arrays with no repeated postsynaptic neurons """ from numpy import * from time import time ''' Testing whether we have repeated indexes. OPTION 1 -------- x=all(bincount(post)<2) This is very slow with bincount if the number of synapses is large. And it's a memory expensive way to do it. In principle, we don't need memory allocation for this test. OPTION 2 -------- x=len(unique(post))1): post[i]=-1 # flag meaning we've processed them u,i=unique(post,return_index=True) #k+=1 print synapses[i[1:]],u[1:] ''' Same thing as above, but in a way closer to the final code. Seems to work (yay) ''' spikes = [0, 1, 2, 3, 4] pre = array([0, 1, 2, 2, 3, 3, 4, 4]) post = array([0, 1, 0, 1, 0, 0, 1, 1]) vout = zeros(2) w = array([1, 2, 1, 2, 1, 2]) def update(weights, voltage, synapses): voltage[post[synapses]] += w[synapses] print voltage for j in spikes: synapses = nonzero(pre == j)[0] post_neurons = post[synapses] u,i = unique(post_neurons, return_index=True) print 'base update' print 'concerned synapses', synapses print 'u, i', u, i update(w, vout, i) if len(u) < len(post_neurons): while len(u) < len(post_neurons) and (post_neurons > -1).any(): post_neurons[i] = -1 u, i = unique(post_neurons, return_index = True) print 'repeated update' print 'concerned synapses', synapses print 'u, i', u, i update(w, vout, i[1:]) brian-1.4.3/dev/ideas/synapses/old/synapses.py000066400000000000000000000434451266062647100213430ustar00rootroot00000000000000''' Here is defined the main Synapses code. Two important things: Synapse object EventSynapseUpdater I tried to write as much as I could in the classdoc of each object. ''' from brian import * import numpy as np from brian.inspection import * from brian.equations import * from brian.utils.documentation import flattened_docstring from dev.ideas.synapses.statevectors import * from dev.ideas.synapses.spikequeue import * import re __all__ = ['Synapses'] class Synapses(NetworkOperation): ''' Synapses object State: - the model kwdarg can only declare variables for now - only the 'pre' code is implemented - multiple synapses (between same pair of neurons), with multiple delays seem to work fine! TODO: - postsynaptic delays ** Initialization ** synapses = syn.Synapses(gin, gout, model = 'w : 1', pre = 'v += w', post = '') * Arguments * ``gin'' source NeuronGroup ``gout'' target NeuronGroup * Keywords * ``model'' for now only declares variables ``pre'' code to be executed when a presynaptic spike arrives ``post'' code to be executed when a postsynaptic spike arrives ``max_delay'' If not set then the synapse is without delays, otherwise sets the maximum acceptable delay for the Synapses. Note that the delays are only axonal (i.e. presynaptic), this could easily be changed in the future. * Attributes * All the variables of the model (see below), plus ``existing_synapses'' a 2 D array of shape (2, len(synapses)) that hold the pre and post neuron numbers for each synapse. * Adding Synapses * When it's created, a Synapses object doesn't have any instantiated synapses. To add some, one must do: synapses[:,0] = True Which connects all the presynaptic neurons to the postsynaptic neuron Alternatively, one may want to add 2 synapses between the same neurons, to do so, one can run the above code line a few times, of just write synapses[:,0] = 2 * Setting variables * All of the variables defined in the model code (except the reserved ones), can (and should) be instantiated. In the above example, to set the weights, one can do: synapses.w[:,0] = 1 (or array, etc...) To set all the weights of the *EXISTING* synapses to 1. If there are multiple synapses, then they can be accessed with a third field: synapses.w[:,0,1] = 2 By default the previous call sets all synapses to the same value. * Delays * If the delays are activated (setting the kwdarg max_delay) then a field of delays (``delay'') is created and can be used as any other field by setting it: synapses.delay[:,0] = 5*ms ''' def __init__(self, source, target = None, clock = None, max_delay = 0, level = 0, model = None, check_units = True, method = None, freeze = False, implicit = False, order = 1, # model (state updater) related pre = '', post = ''): NetworkOperation.__init__(self, lambda:None, clock=clock) # Arguments parsing if target is None: self.source = self.target = source else: self.source = source self.target = target ########### CODE PARSING # model equations parsing if isinstance(model, Equations): model_obj = model else: if '\n' in model: model = flattened_docstring(model) elif ';' in model: model = '\n'.join([line.strip() for line in model.split(';')]) model_obj = Equations(model, level = level + 1) # stolen from NeuronGroup # !! This will not work with postsynaptic variables in the model eqs !! if isinstance(model_obj, StateUpdater): self._state_updater = model_obj self._all_units = defaultdict() # what is that elif isinstance(model_obj, Equations): self._eqs = model_obj self._state_updater, var_names = magic_state_updater(model_obj, clock=clock, order=order, check_units=check_units, implicit=implicit, compile=compile, freeze=freeze, method=method) self._state_updater_varnames = var_names # TODO: # - Maybe stateupdaters should be aware of the dtype of the data (or else my state vectors thing is sort of useless # - or ensure fast access to different data types in a vectorized way? # handle multi-line pre, post equations and multi-statement equations separated by ; if '\n' in pre: pre = flattened_docstring(pre) elif ';' in pre: pre = '\n'.join([line.strip() for line in pre.split(';')]) if '\n' in post: post = flattened_docstring(post) elif ';' in post: post = '\n'.join([line.strip() for line in post.split(';')]) # Check units model_obj.compile_functions() #model_obj.check_units() # Get variable names self.vars = model_obj._diffeq_names # !! there are also static variables and aliases !! ############# Setting up the data structure # Mandatory fields: 3 for pre/post/delay_pre, all int32 TODO: finer pick of dtype! # REPLACE THAT BY MORE SENSIBLE DATA STRUCTURE dtypes = (np.int32, ) * 3 default_labels = ['_pre', '_post', 'delay'] # Equation defined fields (float32) dtypes += (np.float32, ) * len(self.vars) # construction of the structure self._statevector = ConstructionSparseStateVector(len(dtypes), dtype = dtypes, labels = default_labels+self.vars) ############# Code!!! # create namespace pre_namespace = namespace(pre, level = level + 1) pre_namespace['target'] = self.target pre_namespace['unique'] = np.unique pre_namespace['nonzero'] = np.nonzero pre_namespace['_pre'] = self._statevector._pre pre_namespace['_post'] = self._statevector._post for var in self.vars: pre_namespace[var] = self._statevector[var] # !! also add postsynaptic variables !! def update_code(pre, indices): res = pre # given the synapse indices, write the update code, # this is here because in the code we generate we need to write this twice (because of the multiple presyn spikes for the same postsyn neuron problem) for postsyn_var in self.target.var_index: if isinstance(postsyn_var, str): res = re.sub(r'\b' + postsyn_var + r'\b', 'target.' + postsyn_var + '[_post['+indices+']]', res)# postsyn variable, indexed by post syn neuron numbers for var in self.vars: res = re.sub(r'\b' + var + r'\b', var + '['+indices+']', res) # synaptic variable, indexed by the synapse number return res # pre code pre_code = "_post_neurons = _post[_synapses]\n" # which post syn neurons pre_code += "_u, _i = unique(_post_neurons, return_index = True)\n" pre_code += update_code(pre, '_synapses[_i]') + "\n" pre_code += "if len(_u) < len(_post_neurons):\n" pre_code += " _post_neurons[_i] = -1\n" pre_code += " while (len(_u) < len(_post_neurons)) & (_post_neurons>-1).any():\n" # !! the any() is time consuming (len(u)>=1??) pre_code += " _u, _i = unique(_post_neurons, return_index = True)\n" pre_code += " " + update_code(pre, '_synapses[_i[1:]]') + "\n" pre_code += " _post_neurons[_i[1:]] = -1 \n" log_debug('brian.synapses', '\nPRE CODE:\n'+pre_code) pre_code = compile(pre_code, "Presynaptic code", "exec") self.pre_namespace = pre_namespace self.pre_code = pre_code self.pre_queue = SpikeQueue(self.source, self, max_delay = max_delay) self.contained_objects = [self.pre_queue] # wtf is this for: so that pre_queue.propagate is called self._pre_to_synapse = np.zeros(len(self.source), dtype = object) self._post_to_synapse = np.zeros(len(self.target), dtype = object) # presyn neuron to postsyn neuron mapping def __setitem__(self, key, value): if not isinstance(key, tuple): # do they? raise ValueError('Synapses behave as 2-D objects') if isinstance(value, (int, bool)): # Simple case, either one or multiple synapses between different neurons if not (value is True): n_synapses = value else: # !! and False? !! n_synapses = 1 pre_slice = slice2range(key[0], len(self.source)) post_slice = slice2range(key[1], len(self.target)) # Construction of the pre/post neurons indices list # This needs speed up!!!! # BTW not sure that lists are really necessary, maybe at build time though pre, post = [], [] for i in post_slice: post += [i]*len(pre_slice)*n_synapses pre += list(pre_slice)*n_synapses elif isinstance(value, str): # !! Other more efficient option for memory consumption: !! # vectorize only over post neurons, loop over pre # transform boolean to sparse vector after every pre neuron # # more complex case where the value is specified as a string, hence code # The code is evaluated and this yields a matrix that is then passed code = re.sub(r'\b' + 'rand\(\)', 'rand(len(_i), len(_j))', value) # replacing rand() pre_slice = slice2range(key[0], len(self.source)) post_slice = slice2range(key[1], len(self.target)) pre_grid, post_grid = np.meshgrid(pre_slice, post_slice) namespace = {'_i' : pre_slice, '_j' : post_slice, 'i' : pre_grid, 'j' : post_grid, 'rand': np.random.rand} log_debug('synapses.setitem', code) res = eval(code, namespace) log_debug('synapses.setitem', 'created synapses: \n '+ str(res)) self[key] = res return elif isinstance(value, np.ndarray): n_syn = array(value.copy(), dtype = int) pre, post = np.nonzero(n_syn) if (n_syn >= 1).any(): # no need to go through this if there is never multiple synapses pre = list(pre) post = list(post) n_syn -= 1 n_syn[n_syn < 0] = 0 _pre, _post = np.nonzero(n_syn) while len(_pre) != 0: pre += list(_pre) post += list(_post) n_syn -= 1 n_syn[n_syn < 0] = 0 _pre, _post = np.nonzero(n_syn) # !! add: sparse matrices values = np.vstack((pre, post)) # append values and get initial index (for the _pre_to_synapse struct) initial_index = self._statevector.append(values) # build the synapse reference arrays for i in np.unique(values[0,:]): newindices = initial_index + np.nonzero(values[0,:] == i)[0] self._pre_to_synapse[i] = np.hstack((self._pre_to_synapse[i], newindices)) for i in np.unique(values[1,:]): newindices = initial_index + np.nonzero(values[1,:] == i)[0] self._post_to_synapse[i] = np.hstack((self._post_to_synapse[i], newindices)) def __getattr__(self, name): if hasattr(self, '_statevector'): # always True? if name == '_S': # for the state updater, I should do this better I guess return self._statevector._allstates if name in self._statevector.labels: # Have to return a special kind of Vector, for the slicing to work data = getattr(self._statevector, name) groups_shape = (len(self.source), len(self.target)) # dt handling (see the ParameterVector doc) dt = None if name == 'delay': dt = float(self.source.clock.dt) return ParameterVector(data, self, delay_dt = dt) try: self.__dict__[name] except KeyError: raise AttributeError('Synapses object doesn\'t have a '+name+' attribute') def __len__(self): ''' Returns the number of existing synapses. ''' return self._statevector.nvalues def __call__(self): # presynaptic spikes synapses_current = self.pre_queue.peek() if len(synapses_current): _namespace = self.pre_namespace _namespace['_synapses'] = np.array(synapses_current) for var in self.vars: _namespace[var] = self._statevector[var] _namespace['t'] = self.source.clock._t _namespace['_post'] = self._statevector._post exec self.pre_code in _namespace self.pre_queue.next() # state update self._state_updater(self) def pre2synapses(self, presyn): return np.hstack(self._pre_to_synapse[presyn]) def post2synapses(self, postsyn): return np.hstack(self._pre_to_synapse[postsyn]) @property def existing_synapses(self): return np.vstack((self._statevector._pre, self._statevector._post)) def __repr__(self): s = 'Synapses:\n' s+= str(self.existing_synapses) return s # DEPRECATED STUFF: # # ########################## SynapseUpdater ########################## # class EventSynapseUpdater(SpikeMonitor): # ''' # Mimicked after the eventstdpupdater in dev/ideas/stdp/eventdriven_stdp # A SpikeMonitor that executes the code it's given at initialization. # The interesting code is rather in the Synapses object itself (search for pre_code) # ''' # def __init__(self, source, S, code, namespace, delay=0 * ms): # ''' # source = source group # S = base Synapse object # vars = variable names # M = matrix of the linear differential system # code = code to execute for every spike # namespace = namespace for the code # delay = transmission delay # ''' # super(EventSynapseUpdater, self).__init__(source, # record = False) # self._code = code # update code # self._namespace = namespace # code namespace # self.S = S # def propagate(self, spikes): # if len(spikes): # synapses = [] # for i in spikes: # synapses += list(nonzero(self.S._statevector._pre == i)[0]) # self._namespace['_synapses'] = np.array(synapses) # for var in self.S.vars: # self._namespace[var] = self.S._statevector[var] # self._namespace['t'] = self.S.source.clock._t # self._namespace['_post'] = self.S._statevector._post # exec self._code in self._namespace # INTIAL_MAXSPIKESPER_DT = 10 # Because SpikeQueue doesn't have a dynamic structure (yet) # class DelayEventSynapseUpdater(SpikeMonitor): # ''' # Same as above but includes delays (yay!) # ''' # def __init__(self, source, S, code, namespace, max_delay): # ''' # source = source group # S = base Synapse object # vars = variable names # M = matrix of the linear differential system # code = code to execute for every spike # namespace = namespace for the code # max_delay = maximum delay # ''' # super(DelayEventSynapseUpdater, self).__init__(source, # record = False) # self._code = code # update code # self._namespace = namespace # code namespace # self.S = S # # SpikeQueue initialization # nsteps = int(np.ceil((max_delay)/(self.S.source.clock.dt))) # self.spike_queue = SpikeQueue(source, INTIAL_MAXSPIKESPER_DT) # def propagate(self, spikes): # # step 1: insert spikes in the event queue # if len(spikes): # # synapse identification, # # this seems ok in terms of speed even though I dont like the for loop. # # any idea? see stest_fastsynapseidentification.py # synapses = [] # for i in spikes: # synapses += list(nonzero(self.S._statevector._pre == i)[0]) # # delay getting: # delays = self.S._statevector.delay[synapses] # offsets = self.spike_queue.offsets(delays) # self.spike_queue.insert(delays, offsets, synapses) # # step 2: execute code on current spikes # synapses_current = self.spike_queue.peek() # if len(synapses_current): # self._namespace['_synapses'] = np.array(synapses_current) # for var in self.S.vars: # self._namespace[var] = self.S._statevector[var] # self._namespace['t'] = self.S.source.clock._t # self._namespace['_post'] = self.S._statevector._post # exec self._code in self._namespace # self.spike_queue.next() brian-1.4.3/dev/ideas/synapses/speed tests/000077500000000000000000000000001266062647100205575ustar00rootroot00000000000000brian-1.4.3/dev/ideas/synapses/speed tests/CUBA_bench.py000066400000000000000000000044771266062647100230160ustar00rootroot00000000000000 from brian import * import time from brian.experimental.synapses import * #log_level_debug() set_global_preferences(useweave = True) import random as pyrand from numpy.random import seed def one_run(use_synapses = True): # coding: latin-1 """ CUBA example with delays. Connection (no delay): 3.5 s DelayConnection: 5.7 s Synapses (with precomputed offsets): 6.6 s # 6.9 s Synapses with weave: 6.4 s Synapses with zero delays: 5.2 s """ clear(True, True) reinit_default_clock() seed(3240832) pyrand.seed(324331) start_time = time.time() taum = 20 * ms taue = 5 * ms taui = 10 * ms Vt = -50 * mV Vr = -60 * mV El = -49 * mV eqs = Equations(''' dv/dt = (ge+gi-(v-El))/taum : volt dge/dt = -ge/taue : volt dgi/dt = -gi/taui : volt ''') P = NeuronGroup(4000, model=eqs, threshold=Vt, reset=Vr, refractory=5 * ms) P.v = Vr P.ge = 0 * mV P.gi = 0 * mV Pe = P.subgroup(3200) Pi = P.subgroup(800) we = (60 * 0.27 / 10) * mV # excitatory synaptic weight (voltage) wi = (-20 * 4.5 / 10) * mV # inhibitory synaptic weight if use_synapses: ########### NEW SYNAPSE CODE Se = Synapses(Pe, P, model = 'w : 1', pre = 'ge += we') Si = Synapses(Pi, P, model = 'w : 1', pre = 'gi += wi') Se[:,:]=0.02 Si[:,:]=0.02 Se.delay='rand()*ms' Si.delay='rand()*ms' else: ########### OLD CODE Ce = Connection(Pe, P, 'ge', weight=we, sparseness=0.02, delay=(0*ms,1*ms)) Ci = Connection(Pi, P, 'gi', weight=wi, sparseness=0.02, delay=(0*ms,1*ms)) P.v = Vr + rand(len(P)) * (Vt - Vr) # Record the number of spikes Me = PopulationSpikeCounter(Pe) Mi = PopulationSpikeCounter(Pi) # A population rate monitor M = PopulationRateMonitor(P) run(1 * msecond) start_time = time.time() run(1 * second) duration = time.time() - start_time return duration if __name__ == '__main__': Niter = 5 perf_synapses = np.zeros(Niter) perf_nosynapses = np.zeros(Niter) for k in range(Niter): print k perf_synapses[k] = one_run() # perf_nosynapses[k] = one_run(use_synapses = False) from matplotlib.pyplot import * hist([perf_synapses, perf_nosynapses]) show() brian-1.4.3/dev/ideas/synapses/speed tests/CUBA_speedtest.py000066400000000000000000000070361266062647100237310ustar00rootroot00000000000000# coding: latin-1 """ CUBA example with delays. Connection (no delay): 3.5 s DelayConnection: 5.7 s Synapses (with precomputed offsets): 6.6 s # 6.9 s Synapses with weave: 6.4 s Synapses with zero delays: 5.2 s """ from brian import * import time from brian.experimental.synapses import * #log_level_debug() #set_global_preferences(useweave=True, usecodegen=False) set_global_preferences(useweave=False, usecodegen=False) use_synapses = False reproduce_connection = True do_callgraph = False if do_callgraph: import pycallgraph cg_func = 'synapses' def ff(pat): def f(call_stack, module_name, class_name, func_name, full_name): if not 'brian' in module_name: return False for n in call_stack + [full_name]: if pat in n: return True return False return f import random as pyrand from numpy.random import seed seed(3240832) pyrand.seed(324331) start_time = time.time() taum = 20 * ms taue = 5 * ms taui = 10 * ms Vt = -50 * mV Vr = -60 * mV El = -49 * mV eqs = Equations(''' dv/dt = (ge+gi-(v-El))/taum : volt dge/dt = -ge/taue : volt dgi/dt = -gi/taui : volt ''') P = NeuronGroup(4000, model=eqs, threshold=Vt, reset=Vr, refractory=5 * ms) P.v = Vr P.ge = 0 * mV P.gi = 0 * mV P.v = Vr + rand(len(P)) * (Vt - Vr) Pe = P.subgroup(3200) Pi = P.subgroup(800) we = (60 * 0.27 / 10) * mV # excitatory synaptic weight (voltage) wi = (-20 * 4.5 / 10) * mV # inhibitory synaptic weight if not use_synapses or reproduce_connection: Ce = Connection(Pe, P, 'ge', weight=we, sparseness=0.02, delay=(1*ms, 2*ms)) Ci = Connection(Pi, P, 'gi', weight=wi, sparseness=0.02, delay=(1*ms, 2*ms)) Ce.compress() Ci.compress() if use_synapses: set_global_preferences(useweave=False) Se = Synapses(Pe, P, model = 'w : 1', pre = 'ge += we') Si = Synapses(Pi, P, model = 'w : 1', pre = 'gi += wi') if reproduce_connection: def f(i, j): x = zeros(len(j), dtype=bool) x[C[i, :].ind] = True return x C = Ce Se[:, :] = 'f(i,j)' C = Ci Si[:, :] = 'f(i,j)' for i in xrange(len(Pe)): Se.delay[i, :] = Ce.delay[i, :] for i in xrange(len(Pi)): Si.delay[i, :] = Ci.delay[i, :] del Ce, Ci import gc gc.collect() else: Se[:,:]=0.02 Si[:,:]=0.02 Se.delay='rand()*ms' Si.delay='rand()*ms' set_global_preferences(useweave=False) # Record the number of spikes Me = PopulationSpikeCounter(Pe) Mi = PopulationSpikeCounter(Pi) # A population rate monitor M = PopulationRateMonitor(P) #M0 = RecentStateMonitor(P, 'v', record=[0,1,2,3,4], duration=100*ms) print "Network construction time:", time.time() - start_time, "seconds" print len(P), "neurons in the network" print "Simulation running..." if use_synapses: net = Network(P, Se, Si, Me, Mi, M)#, M0) else: net = Network(P, Ce, Ci, Me, Mi, M)#, M0) net.run(100 * msecond) #M0.plot() #show() #exit() start_time = time.time() if do_callgraph: pycallgraph.start_trace(filter_func=ff(cg_func)) net.run(1 * second) duration = time.time() - start_time if do_callgraph: pycallgraph.stop_trace() pycallgraph.make_dot_graph('cuba-callgraph.png') print "Simulation time:", duration, "seconds" print Me.nspikes, "excitatory spikes" print Mi.nspikes, "inhibitory spikes" #plot(M.times / ms, M.smooth_rate(2 * ms, 'gaussian')) #show() brian-1.4.3/dev/ideas/synapses/speed tests/basic_speed_test.py000066400000000000000000000026711266062647100244370ustar00rootroot00000000000000""" Speed test for Synapses ----------------------- Results look ok. With M = 10 740 000 spikes/second No synapses: 0.5s Only the spike queue: 6.5s (46%) Complete: 13.5s (54%) (but this is a case with many simultaneous updates on the same post neuron) With M = 1 311 000 spikes/second No synapses: 0.5s Only the spike queue: 2.4s (70%) Complete: 3.2s (30%) Thus in practical cases, the spike queue is probably the dominant computational cost. With a C implementation spike queue, we will then probably get delay connections that are as fast as normal connections. For comparison: CUBA:320000 synapses, about 5 Hz -> 1,600,000 events / biological second non-spiking CUBA: 1.5s CUBA with Connection: 3s CUBA with DelayConnection: 5.3s -> about 4s for spikes -> 400,000 events/second Nemo: 400 000 000 spikes/second Jayram: 80 000 000 spikes/second """ from brian import * from brian.experimental.synapses import * from time import time #log_level_debug() N=100 M=10 rate=10000*Hz duration=1*second P=NeuronGroup(1,model='dv/dt=rate :1',threshold=1,reset=0) Q=NeuronGroup(N,model='v:1') S=Synapses(P,Q,model='w:1',pre='v+=w',max_delay=2*ms) #S[0,:]=True S[P,Q]=M S.w=rand(N*M) S.delay_pre[:]=rand(N*M)*1*ms #S.pre_queue.precompute_offsets() run(1*ms) t1=time() run(duration) t2=time() print "Simulation time:",t2-t1,"s" print rate*duration*N*M,"events" print rate*duration*N*M/(t2-t1),"events/s" brian-1.4.3/dev/ideas/synapses/speed tests/construction.py000066400000000000000000000005161266062647100236650ustar00rootroot00000000000000""" Speed test for construction """ from brian import * from time import time from brian.experimental.synapses import * N=1000 neurons=NeuronGroup(N,model='dv/dt=1/(10*ms):1', threshold=1, reset=0) S=Synapses(neurons,model='w:1') S[:,:]=0.2 t1=time() for i in range(1000): S.w[i,10:30]=1. t2=time() print t2-t1 brian-1.4.3/dev/ideas/synapses/tests/000077500000000000000000000000001266062647100174765ustar00rootroot00000000000000brian-1.4.3/dev/ideas/synapses/tests/test_consistency.py000066400000000000000000000013201266062647100234440ustar00rootroot00000000000000""" Test the consistency of synapses_post and postsynaptic """ from brian import * from brian.experimental.synapses import * N=100 P=NeuronGroup(N,'v:1') S=Synapses(P,P,model="w:1",pre="v+=w") S[:,:]=0.1 for i in range(N): if (S.presynaptic[S.synapses_pre[i][:]]!=i).any(): print "pre: inconsistency!" for i in range(N): if (S.postsynaptic[S.synapses_post[i][:]]!=i).any(): print S.postsynaptic[S.synapses_post[i][:]] #postsynaptic=randint(5,size=20) #synapses_post=invert_array(postsynaptic) #print postsynaptic,synapses_post #print # #for i,synapses in synapses_post.iteritems(): # if (postsynaptic[synapses]!=i).any(): # print i,postsynaptic[synapses] brian-1.4.3/dev/ideas/synapses/tests/test_fastsynapseidentification.py000066400000000000000000000016061266062647100263640ustar00rootroot00000000000000from numpy import * import time spikes = arange(10000) # presynaptic neurons that spiked pre = ones(100) # presyn neuron per synapse pre[50:] = arange(50) pre[0] = 1 ''' Spec: function(spikes, pre) should return the array of synapse numbers concerned by the spikes in spikes (a list) Issues: - multiple synapse per same presyn neuron - speed ''' def dummy_method(s, p): synapses = [] for i in s: idx = list(nonzero(p == i)[0]) synapses+=idx return synapses if True: t0 = time.time() resdummy = dummy_method(spikes, pre) print 'dummy method in ', time.time()-t0 def meshgrid_method(s, p): ss, pp = meshgrid(s, p) synapses = nonzero(sum(ss == pp, axis = 1))[0] return synapses if True: t0 = time.time() resmesh = meshgrid_method(spikes, pre) print 'mesh method in ', time.time()-t0 print (sort(resmesh) == sort(resdummy)).all() brian-1.4.3/dev/ideas/synapses/tests/test_lenuthing.py000066400000000000000000000010411266062647100231000ustar00rootroot00000000000000from brian import * from brian.experimental.synapses import * reinit_default_clock() source = SpikeGeneratorGroup(3, [(0, 1*ms), (1, 1*ms), (2, 1*ms)]) target = NeuronGroup(2, model = '''dv/dt = -v/(10*ms):1''') syn = Synapses(source, target, model = 'w:1', pre = 'v+=w') syn[1,1] = 1 syn[2,1] = 1. syn[0,0] = 1 syn[1,0] = 1 syn[2,0] = 1 syn.w = 50 M = StateMonitor(target, 'v', record = True) print syn print syn.w[:],syn.presynaptic,syn.postsynaptic run(40*ms) plot(M.times/ms, M[0]) plot(M.times/ms, M[1]) show() brian-1.4.3/dev/ideas/synapses/tests/test_parametervector.py000066400000000000000000000005161266062647100243140ustar00rootroot00000000000000from brian import * import dev.ideas.synapses.synapses as syn reload(syn) existing_synapses = np.array([[1,2,3], [4,5,6]]) groups_shape = (4, 7) data = np.zeros(existing_synapses.shape[1]) pv = syn.ParameterVector(data, groups_shape, existing_synapses) print 'initial', pv.data pv[1,:] = 3 print 'modified', pv.data print data brian-1.4.3/dev/ideas/theano/000077500000000000000000000000001266062647100157455ustar00rootroot00000000000000brian-1.4.3/dev/ideas/theano/theano_test.py000066400000000000000000000000171266062647100206320ustar00rootroot00000000000000import theano brian-1.4.3/dev/ideas/vanrossum.py000066400000000000000000000032401266062647100170750ustar00rootroot00000000000000""" Van Rossum metric. We express the metric as an STDP rule + counter. I think for Poisson inputs we get (Fi+Fj)*tau/2 (after normalization by the duration). """ from brian import * tau = 20 * ms N = 100 b = 1.2 # constant current mean, the modulation varies f = 10 * Hz tau_VR=2*ms eqs = ''' dv/dt=(-v+a*sin(2*pi*f*t)+b)/tau : 1 a : 1 ''' neurons = NeuronGroup(N, model=eqs, threshold=1, reset=0) neurons.v = rand(N) neurons.a = linspace(.05, 0.75, N) S = SpikeMonitor(neurons) trace = StateMonitor(neurons, 'v', record=50) VR=VanRossumMetric(neurons,tau=tau_VR) # Alternative implementation class NewVanRossumMetric(STDP): def __init__(self,source,tau=2*ms): self.counter=SpikeCounter(neurons) self.N=len(neurons) class Fake(object): def __init__(self,source): self.source=source self.target=source self.delay=0 self.W=zeros((len(source),len(source))) self.synapses=Fake(neurons) eqs_stdp=''' dApost/dt=-Apost/tau_VR : 1 dApre/dt=-Apre/tau_VR : 1 ''' STDP.__init__(self,self.synapses,eqs_stdp,pre='Apre+=1;w+=Apost',post='Apost+=1;w+=Apre') self.contained_objects.append(self.counter) def get_distance(self): c=self.counter.count*ones((self.N,1)) return c+c.T-2*self.synapses.W # divide by duration? VR2=NewVanRossumMetric(neurons,tau=tau_VR) run(1000 * ms) subplot(221) raster_plot(S) subplot(222) plot(trace.times / ms, trace[50]) subplot(223) imshow(VR.get_distance()) subplot(224) imshow(VR2.get_distance()) show() brian-1.4.3/dev/jenkins/000077500000000000000000000000001266062647100150435ustar00rootroot00000000000000brian-1.4.3/dev/jenkins/linux_scripts/000077500000000000000000000000001266062647100177515ustar00rootroot00000000000000brian-1.4.3/dev/jenkins/linux_scripts/build_brian.sh000066400000000000000000000041521266062647100225610ustar00rootroot00000000000000#! /bin/bash echo Using $(which python) OLD_DIR="$(pwd)" # change into the virtualenv directory cd ~/.jenkins/virtual_envs/$PythonVersion/$packages # if no system-wide packages are used, update numpy etc. to newest version if [[ $packages == "newest" ]]; then echo "Using newest available package versions" bin/pip install --upgrade numpy bin/pip install --upgrade scipy if [[ ${PythonVersion:0:1} == '2' ]]; then bin/pip install --upgrade sympy else bin/pip http://sympy.googlecode.com/files/sympy-0.7.2-py3.2.tar.gz fi if [[ $PythonVersion == "python2.5" ]]; then # matplotlib 1.2 is no longer compatible with Python 2.5 bin/pip install --upgrade matplotlib==1.1 else bin/pip install --upgrade matplotlib fi elif [[ $packages == "oldest" ]]; then echo "Using oldest available package versions supported by Brian" bin/pip install numpy==1.4.1 # scipy 0.7 has a bug that makes it impossible to use weave, download the # package and apply the patch before installing mkdir downloads || : wget -O downloads/scipy-0.7.0.tar.gz http://sourceforge.net/projects/scipy/files/scipy/0.7.0/scipy-0.7.0.tar.gz/download cd downloads tar xvf scipy-0.7.0.tar.gz # get and apply patch cd scipy-0.7.0 wget http://projects.scipy.org/scipy/raw-attachment/ticket/739/weave-739.patch patch -p1 < weave-739.patch # build scipy ../../bin/python setup.py install cd ../.. bin/pip install sympy # Brian depencies state matplotlib>=0.90.1 but 0.98.5.3 seems to be the oldest one installable bin/pip install http://sourceforge.net/projects/matplotlib/files/matplotlib/matplotlib-0.98.5/matplotlib-0.98.5.3.tar.gz/download fi # Print the version numbers for the dependencies bin/python -c "import numpy; print 'numpy version: ', numpy.__version__" bin/python -c "import scipy; print 'scipy version: ', scipy.__version__" bin/python -c "import sympy; print 'sympy version: ', sympy.__version__" bin/python -c "import matplotlib; print 'matplotlib version: ', matplotlib.__version__" cd "$OLD_DIR" # Make sure the build ends up in the build/lib directory python setup.py build --build-lib=build/lib brian-1.4.3/dev/jenkins/linux_scripts/create_virtualenvs.sh000066400000000000000000000004321266062647100242110ustar00rootroot00000000000000#! /bin/sh mkdir -p ~/.jenkins/virtual_envs # replace this by the list of python versions available for PYTHON in python2.6 python2.7; do for PACKAGES in oldest newest; do virtualenv --python=$PYTHON --no-site-packages ~/.jenkins/virtual_envs/$PYTHON/$PACKAGES done done brian-1.4.3/dev/jenkins/linux_scripts/make_doc.sh000077500000000000000000000011661266062647100220560ustar00rootroot00000000000000#!/bin/bash -x # Use the source directory export PYTHONPATH="$(pwd)" # Generates the HTML and PDF documentation for brian cd dev/tools/docs # Generate HTML documentation python generate_html.py || exit 1 # Generate PDF documentation python build_latex.py || exit 1 cd ../../.. # Copy the PDF to the doc directory cp "$DOCROOT"/docs_sphinx/_latexbuild/Brian*.pdf "$DOCROOT"/docs/ # Delete old docs zip file if it exists rm docs.zip 2> /dev/null || : # copy the docs to the directory served by the local webserver cp -r "$DOCROOT"/docs/* ~/www-doc/"$DOCNAME" # Create new zip file cd "$DOCROOT" zip -r -q -9 docs.zip docs/ brian-1.4.3/dev/jenkins/linux_scripts/run_clonedigger.sh000066400000000000000000000002741266062647100234560ustar00rootroot00000000000000#!/bin/bash if [[ "$BRIAN_PACKAGE" == "" ]]; then BRIAN_PACKAGE=brian fi clonedigger --cpd-output --ignore-dir=tests --ignore-dir=experimental -o clonedigger.xml $BRIAN_PACKAGE || : brian-1.4.3/dev/jenkins/linux_scripts/run_examples.sh000066400000000000000000000006051266062647100230100ustar00rootroot00000000000000# Use previously built Brian export PYTHONPATH=$(pwd)/build/lib:$PYTHONPATH cd dev/tests/ rm examples_completed.txt || : # get the newest version of nose BASEDIR=$(dirname $PYTHON_EXE) $BASEDIR/pip install --upgrade -I nose || : # Do not fail if one of the examples fails, instead the run will be marked as # "unstable" by the violations plugin python -u allexamples_cumulative.py || : brian-1.4.3/dev/jenkins/linux_scripts/run_pyflakes.sh000077500000000000000000000003751266062647100230170ustar00rootroot00000000000000#!/bin/bash if [[ "$BRIAN_PACKAGE" == "" ]]; then BRIAN_PACKAGE=brian fi find $BRIAN_PACKAGE -not -path "$BRIAN_PACKAGE/tests/*" -name '*.py' -not -name '__init__.py' -print0 | xargs -0 pyflakes > pyflakes_warnings.log 2> pyflakes_errors.log || : brian-1.4.3/dev/jenkins/linux_scripts/run_pylint.sh000066400000000000000000000002261266062647100225100ustar00rootroot00000000000000#!/bin/bash if [[ "$BRIAN_PACKAGE" == "" ]]; then BRIAN_PACKAGE=brian fi pylint --rcfile=dev/jenkins/pylint.rc $BRIAN_PACKAGE > pylint.log || : brian-1.4.3/dev/jenkins/linux_scripts/test_brian.sh000066400000000000000000000015171266062647100224430ustar00rootroot00000000000000#! /bin/bash # get the newest version of nose and coverage, ignoring installed packages /home/jenkins/.jenkins/virtual_envs/$PythonVersion/$packages/bin/pip install --upgrade -I nose coverage || : # Use the previously built brian version export PYTHONPATH=$(pwd)/build/lib:$PYTHONPATH # delete remaining compiled code from previous runs echo deleting '~/.python*_compiled' if it exists rm -r ~/.python*_compiled || : # Run unit tests and record coverage but do not fail the build if anything goes wrong here ~/.jenkins/virtual_envs/$PythonVersion/$packages/bin/coverage -e || : ~/.jenkins/virtual_envs/$PythonVersion/$packages/bin/coverage run ~/.jenkins/virtual_envs/$PythonVersion/$packages/bin/nosetests --with-xunit --logging-clear-handlers --verbose brian/tests || : ~/.jenkins/virtual_envs/$PythonVersion/$packages/bin/coverage xml || : brian-1.4.3/dev/jenkins/linux_scripts/test_brian2.sh000066400000000000000000000035651266062647100225320ustar00rootroot00000000000000CURRENT=$(pwd) cd /home/jenkins/.jenkins/virtual_envs/$PythonVersion/$packages # get the newest version of nose and coverage, ignoring installed packages bin/pip install --upgrade -I nose coverage || : # Make sure pyparsing and ipython (used for pretty printing) are installed bin/pip install pyparsing bin/pip install ipython # Make sure we have sphinx (for testing the sphinxext) bin/pip install sphinx # This is copied from the build_brian.sh script. When brian2 gets a setup.py script, this script # should be used and the following lines removed. ##### From build_brian.sh echo "Using newest available package versions" bin/pip install --upgrade numpy bin/pip install --upgrade scipy bin/pip install sympy==0.7.1 bin/pip install --upgrade matplotlib # Print the version numbers for the dependencies bin/python -c "import numpy; print 'numpy version: ', numpy.__version__" bin/python -c "import scipy; print 'scipy version: ', scipy.__version__" bin/python -c "import sympy; print 'sympy version: ', sympy.__version__" bin/python -c "import matplotlib; print 'matplotlib version: ', matplotlib.__version__" ##### End build_brian.sh cd "$CURRENT" # Directly use the source directory (no setup.py yet) export PYTHONPATH="$(pwd)":$PYTHONPATH # delete remaining compiled code from previous runs echo deleting '~/.python*_compiled' if it exists rm -r ~/.python*_compiled || : # Run unit tests and record coverage but do not fail the build if anything goes wrong here ~/.jenkins/virtual_envs/$PythonVersion/$packages/bin/coverage erase --rcfile=.coveragerc_brian2 || : ~/.jenkins/virtual_envs/$PythonVersion/$packages/bin/coverage run --rcfile=.coveragerc_brian2 ~/.jenkins/virtual_envs/$PythonVersion/$packages/bin/nosetests --with-xunit --logging-clear-handlers --verbose --with-doctest brian2 || : ~/.jenkins/virtual_envs/$PythonVersion/$packages/bin/coverage xml --rcfile=.coveragerc_brian2 || : brian-1.4.3/dev/jenkins/linux_scripts/upload_pypi.sh000066400000000000000000000005321266062647100226320ustar00rootroot00000000000000#! /bin/bash # Create a .pypirc file in the home directory echo "[distutils] index-servers = pypi [pypi]" > ~/.pypirc echo "username: " "$USERNAME" >> ~/.pypirc echo "password: " "$PASSWORD" >> ~/.pypirc python dev/tools/newrelease/register_pypi.py # Delete the file again, we do not want to have the password lying around rm -f ~/.pypirc brian-1.4.3/dev/jenkins/linux_slave_configuration.txt000066400000000000000000000053001266062647100230620ustar00rootroot00000000000000Setting up a slave ------------------ ### Linux slave It needs all the packages necessary for running brian, compiling numpy, etc. On a Ubuntu/Debian system the following should work (similar to the setup of the build master (see above)): # Needed for running brian (including weave support, etc.) sudo apt-get install python python-scipy python-sympy build-essential # Needed for building numpy etc. sudo apt-get build-dep python-numpy python-scipy python-sympy python-matplotlib # Install virtualenv with pip because there are differences in command line arguments sudo apt-get install python-pip sudo pip install virtualenv In addition, install any python version available, e.g. # Ubuntu provides a python-all package depending on all supported python versions sudo apt-get install python-all It does not have to have Jenkins installed, but a java version is needed, try sudo apt-get install openjdk-6-jre Recent versions of scipy do not seem to work with the BLAS reference implementation, installing the ATLAS libraries should make it work, e.g.: sudo apt-get install libatlas3gf-base liblapack3gf libatlas-dev liblapack-dev The ATLAS lapack libraries are sometimes not automatically activated, try the following to make it work (select the version with "atlas" in the path): sudo update-alternatives --config liblapack.so sudo update-alternatives --config liblapack.so.3 Other necessary steps: * Prepare the virtual environments (see Master configuration) — the build scripts assume that there is a .jenkins directory in $HOME ### Adding a new slave to Jenkins * Go to Manage Jenkins → Manage Nodes * Add a new node, for real computers use "Dumb Slave", for virtual machine use "VirtualBox Slave" * Set the labels to "linux" and the supported Python versions, e.g. "python2.6 python2.7" * Configure the SSH connection, using key-based authentification (copy the key — while being logged in at hal — with ssh-copy-id username@hostname ) * For VirtualBox machines, configure them as being started and stopped on demand Virtual Machines (Linux Client) ------------------------------- http://www.wiredrevolution.com/virtualbox/setup-ssh-access-between-virtualbox-host-and-guest-vms Make sure that the Virtual machine is reachable by SSH, the simplest way for a virtual machine on the same machine as the build master is: * Add a second network adapter as a "Host-only adapter" * Edit /etc/network/interfaces and add something like: auto eth1 iface eth1 inet static address 192.168.56.101 netmask 255.255.255.0 You can get an IP address if you do iface eth1 inet dhcp and check for the assigned address with ifconfig eth1 SSH to VMs on a different host ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ http://blog.bashton.com/2009/ssh-via-another-host/brian-1.4.3/dev/jenkins/master_configuration.txt000066400000000000000000000155351266062647100220370ustar00rootroot00000000000000Master configuration ==================== Created Tuesday 06 March 2012 Setting up the server --------------------- ### Basic installation (Instructions for Ubuntu 10.04 LTS) * Install necessary packages: # Needed for jenkins sudo apt-get install openjdk-6-jdk openjdk-6-jre subversion # Needed for running brian (including weave support, etc.) sudo apt-get install python python-scipy python-sympy build-essential # Needed for building numpy etc. sudo apt-get build-dep python-numpy python-scipy python-sympy python-matplotlib # Needed for code quality metrics and testing sudo apt-get install python-nose python-coverage python-pip pyflakes # Install virtualenv with pip because there are differences in command line arguments sudo pip install virtualenv # Also install pylint (remove distribution package before if it exists), because the old version is buggy... sudo pip install pylint # For setting up virtual machines sudo apt-get install virtualbox virtualbox-dkms * Install Jenkins from Website (Ubuntu package is too old), following instructions here: https://wiki.jenkins-ci.org/display/JENKINS/Installing+Jenkins+on+Ubuntu wget -q -O - http://pkg.jenkins-ci.org/debian/jenkins-ci.org.key | sudo apt-key add - sudo sh -c 'echo deb http://pkg.jenkins-ci.org/debian binary/ > /etc/apt/sources.list.d/jenkins.list' sudo apt-get update sudo apt-get install jenkins * Setup authentification via the Unix database (there are other options for this but this server already had some users on it): * Add the jenkins user to the shadow group: usermod -a -G shadow jenkins * Log out and back in and restart the jenkins server (or reboot...): sudo /etc/init.d/jenkins restart ### Preparing virtual environments for Python versions * Create a directory ~/.jenkins/virtual_envs: mkdir -p ~/.jenkins/virtual_envs * For every python version (replace $PYTHON by python2.6 for example): virtualenv --python=$PYTHON --no-site-packages ~/.jenkins/virtual_envs/$PYTHON/oldest virtualenv --python=$PYTHON --system-site-packages ~/.jenkins/virtual_envs/$PYTHON/oldest This could be done in a script of course (see linux_scripts/create_virtualenvs.sh) ### Jenkins configuration (can be mostly done via the browser, browse to http://hal.dec.ens.fr:8080) Manage Jenkins → Configure System: #### Security * Enable "Enable Security" * Chose Security Realm "Unix user/group database" (use "Test" for checking whether it works) * Set Authorization to "Logged-in users can do anything" #### Email * Set SMTP server: lumiere.ens.fr * Default user e-mail suffix: @ens.fr * Sender E-mail Adress:** Jenkins Build System ™w÷Ùþîhh‘uNÃrêwtV½®à¬Ù€«&úü·‹þ̶ôJÇSÇÕ—WæXÂÂëÔ¬ÿØkÇÿ¾:SÇþÒú¯ÜZpËÀ»‰¥e~Ù©Õ“»vÙSãsÎ×möû›îcÙìz§wÖ*i¬tC@¸’㱡¬l»é9k ÀÀOˆp$'~Ž|²âc£uZöz$– ëô³ùâê¿ãXö~65V±º¾5æ+±–QÁßõ%(N3 Žð<_ôTA‚<ÝÛsÔÂFúæ%9 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Quality Day.txt000066400000000000000000000053031266062647100204360ustar00rootroot00000000000000Brian Quality Day ================= 7 June 2010 ----------- Done: * More tests for equations * Style fixes * Manual for tools.io * Remove unused comments * New folder tools * stdp cleanup TODO: Victor: * BEP on Simulation class * Connectivity analysis (new module) * A few examples from literature Bertrand: * Brianhears: examples and style fixes * A few statistical functions Cyrille: * LinearStateUpdater * Examples and manual for statistics Dan/Romain: * Brianhears clean-up + syntax ------------------------------------------------- * Remove old commented code [Dan/Romain] * Remove deprecated/unused code [Dan/Romain] * Check style (PEP-8): patch for blank lines before def f() [Dan] * Check if everything is documented and has examples (especially new features) [Cyrille/Bertrand] Focus on: * connection * stdp * neurongroup (especially init) * equations and for these modules: * reorganize [Dan/Romain] * add docstrings [Dan/Romain] * write tests [Cyrille/Bertrand/Dan/Romain] For brianhears: * check docstrings [Bertrand/Dan] * write manual, examples and tests [Bertrand/Dan] Some other ideas: * Work out why static code analysis tools don't work with Brian in PyDev and fix that. This may involve removing exec and eval statements from Brian import. This would mean writing out all the units explicitly, for example, which could be done with a tool to generate that code. Initial planning ---------------- Dan: style patch for blank lines before def f() (PEP-8), use the 'pep8' Python module to help with this. Romain: tests for equations Bertrand: examples/docs for brianhears Cyrille: check examples/docs for new features -- Initial message -- I was thinking of organising a "Brian Quality Day". Basically it would be an afternoon that we would devote to improving the code quality of Brian. Here's a list of possible things to do: * writing docstrings, docs and comments * writing examples (for features that are currently not illustrated) * writing tests (in particular for the modules that we want to change, like equations) * checking style (according to PEP-8) * removing deprecated code or old comments * checking TODOs in the code * simplifying/reorganizing some code (specifically: connection and stdp) * writing some docs in the developer's guide There are also some thoughts in dev.Refactoring.txt. We could even advertise that day on the google group, so that people could send us some tests for example. The goal is that the code is more readable and easier to modify (which requires a better test coverage). This could also include brian.hears. Hopefully this should also give everyone a better idea of the structure of the code. brian-1.4.3/dev/notes/Brian compiler.txt000066400000000000000000000104171266062647100201240ustar00rootroot00000000000000The Brian compiler ================== These are a few notes about how to have Brian generate and compile C code (and possibly GPU code). There are several motivations/uses: * Faster simulations, run from a Python script. This is especially important for STDP simulations, which are currently extremely long. * Embedded real-time simulations, for example running on a robot, or working on real-time inputs (e.g. sounds). In any case, to get very significant speed-ups, I believe it is necessary to have the main loop (Network.run()) not in Python. This may be a problem because some objects (network operations) are defined by the users. To deal with this, we can simply ask the user to provide target code (C), possibly with a simple API. Alternatively, these operations may not need to be called at each timestep. This alternative is possible for the first application only, but perhaps not for embedded simulations. An overview of what we have and what needs to be done: * Network (main loop) Each object in the network must have a target code. This could be a C object, with a run() method and various variables. We write a simple C code that calls these codes in turn. When a non-callable object must be processed, the C code exits. * NeuronGroup The neuron group is a simple object, because it is independent of all other objects in the network. With code generation, we get a C update code from the equations. We also need code generation for threshold and reset. The neuron group does not need to know about other objects in the network. * Synapses A Synapses object must read spikes from NeuronGroup. Therefore, the C code must have a reference to the corresponding object. These spikes are sent to a SpikeQueue. This could be a predefined, independent, object. Synaptic events are then obtained at every timestep. Operations are then applied, which involve local synaptic variables, a source or target neuron group. This also requires references to external objects. * Monitor We will need to code the basic types of monitors. It could be important also to have a way to save to disk, either at the end of the simulation or during the simulation. A simple way could be to have an option in the monitor to save to disk. * NetworkOperation Here we need a simple way for users to write C code. By the way, perhaps we can use packages that convert Python to C. * Saving and analyzing Typically, after a run, the user analyses the results or plots things. If we have a complete Brian simulation running, it could be also important to save the status of the entire network, which could be retrieved by a Python script. This should be possible. Perhaps the script could not be resumed, but at least the state variables could loaded. This could also be a way to save the monitors. * Inputs to the models One major problem I see is that in most cases we need inputs, which can be sounds or other types of stimulations that are much easier to program with Python. For sounds, this would require to also have a Brian Hears compiler. Summary ------- We need general mechanisms to produce code from strings: * differential equations * code (reset) * conditions (threshold) Each object must have * a method providing C code for running one timestep. * a method providing the C reference to the object (or name of instance). * a number of standard variables and mechanisms (store spikes etc). So in fact, I would suggest that what needs to be done is a complete C++ framework, classes etc. Brian would simply produce additional C++ text that would be compiled together with the framework. Suggested plan -------------- ** We could start by writing a pure C++ simulator, for an example ** This means writing C++ classes for each Brian class. For the update code we could just copy and paste code generated by Brian. Then we go for option 2: running a *complete* Brian simulation in C. It makes it less flexible because we cannot do any hybrid script. But it also makes the design much simpler. So what this will make is really a Brian compiler producing stand-alone code, rather than doing code generation to accelerate some parts. More ideas ---------- GPU: these C++ classes could also include GPU code, it might not be much more difficult. brian-1.4.3/dev/notes/Brian meeting 25 Oct 2011000066400000000000000000000040221266062647100204000ustar00rootroot00000000000000Brian meeting, 25 Oct 2011 -------------------------- 1. Debriefing of Brian survey ----------------------------- Docs and examples: * More about using C * Clarify the use of freeze (does it cause errors or wrong results?) * Example without magic; more about how to turn it off in the docs * More docs about model fitting (how to set parameters etc) * Examples of extending Brian Other comments: * Memory problems: optimize creation, use floats, have lighter StateMonitors (e.g. with only population-averaged statistics). Here's an idea for StateMonitor: how about having a user-defined function that takes a vector of state variables and returns a single value (or tuple of values)? (e.g. mean or variance) - this would be a PopulationStateMonitor) * More flexibility for synapses and parallel computing are high on the list * Stiff integration methods * Parameters in noise and multiplicative noise User-contributed examples: Let's do a competition! "Brian twister"? We could announce it at the SfN social, on a Brian poster (oops we have to do one). There could be prizes (maybe 3?), with some Brian merchandise (tee-shirt etc). People have to contribute short scripts that do something cool (possibly an implementation of a model in a paper). 2. Plan for GPU --------------- We might have 1 student in Jan/Feb (from Vilnius). He would work on general stuff (spike queues etc). I will also ask for 1 more student from March to June (ENS Lyon M1). Let's do it by next summer! 3. Next releases ---------------- * Version 1.3.1 (10 November) * New SpikeGeneratorGroup: tests * Metrics: syntax/tests/examples * Brian Hears updates (Tan/Carney) * Little bit of docs + new features (see txt file about this) [DONE] * Brian examples for our papers * Version 1.4 (January) * New model fitting * MultiLinear... * Refractoriness * Victor's spatializer: syntax [DONE] * Brian examples for our papers 4. Synapses: 2012? ------------------ (Dan will have a look at Victor's code) brian-1.4.3/dev/notes/Brian meeting 30 Jul 2012000066400000000000000000000011271266062647100204050ustar00rootroot00000000000000Brian meeting 30 July 2012 -------------------------- Brian 1.4 --------- [DONE]* Examples from the twister (Romain) * Synapses [DONE]- clean examples (Romain) [DONE]- move from experimental (Romain) [DONE] some tests (Marcel) [DONE] 2.5 compatibility (Marcel) [DONE]* Saving spikes from a SpikeMonitor - docs (Victor) For later --------- * Synapses: C++: Victor+Dan * all the other stuff we wanted to do Brian 2.0 --------- * no relative imports * no wildcard imports * tests * maybe replace library models by showcase with copy/paste (catalogue) * New NeuronGroup brian-1.4.3/dev/notes/Development plan.txt000066400000000000000000000065771266062647100205070ustar00rootroot00000000000000Brian Development plan ---------------------- How about we launch a survey? We can easily create a web form with google, and have questions about what users would like to see in next versions. Meeting notes: * First thing: complete GPUNeuronGroup, using threshold/reset in model fitting (B/C) + Issam's compaction algorithms. -> October * Brian Hears: remove all transfers. -> start a BEP [R]* Analyse weblogs on documentation's website I uploaded brian_docs_analysis.txt with this information. * Examples for our papers [R]* BEP on event-driven STDP / nonlinear synapses + examples * Survey on comp-neuro + at installation time + analysis of user's code -> October This is in a google form (check in your google docs to edit it): https://spreadsheets.google.com/spreadsheet/viewform?formkey=dEVlaTVFczBDS1FtZW5wYXl3cmFpVFE6MQ * Test new model fitting * Version 1.4 (October): * MultiLinear... * New model fitting * Refractoriness * Metrics * New SpikeGeneratorGroup 1) GPU ------ [BDV]* Start with Brian Hears. Essentially, GPU kernels are done but we need to remove all GPU<->CPU transfers. Ideally, we would just transfer spikes (and so it would be good if neuron groups can include noise/Poisson firing). [BCD]* For Brian: maybe have a first version that is not entirely transparent to the user? * First a non-transparent version. Possibly several objects for GPU and CPU. * Example scripts: CUBA (simple), barrel cortex (complex - Kremer_et_al_2011) * Specific objects on GPU (GPUNeuronGroup, GPUConnection, etc) ** Thresholding in GPUNeuronGroup ** (compaction) + threshold/reset * GPUConnection [VDR]2) Synapses: ------------ Currently missing: * nonlinear synapses (NMDA) -> using fake NeuronGroup as a proxy to synaptic weights + state updater issue: several synaptic variables using a slower clocks? (then we can define differential equations on synaptic weights) * probabilistic synapses - implications for STP/STDP -> complicated! (is it very useful?) * multiple synapses (with different delays) between the same pre/post neurons -> think of nice syntax * STDP: access to postsynaptic variables * STDP: rules with local synaptic variables -> event queues? -> example from STDP1/2 First, we should at least provide a way, even not clean and elegant, to implement these things. Then essentially what will be needed is: 1) local synaptic variables (distinct from weights), 2) event-driven synaptic dynamics (taking delays into account). 3) Miscellaneous ---------------- [V]* Victor's spatialiser -> Brian Hears -> check final_examples [D]* Refractoriness -> examples (syntax) -> BEP [C]* Cyrille's new LinearStateUpdater code -> test * Stochastic DE integrators * Compound integrators (linear/nonlinear parts) [D]* Multilinear state updater (new syntax for Equations) -> syntax: (constant) [D]* Code generation * Cluster simulations -> ask Eilif -> quick multicore test (CUBA?) to check if it's worth it [B]* Cable equations (a lot is done, but we need to implement the integration method) -> try a HH axon (just Numpy) [B]* Metrics in analysis (Bertrand) -> talk about syntax * New SpikeGeneratorGroup 4) Examples and docs -------------------- * More examples from our papers. * Examples from papers should also be posted in modeldb. * Examples using Playdoh. * Electrode compensation example.brian-1.4.3/dev/notes/New Brian examples.txt000066400000000000000000000000761266062647100206420ustar00rootroot00000000000000Brian examples to write ----------------------- * Pi-limit brian-1.4.3/dev/notes/Next release.txt000066400000000000000000000106521266062647100176160ustar00rootroot00000000000000Next releases ============= Version 1.4 ----------- Initial deadline: January * Examples from the twister * New model fitting * MultiLinear... * Refractoriness * Victor's spatializer: syntax * ReverseCorrelationOnline (with online stimulus using RecentStateMonitor) * VanRossum * More docs (see Brian meeting 25 Oct 2011) * Trace analysis: are there other functions we might want to insert? (some used to process Anna's traces?) * Saving spikes from a SpikeMonitor * IRNS/IRNO (in Sound, by Jonathan) * Example: model fitting * PositiveCrossing (currently in dev/ideas/multiple_events) SpikeGeneratorGroup ------------------- Old one is in DirectControl. Slow for many spikes. New one is in experimental/neuromorphic/AER and works for fixed arrays of spikes (not iterators). * Write NewSpikeGeneratorGroup * Have new threshold (FastDC???) and choose at init time depending on argument type * [Marcel] Tests and make sure it works and is fast enough! * Docs * FastDCThreshold: other name? * AERSpikeMonitor -> in main distribution (* Syntax that combines MultipleGG and the basic one? (using MultipleSpikeGeneratorThreshold)) (* Add SpikeMonitor.save?) Tests: in tests/testinterface/test_directcontrol and neuromorphic/test_AERSpikeMonitor.py Metrics ------- Examples in monitor: CoincidenceCounter: compares each model spike train with the corresponding target spike train (or more complex) data should be: 1 spike train N spike trains dictionary of spike trains with keys = subgroups remove spikedelays model_length -> count (as in SpikeCounter) remove target_length gamma_factor method CoincidenceMatrixCounter: compares each model spike train with all model spike trains name: PopulationCoincidenceCounter update docstring group1, group2 (optional) instead of source gamma_factor method VanRossumMetric: use Romain's new version in tools/statistics: get_gamma_factor: remove and copy in gamma_factor and CoincidenceCounter.gamma_factor() get_gamma_factor_matrix: move to CoincidenceMatrixCounter.gamma_factor() in Audition/electrophysiology/Joris/utils -> in tools/statistics compute_sac same keywords as in correlogram spike_times -> spiketrains compute_xsac same thing DONE revcor ignore_transient -> onset only one spike train spike_times -> spikes name: spike_triggered_average ------------- Version 1.3.1 ------------- TODO: [OK] * check Brian Hears docs [OK] * check PoissonInput [OK (Rossantetal2011bis)] do we have an example? [OK] do we have docs? (should be documented in inputs.txt) [OK] init(**kwargs) should be avoided: does not show in help() and no argument control [OK] argument names should be consistent with other functions: [? here, n is the number of independent Poisson inputs, not the number of neurons in the NeuronGroup] n -> N [OK] w -> weight [OK] var -> state [OK] frozen -> freeze [OK: removed] is add_input needed now? [OK: should be clearer now] jitter, reliability: not clear what is meant by "number of synchronous spikes", plus it is doubly defined (jitter + reliability) [OK] * SpikeGeneratorGroup: replace with new class [OK] * check svn history for new features Initial deadline: 10 November [DONE] * Brian examples for our papers [add: pi-limit] [DONE] * New SpikeGeneratorGroup: tests [DONE] - check syntax - test speed [DONE] - test bugs [V] : I haven't tested speed yet. but as far as I remember the gain is quite big. [DONE] * Brian Hears updates (Tan/Carney) - name: TanCarney - doc [DONE] * Bug fixes added by Marcel (put in new_features.txt) * Trace analysis library (new electrophysiology library package) [DONE] - docs [DONE] - update our own scripts to use this library instead of the experimental code [DONE] - remove functions taken from experimental/trace_analysis [DONE] - examples (examples/electrophysiology) [DONE] * PoissonInputs: docs and syntax [DONE] * Maybe: Sompolinsky example? (if it doesn't require a sound database) * What is the state of the experimental neuromorphic module, anything stable there? [V] : We could add the ability to save spikes from a SpikeMonitor, and load the same files. This is implemented, and works on linux and mac, not on windows. -> Marcel suggested we drop the support for windows. [DONE] * Check all new features (see svn history) and add in new_features.txt brian-1.4.3/dev/notes/Refactoring.txt000066400000000000000000000057061266062647100175460ustar00rootroot00000000000000Refactoring Brian ================= Abstract: I propose to do some refactoring to improve the readability of the code, in order to welcome new developers in the future (see also BEP 4). Priority #1 = developer's guide *** Simplify connection.py and equations.py *** Increase modularity ------------------- As much as possible, you should try to have independent modules, which could be developed and maintained by people who don't know the rest of the code. These should be all those that do not import any Brian module. Here is a list of such modules: * globalprefs * inspection * log * magic * optimiser * units * unitsafefunctions * stdunits * library.* * utils.* Those modules should be better documented/tested, so that external people could start working on it. The plotting module should be made more independent. A list of tests for those modules could quickly be established by searching for places where methods are called (in the rest of Brian). Unify syntax ------------ See BEP 4. Separate C code from pure Python -------------------------------- Each class could have a separate pure Python implementation and C implementation. For example, there could be a CircularVector class and a CircularVector_C class, and using the latter would simply be done by the line: CircularVector=CircularVector_C Alternatively, the separation could be at the level of methods rather than classes, and the switch could be done at initialization. Another option is to have a separate module for the C implementation, e.g. circular_c.py, which would redefine some of the classes, and the last lines of circular.py would import circular_c if C code is switched on. The advantage of this option is that it simplifies the code. The C modules could also be gathered in a specific folder. Problem: synchronization of C and Python code. Solution(?): testing with and without weave/swig. Integration methods ------------------- A template mechanism could be provided, which would help people write their own integration methods. Code generation --------------- Instead of doing code generation ourselves, maybe we could leave that to Numpy, e.g. with the lazy evaluation ideas. There is a great project named Theano which seem to do what we need: http://www.pylearn.org/theano/ Unfortunately there is no Windows build at the moment. There is another module called numexpr, which accelerates Numpy evaluations, and it seems rather stable. Versions > 1.2 crash. The acceleration is significant only for very large vectors (>100 000) or transcendental functions. Cleaning -------- * Move obsolete modules/methods to a specific folder (which ones?) * Remove dead code (tool?) Obsolete modules: * quantityarray: TODO after release For the cleaning operations, we should find automation tools. Tests ----- It should be easier for people to add new tests. Maybe the tests should be outside the modules (possibly in a specific folder). brian-1.4.3/dev/notes/TODO000066400000000000000000000053201266062647100152220ustar00rootroot00000000000000* Multiple events (not just spike initiation): currently in dev/ideas/multiple_events * Brian examples: Abbott delayed STDP (PLoS CB), Gerstner 1996 * Progress reporting could report memory usage as well, and warn if we might run out of memory * Online saving for monitors (direct to disk), possibly with a buffer * Save/load methods for objects and Network (perhaps starting with monitors) * library.ionic_currents: might not work if units are off (looks for amp to recognize the current) * eqs = MembraneEquation(C=200*pF) + leak + K_current + Na_current works, but not eqs = leak + K_current + Na_current + MembraneEquation(C=200*pF). Solution: have a new class that is a set of currents, or (easier) use a new variable with the sum of currents. * Better progress reporting: the estimated time should adapt, maybe use only the last few seconds rather the entire simulation. (* Modulation keyword with variables defined as functions) * Clean NeuronGroup init, in particular S0 is strangely handled; remove the init keyword? (in fact S0 is used mostly for unit checking) (* Neuron = NeuronGroup(1,..)) (* Unit checking in string resets and thresholds) (* Change default values of compile and freeze to true in NeuronGroup? [I would say no, it is less reliable]) (* Cluster computing) Idea: use pypar, it seems really simple to use http://datamining.anu.edu.au/~ole/pypar/ (* Compound linear/nonlinear state updaters?) For example, often there is a linear and nonlinear part of the equation, e.g. dv/dt is nonlinear, but you have some auxiliary variables to define an alpha synapse that can be considered separately and are linear. The separate_equations() function in dev/ideas/separate_equations.py could be used for this purpose. (* Better LinearStateUpdater) In most cases, the update matrix is degenerate only because of the parameters. It would be better to take care of this directly (separate dynamical variables and parameters, and calculate the matrix). Other thing: use A**100 instead of 100 time steps. (* Add MultiLinearStateUpdater to Brian) Need to decide what the syntax should be, e.g. could have specification of constant parameters in the Equations, e.g. eqs = ''' dV/dt = -V/tau : volt const tau : second ''' At first this would only be used by MultiLinearStateUpdater but in principle could also be used by nonlinear mechanisms for better optimisation. (* Why is t a vector?) Figure out why group.t is a vector. It might have to do with units. (* Turning off magic? ) (* Finish autocorrelogram object ) (* Meaningful error messages: Improve catching of errors and usefulness of error messages, also look into logging which is very inconsistent at the moment.) brian-1.4.3/dev/notes/gpu_multiprocessing_notes.txt000066400000000000000000000016621266062647100226120ustar00rootroot00000000000000On Linux, make sure not to call pycuda.autoinit before creating multiple processes. * brian.library.modelfitting.gpu_modelfitting.py does this (and references to autoinit.context need to be removed) * playdoh 0.2 clustertools too Playdoh 0.2 clustertools also has some other problems: In function cluster_worker: if use_gpu: try: if True:#do_redirect: sys.stdin = file(os.devnull, 'w') sys.stdout = file(os.devnull, 'w') set_gpu_device(2-process_number-1) #drv.Device.count()-process_number-1) except Exception, e: print e warnings.warn("Problem when loading Cuda. use_gpu is now set to False.") use_gpu = False do_redirect is never set to True as far as I can tell, but it needs to be on (some versions of) Linux. drv.Device.count() is computed in a couple of places, but this requires initialisation.brian-1.4.3/dev/optimising/000077500000000000000000000000001266062647100155645ustar00rootroot00000000000000brian-1.4.3/dev/optimising/CUBA/000077500000000000000000000000001266062647100162765ustar00rootroot00000000000000brian-1.4.3/dev/optimising/CUBA/CUBA-optimising.py000066400000000000000000000022711266062647100215440ustar00rootroot00000000000000""" CUBA optimising """ import BrianNoUnits from brian import * from brian.stdunits import * from brian.globalprefs import * from scipy import rand import pylab import time import c_profile import profile def main(): taum = 20 * ms taue = 5 * ms taui = 10 * ms Vt = -50 * mV Vr = -60 * mV dv = lambda v, ge, gi: (ge + gi - (v + 49 * mV)) / taum dge = lambda v, ge, gi:-ge / taue dgi = lambda v, ge, gi:-gi / taui P = NeuronGroup(16000, model=(dv, dge, dgi), threshold=Vt, \ reset=Vr, init=(0 * volt, 0 * volt, 0 * volt)) Pe = P.subgroup(12800) Pi = P.subgroup(3200) Ce = Connection(Pe, P, 1) Ci = Connection(Pi, P, 2) we = (60 * 0.27 / 10) * mV # excitatory synaptic weight (voltage) wi = (-20 * 4.5 / 10) * mV # inhibitory synaptic weight Ce.connect_random(Pe, P, 0.02, weight=we) Ci.connect_random(Pi, P, 0.02, weight=wi) P['v'] = Vr + rand(1, len(P)) * (Vt - Vr) # Or P.S[0]=... net = MagicNetwork() start = time.time() net.run(10 * second) print time.time() - start set_global_preferences(useweave=True) main() #c_profile.run('main()') #main() brian-1.4.3/dev/optimising/CUBA/CUBA.cpp000066400000000000000000000056251266062647100175240ustar00rootroot00000000000000/* This is an implementation of a benchmark described in the following review paper: Simulation of networks of spiking neurons: A review of tools and strategies (2006). Brette, Rudolph, Carnevale, Hines, Beeman, Bower, Diesmann, Goodman, Harris, Zirpe, Natschl�ger, Pecevski, Ermentrout, Djurfeldt, Lansner, Rochel, Vibert, Alvarez, Muller, Davison, El Boustani and Destexhe. Journal of Computational Neuroscience Benchmark 2: random network of integrate-and-fire neurons with exponential synaptic currents Clock-driven implementation with exact subthreshold integration (but spike times are aligned to the grid) You need the (free) GSL library (for the random numbers; alternatively, replace then random generators with standard functions). R. Brette - Jan 2008 */ #include #include using namespace std; //#include //#include #include #define taum 20. #define taue 5. #define taui 10. #define Vt -50. #define Vr -60. #define El -49. #define Ne 6400 #define Ni 1600 #define N (Ne+Ni) #define we (60*0.27/10.) #define wi (-20*4.5/10.) #define dt .1 #define refrac 5. int main() { cout << "CUBA model" << endl; // Initialization of random numbers //const gsl_rng_type * T; //gsl_rng_env_setup(); //T = gsl_rng_default; //gsl_rng *rnd = gsl_rng_alloc(T); //gsl_rng_set(rnd,1000); srand((unsigned int)time(NULL)); // State variables double v[N],ge[N],gi[N]; for(int i=0;ilastspike[i]+refrac) { v[i]+=dt*((ge[i]+gi[i]-(v[i]-El))/taum); ge[i]+=-dt*ge[i]/taue; gi[i]+=-dt*gi[i]/taui; if (v[i]>Vt) { nspikes++; for(int j=0;j:1() 1 0.000 0.000 270.783 270.783 CUBA-optimising.py:15(main) 7 0.000 0.000 0.000 0.000 CUBA-optimising.py:21() 7 0.000 0.000 0.000 0.000 CUBA-optimising.py:22() 7 0.000 0.000 0.000 0.000 CUBA-optimising.py:23() 2 0.000 0.000 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20.447 0.000 neurongroup.py:199(reset) 2 0.000 0.000 0.082 0.041 neurongroup.py:205(subgroup) 15 0.000 0.000 0.000 0.000 neurongroup.py:212(__len__) 1 0.000 0.000 0.000 0.000 neurongroup.py:235(__setitem__) 2 0.000 0.000 0.082 0.041 neurongroup.py:246(__getslice__) 1 0.000 0.000 0.315 0.315 neurongroup.py:36(__init__) 915 0.003 0.000 0.003 0.000 ntpath.py:116(splitdrive) 793 0.004 0.000 0.031 0.000 ntpath.py:252(exists) 2 0.000 0.000 0.000 0.000 ntpath.py:267(isdir) 2 0.000 0.000 0.000 0.000 ntpath.py:346(expanduser) 613 0.004 0.000 0.007 0.000 ntpath.py:38(normcase) 2 0.000 0.000 0.000 0.000 ntpath.py:380(expandvars) 913 0.031 0.000 0.056 0.000 ntpath.py:439(normpath) 913 0.007 0.000 0.070 0.000 ntpath.py:495(abspath) 2 0.000 0.000 0.000 0.000 ntpath.py:51(isabs) 2 0.000 0.000 0.000 0.000 ntpath.py:59(join) 9 0.000 0.000 0.000 0.000 numeric.py:126(asarray) 1 0.000 0.000 0.000 0.000 numeric.py:134(asanyarray) 2 0.000 0.000 0.000 0.000 numeric.py:181(isfortran) 1 0.000 0.000 0.000 0.000 numeric.py:516(isscalar) 2 0.000 0.000 0.000 0.000 os.py:439(has_key) 8000 3.813 0.000 6.884 0.001 random.py:264(sample) 234 0.001 0.000 0.003 0.000 re.py:178(compile) 234 0.001 0.000 0.002 0.000 re.py:219(_compile) 1 0.000 0.000 0.000 0.000 reset.py:15(__init__) 400000 8.741 0.000 18.141 0.000 reset.py:18(__call__) 4 0.000 0.000 0.209 0.052 shelve.py:108(__getitem__) 4 0.000 0.000 0.002 0.001 shelve.py:133(close) 2 0.000 0.000 0.000 0.000 shelve.py:141(__del__) 4 0.000 0.000 0.000 0.000 shelve.py:147(sync) 2 0.012 0.006 0.109 0.055 shelve.py:207(__init__) 2 0.000 0.000 0.109 0.055 shelve.py:212(open) 2 0.000 0.000 0.000 0.000 shelve.py:83(__init__) 2 0.000 0.000 0.000 0.000 shelve.py:97(has_key) 4 0.000 0.000 0.000 0.000 sparse.py:102(set_shape) 4 0.069 0.017 0.069 0.017 sparse.py:2189(__init__) 8 0.000 0.000 0.000 0.000 sparse.py:2616(isintlike) 4 0.000 0.000 0.000 0.000 sparse.py:2628(isshape) 4 0.000 0.000 0.000 0.000 sparse.py:2641(getdtype) 4 0.000 0.000 0.000 0.000 sparse.py:93(__init__) 2 0.000 0.000 0.000 0.000 stat.py:29(S_IFMT) 2 0.000 0.000 0.000 0.000 stat.py:45(S_ISDIR) 2427 0.010 0.000 0.015 0.000 string.py:218(lower) 1 0.000 0.000 0.000 0.000 struct.py:35(_compile) 4 0.000 0.000 0.000 0.000 struct.py:77(unpack) 1 0.000 0.000 0.000 0.000 threshold.py:28(__init__) 400000 5.104 0.000 18.639 0.000 threshold.py:32(__call__) 2 0.000 0.000 0.000 0.000 twodim_base.py:43(eye) 1 0.000 0.000 0.000 0.000 ufunclike.py:49(log2) 2 0.000 0.000 0.000 0.000 units.py:307(isScalarType) 2 0.000 0.000 0.004 0.002 whichdb.py:17(whichdb) 1 0.019 0.019 0.019 0.019 whichdb.py:2() 6 0.054 0.009 0.054 0.009 {__import__} 8000 0.035 0.000 0.035 0.000 {_bisect.bisect} 2 0.000 0.000 0.000 0.000 {_bsddb.DBEnv} 2 0.000 0.000 0.000 0.000 {_bsddb.DB} 1 0.000 0.000 0.000 0.000 {abs} 3 0.000 0.000 0.000 0.000 {all} 1200000 12.490 0.000 12.490 0.000 {apply} 1 0.000 0.000 0.000 0.000 {built-in method fromkeys} 4 0.209 0.052 0.209 0.052 {built-in method load} 261 0.001 0.000 0.001 0.000 {built-in method match} 2 0.004 0.002 0.004 0.002 {built-in method open} 2 0.000 0.000 0.000 0.000 {built-in method set_flags} 2 0.000 0.000 0.000 0.000 {built-in method set_get_returns_none} 2 0.000 0.000 0.000 0.000 {built-in method set_lk_detect} 4 0.000 0.000 0.000 0.000 {cPickle.Unpickler} 4 0.000 0.000 0.000 0.000 {cStringIO.StringIO} 3 0.000 0.000 0.000 0.000 {filter} 9 0.000 0.000 0.000 0.000 {getattr} 47340 0.121 0.000 0.121 0.000 {hasattr} 25 0.000 0.000 0.000 0.000 {id} 820 0.003 0.000 0.003 0.000 {imp.get_suffixes} 868218 2.402 0.000 2.402 0.000 {isinstance} 8 0.000 0.000 0.000 0.000 {issubclass} 415257 0.856 0.000 0.856 0.000 {len} 38 0.000 0.000 0.001 0.000 {map} 8000 0.019 0.000 0.019 0.000 {math.ceil} 8000 0.027 0.000 0.027 0.000 {math.log} 469 0.001 0.000 0.001 0.000 {max} 2 0.000 0.000 0.000 0.000 {method '__reduce_ex__' of 'object' objects} 640300 1.592 0.000 1.592 0.000 {method 'add' of 'set' objects} 10 0.000 0.000 0.000 0.000 {method 'any' of 'numpy.ndarray' objects} 271 0.001 0.000 0.001 0.000 {method 'append' of 'list' objects} 2 0.000 0.000 0.000 0.000 {method 'astype' of 'numpy.ndarray' objects} 8000 0.061 0.000 0.061 0.000 {method 'binomial' of 'mtrand.RandomState' objects} 7 0.000 0.000 0.000 0.000 {method 'close' of 'file' objects} 1 0.000 0.000 0.000 0.000 {method 'disable' of '_lsprof.Profiler' objects} 35073 0.073 0.000 0.073 0.000 {method 'get' of 'dict' objects} 1 0.000 0.000 0.000 0.000 {method 'get' of 'dictproxy' objects} 12 0.000 0.000 0.000 0.000 {method 'has_key' of 'dict' objects} 81 0.005 0.000 0.005 0.000 {method 'items' of 'dict' objects} 913 0.002 0.000 0.002 0.000 {method 'join' of 'str' objects} 3040 0.007 0.000 0.007 0.000 {method 'lower' of 'str' objects} 913 0.002 0.000 0.002 0.000 {method 'lstrip' of 'str' objects} 1 0.000 0.000 0.000 0.000 {method 'max' of 'numpy.ndarray' objects} 2 0.000 0.000 0.000 0.000 {method 'outer' of 'numpy.ufunc' objects} 1 0.000 0.000 0.000 0.000 {method 'rand' of 'mtrand.RandomState' objects} 646843 1.389 0.000 1.389 0.000 {method 'random' of '_random.Random' objects} 2 0.000 0.000 0.000 0.000 {method 'read' of 'file' objects} 5 0.002 0.000 0.002 0.000 {method 'readlines' of 'file' objects} 1526 0.004 0.000 0.004 0.000 {method 'replace' of 'str' objects} 2 0.000 0.000 0.000 0.000 {method 'sort' of 'list' objects} 917 0.003 0.000 0.003 0.000 {method 'split' of 'str' objects} 913 0.002 0.000 0.002 0.000 {method 'startswith' of 'str' objects} 1 0.000 0.000 0.000 0.000 {method 'sum' of 'numpy.ndarray' objects} 4 0.000 0.000 0.000 0.000 {method 'unpack' of 'Struct' objects} 2 0.000 0.000 0.000 0.000 {method 'update' of 'dict' objects} 2 0.000 0.000 0.000 0.000 {method 'upper' of 'str' objects} 5 0.000 0.000 0.000 0.000 {method 'values' of 'dict' objects} 234 0.001 0.000 0.001 0.000 {min} 913 0.008 0.000 0.008 0.000 {nt._getfullpathname} 802 0.027 0.000 0.027 0.000 {nt.stat} 400007 76.554 0.000 76.554 0.000 {numpy.core._dotblas.dot} 4 0.000 0.000 0.000 0.000 {numpy.core.multiarray.arange} 8007 0.224 0.000 0.224 0.000 {numpy.core.multiarray.array} 9 0.000 0.000 0.000 0.000 {numpy.core.multiarray.zeros} 11 0.009 0.001 0.009 0.001 {open} 2 0.000 0.000 0.000 0.000 {operator.isNumberType} 4 0.000 0.000 0.000 0.000 {operator.isSequenceType} 592 0.002 0.000 0.002 0.000 {range} 1200027 2.873 0.000 2.873 0.000 {sys._getframe} 2 0.000 0.000 0.000 0.000 {time.time} 2 0.000 0.000 0.000 0.000 {zip} brian-1.4.3/dev/optimising/CUBA/cuba-profile.txt000066400000000000000000000605571266062647100214240ustar00rootroot00000000000000WARNING: Turning off units. 18399068 function calls (18398770 primitive calls) in 358.309 CPU seconds t=40s, dt = 0.1ms, so update steps = 400000 NOTE: it's not clear to me that all of NumPy's functions are being properly profiled by cProfile. For instance, I suspect that array addition, etc. are not being profiled. NOTE: some of these timings have been changed over multiple runs so it might not be entirely consistent, but it should be mostly so. SUMMARY: 22.6% BLAS dot routine No optimisation possible None possible. 15.7% NeuronGroup.getSpikes 4-5% likely 1.7% achieved, rewrite circular in C++ for more 14.7% Threshold.__call__ 3-6% likely 10-15% achieved (3x) 12.0% LinearModel.__call__, even Optimisable, not clear how much. Small improvement (2-5%?) without the BLAS dot 7.1% Connection.propagate Not analysed yet 5.7% NeuronGroup.reset Not analysed yet 3.9% SpikeContainer.push Not analysed yet 3.7% Network.update Not analysed yet = 85% accounted for approx 19% improvement Note: the circular module appears to add up to a lot, and seems like a good candidate for turning into a C++ module. ncalls tottime percall cumtime percall filename:lineno(function) The worst: 400006 78.774 0.000 78.774 0.000 {numpy.core._dotblas.dot} 400000 42.764 0.000 121.538 0.000 model.py:256(__call__) Code: if not self.B==None: P.S[:]=dot(self.A,P.S-self.B)+self.B else: P.S[:]=dot(self.A,P.S) Comment: The dot routine presumably cannot be optimised, and this amounts to approximately 22.6% of the running time. The call function is taking 42s which seems on the long side given what it's doing. Perhaps the profiler is counting operations like - and + which are done by NumPy in too? One way to reduce the calling time is to remove the if else check. This can be done by dynamically redefining the __call__ method depending on whether or not there is a B variable. Also, the expression could be revaluated to: dot(self.A,P.S)+(self.B-dot(self.A,self.B)) where the latter expression can be precalculated. I haven't profiled this to see how much difference it would make though. NOTE: __call__ cannot be dynamically redefined because special methods are looked up in the class definition not the instance. ---- Prospects: unclear, although some of the 42s (12% of the total running time) could be reduced. ------------------------------ **** Progress: rewrote expression as above, saved maybe 2-5% ****** 400000 12.112 0.000 51.883 0.000 threshold.py:27(__call__) 400000 39.529 0.000 39.529 0.000 {method 'nonzero' of 'numpy.ndarray' objects} The code: threshold call: return ((P.S[0]>self.Vt).nonzero())[0] Comment: there are various candidates for optimisation here. There are two candidate algorithms for the nonzero() operation, the first is to search through the array and construct a linked list of indices. The alternative is to search through the array twice, the first time just counting how many are nonzero, then allocating a fixed length array, and then searching again filling out the array. It turns out that the first method is faster if the number of nonzero items is small, and the second method is (much) faster if the number is large. The second algorithm is the one that the NumPy nonzero() method uses. Both algorithms increase linearly in running time as the number of nonzero items increases. My C++ implementation of both algorithms suggests that if the number of nonzero items is less than about 15-20 then the first algorithm is about twice as fast, otherwise the second algorithm is faster. For the CUBA network with dt=0.1ms there are roughly 2 spikes per time step, so the first algorithm would be roughly twice as fast. For an equivalent network size and average firing rate, the linked list algorithm takes roughly 11s to complete, instead of the 40s of the numpy algorithm (although this doesn't include overheads). We should expect that NumPy's core nonzero() algorithm is taking at least 22s to complete, giving about 18s of overheads of one sort or another. We might be able to avoid some of these overheads because we can make explicit assumptions about the type of array, etc. but it's unlikely we can make much difference here. This suggests that custom coding just this in C++ would save about 11s, taking the total from 51s to 40s. On the other hand, custom coding the entire threshold call mechanism would (assuming the same 18s overhead of the NumPy routine) reduce it from 51s to 29s. Ideally, we wouldn't want to be relying on writing C++ code for things which can be extended like the Threshold mechanism, but since a fixed threshold is a fairly common type, perhaps it's justified? See threshold-call.py, threshold-call.cpp and threshold-call-c-times-for-different-rates.txt for more. NOTE: the time spent in threshold call function itself seems to be very variable, from 10s to 40s, from run to run. ----- Prospects: 51s ---> 40s or 29s = 3-6% of total time ------ ***** Progress: wrote inline accelerated version with weave for a substantial speed increase (3x, or about 10-15% of the total running time) 800000 25.145 0.000 25.145 0.000 connection.py:39(propagate) Comment: not analysed yet 800000 17.227 0.000 54.762 0.000 neurongroup.py:178(getSpikes) 1600000 6.420 0.000 6.420 0.000 {_bisect.bisect_left} 800000 14.782 0.000 24.617 0.000 circular.py:128(__getitem__) 800015 2.434 0.000 2.434 0.000 neurongroup.py:204(__len__) 1212411 4.927 0.000 7.361 0.000 {len} (of which 800000 due to getSpikes, or 4.9s cumulative) Code for neuron group getSpikes: if self._owner==self: # Group return self.LS[delay] # last spikes else: # Subgroup spikes=self.LS[delay]-self._origin return spikes[bisect.bisect_left(spikes,0):\ bisect.bisect_left(spikes,len(self))] Comments: getSpikes called twice at each update, once for the excitatory, and once for the inhibitory. Each time, it runs the second version of the if statement above, involving calling bisect_left twice, len and neurongroup.len once. Replacing the len(self) with neurongroup.N (and making sure that neurongroup has such an N) would shave off up to 4.9s or 1.4% of the total. Avoiding the bisect method by restructing the program so that it wasn't necessary would save up to 6.4s or 1.8% of the total, although probably not all of that. Romain has a note that the SpikeContainer __getitem__ method can be optimised, which could be well worth doing as it is taking 24.6s or 7% of the time. The if else is the main Python section could be avoided by having the getSpikes method assume it isn't a subgroup, and having the subgroup creation mechanism overwrite the getSpikes method with another method that assumes it isn't the owner. Equivalently, if subgroups were a different class to groups you wouldn't need this inner loop if else check (which is apparently slow in Python although I haven't profiled it). The potential saving here is unclear, but it's unlikely to be huge. ---- Prospects: 54s, of which 11.3s can definitely be saved (3.2%) 24.6s (7%) can likely be reduced (Romain?), but not sure by how much 17s (4.8%) can possibly be reduced, although probably not much Let's say a 4-5% reduction seems possible here. ------------ **** Progress: 54s down to 48s by removing the len and rewriting the getSpikes method to use new methods added to SpikeContainer and CircularVector, the latter with inline weave. The total improvement is about 1.7%. It's probably better to wait and rewrite the whole of the circular library in C++. ******************************* 400000 2.281 0.000 19.995 0.000 neurongroup.py:191(reset) 400000 8.821 0.000 17.714 0.000 reset.py:18(__call__) 400000 12.836 0.000 347.666 0.001 network.py:125(update) 400000 5.484 0.000 232.821 0.001 neurongroup.py:170(update) 400000 7.001 0.000 13.515 0.000 circular.py:95(push) Code: self._model(self) # update the variables spikes=self._threshold(self) # get spikes self.LS.push(spikes) # Store spikes Comments: calling the model takes 121s, the threshold takes 52s. Is the SpikeContainer push method optimisable? It's currently taking 13.5s or about 3.9%. ---- Prospects: no more than 3.9%, and that's unlikely --------------- Middle: 2000000 6.445 0.000 6.445 0.000 circular.py:34(__getitem__) 400000 5.234 0.000 8.893 0.000 circular.py:122(lastspikes) 1212411 4.927 0.000 7.361 0.000 {len} 1200000 4.747 0.000 4.750 0.000 circular.py:46(__getslice__) The rest: 1 0.007 0.007 358.309 358.309 :1() 1 0.000 0.000 358.302 358.302 CUBA.py:14(main) 800000 2.281 0.000 2.281 0.000 circular.py:28(advance) 1081359 2.299 0.000 2.299 0.000 circular.py:31(__len__) 400000 1.307 0.000 1.307 0.000 circular.py:40(__setitem__) 400000 2.085 0.000 2.085 0.000 circular.py:55(__setslice__) 400000 1.407 0.000 1.407 0.000 clock.py:119(tick) 400002 1.104 0.000 1.104 0.000 clock.py:143(stillRunning) 2 0.051 0.025 3.048 1.524 connection.py:256(randomMatrix) 62206 0.265 0.000 0.390 0.000 inspect.py:35(ismodule) 820/522 0.021 0.000 0.742 0.001 inspect.py:381(getsourcefile) 1 3.624 3.624 354.001 354.001 network.py:217(run) 1 0.000 0.000 354.529 354.529 network.py:450(run) 4000 1.676 0.000 2.964 0.001 random.py:264(sample) 867851 2.244 0.000 2.244 0.000 {isinstance} 319824 0.618 0.000 0.618 0.000 {method 'add' of 'set' objects} 35059 0.069 0.000 0.069 0.000 {method 'get' of 'dict' objects} 323104 0.629 0.000 0.629 0.000 {method 'random' of '_random.Random' objects} Note: there are more functions, but I've deleted all the ones that are more neglible than the ones above. Complete output from a separate run given below. -- A separate run ---------------------------------------------------------------------- 18384482 function calls (18384184 primitive calls) in 343.819 CPU seconds Ordered by: standard name ncalls tottime percall cumtime percall filename:lineno(function) 1 0.008 0.008 343.819 343.819 :1() 1 0.000 0.000 343.811 343.811 CUBA.py:14(main) 7 0.001 0.000 0.001 0.000 CUBA.py:20() 7 0.000 0.000 0.000 0.000 CUBA.py:21() 7 0.000 0.000 0.000 0.000 CUBA.py:22() 1 0.000 0.000 0.000 0.000 basic.py:314(norm) 1 0.176 0.176 0.176 0.176 basic.py:401(lstsq) 1 0.046 0.046 0.046 0.046 basic.py:82(solve) 400000 5.085 0.000 8.751 0.000 circular.py:122(lastspikes) 800000 14.290 0.000 23.814 0.000 circular.py:128(__getitem__) 2 0.000 0.000 0.000 0.000 circular.py:15(__init__) 800000 2.265 0.000 2.265 0.000 circular.py:28(advance) 1067427 2.241 0.000 2.241 0.000 circular.py:31(__len__) 2000000 6.268 0.000 6.268 0.000 circular.py:34(__getitem__) 400000 1.216 0.000 1.216 0.000 circular.py:40(__setitem__) 1200000 4.677 0.000 4.681 0.000 circular.py:46(__getslice__) 400000 2.050 0.000 2.050 0.000 circular.py:55(__setslice__) 1 0.000 0.000 0.000 0.000 circular.py:83(__init__) 400000 6.887 0.000 13.245 0.000 circular.py:95(push) 1 0.000 0.000 0.000 0.000 clock.py:103() 6 0.000 0.000 0.000 0.000 clock.py:104() 400000 1.365 0.000 1.365 0.000 clock.py:119(tick) 1 0.000 0.000 0.000 0.000 clock.py:137(setDuration) 400002 1.150 0.000 1.150 0.000 clock.py:143(stillRunning) 1 0.000 0.000 0.296 0.296 clock.py:148(guessClock) 2 0.000 0.000 0.008 0.004 connection.py:19(__init__) 2 0.055 0.027 3.063 1.532 connection.py:256(randomMatrix) 800000 24.118 0.000 24.118 0.000 connection.py:39(propagate) 2 0.000 0.000 0.000 0.000 connection.py:55(origin) 2 0.090 0.045 0.198 0.099 connection.py:62(compress) 2 0.104 0.052 0.123 0.061 connection.py:74(connect) 2 0.013 0.007 3.200 1.600 connection.py:98(connectRandom) 2 0.000 0.000 0.103 0.052 copy.py:299(_reconstruct) 2 0.000 0.000 0.104 0.052 copy.py:65(copy) 2 0.000 0.000 0.103 0.052 copy_reg.py:91(__newobj__) 1 0.000 0.000 0.000 0.000 copy_reg.py:94(_slotnames) 1 0.000 0.000 0.000 0.000 fromnumeric.py:1025(amax) 2 0.000 0.000 0.000 0.000 fromnumeric.py:32(_wrapit) 1 0.000 0.000 0.000 0.000 fromnumeric.py:812(sum) 1 0.000 0.000 0.000 0.000 fromnumeric.py:954(any) 1 0.000 0.000 0.000 0.000 fromnumeric.py:969(all) 5 0.000 0.000 0.000 0.000 function_base.py:392(asarray_chkfinite) 1 0.000 0.000 0.000 0.000 globalprefs.py:29(getGlobalPreference) 1 0.000 0.000 0.000 0.000 globalprefs.py:34(existsGlobalPreference) 810 0.003 0.000 0.005 0.000 inspect.py:126(isfunction) 1071 0.004 0.000 0.007 0.000 inspect.py:139(istraceback) 1071 0.004 0.000 0.007 0.000 inspect.py:149(isframe) 810 0.003 0.000 0.005 0.000 inspect.py:167(iscode) 907 0.014 0.000 0.047 0.000 inspect.py:342(getfile) 62206 0.263 0.000 0.388 0.000 inspect.py:35(ismodule) 820/522 0.025 0.000 0.787 0.002 inspect.py:381(getsourcefile) 613 0.007 0.000 0.134 0.000 inspect.py:396(getabsfile) 315 0.315 0.001 1.017 0.003 inspect.py:408(getmodule) 810 0.005 0.000 0.009 0.000 inspect.py:43(isclass) 261 0.009 0.000 0.691 0.003 inspect.py:454(findsource) 810 0.003 0.000 0.005 0.000 inspect.py:51(ismethod) 261 0.006 0.000 1.170 0.004 inspect.py:806(getframeinfo) 27 0.002 0.000 1.172 0.043 inspect.py:844(getouterframes) 3 0.000 0.000 0.000 0.000 inspection.py:118(__div__) 3 0.000 0.000 0.000 0.000 inspection.py:164(isaffine) 3 0.000 0.000 0.000 0.000 inspection.py:182(dependson) 1 0.000 0.000 0.000 0.000 inspection.py:42(checkModelUnits) 1 0.000 0.000 0.000 0.000 inspection.py:55(getvarnames) 11 0.000 0.000 0.000 0.000 inspection.py:72(__init__) 2 0.000 0.000 0.000 0.000 inspection.py:79(__add__) 2 0.000 0.000 0.000 0.000 inspection.py:91(__neg__) 1 0.000 0.000 0.000 0.000 inspection.py:94(__sub__) 4 0.000 0.000 0.000 0.000 lapack.py:26(cast_to_lapack_prefix) 2 0.000 0.000 0.000 0.000 lapack.py:48(find_best_lapack_type) 2 0.000 0.000 0.000 0.000 lapack.py:63(get_lapack_funcs) 261 0.001 0.000 0.031 0.000 linecache.py:33(getlines) 32 0.000 0.000 0.030 0.001 linecache.py:68(updatecache) 15 0.000 0.000 0.000 0.000 magic.py:149(__init__) 15 0.000 0.000 0.000 0.000 magic.py:152(setID) 10 0.000 0.000 0.000 0.000 magic.py:154(getID) 15 0.000 0.000 0.000 0.000 magic.py:161(add) 4 0.000 0.000 0.000 0.000 magic.py:170(get) 5 0.000 0.000 0.000 0.000 magic.py:181(add) 18 0.000 0.000 0.000 0.000 magic.py:190(get) 5 0.000 0.000 0.000 0.000 magic.py:202(_trackInstances) 5 0.000 0.000 0.358 0.072 magic.py:208(__new__) 18 0.000 0.000 0.668 0.037 magic.py:215(getInstances) 4 0.000 0.000 0.815 0.204 magic.py:228(findInstances) 1 0.000 0.000 0.059 0.059 matfuncs.py:22(expm) 1 0.000 0.000 0.259 0.259 model.py:21(magicModel) 1 0.022 0.022 0.258 0.258 model.py:223(__init__) 400000 42.302 0.000 119.909 0.000 model.py:256(__call__) 2 0.000 0.000 0.000 0.000 model.py:270(__len__) 400000 12.699 0.000 332.921 0.001 network.py:125(update) 1 3.535 3.535 339.170 339.170 network.py:217(run) 1 0.000 0.000 0.000 0.000 network.py:25(__init__) 3 0.000 0.000 0.000 0.000 network.py:257(sameClocks) 1 0.000 0.000 0.000 0.000 network.py:265(setClock) 1 0.000 0.000 0.520 0.520 network.py:435(__init__) 1 0.000 0.000 339.690 339.690 network.py:450(run) 1 0.000 0.000 0.198 0.198 network.py:89(prepare) 400000 5.351 0.000 220.858 0.001 neurongroup.py:170(update) 800000 17.095 0.000 53.535 0.000 neurongroup.py:178(getSpikes) 400000 2.270 0.000 19.620 0.000 neurongroup.py:191(reset) 2 0.000 0.000 0.104 0.052 neurongroup.py:197(subgroup) 800015 2.464 0.000 2.464 0.000 neurongroup.py:204(__len__) 1 0.000 0.000 0.000 0.000 neurongroup.py:226(__setitem__) 2 0.000 0.000 0.104 0.052 neurongroup.py:237(__getslice__) 1 0.000 0.000 0.555 0.555 neurongroup.py:34(__init__) 911 0.003 0.000 0.003 0.000 ntpath.py:116(splitdrive) 787 0.005 0.000 0.045 0.000 ntpath.py:252(exists) 613 0.004 0.000 0.009 0.000 ntpath.py:38(normcase) 911 0.044 0.000 0.076 0.000 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0.000 0.000 0.001 0.000 twodim_base.py:43(eye) 1 0.000 0.000 0.000 0.000 ufunclike.py:49(log2) 2 0.000 0.000 0.000 0.000 units.py:307(isScalarType) 1600000 6.165 0.000 6.165 0.000 {_bisect.bisect_left} 4000 0.019 0.000 0.019 0.000 {_bisect.bisect} 1 0.000 0.000 0.000 0.000 {abs} 3 0.000 0.000 0.000 0.000 {all} 1 0.000 0.000 0.000 0.000 {built-in method fromkeys} 261 0.001 0.000 0.001 0.000 {built-in method match} 3 0.000 0.000 0.000 0.000 {filter} 9 0.000 0.000 0.000 0.000 {getattr} 43332 0.102 0.000 0.102 0.000 {hasattr} 25 0.000 0.000 0.000 0.000 {id} 820 0.004 0.000 0.004 0.000 {imp.get_suffixes} 867839 2.227 0.000 2.227 0.000 {isinstance} 8 0.000 0.000 0.000 0.000 {issubclass} 1212411 4.849 0.000 7.313 0.000 {len} 38 0.000 0.000 0.000 0.000 {map} 4000 0.009 0.000 0.009 0.000 {math.ceil} 4000 0.014 0.000 0.014 0.000 {math.log} 469 0.001 0.000 0.001 0.000 {max} 2 0.000 0.000 0.000 0.000 {method '__reduce_ex__' of 'object' objects} 319553 0.614 0.000 0.614 0.000 {method 'add' of 'set' objects} 10 0.000 0.000 0.000 0.000 {method 'any' of 'numpy.ndarray' objects} 267 0.001 0.000 0.001 0.000 {method 'append' of 'list' objects} 2 0.000 0.000 0.000 0.000 {method 'astype' of 'numpy.ndarray' objects} 4000 0.028 0.000 0.028 0.000 {method 'binomial' of 'mtrand.RandomState' objects} 5 0.000 0.000 0.000 0.000 {method 'close' of 'file' objects} 1 0.000 0.000 0.000 0.000 {method 'disable' of '_lsprof.Profiler' objects} 35059 0.067 0.000 0.067 0.000 {method 'get' of 'dict' objects} 1 0.000 0.000 0.000 0.000 {method 'get' of 'dictproxy' objects} 81 0.005 0.000 0.005 0.000 {method 'items' of 'dict' objects} 911 0.003 0.000 0.003 0.000 {method 'join' of 'str' objects} 3040 0.008 0.000 0.008 0.000 {method 'lower' of 'str' objects} 911 0.003 0.000 0.003 0.000 {method 'lstrip' of 'str' objects} 1 0.000 0.000 0.000 0.000 {method 'max' of 'numpy.ndarray' objects} 400000 39.242 0.000 39.242 0.000 {method 'nonzero' of 'numpy.ndarray' objects} 2 0.000 0.000 0.000 0.000 {method 'outer' of 'numpy.ufunc' objects} 1 0.000 0.000 0.000 0.000 {method 'rand' of 'mtrand.RandomState' objects} 322805 0.586 0.000 0.586 0.000 {method 'random' of '_random.Random' objects} 5 0.026 0.005 0.026 0.005 {method 'readlines' of 'file' objects} 1524 0.005 0.000 0.005 0.000 {method 'replace' of 'str' objects} 2 0.000 0.000 0.000 0.000 {method 'sort' of 'list' objects} 915 0.004 0.000 0.004 0.000 {method 'split' of 'str' objects} 911 0.003 0.000 0.003 0.000 {method 'startswith' of 'str' objects} 1 0.000 0.000 0.000 0.000 {method 'sum' of 'numpy.ndarray' objects} 2 0.000 0.000 0.000 0.000 {method 'update' of 'dict' objects} 1 0.000 0.000 0.000 0.000 {method 'values' of 'dict' objects} 234 0.000 0.000 0.000 0.000 {min} 911 0.013 0.000 0.013 0.000 {nt._getfullpathname} 792 0.040 0.000 0.040 0.000 {nt.stat} 400006 77.618 0.000 77.618 0.000 {numpy.core._dotblas.dot} 4 0.001 0.000 0.001 0.000 {numpy.core.multiarray.arange} 4007 0.108 0.000 0.108 0.000 {numpy.core.multiarray.array} 9 0.000 0.000 0.000 0.000 {numpy.core.multiarray.zeros} 5 0.003 0.001 0.003 0.001 {open} 2 0.000 0.000 0.000 0.000 {operator.isNumberType} 4 0.000 0.000 0.000 0.000 {operator.isSequenceType} 1402 0.004 0.000 0.004 0.000 {range} 27 0.000 0.000 0.000 0.000 {sys._getframe} 2 0.000 0.000 0.000 0.000 {zip}brian-1.4.3/dev/optimising/CUBA/threshold-call-c-times-for-different-rates.txt000066400000000000000000000012741266062647100271530ustar00rootroot000000000000001, 1.11, 2.266 2, 1.109, 2.203 3, 1.109, 2.235 4, 1.109, 2.25 5, 1.125, 2.235 6, 1.094, 2.265 7, 1.344, 2.266 8, 1.485, 2.25 9, 1.578, 2.25 10, 1.578, 2.234 20, 2.484, 2.297 30, 3.234, 2.329 40, 4.438, 2.312 50, 5.391, 2.375 60, 6.141, 2.343 70, 7.218, 2.391 80, 8.078, 2.391 90, 9.031, 2.438 100, 9.734, 2.469 150, 13.468, 2.594 200, 17.328, 2.657 250, 21.453, 2.75 300, 26.047, 2.906 350, 31.453, 3.016 400, 34.672, 3.078 These values strongly suggest that both algorithms have a linear running term as a function of the network rate. The first one is better if the firing rate per time step is less than 20, the second one is better otherwise.brian-1.4.3/dev/optimising/CUBA/threshold-call.cpp000066400000000000000000000031271266062647100217120ustar00rootroot00000000000000/* * Calling with netsize = 4000 and repeats = 100000 takes about 11s compared to * Python's 92s of which NumPy's 40s so this looks to be a candidate for optimisation * */ #include #include #include #include const int netsize = 4000; // 4000 for comparison with cuba-profile.txt const int repeats = 50000; // 400000 for comparison with cuba-profile.txt using namespace std; double timeforfiringfraction(double *V, double Vt) { clock_t start = clock(); list l; for(int i=0;iVt) l.push_back(j); } } return (clock()-start)*1./CLOCKS_PER_SEC; } // This is the method used by the .nonzero() method of NumPy double timeforfiringfraction2(double *V, double Vt) { clock_t start = clock(); for(int i=0;iVt) count++; int *l = new int [count]; int *lptr = l; for(int j=0;jVt) *lptr++ = j; } delete l; } return (clock()-start)*1./CLOCKS_PER_SEC; } int main(void) { double V[netsize]; srand((unsigned int)time(NULL)); for(int i=0;i self.Vt).nonzero())[0] V = random.uniform(size=netsize) Vt = 1 - 0.0005 P = FakeNeuronGroup() for i in range(40): exec 'P.a' + str(i) + '=1' P.S = zeros((3, netsize)) P.S[0] = V T = FakeThreshold() #T.Vt = Vt for i in range(40): exec 'T.a' + str(i) + '=1' def main(): for i in xrange(repeats): spikes = T(P) c_profile.run('main()') #def timeforfiringfraction(Vt): # global T # T.Vt = Vt # start = time.time() # for i in xrange(repeats): # spikes = T(P) # return time.time()-start # #def main(): # for rate in [1,5,10,50,100,400,4000]: # Vt = 1 - float(rate)/4000. # print rate, ",", timeforfiringfraction(Vt) # #c_profile.run('main()') brian-1.4.3/dev/optimising/inplace.py000066400000000000000000000015261266062647100175550ustar00rootroot00000000000000from numpy import * from numpy.random import * import time N = 150 bx = randn(N, N) by = randn(N, N) t = time.time() for i in xrange(1000): x = array(bx, copy=True) y = array(by, copy=True) baset = time.time() - t t = time.time() for i in xrange(1000): x = matrix(bx, copy=True) y = matrix(by, copy=True) basebt = time.time() - t t = time.time() for i in xrange(1000): x = array(bx, copy=True) y = array(by, copy=True) z = dot(x, y) s = time.time() - t print s, s - baset t = time.time() for i in xrange(1000): x = matrix(bx, copy=True) y = matrix(by, copy=True) z = x * y s = time.time() - t print s, s - basebt t = time.time() for i in xrange(1000): x = matrix(bx, copy=True) y = matrix(by, copy=True) x *= y s = time.time() - t print s, s - basebt brian-1.4.3/dev/optimising/matrix_product.py000066400000000000000000000007331266062647100212050ustar00rootroot00000000000000# Matrix product testing from scipy import * from scipy import weave from time import time from scipy import linalg import numpy gemm, = linalg.blas.get_blas_funcs(['gemm']) iterations = 10000 N = 4000 m = 3 A = array(rand(m, m)) M = array(rand(m, N)) B = array(zeros((m, N))) V = array(rand(m, 1)) t1 = time() for _ in xrange(iterations): B = numpy.core._dotblas.dot(A, M) #B=gemm(1.0,A,M,0,M,0,0,overwrite_c=0) t2 = time() print t2 - t1 brian-1.4.3/dev/optimising/newspikespeed.py000066400000000000000000000107231266062647100210070ustar00rootroot00000000000000''' Comparison of two algorithms for the new spike/refractoriness mechanism. These algorithms are capable of arbitrarily long refractory periods which don't need to be specified in advance, can change from moment to moment (although once a spike is fired the refractory period for that firing is fixed), and can signal differently for each condition (1) spike fired, (2) in refractory period, (3) leaving refractory period. These could be used by the refractoriness mechanism in different ways. Results: N = 100, duration = 10000, thr=0.5, refracperiod = arange(N) algo 1: .7 algo 2: .96 N = 100, duration = 10000, thr=0.5, refracperiod = zeros(N) algo 1: .72 algo 2: .97 N = 100, duration = 10000, thr=0.5, refracperiod = 10*ones(N) algo 1: .75 algo 2: .99 N = 100, duration = 10000, thr=0.5, refracperiod = 500*ones(N) algo 1: .7 algo 2: .95 So it looks like distribution of refrac periods doesn't count much. N = 100, duration = 10000, thr=0.1, refracperiod = arange(N) algo 1: .69 algo 2: .91 N = 100, duration = 10000, thr=0.01, refracperiod = arange(N) algo 1: .67 algo 2: .875 N = 100, duration = 10000, thr=0.9, refracperiod = arange(N) algo 1: .72 algo 2: .97 So it looks like firing rate doesn't matter much. N = 1000, duration = 1000, thr=0.5, refracperiod = arange(N) algo 1: .19 algo 2: .24 N = 10000, duration = 10000, thr=0.5, refracperiod = arange(N) algo 1: 14.3 algo 2: 16.5 So it looks like number of neurons doesn't make much difference. Conclusion: algo 1 > algo 2 For large N, it appears to take about as long to do refractoriness as it does to do thresholding. I think this seems relatively inexpensive. ''' from brian import * import time N = 10000 duration = 10000 thr = 0.5 dorecord = False algo = 1 refracperiod = zeros(N, dtype=int)#arange(N) staterec = [] active = ones(N, dtype=bool) start = time.time() for t in xrange(duration): threshold = (rand(N)=refend[:numref]) notI = -I leaving = ref[I.nonzero()[0]] staying = notI.nonzero()[0] active[leaving] = True if dorecord: state[leaving] = 3 nstaying = len(staying) ref[:nstaying] = ref[staying] refend[:nstaying] = refend[staying] numref = nstaying if dorecord: state += active*0.3 if dorecord: staterec.append(state) end = time.time() elif algo==2: refend = zeros(N, dtype=int) ref = zeros(0, dtype=int) numref = 0 start = time.time() for t in xrange(duration): if dorecord: state = zeros(N) threshold = (rand(N)=refend[ref]) notI = -I leaving = ref[I] active[leaving] = True if dorecord: state[leaving] = 3 ref = ref[notI] if dorecord: state += active*0.3 if dorecord: staterec.append(state) end = time.time() print 'Time spent total:', end-start print 'Time spent on algorithm:', end-start-t_thresh if dorecord: imshow(array(staterec).T, interpolation='nearest', origin='bottom left', aspect='auto') colorbar() show() brian-1.4.3/dev/optimising/profiling.py000066400000000000000000000016711266062647100201340ustar00rootroot00000000000000''' Very short example program. Profiling: * state update = 0.17 s, including 0.11 s in state updater but only 0.016 if no network update * threshold = 0.22 s * reset = 0.06 s * each connection = about 0.20 s (even with no spike) ''' #import brian_no_units from brian import * from time import time N = 5 # number of neurons Ne = int(N * 0.8) # excitatory neurons Ni = N - Ne # inhibitory neurons p = .1 duration = 1000 * ms eqs = ''' dv/dt = (ge+gi-(v+49*mV))/(20*ms) : volt dge/dt = -ge/(5*ms) : volt dgi/dt = -gi/(10*ms) : volt ''' P = NeuronGroup(N, model=eqs)#,threshold=-50*mV,reset=-60*mV) P.v = -60 * mV + 10 * mV * rand(len(P)) #P._state_updater=lambda _:0 #Pe=P.subgroup(Ne) #Pi=P.subgroup(Ni) #Ce=Connection(Pe,P,'ge',weight=0*mV,sparseness=p) #Ci=Connection(Pi,P,'gi',weight=0*mV,sparseness=p) run(1 * ms) t1 = time() run(duration) t2 = time() print "Simulated in", t2 - t1, "s" brian-1.4.3/dev/optimising/python_spike_container.py000066400000000000000000000032741266062647100227220ustar00rootroot00000000000000#return self.X[range(i0,n)+range(0,j0)] # Costly operation! ##return concatenate((self.X[i0:],self.X[0:j0])) # might be slightly faster ##return self.X.take(range(i0,j0+n),mode='wrap') # does not seem to work from pylab import * import time, timeit from numpy import zeros, hstack, concatenate def f_hstack(n, m, repeats=100000): x = zeros(n) start = time.time() for _ in xrange(repeats): hstack((x[m:], x[:m])) end = time.time() return (end - start) / repeats def f_concatenate(n, m, repeats=100000): x = zeros(n) start = time.time() for _ in xrange(repeats): concatenate((x[m:], x[:m])) end = time.time() return (end - start) / repeats def f_range(n, m, repeats=100000): x = zeros(n) start = time.time() for _ in xrange(repeats): x[range(m, n) + range(m)] end = time.time() return (end - start) / repeats def f_take(n, m, repeats=100000): x = zeros(n) start = time.time() for _ in xrange(repeats): x.take(arange(m, m + n), mode='wrap') end = time.time() return (end - start) / repeats N = [1, 2, 3, 4, 5, 10, 100] P = [.1, .25, .5, .75] for p in P: print p t_hstack = [] t_concatenate = [] t_range = [] t_take = [] for n in N: print n m = int(n * p) t_hstack.append(f_hstack(n, m)) t_concatenate.append(f_concatenate(n, m)) t_range.append(f_range(n, m)) t_take.append(f_take(n, m)) plot(N, t_hstack, '-r', label=str(p)) plot(N, t_concatenate, '-g', label=str(p)) plot(N, t_range, '-b', label=str(p)) plot(N, t_take, '-k', label=str(p)) #legend() show() brian-1.4.3/dev/optimising/stdp1_synapses.py000066400000000000000000000530141266062647100211210ustar00rootroot00000000000000''' stdp1_bis / synapses: 260s (this version) stdp1 / synapses: 245s stdp1 / connection / c: 121s stdp1 / connection / python stdp, c propagate: 175s stdp1 / connection / python stdp, propagate: 178s Partial profiling results: 1 0.000 0.000 306.769 306.769 C:\Users\Dan\programming\brian\dev\optimising\stdp1_synapses.py:47(f) 1000000 6.584 0.000 152.892 0.000 C:\Users\Dan\programming\brian\brian\synapses\synapses.py:707(update) 2000000 8.203 0.000 88.519 0.000 C:\Users\Dan\programming\brian\brian\neurongroup.py:474(update) 1000000 1.859 0.000 63.485 0.000 C:\Users\Dan\programming\brian\brian\directcontrol.py:438(update) 1000000 14.161 0.000 55.861 0.000 C:\Users\Dan\programming\brian\brian\threshold.py:463(__call__) 2000000 3.194 0.000 45.535 0.000 C:\Users\Dan\programming\brian\brian\connections\connection.py:295(do_propagate) 2000000 5.157 0.000 34.137 0.000 C:\Users\Dan\programming\brian\brian\synapses\spikequeue.py:323(propagate) 1000000 19.955 0.000 19.955 0.000 {method 'rand' of 'mtrand.RandomState' objects} 780552 18.013 0.000 18.195 0.000 C:\Users\Dan\programming\brian\brian\synapses\spikequeue.py:287(insert_homogeneous) 1501622 3.357 0.000 14.722 0.000 C:\Python27\lib\site-packages\numpy\lib\function_base.py:1258(extract) 1000000 5.420 0.000 14.233 0.000 C:\Users\Dan\programming\brian\brian\threshold.py:153(__call__) 780552 0.817 0.000 9.802 0.000 C:\Python27\lib\site-packages\numpy\core\shape_base.py:228(hstack) 3003244 2.292 0.000 7.782 0.000 C:\Python27\lib\site-packages\numpy\core\fromnumeric.py:1044(ravel) 7561480 5.508 0.000 5.508 0.000 {method 'take' of 'numpy.ndarray' objects} Analysis: Numbers are percentage of time spent in that function/class, indented numbers give times as a percentage of total (left) and of parent (right) 50% Synapses.update 5% 9% numpy.extract 2% 4% numpy.take 15% Connection.do_propagate 11% 73% SpikeQueue.propagate 6% 53% SpikeQueue.insert_homogeneous 29% NeuronGroup.update 21% 72% PoissonGroup 18% 87% PoissonThreshold 6% 35% rand() 16% Threshold.__call__ Synapses together comprises 65% of the time while NeuronGroup (which is the same in all versions) comprises 15%, and another 5% is also presumably more or less the same in both. From this we can deduce that Synapses is the following amount slower than: Version Faster than Synapses stdp1 / connection / c: 3x stdp1 / connection / python stdp, c propagate: 1.69x stdp1 / connection / python stdp, propagate: 178s 1.65x Based on stdp1_bis taking 260s of which 20% is shared, i.e. 52s. So compute amount faster by (time_with_synapses-52)/(time_with_alternative-52) Note that the argsort, extract, take routines take less than 8% of the total time, with the argsort itself not even appearing (i.e. taking less than 0.5% of the total time). Complete profiling results: 134570775 function calls (134569741 primitive calls) in 204.547 seconds Ordered by: cumulative time, call count List reduced from 265 to 50 due to restriction <50> ncalls tottime percall cumtime percall filename:lineno(function) 1 0.000 0.000 306.769 306.769 :1() 1 0.000 0.000 306.769 306.769 C:\Users\Dan\programming\brian\dev\optimising\stdp1_synapses.py:47(f) 1 0.000 0.000 306.769 306.769 C:\Users\Dan\programming\brian\brian\network.py:892(run) 1 2.432 2.432 306.626 306.626 C:\Users\Dan\programming\brian\brian\network.py:532(run) 1000000 5.179 0.000 302.700 0.000 C:\Users\Dan\programming\brian\brian\network.py:507(update) 1000000 6.584 0.000 152.892 0.000 C:\Users\Dan\programming\brian\brian\synapses\synapses.py:707(update) 2000000 8.203 0.000 88.519 0.000 C:\Users\Dan\programming\brian\brian\neurongroup.py:474(update) 1000000 1.859 0.000 63.485 0.000 C:\Users\Dan\programming\brian\brian\directcontrol.py:438(update) 1000000 14.161 0.000 55.861 0.000 C:\Users\Dan\programming\brian\brian\threshold.py:463(__call__) 2000000 3.194 0.000 45.535 0.000 C:\Users\Dan\programming\brian\brian\connections\connection.py:295(do_propagate) 2000000 5.157 0.000 34.137 0.000 C:\Users\Dan\programming\brian\brian\synapses\spikequeue.py:323(propagate) 1000000 19.955 0.000 19.955 0.000 {method 'rand' of 'mtrand.RandomState' objects} 780552 18.013 0.000 18.195 0.000 C:\Users\Dan\programming\brian\brian\synapses\spikequeue.py:287(insert_homogeneous) 1501622 3.357 0.000 14.722 0.000 C:\Python27\lib\site-packages\numpy\lib\function_base.py:1258(extract) 1000000 5.420 0.000 14.233 0.000 C:\Users\Dan\programming\brian\brian\threshold.py:153(__call__) 1000000 1.409 0.000 10.055 0.000 C:\Users\Dan\programming\brian\brian\clock.py:194() 780552 0.817 0.000 9.802 0.000 C:\Python27\lib\site-packages\numpy\core\shape_base.py:228(hstack) 3279118 9.105 0.000 9.105 0.000 {method 'nonzero' of 'numpy.ndarray' objects} 1000271 3.453 0.000 8.649 0.000 C:\Users\Dan\programming\brian\brian\fundamentalunits.py:559(with_dimensions) 3000000 2.613 0.000 8.607 0.000 C:\Users\Dan\programming\brian\brian\neurongroup.py:524(reset) 3009356 6.773 0.000 8.319 0.000 C:\Users\Dan\programming\brian\brian\unitsafefunctions.py:81(f) 2000000 2.892 0.000 8.203 0.000 C:\Users\Dan\programming\brian\brian\neurongroup.py:500(get_spikes) 3003244 2.292 0.000 7.782 0.000 C:\Python27\lib\site-packages\numpy\core\fromnumeric.py:1044(ravel) 1000000 5.295 0.000 7.380 0.000 C:\Users\Dan\programming\brian\brian\stateupdater.py:398(__call__) 780594 1.233 0.000 7.359 0.000 {map} 19632964/19631963 4.345 0.000 6.758 0.000 {len} 1504678 2.925 0.000 6.126 0.000 C:\Python27\lib\site-packages\numpy\core\shape_base.py:6(atleast_1d) 1000000 3.726 0.000 5.701 0.000 C:\Users\Dan\programming\brian\brian\reset.py:139(__call__) 7561480 5.508 0.000 5.508 0.000 {method 'take' of 'numpy.ndarray' objects} 2000010 2.611 0.000 5.053 0.000 C:\Users\Dan\programming\brian\brian\neurongroup.py:553(state_) 1000000 2.881 0.000 4.970 0.000 C:\Python27\lib\site-packages\scipy\weave\inline_tools.py:133(inline) 2000000 4.554 0.000 4.554 0.000 C:\Users\Dan\programming\brian\brian\synapses\spikequeue.py:207(peek) 1000271 2.520 0.000 4.461 0.000 C:\Users\Dan\programming\brian\brian\fundamentalunits.py:554(__init__) 3003304 1.827 0.000 3.967 0.000 C:\Python27\lib\site-packages\numpy\core\numeric.py:167(asarray) 1504678 1.182 0.000 3.794 0.000 C:\Python27\lib\site-packages\numpy\core\fromnumeric.py:1322(clip) 4507982 3.596 0.000 3.596 0.000 {numpy.core.multiarray.array} 2000000 1.008 0.000 2.879 0.000 C:\Users\Dan\programming\brian\brian\utils\ccircular\ccircular.py:112(get_spikes) 1504678 1.190 0.000 2.647 0.000 C:\Python27\lib\site-packages\numpy\core\numeric.py:237(asanyarray) 1504678 2.612 0.000 2.612 0.000 {method 'clip' of 'numpy.ndarray' objects} 2000000 2.345 0.000 2.503 0.000 C:\Users\Dan\programming\brian\brian\synapses\spikequeue.py:200(next) 2000010 2.162 0.000 2.442 0.000 C:\Users\Dan\programming\brian\brian\group.py:46(state_) 4000001 2.412 0.000 2.412 0.000 C:\Users\Dan\programming\brian\brian\group.py:37(__len__) 2000000 1.048 0.000 2.401 0.000 C:\Users\Dan\programming\brian\brian\utils\ccircular\ccircular.py:109(push) 8095487 2.243 0.000 2.243 0.000 {isinstance} 1000000 2.085 0.000 2.085 0.000 {numpy.core._dotblas.dot} 1501622 0.756 0.000 1.952 0.000 C:\Python27\lib\site-packages\numpy\core\fromnumeric.py:45(take) 1000391 1.644 0.000 1.941 0.000 C:\Users\Dan\programming\brian\brian\fundamentalunits.py:172(__init__) 2000000 1.871 0.000 1.871 0.000 {brian.utils.ccircular._ccircular.SpikeContainer_get_spikes} 1000000 1.808 0.000 1.808 0.000 {apply} 1501622 0.790 0.000 1.631 0.000 C:\Python27\lib\site-packages\numpy\core\fromnumeric.py:1129(nonzero) Remaining functions take less than 0.5% of the total time each Generated code for Synapses.update: _post_neurons = _post.data.take(_synapses) _perm = _post_neurons.argsort() _aux = _post_neurons.take(_perm) _flag = empty(len(_aux)+1, dtype=bool) _flag[0] = _flag[-1] = 1 not_equal(_aux[1:], _aux[:-1], _flag[1:-1]) _F = _flag.nonzero()[0][:-1] logical_not(_flag, _flag) while len(_F): _u = _aux.take(_F) _i = _perm.take(_F) Apost[_synapses[_i]]=Apost[_synapses[_i]]*exp(-(-lastupdate[_synapses[_i]] + t)/taupost) Apre[_synapses[_i]]=Apre[_synapses[_i]]*exp(-(-lastupdate[_synapses[_i]] + t)/taupre) _target_ge[_u]+=w[_synapses[_i]] Apre[_synapses[_i]]+=dApre w[_synapses[_i]]=clip(w[_synapses[_i]]+Apost[_synapses[_i]],0,gmax) lastupdate[_synapses[_i]]=t _F += 1 _F = extract(_flag.take(_F), _F) Optimisation attempts: Cache _synapses[_i]: _post_neurons = _post.data.take(_synapses) _perm = _post_neurons.argsort() _aux = _post_neurons.take(_perm) _flag = empty(len(_aux)+1, dtype=bool) _flag[0] = _flag[-1] = 1 not_equal(_aux[1:], _aux[:-1], _flag[1:-1]) _F = _flag.nonzero()[0][:-1] logical_not(_flag, _flag) while len(_F): _u = _aux.take(_F) _i = _perm.take(_F) _synapses_i = _synapses[_i] Apost[_synapses_i]=Apost[_synapses_i]*exp(-(-lastupdate[_synapses_i] + t)/taupost) Apre[_synapses_i]=Apre[_synapses_i]*exp(-(-lastupdate[_synapses_i] + t)/taupre) _target_ge[_u]+=w[_synapses_i] Apre[_synapses_i]+=dApre w[_synapses_i]=clip(w[_synapses_i]+Apost[_synapses_i],0,gmax) lastupdate[_synapses_i]=t _F += 1 _F = extract(_flag.take(_F), _F) Time taken: 244s (2.8x, 1.6x, 1.5x slower than connection versions) Do it directly and the old way (to gauge by the difference how much time the fiddling around with argsort/etc. adds). We have to do this because the number of spikes should be about the same for a fair comparison. First we compute the time taken doing the direct operation, even though the computation is not correct the total number of operations and memory accesses is the same, but in a different order and with the messing around in Python removed. _u = _post[_synapses] _synapses_i = _synapses #Apost[_synapses_i]=Apost[_synapses_i]*exp(-(-lastupdate[_synapses_i] + t)/taupost) Apost[_synapses_i]*exp(-(-lastupdate[_synapses_i] + t)/taupost) Apost[_synapses_i] #Apre[_synapses_i]=Apre[_synapses_i]*exp(-(-lastupdate[_synapses_i] + t)/taupre) Apre[_synapses_i]*exp(-(-lastupdate[_synapses_i] + t)/taupre) Apre[_synapses_i] #_target_ge[_u]+=w[_synapses_i] _target_ge[_u]+w[_synapses_i] _target_ge[_u] #Apre[_synapses_i]+=dApre Apre[_synapses_i]+dApre Apre[_synapses_i] #w[_synapses_i]=clip(w[_synapses_i]+Apost[_synapses_i],0,gmax) clip(w[_synapses_i]+Apost[_synapses_i],0,gmax) w[_synapses_i] #lastupdate[_synapses_i]=t lastupdate[_synapses_i] _post_neurons = _post.data.take(_synapses) _perm = _post_neurons.argsort() _aux = _post_neurons.take(_perm) _flag = empty(len(_aux)+1, dtype=bool) _flag[0] = _flag[-1] = 1 not_equal(_aux[1:], _aux[:-1], _flag[1:-1]) _F = _flag.nonzero()[0][:-1] logical_not(_flag, _flag) while len(_F): _u = _aux.take(_F) _i = _perm.take(_F) _synapses_i = _synapses[_i] Apost[_synapses_i]=Apost[_synapses_i]*exp(-(-lastupdate[_synapses_i] + t)/taupost) Apre[_synapses_i]=Apre[_synapses_i]*exp(-(-lastupdate[_synapses_i] + t)/taupre) _target_ge[_u]+=w[_synapses_i] Apre[_synapses_i]+=dApre w[_synapses_i]=clip(w[_synapses_i]+Apost[_synapses_i],0,gmax) lastupdate[_synapses_i]=t _F += 1 _F = extract(_flag.take(_F), _F) Time taken: 289s versus 244s, i.e. extra takes 45s. Now previously Synapse.update was taking 50% of the 260s, i.e. 130s, the first optimisation improved the speed to 244s, so it was taking 114s. Around 10% of that is not the generated code, so the generated code was taking about 102s. Doing it directly would take 45s, so this should reduce the total by 102-45=57s, i.e. total time would be 187s. This would then be [2x, 1.1x, 1.1x] slower than the Connection alternatives. In other words, essentially the same speed when a like-for-like comparison is done, suggesting that the C++ version should be as fast as the previous version. Hacked C++ version: Time taken 136s. This is [1.2, 0.7]x the time taken by the (C/Python) Connection alternatives. ''' from brian import * from time import time log_level_debug() do_monitor = False N = 1000 taum = 10 * ms taupre = 20 * ms taupost = taupre Ee = 0 * mV vt = -54 * mV vr = -60 * mV El = -74 * mV taue = 5 * ms F = 15 * Hz gmax = .01 dApre = .01 dApost = -dApre * taupre / taupost * 1.05 dApost *= gmax dApre *= gmax eqs_neurons = ''' dv/dt=(ge*(Ee-vr)+El-v)/taum : volt # the synaptic current is linearized dge/dt=-ge/taue : 1 ''' if True: from scipy import weave class Synapses(Synapses): def update(self): if not hasattr(self, '_prepared_hack'): self._QNCV = [] for queue, _namespace, code in zip(self.queues, self.namespaces, self.codes): for k, v in _namespace.items(): if isinstance(v, float64): _namespace[k] = float(v) # we hack it to use C++, only works if the code below is # updated when the Synapse equations change, and there is # only one queue/namespace/code item. orig_code_str = _namespace['_original_code_string'] code_str = None if 'Apost[_synapses[_i]]=Apost[_synapses[_i]]*exp(-(-lastupdate[_synapses[_i]] + t)/taupost)' in orig_code_str: code_str = ''' for(int _spiking_synapse_idx=0; _spiking_synapse_idx<_num_spiking_synapses; _spiking_synapse_idx++) { const int _synapse_idx = _synapses[_spiking_synapse_idx]; const int _postsynaptic_idx = _postdata[_synapse_idx]; /* ORIGINAL CODE STRING WAS: Apost[_synapses[_i]]=Apost[_synapses[_i]]*exp(-(-lastupdate[_synapses[_i]] + t)/taupost) Apre[_synapses[_i]]=Apre[_synapses[_i]]*exp(-(-lastupdate[_synapses[_i]] + t)/taupre) _target_ge[_u]+=w[_synapses[_i]] Apre[_synapses[_i]]+=dApre w[_synapses[_i]]=clip(w[_synapses[_i]]+Apost[_synapses[_i]],0,gmax) lastupdate[_synapses[_i]]=t */ double w_i = w[_synapse_idx]; if(lastupdate[_synapse_idx]gmax) w_i = gmax; w[_synapse_idx] = w_i; } ''' vars = ['Apost', 'Apre', '_synapses', 't', 'taupost', 'taupre', '_target_ge', 'w', 'dApre', 'gmax', '_postdata', 'lastupdate', '_num_spiking_synapses'] elif 'Apost[_synapses]=Apost[_synapses]*exp(-(-lastupdate[_synapses] + t)/taupost)' in orig_code_str: code_str = ''' for(int _spiking_synapse_idx=0; _spiking_synapse_idx<_num_spiking_synapses; _spiking_synapse_idx++) { const int _synapse_idx = _synapses[_spiking_synapse_idx]; const int _postsynaptic_idx = _postdata[_synapse_idx]; /* ORIGINAL CODE STRING WAS: _post_neurons = _postdata[_synapses] Apost[_synapses]=Apost[_synapses]*exp(-(-lastupdate[_synapses] + t)/taupost) Apre[_synapses]=Apre[_synapses]*exp(-(-lastupdate[_synapses] + t)/taupre) Apost[_synapses]+=dApost w[_synapses]=clip(w[_synapses]+Apre[_synapses],0,gmax) lastupdate[_synapses]=t */ double w_i = w[_synapse_idx]; if(lastupdate[_synapse_idx]gmax) w_i = gmax; w[_synapse_idx] = w_i; } ''' vars = ['Apost', 'Apre', '_synapses', 't', 'taupost', 'taupre', 'w', 'dApost', 'gmax', '_postdata', 'lastupdate', '_num_spiking_synapses'] else: raise ValueError("Unknown code string") self._QNCV.append((queue, _namespace, code_str, vars)) self._prepared_hack = True if self._state_updater is not None: self._state_updater(self) for queue, _namespace, code_str, vars in self._QNCV: synaptic_events = queue.peek() # print len(synaptic_events) if len(synaptic_events): # Build the namespace - Here we don't consider static equations _namespace['_synapses'] = synaptic_events _namespace['t'] = float(self.clock._t) _namespace['_num_spiking_synapses'] = len(synaptic_events) _namespace['_postdata'] = _namespace['_post'].data weave.inline(code=code_str, arg_names=vars, local_dict=_namespace, compiler='gcc', extra_compile_args=['-O3', '-ffast-math', '-march=native', # remove on old versions of gcc ]) queue.next() input = PoissonGroup(N, rates=F) neurons = NeuronGroup(1, model=eqs_neurons, threshold=vt, reset=vr) S = Synapses(input, neurons, model='''w:1 dApre/dt=-Apre/taupre : 1 (event-driven) dApost/dt=-Apost/taupost : 1 (event-driven)''', pre='''ge+=w Apre+=dApre w=clip(w+Apost,0,gmax)''', post='''Apost+=dApost w=clip(w+Apre,0,gmax)''') neurons.v = vr S[:,:]=True S.w='rand()*gmax' if do_monitor: M = SpikeMonitor(neurons) def f(): start = time() run(100 * second, report='text') print 'Time taken:', time()-start if do_monitor: print 'Num spikes:', M.nspikes ## do profiling #import cProfile as profile #import pstats #profile.run('f()','stdp1_synapses.prof') #stats = pstats.Stats('stdp1_synapses.prof') ##stats.strip_dirs() #stats.sort_stats('cumulative', 'calls') #stats.print_stats(50) # do simple run f() brian-1.4.3/dev/optimising/stdp_speed_test.py000066400000000000000000000020361266062647100213300ustar00rootroot00000000000000import brian_no_units_no_warnings from brian import * import cProfile as profile N = 1000 taum = 10 * ms tau_pre = 20 * ms tau_post = tau_pre Ee = 0 * mV vt = -54 * mV vr = -60 * mV El = -74 * mV taue = 5 * ms F = 15 * Hz gmax = .01 dA_pre = .01 dA_post = -dA_pre * tau_pre / tau_post * 1.05 eqs_neurons = ''' dv/dt=(ge*(Ee-vr)+El-v)/taum : volt # the synaptic current is linearized dge/dt=-ge/taue : 1 ''' input = PoissonGroup(N, rates=F) neurons = NeuronGroup(1, model=eqs_neurons, threshold=vt, reset=vr) synapses = Connection(input, neurons, 'ge', weight=rand(len(input), len(neurons)) * gmax, delay=True, ) neurons.v = vr eqs_stdp = ''' dA_pre/dt=-A_pre/tau_pre : 1 dA_post/dt=-A_post/tau_post : 1 ''' dA_post *= gmax dA_pre *= gmax stdp = STDP(synapses, eqs=eqs_stdp, pre='A_pre+=dA_pre;w+=A_post', post='A_post+=dA_post;w+=A_pre', wmax=gmax) run(1 * ms) net = MagicNetwork() def main(): net.run(1 * second, report='text') profile.run('main()') brian-1.4.3/dev/optimising/swigtest/000077500000000000000000000000001266062647100174355ustar00rootroot00000000000000brian-1.4.3/dev/optimising/swigtest/buildtest.bat000066400000000000000000000000641266062647100221240ustar00rootroot00000000000000@echo off setup.py build_ext --inplace -c mingw32 brian-1.4.3/dev/optimising/swigtest/setup.py000066400000000000000000000007021266062647100211460ustar00rootroot00000000000000#!/usr/bin/env python """ setup.py file for SWIG example """ from distutils.core import setup, Extension test_module = Extension('_test', sources=['test_wrap.c', 'test.c'], ) setup (name='test', version='0.1', author="SWIG Docs", description="""Simple swig example from docs""", ext_modules=[test_module], py_modules=["test"], ) brian-1.4.3/dev/optimising/swigtest/test.c000066400000000000000000000004171266062647100205620ustar00rootroot00000000000000#include double My_variable = 3.0; int fact(int n) { if (n <= 1) return 1; else return n*fact(n-1); } int my_mod(int x, int y) { return (x%y); } char *get_time() { time_t ltime; time(<ime); return ctime(<ime); }brian-1.4.3/dev/optimising/swigtest/test.i000066400000000000000000000005121266062647100205640ustar00rootroot00000000000000/* example.i */ %module test %{ /* Put header files here or function declarations like below */ extern double My_variable; extern int fact(int n); extern int my_mod(int x, int y); extern char *get_time(); %} extern double My_variable; extern int fact(int n); extern int my_mod(int x, int y); extern char *get_time();brian-1.4.3/dev/optimising/swigtest/test.py000066400000000000000000000033051266062647100207670ustar00rootroot00000000000000# This file was automatically generated by SWIG (http://www.swig.org). # Version 1.3.33 # # Don't modify this file, modify the SWIG interface instead. # This file is compatible with both classic and new-style classes. import _test import new new_instancemethod = new.instancemethod try: _swig_property = property except NameError: pass # Python < 2.2 doesn't have 'property'. def _swig_setattr_nondynamic(self, class_type, name, value, static=1): if (name == "thisown"): return self.this.own(value) if (name == "this"): if type(value).__name__ == 'PySwigObject': self.__dict__[name] = value return method = class_type.__swig_setmethods__.get(name, None) if method: return method(self, value) if (not static) or hasattr(self, name): self.__dict__[name] = value else: raise AttributeError("You cannot add attributes to %s" % self) def _swig_setattr(self, class_type, name, value): return _swig_setattr_nondynamic(self, class_type, name, value, 0) def _swig_getattr(self, class_type, name): if (name == "thisown"): return self.this.own() method = class_type.__swig_getmethods__.get(name, None) if method: return method(self) raise AttributeError, name def _swig_repr(self): try: strthis = "proxy of " + self.this.__repr__() except: strthis = "" return "<%s.%s; %s >" % (self.__class__.__module__, self.__class__.__name__, strthis,) import types try: _object = types.ObjectType _newclass = 1 except AttributeError: class _object : pass _newclass = 0 del types fact = _test.fact my_mod = _test.my_mod get_time = _test.get_time cvar = _test.cvar brian-1.4.3/dev/optimising/swigtest/test_wrap.c000066400000000000000000003021101266062647100216060ustar00rootroot00000000000000/* ---------------------------------------------------------------------------- * This file was automatically generated by SWIG (http://www.swig.org). * Version 1.3.33 * * This file is not intended to be easily readable and contains a number of * coding conventions designed to improve portability and efficiency. Do not make * changes to this file unless you know what you are doing--modify the SWIG * interface file instead. * ----------------------------------------------------------------------------- */ #define SWIGPYTHON #define SWIG_PYTHON_DIRECTOR_NO_VTABLE /* ----------------------------------------------------------------------------- * This section contains generic SWIG labels for method/variable * declarations/attributes, and other compiler dependent labels. * ----------------------------------------------------------------------------- */ /* template workaround for compilers that cannot correctly implement the C++ standard */ #ifndef SWIGTEMPLATEDISAMBIGUATOR # if defined(__SUNPRO_CC) && (__SUNPRO_CC <= 0x560) # define SWIGTEMPLATEDISAMBIGUATOR template # elif defined(__HP_aCC) /* Needed even with `aCC -AA' when `aCC -V' reports HP ANSI C++ B3910B A.03.55 */ /* If we find a maximum version that requires this, the test would be __HP_aCC <= 35500 for A.03.55 */ # define SWIGTEMPLATEDISAMBIGUATOR template # else # define SWIGTEMPLATEDISAMBIGUATOR # endif #endif /* inline attribute */ #ifndef SWIGINLINE # if defined(__cplusplus) || (defined(__GNUC__) && !defined(__STRICT_ANSI__)) # define SWIGINLINE inline # else # define SWIGINLINE # endif #endif /* attribute recognised by some compilers to avoid 'unused' warnings */ #ifndef SWIGUNUSED # if defined(__GNUC__) # if !(defined(__cplusplus)) || (__GNUC__ > 3 || (__GNUC__ == 3 && __GNUC_MINOR__ >= 4)) # define SWIGUNUSED __attribute__ ((__unused__)) # else # define SWIGUNUSED # endif # elif defined(__ICC) # define SWIGUNUSED __attribute__ ((__unused__)) # else # define SWIGUNUSED # endif #endif #ifndef SWIGUNUSEDPARM # ifdef __cplusplus # define SWIGUNUSEDPARM(p) # else # define SWIGUNUSEDPARM(p) p SWIGUNUSED # endif #endif /* internal SWIG method */ #ifndef SWIGINTERN # define SWIGINTERN static SWIGUNUSED #endif /* internal inline SWIG method */ #ifndef SWIGINTERNINLINE # define SWIGINTERNINLINE SWIGINTERN SWIGINLINE #endif /* exporting methods */ #if (__GNUC__ >= 4) || (__GNUC__ == 3 && __GNUC_MINOR__ >= 4) # ifndef GCC_HASCLASSVISIBILITY # define GCC_HASCLASSVISIBILITY # endif #endif #ifndef SWIGEXPORT # if defined(_WIN32) || defined(__WIN32__) || defined(__CYGWIN__) # if defined(STATIC_LINKED) # define SWIGEXPORT # else # define SWIGEXPORT __declspec(dllexport) # endif # else # if defined(__GNUC__) && defined(GCC_HASCLASSVISIBILITY) # define SWIGEXPORT __attribute__ ((visibility("default"))) # else # define SWIGEXPORT # endif # endif #endif /* calling conventions for Windows */ #ifndef SWIGSTDCALL # if defined(_WIN32) || defined(__WIN32__) || defined(__CYGWIN__) # define SWIGSTDCALL __stdcall # else # define SWIGSTDCALL # endif #endif /* Deal with Microsoft's attempt at deprecating C standard runtime functions */ #if !defined(SWIG_NO_CRT_SECURE_NO_DEPRECATE) && defined(_MSC_VER) && !defined(_CRT_SECURE_NO_DEPRECATE) # define _CRT_SECURE_NO_DEPRECATE #endif /* Deal with Microsoft's attempt at deprecating methods in the standard C++ library */ #if !defined(SWIG_NO_SCL_SECURE_NO_DEPRECATE) && defined(_MSC_VER) && !defined(_SCL_SECURE_NO_DEPRECATE) # define _SCL_SECURE_NO_DEPRECATE #endif /* Python.h has to appear first */ #include /* ----------------------------------------------------------------------------- * swigrun.swg * * This file contains generic CAPI SWIG runtime support for pointer * type checking. * ----------------------------------------------------------------------------- */ /* This should only be incremented when either the layout of swig_type_info changes, or for whatever reason, the runtime changes incompatibly */ #define SWIG_RUNTIME_VERSION "3" /* define SWIG_TYPE_TABLE_NAME as "SWIG_TYPE_TABLE" */ #ifdef SWIG_TYPE_TABLE # define SWIG_QUOTE_STRING(x) #x # define SWIG_EXPAND_AND_QUOTE_STRING(x) SWIG_QUOTE_STRING(x) # define SWIG_TYPE_TABLE_NAME SWIG_EXPAND_AND_QUOTE_STRING(SWIG_TYPE_TABLE) #else # define SWIG_TYPE_TABLE_NAME #endif /* You can use the SWIGRUNTIME and SWIGRUNTIMEINLINE macros for creating a static or dynamic library from the swig runtime code. In 99.9% of the cases, swig just needs to declare them as 'static'. But only do this if is strictly necessary, ie, if you have problems with your compiler or so. */ #ifndef SWIGRUNTIME # define SWIGRUNTIME SWIGINTERN #endif #ifndef SWIGRUNTIMEINLINE # define SWIGRUNTIMEINLINE SWIGRUNTIME SWIGINLINE #endif /* Generic buffer size */ #ifndef SWIG_BUFFER_SIZE # define SWIG_BUFFER_SIZE 1024 #endif /* Flags for pointer conversions */ #define SWIG_POINTER_DISOWN 0x1 /* Flags for new pointer objects */ #define SWIG_POINTER_OWN 0x1 /* Flags/methods for returning states. The swig conversion methods, as ConvertPtr, return and integer that tells if the conversion was successful or not. And if not, an error code can be returned (see swigerrors.swg for the codes). Use the following macros/flags to set or process the returning states. In old swig versions, you usually write code as: if (SWIG_ConvertPtr(obj,vptr,ty.flags) != -1) { // success code } else { //fail code } Now you can be more explicit as: int res = SWIG_ConvertPtr(obj,vptr,ty.flags); if (SWIG_IsOK(res)) { // success code } else { // fail code } that seems to be the same, but now you can also do Type *ptr; int res = SWIG_ConvertPtr(obj,(void **)(&ptr),ty.flags); if (SWIG_IsOK(res)) { // success code if (SWIG_IsNewObj(res) { ... delete *ptr; } else { ... } } else { // fail code } I.e., now SWIG_ConvertPtr can return new objects and you can identify the case and take care of the deallocation. Of course that requires also to SWIG_ConvertPtr to return new result values, as int SWIG_ConvertPtr(obj, ptr,...) { if () { if () { *ptr = ; return SWIG_NEWOBJ; } else { *ptr = ; return SWIG_OLDOBJ; } } else { return SWIG_BADOBJ; } } Of course, returning the plain '0(success)/-1(fail)' still works, but you can be more explicit by returning SWIG_BADOBJ, SWIG_ERROR or any of the swig errors code. Finally, if the SWIG_CASTRANK_MODE is enabled, the result code allows to return the 'cast rank', for example, if you have this int food(double) int fooi(int); and you call food(1) // cast rank '1' (1 -> 1.0) fooi(1) // cast rank '0' just use the SWIG_AddCast()/SWIG_CheckState() */ #define SWIG_OK (0) #define SWIG_ERROR (-1) #define SWIG_IsOK(r) (r >= 0) #define SWIG_ArgError(r) ((r != SWIG_ERROR) ? r : SWIG_TypeError) /* The CastRankLimit says how many bits are used for the cast rank */ #define SWIG_CASTRANKLIMIT (1 << 8) /* The NewMask denotes the object was created (using new/malloc) */ #define SWIG_NEWOBJMASK (SWIG_CASTRANKLIMIT << 1) /* The TmpMask is for in/out typemaps that use temporal objects */ #define SWIG_TMPOBJMASK (SWIG_NEWOBJMASK << 1) /* Simple returning values */ #define SWIG_BADOBJ (SWIG_ERROR) #define SWIG_OLDOBJ (SWIG_OK) #define SWIG_NEWOBJ (SWIG_OK | SWIG_NEWOBJMASK) #define SWIG_TMPOBJ (SWIG_OK | SWIG_TMPOBJMASK) /* Check, add and del mask methods */ #define SWIG_AddNewMask(r) (SWIG_IsOK(r) ? (r | SWIG_NEWOBJMASK) : r) #define SWIG_DelNewMask(r) (SWIG_IsOK(r) ? (r & ~SWIG_NEWOBJMASK) : r) #define SWIG_IsNewObj(r) (SWIG_IsOK(r) && (r & SWIG_NEWOBJMASK)) #define SWIG_AddTmpMask(r) (SWIG_IsOK(r) ? (r | SWIG_TMPOBJMASK) : r) #define SWIG_DelTmpMask(r) (SWIG_IsOK(r) ? (r & ~SWIG_TMPOBJMASK) : r) #define SWIG_IsTmpObj(r) (SWIG_IsOK(r) && (r & SWIG_TMPOBJMASK)) /* Cast-Rank Mode */ #if defined(SWIG_CASTRANK_MODE) # ifndef SWIG_TypeRank # define SWIG_TypeRank unsigned long # endif # ifndef SWIG_MAXCASTRANK /* Default cast allowed */ # define SWIG_MAXCASTRANK (2) # endif # define SWIG_CASTRANKMASK ((SWIG_CASTRANKLIMIT) -1) # define SWIG_CastRank(r) (r & SWIG_CASTRANKMASK) SWIGINTERNINLINE int SWIG_AddCast(int r) { return SWIG_IsOK(r) ? ((SWIG_CastRank(r) < SWIG_MAXCASTRANK) ? (r + 1) : SWIG_ERROR) : r; } SWIGINTERNINLINE int SWIG_CheckState(int r) { return SWIG_IsOK(r) ? SWIG_CastRank(r) + 1 : 0; } #else /* no cast-rank mode */ # define SWIG_AddCast # define SWIG_CheckState(r) (SWIG_IsOK(r) ? 1 : 0) #endif #include #ifdef __cplusplus extern "C" { #endif typedef void *(*swig_converter_func)(void *); typedef struct swig_type_info *(*swig_dycast_func)(void **); /* Structure to store inforomation on one type */ typedef struct swig_type_info { const char *name; /* mangled name of this type */ const char *str; /* human readable name of this type */ swig_dycast_func dcast; /* dynamic cast function down a hierarchy */ struct swig_cast_info *cast; /* linked list of types that can cast into this type */ void *clientdata; /* language specific type data */ int owndata; /* flag if the structure owns the clientdata */ } swig_type_info; /* Structure to store a type and conversion function used for casting */ typedef struct swig_cast_info { swig_type_info *type; /* pointer to type that is equivalent to this type */ swig_converter_func converter; /* function to cast the void pointers */ struct swig_cast_info *next; /* pointer to next cast in linked list */ struct swig_cast_info *prev; /* pointer to the previous cast */ } swig_cast_info; /* Structure used to store module information * Each module generates one structure like this, and the runtime collects * all of these structures and stores them in a circularly linked list.*/ typedef struct swig_module_info { swig_type_info **types; /* Array of pointers to swig_type_info structures that are in this module */ size_t size; /* Number of types in this module */ struct swig_module_info *next; /* Pointer to next element in circularly linked list */ swig_type_info **type_initial; /* Array of initially generated type structures */ swig_cast_info **cast_initial; /* Array of initially generated casting structures */ void *clientdata; /* Language specific module data */ } swig_module_info; /* Compare two type names skipping the space characters, therefore "char*" == "char *" and "Class" == "Class", etc. Return 0 when the two name types are equivalent, as in strncmp, but skipping ' '. */ SWIGRUNTIME int SWIG_TypeNameComp(const char *f1, const char *l1, const char *f2, const char *l2) { for (;(f1 != l1) && (f2 != l2); ++f1, ++f2) { while ((*f1 == ' ') && (f1 != l1)) ++f1; while ((*f2 == ' ') && (f2 != l2)) ++f2; if (*f1 != *f2) return (*f1 > *f2) ? 1 : -1; } return (int)((l1 - f1) - (l2 - f2)); } /* Check type equivalence in a name list like ||... Return 0 if not equal, 1 if equal */ SWIGRUNTIME int SWIG_TypeEquiv(const char *nb, const char *tb) { int equiv = 0; const char* te = tb + strlen(tb); const char* ne = nb; while (!equiv && *ne) { for (nb = ne; *ne; ++ne) { if (*ne == '|') break; } equiv = (SWIG_TypeNameComp(nb, ne, tb, te) == 0) ? 1 : 0; if (*ne) ++ne; } return equiv; } /* Check type equivalence in a name list like ||... Return 0 if equal, -1 if nb < tb, 1 if nb > tb */ SWIGRUNTIME int SWIG_TypeCompare(const char *nb, const char *tb) { int equiv = 0; const char* te = tb + strlen(tb); const char* ne = nb; while (!equiv && *ne) { for (nb = ne; *ne; ++ne) { if (*ne == '|') break; } equiv = (SWIG_TypeNameComp(nb, ne, tb, te) == 0) ? 1 : 0; if (*ne) ++ne; } return equiv; } /* think of this as a c++ template<> or a scheme macro */ #define SWIG_TypeCheck_Template(comparison, ty) \ if (ty) { \ swig_cast_info *iter = ty->cast; \ while (iter) { \ if (comparison) { \ if (iter == ty->cast) return iter; \ /* Move iter to the top of the linked list */ \ iter->prev->next = iter->next; \ if (iter->next) \ iter->next->prev = iter->prev; \ iter->next = ty->cast; \ iter->prev = 0; \ if (ty->cast) ty->cast->prev = iter; \ ty->cast = iter; \ return iter; \ } \ iter = iter->next; \ } \ } \ return 0 /* Check the typename */ SWIGRUNTIME swig_cast_info * SWIG_TypeCheck(const char *c, swig_type_info *ty) { SWIG_TypeCheck_Template(strcmp(iter->type->name, c) == 0, ty); } /* Same as previous function, except strcmp is replaced with a pointer comparison */ SWIGRUNTIME swig_cast_info * SWIG_TypeCheckStruct(swig_type_info *from, swig_type_info *into) { SWIG_TypeCheck_Template(iter->type == from, into); } /* Cast a pointer up an inheritance hierarchy */ SWIGRUNTIMEINLINE void * SWIG_TypeCast(swig_cast_info *ty, void *ptr) { return ((!ty) || (!ty->converter)) ? ptr : (*ty->converter)(ptr); } /* Dynamic pointer casting. Down an inheritance hierarchy */ SWIGRUNTIME swig_type_info * SWIG_TypeDynamicCast(swig_type_info *ty, void **ptr) { swig_type_info *lastty = ty; if (!ty || !ty->dcast) return ty; while (ty && (ty->dcast)) { ty = (*ty->dcast)(ptr); if (ty) lastty = ty; } return lastty; } /* Return the name associated with this type */ SWIGRUNTIMEINLINE const char * SWIG_TypeName(const swig_type_info *ty) { return ty->name; } /* Return the pretty name associated with this type, that is an unmangled type name in a form presentable to the user. */ SWIGRUNTIME const char * SWIG_TypePrettyName(const swig_type_info *type) { /* The "str" field contains the equivalent pretty names of the type, separated by vertical-bar characters. We choose to print the last name, as it is often (?) the most specific. */ if (!type) return NULL; if (type->str != NULL) { const char *last_name = type->str; const char *s; for (s = type->str; *s; s++) if (*s == '|') last_name = s+1; return last_name; } else return type->name; } /* Set the clientdata field for a type */ SWIGRUNTIME void SWIG_TypeClientData(swig_type_info *ti, void *clientdata) { swig_cast_info *cast = ti->cast; /* if (ti->clientdata == clientdata) return; */ ti->clientdata = clientdata; while (cast) { if (!cast->converter) { swig_type_info *tc = cast->type; if (!tc->clientdata) { SWIG_TypeClientData(tc, clientdata); } } cast = cast->next; } } SWIGRUNTIME void SWIG_TypeNewClientData(swig_type_info *ti, void *clientdata) { SWIG_TypeClientData(ti, clientdata); ti->owndata = 1; } /* Search for a swig_type_info structure only by mangled name Search is a O(log #types) We start searching at module start, and finish searching when start == end. Note: if start == end at the beginning of the function, we go all the way around the circular list. */ SWIGRUNTIME swig_type_info * SWIG_MangledTypeQueryModule(swig_module_info *start, swig_module_info *end, const char *name) { swig_module_info *iter = start; do { if (iter->size) { register size_t l = 0; register size_t r = iter->size - 1; do { /* since l+r >= 0, we can (>> 1) instead (/ 2) */ register size_t i = (l + r) >> 1; const char *iname = iter->types[i]->name; if (iname) { register int compare = strcmp(name, iname); if (compare == 0) { return iter->types[i]; } else if (compare < 0) { if (i) { r = i - 1; } else { break; } } else if (compare > 0) { l = i + 1; } } else { break; /* should never happen */ } } while (l <= r); } iter = iter->next; } while (iter != end); return 0; } /* Search for a swig_type_info structure for either a mangled name or a human readable name. It first searches the mangled names of the types, which is a O(log #types) If a type is not found it then searches the human readable names, which is O(#types). We start searching at module start, and finish searching when start == end. Note: if start == end at the beginning of the function, we go all the way around the circular list. */ SWIGRUNTIME swig_type_info * SWIG_TypeQueryModule(swig_module_info *start, swig_module_info *end, const char *name) { /* STEP 1: Search the name field using binary search */ swig_type_info *ret = SWIG_MangledTypeQueryModule(start, end, name); if (ret) { return ret; } else { /* STEP 2: If the type hasn't been found, do a complete search of the str field (the human readable name) */ swig_module_info *iter = start; do { register size_t i = 0; for (; i < iter->size; ++i) { if (iter->types[i]->str && (SWIG_TypeEquiv(iter->types[i]->str, name))) return iter->types[i]; } iter = iter->next; } while (iter != end); } /* neither found a match */ return 0; } /* Pack binary data into a string */ SWIGRUNTIME char * SWIG_PackData(char *c, void *ptr, size_t sz) { static const char hex[17] = "0123456789abcdef"; register const unsigned char *u = (unsigned char *) ptr; register const unsigned char *eu = u + sz; for (; u != eu; ++u) { register unsigned char uu = *u; *(c++) = hex[(uu & 0xf0) >> 4]; *(c++) = hex[uu & 0xf]; } return c; } /* Unpack binary data from a string */ SWIGRUNTIME const char * SWIG_UnpackData(const char *c, void *ptr, size_t sz) { register unsigned char *u = (unsigned char *) ptr; register const unsigned char *eu = u + sz; for (; u != eu; ++u) { register char d = *(c++); register unsigned char uu; if ((d >= '0') && (d <= '9')) uu = ((d - '0') << 4); else if ((d >= 'a') && (d <= 'f')) uu = ((d - ('a'-10)) << 4); else return (char *) 0; d = *(c++); if ((d >= '0') && (d <= '9')) uu |= (d - '0'); else if ((d >= 'a') && (d <= 'f')) uu |= (d - ('a'-10)); else return (char *) 0; *u = uu; } return c; } /* Pack 'void *' into a string buffer. */ SWIGRUNTIME char * SWIG_PackVoidPtr(char *buff, void *ptr, const char *name, size_t bsz) { char *r = buff; if ((2*sizeof(void *) + 2) > bsz) return 0; *(r++) = '_'; r = SWIG_PackData(r,&ptr,sizeof(void *)); if (strlen(name) + 1 > (bsz - (r - buff))) return 0; strcpy(r,name); return buff; } SWIGRUNTIME const char * SWIG_UnpackVoidPtr(const char *c, void **ptr, const char *name) { if (*c != '_') { if (strcmp(c,"NULL") == 0) { *ptr = (void *) 0; return name; } else { return 0; } } return SWIG_UnpackData(++c,ptr,sizeof(void *)); } SWIGRUNTIME char * SWIG_PackDataName(char *buff, void *ptr, size_t sz, const char *name, size_t bsz) { char *r = buff; size_t lname = (name ? strlen(name) : 0); if ((2*sz + 2 + lname) > bsz) return 0; *(r++) = '_'; r = SWIG_PackData(r,ptr,sz); if (lname) { strncpy(r,name,lname+1); } else { *r = 0; } return buff; } SWIGRUNTIME const char * SWIG_UnpackDataName(const char *c, void *ptr, size_t sz, const char *name) { if (*c != '_') { if (strcmp(c,"NULL") == 0) { memset(ptr,0,sz); return name; } else { return 0; } } return SWIG_UnpackData(++c,ptr,sz); } #ifdef __cplusplus } #endif /* Errors in SWIG */ #define SWIG_UnknownError -1 #define SWIG_IOError -2 #define SWIG_RuntimeError -3 #define SWIG_IndexError -4 #define SWIG_TypeError -5 #define SWIG_DivisionByZero -6 #define SWIG_OverflowError -7 #define SWIG_SyntaxError -8 #define SWIG_ValueError -9 #define SWIG_SystemError -10 #define SWIG_AttributeError -11 #define SWIG_MemoryError -12 #define SWIG_NullReferenceError -13 /* Add PyOS_snprintf for old Pythons */ #if PY_VERSION_HEX < 0x02020000 # if defined(_MSC_VER) || defined(__BORLANDC__) || defined(_WATCOM) # define PyOS_snprintf _snprintf # else # define PyOS_snprintf snprintf # endif #endif /* A crude PyString_FromFormat implementation for old Pythons */ #if PY_VERSION_HEX < 0x02020000 #ifndef SWIG_PYBUFFER_SIZE # define SWIG_PYBUFFER_SIZE 1024 #endif static PyObject * PyString_FromFormat(const char *fmt, ...) { va_list ap; char buf[SWIG_PYBUFFER_SIZE * 2]; int res; va_start(ap, fmt); res = vsnprintf(buf, sizeof(buf), fmt, ap); va_end(ap); return (res < 0 || res >= (int)sizeof(buf)) ? 0 : PyString_FromString(buf); } #endif /* Add PyObject_Del for old Pythons */ #if PY_VERSION_HEX < 0x01060000 # define PyObject_Del(op) PyMem_DEL((op)) #endif #ifndef PyObject_DEL # define PyObject_DEL PyObject_Del #endif /* A crude PyExc_StopIteration exception for old Pythons */ #if PY_VERSION_HEX < 0x02020000 # ifndef PyExc_StopIteration # define PyExc_StopIteration PyExc_RuntimeError # endif # ifndef PyObject_GenericGetAttr # define PyObject_GenericGetAttr 0 # endif #endif /* Py_NotImplemented is defined in 2.1 and up. */ #if PY_VERSION_HEX < 0x02010000 # ifndef Py_NotImplemented # define Py_NotImplemented PyExc_RuntimeError # endif #endif /* A crude PyString_AsStringAndSize implementation for old Pythons */ #if PY_VERSION_HEX < 0x02010000 # ifndef PyString_AsStringAndSize # define PyString_AsStringAndSize(obj, s, len) {*s = PyString_AsString(obj); *len = *s ? strlen(*s) : 0;} # endif #endif /* PySequence_Size for old Pythons */ #if PY_VERSION_HEX < 0x02000000 # ifndef PySequence_Size # define PySequence_Size PySequence_Length # endif #endif /* PyBool_FromLong for old Pythons */ #if PY_VERSION_HEX < 0x02030000 static PyObject *PyBool_FromLong(long ok) { PyObject *result = ok ? Py_True : Py_False; Py_INCREF(result); return result; } #endif /* Py_ssize_t for old Pythons */ /* This code is as recommended by: */ /* http://www.python.org/dev/peps/pep-0353/#conversion-guidelines */ #if PY_VERSION_HEX < 0x02050000 && !defined(PY_SSIZE_T_MIN) typedef int Py_ssize_t; # define PY_SSIZE_T_MAX INT_MAX # define PY_SSIZE_T_MIN INT_MIN #endif /* ----------------------------------------------------------------------------- * error manipulation * ----------------------------------------------------------------------------- */ SWIGRUNTIME PyObject* SWIG_Python_ErrorType(int code) { PyObject* type = 0; switch(code) { case SWIG_MemoryError: type = PyExc_MemoryError; break; case SWIG_IOError: type = PyExc_IOError; break; case SWIG_RuntimeError: type = PyExc_RuntimeError; break; case SWIG_IndexError: type = PyExc_IndexError; break; case SWIG_TypeError: type = PyExc_TypeError; break; case SWIG_DivisionByZero: type = PyExc_ZeroDivisionError; break; case SWIG_OverflowError: type = PyExc_OverflowError; break; case SWIG_SyntaxError: type = PyExc_SyntaxError; break; case SWIG_ValueError: type = PyExc_ValueError; break; case SWIG_SystemError: type = PyExc_SystemError; break; case SWIG_AttributeError: type = PyExc_AttributeError; break; default: type = PyExc_RuntimeError; } return type; } SWIGRUNTIME void SWIG_Python_AddErrorMsg(const char* mesg) { PyObject *type = 0; PyObject *value = 0; PyObject *traceback = 0; if (PyErr_Occurred()) PyErr_Fetch(&type, &value, &traceback); if (value) { PyObject *old_str = PyObject_Str(value); PyErr_Clear(); Py_XINCREF(type); PyErr_Format(type, "%s %s", PyString_AsString(old_str), mesg); Py_DECREF(old_str); Py_DECREF(value); } else { PyErr_Format(PyExc_RuntimeError, mesg); } } #if defined(SWIG_PYTHON_NO_THREADS) # if defined(SWIG_PYTHON_THREADS) # undef SWIG_PYTHON_THREADS # endif #endif #if defined(SWIG_PYTHON_THREADS) /* Threading support is enabled */ # if !defined(SWIG_PYTHON_USE_GIL) && !defined(SWIG_PYTHON_NO_USE_GIL) # if (PY_VERSION_HEX >= 0x02030000) /* For 2.3 or later, use the PyGILState calls */ # define SWIG_PYTHON_USE_GIL # endif # endif # if defined(SWIG_PYTHON_USE_GIL) /* Use PyGILState threads calls */ # ifndef SWIG_PYTHON_INITIALIZE_THREADS # define SWIG_PYTHON_INITIALIZE_THREADS PyEval_InitThreads() # endif # ifdef __cplusplus /* C++ code */ class SWIG_Python_Thread_Block { bool status; PyGILState_STATE state; public: void end() { if (status) { PyGILState_Release(state); status = false;} } SWIG_Python_Thread_Block() : status(true), state(PyGILState_Ensure()) {} ~SWIG_Python_Thread_Block() { end(); } }; class SWIG_Python_Thread_Allow { bool status; PyThreadState *save; public: void end() { if (status) { PyEval_RestoreThread(save); status = false; }} SWIG_Python_Thread_Allow() : status(true), save(PyEval_SaveThread()) {} ~SWIG_Python_Thread_Allow() { end(); } }; # define SWIG_PYTHON_THREAD_BEGIN_BLOCK SWIG_Python_Thread_Block _swig_thread_block # define SWIG_PYTHON_THREAD_END_BLOCK _swig_thread_block.end() # define SWIG_PYTHON_THREAD_BEGIN_ALLOW SWIG_Python_Thread_Allow _swig_thread_allow # define SWIG_PYTHON_THREAD_END_ALLOW _swig_thread_allow.end() # else /* C code */ # define SWIG_PYTHON_THREAD_BEGIN_BLOCK PyGILState_STATE _swig_thread_block = PyGILState_Ensure() # define SWIG_PYTHON_THREAD_END_BLOCK PyGILState_Release(_swig_thread_block) # define SWIG_PYTHON_THREAD_BEGIN_ALLOW PyThreadState *_swig_thread_allow = PyEval_SaveThread() # define SWIG_PYTHON_THREAD_END_ALLOW PyEval_RestoreThread(_swig_thread_allow) # endif # else /* Old thread way, not implemented, user must provide it */ # if !defined(SWIG_PYTHON_INITIALIZE_THREADS) # define SWIG_PYTHON_INITIALIZE_THREADS # endif # if !defined(SWIG_PYTHON_THREAD_BEGIN_BLOCK) # define SWIG_PYTHON_THREAD_BEGIN_BLOCK # endif # if !defined(SWIG_PYTHON_THREAD_END_BLOCK) # define SWIG_PYTHON_THREAD_END_BLOCK # endif # if !defined(SWIG_PYTHON_THREAD_BEGIN_ALLOW) # define SWIG_PYTHON_THREAD_BEGIN_ALLOW # endif # if !defined(SWIG_PYTHON_THREAD_END_ALLOW) # define SWIG_PYTHON_THREAD_END_ALLOW # endif # endif #else /* No thread support */ # define SWIG_PYTHON_INITIALIZE_THREADS # define SWIG_PYTHON_THREAD_BEGIN_BLOCK # define SWIG_PYTHON_THREAD_END_BLOCK # define SWIG_PYTHON_THREAD_BEGIN_ALLOW # define SWIG_PYTHON_THREAD_END_ALLOW #endif /* ----------------------------------------------------------------------------- * Python API portion that goes into the runtime * ----------------------------------------------------------------------------- */ #ifdef __cplusplus extern "C" { #if 0 } /* cc-mode */ #endif #endif /* ----------------------------------------------------------------------------- * Constant declarations * ----------------------------------------------------------------------------- */ /* Constant Types */ #define SWIG_PY_POINTER 4 #define SWIG_PY_BINARY 5 /* Constant information structure */ typedef struct swig_const_info { int type; char *name; long lvalue; double dvalue; void *pvalue; swig_type_info **ptype; } swig_const_info; #ifdef __cplusplus #if 0 { /* cc-mode */ #endif } #endif /* ----------------------------------------------------------------------------- * See the LICENSE file for information on copyright, usage and redistribution * of SWIG, and the README file for authors - http://www.swig.org/release.html. * * pyrun.swg * * This file contains the runtime support for Python modules * and includes code for managing global variables and pointer * type checking. * * ----------------------------------------------------------------------------- */ /* Common SWIG API */ /* for raw pointers */ #define SWIG_Python_ConvertPtr(obj, pptr, type, flags) SWIG_Python_ConvertPtrAndOwn(obj, pptr, type, flags, 0) #define SWIG_ConvertPtr(obj, pptr, type, flags) SWIG_Python_ConvertPtr(obj, pptr, type, flags) #define SWIG_ConvertPtrAndOwn(obj,pptr,type,flags,own) SWIG_Python_ConvertPtrAndOwn(obj, pptr, type, flags, own) #define SWIG_NewPointerObj(ptr, type, flags) SWIG_Python_NewPointerObj(ptr, type, flags) #define SWIG_CheckImplicit(ty) SWIG_Python_CheckImplicit(ty) #define SWIG_AcquirePtr(ptr, src) SWIG_Python_AcquirePtr(ptr, src) #define swig_owntype int /* for raw packed data */ #define SWIG_ConvertPacked(obj, ptr, sz, ty) SWIG_Python_ConvertPacked(obj, ptr, sz, ty) #define SWIG_NewPackedObj(ptr, sz, type) SWIG_Python_NewPackedObj(ptr, sz, type) /* for class or struct pointers */ #define SWIG_ConvertInstance(obj, pptr, type, flags) SWIG_ConvertPtr(obj, pptr, type, flags) #define SWIG_NewInstanceObj(ptr, type, flags) SWIG_NewPointerObj(ptr, type, flags) /* for C or C++ function pointers */ #define SWIG_ConvertFunctionPtr(obj, pptr, type) SWIG_Python_ConvertFunctionPtr(obj, pptr, type) #define SWIG_NewFunctionPtrObj(ptr, type) SWIG_Python_NewPointerObj(ptr, type, 0) /* for C++ member pointers, ie, member methods */ #define SWIG_ConvertMember(obj, ptr, sz, ty) SWIG_Python_ConvertPacked(obj, ptr, sz, ty) #define SWIG_NewMemberObj(ptr, sz, type) SWIG_Python_NewPackedObj(ptr, sz, type) /* Runtime API */ #define SWIG_GetModule(clientdata) SWIG_Python_GetModule() #define SWIG_SetModule(clientdata, pointer) SWIG_Python_SetModule(pointer) #define SWIG_NewClientData(obj) PySwigClientData_New(obj) #define SWIG_SetErrorObj SWIG_Python_SetErrorObj #define SWIG_SetErrorMsg SWIG_Python_SetErrorMsg #define SWIG_ErrorType(code) SWIG_Python_ErrorType(code) #define SWIG_Error(code, msg) SWIG_Python_SetErrorMsg(SWIG_ErrorType(code), msg) #define SWIG_fail goto fail /* Runtime API implementation */ /* Error manipulation */ SWIGINTERN void SWIG_Python_SetErrorObj(PyObject *errtype, PyObject *obj) { SWIG_PYTHON_THREAD_BEGIN_BLOCK; PyErr_SetObject(errtype, obj); Py_DECREF(obj); SWIG_PYTHON_THREAD_END_BLOCK; } SWIGINTERN void SWIG_Python_SetErrorMsg(PyObject *errtype, const char *msg) { SWIG_PYTHON_THREAD_BEGIN_BLOCK; PyErr_SetString(errtype, (char *) msg); SWIG_PYTHON_THREAD_END_BLOCK; } #define SWIG_Python_Raise(obj, type, desc) SWIG_Python_SetErrorObj(SWIG_Python_ExceptionType(desc), obj) /* Set a constant value */ SWIGINTERN void SWIG_Python_SetConstant(PyObject *d, const char *name, PyObject *obj) { PyDict_SetItemString(d, (char*) name, obj); Py_DECREF(obj); } /* Append a value to the result obj */ SWIGINTERN PyObject* SWIG_Python_AppendOutput(PyObject* result, PyObject* obj) { #if !defined(SWIG_PYTHON_OUTPUT_TUPLE) if (!result) { result = obj; } else if (result == Py_None) { Py_DECREF(result); result = obj; } else { if (!PyList_Check(result)) { PyObject *o2 = result; result = PyList_New(1); PyList_SetItem(result, 0, o2); } PyList_Append(result,obj); Py_DECREF(obj); } return result; #else PyObject* o2; PyObject* o3; if (!result) { result = obj; } else if (result == Py_None) { Py_DECREF(result); result = obj; } else { if (!PyTuple_Check(result)) { o2 = result; result = PyTuple_New(1); PyTuple_SET_ITEM(result, 0, o2); } o3 = PyTuple_New(1); PyTuple_SET_ITEM(o3, 0, obj); o2 = result; result = PySequence_Concat(o2, o3); Py_DECREF(o2); Py_DECREF(o3); } return result; #endif } /* Unpack the argument tuple */ SWIGINTERN int SWIG_Python_UnpackTuple(PyObject *args, const char *name, Py_ssize_t min, Py_ssize_t max, PyObject **objs) { if (!args) { if (!min && !max) { return 1; } else { PyErr_Format(PyExc_TypeError, "%s expected %s%d arguments, got none", name, (min == max ? "" : "at least "), (int)min); return 0; } } if (!PyTuple_Check(args)) { PyErr_SetString(PyExc_SystemError, "UnpackTuple() argument list is not a tuple"); return 0; } else { register Py_ssize_t l = PyTuple_GET_SIZE(args); if (l < min) { PyErr_Format(PyExc_TypeError, "%s expected %s%d arguments, got %d", name, (min == max ? "" : "at least "), (int)min, (int)l); return 0; } else if (l > max) { PyErr_Format(PyExc_TypeError, "%s expected %s%d arguments, got %d", name, (min == max ? "" : "at most "), (int)max, (int)l); return 0; } else { register int i; for (i = 0; i < l; ++i) { objs[i] = PyTuple_GET_ITEM(args, i); } for (; l < max; ++l) { objs[l] = 0; } return i + 1; } } } /* A functor is a function object with one single object argument */ #if PY_VERSION_HEX >= 0x02020000 #define SWIG_Python_CallFunctor(functor, obj) PyObject_CallFunctionObjArgs(functor, obj, NULL); #else #define SWIG_Python_CallFunctor(functor, obj) PyObject_CallFunction(functor, "O", obj); #endif /* Helper for static pointer initialization for both C and C++ code, for example static PyObject *SWIG_STATIC_POINTER(MyVar) = NewSomething(...); */ #ifdef __cplusplus #define SWIG_STATIC_POINTER(var) var #else #define SWIG_STATIC_POINTER(var) var = 0; if (!var) var #endif /* ----------------------------------------------------------------------------- * Pointer declarations * ----------------------------------------------------------------------------- */ /* Flags for new pointer objects */ #define SWIG_POINTER_NOSHADOW (SWIG_POINTER_OWN << 1) #define SWIG_POINTER_NEW (SWIG_POINTER_NOSHADOW | SWIG_POINTER_OWN) #define SWIG_POINTER_IMPLICIT_CONV (SWIG_POINTER_DISOWN << 1) #ifdef __cplusplus extern "C" { #if 0 } /* cc-mode */ #endif #endif /* How to access Py_None */ #if defined(_WIN32) || defined(__WIN32__) || defined(__CYGWIN__) # ifndef SWIG_PYTHON_NO_BUILD_NONE # ifndef SWIG_PYTHON_BUILD_NONE # define SWIG_PYTHON_BUILD_NONE # endif # endif #endif #ifdef SWIG_PYTHON_BUILD_NONE # ifdef Py_None # undef Py_None # define Py_None SWIG_Py_None() # endif SWIGRUNTIMEINLINE PyObject * _SWIG_Py_None(void) { PyObject *none = Py_BuildValue((char*)""); Py_DECREF(none); return none; } SWIGRUNTIME PyObject * SWIG_Py_None(void) { static PyObject *SWIG_STATIC_POINTER(none) = _SWIG_Py_None(); return none; } #endif /* The python void return value */ SWIGRUNTIMEINLINE PyObject * SWIG_Py_Void(void) { PyObject *none = Py_None; Py_INCREF(none); return none; } /* PySwigClientData */ typedef struct { PyObject *klass; PyObject *newraw; PyObject *newargs; PyObject *destroy; int delargs; int implicitconv; } PySwigClientData; SWIGRUNTIMEINLINE int SWIG_Python_CheckImplicit(swig_type_info *ty) { PySwigClientData *data = (PySwigClientData *)ty->clientdata; return data ? data->implicitconv : 0; } SWIGRUNTIMEINLINE PyObject * SWIG_Python_ExceptionType(swig_type_info *desc) { PySwigClientData *data = desc ? (PySwigClientData *) desc->clientdata : 0; PyObject *klass = data ? data->klass : 0; return (klass ? klass : PyExc_RuntimeError); } SWIGRUNTIME PySwigClientData * PySwigClientData_New(PyObject* obj) { if (!obj) { return 0; } else { PySwigClientData *data = (PySwigClientData *)malloc(sizeof(PySwigClientData)); /* the klass element */ data->klass = obj; Py_INCREF(data->klass); /* the newraw method and newargs arguments used to create a new raw instance */ if (PyClass_Check(obj)) { data->newraw = 0; data->newargs = obj; Py_INCREF(obj); } else { #if (PY_VERSION_HEX < 0x02020000) data->newraw = 0; #else data->newraw = PyObject_GetAttrString(data->klass, (char *)"__new__"); #endif if (data->newraw) { Py_INCREF(data->newraw); data->newargs = PyTuple_New(1); PyTuple_SetItem(data->newargs, 0, obj); } else { data->newargs = obj; } Py_INCREF(data->newargs); } /* the destroy method, aka as the C++ delete method */ data->destroy = PyObject_GetAttrString(data->klass, (char *)"__swig_destroy__"); if (PyErr_Occurred()) { PyErr_Clear(); data->destroy = 0; } if (data->destroy) { int flags; Py_INCREF(data->destroy); flags = PyCFunction_GET_FLAGS(data->destroy); #ifdef METH_O data->delargs = !(flags & (METH_O)); #else data->delargs = 0; #endif } else { data->delargs = 0; } data->implicitconv = 0; return data; } } SWIGRUNTIME void PySwigClientData_Del(PySwigClientData* data) { Py_XDECREF(data->newraw); Py_XDECREF(data->newargs); Py_XDECREF(data->destroy); } /* =============== PySwigObject =====================*/ typedef struct { PyObject_HEAD void *ptr; swig_type_info *ty; int own; PyObject *next; } PySwigObject; SWIGRUNTIME PyObject * PySwigObject_long(PySwigObject *v) { return PyLong_FromVoidPtr(v->ptr); } SWIGRUNTIME PyObject * PySwigObject_format(const char* fmt, PySwigObject *v) { PyObject *res = NULL; PyObject *args = PyTuple_New(1); if (args) { if (PyTuple_SetItem(args, 0, PySwigObject_long(v)) == 0) { PyObject *ofmt = PyString_FromString(fmt); if (ofmt) { res = PyString_Format(ofmt,args); Py_DECREF(ofmt); } Py_DECREF(args); } } return res; } SWIGRUNTIME PyObject * PySwigObject_oct(PySwigObject *v) { return PySwigObject_format("%o",v); } SWIGRUNTIME PyObject * PySwigObject_hex(PySwigObject *v) { return PySwigObject_format("%x",v); } SWIGRUNTIME PyObject * #ifdef METH_NOARGS PySwigObject_repr(PySwigObject *v) #else PySwigObject_repr(PySwigObject *v, PyObject *args) #endif { const char *name = SWIG_TypePrettyName(v->ty); PyObject *hex = PySwigObject_hex(v); PyObject *repr = PyString_FromFormat("", name, PyString_AsString(hex)); Py_DECREF(hex); if (v->next) { #ifdef METH_NOARGS PyObject *nrep = PySwigObject_repr((PySwigObject *)v->next); #else PyObject *nrep = PySwigObject_repr((PySwigObject *)v->next, args); #endif PyString_ConcatAndDel(&repr,nrep); } return repr; } SWIGRUNTIME int PySwigObject_print(PySwigObject *v, FILE *fp, int SWIGUNUSEDPARM(flags)) { #ifdef METH_NOARGS PyObject *repr = PySwigObject_repr(v); #else PyObject *repr = PySwigObject_repr(v, NULL); #endif if (repr) { fputs(PyString_AsString(repr), fp); Py_DECREF(repr); return 0; } else { return 1; } } SWIGRUNTIME PyObject * PySwigObject_str(PySwigObject *v) { char result[SWIG_BUFFER_SIZE]; return SWIG_PackVoidPtr(result, v->ptr, v->ty->name, sizeof(result)) ? PyString_FromString(result) : 0; } SWIGRUNTIME int PySwigObject_compare(PySwigObject *v, PySwigObject *w) { void *i = v->ptr; void *j = w->ptr; return (i < j) ? -1 : ((i > j) ? 1 : 0); } SWIGRUNTIME PyTypeObject* _PySwigObject_type(void); SWIGRUNTIME PyTypeObject* PySwigObject_type(void) { static PyTypeObject *SWIG_STATIC_POINTER(type) = _PySwigObject_type(); return type; } SWIGRUNTIMEINLINE int PySwigObject_Check(PyObject *op) { return ((op)->ob_type == PySwigObject_type()) || (strcmp((op)->ob_type->tp_name,"PySwigObject") == 0); } SWIGRUNTIME PyObject * PySwigObject_New(void *ptr, swig_type_info *ty, int own); SWIGRUNTIME void PySwigObject_dealloc(PyObject *v) { PySwigObject *sobj = (PySwigObject *) v; PyObject *next = sobj->next; if (sobj->own) { swig_type_info *ty = sobj->ty; PySwigClientData *data = ty ? (PySwigClientData *) ty->clientdata : 0; PyObject *destroy = data ? data->destroy : 0; if (destroy) { /* destroy is always a VARARGS method */ PyObject *res; if (data->delargs) { /* we need to create a temporal object to carry the destroy operation */ PyObject *tmp = PySwigObject_New(sobj->ptr, ty, 0); res = SWIG_Python_CallFunctor(destroy, tmp); Py_DECREF(tmp); } else { PyCFunction meth = PyCFunction_GET_FUNCTION(destroy); PyObject *mself = PyCFunction_GET_SELF(destroy); res = ((*meth)(mself, v)); } Py_XDECREF(res); } else { const char *name = SWIG_TypePrettyName(ty); #if !defined(SWIG_PYTHON_SILENT_MEMLEAK) printf("swig/python detected a memory leak of type '%s', no destructor found.\n", name); #endif } } Py_XDECREF(next); PyObject_DEL(v); } SWIGRUNTIME PyObject* PySwigObject_append(PyObject* v, PyObject* next) { PySwigObject *sobj = (PySwigObject *) v; #ifndef METH_O PyObject *tmp = 0; if (!PyArg_ParseTuple(next,(char *)"O:append", &tmp)) return NULL; next = tmp; #endif if (!PySwigObject_Check(next)) { return NULL; } sobj->next = next; Py_INCREF(next); return SWIG_Py_Void(); } SWIGRUNTIME PyObject* #ifdef METH_NOARGS PySwigObject_next(PyObject* v) #else PySwigObject_next(PyObject* v, PyObject *SWIGUNUSEDPARM(args)) #endif { PySwigObject *sobj = (PySwigObject *) v; if (sobj->next) { Py_INCREF(sobj->next); return sobj->next; } else { return SWIG_Py_Void(); } } SWIGINTERN PyObject* #ifdef METH_NOARGS PySwigObject_disown(PyObject *v) #else PySwigObject_disown(PyObject* v, PyObject *SWIGUNUSEDPARM(args)) #endif { PySwigObject *sobj = (PySwigObject *)v; sobj->own = 0; return SWIG_Py_Void(); } SWIGINTERN PyObject* #ifdef METH_NOARGS PySwigObject_acquire(PyObject *v) #else PySwigObject_acquire(PyObject* v, PyObject *SWIGUNUSEDPARM(args)) #endif { PySwigObject *sobj = (PySwigObject *)v; sobj->own = SWIG_POINTER_OWN; return SWIG_Py_Void(); } SWIGINTERN PyObject* PySwigObject_own(PyObject *v, PyObject *args) { PyObject *val = 0; #if (PY_VERSION_HEX < 0x02020000) if (!PyArg_ParseTuple(args,(char *)"|O:own",&val)) #else if (!PyArg_UnpackTuple(args, (char *)"own", 0, 1, &val)) #endif { return NULL; } else { PySwigObject *sobj = (PySwigObject *)v; PyObject *obj = PyBool_FromLong(sobj->own); if (val) { #ifdef METH_NOARGS if (PyObject_IsTrue(val)) { PySwigObject_acquire(v); } else { PySwigObject_disown(v); } #else if (PyObject_IsTrue(val)) { PySwigObject_acquire(v,args); } else { PySwigObject_disown(v,args); } #endif } return obj; } } #ifdef METH_O static PyMethodDef swigobject_methods[] = { {(char *)"disown", (PyCFunction)PySwigObject_disown, METH_NOARGS, (char *)"releases ownership of the pointer"}, {(char *)"acquire", (PyCFunction)PySwigObject_acquire, METH_NOARGS, (char *)"aquires ownership of the pointer"}, {(char *)"own", (PyCFunction)PySwigObject_own, METH_VARARGS, (char *)"returns/sets ownership of the pointer"}, {(char *)"append", (PyCFunction)PySwigObject_append, METH_O, (char *)"appends another 'this' object"}, {(char *)"next", (PyCFunction)PySwigObject_next, METH_NOARGS, (char *)"returns the next 'this' object"}, {(char *)"__repr__",(PyCFunction)PySwigObject_repr, METH_NOARGS, (char *)"returns object representation"}, {0, 0, 0, 0} }; #else static PyMethodDef swigobject_methods[] = { {(char *)"disown", (PyCFunction)PySwigObject_disown, METH_VARARGS, (char *)"releases ownership of the pointer"}, {(char *)"acquire", (PyCFunction)PySwigObject_acquire, METH_VARARGS, (char *)"aquires ownership of the pointer"}, {(char *)"own", (PyCFunction)PySwigObject_own, METH_VARARGS, (char *)"returns/sets ownership of the pointer"}, {(char *)"append", (PyCFunction)PySwigObject_append, METH_VARARGS, (char *)"appends another 'this' object"}, {(char *)"next", (PyCFunction)PySwigObject_next, METH_VARARGS, (char *)"returns the next 'this' object"}, {(char *)"__repr__",(PyCFunction)PySwigObject_repr, METH_VARARGS, (char *)"returns object representation"}, {0, 0, 0, 0} }; #endif #if PY_VERSION_HEX < 0x02020000 SWIGINTERN PyObject * PySwigObject_getattr(PySwigObject *sobj,char *name) { return Py_FindMethod(swigobject_methods, (PyObject *)sobj, name); } #endif SWIGRUNTIME PyTypeObject* _PySwigObject_type(void) { static char swigobject_doc[] = "Swig object carries a C/C++ instance pointer"; static PyNumberMethods PySwigObject_as_number = { (binaryfunc)0, /*nb_add*/ (binaryfunc)0, /*nb_subtract*/ (binaryfunc)0, /*nb_multiply*/ (binaryfunc)0, /*nb_divide*/ (binaryfunc)0, /*nb_remainder*/ (binaryfunc)0, /*nb_divmod*/ (ternaryfunc)0,/*nb_power*/ (unaryfunc)0, /*nb_negative*/ (unaryfunc)0, /*nb_positive*/ (unaryfunc)0, /*nb_absolute*/ (inquiry)0, /*nb_nonzero*/ 0, /*nb_invert*/ 0, /*nb_lshift*/ 0, /*nb_rshift*/ 0, /*nb_and*/ 0, /*nb_xor*/ 0, /*nb_or*/ (coercion)0, /*nb_coerce*/ (unaryfunc)PySwigObject_long, /*nb_int*/ (unaryfunc)PySwigObject_long, /*nb_long*/ (unaryfunc)0, /*nb_float*/ (unaryfunc)PySwigObject_oct, /*nb_oct*/ (unaryfunc)PySwigObject_hex, /*nb_hex*/ #if PY_VERSION_HEX >= 0x02050000 /* 2.5.0 */ 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 /* nb_inplace_add -> nb_index */ #elif PY_VERSION_HEX >= 0x02020000 /* 2.2.0 */ 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 /* nb_inplace_add -> nb_inplace_true_divide */ #elif PY_VERSION_HEX >= 0x02000000 /* 2.0.0 */ 0,0,0,0,0,0,0,0,0,0,0 /* nb_inplace_add -> nb_inplace_or */ #endif }; static PyTypeObject pyswigobject_type; static int type_init = 0; if (!type_init) { const PyTypeObject tmp = { PyObject_HEAD_INIT(NULL) 0, /* ob_size */ (char *)"PySwigObject", /* tp_name */ sizeof(PySwigObject), /* tp_basicsize */ 0, /* tp_itemsize */ (destructor)PySwigObject_dealloc, /* tp_dealloc */ (printfunc)PySwigObject_print, /* tp_print */ #if PY_VERSION_HEX < 0x02020000 (getattrfunc)PySwigObject_getattr, /* tp_getattr */ #else (getattrfunc)0, /* tp_getattr */ #endif (setattrfunc)0, /* tp_setattr */ (cmpfunc)PySwigObject_compare, /* tp_compare */ (reprfunc)PySwigObject_repr, /* tp_repr */ &PySwigObject_as_number, /* tp_as_number */ 0, /* tp_as_sequence */ 0, /* tp_as_mapping */ (hashfunc)0, /* tp_hash */ (ternaryfunc)0, /* tp_call */ (reprfunc)PySwigObject_str, /* tp_str */ PyObject_GenericGetAttr, /* tp_getattro */ 0, /* tp_setattro */ 0, /* tp_as_buffer */ Py_TPFLAGS_DEFAULT, /* tp_flags */ swigobject_doc, /* tp_doc */ 0, /* tp_traverse */ 0, /* tp_clear */ 0, /* tp_richcompare */ 0, /* tp_weaklistoffset */ #if PY_VERSION_HEX >= 0x02020000 0, /* tp_iter */ 0, /* tp_iternext */ swigobject_methods, /* tp_methods */ 0, /* tp_members */ 0, /* tp_getset */ 0, /* tp_base */ 0, /* tp_dict */ 0, /* tp_descr_get */ 0, /* tp_descr_set */ 0, /* tp_dictoffset */ 0, /* tp_init */ 0, /* tp_alloc */ 0, /* tp_new */ 0, /* tp_free */ 0, /* tp_is_gc */ 0, /* tp_bases */ 0, /* tp_mro */ 0, /* tp_cache */ 0, /* tp_subclasses */ 0, /* tp_weaklist */ #endif #if PY_VERSION_HEX >= 0x02030000 0, /* tp_del */ #endif #ifdef COUNT_ALLOCS 0,0,0,0 /* tp_alloc -> tp_next */ #endif }; pyswigobject_type = tmp; pyswigobject_type.ob_type = &PyType_Type; type_init = 1; } return &pyswigobject_type; } SWIGRUNTIME PyObject * PySwigObject_New(void *ptr, swig_type_info *ty, int own) { PySwigObject *sobj = PyObject_NEW(PySwigObject, PySwigObject_type()); if (sobj) { sobj->ptr = ptr; sobj->ty = ty; sobj->own = own; sobj->next = 0; } return (PyObject *)sobj; } /* ----------------------------------------------------------------------------- * Implements a simple Swig Packed type, and use it instead of string * ----------------------------------------------------------------------------- */ typedef struct { PyObject_HEAD void *pack; swig_type_info *ty; size_t size; } PySwigPacked; SWIGRUNTIME int PySwigPacked_print(PySwigPacked *v, FILE *fp, int SWIGUNUSEDPARM(flags)) { char result[SWIG_BUFFER_SIZE]; fputs("pack, v->size, 0, sizeof(result))) { fputs("at ", fp); fputs(result, fp); } fputs(v->ty->name,fp); fputs(">", fp); return 0; } SWIGRUNTIME PyObject * PySwigPacked_repr(PySwigPacked *v) { char result[SWIG_BUFFER_SIZE]; if (SWIG_PackDataName(result, v->pack, v->size, 0, sizeof(result))) { return PyString_FromFormat("", result, v->ty->name); } else { return PyString_FromFormat("", v->ty->name); } } SWIGRUNTIME PyObject * PySwigPacked_str(PySwigPacked *v) { char result[SWIG_BUFFER_SIZE]; if (SWIG_PackDataName(result, v->pack, v->size, 0, sizeof(result))){ return PyString_FromFormat("%s%s", result, v->ty->name); } else { return PyString_FromString(v->ty->name); } } SWIGRUNTIME int PySwigPacked_compare(PySwigPacked *v, PySwigPacked *w) { size_t i = v->size; size_t j = w->size; int s = (i < j) ? -1 : ((i > j) ? 1 : 0); return s ? s : strncmp((char *)v->pack, (char *)w->pack, 2*v->size); } SWIGRUNTIME PyTypeObject* _PySwigPacked_type(void); SWIGRUNTIME PyTypeObject* PySwigPacked_type(void) { static PyTypeObject *SWIG_STATIC_POINTER(type) = _PySwigPacked_type(); return type; } SWIGRUNTIMEINLINE int PySwigPacked_Check(PyObject *op) { return ((op)->ob_type == _PySwigPacked_type()) || (strcmp((op)->ob_type->tp_name,"PySwigPacked") == 0); } SWIGRUNTIME void PySwigPacked_dealloc(PyObject *v) { if (PySwigPacked_Check(v)) { PySwigPacked *sobj = (PySwigPacked *) v; free(sobj->pack); } PyObject_DEL(v); } SWIGRUNTIME PyTypeObject* _PySwigPacked_type(void) { static char swigpacked_doc[] = "Swig object carries a C/C++ instance pointer"; static PyTypeObject pyswigpacked_type; static int type_init = 0; if (!type_init) { const PyTypeObject tmp = { PyObject_HEAD_INIT(NULL) 0, /* ob_size */ (char *)"PySwigPacked", /* tp_name */ sizeof(PySwigPacked), /* tp_basicsize */ 0, /* tp_itemsize */ (destructor)PySwigPacked_dealloc, /* tp_dealloc */ (printfunc)PySwigPacked_print, /* tp_print */ (getattrfunc)0, /* tp_getattr */ (setattrfunc)0, /* tp_setattr */ (cmpfunc)PySwigPacked_compare, /* tp_compare */ (reprfunc)PySwigPacked_repr, /* tp_repr */ 0, /* tp_as_number */ 0, /* tp_as_sequence */ 0, /* tp_as_mapping */ (hashfunc)0, /* tp_hash */ (ternaryfunc)0, /* tp_call */ (reprfunc)PySwigPacked_str, /* tp_str */ PyObject_GenericGetAttr, /* tp_getattro */ 0, /* tp_setattro */ 0, /* tp_as_buffer */ Py_TPFLAGS_DEFAULT, /* tp_flags */ swigpacked_doc, /* tp_doc */ 0, /* tp_traverse */ 0, /* tp_clear */ 0, /* tp_richcompare */ 0, /* tp_weaklistoffset */ #if PY_VERSION_HEX >= 0x02020000 0, /* tp_iter */ 0, /* tp_iternext */ 0, /* tp_methods */ 0, /* tp_members */ 0, /* tp_getset */ 0, /* tp_base */ 0, /* tp_dict */ 0, /* tp_descr_get */ 0, /* tp_descr_set */ 0, /* tp_dictoffset */ 0, /* tp_init */ 0, /* tp_alloc */ 0, /* tp_new */ 0, /* tp_free */ 0, /* tp_is_gc */ 0, /* tp_bases */ 0, /* tp_mro */ 0, /* tp_cache */ 0, /* tp_subclasses */ 0, /* tp_weaklist */ #endif #if PY_VERSION_HEX >= 0x02030000 0, /* tp_del */ #endif #ifdef COUNT_ALLOCS 0,0,0,0 /* tp_alloc -> tp_next */ #endif }; pyswigpacked_type = tmp; pyswigpacked_type.ob_type = &PyType_Type; type_init = 1; } return &pyswigpacked_type; } SWIGRUNTIME PyObject * PySwigPacked_New(void *ptr, size_t size, swig_type_info *ty) { PySwigPacked *sobj = PyObject_NEW(PySwigPacked, PySwigPacked_type()); if (sobj) { void *pack = malloc(size); if (pack) { memcpy(pack, ptr, size); sobj->pack = pack; sobj->ty = ty; sobj->size = size; } else { PyObject_DEL((PyObject *) sobj); sobj = 0; } } return (PyObject *) sobj; } SWIGRUNTIME swig_type_info * PySwigPacked_UnpackData(PyObject *obj, void *ptr, size_t size) { if (PySwigPacked_Check(obj)) { PySwigPacked *sobj = (PySwigPacked *)obj; if (sobj->size != size) return 0; memcpy(ptr, sobj->pack, size); return sobj->ty; } else { return 0; } } /* ----------------------------------------------------------------------------- * pointers/data manipulation * ----------------------------------------------------------------------------- */ SWIGRUNTIMEINLINE PyObject * _SWIG_This(void) { return PyString_FromString("this"); } SWIGRUNTIME PyObject * SWIG_This(void) { static PyObject *SWIG_STATIC_POINTER(swig_this) = _SWIG_This(); return swig_this; } /* #define SWIG_PYTHON_SLOW_GETSET_THIS */ SWIGRUNTIME PySwigObject * SWIG_Python_GetSwigThis(PyObject *pyobj) { if (PySwigObject_Check(pyobj)) { return (PySwigObject *) pyobj; } else { PyObject *obj = 0; #if (!defined(SWIG_PYTHON_SLOW_GETSET_THIS) && (PY_VERSION_HEX >= 0x02030000)) if (PyInstance_Check(pyobj)) { obj = _PyInstance_Lookup(pyobj, SWIG_This()); } else { PyObject **dictptr = _PyObject_GetDictPtr(pyobj); if (dictptr != NULL) { PyObject *dict = *dictptr; obj = dict ? PyDict_GetItem(dict, SWIG_This()) : 0; } else { #ifdef PyWeakref_CheckProxy if (PyWeakref_CheckProxy(pyobj)) { PyObject *wobj = PyWeakref_GET_OBJECT(pyobj); return wobj ? SWIG_Python_GetSwigThis(wobj) : 0; } #endif obj = PyObject_GetAttr(pyobj,SWIG_This()); if (obj) { Py_DECREF(obj); } else { if (PyErr_Occurred()) PyErr_Clear(); return 0; } } } #else obj = PyObject_GetAttr(pyobj,SWIG_This()); if (obj) { Py_DECREF(obj); } else { if (PyErr_Occurred()) PyErr_Clear(); return 0; } #endif if (obj && !PySwigObject_Check(obj)) { /* a PyObject is called 'this', try to get the 'real this' PySwigObject from it */ return SWIG_Python_GetSwigThis(obj); } return (PySwigObject *)obj; } } /* Acquire a pointer value */ SWIGRUNTIME int SWIG_Python_AcquirePtr(PyObject *obj, int own) { if (own) { PySwigObject *sobj = SWIG_Python_GetSwigThis(obj); if (sobj) { int oldown = sobj->own; sobj->own = own; return oldown; } } return 0; } /* Convert a pointer value */ SWIGRUNTIME int SWIG_Python_ConvertPtrAndOwn(PyObject *obj, void **ptr, swig_type_info *ty, int flags, int *own) { if (!obj) return SWIG_ERROR; if (obj == Py_None) { if (ptr) *ptr = 0; return SWIG_OK; } else { PySwigObject *sobj = SWIG_Python_GetSwigThis(obj); while (sobj) { void *vptr = sobj->ptr; if (ty) { swig_type_info *to = sobj->ty; if (to == ty) { /* no type cast needed */ if (ptr) *ptr = vptr; break; } else { swig_cast_info *tc = SWIG_TypeCheck(to->name,ty); if (!tc) { sobj = (PySwigObject *)sobj->next; } else { if (ptr) *ptr = SWIG_TypeCast(tc,vptr); break; } } } else { if (ptr) *ptr = vptr; break; } } if (sobj) { if (own) *own = sobj->own; if (flags & SWIG_POINTER_DISOWN) { sobj->own = 0; } return SWIG_OK; } else { int res = SWIG_ERROR; if (flags & SWIG_POINTER_IMPLICIT_CONV) { PySwigClientData *data = ty ? (PySwigClientData *) ty->clientdata : 0; if (data && !data->implicitconv) { PyObject *klass = data->klass; if (klass) { PyObject *impconv; data->implicitconv = 1; /* avoid recursion and call 'explicit' constructors*/ impconv = SWIG_Python_CallFunctor(klass, obj); data->implicitconv = 0; if (PyErr_Occurred()) { PyErr_Clear(); impconv = 0; } if (impconv) { PySwigObject *iobj = SWIG_Python_GetSwigThis(impconv); if (iobj) { void *vptr; res = SWIG_Python_ConvertPtrAndOwn((PyObject*)iobj, &vptr, ty, 0, 0); if (SWIG_IsOK(res)) { if (ptr) { *ptr = vptr; /* transfer the ownership to 'ptr' */ iobj->own = 0; res = SWIG_AddCast(res); res = SWIG_AddNewMask(res); } else { res = SWIG_AddCast(res); } } } Py_DECREF(impconv); } } } } return res; } } } /* Convert a function ptr value */ SWIGRUNTIME int SWIG_Python_ConvertFunctionPtr(PyObject *obj, void **ptr, swig_type_info *ty) { if (!PyCFunction_Check(obj)) { return SWIG_ConvertPtr(obj, ptr, ty, 0); } else { void *vptr = 0; /* here we get the method pointer for callbacks */ const char *doc = (((PyCFunctionObject *)obj) -> m_ml -> ml_doc); const char *desc = doc ? strstr(doc, "swig_ptr: ") : 0; if (desc) { desc = ty ? SWIG_UnpackVoidPtr(desc + 10, &vptr, ty->name) : 0; if (!desc) return SWIG_ERROR; } if (ty) { swig_cast_info *tc = SWIG_TypeCheck(desc,ty); if (!tc) return SWIG_ERROR; *ptr = SWIG_TypeCast(tc,vptr); } else { *ptr = vptr; } return SWIG_OK; } } /* Convert a packed value value */ SWIGRUNTIME int SWIG_Python_ConvertPacked(PyObject *obj, void *ptr, size_t sz, swig_type_info *ty) { swig_type_info *to = PySwigPacked_UnpackData(obj, ptr, sz); if (!to) return SWIG_ERROR; if (ty) { if (to != ty) { /* check type cast? */ swig_cast_info *tc = SWIG_TypeCheck(to->name,ty); if (!tc) return SWIG_ERROR; } } return SWIG_OK; } /* ----------------------------------------------------------------------------- * Create a new pointer object * ----------------------------------------------------------------------------- */ /* Create a new instance object, whitout calling __init__, and set the 'this' attribute. */ SWIGRUNTIME PyObject* SWIG_Python_NewShadowInstance(PySwigClientData *data, PyObject *swig_this) { #if (PY_VERSION_HEX >= 0x02020000) PyObject *inst = 0; PyObject *newraw = data->newraw; if (newraw) { inst = PyObject_Call(newraw, data->newargs, NULL); if (inst) { #if !defined(SWIG_PYTHON_SLOW_GETSET_THIS) PyObject **dictptr = _PyObject_GetDictPtr(inst); if (dictptr != NULL) { PyObject *dict = *dictptr; if (dict == NULL) { dict = PyDict_New(); *dictptr = dict; PyDict_SetItem(dict, SWIG_This(), swig_this); } } #else PyObject *key = SWIG_This(); PyObject_SetAttr(inst, key, swig_this); #endif } } else { PyObject *dict = PyDict_New(); PyDict_SetItem(dict, SWIG_This(), swig_this); inst = PyInstance_NewRaw(data->newargs, dict); Py_DECREF(dict); } return inst; #else #if (PY_VERSION_HEX >= 0x02010000) PyObject *inst; PyObject *dict = PyDict_New(); PyDict_SetItem(dict, SWIG_This(), swig_this); inst = PyInstance_NewRaw(data->newargs, dict); Py_DECREF(dict); return (PyObject *) inst; #else PyInstanceObject *inst = PyObject_NEW(PyInstanceObject, &PyInstance_Type); if (inst == NULL) { return NULL; } inst->in_class = (PyClassObject *)data->newargs; Py_INCREF(inst->in_class); inst->in_dict = PyDict_New(); if (inst->in_dict == NULL) { Py_DECREF(inst); return NULL; } #ifdef Py_TPFLAGS_HAVE_WEAKREFS inst->in_weakreflist = NULL; #endif #ifdef Py_TPFLAGS_GC PyObject_GC_Init(inst); #endif PyDict_SetItem(inst->in_dict, SWIG_This(), swig_this); return (PyObject *) inst; #endif #endif } SWIGRUNTIME void SWIG_Python_SetSwigThis(PyObject *inst, PyObject *swig_this) { PyObject *dict; #if (PY_VERSION_HEX >= 0x02020000) && !defined(SWIG_PYTHON_SLOW_GETSET_THIS) PyObject **dictptr = _PyObject_GetDictPtr(inst); if (dictptr != NULL) { dict = *dictptr; if (dict == NULL) { dict = PyDict_New(); *dictptr = dict; } PyDict_SetItem(dict, SWIG_This(), swig_this); return; } #endif dict = PyObject_GetAttrString(inst, (char*)"__dict__"); PyDict_SetItem(dict, SWIG_This(), swig_this); Py_DECREF(dict); } SWIGINTERN PyObject * SWIG_Python_InitShadowInstance(PyObject *args) { PyObject *obj[2]; if (!SWIG_Python_UnpackTuple(args,(char*)"swiginit", 2, 2, obj)) { return NULL; } else { PySwigObject *sthis = SWIG_Python_GetSwigThis(obj[0]); if (sthis) { PySwigObject_append((PyObject*) sthis, obj[1]); } else { SWIG_Python_SetSwigThis(obj[0], obj[1]); } return SWIG_Py_Void(); } } /* Create a new pointer object */ SWIGRUNTIME PyObject * SWIG_Python_NewPointerObj(void *ptr, swig_type_info *type, int flags) { if (!ptr) { return SWIG_Py_Void(); } else { int own = (flags & SWIG_POINTER_OWN) ? SWIG_POINTER_OWN : 0; PyObject *robj = PySwigObject_New(ptr, type, own); PySwigClientData *clientdata = type ? (PySwigClientData *)(type->clientdata) : 0; if (clientdata && !(flags & SWIG_POINTER_NOSHADOW)) { PyObject *inst = SWIG_Python_NewShadowInstance(clientdata, robj); if (inst) { Py_DECREF(robj); robj = inst; } } return robj; } } /* Create a new packed object */ SWIGRUNTIMEINLINE PyObject * SWIG_Python_NewPackedObj(void *ptr, size_t sz, swig_type_info *type) { return ptr ? PySwigPacked_New((void *) ptr, sz, type) : SWIG_Py_Void(); } /* -----------------------------------------------------------------------------* * Get type list * -----------------------------------------------------------------------------*/ #ifdef SWIG_LINK_RUNTIME void *SWIG_ReturnGlobalTypeList(void *); #endif SWIGRUNTIME swig_module_info * SWIG_Python_GetModule(void) { static void *type_pointer = (void *)0; /* first check if module already created */ if (!type_pointer) { #ifdef SWIG_LINK_RUNTIME type_pointer = SWIG_ReturnGlobalTypeList((void *)0); #else type_pointer = PyCObject_Import((char*)"swig_runtime_data" SWIG_RUNTIME_VERSION, (char*)"type_pointer" SWIG_TYPE_TABLE_NAME); if (PyErr_Occurred()) { PyErr_Clear(); type_pointer = (void *)0; } #endif } return (swig_module_info *) type_pointer; } #if PY_MAJOR_VERSION < 2 /* PyModule_AddObject function was introduced in Python 2.0. The following function is copied out of Python/modsupport.c in python version 2.3.4 */ SWIGINTERN int PyModule_AddObject(PyObject *m, char *name, PyObject *o) { PyObject *dict; if (!PyModule_Check(m)) { PyErr_SetString(PyExc_TypeError, "PyModule_AddObject() needs module as first arg"); return SWIG_ERROR; } if (!o) { PyErr_SetString(PyExc_TypeError, "PyModule_AddObject() needs non-NULL value"); return SWIG_ERROR; } dict = PyModule_GetDict(m); if (dict == NULL) { /* Internal error -- modules must have a dict! */ PyErr_Format(PyExc_SystemError, "module '%s' has no __dict__", PyModule_GetName(m)); return SWIG_ERROR; } if (PyDict_SetItemString(dict, name, o)) return SWIG_ERROR; Py_DECREF(o); return SWIG_OK; } #endif SWIGRUNTIME void SWIG_Python_DestroyModule(void *vptr) { swig_module_info *swig_module = (swig_module_info *) vptr; swig_type_info **types = swig_module->types; size_t i; for (i =0; i < swig_module->size; ++i) { swig_type_info *ty = types[i]; if (ty->owndata) { PySwigClientData *data = (PySwigClientData *) ty->clientdata; if (data) PySwigClientData_Del(data); } } Py_DECREF(SWIG_This()); } SWIGRUNTIME void SWIG_Python_SetModule(swig_module_info *swig_module) { static PyMethodDef swig_empty_runtime_method_table[] = { {NULL, NULL, 0, NULL} };/* Sentinel */ PyObject *module = Py_InitModule((char*)"swig_runtime_data" SWIG_RUNTIME_VERSION, swig_empty_runtime_method_table); PyObject *pointer = PyCObject_FromVoidPtr((void *) swig_module, SWIG_Python_DestroyModule); if (pointer && module) { PyModule_AddObject(module, (char*)"type_pointer" SWIG_TYPE_TABLE_NAME, pointer); } else { Py_XDECREF(pointer); } } /* The python cached type query */ SWIGRUNTIME PyObject * SWIG_Python_TypeCache(void) { static PyObject *SWIG_STATIC_POINTER(cache) = PyDict_New(); return cache; } SWIGRUNTIME swig_type_info * SWIG_Python_TypeQuery(const char *type) { PyObject *cache = SWIG_Python_TypeCache(); PyObject *key = PyString_FromString(type); PyObject *obj = PyDict_GetItem(cache, key); swig_type_info *descriptor; if (obj) { descriptor = (swig_type_info *) PyCObject_AsVoidPtr(obj); } else { swig_module_info *swig_module = SWIG_Python_GetModule(); descriptor = SWIG_TypeQueryModule(swig_module, swig_module, type); if (descriptor) { obj = PyCObject_FromVoidPtr(descriptor, NULL); PyDict_SetItem(cache, key, obj); Py_DECREF(obj); } } Py_DECREF(key); return descriptor; } /* For backward compatibility only */ #define SWIG_POINTER_EXCEPTION 0 #define SWIG_arg_fail(arg) SWIG_Python_ArgFail(arg) #define SWIG_MustGetPtr(p, type, argnum, flags) SWIG_Python_MustGetPtr(p, type, argnum, flags) SWIGRUNTIME int SWIG_Python_AddErrMesg(const char* mesg, int infront) { if (PyErr_Occurred()) { PyObject *type = 0; PyObject *value = 0; PyObject *traceback = 0; PyErr_Fetch(&type, &value, &traceback); if (value) { PyObject *old_str = PyObject_Str(value); Py_XINCREF(type); PyErr_Clear(); if (infront) { PyErr_Format(type, "%s %s", mesg, PyString_AsString(old_str)); } else { PyErr_Format(type, "%s %s", PyString_AsString(old_str), mesg); } Py_DECREF(old_str); } return 1; } else { return 0; } } SWIGRUNTIME int SWIG_Python_ArgFail(int argnum) { if (PyErr_Occurred()) { /* add information about failing argument */ char mesg[256]; PyOS_snprintf(mesg, sizeof(mesg), "argument number %d:", argnum); return SWIG_Python_AddErrMesg(mesg, 1); } else { return 0; } } SWIGRUNTIMEINLINE const char * PySwigObject_GetDesc(PyObject *self) { PySwigObject *v = (PySwigObject *)self; swig_type_info *ty = v ? v->ty : 0; return ty ? ty->str : (char*)""; } SWIGRUNTIME void SWIG_Python_TypeError(const char *type, PyObject *obj) { if (type) { #if defined(SWIG_COBJECT_TYPES) if (obj && PySwigObject_Check(obj)) { const char *otype = (const char *) PySwigObject_GetDesc(obj); if (otype) { PyErr_Format(PyExc_TypeError, "a '%s' is expected, 'PySwigObject(%s)' is received", type, otype); return; } } else #endif { const char *otype = (obj ? obj->ob_type->tp_name : 0); if (otype) { PyObject *str = PyObject_Str(obj); const char *cstr = str ? PyString_AsString(str) : 0; if (cstr) { PyErr_Format(PyExc_TypeError, "a '%s' is expected, '%s(%s)' is received", type, otype, cstr); } else { PyErr_Format(PyExc_TypeError, "a '%s' is expected, '%s' is received", type, otype); } Py_XDECREF(str); return; } } PyErr_Format(PyExc_TypeError, "a '%s' is expected", type); } else { PyErr_Format(PyExc_TypeError, "unexpected type is received"); } } /* Convert a pointer value, signal an exception on a type mismatch */ SWIGRUNTIME void * SWIG_Python_MustGetPtr(PyObject *obj, swig_type_info *ty, int argnum, int flags) { void *result; if (SWIG_Python_ConvertPtr(obj, &result, ty, flags) == -1) { PyErr_Clear(); if (flags & SWIG_POINTER_EXCEPTION) { SWIG_Python_TypeError(SWIG_TypePrettyName(ty), obj); SWIG_Python_ArgFail(argnum); } } return result; } #ifdef __cplusplus #if 0 { /* cc-mode */ #endif } #endif #define SWIG_exception_fail(code, msg) do { SWIG_Error(code, msg); SWIG_fail; } while(0) #define SWIG_contract_assert(expr, msg) if (!(expr)) { SWIG_Error(SWIG_RuntimeError, msg); SWIG_fail; } else /* -------- TYPES TABLE (BEGIN) -------- */ #define SWIGTYPE_p_char swig_types[0] static swig_type_info *swig_types[2]; static swig_module_info swig_module = {swig_types, 1, 0, 0, 0, 0}; #define SWIG_TypeQuery(name) SWIG_TypeQueryModule(&swig_module, &swig_module, name) #define SWIG_MangledTypeQuery(name) SWIG_MangledTypeQueryModule(&swig_module, &swig_module, name) /* -------- TYPES TABLE (END) -------- */ #if (PY_VERSION_HEX <= 0x02000000) # if !defined(SWIG_PYTHON_CLASSIC) # error "This python version requires swig to be run with the '-classic' option" # endif #endif /*----------------------------------------------- @(target):= _test.so ------------------------------------------------*/ #define SWIG_init init_test #define SWIG_name "_test" #define SWIGVERSION 0x010333 #define SWIG_VERSION SWIGVERSION #define SWIG_as_voidptr(a) (void *)((const void *)(a)) #define SWIG_as_voidptrptr(a) ((void)SWIG_as_voidptr(*a),(void**)(a)) /* Put header files here or function declarations like below */ extern double My_variable; extern int fact(int n); extern int my_mod(int x, int y); extern char *get_time(); SWIGINTERN int SWIG_AsVal_double (PyObject *obj, double *val) { int res = SWIG_TypeError; if (PyFloat_Check(obj)) { if (val) *val = PyFloat_AsDouble(obj); return SWIG_OK; } else if (PyInt_Check(obj)) { if (val) *val = PyInt_AsLong(obj); return SWIG_OK; } else if (PyLong_Check(obj)) { double v = PyLong_AsDouble(obj); if (!PyErr_Occurred()) { if (val) *val = v; return SWIG_OK; } else { PyErr_Clear(); } } #ifdef SWIG_PYTHON_CAST_MODE { int dispatch = 0; double d = PyFloat_AsDouble(obj); if (!PyErr_Occurred()) { if (val) *val = d; return SWIG_AddCast(SWIG_OK); } else { PyErr_Clear(); } if (!dispatch) { long v = PyLong_AsLong(obj); if (!PyErr_Occurred()) { if (val) *val = v; return SWIG_AddCast(SWIG_AddCast(SWIG_OK)); } else { PyErr_Clear(); } } } #endif return res; } #define SWIG_From_double PyFloat_FromDouble #include #if !defined(SWIG_NO_LLONG_MAX) # if !defined(LLONG_MAX) && defined(__GNUC__) && defined (__LONG_LONG_MAX__) # define LLONG_MAX __LONG_LONG_MAX__ # define LLONG_MIN (-LLONG_MAX - 1LL) # define ULLONG_MAX (LLONG_MAX * 2ULL + 1ULL) # endif #endif #include #include SWIGINTERNINLINE int SWIG_CanCastAsInteger(double *d, double min, double max) { double x = *d; if ((min <= x && x <= max)) { double fx = floor(x); double cx = ceil(x); double rd = ((x - fx) < 0.5) ? fx : cx; /* simple rint */ if ((errno == EDOM) || (errno == ERANGE)) { errno = 0; } else { double summ, reps, diff; if (rd < x) { diff = x - rd; } else if (rd > x) { diff = rd - x; } else { return 1; } summ = rd + x; reps = diff/summ; if (reps < 8*DBL_EPSILON) { *d = rd; return 1; } } } return 0; } SWIGINTERN int SWIG_AsVal_long (PyObject *obj, long* val) { if (PyInt_Check(obj)) { if (val) *val = PyInt_AsLong(obj); return SWIG_OK; } else if (PyLong_Check(obj)) { long v = PyLong_AsLong(obj); if (!PyErr_Occurred()) { if (val) *val = v; return SWIG_OK; } else { PyErr_Clear(); } } #ifdef SWIG_PYTHON_CAST_MODE { int dispatch = 0; long v = PyInt_AsLong(obj); if (!PyErr_Occurred()) { if (val) *val = v; return SWIG_AddCast(SWIG_OK); } else { PyErr_Clear(); } if (!dispatch) { double d; int res = SWIG_AddCast(SWIG_AsVal_double (obj,&d)); if (SWIG_IsOK(res) && SWIG_CanCastAsInteger(&d, LONG_MIN, LONG_MAX)) { if (val) *val = (long)(d); return res; } } } #endif return SWIG_TypeError; } SWIGINTERN int SWIG_AsVal_int (PyObject * obj, int *val) { long v; int res = SWIG_AsVal_long (obj, &v); if (SWIG_IsOK(res)) { if ((v < INT_MIN || v > INT_MAX)) { return SWIG_OverflowError; } else { if (val) *val = (int)(v); } } return res; } #define SWIG_From_long PyInt_FromLong SWIGINTERNINLINE PyObject * SWIG_From_int (int value) { return SWIG_From_long (value); } SWIGINTERN swig_type_info* SWIG_pchar_descriptor(void) { static int init = 0; static swig_type_info* info = 0; if (!init) { info = SWIG_TypeQuery("_p_char"); init = 1; } return info; } SWIGINTERNINLINE PyObject * SWIG_FromCharPtrAndSize(const char* carray, size_t size) { if (carray) { if (size > INT_MAX) { swig_type_info* pchar_descriptor = SWIG_pchar_descriptor(); return pchar_descriptor ? SWIG_NewPointerObj((char *)(carray), pchar_descriptor, 0) : SWIG_Py_Void(); } else { return PyString_FromStringAndSize(carray, (int)(size)); } } else { return SWIG_Py_Void(); } } SWIGINTERNINLINE PyObject * SWIG_FromCharPtr(const char *cptr) { return SWIG_FromCharPtrAndSize(cptr, (cptr ? strlen(cptr) : 0)); } #ifdef __cplusplus extern "C" { #endif SWIGINTERN int Swig_var_My_variable_set(PyObject *_val) { { double val; int res = SWIG_AsVal_double(_val, &val); if (!SWIG_IsOK(res)) { SWIG_exception_fail(SWIG_ArgError(res), "in variable '""My_variable""' of type '""double""'"); } My_variable = (double)(val); } return 0; fail: return 1; } SWIGINTERN PyObject *Swig_var_My_variable_get(void) { PyObject *pyobj = 0; pyobj = SWIG_From_double((double)(My_variable)); return pyobj; } SWIGINTERN PyObject *_wrap_fact(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; int arg1 ; int result; int val1 ; int ecode1 = 0 ; PyObject * obj0 = 0 ; if (!PyArg_ParseTuple(args,(char *)"O:fact",&obj0)) SWIG_fail; ecode1 = SWIG_AsVal_int(obj0, &val1); if (!SWIG_IsOK(ecode1)) { SWIG_exception_fail(SWIG_ArgError(ecode1), "in method '" "fact" "', argument " "1"" of type '" "int""'"); } arg1 = (int)(val1); result = (int)fact(arg1); resultobj = SWIG_From_int((int)(result)); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_my_mod(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; int arg1 ; int arg2 ; int result; int val1 ; int ecode1 = 0 ; int val2 ; int ecode2 = 0 ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; if (!PyArg_ParseTuple(args,(char *)"OO:my_mod",&obj0,&obj1)) SWIG_fail; ecode1 = SWIG_AsVal_int(obj0, &val1); if (!SWIG_IsOK(ecode1)) { SWIG_exception_fail(SWIG_ArgError(ecode1), "in method '" "my_mod" "', argument " "1"" of type '" "int""'"); } arg1 = (int)(val1); ecode2 = SWIG_AsVal_int(obj1, &val2); if (!SWIG_IsOK(ecode2)) { SWIG_exception_fail(SWIG_ArgError(ecode2), "in method '" "my_mod" "', argument " "2"" of type '" "int""'"); } arg2 = (int)(val2); result = (int)my_mod(arg1,arg2); resultobj = SWIG_From_int((int)(result)); return resultobj; fail: return NULL; } SWIGINTERN PyObject *_wrap_get_time(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; char *result = 0 ; if (!PyArg_ParseTuple(args,(char *)":get_time")) SWIG_fail; result = (char *)get_time(); resultobj = SWIG_FromCharPtr((const char *)result); return resultobj; fail: return NULL; } static PyMethodDef SwigMethods[] = { { (char *)"fact", _wrap_fact, METH_VARARGS, NULL}, { (char *)"my_mod", _wrap_my_mod, METH_VARARGS, NULL}, { (char *)"get_time", _wrap_get_time, METH_VARARGS, NULL}, { NULL, NULL, 0, NULL } }; /* -------- TYPE CONVERSION AND EQUIVALENCE RULES (BEGIN) -------- */ static swig_type_info _swigt__p_char = {"_p_char", "char *", 0, 0, (void*)0, 0}; static swig_type_info *swig_type_initial[] = { &_swigt__p_char, }; static swig_cast_info _swigc__p_char[] = { {&_swigt__p_char, 0, 0, 0},{0, 0, 0, 0}}; static swig_cast_info *swig_cast_initial[] = { _swigc__p_char, }; /* -------- TYPE CONVERSION AND EQUIVALENCE RULES (END) -------- */ static swig_const_info swig_const_table[] = { {0, 0, 0, 0.0, 0, 0}}; #ifdef __cplusplus } #endif /* ----------------------------------------------------------------------------- * Type initialization: * This problem is tough by the requirement that no dynamic * memory is used. Also, since swig_type_info structures store pointers to * swig_cast_info structures and swig_cast_info structures store pointers back * to swig_type_info structures, we need some lookup code at initialization. * The idea is that swig generates all the structures that are needed. * The runtime then collects these partially filled structures. * The SWIG_InitializeModule function takes these initial arrays out of * swig_module, and does all the lookup, filling in the swig_module.types * array with the correct data and linking the correct swig_cast_info * structures together. * * The generated swig_type_info structures are assigned staticly to an initial * array. We just loop through that array, and handle each type individually. * First we lookup if this type has been already loaded, and if so, use the * loaded structure instead of the generated one. Then we have to fill in the * cast linked list. The cast data is initially stored in something like a * two-dimensional array. Each row corresponds to a type (there are the same * number of rows as there are in the swig_type_initial array). Each entry in * a column is one of the swig_cast_info structures for that type. * The cast_initial array is actually an array of arrays, because each row has * a variable number of columns. So to actually build the cast linked list, * we find the array of casts associated with the type, and loop through it * adding the casts to the list. The one last trick we need to do is making * sure the type pointer in the swig_cast_info struct is correct. * * First off, we lookup the cast->type name to see if it is already loaded. * There are three cases to handle: * 1) If the cast->type has already been loaded AND the type we are adding * casting info to has not been loaded (it is in this module), THEN we * replace the cast->type pointer with the type pointer that has already * been loaded. * 2) If BOTH types (the one we are adding casting info to, and the * cast->type) are loaded, THEN the cast info has already been loaded by * the previous module so we just ignore it. * 3) Finally, if cast->type has not already been loaded, then we add that * swig_cast_info to the linked list (because the cast->type) pointer will * be correct. * ----------------------------------------------------------------------------- */ #ifdef __cplusplus extern "C" { #if 0 } /* c-mode */ #endif #endif #if 0 #define SWIGRUNTIME_DEBUG #endif SWIGRUNTIME void SWIG_InitializeModule(void *clientdata) { size_t i; swig_module_info *module_head, *iter; int found; clientdata = clientdata; /* check to see if the circular list has been setup, if not, set it up */ if (swig_module.next==0) { /* Initialize the swig_module */ swig_module.type_initial = swig_type_initial; swig_module.cast_initial = swig_cast_initial; swig_module.next = &swig_module; } /* Try and load any already created modules */ module_head = SWIG_GetModule(clientdata); if (!module_head) { /* This is the first module loaded for this interpreter */ /* so set the swig module into the interpreter */ SWIG_SetModule(clientdata, &swig_module); module_head = &swig_module; } else { /* the interpreter has loaded a SWIG module, but has it loaded this one? */ found=0; iter=module_head; do { if (iter==&swig_module) { found=1; break; } iter=iter->next; } while (iter!= module_head); /* if the is found in the list, then all is done and we may leave */ if (found) return; /* otherwise we must add out module into the list */ swig_module.next = module_head->next; module_head->next = &swig_module; } /* Now work on filling in swig_module.types */ #ifdef SWIGRUNTIME_DEBUG printf("SWIG_InitializeModule: size %d\n", swig_module.size); #endif for (i = 0; i < swig_module.size; ++i) { swig_type_info *type = 0; swig_type_info *ret; swig_cast_info *cast; #ifdef SWIGRUNTIME_DEBUG printf("SWIG_InitializeModule: type %d %s\n", i, swig_module.type_initial[i]->name); #endif /* if there is another module already loaded */ if (swig_module.next != &swig_module) { type = SWIG_MangledTypeQueryModule(swig_module.next, &swig_module, swig_module.type_initial[i]->name); } if (type) { /* Overwrite clientdata field */ #ifdef SWIGRUNTIME_DEBUG printf("SWIG_InitializeModule: found type %s\n", type->name); #endif if (swig_module.type_initial[i]->clientdata) { type->clientdata = swig_module.type_initial[i]->clientdata; #ifdef SWIGRUNTIME_DEBUG printf("SWIG_InitializeModule: found and overwrite type %s \n", type->name); #endif } } else { type = swig_module.type_initial[i]; } /* Insert casting types */ cast = swig_module.cast_initial[i]; while (cast->type) { /* Don't need to add information already in the list */ ret = 0; #ifdef SWIGRUNTIME_DEBUG printf("SWIG_InitializeModule: look cast %s\n", cast->type->name); #endif if (swig_module.next != &swig_module) { ret = SWIG_MangledTypeQueryModule(swig_module.next, &swig_module, cast->type->name); #ifdef SWIGRUNTIME_DEBUG if (ret) printf("SWIG_InitializeModule: found cast %s\n", ret->name); #endif } if (ret) { if (type == swig_module.type_initial[i]) { #ifdef SWIGRUNTIME_DEBUG printf("SWIG_InitializeModule: skip old type %s\n", ret->name); #endif cast->type = ret; ret = 0; } else { /* Check for casting already in the list */ swig_cast_info *ocast = SWIG_TypeCheck(ret->name, type); #ifdef SWIGRUNTIME_DEBUG if (ocast) printf("SWIG_InitializeModule: skip old cast %s\n", ret->name); #endif if (!ocast) ret = 0; } } if (!ret) { #ifdef SWIGRUNTIME_DEBUG printf("SWIG_InitializeModule: adding cast %s\n", cast->type->name); #endif if (type->cast) { type->cast->prev = cast; cast->next = type->cast; } type->cast = cast; } cast++; } /* Set entry in modules->types array equal to the type */ swig_module.types[i] = type; } swig_module.types[i] = 0; #ifdef SWIGRUNTIME_DEBUG printf("**** SWIG_InitializeModule: Cast List ******\n"); for (i = 0; i < swig_module.size; ++i) { int j = 0; swig_cast_info *cast = swig_module.cast_initial[i]; printf("SWIG_InitializeModule: type %d %s\n", i, swig_module.type_initial[i]->name); while (cast->type) { printf("SWIG_InitializeModule: cast type %s\n", cast->type->name); cast++; ++j; } printf("---- Total casts: %d\n",j); } printf("**** SWIG_InitializeModule: Cast List ******\n"); #endif } /* This function will propagate the clientdata field of type to * any new swig_type_info structures that have been added into the list * of equivalent types. It is like calling * SWIG_TypeClientData(type, clientdata) a second time. */ SWIGRUNTIME void SWIG_PropagateClientData(void) { size_t i; swig_cast_info *equiv; static int init_run = 0; if (init_run) return; init_run = 1; for (i = 0; i < swig_module.size; i++) { if (swig_module.types[i]->clientdata) { equiv = swig_module.types[i]->cast; while (equiv) { if (!equiv->converter) { if (equiv->type && !equiv->type->clientdata) SWIG_TypeClientData(equiv->type, swig_module.types[i]->clientdata); } equiv = equiv->next; } } } } #ifdef __cplusplus #if 0 { /* c-mode */ #endif } #endif #ifdef __cplusplus extern "C" { #endif /* Python-specific SWIG API */ #define SWIG_newvarlink() SWIG_Python_newvarlink() #define SWIG_addvarlink(p, name, get_attr, set_attr) SWIG_Python_addvarlink(p, name, get_attr, set_attr) #define SWIG_InstallConstants(d, constants) SWIG_Python_InstallConstants(d, constants) /* ----------------------------------------------------------------------------- * global variable support code. * ----------------------------------------------------------------------------- */ typedef struct swig_globalvar { char *name; /* Name of global variable */ PyObject *(*get_attr)(void); /* Return the current value */ int (*set_attr)(PyObject *); /* Set the value */ struct swig_globalvar *next; } swig_globalvar; typedef struct swig_varlinkobject { PyObject_HEAD swig_globalvar *vars; } swig_varlinkobject; SWIGINTERN PyObject * swig_varlink_repr(swig_varlinkobject *SWIGUNUSEDPARM(v)) { return PyString_FromString(""); } SWIGINTERN PyObject * swig_varlink_str(swig_varlinkobject *v) { PyObject *str = PyString_FromString("("); swig_globalvar *var; for (var = v->vars; var; var=var->next) { PyString_ConcatAndDel(&str,PyString_FromString(var->name)); if (var->next) PyString_ConcatAndDel(&str,PyString_FromString(", ")); } PyString_ConcatAndDel(&str,PyString_FromString(")")); return str; } SWIGINTERN int swig_varlink_print(swig_varlinkobject *v, FILE *fp, int SWIGUNUSEDPARM(flags)) { PyObject *str = swig_varlink_str(v); fprintf(fp,"Swig global variables "); fprintf(fp,"%s\n", PyString_AsString(str)); Py_DECREF(str); return 0; } SWIGINTERN void swig_varlink_dealloc(swig_varlinkobject *v) { swig_globalvar *var = v->vars; while (var) { swig_globalvar *n = var->next; free(var->name); free(var); var = n; } } SWIGINTERN PyObject * swig_varlink_getattr(swig_varlinkobject *v, char *n) { PyObject *res = NULL; swig_globalvar *var = v->vars; while (var) { if (strcmp(var->name,n) == 0) { res = (*var->get_attr)(); break; } var = var->next; } if (res == NULL && !PyErr_Occurred()) { PyErr_SetString(PyExc_NameError,"Unknown C global variable"); } return res; } SWIGINTERN int swig_varlink_setattr(swig_varlinkobject *v, char *n, PyObject *p) { int res = 1; swig_globalvar *var = v->vars; while (var) { if (strcmp(var->name,n) == 0) { res = (*var->set_attr)(p); break; } var = var->next; } if (res == 1 && !PyErr_Occurred()) { PyErr_SetString(PyExc_NameError,"Unknown C global variable"); } return res; } SWIGINTERN PyTypeObject* swig_varlink_type(void) { static char varlink__doc__[] = "Swig var link object"; static PyTypeObject varlink_type; static int type_init = 0; if (!type_init) { const PyTypeObject tmp = { PyObject_HEAD_INIT(NULL) 0, /* Number of items in variable part (ob_size) */ (char *)"swigvarlink", /* Type name (tp_name) */ sizeof(swig_varlinkobject), /* Basic size (tp_basicsize) */ 0, /* Itemsize (tp_itemsize) */ (destructor) swig_varlink_dealloc, /* Deallocator (tp_dealloc) */ (printfunc) swig_varlink_print, /* Print (tp_print) */ (getattrfunc) swig_varlink_getattr, /* get attr (tp_getattr) */ (setattrfunc) swig_varlink_setattr, /* Set attr (tp_setattr) */ 0, /* tp_compare */ (reprfunc) swig_varlink_repr, /* tp_repr */ 0, /* tp_as_number */ 0, /* tp_as_sequence */ 0, /* tp_as_mapping */ 0, /* tp_hash */ 0, /* tp_call */ (reprfunc)swig_varlink_str, /* tp_str */ 0, /* tp_getattro */ 0, /* tp_setattro */ 0, /* tp_as_buffer */ 0, /* tp_flags */ varlink__doc__, /* tp_doc */ 0, /* tp_traverse */ 0, /* tp_clear */ 0, /* tp_richcompare */ 0, /* tp_weaklistoffset */ #if PY_VERSION_HEX >= 0x02020000 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, /* tp_iter -> tp_weaklist */ #endif #if PY_VERSION_HEX >= 0x02030000 0, /* tp_del */ #endif #ifdef COUNT_ALLOCS 0,0,0,0 /* tp_alloc -> tp_next */ #endif }; varlink_type = tmp; varlink_type.ob_type = &PyType_Type; type_init = 1; } return &varlink_type; } /* Create a variable linking object for use later */ SWIGINTERN PyObject * SWIG_Python_newvarlink(void) { swig_varlinkobject *result = PyObject_NEW(swig_varlinkobject, swig_varlink_type()); if (result) { result->vars = 0; } return ((PyObject*) result); } SWIGINTERN void SWIG_Python_addvarlink(PyObject *p, char *name, PyObject *(*get_attr)(void), int (*set_attr)(PyObject *p)) { swig_varlinkobject *v = (swig_varlinkobject *) p; swig_globalvar *gv = (swig_globalvar *) malloc(sizeof(swig_globalvar)); if (gv) { size_t size = strlen(name)+1; gv->name = (char *)malloc(size); if (gv->name) { strncpy(gv->name,name,size); gv->get_attr = get_attr; gv->set_attr = set_attr; gv->next = v->vars; } } v->vars = gv; } SWIGINTERN PyObject * SWIG_globals(void) { static PyObject *_SWIG_globals = 0; if (!_SWIG_globals) _SWIG_globals = SWIG_newvarlink(); return _SWIG_globals; } /* ----------------------------------------------------------------------------- * constants/methods manipulation * ----------------------------------------------------------------------------- */ /* Install Constants */ SWIGINTERN void SWIG_Python_InstallConstants(PyObject *d, swig_const_info constants[]) { PyObject *obj = 0; size_t i; for (i = 0; constants[i].type; ++i) { switch(constants[i].type) { case SWIG_PY_POINTER: obj = SWIG_NewPointerObj(constants[i].pvalue, *(constants[i]).ptype,0); break; case SWIG_PY_BINARY: obj = SWIG_NewPackedObj(constants[i].pvalue, constants[i].lvalue, *(constants[i].ptype)); break; default: obj = 0; break; } if (obj) { PyDict_SetItemString(d, constants[i].name, obj); Py_DECREF(obj); } } } /* -----------------------------------------------------------------------------*/ /* Fix SwigMethods to carry the callback ptrs when needed */ /* -----------------------------------------------------------------------------*/ SWIGINTERN void SWIG_Python_FixMethods(PyMethodDef *methods, swig_const_info *const_table, swig_type_info **types, swig_type_info **types_initial) { size_t i; for (i = 0; methods[i].ml_name; ++i) { const char *c = methods[i].ml_doc; if (c && (c = strstr(c, "swig_ptr: "))) { int j; swig_const_info *ci = 0; const char *name = c + 10; for (j = 0; const_table[j].type; ++j) { if (strncmp(const_table[j].name, name, strlen(const_table[j].name)) == 0) { ci = &(const_table[j]); break; } } if (ci) { size_t shift = (ci->ptype) - types; swig_type_info *ty = types_initial[shift]; size_t ldoc = (c - methods[i].ml_doc); size_t lptr = strlen(ty->name)+2*sizeof(void*)+2; char *ndoc = (char*)malloc(ldoc + lptr + 10); if (ndoc) { char *buff = ndoc; void *ptr = (ci->type == SWIG_PY_POINTER) ? ci->pvalue : 0; if (ptr) { strncpy(buff, methods[i].ml_doc, ldoc); buff += ldoc; strncpy(buff, "swig_ptr: ", 10); buff += 10; SWIG_PackVoidPtr(buff, ptr, ty->name, lptr); methods[i].ml_doc = ndoc; } } } } } } #ifdef __cplusplus } #endif /* -----------------------------------------------------------------------------* * Partial Init method * -----------------------------------------------------------------------------*/ #ifdef __cplusplus extern "C" #endif SWIGEXPORT void SWIG_init(void) { PyObject *m, *d; /* Fix SwigMethods to carry the callback ptrs when needed */ SWIG_Python_FixMethods(SwigMethods, swig_const_table, swig_types, swig_type_initial); m = Py_InitModule((char *) SWIG_name, SwigMethods); d = PyModule_GetDict(m); SWIG_InitializeModule(0); SWIG_InstallConstants(d,swig_const_table); PyDict_SetItemString(d,(char*)"cvar", SWIG_globals()); SWIG_addvarlink(SWIG_globals(),(char*)"My_variable",Swig_var_My_variable_get, Swig_var_My_variable_set); } brian-1.4.3/dev/optimising/swigtest/testtest.py000066400000000000000000000000411266062647100216610ustar00rootroot00000000000000import test print test.fact(4) brian-1.4.3/dev/optimising/units_speedup.py000066400000000000000000000014571266062647100210340ustar00rootroot00000000000000from brian import * from time import time m = 3 k = 1 # number of time steps class Fake(float): pass #def __init__(self,value): # #super(Fake,self).__init__(value) # #self.x=value # pass if m == 1: t1 = time() N = 10000000 for _ in xrange(N): x = 5. t2 = time() print k * (t2 - t1) * second / N elif m == 2: t1 = time() N = 100000 for _ in xrange(N): #x=1*second #x=second.copy() #x=Quantity(2.) #x=Fake() #x.dim=D x = Quantity.with_dimensions(2., second.dim) t2 = time() print k * (t2 - t1) * second / N elif m == 3: t1 = time() N = 100000 for _ in xrange(N): x = Fake(5.) t2 = time() print k * (t2 - t1) * second / N brian-1.4.3/dev/speedtracking/000077500000000000000000000000001266062647100162255ustar00rootroot00000000000000brian-1.4.3/dev/speedtracking/benchmark_connections.py000066400000000000000000000113771266062647100231440ustar00rootroot00000000000000from vbench.benchmark import Benchmark from datetime import datetime common_setup = """ from brian import * """ python_only_setup = """ set_global_preferences(useweave=False, usecodegen=False, usecodegenweave=False) """ weave_setup = """ set_global_preferences(useweave=True, gcc_options=['-march=native', '-ffast-math', '-O3']) """ codegen_setup = """ set_global_preferences(usecodegenweave=True, usecodegen=True, gcc_options=['-march=native', '-ffast-math', '-O3']) """ setup_template = """ s = arange(%(neurons)d) class thr(Threshold): def __call__(self, P): return s G = NeuronGroup(%(neurons)d, 'V:1', threshold=thr()) H = NeuronGroup(%(neurons)d, 'V:1') C = Connection(G, H, structure='%(structure)s' ) C.connect_full(G, H, weight=1) net = Network(G, H, C) net.prepare() net.run(defaultclock.dt) """ statement = "net.run(1 * second)" # Code generation was introduced here start_codegen = datetime(2010, 2, 6) # Sparse matrices bench_sparse = Benchmark(statement, common_setup + python_only_setup + \ setup_template % {'neurons' : 10, 'structure' : 'sparse'}, name='sparse connection matrix (10x10)') bench_sparse100 = Benchmark(statement, common_setup + python_only_setup + \ setup_template % {'neurons' : 100, 'structure' : 'sparse'}, name='sparse connection matrix (100x100)') bench_sparse100w = Benchmark(statement, common_setup + weave_setup + \ setup_template % {'neurons' : 100, 'structure' : 'sparse'}, name='sparse connection matrix (100x100) with weave') bench_sparse100wc = Benchmark(statement, common_setup + weave_setup + codegen_setup +\ setup_template % {'neurons' : 100, 'structure' : 'sparse'}, name='sparse connection matrix (100x100) with weave + codegen', start_date=start_codegen) # Dynamic matrices # Set a start date here because the benchmark fails for earlier revisions start_dynamic = datetime(2010, 2, 4) bench_dynamic = Benchmark(statement, common_setup + python_only_setup + \ setup_template % {'neurons' : 5, 'structure' : 'dynamic'}, name='dynamic connection matrix (5x5)', start_date=start_dynamic) bench_dynamic50 = Benchmark(statement, common_setup + python_only_setup + \ setup_template % {'neurons' : 50, 'structure' : 'dynamic'}, name='dynamic connection matrix (50x50)', start_date=start_dynamic) bench_dynamic50w = Benchmark(statement, common_setup + weave_setup + \ setup_template % {'neurons' : 50, 'structure' : 'dynamic'}, name='dynamic connection matrix (50x50) with weave', start_date=start_dynamic) # Dense matrices bench_dense = Benchmark(statement, common_setup + python_only_setup + \ setup_template % {'neurons' : 10, 'structure' : 'dense'}, name='dense connection matrix (10x10)') bench_dense100 = Benchmark(statement, common_setup + python_only_setup + \ setup_template % {'neurons' : 100, 'structure' : 'dense'}, name='dense connection matrix (100x100)') bench_dense100w = Benchmark(statement, common_setup + weave_setup + \ setup_template % {'neurons' : 100, 'structure' : 'dense'}, name='dense connection matrix (100x100) with weave') bench_dense100wc = Benchmark(statement, common_setup + weave_setup + codegen_setup +\ setup_template % {'neurons' : 100, 'structure' : 'dense'}, name='dense connection matrix (100x100) with weave + codegen', start_date=start_codegen)brian-1.4.3/dev/speedtracking/benchmark_spikegenerator.py000066400000000000000000000041531266062647100236360ustar00rootroot00000000000000from vbench.benchmark import Benchmark common_setup = """ from brian import * set_global_preferences(useweave=False, usecodegen=False, usecodegenweave=False) log_level_error() # do not show warnings """ setup_template = """ N = %(N)d rate = 250 dt = defaultclock.dt defaultclock.reinit() #The spike times are shifted by half a dt to center the spikes in the bins times = arange(0, 1., 1. / rate) stim_type = '%(stim_type)s' #will be replaced from outside if stim_type == 'pairs': spiketimes = [(idx, t*second +dt/2) for idx in xrange(N) for t in times] elif stim_type == 'array': spiketimes = vstack([array([n, t]) for t in times for n in xrange(N)]) """ # This tests the overhead of the SpikeGeneratorGroup if no spikes are generated setup_empty = """ N = 1 spiketimes = [] """ #to do a fair comparison between old and new versions, this includes the setting #up of the SpikeGeneratorGroup, because the newer version does quite some #preprocessing of the data at this point. statement = ''' G = SpikeGeneratorGroup(N, spiketimes) net = Network(G) net.prepare() net.run(1 * second) ''' bench_pairs = Benchmark(statement, common_setup + (setup_template % {'stim_type' : 'pairs', 'N' : 100}), name='SpikeGeneratorGroup with (index, time) pairs') bench_array = Benchmark(statement, common_setup + (setup_template % {'stim_type' : 'array', 'N' : 200}), name='SpikeGeneratorGroup with an array of indices/times') bench_bigarray = Benchmark(statement, common_setup + (setup_template % {'stim_type' : 'array', 'N' : 1000}), name='SpikeGeneratorGroup with a big array of indices/times') bench_empty = Benchmark(statement, common_setup + setup_empty, name='SpikeGeneratorGroup without any spikes') brian-1.4.3/dev/speedtracking/benchmark_stdp.py000066400000000000000000000022671266062647100215720ustar00rootroot00000000000000from datetime import datetime from vbench.benchmark import Benchmark common_setup = """ from brian import * log_level_error() # do not show warnings """ python_only_setup = """ set_global_preferences(useweave=False, usecodegen=False, usecodegenweave=False) """ setup= """ N = 1000 taum = 10 * ms tau_pre = 20 * ms tau_post = tau_pre Ee = 0 * mV vt = -54 * mV vr = -60 * mV El = -74 * mV taue = 5 * ms gmax = 0.01 F = 15 * Hz dA_pre = .01 dA_post = -dA_pre * tau_pre / tau_post * 2.5 eqs_neurons = ''' dv/dt=(ge*(Ee-vr)+El-v)/taum : volt # the synaptic current is linearized dge/dt=-ge/taue : 1 ''' input = PoissonGroup(N, rates=F) neurons = NeuronGroup(1, model=Equations(eqs_neurons), threshold=vt, reset=vr) synapses = Connection(input, neurons, 'ge', weight=rand(len(input), len(neurons)) * gmax, structure='dense') neurons.v = vr stdp = ExponentialSTDP(synapses, tau_pre, tau_post, dA_pre, dA_post, wmax=gmax, update='mixed') run(defaultclock.dt) """ statement = ''' run(5 * second) ''' bench_stdp = Benchmark(statement, common_setup + python_only_setup + setup, name='Exponential STDP (Python only)') brian-1.4.3/dev/speedtracking/main_loop.py000066400000000000000000000003421266062647100205530ustar00rootroot00000000000000''' Main loop: 1.6 s / 100 s (= 1e6 time steps) With a neuron group: 12.5 s ''' from time import time from brian import * #N=NeuronGroup(1000,'v:volt') run(1*ms) t1=time() run(100*second) t2=time() print t2-t1 brian-1.4.3/dev/speedtracking/publish_benchmarks.py000066400000000000000000000130241266062647100224420ustar00rootroot00000000000000import os, sys, getpass, xmlrpclib, tempfile from datetime import date, datetime import matplotlib as mpl mpl.use('Agg') from matplotlib import pyplot as plt from matplotlib.dates import DateFormatter, MonthLocator from run_benchmarks import benchmarks if __name__ == '__main__': if len(sys.argv) != 2: print 'Need the database filename as an argument' sys.exit(1) DB_PATH = sys.argv[1] # BRIAN releases (some minor left out that occurred in too close succession) releases = {datetime(2008, 9, 23) : '1.0', datetime(2009, 12, 8) : '1.1.3', datetime(2010, 1, 19) : '1.2.0', datetime(2010, 9, 27) : '1.2.1', datetime(2011, 2, 18) : '1.3.0', datetime(2011, 12, 22) : '1.3.1', datetime(2012, 8, 29) : '1.4.0'} TRAC_URL = 'https://%s:%s@neuralensemble.org/trac/brian/login/xmlrpc' # Ask for username and password to authenticate at the TRAC server # First look for environment variables (set in the Jenkins job) username = os.getenv('TRAC_USERNAME', None) password = os.getenv('TRAC_PASSWORD', None) if username is None: username = raw_input('TRAC username [%s]: ' % getpass.getuser()) if not username: username = getpass.getuser() if password is None: password = getpass.getpass('TRAC password: ') server = xmlrpclib.ServerProxy(TRAC_URL % (username, password)) print 'Getting the main wiki page.' # get the benchmark start page: start_page = server.wiki.getPage('benchmarks') # assume there is a header: == List of benchmarks == header_index = start_page.find('== List of benchmarks ==') if header_index == -1: raise ValueError('Wiki page does not contain "== List of benchmarks =="') # leave everything before the header untouched new_page = start_page[:header_index] + '== List of benchmarks ==\n' + \ "''Last update:'' " + date.today().isoformat() + '\n' # sort benchmarks according to name benchmarks.sort(key=lambda b: b.name) for idx, benchmark in enumerate(benchmarks): # add an entry with a link to the benchmark (the name for the benchmark # wiki page is 'benchmark_' + its checksum) checksum, name = benchmark.checksum, benchmark.name wiki_name = 'benchmark_' + checksum print 'Adding/updating wiki page for Benchmark: ', name new_page += ' * [wiki:%s %s]\n' % (wiki_name, name) # Create the wikipage for the individual benchmark, including a link to the # graph benchmark_page = ''' = %(name)s = ''Description'': %(description)s == Setup code == {{{ #!python %(setup)s }}} == Benchmarked code == {{{ #!python %(statement)s }}} == Benchmark results == [[Image(results.png, max-width:"100%%")]] ''' % {'name': benchmark.name, 'description': benchmark.description, 'setup': benchmark.setup, 'statement': benchmark.code} try: old_page = server.wiki.getPage(wiki_name) except: old_page = None if old_page != benchmark_page: server.wiki.putPage(wiki_name, benchmark_page, {'minoredit' : True}) # prepare the graph print '\tCreating graph' tmp_path = tempfile.mktemp(suffix='.png') results = benchmark.get_results(DB_PATH)['timing'] # make basic plot with grid in background fig = plt.figure() ax = fig.add_subplot(111) ax.set_axis_bgcolor('#eeeeec') if results.max() > 2500: divider = 1000. unit = 's' else: divider = 1. unit = 'ms' ax.plot(results.index, results / divider, color='#2e3436', linewidth=1.5) max_y = ax.get_ylim()[1] ax.set_ylim(0, max_y) ax.grid(True, color='white', linestyle='-', linewidth=3) ax.set_axisbelow(True) # Disable spines. ax.spines['right'].set_color('none') ax.spines['top'].set_color('none') # Disable ticks. ax.xaxis.set_ticks_position('bottom') ax.yaxis.set_ticks_position('left') # format dates formatter = DateFormatter("%m/%Y") ax.xaxis.set_major_locator(MonthLocator(bymonth=(1, 4, 7, 10))) ax.xaxis.set_major_formatter(formatter) fig.autofmt_xdate(rotation=45) ax.set_title(benchmark.name) ax.set_xlabel('Date') ax.set_ylabel('Run time (%s)' % unit) # add information about releases for date, release in releases.iteritems(): ax.plot([date, date], [0, max_y], color='#ef2929') ax.annotate(release, (date, 0.025 * max_y), color='#a40000', verticalalignment='left', rotation=45) plt.tight_layout() plt.savefig(tmp_path, dpi=150) print '\tUploading graph' # upload the graph as an attachment to the wiki page server.wiki.putAttachment('%s/results.png' % wiki_name, xmlrpclib.Binary(open(tmp_path).read())) #delete the temporary file os.remove(tmp_path) # upload the main benchmark wiki page if start_page != new_page: print 'Updating the main wiki page' server.wiki.putPage('benchmarks', new_page, {'comment': 'benchmark update by %s' % username, 'minoredit' : True}) print 'All done' brian-1.4.3/dev/speedtracking/run_benchmarks.py000066400000000000000000000103451266062647100216030ustar00rootroot00000000000000''' Run benchmarks for all revisions of Brian in the repository, starting with version 1.0.0 and only using every tenth revision. To run the benchmark on your machine you need the following python packages: * vbench: https://github.com/pydata/vbench You have to download/clone it from the github page and use "python setup.py install" vbench has two more dependencies (those are available on pypi.python.org so you can simply use easy_install or pip) * sqlalchemy: http://pypi.python.org/pypi/SQLAlchemy * pandas: http://pypi.python.org/pypi/pandas vbench also needs the pstats module that is part of the Python standard library but sometimes has to be installed separately, e.g. as part of the python-profiler package on Debian/Ubuntu systems. vbench currently only supports git repositories. As Brian uses a SVN repository, it has to use "git svn clone" first to convert it to git -- this should be done automatically but you obviously need a working installation of git together with the git-svn extension. Benchmark results are saved in a database, therefore if you add new benchmarks or if new revisions appear, only the new things will be run. It might make sense to delete the database and re-run all benchmarks from time to time to avoid treating improvements/regressions in external libraries as numpy/scipy as improvements /regressions in Brian. ''' import os, sys import tempfile from datetime import datetime import pstats try: import sqlalchemy except ImportError: raise ImportError('You need to have sqlalchemy installed: http://pypi.python.org/pypi/SQLAlchemy/') try: import pandas except ImportError: raise ImportError('You need to have pandas installed: http://pypi.python.org/pypi/pandas') try: from vbench.api import Benchmark, BenchmarkRunner, GitRepo except ImportError: raise ImportError('You need to have vbench installed: https://github.com/pydata/vbench') # inspired by https://github.com/wesm/pandas/blob/master/vb_suite/suite.py modules = ['benchmark_connections', 'benchmark_spikegenerator', 'benchmark_stdp'] by_module = {} benchmarks = [] # automatically adds all the benchmarks from the above modules for modname in modules: ref = __import__(modname) by_module[modname] = [v for v in ref.__dict__.values() if isinstance(v, Benchmark)] benchmarks.extend(by_module[modname]) if __name__ == '__main__': if len(sys.argv) < 2 or len(sys.argv) > 3 or not os.path.isabs(sys.argv[1]): sys.stderr.write('Usage: python run_benchmarks.py PATH [n]\n' + 'where PATH is an absolute path that will be used as the base path for saving results.\n') sys.exit(1) # if the directory does not exist it will be created PATH = sys.argv[1] if len(sys.argv) == 3: try: run_option = int(sys.argv[2]) if run_option < 1: raise ValueError() except ValueError: sys.stderr.write('n has to be an integer number > 0 not %r\n' % sys.argv[2]) sys.exit(2) else: run_option = 10 DB_PATH = os.path.join(PATH, 'benchmarks.db') SVN_URL = 'https://neuralensemble.org/svn/brian/trunk' REPO_PATH = os.path.join(PATH, 'brian-trunk') if not os.path.exists(REPO_PATH): # create the repository os.makedirs(REPO_PATH) os.system('git svn clone %s %s' % (SVN_URL, REPO_PATH)) else: # update the repository (can only be called from the directory...) os.chdir(REPO_PATH) os.system('git svn rebase') os.system('git svn fetch') REPO_URL = 'file://' + REPO_PATH TMP_DIR = tempfile.mkdtemp(suffix='vbench') # Those two are not really needed at the moment as no C extensions are # compiled by default # TODO: Does using sys.executable here work on Windows? PREPARE = "%s setup.py clean" % sys.executable BUILD = "%s setup.py build_ext" % sys.executable START_DATE = datetime(2008, 9, 23) # Brian version 1.0.0 repo = GitRepo(REPO_PATH) runner = BenchmarkRunner(benchmarks, REPO_PATH, REPO_URL, BUILD, DB_PATH, TMP_DIR, PREPARE, run_option=run_option, start_date=START_DATE) runner.run() brian-1.4.3/dev/tests/000077500000000000000000000000001266062647100145445ustar00rootroot00000000000000brian-1.4.3/dev/tests/allexamples.py000066400000000000000000000004571266062647100174330ustar00rootroot00000000000000''' Runs all of the examples in the examples/ folder. This version clears any history of which examples did or didn't pass. Better to use allexamples_cumulative but delete the examples_completed.txt file first. ''' open('examples_completed.txt', 'w').write('') import allexamples_cumulative brian-1.4.3/dev/tests/allexamples_cumulative.py000066400000000000000000000110321266062647100216600ustar00rootroot00000000000000''' Runs all of the examples in the examples/ folder, except those excluded in examples_excluded.txt or those run previously (as recorded in examples_completed.txt). Note: Uses the 'Agg' backend, therefore no plots are displayed on the screen and it is possible to run this script on a headless server. ''' import os, nose, sys, subprocess, warnings, unittest import tempfile, pickle from nose.plugins import Plugin from nose.plugins.capture import Capture from nose.plugins.xunit import Xunit warnings.simplefilter('ignore') exclude_list = open('examples_exclude.txt', 'U').read().split('\n') exclude_list = [f for f in exclude_list if not f.startswith('#') and f] if os.path.exists('examples_completed.txt'): examples_completed = [f for f in open('examples_completed.txt', 'U').read().split('\n') if f] else: examples_completed = [] class RunTestCase(unittest.TestCase): ''' A test case that simply executes a python script and notes the execution of the script in a file `examples_completed.txt`. ''' def __init__(self, filename): unittest.TestCase.__init__(self) self.filename = filename def id(self): # Remove the .py and pretend the dirname is a package and the filename # is a class. name = os.path.splitext(os.path.split(self.filename)[1])[0] pkgname = os.path.split(os.path.split(self.filename)[0])[1] return pkgname + '.' + name.replace('.', '_') def shortDescription(self): return str(self) def runTest(self): # a simpler version of what the nosepipe plugin achieves: # isolate test execution in a subprocess: tempfilename = tempfile.mktemp('exception') # Catch any exception and save it to a temporary file code_string = """ # needed for some scripts that load data __file__ = '%(fname)s' import matplotlib as _mpl _mpl.use('Agg') import warnings, traceback, pickle, sys warnings.simplefilter('ignore') try: execfile('%(fname)s') except Exception, ex: traceback.print_exc(file=sys.stdout) f = open('%(tempfname)s', 'w') pickle.dump(ex, f, -1) f.close() """ % {'fname': self.filename, 'tempfname': tempfilename} args = [sys.executable, '-c', code_string] # Run the example in a new process and make sure that stdout gets # redirected into the capture plugin p = subprocess.Popen(args, stdout=subprocess.PIPE, stderr=subprocess.PIPE) stdout, stderr = p.communicate() sys.stdout.write(stdout) sys.stderr.write(stderr) # Re-raise any exception that occured if os.path.exists(tempfilename): f = open(tempfilename, 'r') ex = pickle.load(f) self.successful = False raise ex else: self.successful = True def tearDown(self): if self.successful: open('examples_completed.txt', 'a').write(self.filename + '\n') def __str__(self): return 'Example: ' + self.filename class SelectFilesPlugin(Plugin): ''' This plugin makes nose descend into all directories but skips files that are mentioned in examples_exclude.txt or examples_completed.txt Test cases simply consist of executing the script. ''' # no command line arg needed to activate plugin enabled = True name = "select-files" def configure(self, options, conf): pass # always on def wantDirectory(self, dirname): # we want all directories return True def find_examples(self, name): short_name = os.path.split(name)[1] examples = [] if short_name in exclude_list or name in examples_completed: return [] elif os.path.isdir(name): for subname in os.listdir(name): examples.extend(self.find_examples(os.path.join(name, subname))) return examples elif name.endswith('.py'): # only execute Python scripts return [name] else: return [] def loadTestsFromName(self, name, module=None, discovered=False): all_examples = self.find_examples(name) return [RunTestCase(example) for example in all_examples] argv = [__file__, '-v', '--with-xunit', '--verbose', '../../examples'] nose.main(argv=argv, plugins=[SelectFilesPlugin(), Capture(), Xunit()]) brian-1.4.3/dev/tests/alltests.py000066400000000000000000000001331266062647100167460ustar00rootroot00000000000000import os os.chdir('../../brian/tests') os.system('nosetests --logging-clear-handlers') brian-1.4.3/dev/tests/correctness/000077500000000000000000000000001266062647100170765ustar00rootroot00000000000000brian-1.4.3/dev/tests/correctness/COBAHH_spikes.txt.gz000066400000000000000000023347451266062647100226020ustar00rootroot00000000000000‹»ì$PÿCOBAHH_spikes.txt¬½Ir&;v9¯U伬ÂH€èTûßBÉïw¥üã yÒJCÉŽ@4A:›õkýñóÿþßkýW|þa¯óÿþ?Ëÿ¯ Äcâ`"0‘˜(L4&æÿ/î±1a˜pLL&…‰ÆÄ(âÃQžyëƒWbcÂ0á˜8˜L$&  êsÇ>wìsÇ>wìsÇ>wìsÇ>wìsÇ>wìóƒ}~°ÏöùÁ>?Øçûü`Ÿìóƒ}~°Ïû<°Ïû<°ÏCûüCxÚüK&…‰ÆÄ@"µÏ%±1a˜pLL&…‰ÆõyaŸöyaŸöyaŸöyaŸöyaŸöycŸ7öycŸ7öykŸþ+ûž &…‰ÆÄ@b´Ï%±1a˜pLL&…‰Æõ¹X“±+±1a˜pLL&…‰ÆÄPbcŸoåó«[ç7a˜pLL&…‰ÆÄPÂ&°Ï ûÜ°Ï ûÜ°Ï ûÜ°Ï ûÜ°ÏûܱÏûܱÏûÜ•Ï?~§¿‰ÄDa¢11”8 † ÇÄÁöùÁ>?Øçûü`Ÿöy`Ÿöy`Ÿöy`Ÿöy`ŸöyHŸÏï?ÇoßïÄÆ„aÂ1q0˜HL&Øç…}^Øç…}^Øç…}^Øç…}^Øç…}^Øç}Þ؃­¢äÃ"¶â›pLL&…‰ÆÄPb&°Ç0}>Øçƒ}>Øçƒ}>Ѓ¦×}4ãjññû“}Û‹s'6& Ž‰ƒ‰ÀDbb(±±?6öÇÆþØØûccl칧 Ü£ {а­ ÛÊy”ÈØý½dèôïVmL&‰‰ÂDc[W®Üi[×±uå:œ&غŽ­{°u¶îÁÖ=8vŽ]±ÊùǶžmßu‰Xå¼b=ñFL&C ±w#°­Ä:ÜpL`Ž«Àq•XÄ­JÞ*W…õh‰ƒ#q¸UüÜgÙõM %rabcÂ0q0m•ØV…5/¬G&XóÆ­ìó¶rµp#††[e¸U2‹ê~®|þ¹KïÌíËL$%zcÂ0q05o®9öà8&°[åjÅëFlL&‚뱋†e¶•c[ER"±‰c·”ŸÿéœÛ¹»+1…‰a„«Uç ± Ž‰ƒ‰À×[×p«,)áØŽýáغëq°uÇnâVnÕ@Ÿ=Fib(±q«Ì)Ž³¨">"Ô×OݾáêëÒ…ØX†´®$Ì11”ðÀÖC®'Jâ`Ín•\Ô¶n`붕\ËÐŽöÄzÈÙ&p*¬ycë”qô€$,)áX†7%–Ðç¡ç’pWKå’Ï™ù3WKJÈVib(qŒ[%Wî$ÑXFcÍå:œ& Œ£W 4˜hJlÇn•cÍË8XÆÁšËïj’H¬GbÖcp«æ«ÀÑ8'†%q%gj“üØ)›çv#Ð8˜(JÖ#91”(¬yaÍåz»&‚mu.U†"ä R¶0Áe& C Ç­ L¶U&p\5lU\êEì… Üªm˜JÈ5I¶Í¢³h\j†w"qfH½Š#‰„¹½ô ¤$gj™¯>÷ÐÛÏ}}Ž‰ƒ‰¡DáV–!s‰&°æò»³$k.¿’ âèïµ’ØC Ã2ä×%Iœ l«ƒõ8ØV5¤„üŠ¬ lݱ+kQA„þš! 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À4­Ï Š>¢ wÀÕº(ÀJH€w”ûŒ;› @ pú•X‰•èÀز¤G݇Â@áf6d”`â` àsÂÉÄrvQ@–|³ ïºAÀ€t@Ðõ]S t8€Î3à÷œ SÐw0»ØЄð¨hdB @ËÂì¢6dÀW'(gš¾“€¾ke˜8@^yš ‰@Ø@CîÈÐ,PBœûzîïtQ!nzãxG`u ¹Ô§ž€ŠrÀ>AÔ^Öçä|HŸ’Ôx( Ìsàλ†Dt%ƒ,¿jN,ü£šÎ@o®V¬$ƒèÎÿ¸²ÿŒf逨X?hÈ.JH€2T£]”;WQvИ@t°é ,E62€~H-gÀ¶ðþ’Hš>v€î€¤Xè@ßD°'ˆÂ¼€èrÖdðÌðÌD }`Y€2@Š6¾W ²DúôD!°_ß–`l€ÖЖPªŒ Æ„Àû¹ ¤(>BÀ`@—Õ „ 'ˆÀ÷+Ð2€¼]±è B"VA'—» YH`ý’¸A7˜Q(Ð^sX …V E@ë]¨(¦ò – TÐø‰ìí €ã Ä @À+ux;;9ÓÈI €&ÈDõ¬ƒnPHCXtÌ(ð*ΰ´%€/w4} h–(ÝÿSÀk<° Ã €U ðVËÐdP›×(û^EÀr€R@^ðÿ‘ð.Ôz ').lower().strip() if len(inp) == 0: continue if not inp == '+' and not inp == '*' and not inp == '-': print 'Bad input, skipping' files[f] = inp out = open('dev/tools/newrelease/tracking_data/' + name + '_files.txt', 'w') sorted_files = files.items() sorted_files.sort(key=lambda x:x[0]) for f, c in sorted_files: print >> out, c, f out.close() return files def get_files(name): files = apply_rules(name, allfiles) files = load_files(name, files) return files #### Compare files def compare_files(acc, files): ok = True strange = [] too_many = [] ask = [] for f in acc: try: c = files[f] if c == '-': too_many.append(f) elif c == '*': ask.append(f) del files[f] except KeyError: if 'docs/' not in f: strange.append(f) too_few = [f for f, c in files.iteritems() if c == '+'] ask_not_inc = [f for f, c in files.iteritems() if c == '*'] if len(strange): ok = False print 'Files included which are not tracked (check rules):' print for f in strange: print f print if len(too_many): ok = False print 'Files included which should not be:' print for f in too_many: print f print if len(ask): ok = False print 'Files included which you have marked to be asked about each time:' print for f in ask: print f print if len(too_few): ok = False print 'Files not included which should be:' print for f in too_few: print f print if len(ask_not_inc): ok = False print 'Files not included which you have marked to be asked about each time:' print for f in ask_not_inc: print f print return ok print 'RELEASE' print '=======' print if compare_files(release_files, get_files('release')): print 'OK' print print 'EXTRAS' print '======' print if compare_files(extras_files, get_files('extras')): print 'OK' print brian-1.4.3/dev/tools/newrelease/tracking_data/000077500000000000000000000000001266062647100214675ustar00rootroot00000000000000brian-1.4.3/dev/tools/newrelease/tracking_data/extras_files.txt000066400000000000000000000123361266062647100247250ustar00rootroot00000000000000+ examples/audition/filterbank.py + examples/audition/jeffress.py + examples/audition/licklider.py + examples/electrophysiology/aec.py + examples/electrophysiology/bridge.py + examples/electrophysiology/compensation_i.npy + examples/electrophysiology/compensation_vm.npy + examples/electrophysiology/current.npy + examples/electrophysiology/dcc.py + examples/electrophysiology/lp_compensation.py + examples/electrophysiology/offline_electrode_compensation.py + examples/electrophysiology/sevc.py + examples/electrophysiology/threshold_analysis.py + examples/electrophysiology/trace.npy + examples/electrophysiology/voltageclamp.py + examples/examples_guide.txt + examples/frompapers/brette_2004.py + examples/frompapers/brette_gerstner_2005.py + examples/frompapers/brette_guigon_2003.py + examples/frompapers/brunel_hakim_1999.py + examples/frompapers/diesmann_et_al_1999.py + examples/frompapers/diesmann_et_al_1999_longer.py + examples/frompapers/goodman_brette_2010.py + examples/frompapers/guetig_sompolinsky_2009.py + examples/frompapers/kremer_et_al_2011.py + examples/frompapers/muller_et_al_2011.py + examples/frompapers/plakiewicz_brette_2010.py + examples/frompapers/platkiewicz_brette_2011.py + examples/frompapers/rossant_et_al_2011.py + examples/frompapers/rossant_et_al_2011bis.py + examples/frompapers/rothman_manis_2003.py + examples/frompapers/sturzl_et_al_2000.py + examples/frompapers/touboul_brette_2008.py + examples/hears/approximate_gammatone.py + examples/hears/artificial_vowels.py + examples/hears/butterworth.py + examples/hears/cochleagram.py + examples/hears/cochlear_models.py + examples/hears/dcgc.py + examples/hears/drnl.py + examples/hears/gammatone.py + examples/hears/iirfilterbank.py + examples/hears/ircam_hrtf.py + examples/hears/linear_gammachirp.py + examples/hears/log_gammachirp.py + examples/hears/online_computation.py + examples/hears/simple_anf.py + examples/hears/sound_localisation_model.py + examples/hears/sounds.py + examples/hears/time_varying_filter1.py + examples/hears/time_varying_filter2.py + examples/interface/index.html + examples/interface/interface.py + examples/interface/results.html + examples/misc/adaptive.py + examples/misc/adaptive_threshold.py + examples/misc/after_potential.py + examples/misc/cable.py + examples/misc/coba.py + examples/misc/cobahh.py + examples/misc/correlated_inputs.py + examples/misc/correlated_inputs2.py + examples/misc/cuba.py + examples/misc/current_clamp.py - examples/misc/data.pkl + examples/misc/delays.py + examples/misc/expif_network.py + examples/misc/gap_junctions.py + examples/misc/heterogeneous_delays.py + examples/misc/hodgkinhuxley.py + examples/misc/i-f_curve.py + examples/misc/i-f_curve2.py + examples/misc/if.py - examples/misc/image.png + examples/misc/leaky_if.py + examples/misc/linked_var.py + examples/misc/minimalexample.py + examples/misc/mirollo_strogatz.py + examples/misc/multipleclocks.py + examples/misc/named_threshold.py + examples/misc/noisy_ring.py + examples/misc/non_reliability.py + examples/misc/phase_locking.py + examples/misc/poisson.py + examples/misc/poissongroup.py + examples/misc/pulsepacket.py + examples/misc/rate_model.py + examples/misc/realtime_plotting.py + examples/misc/reliability.py + examples/misc/remotecontrolclient.py + examples/misc/remotecontrolserver.py + examples/misc/ring.py + examples/misc/spike_triggered_average.py + examples/misc/stim2d.py + examples/misc/stopping.py + examples/misc/timed_array.py + examples/misc/topographic_map.py + examples/misc/topographic_map2.py - examples/misc/trace.txt + examples/misc/transient_sync.py + examples/misc/two_neurons.py + examples/misc/using_classes.py + examples/misc/van_rossum_metric.py - examples/modelfitting/current.pickle + examples/modelfitting/current.txt + examples/modelfitting/modelfitting.py + examples/modelfitting/modelfitting_groups.py + examples/modelfitting/modelfitting_machines.py + examples/modelfitting/spikes.txt - examples/mono_sound.wav + examples/multiprocessing/multiple_runs_simple.py + examples/multiprocessing/multiple_runs_with_gui.py + examples/multiprocessing/taskfarm.py - examples/multiprocessing/taskfarmexample.data/dan.dandell01 + examples/plasticity/short_term_plasticity.py + examples/plasticity/short_term_plasticity2.py + examples/plasticity/stdp1.py + examples/plasticity/stdp2.py - examples/stereo_sound.wav + tutorials/tutorial1_basic_concepts/introduction.txt + tutorials/tutorial1_basic_concepts/tutorial_1a_the_simplest_brian_program.py + tutorials/tutorial1_basic_concepts/tutorial_1b_counting_spikes.py + tutorials/tutorial1_basic_concepts/tutorial_1c_making_some_activity.py + tutorials/tutorial1_basic_concepts/tutorial_1d_introducing_randomness.py + tutorials/tutorial1_basic_concepts/tutorial_1e_connecting_neurons.py + tutorials/tutorial1_basic_concepts/tutorial_1f_recording_spikes.py + tutorials/tutorial1_basic_concepts/tutorial_1g_recording_membrane_potentials.py + tutorials/tutorial2_connections/introduction.txt + tutorials/tutorial2_connections/tutorial_2a_the_concept_of_a_connection.py + tutorials/tutorial2_connections/tutorial_2b_excitatory_and_inhibitory_currents.py + tutorials/tutorial2_connections/tutorial_2c_the_cuba_network.py brian-1.4.3/dev/tools/newrelease/tracking_data/extras_rules.txt000066400000000000000000000007551266062647100247570ustar00rootroot00000000000000# Each line is of the form include pattern or exclude pattern where pattern is # a regular expression, include adds files from the list of all files, and # exclude removes them, the includes should probably all go before the excludes include ^tutorials/ include ^examples/ exclude \.svn exclude \.pyc exclude \.o exclude \.obj exclude \.def exclude \.lib exclude build/ exclude \.pyd exclude ^\.buildinfo exclude ^examples/frompapers/data/ exclude ^examples/frompapers/imgs/ brian-1.4.3/dev/tools/newrelease/tracking_data/release_files.txt000066400000000000000000000220311266062647100250300ustar00rootroot00000000000000+ brian/__init__.py + brian/base.py + brian/clock.py + brian/compartments.py + brian/connection.py + brian/connections/__init__.py + brian/connections/base.py + brian/connections/connection.py + brian/connections/connectionmatrix.py + brian/connections/connectionvector.py + brian/connections/construction.py + brian/connections/constructionmatrix.py + brian/connections/delayconnection.py + brian/connections/otherconnections.py + brian/connections/propagation_c_code.py + brian/connections/sparsematrix.py + brian/deprecated/__init__.py + brian/directcontrol.py + brian/equations.py + brian/experimental/__init__.py + brian/experimental/c_stdp.py + brian/experimental/codegen/__init__.py + brian/experimental/codegen/c_support_code.py + brian/experimental/codegen/codegen.py + brian/experimental/codegen/codegen_c.py + brian/experimental/codegen/codegen_gpu.py + brian/experimental/codegen/codegen_python.py + brian/experimental/codegen/demo.py + brian/experimental/codegen/expressions.py + brian/experimental/codegen/integration_schemes.py - brian/experimental/codegen/known_issues.txt + brian/experimental/codegen/reset.py + brian/experimental/codegen/rewriting.py + brian/experimental/codegen/stateupdaters.py + brian/experimental/codegen/test_hh.py + brian/experimental/codegen/test_reset.py + brian/experimental/codegen/test_threshold.py + brian/experimental/codegen/threshold.py + brian/experimental/coincidence_detection.py + brian/experimental/compensation/__init__.py + brian/experimental/compensation/compare.py + brian/experimental/compensation/fast_compensation.py + brian/experimental/compensation/filter.py + brian/experimental/connectionmonitor.py + brian/experimental/cuda/__init__.py + brian/experimental/cuda/buffering.py + brian/experimental/cuda/gpucodegen.py - brian/experimental/dana/__init__.py - brian/experimental/dana/convolution_matrix.py + brian/experimental/integrodiff.py + brian/experimental/modelfitting/__init__.py + brian/experimental/modelfitting/benchmark.py + brian/experimental/modelfitting/compensation.py + brian/experimental/modelfitting/criteria.py + brian/experimental/modelfitting/gpu_modelfitting.py + brian/experimental/modelfitting/lp2bert.py + brian/experimental/modelfitting/modelfitting.py + brian/experimental/modelfitting/modelfitting_old.py + brian/experimental/modelfitting/simulator.py + brian/experimental/morphology.py - brian/experimental/mp_ma_40984_gc2.cng.swc + brian/experimental/multilinearstateupdater.py + brian/experimental/neuromorphic/__init__.py + brian/experimental/neuromorphic/aer.py + brian/experimental/neuromorphic/example_aer.py - brian/experimental/neuromorphic/notes.txt + brian/experimental/neuromorphic/realtime.py + brian/experimental/neuromorphic/spikequeue.py + brian/experimental/neuromorphic/test_aerspikemonitor.py + brian/experimental/neuromorphic/test_newspikegengroup.py + brian/experimental/neuromorphic/test_struct_vs_np.py + brian/experimental/new_c_propagate.py - brian/experimental/oi24rpy1.cng.swc + brian/experimental/realtime_monitor.py + brian/experimental/spatialneuron.py + brian/experimental/stdp/__init__.py + brian/experimental/stdp/eventbased_stdp.py + brian/experimental/stdp/stdp1.py + brian/experimental/stochasticconnection.py + brian/experimental/trace_analysis.py + brian/globalprefs.py + brian/group.py + brian/hears/__init__.py + brian/hears/bufferable.py + brian/hears/db.py + brian/hears/erb.py + brian/hears/filtering/__init__.py + brian/hears/filtering/dcgc.py + brian/hears/filtering/drnl.py + brian/hears/filtering/filterbank.py + brian/hears/filtering/filterbankgroup.py + brian/hears/filtering/filterbanklibrary.py + brian/hears/filtering/firfilterbank.py + brian/hears/filtering/fractionaldelay.py + brian/hears/filtering/gpulinearfilterbank.py + brian/hears/filtering/gputools.py + brian/hears/filtering/linearfilterbank.py + brian/hears/filtering/tan_carney.py + brian/hears/filtering/test.py + brian/hears/filtering/test2.py + brian/hears/filtering/zilany.py + brian/hears/hrtf/__init__.py + brian/hears/hrtf/hrtf.py + brian/hears/hrtf/ircam.py + brian/hears/hrtf/itd.py + brian/hears/onlinesounds.py + brian/hears/prefs.py + brian/hears/sounds.py - brian/hears/todo.txt + brian/inspection.py + brian/library/__init__.py + brian/library/electrophysiology/__init__.py + brian/library/electrophysiology/electrode_compensation.py + brian/library/electrophysiology/electrophysiology_models.py + brian/library/electrophysiology/trace_analysis.py + brian/library/if.py + brian/library/ionic_currents.py + brian/library/modelfitting/__init__.py - brian/library/modelfitting/current_long.txt + brian/library/modelfitting/gpu_modelfitting.py + brian/library/modelfitting/gputools.py + brian/library/modelfitting/modelfitting.py - brian/library/modelfitting/new/clusterfitting.py - brian/library/modelfitting/new/clustersplitting.py - brian/library/modelfitting/new/current.txt - brian/library/modelfitting/new/fittingmanager.py - brian/library/modelfitting/new/fittingoptimization.py - brian/library/modelfitting/new/fittingoptimization_sample.py - brian/library/modelfitting/new/fittingparameters.py - brian/library/modelfitting/new/fittingsimulation.py - brian/library/modelfitting/new/fittingworker.py - brian/library/modelfitting/new/modelfitting.py - brian/library/modelfitting/new/new.txt - brian/library/modelfitting/new/spikes.txt - brian/library/modelfitting/old/__init__.py - brian/library/modelfitting/old/cluster_splitting.py - brian/library/modelfitting/old/clustertools.py - brian/library/modelfitting/old/fittingparameters.py - brian/library/modelfitting/old/gpu_modelfitting.py - brian/library/modelfitting/old/modelfitting.py - brian/library/modelfitting/old/singleprocessing.py - brian/library/modelfitting/singleprocessing.py - brian/library/modelfitting/spikes_long.txt + brian/library/random_processes.py + brian/library/synapses.py - brian/license.txt + brian/log.py + brian/magic.py + brian/membrane_equations.py + brian/monitor.py + brian/network.py + brian/neurongroup.py - brian/new_features.txt + brian/optimiser.py + brian/plotting.py + brian/reset.py + brian/stateupdater.py + brian/stdp.py + brian/stdunits.py + brian/stp.py + brian/tests/__init__.py + brian/tests/simpletest.py + brian/tests/testcorrectness/__init__.py + brian/tests/testcorrectness/test_epsp.py + brian/tests/testcorrectness/test_exponential_current.py + brian/tests/testcorrectness/test_from_tutorial1c.py + brian/tests/testfeatures/__init__.py + brian/tests/testfeatures/test_connect.py + brian/tests/testfeatures/test_propagation.py + brian/tests/testinterface/__init__.py + brian/tests/testinterface/test_clock.py + brian/tests/testinterface/test_connection.py + brian/tests/testinterface/test_directcontrol.py + brian/tests/testinterface/test_equations.py + brian/tests/testinterface/test_inspection.py + brian/tests/testinterface/test_magic.py + brian/tests/testinterface/test_monitor.py + brian/tests/testinterface/test_neurongroup.py + brian/tests/testinterface/test_reset.py + brian/tests/testinterface/test_statistics.py + brian/tests/testinterface/test_threshold.py + brian/tests/testinterface/test_units.py + brian/tests/testinterface/test_unitsafefunctions.py + brian/tests/testutils/__init__.py + brian/tests/testutils/test_sparse.py + brian/tests/testutils/test_statistics.py + brian/threshold.py + brian/timedarray.py + brian/tools/__init__.py + brian/tools/autodiff.py + brian/tools/correlatedspikes.py + brian/tools/datamanager.py + brian/tools/information_theory.py + brian/tools/io.py + brian/tools/parameters.py + brian/tools/particle_swarm.py + brian/tools/remotecontrol.py + brian/tools/statistics.py + brian/tools/tabulate.py + brian/tools/taskfarm.py + brian/units.py + brian/unitsafefunctions.py + brian/utils/__init__.py + brian/utils/approximatecomparisons.py + brian/utils/ccircular/__init__.py + brian/utils/ccircular/ccircular.h + brian/utils/ccircular/ccircular.i + brian/utils/ccircular/ccircular.py + brian/utils/ccircular/ccircular_wrap.cxx + brian/utils/ccircular/circular.cpp + brian/utils/ccircular/numpy.i + brian/utils/ccircular/setup.py + brian/utils/ccircular/swigcompile.bat + brian/utils/circular.py + brian/utils/documentation.py + brian/utils/fastexp/__init__.py + brian/utils/fastexp/fastexp.cpp + brian/utils/fastexp/fastexp.h + brian/utils/fastexp/fastexp.i + brian/utils/fastexp/fastexp.py + brian/utils/fastexp/fastexp_wrap.cxx + brian/utils/fastexp/fexp.c + brian/utils/fastexp/fexp.h + brian/utils/fastexp/numpy.i + brian/utils/fastexp/setup.py + brian/utils/fastexp/swigcompile.bat + brian/utils/fastexp/testfastexp.py + brian/utils/fastexp/testfastexp2.py - brian/utils/parameters.py + brian/utils/progressreporting.py + brian/utils/separate_equations.py + brian/utils/sparse_patch/0_6_0.py + brian/utils/sparse_patch/0_7_0.py + brian/utils/sparse_patch/0_7_1.py + brian/utils/sparse_patch/__init__.py + brian_no_units.py + brian_no_units_no_warnings.py + brian_unit_prefs.py - license.txt - manifest.in + readme.txt + setup.py brian-1.4.3/dev/tools/newrelease/tracking_data/release_rules.txt000066400000000000000000000007131266062647100250630ustar00rootroot00000000000000# Each line is of the form include pattern or exclude pattern where pattern is # a regular expression, include adds files from the list of all files, and # exclude removes them, the includes should probably all go before the excludes include ^[^/]*\. include ^brian/ exclude \.svn exclude \.pyc exclude \.o exclude \.obj exclude \.def exclude \.lib exclude build/ exclude \.pyd exclude ^\.project exclude ^\.settings/ exclude ^brian/unused/ brian-1.4.3/dev/tools/newrelease/update_release_date.py000066400000000000000000000007731266062647100232340ustar00rootroot00000000000000import os, sys, brian, re, datetime def setreleasedate(): releasedate = str(datetime.date.today()) pathname = os.path.abspath(os.path.dirname(__file__)) os.chdir(pathname) os.chdir('../../../') # update __init__.py init_py = open('brian/__init__.py', 'r').read() init_py = re.sub("__release_date__\s*=\s*'.*?'", "__release_date__ = '" + releasedate + "'", init_py) open('brian/__init__.py', 'w').write(init_py) if __name__ == '__main__': setreleasedate() brian-1.4.3/dev/tools/searchreplace/000077500000000000000000000000001266062647100173435ustar00rootroot00000000000000brian-1.4.3/dev/tools/searchreplace/insert_licence.py000066400000000000000000000020251266062647100227020ustar00rootroot00000000000000''' Inserts licence information in Brian modules. ''' import os, re os.chdir('../../../brian') # work from brian/ package directory exclude = ['insert_licence.py', 'autodiff.py'] license = open('license.txt').read() # Add comments pattern = re.compile('^', flags=re.M) license = pattern.sub('# ', license) + '\n' def insert_license(filename): f_in = open(filename) text = f_in.read() f_in.close() if re.search('Copyright', text) is None: f_out = open(filename, 'w') f_out.write(license + text) f_out.close() return True else: return False def insert_all(verbose=True): # Get all source files files = [file for file in os.listdir('') if file[-3:] == '.py' and (file not in exclude)] + \ ['utils/' + file for file in os.listdir('utils') if file[-3:] == '.py' and (file not in exclude)] for file in files: if insert_license(file) and verbose: print file if __name__ == '__main__': insert_all() brian-1.4.3/dev/tools/searchreplace/stylepatch.py000066400000000000000000000125011266062647100220740ustar00rootroot00000000000000''' Apply style changes to all Brian modules. From PEP-8 (Python naming conventions): --------------------------------------- Package and Module Names Modules should have short, all-lowercase names. Underscores can be used in the module name if it improves readability. Python packages should also have short, all-lowercase names, although the use of underscores is discouraged. Since module names are mapped to file names, and some file systems are case insensitive and truncate long names, it is important that module names be chosen to be fairly short -- this won't be a problem on Unix, but it may be a problem when the code is transported to older Mac or Windows versions, or DOS. When an extension module written in C or C++ has an accompanying Python module that provides a higher level (e.g. more object oriented) interface, the C/C++ module has a leading underscore (e.g. _socket). Class Names Almost without exception, class names use the CapWords convention. Classes for internal use have a leading underscore in addition. Exception Names Because exceptions should be classes, the class naming convention applies here. However, you should use the suffix "Error" on your exception names (if the exception actually is an error). Global Variable Names (Let's hope that these variables are meant for use inside one module only.) The conventions are about the same as those for functions. Modules that are designed for use via "from M import *" should use the __all__ mechanism to prevent exporting globals, or use the older convention of prefixing such globals with an underscore (which you might want to do to indicate these globals are "module non-public"). Function Names Function names should be lowercase, with words separated by underscores as necessary to improve readability. mixed_case is allowed only in contexts where that's already the prevailing style (e.g. threading.py), to retain backwards compatibility. Function and method arguments Always use 'self' for the first argument to instance methods. Always use 'cls' for the first argument to class methods. If a function argument's name clashes with a reserved keyword, it is generally better to append a single trailing underscore rather than use an abbreviation or spelling corruption. Thus "print_" is better than "prnt". (Perhaps better is to avoid such clashes by using a synonym.) Method Names and Instance Variables Use the function naming rules: lowercase with words separated by underscores as necessary to improve readability. Use one leading underscore only for non-public methods and instance variables. To avoid name clashes with subclasses, use two leading underscores to invoke Python's name mangling rules. Python mangles these names with the class name: if class Foo has an attribute named __a, it cannot be accessed by Foo.__a. (An insistent user could still gain access by calling Foo._Foo__a.) Generally, double leading underscores should be used only to avoid name conflicts with attributes in classes designed to be subclassed. Note: there is some controversy about the use of __names (see below). ''' import os, re os.chdir('..') os.chdir('..') os.chdir('..') # work from Brian's root #package_names={'Brian':'brian'} #module_names={'BrianNoUnits':'brian_no_units'} specific_changes = {'is_number_type':'isNumberType', 'is_sequence_type':'isSequenceType', 'assert_raises':'assertRaises', 'BrianNoUnits':'brian_no_units', 'BrianNoUnitsNoWarnings':'brian_no_units_no_warnings', 'BrianUnitPrefs':'brian_unit_prefs', 'from brian':'from brian', 'Library':'library'} def bad_identifiers(text): return set(re.findall(r'\b[a-z0-9_]+[A-Z]\w+\b', text)) def replace_bad_identifiers(text, names): text2 = text for name in names: new_name = re.sub('[A-Z]', lambda c:'_' + c.group(0).lower(), name) text2 = re.sub(name, new_name, text2) return text2 def make_specific_changes(text): text2 = text for name1, name2 in specific_changes.iteritems(): text2 = re.sub(name1, name2, text2) return text2 def patch_file(filename): f_in = open(filename) text = f_in.read() f_in.close() names = bad_identifiers(text) text_out = text if names != set([]): text_out = replace_bad_identifiers(text_out, names) text_out = make_specific_changes(text_out) if text != text_out: f_out = open(filename, 'w') f_out.write(text_out) f_out.close() def patch_all_files(verbose=True): # Get all source files files = [] for dirpath, dirnames, filenames in os.walk('.'): files.extend([dirpath + '\\' + file for file in filenames if file[-3:] == '.py']) for file in files: if file[-13:] != 'stylepatch.py': if verbose: print file patch_file(file) if __name__ == '__main__': patch_all_files() brian-1.4.3/dev/tools/static_codegen/000077500000000000000000000000001266062647100175155ustar00rootroot00000000000000brian-1.4.3/dev/tools/static_codegen/units.py000066400000000000000000000122431266062647100212330ustar00rootroot00000000000000import os, re curdir, _ = os.path.split(__file__) fundamentalunits_fname = os.path.normpath(os.path.join(curdir, '../../../brian/fundamentalunits.py')) units_fname = os.path.normpath(os.path.join(curdir, '../../../brian/units.py')) _siprefixes = {"y":1e-24, "z":1e-21, "a":1e-18, "f":1e-15, "p":1e-12, "n":1e-9, "u":1e-6, "m":1e-3, "c":1e-2, "d":1e-1, \ "":1, \ "da":1e1, "h":1e2, "k":1e3, "M":1e6, "G":1e9, "T":1e12, "P":1e15, "E":1e18, "Z":1e21, "Y":1e24} fundamental_units = [ 'metre', 'meter', 'gram', 'second', 'amp', 'kelvin', 'mole', 'candle' ] #### DERIVED UNITS, from http://physics.nist.gov/cuu/Units/units.html derived_unit_table = [ [ 'radian', 'rad', 'Dimension()' ], [ 'steradian', 'sr', 'Dimension()' ], [ 'hertz', 'Hz', 'Dimension(s= -1)' ], [ 'newton', 'N', 'Dimension(m=1, kg=1, s= -2)' ], [ 'pascal', 'Pa', 'Dimension(m= -1, kg=1, s= -2)' ], [ 'joule', 'J', 'Dimension(m=2, kg=1, s= -2)' ], [ 'watt', 'W', 'Dimension(m=2, kg=1, s= -3)' ], [ 'coulomb', 'C', 'Dimension(s=1, A=1)' ], [ 'volt', 'V', 'Dimension(m=2, kg=1, s= -3, A= -1)' ], [ 'farad', 'F', 'Dimension(m= -2, kg= -1, s=4, A=2)' ], [ 'ohm', 'ohm', 'Dimension(m=2, kg=1, s= -3, A= -2)' ], [ 'siemens', 'S', 'Dimension(m= -2, kg= -1, s=3, A=2)' ], [ 'weber', 'Wb', 'Dimension(m=2, kg=1, s= -2, A= -1)' ], [ 'tesla', 'T', 'Dimension(kg=1, s= -2, A= -1)' ], [ 'henry', 'H', 'Dimension(m=2, kg=1, s= -2, A= -2)' ], [ 'celsius', 'degC', 'Dimension(K=1)' ], [ 'lumen', 'lm', 'Dimension(cd=1)' ], [ 'lux', 'lx', 'Dimension(m= -2, cd=1)' ], [ 'becquerel', 'Bq', 'Dimension(s= -1)' ], [ 'gray', 'Gy', 'Dimension(m=2, s= -2)' ], [ 'sievert', 'Sv', 'Dimension(m=2, s= -2)' ], [ 'katal', 'kat', 'Dimension(s= -1, mol=1)' ], ] additional_units = ''' # Current list from http://physics.nist.gov/cuu/Units/units.html, far from complete additional_units = [ pascal * second, newton * metre, watt / metre ** 2, joule / kelvin, joule / (kilogram * kelvin), joule / kilogram, watt / (metre * kelvin), joule / metre ** 3, volt / metre ** 3, coulomb / metre ** 3, coulomb / metre ** 2, farad / metre, henry / metre, joule / mole, joule / (mole * kelvin), coulomb / kilogram, gray / second, katal / metre ** 3 ] ''' ## Generate derived unit objects and make a table of base units from these and the fundamental ones base_units = fundamental_units+['gramme', 'kilogram'] derived = '' for longname, shortname, definition in derived_unit_table: derived += '{longname} = Unit.create({definition}, "{longname}", "{shortname}")\n'.format( longname=longname, shortname=shortname, definition=definition) base_units.append(longname) all_units = base_units + [] definitions = '######### SCALED BASE UNITS ###########\n' # Generate scaled units for all base units scaled_units = [] excluded_scaled_units = set() for _bu in base_units: for _k in _siprefixes.keys(): if len(_k): _u = _k+_bu all_units.append(_u) definitions += '{_u} = Unit.create_scaled_unit({_bu}, "{_k}")\n'.format( _u=_u, _bu=_bu, _k=_k) if not _k in ["da", "d", "c", "h"]: scaled_units.append(_u) else: excluded_scaled_units.add(_u) # Generate 2nd and 3rd powers for all scaled base units definitions += '######### SCALED BASE UNITS TO POWERS ###########\n' powered_units = [] for bu in all_units + []: for i in [2, 3]: u = bu+str(i) definitions += '{u} = {bu}**{i}\n'.format(u=u, bu=bu, i=i) definitions += '{u}.name = "{u}"\n'.format(u=u) all_units.append(u) if bu not in excluded_scaled_units: powered_units.append(u) # Handle the removal of all units definitions += '\nif not bup.use_units:\n' for u in all_units: definitions += ' {u} = float({u})\n'.format(u=u) # Add unit names to __all__ # start with __all__ from fundamentalunits fu = open(fundamentalunits_fname, 'r').read() fundamental_all = re.findall(r'\_\_all\_\_ = \[(.*?)\]', fu, re.MULTILINE|re.DOTALL)[0] all = ''' __all__ = [{fundamental_all} {allunits} ] '''.format(fundamental_all=fundamental_all, allunits='\n'.join(' "'+u+'",' for u in all_units)) def to_definition(name, x): return ''' {name} = [ {items} ] '''.format(name=name, items='\n'.join(' '+i+',' for i in x)) template = open('units_template.py').read() units_str = template.format( derived=derived, all=all, definitions=definitions, base_units=to_definition('base_units', base_units), scaled_units=to_definition('scaled_units', scaled_units), powered_units=to_definition('powered_units', powered_units), all_units=to_definition('all_units', all_units), additional_units=additional_units, ) open(units_fname, 'w').write(units_str) brian-1.4.3/dev/tools/static_codegen/units_template.py000066400000000000000000000062311266062647100231260ustar00rootroot00000000000000''' THIS FILE IS AUTOMATICALLY GENERATED BY A STATIC CODE GENERATION TOOL DO NOT EDIT BY HAND Instead edit the template in the SVN: /dec/tools/static_codegen/units_template.py ''' from fundamentalunits import * from fundamentalunits import standard_unit_register, additional_unit_register import fundamentalunits from brian_unit_prefs import bup fundamentalunits.automatically_register_units = False #### FUNDAMENTAL UNITS metre = Unit.create(Dimension(m=1), "metre", "m") meter = Unit.create(Dimension(m=1), "meter", "m") kilogram = Unit.create(Dimension(kg=1), "kilogram", "kg") gram = Unit.create_scaled_unit(kilogram, "m") gram.set_name('gram') gram.set_display_name('g') gramme = Unit.create_scaled_unit(kilogram, "m") gramme.set_name('gramme') gramme.set_display_name('g') second = Unit.create(Dimension(s=1), "second", "s") amp = Unit.create(Dimension(A=1), "amp", "A") kelvin = Unit.create(Dimension(K=1), "kelvin", "K") mole = Unit.create(Dimension(mol=1), "mole", "mol") candle = Unit.create(Dimension(candle=1), "candle", "cd") fundamental_units = [ metre, meter, gram, second, amp, kelvin, mole, candle ] {derived} {all} {definitions} {base_units} {scaled_units} {powered_units} {additional_units} {all_units} fundamentalunits.automatically_register_units = True map(standard_unit_register.add, base_units + scaled_units + powered_units) map(additional_unit_register.add, additional_units) if __name__ == "__main__": from numpy import * # shorthand function used for example code below def pE(vname, str): uname = vname if vname == "": uname = "temp" exec(uname + "=" + str) if vname != "": print vname, "=", print str, if locals()[uname] != None: print '=', locals()[uname] else: print return locals()[uname] V = pE("V", "3 * volt") I = pE("I", "2 * amp") a = pE("a", "array([1,2,3])") print R = pE("R", "V/I") pE("", "I*R") print pE("", "a*V") pE("", "V*a") pE("", "a+V") print pE("", "1000*metre") pE("", "1000*mmetre") print pE("", "(2*volt).in_unit(mvolt)") pE("", "(2*volt)/mvolt") print "(2*volt).in_unit(amp) =", try: print (2 * volt).in_unit(amp) except DimensionMismatchError, i: print "DimensionMismatchError:", i print pE("", "have_same_dimensions(1*volt,1*amp*ohm)") pE("", "have_same_dimensions(1*volt*second,1*amp*ohm)") print pE("", "(ufarad/nmetre)**2") print pE("", "2.0*farad/metre**2") pE("", "register_new_unit(pfarad / mmetre**2)") pE("", "2.0*farad/metre**2") print print "Some decorator examples (see code):" print @check_units(I=amp, R=ohm, wibble=metre, result=volt) def getvoltage(I, R, *args, **k): return I * R try: print getvoltage(1 * amp, 2 * ohm, 20) print getvoltage(R=2 * ohm, I=1 * amp, wibble=7 * mmetre) print getvoltage(1 * amp, 2 * ohm * metre) except DimensionMismatchError, inst: print "DME:", inst print pE("", "get_unit(3*msecond)") brian-1.4.3/dev/unfinished_examples/000077500000000000000000000000001266062647100174345ustar00rootroot00000000000000brian-1.4.3/dev/unfinished_examples/frompapers/000077500000000000000000000000001266062647100216125ustar00rootroot00000000000000brian-1.4.3/dev/unfinished_examples/frompapers/Izhikevich_2006_Polychronization.py000066400000000000000000000145431266062647100303720ustar00rootroot00000000000000''' Polychronization: Computation with Spikes Eugene M. Izhikevich Neural Computation, 2006 This script shows a way to reproduce Izhikevich's network using Brian. Note that there are a couple of slightly tricky technical points necessary to reproduce Izhikevich's particular numerical integration scheme and STDP rule. These are explained below. This version of the script demonstrates the simplest version of the code possible, the longer version of this script shows a much faster version which saves the output and analyses the polychronous groups formed. ''' # This is to use interactive plotting import matplotlib matplotlib.use('WXAgg') # You may need to experiment, try WXAgg, GTKAgg, QTAgg, TkAgg from brian import * import random dt = defaultclock.dt = .5 * ms I_factor = float((1*ms)/dt) M = 100 # number of synapses per neuron D = 20 * ms # maximal conduction delay D_min = 1 * ms # minimal conduction delay D_i = 1 * ms # inhibitory neuron conduction delay # excitatory neurons Ne = 800 a_e = 0.02 / ms d_e = 8 * mV / ms s_e = 6 * mV / ms * I_factor # inhibitory neurons Ni = 200 a_i = 0.1 / ms d_i = 2 * mV / ms s_i = -5 * mV / ms * I_factor # all neurons b = 0.2 / ms c = -65 * mV sm = 10 * mV / ms * I_factor # maximal synaptic strength N = Ne + Ni thalamic_input_weight = 20 * mV / ms# * I_factor eqs = Equations(''' dv/dt = (0.04/ms/mV)*v**2+(5/ms)*v+140*mV/ms-u+I : volt du/dt = a*(b*v-u) : volt/second a : 1/second d : volt/second I : volt/second ''') reset = ''' v = c u += d ''' threshold = 30 * mV G = NeuronGroup(N, eqs, threshold=threshold, reset=reset) # Izhikevich's numerical integration scheme is not built in to Brian, but # we can specify it explicitly. By setting the NeuronGroup._state_updater # attribute to a function f(G), we replace the default numerical integration # scheme (Euler) defined by Brian. This function can run arbitrary Python # code. def Izhikevich_scheme(G): G.v += 0.5 * dt * ((0.04 / ms / mV) * G.v ** 2 + (5 / ms) * G.v + 140 * mV / ms - G.u + G.I) G.v += 0.5 * dt * ((0.04 / ms / mV) * G.v ** 2 + (5 / ms) * G.v + 140 * mV / ms - G.u + G.I) G.u += dt * G.a * (b * G.v - G.u) G._state_updater = Izhikevich_scheme Ge = G.subgroup(Ne) Gi = G.subgroup(Ni) G.v = c G.u = b * c Ge.a = a_e Ge.d = d_e Gi.a = a_i Gi.d = d_i # In Izhikevich's simulation, the value of I is set to zero each time step, # then a 'thalamic input' is added to I, then spikes cause a postsynaptic # effect on I, and then the numerical integration step is performed. In # Brian, the numerical integration step is performed before spike propagation, # so we insert the thalamic input after the numerical integration but before # the spike propagation, using the when='before_connections' setting. change_every = int(1*ms/dt) change_time = 0 thal_ind = 0 @network_operation(when='before_connections') def thalamic_input(): global change_time, thal_ind G.I = 0 * mV / ms G.I[thal_ind] = float(thalamic_input_weight) if change_time==0: change_time = change_every thal_ind = randint(N) #G.I[randint(N)] = float(thalamic_input_weight*1*ms/dt) change_time -= 1 Ce = Connection(Ge, G, 'I', delay=True, max_delay=D) Ci = Connection(Gi, Ge, 'I', delay=D_i) # In order to implement Izhikevich's STDP rule, we need two weight matrices, # one is the actual weight matrix and the second is a weight matrix derivative. # To do this with Brian's sparse matrices, we create a second Connection # Ce_deriv, which we initialise to have the same shape as Ce, but we use the # forget() function on the Connection so that although the object exists, # no spikes will be propagated by that Connection. We need the object to exist # because we create an ExponentialSTDP object that acts on Ce_deriv not # directly on Ce. Ce_deriv = Connection(Ge, G, 'I', delay=True, max_delay=D) forget(Ce_deriv) for i in xrange(Ne): inds = random.sample(xrange(N), M) Ce[i, inds] = s_e * ones(len(inds)) Ce.delay[i, inds] = rand(len(inds)) * (D - D_min) + D_min for i in xrange(Ni): inds = random.sample(xrange(Ne), M) Ci[i, inds] = s_i * ones(len(inds)) # Now we duplicate Ce to Ce_deriv for i in xrange(Ne): Ce_deriv[i, :] = Ce[i, :] Ce_deriv.delay[i, :] = Ce.delay[i, :] # STDP acts directly on Ce_deriv rather than Ce. With this STDP rule alone, # we would not see any learning, the network operation below propagates changes # in Ce_deriv to Ce. artificial_wmax = 1e10 * mV / ms stdp = ExponentialSTDP(Ce_deriv, taup=20 * ms, taum=20 * ms, Ap=(0.1 * mV / ms / artificial_wmax), Am=(-0.12 * mV / ms / artificial_wmax), wmin= -artificial_wmax, wmax=artificial_wmax, interactions='nearest' ) # Izhikevich's STDP rule has STDP acting on a matrix sd of derivatives, and # then each second the weight matrix s and derivates sd are updated according # to the rule: # s <- s+0.01+sd # sd <- sd*0.9 # Note also that we are using Brian's sparse matrices, but they are have # exactly the same nonzero entries, and therefore we can do arithmetic # operations on these matrices using the alldata attribute of Brian sparse # matrices. The compress() method converts the ConstructionMatrix into a # ConnectionMatrix, thus freezing the nonzero entries. In the next line, we # want the actual starting values of Ce_deriv to be zero, but if we had done # this before compressing to a ConnectionMatrix, all entries would be # considered zero entries in the sparse matrix, and then Ce and Ce_deriv would # have a different pattern of non-zero entries. Ce_deriv.compress() Ce_deriv.W.alldata[:] = 0 @network_operation(clock=EventClock(dt=1 * second)) def update_weights_from_derivative(): Ce.W.alldata[:] = clip(Ce.W.alldata + 0.01 * mV / ms + Ce_deriv.W.alldata, 0, sm) Ce_deriv.W.alldata[:] *= 0.9 M = SpikeMonitor(G) ion() raster_plot(M, refresh=.2 * second, showlast=1 * second) run(50 * second, report='stderr') ioff() show() brian-1.4.3/dev/unfinished_examples/frompapers/Izhikevich_2006_Polychronization_longer.py000066400000000000000000000166721266062647100317450ustar00rootroot00000000000000''' Polychronization: Computation with Spikes Eugene M. Izhikevich Neural Computation, 2006 See Izhikevich_2006_Polychronization.py for an explanation of this code. In this version, we use various tricks to make the simulation much faster, including inline C++ code. We also save the progress as the simulation runs, and analyse it at the end for polychronous groups. Note that you will need to create two folders data and imgs to run this script. ''' import brian_no_units_no_warnings from brian import * import random from scipy import weave import cPickle as pickle try: imported_data = pickle.load(open('data/izhikevich.pickle', 'rb')) defaultclock.t = imported_data['t'] print 'Starting from saved progress at time', defaultclock.t except IOError: imported_data = {} dt = defaultclock.dt = 0.1 * ms M = 100 # number of synapses per neuron D = 20 * ms # maximal conduction delay D_min = 1 * ms # minimal conduction delay D_i = 1 * ms # inhibitory neuron conduction delay # excitatory neurons Ne = 800 a_e = 0.02 / ms d_e = 8 * mV / ms s_e = 6 * mV / dt # ms # inhibitory neurons Ni = 200 a_i = 0.1 / ms d_i = 2 * mV / ms s_i = -5 * mV / dt # ms # all neurons b = 0.2 / ms c = -65 * mV sm = 10 * mV / dt # ms # maximal synaptic strength N = Ne + Ni thalamic_input_weight = 20 * mV / dt # ms eqs = Equations(''' dv/dt = (0.04/ms/mV)*v**2+(5/ms)*v+140*mV/ms-u+I : volt du/dt = a*(b*v-u) : volt/second a : 1/second d : volt/second I : volt/second ''') reset = ''' v = c u += d ''' threshold = 30 * mV G = NeuronGroup(N, eqs, threshold=threshold, reset=reset) def make_Izhikevich_scheme(): c1 = 0.04 / ms / mV c2 = 5 / ms c3 = 140 * mV / ms # Izhikevich's numerical integration scheme def Izhikevich_scheme(G): G.v += (0.5 * dt) * (c1 * G.v ** 2 + c2 * G.v + c3 - G.u + G.I) G.v += (0.5 * dt) * (c1 * G.v ** 2 + c2 * G.v + c3 - G.u + G.I) G.u += dt * G.a * (b * G.v - G.u) weave_code = ''' for(int i=0; i 1. / 100).nonzero()[0]) plot(1 / edges[emin:], ac[emin:]) gcf().savefig('imgs/iz' + s + '.png') pickle.dump(M.spikes, open('data/iz_spikes' + s + '.pickle', 'wb'), -1) M.reinit() print 'Plotted up to time', s @network_operation(clock=EventClock(dt=100 * second, t=defaultclock.t + 100 * second)) def save_progress(): s = str(int((defaultclock.t + .5 * ms) / second)) imported_data['G._S'] = G._S imported_data['t'] = defaultclock.t exc_inds = [Ce.W[i, :].ind for i in range(Ne)] exc_weights = [asarray(Ce.W[i, :]) for i in range(Ne)] exc_delays = [asarray(Ce.delay[i, :]) for i in range(Ne)] inh_inds = [Ci.W[i, :].ind for i in range(Ni)] inh_weights = [asarray(Ci.W[i, :]) for i in range(Ni)] imported_data['exc_inds'] = exc_inds imported_data['exc_weights'] = exc_weights imported_data['exc_delays'] = exc_delays imported_data['inh_inds'] = inh_inds imported_data['inh_weights'] = inh_weights pickle.dump(imported_data, open('data/izhikevich.pickle', 'wb'), -1) print 'Saved progress up to time', s run(3600 * second - defaultclock.t, report='stderr') brian-1.4.3/dev/unfinished_examples/frompapers/Naundorf.py000066400000000000000000000017601266062647100237440ustar00rootroot00000000000000""" Naundorf et al. (2006) Unique features... Nature. Doesn't work... typos? """ from brian import * mS=msiemens v12A=-35*mV kA=6*mV invtauA=1/(0.1*ms) invtauI=1/(0.5*ms) v12C=80*mV # -80 mV? kC=4*mV invtauC=1/(30*ms) C=1*uF gL=2*mS VL=-80*mV gNa=68.4*mS VNa=50*mV I0=0*uA sigma=12*uA K=1000 J=3.2*mV tau=50*ms eqs=""" dv/dt=(gL*(VL-v)+gNa*o*(VNa-v)+I0+sigma*z)/C : volt dz/dt=-z/tau+tau**-.5*xi : 1 # noisy current do/dt=alphaA*(h-o)-(invtauI+betaA)*o : 1 # open channels dh/dt=alphaC*(1-h)-betaC*(h-o)-invtauI*o : 1 # available channels # Channel kinetics alphaA=invtauA/(1+exp(-(v+K*J*o-v12A)/kA)) : Hz betaA=invtauA/(1+exp((v+K*J*o-v12A)/kA)) : Hz alphaC=invtauC/(1+exp(-(v-v12C)/kC)) : Hz # I corrected * to / betaC=invtauC/(1+exp((v-v12C)/kC)) : Hz # idem """ neuron=NeuronGroup(1,eqs,threshold=0*mV,reset=VL) neuron.v=VL neuron.o=0 neuron.h=1 M=StateMonitor(neuron,'v',record=0) run(2*second,report='text') plot(M.times/ms,M[0]/mV) show() brian-1.4.3/dev/unfinished_examples/frompapers/Naundorf2.py000066400000000000000000000021421266062647100240210ustar00rootroot00000000000000""" Naundorf et al. (2006), Nature Unique features... Cooperative model of spike initiation. Note that implicit integration is not possible here. """ from brian import * defaultclock.dt=0.005*ms Cm=1*uF # /cm**2 Iapp=2*uA gL=0.1*msiemens EL=-65*mV ENa=55*mV EK=-90*mV gNa=35*msiemens gK=9*msiemens KJ=500*mV # coupling factor eqs=''' dv/dt=(-gNa*m**3*h*(v-ENa)-gK*n**4*(v-EK)-gL*(v-EL)+Iapp)/Cm : volt dm/dt=10*(alpham*(1-m)-betam*m) : 1 alpham=-0.1/mV*(vshift+35*mV)/(exp(-0.1/mV*(vshift+35*mV))-1)/ms : Hz betam=4*exp(-(vshift+60*mV)/(18*mV))/ms : Hz vshift=v+KJ*m**3*h : volt dh/dt=5*(alphah*(1-h)-betah*h) : 1 alphah=0.07*exp(-(v+58*mV)/(20*mV))/ms : Hz betah=1./(exp(-0.1/mV*(v+28*mV))+1)/ms : Hz dn/dt=5*(alphan*(1-n)-betan*n) : 1 alphan=-0.01/mV*(v+34*mV)/(exp(-0.1/mV*(v+34*mV))-1)/ms : Hz betan=0.125*exp(-(v+44*mV)/(80*mV))/ms : Hz ''' neuron=NeuronGroup(1,eqs) neuron.v=-70*mV neuron.h=1 M=StateMonitor(neuron,'v',record=0) run(50*ms,report='text') subplot(211) plot(M.times/ms,M[0]/mV) subplot(212) plot((M[0]/mV)[:-1],diff(M[0])/defaultclock.dt) show() brian-1.4.3/dev/unfinished_examples/frompapers/masquelier_et_al_2008.py000066400000000000000000000146751266062647100261650ustar00rootroot00000000000000''' This program duplicates the results of Masquelier et al. 2008 - STDP finds the start of repeating patterns in continuous spike trains. 1000 neurons are connected to a single neuron by STDP synapses. Every 200ms, the pattern RANDOM 100ms, PATTERN 50ms RANDOM 50ms is repeated, where RANDOM is Poisson noise, and PATTERN is the same pattern repeated. More exactly, half the neurons are patterned, and half are random all the time. The STDP model is standard exponential STDP, not the one from that paper, and so the results are slightly different here. ''' from brian import * from brian.utils.progressreporting import ProgressReporter import time import pylab class TimedPoissonGroup(PoissonGroup): ''' Fires Poisson spikes during a certain interval See documentation for :class:`PoissonGroup`, with the additional feature that spikes will only be fired between ``start`` and ``start+duration``. If ``period`` is specified, it will repeat with that period. ''' def __init__(self, N=1, rates=0*Hz, start=0*second, duration=1*second, clock=None, period=None): end = float(start+duration) start = float(start) if period is not None: period = float(period) def f_rates(t): t = float(t) if period is not None: t = t%period if startpattern_segment_duration]=0*ms latency_reduction_t = t.copy() plot(latency_reduction_t,'.') title('Latency reduction (fig 5 in paper)') subplot(122) i, t = zip(*M_post.spikes) m = int(len(t)/20) if m==0: m=1 t = array(t)-initial_segment_duration s = [t[n:n+m] for n in range(0,len(t),m)] ps = [float(sum((si>0*ms) & (si20 for d in docs_data_html_revisited['views']])] write_data(docs_data_html_revisited, 'topdocs_revisited_html.txt') I = argsort(-docs_data_html['duration']) docs_data_html_duration = docs_data_html[I] docs_data_html_duration = docs_data_html_duration[array([d>20 for d in docs_data_html_duration['views']])] write_data(docs_data_html_duration, 'topdocs_duration_html.txt') brian-1.4.3/dev/usage_analysis/weblogs/brian_docs_analysis.txt000066400000000000000000002040051266062647100246230ustar00rootroot00000000000000 #-------------------------------------------------------- # Nom du profil: briansimulator.org # Période: 06/28/2006 - 07/13/2011 #-------------------------------------------------------- #-------------------------------------------------------- # Pages les plus consultées #-------------------------------------------------------- Contenu Visites Pages vues Durée moy. 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Mohm, mvolt, umetre, ... mV, mS, um, cm2, ...Arithmetic(2*amp)*(3*ohm) == 6*volt (2*amp)+(3*ohm) raises DimensionMismatchErrorIPythonRun script%run example.pyShellcd examples lsHelpNeuronGroup? help(Connection)SourceNeuronGroup??EquationsEquations objecteqs = Equations(’’’ dV/dt=-V/(10*ms) : volt ’’’)SyntaxdV/dt = -(V-V0)/(10*ms) : voltDifferential eqn.I = I0+I1 : ampEquationu = VAliasV0 : voltParameterNoisedV/dt = xi*sigma/tau**0.5 : voltNeuronGroupCreationG = NeuronGroup(100, model=eqns, threshold=10*mV, reset=0*mV) G = NeuronGroup(100, model=eqns, threshold=’v>=vt’, reset=’v=vr’, refractory=5*ms)State variablesG.V = Vr+(Vt-Vr)*rand(len(G))SubgroupsGe = G[0:50]ConnectionCreationC = Connection(P, Q, ‘V’) Connection from group P to state variable V of group Q. WeightsC.connect_full(weight=2*mV) C.connect_random(sparseness=0.1, weight=lambda i,j: exp(-abs(i-j)*.1)*3*mV) C.connect(W=eye(len(P),len(Q))) C.connect_full(Pe, Q, weight=2*mV) C[0,3]=4*mVConstruction by blockPe = P[0:50] Pi = P[50:100] C.connect_full(Pe, Pi, weight=1*mV)One line definitionC = Connection(P, Q, ‘V’, weight=1*mV, sparseness=0.1)DelaysC = Connection(P, Q, ‘V’, delay=2*ms) C = Connection(P, Q, ‘V’, delay=True) C.delay[1,3]=3*msHomogeneous delays Heterogeneous delaysSpike-timing dependent plasticityGeneral STDPC = Connection(P, Q, ‘ge’) eqs_stdp=''' dA_pre/dt=-A_pre/tau_pre : 1 dA_post/dt=-A_post/tau_post : 1 ''' stdp=STDP(C, eqs=eqs, pre= 'A_pre+=dA_pre;w+=A_post’, post='A_post+=dA_post;w+=A_pre',wmax=gmax)Dynamics of synaptic variables pre (post) is executed for each presynaptic (postsynaptic) spike.Exponential STDPstdp=ExponentialSTDP(synapses, taup, taum, Ap, Am, wmax=gmax, interactions='all', update='additive') Interactions can be ‘all’, ‘nearest’, ‘nearest_post’, ‘nearest_pre’; update can be ‘additive’, ‘multiplicative’, ‘mixed’.Short-term plasticityCreationC = Connection(P, Q, ‘ge’) stp = STP(C, taud=100*ms, tauf=5*ms, U=.6)SpikeMonitorCreationM = SpikeMonitor(G)AttributesM.nspikes M.spikes def f(spikes): print spikes M = SpikeMonitor(G, function=f)M.spikes is a list of pairs (i,t) for neuron i at time t. Custom monitoring.StateMonitorCreationM = StateMonitor(G, ‘V’)Just record summary statsM = StateMonitor(G, ‘V’, record=5)Record neuron 5M = StateMonitor(G, ‘V’, record=[5,6,7])Record neurons 5, 6, 7M = StateMonitor(G, ‘V’, record=True)Record all neuronsAttributesM.times M.mean, M.var, M.std M[i]Recording times (array of length nsteps) Summary statistics (array of length num neurons) Recorded values of neuron i (length nsteps)PopulationRateMonitorCreationM = PopulationRateMonitor(G) M = PopulationRateMonitor(G, bin=5*ms)AttributesM.times M.ratesMethodsM.smooth_rate(width=10*ms) M.smooth_rate(width=10*ms, filter=’flat’)NetworkRunningrun(10*second)Uses all defined objects from the current file or function, for finer control use the Network objectOperations@network_operation def f(): ...Calls the function f every update stepNetwork objectnet = Network(G, C, f) net.run(10*second)To specify exactly which objects should be used.PlottingRaster plotraster_plot() raster_plot(M) raster_plot(M1, M2)Graphsplot(M.times/ms, M[i]/mV)See pylab docs for detailsConnection strengthsimshow(C.W.todense(), interpolation='nearest', origin='lower')C.W.todense() is the connection matrixShowingshow() Built-in NeuronGroup typesPoisson groupP=PoissonGroup(N, rates) P=PoissonGroup(100, 10*Hz) P=PoissonGroup(3, array([1,2,3])*Hz) P=PoissonGroup(1, lambda t : (1+sin(t))*50*Hz)rates can be a value, array or function of time returning a value or array, all in HzPulse packetP=PulsePacket(t, n, sigma) P=PulsePacket(50*ms, 100, 5*ms)Spike generatorP=SpikeGeneratorGroup(N, spiketimes) P=SpikeGeneratorGroup(2,[(0,1*ms), (1,3*ms), (0,5*ms)])spiketimes should be a list of pairs (i,t) for neuron i firing at time t For more information, see the online manual: http://www.briansimulator.org/docs cdepqš ·ºÂÎÞæõü    " # 0 ïãØÃظ­œ‡v‡­j­‡­‡­‡X‡­PEXh£ÑhdmH sH hdmH sH "hdCJOJQJ^JaJmH sH h£Ñh“=ã5mH sH  h£Ñh“=ãCJ^JaJmH sH (h£Ñh“=ãCJOJQJ^JaJmH sH  h£Ñh“=ãOJQJ^JmH sH h£Ñh“=ãmH sH h£Ñh*SªmH sH (h£ÑhPÎCJOJQJ^JaJmH sH h£ÑhPÎmH sH h£ÑhPÎ5mH sH hPÎhPÎ5CJaJmH sH 5Ncdðߌ~~~~~dð¤$Ifgd£ÑSkd$$If–FÖ”ÿŒ$ø$ Ö t Ö ÿÙÙÙöø$6ööÖÿÖÿÖÿÖÿ4Ö4Ö laöpÖ ÿÙÙÙyt‚?Õ$dð¤$Ifa$gd£Ñ&d @&PÆÿ gd‚?ÕdepŠ¸¹•‡‡‡ydð¤$Ifgd£Ñdð¤$Ifgd^ šikdn$$If–FÖF”ÿÀRŒ$,’: t öø$6ööÖ ÿÿÿÖ ÿÿÿÖ ÿÿÿÖ ÿÿÿ4Ö4Ö laöyt‚?Õ¹ºÂp_$dð¤$Ifa$gd£Ñkdé$$If–FÖF”ÿÀRŒ$,’: Ö t Öÿòòòÿòòòÿòòòöø$6ööÖ ÿÿÿÖ ÿÿÿÖ ÿÿÿÖ ÿÿÿ4Ö4Ö laöpÖÿòòòÿòòòÿòòòyt‚?ÕÂÃÎÞßଞžž4ikd$$If–FÖF”ÿÀuŒ$,µ t öø$6ööÖ ÿÿÿÖ ÿÿÿÖ ÿÿÿÖ ÿÿÿ4Ö4Ö laöyt‚?Õdð¤$Ifgd£ÑSkd¯$$If–FÖ”ÿŒ$ø$ Ö t Ö ÿÙÙÙöø$6ööÖÿÖÿÖÿÖÿ4Ö4Ö laöpÖ ÿÙÙÙyt‚?Õàæòõö÷ü   ññññbññTñdð¤$Ifgddkd˜$$If–FÖF”ÿÀuŒ$,µ Ö t Öÿòòòÿòòòÿòòòöø$6ööÖ ÿÿÿÖ ÿÿÿÖ ÿÿÿÖ ÿÿÿ4Ö4Ö laöpÖÿòòòÿòòòÿòòòyt‚?Õdð¤$Ifgd£Ñ   # 1 2 •‡‡‡dð¤$Ifgd£Ñikd^$$If–FÖF”ÿÀuŒ$,µ t öø$6ööÖ ÿÿÿÖ ÿÿÿÖ ÿÿÿÖ ÿÿÿ4Ö4Ö laöyt‚?Õ0 1 3 = O € ‰ ¨ ¼ Ì × Ý å ï  ! # / 9 ¢ £ ¨ © ± · Ê Û ù û   ëàÔÉ´É´É´É´É´É£•£Ž†Ž´t´t´t´É´Élah£Ñh– ØmH sH h– ØmH sH "h[.ÀCJOJQJ^JaJmH sH h£Ñh“=ã5 h£Ñh“=ãh[.ÀCJOJQJ^JaJ h£Ñh“=ãCJOJQJ^JaJ(h£Ñh“=ãCJOJQJ^JaJmH sH h£Ñh“=ãmH sH h£Ñh“=ã5mH sH h£ÑhdmH sH (h£ÑhdCJOJQJ^JaJmH sH  2 3 = p_$dð¤$Ifa$gd£ÑkdÙ$$If–FÖF”ÿÀuŒ$,µ Ö t Öÿòòòÿòòòÿòòòöø$6ööÖ ÿÿÿÖ ÿÿÿÖ ÿÿÿÖ ÿÿÿ4Ö4Ö laöpÖÿòòòÿòòòÿòòòyt‚?Õ= > O c { € ¬žžžžždð¤$Ifgd£ÑSkdŸ$$If–FÖ”ÿŒ$ø$ Ö t Ö ÿÙÙÙöø$6ööÖÿÖÿÖÿÖÿ4Ö4Ö laöpÖ ÿÙÙÙyt‚?Õ ‚ ‰ ¨ º •‡‡‡dð¤$Ifgd£Ñikd $$If–FÖF”ÿÀuŒ$,µ t öø$6ööÖ ÿÿÿÖ ÿÿÿÖ ÿÿÿÖ ÿÿÿ4Ö4Ö laöyt‚?Õº » ¼ Ì Õ l^^^dð¤$Ifgd£Ñ’kdˆ$$If–F4”6ÖF”ÿÀuŒ$`,µ Ö t Öÿòòòÿòòòÿòòòöø$6ööÖ ÿÿÿÖ ÿÿÿÖ ÿÿÿÖ ÿÿÿ4Ö4Ö laöpÖÿòòòÿòòòÿòòòyt‚?ÕÕ Ö × Ý ã l^^^dð¤$Ifgd£Ñ’kdZ$$If–F4”6ÖF”ÿÀuŒ$ ,µ Ö t Öÿòòòÿòòòÿòòòöø$6ööÖ ÿÿÿÖ ÿÿÿÖ ÿÿÿÖ ÿÿÿ4Ö4Ö laöpÖÿòòòÿòòòÿòòòyt‚?Õã ä å ï ù l^^^dð¤$Ifgd£Ñ’kd,$$If–F4”6ÖF”ÿÀuŒ$ ,µ Ö t Öÿòòòÿòòòÿòòòöø$6ööÖ ÿÿÿÖ ÿÿÿÖ ÿÿÿÖ ÿÿÿ4Ö4Ö laöpÖÿòòòÿòòòÿòòòyt‚?Õù ú ! 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TODO: advanced sections interfaces compartments brian-1.4.3/docs_sphinx/advanced_connection_matrices.txt000066400000000000000000000057631266062647100235740ustar00rootroot00000000000000.. currentmodule:: brian Connection matrices =================== A :class:`Connection` object has an attribute ``W`` which is its connection matrix. Brian's system for connection matrices can be slightly confusing. The way it works is roughly as follows. There are two types of connection matrix data structures, :class:`ConstructionMatrix` and :class:`ConnectionMatrix`. The construction matrix types are used for building connectivity, and are optimised for insertion and deletion of elements, but access is slow. The connection matrix types are used when the simulation is running, and are optimised for fast access, but not for adding/removing or modifying elements. When a :class:`Connection` object is created, it is given a construction matrix data type, and when the network is run, this matrix is converted to its corresponding connection matrix type. As well as this construction/connection matrix type distinction, there is also the distinction between dense/sparse/dynamic matrices, each of which have their own construction and connection versions. The dense matrix structure is very simple, both the construction and connection types are basically just 2D numpy arrays. The sparse and dynamic matrix structures are very different for construction and connection. Both the sparse and dynamic construction matrices are essentially just the ``scipy.lil_matrix`` sparse matrix type, however we add some slight improvements to scipy's matrix data type to make it more efficient for our case. The sparse and dynamic connection matrix structures are documented in more detail in the reference pages for :class:`SparseConnectionMatrix` and :class:`DynamicConnectionMatrix`. For customised run-time modifications to sparse and dense connection matrices you have two options. You can modify the data structures directly using the information in the reference pages linked to in the paragraph above, or you can use the methods defined in the :class:`ConnectionMatrix` class, which work for dense, sparse and dynamic matrix structures, and do not depend on implementation specific details. These methods provide element, row and column access. The row and column access methods use either :class:`DenseConnectionVector` or :class:`SparseConnectionVector` objects. The dense connection vector is just a 1D numpy array of length the size of the row/column. The sparse connection vector is slightly more complicated (but not much), see its documentation for details. The idea is that in most cases, both dense and sparse connection vectors can be operated on without having to know how they work, so for example if ``v`` is a :class:`ConnectionVector` then ``2*v`` is of the same type. So for a :class:`ConnectionMatrix` ``W``, this should work, whatever the structure of ``W``:: W.set_row(i, 2*W.get_row(i)) Or equivalently:: W[i,:] = 2*W[i,:] The syntax ``W[i,:]``, ``W[:,i]`` and ``W[i,j]`` is supported for integers ``i`` and ``j`` for (respectively) row, column and element access. brian-1.4.3/docs_sphinx/analysis.txt000066400000000000000000000117201266062647100175320ustar00rootroot00000000000000.. currentmodule:: brian .. index:: plotting Analysis and plotting ===================== .. index:: pair: plotting; pylab Most plotting should be done with the PyLab commands, all of which are loaded when you import Brian. See: http://matplotlib.sourceforge.net/matplotlib.pylab.html for help on PyLab. The scientific library `Scipy `__ is also automatically imported by the instruction ``from brian import *``. The most useful plotting instruction is the Pylab function ``plot``. A typical use with Brian is:: plot(t/ms,vm/mV) where t is a vector of times with units ms and vm is a vector of voltage values with units mV. To display the figures on the screen, the function ``show()`` must be called once (this should be the last line of your script), except when using IPython with the Pylab mode (``ipython -pylab``). Brian currently defines just two plotting functions of its own, :func:`raster_plot` and :func:`hist_plot`. In addition, the :class:`StateMonitor` object has a :meth:`~StateMonitor.plot` method. Raster plots ------------ Spike trains recorded by a :class:`SpikeMonitor` can be displayed as raster plots:: S=SpikeMonitor(group) ... raster_plot(S) Usual options of the ``plot`` command can also be passed to :func:`raster_plot`. One may also pass several spike monitors as arguments. State variable plots -------------------- State values recorded by a :class:`StateMonitor` can also be plotted as follows:: M = StateMonitor(group, 'V', record=[0,1,2]) ... M.plot() Realtime plotting ----------------- Both :func:`raster_plot` and :meth:`StateMonitor.plot` have real-time versions which update as the simulation runs, for example:: G = NeuronGroup(...) spikemon = SpikeMonitor(G) statemon = StateMonitor(G, 'V', record=range(5)) ion() subplot(211) raster_plot(spikemon, refresh=10*ms, showlast=200*ms) subplot(212) statemon.plot(refresh=10*ms, showlast=200*ms) run(1*second) ioff() show() The ``ion()`` and ``ioff()`` command activate and deactivate Pylab's interactive plotting mode. The ``refresh`` parameter specifies how often (in simulation time) to refresh the plot - smaller values will slow down the simulation. The ``showlast`` option only plots the most recent values. With some IDEs, you may need to do something like the following at the beginning of your script to make interactive mode work:: import matplotlib matplotlib.use('WXAgg') This is because the default graphical backend can sometimes interact badly with the IDE. Other options to try are ``GTKAgg``, ``QTAgg``, ``TkAgg``. Statistics ---------- Here are a few functions to analyse first and second order statistical properties of spike trains, defined as ordered lists of spike times: * Firing rate: ``firing_rate(spikes)`` where ``spikes`` is a spike train (list of spike times). * Coefficient of variation: ``CV(spikes)``. * Cross-correlogram: ``correlogram(T1,T2,width=20*ms,bin=1*ms,T=None)`` returns the cross-correlogram of spike trains T1 and T2 with lag in [-width,width] and given bin size. T is the total duration (optional) and should be greater than the duration of T1 and T2. The result the rate of coincidences in each bin, returned as an array. * Autocorrelogram: ``autocorrelogram(T0,width=20*ms,bin=1*ms,T=None)`` is the same as ``correlogram(T0,T0,width=20*ms,bin=1*ms,T=None)``. * Cross-correlation function: ``CCF(T1,T2,width=20*ms,bin=1*ms,T=None)`` returns the cross-correlation function of T1 and T2, which is the same as the cross-correlogram divided by the bin size (which makes the result independent of the bin size). * Autocorrelation function: ``ACF(T0,width=20*ms,bin=1*ms,T=None)``, same as ``CCF(T0,T0,width=20*ms,bin=1*ms,T=None)``. * Cross-covariance function: ``CCVF(T1,T2,width=20*ms,bin=1*ms,T=None)`` is the cross-correlation function of T1 and T2 minus for the cross-correlation of independent spike trains with the same rates (product of rates). * Auto-covariance function: ``ACVF(T0,width=20*ms,bin=1*ms,T=None)`` is the same as ``CCVF(T0,T0,width=20*ms,bin=1*ms,T=None)``. * Total correlation coefficient: ``total_correlation(T1,T2,width=20*ms,T=None)`` is the integral of the cross-covariance function divided by the rate of T1, typically (but not always) between 0 and 1. * Vector strength: ``vector_strength(spikes,period)`` returns the vector strength of the given spike train with respect to the period. If each spike time with phase phi is represented by a vector with angle phi, then the vector strength is the length of the average vector. It equals 1 for spikes with constant phase and 0 for homogeneous phase distributions. * Gamma precision factor: ``gamma_factor(source, target, delta)`` returns the gamma precision factor between source and target trains, with precision delta. These functions return NaN (not a number) when a spike train is empty. .. TODO: docs:histograms Histograms ---------- brian-1.4.3/docs_sphinx/brian-logo.png000066400000000000000000000465541266062647100177220ustar00rootroot00000000000000‰PNG  IHDRÈilÕ EiCCPICC profilexÚSgTSé=÷ÞôBKˆ€”KoR RB‹€‘&*! 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They are copied after the builtin static files, # so a file named "default.css" will overwrite the builtin "default.css". html_static_path = ['_static'] # If not '', a 'Last updated on:' timestamp is inserted at every page bottom, # using the given strftime format. html_last_updated_fmt = '%b %d, %Y' # If true, SmartyPants will be used to convert quotes and dashes to # typographically correct entities. #html_use_smartypants = True # Custom sidebar templates, maps document names to template names. #html_sidebars = {} # Additional templates that should be rendered to pages, maps page names to # template names. #html_additional_pages = {} # If false, no module index is generated. html_use_modindex = False # If true, the reST sources are included in the HTML build as _sources/. #html_copy_source = True # If true, an OpenSearch description file will be output, and all pages will # contain a tag referring to it. #html_use_opensearch = False # Output file base name for HTML help builder. htmlhelp_basename = 'Briandoc' # Options for LaTeX output # ------------------------ # The paper size ('letter' or 'a4'). #latex_paper_size = 'letter' # The font size ('10pt', '11pt' or '12pt'). #latex_font_size = '10pt' # Grouping the document tree into LaTeX files. List of tuples # (source start file, target name, title, author, document class [howto/manual]). latex_documents = [ ('index', 'Brian.tex', 'Brian Documentation', 'Romain Brette, Dan Goodman', 'manual'), ] # The name of an image file (relative to this directory) to place at the top of # the title page. #latex_logo = None # Additional stuff for the LaTeX preamble. #latex_preamble = '' # Documents to append as an appendix to all manuals. #latex_appendices = [] # If false, no module index is generated. #latex_use_modindex = True brian-1.4.3/docs_sphinx/connections.txt000066400000000000000000000204751266062647100202400ustar00rootroot00000000000000.. currentmodule:: brian Connections =========== .. note:: Consider using the newer :class:`Synapses` class instead of :class:`Connection`, in particular when you are modelling plastic connections. Building connections -------------------- First, one must define which neuron groups are connected and which state variable receives the spikes. The following instruction:: myconnection=Connection(group1,group2,'ge') defines a connection from group ``group1`` to ``group2``, acting on variable ``ge``. When neurons from group ``group1`` spike, the variable ``ge`` of the target neurons in group ``group2`` are incremented. When the connection object is initialised, the list of connections is empty. It can be created in several ways. First, explicitly:: myconnection[2,5]=3*nS This instruction connects neuron 2 from ``group1`` to neuron 5 from ``group2`` with synaptic weight 3 nS. Units should match the units of the variable defined at initialisation time (``ge``). The matrix of synaptic weights can be defined directly with the method :meth:`Connection.connect`:: W=rand(len(group1),len(group2))*nS myconnection.connect(group1,group2,W) Here a matrix with random elements is used to define the synaptic weights from ``group1`` to ``group2``. It is possible to build the matrix by block by using subgroups, e.g.:: W=rand(20,30)*nS myconnection.connect(group1[0:20],group2[10:40],W=W) There are several handy functions available to set the synaptic weights: :meth:`~Connection.connect_full`, :meth:`~Connection.connect_random` and :meth:`~Connection.connect_one_to_one`. The first one is used to set uniform weights for all pairs of neurons in the (sub)groups:: myconnection.connect_full(group1[0:20],group2[10:40],weight=5*nS) The second one is used to set uniform weights for random pairs of neurons in the (sub)groups:: myconnection.connect_random(group1[0:20],group2[10:40],sparseness=0.02,weight=5*nS) Here the third argument (0.02) is the probability that a synaptic connection exists between two neurons. The number of presynaptic neurons can be made constant by setting the keyword ``fixed=True`` (probability * number of neurons in ``group1``). Finally, the method :meth:`~Connection.connect_one_to_one` connects neuron i from the first group to neuron i from the second group:: myconnection.connect_one_to_one(group1,group2,weight=3*nS) Both groups must have the same number of neurons. If you are connecting the whole groups, you can omit the first two arguments, e.g.:: myconnection.connect_full(weight=5*nS) connects ``group1`` to ``group2`` with weights 5 nS. Building connections with connectivity functions ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ There is a simple and efficient way to build heterogeneous connections, by passing functions instead of constants to the methods :meth:`~Connection.connect_full` and :meth:`~Connection.connect_random`. The function must return the synaptic weight for a given pair of neuron (i,j). For example:: myconnection.connect_full(group1,group2,weight=lambda i,j:(1+cos(i-j))*2*nS) where i (j) indexes neurons in ``group1`` (``group2``). This is the same as doing by hand:: for i in range(len(group1)): for j in range(len(group2)): myconnection[i,j]=(1+cos(i-j))*2*nS but it is much faster because the construction is vectorised, i.e., the function is called for every i with j being the entire row of target indexes. Thus, the implementation is closer to:: for i in range(len(group1)): myconnection[i,j]=(1+cos(i-arange(len(group2)))*2*nS The method :meth:`~Connection.connect_random` also accepts functional arguments for the weights and connection probability. For that method, it is possible to pass a function with no argument, as in the following example:: myconnection.connect_random(group1,group2,0.1,weight=lambda:rand()*nS) Here each synaptic weight is random (between 0 and 1 nS). Alternatively, the connection probability can also be a function, e.g.:: myconnection.connect_random(group1,group2,0.1,weight=1*nS,sparseness=lambda i,j:exp(-abs(i-j)*.1)) The weight or the connection probability may both be functions (with 0 or 2 arguments). Delays ------ Transmission delays can be introduced with the keyword ``delay``, passed at initialisation time. There are two types of delays, homogeneous (all synapses have the same delay) and heterogeneous (all synapses can have different delays). The former is more computationally efficient than the latter. An example of homogeneous delays:: myconnection=Connection(group1,group2,'ge',delay=3*ms) If you have limited heterogeneity, you can use several Connection objects, e.g.:: myconnection_fast=Connection(group1,group2,'ge',delay=1*ms) myconnection_slow=Connection(group1,group2,'ge',delay=5*ms) For a highly heterogeneous set of delays, initialise the connection with ``delay=True``, set a maximum delay with for example ``max_delay=5*ms`` and then use the ``delay`` keyword in the :meth:`~DelayConnection.connect_random` and :meth:`~DelayConnection.connect_full` methods:: myconnection=Connection(group1,group2,'ge',delay=True,max_delay=5*ms) myconnection.connect_full(group1,group2,weight=3*nS,delay=(0*ms,5*ms)) The code above initialises the delays uniformly randomly between 0ms and 5ms. You can also set ``delay`` to be a function of no variables, where it will be called once for each synapse, or of two variables ``i, j`` where it will be called once for each row, as in the case of the weights in the section above. Alternatively, you can set the delays as follows:: myconnection.delay[i,j] = 3*ms See the reference documentation for :class:`Connection` and :class:`DelayConnection` for more details. Connection structure -------------------- The underlying data structure used to store the synaptic connections is by default a sparse matrix. If the connections are dense, it is more efficient to use a dense matrix, which can be set at initialisation time:: myconnection=Connection(group1,group2,'ge',structure='dense') The sparse matrix structure is fixed during a run, new synapses cannot be added or deleted, but there is a dynamic sparse matrix structure. It is less computationally efficient, but allows runtime adding and deleting of synaptic connections. Use the ``structure='dynamic'`` keyword. For more details see the reference documentation for :class:`Connection`. Modulation ---------- The synaptic weights can be modulated by a state variable of the presynaptic neurons with the keyword ``modulation``:: myconnection=Connection(group1,group2,'ge',modulation='u') When a spike is produced by a presynaptic neuron (``group1``), the variable ge of each postsynaptic neuron (``group2``) is incremented by the synaptic weight multiplied by the value of the variable u of the presynaptic neuron. This is useful to implement short-term plasticity. Direct connection ----------------- In some cases, it is useful to connect a group directly to another one, in a one-to-one fashion. The most efficient way to implement it is with the class :class:`IdentityConnection`:: myconnection=IdentityConnection(group1,group2,'ge',weight=1*nS) With this structure, the synaptic weights are homogeneous (it is not possible to define them independently). When neuron i from ``group1`` spikes, the variable ge of neuron i from ``group2`` is increased by 1 nS. A typical application is when defining inputs to a network. Simple connections ------------------ If your connection just connects one group to another in a simple way, you can initialise the weights and delays at the time you initialise the :class:`Connection` object by using the ``weight``, ``sparseness`` and ``delay`` keywords. For example:: myconnection = Connection(group1, group2, 'ge', weight=1*nS, sparseness=0.1, delay=(0*ms, 5*ms), max_delay=5*ms) This would be equivalent to:: myconnection = Connection(group1, group2, 'ge', delay=True, max_delay=5*ms) myconnection.connect_random(group1, group2, weight=1*nS, delay=(0*ms, 5*ms)) If the ``sparseness`` value is omitted or set to value 1, full connectivity is assumed, otherwise random connectivity. NOTE: in this case the ``delay`` keyword used without the ``weight`` keyword has no effect.brian-1.4.3/docs_sphinx/developer-codegen.txt000066400000000000000000000434601266062647100213040ustar00rootroot00000000000000.. currentmodule:: brian.experimental.codegen2 .. index:: pair: code; generation Code generation ^^^^^^^^^^^^^^^ .. warning:: This section is a work in progress, documenting the most recent code generation framework, which is in the package ``brian.experimental.codegen2``. Overview ======== To generate code, we start with a basic statement or set of statements we want to evaluate for all neurons, or for all synapses, and then apply various transformations to generate code that will do this. We start from a structured, language-invariant representation of the set of basic statements. We then 'resolve' the unknown symbols in it. This is done recursively, the resolution of each symbol can add vectorised statements or loops to the current representation, and add data to a namespace that will be associated to the final code. Symbols will be things like a :class:`~brian.NeuronGroup` state variable, or a synaptic weight value. The output of this process is a new, more complicated structured representation, including things like loops if necessary. Next, we convert this structured representation into a code string. Finally, this code string is JIT-compiled into an executable object. Using numerical integration generation -------------------------------------- You can use Brian's equations format to generate C/C++ code for a numerical integration step, for example:: eqs = ''' dv/dt = (ge+gi-(v+49*mV))/(20*ms) : volt dge/dt = -ge/(5*ms) : volt dgi/dt = -gi/(10*ms) : volt ''' code, vars, params = make_c_integrator(eqs, method=euler, dt=0.1*ms) print code has output:: double _temp_v = 50.0*ge + 50.0*gi - 50.0*v - 2.45; double _temp_ge = -200.0*ge; double _temp_gi = -100.0*gi; v += _temp_v*0.0001; ge += _temp_ge*0.0001; gi += _temp_gi*0.0001; See the documentation for the function :func:`make_c_integrator`. Using the code generation package --------------------------------- The basic way to use the code generation module is as follows: 1. Create a :class:`Block` of :class:`Statement` objects which you want to execute. You can use :func:`statements_from_codestring` to do this. 2. Create a dictionary of :class:`Symbol` objects corresponding to the symbols in the block above. 3. Call :meth:`CodeItem.generate` with the specified language and symbols, to give you a :class:`Code` object. 4. Optionally, insert additional data into the namespace of the :class:`Code` object. 5. Use the :class:`Code` object via ``code(name1=val1, name2=val2)`` where the ``name=val`` are to be inserted into the namespace before the code is called. This process is very clearly illustrated in the source code for :class:`CodeGenStateUpdater`. Structure of the package ------------------------ The following are the main elements of the code generation package: :class:`Code` This is the output of the code generation package, a compilable/compiled code fragment, along with a namespace in which it is executed. :class:`Language` Used to specify which language the output should have. :class:`CodeItem` Before code is converted into a specific language, it is stored in a language-invariant format consisting of :class:`CodeItem` objects, which can in turn contain other :class:`CodeItem` objects. The main derived classes from this are :class:`Block` and :class:`Statement`. The first can contain a series of statements, or it can be a for loop, an if block, etc. A :class:`Statement` can be a :class:`MathematicalStatement` or :class:`CodeStatement`. The former is for things like ``x=y*z`` and the latter for things like ``x = arr[index]``. :class:`Symbol`, :func:`resolve` A :class:`CodeItem` with unresolved dependencies needs to be resolved by the function :func:`resolve`. Each unresolved depdendency should correspond to a :class:`Symbol` which knows how to modify a :class:`CodeItem` in order to resolve itself. For example, a :class:`NeuronGroupStateVariableSymbol` will insert the :class:`~brian.NeuronGroup` state variable value into the namespace, create a new array symbol like ``__arr_V`` for symbol ``V``, and resolve itself either by doing nothing (in Python, as the variable is already vectorised), or by introducing a loop (in C++), or by setting the index variable as the kernel thread (for GPU). For more details, see the section on resolution below. :func:`make_integration_step`, :func:`euler`, :func:`rk2`, :func:`exp_euler` Numerical integration schemes, each integration scheme (such as :func:`euler`) converts a set of differential equations into a sequence of :class:`MathematicalStatement` objects comprising an integration step. :class:`CodeGenStateUpdater`, :class:`CodeGenThreshold`, :class:`CodeGenReset`, :class:`CodeGenConnection` Brian objects using code generation. Resolution process ================== Example ------- We start with a worked example. Consider the statement:: V = V+1 Here ``V`` is a :class:`NeuronGroup` state variable. We wish to transform this into code that can be executed. In the case of Python, the output would look like:: _neuron_index = slice(None) V = _arr_V[_neuron_index] _arr_V[_neuron_index] = V+1 The symbol ``_arr_V`` would be added directly to the namespace. In the case of C++ it would look like:: for(int _neuron_index=0; _neuron_index<_len__arr_V; _neuron_index++) { double &V = _arr_V[_neuron_index]; V = V+1; } Here the symbols ``_arr_V`` and `_len__arr_V`` would be added to the namespace. The reason for these complicated names is to do with making the code as generic as possible, not introducing namespace clashes (symbols starting with ``_`` are reserved), etc. The way the process works is we start with the statement ``V=V+1`` and a :class:`Symbol` object with name ``V``, specifically a :class:`NeuronGroupStateVariableSymbol`. The statement ``V=V+1`` depends on ``V`` with both a :class:`Read` and :class:`Write` dependency. We therefore have to 'resolve' the symbol ``V``. To do this we call the method :meth:`~ArraySymbol.resolve` on ``V``. In the case of Python, this gives us:: V = _arr_V[_neuron_index] _arr_V[_neuron_index] = V+1 It adds ``_arr_V`` to the namespace, and creates a dependency on ``_neuron_index``. The reason that ``V=V+1`` is translated to ``_arr_V[_neuron_index] = V+1`` is that on the left hand side we have a write variable, and on the right hand side we have a read variable. In Python, when vectorising, we have no choice but to give the underlying array with its slice when writing to an array. However, at this point the code generation framework doesn't know what ``_neuron_index`` will be, so it could be, for example, an array of indices. In this case, suppose we did ``V*V`` it would be more efficient to compute ``V=_arr_V[_neuron_index]`` and then compute ``V*V`` than to compute ``_arr_V[_neuron_index]*_arr_V[_neuron_index]``, and in the case where ``_neuron_index=slice(None)`` it is no slower, so we always do this. In the case of C++, the first resolution step gives us:: double &V = _arr_V[_neuron_index]; V = V+1; For the second resolution step, we need to resolve ``_neuron_index``, which is a symbol of type :class:`SliceIndex`, telling us that ``_neuron_index`` varies over all neurons. Note that we could also have ``_neuron_index`` being an :class:`ArrayIndex`, for examples ``spikes``, and then this could be used for a reset operation (we would iterate only over those indices of neurons which had spiked). Here though, we iterate over all neurons. In Python, calling the :meth:`~ArrayIndex.resolve` method of ``_neuron_index`` gives us:: _neuron_index = slice(None) V = _arr_V[_neuron_index] _arr_V[_neuron_index] = V+1 and in C++:: for(int _neuron_index=0; _neuron_index<_len__arr_V; _neuron_index++) { double &V = _arr_V[_neuron_index]; V = V+1; } In both cases, the ``_neuron_index`` symbol is resolved and the process is complete. Note that we have actually mixed two stages here, the stage of generating a structured representation of the code using :class:`CodeItem` objects, and the stage of generating code strings using :meth:`CodeItem.convert_to`. In fact, the converting of, for example, ``V`` to ``_arr_V[_neuron_index]`` only happens at the second stage. :func:`resolve` --------------- The first stage, acting on the structured representation of nested :class:`CodeItem` objects is resolved using the function :func:`resolve`. This calls :meth:`Symbol.resolve` for each of the symbols in turn. The resolution order is determined by an optimal efficiency algorithm, see the reference documentation for :func:`resolve` for the full algorithm description. :meth:`Symbol.resolve` can do an arbitrary transformation of the input :class:`CodeItem`, but typically it will either do something like:: load() item save() Or something like:: for name in array: item See the reference documentation for :meth:`Symbol.resolve`, and the documentation for the most important symbols: * :class:`SliceIndex` * :class:`ArraySymbol` * :class:`ArrayIndex` * :class:`NeuronGroupStateVariableSymbol` :meth:`~CodeItem.convert_to` ---------------------------- This step is relatively straightforward, each :class:`CodeItem` object has its ``convert_to`` method called iteratively. The important one is in :class:`MathematicalStatement`, where the left hand side usage is replaced by :meth:`Symbol.write` and the right hand side usage is replaced by :meth:`Symbol.read`. In addition, at this stage the syntax of mathematical statements is corrected, e.g. Python's ``x**y`` is replaced by C++'s ``pow(x,y)`` using ``sympy``. Code generation in Brian ======================== The four objects used for code generation in Brian are: :class:`CodeGenStateUpdater` Used for numerical integration, see above and reference documentation. :class:`CodeGenThreshold` Used for computing a threshold function. :class:`CodeGenReset` Used for computing post-spike reset. :class:`CodeGenConnection` Used for synaptic propagation. Numerical integration --------------------- An integration scheme is generated from an :class:`Equations` object using the :func:`make_integration_step` function. See reference documentation for that function for details. This is carried out by :class:`CodeGenStateUpdater`, which can be used as a Brian :class:`brian.StateUpdater` object in :class:`brian.NeuronGroup`. As an example, for Euler integration, the differential equations:: dx/dt = expr are separated by :class:`Equations` into variable ``x`` with expression ``expr``. This then becomes:: _temp_x := expr x += _temp_x*dt This can then be resolved by the code generation mechanisms described already. Synaptic propagation -------------------- TODO: synaptic propagation, including docstrings and code comments NOTE: GPU functionality not included for synaptic propagation yet. GPU === .. warning:: GPU code is highly transitional, many details may change in the future. GPU code is handled by five classes: :class:`GPULanguage` (derived from :class:`CLanguage`) Identifies the language as CUDA, and stores a singleton :class:`GPUManager` object which is used to manage the GPU. :class:`GPUCode` (derived from :class:`Code`) Returned from the code generation process, but mostly just acts as a proxy to :class:`GPUManager`. :class:`GPUKernel` Handles the final stage of taking a partially generated kernel (without the vectorisation over threads) and computing the final kernel (using vectorisation over threads). Also adds data to the :class:`GPUSymbolMemoryManager`. :class:`GPUManager` Manages the GPU generally. Stores a set of kernels (:class:`GPUKernel`) and manages memory via :class:`GPUSymbolMemoryManager`. Handles joining the memory management code and kernel code into a single source file, and compiling it. :class:`GPUSymbolMemoryManager` Handles allocation of GPU memory for symbols. For more details, see the reference documentation for the classes in the order above. Note that :class:`CodeGenConnection` is the only code generation version of a Brian class which is not GPU enabled at present. Extending code generation ========================= To extend code generation, you will probably need to add new :class:`Symbol` classes. Read the documentation for this class to start, and the documentation for the most important symbols: * :class:`SliceIndex` * :class:`ArraySymbol` * :class:`ArrayIndex` * :class:`NeuronGroupStateVariableSymbol` See also :class:`CodeItem`, particularly the process described in :meth:`CodeItem.generate`. Inheritance diagrams ==================== The overall structure of the classes in the code generation package are included below, for reference. Languages --------- .. inheritance-diagram:: brian.experimental.codegen2.languages GPULanguage :parts: 2 Code objects ------------ .. inheritance-diagram:: brian.experimental.codegen2.codeobject GPUCode :parts: 2 Code items ---------- .. inheritance-diagram:: brian.experimental.codegen2.codeitems brian.experimental.codegen2.statements brian.experimental.codegen2.blocks :parts: 2 Equations --------- .. inheritance-diagram:: brian.experimental.codegen2.equations brian.experimental.codegen2.expressions :parts: 2 Symbols ------- .. inheritance-diagram:: brian.experimental.codegen2.symbols :parts: 2 Resolution and code output -------------------------- .. inheritance-diagram:: brian.experimental.codegen2.dependencies brian.experimental.codegen2.formatting :parts: 2 Integration ----------- .. inheritance-diagram:: brian.experimental.codegen2.integration :parts: 2 GPU --- .. inheritance-diagram:: brian.experimental.codegen2.gpu.management :parts: 2 Brian objects ------------- Connection ~~~~~~~~~~ .. inheritance-diagram:: brian.experimental.codegen2.connection :parts: 3 Reset ~~~~~ .. inheritance-diagram:: brian.experimental.codegen2.reset :parts: 3 State updater ~~~~~~~~~~~~~ .. inheritance-diagram:: brian.experimental.codegen2.stateupdater :parts: 3 Threshold ~~~~~~~~~ .. inheritance-diagram:: brian.experimental.codegen2.threshold :parts: 3 Reference ========= blocks ------ .. autoclass:: Block :members: :undoc-members: .. autoclass:: ControlBlock :members: :undoc-members: .. autoclass:: ForBlock :members: :undoc-members: .. autoclass:: PythonForBlock :members: :undoc-members: .. autoclass:: CForBlock :members: :undoc-members: .. autoclass:: IfBlock :members: :undoc-members: .. autoclass:: PythonIfBlock :members: :undoc-members: .. autoclass:: CIfBlock :members: :undoc-members: codeitems --------- .. autoclass:: CodeItem :members: :undoc-members: codeobject ---------- .. autoclass:: Code :members: :undoc-members: .. autoclass:: PythonCode :members: :undoc-members: .. autoclass:: CCode :members: :undoc-members: connection ---------- .. autoclass:: CodeGenConnection :members: :undoc-members: .. autoclass:: DenseMatrixSymbols :members: :undoc-members: .. autoclass:: SparseMatrixSymbols :members: :undoc-members: dependencies ------------ .. autoclass:: Dependency :members: :undoc-members: .. autoclass:: Read :members: :undoc-members: .. autoclass:: Write :members: :undoc-members: .. autofunction:: get_read_or_write_dependencies equations --------- .. autofunction:: freeze_with_equations .. autofunction:: frozen_equations expressions ----------- .. autoclass:: Expression :members: :undoc-members: formatting ---------- .. autofunction:: word_substitute .. autofunction:: flattened_docstring .. autofunction:: indent_string .. autofunction:: get_identifiers .. autofunction:: strip_empty_lines gpu --- .. warning:: GPU code is highly transitional, many details may change in the future. .. autoclass:: GPUKernel :members: :undoc-members: .. autoclass:: GPUManager :members: :undoc-members: .. autoclass:: GPUSymbolMemoryManager :members: :undoc-members: .. autoclass:: GPUCode :members: :undoc-members: .. autoclass:: GPULanguage :members: :undoc-members: integration ----------- .. autoclass:: EquationsContainer :members: :undoc-members: .. autofunction:: make_integration_step .. autofunction:: euler .. autofunction:: rk2 .. autofunction:: exp_euler languages --------- .. autoclass:: Language :members: :undoc-members: .. autoclass:: PythonLanguage :members: :undoc-members: .. autoclass:: CLanguage :members: :undoc-members: makeintegrator -------------- .. autofunction:: make_c_integrator reset ----- .. autoclass:: CodeGenReset :members: :undoc-members: resolution ---------- .. autofunction:: resolve statements ---------- .. autoclass:: Statement :members: :undoc-members: .. autoclass:: CodeStatement :members: :undoc-members: .. autoclass:: CDefineFromArray :members: :undoc-members: .. autoclass:: MathematicalStatement :members: :undoc-members: .. autofunction:: statements_from_codestring .. autofunction:: c_data_type stateupdater ------------ .. autoclass:: CodeGenStateUpdater :members: :undoc-members: symbols ------- .. autoclass:: Symbol :members: :undoc-members: .. autoclass:: RuntimeSymbol :members: :undoc-members: .. autoclass:: ArraySymbol :members: :undoc-members: .. autoclass:: NeuronGroupStateVariableSymbol :members: :undoc-members: .. autoclass:: SliceIndex :members: :undoc-members: .. autoclass:: ArrayIndex :members: :undoc-members: .. autofunction:: language_invariant_symbol_method .. autofunction:: get_neuron_group_symbols threshold --------- .. autoclass:: CodeGenThreshold :members: :undoc-members: brian-1.4.3/docs_sphinx/developer-codestructure.txt000066400000000000000000000344151266062647100225730ustar00rootroot00000000000000Main code structure =================== Overview -------- Brian features can be broadly categorised into *construction* of the network, and *running* the network. Constructing the network ~~~~~~~~~~~~~~~~~~~~~~~~ The following objects need to be specified by the user explicitly or implicitly: * :class:`NeuronGroup` * :class:`Connection` * Monitors * :class:`Network` After that, the network needs to be *prepared*. Preparation of the network involves initialising objects' data structures appropriately, in particular compressing the :class:`Connection` matrices. Connection matrices are initially stored as instances of a :class:`ConstructionMatrix` class (sparse, dense, etc.), and then later *compressed* into an instance of a :class:`ConnectionMatrix` class. Two levels are necessary, because at construction time, all matrices have to be editable, whereas at runtime, for efficiency reasons, some matrix types are read-only or partially read-only. Data structures appropriate to the construction of a matrix, particularly sparse matrices, are not the most efficient for runtime access. Constructing the :class:`NeuronGroup` object is a rather complicated operation, involving the construction of many subsididary objects. The most complicated aspect is the creation, manipulation and analysis of an :class:`Equations` object. Running the network ~~~~~~~~~~~~~~~~~~~ The network is run by repeatedly evaluating the 'update schedule' and updating the clock or clocks. The 'update schedule' is user specifiable, but usually consists of the following sequence of operations (interspersed with optional user network operation calls): * Update state variables of :class:`NeuronGroup` * Call thresholding function * Push spikes into :class:`SpikeContainer` * Propagate spikes (possibly with delays) via :class:`Connection` * Update state variables of :class:`Synapses` (possibly includes updating the state of targeted :class:`NeuronGroup` objects * Call reset function on neurons which have spiked Details of network construction ------------------------------- Construction of :class:`NeuronGroup` ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ The :class:`NeuronGroup` object is responsible for storing the state variables of each of its neurons, for updating them each time step, generating spikes via a thresholding mechanism, storing spikes so that they can be accessed with a delay, and resetting state variables after spiking. State variable update is done by a ``StateUpdater`` class defined in ``brian/stateupdater.py``. Thresholding is done by a :class:`Threshold` class defined in ``brian/threshold.py`` and resetting is done by a :class:`Reset` class defined in ``brian/reset.py``. The ``__init__`` method of :class:`NeuronGroup` takes these objects as arguments, but it also has various additional keywords which can be used more intuitively. In this case, the appropriate object is selected automatically. For example, if you specify ``reset=0*mV`` in the keyword arguments, Brian generates a ``Reset(0*mV)`` object. The :meth:`NeuronGroup.__init__` method code is rather complicated and deals with many special cases. The most complicated aspect of this is the definition of the state variables and the update procedure. Typically, the user simply gives a list of differential equations, and Brian attempts to automatically extract the appropriate state variable definitions, and creates a differential equation solver appropriate to them (it needs some help in this at the moment, e.g. specifying the order or type of the solver). The main work in this is done by the :func:`magic_state_updater` function, which uses the :class:`Equations` object (see next section). Once the state variables are defined, various internal objects are created. The state variables are stored in the ``_S`` attribute of a :class:`NeuronGroup`. This is an MxN matrix where M is the number of variables and N is the number of neurons. The other major data structure generated is the ``LS`` attribute (last spikes). This is a ``SpikeContainer`` instance, a circular array used to contain spikes. See ``brian/utils/circular.py``. Finally note that the construction of many of these objects requires a :class:`Clock` object, which can either be specified explicitly, or is guessed by the :func:`guess_clock` function which searches for clocks using the magic module (see below). :class:`EventClock` objects are excluded from this guessing. The :func:`magic_state_updater` function and the :class:`Equations` object ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ This function returns an appropriate ``StateUpdater`` object and a list of the dynamic variables of an :class:`Equations` object. It uses methods of the :class:`Equations` object to do this (such as :meth:`Equations.is_linear`). The :class:`Equations` object can be constructed in various ways. It can be constructed from a multi-line string or by adding (concatenating) other :class:`Equations` objects. Construction by multi-line string is done by pattern matching. The four forms are: 1. ``dx/dt = f : unit`` (differential equation) 2. ``x = f : unit`` (equation) 3. ``x = y`` (alias) 4. ``x : unit`` (parameter) Differential equations and parameters are dynamic variables, equations and aliases are just computed and substituted when necessary. The ``f`` patterns in the forms above are stored for differential equations and parameters. For the solution of nonlinear equations, these ``f`` patterns are executed as Python code in the evaluation of the state update. For example, the equations ``dV/dt = -V*V/(10*ms) : 1`` and ``dW/dt = cos(W)/(20*ms) : 1`` are numerically evaluated with an Euler method as the following code (generated from the list of dynamic variables and their defining strings):: V, W = P._S V__tmp, W__tmp = P._dS V__tmp[:] = -V*V/(10*ms) W__tmp[:] = cos(W)/(20*ms) P._S += dt*P._dS This code generation is done by the :meth:`Equations.forward_euler` family of methods. In the case where the equations are linear, they are solved by a matrix exponential using the code in :func:`get_linear_equations` defined in ``brian/stateupdater.py``. Finally, note that equations strings can contain references to names of objects that are defined in the namespace of the string, and the :class:`Equations` object can pick these out. It does this by inspecting the call stack, extracting the namespace for the appropriate level (which has to be kept track of), and plucking out the appropriate name. The ``level=...`` keywords you see dotted around Brian's code are there to keep track of these levels for this reason. Construction of :class:`Connection` ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ :class:`Connection` objects provide methods for storing weight matrices and propagating spikes. Spike propagation is done via the :meth:`Connection.do_propagate` and :meth:`Connection.propagate` methods. Weight matrices are stored in the ``W`` attribute. Initially, weight matrices are :class:`ConstructionMatrix` objects and are converted by the :meth:`Connection.compress` method, via the matrices' ``connection_matrix()`` methods to :class:`ConnectionMatrix` objects. The idea is to have two data structures, one appropriate to the construction of a matrix, supporting adding and removing new synapses, and one appropriate to runtime behaviour, focussing on fast row access above all else. There are three matrix structures, 'dense', 'sparse' and 'dynamic' (and 'computed' may be added later). The 'dense' matrix is just a full 2D array, and the matrix objects just reproduce the functionality of numpy arrays. The 'sparse' and 'dynamic' structures are sparse matrices. The first doesn't allow you to add or remove elements at runtime and is very optimised, the second does allow you to and is less optimised. Both are more optimised than scipy's sparse matrices, which are used as the basis for the construction phase. :class:`Connection` objects can handle homogeneous delays (all synapses with the same delay) by pulling spikes from :class:`NeuronGroup`'s ``LS`` object with a delay. Heterogeneous delays (each synapse with a different delay) are done by the :class:`DelayConnection` object, which stores a ``_delayvec`` attribute alongside the ``W`` attribute. The delay matrix is of the same type as the weight matrix and in the case of sparse matrices must have nonzero elements at the same places. The :class:`Connection` object automatically turns itself into a :class:`DelayConnection` object if heterogeneous delays are requested, so that the user doesn't even need to know of the existence of :class:`DelayConnection`. The :class:`Connection` object also provides methods for initialising the weight matrices either fully, randomly, etc. (See the user docs.) Construction of monitors ~~~~~~~~~~~~~~~~~~~~~~~~ The :class:`SpikeMonitor` class of monitors derive from :class:`Connection`. Rather than propagating spikes to another group, they store them in a list. The work is done in the :meth:`SpikeMonitor.propagate` method. The :class:`StateMonitor` class of monitors derive from :class:`NetworkOperation`. Network operations are called once every time step and execute arbitrary Python code. The :class:`StateMonitor` class of monitors record state variables each time step to a list. Construction of :class:`Network` ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ When the user calls the function :func:`run`, a :class:`MagicNetwork` object is created, and the :meth:`Network.run` method is called. :class:`MagicNetwork` gathers, using the magic module, a list of all appropriate objects and runs them together. Alternatively, the user can specify their own list of objects using a :class:`Network` object. Each time an object is added to a :class:`Network` either via the initialiser or the :meth:`Network.add` method, it checks to see if it has an attribute ``contained_objects``, and if so it adds all the objects in that to the network too. This allows, for example, the :class:`STDP` object to contained :class:`NeuronGroup` and :class:`Connection` objects which the user doesn't see, but are used to implement the STDP functionality. The :meth:`Network.run` method calls the :meth:`Connection.compress` method on every :class:`Connection` object to convert construction matrices to connection matrices. It also builds an update schedule (see below). The magic module ~~~~~~~~~~~~~~~~ The magic module is used for tracking instances of objects. A class that derives from the :class:`magic.InstanceTracker` class can be tracked in this way, including :class:`NeuronGroup`, :class:`Connection`, :class:`NetworkOperation` and :class:`Clock`. The :func:`find_instances` function can be used to search for instances. Note that :func:`find_instances` will only return instances which were instantiated in the same execution frame as the :func:`find_instances` calling frame, or (if the ``level`` keyword is used) one of the frames higher up in the call stack. The idea is that you may have modular code with objects defined in different places, but that you don't want to use all objects that exist at all in the network. This system causes a bit of trouble, but seems unavoidable. See the user manual section :ref:`projects-with-multiple-files` for details on getting around this. Details of network running -------------------------- Update schedules ~~~~~~~~~~~~~~~~ An update schedule gives the sequence of operations to be carried out each time step of the simulation. Typically this is: state update and threshold; propagation; reset, although an option is available for switching propagation and reset around. Network operations can be weaved in anywhere amongst these basic steps. See the reference documentation for the :class:`Network` object for more details. Simulation proceeds by choosing the clock with the lowest current time, selecting all objects which have that clock as their clock, and performing the update schedule on those objects, before applying the :meth:`Clock.tick` method to increment the clock time by ``dt``. Network operations ~~~~~~~~~~~~~~~~~~ A :class:`NetworkOperation` object is called as if it were a function, i.e. the ``__call__()`` method is called with no arguments. The :func:`network_operation` decorator exists to convert a function into a suitable :class:`NetworkOperation` object. This technique is used for the internal functioning of many of Brian's features (such as :class:`StateMonitor`). :class:`NeuronGroup` update ~~~~~~~~~~~~~~~~~~~~~~~~~~~ The :meth:`NeuronGroup.update` method does the following three things. First of all it calls the ``StateUpdater`` to update the state variables. Then it calls its :class:`Threshold` object if one exists to extract the indices of the spiking neurons. Finally it pushes these into the ``LS`` attribute for extraction by any :class:`Connection` objects. :class:`NeuronGroup` reset ~~~~~~~~~~~~~~~~~~~~~~~~~~ The :meth:`Reset.__call__` method pulls spike from the :class:`NeuronGroup`'s ``LS`` attribute and then resets the state variables for those. Spike propagation ~~~~~~~~~~~~~~~~~ The :meth:`Connection.do_propagate` method does two things, it gets the spike indices to propagate (with homogeneous delays if chosen) from the ``LS`` attribute of the :class:`NeuronGroup` and then passes these to its :meth:`Connection.propagate` method. This method extracts a list of connection matrix rows using the :meth:`ConnectionMatrix.get_rows` method. This method returns a list of :class:`ConnectionVector` instances. There are two types of :class:`ConnectionVector`, dense and sparse. Dense ones are simply numpy arrays, sparse ones consist of two numpy arrays, an array of values and an array of corresponding column indices. The :class:`SparseConnectionVector` class has some methods which make this distinction seamless to the user in most instances, although developers need to be aware of it. Finally, the :meth:`Connection.propagate` method goes through this list applying the row vectors one by one. The pure Python version of this is straightforward, but there is also a ``C++`` accelerated version which uses the scipy Weave package if the user has a suitable compiler on their system. This version is much more efficient, but the code is rather dense and difficult to understand. brian-1.4.3/docs_sphinx/developer-equations.txt000066400000000000000000000226271266062647100217120ustar00rootroot00000000000000Equations ========= An Equation is a set of single lines in a string: (1) ``dx/dt = f : unit`` (differential equation) (2) ``x = f : unit`` (equation) (3) ``x = y`` (alias) (4) ``x : unit`` (parameter) The equations may be defined on multiple lines with the character \. Comments using # may also be included. Two special variables are defined: t (time) and xi (white noise). Ultimately, it should be possible (using Sympy) to define equations implicitly, e.g.: 'tau*dv/dt=-v : unit' (although it makes unit specification ambiguous). An equation can be seen as a set of functions or code and a namespace to evaluate them. A key part of object construction is the construction of the namespace (after parsing). Namespace construction ---------------------- The namespaces are stored in eq._namespace. Each equation (string) has a specific namespace. Proposition for a simplification: there could be just one namespace per Equation object rather than per string. Possible conflicts would be dealt with when equations are added (with prefix as when inserting static variables, see below). Variable substitution ~~~~~~~~~~~~~~~~~~~~~ These are simply string substitutions. * Equation('dv/dt=-v/tau:volt',tau='taum') The name of the variable (tau) is changed in the string to taum. * Equation('dv/dt=-v/tau:volt',tau=None) The name of the variable (tau) is changed in the string to a unique identifier. Explicit namespace ~~~~~~~~~~~~~~~~~~ * Equation('dv/dt=-v/tau:volt',tau=2*ms) The namespace is explicitly given: {'tau':2*ms}. In this case, Brian does not try to build a namespace "magically", so the namespace must be exhaustive. Units need not be passed. Implicit namespace ~~~~~~~~~~~~~~~~~~ * Equation('dv/dt=-v/tau:volt') The namespace is built from the globals and locals in the caller's frame. For each identifier in the string, the name is looked up in: 1) locals of caller, 2) globals of caller, 3) globals of equations.py module (typically units). Identifiers can be any Python object (for example functions). Issues ~~~~~~ * Special variables (xi and t) are not taken into account at this stage, i.e., they are integrated in the namespace if present. This should probably be fixed and a warning should be raised. A warning is raised for t at the preparation stage (see below). * If identifiers are not found, then no error is raised. This is to allow equations to be built in several pieces, which is useful in particular for library objects. * If an identifier is found whose name is the same as the name of a variable, then no error is raised here and it is included in the namespace. This is difficult to avoid in the case when equations are built in several pieces (e.g. the conflict appears only when the pieces are put together). A warning is issued at the preparation stage (see below). Attributes after initialisation ------------------------------- After initialisation, an Equation object contains: * a namespace (_namespace) * a dictionary of units for all variables (_units) * a dictionary of strings corresponding to each variable (right hand side of each equation), including parameters and aliases (_string). Parameters are defined as differential equations with RHS 0*unit/second. All comments are removed and multiline strings are concatenated. * a list of variables of non-differential equations (_eq_names) * a list of variables of differential equations, including parameters (_diffeq_names) * a list of variables of differential equations, excluding parameters (_diffeq_names_nonzero) * a dictionary of aliases (_alias), mapping a variable name to its alias There is no explicit list of parameters, maybe it should be added. Nothing more is done at initialisation time (no units checking, etc). The reason is that the equation set might not be complete at this time, in the case when equations are built in several pieces. Various checks are done using the prepare() method. Finalisation (prepare()) ------------------------ The Equation object is finalised by an explicit call to the prepare() method. Finding Vm ~~~~~~~~~~ The first step is to find the name of the membrane potential variable (getVm()). This is useful when the variable name for threshold or reset is not given (e.g. threshold=10*mV). The method looks for one these names: 'v','V','vm','Vm'. If one is present, it is the membrane potential variable. If none or more than one is present, no variable is found. If it is found, the corresponding variable is swapped with the first variable in the _diffeq_names list (note: not in the _diffeq_names_nonzero list). Otherwise, nothing happens. This way, the first variable in the list is the membrane potential. Possibly, a warning could be issued if it is not found. The problem it might issue warnings too often. A better way would be to issue warnings only when threshold and reset are used ambiguously (i.e., no Vm found and more than 1 variable). Cleaning the namespace ~~~~~~~~~~~~~~~~~~~~~~ Then variables and t are removed from the namespace if present (N.B.: xi does not appear to be removed), and warnings are issued using log_warn (method clean_namespace()). Compiling functions ~~~~~~~~~~~~~~~~~~~ This is done by the compile_functions() method. Python functions are created from the string definition of equations. For each equation/differential equation, the list of identifiers is obtained from the string definition, then only those referring to variables are kept. A Python lambda function of these remaining identifiers is then compiled (using eval) and put in the _function dictionary. Compiled functions are used for: * checking units * obtaining the list of arguments (this could be done independently) * state updates This step might be avoided and replaced by eval calls. It might actually be a little simpler because arguments would be replaced by namespace. It seems to be faster with the current implementation, but the string could be compiled with compile() (then evaluated in the relevant namespace). Besides, with the way it is currently evaluated in the Euler update: ``f(*[S[var] for var in f.func_code.co_varnames])``, it is not faster than direct evaluation in the namespace. Checking units ~~~~~~~~~~~~~~ This is done by the check_units() method. First, the static equations are ordered (see next section). To check the units of a static equation, one calls the associated function (giving the RHS) where the arguments are units (e.g., 1*volt for v, etc.) and adds the units of the LHS. A dimension error is raised if it is not homogeneous. Currently, the message states "The differential equation is not homogeneous" but it should be adapted to non-differential equations. One problem with this way of checking units is that the RHS function may not be defined at the point it is checked. Differential equations are checked in the same way, with two specificities: the units of RHS should be the units of the variable divided by second (dx/dt), and noise (xi) has units of second**-.5 (this is put in the globals of the function, which might not be a very clean way to do it). Ordering static variables ~~~~~~~~~~~~~~~~~~~~~~~~~ It seems that this method (set_eq_order()) is already called by check_units() and therefore it is probably not necessary to call it here. This method computes the dependency graph of (static) equations on other static variables, which must have no cycle (otherwise an error is raised). From that graph, an update list is built and put in _eq_names. Then for each variable (static or differential), the list of dependent static variables is built and sorted in update order. The result is put in the _dependencies dictionary. This is a necessary step to calculate the RHS of any equation: it gives the ordered list of static variables to calculate first before calculating the RHS. Inserting static variables into differential equations ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ The value of static variables are then replaced by their string value (RHS) in all differential equations (substitute_eq()). The previous step (ordering) ensures that the result if correct and does not depend on static variables anymore. To avoid namespace conflicts, all identifiers in the namespace of a static variable is augmented by a prefix: name+'_' (e.g. 'x_y' for identifier y in equation 'x=2*y'). Then namespaces are merged. It might not be optimal to do it in this way, because some of calculations will be done several times in an update step. It might be better to keep the static variables separate. Recompiling functions ~~~~~~~~~~~~~~~~~~~~~ Functions are then recompiled so that differential equations are now independent of static variables. Checking free variables ~~~~~~~~~~~~~~~~~~~~~~~ Finally, the list of undefined identifiers is checked (free_variables()) and a warning is issued if any is found. Freezing -------- Freezing is done by calling compile_functions(freeze=True). Each string expression is then frozen with optimiser.freeze(), which replaces identifiers by their float value. This step does not necessarily succeed, in which case a warning (not an error) is issued. Adding Equation objects ----------------------- Adding equations consists simply in merging the lists/dictionaries of variables, namespaces, strings, units and functions. Conflicts raise an error. This step must precede preparation of the object. brian-1.4.3/docs_sphinx/developer-guidelines.txt000066400000000000000000000120201266062647100220140ustar00rootroot00000000000000Guidelines ---------- The basic principles of developing Brian are: 1. For the user, the emphasis is on making the package flexible, readable and easy to use. See the paper "The Brian simulator" in Frontiers in Neuroscience for more details. 2. For the developer, the emphasis is on keeping the package maintainable by a small number of people. To this end, we use stable, well maintained, existing open source packages whenever possible, rather than writing our own code. *Coding conventions.* We use the `PEP-8 coding conventions `__ for our code. Syntax is chosen as much as possible from the user point of view, to reflect the concepts as directly as possible. Ideally, a Brian script should be readable by someone who doesn't know Python or Brian, although this isn't always possible. Function and class names should be explicit rather than abbreviated. *Documentation.* It is very important to maintain documentation. We use the `Sphinx documentation generator `__ tools. The documentation is all hand written. Sphinx source files are stored in the ``docs_sphinx`` folder in the repository, and compiled HTML files are stored in the ``docs`` folder. Most of the documentation is stored directly in the Sphinx source text files, but reference documentation for important Brian classes and functions are kept in the documentation strings of those classes themselves. This is automatically pulled from these classes for the reference manual section of the documentation. The idea is to keep the definitive reference documentation near the code that it documents, serving as both a comment for the code itself, and to keep the documentation up to date with the code. In the code, every class or function should start with an explanation of what it does, unless it is trivial. A good idea is to use explicit names rather than abbreviations, so that you instantly understand what it is about. Inside a function, important chunks should also be commented. *Testing.* Brian uses the `nose package `__ for its testing framework. Tests are stored in the ``brian/tests`` directory. Tests associated to a Brian module are stored in ``brian/tests/testinterface`` and tests of the mathematical correctness of Brian's algorithms are stored in ``brian/tests/testcorrectness``. *Errors.* It is a good idea to start an important function (e.g. object initialisation) with a check of the arguments, and possibly issue errors. This way errors are more understandable by the user. *Enhancements.* Brian uses a system parallel to the `Python Enhancement Proposals (PEPs) `__ system for Python, called *Brian Enhancement Proposals* (BEPs). These are stored in ``dev/BEPs``. Ideas for new functionality for Brian are put in here for comment and discussion. A BEP typically includes: * How the feature will look from user point of view, with example scripts. * Detailed implementation ideas and options. We also use the `Brian development `__ mailing list. Contributing code ~~~~~~~~~~~~~~~~~ First of all, you should register to the `developers mailing list `__. If you want to modify existing modules, you should make sure that you work on the latest SVN version. We use the Eclipse IDE because it has a nice Python plugin (Pydev) and SVN plugin, but of course you can use your preferred IDE. The next step is to carefully read the guidelines in this guide. Now that you wrote your code: * Write a test for it in ``brian/tests/testinterface``. If it is a new module, create a new file ``test_mymodule.py``; * Write documentation, both in the file (see how it's done in existing modules) and, if appropriate, in the relevant file in ``docs_sphinx``. We use the `Sphinx documentation generator `__ tools. If you want to see how it looks, generate the html docs by executing ``dev/tools/docs/build_html.py``. The html files will then be in ``docs``. * If it is a significant feature, write an example script in ``examples`` and insert a line in ``examples/examples_guide.txt``. * Create a patch file. For example with Eclipse, right-click on the Brian project, then Team > Create Patch > Save in filesystem, then Next > Project. * Send your patch as an attachment to the `developers mailing list `__ and make sure the subject of your message starts with [PATCH]. Then describe your patch in your message. From that point, your patch will either be directly included in the svn or (more likely) will be first discussed in the mailing list. *New modules.* New Brian modules typically start in the ``dev/ideas`` folder, then go to ``brian/experimental`` when they starting looking like modules. They move to the main folder when they are stable (especially the user syntax). brian-1.4.3/docs_sphinx/developer-packagestructure.txt000066400000000000000000000110761266062647100232520ustar00rootroot00000000000000Brian package structure ----------------------- List of modules with descriptions of contents: **Root package** ``base`` Shared base classes for some Brian clases. At the moment, just the :class:`ObjectContainer` class used to implement the ``contained_objects`` protocol. ``clock`` The :class:`Clock` object, :func:`guess_clock` function, and other clock manipulation functions. ``compartments`` A class used in compartmental modelling (see user documentation). ``connection`` Everything to do with connections, including the :class:`Connection` and :class:`DelayConnection` classes, but also construction/connection matrices and connection vector code. One of the longest and most technical parts of Brian. ``correlatedspikes`` A tool for producing correlated spike trains. ``directcontrol`` Classes for producing groups which fire spikes at user specified times. ``equations`` Everything to do with the :class:`Equations` class. ``globalprefs`` Global preferences for Brian, a few routines for getting and setting. ``group`` A base class for :class:`NeuronGroup` which creates an ``_S`` attribute from an :class:`Equations` object with the appropriate dynamical variables, and allows these variables to be accessed by e.g. ``grp.V`` by overriding the ``__getattr__`` and ``__setattr__`` methods. ``inspection`` Utility functions for inspecting namespaces, checking consistency of equations, some code manipulation, etc. ``log`` Brian's somewhat under-developed logging capabilities. ``magic`` Classes and functions for tracking and finding instances of classes. ``membrane_equations`` More code for compartmental modelling (see user docs). ``monitor`` All the monitors, including :class:`SpikeMonitor` and :class:`StateMonitor`. ``network`` The :class:`Network` and :class:`MagicNetwork` classes as well as the :class:`NetworkOperation` class. Also includes the :func:`run`, etc. functions. ``neurongroup`` The :class:`NeuronGroup` definition and some related stuff, including linked variables (the :class:`LinkedVar` class) and :class:`PoissonGroup`. ``optimiser`` Some tools for freezing expressions (converting e.g. ``3*ms`` into 0.003) and simplifying some equations (e.g. ``a/(10*ms)`` converted to ``a*100``). ``plotting`` Plotting tools, mostly ``raster_plot``. ``quantityarray`` A leftover from the day when Brian had support for arrays with units, will be removed when practical. ``reset`` Reset classes. ``stateupdater`` State update classes and the :func:`magic_state_updater` function. ``stdp`` STDP features. ``stdunits`` Standard unit names such as ``mV`` for ``mvolt``, etc. ``stp`` Short term plasticity features. ``threshold`` Threshold classes. ``timedarray`` The :class:`TimedArray` class and related functions. ``units`` The Brian units package, including the :class:`Quantity` class. ``unitsafefunctions`` Some functions which override the numpy ones which are safe to use with units, e.g. ``sin(3*volt)`` raises a dimensionality error. **``library`` subpackage** ``electrophysiology`` Electrophysiology library with electrode and amplifier models. ``IF`` Integrate-and-fire models (leaky, quadratic, exponential...). ``ionic_currents`` Ionic current models (K+, Na+...). ``random_processes`` Currently only Ornstein-Uhlenbeck. ``synapses`` Synaptic models (exponential, alpha and biexponential models). **``utils`` subpackage** ``approximatecomparisons`` Some tools for doing approximate comparisons with floating point numbers (because they are inexact). ``autodiff`` Automatic differentiation routines (for single-valued functions). ``circular`` and the ``ccircular`` subpackage The important :class:`SpikeContainer` and related classes. The ``C`` version uses SWIG and is much faster but requires the user to compile themselves at the moment (this will be addressed at some point in the future). ``documentation`` Some utility functions related to documentation. ``information_theory`` Entropy and mutual information estimators. Requires the ANN wrapper in scikits. ``parallelpython`` A utility function for using the Parallel Python module. ``parameters`` The :class:`Parameters` class, basically independent of Brian but potentially useful. ``progressreporting`` A progress reporting framework which :meth:`Network.run` can use to report how long it is taking to run, with text or graphical options. ``statistics`` Statistics of spike trains (CV, vector strength, correlograms...). ``tabulate`` Tabulation of numerical functions (precalculation). brian-1.4.3/docs_sphinx/developer-repositorystructure.txt000066400000000000000000000056741266062647100241050ustar00rootroot00000000000000Repository structure -------------------- The Brian source code repository is broken into the following directories: ``brian`` The main package, documented above, with the following additional directories: ``deprecated`` For code that is no longer up to date, but that we keep for backwards compatibility. ``experimental`` Package for storing experimental code that can be used but whose syntax and functionality may change. ``library`` Modules where specific models are defined (e.g. neuron and synaptic models). ``tests`` Package for storing tests, composed of: ``testcorrectness`` Package for tests of mathematical correctness of algorithms, etc. ``testinterface`` Package for tests of individual Brian modules. Module names are the names of the module being tested prepended by 'test'. ``unused`` Old stuff ``utils`` Modules that are not Brian-specific, for example circular.py defines circular arrays used for storing spiking events. ``dev`` The main development folder, for works in progress, debugging stuff, tools, etc. Consists of: ``benchmarking`` Code for benchmarking performance against other languages and simulators. ``BEPs`` The Brian Enhancement Proposals. ``debugging`` Dumping ground for files used for debugging a problem. ``troubleshooting`` Used for debugging problems from the ``brian-support`` mailing list. ``ideas`` For ideas for new features, incomplete implementations, etc. This is where new things go before going into the main Brian package or the ``experimental`` package. ``logo`` The Brian logo in various sizes. ``optimising`` Ideas for making Brian faster. ``speedtracking`` A sort of testing framework which tracks, over time, the speed of various Brian features. ``tests`` A few scripts to run Brian's tests. ``tools`` The main folder for developer tools. ``docs`` Scripts for invoking Sphinx and building the documentation. Includes script to automatically generate documentation for examples and tutorials, and to build index entries for these. ``newrelease`` Tools for creating a new public release of Brian. ``searchreplace`` Some tools for doing global changes to the code (e.g. syntax changes). ``dist`` Automatically generated distribution files. ``docs`` Automatically generated documentation files in HTML/PDF format. ``docs_sphinx`` Sources for Sphinx documentation. ``examples`` Examples of Brian's use. Documentation is automatically generated from all of these examples. ``tutorials`` Source files for the tutorials, documentation is automatically generated from these. Each tutorial has a directory, possibly containing an ``introduction.txt`` Sphinx source, followed by a series of files in alphabetical order (e.g. 1a, 1b, 1c, etc.). Multi-line strings are treated as Sphinx source code (take a look at a few examples to get the idea). brian-1.4.3/docs_sphinx/developer-simulationprinciples.txt000066400000000000000000000130501266062647100241450ustar00rootroot00000000000000Simulation principles --------------------- The following paper outlines the principles of Brian simulation: Goodman, D and Brette R (2008), `Brian: a simulator for spiking neural networks in Python `__, Front. Neuroinform. doi:10.3389/neuro.11.005.2008. This one describes the simulation algorithms, which are based on vectorisation: Brette R and Goodman, DF, `Vectorised algorithms for spiking neural network simulation `__, Neural Computation (in press). Sample script ~~~~~~~~~~~~~ Below we present a Brian script, and a translation into pure Python to illustrate the basic principles of Brian simulations. Original Brian script ..................... A script in Brian:: ''' Very short example program. ''' from brian import * from time import time N=10000 # number of neurons Ne=int(N*0.8) # excitatory neurons Ni=N-Ne # inhibitory neurons p=80./N duration=1000*ms eqs=''' dv/dt = (ge+gi-(v+49*mV))/(20*ms) : volt dge/dt = -ge/(5*ms) : volt dgi/dt = -gi/(10*ms) : volt ''' P=NeuronGroup(N,model=eqs, threshold=-50*mV,reset=-60*mV) P.v=-60*mV+10*mV*rand(len(P)) Pe=P.subgroup(Ne) Pi=P.subgroup(Ni) Ce=Connection(Pe,P,'ge',weight=1.62*mV,sparseness=p) Ci=Connection(Pi,P,'gi',weight=-9*mV,sparseness=p) M=SpikeMonitor(P) trace=StateMonitor(P,'v',record=0) t1=time() run(1*second) t2=time() print "Simulated in",t2-t1,"s" print len(M.spikes),"spikes" subplot(211) raster_plot(M) subplot(212) plot(trace.times/ms,trace[0]/mV) show() Equivalent in pure Python ......................... The script above translated into pure Python (no Brian):: ''' A pure Python version of the CUBA example, that reproduces basic Brian principles. ''' from pylab import * from time import time from random import sample from scipy import random as scirandom """ Parameters """ N=10000 # number of neurons Ne=int(N*0.8) # excitatory neurons Ni=N-Ne # inhibitory neurons mV=ms=1e-3 # units dt=0.1*ms # timestep taum=20*ms # membrane time constant taue=5*ms taui=10*ms p=80.0/N # connection probability (80 synapses per neuron) Vt=-1*mV # threshold = -50+49 Vr=-11*mV # reset = -60+49 we=60*0.27/10 # excitatory weight wi=-20*4.5/10 # inhibitory weight duration=1000*ms """ Equations --------- eqs=''' dv/dt = (ge+gi-(v+49*mV))/(20*ms) : volt dge/dt = -ge/(5*ms) : volt dgi/dt = -gi/(10*ms) : volt ''' This is a linear system, so each update corresponds to multiplying the state matrix by a (3,3) 'update matrix' """ # Update matrix A=array([[exp(-dt/taum),0,0], [taue/(taum-taue)*(exp(-dt/taum)-exp(-dt/taue)),exp(-dt/taue),0], [taui/(taum-taui)*(exp(-dt/taum)-exp(-dt/taui)),0,exp(-dt/taui)]]).T """ State variables --------------- P=NeuronGroup(4000,model=eqs, threshold=-50*mV,reset=-60*mV) """ S=zeros((3,N)) """ Initialisation -------------- P.v=-60*mV+10*mV*rand(len(P)) """ S[0,:]=rand(N)*(Vt-Vr)+Vr # Potential: uniform between reset and threshold """ Connectivity matrices --------------------- Pe=P.subgroup(3200) # excitatory group Pi=P.subgroup(800) # inhibitory group Ce=Connection(Pe,P,'ge',weight=1.62*mV,sparseness=p) Ci=Connection(Pi,P,'gi',weight=-9*mV,sparseness=p) """ We_target=[] We_weight=[] for _ in range(Ne): k=scirandom.binomial(N,p,1)[0] target=sample(xrange(N),k) target.sort() We_target.append(target) We_weight.append([1.62*mV]*k) Wi_target=[] Wi_weight=[] for _ in range(Ni): k=scirandom.binomial(N,p,1)[0] target=sample(xrange(N),k) target.sort() Wi_target.append(target) Wi_weight.append([-9*mV]*k) """ Spike monitor ------------- M=SpikeMonitor(P) will contain a list of (i,t), where neuron i spiked at time t. """ spike_monitor=[] # Empty list of spikes """ State monitor ------------- trace=StateMonitor(P,'v',record=0) # record only neuron 0 """ trace=[] # Will contain v(t) for each t (for neuron 0) """ Simulation ---------- run(duration) """ t1=time() t=0*ms while tVt).nonzero()[0] # List of neurons that meet threshold condition # PROPAGATION OF SPIKES # Excitatory neurons spikes=(S[0,:Ne]>Vt).nonzero()[0] # In Brian we actually use bisection to speed it up for i in spikes: S[1,We_target[i]]+=We_weight[i] # Inhibitory neurons spikes=(S[0,Ne:N]>Vt).nonzero()[0] for i in spikes: S[2,Wi_target[i]]+=Wi_weight[i] # Reset neurons after spiking S[0,all_spikes]=Vr # Reset membrane potential # Spike monitor spike_monitor+=[(i,t) for i in all_spikes] # State monitor trace.append(S[0,0]) t+=dt t2=time() print "Simulated in",t2-t1,"s" print len(spike_monitor),"spikes" """ Plot ---- subplot(211) raster_plot(M) subplot(212) plot(trace.times/ms,trace[0]/mV) show() Here we cheat a little. """ from brian import raster_plot class M: pass M.spikes=spike_monitor subplot(211) raster_plot(M) subplot(212) plot(arange(len(trace))*dt/ms,array(trace)/mV) show() brian-1.4.3/docs_sphinx/developer.txt000066400000000000000000000007721266062647100177010ustar00rootroot00000000000000Developer's guide ================= This section is intended as a guide to how Brian functions internally for people developing Brian itself, or extensions to Brian. It may also be of some interest to others wishing to better understand how Brian works internally. .. toctree:: :maxdepth: 2 developer-guidelines developer-simulationprinciples developer-codestructure developer-equations developer-codegen developer-packagestructure developer-repositorystructure brian-1.4.3/docs_sphinx/efficient.txt000066400000000000000000000200161266062647100176410ustar00rootroot00000000000000.. currentmodule:: brian .. index:: single: efficient code How to write efficient Brian code ================================= There are a few keys to writing fast and efficient Brian code. The first is to use Brian itself efficiently. The second is to write good vectorised code, which is using Python and NumPy efficiently. For more performance tips, see also :ref:`compiled-code`. Brian specifics --------------- .. removed for Brian 1.0 The slowest part of Brian is the :class:`QuantityArray` class. This is an extension of the NumPy ``ndarray`` array class with added support for physical units, unfortunately it's very slow in comparison to NumPy. For writing code which runs once, these arrays with units are a good idea because they check that you are using the right units, thus avoiding bugs, but for code that runs repeatedly (in a loop for example), it should be avoided. If you have a :class:`QuantityArray` object ``x``, you can convert it to a NumPy array by writing ``y=asarray(x)``. If you do this only once and from then on use ``y`` instead of ``x`` you will see a significant increase in speed. Also note that most Brian code that returns arrays of data supports a mechanism for returning a version without units. For example, if ``G`` is a :class:`NeuronGroup` with a variable ``V`` you would write ``G.V`` to get the array of values of ``V`` for each neuron in the group. This array will be a :class:`QuantityArray` and so will be slow. For code that is running often or in a loop, you can write ``G.V_`` to return the NumPy array version, which will be much faster. This same mechanism works for any :class:`NeuronGroup` neuron variable, just add an underscore ``_`` character to the end of the variable name to refer to the NumPy version without units. As an example, suppose you were writing a custom reset function, you might do something like this:: def myreset(P, spikes): P.V[spikes] = 0*mV P.Vt[spikes] += 2*mV This will work, but it will be very slow, so you should write this instead:: def myreset(P, spikes): P.V_[spikes] = 0*mV P.Vt_[spikes] += 2*mV Note that the constants can still be given with units, if you set a NumPy array to a value with units it just ignores the units. It's much better to write ``2*mV`` than ``0.002`` even though they're equivalent. You can switch off Brian's entire unit checking module by including the line:: import brian_no_units before importing Brian itself. Good practice is to leave unit checking on most of the time when developing and debugging a model, but switching it off for long runs once the basic model is stable. Another way to speed up code is to store references to arrays rather than extracting them from Brian objects each time you need them. For example, if you know the custom reset object in the code above is only ever applied to a group ``custom_group`` say, then you could do something like this:: def myreset(P, spikes): custom_group_V_[spikes] = 0*mV custom_group_Vt_[spikes] = 2*mV custom_group = ... custom_group_V_ = custom_group.V_ custom_group_Vt_ = custom_group.Vt_ In this case, the speed increase will be quite small, and probably not worth doing because it makes it less readable, but in more complicated examples where you repeatedly refer to ``custom_group.V_`` it could add up. .. index:: pair: efficient code; vectorisation single: vectorisation single: speed; vectorisation .. _efficiency-vectorisation: Vectorisation ------------- Python is a fast language, but each line of Python code has an associated overhead attached to it. Sometimes you can get considerable increases in speed by writing a vectorised version of it. A good guide to this in general is the `Performance Python `__ page. Here we will do a single worked example in Brian. Suppose you wanted to multiplicatively depress the connection strengths every time step by some amount, you might do something like this:: C = Connection(G1, G2, 'V', structure='dense') ... @network_operation(when='end') def depress_C(): for i in range(len(G1)): for j in range(len(G2)): C[i,j] = C[i,j]*depression_factor This will work, but it will be very, very slow. The first thing to note is that the Python expression ``range(N)`` actually constructs a list ``[0,1,2,...,N-1]`` each time it is called, which is not really necessary if you are only iterating over the list. Instead, use the ``xrange`` iterator which doesn't construct the list explicitly:: for i in xrange(len(G1)): for j in xrange(len(G2)): C[i,j] = C[i,j]*depression_factor The next thing to note is that when you call C[i,j] you are doing an operation on the :class:`Connection` object, not directly on the underlying matrix. Instead, do something like this:: C = Connection(G1, G2, 'V', structure='dense') C_matrix = asarray(C.W) ... @network_operation(when='end') def depress_C(): for i in xrange(len(G1)): for j in xrange(len(G2)): C_matrix[i,j] *= depression_factor What's going on here? First of all, ``C.W`` refers to the :class:`ConnectionMatrix` object, which is a 2D NumPy array with some extra stuff - we don't need the extra stuff so we convert it to a straight NumPy array ``asarray(C.W)``. We also store a copy of this as the variable ``C_matrix`` so we don't need to do this every time step. The other thing we do is to use the ``*=`` operator instead of the ``*`` operator. The most important step of all though is to vectorise the entire operation. You don't need to loop over ``i`` and ``j`` at all, you can manipulate the array object with a single NumPy expression:: C = Connection(G1, G2, 'V', structure='dense') C_matrix = asarray(C.W) ... @network_operation(when='end') def depress_C(): C_matrix *= depression_factor This final version will probably be hundreds if not thousands of times faster than the original. It's usually possible to work out a way using NumPy expressions only to do whatever you want in a vectorised way, but in some very rare instances it might be necessary to have a loop. In this case, if this loop is slowing your code down, you might want to try writing that loop in inline C++ using the `SciPy Weave `__ package. See the documentation at that link for more details, but as an example we could rewrite the code above using inline C++ as follows:: from scipy import weave ... C = Connection(G1, G2, 'V', structure='dense') C_matrix = asarray(C.W) ... @network_operation(when='end') def depress_C(): n = len(G1) m = len(G2) code = ''' for(int i=0;i`__, Neuron 59(3):379-91. Given a digital current-clamp recording of the (uncompensated) potential ``v`` (vector of values) and injected current ``i``, the following instructions calculate the full kernel of the system and the electrode kernel:: K=full_kernel(v,i,ksize) Ke=electrode_kernel_soma(K,start_tail) ``ksize`` is the size of the full kernel (number of sampling steps; typical size is about 15 ms) and ``start_tail`` is the size of the electrode kernel (start point of the ''tail'' of the full kernel; typical size if about 4 ms). The electrode should be compensated for capacitance (capacitance neutralization) but not resistance (bridge compensation). The best choice for the input current is a series of independent random values, and the last ``ksize`` steps of ``v`` should be null (i.e., the injection should stop before the end). Here it was assumed that the recording was done at the soma; if it is done in a thin process such as a dendrite or axon, the function ``electrode_kernel_dendrite`` should be used instead. The full kernel can also be obtained from a step current injection:: K=full_kernel_from_step(v,i,ksize) Ke=electrode_kernel_soma(K,start_tail) where ``i`` is a constant value in this case (note that this is not the best choice for real recordings). Once the electrode kernel has been found, any recording can be compensated as follows:: vcomp=AEC_compensate(v,i,ke) where ``v`` is the raw voltage recording, ``i`` is the injected current and ``ke`` is the electrode kernel. Lp compensation --------------- The Lp compensation is an other electrode compensation method. It is based on linear model fitting of an electrode and a neuron in response to an injected current. The fitness function is the Lp error between the full model response and the raw trace, with p<2 to minimize the bias due to the nonlinear voltage excursions of action potentials. You can use it like this:: Vcomp, params = Lp_compensate(I, Vraw, .1*ms) where ``I`` is the injected current, ``Vraw`` is the raw voltage trace, and the last parameter is the inverse of the sampling frequency. ``I`` and ``Vraw`` must be 1D Numpy arrays with the same length. The ``Lp_compensate`` function returns the compensated trace ``Vcomp`` and the best parameters ``params``, which is a 2D Numpy array where each column contains the parameters R, tau, Vr, Re, taue. Columns correspond to consecutive slices of the current and the voltage, the compensation is performed independently on each slice. The duration of the slices can be specified with the ``slice_duration`` keyword argument. Also, the ``p`` parameter can also be specified as a keyword argument. brian-1.4.3/docs_sphinx/electrophysiology_models.txt000066400000000000000000000214671266062647100230470ustar00rootroot00000000000000.. currentmodule:: brian Electrophysiology: models ========================= The electrophysiology library contains a number of models of electrodes, amplifiers and recording protocols to simulate intracellular electrophysiological recordings. To import the electrophysiology library:: from brian.library.electrophysiology import * There is a series of example scripts in the examples/electrophysiology folder. Electrodes ---------- Electrodes are defined as resistor/capacitor (RC) circuits, or multiple RC circuits in series. Define a simple RC electrode with resistance Re and capacitance Ce (possibly 0 pF) as follows:: el=electrode(Re,Ce) The ``electrode`` function returns an :class:`Equations` object containing the electrode model, where the electrode potential is ``v_el`` (the recording), the membrane potential is ``vm``, the electrode current entering the membrane is ``i_inj`` and command current is ``i_cmd``. These names can be overriden using the corresponding keywords. For example, a membrane equation with a .5 nA current injected through an electrode is defined as follows:: eqs=Equations('dv/dt=(-gl*v+i_inj)/Cm : volt')+electrode(50*Mohm,10*pF,vm='v',i_cmd=.5*nA) Specify ``i_cmd=None`` if the electrode is only used to record (no current injection). More complex electrodes can be defined by passing lists of resistances and capacitances, e.g.:: el=electrode([50*Mohm,20*Mohm],[5*pF,3*pF]) Amplifiers ---------- Current-clamp amplifier ^^^^^^^^^^^^^^^^^^^^^^^ A current-clamp amplifier injects a current through an intracellular electrode and records the membrane potential. Two standard circuits are included to compensate for the electrode voltage: bridge compensation and capacitance neutralization (see e.g. the `Axon guide `__). The following command:: amp=current_clamp(Re=80*Mohm,Ce=10*pF) defines a current-clamp amplifier with an electrode modelled as a RC circuit. The function returns an :class:`Equations` object, where the recording potential is ``v_rec``, the membrane potential is ``vm``, the electrode current entering the membrane is ``i_inj`` and command current is ``i_cmd``. These names can be overriden using the corresponding keywords. For implementation reasons, the amplifier always includes an electrode. Optionally, bridge compensation, can be used with the ``bridge`` keyword and capacitance neutralization with the ``capa_comp`` keyword. For example, the following instruction defines a partially compensated recording:: amp=current_clamp(Re=80*Mohm,Ce=10*pF,bridge=78*Mohm,capa_comp=8*pF) The capacitance neutralization is a feedback circuit, so that it becomes unstable if the feedback capacitance is larger than the actual capacitance of the electrode. The bridge compensation is an input-dependent voltage offset (``bridge*i_cmd``), and thus is always stable (unless an additional feedback, such as dynamic clamp, is provided). Note that the bridge and capacitance neutralization parameters can be variable names, e.g.:: amp=current_clamp(Re=80*Mohm,Ce=10*pF,bridge='Rbridge',capa_comp=8*pF) and then the bridge compensation can be changed dynamically during the simulation. Voltage-clamp amplifier ^^^^^^^^^^^^^^^^^^^^^^^ The library includes a single-electrode voltage-clamp amplifier, which clamps the potential at a given value and records the current going through the electrode. The following command:: amp=voltage_clamp(Re=20*Mohm) defines a voltage-clamp amplifier with an electrode modelled as a pure resistance. The function returns an :class:`Equations` object, where the recording current is ``i_rec``, the membrane potential is ``vm``, the electrode current entering the membrane is ``i_inj`` and command voltage is ``v_cmd`` (note that ``i_rec`` = - ``i_inj``). These names can be overriden using the corresponding keywords. For implementation reasons, the amplifier always includes an electrode. Electrode capacitance is not included, meaning that the capacitance neutralization circuit is always set at the maximum value. The quality of the clamp is limited by the electrode or ''series'' resistance, which can be compensated in a similar way as bridge compensation in current-clamp recordings. Series resistance compensation consists in adding a current-dependent voltage offset to the voltage command. Because of the feedback, that compensation needs to be slightly delayed (with a low-pass circuit). The following example defines a voltage-clamp amplifier with half-compensated series resistance and compensation delay 1 ms:: amp=voltage_clamp(Re=20*Mohm,Rs=10*Mohm,tau_u=1*ms) The ``tau_u`` keyword is optional and defaults to 1 ms. Acquisition board ^^^^^^^^^^^^^^^^^ An acquisition board samples a recording and sends a command (e.g. injected current) at regular times. It is defined as a :class:`NeuronGroup`. Use:: board=AcquisitionBoard(P=neuron,V='V',I='I',clock) where ``P`` = neuron group (possibly containing amplifier and electrode), ``V`` = potential variable name, ``I`` = current variable name, ``clock`` = acquisition clock. The recording variable is then stored in ``board.record`` and a command is sent with the instruction ``board.command=I``. Discontinuous current clamp ^^^^^^^^^^^^^^^^^^^^^^^^^^^ The discontinuous current clamp (DCC) consists in alternatively injecting current and measuring the potential, in order to measure the potential when the voltage across the electrode has vanished. The sampling clock is mainly determined by the electrode time constant (the sampling period should be two orders of magnitude larger than the electrode time constant). It is defined and used in the same way as an acquisition board (above):: board=DCC(P=neuron,V='V',I='I',frequency=2*kHz) where ``frequency`` is the sampling frequency. The duty cycle is 1/3 (meaning current is injected during 1/3 of each sampling step). Discontinuous voltage clamp ^^^^^^^^^^^^^^^^^^^^^^^^^^^ The discontinuous voltage clamp or single-electrode voltage clamp (SEVC) is an implementation of the voltage clamp using a feedback current with a DCC amplifier. It is defined as the DCC:: board=SEVC(P=neuron,V='V',I='I',frequency=2*kHz,gain=10*nS) except that a gain parameter is included. The SEVC injects a negative feedback current I=gain*(Vcommand-V). The quality of the clamp improves with higher gains, but there is a maximum value above which the system is unstable, because of the finite temporal resolution. The recorded current is stored in ``board.record`` and the command voltage is sent with the instruction ``board.command=-20*mV``. With this implementation of the SEVC, the membrane is never perfectly clamped. A better clamp is obtained by adding an integral controller with the keyword ``gain2=10*nS/ms``. The additional current J(t) is governed by the differential equation dJ/dt=gain2*(Vcommand-V), so that it ensures perfect clamping in the stationary state. However, this controller does not improve the settling time of the clamp, but only the final voltage value. Active Electrode Compensation ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ The electrophysiology library includes the Active Electrode Compensation (AEC) technique described in Brette et al (2008), `High-resolution intracellular recordings using a real-time computational model of the electrode `__, Neuron 59(3):379-91. It can be applied offline, or online, using the models of experimental setup described above. (for dynamic-clamp or voltage-clamp recordings, the electrode compensation must be done online). An AEC board is initialized in the same way as an acquisition board:: board=AEC(neuron,'V','I',clock) where ``clock`` is the acquisition clock. The estimation phase typically looks like:: board.start_injection() run(2*second) board.start_injection() run(100*ms) board.estimate() where white noise is injected for 2 seconds (default amplitude .5 nA). You can change the default amplitude and DC current as follows: ``board.start_injection(amp=.5*nA,DC=1*nA)``. After estimation, the kernel is stored in ``board.Ke``. The following options can be passed to the function ``estimate``: ``ksize`` (default 150 sampling steps), ``ktail`` (default 50 sampling steps) and ``dendritic`` (default ``False``, use ``True`` is the recording is a thin process, i.e., axon or dendrite). Online compensation is then switched on with ``board.switch_on()`` and off with ``board.switch_off()``. For example, to inject a .5 nA current pulse for 200 ms, use the following instructions:: board.switch_on() board.command=.5*nA run(200*ms) board.command=0*nA run(150*ms) board.switch_off() During the simulation, the variable ``board.record`` stores the compensated potential. brian-1.4.3/docs_sphinx/equations.txt000066400000000000000000000333431266062647100177240ustar00rootroot00000000000000.. currentmodule:: brian .. index:: equations .. _moreonequations: More on equations ================= The :class:`Equations` class is a central part of Brian, since models are generally specified with an :class:`Equations` object. Here we explain advanced aspects of this class. .. index:: pair: equations; external variables pair: equations; namespaces External variables ------------------ Equations may contain external variables. When an :class:`Equations` object is initialised, a dictionary is built with the values of all external variables. These values are taken from the namespace where the :class:`Equations` object was defined. It is possible to go one or several levels up in the namespaces by specifying the keyword ``level`` (default=0). The value of these parameters can in general be changed during the simulation and the modifications are taken into account, except in two situations: when the equations are frozen (see below) or when the integration is exact (linear equations). In those cases, the values of the parameters are the ones at initialisation time. Alternatively, the string defining the equations can be evaluated within a given namespace by providing keywords at initialisation time, e.g.:: eqs=Equations('dx/dt=-x/tau : volt',tau=10*ms) In that case, the values of all external variables are taken from the specified dictionary (given by the keyword arguments), even if variables with the same name exist in the namespace where the string was defined. The two methods for passing the values of external variables are mutually exclusive, that is, either all external variables are explicitly specified with keywords (if not, they are left unspecified even if there are variables with the same names in the namespace where the string was defined), or all values are taken from the calling namespace. More can be done with keyword arguments. If the value is a string, then the name of the variable is replaced, e.g.:: eqs=Equations('dx/dt=-x/tau : volt',tau=10*ms,x='Vm') changes the variable name x to Vm. This is useful for writing functions which return equations where the variable name is provided by the user. Finally, if the value is ``None`` then the name of the variable is replaced by a unique name, e.g.:: eqs=Equations('dx/dt=-x/tau : volt',tau=10*ms,x=None) This is useful to avoid conflicts in the names of hidden variables. Issues ^^^^^^ * There can be problems if a variable with the same name as the variable of a differential equation exists in the namespace where the :class:`Equations` object was defined. .. index:: pair: equations; combining Combining equations ------------------- :class:`Equations` can be combined using the sum operator. For example:: eqs=Equations('dx/dt=(y-x)/tau : volt') eqs+=Equations('dy/dt=-y/tau: volt') Note that some variables may be undefined when defining the first equation. No error is raised when variables are undefined and absent from the calling namespace. When two :class:`Equations` objects are added, the consistency is checked. For example it is not possible to add two :class:`Equations` objects which define the same variable. .. index:: pair: equations; membrane potential Which variable is the membrane potential? ----------------------------------------- Several objects, such as :class:`Threshold` or :class:`Reset` objects can be initialised without specifying which variable is the membrane potential, in which case it is assumed that it is the first variable. Internally, the variables of an :class:`Equations` object are reorderered so that the first one is most likely to be the membrane potential (using :meth:`Equations.get_Vm`). The first variable is, with decreasing priority : * v * V * vm * Vm * the first defined variable. .. index:: pair: equations; numerical integration .. _numerical-integration: Numerical integration --------------------- The currently available integration methods are: * Exact integration when the equations are linear. * Euler integration (explicit, first order). * Runge-Kutta integration (explicit, second order). * Exponential Euler integration (implicit, first order). The method is selected when a :class:`NeuronGroup` is initialized. If the equations are linear, exact integration is automatically selected. Otherwise, Euler integration is selected by default, unless the keyword ``implicit=True`` is passed, which selects the exponential Euler method. A second-order method can be selected using the keyword ``order=2`` (explicit Runge-Kutta method, midpoint estimation). It is possible to override this behaviour with the ``method`` keyword when initialising a :class:`NeuronGroup`. Possible values are ``linear``, ``nonlinear``, ``Euler``, ``RK``, ``exponential_Euler``. .. index:: pair: equations; linear pair: numerical integration; exact pair: numerical integration; semi-exact Exact integration ^^^^^^^^^^^^^^^^^ If the differential equations are linear, then the update phase X(t)->X(t+dt) can be calculated exactly with a matrix product. First, the equations are examined to determine whether they are linear with the method :meth:`~equations.Equations.islinear` and the function :func:`~inspection.is_affine` (this is currently done using dynamic typing). Second, the matrix M and the vector B such that dX/dt=M(X-B) are calculated with the function :func:`~stateupdater.get_linear_equations` [1]_. Third, the matrix A such that X(t+dt)=A*(X(t)-B)+B is calculated at initialisation of a specific state updater object, :class:`LinearStateUpdater`, as A=expm(M*dt), where expm is the matrix exponential. **Important remark**: since the update matrix and vector are precalculated, the values of all external variables in the equations are frozen at initialisation. If external variables are modified after initialisation, those modifications are *not* taken into account during the simulation. **Inexact exact integration**: If the equation cannot be put into the form dX/dt=M(X-B), for example if the equation is dX/dt=MX+A where M is not invertible, then the equations are not integrated exactly, but using a system equivalent to Euler integration but with dt 100 times smaller than specified. Updates are of the form X(t+dt)=A*X(t)+C where the matrix A and vector C are computed by applying Euler integration 100 times to the differential equations. .. index:: pair: numerical integration; Euler Euler integration ^^^^^^^^^^^^^^^^^ The Euler is a first order explicit integration method. It is the default one for nonlinear equations. It is simply implemented as X(t+dt)=X(t)+f(X)*dt. .. index:: pair: numerical integration; exponential Euler pair: numerical integration; Hodgin-Huxley type equations Exponential Euler integration ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ The exponential Euler method is used for Hodgkin-Huxley type equations, are which stiff. Equations of that type are conditionally linear, that is, the differential equation for each variable is linear in that variable (i.e., linear if all other variables are considered constant). The idea is thus to solve the differential equation for each variable over one time step, assuming that all other variables are constant over that time step. The numerical scheme is still first order, but it is more stable than the forward Euler method. Each equation can be written as dx/dt=a*x+b, where a and b depend on the other variables and thus change after each time step. The values of a and b are obtained during the update phase by calculating a*x+b for x=0 and x=1 (note that these values are different for every neuron, thus we calculate vectors A and B). Then x(t+dt) is calculated in the same way as for the exact integration method above. .. index:: single: equations; stochastic pair: differential equations; stochastic Stochastic differential equations --------------------------------- Noise is introduced in differential equations with the keyword ``xi``, which means normalised gaussian noise (the derivative of the Brownian term). Currently, this is implemented simply by adding a normal random number to the variable at the end of the integration step (independently for each neuron). The unit of white noise is non-trivial, it is ``second**(-.5)``. Thus, a typical stochastic equation reads:: dx/dt=-x/tau+sigma*xi/tau**.5 where ``sigma`` is in the same units as ``x``. We note the following two facts: * The noise term is independent between neurons. Thus, one cannot use this method to analyse the response to frozen noise (where all neurons receive the same input noise). One would need to use an external variable representing the input, updated by a user-defined operation. * The noise term is independent between equations. This can however be solved by the following trick:: dx/dt=-x/tau+sigmax*u/tau**.5 : volt dy/dt=-y/tau+sigmay*u/tau**.5 : volt u=xi : second**(-.5) Important notice ^^^^^^^^^^^^^^^^ It is not possible to modulate the noise term with a variable (e.g. ``v*xi/tau**.5``). One reason is that, with multiplicative noise, there is an ambiguity between the Ito and the Stratonovich interpretation. Unfortunately, this limitation also applies to parameters, i.e., ``sigma*xi/tau**.5`` is not possible if sigma is a parameter, as in the following example:: eqs=Equations('''dx/dt=-x/tau + sigma*xi/tau**.5 : volt sigma : volt''') group = NeuronGroup(1, eqs, threshold='x>vt') However, the problem can usually be solved by some rewriting:: eqs=Equations('''dy/dt=-y/tau + xi/tau**.5 : 1 x=sigma*y : volt sigma : volt''') group = NeuronGroup(1, eqs, threshold = 'x>vt') .. index:: pair: differential equations; non-autonomous pair: differential equations; time-dependent pair: equations; time-dependent pair: equations; non-autonomous Non-autonomous equations ------------------------ The time variable ``t`` can be directly inserted into an equation string. It is replaced at run time by the current value of the time variable for the relevant neuron group, and also appears as a state variable of the neuron group. .. index:: pair: equations; freezing pair: differential equations; freezing Freezing -------- External variables can be frozen by passing the keyword ``freeze=True`` (default = ``False``) at initialization of a :class:`NeuronGroup` object. Then when the string defining the equations are compiled into Python functions (method :meth:`~equations.Equations.compile_functions`), the external variables are replaced by their float values (units are discarded). This can result in a significant speed-up. TODO: more on the implementation. .. index:: pair: equations; compilation pair: differential equations; compilation Compilation ----------- State updates can be compiled into Python code objects by passing the keyword ``compile=True`` at initialization of a a :class:`NeuronGroup`. Note that this is different from the method :meth:`~equations.Equations.compile_functions`, which compiles the equation for every variable into a Python function (not the whole state update process). When the ``compile`` keyword is set, the method :meth:`~equations.Equations.forward_euler_code` or :meth:`~equations.Equations.exponential_euler_code` is called. It generates a string containing the Python code for the update of all state variables (one time step), then compiles it into Python code object. That compiled object is then called at every time step. All external variables are frozen in the process (regardless of the value of the ``freeze`` keyword). This results in a significant speed-up (although the exponential Euler code is not quite optimised yet). Note that only Python code is generated, thus a C compiler is not required. Working with equations ---------------------- :class:`Equations` object can also be used outside simulations. In the following, we suppose that an :class:`Equations` object is defined as follows:: eqs=Equations(''' dx/dt=(y-x)/(10*ms) : volt dy/dt=-z/(5*ms) : volt z=2*(x+y) : volt ''') .. index:: pair: equations; applying Applying an equation ^^^^^^^^^^^^^^^^^^^^ The value of z can be calculated using the :meth:`~equations.Equations.apply` method:: z=eqs.apply('z',dict(x=3*mV,y=5*mV)) The second argument is a dictionary containing the values of all dependent variables (here the result is ``8*mV``). The right-hand side of differential equations can also be calculated in the same way:: x=eqs.apply('x',dict(x=2*mV,y=3*mV)) y=eqs.apply('y',dict(x=2*mV,y=3*mV)) Note in the second case that only the values of the dynamic variables should be passed. .. index:: pair: equations; fixed points Calculating a fixed point ^^^^^^^^^^^^^^^^^^^^^^^^^ A fixed point of the equations can be calculated as follows:: fp=eqs.fixedpoint(x=2*mV,y=3*mV) where the optional keywords give the initial point (zero if not provided). Internally, the function ``optimize.fsolve`` from the Scipy package is used to find a zero of the set of differential equations (thus, convergence is not guaranteed; in that case, the initial values are returned). A dictionary with the values of the dynamic variables at the fixed point is returned. Issues ^^^^^^ * If the equations were previously frozen, then the units disappear from the equations and unit consistency problems may arise. * :class:`Equations` objects need to be "prepared" before use, as follows:: eqs.prepare() This is automatically called by the NeuronGroup initialiser. .. rubric:: Footnotes .. [1] Note that this approach raises an issue when dX/dt=B. We currently (temporarily) solve this problem by adding a small diagonal matrix to M to make it invertible. brian-1.4.3/docs_sphinx/examples-audition_filterbank.txt000066400000000000000000000032371266062647100235440ustar00rootroot00000000000000.. currentmodule:: brian .. index:: pair: example usage; subplot pair: example usage; plot pair: example usage; run pair: example usage; show pair: example usage; PoissonThreshold pair: example usage; xlabel pair: example usage; linspace pair: example usage; ylabel pair: example usage; SpikeMonitor pair: example usage; NeuronGroup pair: example usage; SpikeCounter pair: example usage; gain .. _example-audition_filterbank: Example: filterbank (audition) ============================== An auditory filterbank implemented with Poisson neurons The input sound has a missing fundamental (only harmonics 2 and 3) :: from brian import * defaultclock.dt = .01 * ms N = 1500 tau = 1 * ms # Decay time constant of filters = 2*tau freq = linspace(100 * Hz, 2000 * Hz, N) # characteristic frequencies f_stimulus = 500 * Hz # stimulus frequency gain = 500 * Hz eqs = ''' dv/dt=(-a*w-v+I)/tau : Hz dw/dt=(v-w)/tau : Hz # e.g. linearized potassium channel with conductance a a : 1 I = gain*(sin(4*pi*f_stimulus*t)+sin(6*pi*f_stimulus*t)) : Hz ''' neurones = NeuronGroup(N, model=eqs, threshold=PoissonThreshold()) neurones.a = (2 * pi * freq * tau) ** 2 spikes = SpikeMonitor(neurones) counter = SpikeCounter(neurones) run(100 * ms) subplot(121) CF = array([freq[i] for i, _ in spikes.spikes]) timings = array([t for _, t in spikes.spikes]) plot(timings / ms, CF, '.') xlabel('Time (ms)') ylabel('Characteristic frequency (Hz)') subplot(122) plot(counter.count / (300 * ms), freq) xlabel('Firing rate (Hz)') show() brian-1.4.3/docs_sphinx/examples-audition_jeffress.txt000066400000000000000000000044451266062647100232340ustar00rootroot00000000000000.. currentmodule:: brian .. index:: pair: example usage; NeuronGroup pair: example usage; run pair: example usage; TimedArray pair: example usage; show pair: example usage; raster_plot pair: example usage; Connection pair: example usage; SpikeMonitor pair: example usage; linspace pair: example usage; StateMonitor .. _example-audition_jeffress: Example: jeffress (audition) ============================ Jeffress model, adapted with spiking neuron models. A sound source (white noise) is moving around the head. Delay differences between the two ears are used to determine the azimuth of the source. Delays are mapped to a neural place code using delay lines (each neuron receives input from both ears, with different delays). Romain Brette :: from brian import * defaultclock.dt = .02 * ms dt = defaultclock.dt # Sound sound = TimedArray(10 * randn(50000)) # white noise # Ears and sound motion around the head (constant angular speed) sound_speed = 300 * metre / second interaural_distance = 20 * cm # big head! max_delay = interaural_distance / sound_speed print "Maximum interaural delay:", max_delay angular_speed = 2 * pi * radian / second # 1 turn/second tau_ear = 1 * ms sigma_ear = .1 eqs_ears = ''' dx/dt=(sound(t-delay)-x)/tau_ear+sigma_ear*(2./tau_ear)**.5*xi : 1 delay=distance*sin(theta) : second distance : second # distance to the centre of the head in time units dtheta/dt=angular_speed : radian ''' ears = NeuronGroup(2, model=eqs_ears, threshold=1, reset=0, refractory=2.5 * ms) ears.distance = [-.5 * max_delay, .5 * max_delay] traces = StateMonitor(ears, 'x', record=True) # Coincidence detectors N = 300 tau = 1 * ms sigma = .1 eqs_neurons = ''' dv/dt=-v/tau+sigma*(2./tau)**.5*xi : 1 ''' neurons = NeuronGroup(N, model=eqs_neurons, threshold=1, reset=0) synapses = Connection(ears, neurons, 'v', structure='dense', delay=True, max_delay=1.1 * max_delay) synapses.connect_full(ears, neurons, weight=.5) synapses.delay[0, :] = linspace(0 * ms, 1.1 * max_delay, N) synapses.delay[1, :] = linspace(0 * ms, 1.1 * max_delay, N)[::-1] spikes = SpikeMonitor(neurons) run(1000 * ms) raster_plot(spikes) show() brian-1.4.3/docs_sphinx/examples-audition_licklider.txt000066400000000000000000000040111266062647100233540ustar00rootroot00000000000000.. currentmodule:: brian .. index:: pair: example usage; NeuronGroup pair: example usage; run pair: example usage; log pair: example usage; show pair: example usage; raster_plot pair: example usage; yticks pair: example usage; Connection pair: example usage; SpikeMonitor pair: example usage; linspace pair: example usage; exp pair: example usage; ylabel pair: example usage; StateMonitor .. _example-audition_licklider: Example: licklider (audition) ============================= Spike-based adaptation of Licklider's model of pitch processing (autocorrelation with delay lines) with phase locking. Romain Brette :: from brian import * defaultclock.dt = .02 * ms # Ear and sound max_delay = 20 * ms # 50 Hz tau_ear = 1 * ms sigma_ear = .1 eqs_ear = ''' dx/dt=(sound-x)/tau_ear+sigma_ear*(2./tau_ear)**.5*xi : 1 sound=5*sin(2*pi*frequency*t)**3 : 1 # nonlinear distorsion #sound=5*(sin(4*pi*frequency*t)+.5*sin(6*pi*frequency*t)) : 1 # missing fundamental frequency=(200+200*t*Hz)*Hz : Hz # increasing pitch ''' receptors = NeuronGroup(2, model=eqs_ear, threshold=1, reset=0, refractory=2 * ms) traces = StateMonitor(receptors, 'x', record=True) sound = StateMonitor(receptors, 'sound', record=0) # Coincidence detectors min_freq = 50 * Hz max_freq = 1000 * Hz N = 300 tau = 1 * ms sigma = .1 eqs_neurons = ''' dv/dt=-v/tau+sigma*(2./tau)**.5*xi : 1 ''' neurons = NeuronGroup(N, model=eqs_neurons, threshold=1, reset=0) synapses = Connection(receptors, neurons, 'v', structure='dense', max_delay=1.1 * max_delay, delay=True) synapses.connect_full(receptors, neurons, weight=.5) synapses.delay[1, :] = 1. / exp(linspace(log(min_freq / Hz), log(max_freq / Hz), N)) spikes = SpikeMonitor(neurons) run(500 * ms) raster_plot(spikes) ylabel('Frequency') yticks([0, 99, 199, 299], array(1. / synapses.delay.todense()[1, [0, 99, 199, 299]], dtype=int)) show() brian-1.4.3/docs_sphinx/examples-electrophysiology_AEC.txt000066400000000000000000000034661266062647100237670ustar00rootroot00000000000000.. currentmodule:: brian .. index:: pair: example usage; plot pair: example usage; run pair: example usage; figure pair: example usage; show pair: example usage; sum pair: example usage; Clock pair: example usage; NeuronGroup pair: example usage; Equations pair: example usage; StateMonitor .. _example-electrophysiology_AEC: Example: AEC (electrophysiology) ================================ AEC experiment (current-clamp) :: from brian import * from brian.library.electrophysiology import * from time import time myclock = Clock(dt=.1 * ms) clock_rec = Clock(dt=.1 * ms) #log_level_debug() taum = 20 * ms gl = 20 * nS Cm = taum * gl Re = 50 * Mohm Ce = 0.1 * ms / Re eqs = Equations(''' dvm/dt=(-gl*vm+i_inj)/Cm : volt I:amp ''') eqs += electrode(.6 * Re, Ce) eqs += current_clamp(vm='v_el', i_inj='i_cmd', i_cmd='I', Re=.4 * Re, Ce=Ce) setup = NeuronGroup(1, model=eqs, clock=myclock) board = AEC(setup, 'v_rec', 'I', clock_rec) recording = StateMonitor(board, 'record', record=True, clock=myclock) soma = StateMonitor(setup, 'vm', record=True, clock=myclock) run(50 * ms) board.command = .5 * nA run(200 * ms) board.command = 0 * nA run(150 * ms) board.start_injection() t1 = time() run(1 * second) t2 = time() print 'Duration:', t2 - t1, 's' board.stop_injection() run(100 * ms) board.estimate() print 'Re=', sum(board.Ke) * ohm board.switch_on() run(50 * ms) board.command = .5 * nA run(200 * ms) board.command = 0 * nA run(150 * ms) board.switch_off() figure() plot(recording.times / ms, recording[0] / mV, 'b') plot(soma.times / ms, soma[0] / mV, 'r') figure() plot(board.Ke) show() brian-1.4.3/docs_sphinx/examples-electrophysiology_DCC.txt000066400000000000000000000032651266062647100237650ustar00rootroot00000000000000.. currentmodule:: brian .. index:: pair: example usage; NeuronGroup pair: example usage; run pair: example usage; plot pair: example usage; show pair: example usage; Equations pair: example usage; StateMonitor .. _example-electrophysiology_DCC: Example: DCC (electrophysiology) ================================ An example of single-electrode current clamp recording with discontinuous current clamp (using the electrophysiology library). :: from brian import * from brian.library.electrophysiology import * defaultclock.dt = 0.01 * ms taum = 20 * ms # membrane time constant gl = 1. / (50 * Mohm) # leak conductance Cm = taum * gl # membrane capacitance Re = 50 * Mohm # electrode resistance Ce = 0.1 * ms / Re # electrode capacitance eqs = Equations(''' dvm/dt=(-gl*vm+i_inj)/Cm : volt Rbridge:ohm # bridge resistance I:amp # command current ''') eqs += current_clamp(i_cmd='I', Re=Re, Ce=Ce) setup = NeuronGroup(1, model=eqs) ampli = DCC(setup, 'v_rec', 'I', 1 * kHz) soma = StateMonitor(setup, 'vm', record=True) recording = StateMonitor(setup, 'v_rec', record=True) DCCrecording = StateMonitor(ampli, 'record', record=True) # No compensation run(50 * ms) ampli.command = .5 * nA run(100 * ms) ampli.command = 0 * nA run(50 * ms) ampli.set_frequency(2 * kHz) ampli.command = .5 * nA run(100 * ms) ampli.command = 0 * nA run(50 * ms) plot(recording.times / ms, recording[0] / mV, 'b') plot(DCCrecording.times / ms, DCCrecording[0] / mV, 'k') plot(soma.times / ms, soma[0] / mV, 'r') show() brian-1.4.3/docs_sphinx/examples-electrophysiology_SEVC.txt000066400000000000000000000024551266062647100241340ustar00rootroot00000000000000.. currentmodule:: brian .. index:: pair: example usage; plot pair: example usage; run pair: example usage; figure pair: example usage; show pair: example usage; NeuronGroup pair: example usage; Equations pair: example usage; StateMonitor .. _example-electrophysiology_SEVC: Example: SEVC (electrophysiology) ================================= Voltage-clamp experiment (SEVC) :: from brian import * from brian.library.electrophysiology import * defaultclock.dt = .01 * ms taum = 20 * ms # membrane time constant gl = 1. / (50 * Mohm) # leak conductance Cm = taum * gl # membrane capacitance Re = 50 * Mohm # electrode resistance Ce = 0.1 * ms / Re # electrode capacitance eqs = Equations(''' dvm/dt=(-gl*vm+i_inj)/Cm : volt I:amp ''') eqs += current_clamp(i_cmd='I', Re=Re, Ce=Ce) setup = NeuronGroup(1, model=eqs) ampli = SEVC(setup, 'v_rec', 'I', 1 * kHz, gain=250 * nS, gain2=50 * nS / ms) recording = StateMonitor(ampli, 'record', record=True) soma = StateMonitor(setup, 'vm', record=True) ampli.command = 20 * mV run(200 * ms) figure() plot(recording.times / ms, recording[0] / nA, 'k') figure() plot(soma.times / ms, soma[0] / mV, 'b') show() brian-1.4.3/docs_sphinx/examples-electrophysiology_bridge.txt000066400000000000000000000027261266062647100246310ustar00rootroot00000000000000.. currentmodule:: brian .. index:: pair: example usage; plot pair: example usage; run pair: example usage; show pair: example usage; linspace pair: example usage; NeuronGroup pair: example usage; Equations pair: example usage; StateMonitor .. _example-electrophysiology_bridge: Example: bridge (electrophysiology) =================================== Bridge experiment (current-clamp) :: from brian import * from brian.library.electrophysiology import * defaultclock.dt = .01 * ms #log_level_debug() taum = 20 * ms gl = 20 * nS Cm = taum * gl Re = 50 * Mohm Ce = 0.5 * ms / Re N = 10 eqs = Equations(''' dvm/dt=(-gl*vm+i_inj)/Cm : volt #Rbridge:ohm CC:farad I:amp ''') eqs += electrode(.6 * Re, Ce) #eqs+=current_clamp(vm='v_el',i_inj='i_cmd',i_cmd='I',Re=.4*Re,Ce=Ce, # bridge='Rbridge') eqs += current_clamp(vm='v_el', i_inj='i_cmd', i_cmd='I', Re=.4 * Re, Ce=Ce, bridge=Re, capa_comp='CC') setup = NeuronGroup(N, model=eqs) setup.I = 0 * nA setup.v = 0 * mV #setup.Rbridge=linspace(0*Mohm,60*Mohm,N) setup.CC = linspace(0 * Ce, Ce, N) recording = StateMonitor(setup, 'v_rec', record=True) run(50 * ms) setup.I = .5 * nA run(200 * ms) setup.I = 0 * nA run(150 * ms) for i in range(N): plot(recording.times / ms + i * 400, recording[i] / mV, 'k') show() brian-1.4.3/docs_sphinx/examples-electrophysiology_compensation_ex1.txt000066400000000000000000000026471266062647100266530ustar00rootroot00000000000000.. currentmodule:: brian .. index:: pair: example usage; load pair: example usage; plot pair: example usage; subplot pair: example usage; linspace pair: example usage; show .. _example-electrophysiology_compensation_ex1: Example: compensation_ex1 (electrophysiology) ============================================= Example of L^p electrode compensation method. Requires binary files "current.npy" and "rawtrace.npy". Rossant et al., "A calibration-free electrode compensation method" J. Neurophysiol 2012 :: import os from brian import * import numpy as np from brian.library.electrophysiology import * working_dir = os.path.dirname(__file__) # load data dt = 0.1*ms current = np.load(os.path.join(working_dir, "current.npy")) # 10000-long vector, 1s duration rawtrace = np.load(os.path.join(working_dir, "trace.npy")) # 10000-long vector, 1s duration t = linspace(0., 1., len(current)) # launch compensation r = Lp_compensate(current, rawtrace, dt, p=1.0, full=True) # print best parameters print "Best parameters: R, tau, Vr, Re, taue:" print r["params"] # plot traces subplot(211) plot(t, current, 'k') subplot(212) plot(t, rawtrace, 'k') # raw trace plot(t, r["Vfull"], 'b') # full model trace (neuron and electrode) plot(t, r["Vcompensated"], 'g') # compensated trace show() brian-1.4.3/docs_sphinx/examples-electrophysiology_compensation_ex2_spikes.txt000066400000000000000000000022441266062647100302230ustar00rootroot00000000000000.. currentmodule:: brian .. index:: pair: example usage; load pair: example usage; plot pair: example usage; linspace pair: example usage; show .. _example-electrophysiology_compensation_ex2_spikes: Example: compensation_ex2_spikes (electrophysiology) ==================================================== Example of spike detection method. Requires binary files "current.npy" and "rawtrace.npy". Rossant et al., "A calibration-free electrode compensation method" J. Neurophysiol 2012 :: import os from brian import * import numpy as np from brian.library.electrophysiology import * working_dir = os.path.dirname(__file__) # load data dt = 0.1*ms current = np.load(os.path.join(working_dir, "current.npy")) # 10000-long vector, 1s duration rawtrace = np.load(os.path.join(working_dir, "trace.npy")) # 10000-long vector, 1s duration t = linspace(0., 1., len(current)) # find spikes and compute score spikes, scores = find_spikes(rawtrace, dt=dt, check_quality=True) # plot trace and spikes plot(t, rawtrace, 'k') plot(t[spikes], rawtrace[spikes], 'or') show() brian-1.4.3/docs_sphinx/examples-electrophysiology_compensation_ex3_quality.txt000066400000000000000000000031111266062647100304100ustar00rootroot00000000000000.. currentmodule:: brian .. index:: pair: example usage; load pair: example usage; plot pair: example usage; linspace pair: example usage; show .. _example-electrophysiology_compensation_ex3_quality: Example: compensation_ex3_quality (electrophysiology) ===================================================== Example of quality check method. Requires binary files "current.npy" and "rawtrace.npy". Rossant et al., "A calibration-free electrode compensation method" J. Neurophysiol 2012 :: import os from brian import * import numpy as np from brian.library.electrophysiology import * working_dir = os.path.dirname(__file__) # load data dt = 0.1*ms current = np.load(os.path.join(working_dir, "current.npy")) # 10000-long vector, 1s duration rawtrace = np.load(os.path.join(working_dir, "trace.npy")) # 10000-long vector, 1s duration compensatedtrace = np.load(os.path.join(working_dir, "compensatedtrace.npy")) # obtained with example1 t = linspace(0., 1., len(current)) # get trace quality of both raw and compensated traces r = get_trace_quality(rawtrace, current, full=True) rcomp = get_trace_quality(compensatedtrace, current, full=True) spikes = r["spikes"] print "Quality coefficient for raw: %.3f and for compensated trace: %.3f" % \ (r["correlation"], rcomp["correlation"]) # plot trace and spikes plot(t, rawtrace, 'k') plot(t, compensatedtrace, 'g') plot(t[spikes], rawtrace[spikes], 'ok') plot(t[spikes], compensatedtrace[spikes], 'og') show() brian-1.4.3/docs_sphinx/examples-electrophysiology_threshold_analysis.txt000066400000000000000000000034121266062647100272650ustar00rootroot00000000000000.. currentmodule:: brian .. index:: pair: example usage; load pair: example usage; plot pair: example usage; subplot pair: example usage; show .. _example-electrophysiology_threshold_analysis: Example: threshold_analysis (electrophysiology) =============================================== Analysis of spike threshold. Loads a current clamp voltage trace, compensates (remove electrode voltage) and analyses the spikes. :: from brian import * from brian.library.electrophysiology import * import numpy dt=.1*ms Vraw = numpy.load("trace.npy") # Raw current clamp trace I = numpy.load("current.npy") V, _ = Lp_compensate(I, Vraw, dt) # Electrode compensation # Peaks spike_criterion=find_spike_criterion(V) print "Spike detected when V exceeds",float(spike_criterion/mV),"mV" peaks=spike_peaks(V,vc=spike_criterion) # vc is optional # Onsets (= spike threshold) onsets=spike_onsets(V,criterion=3*dt,vc=spike_criterion) # Criterion: dV/dt>3 V/s # Spike-triggered average of V STA=spike_shape(V, onsets=onsets, before=100, after=100) print "Spike duration:",float(spike_duration(V,onsets=onsets)*dt/ms),"ms" print "Reset potential:",float(reset_potential(V,peaks=peaks)/mV),"mV" # Spike threshold statistics slope=slope_threshold(V,onsets=onsets,T=int(5*ms/dt)) # Subthreshold trace subthreshold=-spike_mask(V) t=arange(len(V))*dt subplot(221) plot(t/ms,V/mV,'k') plot(t[peaks]/ms,V[peaks]/mV,".b") plot(t[onsets]/ms,V[onsets]/mV,".r") subplot(222) plot(((arange(len(STA))-100)*dt)/ms,STA/mV,'k') subplot(223) plot(t[subthreshold]/ms,V[subthreshold]/mV,'k') subplot(224) plot(slope/ms,V[onsets]/mV,'.') show() brian-1.4.3/docs_sphinx/examples-electrophysiology_voltageclamp.txt000066400000000000000000000023731266062647100260510ustar00rootroot00000000000000.. currentmodule:: brian .. index:: pair: example usage; plot pair: example usage; run pair: example usage; figure pair: example usage; show pair: example usage; NeuronGroup pair: example usage; Equations pair: example usage; StateMonitor .. _example-electrophysiology_voltageclamp: Example: voltageclamp (electrophysiology) ========================================= Voltage-clamp experiment :: from brian import * from brian.library.electrophysiology import * defaultclock.dt = .01 * ms taum = 20 * ms gl = 20 * nS Cm = taum * gl Re = 50 * Mohm Ce = 0.2 * ms / Re N = 1 Rs = .9 * Re tauc = Rs * Ce # critical tau_u eqs = Equations(''' dvm/dt=(-gl*vm+i_inj)/Cm : volt ''') eqs += electrode(.2 * Re, Ce) eqs += voltage_clamp(vm='v_el', v_cmd=20 * mV, i_inj='i_cmd', i_rec='ic', Re=.8 * Re, Rs=.9 * Re, tau_u=.2 * ms) setup = NeuronGroup(N, model=eqs) setup.v = 0 * mV recording = StateMonitor(setup, 'ic', record=True) soma = StateMonitor(setup, 'vm', record=True) run(200 * ms) figure() plot(recording.times / ms, recording[0] / nA, 'k') figure() plot(soma.times / ms, soma[0] / mV, 'b') show() examples-frompapers-computing with neural synchrony-coincidence detection and synchrony_Fig1C.txt000066400000000000000000000027451266062647100377650ustar00rootroot00000000000000brian-1.4.3/docs_sphinx.. currentmodule:: brian .. index:: pair: example usage; subplot pair: example usage; plot pair: example usage; loadtxt pair: example usage; log pair: example usage; figure pair: example usage; show pair: example usage; savefig pair: example usage; rc pair: example usage; xlabel pair: example usage; ylabel pair: example usage; ylim .. _example-frompapers-computing with neural synchrony-coincidence detection and synchrony_Fig1C: Example: Fig1C (frompapers/computing with neural synchrony/coincidence detection and synchrony) =============================================================================================== Timescale and strength :: from brian import * rc('lines',linewidth=2) rc('font',size=16) rc('xtick',labelsize=16) rc('ytick',labelsize=16) rc('legend',fontsize=16) rc('axes',labelsize=16,titlesize=16) w,h=rcParamsDefault['figure.figsize'] fontsize=16 SNR,timescale,strength=loadtxt('reproducibility.txt') SNR=10*log(SNR)/log(10) #SNR=SNR[1:] #timescale=timescale[1:] #strength=strength[1:] figure(figsize=(w*1.5,h*.5)) subplot(121) plot(SNR,timescale/ms,'k') #plot(SNR,timescale/ms,'r.') plot(SNR,0*SNR+7,'k--') xlabel('SNR (dB)') ylabel('Precision (ms)') subplot(122) plot(SNR,strength*100,'k') ylim(0,100) #plot(SNR,strength*100,'r.') xlabel('SNR (dB)') ylabel('Reliability (%)') savefig('FigC.eps') show() 54af0806af57cd89d76e87aa466f14a262f736eb.paxheader00006660000000000000000000000207126606264710020546xustar00rootroot00000000000000135 path=brian-1.4.3/docs_sphinx/examples-frompapers-computing with neural synchrony-coincidence detection and synchrony_Fig1F_ROC.txt 54af0806af57cd89d76e87aa466f14a262f736eb.data000066400000000000000000000076331266062647100174160ustar00rootroot00000000000000.. currentmodule:: brian .. index:: pair: example usage; plot pair: example usage; run pair: example usage; SpikeGeneratorGroup pair: example usage; xlim pair: example usage; show pair: example usage; reinit pair: example usage; sqrt pair: example usage; Connection pair: example usage; xlabel pair: example usage; linspace pair: example usage; ylabel pair: example usage; NeuronGroup pair: example usage; ylim pair: example usage; SpikeCounter .. _example-frompapers-computing with neural synchrony-coincidence detection and synchrony_Fig1F_ROC: Example: Fig1F_ROC (frompapers/computing with neural synchrony/coincidence detection and synchrony) =================================================================================================== Brette R (2012). Computing with neural synchrony. PLoS Comp Biol. 8(6): e1002561. doi:10.1371/journal.pcbi.1002561 ------------------------------------------------------------------------------------------------------------------ Figure 1F. (simulation takes about 10 mins) Coincidence detection is seen as a signal detection problem, that of detecting a given depolarization in a background of noise, within one characteristic time constant. The choice of the spike threshold implements a particular trade-off between false alarms (depolarization was due to noise) and misses (depolarization was not detected). Caption (Fig 1F). Receiver-operation characteristic (ROC) for one level of noise, obtained by varying the threshold (black curve). The hit rate is the probability that the neuron fires within one integration time constant t when depolarized by Dv, and the false alarm rate is the firing probability without depolarization. The corresponding theoretical curve, with sensitivity index d' =Dv/sigma, is shown in red. :: from brian import * from scipy.special import erf def spike_probability(x): # firing probability for unit variance and zero mean, and threshold = x return .5*(1-erf(x/sqrt(2.))) tau_cd=5*ms # membrane time constant (cd for coincidence detector) tau_n=tau_cd # input is an Ornstein-Uhlenbeck process with the same time constant as the membrane time constant T=3*tau_n # neurons are depolarized by w at regular intervales, T is the spacing Nspikes=10000 # number of input spikes T0=T*Nspikes # initial period without inputs, to calculate the false alarm rate N=500 # number of neurons, each neuron has a different threshold between 0. and 3. w=1 # synaptic weight (depolarization) sigma=1. # input noise s.d. sigmav=sigma*sqrt(tau_n/(tau_n+tau_cd)) # noise s.d. on the membrane potential print "d'=",1./sigmav # discriminability index # Integrate-and-fire neurons eqs=''' dv/dt=(sigma*n-v)/tau_cd : 1 dn/dt=-n/tau_n+(2/tau_n)**.5*xi : 1 vt : 1 # spike threshold ''' neurons=NeuronGroup(N,model=eqs,threshold='v>vt',reset='v=0',refractory=tau_cd) neurons.vt=linspace(0.,3,N) # spike threshold varies across neurons counter=SpikeCounter(neurons) # Inputs are regular spikes, starting at T0 input=SpikeGeneratorGroup(1,[(0,n*T+T0) for n in range(Nspikes)]) C=Connection(input,neurons,'v',weight=w) # Calculate the false alarm rate run(T0,report='text') FR=tau_cd*counter.count*1./T0 # Calculate the hit rate counter.reinit() run(Nspikes*T,report='text') HR=counter.count*1./Nspikes-FR*(T-tau_cd)/tau_cd # Prediction based on Gaussian statistics FRpred=spike_probability(neurons.vt/sigmav) HRpred=spike_probability((neurons.vt-w)/sigmav) # Figure plot(FR*100,HR*100,'k') # simulations plot(FRpred*100,HRpred*100,'r') # theoretical predictions plot([0,100],[0,100],'k--') plot([0,100],[50,50],'k--') xlim(0,100) ylim(0,100) xlabel('False alarm rate (%)') ylabel('Hit rate (%)') show() fbbaffb91d142a45ed5591ca5facc0dd0382839e.paxheader00006660000000000000000000000223126606264710021072xustar00rootroot00000000000000147 path=brian-1.4.3/docs_sphinx/examples-frompapers-computing with neural synchrony-coincidence detection and synchrony_Fig5D_reproducibility.txt fbbaffb91d142a45ed5591ca5facc0dd0382839e.data000066400000000000000000000036231266062647100177370ustar00rootroot00000000000000.. currentmodule:: brian .. index:: pair: example usage; NeuronGroup pair: example usage; run pair: example usage; show pair: example usage; raster_plot pair: example usage; linked_var pair: example usage; sqrt pair: example usage; SpikeMonitor .. _example-frompapers-computing with neural synchrony-coincidence detection and synchrony_Fig5D_reproducibility: Example: Fig5D_reproducibility (frompapers/computing with neural synchrony/coincidence detection and synchrony) =============================================================================================================== Brette R (2012). Computing with neural synchrony. PLoS Comp Biol. 8(6): e1002561. doi:10.1371/journal.pcbi.1002561 ------------------------------------------------------------------------------------------------------------------ Figure 5D, left. Caption (Fig 5D). Responses of a noisy integrate-and-fire model in repeated trials. Protocol: neuron receives input = signal + noise, both O-U processes, signal is identical in all trials (frozen noise). The total variance is held fixed. Signal-to-noise ratio is 3 in this simulation. :: from brian import * # The common noisy input tau_noise=5*ms input=NeuronGroup(1,model='dx/dt=-x/tau_noise+(2./tau_noise)**.5*xi:1') # The noisy neurons receiving the same input + independent noise tau=10*ms SNR=3. # signal to noise ratio sigma=.5 # total input amplitude Z=sigma*sqrt((tau_noise+tau)/(tau_noise*(SNR**2+1))) # normalizing factor eqs_neurons=''' dx/dt=(Z*(SNR*I+u)-x)/tau:1 du/dt=-u/tau_noise+(2./tau_noise)**.5*xi:1 I : 1 ''' neurons=NeuronGroup(25,model=eqs_neurons,threshold=1,reset=0,refractory=5*ms) neurons.x=rand(25) # random initial conditions neurons.I=linked_var(input,'x') spikes=SpikeMonitor(neurons) run(2*second) # Figure raster_plot(spikes) show() 3855cb3ef2754b2bc0b88455ff8e73703516fe2c.paxheader00006660000000000000000000000231126606264710020454xustar00rootroot00000000000000153 path=brian-1.4.3/docs_sphinx/examples-frompapers-computing with neural synchrony-coincidence detection and synchrony_Fig5E_precision_reliability.txt 3855cb3ef2754b2bc0b88455ff8e73703516fe2c.data000066400000000000000000000102621266062647100173170ustar00rootroot00000000000000.. currentmodule:: brian .. index:: pair: example usage; subplot pair: example usage; plot pair: example usage; show pair: example usage; xlabel pair: example usage; linspace pair: example usage; ylabel .. _example-frompapers-computing with neural synchrony-coincidence detection and synchrony_Fig5E_precision_reliability: Example: Fig5E_precision_reliability (frompapers/computing with neural synchrony/coincidence detection and synchrony) ===================================================================================================================== Brette R (2012). Computing with neural synchrony. PLoS Comp Biol. 8(6): e1002561. doi:10.1371/journal.pcbi.1002561 ------------------------------------------------------------------------------------------------------------------ Figure 5E. (very long simulation) Caption (Fig 5E). Precision and reliability of spike timing as a function of SNR. Simulations are run in parallel on all cores but one. :: from brian import * import multiprocessing def autocor(PSTH,N=None,T=20*ms,bin=None): ''' Autocorrelogram of PSTH, to calculate a shuffled autocorrelogram N = number of spike trains T = temporal window bin = PSTH bin The baseline is not subtracted. Returns times,SAC ''' if bin is None: bin=defaultclock.dt n=len(PSTH) p=int(T/bin) SAC=zeros(p) if N is None: SAC[0]=mean(PSTH*PSTH) else: # correction to exclude self-coincidences PSTHnoself=clip(PSTH-1./(bin*N),0,Inf) SAC[0]=mean(PSTH*PSTHnoself)*N/(N-1.) SAC[1:]=[mean(PSTH[:-i]*PSTH[i:]) for i in range(1,p)] SAC=hstack((SAC[::-1],SAC[1:])) return (arange(len(SAC))-len(SAC)/2)*bin,SAC def halfwidth(x): ''' Returns half-width of function given by x in bin numbers. This is used to calculate the precision (left panel). ''' M,n=max(x),argmax(x) return find(x[n:]=N) & (i=2*N) & (i<2*N+25)]) plot(t,array(i)-2*N,'.b') ylim(0,75) xlabel('Time (s)') ylabel('Trials') # Cross-correlograms (CC) width=100*ms bin=1*ms spikes=spikes.spiketimes C_AB=correlogram(spikes[0],spikes[N],width=width,T=duration) for i in range(1,N): C_AB+=correlogram(spikes[i],spikes[N+i],width=width,T=duration) C_AC=correlogram(spikes[0],spikes[2*N],width=width,T=duration) for i in range(1,N): C_AC+=correlogram(spikes[i],spikes[2*N+i],width=width,T=duration) # Shuffled auto-correlogram (SAC) C=0*C_AB for i in range(0,N): for j in range(0,N): if i!=j: C+=correlogram(spikes[i],spikes[j],width=width,T=duration) lag=(arange(len(C))-len(C)/2)*bin subplot(223) # Fig. 6C plot(lag/ms,C/(bin*N*(N-1)),'k') ylim(0,1.1*max(C_AB/(bin*N))) xlabel('Lag (ms)') ylabel('Coincidences') subplot(224) # Fig. 6D plot(lag/ms,C_AB/(bin*N),'k') # A vs. B plot(lag/ms,C_AC/(bin*N),'r') # A vs. C ylim(0,1.1*max(C_AB/(bin*N))) xlabel('Lag (ms)') ylabel('Coincidences') show() examples-frompapers-computing with neural synchrony-duration selectivity_Fig1A_rebound_neurons.txt000066400000000000000000000061101266062647100404500ustar00rootroot00000000000000brian-1.4.3/docs_sphinx.. currentmodule:: brian .. index:: pair: example usage; subplot pair: example usage; plot pair: example usage; run pair: example usage; xlim pair: example usage; show pair: example usage; SpikeMonitor pair: example usage; linspace pair: example usage; exp pair: example usage; ylabel pair: example usage; xlabel pair: example usage; NeuronGroup pair: example usage; StateMonitor .. _example-frompapers-computing with neural synchrony-duration selectivity_Fig1A_rebound_neurons: Example: Fig1A_rebound_neurons (frompapers/computing with neural synchrony/duration selectivity) ================================================================================================ Brette R (2012). Computing with neural synchrony. PLoS Comp Biol. 8(6): e1002561. doi:10.1371/journal.pcbi.1002561 ------------------------------------------------------------------------------------------------------------------ Figure 1A, B. Caption (Fig. 1A,B) A, When neuron A is hyperpolarized by an inhibitory input (top), its low-voltage-activated K channels slowly close (bottom), which makes the neuron fire when inhibition is released (neuron models are used in this and other figures). B, Spike latency is negatively correlated with the duration of inhibition (black line). :: from brian import * # Parameters and equations of the rebound neurons Vt=-55*mV Vr=-70*mV El=-35*mV EK=-90*mV Va=Vr ka=5*mV gmax=1 gmax2=2 tau=20*ms ginh_max=5. tauK=400*ms tauK2=100*ms N=100 # number of neurons (= different durations, for plot 1B) plotted_neuron=N/4 rest_time=1*second # initial time (to start at equilibrium) tmin=rest_time-20*ms # for plots tmax=rest_time+600*ms eqs=''' dv/dt=(El-v+(gmax*gK+gmax2*gK2+ginh)*(EK-v))/tau : volt dgK/dt=(gKinf-gK)/tauK : 1 # IKLT dgK2/dt=-gK2/tauK2 : 1 # Delayed rectifier gKinf=1./(1+exp((Va-v)/ka)) : 1 duration : second # duration of inhibition, varies across neurons ginh = ginh_max*((t>rest_time) & (t<(rest_time+duration))) : 1 ''' neurons=NeuronGroup(N,model=eqs,threshold='v>Vt',reset='v=Vr;gK2=1') neurons.v=Vr neurons.gK=1./(1+exp((Va-El)/ka)) neurons.duration=linspace(100*ms,1*second,N) M=StateMonitor(neurons,'v',record=plotted_neuron) Mg=StateMonitor(neurons,'gK',record=plotted_neuron) spikes=SpikeMonitor(neurons) run(rest_time+1.1*second) M.insert_spikes(spikes) # draw spikes for a nicer display # Figure subplot(221) # Fig. 1A, top plot((M.times-tmin)/ms,M[plotted_neuron]/mV,'k') xlim(0,(tmax-tmin)/ms) ylabel('V (mV)') subplot(223) # Fig. 1A, bottom plot((Mg.times-tmin)/ms,Mg[plotted_neuron],'k') xlim(0,(tmax-tmin)/ms) xlabel('Time (ms)') ylabel('g/gmax') subplot(122) # Fig. 1B times=array([t-neurons.duration[i]*second-rest_time for i,t in spikes.spikes]) duration=array([neurons.duration[i]*second for i,_ in spikes.spikes]) plot(duration/ms,times/ms,'k') xlabel('Duration (ms)') ylabel('Latency (ms)') show() 3c27b891ac975346809b85dee9d962a88d5560ff.paxheader00006660000000000000000000000211126606264710020424xustar00rootroot00000000000000137 path=brian-1.4.3/docs_sphinx/examples-frompapers-computing with neural synchrony-duration selectivity_Fig1D_duration_selectivity.txt 3c27b891ac975346809b85dee9d962a88d5560ff.data000066400000000000000000000073221266062647100172740ustar00rootroot00000000000000.. currentmodule:: brian .. index:: pair: example usage; IdentityConnection pair: example usage; plot pair: example usage; run pair: example usage; xlim pair: example usage; show pair: example usage; rate pair: example usage; subplot pair: example usage; xlabel pair: example usage; linspace pair: example usage; exp pair: example usage; ylabel pair: example usage; mean pair: example usage; NeuronGroup pair: example usage; ylim pair: example usage; SpikeCounter pair: example usage; StateMonitor .. _example-frompapers-computing with neural synchrony-duration selectivity_Fig1D_duration_selectivity: Example: Fig1D_duration_selectivity (frompapers/computing with neural synchrony/duration selectivity) ===================================================================================================== Brette R (2012). Computing with neural synchrony. PLoS Comp Biol. 8(6): e1002561. doi:10.1371/journal.pcbi.1002561 ------------------------------------------------------------------------------------------------------------------ Figure 1C,D. Duration selectivity. (takes about 1 min) Caption (Fig. 1C,D). A postsynaptic neuron receives inputs from A and B. It is more likely to fire when the stimulus in the synchrony receptive field of A and B. :: from brian import * # Parameters and equations of the rebound neurons Vt=-55*mV Vr=-70*mV El=-35*mV EK=-90*mV Va=Vr ka=5*mV gmax2=2 tau=20*ms ginh_max=5. tauK2=100*ms N=10000 # number of neurons (= different durations) rest_time=1*second # initial time tmin=rest_time-20*ms #for plots tmax=rest_time+600*ms eqs=''' dv/dt=(El-v+(gmax*gK+gmax2*gK2+ginh)*(EK-v))/tau : volt dgK/dt=(gKinf-gK)/tauK : 1 # IKLT dgK2/dt=-gK2/tauK2 : 1 # Delayed rectifier gKinf=1./(1+exp((Va-v)/ka)) : 1 duration : second ginh = ginh_max*((t>rest_time) & (t<(rest_time+duration))) : 1 tauK : ms gmax : 1 theta : volt # threshold ''' neurons=NeuronGroup(2*N,model=eqs,threshold='v>theta',reset='v=Vr;gK2=1') neurons.v=Vr neurons.theta=Vt neurons.gK=1./(1+exp((Va-El)/ka)) # Neuron A, duplicated to simulate multiple input durations simultaneously neuronsA=neurons[:N] neuronsA.tauK=400*ms neuronsA.gmax=1 neuronsA.theta=-55*mV neuronsA.duration=linspace(100*ms,1*second,N) # Neuron B, duplicated to simulate multiple input durations simultaneously neuronsB=neurons[N:] neuronsB.tauK=100*ms neuronsB.gmax=1.5 neuronsB.theta=-54*mV neuronsB.duration=linspace(100*ms,1*second,N) # Noisy coincidence detectors tau_cd=5*ms tau_n=tau_cd sigma=0.2 # noise s.d. in units of the threshold eqs_post=''' dv/dt=(n-v)/tau_cd : 1 dn/dt=-n/tau_n+sigma*(2/tau_n)**.5*xi : 1 ''' postneurons=NeuronGroup(N,model=eqs_post,threshold=1,reset=0) CA=IdentityConnection(neuronsA,postneurons,'v',weight=0.5) CB=IdentityConnection(neuronsB,postneurons,'v',weight=0.5) spikes=SpikeCounter(postneurons) M=StateMonitor(postneurons,'v',record=N/3) run(rest_time+1.1*second,report='text') # Figure subplot(121) # Fig. 1C, example trace plot(M.times/ms,M[N/3],'k') xlim(1350,1500) ylim(-.3,1) xlabel('Time (ms)') ylabel('V') subplot(122) # Fig. 1D, duration tuning curve count=spikes.count # Smooth the tuning curve window=200 rate=zeros(len(count)-window) for i in range(0,len(count)-window): rate[i]=mean(count[i:i+window]) plot((neuronsA.duration[window/2:-window/2]/ms)[::10],rate[::10],'k') xlim(0,1000) ylim(0,0.5) xlabel('Duration (ms)') ylabel('Spiking probability') show() 01c264006fb3ac19b87bca87c57736a74c2aea88.paxheader00006660000000000000000000000210126606264710020511xustar00rootroot00000000000000136 path=brian-1.4.3/docs_sphinx/examples-frompapers-computing with neural synchrony-duration selectivity_Fig2A_synchrony_partition.txt 01c264006fb3ac19b87bca87c57736a74c2aea88.data000066400000000000000000000127441266062647100173660ustar00rootroot00000000000000.. currentmodule:: brian .. index:: pair: example usage; gca pair: example usage; NeuronGroup pair: example usage; run pair: example usage; jet pair: example usage; xlim pair: example usage; show pair: example usage; sum pair: example usage; axes pair: example usage; xticks pair: example usage; SpikeMonitor pair: example usage; linspace pair: example usage; exp pair: example usage; yticks pair: example usage; close pair: example usage; Circle pair: example usage; ylim pair: example usage; append pair: example usage; axis .. _example-frompapers-computing with neural synchrony-duration selectivity_Fig2A_synchrony_partition: Example: Fig2A_synchrony_partition (frompapers/computing with neural synchrony/duration selectivity) ==================================================================================================== Brette R (2012). Computing with neural synchrony. PLoS Comp Biol. 8(6): e1002561. doi:10.1371/journal.pcbi.1002561 ------------------------------------------------------------------------------------------------------------------ Figure 2A. Synchrony partition for duration selective neurons. Caption (Fig. 2A) Decoding synchrony patterns in a heterogeneous population. Color represents the latency of the spike produced by each neuron responding to the stimulus (white if the neuron did not spike). Thus, neurons with the same color are synchronous for that specific stimulus (duration). The population can be divided in groups of synchronous neurons (i.e., with the same color), forming the "synchrony partition". Circled neurons belong to the same synchronous group. This script calculates and displays the synchrony partition for one particular duration. It also saves the results in file, that is required by the script Fig2C_decoding_synchrony. The synchrony partition is calculated empirically, by simulating the responses of the neurons at the specific inhibitory duration and grouping neurons that respond in synchrony (+- 2 ms). :: from brian import * from numpy.random import seed from params import * from pylab import cm # Graphics radius=.15 selected_neuron=7 # Parameters ginh_max=5. Nx=5 # number of neurons per row N=Nx*Nx # number of neurons rest_time=1*second # initial time duration=500*ms delta_t=2*ms # Size of synchronous groups (maximum time difference) # Duration-selective neurons eqs=''' dv/dt=(El-v+(gmax*gK+gmax2*gK2+ginh)*(EK-v))/tau : volt dgK/dt=(gKinf-gK)/tauK : 1 # IKLT dgK2/dt=-gK2/tauK2 : 1 # Delayed rectifier gKinf=1./(1+exp((Va-v)/ka)) : 1 ginh = ginh_max*((t>rest_time) & (t<(rest_time+duration))) : 1 tauK : ms tau : ms gmax : 1 ''' uniform=lambda N:(rand(N)-.5)*2 # uniform between -1 and 1 seed(31418) # Get the same neurons every time neurons=NeuronGroup(N,model=eqs,threshold='v>Vt',reset='v=Vr;gK2=1') neurons.v=Vr neurons.gK=1./(1+exp((Va-El)/ka)) neurons.tauK=400*ms+uniform(N)*tauK_spread alpha=(El-Vt)/(Vt-EK) neurons.gmax=alpha*(minx+(maxx-minx)*rand(N)) neurons.tau=30*ms+uniform(N)*tau_spread spikes=SpikeMonitor(neurons) run(rest_time+1.1*second) # Calculate first spike time of each neuron times=zeros(N) # First spike time of each neuron times[:]=Inf # Inf means: no response, or response before the start of the stimulus blacklist=[] # neurons that fire spontaneously for i,t in spikes.spikes: if times[i]==Inf: times[i]=t-duration-rest_time if times[i]<0: blacklist.append(i) times[blacklist]=Inf tmin,tmax=min(times[times!=Inf]),max(times[times!=Inf]) # Color of each neuron between 0 and 1 color=(times-tmin)/(tmax+1e-10-tmin) # (to avoid zero division) # Assign groups; each responding neuron gets a group number group_size=delta_t/(tmax-tmin) # size of a group, as a proportion of the timing range group_number=array(color/group_size,dtype=int) group_number[color==Inf]=-1 # Get the size of each group count=zeros(max(group_number)+1) # number of neurons in each group for i in range(len(group_number)): if group_number[i]!=-1: count[group_number[i]]+=1 selected_group=group_number[selected_neuron] # Display the synchrony partition (Fig. 2A) axes(frameon=False) axis('scaled') xticks([]) yticks([]) i=0 for y in linspace(0,1,Nx): for x in linspace(0,1,Nx): if color[i]!=Inf: if group_number[i]==selected_group: w=4 ec="k" # edge color else: w=1 ec='k' cir=Circle((x,y),radius,fc=cm.jet(color[i]),linewidth=w,ec=ec) else: cir=Circle((x,y),radius,fc='w') i+=1 gca().add_patch(cir) xlim(0-2*radius,1+2*radius) ylim(0-2*radius,1+2*radius) # Remove groups with fewer than two neurons and recalculate group numbers for i in range(len(group_number)): if group_number[i]>=0: if count[group_number[i]]>=2: group_number[i]=sum(count[:group_number[i]]>=2) else: group_number[i]=-1 # Save assignment to groups f=open('groups'+str(int(duration/ms))+'.txt','w') f.write(' '.join([str(x) for x in group_number])) f.close() show() a3decdc80cb7cc66da95e9a52d2b9d7567a9040f.paxheader00006660000000000000000000000207126606264710021033xustar00rootroot00000000000000135 path=brian-1.4.3/docs_sphinx/examples-frompapers-computing with neural synchrony-duration selectivity_Fig2C_decoding_synchrony.txt a3decdc80cb7cc66da95e9a52d2b9d7567a9040f.data000066400000000000000000000111141266062647100176700ustar00rootroot00000000000000.. currentmodule:: brian .. index:: pair: example usage; plot pair: example usage; run pair: example usage; xlim pair: example usage; NeuronGroup pair: example usage; show pair: example usage; rate pair: example usage; Connection pair: example usage; xlabel pair: example usage; split pair: example usage; exp pair: example usage; ylabel pair: example usage; close pair: example usage; SpikeCounter pair: example usage; linspace pair: example usage; ylim pair: example usage; append pair: example usage; mean .. _example-frompapers-computing with neural synchrony-duration selectivity_Fig2C_decoding_synchrony: Example: Fig2C_decoding_synchrony (frompapers/computing with neural synchrony/duration selectivity) =================================================================================================== Brette R (2012). Computing with neural synchrony. PLoS Comp Biol. 8(6): e1002561. doi:10.1371/journal.pcbi.1002561 ------------------------------------------------------------------------------------------------------------------ Figure 2C. Decoding synchrony patterns. Caption (Fig. 2C). Activation of the postsynaptic assembly as a function of duration (grey: individual neurons; black: average). The script Fig2A_synchrony_partition must be run first (it produces a file). :: from brian import * from numpy.random import seed from params import * from pylab import cm Ndur=2000*5 # number of stimulus durations (durations are multiplexed) sigma=0.1 # More/less noise best_duration=500*ms # Read group index for each neuron f=open('groups'+str(int(best_duration/ms))+'.txt') group_number=array([int(x) for x in f.read().split(' ')]) nneurons=max(group_number)+1 # one postsynaptic neuron per group f.close() # Calculate group size count=zeros(nneurons) # number of presynaptic neurons for each postsynaptic neuron for i in range(len(group_number)): if group_number[i]!=-1: count[group_number[i]]+=1 # Presynaptic neurons ginh_max=5. Nx=5 # number of neurons per row N=Nx*Nx # number of neurons rest_time=1*second # initial time eqs=''' dv/dt=(El-v+(gmax*gK+gmax2*gK2+ginh)*(EK-v))/tau : volt dgK/dt=(gKinf-gK)/tauK : 1 # IKLT dgK2/dt=-gK2/tauK2 : 1 # Delayed rectifier gKinf=1./(1+exp((Va-v)/ka)) : 1 ginh = ginh_max*((t>rest_time) & (t<(rest_time+duration))) : 1 tauK : ms tau : ms gmax : 1 duration : second ''' uniform=lambda N:(rand(N)-.5)*2 #uniform between -1 and 1 seed(31418) # Get the same neurons every time _tauK=400*ms+uniform(N)*tauK_spread alpha=(El-Vt)/(Vt-EK) _gmax=alpha*(minx+(maxx-minx)*rand(N)) _tau=30*ms+uniform(N)*tau_spread neurons=NeuronGroup(N*Ndur,model=eqs,threshold='v>Vt',reset='v=Vr;gK2=1') neurons.v=Vr neurons.gK=1./(1+exp((Va-El)/ka)) # Postsynaptic neurons (noisy coincidence detectors) eqs_post=''' dv/dt=(n-v)/tau_cd : 1 dn/dt=-n/tau_n+sigma*(2/tau_n)**.5*xi : 1 ''' postneurons=NeuronGroup(Ndur*nneurons,model=eqs_post,threshold=1,reset=0) C=Connection(neurons,postneurons) # Divide into subgroups, each group corresponds to one postsynaptic neuron with all stimulus durations postgroup=[] for i in range(nneurons): postgroup.append(postneurons.subgroup(Ndur)) # Connections according to the synchrony partition group=[] for i in range(N): group.append(neurons.subgroup(Ndur)) group[i].tauK=_tauK[i] group[i].gmax=_gmax[i] group[i].tau=_tau[i] group[i].duration=linspace(100*ms,1*second,Ndur) if group_number[i]>=0: C.connect_one_to_one(group[i],postgroup[group_number[i]],weight=1./count[group_number[i]]) spikes=SpikeCounter(postneurons) run(rest_time+1.1*second,report='text') # Figure (2C) window=100*5 # smoothing window rate=zeros(Ndur-window) totrate=zeros(Ndur-window) for i in range(nneurons): # display tuning curve for each neuron, in grey count=spikes.count[i*Ndur:(i+1)*Ndur] # Smooth for j in range(0,len(count)-window): rate[j]=mean(count[j:j+window]) totrate+=rate if i<5: # plot only 5 individual curves plot((group[0].duration[window/2:-window/2]/ms),rate,'grey',linewidth=1) # Mean tuning curve plot((group[0].duration[window/2:-window/2]/ms),totrate/nneurons,'k',linewidth=2) xlim(100,600) ylim(0,0.5) xlabel('Duration (ms)') ylabel('Spiking probability') show() examples-frompapers-computing with neural synchrony-duration selectivity_Fig4_duration_stdp.txt000066400000000000000000000074431266062647100400340ustar00rootroot00000000000000brian-1.4.3/docs_sphinx.. currentmodule:: brian .. index:: pair: example usage; plot pair: example usage; run pair: example usage; xlim pair: example usage; show pair: example usage; shape pair: example usage; STDP pair: example usage; EventClock pair: example usage; Connection pair: example usage; exp pair: example usage; NeuronGroup pair: example usage; ylim .. _example-frompapers-computing with neural synchrony-duration selectivity_Fig4_duration_stdp: Example: Fig4_duration_stdp (frompapers/computing with neural synchrony/duration selectivity) ============================================================================================= Brette R (2012). Computing with neural synchrony. PLoS Comp Biol. 8(6): e1002561. doi:10.1371/journal.pcbi.1002561 ------------------------------------------------------------------------------------------------------------------ Figure 4D. STDP in the duration model. (very long simulation, default: 5000 stimuli) Caption (Fig. 4D). Temporal evolution of the synaptic weights of the neuron corresponding to the blue curves in Fig. 4C. The script runs the simulation with STDP for a long time, then displays the evolution of synaptic weights for one neuron. :: from brian import * from pylab import cm from numpy.random import seed from brian.experimental.connectionmonitor import * import numpy from params import * # Rebound neurons eqs=''' dv/dt=(El-v+(gmax*gK+gmax2*gK2+ginh)*(EK-v))/tau : volt dgK/dt=(gKinf-gK)/tauK : 1 # IKLT dgK2/dt=-gK2/tauK2 : 1 # Delayed rectifier gKinf=1./(1+exp((Va-v)/ka)) : 1 tauK : ms tau : ms gmax : 1 ginh : 1 ''' uniform=lambda N:(rand(N)-.5)*2 #uniform between -1 and 1 seed(31415) # Get the same neurons every time neurons=NeuronGroup(N,model=eqs,threshold='v>Vt',reset='v=Vr;gK2=1') neurons.v=Vr neurons.gK=1./(1+exp((Va-El)/ka)) neurons.tauK=400*ms+uniform(N)*tauK_spread alpha=(El-Vt)/(Vt-EK) neurons.gmax=alpha*(minx+(maxx-minx)*rand(N)) neurons.tau=30*ms+uniform(N)*tau_spread # Store the value of state variables at rest print "Calculate resting state" run(2*second) rest=zeros(neurons._S.shape) rest[:]=neurons._S # Postsynaptic neurons (noisy coincidence detectors) eqs_post=''' dv/dt=(n-v)/tau_cd : 1 dn/dt=-n/tau_n+sigma*(2/tau_n)**.5*xi : 1 ''' postneurons=NeuronGroup(Nout,model=eqs_post,threshold=1,reset=0,refractory=refractory) # Random connections between pre and post-synaptic neurons C=Connection(neurons,postneurons,'v',sparseness=Nsynapses*1./N,weight=w0) # STDP eqs_stdp=''' dApre/dt=-Apre/tau_pre : 1 Apost : 1 ''' pre=''' Apre+=a_pre w+=0 #b_pre*w ''' post=''' Apost+=0 w+=Apre+b_post*w ''' stdp=STDP(C,eqs_stdp,pre=pre,post=post,wmax=Inf) # Record the evolution of synaptic weights MC=ConnectionMonitor(C,store=True,clock=EventClock(dt=record_period)) print "Learning..." # Series of inhibitory pulses for i in range(Npulses): print "pulse",i+1 duration=200*ms+rand()*300*ms # random stimulus duration neurons.ginh=ginh_max run(duration) C.W.alldata[:]=C.W.alldata+C.W.alldata*b_pre # homeostasis (synaptic scaling) neurons.ginh=0 run(rest_time) # let neurons spike neurons._S[:]=rest # reset (to save time) # Figure (4D) neuron=0 wsave=[(t,M.todense()) for (t,M) in MC.values] W=array(zip(*wsave)[1]) weights=W[:,:,neuron] # Evolution of all synaptic weights for this neuron for i in range(weights.shape[1]): plot(arange(len(weights[:,i,]))*record_period,weights[:,i,],'k') xlim(0,weights.shape[0]*float(record_period)) ylim(0,1) show() examples-frompapers-computing with neural synchrony-duration selectivity_params.txt000066400000000000000000000024421266062647100355610ustar00rootroot00000000000000brian-1.4.3/docs_sphinx.. currentmodule:: brian .. _example-frompapers-computing with neural synchrony-duration selectivity_params: Example: params (frompapers/computing with neural synchrony/duration selectivity) ================================================================================= Brette R (2012). Computing with neural synchrony. PLoS Comp Biol. 8(6): e1002561. doi:10.1371/journal.pcbi.1002561 ------------------------------------------------------------------------------------------------------------------ Duration selectivity, parameters :: from brian import * # Simulation control Npulses=5000 Ntest=20 record_period=15*second rest_time=200*ms # Encoding neurons Vt=-55*mV Vr=-70*mV El=-35*mV EK=-90*mV Va=Vr ka=5*mV gmax2=2 tauK2=300*ms N=100 # number of encoding neurons Nout=30 # number of decoding neurons ginh_max=5. tauK_spread=200*ms tau_spread=20*ms minx=1.7 # range of gmax for K+ maxx=2.5 # Coincidence detectors sigma=0.1 # noise s.d. tau_cd=5*ms tau_n=tau_cd # slow noise refractory=0*ms # Connections Nsynapses=5 # synapses per neuron w0=lambda i,j:rand() # STDP factor=0.05 a_pre=.06*factor b_post=-1.*factor b_pre=0.1*factor tau_pre=tau_cd examples-frompapers-computing with neural synchrony-hearing_Fig12A_Goodman_Brette_2010.txt000066400000000000000000000125551266062647100361330ustar00rootroot00000000000000brian-1.4.3/docs_sphinx.. currentmodule:: brian .. index:: pair: example usage; xlim pair: example usage; show pair: example usage; argmax pair: example usage; erbspace pair: example usage; shape pair: example usage; ylim pair: example usage; plot pair: example usage; Repeat pair: example usage; FunctionFilterbank pair: example usage; sum pair: example usage; FilterbankGroup pair: example usage; ylabel pair: example usage; tile pair: example usage; SpikeCounter pair: example usage; Sound pair: example usage; run pair: example usage; Connection pair: example usage; IRCAM_LISTEN pair: example usage; amax pair: example usage; NeuronGroup pair: example usage; Gammatone pair: example usage; RestructureFilterbank pair: example usage; xlabel pair: example usage; whitenoise pair: example usage; scatter .. _example-frompapers-computing with neural synchrony-hearing_Fig12A_Goodman_Brette_2010: Example: Fig12A_Goodman_Brette_2010 (frompapers/computing with neural synchrony/hearing) ======================================================================================== Sound localization with HRTFs ----------------------------- Goodman DF and R Brette (2010). Spike-timing-based computation in sound localization. PLoS Comp Biol 6(11): e1000993. doi:10.1371/journal.pcbi.1000993. Corresponds to Fig. 12A in: Brette R (2012). Computing with neural synchrony. PLoS Comp Biol. 8(6): e1002561. doi:10.1371/journal.pcbi.1002561 Simplified version of the "ideal" sound localisation model. The sound is played at a particular spatial location (indicated on the final plot by a red +). Each location has a corresponding assembly of neurons, whose summed firing rates give the sizes of the blue circles in the plot. The most strongly responding assembly is indicated by the green x, which is the estimate of the location by the model. :: from brian import * from brian.hears import * # Download the IRCAM database # http://recherche.ircam.fr/equipes/salles/listen/download.html # and replace this filename with the location you downloaded it to hrtfdb = IRCAM_LISTEN(r'Z:\HRTF\IRCAM') subject = 1002 hrtfset = hrtfdb.load_subject(subject) # This gives the number of spatial locations in the set of HRTFs num_indices = hrtfset.num_indices # Choose a random location for the sound to come from index = randint(num_indices) # A sound to test the model with sound = Sound.whitenoise(500*ms) # This is the specific HRTF for the chosen location hrtf = hrtfset.hrtf[index] # We apply the chosen HRTF to the sound, the output has 2 channels hrtf_fb = hrtf.filterbank(sound) # We swap these channels (equivalent to swapping the channels in the # subsequent filters, but simpler to do it with the inputs) swapped_channels = RestructureFilterbank(hrtf_fb, indexmapping=[1, 0]) # Now we apply all of the possible pairs of HRTFs in the set to these # swapped channels, which means repeating them num_indices times first hrtfset_fb = hrtfset.filterbank(Repeat(swapped_channels, num_indices)) # Now we apply cochlear filtering (logically, this comes before the HRTF # filtering, but since convolution is commutative it is more efficient to # do the cochlear filtering afterwards cfmin, cfmax, cfN = 150*Hz, 5*kHz, 40 cf = erbspace(cfmin, cfmax, cfN) # We repeat each of the HRTFSet filterbank channels cfN times, so that # for each location we will apply each possible cochlear frequency gfb = Gammatone(Repeat(hrtfset_fb, cfN), tile(cf, hrtfset_fb.nchannels)) # Half wave rectification and compression cochlea = FunctionFilterbank(gfb, lambda x:15*clip(x, 0, Inf)**(1.0/3.0)) # Leaky integrate and fire neuron model eqs = ''' dV/dt = (I-V)/(1*ms)+0.1*xi/(0.5*ms)**.5 : 1 I : 1 ''' G = FilterbankGroup(cochlea, 'I', eqs, reset=0, threshold=1, refractory=5*ms) # The coincidence detector (cd) neurons cd = NeuronGroup(num_indices*cfN, eqs, reset=0, threshold=1, clock=G.clock) # Each CD neuron receives precisely two inputs, one from the left ear and # one from the right, for each location and each cochlear frequency C = Connection(G, cd, 'V') for i in xrange(num_indices*cfN): C[i, i] = 0.5 # from right ear C[i+num_indices*cfN, i] = 0.5 # from left ear # We want to just count the number of CD spikes counter = SpikeCounter(cd) # Run the simulation, giving a report on how long it will take as we run run(sound.duration, report='stderr') # We take the array of counts, and reshape them into a 2D array which we sum # across frequencies to get the spike count of each location-specific assembly count = counter.count count.shape = (num_indices, cfN) count = sum(count, axis=1) count = array(count, dtype=float)/amax(count) # Our guess of the location is the index of the strongest firing assembly index_guess = argmax(count) # Now we plot the output, using the coordinates of the HRTFSet coords = hrtfset.coordinates azim, elev = coords['azim'], coords['elev'] scatter(azim, elev, 100*count) plot([azim[index]], [elev[index]], '+r', ms=15, mew=2) plot([azim[index_guess]], [elev[index_guess]], 'xg', ms=15, mew=2) xlabel('Azimuth (deg)') ylabel('Elevation (deg)') xlim(-5, 350) ylim(-50, 95) show() examples-frompapers-computing with neural synchrony-hearing_Fig7A_Jeffress.txt000066400000000000000000000052311266062647100342640ustar00rootroot00000000000000brian-1.4.3/docs_sphinx.. currentmodule:: brian .. index:: pair: example usage; NeuronGroup pair: example usage; run pair: example usage; TimedArray pair: example usage; show pair: example usage; raster_plot pair: example usage; Connection pair: example usage; SpikeMonitor pair: example usage; linspace pair: example usage; StateMonitor .. _example-frompapers-computing with neural synchrony-hearing_Fig7A_Jeffress: Example: Fig7A_Jeffress (frompapers/computing with neural synchrony/hearing) ============================================================================ Brette R (2012). Computing with neural synchrony. PLoS Comp Biol. 8(6): e1002561. doi:10.1371/journal.pcbi.1002561 ------------------------------------------------------------------------------------------------------------------ Figure 7A. Jeffress model, adapted with spiking neuron models. A sound source (white noise) is moving around the head. Delay differences between the two ears are used to determine the azimuth of the source. Delays are mapped to a neural place code using delay lines (each neuron receives input from both ears, with different delays). :: from brian import * defaultclock.dt = .02 * ms dt = defaultclock.dt # Sound sound = TimedArray(10 * randn(50000)) # white noise # Ears and sound motion around the head (constant angular speed) sound_speed = 300 * metre / second interaural_distance = 20 * cm # big head! max_delay = interaural_distance / sound_speed print "Maximum interaural delay:", max_delay angular_speed = 2 * pi * radian / second # 1 turn/second tau_ear = 1 * ms sigma_ear = .05 eqs_ears = ''' dx/dt=(sound(t-delay)-x)/tau_ear+sigma_ear*(2./tau_ear)**.5*xi : 1 delay=distance*sin(theta) : second distance : second # distance to the centre of the head in time units dtheta/dt=angular_speed : radian ''' ears = NeuronGroup(2, model=eqs_ears, threshold=1, reset=0, refractory=2.5 * ms) ears.distance = [-.5 * max_delay, .5 * max_delay] traces = StateMonitor(ears, 'x', record=True) # Coincidence detectors N = 300 tau = 1 * ms sigma = .05 eqs_neurons = ''' dv/dt=-v/tau+sigma*(2./tau)**.5*xi : 1 ''' neurons = NeuronGroup(N, model=eqs_neurons, threshold=1, reset=0) synapses = Connection(ears, neurons, 'v', structure='dense', delay=True, max_delay=1.1 * max_delay) synapses.connect_full(ears, neurons, weight=.5) synapses.delay[0, :] = linspace(0 * ms, 1.1 * max_delay, N) synapses.delay[1, :] = linspace(0 * ms, 1.1 * max_delay, N)[::-1] spikes = SpikeMonitor(neurons) run(1000 * ms) raster_plot(spikes) show() examples-frompapers-computing with neural synchrony-hearing_Fig7B_Licklider.txt000066400000000000000000000045511266062647100344240ustar00rootroot00000000000000brian-1.4.3/docs_sphinx.. currentmodule:: brian .. index:: pair: example usage; NeuronGroup pair: example usage; run pair: example usage; log pair: example usage; show pair: example usage; raster_plot pair: example usage; yticks pair: example usage; Connection pair: example usage; SpikeMonitor pair: example usage; linspace pair: example usage; exp pair: example usage; ylabel pair: example usage; StateMonitor .. _example-frompapers-computing with neural synchrony-hearing_Fig7B_Licklider: Example: Fig7B_Licklider (frompapers/computing with neural synchrony/hearing) ============================================================================= Brette R (2012). Computing with neural synchrony. PLoS Comp Biol. 8(6): e1002561. doi:10.1371/journal.pcbi.1002561 ------------------------------------------------------------------------------------------------------------------ Figure 12B. Spike-based adaptation of Licklider's model of pitch processing (autocorrelation with delay lines). :: from brian import * defaultclock.dt = .02 * ms # Ear and sound max_delay = 20 * ms # 50 Hz tau_ear = 1 * ms sigma_ear = .1 eqs_ear = ''' dx/dt=(sound-x)/tau_ear+sigma_ear*(2./tau_ear)**.5*xi : 1 sound=5*sin(2*pi*frequency*t)**3 : 1 # nonlinear distorsion #sound=5*(sin(4*pi*frequency*t)+.5*sin(6*pi*frequency*t)) : 1 # missing fundamental frequency=(200+200*t*Hz)*Hz : Hz # increasing pitch ''' receptors = NeuronGroup(2, model=eqs_ear, threshold=1, reset=0, refractory=2 * ms) traces = StateMonitor(receptors, 'x', record=True) sound = StateMonitor(receptors, 'sound', record=0) # Coincidence detectors min_freq = 50 * Hz max_freq = 1000 * Hz N = 300 tau = 1 * ms sigma = .1 eqs_neurons = ''' dv/dt=-v/tau+sigma*(2./tau)**.5*xi : 1 ''' neurons = NeuronGroup(N, model=eqs_neurons, threshold=1, reset=0) synapses = Connection(receptors, neurons, 'v', structure='dense', max_delay=1.1 * max_delay, delay=True) synapses.connect_full(receptors, neurons, weight=.5) synapses.delay[1, :] = 1. / exp(linspace(log(min_freq / Hz), log(max_freq / Hz), N)) spikes = SpikeMonitor(neurons) run(500 * ms) raster_plot(spikes) ylabel('Frequency') yticks([0, 99, 199, 299], array(1. / synapses.delay.todense()[1, [0, 99, 199, 299]], dtype=int)) show() examples-frompapers-computing with neural synchrony-olfaction_Fig11B_olfaction_stdp_learning.txt000066400000000000000000000101771266062647100400260ustar00rootroot00000000000000brian-1.4.3/docs_sphinx.. currentmodule:: brian .. index:: pair: example usage; NeuronGroup pair: example usage; run pair: example usage; network_operation pair: example usage; EventClock pair: example usage; Connection pair: example usage; SpikeMonitor pair: example usage; exp pair: example usage; save pair: example usage; STDP .. _example-frompapers-computing with neural synchrony-olfaction_Fig11B_olfaction_stdp_learning: Example: Fig11B_olfaction_stdp_learning (frompapers/computing with neural synchrony/olfaction) ============================================================================================== Brette R (2012). Computing with neural synchrony. PLoS Comp Biol. 8(6): e1002561. doi:10.1371/journal.pcbi.1002561 ------------------------------------------------------------------------------------------------------------------ Figure 11B. Learning to recognize odors. (long simulation) Caption (Fig. 11B). After learning, responses of postsynaptic neurons, ordered by tuning ratio, to odor A (blue) and odor B (red), with an increasing concentration (0.1 to 10, where 1 is odor concentration in the learning phase). After this script, run the other file: Fig11B_olfaction_stdp_testing.py. :: from brian import * from params import * from brian.experimental.connectionmonitor import * import numpy bmin,bmax=-7,-1 def odor(N): # Returns a random vector of binding constants return 10**(rand(N)*(bmax-bmin)+bmin) def hill_function(c,K=1.,n=3.): ''' Hill function: * c = concentration * K = half activation constant (choose K=1 for relative concentrations) * n = Hill coefficient ''' return (c**n)/(c**n+K**n) N=5000 # number of receptors seed(31415) # Get the same neurons every time intensity=3000. # Odor plumes tau_plume=75*ms eq_plumes=''' dx/dt=-x/tau_plume+(2./tau_plume)**.5*xi : 1 y=clip(x,0,inf) : 1 ''' plume=NeuronGroup(1,model=eq_plumes) # 1 odor # Receptor neurons Fmax=40*Hz # maximum firing rate tau=20*ms Imax=1/(1-exp(-1/(Fmax*tau))) # maximum input current eq_receptors=''' dv/dt=(Imax*hill_function(c)-v)/tau : 1 c : 1 # concentrations (relative to activation constant) ''' receptors=NeuronGroup(N,model=eq_receptors,threshold=1,reset=0) @network_operation def odor_to_nose(): # Send odor plume to the receptors receptors.c=I1*c1*clip(plume.x[0],0,Inf) odors=[odor(N),odor(N)] # two odors c1=odors[0] stimuli=[] # A random odor is presented every 200 ms @network_operation(clock=EventClock(dt=200*ms)) def change_odor(): global c1 nodor=randint(len(odors)) c1=odors[nodor] stimuli.append((float(defaultclock.t),float(nodor))) # Decoder neurons M=30 eq_decoders=''' dv/dt=-v/taud + sigma*(2/taud)**.5*xi : 1 ''' decoders=NeuronGroup(M,model=eq_decoders,threshold=1,reset=0) S2=SpikeMonitor(decoders) # Random synapses syn=Connection(receptors,decoders,'v',sparseness=Nsynapses*1./N,weight=w0) # STDP eqs_stdp=''' dApre/dt=-Apre/tau_pre : 1 Apost : 1 ''' pre=''' Apre+=a_pre #w+=0 ''' post=''' Apost+=0 w+=Apre+b_post*w ''' stdp=STDP(syn,eqs_stdp,pre=pre,post=post,wmax=Inf) MC=ConnectionMonitor(syn,store=True,clock=EventClock(dt=record_period)) @network_operation(EventClock(dt=IP_period)) def intrinsic_plasticity(): # synaptic scaling # Increases weights of all synapses syn.W.alldata+=syn.W.alldata*IP_rate*IP_period # Record the evolution of weights weights=[] @network_operation(EventClock(dt=record_period)) def recordW(): Z=syn.W[:,0].copy() weights.append(Z) I1=intensity print "Started" run(duration,report="text") # Save data wsave=[(t,M.todense()) for (t,M) in MC.values] numpy.save("weights.npy",array(zip(*wsave)[1])) # 3D array (t,i,j) numpy.save("spikesout.npy",array(S2.spikes)) numpy.save("stimuli.npy",array(stimuli)) examples-frompapers-computing with neural synchrony-olfaction_Fig11B_olfaction_stdp_testing.txt000066400000000000000000000114501266062647100376770ustar00rootroot00000000000000brian-1.4.3/docs_sphinx.. currentmodule:: brian .. index:: pair: example usage; load pair: example usage; plot pair: example usage; run pair: example usage; log pair: example usage; show pair: example usage; network_operation pair: example usage; sum pair: example usage; semilogy pair: example usage; argsort pair: example usage; Connection pair: example usage; subplot pair: example usage; SpikeMonitor pair: example usage; linspace pair: example usage; exp pair: example usage; NeuronGroup pair: example usage; nonzero .. _example-frompapers-computing with neural synchrony-olfaction_Fig11B_olfaction_stdp_testing: Example: Fig11B_olfaction_stdp_testing (frompapers/computing with neural synchrony/olfaction) ============================================================================================= Brette R (2012). Computing with neural synchrony. PLoS Comp Biol. 8(6): e1002561. doi:10.1371/journal.pcbi.1002561 ------------------------------------------------------------------------------------------------------------------ Figure 11B. Learning to recognize odors. Caption (Fig. 11B). After learning, responses of postsynaptic neurons, ordered by tuning ratio, to odor A (blue) and odor B (red), with an increasing concentration (0.1 to 10, where 1 is odor concentration in the learning phase). Run the other file first: Fig11B_olfaction_stdp_learning.py :: from brian import * from params import * import numpy from scipy.sparse import lil_matrix bmin,bmax=-7,-1 # Loads information from the STDP simulation t,odor=numpy.load("stimuli.npy").T W=numpy.load("weights.npy") spikes_out=numpy.load("spikesout.npy") weights=W[-1,:,:] # final weights # Analyze selectivity at the end of the STDP simulation ispikes=spikes_out[:,0] # indexes of neurons that spiked tspikes=spikes_out[:,1] # spike timings # Select only the end of the STDP simulation end=tspikes>.8*max(tspikes) ispikes=ispikes[end] tspikes=tspikes[end] odors=odor[digitize(tspikes,t)-1] # odor (0/1) presented at the time of spikes tuning=zeros(30) # Tuning ratio of the postsynaptic neurons n0,n1=zeros(30),zeros(30) # number of spikes for odor 0 and for odor 1 for k in range(len(tuning)): o=odors[ispikes==k] n0[k]=sum(o==0) n1[k]=sum(o==1) tuning[k]=n0[k]*1./(n0[k]+n1[k]) # Sort the postsynaptic neurons by odor tuning weights=weights[:,argsort(tuning)] ''' Run the simulation ''' def odor(N): # Returns a random vector of binding constants return 10**(rand(N)*(bmax-bmin)+bmin) def hill_function(c,K=1.,n=3.): ''' Hill function: * c = concentration * K = half activation constant (choose K=1 for relative concentrations) * n = Hill coefficient ''' return (c**n)/(c**n+K**n) N=5000 # number of receptors seed(31415) # Get the same neurons every time intensity=3000. # Odor plumes tau_plume=75*ms eq_plumes=''' dx/dt=-x/tau_plume+(2./tau_plume)**.5*xi : 1 y=clip(x,0,inf) : 1 ''' plume=NeuronGroup(2,model=eq_plumes) # 1 odor # Receptor neurons Fmax=40*Hz # maximum firing rate tau=20*ms Imax=1/(1-exp(-1/(Fmax*tau))) # maximum input current eq_receptors=''' dv/dt=(Imax*hill_function(c)-v)/tau : 1 c : 1 # concentrations (relative to activation constant) ''' receptors=NeuronGroup(N,model=eq_receptors,threshold=1,reset=0) @network_operation def odor_to_nose(): # Send odor plume to the receptors receptors.c=I1*c1*clip(plume.x[0],0,Inf)+I2*c2*clip(plume.x[0],0,Inf) odors=[odor(N),odor(N)] c1,c2=odors # Decoder neurons M=len(tuning) eq_decoders=''' dv/dt=-v/taud + sigma*(2/taud)**.5*xi : 1 ''' decoders=NeuronGroup(M,model=eq_decoders,threshold=1,reset=0) S2=SpikeMonitor(decoders) # Synapses syn=Connection(receptors,decoders,'v') for i in range(len(decoders)): for j in weights[:,i].nonzero()[0]: syn[j,i]=weights[j,i] # Run I1,I2=intensity,0 print "Started" # Odor A, increasing concentration for I1 in intensity*exp(linspace(log(.1),log(10),20)): run(1*second,report="text") I1=0 # Odor B, increasing concentration for I2 in intensity*exp(linspace(log(.1),log(10),20)): run(1*second,report="text") # Figure (11B) spikes=array(S2.spikes) # i,t n,t=spikes[:,0],spikes[:,1] subplot(211) # Raster plot plot(t,n,'k.') subplot(212) # Odor concentrations semilogy(linspace(0,20,20),exp(linspace(log(.1),log(10),20)),'b') semilogy(linspace(20,40,20),exp(linspace(log(.1),log(10),20)),'r') plot([0,40],[1,1],'k--') show() examples-frompapers-computing with neural synchrony-olfaction_Fig9B_olfaction.txt000066400000000000000000000121521266062647100350370ustar00rootroot00000000000000brian-1.4.3/docs_sphinx.. currentmodule:: brian .. index:: pair: example usage; subplot pair: example usage; plot pair: example usage; run pair: example usage; log pair: example usage; xlim pair: example usage; show pair: example usage; raster_plot pair: example usage; network_operation pair: example usage; sum pair: example usage; xticks pair: example usage; Connection pair: example usage; SpikeMonitor pair: example usage; exp pair: example usage; NeuronGroup pair: example usage; ylim pair: example usage; StateMonitor .. _example-frompapers-computing with neural synchrony-olfaction_Fig9B_olfaction: Example: Fig9B_olfaction (frompapers/computing with neural synchrony/olfaction) =============================================================================== Brette R (2012). Computing with neural synchrony. PLoS Comp Biol. 8(6): e1002561. doi:10.1371/journal.pcbi.1002561 ------------------------------------------------------------------------------------------------------------------ Figure 9B. Caption (Fig. 9B). Top, Fluctuating concentration of three odors (A: blue, B: red, C: black). Middle, spiking responses of olfactory receptors. Bottom, Responses of postsynaptic neurons from the assembly selective to A (blue) and to B (red). Stimuli are presented is sequence: 1) odor A alone, 2) odor B alone, 3) odor B alone with twice stronger intensity, 4) odor A with distracting odor C (same intensity), 5) odors A and B (same intensity). :: from brian import * bmin,bmax=-7,-1 def odor(N): # Returns a random vector of binding constants return 10**(rand(N)*(bmax-bmin)+bmin) def hill_function(c,K=1.,n=3.): ''' Hill function: * c = concentration * K = half activation constant (choose K=1 for relative concentrations) * n = Hill coefficient ''' return (c**n)/(c**n+K**n) N=5000 # number of receptors # Odors seed(31415) # Get the same neurons every time intensity=3000. c1=odor(N) c2=odor(N) c0=c1 I1,I2=intensity,intensity # Odor plumes (fluctuating concentrations) tau_plume=75*ms eq_plumes=''' dx/dt=-x/tau_plume+(2./tau_plume)**.5*xi : 1 y=clip(x,0,inf) : 1 ''' plume=NeuronGroup(2,model=eq_plumes) # 2 odors # Receptor neurons Fmax=40*Hz # maximum firing rate tau=20*ms Imax=1/(1-exp(-1/(Fmax*tau))) # maximum input current eq_receptors=''' dv/dt=(Imax*hill_function(c)-v)/tau : 1 c : 1 # concentrations (relative to activation constant) ''' receptors=NeuronGroup(N,model=eq_receptors,threshold=1,reset=0) receptors.c=c1 @network_operation def odor_to_nose(): # Send odor plume to the receptors receptors.c=I1*c1*clip(plume.x[0],0,Inf)+I2*c2*clip(plume.x[1],0,Inf) # Decoder neurons M=200 taud=8*ms sigma=.15 eq_decoders=''' dv/dt=-v/taud + sigma*(2/taud)**.5*xi : 1 ''' decoders=NeuronGroup(2*M,model=eq_decoders,threshold=1,reset=0) # First M neurons encode odor A, next M neurons encode odor B # Synapses syn=Connection(receptors,decoders,'v') # Connectivity according to synchrony partitions bhalf=.5*(bmin+bmax) # select only those that are well activated u=2*(log(c1)/log(10)-bhalf)/(bmax-bmin) # normalized binding constants for odor A for i in range(M): which=((u>=i*1./M) & (u<(i+1)*1./M)) # we divide in M groups with similar values if sum(which)>0: w=1./sum(which) # total synaptic weight for a postsynaptic neuron is 1 syn[:,i]=w*which u=2*(log(c2)/log(10)-bhalf)/(bmax-bmin) for i in range(M): # normalized binding constants for odor B which=((u>=i*1./M) & (u<(i+1)*1./M)) if sum(which)>0: w=1./sum(which) syn[:,2*M-1-i]=w*which # Record odor concentration and output spikes O=StateMonitor(plume,'y',record=True) S=SpikeMonitor(receptors) S2=SpikeMonitor(decoders) print "Odor A" I1,I2=intensity,0 run(2*second) print "Odor B" I1,I2=0,intensity run(2*second) print "Odor B x2" I1,I2=0,2*intensity run(2*second) print "Odor A + odor C" I1,I2=intensity,intensity old_c2=c2 c2=odor(N) # different odor run(2*second) print "Odor A + odor B" I1,I2=intensity,intensity c2=old_c2 run(2*second) t=O.times/ms # Figure (9B) subplot(311) # odor fluctuations plot(t[t<2000],O[0][t<2000],'b') plot(t[(t>=2000) & (t<4000)],O[1][(t>=2000) & (t<4000)],'r') plot(t[(t>=4000) & (t<6000)],2*O[1][(t>=4000) & (t<6000)],'r') plot(t[(t>=6000) & (t<8000)],O[0][(t>=6000) & (t<8000)],'b') plot(t[(t>=6000) & (t<8000)],O[1][(t>=6000) & (t<8000)],'k') plot(t[(t>=8000) & (t<10000)],O[1][(t>=8000) & (t<10000)],'r') plot(t[(t>=8000) & (t<10000)],O[0][(t>=8000) & (t<10000)],'b') xlim(0,10000) xticks([]) subplot(312) raster_plot(S) xlim(0,10000) ylim(2500,2600) # 100 random neurons xticks([]) subplot(313) raster_plot(S2) ylim(100,300) xlim(0,10000) show() brian-1.4.3/docs_sphinx/examples-frompapers-computing with neural synchrony-olfaction_params.txt000066400000000000000000000017171266062647100334500ustar00rootroot00000000000000.. currentmodule:: brian .. _example-frompapers-computing with neural synchrony-olfaction_params: Example: params (frompapers/computing with neural synchrony/olfaction) ====================================================================== Brette R (2012). Computing with neural synchrony. PLoS Comp Biol. 8(6): e1002561. doi:10.1371/journal.pcbi.1002561 ------------------------------------------------------------------------------------------------------------------ Parameters for the olfactory model with STDP :: from brian import * N=5000 # Coincidence detectors sigma=.15 taud=8*ms # Connections Nsynapses=50 w0=150./(0.02*N) # STDP factor=0.05 a_pre=0.06*factor b_post=-1.*factor tau_pre=3*ms # Intrinsic plasticity: non-specific weight increase IP_period=10*ms IP_rate=-b_post*5*Hz # target firing rate = 5 Hz # Simulation control record_period=1*second duration=100*second brian-1.4.3/docs_sphinx/examples-frompapers_Brette_2004.txt000066400000000000000000000026271266062647100236610ustar00rootroot00000000000000.. currentmodule:: brian .. index:: pair: example usage; plot pair: example usage; run pair: example usage; show pair: example usage; yticks pair: example usage; xlabel pair: example usage; linspace pair: example usage; ylabel pair: example usage; SpikeMonitor pair: example usage; NeuronGroup .. _example-frompapers_Brette_2004: Example: Brette_2004 (frompapers) ================================= Phase locking in leaky integrate-and-fire model ----------------------------------------------- Fig. 2A from: Brette R (2004). Dynamics of one-dimensional spiking neuron models. J Math Biol 48(1): 38-56. This shows the phase-locking structure of a LIF driven by a sinusoidal current. When the current crosses the threshold (a<3), the model almost always phase locks (in a measure-theoretical sense). :: from brian import * #defaultclock.dt=0.01*ms # for a more precise picture N=2000 tau=100*ms freq=1/tau eqs=''' dv/dt=(-v+a+2*sin(2*pi*t/tau))/tau : 1 a : 1 ''' neurons=NeuronGroup(N,eqs,threshold=1,reset=0) neurons.a=linspace(2,4,N) run(5*second,report='text') # discard the first spikes (wait for convergence) S=SpikeMonitor(neurons) run(5*second,report='text') i,t=zip(*S.spikes) plot((t % tau)/tau,i,'.') xlabel('Spike phase') ylabel('Parameter a') yticks([0,N/2,N],[2,3,4]) show() brian-1.4.3/docs_sphinx/examples-frompapers_Brette_Gerstner_2005.txt000066400000000000000000000034121266062647100255240ustar00rootroot00000000000000.. currentmodule:: brian .. index:: pair: example usage; NeuronGroup pair: example usage; run pair: example usage; trace pair: example usage; plot pair: example usage; show pair: example usage; SpikeMonitor pair: example usage; StateMonitor .. _example-frompapers_Brette_Gerstner_2005: Example: Brette_Gerstner_2005 (frompapers) ========================================== Adaptive exponential integrate-and-fire model. http://www.scholarpedia.org/article/Adaptive_exponential_integrate-and-fire_model Introduced in Brette R. and Gerstner W. (2005), Adaptive Exponential Integrate-and-Fire Model as an Effective Description of Neuronal Activity, J. Neurophysiol. 94: 3637 - 3642. :: from brian import * # Parameters C = 281 * pF gL = 30 * nS taum = C / gL EL = -70.6 * mV VT = -50.4 * mV DeltaT = 2 * mV Vcut = VT + 5 * DeltaT # Pick an electrophysiological behaviour tauw, a, b, Vr = 144 * ms, 4 * nS, 0.0805 * nA, -70.6 * mV # Regular spiking (as in the paper) #tauw,a,b,Vr=20*ms,4*nS,0.5*nA,VT+5*mV # Bursting #tauw,a,b,Vr=144*ms,2*C/(144*ms),0*nA,-70.6*mV # Fast spiking eqs = """ dvm/dt=(gL*(EL-vm)+gL*DeltaT*exp((vm-VT)/DeltaT)+I-w)/C : volt dw/dt=(a*(vm-EL)-w)/tauw : amp I : amp """ neuron = NeuronGroup(1, model=eqs, threshold=Vcut, reset="vm=Vr;w+=b", freeze=True) neuron.vm = EL trace = StateMonitor(neuron, 'vm', record=0) spikes = SpikeMonitor(neuron) run(20 * ms) neuron.I = 1 * nA run(100 * ms) neuron.I = 0 * nA run(20 * ms) # We draw nicer spikes vm = trace[0] for _, t in spikes.spikes: i = int(t / defaultclock.dt) vm[i] = 20 * mV plot(trace.times / ms, vm / mV) show() brian-1.4.3/docs_sphinx/examples-frompapers_Brette_Guigon_2003.txt000066400000000000000000000036601266062647100251660ustar00rootroot00000000000000.. currentmodule:: brian .. index:: pair: example usage; subplot pair: example usage; plot pair: example usage; run pair: example usage; show pair: example usage; raster_plot pair: example usage; linked_var pair: example usage; SpikeMonitor pair: example usage; linspace pair: example usage; NeuronGroup pair: example usage; StateMonitor .. _example-frompapers_Brette_Guigon_2003: Example: Brette_Guigon_2003 (frompapers) ======================================== Reliability of spike timing --------------------------- Adapted from Fig. 10D,E of Brette R and E Guigon (2003). Reliability of Spike Timing Is a General Property of Spiking Model Neurons. Neural Computation 15, 279-308. This shows that reliability of spike timing is a generic property of spiking neurons, even those that are not leaky. This is a non-physiological model which can be leaky or anti-leaky depending on the sign of the input I. All neurons receive the same fluctuating input, scaled by a parameter p that varies across neurons. This shows: 1. reproducibility of spike timing 2. robustness with respect to deterministic changes (parameter) 3. increased reproducibility in the fluctuation-driven regime (input crosses the threshold) :: from brian import * N=500 tau=33*ms taux=20*ms sigma=0.02 eqs_input=''' B=2./(1+exp(-2*x))-1 : 1 dx/dt=-x/taux+(2/taux)**.5*xi : 1 ''' eqs=''' dv/dt=(v*I+1)/tau + sigma*(2/tau)**.5*xi : 1 I=0.5+3*p*B : 1 B : 1 p : 1 ''' input=NeuronGroup(1,eqs_input) neurons=NeuronGroup(N,eqs,threshold=1,reset=0) neurons.p=linspace(0,1,N) neurons.v=rand(N) neurons.B=linked_var(input,'B') M=StateMonitor(input,'B',record=0) S=SpikeMonitor(neurons) run(1000*ms) subplot(211) # The input plot(M.times/ms,M[0]) subplot(212) raster_plot(S) plot([0,1000],[250,250],'r') show() brian-1.4.3/docs_sphinx/examples-frompapers_Brunel_Hakim_1999.txt000066400000000000000000000035111266062647100250130ustar00rootroot00000000000000.. currentmodule:: brian .. index:: pair: example usage; subplot pair: example usage; plot pair: example usage; run pair: example usage; PopulationRateMonitor pair: example usage; xlim pair: example usage; show pair: example usage; raster_plot pair: example usage; Connection pair: example usage; rate pair: example usage; SpikeMonitor pair: example usage; NeuronGroup .. _example-frompapers_Brunel_Hakim_1999: Example: Brunel_Hakim_1999 (frompapers) ======================================= Dynamics of a network of sparsely connected inhibitory current-based integrate-and-fire neurons. Individual neurons fire irregularly at low rate but the network is in an oscillatory global activity regime where neurons are weakly synchronized. Reference: "Fast Global Oscillations in Networks of Integrate-and-Fire Neurons with Low Firing Rates" Nicolas Brunel & Vincent Hakim Neural Computation 11, 1621-1671 (1999) :: from brian import * N = 5000 Vr = 10 * mV theta = 20 * mV tau = 20 * ms delta = 2 * ms taurefr = 2 * ms duration = .1 * second C = 1000 sparseness = float(C)/N J = .1 * mV muext = 25 * mV sigmaext = 1 * mV eqs = """ dV/dt = (-V+muext + sigmaext * sqrt(tau) * xi)/tau : volt """ group = NeuronGroup(N, eqs, threshold=theta, reset=Vr, refractory=taurefr) group.V = Vr conn = Connection(group, group, state='V', delay=delta, weight = -J, sparseness=sparseness) M = SpikeMonitor(group) LFP = PopulationRateMonitor(group, bin=0.4 * ms) run(duration) subplot(211) raster_plot(M) xlim(0, duration/ms) subplot(212) plot(LFP.times_/ms, LFP.rate) xlim(0, duration/ms) show() brian-1.4.3/docs_sphinx/examples-frompapers_Diesmann_et_al_1999.txt000066400000000000000000000032301266062647100253530ustar00rootroot00000000000000.. currentmodule:: brian .. index:: pair: example usage; NeuronGroup pair: example usage; run pair: example usage; show pair: example usage; raster_plot pair: example usage; Connection pair: example usage; SpikeMonitor pair: example usage; PulsePacket pair: example usage; Equations .. _example-frompapers_Diesmann_et_al_1999: Example: Diesmann_et_al_1999 (frompapers) ========================================= Synfire chains -------------- M. Diesmann et al. (1999). Stable propagation of synchronous spiking in cortical neural networks. Nature 402, 529-533. :: from brian import * # Neuron model parameters Vr = -70 * mV Vt = -55 * mV taum = 10 * ms taupsp = 0.325 * ms weight = 4.86 * mV # Neuron model eqs = Equations(''' dV/dt=(-(V-Vr)+x)*(1./taum) : volt dx/dt=(-x+y)*(1./taupsp) : volt dy/dt=-y*(1./taupsp)+25.27*mV/ms+\ (39.24*mV/ms**0.5)*xi : volt ''') # Neuron groups P = NeuronGroup(N=1000, model=eqs, threshold=Vt, reset=Vr, refractory=1 * ms) Pinput = PulsePacket(t=50 * ms, n=85, sigma=1 * ms) # The network structure Pgp = [ P.subgroup(100) for i in range(10)] C = Connection(P, P, 'y') for i in range(9): C.connect_full(Pgp[i], Pgp[i + 1], weight) Cinput = Connection(Pinput, Pgp[0], 'y') Cinput.connect_full(weight=weight) # Record the spikes Mgp = [SpikeMonitor(p) for p in Pgp] Minput = SpikeMonitor(Pinput) monitors = [Minput] + Mgp # Setup the network, and run it P.V = Vr + rand(len(P)) * (Vt - Vr) run(100 * ms) # Plot result raster_plot(showgrouplines=True, *monitors) show() brian-1.4.3/docs_sphinx/examples-frompapers_Diesmann_et_al_1999_longer.txt000066400000000000000000000211611266062647100267240ustar00rootroot00000000000000.. currentmodule:: brian .. index:: pair: example usage; Network pair: example usage; Parameters .. _example-frompapers_Diesmann_et_al_1999_longer: Example: Diesmann_et_al_1999_longer (frompapers) ================================================ Implementation of synfire chain from Diesmann et al. 1999 Dan Goodman - Dec. 2007 :: #import brian_no_units from brian import * import time from brian.library.IF import * from brian.library.synapses import * def minimal_example(): # Neuron model parameters Vr = -70 * mV Vt = -55 * mV taum = 10 * ms taupsp = 0.325 * ms weight = 4.86 * mV # Neuron model equations = Equations(''' dV/dt = (-(V-Vr)+x)*(1./taum) : volt dx/dt = (-x+y)*(1./taupsp) : volt dy/dt = -y*(1./taupsp)+25.27*mV/ms+(39.24*mV/ms**0.5)*xi : volt ''') # Neuron groups P = NeuronGroup(N=1000, model=equations, threshold=Vt, reset=Vr, refractory=1 * ms) # P = NeuronGroup(N=1000, model=(dV,dx,dy),init=(0*volt,0*volt,0*volt), # threshold=Vt,reset=Vr,refractory=1*ms) Pinput = PulsePacket(t=50 * ms, n=85, sigma=1 * ms) # The network structure Pgp = [ P.subgroup(100) for i in range(10)] C = Connection(P, P, 'y') for i in range(9): C.connect_full(Pgp[i], Pgp[i + 1], weight) Cinput = Connection(Pinput, P, 'y') Cinput.connect_full(Pinput, Pgp[0], weight) # Record the spikes Mgp = [SpikeMonitor(p, record=True) for p in Pgp] Minput = SpikeMonitor(Pinput, record=True) monitors = [Minput] + Mgp # Setup the network, and run it P.V = Vr + rand(len(P)) * (Vt - Vr) run(100 * ms) # Plot result raster_plot(showgrouplines=True, *monitors) show() # DEFAULT PARAMATERS FOR SYNFIRE CHAIN # Approximates those in Diesman et al. 1999 model_params = Parameters( # Simulation parameters dt=0.1 * ms, duration=100 * ms, # Neuron model parameters taum=10 * ms, taupsp=0.325 * ms, Vt= -55 * mV, Vr= -70 * mV, abs_refrac=1 * ms, we=34.7143, wi= -34.7143, psp_peak=0.14 * mV, # Noise parameters noise_neurons=20000, noise_exc=0.88, noise_inh=0.12, noise_exc_rate=2 * Hz, noise_inh_rate=12.5 * Hz, computed_model_parameters=""" noise_mu = noise_neurons * (noise_exc * noise_exc_rate - noise_inh * noise_inh_rate ) * psp_peak * we noise_sigma = (noise_neurons * (noise_exc * noise_exc_rate + noise_inh * noise_inh_rate ))**.5 * psp_peak * we """ ) # MODEL FOR SYNFIRE CHAIN # Excitatory PSPs only def Model(p): equations = Equations(''' dV/dt = (-(V-p.Vr)+x)*(1./p.taum) : volt dx/dt = (-x+y)*(1./p.taupsp) : volt dy/dt = -y*(1./p.taupsp)+25.27*mV/ms+(39.24*mV/ms**0.5)*xi : volt ''') return Parameters(model=equations, threshold=p.Vt, reset=p.Vr, refractory=p.abs_refrac) default_params = Parameters( # Network parameters num_layers=10, neurons_per_layer=100, neurons_in_input_layer=100, # Initiating burst parameters initial_burst_t=50 * ms, initial_burst_a=85, initial_burst_sigma=1 * ms, # these values are recomputed whenever another value changes computed_network_parameters=""" total_neurons = neurons_per_layer * num_layers """, # plus we also use the default model parameters ** model_params ) # DEFAULT NETWORK STRUCTURE # Single input layer, multiple chained layers class DefaultNetwork(Network): def __init__(self, p): # define groups chaingroup = NeuronGroup(N=p.total_neurons, **Model(p)) inputgroup = PulsePacket(p.initial_burst_t, p.neurons_in_input_layer, p.initial_burst_sigma) layer = [ chaingroup.subgroup(p.neurons_per_layer) for i in range(p.num_layers) ] # connections chainconnect = Connection(chaingroup, chaingroup, 2) for i in range(p.num_layers - 1): chainconnect.connect_full(layer[i], layer[i + 1], p.psp_peak * p.we) inputconnect = Connection(inputgroup, chaingroup, 2) inputconnect.connect_full(inputgroup, layer[0], p.psp_peak * p.we) # monitors chainmon = [SpikeMonitor(g, True) for g in layer] inputmon = SpikeMonitor(inputgroup, True) mon = [inputmon] + chainmon # network Network.__init__(self, chaingroup, inputgroup, chainconnect, inputconnect, mon) # add additional attributes to self self.mon = mon self.inputgroup = inputgroup self.chaingroup = chaingroup self.layer = layer self.params = p def prepare(self): Network.prepare(self) self.reinit() def reinit(self, p=None): Network.reinit(self) q = self.params if p is None: p = q self.inputgroup.generate(p.initial_burst_t, p.initial_burst_a, p.initial_burst_sigma) self.chaingroup.V = q.Vr + rand(len(self.chaingroup)) * (q.Vt - q.Vr) def run(self): Network.run(self, self.params.duration) def plot(self): raster_plot(ylabel="Layer", title="Synfire chain raster plot", color=(1, 0, 0), markersize=3, showgrouplines=True, spacebetweengroups=0.2, grouplinecol=(0.5, 0.5, 0.5), *self.mon) def estimate_params(mon, time_est): # Quick and dirty algorithm for the moment, for a more decent algorithm # use leastsq algorithm from scipy.optimize.minpack to fit const+Gaussian # http://www.scipy.org/doc/api_docs/SciPy.optimize.minpack.html#leastsq i, times = zip(*mon.spikes) times = array(times) times = times[abs(times - time_est) < 15 * ms] if len(times) == 0: return (0, 0 * ms) better_time_est = times.mean() times = times[abs(times - time_est) < 5 * ms] if len(times) == 0: return (0, 0 * ms) return (len(times), times.std()*second) def single_sfc(): net = DefaultNetwork(default_params) net.run() net.plot() def state_space(grid, neuron_multiply, verbose=True): amin = 0 amax = 100 sigmamin = 0. * ms sigmamax = 3. * ms params = default_params() params.num_layers = 1 params.neurons_per_layer = params.neurons_per_layer * neuron_multiply net = DefaultNetwork(params) i = 0 # uncomment these 2 lines for TeX labels #import pylab #pylab.rc_params.update({'text.usetex': True}) if verbose: print "Completed:" start_time = time.time() figure() for ai in range(grid + 1): for sigmai in range(grid + 1): a = int(amin + (ai * (amax - amin)) / grid) if a > amax: a = amax sigma = sigmamin + sigmai * (sigmamax - sigmamin) / grid params.initial_burst_a, params.initial_burst_sigma = a, sigma net.reinit(params) net.run() (newa, newsigma) = estimate_params(net.mon[-1], params.initial_burst_t) newa = float(newa) / float(neuron_multiply) col = (float(ai) / float(grid), float(sigmai) / float(grid), 0.5) plot([sigma / ms, newsigma / ms], [a, newa], color=col) plot([sigma / ms], [a], marker='.', color=col, markersize=15) i += 1 if verbose: print str(int(100. * float(i) / float((grid + 1) ** 2))) + "%", if verbose: print if verbose: print "Evaluation time:", time.time() - start_time, "seconds" xlabel(r'$\sigma$ (ms)') ylabel('a') title('Synfire chain state space') axis([sigmamin / ms, sigmamax / ms, amin, amax]) minimal_example() #print 'Computing SFC with multiple layers' #single_sfc() #print 'Plotting SFC state space' #state_space(3,1) #state_space(8,10) #state_space(10,50) #state_space(10,150) #show() brian-1.4.3/docs_sphinx/examples-frompapers_Goodman_Brette_2010.txt000066400000000000000000000121201266062647100253070ustar00rootroot00000000000000.. currentmodule:: brian .. index:: pair: example usage; xlim pair: example usage; show pair: example usage; argmax pair: example usage; erbspace pair: example usage; shape pair: example usage; ylim pair: example usage; plot pair: example usage; Repeat pair: example usage; FunctionFilterbank pair: example usage; sum pair: example usage; FilterbankGroup pair: example usage; ylabel pair: example usage; tile pair: example usage; SpikeCounter pair: example usage; Sound pair: example usage; run pair: example usage; Connection pair: example usage; IRCAM_LISTEN pair: example usage; amax pair: example usage; NeuronGroup pair: example usage; Gammatone pair: example usage; RestructureFilterbank pair: example usage; xlabel pair: example usage; whitenoise pair: example usage; scatter .. _example-frompapers_Goodman_Brette_2010: Example: Goodman_Brette_2010 (frompapers) ========================================= Sound localization with HRTFs ----------------------------- Goodman DF and R Brette (2010). Spike-timing-based computation in sound localization. PLoS Comp Biol 6(11): e1000993. doi:10.1371/journal.pcbi.1000993. Simplified version of the "ideal" sound localisation model. The sound is played at a particular spatial location (indicated on the final plot by a red +). Each location has a corresponding assembly of neurons, whose summed firing rates give the sizes of the blue circles in the plot. The most strongly responding assembly is indicated by the green x, which is the estimate of the location by the model. :: from brian import * from brian.hears import * # Download the IRCAM database # http://recherche.ircam.fr/equipes/salles/listen/download.html # and replace this filename with the location you downloaded it to hrtfdb = IRCAM_LISTEN(r'Z:\HRTF\IRCAM') subject = 1002 hrtfset = hrtfdb.load_subject(subject) # This gives the number of spatial locations in the set of HRTFs num_indices = hrtfset.num_indices # Choose a random location for the sound to come from index = randint(num_indices) # A sound to test the model with sound = Sound.whitenoise(500*ms) # This is the specific HRTF for the chosen location hrtf = hrtfset.hrtf[index] # We apply the chosen HRTF to the sound, the output has 2 channels hrtf_fb = hrtf.filterbank(sound) # We swap these channels (equivalent to swapping the channels in the # subsequent filters, but simpler to do it with the inputs) swapped_channels = RestructureFilterbank(hrtf_fb, indexmapping=[1, 0]) # Now we apply all of the possible pairs of HRTFs in the set to these # swapped channels, which means repeating them num_indices times first hrtfset_fb = hrtfset.filterbank(Repeat(swapped_channels, num_indices)) # Now we apply cochlear filtering (logically, this comes before the HRTF # filtering, but since convolution is commutative it is more efficient to # do the cochlear filtering afterwards cfmin, cfmax, cfN = 150*Hz, 5*kHz, 40 cf = erbspace(cfmin, cfmax, cfN) # We repeat each of the HRTFSet filterbank channels cfN times, so that # for each location we will apply each possible cochlear frequency gfb = Gammatone(Repeat(hrtfset_fb, cfN), tile(cf, hrtfset_fb.nchannels)) # Half wave rectification and compression cochlea = FunctionFilterbank(gfb, lambda x:15*clip(x, 0, Inf)**(1.0/3.0)) # Leaky integrate and fire neuron model eqs = ''' dV/dt = (I-V)/(1*ms)+0.1*xi/(0.5*ms)**.5 : 1 I : 1 ''' G = FilterbankGroup(cochlea, 'I', eqs, reset=0, threshold=1, refractory=5*ms) # The coincidence detector (cd) neurons cd = NeuronGroup(num_indices*cfN, eqs, reset=0, threshold=1, clock=G.clock) # Each CD neuron receives precisely two inputs, one from the left ear and # one from the right, for each location and each cochlear frequency C = Connection(G, cd, 'V') for i in xrange(num_indices*cfN): C[i, i] = 0.5 # from right ear C[i+num_indices*cfN, i] = 0.5 # from left ear # We want to just count the number of CD spikes counter = SpikeCounter(cd) # Run the simulation, giving a report on how long it will take as we run run(sound.duration, report='stderr') # We take the array of counts, and reshape them into a 2D array which we sum # across frequencies to get the spike count of each location-specific assembly count = counter.count count.shape = (num_indices, cfN) count = sum(count, axis=1) count = array(count, dtype=float)/amax(count) # Our guess of the location is the index of the strongest firing assembly index_guess = argmax(count) # Now we plot the output, using the coordinates of the HRTFSet coords = hrtfset.coordinates azim, elev = coords['azim'], coords['elev'] scatter(azim, elev, 100*count) plot([azim[index]], [elev[index]], '+r', ms=15, mew=2) plot([azim[index_guess]], [elev[index_guess]], 'xg', ms=15, mew=2) xlabel('Azimuth (deg)') ylabel('Elevation (deg)') xlim(-5, 350) ylim(-50, 95) show() brian-1.4.3/docs_sphinx/examples-frompapers_Guetig_Sompolinsky_2009.txt000066400000000000000000000152571266062647100262770ustar00rootroot00000000000000.. currentmodule:: brian .. index:: pair: example usage; subplot pair: example usage; plot pair: example usage; run pair: example usage; figure pair: example usage; show pair: example usage; title pair: example usage; xticks pair: example usage; colors pair: example usage; xlabel pair: example usage; yticks pair: example usage; append pair: example usage; axis .. _example-frompapers_Guetig_Sompolinsky_2009: Example: Guetig_Sompolinsky_2009 (frompapers) ============================================= Implementation of the basic model (no speech recognition, no learning) described in: Gutig and Sompolinsky (2009): "Time-Warp-Invariant Neuronal Processing" PLoS Biology, Vol. 7 (7), e1000141 :: from brian import * class TimeWarpModel(object): ''' A simple neuron model for testing the "time-warp invariance" with conductance based or current based synapses. The neuron receives balanced excitatory and inhibitory input from a random spike train. The same spike train can be fed into the model with different time warps. ''' def __init__(self, conductance_based=True): ''' Create a new model with conductance based or current based synapses ''' # Model parameters E_e = 5 E_i = -1 E_L = 0 g_L = 1/(100.0*msecond) tau_syn = 1*ms N_ex = 250 N_inh = 250 self.N = N_ex + N_inh # Equations if conductance_based: eqs = ''' dV/dt = -(V - E_L) * g_L - I_syn : 1 I_syn = I_ge + I_gi : second**-1 I_ge = (V - E_e) * g_e : second**-1 I_gi = (V - E_i) * g_i : second**-1 dg_e/dt = -g_e/tau_syn : second**-1 dg_i/dt = -g_i/tau_syn : second**-1 ''' else: eqs = ''' dV/dt = -(V - E_L) * g_L - I_syn : 1 I_syn = -5 * g_e + g_i : second**-1 dg_e/dt = -g_e/tau_syn : second**-1 dg_i/dt = -g_i/tau_syn : second**-1 ''' # for simpler voltage traces: no spiking neuron = NeuronGroup(1, model=eqs, threshold=None) # every input neuron fires once in a random interval self.unwarped_spiketimes = [(i, t * 250 * ms) for i, t in zip(range(0, self.N), rand(self.N))] # final spiketimes will be set in the run function self.input = SpikeGeneratorGroup(self.N, []) e_input = self.input.subgroup(N_ex) i_input = self.input.subgroup(N_inh) e_conn = Connection(e_input, neuron, 'g_e', weight=6 / (N_ex * tau_syn)) i_conn = Connection(i_input, neuron, 'g_i', weight=5 * 6 / (N_ex * tau_syn)) # record membrane potential self.monitor = StateMonitor(neuron, varname='V', record=True) # putting everything together self.net = Network(neuron, self.input, e_conn, i_conn, self.monitor) def run(self, beta=1.0): ''' Run the network with the original spike train warped by a certain factor beta. Beta > 1 corresponds to an extended and beta < 1 to a shrinked input spike train. ''' self.net.reinit() #warp spike train in time self.input.spiketimes = [(i, beta*t) for i, t in self.unwarped_spiketimes] self.net.run(beta * 250*ms) #Return the voltage trace return (self.monitor.times, self.monitor[0]) if __name__ == '__main__': cond_model = TimeWarpModel(True) curr_model = TimeWarpModel(False) N = cond_model.N # ######################################################################### # Reproduce Fig. 2 from Gütig and Sompolinsky (2009) # ######################################################################### beta = 2.0 times1, v1 = cond_model.run(beta=1.0) times2, v2 = cond_model.run(beta=beta) maxtime = 250 * beta subplot(4, 1, 1) (neurons, times) = zip(*cond_model.unwarped_spiketimes) plot(array(times) / ms, neurons, 'g.') axis([0, maxtime, 0, N]) xticks([]) yticks([]) title('Time-warp-invariant voltage traces (conductance-based)') subplot(4, 1, 2) plot(times1 / ms, v1, 'g') axis([0, maxtime, -1.5, 1.5]) xticks([]) yticks([]) subplot(4, 1, 3) plot(array(times) * beta / ms, neurons, 'b.') axis([0, maxtime, 0, N]) xticks([]) yticks([1, 500]) subplot(4, 1, 4) plot(times2 / ms, v2, 'b') plot(times1 / ms * beta, v1, 'g') axis([0, maxtime, -1.5, 1.5]) xlabel('Time (ms)') xticks([0, 250, 500]) yticks([-1, 1]) show() # ######################################################################### # Reproduce Fig. 3(C) from Gütig and Sompolinsky (2009), but for random # spike trains and not in a speech recognition task # ######################################################################### betas = arange(0.2, 3.1, 0.1) #betas = array([1.0, 2.0]) cond_results = [] curr_results = [] for beta in betas: print 'Testing warp factor %.1f' % beta cond_results.append(cond_model.run(beta)) curr_results.append(curr_model.run(beta)) figure() colors = mpl.cm.gist_earth((betas - betas[0]) / (betas[-1] - betas[0])) lookup = dict(zip(betas, colors)) for beta, cond_result, curr_result in zip(betas, cond_results, curr_results): times_cond, v_cond = cond_result times_curr, v_curr = curr_result subplot(1,2,1) plot(times_cond / ms / beta, v_cond, color=lookup[beta]) axis([0, 250, -1.5, 1.5]) subplot(1,2,2) plot(times_curr / ms / beta, v_curr, color=lookup[beta]) axis([0, 250, -1.5, 1.5]) subplot(1,2,1) title('conductance based') subplot(1,2,2) title('current based') show() brian-1.4.3/docs_sphinx/examples-frompapers_Kremer_et_al_2011.txt000066400000000000000000000160411266062647100250160ustar00rootroot00000000000000.. currentmodule:: brian .. index:: pair: example usage; figure pair: example usage; show pair: example usage; flatten pair: example usage; plot pair: example usage; imshow pair: example usage; hist pair: example usage; PoissonGroup pair: example usage; real pair: example usage; run pair: example usage; around pair: example usage; imag pair: example usage; ExponentialSTDP pair: example usage; Connection pair: example usage; ones pair: example usage; hsv pair: example usage; linspace pair: example usage; NeuronGroup pair: example usage; network_operation pair: example usage; colorbar pair: example usage; meshgrid pair: example usage; exp pair: example usage; mean .. _example-frompapers_Kremer_et_al_2011: Example: Kremer_et_al_2011 (frompapers) ======================================= Late Emergence of the Whisker Direction Selectivity Map in the Rat Barrel Cortex Kremer Y, Leger JF, Goodman DF, Brette R, Bourdieu L (2011). J Neurosci 31(29):10689-700. Development of direction maps with pinwheels in the barrel cortex. Whiskers are deflected with random moving bars. N.B.: network construction can be long. In this version, STDP is faster than in the paper so that the script runs in just a few minutes. :: from brian import * # Uncomment if you have a C compiler # set_global_preferences(useweave=True,usecodegen=True,usecodegenweave=True,usenewpropagate=True,usecstdp=True) # PARAMETERS # Neuron numbers M4,M23exc,M23inh=22,25,12 # side of each barrel (in neurons) N4,N23exc,N23inh=M4**2,M23exc**2,M23inh**2 # neurons per barrel barrelarraysize=5 # Choose 3 or 4 if memory error Nbarrels=barrelarraysize**2 # Stimulation stim_change_time = 5*ms Fmax=.5/stim_change_time # maximum firing rate in layer 4 (.5 spike / stimulation) # Neuron parameters taum,taue,taui=10*ms,2*ms,25*ms El=-70*mV Vt,vt_inc,tauvt=-55*mV,2*mV,50*ms # adaptive threshold # STDP taup,taud=5*ms,25*ms Ap,Ad=.05,-.04 # EPSPs/IPSPs EPSP,IPSP = 1*mV,-1*mV EPSC = EPSP * (taue/taum)**(taum/(taue-taum)) IPSC = IPSP * (taui/taum)**(taum/(taui-taum)) # Model: IF with adaptive threshold eqs=''' dv/dt=(ge+gi+El-v)/taum : volt dge/dt=-ge/taue : volt dgi/dt=-gi/taui : volt dvt/dt=(Vt-vt)/tauvt : volt # adaptation x : 1 y : 1 ''' # Tuning curve tuning=lambda theta:clip(cos(theta),0,Inf)*Fmax # Layer 4 layer4=PoissonGroup(N4*Nbarrels) barrels4 = dict(((i, j), layer4.subgroup(N4)) for i in xrange(barrelarraysize) for j in xrange(barrelarraysize)) barrels4active = dict((ij, False) for ij in barrels4) barrelindices = dict((ij, slice(b._origin, b._origin+len(b))) for ij, b in barrels4.iteritems()) layer4.selectivity = zeros(len(layer4)) for (i, j), inds in barrelindices.iteritems(): layer4.selectivity[inds]=linspace(0,2*pi,N4) # Layer 2/3 layer23=NeuronGroup(Nbarrels*(N23exc+N23inh),model=eqs,threshold='v>vt',reset='v=El;vt+=vt_inc',refractory=2*ms) layer23.v=El layer23.vt=Vt # Layer 2/3 excitatory layer23exc=layer23.subgroup(Nbarrels*N23exc) x,y=meshgrid(arange(M23exc)*1./M23exc,arange(M23exc)*1./M23exc) x,y=x.flatten(),y.flatten() barrels23 = dict(((i, j), layer23exc.subgroup(N23exc)) for i in xrange(barrelarraysize) for j in xrange(barrelarraysize)) for i in range(barrelarraysize): for j in range(barrelarraysize): barrels23[i,j].x=x+i barrels23[i,j].y=y+j # Layer 2/3 inhibitory layer23inh=layer23.subgroup(Nbarrels*N23inh) x,y=meshgrid(arange(M23inh)*1./M23inh,arange(M23inh)*1./M23inh) x,y=x.flatten(),y.flatten() barrels23inh = dict(((i, j), layer23inh.subgroup(N23inh)) for i in xrange(barrelarraysize) for j in xrange(barrelarraysize)) for i in range(barrelarraysize): for j in range(barrelarraysize): barrels23inh[i,j].x=x+i barrels23inh[i,j].y=y+j print "Building synapses, please wait..." # Feedforward connections feedforward=Connection(layer4,layer23exc,'ge') for i in range(barrelarraysize): for j in range(barrelarraysize): feedforward.connect_random(barrels4[i,j],barrels23[i,j],sparseness=.5,weight=EPSC*.5) stdp=ExponentialSTDP(feedforward,taup,taud,Ap,Ad,wmax=EPSC) # Excitatory lateral connections recurrent_exc=Connection(layer23exc,layer23,'ge') recurrent_exc.connect_random(layer23exc,layer23exc,weight=EPSC*.3, sparseness=lambda i,j:.15*exp(-.5*(((layer23exc.x[i]-layer23exc.x[j])/.4)**2+((layer23exc.y[i]-layer23exc.y[j])/.4)**2))) recurrent_exc.connect_random(layer23exc,layer23inh,weight=EPSC, sparseness=lambda i,j:.15*exp(-.5*(((layer23exc.x[i]-layer23inh.x[j])/.4)**2+((layer23exc.y[i]-layer23inh.y[j])/.4)**2))) # Inhibitory lateral connections recurrent_inh=Connection(layer23inh,layer23exc,'gi') recurrent_inh.connect_random(layer23inh,layer23exc,weight=IPSC, sparseness=lambda i,j:exp(-.5*(((layer23inh.x[i]-layer23exc.x[j])/.2)**2+((layer23inh.y[i]-layer23exc.y[j])/.2)**2))) # Stimulation stimspeed = 1./stim_change_time # speed at which the bar of stimulation moves direction = 0.0 stimzonecentre = ones(2)*barrelarraysize/2. stimcentre,stimnorm = zeros(2),zeros(2) stimradius = (11*stim_change_time*stimspeed+1)*.5 stimradius2 = stimradius**2 def new_direction(): global direction direction = rand()*2*pi stimnorm[:] = (cos(direction), sin(direction)) stimcentre[:] = stimzonecentre-stimnorm*stimradius @network_operation def stimulation(): global direction, stimcentre stimcentre += stimspeed*stimnorm*defaultclock.dt if sum((stimcentre-stimzonecentre)**2)>stimradius2: new_direction() for (i, j), b in barrels4.iteritems(): whiskerpos = array([i,j], dtype=float)+0.5 isactive = abs(dot(whiskerpos-stimcentre, stimnorm))<.5 if barrels4active[i, j]!=isactive: barrels4active[i, j] = isactive b.rate = float(isactive)*tuning(layer4.selectivity[barrelindices[i, j]]-direction) new_direction() run(5*second,report='text') figure() # Preferred direction selectivity=array([mean(array(feedforward[:,i].todense())*exp(layer4.selectivity*1j)) for i in range(len(layer23exc))]) selectivity=(arctan2(selectivity.imag,selectivity.real) % (2*pi))*180./pi I=zeros((barrelarraysize*M23exc,barrelarraysize*M23exc)) ix=array(around(layer23exc.x*M23exc),dtype=int) iy=array(around(layer23exc.y*M23exc),dtype=int) I[iy,ix]=selectivity imshow(I) hsv() colorbar() for i in range(1,barrelarraysize+1): plot([i*max(ix)/barrelarraysize,i*max(ix)/barrelarraysize],[0,max(iy)],'k') plot([0,max(ix)],[i*max(iy)/barrelarraysize,i*max(iy)/barrelarraysize],'k') figure() hist(selectivity) show() brian-1.4.3/docs_sphinx/examples-frompapers_Muller_et_al_2011.txt000066400000000000000000000077621266062647100250430ustar00rootroot00000000000000.. currentmodule:: brian .. index:: pair: example usage; std pair: example usage; xlim pair: example usage; title pair: example usage; show pair: example usage; errorbar pair: example usage; sqrt pair: example usage; xlabel pair: example usage; linspace pair: example usage; ylabel pair: example usage; ylim pair: example usage; mean .. _example-frompapers_Muller_et_al_2011: Example: Muller_et_al_2011 (frompapers) ======================================= Interplay of STDP and input oscillations ---------------------------------------- Figure 4 from: Muller L, Brette R and Gutkin B (2011) Spike-timing dependent plasticity and feed-forward input oscillations produce precise and invariant spike phase-locking. Front. Comput. Neurosci. 5:45. doi: 10.3389/fncom.2011.00045 Description: In this simulation, a group of IF neurons is given a tonic DC input and a tonic AC input. The DC input is mediated by current injection (neurons.I, line 62), and the AC input is mediated by Poisson processes whose rate parameters are oscillating in time. Each neuron in the group is given a different DC input, ensuring a unique initial phase. After two seconds of simulation (to integrate out any initial transients), the STDP rule is turned on (ExponentialSTDP, line 68), and the population of neurons converges to the theoretically predicted fixed point. As there is some noise in the phase due to the random inputs, the simulation is averaged over trials (50 in Figure 4, though 10 trials should be fine for testing). The trials run in parallel on all available processors (10 trials take about 2 minutes on a modern PC). :: ### IMPORTS from brian import * import multiprocessing ### PARAMETERS N=5000 M=10 taum=33*ms tau_pre=20*ms tau_post=tau_pre Ee=0*mV vt=-54*mV vr=-70*mV El=-70*mV taue=5*ms f=20*Hz theta_period = 1/f Rm=200*Mohm a = linspace(51,65,num=M) weights = .001 ratio=1.50 dA_pre=.01 dA_post=.01*ratio trials=10 ### SIMULATION LOOP def trial(n): # n is the trial number reinit_default_clock() clear(True) eqs_neurons=''' dv/dt=((ge*(Ee-vr))+Rm*I+(El-v))/taum : volt dge/dt=-ge/taue : 1 I : amp ''' inputs = PoissonGroup(N,rates=lambda t:((.5-.5*cos(2*pi*f*t)))*10*Hz) neurons=NeuronGroup(M,model=eqs_neurons,threshold=vt,reset=vr) neurons.I = a*pA synapses=Connection(inputs,neurons,'ge',weight=weights) neurons.v=vr S = SpikeMonitor(neurons) run(2*second) stdp=ExponentialSTDP(synapses,tau_pre,tau_post,dA_pre,-dA_post,wmax=10*weights,interactions='all',update='additive') run(5*second) phase=zeros((M,200)) for b in range(0,M): tmp_phase=(S[b]%theta_period)*(360/theta_period) phase[b,range(0,len(tmp_phase))] = tmp_phase return phase if __name__=='__main__': # This is very important on Windows, otherwise the machine crashes! phase = zeros((M,200,trials)) print "This will take approximately 2 minutes." pool=multiprocessing.Pool() # uses all available processors b results=pool.map(trial,range(trials)) for i in range(trials): phase[:,:,i]=results[i] ### PLOTTING for b in range(0,M): m = mean(phase[b,:,:],axis=1) st = std(phase[b,:,:],axis=1)/sqrt(trials) errorbar(range(0,135), m[range(0,135)], yerr=st[range(0,135)], xerr=None, fmt='-', ecolor=None, elinewidth=None, capsize=3, barsabove=False, lolims=False, uplims=False, xlolims=False, xuplims=False) title('STDP + Oscillations Simulation') xlabel('Spike Number') ylabel('Spike Phase (deg)') xlim([0, 135]) ylim([140, 280]) show() brian-1.4.3/docs_sphinx/examples-frompapers_Plakiewicz_Brette_2010.txt000066400000000000000000000166561266062647100260470ustar00rootroot00000000000000.. currentmodule:: brian .. index:: pair: example usage; subplot pair: example usage; NeuronGroup pair: example usage; run pair: example usage; log pair: example usage; xlim pair: example usage; show pair: example usage; plot pair: example usage; xticks pair: example usage; hist pair: example usage; xlabel pair: example usage; flatten pair: example usage; ylabel pair: example usage; ylim pair: example usage; StateMonitor .. _example-frompapers_Plakiewicz_Brette_2010: Example: Plakiewicz_Brette_2010 (frompapers) ============================================ Spike threshold variability in single compartment model ------------------------------------------------------- Figure 7 from: Platkiewicz J, Brette R (2010). A Threshold Equation for Action Potential Initiation. PLoS Comput Biol 6(7): e1000850. doi:10.1371/journal.pcbi.1000850 The original HH model is from Traub and Miles 1991, modified by Destexhe et al. 2001, and we shift Na inactivation to -62 mV. This shift produces threshold variability, but also spikes with variable shapes (unavoidable in a single-compartment model). The script demonstrates that the spike threshold is proportional to the logarithm of h. :: from brian import * from scipy import stats from brian.library.electrophysiology import * defaultclock.dt=0.05*ms duration=500*ms N=1000 # we simulate 1000 neurons to have more threshold statistics rectify=lambda x:clip(x,0,inf)*siemens # Biophysical parameters #--Passive parameters area=pi*(105*umetre)**2 C=1*uF GL=0.0452*msiemens EL=-80*mV #--Active fixed parameters celsius=36 temp=23 q10=2.3 #----Sodium channel parameters ENa=50*mV GNa=51.6*msiemens vTraub_Na=-63*mV #Traub convention vshift=-20*mV #Inactivation shift (-62 mV instead of -42 mV) #--------activation A_alpham=0.32/(ms*mV) #open (V) A_betam=0.28/(ms*mV) #close (V) v12_alpham=13*mV #v1/2 for act v12_betam=40*mV #v1/2 for act k_alpham=4*mV #act slope k_betam=5*mV #act slope #--------inactivation A_alphah=0.128/ms #inact recov (V) A_betah=4/ms #inact (V) v12_alphah=17*mV #v1/2 for inact v12_betah=40*mV #v1/2 for inact k_alphah=18*mV #inact tau slope k_betah=5*mV #inact tau slope #----Potassium channel parameters EK=-90*mV #--------"delay-rectifier" GK=10*msiemens vTraub_K=-63*mV A_alphan=0.032/(ms*mV) #open (V) A_betan=0.5/ms #close (V) v12_alphan=15*mV #v1/2 for act v12_betan=10*mV #v1/2 for act k_alphan=5*mV #act slope k_betan=40*mV #act slope #--------muscarinic GK_m=0.5*msiemens A_alphan_m=1e-4/(ms*mV) A_betan_m=1e-4/(ms*mV) v12_alphan_m=-30*mV v12_betan_m=-30*mV k_alphan_m=9*mV k_betan_m=9*mV # Input parameters Ee=0*mV Ei=-75*mV taue=2.728*ms taui=10.49*ms Ge0=0.0121*usiemens*cm**2 Gi0=0.0573*usiemens*cm**2 Sigmae=0.012*usiemens*cm**2 Sigmai=0.0264*usiemens*cm**2 tadj=q10**((celsius-temp)/10) ge0=Ge0/area gi0=Gi0/area sigmae=Sigmae/area sigmai=Sigmai/area Traubm=lambda v:v-vTraub_Na alpham=lambda v:A_alpham*(Traubm(v)-v12_alpham)/(1-exp((v12_alpham-Traubm(v))/k_alpham)) betam=lambda v:-A_betam*(Traubm(v)-v12_betam)/(1-exp(-(v12_betam-Traubm(v))/k_betam)) minf=lambda v:alpham(v)/(alpham(v)+betam(v)) taum=lambda v:1/(alpham(v)+betam(v)) Shift=lambda v:Traubm(v)-vshift alphah=lambda v:A_alphah*exp((v12_alphah-Shift(v))/k_alphah) betah=lambda v:A_betah/(1+exp((v12_betah-Shift(v))/k_betah)) hinf=lambda v:alphah(v)/(alphah(v)+betah(v)) tauh=lambda v:1/(alphah(v)+betah(v)) TraubK=lambda v:v-vTraub_K alphan= lambda v:A_alphan*(TraubK(v)-v12_alphan)/(1-exp((v12_alphan-TraubK(v))/k_alphan)) betan= lambda v:A_betan*exp((v12_betan-TraubK(v))/k_betan) ninf= lambda v:alphan(v)/(alphan(v)+betan(v)) taun= lambda v:1/(alphan(v)+betan(v))/tadj eqs=""" dv/dt=(3*GNa*h*m**3*(ENa-v)+(GK*n**4+GK_m*n_m)*(EK-v)+GL*(EL-v)+I)/C : volt # Sodium activation m_inf=minf(v) : 1 #minf(v) tau_m=taum(v) : second dm/dt=(m_inf-m)/tau_m : 1 # Sodium inactivation h_inf=hinf(v) : 1 tau_h=tauh(v) : second dh/dt=(h_inf-h)/tau_h : 1 # Potassium : delay-rectifier n_inf=ninf(v) : 1 tau_n=taun(v) : second dn/dt=(n_inf-n)/tau_n : 1 gK=GK*n**4 : siemens # Potassium : muscarinic alphan_m=A_alphan_m*(v-v12_alphan_m)/(1-exp((v12_alphan_m-v)/k_alphan_m)) : hertz betan_m=-A_alphan_m*(v-v12_alphan_m)/(1-exp(-(v12_alphan_m-v)/k_alphan_m)) : hertz n_minf=alphan_m/(alphan_m+betan_m) : 1 taun_m=1/(alphan_m+betan_m)/tadj : second dn_m/dt=(n_minf-n_m)/taun_m : 1 gK_m=GK_m*n_m : siemens # Fluctuating synaptic conductances I=rectify(ge)*(Ee-v)+rectify(gi)*(Ei-v) : amp dge/dt=(1.5*ge0-ge)/taue+1.5*sigmae*(2./taue)**.5*xi : siemens dgi/dt=(gi0-gi)/taui+2*sigmai*(2./taui)**.5*xi : siemens gtot=GL+rectify(ge)+rectify(gi)+gK+gK_m : siemens """ neurons=NeuronGroup(N,model=eqs,implicit=True) neurons.v=EL neurons.m=minf(EL) neurons.h=hinf(EL) neurons.n=ninf(EL) neurons.n_m=0 M=StateMonitor(neurons,'v',record=True) Mh=StateMonitor(neurons,'h',record=True) run(duration,report='text') # Collect spike thresholds and values of h threshold,logh=[],[] valuesv,valuesh=array(M._values),array(Mh._values) criterion=10*mV/ms # criterion for spike threshold for i in range(N): v=valuesv[:,i] h=valuesh[:,i] onsets=spike_onsets(v,criterion=defaultclock.dt*criterion,vc=-35*mV) threshold.extend(v[onsets]) logh.extend(-log(h[onsets]))#+log(g[onsets])) threshold=array(threshold)/mV logh=array(logh)/log(10.) # for display # Voltage trace with spike onsets subplot(311) v=valuesv[:,0] onsets=spike_onsets(v,criterion=defaultclock.dt*criterion,vc=-35*mV) t=M.times[onsets]/ms plot(M.times/ms,M[0]/mV,'k') plot(t,v[onsets]/mV,'.r') xlabel('t (ms)') ylabel('V (mV)') # Distribution of Vm and spike onsets subplot(312) hist(threshold,30,normed=1,histtype='stepfilled',alpha=0.6,facecolor='r') hist(valuesv.flatten()/mV,100,normed=1,histtype='stepfilled',alpha=0.6,facecolor='k') xlabel('V (mV)') xlim(-80,-40) # Relationship between h and spike threshold subplot(313) slope,intercept=3.1,-54. # linear regression for the prediction of threshold p1,p2=min(logh),max(logh) plot(logh[:len(logh)/10],threshold[:len(logh)/10],'.k') # don't show everything plot([p1,p2],intercept+slope*array([p1,p2])*log(10.),'r') xlabel('h') ylabel('Threshold (mV)') ylim(-55,-40) xticks([0,-log(5e-1)/log(10),1,-log(5e-2)/log(10)],[1,5e-1,1e-1,5e-2]) xlim(0,1.5) show() brian-1.4.3/docs_sphinx/examples-frompapers_Platkiewicz_Brette_2011.txt000066400000000000000000000061351266062647100262230ustar00rootroot00000000000000.. currentmodule:: brian .. index:: pair: example usage; subplot pair: example usage; plot pair: example usage; run pair: example usage; xlim pair: example usage; show pair: example usage; sqrt pair: example usage; SpikeMonitor pair: example usage; linspace pair: example usage; ylabel pair: example usage; xlabel pair: example usage; NeuronGroup pair: example usage; append pair: example usage; StateMonitor .. _example-frompapers_Platkiewicz_Brette_2011: Example: Platkiewicz_Brette_2011 (frompapers) ============================================= Slope-threshold relationship with noisy inputs, in the adaptive threshold model ------------------------------------------------------------------------------- Fig. 5E,F from: Platkiewicz J and R Brette (2011). Impact of Fast Sodium Channel Inactivation on Spike Threshold Dynamics and Synaptic Integration. PLoS Comp Biol 7(5): e1001129. doi:10.1371/journal.pcbi.1001129 :: from brian import * from scipy import stats,optimize from scipy.stats import linregress rectify=lambda x:clip(x/volt,0,Inf)*volt N=200 # 200 neurons to get more statistics, only one is shown duration=1*second #--Biophysical parameters ENa=60*mV EL=-70*mV vT=-55*mV Vi=-63*mV tauh=5*ms tau=5*ms ka=5*mV ki=6*mV a=ka/ki tauI=5*ms mu=15*mV sigma=6*mV/sqrt(tauI/(tauI+tau)) #--Theoretical prediction for the slope-threshold relationship (approximation: a=1+epsilon) thresh=lambda slope,a: Vi-slope*tauh*log(1+(Vi-vT/a)/(slope*tauh)) #-----Exact calculation of the slope-threshold relationship thresh_ex=lambda s: optimize.fsolve(lambda th: a*s*tauh*exp((Vi-th)/(s*tauh))-th*(1-a)-a*(s*tauh+Vi)+vT,thresh(s,a))*volt eqs=""" dv/dt=(EL-v+mu+sigma*I)/tau : volt dtheta/dt=(vT+a*rectify(v-Vi)-theta)/tauh : volt dI/dt=-I/tauI+(2/tauI)**.5*xi : 1 # Ornstein-Uhlenbeck """ neurons=NeuronGroup(N,eqs,threshold="v>theta",reset='v=EL',refractory=5*ms) neurons.v=EL neurons.theta=vT neurons.I=0 S=SpikeMonitor(neurons) M=StateMonitor(neurons,'v',record=True) Mt=StateMonitor(neurons,'theta',record=0) run(duration,report='text') # Linear regression gives depolarization slope before spikes tx=M.times[(M.times>0) & (M.times<1.5*tauh)] slope,threshold=[],[] v=array(M._values) for (i,t) in S.spikes: ind=(M.timest-tauh) mx=v[:,i][ind] s,_,_,_,_=linregress(tx[:len(mx)],mx) slope.append(s) threshold.append(mx[-1]) # Figure M.insert_spikes(S) # displays spikes on the trace subplot(221) ind=M.times<500*ms plot(M.times[ind]/ms,M[0][ind]/mV,'k') plot(Mt.times[ind]/ms,Mt[0][ind]/mV,'r') xlabel('Time (ms)') ylabel('Voltage (mV)') subplot(222) plot(slope,array(threshold)/mV,'r.') sx=linspace(0.5*volt/second,4*volt/second,100) t=array([thresh_ex(s*volt/second) for s in sx]) plot(sx,t/mV,'k') xlim(0.5,4) xlabel('Depolarization slope (ms)') ylabel('Threshold (mV/ms)') show() brian-1.4.3/docs_sphinx/examples-frompapers_Rossant_et_al_2011.txt000066400000000000000000000075601266062647100252300ustar00rootroot00000000000000.. currentmodule:: brian .. index:: pair: example usage; subplot pair: example usage; plot pair: example usage; PopulationRateMonitor pair: example usage; run pair: example usage; SpikeGeneratorGroup pair: example usage; xlim pair: example usage; show pair: example usage; sum pair: example usage; title pair: example usage; Connection pair: example usage; rate pair: example usage; reinit_default_clock pair: example usage; figure pair: example usage; NeuronGroup pair: example usage; ylim pair: example usage; mean .. _example-frompapers_Rossant_et_al_2011: Example: Rossant_et_al_2011 (frompapers) ======================================== Coincidence detection example ============================= Fig. 4 from: Rossant C, Leijon S, Magnusson AK, Brette R (2011). "Sensitivity of noisy neurons to coincident inputs". Journal of Neuroscience, 31(47). Two distant or coincident spikes are injected into a noisy balanced leaky integrate-and-fire neuron. The PSTH of the neuron in response to these inputs is calculated along with the extra number of spikes in the two cases. This number is higher for the coincident spikes, showing the sensitivity of a noisy neuron to coincident inputs. :: from brian import * import matplotlib.patches as patches import matplotlib.path as path def histo(bins, cc, ax): # get the corners of the rectangles for the histogram left = array(bins[:-1]) right = array(bins[1:]) bottom = zeros(len(left)) top = bottom + cc # we need a (numrects x numsides x 2) numpy array for the path helper # function to build a compound path XY = array([[left,left,right,right], [bottom,top,top,bottom]]).T # get the Path object barpath = path.Path.make_compound_path_from_polys(XY) # make a patch out of it patch = patches.PathPatch(barpath, facecolor='blue', edgecolor='gray', alpha=0.8) ax.add_patch(patch) # update the view limits ax.set_xlim(left[0], right[-1]) ax.set_ylim(bottom.min(), top.max()) # neuron parameters theta = -55*mV vmean = -65*mV taum = 5*ms taue = 3*ms taun = 15*ms sigma = 4*mV # input times t1 = 100*ms t2 = 120*ms # simulation duration dur = 200*ms # number of neuron N = 10000 bin = 2*ms # EPSP size int_EPSP=taue int_EPSP2=taue*taue/(2*(taum+taue)) max_EPSP=(taum/taue)**(taum/(taue-taum)) we = 3.0*mV/max_EPSP # model equations eqs = ''' V=V0+noise : volt dV0/dt=(-V0+psp)/taum : volt dpsp/dt=-psp/taue : volt dnoise/dt=(vmean-noise)/taun+sigma*(2./taun)**.5*xi : volt ''' threshold = 'V>theta' reset = vmean # initialization of the NeuronGroup reinit_default_clock() group = NeuronGroup(2*N, model=eqs, reset=reset, threshold=threshold) group.V0 = group.psp = 0*volt group.noise = vmean + sigma * randn(2*N) # input spikes input_spikes = [(0, t1), (0, t2), (1, t1)] input = SpikeGeneratorGroup(2, array(input_spikes)) # connections C = Connection(input, group, 'psp') C.connect_full(input[0], group[:N], weight=we) C.connect_full(input[1], group[N:], weight=2*we) # monitors prM1 = PopulationRateMonitor(group[:N], bin=bin) prM2 = PopulationRateMonitor(group[N:], bin=bin) # launch simulation run(dur) # PSTH plot figure(figsize=(10,10)) prMs = [prM1, prM2] for i in [0,1]: prM = prMs[i] r = prM.rate[:-1]*bin m = mean(r[:len(r)/2]) ax = subplot(211+i) histo(prM.times, r, ax) plot([0,dur],[m,m],'--r') title("%.2f extra spikes" % sum(r[t1/bin:(t2+20*ms)/bin]-m)) xlim(.05, .2) ylim(0, .125) show() brian-1.4.3/docs_sphinx/examples-frompapers_Rossant_et_al_2011bis.txt000066400000000000000000000061501266062647100257200ustar00rootroot00000000000000.. currentmodule:: brian .. index:: pair: example usage; subplot pair: example usage; plot pair: example usage; run pair: example usage; title pair: example usage; show pair: example usage; vstack pair: example usage; reinit_default_clock pair: example usage; PoissonInput pair: example usage; tile pair: example usage; SpikeMonitor pair: example usage; NeuronGroup pair: example usage; Equations pair: example usage; StateMonitor .. _example-frompapers_Rossant_et_al_2011bis: Example: Rossant_et_al_2011bis (frompapers) =========================================== Distributed synchrony example ============================= Fig. 14 from: Rossant C, Leijon S, Magnusson AK, Brette R (2011). "Sensitivity of noisy neurons to coincident inputs". Journal of Neuroscience, 31(47). 5000 independent E/I Poisson inputs are injected into a leaky integrate-and-fire neuron. Synchronous events, following an independent Poisson process at 40 Hz, are considered, where 15 E Poisson spikes are randomly shifted to be synchronous at those events. The output firing rate is then significantly higher, showing that the spike timing of less than 1% of the excitatory synapses have an important impact on the postsynaptic firing. :: from brian import * # neuron parameters theta = -55*mV El = -65*mV vmean = -65*mV taum = 5*ms taue = 3*ms taui = 10*ms eqs = Equations(""" dv/dt = (ge+gi-(v-El))/taum : volt dge/dt = -ge/taue : volt dgi/dt = -gi/taui : volt """) # input parameters p = 15 ne = 4000 ni = 1000 lambdac = 40*Hz lambdae = lambdai = 1*Hz # synapse parameters we = .5*mV/(taum/taue)**(taum/(taue-taum)) wi = (vmean-El-lambdae*ne*we*taue)/(lambdae*ni*taui) # NeuronGroup definition group = NeuronGroup(N=2, model=eqs, reset=El, threshold=theta, refractory=5*ms) group.v = El group.ge = group.gi = 0 # independent E/I Poisson inputs p1 = PoissonInput(group[0], N=ne, rate=lambdae, weight=we, state='ge') p2 = PoissonInput(group[0], N=ni, rate=lambdai, weight=wi, state='gi') # independent E/I Poisson inputs + synchronous E events p3 = PoissonInput(group[1], N=ne, rate=lambdae-(p*1.0/ne)*lambdac, weight=we, state='ge') p4 = PoissonInput(group[1], N=ni, rate=lambdai, weight=wi, state='gi') p5 = PoissonInput(group[1], N=1, rate=lambdac, weight=p*we, state='ge') # run the simulation reinit_default_clock() M = SpikeMonitor(group) SM = StateMonitor(group, 'v', record=True) run(1*second) # plot trace and spikes for i in [0,1]: spikes = M.spiketimes[i]-.0001 val = SM.values[i] subplot(2,1,i+1) plot(SM.times, val) plot(tile(spikes, (2,1)), vstack((val[array(spikes*10000, dtype=int)], zeros(len(spikes)))), 'b') title("%s: %d spikes/second" % (["uncorrelated inputs", "correlated inputs"][i], len(M.spiketimes[i]))) show() brian-1.4.3/docs_sphinx/examples-frompapers_Rothman_Manis_2003.txt000066400000000000000000000120471266062647100251670ustar00rootroot00000000000000.. currentmodule:: brian .. index:: pair: example usage; NeuronGroup pair: example usage; plot pair: example usage; StateMonitor pair: example usage; run pair: example usage; show .. _example-frompapers_Rothman_Manis_2003: Example: Rothman_Manis_2003 (frompapers) ======================================== Cochlear neuron model of Rothman & Manis ---------------------------------------- Rothman JS, Manis PB (2003) The roles potassium currents play in regulating the electrical activity of ventral cochlear nucleus neurons. J Neurophysiol 89:3097-113. All model types differ only by the maximal conductances. Adapted from their Neuron implementation by Romain Brette :: from brian import * #defaultclock.dt=0.025*ms # for better precision ''' Simulation parameters: choose current amplitude and neuron type (from type1c, type1t, type12, type 21, type2, type2o) ''' neuron_type = 'type1c' Ipulse = 250 * pA C = 12 * pF Eh = -43 * mV EK = -70 * mV # -77*mV in mod file El = -65 * mV ENa = 50 * mV nf = 0.85 # proportion of n vs p kinetics zss = 0.5 # steady state inactivation of glt celsius = 22. # temperature q10 = 3. ** ((celsius - 22) / 10.) # hcno current (octopus cell) frac = 0.0 qt = 4.5 ** ((celsius - 33.) / 10.) # Maximal conductances of different cell types in nS maximal_conductances = dict( type1c=(1000, 150, 0, 0, 0.5, 0, 2), type1t=(1000, 80, 0, 65, 0.5, 0, 2), type12=(1000, 150, 20, 0, 2, 0, 2), type21=(1000, 150, 35, 0, 3.5, 0, 2), type2=(1000, 150, 200, 0, 20, 0, 2), type2o=(1000, 150, 600, 0, 0, 40, 2) # octopus cell ) gnabar, gkhtbar, gkltbar, gkabar, ghbar, gbarno, gl = [x * nS for x in maximal_conductances[neuron_type]] # Classical Na channel eqs_na = """ ina = gnabar*m**3*h*(ENa-v) : amp dm/dt=q10*(minf-m)/mtau : 1 dh/dt=q10*(hinf-h)/htau : 1 minf = 1./(1+exp(-(vu + 38.) / 7.)) : 1 hinf = 1./(1+exp((vu + 65.) / 6.)) : 1 mtau = ((10. / (5*exp((vu+60.) / 18.) + 36.*exp(-(vu+60.) / 25.))) + 0.04)*ms : ms htau = ((100. / (7*exp((vu+60.) / 11.) + 10.*exp(-(vu+60.) / 25.))) + 0.6)*ms : ms """ # KHT channel (delayed-rectifier K+) eqs_kht = """ ikht = gkhtbar*(nf*n**2 + (1-nf)*p)*(EK-v) : amp dn/dt=q10*(ninf-n)/ntau : 1 dp/dt=q10*(pinf-p)/ptau : 1 ninf = (1 + exp(-(vu + 15) / 5.))**-0.5 : 1 pinf = 1. / (1 + exp(-(vu + 23) / 6.)) : 1 ntau = ((100. / (11*exp((vu+60) / 24.) + 21*exp(-(vu+60) / 23.))) + 0.7)*ms : ms ptau = ((100. / (4*exp((vu+60) / 32.) + 5*exp(-(vu+60) / 22.))) + 5)*ms : ms """ # Ih channel (subthreshold adaptive, non-inactivating) eqs_ih = """ ih = ghbar*r*(Eh-v) : amp dr/dt=q10*(rinf-r)/rtau : 1 rinf = 1. / (1+exp((vu + 76.) / 7.)) : 1 rtau = ((100000. / (237.*exp((vu+60.) / 12.) + 17.*exp(-(vu+60.) / 14.))) + 25.)*ms : ms """ # KLT channel (low threshold K+) eqs_klt = """ iklt = gkltbar*w**4*z*(EK-v) : amp dw/dt=q10*(winf-w)/wtau : 1 dz/dt=q10*(zinf-z)/wtau : 1 winf = (1. / (1 + exp(-(vu + 48.) / 6.)))**0.25 : 1 zinf = zss + ((1.-zss) / (1 + exp((vu + 71.) / 10.))) : 1 wtau = ((100. / (6.*exp((vu+60.) / 6.) + 16.*exp(-(vu+60.) / 45.))) + 1.5)*ms : ms ztau = ((1000. / (exp((vu+60.) / 20.) + exp(-(vu+60.) / 8.))) + 50)*ms : ms """ # Ka channel (transient K+) eqs_ka = """ ika = gkabar*a**4*b*c*(EK-v): amp da/dt=q10*(ainf-a)/atau : 1 db/dt=q10*(binf-b)/btau : 1 dc/dt=q10*(cinf-c)/ctau : 1 ainf = (1. / (1 + exp(-(vu + 31) / 6.)))**0.25 : 1 binf = 1. / (1 + exp((vu + 66) / 7.))**0.5 : 1 cinf = 1. / (1 + exp((vu + 66) / 7.))**0.5 : 1 atau = ((100. / (7*exp((vu+60) / 14.) + 29*exp(-(vu+60) / 24.))) + 0.1)*ms : ms btau = ((1000. / (14*exp((vu+60) / 27.) + 29*exp(-(vu+60) / 24.))) + 1)*ms : ms ctau = ((90. / (1 + exp((-66-vu) / 17.))) + 10)*ms : ms """ # Leak eqs_leak = """ ileak = gl*(El-v) : amp """ # h current for octopus cells eqs_hcno = """ ihcno = gbarno*(h1*frac + h2*(1-frac))*(Eh-v) : amp dh1/dt=(hinfno-h1)/tau1 : 1 dh2/dt=(hinfno-h2)/tau2 : 1 hinfno = 1./(1+exp((vu+66.)/7.)) : 1 tau1 = bet1/(qt*0.008*(1+alp1))*ms : ms tau2 = bet2/(qt*0.0029*(1+alp2))*ms : ms alp1 = exp(1e-3*3*(vu+50)*9.648e4/(8.315*(273.16+celsius))) : 1 bet1 = exp(1e-3*3*0.3*(vu+50)*9.648e4/(8.315*(273.16+celsius))) : 1 alp2 = exp(1e-3*3*(vu+84)*9.648e4/(8.315*(273.16+celsius))) : 1 bet2 = exp(1e-3*3*0.6*(vu+84)*9.648e4/(8.315*(273.16+celsius))) : 1 """ eqs = """ dv/dt=(ileak+ina+ikht+iklt+ika+ih+ihcno+I)/C : volt vu = v/mV : 1 # unitless v I : amp """ eqs += eqs_leak + eqs_ka + eqs_na + eqs_ih + eqs_klt + eqs_kht + eqs_hcno neuron = NeuronGroup(1, eqs, implicit=True) neuron.v = El run(50 * ms) # Go to rest M = StateMonitor(neuron, 'v', record=0) neuron.I = Ipulse run(100 * ms, report='text') plot(M.times / ms, M[0] / mV) show() brian-1.4.3/docs_sphinx/examples-frompapers_Sturzl_et_al_2000.txt000066400000000000000000000053241266062647100250740ustar00rootroot00000000000000.. currentmodule:: brian .. index:: pair: example usage; IdentityConnection pair: example usage; polar pair: example usage; cos pair: example usage; run pair: example usage; show pair: example usage; sum pair: example usage; arctan pair: example usage; Connection pair: example usage; exp pair: example usage; real pair: example usage; NeuronGroup pair: example usage; imag pair: example usage; SpikeCounter .. _example-frompapers_Sturzl_et_al_2000: Example: Sturzl_et_al_2000 (frompapers) ======================================= Adapted from Theory of Arachnid Prey Localization W. Sturzl, R. Kempter, and J. L. van Hemmen PRL 2000 Poisson inputs are replaced by integrate-and-fire neurons Romain Brette :: from brian import * # Parameters degree = 2 * pi / 360. duration = 500 * ms R = 2.5 * cm # radius of scorpion vr = 50 * meter / second # Rayleigh wave speed phi = 144 * degree # angle of prey A = 250 * Hz deltaI = .7 * ms # inhibitory delay gamma = (22.5 + 45 * arange(8)) * degree # leg angle delay = R / vr * (1 - cos(phi - gamma)) # wave delay # Wave (vector w) t = arange(int(duration / defaultclock.dt) + 1) * defaultclock.dt Dtot = 0. w = 0. for f in range(150, 451): D = exp(-(f - 300) ** 2 / (2 * (50 ** 2))) xi = 2 * pi * rand() w += 100 * D * cos(2 * pi * f * t + xi) Dtot += D w = .01 * w / Dtot # Rates from the wave def rates(t): return w[array(t / defaultclock.dt, dtype=int)] # Leg mechanical receptors tau_legs = 1 * ms sigma = .01 eqs_legs = """ dv/dt=(1+rates(t-d)-v)/tau_legs+sigma*(2./tau_legs)**.5*xi:1 d : second """ legs = NeuronGroup(8, model=eqs_legs, threshold=1, reset=0, refractory=1 * ms) legs.d = delay spikes_legs = SpikeCounter(legs) # Command neurons tau = 1 * ms taus = 1 * ms wex = 7 winh = -2 eqs_neuron = ''' dv/dt=(x-v)/tau : 1 dx/dt=(y-x)/taus : 1 # alpha currents dy/dt=-y/taus : 1 ''' neurons = NeuronGroup(8, model=eqs_neuron, threshold=1, reset=0) synapses_ex = IdentityConnection(legs, neurons, 'y', weight=wex) synapses_inh = Connection(legs, neurons, 'y', delay=deltaI) for i in range(8): synapses_inh[i, (4 + i - 1) % 8] = winh synapses_inh[i, (4 + i) % 8] = winh synapses_inh[i, (4 + i + 1) % 8] = winh spikes = SpikeCounter(neurons) run(duration) nspikes = spikes.count x = sum(nspikes * exp(gamma * 1j)) print "Angle (deg):", arctan(imag(x) / real(x)) / degree polar(concatenate((gamma, [gamma[0] + 2 * pi])), concatenate((nspikes, [nspikes[0]])) / duration) show() brian-1.4.3/docs_sphinx/examples-frompapers_Touboul_Brette_2008.txt000066400000000000000000000033121266062647100253660ustar00rootroot00000000000000.. currentmodule:: brian .. index:: pair: example usage; plot pair: example usage; run pair: example usage; figure pair: example usage; show pair: example usage; StateSpikeMonitor pair: example usage; xlabel pair: example usage; linspace pair: example usage; ylabel pair: example usage; NeuronGroup .. _example-frompapers_Touboul_Brette_2008: Example: Touboul_Brette_2008 (frompapers) ========================================= Chaos in the AdEx model ----------------------- Fig. 8B from: Touboul, J. and Brette, R. (2008). Dynamics and bifurcations of the adaptive exponential integrate-and-fire model. Biological Cybernetics 99(4-5):319-34. This shows the bifurcation structure when the reset value is varied (vertical axis shows the values of w at spike times for a given a reset value Vr). :: from brian import * defaultclock.dt=0.01*ms C=281*pF gL=30*nS EL=-70.6*mV VT=-50.4*mV DeltaT=2*mV tauw=40*ms a=4*nS b=0.08*nA I=.8*nA Vcut=VT+5*DeltaT # practical threshold condition N=500 eqs=""" dvm/dt=(gL*(EL-vm)+gL*DeltaT*exp((vm-VT)/DeltaT)+I-w)/C : volt dw/dt=(a*(vm-EL)-w)/tauw : amp Vr:volt """ neuron=NeuronGroup(N,model=eqs,threshold=Vcut,reset="vm=Vr;w+=b") neuron.vm=EL neuron.w=a*(neuron.vm-EL) neuron.Vr=linspace(-48.3*mV,-47.7*mV,N) # bifurcation parameter run(3*second,report='text') # we discard the first spikes M=StateSpikeMonitor(neuron,("Vr","w")) # record Vr and w at spike times run(2*second,report='text') Vr,w=M.values("Vr"),M.values("w") figure() plot(Vr/mV,w/nA,'.k') xlabel('Vr (mV)') ylabel('w (nA)') show() brian-1.4.3/docs_sphinx/examples-frompapers_Vogels_et_al_2011.txt000066400000000000000000000101261266062647100250260ustar00rootroot00000000000000.. currentmodule:: brian .. index:: pair: example usage; subplot pair: example usage; NeuronGroup pair: example usage; run pair: example usage; xlim pair: example usage; title pair: example usage; raster_plot pair: example usage; show pair: example usage; Connection pair: example usage; xlabel pair: example usage; SpikeMonitor pair: example usage; STDP .. _example-frompapers_Vogels_et_al_2011: Example: Vogels_et_al_2011 (frompapers) ======================================= Inhibitory synaptic plasticity in a recurrent network model ----------------------------------------------------------- (F. Zenke, 2011) (from the 2012 Brian twister) Adapted from: Vogels, T. P., H. Sprekeler, F. Zenke, C. Clopath, and W. Gerstner. Inhibitory Plasticity Balances Excitation and Inhibition in Sensory Pathways and Memory Networks. Science (November 10, 2011). :: from brian import * # ########################################### # Defining network model parameters # ########################################### NE = 8000 # Number of excitatory cells NI = NE/4 # Number of inhibitory cells w = 1.*nS # Basic weight unit tau_ampa = 5.0*ms # Glutamatergic synaptic time constant tau_gaba = 10.0*ms # GABAergic synaptic time constant epsilon = 0.02 # Sparseness of synaptic connections eta = 1e-2 # Learning rate tau_stdp = 20*ms # STDP time constant simtime = 10*second # Simulation time # ########################################### # Neuron model # ########################################### gl = 10.0*nsiemens # Leak conductance el = -60*mV # Resting potential er = -80*mV # Inhibitory reversal potential vt = -50.*mV # Spiking threshold memc = 200.0*pfarad # Membrane capacitance bgcurrent = 200*pA # External current eqs_neurons=''' dv/dt=(-gl*(v-el)-(g_ampa*w*v+g_gaba*(v-er)*w)+bgcurrent)/memc : volt dg_ampa/dt = -g_ampa/tau_ampa : 1 dg_gaba/dt = -g_gaba/tau_gaba : 1 ''' # ########################################### # Initialize neuron group # ########################################### neurons=NeuronGroup(NE+NI,model=eqs_neurons,threshold=vt,reset=el,refractory=5*ms) Pe=neurons.subgroup(NE) Pi=neurons.subgroup(NI) # ########################################### # Connecting the network # ########################################### con_e = Connection(Pe,neurons,'g_ampa',weight=0.3,sparseness=epsilon) con_ie = Connection(Pi,Pe,'g_gaba',weight=1e-10,sparseness=epsilon) con_ii = Connection(Pi,Pi,'g_gaba',weight=3,sparseness=epsilon) # ########################################### # Setting up monitors # ########################################### sm = SpikeMonitor(Pe) # ########################################### # Run without plasticity # ########################################### run(1*second) # ########################################### # Inhibitory Plasticity # ########################################### alpha = 3*Hz*tau_stdp*2 # Target rate parameter gmax = 100 # Maximum inhibitory weight eqs_stdp_inhib = ''' dA_pre/dt=-A_pre/tau_stdp : 1 dA_post/dt=-A_post/tau_stdp : 1 ''' stdp_ie = STDP(con_ie, eqs=eqs_stdp_inhib, pre='A_pre+=1.; w+=(A_post-alpha)*eta;', post='A_post+=1.; w+=A_pre*eta;', wmax=gmax) # ########################################### # Run with plasticity # ########################################### run(simtime-1*second,report='text') # ########################################### # Make plots # ########################################### subplot(211) raster_plot(sm,ms=1.) title("Before") xlabel("") xlim(float(0.8*second/ms), float(1*second/ms)) subplot(212) raster_plot(sm,ms=1.) title("After") xlim(float((simtime-0.2*second)/ms), float(simtime/ms)) show() brian-1.4.3/docs_sphinx/examples-frompapers_Wang_Buszaki_1996.txt000066400000000000000000000030021266062647100250270ustar00rootroot00000000000000.. currentmodule:: brian .. index:: pair: example usage; NeuronGroup pair: example usage; plot pair: example usage; StateMonitor pair: example usage; run pair: example usage; show .. _example-frompapers_Wang_Buszaki_1996: Example: Wang_Buszaki_1996 (frompapers) ======================================= Wang-Buszaki model ------------------ J Neurosci. 1996 Oct 15;16(20):6402-13. Gamma oscillation by synaptic inhibition in a hippocampal interneuronal network model. Wang XJ, Buzsaki G. Note that implicit integration (exponential Euler) cannot be used, and therefore simulation is rather slow. :: from brian import * defaultclock.dt=0.01*ms Cm=1*uF # /cm**2 Iapp=2*uA gL=0.1*msiemens EL=-65*mV ENa=55*mV EK=-90*mV gNa=35*msiemens gK=9*msiemens eqs=''' dv/dt=(-gNa*m**3*h*(v-ENa)-gK*n**4*(v-EK)-gL*(v-EL)+Iapp)/Cm : volt m=alpham/(alpham+betam) : 1 alpham=-0.1/mV*(v+35*mV)/(exp(-0.1/mV*(v+35*mV))-1)/ms : Hz betam=4*exp(-(v+60*mV)/(18*mV))/ms : Hz dh/dt=5*(alphah*(1-h)-betah*h) : 1 alphah=0.07*exp(-(v+58*mV)/(20*mV))/ms : Hz betah=1./(exp(-0.1/mV*(v+28*mV))+1)/ms : Hz dn/dt=5*(alphan*(1-n)-betan*n) : 1 alphan=-0.01/mV*(v+34*mV)/(exp(-0.1/mV*(v+34*mV))-1)/ms : Hz betan=0.125*exp(-(v+44*mV)/(80*mV))/ms : Hz ''' neuron=NeuronGroup(1,eqs) neuron.v=-70*mV neuron.h=1 M=StateMonitor(neuron,'v',record=0) run(100*ms,report='text') plot(M.times/ms,M[0]/mV) show() brian-1.4.3/docs_sphinx/examples-hears-tan_carney_2003_tan_carney_Fig11.txt000066400000000000000000000101761266062647100265500ustar00rootroot00000000000000.. currentmodule:: brian .. index:: pair: example usage; Sound pair: example usage; tone pair: example usage; Network pair: example usage; xlim pair: example usage; sequence pair: example usage; TanCarney pair: example usage; shape pair: example usage; ylim pair: example usage; plot pair: example usage; angle pair: example usage; reshape pair: example usage; sum pair: example usage; show pair: example usage; ylabel pair: example usage; StateMonitor pair: example usage; subplot pair: example usage; product pair: example usage; run pair: example usage; ZhangSynapse pair: example usage; legend pair: example usage; rate pair: example usage; xlabel pair: example usage; exp pair: example usage; set_default_samplerate pair: example usage; MiddleEar pair: example usage; mean .. _example-hears-tan_carney_2003_tan_carney_Fig11: Example: tan_carney_Fig11 (hears/tan_carney_2003) ================================================= Response area and phase response of a model fiber with CF=2200Hz in the Tan&Carney model. Reproduces Fig. 11 from: Tan, Q., and L. H. Carney. "A Phenomenological Model for the Responses of Auditory-nerve Fibers. II. Nonlinear Tuning with a Frequency Glide". The Journal of the Acoustical Society of America 114 (2003): 2007. :: import matplotlib.pyplot as plt import numpy as np from brian import * # set_global_preferences(useweave=True) from brian.hears import * def product(*args): # Simple (and inefficient) variant of itertools.product that works for # Python 2.5 (directly returns a list instead of yielding one item at a # time) pools = map(tuple, args) result = [[]] for pool in pools: result = [x+[y] for x in result for y in pool] return result duration = 50*ms samplerate = 50*kHz set_default_samplerate(samplerate) CF = 2200 freqs = np.arange(250.0, 3501., 50.) levels = [10, 30, 50, 70, 90] cf_level = product(freqs, levels) tones = Sound([Sound.sequence([tone(freq * Hz, duration).atlevel(level*dB).ramp(when='both', duration=2.5*ms, inplace=False)]) for freq, level in cf_level]) ihc = TanCarney(MiddleEar(tones), [CF] * len(cf_level), update_interval=2) syn = ZhangSynapse(ihc, CF) s_mon = StateMonitor(syn, 's', record=True, clock=syn.clock) net = Network(syn, s_mon) net.run(duration) reshaped = s_mon.values.reshape((len(freqs), len(levels), -1)) # calculate the phase with respect to the stimulus pi = np.pi min_freq, max_freq = 1100, 2900 freq_subset = freqs[(freqs>=min_freq) & (freqs<=max_freq)] reshaped_subset = reshaped[(freqs>=min_freq) & (freqs<=max_freq), :, :] phases = np.zeros((reshaped_subset.shape[0], len(levels))) for f_idx, freq in enumerate(freq_subset): period = 1.0 / freq for l_idx in xrange(len(levels)): phase_angles = np.arange(reshaped_subset.shape[2])/samplerate % period / period * 2*pi temp_phases = (np.exp(1j * phase_angles) * reshaped_subset[f_idx, l_idx, :]) phases[f_idx, l_idx] = np.angle(np.sum(temp_phases)) plt.subplot(2, 1, 1) rate = reshaped.mean(axis=2) plt.plot(freqs, rate) plt.ylabel('Spikes/sec') plt.legend(['%.0f dB' % level for level in levels], loc='best') plt.xlim(0, 4000) plt.ylim(0, 250) plt.subplot(2, 1, 2) relative_phases = (phases.T - phases[:, -1]).T relative_phases[relative_phases > pi] = relative_phases[relative_phases > pi] - 2*pi relative_phases[relative_phases < -pi] = relative_phases[relative_phases < -pi] + 2*pi plt.plot(freq_subset, relative_phases / pi) plt.ylabel("Phase Re:90dB (pi radians)") plt.xlabel('Frequency (Hz)') plt.legend(['%.0f dB' % level for level in levels], loc='best') plt.xlim(0, 4000) plt.ylim(-0.5, 0.75) plt.show() brian-1.4.3/docs_sphinx/examples-hears-tan_carney_2003_tan_carney_Fig7.txt000066400000000000000000000114411266062647100264710ustar00rootroot00000000000000.. currentmodule:: brian .. index:: pair: example usage; Sound pair: example usage; plot pair: example usage; product pair: example usage; xlim pair: example usage; show pair: example usage; reshape pair: example usage; yscale pair: example usage; set_default_samplerate pair: example usage; sqrt pair: example usage; legend pair: example usage; xlabel pair: example usage; flatten pair: example usage; ylabel pair: example usage; MiddleEar pair: example usage; mean pair: example usage; ylim pair: example usage; linspace .. _example-hears-tan_carney_2003_tan_carney_Fig7: Example: tan_carney_Fig7 (hears/tan_carney_2003) ================================================ CF-dependence of compressive nonlinearity in the Tan&Carney model. Reproduces Fig. 7 from: Tan, Q., and L. H. Carney. "A Phenomenological Model for the Responses of Auditory-nerve Fibers. II. Nonlinear Tuning with a Frequency Glide". The Journal of the Acoustical Society of America 114 (2003): 2007. :: import numpy as np import matplotlib.pyplot as plt from scipy.interpolate import interp1d from brian import * #set_global_preferences(useweave=True) from brian.hears import * from brian.hears.filtering.tan_carney import TanCarneySignal, MiddleEar samplerate = 50*kHz set_default_samplerate(samplerate) duration = 50*ms def product(*args): # Simple (and inefficient) variant of itertools.product that works for # Python 2.5 (directly returns a list instead of yielding one item at a # time) pools = map(tuple, args) result = [[]] for pool in pools: result = [x+[y] for x in result for y in pool] return result def gen_tone(freq, level): ''' Little helper function to generate a pure tone at frequency `freq` with the given `level`. The tone has a duration of 50ms and is ramped with two ramps of 2.5ms. ''' freq = float(freq) * Hz level = float(level) * dB return tone(freq, duration).ramp(when='both', duration=2.5*ms, inplace=False).atlevel(level) freqs = [500, 1100, 2000, 4000] levels = np.arange(-10, 100.1, 5) cf_level = product(freqs, levels) # steady-state start = 10*ms*samplerate end = 45*ms*samplerate # For Figure 7 we have manually adjusts the gain for different CFs -- otherwise # the RMS values wouldn't be identical for low CFs. Therefore, try to estimate # suitable gain values first using the lowest CF as a reference ref_tone = gen_tone(freqs[0], levels[0]) F_out_reference = TanCarneySignal(MiddleEar(ref_tone, gain=1), freqs[0], update_interval=1).process().flatten() ref_rms = np.sqrt(np.mean((F_out_reference[start:end] - np.mean(F_out_reference[start:end]))**2)) gains = np.linspace(0.1, 1, 50) # for higher CFs we need lower gains cf_gains = product(freqs[1:], gains) tones = Sound([gen_tone(freq, levels[0]) for freq, _ in cf_gains]) F_out_test = TanCarneySignal(MiddleEar(tones, gain=np.array([g for _, g in cf_gains])), [cf for cf,_ in cf_gains], update_interval=1).process() reshaped_Fout = F_out_test.T.reshape((len(freqs[1:]), len(gains), -1)) rms = np.sqrt(np.mean((reshaped_Fout[:, :, start:end].T - np.mean(reshaped_Fout[:, :, start:end], axis=2).T).T**2, axis=2)) # get the best gain for each CF using simple linear interpolation gain_dict = {freqs[0]: 1.} # reference gain for idx, freq in enumerate(freqs[1:]): gain_dict[freq] = interp1d(rms[idx, :], gains)(ref_rms) # now do the real test: tones at different levels for different CFs tones = Sound([gen_tone(freq, level) for freq, level in cf_level]) F_out = TanCarneySignal(MiddleEar(tones, gain=np.array([gain_dict[cf] for cf, _ in cf_level])), [cf for cf, _ in cf_level], update_interval=1).process() reshaped_Fout = F_out.T.reshape((len(freqs), len(levels), -1)) rms = np.sqrt(np.mean((reshaped_Fout[:, :, start:end].T - np.mean(reshaped_Fout[:, :, start:end], axis=2).T).T**2, axis=2)) # This should more or less reproduce Fig. 7 plt.plot(levels, rms.T) plt.legend(['%.0f Hz' % cf for cf in freqs], loc='best') plt.xlim(-20, 100) plt.ylim(1e-6, 1) plt.yscale('log') plt.xlabel('input signal SPL (dB)') plt.ylabel('rms of AC component of Fout') plt.show() brian-1.4.3/docs_sphinx/examples-hears-tan_carney_2003_tan_carney_simple_test.txt000066400000000000000000000133031266062647100302240ustar00rootroot00000000000000.. currentmodule:: brian .. index:: pair: example usage; subplot pair: example usage; tone pair: example usage; Network pair: example usage; figure pair: example usage; sequence pair: example usage; text pair: example usage; TanCarney pair: example usage; ylim pair: example usage; plot pair: example usage; xlim pair: example usage; title pair: example usage; show pair: example usage; click pair: example usage; ylabel pair: example usage; SpikeMonitor pair: example usage; set_global_preferences pair: example usage; nanmax pair: example usage; StateMonitor pair: example usage; Sound pair: example usage; run pair: example usage; ZhangSynapse pair: example usage; reinit_default_clock pair: example usage; get_samplerate pair: example usage; ones pair: example usage; xlabel pair: example usage; set_default_samplerate pair: example usage; MiddleEar pair: example usage; silence .. _example-hears-tan_carney_2003_tan_carney_simple_test: Example: tan_carney_simple_test (hears/tan_carney_2003) ======================================================= Fig. 1 and 3 (spking output without spiking/refractory period) should reproduce the output of the AN3_test_tone.m and AN3_test_click.m scripts, available in the code accompanying the paper Tan & Carney (2003). This matlab code is available from http://www.urmc.rochester.edu/labs/Carney-Lab/publications/auditory-models.cfm Tan, Q., and L. H. Carney. "A Phenomenological Model for the Responses of Auditory-nerve Fibers. II. Nonlinear Tuning with a Frequency Glide". The Journal of the Acoustical Society of America 114 (2003): 2007. :: import numpy as np import matplotlib.pyplot as plt from brian.stdunits import kHz, Hz, ms from brian.network import Network from brian.monitor import StateMonitor, SpikeMonitor from brian.globalprefs import set_global_preferences #set_global_preferences(useweave=True) from brian.hears import (Sound, get_samplerate, set_default_samplerate, tone, click, silence, dB, TanCarney, MiddleEar, ZhangSynapse) from brian.clock import reinit_default_clock set_default_samplerate(50*kHz) sample_length = 1 / get_samplerate(None) cf = 1000 * Hz print 'Testing click response' duration = 25*ms levels = [40, 60, 80, 100, 120] # a click of two samples tones = Sound([Sound.sequence([click(sample_length*2, peak=level*dB), silence(duration=duration - sample_length)]) for level in levels]) ihc = TanCarney(MiddleEar(tones), [cf] * len(levels), update_interval=1) syn = ZhangSynapse(ihc, cf) s_mon = StateMonitor(syn, 's', record=True, clock=syn.clock) R_mon = StateMonitor(syn, 'R', record=True, clock=syn.clock) spike_mon = SpikeMonitor(syn) net = Network(syn, s_mon, R_mon, spike_mon) net.run(duration * 1.5) for idx, level in enumerate(levels): plt.figure(1) plt.subplot(len(levels), 1, idx + 1) plt.plot(s_mon.times / ms, s_mon[idx]) plt.xlim(0, 25) plt.xlabel('Time (msec)') plt.ylabel('Sp/sec') plt.text(15, np.nanmax(s_mon[idx])/2., 'Peak SPL=%s SPL' % str(level*dB)); ymin, ymax = plt.ylim() if idx == 0: plt.title('Click responses') plt.figure(2) plt.subplot(len(levels), 1, idx + 1) plt.plot(R_mon.times / ms, R_mon[idx]) plt.xlabel('Time (msec)') plt.xlabel('Time (msec)') plt.text(15, np.nanmax(s_mon[idx])/2., 'Peak SPL=%s SPL' % str(level*dB)); plt.ylim(ymin, ymax) if idx == 0: plt.title('Click responses (with spikes and refractoriness)') plt.plot(spike_mon.spiketimes[idx] / ms, np.ones(len(spike_mon.spiketimes[idx])) * np.nanmax(R_mon[idx]), 'rx') print 'Testing tone response' reinit_default_clock() duration = 60*ms levels = [0, 20, 40, 60, 80] tones = Sound([Sound.sequence([tone(cf, duration).atlevel(level*dB).ramp(when='both', duration=10*ms, inplace=False), silence(duration=duration/2)]) for level in levels]) ihc = TanCarney(MiddleEar(tones), [cf] * len(levels), update_interval=1) syn = ZhangSynapse(ihc, cf) s_mon = StateMonitor(syn, 's', record=True, clock=syn.clock) R_mon = StateMonitor(syn, 'R', record=True, clock=syn.clock) spike_mon = SpikeMonitor(syn) net = Network(syn, s_mon, R_mon, spike_mon) net.run(duration * 1.5) for idx, level in enumerate(levels): plt.figure(3) plt.subplot(len(levels), 1, idx + 1) plt.plot(s_mon.times / ms, s_mon[idx]) plt.xlim(0, 120) plt.xlabel('Time (msec)') plt.ylabel('Sp/sec') plt.text(1.25 * duration/ms, np.nanmax(s_mon[idx])/2., '%s SPL' % str(level*dB)); ymin, ymax = plt.ylim() if idx == 0: plt.title('CF=%.0f Hz - Response to Tone at CF' % cf) plt.figure(4) plt.subplot(len(levels), 1, idx + 1) plt.plot(R_mon.times / ms, R_mon[idx]) plt.xlabel('Time (msec)') plt.xlabel('Time (msec)') plt.text(1.25 * duration/ms, np.nanmax(R_mon[idx])/2., '%s SPL' % str(level*dB)); plt.ylim(ymin, ymax) if idx == 0: plt.title('CF=%.0f Hz - Response to Tone at CF (with spikes and refractoriness)' % cf) plt.plot(spike_mon.spiketimes[idx] / ms, np.ones(len(spike_mon.spiketimes[idx])) * np.nanmax(R_mon[idx]), 'rx') plt.show() brian-1.4.3/docs_sphinx/examples-hears_IIRfilterbank.txt000066400000000000000000000056001266062647100233720ustar00rootroot00000000000000.. currentmodule:: brian .. index:: pair: example usage; subplot pair: example usage; figure pair: example usage; show pair: example usage; flipud pair: example usage; imshow pair: example usage; vstack pair: example usage; linspace pair: example usage; whitenoise pair: example usage; IIRFilterbank .. _example-hears_IIRfilterbank: Example: IIRfilterbank (hears) ============================== Example of the use of the class :class:`~brian.hears.IIRFilterbank` available in the library. In this example, a white noise is filtered by a bank of chebyshev bandpass filters and lowpass filters which are different for every channels. The centre frequencies of the filters are linearly taken between 100kHz and 1000kHz and its bandwidth or cutoff frequency increases linearly with frequency. :: from brian import * from brian.hears import * sound = whitenoise(100*ms).ramp() sound.level = 50*dB ### example of a bank of bandpass filter ################ nchannels = 50 center_frequencies = linspace(200*Hz, 1000*Hz, nchannels) #center frequencies bw = linspace(50*Hz, 300*Hz, nchannels) #bandwidth of the filters # The maximum loss in the passband in dB. Can be a scalar or an array of length # nchannels gpass = 1.*dB # The minimum attenuation in the stopband in dB. Can be a scalar or an array # of length nchannels gstop = 10.*dB #arrays of shape (2 x nchannels) defining the passband frequencies (Hz) passband = vstack((center_frequencies-bw/2, center_frequencies+bw/2)) #arrays of shape (2 x nchannels) defining the stopband frequencies (Hz) stopband = vstack((center_frequencies-1.1*bw, center_frequencies+1.1*bw)) filterbank = IIRFilterbank(sound, nchannels, passband, stopband, gpass, gstop, 'bandstop', 'cheby1') filterbank_mon = filterbank.process() figure() subplot(211) imshow(flipud(filterbank_mon.T), aspect='auto') #### example of a bank of lowpass filter ################ nchannels = 50 cutoff_frequencies = linspace(100*Hz, 1000*Hz, nchannels) #bandwidth of the transition region between the en of the pass band and the #begin of the stop band width_transition = linspace(50*Hz, 300*Hz, nchannels) # The maximum loss in the passband in dB. Can be a scalar or an array of length # nchannels gpass = 1*dB # The minimum attenuation in the stopband in dB. Can be a scalar or an array of # length nchannels gstop = 10*dB passband = cutoff_frequencies-width_transition/2 stopband = cutoff_frequencies+width_transition/2 filterbank = IIRFilterbank(sound, nchannels, passband, stopband, gpass, gstop, 'low','cheby1') filterbank_mon=filterbank.process() subplot(212) imshow(flipud(filterbank_mon.T), aspect='auto') show() brian-1.4.3/docs_sphinx/examples-hears_approximate_gammatone.txt000066400000000000000000000031061266062647100252650ustar00rootroot00000000000000.. currentmodule:: brian .. index:: pair: example usage; figure pair: example usage; show pair: example usage; flipud pair: example usage; imshow pair: example usage; erbspace pair: example usage; whitenoise pair: example usage; ApproximateGammatone .. _example-hears_approximate_gammatone: Example: approximate_gammatone (hears) ====================================== Example of the use of the class :class:`~brian.hears.ApproximateGammatone` available in the library. It implements a filterbank of approximate gammatone filters as described in Hohmann, V., 2002, "Frequency analysis and synthesis using a Gammatone filterbank", Acta Acustica United with Acustica. In this example, a white noise is filtered by a gammatone filterbank and the resulting cochleogram is plotted. :: from brian import * from brian.hears import * level=50*dB # level of the input sound in rms dB SPL sound = whitenoise(100*ms).ramp() # generation of a white noise sound = sound.atlevel(level) # set the sound to a certain dB level nbr_center_frequencies = 50 # number of frequency channels in the filterbank # center frequencies with a spacing following an ERB scale center_frequencies = erbspace(100*Hz, 1000*Hz, nbr_center_frequencies) # bandwidth of the filters (different in each channel) bw = 10**(0.037+0.785*log10(center_frequencies)) gammatone = ApproximateGammatone(sound, center_frequencies, bw, order=3) gt_mon = gammatone.process() figure() imshow(flipud(gt_mon.T), aspect='auto') show() brian-1.4.3/docs_sphinx/examples-hears_artificial_vowels.txt000066400000000000000000000036341266062647100244200ustar00rootroot00000000000000.. currentmodule:: brian .. index:: pair: example usage; subplot pair: example usage; plot pair: example usage; play pair: example usage; show pair: example usage; set_default_samplerate pair: example usage; Sound pair: example usage; save .. _example-hears_artificial_vowels: Example: artificial_vowels (hears) ================================== This example implements the artificial vowels from Culling, J. F. and Summerfield, Q. (1995a). "Perceptual segregation of concurrent speech sounds: absence of across-frequency grouping by common interaural delay" J. Acoust. Soc. Am. 98, 785-797. :: from brian import * from brian.hears import * duration = 409.6*ms width = 150*Hz/2 samplerate = 10*kHz set_default_samplerate(samplerate) centres = [225*Hz, 625*Hz, 975*Hz, 1925*Hz] vowels = { 'ee':[centres[0], centres[3]], 'ar':[centres[1], centres[2]], 'oo':[centres[0], centres[2]], 'er':[centres[1], centres[3]] } def generate_vowel(vowel): vowel = vowels[vowel] x = whitenoise(duration) y = fft(asarray(x).flatten()) f = fftfreq(len(x), 1/samplerate) I = zeros(len(f), dtype=bool) for cf in vowel: I = I|((abs(f)cf-width)) I = -I y[I] = 0 x = ifft(y) return Sound(x.real) v1 = generate_vowel('ee').ramp() v2 = generate_vowel('ar').ramp() v3 = generate_vowel('oo').ramp() v4 = generate_vowel('er').ramp() for s in [v1, v2, v3, v4]: s.play(normalise=True, sleep=True) s1 = Sound((v1, v2)) #s1.play(normalise=True, sleep=True) s2 = Sound((v3, v4)) #s2.play(normalise=True, sleep=True) v1.save('mono_sound.wav') s1.save('stereo_sound.wav') subplot(211) plot(v1.times, v1) subplot(212) v1.spectrogram() show() brian-1.4.3/docs_sphinx/examples-hears_butterworth.txt000066400000000000000000000037131266062647100233010ustar00rootroot00000000000000.. currentmodule:: brian .. index:: pair: example usage; subplot pair: example usage; Butterworth pair: example usage; figure pair: example usage; show pair: example usage; flipud pair: example usage; imshow pair: example usage; vstack pair: example usage; linspace pair: example usage; whitenoise .. _example-hears_butterworth: Example: butterworth (hears) ============================ Example of the use of the class :class:`~brian.hears.Butterworth` available in the library. In this example, a white noise is filtered by a bank of butterworth bandpass filters and lowpass filters which are different for every channels. The centre or cutoff frequency of the filters are linearly taken between 100kHz and 1000kHz and its bandwidth frequency increases linearly with frequency. :: from brian import * from brian.hears import * level = 50*dB # level of the input sound in rms dB SPL sound = whitenoise(100*ms).ramp() sound = sound.atlevel(level) order = 2 #order of the filters #### example of a bank of bandpass filter ################ nchannels = 50 center_frequencies = linspace(100*Hz, 1000*Hz, nchannels) bw = linspace(50*Hz, 300*Hz, nchannels) # bandwidth of the filters #arrays of shape (2 x nchannels) defining the passband frequencies (Hz) fc = vstack((center_frequencies-bw/2, center_frequencies+bw/2)) filterbank = Butterworth(sound, nchannels, order, fc, 'bandpass') filterbank_mon = filterbank.process() figure() subplot(211) imshow(flipud(filterbank_mon.T), aspect='auto') ### example of a bank of lowpass filter ################ nchannels = 50 cutoff_frequencies = linspace(200*Hz, 1000*Hz, nchannels) filterbank = Butterworth(sound, nchannels, order, cutoff_frequencies, 'low') filterbank_mon = filterbank.process() subplot(212) imshow(flipud(filterbank_mon.T), aspect='auto') show() brian-1.4.3/docs_sphinx/examples-hears_cochleagram.txt000066400000000000000000000021371266062647100231540ustar00rootroot00000000000000.. currentmodule:: brian .. index:: pair: example usage; tone pair: example usage; Gammatone pair: example usage; FunctionFilterbank pair: example usage; show pair: example usage; imshow pair: example usage; erbspace pair: example usage; LowPass pair: example usage; whitenoise .. _example-hears_cochleagram: Example: cochleagram (hears) ============================ Example of basic filtering of a sound with Brian hears. This example implements a cochleagram based on a gammatone filterbank followed by halfwave rectification, cube root compression and 10 Hz low pass filtering. :: from brian import * from brian.hears import * sound1 = tone(1*kHz, .1*second) sound2 = whitenoise(.1*second) sound = sound1+sound2 sound = sound.ramp() cf = erbspace(20*Hz, 20*kHz, 3000) gammatone = Gammatone(sound, cf) cochlea = FunctionFilterbank(gammatone, lambda x: clip(x, 0, Inf)**(1.0/3.0)) lowpass = LowPass(cochlea, 10*Hz) output = lowpass.process() imshow(output.T, origin='lower left', aspect='auto', vmin=0) show() brian-1.4.3/docs_sphinx/examples-hears_cochlear_models.txt000066400000000000000000000026561266062647100240400ustar00rootroot00000000000000.. currentmodule:: brian .. index:: pair: example usage; figure pair: example usage; show pair: example usage; flipud pair: example usage; imshow pair: example usage; TanCarney pair: example usage; erbspace pair: example usage; set_default_samplerate pair: example usage; whitenoise .. _example-hears_cochlear_models: Example: cochlear_models (hears) ================================ Example of the use of the cochlear models (:class:`~brian.hears.DRNL`, :class:`~brian.hears.DCGC` and :class:`~brian.hears.TanCarney`) available in the library. :: from brian import * from brian.hears import * simulation_duration = 50*ms set_default_samplerate(50*kHz) sound = whitenoise(simulation_duration) sound = sound.atlevel(50*dB) # level in rms dB SPL cf = erbspace(100*Hz, 1000*Hz, 50) # centre frequencies interval = 16 #update interval of the time varying filters ## DNRL #param_drnl = {} #param_drnl['lp_nl_cutoff_m'] = 1.1 #drnl_filter=DRNL(sound, cf, type='human', param=param_drnl) #out = drnl_filter.process() ## DCGC #param_dcgc = {} #param_dcgc['c1'] = -2.96 #dcgc_filter = DCGC(sound, cf, interval, param=param_dcgc) #out = dcgc_filter.process() ## Tan and Carney 2003 tan_filter = TanCarney(sound, cf, interval) out = tan_filter.process() figure() imshow(flipud(out.T), aspect='auto') show() brian-1.4.3/docs_sphinx/examples-hears_dcgc.txt000066400000000000000000000153221266062647100216070ustar00rootroot00000000000000.. currentmodule:: brian .. index:: pair: example usage; RestructureFilterbank pair: example usage; figure pair: example usage; AsymmetricCompensation pair: example usage; show pair: example usage; flipud pair: example usage; imshow pair: example usage; erbspace pair: example usage; exp pair: example usage; whitenoise pair: example usage; ControlFilterbank pair: example usage; diff pair: example usage; mean pair: example usage; LogGammachirp pair: example usage; log .. _example-hears_dcgc: Example: dcgc (hears) ===================== Implementation example of the compressive gammachirp auditory filter as described in Irino, T. and Patterson R., "A compressive gammachirp auditory filter for both physiological and psychophysical data", JASA 2001. A class called :class:`~brian.hears.DCGC` implementing this model is available in the library. Technical implementation details and notation can be found in Irino, T. and Patterson R., "A Dynamic Compressive Gammachirp Auditory Filterbank", IEEE Trans Audio Speech Lang Processing. :: from brian import * from brian.hears import * simulation_duration = 50*ms samplerate = 50*kHz level = 50*dB # level of the input sound in rms dB SPL sound = whitenoise(simulation_duration, samplerate).ramp() sound = sound.atlevel(level) nbr_cf = 50 # number of centre frequencies # center frequencies with a spacing following an ERB scale cf = erbspace(100*Hz, 1000*Hz, nbr_cf) c1 = -2.96 #glide slope of the first filterbank b1 = 1.81 #factor determining the time constant of the first filterbank c2 = 2.2 #glide slope of the second filterbank b2 = 2.17 #factor determining the time constant of the second filterbank order_ERB = 4 ERBrate = 21.4*log10(4.37*cf/1000+1) ERBwidth = 24.7*(4.37*cf/1000 + 1) ERBspace = mean(diff(ERBrate)) # the filter coefficients are updated every update_interval (here in samples) update_interval = 1 #bank of passive gammachirp filters. As the control path uses the same passive #filterbank than the signal path (but shifted in frequency) #this filterbank is used by both pathway. pGc = LogGammachirp(sound, cf, b=b1, c=c1) fp1 = cf + c1*ERBwidth*b1/order_ERB #centre frequency of the signal path #### Control Path #### #the first filterbank in the control path consists of gammachirp filters #value of the shift in ERB frequencies of the control path with respect to the signal path lct_ERB = 1.5 n_ch_shift = round(lct_ERB/ERBspace) #value of the shift in channels #index of the channel of the control path taken from pGc indch1_control = minimum(maximum(1, arange(1, nbr_cf+1)+n_ch_shift), nbr_cf).astype(int)-1 fp1_control = fp1[indch1_control] #the control path bank pass filter uses the channels of pGc indexed by indch1_control pGc_control = RestructureFilterbank(pGc, indexmapping=indch1_control) #the second filterbank in the control path consists of fixed asymmetric compensation filters frat_control = 1.08 fr2_control = frat_control*fp1_control asym_comp_control = AsymmetricCompensation(pGc_control, fr2_control, b=b2, c=c2) #definition of the pole of the asymmetric comensation filters p0 = 2 p1 = 1.7818*(1-0.0791*b2)*(1-0.1655*abs(c2)) p2 = 0.5689*(1-0.1620*b2)*(1-0.0857*abs(c2)) p3 = 0.2523*(1-0.0244*b2)*(1+0.0574*abs(c2)) p4 = 1.0724 #definition of the parameters used in the control path output levels computation #(see IEEE paper for details) decay_tcst = .5*ms order = 1. lev_weight = .5 level_ref = 50. level_pwr1 = 1.5 level_pwr2 = .5 RMStoSPL = 30. frat0 = .2330 frat1 = .005 exp_deca_val = exp(-1/(decay_tcst*samplerate)*log(2)) level_min = 10**(-RMStoSPL/20) #definition of the controller class. What is does it take the outputs of the #first and second fitlerbanks of the control filter as input, compute an overall #intensity level for each frequency channel. It then uses those level to update #the filter coefficient of its target, the asymmetric compensation filterbank of #the signal path. class CompensensationFilterUpdater(object): def __init__(self, target): self.target = target self.level1_prev = -100 self.level2_prev = -100 def __call__(self, *input): value1 = input[0][-1,:] value2 = input[1][-1,:] #the current level value is chosen as the max between the current #output and the previous one decreased by a decay level1 = maximum(maximum(value1, 0), self.level1_prev*exp_deca_val) level2 = maximum(maximum(value2, 0), self.level2_prev*exp_deca_val) self.level1_prev = level1 #the value is stored for the next iteration self.level2_prev = level2 #the overall intensity is computed between the two filterbank outputs level_total = lev_weight*level_ref*(level1/level_ref)**level_pwr1+\ (1-lev_weight)*level_ref*(level2/level_ref)**level_pwr2 #then it is converted in dB level_dB = 20*log10(maximum(level_total, level_min))+RMStoSPL #the frequency factor is calculated frat = frat0 + frat1*level_dB #the centre frequency of the asymmetric compensation filters are updated fr2 = fp1*frat coeffs = asymmetric_compensation_coeffs(samplerate, fr2, self.target.filt_b, self.target.filt_a, b2, c2, p0, p1, p2, p3, p4) self.target.filt_b, self.target.filt_a = coeffs #### Signal Path #### #the signal path consists of the passive gammachirp filterbank pGc previously #defined followed by a asymmetric compensation filterbank fr1 = fp1*frat0 varyingfilter_signal_path = AsymmetricCompensation(pGc, fr1, b=b2, c=c2) updater = CompensensationFilterUpdater(varyingfilter_signal_path) #the controler which takes the two filterbanks of the control path as inputs #and the varying filter of the signal path as target is instantiated control = ControlFilterbank(varyingfilter_signal_path, [pGc_control, asym_comp_control], varyingfilter_signal_path, updater, update_interval) #run the simulation #Remember that the controler are at the end of the chain and the output of the #whole path comes from them signal = control.process() figure() imshow(flipud(signal.T), aspect='auto') show() brian-1.4.3/docs_sphinx/examples-hears_drnl.txt000066400000000000000000000102321266062647100216410ustar00rootroot00000000000000.. currentmodule:: brian .. index:: pair: example usage; FunctionFilterbank pair: example usage; figure pair: example usage; show pair: example usage; flipud pair: example usage; imshow pair: example usage; erbspace pair: example usage; gain pair: example usage; LowPass pair: example usage; ApproximateGammatone pair: example usage; whitenoise pair: example usage; Cascade .. _example-hears_drnl: Example: drnl (hears) ===================== Implementation example of the dual resonance nonlinear (DRNL) filter with parameters fitted for human as described in Lopez-Paveda, E. and Meddis, R., A human nonlinear cochlear filterbank, JASA 2001. A class called :class:`~brian.hears.DRNL` implementing this model is available in the library. The entire pathway consists of the sum of a linear and a nonlinear pathway. The linear path consists of a bank of bandpass filters (second order gammatone), a low pass function, and a gain/attenuation factor, g, in a cascade. The nonlinear path is a cascade consisting of a bank of gammatone filters, a compression function, a second bank of gammatone filters, and a low pass function, in that order. The parameters are given in the form ``10**(p0+mlog10(cf))``. :: from brian import * from brian.hears import * simulation_duration = 50*ms samplerate = 50*kHz level = 50*dB # level of the input sound in rms dB SPL sound = whitenoise(simulation_duration, samplerate).ramp() sound.level = level nbr_cf = 50 #number of centre frequencies #center frequencies with a spacing following an ERB scale center_frequencies = erbspace(100*Hz,1000*Hz, nbr_cf) #conversion to stape velocity (which are the units needed by the following centres) sound = sound*0.00014 #### Linear Pathway #### #bandpass filter (second order gammatone filter) center_frequencies_linear = 10**(-0.067+1.016*log10(center_frequencies)) bandwidth_linear = 10**(0.037+0.785*log10(center_frequencies)) order_linear = 3 gammatone = ApproximateGammatone(sound, center_frequencies_linear, bandwidth_linear, order=order_linear) #linear gain g = 10**(4.2-0.48*log10(center_frequencies)) func_gain = lambda x:g*x gain = FunctionFilterbank(gammatone, func_gain) #low pass filter(cascade of 4 second order lowpass butterworth filters) cutoff_frequencies_linear = center_frequencies_linear order_lowpass_linear = 2 lp_l = LowPass(gain, cutoff_frequencies_linear) lowpass_linear = Cascade(gain, lp_l, 4) #### Nonlinear Pathway #### #bandpass filter (third order gammatone filters) center_frequencies_nonlinear = center_frequencies bandwidth_nonlinear = 10**(-0.031+0.774*log10(center_frequencies)) order_nonlinear = 3 bandpass_nonlinear1 = ApproximateGammatone(sound, center_frequencies_nonlinear, bandwidth_nonlinear, order=order_nonlinear) #compression (linear at low level, compress at high level) a = 10**(1.402+0.819*log10(center_frequencies)) #linear gain b = 10**(1.619-0.818*log10(center_frequencies)) v = .2 #compression exponent func_compression = lambda x: sign(x)*minimum(a*abs(x), b*abs(x)**v) compression = FunctionFilterbank(bandpass_nonlinear1, func_compression) #bandpass filter (third order gammatone filters) bandpass_nonlinear2 = ApproximateGammatone(compression, center_frequencies_nonlinear, bandwidth_nonlinear, order=order_nonlinear) #low pass filter cutoff_frequencies_nonlinear = center_frequencies_nonlinear order_lowpass_nonlinear = 2 lp_nl = LowPass(bandpass_nonlinear2, cutoff_frequencies_nonlinear) lowpass_nonlinear = Cascade(bandpass_nonlinear2, lp_nl, 3) #adding the two pathways dnrl_filter = lowpass_linear+lowpass_nonlinear dnrl = dnrl_filter.process() figure() imshow(flipud(dnrl.T), aspect='auto') show() brian-1.4.3/docs_sphinx/examples-hears_gammatone.txt000066400000000000000000000031661266062647100226620ustar00rootroot00000000000000.. currentmodule:: brian .. index:: pair: example usage; Gammatone pair: example usage; figure pair: example usage; title pair: example usage; imshow pair: example usage; erbspace pair: example usage; yscale pair: example usage; ylabel pair: example usage; show pair: example usage; xlabel pair: example usage; whitenoise .. _example-hears_gammatone: Example: gammatone (hears) ========================== Example of the use of the class :class:`~brian.hears.Gammatone` available in the library. It implements a fitlerbank of IIR gammatone filters as described in Slaney, M., 1993, "An Efficient Implementation of the Patterson-Holdsworth Auditory Filter Bank". Apple Computer Technical Report #35. In this example, a white noise is filtered by a gammatone filterbank and the resulting cochleogram is plotted. :: from brian import * from brian.hears import * from matplotlib import pyplot sound = whitenoise(100*ms).ramp() sound.level = 50*dB nbr_center_frequencies = 50 b1 = 1.019 #factor determining the time constant of the filters #center frequencies with a spacing following an ERB scale center_frequencies = erbspace(100*Hz, 1000*Hz, nbr_center_frequencies) gammatone = Gammatone(sound, center_frequencies, b=b1) gt_mon = gammatone.process() figure() imshow(gt_mon.T, aspect='auto', origin='lower left', extent=(0, sound.duration/ms, center_frequencies[0], center_frequencies[-1])) pyplot.yscale('log') title('Cochleogram') ylabel('Frequency (Hz)') xlabel('Time (ms)') show() brian-1.4.3/docs_sphinx/examples-hears_ircam_hrtf.txt000066400000000000000000000026251266062647100230270ustar00rootroot00000000000000.. currentmodule:: brian .. index:: pair: example usage; subplot pair: example usage; title pair: example usage; imshow pair: example usage; shape pair: example usage; xlabel pair: example usage; ylabel pair: example usage; show pair: example usage; whitenoise pair: example usage; IRCAM_LISTEN .. _example-hears_ircam_hrtf: Example: ircam_hrtf (hears) =========================== Example showing the use of HRTFs in Brian hears. Note that you will need to download the :class:`IRCAM_LISTEN` database. :: from brian import * from brian.hears import * # Load database hrtfdb = IRCAM_LISTEN(r'F:\HRTF\IRCAM') hrtfset = hrtfdb.load_subject(1002) # Select only the horizontal plane hrtfset = hrtfset.subset(lambda elev: elev==0) # Set up a filterbank sound = whitenoise(10*ms) fb = hrtfset.filterbank(sound) # Extract the filtered response and plot img = fb.process().T img_left = img[:img.shape[0]/2, :] img_right = img[img.shape[0]/2:, :] subplot(121) imshow(img_left, origin='lower left', aspect='auto', extent=(0, sound.duration/ms, 0, 360)) xlabel('Time (ms)') ylabel('Azimuth') title('Left ear') subplot(122) imshow(img_right, origin='lower left', aspect='auto', extent=(0, sound.duration/ms, 0, 360)) xlabel('Time (ms)') ylabel('Azimuth') title('Right ear') show() brian-1.4.3/docs_sphinx/examples-hears_linear_gammachirp.txt000066400000000000000000000031661266062647100243540ustar00rootroot00000000000000.. currentmodule:: brian .. index:: pair: example usage; plot pair: example usage; figure pair: example usage; show pair: example usage; imshow pair: example usage; erbspace pair: example usage; linspace pair: example usage; whitenoise pair: example usage; LinearGammachirp .. _example-hears_linear_gammachirp: Example: linear_gammachirp (hears) ================================== Example of the use of the class :class:`~brian.hears.LinearGammachirp` available in the library. It implements a filterbank of FIR gammatone filters with linear frequency sweeps as described in Wagner et al. 2009, "Auditory responses in the barn owl's nucleus laminaris to clicks: impulse response and signal analysis of neurophonic potential", J. Neurophysiol. In this example, a white noise is filtered by a gammachirp filterbank and the resulting cochleogram is plotted. The different impulse responses are also plotted. :: from brian import * from brian.hears import * sound = whitenoise(100*ms).ramp() sound.level = 50*dB nbr_center_frequencies = 10 #number of frequency channels in the filterbank #center frequencies with a spacing following an ERB scale center_frequencies = erbspace(100*Hz, 1000*Hz, nbr_center_frequencies) c = 0.0 #glide slope time_constant = linspace(3, 0.3, nbr_center_frequencies)*ms gamma_chirp = LinearGammachirp(sound, center_frequencies, time_constant, c) gamma_chirp_mon = gamma_chirp.process() figure() imshow(gamma_chirp_mon.T, aspect='auto') figure() plot(gamma_chirp.impulse_response.T) show() brian-1.4.3/docs_sphinx/examples-hears_log_gammachirp.txt000066400000000000000000000026111266062647100236550ustar00rootroot00000000000000.. currentmodule:: brian .. index:: pair: example usage; figure pair: example usage; show pair: example usage; flipud pair: example usage; imshow pair: example usage; erbspace pair: example usage; LogGammachirp pair: example usage; whitenoise .. _example-hears_log_gammachirp: Example: log_gammachirp (hears) =============================== Example of the use of the class :class:`~brian.hears.LogGammachirp` available in the library. It implements a filterbank of IIR gammachirp filters as Unoki et al. 2001, "Improvement of an IIR asymmetric compensation gammachirp filter". In this example, a white noise is filtered by a linear gammachirp filterbank and the resulting cochleogram is plotted. The different impulse responses are also plotted. :: from brian import * from brian.hears import * sound = whitenoise(100*ms).ramp() sound.level = 50*dB nbr_center_frequencies = 50 #number of frequency channels in the filterbank c1 = -2.96 #glide slope b1 = 1.81 #factor determining the time constant of the filters #center frequencies with a spacing following an ERB scale cf = erbspace(100*Hz, 1000*Hz, nbr_center_frequencies) gamma_chirp = LogGammachirp(sound, cf, c=c1, b=b1) gamma_chirp_mon = gamma_chirp.process() figure() imshow(flipud(gamma_chirp_mon.T), aspect='auto') show() brian-1.4.3/docs_sphinx/examples-hears_online_computation.txt000066400000000000000000000023361266062647100246160ustar00rootroot00000000000000.. currentmodule:: brian .. index:: pair: example usage; plot pair: example usage; tone pair: example usage; Gammatone pair: example usage; show pair: example usage; axhline pair: example usage; sqrt pair: example usage; erbspace pair: example usage; xlabel pair: example usage; ylabel pair: example usage; whitenoise pair: example usage; mean .. _example-hears_online_computation: Example: online_computation (hears) =================================== Example of online computation using :meth:`~brian.hears.Filterbank.process`. Plots the RMS value of each channel output by a gammatone filterbank. :: from brian import * from brian.hears import * sound1 = tone(1*kHz, .1*second) sound2 = whitenoise(.1*second) sound = sound1+sound2 sound = sound.ramp() sound.level = 60*dB cf = erbspace(20*Hz, 20*kHz, 3000) fb = Gammatone(sound, cf) def sum_of_squares(input, running): return running+sum(input**2, axis=0) rms = sqrt(fb.process(sum_of_squares)/sound.nsamples) sound_rms = sqrt(mean(sound**2)) axhline(sound_rms, ls='--') plot(cf, rms) xlabel('Frequency (Hz)') ylabel('RMS') show() brian-1.4.3/docs_sphinx/examples-hears_simple_anf.txt000066400000000000000000000023661266062647100230300ustar00rootroot00000000000000.. currentmodule:: brian .. index:: pair: example usage; tone pair: example usage; Gammatone pair: example usage; FunctionFilterbank pair: example usage; show pair: example usage; raster_plot pair: example usage; erbspace pair: example usage; FilterbankGroup pair: example usage; SpikeMonitor pair: example usage; whitenoise pair: example usage; run .. _example-hears_simple_anf: Example: simple_anf (hears) =========================== Example of a simple auditory nerve fibre model with Brian hears. :: from brian import * from brian.hears import * sound1 = tone(1*kHz, .1*second) sound2 = whitenoise(.1*second) sound = sound1+sound2 sound = sound.ramp() cf = erbspace(20*Hz, 20*kHz, 3000) cochlea = Gammatone(sound, cf) # Half-wave rectification and compression [x]^(1/3) ihc = FunctionFilterbank(cochlea, lambda x: 3*clip(x, 0, Inf)**(1.0/3.0)) # Leaky integrate-and-fire model with noise and refractoriness eqs = ''' dv/dt = (I-v)/(1*ms)+0.2*xi*(2/(1*ms))**.5 : 1 I : 1 ''' anf = FilterbankGroup(ihc, 'I', eqs, reset=0, threshold=1, refractory=5*ms) M = SpikeMonitor(anf) run(sound.duration) raster_plot(M) show() brian-1.4.3/docs_sphinx/examples-hears_sound_localisation_model.txt000066400000000000000000000122711266062647100257600ustar00rootroot00000000000000.. currentmodule:: brian .. index:: pair: example usage; xlim pair: example usage; show pair: example usage; argmax pair: example usage; erbspace pair: example usage; shape pair: example usage; ylim pair: example usage; plot pair: example usage; Repeat pair: example usage; FunctionFilterbank pair: example usage; sum pair: example usage; FilterbankGroup pair: example usage; ylabel pair: example usage; tile pair: example usage; SpikeCounter pair: example usage; Sound pair: example usage; run pair: example usage; Connection pair: example usage; IRCAM_LISTEN pair: example usage; amax pair: example usage; NeuronGroup pair: example usage; Gammatone pair: example usage; RestructureFilterbank pair: example usage; xlabel pair: example usage; whitenoise pair: example usage; scatter .. _example-hears_sound_localisation_model: Example: sound_localisation_model (hears) ========================================= Example demonstrating the use of many features of Brian hears, including HRTFs, restructuring filters and integration with Brian. Implements a simplified version of the "ideal" sound localisation model from Goodman and Brette (2010). The sound is played at a particular spatial location (indicated on the final plot by a red +). Each location has a corresponding assembly of neurons, whose summed firing rates give the sizes of the blue circles in the plot. The most strongly responding assembly is indicated by the green x, which is the estimate of the location by the model. Reference: `Goodman DFM, Brette R (2010). Spike-timing-based computation in sound localization. PLoS Comput. Biol. 6(11) `__. :: from brian import * from brian.hears import * # Download the IRCAM database, and replace this filename with the location # you downloaded it to hrtfdb = IRCAM_LISTEN(r'F:\HRTF\IRCAM') subject = 1002 hrtfset = hrtfdb.load_subject(subject) # This gives the number of spatial locations in the set of HRTFs num_indices = hrtfset.num_indices # Choose a random location for the sound to come from index = randint(hrtfset.num_indices) # A sound to test the model with sound = Sound.whitenoise(500*ms) # This is the specific HRTF for the chosen location hrtf = hrtfset.hrtf[index] # We apply the chosen HRTF to the sound, the output has 2 channels hrtf_fb = hrtf.filterbank(sound) # We swap these channels (equivalent to swapping the channels in the # subsequent filters, but simpler to do it with the inputs) swapped_channels = RestructureFilterbank(hrtf_fb, indexmapping=[1, 0]) # Now we apply all of the possible pairs of HRTFs in the set to these # swapped channels, which means repeating them num_indices times first hrtfset_fb = hrtfset.filterbank(Repeat(swapped_channels, num_indices)) # Now we apply cochlear filtering (logically, this comes before the HRTF # filtering, but since convolution is commutative it is more efficient to # do the cochlear filtering afterwards cfmin, cfmax, cfN = 150*Hz, 5*kHz, 40 cf = erbspace(cfmin, cfmax, cfN) # We repeat each of the HRTFSet filterbank channels cfN times, so that # for each location we will apply each possible cochlear frequency gfb = Gammatone(Repeat(hrtfset_fb, cfN), tile(cf, hrtfset_fb.nchannels)) # Half wave rectification and compression cochlea = FunctionFilterbank(gfb, lambda x:15*clip(x, 0, Inf)**(1.0/3.0)) # Leaky integrate and fire neuron model eqs = ''' dV/dt = (I-V)/(1*ms)+0.1*xi/(0.5*ms)**.5 : 1 I : 1 ''' G = FilterbankGroup(cochlea, 'I', eqs, reset=0, threshold=1, refractory=5*ms) # The coincidence detector (cd) neurons cd = NeuronGroup(num_indices*cfN, eqs, reset=0, threshold=1, clock=G.clock) # Each CD neuron receives precisely two inputs, one from the left ear and # one from the right, for each location and each cochlear frequency C = Connection(G, cd, 'V') for i in xrange(num_indices*cfN): C[i, i] = 0.5 # from right ear C[i+num_indices*cfN, i] = 0.5 # from left ear # We want to just count the number of CD spikes counter = SpikeCounter(cd) # Run the simulation, giving a report on how long it will take as we run run(sound.duration, report='stderr') # We take the array of counts, and reshape them into a 2D array which we sum # across frequencies to get the spike count of each location-specific assembly count = counter.count count.shape = (num_indices, cfN) count = sum(count, axis=1) count = array(count, dtype=float)/amax(count) # Our guess of the location is the index of the strongest firing assembly index_guess = argmax(count) # Now we plot the output, using the coordinates of the HRTFSet coords = hrtfset.coordinates azim, elev = coords['azim'], coords['elev'] scatter(azim, elev, 100*count) plot([azim[index]], [elev[index]], '+r', ms=15, mew=2) plot([azim[index_guess]], [elev[index_guess]], 'xg', ms=15, mew=2) xlabel('Azimuth (deg)') ylabel('Elevation (deg)') xlim(-5, 350) ylim(-50, 95) show() brian-1.4.3/docs_sphinx/examples-hears_sounds.txt000066400000000000000000000014721266062647100222230ustar00rootroot00000000000000.. currentmodule:: brian .. index:: pair: example usage; subplot pair: example usage; plot pair: example usage; play pair: example usage; tone pair: example usage; show pair: example usage; whitenoise .. _example-hears_sounds: Example: sounds (hears) ======================= Example of basic use and manipulation of sounds with Brian hears. :: from brian import * from brian.hears import * sound1 = tone(1*kHz, 1*second) sound2 = whitenoise(1*second) sound = sound1+sound2 sound = sound.ramp() # Comment this line out if you don't have pygame installed sound.play() # The first 20ms of the sound startsound = sound[:20*ms] subplot(121) plot(startsound.times, startsound) subplot(122) sound.spectrogram() show() brian-1.4.3/docs_sphinx/examples-hears_time_varying_filter1.txt000066400000000000000000000106211266062647100250270ustar00rootroot00000000000000.. currentmodule:: brian .. index:: pair: example usage; cos pair: example usage; LinearFilterbank pair: example usage; log pair: example usage; FunctionFilterbank pair: example usage; show pair: example usage; flipud pair: example usage; imshow pair: example usage; specgram pair: example usage; sqrt pair: example usage; sinh pair: example usage; whitenoise pair: example usage; ControlFilterbank pair: example usage; figure pair: example usage; squeeze pair: example usage; sin pair: example usage; arcsinh .. _example-hears_time_varying_filter1: Example: time_varying_filter1 (hears) ===================================== This example implements a band pass filter whose center frequency is modulated by an Ornstein-Uhlenbeck. The white noise term used for this process is output by a FunctionFilterbank. The bandpass filter coefficients update is an example of how to use a :class:`~brian.hears.ControlFilterbank`. The bandpass filter is a basic biquadratic filter for which the Q factor and the center frequency must be given. The input is a white noise. :: from brian import * from brian.hears import * samplerate = 20*kHz SoundDuration = 300*ms sound = whitenoise(SoundDuration, samplerate).ramp() #number of frequency channel (here it must be one as a spectrogram of the #output is plotted) nchannels = 1 fc_init = 5000*Hz #initial center frequency of the band pass filter Q = 5 #quality factor of the band pass filter update_interval = 4 # the filter coefficients are updated every 4 samples #parameters of the Ornstein-Uhlenbeck process s_i = 1200*Hz tau_i = 100*ms mu_i = fc_init/tau_i sigma_i = sqrt(2)*s_i/sqrt(tau_i) deltaT = defaultclock.dt #this function is used in a FunctionFilterbank. It outputs a noise term that #will be later used by the controler to update the center frequency noise = lambda x: mu_i*deltaT+sigma_i*randn(1)*sqrt(deltaT) noise_generator = FunctionFilterbank(sound, noise) #this class will take as input the output of the noise generator and as target #the bandpass filter center frequency class CoeffController(object): def __init__(self, target): self.target = target self.deltaT = 1./samplerate self.BW = 2*arcsinh(1./2/Q)*1.44269 self.fc = fc_init def __call__(self, input): #the control variables are taken as the last of the buffer noise_term = input[-1,:] #update the center frequency by updateing the OU process self.fc = self.fc-self.fc/tau_i*self.deltaT+noise_term w0 = 2*pi*self.fc/samplerate #update the coefficient of the biquadratic filterbank alpha = sin(w0)*sinh(log(2)/2*self.BW*w0/sin(w0)) self.target.filt_b[:, 0, 0] = sin(w0)/2 self.target.filt_b[:, 1, 0] = 0 self.target.filt_b[:, 2, 0] = -sin(w0)/2 self.target.filt_a[:, 0, 0] = 1+alpha self.target.filt_a[:, 1, 0] = -2*cos(w0) self.target.filt_a[:, 2, 0] = 1-alpha # In the present example the time varying filter is a LinearFilterbank therefore #we must initialise the filter coefficients; the one used for the first buffer computation w0 = 2*pi*fc_init/samplerate BW = 2*arcsinh(1./2/Q)*1.44269 alpha = sin(w0)*sinh(log(2)/2*BW*w0/sin(w0)) filt_b = zeros((nchannels, 3, 1)) filt_a = zeros((nchannels, 3, 1)) filt_b[:, 0, 0] = sin(w0)/2 filt_b[:, 1, 0] = 0 filt_b[:, 2, 0] = -sin(w0)/2 filt_a[:, 0, 0] = 1+alpha filt_a[:, 1, 0] = -2*cos(w0) filt_a[:, 2, 0] = 1-alpha #the filter which will have time varying coefficients bandpass_filter = LinearFilterbank(sound, filt_b, filt_a) #the updater updater = CoeffController(bandpass_filter) #the controller. Remember it must be the last of the chain control = ControlFilterbank(bandpass_filter, noise_generator, bandpass_filter, updater, update_interval) time_varying_filter_mon = control.process() figure(1) pxx, freqs, bins, im = specgram(squeeze(time_varying_filter_mon), NFFT=256, Fs=samplerate, noverlap=240) imshow(flipud(pxx), aspect='auto') show() brian-1.4.3/docs_sphinx/examples-hears_time_varying_filter2.txt000066400000000000000000000112241266062647100250300ustar00rootroot00000000000000.. currentmodule:: brian .. index:: pair: example usage; cos pair: example usage; LinearFilterbank pair: example usage; log pair: example usage; FunctionFilterbank pair: example usage; show pair: example usage; flipud pair: example usage; imshow pair: example usage; specgram pair: example usage; sinh pair: example usage; whitenoise pair: example usage; ControlFilterbank pair: example usage; figure pair: example usage; squeeze pair: example usage; sin pair: example usage; arcsinh .. _example-hears_time_varying_filter2: Example: time_varying_filter2 (hears) ===================================== This example implements a band pass filter whose center frequency is modulated by a sinusoid function. This modulator is implemented as a :class:`~brian.hears.FunctionFilterbank`. One state variable (here time) must be kept; it is therefore implemented with a class. The bandpass filter coefficients update is an example of how to use a :class:`~brian.hears.ControlFilterbank`. The bandpass filter is a basic biquadratic filter for which the Q factor and the center frequency must be given. The input is a white noise. :: from brian import * from brian.hears import * samplerate = 20*kHz SoundDuration = 300*ms sound = whitenoise(SoundDuration, samplerate).ramp() #number of frequency channel (here it must be one as a spectrogram of the #output is plotted) nchannels = 1 fc_init = 5000*Hz #initial center frequency of the band pass filter Q = 5 #quality factor of the band pass filter update_interval = 1 # the filter coefficients are updated every sample mean_center_freq = 4*kHz #mean frequency around which the CF will oscillate amplitude = 1500*Hz #amplitude of the oscillation frequency = 10*Hz #frequency of the oscillation #this class is used in a FunctionFilterbank (via its __call__). It outputs the #center frequency of the band pass filter. Its output is thus later passed as #input to the controler. class CenterFrequencyGenerator(object): def __init__(self): self.t=0*second def __call__(self, input): #update of the center frequency fc = mean_center_freq+amplitude*sin(2*pi*frequency*self.t) #update of the state variable self.t = self.t+1./samplerate return fc center_frequency = CenterFrequencyGenerator() fc_generator = FunctionFilterbank(sound, center_frequency) #the updater of the controller generates new filter coefficient of the band pass #filter based on the center frequency it receives from the fc_generator #(its input) class CoeffController(object): def __init__(self, target): self.BW = 2*arcsinh(1./2/Q)*1.44269 self.target=target def __call__(self, input): fc = input[-1,:] #the control variables are taken as the last of the buffer w0 = 2*pi*fc/array(samplerate) alpha = sin(w0)*sinh(log(2)/2*self.BW*w0/sin(w0)) self.target.filt_b[:, 0, 0] = sin(w0)/2 self.target.filt_b[:, 1, 0] = 0 self.target.filt_b[:, 2, 0] = -sin(w0)/2 self.target.filt_a[:, 0, 0] = 1+alpha self.target.filt_a[:, 1, 0] = -2*cos(w0) self.target.filt_a[:, 2, 0] = 1-alpha # In the present example the time varying filter is a LinearFilterbank therefore #we must initialise the filter coefficients; the one used for the first buffer computation w0 = 2*pi*fc_init/samplerate BW = 2*arcsinh(1./2/Q)*1.44269 alpha = sin(w0)*sinh(log(2)/2*BW*w0/sin(w0)) filt_b = zeros((nchannels, 3, 1)) filt_a = zeros((nchannels, 3, 1)) filt_b[:, 0, 0] = sin(w0)/2 filt_b[:, 1, 0] = 0 filt_b[:, 2, 0] = -sin(w0)/2 filt_a[:, 0, 0] = 1+alpha filt_a[:, 1, 0] = -2*cos(w0) filt_a[:, 2, 0] = 1-alpha #the filter which will have time varying coefficients bandpass_filter = LinearFilterbank(sound, filt_b, filt_a) #the updater updater = CoeffController(bandpass_filter) #the controller. Remember it must be the last of the chain control = ControlFilterbank(bandpass_filter, fc_generator, bandpass_filter, updater, update_interval) time_varying_filter_mon = control.process() figure(1) pxx, freqs, bins, im = specgram(squeeze(time_varying_filter_mon), NFFT=256, Fs=samplerate, noverlap=240) imshow(flipud(pxx), aspect='auto') show() brian-1.4.3/docs_sphinx/examples-interface_interface.txt000066400000000000000000000037651266062647100235150ustar00rootroot00000000000000.. currentmodule:: brian .. _example-interface_interface: Example: interface (interface) ============================== Interface example Install cherrypy for this example Then run the script and go to http://localhost:8080 on your web browser You can use cherrypy to write html interfaces to your code. :: from brian import * import cherrypy import os.path # The server is defined here class MyInterface(object): @cherrypy.expose def index(self): # redirect to the html page we wrote return '' @cherrypy.expose def runscript(self, we="1.62", wi="-9", **kwd): # 'runscript' is the script name # we and wi are the names of form fields we = float(we) wi = float(wi) # From minimalexample reinit_default_clock() eqs = ''' dv/dt = (ge+gi-(v+49*mV))/(20*ms) : volt dge/dt = -ge/(5*ms) : volt dgi/dt = -gi/(10*ms) : volt ''' P = NeuronGroup(4000, model=eqs, threshold= -50 * mV, reset= -60 * mV) P.v = -60 * mV + 10 * mV * rand(len(P)) Pe = P.subgroup(3200) Pi = P.subgroup(800) Ce = Connection(Pe, P, 'ge') Ci = Connection(Pi, P, 'gi') Ce.connect_random(Pe, P, 0.02, weight=we * mV) Ci.connect_random(Pi, P, 0.02, weight=wi * mV) M = SpikeMonitor(P) run(.5 * second) clf() raster_plot(M) savefig('image.png') # Redirect to the html page we wrote return '' # Set the directory for static files current_dir = os.path.dirname(os.path.abspath(__file__)) conf = {'/': {'tools.staticdir.on':True, 'tools.staticdir.dir':current_dir}} # Start the server cherrypy.quickstart(MyInterface(), config=conf) brian-1.4.3/docs_sphinx/examples-misc_COBA.txt000066400000000000000000000052151266062647100212440ustar00rootroot00000000000000.. currentmodule:: brian .. index:: pair: example usage; PopulationSpikeCounter pair: example usage; Connection pair: example usage; Equations pair: example usage; run pair: example usage; NeuronGroup .. _example-misc_COBA: Example: COBA (misc) ==================== This is a Brian script implementing a benchmark described in the following review paper: Simulation of networks of spiking neurons: A review of tools and strategies (2007). Brette, Rudolph, Carnevale, Hines, Beeman, Bower, Diesmann, Goodman, Harris, Zirpe, Natschlager, Pecevski, Ermentrout, Djurfeldt, Lansner, Rochel, Vibert, Alvarez, Muller, Davison, El Boustani and Destexhe. Journal of Computational Neuroscience 23(3):349-98 Benchmark 1: random network of integrate-and-fire neurons with exponential synaptic conductances Clock-driven implementation with Euler integration (no spike time interpolation) R. Brette - Dec 2007 -------------------------------------------------------------------------------------- Brian is a simulator for spiking neural networks written in Python, developed by R. Brette and D. Goodman. http://brian.di.ens.fr :: from brian import * import time # Time constants taum = 20 * msecond taue = 5 * msecond taui = 10 * msecond # Reversal potentials Ee = (0. + 60.) * mvolt Ei = (-80. + 60.) * mvolt start_time = time.time() eqs = Equations(''' dv/dt = (-v+ge*(Ee-v)+gi*(Ei-v))*(1./taum) : volt dge/dt = -ge*(1./taue) : 1 dgi/dt = -gi*(1./taui) : 1 ''') # NB 1: conductances are in units of the leak conductance # NB 2: multiplication is faster than division P = NeuronGroup(4000, model=eqs, threshold=10 * mvolt, \ reset=0 * mvolt, refractory=5 * msecond, order=1, compile=True) Pe = P.subgroup(3200) Pi = P.subgroup(800) we = 6. / 10. # excitatory synaptic weight (voltage) wi = 67. / 10. # inhibitory synaptic weight Ce = Connection(Pe, P, 'ge', weight=we, sparseness=0.02) Ci = Connection(Pi, P, 'gi', weight=wi, sparseness=0.02) # Initialization P.v = (randn(len(P)) * 5 - 5) * mvolt P.ge = randn(len(P)) * 1.5 + 4 P.gi = randn(len(P)) * 12 + 20 # Record the number of spikes Me = PopulationSpikeCounter(Pe) Mi = PopulationSpikeCounter(Pi) print "Network construction time:", time.time() - start_time, "seconds" print "Simulation running..." start_time = time.time() run(1 * second) duration = time.time() - start_time print "Simulation time:", duration, "seconds" print Me.nspikes, "excitatory spikes" print Mi.nspikes, "inhibitory spikes" brian-1.4.3/docs_sphinx/examples-misc_COBAHH.txt000066400000000000000000000060411266062647100214620ustar00rootroot00000000000000.. currentmodule:: brian .. index:: pair: example usage; plot pair: example usage; run pair: example usage; trace pair: example usage; show pair: example usage; Connection pair: example usage; EmpiricalThreshold pair: example usage; NeuronGroup pair: example usage; Equations pair: example usage; StateMonitor .. _example-misc_COBAHH: Example: COBAHH (misc) ====================== This is an implementation of a benchmark described in the following review paper: Simulation of networks of spiking neurons: A review of tools and strategies (2006). Brette, Rudolph, Carnevale, Hines, Beeman, Bower, Diesmann, Goodman, Harris, Zirpe, NatschlAger, Pecevski, Ermentrout, Djurfeldt, Lansner, Rochel, Vibert, Alvarez, Muller, Davison, El Boustani and Destexhe. Journal of Computational Neuroscience Benchmark 3: random network of HH neurons with exponential synaptic conductances Clock-driven implementation (no spike time interpolation) R. Brette - Dec 2007 70s for dt=0.1 ms with exponential Euler :: from brian import * # Parameters area = 20000 * umetre ** 2 Cm = (1 * ufarad * cm ** -2) * area gl = (5e-5 * siemens * cm ** -2) * area El = -60 * mV EK = -90 * mV ENa = 50 * mV g_na = (100 * msiemens * cm ** -2) * area g_kd = (30 * msiemens * cm ** -2) * area VT = -63 * mV # Time constants taue = 5 * ms taui = 10 * ms # Reversal potentials Ee = 0 * mV Ei = -80 * mV we = 6 * nS # excitatory synaptic weight (voltage) wi = 67 * nS # inhibitory synaptic weight # The model eqs = Equations(''' dv/dt = (gl*(El-v)+ge*(Ee-v)+gi*(Ei-v)-\ g_na*(m*m*m)*h*(v-ENa)-\ g_kd*(n*n*n*n)*(v-EK))/Cm : volt dm/dt = alpham*(1-m)-betam*m : 1 dn/dt = alphan*(1-n)-betan*n : 1 dh/dt = alphah*(1-h)-betah*h : 1 dge/dt = -ge*(1./taue) : siemens dgi/dt = -gi*(1./taui) : siemens alpham = 0.32*(mV**-1)*(13*mV-v+VT)/ \ (exp((13*mV-v+VT)/(4*mV))-1.)/ms : Hz betam = 0.28*(mV**-1)*(v-VT-40*mV)/ \ (exp((v-VT-40*mV)/(5*mV))-1)/ms : Hz alphah = 0.128*exp((17*mV-v+VT)/(18*mV))/ms : Hz betah = 4./(1+exp((40*mV-v+VT)/(5*mV)))/ms : Hz alphan = 0.032*(mV**-1)*(15*mV-v+VT)/ \ (exp((15*mV-v+VT)/(5*mV))-1.)/ms : Hz betan = .5*exp((10*mV-v+VT)/(40*mV))/ms : Hz ''') P = NeuronGroup(4000, model=eqs, threshold=EmpiricalThreshold(threshold= -20 * mV, refractory=3 * ms), implicit=True, freeze=True) Pe = P.subgroup(3200) Pi = P.subgroup(800) Ce = Connection(Pe, P, 'ge', weight=we, sparseness=0.02) Ci = Connection(Pi, P, 'gi', weight=wi, sparseness=0.02) # Initialization P.v = El + (randn(len(P)) * 5 - 5) * mV P.ge = (randn(len(P)) * 1.5 + 4) * 10. * nS P.gi = (randn(len(P)) * 12 + 20) * 10. * nS # Record the number of spikes and a few traces trace = StateMonitor(P, 'v', record=[1, 10, 100]) run(1 * second) plot(trace[1]) plot(trace[10]) plot(trace[100]) show() brian-1.4.3/docs_sphinx/examples-misc_CUBA.txt000066400000000000000000000052601266062647100212520ustar00rootroot00000000000000.. currentmodule:: brian .. index:: pair: example usage; plot pair: example usage; run pair: example usage; PopulationRateMonitor pair: example usage; NeuronGroup pair: example usage; PopulationSpikeCounter pair: example usage; show pair: example usage; Connection pair: example usage; Equations .. _example-misc_CUBA: Example: CUBA (misc) ==================== This is a Brian script implementing a benchmark described in the following review paper: Simulation of networks of spiking neurons: A review of tools and strategies (2007). Brette, Rudolph, Carnevale, Hines, Beeman, Bower, Diesmann, Goodman, Harris, Zirpe, Natschlager, Pecevski, Ermentrout, Djurfeldt, Lansner, Rochel, Vibert, Alvarez, Muller, Davison, El Boustani and Destexhe. Journal of Computational Neuroscience 23(3):349-98 Benchmark 2: random network of integrate-and-fire neurons with exponential synaptic currents Clock-driven implementation with exact subthreshold integration (but spike times are aligned to the grid) R. Brette - Oct 2007 -------------------------------------------------------------------------------------- Brian is a simulator for spiking neural networks written in Python, developed by R. Brette and D. Goodman. http://brian.di.ens.fr :: from brian import * import time start_time = time.time() taum = 20 * ms taue = 5 * ms taui = 10 * ms Vt = -50 * mV Vr = -60 * mV El = -49 * mV eqs = Equations(''' dv/dt = (ge+gi-(v-El))/taum : volt dge/dt = -ge/taue : volt dgi/dt = -gi/taui : volt ''') P = NeuronGroup(4000, model=eqs, threshold=Vt, reset=Vr, refractory=5 * ms) P.v = Vr P.ge = 0 * mV P.gi = 0 * mV Pe = P.subgroup(3200) Pi = P.subgroup(800) we = (60 * 0.27 / 10) * mV # excitatory synaptic weight (voltage) wi = (-20 * 4.5 / 10) * mV # inhibitory synaptic weight Ce = Connection(Pe, P, 'ge', weight=we, sparseness=0.02) Ci = Connection(Pi, P, 'gi', weight=wi, sparseness=0.02) P.v = Vr + rand(len(P)) * (Vt - Vr) # Record the number of spikes Me = PopulationSpikeCounter(Pe) Mi = PopulationSpikeCounter(Pi) # A population rate monitor M = PopulationRateMonitor(P) print "Network construction time:", time.time() - start_time, "seconds" print len(P), "neurons in the network" print "Simulation running..." run(1 * msecond) start_time = time.time() run(1 * second) duration = time.time() - start_time print "Simulation time:", duration, "seconds" print Me.nspikes, "excitatory spikes" print Mi.nspikes, "inhibitory spikes" plot(M.times / ms, M.smooth_rate(2 * ms, 'gaussian')) show() brian-1.4.3/docs_sphinx/examples-misc_HodgkinHuxley.txt000066400000000000000000000020251266062647100233160ustar00rootroot00000000000000.. currentmodule:: brian .. index:: pair: example usage; plot pair: example usage; run pair: example usage; trace pair: example usage; show pair: example usage; MembraneEquation pair: example usage; Current pair: example usage; NeuronGroup pair: example usage; StateMonitor .. _example-misc_HodgkinHuxley: Example: HodgkinHuxley (misc) ============================= Hodgkin-Huxley model Assuming area 1*cm**2 :: from brian import * from brian.library.ionic_currents import * #defaultclock.dt=.01*ms # more precise El = 10.6 * mV EK = -12 * mV ENa = 120 * mV eqs = MembraneEquation(1 * uF) + leak_current(.3 * msiemens, El) eqs += K_current_HH(36 * msiemens, EK) + Na_current_HH(120 * msiemens, ENa) eqs += Current('I:amp') neuron = NeuronGroup(1, eqs, implicit=True, freeze=True) trace = StateMonitor(neuron, 'vm', record=True) run(100 * ms) neuron.I = 10 * uA run(100 * ms) plot(trace.times / ms, trace[0] / mV) show() brian-1.4.3/docs_sphinx/examples-misc_I-F_curve.txt000066400000000000000000000030311266062647100223110ustar00rootroot00000000000000.. currentmodule:: brian .. index:: pair: example usage; subplot pair: example usage; plot pair: example usage; run pair: example usage; trace pair: example usage; show pair: example usage; IdentityConnection pair: example usage; Current pair: example usage; xlabel pair: example usage; linspace pair: example usage; ylabel pair: example usage; NeuronGroup pair: example usage; StateMonitor .. _example-misc_I-F_curve: Example: I-F_curve (misc) ========================= Input-Frequency curve of a neuron (cortical RS type) Network: 1000 unconnected integrate-and-fire neurons (Brette-Gerstner) with an input parameter I. The input is set differently for each neuron. Spikes are sent to a 'neuron' group with the same size and variable n, which has the role of a spike counter. :: from brian import * from brian.library.IF import * N = 1000 eqs = Brette_Gerstner() + Current('I:amp') print eqs group = NeuronGroup(N, model=eqs, threshold= -20 * mV, reset=AdaptiveReset()) group.vm = -70 * mV group.I = linspace(0 * nA, 1 * nA, N) counter = NeuronGroup(N, model='n:1') C = IdentityConnection(group, counter, 'n') i = N * 8 / 10 trace = StateMonitor(group, 'vm', record=i) duration = 5 * second run(duration) subplot(211) plot(group.I / nA, counter.n / duration) xlabel('I (nA)') ylabel('Firing rate (Hz)') subplot(212) plot(trace.times / ms, trace[i] / mV) xlabel('Time (ms)') ylabel('Vm (mV)') show() brian-1.4.3/docs_sphinx/examples-misc_I-F_curve2.txt000066400000000000000000000017571266062647100224100ustar00rootroot00000000000000.. currentmodule:: brian .. index:: pair: example usage; NeuronGroup pair: example usage; run pair: example usage; plot pair: example usage; show pair: example usage; linspace pair: example usage; SpikeCounter .. _example-misc_I-F_curve2: Example: I-F_curve2 (misc) ========================== Input-Frequency curve of a IF model Network: 1000 unconnected integrate-and-fire neurons (leaky IF) with an input parameter v0. The input is set differently for each neuron. Spikes are sent to a spike counter (counts the spikes emitted by each neuron). :: from brian import * N = 1000 tau = 10 * ms eqs = ''' dv/dt=(v0-v)/tau : volt v0 : volt ''' group = NeuronGroup(N, model=eqs, threshold=10 * mV, reset=0 * mV, refractory=5 * ms) group.v = 0 * mV group.v0 = linspace(0 * mV, 20 * mV, N) counter = SpikeCounter(group) duration = 5 * second run(duration) plot(group.v0 / mV, counter.count / duration) show() brian-1.4.3/docs_sphinx/examples-misc_adaptive.txt000066400000000000000000000021241266062647100223310ustar00rootroot00000000000000.. currentmodule:: brian .. index:: pair: example usage; NeuronGroup pair: example usage; run pair: example usage; plot pair: example usage; show pair: example usage; Connection pair: example usage; PoissonGroup pair: example usage; SpikeMonitor pair: example usage; StateMonitor .. _example-misc_adaptive: Example: adaptive (misc) ======================== An adaptive neuron model :: from brian import * PG = PoissonGroup(1, 500 * Hz) eqs = ''' dv/dt = (-w-v)/(10*ms) : volt # the membrane equation dw/dt = -w/(30*ms) : volt # the adaptation current ''' # The adaptation variable increases with each spike IF = NeuronGroup(1, model=eqs, threshold=20 * mV, reset='''v = 0*mV w += 3*mV ''') C = Connection(PG, IF, 'v', weight=3 * mV) MS = SpikeMonitor(PG, True) Mv = StateMonitor(IF, 'v', record=True) Mw = StateMonitor(IF, 'w', record=True) run(100 * ms) plot(Mv.times / ms, Mv[0] / mV) plot(Mw.times / ms, Mw[0] / mV) show() brian-1.4.3/docs_sphinx/examples-misc_adaptive_threshold.txt000066400000000000000000000017571266062647100244200ustar00rootroot00000000000000.. currentmodule:: brian .. index:: pair: example usage; plot pair: example usage; run pair: example usage; show pair: example usage; Connection pair: example usage; PoissonGroup pair: example usage; NeuronGroup pair: example usage; StateMonitor .. _example-misc_adaptive_threshold: Example: adaptive_threshold (misc) ================================== A model with adaptive threshold (increases with each spike) :: from brian import * eqs = ''' dv/dt = -v/(10*ms) : volt dvt/dt = (10*mV-vt)/(15*ms) : volt ''' reset = ''' v=0*mV vt+=3*mV ''' IF = NeuronGroup(1, model=eqs, reset=reset, threshold='v>vt') IF.rest() PG = PoissonGroup(1, 500 * Hz) C = Connection(PG, IF, 'v', weight=3 * mV) Mv = StateMonitor(IF, 'v', record=True) Mvt = StateMonitor(IF, 'vt', record=True) run(100 * ms) plot(Mv.times / ms, Mv[0] / mV) plot(Mvt.times / ms, Mvt[0] / mV) show() brian-1.4.3/docs_sphinx/examples-misc_after_potential.txt000066400000000000000000000014331266062647100237160ustar00rootroot00000000000000.. currentmodule:: brian .. index:: pair: example usage; NeuronGroup pair: example usage; show pair: example usage; run pair: example usage; StateMonitor pair: example usage; plot .. _example-misc_after_potential: Example: after_potential (misc) =============================== A model with depolarizing after-potential. :: from brian import * v0=20.5*mV eqs = ''' dv/dt = (v0-v)/(30*ms) : volt # the membrane equation dAP/dt = -AP/(3*ms) : volt # the after-potential vm = v+AP : volt # total membrane potential ''' IF = NeuronGroup(1, model=eqs, threshold='vm>20*mV', reset='v=0*mV; AP=10*mV') Mv = StateMonitor(IF, 'vm', record=True) run(500 * ms) plot(Mv.times / ms, Mv[0] / mV) show() brian-1.4.3/docs_sphinx/examples-misc_cable.txt000066400000000000000000000030501266062647100216010ustar00rootroot00000000000000.. currentmodule:: brian .. index:: pair: example usage; plot pair: example usage; run pair: example usage; trace pair: example usage; show pair: example usage; MembraneEquation pair: example usage; Compartments pair: example usage; Current pair: example usage; connect pair: example usage; NeuronGroup pair: example usage; append pair: example usage; StateMonitor .. _example-misc_cable: Example: cable (misc) ===================== Dendrite with 100 compartments :: from brian import * from brian.compartments import * from brian.library.ionic_currents import * length = 1 * mm nseg = 100 dx = length / nseg Cm = 1 * uF / cm ** 2 gl = 0.02 * msiemens / cm ** 2 diam = 1 * um area = pi * diam * dx El = 0 * mV Ri = 100 * ohm * cm ra = Ri * 4 / (pi * diam ** 2) print "Time constant =", Cm / gl print "Space constant =", .5 * (diam / (gl * Ri)) ** .5 segments = {} for i in range(nseg): segments[i] = MembraneEquation(Cm * area) + leak_current(gl * area, El) segments[0] += Current('I:nA') cable = Compartments(segments) for i in range(nseg - 1): cable.connect(i, i + 1, ra * dx) neuron = NeuronGroup(1, model=cable) #neuron.vm_0=10*mV neuron.I_0 = .05 * nA trace = [] for i in range(10): trace.append(StateMonitor(neuron, 'vm_' + str(10 * i), record=True)) run(200 * ms) for i in range(10): plot(trace[i].times / ms, trace[i][0] / mV) show() brian-1.4.3/docs_sphinx/examples-misc_correlated_inputs.txt000066400000000000000000000023531266062647100242660ustar00rootroot00000000000000.. currentmodule:: brian .. index:: pair: example usage; run pair: example usage; SpikeGeneratorGroup pair: example usage; show pair: example usage; raster_plot pair: example usage; reshape pair: example usage; mixture_process pair: example usage; SpikeMonitor pair: example usage; linspace pair: example usage; mean .. _example-misc_correlated_inputs: Example: correlated_inputs (misc) ================================= An example with correlated spike trains From: Brette, R. (2007). Generation of correlated spike trains. :: from brian import * N = 100 #input = HomogeneousCorrelatedSpikeTrains(N, r=10 * Hz, c=0.1, tauc=10 * ms) c = .2 nu = linspace(1*Hz, 10*Hz, N) P = c*dot(nu.reshape((N,1)), nu.reshape((1,N)))/mean(nu**2) tauc = 5*ms spikes = mixture_process(nu, P, tauc, 1*second) #spikes = [(i,t*second) for i,t in spikes] input = SpikeGeneratorGroup(N, spikes) S = SpikeMonitor(input) #S2 = PopulationRateMonitor(input) #M = StateMonitor(input, 'rate', record=0) run(1000 * ms) #subplot(211) raster_plot(S) #subplot(212) #plot(S2.times / ms, S2.smooth_rate(5 * ms)) #plot(M.times / ms, M[0] / Hz) show() brian-1.4.3/docs_sphinx/examples-misc_correlated_inputs2.txt000066400000000000000000000016331266062647100243500ustar00rootroot00000000000000.. currentmodule:: brian .. index:: pair: example usage; run pair: example usage; SpikeGeneratorGroup pair: example usage; show pair: example usage; raster_plot pair: example usage; reshape pair: example usage; mixture_process pair: example usage; SpikeMonitor pair: example usage; linspace pair: example usage; mean .. _example-misc_correlated_inputs2: Example: correlated_inputs2 (misc) ================================== An example with correlated spike trains From: Brette, R. (2007). Generation of correlated spike trains. :: from brian import * N = 100 c = .2 nu = linspace(1*Hz, 10*Hz, N) P = c*dot(nu.reshape((N,1)), nu.reshape((1,N)))/mean(nu**2) tauc = 5*ms spikes = mixture_process(nu, P, tauc, 1*second) input = SpikeGeneratorGroup(N, spikes) S = SpikeMonitor(input) run(1000 * ms) raster_plot(S) show() brian-1.4.3/docs_sphinx/examples-misc_current_clamp.txt000066400000000000000000000027571266062647100234060ustar00rootroot00000000000000.. currentmodule:: brian .. index:: pair: example usage; NeuronGroup pair: example usage; run pair: example usage; plot pair: example usage; show pair: example usage; Equations pair: example usage; StateMonitor .. _example-misc_current_clamp: Example: current_clamp (misc) ============================= An example of single-electrode current clamp recording with bridge compensation (using the electrophysiology library). :: from brian import * from brian.library.electrophysiology import * taum = 20 * ms # membrane time constant gl = 1. / (50 * Mohm) # leak conductance Cm = taum * gl # membrane capacitance Re = 50 * Mohm # electrode resistance Ce = 0.5 * ms / Re # electrode capacitance eqs = Equations(''' dvm/dt=(-gl*vm+i_inj)/Cm : volt Rbridge:ohm # bridge resistance I:amp # command current ''') eqs += current_clamp(i_cmd='I', Re=Re, Ce=Ce, bridge='Rbridge') setup = NeuronGroup(1, model=eqs) soma = StateMonitor(setup, 'vm', record=True) recording = StateMonitor(setup, 'v_rec', record=True) # No compensation run(50 * ms) setup.I = .5 * nA run(100 * ms) setup.I = 0 * nA run(50 * ms) # Full compensation setup.Rbridge = Re run(50 * ms) setup.I = .5 * nA run(100 * ms) setup.I = 0 * nA run(50 * ms) plot(recording.times / ms, recording[0] / mV, 'b') plot(soma.times / ms, soma[0] / mV, 'r') show() brian-1.4.3/docs_sphinx/examples-misc_delays.txt000066400000000000000000000016701266062647100220220ustar00rootroot00000000000000.. currentmodule:: brian .. index:: pair: example usage; NeuronGroup pair: example usage; run pair: example usage; show pair: example usage; raster_plot pair: example usage; Connection pair: example usage; SpikeMonitor .. _example-misc_delays: Example: delays (misc) ====================== Random network with external noise and transmission delays :: from brian import * tau = 10 * ms sigma = 5 * mV eqs = 'dv/dt = -v/tau+sigma*xi/tau**.5 : volt' P = NeuronGroup(4000, model=eqs, threshold=10 * mV, reset=0 * mV, \ refractory=5 * ms) P.v = -60 * mV Pe = P.subgroup(3200) Pi = P.subgroup(800) C = Connection(P, P, 'v', delay=2 * ms) C.connect_random(Pe, P, 0.05, weight=.7 * mV) C.connect_random(Pi, P, 0.05, weight= -2.8 * mV) M = SpikeMonitor(P, True) run(1 * second) print 'Mean rate =', M.nspikes / 4000. / second raster_plot(M) show() brian-1.4.3/docs_sphinx/examples-misc_expIF_network.txt000066400000000000000000000047501266062647100233270ustar00rootroot00000000000000.. currentmodule:: brian .. index:: pair: example usage; IdentityConnection pair: example usage; NeuronGroup pair: example usage; run pair: example usage; show pair: example usage; raster_plot pair: example usage; Connection pair: example usage; Current pair: example usage; PoissonGroup pair: example usage; SpikeMonitor .. _example-misc_expIF_network: Example: expIF_network (misc) ============================= A network of exponential IF models with synaptic conductances :: from brian import * from brian.library.IF import * from brian.library.synapses import * import time C = 200 * pF taum = 10 * msecond gL = C / taum EL = -70 * mV VT = -55 * mV DeltaT = 3 * mV # Synapse parameters Ee = 0 * mvolt Ei = -80 * mvolt taue = 5 * msecond taui = 10 * msecond eqs = exp_IF(C, gL, EL, VT, DeltaT) # Two different ways of adding synaptic currents: eqs += Current(''' Ie=ge*(Ee-vm) : amp dge/dt=-ge/taue : siemens ''') eqs += exp_conductance('gi', Ei, taui) # from library.synapses P = NeuronGroup(4000, model=eqs, threshold= -20 * mvolt, reset=EL, refractory=2 * ms) Pe = P.subgroup(3200) Pi = P.subgroup(800) we = 1.5 * nS # excitatory synaptic weight wi = 2.5 * we # inhibitory synaptic weight Ce = Connection(Pe, P, 'ge', weight=we, sparseness=0.05) Ci = Connection(Pi, P, 'gi', weight=wi, sparseness=0.05) # Initialization P.vm = randn(len(P)) * 10 * mV - 70 * mV P.ge = (randn(len(P)) * 2 + 5) * we P.gi = (randn(len(P)) * 2 + 5) * wi # Excitatory input to a subset of excitatory and inhibitory neurons # Excitatory neurons are excited for the first 200 ms # Inhibitory neurons are excited for the first 100 ms input_layer1 = Pe.subgroup(200) input_layer2 = Pi.subgroup(200) input1 = PoissonGroup(200, rates=lambda t: (t < 200 * ms and 2000 * Hz) or 0 * Hz) input2 = PoissonGroup(200, rates=lambda t: (t < 100 * ms and 2000 * Hz) or 0 * Hz) input_co1 = IdentityConnection(input1, input_layer1, 'ge', weight=we) input_co2 = IdentityConnection(input2, input_layer2, 'ge', weight=we) # Record the number of spikes M = SpikeMonitor(P) print "Simulation running..." start_time = time.time() run(500 * ms) duration = time.time() - start_time print "Simulation time:", duration, "seconds" print M.nspikes / 4000., "spikes per neuron" raster_plot(M) show() brian-1.4.3/docs_sphinx/examples-misc_gap_junctions.txt000066400000000000000000000032221266062647100233770ustar00rootroot00000000000000.. currentmodule:: brian .. index:: pair: example usage; subplot pair: example usage; plot pair: example usage; run pair: example usage; PopulationRateMonitor pair: example usage; trace pair: example usage; show pair: example usage; raster_plot pair: example usage; network_operation pair: example usage; rate pair: example usage; SpikeMonitor pair: example usage; NeuronGroup pair: example usage; StateMonitor .. _example-misc_gap_junctions: Example: gap_junctions (misc) ============================= Network of noisy IF neurons with gap junctions :: from brian import * N = 300 v0 = 5 * mV tau = 20 * ms sigma = 5 * mV vt = 10 * mV vr = 0 * mV g_gap = 1. / N beta = 60 * mV * 2 * ms delta = vt - vr eqs = ''' dv/dt=(v0-v)/tau+g_gap*(u-N*v)/tau : volt du/dt=(N*v0-u)/tau : volt # input from other neurons ''' def myreset(P, spikes): P.v[spikes] = vr # reset P.v += g_gap * beta * len(spikes) # spike effect P.u -= delta * len(spikes) group = NeuronGroup(N, model=eqs, threshold=vt, reset=myreset) @network_operation def noise(cl): x = randn(N) * sigma * (cl.dt / tau) ** .5 group.v += x group.u += sum(x) trace = StateMonitor(group, 'v', record=[0, 1]) spikes = SpikeMonitor(group) rate = PopulationRateMonitor(group) run(1 * second) subplot(311) raster_plot(spikes) subplot(312) plot(trace.times / ms, trace[0] / mV) plot(trace.times / ms, trace[1] / mV) subplot(313) plot(rate.times / ms, rate.smooth_rate(5 * ms) / Hz) show() brian-1.4.3/docs_sphinx/examples-misc_heterogeneous_delays.txt000066400000000000000000000042651266062647100247610ustar00rootroot00000000000000.. currentmodule:: brian .. index:: pair: example usage; subplot pair: example usage; plot pair: example usage; run pair: example usage; title pair: example usage; imshow pair: example usage; Connection pair: example usage; xlabel pair: example usage; ylabel pair: example usage; show pair: example usage; NeuronGroup pair: example usage; StateMonitor .. _example-misc_heterogeneous_delays: Example: heterogeneous_delays (misc) ==================================== Script demonstrating use of a :class:`Connection` with homogenenous delays The network consists of a 'starter' neuron which fires a single spike at time t=0, connected to 100 leaky integrate and fire neurons with different delays for each target neuron, with the delays forming a quadratic curve centred at neuron 50. The longest delay is 10ms, and the network is run for 40ms. At the end, the delays are plotted above a colour plot of the membrane potential of each of the target neurons as a function of time (demonstrating the delays). :: from brian import * # Starter neuron, threshold is below 0 so it fires immediately, reset is below # threshold so it fires only once. G = NeuronGroup(1, model='V:1', threshold= -1.0, reset= -2.0) # 100 LIF neurons, no reset or threshold so they will not spike H = NeuronGroup(100, model='dV/dt=-V/(10*ms):volt') # Connection with delays, here the delays are specified as a function of (i,j) # giving the delay from neuron i to neuron j. In this case there is only one # presynaptic neuron so i will be 0. C = Connection(G, H, weight=5 * mV, max_delay=10 * ms, delay=lambda i, j:10 * ms * (j / 50. - 1) ** 2) M = StateMonitor(H, 'V', record=True) run(40 * ms) subplot(211) # These are the delays from neuron 0 to neuron i in ms plot([C.delay[0, i] / ms for i in range(100)]) ylabel('Delay (ms)') title('Delays') subplot(212) # M.values is an array of all the recorded values, here transposed to make # it fit with the plot above. imshow(M.values.T, aspect='auto', extent=(0, 100, 40, 0)) xlabel('Neuron number') ylabel('Time (ms)') title('Potential') show() brian-1.4.3/docs_sphinx/examples-misc_if.txt000066400000000000000000000020021266062647100211250ustar00rootroot00000000000000.. currentmodule:: brian .. index:: pair: example usage; plot pair: example usage; run pair: example usage; SpikeGeneratorGroup pair: example usage; show pair: example usage; Connection pair: example usage; linspace pair: example usage; NeuronGroup pair: example usage; StateMonitor .. _example-misc_if: Example: if (misc) ================== A very simple example Brian script to show how to implement an integrate and fire model. In this example, we also drive the single integrate and fire neuron with regularly spaced spikes from the :class:`SpikeGeneratorGroup`. :: from brian import * tau = 10 * ms Vr = -70 * mV Vt = -55 * mV G = NeuronGroup(1, model='V:volt', threshold=Vt, reset=Vr) input = SpikeGeneratorGroup(1, [(0, t * ms) for t in linspace(10, 100, 25)]) C = Connection(input, G) C[0, 0] = 2 * mV M = StateMonitor(G, 'V', record=True) G.V = Vr run(100 * ms) plot(M.times / ms, M[0] / mV) show() brian-1.4.3/docs_sphinx/examples-misc_leaky_if.txt000066400000000000000000000021231266062647100223160ustar00rootroot00000000000000.. currentmodule:: brian .. index:: pair: example usage; plot pair: example usage; run pair: example usage; SpikeGeneratorGroup pair: example usage; show pair: example usage; Connection pair: example usage; linspace pair: example usage; NeuronGroup pair: example usage; StateMonitor .. _example-misc_leaky_if: Example: leaky_if (misc) ======================== A very simple example Brian script to show how to implement a leaky integrate and fire model. In this example, we also drive the single leaky integrate and fire neuron with regularly spaced spikes from the :class:`SpikeGeneratorGroup`. :: from brian import * tau = 10 * ms Vr = -70 * mV Vt = -55 * mV G = NeuronGroup(1, model='dV/dt = -(V-Vr)/tau : volt', threshold=Vt, reset=Vr) spikes = [(0, t*second) for t in linspace(10 * ms, 100 * ms, 25)] input = SpikeGeneratorGroup(1, spikes) C = Connection(input, G) C[0, 0] = 5 * mV M = StateMonitor(G, 'V', record=True) G.V = Vr run(100 * ms) plot(M.times / ms, M[0] / mV) show() brian-1.4.3/docs_sphinx/examples-misc_linked_var.txt000066400000000000000000000036621266062647100226620ustar00rootroot00000000000000.. currentmodule:: brian .. index:: pair: example usage; subplot pair: example usage; plot pair: example usage; run pair: example usage; linked_var pair: example usage; show pair: example usage; linspace pair: example usage; ylabel pair: example usage; MultiStateMonitor pair: example usage; NeuronGroup .. _example-misc_linked_var: Example: linked_var (misc) ========================== Example showing :func:`linked_var`, connecting two different :class:`NeuronGroup` variables. Here we show something like a simplified haircell and auditory nerve fibre model where the hair cells and ANFs are implemented as two separate :class:`NeuronGroup` objects. The hair cells filter their inputs via a differential equation, and then emit graded amounts of neurotransmitter (variable 'y') to the auditory nerve fibres input current (variable 'I'). :: from brian import * N = 5 f = 50 * Hz a_min = 1.0 a_max = 100.0 tau_haircell = 50 * ms tau = 10 * ms duration = 100 * ms eqs_haircells = ''' input = a*sin(2*pi*f*t) : 1 x = clip(input, 0, Inf)**(1.0/3.0) : 1 a : 1 dy/dt = (x-y)/tau_haircell : 1 ''' haircells = NeuronGroup(N, eqs_haircells) haircells.a = linspace(a_min, a_max, N) M_haircells = MultiStateMonitor(haircells, vars=('input', 'y'), record=True) eqs_nervefibres = ''' dV/dt = (I-V)/tau : 1 I : 1 ''' nervefibres = NeuronGroup(N, eqs_nervefibres, reset=0, threshold=1) nervefibres.I = linked_var(haircells, 'y') M_nervefibres = MultiStateMonitor(nervefibres, record=True) run(duration) subplot(221) M_haircells['input'].plot() ylabel('haircell.input') subplot(222) M_haircells['y'].plot() ylabel('haircell.y') subplot(223) M_nervefibres['I'].plot() ylabel('nervefibres.I') subplot(224) M_nervefibres['V'].plot() ylabel('nervefibres.V') show() brian-1.4.3/docs_sphinx/examples-misc_minimalexample.txt000066400000000000000000000022631266062647100235420ustar00rootroot00000000000000.. currentmodule:: brian .. index:: pair: example usage; NeuronGroup pair: example usage; run pair: example usage; firing_rate pair: example usage; show pair: example usage; raster_plot pair: example usage; Connection pair: example usage; SpikeMonitor pair: example usage; CV .. _example-misc_minimalexample: Example: minimalexample (misc) ============================== Very short example program. :: from brian import * eqs = ''' dv/dt = (ge+gi-(v+49*mV))/(20*ms) : volt dge/dt = -ge/(5*ms) : volt dgi/dt = -gi/(10*ms) : volt ''' P = NeuronGroup(4000, model=eqs, threshold= -50 * mV, reset= -60 * mV) P.v = -60 * mV + 10 * mV * rand(len(P)) Pe = P.subgroup(3200) Pi = P.subgroup(800) Ce = Connection(Pe, P, 'ge', weight=1.62 * mV, sparseness=0.02) Ci = Connection(Pi, P, 'gi', weight= -9 * mV, sparseness=0.02) M = SpikeMonitor(P) run(1 * second) i = 0 while len(M[i]) <= 1: i += 1 print "The firing rate of neuron", i, "is", firing_rate(M[i]) * Hz print "The coefficient of variation neuron", i, "is", CV(M[i]) raster_plot(M) show() brian-1.4.3/docs_sphinx/examples-misc_mirollo_strogatz.txt000066400000000000000000000013541266062647100241520ustar00rootroot00000000000000.. currentmodule:: brian .. index:: pair: example usage; NeuronGroup pair: example usage; run pair: example usage; show pair: example usage; raster_plot pair: example usage; Connection pair: example usage; SpikeMonitor .. _example-misc_mirollo_strogatz: Example: mirollo_strogatz (misc) ================================ Mirollo-Strogatz network :: from brian import * tau = 10 * ms v0 = 11 * mV N = 20 w = .1 * mV group = NeuronGroup(N, model='dv/dt=(v0-v)/tau : volt', threshold=10 * mV, reset=0 * mV) W = Connection(group, group, 'v', weight=w) group.v = rand(N) * 10 * mV S = SpikeMonitor(group) run(300 * ms) raster_plot(S) show() brian-1.4.3/docs_sphinx/examples-misc_multipleclocks.txt000066400000000000000000000042601266062647100235710ustar00rootroot00000000000000.. currentmodule:: brian .. index:: pair: example usage; NeuronGroup pair: example usage; run pair: example usage; show pair: example usage; network_operation pair: example usage; Clock pair: example usage; plot pair: example usage; StateMonitor .. _example-misc_multipleclocks: Example: multipleclocks (misc) ============================== This example demonstrates using different clocks for different objects in the network. The clock ``simclock`` is the clock used for the underlying simulation. The clock ``monclock`` is the clock used for monitoring the membrane potential. This monitoring takes place less frequently than the simulation update step to save time and memory. Finally, the clock ``inputclock`` controls when the external 'current' ``Iext`` should be updated. In this case, we update it infrequently so we can see the effect on the network. This example also demonstrates the @network_operation decorator. A function with this decorator will be run as part of the network update step, in sync with the clock provided (or the default one if none is provided). :: from brian import * # define the three clocks simclock = Clock(dt=0.1 * ms) monclock = Clock(dt=0.3 * ms) inputclock = Clock(dt=100 * ms) # simple leaky I&F model with external 'current' Iext as a parameter tau = 10 * ms eqs = ''' dV/dt = (-V+Iext)/tau : volt Iext: volt ''' # A single leaky I&F neuron with simclock as its clock G = NeuronGroup(1, model=eqs, reset=0 * mV, threshold=10 * mV, clock=simclock) G.V = 5 * mV # This function will be run in sync with inputclock i.e. every 100 ms @network_operation(clock=inputclock) def update_Iext(): G.Iext = rand(len(G)) * 20 * mV # V is monitored in sync with monclock MV = StateMonitor(G, 'V', record=0, clock=monclock) # run and plot run(1000 * ms) plot(MV.times / ms, MV[0] / mV) show() # You should see 10 different regions, sometimes Iext will be above threshold # in which case you will see regular spiking at different rates, and sometimes # it will be below threshold in which case you'll see exponential decay to that # value brian-1.4.3/docs_sphinx/examples-misc_named_threshold.txt000066400000000000000000000021761266062647100237030ustar00rootroot00000000000000.. currentmodule:: brian .. index:: pair: example usage; NeuronGroup pair: example usage; run pair: example usage; show pair: example usage; raster_plot pair: example usage; Connection pair: example usage; SpikeMonitor pair: example usage; Refractoriness .. _example-misc_named_threshold: Example: named_threshold (misc) =============================== Example with named threshold and reset variables :: from brian import * eqs = ''' dge/dt = -ge/(5*ms) : volt dgi/dt = -gi/(10*ms) : volt dx/dt = (ge+gi-(x+49*mV))/(20*ms) : volt ''' P = NeuronGroup(4000, model=eqs, threshold='x>-50*mV', \ reset=Refractoriness(-60 * mV, 5 * ms, state='x')) #P=NeuronGroup(4000,model=eqs,threshold=Threshold(-50*mV,state='x'),\ # reset=Reset(-60*mV,state='x')) # without refractoriness P.x = -60 * mV Pe = P.subgroup(3200) Pi = P.subgroup(800) Ce = Connection(Pe, P, 'ge', weight=1.62 * mV, sparseness=0.02) Ci = Connection(Pi, P, 'gi', weight= -9 * mV, sparseness=0.02) M = SpikeMonitor(P) run(1 * second) raster_plot(M) show() brian-1.4.3/docs_sphinx/examples-misc_noisy_ring.txt000066400000000000000000000021741266062647100227210ustar00rootroot00000000000000.. currentmodule:: brian .. index:: pair: example usage; subplot pair: example usage; plot pair: example usage; run pair: example usage; trace pair: example usage; show pair: example usage; raster_plot pair: example usage; Connection pair: example usage; SpikeMonitor pair: example usage; NeuronGroup pair: example usage; StateMonitor .. _example-misc_noisy_ring: Example: noisy_ring (misc) ========================== Integrate-and-fire neurons with noise :: from brian import * tau = 10 * ms sigma = .5 N = 100 J = -1 mu = 2 eqs = """ dv/dt=mu/tau+sigma/tau**.5*xi : 1 """ group = NeuronGroup(N, model=eqs, threshold=1, reset=0) C = Connection(group, group, 'v') for i in range(N): C[i, (i + 1) % N] = J #C.connect_full(group,group,weight=J) #for i in range(N): # C[i,i]=0 S = SpikeMonitor(group) trace = StateMonitor(group, 'v', record=True) run(500 * ms) i, t = S.spikes[-1] subplot(211) raster_plot(S) subplot(212) plot(trace.times / ms, trace[0]) show() brian-1.4.3/docs_sphinx/examples-misc_non_reliability.txt000066400000000000000000000014531266062647100237230ustar00rootroot00000000000000.. currentmodule:: brian .. index:: pair: example usage; raster_plot pair: example usage; NeuronGroup pair: example usage; run pair: example usage; SpikeMonitor pair: example usage; show .. _example-misc_non_reliability: Example: non_reliability (misc) =============================== Reliability of spike timing. See e.g. Mainen & Sejnowski (1995) for experimental results in vitro. Here: a constant current is injected in all trials. R. Brette :: from brian import * N = 25 tau = 20 * ms sigma = .015 eqs_neurons = ''' dx/dt=(1.1-x)/tau+sigma*(2./tau)**.5*xi:1 ''' neurons = NeuronGroup(N, model=eqs_neurons, threshold=1, reset=0, refractory=5 * ms) spikes = SpikeMonitor(neurons) run(500 * ms) raster_plot(spikes) show() brian-1.4.3/docs_sphinx/examples-misc_phase_locking.txt000066400000000000000000000020521266062647100233420ustar00rootroot00000000000000.. currentmodule:: brian .. index:: pair: example usage; subplot pair: example usage; plot pair: example usage; run pair: example usage; trace pair: example usage; show pair: example usage; raster_plot pair: example usage; SpikeMonitor pair: example usage; linspace pair: example usage; NeuronGroup pair: example usage; StateMonitor .. _example-misc_phase_locking: Example: phase_locking (misc) ============================= Phase locking of IF neurons to a periodic input :: from brian import * tau = 20 * ms N = 100 b = 1.2 # constant current mean, the modulation varies f = 10 * Hz eqs = ''' dv/dt=(-v+a*sin(2*pi*f*t)+b)/tau : 1 a : 1 ''' neurons = NeuronGroup(N, model=eqs, threshold=1, reset=0) neurons.v = rand(N) neurons.a = linspace(.05, 0.75, N) S = SpikeMonitor(neurons) trace = StateMonitor(neurons, 'v', record=50) run(1000 * ms) subplot(211) raster_plot(S) subplot(212) plot(trace.times / ms, trace[50]) show() brian-1.4.3/docs_sphinx/examples-misc_poisson.txt000066400000000000000000000024651266062647100222360ustar00rootroot00000000000000.. currentmodule:: brian .. index:: pair: example usage; raster_plot pair: example usage; SpikeMonitor pair: example usage; PoissonGroup pair: example usage; run pair: example usage; show .. _example-misc_poisson: Example: poisson (misc) ======================= This example demonstrates the PoissonGroup object. Here we have used a custom function to generate different rates at different times. This example also demonstrates a custom SpikeMonitor. :: #import brian_no_units # uncomment to run faster from brian import * # Rates r1 = arange(101, 201) * 0.1 * Hz r2 = arange(1, 101) * 0.1 * Hz def myrates(t): if t < 10 * second: return r1 else: return r2 # More compact: myrates=lambda t: (t<10*second and r1) or r2 # Neuron group P = PoissonGroup(100, myrates) # Calculation of rates ns = zeros(len(P)) def ratemonitor(spikes): ns[spikes] += 1 Mf = SpikeMonitor(P, function=ratemonitor) M = SpikeMonitor(P) # Simulation and plotting run(10 * second) print "Rates after 10s:" print ns / (10 * second) ns[:] = 0 run(10 * second) print "Rates after 20s:" print ns / (10 * second) raster_plot() show() brian-1.4.3/docs_sphinx/examples-misc_poissongroup.txt000066400000000000000000000017571266062647100233160ustar00rootroot00000000000000.. currentmodule:: brian .. index:: pair: example usage; subplot pair: example usage; plot pair: example usage; run pair: example usage; show pair: example usage; Connection pair: example usage; PoissonGroup pair: example usage; SpikeMonitor pair: example usage; NeuronGroup pair: example usage; StateMonitor .. _example-misc_poissongroup: Example: poissongroup (misc) ============================ Poisson input to an IF model :: from brian import * PG = PoissonGroup(1, lambda t:200 * Hz * (1 + cos(2 * pi * t * 50 * Hz))) IF = NeuronGroup(1, model='dv/dt=-v/(10*ms) : volt', reset=0 * volt, threshold=10 * mV) C = Connection(PG, IF, 'v', weight=3 * mV) MS = SpikeMonitor(PG, True) Mv = StateMonitor(IF, 'v', record=True) rates = StateMonitor(PG, 'rate', record=True) run(100 * ms) subplot(211) plot(rates.times / ms, rates[0] / Hz) subplot(212) plot(Mv.times / ms, Mv[0] / mV) show() brian-1.4.3/docs_sphinx/examples-misc_pulsepacket.txt000066400000000000000000000027711266062647100230640ustar00rootroot00000000000000.. currentmodule:: brian .. index:: pair: example usage; subplot pair: example usage; plot pair: example usage; run pair: example usage; SpikeGeneratorGroup pair: example usage; PopulationRateMonitor pair: example usage; show pair: example usage; raster_plot pair: example usage; rate pair: example usage; PulsePacket pair: example usage; SpikeMonitor .. _example-misc_pulsepacket: Example: pulsepacket (misc) =========================== This example basically replicates what the Brian PulsePacket object does, and then compares to that object. :: from brian import * from random import gauss, shuffle # Generator for pulse packet def pulse_packet(t, n, sigma): # generate a list of n times with Gaussian distribution, sort them in time, and # then randomly assign the neuron numbers to them times = [gauss(t, sigma) for i in range(n)] times.sort() neuron = range(n) shuffle(neuron) return zip(neuron, times) # returns a list of pairs (i,t) G1 = SpikeGeneratorGroup(1000, pulse_packet(50 * ms, 1000, 5 * ms)) M1 = SpikeMonitor(G1) PRM1 = PopulationRateMonitor(G1, bin=1 * ms) G2 = PulsePacket(50 * ms, 1000, 5 * ms) M2 = SpikeMonitor(G2) PRM2 = PopulationRateMonitor(G2, bin=1 * ms) run(100 * ms) subplot(221) raster_plot(M1) subplot(223) plot(PRM1.rate) subplot(222) raster_plot(M2) subplot(224) plot(PRM2.rate) show() brian-1.4.3/docs_sphinx/examples-misc_rate_model.txt000066400000000000000000000013221266062647100226460ustar00rootroot00000000000000.. currentmodule:: brian .. index:: pair: example usage; NeuronGroup pair: example usage; run pair: example usage; PopulationRateMonitor pair: example usage; plot pair: example usage; PoissonThreshold pair: example usage; show pair: example usage; rate .. _example-misc_rate_model: Example: rate_model (misc) ========================== A rate model :: from brian import * N = 50000 tau = 20 * ms I = 10 * Hz eqs = ''' dv/dt=(I-v)/tau : Hz # note the unit here: this is the output rate ''' group = NeuronGroup(N, eqs, threshold=PoissonThreshold()) S = PopulationRateMonitor(group, bin=1 * ms) run(100 * ms) plot(S.rate) show() brian-1.4.3/docs_sphinx/examples-misc_realtime_plotting.txt000066400000000000000000000033551266062647100242650ustar00rootroot00000000000000.. currentmodule:: brian .. index:: pair: example usage; subplot pair: example usage; plot pair: example usage; run pair: example usage; trace pair: example usage; show pair: example usage; raster_plot pair: example usage; ioff pair: example usage; ion pair: example usage; Connection pair: example usage; RecentStateMonitor pair: example usage; SpikeMonitor pair: example usage; NeuronGroup .. _example-misc_realtime_plotting: Example: realtime_plotting (misc) ================================= Realtime plotting example :: # These lines are necessary for interactive plotting when launching from the # Eclipse IDE, they may not be necessary in every environment. import matplotlib matplotlib.use('WXAgg') # You may need to experiment, try WXAgg, GTKAgg, QTAgg, TkAgg from brian import * ###### Set up the standard CUBA example ###### N = 4000 eqs = ''' dv/dt = (ge+gi-(v+49*mV))/(20*ms) : volt dge/dt = -ge/(5*ms) : volt dgi/dt = -gi/(10*ms) : volt ''' P = NeuronGroup(N, eqs, threshold= -50 * mV, reset= -60 * mV) P.v = -60 * mV + 10 * mV * rand(len(P)) Pe = P.subgroup(3200) Pi = P.subgroup(800) Ce = Connection(Pe, P, 'ge', weight=1.62 * mV, sparseness=0.02) Ci = Connection(Pi, P, 'gi', weight= -9 * mV, sparseness=0.02) M = SpikeMonitor(P) trace = RecentStateMonitor(P, 'v', record=range(5), duration=200 * ms) ion() subplot(211) raster_plot(M, refresh=10 * ms, showlast=200 * ms, redraw=False) subplot(212) trace.plot(refresh=10 * ms, showlast=200 * ms) run(1 * second) ioff() # switch interactive mode off show() # and wait for user to close the window before shutting down brian-1.4.3/docs_sphinx/examples-misc_reliability.txt000066400000000000000000000021001266062647100230370ustar00rootroot00000000000000.. currentmodule:: brian .. index:: pair: example usage; NeuronGroup pair: example usage; run pair: example usage; show pair: example usage; raster_plot pair: example usage; linked_var pair: example usage; SpikeMonitor .. _example-misc_reliability: Example: reliability (misc) =========================== Reliability of spike timing. See e.g. Mainen & Sejnowski (1995) for experimental results in vitro. R. Brette :: from brian import * # The common noisy input N = 25 tau_input = 5 * ms input = NeuronGroup(1, model='dx/dt=-x/tau_input+(2./tau_input)**.5*xi:1') # The noisy neurons receiving the same input tau = 10 * ms sigma = .015 eqs_neurons = ''' dx/dt=(0.9+.5*I-x)/tau+sigma*(2./tau)**.5*xi:1 I : 1 ''' neurons = NeuronGroup(N, model=eqs_neurons, threshold=1, reset=0, refractory=5 * ms) neurons.x = rand(N) neurons.I = linked_var(input,'x') # input.x is continuously fed into neurons.I spikes = SpikeMonitor(neurons) run(500 * ms) raster_plot(spikes) show() brian-1.4.3/docs_sphinx/examples-misc_remotecontrolclient.txt000066400000000000000000000015261266062647100246340ustar00rootroot00000000000000.. currentmodule:: brian .. index:: pair: example usage; subplot pair: example usage; plot pair: example usage; RemoteControlClient pair: example usage; stop pair: example usage; show .. _example-misc_remotecontrolclient: Example: remotecontrolclient (misc) =================================== Example of using :class:`RemoteControlServer` and :class:`RemoteControlClient` to control a simulation as it runs in Brian. Run the script remotecontrolserver.py before running this. :: from brian import * import time client = RemoteControlClient() time.sleep(1) subplot(121) plot(*client.evaluate('(M.times, M.values)')) client.execute('G.I = 1.1') time.sleep(1) subplot(122) plot(*client.evaluate('(M.times, M.values)')) client.stop() show() brian-1.4.3/docs_sphinx/examples-misc_remotecontrolserver.txt000066400000000000000000000015331266062647100246620ustar00rootroot00000000000000.. currentmodule:: brian .. index:: pair: example usage; RecentStateMonitor pair: example usage; NeuronGroup pair: example usage; RemoteControlServer pair: example usage; run .. _example-misc_remotecontrolserver: Example: remotecontrolserver (misc) =================================== Example of using :class:`RemoteControlServer` and :class:`RemoteControlClient` to control a simulation as it runs in Brian. After running this script, run remotecontrolclient.py or paste the code from that script into an IPython shell for interactive control. :: from brian import * eqs = ''' dV/dt = (I-V)/(10*ms)+0.1*xi*(2/(10*ms))**.5 : 1 I : 1 ''' G = NeuronGroup(3, eqs, reset=0, threshold=1) M = RecentStateMonitor(G, 'V', duration=50*ms) server = RemoteControlServer() run(1e10*second) brian-1.4.3/docs_sphinx/examples-misc_ring.txt000066400000000000000000000013721266062647100214770ustar00rootroot00000000000000.. currentmodule:: brian .. index:: pair: example usage; NeuronGroup pair: example usage; run pair: example usage; show pair: example usage; raster_plot pair: example usage; Connection pair: example usage; SpikeMonitor .. _example-misc_ring: Example: ring (misc) ==================== A ring of integrate-and-fire neurons. :: from brian import * tau = 10 * ms v0 = 11 * mV N = 20 w = 1 * mV ring = NeuronGroup(N, model='dv/dt=(v0-v)/tau : volt', threshold=10 * mV, reset=0 * mV) W = Connection(ring, ring, 'v') for i in range(N): W[i, (i + 1) % N] = w ring.v = rand(N) * 10 * mV S = SpikeMonitor(ring) run(300 * ms) raster_plot(S) show() brian-1.4.3/docs_sphinx/examples-misc_spike_triggered_average.txt000066400000000000000000000036371266062647100254070ustar00rootroot00000000000000.. currentmodule:: brian .. index:: pair: example usage; plot pair: example usage; run pair: example usage; figure pair: example usage; show pair: example usage; spike_triggered_average pair: example usage; PoissonGroup pair: example usage; SpikeMonitor pair: example usage; linspace pair: example usage; exp pair: example usage; ylabel pair: example usage; convolve pair: example usage; xlabel pair: example usage; legend pair: example usage; norm .. _example-misc_spike_triggered_average: Example: spike_triggered_average (misc) ======================================= Example of the use of the function spike_triggered_average. A white noise is filtered by a gaussian filter (low pass filter) which output is used to generate spikes (poission process) Those spikes are used in conjunction with the input signal to retrieve the filter function. :: from brian import * from brian.hears import * from numpy.random import randn from numpy.linalg import norm from matplotlib import pyplot dt = 0.1*ms defaultclock.dt = dt stimulus_duration = 15000*ms stimulus = randn(int(stimulus_duration/ dt)) #filter n=200 filt = exp(-((linspace(0.5,n,n))-(n+5)/2)**2/(n/3)); filt = filt/norm(filt)*1000; filtered_stimulus = convolve(stimulus,filt) neuron = PoissonGroup(1,lambda t:filtered_stimulus[int(t/dt)]) spikes = SpikeMonitor(neuron) run(stimulus_duration,report='text') spikes = spikes[0] #resulting spikes max_interval = 20*ms #window duration of the spike triggered average onset = 10*ms sta,time_axis = spike_triggered_average(spikes,stimulus,max_interval,dt,onset=onset,display=True) figure() plot(time_axis,filt/max(filt)) plot(time_axis,sta/max(sta)) xlabel('time axis') ylabel('sta') legend(('real filter','estimated filter')) show() brian-1.4.3/docs_sphinx/examples-misc_spikes_io.txt000066400000000000000000000031751266062647100225300ustar00rootroot00000000000000.. currentmodule:: brian .. index:: pair: example usage; run pair: example usage; SpikeGeneratorGroup pair: example usage; show pair: example usage; raster_plot pair: example usage; clear pair: example usage; SpikeMonitor pair: example usage; PoissonGroup pair: example usage; reinit_default_clock pair: example usage; AERSpikeMonitor pair: example usage; close pair: example usage; load_aer .. _example-misc_spikes_io: Example: spikes_io (misc) ========================= This script demonstrates how to save/load spikes in AER format from inside Brian. :: from brian import * ####################### SAVING ######################### # First we need to generate some spikes N = 1000 g = PoissonGroup(N, 200*Hz) # And set up a monitor to record those spikes to the disk Maer = AERSpikeMonitor(g, './dummy.aedat') # Now we can run run(100*ms) # This line executed automatically when the script ends, but here we # need to close the file because we re-use it from within the same script Maer.close() clear(all = True) reinit_default_clock() ####################### LOADING ######################## # Now we can re-load the spikes addr, timestamps = load_aer('./dummy.aedat') # Feed them to a SpikeGeneratorGroup group = SpikeGeneratorGroup(N, (addr, timestamps)) # The group can now be used as any other, here we choose to monitor # the spikes newM = SpikeMonitor(group, record = True) run(100*ms) # And plot the result raster_plot(newM) show() brian-1.4.3/docs_sphinx/examples-misc_stim2d.txt000066400000000000000000000044701266062647100217440ustar00rootroot00000000000000.. currentmodule:: brian .. index:: pair: example usage; subplot pair: example usage; run pair: example usage; show pair: example usage; raster_plot pair: example usage; imshow pair: example usage; PoissonGroup pair: example usage; SpikeMonitor pair: example usage; bar pair: example usage; axis .. _example-misc_stim2d: Example: stim2d (misc) ====================== Example of a 2D stimulus, see the `complete description `__ at the Brian Cookbook. :: from brian import * import scipy.ndimage as im __all__ = ['bar', 'StimulusArrayGroup'] def bar(width, height, thickness, angle): ''' An array of given dimensions with a bar of given thickness and angle ''' stimulus = zeros((width, height)) stimulus[:, int(height / 2. - thickness / 2.):int(height / 2. + thickness / 2.)] = 1. stimulus = im.rotate(stimulus, angle, reshape=False) return stimulus class StimulusArrayGroup(PoissonGroup): ''' A group of neurons which fire with a given stimulus at a given rate The argument ``stimulus`` should be a 2D array with values between 0 and 1. The point in the stimulus array at position (y,x) will correspond to the neuron with index i=y*width+x. This neuron will fire Poisson spikes at ``rate*stimulus[y,x]`` Hz. The stimulus will start at time ``onset`` for ``duration``. ''' def __init__(self, stimulus, rate, onset, duration): height, width = stimulus.shape stim = stimulus.ravel()*rate self.stimulus = stim def stimfunc(t): if onset < t < (onset + duration): return stim else: return 0. * Hz PoissonGroup.__init__(self, width * height, stimfunc) if __name__ == '__main__': import pylab subplot(121) stim = bar(100, 100, 10, 90) * 0.9 + 0.1 pylab.imshow(stim, origin='lower') pylab.gray() G = StimulusArrayGroup(stim, 50 * Hz, 100 * ms, 100 * ms) M = SpikeMonitor(G) run(300 * ms) subplot(122) raster_plot(M) axis(xmin=0, xmax=300) show() brian-1.4.3/docs_sphinx/examples-misc_stopping.txt000066400000000000000000000034261266062647100224050ustar00rootroot00000000000000.. currentmodule:: brian .. index:: pair: example usage; NeuronGroup pair: example usage; run pair: example usage; PopulationRateMonitor pair: example usage; Clock pair: example usage; network_operation pair: example usage; Connection pair: example usage; PoissonGroup .. _example-misc_stopping: Example: stopping (misc) ======================== Network to demonstrate stopping a simulation during a run Have a fully connected network of integrate and fire neurons with input fed by a group of Poisson neurons with a steadily increasing rate, want to determine the point in time at which the network of integrate and fire neurons switches from no firing to all neurons firing, so we have a network_operation called stop_condition that calls the stop() function if the monitored network firing rate is above a minimum threshold. :: from brian import * clk = Clock() Vr = 0 * mV El = 0 * mV Vt = 10 * mV tau = 10 * ms weight = 0.2 * mV duration = 100 * msecond max_input_rate = 10000 * Hz num_input_neurons = 1000 input_connection_p = 0.1 rate_per_neuron = max_input_rate / (num_input_neurons * input_connection_p) P = PoissonGroup(num_input_neurons, lambda t: rate_per_neuron * (t / duration)) G = NeuronGroup(1000, model='dV/dt=-(V-El)/tau : volt', threshold=Vt, reset=Vr) G.V = Vr + (Vt - Vr) * rand(len(G)) CPG = Connection(P, G, weight=weight, sparseness=input_connection_p) CGG = Connection(G, G, weight=weight) MP = PopulationRateMonitor(G, bin=1 * ms) @network_operation def stop_condition(): if MP.rate[-1] * Hz > 10 * Hz: stop() run(duration) print "Reached population rate>10 Hz by time", clk.t, "+/- 1 ms." brian-1.4.3/docs_sphinx/examples-misc_timed_array.txt000066400000000000000000000024351266062647100230410ustar00rootroot00000000000000.. currentmodule:: brian .. index:: pair: example usage; subplot pair: example usage; plot pair: example usage; run pair: example usage; TimedArray pair: example usage; clip pair: example usage; show pair: example usage; linspace pair: example usage; ylabel pair: example usage; MultiStateMonitor pair: example usage; NeuronGroup pair: example usage; sin .. _example-misc_timed_array: Example: timed_array (misc) =========================== An example of the :class:`TimedArray` class used for applying input currents to neurons. :: from brian import * N = 5 duration = 100 * ms Vr = -60 * mV Vt = -50 * mV tau = 10 * ms Rmin = 1 * Mohm Rmax = 10 * Mohm freq = 50 * Hz k = 10 * nA eqs = ''' dV/dt = (-(V-Vr)+R*I)/tau : volt R : ohm I : amp ''' G = NeuronGroup(N, eqs, reset='V=Vr', threshold='V>Vt') G.R = linspace(Rmin, Rmax, N) t = linspace(0 * second, duration, int(duration / defaultclock.dt)) I = clip(k * sin(2 * pi * freq * t), 0, Inf) G.I = TimedArray(I) M = MultiStateMonitor(G, record=True) run(duration) subplot(211) M['I'].plot() ylabel('I (amp)') subplot(212) M['V'].plot() ylabel('V (volt)') show() brian-1.4.3/docs_sphinx/examples-misc_topographic_map.txt000066400000000000000000000033731266062647100237170ustar00rootroot00000000000000.. currentmodule:: brian .. index:: pair: example usage; subplot pair: example usage; NeuronGroup pair: example usage; imshow pair: example usage; run pair: example usage; title pair: example usage; raster_plot pair: example usage; show pair: example usage; Connection pair: example usage; PoissonGroup pair: example usage; ones pair: example usage; SpikeMonitor .. _example-misc_topographic_map: Example: topographic_map (misc) =============================== Topographic map - an example of complicated connections. Two layers of neurons. The first layer is connected randomly to the second one in a topographical way. The second layer has random lateral connections. :: from brian import * N = 100 tau = 10 * ms tau_e = 2 * ms # AMPA synapse eqs = ''' dv/dt=(I-v)/tau : volt dI/dt=-I/tau_e : volt ''' rates = zeros(N) * Hz rates[N / 2 - 10:N / 2 + 10] = ones(20) * 30 * Hz layer1 = PoissonGroup(N, rates=rates) layer2 = NeuronGroup(N, model=eqs, threshold=10 * mV, reset=0 * mV) topomap = lambda i, j:exp(-abs(i - j) * .1) * 3 * mV feedforward = Connection(layer1, layer2, sparseness=.5, weight=topomap) #feedforward[2,3]=1*mV lateralmap = lambda i, j:exp(-abs(i - j) * .05) * 0.5 * mV recurrent = Connection(layer2, layer2, sparseness=.5, weight=lateralmap) spikes = SpikeMonitor(layer2) run(1 * second) subplot(211) raster_plot(spikes) subplot(223) imshow(feedforward.W.todense(), interpolation='nearest', origin='lower') title('Feedforward connection strengths') subplot(224) imshow(recurrent.W.todense(), interpolation='nearest', origin='lower') title('Recurrent connection strengths') show() brian-1.4.3/docs_sphinx/examples-misc_topographic_map2.txt000066400000000000000000000040731266062647100237770ustar00rootroot00000000000000.. currentmodule:: brian .. index:: pair: example usage; subplot pair: example usage; NeuronGroup pair: example usage; imshow pair: example usage; run pair: example usage; show pair: example usage; raster_plot pair: example usage; title pair: example usage; Connection pair: example usage; PoissonGroup pair: example usage; ones pair: example usage; SpikeMonitor pair: example usage; linspace .. _example-misc_topographic_map2: Example: topographic_map2 (misc) ================================ Topographic map - an example of complicated connections. Two layers of neurons. The first layer is connected randomly to the second one in a topographical way. The second layer has random lateral connections. Each neuron has a position x[i]. :: from brian import * N = 100 tau = 10 * ms tau_e = 2 * ms # AMPA synapse eqs = ''' dv/dt=(I-v)/tau : volt dI/dt=-I/tau_e : volt ''' rates = zeros(N) * Hz rates[N / 2 - 10:N / 2 + 10] = ones(20) * 30 * Hz layer1 = PoissonGroup(N, rates=rates) layer1.x = linspace(0., 1., len(layer1)) # abstract position between 0 and 1 layer2 = NeuronGroup(N, model=eqs, threshold=10 * mV, reset=0 * mV) layer2.x = linspace(0., 1., len(layer2)) # Generic connectivity function topomap = lambda i, j, x, y, sigma: exp(-abs(x[i] - y[j]) / sigma) feedforward = Connection(layer1, layer2, sparseness=.5, weight=lambda i, j:topomap(i, j, layer1.x, layer2.x, .3) * 3 * mV) recurrent = Connection(layer2, layer2, sparseness=.5, weight=lambda i, j:topomap(i, j, layer1.x, layer2.x, .2) * .5 * mV) spikes = SpikeMonitor(layer2) run(1 * second) subplot(211) raster_plot(spikes) subplot(223) imshow(feedforward.W.todense(), interpolation='nearest', origin='lower') title('Feedforward connection strengths') subplot(224) imshow(recurrent.W.todense(), interpolation='nearest', origin='lower') title('Recurrent connection strengths') show() brian-1.4.3/docs_sphinx/examples-misc_transient_sync.txt000066400000000000000000000024771266062647100236120ustar00rootroot00000000000000.. currentmodule:: brian .. index:: pair: example usage; subplot pair: example usage; plot pair: example usage; run pair: example usage; PopulationRateMonitor pair: example usage; show pair: example usage; raster_plot pair: example usage; imshow pair: example usage; Connection pair: example usage; SpikeMonitor pair: example usage; title pair: example usage; NeuronGroup .. _example-misc_transient_sync: Example: transient_sync (misc) ============================== Transient synchronisation in a population of noisy IF neurons with distance-dependent synaptic weights (organised as a ring) :: from brian import * tau = 10 * ms N = 100 v0 = 5 * mV sigma = 4 * mV group = NeuronGroup(N, model='dv/dt=(v0-v)/tau + sigma*xi/tau**.5 : volt', \ threshold=10 * mV, reset=0 * mV) C = Connection(group, group, 'v', weight=lambda i, j:.4 * mV * cos(2. * pi * (i - j) * 1. / N)) S = SpikeMonitor(group) R = PopulationRateMonitor(group) group.v = rand(N) * 10 * mV run(5000 * ms) subplot(211) raster_plot(S) subplot(223) imshow(C.W.todense(), interpolation='nearest') title('Synaptic connections') subplot(224) plot(R.times / ms, R.smooth_rate(2 * ms, filter='flat')) title('Firing rate') show() brian-1.4.3/docs_sphinx/examples-misc_two_neurons.txt000066400000000000000000000017321266062647100231220ustar00rootroot00000000000000.. currentmodule:: brian .. index:: pair: example usage; NeuronGroup pair: example usage; run pair: example usage; plot pair: example usage; PopulationSpikeCounter pair: example usage; show pair: example usage; Connection pair: example usage; StateMonitor .. _example-misc_two_neurons: Example: two_neurons (misc) =========================== Two connected neurons with delays :: from brian import * tau = 10 * ms w = -1 * mV v0 = 11 * mV neurons = NeuronGroup(2, model='dv/dt=(v0-v)/tau : volt', threshold=10 * mV, reset=0 * mV, \ max_delay=5 * ms) neurons.v = rand(2) * 10 * mV W = Connection(neurons, neurons, 'v', delay=2 * ms) W[0, 1] = w W[1, 0] = w S = StateMonitor(neurons, 'v', record=True) #mymonitor=SpikeMonitor(neurons[0]) mymonitor = PopulationSpikeCounter(neurons) run(500 * ms) plot(S.times / ms, S[0] / mV) plot(S.times / ms, S[1] / mV) show() brian-1.4.3/docs_sphinx/examples-misc_using_classes.txt000066400000000000000000000043601266062647100234020ustar00rootroot00000000000000.. currentmodule:: brian .. index:: pair: example usage; subplot pair: example usage; NeuronGroup pair: example usage; run pair: example usage; trace pair: example usage; plot pair: example usage; raster_plot pair: example usage; title pair: example usage; SpikeMonitor pair: example usage; show pair: example usage; StateMonitor .. _example-misc_using_classes: Example: using_classes (misc) ============================= Example of using derived classes in Brian Using a class derived from one of Brian's classes can be a useful way of organising code in complicated simulations. A class such as a :class:`NeuronGroup` can itself create further :class:`NeuronGroup`, :class:`Connection` and :class:`NetworkOperation` objects. In order to have these objects included in the simulation, the derived class has to include them in its ``contained_objects`` list (this tells Brian to add these to the :class:`Network` when the derived class object is added to the network). :: from brian import * class PoissonDrivenGroup(NeuronGroup): ''' This class is a group of leaky integrate-and-fire neurons driven by external Poisson inputs. The class creates the Poisson inputs and connects them to itself. ''' def __init__(self, N, rate, weight): tau = 10 * ms eqs = ''' dV/dt = -V/tau : 1 ''' # It's essential to call the initialiser of the base class super(PoissonDrivenGroup, self).__init__(N, eqs, reset=0, threshold=1) self.poisson_group = PoissonGroup(N, rate) self.conn = Connection(self.poisson_group, self, 'V') self.conn.connect_one_to_one(weight=weight) self.contained_objects += [self.poisson_group, self.conn] G = PoissonDrivenGroup(100, 100 * Hz, .3) M = SpikeMonitor(G) M_pg = SpikeMonitor(G.poisson_group) trace = StateMonitor(G, 'V', record=0) run(1 * second) subplot(311) raster_plot(M_pg) title('Input spikes') subplot(312) raster_plot(M) title('Output spikes') subplot(313) plot(trace.times, trace[0]) title('Sample trace') show() brian-1.4.3/docs_sphinx/examples-misc_van_rossum_metric.txt000066400000000000000000000035111266062647100242740ustar00rootroot00000000000000.. currentmodule:: brian .. index:: pair: example usage; NeuronGroup pair: example usage; run pair: example usage; trace pair: example usage; figure pair: example usage; show pair: example usage; raster_plot pair: example usage; imshow pair: example usage; colorbar pair: example usage; SpikeMonitor pair: example usage; linspace pair: example usage; title pair: example usage; VanRossumMetric pair: example usage; StateMonitor .. _example-misc_van_rossum_metric: Example: van_rossum_metric (misc) ================================= Example of how to use the van Rossum metric. The VanRossumMetric function, which is defined as a monitor and therefore works online, computes the metric between every neuron in a given population. The present example show the concept of phase locking: N neurons are driven by sinusoidal inputs with different amplitude. Use: output=VanRossumMetric(source, tau=4 * ms) source is a NeuronGroup of N neurons tau is the time constant of the kernel used in the metric output is a monitor with attribute distance which is the distance matrix between the neurons in source :: from brian import * from time import time tau=20*ms N=100 b=1.2 # constant current mean, the modulation varies f=10*Hz delta =2*ms eqs=''' dv/dt=(-v+a*sin(2*pi*f*t)+b)/tau : 1 a : 1 ''' neurons=NeuronGroup(N,model=eqs,threshold=1,reset=0) neurons.v=rand(N) neurons.a=linspace(.05,0.75,N) S=SpikeMonitor(neurons) trace=StateMonitor(neurons,'v',record=50) van_rossum_metric=VanRossumMetric(neurons, tau=4 * ms) run(1000*ms) raster_plot(S) title('Raster plot') figure() title('Distance matrix between spike trains') imshow(van_rossum_metric.distance) colorbar() show() brian-1.4.3/docs_sphinx/examples-modelfitting_modelfitting.txt000066400000000000000000000024361266062647100247610ustar00rootroot00000000000000.. currentmodule:: brian .. index:: pair: example usage; loadtxt pair: example usage; Equations .. _example-modelfitting_modelfitting: Example: modelfitting (modelfitting) ==================================== Model fitting example. Fit an integrate-and-fire model to an in-vitro electrophysiological recording during one second. :: from brian import loadtxt, ms, Equations from brian.library.modelfitting import * if __name__ == '__main__': equations = Equations(''' dV/dt=(R*I-V)/tau : 1 I : 1 R : 1 tau : second ''') input = loadtxt('current.txt') spikes = loadtxt('spikes.txt') results = modelfitting( model = equations, reset = 0, threshold = 1, data = spikes, input = input, dt = .1*ms, popsize = 1000, maxiter = 3, delta = 4*ms, R = [1.0e9, 9.0e9], tau = [10*ms, 40*ms], refractory = [0*ms, 10*ms]) print_table(results) brian-1.4.3/docs_sphinx/examples-modelfitting_modelfitting_groups.txt000066400000000000000000000027221266062647100263560ustar00rootroot00000000000000.. currentmodule:: brian .. index:: pair: example usage; loadtxt pair: example usage; Equations .. _example-modelfitting_modelfitting_groups: Example: modelfitting_groups (modelfitting) =========================================== Example showing how to fit a single model with different target spike trains (several groups). :: from brian import loadtxt, ms, Equations, second from brian.library.modelfitting import * if __name__ == '__main__': model = Equations(''' dV/dt=(R*I-V)/tau : 1 I : 1 R : 1 tau : second ''') input = loadtxt('current.txt') spikes0 = loadtxt('spikes.txt') spikes = [] for i in xrange(2): spikes.extend([(i, spike*second + 5*i*ms) for spike in spikes0]) results = modelfitting( model = model, reset = 0, threshold = 1, data = spikes, input = input, dt = .1*ms, popsize = 1000, maxiter = 3, cpu = 1, delta = 4*ms, R = [1.0e9, 9.0e9], tau = [10*ms, 40*ms], delays = [-10*ms, 10*ms]) print_table(results) brian-1.4.3/docs_sphinx/examples-modelfitting_modelfitting_machines.txt000066400000000000000000000031041266062647100266210ustar00rootroot00000000000000.. currentmodule:: brian .. index:: pair: example usage; loadtxt pair: example usage; Equations .. _example-modelfitting_modelfitting_machines: Example: modelfitting_machines (modelfitting) ============================================= Model fitting example using several machines. Before running this example, you must start the Playdoh server on the remote machines. :: from brian import loadtxt, ms, Equations from brian.library.modelfitting import * if __name__ == '__main__': # List of machines IP addresses machines = ['bobs-machine.university.com', 'jims-machine.university.com'] equations = Equations(''' dV/dt=(R*I-V)/tau : 1 I : 1 R : 1 tau : second ''') input = loadtxt('current.txt') spikes = loadtxt('spikes.txt') results = modelfitting( model = equations, reset = 0, threshold = 1, data = spikes, input = input, dt = .1*ms, popsize = 1000, maxiter = 3, delta = 4*ms, unit_type = 'CPU', machines = machines, R = [1.0e9, 9.0e9], tau = [10*ms, 40*ms], refractory = [0*ms, 10*ms]) print_table(results) brian-1.4.3/docs_sphinx/examples-multiprocessing_multiple_runs_simple.txt000066400000000000000000000043571266062647100273150ustar00rootroot00000000000000.. currentmodule:: brian .. index:: pair: example usage; plot pair: example usage; linspace pair: example usage; show .. _example-multiprocessing_multiple_runs_simple: Example: multiple_runs_simple (multiprocessing) =============================================== Example of using Python multiprocessing module to distribute simulations over multiple processors. The general procedure for using multiprocessing is to define and run a network inside a function, and then use multiprocessing.Pool.map to call the function with multiple parameter values. Note that on Windows, any code that should only run once should be placed inside an if __name__=='__main__' block. :: from brian import * import multiprocessing # This is the function that we want to compute for various different parameters def how_many_spikes(excitatory_weight): # These two lines reset the clock to 0 and clear any remaining data so that # memory use doesn't build up over multiple runs. reinit_default_clock() clear(True) eqs = ''' dv/dt = (ge+gi-(v+49*mV))/(20*ms) : volt dge/dt = -ge/(5*ms) : volt dgi/dt = -gi/(10*ms) : volt ''' P = NeuronGroup(4000, eqs, threshold= -50 * mV, reset= -60 * mV) P.v = -60 * mV + 10 * mV * rand(len(P)) Pe = P.subgroup(3200) Pi = P.subgroup(800) Ce = Connection(Pe, P, 'ge') Ci = Connection(Pi, P, 'gi') Ce.connect_random(Pe, P, 0.02, weight=excitatory_weight) Ci.connect_random(Pi, P, 0.02, weight= -9 * mV) M = SpikeMonitor(P) run(100 * ms) return M.nspikes if __name__ == '__main__': # Note that on Windows platforms, all code that is executed rather than # just defining functions and classes has to be in the if __name__=='__main__' # block, otherwise it will be executed by each process that starts. This # isn't a problem on Linux. pool = multiprocessing.Pool() # uses num_cpu processes by default weights = linspace(0, 3.5, 100) * mV args = [w * volt for w in weights] results = pool.map(how_many_spikes, args) # launches multiple processes plot(weights, results, '.') show() brian-1.4.3/docs_sphinx/examples-multiprocessing_multiple_runs_with_gui.txt000066400000000000000000000213021266062647100276300ustar00rootroot00000000000000.. currentmodule:: brian .. index:: pair: example usage; ion .. _example-multiprocessing_multiple_runs_with_gui: Example: multiple_runs_with_gui (multiprocessing) ================================================= A complicated example of using multiprocessing for multiple runs of a simulation with different parameters, using a GUI to monitor and control the runs. This example features: * An indefinite number of runs, with a set of parameters for each run generated at random for each run. * A plot of the output of all the runs updated as soon as each run is completed. * A GUI showing how long each process has been running for and how long until it completes, and with a button allowing you to terminate the runs. A simpler example is in ``examples/multiprocessing/multiple_runs_simple.py``. :: # We use Tk as the backend for the GUI and matplotlib so as to avoid any # threading conflicts import matplotlib matplotlib.use('TkAgg') from brian import * import Tkinter, time, multiprocessing, os from brian.utils.progressreporting import make_text_report from Queue import Empty as QueueEmpty class SimulationController(Tkinter.Tk): ''' GUI, uses Tkinter and features a progress bar for each process, and a callback function for when the terminate button is clicked. ''' def __init__(self, processes, terminator, width=600): Tkinter.Tk.__init__(self, None) self.parent = None self.grid() button = Tkinter.Button(self, text='Terminate simulation', command=terminator) button.grid(column=0, row=0) self.pb_width = width self.progressbars = [] for i in xrange(processes): can = Tkinter.Canvas(self, width=width, height=30) can.grid(column=0, row=1 + i) can.create_rectangle(0, 0, width, 30, fill='#aaaaaa') r = can.create_rectangle(0, 0, 0, 30, fill='#ffaaaa', width=0) t = can.create_text(width / 2, 15, text='') self.progressbars.append((can, r, t)) self.results_text = Tkinter.Label(self, text='Computed 0 results, time taken: 0s') self.results_text.grid(column=0, row=processes + 1) self.title('Simulation control') def update_results(self, elapsed, complete): ''' Method to update the total number of results computed and the amount of time taken. ''' self.results_text.config(text='Computed ' + str(complete) + ', time taken: ' + str(int(elapsed)) + 's') self.update() def update_process(self, i, elapsed, complete, msg): ''' Method to update the status of a given process. ''' can, r, t = self.progressbars[i] can.itemconfigure(t, text='Process ' + str(i) + ': ' + make_text_report(elapsed, complete) + ': ' + msg) can.coords(r, 0, 0, int(self.pb_width * complete), 30) self.update() def sim_mainloop(pool, results, message_queue): ''' Monitors results of a simulation as they arrive pool is the multiprocessing.Pool that the processes are running in, results is the AsyncResult object returned by Pool.imap_unordered which returns simulation results asynchronously as and when they are ready, and message_queue is a multiprocessing.Queue used to communicate between child processes and the server process. In this case, we use this Queue to send messages about the percent complete and time elapsed for each run. ''' # We use this to enumerate the processes, mapping their process IDs to an int # in the range 0:num_processes. pid_to_id = dict((pid, i) for i, pid in enumerate([p.pid for p in pool._pool])) num_processes = len(pid_to_id) start = time.time() stoprunningsim = [False] # This function terminates all the pool's child processes, it is used as # the callback function called when the terminate button on the GUI is clicked. def terminate_sim(): pool.terminate() stoprunningsim[0] = True controller = SimulationController(num_processes, terminate_sim) for i in range(num_processes): controller.update_process(i, 0, 0, 'no info yet') i = 0 while True: try: # If there is a new result (the 0.1 means wait 0.1 seconds for a # result before giving up) then this try clause will execute, otherwise # a TimeoutError will occur and the except clause afterwards will # execute. weight, numspikes = results.next(0.1) # if we reach here, we have a result to plot, so we plot it and # update the GUI plot_result(weight, numspikes) i = i + 1 controller.update_results(time.time() - start, i) except multiprocessing.TimeoutError: # if we're still waiting for a new result, we can process events in # the message_queue and update the GUI if there are any. while not message_queue.empty(): try: # messages here are of the form: (pid, elapsed, complete) # where pid is the process ID of the child process, elapsed # is the amount of time elapsed, and complete is the # fraction of the run completed. See function how_many_spikes # to see where these messages come from. pid, elapsed, complete = message_queue.get_nowait() controller.update_process(pid_to_id[pid], elapsed, complete, '') except QueueEmpty: break controller.update() if stoprunningsim[0]: print 'Terminated simulation processes' break controller.destroy() def plot_result(weight, numspikes): plot([weight], [numspikes], '.', color=(0, 0, 0.5)) axis('tight') draw() # this forces matplotlib to redraw # Note that how_many_spikes only takes one argument, which is a tuple of # its actual arguments. The reason for this is that Pool.imap_unordered can only # pass a single argument to the function its applied to, but that argument can # be a tuple... def how_many_spikes((excitatory_weight, message_queue)): reinit_default_clock() clear(True) eqs = ''' dv/dt = (ge+gi-(v+49*mV))/(20*ms) : volt dge/dt = -ge/(5*ms) : volt dgi/dt = -gi/(10*ms) : volt ''' P = NeuronGroup(4000, eqs, threshold= -50 * mV, reset= -60 * mV) P.v = -60 * mV + 10 * mV * rand(len(P)) Pe = P.subgroup(3200) Pi = P.subgroup(800) Ce = Connection(Pe, P, 'ge') Ci = Connection(Pi, P, 'gi') Ce.connect_random(Pe, P, 0.02, weight=excitatory_weight) Ci.connect_random(Pi, P, 0.02, weight= -9 * mV) M = SpikeMonitor(P) # This reporter function is called every second, and it sends a message to # the server process updating the status of the current run. def reporter(elapsed, complete): message_queue.put((os.getpid(), elapsed, complete)) run(4000 * ms, report=reporter, report_period=1 * second) return (excitatory_weight, M.nspikes) if __name__ == '__main__': numprocesses = None # number of processes to use, set to None to have one per CPU # We have to use a Queue from the Manager to send messages from client # processes to the server process manager = multiprocessing.Manager() message_queue = manager.Queue() pool = multiprocessing.Pool(processes=numprocesses) # This generator function repeatedly generates random sets of parameters # to pass to the how_many_spikes function def args(): while True: weight = rand()*3.5 * mV yield (weight, message_queue) # imap_unordered returns an AsyncResult object which returns results as # and when they are ready, we pass this results object which is returned # immediately to the sim_mainloop function which monitors this, updates the # GUI and plots the results as they come in. results = pool.imap_unordered(how_many_spikes, args()) ion() # this puts matplotlib into interactive mode to plot as we go sim_mainloop(pool, results, message_queue) brian-1.4.3/docs_sphinx/examples-multiprocessing_taskfarm.txt000066400000000000000000000022241266062647100246410ustar00rootroot00000000000000.. currentmodule:: brian .. index:: pair: example usage; plot pair: example usage; ylabel pair: example usage; xlabel pair: example usage; show .. _example-multiprocessing_taskfarm: Example: taskfarm (multiprocessing) =================================== Uses the :func:`~brian.tools.taskfarm.run_tasks` function to run a task on multiple CPUs and save the results to a :class:`~brian.tools.datamanager.DataManager` object. :: from brian import * from brian.tools.datamanager import * from brian.tools.taskfarm import * def find_rate(k, report): eqs = ''' dV/dt = (k-V)/(10*ms) : 1 ''' G = NeuronGroup(1000, eqs, reset=0, threshold=1) M = SpikeCounter(G) run(30*second, report=report) return (k, mean(M.count)/30) if __name__=='__main__': N = 20 dataman = DataManager('taskfarmexample') if dataman.itemcount()vt',reset='v=El;vt+=vt_inc',refractory=2*ms) layer23.v=El layer23.vt=Vt # Layer 2/3 excitatory layer23exc=layer23.subgroup(Nbarrels*N23exc) x,y=meshgrid(arange(M23exc)*1./M23exc,arange(M23exc)*1./M23exc) x,y=x.flatten(),y.flatten() barrels23 = dict(((i, j), layer23exc.subgroup(N23exc)) for i in xrange(barrelarraysize) for j in xrange(barrelarraysize)) for i in range(barrelarraysize): for j in range(barrelarraysize): barrels23[i,j].x=x+i barrels23[i,j].y=y+j # Layer 2/3 inhibitory layer23inh=layer23.subgroup(Nbarrels*N23inh) x,y=meshgrid(arange(M23inh)*1./M23inh,arange(M23inh)*1./M23inh) x,y=x.flatten(),y.flatten() barrels23inh = dict(((i, j), layer23inh.subgroup(N23inh)) for i in xrange(barrelarraysize) for j in xrange(barrelarraysize)) for i in range(barrelarraysize): for j in range(barrelarraysize): barrels23inh[i,j].x=x+i barrels23inh[i,j].y=y+j print "Building synapses, please wait..." # Feedforward connections feedforward=Synapses(layer4,layer23exc, model='''w:volt A_pre:1 A_post:1''', pre='''ge+=w A_pre=A_pre*exp((lastupdate-t)/taup)+Ap A_post=A_post*exp((lastupdate-t)/taud) w=clip(w+A_post,0,EPSC)''', post=''' A_pre=A_pre*exp((lastupdate-t)/taup) A_post=A_post*exp((lastupdate-t)/taud)+Ad w=clip(w+A_pre,0,EPSC)''') for i in range(barrelarraysize): for j in range(barrelarraysize): feedforward[barrels4[i,j],barrels23[i,j]]=.5 feedforward.w[barrels4[i,j],barrels23[i,j]]=EPSC*.5 # Excitatory lateral connections recurrent_exc=Synapses(layer23exc,layer23,model='w:volt',pre='ge+=w') recurrent_exc[layer23exc,layer23exc]='.15*exp(-.5*(((layer23exc.x[i]-layer23exc.x[j])/.4)**2+((layer23exc.y[i]-layer23exc.y[j])/.4)**2))' recurrent_exc.w[layer23exc,layer23exc]=EPSC*.3 recurrent_exc[layer23exc,layer23inh]='.15*exp(-.5*(((layer23exc.x[i]-layer23inh.x[j])/.4)**2+((layer23exc.y[i]-layer23inh.y[j])/.4)**2))' recurrent_exc.w[layer23exc,layer23inh]=EPSC # Inhibitory lateral connections recurrent_inh=Synapses(layer23inh,layer23exc,model='w:volt',pre='gi+=w') recurrent_inh[:,:]='exp(-.5*(((layer23inh.x[i]-layer23exc.x[j])/.2)**2+((layer23inh.y[i]-layer23exc.y[j])/.2)**2))' recurrent_inh.w=IPSC # Stimulation stimspeed = 1./stim_change_time # speed at which the bar of stimulation moves direction = 0.0 stimzonecentre = ones(2)*barrelarraysize/2. stimcentre,stimnorm = zeros(2),zeros(2) stimradius = (11*stim_change_time*stimspeed+1)*.5 stimradius2 = stimradius**2 def new_direction(): global direction direction = rand()*2*pi stimnorm[:] = (cos(direction), sin(direction)) stimcentre[:] = stimzonecentre-stimnorm*stimradius @network_operation def stimulation(): global direction, stimcentre stimcentre += stimspeed*stimnorm*defaultclock.dt if sum((stimcentre-stimzonecentre)**2)>stimradius2: new_direction() for (i, j), b in barrels4.iteritems(): whiskerpos = array([i,j], dtype=float)+0.5 isactive = abs(dot(whiskerpos-stimcentre, stimnorm))<.5 if barrels4active[i, j]!=isactive: barrels4active[i, j] = isactive b.rate = float(isactive)*tuning(layer4.selectivity[barrelindices[i, j]]-direction) new_direction() t2=time.time() print "Construction time:",t2-t1,"s" run(5*second,report='text') figure() # Preferred direction # perhaps we need to add presynaptic and postsynaptic with 2D/3D access selectivity=array([mean(array(feedforward.w[feedforward.synapses_post[i][:]])*exp(layer4.selectivity[feedforward.presynaptic[feedforward.synapses_post[i][:]]]*1j)) for i in range(len(layer23exc))]) selectivity=(arctan2(selectivity.imag,selectivity.real) % (2*pi))*180./pi I=zeros((barrelarraysize*M23exc,barrelarraysize*M23exc)) ix=array(around(layer23exc.x*M23exc),dtype=int) iy=array(around(layer23exc.y*M23exc),dtype=int) I[iy,ix]=selectivity imshow(I) hsv() colorbar() for i in range(1,barrelarraysize+1): plot([i*max(ix)/barrelarraysize,i*max(ix)/barrelarraysize],[0,max(iy)],'k') plot([0,max(ix)],[i*max(iy)/barrelarraysize,i*max(iy)/barrelarraysize],'k') figure() hist(selectivity) show() brian-1.4.3/docs_sphinx/examples-synapses_delayed_stdp.txt000066400000000000000000000035131266062647100241120ustar00rootroot00000000000000.. currentmodule:: brian .. index:: pair: example usage; subplot pair: example usage; plot pair: example usage; run pair: example usage; show pair: example usage; PoissonGroup pair: example usage; Synapses pair: example usage; NeuronGroup pair: example usage; StateMonitor .. _example-synapses_delayed_stdp: Example: delayed_stdp (synapses) ================================ Delayed STDP :: from brian import * import time N = 1 taum = 10 * ms taupre = 20 * ms taupost = taupre Ee = 0 * mV vt = -54 * mV vr = -74 * mV El = -74 * mV taue = 5 * ms F = 20 * Hz dApre = .1 dApost = -dApre * taupre / taupost * 2. eqs_neurons = ''' dv/dt=(ge*(Ee-vr)+El-v)/taum : volt # the synaptic current is linearized dge/dt=-ge/taue : 1 ''' input = PoissonGroup(N, rates=F) neurons = NeuronGroup(1, model=eqs_neurons, threshold=vt, reset=vr) S = Synapses(input, neurons, model='''w:1 dApre/dt=-Apre/taupre : 1 #(event-driven) dApost/dt=-Apost/taupost : 1 #(event-driven)''', pre=('ge+=w', '''w=clip(w+Apost,0,inf) Apre+=dApre'''), post='''Apost+=dApost w=clip(w+Apre,0,inf)''') neurons.v = vr S[:,:]=True S.w=10 S.delay[1][0,0]=3*ms # delayed trace (try 0 ms to see the difference) M=StateMonitor(S,'w',record=0) Mpre=StateMonitor(S,'Apre',record=0) Mpost=StateMonitor(S,'Apost',record=0) Mv=StateMonitor(neurons,'v',record=0) run(10*second,report='text') subplot(211) plot(M.times/ms,M[0]) plot(M.times/ms,Mpre[0],'r') plot(M.times/ms,Mpost[0],'k') subplot(212) plot(Mv.times/ms,Mv[0]) show() brian-1.4.3/docs_sphinx/examples-synapses_gapjunctions.txt000066400000000000000000000020141266062647100241500ustar00rootroot00000000000000.. currentmodule:: brian .. index:: pair: example usage; plot pair: example usage; run pair: example usage; trace pair: example usage; show pair: example usage; Synapses pair: example usage; linspace pair: example usage; NeuronGroup pair: example usage; StateMonitor .. _example-synapses_gapjunctions: Example: gapjunctions (synapses) ================================ Neurons with gap junctions :: from brian import * N = 10 v0=1.05 tau=10*ms eqs = ''' dv/dt=(v0-v+Igap)/tau : 1 Igap : 1 # gap junction current ''' neurons = NeuronGroup(N, model=eqs, threshold=1, reset=0) neurons.v=linspace(0,1,N) trace = StateMonitor(neurons, 'v', record=[0, 5]) S=Synapses(neurons,model='''w:1 # gap junction conductance Igap=w*(v_pre-v_post): 1''') S[:,:]=True neurons.Igap=S.Igap S.w=.02 run(500*ms) plot(trace.times / ms, trace[0]) plot(trace.times / ms, trace[5]) show() brian-1.4.3/docs_sphinx/examples-synapses_jeffress.txt000066400000000000000000000045351266062647100232650ustar00rootroot00000000000000.. currentmodule:: brian .. index:: pair: example usage; NeuronGroup pair: example usage; run pair: example usage; TimedArray pair: example usage; show pair: example usage; raster_plot pair: example usage; Synapses pair: example usage; linspace pair: example usage; SpikeMonitor pair: example usage; StateMonitor .. _example-synapses_jeffress: Example: jeffress (synapses) ============================ Jeffress model, adapted with spiking neuron models. A sound source (white noise) is moving around the head. Delay differences between the two ears are used to determine the azimuth of the source. Delays are mapped to a neural place code using delay lines (each neuron receives input from both ears, with different delays). Romain Brette :: from brian import * from time import time defaultclock.dt = .02 * ms dt = defaultclock.dt # Sound sound = TimedArray(10 * randn(50000)) # white noise # Ears and sound motion around the head (constant angular speed) sound_speed = 300 * metre / second interaural_distance = 20 * cm # big head! max_delay = interaural_distance / sound_speed print "Maximum interaural delay:", max_delay angular_speed = 2 * pi * radian / second # 1 turn/second tau_ear = 1 * ms sigma_ear = .1 eqs_ears = ''' dx/dt=(sound(t-delay)-x)/tau_ear+sigma_ear*(2./tau_ear)**.5*xi : 1 delay=distance*sin(theta) : second distance : second # distance to the centre of the head in time units dtheta/dt=angular_speed : radian ''' ears = NeuronGroup(2, model=eqs_ears, threshold=1, reset=0, refractory=2.5 * ms) ears.distance = [-.5 * max_delay, .5 * max_delay] traces = StateMonitor(ears, 'x', record=True) # Coincidence detectors N = 300 tau = 1 * ms sigma = .1 eqs_neurons = ''' dv/dt=-v/tau+sigma*(2./tau)**.5*xi : 1 ''' neurons = NeuronGroup(N, model=eqs_neurons, threshold=1, reset=0) synapses = Synapses(ears,neurons,model='w:1',pre='v+=w') synapses[:,:]=True synapses.w=.5 synapses.delay[0, :] = linspace(0 * ms, 1.1 * max_delay, N) synapses.delay[1, :] = linspace(0 * ms, 1.1 * max_delay, N)[::-1] spikes = SpikeMonitor(neurons) run(1*ms) t1=time() run(1000 * ms) t2=time() print "It took",t2-t1,"s" raster_plot(spikes) show() brian-1.4.3/docs_sphinx/examples-synapses_licklider.txt000066400000000000000000000034651266062647100234210ustar00rootroot00000000000000.. currentmodule:: brian .. index:: pair: example usage; NeuronGroup pair: example usage; run pair: example usage; log pair: example usage; show pair: example usage; raster_plot pair: example usage; yticks pair: example usage; Synapses pair: example usage; linspace pair: example usage; exp pair: example usage; ylabel pair: example usage; SpikeMonitor .. _example-synapses_licklider: Example: licklider (synapses) ============================= Spike-based adaptation of Licklider's model of pitch processing (autocorrelation with delay lines) with phase locking. Romain Brette :: from brian import * defaultclock.dt = .02 * ms # Ear and sound max_delay = 20 * ms # 50 Hz tau_ear = 1 * ms sigma_ear = .1 eqs_ear = ''' dx/dt=(sound-x)/tau_ear+sigma_ear*(2./tau_ear)**.5*xi : 1 sound=5*sin(2*pi*frequency*t)**3 : 1 # nonlinear distorsion #sound=5*(sin(4*pi*frequency*t)+.5*sin(6*pi*frequency*t)) : 1 # missing fundamental frequency=(200+200*t*Hz)*Hz : Hz # increasing pitch ''' receptors = NeuronGroup(2, model=eqs_ear, threshold=1, reset=0, refractory=2 * ms) # Coincidence detectors min_freq = 50 * Hz max_freq = 1000 * Hz N = 300 tau = 1 * ms sigma = .1 eqs_neurons = ''' dv/dt=-v/tau+sigma*(2./tau)**.5*xi : 1 ''' neurons = NeuronGroup(N, model=eqs_neurons, threshold=1, reset=0) synapses = Synapses(receptors, neurons, model='w : 1', pre='v+=w') synapses[:,:]=True synapses.w=0.5 synapses.delay[1, :] = 1. / exp(linspace(log(min_freq / Hz), log(max_freq / Hz), N)) spikes = SpikeMonitor(neurons) run(500 * ms) raster_plot(spikes) ylabel('Frequency') yticks([0, 99, 199, 299], array(1. / synapses.delay[1, [0, 99, 199, 299]], dtype=int)) show() brian-1.4.3/docs_sphinx/examples-synapses_multiple_delays.txt000066400000000000000000000013501266062647100246420ustar00rootroot00000000000000.. currentmodule:: brian .. index:: pair: example usage; NeuronGroup pair: example usage; run pair: example usage; plot pair: example usage; show pair: example usage; Synapses pair: example usage; StateMonitor .. _example-synapses_multiple_delays: Example: multiple_delays (synapses) =================================== Multiple delays :: from brian import * P=NeuronGroup(1,model='dv/dt=1/(20*ms):1',threshold=1,reset=0) Q=NeuronGroup(1,model='v:1') S=Synapses(P,Q,model='w:1',pre='v+=w') M=StateMonitor(Q,'v',record=True) S[0,0]=2 S.w[0,0,0]=1. S.w[0,0,1]=.5 S.delay[0,0,0]=.5*ms S.delay[0,0,1]=1*ms run(60*ms) plot(M.times/ms,M[0]) show() brian-1.4.3/docs_sphinx/examples-synapses_noisy_ring.txt000066400000000000000000000020471266062647100236320ustar00rootroot00000000000000.. currentmodule:: brian .. index:: pair: example usage; subplot pair: example usage; plot pair: example usage; run pair: example usage; trace pair: example usage; show pair: example usage; raster_plot pair: example usage; SpikeMonitor pair: example usage; Synapses pair: example usage; NeuronGroup pair: example usage; StateMonitor .. _example-synapses_noisy_ring: Example: noisy_ring (synapses) ============================== Integrate-and-fire neurons with noise :: from brian import * tau = 10 * ms sigma = .5 N = 100 J = -1 mu = 2 eqs = """ dv/dt=mu/tau+sigma/tau**.5*xi : 1 """ group = NeuronGroup(N, model=eqs, threshold=1, reset=0) C = Synapses(group, model='w:1', pre='v+=w') C[:,:]='j==((i+1)%N)' C.w=J S = SpikeMonitor(group) trace = StateMonitor(group, 'v', record=True) run(500 * ms) i, t = S.spikes[-1] subplot(211) raster_plot(S) subplot(212) plot(trace.times / ms, trace[0]) show() brian-1.4.3/docs_sphinx/examples-synapses_nonlinear_synapses.txt000066400000000000000000000023361266062647100253650ustar00rootroot00000000000000.. currentmodule:: brian .. index:: pair: example usage; subplot pair: example usage; plot pair: example usage; run pair: example usage; show pair: example usage; Synapses pair: example usage; NeuronGroup pair: example usage; StateMonitor .. _example-synapses_nonlinear_synapses: Example: nonlinear_synapses (synapses) ====================================== NMDA synapses :: from brian import * import time a=1/(10*ms) b=1/(10*ms) c=1/(10*ms) input=NeuronGroup(2,model='dv/dt=1/(10*ms):1', threshold=1, reset=0) neurons = NeuronGroup(1, model="""dv/dt=(gtot-v)/(10*ms) : 1 gtot : 1""") S=Synapses(input,neurons, model='''dg/dt=-a*g+b*x*(1-g) : 1 dx/dt=-c*x : 1 w : 1 # synaptic weight ''', pre='x+=w') # NMDA synapses neurons.gtot=S.g S[:,:]=True S.w=[1.,10.] input.v=[0.,0.5] M=StateMonitor(S,'g',record=True) Mn=StateMonitor(neurons,'v',record=0) run(100*ms) subplot(211) plot(M.times/ms,M[0]) plot(M.times/ms,M[1]) subplot(212) plot(Mn.times/ms,Mn[0]) show() brian-1.4.3/docs_sphinx/examples-synapses_one_synapse.txt000066400000000000000000000012551266062647100237750ustar00rootroot00000000000000.. currentmodule:: brian .. index:: pair: example usage; NeuronGroup pair: example usage; run pair: example usage; plot pair: example usage; show pair: example usage; Synapses pair: example usage; StateMonitor .. _example-synapses_one_synapse: Example: one_synapse (synapses) =============================== One synapse :: from brian import * P=NeuronGroup(1,model='dv/dt=1/(10*ms):1',threshold=1,reset=0) Q=NeuronGroup(1,model='v:1') S=Synapses(P,Q,model='w:1',pre='v+=w') M=StateMonitor(Q,'v',record=True) S[0,0]=True S.w[0,0]=1. S.delay[0,0]=.5*ms run(40*ms) plot(M.times/ms,M[0]) show() brian-1.4.3/docs_sphinx/examples-synapses_one_synapse_bis.txt000066400000000000000000000014151266062647100246300ustar00rootroot00000000000000.. currentmodule:: brian .. index:: pair: example usage; NeuronGroup pair: example usage; run pair: example usage; show pair: example usage; plot pair: example usage; Synapses pair: example usage; StateMonitor .. _example-synapses_one_synapse_bis: Example: one_synapse_bis (synapses) =================================== One synapse within several possibilities. Synapse from 2->3. :: from brian import * P=NeuronGroup(5,model='dv/dt=1/(10*ms):1',threshold=1,reset=0) Q=NeuronGroup(4,model='v:1') S=Synapses(P,Q,model='w:1',pre='v+=w') M=StateMonitor(Q,'v',record=True) S[2,3]=True S.w[2,3]=1. S.delay[2,3]=.5*ms run(40*ms) for i in range(4): plot(M.times/ms,M[i]+i*2,'k') show() brian-1.4.3/docs_sphinx/examples-synapses_poisson_synapses.txt000066400000000000000000000037001266062647100250660ustar00rootroot00000000000000.. currentmodule:: brian .. index:: pair: example usage; NeuronGroup pair: example usage; run pair: example usage; plot pair: example usage; show pair: example usage; Synapses pair: example usage; StateMonitor .. _example-synapses_poisson_synapses: Example: poisson_synapses (synapses) ==================================== This example shows how to efficiently simulate neurons with a large number of Poisson inputs targetting arbitrarily complex synapses. The approach is very similiar to what the :class:`PoissonInput` class does internally, but :class:`PoissonInput` cannot be combined with the :class:`Synapses` class. You could also just use many :class:`PoissonGroup` objects as inputs, but this is very slow and memory consuming. :: from brian import * # Poisson inputs M = 1000 # number of Poisson inputs max_rate = 100 # Neurons N = 50 # number of neurons tau = 10 * ms E_exc = 0 * mV E_L = -70 * mV G = NeuronGroup(N, model='dvm/dt = -(vm - E_L)/tau : mV') G.rest() # Dummy neuron group P = NeuronGroup(1, 'v : 1', threshold= -1, reset=0) # spikes every timestep # time varying rate def varying_rate(t): return defaultclock.dt * max_rate * (0.5 + 0.5 * sin(2 * pi * 5 * t)) # Synaptic connections: binomial(cellM, varying_rate(t)) gives the number of # events per timestep. The synapse model is a conductance-based instanteneous # jump in postsynaptic membrane potential S = Synapses(P, G, model=''' J : 1 cellM : 1 ''', pre='vm += binomial(cellM, varying_rate(t)) * J * (E_exc - vm)') S[:, :] = True S.cellM = M #we need one value for M per cell, so that binomial is vectorized S.J = 0.0005 mon = StateMonitor(G, 'vm', record=True) run(1 * second, report='text') mon.plot() show() brian-1.4.3/docs_sphinx/examples-synapses_probabilistic_synapses.txt000066400000000000000000000021761266062647100262300ustar00rootroot00000000000000.. currentmodule:: brian .. index:: pair: example usage; plot pair: example usage; run pair: example usage; show pair: example usage; PoissonGroup pair: example usage; Synapses pair: example usage; linspace pair: example usage; NeuronGroup pair: example usage; StateMonitor .. _example-synapses_probabilistic_synapses: Example: probabilistic_synapses (synapses) ========================================== Probabilistic synapses Seems to work. :: from brian import * N=20 tau=5*ms input=PoissonGroup(2,rates=20*Hz) neurons=NeuronGroup(N,model='dv/dt=-v/tau : 1') S=Synapses(input,neurons,model="""w : 1 p : 1 # transmission probability""", pre="v+=w*(rand()2,3. :: from brian import * P=NeuronGroup(2,model='dv/dt=1/(10*ms):1',threshold=1,reset=0) Q=NeuronGroup(4,model='v:1') S=Synapses(P,Q,model='w:1',pre='v+=w') M=StateMonitor(Q,'v',record=True) S[0,2]=True S[0,3]=True S.w[0,:]=[1.,.7] S.delay[0,:]=[.5*ms,.7*ms] run(40*ms) for i in range(4): plot(M.times/ms,M[i]+i*2,'k') show() brian-1.4.3/docs_sphinx/examples-synapses_weightmonitor.txt000066400000000000000000000043441266062647100243530ustar00rootroot00000000000000.. currentmodule:: brian .. index:: pair: example usage; subplot pair: example usage; plot pair: example usage; run pair: example usage; PopulationRateMonitor pair: example usage; figure pair: example usage; show pair: example usage; rate pair: example usage; hist pair: example usage; PoissonGroup pair: example usage; Synapses pair: example usage; NeuronGroup pair: example usage; StateMonitor .. _example-synapses_weightmonitor: Example: weightmonitor (synapses) ================================= Monitoring synaptic variables. STDP example. :: from brian import * from time import time N = 1000 taum = 10 * ms tau_pre = 20 * ms tau_post = tau_pre Ee = 0 * mV vt = -54 * mV vr = -60 * mV El = -74 * mV taue = 5 * ms F = 15 * Hz gmax = .01 dA_pre = .01 dA_post = -dA_pre * tau_pre / tau_post * 1.05 dA_post *= gmax dA_pre *= gmax eqs_neurons = ''' dv/dt=(ge*(Ee-vr)+El-v)/taum : volt # the synaptic current is linearized dge/dt=-ge/taue : 1 ''' input = PoissonGroup(N, rates=F) neurons = NeuronGroup(1, model=eqs_neurons, threshold=vt, reset=vr) S = Synapses(input, neurons, model='''w:1 A_pre:1 A_post:1''', pre='''ge+=w A_pre=A_pre*exp((lastupdate-t)/tau_pre)+dA_pre A_post=A_post*exp((lastupdate-t)/tau_post) w=clip(w+A_post,0,gmax)''', post=''' A_pre=A_pre*exp((lastupdate-t)/tau_pre) A_post=A_post*exp((lastupdate-t)/tau_post)+dA_post w=clip(w+A_pre,0,gmax)''') neurons.v = vr S[:,:]=True S.w='rand()*gmax' rate = PopulationRateMonitor(neurons) M = StateMonitor(S,'w',record=[0,1]) # monitors synapses number 0 and 1 start_time = time() run(10 * second, report='text') print "Simulation time:", time() - start_time figure() subplot(311) plot(rate.times / second, rate.smooth_rate(100 * ms)) subplot(312) plot(S.w[:] / gmax, '.') subplot(313) hist(S.w[:] / gmax, 20) figure() plot(M.times,M[0]/gmax) plot(M.times,M[1]/gmax) show() brian-1.4.3/docs_sphinx/examples-twister_FriedemannZenke.txt000066400000000000000000000103041266062647100243460ustar00rootroot00000000000000.. currentmodule:: brian .. index:: pair: example usage; subplot pair: example usage; NeuronGroup pair: example usage; run pair: example usage; xlim pair: example usage; title pair: example usage; raster_plot pair: example usage; show pair: example usage; Connection pair: example usage; xlabel pair: example usage; SpikeMonitor pair: example usage; STDP .. _example-twister_FriedemannZenke: Example: FriedemannZenke (twister) ================================== Friedemann Zenke's winning entry for the 2012 Brian twister. :: # ########################################### # # Inhibitory synaptic plasticity in a recurrent network model # (F. Zenke, 2011) # # Adapted from: # Vogels, T. P., H. Sprekeler, F. Zenke, C. Clopath, and W. Gerstner. # 'Inhibitory Plasticity Balances Excitation and Inhibition in Sensory # Pathways and Memory Networks.' Science (November 10, 2011). # # ########################################### from brian import * # ########################################### # Defining network model parameters # ########################################### NE = 8000 # Number of excitatory cells NI = NE/4 # Number of inhibitory cells w = 1.*nS # Basic weight unit tau_ampa = 5.0*ms # Glutamatergic synaptic time constant tau_gaba = 10.0*ms # GABAergic synaptic time constant epsilon = 0.02 # Sparseness of synaptic connections eta = 1e-2 # Learning rate tau_stdp = 20*ms # STDP time constant simtime = 10*second # Simulation time # ########################################### # Neuron model # ########################################### gl = 10.0*nsiemens # Leak conductance el = -60*mV # Resting potential er = -80*mV # Inhibitory reversal potential vt = -50.*mV # Spiking threshold memc = 200.0*pfarad # Membrane capacitance bgcurrent = 200*pA # External current eqs_neurons=''' dv/dt=(-gl*(v-el)-(g_ampa*w*v+g_gaba*(v-er)*w)+bgcurrent)/memc : volt dg_ampa/dt = -g_ampa/tau_ampa : 1 dg_gaba/dt = -g_gaba/tau_gaba : 1 ''' # ########################################### # Initialize neuron group # ########################################### neurons=NeuronGroup(NE+NI,model=eqs_neurons,threshold=vt,reset=el,refractory=5*ms) Pe=neurons.subgroup(NE) Pi=neurons.subgroup(NI) # ########################################### # Connecting the network # ########################################### con_e = Connection(Pe,neurons,'g_ampa',weight=0.3,sparseness=epsilon) con_ie = Connection(Pi,Pe,'g_gaba',weight=1e-10,sparseness=epsilon) con_ii = Connection(Pi,Pi,'g_gaba',weight=3,sparseness=epsilon) # ########################################### # Setting up monitors # ########################################### sm = SpikeMonitor(Pe) # ########################################### # Run without plasticity # ########################################### run(1*second) # ########################################### # Inhibitory Plasticity # ########################################### alpha = 3*Hz*tau_stdp*2 # Target rate parameter gmax = 100 # Maximum inhibitory weight eqs_stdp_inhib = ''' dA_pre/dt=-A_pre/tau_stdp : 1 dA_post/dt=-A_post/tau_stdp : 1 ''' stdp_ie = STDP(con_ie, eqs=eqs_stdp_inhib, pre='A_pre+=1.; w+=(A_post-alpha)*eta;', post='A_post+=1.; w+=A_pre*eta;', wmax=gmax) # ########################################### # Run with plasticity # ########################################### run(simtime-1*second,report='text') # ########################################### # Make plots # ########################################### subplot(211) raster_plot(sm,ms=1.) title("Before") xlabel("") xlim(float(0.8*second/ms), float(1*second/ms)) subplot(212) raster_plot(sm,ms=1.) title("After") xlim(float((simtime-0.2*second)/ms), float(simtime/ms)) show() brian-1.4.3/docs_sphinx/examples-twister_MicheleGiugliano.txt000066400000000000000000000117031266062647100245120ustar00rootroot00000000000000.. currentmodule:: brian .. index:: pair: example usage; subplot pair: example usage; plot pair: example usage; run pair: example usage; PopulationRateMonitor pair: example usage; trace pair: example usage; show pair: example usage; raster_plot pair: example usage; SimpleCustomRefractoriness pair: example usage; Connection pair: example usage; SpikeMonitor pair: example usage; NeuronGroup pair: example usage; ylim pair: example usage; StateMonitor .. _example-twister_MicheleGiugliano: Example: MicheleGiugliano (twister) =================================== Michele Giugliano's entry for the 2012 Brian twister. :: # # Figure5B - from Giugliano et al., 2004 # Journal of Neurophysiology 92(2):977-96 # # implemented by Eleni Vasilaki and # Michele Giugliano # # A sparsely connected network of excitatory neurons, interacting # via current-based synaptic interactions, and incorporating # spike-frequency adaptation, is simulated. # # Its overall emerging firing rate activity replicates some of the features of # spontaneous patterned electrical activity, observed experimentally in cultured # networks of neurons dissociated from the neocortex. # from brian import * # Parameters of the simulation T = 30000 *ms # life time of the simulation N = 100 # total number of (excitatory) integrate-and-fire model neurons in the network # Parameters of each model neuron, voltage dynamics C = 67.95 *pF # Membrane capacitance of single model neurons tau = 22.25 *ms # Membrane time-constant of single model neurons H = 2.39 *mV # Reset voltage, mimicking hyperpolarization potential following a spike theta= 20 *mV # Threshold voltage for spike initiation tauarp=7.76 *ms # Absolute refractory period # Parameters of each model neuron, spike-frequency adaptation dynamics taua = 2100 *ms # Adaptation time constant a = 0.75 *pA # Adaptation scaling factor - NO ADAPTATION D = 1*ms # Unit consistency factor temp = 1. *ms**(-.5) # Unit consistency factor # Parameters of network connectivity Cee = 0.38 # Sparseness of all-to-all random connectivity taue = 5 *ms # Decay time constant of excitatory EPSPs delta= 1.5 * ms # Conduction+synaptic propagation delay J = 14.5* pA # Strenght of synaptic coupling, up to 18 *pA # Parameters of background synaptic activity, modelled as a identical and independent noisy extra-input to each model neuron m0 = 25.1 *pA # Mean background input current s0 = 92 *pA # Std dev of the (noisy) background input current # Each model neuron is described as a leaky integrate-and-fire with adaptation and current-driven synapses eqs = """ dv/dt = - v / tau - a/C * x + Ie/C + (m0 + s0 * xi / temp)/C : mV dx/dt = -x/taua : 1 dIe/dt = -Ie/taue : pA """ # Custom refractory mechanisms are employed here, to allow the membrane potential to be clamped to the reset value H def myresetfunc(P, spikes): P.v[spikes] = H #reset voltage P.x[spikes] += 1 #low pass filter of spikes (adaptation mechanism) SCR = SimpleCustomRefractoriness(myresetfunc, tauarp, state='v') # The population of identical N model neuon is defined now P = NeuronGroup(N, model=eqs, threshold=theta, reset=SCR) # The interneuronal connectivity is defined now Ce = Connection(P, P, 'Ie', weight=J, sparseness=Cee, delay=delta) # Initialization of the state variables, for each model neuron P.v = rand(len(P)) * 20 * mV #membrane potential P.x = rand(len(P)) * 2 #low pass filter of spikes P.Ie = 0 *pA #excitatory synaptic input # Definition of tools for plotting and visualization of single neuron and population quantities R = PopulationRateMonitor(P) M = SpikeMonitor(P) trace = StateMonitor(P, 'v', record=0) tracex = StateMonitor(P, 'x', record=0) print "Simulation running... (long-lasting simulation: be patient)" run(T) print "Simulation completed! If you did not see any firing rate population burst (lower panel), then slightly increase J!" # Plot nice spikes - adapted from Brette's code vm = trace[0] spikes0 = [t for i,t in M.spikes if i==0] for i in range(0,len(spikes0)): k = int(spikes0[i] / defaultclock.dt) vm[k] = 80 * mV subplot(311) #membrane potential of neuron 0 plot(trace.times / ms, vm / mV - 60) subplot(312) #raster plot raster_plot(M) subplot(313) #smoothed population rate plot(R.times / ms, R.smooth_rate(5*ms) / Hz, tracex.times / ms, tracex[0] * 10) ylim(0, 120) show() brian-1.4.3/docs_sphinx/examples-twister_PeterDiehl.txt000066400000000000000000000034661266062647100233410ustar00rootroot00000000000000.. currentmodule:: brian .. index:: pair: example usage; plot pair: example usage; run pair: example usage; PopulationRateMonitor pair: example usage; NeuronGroup pair: example usage; show pair: example usage; rate pair: example usage; Connection pair: example usage; STDP .. _example-twister_PeterDiehl: Example: PeterDiehl (twister) ============================= Peter Diehl's entry for the 2012 Brian twister. :: from brian import * eqs = ''' dv/dt = ((-60.*mV-v)+(I_synE+I_synI+I_b)/(10.*nS))/(20*ms) : volt I_synE = 3.*nS*ge*( 0.*mV-v) : amp I_synI = 30.*nS*gi*(-80.*mV-v) : amp I_b : amp dge/dt = -ge/( 5.*ms) : 1 dgi/dt = -gi/(10.*ms) : 1 ''' P = NeuronGroup(10000, eqs, threshold=-50.*mV, refractory=5.*ms, reset=-60.*mV) Pe = P.subgroup(8000) Pi = P.subgroup(2000) Ce = Connection(Pe, P, 'ge', weight=1., sparseness=0.02) Cie = Connection(Pi, Pe, 'gi', weight=1., sparseness=0.02) Cii = Connection(Pi, Pi, 'gi', weight=1., sparseness=0.02) eqs_stdp = ''' dpre/dt = -pre/(20.*ms) : 1.0 dpost/dt = -post/(20.*ms) : 1.0 ''' nu = 0.1 # learning rate alpha = 0.12 # controls the firing rate stdp = STDP(Cie, eqs=eqs_stdp, pre='pre+= 1.; w+= nu*(post-alpha)', post='post+= 1.; w+= nu*pre', wmin=0., wmax= 10.) M = PopulationRateMonitor(Pe, bin = 1.) P.I_b = 200.*pA #set the input current run(10*second) P.I_b = 600.*pA #increase the input and see how the rate adapts run(10*second) plot(M.times[0:-1]/second, M.rate[0:-1]) show() brian-1.4.3/docs_sphinx/examples-twister_PierreYger.txt000066400000000000000000000217511266062647100233660ustar00rootroot00000000000000.. currentmodule:: brian .. index:: pair: example usage; figure pair: example usage; Clock pair: example usage; savefig pair: example usage; text pair: example usage; imread pair: example usage; close pair: example usage; size pair: example usage; gca pair: example usage; get_current_fig_manager pair: example usage; sum pair: example usage; cla pair: example usage; sqrt pair: example usage; PoissonGroup pair: example usage; RecentStateMonitor pair: example usage; Equations pair: example usage; SpikeCounter pair: example usage; subplot pair: example usage; draw pair: example usage; run pair: example usage; flipud pair: example usage; ion pair: example usage; Connection pair: example usage; NeuronGroup pair: example usage; ioff pair: example usage; colorbar pair: example usage; setp pair: example usage; scatter pair: example usage; mean .. _example-twister_PierreYger: Example: PierreYger (twister) ============================= Pierre Yger's winning entry for the 2012 Brian twister. :: from brian import * import numpy, os, pylab """ An implementation of a simple topographical network, like those used in Mehring 2005 or Yger 2011. Cells are aranged randomly on a 2D plane and connected according to a gaussian profile P(r) = exp(-d**2/(2*sigma**2)), with delays depending linearly on the distances. Note that the exact number of synapses per neuron is not fixed here. To avoid any border conditions, the plane is considered to be toroidal. Script will generate an Synchronous Irregular (SI) slow regime with propagating waves that will spread in various directions, wandering over the network. In addition, an external layer of Poisson sources will stimulates some cells on the network, with a wiring scheme such that the word BRIAN will pop up. External rates can be turned off to observed the spontaneous activity of the 2D layer. One can observe that despite the inputs is constant, the network is not always responding to it. The script will display, while running, the spikes and Vm of the excitatory cells. Varying sigma will show the various activity structures from a random network (s_lat > 1), to a very locally connected one (s_lat < 0.1) """ ### We are setting the global timestep of the simulation Clock(0.1 * ms) ### Cell parameters ### tau_m = 20. * ms # Membrane time constant c_m = 0.2 * nF # Capacitance tau_exc = 3. * ms # Synaptic time constant (excitatory) tau_inh = 7. * ms # Synaptic time constant (inhibitory) tau_ref = 5. * ms # Refractory period El = -80 * mV # Leak potential Ee = 0. * mV # Reversal potential (excitation) Ei = -70.* mV # Reversal potential (inhibition) Vt = -50 * mV # Spike Threhold Vr = -60 * mV # Spike Reset ### Equation for a Conductance-based IAF #### eqs = Equations(''' dv/dt = (El-v)/tau_m + (ge*(Ee-v)+gi*(Ei-v))/c_m : volt dge/dt = -ge*(1./tau_exc) : uS dgi/dt = -gi*(1./tau_inh) : uS ''') n_cells = 12500 # Total number of cells n_exc = int(0.8 * n_cells) # 4:1 ratio for exc/inh size = 1. # Size of the network simtime = 1000 * ms # Simulation time sim_step = 1 * ms # Display snapshots every sim_step ms epsilon = 0.02 # Probability density s_lat = 0.2 # Spread of the lateral connections g_exc = 4. * nS # Excitatory conductance g_inh = 64. * nS # Inhibitory conductance g_ext = 200 * nS # External drive velocity = 0.3 * mm/ms # velocity ext_rate = 100 * Hz # Rate of the external source max_distance = size * mm/numpy.sqrt(2) # Since this is a torus max_delay = max_distance/velocity # Needed for the connectors ### Generate the images with the letters B, R, I, A, N ### To do that, we create a png image and read it as a matrix pylab.figure() pylab.text(0.125, 0.4, "B R I A N", size=80) pylab.setp(gca(), xticks=[], yticks=[]) pylab.savefig("BRIAN.png") brian_letters = imread("BRIAN.png") os.remove("BRIAN.png") brian_letters = numpy.flipud(mean(brian_letters,2)).T pylab.close() ### We create the cells and generate random positons in [0, size]x[0, size] all_cells = NeuronGroup(n_cells, model=eqs, threshold=Vt, reset=Vr, refractory=tau_ref) all_cells.position = size*numpy.random.rand(n_cells, 2) exc_cells = all_cells[0:n_exc] inh_cells = all_cells[n_exc:n_cells] ### We initialize v values slightly above Vt, to have initial spikes all_cells.v = El + 1.1*numpy.random.rand(n_cells) * (Vt - El) ### Now we create the source that will write the word BRIAN sources = PoissonGroup(1, ext_rate) sources.position = array([[0.5, 0.5]]) ### Function to get the distance between one position and an array of positions ### This is needed to used the vectorized form of the connections in the brian.Connection objects ### Note that the plane is wrapped, to avoid any border effects. def get_distance(x, y): d1 = abs(x - y) min_d = numpy.minimum(d1, size - d1) return numpy.sqrt(numpy.sum(min_d**2, 1)) ### Function returning the probabilities of connections as a functions of distances def probas(i, j, x, y): distance = get_distance(x[i], y[j]) return epsilon * numpy.exp(-distance**2/(2*s_lat**2)) ### Function returning linear delays as function of distances def delays(i, j, x, y): distance = get_distance(x[i], y[j]) return 0.1*ms + (distance * mm )/ velocity ### Function assessing if a cell is located in a particular letter of the word BRIAN ### Return 0 if not, and 1 if yes. def is_in_brian(i, j, x, y): a, b = brian_letters.shape tmp_x, tmp_y = (y[j][:, 0]*a).astype(int), (y[j][:, 1]*b).astype(int) return 1 - brian_letters[tmp_x, tmp_y] print "Building network with wrapped 2D gaussian profiles..." Ce = Connection(exc_cells, all_cells, 'ge', weight=g_exc, max_delay=max_delay, sparseness=lambda i, j : probas(i, j, exc_cells.position, all_cells.position), delay =lambda i, j : delays(i, j, exc_cells.position, all_cells.position)) Ci = Connection(inh_cells, all_cells, 'gi', weight=g_inh, max_delay=max_delay, sparseness=lambda i, j : probas(i, j, inh_cells.position, all_cells.position), delay =lambda i, j : delays(i, j, inh_cells.position, all_cells.position)) Cext = Connection(sources, all_cells, 'ge', weight=g_ext, max_delay=max_delay, sparseness=lambda i, j : is_in_brian(i, j, sources.position, all_cells.position)) print "--> mean probability from excitatory synapses:", Ce.W.getnnz()/float(n_exc*n_cells) * 100, "%" print "--> mean probability from inhibitory synapses:", Ci.W.getnnz()/float((n_cells - n_exc)*n_cells) * 100, "%" print "Setting the recorders..." v_exc = RecentStateMonitor(exc_cells, 'v', record=True) s_exc = SpikeCounter(exc_cells) ion() # To enter the interactive mode print "Initializing the plots..." fig1 = pylab.subplot(211) im = fig1.scatter(all_cells.position[:n_exc, 0], all_cells.position[:n_exc, 1], c=[0]*n_exc) im.set_clim(0, 1) fig1.set_ylabel("spikes") pylab.colorbar(im) fig2 = pylab.subplot(212) im = fig2.scatter(all_cells.position[:n_exc, 0], all_cells.position[:n_exc, 1], c=[0]*n_exc) im.set_clim(El, Vt) fig2.set_ylabel("v") pylab.colorbar(im) manager = pylab.get_current_fig_manager() print "Running network ..." for time in xrange(int((simtime/sim_step)/ms)): run(sim_step) fig1.cla() fig1.set_title("t = %g s" %((sim_step * time)/ms)) idx = s_exc.count > 0 if numpy.sum(idx) > 0: im = fig1.scatter(all_cells.position[:n_exc, 0][idx], all_cells.position[:n_exc, 1][idx], c=[0]*n_exc) s_exc.count = numpy.zeros(n_exc) ## We reset the spike counter fig1.set_xlim(0, size) fig1.set_ylim(0, size) fig1.set_ylabel("spikes") im.set_clim(0, 1) setp(fig1, xticks=[], yticks=[]) fig2.cla() im = fig2.scatter(all_cells.position[:n_exc, 0], all_cells.position[:n_exc, 1], c=v_exc.values[-1]) fig2.set_xlim(0, size) fig2.set_ylim(0, size) fig2.set_ylabel("v") im.set_clim(El, Vt) setp(fig2, xticks=[], yticks=[]) manager.canvas.draw() manager.canvas.flush_events() ioff() # To leave the interactive mode brian-1.4.3/docs_sphinx/examples-twister_QuentinPauluis.txt000066400000000000000000000045051266062647100242750ustar00rootroot00000000000000.. currentmodule:: brian .. index:: pair: example usage; subplot pair: example usage; plot pair: example usage; run pair: example usage; show pair: example usage; raster_plot pair: example usage; Connection pair: example usage; SpikeMonitor pair: example usage; ylabel pair: example usage; xlabel pair: example usage; NeuronGroup pair: example usage; Equations pair: example usage; legend pair: example usage; StateMonitor .. _example-twister_QuentinPauluis: Example: QuentinPauluis (twister) ================================= Quentin Pauluis's entry for the 2012 Brian twister. :: from brian import * taum = 20 * ms # membrane time constant taue = 5 * ms # excitatory synaptic time constant taui = 10 * ms # inhibitory synaptic time constant Vt = -50 * mV # spike threshold Vr = -60 * mV # reset value El = -49 * mV # resting potential we = (60 * 0.27 / 10) * mV # excitatory synaptic weight wi = (20 * 4.5 / 10) * mV # inhibitory synaptic weight eqs = Equations(''' dV/dt = (ge-gi-(V-El))/taum : volt dge/dt = -ge/taue : volt dgi/dt = -gi/taui : volt ''') G = NeuronGroup(4000, model=eqs, threshold=Vt, reset=Vr) Ge = G.subgroup(3200) # Excitatory neurons Gi = G.subgroup(800) # Inhibitory neurons Ce = Connection(Ge, G, 'ge', sparseness=0.2, weight=we) Ci = Connection(Gi, G, 'gi', sparseness=0.2, weight=wi*10) Cii=Connection(Gi,Gi,'gi',sparseness=0.2, weight=wi) M = SpikeMonitor(G) E=SpikeMonitor(Ge,'+') I=SpikeMonitor(Gi,'o') MV = StateMonitor(G, 'V', record=0) Mge = StateMonitor(G, 'ge', record=0) Mgi = StateMonitor(G, 'gi', record=0) G.V = Vr + (Vt - Vr) * rand(len(G)) run(2500 * ms) subplot(211) raster_plot(M, title='The CUBA network', newfigure=False) raster_plot(E) raster_plot(I) subplot(223) plot(MV.times / ms, MV[0] / mV) xlabel('Time (ms)') ylabel('V (mV)') subplot(224) plot(Mge.times / ms, Mge[0] / mV) plot(Mgi.times / ms, Mgi[0] / mV) xlabel('Time (ms)') ylabel('ge and gi (mV)') legend(('ge', 'gi'), 'upper right') show() #new.Figure brian-1.4.3/docs_sphinx/examples-twister_anonymous.txt000066400000000000000000000104421266062647100233340ustar00rootroot00000000000000.. currentmodule:: brian .. index:: pair: example usage; SpikeMonitor .. _example-twister_anonymous: Example: anonymous (twister) ============================ Anonymous entry for the 2012 Brian twister. :: ''' My contribution to the brian twister! I meant to give it more thought, but I forgot about the deadline! ''' from brian import * from brian.hears import * import pygame _mixer_status = [-1,-1] class SoundMonitor(SpikeMonitor): """ Listen to you networks! Plays pure tones whenever a neuron spikes, frequency is set according to the neuron number. """ def __init__(self, source, record=False, delay=0, frange = (100.*Hz, 5000.*Hz), duration = 50*ms, samplerate = 44100*Hz): super(SoundMonitor, self).__init__(source, record = record, delay = delay) self.samplerate = samplerate self.nsamples = np.rint(duration * samplerate) p = linspace(0, 1, len(source)).reshape((1, len(source))) p = np.tile(p, (self.nsamples, 1)) freqs = frange[0] * p + (1-p) * frange[1] del p times = linspace(0*ms, duration, self.nsamples).reshape((self.nsamples, 1)) times = np.tile(times, (1, len(source))) self.sounds = np.sin(2 * np.pi * freqs * times) self._init_mixer() def propagate(self, spikes): if len(spikes): data = np.sum(self.sounds[:,spikes], axis = 1) x = array((2 ** 15 - 1) * clip(data/amax(data), -1, 1), dtype=int16) x.shape = x.size # Make sure pygame receives an array in C-order x = pygame.sndarray.make_sound(np.ascontiguousarray(x)) x.play() def _init_mixer(self): global _mixer_status if _mixer_status==[-1,-1] or _mixer_status[0]!=1 or _mixer_status != self.samplerate: pygame.mixer.quit() pygame.mixer.init(int(self.samplerate), -16, 1) _mixer_status=[1,self.samplerate] def test_cuba(): # The CUBA example with sound! taum = 20 * ms taue = 5 * ms taui = 10 * ms Vt = -50 * mV Vr = -60 * mV El = -49 * mV eqs = Equations(''' dv/dt = (ge+gi-(v-El))/taum : volt dge/dt = -ge/taue : volt dgi/dt = -gi/taui : volt ''') P = NeuronGroup(4000, model=eqs, threshold=Vt, reset=Vr, refractory=5 * ms) P.v = Vr P.ge = 0 * mV P.gi = 0 * mV Pe = P.subgroup(3200) Pi = P.subgroup(800) we = (60 * 0.27 / 10) * mV # excitatory synaptic weight (voltage) wi = (-20 * 4.5 / 10) * mV # inhibitory synaptic weight Ce = Connection(Pe, P, 'ge', weight=we, sparseness=0.5) Ci = Connection(Pi, P, 'gi', weight=wi, sparseness=0.5) P.v = Vr + rand(len(P)) * (Vt - Vr) # Record the number of spikes M = SoundMonitor(P) run(10 * second) def test_synfire(): from brian import * # Neuron model parameters Vr = -70 * mV Vt = -55 * mV taum = 10 * ms taupsp = 0.325 * ms weight = 4.86 * mV # Neuron model eqs = Equations(''' dV/dt=(-(V-Vr)+x)*(1./taum) : volt dx/dt=(-x+y)*(1./taupsp) : volt dy/dt=-y*(1./taupsp)+25.27*mV/ms+\ (39.24*mV/ms**0.5)*xi : volt ''') # Neuron groups P = NeuronGroup(N=1000, model=eqs, threshold=Vt, reset=Vr, refractory=1 * ms) Pinput = PulsePacket(t=50 * ms, n=85, sigma=1 * ms) # The network structure Pgp = [ P.subgroup(100) for i in range(10)] C = Connection(P, P, 'y') for i in range(9): C.connect_full(Pgp[i], Pgp[i + 1], weight) Cinput = Connection(Pinput, Pgp[0], 'y') Cinput.connect_full(weight=weight) monitor = SoundMonitor(P) # Setup the network, and run it P.V = Vr + rand(len(P)) * (Vt - Vr) run(1*second) # Plot result show() if __name__ == '__main__': test_synfire() brian-1.4.3/docs_sphinx/examples.txt000066400000000000000000000211771266062647100175340ustar00rootroot00000000000000.. currentmodule:: brian Examples ======== These examples cover some basic topics in writing Brian scripts in Python. The complete source code for the examples is available in the examples folder in the extras package. .. EXAMPLES TOCTREE .. toctree:: :maxdepth: 1 .. _examples-electrophysiology: electrophysiology ----------------- .. toctree:: :maxdepth: 1 examples-electrophysiology_voltageclamp examples-electrophysiology_compensation_ex3_quality examples-electrophysiology_compensation_ex2_spikes examples-electrophysiology_threshold_analysis examples-electrophysiology_AEC examples-electrophysiology_SEVC examples-electrophysiology_bridge examples-electrophysiology_DCC examples-electrophysiology_compensation_ex1 .. _examples-misc: misc ---- .. toctree:: :maxdepth: 1 examples-misc_cable examples-misc_stopping examples-misc_topographic_map2 examples-misc_transient_sync examples-misc_pulsepacket examples-misc_mirollo_strogatz examples-misc_CUBA examples-misc_current_clamp examples-misc_phase_locking examples-misc_correlated_inputs2 examples-misc_rate_model examples-misc_timed_array examples-misc_noisy_ring examples-misc_delays examples-misc_after_potential examples-misc_named_threshold examples-misc_ring examples-misc_I-F_curve2 examples-misc_adaptive_threshold examples-misc_spike_triggered_average examples-misc_leaky_if examples-misc_I-F_curve examples-misc_heterogeneous_delays examples-misc_spikes_io examples-misc_COBAHH examples-misc_poissongroup examples-misc_two_neurons examples-misc_multipleclocks examples-misc_HodgkinHuxley examples-misc_stim2d examples-misc_reliability examples-misc_minimalexample examples-misc_using_classes examples-misc_poisson examples-misc_adaptive examples-misc_van_rossum_metric examples-misc_realtime_plotting examples-misc_topographic_map examples-misc_if examples-misc_remotecontrolclient examples-misc_linked_var examples-misc_gap_junctions examples-misc_remotecontrolserver examples-misc_non_reliability examples-misc_correlated_inputs examples-misc_expIF_network examples-misc_COBA .. _examples-audition: audition -------- .. toctree:: :maxdepth: 1 examples-audition_licklider examples-audition_filterbank examples-audition_jeffress .. _examples-synapses: synapses -------- .. toctree:: :maxdepth: 1 examples-synapses_licklider examples-synapses_two_synapses examples-synapses_transient_sync examples-synapses_synapse_construction examples-synapses_CUBA examples-synapses_poisson_synapses examples-synapses_noisy_ring examples-synapses_barrelcortex examples-synapses_short_term_plasticity2 examples-synapses_one_synapse examples-synapses_one_synapse_bis examples-synapses_STDP1_bis examples-synapses_delayed_stdp examples-synapses_nonlinear_synapses examples-synapses_STDP1 examples-synapses_short_term_plasticity examples-synapses_gapjunctions examples-synapses_jeffress examples-synapses_probabilistic_synapses2 examples-synapses_Diesmann_et_al_1999 examples-synapses_multiple_delays examples-synapses_probabilistic_synapses examples-synapses_weightmonitor .. _examples-plasticity: plasticity ---------- .. toctree:: :maxdepth: 1 examples-plasticity_STDP2 examples-plasticity_short_term_plasticity2 examples-plasticity_STDP1 examples-plasticity_short_term_plasticity .. _examples-interface: interface --------- .. toctree:: :maxdepth: 1 examples-interface_interface .. _examples-twister: twister ------- .. toctree:: :maxdepth: 1 examples-twister_PeterDiehl examples-twister_FriedemannZenke examples-twister_QuentinPauluis examples-twister_anonymous examples-twister_PierreYger examples-twister_MicheleGiugliano .. _examples-multiprocessing: multiprocessing --------------- .. toctree:: :maxdepth: 1 examples-multiprocessing_taskfarm examples-multiprocessing_multiple_runs_simple examples-multiprocessing_multiple_runs_with_gui .. _examples-hears: hears ----- .. toctree:: :maxdepth: 1 examples-hears_online_computation examples-hears_sound_localisation_model examples-hears_dcgc examples-hears_butterworth examples-hears_time_varying_filter2 examples-hears_log_gammachirp examples-hears_drnl examples-hears_simple_anf examples-hears_time_varying_filter1 examples-hears_gammatone examples-hears_cochlear_models examples-hears_approximate_gammatone examples-hears_artificial_vowels examples-hears_linear_gammachirp examples-hears_IIRfilterbank examples-hears_ircam_hrtf examples-hears_cochleagram examples-hears_sounds .. _examples-hears-tan_carney_2003: hears/tan_carney_2003 --------------------- .. toctree:: :maxdepth: 1 examples-hears-tan_carney_2003_tan_carney_Fig7 examples-hears-tan_carney_2003_tan_carney_simple_test examples-hears-tan_carney_2003_tan_carney_Fig11 .. _examples-modelfitting: modelfitting ------------ .. toctree:: :maxdepth: 1 examples-modelfitting_modelfitting examples-modelfitting_modelfitting_groups examples-modelfitting_modelfitting_machines .. _examples-frompapers: frompapers ---------- .. toctree:: :maxdepth: 1 examples-frompapers_Touboul_Brette_2008 examples-frompapers_Platkiewicz_Brette_2011 examples-frompapers_Brette_2004 examples-frompapers_Plakiewicz_Brette_2010 examples-frompapers_Guetig_Sompolinsky_2009 examples-frompapers_Diesmann_et_al_1999_longer examples-frompapers_Kremer_et_al_2011 examples-frompapers_Rossant_et_al_2011bis examples-frompapers_Rossant_et_al_2011 examples-frompapers_Brunel_Hakim_1999 examples-frompapers_Vogels_et_al_2011 examples-frompapers_Rothman_Manis_2003 examples-frompapers_Brette_Guigon_2003 examples-frompapers_Sturzl_et_al_2000 examples-frompapers_Wang_Buszaki_1996 examples-frompapers_Goodman_Brette_2010 examples-frompapers_Muller_et_al_2011 examples-frompapers_Diesmann_et_al_1999 examples-frompapers_Brette_Gerstner_2005 .. _examples-frompapers-computing with neural synchrony-coincidence detection and synchrony: frompapers/computing with neural synchrony/coincidence detection and synchrony ------------------------------------------------------------------------------ .. toctree:: :maxdepth: 1 examples-frompapers-computing with neural synchrony-coincidence detection and synchrony_Fig6_shared_variability examples-frompapers-computing with neural synchrony-coincidence detection and synchrony_Fig1F_ROC examples-frompapers-computing with neural synchrony-coincidence detection and synchrony_Fig5D_reproducibility examples-frompapers-computing with neural synchrony-coincidence detection and synchrony_Fig5E_precision_reliability examples-frompapers-computing with neural synchrony-coincidence detection and synchrony_Fig1C .. _examples-frompapers-computing with neural synchrony-hearing: frompapers/computing with neural synchrony/hearing -------------------------------------------------- .. toctree:: :maxdepth: 1 examples-frompapers-computing with neural synchrony-hearing_Fig12A_Goodman_Brette_2010 examples-frompapers-computing with neural synchrony-hearing_Fig7A_Jeffress examples-frompapers-computing with neural synchrony-hearing_Fig7B_Licklider .. _examples-frompapers-computing with neural synchrony-duration selectivity: frompapers/computing with neural synchrony/duration selectivity --------------------------------------------------------------- .. toctree:: :maxdepth: 1 examples-frompapers-computing with neural synchrony-duration selectivity_params examples-frompapers-computing with neural synchrony-duration selectivity_Fig2C_decoding_synchrony examples-frompapers-computing with neural synchrony-duration selectivity_Fig1A_rebound_neurons examples-frompapers-computing with neural synchrony-duration selectivity_Fig4_duration_stdp examples-frompapers-computing with neural synchrony-duration selectivity_Fig1D_duration_selectivity examples-frompapers-computing with neural synchrony-duration selectivity_Fig2A_synchrony_partition .. _examples-frompapers-computing with neural synchrony-olfaction: frompapers/computing with neural synchrony/olfaction ---------------------------------------------------- .. toctree:: :maxdepth: 1 examples-frompapers-computing with neural synchrony-olfaction_params examples-frompapers-computing with neural synchrony-olfaction_Fig11B_olfaction_stdp_learning examples-frompapers-computing with neural synchrony-olfaction_Fig9B_olfaction examples-frompapers-computing with neural synchrony-olfaction_Fig11B_olfaction_stdp_testing brian-1.4.3/docs_sphinx/experimental-codegen.txt000066400000000000000000000020011266062647100217760ustar00rootroot00000000000000.. currentmodule:: brian.experimental.codegen .. _experimental-codegen: Code generation =============== Brian has support for automatic generation of C code, detailed in :ref:`compiled-code`. We also have experimental support for C code generation more widely, implementing the algorithms described in `Goodman (2010) `__. This support can be activated using the ``usecodegen*``, ``usenewpropagate`` and ``usecstdp`` global preferences (see :ref:`preferences`). Note that not all code will run without problems using code generation yet, but in most cases it will and speed improvements can be very substantial, especially for STDP. References ---------- * `Goodman DFM (2010) `__. Code Generation: A Strategy for Neural Network Simulators. Neuroinformatics 8, no. 3 (9). doi:10.1007/s12021-010-9082-x. [`pdf `__] brian-1.4.3/docs_sphinx/experimental-cuda.txt000066400000000000000000000014311266062647100213140ustar00rootroot00000000000000.. currentmodule:: brian.experimental.cuda GPU/CUDA ======== Brian has some experimental support for doing numerical integration only using GPUs, using the `PyCUDA package `__. Note that only numerical integration is done on the GPU, which means that variables that can be altered on the CPU (via synapses or user operations) need to be copied to and from the GPU each time step, as well as variables that are used for thresholding and reset operations. This creates a memory bandwidth bottleneck, which means that for the moment the GPU code is only useful for complicated neuron models such as Hodgkin-Huxley type neurons (although in this case it can lead to very substantial speed improvements). .. autoclass:: GPUNeuronGroup brian-1.4.3/docs_sphinx/experimental-modeldocumentation.txt000066400000000000000000000002061266062647100242710ustar00rootroot00000000000000Automatic Model Documentation ============================= .. automodule:: brian.experimental.model_documentation :members: brian-1.4.3/docs_sphinx/experimental-multilinearstateupdater.txt000066400000000000000000000010061266062647100253510ustar00rootroot00000000000000.. currentmodule:: brian.experimental.multilinearstateupdater Multilinear state updater ========================= .. autoclass:: MultiLinearNeuronGroup Example:: eqs = ''' dv/dt = k*v/(1*second) : 1 dw/dt = k*w/(1*second) : 1 k : 1 ''' k = array([-1,-2,-3]) subs = ['k'] G = MultiLinearNeuronGroup(eqs, subs) G.v = 1 G.w = 0 M = StateMonitor(G, 'v', record=True) run(1*second) for i in range(len(G)): plot(M.times, M[i]) show()brian-1.4.3/docs_sphinx/experimental-realtimeconnectionmonitor.txt000066400000000000000000000004401266062647100256710ustar00rootroot00000000000000.. currentmodule:: brian.experimental.realtime_monitor Realtime Connection Monitor =========================== You can visual weight matrices in realtime, using the `PyGame `__ package with the following class. .. autoclass:: RealtimeConnectionMonitor brian-1.4.3/docs_sphinx/experimental.txt000066400000000000000000000010011266062647100203730ustar00rootroot00000000000000.. currentmodule:: brian Experimental features ********************* The following features are located in the ``experimental`` package inside Brian, and are subject to change without notice. The most likely changes are ones of syntax and naming, although functionality may also be subject to change. .. toctree:: :maxdepth: 1 experimental-codegen experimental-cuda experimental-multilinearstateupdater experimental-realtimeconnectionmonitor experimental-modeldocumentationbrian-1.4.3/docs_sphinx/extending.txt000066400000000000000000000004061266062647100176730ustar00rootroot00000000000000Extending Brian =============== TODO: Description of how to extend Brian, add new model types, and maybe at some point how to upload them to a database, share with others, etc. For the moment, see the documentation on :ref:`projects-with-multiple-files`.brian-1.4.3/docs_sphinx/files.txt000066400000000000000000000013511266062647100170100ustar00rootroot00000000000000.. currentmodule:: brian File management =============== A few functions are provided to read files with common formats. The function :func:`read_neuron_dat` reads a Neuron .dat text file and returns a vector of times and a vector of values. This is the format used by the Neuron simulator when saving the time-varying value of a variable from the GUI. For example:: t, v = read_neuron_dat('myfile.dat') The function :func:`read_atf` reads an Axon .atf text file and returns a vector of times and a vector of values. This is a format used to store data recorded with Axon amplifiers. Note that metadata stored in the file are not extracted. Binary .abf files are currently not supported. See also :ref:`reference-io`. brian-1.4.3/docs_sphinx/hears.txt000066400000000000000000000651311266062647100170160ustar00rootroot00000000000000.. currentmodule:: brian.hears .. index:: pair: auditory; modelling .. _brianhears: Brian hears =========== .. image:: images/brianhearslogo.png :align: right Brian hears is an auditory modelling library for Python. It is part of the neural network simulator package Brian, but can also be used on its own. To download Brian hears, simply :ref:`download Brian `: Brian hears is included as part of the package. Brian hears is primarily designed for generating and manipulating sounds, and applying large banks of filters. We import the package by writing:: from brian import * from brian.hears import * Then, for example, to generate a tone or a whitenoise we would write:: sound1 = tone(1*kHz, .1*second) sound2 = whitenoise(.1*second) These sounds can then be manipulated in various ways, for example:: sound = sound1+sound2 sound = sound.ramp() If you have the `pygame `__ package installed, you can also play these sounds:: sound.play() We can filter these sounds through a bank of 3000 gammatone filters covering the human auditory range as follows:: cf = erbspace(20*Hz, 20*kHz, 3000) fb = Gammatone(sound, cf) output = fb.process() The output of this would look something like this (zoomed into one region): .. image:: images/cochleagram.png Alternatively, if we're interested in modelling auditory nerve fibres, we could feed the output of this filterbank directly into a group of neurons defined with Brian:: # Half-wave rectification and compression [x]^(1/3) ihc = FunctionFilterbank(fb, lambda x: 3*clip(x, 0, Inf)**(1.0/3.0)) # Leaky integrate-and-fire model with noise and refractoriness eqs = ''' dv/dt = (I-v)/(1*ms)+0.2*xi*(2/(1*ms))**.5 : 1 I : 1 ''' anf = FilterbankGroup(ihc, 'I', eqs, reset=0, threshold=1, refractory=5*ms) This model would give output something like this: .. image:: images/auditory-nerve-fibre-rasterplot.png The human cochlea applies the equivalent of 3000 auditory filters, which causes a technical problem for modellers which this package is designed to address. At a typical sample rate, the output of 3000 filters would saturate the computer's RAM in a few seconds. To deal with this, we use online computation, that is we only ever keep in memory the output of the filters for a relatively short duration (say, the most recent 20ms), do our modelling with these values, and then discard them. Although this requires that some models be rewritten for online rather than offline computation, it allows us to easily handle models with very large numbers of channels. 3000 or 6000 for human monaural or binaural processing is straightforward, and even much larger banks of filters can be used (for example, around 30,000 in `Goodman DFM, Brette R (2010). Spike-timing-based computation in sound localization. PLoS Comput. Biol. 6(11): e1000993. doi:10.1371/journal.pcbi.1000993 `__). Techniques for online computation are discussed below in the section `Online computation`_. Brian hears consists of classes and functions for defining `sounds`_, `filter chains`_, cochlear models, neuron models and `head-related transfer functions`_. These classes are designed to be modular and easily extendable. Typically, a model will consist of a chain starting with a sound which is plugged into a chain of filter banks, which are then plugged into a neuron model. The two main classes in Brian hears are :class:`Sound` and :class:`Filterbank`, which function very similarly. Each consists of multiple channels (typically just 1 or 2 in the case of sounds, and many in the case of filterbanks, but in principle any number of channels is possible for either). The difference is that a filterbank has an input source, which can be either a sound or another filterbank. All scripts using Brian hears should start by importing the Brian and Brian hears packages as follows:: from brian import * from brian.hears import * To download Brian hears, simply :ref:`download Brian `: Brian hears is included as part of the package. .. seealso:: Reference documentation for :ref:`brian-hears-reference`, which covers everything in this overview in detail, and more. List of :ref:`examples of using Brian hears `. .. index:: single: sound pair: sound; wav pair: sound; aiff .. _sounds_overview: Sounds ------ Sounds can be loaded from a WAV or AIFF file with the :func:`loadsound` function (and saved with the :func:`savesound` function or :meth:`Sound.save` method), or by initialising with a filename:: sound = loadsound('test.wav') sound = Sound('test.aif') sound.save('test.wav') Various standard types of sounds can also be constructed, e.g. pure tones, white noise, clicks and silence:: sound = tone(1*kHz, 1*second) sound = whitenoise(1*second) sound = click(1*ms) sound = silence(1*second) You can pass a function of time or an array to initialise a sound:: # Equivalent to Sound.tone sound = Sound(lambda t:sin(50*Hz*2*pi*t), duration=1*second) # Equivalent to Sound.whitenoise sound = Sound(randn(int(1*second*44.1*kHz)), samplerate=44.1*kHz) Multiple channel sounds can be passed as a list or tuple of filenames, arrays or :class:`Sound` objects:: sound = Sound(('left.wav', 'right.wav')) sound = Sound((randn(44100), randn(44100)), samplerate=44.1*kHz) sound = Sound((Sound.tone(1*kHz, 1*second), Sound.tone(2*kHz, 1*second))) A multi-channel sound is also a numpy array of shape ``(nsamples, nchannels)``, and can be initialised as this (or converted to a standard numpy array):: sound = Sound(randn(44100, 2), samplerate=44.1*kHz) arr = array(sound) Sounds can be added and multiplied:: sound = Sound.tone(1*kHz, 1*second)+0.1*Sound.whitenoise(1*second) For more details on combining and operating on sounds, including shifting them in time, repeating them, resampling them, ramping them, finding and setting intensities, plotting spectrograms, etc., see :class:`Sound`. Sounds can be played using the :func:`play` function or :meth:`Sound.play` method:: play(sound) sound.play() .. index:: pair: sound; sequence Sequences of sounds can be played as:: play(sound1, sound2, sound3) .. index:: pair: sound; stereo single: sound; multiple channels The number of channels in a sound can be found using the ``nchannels`` attribute, and individual channels can be extracted using the :meth:`Sound.channel` method, or using the ``left`` and ``right`` attributes in the case of stereo sounds:: print sound.nchannels print amax(abs(sound.left-sound.channel(0))) As an example of using this, the following swaps the channels in a stereo sound:: sound = Sound('test_stereo.wav') swappedsound = Sound((sound.right, sound.left)) swappedsound.play() .. index:: pair: sound; level pair: sound; dB The level of the sound can be computed and changed with the ``sound.level`` attribute. Levels are returned in dB which is a special unit in Brian hears. For example, ``10*dB+10`` will raise an error because ``10`` does not have units of dB. The multiplicative gain of a value in dB can be computed with the function ``gain(level)``. All dB values are measured as RMS dB SPL assuming that the values of the sound object are measured in Pascals. Some examples:: sound = whitenoise(100*ms) print sound.level sound.level = 60*dB sound.level += 10*dB sound *= gain(-10*dB) .. index:: single: filter single: filter bank pair: cochlea; modelling Filter chains ------------- The standard way to set up a model based on filterbanks is to start with a sound and then construct a chain of filterbanks that modify it, for example a common model of cochlear filtering is to apply a bank of gammatone filters, and then half wave rectify and compress it (for example, with a 1/3 power law). This can be achieved in Brian hears as follows (for 3000 channels in the human hearing range from 20 Hz to 20 kHz):: cfmin, cfmax, cfN = 20*Hz, 20*kHz, 3000 cf = erbspace(cfmin, cfmax, cfN) sound = Sound('test.wav') gfb = GammatoneFilterbank(sound, cf) ihc = FunctionFilterbank(gfb, lambda x: clip(x, 0, Inf)**(1.0/3.0)) The :func:`erbspace` function constructs an array of centre frequencies on the ERB scale. The ``GammatoneFilterbank(source, cf)`` class creates a bank of gammatone filters with inputs coming from ``source`` and the centre frequencies in the array ``cf``. The ``FunctionFilterbank(source, func)`` creates a bank of filters that applies the given function ``func`` to the inputs in ``source``. Filterbanks can be added and multiplied, for example for creating a linear and nonlinear path, e.g.:: sum_path_fb = 0.1*linear_path_fb+0.2*nonlinear_path_fb A filterbank must have an input with either a single channel or an equal number of channels. In the former case, the single channel is duplicated for each of the output channels. However, you might want to apply gammatone filters to a stereo sound, for example, but in this case it's not clear how to duplicate the channels and you have to specify it explicitly. You can do this using the :class:`Repeat`, :class:`Tile`, :class:`Join` and :class:`Interleave` filterbanks. For example, if the input is a stereo sound with channels LR then you can get an output with channels LLLRRR or LRLRLR by writing (respectively):: fb = Repeat(sound, 3) fb = Tile(sound, 3) To combine multiple filterbanks into one, you can either join them in series or interleave them, as follows:: fb = Join(source1, source2) fb = Interleave(source1, source2) For a more general (but more complicated) approach, see :class:`RestructureFilterbank`. Two of the most important generic filterbanks (upon which many of the others are based) are :class:`LinearFilterbank` and :class:`FIRFilterbank`. The former is a generic digital filter for FIR and IIR filters. The latter is specifically for FIR filters. These can be implemented with the former, but the implementation is optimised using FFTs with the latter (which can often be hundreds of times faster, particularly for long impulse responses). IIR filter banks can be designed using :class:`IIRFilterbank` which is based on the syntax of the ``iirdesign`` scipy function. You can change the input source to a :class:`Filterbank` by modifying its ``source`` attribute, e.g. to change the input sound of a filterbank ``fb`` you might do:: fb.source = newsound Note that the new source should have the same number of channels. .. index:: pair: filtering; control path You can implement control paths (using the output of one filter chain path to modify the parameters of another filter chain path) using :class:`ControlFilterbank` (see reference documentation for more details). For examples of this in action, see the following: * :ref:`example-hears_time_varying_filter1`. * :ref:`example-hears_time_varying_filter2`. * :ref:`example-hears_dcgc`. Connecting with Brian --------------------- To create spiking neuron models based on filter chains, you use the :class:`FilterbankGroup` class. This acts exactly like a standard Brian :class:`~brian.NeuronGroup` except that you give a source filterbank and choose a state variable in the target equations for the output of the filterbank. A simple auditory nerve fibre model would take the inner hair cell model from earlier, and feed it into a noisy leaky integrate-and-fire model as follows:: # Inner hair cell model as before cfmin, cfmax, cfN = 20*Hz, 20*kHz, 3000 cf = erbspace(cfmin, cfmax, cfN) sound = Sound.whitenoise(100*ms) gfb = Gammatone(sound, cf) ihc = FunctionFilterbank(gfb, lambda x: 3*clip(x, 0, Inf)**(1.0/3.0)) # Leaky integrate-and-fire model with noise and refractoriness eqs = ''' dv/dt = (I-v)/(1*ms)+0.2*xi*(2/(1*ms))**.5 : 1 I : 1 ''' G = FilterbankGroup(ihc, 'I', eqs, reset=0, threshold=1, refractory=5*ms) # Run, and raster plot of the spikes M = SpikeMonitor(G) run(sound.duration) raster_plot(M) show() And here's the output (after 6 seconds of computation on a 2GHz laptop): .. image:: images/auditory-nerve-fibre-rasterplot.png Plotting -------- Often, you want to use log-scaled axes for frequency in plots, but the built-in matplotlib axis labelling for log-scaled axes doesn't work well for frequencies. We provided two functions (:func:`log_frequency_xaxis_labels` and :func:`log_frequency_yaxis_labels`) to automatically set useful axis labels. For example:: cf = erbspace(100*Hz, 10*kHz) ... semilogx(cf, response) axis('tight') log_frequency_xaxis_labels() .. index:: pair: online; computation Online computation ------------------ Typically in auditory modelling, we precompute the entire output of each channel of the filterbank ("offline computation"), and then work with that. This is straightforward, but puts a severe limit on the number of channels we can use or the length of time we can work with (otherwise the RAM would be quickly exhausted). Brian hears allows us to use a very large number of channels in filterbanks, but at the cost of only storing the output of the filterbanks for a relatively short period of time ("online computation"). This requires a slight change in the way we use the output of the filterbanks, but is actually not too difficult. For example, suppose we wanted to compute the vector of RMS values for each channel of the output of the filterbank. Traditionally, or if we just use the syntax ``output = fb.process()`` in Brian hears, we have an array ``output`` of shape ``(nsamples, nchannels)``. We could compute the vector of RMS values as:: rms = sqrt(mean(output**2, axis=0)) To do the same thing with online computation, we simply store a vector of the running sum of squares, and update it for each buffered segment as it is computed. At the end of the processing, we divide the sum of squares by the number of samples and take the square root. The :meth:`Filterbank.process` method allows us to pass an optional function ``f(output, running)`` of two arguments. In this case, :meth:`~Filterbank.process` will first call ``running = f(output, 0)`` for the first buffered segment ``output``. It will then call ``running = f(output, running)`` for each subsequent segment. In other words, it will "accumulate" the output of ``f``, passing the output of each call to the subsequent call. To compute the vector of RMS values then, we simply do:: def sum_of_squares(input, running): return running+sum(input**2, axis=0) rms = sqrt(fb.process(sum_of_squares)/nsamples) If the computation you wish to perform is more complicated than can be achieved with the :meth:`~Filterbank.process` method, you can derive a class from :class:`Filterbank` (see that class' reference documentation for more details on this). .. index:: pair: buffering; interface Buffering interface ------------------- The :class:`Sound`, :class:`OnlineSound` and :class:`Filterbank` classes (and all classes derived from them) all implement the same buffering mechanism. The purpose of this is to allow for efficient processing of multiple channels in buffers. Rather than precomputing the application of filters to all channels (which for large numbers of channels or long sounds would not fit in memory), we process small chunks at a time. The entire design of these classes is based on the idea of buffering, as defined by the base class :class:`Bufferable` (see section :ref:`brian-hears-class-diagram`). Each class has two methods, ``buffer_init()`` to initialise the buffer, and ``buffer_fetch(start, end)`` to fetch the portion of the buffer from samples with indices from ``start`` to ``end`` (not including ``end`` as standard for Python). The ``buffer_fetch(start, end)`` method should return a 2D array of shape ``(end-start, nchannels)`` with the buffered values. From the user point of view, all you need to do, having set up a chain of :class:`Sound` and :class:`Filterbank` objects, is to call ``buffer_fetch(start, end)`` repeatedly. If the output of a :class:`Filterbank` is being plugged into a :class:`FilterbankGroup` object, everything is handled automatically. For cases where the number of channels is small or the length of the input source is short, you can use the :meth:`Filterbank.fetch(duration)` method to automatically handle the initialisation and repeated application of ``buffer_fetch``. To extend :class:`Filterbank`, it is often sufficient just to implement the ``buffer_apply(input)`` method. See the documentation for :class:`Filterbank` for more details. Library ------- Brian hears comes with a package of predefined filter classes to be used as basic blocks by the user. All of them are implemented as filterbanks. First, a series of standard filters widely used in audio processing are available: .. tabularcolumns::|p{3cm}|p{15cm}|p{3cm}| +------------------------------------+---------------------------------------------------------------------------------------------------+----------------------------------------------+ | Class | Descripition | Example | +====================================+===================================================================================================+==============================================+ | :class:`IIRFilterbank` | Bank of low, high, bandpass or bandstop filter of type Chebyshef, Elliptic, etc... | :ref:`example-hears_IIRfilterbank` | +------------------------------------+---------------------------------------------------------------------------------------------------+----------------------------------------------+ | :class:`Butterworth` | Bank of low, high, bandpass or bandstop Butterworth filters | :ref:`example-hears_butterworth` | +------------------------------------+---------------------------------------------------------------------------------------------------+----------------------------------------------+ | :class:`LowPass` | Bank of lowpass filters of order 1 | :ref:`example-hears_cochleagram` | +------------------------------------+---------------------------------------------------------------------------------------------------+----------------------------------------------+ Second, the library provides linear auditory filters developed to model the middle ear transfer function and the frequency analysis of the cochlea: .. tabularcolumns::|p{3cm}|p{15cm}|p{3cm}| +------------------------------------+---------------------------------------------------------------------------------------------------+------------------------------------------------------------+ | Class | Description | Example | +====================================+===================================================================================================+============================================================+ | :class:`MiddleEar` | Linear bandpass filter, based on middle-ear frequency response properties | :ref:`example-hears-tan_carney_2003_tan_carney_simple_test`| +------------------------------------+---------------------------------------------------------------------------------------------------+------------------------------------------------------------+ | :class:`Gammatone` | Bank of IIR gammatone filters (based on Slaney implementation) | :ref:`example-hears_gammatone` | +------------------------------------+---------------------------------------------------------------------------------------------------+------------------------------------------------------------+ | :class:`ApproximateGammatone` | Bank of IIR gammatone filters (based on Hohmann implementation) | :ref:`example-hears_approximate_gammatone` | +------------------------------------+---------------------------------------------------------------------------------------------------+------------------------------------------------------------+ | :class:`LogGammachirp` | Bank of IIR gammachirp filters with logarithmic sweep (based on Irino implementation) | :ref:`example-hears_log_gammachirp` | +------------------------------------+---------------------------------------------------------------------------------------------------+------------------------------------------------------------+ | :class:`LinearGammachirp` | Bank of FIR chirp filters with linear sweep and gamma envelope | :ref:`example-hears_linear_gammachirp` | +------------------------------------+---------------------------------------------------------------------------------------------------+------------------------------------------------------------+ | :class:`LinearGaborchirp` | Bank of FIR chirp filters with linear sweep and gaussian envelope | | +------------------------------------+---------------------------------------------------------------------------------------------------+------------------------------------------------------------+ Finally, Brian hears comes with a series of complex nonlinear cochlear models developed to model nonlinear effects such as filter bandwith level dependency, two-tones suppression, peak position level dependency, etc. .. tabularcolumns::|p{3cm}|p{15cm}|p{3cm}| +------------------------------------+---------------------------------------------------------------------------------------------------+------------------------------------------------------------+ | Class | Description | Example | +====================================+===================================================================================================+============================================================+ | :class:`DRNL` | Dual resonance nonlinear filter as described in Lopez-Paveda and Meddis, JASA 2001 | :ref:`example-hears_drnl` | +------------------------------------+---------------------------------------------------------------------------------------------------+------------------------------------------------------------+ | :class:`DCGC` | Compressive gammachirp auditory filter as described in Irino and Patterson, JASA 2001 | :ref:`example-hears_dcgc` | +------------------------------------+---------------------------------------------------------------------------------------------------+------------------------------------------------------------+ | :class:`TanCarney` | Auditory phenomenological model as described in Tan and Carney, JASA 2003 | :ref:`example-hears-tan_carney_2003_tan_carney_simple_test`| +------------------------------------+---------------------------------------------------------------------------------------------------+------------------------------------------------------------+ | :class:`ZhangSynapse` | Model of an inner hair cell -- auditory nerve synapse (Zhang et al., JASA 2001) | :ref:`example-hears-tan_carney_2003_tan_carney_simple_test`| +------------------------------------+---------------------------------------------------------------------------------------------------+------------------------------------------------------------+ .. index:: single: HRTF pair: IRCAM; HRTF pair: database; HRTF Head-related transfer functions ------------------------------- You can work with head-related transfer functions (HRTFs) using the three classes :class:`HRTF` (a single pair of left/right ear HRTFs), :class:`HRTFSet` (a set of HRTFs, typically for a single individual), and :class:`HRTFDatabase` (for working with databases of individuals). At the moment, we have included only one HRTF database, the :class:`IRCAM_LISTEN` public HRTF database. However, we will add support for the CIPIC and MIT-KEMAR databases in a subsequent release. There is also one artificial HRTF database, :class:`HeadlessDatabase` used for generating HRTFs of artifically introduced ITDs. An example of loading the IRCAM database, selecting a subject and plotting the pair of impulse responses for a particular direction:: hrtfdb = IRCAM_LISTEN(r'F:\HRTF\IRCAM') hrtfset = hrtfdb.load_subject(1002) hrtf = hrtfset(azim=30, elev=15) plot(hrtf.left) plot(hrtf.right) show() :class:`HRTFSet` has a set of coordinates, which can be accessed via the ``coordinates`` attribute, e.g.:: print hrtfset.coordinates['azim'] print hrtfset.coordinates['elev'] You can also generated filterbanks associated either to an :class:`HRTF` or an entire :class:`HRTFSet`. Here is an example of doing this with the IRCAM database, and applying this filterbank to some white noise and plotting the response as an image:: # Load database hrtfdb = IRCAM_LISTEN(r'D:\HRTF\IRCAM') hrtfset = hrtfdb.load_subject(1002) # Select only the horizontal plane hrtfset = hrtfset.subset(lambda elev: elev==0) # Set up a filterbank sound = whitenoise(10*ms) fb = hrtfset.filterbank(sound) # Extract the filtered response and plot img = fb.process().T img_left = img[:img.shape[0]/2, :] img_right = img[img.shape[0]/2:, :] subplot(121) imshow(img_left, origin='lower left', aspect='auto', extent=(0, sound.duration/ms, 0, 360)) xlabel('Time (ms)') ylabel('Azimuth') title('Left ear') subplot(122) imshow(img_right, origin='lower left', aspect='auto', extent=(0, sound.duration/ms, 0, 360)) xlabel('Time (ms)') ylabel('Azimuth') title('Right ear') show() This generates the following output: .. image:: images/hrtfset_response_plot.png For more details, see the reference documentation for :class:`HRTF`, :class:`HRTFSet`, :class:`HRTFDatabase`, :class:`IRCAM_LISTEN` and :class:`HeadlessDatabase`. brian-1.4.3/docs_sphinx/images/000077500000000000000000000000001266062647100164125ustar00rootroot00000000000000brian-1.4.3/docs_sphinx/images/auditory-nerve-fibre-rasterplot.png000066400000000000000000007260531266062647100253740ustar00rootroot00000000000000‰PNG  IHDR€ä®@žò pHYsaa¨?§i OiCCPPhotoshop ICC profilexÚSgTSé=÷ÞôBKˆ€”KoR RB‹€‘&*! 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Œ"o^nåøø¸fggëôô´ªª=zT_¿~­©©©–'}Ÿ?®ÅÅÅáÓH_½zUûûû-Oð÷ì’sì’€Q%^"&ÛOâ% B¼Dˆ—€ñ!^"ÄK@„x ˆ/â% B¼Dˆ—€ñ!^"ÄK@„x ˆ/â% B¼Dˆ—€ñ!^"ÄK@„x ˆ/â% B¼Dˆ—€ñ!^"ÄK@„x ˆ/â% B¼Dˆ—€ñ!^"ÄK@„x ˆ/â% B¼Dˆ—€ñ!^"ÄK@„x ˆ/â% B¼Dˆ—€ñ!^"ÄK@„x ˆ/â% B¼Dˆ—€ñ!^"ÄK@„x ˆ/â% B¼Dˆ—€ñ!^"ÄK@„x ˆ/â% B¼Dˆ—€ñ!^"ÄK@„x ˆ/â% B¼Dˆ—€ñ!^"ÄK@„x ˆ/â% B¼Dˆ—€ñ!^"ÄK@„x ˆ/â% B¼Dˆ—€ñ!^"ÄK@„x ˆ/â% B¼Dˆ—€ñ!^"ÄK@„x ˆ/â% B¼Dˆ—€ñ!^"ÄK@„x ˆ/â% B¼Dˆ—€ñ!^"ÄK@„x ˆ/â% B¼Dˆ—€ñ!^"ÄK@„x ˆ/â% B¼Dˆ—€ñ!^"~)—¹ùF;mIEND®B`‚brian-1.4.3/docs_sphinx/index.txt000066400000000000000000000016231266062647100170170ustar00rootroot00000000000000Welcome to Brian's documentation! ********************************* .. module:: brian :synopsis: The Brian package .. moduleauthor:: Romain Brette .. moduleauthor:: Dan Goodman .. toctree:: :maxdepth: 2 introduction installation slowstart usermanual library advanced extending reference typicaltasks experimental developer See also the `reference sheet `__. You can download a PDF version of the documentation `here `__. .. current removed because it's not written yet quickstart .. TODO: where should these things go? The manual is currently undergoing a restructuring, the following pages have not yet been placed. .. toctree:: :maxdepth: 2 inside channels magic moreunits concepts reference-scipy-numpy-pylab brian-1.4.3/docs_sphinx/inputs.txt000066400000000000000000000164401266062647100172350ustar00rootroot00000000000000.. currentmodule:: brian Inputs ====== Some specific types of neuron groups are available to provide inputs to a network. Poisson inputs -------------- Poisson spike trains can be generated as follows:: group=PoissonGroup(100,rates=10*Hz) Here 100 neurons are defined, which emit spikes independently according to Poisson processes with rates 10 Hz. To have different rates across the group, initialise with an array of rates: group=PoissonGroup(100,rates=linspace(0*Hz,10*Hz,100)) Inhomogeneous Poisson processes can be defined by passing a function of time that returns the rates:: group=PoissonGroup(100,rates=lambda t:(1+cos(t))*10*Hz) or:: r0=linspace(0*Hz,10*Hz,100) group=PoissonGroup(100,rates=lambda t:(1+cos(t))*r0) There is another class for Poisson inputs: :class:`PoissonInput`, which updates the state variable of a :class:`NeuronGroup` dynamically without storing in memory all the Poisson events. It can be used like this:: input = PoissonInput(group, N=N, rate=rate, weight=w, state='I') In this case, the variable ``I`` represents the sum of ``N`` independent Poisson spike inputs with rate ``rate``, where each individual synaptic event increases the variable ``I`` by ``w``. Several :class:`PoissonInput` objects can be created for a given :class:`NeuronGroup`, in which case all the independent inputs are linearly superimposed. Other features of the :class:`PoissonInput` class include the following (see the reference): * record the individual Poisson events (``record=True`` keyword), * having identical Poisson events for all neurons, instead of having independent copies for every neuron (``freeze=True`` keyword) * copying every Poisson input a specified number of times (``copies=p`` keyword). This is equivalent of specifying ``weight=p*w``, except that those copies can be randomly shifted (``jitter`` keyword), or can be unreliable to model synapse unreliability (``reliability`` keyword). The latter case corresponds to a Binomial synaptic weight. Correlated inputs ----------------- Generation of correlated spike trains is partially implemented, using algorithms from the the following paper: Brette, R. (2009) `Generation of correlated spike trains `__, Neural Computation 21(1): 188-215. Currently, only the method with Cox processes (or doubly stochastic processes, first method in the paper) is fully implemented. Doubly stochastic processes ^^^^^^^^^^^^^^^^^^^^^^^^^^^ To generate correlated spike trains with identical rates and homogeneous exponential correlations, use the class :class:`HomogeneousCorrelatedSpikeTrains`:: group=HomogeneousCorrelatedSpikeTrains(100,r=10*Hz,c=0.1,tauc=10*ms) where ``r`` is the rate, ``c`` is the total correlation strength and ``tauc`` is the correlation time constant. The cross-covariance functions are `(c*r/tauc)*exp(-|s|/tauc)`. To generate correlated spike trains with arbitrary rates r(i) and cross-covariance functions `c(i,j)*exp(-|s|/tauc)`, use the class :class:`CorrelatedSpikeTrains`:: group=CorrelatedSpikeTrains(rates,C,tauc) where ``rates`` is the vector of rates r(i), ``C`` is the correlation matrix (which must be symmetrical) and ``tauc`` is the correlation time constant. Note that distortions are introduced with strong correlations and short correlation time constants. For short time constants, the mixture method is more appropriate (see the paper above). The two classes :class:`HomogeneousCorrelatedSpikeTrains` and :class:`CorrelatedSpikeTrains` define neuron groups, which can be directly used with :class:`Connection` objects. Mixture method ^^^^^^^^^^^^^^ The mixture method to generate correlated spike trains is only partially implemented and the interface may change in future releases. Currently, one can use the function :func:`mixture_process` to generate spike trains:: spiketrains=mixture_process(nu,P,tauc,t) where ``nu`` is the vector of rates of the source spike trains, ``P`` is the mixture matrix (entries between 0 and 1), ``tauc`` is the correlation time constant, ``t`` is the duration. It returns a list of (neuron_number,spike_time), which can be passed to ``SpikeGeneratorGroup``. This method is appropriate for short time constants and is explained in the paper mentioned above. Input spike trains ------------------ A set of spike trains can be explicitly defined as list of pairs (i,t) (meaning neuron i fires at time t), which used to initialise a :class:`SpikeGeneratorGroup`:: spiketimes=[(0,1*ms), (1,2*ms)] input=SpikeGeneratorGroup(5,spiketimes) The neuron 0 fires at time 1 ms and neuron 1 fires at time 2 ms (there are 5 neurons, but 3 of them never spike). One may also pass a generator instead of a list (in that case the pairs should be ordered in time). Gaussian spike packets ^^^^^^^^^^^^^^^^^^^^^^ There is a subclass of :class:`SpikeGeneratorGroup` for generating spikes with a Gaussian distribution:: input=PulsePacket(t=10*ms,n=10,sigma=3*ms) Here 10 spikes are produced, with spike times distributed according a Gaussian distribution with mean 10 ms and standard deviation 3 ms. Direct input ------------ Inputs may also be defined by accessing directly the state variables of a neuron group. The standard way to do this is to insert parameters in the equations:: eqs = ''' dv/dt = (I-v)/tau : volt I : volt ''' group = NeuronGroup(100, model=eqs, reset=0*mV, threshold=15*mV) group.I = linspace(0*mV, 20*mV, 100) Here the value of the parameter I for each neuron is provided at initialisation time (evenly distributed between 0 mV and 20 mV). Time varying inputs ^^^^^^^^^^^^^^^^^^^ It is possible to change the value of I every timestep by using a user-defined operation (see next section). Alternatively, you can use a :class:`TimedArray` to specify the values the variable will have at each time interval, for example:: eqs = ''' dv/dt = (I-v)/tau : volt I : volt ''' group = NeuronGroup(1, model=eqs, reset=0*mV, threshold=15*mV) group.I = TimedArray(linspace(0*mV, 20*mV, 100), dt=10*ms) Here ``I`` will have value ``0*mV`` for t between 0 and ``10*ms`, ``0.2*mV`` between ``10*ms`` and ``20*ms``, and so on. A more intuitive syntax is:: I = TimedArray(linspace(0*mV, 20*mV, 100), dt=10*ms) eqs = ''' dv/dt = (I(t) * volt - v)/tau : volt ''' group = NeuronGroup(1, model=eqs, reset=0*mV, threshold=15*mV) Note however that the more efficient exact linear differential equations solver won't be used in this case because ``I(t)`` could be any function, so the previous mechanism is often preferable. Additionally, be aware that the call to ``I(t)`` does return a value without units (as units cannot be stored in arrays), therefore you have to explicitly multiply it with the respective unit. Linked variables ^^^^^^^^^^^^^^^^ Another option is to link the variable of one group to the variables of another group using :func:`linked_var`, for example:: G = NeuronGroup(...) H = NeuronGroup(...) G.V = linked_var(H, 'W') In this scenario, the variable V in group G will always be updated with the values from variable W in group H. The groups G and H must be the same size (although subgroups can be used if they are not the same size). brian-1.4.3/docs_sphinx/installation.txt000066400000000000000000000164131266062647100204140ustar00rootroot00000000000000.. _installation: Installation ============ If you already have a copy of Python 2.5-2.7, try the `Quick installation`_ below, otherwise take a look at `Manual installation`_. Quick installation ------------------ easy_install / pip ^^^^^^^^^^^^^^^^^^ The easiest way to install the most recent version of Brian if you already have a version of Python 2.5-2.7 including the ``easy_install`` script is to simply run the following in a shell:: easy_install brian This will download and install Brian and all its required packages (NumPy, SciPy, etc.). Similarly, you can use the ``pip`` utility:: pip install brian Note that there are some optimisations you can make after installation, see the section below on `Optimisations`_. Debian/Ubuntu packages ^^^^^^^^^^^^^^^^^^^^^^ If you use a Debian-based Linux distribution (in addition to Debian itself, this includes for example Ubuntu or Linux Mint), you can install Brian directly from your favourite package manager (e.g. Synaptic or the Ubuntu Software Centre), thanks to the packages provided by the `NeuroDebian `__ team. The package is called ``python-brian``, the documentation and tutorials can be found in ``python-brian-doc``. To install these packages from the command-line use:: sudo apt-get install python-brian python-brian-doc Note that in contrast to the procedure described above for `easy_install / pip`_, you will not necessarily get the most recent version of Brian this way. On the other hand, you do not have to take care of future updates yourself, as the Brian package gets updated with the standard update process. Additionally, the Brian package already includes all the compiled C code mentioned in the `Optimisations`_ section. Another way to install Brian which combines these advantages with up-to-date versions is to directly add the `NeuroDebian repository `__ to your software sources. Manual installation ------------------- Installing Brian requires the following components: #. `Python `__ version 2.5-2.7. #. `NumPy and Scipy `__ packages for Python: an efficient scientific library. #. `PyLab `__ package for Python: a plotting library similar to Matlab (see the `detailed installation instructions `__). #. `SymPy `__ package for Python: a library for symbolic mathematics (not mandatory yet for Brian). #. `Brian `__ itself (don't forget to download the extras.zip file, which includes examples, tutorials, and a complete copy of the documentation). Brian is also a Python package and can be installed as explained below. Fortunately, Python packages are very quick and easy to install, so the whole process shouldn't take very long. We also recommend using the following for writing programs in Python (see details below): #. `Eclipse `__ IDE with `PyDev `__ #. `IPython `__ shell Finally, if you want to use the (optional) automatic C++ code generation features of Brian, you should have the ``gcc`` compiler installed (on `Cygwin `__ if you are running on Windows). Mac users: The Enthought Python Distribution (`EPD `__ ) is free for academics and contains all the libraries necessary to run Brian. Otherwise, the `Scipy Superpack for Intel OS X `__ also includes versions of Numpy, Scipy, Pylab and IPython. Windows users: the `Python(x,y) `__ distribution includes all the packages (including Eclipse and IPython) above except Brian (which is available as an optional plugin). Another option is the `Anaconda distribution `__, which also includes all the packages above except Brian and Eclipse. Installing Python packages ^^^^^^^^^^^^^^^^^^^^^^^^^^ On Windows, Python packages (including Brian) are generally installed simply by running an .exe file. On other operating systems, you can download the source release (typically a compressed archive .tar.gz or .zip that you need to unzip) and then install the package by typing the following in your shell:: python setup.py install Installing Eclipse ^^^^^^^^^^^^^^^^^^ Eclipse is an Integrated Development Environment (IDE) for any programming language. PyDev is a plugin for Eclipse with features specifically for Python development. The combination of these two is excellent for Python development (it's what we use for writing Brian). To install Eclipse, go to `their web page `__ and download any of the base language IDEs. It doesn't matter which one, but Python is not one of the base languages so you have to choose an alternative language. Probably the most useful is the C++ one or the Java one. The C++ one can be downloaded `here `__. Having downloaded and installed Eclipse, you should download and install the PyDev plugin from `their web site `__. The best way to do this is directly from within the Eclipse IDE. Follow the instructions on the `PyDev manual page `__. Installing IPython ^^^^^^^^^^^^^^^^^^ `IPython `__ is an interactive shell for Python. It has features for SciPy and PyLab built in, so it is a good choice for scientific work. Download from `their page `__. If you are using Windows, you will also need to download PyReadline from the same page. C++ compilers ^^^^^^^^^^^^^ The default for Brian is to use the ``gcc`` compiler which will be installed already on most unix or linux distributions. If you are using Windows, you can install `cygwin `__ (make sure to include the ``gcc`` package). Alternatively, some but not all versions of Microsoft Visual C++ should be compatible, but this is untested so far. See the documentation for the `SciPy Weave `__ package for more information on this. Mac users should have XCode installed so as to have access to gcc and hence take advantage of brian compiled code. See also the section on :ref:`compiled-code`. Testing ------- You can test whether Brian has installed properly by running Python and typing the following two lines:: from brian import * brian_sample_run() A sample network should run and produce a raster plot. Optimisations ------------- After a successful installation, there are some optimisations you can make to your Brian installation to get it running faster using compiled C code. We do not include these as standard because they do not work on all computers, and we want Brian to install without problems on all computers. Note that including all the optimisations can result in significant speed increases (around 30%). These optimisations are described in detail in the section on :ref:`compiled-code`. brian-1.4.3/docs_sphinx/introduction.txt000066400000000000000000000062651266062647100204400ustar00rootroot00000000000000.. currentmodule:: brian Introduction ============ Brian is a clock driven simulator for spiking neural networks, written in the `Python `__ programming language. The simulator is written almost entirely in Python. The idea is that it can be used at various levels of abstraction without the steep learning curve of software like `Neuron `__, where you have to learn their own programming language to extend their models. As a language, Python is well suited to this task because it is easy to learn, well known and supported, and allows a great deal of flexibility in usage and in designing interfaces and abstraction mechanisms. As an interpreted language, and therefore slower than say C++, Python is not the obvious choice for writing a computationally demanding scientific application. However, the `SciPy `__ module for Python provides very efficient linear algebra routines, which means that vectorised code can be very fast. Here's what the Python web site has to say about themselves: Python is an easy to learn, powerful programming language. It has efficient high-level data structures and a simple but effective approach to object-oriented programming. Python's elegant syntax and dynamic typing, together with its interpreted nature, make it an ideal language for scripting and rapid application development in many areas on most platforms. The Python interpreter and the extensive standard library are freely available in source or binary form for all major platforms from the Python Web site, http://www.python.org/, and may be freely distributed. The same site also contains distributions of and pointers to many free third party Python modules, programs and tools, and additional documentation. As an example of the ease of use and clarity of programs written in Brian, the following script defines and runs a randomly connected network of 4000 integrate and fire neurons with exponential currents:: from brian import * eqs=''' dv/dt = (ge+gi-(v+49*mV))/(20*ms) : volt dge/dt = -ge/(5*ms) : volt dgi/dt = -gi/(10*ms) : volt ''' P=NeuronGroup(4000,model=eqs,threshold=-50*mV,reset=-60*mV) P.v=-60*mV Pe=P.subgroup(3200) Pi=P.subgroup(800) Ce=Connection(Pe,P,'ge',weight=1.62*mV,sparseness=0.02) Ci=Connection(Pi,P,'gi',weight=-9*mV,sparseness=0.02) M=SpikeMonitor(P) run(1*second) raster_plot(M) show() As an example of the output of Brian, the following two images reproduce figures from Diesmann et al. 1999 on synfire chains. The first is a raster plot of a synfire chain showing the stabilisation of the chain. .. image:: images/synfirechain-example.jpg The simulation of 1000 neurons in 10 layers, each all-to-all connected to the next, using integrate and fire neurons with synaptic noise for 100ms of simulated time took 1 second to run with a timestep of 0.1ms on a 2.4GHz Intel Xeon dual-core processor. The next image is of the state space, figure 3: .. image:: images/sfc-statespace.jpg The figure computed 50 averages for each of 121 starting points over 100ms at a timestep of 0.1ms and took 201s to run on the same processor as above. brian-1.4.3/docs_sphinx/library.txt000066400000000000000000000010161266062647100173500ustar00rootroot00000000000000.. currentmodule:: brian The library =========== A number of standard models is defined in the library folder. To use library elements, use the following syntax:: from brian.library.module_name import * For example, to import electrophysiology models:: from brian.library.electrophysiology import * .. toctree:: :maxdepth: 1 librarymodels randomprocesses electrophysiology_models electrode_compensation traceanalysis modelfitting hears .. extendinglibrary brian-1.4.3/docs_sphinx/librarymodels.txt000066400000000000000000000177101266062647100205640ustar00rootroot00000000000000.. currentmodule:: brian Library models -------------- Membrane equations ^^^^^^^^^^^^^^^^^^ Library models are defined using the :class:`MembraneEquation` class. This is a subclass of :class:`Equations` which is defined by a capacitance C and a sum of currents. The following instruction:: eqs=MembraneEquation(200*pF) defines the equation C*dvm/dt=0*amp, with the membrane capacitance C=200 pF. The name of the membrane potential variable can be changed as follows:: eqs=MembraneEquation(200*pF,vm='V') The main interest of this class is that one can use it to build models by adding currents to a membrane equation. The :class:`Current` class is a subclass of :class:`Equations` which defines a current to be added to a membrane equation. For example:: eqs=MembraneEquation(200*pF)+Current('I=(V0-vm)/R : amp',current_name='I') defines the same equation as:: eqs=Equations(''' dvm/dt=I/(200*pF) : volt I=(V0-vm)/R : amp ''') The keyword ``current_name`` is optional if there is no ambiguity, i.e., if there is only one variable or only one variable with amp units. As for standard equations, :class:`Current` objects can be initialised with a multiline string (several equations). By default, the convention for the current direction is the one for injected current. For the ionic current convention, use the :class:`IonicCurrent` class:: eqs=MembraneEquation(200*pF)+IonicCurrent('I=(vm-V0)/R : amp') Compartmental modelling ^^^^^^^^^^^^^^^^^^^^^^^ Compartmental neuron models can be created by merging several :class:`MembraneEquation` objects, with the ``compartments`` module. If ``soma`` and ``dendrite`` are two compartments defined as :class:`MembraneEquation` objects, then a neuron with those 2 compartments can be created as follows:: neuron_eqs=Compartments({'soma':soma,'dendrite':dendrite}) neuron_eqs.connect('soma','dendrite',Ra) neuron=NeuronGroup(1,model=neuron_eqs) The :class:`Compartments` object is initialised with a dictionary of :class:`MembraneEquation` objects. The returned object ``neuron_eqs`` is also a :class:`MembraneEquation` object, where the name of each compartment has been appended to variable names (with a leading underscore). For example, ``neuron.vm_soma`` refers to variable ``vm`` of the somatic compartment. The ``connect`` method adds a coupling current between the two named compartments, with the given resistance ``Ra``. Integrate-and-Fire models ^^^^^^^^^^^^^^^^^^^^^^^^^ A few standard Integrate-and-Fire models are implemented in the ``IF`` library module:: from brian.library.IF import * All these functions return :class:`Equations` objects (more precisely, :class:`MembraneEquation` objects). * Leaky integrate-and-fire model (``dvm/dt=(El-vm)/tau : volt``):: eqs=leaky_IF(tau=10*ms,El=-70*mV) * Perfect integrator (``dvm/dt=Im/tau : volt``):: eqs=perfect_IF(tau=10*ms) * Quadratic integrate-and-fire model (``C*dvm/dt=a*(vm-EL)*(vm-VT) : volt``):: eqs=quadratic_IF(C=200*pF,a=10*nS/mV,EL=-70*mV,VT=-50*mV) * Exponential integrate-and-fire model (``C*dvm/dt=gL*(EL-vm)+gL*DeltaT*exp((vm-VT)/DeltaT) :volt``):: eqs=exp_IF(C=200*pF,gL=10*nS,EL=-70*mV,VT=-55*mV,DeltaT=3*mV) In general, it is possible to define a neuron group with different parameter values for each neuron, by passing strings at initialisation. For example, the following code defines leaky integrate-and-fire models with heterogeneous resting potential values:: eqs=leaky_IF(tau=10*ms,El='V0')+Equations('V0:volt') group=NeuronGroup(100,model=eqs,reset=0*mV,threshold=15*mV) Two-dimensional IF models ^^^^^^^^^^^^^^^^^^^^^^^^^ Integrate-and-fire models with two variables can display a very rich set of electrophysiological behaviours. In Brian, two such models have been implemented: Izhikevich model and Brette-Gerstner adaptive exponential integrate-and-fire model (also included in the ``IF`` module). The equations are obtained in the same way as for one-dimensional models:: eqs=Izhikevich(a=0.02/ms,b=0.2/ms) eqs=Brette_Gerstner(C=281*pF,gL=30*nS,EL=-70.6*mV,VT=-50.4*mV,DeltaT=2*mV,tauw=144*ms,a=4*nS) eqs=aEIF(C=281*pF,gL=30*nS,EL=-70.6*mV,VT=-50.4*mV,DeltaT=2*mV,tauw=144*ms,a=4*nS) # equivalent and two state variables are defined: ``vm`` (membrane potential) and ``w`` (adaptation variable). The equivalent equations for Izhikevich model are:: dvm/dt=(0.04/ms/mV)*vm**2+(5/ms)*vm+140*mV/ms-w : volt dw/dt=a*(b*vm-w) : volt/second and for Brette-Gerstner model:: C*dvm/dt=gL*(EL-vm)+gL*DeltaT*exp((vm-VT)/DeltaT)-w :volt dw/dt=(a*(vm-EL)-w)/tauw : amp To simulate these models, one needs to specify a threshold value, and a good choice is ``VT+4*DeltaT``. The reset is particular in these models since it is bidimensional: vm->Vr and w->w+b. A specific reset class has been implemented for this purpose: :class:`AdaptiveReset`, initialised with Vr and b. Thus, a typical construction of a group of such models is:: eqs=Brette_Gerstner(C=281*pF,gL=30*nS,EL=-70.6*mV,VT=-50.4*mV,DeltaT=2*mV,tauw=144*ms,a=4*nS) group=NeuronGroup(100,model=eqs,threshold=-43*mV,reset=AdaptiveReset(Vr=-70.6*mvolt,b=0.0805*nA)) Synapses ^^^^^^^^ A few simple synaptic models are implemented in the module ``synapses``:: from brian.library.synapses import * All the following functions need to be passed the name of the variable upon which the received spikes will act, and the name of the variable representing the current or conductance. The simplest one is the exponential synapse:: eqs=exp_synapse(input='x',tau=10*ms,unit=amp,output='x_current') It is equivalent to:: eqs=Equations(''' dx/dt=-x/tau : amp x_out=x ''') Here, ``x`` is the variable which receives the spikes and ``x_current`` is the variable to be inserted in the membrane equation (since it is a one-dimensional synaptic model, the variables are the same). If the output variable name is not defined, then it will be automatically generated by adding the suffix ``_out`` to the input name. Two other types of synapses are implemented. The alpha synapse (``x(t)=alpha*(t/tau)*exp(1-t/tau)``, where ``alpha`` is a normalising factor) is defined with the same syntax by:: eqs=alpha_synapse(input='x',tau=10*ms,unit=amp) and the bi-exponential synapse is defined by (``x(t)=(tau2/(tau2-tau1))*(exp(-t/tau1)-exp(-t/tau2))``, up to a normalising factor):: eqs=biexp_synapse(input='x',tau1=10*ms,tau2=5*ms,unit=amp) For all types of synapses, the normalising factor is such that the maximum of x(t) is 1. These functions can be used as in the following example:: eqs=MembraneEquation(C=200*pF)+Current('I=gl*(El-vm)+ge*(Ee-vm):amp') eqs+=alpha_synapse(input='ge_in',tau=10*ms,unit=siemens,output='ge') where alpha conductances have been inserted in the membrane equation. One can directly insert synaptic currents with the functions ``exp_current``, ``alpha_current`` and ``biexp_current``:: eqs=MembraneEquation(C=200*pF)+Current('I=gl*(El-vm):amp')+\ alpha_current(input='ge',tau=10*ms) (the units is amp by default), or synaptic conductances with the functions ``exp_conductance``, ``alpha_conductance`` and ``biexp_conductance``:: eqs=MembraneEquation(C=200*pF)+Current('I=gl*(El-vm):amp')+\ alpha_conductance(input='ge',E=0*mV,tau=10*ms) where ``E`` is the reversal potential. Ionic currents ^^^^^^^^^^^^^^ A few standard ionic currents have implemented in the module ``ionic_currents``:: from brian.library.ionic_currents import * When the current name is not specified, a unique name is generated automatically. Models can be constructed by adding currents to a :class:`MembraneEquation`. * Leak current (``gl*(El-vm)``):: current=leak_current(gl=10*nS,El=-70*mV,current_name='I') * Hodgkin-Huxley K+ current:: current=K_current_HH(gmax,EK,current_name='IK'): * Hodgkin-Huxley Na+ current:: current=Na_current_HH(gmax,ENa,current_name='INa'): brian-1.4.3/docs_sphinx/managing_sims.txt000066400000000000000000000111101266062647100205140ustar00rootroot00000000000000.. currentmodule:: brian Managing simulation runs and data ================================= Often, you want to run a simulation multiple times with different parameters to generate data for a plot. There are many different ways to manage this, and Brian has a few tools to make it easier. Saving data by hand ------------------- The simplest strategy is to run your simulation, and then save the data with a unique filename using either `pickle `__, writing text or binary data to a file `with Python `__, or with `Numpy `__ and `Scipy `__. Structured data formats ----------------------- Another option is to use a more structured file type, for example, you could use the high performance `HDF5 `__ scientific data file format with `PyTables `__. Python also includes an object for storing data in a dictionary like database object with the `shelve module `__. Brian includes a simple modification of Python's shelves to make it easy to generate data in parallel on a single machine or across several machines. The problem with Python shelves and HDF5 is that they cannot be accessed by several processes on a single machine concurrently (if two processes attempt to write to the file at the same time it gets corrupted). In addition, if you want to run simulations on two computers at once and merge them you have to write a separate program to merge the databases produced. With the :class:`~brian.tools.datamanager.DataManager` class, you generate a directory containing multiple files, and the data can be distributed amongst these files. To merge the results generated on two different computers just copy the contents of one directory into the other. The way it works is that to write data to a :class:`~brian.tools.datamanager.DataManager`, you first generate a ''session'' object (which is essentially a Python shelf object) and then write data to that. However, when you want to read data, it will look in all the files in the directory and return merged data from them. Typically, a session file will have the form ``username.computername`` so that merging directories across multiple computers/users is straightforward (no name conflicts). You can also create a ''locking session''. This object can be used in multiple processes concurrently without danger of losing data. Multiple runs in parallel ------------------------- The Python `multiprocessing `__ module can be used for relatively simply distributing simulation runs over multiple CPUs. Alternatively, you could use `Playdoh `__ (produced by our group) to distribute work over multiple CPUs and multiple machines. For other solutions, see the "Parallel and distributed programming" section of the `Scipy Topical Software `__ page. Brian provides a simple, single machine technique that works with the :class:`~brian.tools.datamanager.DataManager` object, :func:`~brian.tools.taskfarm.run_tasks`. With this, you provide a function and a sequence of arguments to that function, and the function calls will be evaluated across multiple CPUs, with the results being stored in the data manager. It also features a GUI which gives feedback on simulations as they run, and can be used to safely stop the processes without risking losing any data. A simple example of using this technique:: from brian import * from brian.tools.datamanager import * from brian.tools.taskfarm import * def find_rate(k, report): eqs = ''' dV/dt = (k-V)/(10*ms) : 1 ''' G = NeuronGroup(1000, eqs, reset=0, threshold=1) M = SpikeCounter(G) run(30*second, report=report) return (k, mean(M.count)/30) if __name__=='__main__': N = 20 dataman = DataManager('taskfarmexample') if dataman.itemcount()`__. brian-1.4.3/docs_sphinx/modelfitting.txt000066400000000000000000000223641266062647100204020ustar00rootroot00000000000000.. currentmodule:: brian.library.modelfitting .. _model-fitting-library: Model fitting ============= The modelfitting library is used for fitting a neuron model to data. The library provides a single function :func:`modelfitting`, which accepts the model and the data as arguments and returns the model parameters that fit best the data. The model is a spiking neuron model, whereas the data consists of both an input (time-varying signal, for example an injected current) and a set of spike trains. Only spikes are considered for the fitness. Several target spike trains can be specified in order to fit independently several data sets. In this case, the :func:`modelfitting` function returns as many parameters sets as there are target spike trains. The model is defined as any spiking neuron model in Brian, by giving the equations as mathematical equations, and the reset and threshold values. The free parameters of the model that shall be fitted by the library are also specified. The data is specified by the input (a vector containing the time-varying injected current), the timestep of the input, and the data as a list of spike times. How it works ------------ Fitting a spiking neuron model to electrophysiological data is performed by maximizing a fitness function measuring the adequacy of the model to the data. This function is defined as the gamma factor, which is based on the number of coincidences between the model spikes and the experimentally-recorded spikes, defined as the number of spikes in the experimental train such that there is at least one spike in the model train within plus or minus ``delta``, where ``delta`` is the size of the temporal window (typically a few milliseconds). For more details on the gamma factor, see `Jolivet et al. 2008, "A benchmark test for a quantitative assessment of simple neuron models", J. Neurosci. Methods `__ (available in PDF `here `__). The optimization procedure is performed by an optimization algorithm. The optimization toolbox used by modelfitting is implemented in the external Python package `Playdoh `__. It also supports distributed and parallel optimization across CPUs and machines. Different optimization algorithms are supported, the default one is :class:`CMAES`. All those algorithms require the evaluation of the fitness function for a large number of parameter sets. Each iteration of the algorithm involves the simulation of a large number of neurons (one neuron corresponding to one parameter set) as well as the computation of the gamma factor for each neuron. The quality of the result depends on the number of neurons used, which is specified in the :func:`modelfitting` function. Playdoh supports the use of graphical processing units (GPUs) in order to accelerate the speed of convergence of the algorithm. If multiple cores are detected, the library will use all of them by default. Also, if a CUDA-enabled GPU is present on the system, and if PyCUDA is installed, the library will automatically use the GPU by default. In addition, several computers can be networked over IP, see :ref:`modelfitting-clusters`. Usage example ------------- To import the library, use :: from brian.library.modelfitting import * To fit the parameters of a neuron model with respect to some data, use the :func:`modelfitting` function :: results = modelfitting(model = equations, reset = 0, threshold = 1, data = spikes, input = input, dt = .1*ms, popsize = 1000, maxiter = 10, R = [1.0e9, 1.0e10], tau = [1*ms, 50*ms]) print_table(results) .. warning:: Windows users should read the section :ref:`note-for-windows-users`. The model is defined by ``equations`` (an :class:`~brian.Equations` object), ``reset`` (a scalar value or a set of equations as a string) and ``threshold`` (a scalar value or a set of equations as a string). The target spike trains are defined by ``data`` (a list of pairs ``(neuron index, spike time)`` or a list of spike times if there is only one target spike train). The input is specified with ``input`` (a vector containing the time-varying signal) and ``dt`` (the time step of the signal). The input variable should be ``I`` in the equations, although the input variable name can be specified with ``input_var``. The number of particles per target train used in the optimization algorithm is specified with ``popsize``. The total number of neurons is ``popsize`` multiplied by the number of target spike trains. The number of iterations in the algorithm is specified with ``maxiter``. Each free parameter of the model that shall be fitted is defined by two values :: param_name = [min, max] ``param_name`` should correspond to the parameter name in the model equations. ``min`` and ``max`` specify the initial interval from which the parameter values will be uniformly sampled at the beginning of the optimization algorithm. A boundary interval can also be specified by giving four values :: param_name = [bound_min, min, max, bound_max] The parameter values will be forced to stay inside the interval [bound_min, bound_max] during the optimization. The complete list of arguments can be found in the reference section of the :func:`modelfitting` function. The best parameters and the corresponding best fitness values found by the optimization procedure are returned in the :class:`OptimizationResult` object ``result``. .. _note-for-windows-users: Important note for Windows users -------------------------------- The model fitting library uses the Python `multiprocessing `__ package to distribute fitting across processors in a single computer or across multiple computers. However, there is a limitation of the Windows version of multiprocessing which you can read about `here `__. The end result is that a script like this:: from brian.library.modelfitting import * ... results = modelfitting(...) will crash, going into an endless loop and creating hundreds of Python processes that have to be shut down by hand. Instead, you have to do this:: from brian.library.modelfitting import * ... if __name__=='__main__': results = modelfitting(...) .. _modelfitting-clusters: Clusters -------- The model fitting package can be used with a cluster of computers connected over IP. Every computer must have Brian and Playdoh installed, and they must run the Playdoh server: see `the Playdoh documentation `__. Then, you can launch the ```modelfitting`` function with the ``machines`` keyword, which is the list of the IP addresses of the machines to use in parallel for the fitting procedure. You must also specify the ``unit_type`` keyword, which is ``CPU`` or ``GPU``, to indicate whether you want to use CPUs or GPUs on these computers. You can't mix CPUs and GPUs for the same optimization. IP ~~ To connect several machines via IP, pass a list of host names or IP addresses as strings to the ``machines`` keyword of the :func:`modelfitting` function. To specify a specific port, use a tuple ``(IP, port)`` instead of a string. You can also specify a default port in the Playdoh user preferences, see `the Playdoh documentation `__. Authentication ~~~~~~~~~~~~~~ You can specify an authentication string on all the computers running the Playdoh server to secure communications. See `the Playdoh documentation `__. Example ~~~~~~~ The following script launches a fitting procedure in parallel on two machines:: from brian import loadtxt, ms, Equations from brian.library.modelfitting import * if __name__ == '__main__': # List of machines IP addresses machines = ['bobs-machine.university.com', 'jims-machine.university.com'] equations = Equations(''' dV/dt=(R*I-V)/tau : 1 I : 1 R : 1 tau : second ''') input = loadtxt('current.txt') spikes = loadtxt('spikes.txt') results = modelfitting( model = equations, reset = 0, threshold = 1, data = spikes, input = input, dt = .1*ms, popsize = 1000, maxiter = 3, delta = 4*ms, unit_type = 'CPU', machines = machines, R = [1.0e9, 9.0e9], tau = [10*ms, 40*ms], refractory = [0*ms, 10*ms]) print_table(results) The two remote machines would run the `Playdoh server `__. brian-1.4.3/docs_sphinx/models.txt000066400000000000000000000257701266062647100172040ustar00rootroot00000000000000.. currentmodule:: brian Models and neuron groups ======================== :class:`Equations` ------------------ :class:`Equations` objects are initialised with a string as follows:: eqs=Equations(''' dx/dt=(y-x)/tau + a : volt # differential equation y=2*x : volt # equation z=x # alias a : volt/second # parameter ''') .. index:: pair: equations; differential pair: equations; equation single: equation pair: equations; alias pair: equations; parameter It is possible to pass a string instead of an :class:`Equations` object when initialising a neuron group. In that case, the string is implicitly converted to an :class:`Equations` object. There are 4 different types of equations: * Differential equations: a differential equation, also defining the variable as a state variable in neuron groups. * Equations: a non-differential equation, which is useful for defining complicated models. The variables are also accessible for reading in neuron groups, which is useful for monitoring. The graph of dependencies of all equations must have no cycle. * Aliases: the two variables are equivalent. This is implemented as an equation, with write access in neuron groups. * Parameters: these are constant variables, but their values can differ from one neuron to the next. They are implemented internally as differential equations with zero derivative. Right hand sides must be valid Python expressions, possibly including comments and multiline characters (``\``). The units of all variables except aliases must be specified. Note that in first line, the units *volt* are meant for x, not dx/dt. The consistency of all units is checked with the method :meth:`~Equations.check_units`, which is automatically called when initialising a neuron group (through the method :meth:`~Equations.prepare`). When an :class:`Equations` object is finalised (through the method :meth:`~equations.Equations.prepare`, automatically called the :class:`NeuronGroup` initialiser), the names of variables defined by non-differential equations are replaced by their (string) values, so that differential equations are self-consistent. In the process, names of external variables are also modified to avoid conflicts (by adding a prefix). Neuron groups ------------- The key idea for efficient simulations is to update synchronously the state variables of all identical neuron models. A neuron group is defined by the model equations, and optionally a threshold condition and a reset. For example for 100 neurons:: eqs=Equations('dv/dt=-v/tau : volt') group=NeuronGroup(100,model=eqs,reset=0*mV,threshold=10*mV) The ``model`` keyword also accepts strings (in that case it is converted to an :class:`Equations` object), e.g.:: group=NeuronGroup(100,model='dv/dt=-v/tau : volt',reset=0*mV,threshold=10*mV) The units of both the reset and threshold are checked for consistency with the equations. The code above defines a group of 100 integrate-and-fire neurons with threshold 10 mV and reset 0 mV. The second line defines an object named ``group`` which contains all the state variables, which can be accessed with the dot notation, i.e. ``group.v`` is a vector with the values of variable ``v`` for all of the 100 neurons. It is an array with units as defined in the equations (here, volt). By default, all state variables are initialised at value 0. It can be initialised by the user as in the following example:: group.v=linspace(0*mV,10*mV,100) Here the values of ``v`` for all the neurons are evenly spaced between 0 mV and 10 mV (``linspace`` is a NumPy function). The method ``group.rest()`` may also be used to set the resting point of the equations, but convergence is not always guaranteed. Important options ^^^^^^^^^^^^^^^^^ * ``refractory``: a refractory period (default 0 ms), to be used in combination with the ``reset`` value. * ``implicit`` (default ``False``): if True, then an implicit method is used. This is useful for Hodgkin-Huxley equations, which are stiff. Subgroups ^^^^^^^^^ Subgroups can be created with the slice operator:: subgroup1=group[0:50] subgroup2=group[50:100] Then ``subgroup2.v[i]`` equals ``group.v[50+i]``. An alternative equivalent method is the following:: subgroup1=group.subgroup(50) subgroup2=group.subgroup(50) The parent group keeps track of the allocated subgroups. But note that the two methods are mutually exclusive, e.g. in the following example:: subgroup1=group[0:50] subgroup2=group.subgroup(50) both subgroups are actually identical. Subgroups are useful when creating connections or monitoring the state variables or spikes. The best practice is to define groups as large as possible, then divide them in subgroups if necessary. Indeed, the larger the groups are, the faster the simulation runs. For example, for a network with a feedforward architecture, one should first define one group holding all the neurons in the network, then define the layers as subgroups of this big group. .. removed for Brian 1.0 Unitless variables ^^^^^^^^^^^^^^^^^^ If an underscore is added to the name of a state variable (``P.v_``) then the units are removed. This can be useful when writing custom reset or threshold functions, to make the code faster. Details ^^^^^^^ For details, see the reference documentation for :class:`NeuronGroup`. Reset ----- More complex resets can be defined. The value of the ``reset`` keyword can be: * a quantity (``0*mV``) * a string * a function * a :class:`Reset` object, which can be used for resetting a specific state variable or for resetting a state variable to the value of another variable. Reset as Python code ^^^^^^^^^^^^^^^^^^^^ The simplest way to customise the reset is to define it as a Python statement, e.g.:: eqs=''' dv/dt=-v/tau : volt dw/dt=-w/tau : volt ''' group=NeuronGroup(100,model=eqs,reset="v=0*mV;w+=3*mV",threshold=10*mV) The string must be a valid Python statement (possibly a multiline string). It can contain variables from the neuron group, units and any variable defined in the namespace (e.g. tau), as for equations. Be aware that if a variable in the namespace has the same name as a neuron group variable, then it masks the neuron variable. The way it works is that the code is evaluated with each neuron variable ``v`` replaced by ``v[spikes]``, where ``spikes`` is the array of indexes of the neurons that just spiked. Functional reset ^^^^^^^^^^^^^^^^ To define a specific reset, the generic method is define a function as follows:: def myreset(P,spikes): P.v[spikes]=rand(len(spikes))*5*mV group=NeuronGroup(100,model=eqs,reset=myreset,threshold=10*mV) or faster:: def myreset(P,spikes): P.v_[spikes]=rand(len(spikes))*5*mV Every time step, the user-defined function is called with arguments ``P``, the neuron group, and ``spikes``, the list of indexes of the neurons that just spiked. The function above resets the neurons that just spiked to a random value. Resetting another variable ^^^^^^^^^^^^^^^^^^^^^^^^^^ It is possible to specify the reset variable explicitly:: group=NeuronGroup(100,model=eqs,reset=Reset(0*mV,state='w'),threshold=10*mV) Here the variable ``w`` is reset. Resetting to the value of another variable ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ The value of the reset can be given by another state variable:: group=NeuronGroup(100,model=eqs,reset=VariableReset(0*mV,state='v',resetvaluestate='w'),threshold=10*mV) Here the value of the variable ``w`` is used to reset the variable ``v``. Threshold --------- As for the reset, the threshold can be customised. Threshold as Python expression ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ The simplest way to customise the threshold is to define it as a Python expression, e.g.:: eqs=''' dv/dt=-v/tau : volt dw/dt=(v-w)/tau : volt ''' group=NeuronGroup(100,model=eqs,reset=0*mV,threshold="v>=w") The string must be an expression which evaluates to a boolean. It can contain variables from the neuron group, units and any variable defined in the namespace (e.g. tau), as for equations. Be aware that if a variable in the namespace has the same name as a neuron group variable, then it masks the neuron variable. The way it works is that the expression is evaluated with the neuron variables replaced by their vector values (values for all neurons), so that the expression returns a boolean vector. Functional threshold ^^^^^^^^^^^^^^^^^^^^ The generic method to define a custom threshold condition is to pass a function of the state variables which returns a boolean (true if the threshold condition is met), for example:: eqs=''' dv/dt=-v/tau : volt dw/dt=(v-w)/tau : volt ''' group=NeuronGroup(100,model=eqs,reset=0*mV,threshold=lambda v,w:v>=w) Here we used an anonymous function (``lambda`` keyword) but of course a named function can also be used. In this example, spikes are generated when v is greater than w. Note that the arguments of the function must be the state variables with the same order as in the :class:`Equations` string. Thresholding another variable ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ It is possible to specify the threshold variable explicitly:: group=NeuronGroup(100,model=eqs,reset=0*mV,threshold=Threshold(0*mV,state='w')) Here the variable ``w`` is checked. Using another variable as the threshold value ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ The same model as in the functional threshold example can be defined as follows:: group=NeuronGroup(100,model=eqs,reset=0*mV,threshold=\ VariableThreshold(state='v',threshold_state='w')) Empirical threshold ^^^^^^^^^^^^^^^^^^^ For Hodgkin-Huxley models, one needs to determine the threshold empirically. Here the *threshold* should really be understood rather as the onset of the spikes (used to propagate the spikes to the other neurons), since there is no explicit reset. There is a :class:`Threshold` subclass for this purpose:: group=NeuronGroup(100,model=eqs,threshold=EmpiricalThreshold(threshold=-20*mV,refractory=3*ms)) Spikes are triggered when the membrane potential reaches the value -20 mV, but only if it has not spiked in the last 3 ms (otherwise there would be spikes every time step during the action potential). The ``state`` keyword may be used to specify the state variable which should be checked for the threshold condition. Poisson threshold ^^^^^^^^^^^^^^^^^ It is possible to generate spikes with a given probability rather than when a threshold condition is met, by using the class :class:`PoissonThreshold`, as in the following example:: group=NeuronGroup(100,model='x : Hz',threshold=PoissonThreshold(state='x')) x=linspace(0*Hz,10*Hz,100) Here spikes are generated as Poisson processes with rates given by the variable x (the ``state`` keyword is optional: default = first variable defined). Note that x can change over time (inhomogeneous Poisson processes). The units of variable x must be Hertz. brian-1.4.3/docs_sphinx/operations.txt000066400000000000000000000010161266062647100200670ustar00rootroot00000000000000.. currentmodule:: brian User-defined operations ======================= In addition to neuron models, the user can provide functions that are to be called every timestep during the simulation, using the decorator :func:`network_operation`:: @network_operation def myoperation(): do_something_every_timestep() The operation may be called at regular intervals by defining a clock:: myclock=Clock(dt=1*ms) @network_operation(myclock) def myoperation(): do_something_every_ms() brian-1.4.3/docs_sphinx/othertutorials.txt000066400000000000000000000025171266062647100210030ustar00rootroot00000000000000.. currentmodule:: brian Tutorials for Python and Scipy ============================== Python ------ The first thing to do in learning how to use Brian is to have a basic grasp of the Python programming language. There are lots of good tutorials already out there. The best one is probably `the official Python tutorial `__. There is also a course for biologists at the Pasteur Institute: `Introduction to programming using Python `__. NumPy, SciPy and Pylab ---------------------- The first place to look is the `SciPy documentation website `__. To start using Brian, you do not need to understand much about how NumPy and SciPy work, although understanding how their array structures work will be useful for more advanced uses of Brian. The syntax of the Numpy and Pylab functions is very similar to Matlab. If you already know Matlab, you could read this tutorial: `NumPy for Matlab users `__ and this list of `Matlab-Python translations `__ (`pdf version here `__). A `tutorial `__ is also available on the web site of Pylab. brian-1.4.3/docs_sphinx/parameters.txt000066400000000000000000000010121266062647100200430ustar00rootroot00000000000000.. currentmodule:: brian Parameters ---------- Brian includes a simple tool for keeping track of parameters. If you only need something simple, then a dict or an empty class could be used. The point of the parameters class is that allows you to define a cascade of computed parameters that depend on the values of other parameters, so that changing one will automatically update the others. See the synfire chain example ``examples/sfc.py`` for a demonstration of how it can be used. .. autoclass:: Parametersbrian-1.4.3/docs_sphinx/randomprocesses.txt000066400000000000000000000012201266062647100211100ustar00rootroot00000000000000.. currentmodule:: brian Random processes ---------------- To import the random processes library:: from brian.library.random_processes import * For the moment, only the Ornstein-Uhlenbeck process has been included. The function :func:`OrnsteinUhlenbeck` returns an :class:`Equations` object. The following example defines a membrane equation with an Ornstein-Uhlenbeck current ``I`` (= coloured noise):: eqs=Equations('dv/dt=-v/tau+I/C : volt') eqs+=OrnsteinUhlenbeck('I',mu=1*nA,sigma=2*nA,tau=10*ms) where ``mu`` is the mean of the current, ``sigma`` is the standard deviation and ``tau`` is autocorrelation time constant. brian-1.4.3/docs_sphinx/realtime.txt000066400000000000000000000013301266062647100175050ustar00rootroot00000000000000.. currentmodule:: brian Realtime control **************** A running Brian simulation can be controlled, for example using an IPython shell. This can work either on a single computer, or over IP from another computer. The process running the simulation calls something like:: server = RemoteControlServer() and the IPython shell calls:: client = RemoteControlClient() The shell can now execute and evaluate in the server process via:: spikes = client.evaluate('M.spikes') i, t = zip(*spikes) plot(t, i, '.') client.stop() Parameters can be changed as the simulation runs. For more details, see the reference documentation for :class:`RemoteControlServer` and :class:`RemoteControlClient`. brian-1.4.3/docs_sphinx/recording.txt000066400000000000000000000125001266062647100176600ustar00rootroot00000000000000.. currentmodule:: brian Recording ========= The activity of the network can be recorded by defining *monitors*. Recording spikes ---------------- To record the spikes from a given group, define a :class:`SpikeMonitor` object:: M=SpikeMonitor(group) At the end of the simulation, the spike times are stored in the variable ``spikes`` as a list of pairs (i,t) where neuron i fired at time t. For example, the following code extracts the list of spike times for neuron 3:: spikes3=[t for i,t in M.spikes if i==3] but this operation can be done directly as follows:: spikes3=M[3] The total number of spikes is ``M.nspikes``. Custom monitoring ^^^^^^^^^^^^^^^^^ To process the spikes in a specific way, one can pass a function at initialisation of the :class:`SpikeMonitor` object:: def f(spikes): print spikes M=SpikeMonitor(group,function=f) The function ``f`` is called every time step with the argument ``spikes`` being the list of indexes of neurons that just spiked. .. TODO: docs: recording directly to a file Recording directly to a file ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Recording state variables ------------------------- State variables can be recorded continuously by defining a :class:`StateMonitor` object, as follows:: M=StateMonitor(group,'v') Here the state variables ``v`` of the defined group are monitored. By default, only the statistics are recorded. The list of time averages for all neurons is ``M.mean``; the standard deviations are stored in ``M.std`` and the variances in ``M.var``. Note that these are averages over time, not over the neurons. To record the values of the state variables over the whole simulation, use the keyword ``record``:: M1=StateMonitor(group,'v',record=True) M2=StateMonitor(group,'v',record=[3,5,9]) The first monitor records the value of ``v`` for all neurons while the second one records ``v`` for neurons 3, 5 and 9 only. The list of times is stored in ``M1.times`` and the lists of values are stored in ``M1[i]``, where i the index of the neuron. Means and variances are no longer recorded if you record traces. By default, the values of the state variables are recorded every timestep, but one may record every n timesteps by setting the keyword ``timestep``:: M=StateMonitor(group,'v',record=True,timestep=n) Recording spike triggered state values ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ You can record the value of a state variable at each spike using :class:`StateSpikeMonitor`:: M = StateSpikeMonitor(group, 'V') The ``spikes`` attribute of ``M`` consists of a series of tuples ``(i,t,V)`` where ``V`` is the value at the time of the spike. Recording multiple state variables ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ You can either use multiple :class:`StateMonitor` objects or use the :class:`MultiStateMonitor` object:: M = MultiStateMonitor(group, record=True) ... run(...) ... plot(M['V'].times, M['V'][0]) figure() for name, m in M.iteritems(): plot(m.times, m[0], label=name) legend() show() Recording only recent values ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ You can use the :class:`RecentStateMonitor` object, e.g.:: G = NeuronGroup(1, 'dV/dt = xi/(10*ms)**0.5 : 1') MR = RecentStateMonitor(G, 'V', duration=5*ms) run(7*ms) MR.plot() show() Counting spikes --------------- To count the total number of spikes produced by a group, use a :class:`PopulationSpikeCounter` object:: M=PopulationSpikeCounter(group) Then the number of spikes after the simulation is ``M.nspikes``. If you need to count the spikes separately for each neuron, use a :class:`SpikeCounter` object:: M=SpikeCounter(group) Then ``M[i]`` is the number of spikes produced by neuron i. Counting coincidences --------------------- To count the number of coincident spikes between the neurons of a group and given target spike trains, use a :class:`CoincidenceCounter` object:: C=CoincidenceCounter(source=group, data=data, delta=delta) ``data`` is a list of pairs (neuron_index, spike time), and delta is the time window in second. To get the number of coincidences for each neuron of the group, use :: coincidences = C.coincidences The gamma precision factor can be obtained with :: gamma = C.gamma Recording population rates -------------------------- The population rate can be monitored with a :class:`PopulationRateMonitor` object:: M=PopulationRateMonitor(group) After the simulation, ``M.times`` contains the list of recording times and ``M.rate`` is the list of rate values (where the rate is meant in the spatial sense: average rate over the whole group at some given time). The bin size is set with the ``bin`` keyword (in seconds):: M=PopulationRateMonitor(group,bin=1*ms) Here the averages are calculated over 1 ms time windows. Alternatively, one can use the :meth:`~PopulationRateMonitor.smooth_rate` method to smooth the rates:: rates=M.smooth_rate(width=1*ms,filter='gaussian') The rates are convolved with a linear filter, which is either a Gaussian function (``gaussian``, default) or a box function ('flat'). Van Rossum Metric ----------------- The Van Rossum metric can be computed by monitoring a group with a :class:`VanRossumMetric` object:: M = VanRossumMetric(G, tau=4*ms) ... imshow(M.distance) brian-1.4.3/docs_sphinx/reference-analysis.txt000066400000000000000000000006171266062647100214710ustar00rootroot00000000000000.. currentmodule:: brian Analysis ======== Statistics of spike trains -------------------------- .. autofunction:: firing_rate .. autofunction:: CV .. autofunction:: correlogram .. autofunction:: autocorrelogram .. autofunction:: CCF .. autofunction:: ACF .. autofunction:: CCVF .. autofunction:: ACVF .. autofunction:: total_correlation .. autofunction:: spike_triggered_average brian-1.4.3/docs_sphinx/reference-clocks.txt000066400000000000000000000042231266062647100211210ustar00rootroot00000000000000.. index:: clock Clocks ------ .. currentmodule:: brian .. index:: single: clock; default clock single: default clock Many Brian objects store a clock object (always passed in the initialiser with the keyword ``clock=``...). If no clock is specified, the program uses the global default clock. When Brian is initially imported, this is the object :data:`defaultclock`, and it has a default time step of 0.1ms. In a simple script, you can override this by writing (for example):: defaultclock.dt = 1*ms However, there are other ways to access or redefine the default clock (see functions below). .. index:: single: clock; multiple clocks single: multiple clocks You may wish to use multiple clocks in your program. In this case, for each object which requires one, you have to pass a copy of its :class:`Clock` object. The network run function automatically handles objects with different clocks, updating them all at the appropriate time according to their time steps (value of :attr:`~Clock.dt`). Multiple clocks can be useful, for example, for defining a simulation that runs with a very small ``dt``, but with some computationally expensive operation running at a lower frequency. The :class:`Clock` class ~~~~~~~~~~~~~~~~~~~~~~~~ .. autoclass:: Clock .. autoclass:: EventClock .. autoclass:: FloatClock .. autoclass:: NaiveClock .. autoclass:: RegularClock .. index:: single: clock; default clock single: default clock The default clock ~~~~~~~~~~~~~~~~~ .. data:: defaultclock The default clock object Note that this is only the default clock object if you haven't redefined it with the :func:`define_default_clock` function or the ``makedefaultclock=True`` option of a :class:`Clock` object. A safe way to get hold of the default clock is to use the functions: * :func:`get_default_clock` * :func:`reinit_default_clock` However, it is suitable for short scripts, e.g.:: defaultclock.dt = 1*ms ... .. autofunction:: define_default_clock .. autofunction:: reinit_default_clock .. autofunction:: get_default_clock brian-1.4.3/docs_sphinx/reference-connections.txt000066400000000000000000000026341266062647100221710ustar00rootroot00000000000000.. currentmodule:: brian Connections ----------- The best way to understand the concept of a :class:`Connection` in Brian is to work through Tutorial 2: Connections. .. autoclass:: Connection .. autoclass:: DelayConnection .. autoclass:: IdentityConnection .. index:: pair: connection; matrix single: connection matrix Connection matrix types ~~~~~~~~~~~~~~~~~~~~~~~ .. autoclass:: ConnectionMatrix .. autoclass:: DenseConnectionMatrix .. autoclass:: SparseConnectionMatrix .. autoclass:: DynamicConnectionMatrix .. temporarily removed .. index:: pair: computed; connection Computed connections ^^^^^^^^^^^^^^^^^^^^ The following two connection matrix types can be given for a :class:`Connection` object. .. autoclass:: UserComputedConnectionMatrix .. autoclass:: UserComputedSparseConnectionMatrix These standard functions can be used to define their behaviour as an alternative to specifying your own. .. autofunction:: random_row_func .. autofunction:: random_sparse_row_func Construction matrix types ~~~~~~~~~~~~~~~~~~~~~~~~~ .. autoclass:: ConstructionMatrix .. autoclass:: DenseConstructionMatrix .. autoclass:: SparseConstructionMatrix .. autoclass:: DynamicConstructionMatrix Connection vector types ~~~~~~~~~~~~~~~~~~~~~~~ .. autoclass:: ConnectionVector .. autoclass:: DenseConnectionVector .. autoclass:: SparseConnectionVector brian-1.4.3/docs_sphinx/reference-electrodecompensation.txt000066400000000000000000000005251266062647100242320ustar00rootroot00000000000000.. currentmodule:: brian.library.electrophysiology Electrode compensation ====================== The L^p electrode compensation method is implemented, along with a spike detection method and a quality test (Rossant et al 2012). .. autofunction:: Lp_compensate .. autofunction:: find_spikes .. autofunction:: get_trace_quality brian-1.4.3/docs_sphinx/reference-hears.txt000066400000000000000000000067721266062647100207600ustar00rootroot00000000000000.. currentmodule:: brian.hears .. _brian-hears-reference: Brian hears =========== .. seealso:: User guide for :ref:`brianhears`. .. autofunction:: set_default_samplerate Sounds ------ .. autoclass:: Sound .. autofunction:: savesound .. autofunction:: loadsound .. autofunction:: play(*sounds, normalise=False, sleep=False) .. autofunction:: whitenoise .. autofunction:: powerlawnoise .. autofunction:: brownnoise .. autofunction:: pinknoise .. autofunction:: irns .. autofunction:: irno .. autofunction:: tone .. autofunction:: click .. autofunction:: clicks .. autofunction:: harmoniccomplex .. autofunction:: silence .. autofunction:: sequence(*sounds, samplerate=None) .. index:: single: dB single: decibel pair: level; sound single: sound; dB single: sound; decibel .. _dB: dB ~~ .. autoclass:: dB_type .. autoclass:: dB_error Filterbanks ----------- .. autoclass:: LinearFilterbank .. autoclass:: FIRFilterbank .. autoclass:: RestructureFilterbank .. autoclass:: Join .. autoclass:: Interleave .. autoclass:: Repeat .. autoclass:: Tile .. autoclass:: FunctionFilterbank .. autoclass:: SumFilterbank .. autoclass:: DoNothingFilterbank .. autoclass:: ControlFilterbank .. autoclass:: CombinedFilterbank Filterbank library ------------------ .. autoclass:: Gammatone(source, cf,b=1.019,erb_order=1,ear_Q=9.26449,min_bw=24.7) .. autoclass:: ApproximateGammatone .. autoclass:: LogGammachirp(source, f, b=1.019, c=1, ncascades=4) .. autoclass:: LinearGammachirp .. autoclass:: LinearGaborchirp .. autoclass:: IIRFilterbank .. autoclass:: Butterworth .. autoclass:: Cascade .. autoclass:: LowPass .. autoclass:: AsymmetricCompensation Auditory model library ---------------------- .. autoclass:: DRNL .. autoclass:: DCGC .. autoclass:: MiddleEar .. autoclass:: TanCarney .. autoclass:: ZhangSynapse Filterbank group ---------------- .. autoclass:: FilterbankGroup Functions --------- .. autofunction:: erbspace .. autofunction:: asymmetric_compensation_coeffs Plotting -------- .. autofunction:: log_frequency_xaxis_labels .. autofunction:: log_frequency_yaxis_labels HRTFs ----- .. autoclass:: HRTF .. autoclass:: HRTFSet .. autoclass:: HRTFDatabase .. autofunction:: make_coordinates .. autoclass:: IRCAM_LISTEN .. autoclass:: HeadlessDatabase Base classes ------------ Useful for understanding more about the internals. .. autoclass:: Bufferable .. autoclass:: Filterbank .. autoclass:: BaseSound .. autoclass:: OnlineSound .. _brian-hears-class-diagram: Options ------- There are several relevant global options for Brian hears. In particular, activating scipy Weave support with the ``useweave=True`` will give considerably faster linear filtering. See :ref:`preferences` and :ref:`compiled-code` for more details. Class diagram ------------- .. inheritance-diagram:: Sound OnlineSound Filterbank LinearFilterbank Gammatone ApproximateGammatone LogGammachirp LinearGammachirp LinearGaborchirp Cascade IIRFilterbank Butterworth LowPass FIRFilterbank RestructureFilterbank Join Interleave Repeat Tile FunctionFilterbank SumFilterbank DoNothingFilterbank ControlFilterbank CombinedFilterbank DRNL DCGC TanCarney AsymmetricCompensation HRTFDatabase IRCAM_LISTEN HeadlessDatabase :parts: 1 brian-1.4.3/docs_sphinx/reference-integration.txt000066400000000000000000000013711266062647100221670ustar00rootroot00000000000000.. currentmodule:: brian .. index:: pair: integration; methods .. _integration: Integration =========== See :ref:`numerical-integration` for an overview. StateUpdaters ------------- Typically you don't need to worry about :class:`StateUpdater` objects because they are automatically created from the differential equations defining your model. TODO: more details about this. .. index:: pair: integration; linear .. autoclass:: LinearStateUpdater .. autoclass:: LazyStateUpdater TODO: write docs for these StateUpdaters: * StateUpdater, LinearStateUpdater more details, NonlinearStateUpdater, NonlinearStateUpdater2, ExponentialEulerStateUpdater, NonlinearStateUpdaterRK2, NonlinearStateUpdaterBE, SynapticNoise brian-1.4.3/docs_sphinx/reference-io.txt000066400000000000000000000013351266062647100202530ustar00rootroot00000000000000.. currentmodule:: brian .. _reference-io: Input/output ============ General data management ----------------------- .. autoclass:: brian.tools.datamanager.DataManager Spikes management ----------------- The following function describes how to load Address-Event Representation files (AER files). See also the :class:`AERSpikeMonitor` for saving spikes in that format, and :class:`SpikeGeneratorGroup` for reusing them in a simulation. .. index:: pair: Spikes; aer file .. autofunction:: load_aer Other external datatypes ------------------------ .. index:: pair: Neuron; dat file .. autofunction:: read_neuron_dat .. index:: pair: Axon; atf file .. autofunction:: read_atf brian-1.4.3/docs_sphinx/reference-logging.txt000066400000000000000000000016131266062647100212710ustar00rootroot00000000000000.. currentmodule:: brian .. index:: log, logging Logging ------- Brian uses the standard Python ``logging`` package to generate information and warnings. All messages are sent to the logger named ``brian`` or loggers derived from this one, and you can use the standard logging functions to set options, write the logs to files, etc. Alternatively, Brian has four simple functions to set the level of the displayed log (see below). There are four different levels for log messages, in decreasing order of severity they are ERROR, WARN, INFO and DEBUG. By default, Brian displays only the WARN and ERROR level messages. Some useful information is at the INFO level, so if you are having problems with your program, setting the level to INFO may help. .. autofunction:: log_level_error .. autofunction:: log_level_warn .. autofunction:: log_level_info .. autofunction:: log_level_debugbrian-1.4.3/docs_sphinx/reference-magic.txt000066400000000000000000000004751266062647100207300ustar00rootroot00000000000000.. currentmodule:: brian .. index:: magic Magic in Brian -------------- .. autofunction:: magic_return .. autofunction:: magic_register .. seealso:: :ref:`projects-with-multiple-files` Describes difficulties and solutions for using magic functions on projects with multiple files or functions.brian-1.4.3/docs_sphinx/reference-modelfitting.txt000066400000000000000000000010031266062647100223210ustar00rootroot00000000000000.. currentmodule:: brian.library.modelfitting Model fitting toolbox ===================== The model fitting toolbox uses the package `Playdoh `__, you can see the documentation `here `__. .. autofunction:: modelfitting(**kwds) .. autofunction:: print_table .. autofunction:: open_server .. autofunction:: get_spikes .. autofunction:: predict .. autoclass:: PSO() .. autoclass:: GA() .. autoclass:: CMAES() brian-1.4.3/docs_sphinx/reference-models-and-groups.txt000066400000000000000000000030321266062647100232000ustar00rootroot00000000000000.. currentmodule:: brian .. index:: pair: neuron; model pair: neuron; equations pair: model; equations pair: neuron; group Neuron models and groups ======================== .. index:: single: equations The :class:`Equations` object ----------------------------- .. autoclass:: Equations For information on integration methods, and the :class:`StateUpdater` class, see :ref:`integration`. The :class:`NeuronGroup` object ------------------------------- .. autoclass:: NeuronGroup .. index:: single: model .. index:: reset Resets ------ Reset objects are called each network update step to reset specified state variables of neurons that have fired. .. autoclass:: Reset .. autoclass:: StringReset .. index:: pair: variable; reset .. autoclass:: VariableReset .. index:: refractory .. autoclass:: Refractoriness .. autoclass:: SimpleCustomRefractoriness .. autoclass:: CustomRefractoriness .. autoclass:: FunReset .. autoclass:: NoReset .. index:: threshold Thresholds ---------- A threshold mechanism checks which neurons have fired a spike. .. index:: pair: threshold; linear .. autoclass:: Threshold .. autoclass:: StringThreshold .. index:: pair: threshold; variable .. autoclass:: VariableThreshold .. index:: pair: threshold; empirical pair: threshold; hodgkin-huxley .. autoclass:: EmpiricalThreshold .. index:: single: threshold; functional .. autoclass:: SimpleFunThreshold .. autoclass:: FunThreshold .. autoclass:: NoThreshold brian-1.4.3/docs_sphinx/reference-monitors.txt000066400000000000000000000026041266062647100215160ustar00rootroot00000000000000.. currentmodule:: brian Monitors -------- Monitors are used to record properties of your network. The two most important are :class:`SpikeMonitor` which records spikes, and :class:`StateMonitor` which records values of state variables. These objects are just added to the network like a :class:`NeuronGroup` or :class:`Connection`. Implementation note: monitors that record spikes are classes derived from :class:`Connection`, and overwrite the ``propagate`` method to store spikes. If you want to write your own custom spike monitors, you can do the same (or just use :class:`SpikeMonitor` with a custom function). Monitors that record values are classes derived from :class:`NetworkOperation` and implement the ``__call__`` method to store values each time the network updates. Custom state monitors are most easily written by just writing your own network operation using the ``network_operation`` decorator. .. autoclass:: SpikeMonitor .. autoclass:: SpikeCounter .. autoclass:: PopulationSpikeCounter .. autoclass:: StateSpikeMonitor .. autoclass:: StateMonitor .. autoclass:: MultiStateMonitor .. autoclass:: RecentStateMonitor .. autoclass:: AERSpikeMonitor .. autoclass:: FileSpikeMonitor .. autoclass:: ISIHistogramMonitor .. autoclass:: PopulationRateMonitor .. autoclass:: VanRossumMetric .. autoclass:: CoincidenceCounter .. autoclass:: StateHistogramMonitor brian-1.4.3/docs_sphinx/reference-network.txt000066400000000000000000000022371266062647100213370ustar00rootroot00000000000000.. currentmodule:: brian Network ------- The :class:`Network` object stores simulation objects and runs simulations. Usage is described in detail below. For simple scripts, you don't even need to use the :class:`Network` object itself, just directly use the ''magic'' functions :func:`run` and :func:`reinit` described below. .. autoclass:: Network .. autofunction:: network_operation .. autoclass:: NetworkOperation The ''magic'' functions :func:`run` and :func:`reinit` work by searching for objects which could be added to a network, constructing a network with all these objects, and working with that. They are suitable for simple scripts only. If you have problems where objects are unexpectedly not being added to the network, the best thing to do would probably be to just use an explicit :class:`Network` object as above rather than trying to tweak your program to make the magic functions work. However, details are available in the ``brian/magic.py`` source code. .. autofunction:: run .. autofunction:: reinit .. autofunction:: stop .. autofunction:: clear .. autofunction:: forget .. autofunction:: recall .. autoclass:: MagicNetwork brian-1.4.3/docs_sphinx/reference-plasticity.txt000066400000000000000000000013301266062647100220240ustar00rootroot00000000000000.. currentmodule:: brian Plasticity ========== .. note:: The classes below are only working with the :class:`Connection` class. Consider using the newer :class:`Synapses` class as a replacement, it allows you to flexibily express plasticity rules in a very similar way to the classes below. A single :class:`Synapses` object can therefore completely replace the combination of :class:`Connection` + :class:`STDP`, for example. See :doc:`synapses` for more details. Spike timing dependent plasticity (STDP) ---------------------------------------- .. autoclass:: STDP .. autoclass:: ExponentialSTDP Short term plasticity (STP) --------------------------- .. autoclass:: STP brian-1.4.3/docs_sphinx/reference-plotting.txt000066400000000000000000000010571266062647100215050ustar00rootroot00000000000000.. currentmodule:: brian .. index:: plotting Plotting -------- .. index:: pair: plotting; pylab Most plotting should be done with the PyLab commands, all of which are loaded when you import Brian. See: http://matplotlib.sourceforge.net/matplotlib.pylab.html for help on PyLab. Brian currently defines just two plotting functions of its own, :func:`raster_plot` and :func:`hist_plot`. .. index:: pair: plotting; raster .. autofunction:: raster_plot .. index:: pair: plotting; histogram .. autofunction:: hist_plot brian-1.4.3/docs_sphinx/reference-preferences.txt000066400000000000000000000074351266062647100221540ustar00rootroot00000000000000.. currentmodule:: brian .. index:: single: preferences .. _preferences: Preferences =========== Functions --------- Setting and getting global preferences is done with the following functions: .. autofunction:: set_global_preferences .. autofunction:: get_global_preference Global configuration file ------------------------- If you have a module named ``brian_global_config`` anywhere on your Python path, Brian will attempt to import it to define global preferences. For example, to automatically enable weave compilation for all your Brian projects, create a file ``brian_global_config.py`` somewhere in the Python path with the following contents:: from brian.globalprefs import * set_global_preferences(useweave=True) .. INSERT_GLOBAL_PREFERENCES_HERE Global preferences for Brian ---------------------------- The following global preferences have been defined: ``defaultclock = Clock(dt=0.1*msecond)`` The default clock to use if none is provided or defined in any enclosing scope. ``useweave_linear_diffeq = False`` Whether to use weave C++ acceleration for the solution of linear differential equations. Note that on some platforms, typically older ones, this is faster and on some platforms, typically new ones, this is actually slower. ``useweave = False`` Defines whether or not functions should use inlined compiled C code where defined. Requires a compatible C++ compiler. The ``gcc`` and ``g++`` compilers are probably the easiest option (use Cygwin on Windows machines). See also the ``weavecompiler`` global preference. ``weavecompiler = gcc`` Defines the compiler to use for weave compilation. On Windows machines, installing Cygwin is the easiest way to get access to the gcc compiler. ``gcc_options = ['-ffast-math']`` Defines the compiler switches passed to the gcc compiler. For gcc versions 4.2+ we recommend using ``-march=native``. By default, the ``-ffast-math`` optimisations are turned on - if you need IEEE guaranteed results, turn this switch off. ``openmp = False`` Whether or not to use OpenMP pragmas in generated C code. If supported on your compiler (gcc 4.2+) it will use multiple CPUs and can run substantially faster. However, if you are already running several simulations in parallel this will not improve the speed and may even slow it down. In addition, for smaller networks or for simpler neuron models the parallelisation overheads can make it take longer. ``usecodegen = False`` Whether or not to use experimental code generation support. ``usecodegenweave = False`` Whether or not to use C with experimental code generation support. ``usecodegenstateupdate = True`` Whether or not to use experimental code generation support on state updaters. ``usecodegenreset = False`` Whether or not to use experimental code generation support on resets. Typically slower due to weave overheads, so usually leave this off. ``usecodegenthreshold = True`` Whether or not to use experimental code generation support on thresholds. ``usenewpropagate = False`` Whether or not to use experimental new C propagation functions. ``usecstdp = False`` Whether or not to use experimental new C STDP. ``brianhears_usegpu = False`` Whether or not to use the GPU (if available) in Brian.hears. Support is experimental at the moment, and requires the PyCUDA package to be installed. ``magic_useframes = True`` Defines whether or not the magic functions should search for objects defined only in the calling frame or if they should find all objects defined in any frame. This should be set to ``False`` if you are using Brian from an interactive shell like IDLE or IPython where each command has its own frame, otherwise set it to ``True``. brian-1.4.3/docs_sphinx/reference-progressreporting.txt000066400000000000000000000002551266062647100234420ustar00rootroot00000000000000.. currentmodule:: brian .. index:: pair: progress; reporting Progress reporting ================== .. autoclass:: brian.utils.progressreporting.ProgressReporterbrian-1.4.3/docs_sphinx/reference-remotecontrol.txt000066400000000000000000000002551266062647100225400ustar00rootroot00000000000000.. currentmodule:: brian .. index:: single: remote control Remote control ============== .. autoclass:: RemoteControlServer .. autoclass:: RemoteControlClient brian-1.4.3/docs_sphinx/reference-scipy.txt000066400000000000000000000005641266062647100207760ustar00rootroot00000000000000.. currentmodule:: brian SciPy, NumPy and PyLab ====================== .. index:: pair: scipy; analysis pair: numpy; numerical computation pair: numpy; analysis pair: pylab; plotting See the following web sites: * http://www.scipy.org/Getting_Started * http://www.scipy.org/Documentation * http://matplotlib.sourceforge.net/matplotlib.pylab.html brian-1.4.3/docs_sphinx/reference-standard-groups.txt000066400000000000000000000013451266062647100227620ustar00rootroot00000000000000.. currentmodule:: brian Standard Groups =============== Some standard types of :class:`NeuronGroup` have already been defined. :class:`PoissonGroup` and :class:`PoissonInput` to generate spikes with Poisson statistics, :class:`PulsePacket` to generate pulse packets with specified parameters, and :class:`SpikeGeneratorGroup` to generate spikes which fire at prespecified times. .. index:: pair: poisson; group pair: poisson; input .. autoclass:: PoissonGroup .. autoclass:: PoissonInput .. index:: single: input; pulse packet single: pulse packet .. autoclass:: PulsePacket .. index:: pair: direct control; spikes .. autoclass:: SpikeGeneratorGroup .. autoclass:: MultipleSpikeGeneratorGroup brian-1.4.3/docs_sphinx/reference-synapses.txt000066400000000000000000000012421266062647100215060ustar00rootroot00000000000000.. currentmodule:: brian Synapses -------- The :class:`Synapses` class was introduced in Brian version 1.4 and can be used to define everything around synaptic interactions. It can replace the :class:`Connection` class but is much more flexible, in particular it allows to directly incorporate descriptions of synaptic plasticity. See section :ref:`usermanual-synapses` in the user manual for instructions how to use the class. .. autoclass:: Synapses .. autoclass:: SynapticEquations .. autoclass:: brian.synapses.synapticvariable.SynapticVariable .. autoclass:: brian.synapses.synapticvariable.SynapticDelayVariable .. autoclass:: brian.synapses.spikequeue.SpikeQueue brian-1.4.3/docs_sphinx/reference-taskfarm.txt000066400000000000000000000001461266062647100214530ustar00rootroot00000000000000.. currentmodule:: brian.tools.taskfarm Task farming ============ .. autofunction:: run_tasks brian-1.4.3/docs_sphinx/reference-tests.txt000066400000000000000000000001551266062647100210050ustar00rootroot00000000000000.. currentmodule:: brian .. index:: tests, unit tests Tests ----- .. autofunction:: run_all_tests brian-1.4.3/docs_sphinx/reference-units.txt000066400000000000000000000014361266062647100210100ustar00rootroot00000000000000.. currentmodule:: brian .. index:: single: units single: quantity single: dimensions; units Units system ------------ .. autofunction:: have_same_dimensions .. autofunction:: is_dimensionless .. index:: single: dimensions; inconsistent single: units; inconsistent .. autoexception:: DimensionMismatchError .. autofunction:: check_units Typically, you shouldn't need to use any details about the following two classes, and their implementations are subject to change in future releases of Brian. .. autoclass:: Quantity .. autoclass:: Unit .. index:: pair: array; units pair: array; quantity .. removed for Brian 1.0 You can also work with arrays of quantities. .. autoclass:: QuantityArray .. autoclass:: qarray .. autoclass:: safeqarray brian-1.4.3/docs_sphinx/reference-variableupdating.txt000066400000000000000000000004161266062647100231640ustar00rootroot00000000000000.. currentmodule:: brian Variable updating ----------------- Timed Arrays ============ .. autoclass:: TimedArray .. autoclass:: TimedArraySetter .. autofunction:: set_group_var_by_array Linked variables ================ .. autofunction:: linked_var brian-1.4.3/docs_sphinx/reference.txt000066400000000000000000000013521266062647100176450ustar00rootroot00000000000000.. currentmodule:: brian Reference ========= For an overview of Brian, see the :ref:`user-manual` section. .. toctree:: :maxdepth: 2 reference-scipy reference-units reference-clocks reference-models-and-groups reference-integration reference-standard-groups reference-connections reference-plasticity reference-synapses reference-network reference-monitors reference-plotting reference-variableupdating reference-analysis reference-io reference-taskfarm reference-remotecontrol reference-progressreporting reference-modelfitting reference-electrodecompensation reference-hears reference-magic reference-tests brian-1.4.3/docs_sphinx/simulation.txt000066400000000000000000000072341266062647100201000ustar00rootroot00000000000000.. currentmodule:: brian .. index:: pair: simulation; control Simulation control ================== .. index:: single: simulation; update schedule The update schedule ------------------- When a simulation is run, the operations are done in the following order by default: #. Update every :class:`NeuronGroup`, this typically performs an integration time step for the differential equations defining the neuron model. #. Check the threshold condition and propagate the spikes to the target neurons. #. Update every :class:`Synapses`, this may include updating the state of targeted :class:`NeuronGroup` objects #. Reset all neurons that spiked. #. Call all user-defined operations and state monitors. The user-defined operations and state monitors can be placed at other places in this schedule, by using the keyword ``when``. The values can be ``start``, ``before_groups``, ``after_groups``, ``middle``, ``before_connections``, ``after_connections``, ``before_resets``, ``after_resets`` or ``end`` (default: end). For example, to call a function ``f`` at the beginning of every timestep:: @network_operation(when='start') def f(): do_something() or to record the value of a state variable just before the resets:: M=StateMonitor(group,'x',record=True,when='before_resets') Basic simulation control ------------------------ The simulation is run simply as follows:: run(1000*ms) where 1000 ms is the duration of the run. It can be stopped during the simulation with the instruction :func:`stop`, and the network can be reinitialised with the instruction :func:`reinit`. The :func:`run` function also has some options for reporting the progress of the simulation as it runs, for example this will print out the elapsed time, percentage of the simulation this is complete, and an estimate of the remaining time every 10s:: run(100*second, report='text') When the :func:`run` function is called, Brian looks for all relevant objects in the namespace (groups, connections, monitors, user operations), and runs them. In complex scripts, the user might want to run only selected objects. In that case, there are two options. The first is to create a :class:`Network` object (see next section). The second is to use the :func:`forget` function on objects you want to exclude from being used. These can then be later added back using the :func:`recall` function. Users of ``ipython`` may also want to make use of the :func:`clear` function which removes all Brian objects and deletes their data. This is useful because ``ipython`` keeps persistent references to these objects which stops memory from being freed. The Network class ----------------- A :class:`Network` object holds a collection of objets that can be run, i.e., objects with class :class:`NeuronGroup`, :class:`Connection`, :class:`SpikeMonitor`, :class:`StateMonitor` (or subclasses) or any user-defined operation with the decorator :func:`network_operation`. Thoses objects can then be simulated. Example:: G = NeuronGroup(...) C = Connection(...) net = Network(G,C) net.run(1*second) You can also pass lists of objects. The simulation can be controlled with the methods ``stop`` and ``reinit``. The :class:`MagicNetwork` object -------------------------------- When :func:`run`, :func:`reinit` and :func:`stop` are called, they act on the "magic network" (the network consisting of all relevant objects such as groups, connections, monitors and user operations). This "magic network" can be explicitly constructed using the :class:`MagicNetwork` object:: G = NeuronGroup(...) C = Connection(...) net = MagicNetwork() net.run(1*second) brian-1.4.3/docs_sphinx/slowstart.txt000066400000000000000000000002021266062647100177420ustar00rootroot00000000000000.. currentmodule:: brian Getting started =============== .. toctree:: :maxdepth: 2 tutorials examples brian-1.4.3/docs_sphinx/stdp.txt000066400000000000000000000076441266062647100166730ustar00rootroot00000000000000.. currentmodule:: brian Spike-timing-dependent plasticity ================================= .. note:: The classes below are only working with the :class:`Connection` class. Consider using the newer :class:`Synapses` class as a replacement, it allows you to flexibily express plasticity rules in a very similar way to the classes below. A single :class:`Synapses` object can therefore completely replace the combination of :class:`Connection` + :class:`STDP`, for example. See :doc:`synapses` for more details. Synaptic weights can be modified by spiking activity. Weight modifications at a given synapse depend on the relative timing between presynaptic and postsynaptic spikes. Down to the biophysical level, there is a number of synaptic variables which are continuously evolving according to some differential equations, and those variables can be modified by presynaptic and postsynaptic spikes. In spike-timing-dependent plasticity (STDP) rules, the synaptic weight changes at the times of presynaptic and postsynaptic spikes only, as a function of the other synaptic variables. In Brian, an STDP rule can be specified by defining an :class:`STDP` object, as in the following example:: eqs_stdp=''' dA_pre/dt=-A_pre/tau_pre : 1 dA_post/dt=-A_post/tau_post : 1 ''' stdp=STDP(myconnection,eqs=eqs_stdp,pre='A_pre+=dA_pre;w+=A_post', post='A_post+=dA_post;w+=A_pre',wmax=gmax) The :class:`STDP` object acts on the :class:`Connection` object ``myconnection``. Equations of the synaptic variables are given in a string (argument ``eqs``) as for defining neuron models. When a presynaptic (postsynaptic) spike is received, the code ``pre`` (``post``) is executed, where the special identifier ``w`` stands for the synaptic weight (from the specified connection matrix). Optionally, an upper limit can be specified for the synaptic weights (``wmax``). The example above defines an exponential STDP rule with hard bounds and all-to-all pair interactions. Current limitations ------------------- * The differential equations must be linear. * Presynaptic and postsynaptic variables must not interact, that is, a variable cannot be modified by both presynaptic and postsynaptic spikes. However, synaptic weight modifications can depend on all variables. * STDP currently works only with homogeneous delays, not heterogeneous ones. Exponential STDP ^^^^^^^^^^^^^^^^ In many applications, the STDP function is piecewise exponential. In that case, one can use the :class:`ExponentialSTDP` class:: stdp=ExponentialSTDP(connection,taup,taum,Ap,Am,wmax=gmax,interactions='all',update='additive') Here the synaptic weight modification function is:: f(s) = Ap*exp(-s/taup) if s >0 Am*exp(s/taum) if s <0 where s is the time of the postsynaptic spike minus the time of the presynaptic spike. The modification is generally relative to the maximum weight ``wmax`` (see below). The ``interactions`` keyword determines how pairs of pre/post synaptic spikes interact: ``all`` if contributions from all pairs are added, ``nearest`` for only nearest neighbour interactions, ``nearest_pre`` if only the nearest presynaptic spike and all postsynaptic spikes are taken into account and ``nearest_post`` for the symmetrical situation. The weight update can be ``additive``, i.e., w=w+wmax*f(s), or ``multiplicative``: w=w+w*f(s) for depression (usually s<0) and w=w+(wmax-w)*f(s) for potentiation (usually s>0). It can also be ``mixed``: multiplicative for depression, additive for potentiation. Delays ^^^^^^ By default, transmission delays are assumed to be axonal, i.e., synapses are located on the soma: if the delay of the connection C is d, then presynaptic spikes act after a delay d while postsynaptic spikes act immediately. This behaviour can be overriden with the keywords ``delay_pre`` and ``delay_post``, in both classes ``STDP`` and ``Exponential STDP``. brian-1.4.3/docs_sphinx/stp.txt000066400000000000000000000017051266062647100165170ustar00rootroot00000000000000.. currentmodule:: brian Short-term plasticity ===================== Brian implements the short-term plasticity model described in: Markram et al (1998). Differential signaling via the same axon of neocortical pyramidal neurons, PNAS 95(9):5323-8. Synaptic dynamics is described by two variables x and u, which follows the following differential equations:: dx/dt=(1-x)/taud (depression) du/dt=(U-u)/tauf (facilitation) where taud, tauf are time constants and U is a parameter in 0..1. Each a presynaptic spike triggers modifications of the variables:: x->x*(1-u) u->u+U*(1-u) Note that the update order is important. Synaptic weights are modulated by the product u*x (in 0..1), which is taken before updating the variables. This model describes both depression and facilitation. To introduce short-term plasticity into an existing connection C, use the class :class:`STP`:: mystp=STP(C,taud=100*ms,tauf=5*ms,U=.6) brian-1.4.3/docs_sphinx/synapses.txt000066400000000000000000000264241266062647100175630ustar00rootroot00000000000000.. currentmodule:: brian .. _usermanual-synapses: Synapses ======== Starting from Brian 1.4, there is a new class, :class:`Synapses`, in which everything synaptic can be defined. The :class:`Synapses` is similar to the :class:`Connection` class, but it is more general and flexible. In particular, synaptic plasticity can be defined in the same object. Defining synaptic models ------------------------ The basic syntax is as follows:: S=Synapses(P,Q,model='w:1',pre='v+=w') This defines a set of synapses between :class:`NeuronGroup` P and :class:`NeuronGroup` Q. If the target group is not specified, it is identical to the source group by default. The ``model`` keyword is similar as in :class:`NeuronGroup`: it defines synaptic variables and possibly their dynamics (with differential equations, as in :class:`NeuronGroup`). Here, synaptic variable ``w`` is created: there is one value for each synapse. The ``pre`` keyword defines what happens when a presynaptic spike arrives at a synapse. In this case, variable ``w`` is added to variable ``v``. Because ``v`` is not defined as a synaptic variable, it is assumed by default that it is a postsynaptic variable, defined in the target :class:`NeuronGroup` Q. Note that this does not does create synapses (see next section), only the synaptic models. The more general syntax is:: S=Synapses(P,Q,model=model_string,pre=pre_code,post=post_code) Model syntax ^^^^^^^^^^^^ The model follows exactly the same syntax as for :class:`NeuronGroup`. There can be parameters (e.g. synaptic variable ``w`` above), but there can also be static equations and differential equations, describing the dynamics of synaptic variables. In all cases, synaptic variables are created, one value per synapse. Internally, these are stored as arrays. There are a few specificities: * A variable with the ``_post`` suffix is looked up in the postsynaptic (target) neuron. That is, ``v_post`` means variable ``v`` in the postsynaptic neuron. * A variable with the ``_pre`` suffix is looked up in the presynaptic (source) neuron. * A variable not defined as a synaptic variable is considered to be postsynaptic. * A variable not defined as a synaptic variable and not defined in the postsynaptic neuron is considered external. For the integration of differential equations, one can use the same keywords as for :class:`NeuronGroup`. Event-driven updates ^^^^^^^^^^^^^^^^^^^^ By default, differential equations are integrated in a clock-driven fashion, as for a :class:`NeuronGroup`. This is potentially very time consuming, because all synapses are updated at every timestep. It is possible to ask Brian to simulate differential equations in an event-driven fashion, for one-dimensional linear equations, using the keyword ``(event-driven)``. A typical example is pre and postsynaptic traces in STDP:: model='''w:1 dApre/dt=-Apre/taupre : 1 (event-driven) dApost/dt=-Apost/taupost : 1 (event-driven)''' Here, Brian updates the value of ``Apre`` for a given synapse only when this synapse receives a spike, whether it is presynaptic or postsynaptic. More precisely, the variables are updated every time either the ``pre`` or ``post`` code is called for the synapse, so that the values are always up to date when these codes are executed. Automatic event-driven updates are only possible for one-dimensional linear equations. These equations must also be independent of the other ones, that is, a differential equation that is not event-driven cannot depend on an event-driven equation (since the values are not continuously updated). In other cases, the user can write event-driven code explicitly in the update codes (see below). Pre and post codes ^^^^^^^^^^^^^^^^^^ The ``pre`` (``post``) code is executed at each synapse receiving a presynaptic spike. For example: pre='v+=w' adds the value of synaptic variable ``w`` to postsynaptic variable ``v``. As for the model equations, the ``_post`` (``_pre``) suffix indicates a postsynaptic (presynaptic) variable, and variables not found in the synaptic variables are considered postsynaptic by default. Internally, the execution of the code is vectorized (simultaneously executed) for all synapses receiving presynaptic spikes during the current timestep. Therefore, the code should be understood as acting on arrays rather than single values. Any sort of code can be executed. For example, the following code defines stochastic synapses, with a synaptic weight ``w`` and transmission probability ``p``:: S=Synapses(input,neurons,model="""w : 1 p : 1""", pre="v+=w*(rand()3 S.w[2,3]=(1*nS,2*nS) S.w[group1,group2]="(1+cos(i-j))*2*nS" S.w[:,:]='rand()*nS' Delays ------ There is a special synaptic variable that is automatically created: ``delay``. It is the propagation delay from the presynaptic neuron to the synapse, i.e., the presynaptic delay. An alias is ``delay_pre``. When there is a postsynaptic code (keyword ``post``), the variable ``delay_post`` is created. These can be accessed and modified in the same way as other synaptic variables. If delays can change during the simulation, one should specify the maximum allowed delay with the keyword ``max_delay``:: synapses = Synapses(P,Q,model='w:1',pre='v+=w',max_delay=1*ms) Otherwise, this maximum delay is automatically calculated the first time the model is run. Multiple pathways ----------------- It is possible to have multiple pathways with different update codes from the same presynaptic neuron group. This may be interesting in cases when different operations must be applied at different times for the same presynaptic spike. To do this, simply specify a tuple or list of ``pre`` codes:: pre=('ge+=w', '''w=clip(w+Apost,0,inf) Apre+=dApre''') This creates two sets of delay variables, one for each pathway. They can be accessed by first indexing with the pathway number. The following statement, for example, sets the delay of the synapse between the first neurons of the source and target groups, in the second pathway:: S.delay[1][0,0]=3*ms Monitoring synaptic variables ----------------------------- A :class:`StateMonitor` object can be used to monitor synaptic variables. For example, the following statement creates a monitor for variable ``w`` for the synapses 0 and 1:: M = StateMonitor(S,'w',record=[0,1]) Note that these are synapse indexes, not neuron indexes. These can be obtained with the :meth:`~Synapses.synapse_index` method:: s=S.synapse_index((i,j)) where ``i`` and ``j`` may be integers, arrays or slices. A third index can also be given. The recorded traces can then be accessed in the usual way, for example:: plot(M.times,M[0]) brian-1.4.3/docs_sphinx/tabulate.txt000066400000000000000000000007411266062647100175110ustar00rootroot00000000000000.. currentmodule:: brian Precalculated tables -------------------- One way to speed up simulations is to use precalculated tables for complicated functions. The :class:`Tabulate` class defines a table of values of the given function at regularly sampled points. The :class:`TabulateInterp` class defines a table with linear interpolation, which is much more precise. Both work with scalar and vector arguments. .. autoclass:: Tabulate .. autoclass:: TabulateInterp brian-1.4.3/docs_sphinx/tasks_multiplefiles.txt000066400000000000000000000126071266062647100217770ustar00rootroot00000000000000.. currentmodule:: brian .. index:: single: multiple files pair: multiple files; magic functions .. _projects-with-multiple-files: Projects with multiple files or functions ========================================= Brian works with the minimal hassle if the whole of your code is in a single Python module (``.py`` file). This is fine when learning Brian or for quick projects, but for larger, more realistic projects with the source code separated into multiple files, there are some small issues you need to be aware of. These issues essentially revolve around the use of the ''magic'' functions :func:`run`, etc. The way these functions work is to look for objects of the required type that have been instantiated (created) in the same ''execution frame'' as the :func:`run` function. In a small script, that is normally just any objects that have been defined in that script. However, if you define objects in a different module, or in a function, then the magic functions won't be able to find them. There are three main approaches then to splitting code over multiple files (or functions). .. index:: pair: multiple files; network Use the :class:`Network` object explicitly ------------------------------------------ The magic :func:`run` function works by creating a :class:`Network` object automatically, and then running that network. Instead of doing this automatically, you can create your own :class:`Network` object. Rather than writing something like:: group1 = ... group2 = ... C = Connection(group1,group2) ... run(1*second) You do this:: group1 = ... group2 = ... C = Connection(group1, group2) ... net = Network(group1, group2, C) net.run(1*second) In other words, you explicitly say which objects are in your network. Note that any :class:`NeuronGroup`, :class:`Connection`, :class:`Monitor` or function decorated with :func:`network_operation` should be included in the :class:`Network`. See the documentation for :class:`Network` for more details. This is the preferred solution for almost all cases. You may want to use either of the following two solutions if you think your code may be used by someone else, or if you want to make it into an extension to Brian. .. index:: pair: extending brian; magic functions pair: extending brian; magic_return pair: extending brian; magic_register pair: multiple files; magic_return pair: multiple files; magic_register Use the :func:`magic_return` decorator or :func:`magic_register` function ------------------------------------------------------------------------- The :func:`magic_return` decorator is used as follows:: @magic_return def f(): ... return obj Any object returned by a function decorated by :func:`magic_return` will be considered to have been instantiated in the execution frame that called the function. In other words, the magic functions will find that object even though it was really instantiated in a different execution frame. In more complicated scenarios, you may want to use the :func:`magic_register` function. For example:: def f(): ... magic_register(obj1, obj2) return (obj1, obj2) This does the same thing as :func:`magic_return` but can be used with multiple objects. Also, you can specify a ``level`` (see documentation on :func:`magic_register` for more details). .. index:: pair: extending brian; derived classes pair: multiple files; derived classes Use derived classes ------------------- Rather than writing a function which returns an object, you could instead write a derived class of the object type. So, suppose you wanted to have an object that emitted N equally spaced spikes, with an interval dt between them, you could use the :class:`SpikeGeneratorGroup` class as follows:: @magic_return def equally_spaced_spike_group(N, dt): spikes = [(0,i*dt) for i in range(N)] return SpikeGeneratorGroup(spikes) Or alternatively, you could derive a class from :class:`SpikeGeneratorGroup` as follows:: class EquallySpacedSpikeGroup(SpikeGeneratorGroup): def __init__(self, N, t): spikes = [(0,i*dt) for i in range(N)] SpikeGeneratorGroup.__init__(self, spikes) You would use these objects in the following ways:: obj1 = equally_spaced_spike_group(100, 10*ms) obj2 = EquallySpacedSpikeGroup(100, 10*ms) For simple examples like the one above, there's no particular benefit to using derived classes, but using derived classes allows you to add methods to your derived class for example, which might be useful. For more experienced Python programmers, or those who are thinking about making their code into an extension for Brian, this is probably the preferred approach. .. index:: pair: extending brian; contained objects protocol Finally, it may be useful to note that there is a protocol for one object to 'contain' other objects. That is, suppose you want to have an object that can be treated as a simple :class:`NeuronGroup` by the person using it, but actually instantiates several objects (perhaps internal :class:`Connection` objects). These objects need to be added to the :class:`Network` object in order for them to be run with the simulation, but the user shouldn't need to have to know about them. To this end, for any object added to a :class:`Network`, if it has an attribute ``contained_objects``, then any objects in that container will also be added to the network.brian-1.4.3/docs_sphinx/traceanalysis.txt000066400000000000000000000145521266062647100205570ustar00rootroot00000000000000.. currentmodule:: brian Electrophysiology: trace analysis ================================= The electrophysiology library also contains methods to analyze intracellular recordings. To import the electrophysiology library:: from brian.library.electrophysiology import * There is a series of example scripts in the examples/electrophysiology folder. Currently, most methods are related to the analysis of spike shape. Miscellaneous ------------- You can low-pass filter a trace as follows:: v_lp=lowpass(v, tau) where tau is the time constant (cut-off frequency 1/(2*pi*tau)) and v is the trace (a vector of values). By default, tau is in units of the timestep. Alternatively, one can specify the timestep:: v_lp=lowpass(v, tau, dt=0.1*ms) Spike analysis -------------- Detecting spikes ^^^^^^^^^^^^^^^^ The following function returns the time indexes of spike peaks in a trace v:: peaks=spike_peaks(v, vc=-10*mV) where vc is the voltage criterion (we consider that there is a spike when v>vc). The algorithm works as follows. First, we identify positive crossings of the voltage criterion. Then, after each positive crossing, we look for the first local maximum (that is, when the voltage first starts decreasing). The last spike is treated differently because the peak may occur after the end of the recording, in which case the last element is considered as the peak. It is possible to omit the voltage criterion vc. In this case, it is guessed with the following (rather complex) function:: vc=find_spike_criterion(v) The idea of this algorithm is to look at the trace in phase space (v,dv/dt). In this space, spikes tend to circle around some area which contains no trajectory. It appears that, somewhere in the middle of these circles, there is a voltage vc for which trajectories are either increasing (dv>0, upstroke of a spike) or decreasing (dv<0, downstroke of a spike) but never still (dv=0). This means that a positive crossing of this voltage always leads to a spike. We identify this voltage by looking for the largest interval of voltages (v1,v2) for which there is no sign change of dv/dt (over two successive timesteps), and we set vc=(v1+v2)/2, the middle of this interval. As this method is rather complex, it is strongly advised to manually check whether it gives reasonable results. Voltage reset ^^^^^^^^^^^^^ The average voltage reset after a spike is calculated as the average first minimum after a spike, with the following function:: reset=reset_potential(v, peaks=None, full=False) The time indexes of spike peaks can be given (this may save some computation time). With the ``full=True`` option, the standard deviation is also returned. Spike threshold ^^^^^^^^^^^^^^^ There are 3 ways to measure the spike threshold. The first derivative method uses a threshold criterion on the first derivative dv/dt to identify spike onsets:: onsets=spike_onsets(v, criterion=None, vc=None) where ``criterion`` is the derivative criterion and ``vc`` is the voltage criterion to detect spikes. Note that the criterion is in units of voltage per time step. First, the algorithm detects spike peaks. Then for each spike, we look for the last local maximum of dv/dt before the spike, which should be the inflexion point of the spike. Then we identify the last time before the inflexion point when dv/dt is smaller than the criterion. The function returns the time indexes of the onsets, not their values (which are ``v[onsets]``). The derivative criterion may be automatically determined, using the following function:: criterion=find_onset_criterion(v, guess=0.1, vc=None) where ``guess`` is an optional initial guess for the optimization method. The algorithm is simple: find the criterion that minimizes the variability of onsets. There are two other methods to measure spike thresholds, but they do not always give very good results (perhaps the trace should be preliminary filtered):: onsets2=spike_onsets_dv2(v, vc=None) onsets3=spike_onsets_dv3(v, vc=None) The first one finds the maximum of the second derivative d2v/dt2, the second one finds the maximum of d3v/dt3. These are global maxima in each interspike interval (it could be that looking for the last local maximum gives better results). The following function returns the depolarization slope preceding each spike as an array:: slopes=slope_threshold(v, onsets=None, T=None) In this function, spike onset indexes are passed through the ``onset`` keyword. The depolarization slope is calculated by linear regression over the ``T`` time bins preceding each spike. The result is in units of the time bin. In a similar way, the following function returns the average membrane potential preceding each spike as an array:: vm_threshold(v, onsets=None, T=None): Spike shape ^^^^^^^^^^^ The following function returns the average spike duration, defined as the time from onset to reset (next voltage minimum):: duration=spike_duration(v) The onsets can be passed to save computation time, with the ``onsets`` keyword. With the option ``full=True``, the function returns: the mean time from onset to peak, the mean time from onset down to same value (note that this may not be meaningful for some neurons), mean time from onset to next minimum, and standard deviations for these 3 values. Note: this function may change. The following function returns the average spike-triggered voltage:: shape=spike_shape(v, onsets=None, before=100, after=100) If ``onsets`` is unspecified, it is calculated with the ``spike_onsets`` function. Note that you can align spikes on other times, for example peaks. The arguments ``before`` and ``after`` specify the number of time steps before and after the triger times. Note: this should not be specific to spikes, it's a stimulus-triggered average. Spike mask ^^^^^^^^^^ It is often useful to discard spikes from the trace to analyse it. The following function returns an array of booleans which are True in spikes:: spike_mask(v, spikes=None, T=None) The starting point of each spike (time bin) is given by the ``spikes`` variable (default: onsets), and ``T`` is the duration of each spike in time bins. This function can then be used to select the subthreshold trace or the spikes:: v_subthreshold=v[-spike_mask(v,T=100)] v_spikes=v[spike_mask(v,T=100)] brian-1.4.3/docs_sphinx/tutorial1_basic_concepts.txt000066400000000000000000000030031266062647100226650ustar00rootroot00000000000000.. currentmodule:: brian .. _tutorial1_basic_concepts: Tutorial 1: Basic Concepts -------------------------- In this tutorial, we introduce some of the basic concepts of a Brian simulation: * Importing the Brian module into Python * Using quantities with units * Defining a neuron model by its differential equation * Creating a group of neurons * Running a network * Looking at the output of the network * Modifying the state variables of the network directly * Defining the network structure by connecting neurons * Doing a raster plot of the output * Plotting the membrane potential of an individual neuron The following Brian classes will be introduced: * :class:`NeuronGroup` * :class:`Connection` * :class:`SpikeMonitor` * :class:`StateMonitor` We will build a Brian program that defines a randomly connected network of integrate and fire neurons and plot its output. This tutorial assumes you know: * The very basics of Python, the ``import`` keyword, variables, basic arithmetical expressions, calling functions, lists * The simplest leaky integrate and fire neuron model The best place to start learning Python is the official tutorial: http://docs.python.org/tut/ **Tutorial contents** .. toctree:: :maxdepth: 2 tutorial_1g_recording_membrane_potentials tutorial_1a_the_simplest_brian_program tutorial_1b_counting_spikes tutorial_1d_introducing_randomness tutorial_1c_making_some_activity tutorial_1e_connecting_neurons tutorial_1f_recording_spikes brian-1.4.3/docs_sphinx/tutorial2_connections.txt000066400000000000000000000005761266062647100222450ustar00rootroot00000000000000.. currentmodule:: brian .. _tutorial2_connections: Tutorial 2: Connections ----------------------- In this tutorial, we will cover in more detail the concept of a :class:`Connection` in Brian. **Tutorial contents** .. toctree:: :maxdepth: 2 tutorial_2a_the_concept_of_a_connection tutorial_2b_excitatory_and_inhibitory_currents tutorial_2c_the_cuba_network brian-1.4.3/docs_sphinx/tutorial_1a_the_simplest_brian_program.txt000066400000000000000000000073041266062647100256200ustar00rootroot00000000000000.. currentmodule:: brian Tutorial 1a: The simplest Brian program *************************************** Importing the Brian module ~~~~~~~~~~~~~~~~~~~~~~~~~~ The first thing to do in any Brian program is to load Brian and the names of its functions and classes. The standard way to do this is to use the Python ``from ... import *`` statement. :: from brian import * Integrate and Fire model ~~~~~~~~~~~~~~~~~~~~~~~~ The neuron model we will use in this tutorial is the simplest possible leaky integrate and fire neuron, defined by the differential equation: tau dV/dt = -(V-El) and with a threshold value Vt and reset value Vr. Parameters ~~~~~~~~~~ Brian has a system for defining physical quantities (quantities with a physical dimension such as time). The code below illustrates how to use this system, which (mostly) works just as you'd expect. :: tau = 20 * msecond # membrane time constant Vt = -50 * mvolt # spike threshold Vr = -60 * mvolt # reset value El = -60 * mvolt # resting potential (same as the reset) The built in standard units in Brian consist of all the fundamental SI units like second and metre, along with a selection of derived SI units such as volt, farad, coulomb. All names are lowercase following the SI standard. In addition, there are scaled versions of these units using the standard SI prefixes m=1/1000, K=1000, etc. Neuron model and equations ~~~~~~~~~~~~~~~~~~~~~~~~~~ The simplest way to define a neuron model in Brian is to write a list of the differential equations that define it. For the moment, we'll just give the simplest possible example, a single differential equation. You write it in the following form:: dx/dt = f(x) : unit where ``x`` is the name of the variable, ``f(x)`` can be any valid Python expression, and ``unit`` is the physical units of the variable ``x``. In our case we will write:: dV/dt = -(V-El)/tau : volt to define the variable ``V`` with units ``volt``. To complete the specification of the model, we also define a threshold and reset value and create a group of 40 neurons with this model. :: G = NeuronGroup(N=40, model='dV/dt = -(V-El)/tau : volt', threshold=Vt, reset=Vr) The statement creates a new object 'G' which is an instance of the Brian class :class:`NeuronGroup`, initialised with the values in the line above and 40 neurons. In Python, you can call a function or initialise a class using keyword arguments as well as ordered arguments, so if I defined a function ``f(x,y)`` I could call it as ``f(1,2)`` or as ``f(y=2,x=1)`` and get the same effect. See the Python tutorial for more information on this. For the moment, we leave the neurons in this group unconnected to each other, each evolves separately from the others. Simulation ~~~~~~~~~~ Finally, we run the simulation for 1 second of simulated time. By default, the simulator uses a timestep dt = 0.1 ms. :: run(1 * second) And that's it! To see some of the output of this network, go to the next part of the tutorial. Exercise ~~~~~~~~ The units system of Brian is useful for ensuring that everything is consistent, and that you don't make hard to find mistakes in your code by using the wrong units. Try changing the units of one of the parameters and see what happens. Solution ~~~~~~~~ You should see an error message with a Python traceback (telling you which functions were being called when the error happened), ending in a line something like:: Brian.units.DimensionMismatchError: The differential equations are not homogeneous!, dimensions were (m^2 kg s^-3 A^-1) (m^2 kg s^-4 A^-1) brian-1.4.3/docs_sphinx/tutorial_1b_counting_spikes.txt000066400000000000000000000033271266062647100234240ustar00rootroot00000000000000.. currentmodule:: brian Tutorial 1b: Counting spikes **************************** In the previous part of the tutorial we looked at the following: * Importing the Brian module into Python * Using quantities with units * Defining a neuron model by its differential equation * Creating a group of neurons * Running a network In this part, we move on to looking at the output of the network. The first part of the code is the same. :: from brian import * tau = 20 * msecond # membrane time constant Vt = -50 * mvolt # spike threshold Vr = -60 * mvolt # reset value El = -60 * mvolt # resting potential (same as the reset) G = NeuronGroup(N=40, model='dV/dt = -(V-El)/tau : volt', threshold=Vt, reset=Vr) Counting spikes ~~~~~~~~~~~~~~~ Now we would like to have some idea of what this network is doing. In Brian, we use monitors to keep track of the behaviour of the network during the simulation. The simplest monitor of all is the :class:`SpikeMonitor`, which just records the spikes from a given :class:`NeuronGroup`. :: M = SpikeMonitor(G) Results ~~~~~~~ Now we run the simulation as before: :: run(1 * second) And finally, we print out how many spikes there were: :: print M.nspikes So what's going on? Why are there 40 spikes? Well, the answer is that the initial value of the membrane potential for every neuron is 0 mV, which is above the threshold potential of -50 mV and so there is an initial spike at t=0 and then it resets to -60 mV and stays there, below the threshold potential. In the next part of this tutorial, we'll make sure there are some more spikes to see. brian-1.4.3/docs_sphinx/tutorial_1c_making_some_activity.txt000066400000000000000000000043131266062647100244220ustar00rootroot00000000000000.. currentmodule:: brian Tutorial 1c: Making some activity ********************************* In the previous part of the tutorial we found that each neuron was producing only one spike. In this part, we alter the model so that some more spikes will be generated. What we'll do is alter the resting potential ``El`` so that it is above threshold, this will ensure that some spikes are generated. The first few lines remain the same: :: from brian import * tau = 20 * msecond # membrane time constant Vt = -50 * mvolt # spike threshold Vr = -60 * mvolt # reset value But we change the resting potential to -49 mV, just above the spike threshold: :: El = -49 * mvolt # resting potential (same as the reset) And then continue as before: :: G = NeuronGroup(N=40, model='dV/dt = -(V-El)/tau : volt', threshold=Vt, reset=Vr) M = SpikeMonitor(G) run(1 * second) print M.nspikes Running this program gives the output ``840``. That's because every neuron starts at the same initial value and proceeds deterministically, so that each neuron fires at exactly the same time, in total 21 times during the 1s of the run. In the next part, we'll introduce a random element into the behaviour of the network. Exercises ~~~~~~~~~ 1. Try varying the parameters and seeing how the number of spikes generated varies. 2. Solve the differential equation by hand and compute a formula for the number of spikes generated. Compare this with the program output and thereby partially verify it. (Hint: each neuron starts at above the threshold and so fires a spike immediately.) Solution ~~~~~~~~ Solving the differential equation gives: V = El + (Vr-El) exp (-t/tau) Setting V=Vt at time t gives: t = tau log( (Vr-El) / (Vt-El) ) If the simulator runs for time T, and fires a spike immediately at the beginning of the run it will then generate n spikes, where: n = [T/t] + 1 If you have m neurons all doing the same thing, you get nm spikes. This calculation with the parameters above gives: t = 48.0 ms n = 21 nm = 840 As predicted. brian-1.4.3/docs_sphinx/tutorial_1d_introducing_randomness.txt000066400000000000000000000032121266062647100247710ustar00rootroot00000000000000.. currentmodule:: brian Tutorial 1d: Introducing randomness *********************************** In the previous part of the tutorial, all the neurons start at the same values and proceed deterministically, so they all spike at exactly the same times. In this part, we introduce some randomness by initialising all the membrane potentials to uniform random values between the reset and threshold values. We start as before: :: from brian import * tau = 20 * msecond # membrane time constant Vt = -50 * mvolt # spike threshold Vr = -60 * mvolt # reset value El = -49 * mvolt # resting potential (same as the reset) G = NeuronGroup(N=40, model='dV/dt = -(V-El)/tau : volt', threshold=Vt, reset=Vr) M = SpikeMonitor(G) But before we run the simulation, we set the values of the membrane potentials directly. The notation ``G.V`` refers to the array of values for the variable ``V`` in group ``G``. In our case, this is an array of length 40. We set its values by generating an array of random numbers using Brian's ``rand`` function. The syntax is ``rand(size)`` generates an array of length ``size`` consisting of uniformly distributed random numbers in the interval 0, 1. :: G.V = Vr + rand(40) * (Vt - Vr) And now we run the simulation as before. :: run(1 * second) print M.nspikes But this time we get a varying number of spikes each time we run it, roughly between 800 and 850 spikes. In the next part of this tutorial, we introduce a bit more interest into this network by connecting the neurons together. brian-1.4.3/docs_sphinx/tutorial_1e_connecting_neurons.txt000066400000000000000000000061301266062647100241160ustar00rootroot00000000000000.. currentmodule:: brian Tutorial 1e: Connecting neurons ******************************* In the previous parts of this tutorial, the neurons are still all unconnected. We add in connections here. The model we use is that when neuron i is connected to neuron j and neuron i fires a spike, then the membrane potential of neuron j is instantaneously increased by a value ``psp``. We start as before: :: from brian import * tau = 20 * msecond # membrane time constant Vt = -50 * mvolt # spike threshold Vr = -60 * mvolt # reset value El = -49 * mvolt # resting potential (same as the reset) Now we include a new parameter, the PSP size: :: psp = 0.5 * mvolt # postsynaptic potential size And continue as before: :: G = NeuronGroup(N=40, model='dV/dt = -(V-El)/tau : volt', threshold=Vt, reset=Vr) Connections ~~~~~~~~~~~ We now proceed to connect these neurons. Firstly, we declare that there is a connection from neurons in ``G`` to neurons in ``G``. For the moment, this is just something that is necessary to do, the reason for doing it this way will become clear in the next tutorial. :: C = Connection(G, G) Now the interesting part, we make these neurons be randomly connected with probability 0.1 and weight ``psp``. Each neuron i in ``G`` will be connected to each neuron j in ``G`` with probability 0.1. The weight of the connection is the amount that is added to the membrane potential of the target neuron when the source neuron fires a spike. :: C.connect_random(sparseness=0.1, weight=psp) These two previous lines could be done in one line:: C = Connection(G,G,sparseness=0.1,weight=psp) Now we continue as before: :: M = SpikeMonitor(G) G.V = Vr + rand(40) * (Vt - Vr) run(1 * second) print M.nspikes You can see that the number of spikes has jumped from around 800-850 to around 1000-1200. In the next part of the tutorial, we'll look at a way to plot the output of the network. Exercise ~~~~~~~~ Try varying the parameter ``psp`` and see what happens. How large can you make the number of spikes output by the network? Why? Solution ~~~~~~~~ The logically maximum number of firings is 400,000 = 40 * 1000 / 0.1, the number of neurons in the network * the time it runs for / the integration step size (you cannot have more than one spike per step). In fact, the number of firings is bounded above by 200,000. The reason for this is that the network updates in the following way: 1. Integration step 2. Find neurons above threshold 3. Propagate spikes 4. Reset neurons which spiked You can see then that if neuron i has spiked at time t, then it will not spike at time t+dt, even if it receives spikes from another neuron. Those spikes it receives will be added at step 3 at time t, then reset to ``Vr`` at step 4 of time t, then the thresholding function at time t+dt is applied at step 2, before it has received any subsequent inputs. So the most a neuron can spike is every other time step. brian-1.4.3/docs_sphinx/tutorial_1f_recording_spikes.txt000066400000000000000000000027521266062647100235570ustar00rootroot00000000000000.. currentmodule:: brian Tutorial 1f: Recording spikes ***************************** In the previous part of the tutorial, we defined a network with not entirely trivial behaviour, and printed the number of spikes. In this part, we'll record every spike that the network generates and display a raster plot of them. We start as before: :: from brian import * tau = 20 * msecond # membrane time constant Vt = -50 * mvolt # spike threshold Vr = -60 * mvolt # reset value El = -49 * mvolt # resting potential (same as the reset) psp = 0.5 * mvolt # postsynaptic potential size G = NeuronGroup(N=40, model='dV/dt = -(V-El)/tau : volt', threshold=Vt, reset=Vr) C = Connection(G, G) C.connect_random(sparseness=0.1, weight=psp) M = SpikeMonitor(G) G.V = Vr + rand(40) * (Vt - Vr) run(1 * second) print M.nspikes Having run the network, we simply use the :func:`raster_plot` function provided by Brian. After creating plots, we have to use the ``show()`` function to display them. This function is from the PyLab module that Brian uses for its built in plotting routines. :: raster_plot() show() .. image:: images/tutorials/1f.jpg As you can see, despite having introduced some randomness into our network, the output is very regular indeed. In the next part we introduce one more way to plot the output of a network. brian-1.4.3/docs_sphinx/tutorial_1g_recording_membrane_potentials.txt000066400000000000000000000052571266062647100263150ustar00rootroot00000000000000.. currentmodule:: brian Tutorial 1g: Recording membrane potentials ****************************************** In the previous part of this tutorial, we plotted a raster plot of the firing times of the network. In this tutorial, we introduce a way to record the value of the membrane potential for a neuron during the simulation, and plot it. We continue as before: :: from brian import * tau = 20 * msecond # membrane time constant Vt = -50 * mvolt # spike threshold Vr = -60 * mvolt # reset value El = -49 * mvolt # resting potential (same as the reset) psp = 0.5 * mvolt # postsynaptic potential size G = NeuronGroup(N=40, model='dV/dt = -(V-El)/tau : volt', threshold=Vt, reset=Vr) C = Connection(G, G) C.connect_random(sparseness=0.1, weight=psp) This time we won't record the spikes. Recording states ~~~~~~~~~~~~~~~~ Now we introduce a second type of monitor, the :class:`StateMonitor`. The first argument is the group to monitor, and the second is the state variable to monitor. The keyword ``record`` can be an integer, list or the value ``True``. If it is an integer ``i``, the monitor will record the state of the variable for neuron ``i``. If it's a list of integers, it will record the states for each neuron in the list. If it's set to ``True`` it will record for all the neurons in the group. :: M = StateMonitor(G, 'V', record=0) And then we continue as before: :: G.V = Vr + rand(40) * (Vt - Vr) But this time we run it for a shorter time so we can look at the output in more detail: :: run(200 * msecond) Having run the simulation, we plot the results using the ``plot`` command from PyLab which has the same syntax as the Matlab :class:`plot`` command, i.e. ``plot(xvals,yvals,...)``. The :class:`StateMonitor` monitors the times at which it monitored a value in the array ``M.times``, and the values in the array ``M[0]``. The notation ``M[i]`` means the array of values of the monitored state variable for neuron ``i``. In the following lines, we scale the times so that they're measured in ms and the values so that they're measured in mV. We also label the plot using PyLab's ``xlabel``, ``ylabel`` and ``title`` functions, which again mimic the Matlab equivalents. :: plot(M.times / ms, M[0] / mV) xlabel('Time (in ms)') ylabel('Membrane potential (in mV)') title('Membrane potential for neuron 0') show() .. image:: images/tutorials/1g.jpg You can clearly see the leaky integration exponential decay toward the resting potential, as well as the jumps when a spike was received. brian-1.4.3/docs_sphinx/tutorial_2a_the_concept_of_a_connection.txt000066400000000000000000000147641266062647100257250ustar00rootroot00000000000000.. currentmodule:: brian Tutorial 2a: The concept of a Connection **************************************** The network ~~~~~~~~~~~ In this first part, we'll build a network consisting of three neurons. The first two neurons will be under direct control and have no equations defining them, they'll just produce spikes which will feed into the third neuron. This third neuron has two different state variables, called Va and Vb. The first two neurons will be connected to the third neuron, but a spike arriving at the third neuron will be treated differently according to whether it came from the first or second neuron (which you can consider as meaning that the first two neurons have different types of synapses on to the third neuron). The program starts as follows. :: from brian import * tau_a = 1 * ms tau_b = 10 * ms Vt = 10 * mV Vr = 0 * mV Differential equations ~~~~~~~~~~~~~~~~~~~~~~ This time, we will have multiple differential equations. We will use the :class:`Equations` object, although you could equally pass the multi-line string defining the differential equations directly when initialising the :class:`NeuronGroup` object (see the next part of the tutorial for an example of this). :: eqs = Equations(''' dVa/dt = -Va/tau_a : volt dVb/dt = -Vb/tau_b : volt ''') So far, we have defined a model neuron with two state variables, ``Va`` and ``Vb``, which both decay exponentially towards 0, but with different time constants ``tau_a`` and ``tau_b``. This is just so that you can see the difference between them more clearly in the plot later on. SpikeGeneratorGroup ~~~~~~~~~~~~~~~~~~~ Now we introduce the :class:`SpikeGeneratorGroup` class. This is a group of neurons without a model, which just produces spikes at the times that you specify. You create a group like this by writing:: G = SpikeGeneratorGroup(N,spiketimes) where ``N`` is the number of neurons in the group, and ``spiketimes`` is a list of pairs ``(i,t)`` indicating that neuron ``i`` should fire at time ``t``. In fact, ``spiketimes`` can be any 'iterable container' or 'generator', but we don't cover that here (see the detailed documentation for :class:`SpikeGeneratorGroup`). In our case, we want to create a group with two neurons, the first of which (neuron 0) fires at times 1 ms and 4 ms, and the second of which (neuron 1) fires at times 2 ms and 3 ms. The list of ``spiketimes`` then is: :: spiketimes = [(0, 1 * ms), (0, 4 * ms), (1, 2 * ms), (1, 3 * ms)] and we create the group as follows: :: G1 = SpikeGeneratorGroup(2, spiketimes) Now we create a second group, with one neuron, according to the model we defined earlier. :: G2 = NeuronGroup(N=1, model=eqs, threshold=Vt, reset=Vr) Connections ~~~~~~~~~~~ In Brian, a :class:`Connection` from one :class:`NeuronGroup` to another is defined by writing:: C = Connection(G,H,state) Here ``G`` is the source group, ``H`` is the target group, and ``state`` is the name of the target state variable. When a neuron ``i`` in ``G`` fires, Brian finds all the neurons ``j`` in ``H`` that ``i`` in ``G`` is connected to, and adds the amount ``C[i,j]`` to the specified state variable of neuron ``j`` in ``H``. Here ``C[i,j]`` is the (i,j)th entry of the connection matrix of ``C`` (which is initially all zero). To start with, we create two connections from the group of two directly controlled neurons to the group of one neuron with the differential equations. The first connection has the target state ``Va`` and the second has the target state ``Vb``. :: C1 = Connection(G1, G2, 'Va') C2 = Connection(G1, G2, 'Vb') So far, this only declares our intention to connect neurons in group ``G1`` to neurons in group ``G2``, because the connection matrix is initially all zeros. Now, with connection ``C1`` we connect neuron 0 in group ``G1`` to neuron 0 in group ``G2``, with weight 3 mV. This means that when neuron 0 in group ``G1`` fires, the state variable ``Va`` of the neuron in group ``G2`` will be increased by 6 mV. Then we use connection ``C2`` to connection neuron 1 in group ``G1`` to neuron 0 in group ``G2``, this time with weight 3 mV. :: C1[0, 0] = 6 * mV C2[1, 0] = 3 * mV The net effect of this is that when neuron 0 of ``G1`` fires, ``Va`` for the neuron in ``G2`` will increase 6 mV, and when neuron 1 of ``G1`` fires, ``Vb`` for the neuron in ``G2`` will increase 3 mV. Now we set up monitors to record the activity of the network, run it and plot it. :: Ma = StateMonitor(G2, 'Va', record=True) Mb = StateMonitor(G2, 'Vb', record=True) run(10 * ms) plot(Ma.times, Ma[0]) plot(Mb.times, Mb[0]) show() .. image:: images/tutorials/2a.jpg The two plots show the state variables ``Va`` and ``Vb`` for the single neuron in group ``G2``. ``Va`` is shown in blue, and ``Vb`` in green. According to the differential equations, ``Va`` decays much faster than ``Vb`` (time constant 1 ms rather than 10 ms), but we have set it up (through the connection strengths) that an incoming spike from neuron 0 of ``G1`` causes a large increase of 6 mV to ``Va``, whereas a spike from neuron 1 of ``G1`` causes a smaller increase of 3 mV to Vb. The value for ``Va`` then jumps at times 1 ms and 4 ms, when we defined neuron 0 of ``G1`` to fire, and decays almost back to rest in between. The value for ``Vb`` jumps at times 2 ms and 3 ms, and because the times are closer together and the time constant is longer, they add together. In the next part of this tutorial, we'll see how to use this system to do something useful. Exercises ~~~~~~~~~ 1. Try playing with the parameters ``tau_a``, ``tau_b`` and the connection strengths, ``C1[0,0]`` and ``C2[0,1]``. Try changing the list of spike times. 2. In this part of the tutorial, the states ``Va`` and ``Vb`` are independent of one another. Try rewriting the differential equations so that they're not independent and play around with that. 3. Write a network with inhibitory and excitatory neurons. Hint: you only need one connection. 4. Write a network with inhibitory and excitatory neurons whose actions have different time constants (for example, excitatory neurons have a slower effect than inhibitory ones). Solutions ~~~~~~~~~ 3. Simple write ``C[i,j]=-3*mV`` to make the connection from neuron i to neuron j inhibitory. 4. See the next part of this tutorial. brian-1.4.3/docs_sphinx/tutorial_2b_excitatory_and_inhibitory_currents.txt000066400000000000000000000113771266062647100274270ustar00rootroot00000000000000.. currentmodule:: brian Tutorial 2b: Excitatory and inhibitory currents *********************************************** In this tutorial, we use multiple connections to solve a real problem, how to implement two types of synapses with excitatory and inhibitory currents with different time constants. The scheme ~~~~~~~~~~ The scheme we implement is the following diffential equations: | taum dV/dt = -V + ge - gi | taue dge/dt = -ge | taui dgi/dt = -gi An excitatory neuron connects to state ge, and an inhibitory neuron connects to state gi. When an excitatory spike arrives, ge instantaneously increases, then decays exponentially. Consequently, V will initially but continuously rise and then fall. Solving these equations, if V(0)=0, ge(0)=g0 corresponding to an excitatory spike arriving at time 0, and gi(0)=0 then: | gi = 0 | ge = g0 exp(-t/taue) | V = (exp(-t/taum) - exp(-t/taue)) taue g0 / (taum-taue) We use a very short time constant for the excitatory currents, a longer one for the inhibitory currents, and an even longer one for the membrane potential. :: from brian import * taum = 20 * ms taue = 1 * ms taui = 10 * ms Vt = 10 * mV Vr = 0 * mV eqs = Equations(''' dV/dt = (-V+ge-gi)/taum : volt dge/dt = -ge/taue : volt dgi/dt = -gi/taui : volt ''') Connections ~~~~~~~~~~~ As before, we'll have a group of two neurons under direct control, the first of which will be excitatory this time, and the second will be inhibitory. To demonstrate the effect, we'll have two excitatory spikes reasonably close together, followed by an inhibitory spike later on, and then shortly after that two excitatory spikes close together. :: spiketimes = [(0, 1 * ms), (0, 10 * ms), (1, 40 * ms), (0, 50 * ms), (0, 55 * ms)] G1 = SpikeGeneratorGroup(2, spiketimes) G2 = NeuronGroup(N=1, model=eqs, threshold=Vt, reset=Vr) C1 = Connection(G1, G2, 'ge') C2 = Connection(G1, G2, 'gi') The weights are the same - when we increase ``ge`` the effect on ``V`` is excitatory and when we increase ``gi`` the effect on ``V`` is inhibitory. :: C1[0, 0] = 3 * mV C2[1, 0] = 3 * mV We set up monitors and run as normal. :: Mv = StateMonitor(G2, 'V', record=True) Mge = StateMonitor(G2, 'ge', record=True) Mgi = StateMonitor(G2, 'gi', record=True) run(100 * ms) This time we do something a little bit different when plotting it. We want a plot with two subplots, the top one will show ``V``, and the bottom one will show both ``ge`` and ``gi``. We use the ``subplot`` command from pylab which mimics the same command from Matlab. :: figure() subplot(211) plot(Mv.times, Mv[0]) subplot(212) plot(Mge.times, Mge[0]) plot(Mgi.times, Mgi[0]) show() .. image:: images/tutorials/2b.jpg The top figure shows the voltage trace, and the bottom figure shows ``ge`` in blue and ``gi`` in green. You can see that although the inhibitory and excitatory weights are the same, the inhibitory current is much more powerful. This is because the effect of ``ge`` or ``gi`` on ``V`` is related to the integral of the differential equation for those variables, and ``gi`` decays much more slowly than ``ge``. Thus the size of the negative deflection at 40 ms is much bigger than the excitatory ones, and even the double excitatory spike after the inhibitory one can't cancel it out. In the next part of this tutorial, we set up our first serious network, with 4000 neurons, excitatory and inhibitory. Exercises ~~~~~~~~~ 1. Try changing the parameters and spike times to get a feel for how it works. 2. Try an equivalent implementation with the equation taum dV/dt = -V+ge+gi 3. Verify that the differential equation has been solved correctly. Solutions ~~~~~~~~~ Solution for 2: Simply use the line ``C2[1,0] = -3*mV`` to get the same effect. Solution for 3: First, set up the situation we described at the top for which we already know the solution of the differential equations, by changing the spike times as follows:: spiketimes = [(0,0*ms)] Now we compute what the values ought to be as follows:: t = Mv.times Vpredicted = (exp(-t/taum) - exp(-t/taue))*taue*(3*mV) / (taum-taue) Now we can compute the difference between the predicted and actual values:: Vdiff = abs(Vpredicted - Mv[0]) This should be zero:: print max(Vdiff) Sure enough, it's as close as you can expect on a computer. When I run this it gives me the value 1.3 aV, which is 1.3 * 10^-18 volts, i.e. effectively zero given the finite precision of the calculations involved. brian-1.4.3/docs_sphinx/tutorial_2c_the_cuba_network.txt000066400000000000000000000107341266062647100235450ustar00rootroot00000000000000.. currentmodule:: brian Tutorial 2c: The CUBA network ***************************** In this part of the tutorial, we set up our first serious network that actually does something. It implements the CUBA network, Benchmark 2 from: Simulation of networks of spiking neurons: A review of tools and strategies (2006). Brette, Rudolph, Carnevale, Hines, Beeman, Bower, Diesmann, Goodman, Harris, Zirpe, Natschlager, Pecevski, Ermentrout, Djurfeldt, Lansner, Rochel, Vibert, Alvarez, Muller, Davison, El Boustani and Destexhe. Journal of Computational Neuroscience This is a network of 4000 neurons, of which 3200 excitatory, and 800 inhibitory, with exponential synaptic currents. The neurons are randomly connected with probability 0.02. :: from brian import * taum = 20 * ms # membrane time constant taue = 5 * ms # excitatory synaptic time constant taui = 10 * ms # inhibitory synaptic time constant Vt = -50 * mV # spike threshold Vr = -60 * mV # reset value El = -49 * mV # resting potential we = (60 * 0.27 / 10) * mV # excitatory synaptic weight wi = (20 * 4.5 / 10) * mV # inhibitory synaptic weight eqs = Equations(''' dV/dt = (ge-gi-(V-El))/taum : volt dge/dt = -ge/taue : volt dgi/dt = -gi/taui : volt ''') So far, this has been pretty similar to the previous part, the only difference is we have a couple more parameters, and we've added a resting potential ``El`` into the equation for ``V``. Now we make lots of neurons: :: G = NeuronGroup(4000, model=eqs, threshold=Vt, reset=Vr) Next, we divide them into subgroups. The :meth:`~NeuronGroup.subgroup` method of a :class:`NeuronGroup` returns a new :class:`NeuronGroup` that can be used in exactly the same way as its parent group. At the moment, the subgrouping mechanism can only be used to create contiguous groups of neurons (so you can't have a subgroup consisting of neurons 0-100 and also 200-300 say). We designate the first 3200 neurons as ``Ge`` and the second 800 as ``Gi``, these will be the excitatory and inhibitory neurons. :: Ge = G.subgroup(3200) # Excitatory neurons Gi = G.subgroup(800) # Inhibitory neurons Now we define the connections. As in the previous part of the tutorial, ``ge`` is the excitatory current and ``gi`` is the inhibitory one. ``Ce`` says that an excitatory neuron can synapse onto any neuron in ``G``, be it excitatory or inhibitory. Similarly for inhibitory neurons. We also randomly connect ``Ge`` and ``Gi`` to the whole of ``G`` with probability 0.02 and the weights given in the list of parameters at the top. :: Ce = Connection(Ge, G, 'ge', sparseness=0.02, weight=we) Ci = Connection(Gi, G, 'gi', sparseness=0.02, weight=wi) Set up some monitors as usual. The line ``record=0`` in the :class:`StateMonitor` declarations indicates that we only want to record the activity of neuron 0. This saves time and memory. :: M = SpikeMonitor(G) MV = StateMonitor(G, 'V', record=0) Mge = StateMonitor(G, 'ge', record=0) Mgi = StateMonitor(G, 'gi', record=0) And in order to start the network off in a somewhat more realistic state, we initialise the membrane potentials uniformly randomly between the reset and the threshold. :: G.V = Vr + (Vt - Vr) * rand(len(G)) Now we run. :: run(500 * ms) And finally we plot the results. Just for fun, we do a rather more complicated plot than we've been doing so far, with three subplots. The upper one is the raster plot of the whole network, and the lower two are the values of ``V`` (on the left) and ``ge`` and ``gi`` (on the right) for the neuron we recorded from. See the PyLab documentation for an explanation of the plotting functions, but note that the :func:`raster_plot` keyword ``newfigure=False`` instructs the (Brian) function :func:`raster_plot` not to create a new figure (so that it can be placed as a subplot of a larger figure). :: subplot(211) raster_plot(M, title='The CUBA network', newfigure=False) subplot(223) plot(MV.times / ms, MV[0] / mV) xlabel('Time (ms)') ylabel('V (mV)') subplot(224) plot(Mge.times / ms, Mge[0] / mV) plot(Mgi.times / ms, Mgi[0] / mV) xlabel('Time (ms)') ylabel('ge and gi (mV)') legend(('ge', 'gi'), 'upper right') show() .. image:: images/tutorials/2c.jpg brian-1.4.3/docs_sphinx/tutorials.txt000066400000000000000000000005301266062647100177320ustar00rootroot00000000000000.. currentmodule:: brian Tutorials ========= These tutorials cover some basic topics in writing Brian scripts in Python. The complete source code for the tutorials is available in the tutorials folder in the extras package. .. toctree:: :maxdepth: 2 othertutorials tutorial1_basic_concepts tutorial2_connectionsbrian-1.4.3/docs_sphinx/typicaltasks.txt000066400000000000000000000003411266062647100204170ustar00rootroot00000000000000.. currentmodule:: brian Typical Tasks ------------- TODO: typical things you want to achieve in running your simulation, and how to go about doing them. .. toctree:: :maxdepth: 2 tasks_multiplefiles brian-1.4.3/docs_sphinx/units.txt000066400000000000000000000153211266062647100170520ustar00rootroot00000000000000.. currentmodule:: brian Units ===== Basics ------ Brian has a system for physical quantities with units built in, and most of the library functions require that variables have the right units. This restriction is useful in catching hard to find errors based on using incorrect units, and ensures that simulated models are physically meaningful. For example, running the following code causes an error:: >>> from brian import * >>> c = Clock(t=0) Traceback (most recent call last): File "", line 1, in c = Clock(t=0) File "C:\Documents and Settings\goodman\Mes documents\Programming\Python simulator\Brian\units.py", line 1126, in new_f raise DimensionMismatchError("Function " + f.__name__ + " variable " + k + " should have dimensions of " + str(au[k]), getDimensions(newkeyset[k])) DimensionMismatchError: Function __init__ variable t should have dimensions of s, dimensions were (1) You can see that Brian raises a ``DimensionMismatchError`` exception, because the :class:`Clock` object expects ``t`` to have units of time. The correct thing to write is:: >>> from brian import * >>> c = Clock(t=0*second) Similarly, attempting to do numerical operations with inconsistent units will raise an error:: >>> from brian import * >>> 3*second+2*metre Traceback (most recent call last): File "", line 1, in 3*second+2*metre File "C:\Documents and Settings\goodman\Mes documents\Programming\Python simulator\Brian\units.py", line 600, in __add__ if dim==self.dim: DimensionMismatchError: Addition, dimensions were (s) (m) Units defined in Brian ---------------------- The following fundamental SI unit names are defined: ``metre``, ``meter`` (US spelling), ``kilogram``, ``second``, ``amp``, ``kelvin``, ``mole``, ``candle`` These derived SI unit names are also defined: ``radian``, ``steradian``, ``hertz``, ``newton``, ``pascal``, ``joule``, ``watt``, ``coulomb``, ``volt``, ``farad``, ``ohm``, ``siemens``, ``weber``, ``tesla``, ``henry``, ``celsius``, ``lumen``, ``lux``, ``becquerel``, ``gray``, ``sievert``, ``katal`` In addition, you can form scaled versions of these units with any of the standard SI prefixes: ========== ======= ================ ========== ======= ================ Factor Name Symbol Factor Name Symbol ========== ======= ================ ========== ======= ================ 10^24 yotta Y 10^-24 yocto y 10^21 zetta Z 10^-21 zepto z 10^18 exa E 10^-21 zepto z 10^15 peta P 10^-15 femto f 10^12 tera T 10^-12 pico p 10^9 giga G 10^-9 nano n 10^6 mega M 10^-6 micro u (mu in SI) 10^3 kilo k 10^-3 milli m 10^2 hecto h 10^-2 centi c 10^1 deka da 10^-1 deci d ========== ======= ================ ========== ======= ================ So for example, you could write ``fnewton`` for femto-newtons, ``Mwatt`` for megawatt, etc. There are also units for 2nd and 3rd powers of each of the above units, for example ``metre3 = metre**3``, ``watt2 = watt*watt``, etc. You can optionally use short names for some units derived from volts, amps, farads, siemens, seconds, hertz and metres: ``mV, mA, uA, nA, pA, mF, uF, nF, mS, uS, ms, Hz, kHz, MHz, cm, cm2, cm3,`` ``mm, mm2, mm3, um, um2, um3``. Since these names are so short, there is a danger that they might clash with your own variables names, so watch out for that. .. removed in Brian 1.0 Arrays with units ----------------- In Brian, arrays have units by default. In fact, a new class named :class:`qarray` replaces the standard ``array`` class. For example, one can define a vector with each element being ``1*mV`` as follows:: x=ones(5)*mV Attempts to assign one element of x with a different units (e.g. ``x[2]=1*nA``) raises an error. Arrays and units ---------------- Versions of Brian before 1.0 had a system for allowing arrays to have units, this has been removed for the 1.0 release because of stability problems - as new releases of NumPy, SciPy and PyLab came out it required changes to the units code. Now all arrays used by Brian are standard NumPy arrays and have no units. Checking units -------------- Units are automatically checked when arithmetic operations are performed, and when a neuron group is initialised (the consistency of the differential equations is checked). They can also be checked explictly when a user-defined function is called by using the decorator ``@check_units``, which can be used as follows:: @check_units(I=amp,R=ohm,wibble=metre,result=volt) def getvoltage(I,R,**k): return I*R Remarks: * not all arguments need to be checked * keyword arguments may be checked * the result can optionnally be checked * no error is raised if the values are strings. Disabling units --------------- Unit checking can slow down the simulations. The units system can be disabled by inserting ``import brian_no_units`` as the *first line* of the script, e.g.:: import brian_no_units from brian import * # etc Internally, physical quantities are floats with an additional units information. The float value is the value in the SI system. For example, ``float(mV)`` returns ``0.001``. After importing ``brian_no_units``, all units are converted to their float values. For example, ``mV`` is simply the number ``0.001``. This may also be a solution when using external libraries which are not compatible with units (but see next section). Unit checking can also be turned down locally when initializing a neuron group by passing the argument ``check_units=False``. In that case, no error is raised if the differential equations are not homogeneous. A good practice is to develop the script with units on, then switch them off once the script runs correctly. Converting quantities --------------------- In many situations, physical quantities need to be expressed with given units. For example, one might want to plot a graph of the membrane potential in mV as a function of time in ms. The following code:: plot(t,V) displays the trace with time in seconds and potential in volts. The simplest solution to have time in ms and potential in mV is to use units operations:: plot(t/ms,V/mV) Here, t/ms is a unitless array containing the values of t in ms. The same trick may be applied to use external functions which do not work with units (convert the arguments to unitless quantities as above). brian-1.4.3/docs_sphinx/usermanual.txt000066400000000000000000000014151266062647100200630ustar00rootroot00000000000000.. currentmodule:: brian .. _user-manual: User manual *********** .. index:: pair: scipy; analysis pair: numpy; numerical computation pair: numpy; analysis pair: pylab; plotting The SciPy, NumPy and PyLab packages are documented on the following web sites: * http://www.scipy.org/Getting_Started * http://www.scipy.org/Documentation * http://docs.scipy.org/ * http://matplotlib.sourceforge.net/ Brian itself is documented in the following sections: .. toctree:: :maxdepth: 1 units models connections stdp stp synapses recording inputs operations analysis realtime clocks simulation equations files managing_sims For more detailed information, see the reference chapter. brian-1.4.3/examples/000077500000000000000000000000001266062647100144425ustar00rootroot00000000000000brian-1.4.3/examples/audition/000077500000000000000000000000001266062647100162565ustar00rootroot00000000000000brian-1.4.3/examples/audition/filterbank.py000077500000000000000000000021011266062647100207460ustar00rootroot00000000000000#!/usr/bin/env python ''' An auditory filterbank implemented with Poisson neurons The input sound has a missing fundamental (only harmonics 2 and 3) ''' from brian import * defaultclock.dt = .01 * ms N = 1500 tau = 1 * ms # Decay time constant of filters = 2*tau freq = linspace(100 * Hz, 2000 * Hz, N) # characteristic frequencies f_stimulus = 500 * Hz # stimulus frequency gain = 500 * Hz eqs = ''' dv/dt=(-a*w-v+I)/tau : Hz dw/dt=(v-w)/tau : Hz # e.g. linearized potassium channel with conductance a a : 1 I = gain*(sin(4*pi*f_stimulus*t)+sin(6*pi*f_stimulus*t)) : Hz ''' neurones = NeuronGroup(N, model=eqs, threshold=PoissonThreshold()) neurones.a = (2 * pi * freq * tau) ** 2 spikes = SpikeMonitor(neurones) counter = SpikeCounter(neurones) run(100 * ms) subplot(121) CF = array([freq[i] for i, _ in spikes.spikes]) timings = array([t for _, t in spikes.spikes]) plot(timings / ms, CF, '.') xlabel('Time (ms)') ylabel('Characteristic frequency (Hz)') subplot(122) plot(counter.count / (300 * ms), freq) xlabel('Firing rate (Hz)') show() brian-1.4.3/examples/audition/jeffress.py000077500000000000000000000034151266062647100204450ustar00rootroot00000000000000#!/usr/bin/env python ''' Jeffress model, adapted with spiking neuron models. A sound source (white noise) is moving around the head. Delay differences between the two ears are used to determine the azimuth of the source. Delays are mapped to a neural place code using delay lines (each neuron receives input from both ears, with different delays). Romain Brette ''' from brian import * defaultclock.dt = .02 * ms dt = defaultclock.dt # Sound sound = TimedArray(10 * randn(50000)) # white noise # Ears and sound motion around the head (constant angular speed) sound_speed = 300 * metre / second interaural_distance = 20 * cm # big head! max_delay = interaural_distance / sound_speed print "Maximum interaural delay:", max_delay angular_speed = 2 * pi * radian / second # 1 turn/second tau_ear = 1 * ms sigma_ear = .1 eqs_ears = ''' dx/dt=(sound(t-delay)-x)/tau_ear+sigma_ear*(2./tau_ear)**.5*xi : 1 delay=distance*sin(theta) : second distance : second # distance to the centre of the head in time units dtheta/dt=angular_speed : radian ''' ears = NeuronGroup(2, model=eqs_ears, threshold=1, reset=0, refractory=2.5 * ms) ears.distance = [-.5 * max_delay, .5 * max_delay] traces = StateMonitor(ears, 'x', record=True) # Coincidence detectors N = 300 tau = 1 * ms sigma = .1 eqs_neurons = ''' dv/dt=-v/tau+sigma*(2./tau)**.5*xi : 1 ''' neurons = NeuronGroup(N, model=eqs_neurons, threshold=1, reset=0) synapses = Connection(ears, neurons, 'v', structure='dense', delay=True, max_delay=1.1 * max_delay) synapses.connect_full(ears, neurons, weight=.5) synapses.delay[0, :] = linspace(0 * ms, 1.1 * max_delay, N) synapses.delay[1, :] = linspace(0 * ms, 1.1 * max_delay, N)[::-1] spikes = SpikeMonitor(neurons) run(1000 * ms) raster_plot(spikes) show() brian-1.4.3/examples/audition/licklider.py000077500000000000000000000026471266062647100206060ustar00rootroot00000000000000#!/usr/bin/env python ''' Spike-based adaptation of Licklider's model of pitch processing (autocorrelation with delay lines) with phase locking. Romain Brette ''' from brian import * defaultclock.dt = .02 * ms # Ear and sound max_delay = 20 * ms # 50 Hz tau_ear = 1 * ms sigma_ear = .1 eqs_ear = ''' dx/dt=(sound-x)/tau_ear+sigma_ear*(2./tau_ear)**.5*xi : 1 sound=5*sin(2*pi*frequency*t)**3 : 1 # nonlinear distorsion #sound=5*(sin(4*pi*frequency*t)+.5*sin(6*pi*frequency*t)) : 1 # missing fundamental frequency=(200+200*t*Hz)*Hz : Hz # increasing pitch ''' receptors = NeuronGroup(2, model=eqs_ear, threshold=1, reset=0, refractory=2 * ms) traces = StateMonitor(receptors, 'x', record=True) sound = StateMonitor(receptors, 'sound', record=0) # Coincidence detectors min_freq = 50 * Hz max_freq = 1000 * Hz N = 300 tau = 1 * ms sigma = .1 eqs_neurons = ''' dv/dt=-v/tau+sigma*(2./tau)**.5*xi : 1 ''' neurons = NeuronGroup(N, model=eqs_neurons, threshold=1, reset=0) synapses = Connection(receptors, neurons, 'v', structure='dense', max_delay=1.1 * max_delay, delay=True) synapses.connect_full(receptors, neurons, weight=.5) synapses.delay[1, :] = 1. / exp(linspace(log(min_freq / Hz), log(max_freq / Hz), N)) spikes = SpikeMonitor(neurons) run(500 * ms) raster_plot(spikes) ylabel('Frequency') yticks([0, 99, 199, 299], array(1. / synapses.delay.todense()[1, [0, 99, 199, 299]], dtype=int)) show() brian-1.4.3/examples/electrophysiology/000077500000000000000000000000001266062647100202265ustar00rootroot00000000000000brian-1.4.3/examples/electrophysiology/AEC.py000077500000000000000000000024061266062647100211750ustar00rootroot00000000000000#!/usr/bin/env python ''' AEC experiment (current-clamp) ''' from brian import * from brian.library.electrophysiology import * from time import time myclock = Clock(dt=.1 * ms) clock_rec = Clock(dt=.1 * ms) #log_level_debug() taum = 20 * ms gl = 20 * nS Cm = taum * gl Re = 50 * Mohm Ce = 0.1 * ms / Re eqs = Equations(''' dvm/dt=(-gl*vm+i_inj)/Cm : volt I:amp ''') eqs += electrode(.6 * Re, Ce) eqs += current_clamp(vm='v_el', i_inj='i_cmd', i_cmd='I', Re=.4 * Re, Ce=Ce) setup = NeuronGroup(1, model=eqs, clock=myclock) board = AEC(setup, 'v_rec', 'I', clock_rec) recording = StateMonitor(board, 'record', record=True, clock=myclock) soma = StateMonitor(setup, 'vm', record=True, clock=myclock) run(50 * ms) board.command = .5 * nA run(200 * ms) board.command = 0 * nA run(150 * ms) board.start_injection() t1 = time() run(1 * second) t2 = time() print 'Duration:', t2 - t1, 's' board.stop_injection() run(100 * ms) board.estimate() print 'Re=', sum(board.Ke) * ohm board.switch_on() run(50 * ms) board.command = .5 * nA run(200 * ms) board.command = 0 * nA run(150 * ms) board.switch_off() figure() plot(recording.times / ms, recording[0] / mV, 'b') plot(soma.times / ms, soma[0] / mV, 'r') figure() plot(board.Ke) show() brian-1.4.3/examples/electrophysiology/DCC.py000077500000000000000000000024061266062647100211760ustar00rootroot00000000000000#!/usr/bin/env python ''' An example of single-electrode current clamp recording with discontinuous current clamp (using the electrophysiology library). ''' from brian import * from brian.library.electrophysiology import * defaultclock.dt = 0.01 * ms taum = 20 * ms # membrane time constant gl = 1. / (50 * Mohm) # leak conductance Cm = taum * gl # membrane capacitance Re = 50 * Mohm # electrode resistance Ce = 0.1 * ms / Re # electrode capacitance eqs = Equations(''' dvm/dt=(-gl*vm+i_inj)/Cm : volt Rbridge:ohm # bridge resistance I:amp # command current ''') eqs += current_clamp(i_cmd='I', Re=Re, Ce=Ce) setup = NeuronGroup(1, model=eqs) ampli = DCC(setup, 'v_rec', 'I', 1 * kHz) soma = StateMonitor(setup, 'vm', record=True) recording = StateMonitor(setup, 'v_rec', record=True) DCCrecording = StateMonitor(ampli, 'record', record=True) # No compensation run(50 * ms) ampli.command = .5 * nA run(100 * ms) ampli.command = 0 * nA run(50 * ms) ampli.set_frequency(2 * kHz) ampli.command = .5 * nA run(100 * ms) ampli.command = 0 * nA run(50 * ms) plot(recording.times / ms, recording[0] / mV, 'b') plot(DCCrecording.times / ms, DCCrecording[0] / mV, 'k') plot(soma.times / ms, soma[0] / mV, 'r') show() brian-1.4.3/examples/electrophysiology/SEVC.py000077500000000000000000000015771266062647100213550ustar00rootroot00000000000000#!/usr/bin/env python ''' Voltage-clamp experiment (SEVC) ''' from brian import * from brian.library.electrophysiology import * defaultclock.dt = .01 * ms taum = 20 * ms # membrane time constant gl = 1. / (50 * Mohm) # leak conductance Cm = taum * gl # membrane capacitance Re = 50 * Mohm # electrode resistance Ce = 0.1 * ms / Re # electrode capacitance eqs = Equations(''' dvm/dt=(-gl*vm+i_inj)/Cm : volt I:amp ''') eqs += current_clamp(i_cmd='I', Re=Re, Ce=Ce) setup = NeuronGroup(1, model=eqs) ampli = SEVC(setup, 'v_rec', 'I', 1 * kHz, gain=250 * nS, gain2=50 * nS / ms) recording = StateMonitor(ampli, 'record', record=True) soma = StateMonitor(setup, 'vm', record=True) ampli.command = 20 * mV run(200 * ms) figure() plot(recording.times / ms, recording[0] / nA, 'k') figure() plot(soma.times / ms, soma[0] / mV, 'b') show() brian-1.4.3/examples/electrophysiology/bridge.py000077500000000000000000000017741266062647100220500ustar00rootroot00000000000000#!/usr/bin/env python ''' Bridge experiment (current-clamp) ''' from brian import * from brian.library.electrophysiology import * defaultclock.dt = .01 * ms #log_level_debug() taum = 20 * ms gl = 20 * nS Cm = taum * gl Re = 50 * Mohm Ce = 0.5 * ms / Re N = 10 eqs = Equations(''' dvm/dt=(-gl*vm+i_inj)/Cm : volt #Rbridge:ohm CC:farad I:amp ''') eqs += electrode(.6 * Re, Ce) #eqs+=current_clamp(vm='v_el',i_inj='i_cmd',i_cmd='I',Re=.4*Re,Ce=Ce, # bridge='Rbridge') eqs += current_clamp(vm='v_el', i_inj='i_cmd', i_cmd='I', Re=.4 * Re, Ce=Ce, bridge=Re, capa_comp='CC') setup = NeuronGroup(N, model=eqs) setup.I = 0 * nA setup.v = 0 * mV #setup.Rbridge=linspace(0*Mohm,60*Mohm,N) setup.CC = linspace(0 * Ce, Ce, N) recording = StateMonitor(setup, 'v_rec', record=True) run(50 * ms) setup.I = .5 * nA run(200 * ms) setup.I = 0 * nA run(150 * ms) for i in range(N): plot(recording.times / ms + i * 400, recording[i] / mV, 'k') show() brian-1.4.3/examples/electrophysiology/compensatedtrace.npy000066400000000000000000002343201266062647100243030ustar00rootroot00000000000000“NUMPYF{'descr': 'àɱ¿aYûŸÀ±¿¾j ]’½±¿TfY™¾±¿óB2j8º±¿†Ã˜‡™¶±¿­_Íu*³±¿ßˆåû©±¿¥¨WL«±¿Žv× ±¿;(Ð㣱¿ñpd†¡±¿ÞJ‰É–”±¿JåÂî³™±¿g>Ýãû‡±¿6îþmò€±¿Ã`ð‚±¿uw¯ð2~±¿ëÛíÀx±¿eÊ“ ¬r±¿`×Ýño±¿”Fê|¸k±¿¢'•un±¿®! úHb±¿hä[ U^±¿;à¯Ó˜Y±¿©´C4PQ±¿¸žLêH±¿à踔°=±¿E|'{Ê:±¿$ìiý9±¿wÎØQù6±¿—È2±¿3toÊ#±¿(öc¹$%±¿¾zôÍͱ¿Œ–Ö Ì±¿üikÍ£ ±¿Ö/MÊ ±¿ä˜Oj±¿¢Òqö°¿…Ÿn õ°¿rìfÓöù°¿´u=ý:ë°¿#µ2á°¿hÞf~5×°¿š§ uÖ°¿F s|Ú°¿¡‹¼éñÒ°¿p0ÆhÒ°¿ØÀÊÉ)Í°¿MÑøpÊ°¿ŽÐbcÁ°¿°Ls9;À°¿çI~Á¿°¿˜¡ ³®¸°¿½w(ãG¶°¿8*¨½ÀÀ°¿ø1èŸ ¼°¿–=t)M¶°¿ùm:®°¿¸³¾Z¿´°¿Ü©%…¬°¿uÎþ§°°¿Ð—®Uͯ°¿‚Pú£©°¿’|`Þ?²°¿¡¹eå °¿æz¤X¬°¿Þ’›ϧ°¿+/¨°v£°¿ Jj"°©°¿†xMüâ¡°¿Æfªà ›°¿š{Dð8¥°¿†«¾˜°¿l[OΛ°¿˜"=—°¿ø}ÏXý›°¿Qà¤[—°¿ZïZ¸h‘°¿*Š\îÞ°¿@Î !bž°¿“¼úÚÿŽ°¿Úð´úŽ°¿F“A°¿˜›ý[‹°¿2(r¢`°¿QeΧ°¿û͈"“°¿äF:ƒ°¿‚¤öcn—°¿²ÑM¯WŒ°¿ƒ#«à‹°¿Ê¶kSEŽ°¿f cè„°¿-Üᶋ°¿<•az£—°¿‚²<¼Ö‘°¿©¿¹éˆ°¿U(¡î¯¿.ŽbÄ𯿶l†Aƒß¯¿‡võìǯ¿AÿÀiPï¿Z6‘ƒ´¯¿ 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Requires binary files "current.npy" and "rawtrace.npy". Rossant et al., "A calibration-free electrode compensation method" J. Neurophysiol 2012 """ import os from brian import * import numpy as np from brian.library.electrophysiology import * working_dir = os.path.dirname(__file__) # load data dt = 0.1*ms current = np.load(os.path.join(working_dir, "current.npy")) # 10000-long vector, 1s duration rawtrace = np.load(os.path.join(working_dir, "trace.npy")) # 10000-long vector, 1s duration t = linspace(0., 1., len(current)) # launch compensation r = Lp_compensate(current, rawtrace, dt, p=1.0, full=True) # print best parameters print "Best parameters: R, tau, Vr, Re, taue:" print r["params"] # plot traces subplot(211) plot(t, current, 'k') subplot(212) plot(t, rawtrace, 'k') # raw trace plot(t, r["Vfull"], 'b') # full model trace (neuron and electrode) plot(t, r["Vcompensated"], 'g') # compensated trace show()brian-1.4.3/examples/electrophysiology/compensation_ex2_spikes.py000077500000000000000000000014731266062647100254430ustar00rootroot00000000000000#!/usr/bin/env python """ Example of spike detection method. Requires binary files "current.npy" and "rawtrace.npy". Rossant et al., "A calibration-free electrode compensation method" J. Neurophysiol 2012 """ import os from brian import * import numpy as np from brian.library.electrophysiology import * working_dir = os.path.dirname(__file__) # load data dt = 0.1*ms current = np.load(os.path.join(working_dir, "current.npy")) # 10000-long vector, 1s duration rawtrace = np.load(os.path.join(working_dir, "trace.npy")) # 10000-long vector, 1s duration t = linspace(0., 1., len(current)) # find spikes and compute score spikes, scores = find_spikes(rawtrace, dt=dt, check_quality=True) # plot trace and spikes plot(t, rawtrace, 'k') plot(t[spikes], rawtrace[spikes], 'or') show() brian-1.4.3/examples/electrophysiology/compensation_ex3_quality.py000077500000000000000000000023161266062647100256330ustar00rootroot00000000000000#!/usr/bin/env python """ Example of quality check method. Requires binary files "current.npy" and "rawtrace.npy". Rossant et al., "A calibration-free electrode compensation method" J. Neurophysiol 2012 """ import os from brian import * import numpy as np from brian.library.electrophysiology import * working_dir = os.path.dirname(__file__) # load data dt = 0.1*ms current = np.load(os.path.join(working_dir, "current.npy")) # 10000-long vector, 1s duration rawtrace = np.load(os.path.join(working_dir, "trace.npy")) # 10000-long vector, 1s duration compensatedtrace = np.load(os.path.join(working_dir, "compensatedtrace.npy")) # obtained with example1 t = linspace(0., 1., len(current)) # get trace quality of both raw and compensated traces r = get_trace_quality(rawtrace, current, full=True) rcomp = get_trace_quality(compensatedtrace, current, full=True) spikes = r["spikes"] print "Quality coefficient for raw: %.3f and for compensated trace: %.3f" % \ (r["correlation"], rcomp["correlation"]) # plot trace and spikes plot(t, rawtrace, 'k') plot(t, compensatedtrace, 'g') plot(t[spikes], rawtrace[spikes], 'ok') plot(t[spikes], compensatedtrace[spikes], 'og') show() brian-1.4.3/examples/electrophysiology/current.npy000066400000000000000000002343201266062647100224440ustar00rootroot00000000000000“NUMPYF{'descr': 'Ó=J•øëáÑ=®]€3MÓ=€F°„4Ô=m}yÒWÑ=Zba†CýÓ=i§q5\ÒÎ=>-­™jÐ=º>s¥NÑ='Ö18Ð=s™í5’œÒ=ØœugfãÒ= ëïþÐ=Ö`zÆ4Õ=†àóº˜1Ó=J[>Ó=2òm†Z†Ï=ê®àT»0Ñ=…j3)Ð=Q]iøÍ=|E(qûÅ=çm!à`ÓÏ=>Ïõ"Ë=& ÅÙ¬ôÆ=¸i)ØËgÅ=FÜÐøÕ&Ä=$!E:Â=•¿¥¤gÃ=,`]ÓÈ=l¹ù9¢Ê¿=JM>†5Æ=Ï¥îÄ=œ G¿ûÄ=Eåõ¤ËêÅ=nq%ÿ„Ͻ=]à:Ÿdb¶=A¢Ã0|ˆÄ=èZ]éÜÇ=°ñjQšÇ=$@œ¹:Ê=ÁN¯ZÉ=}Q Ð)Ç=NvYcÍÅ=<ŸÙDÍÆ=ÞÏ¥Ú½Á=km{7l¸=£Ö‹Íý¿=6¡Ð/+Ã=,¸¬=£àÅ=î^éþ¢Ã=‚ÖÛHŽ¾=ïÈwØÅ=ÙS™¯I:É=ÃPªÿ4Ñ=FLÒƒNÐ=“i¥ÜtÜÒ=ÉŒa3™Ó=Š-«ÇIÏ=Ü]513-Ï=POlèÓ=:õ}*y\Ó=î2ÅÕ=2ÿ±«H÷Õ=ÿ —ç©\Ú=2—ð‘Ü=Ê®Œ˜ˆzß=˜°€-Ý=H5íôq1á=3YL,]7â=µ Òʾvâ=íRŽWÕá=à;©çÖá=²MãHRsß=Žšª½ˆôÞ=‰ò<‘ÙÞ=¡R­(Þ=š}Xëà=}£'ߤ”à=ÒK$âç@â=ͪ¬¢ ã=ëF‚ã=¶uÓ /â=€U¹ã=é{gvÙòä=\Læ=(’ÿ?ìæ=ÑÇ­K‘‹å=ºûÔ©¶ä=F£„¬º®ã=œA¹ÅÈ;å=ÕØ‚É…å=` ,òŠ¡ä=ÊN¦iRå=òïK̾Öæ=îkaU …å=¡•Ñàˆä=ÁeGõaå="ÖL™Pä=¼0ãhµSâ=’ò7PÛâ=¦¢Ø “ ã=üO-ZËBâ=~ì]œTâ=²'à=ßóÍÞ¥Û=°‰ðÚ”Ý=¡§ÔǨ‡Ü=UC÷ºÝ= óãÁnïÛ=¤0oEiGÛ=k©, «%Û=†'$Å¡ß=¥×A@@7à= mÕ<*Û=f-±ê0Ý=ËYtÅðqÜ=“…´‹Û=Cgonã{Ø=œm%~ØÃÛ=,c‘>Û=åeÕ§‡Ú=ßÆ©i ÑÙ=ºmc%‡Ø=¢Á=’×MØ=˜ Hvt÷Ù=|Ôâ¡ +Ù=â4{d …Ø=·=‹ 8Ù=w‘G ×=þöbáÔ=NSioåÕ=ÔÃF†×=0%`Õ=iñ:©6²Ó=|üò`ôÑ=ÇÀ„…ÞµÏ=̼–¦,Ë=¿=©ˆ"Ð= ËXj=ºÒ=ø]XÎbÑ=Oÿ–A{Ò=n3 øDÖ=«á·˜Õ=o[Ö Íc×=|×òÈØ=— Š“o?×=C!ÊĽÛ=ë8XñýÛ=΂ôßÀÅÜ=Çùÿ[ñ×=Œ%eØ=ê·Á¢xÊÕ=@°#¨ìÐÎ=î# *vÐ=ßûÐœ×Î=<p»tÒ=GÏû8Å=Ì=5ï$b²¿Ï=ñY¹Þã]Ñ="/˜/ØÑ=û•‘i†NÒ=Y›PŸæhÓ=¢ðÛó£Ï=PÑåq“Ñ=·#÷Š£ôÍ=¾É5WÂÍ=ÒC×À¬Ê=€Û”WÄ•È=\ÚkgÉ=ôDÞïVÉ=ï?f3“Å=o¢()EX½=Ô}E] ^·=þ|Z¼=83L¬&³³=‰üûËO|=\Öíå,Ž˜=j[Žlzä|=;—×Áï°=hAËöë¥=UœLf¡=K«óž±=ÐÅn|‘?µ=¾:ô‹ ²=¸h¯ ~¸=éJÏŒ`«=ˆÒÓ£fF¿=tÙÈßÂ=hÜÛ GrÃ=$FÝ’íÀ=<¥cÂ=W§ß1ößÅ=ðÒ_<1_É=BNÉÅGoÆ=zÍB£FÄ=+b ÷|¡Å=ÏczŠ8Æ=S}æÃÉ=?®«ÞGoË=aÖ«¨«¥Ò=ÐÏ?óÙ¡Ô=Ä`µÖ+Ñ=–íPÖÂÑ=Ò4_\éÑ=î¦)ÀÓ=o§ÆÎÑ=vT:©Û7Ó=û€å± Ð= ªëþ Ï=ܳ&ˆf˜Ò=BÜ£1”‰Ð=¸*NiàÉÌ=Dþ¡¶´ÙÒ=tû‹ÄÕ€Î=@k¬FâÄÌ=aÐTnÔ8Ã=EÍ?J¨£Â=ºÇ\œœ)È=…%îßÈ=[Qq­ŠÁ=ôÑ•Ò»=u•÷Tá¼=Ž „Â̬À==¯†LuÇ=z]y_Á=QK 7ÃÃ=óƒÕd…¾=´ÙTì(ŒÎ=Òò‚ÏÌ=ÍñVÈ8ßÐ=Œp(‹¬ÿÌ=$÷²æ×úÎ= aÉýÆ=FâU—@(É=J;À÷fÉ=Ö—¼)¦Ë=”,iÛöÆ=úKO–‰Å=ìK¥PjÂ=^{ÎýÊUÉ=‚Ö?BÇ=J#³[ëYÇ= §ãˆÆ=´rêD8È=€Ü7ß'Á=pG)7DÚ»=’Ìs²]èÂ=5i9Ä=\`&ÙÕÅ=“ìHXeÄ=êÑmŸÓ¤È=@ÃËÂ`Ä=‹ŒÕ¾ó¹Á=òl®ƒ +Æ=ø7+<{þÇ=œuÓ¹!Á=Âü$h!º=Uk~»þv£=Mϲ¿=ò¾õ§vÃ=ççõ¾àÑ¿=e‰Ÿzâß³=äã8}¯·=˜ý+C¦pÄ=9ª‰B?/È=¤§§KOÉ=àc€Ë=§¯áÌ)WÆ=(Ê$H³Ä=9 1„Î#Å=Ë;ŸÃk½=V,“9’,º=þ¢qz†ð½=»ž»ÁÜÜ¿=‹›™’ž¶=ˆô¢ž¶=úyŸ[ål¿=ûÚµ±ïÂ=hÓŽî ·¸=@{Xr´=€q·÷.«=òb낺±=‡)u¤ ¹=‡ ŒåÅ=r#³¿$Ä=ÀŠÙêîåÌ=ÌI•Ð=™¾&ùF!Ï=0—²ák}Ç=t(ŒI@Å=°ˆ’¬‚WÆ=E¶ìIÇÊ=*¥»øtàÌ=7ø‰q ïÏ=ä7‘R‘Ï=àêÁ½ýõÐ=6Bëö‹ÞÑ==è8…ÛqÑ=„¢ƒøºóÎ=yðÍ£6KÏ=±NH0þ¢Î=ñeÏæòˆÓ=Ì¿Œ’Õ’Ó= &¥¦gÒ=XŒ‹Î¦Ó=Œ N©’7Õ=fË>brùÕ=ËO|bÒáÙ=Š½à^ÕÿÕ= )UÆG×=-?+žÚ=t.½0u°Û=„šÎ/CÙ=òµÜWÊ£×=(§ô›³Ú=ÄpÀ=r°²~~3º=îkÆôÜÃ=éôè¹Òz¼=¥íÜa¼=Õ 7}|߸=(ž:k›µ=I~<¾µ=ÜMû”¼;´=F;‘[=·ª=Tû!æ~¥=\#C… ±=6Š¨6Å!¶=YHüY±=¿T9¯Õº=È(FP¡¨É=‹W•ÉVÏ=e‹&E™§Ð=ƒ8ë‡%Ñ=Ð9ìöTXÔ=¢$;F3Ö= ïíyòÚ=´@ã–H¡Û=ì‚EcadÛ=--‘˜Ý=$¢3ÅÙ=Ù„%nòÜ=#‰-;RçÛ=)Q‡ÎÇ Û=ÍÓ²×Ø=êÿLÂU¡¤Œ—À=yjÏjc¾=“ ë6=<´= +š#©=´±­4ó²=Àyng¤=í³"€½®=Bäóf¼=Ö”/×`ãu=M«jÕ‘¬t½Ó‚ çâ r½¨&ðüOqš½™APû­™ž½ë(`G]S=w§ôÚœ½Ҕͭœ­½ýë«ÊÙ½³{ê» ¢½øÆ`”s㊽üÒyDâ¨=­÷,æ#¸=TúIë½ÀÃ=Ã;T}” Ä=·ý6Õ ðÁ=1*5ÖòÅ=z/‡QÊ=,U? 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Loads a current clamp voltage trace, compensates (remove electrode voltage) and analyses the spikes. """ from brian import * from brian.library.electrophysiology import * import numpy dt=.1*ms Vraw = numpy.load("trace.npy") # Raw current clamp trace I = numpy.load("current.npy") V, _ = Lp_compensate(I, Vraw, dt) # Electrode compensation # Peaks spike_criterion=find_spike_criterion(V) print "Spike detected when V exceeds",float(spike_criterion/mV),"mV" peaks=spike_peaks(V,vc=spike_criterion) # vc is optional # Onsets (= spike threshold) onsets=spike_onsets(V,criterion=3*dt,vc=spike_criterion) # Criterion: dV/dt>3 V/s # Spike-triggered average of V STA=spike_shape(V, onsets=onsets, before=100, after=100) print "Spike duration:",float(spike_duration(V,onsets=onsets)*dt/ms),"ms" print "Reset potential:",float(reset_potential(V,peaks=peaks)/mV),"mV" # Spike threshold statistics slope=slope_threshold(V,onsets=onsets,T=int(5*ms/dt)) # Subthreshold trace subthreshold=-spike_mask(V) t=arange(len(V))*dt subplot(221) plot(t/ms,V/mV,'k') plot(t[peaks]/ms,V[peaks]/mV,".b") plot(t[onsets]/ms,V[onsets]/mV,".r") subplot(222) plot(((arange(len(STA))-100)*dt)/ms,STA/mV,'k') subplot(223) plot(t[subthreshold]/ms,V[subthreshold]/mV,'k') subplot(224) plot(slope/ms,V[onsets]/mV,'.') show() brian-1.4.3/examples/electrophysiology/trace.npy000066400000000000000000002343201266062647100220600ustar00rootroot00000000000000“NUMPYF{'descr': '°¿:°¿8°¿<°¿B°¿:°¿2°¿°¿°¿°¿°¿ °¿ °¿ü¯¿ð¯¿Ø¯¿È¯¿¤¯¿„¯¿X¯¿H¯¿¯¿ð®¿Ô®¿Ä®¿°®¿¼®¿´®¿¬®¿¬®¿˜®¿„®¿X®¿<®¿®¿ ®¿®¿Ø­¿°­¿ˆ­¿x­¿t­¿p­¿X­¿@­¿8­¿­¿ü¬¿ô¬¿ì¬¿Ô¬¿Ô¬¿è¬¿Ü¬¿Ì¬¿Ì¬¿À¬¿¸¬¿¤¬¿x¬¿x¬¿`¬¿p¬¿x¬¿p¬¿€¬¿d¬¿`¬¿T¬¿l¬¿€¬¿|¬¿|¬¿x¬¿„¬¿|¬¿h¬¿X¬¿d¬¿H¬¿X¬¿H¬¿H¬¿<¬¿@¬¿@¬¿D¬¿¬¿¬¿ ¬¿¬¿ ¬¿¬¿$¬¿¬¿ ¬¿¬¿¬¿¬¿¬¿¬¿¬¿¬¿ø«¿¬¿¬¿¬¿ ¬¿¬¿ ¬¿0¬¿D¬¿X¬¿T¬¿H¬¿L¬¿D¬¿,¬¿$¬¿ ¬¿¬¿¬¿Ü«¿Ì«¿¸«¿Ð«¿Ô«¿Ð«¿Ü«¿¬¿ ¬¿¬¿¬¿,¬¿(¬¿$¬¿ ¬¿ ¬¿¬¿0¬¿ ¬¿,¬¿8¬¿D¬¿T¬¿T¬¿\¬¿l¬¿Œ¬¿¤¬¿¬¬¿¼¬¿è¬¿ð¬¿ ­¿ü¬¿ ­¿­¿­¿­¿­¿­¿(­¿­¿ 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¥¿@¥¿T¥¿l¥¿ˆ¥¿¤¥¿°¥¿°¥¿À¥¿´¥¿È¥¿è¥¿è¥¿à¥¿ð¥¿¦¿¦¿D¦¿`¦¿d¦¿d¦¿d¦¿\¦¿H¦¿H¦¿P¦¿`¦¿h¦¿\¦¿p¦¿d¦¿d¦¿p¦¿|¦¿x¦¿„¦¿„¦¿ˆ¦¿œ¦¿¨¦¿´¦¿ ¦¿¬¦¿¸¦¿Ð¦¿ø¦¿§¿§¿$§¿<§¿H§¿t§¿”§¿ˆ§¿¤§¿¬§¿”§¿„§¿ˆ§¿ˆ§¿¤§¿¸§¿À§¿ð§¿¨¿à§¿ì§¿à§¿ü§¿¨¿@¨¿P¨¿`¨¿x¨¿”¨¿”¨¿´¨¿°¨¿¼¨¿Ü¨¿à¨¿©¿©¿©¿(©¿©¿©¿©¿D©¿d©¿l©¿©¿´©¿Ô©¿è©¿ª¿(ª¿hª¿¨ª¿àª¿$«¿@«¿d«¿|«¿¨«¿¬«¿Ä«¿Ø«¿ì«¿¬¿8¬¿x¬¿ ¬¿¸¬¿Ì¬¿ì¬¿­¿­¿4­¿@­¿T­¿d­¿l­¿l­¿ˆ­¿„­¿˜­¿Ð­¿®¿<®¿H®¿x®¿„®¿|®¿„®¿„®¿ˆ®¿ˆ®¿˜®¿´®¿À®¿Ð®¿Ì®¿¼®¿à®¿ü®¿¯¿4¯¿h¯¿|¯¿¤¯¿œ¯¿”¯¿¨¯¿¸¯¿¸¯¿À¯¿´¯¿°¯¿œ¯¿ ¯¿x¯¿|¯¿t¯¿l¯¿„¯¿ˆ¯¿ˆ¯¿”¯¿¯¿˜¯¿¸¯¿Ì¯¿Ð¯¿à¯¿Ø¯¿Ô¯¿ð¯¿ô¯¿è¯¿ü¯¿°¿ °¿ °¿°¿°¿°¿$°¿*°¿*°¿&°¿0°¿6°¿<°¿:°¿,°¿&°¿$°¿ °¿&°¿(°¿"°¿°¿ °¿°¿ü¯¿°¿ø¯¿ð¯¿ä¯¿Ô¯¿Ü¯¿Ü¯¿à¯¿Ð¯¿Ü¯¿Ø¯¿Ü¯¿¼¯¿À¯¿Ä¯¿¸¯¿À¯¿È¯¿È¯¿œ¯¿˜¯¿¯¿x¯¿|¯¿h¯¿X¯¿D¯¿H¯¿4¯¿¯¿ü®¿¯¿¯¿ü®¿ð®¿ä®¿´®¿”®¿Œ®¿t®¿p®¿T®¿T®¿T®¿D®¿`®¿d®¿L®¿H®¿H®¿@®¿<®¿(®¿ü­¿®¿ä­¿ð­¿à­¿¼­¿¸­¿¼­¿Ä­¿°­¿Ð­¿Ô­¿È­¿Ð­¿¨­¿¬­¿­¿„­¿­¿€­¿€­¿­¿­¿|­¿h­¿`­¿d­¿l­¿T­¿L­¿\­¿L­¿<­¿H­¿@­¿<­¿H­¿@­¿D­¿H­¿P­¿X­¿T­¿H­¿X­¿p­¿\­¿d­¿`­¿h­¿€­¿x­¿Œ­¿x­¿ˆ­¿„­¿­¿°­¿Ì­¿ø­¿ü­¿®¿ ®¿®¿®¿®¿®¿®¿$®¿,®¿L®¿p®¿x®¿€®¿Œ®¿¤®¿˜®¿”®¿˜®¿°®¿°®¿À®¿¼®¿¸®¿¼®¿Ä®¿Ð®¿Ì®¿Ì®¿¸®¿Ä®¿¸®¿¼®¿Ä®¿Ô®¿ø®¿¯¿,¯¿,¯¿@¯¿\¯¿d¯¿\¯¿\¯¿8¯¿<¯¿0¯¿$¯¿¯¿¯¿ü®¿ð®¿ü®¿¯¿¯¿ô®¿è®¿ø®¿ä®¿ô®¿ø®¿è®¿ô®¿ð®¿ü®¿ ¯¿ ¯¿(¯¿(¯¿4¯¿H¯¿H¯¿D¯¿D¯¿@¯¿<¯¿(¯¿4¯¿¯¿ ¯¿(¯¿¯¿ü®¿ä®¿´®¿®¿T®¿4®¿®¿ø­¿Ä­¿ ­¿|­¿l­¿`­¿D­¿D­¿L­¿8­¿$­¿­¿(­¿­¿ ­¿­¿ü¬¿ð¬¿Ð¬¿À¬¿È¬¿È¬¿ì¬¿ì¬¿­¿­¿,­¿,­¿,­¿­¿­¿8­¿H­¿t­¿t­¿p­¿t­¿d­¿„­¿­¿¨­¿´­¿¬­¿Ø­¿Ô­¿Ì­¿ð­¿®¿ ®¿ ®¿®¿®¿ø­¿Ø­¿à­¿ä­¿ä­¿à­¿à­¿ì­¿ð­¿ì­¿à­¿ ­¿ ­¿”­¿`­¿T­¿@­¿,­¿4­¿$­¿ ­¿(­¿ ­¿­¿­¿­¿­¿ô¬¿Ð¬¿Ä¬¿ ¬¿¨¬¿ˆ¬¿X¬¿4¬¿<¬¿(¬¿¬¿¬¿¬¿ ¬¿4¬¿D¬¿X¬¿<¬¿H¬¿T¬¿X¬¿D¬¿T¬¿H¬¿4¬¿(¬¿8¬¿$¬¿(¬¿ ¬¿ ¬¿(¬¿(¬¿¬¿¬¿¬¿ø«¿ì«¿à«¿è«¿è«¿ü«¿¬¿¬¿¬¿¬¿ü«¿ð«¿Ø«¿´«¿¬«¿¨«¿œ«¿˜«¿œ«¿Œ«¿˜«¿”«¿x«¿«¿|«¿”«¿˜«¿ «¿¨«¿¤«¿ «¿°«¿¨«¿˜«¿t«¿\«¿8«¿«¿üª¿ôª¿ðª¿üª¿«¿ôª¿«¿øª¿«¿«¿øª¿ìª¿«¿ìª¿«¿«¿(«¿«¿ «¿ìª¿«¿øª¿ìª¿àª¿äª¿Øª¿Øª¿àª¿Øª¿Üª¿Äª¿äª¿èª¿èª¿Üª¿èª¿äª¿èª¿Äª¿¬ª¿Œª¿ˆª¿lª¿\ª¿@ª¿Dª¿ ª¿ð©¿Ø©¿Ð©¿¼©¿´©¿œ©¿Œ©¿ˆ©¿p©¿d©¿X©¿L©¿H©¿$©¿0©¿©¿©¿©¿ø¨¿è¨¿Ô¨¿¼¨¿°¨¿œ¨¿|¨¿”¨¿ˆ¨¿|¨¿x¨¿h¨¿l¨¿p¨¿t¨¿x¨¿€¨¿t¨¿l¨¿\¨¿l¨¿„¨¿Œ¨¿Œ¨¿ ¨¿œ¨¿˜¨¿¤¨¿„¨¿„¨¿”¨¿ˆ¨¿”¨¿|¨¿x¨¿p¨¿p¨¿„¨¿¨¨¿¸¨¿Ð¨¿Ì¨¿Ð¨¿´¨¿Ü¨¿ì¨¿©¿$©¿@©¿L©¿X©¿<©¿(©¿©¿©¿©¿ð¨¿ì¨¿Ø¨¿´¨¿Ä¨¿´¨¿ ¨¿„¨¿ˆ¨¿t¨¿d¨¿T¨¿8¨¿,¨¿ ¨¿¨¿(¨¿¨¿ ¨¿¨¿è§¿à§¿Ô§¿À§¿¸§¿”§¿¤§¿”§¿Œ§¿Œ§¿t§¿`§¿L§¿<§¿4§¿§¿ô¦¿è¦¿ä¦¿Ü¦¿Ø¦¿Ü¦¿brian-1.4.3/examples/electrophysiology/voltageclamp.py000077500000000000000000000014541266062647100232650ustar00rootroot00000000000000#!/usr/bin/env python ''' Voltage-clamp experiment ''' from brian import * from brian.library.electrophysiology import * defaultclock.dt = .01 * ms taum = 20 * ms gl = 20 * nS Cm = taum * gl Re = 50 * Mohm Ce = 0.2 * ms / Re N = 1 Rs = .9 * Re tauc = Rs * Ce # critical tau_u eqs = Equations(''' dvm/dt=(-gl*vm+i_inj)/Cm : volt ''') eqs += electrode(.2 * Re, Ce) eqs += voltage_clamp(vm='v_el', v_cmd=20 * mV, i_inj='i_cmd', i_rec='ic', Re=.8 * Re, Rs=.9 * Re, tau_u=.2 * ms) setup = NeuronGroup(N, model=eqs) setup.v = 0 * mV recording = StateMonitor(setup, 'ic', record=True) soma = StateMonitor(setup, 'vm', record=True) run(200 * ms) figure() plot(recording.times / ms, recording[0] / nA, 'k') figure() plot(soma.times / ms, soma[0] / mV, 'b') show() brian-1.4.3/examples/examples_guide.txt000066400000000000000000000103051266062647100201750ustar00rootroot00000000000000List of examples ---------------- misc/ (miscellaneous) * adaptive_threshold: an integrate-and-fire model with a threshold that increases with each spike and relaxes exponentially, and Poisson input. * adaptive: an integrate-and-fire model with adaptation. * cable: a passive dendrite. * COBA: a network of IF models with synaptic conductances. * cobahh_simplified: a network of Hodgkin-Huxley models. * COBAHH: a network of Hodgkin-Huxley models. * correlated_inputs: an example of correlated spike trains. * current_clamp: a current clamp recording with bridge compensation. * CUBA: a network of IF models with synaptic currents. * delays: a network with external noise and transmission delays. * expIF_network: a network of exponential IF models with synaptic conductances and Poisson input to subsets of the network. * HodgkinHuxley: the original Hodgkin-Huxley model. * I-F_curve: current-frequency curve of the Brette-Gerstner model. * I-F_curve2: current-frequency curve of the leaky integrator. * if: demonstrating a single integrate and fire neuron * interface/*: how to write a html interface to a script using CherryPy. * leaky_if: demonstrating a single leaky integrate and fire neuron * minimalexample: same as CUBA. * mirollo_strogatz: fully connected network of IF oscillators. * multipleclocks: an example demonstrating the use of multiple clocks. * named_threshold: example with named threshold and reset variables. * noisy_ring: a ring of noisy perfect IF neurons. * online_imaging: an example demonstrating online imaging of state variables (experimental). * parallelpython: how to run scripts on a cluster using parallelpython. * phase_locking: IF models phase locking to a periodic input. * pickle_loadnet: how to load and continue running an incomplete simulation. * pickle_savenet: how to save an incomplete simulation. * poisson: Poisson spike trains with variable rates. * poissongroup: Poisson input to an IF neuron. * pulsepacket: demonstrates the use of PulsePacket and SpikeGenerator objects. * rate_model: model where spikes are produced with probability v*dt. * reliability: reliability of spike timing. * ring: a ring of IF oscillators. * stim2d: how to do a layer of neurons responding to a 2D stimulus * stopping: demonstration of how to stop a stimulation during a run. * topographic_map: a two-layer network with topographic random connections, including lateral connections. * topographic_map2: a two-layer network with topographic random connections, including lateral connections. * transient_sync: a ring network of noisy IF neurons with distance-dependent synaptic weights. * two_neurons: two connected neurons with delays. audition/ (auditory models) * filterbank: an auditory filterbank implemented with Poisson neurons * jeffress: Jeffress model of sound localisation * licklider: Licklider model of pitch perception electrophysiology/ (simulation of intracellular recordings) * AEC: active electrode compensation * bridge: bridge compensation * DCC: discontinuous current clamp * SEVC: single-electrode discontinuous voltage clamp * voltageclamp: single-electrode continuous voltage clamp frompapers/ (models adapted from published papers) * Brette_Gerstner_2005: adaptive exponential integrate-and-fire model. * Diesmann_et_al_1999: synfire chains * Diesmann_et_al_1999_longer: synfire chains * Sturzl_et_al_2000: Theory of Arachnid Prey Localization * Rothman_Manis_2003: Cochlear neuron model of Rothman & Manis * Izhikevich_2006_Polychronization: polychronization * Izhikevich_2006_Polychronization_longer: polychronization * Guetig_Sompolinsky_2009.py: Time-warp invariant neuronal voltage traces interface/ (web interface) * interface: web interface (requires cherrypy) modelfitting/ (demo of model fitting library) multiprocessing/ (use of python 2.6 multiprocessing package for multiple cores) plasticity/ (spike-timing dependent and short term plasticity) * STDP1: STDP example adapted from Song, Miller and Abbott (2000), additive rule. * STDP2: STDP example adapted from van Rossum et al (2000), mixed rule. * short_term_plasticity: neuron with short term plasticity * short_term_plasticity2: network with short term plasticity brian-1.4.3/examples/frompapers/000077500000000000000000000000001266062647100166205ustar00rootroot00000000000000brian-1.4.3/examples/frompapers/Brette_2004.py000077500000000000000000000016751266062647100211000ustar00rootroot00000000000000#!/usr/bin/env python ''' Phase locking in leaky integrate-and-fire model ----------------------------------------------- Fig. 2A from: Brette R (2004). Dynamics of one-dimensional spiking neuron models. J Math Biol 48(1): 38-56. This shows the phase-locking structure of a LIF driven by a sinusoidal current. When the current crosses the threshold (a<3), the model almost always phase locks (in a measure-theoretical sense). ''' from brian import * #defaultclock.dt=0.01*ms # for a more precise picture N=2000 tau=100*ms freq=1/tau eqs=''' dv/dt=(-v+a+2*sin(2*pi*t/tau))/tau : 1 a : 1 ''' neurons=NeuronGroup(N,eqs,threshold=1,reset=0) neurons.a=linspace(2,4,N) run(5*second,report='text') # discard the first spikes (wait for convergence) S=SpikeMonitor(neurons) run(5*second,report='text') i,t=zip(*S.spikes) plot((t % tau)/tau,i,'.') xlabel('Spike phase') ylabel('Parameter a') yticks([0,N/2,N],[2,3,4]) show() brian-1.4.3/examples/frompapers/Brette_Gerstner_2005.py000077500000000000000000000024351266062647100227450ustar00rootroot00000000000000#!/usr/bin/env python ''' Adaptive exponential integrate-and-fire model. http://www.scholarpedia.org/article/Adaptive_exponential_integrate-and-fire_model Introduced in Brette R. and Gerstner W. (2005), Adaptive Exponential Integrate-and-Fire Model as an Effective Description of Neuronal Activity, J. Neurophysiol. 94: 3637 - 3642. ''' from brian import * # Parameters C = 281 * pF gL = 30 * nS taum = C / gL EL = -70.6 * mV VT = -50.4 * mV DeltaT = 2 * mV Vcut = VT + 5 * DeltaT # Pick an electrophysiological behaviour tauw, a, b, Vr = 144 * ms, 4 * nS, 0.0805 * nA, -70.6 * mV # Regular spiking (as in the paper) #tauw,a,b,Vr=20*ms,4*nS,0.5*nA,VT+5*mV # Bursting #tauw,a,b,Vr=144*ms,2*C/(144*ms),0*nA,-70.6*mV # Fast spiking eqs = """ dvm/dt=(gL*(EL-vm)+gL*DeltaT*exp((vm-VT)/DeltaT)+I-w)/C : volt dw/dt=(a*(vm-EL)-w)/tauw : amp I : amp """ neuron = NeuronGroup(1, model=eqs, threshold=Vcut, reset="vm=Vr;w+=b", freeze=True) neuron.vm = EL trace = StateMonitor(neuron, 'vm', record=0) spikes = SpikeMonitor(neuron) run(20 * ms) neuron.I = 1 * nA run(100 * ms) neuron.I = 0 * nA run(20 * ms) # We draw nicer spikes vm = trace[0] for _, t in spikes.spikes: i = int(t / defaultclock.dt) vm[i] = 20 * mV plot(trace.times / ms, vm / mV) show() brian-1.4.3/examples/frompapers/Brette_Guigon_2003.py000077500000000000000000000025721266062647100224040ustar00rootroot00000000000000#!/usr/bin/env python """ Reliability of spike timing --------------------------- Adapted from Fig. 10D,E of Brette R and E Guigon (2003). Reliability of Spike Timing Is a General Property of Spiking Model Neurons. Neural Computation 15, 279-308. This shows that reliability of spike timing is a generic property of spiking neurons, even those that are not leaky. This is a non-physiological model which can be leaky or anti-leaky depending on the sign of the input I. All neurons receive the same fluctuating input, scaled by a parameter p that varies across neurons. This shows: 1. reproducibility of spike timing 2. robustness with respect to deterministic changes (parameter) 3. increased reproducibility in the fluctuation-driven regime (input crosses the threshold) """ from brian import * N=500 tau=33*ms taux=20*ms sigma=0.02 eqs_input=''' B=2./(1+exp(-2*x))-1 : 1 dx/dt=-x/taux+(2/taux)**.5*xi : 1 ''' eqs=''' dv/dt=(v*I+1)/tau + sigma*(2/tau)**.5*xi : 1 I=0.5+3*p*B : 1 B : 1 p : 1 ''' input=NeuronGroup(1,eqs_input) neurons=NeuronGroup(N,eqs,threshold=1,reset=0) neurons.p=linspace(0,1,N) neurons.v=rand(N) neurons.B=linked_var(input,'B') M=StateMonitor(input,'B',record=0) S=SpikeMonitor(neurons) run(1000*ms) subplot(211) # The input plot(M.times/ms,M[0]) subplot(212) raster_plot(S) plot([0,1000],[250,250],'r') show() brian-1.4.3/examples/frompapers/Brunel_Hakim_1999.py000077500000000000000000000022541266062647100222330ustar00rootroot00000000000000#!/usr/bin/env python """ Dynamics of a network of sparsely connected inhibitory current-based integrate-and-fire neurons. Individual neurons fire irregularly at low rate but the network is in an oscillatory global activity regime where neurons are weakly synchronized. Reference: "Fast Global Oscillations in Networks of Integrate-and-Fire Neurons with Low Firing Rates" Nicolas Brunel & Vincent Hakim Neural Computation 11, 1621-1671 (1999) """ from brian import * N = 5000 Vr = 10 * mV theta = 20 * mV tau = 20 * ms delta = 2 * ms taurefr = 2 * ms duration = .1 * second C = 1000 sparseness = float(C)/N J = .1 * mV muext = 25 * mV sigmaext = 1 * mV eqs = """ dV/dt = (-V+muext + sigmaext * sqrt(tau) * xi)/tau : volt """ group = NeuronGroup(N, eqs, threshold=theta, reset=Vr, refractory=taurefr) group.V = Vr conn = Connection(group, group, state='V', delay=delta, weight = -J, sparseness=sparseness) M = SpikeMonitor(group) LFP = PopulationRateMonitor(group, bin=0.4 * ms) run(duration) subplot(211) raster_plot(M) xlim(0, duration/ms) subplot(212) plot(LFP.times_/ms, LFP.rate) xlim(0, duration/ms) show() brian-1.4.3/examples/frompapers/Diesmann_et_al_1999.py000077500000000000000000000022211266062647100225670ustar00rootroot00000000000000#!/usr/bin/env python ''' Synfire chains -------------- M. Diesmann et al. (1999). Stable propagation of synchronous spiking in cortical neural networks. Nature 402, 529-533. ''' from brian import * # Neuron model parameters Vr = -70 * mV Vt = -55 * mV taum = 10 * ms taupsp = 0.325 * ms weight = 4.86 * mV # Neuron model eqs = Equations(''' dV/dt=(-(V-Vr)+x)*(1./taum) : volt dx/dt=(-x+y)*(1./taupsp) : volt dy/dt=-y*(1./taupsp)+25.27*mV/ms+\ (39.24*mV/ms**0.5)*xi : volt ''') # Neuron groups P = NeuronGroup(N=1000, model=eqs, threshold=Vt, reset=Vr, refractory=1 * ms) Pinput = PulsePacket(t=50 * ms, n=85, sigma=1 * ms) # The network structure Pgp = [ P.subgroup(100) for i in range(10)] C = Connection(P, P, 'y') for i in range(9): C.connect_full(Pgp[i], Pgp[i + 1], weight) Cinput = Connection(Pinput, Pgp[0], 'y') Cinput.connect_full(weight=weight) # Record the spikes Mgp = [SpikeMonitor(p) for p in Pgp] Minput = SpikeMonitor(Pinput) monitors = [Minput] + Mgp # Setup the network, and run it P.V = Vr + rand(len(P)) * (Vt - Vr) run(100 * ms) # Plot result raster_plot(showgrouplines=True, *monitors) show() brian-1.4.3/examples/frompapers/Diesmann_et_al_1999_longer.py000077500000000000000000000173521266062647100241500ustar00rootroot00000000000000#!/usr/bin/env python """ Implementation of synfire chain from Diesmann et al. 1999 Dan Goodman - Dec. 2007 """ #import brian_no_units from brian import * import time from brian.library.IF import * from brian.library.synapses import * def minimal_example(): # Neuron model parameters Vr = -70 * mV Vt = -55 * mV taum = 10 * ms taupsp = 0.325 * ms weight = 4.86 * mV # Neuron model equations = Equations(''' dV/dt = (-(V-Vr)+x)*(1./taum) : volt dx/dt = (-x+y)*(1./taupsp) : volt dy/dt = -y*(1./taupsp)+25.27*mV/ms+(39.24*mV/ms**0.5)*xi : volt ''') # Neuron groups P = NeuronGroup(N=1000, model=equations, threshold=Vt, reset=Vr, refractory=1 * ms) # P = NeuronGroup(N=1000, model=(dV,dx,dy),init=(0*volt,0*volt,0*volt), # threshold=Vt,reset=Vr,refractory=1*ms) Pinput = PulsePacket(t=50 * ms, n=85, sigma=1 * ms) # The network structure Pgp = [ P.subgroup(100) for i in range(10)] C = Connection(P, P, 'y') for i in range(9): C.connect_full(Pgp[i], Pgp[i + 1], weight) Cinput = Connection(Pinput, P, 'y') Cinput.connect_full(Pinput, Pgp[0], weight) # Record the spikes Mgp = [SpikeMonitor(p, record=True) for p in Pgp] Minput = SpikeMonitor(Pinput, record=True) monitors = [Minput] + Mgp # Setup the network, and run it P.V = Vr + rand(len(P)) * (Vt - Vr) run(100 * ms) # Plot result raster_plot(showgrouplines=True, *monitors) show() # DEFAULT PARAMATERS FOR SYNFIRE CHAIN # Approximates those in Diesman et al. 1999 model_params = Parameters( # Simulation parameters dt=0.1 * ms, duration=100 * ms, # Neuron model parameters taum=10 * ms, taupsp=0.325 * ms, Vt= -55 * mV, Vr= -70 * mV, abs_refrac=1 * ms, we=34.7143, wi= -34.7143, psp_peak=0.14 * mV, # Noise parameters noise_neurons=20000, noise_exc=0.88, noise_inh=0.12, noise_exc_rate=2 * Hz, noise_inh_rate=12.5 * Hz, computed_model_parameters=""" noise_mu = noise_neurons * (noise_exc * noise_exc_rate - noise_inh * noise_inh_rate ) * psp_peak * we noise_sigma = (noise_neurons * (noise_exc * noise_exc_rate + noise_inh * noise_inh_rate ))**.5 * psp_peak * we """ ) # MODEL FOR SYNFIRE CHAIN # Excitatory PSPs only def Model(p): equations = Equations(''' dV/dt = (-(V-p.Vr)+x)*(1./p.taum) : volt dx/dt = (-x+y)*(1./p.taupsp) : volt dy/dt = -y*(1./p.taupsp)+25.27*mV/ms+(39.24*mV/ms**0.5)*xi : volt ''') return Parameters(model=equations, threshold=p.Vt, reset=p.Vr, refractory=p.abs_refrac) default_params = Parameters( # Network parameters num_layers=10, neurons_per_layer=100, neurons_in_input_layer=100, # Initiating burst parameters initial_burst_t=50 * ms, initial_burst_a=85, initial_burst_sigma=1 * ms, # these values are recomputed whenever another value changes computed_network_parameters=""" total_neurons = neurons_per_layer * num_layers """, # plus we also use the default model parameters ** model_params ) # DEFAULT NETWORK STRUCTURE # Single input layer, multiple chained layers class DefaultNetwork(Network): def __init__(self, p): # define groups chaingroup = NeuronGroup(N=p.total_neurons, **Model(p)) inputgroup = PulsePacket(p.initial_burst_t, p.neurons_in_input_layer, p.initial_burst_sigma) layer = [ chaingroup.subgroup(p.neurons_per_layer) for i in range(p.num_layers) ] # connections chainconnect = Connection(chaingroup, chaingroup, 2) for i in range(p.num_layers - 1): chainconnect.connect_full(layer[i], layer[i + 1], p.psp_peak * p.we) inputconnect = Connection(inputgroup, chaingroup, 2) inputconnect.connect_full(inputgroup, layer[0], p.psp_peak * p.we) # monitors chainmon = [SpikeMonitor(g, True) for g in layer] inputmon = SpikeMonitor(inputgroup, True) mon = [inputmon] + chainmon # network Network.__init__(self, chaingroup, inputgroup, chainconnect, inputconnect, mon) # add additional attributes to self self.mon = mon self.inputgroup = inputgroup self.chaingroup = chaingroup self.layer = layer self.params = p def prepare(self): Network.prepare(self) self.reinit() def reinit(self, p=None): Network.reinit(self) q = self.params if p is None: p = q self.inputgroup.generate(p.initial_burst_t, p.initial_burst_a, p.initial_burst_sigma) self.chaingroup.V = q.Vr + rand(len(self.chaingroup)) * (q.Vt - q.Vr) def run(self): Network.run(self, self.params.duration) def plot(self): raster_plot(ylabel="Layer", title="Synfire chain raster plot", color=(1, 0, 0), markersize=3, showgrouplines=True, spacebetweengroups=0.2, grouplinecol=(0.5, 0.5, 0.5), *self.mon) def estimate_params(mon, time_est): # Quick and dirty algorithm for the moment, for a more decent algorithm # use leastsq algorithm from scipy.optimize.minpack to fit const+Gaussian # http://www.scipy.org/doc/api_docs/SciPy.optimize.minpack.html#leastsq i, times = zip(*mon.spikes) times = array(times) times = times[abs(times - time_est) < 15 * ms] if len(times) == 0: return (0, 0 * ms) better_time_est = times.mean() times = times[abs(times - time_est) < 5 * ms] if len(times) == 0: return (0, 0 * ms) return (len(times), times.std()*second) def single_sfc(): net = DefaultNetwork(default_params) net.run() net.plot() def state_space(grid, neuron_multiply, verbose=True): amin = 0 amax = 100 sigmamin = 0. * ms sigmamax = 3. * ms params = default_params() params.num_layers = 1 params.neurons_per_layer = params.neurons_per_layer * neuron_multiply net = DefaultNetwork(params) i = 0 # uncomment these 2 lines for TeX labels #import pylab #pylab.rc_params.update({'text.usetex': True}) if verbose: print "Completed:" start_time = time.time() figure() for ai in range(grid + 1): for sigmai in range(grid + 1): a = int(amin + (ai * (amax - amin)) / grid) if a > amax: a = amax sigma = sigmamin + sigmai * (sigmamax - sigmamin) / grid params.initial_burst_a, params.initial_burst_sigma = a, sigma net.reinit(params) net.run() (newa, newsigma) = estimate_params(net.mon[-1], params.initial_burst_t) newa = float(newa) / float(neuron_multiply) col = (float(ai) / float(grid), float(sigmai) / float(grid), 0.5) plot([sigma / ms, newsigma / ms], [a, newa], color=col) plot([sigma / ms], [a], marker='.', color=col, markersize=15) i += 1 if verbose: print str(int(100. * float(i) / float((grid + 1) ** 2))) + "%", if verbose: print if verbose: print "Evaluation time:", time.time() - start_time, "seconds" xlabel(r'$\sigma$ (ms)') ylabel('a') title('Synfire chain state space') axis([sigmamin / ms, sigmamax / ms, amin, amax]) minimal_example() #print 'Computing SFC with multiple layers' #single_sfc() #print 'Plotting SFC state space' #state_space(3,1) #state_space(8,10) #state_space(10,50) #state_space(10,150) #show() brian-1.4.3/examples/frompapers/Goodman_Brette_2010.py000077500000000000000000000076701266062647100225420ustar00rootroot00000000000000#!/usr/bin/env python ''' Sound localization with HRTFs ----------------------------- Goodman DF and R Brette (2010). Spike-timing-based computation in sound localization. PLoS Comp Biol 6(11): e1000993. doi:10.1371/journal.pcbi.1000993. Simplified version of the "ideal" sound localisation model. The sound is played at a particular spatial location (indicated on the final plot by a red +). Each location has a corresponding assembly of neurons, whose summed firing rates give the sizes of the blue circles in the plot. The most strongly responding assembly is indicated by the green x, which is the estimate of the location by the model. ''' from brian import * from brian.hears import * # Download the IRCAM database # http://recherche.ircam.fr/equipes/salles/listen/download.html # and replace this filename with the location you downloaded it to hrtfdb = IRCAM_LISTEN(r'Z:\HRTF\IRCAM') subject = 1002 hrtfset = hrtfdb.load_subject(subject) # This gives the number of spatial locations in the set of HRTFs num_indices = hrtfset.num_indices # Choose a random location for the sound to come from index = randint(num_indices) # A sound to test the model with sound = Sound.whitenoise(500*ms) # This is the specific HRTF for the chosen location hrtf = hrtfset.hrtf[index] # We apply the chosen HRTF to the sound, the output has 2 channels hrtf_fb = hrtf.filterbank(sound) # We swap these channels (equivalent to swapping the channels in the # subsequent filters, but simpler to do it with the inputs) swapped_channels = RestructureFilterbank(hrtf_fb, indexmapping=[1, 0]) # Now we apply all of the possible pairs of HRTFs in the set to these # swapped channels, which means repeating them num_indices times first hrtfset_fb = hrtfset.filterbank(Repeat(swapped_channels, num_indices)) # Now we apply cochlear filtering (logically, this comes before the HRTF # filtering, but since convolution is commutative it is more efficient to # do the cochlear filtering afterwards cfmin, cfmax, cfN = 150*Hz, 5*kHz, 40 cf = erbspace(cfmin, cfmax, cfN) # We repeat each of the HRTFSet filterbank channels cfN times, so that # for each location we will apply each possible cochlear frequency gfb = Gammatone(Repeat(hrtfset_fb, cfN), tile(cf, hrtfset_fb.nchannels)) # Half wave rectification and compression cochlea = FunctionFilterbank(gfb, lambda x:15*clip(x, 0, Inf)**(1.0/3.0)) # Leaky integrate and fire neuron model eqs = ''' dV/dt = (I-V)/(1*ms)+0.1*xi/(0.5*ms)**.5 : 1 I : 1 ''' G = FilterbankGroup(cochlea, 'I', eqs, reset=0, threshold=1, refractory=5*ms) # The coincidence detector (cd) neurons cd = NeuronGroup(num_indices*cfN, eqs, reset=0, threshold=1, clock=G.clock) # Each CD neuron receives precisely two inputs, one from the left ear and # one from the right, for each location and each cochlear frequency C = Connection(G, cd, 'V') for i in xrange(num_indices*cfN): C[i, i] = 0.5 # from right ear C[i+num_indices*cfN, i] = 0.5 # from left ear # We want to just count the number of CD spikes counter = SpikeCounter(cd) # Run the simulation, giving a report on how long it will take as we run run(sound.duration, report='stderr') # We take the array of counts, and reshape them into a 2D array which we sum # across frequencies to get the spike count of each location-specific assembly count = counter.count count.shape = (num_indices, cfN) count = sum(count, axis=1) count = array(count, dtype=float)/amax(count) # Our guess of the location is the index of the strongest firing assembly index_guess = argmax(count) # Now we plot the output, using the coordinates of the HRTFSet coords = hrtfset.coordinates azim, elev = coords['azim'], coords['elev'] scatter(azim, elev, 100*count) plot([azim[index]], [elev[index]], '+r', ms=15, mew=2) plot([azim[index_guess]], [elev[index_guess]], 'xg', ms=15, mew=2) xlabel('Azimuth (deg)') ylabel('Elevation (deg)') xlim(-5, 350) ylim(-50, 95) show() brian-1.4.3/examples/frompapers/Guetig_Sompolinsky_2009.py000077500000000000000000000131331266062647100235030ustar00rootroot00000000000000#!/usr/bin/env python # -*- coding: utf-8 -*- ''' Implementation of the basic model (no speech recognition, no learning) described in: Gutig and Sompolinsky (2009): "Time-Warp-Invariant Neuronal Processing" PLoS Biology, Vol. 7 (7), e1000141 ''' from brian import * class TimeWarpModel(object): ''' A simple neuron model for testing the "time-warp invariance" with conductance based or current based synapses. The neuron receives balanced excitatory and inhibitory input from a random spike train. The same spike train can be fed into the model with different time warps. ''' def __init__(self, conductance_based=True): ''' Create a new model with conductance based or current based synapses ''' # Model parameters E_e = 5 E_i = -1 E_L = 0 g_L = 1/(100.0*msecond) tau_syn = 1*ms N_ex = 250 N_inh = 250 self.N = N_ex + N_inh # Equations if conductance_based: eqs = ''' dV/dt = -(V - E_L) * g_L - I_syn : 1 I_syn = I_ge + I_gi : second**-1 I_ge = (V - E_e) * g_e : second**-1 I_gi = (V - E_i) * g_i : second**-1 dg_e/dt = -g_e/tau_syn : second**-1 dg_i/dt = -g_i/tau_syn : second**-1 ''' else: eqs = ''' dV/dt = -(V - E_L) * g_L - I_syn : 1 I_syn = -5 * g_e + g_i : second**-1 dg_e/dt = -g_e/tau_syn : second**-1 dg_i/dt = -g_i/tau_syn : second**-1 ''' # for simpler voltage traces: no spiking neuron = NeuronGroup(1, model=eqs, threshold=None) # every input neuron fires once in a random interval self.unwarped_spiketimes = [(i, t * 250 * ms) for i, t in zip(range(0, self.N), rand(self.N))] # final spiketimes will be set in the run function self.input = SpikeGeneratorGroup(self.N, []) e_input = self.input.subgroup(N_ex) i_input = self.input.subgroup(N_inh) e_conn = Connection(e_input, neuron, 'g_e', weight=6 / (N_ex * tau_syn)) i_conn = Connection(i_input, neuron, 'g_i', weight=5 * 6 / (N_ex * tau_syn)) # record membrane potential self.monitor = StateMonitor(neuron, varname='V', record=True) # putting everything together self.net = Network(neuron, self.input, e_conn, i_conn, self.monitor) def run(self, beta=1.0): ''' Run the network with the original spike train warped by a certain factor beta. Beta > 1 corresponds to an extended and beta < 1 to a shrinked input spike train. ''' self.net.reinit() #warp spike train in time self.input.spiketimes = [(i, beta*t) for i, t in self.unwarped_spiketimes] self.net.run(beta * 250*ms) #Return the voltage trace return (self.monitor.times, self.monitor[0]) if __name__ == '__main__': cond_model = TimeWarpModel(True) curr_model = TimeWarpModel(False) N = cond_model.N # ######################################################################### # Reproduce Fig. 2 from Gütig and Sompolinsky (2009) # ######################################################################### beta = 2.0 times1, v1 = cond_model.run(beta=1.0) times2, v2 = cond_model.run(beta=beta) maxtime = 250 * beta subplot(4, 1, 1) (neurons, times) = zip(*cond_model.unwarped_spiketimes) plot(array(times) / ms, neurons, 'g.') axis([0, maxtime, 0, N]) xticks([]) yticks([]) title('Time-warp-invariant voltage traces (conductance-based)') subplot(4, 1, 2) plot(times1 / ms, v1, 'g') axis([0, maxtime, -1.5, 1.5]) xticks([]) yticks([]) subplot(4, 1, 3) plot(array(times) * beta / ms, neurons, 'b.') axis([0, maxtime, 0, N]) xticks([]) yticks([1, 500]) subplot(4, 1, 4) plot(times2 / ms, v2, 'b') plot(times1 / ms * beta, v1, 'g') axis([0, maxtime, -1.5, 1.5]) xlabel('Time (ms)') xticks([0, 250, 500]) yticks([-1, 1]) show() # ######################################################################### # Reproduce Fig. 3(C) from Gütig and Sompolinsky (2009), but for random # spike trains and not in a speech recognition task # ######################################################################### betas = arange(0.2, 3.1, 0.1) #betas = array([1.0, 2.0]) cond_results = [] curr_results = [] for beta in betas: print 'Testing warp factor %.1f' % beta cond_results.append(cond_model.run(beta)) curr_results.append(curr_model.run(beta)) figure() colors = mpl.cm.gist_earth((betas - betas[0]) / (betas[-1] - betas[0])) lookup = dict(zip(betas, colors)) for beta, cond_result, curr_result in zip(betas, cond_results, curr_results): times_cond, v_cond = cond_result times_curr, v_curr = curr_result subplot(1,2,1) plot(times_cond / ms / beta, v_cond, color=lookup[beta]) axis([0, 250, -1.5, 1.5]) subplot(1,2,2) plot(times_curr / ms / beta, v_curr, color=lookup[beta]) axis([0, 250, -1.5, 1.5]) subplot(1,2,1) title('conductance based') subplot(1,2,2) title('current based') show()brian-1.4.3/examples/frompapers/Kremer_et_al_2011.py000077500000000000000000000134661266062647100222430ustar00rootroot00000000000000#!/usr/bin/env python ''' Late Emergence of the Whisker Direction Selectivity Map in the Rat Barrel Cortex Kremer Y, Leger JF, Goodman DF, Brette R, Bourdieu L (2011). J Neurosci 31(29):10689-700. Development of direction maps with pinwheels in the barrel cortex. Whiskers are deflected with random moving bars. N.B.: network construction can be long. In this version, STDP is faster than in the paper so that the script runs in just a few minutes. ''' from brian import * # Uncomment if you have a C compiler # set_global_preferences(useweave=True,usecodegen=True,usecodegenweave=True,usenewpropagate=True,usecstdp=True) # PARAMETERS # Neuron numbers M4,M23exc,M23inh=22,25,12 # side of each barrel (in neurons) N4,N23exc,N23inh=M4**2,M23exc**2,M23inh**2 # neurons per barrel barrelarraysize=5 # Choose 3 or 4 if memory error Nbarrels=barrelarraysize**2 # Stimulation stim_change_time = 5*ms Fmax=.5/stim_change_time # maximum firing rate in layer 4 (.5 spike / stimulation) # Neuron parameters taum,taue,taui=10*ms,2*ms,25*ms El=-70*mV Vt,vt_inc,tauvt=-55*mV,2*mV,50*ms # adaptive threshold # STDP taup,taud=5*ms,25*ms Ap,Ad=.05,-.04 # EPSPs/IPSPs EPSP,IPSP = 1*mV,-1*mV EPSC = EPSP * (taue/taum)**(taum/(taue-taum)) IPSC = IPSP * (taui/taum)**(taum/(taui-taum)) # Model: IF with adaptive threshold eqs=''' dv/dt=(ge+gi+El-v)/taum : volt dge/dt=-ge/taue : volt dgi/dt=-gi/taui : volt dvt/dt=(Vt-vt)/tauvt : volt # adaptation x : 1 y : 1 ''' # Tuning curve tuning=lambda theta:clip(cos(theta),0,Inf)*Fmax # Layer 4 layer4=PoissonGroup(N4*Nbarrels) barrels4 = dict(((i, j), layer4.subgroup(N4)) for i in xrange(barrelarraysize) for j in xrange(barrelarraysize)) barrels4active = dict((ij, False) for ij in barrels4) barrelindices = dict((ij, slice(b._origin, b._origin+len(b))) for ij, b in barrels4.iteritems()) layer4.selectivity = zeros(len(layer4)) for (i, j), inds in barrelindices.iteritems(): layer4.selectivity[inds]=linspace(0,2*pi,N4) # Layer 2/3 layer23=NeuronGroup(Nbarrels*(N23exc+N23inh),model=eqs,threshold='v>vt',reset='v=El;vt+=vt_inc',refractory=2*ms) layer23.v=El layer23.vt=Vt # Layer 2/3 excitatory layer23exc=layer23.subgroup(Nbarrels*N23exc) x,y=meshgrid(arange(M23exc)*1./M23exc,arange(M23exc)*1./M23exc) x,y=x.flatten(),y.flatten() barrels23 = dict(((i, j), layer23exc.subgroup(N23exc)) for i in xrange(barrelarraysize) for j in xrange(barrelarraysize)) for i in range(barrelarraysize): for j in range(barrelarraysize): barrels23[i,j].x=x+i barrels23[i,j].y=y+j # Layer 2/3 inhibitory layer23inh=layer23.subgroup(Nbarrels*N23inh) x,y=meshgrid(arange(M23inh)*1./M23inh,arange(M23inh)*1./M23inh) x,y=x.flatten(),y.flatten() barrels23inh = dict(((i, j), layer23inh.subgroup(N23inh)) for i in xrange(barrelarraysize) for j in xrange(barrelarraysize)) for i in range(barrelarraysize): for j in range(barrelarraysize): barrels23inh[i,j].x=x+i barrels23inh[i,j].y=y+j print "Building synapses, please wait..." # Feedforward connections feedforward=Connection(layer4,layer23exc,'ge') for i in range(barrelarraysize): for j in range(barrelarraysize): feedforward.connect_random(barrels4[i,j],barrels23[i,j],sparseness=.5,weight=EPSC*.5) stdp=ExponentialSTDP(feedforward,taup,taud,Ap,Ad,wmax=EPSC) # Excitatory lateral connections recurrent_exc=Connection(layer23exc,layer23,'ge') recurrent_exc.connect_random(layer23exc,layer23exc,weight=EPSC*.3, sparseness=lambda i,j:.15*exp(-.5*(((layer23exc.x[i]-layer23exc.x[j])/.4)**2+((layer23exc.y[i]-layer23exc.y[j])/.4)**2))) recurrent_exc.connect_random(layer23exc,layer23inh,weight=EPSC, sparseness=lambda i,j:.15*exp(-.5*(((layer23exc.x[i]-layer23inh.x[j])/.4)**2+((layer23exc.y[i]-layer23inh.y[j])/.4)**2))) # Inhibitory lateral connections recurrent_inh=Connection(layer23inh,layer23exc,'gi') recurrent_inh.connect_random(layer23inh,layer23exc,weight=IPSC, sparseness=lambda i,j:exp(-.5*(((layer23inh.x[i]-layer23exc.x[j])/.2)**2+((layer23inh.y[i]-layer23exc.y[j])/.2)**2))) # Stimulation stimspeed = 1./stim_change_time # speed at which the bar of stimulation moves direction = 0.0 stimzonecentre = ones(2)*barrelarraysize/2. stimcentre,stimnorm = zeros(2),zeros(2) stimradius = (11*stim_change_time*stimspeed+1)*.5 stimradius2 = stimradius**2 def new_direction(): global direction direction = rand()*2*pi stimnorm[:] = (cos(direction), sin(direction)) stimcentre[:] = stimzonecentre-stimnorm*stimradius @network_operation def stimulation(): global direction, stimcentre stimcentre += stimspeed*stimnorm*defaultclock.dt if sum((stimcentre-stimzonecentre)**2)>stimradius2: new_direction() for (i, j), b in barrels4.iteritems(): whiskerpos = array([i,j], dtype=float)+0.5 isactive = abs(dot(whiskerpos-stimcentre, stimnorm))<.5 if barrels4active[i, j]!=isactive: barrels4active[i, j] = isactive b.rate = float(isactive)*tuning(layer4.selectivity[barrelindices[i, j]]-direction) new_direction() run(5*second,report='text') figure() # Preferred direction selectivity=array([mean(array(feedforward[:,i].todense())*exp(layer4.selectivity*1j)) for i in range(len(layer23exc))]) selectivity=(arctan2(selectivity.imag,selectivity.real) % (2*pi))*180./pi I=zeros((barrelarraysize*M23exc,barrelarraysize*M23exc)) ix=array(around(layer23exc.x*M23exc),dtype=int) iy=array(around(layer23exc.y*M23exc),dtype=int) I[iy,ix]=selectivity imshow(I) hsv() colorbar() for i in range(1,barrelarraysize+1): plot([i*max(ix)/barrelarraysize,i*max(ix)/barrelarraysize],[0,max(iy)],'k') plot([0,max(ix)],[i*max(iy)/barrelarraysize,i*max(iy)/barrelarraysize],'k') figure() hist(selectivity) show() brian-1.4.3/examples/frompapers/Muller_et_al_2011.py000077500000000000000000000065011266062647100222460ustar00rootroot00000000000000#!/usr/bin/env python """ Interplay of STDP and input oscillations ---------------------------------------- Figure 4 from: Muller L, Brette R and Gutkin B (2011) Spike-timing dependent plasticity and feed-forward input oscillations produce precise and invariant spike phase-locking. Front. Comput. Neurosci. 5:45. doi: 10.3389/fncom.2011.00045 Description: In this simulation, a group of IF neurons is given a tonic DC input and a tonic AC input. The DC input is mediated by current injection (neurons.I, line 62), and the AC input is mediated by Poisson processes whose rate parameters are oscillating in time. Each neuron in the group is given a different DC input, ensuring a unique initial phase. After two seconds of simulation (to integrate out any initial transients), the STDP rule is turned on (ExponentialSTDP, line 68), and the population of neurons converges to the theoretically predicted fixed point. As there is some noise in the phase due to the random inputs, the simulation is averaged over trials (50 in Figure 4, though 10 trials should be fine for testing). The trials run in parallel on all available processors (10 trials take about 2 minutes on a modern PC). """ ### IMPORTS from brian import * import multiprocessing ### PARAMETERS N=5000 M=10 taum=33*ms tau_pre=20*ms tau_post=tau_pre Ee=0*mV vt=-54*mV vr=-70*mV El=-70*mV taue=5*ms f=20*Hz theta_period = 1/f Rm=200*Mohm a = linspace(51,65,num=M) weights = .001 ratio=1.50 dA_pre=.01 dA_post=.01*ratio trials=10 ### SIMULATION LOOP def trial(n): # n is the trial number reinit_default_clock() clear(True) eqs_neurons=''' dv/dt=((ge*(Ee-vr))+Rm*I+(El-v))/taum : volt dge/dt=-ge/taue : 1 I : amp ''' inputs = PoissonGroup(N,rates=lambda t:((.5-.5*cos(2*pi*f*t)))*10*Hz) neurons=NeuronGroup(M,model=eqs_neurons,threshold=vt,reset=vr) neurons.I = a*pA synapses=Connection(inputs,neurons,'ge',weight=weights) neurons.v=vr S = SpikeMonitor(neurons) run(2*second) stdp=ExponentialSTDP(synapses,tau_pre,tau_post,dA_pre,-dA_post,wmax=10*weights,interactions='all',update='additive') run(5*second) phase=zeros((M,200)) for b in range(0,M): tmp_phase=(S[b]%theta_period)*(360/theta_period) phase[b,range(0,len(tmp_phase))] = tmp_phase return phase if __name__=='__main__': # This is very important on Windows, otherwise the machine crashes! phase = zeros((M,200,trials)) print "This will take approximately 2 minutes." pool=multiprocessing.Pool() # uses all available processors b results=pool.map(trial,range(trials)) for i in range(trials): phase[:,:,i]=results[i] ### PLOTTING for b in range(0,M): m = mean(phase[b,:,:],axis=1) st = std(phase[b,:,:],axis=1)/sqrt(trials) errorbar(range(0,135), m[range(0,135)], yerr=st[range(0,135)], xerr=None, fmt='-', ecolor=None, elinewidth=None, capsize=3, barsabove=False, lolims=False, uplims=False, xlolims=False, xuplims=False) title('STDP + Oscillations Simulation') xlabel('Spike Number') ylabel('Spike Phase (deg)') xlim([0, 135]) ylim([140, 280]) show()brian-1.4.3/examples/frompapers/Plakiewicz_Brette_2010.py000077500000000000000000000145331266062647100232540ustar00rootroot00000000000000#!/usr/bin/env python ''' Spike threshold variability in single compartment model ------------------------------------------------------- Figure 7 from: Platkiewicz J, Brette R (2010). A Threshold Equation for Action Potential Initiation. PLoS Comput Biol 6(7): e1000850. doi:10.1371/journal.pcbi.1000850 The original HH model is from Traub and Miles 1991, modified by Destexhe et al. 2001, and we shift Na inactivation to -62 mV. This shift produces threshold variability, but also spikes with variable shapes (unavoidable in a single-compartment model). The script demonstrates that the spike threshold is proportional to the logarithm of h. ''' from brian import * from scipy import stats from brian.library.electrophysiology import * defaultclock.dt=0.05*ms duration=500*ms N=1000 # we simulate 1000 neurons to have more threshold statistics rectify=lambda x:clip(x,0,inf)*siemens # Biophysical parameters #--Passive parameters area=pi*(105*umetre)**2 C=1*uF GL=0.0452*msiemens EL=-80*mV #--Active fixed parameters celsius=36 temp=23 q10=2.3 #----Sodium channel parameters ENa=50*mV GNa=51.6*msiemens vTraub_Na=-63*mV #Traub convention vshift=-20*mV #Inactivation shift (-62 mV instead of -42 mV) #--------activation A_alpham=0.32/(ms*mV) #open (V) A_betam=0.28/(ms*mV) #close (V) v12_alpham=13*mV #v1/2 for act v12_betam=40*mV #v1/2 for act k_alpham=4*mV #act slope k_betam=5*mV #act slope #--------inactivation A_alphah=0.128/ms #inact recov (V) A_betah=4/ms #inact (V) v12_alphah=17*mV #v1/2 for inact v12_betah=40*mV #v1/2 for inact k_alphah=18*mV #inact tau slope k_betah=5*mV #inact tau slope #----Potassium channel parameters EK=-90*mV #--------"delay-rectifier" GK=10*msiemens vTraub_K=-63*mV A_alphan=0.032/(ms*mV) #open (V) A_betan=0.5/ms #close (V) v12_alphan=15*mV #v1/2 for act v12_betan=10*mV #v1/2 for act k_alphan=5*mV #act slope k_betan=40*mV #act slope #--------muscarinic GK_m=0.5*msiemens A_alphan_m=1e-4/(ms*mV) A_betan_m=1e-4/(ms*mV) v12_alphan_m=-30*mV v12_betan_m=-30*mV k_alphan_m=9*mV k_betan_m=9*mV # Input parameters Ee=0*mV Ei=-75*mV taue=2.728*ms taui=10.49*ms Ge0=0.0121*usiemens*cm**2 Gi0=0.0573*usiemens*cm**2 Sigmae=0.012*usiemens*cm**2 Sigmai=0.0264*usiemens*cm**2 tadj=q10**((celsius-temp)/10) ge0=Ge0/area gi0=Gi0/area sigmae=Sigmae/area sigmai=Sigmai/area Traubm=lambda v:v-vTraub_Na alpham=lambda v:A_alpham*(Traubm(v)-v12_alpham)/(1-exp((v12_alpham-Traubm(v))/k_alpham)) betam=lambda v:-A_betam*(Traubm(v)-v12_betam)/(1-exp(-(v12_betam-Traubm(v))/k_betam)) minf=lambda v:alpham(v)/(alpham(v)+betam(v)) taum=lambda v:1/(alpham(v)+betam(v)) Shift=lambda v:Traubm(v)-vshift alphah=lambda v:A_alphah*exp((v12_alphah-Shift(v))/k_alphah) betah=lambda v:A_betah/(1+exp((v12_betah-Shift(v))/k_betah)) hinf=lambda v:alphah(v)/(alphah(v)+betah(v)) tauh=lambda v:1/(alphah(v)+betah(v)) TraubK=lambda v:v-vTraub_K alphan= lambda v:A_alphan*(TraubK(v)-v12_alphan)/(1-exp((v12_alphan-TraubK(v))/k_alphan)) betan= lambda v:A_betan*exp((v12_betan-TraubK(v))/k_betan) ninf= lambda v:alphan(v)/(alphan(v)+betan(v)) taun= lambda v:1/(alphan(v)+betan(v))/tadj eqs=""" dv/dt=(3*GNa*h*m**3*(ENa-v)+(GK*n**4+GK_m*n_m)*(EK-v)+GL*(EL-v)+I)/C : volt # Sodium activation m_inf=minf(v) : 1 #minf(v) tau_m=taum(v) : second dm/dt=(m_inf-m)/tau_m : 1 # Sodium inactivation h_inf=hinf(v) : 1 tau_h=tauh(v) : second dh/dt=(h_inf-h)/tau_h : 1 # Potassium : delay-rectifier n_inf=ninf(v) : 1 tau_n=taun(v) : second dn/dt=(n_inf-n)/tau_n : 1 gK=GK*n**4 : siemens # Potassium : muscarinic alphan_m=A_alphan_m*(v-v12_alphan_m)/(1-exp((v12_alphan_m-v)/k_alphan_m)) : hertz betan_m=-A_alphan_m*(v-v12_alphan_m)/(1-exp(-(v12_alphan_m-v)/k_alphan_m)) : hertz n_minf=alphan_m/(alphan_m+betan_m) : 1 taun_m=1/(alphan_m+betan_m)/tadj : second dn_m/dt=(n_minf-n_m)/taun_m : 1 gK_m=GK_m*n_m : siemens # Fluctuating synaptic conductances I=rectify(ge)*(Ee-v)+rectify(gi)*(Ei-v) : amp dge/dt=(1.5*ge0-ge)/taue+1.5*sigmae*(2./taue)**.5*xi : siemens dgi/dt=(gi0-gi)/taui+2*sigmai*(2./taui)**.5*xi : siemens gtot=GL+rectify(ge)+rectify(gi)+gK+gK_m : siemens """ neurons=NeuronGroup(N,model=eqs,implicit=True) neurons.v=EL neurons.m=minf(EL) neurons.h=hinf(EL) neurons.n=ninf(EL) neurons.n_m=0 M=StateMonitor(neurons,'v',record=True) Mh=StateMonitor(neurons,'h',record=True) run(duration,report='text') # Collect spike thresholds and values of h threshold,logh=[],[] valuesv,valuesh=array(M._values),array(Mh._values) criterion=10*mV/ms # criterion for spike threshold for i in range(N): v=valuesv[:,i] h=valuesh[:,i] onsets=spike_onsets(v,criterion=defaultclock.dt*criterion,vc=-35*mV) threshold.extend(v[onsets]) logh.extend(-log(h[onsets]))#+log(g[onsets])) threshold=array(threshold)/mV logh=array(logh)/log(10.) # for display # Voltage trace with spike onsets subplot(311) v=valuesv[:,0] onsets=spike_onsets(v,criterion=defaultclock.dt*criterion,vc=-35*mV) t=M.times[onsets]/ms plot(M.times/ms,M[0]/mV,'k') plot(t,v[onsets]/mV,'.r') xlabel('t (ms)') ylabel('V (mV)') # Distribution of Vm and spike onsets subplot(312) hist(threshold,30,normed=1,histtype='stepfilled',alpha=0.6,facecolor='r') hist(valuesv.flatten()/mV,100,normed=1,histtype='stepfilled',alpha=0.6,facecolor='k') xlabel('V (mV)') xlim(-80,-40) # Relationship between h and spike threshold subplot(313) slope,intercept=3.1,-54. # linear regression for the prediction of threshold p1,p2=min(logh),max(logh) plot(logh[:len(logh)/10],threshold[:len(logh)/10],'.k') # don't show everything plot([p1,p2],intercept+slope*array([p1,p2])*log(10.),'r') xlabel('h') ylabel('Threshold (mV)') ylim(-55,-40) xticks([0,-log(5e-1)/log(10),1,-log(5e-2)/log(10)],[1,5e-1,1e-1,5e-2]) xlim(0,1.5) show() brian-1.4.3/examples/frompapers/Platkiewicz_Brette_2011.py000077500000000000000000000045201266062647100234340ustar00rootroot00000000000000#!/usr/bin/env python ''' Slope-threshold relationship with noisy inputs, in the adaptive threshold model ------------------------------------------------------------------------------- Fig. 5E,F from: Platkiewicz J and R Brette (2011). Impact of Fast Sodium Channel Inactivation on Spike Threshold Dynamics and Synaptic Integration. PLoS Comp Biol 7(5): e1001129. doi:10.1371/journal.pcbi.1001129 ''' from brian import * from scipy import stats,optimize from scipy.stats import linregress rectify=lambda x:clip(x/volt,0,Inf)*volt N=200 # 200 neurons to get more statistics, only one is shown duration=1*second #--Biophysical parameters ENa=60*mV EL=-70*mV vT=-55*mV Vi=-63*mV tauh=5*ms tau=5*ms ka=5*mV ki=6*mV a=ka/ki tauI=5*ms mu=15*mV sigma=6*mV/sqrt(tauI/(tauI+tau)) #--Theoretical prediction for the slope-threshold relationship (approximation: a=1+epsilon) thresh=lambda slope,a: Vi-slope*tauh*log(1+(Vi-vT/a)/(slope*tauh)) #-----Exact calculation of the slope-threshold relationship thresh_ex=lambda s: optimize.fsolve(lambda th: a*s*tauh*exp((Vi-th)/(s*tauh))-th*(1-a)-a*(s*tauh+Vi)+vT,thresh(s,a))*volt eqs=""" dv/dt=(EL-v+mu+sigma*I)/tau : volt dtheta/dt=(vT+a*rectify(v-Vi)-theta)/tauh : volt dI/dt=-I/tauI+(2/tauI)**.5*xi : 1 # Ornstein-Uhlenbeck """ neurons=NeuronGroup(N,eqs,threshold="v>theta",reset='v=EL',refractory=5*ms) neurons.v=EL neurons.theta=vT neurons.I=0 S=SpikeMonitor(neurons) M=StateMonitor(neurons,'v',record=True) Mt=StateMonitor(neurons,'theta',record=0) run(duration,report='text') # Linear regression gives depolarization slope before spikes tx=M.times[(M.times>0) & (M.times<1.5*tauh)] slope,threshold=[],[] v=array(M._values) for (i,t) in S.spikes: ind=(M.timest-tauh) mx=v[:,i][ind] s,_,_,_,_=linregress(tx[:len(mx)],mx) slope.append(s) threshold.append(mx[-1]) # Figure M.insert_spikes(S) # displays spikes on the trace subplot(221) ind=M.times<500*ms plot(M.times[ind]/ms,M[0][ind]/mV,'k') plot(Mt.times[ind]/ms,Mt[0][ind]/mV,'r') xlabel('Time (ms)') ylabel('Voltage (mV)') subplot(222) plot(slope,array(threshold)/mV,'r.') sx=linspace(0.5*volt/second,4*volt/second,100) t=array([thresh_ex(s*volt/second) for s in sx]) plot(sx,t/mV,'k') xlim(0.5,4) xlabel('Depolarization slope (ms)') ylabel('Threshold (mV/ms)') show() brian-1.4.3/examples/frompapers/Rossant_et_al_2011.py000077500000000000000000000055011266062647100224360ustar00rootroot00000000000000#!/usr/bin/env python """ Coincidence detection example ============================= Fig. 4 from: Rossant C, Leijon S, Magnusson AK, Brette R (2011). "Sensitivity of noisy neurons to coincident inputs". Journal of Neuroscience, 31(47). Two distant or coincident spikes are injected into a noisy balanced leaky integrate-and-fire neuron. The PSTH of the neuron in response to these inputs is calculated along with the extra number of spikes in the two cases. This number is higher for the coincident spikes, showing the sensitivity of a noisy neuron to coincident inputs. """ from brian import * import matplotlib.patches as patches import matplotlib.path as path def histo(bins, cc, ax): # get the corners of the rectangles for the histogram left = array(bins[:-1]) right = array(bins[1:]) bottom = zeros(len(left)) top = bottom + cc # we need a (numrects x numsides x 2) numpy array for the path helper # function to build a compound path XY = array([[left,left,right,right], [bottom,top,top,bottom]]).T # get the Path object barpath = path.Path.make_compound_path_from_polys(XY) # make a patch out of it patch = patches.PathPatch(barpath, facecolor='blue', edgecolor='gray', alpha=0.8) ax.add_patch(patch) # update the view limits ax.set_xlim(left[0], right[-1]) ax.set_ylim(bottom.min(), top.max()) # neuron parameters theta = -55*mV vmean = -65*mV taum = 5*ms taue = 3*ms taun = 15*ms sigma = 4*mV # input times t1 = 100*ms t2 = 120*ms # simulation duration dur = 200*ms # number of neuron N = 10000 bin = 2*ms # EPSP size int_EPSP=taue int_EPSP2=taue*taue/(2*(taum+taue)) max_EPSP=(taum/taue)**(taum/(taue-taum)) we = 3.0*mV/max_EPSP # model equations eqs = ''' V=V0+noise : volt dV0/dt=(-V0+psp)/taum : volt dpsp/dt=-psp/taue : volt dnoise/dt=(vmean-noise)/taun+sigma*(2./taun)**.5*xi : volt ''' threshold = 'V>theta' reset = vmean # initialization of the NeuronGroup reinit_default_clock() group = NeuronGroup(2*N, model=eqs, reset=reset, threshold=threshold) group.V0 = group.psp = 0*volt group.noise = vmean + sigma * randn(2*N) # input spikes input_spikes = [(0, t1), (0, t2), (1, t1)] input = SpikeGeneratorGroup(2, array(input_spikes)) # connections C = Connection(input, group, 'psp') C.connect_full(input[0], group[:N], weight=we) C.connect_full(input[1], group[N:], weight=2*we) # monitors prM1 = PopulationRateMonitor(group[:N], bin=bin) prM2 = PopulationRateMonitor(group[N:], bin=bin) # launch simulation run(dur) # PSTH plot figure(figsize=(10,10)) prMs = [prM1, prM2] for i in [0,1]: prM = prMs[i] r = prM.rate[:-1]*bin m = mean(r[:len(r)/2]) ax = subplot(211+i) histo(prM.times, r, ax) plot([0,dur],[m,m],'--r') title("%.2f extra spikes" % sum(r[t1/bin:(t2+20*ms)/bin]-m)) xlim(.05, .2) ylim(0, .125) show() brian-1.4.3/examples/frompapers/Rossant_et_al_2011bis.py000077500000000000000000000045741266062647100231450ustar00rootroot00000000000000#!/usr/bin/env python """ Distributed synchrony example ============================= Fig. 14 from: Rossant C, Leijon S, Magnusson AK, Brette R (2011). "Sensitivity of noisy neurons to coincident inputs". Journal of Neuroscience, 31(47). 5000 independent E/I Poisson inputs are injected into a leaky integrate-and-fire neuron. Synchronous events, following an independent Poisson process at 40 Hz, are considered, where 15 E Poisson spikes are randomly shifted to be synchronous at those events. The output firing rate is then significantly higher, showing that the spike timing of less than 1% of the excitatory synapses have an important impact on the postsynaptic firing. """ from brian import * # neuron parameters theta = -55*mV El = -65*mV vmean = -65*mV taum = 5*ms taue = 3*ms taui = 10*ms eqs = Equations(""" dv/dt = (ge+gi-(v-El))/taum : volt dge/dt = -ge/taue : volt dgi/dt = -gi/taui : volt """) # input parameters p = 15 ne = 4000 ni = 1000 lambdac = 40*Hz lambdae = lambdai = 1*Hz # synapse parameters we = .5*mV/(taum/taue)**(taum/(taue-taum)) wi = (vmean-El-lambdae*ne*we*taue)/(lambdae*ni*taui) # NeuronGroup definition group = NeuronGroup(N=2, model=eqs, reset=El, threshold=theta, refractory=5*ms) group.v = El group.ge = group.gi = 0 # independent E/I Poisson inputs p1 = PoissonInput(group[0], N=ne, rate=lambdae, weight=we, state='ge') p2 = PoissonInput(group[0], N=ni, rate=lambdai, weight=wi, state='gi') # independent E/I Poisson inputs + synchronous E events p3 = PoissonInput(group[1], N=ne, rate=lambdae-(p*1.0/ne)*lambdac, weight=we, state='ge') p4 = PoissonInput(group[1], N=ni, rate=lambdai, weight=wi, state='gi') p5 = PoissonInput(group[1], N=1, rate=lambdac, weight=p*we, state='ge') # run the simulation reinit_default_clock() M = SpikeMonitor(group) SM = StateMonitor(group, 'v', record=True) run(1*second) # plot trace and spikes for i in [0,1]: spikes = M.spiketimes[i]-.0001 val = SM.values[i] subplot(2,1,i+1) plot(SM.times, val) plot(tile(spikes, (2,1)), vstack((val[array(spikes*10000, dtype=int)], zeros(len(spikes)))), 'b') title("%s: %d spikes/second" % (["uncorrelated inputs", "correlated inputs"][i], len(M.spiketimes[i]))) show() brian-1.4.3/examples/frompapers/Rothman_Manis_2003.py000077500000000000000000000106011266062647100223760ustar00rootroot00000000000000#!/usr/bin/env python """ Cochlear neuron model of Rothman & Manis ---------------------------------------- Rothman JS, Manis PB (2003) The roles potassium currents play in regulating the electrical activity of ventral cochlear nucleus neurons. J Neurophysiol 89:3097-113. All model types differ only by the maximal conductances. Adapted from their Neuron implementation by Romain Brette """ from brian import * #defaultclock.dt=0.025*ms # for better precision ''' Simulation parameters: choose current amplitude and neuron type (from type1c, type1t, type12, type 21, type2, type2o) ''' neuron_type = 'type1c' Ipulse = 250 * pA C = 12 * pF Eh = -43 * mV EK = -70 * mV # -77*mV in mod file El = -65 * mV ENa = 50 * mV nf = 0.85 # proportion of n vs p kinetics zss = 0.5 # steady state inactivation of glt celsius = 22. # temperature q10 = 3. ** ((celsius - 22) / 10.) # hcno current (octopus cell) frac = 0.0 qt = 4.5 ** ((celsius - 33.) / 10.) # Maximal conductances of different cell types in nS maximal_conductances = dict( type1c=(1000, 150, 0, 0, 0.5, 0, 2), type1t=(1000, 80, 0, 65, 0.5, 0, 2), type12=(1000, 150, 20, 0, 2, 0, 2), type21=(1000, 150, 35, 0, 3.5, 0, 2), type2=(1000, 150, 200, 0, 20, 0, 2), type2o=(1000, 150, 600, 0, 0, 40, 2) # octopus cell ) gnabar, gkhtbar, gkltbar, gkabar, ghbar, gbarno, gl = [x * nS for x in maximal_conductances[neuron_type]] # Classical Na channel eqs_na = """ ina = gnabar*m**3*h*(ENa-v) : amp dm/dt=q10*(minf-m)/mtau : 1 dh/dt=q10*(hinf-h)/htau : 1 minf = 1./(1+exp(-(vu + 38.) / 7.)) : 1 hinf = 1./(1+exp((vu + 65.) / 6.)) : 1 mtau = ((10. / (5*exp((vu+60.) / 18.) + 36.*exp(-(vu+60.) / 25.))) + 0.04)*ms : ms htau = ((100. / (7*exp((vu+60.) / 11.) + 10.*exp(-(vu+60.) / 25.))) + 0.6)*ms : ms """ # KHT channel (delayed-rectifier K+) eqs_kht = """ ikht = gkhtbar*(nf*n**2 + (1-nf)*p)*(EK-v) : amp dn/dt=q10*(ninf-n)/ntau : 1 dp/dt=q10*(pinf-p)/ptau : 1 ninf = (1 + exp(-(vu + 15) / 5.))**-0.5 : 1 pinf = 1. / (1 + exp(-(vu + 23) / 6.)) : 1 ntau = ((100. / (11*exp((vu+60) / 24.) + 21*exp(-(vu+60) / 23.))) + 0.7)*ms : ms ptau = ((100. / (4*exp((vu+60) / 32.) + 5*exp(-(vu+60) / 22.))) + 5)*ms : ms """ # Ih channel (subthreshold adaptive, non-inactivating) eqs_ih = """ ih = ghbar*r*(Eh-v) : amp dr/dt=q10*(rinf-r)/rtau : 1 rinf = 1. / (1+exp((vu + 76.) / 7.)) : 1 rtau = ((100000. / (237.*exp((vu+60.) / 12.) + 17.*exp(-(vu+60.) / 14.))) + 25.)*ms : ms """ # KLT channel (low threshold K+) eqs_klt = """ iklt = gkltbar*w**4*z*(EK-v) : amp dw/dt=q10*(winf-w)/wtau : 1 dz/dt=q10*(zinf-z)/wtau : 1 winf = (1. / (1 + exp(-(vu + 48.) / 6.)))**0.25 : 1 zinf = zss + ((1.-zss) / (1 + exp((vu + 71.) / 10.))) : 1 wtau = ((100. / (6.*exp((vu+60.) / 6.) + 16.*exp(-(vu+60.) / 45.))) + 1.5)*ms : ms ztau = ((1000. / (exp((vu+60.) / 20.) + exp(-(vu+60.) / 8.))) + 50)*ms : ms """ # Ka channel (transient K+) eqs_ka = """ ika = gkabar*a**4*b*c*(EK-v): amp da/dt=q10*(ainf-a)/atau : 1 db/dt=q10*(binf-b)/btau : 1 dc/dt=q10*(cinf-c)/ctau : 1 ainf = (1. / (1 + exp(-(vu + 31) / 6.)))**0.25 : 1 binf = 1. / (1 + exp((vu + 66) / 7.))**0.5 : 1 cinf = 1. / (1 + exp((vu + 66) / 7.))**0.5 : 1 atau = ((100. / (7*exp((vu+60) / 14.) + 29*exp(-(vu+60) / 24.))) + 0.1)*ms : ms btau = ((1000. / (14*exp((vu+60) / 27.) + 29*exp(-(vu+60) / 24.))) + 1)*ms : ms ctau = ((90. / (1 + exp((-66-vu) / 17.))) + 10)*ms : ms """ # Leak eqs_leak = """ ileak = gl*(El-v) : amp """ # h current for octopus cells eqs_hcno = """ ihcno = gbarno*(h1*frac + h2*(1-frac))*(Eh-v) : amp dh1/dt=(hinfno-h1)/tau1 : 1 dh2/dt=(hinfno-h2)/tau2 : 1 hinfno = 1./(1+exp((vu+66.)/7.)) : 1 tau1 = bet1/(qt*0.008*(1+alp1))*ms : ms tau2 = bet2/(qt*0.0029*(1+alp2))*ms : ms alp1 = exp(1e-3*3*(vu+50)*9.648e4/(8.315*(273.16+celsius))) : 1 bet1 = exp(1e-3*3*0.3*(vu+50)*9.648e4/(8.315*(273.16+celsius))) : 1 alp2 = exp(1e-3*3*(vu+84)*9.648e4/(8.315*(273.16+celsius))) : 1 bet2 = exp(1e-3*3*0.6*(vu+84)*9.648e4/(8.315*(273.16+celsius))) : 1 """ eqs = """ dv/dt=(ileak+ina+ikht+iklt+ika+ih+ihcno+I)/C : volt vu = v/mV : 1 # unitless v I : amp """ eqs += eqs_leak + eqs_ka + eqs_na + eqs_ih + eqs_klt + eqs_kht + eqs_hcno neuron = NeuronGroup(1, eqs, implicit=True) neuron.v = El run(50 * ms) # Go to rest M = StateMonitor(neuron, 'v', record=0) neuron.I = Ipulse run(100 * ms, report='text') plot(M.times / ms, M[0] / mV) show() brian-1.4.3/examples/frompapers/Sturzl_et_al_2000.py000077500000000000000000000037641266062647100223170ustar00rootroot00000000000000#!/usr/bin/env python ''' Adapted from Theory of Arachnid Prey Localization W. Sturzl, R. Kempter, and J. L. van Hemmen PRL 2000 Poisson inputs are replaced by integrate-and-fire neurons Romain Brette ''' from brian import * # Parameters degree = 2 * pi / 360. duration = 500 * ms R = 2.5 * cm # radius of scorpion vr = 50 * meter / second # Rayleigh wave speed phi = 144 * degree # angle of prey A = 250 * Hz deltaI = .7 * ms # inhibitory delay gamma = (22.5 + 45 * arange(8)) * degree # leg angle delay = R / vr * (1 - cos(phi - gamma)) # wave delay # Wave (vector w) t = arange(int(duration / defaultclock.dt) + 1) * defaultclock.dt Dtot = 0. w = 0. for f in range(150, 451): D = exp(-(f - 300) ** 2 / (2 * (50 ** 2))) xi = 2 * pi * rand() w += 100 * D * cos(2 * pi * f * t + xi) Dtot += D w = .01 * w / Dtot # Rates from the wave def rates(t): return w[array(t / defaultclock.dt, dtype=int)] # Leg mechanical receptors tau_legs = 1 * ms sigma = .01 eqs_legs = """ dv/dt=(1+rates(t-d)-v)/tau_legs+sigma*(2./tau_legs)**.5*xi:1 d : second """ legs = NeuronGroup(8, model=eqs_legs, threshold=1, reset=0, refractory=1 * ms) legs.d = delay spikes_legs = SpikeCounter(legs) # Command neurons tau = 1 * ms taus = 1 * ms wex = 7 winh = -2 eqs_neuron = ''' dv/dt=(x-v)/tau : 1 dx/dt=(y-x)/taus : 1 # alpha currents dy/dt=-y/taus : 1 ''' neurons = NeuronGroup(8, model=eqs_neuron, threshold=1, reset=0) synapses_ex = IdentityConnection(legs, neurons, 'y', weight=wex) synapses_inh = Connection(legs, neurons, 'y', delay=deltaI) for i in range(8): synapses_inh[i, (4 + i - 1) % 8] = winh synapses_inh[i, (4 + i) % 8] = winh synapses_inh[i, (4 + i + 1) % 8] = winh spikes = SpikeCounter(neurons) run(duration) nspikes = spikes.count x = sum(nspikes * exp(gamma * 1j)) print "Angle (deg):", arctan(imag(x) / real(x)) / degree polar(concatenate((gamma, [gamma[0] + 2 * pi])), concatenate((nspikes, [nspikes[0]])) / duration) show() brian-1.4.3/examples/frompapers/Touboul_Brette_2008.py000077500000000000000000000022511266062647100226040ustar00rootroot00000000000000#!/usr/bin/env python ''' Chaos in the AdEx model ----------------------- Fig. 8B from: Touboul, J. and Brette, R. (2008). Dynamics and bifurcations of the adaptive exponential integrate-and-fire model. Biological Cybernetics 99(4-5):319-34. This shows the bifurcation structure when the reset value is varied (vertical axis shows the values of w at spike times for a given a reset value Vr). ''' from brian import * defaultclock.dt=0.01*ms C=281*pF gL=30*nS EL=-70.6*mV VT=-50.4*mV DeltaT=2*mV tauw=40*ms a=4*nS b=0.08*nA I=.8*nA Vcut=VT+5*DeltaT # practical threshold condition N=500 eqs=""" dvm/dt=(gL*(EL-vm)+gL*DeltaT*exp((vm-VT)/DeltaT)+I-w)/C : volt dw/dt=(a*(vm-EL)-w)/tauw : amp Vr:volt """ neuron=NeuronGroup(N,model=eqs,threshold=Vcut,reset="vm=Vr;w+=b") neuron.vm=EL neuron.w=a*(neuron.vm-EL) neuron.Vr=linspace(-48.3*mV,-47.7*mV,N) # bifurcation parameter run(3*second,report='text') # we discard the first spikes M=StateSpikeMonitor(neuron,("Vr","w")) # record Vr and w at spike times run(2*second,report='text') Vr,w=M.values("Vr"),M.values("w") figure() plot(Vr/mV,w/nA,'.k') xlabel('Vr (mV)') ylabel('w (nA)') show() brian-1.4.3/examples/frompapers/Vogels_et_al_2011.py000077500000000000000000000065071266062647100222530ustar00rootroot00000000000000#!/usr/bin/env python ''' Inhibitory synaptic plasticity in a recurrent network model ----------------------------------------------------------- (F. Zenke, 2011) (from the 2012 Brian twister) Adapted from: Vogels, T. P., H. Sprekeler, F. Zenke, C. Clopath, and W. Gerstner. Inhibitory Plasticity Balances Excitation and Inhibition in Sensory Pathways and Memory Networks. Science (November 10, 2011). ''' from brian import * # ########################################### # Defining network model parameters # ########################################### NE = 8000 # Number of excitatory cells NI = NE/4 # Number of inhibitory cells w = 1.*nS # Basic weight unit tau_ampa = 5.0*ms # Glutamatergic synaptic time constant tau_gaba = 10.0*ms # GABAergic synaptic time constant epsilon = 0.02 # Sparseness of synaptic connections eta = 1e-2 # Learning rate tau_stdp = 20*ms # STDP time constant simtime = 10*second # Simulation time # ########################################### # Neuron model # ########################################### gl = 10.0*nsiemens # Leak conductance el = -60*mV # Resting potential er = -80*mV # Inhibitory reversal potential vt = -50.*mV # Spiking threshold memc = 200.0*pfarad # Membrane capacitance bgcurrent = 200*pA # External current eqs_neurons=''' dv/dt=(-gl*(v-el)-(g_ampa*w*v+g_gaba*(v-er)*w)+bgcurrent)/memc : volt dg_ampa/dt = -g_ampa/tau_ampa : 1 dg_gaba/dt = -g_gaba/tau_gaba : 1 ''' # ########################################### # Initialize neuron group # ########################################### neurons=NeuronGroup(NE+NI,model=eqs_neurons,threshold=vt,reset=el,refractory=5*ms) Pe=neurons.subgroup(NE) Pi=neurons.subgroup(NI) # ########################################### # Connecting the network # ########################################### con_e = Connection(Pe,neurons,'g_ampa',weight=0.3,sparseness=epsilon) con_ie = Connection(Pi,Pe,'g_gaba',weight=1e-10,sparseness=epsilon) con_ii = Connection(Pi,Pi,'g_gaba',weight=3,sparseness=epsilon) # ########################################### # Setting up monitors # ########################################### sm = SpikeMonitor(Pe) # ########################################### # Run without plasticity # ########################################### run(1*second) # ########################################### # Inhibitory Plasticity # ########################################### alpha = 3*Hz*tau_stdp*2 # Target rate parameter gmax = 100 # Maximum inhibitory weight eqs_stdp_inhib = ''' dA_pre/dt=-A_pre/tau_stdp : 1 dA_post/dt=-A_post/tau_stdp : 1 ''' stdp_ie = STDP(con_ie, eqs=eqs_stdp_inhib, pre='A_pre+=1.; w+=(A_post-alpha)*eta;', post='A_post+=1.; w+=A_pre*eta;', wmax=gmax) # ########################################### # Run with plasticity # ########################################### run(simtime-1*second,report='text') # ########################################### # Make plots # ########################################### subplot(211) raster_plot(sm,ms=1.) title("Before") xlabel("") xlim(float(0.8*second/ms), float(1*second/ms)) subplot(212) raster_plot(sm,ms=1.) title("After") xlim(float((simtime-0.2*second)/ms), float(simtime/ms)) show() brian-1.4.3/examples/frompapers/Wang_Buszaki_1996.py000077500000000000000000000021661266062647100222560ustar00rootroot00000000000000#!/usr/bin/env python """ Wang-Buszaki model ------------------ J Neurosci. 1996 Oct 15;16(20):6402-13. Gamma oscillation by synaptic inhibition in a hippocampal interneuronal network model. Wang XJ, Buzsaki G. Note that implicit integration (exponential Euler) cannot be used, and therefore simulation is rather slow. """ from brian import * defaultclock.dt=0.01*ms Cm=1*uF # /cm**2 Iapp=2*uA gL=0.1*msiemens EL=-65*mV ENa=55*mV EK=-90*mV gNa=35*msiemens gK=9*msiemens eqs=''' dv/dt=(-gNa*m**3*h*(v-ENa)-gK*n**4*(v-EK)-gL*(v-EL)+Iapp)/Cm : volt m=alpham/(alpham+betam) : 1 alpham=-0.1/mV*(v+35*mV)/(exp(-0.1/mV*(v+35*mV))-1)/ms : Hz betam=4*exp(-(v+60*mV)/(18*mV))/ms : Hz dh/dt=5*(alphah*(1-h)-betah*h) : 1 alphah=0.07*exp(-(v+58*mV)/(20*mV))/ms : Hz betah=1./(exp(-0.1/mV*(v+28*mV))+1)/ms : Hz dn/dt=5*(alphan*(1-n)-betan*n) : 1 alphan=-0.01/mV*(v+34*mV)/(exp(-0.1/mV*(v+34*mV))-1)/ms : Hz betan=0.125*exp(-(v+44*mV)/(80*mV))/ms : Hz ''' neuron=NeuronGroup(1,eqs) neuron.v=-70*mV neuron.h=1 M=StateMonitor(neuron,'v',record=0) run(100*ms,report='text') plot(M.times/ms,M[0]/mV) show() brian-1.4.3/examples/frompapers/computing with neural synchrony/000077500000000000000000000000001266062647100250455ustar00rootroot00000000000000brian-1.4.3/examples/frompapers/computing with neural synchrony/README.txt000066400000000000000000000007001266062647100265400ustar00rootroot00000000000000These are Brian scripts corresponding to the following paper: Brette R (2012). Computing with neural synchrony. PLoS Comp Biol. 8(6): e1002561. doi:10.1371/journal.pcbi.1002561 Files are organized by topic. There can be dependencies within folders, but not across folders. A few scripts need to be run in a specific order, but this is specified in the header of the files. Note that a few simulations are very long (mostly the STDP ones). brian-1.4.3/examples/frompapers/computing with neural synchrony/coincidence detection and synchrony/000077500000000000000000000000001266062647100340075ustar00rootroot00000000000000Fig1C.py000077500000000000000000000014361266062647100352020ustar00rootroot00000000000000brian-1.4.3/examples/frompapers/computing with neural synchrony/coincidence detection and synchrony#!/usr/bin/env python # -*- coding:utf-8 -*- """ Timescale and strength """ from brian import * rc('lines',linewidth=2) rc('font',size=16) rc('xtick',labelsize=16) rc('ytick',labelsize=16) rc('legend',fontsize=16) rc('axes',labelsize=16,titlesize=16) w,h=rcParamsDefault['figure.figsize'] fontsize=16 SNR,timescale,strength=loadtxt('reproducibility.txt') SNR=10*log(SNR)/log(10) #SNR=SNR[1:] #timescale=timescale[1:] #strength=strength[1:] figure(figsize=(w*1.5,h*.5)) subplot(121) plot(SNR,timescale/ms,'k') #plot(SNR,timescale/ms,'r.') plot(SNR,0*SNR+7,'k--') xlabel('SNR (dB)') ylabel('Precision (ms)') subplot(122) plot(SNR,strength*100,'k') ylim(0,100) #plot(SNR,strength*100,'r.') xlabel('SNR (dB)') ylabel('Reliability (%)') savefig('FigC.eps') show() Fig1F_ROC.py000077500000000000000000000060041266062647100357040ustar00rootroot00000000000000brian-1.4.3/examples/frompapers/computing with neural synchrony/coincidence detection and synchrony#!/usr/bin/env python """ Brette R (2012). Computing with neural synchrony. PLoS Comp Biol. 8(6): e1002561. doi:10.1371/journal.pcbi.1002561 ------------------------------------------------------------------------------------------------------------------ Figure 1F. (simulation takes about 10 mins) Coincidence detection is seen as a signal detection problem, that of detecting a given depolarization in a background of noise, within one characteristic time constant. The choice of the spike threshold implements a particular trade-off between false alarms (depolarization was due to noise) and misses (depolarization was not detected). Caption (Fig 1F). Receiver-operation characteristic (ROC) for one level of noise, obtained by varying the threshold (black curve). The hit rate is the probability that the neuron fires within one integration time constant t when depolarized by Dv, and the false alarm rate is the firing probability without depolarization. The corresponding theoretical curve, with sensitivity index d' =Dv/sigma, is shown in red. """ from brian import * from scipy.special import erf def spike_probability(x): # firing probability for unit variance and zero mean, and threshold = x return .5*(1-erf(x/sqrt(2.))) tau_cd=5*ms # membrane time constant (cd for coincidence detector) tau_n=tau_cd # input is an Ornstein-Uhlenbeck process with the same time constant as the membrane time constant T=3*tau_n # neurons are depolarized by w at regular intervales, T is the spacing Nspikes=10000 # number of input spikes T0=T*Nspikes # initial period without inputs, to calculate the false alarm rate N=500 # number of neurons, each neuron has a different threshold between 0. and 3. w=1 # synaptic weight (depolarization) sigma=1. # input noise s.d. sigmav=sigma*sqrt(tau_n/(tau_n+tau_cd)) # noise s.d. on the membrane potential print "d'=",1./sigmav # discriminability index # Integrate-and-fire neurons eqs=''' dv/dt=(sigma*n-v)/tau_cd : 1 dn/dt=-n/tau_n+(2/tau_n)**.5*xi : 1 vt : 1 # spike threshold ''' neurons=NeuronGroup(N,model=eqs,threshold='v>vt',reset='v=0',refractory=tau_cd) neurons.vt=linspace(0.,3,N) # spike threshold varies across neurons counter=SpikeCounter(neurons) # Inputs are regular spikes, starting at T0 input=SpikeGeneratorGroup(1,[(0,n*T+T0) for n in range(Nspikes)]) C=Connection(input,neurons,'v',weight=w) # Calculate the false alarm rate run(T0,report='text') FR=tau_cd*counter.count*1./T0 # Calculate the hit rate counter.reinit() run(Nspikes*T,report='text') HR=counter.count*1./Nspikes-FR*(T-tau_cd)/tau_cd # Prediction based on Gaussian statistics FRpred=spike_probability(neurons.vt/sigmav) HRpred=spike_probability((neurons.vt-w)/sigmav) # Figure plot(FR*100,HR*100,'k') # simulations plot(FRpred*100,HRpred*100,'r') # theoretical predictions plot([0,100],[0,100],'k--') plot([0,100],[50,50],'k--') xlim(0,100) ylim(0,100) xlabel('False alarm rate (%)') ylabel('Hit rate (%)') show() Fig5D_reproducibility.py000077500000000000000000000024031266062647100404730ustar00rootroot00000000000000brian-1.4.3/examples/frompapers/computing with neural synchrony/coincidence detection and synchrony#!/usr/bin/env python ''' Brette R (2012). Computing with neural synchrony. PLoS Comp Biol. 8(6): e1002561. doi:10.1371/journal.pcbi.1002561 ------------------------------------------------------------------------------------------------------------------ Figure 5D, left. Caption (Fig 5D). Responses of a noisy integrate-and-fire model in repeated trials. Protocol: neuron receives input = signal + noise, both O-U processes, signal is identical in all trials (frozen noise). The total variance is held fixed. Signal-to-noise ratio is 3 in this simulation. ''' from brian import * # The common noisy input tau_noise=5*ms input=NeuronGroup(1,model='dx/dt=-x/tau_noise+(2./tau_noise)**.5*xi:1') # The noisy neurons receiving the same input + independent noise tau=10*ms SNR=3. # signal to noise ratio sigma=.5 # total input amplitude Z=sigma*sqrt((tau_noise+tau)/(tau_noise*(SNR**2+1))) # normalizing factor eqs_neurons=''' dx/dt=(Z*(SNR*I+u)-x)/tau:1 du/dt=-u/tau_noise+(2./tau_noise)**.5*xi:1 I : 1 ''' neurons=NeuronGroup(25,model=eqs_neurons,threshold=1,reset=0,refractory=5*ms) neurons.x=rand(25) # random initial conditions neurons.I=linked_var(input,'x') spikes=SpikeMonitor(neurons) run(2*second) # Figure raster_plot(spikes) show() Fig5E_precision_reliability.py000077500000000000000000000066261266062647100416620ustar00rootroot00000000000000brian-1.4.3/examples/frompapers/computing with neural synchrony/coincidence detection and synchrony#!/usr/bin/env python # -*- coding:utf-8 -*- """ Brette R (2012). Computing with neural synchrony. PLoS Comp Biol. 8(6): e1002561. doi:10.1371/journal.pcbi.1002561 ------------------------------------------------------------------------------------------------------------------ Figure 5E. (very long simulation) Caption (Fig 5E). Precision and reliability of spike timing as a function of SNR. Simulations are run in parallel on all cores but one. """ from brian import * import multiprocessing def autocor(PSTH,N=None,T=20*ms,bin=None): ''' Autocorrelogram of PSTH, to calculate a shuffled autocorrelogram N = number of spike trains T = temporal window bin = PSTH bin The baseline is not subtracted. Returns times,SAC ''' if bin is None: bin=defaultclock.dt n=len(PSTH) p=int(T/bin) SAC=zeros(p) if N is None: SAC[0]=mean(PSTH*PSTH) else: # correction to exclude self-coincidences PSTHnoself=clip(PSTH-1./(bin*N),0,Inf) SAC[0]=mean(PSTH*PSTHnoself)*N/(N-1.) SAC[1:]=[mean(PSTH[:-i]*PSTH[i:]) for i in range(1,p)] SAC=hstack((SAC[::-1],SAC[1:])) return (arange(len(SAC))-len(SAC)/2)*bin,SAC def halfwidth(x): ''' Returns half-width of function given by x in bin numbers. This is used to calculate the precision (left panel). ''' M,n=max(x),argmax(x) return find(x[n:]=N) & (i=2*N) & (i<2*N+25)]) plot(t,array(i)-2*N,'.b') ylim(0,75) xlabel('Time (s)') ylabel('Trials') # Cross-correlograms (CC) width=100*ms bin=1*ms spikes=spikes.spiketimes C_AB=correlogram(spikes[0],spikes[N],width=width,T=duration) for i in range(1,N): C_AB+=correlogram(spikes[i],spikes[N+i],width=width,T=duration) C_AC=correlogram(spikes[0],spikes[2*N],width=width,T=duration) for i in range(1,N): C_AC+=correlogram(spikes[i],spikes[2*N+i],width=width,T=duration) # Shuffled auto-correlogram (SAC) C=0*C_AB for i in range(0,N): for j in range(0,N): if i!=j: C+=correlogram(spikes[i],spikes[j],width=width,T=duration) lag=(arange(len(C))-len(C)/2)*bin subplot(223) # Fig. 6C plot(lag/ms,C/(bin*N*(N-1)),'k') ylim(0,1.1*max(C_AB/(bin*N))) xlabel('Lag (ms)') ylabel('Coincidences') subplot(224) # Fig. 6D plot(lag/ms,C_AB/(bin*N),'k') # A vs. B plot(lag/ms,C_AC/(bin*N),'r') # A vs. C ylim(0,1.1*max(C_AB/(bin*N))) xlabel('Lag (ms)') ylabel('Coincidences') show() brian-1.4.3/examples/frompapers/computing with neural synchrony/duration selectivity/000077500000000000000000000000001266062647100312175ustar00rootroot00000000000000Fig1A_rebound_neurons.py000077500000000000000000000044021266062647100356730ustar00rootroot00000000000000brian-1.4.3/examples/frompapers/computing with neural synchrony/duration selectivity#!/usr/bin/env python # -*- coding:utf-8 -*- """ Brette R (2012). Computing with neural synchrony. PLoS Comp Biol. 8(6): e1002561. doi:10.1371/journal.pcbi.1002561 ------------------------------------------------------------------------------------------------------------------ Figure 1A, B. Caption (Fig. 1A,B) A, When neuron A is hyperpolarized by an inhibitory input (top), its low-voltage-activated K channels slowly close (bottom), which makes the neuron fire when inhibition is released (neuron models are used in this and other figures). B, Spike latency is negatively correlated with the duration of inhibition (black line). """ from brian import * # Parameters and equations of the rebound neurons Vt=-55*mV Vr=-70*mV El=-35*mV EK=-90*mV Va=Vr ka=5*mV gmax=1 gmax2=2 tau=20*ms ginh_max=5. tauK=400*ms tauK2=100*ms N=100 # number of neurons (= different durations, for plot 1B) plotted_neuron=N/4 rest_time=1*second # initial time (to start at equilibrium) tmin=rest_time-20*ms # for plots tmax=rest_time+600*ms eqs=''' dv/dt=(El-v+(gmax*gK+gmax2*gK2+ginh)*(EK-v))/tau : volt dgK/dt=(gKinf-gK)/tauK : 1 # IKLT dgK2/dt=-gK2/tauK2 : 1 # Delayed rectifier gKinf=1./(1+exp((Va-v)/ka)) : 1 duration : second # duration of inhibition, varies across neurons ginh = ginh_max*((t>rest_time) & (t<(rest_time+duration))) : 1 ''' neurons=NeuronGroup(N,model=eqs,threshold='v>Vt',reset='v=Vr;gK2=1') neurons.v=Vr neurons.gK=1./(1+exp((Va-El)/ka)) neurons.duration=linspace(100*ms,1*second,N) M=StateMonitor(neurons,'v',record=plotted_neuron) Mg=StateMonitor(neurons,'gK',record=plotted_neuron) spikes=SpikeMonitor(neurons) run(rest_time+1.1*second) M.insert_spikes(spikes) # draw spikes for a nicer display # Figure subplot(221) # Fig. 1A, top plot((M.times-tmin)/ms,M[plotted_neuron]/mV,'k') xlim(0,(tmax-tmin)/ms) ylabel('V (mV)') subplot(223) # Fig. 1A, bottom plot((Mg.times-tmin)/ms,Mg[plotted_neuron],'k') xlim(0,(tmax-tmin)/ms) xlabel('Time (ms)') ylabel('g/gmax') subplot(122) # Fig. 1B times=array([t-neurons.duration[i]*second-rest_time for i,t in spikes.spikes]) duration=array([neurons.duration[i]*second for i,_ in spikes.spikes]) plot(duration/ms,times/ms,'k') xlabel('Duration (ms)') ylabel('Latency (ms)') show() Fig1D_duration_selectivity.py000077500000000000000000000052571266062647100367510ustar00rootroot00000000000000brian-1.4.3/examples/frompapers/computing with neural synchrony/duration selectivity#!/usr/bin/env python # -*- coding:utf-8 -*- """ Brette R (2012). Computing with neural synchrony. PLoS Comp Biol. 8(6): e1002561. doi:10.1371/journal.pcbi.1002561 ------------------------------------------------------------------------------------------------------------------ Figure 1C,D. Duration selectivity. (takes about 1 min) Caption (Fig. 1C,D). A postsynaptic neuron receives inputs from A and B. It is more likely to fire when the stimulus in the synchrony receptive field of A and B. """ from brian import * # Parameters and equations of the rebound neurons Vt=-55*mV Vr=-70*mV El=-35*mV EK=-90*mV Va=Vr ka=5*mV gmax2=2 tau=20*ms ginh_max=5. tauK2=100*ms N=10000 # number of neurons (= different durations) rest_time=1*second # initial time tmin=rest_time-20*ms #for plots tmax=rest_time+600*ms eqs=''' dv/dt=(El-v+(gmax*gK+gmax2*gK2+ginh)*(EK-v))/tau : volt dgK/dt=(gKinf-gK)/tauK : 1 # IKLT dgK2/dt=-gK2/tauK2 : 1 # Delayed rectifier gKinf=1./(1+exp((Va-v)/ka)) : 1 duration : second ginh = ginh_max*((t>rest_time) & (t<(rest_time+duration))) : 1 tauK : ms gmax : 1 theta : volt # threshold ''' neurons=NeuronGroup(2*N,model=eqs,threshold='v>theta',reset='v=Vr;gK2=1') neurons.v=Vr neurons.theta=Vt neurons.gK=1./(1+exp((Va-El)/ka)) # Neuron A, duplicated to simulate multiple input durations simultaneously neuronsA=neurons[:N] neuronsA.tauK=400*ms neuronsA.gmax=1 neuronsA.theta=-55*mV neuronsA.duration=linspace(100*ms,1*second,N) # Neuron B, duplicated to simulate multiple input durations simultaneously neuronsB=neurons[N:] neuronsB.tauK=100*ms neuronsB.gmax=1.5 neuronsB.theta=-54*mV neuronsB.duration=linspace(100*ms,1*second,N) # Noisy coincidence detectors tau_cd=5*ms tau_n=tau_cd sigma=0.2 # noise s.d. in units of the threshold eqs_post=''' dv/dt=(n-v)/tau_cd : 1 dn/dt=-n/tau_n+sigma*(2/tau_n)**.5*xi : 1 ''' postneurons=NeuronGroup(N,model=eqs_post,threshold=1,reset=0) CA=IdentityConnection(neuronsA,postneurons,'v',weight=0.5) CB=IdentityConnection(neuronsB,postneurons,'v',weight=0.5) spikes=SpikeCounter(postneurons) M=StateMonitor(postneurons,'v',record=N/3) run(rest_time+1.1*second,report='text') # Figure subplot(121) # Fig. 1C, example trace plot(M.times/ms,M[N/3],'k') xlim(1350,1500) ylim(-.3,1) xlabel('Time (ms)') ylabel('V') subplot(122) # Fig. 1D, duration tuning curve count=spikes.count # Smooth the tuning curve window=200 rate=zeros(len(count)-window) for i in range(0,len(count)-window): rate[i]=mean(count[i:i+window]) plot((neuronsA.duration[window/2:-window/2]/ms)[::10],rate[::10],'k') xlim(0,1000) ylim(0,0.5) xlabel('Duration (ms)') ylabel('Spiking probability') show() Fig2A_synchrony_partition.py000077500000000000000000000105511266062647100366140ustar00rootroot00000000000000brian-1.4.3/examples/frompapers/computing with neural synchrony/duration selectivity#!/usr/bin/env python # -*- coding:utf-8 -*- """ Brette R (2012). Computing with neural synchrony. PLoS Comp Biol. 8(6): e1002561. doi:10.1371/journal.pcbi.1002561 ------------------------------------------------------------------------------------------------------------------ Figure 2A. Synchrony partition for duration selective neurons. Caption (Fig. 2A) Decoding synchrony patterns in a heterogeneous population. Color represents the latency of the spike produced by each neuron responding to the stimulus (white if the neuron did not spike). Thus, neurons with the same color are synchronous for that specific stimulus (duration). The population can be divided in groups of synchronous neurons (i.e., with the same color), forming the "synchrony partition". Circled neurons belong to the same synchronous group. This script calculates and displays the synchrony partition for one particular duration. It also saves the results in file, that is required by the script Fig2C_decoding_synchrony. The synchrony partition is calculated empirically, by simulating the responses of the neurons at the specific inhibitory duration and grouping neurons that respond in synchrony (+- 2 ms). """ from brian import * from numpy.random import seed from params import * from pylab import cm # Graphics radius=.15 selected_neuron=7 # Parameters ginh_max=5. Nx=5 # number of neurons per row N=Nx*Nx # number of neurons rest_time=1*second # initial time duration=500*ms delta_t=2*ms # Size of synchronous groups (maximum time difference) # Duration-selective neurons eqs=''' dv/dt=(El-v+(gmax*gK+gmax2*gK2+ginh)*(EK-v))/tau : volt dgK/dt=(gKinf-gK)/tauK : 1 # IKLT dgK2/dt=-gK2/tauK2 : 1 # Delayed rectifier gKinf=1./(1+exp((Va-v)/ka)) : 1 ginh = ginh_max*((t>rest_time) & (t<(rest_time+duration))) : 1 tauK : ms tau : ms gmax : 1 ''' uniform=lambda N:(rand(N)-.5)*2 # uniform between -1 and 1 seed(31418) # Get the same neurons every time neurons=NeuronGroup(N,model=eqs,threshold='v>Vt',reset='v=Vr;gK2=1') neurons.v=Vr neurons.gK=1./(1+exp((Va-El)/ka)) neurons.tauK=400*ms+uniform(N)*tauK_spread alpha=(El-Vt)/(Vt-EK) neurons.gmax=alpha*(minx+(maxx-minx)*rand(N)) neurons.tau=30*ms+uniform(N)*tau_spread spikes=SpikeMonitor(neurons) run(rest_time+1.1*second) # Calculate first spike time of each neuron times=zeros(N) # First spike time of each neuron times[:]=Inf # Inf means: no response, or response before the start of the stimulus blacklist=[] # neurons that fire spontaneously for i,t in spikes.spikes: if times[i]==Inf: times[i]=t-duration-rest_time if times[i]<0: blacklist.append(i) times[blacklist]=Inf tmin,tmax=min(times[times!=Inf]),max(times[times!=Inf]) # Color of each neuron between 0 and 1 color=(times-tmin)/(tmax+1e-10-tmin) # (to avoid zero division) # Assign groups; each responding neuron gets a group number group_size=delta_t/(tmax-tmin) # size of a group, as a proportion of the timing range group_number=array(color/group_size,dtype=int) group_number[color==Inf]=-1 # Get the size of each group count=zeros(max(group_number)+1) # number of neurons in each group for i in range(len(group_number)): if group_number[i]!=-1: count[group_number[i]]+=1 selected_group=group_number[selected_neuron] # Display the synchrony partition (Fig. 2A) axes(frameon=False) axis('scaled') xticks([]) yticks([]) i=0 for y in linspace(0,1,Nx): for x in linspace(0,1,Nx): if color[i]!=Inf: if group_number[i]==selected_group: w=4 ec="k" # edge color else: w=1 ec='k' cir=Circle((x,y),radius,fc=cm.jet(color[i]),linewidth=w,ec=ec) else: cir=Circle((x,y),radius,fc='w') i+=1 gca().add_patch(cir) xlim(0-2*radius,1+2*radius) ylim(0-2*radius,1+2*radius) # Remove groups with fewer than two neurons and recalculate group numbers for i in range(len(group_number)): if group_number[i]>=0: if count[group_number[i]]>=2: group_number[i]=sum(count[:group_number[i]]>=2) else: group_number[i]=-1 # Save assignment to groups f=open('groups'+str(int(duration/ms))+'.txt','w') f.write(' '.join([str(x) for x in group_number])) f.close() show() Fig2C_decoding_synchrony.py000077500000000000000000000067751266062647100363760ustar00rootroot00000000000000brian-1.4.3/examples/frompapers/computing with neural synchrony/duration selectivity#!/usr/bin/env python # -*- coding:utf-8 -*- """ Brette R (2012). Computing with neural synchrony. PLoS Comp Biol. 8(6): e1002561. doi:10.1371/journal.pcbi.1002561 ------------------------------------------------------------------------------------------------------------------ Figure 2C. Decoding synchrony patterns. Caption (Fig. 2C). Activation of the postsynaptic assembly as a function of duration (grey: individual neurons; black: average). The script Fig2A_synchrony_partition must be run first (it produces a file). """ from brian import * from numpy.random import seed from params import * from pylab import cm Ndur=2000*5 # number of stimulus durations (durations are multiplexed) sigma=0.1 # More/less noise best_duration=500*ms # Read group index for each neuron f=open('groups'+str(int(best_duration/ms))+'.txt') group_number=array([int(x) for x in f.read().split(' ')]) nneurons=max(group_number)+1 # one postsynaptic neuron per group f.close() # Calculate group size count=zeros(nneurons) # number of presynaptic neurons for each postsynaptic neuron for i in range(len(group_number)): if group_number[i]!=-1: count[group_number[i]]+=1 # Presynaptic neurons ginh_max=5. Nx=5 # number of neurons per row N=Nx*Nx # number of neurons rest_time=1*second # initial time eqs=''' dv/dt=(El-v+(gmax*gK+gmax2*gK2+ginh)*(EK-v))/tau : volt dgK/dt=(gKinf-gK)/tauK : 1 # IKLT dgK2/dt=-gK2/tauK2 : 1 # Delayed rectifier gKinf=1./(1+exp((Va-v)/ka)) : 1 ginh = ginh_max*((t>rest_time) & (t<(rest_time+duration))) : 1 tauK : ms tau : ms gmax : 1 duration : second ''' uniform=lambda N:(rand(N)-.5)*2 #uniform between -1 and 1 seed(31418) # Get the same neurons every time _tauK=400*ms+uniform(N)*tauK_spread alpha=(El-Vt)/(Vt-EK) _gmax=alpha*(minx+(maxx-minx)*rand(N)) _tau=30*ms+uniform(N)*tau_spread neurons=NeuronGroup(N*Ndur,model=eqs,threshold='v>Vt',reset='v=Vr;gK2=1') neurons.v=Vr neurons.gK=1./(1+exp((Va-El)/ka)) # Postsynaptic neurons (noisy coincidence detectors) eqs_post=''' dv/dt=(n-v)/tau_cd : 1 dn/dt=-n/tau_n+sigma*(2/tau_n)**.5*xi : 1 ''' postneurons=NeuronGroup(Ndur*nneurons,model=eqs_post,threshold=1,reset=0) C=Connection(neurons,postneurons) # Divide into subgroups, each group corresponds to one postsynaptic neuron with all stimulus durations postgroup=[] for i in range(nneurons): postgroup.append(postneurons.subgroup(Ndur)) # Connections according to the synchrony partition group=[] for i in range(N): group.append(neurons.subgroup(Ndur)) group[i].tauK=_tauK[i] group[i].gmax=_gmax[i] group[i].tau=_tau[i] group[i].duration=linspace(100*ms,1*second,Ndur) if group_number[i]>=0: C.connect_one_to_one(group[i],postgroup[group_number[i]],weight=1./count[group_number[i]]) spikes=SpikeCounter(postneurons) run(rest_time+1.1*second,report='text') # Figure (2C) window=100*5 # smoothing window rate=zeros(Ndur-window) totrate=zeros(Ndur-window) for i in range(nneurons): # display tuning curve for each neuron, in grey count=spikes.count[i*Ndur:(i+1)*Ndur] # Smooth for j in range(0,len(count)-window): rate[j]=mean(count[j:j+window]) totrate+=rate if i<5: # plot only 5 individual curves plot((group[0].duration[window/2:-window/2]/ms),rate,'grey',linewidth=1) # Mean tuning curve plot((group[0].duration[window/2:-window/2]/ms),totrate/nneurons,'k',linewidth=2) xlim(100,600) ylim(0,0.5) xlabel('Duration (ms)') ylabel('Spiking probability') show() Fig4_duration_stdp.py000077500000000000000000000056401266062647100352520ustar00rootroot00000000000000brian-1.4.3/examples/frompapers/computing with neural synchrony/duration selectivity#!/usr/bin/env python """ Brette R (2012). Computing with neural synchrony. PLoS Comp Biol. 8(6): e1002561. doi:10.1371/journal.pcbi.1002561 ------------------------------------------------------------------------------------------------------------------ Figure 4D. STDP in the duration model. (very long simulation, default: 5000 stimuli) Caption (Fig. 4D). Temporal evolution of the synaptic weights of the neuron corresponding to the blue curves in Fig. 4C. The script runs the simulation with STDP for a long time, then displays the evolution of synaptic weights for one neuron. """ from brian import * from pylab import cm from numpy.random import seed from brian.experimental.connectionmonitor import * import numpy from params import * # Rebound neurons eqs=''' dv/dt=(El-v+(gmax*gK+gmax2*gK2+ginh)*(EK-v))/tau : volt dgK/dt=(gKinf-gK)/tauK : 1 # IKLT dgK2/dt=-gK2/tauK2 : 1 # Delayed rectifier gKinf=1./(1+exp((Va-v)/ka)) : 1 tauK : ms tau : ms gmax : 1 ginh : 1 ''' uniform=lambda N:(rand(N)-.5)*2 #uniform between -1 and 1 seed(31415) # Get the same neurons every time neurons=NeuronGroup(N,model=eqs,threshold='v>Vt',reset='v=Vr;gK2=1') neurons.v=Vr neurons.gK=1./(1+exp((Va-El)/ka)) neurons.tauK=400*ms+uniform(N)*tauK_spread alpha=(El-Vt)/(Vt-EK) neurons.gmax=alpha*(minx+(maxx-minx)*rand(N)) neurons.tau=30*ms+uniform(N)*tau_spread # Store the value of state variables at rest print "Calculate resting state" run(2*second) rest=zeros(neurons._S.shape) rest[:]=neurons._S # Postsynaptic neurons (noisy coincidence detectors) eqs_post=''' dv/dt=(n-v)/tau_cd : 1 dn/dt=-n/tau_n+sigma*(2/tau_n)**.5*xi : 1 ''' postneurons=NeuronGroup(Nout,model=eqs_post,threshold=1,reset=0,refractory=refractory) # Random connections between pre and post-synaptic neurons C=Connection(neurons,postneurons,'v',sparseness=Nsynapses*1./N,weight=w0) # STDP eqs_stdp=''' dApre/dt=-Apre/tau_pre : 1 Apost : 1 ''' pre=''' Apre+=a_pre w+=0 #b_pre*w ''' post=''' Apost+=0 w+=Apre+b_post*w ''' stdp=STDP(C,eqs_stdp,pre=pre,post=post,wmax=Inf) # Record the evolution of synaptic weights MC=ConnectionMonitor(C,store=True,clock=EventClock(dt=record_period)) print "Learning..." # Series of inhibitory pulses for i in range(Npulses): print "pulse",i+1 duration=200*ms+rand()*300*ms # random stimulus duration neurons.ginh=ginh_max run(duration) C.W.alldata[:]=C.W.alldata+C.W.alldata*b_pre # homeostasis (synaptic scaling) neurons.ginh=0 run(rest_time) # let neurons spike neurons._S[:]=rest # reset (to save time) # Figure (4D) neuron=0 wsave=[(t,M.todense()) for (t,M) in MC.values] W=array(zip(*wsave)[1]) weights=W[:,:,neuron] # Evolution of all synaptic weights for this neuron for i in range(weights.shape[1]): plot(arange(len(weights[:,i,]))*record_period,weights[:,i,],'k') xlim(0,weights.shape[0]*float(record_period)) ylim(0,1) show() brian-1.4.3/examples/frompapers/computing with neural synchrony/duration selectivity/groups500.txt000066400000000000000000000000771266062647100335300ustar00rootroot000000000000003 1 2 -1 3 -1 0 3 2 -1 -1 1 2 -1 -1 -1 -1 -1 0 -1 -1 -1 2 -1 -1brian-1.4.3/examples/frompapers/computing with neural synchrony/duration selectivity/params.py000077500000000000000000000016671266062647100330710ustar00rootroot00000000000000#!/usr/bin/env python ''' Brette R (2012). Computing with neural synchrony. PLoS Comp Biol. 8(6): e1002561. doi:10.1371/journal.pcbi.1002561 ------------------------------------------------------------------------------------------------------------------ Duration selectivity, parameters ''' from brian import * # Simulation control Npulses=5000 Ntest=20 record_period=15*second rest_time=200*ms # Encoding neurons Vt=-55*mV Vr=-70*mV El=-35*mV EK=-90*mV Va=Vr ka=5*mV gmax2=2 tauK2=300*ms N=100 # number of encoding neurons Nout=30 # number of decoding neurons ginh_max=5. tauK_spread=200*ms tau_spread=20*ms minx=1.7 # range of gmax for K+ maxx=2.5 # Coincidence detectors sigma=0.1 # noise s.d. tau_cd=5*ms tau_n=tau_cd # slow noise refractory=0*ms # Connections Nsynapses=5 # synapses per neuron w0=lambda i,j:rand() # STDP factor=0.05 a_pre=.06*factor b_post=-1.*factor b_pre=0.1*factor tau_pre=tau_cd brian-1.4.3/examples/frompapers/computing with neural synchrony/hearing/000077500000000000000000000000001266062647100264625ustar00rootroot00000000000000Fig12A_Goodman_Brette_2010.py000077500000000000000000000101131266062647100333400ustar00rootroot00000000000000brian-1.4.3/examples/frompapers/computing with neural synchrony/hearing#!/usr/bin/env python ''' Sound localization with HRTFs ----------------------------- Goodman DF and R Brette (2010). Spike-timing-based computation in sound localization. PLoS Comp Biol 6(11): e1000993. doi:10.1371/journal.pcbi.1000993. Corresponds to Fig. 12A in: Brette R (2012). Computing with neural synchrony. PLoS Comp Biol. 8(6): e1002561. doi:10.1371/journal.pcbi.1002561 Simplified version of the "ideal" sound localisation model. The sound is played at a particular spatial location (indicated on the final plot by a red +). Each location has a corresponding assembly of neurons, whose summed firing rates give the sizes of the blue circles in the plot. The most strongly responding assembly is indicated by the green x, which is the estimate of the location by the model. ''' from brian import * from brian.hears import * # Download the IRCAM database # http://recherche.ircam.fr/equipes/salles/listen/download.html # and replace this filename with the location you downloaded it to hrtfdb = IRCAM_LISTEN(r'Z:\HRTF\IRCAM') subject = 1002 hrtfset = hrtfdb.load_subject(subject) # This gives the number of spatial locations in the set of HRTFs num_indices = hrtfset.num_indices # Choose a random location for the sound to come from index = randint(num_indices) # A sound to test the model with sound = Sound.whitenoise(500*ms) # This is the specific HRTF for the chosen location hrtf = hrtfset.hrtf[index] # We apply the chosen HRTF to the sound, the output has 2 channels hrtf_fb = hrtf.filterbank(sound) # We swap these channels (equivalent to swapping the channels in the # subsequent filters, but simpler to do it with the inputs) swapped_channels = RestructureFilterbank(hrtf_fb, indexmapping=[1, 0]) # Now we apply all of the possible pairs of HRTFs in the set to these # swapped channels, which means repeating them num_indices times first hrtfset_fb = hrtfset.filterbank(Repeat(swapped_channels, num_indices)) # Now we apply cochlear filtering (logically, this comes before the HRTF # filtering, but since convolution is commutative it is more efficient to # do the cochlear filtering afterwards cfmin, cfmax, cfN = 150*Hz, 5*kHz, 40 cf = erbspace(cfmin, cfmax, cfN) # We repeat each of the HRTFSet filterbank channels cfN times, so that # for each location we will apply each possible cochlear frequency gfb = Gammatone(Repeat(hrtfset_fb, cfN), tile(cf, hrtfset_fb.nchannels)) # Half wave rectification and compression cochlea = FunctionFilterbank(gfb, lambda x:15*clip(x, 0, Inf)**(1.0/3.0)) # Leaky integrate and fire neuron model eqs = ''' dV/dt = (I-V)/(1*ms)+0.1*xi/(0.5*ms)**.5 : 1 I : 1 ''' G = FilterbankGroup(cochlea, 'I', eqs, reset=0, threshold=1, refractory=5*ms) # The coincidence detector (cd) neurons cd = NeuronGroup(num_indices*cfN, eqs, reset=0, threshold=1, clock=G.clock) # Each CD neuron receives precisely two inputs, one from the left ear and # one from the right, for each location and each cochlear frequency C = Connection(G, cd, 'V') for i in xrange(num_indices*cfN): C[i, i] = 0.5 # from right ear C[i+num_indices*cfN, i] = 0.5 # from left ear # We want to just count the number of CD spikes counter = SpikeCounter(cd) # Run the simulation, giving a report on how long it will take as we run run(sound.duration, report='stderr') # We take the array of counts, and reshape them into a 2D array which we sum # across frequencies to get the spike count of each location-specific assembly count = counter.count count.shape = (num_indices, cfN) count = sum(count, axis=1) count = array(count, dtype=float)/amax(count) # Our guess of the location is the index of the strongest firing assembly index_guess = argmax(count) # Now we plot the output, using the coordinates of the HRTFSet coords = hrtfset.coordinates azim, elev = coords['azim'], coords['elev'] scatter(azim, elev, 100*count) plot([azim[index]], [elev[index]], '+r', ms=15, mew=2) plot([azim[index_guess]], [elev[index_guess]], 'xg', ms=15, mew=2) xlabel('Azimuth (deg)') ylabel('Elevation (deg)') xlim(-5, 350) ylim(-50, 95) show() brian-1.4.3/examples/frompapers/computing with neural synchrony/hearing/Fig7A_Jeffress.py000077500000000000000000000037611266062647100315720ustar00rootroot00000000000000#!/usr/bin/env python ''' Brette R (2012). Computing with neural synchrony. PLoS Comp Biol. 8(6): e1002561. doi:10.1371/journal.pcbi.1002561 ------------------------------------------------------------------------------------------------------------------ Figure 7A. Jeffress model, adapted with spiking neuron models. A sound source (white noise) is moving around the head. Delay differences between the two ears are used to determine the azimuth of the source. Delays are mapped to a neural place code using delay lines (each neuron receives input from both ears, with different delays). ''' from brian import * defaultclock.dt = .02 * ms dt = defaultclock.dt # Sound sound = TimedArray(10 * randn(50000)) # white noise # Ears and sound motion around the head (constant angular speed) sound_speed = 300 * metre / second interaural_distance = 20 * cm # big head! max_delay = interaural_distance / sound_speed print "Maximum interaural delay:", max_delay angular_speed = 2 * pi * radian / second # 1 turn/second tau_ear = 1 * ms sigma_ear = .05 eqs_ears = ''' dx/dt=(sound(t-delay)-x)/tau_ear+sigma_ear*(2./tau_ear)**.5*xi : 1 delay=distance*sin(theta) : second distance : second # distance to the centre of the head in time units dtheta/dt=angular_speed : radian ''' ears = NeuronGroup(2, model=eqs_ears, threshold=1, reset=0, refractory=2.5 * ms) ears.distance = [-.5 * max_delay, .5 * max_delay] traces = StateMonitor(ears, 'x', record=True) # Coincidence detectors N = 300 tau = 1 * ms sigma = .05 eqs_neurons = ''' dv/dt=-v/tau+sigma*(2./tau)**.5*xi : 1 ''' neurons = NeuronGroup(N, model=eqs_neurons, threshold=1, reset=0) synapses = Connection(ears, neurons, 'v', structure='dense', delay=True, max_delay=1.1 * max_delay) synapses.connect_full(ears, neurons, weight=.5) synapses.delay[0, :] = linspace(0 * ms, 1.1 * max_delay, N) synapses.delay[1, :] = linspace(0 * ms, 1.1 * max_delay, N)[::-1] spikes = SpikeMonitor(neurons) run(1000 * ms) raster_plot(spikes) show() brian-1.4.3/examples/frompapers/computing with neural synchrony/hearing/Fig7B_Licklider.py000077500000000000000000000031671266062647100317260ustar00rootroot00000000000000#!/usr/bin/env python ''' Brette R (2012). Computing with neural synchrony. PLoS Comp Biol. 8(6): e1002561. doi:10.1371/journal.pcbi.1002561 ------------------------------------------------------------------------------------------------------------------ Figure 12B. Spike-based adaptation of Licklider's model of pitch processing (autocorrelation with delay lines). ''' from brian import * defaultclock.dt = .02 * ms # Ear and sound max_delay = 20 * ms # 50 Hz tau_ear = 1 * ms sigma_ear = .1 eqs_ear = ''' dx/dt=(sound-x)/tau_ear+sigma_ear*(2./tau_ear)**.5*xi : 1 sound=5*sin(2*pi*frequency*t)**3 : 1 # nonlinear distorsion #sound=5*(sin(4*pi*frequency*t)+.5*sin(6*pi*frequency*t)) : 1 # missing fundamental frequency=(200+200*t*Hz)*Hz : Hz # increasing pitch ''' receptors = NeuronGroup(2, model=eqs_ear, threshold=1, reset=0, refractory=2 * ms) traces = StateMonitor(receptors, 'x', record=True) sound = StateMonitor(receptors, 'sound', record=0) # Coincidence detectors min_freq = 50 * Hz max_freq = 1000 * Hz N = 300 tau = 1 * ms sigma = .1 eqs_neurons = ''' dv/dt=-v/tau+sigma*(2./tau)**.5*xi : 1 ''' neurons = NeuronGroup(N, model=eqs_neurons, threshold=1, reset=0) synapses = Connection(receptors, neurons, 'v', structure='dense', max_delay=1.1 * max_delay, delay=True) synapses.connect_full(receptors, neurons, weight=.5) synapses.delay[1, :] = 1. / exp(linspace(log(min_freq / Hz), log(max_freq / Hz), N)) spikes = SpikeMonitor(neurons) run(500 * ms) raster_plot(spikes) ylabel('Frequency') yticks([0, 99, 199, 299], array(1. / synapses.delay.todense()[1, [0, 99, 199, 299]], dtype=int)) show() brian-1.4.3/examples/frompapers/computing with neural synchrony/olfaction/000077500000000000000000000000001266062647100270235ustar00rootroot00000000000000Fig11B_olfaction_stdp_learning.py000077500000000000000000000063461266062647100352520ustar00rootroot00000000000000brian-1.4.3/examples/frompapers/computing with neural synchrony/olfaction#!/usr/bin/env python ''' Brette R (2012). Computing with neural synchrony. PLoS Comp Biol. 8(6): e1002561. doi:10.1371/journal.pcbi.1002561 ------------------------------------------------------------------------------------------------------------------ Figure 11B. Learning to recognize odors. (long simulation) Caption (Fig. 11B). After learning, responses of postsynaptic neurons, ordered by tuning ratio, to odor A (blue) and odor B (red), with an increasing concentration (0.1 to 10, where 1 is odor concentration in the learning phase). After this script, run the other file: Fig11B_olfaction_stdp_testing.py. ''' from brian import * from params import * from brian.experimental.connectionmonitor import * import numpy bmin,bmax=-7,-1 def odor(N): # Returns a random vector of binding constants return 10**(rand(N)*(bmax-bmin)+bmin) def hill_function(c,K=1.,n=3.): ''' Hill function: * c = concentration * K = half activation constant (choose K=1 for relative concentrations) * n = Hill coefficient ''' return (c**n)/(c**n+K**n) N=5000 # number of receptors seed(31415) # Get the same neurons every time intensity=3000. # Odor plumes tau_plume=75*ms eq_plumes=''' dx/dt=-x/tau_plume+(2./tau_plume)**.5*xi : 1 y=clip(x,0,inf) : 1 ''' plume=NeuronGroup(1,model=eq_plumes) # 1 odor # Receptor neurons Fmax=40*Hz # maximum firing rate tau=20*ms Imax=1/(1-exp(-1/(Fmax*tau))) # maximum input current eq_receptors=''' dv/dt=(Imax*hill_function(c)-v)/tau : 1 c : 1 # concentrations (relative to activation constant) ''' receptors=NeuronGroup(N,model=eq_receptors,threshold=1,reset=0) @network_operation def odor_to_nose(): # Send odor plume to the receptors receptors.c=I1*c1*clip(plume.x[0],0,Inf) odors=[odor(N),odor(N)] # two odors c1=odors[0] stimuli=[] # A random odor is presented every 200 ms @network_operation(clock=EventClock(dt=200*ms)) def change_odor(): global c1 nodor=randint(len(odors)) c1=odors[nodor] stimuli.append((float(defaultclock.t),float(nodor))) # Decoder neurons M=30 eq_decoders=''' dv/dt=-v/taud + sigma*(2/taud)**.5*xi : 1 ''' decoders=NeuronGroup(M,model=eq_decoders,threshold=1,reset=0) S2=SpikeMonitor(decoders) # Random synapses syn=Connection(receptors,decoders,'v',sparseness=Nsynapses*1./N,weight=w0) # STDP eqs_stdp=''' dApre/dt=-Apre/tau_pre : 1 Apost : 1 ''' pre=''' Apre+=a_pre #w+=0 ''' post=''' Apost+=0 w+=Apre+b_post*w ''' stdp=STDP(syn,eqs_stdp,pre=pre,post=post,wmax=Inf) MC=ConnectionMonitor(syn,store=True,clock=EventClock(dt=record_period)) @network_operation(EventClock(dt=IP_period)) def intrinsic_plasticity(): # synaptic scaling # Increases weights of all synapses syn.W.alldata+=syn.W.alldata*IP_rate*IP_period # Record the evolution of weights weights=[] @network_operation(EventClock(dt=record_period)) def recordW(): Z=syn.W[:,0].copy() weights.append(Z) I1=intensity print "Started" run(duration,report="text") # Save data wsave=[(t,M.todense()) for (t,M) in MC.values] numpy.save("weights.npy",array(zip(*wsave)[1])) # 3D array (t,i,j) numpy.save("spikesout.npy",array(S2.spikes)) numpy.save("stimuli.npy",array(stimuli)) Fig11B_olfaction_stdp_testing.py000077500000000000000000000072451266062647100351270ustar00rootroot00000000000000brian-1.4.3/examples/frompapers/computing with neural synchrony/olfaction#!/usr/bin/env python ''' Brette R (2012). Computing with neural synchrony. PLoS Comp Biol. 8(6): e1002561. doi:10.1371/journal.pcbi.1002561 ------------------------------------------------------------------------------------------------------------------ Figure 11B. Learning to recognize odors. Caption (Fig. 11B). After learning, responses of postsynaptic neurons, ordered by tuning ratio, to odor A (blue) and odor B (red), with an increasing concentration (0.1 to 10, where 1 is odor concentration in the learning phase). Run the other file first: Fig11B_olfaction_stdp_learning.py ''' from brian import * from params import * import numpy from scipy.sparse import lil_matrix bmin,bmax=-7,-1 # Loads information from the STDP simulation t,odor=numpy.load("stimuli.npy").T W=numpy.load("weights.npy") spikes_out=numpy.load("spikesout.npy") weights=W[-1,:,:] # final weights # Analyze selectivity at the end of the STDP simulation ispikes=spikes_out[:,0] # indexes of neurons that spiked tspikes=spikes_out[:,1] # spike timings # Select only the end of the STDP simulation end=tspikes>.8*max(tspikes) ispikes=ispikes[end] tspikes=tspikes[end] odors=odor[digitize(tspikes,t)-1] # odor (0/1) presented at the time of spikes tuning=zeros(30) # Tuning ratio of the postsynaptic neurons n0,n1=zeros(30),zeros(30) # number of spikes for odor 0 and for odor 1 for k in range(len(tuning)): o=odors[ispikes==k] n0[k]=sum(o==0) n1[k]=sum(o==1) tuning[k]=n0[k]*1./(n0[k]+n1[k]) # Sort the postsynaptic neurons by odor tuning weights=weights[:,argsort(tuning)] ''' Run the simulation ''' def odor(N): # Returns a random vector of binding constants return 10**(rand(N)*(bmax-bmin)+bmin) def hill_function(c,K=1.,n=3.): ''' Hill function: * c = concentration * K = half activation constant (choose K=1 for relative concentrations) * n = Hill coefficient ''' return (c**n)/(c**n+K**n) N=5000 # number of receptors seed(31415) # Get the same neurons every time intensity=3000. # Odor plumes tau_plume=75*ms eq_plumes=''' dx/dt=-x/tau_plume+(2./tau_plume)**.5*xi : 1 y=clip(x,0,inf) : 1 ''' plume=NeuronGroup(2,model=eq_plumes) # 1 odor # Receptor neurons Fmax=40*Hz # maximum firing rate tau=20*ms Imax=1/(1-exp(-1/(Fmax*tau))) # maximum input current eq_receptors=''' dv/dt=(Imax*hill_function(c)-v)/tau : 1 c : 1 # concentrations (relative to activation constant) ''' receptors=NeuronGroup(N,model=eq_receptors,threshold=1,reset=0) @network_operation def odor_to_nose(): # Send odor plume to the receptors receptors.c=I1*c1*clip(plume.x[0],0,Inf)+I2*c2*clip(plume.x[0],0,Inf) odors=[odor(N),odor(N)] c1,c2=odors # Decoder neurons M=len(tuning) eq_decoders=''' dv/dt=-v/taud + sigma*(2/taud)**.5*xi : 1 ''' decoders=NeuronGroup(M,model=eq_decoders,threshold=1,reset=0) S2=SpikeMonitor(decoders) # Synapses syn=Connection(receptors,decoders,'v') for i in range(len(decoders)): for j in weights[:,i].nonzero()[0]: syn[j,i]=weights[j,i] # Run I1,I2=intensity,0 print "Started" # Odor A, increasing concentration for I1 in intensity*exp(linspace(log(.1),log(10),20)): run(1*second,report="text") I1=0 # Odor B, increasing concentration for I2 in intensity*exp(linspace(log(.1),log(10),20)): run(1*second,report="text") # Figure (11B) spikes=array(S2.spikes) # i,t n,t=spikes[:,0],spikes[:,1] subplot(211) # Raster plot plot(t,n,'k.') subplot(212) # Odor concentrations semilogy(linspace(0,20,20),exp(linspace(log(.1),log(10),20)),'b') semilogy(linspace(20,40,20),exp(linspace(log(.1),log(10),20)),'r') plot([0,40],[1,1],'k--') show() brian-1.4.3/examples/frompapers/computing with neural synchrony/olfaction/Fig9B_olfaction.py000077500000000000000000000077471266062647100323550ustar00rootroot00000000000000#!/usr/bin/env python ''' Brette R (2012). Computing with neural synchrony. PLoS Comp Biol. 8(6): e1002561. doi:10.1371/journal.pcbi.1002561 ------------------------------------------------------------------------------------------------------------------ Figure 9B. Caption (Fig. 9B). Top, Fluctuating concentration of three odors (A: blue, B: red, C: black). Middle, spiking responses of olfactory receptors. Bottom, Responses of postsynaptic neurons from the assembly selective to A (blue) and to B (red). Stimuli are presented is sequence: 1) odor A alone, 2) odor B alone, 3) odor B alone with twice stronger intensity, 4) odor A with distracting odor C (same intensity), 5) odors A and B (same intensity). ''' from brian import * bmin,bmax=-7,-1 def odor(N): # Returns a random vector of binding constants return 10**(rand(N)*(bmax-bmin)+bmin) def hill_function(c,K=1.,n=3.): ''' Hill function: * c = concentration * K = half activation constant (choose K=1 for relative concentrations) * n = Hill coefficient ''' return (c**n)/(c**n+K**n) N=5000 # number of receptors # Odors seed(31415) # Get the same neurons every time intensity=3000. c1=odor(N) c2=odor(N) c0=c1 I1,I2=intensity,intensity # Odor plumes (fluctuating concentrations) tau_plume=75*ms eq_plumes=''' dx/dt=-x/tau_plume+(2./tau_plume)**.5*xi : 1 y=clip(x,0,inf) : 1 ''' plume=NeuronGroup(2,model=eq_plumes) # 2 odors # Receptor neurons Fmax=40*Hz # maximum firing rate tau=20*ms Imax=1/(1-exp(-1/(Fmax*tau))) # maximum input current eq_receptors=''' dv/dt=(Imax*hill_function(c)-v)/tau : 1 c : 1 # concentrations (relative to activation constant) ''' receptors=NeuronGroup(N,model=eq_receptors,threshold=1,reset=0) receptors.c=c1 @network_operation def odor_to_nose(): # Send odor plume to the receptors receptors.c=I1*c1*clip(plume.x[0],0,Inf)+I2*c2*clip(plume.x[1],0,Inf) # Decoder neurons M=200 taud=8*ms sigma=.15 eq_decoders=''' dv/dt=-v/taud + sigma*(2/taud)**.5*xi : 1 ''' decoders=NeuronGroup(2*M,model=eq_decoders,threshold=1,reset=0) # First M neurons encode odor A, next M neurons encode odor B # Synapses syn=Connection(receptors,decoders,'v') # Connectivity according to synchrony partitions bhalf=.5*(bmin+bmax) # select only those that are well activated u=2*(log(c1)/log(10)-bhalf)/(bmax-bmin) # normalized binding constants for odor A for i in range(M): which=((u>=i*1./M) & (u<(i+1)*1./M)) # we divide in M groups with similar values if sum(which)>0: w=1./sum(which) # total synaptic weight for a postsynaptic neuron is 1 syn[:,i]=w*which u=2*(log(c2)/log(10)-bhalf)/(bmax-bmin) for i in range(M): # normalized binding constants for odor B which=((u>=i*1./M) & (u<(i+1)*1./M)) if sum(which)>0: w=1./sum(which) syn[:,2*M-1-i]=w*which # Record odor concentration and output spikes O=StateMonitor(plume,'y',record=True) S=SpikeMonitor(receptors) S2=SpikeMonitor(decoders) print "Odor A" I1,I2=intensity,0 run(2*second) print "Odor B" I1,I2=0,intensity run(2*second) print "Odor B x2" I1,I2=0,2*intensity run(2*second) print "Odor A + odor C" I1,I2=intensity,intensity old_c2=c2 c2=odor(N) # different odor run(2*second) print "Odor A + odor B" I1,I2=intensity,intensity c2=old_c2 run(2*second) t=O.times/ms # Figure (9B) subplot(311) # odor fluctuations plot(t[t<2000],O[0][t<2000],'b') plot(t[(t>=2000) & (t<4000)],O[1][(t>=2000) & (t<4000)],'r') plot(t[(t>=4000) & (t<6000)],2*O[1][(t>=4000) & (t<6000)],'r') plot(t[(t>=6000) & (t<8000)],O[0][(t>=6000) & (t<8000)],'b') plot(t[(t>=6000) & (t<8000)],O[1][(t>=6000) & (t<8000)],'k') plot(t[(t>=8000) & (t<10000)],O[1][(t>=8000) & (t<10000)],'r') plot(t[(t>=8000) & (t<10000)],O[0][(t>=8000) & (t<10000)],'b') xlim(0,10000) xticks([]) subplot(312) raster_plot(S) xlim(0,10000) ylim(2500,2600) # 100 random neurons xticks([]) subplot(313) raster_plot(S2) ylim(100,300) xlim(0,10000) show() brian-1.4.3/examples/frompapers/computing with neural synchrony/olfaction/params.py000077500000000000000000000012701266062647100306630ustar00rootroot00000000000000#!/usr/bin/env python ''' Brette R (2012). Computing with neural synchrony. PLoS Comp Biol. 8(6): e1002561. doi:10.1371/journal.pcbi.1002561 ------------------------------------------------------------------------------------------------------------------ Parameters for the olfactory model with STDP ''' from brian import * N=5000 # Coincidence detectors sigma=.15 taud=8*ms # Connections Nsynapses=50 w0=150./(0.02*N) # STDP factor=0.05 a_pre=0.06*factor b_post=-1.*factor tau_pre=3*ms # Intrinsic plasticity: non-specific weight increase IP_period=10*ms IP_rate=-b_post*5*Hz # target firing rate = 5 Hz # Simulation control record_period=1*second duration=100*second brian-1.4.3/examples/hears/000077500000000000000000000000001266062647100155445ustar00rootroot00000000000000brian-1.4.3/examples/hears/IIRfilterbank.py000077500000000000000000000044351266062647100206140ustar00rootroot00000000000000#!/usr/bin/env python ''' Example of the use of the class :class:`~brian.hears.IIRFilterbank` available in the library. In this example, a white noise is filtered by a bank of chebyshev bandpass filters and lowpass filters which are different for every channels. The centre frequencies of the filters are linearly taken between 100kHz and 1000kHz and its bandwidth or cutoff frequency increases linearly with frequency. ''' from brian import * from brian.hears import * sound = whitenoise(100*ms).ramp() sound.level = 50*dB ### example of a bank of bandpass filter ################ nchannels = 50 center_frequencies = linspace(200*Hz, 1000*Hz, nchannels) #center frequencies bw = linspace(50*Hz, 300*Hz, nchannels) #bandwidth of the filters # The maximum loss in the passband in dB. Can be a scalar or an array of length # nchannels gpass = 1.*dB # The minimum attenuation in the stopband in dB. Can be a scalar or an array # of length nchannels gstop = 10.*dB #arrays of shape (2 x nchannels) defining the passband frequencies (Hz) passband = vstack((center_frequencies-bw/2, center_frequencies+bw/2)) #arrays of shape (2 x nchannels) defining the stopband frequencies (Hz) stopband = vstack((center_frequencies-1.1*bw, center_frequencies+1.1*bw)) filterbank = IIRFilterbank(sound, nchannels, passband, stopband, gpass, gstop, 'bandstop', 'cheby1') filterbank_mon = filterbank.process() figure() subplot(211) imshow(flipud(filterbank_mon.T), aspect='auto') #### example of a bank of lowpass filter ################ nchannels = 50 cutoff_frequencies = linspace(100*Hz, 1000*Hz, nchannels) #bandwidth of the transition region between the en of the pass band and the #begin of the stop band width_transition = linspace(50*Hz, 300*Hz, nchannels) # The maximum loss in the passband in dB. Can be a scalar or an array of length # nchannels gpass = 1*dB # The minimum attenuation in the stopband in dB. Can be a scalar or an array of # length nchannels gstop = 10*dB passband = cutoff_frequencies-width_transition/2 stopband = cutoff_frequencies+width_transition/2 filterbank = IIRFilterbank(sound, nchannels, passband, stopband, gpass, gstop, 'low','cheby1') filterbank_mon=filterbank.process() subplot(212) imshow(flipud(filterbank_mon.T), aspect='auto') show() brian-1.4.3/examples/hears/approximate_gammatone.py000077500000000000000000000021771266062647100225110ustar00rootroot00000000000000#!/usr/bin/env python ''' Example of the use of the class :class:`~brian.hears.ApproximateGammatone` available in the library. It implements a filterbank of approximate gammatone filters as described in Hohmann, V., 2002, "Frequency analysis and synthesis using a Gammatone filterbank", Acta Acustica United with Acustica. In this example, a white noise is filtered by a gammatone filterbank and the resulting cochleogram is plotted. ''' from brian import * from brian.hears import * level=50*dB # level of the input sound in rms dB SPL sound = whitenoise(100*ms).ramp() # generation of a white noise sound = sound.atlevel(level) # set the sound to a certain dB level nbr_center_frequencies = 50 # number of frequency channels in the filterbank # center frequencies with a spacing following an ERB scale center_frequencies = erbspace(100*Hz, 1000*Hz, nbr_center_frequencies) # bandwidth of the filters (different in each channel) bw = 10**(0.037+0.785*log10(center_frequencies)) gammatone = ApproximateGammatone(sound, center_frequencies, bw, order=3) gt_mon = gammatone.process() figure() imshow(flipud(gt_mon.T), aspect='auto') show() brian-1.4.3/examples/hears/artificial_vowels.py000077500000000000000000000026451266062647100216360ustar00rootroot00000000000000#!/usr/bin/env python ''' This example implements the artificial vowels from Culling, J. F. and Summerfield, Q. (1995a). "Perceptual segregation of concurrent speech sounds: absence of across-frequency grouping by common interaural delay" J. Acoust. Soc. Am. 98, 785-797. ''' from brian import * from brian.hears import * duration = 409.6*ms width = 150*Hz/2 samplerate = 10*kHz set_default_samplerate(samplerate) centres = [225*Hz, 625*Hz, 975*Hz, 1925*Hz] vowels = { 'ee':[centres[0], centres[3]], 'ar':[centres[1], centres[2]], 'oo':[centres[0], centres[2]], 'er':[centres[1], centres[3]] } def generate_vowel(vowel): vowel = vowels[vowel] x = whitenoise(duration) y = fft(asarray(x).flatten()) f = fftfreq(len(x), 1/samplerate) I = zeros(len(f), dtype=bool) for cf in vowel: I = I|((abs(f)cf-width)) I = -I y[I] = 0 x = ifft(y) return Sound(x.real) v1 = generate_vowel('ee').ramp() v2 = generate_vowel('ar').ramp() v3 = generate_vowel('oo').ramp() v4 = generate_vowel('er').ramp() for s in [v1, v2, v3, v4]: s.play(normalise=True, sleep=True) s1 = Sound((v1, v2)) #s1.play(normalise=True, sleep=True) s2 = Sound((v3, v4)) #s2.play(normalise=True, sleep=True) v1.save('mono_sound.wav') s1.save('stereo_sound.wav') subplot(211) plot(v1.times, v1) subplot(212) v1.spectrogram() show() brian-1.4.3/examples/hears/butterworth.py000077500000000000000000000026641266062647100205220ustar00rootroot00000000000000#!/usr/bin/env python ''' Example of the use of the class :class:`~brian.hears.Butterworth` available in the library. In this example, a white noise is filtered by a bank of butterworth bandpass filters and lowpass filters which are different for every channels. The centre or cutoff frequency of the filters are linearly taken between 100kHz and 1000kHz and its bandwidth frequency increases linearly with frequency. ''' from brian import * from brian.hears import * level = 50*dB # level of the input sound in rms dB SPL sound = whitenoise(100*ms).ramp() sound = sound.atlevel(level) order = 2 #order of the filters #### example of a bank of bandpass filter ################ nchannels = 50 center_frequencies = linspace(100*Hz, 1000*Hz, nchannels) bw = linspace(50*Hz, 300*Hz, nchannels) # bandwidth of the filters #arrays of shape (2 x nchannels) defining the passband frequencies (Hz) fc = vstack((center_frequencies-bw/2, center_frequencies+bw/2)) filterbank = Butterworth(sound, nchannels, order, fc, 'bandpass') filterbank_mon = filterbank.process() figure() subplot(211) imshow(flipud(filterbank_mon.T), aspect='auto') ### example of a bank of lowpass filter ################ nchannels = 50 cutoff_frequencies = linspace(200*Hz, 1000*Hz, nchannels) filterbank = Butterworth(sound, nchannels, order, cutoff_frequencies, 'low') filterbank_mon = filterbank.process() subplot(212) imshow(flipud(filterbank_mon.T), aspect='auto') show() brian-1.4.3/examples/hears/cochleagram.py000077500000000000000000000012721266062647100203700ustar00rootroot00000000000000#!/usr/bin/env python ''' Example of basic filtering of a sound with Brian hears. This example implements a cochleagram based on a gammatone filterbank followed by halfwave rectification, cube root compression and 10 Hz low pass filtering. ''' from brian import * from brian.hears import * sound1 = tone(1*kHz, .1*second) sound2 = whitenoise(.1*second) sound = sound1+sound2 sound = sound.ramp() cf = erbspace(20*Hz, 20*kHz, 3000) gammatone = Gammatone(sound, cf) cochlea = FunctionFilterbank(gammatone, lambda x: clip(x, 0, Inf)**(1.0/3.0)) lowpass = LowPass(cochlea, 10*Hz) output = lowpass.process() imshow(output.T, origin='lower left', aspect='auto', vmin=0) show() brian-1.4.3/examples/hears/cochlear_models.py000077500000000000000000000016611266062647100212500ustar00rootroot00000000000000#!/usr/bin/env python ''' Example of the use of the cochlear models (:class:`~brian.hears.DRNL`, :class:`~brian.hears.DCGC` and :class:`~brian.hears.TanCarney`) available in the library. ''' from brian import * from brian.hears import * simulation_duration = 50*ms set_default_samplerate(50*kHz) sound = whitenoise(simulation_duration) sound = sound.atlevel(50*dB) # level in rms dB SPL cf = erbspace(100*Hz, 1000*Hz, 50) # centre frequencies interval = 16 #update interval of the time varying filters ## DNRL #param_drnl = {} #param_drnl['lp_nl_cutoff_m'] = 1.1 #drnl_filter=DRNL(sound, cf, type='human', param=param_drnl) #out = drnl_filter.process() ## DCGC #param_dcgc = {} #param_dcgc['c1'] = -2.96 #dcgc_filter = DCGC(sound, cf, interval, param=param_dcgc) #out = dcgc_filter.process() ## Tan and Carney 2003 tan_filter = TanCarney(sound, cf, interval) out = tan_filter.process() figure() imshow(flipud(out.T), aspect='auto') show() brian-1.4.3/examples/hears/dcgc.py000077500000000000000000000132341266062647100170240ustar00rootroot00000000000000#!/usr/bin/env python ''' Implementation example of the compressive gammachirp auditory filter as described in Irino, T. and Patterson R., "A compressive gammachirp auditory filter for both physiological and psychophysical data", JASA 2001. A class called :class:`~brian.hears.DCGC` implementing this model is available in the library. Technical implementation details and notation can be found in Irino, T. and Patterson R., "A Dynamic Compressive Gammachirp Auditory Filterbank", IEEE Trans Audio Speech Lang Processing. ''' from brian import * from brian.hears import * simulation_duration = 50*ms samplerate = 50*kHz level = 50*dB # level of the input sound in rms dB SPL sound = whitenoise(simulation_duration, samplerate).ramp() sound = sound.atlevel(level) nbr_cf = 50 # number of centre frequencies # center frequencies with a spacing following an ERB scale cf = erbspace(100*Hz, 1000*Hz, nbr_cf) c1 = -2.96 #glide slope of the first filterbank b1 = 1.81 #factor determining the time constant of the first filterbank c2 = 2.2 #glide slope of the second filterbank b2 = 2.17 #factor determining the time constant of the second filterbank order_ERB = 4 ERBrate = 21.4*log10(4.37*cf/1000+1) ERBwidth = 24.7*(4.37*cf/1000 + 1) ERBspace = mean(diff(ERBrate)) # the filter coefficients are updated every update_interval (here in samples) update_interval = 1 #bank of passive gammachirp filters. As the control path uses the same passive #filterbank than the signal path (but shifted in frequency) #this filterbank is used by both pathway. pGc = LogGammachirp(sound, cf, b=b1, c=c1) fp1 = cf + c1*ERBwidth*b1/order_ERB #centre frequency of the signal path #### Control Path #### #the first filterbank in the control path consists of gammachirp filters #value of the shift in ERB frequencies of the control path with respect to the signal path lct_ERB = 1.5 n_ch_shift = round(lct_ERB/ERBspace) #value of the shift in channels #index of the channel of the control path taken from pGc indch1_control = minimum(maximum(1, arange(1, nbr_cf+1)+n_ch_shift), nbr_cf).astype(int)-1 fp1_control = fp1[indch1_control] #the control path bank pass filter uses the channels of pGc indexed by indch1_control pGc_control = RestructureFilterbank(pGc, indexmapping=indch1_control) #the second filterbank in the control path consists of fixed asymmetric compensation filters frat_control = 1.08 fr2_control = frat_control*fp1_control asym_comp_control = AsymmetricCompensation(pGc_control, fr2_control, b=b2, c=c2) #definition of the pole of the asymmetric comensation filters p0 = 2 p1 = 1.7818*(1-0.0791*b2)*(1-0.1655*abs(c2)) p2 = 0.5689*(1-0.1620*b2)*(1-0.0857*abs(c2)) p3 = 0.2523*(1-0.0244*b2)*(1+0.0574*abs(c2)) p4 = 1.0724 #definition of the parameters used in the control path output levels computation #(see IEEE paper for details) decay_tcst = .5*ms order = 1. lev_weight = .5 level_ref = 50. level_pwr1 = 1.5 level_pwr2 = .5 RMStoSPL = 30. frat0 = .2330 frat1 = .005 exp_deca_val = exp(-1/(decay_tcst*samplerate)*log(2)) level_min = 10**(-RMStoSPL/20) #definition of the controller class. What is does it take the outputs of the #first and second fitlerbanks of the control filter as input, compute an overall #intensity level for each frequency channel. It then uses those level to update #the filter coefficient of its target, the asymmetric compensation filterbank of #the signal path. class CompensensationFilterUpdater(object): def __init__(self, target): self.target = target self.level1_prev = -100 self.level2_prev = -100 def __call__(self, *input): value1 = input[0][-1,:] value2 = input[1][-1,:] #the current level value is chosen as the max between the current #output and the previous one decreased by a decay level1 = maximum(maximum(value1, 0), self.level1_prev*exp_deca_val) level2 = maximum(maximum(value2, 0), self.level2_prev*exp_deca_val) self.level1_prev = level1 #the value is stored for the next iteration self.level2_prev = level2 #the overall intensity is computed between the two filterbank outputs level_total = lev_weight*level_ref*(level1/level_ref)**level_pwr1+\ (1-lev_weight)*level_ref*(level2/level_ref)**level_pwr2 #then it is converted in dB level_dB = 20*log10(maximum(level_total, level_min))+RMStoSPL #the frequency factor is calculated frat = frat0 + frat1*level_dB #the centre frequency of the asymmetric compensation filters are updated fr2 = fp1*frat coeffs = asymmetric_compensation_coeffs(samplerate, fr2, self.target.filt_b, self.target.filt_a, b2, c2, p0, p1, p2, p3, p4) self.target.filt_b, self.target.filt_a = coeffs #### Signal Path #### #the signal path consists of the passive gammachirp filterbank pGc previously #defined followed by a asymmetric compensation filterbank fr1 = fp1*frat0 varyingfilter_signal_path = AsymmetricCompensation(pGc, fr1, b=b2, c=c2) updater = CompensensationFilterUpdater(varyingfilter_signal_path) #the controler which takes the two filterbanks of the control path as inputs #and the varying filter of the signal path as target is instantiated control = ControlFilterbank(varyingfilter_signal_path, [pGc_control, asym_comp_control], varyingfilter_signal_path, updater, update_interval) #run the simulation #Remember that the controler are at the end of the chain and the output of the #whole path comes from them signal = control.process() figure() imshow(flipud(signal.T), aspect='auto') show() brian-1.4.3/examples/hears/drnl.py000077500000000000000000000066521266062647100170710ustar00rootroot00000000000000#!/usr/bin/env python ''' Implementation example of the dual resonance nonlinear (DRNL) filter with parameters fitted for human as described in Lopez-Paveda, E. and Meddis, R., A human nonlinear cochlear filterbank, JASA 2001. A class called :class:`~brian.hears.DRNL` implementing this model is available in the library. The entire pathway consists of the sum of a linear and a nonlinear pathway. The linear path consists of a bank of bandpass filters (second order gammatone), a low pass function, and a gain/attenuation factor, g, in a cascade. The nonlinear path is a cascade consisting of a bank of gammatone filters, a compression function, a second bank of gammatone filters, and a low pass function, in that order. The parameters are given in the form ``10**(p0+mlog10(cf))``. ''' from brian import * from brian.hears import * simulation_duration = 50*ms samplerate = 50*kHz level = 50*dB # level of the input sound in rms dB SPL sound = whitenoise(simulation_duration, samplerate).ramp() sound.level = level nbr_cf = 50 #number of centre frequencies #center frequencies with a spacing following an ERB scale center_frequencies = erbspace(100*Hz,1000*Hz, nbr_cf) #conversion to stape velocity (which are the units needed by the following centres) sound = sound*0.00014 #### Linear Pathway #### #bandpass filter (second order gammatone filter) center_frequencies_linear = 10**(-0.067+1.016*log10(center_frequencies)) bandwidth_linear = 10**(0.037+0.785*log10(center_frequencies)) order_linear = 3 gammatone = ApproximateGammatone(sound, center_frequencies_linear, bandwidth_linear, order=order_linear) #linear gain g = 10**(4.2-0.48*log10(center_frequencies)) func_gain = lambda x:g*x gain = FunctionFilterbank(gammatone, func_gain) #low pass filter(cascade of 4 second order lowpass butterworth filters) cutoff_frequencies_linear = center_frequencies_linear order_lowpass_linear = 2 lp_l = LowPass(gain, cutoff_frequencies_linear) lowpass_linear = Cascade(gain, lp_l, 4) #### Nonlinear Pathway #### #bandpass filter (third order gammatone filters) center_frequencies_nonlinear = center_frequencies bandwidth_nonlinear = 10**(-0.031+0.774*log10(center_frequencies)) order_nonlinear = 3 bandpass_nonlinear1 = ApproximateGammatone(sound, center_frequencies_nonlinear, bandwidth_nonlinear, order=order_nonlinear) #compression (linear at low level, compress at high level) a = 10**(1.402+0.819*log10(center_frequencies)) #linear gain b = 10**(1.619-0.818*log10(center_frequencies)) v = .2 #compression exponent func_compression = lambda x: sign(x)*minimum(a*abs(x), b*abs(x)**v) compression = FunctionFilterbank(bandpass_nonlinear1, func_compression) #bandpass filter (third order gammatone filters) bandpass_nonlinear2 = ApproximateGammatone(compression, center_frequencies_nonlinear, bandwidth_nonlinear, order=order_nonlinear) #low pass filter cutoff_frequencies_nonlinear = center_frequencies_nonlinear order_lowpass_nonlinear = 2 lp_nl = LowPass(bandpass_nonlinear2, cutoff_frequencies_nonlinear) lowpass_nonlinear = Cascade(bandpass_nonlinear2, lp_nl, 3) #adding the two pathways dnrl_filter = lowpass_linear+lowpass_nonlinear dnrl = dnrl_filter.process() figure() imshow(flipud(dnrl.T), aspect='auto') show() brian-1.4.3/examples/hears/gammatone.py000077500000000000000000000021561266062647100200750ustar00rootroot00000000000000#!/usr/bin/env python ''' Example of the use of the class :class:`~brian.hears.Gammatone` available in the library. It implements a fitlerbank of IIR gammatone filters as described in Slaney, M., 1993, "An Efficient Implementation of the Patterson-Holdsworth Auditory Filter Bank". Apple Computer Technical Report #35. In this example, a white noise is filtered by a gammatone filterbank and the resulting cochleogram is plotted. ''' from brian import * from brian.hears import * from matplotlib import pyplot sound = whitenoise(100*ms).ramp() sound.level = 50*dB nbr_center_frequencies = 50 b1 = 1.019 #factor determining the time constant of the filters #center frequencies with a spacing following an ERB scale center_frequencies = erbspace(100*Hz, 1000*Hz, nbr_center_frequencies) gammatone = Gammatone(sound, center_frequencies, b=b1) gt_mon = gammatone.process() figure() imshow(gt_mon.T, aspect='auto', origin='lower left', extent=(0, sound.duration/ms, center_frequencies[0], center_frequencies[-1])) pyplot.yscale('log') title('Cochleogram') ylabel('Frequency (Hz)') xlabel('Time (ms)') show() brian-1.4.3/examples/hears/ircam_hrtf.py000077500000000000000000000016771266062647100202520ustar00rootroot00000000000000#!/usr/bin/env python ''' Example showing the use of HRTFs in Brian hears. Note that you will need to download the :class:`IRCAM_LISTEN` database. ''' from brian import * from brian.hears import * # Load database hrtfdb = IRCAM_LISTEN(r'F:\HRTF\IRCAM') hrtfset = hrtfdb.load_subject(1002) # Select only the horizontal plane hrtfset = hrtfset.subset(lambda elev: elev==0) # Set up a filterbank sound = whitenoise(10*ms) fb = hrtfset.filterbank(sound) # Extract the filtered response and plot img = fb.process().T img_left = img[:img.shape[0]/2, :] img_right = img[img.shape[0]/2:, :] subplot(121) imshow(img_left, origin='lower left', aspect='auto', extent=(0, sound.duration/ms, 0, 360)) xlabel('Time (ms)') ylabel('Azimuth') title('Left ear') subplot(122) imshow(img_right, origin='lower left', aspect='auto', extent=(0, sound.duration/ms, 0, 360)) xlabel('Time (ms)') ylabel('Azimuth') title('Right ear') show() brian-1.4.3/examples/hears/linear_gammachirp.py000077500000000000000000000022241266062647100215630ustar00rootroot00000000000000#!/usr/bin/env python ''' Example of the use of the class :class:`~brian.hears.LinearGammachirp` available in the library. It implements a filterbank of FIR gammatone filters with linear frequency sweeps as described in Wagner et al. 2009, "Auditory responses in the barn owl's nucleus laminaris to clicks: impulse response and signal analysis of neurophonic potential", J. Neurophysiol. In this example, a white noise is filtered by a gammachirp filterbank and the resulting cochleogram is plotted. The different impulse responses are also plotted. ''' from brian import * from brian.hears import * sound = whitenoise(100*ms).ramp() sound.level = 50*dB nbr_center_frequencies = 10 #number of frequency channels in the filterbank #center frequencies with a spacing following an ERB scale center_frequencies = erbspace(100*Hz, 1000*Hz, nbr_center_frequencies) c = 0.0 #glide slope time_constant = linspace(3, 0.3, nbr_center_frequencies)*ms gamma_chirp = LinearGammachirp(sound, center_frequencies, time_constant, c) gamma_chirp_mon = gamma_chirp.process() figure() imshow(gamma_chirp_mon.T, aspect='auto') figure() plot(gamma_chirp.impulse_response.T) show() brian-1.4.3/examples/hears/log_gammachirp.py000077500000000000000000000017321266062647100210750ustar00rootroot00000000000000#!/usr/bin/env python ''' Example of the use of the class :class:`~brian.hears.LogGammachirp` available in the library. It implements a filterbank of IIR gammachirp filters as Unoki et al. 2001, "Improvement of an IIR asymmetric compensation gammachirp filter". In this example, a white noise is filtered by a linear gammachirp filterbank and the resulting cochleogram is plotted. The different impulse responses are also plotted. ''' from brian import * from brian.hears import * sound = whitenoise(100*ms).ramp() sound.level = 50*dB nbr_center_frequencies = 50 #number of frequency channels in the filterbank c1 = -2.96 #glide slope b1 = 1.81 #factor determining the time constant of the filters #center frequencies with a spacing following an ERB scale cf = erbspace(100*Hz, 1000*Hz, nbr_center_frequencies) gamma_chirp = LogGammachirp(sound, cf, c=c1, b=b1) gamma_chirp_mon = gamma_chirp.process() figure() imshow(flipud(gamma_chirp_mon.T), aspect='auto') show() brian-1.4.3/examples/hears/online_computation.py000077500000000000000000000012741266062647100220330ustar00rootroot00000000000000#!/usr/bin/env python ''' Example of online computation using :meth:`~brian.hears.Filterbank.process`. Plots the RMS value of each channel output by a gammatone filterbank. ''' from brian import * from brian.hears import * sound1 = tone(1*kHz, .1*second) sound2 = whitenoise(.1*second) sound = sound1+sound2 sound = sound.ramp() sound.level = 60*dB cf = erbspace(20*Hz, 20*kHz, 3000) fb = Gammatone(sound, cf) def sum_of_squares(input, running): return running+sum(input**2, axis=0) rms = sqrt(fb.process(sum_of_squares)/sound.nsamples) sound_rms = sqrt(mean(sound**2)) axhline(sound_rms, ls='--') plot(cf, rms) xlabel('Frequency (Hz)') ylabel('RMS') show() brian-1.4.3/examples/hears/simple_anf.py000077500000000000000000000013501266062647100202350ustar00rootroot00000000000000#!/usr/bin/env python ''' Example of a simple auditory nerve fibre model with Brian hears. ''' from brian import * from brian.hears import * sound1 = tone(1*kHz, .1*second) sound2 = whitenoise(.1*second) sound = sound1+sound2 sound = sound.ramp() cf = erbspace(20*Hz, 20*kHz, 3000) cochlea = Gammatone(sound, cf) # Half-wave rectification and compression [x]^(1/3) ihc = FunctionFilterbank(cochlea, lambda x: 3*clip(x, 0, Inf)**(1.0/3.0)) # Leaky integrate-and-fire model with noise and refractoriness eqs = ''' dv/dt = (I-v)/(1*ms)+0.2*xi*(2/(1*ms))**.5 : 1 I : 1 ''' anf = FilterbankGroup(ihc, 'I', eqs, reset=0, threshold=1, refractory=5*ms) M = SpikeMonitor(anf) run(sound.duration) raster_plot(M) show() brian-1.4.3/examples/hears/sound_localisation_model.py000077500000000000000000000100471266062647100231740ustar00rootroot00000000000000#!/usr/bin/env python ''' Example demonstrating the use of many features of Brian hears, including HRTFs, restructuring filters and integration with Brian. Implements a simplified version of the "ideal" sound localisation model from Goodman and Brette (2010). The sound is played at a particular spatial location (indicated on the final plot by a red +). Each location has a corresponding assembly of neurons, whose summed firing rates give the sizes of the blue circles in the plot. The most strongly responding assembly is indicated by the green x, which is the estimate of the location by the model. Reference: `Goodman DFM, Brette R (2010). Spike-timing-based computation in sound localization. PLoS Comput. Biol. 6(11) `__. ''' from brian import * from brian.hears import * # Download the IRCAM database, and replace this filename with the location # you downloaded it to hrtfdb = IRCAM_LISTEN(r'F:\HRTF\IRCAM') subject = 1002 hrtfset = hrtfdb.load_subject(subject) # This gives the number of spatial locations in the set of HRTFs num_indices = hrtfset.num_indices # Choose a random location for the sound to come from index = randint(hrtfset.num_indices) # A sound to test the model with sound = Sound.whitenoise(500*ms) # This is the specific HRTF for the chosen location hrtf = hrtfset.hrtf[index] # We apply the chosen HRTF to the sound, the output has 2 channels hrtf_fb = hrtf.filterbank(sound) # We swap these channels (equivalent to swapping the channels in the # subsequent filters, but simpler to do it with the inputs) swapped_channels = RestructureFilterbank(hrtf_fb, indexmapping=[1, 0]) # Now we apply all of the possible pairs of HRTFs in the set to these # swapped channels, which means repeating them num_indices times first hrtfset_fb = hrtfset.filterbank(Repeat(swapped_channels, num_indices)) # Now we apply cochlear filtering (logically, this comes before the HRTF # filtering, but since convolution is commutative it is more efficient to # do the cochlear filtering afterwards cfmin, cfmax, cfN = 150*Hz, 5*kHz, 40 cf = erbspace(cfmin, cfmax, cfN) # We repeat each of the HRTFSet filterbank channels cfN times, so that # for each location we will apply each possible cochlear frequency gfb = Gammatone(Repeat(hrtfset_fb, cfN), tile(cf, hrtfset_fb.nchannels)) # Half wave rectification and compression cochlea = FunctionFilterbank(gfb, lambda x:15*clip(x, 0, Inf)**(1.0/3.0)) # Leaky integrate and fire neuron model eqs = ''' dV/dt = (I-V)/(1*ms)+0.1*xi/(0.5*ms)**.5 : 1 I : 1 ''' G = FilterbankGroup(cochlea, 'I', eqs, reset=0, threshold=1, refractory=5*ms) # The coincidence detector (cd) neurons cd = NeuronGroup(num_indices*cfN, eqs, reset=0, threshold=1, clock=G.clock) # Each CD neuron receives precisely two inputs, one from the left ear and # one from the right, for each location and each cochlear frequency C = Connection(G, cd, 'V') for i in xrange(num_indices*cfN): C[i, i] = 0.5 # from right ear C[i+num_indices*cfN, i] = 0.5 # from left ear # We want to just count the number of CD spikes counter = SpikeCounter(cd) # Run the simulation, giving a report on how long it will take as we run run(sound.duration, report='stderr') # We take the array of counts, and reshape them into a 2D array which we sum # across frequencies to get the spike count of each location-specific assembly count = counter.count count.shape = (num_indices, cfN) count = sum(count, axis=1) count = array(count, dtype=float)/amax(count) # Our guess of the location is the index of the strongest firing assembly index_guess = argmax(count) # Now we plot the output, using the coordinates of the HRTFSet coords = hrtfset.coordinates azim, elev = coords['azim'], coords['elev'] scatter(azim, elev, 100*count) plot([azim[index]], [elev[index]], '+r', ms=15, mew=2) plot([azim[index_guess]], [elev[index_guess]], 'xg', ms=15, mew=2) xlabel('Azimuth (deg)') ylabel('Elevation (deg)') xlim(-5, 350) ylim(-50, 95) show() brian-1.4.3/examples/hears/sounds.py000077500000000000000000000007531266062647100174410ustar00rootroot00000000000000#!/usr/bin/env python ''' Example of basic use and manipulation of sounds with Brian hears. ''' from brian import * from brian.hears import * sound1 = tone(1*kHz, 1*second) sound2 = whitenoise(1*second) sound = sound1+sound2 sound = sound.ramp() # Comment this line out if you don't have pygame installed sound.play() # The first 20ms of the sound startsound = sound[:20*ms] subplot(121) plot(startsound.times, startsound) subplot(122) sound.spectrogram() show() brian-1.4.3/examples/hears/tan_carney_2003/000077500000000000000000000000001266062647100203335ustar00rootroot00000000000000brian-1.4.3/examples/hears/tan_carney_2003/tan_carney_Fig11.py000066400000000000000000000055531266062647100237670ustar00rootroot00000000000000''' Response area and phase response of a model fiber with CF=2200Hz in the Tan&Carney model. Reproduces Fig. 11 from: Tan, Q., and L. H. Carney. "A Phenomenological Model for the Responses of Auditory-nerve Fibers. II. Nonlinear Tuning with a Frequency Glide". The Journal of the Acoustical Society of America 114 (2003): 2007. ''' import matplotlib.pyplot as plt import numpy as np from brian import * # set_global_preferences(useweave=True) from brian.hears import * def product(*args): # Simple (and inefficient) variant of itertools.product that works for # Python 2.5 (directly returns a list instead of yielding one item at a # time) pools = map(tuple, args) result = [[]] for pool in pools: result = [x+[y] for x in result for y in pool] return result duration = 50*ms samplerate = 50*kHz set_default_samplerate(samplerate) CF = 2200 freqs = np.arange(250.0, 3501., 50.) levels = [10, 30, 50, 70, 90] cf_level = product(freqs, levels) tones = Sound([Sound.sequence([tone(freq * Hz, duration).atlevel(level*dB).ramp(when='both', duration=2.5*ms, inplace=False)]) for freq, level in cf_level]) ihc = TanCarney(MiddleEar(tones), [CF] * len(cf_level), update_interval=2) syn = ZhangSynapse(ihc, CF) s_mon = StateMonitor(syn, 's', record=True, clock=syn.clock) net = Network(syn, s_mon) net.run(duration) reshaped = s_mon.values.reshape((len(freqs), len(levels), -1)) # calculate the phase with respect to the stimulus pi = np.pi min_freq, max_freq = 1100, 2900 freq_subset = freqs[(freqs>=min_freq) & (freqs<=max_freq)] reshaped_subset = reshaped[(freqs>=min_freq) & (freqs<=max_freq), :, :] phases = np.zeros((reshaped_subset.shape[0], len(levels))) for f_idx, freq in enumerate(freq_subset): period = 1.0 / freq for l_idx in xrange(len(levels)): phase_angles = np.arange(reshaped_subset.shape[2])/samplerate % period / period * 2*pi temp_phases = (np.exp(1j * phase_angles) * reshaped_subset[f_idx, l_idx, :]) phases[f_idx, l_idx] = np.angle(np.sum(temp_phases)) plt.subplot(2, 1, 1) rate = reshaped.mean(axis=2) plt.plot(freqs, rate) plt.ylabel('Spikes/sec') plt.legend(['%.0f dB' % level for level in levels], loc='best') plt.xlim(0, 4000) plt.ylim(0, 250) plt.subplot(2, 1, 2) relative_phases = (phases.T - phases[:, -1]).T relative_phases[relative_phases > pi] = relative_phases[relative_phases > pi] - 2*pi relative_phases[relative_phases < -pi] = relative_phases[relative_phases < -pi] + 2*pi plt.plot(freq_subset, relative_phases / pi) plt.ylabel("Phase Re:90dB (pi radians)") plt.xlabel('Frequency (Hz)') plt.legend(['%.0f dB' % level for level in levels], loc='best') plt.xlim(0, 4000) plt.ylim(-0.5, 0.75) plt.show() brian-1.4.3/examples/hears/tan_carney_2003/tan_carney_Fig7.py000066400000000000000000000073271266062647100237150ustar00rootroot00000000000000''' CF-dependence of compressive nonlinearity in the Tan&Carney model. Reproduces Fig. 7 from: Tan, Q., and L. H. Carney. "A Phenomenological Model for the Responses of Auditory-nerve Fibers. II. Nonlinear Tuning with a Frequency Glide". The Journal of the Acoustical Society of America 114 (2003): 2007. ''' import numpy as np import matplotlib.pyplot as plt from scipy.interpolate import interp1d from brian import * #set_global_preferences(useweave=True) from brian.hears import * from brian.hears.filtering.tan_carney import TanCarneySignal, MiddleEar samplerate = 50*kHz set_default_samplerate(samplerate) duration = 50*ms def product(*args): # Simple (and inefficient) variant of itertools.product that works for # Python 2.5 (directly returns a list instead of yielding one item at a # time) pools = map(tuple, args) result = [[]] for pool in pools: result = [x+[y] for x in result for y in pool] return result def gen_tone(freq, level): ''' Little helper function to generate a pure tone at frequency `freq` with the given `level`. The tone has a duration of 50ms and is ramped with two ramps of 2.5ms. ''' freq = float(freq) * Hz level = float(level) * dB return tone(freq, duration).ramp(when='both', duration=2.5*ms, inplace=False).atlevel(level) freqs = [500, 1100, 2000, 4000] levels = np.arange(-10, 100.1, 5) cf_level = product(freqs, levels) # steady-state start = 10*ms*samplerate end = 45*ms*samplerate # For Figure 7 we have manually adjusts the gain for different CFs -- otherwise # the RMS values wouldn't be identical for low CFs. Therefore, try to estimate # suitable gain values first using the lowest CF as a reference ref_tone = gen_tone(freqs[0], levels[0]) F_out_reference = TanCarneySignal(MiddleEar(ref_tone, gain=1), freqs[0], update_interval=1).process().flatten() ref_rms = np.sqrt(np.mean((F_out_reference[start:end] - np.mean(F_out_reference[start:end]))**2)) gains = np.linspace(0.1, 1, 50) # for higher CFs we need lower gains cf_gains = product(freqs[1:], gains) tones = Sound([gen_tone(freq, levels[0]) for freq, _ in cf_gains]) F_out_test = TanCarneySignal(MiddleEar(tones, gain=np.array([g for _, g in cf_gains])), [cf for cf,_ in cf_gains], update_interval=1).process() reshaped_Fout = F_out_test.T.reshape((len(freqs[1:]), len(gains), -1)) rms = np.sqrt(np.mean((reshaped_Fout[:, :, start:end].T - np.mean(reshaped_Fout[:, :, start:end], axis=2).T).T**2, axis=2)) # get the best gain for each CF using simple linear interpolation gain_dict = {freqs[0]: 1.} # reference gain for idx, freq in enumerate(freqs[1:]): gain_dict[freq] = interp1d(rms[idx, :], gains)(ref_rms) # now do the real test: tones at different levels for different CFs tones = Sound([gen_tone(freq, level) for freq, level in cf_level]) F_out = TanCarneySignal(MiddleEar(tones, gain=np.array([gain_dict[cf] for cf, _ in cf_level])), [cf for cf, _ in cf_level], update_interval=1).process() reshaped_Fout = F_out.T.reshape((len(freqs), len(levels), -1)) rms = np.sqrt(np.mean((reshaped_Fout[:, :, start:end].T - np.mean(reshaped_Fout[:, :, start:end], axis=2).T).T**2, axis=2)) # This should more or less reproduce Fig. 7 plt.plot(levels, rms.T) plt.legend(['%.0f Hz' % cf for cf in freqs], loc='best') plt.xlim(-20, 100) plt.ylim(1e-6, 1) plt.yscale('log') plt.xlabel('input signal SPL (dB)') plt.ylabel('rms of AC component of Fout') plt.show() brian-1.4.3/examples/hears/tan_carney_2003/tan_carney_simple_test.py000066400000000000000000000103031266062647100254350ustar00rootroot00000000000000''' Fig. 1 and 3 (spking output without spiking/refractory period) should reproduce the output of the AN3_test_tone.m and AN3_test_click.m scripts, available in the code accompanying the paper Tan & Carney (2003). This matlab code is available from http://www.urmc.rochester.edu/labs/Carney-Lab/publications/auditory-models.cfm Tan, Q., and L. H. Carney. "A Phenomenological Model for the Responses of Auditory-nerve Fibers. II. Nonlinear Tuning with a Frequency Glide". The Journal of the Acoustical Society of America 114 (2003): 2007. ''' import numpy as np import matplotlib.pyplot as plt from brian.stdunits import kHz, Hz, ms from brian.network import Network from brian.monitor import StateMonitor, SpikeMonitor from brian.globalprefs import set_global_preferences #set_global_preferences(useweave=True) from brian.hears import (Sound, get_samplerate, set_default_samplerate, tone, click, silence, dB, TanCarney, MiddleEar, ZhangSynapse) from brian.clock import reinit_default_clock set_default_samplerate(50*kHz) sample_length = 1 / get_samplerate(None) cf = 1000 * Hz print 'Testing click response' duration = 25*ms levels = [40, 60, 80, 100, 120] # a click of two samples tones = Sound([Sound.sequence([click(sample_length*2, peak=level*dB), silence(duration=duration - sample_length)]) for level in levels]) ihc = TanCarney(MiddleEar(tones), [cf] * len(levels), update_interval=1) syn = ZhangSynapse(ihc, cf) s_mon = StateMonitor(syn, 's', record=True, clock=syn.clock) R_mon = StateMonitor(syn, 'R', record=True, clock=syn.clock) spike_mon = SpikeMonitor(syn) net = Network(syn, s_mon, R_mon, spike_mon) net.run(duration * 1.5) for idx, level in enumerate(levels): plt.figure(1) plt.subplot(len(levels), 1, idx + 1) plt.plot(s_mon.times / ms, s_mon[idx]) plt.xlim(0, 25) plt.xlabel('Time (msec)') plt.ylabel('Sp/sec') plt.text(15, np.nanmax(s_mon[idx])/2., 'Peak SPL=%s SPL' % str(level*dB)); ymin, ymax = plt.ylim() if idx == 0: plt.title('Click responses') plt.figure(2) plt.subplot(len(levels), 1, idx + 1) plt.plot(R_mon.times / ms, R_mon[idx]) plt.xlabel('Time (msec)') plt.xlabel('Time (msec)') plt.text(15, np.nanmax(s_mon[idx])/2., 'Peak SPL=%s SPL' % str(level*dB)); plt.ylim(ymin, ymax) if idx == 0: plt.title('Click responses (with spikes and refractoriness)') plt.plot(spike_mon.spiketimes[idx] / ms, np.ones(len(spike_mon.spiketimes[idx])) * np.nanmax(R_mon[idx]), 'rx') print 'Testing tone response' reinit_default_clock() duration = 60*ms levels = [0, 20, 40, 60, 80] tones = Sound([Sound.sequence([tone(cf, duration).atlevel(level*dB).ramp(when='both', duration=10*ms, inplace=False), silence(duration=duration/2)]) for level in levels]) ihc = TanCarney(MiddleEar(tones), [cf] * len(levels), update_interval=1) syn = ZhangSynapse(ihc, cf) s_mon = StateMonitor(syn, 's', record=True, clock=syn.clock) R_mon = StateMonitor(syn, 'R', record=True, clock=syn.clock) spike_mon = SpikeMonitor(syn) net = Network(syn, s_mon, R_mon, spike_mon) net.run(duration * 1.5) for idx, level in enumerate(levels): plt.figure(3) plt.subplot(len(levels), 1, idx + 1) plt.plot(s_mon.times / ms, s_mon[idx]) plt.xlim(0, 120) plt.xlabel('Time (msec)') plt.ylabel('Sp/sec') plt.text(1.25 * duration/ms, np.nanmax(s_mon[idx])/2., '%s SPL' % str(level*dB)); ymin, ymax = plt.ylim() if idx == 0: plt.title('CF=%.0f Hz - Response to Tone at CF' % cf) plt.figure(4) plt.subplot(len(levels), 1, idx + 1) plt.plot(R_mon.times / ms, R_mon[idx]) plt.xlabel('Time (msec)') plt.xlabel('Time (msec)') plt.text(1.25 * duration/ms, np.nanmax(R_mon[idx])/2., '%s SPL' % str(level*dB)); plt.ylim(ymin, ymax) if idx == 0: plt.title('CF=%.0f Hz - Response to Tone at CF (with spikes and refractoriness)' % cf) plt.plot(spike_mon.spiketimes[idx] / ms, np.ones(len(spike_mon.spiketimes[idx])) * np.nanmax(R_mon[idx]), 'rx') plt.show() brian-1.4.3/examples/hears/time_varying_filter1.py000077500000000000000000000066461266062647100222600ustar00rootroot00000000000000#!/usr/bin/env python ''' This example implements a band pass filter whose center frequency is modulated by an Ornstein-Uhlenbeck. The white noise term used for this process is output by a FunctionFilterbank. The bandpass filter coefficients update is an example of how to use a :class:`~brian.hears.ControlFilterbank`. The bandpass filter is a basic biquadratic filter for which the Q factor and the center frequency must be given. The input is a white noise. ''' from brian import * from brian.hears import * samplerate = 20*kHz SoundDuration = 300*ms sound = whitenoise(SoundDuration, samplerate).ramp() #number of frequency channel (here it must be one as a spectrogram of the #output is plotted) nchannels = 1 fc_init = 5000*Hz #initial center frequency of the band pass filter Q = 5 #quality factor of the band pass filter update_interval = 4 # the filter coefficients are updated every 4 samples #parameters of the Ornstein-Uhlenbeck process s_i = 1200*Hz tau_i = 100*ms mu_i = fc_init/tau_i sigma_i = sqrt(2)*s_i/sqrt(tau_i) deltaT = defaultclock.dt #this function is used in a FunctionFilterbank. It outputs a noise term that #will be later used by the controler to update the center frequency noise = lambda x: mu_i*deltaT+sigma_i*randn(1)*sqrt(deltaT) noise_generator = FunctionFilterbank(sound, noise) #this class will take as input the output of the noise generator and as target #the bandpass filter center frequency class CoeffController(object): def __init__(self, target): self.target = target self.deltaT = 1./samplerate self.BW = 2*arcsinh(1./2/Q)*1.44269 self.fc = fc_init def __call__(self, input): #the control variables are taken as the last of the buffer noise_term = input[-1,:] #update the center frequency by updateing the OU process self.fc = self.fc-self.fc/tau_i*self.deltaT+noise_term w0 = 2*pi*self.fc/samplerate #update the coefficient of the biquadratic filterbank alpha = sin(w0)*sinh(log(2)/2*self.BW*w0/sin(w0)) self.target.filt_b[:, 0, 0] = sin(w0)/2 self.target.filt_b[:, 1, 0] = 0 self.target.filt_b[:, 2, 0] = -sin(w0)/2 self.target.filt_a[:, 0, 0] = 1+alpha self.target.filt_a[:, 1, 0] = -2*cos(w0) self.target.filt_a[:, 2, 0] = 1-alpha # In the present example the time varying filter is a LinearFilterbank therefore #we must initialise the filter coefficients; the one used for the first buffer computation w0 = 2*pi*fc_init/samplerate BW = 2*arcsinh(1./2/Q)*1.44269 alpha = sin(w0)*sinh(log(2)/2*BW*w0/sin(w0)) filt_b = zeros((nchannels, 3, 1)) filt_a = zeros((nchannels, 3, 1)) filt_b[:, 0, 0] = sin(w0)/2 filt_b[:, 1, 0] = 0 filt_b[:, 2, 0] = -sin(w0)/2 filt_a[:, 0, 0] = 1+alpha filt_a[:, 1, 0] = -2*cos(w0) filt_a[:, 2, 0] = 1-alpha #the filter which will have time varying coefficients bandpass_filter = LinearFilterbank(sound, filt_b, filt_a) #the updater updater = CoeffController(bandpass_filter) #the controller. Remember it must be the last of the chain control = ControlFilterbank(bandpass_filter, noise_generator, bandpass_filter, updater, update_interval) time_varying_filter_mon = control.process() figure(1) pxx, freqs, bins, im = specgram(squeeze(time_varying_filter_mon), NFFT=256, Fs=samplerate, noverlap=240) imshow(flipud(pxx), aspect='auto') show() brian-1.4.3/examples/hears/time_varying_filter2.py000077500000000000000000000072461266062647100222560ustar00rootroot00000000000000#!/usr/bin/env python ''' This example implements a band pass filter whose center frequency is modulated by a sinusoid function. This modulator is implemented as a :class:`~brian.hears.FunctionFilterbank`. One state variable (here time) must be kept; it is therefore implemented with a class. The bandpass filter coefficients update is an example of how to use a :class:`~brian.hears.ControlFilterbank`. The bandpass filter is a basic biquadratic filter for which the Q factor and the center frequency must be given. The input is a white noise. ''' from brian import * from brian.hears import * samplerate = 20*kHz SoundDuration = 300*ms sound = whitenoise(SoundDuration, samplerate).ramp() #number of frequency channel (here it must be one as a spectrogram of the #output is plotted) nchannels = 1 fc_init = 5000*Hz #initial center frequency of the band pass filter Q = 5 #quality factor of the band pass filter update_interval = 1 # the filter coefficients are updated every sample mean_center_freq = 4*kHz #mean frequency around which the CF will oscillate amplitude = 1500*Hz #amplitude of the oscillation frequency = 10*Hz #frequency of the oscillation #this class is used in a FunctionFilterbank (via its __call__). It outputs the #center frequency of the band pass filter. Its output is thus later passed as #input to the controler. class CenterFrequencyGenerator(object): def __init__(self): self.t=0*second def __call__(self, input): #update of the center frequency fc = mean_center_freq+amplitude*sin(2*pi*frequency*self.t) #update of the state variable self.t = self.t+1./samplerate return fc center_frequency = CenterFrequencyGenerator() fc_generator = FunctionFilterbank(sound, center_frequency) #the updater of the controller generates new filter coefficient of the band pass #filter based on the center frequency it receives from the fc_generator #(its input) class CoeffController(object): def __init__(self, target): self.BW = 2*arcsinh(1./2/Q)*1.44269 self.target=target def __call__(self, input): fc = input[-1,:] #the control variables are taken as the last of the buffer w0 = 2*pi*fc/array(samplerate) alpha = sin(w0)*sinh(log(2)/2*self.BW*w0/sin(w0)) self.target.filt_b[:, 0, 0] = sin(w0)/2 self.target.filt_b[:, 1, 0] = 0 self.target.filt_b[:, 2, 0] = -sin(w0)/2 self.target.filt_a[:, 0, 0] = 1+alpha self.target.filt_a[:, 1, 0] = -2*cos(w0) self.target.filt_a[:, 2, 0] = 1-alpha # In the present example the time varying filter is a LinearFilterbank therefore #we must initialise the filter coefficients; the one used for the first buffer computation w0 = 2*pi*fc_init/samplerate BW = 2*arcsinh(1./2/Q)*1.44269 alpha = sin(w0)*sinh(log(2)/2*BW*w0/sin(w0)) filt_b = zeros((nchannels, 3, 1)) filt_a = zeros((nchannels, 3, 1)) filt_b[:, 0, 0] = sin(w0)/2 filt_b[:, 1, 0] = 0 filt_b[:, 2, 0] = -sin(w0)/2 filt_a[:, 0, 0] = 1+alpha filt_a[:, 1, 0] = -2*cos(w0) filt_a[:, 2, 0] = 1-alpha #the filter which will have time varying coefficients bandpass_filter = LinearFilterbank(sound, filt_b, filt_a) #the updater updater = CoeffController(bandpass_filter) #the controller. Remember it must be the last of the chain control = ControlFilterbank(bandpass_filter, fc_generator, bandpass_filter, updater, update_interval) time_varying_filter_mon = control.process() figure(1) pxx, freqs, bins, im = specgram(squeeze(time_varying_filter_mon), NFFT=256, Fs=samplerate, noverlap=240) imshow(flipud(pxx), aspect='auto') show() brian-1.4.3/examples/interface/000077500000000000000000000000001266062647100164025ustar00rootroot00000000000000brian-1.4.3/examples/interface/index.html000066400000000000000000000020221266062647100203730ustar00rootroot00000000000000 Untitled Document

Random network example with Brian

This is an example with 4000 randomly connected neurons (integrate-and-fire neurons with synaptic currents).

Excitatory weight (mV):

Inhibitory weight (mV):

 

brian-1.4.3/examples/interface/interface.py000077500000000000000000000034111266062647100207160ustar00rootroot00000000000000#!/usr/bin/env python ''' Interface example Install cherrypy for this example Then run the script and go to http://localhost:8080 on your web browser You can use cherrypy to write html interfaces to your code. ''' from brian import * import cherrypy import os.path # The server is defined here class MyInterface(object): @cherrypy.expose def index(self): # redirect to the html page we wrote return '' @cherrypy.expose def runscript(self, we="1.62", wi="-9", **kwd): # 'runscript' is the script name # we and wi are the names of form fields we = float(we) wi = float(wi) # From minimalexample reinit_default_clock() eqs = ''' dv/dt = (ge+gi-(v+49*mV))/(20*ms) : volt dge/dt = -ge/(5*ms) : volt dgi/dt = -gi/(10*ms) : volt ''' P = NeuronGroup(4000, model=eqs, threshold= -50 * mV, reset= -60 * mV) P.v = -60 * mV + 10 * mV * rand(len(P)) Pe = P.subgroup(3200) Pi = P.subgroup(800) Ce = Connection(Pe, P, 'ge') Ci = Connection(Pi, P, 'gi') Ce.connect_random(Pe, P, 0.02, weight=we * mV) Ci.connect_random(Pi, P, 0.02, weight=wi * mV) M = SpikeMonitor(P) run(.5 * second) clf() raster_plot(M) savefig('image.png') # Redirect to the html page we wrote return '' # Set the directory for static files current_dir = os.path.dirname(os.path.abspath(__file__)) conf = {'/': {'tools.staticdir.on':True, 'tools.staticdir.dir':current_dir}} # Start the server cherrypy.quickstart(MyInterface(), config=conf) brian-1.4.3/examples/interface/results.html000066400000000000000000000005621266062647100207740ustar00rootroot00000000000000 Untitled Document

Results:

brian-1.4.3/examples/misc/000077500000000000000000000000001266062647100153755ustar00rootroot00000000000000brian-1.4.3/examples/misc/COBA.py000077500000000000000000000044331266062647100164620ustar00rootroot00000000000000#!/usr/bin/env python # coding: latin-1 """ This is a Brian script implementing a benchmark described in the following review paper: Simulation of networks of spiking neurons: A review of tools and strategies (2007). Brette, Rudolph, Carnevale, Hines, Beeman, Bower, Diesmann, Goodman, Harris, Zirpe, Natschläger, Pecevski, Ermentrout, Djurfeldt, Lansner, Rochel, Vibert, Alvarez, Muller, Davison, El Boustani and Destexhe. Journal of Computational Neuroscience 23(3):349-98 Benchmark 1: random network of integrate-and-fire neurons with exponential synaptic conductances Clock-driven implementation with Euler integration (no spike time interpolation) R. Brette - Dec 2007 -------------------------------------------------------------------------------------- Brian is a simulator for spiking neural networks written in Python, developed by R. Brette and D. Goodman. http://brian.di.ens.fr """ from brian import * import time # Time constants taum = 20 * msecond taue = 5 * msecond taui = 10 * msecond # Reversal potentials Ee = (0. + 60.) * mvolt Ei = (-80. + 60.) * mvolt start_time = time.time() eqs = Equations(''' dv/dt = (-v+ge*(Ee-v)+gi*(Ei-v))*(1./taum) : volt dge/dt = -ge*(1./taue) : 1 dgi/dt = -gi*(1./taui) : 1 ''') # NB 1: conductances are in units of the leak conductance # NB 2: multiplication is faster than division P = NeuronGroup(4000, model=eqs, threshold=10 * mvolt, \ reset=0 * mvolt, refractory=5 * msecond, order=1, compile=True) Pe = P.subgroup(3200) Pi = P.subgroup(800) we = 6. / 10. # excitatory synaptic weight (voltage) wi = 67. / 10. # inhibitory synaptic weight Ce = Connection(Pe, P, 'ge', weight=we, sparseness=0.02) Ci = Connection(Pi, P, 'gi', weight=wi, sparseness=0.02) # Initialization P.v = (randn(len(P)) * 5 - 5) * mvolt P.ge = randn(len(P)) * 1.5 + 4 P.gi = randn(len(P)) * 12 + 20 # Record the number of spikes Me = PopulationSpikeCounter(Pe) Mi = PopulationSpikeCounter(Pi) print "Network construction time:", time.time() - start_time, "seconds" print "Simulation running..." start_time = time.time() run(1 * second) duration = time.time() - start_time print "Simulation time:", duration, "seconds" print Me.nspikes, "excitatory spikes" print Mi.nspikes, "inhibitory spikes" brian-1.4.3/examples/misc/COBAHH.py000077500000000000000000000047741266062647100167120ustar00rootroot00000000000000#!/usr/bin/env python # coding: latin-1 """ This is an implementation of a benchmark described in the following review paper: Simulation of networks of spiking neurons: A review of tools and strategies (2006). Brette, Rudolph, Carnevale, Hines, Beeman, Bower, Diesmann, Goodman, Harris, Zirpe, Natschläger, Pecevski, Ermentrout, Djurfeldt, Lansner, Rochel, Vibert, Alvarez, Muller, Davison, El Boustani and Destexhe. Journal of Computational Neuroscience Benchmark 3: random network of HH neurons with exponential synaptic conductances Clock-driven implementation (no spike time interpolation) R. Brette - Dec 2007 70s for dt=0.1 ms with exponential Euler """ from brian import * # Parameters area = 20000 * umetre ** 2 Cm = (1 * ufarad * cm ** -2) * area gl = (5e-5 * siemens * cm ** -2) * area El = -60 * mV EK = -90 * mV ENa = 50 * mV g_na = (100 * msiemens * cm ** -2) * area g_kd = (30 * msiemens * cm ** -2) * area VT = -63 * mV # Time constants taue = 5 * ms taui = 10 * ms # Reversal potentials Ee = 0 * mV Ei = -80 * mV we = 6 * nS # excitatory synaptic weight (voltage) wi = 67 * nS # inhibitory synaptic weight # The model eqs = Equations(''' dv/dt = (gl*(El-v)+ge*(Ee-v)+gi*(Ei-v)-\ g_na*(m*m*m)*h*(v-ENa)-\ g_kd*(n*n*n*n)*(v-EK))/Cm : volt dm/dt = alpham*(1-m)-betam*m : 1 dn/dt = alphan*(1-n)-betan*n : 1 dh/dt = alphah*(1-h)-betah*h : 1 dge/dt = -ge*(1./taue) : siemens dgi/dt = -gi*(1./taui) : siemens alpham = 0.32*(mV**-1)*(13*mV-v+VT)/ \ (exp((13*mV-v+VT)/(4*mV))-1.)/ms : Hz betam = 0.28*(mV**-1)*(v-VT-40*mV)/ \ (exp((v-VT-40*mV)/(5*mV))-1)/ms : Hz alphah = 0.128*exp((17*mV-v+VT)/(18*mV))/ms : Hz betah = 4./(1+exp((40*mV-v+VT)/(5*mV)))/ms : Hz alphan = 0.032*(mV**-1)*(15*mV-v+VT)/ \ (exp((15*mV-v+VT)/(5*mV))-1.)/ms : Hz betan = .5*exp((10*mV-v+VT)/(40*mV))/ms : Hz ''') P = NeuronGroup(4000, model=eqs, threshold=EmpiricalThreshold(threshold= -20 * mV, refractory=3 * ms), implicit=True, freeze=True) Pe = P.subgroup(3200) Pi = P.subgroup(800) Ce = Connection(Pe, P, 'ge', weight=we, sparseness=0.02) Ci = Connection(Pi, P, 'gi', weight=wi, sparseness=0.02) # Initialization P.v = El + (randn(len(P)) * 5 - 5) * mV P.ge = (randn(len(P)) * 1.5 + 4) * 10. * nS P.gi = (randn(len(P)) * 12 + 20) * 10. * nS # Record the number of spikes and a few traces trace = StateMonitor(P, 'v', record=[1, 10, 100]) run(1 * second) plot(trace[1]) plot(trace[10]) plot(trace[100]) show() brian-1.4.3/examples/misc/CUBA.py000077500000000000000000000043151266062647100164670ustar00rootroot00000000000000#!/usr/bin/env python # coding: latin-1 """ This is a Brian script implementing a benchmark described in the following review paper: Simulation of networks of spiking neurons: A review of tools and strategies (2007). Brette, Rudolph, Carnevale, Hines, Beeman, Bower, Diesmann, Goodman, Harris, Zirpe, Natschläger, Pecevski, Ermentrout, Djurfeldt, Lansner, Rochel, Vibert, Alvarez, Muller, Davison, El Boustani and Destexhe. Journal of Computational Neuroscience 23(3):349-98 Benchmark 2: random network of integrate-and-fire neurons with exponential synaptic currents Clock-driven implementation with exact subthreshold integration (but spike times are aligned to the grid) R. Brette - Oct 2007 -------------------------------------------------------------------------------------- Brian is a simulator for spiking neural networks written in Python, developed by R. Brette and D. Goodman. http://brian.di.ens.fr """ from brian import * import time start_time = time.time() taum = 20 * ms taue = 5 * ms taui = 10 * ms Vt = -50 * mV Vr = -60 * mV El = -49 * mV eqs = Equations(''' dv/dt = (ge+gi-(v-El))/taum : volt dge/dt = -ge/taue : volt dgi/dt = -gi/taui : volt ''') P = NeuronGroup(4000, model=eqs, threshold=Vt, reset=Vr, refractory=5 * ms) P.v = Vr P.ge = 0 * mV P.gi = 0 * mV Pe = P.subgroup(3200) Pi = P.subgroup(800) we = (60 * 0.27 / 10) * mV # excitatory synaptic weight (voltage) wi = (-20 * 4.5 / 10) * mV # inhibitory synaptic weight Ce = Connection(Pe, P, 'ge', weight=we, sparseness=0.02) Ci = Connection(Pi, P, 'gi', weight=wi, sparseness=0.02) P.v = Vr + rand(len(P)) * (Vt - Vr) # Record the number of spikes Me = PopulationSpikeCounter(Pe) Mi = PopulationSpikeCounter(Pi) # A population rate monitor M = PopulationRateMonitor(P) print "Network construction time:", time.time() - start_time, "seconds" print len(P), "neurons in the network" print "Simulation running..." run(1 * msecond) start_time = time.time() run(1 * second) duration = time.time() - start_time print "Simulation time:", duration, "seconds" print Me.nspikes, "excitatory spikes" print Mi.nspikes, "inhibitory spikes" plot(M.times / ms, M.smooth_rate(2 * ms, 'gaussian')) show() brian-1.4.3/examples/misc/HodgkinHuxley.py000077500000000000000000000011431266062647100205330ustar00rootroot00000000000000#!/usr/bin/env python ''' Hodgkin-Huxley model Assuming area 1*cm**2 ''' from brian import * from brian.library.ionic_currents import * #defaultclock.dt=.01*ms # more precise El = 10.6 * mV EK = -12 * mV ENa = 120 * mV eqs = MembraneEquation(1 * uF) + leak_current(.3 * msiemens, El) eqs += K_current_HH(36 * msiemens, EK) + Na_current_HH(120 * msiemens, ENa) eqs += Current('I:amp') neuron = NeuronGroup(1, eqs, implicit=True, freeze=True) trace = StateMonitor(neuron, 'vm', record=True) run(100 * ms) neuron.I = 10 * uA run(100 * ms) plot(trace.times / ms, trace[0] / mV) show() brian-1.4.3/examples/misc/I-F_curve.py000077500000000000000000000017441266062647100175370ustar00rootroot00000000000000#!/usr/bin/env python ''' Input-Frequency curve of a neuron (cortical RS type) Network: 1000 unconnected integrate-and-fire neurons (Brette-Gerstner) with an input parameter I. The input is set differently for each neuron. Spikes are sent to a 'neuron' group with the same size and variable n, which has the role of a spike counter. ''' from brian import * from brian.library.IF import * N = 1000 eqs = Brette_Gerstner() + Current('I:amp') print eqs group = NeuronGroup(N, model=eqs, threshold= -20 * mV, reset=AdaptiveReset()) group.vm = -70 * mV group.I = linspace(0 * nA, 1 * nA, N) counter = NeuronGroup(N, model='n:1') C = IdentityConnection(group, counter, 'n') i = N * 8 / 10 trace = StateMonitor(group, 'vm', record=i) duration = 5 * second run(duration) subplot(211) plot(group.I / nA, counter.n / duration) xlabel('I (nA)') ylabel('Firing rate (Hz)') subplot(212) plot(trace.times / ms, trace[i] / mV) xlabel('Time (ms)') ylabel('Vm (mV)') show() brian-1.4.3/examples/misc/I-F_curve2.py000077500000000000000000000012301266062647100176070ustar00rootroot00000000000000#!/usr/bin/env python ''' Input-Frequency curve of a IF model Network: 1000 unconnected integrate-and-fire neurons (leaky IF) with an input parameter v0. The input is set differently for each neuron. Spikes are sent to a spike counter (counts the spikes emitted by each neuron). ''' from brian import * N = 1000 tau = 10 * ms eqs = ''' dv/dt=(v0-v)/tau : volt v0 : volt ''' group = NeuronGroup(N, model=eqs, threshold=10 * mV, reset=0 * mV, refractory=5 * ms) group.v = 0 * mV group.v0 = linspace(0 * mV, 20 * mV, N) counter = SpikeCounter(group) duration = 5 * second run(duration) plot(group.v0 / mV, counter.count / duration) show() brian-1.4.3/examples/misc/adaptive.py000077500000000000000000000012411266062647100175450ustar00rootroot00000000000000#!/usr/bin/env python ''' An adaptive neuron model ''' from brian import * PG = PoissonGroup(1, 500 * Hz) eqs = ''' dv/dt = (-w-v)/(10*ms) : volt # the membrane equation dw/dt = -w/(30*ms) : volt # the adaptation current ''' # The adaptation variable increases with each spike IF = NeuronGroup(1, model=eqs, threshold=20 * mV, reset='''v = 0*mV w += 3*mV ''') C = Connection(PG, IF, 'v', weight=3 * mV) MS = SpikeMonitor(PG, True) Mv = StateMonitor(IF, 'v', record=True) Mw = StateMonitor(IF, 'w', record=True) run(100 * ms) plot(Mv.times / ms, Mv[0] / mV) plot(Mw.times / ms, Mw[0] / mV) show() brian-1.4.3/examples/misc/adaptive_threshold.py000077500000000000000000000010721266062647100216230ustar00rootroot00000000000000#!/usr/bin/env python ''' A model with adaptive threshold (increases with each spike) ''' from brian import * eqs = ''' dv/dt = -v/(10*ms) : volt dvt/dt = (10*mV-vt)/(15*ms) : volt ''' reset = ''' v=0*mV vt+=3*mV ''' IF = NeuronGroup(1, model=eqs, reset=reset, threshold='v>vt') IF.rest() PG = PoissonGroup(1, 500 * Hz) C = Connection(PG, IF, 'v', weight=3 * mV) Mv = StateMonitor(IF, 'v', record=True) Mvt = StateMonitor(IF, 'vt', record=True) run(100 * ms) plot(Mv.times / ms, Mv[0] / mV) plot(Mvt.times / ms, Mvt[0] / mV) show() brian-1.4.3/examples/misc/after_potential.py000077500000000000000000000007331266062647100211350ustar00rootroot00000000000000#!/usr/bin/env python """ A model with depolarizing after-potential. """ from brian import * v0=20.5*mV eqs = ''' dv/dt = (v0-v)/(30*ms) : volt # the membrane equation dAP/dt = -AP/(3*ms) : volt # the after-potential vm = v+AP : volt # total membrane potential ''' IF = NeuronGroup(1, model=eqs, threshold='vm>20*mV', reset='v=0*mV; AP=10*mV') Mv = StateMonitor(IF, 'vm', record=True) run(500 * ms) plot(Mv.times / ms, Mv[0] / mV) show() brian-1.4.3/examples/misc/cable.py000077500000000000000000000017551266062647100170300ustar00rootroot00000000000000#!/usr/bin/env python ''' Dendrite with 100 compartments ''' from brian import * from brian.compartments import * from brian.library.ionic_currents import * length = 1 * mm nseg = 100 dx = length / nseg Cm = 1 * uF / cm ** 2 gl = 0.02 * msiemens / cm ** 2 diam = 1 * um area = pi * diam * dx El = 0 * mV Ri = 100 * ohm * cm ra = Ri * 4 / (pi * diam ** 2) print "Time constant =", Cm / gl print "Space constant =", .5 * (diam / (gl * Ri)) ** .5 segments = {} for i in range(nseg): segments[i] = MembraneEquation(Cm * area) + leak_current(gl * area, El) segments[0] += Current('I:nA') cable = Compartments(segments) for i in range(nseg - 1): cable.connect(i, i + 1, ra * dx) neuron = NeuronGroup(1, model=cable) #neuron.vm_0=10*mV neuron.I_0 = .05 * nA trace = [] for i in range(10): trace.append(StateMonitor(neuron, 'vm_' + str(10 * i), record=True)) run(200 * ms) for i in range(10): plot(trace[i].times / ms, trace[i][0] / mV) show() brian-1.4.3/examples/misc/correlated_inputs.py000077500000000000000000000013631266062647100215030ustar00rootroot00000000000000#!/usr/bin/env python ''' An example with correlated spike trains From: Brette, R. (2007). Generation of correlated spike trains. ''' from brian import * N = 100 #input = HomogeneousCorrelatedSpikeTrains(N, r=10 * Hz, c=0.1, tauc=10 * ms) c = .2 nu = linspace(1*Hz, 10*Hz, N) P = c*dot(nu.reshape((N,1)), nu.reshape((1,N)))/mean(nu**2) tauc = 5*ms spikes = mixture_process(nu, P, tauc, 1*second) #spikes = [(i,t*second) for i,t in spikes] input = SpikeGeneratorGroup(N, spikes) S = SpikeMonitor(input) #S2 = PopulationRateMonitor(input) #M = StateMonitor(input, 'rate', record=0) run(1000 * ms) #subplot(211) raster_plot(S) #subplot(212) #plot(S2.times / ms, S2.smooth_rate(5 * ms)) #plot(M.times / ms, M[0] / Hz) show() brian-1.4.3/examples/misc/correlated_inputs2.py000077500000000000000000000006731266062647100215700ustar00rootroot00000000000000#!/usr/bin/env python ''' An example with correlated spike trains From: Brette, R. (2007). Generation of correlated spike trains. ''' from brian import * N = 100 c = .2 nu = linspace(1*Hz, 10*Hz, N) P = c*dot(nu.reshape((N,1)), nu.reshape((1,N)))/mean(nu**2) tauc = 5*ms spikes = mixture_process(nu, P, tauc, 1*second) input = SpikeGeneratorGroup(N, spikes) S = SpikeMonitor(input) run(1000 * ms) raster_plot(S) show() brian-1.4.3/examples/misc/current_clamp.py000077500000000000000000000021221266062647100206050ustar00rootroot00000000000000#!/usr/bin/env python ''' An example of single-electrode current clamp recording with bridge compensation (using the electrophysiology library). ''' from brian import * from brian.library.electrophysiology import * taum = 20 * ms # membrane time constant gl = 1. / (50 * Mohm) # leak conductance Cm = taum * gl # membrane capacitance Re = 50 * Mohm # electrode resistance Ce = 0.5 * ms / Re # electrode capacitance eqs = Equations(''' dvm/dt=(-gl*vm+i_inj)/Cm : volt Rbridge:ohm # bridge resistance I:amp # command current ''') eqs += current_clamp(i_cmd='I', Re=Re, Ce=Ce, bridge='Rbridge') setup = NeuronGroup(1, model=eqs) soma = StateMonitor(setup, 'vm', record=True) recording = StateMonitor(setup, 'v_rec', record=True) # No compensation run(50 * ms) setup.I = .5 * nA run(100 * ms) setup.I = 0 * nA run(50 * ms) # Full compensation setup.Rbridge = Re run(50 * ms) setup.I = .5 * nA run(100 * ms) setup.I = 0 * nA run(50 * ms) plot(recording.times / ms, recording[0] / mV, 'b') plot(soma.times / ms, soma[0] / mV, 'r') show() brian-1.4.3/examples/misc/data.pkl000066400000000000000000002756201266062647100170320ustar00rootroot00000000000000€(cbrian.clock Clock q)q}q(U_startqGU_endqG?¹™™™™™šU_tqG?¹™™™™šU_dtqG?6âëC-ubUa dv/dt = (ge+gi-(v+49*mV))/(20*ms) : volt dge/dt = -ge/(5*ms) : volt dgi/dt = -gi/(10*ms) : volt qcbrian.neurongroup NeuronGroup q)q }q (U_Sq cnumpy.core.multiarray _reconstruct q cnumpy ndarray q K…qUbq‡qRq(KKM †qcnumpy dtype qUf8qKK‡qRq(KUd”`6®¿[ó3o†«¿ÃÔÒ®¿ùE ÿi¬¿‘žíÙµ¬¿òç¤Á­¿F^9@—®¿&Þ‚¸ˆ«¿öüêÂŽ†¬¿fÁ–éh ¬¿ê‚ü»Ÿ¬¿œ Ì;qš­¿fÁ–éh ¬¿ªéý>ÿi¬¿, Ó ¹O¬¿Ùm¤Í«¿ùJ µ°z®¿¨u…“ ®¿\ ¸4¦¬¿²¥p›Æ(­¿Òõ–"Ù®¿\LÜŠÿ­¿*(˜é«¿®ÌòÆÿ0­¿œR§V ®¿;Îäj¸¾­¿©'Íu¯¿’ù”A6­¿o‡€ùi™­¿ô‰JM$F®¿m‚ìQõ?¬¿ÿš™:¯³­¿âŸCÐcª¿_­gÔ\ª¿ÇÓ÷e„­¿, Ó ¹O¬¿Xhn笿é]ƒÞÆ«¿, Ó ¹O¬¿ƒü©éͬ­¿:iW ®¿RĹüìj®¿ “hA”b­¿¼š@¦‰­¿kNösþÊ­¿š-<~玭¿Ýîà[ù«¿ªéý>ÿi¬¿rœ¤ûL°¿STeêVä«¿{â©žIú­¿ßˆy*"䮿3L—­¿-_%CÊ(°¿ÕB© °¿?B]$¬¿aåø¯ÀÜ®¿$–2•N®¿ï»yÇÐ1°¿¤(¨Ör5¬¿á”Mkñ¸­¿ýl?Õ$ ¯¿öüêÂŽ†¬¿Ÿº¹¨>­¿—Â{Æa¬¿\ ¸4¦¬¿"ç3ëD½«¿E7ѳb®¿œR§V ®¿æÁ]៦«¿U$ò«„Ĭ¿ñb†;Z¬¿rÆ*‘Ýd­¿ÕÜ,ÂÚ­¿uàm ¬¿ üôªÆ¯¿“º›}N¯¿÷{L6m®¿é„¶¥¯ì¬¿œß†‹Åš«¿üIÏ’®¯¿Î¬Ä;|˜«¿ùg«-°³­¿IkÏWB °¿Ð‚ö¬nÖ¯¿%g²-©­¿¬YRQ•ð®¿Å`WúC¯¿®ÌòÆÿ0­¿ŽZÞ»(”­¿âZ9矪¿o‡€ùi™­¿6þÏìëܬ¿; ðn®­¿¨ íl ¬¿€™ˆSœ_­¿Î¬Ä;|˜«¿° ^sü¬¿tDŠ`¯¿æ´ú Q­¿%g²-©­¿‚&7B‚;­¿a6£s믿Ë|Ù¹Hz¬¿ê ‚ˆÐR­¿e¤‹qiÒ¬¿Þ$ùX[2«¿&›½^Z°¿:1@m­¿Íšm}ùµ­¿ú~–“§ï¬¿Î)Ћ¬¿{Ý÷Ô ¬¿·E.«¿ÙFS5«¿Ô»€2Ú¤¯¿e¤‹qiÒ¬¿ „¦h3®¿KæXaý®¿ìx.iú®¿öüêÂŽ†¬¿£µaõ­¿é„¶¥¯ì¬¿=˜£)? 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]q!(KKJýÿÿÿJÿÿÿÿKKKeb†q"Rq#hGh †q$Rq%hGh †q&Rq'eU_state_updaterq(cbrian.stateupdater LinearStateUpdater q))q*}q+(UAq,h h K…q-h‡q.Rq/(KKK†q0h‰UHŒÒ'i$×ï?@]¦éÌ9t?¶ÆþµSt?[ì­›É]ï?ÿœ+ý|®ï?q1tq2bUBq3h h K…q4h‡q5Rq6(KKK†q7hˆUL +‡©¿€€q8tq9bU_useBq:ˆU _cpp_compilerq;Ugccqh‡q?Rq@(KKK†qAh‰Uó²(0¿qBtqCbU _useaccelqD‰ubU _subgroup_setqEcbrian.magic WeakSet qF]qG(cbrian.magic ExtendedRef qH)qI}qJU_ExtendedRef__idqKKsbhH)qL}qMhKKsbe…qNRqO}qPbU _max_delayqQKUclockqRhU_spikingqSˆULSqTcbrian.utils.circular SpikeContainer qU)qV}qWUthisqXbrian-1.4.3/examples/misc/delays.py000077500000000000000000000011431266062647100172320ustar00rootroot00000000000000#!/usr/bin/env python ''' Random network with external noise and transmission delays ''' from brian import * tau = 10 * ms sigma = 5 * mV eqs = 'dv/dt = -v/tau+sigma*xi/tau**.5 : volt' P = NeuronGroup(4000, model=eqs, threshold=10 * mV, reset=0 * mV, \ refractory=5 * ms) P.v = -60 * mV Pe = P.subgroup(3200) Pi = P.subgroup(800) C = Connection(P, P, 'v', delay=2 * ms) C.connect_random(Pe, P, 0.05, weight=.7 * mV) C.connect_random(Pi, P, 0.05, weight= -2.8 * mV) M = SpikeMonitor(P, True) run(1 * second) print 'Mean rate =', M.nspikes / 4000. / second raster_plot(M) show() brian-1.4.3/examples/misc/expIF_network.py000077500000000000000000000036201266062647100205370ustar00rootroot00000000000000#!/usr/bin/env python ''' A network of exponential IF models with synaptic conductances ''' from brian import * from brian.library.IF import * from brian.library.synapses import * import time C = 200 * pF taum = 10 * msecond gL = C / taum EL = -70 * mV VT = -55 * mV DeltaT = 3 * mV # Synapse parameters Ee = 0 * mvolt Ei = -80 * mvolt taue = 5 * msecond taui = 10 * msecond eqs = exp_IF(C, gL, EL, VT, DeltaT) # Two different ways of adding synaptic currents: eqs += Current(''' Ie=ge*(Ee-vm) : amp dge/dt=-ge/taue : siemens ''') eqs += exp_conductance('gi', Ei, taui) # from library.synapses P = NeuronGroup(4000, model=eqs, threshold= -20 * mvolt, reset=EL, refractory=2 * ms) Pe = P.subgroup(3200) Pi = P.subgroup(800) we = 1.5 * nS # excitatory synaptic weight wi = 2.5 * we # inhibitory synaptic weight Ce = Connection(Pe, P, 'ge', weight=we, sparseness=0.05) Ci = Connection(Pi, P, 'gi', weight=wi, sparseness=0.05) # Initialization P.vm = randn(len(P)) * 10 * mV - 70 * mV P.ge = (randn(len(P)) * 2 + 5) * we P.gi = (randn(len(P)) * 2 + 5) * wi # Excitatory input to a subset of excitatory and inhibitory neurons # Excitatory neurons are excited for the first 200 ms # Inhibitory neurons are excited for the first 100 ms input_layer1 = Pe.subgroup(200) input_layer2 = Pi.subgroup(200) input1 = PoissonGroup(200, rates=lambda t: (t < 200 * ms and 2000 * Hz) or 0 * Hz) input2 = PoissonGroup(200, rates=lambda t: (t < 100 * ms and 2000 * Hz) or 0 * Hz) input_co1 = IdentityConnection(input1, input_layer1, 'ge', weight=we) input_co2 = IdentityConnection(input2, input_layer2, 'ge', weight=we) # Record the number of spikes M = SpikeMonitor(P) print "Simulation running..." start_time = time.time() run(500 * ms) duration = time.time() - start_time print "Simulation time:", duration, "seconds" print M.nspikes / 4000., "spikes per neuron" raster_plot(M) show() brian-1.4.3/examples/misc/gap_junctions.py000077500000000000000000000020101266062647100206060ustar00rootroot00000000000000#!/usr/bin/env python ''' Network of noisy IF neurons with gap junctions ''' from brian import * N = 300 v0 = 5 * mV tau = 20 * ms sigma = 5 * mV vt = 10 * mV vr = 0 * mV g_gap = 1. / N beta = 60 * mV * 2 * ms delta = vt - vr eqs = ''' dv/dt=(v0-v)/tau+g_gap*(u-N*v)/tau : volt du/dt=(N*v0-u)/tau : volt # input from other neurons ''' def myreset(P, spikes): P.v[spikes] = vr # reset P.v += g_gap * beta * len(spikes) # spike effect P.u -= delta * len(spikes) group = NeuronGroup(N, model=eqs, threshold=vt, reset=myreset) @network_operation def noise(cl): x = randn(N) * sigma * (cl.dt / tau) ** .5 group.v += x group.u += sum(x) trace = StateMonitor(group, 'v', record=[0, 1]) spikes = SpikeMonitor(group) rate = PopulationRateMonitor(group) run(1 * second) subplot(311) raster_plot(spikes) subplot(312) plot(trace.times / ms, trace[0] / mV) plot(trace.times / ms, trace[1] / mV) subplot(313) plot(rate.times / ms, rate.smooth_rate(5 * ms) / Hz) show() brian-1.4.3/examples/misc/heterogeneous_delays.py000077500000000000000000000032171266062647100221720ustar00rootroot00000000000000#!/usr/bin/env python ''' Script demonstrating use of a :class:`Connection` with homogenenous delays The network consists of a 'starter' neuron which fires a single spike at time t=0, connected to 100 leaky integrate and fire neurons with different delays for each target neuron, with the delays forming a quadratic curve centred at neuron 50. The longest delay is 10ms, and the network is run for 40ms. At the end, the delays are plotted above a colour plot of the membrane potential of each of the target neurons as a function of time (demonstrating the delays). ''' from brian import * # Starter neuron, threshold is below 0 so it fires immediately, reset is below # threshold so it fires only once. G = NeuronGroup(1, model='V:1', threshold= -1.0, reset= -2.0) # 100 LIF neurons, no reset or threshold so they will not spike H = NeuronGroup(100, model='dV/dt=-V/(10*ms):volt') # Connection with delays, here the delays are specified as a function of (i,j) # giving the delay from neuron i to neuron j. In this case there is only one # presynaptic neuron so i will be 0. C = Connection(G, H, weight=5 * mV, max_delay=10 * ms, delay=lambda i, j:10 * ms * (j / 50. - 1) ** 2) M = StateMonitor(H, 'V', record=True) run(40 * ms) subplot(211) # These are the delays from neuron 0 to neuron i in ms plot([C.delay[0, i] / ms for i in range(100)]) ylabel('Delay (ms)') title('Delays') subplot(212) # M.values is an array of all the recorded values, here transposed to make # it fit with the plot above. imshow(M.values.T, aspect='auto', extent=(0, 100, 40, 0)) xlabel('Neuron number') ylabel('Time (ms)') title('Potential') show() brian-1.4.3/examples/misc/if.py000077500000000000000000000011601266062647100163460ustar00rootroot00000000000000#!/usr/bin/env python ''' A very simple example Brian script to show how to implement an integrate and fire model. In this example, we also drive the single integrate and fire neuron with regularly spaced spikes from the :class:`SpikeGeneratorGroup`. ''' from brian import * tau = 10 * ms Vr = -70 * mV Vt = -55 * mV G = NeuronGroup(1, model='V:volt', threshold=Vt, reset=Vr) input = SpikeGeneratorGroup(1, [(0, t * ms) for t in linspace(10, 100, 25)]) C = Connection(input, G) C[0, 0] = 2 * mV M = StateMonitor(G, 'V', record=True) G.V = Vr run(100 * ms) plot(M.times / ms, M[0] / mV) show() brian-1.4.3/examples/misc/image.png000066400000000000000000003235411266062647100171750ustar00rootroot00000000000000‰PNG  IHDR Xšv‚psBIT|dˆ pHYsaa¨?§i IDATxœì½}¬¦WU7|Ýçã>§--içÐ΀ÔDhù ÑB-”¤íL0Hu`Hm¥_±´Ø‰ÌÁ’Vl€HQ‚¶”6C¿!|*ejcžÈ%Ó™ýþq¿ûœu¯{}{n§ë—œ´sÎuíkí¯µÖoíµ÷¥”R@ sÀÒ© @ ó™ÏtùÈGºsÎ9§FSûÀ>Ð]}õÕÝÅ_Ü}êSŸê~ë·~«;pà@÷ë¿þëSÏõ}ß½à/èî½÷ÞîOþäOº~ô£ÝÉ“'»K/½´ûò—¿\Tf @ ðHÂ(¥”NµCãG?úQwÑEu¯~õ«»î‰O|b÷7ó7]×uÝÃ?Ü=þñï.ºè¢î¯ÿú¯·ÞyË[ÞÒ½ñoìþéŸþ©{êSŸÚu]×>|¸ûÍßüÍîsŸû\÷ó?ÿó]×uÝþçvO|⻾ð…݇?üaw™@ À# ˆ[n¹¥;û쳻믿¾Ã|ëèѣݱcǺýû÷Oýþ•¯|e×u]÷ñ|ëwûØǺ;wn‘®ëºsÎ9§Û³gO÷ÉO~²;yò¤»Ì@ @à‘„Óž€|á _èþøÿ¸;räH·¼¼<ó÷/}éK]×uÝ…^8õû]»vugžyæÖßó³ø¹®ëº§<å)Ý~ô£îk_ûš»Ì@ 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In this example, we also drive the single leaky integrate and fire neuron with regularly spaced spikes from the :class:`SpikeGeneratorGroup`. ''' from brian import * tau = 10 * ms Vr = -70 * mV Vt = -55 * mV G = NeuronGroup(1, model='dV/dt = -(V-Vr)/tau : volt', threshold=Vt, reset=Vr) spikes = [(0, t*second) for t in linspace(10 * ms, 100 * ms, 25)] input = SpikeGeneratorGroup(1, spikes) C = Connection(input, G) C[0, 0] = 5 * mV M = StateMonitor(G, 'V', record=True) G.V = Vr run(100 * ms) plot(M.times / ms, M[0] / mV) show() brian-1.4.3/examples/misc/linked_var.py000077500000000000000000000026471266062647100201010ustar00rootroot00000000000000#!/usr/bin/env python ''' Example showing :func:`linked_var`, connecting two different :class:`NeuronGroup` variables. Here we show something like a simplified haircell and auditory nerve fibre model where the hair cells and ANFs are implemented as two separate :class:`NeuronGroup` objects. The hair cells filter their inputs via a differential equation, and then emit graded amounts of neurotransmitter (variable 'y') to the auditory nerve fibres input current (variable 'I'). ''' from brian import * N = 5 f = 50 * Hz a_min = 1.0 a_max = 100.0 tau_haircell = 50 * ms tau = 10 * ms duration = 100 * ms eqs_haircells = ''' input = a*sin(2*pi*f*t) : 1 x = clip(input, 0, Inf)**(1.0/3.0) : 1 a : 1 dy/dt = (x-y)/tau_haircell : 1 ''' haircells = NeuronGroup(N, eqs_haircells) haircells.a = linspace(a_min, a_max, N) M_haircells = MultiStateMonitor(haircells, vars=('input', 'y'), record=True) eqs_nervefibres = ''' dV/dt = (I-V)/tau : 1 I : 1 ''' nervefibres = NeuronGroup(N, eqs_nervefibres, reset=0, threshold=1) nervefibres.I = linked_var(haircells, 'y') M_nervefibres = MultiStateMonitor(nervefibres, record=True) run(duration) subplot(221) M_haircells['input'].plot() ylabel('haircell.input') subplot(222) M_haircells['y'].plot() ylabel('haircell.y') subplot(223) M_nervefibres['I'].plot() ylabel('nervefibres.I') subplot(224) M_nervefibres['V'].plot() ylabel('nervefibres.V') show() brian-1.4.3/examples/misc/minimalexample.py000077500000000000000000000013511266062647100207540ustar00rootroot00000000000000#!/usr/bin/env python ''' Very short example program. ''' from brian import * eqs = ''' dv/dt = (ge+gi-(v+49*mV))/(20*ms) : volt dge/dt = -ge/(5*ms) : volt dgi/dt = -gi/(10*ms) : volt ''' P = NeuronGroup(4000, model=eqs, threshold= -50 * mV, reset= -60 * mV) P.v = -60 * mV + 10 * mV * rand(len(P)) Pe = P.subgroup(3200) Pi = P.subgroup(800) Ce = Connection(Pe, P, 'ge', weight=1.62 * mV, sparseness=0.02) Ci = Connection(Pi, P, 'gi', weight= -9 * mV, sparseness=0.02) M = SpikeMonitor(P) run(1 * second) i = 0 while len(M[i]) <= 1: i += 1 print "The firing rate of neuron", i, "is", firing_rate(M[i]) * Hz print "The coefficient of variation neuron", i, "is", CV(M[i]) raster_plot(M) show() brian-1.4.3/examples/misc/mirollo_strogatz.py000077500000000000000000000005631266062647100213700ustar00rootroot00000000000000#!/usr/bin/env python """ Mirollo-Strogatz network """ from brian import * tau = 10 * ms v0 = 11 * mV N = 20 w = .1 * mV group = NeuronGroup(N, model='dv/dt=(v0-v)/tau : volt', threshold=10 * mV, reset=0 * mV) W = Connection(group, group, 'v', weight=w) group.v = rand(N) * 10 * mV S = SpikeMonitor(group) run(300 * ms) raster_plot(S) show() brian-1.4.3/examples/misc/multipleclocks.py000077500000000000000000000034151266062647100210070ustar00rootroot00000000000000#!/usr/bin/env python ''' This example demonstrates using different clocks for different objects in the network. The clock ``simclock`` is the clock used for the underlying simulation. The clock ``monclock`` is the clock used for monitoring the membrane potential. This monitoring takes place less frequently than the simulation update step to save time and memory. Finally, the clock ``inputclock`` controls when the external 'current' ``Iext`` should be updated. In this case, we update it infrequently so we can see the effect on the network. This example also demonstrates the @network_operation decorator. A function with this decorator will be run as part of the network update step, in sync with the clock provided (or the default one if none is provided). ''' from brian import * # define the three clocks simclock = Clock(dt=0.1 * ms) monclock = Clock(dt=0.3 * ms) inputclock = Clock(dt=100 * ms) # simple leaky I&F model with external 'current' Iext as a parameter tau = 10 * ms eqs = ''' dV/dt = (-V+Iext)/tau : volt Iext: volt ''' # A single leaky I&F neuron with simclock as its clock G = NeuronGroup(1, model=eqs, reset=0 * mV, threshold=10 * mV, clock=simclock) G.V = 5 * mV # This function will be run in sync with inputclock i.e. every 100 ms @network_operation(clock=inputclock) def update_Iext(): G.Iext = rand(len(G)) * 20 * mV # V is monitored in sync with monclock MV = StateMonitor(G, 'V', record=0, clock=monclock) # run and plot run(1000 * ms) plot(MV.times / ms, MV[0] / mV) show() # You should see 10 different regions, sometimes Iext will be above threshold # in which case you will see regular spiking at different rates, and sometimes # it will be below threshold in which case you'll see exponential decay to that # value brian-1.4.3/examples/misc/named_threshold.py000077500000000000000000000013411266062647100211110ustar00rootroot00000000000000#!/usr/bin/env python ''' Example with named threshold and reset variables ''' from brian import * eqs = ''' dge/dt = -ge/(5*ms) : volt dgi/dt = -gi/(10*ms) : volt dx/dt = (ge+gi-(x+49*mV))/(20*ms) : volt ''' P = NeuronGroup(4000, model=eqs, threshold='x>-50*mV', \ reset=Refractoriness(-60 * mV, 5 * ms, state='x')) #P=NeuronGroup(4000,model=eqs,threshold=Threshold(-50*mV,state='x'),\ # reset=Reset(-60*mV,state='x')) # without refractoriness P.x = -60 * mV Pe = P.subgroup(3200) Pi = P.subgroup(800) Ce = Connection(Pe, P, 'ge', weight=1.62 * mV, sparseness=0.02) Ci = Connection(Pi, P, 'gi', weight= -9 * mV, sparseness=0.02) M = SpikeMonitor(P) run(1 * second) raster_plot(M) show() brian-1.4.3/examples/misc/noisy_ring.py000077500000000000000000000011531266062647100201320ustar00rootroot00000000000000#!/usr/bin/env python ''' Integrate-and-fire neurons with noise ''' from brian import * tau = 10 * ms sigma = .5 N = 100 J = -1 mu = 2 eqs = """ dv/dt=mu/tau+sigma/tau**.5*xi : 1 """ group = NeuronGroup(N, model=eqs, threshold=1, reset=0) C = Connection(group, group, 'v') for i in range(N): C[i, (i + 1) % N] = J #C.connect_full(group,group,weight=J) #for i in range(N): # C[i,i]=0 S = SpikeMonitor(group) trace = StateMonitor(group, 'v', record=True) run(500 * ms) i, t = S.spikes[-1] subplot(211) raster_plot(S) subplot(212) plot(trace.times / ms, trace[0]) show() brian-1.4.3/examples/misc/non_reliability.py000077500000000000000000000007541266062647100211430ustar00rootroot00000000000000#!/usr/bin/env python ''' Reliability of spike timing. See e.g. Mainen & Sejnowski (1995) for experimental results in vitro. Here: a constant current is injected in all trials. R. Brette ''' from brian import * N = 25 tau = 20 * ms sigma = .015 eqs_neurons = ''' dx/dt=(1.1-x)/tau+sigma*(2./tau)**.5*xi:1 ''' neurons = NeuronGroup(N, model=eqs_neurons, threshold=1, reset=0, refractory=5 * ms) spikes = SpikeMonitor(neurons) run(500 * ms) raster_plot(spikes) show() brian-1.4.3/examples/misc/phase_locking.py000077500000000000000000000010551266062647100205610ustar00rootroot00000000000000#!/usr/bin/env python ''' Phase locking of IF neurons to a periodic input ''' from brian import * tau = 20 * ms N = 100 b = 1.2 # constant current mean, the modulation varies f = 10 * Hz eqs = ''' dv/dt=(-v+a*sin(2*pi*f*t)+b)/tau : 1 a : 1 ''' neurons = NeuronGroup(N, model=eqs, threshold=1, reset=0) neurons.v = rand(N) neurons.a = linspace(.05, 0.75, N) S = SpikeMonitor(neurons) trace = StateMonitor(neurons, 'v', record=50) run(1000 * ms) subplot(211) raster_plot(S) subplot(212) plot(trace.times / ms, trace[50]) show() brian-1.4.3/examples/misc/poisson.py000077500000000000000000000016731266062647100174530ustar00rootroot00000000000000#!/usr/bin/env python """ This example demonstrates the PoissonGroup object. Here we have used a custom function to generate different rates at different times. This example also demonstrates a custom SpikeMonitor. """ #import brian_no_units # uncomment to run faster from brian import * # Rates r1 = arange(101, 201) * 0.1 * Hz r2 = arange(1, 101) * 0.1 * Hz def myrates(t): if t < 10 * second: return r1 else: return r2 # More compact: myrates=lambda t: (t<10*second and r1) or r2 # Neuron group P = PoissonGroup(100, myrates) # Calculation of rates ns = zeros(len(P)) def ratemonitor(spikes): ns[spikes] += 1 Mf = SpikeMonitor(P, function=ratemonitor) M = SpikeMonitor(P) # Simulation and plotting run(10 * second) print "Rates after 10s:" print ns / (10 * second) ns[:] = 0 run(10 * second) print "Rates after 20s:" print ns / (10 * second) raster_plot() show() brian-1.4.3/examples/misc/poissongroup.py000077500000000000000000000010371266062647100205220ustar00rootroot00000000000000#!/usr/bin/env python ''' Poisson input to an IF model ''' from brian import * PG = PoissonGroup(1, lambda t:200 * Hz * (1 + cos(2 * pi * t * 50 * Hz))) IF = NeuronGroup(1, model='dv/dt=-v/(10*ms) : volt', reset=0 * volt, threshold=10 * mV) C = Connection(PG, IF, 'v', weight=3 * mV) MS = SpikeMonitor(PG, True) Mv = StateMonitor(IF, 'v', record=True) rates = StateMonitor(PG, 'rate', record=True) run(100 * ms) subplot(211) plot(rates.times / ms, rates[0] / Hz) subplot(212) plot(Mv.times / ms, Mv[0] / mV) show() brian-1.4.3/examples/misc/pulsepacket.py000077500000000000000000000017251266062647100202770ustar00rootroot00000000000000#!/usr/bin/env python ''' This example basically replicates what the Brian PulsePacket object does, and then compares to that object. ''' from brian import * from random import gauss, shuffle # Generator for pulse packet def pulse_packet(t, n, sigma): # generate a list of n times with Gaussian distribution, sort them in time, and # then randomly assign the neuron numbers to them times = [gauss(t, sigma) for i in range(n)] times.sort() neuron = range(n) shuffle(neuron) return zip(neuron, times) # returns a list of pairs (i,t) G1 = SpikeGeneratorGroup(1000, pulse_packet(50 * ms, 1000, 5 * ms)) M1 = SpikeMonitor(G1) PRM1 = PopulationRateMonitor(G1, bin=1 * ms) G2 = PulsePacket(50 * ms, 1000, 5 * ms) M2 = SpikeMonitor(G2) PRM2 = PopulationRateMonitor(G2, bin=1 * ms) run(100 * ms) subplot(221) raster_plot(M1) subplot(223) plot(PRM1.rate) subplot(222) raster_plot(M2) subplot(224) plot(PRM2.rate) show() brian-1.4.3/examples/misc/rate_model.py000077500000000000000000000005221266062647100200640ustar00rootroot00000000000000#!/usr/bin/env python ''' A rate model ''' from brian import * N = 50000 tau = 20 * ms I = 10 * Hz eqs = ''' dv/dt=(I-v)/tau : Hz # note the unit here: this is the output rate ''' group = NeuronGroup(N, eqs, threshold=PoissonThreshold()) S = PopulationRateMonitor(group, bin=1 * ms) run(100 * ms) plot(S.rate) show() brian-1.4.3/examples/misc/realtime_plotting.py000077500000000000000000000022051266062647100214730ustar00rootroot00000000000000#!/usr/bin/env python ''' Realtime plotting example ''' # These lines are necessary for interactive plotting when launching from the # Eclipse IDE, they may not be necessary in every environment. import matplotlib matplotlib.use('WXAgg') # You may need to experiment, try WXAgg, GTKAgg, QTAgg, TkAgg from brian import * ###### Set up the standard CUBA example ###### N = 4000 eqs = ''' dv/dt = (ge+gi-(v+49*mV))/(20*ms) : volt dge/dt = -ge/(5*ms) : volt dgi/dt = -gi/(10*ms) : volt ''' P = NeuronGroup(N, eqs, threshold= -50 * mV, reset= -60 * mV) P.v = -60 * mV + 10 * mV * rand(len(P)) Pe = P.subgroup(3200) Pi = P.subgroup(800) Ce = Connection(Pe, P, 'ge', weight=1.62 * mV, sparseness=0.02) Ci = Connection(Pi, P, 'gi', weight= -9 * mV, sparseness=0.02) M = SpikeMonitor(P) trace = RecentStateMonitor(P, 'v', record=range(5), duration=200 * ms) ion() subplot(211) raster_plot(M, refresh=10 * ms, showlast=200 * ms, redraw=False) subplot(212) trace.plot(refresh=10 * ms, showlast=200 * ms) run(1 * second) ioff() # switch interactive mode off show() # and wait for user to close the window before shutting down brian-1.4.3/examples/misc/reliability.py000077500000000000000000000013201266062647100202570ustar00rootroot00000000000000#!/usr/bin/env python ''' Reliability of spike timing. See e.g. Mainen & Sejnowski (1995) for experimental results in vitro. R. Brette ''' from brian import * # The common noisy input N = 25 tau_input = 5 * ms input = NeuronGroup(1, model='dx/dt=-x/tau_input+(2./tau_input)**.5*xi:1') # The noisy neurons receiving the same input tau = 10 * ms sigma = .015 eqs_neurons = ''' dx/dt=(0.9+.5*I-x)/tau+sigma*(2./tau)**.5*xi:1 I : 1 ''' neurons = NeuronGroup(N, model=eqs_neurons, threshold=1, reset=0, refractory=5 * ms) neurons.x = rand(N) neurons.I = linked_var(input,'x') # input.x is continuously fed into neurons.I spikes = SpikeMonitor(neurons) run(500 * ms) raster_plot(spikes) show() brian-1.4.3/examples/misc/remotecontrolclient.py000077500000000000000000000007721266062647100220530ustar00rootroot00000000000000#!/usr/bin/env python ''' Example of using :class:`RemoteControlServer` and :class:`RemoteControlClient` to control a simulation as it runs in Brian. Run the script remotecontrolserver.py before running this. ''' from brian import * import time client = RemoteControlClient() time.sleep(1) subplot(121) plot(*client.evaluate('(M.times, M.values)')) client.execute('G.I = 1.1') time.sleep(1) subplot(122) plot(*client.evaluate('(M.times, M.values)')) client.stop() show() brian-1.4.3/examples/misc/remotecontrolserver.py000077500000000000000000000010411266062647100220710ustar00rootroot00000000000000#!/usr/bin/env python ''' Example of using :class:`RemoteControlServer` and :class:`RemoteControlClient` to control a simulation as it runs in Brian. After running this script, run remotecontrolclient.py or paste the code from that script into an IPython shell for interactive control. ''' from brian import * eqs = ''' dV/dt = (I-V)/(10*ms)+0.1*xi*(2/(10*ms))**.5 : 1 I : 1 ''' G = NeuronGroup(3, eqs, reset=0, threshold=1) M = RecentStateMonitor(G, 'V', duration=50*ms) server = RemoteControlServer() run(1e10*second) brian-1.4.3/examples/misc/ring.py000077500000000000000000000006371266062647100167170ustar00rootroot00000000000000#!/usr/bin/env python """ A ring of integrate-and-fire neurons. """ from brian import * tau = 10 * ms v0 = 11 * mV N = 20 w = 1 * mV ring = NeuronGroup(N, model='dv/dt=(v0-v)/tau : volt', threshold=10 * mV, reset=0 * mV) W = Connection(ring, ring, 'v') for i in range(N): W[i, (i + 1) % N] = w ring.v = rand(N) * 10 * mV S = SpikeMonitor(ring) run(300 * ms) raster_plot(S) show() brian-1.4.3/examples/misc/spike_triggered_average.py000077500000000000000000000022651266062647100226200ustar00rootroot00000000000000#!/usr/bin/env python ''' Example of the use of the function spike_triggered_average. A white noise is filtered by a gaussian filter (low pass filter) which output is used to generate spikes (poission process) Those spikes are used in conjunction with the input signal to retrieve the filter function. ''' from brian import * from brian.hears import * from numpy.random import randn from numpy.linalg import norm from matplotlib import pyplot dt = 0.1*ms defaultclock.dt = dt stimulus_duration = 15000*ms stimulus = randn(int(stimulus_duration/ dt)) #filter n=200 filt = exp(-((linspace(0.5,n,n))-(n+5)/2)**2/(n/3)); filt = filt/norm(filt)*1000; filtered_stimulus = convolve(stimulus,filt) neuron = PoissonGroup(1,lambda t:filtered_stimulus[int(t/dt)]) spikes = SpikeMonitor(neuron) run(stimulus_duration,report='text') spikes = spikes[0] #resulting spikes max_interval = 20*ms #window duration of the spike triggered average onset = 10*ms sta,time_axis = spike_triggered_average(spikes,stimulus,max_interval,dt,onset=onset,display=True) figure() plot(time_axis,filt/max(filt)) plot(time_axis,sta/max(sta)) xlabel('time axis') ylabel('sta') legend(('real filter','estimated filter')) show() brian-1.4.3/examples/misc/spikes_io.py000077500000000000000000000017631266062647100177460ustar00rootroot00000000000000#!/usr/bin/env python ''' This script demonstrates how to save/load spikes in AER format from inside Brian. ''' from brian import * ####################### SAVING ######################### # First we need to generate some spikes N = 1000 g = PoissonGroup(N, 200*Hz) # And set up a monitor to record those spikes to the disk Maer = AERSpikeMonitor(g, './dummy.aedat') # Now we can run run(100*ms) # This line executed automatically when the script ends, but here we # need to close the file because we re-use it from within the same script Maer.close() clear(all = True) reinit_default_clock() ####################### LOADING ######################## # Now we can re-load the spikes addr, timestamps = load_aer('./dummy.aedat') # Feed them to a SpikeGeneratorGroup group = SpikeGeneratorGroup(N, (addr, timestamps)) # The group can now be used as any other, here we choose to monitor # the spikes newM = SpikeMonitor(group, record = True) run(100*ms) # And plot the result raster_plot(newM) show() brian-1.4.3/examples/misc/stim2d.py000077500000000000000000000034531266062647100171610ustar00rootroot00000000000000#!/usr/bin/env python ''' Example of a 2D stimulus, see the `complete description `__ at the Brian Cookbook. ''' from brian import * import scipy.ndimage as im __all__ = ['bar', 'StimulusArrayGroup'] def bar(width, height, thickness, angle): ''' An array of given dimensions with a bar of given thickness and angle ''' stimulus = zeros((width, height)) stimulus[:, int(height / 2. - thickness / 2.):int(height / 2. + thickness / 2.)] = 1. stimulus = im.rotate(stimulus, angle, reshape=False) return stimulus class StimulusArrayGroup(PoissonGroup): ''' A group of neurons which fire with a given stimulus at a given rate The argument ``stimulus`` should be a 2D array with values between 0 and 1. The point in the stimulus array at position (y,x) will correspond to the neuron with index i=y*width+x. This neuron will fire Poisson spikes at ``rate*stimulus[y,x]`` Hz. The stimulus will start at time ``onset`` for ``duration``. ''' def __init__(self, stimulus, rate, onset, duration): height, width = stimulus.shape stim = stimulus.ravel()*rate self.stimulus = stim def stimfunc(t): if onset < t < (onset + duration): return stim else: return 0. * Hz PoissonGroup.__init__(self, width * height, stimfunc) if __name__ == '__main__': import pylab subplot(121) stim = bar(100, 100, 10, 90) * 0.9 + 0.1 pylab.imshow(stim, origin='lower') pylab.gray() G = StimulusArrayGroup(stim, 50 * Hz, 100 * ms, 100 * ms) M = SpikeMonitor(G) run(300 * ms) subplot(122) raster_plot(M) axis(xmin=0, xmax=300) show() brian-1.4.3/examples/misc/stopping.py000077500000000000000000000025301266062647100176150ustar00rootroot00000000000000#!/usr/bin/env python ''' Network to demonstrate stopping a simulation during a run Have a fully connected network of integrate and fire neurons with input fed by a group of Poisson neurons with a steadily increasing rate, want to determine the point in time at which the network of integrate and fire neurons switches from no firing to all neurons firing, so we have a network_operation called stop_condition that calls the stop() function if the monitored network firing rate is above a minimum threshold. ''' from brian import * clk = Clock() Vr = 0 * mV El = 0 * mV Vt = 10 * mV tau = 10 * ms weight = 0.2 * mV duration = 100 * msecond max_input_rate = 10000 * Hz num_input_neurons = 1000 input_connection_p = 0.1 rate_per_neuron = max_input_rate / (num_input_neurons * input_connection_p) P = PoissonGroup(num_input_neurons, lambda t: rate_per_neuron * (t / duration)) G = NeuronGroup(1000, model='dV/dt=-(V-El)/tau : volt', threshold=Vt, reset=Vr) G.V = Vr + (Vt - Vr) * rand(len(G)) CPG = Connection(P, G, weight=weight, sparseness=input_connection_p) CGG = Connection(G, G, weight=weight) MP = PopulationRateMonitor(G, bin=1 * ms) @network_operation def stop_condition(): if MP.rate[-1] * Hz > 10 * Hz: stop() run(duration) print "Reached population rate>10 Hz by time", clk.t, "+/- 1 ms." brian-1.4.3/examples/misc/timed_array.py000077500000000000000000000013521266062647100202530ustar00rootroot00000000000000#!/usr/bin/env python """ An example of the :class:`TimedArray` class used for applying input currents to neurons. """ from brian import * N = 5 duration = 100 * ms Vr = -60 * mV Vt = -50 * mV tau = 10 * ms Rmin = 1 * Mohm Rmax = 10 * Mohm freq = 50 * Hz k = 10 * nA eqs = ''' dV/dt = (-(V-Vr)+R*I)/tau : volt R : ohm I : amp ''' G = NeuronGroup(N, eqs, reset='V=Vr', threshold='V>Vt') G.R = linspace(Rmin, Rmax, N) t = linspace(0 * second, duration, int(duration / defaultclock.dt)) I = clip(k * sin(2 * pi * freq * t), 0, Inf) G.I = TimedArray(I) M = MultiStateMonitor(G, record=True) run(duration) subplot(211) M['I'].plot() ylabel('I (amp)') subplot(212) M['V'].plot() ylabel('V (volt)') show() brian-1.4.3/examples/misc/topographic_map.py000077500000000000000000000022751266062647100211340ustar00rootroot00000000000000#!/usr/bin/env python ''' Topographic map - an example of complicated connections. Two layers of neurons. The first layer is connected randomly to the second one in a topographical way. The second layer has random lateral connections. ''' from brian import * N = 100 tau = 10 * ms tau_e = 2 * ms # AMPA synapse eqs = ''' dv/dt=(I-v)/tau : volt dI/dt=-I/tau_e : volt ''' rates = zeros(N) * Hz rates[N / 2 - 10:N / 2 + 10] = ones(20) * 30 * Hz layer1 = PoissonGroup(N, rates=rates) layer2 = NeuronGroup(N, model=eqs, threshold=10 * mV, reset=0 * mV) topomap = lambda i, j:exp(-abs(i - j) * .1) * 3 * mV feedforward = Connection(layer1, layer2, sparseness=.5, weight=topomap) #feedforward[2,3]=1*mV lateralmap = lambda i, j:exp(-abs(i - j) * .05) * 0.5 * mV recurrent = Connection(layer2, layer2, sparseness=.5, weight=lateralmap) spikes = SpikeMonitor(layer2) run(1 * second) subplot(211) raster_plot(spikes) subplot(223) imshow(feedforward.W.todense(), interpolation='nearest', origin='lower') title('Feedforward connection strengths') subplot(224) imshow(recurrent.W.todense(), interpolation='nearest', origin='lower') title('Recurrent connection strengths') show() brian-1.4.3/examples/misc/topographic_map2.py000077500000000000000000000027161266062647100212160ustar00rootroot00000000000000#!/usr/bin/env python ''' Topographic map - an example of complicated connections. Two layers of neurons. The first layer is connected randomly to the second one in a topographical way. The second layer has random lateral connections. Each neuron has a position x[i]. ''' from brian import * N = 100 tau = 10 * ms tau_e = 2 * ms # AMPA synapse eqs = ''' dv/dt=(I-v)/tau : volt dI/dt=-I/tau_e : volt ''' rates = zeros(N) * Hz rates[N / 2 - 10:N / 2 + 10] = ones(20) * 30 * Hz layer1 = PoissonGroup(N, rates=rates) layer1.x = linspace(0., 1., len(layer1)) # abstract position between 0 and 1 layer2 = NeuronGroup(N, model=eqs, threshold=10 * mV, reset=0 * mV) layer2.x = linspace(0., 1., len(layer2)) # Generic connectivity function topomap = lambda i, j, x, y, sigma: exp(-abs(x[i] - y[j]) / sigma) feedforward = Connection(layer1, layer2, sparseness=.5, weight=lambda i, j:topomap(i, j, layer1.x, layer2.x, .3) * 3 * mV) recurrent = Connection(layer2, layer2, sparseness=.5, weight=lambda i, j:topomap(i, j, layer1.x, layer2.x, .2) * .5 * mV) spikes = SpikeMonitor(layer2) run(1 * second) subplot(211) raster_plot(spikes) subplot(223) imshow(feedforward.W.todense(), interpolation='nearest', origin='lower') title('Feedforward connection strengths') subplot(224) imshow(recurrent.W.todense(), interpolation='nearest', origin='lower') title('Recurrent connection strengths') show() brian-1.4.3/examples/misc/trace.txt000066400000000000000000007737261266062647100172620ustar00rootroot000000000000000.000000000000000000e+000 1.000000000000000100e-001 2.000000000000000100e-001 3.000000000000000400e-001 4.000000000000000200e-001 5.000000000000000000e-001 6.000000000000000900e-001 7.000000000000000700e-001 8.000000000000001600e-001 9.000000000000001300e-001 1.000000000000000200e+000 1.100000000000000300e+000 1.200000000000000400e+000 1.300000000000000300e+000 1.400000000000000400e+000 1.500000000000000400e+000 1.600000000000000500e+000 1.700000000000000600e+000 1.800000000000000500e+000 1.900000000000000600e+000 2.000000000000000400e+000 2.100000000000000100e+000 2.200000000000000200e+000 2.299999999999999800e+000 2.399999999999999900e+000 2.499999999999999600e+000 2.599999999999999200e+000 2.699999999999999300e+000 2.799999999999998900e+000 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7.374851428172865700e+000 7.410922259146511600e+000 7.446634179355164000e+000 7.481990760020603500e+000 7.516995536830361100e+000 7.551652010291288500e+000 7.585963646079611600e+000 7.619933875387503400e+000 7.653566095266202600e+000 7.686863668965724400e+000 7.719829926271186700e+000 7.752468163835791500e+000 7.784781645510493700e+000 7.816773602670391300e+000 7.848447234537857800e+000 7.879805708502475500e+000 7.910852160437773500e+000 brian-1.4.3/examples/misc/transient_sync.py000077500000000000000000000014311266062647100210140ustar00rootroot00000000000000#!/usr/bin/env python ''' Transient synchronisation in a population of noisy IF neurons with distance-dependent synaptic weights (organised as a ring) ''' from brian import * tau = 10 * ms N = 100 v0 = 5 * mV sigma = 4 * mV group = NeuronGroup(N, model='dv/dt=(v0-v)/tau + sigma*xi/tau**.5 : volt', \ threshold=10 * mV, reset=0 * mV) C = Connection(group, group, 'v', weight=lambda i, j:.4 * mV * cos(2. * pi * (i - j) * 1. / N)) S = SpikeMonitor(group) R = PopulationRateMonitor(group) group.v = rand(N) * 10 * mV run(5000 * ms) subplot(211) raster_plot(S) subplot(223) imshow(C.W.todense(), interpolation='nearest') title('Synaptic connections') subplot(224) plot(R.times / ms, R.smooth_rate(2 * ms, filter='flat')) title('Firing rate') show() brian-1.4.3/examples/misc/two_neurons.py000077500000000000000000000011131266062647100203300ustar00rootroot00000000000000#!/usr/bin/env python ''' Two connected neurons with delays ''' from brian import * tau = 10 * ms w = -1 * mV v0 = 11 * mV neurons = NeuronGroup(2, model='dv/dt=(v0-v)/tau : volt', threshold=10 * mV, reset=0 * mV, \ max_delay=5 * ms) neurons.v = rand(2) * 10 * mV W = Connection(neurons, neurons, 'v', delay=2 * ms) W[0, 1] = w W[1, 0] = w S = StateMonitor(neurons, 'v', record=True) #mymonitor=SpikeMonitor(neurons[0]) mymonitor = PopulationSpikeCounter(neurons) run(500 * ms) plot(S.times / ms, S[0] / mV) plot(S.times / ms, S[1] / mV) show() brian-1.4.3/examples/misc/using_classes.py000077500000000000000000000033151266062647100206160ustar00rootroot00000000000000#!/usr/bin/env python ''' Example of using derived classes in Brian Using a class derived from one of Brian's classes can be a useful way of organising code in complicated simulations. A class such as a :class:`NeuronGroup` can itself create further :class:`NeuronGroup`, :class:`Connection` and :class:`NetworkOperation` objects. In order to have these objects included in the simulation, the derived class has to include them in its ``contained_objects`` list (this tells Brian to add these to the :class:`Network` when the derived class object is added to the network). ''' from brian import * class PoissonDrivenGroup(NeuronGroup): ''' This class is a group of leaky integrate-and-fire neurons driven by external Poisson inputs. The class creates the Poisson inputs and connects them to itself. ''' def __init__(self, N, rate, weight): tau = 10 * ms eqs = ''' dV/dt = -V/tau : 1 ''' # It's essential to call the initialiser of the base class super(PoissonDrivenGroup, self).__init__(N, eqs, reset=0, threshold=1) self.poisson_group = PoissonGroup(N, rate) self.conn = Connection(self.poisson_group, self, 'V') self.conn.connect_one_to_one(weight=weight) self.contained_objects += [self.poisson_group, self.conn] G = PoissonDrivenGroup(100, 100 * Hz, .3) M = SpikeMonitor(G) M_pg = SpikeMonitor(G.poisson_group) trace = StateMonitor(G, 'V', record=0) run(1 * second) subplot(311) raster_plot(M_pg) title('Input spikes') subplot(312) raster_plot(M) title('Output spikes') subplot(313) plot(trace.times, trace[0]) title('Sample trace') show() brian-1.4.3/examples/misc/van_rossum_metric.py000077500000000000000000000022411266062647100215100ustar00rootroot00000000000000#!/usr/bin/env python ''' Example of how to use the van Rossum metric. The VanRossumMetric function, which is defined as a monitor and therefore works online, computes the metric between every neuron in a given population. The present example show the concept of phase locking: N neurons are driven by sinusoidal inputs with different amplitude. Use: output=VanRossumMetric(source, tau=4 * ms) source is a NeuronGroup of N neurons tau is the time constant of the kernel used in the metric output is a monitor with attribute distance which is the distance matrix between the neurons in source ''' from brian import * from time import time tau=20*ms N=100 b=1.2 # constant current mean, the modulation varies f=10*Hz delta =2*ms eqs=''' dv/dt=(-v+a*sin(2*pi*f*t)+b)/tau : 1 a : 1 ''' neurons=NeuronGroup(N,model=eqs,threshold=1,reset=0) neurons.v=rand(N) neurons.a=linspace(.05,0.75,N) S=SpikeMonitor(neurons) trace=StateMonitor(neurons,'v',record=50) van_rossum_metric=VanRossumMetric(neurons, tau=4 * ms) run(1000*ms) raster_plot(S) title('Raster plot') figure() title('Distance matrix between spike trains') imshow(van_rossum_metric.distance) colorbar() 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brian-1.4.3/examples/modelfitting/modelfitting.py000077500000000000000000000020201266062647100221630ustar00rootroot00000000000000#!/usr/bin/env python ''' Model fitting example. Fit an integrate-and-fire model to an in-vitro electrophysiological recording during one second. ''' from brian import loadtxt, ms, Equations from brian.library.modelfitting import * if __name__ == '__main__': equations = Equations(''' dV/dt=(R*I-V)/tau : 1 I : 1 R : 1 tau : second ''') input = loadtxt('current.txt') spikes = loadtxt('spikes.txt') results = modelfitting( model = equations, reset = 0, threshold = 1, data = spikes, input = input, dt = .1*ms, popsize = 1000, maxiter = 3, delta = 4*ms, R = [1.0e9, 9.0e9], tau = [10*ms, 40*ms], refractory = [0*ms, 10*ms]) print_table(results) brian-1.4.3/examples/modelfitting/modelfitting_groups.py000077500000000000000000000022261266062647100235720ustar00rootroot00000000000000#!/usr/bin/env python ''' Example showing how to fit a single model with different target spike trains (several groups). ''' from brian import loadtxt, ms, Equations, second from brian.library.modelfitting import * if __name__ == '__main__': model = Equations(''' dV/dt=(R*I-V)/tau : 1 I : 1 R : 1 tau : second ''') input = loadtxt('current.txt') spikes0 = loadtxt('spikes.txt') spikes = [] for i in xrange(2): spikes.extend([(i, spike*second + 5*i*ms) for spike in spikes0]) results = modelfitting( model = model, reset = 0, threshold = 1, data = spikes, input = input, dt = .1*ms, popsize = 1000, maxiter = 3, cpu = 1, delta = 4*ms, R = [1.0e9, 9.0e9], tau = [10*ms, 40*ms], delays = [-10*ms, 10*ms]) print_table(results) brian-1.4.3/examples/modelfitting/modelfitting_machines.py000077500000000000000000000023451266062647100240440ustar00rootroot00000000000000#!/usr/bin/env python ''' Model fitting example using several machines. Before running this example, you must start the Playdoh server on the remote machines. ''' from brian import loadtxt, ms, Equations from brian.library.modelfitting import * if __name__ == '__main__': # List of machines IP addresses machines = ['bobs-machine.university.com', 'jims-machine.university.com'] equations = Equations(''' dV/dt=(R*I-V)/tau : 1 I : 1 R : 1 tau : second ''') input = loadtxt('current.txt') spikes = loadtxt('spikes.txt') results = modelfitting( model = equations, reset = 0, threshold = 1, data = spikes, input = input, dt = .1*ms, popsize = 1000, maxiter = 3, delta = 4*ms, unit_type = 'CPU', machines = machines, R = [1.0e9, 9.0e9], tau = [10*ms, 40*ms], refractory = [0*ms, 10*ms]) print_table(results) brian-1.4.3/examples/modelfitting/spikes.txt000066400000000000000000000007561266062647100211760ustar00rootroot000000000000005.890000000000128500e-02 1.259000000000014600e-01 2.564999999999990600e-01 2.784000000000013100e-01 3.195000000000014500e-01 3.524999999999991500e-01 3.848999999999982400e-01 4.194999999999993200e-01 4.557999999999999800e-01 4.826000000000014700e-01 5.149000000000008000e-01 5.381999999999997900e-01 5.676999999999985400e-01 6.749000000000009400e-01 7.154999999999986900e-01 7.504999999999988300e-01 8.883000000000009800e-01 9.280000000000008200e-01 9.682999999999992700e-01 brian-1.4.3/examples/multiprocessing/000077500000000000000000000000001266062647100176715ustar00rootroot00000000000000brian-1.4.3/examples/multiprocessing/multiple_runs_simple.py000077500000000000000000000036041266062647100245240ustar00rootroot00000000000000#!/usr/bin/env python ''' Example of using Python multiprocessing module to distribute simulations over multiple processors. The general procedure for using multiprocessing is to define and run a network inside a function, and then use multiprocessing.Pool.map to call the function with multiple parameter values. Note that on Windows, any code that should only run once should be placed inside an if __name__=='__main__' block. ''' from brian import * import multiprocessing # This is the function that we want to compute for various different parameters def how_many_spikes(excitatory_weight): # These two lines reset the clock to 0 and clear any remaining data so that # memory use doesn't build up over multiple runs. reinit_default_clock() clear(True) eqs = ''' dv/dt = (ge+gi-(v+49*mV))/(20*ms) : volt dge/dt = -ge/(5*ms) : volt dgi/dt = -gi/(10*ms) : volt ''' P = NeuronGroup(4000, eqs, threshold= -50 * mV, reset= -60 * mV) P.v = -60 * mV + 10 * mV * rand(len(P)) Pe = P.subgroup(3200) Pi = P.subgroup(800) Ce = Connection(Pe, P, 'ge') Ci = Connection(Pi, P, 'gi') Ce.connect_random(Pe, P, 0.02, weight=excitatory_weight) Ci.connect_random(Pi, P, 0.02, weight= -9 * mV) M = SpikeMonitor(P) run(100 * ms) return M.nspikes if __name__ == '__main__': # Note that on Windows platforms, all code that is executed rather than # just defining functions and classes has to be in the if __name__=='__main__' # block, otherwise it will be executed by each process that starts. This # isn't a problem on Linux. pool = multiprocessing.Pool() # uses num_cpu processes by default weights = linspace(0, 3.5, 100) * mV args = [w * volt for w in weights] results = pool.map(how_many_spikes, args) # launches multiple processes plot(weights, results, '.') show() brian-1.4.3/examples/multiprocessing/multiple_runs_with_gui.py000077500000000000000000000200111266062647100250410ustar00rootroot00000000000000#!/usr/bin/env python ''' A complicated example of using multiprocessing for multiple runs of a simulation with different parameters, using a GUI to monitor and control the runs. This example features: * An indefinite number of runs, with a set of parameters for each run generated at random for each run. * A plot of the output of all the runs updated as soon as each run is completed. * A GUI showing how long each process has been running for and how long until it completes, and with a button allowing you to terminate the runs. A simpler example is in ``examples/multiprocessing/multiple_runs_simple.py``. ''' # We use Tk as the backend for the GUI and matplotlib so as to avoid any # threading conflicts import matplotlib matplotlib.use('TkAgg') from brian import * import Tkinter, time, multiprocessing, os from brian.utils.progressreporting import make_text_report from Queue import Empty as QueueEmpty class SimulationController(Tkinter.Tk): ''' GUI, uses Tkinter and features a progress bar for each process, and a callback function for when the terminate button is clicked. ''' def __init__(self, processes, terminator, width=600): Tkinter.Tk.__init__(self, None) self.parent = None self.grid() button = Tkinter.Button(self, text='Terminate simulation', command=terminator) button.grid(column=0, row=0) self.pb_width = width self.progressbars = [] for i in xrange(processes): can = Tkinter.Canvas(self, width=width, height=30) can.grid(column=0, row=1 + i) can.create_rectangle(0, 0, width, 30, fill='#aaaaaa') r = can.create_rectangle(0, 0, 0, 30, fill='#ffaaaa', width=0) t = can.create_text(width / 2, 15, text='') self.progressbars.append((can, r, t)) self.results_text = Tkinter.Label(self, text='Computed 0 results, time taken: 0s') self.results_text.grid(column=0, row=processes + 1) self.title('Simulation control') def update_results(self, elapsed, complete): ''' Method to update the total number of results computed and the amount of time taken. ''' self.results_text.config(text='Computed ' + str(complete) + ', time taken: ' + str(int(elapsed)) + 's') self.update() def update_process(self, i, elapsed, complete, msg): ''' Method to update the status of a given process. ''' can, r, t = self.progressbars[i] can.itemconfigure(t, text='Process ' + str(i) + ': ' + make_text_report(elapsed, complete) + ': ' + msg) can.coords(r, 0, 0, int(self.pb_width * complete), 30) self.update() def sim_mainloop(pool, results, message_queue): ''' Monitors results of a simulation as they arrive pool is the multiprocessing.Pool that the processes are running in, results is the AsyncResult object returned by Pool.imap_unordered which returns simulation results asynchronously as and when they are ready, and message_queue is a multiprocessing.Queue used to communicate between child processes and the server process. In this case, we use this Queue to send messages about the percent complete and time elapsed for each run. ''' # We use this to enumerate the processes, mapping their process IDs to an int # in the range 0:num_processes. pid_to_id = dict((pid, i) for i, pid in enumerate([p.pid for p in pool._pool])) num_processes = len(pid_to_id) start = time.time() stoprunningsim = [False] # This function terminates all the pool's child processes, it is used as # the callback function called when the terminate button on the GUI is clicked. def terminate_sim(): pool.terminate() stoprunningsim[0] = True controller = SimulationController(num_processes, terminate_sim) for i in range(num_processes): controller.update_process(i, 0, 0, 'no info yet') i = 0 while True: try: # If there is a new result (the 0.1 means wait 0.1 seconds for a # result before giving up) then this try clause will execute, otherwise # a TimeoutError will occur and the except clause afterwards will # execute. weight, numspikes = results.next(0.1) # if we reach here, we have a result to plot, so we plot it and # update the GUI plot_result(weight, numspikes) i = i + 1 controller.update_results(time.time() - start, i) except multiprocessing.TimeoutError: # if we're still waiting for a new result, we can process events in # the message_queue and update the GUI if there are any. while not message_queue.empty(): try: # messages here are of the form: (pid, elapsed, complete) # where pid is the process ID of the child process, elapsed # is the amount of time elapsed, and complete is the # fraction of the run completed. See function how_many_spikes # to see where these messages come from. pid, elapsed, complete = message_queue.get_nowait() controller.update_process(pid_to_id[pid], elapsed, complete, '') except QueueEmpty: break controller.update() if stoprunningsim[0]: print 'Terminated simulation processes' break controller.destroy() def plot_result(weight, numspikes): plot([weight], [numspikes], '.', color=(0, 0, 0.5)) axis('tight') draw() # this forces matplotlib to redraw # Note that how_many_spikes only takes one argument, which is a tuple of # its actual arguments. The reason for this is that Pool.imap_unordered can only # pass a single argument to the function its applied to, but that argument can # be a tuple... def how_many_spikes((excitatory_weight, message_queue)): reinit_default_clock() clear(True) eqs = ''' dv/dt = (ge+gi-(v+49*mV))/(20*ms) : volt dge/dt = -ge/(5*ms) : volt dgi/dt = -gi/(10*ms) : volt ''' P = NeuronGroup(4000, eqs, threshold= -50 * mV, reset= -60 * mV) P.v = -60 * mV + 10 * mV * rand(len(P)) Pe = P.subgroup(3200) Pi = P.subgroup(800) Ce = Connection(Pe, P, 'ge') Ci = Connection(Pi, P, 'gi') Ce.connect_random(Pe, P, 0.02, weight=excitatory_weight) Ci.connect_random(Pi, P, 0.02, weight= -9 * mV) M = SpikeMonitor(P) # This reporter function is called every second, and it sends a message to # the server process updating the status of the current run. def reporter(elapsed, complete): message_queue.put((os.getpid(), elapsed, complete)) run(4000 * ms, report=reporter, report_period=1 * second) return (excitatory_weight, M.nspikes) if __name__ == '__main__': numprocesses = None # number of processes to use, set to None to have one per CPU # We have to use a Queue from the Manager to send messages from client # processes to the server process manager = multiprocessing.Manager() message_queue = manager.Queue() pool = multiprocessing.Pool(processes=numprocesses) # This generator function repeatedly generates random sets of parameters # to pass to the how_many_spikes function def args(): while True: weight = rand()*3.5 * mV yield (weight, message_queue) # imap_unordered returns an AsyncResult object which returns results as # and when they are ready, we pass this results object which is returned # immediately to the sim_mainloop function which monitors this, updates the # GUI and plots the results as they come in. results = pool.imap_unordered(how_many_spikes, args()) ion() # this puts matplotlib into interactive mode to plot as we go sim_mainloop(pool, results, message_queue) brian-1.4.3/examples/multiprocessing/taskfarm.py000077500000000000000000000015261266062647100220620ustar00rootroot00000000000000#!/usr/bin/env python ''' Uses the :func:`~brian.tools.taskfarm.run_tasks` function to run a task on multiple CPUs and save the results to a :class:`~brian.tools.datamanager.DataManager` object. ''' from brian import * from brian.tools.datamanager import * from brian.tools.taskfarm import * def find_rate(k, report): eqs = ''' dV/dt = (k-V)/(10*ms) : 1 ''' G = NeuronGroup(1000, eqs, reset=0, threshold=1) M = SpikeCounter(G) run(30*second, report=report) return (k, mean(M.count)/30) if __name__=='__main__': N = 20 dataman = DataManager('taskfarmexample') if dataman.itemcount()vt',reset='v=El;vt+=vt_inc',refractory=2*ms) layer23.v=El layer23.vt=Vt # Layer 2/3 excitatory layer23exc=layer23.subgroup(Nbarrels*N23exc) x,y=meshgrid(arange(M23exc)*1./M23exc,arange(M23exc)*1./M23exc) x,y=x.flatten(),y.flatten() barrels23 = dict(((i, j), layer23exc.subgroup(N23exc)) for i in xrange(barrelarraysize) for j in xrange(barrelarraysize)) for i in range(barrelarraysize): for j in range(barrelarraysize): barrels23[i,j].x=x+i barrels23[i,j].y=y+j # Layer 2/3 inhibitory layer23inh=layer23.subgroup(Nbarrels*N23inh) x,y=meshgrid(arange(M23inh)*1./M23inh,arange(M23inh)*1./M23inh) x,y=x.flatten(),y.flatten() barrels23inh = dict(((i, j), layer23inh.subgroup(N23inh)) for i in xrange(barrelarraysize) for j in xrange(barrelarraysize)) for i in range(barrelarraysize): for j in range(barrelarraysize): barrels23inh[i,j].x=x+i barrels23inh[i,j].y=y+j print "Building synapses, please wait..." # Feedforward connections feedforward=Synapses(layer4,layer23exc, model='''w:volt A_pre:1 A_post:1''', pre='''ge+=w A_pre=A_pre*exp((lastupdate-t)/taup)+Ap A_post=A_post*exp((lastupdate-t)/taud) w=clip(w+A_post,0,EPSC)''', post=''' A_pre=A_pre*exp((lastupdate-t)/taup) A_post=A_post*exp((lastupdate-t)/taud)+Ad w=clip(w+A_pre,0,EPSC)''') for i in range(barrelarraysize): for j in range(barrelarraysize): feedforward[barrels4[i,j],barrels23[i,j]]=.5 feedforward.w[barrels4[i,j],barrels23[i,j]]=EPSC*.5 # Excitatory lateral connections recurrent_exc=Synapses(layer23exc,layer23,model='w:volt',pre='ge+=w') recurrent_exc[layer23exc,layer23exc]='.15*exp(-.5*(((layer23exc.x[i]-layer23exc.x[j])/.4)**2+((layer23exc.y[i]-layer23exc.y[j])/.4)**2))' recurrent_exc.w[layer23exc,layer23exc]=EPSC*.3 recurrent_exc[layer23exc,layer23inh]='.15*exp(-.5*(((layer23exc.x[i]-layer23inh.x[j])/.4)**2+((layer23exc.y[i]-layer23inh.y[j])/.4)**2))' recurrent_exc.w[layer23exc,layer23inh]=EPSC # Inhibitory lateral connections recurrent_inh=Synapses(layer23inh,layer23exc,model='w:volt',pre='gi+=w') recurrent_inh[:,:]='exp(-.5*(((layer23inh.x[i]-layer23exc.x[j])/.2)**2+((layer23inh.y[i]-layer23exc.y[j])/.2)**2))' recurrent_inh.w=IPSC # Stimulation stimspeed = 1./stim_change_time # speed at which the bar of stimulation moves direction = 0.0 stimzonecentre = ones(2)*barrelarraysize/2. stimcentre,stimnorm = zeros(2),zeros(2) stimradius = (11*stim_change_time*stimspeed+1)*.5 stimradius2 = stimradius**2 def new_direction(): global direction direction = rand()*2*pi stimnorm[:] = (cos(direction), sin(direction)) stimcentre[:] = stimzonecentre-stimnorm*stimradius @network_operation def stimulation(): global direction, stimcentre stimcentre += stimspeed*stimnorm*defaultclock.dt if sum((stimcentre-stimzonecentre)**2)>stimradius2: new_direction() for (i, j), b in barrels4.iteritems(): whiskerpos = array([i,j], dtype=float)+0.5 isactive = abs(dot(whiskerpos-stimcentre, stimnorm))<.5 if barrels4active[i, j]!=isactive: barrels4active[i, j] = isactive b.rate = float(isactive)*tuning(layer4.selectivity[barrelindices[i, j]]-direction) new_direction() t2=time.time() print "Construction time:",t2-t1,"s" run(5*second,report='text') figure() # Preferred direction # perhaps we need to add presynaptic and postsynaptic with 2D/3D access selectivity=array([mean(array(feedforward.w[feedforward.synapses_post[i][:]])*exp(layer4.selectivity[feedforward.presynaptic[feedforward.synapses_post[i][:]]]*1j)) for i in range(len(layer23exc))]) selectivity=(arctan2(selectivity.imag,selectivity.real) % (2*pi))*180./pi I=zeros((barrelarraysize*M23exc,barrelarraysize*M23exc)) ix=array(around(layer23exc.x*M23exc),dtype=int) iy=array(around(layer23exc.y*M23exc),dtype=int) I[iy,ix]=selectivity imshow(I) hsv() colorbar() for i in range(1,barrelarraysize+1): plot([i*max(ix)/barrelarraysize,i*max(ix)/barrelarraysize],[0,max(iy)],'k') plot([0,max(ix)],[i*max(iy)/barrelarraysize,i*max(iy)/barrelarraysize],'k') figure() hist(selectivity) show() brian-1.4.3/examples/synapses/delayed_stdp.py000077500000000000000000000024661266062647100213350ustar00rootroot00000000000000#!/usr/bin/env python ''' Delayed STDP ''' from brian import * import time N = 1 taum = 10 * ms taupre = 20 * ms taupost = taupre Ee = 0 * mV vt = -54 * mV vr = -74 * mV El = -74 * mV taue = 5 * ms F = 20 * Hz dApre = .1 dApost = -dApre * taupre / taupost * 2. eqs_neurons = ''' dv/dt=(ge*(Ee-vr)+El-v)/taum : volt # the synaptic current is linearized dge/dt=-ge/taue : 1 ''' input = PoissonGroup(N, rates=F) neurons = NeuronGroup(1, model=eqs_neurons, threshold=vt, reset=vr) S = Synapses(input, neurons, model='''w:1 dApre/dt=-Apre/taupre : 1 #(event-driven) dApost/dt=-Apost/taupost : 1 #(event-driven)''', pre=('ge+=w', '''w=clip(w+Apost,0,inf) Apre+=dApre'''), post='''Apost+=dApost w=clip(w+Apre,0,inf)''') neurons.v = vr S[:,:]=True S.w=10 S.delay[1][0,0]=3*ms # delayed trace (try 0 ms to see the difference) M=StateMonitor(S,'w',record=0) Mpre=StateMonitor(S,'Apre',record=0) Mpost=StateMonitor(S,'Apost',record=0) Mv=StateMonitor(neurons,'v',record=0) run(10*second,report='text') subplot(211) plot(M.times/ms,M[0]) plot(M.times/ms,Mpre[0],'r') plot(M.times/ms,Mpost[0],'k') subplot(212) plot(Mv.times/ms,Mv[0]) show() brian-1.4.3/examples/synapses/gapjunctions.py000077500000000000000000000011101266062647100213610ustar00rootroot00000000000000#!/usr/bin/env python ''' Neurons with gap junctions ''' from brian import * N = 10 v0=1.05 tau=10*ms eqs = ''' dv/dt=(v0-v+Igap)/tau : 1 Igap : 1 # gap junction current ''' neurons = NeuronGroup(N, model=eqs, threshold=1, reset=0) neurons.v=linspace(0,1,N) trace = StateMonitor(neurons, 'v', record=[0, 5]) S=Synapses(neurons,model='''w:1 # gap junction conductance Igap=w*(v_pre-v_post): 1''') S[:,:]=True neurons.Igap=S.Igap S.w=.02 run(500*ms) plot(trace.times / ms, trace[0]) plot(trace.times / ms, trace[5]) show() brian-1.4.3/examples/synapses/jeffress.py000077500000000000000000000033631266062647100205000ustar00rootroot00000000000000#!/usr/bin/env python ''' Jeffress model, adapted with spiking neuron models. A sound source (white noise) is moving around the head. Delay differences between the two ears are used to determine the azimuth of the source. Delays are mapped to a neural place code using delay lines (each neuron receives input from both ears, with different delays). Romain Brette ''' from brian import * from time import time defaultclock.dt = .02 * ms dt = defaultclock.dt # Sound sound = TimedArray(10 * randn(50000)) # white noise # Ears and sound motion around the head (constant angular speed) sound_speed = 300 * metre / second interaural_distance = 20 * cm # big head! max_delay = interaural_distance / sound_speed print "Maximum interaural delay:", max_delay angular_speed = 2 * pi * radian / second # 1 turn/second tau_ear = 1 * ms sigma_ear = .1 eqs_ears = ''' dx/dt=(sound(t-delay)-x)/tau_ear+sigma_ear*(2./tau_ear)**.5*xi : 1 delay=distance*sin(theta) : second distance : second # distance to the centre of the head in time units dtheta/dt=angular_speed : radian ''' ears = NeuronGroup(2, model=eqs_ears, threshold=1, reset=0, refractory=2.5 * ms) ears.distance = [-.5 * max_delay, .5 * max_delay] traces = StateMonitor(ears, 'x', record=True) # Coincidence detectors N = 300 tau = 1 * ms sigma = .1 eqs_neurons = ''' dv/dt=-v/tau+sigma*(2./tau)**.5*xi : 1 ''' neurons = NeuronGroup(N, model=eqs_neurons, threshold=1, reset=0) synapses = Synapses(ears,neurons,model='w:1',pre='v+=w') synapses[:,:]=True synapses.w=.5 synapses.delay[0, :] = linspace(0 * ms, 1.1 * max_delay, N) synapses.delay[1, :] = linspace(0 * ms, 1.1 * max_delay, N)[::-1] spikes = SpikeMonitor(neurons) run(1*ms) t1=time() run(1000 * ms) t2=time() print "It took",t2-t1,"s" raster_plot(spikes) show() brian-1.4.3/examples/synapses/licklider.py000077500000000000000000000023751266062647100206350ustar00rootroot00000000000000#!/usr/bin/env python ''' Spike-based adaptation of Licklider's model of pitch processing (autocorrelation with delay lines) with phase locking. Romain Brette ''' from brian import * defaultclock.dt = .02 * ms # Ear and sound max_delay = 20 * ms # 50 Hz tau_ear = 1 * ms sigma_ear = .1 eqs_ear = ''' dx/dt=(sound-x)/tau_ear+sigma_ear*(2./tau_ear)**.5*xi : 1 sound=5*sin(2*pi*frequency*t)**3 : 1 # nonlinear distorsion #sound=5*(sin(4*pi*frequency*t)+.5*sin(6*pi*frequency*t)) : 1 # missing fundamental frequency=(200+200*t*Hz)*Hz : Hz # increasing pitch ''' receptors = NeuronGroup(2, model=eqs_ear, threshold=1, reset=0, refractory=2 * ms) # Coincidence detectors min_freq = 50 * Hz max_freq = 1000 * Hz N = 300 tau = 1 * ms sigma = .1 eqs_neurons = ''' dv/dt=-v/tau+sigma*(2./tau)**.5*xi : 1 ''' neurons = NeuronGroup(N, model=eqs_neurons, threshold=1, reset=0) synapses = Synapses(receptors, neurons, model='w : 1', pre='v+=w') synapses[:,:]=True synapses.w=0.5 synapses.delay[1, :] = 1. / exp(linspace(log(min_freq / Hz), log(max_freq / Hz), N)) spikes = SpikeMonitor(neurons) run(500 * ms) raster_plot(spikes) ylabel('Frequency') yticks([0, 99, 199, 299], array(1. / synapses.delay[1, [0, 99, 199, 299]], dtype=int)) show() brian-1.4.3/examples/synapses/multiple_delays.py000077500000000000000000000005651266062647100220660ustar00rootroot00000000000000#!/usr/bin/env python ''' Multiple delays ''' from brian import * P=NeuronGroup(1,model='dv/dt=1/(20*ms):1',threshold=1,reset=0) Q=NeuronGroup(1,model='v:1') S=Synapses(P,Q,model='w:1',pre='v+=w') M=StateMonitor(Q,'v',record=True) S[0,0]=2 S.w[0,0,0]=1. S.w[0,0,1]=.5 S.delay[0,0,0]=.5*ms S.delay[0,0,1]=1*ms run(60*ms) plot(M.times/ms,M[0]) show() brian-1.4.3/examples/synapses/noisy_ring.py000077500000000000000000000010301266062647100210360ustar00rootroot00000000000000#!/usr/bin/env python ''' Integrate-and-fire neurons with noise ''' from brian import * tau = 10 * ms sigma = .5 N = 100 J = -1 mu = 2 eqs = """ dv/dt=mu/tau+sigma/tau**.5*xi : 1 """ group = NeuronGroup(N, model=eqs, threshold=1, reset=0) C = Synapses(group, model='w:1', pre='v+=w') C[:,:]='j==((i+1)%N)' C.w=J S = SpikeMonitor(group) trace = StateMonitor(group, 'v', record=True) run(500 * ms) i, t = S.spikes[-1] subplot(211) raster_plot(S) subplot(212) plot(trace.times / ms, trace[0]) show() brian-1.4.3/examples/synapses/nonlinear_synapses.py000077500000000000000000000014221266062647100225750ustar00rootroot00000000000000#!/usr/bin/env python """ NMDA synapses """ from brian import * import time a=1/(10*ms) b=1/(10*ms) c=1/(10*ms) input=NeuronGroup(2,model='dv/dt=1/(10*ms):1', threshold=1, reset=0) neurons = NeuronGroup(1, model="""dv/dt=(gtot-v)/(10*ms) : 1 gtot : 1""") S=Synapses(input,neurons, model='''dg/dt=-a*g+b*x*(1-g) : 1 dx/dt=-c*x : 1 w : 1 # synaptic weight ''', pre='x+=w') # NMDA synapses neurons.gtot=S.g S[:,:]=True S.w=[1.,10.] input.v=[0.,0.5] M=StateMonitor(S,'g',record=True) Mn=StateMonitor(neurons,'v',record=0) run(100*ms) subplot(211) plot(M.times/ms,M[0]) plot(M.times/ms,M[1]) subplot(212) plot(Mn.times/ms,Mn[0]) show() brian-1.4.3/examples/synapses/one_synapse.py000077500000000000000000000005141266062647100212070ustar00rootroot00000000000000#!/usr/bin/env python ''' One synapse ''' from brian import * P=NeuronGroup(1,model='dv/dt=1/(10*ms):1',threshold=1,reset=0) Q=NeuronGroup(1,model='v:1') S=Synapses(P,Q,model='w:1',pre='v+=w') M=StateMonitor(Q,'v',record=True) S[0,0]=True S.w[0,0]=1. S.delay[0,0]=.5*ms run(40*ms) plot(M.times/ms,M[0]) show() brian-1.4.3/examples/synapses/one_synapse_bis.py000077500000000000000000000006361266062647100220510ustar00rootroot00000000000000#!/usr/bin/env python ''' One synapse within several possibilities. Synapse from 2->3. ''' from brian import * P=NeuronGroup(5,model='dv/dt=1/(10*ms):1',threshold=1,reset=0) Q=NeuronGroup(4,model='v:1') S=Synapses(P,Q,model='w:1',pre='v+=w') M=StateMonitor(Q,'v',record=True) S[2,3]=True S.w[2,3]=1. S.delay[2,3]=.5*ms run(40*ms) for i in range(4): plot(M.times/ms,M[i]+i*2,'k') show() brian-1.4.3/examples/synapses/poisson_synapses.py000077500000000000000000000027461266062647100223140ustar00rootroot00000000000000#!/usr/bin/env python ''' This example shows how to efficiently simulate neurons with a large number of Poisson inputs targetting arbitrarily complex synapses. The approach is very similiar to what the :class:`PoissonInput` class does internally, but :class:`PoissonInput` cannot be combined with the :class:`Synapses` class. You could also just use many :class:`PoissonGroup` objects as inputs, but this is very slow and memory consuming. ''' from brian import * # Poisson inputs M = 1000 # number of Poisson inputs max_rate = 100 # Neurons N = 50 # number of neurons tau = 10 * ms E_exc = 0 * mV E_L = -70 * mV G = NeuronGroup(N, model='dvm/dt = -(vm - E_L)/tau : mV') G.rest() # Dummy neuron group P = NeuronGroup(1, 'v : 1', threshold= -1, reset=0) # spikes every timestep # time varying rate def varying_rate(t): return defaultclock.dt * max_rate * (0.5 + 0.5 * sin(2 * pi * 5 * t)) # Synaptic connections: binomial(cellM, varying_rate(t)) gives the number of # events per timestep. The synapse model is a conductance-based instanteneous # jump in postsynaptic membrane potential S = Synapses(P, G, model=''' J : 1 cellM : 1 ''', pre='vm += binomial(cellM, varying_rate(t)) * J * (E_exc - vm)') S[:, :] = True S.cellM = M #we need one value for M per cell, so that binomial is vectorized S.J = 0.0005 mon = StateMonitor(G, 'vm', record=True) run(1 * second, report='text') mon.plot() show()brian-1.4.3/examples/synapses/probabilistic_synapses.py000077500000000000000000000012401266062647100234340ustar00rootroot00000000000000#!/usr/bin/env python """ Probabilistic synapses Seems to work. """ from brian import * N=20 tau=5*ms input=PoissonGroup(2,rates=20*Hz) neurons=NeuronGroup(N,model='dv/dt=-v/tau : 1') S=Synapses(input,neurons,model="""w : 1 p : 1 # transmission probability""", pre="v+=w*(rand()2,3. ''' from brian import * P=NeuronGroup(2,model='dv/dt=1/(10*ms):1',threshold=1,reset=0) Q=NeuronGroup(4,model='v:1') S=Synapses(P,Q,model='w:1',pre='v+=w') M=StateMonitor(Q,'v',record=True) S[0,2]=True S[0,3]=True S.w[0,:]=[1.,.7] S.delay[0,:]=[.5*ms,.7*ms] run(40*ms) for i in range(4): plot(M.times/ms,M[i]+i*2,'k') show() brian-1.4.3/examples/synapses/weightmonitor.py000077500000000000000000000030531266062647100215640ustar00rootroot00000000000000#!/usr/bin/env python """ Monitoring synaptic variables. STDP example. """ from brian import * from time import time N = 1000 taum = 10 * ms tau_pre = 20 * ms tau_post = tau_pre Ee = 0 * mV vt = -54 * mV vr = -60 * mV El = -74 * mV taue = 5 * ms F = 15 * Hz gmax = .01 dA_pre = .01 dA_post = -dA_pre * tau_pre / tau_post * 1.05 dA_post *= gmax dA_pre *= gmax eqs_neurons = ''' dv/dt=(ge*(Ee-vr)+El-v)/taum : volt # the synaptic current is linearized dge/dt=-ge/taue : 1 ''' input = PoissonGroup(N, rates=F) neurons = NeuronGroup(1, model=eqs_neurons, threshold=vt, reset=vr) S = Synapses(input, neurons, model='''w:1 A_pre:1 A_post:1''', pre='''ge+=w A_pre=A_pre*exp((lastupdate-t)/tau_pre)+dA_pre A_post=A_post*exp((lastupdate-t)/tau_post) w=clip(w+A_post,0,gmax)''', post=''' A_pre=A_pre*exp((lastupdate-t)/tau_pre) A_post=A_post*exp((lastupdate-t)/tau_post)+dA_post w=clip(w+A_pre,0,gmax)''') neurons.v = vr S[:,:]=True S.w='rand()*gmax' rate = PopulationRateMonitor(neurons) M = StateMonitor(S,'w',record=[0,1]) # monitors synapses number 0 and 1 start_time = time() run(10 * second, report='text') print "Simulation time:", time() - start_time figure() subplot(311) plot(rate.times / second, rate.smooth_rate(100 * ms)) subplot(312) plot(S.w[:] / gmax, '.') subplot(313) hist(S.w[:] / gmax, 20) figure() plot(M.times,M[0]/gmax) plot(M.times,M[1]/gmax) show() brian-1.4.3/examples/twister/000077500000000000000000000000001266062647100161435ustar00rootroot00000000000000brian-1.4.3/examples/twister/FriedemannZenke.py000077500000000000000000000066331266062647100215750ustar00rootroot00000000000000#!/usr/bin/env python ''' Friedemann Zenke's winning entry for the 2012 Brian twister. ''' # ########################################### # # Inhibitory synaptic plasticity in a recurrent network model # (F. Zenke, 2011) # # Adapted from: # Vogels, T. P., H. Sprekeler, F. Zenke, C. Clopath, and W. Gerstner. # 'Inhibitory Plasticity Balances Excitation and Inhibition in Sensory # Pathways and Memory Networks.' Science (November 10, 2011). # # ########################################### from brian import * # ########################################### # Defining network model parameters # ########################################### NE = 8000 # Number of excitatory cells NI = NE/4 # Number of inhibitory cells w = 1.*nS # Basic weight unit tau_ampa = 5.0*ms # Glutamatergic synaptic time constant tau_gaba = 10.0*ms # GABAergic synaptic time constant epsilon = 0.02 # Sparseness of synaptic connections eta = 1e-2 # Learning rate tau_stdp = 20*ms # STDP time constant simtime = 10*second # Simulation time # ########################################### # Neuron model # ########################################### gl = 10.0*nsiemens # Leak conductance el = -60*mV # Resting potential er = -80*mV # Inhibitory reversal potential vt = -50.*mV # Spiking threshold memc = 200.0*pfarad # Membrane capacitance bgcurrent = 200*pA # External current eqs_neurons=''' dv/dt=(-gl*(v-el)-(g_ampa*w*v+g_gaba*(v-er)*w)+bgcurrent)/memc : volt dg_ampa/dt = -g_ampa/tau_ampa : 1 dg_gaba/dt = -g_gaba/tau_gaba : 1 ''' # ########################################### # Initialize neuron group # ########################################### neurons=NeuronGroup(NE+NI,model=eqs_neurons,threshold=vt,reset=el,refractory=5*ms) Pe=neurons.subgroup(NE) Pi=neurons.subgroup(NI) # ########################################### # Connecting the network # ########################################### con_e = Connection(Pe,neurons,'g_ampa',weight=0.3,sparseness=epsilon) con_ie = Connection(Pi,Pe,'g_gaba',weight=1e-10,sparseness=epsilon) con_ii = Connection(Pi,Pi,'g_gaba',weight=3,sparseness=epsilon) # ########################################### # Setting up monitors # ########################################### sm = SpikeMonitor(Pe) # ########################################### # Run without plasticity # ########################################### run(1*second) # ########################################### # Inhibitory Plasticity # ########################################### alpha = 3*Hz*tau_stdp*2 # Target rate parameter gmax = 100 # Maximum inhibitory weight eqs_stdp_inhib = ''' dA_pre/dt=-A_pre/tau_stdp : 1 dA_post/dt=-A_post/tau_stdp : 1 ''' stdp_ie = STDP(con_ie, eqs=eqs_stdp_inhib, pre='A_pre+=1.; w+=(A_post-alpha)*eta;', post='A_post+=1.; w+=A_pre*eta;', wmax=gmax) # ########################################### # Run with plasticity # ########################################### run(simtime-1*second,report='text') # ########################################### # Make plots # ########################################### subplot(211) raster_plot(sm,ms=1.) title("Before") xlabel("") xlim(float(0.8*second/ms), float(1*second/ms)) subplot(212) raster_plot(sm,ms=1.) title("After") xlim(float((simtime-0.2*second)/ms), float(simtime/ms)) show() brian-1.4.3/examples/twister/MicheleGiugliano.py000077500000000000000000000101231266062647100217220ustar00rootroot00000000000000#!/usr/bin/env python ''' Michele Giugliano's entry for the 2012 Brian twister. ''' # # Figure5B - from Giugliano et al., 2004 # Journal of Neurophysiology 92(2):977-96 # # implemented by Eleni Vasilaki and # Michele Giugliano # # A sparsely connected network of excitatory neurons, interacting # via current-based synaptic interactions, and incorporating # spike-frequency adaptation, is simulated. # # Its overall emerging firing rate activity replicates some of the features of # spontaneous patterned electrical activity, observed experimentally in cultured # networks of neurons dissociated from the neocortex. # from brian import * # Parameters of the simulation T = 30000 *ms # life time of the simulation N = 100 # total number of (excitatory) integrate-and-fire model neurons in the network # Parameters of each model neuron, voltage dynamics C = 67.95 *pF # Membrane capacitance of single model neurons tau = 22.25 *ms # Membrane time-constant of single model neurons H = 2.39 *mV # Reset voltage, mimicking hyperpolarization potential following a spike theta= 20 *mV # Threshold voltage for spike initiation tauarp=7.76 *ms # Absolute refractory period # Parameters of each model neuron, spike-frequency adaptation dynamics taua = 2100 *ms # Adaptation time constant a = 0.75 *pA # Adaptation scaling factor - NO ADAPTATION D = 1*ms # Unit consistency factor temp = 1. *ms**(-.5) # Unit consistency factor # Parameters of network connectivity Cee = 0.38 # Sparseness of all-to-all random connectivity taue = 5 *ms # Decay time constant of excitatory EPSPs delta= 1.5 * ms # Conduction+synaptic propagation delay J = 14.5* pA # Strenght of synaptic coupling, up to 18 *pA # Parameters of background synaptic activity, modelled as a identical and independent noisy extra-input to each model neuron m0 = 25.1 *pA # Mean background input current s0 = 92 *pA # Std dev of the (noisy) background input current # Each model neuron is described as a leaky integrate-and-fire with adaptation and current-driven synapses eqs = """ dv/dt = - v / tau - a/C * x + Ie/C + (m0 + s0 * xi / temp)/C : mV dx/dt = -x/taua : 1 dIe/dt = -Ie/taue : pA """ # Custom refractory mechanisms are employed here, to allow the membrane potential to be clamped to the reset value H def myresetfunc(P, spikes): P.v[spikes] = H #reset voltage P.x[spikes] += 1 #low pass filter of spikes (adaptation mechanism) SCR = SimpleCustomRefractoriness(myresetfunc, tauarp, state='v') # The population of identical N model neuon is defined now P = NeuronGroup(N, model=eqs, threshold=theta, reset=SCR) # The interneuronal connectivity is defined now Ce = Connection(P, P, 'Ie', weight=J, sparseness=Cee, delay=delta) # Initialization of the state variables, for each model neuron P.v = rand(len(P)) * 20 * mV #membrane potential P.x = rand(len(P)) * 2 #low pass filter of spikes P.Ie = 0 *pA #excitatory synaptic input # Definition of tools for plotting and visualization of single neuron and population quantities R = PopulationRateMonitor(P) M = SpikeMonitor(P) trace = StateMonitor(P, 'v', record=0) tracex = StateMonitor(P, 'x', record=0) print "Simulation running... (long-lasting simulation: be patient)" run(T) print "Simulation completed! If you did not see any firing rate population burst (lower panel), then slightly increase J!" # Plot nice spikes - adapted from Brette's code vm = trace[0] spikes0 = [t for i,t in M.spikes if i==0] for i in range(0,len(spikes0)): k = int(spikes0[i] / defaultclock.dt) vm[k] = 80 * mV subplot(311) #membrane potential of neuron 0 plot(trace.times / ms, vm / mV - 60) subplot(312) #raster plot raster_plot(M) subplot(313) #smoothed population rate plot(R.times / ms, R.smooth_rate(5*ms) / Hz, tracex.times / ms, tracex[0] * 10) ylim(0, 120) show() brian-1.4.3/examples/twister/PeterDiehl.py000077500000000000000000000025541266062647100205530ustar00rootroot00000000000000#!/usr/bin/env python ''' Peter Diehl's entry for the 2012 Brian twister. ''' from brian import * eqs = ''' dv/dt = ((-60.*mV-v)+(I_synE+I_synI+I_b)/(10.*nS))/(20*ms) : volt I_synE = 3.*nS*ge*( 0.*mV-v) : amp I_synI = 30.*nS*gi*(-80.*mV-v) : amp I_b : amp dge/dt = -ge/( 5.*ms) : 1 dgi/dt = -gi/(10.*ms) : 1 ''' P = NeuronGroup(10000, eqs, threshold=-50.*mV, refractory=5.*ms, reset=-60.*mV) Pe = P.subgroup(8000) Pi = P.subgroup(2000) Ce = Connection(Pe, P, 'ge', weight=1., sparseness=0.02) Cie = Connection(Pi, Pe, 'gi', weight=1., sparseness=0.02) Cii = Connection(Pi, Pi, 'gi', weight=1., sparseness=0.02) eqs_stdp = ''' dpre/dt = -pre/(20.*ms) : 1.0 dpost/dt = -post/(20.*ms) : 1.0 ''' nu = 0.1 # learning rate alpha = 0.12 # controls the firing rate stdp = STDP(Cie, eqs=eqs_stdp, pre='pre+= 1.; w+= nu*(post-alpha)', post='post+= 1.; w+= nu*pre', wmin=0., wmax= 10.) M = PopulationRateMonitor(Pe, bin = 1.) P.I_b = 200.*pA #set the input current run(10*second) P.I_b = 600.*pA #increase the input and see how the rate adapts run(10*second) plot(M.times[0:-1]/second, M.rate[0:-1]) show()brian-1.4.3/examples/twister/PierreYger.py000077500000000000000000000167621266062647100206110ustar00rootroot00000000000000#!/usr/bin/env python ''' Pierre Yger's winning entry for the 2012 Brian twister. ''' from brian import * import numpy, os, pylab """ An implementation of a simple topographical network, like those used in Mehring 2005 or Yger 2011. Cells are aranged randomly on a 2D plane and connected according to a gaussian profile P(r) = exp(-d**2/(2*sigma**2)), with delays depending linearly on the distances. Note that the exact number of synapses per neuron is not fixed here. To avoid any border conditions, the plane is considered to be toroidal. Script will generate an Synchronous Irregular (SI) slow regime with propagating waves that will spread in various directions, wandering over the network. In addition, an external layer of Poisson sources will stimulates some cells on the network, with a wiring scheme such that the word BRIAN will pop up. External rates can be turned off to observed the spontaneous activity of the 2D layer. One can observe that despite the inputs is constant, the network is not always responding to it. The script will display, while running, the spikes and Vm of the excitatory cells. Varying sigma will show the various activity structures from a random network (s_lat > 1), to a very locally connected one (s_lat < 0.1) """ ### We are setting the global timestep of the simulation Clock(0.1 * ms) ### Cell parameters ### tau_m = 20. * ms # Membrane time constant c_m = 0.2 * nF # Capacitance tau_exc = 3. * ms # Synaptic time constant (excitatory) tau_inh = 7. * ms # Synaptic time constant (inhibitory) tau_ref = 5. * ms # Refractory period El = -80 * mV # Leak potential Ee = 0. * mV # Reversal potential (excitation) Ei = -70.* mV # Reversal potential (inhibition) Vt = -50 * mV # Spike Threhold Vr = -60 * mV # Spike Reset ### Equation for a Conductance-based IAF #### eqs = Equations(''' dv/dt = (El-v)/tau_m + (ge*(Ee-v)+gi*(Ei-v))/c_m : volt dge/dt = -ge*(1./tau_exc) : uS dgi/dt = -gi*(1./tau_inh) : uS ''') n_cells = 12500 # Total number of cells n_exc = int(0.8 * n_cells) # 4:1 ratio for exc/inh size = 1. # Size of the network simtime = 1000 * ms # Simulation time sim_step = 1 * ms # Display snapshots every sim_step ms epsilon = 0.02 # Probability density s_lat = 0.2 # Spread of the lateral connections g_exc = 4. * nS # Excitatory conductance g_inh = 64. * nS # Inhibitory conductance g_ext = 200 * nS # External drive velocity = 0.3 * mm/ms # velocity ext_rate = 100 * Hz # Rate of the external source max_distance = size * mm/numpy.sqrt(2) # Since this is a torus max_delay = max_distance/velocity # Needed for the connectors ### Generate the images with the letters B, R, I, A, N ### To do that, we create a png image and read it as a matrix pylab.figure() pylab.text(0.125, 0.4, "B R I A N", size=80) pylab.setp(gca(), xticks=[], yticks=[]) pylab.savefig("BRIAN.png") brian_letters = imread("BRIAN.png") os.remove("BRIAN.png") brian_letters = numpy.flipud(mean(brian_letters,2)).T pylab.close() ### We create the cells and generate random positons in [0, size]x[0, size] all_cells = NeuronGroup(n_cells, model=eqs, threshold=Vt, reset=Vr, refractory=tau_ref) all_cells.position = size*numpy.random.rand(n_cells, 2) exc_cells = all_cells[0:n_exc] inh_cells = all_cells[n_exc:n_cells] ### We initialize v values slightly above Vt, to have initial spikes all_cells.v = El + 1.1*numpy.random.rand(n_cells) * (Vt - El) ### Now we create the source that will write the word BRIAN sources = PoissonGroup(1, ext_rate) sources.position = array([[0.5, 0.5]]) ### Function to get the distance between one position and an array of positions ### This is needed to used the vectorized form of the connections in the brian.Connection objects ### Note that the plane is wrapped, to avoid any border effects. def get_distance(x, y): d1 = abs(x - y) min_d = numpy.minimum(d1, size - d1) return numpy.sqrt(numpy.sum(min_d**2, 1)) ### Function returning the probabilities of connections as a functions of distances def probas(i, j, x, y): distance = get_distance(x[i], y[j]) return epsilon * numpy.exp(-distance**2/(2*s_lat**2)) ### Function returning linear delays as function of distances def delays(i, j, x, y): distance = get_distance(x[i], y[j]) return 0.1*ms + (distance * mm )/ velocity ### Function assessing if a cell is located in a particular letter of the word BRIAN ### Return 0 if not, and 1 if yes. def is_in_brian(i, j, x, y): a, b = brian_letters.shape tmp_x, tmp_y = (y[j][:, 0]*a).astype(int), (y[j][:, 1]*b).astype(int) return 1 - brian_letters[tmp_x, tmp_y] print "Building network with wrapped 2D gaussian profiles..." Ce = Connection(exc_cells, all_cells, 'ge', weight=g_exc, max_delay=max_delay, sparseness=lambda i, j : probas(i, j, exc_cells.position, all_cells.position), delay =lambda i, j : delays(i, j, exc_cells.position, all_cells.position)) Ci = Connection(inh_cells, all_cells, 'gi', weight=g_inh, max_delay=max_delay, sparseness=lambda i, j : probas(i, j, inh_cells.position, all_cells.position), delay =lambda i, j : delays(i, j, inh_cells.position, all_cells.position)) Cext = Connection(sources, all_cells, 'ge', weight=g_ext, max_delay=max_delay, sparseness=lambda i, j : is_in_brian(i, j, sources.position, all_cells.position)) print "--> mean probability from excitatory synapses:", Ce.W.getnnz()/float(n_exc*n_cells) * 100, "%" print "--> mean probability from inhibitory synapses:", Ci.W.getnnz()/float((n_cells - n_exc)*n_cells) * 100, "%" print "Setting the recorders..." v_exc = RecentStateMonitor(exc_cells, 'v', record=True) s_exc = SpikeCounter(exc_cells) ion() # To enter the interactive mode print "Initializing the plots..." fig1 = pylab.subplot(211) im = fig1.scatter(all_cells.position[:n_exc, 0], all_cells.position[:n_exc, 1], c=[0]*n_exc) im.set_clim(0, 1) fig1.set_ylabel("spikes") pylab.colorbar(im) fig2 = pylab.subplot(212) im = fig2.scatter(all_cells.position[:n_exc, 0], all_cells.position[:n_exc, 1], c=[0]*n_exc) im.set_clim(El, Vt) fig2.set_ylabel("v") pylab.colorbar(im) manager = pylab.get_current_fig_manager() print "Running network ..." for time in xrange(int((simtime/sim_step)/ms)): run(sim_step) fig1.cla() fig1.set_title("t = %g s" %((sim_step * time)/ms)) idx = s_exc.count > 0 if numpy.sum(idx) > 0: im = fig1.scatter(all_cells.position[:n_exc, 0][idx], all_cells.position[:n_exc, 1][idx], c=[0]*n_exc) s_exc.count = numpy.zeros(n_exc) ## We reset the spike counter fig1.set_xlim(0, size) fig1.set_ylim(0, size) fig1.set_ylabel("spikes") im.set_clim(0, 1) setp(fig1, xticks=[], yticks=[]) fig2.cla() im = fig2.scatter(all_cells.position[:n_exc, 0], all_cells.position[:n_exc, 1], c=v_exc.values[-1]) fig2.set_xlim(0, size) fig2.set_ylim(0, size) fig2.set_ylabel("v") im.set_clim(El, Vt) setp(fig2, xticks=[], yticks=[]) manager.canvas.draw() manager.canvas.flush_events() ioff() # To leave the interactive mode brian-1.4.3/examples/twister/QuentinPauluis.py000077500000000000000000000030621266062647100215070ustar00rootroot00000000000000#!/usr/bin/env python ''' Quentin Pauluis's entry for the 2012 Brian twister. ''' from brian import * taum = 20 * ms # membrane time constant taue = 5 * ms # excitatory synaptic time constant taui = 10 * ms # inhibitory synaptic time constant Vt = -50 * mV # spike threshold Vr = -60 * mV # reset value El = -49 * mV # resting potential we = (60 * 0.27 / 10) * mV # excitatory synaptic weight wi = (20 * 4.5 / 10) * mV # inhibitory synaptic weight eqs = Equations(''' dV/dt = (ge-gi-(V-El))/taum : volt dge/dt = -ge/taue : volt dgi/dt = -gi/taui : volt ''') G = NeuronGroup(4000, model=eqs, threshold=Vt, reset=Vr) Ge = G.subgroup(3200) # Excitatory neurons Gi = G.subgroup(800) # Inhibitory neurons Ce = Connection(Ge, G, 'ge', sparseness=0.2, weight=we) Ci = Connection(Gi, G, 'gi', sparseness=0.2, weight=wi*10) Cii=Connection(Gi,Gi,'gi',sparseness=0.2, weight=wi) M = SpikeMonitor(G) E=SpikeMonitor(Ge,'+') I=SpikeMonitor(Gi,'o') MV = StateMonitor(G, 'V', record=0) Mge = StateMonitor(G, 'ge', record=0) Mgi = StateMonitor(G, 'gi', record=0) G.V = Vr + (Vt - Vr) * rand(len(G)) run(2500 * ms) subplot(211) raster_plot(M, title='The CUBA network', newfigure=False) raster_plot(E) raster_plot(I) subplot(223) plot(MV.times / ms, MV[0] / mV) xlabel('Time (ms)') ylabel('V (mV)') subplot(224) plot(Mge.times / ms, Mge[0] / mV) plot(Mgi.times / ms, Mgi[0] / mV) xlabel('Time (ms)') ylabel('ge and gi (mV)') legend(('ge', 'gi'), 'upper right') show() #new.Figure brian-1.4.3/examples/twister/README.txt000066400000000000000000000002001266062647100176310ustar00rootroot00000000000000These are the example scripts submitted to the 2012 Brian twister. There were two winners: * Friedemann Zenke * Pierre Yger brian-1.4.3/examples/twister/anonymous.py000077500000000000000000000074301266062647100205540ustar00rootroot00000000000000#!/usr/bin/env python ''' Anonymous entry for the 2012 Brian twister. ''' ''' My contribution to the brian twister! I meant to give it more thought, but I forgot about the deadline! ''' from brian import * from brian.hears import * import pygame _mixer_status = [-1,-1] class SoundMonitor(SpikeMonitor): """ Listen to you networks! Plays pure tones whenever a neuron spikes, frequency is set according to the neuron number. """ def __init__(self, source, record=False, delay=0, frange = (100.*Hz, 5000.*Hz), duration = 50*ms, samplerate = 44100*Hz): super(SoundMonitor, self).__init__(source, record = record, delay = delay) self.samplerate = samplerate self.nsamples = np.rint(duration * samplerate) p = linspace(0, 1, len(source)).reshape((1, len(source))) p = np.tile(p, (self.nsamples, 1)) freqs = frange[0] * p + (1-p) * frange[1] del p times = linspace(0*ms, duration, self.nsamples).reshape((self.nsamples, 1)) times = np.tile(times, (1, len(source))) self.sounds = np.sin(2 * np.pi * freqs * times) self._init_mixer() def propagate(self, spikes): if len(spikes): data = np.sum(self.sounds[:,spikes], axis = 1) x = array((2 ** 15 - 1) * clip(data/amax(data), -1, 1), dtype=int16) x.shape = x.size # Make sure pygame receives an array in C-order x = pygame.sndarray.make_sound(np.ascontiguousarray(x)) x.play() def _init_mixer(self): global _mixer_status if _mixer_status==[-1,-1] or _mixer_status[0]!=1 or _mixer_status != self.samplerate: pygame.mixer.quit() pygame.mixer.init(int(self.samplerate), -16, 1) _mixer_status=[1,self.samplerate] def test_cuba(): # The CUBA example with sound! taum = 20 * ms taue = 5 * ms taui = 10 * ms Vt = -50 * mV Vr = -60 * mV El = -49 * mV eqs = Equations(''' dv/dt = (ge+gi-(v-El))/taum : volt dge/dt = -ge/taue : volt dgi/dt = -gi/taui : volt ''') P = NeuronGroup(4000, model=eqs, threshold=Vt, reset=Vr, refractory=5 * ms) P.v = Vr P.ge = 0 * mV P.gi = 0 * mV Pe = P.subgroup(3200) Pi = P.subgroup(800) we = (60 * 0.27 / 10) * mV # excitatory synaptic weight (voltage) wi = (-20 * 4.5 / 10) * mV # inhibitory synaptic weight Ce = Connection(Pe, P, 'ge', weight=we, sparseness=0.5) Ci = Connection(Pi, P, 'gi', weight=wi, sparseness=0.5) P.v = Vr + rand(len(P)) * (Vt - Vr) # Record the number of spikes M = SoundMonitor(P) run(10 * second) def test_synfire(): from brian import * # Neuron model parameters Vr = -70 * mV Vt = -55 * mV taum = 10 * ms taupsp = 0.325 * ms weight = 4.86 * mV # Neuron model eqs = Equations(''' dV/dt=(-(V-Vr)+x)*(1./taum) : volt dx/dt=(-x+y)*(1./taupsp) : volt dy/dt=-y*(1./taupsp)+25.27*mV/ms+\ (39.24*mV/ms**0.5)*xi : volt ''') # Neuron groups P = NeuronGroup(N=1000, model=eqs, threshold=Vt, reset=Vr, refractory=1 * ms) Pinput = PulsePacket(t=50 * ms, n=85, sigma=1 * ms) # The network structure Pgp = [ P.subgroup(100) for i in range(10)] C = Connection(P, P, 'y') for i in range(9): C.connect_full(Pgp[i], Pgp[i + 1], weight) Cinput = Connection(Pinput, Pgp[0], 'y') Cinput.connect_full(weight=weight) monitor = SoundMonitor(P) # Setup the network, and run it P.V = Vr + rand(len(P)) * (Vt - Vr) run(1*second) # Plot result show() if __name__ == '__main__': test_synfire() brian-1.4.3/license.txt000066400000000000000000000034271266062647100150150ustar00rootroot00000000000000---------------------------------------------------------------------------------- Copyright ENS, INRIA, CNRS Contributors: Romain Brette (brette@di.ens.fr) and Dan Goodman (goodman@di.ens.fr) Brian is a computer program whose purpose is to simulate models of biological neural networks. This software is governed by the CeCILL license under French law and abiding by the rules of distribution of free software. You can use, modify and/ or redistribute the software under the terms of the CeCILL license as circulated by CEA, CNRS and INRIA at the following URL "http://www.cecill.info". As a counterpart to the access to the source code and rights to copy, modify and redistribute granted by the license, users are provided only with a limited warranty and the software's author, the holder of the economic rights, and the successive licensors have only limited liability. In this respect, the user's attention is drawn to the risks associated with loading, using, modifying and/or developing or reproducing the software by the user in light of its specific status of free software, that may mean that it is complicated to manipulate, and that also therefore means that it is reserved for developers and experienced professionals having in-depth computer knowledge. Users are therefore encouraged to load and test the software's suitability as regards their requirements in conditions enabling the security of their systems and/or data to be ensured and, more generally, to use and operate it in the same conditions as regards security. The fact that you are presently reading this means that you have had knowledge of the CeCILL license and that you accept its terms. ---------------------------------------------------------------------------------- brian-1.4.3/rtd-requirements.txt000066400000000000000000000000601266062647100166730ustar00rootroot00000000000000numpy>=1.4.1 scipy>=0.7.0 sympy==0.7.1 playdoh brian-1.4.3/setup.py000066400000000000000000000135401266062647100143410ustar00rootroot00000000000000""" Setup script for Brian """ import sys import os from distutils.core import setup, Extension from distutils.command.build_ext import build_ext from distutils.errors import CCompilerError import numpy # Insert the path of the directory where setup.py is located to sys.path, # we need to import brian_setup_info from there setup_path = os.path.dirname(os.path.abspath(__file__)) sys.path.insert(0, setup_path) from brian_setup_info import version class optional_build_ext(build_ext): ''' This class allows the building of C extensions to fail and still continue with the building process. This ensures that installation never fails, even on systems without a C compiler, for example. If brian is installed in an environment where building C extensions *should* work, set the environment variable BRIAN_SETUP_FAIL_ON_ERROR ''' def build_extension(self, ext): try: build_ext.build_extension(self, ext) except CCompilerError, ex: if os.getenv('BRIAN_SETUP_FAIL_ON_ERROR', False): raise ex else: error_msg = ('Building %s failed (see error message(s) ' 'above) -- pure Python version will be used ' 'instead.') % ext.name sys.stderr.write('*' * len(error_msg) + '\n' + error_msg + '\n' + '*' * len(error_msg) + '\n') long_description = ''' Brian is a simulator for spiking neural networks available on almost all platforms. The motivation for this project is that a simulator should not only save the time of processors, but also the time of scientists. Brian is easy to learn and use, highly flexible and easily extensible. The Brian package itself and simulations using it are all written in the Python programming language, which is an easy, concise and highly developed language with many advanced features and development tools, excellent documentation and a large community of users providing support and extension packages. ''' # Allow switching off extensions to allow building a pure Python Windows # installer ext_modules = [] if not os.getenv('BRIAN_SETUP_NO_EXTENSIONS', False): utils_path = os.path.join('brian', 'utils') ext_modules.append(Extension('brian.utils.fastexp._fastexp', sources=[os.path.join(utils_path, 'fastexp', x) for x in ('fastexp_wrap.cxx', 'fastexp.cpp', 'fexp.c')], include_dirs=[numpy.get_include()] )) ext_modules.append(Extension('brian.utils.ccircular._ccircular', sources=[os.path.join(utils_path, 'ccircular', x) for x in ('ccircular_wrap.cxx', 'circular.cpp')], include_dirs=[numpy.get_include()] )) setup(name='brian', version=version, py_modules=['brian_unit_prefs', 'brian_no_units', 'brian_no_units_no_warnings'], packages=['brian', 'brian.connections', 'brian.synapses', 'brian.deprecated', 'brian.experimental', 'brian.experimental.cuda', 'brian.experimental.codegen', 'brian.experimental.codegen2', 'brian.experimental.codegen2.gpu', 'brian.experimental.compensation', 'brian.experimental.cspikequeue', 'brian.experimental.cuda', 'brian.experimental.genn', 'brian.experimental.nemo', 'brian.experimental.modelfitting', 'brian.experimental.neuromorphic', 'brian.hears', 'brian.hears.filtering', 'brian.hears.hrtf', 'brian.library', 'brian.library.electrophysiology', 'brian.library.modelfitting', 'brian.tests', 'brian.tests.testcorrectness', 'brian.tests.testinterface', 'brian.tests.testutils', 'brian.tests.testfeatures', 'brian.tools', 'brian.utils', 'brian.utils.ccircular', 'brian.utils.fastexp', 'brian.utils.sparse_patch', ], ext_modules=ext_modules, # Add the source files for the CSpikeQueue as package data so they end up # being included in the installation directory for a possible later manual # build. package_data={'brian.experimental.cspikequeue': ['*.h', '*.c??']}, cmdclass={'build_ext': optional_build_ext}, provides=['brian'], requires=['matplotlib(>=0.90.1)', 'numpy(>=1.4.1)', 'scipy(>=0.7.0)' ], url='http://www.briansimulator.org/', description='A clock-driven simulator for spiking neural networks', long_description=long_description, author='Romain Brette, Dan Goodman', author_email='Romain.Brette at inserm.fr', download_url='https://neuralensemble.org/trac/brian/wiki/Downloads', classifiers=[ 'Development Status :: 5 - Production/Stable', 'Intended Audience :: Science/Research', 'License :: Other/Proprietary License', 'Natural Language :: English', 'Operating System :: OS Independent', 'Programming Language :: Python', 'Topic :: Scientific/Engineering :: Bio-Informatics' ] ) brian-1.4.3/tutorials/000077500000000000000000000000001266062647100146525ustar00rootroot00000000000000brian-1.4.3/tutorials/tutorial1_basic_concepts/000077500000000000000000000000001266062647100216355ustar00rootroot00000000000000brian-1.4.3/tutorials/tutorial1_basic_concepts/introduction.txt000066400000000000000000000022211266062647100251140ustar00rootroot00000000000000Tutorial 1: Basic Concepts -------------------------- In this tutorial, we introduce some of the basic concepts of a Brian simulation: * Importing the Brian module into Python * Using quantities with units * Defining a neuron model by its differential equation * Creating a group of neurons * Running a network * Looking at the output of the network * Modifying the state variables of the network directly * Defining the network structure by connecting neurons * Doing a raster plot of the output * Plotting the membrane potential of an individual neuron The following Brian classes will be introduced: * :class:`NeuronGroup` * :class:`Connection` * :class:`SpikeMonitor` * :class:`StateMonitor` We will build a Brian program that defines a randomly connected network of integrate and fire neurons and plot its output. This tutorial assumes you know: * The very basics of Python, the ``import`` keyword, variables, basic arithmetical expressions, calling functions, lists * The simplest leaky integrate and fire neuron model The best place to start learning Python is the official tutorial: http://docs.python.org/tut/ brian-1.4.3/tutorials/tutorial1_basic_concepts/tutorial_1a_the_simplest_brian_program.py000066400000000000000000000072341266062647100321230ustar00rootroot00000000000000''' Tutorial 1a: The simplest Brian program *************************************** Importing the Brian module ~~~~~~~~~~~~~~~~~~~~~~~~~~ The first thing to do in any Brian program is to load Brian and the names of its functions and classes. The standard way to do this is to use the Python ``from ... import *`` statement. ''' from brian import * ''' Integrate and Fire model ~~~~~~~~~~~~~~~~~~~~~~~~ The neuron model we will use in this tutorial is the simplest possible leaky integrate and fire neuron, defined by the differential equation: tau dV/dt = -(V-El) and with a threshold value Vt and reset value Vr. Parameters ~~~~~~~~~~ Brian has a system for defining physical quantities (quantities with a physical dimension such as time). The code below illustrates how to use this system, which (mostly) works just as you'd expect. ''' tau = 20 * msecond # membrane time constant Vt = -50 * mvolt # spike threshold Vr = -60 * mvolt # reset value El = -60 * mvolt # resting potential (same as the reset) ''' The built in standard units in Brian consist of all the fundamental SI units like second and metre, along with a selection of derived SI units such as volt, farad, coulomb. All names are lowercase following the SI standard. In addition, there are scaled versions of these units using the standard SI prefixes m=1/1000, K=1000, etc. Neuron model and equations ~~~~~~~~~~~~~~~~~~~~~~~~~~ The simplest way to define a neuron model in Brian is to write a list of the differential equations that define it. For the moment, we'll just give the simplest possible example, a single differential equation. You write it in the following form:: dx/dt = f(x) : unit where ``x`` is the name of the variable, ``f(x)`` can be any valid Python expression, and ``unit`` is the physical units of the variable ``x``. In our case we will write:: dV/dt = -(V-El)/tau : volt to define the variable ``V`` with units ``volt``. To complete the specification of the model, we also define a threshold and reset value and create a group of 40 neurons with this model. ''' G = NeuronGroup(N=40, model='dV/dt = -(V-El)/tau : volt', threshold=Vt, reset=Vr) ''' The statement creates a new object 'G' which is an instance of the Brian class :class:`NeuronGroup`, initialised with the values in the line above and 40 neurons. In Python, you can call a function or initialise a class using keyword arguments as well as ordered arguments, so if I defined a function ``f(x,y)`` I could call it as ``f(1,2)`` or as ``f(y=2,x=1)`` and get the same effect. See the Python tutorial for more information on this. For the moment, we leave the neurons in this group unconnected to each other, each evolves separately from the others. Simulation ~~~~~~~~~~ Finally, we run the simulation for 1 second of simulated time. By default, the simulator uses a timestep dt = 0.1 ms. ''' run(1 * second) ''' And that's it! To see some of the output of this network, go to the next part of the tutorial. Exercise ~~~~~~~~ The units system of Brian is useful for ensuring that everything is consistent, and that you don't make hard to find mistakes in your code by using the wrong units. Try changing the units of one of the parameters and see what happens. Solution ~~~~~~~~ You should see an error message with a Python traceback (telling you which functions were being called when the error happened), ending in a line something like:: Brian.units.DimensionMismatchError: The differential equations are not homogeneous!, dimensions were (m^2 kg s^-3 A^-1) (m^2 kg s^-4 A^-1) ''' brian-1.4.3/tutorials/tutorial1_basic_concepts/tutorial_1b_counting_spikes.py000066400000000000000000000032411266062647100277200ustar00rootroot00000000000000''' Tutorial 1b: Counting spikes **************************** In the previous part of the tutorial we looked at the following: * Importing the Brian module into Python * Using quantities with units * Defining a neuron model by its differential equation * Creating a group of neurons * Running a network In this part, we move on to looking at the output of the network. The first part of the code is the same. ''' from brian import * tau = 20 * msecond # membrane time constant Vt = -50 * mvolt # spike threshold Vr = -60 * mvolt # reset value El = -60 * mvolt # resting potential (same as the reset) G = NeuronGroup(N=40, model='dV/dt = -(V-El)/tau : volt', threshold=Vt, reset=Vr) ''' Counting spikes ~~~~~~~~~~~~~~~ Now we would like to have some idea of what this network is doing. In Brian, we use monitors to keep track of the behaviour of the network during the simulation. The simplest monitor of all is the :class:`SpikeMonitor`, which just records the spikes from a given :class:`NeuronGroup`. ''' M = SpikeMonitor(G) ''' Results ~~~~~~~ Now we run the simulation as before: ''' run(1 * second) ''' And finally, we print out how many spikes there were: ''' print M.nspikes ''' So what's going on? Why are there 40 spikes? Well, the answer is that the initial value of the membrane potential for every neuron is 0 mV, which is above the threshold potential of -50 mV and so there is an initial spike at t=0 and then it resets to -60 mV and stays there, below the threshold potential. In the next part of this tutorial, we'll make sure there are some more spikes to see. ''' brian-1.4.3/tutorials/tutorial1_basic_concepts/tutorial_1c_making_some_activity.py000066400000000000000000000042151266062647100307240ustar00rootroot00000000000000''' Tutorial 1c: Making some activity ********************************* In the previous part of the tutorial we found that each neuron was producing only one spike. In this part, we alter the model so that some more spikes will be generated. What we'll do is alter the resting potential ``El`` so that it is above threshold, this will ensure that some spikes are generated. The first few lines remain the same: ''' from brian import * tau = 20 * msecond # membrane time constant Vt = -50 * mvolt # spike threshold Vr = -60 * mvolt # reset value ''' But we change the resting potential to -49 mV, just above the spike threshold: ''' El = -49 * mvolt # resting potential (same as the reset) ''' And then continue as before: ''' G = NeuronGroup(N=40, model='dV/dt = -(V-El)/tau : volt', threshold=Vt, reset=Vr) M = SpikeMonitor(G) run(1 * second) print M.nspikes ''' Running this program gives the output ``840``. That's because every neuron starts at the same initial value and proceeds deterministically, so that each neuron fires at exactly the same time, in total 21 times during the 1s of the run. In the next part, we'll introduce a random element into the behaviour of the network. Exercises ~~~~~~~~~ 1. Try varying the parameters and seeing how the number of spikes generated varies. 2. Solve the differential equation by hand and compute a formula for the number of spikes generated. Compare this with the program output and thereby partially verify it. (Hint: each neuron starts at above the threshold and so fires a spike immediately.) Solution ~~~~~~~~ Solving the differential equation gives: V = El + (Vr-El) exp (-t/tau) Setting V=Vt at time t gives: t = tau log( (Vr-El) / (Vt-El) ) If the simulator runs for time T, and fires a spike immediately at the beginning of the run it will then generate n spikes, where: n = [T/t] + 1 If you have m neurons all doing the same thing, you get nm spikes. This calculation with the parameters above gives: t = 48.0 ms n = 21 nm = 840 As predicted. ''' brian-1.4.3/tutorials/tutorial1_basic_concepts/tutorial_1d_introducing_randomness.py000066400000000000000000000031101266062647100312670ustar00rootroot00000000000000''' Tutorial 1d: Introducing randomness *********************************** In the previous part of the tutorial, all the neurons start at the same values and proceed deterministically, so they all spike at exactly the same times. In this part, we introduce some randomness by initialising all the membrane potentials to uniform random values between the reset and threshold values. We start as before: ''' from brian import * tau = 20 * msecond # membrane time constant Vt = -50 * mvolt # spike threshold Vr = -60 * mvolt # reset value El = -49 * mvolt # resting potential (same as the reset) G = NeuronGroup(N=40, model='dV/dt = -(V-El)/tau : volt', threshold=Vt, reset=Vr) M = SpikeMonitor(G) ''' But before we run the simulation, we set the values of the membrane potentials directly. The notation ``G.V`` refers to the array of values for the variable ``V`` in group ``G``. In our case, this is an array of length 40. We set its values by generating an array of random numbers using Brian's ``rand`` function. The syntax is ``rand(size)`` generates an array of length ``size`` consisting of uniformly distributed random numbers in the interval 0, 1. ''' G.V = Vr + rand(40) * (Vt - Vr) ''' And now we run the simulation as before. ''' run(1 * second) print M.nspikes ''' But this time we get a varying number of spikes each time we run it, roughly between 800 and 850 spikes. In the next part of this tutorial, we introduce a bit more interest into this network by connecting the neurons together. ''' brian-1.4.3/tutorials/tutorial1_basic_concepts/tutorial_1e_connecting_neurons.py000066400000000000000000000060201266062647100304150ustar00rootroot00000000000000''' Tutorial 1e: Connecting neurons ******************************* In the previous parts of this tutorial, the neurons are still all unconnected. We add in connections here. The model we use is that when neuron i is connected to neuron j and neuron i fires a spike, then the membrane potential of neuron j is instantaneously increased by a value ``psp``. We start as before: ''' from brian import * tau = 20 * msecond # membrane time constant Vt = -50 * mvolt # spike threshold Vr = -60 * mvolt # reset value El = -49 * mvolt # resting potential (same as the reset) ''' Now we include a new parameter, the PSP size: ''' psp = 0.5 * mvolt # postsynaptic potential size ''' And continue as before: ''' G = NeuronGroup(N=40, model='dV/dt = -(V-El)/tau : volt', threshold=Vt, reset=Vr) ''' Connections ~~~~~~~~~~~ We now proceed to connect these neurons. Firstly, we declare that there is a connection from neurons in ``G`` to neurons in ``G``. For the moment, this is just something that is necessary to do, the reason for doing it this way will become clear in the next tutorial. ''' C = Connection(G, G) ''' Now the interesting part, we make these neurons be randomly connected with probability 0.1 and weight ``psp``. Each neuron i in ``G`` will be connected to each neuron j in ``G`` with probability 0.1. The weight of the connection is the amount that is added to the membrane potential of the target neuron when the source neuron fires a spike. ''' C.connect_random(sparseness=0.1, weight=psp) ''' These two previous lines could be done in one line:: C = Connection(G,G,sparseness=0.1,weight=psp) Now we continue as before: ''' M = SpikeMonitor(G) G.V = Vr + rand(40) * (Vt - Vr) run(1 * second) print M.nspikes ''' You can see that the number of spikes has jumped from around 800-850 to around 1000-1200. In the next part of the tutorial, we'll look at a way to plot the output of the network. Exercise ~~~~~~~~ Try varying the parameter ``psp`` and see what happens. How large can you make the number of spikes output by the network? Why? Solution ~~~~~~~~ The logically maximum number of firings is 400,000 = 40 * 1000 / 0.1, the number of neurons in the network * the time it runs for / the integration step size (you cannot have more than one spike per step). In fact, the number of firings is bounded above by 200,000. The reason for this is that the network updates in the following way: 1. Integration step 2. Find neurons above threshold 3. Propagate spikes 4. Reset neurons which spiked You can see then that if neuron i has spiked at time t, then it will not spike at time t+dt, even if it receives spikes from another neuron. Those spikes it receives will be added at step 3 at time t, then reset to ``Vr`` at step 4 of time t, then the thresholding function at time t+dt is applied at step 2, before it has received any subsequent inputs. So the most a neuron can spike is every other time step. ''' brian-1.4.3/tutorials/tutorial1_basic_concepts/tutorial_1f_recording_spikes.py000066400000000000000000000026111266062647100300520ustar00rootroot00000000000000''' Tutorial 1f: Recording spikes ***************************** In the previous part of the tutorial, we defined a network with not entirely trivial behaviour, and printed the number of spikes. In this part, we'll record every spike that the network generates and display a raster plot of them. We start as before: ''' from brian import * tau = 20 * msecond # membrane time constant Vt = -50 * mvolt # spike threshold Vr = -60 * mvolt # reset value El = -49 * mvolt # resting potential (same as the reset) psp = 0.5 * mvolt # postsynaptic potential size G = NeuronGroup(N=40, model='dV/dt = -(V-El)/tau : volt', threshold=Vt, reset=Vr) C = Connection(G, G) C.connect_random(sparseness=0.1, weight=psp) M = SpikeMonitor(G) G.V = Vr + rand(40) * (Vt - Vr) run(1 * second) print M.nspikes ''' Having run the network, we simply use the :func:`raster_plot` function provided by Brian. After creating plots, we have to use the ``show()`` function to display them. This function is from the PyLab module that Brian uses for its built in plotting routines. ''' raster_plot() show() ''' .. image:: images/tutorials/1f.jpg As you can see, despite having introduced some randomness into our network, the output is very regular indeed. In the next part we introduce one more way to plot the output of a network. ''' brian-1.4.3/tutorials/tutorial1_basic_concepts/tutorial_1g_recording_membrane_potentials.py000066400000000000000000000051301266062647100326040ustar00rootroot00000000000000''' Tutorial 1g: Recording membrane potentials ****************************************** In the previous part of this tutorial, we plotted a raster plot of the firing times of the network. In this tutorial, we introduce a way to record the value of the membrane potential for a neuron during the simulation, and plot it. We continue as before: ''' from brian import * tau = 20 * msecond # membrane time constant Vt = -50 * mvolt # spike threshold Vr = -60 * mvolt # reset value El = -49 * mvolt # resting potential (same as the reset) psp = 0.5 * mvolt # postsynaptic potential size G = NeuronGroup(N=40, model='dV/dt = -(V-El)/tau : volt', threshold=Vt, reset=Vr) C = Connection(G, G) C.connect_random(sparseness=0.1, weight=psp) ''' This time we won't record the spikes. Recording states ~~~~~~~~~~~~~~~~ Now we introduce a second type of monitor, the :class:`StateMonitor`. The first argument is the group to monitor, and the second is the state variable to monitor. The keyword ``record`` can be an integer, list or the value ``True``. If it is an integer ``i``, the monitor will record the state of the variable for neuron ``i``. If it's a list of integers, it will record the states for each neuron in the list. If it's set to ``True`` it will record for all the neurons in the group. ''' M = StateMonitor(G, 'V', record=0) ''' And then we continue as before: ''' G.V = Vr + rand(40) * (Vt - Vr) ''' But this time we run it for a shorter time so we can look at the output in more detail: ''' run(200 * msecond) ''' Having run the simulation, we plot the results using the ``plot`` command from PyLab which has the same syntax as the Matlab :class:`plot`` command, i.e. ``plot(xvals,yvals,...)``. The :class:`StateMonitor` monitors the times at which it monitored a value in the array ``M.times``, and the values in the array ``M[0]``. The notation ``M[i]`` means the array of values of the monitored state variable for neuron ``i``. In the following lines, we scale the times so that they're measured in ms and the values so that they're measured in mV. We also label the plot using PyLab's ``xlabel``, ``ylabel`` and ``title`` functions, which again mimic the Matlab equivalents. ''' plot(M.times / ms, M[0] / mV) xlabel('Time (in ms)') ylabel('Membrane potential (in mV)') title('Membrane potential for neuron 0') show() ''' .. image:: images/tutorials/1g.jpg You can clearly see the leaky integration exponential decay toward the resting potential, as well as the jumps when a spike was received. ''' brian-1.4.3/tutorials/tutorial2_connections/000077500000000000000000000000001266062647100212015ustar00rootroot00000000000000brian-1.4.3/tutorials/tutorial2_connections/introduction.txt000066400000000000000000000002241266062647100244610ustar00rootroot00000000000000Tutorial 2: Connections ----------------------- In this tutorial, we will cover in more detail the concept of a :class:`Connection` in Brian. brian-1.4.3/tutorials/tutorial2_connections/tutorial_2a_the_concept_of_a_connection.py000066400000000000000000000146171266062647100315670ustar00rootroot00000000000000''' Tutorial 2a: The concept of a Connection **************************************** The network ~~~~~~~~~~~ In this first part, we'll build a network consisting of three neurons. The first two neurons will be under direct control and have no equations defining them, they'll just produce spikes which will feed into the third neuron. This third neuron has two different state variables, called Va and Vb. The first two neurons will be connected to the third neuron, but a spike arriving at the third neuron will be treated differently according to whether it came from the first or second neuron (which you can consider as meaning that the first two neurons have different types of synapses on to the third neuron). The program starts as follows. ''' from brian import * tau_a = 1 * ms tau_b = 10 * ms Vt = 10 * mV Vr = 0 * mV ''' Differential equations ~~~~~~~~~~~~~~~~~~~~~~ This time, we will have multiple differential equations. We will use the :class:`Equations` object, although you could equally pass the multi-line string defining the differential equations directly when initialising the :class:`NeuronGroup` object (see the next part of the tutorial for an example of this). ''' eqs = Equations(''' dVa/dt = -Va/tau_a : volt dVb/dt = -Vb/tau_b : volt ''') ''' So far, we have defined a model neuron with two state variables, ``Va`` and ``Vb``, which both decay exponentially towards 0, but with different time constants ``tau_a`` and ``tau_b``. This is just so that you can see the difference between them more clearly in the plot later on. SpikeGeneratorGroup ~~~~~~~~~~~~~~~~~~~ Now we introduce the :class:`SpikeGeneratorGroup` class. This is a group of neurons without a model, which just produces spikes at the times that you specify. You create a group like this by writing:: G = SpikeGeneratorGroup(N,spiketimes) where ``N`` is the number of neurons in the group, and ``spiketimes`` is a list of pairs ``(i,t)`` indicating that neuron ``i`` should fire at time ``t``. In fact, ``spiketimes`` can be any 'iterable container' or 'generator', but we don't cover that here (see the detailed documentation for :class:`SpikeGeneratorGroup`). In our case, we want to create a group with two neurons, the first of which (neuron 0) fires at times 1 ms and 4 ms, and the second of which (neuron 1) fires at times 2 ms and 3 ms. The list of ``spiketimes`` then is: ''' spiketimes = [(0, 1 * ms), (0, 4 * ms), (1, 2 * ms), (1, 3 * ms)] ''' and we create the group as follows: ''' G1 = SpikeGeneratorGroup(2, spiketimes) ''' Now we create a second group, with one neuron, according to the model we defined earlier. ''' G2 = NeuronGroup(N=1, model=eqs, threshold=Vt, reset=Vr) ''' Connections ~~~~~~~~~~~ In Brian, a :class:`Connection` from one :class:`NeuronGroup` to another is defined by writing:: C = Connection(G,H,state) Here ``G`` is the source group, ``H`` is the target group, and ``state`` is the name of the target state variable. When a neuron ``i`` in ``G`` fires, Brian finds all the neurons ``j`` in ``H`` that ``i`` in ``G`` is connected to, and adds the amount ``C[i,j]`` to the specified state variable of neuron ``j`` in ``H``. Here ``C[i,j]`` is the (i,j)th entry of the connection matrix of ``C`` (which is initially all zero). To start with, we create two connections from the group of two directly controlled neurons to the group of one neuron with the differential equations. The first connection has the target state ``Va`` and the second has the target state ``Vb``. ''' C1 = Connection(G1, G2, 'Va') C2 = Connection(G1, G2, 'Vb') ''' So far, this only declares our intention to connect neurons in group ``G1`` to neurons in group ``G2``, because the connection matrix is initially all zeros. Now, with connection ``C1`` we connect neuron 0 in group ``G1`` to neuron 0 in group ``G2``, with weight 3 mV. This means that when neuron 0 in group ``G1`` fires, the state variable ``Va`` of the neuron in group ``G2`` will be increased by 6 mV. Then we use connection ``C2`` to connection neuron 1 in group ``G1`` to neuron 0 in group ``G2``, this time with weight 3 mV. ''' C1[0, 0] = 6 * mV C2[1, 0] = 3 * mV ''' The net effect of this is that when neuron 0 of ``G1`` fires, ``Va`` for the neuron in ``G2`` will increase 6 mV, and when neuron 1 of ``G1`` fires, ``Vb`` for the neuron in ``G2`` will increase 3 mV. Now we set up monitors to record the activity of the network, run it and plot it. ''' Ma = StateMonitor(G2, 'Va', record=True) Mb = StateMonitor(G2, 'Vb', record=True) run(10 * ms) plot(Ma.times, Ma[0]) plot(Mb.times, Mb[0]) show() ''' .. image:: images/tutorials/2a.jpg The two plots show the state variables ``Va`` and ``Vb`` for the single neuron in group ``G2``. ``Va`` is shown in blue, and ``Vb`` in green. According to the differential equations, ``Va`` decays much faster than ``Vb`` (time constant 1 ms rather than 10 ms), but we have set it up (through the connection strengths) that an incoming spike from neuron 0 of ``G1`` causes a large increase of 6 mV to ``Va``, whereas a spike from neuron 1 of ``G1`` causes a smaller increase of 3 mV to Vb. The value for ``Va`` then jumps at times 1 ms and 4 ms, when we defined neuron 0 of ``G1`` to fire, and decays almost back to rest in between. The value for ``Vb`` jumps at times 2 ms and 3 ms, and because the times are closer together and the time constant is longer, they add together. In the next part of this tutorial, we'll see how to use this system to do something useful. Exercises ~~~~~~~~~ 1. Try playing with the parameters ``tau_a``, ``tau_b`` and the connection strengths, ``C1[0,0]`` and ``C2[0,1]``. Try changing the list of spike times. 2. In this part of the tutorial, the states ``Va`` and ``Vb`` are independent of one another. Try rewriting the differential equations so that they're not independent and play around with that. 3. Write a network with inhibitory and excitatory neurons. Hint: you only need one connection. 4. Write a network with inhibitory and excitatory neurons whose actions have different time constants (for example, excitatory neurons have a slower effect than inhibitory ones). Solutions ~~~~~~~~~ 3. Simple write ``C[i,j]=-3*mV`` to make the connection from neuron i to neuron j inhibitory. 4. See the next part of this tutorial. ''' brian-1.4.3/tutorials/tutorial2_connections/tutorial_2b_excitatory_and_inhibitory_currents.py000066400000000000000000000111531266062647100332640ustar00rootroot00000000000000''' Tutorial 2b: Excitatory and inhibitory currents *********************************************** In this tutorial, we use multiple connections to solve a real problem, how to implement two types of synapses with excitatory and inhibitory currents with different time constants. The scheme ~~~~~~~~~~ The scheme we implement is the following diffential equations: | taum dV/dt = -V + ge - gi | taue dge/dt = -ge | taui dgi/dt = -gi An excitatory neuron connects to state ge, and an inhibitory neuron connects to state gi. When an excitatory spike arrives, ge instantaneously increases, then decays exponentially. Consequently, V will initially but continuously rise and then fall. Solving these equations, if V(0)=0, ge(0)=g0 corresponding to an excitatory spike arriving at time 0, and gi(0)=0 then: | gi = 0 | ge = g0 exp(-t/taue) | V = (exp(-t/taum) - exp(-t/taue)) taue g0 / (taum-taue) We use a very short time constant for the excitatory currents, a longer one for the inhibitory currents, and an even longer one for the membrane potential. ''' from brian import * taum = 20 * ms taue = 1 * ms taui = 10 * ms Vt = 10 * mV Vr = 0 * mV eqs = Equations(''' dV/dt = (-V+ge-gi)/taum : volt dge/dt = -ge/taue : volt dgi/dt = -gi/taui : volt ''') ''' Connections ~~~~~~~~~~~ As before, we'll have a group of two neurons under direct control, the first of which will be excitatory this time, and the second will be inhibitory. To demonstrate the effect, we'll have two excitatory spikes reasonably close together, followed by an inhibitory spike later on, and then shortly after that two excitatory spikes close together. ''' spiketimes = [(0, 1 * ms), (0, 10 * ms), (1, 40 * ms), (0, 50 * ms), (0, 55 * ms)] G1 = SpikeGeneratorGroup(2, spiketimes) G2 = NeuronGroup(N=1, model=eqs, threshold=Vt, reset=Vr) C1 = Connection(G1, G2, 'ge') C2 = Connection(G1, G2, 'gi') ''' The weights are the same - when we increase ``ge`` the effect on ``V`` is excitatory and when we increase ``gi`` the effect on ``V`` is inhibitory. ''' C1[0, 0] = 3 * mV C2[1, 0] = 3 * mV ''' We set up monitors and run as normal. ''' Mv = StateMonitor(G2, 'V', record=True) Mge = StateMonitor(G2, 'ge', record=True) Mgi = StateMonitor(G2, 'gi', record=True) run(100 * ms) ''' This time we do something a little bit different when plotting it. We want a plot with two subplots, the top one will show ``V``, and the bottom one will show both ``ge`` and ``gi``. We use the ``subplot`` command from pylab which mimics the same command from Matlab. ''' figure() subplot(211) plot(Mv.times, Mv[0]) subplot(212) plot(Mge.times, Mge[0]) plot(Mgi.times, Mgi[0]) show() ''' .. image:: images/tutorials/2b.jpg The top figure shows the voltage trace, and the bottom figure shows ``ge`` in blue and ``gi`` in green. You can see that although the inhibitory and excitatory weights are the same, the inhibitory current is much more powerful. This is because the effect of ``ge`` or ``gi`` on ``V`` is related to the integral of the differential equation for those variables, and ``gi`` decays much more slowly than ``ge``. Thus the size of the negative deflection at 40 ms is much bigger than the excitatory ones, and even the double excitatory spike after the inhibitory one can't cancel it out. In the next part of this tutorial, we set up our first serious network, with 4000 neurons, excitatory and inhibitory. Exercises ~~~~~~~~~ 1. Try changing the parameters and spike times to get a feel for how it works. 2. Try an equivalent implementation with the equation taum dV/dt = -V+ge+gi 3. Verify that the differential equation has been solved correctly. Solutions ~~~~~~~~~ Solution for 2: Simply use the line ``C2[1,0] = -3*mV`` to get the same effect. Solution for 3: First, set up the situation we described at the top for which we already know the solution of the differential equations, by changing the spike times as follows:: spiketimes = [(0,0*ms)] Now we compute what the values ought to be as follows:: t = Mv.times Vpredicted = (exp(-t/taum) - exp(-t/taue))*taue*(3*mV) / (taum-taue) Now we can compute the difference between the predicted and actual values:: Vdiff = abs(Vpredicted - Mv[0]) This should be zero:: print max(Vdiff) Sure enough, it's as close as you can expect on a computer. When I run this it gives me the value 1.3 aV, which is 1.3 * 10^-18 volts, i.e. effectively zero given the finite precision of the calculations involved. ''' brian-1.4.3/tutorials/tutorial2_connections/tutorial_2c_the_cuba_network.py000066400000000000000000000104761266062647100274150ustar00rootroot00000000000000''' Tutorial 2c: The CUBA network ***************************** In this part of the tutorial, we set up our first serious network that actually does something. It implements the CUBA network, Benchmark 2 from: Simulation of networks of spiking neurons: A review of tools and strategies (2006). Brette, Rudolph, Carnevale, Hines, Beeman, Bower, Diesmann, Goodman, Harris, Zirpe, Natschlager, Pecevski, Ermentrout, Djurfeldt, Lansner, Rochel, Vibert, Alvarez, Muller, Davison, El Boustani and Destexhe. Journal of Computational Neuroscience This is a network of 4000 neurons, of which 3200 excitatory, and 800 inhibitory, with exponential synaptic currents. The neurons are randomly connected with probability 0.02. ''' from brian import * taum = 20 * ms # membrane time constant taue = 5 * ms # excitatory synaptic time constant taui = 10 * ms # inhibitory synaptic time constant Vt = -50 * mV # spike threshold Vr = -60 * mV # reset value El = -49 * mV # resting potential we = (60 * 0.27 / 10) * mV # excitatory synaptic weight wi = (20 * 4.5 / 10) * mV # inhibitory synaptic weight eqs = Equations(''' dV/dt = (ge-gi-(V-El))/taum : volt dge/dt = -ge/taue : volt dgi/dt = -gi/taui : volt ''') ''' So far, this has been pretty similar to the previous part, the only difference is we have a couple more parameters, and we've added a resting potential ``El`` into the equation for ``V``. Now we make lots of neurons: ''' G = NeuronGroup(4000, model=eqs, threshold=Vt, reset=Vr) ''' Next, we divide them into subgroups. The :meth:`~NeuronGroup.subgroup` method of a :class:`NeuronGroup` returns a new :class:`NeuronGroup` that can be used in exactly the same way as its parent group. At the moment, the subgrouping mechanism can only be used to create contiguous groups of neurons (so you can't have a subgroup consisting of neurons 0-100 and also 200-300 say). We designate the first 3200 neurons as ``Ge`` and the second 800 as ``Gi``, these will be the excitatory and inhibitory neurons. ''' Ge = G.subgroup(3200) # Excitatory neurons Gi = G.subgroup(800) # Inhibitory neurons ''' Now we define the connections. As in the previous part of the tutorial, ``ge`` is the excitatory current and ``gi`` is the inhibitory one. ``Ce`` says that an excitatory neuron can synapse onto any neuron in ``G``, be it excitatory or inhibitory. Similarly for inhibitory neurons. We also randomly connect ``Ge`` and ``Gi`` to the whole of ``G`` with probability 0.02 and the weights given in the list of parameters at the top. ''' Ce = Connection(Ge, G, 'ge', sparseness=0.02, weight=we) Ci = Connection(Gi, G, 'gi', sparseness=0.02, weight=wi) ''' Set up some monitors as usual. The line ``record=0`` in the :class:`StateMonitor` declarations indicates that we only want to record the activity of neuron 0. This saves time and memory. ''' M = SpikeMonitor(G) MV = StateMonitor(G, 'V', record=0) Mge = StateMonitor(G, 'ge', record=0) Mgi = StateMonitor(G, 'gi', record=0) ''' And in order to start the network off in a somewhat more realistic state, we initialise the membrane potentials uniformly randomly between the reset and the threshold. ''' G.V = Vr + (Vt - Vr) * rand(len(G)) ''' Now we run. ''' run(500 * ms) ''' And finally we plot the results. Just for fun, we do a rather more complicated plot than we've been doing so far, with three subplots. The upper one is the raster plot of the whole network, and the lower two are the values of ``V`` (on the left) and ``ge`` and ``gi`` (on the right) for the neuron we recorded from. See the PyLab documentation for an explanation of the plotting functions, but note that the :func:`raster_plot` keyword ``newfigure=False`` instructs the (Brian) function :func:`raster_plot` not to create a new figure (so that it can be placed as a subplot of a larger figure). ''' subplot(211) raster_plot(M, title='The CUBA network', newfigure=False) subplot(223) plot(MV.times / ms, MV[0] / mV) xlabel('Time (ms)') ylabel('V (mV)') subplot(224) plot(Mge.times / ms, Mge[0] / mV) plot(Mgi.times / ms, Mgi[0] / mV) xlabel('Time (ms)') ylabel('ge and gi (mV)') legend(('ge', 'gi'), 'upper right') show() ''' .. image:: images/tutorials/2c.jpg '''