PyCorrFit-0.9.9/000077500000000000000000000000001274265304700134245ustar00rootroot00000000000000PyCorrFit-0.9.9/ChangeLog.txt000066400000000000000000000347711274265304700160300ustar00rootroot000000000000000.9.9 - Remove admin-requirement during install (Windows) - Support newer correlator.com .sin file format (experimental, #135) - Add smart progress dialog for fitting (#155) - Statistics: check "filename/title" by default (#151) - Documentation: fix bad LaTeX commands (#163) 0.9.8 - Bugfixes: - Indexing error when saving sessions (#154) - Page number truncated in csv export (#159) - Export of csv files used incorrect normalization (#153) - Normalization parameter was not displayed in the 'Info' tool 0.9.7 - Second triplet time is now larger than first triplet time by default - Remove a hack that causes a run through all pages e.g. when an average is created - Bugfixes: - Opening sessions with user-defined models - Saving sessions with comments containing non-ASCII characters - Windows build: Graphical plot export was misconfigured -> added matplotlibrc patch in .spec file 0.9.6 - Bugfixes: - Fixed minor wx sizer problems for the tools - Fixed `AttributeError` in page.py if no weights are present - New confocal fitting models (#111): - 3D+3D, 2D+2D, 3D+2D; no triplet - T+T+2D+2D, T+T+3D+2D; double triplet - T+3D+3D+3D, T+3D+3D+2D (#40, #59) - Under the hood: - Separation of core and GUI modules - Include tests in distributions for PyPI - Improve automated testing on Windows and Mac OS - More constraint options for fitting 0.9.5 - Bugfixes - Closing the batch control window causes segfault bug (#142) - Closing page causes error when batch control is active (#143) - Plot normalization causes "Save Session" to fail (#144) - Plot normalization not loaded from session (#145) 0.9.4 - Batch control allows to select individual parameters (#108) - Allow to exclude pages from batch fitting (#107) - Bugfixes: - Fix `ValueError` in parameter display - Possibly fixed error with `yaml.safe_dump` on Mac OSx 10.8.5 - Make sure background is lower than signal (#137) 0.9.3 - Fitting: migrate to lmfit - This introduces a new dependency for building PyCorrFit. (e.g. in Debian, the package "python-lmfit" is required) - Improved fitting behavior at parameter boundaries - Removed "Polak-Ribiere" fitting algorithm - Added "Sequential Linear Squares Programming" algorithm - Heuristic fit (#109): - Detect parameters that are stuck during fitting - Fit each curve five times or less and check whether the fit converges. - If two diffusion time parameter exist in a model, always make sure that one parameter is the larger one. This feature can currently not be disabled (#110). - Allow infinity ("inf" and "-inf") parameters for models and boundaries. - New model: confocal T+T+3D+3D - Bugfixes: - Sessions saved with 64bit Windows were not opened (#136) - Old sessions and "KeyError: 'chi2'" - Old session file extension was not recognized (#106) 0.9.2 - Bugfixes: - "Slider Simulation"/"Parm Range" broken (#133) - Computation of average intensity did not work correctly for unequally spaced traces - Update .pt3 reader to version 8399ff7401 - Import traces of .pt3 files (experimental, #118) Warning: Absolute values for intensity might be wrong 0.9.1 - Tool 'Overlay curves': improve UI (#117) - Tool 'Statistics view': improve UI (#113) - Tool 'Trace view': display countrate (#121) - Bugfixes: - Unicode errors in statistics tool (#131) - Load session errors with empty pages 0.9.0 - Improve parameter display (#52, #114) - Display Chi2 on each page (#115) - The displayed Chi2-value for non-weighted fits is now normalized to the expected values of the fit. The documentation has been updated accordingly. - Add "All files" option in save dialogs (#97) - Improved plot export dialog (#99) 0.8.9 - Improved support for "ALV-7004" files (#104) - Increase resolution for image export - Load weights from PyCorrFit csv files - Tool 'Overlay Curves': show cropped correlation curves - Tool 'Trace view': increase size of window (#93) - Tool 'Global fitting': remove forced, joint weights - Session comment dialog: more intuitive behavior (#116) - Improve plot export (#95) - Bugfixes: - Weighted fits at borders of fit interval were computed incorrectly due to integer division - Fitting algorithms did not work (#94) - Creating averages did not work (#123) - ASCII errors in statistics tool (#112) - Under the hood: - Introduce new classes: Correlation, Fit, Trace - Code cleanup and rewrite to support planned features - In some cases support older versions of NumPy 0.8.8 - Improved support for "ALV-7004" files - If you install the GUI with pip, you now need to include the `GUI` requirement: `pip install pycorrfit[GUI]`. The GUI depends on matplotlib and wxPython which is not required for scripting with the pycorrfit module. - Bugfix: missing version string on SuSe linux (#101) - Under the hood: - Python entry point script replaces "bin/" script - Windows build system hosted by appveyor.com - MacOS X build system hosted by travis-ci.org - New builds use wxPython3 (#85) - Unicode support without `reload(sys)` - Error messages are more verbose 0.8.7 - Removed unused fitting parameter d_eva from model 6022 and secured backwards compatibility. - Improved support for ALV700X (#92) - Bugfix: Corrected false display of Unicode characters on Windows - Under the hood: - Code cleanup with pyflakes - Repo cleanup (#98) 0.8.6 - Bugfix: Opening .fcs files with only one AC curve works now - Zip files with measurements may now contain subfolders - Improved pt3-file support from https://github.com/dwaithe/FCS_point_correlator (#89) 0.8.5 - Fixed bug that made it impossible to load data (#88) - Exceptions are now handled by wxPython - Under the hood: - Pythonic repository structure - Relative imports - Windows build machine is now Windows 7 - Removed strict dependency on matplotlib 0.8.4 - Support for PicoQuant data file format Many thanks to Dominic Waithe (@dwaithe) - Improved compatibility with Zeiss .fcs file format - PyCorrFit is now dependent on Cython - The module 'openfile' is now available from within Python - Installer for Windows 0.8.3 - New .pcfs (PyCorrFit Session) file format (#60) - Additional fitting algorithms: Nelder-Mead, BFGS, Powell, Polak-Ribiere (#71) - Improvements - Massive speed-up when working with large data sets (#77) - Plot export: legend position and displayed parameters (#54) - Average tool: traces may now start at time points != 0 - Statistics tool: display on smaller screens - ALV data files: updated parser to identify curve types and segment traces - Zeiss ConfoCor3 data files: some files could not be opened due to dummy AC curves - Models: default parameters were changed to prevent unstable fits - Software: notification dialogs for missing modules or other software - Bugfixes - User could accidently clear a session (#65) - wxPython plotting problem on MacOSx (#64) - Statistics view: some parameters were duplicated (#76) - Caught zero-division warnings (models with triplet component) - Corrected x-axis scaling of statistics view and trace view 0.8.2 - The documentation has been thoroughly reworked - The user is now warned if he does not have a TeX distribution installed - Improvements: - Complete support for installing PyCorrFit with virtualenv and pip (This is documented in the wiki) - Statistics tool now displays average and SD (#43) - Bugfix: TeX did not work on Ubuntu due to missing imports 0.8.1 - Thanks to Alex Mestiashvili for providing initial setup.py files and for debianizing PyCorrFit (@mestia) - Thanks to Thomas Weidemann for his contributions to the documentation (@weidemann) - Bugfixes - Some ConfoCor files were not imported - The cpp was not calculated correctly in case of background correction (#45) - Enabled averaging of single pages (#58) - Background correction for cross-correlation data is now computed (#46) - Improvements of the user interface - The menus have been reordered (#47, #50) - The fitting panel has been optimized (#49) - the slider simulation got a reset button (#51) - The Help menu contains documentation and wiki (#56) - Model functions that are somehow redundant have been removed from the menu, but are still supported - The model doc strings were fully converted to Unicode - Several text messages were modified for better coherence - The background correction tool is more intuitive - Statistics panel improvements (#43) - Run information is included in the Data set title - The page counter starts at "1" instead of "0" (#44) - New handling of background correction (#46, #53) 0.8.0 - Filename/title of each tab now shows up in the notebook (#39) - Statistics tool can plot parameters and page selection with the Overlay tool is possible (#31) 0.7.9 - Support for Mac OSx - Enhancements: - Export file format (.csv) layout improved - Model function info text in UTF-8 - Improved waring message when opening sessions from future versions - New feature lets user set the range for the fitting parameters - Bugfixes: - Cleaned minor tracebacks and exceptions created by the frontend - Mac version now works as expected, but .app bundling failed - Latex plotting features now support more characters such as "[]{}^" 0.7.8 - Enhancements: - Averages can now be calculated from user-selected pages - Pages selected in the Overlay tool are now automatically set for computation of average and for global fitting - Source pages are now displayed in average title - Graph normalization with particle numbers is now possible - Bugfixes: - Errors during fitting with weights equal to zero - Overlay tool displayed last curve after all pages have been removed - Global fit did not work with weights - Session saving now uses 20 digits accuracy - CSV export is now using tab-delimited data for easier Excel-import - Added version checking for session management 0.7.7 - Fixed: Tools windows could not be closed (or moved on MS Windows) - Fixed: .csv export failed in some cases where no weights were used - Enhancement: The user is now asked before a page is closed - Enhancement: Tool "Page Info" and in exported .csv files, variables and values are now separated by a tab stop instead of a "=" - Fixed: Opening a session with an empty page failed in some cases - Fixed: Tool "Statistics" missed to output the column "filename/title" if that key value is empty - replaced empty strings with "NoName" - Enhancement: Tool "Overlay" now asks the user to check kept curves instead of showing the curves to be removed - Enhancement: Tool "Overlay" now has a "Cancel" button 0.7.6 - Improved handling - Tools are now sorted according to a standard work-flow - Renamed "Curve selection" to "Overlay tool" - this is more intuitive - Tools will now stay open or may be opened when there are no open pages (#25) - Filenames and runs are now displayed on each page (also added filename/title tag) (#23) - Notebook: moved close button to each tab to prevent accidental closing of tabs - Improved tool "Statistics" (#21) - Fixed the case where "useless" data was produced - instead we write "NaN" data, removed warning message accordingly - Row-wise ordering according to page numbers (#22) - Column-wise ordering is now more intuitive (Fitted parameters with errors first) - Some columns are now checked by default - PyCorrFit remembers checked parameters for a page (not saved in session) - Improved tool "Overlay" (#23) - New feature: Overlay shows run number of each file (upon import), the run (or index) of an experimental file is unique to PyCorrFit - Upon import, filenames and runs are displayed - In a session, the filename/title is displayed - Web address of PyCorrFit changed from "fcstools.dyndns.org/pycorrfit" to "pycorrfit.craban.de" - Minor bugfixes: Batch control, Global fitting, import dialog 0.7.5 - Added model functions to documentation. - Weights from fitting are now exported in .csv files. - Rework of info panel for fitting - Cleared unintuitive behavior of session saving: The fitting parameters were read from the frontend. This could have led to saving false fit meta data. - During fitting, units are now displayed as "human readable" (#17). - Slider simulation now also uses human readable units (#17). - Secured support for Ubuntu 12.10 and 13.04 - Fixed: new line (\n) characters for LaTeX plotting on Windows 0.7.4 - New tool: Colorful curve selection - Import data: Curve selection possible - Average: Crop average according to current page. - Fixed: Page now displays Chi-squared of global fit. - Fixed: Chi-squared and parameters of global fitting are now stored in sessions. 0.7.3 - Issue closed. External weights from averages saved in session (#11). - Solved minor bugs - Added estimation of errors of fit (Issue #12/#14) - Fixed: Some .fcs files containing averages were not imported. 0.7.2 - Bugfix: Issue #10; we now have a goodness of the fit, if weighted fitting is performed - Bugfix: Weights for fitting not properly calculated (sqrt(std)). - Bugfix: Batch control IndexError with Info window opened - Tool Statistics: Sort values according to page numbers - Tool global: Added weighted fitting - Residuals: According to weighted fitting, weighted residuals are plotted - Average: Variances from averaging can be used for weighted fitting 0.7.1 - Feature: Added Python shell - Bugfix: Saving image was not performed using WXAgg - Bugfix: Notebook pages were drag'n'dropable - Update function now works in its own thread - Code cleanup: documentation of model functions - Added program icon 0.7.0 - File import dialog was enhanced (#4, #5 - subsequently #7, #8): - Now there is only one "load data" dialog in the file menu. - The model function is chosen for each type of data that is to be imported (AC, CC, etc.). - Loading files that do not contain data a pointed out to the user and the program continues with the other files. - Bugfix: Channel selection window causes crash on file import (#1). - Bugfix: Hidden feature changes fixed parameters during fitting (#2). - Feature: Convert TIR model function parameters lambda and NA to sigma (#3). - Code cleanup: Opening data files is now handled internally differently. 0.6.9 - Initital GitHub commit PyCorrFit-0.9.9/LICENSE000066400000000000000000000366071274265304700144450ustar00rootroot00000000000000Copyright (C) 2011-2012 Paul Müller PyCorrFit is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. 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END OF TERMS AND CONDITIONS PyCorrFit-0.9.9/MANIFEST.in000066400000000000000000000005401274265304700151610ustar00rootroot00000000000000include doc/*.tex include doc/*.bib include doc/*.pdf include doc/*.md include doc/Images/* include examples/external_model_functions/*.txt include README.md include ChangeLog.txt include pycorrfit/readfiles/read_pt3_scripts/*.py include pycorrfit/readfiles/read_pt3_scripts/*.pyx include pycorrfit/readfiles/read_pt3_scripts/LICENSE include tests/*.pyPyCorrFit-0.9.9/README.rst000066400000000000000000000100471274265304700151150ustar00rootroot00000000000000|PyCorrFit| =========== |PyPI Version| |Build Status Win| |Build Status Mac| |Coverage Status| A graphical fitting tool for fluorescence correlation spectroscopy (FCS) that comes with support for several file formats, can be applied to a large variety of problems, and attempts to be as user-friendly as possible. Some of the features are - Averaging of curves - Background correction - Batch processing - Overlay tool to identify outliers - Fast simulation of model parameter behavior - Session management - User-defined model functions - High quality plot export using LaTeX (bitmap or vector graphics) Getting started =============== Installation ------------ Installers for PyCorrFit are available at the `release page `__. Documentation ------------- A detailed documentation including an explanation of the graphical user interface and available model functions is available as a `PDF file `__. Wiki ---- If you are interested in a specific topic or wish to contribute with your own HowTo, have a look at the `PyCorrFit wiki `__. There you will also find information on `how to write your own model functions `__. Problems -------- If you find a bug or need help with a specific topic, do not hesitate to ask a question at the `issues page `__. Advanced usage -------------- If you have Python installed you can install PyCorrFit, including its scripting functionalities, from the Python package index: :: pip install pycorrfit[GUI] More information is available in the `PyCorrFit wiki `__. Information for developers ========================== Running from source ------------------- The easiest way to run PyCorrFit from source is to use `Anaconda `__. PyCorrFit requires wxPython which is not available at the Python package index. Make sure you install a unicode version of wxPython. Detailed installation instructions are `here `__. Contributing ------------ The main branch for developing PyCorrFit is *develop*. Small changes that do not break anything can be submitted to this branch. If you want to do big changes, please (fork ShapeOut and) create a separate branch, e.g. ``my_new_feature_dev``, and create a pull-request to *develop* once you are done making your changes. Please make sure to also update the `changelog `__. Tests ----- PyCorrFit is tested using pytest. If you have the time, please write test methods for your code and put them in the ``tests`` directory. You may run the tests manually by issuing: :: python setup.py test Windows test binaries --------------------- After each commit to the PyCorrFit repository, a binary installer is created by `Appveyor `__. Click on a build and navigate to ``ARTIFACTS`` (upper right corner right under the running time of the build). From there you can download the Windows installer of the commit. .. |PyCorrFit| image:: https://raw.github.com/FCS-analysis/PyCorrFit/master/doc/Images/PyCorrFit_logo_dark.png .. |PyPI Version| image:: http://img.shields.io/pypi/v/PyCorrFit.svg :target: https://pypi.python.org/pypi/pycorrfit .. |Build Status Win| image:: https://img.shields.io/appveyor/ci/paulmueller/PyCorrFit/master.svg?label=build_win :target: https://ci.appveyor.com/project/paulmueller/pycorrfit .. |Build Status Mac| image:: https://img.shields.io/travis/FCS-analysis/PyCorrFit/master.svg?label=build_mac :target: https://travis-ci.org/FCS-analysis/PyCorrFit .. |Coverage Status| image:: https://img.shields.io/coveralls/FCS-analysis/PyCorrFit.svg :target: https://coveralls.io/r/FCS-analysis/PyCorrFit PyCorrFit-0.9.9/doc/000077500000000000000000000000001274265304700141715ustar00rootroot00000000000000PyCorrFit-0.9.9/doc/Bibliography.bib000077500000000000000000003601731274265304700172770ustar00rootroot00000000000000% This file was created with JabRef 2.10b2. % Encoding: UTF-8 @Article{Aragon1976, Title = {Fluorescence correlation spectroscopy as a probe of molecular dynamics}, Author = {S. R. Aragon and R. Pecora}, Journal = {The Journal of Chemical Physics}, Year = {1976}, Number = {4}, Pages = {1791-1803}, Volume = {64}, Doi = {10.1063/1.432357}, Owner = {paul}, Publisher = {AIP}, Timestamp = {2012.11.02} } @Article{Ashkin1970, Title = {Acceleration and Trapping of Particles by Radiation Pressure}, Author = {Ashkin, A.}, Journal = {Physical Review Letters}, Year = {1970}, Month = {Jan}, Pages = {156--159}, Volume = {24}, Doi = {10.1103/PhysRevLett.24.156}, Issue = {4}, Owner = {paul}, Publisher = {American Physical Society}, Timestamp = {2012.11.13} } @Article{Axelrod1984, Title = {Total Internal Reflection Fluorescence}, Author = {Axelrod, D and Burghardt, T P and Thompson, N L}, Journal = {Annual Review of Biophysics and Biomolecular Structure}, Year = {1984}, Month = jun, Number = {1}, Pages = {247--268}, Volume = {13}, Booktitle = {Annual Review of Biophysics and Bioengineering}, Comment = {doi: 10.1146/annurev.bb.13.060184.001335}, Doi = {10.1146/annurev.bb.13.060184.001335}, ISSN = {0084-6589}, Owner = {paul}, Publisher = {Annual Reviews}, Timestamp = {2012.02.14} } @Article{Bacia2006, Title = {Fluorescence cross-correlation spectroscopy in living cells}, Author = {Bacia, K. and Kim, S. A. and Schwille, P.}, Journal = {Nat Methods}, Year = {2006}, Number = {2}, Pages = {83--9}, Volume = {3}, Abstract = {Cell biologists strive to characterize molecular interactions directly in the intracellular environment. The intrinsic resolution of optical microscopy, however, allows visualization of only coarse subcellular localization. By extracting information from molecular dynamics, fluorescence cross-correlation spectroscopy (FCCS) grants access to processes on a molecular scale, such as diffusion, binding, enzymatic reactions and codiffusion, and has become a valuable tool for studies in living cells. Here we review basic principles of FCCS and focus on seminal applications, including examples of intracellular signaling and trafficking. We consider FCCS in the context of fluorescence resonance energy transfer and multicolor imaging techniques and discuss application strategies and recent technical advances.}, Doi = {10.1038/nmeth822}, Keywords = {Algorithms Animals Biological Transport Diffusion Endocytosis/physiology Enzymes/metabolism Fluorescence Resonance Energy Transfer/methods Humans Laser Scanning Cytometry/instrumentation/*methods Oligonucleotides/metabolism Protein Binding Protein Transport Proteins/metabolism Signal Transduction/physiology Spectrometry, Fluorescence/instrumentation/methods}, Owner = {paul}, Timestamp = {2014.01.15} } @Article{Bacia2012, Title = {Correcting for spectral cross-talk in dual-color fluorescence cross-correlation spectroscopy}, Author = {Bacia, K. and Petr\'{a}\v{s}ek, Zden\v{e}k and Schwille, P.}, Journal = {Chemphyschem}, Year = {2012}, Number = {5}, Pages = {1221--31}, Volume = {13}, Abstract = {Dual-color fluorescence cross-correlation spectroscopy (dcFCCS) allows one to quantitatively assess the interactions of mobile molecules labeled with distinct fluorophores. The technique is widely applied to both reconstituted and live-cell biological systems. A major drawback of dcFCCS is the risk of an artifactual false-positive or overestimated cross-correlation amplitude arising from spectral cross-talk. Cross-talk can be reduced or prevented by fast alternating excitation, but the technology is not easily implemented in standard commercial setups. An experimental strategy is devised that does not require specialized hardware and software for recognizing and correcting for cross-talk in standard dcFCCS. The dependence of the cross-talk on particle concentrations and brightnesses is quantitatively confirmed. Moreover, it is straightforward to quantitatively correct for cross-talk using quickly accessible parameters, that is, the measured (apparent) fluorescence count rates and correlation amplitudes. Only the bleed-through ratio needs to be determined in a calibration measurement. Finally, the limitations of cross-talk correction and its influence on experimental error are explored.}, Doi = {10.1002/cphc.201100801}, Owner = {paul}, Timestamp = {2014.01.15} } @Article{Bag2012, Title = {Calibration and Limits of Camera-Based Fluorescence Correlation Spectroscopy: A Supported Lipid Bilayer Study}, Author = {Bag, Nirmalya and Sankaran, Jagadish and Paul, Alexandra and Kraut, Rachel S. and Wohland, Thorsten}, Journal = {ChemPhysChem}, Year = {2012}, Number = {11}, Pages = {2784--2794}, Volume = {13}, Doi = {10.1002/cphc.201200032}, ISSN = {1439-7641}, Keywords = {fluorescence spectroscopy, membrane, multiplexing, point spread function, total internal reflection}, Owner = {paul}, Publisher = {WILEY-VCH Verlag}, Timestamp = {2012.09.20} } @Article{Bestvater2010, Title = {EMCCD-based spectrally resolved fluorescence correlation spectroscopy}, Author = {Felix Bestvater and Zahir Seghiri and Moon Sik Kang and Nadine Gr\"{o}ner and Ji Young Lee and Kang-Bin Im and Malte Wachsmuth}, Journal = {Optics Express}, Year = {2010}, Month = {Nov}, Number = {23}, Pages = {23818--23828}, Volume = {18}, Abstract = {We present an implementation of fluorescence correlation spectroscopy with spectrally resolved detection based on a combined commercial confocal laser scanning/fluorescence correlation spectroscopy microscope. We have replaced the conventional detection scheme by a prism-based spectrometer and an electron-multiplying charge-coupled device camera used to record the photons. This allows us to read out more than 80,000 full spectra per second with a signal-to-noise ratio and a quantum efficiency high enough to allow single photon counting. We can identify up to four spectrally different quantum dots in vitro and demonstrate that spectrally resolved detection can be used to characterize photophysical properties of fluorophores by measuring the spectral dependence of quantum dot fluorescence emission intermittence. Moreover, we can confirm intracellular cross-correlation results as acquired with a conventional setup and show that spectral flexibility can help to optimize the choice of the detection windows.}, Doi = {10.1364/OE.18.023818}, Keywords = {CCD, charge-coupled device; Confocal microscopy; Spectroscopy, fluorescence and luminescence}, Owner = {paul}, Publisher = {OSA}, Timestamp = {2012.11.07} } @Article{Blom2009, Title = {Triplet-State Investigations of Fluorescent Dyes at Dielectric Interfaces Using Total Internal Reflection Fluorescence Correlation Spectroscopy}, Author = {Blom, Hans and Chmyrov, Andriy and Hassler, Kai and Davis, Lloyd M. and Widengren, Jerker}, Journal = {The Journal of Physical Chemistry A}, Year = {2009}, Number = {19}, Pages = {5554-5566}, Volume = {113}, Doi = {10.1021/jp8110088}, Owner = {paul}, Timestamp = {2012.11.02} } @Article{Blom2002, Title = {Parallel Fluorescence Detection of Single Biomolecules in Microarrays by a Diffractive-Optical-Designed 2 x 2 Fan-Out Element}, Author = {Hans Blom and Mathias Johansson and Anna-Sara Hedman and Liselotte Lundberg and Anders Hanning and Sverker H{\aa}rd and Rudolf Rigler}, Journal = {Applied Optics}, Year = {2002}, Month = {Jun}, Number = {16}, Pages = {3336--3342}, Volume = {41}, Abstract = {We have developed a multifocal diffractive-optical fluorescence correlation spectroscopy system for parallel excitation and detection of single tetramethylrhodamine biomolecules in microarrays. Multifocal excitation was made possible through the use of a 2 {\texttimes} 2 fan-out diffractive-optical element with uniform intensity in all foci. Characterization of the 2 {\texttimes} 2 fan-out diffractive-optical element shows formation of almost perfect Gaussian foci of submicrometer lateral diameter, as analyzed by thermal motion of tetramethylrhodamine dye molecules in solution. Results of parallel excitation and detection in a high-density microarray of circular wells show single-biomolecule sensitivity in all four foci simultaneously.}, Doi = {10.1364/AO.41.003336}, Keywords = {Diffractive optics; Confocal microscopy; Fluorescence microscopy; Fluorescence, laser-induced}, Owner = {paul}, Publisher = {OSA}, Timestamp = {2012.11.07} } @Article{Brinkmeier1999, Title = {Two-beam cross-correlation:  a method to characterize transport phenomena in micrometer-sized structures.}, Author = {M. Brinkmeier and K. Dörre and J. Stephan and M. Eigen}, Journal = {Analytical Chemistry}, Year = {1999}, Month = {Feb}, Number = {3}, Pages = {609--616}, Volume = {71}, Abstract = {To determine flow properties, namely, the velocity and angle of the flow in microstructured channels, an experimental realization based on fluorescence correlation spectroscopy is described. For this purpose, two micrometer-sized spatially separated volume elements have been created. The cross-correlation signal from these has been recorded and evaluated mathematically. In addition to previous results, two-beam cross-correlation allows for fast and easy determination of even small (down to 200 μm/s) flow velocities, as well as simultaneous measurement of diffusion properties of single dye molecules within a rather short detection time of 5-100 s and an error rate of less than 20\%. The spatial flow resolution is around 1-2 μm, limited by the diameter of the volume element. Furthermore, vectorial flow data can be obtained and evaluated. A discussion of the theoretical background and an experimental verification of the theoretical results is performed. The feasibility of fast and easy data processing is shown if the flow time is the only desired information. Possible applications of this precise and simple method are the determination of transportation effects within artificial microstructures for CE and HPLC, fast chemical kinetics, and high-throughput screening.}, Doi = {10.1021/ac980820i}, Institution = {Max-Planck-Institut für biophysikalische Chemie, Am Fassberg, D-37077 Göttingen, Germany.}, Language = {eng}, Medline-pst = {ppublish}, Owner = {paul}, Pmid = {21662718}, Timestamp = {2012.11.07} } @Article{Brutzer2012, Title = {Scanning Evanescent Fields Using a pointlike Light Source and a Nanomechanical DNA Gear}, Author = {Brutzer, Hergen and Schwarz, Friedrich W. and Seidel, Ralf}, Journal = {Nano Letters}, Year = {2012}, Number = {1}, Pages = {473-478}, Volume = {12}, Doi = {10.1021/nl203876w}, Owner = {paul}, Timestamp = {2012.08.09} } @Article{Buchholz2012, Title = {FPGA implementation of a 32x32 autocorrelator array for analysis of fast image series}, Author = {Jan Buchholz and Jan Wolfgang Krieger and G\'{a}bor Mocs\'{a}r and Bal\'{a}zs Kreith and Edoardo Charbon and Gy\"{o}rgy V\'{a}mosi and Udo Kebschull and J\"{o}rg Langowski}, Journal = {Optics Express}, Year = {2012}, Month = {Jul}, Number = {16}, Pages = {17767--17782}, Volume = {20}, Abstract = {With the evolving technology in CMOS integration, new classes of 2D-imaging detectors have recently become available. In particular, single photon avalanche diode (SPAD) arrays allow detection of single photons at high acquisition rates (\&\#x02265; 100kfps), which is about two orders of magnitude higher than with currently available cameras. Here we demonstrate the use of a SPAD array for imaging fluorescence correlation spectroscopy (imFCS), a tool to create 2D maps of the dynamics of fluorescent molecules inside living cells. Time-dependent fluorescence fluctuations, due to fluorophores entering and leaving the observed pixels, are evaluated by means of autocorrelation analysis. The multi-\&\#x003C4; correlation algorithm is an appropriate choice, as it does not rely on the full data set to be held in memory. Thus, this algorithm can be efficiently implemented in custom logic. We describe a new implementation for massively parallel multi-\&\#x003C4; correlation hardware. Our current implementation can calculate 1024 correlation functions at a resolution of 10\&\#x003BC;s in real-time and therefore correlate real-time image streams from high speed single photon cameras with thousands of pixels.}, Doi = {10.1364/OE.20.017767}, Keywords = {Detectors; Arrays; Cameras; Correlators ; Fluorescence microscopy; Three-dimensional microscopy; Spectroscopy, fluorescence and luminescence; Avalanche photodiodes (APDs)}, Owner = {paul}, Publisher = {OSA}, Timestamp = {2012.10.24} } @PhdThesis{Burkhardt2010, Title = {Electron multiplying CCD – based detection in Fluorescence Correlation Spectroscopy and measurements in living zebrafish embryos}, Author = {Burkhardt, Markus}, School = {Biophysics, BIOTEC, Technische Universität Dresden, Tatzberg 47–51, 01307 Dresden, Germany}, Year = {2010}, Note = {\url{http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-61021}}, Owner = {paul}, Timestamp = {2012.10.24} } @Article{Burkhardt:06, Title = {Electron multiplying CCD based detection for spatially resolved fluorescence correlation spectroscopy}, Author = {Markus Burkhardt and Petra Schwille}, Journal = {Optics Express}, Year = {2006}, Month = {Jun}, Number = {12}, Pages = {5013--5020}, Volume = {14}, Abstract = {Fluorescence correlation spectroscopy (FCS) is carried out with an electron multiplying CCD (EMCCD). This new strategy is compared to standard detection by an avalanche photo diode showing good agreement with respect to the resulting autocorrelation curves. Applying different readout modes, a time resolution of 20 {\textmu}s can be achieved, which is sufficient to resolve the diffusion of free dye in solution. The advantages of implementing EMCCD cameras in wide-field ultra low light imaging, as well as in multi-spot confocal laser scanning microscopy, can consequently also be exploited for spatially resolved FCS. First proof-of-principle FCS measurements with two excitation volumes demonstrate the advantage of the flexible CCD area detection.}, Doi = {10.1364/OE.14.005013}, Keywords = {CCD, charge-coupled device; Medical optics and biotechnology; Fluorescence, laser-induced}, Publisher = {OSA} } @Article{Chiantia2006, Title = {Combined AFM and Two-Focus SFCS Study of Raft-Exhibiting Model Membranes}, Author = {Chiantia , Salvatore and Ries , Jonas and Kahya, Nicoletta and Schwille, Petra}, Journal = {ChemPhysChem}, Year = {2006}, Number = {11}, Pages = {2409--2418}, Volume = {7}, Doi = {10.1002/cphc.200600464}, ISSN = {1439-7641}, Keywords = {fluorescent probes, force measurements, membranes, sphingolipids}, Owner = {paul}, Publisher = {WILEY-VCH Verlag}, Timestamp = {2012.10.24} } @Article{Dertinger2007, Title = {Two-Focus Fluorescence Correlation Spectroscopy: A New Tool for Accurate and Absolute Diffusion Measurements}, Author = {Dertinger, Thomas and Pacheco, Victor and von der Hocht, Iris and Hartmann, Rudolf and Gregor, Ingo and Enderlein, Jörg}, Journal = {ChemPhysChem}, Year = {2007}, Number = {3}, Pages = {433--443}, Volume = {8}, Doi = {10.1002/cphc.200600638}, ISSN = {1439-7641}, Keywords = {diffusion coefficients, fluorescence spectroscopy, fluorescent dyes, time-resolved spectroscopy}, Owner = {paul}, Publisher = {WILEY-VCH Verlag}, Timestamp = {2012.02.14} } @Article{Einstein1905, Title = {Über die von der molekularkinetischen Theorie der Wärme geforderte Bewegung von in ruhenden Flüssigkeiten suspendierten Teilchen}, Author = {Einstein, A.}, Journal = {Annalen der Physik}, Year = {1905}, Number = {8}, Pages = {549--560}, Volume = {322}, Doi = {10.1002/andp.19053220806}, ISSN = {1521-3889}, Owner = {paul}, Publisher = {WILEY-VCH Verlag}, Timestamp = {2012.11.02} } @Article{Elson1974, Title = {Fluorescence correlation spectroscopy. I. Conceptual basis and theory}, Author = {Elson, Elliot L. and Magde, Douglas}, Journal = {Biopolymers}, Year = {1974}, Number = {1}, Pages = {1--27}, Volume = {13}, Doi = {10.1002/bip.1974.360130102}, ISSN = {1097-0282}, Owner = {paul}, Publisher = {Wiley Subscription Services, Inc., A Wiley Company}, Timestamp = {2012.09.24} } @Article{Enderlein1999, Title = {Highly Efficient Optical Detection of Surface-Generated Fluorescence}, Author = {J\"{o}rg Enderlein and Thomas Ruckstuhl and Stefan Seeger}, Journal = {Applied Optics}, Year = {1999}, Month = {Feb}, Number = {4}, Pages = {724--732}, Volume = {38}, Abstract = {We present a theoretical study of a new highly efficient system for optical light collection, designed for ultrasensitive fluorescence detection of surface-bound molecules. The main core of the system is a paraboloid glass segment acting as a mirror for collecting the fluorescence. A special feature of the system is its ability to sample not only fluorescence that is emitted below the angle of total internal reflection (the critical angle) but also particularly the light above the critical angle. As shown, this is especially advantageous for collecting the fluorescence of surface-bound molecules. A comparison is made with conventional high-aperture microscope objectives. Furthermore, it is shown that the system allows not only for highly efficient light collection but also for confocal imaging of the detection region, which is of great importance for rejecting scattered light in potential applications such as the detection of only a few molecules.}, Doi = {10.1364/AO.38.000724}, Keywords = {Geometric optical design; Microscopy; Detection; Fluorescence microscopy}, Owner = {paul}, Publisher = {OSA}, Timestamp = {2012.11.02} } @Article{Foo2012, Title = {Factors Affecting the Quantification of Biomolecular Interactions by Fluorescence Cross-Correlation Spectroscopy}, Author = {Foo, Y. H. and Naredi-Rainer, N. and Lamb, D. C. and Ahmed, S. and Wohland, T.}, Journal = {Biophys J}, Year = {2012}, Number = {5}, Pages = {1174--83}, Volume = {102}, Abstract = {Fluorescence cross-correlation spectroscopy (FCCS) is used to determine interactions and dissociation constants (K(d)s) of biomolecules. The determination of a K(d) depends on the accurate measurement of the auto- and cross-correlation function (ACF and CCF) amplitudes. In the case of complete binding, the ratio of the CCF/ACF amplitudes is expected to be 1. However, measurements performed on tandem fluorescent proteins (FPs), in which two different FPs are linked, yield CCF/ACF amplitude ratios of approximately 0.5 or less for different FCCS schemes. We use single wavelength FCCS and pulsed interleaved excitation FCCS to measure various tandem FPs constituted of different red and green FPs and determine the causes for this suboptimal ratio. The main causes for the reduced CCF/ACF amplitude ratio are differences in observation volumes for the different labels, the existence of dark FPs due to maturation problems, photobleaching, and to a lesser extent Forster (or fluorescence) resonance energy transfer between the labels. We deduce the fraction of nonfluorescent proteins for EGFP, mRFP, and mCherry as well as the differences in observation volumes. We use this information to correct FCCS measurements of the interaction of Cdc42, a small Rho-GTPase, with its effector IQGAP1 in live cell measurements to obtain a label-independent value for the K(d).}, Doi = {10.1016/j.bpj.2012.01.040}, Owner = {paul}, Timestamp = {2014.01.15} } @Article{Hansen1998, Title = {Measuring Reversible Adsorption Kinetics of Small Molecules at Solid/Liquid Interfaces by Total Internal Reflection Fluorescence Correlation Spectroscopy}, Author = {Hansen, Richard L and Harris, Joel M}, Journal = {Analytical Chemistry}, Year = {1998}, Number = {20}, Pages = {4247--4256}, Volume = {70}, Doi = {10.1021/ac980925l}, Owner = {paul}, Timestamp = {2012.02.14} } @Article{Hashmi2007, Title = {Spatially extended FCS for visualizing and quantifying high-speed multiphase flows in microchannels}, Author = {Sara M. Hashmi and Michael Loewenberg and Eric R. Dufresne}, Journal = {Optics Express}, Year = {2007}, Month = {May}, Number = {10}, Pages = {6528--6533}, Volume = {15}, Abstract = {We report the development of spatially extended fluorescence correlation spectroscopy for visualizing and quantifying multiphase flows in microchannels. We employ simultaneous detection with a high-speed camera across the width of the channel, enabling investigation of the dynamics of the flow at short time scales. We take advantage of the flow to scan the sample past the fixed illumination, capturing frames up to 100 KHz. At these rates, we can resolve the motion of sub-micron particles at velocities up to the order of 1 cm/s. We visualize flows with kymographs and quantify velocity profiles by cross-correlations within the focal volume. We demonstrate the efficacy of our approach by measuring the depth-resolved velocity profile of suspensions of sub-micron diameter silica particles flowing up to 1.5 mm/s.}, Doi = {10.1364/OE.15.006528}, Keywords = {Velocimetry; Flow diagnostics; Fluorescence, laser-induced}, Owner = {paul}, Publisher = {OSA}, Timestamp = {2012.11.07} } @Article{Hassler2005, Title = {High Count Rates with Total Internal Reflection Fluorescence Correlation Spectroscopy}, Author = {Hassler, Kai and Anhut, Tiemo and Rigler, Rudolf and G\"{o}sch, Michael and Lasser, Theo}, Journal = {Biophysical Journal}, Year = {2005}, Month = jan, Number = {1}, Pages = {L01--L03}, Volume = {88}, Doi = {10.1529/biophysj.104.053884}, ISSN = {0006-3495}, Owner = {paul}, Publisher = {Cell Press}, Refid = {S0006-3495(05)73079-4 DOI - 10.1529/biophysj.104.053884}, Timestamp = {2012.05.02} } @Article{Hassler2005a, Title = {Total internal reflection fluorescence correlation spectroscopy (TIR-FCS) with low background and high count-rate per molecule}, Author = {Kai Hassler and Marcel Leutenegger and Per Rigler and Ramachandra Rao and Rudolf Rigler and Michael G\"{o}sch and Theo Lasser}, Journal = {Optics Express}, Year = {2005}, Month = {Sep}, Number = {19}, Pages = {7415--7423}, Volume = {13}, Abstract = {We designed a fluorescence correlation spectroscopy (FCS) system for measurements on surfaces. The system consists of an objective-type total internal reflection fluorescence (TIRF) microscopy setup, adapted to measure FCS. Here, the fluorescence exciting evanescent wave is generated by epi-illumination through the periphery of a high NA oil-immersion objective. The main advantages with respect to conventional FCS systems are an improvement in terms of counts per molecule (cpm) and a high signal to background ratio. This is demonstrated by investigating diffusion as well as binding and release of single molecules on a glass surface. Furthermore, the size and shape of the molecule detection efficiency (MDE) function was calculated, using a wave-vectorial approach and taking into account the influence of the dielectric interface on the emission properties of fluorophores.}, Doi = {10.1364/OPEX.13.007415}, Keywords = {Spectroscopy, fluorescence and luminescence; Spectroscopy, surface; Fluorescence, laser-induced}, Owner = {paul}, Publisher = {OSA}, Timestamp = {2012.09.21} } @Article{Haupts1998, Title = {Dynamics of fluorescence fluctuations in green fluorescent protein observed by fluorescence correlation spectroscopy}, Author = {Haupts, Ulrich and Maiti, Sudipta and Schwille, Petra and Webb, Watt W.}, Journal = {Proceedings of the National Academy of Sciences}, Year = {1998}, Number = {23}, Pages = {13573-13578}, Volume = {95}, Abstract = {We have investigated the pH dependence of the dynamics of conformational fluctuations of green fluorescent protein mutants EGFP (F64L/S65T) and GFP-S65T in small ensembles of molecules in solution by using fluorescence correlation spectroscopy (FCS). FCS utilizes time-resolved measurements of fluctuations in the molecular fluorescence emission for determination of the intrinsic dynamics and thermodynamics of all processes that affect the fluorescence. Fluorescence excitation of a bulk solution of EGFP decreases to zero at low pH (pKa = 5.8) paralleled by a decrease of the absorption at 488 nm and an increase at 400 nm. Protonation of the hydroxyl group of Tyr-66, which is part of the chromophore, induces these changes. When FCS is used the fluctuations in the protonation state of the chromophore are time resolved. The autocorrelation function of fluorescence emission shows contributions from two chemical relaxation processes as well as diffusional concentration fluctuations. The time constant of the fast, pH-dependent chemical process decreases with pH from 300 μs at pH 7 to 45 μs at pH 5, while the time-average fraction of molecules in a nonfluorescent state increases to 80% in the same range. A second, pH-independent, process with a time constant of 340 μs and an associated fraction of 13% nonfluorescent molecules is observed between pH 8 and 11, possibly representing an internal proton transfer process and associated conformational rearrangements. The FCS data provide direct measures of the dynamics and the equilibrium properties of the protonation processes. Thus FCS is a convenient, intrinsically calibrated method for pH measurements in subfemtoliter volumes with nanomolar concentrations of EGFP.}, Doi = {10.1073/pnas.95.23.13573}, Owner = {paul}, Timestamp = {2012.11.01} } @Article{Haustein2007, Title = {Fluorescence Correlation Spectroscopy: Novel Variations of an Established Technique}, Author = {Haustein, Elke and Schwille, Petra}, Journal = {Annual Review of Biophysics and Biomolecular Structure}, Year = {2007}, Number = {1}, Pages = {151-169}, Volume = {36}, Doi = {10.1146/annurev.biophys.36.040306.132612}, Owner = {paul}, Timestamp = {2012.02.14} } @Article{Helmers2003, Title = {CMOS vs. CCD sensors in speckle interferometry}, Author = {Heinz Helmers and Markus Schellenberg}, Journal = {Optics \& Laser Technology}, Year = {2003}, Number = {8}, Pages = {587 - 595}, Volume = {35}, Doi = {10.1016/S0030-3992(03)00078-1}, ISSN = {0030-3992}, Keywords = {CCD sensors}, Owner = {paul}, Timestamp = {2012.10.06} } @Article{Holekamp2008, Title = {Fast Three-Dimensional Fluorescence Imaging of Activity in Neural Populations by Objective-Coupled Planar Illumination Microscopy}, Author = {Terrence F. Holekamp and Diwakar Turaga and Timothy E. Holy}, Journal = {Neuron}, Year = {2008}, Number = {5}, Pages = {661 - 672}, Volume = {57}, Doi = {10.1016/j.neuron.2008.01.011}, ISSN = {0896-6273}, Keywords = {SYSBIO}, Owner = {paul}, Timestamp = {2012.11.13} } @Article{Humpolickova2006, Title = {Probing Diffusion Laws within Cellular Membranes by Z-Scan Fluorescence Correlation Spectroscopy}, Author = {Jana Humpol\'{i}\v{c}kov\'{a} and Ellen Gielen and Ale\v{s} Benda and Veronika Fagulova and Jo Vercammen and Martin vandeVen and Martin Hof and Marcel Ameloot and Yves Engelborghs}, Journal = {Biophysical Journal}, Year = {2006}, Number = {3}, Pages = {L23 - L25}, Volume = {91}, Doi = {10.1529/biophysj.106.089474}, ISSN = {0006-3495}, Owner = {paul}, Timestamp = {2012.10.25} } @Book{Nocedal2006, Title = {Numerical Optimization}, Author = {Nocedal J. and Wright S J.}, Publisher = {Springer Berlin Heidelberg}, Year = {2006}, Doi = {10.1007/978-3-540-35447-5}, Owner = {paul}, Timestamp = {2014.03.31} } @Article{Jin2004, Title = {Near-surface velocimetry using evanescent wave illumination}, Author = {Jin, S. and Huang, P. and Park, J. and Yoo, J. Y. and Breuer, K. S.}, Journal = {Experiments in Fluids}, Year = {2004}, Pages = {825-833}, Volume = {37}, Affiliation = {School of Mechanical and Aerospace Engineering Seoul National University Seoul 151-742 Korea}, Doi = {10.1007/s00348-004-0870-7}, ISSN = {0723-4864}, Issue = {6}, Keyword = {Technik}, Owner = {paul}, Publisher = {Springer Berlin / Heidelberg}, Timestamp = {2012.02.14} } @Article{Kannan2006, Title = {Electron Multiplying Charge-Coupled Device Camera Based Fluorescence Correlation Spectroscopy}, Author = {Kannan, Balakrishnan and Har, Jia Yi and Liu, Ping and Maruyama, Ichiro and Ding, Jeak Ling and Wohland, Thorsten}, Journal = {Analytical Chemistry}, Year = {2006}, Number = {10}, Pages = {3444-3451}, Volume = {78}, Doi = {10.1021/ac0600959}, Owner = {paul}, Timestamp = {2012.11.07} } @Article{Kim2007, Title = {Fluorescence correlation spectroscopy in living cells}, Author = {Kim, S. A. and Heinze, K. G. and Schwille, P.}, Journal = {Nat Methods}, Year = {2007}, Number = {11}, Pages = {963--73}, Volume = {4}, Abstract = {Fluorescence correlation spectroscopy (FCS) is an ideal analytical tool for studying concentrations, propagation, interactions and internal dynamics of molecules at nanomolar concentrations in living cells. FCS analyzes minute fluorescence-intensity fluctuations about the equilibrium of a small ensemble (<10(3)) of molecules. These fluctuations act like a 'fingerprint' of a molecular species detected when entering and leaving a femtoliter-sized optically defined observation volume created by a focused laser beam. In FCS the fluorescence fluctuations are recorded as a function of time and then statistically analyzed by autocorrelation analysis. The resulting autocorrelation curve yields a measure of self-similarity of the system after a certain time delay, and its amplitude describes the normalized variance of the fluorescence fluctuations. By fitting the curves to an appropriate physical model, this method provides precise information about a multitude of measurement parameters, including diffusion coefficients, local concentration, states of aggregation and molecular interactions. FCS operates in real time with diffraction-limited spatial and sub-microsecond temporal resolution. Assessing diverse molecular dynamics within the living cell is a challenge well met by FCS because of its single-molecule sensitivity and high dynamic resolution. For these same reasons, however, intracellular FCS measurements also harbor the large risk of collecting artifacts and thus producing erroneous data. Here we provide a step-by-step guide to the application of FCS to cellular systems, including methods for minimizing artifacts, optimizing measurement conditions and obtaining parameter values in the face of diverse and complex conditions of the living cell. A discussion of advantages and disadvantages of one-photon versus two-photon excitation for FCS is available in Supplementary Methods online.}, Doi = {10.1038/nmeth1104}, Keywords = {Animals Biological Transport Cytophotometry/instrumentation/*methods Eukaryotic Cells/*metabolism Fluorescent Dyes/chemistry Humans Kinetics Models, Biological Proteins/chemistry/metabolism Spectrometry, Fluorescence/instrumentation/methods Staining and Labeling}, Owner = {paul}, Timestamp = {2014.01.15} } @InCollection{Kohl2005, Title = {Fluorescence Correlation Spectroscopy with Autofluorescent Proteins}, Author = {Kohl, Tobias and Schwille, Petra}, Booktitle = {Microscopy Techniques}, Publisher = {Springer Berlin / Heidelberg}, Year = {2005}, Editor = {Rietdorf, Jens}, Pages = {1316-1317}, Series = {Advances in Biochemical Engineering/Biotechnology}, Volume = {95}, Affiliation = {Pastor-Sander-Bogen 92 37083 Göttingen Germany}, Doi = {10.1007/b102212}, ISBN = {978-3-540-23698-6}, Keyword = {Chemistry and Materials Science}, Owner = {paul}, Timestamp = {2012.02.14} } @Article{Koppel1974, Title = {Statistical accuracy in fluorescence correlation spectroscopy}, Author = {Koppel, D.}, Journal = {Phys Rev A}, Year = {1974}, Pages = {1938--1945}, Volume = {10}, Doi = {10.1103/physreva.10.1938}, Keywords = {FCS}, Owner = {paul}, Timestamp = {2014.01.15} } @Article{Korlach1999, Title = {Characterization of lipid bilayer phases by confocal microscopy and fluorescence correlation spectroscopy}, Author = {Korlach, J. and Schwille, P. and Webb, W. W. and Feigenson, G. W.}, Journal = {Proc Natl Acad Sci U S A}, Year = {1999}, Number = {15}, Pages = {8461--6}, Volume = {96}, Abstract = {We report the application of confocal imaging and fluorescence correlation spectroscopy (FCS) to characterize chemically well-defined lipid bilayer models for biomembranes. Giant unilamellar vesicles of dilauroyl phosphatidylcholine/dipalmitoyl phosphatidylcholine (DLPC/DPPC)/cholesterol were imaged by confocal fluorescence microscopy with two fluorescent probes, 1, 1'-dieicosanyl-3,3,3',3'-tetramethylindocarbocyanine perchlorate (DiI-C(20)) and 2-(4,4-difluoro-5,7-dimethyl-4-bora-3a, 4a-diaza-s-indacene-3-pentanoyl)-1-hexadecanoyl-sn-glycero-3 -phosphoc holine (Bodipy-PC). Phase separation was visualized by differential probe partition into the coexisting phases. Three-dimensional image reconstructions of confocal z-scans through giant unilamellar vesicles reveal the anisotropic morphology of coexisting phase domains on the surface of these vesicles with full two-dimensional resolution. This method demonstrates by direct visualization the exact superposition of like phase domains in apposing monolayers, thus answering a long-standing open question. Cholesterol was found to induce a marked change in the phase boundary shapes of the coexisting phase domains. To further characterize the phases, the translational diffusion coefficient, D(T), of the DiI-C(20) was measured by FCS. D(T) values at approximately 25 degrees C ranged from approximately 3 x 10(-8) cm(2)/s in the fluid phase, to approximately 2 x 10(-9) cm(2)/s in high-cholesterol-content phases, to approximately 2 x 10(-10) cm(2)/s in the spatially ordered phases that coexist with fluid phases. In favorable cases, FCS could distinguish two different values of D(T) in a region of two-phase coexistence on a single vesicle.}, Doi = {10.1073/pnas.96.15.8461}, Keywords = {Carbocyanines Cholesterol/chemistry Diffusion Fluorescent Dyes Lipid Bilayers/*chemistry Liposomes/chemistry Microscopy, Confocal Phospholipids/chemistry Spectrometry, Fluorescence}, Owner = {paul}, Timestamp = {2014.01.15} } @Article{Korson1969, Title = {Viscosity of water at various temperatures}, Author = {Korson, Lawrence and Drost-Hansen, Walter and Millero, Frank J.}, Journal = {The Journal of Physical Chemistry}, Year = {1969}, Number = {1}, Pages = {34-39}, Volume = {73}, Doi = {10.1021/j100721a006}, Owner = {paul}, Timestamp = {2012.10.29} } @Book{LandauLifshitsStatPhys, Title = {{Statistical Physics, Third Edition, Part 1: Volume 5 (Course of Theoretical Physics, Volume 5)}}, Author = {Landau, L. D. and Lifshitz, E. M.}, Publisher = {Butterworth-Heinemann}, Year = {1980}, Edition = {3}, Month = jan, Abstract = {{A lucid presentation of statistical physics and thermodynamics which develops from the general principles to give a large number of applications of the theory.}}, Citeulike-article-id = {1284487}, Citeulike-linkout-0 = {http://www.amazon.ca/exec/obidos/redirect?tag=citeulike09-20\&path=ASIN/0750633727}, Citeulike-linkout-1 = {http://www.amazon.de/exec/obidos/redirect?tag=citeulike01-21\&path=ASIN/0750633727}, Citeulike-linkout-2 = {http://www.amazon.fr/exec/obidos/redirect?tag=citeulike06-21\&path=ASIN/0750633727}, Citeulike-linkout-3 = {http://www.amazon.jp/exec/obidos/ASIN/0750633727}, Citeulike-linkout-4 = {http://www.amazon.co.uk/exec/obidos/ASIN/0750633727/citeulike00-21}, Citeulike-linkout-5 = {http://www.amazon.com/exec/obidos/redirect?tag=citeulike07-20\&path=ASIN/0750633727}, Citeulike-linkout-6 = {http://www.worldcat.org/isbn/0750633727}, Citeulike-linkout-7 = {http://books.google.com/books?vid=ISBN0750633727}, Citeulike-linkout-8 = {http://www.amazon.com/gp/search?keywords=0750633727\&index=books\&linkCode=qs}, Citeulike-linkout-9 = {http://www.librarything.com/isbn/0750633727}, Day = {15}, HowPublished = {Paperback}, ISBN = {0750633727}, Keywords = {fermi\_statistics, statistical\_physics}, Owner = {paul}, Posted-at = {2011-03-03 11:38:41}, Priority = {2}, Timestamp = {2012.02.03} } @Article{Leutenegger2012, Title = {Fluorescence correlation spectroscopy with a total internal reflection fluorescence STED microscope (TIRF-STED-FCS)}, Author = {Marcel Leutenegger and Christian Ringemann and Theo Lasser and Stefan W. Hell and Christian Eggeling}, Journal = {Optics Express}, Year = {2012}, Month = {Feb}, Number = {5}, Pages = {5243--5263}, Volume = {20}, Abstract = {We characterize a novel fluorescence microscope which combines the high spatial discrimination of a total internal reflection epi-fluorescence (epi-TIRF) microscope with that of stimulated emission depletion (STED) nanoscopy. This combination of high axial confinement and dynamic-active lateral spatial discrimination of the detected fluorescence emission promises imaging and spectroscopy of the structure and function of cell membranes at the macro-molecular scale. Following a full theoretical description of the sampling volume and the recording of images of fluorescent beads, we exemplify the performance and limitations of the TIRF-STED nanoscope with particular attention to the polarization state of the laser excitation light. We demonstrate fluorescence correlation spectroscopy (FCS) with the TIRF-STED nanoscope by observing the diffusion of dye molecules in aqueous solutions and of fluorescent lipid analogs in supported lipid bilayers in the presence of background signal. The nanoscope reduced the out-of-focus background signal. A lateral resolution down to 40--50 nm was attained which was ultimately limited by the low lateral signal-to-background ratio inherent to the confocal epi-TIRF scheme. Together with the estimated axial confinement of about 55 nm, our TIRF-STED nanoscope achieved an almost isotropic and less than 1 attoliter small all-optically induced measurement volume.}, Doi = {10.1364/OE.20.005243}, Keywords = {Diffraction; Fluorescence microscopy; Fluorescence}, Owner = {paul}, Publisher = {OSA}, Timestamp = {2012.09.21} } @Article{Levenberg1944, Title = {A method for the solution of certain non-linear problems in least squares}, Author = {Levenberg, Kenneth}, Journal = {Quarterly Journal of Applied Mathmatics}, Year = {1944}, Number = {2}, Pages = {164--168}, Volume = {II}, __markedentry = {[paul:6]}, Citeulike-article-id = {10796881}, Keywords = {indefinite, nonconvex, optimization}, Owner = {paul}, Posted-at = {2012-06-17 09:00:21}, Priority = {2}, Timestamp = {2014.03.31} } @Article{Lieto2003a, Title = {Ligand-Receptor Kinetics Measured by Total Internal Reflection with Fluorescence Correlation Spectroscopy}, Author = {Lieto, Alena M. and Cush, Randall C. and Thompson, Nancy L.}, Journal = {Biophysical Journal}, Year = {2003}, Month = nov, Number = {5}, Pages = {3294--3302}, Volume = {85}, Doi = {10.1016/S0006-3495(03)74748-1}, ISSN = {0006-3495}, Owner = {paul}, Publisher = {Cell Press}, Refid = {S0006-3495(03)74748-1 DOI - 10.1016/S0006-3495(03)74748-1}, Timestamp = {2012.09.21} } @Article{Lieto2003, Title = {Lateral Diffusion from Ligand Dissociation and Rebinding at Surfaces†}, Author = {Lieto, Alena M. and Lagerholm, B. Christoffer and Thompson, Nancy L.}, Journal = {Langmuir}, Year = {2003}, Number = {5}, Pages = {1782-1787}, Volume = {19}, Doi = {10.1021/la0261601}, Owner = {paul}, Timestamp = {2012.02.14} } @Article{Lieto2004, Title = {Total Internal Reflection with Fluorescence Correlation Spectroscopy: Nonfluorescent Competitors}, Author = {Alena M. Lieto and Nancy L. Thompson}, Journal = {Biophysical Journal}, Year = {2004}, Number = {2}, Pages = {1268 - 1278}, Volume = {87}, Doi = {10.1529/biophysj.103.035030}, ISSN = {0006-3495}, Owner = {paul}, Timestamp = {2012.02.14} } @Article{Muller2014, Title = {Scanning fluorescence correlation spectroscopy ({SFCS}) with a scan path perpendicular to the membrane plane}, Author = {M\"{u}ller, P. and Schwille, P. and Weidemann, T.}, Journal = {Methods Mol Biol}, Year = {2014}, Pages = {635--51}, Volume = {1076}, Abstract = {Scanning fluorescence correlation spectroscopy (SFCS) with a scan path perpendicular to the membrane plane was introduced to measure diffusion and interactions of fluorescent components in free-standing biomembranes. Using a confocal laser scanning microscope (CLSM), the open detection volume is repeatedly scanned through the membrane at a kHz frequency. The fluorescence photons emitted from the detection volume are continuously recorded and stored in a file. While the accessory hardware requirements for a conventional CLSM are minimal, data evaluation can pose a bottleneck. The photon events must be assigned to each scan, in which the maximum signal intensities have to be detected, binned, and aligned between the scans, in order to derive the membrane-related intensity fluctuations of one spot. Finally, this time-dependent signal must be correlated and evaluated by well-known FCS model functions. Here we provide two platform-independent, open source software tools (PyScanFCS and PyCorrFit) that allow to perform all of these steps and to establish perpendicular SFCS in its one- or two-focus as well as its single- or dual-color modality.}, Doi = {10.1007/978-1-62703-649-8_29}, Owner = {paul}, Timestamp = {2014.01.25} } @Article{Magde1972, Title = {Thermodynamic Fluctuations in a Reacting System - Measurement by Fluorescence Correlation Spectroscopy}, Author = {Magde, Douglas and Elson, Elliot and Webb, W. W.}, Journal = {Physical Review Letters}, Year = {1972}, Month = {Sep}, Pages = {705--708}, Volume = {29}, Doi = {10.1103/PhysRevLett.29.705}, Issue = {11}, Owner = {paul}, Publisher = {American Physical Society}, Timestamp = {2012.11.01} } @Article{Magde1974, Title = {Fluorescence correlation spectroscopy. II. An experimental realization}, Author = {Magde, Douglas and Elson, Elliot L. and Webb, Watt W.}, Journal = {Biopolymers}, Year = {1974}, Number = {1}, Pages = {29--61}, Volume = {13}, Doi = {10.1002/bip.1974.360130103}, ISSN = {1097-0282}, Owner = {paul}, Publisher = {Wiley Subscription Services, Inc., A Wiley Company}, Timestamp = {2012.09.21} } @Article{Magde1978, Title = {Fluorescence correlation spectroscopy. III. Uniform translation and laminar flow}, Author = {Magde, D. and Webb, W. W. and Elson, E. L.}, Journal = {Biopolymers}, Year = {1978}, Pages = {361--376}, Volume = {17}, Doi = {10.1002/bip.1978.360170208}, Keywords = {FCS; laminar flow; capillary electrophoresis}, Owner = {paul}, Timestamp = {2014.01.15} } @Article{Meseth1999, Title = {Resolution of fluorescence correlation measurements}, Author = {Meseth, U. and Wohland, T. and Rigler, R. and Vogel, H.}, Journal = {Biophys J}, Year = {1999}, Pages = {1619--1631}, Volume = {76}, Abstract = {The resolution limit of fluorescence correlation spectroscopy for two-component solutions is investigated theoretically and experimentally. The autocorrelation function for two different particles in solution were computed, statistical noise was added, and the resulting curve was fitted with a least squares fit. These simulations show that the ability to distinguish between two different molecular species in solution depends strongly on the number of photons detected from each particle, their difference in size, and the concentration of each component in solution. To distinguish two components, their diffusion times must differ by at least a factor of 1.6 for comparable quantum yields and a high fluorescence signal. Experiments were conducted with Rhodamine 6G and Rhodamine-labeled bovine serum albumin. The experimental results support the simulations. In addition, they show that even with a high fluorescence signal but significantly different quantum yields, the diffusion times must differ by a factor much bigger than 1.6 to distinguish the two components. Depending on the quantum yields and the difference in size, there exists a concentration threshold for the less abundant component below which it is not possible to determine with statistical means alone that two particles are in solution.}, Doi = {10.1016/S0006-3495(99)77321-2}, Keywords = {Diffusion, Multiple components}, Owner = {TW}, Timestamp = {2014.01.27} } @Article{Nelder1965, Title = {A simplex method for function minimization}, Author = {Nelder, John A and Mead, Roger}, Journal = {Computer journal}, Year = {1965}, Number = {4}, Pages = {308--313}, Volume = {7}, Doi = {10.1093/comjnl/7.4.308}, Owner = {paul}, Timestamp = {2014.03.31} } @Article{Nitsche2004, Title = {A Transient Diffusion Model Yields Unitary Gap Junctional Permeabilities from Images of Cell-to-Cell Fluorescent Dye Transfer Between Xenopus Oocytes}, Author = {Johannes M. Nitsche and Hou-Chien Chang and Paul A. Weber and Bruce J. Nicholson}, Journal = {Biophysical Journal}, Year = {2004}, Number = {4}, Pages = {2058 - 2077}, Volume = {86}, Doi = {10.1016/S0006-3495(04)74267-8}, ISSN = {0006-3495}, Owner = {paul}, Timestamp = {2012.11.08} } @Article{Ohsugi2009, Title = {Multipoint fluorescence correlation spectroscopy with total internal reflection fluorescence microscope}, Author = {Ohsugi, Yu and Kinjo, Masataka}, Journal = {Journal of Biomedical Optics}, Year = {2009}, Number = {1}, Pages = {014030-014030-4}, Volume = {14}, Doi = {10.1117/1.3080723}, Owner = {paul}, Timestamp = {2012.11.12} } @Article{Ohsugi2006, Title = {Lateral mobility of membrane-binding proteins in living cells measured by total internal reflection fluorescence correlation spectroscopy.}, Author = {Ohsugi, Yu and Saito, Kenta and Tamura, Mamoru and Kinjo, Masataka}, Journal = {Biophysical Journal}, Year = {2006}, Number = {9}, Pages = {3456--3464}, Volume = {91}, Doi = {10.1529/biophysj.105.074625}, Owner = {paul}, Publisher = {Biophysical Society}, Timestamp = {2012.02.14} } @Article{Palmer1987, Title = {Theory of sample translation in fluorescence correlation spectroscopy.}, Author = {A. G. Palmer and N. L. Thompson}, Journal = {Biophysical Journal}, Year = {1987}, Month = {Feb}, Number = {2}, Pages = {339--343}, Volume = {51}, Abstract = {New applications of the technique of fluorescence correlation spectroscopy (FCS) require lateral translation of the sample through a focused laser beam (Peterson, N.O., D.C. Johnson, and M.J. Schlesinger, 1986, Biophys. J., 49:817-820). Here, the effect of sample translation on the shape of the FCS autocorrelation function is examined in general. It is found that if the lateral diffusion coefficients of the fluorescent species obey certain conditions, then the FCS autocorrelation function is a simple product of one function that depends only on transport coefficients and another function that depends only on the rate constants of chemical reactions that occur in the sample. This simple form should allow manageable data analyses in new FCS experiments that involve sample translation.}, Doi = {10.1016/S0006-3495(87)83340-4}, Keywords = {Kinetics; Lasers; Mathematics; Models, Theoretical; Spectrometry, Fluorescence, methods}, Language = {eng}, Medline-pst = {ppublish}, Owner = {paul}, Pii = {S0006-3495(87)83340-4}, Pmid = {3828464}, Timestamp = {2012.11.02} } @Article{Pero2006-06, Title = {Size dependence of protein diffusion very close to membrane surfaces: measurement by total internal reflection with fluorescence correlation spectroscopy.}, Author = {Pero, JK and Haas, EM and Thompson, NL}, Journal = {The Journal of Physical Chemistry. B}, Year = {2006}, Number = {5}, Pages = {10910-8}, Volume = {110}, Doi = {10.1021/jp056990y}, ISSN = {1520-6106}, Owner = {paul}, Timestamp = {2012.09.21} } @Article{Petrasek2010, Title = {Scanning {FCS} for the characterization of protein dynamics in live cells}, Author = {Petr\'{a}\v{s}ek, Zden\v{e}k and Ries, J. and Schwille, P.}, Journal = {Methods Enzymol}, Year = {2010}, Pages = {317--43}, Volume = {472}, Abstract = {Scanning fluorescence correlation spectroscopy (sFCS) is the generic term for a group of fluorescence correlation techniques where the measurement volume is moved across the sample in a defined way. The introduction of scanning is motivated by its ability to alleviate or remove several distinct problems often encountered in standard FCS, and thus, to extend the range of applicability of fluorescence correlation methods in biological systems. These problems include poor statistical accuracy in measurements with slowly moving molecules, photobleaching, optical distortions affecting the calibration of the measurement volume, membrane instabilities, etc. Here, we present an overview of sFCS methods, explaining their benefits, implementation details, requirements, and limitations, as well as relations to each other. Further, we give examples of different sFCS implementations as applied to cellular systems, namely large-circle sFCS to measure protein dynamics in embryo cortex and line sFCS to measure protein diffusion and interactions in unstable membranes.}, Doi = {10.1016/s0076-6879(10)72005-x}, Owner = {paul}, Timestamp = {2014.01.15} } @Article{Petrasek2008, Title = {Precise Measurement of Diffusion Coefficients using Scanning Fluorescence Correlation Spectroscopy}, Author = {Petr\'{a}\v{s}ek, Zden\v{e}k and Schwille, Petra}, Journal = {Biophysical Journal}, Year = {2008}, Month = feb, Number = {4}, Pages = {1437--1448}, Volume = {94}, Doi = {10.1529/biophysj.107.108811}, ISSN = {0006-3495}, Owner = {paul}, Publisher = {Cell Press}, Refid = {S0006-3495(08)70660-X DOI - 10.1529/biophysj.107.108811}, Timestamp = {2012.05.20} } @InCollection{Petrov:2008, Title = {State of the Art and Novel Trends in Fluorescence Correlation Spectroscopy}, Author = {Petrov, E. P. and Schwille, P.}, Booktitle = {Standardization and Quality Assurance in Fluorescence Measurements II}, Publisher = {Springer Berlin Heidelberg}, Year = {2008}, Editor = {Resch-Genger, Ute}, Pages = {145-197}, Series = {Springer Series on Fluorescence}, Volume = {6}, Affiliation = {Biophysics, BIOTEC, Technische Universität Dresden, Tatzberg 47–51, 01307 Dresden, Germany}, Doi = {10.1007/4243_2008_032}, ISBN = {978-3-540-70571-0}, Keyword = {Chemistry} } @Article{Powell1964, Title = {An efficient method for finding the minimum of a function of several variables without calculating derivatives}, Author = {Powell, M. J. D.}, Journal = {The Computer Journal}, Year = {1964}, Month = {Feb}, Number = {2}, Pages = {155–162}, Volume = {7}, Doi = {10.1093/comjnl/7.2.155}, ISSN = {1460-2067}, Owner = {paul}, Publisher = {Oxford University Press (OUP)}, Timestamp = {2014.03.31} } @Article{Press, Title = {Numerical recipes}, Author = {Press, William and Flannery, Brian P and Teukolsky, SAUL and Vetterling, WT}, Journal = {Cambridge University Press}, Year = {2006}, Pages = {989}, Volume = {1}, __markedentry = {[paul:]}, Owner = {paul}, Publisher = {Cambridge Univ Press}, Timestamp = {2014.03.31} } @Article{Qian1991, Title = {Analysis of confocal laser-microscope optics for 3-D fluorescence correlation spectroscopy}, Author = {Hong Qian and Elliot L. Elson}, Journal = {Applied Optics}, Year = {1991}, Month = {Apr}, Number = {10}, Pages = {1185--1195}, Volume = {30}, Abstract = {Quantitative fluorescence correlation spectroscopy (FCS) and fluorescence photobleaching recovery (FPR) measurements in bulk solution require a well characterized confocal laser microscope optical system. The introduction of a characteristic function, the collection efficiency function (CEF), provides a quantitative theoretical analysis of this system, which yields an interpretation of the FCS and FPR measurements in three dimensions. We demonstrate that when the proper field diaphragm is introduced, the 3-D FCS measurements can be mimicked by a 2-D theory with only minor error. The FPR characteristic recovery time for diffusion is expected to be slightly longer than the corresponding time measured by FCS in the same conditions. This is because the profile of the laser beam used for photobleaching is not affected by the field diaphragm. The CEF is also important for quantitative analysis of standard scanning confocal microscopy when it is carried out using a finite detection pinhole.}, Doi = {10.1364/AO.30.001185}, Owner = {paul}, Publisher = {OSA}, Timestamp = {2012.11.02} } @Article{Richter2006, Title = {Formation of Solid-Supported Lipid Bilayers:  An Integrated View}, Author = {Richter, Ralf P. and Bérat, Rémi and Brisson, Alain R.}, Journal = {Langmuir}, Year = {2006}, Number = {8}, Pages = {3497-3505}, Volume = {22}, Doi = {10.1021/la052687c}, Owner = {paul}, Timestamp = {2012.11.12} } @PhdThesis{Ries:08, Title = {Advanced Fluorescence Correlation Techniques to Study Membrane Dynamics}, Author = {Ries, E.}, School = {Biophysics, BIOTEC, Technische Universität Dresden, Tatzberg 47–51, 01307 Dresden, Germany}, Year = {2008}, Note = {\url{http://nbn-resolving.de/urn:nbn:de:bsz:14-ds-1219846317196-73420}} } @Article{Ries2009, Title = {Accurate Determination of Membrane Dynamics with Line-Scan FCS}, Author = {Jonas Ries and Salvatore Chiantia and Petra Schwille}, Journal = {Biophysical Journal}, Year = {2009}, Number = {5}, Pages = {1999 - 2008}, Volume = {96}, Doi = {10.1016/j.bpj.2008.12.3888}, ISSN = {0006-3495}, Owner = {paul}, Timestamp = {2012.11.08} } @Article{Ries2010, Title = {A comprehensive framework for fluorescence cross-correlation spectroscopy}, Author = {Ries, J. and Petr\'{a}\v{s}ek, Zden\v{e}k and Garcia-Saez, A. J. and Schwille, P.}, Journal = {New Journal of Physics}, Year = {2010}, Number = {11}, Pages = {113009}, Volume = {12}, Abstract = {Dual-colour fluorescence cross-correlation spectroscopy is a powerful method of studying binding between labelled biomolecules in vitro as well as in vivo. However, numerous artefacts and experimental complexities complicate quantitative measurements. Here, we show that a combination of dual-colour fluorescence correlation spectroscopy (FCS) with dual-focus FCS avoids artefacts due to chromatic aberrations or saturation and circumvents the calibration of the detection volumes. In addition, we present a comprehensive mathematical framework that allows us to accurately analyse correlation curves even in the presence of spectral cross-talk, incomplete or stochastic labelling, multiple binding sites, a fluorescent background and depletion due to photobleaching. We demonstrate the merits of this approach using dual-colour dual-focus scanning FCS, which allows binding measurements on membranes not affected by membrane movements.}, Doi = {10.1088/1367-2630/12/11/113009}, Keywords = {living cells membrane dynamics diffusion molecules accurate fret fcs}, Owner = {paul}, Timestamp = {2014.01.15} } @Article{Ries2008390, Title = {Total Internal Reflection Fluorescence Correlation Spectroscopy: Effects of Lateral Diffusion and Surface-Generated Fluorescence}, Author = {Jonas Ries and Eugene P. Petrov and Petra Schwille}, Journal = {Biophysical Journal}, Year = {2008}, Number = {1}, Pages = {390 - 399}, Volume = {95}, Doi = {10.1529/biophysj.107.126193}, ISSN = {0006-3495} } @Article{Ries2008, Title = {New concepts for fluorescence correlation spectroscopy on membranes}, Author = {Ries, Jonas and Schwille, Petra}, Journal = {Physical Chemistry Chemical Physics}, Year = {2008}, Number = {24}, Pages = {--}, Volume = {10}, Abstract = {Fluorescence correlation spectroscopy (FCS) is a powerful tool to measure useful physical quantities such as concentrations, diffusion coefficients, diffusion modes or binding parameters, both in model and cell membranes. However, it can suffer from severe artifacts, especially in non-ideal systems. Here we assess the potential and limitations of standard confocal FCS on lipid membranes and present recent developments which facilitate accurate and quantitative measurements on such systems. In particular, we discuss calibration-free diffusion and concentration measurements using z-scan FCS and two focus FCS and present several approaches using scanning FCS to accurately measure slow dynamics. We also show how surface confined FCS enables the study of membrane dynamics even in presence of a strong cytosolic background and how FCS with a variable detection area can reveal submicroscopic heterogeneities in cell membranes.}, Doi = {10.1039/b718132a}, ISSN = {1463-9076}, Owner = {paul}, Publisher = {The Royal Society of Chemistry}, Timestamp = {2012.02.14} } @Article{Ries2006, Title = {Studying slow membrane dynamics with continuous wave scanning fluorescence correlation spectroscopy}, Author = {Ries, J. and Schwille, P.}, Journal = {Biophys J}, Year = {2006}, Number = {5}, Pages = {1915--24}, Volume = {91}, Abstract = {Here we discuss the application of scanning fluorescence correlation spectroscopy (SFCS) using continuous wave excitation to analyze membrane dynamics. The high count rate per molecule enables the study of very slow diffusion in model and cell membranes, as well as the application of two-foci fluorescence cross-correlation spectroscopy for parameter-free determination of diffusion constants. The combination with dual-color fluorescence cross-correlation spectroscopy with continuous or pulsed interleaved excitation allows binding studies on membranes. Reduction of photobleaching, higher reproducibility, and stability compared to traditional FCS on membranes, and the simple implementation in a commercial microscopy setup make SFCS a valuable addition to the pool of fluorescence fluctuation techniques.}, Doi = {10.1529/biophysj.106.082297}, Keywords = {Biological Transport, Active/physiology Cell Membrane/*chemistry/*metabolism Diffusion Membrane Proteins/analysis/*chemistry/*metabolism Spectrometry, Fluorescence/*methods Time Factors}, Owner = {paul}, Timestamp = {2014.01.25} } @Article{Rigler1993, Title = {Fluorescence correlation spectroscopy with high count rate and low background: analysis of translational diffusion}, Author = {Rigler, R. and Mets, {\"U}. and Widengren, J. and Kask, P.}, Journal = {European Biophysics Journal}, Year = {1993}, Pages = {169-175}, Volume = {22}, Doi = {10.1007/BF00185777}, ISSN = {0175-7571}, Issue = {3}, Keywords = {Fluorescence correlation spectroscopy; Fluorescence intensity fluctuations; Translational diffusion; Epifluorescence microscope; Silicon photon counter}, Language = {English}, Owner = {paul}, Publisher = {Springer-Verlag}, Timestamp = {2012.11.02} } @Article{Rippe2000, Title = {Simultaneous binding of two {DNA} duplexes to the NtrC-enhancer complex studied by two-color fluorescence cross-correlation spectroscopy}, Author = {Rippe, K.}, Journal = {Biochemistry}, Year = {2000}, Number = {9}, Pages = {2131--9}, Volume = {39}, Abstract = {The transcription activator protein NtrC (nitrogen regulatory protein C, also termed NR(I)) can catalyze the transition of Escherichia coli RNA polymerase complexed with the sigma(54) factor (RNAP x sigma(54)) from the closed complex (RNAP x sigma(54) bound at the promoter) to the open complex (melting of the promoter DNA). This process involves phosphorylation of NtrC (NtrC-P), assembly of an octameric NtrC-P complex at the enhancer DNA sequence, interaction of this complex with promoter-bound RNAP x sigma(54) via DNA looping, and hydrolysis of ATP. Here it is demonstrated by two-color fluorescence cross-correlation spectroscopy measurements of 6-carboxyfluorescein and 6-carboxy-X-rhodamine-labeled DNA oligonucleotide duplexes that the NtrC-P complex can bind two DNA duplexes simultaneously. This suggests a model for the conformation of the looped intermediate that is formed between NtrC-P and RNAP. sigma(54) at the glnAp2 promoter during the activation process.}, Doi = {10.1021/bi9922190}, Keywords = {Bacterial Proteins/chemistry/*metabolism Base Sequence DNA, Bacterial/chemistry/*metabolism DNA-Binding Proteins/chemistry/*metabolism Diffusion *Enhancer Elements (Genetics) Escherichia coli/chemistry/genetics Escherichia coli Proteins Models, Chemical Models, Molecular Molecular Sequence Data Nucleic Acid Heteroduplexes/chemistry/*metabolism PII Nitrogen Regulatory Proteins Phosphorylation Protein Binding Reproducibility of Results Spectrometry, Fluorescence/methods *Trans-Activators *Transcription Factors}, Owner = {paul}, Timestamp = {2014.01.15} } @Article{Ruan2004, Title = {Spatial-Temporal Studies of Membrane Dynamics: Scanning Fluorescence Correlation Spectroscopy (SFCS)}, Author = {Ruan, Qiaoqiao and Cheng, Melanie A. and Levi, Moshe and Gratton, Enrico and Mantulin, William W.}, Journal = {Biophysical Journal}, Year = {2004}, Month = aug, Number = {2}, Pages = {1260--1267}, Volume = {87}, Doi = {10.1529/biophysj.103.036483}, ISSN = {0006-3495}, Owner = {paul}, Publisher = {Cell Press}, Refid = {S0006-3495(04)73605-X DOI - 10.1529/biophysj.103.036483}, Timestamp = {2012.02.14} } @Article{Sankaran2009, Title = {Diffusion, Transport, and Cell Membrane Organization Investigated by Imaging Fluorescence Cross-Correlation Spectroscopy}, Author = {Sankaran, Jagadish and Manna, Manoj and Guo, Lin and Kraut, Rachel and Wohland, Thorsten}, Journal = {Biophysical Journal}, Year = {2009}, Month = nov, Number = {9}, Pages = {2630--2639}, Volume = {97}, Doi = {10.1016/j.bpj.2009.08.025}, ISSN = {0006-3495}, Owner = {paul}, Publisher = {Cell Press}, Refid = {S0006-3495(09)01387-3 DOI - 10.1016/j.bpj.2009.08.025}, Timestamp = {2012.09.21} } @Article{Sankaran2010, Title = {ImFCS: a software for imaging FCS data analysis and visualization.}, Author = {Jagadish Sankaran and Xianke Shi and Liang Yoong Ho and Ernst H K Stelzer and Thorsten Wohland}, Journal = {Optics Express}, Year = {2010}, Month = {Dec}, Number = {25}, Pages = {25468--25481}, Volume = {18}, Abstract = {The multiplexing of fluorescence correlation spectroscopy (FCS), especially in imaging FCS using fast, sensitive array detectors, requires the handling of large amounts of data. One can easily collect in excess of 100,000 FCS curves a day, too many to be treated manually. Therefore, ImFCS, an open-source software which relies on standard image files was developed and provides a wide range of options for the calculation of spatial and temporal auto- and cross-correlations, as well as differences in Cross-Correlation Functions (ΔCCF). ImFCS permits fitting of standard models to correlation functions and provides optimized histograms of fitted parameters. Applications include the measurement of diffusion and flow with Imaging Total Internal Reflection FCS (ITIR-FCS) and Single Plane Illumination Microscopy FCS (SPIM-FCS) in biologically relevant samples. As a compromise between ITIR-FCS and SPIM-FCS, we extend the applications to Imaging Variable Angle-FCS (IVA-FCS) where sub-critical oblique illumination provides sample sectioning close to the cover slide.}, Doi = {10.1364/OE.18.025468}, Institution = {Singapore-MIT Alliance, National University of Singapore, E4-04-10, 4 Engineering Drive 3, 117576 Singapore.}, Keywords = {Algorithms; Pattern Recognition, Automated, methods; Software; Spectrometry, Fluorescence, methods}, Language = {eng}, Medline-pst = {ppublish}, Owner = {paul}, Pii = {208325}, Pmid = {21164894}, Timestamp = {2012.10.24} } @Article{SbalzariniSPT, Title = {Feature Point Tracking and Trajectory Analysis for Video Imaging in Cell Biology}, Author = {I. F. Sbalzarini and P. Koumoutsakos}, Journal = {Journal of Structural Biology}, Year = {2005}, Pages = {182-195}, Volume = {151(2)}, Doi = {10.1016/j.jsb.2005.06.002}, Owner = {paul}, Timestamp = {2012.10.16} } @Article{Schatzel1990, Title = {Noise on photon correlation data. I. Autocorrelation functions}, Author = {K. Sch{\"a}tzel}, Journal = {Quantum Optics: Journal of the European Optical Society Part B}, Year = {1990}, Number = {4}, Pages = {287}, Volume = {2}, Abstract = {An adequate analysis of photon correlation data requires knowledge about the statistical accuracy of the measured data. For the model of gamma-distributed intensities, that is including the effect of a finite intercept, the full covariance matrix is calculated for all the channels of the photon autocorrelation functions. A thorough discussion of multiple sample time correlation illuminates the importance of temporal averaging effects at large lag times. A practical estimation scheme is given for the noise in photon correlation data from a multiple sample time measurement.}, Doi = {10.1088/0954-8998/2/4/002}, Owner = {paul}, Timestamp = {2012.11.02} } @Article{Schwille1999, Title = {Molecular dynamics in living cells observed by fluorescence correlation spectroscopy with one- and two-photon excitation}, Author = {Schwille, P. and Haupts, U. and Maiti, S. and Webb, W. W.}, Journal = {Biophys J}, Year = {1999}, Number = {4}, Pages = {2251--65}, Volume = {77}, Abstract = {Multiphoton excitation (MPE) of fluorescent probes has become an attractive alternative in biological applications of laser scanning microscopy because many problems encountered in spectroscopic measurements of living tissue such as light scattering, autofluorescence, and photodamage can be reduced. The present study investigates the characteristics of two-photon excitation (2PE) in comparison with confocal one-photon excitation (1PE) for intracellular applications of fluorescence correlation spectroscopy (FCS). FCS is an attractive method of measuring molecular concentrations, mobility parameters, chemical kinetics, and fluorescence photophysics. Several FCS applications in mammalian and plant cells are outlined, to illustrate the capabilities of both 1PE and 2PE. Photophysical properties of fluorophores required for quantitative FCS in tissues are analyzed. Measurements in live cells and on cell membranes are feasible with reasonable signal-to-noise ratios, even with fluorophore concentrations as low as the single-molecule level in the sampling volume. Molecular mobilities can be measured over a wide range of characteristic time constants from approximately 10(-3) to 10(3) ms. While both excitation alternatives work well for intracellular FCS in thin preparations, 2PE can substantially improve signal quality in turbid preparations like plant cells and deep cell layers in tissue. At comparable signal levels, 2PE minimizes photobleaching in spatially restrictive cellular compartments, thereby preserving long-term signal acquisition.}, Doi = {10.1016/s0006-3495(99)77065-7}, Keywords = {Animals Calibration Cell Line Cell Membrane/*metabolism/radiation effects Cell Wall/*metabolism/radiation effects Cytoplasm/*metabolism/radiation effects Diffusion Fluorescence Fluorescent Dyes/*metabolism Green Fluorescent Proteins Humans Kinetics Luminescent Proteins/metabolism Photochemistry *Photons Plant Leaves/cytology/radiation effects Plants, Toxic Rhodamines/metabolism Spectrometry, Fluorescence/instrumentation/*methods Tobacco}, Owner = {paul}, Timestamp = {2014.01.15} } @Article{Schwille2000, Title = {Fluorescence correlation spectroscopy reveals fast optical excitation-driven intramolecular dynamics of yellow fluorescent proteins}, Author = {Schwille, Petra and Kummer, Susanne and Heikal, Ahmed A. and Moerner, W. E. and Webb, Watt W.}, Journal = {Proceedings of the National Academy of Sciences}, Year = {2000}, Number = {1}, Pages = {151-156}, Volume = {97}, Abstract = {Fast excitation-driven fluctuations in the fluorescence emission of yellow-shifted green fluorescent protein mutants T203Y and T203F, with S65G/S72A, are discovered in the 10−6–10−3-s time range, by using fluorescence correlation spectroscopy at 10−8 M. This intensity-dependent flickering is conspicuous at high pH, with rate constants independent of pH and viscosity with a minor temperature effect. The mean flicker rate increases linearly with excitation intensity for at least three decades, but the mean dark fraction of the molecules undergoing these dynamics is independent of illumination intensity over ≈6 × 102 to 5 × 106 W/cm2. These results suggest that optical excitation establishes an equilibration between two molecular states of different spectroscopic properties that are coupled only via the excited state as a gateway. This reversible excitation-driven transition has a quantum efficiency of ≈10−3. Dynamics of external protonation, reversibly quenching the fluorescence, are also observed at low pH in the 10- to 100-μs time range. The independence of these two bright–dark flicker processes implies the existence of at least two separate dark states of these green fluorescent protein mutants. Time-resolved fluorescence measurements reveal a single exponential decay of the excited state population with 3.8-ns lifetime, after 500-nm excitation, that is pH independent. Our fluorescence correlation spectroscopy results are discussed in terms of recent theoretical studies that invoke isomerization of the chromophore as a nonradiative channel of the excited state relaxation.}, Doi = {10.1073/pnas.97.1.151}, Owner = {paul}, Timestamp = {2012.09.24} } @Article{Schwille1997, Title = {Dual-color fluorescence cross-correlation spectroscopy for multicomponent diffusional analysis in solution}, Author = {Schwille, P. and Meyer-Almes, F.J. and Rigler, R.}, Journal = {Biophysical Journal}, Year = {1997}, Month = apr, Number = {4}, Pages = {1878--1886}, Volume = {72}, Doi = {10.1016/s0006-3495(97)78833-7}, ISSN = {0006-3495}, Owner = {paul}, Publisher = {Cell Press}, Refid = {S0006-3495(97)78833-7 DOI - 10.1016/S0006-3495(97)78833-7}, Timestamp = {2012.02.14} } @Article{Scomparin2009, Title = {Diffusion in supported lipid bilayers: Influence of substrate and preparation technique on the internal dynamics}, Author = {Scomparin, C. and Lecuyer, S. and Ferreira, M. and Charitat, T. and Tinland, B.}, Journal = {The European Physical Journal E: Soft Matter and Biological Physics}, Year = {2009}, Pages = {211-220}, Volume = {28}, Affiliation = {CNRS UPR 3118 CINAM 13288 Marseille Cedex 09 France}, Doi = {10.1140/epje/i2008-10407-3}, ISSN = {1292-8941}, Issue = {2}, Keyword = {Physik und Astronomie}, Owner = {paul}, Publisher = {Springer Berlin / Heidelberg}, Timestamp = {2012.10.22} } @Article{Seu2007, Title = {Effect of Surface Treatment on Diffusion and Domain Formation in Supported Lipid Bilayers}, Author = {Seu, Kalani J. and Pandey, Anjan P. and Haque, Farzin and Proctor, Elizabeth A. and Ribbe, Alexander E. and Hovis, Jennifer S.}, Journal = {Biophysical Journal}, Year = {2007}, Month = apr, Number = {7}, Pages = {2445--2450}, Volume = {92}, Doi = {10.1529/biophysj.106.099721}, ISSN = {0006-3495}, Owner = {paul}, Publisher = {Cell Press}, Refid = {S0006-3495(07)71049-4 DOI - 10.1529/biophysj.106.099721}, Timestamp = {2012.10.22} } @Article{Shannon1984, Title = {Communication in the presence of noise}, Author = {Shannon, C.E.}, Journal = {Proceedings of the IEEE}, Year = {1984}, Month = {sept.}, Number = {9}, Pages = { 1192 - 1201}, Volume = {72}, Doi = {10.1109/PROC.1984.12998}, ISSN = {0018-9219}, Owner = {paul}, Timestamp = {2012.11.12} } @Article{Skinner2005, Title = {Position-sensitive scanning fluorescence correlation spectroscopy.}, Author = {Joseph P Skinner and Yan Chen and Joachim D Müller}, Journal = {Biophysical Journal}, Year = {2005}, Month = {Aug}, Number = {2}, Pages = {1288--1301}, Volume = {89}, Abstract = {Fluorescence correlation spectroscopy (FCS) uses a stationary laser beam to illuminate a small sample volume and analyze the temporal behavior of the fluorescence fluctuations within the stationary observation volume. In contrast, scanning FCS (SFCS) collects the fluorescence signal from a moving observation volume by scanning the laser beam. The fluctuations now contain both temporal and spatial information about the sample. To access the spatial information we synchronize scanning and data acquisition. Synchronization allows us to evaluate correlations for every position along the scanned trajectory. We use a circular scan trajectory in this study. Because the scan radius is constant, the phase angle is sufficient to characterize the position of the beam. We introduce position-sensitive SFCS (PSFCS), where correlations are calculated as a function of lag time and phase. We present the theory of PSFCS and derive expressions for diffusion, diffusion in the presence of flow, and for immobilization. To test PSFCS we compare experimental data with theory. We determine the direction and speed of a flowing dye solution and the position of an immobilized particle. To demonstrate the feasibility of the technique for applications in living cells we present data of enhanced green fluorescent protein measured in the nucleus of COS cells.}, Doi = {10.1529/biophysj.105.060749}, Institution = {School of Physics and Astronomy, University of Minnesota, Minneapolis, 55455, USA. josephs@physics.umn.edu}, Keywords = {Algorithms; Image Enhancement, methods; Image Interpretation, Computer-Assisted, methods; Information Storage and Retrieval, methods; Microscopy, Confocal, methods; Reproducibility of Results; Sensitivity and Specificity; Spectrometry, Fluorescence, methods}, Language = {eng}, Medline-pst = {ppublish}, Owner = {paul}, Pii = {S0006-3495(05)72776-4}, Pmid = {15894645}, Timestamp = {2012.10.28} } @Article{Starr2001, Title = {Total Internal Reflection with Fluorescence Correlation Spectroscopy: Combined Surface Reaction and Solution Diffusion}, Author = {Tammy E. Starr and Nancy L. Thompson}, Journal = {Biophysical Journal}, Year = {2001}, Number = {3}, Pages = {1575 - 1584}, Volume = {80}, Doi = {10.1016/S0006-3495(01)76130-9}, ISSN = {0006-3495} } @Article{Sutherland1905, Title = {A dynamical theory of diffusion for non-electrolytes and the molecular mass of albumin}, Author = {Sutherland, William}, Journal = {Philosophical Magazine Series 6}, Year = {1905}, Number = {54}, Pages = {781-785}, Volume = {9}, __markedentry = {[paul]}, Doi = {10.1080/14786440509463331}, Owner = {paul}, Timestamp = {2012.11.14} } @Article{Tamm1985, Title = {Supported phospholipid bilayers}, Author = {Tamm, L.K. and McConnell, H.M.}, Journal = {Biophysical Journal}, Year = {1985}, Month = jan, Number = {1}, Pages = {105--113}, Volume = {47}, Doi = {10.1016/S0006-3495(85)83882-0}, ISSN = {0006-3495}, Owner = {paul}, Publisher = {Cell Press}, Refid = {S0006-3495(85)83882-0 DOI - 10.1016/S0006-3495(85)83882-0}, Timestamp = {2012.10.29} } @InCollection{Thomps:bookFCS2002, Title = {Fluorescence Correlation Spectroscopy}, Author = {Thompson, Nancy}, Booktitle = {Topics in Fluorescence Spectroscopy}, Publisher = {Springer US}, Year = {2002}, Editor = {Lakowicz, Joseph and Geddes, Chris D. and Lakowicz, Joseph R.}, Pages = {337-378}, Series = {Topics in Fluorescence Spectroscopy}, Volume = {1}, Affiliation = {University of North Carolina at Chapel Hill Department of Chemistry Chapel Hill North Carolina 27599-3290 USA}, Doi = {10.1007/0-306-47057-8_6}, ISBN = {978-0-306-47057-8}, Keyword = {Biomedical and Life Sciences}, Owner = {paul}, Timestamp = {2012.01.10} } @InBook{Thompson1991, Title = {Fluorescence Correlation Spectroscopy}, Author = {Thompson, N.L.}, Editor = {Lankowicz, J.R.}, Pages = {337--378}, Publisher = {Plenum Press}, Year = {1991}, Address = {New York}, Edition = {Techniques}, Series = {Topics in Fluorescence Spectroscopy}, Volume = {1}, Booktitle = {Topics in Fluorescence Spectroscopy}, Owner = {paul}, Timestamp = {2014.01.15} } @Article{Thompson1983, Title = {Immunoglobulin surface-binding kinetics studied by total internal reflection with fluorescence correlation spectroscopy}, Author = {N.L. Thompson and D. Axelrod}, Journal = {Biophysical Journal}, Year = {1983}, Number = {1}, Pages = {103 - 114}, Volume = {43}, Doi = {10.1016/S0006-3495(83)84328-8}, ISSN = {0006-3495}, Owner = {paul}, Timestamp = {2012.02.14} } @Article{Thompson1981, Title = {Measuring surface dynamics of biomolecules by total internal reflection fluorescence with photobleaching recovery or correlation spectroscopy}, Author = {N.L. Thompson and T.P. Burghardt and D. Axelrod}, Journal = {Biophysical Journal}, Year = {1981}, Number = {3}, Pages = {435 - 454}, Volume = {33}, Doi = {10.1016/S0006-3495(81)84905-3}, ISSN = {0006-3495}, Owner = {paul}, Timestamp = {2012.02.14} } @Article{Thompson1997, Title = {Equilibrium, Kinetics, Diffusion and Self-Association of Proteins at Membrane Surfaces: Measurement by Total Internal Reflection Fluorescence Microscopy}, Author = {Thompson, Nancy L. and Drake, Andrew W. and Chen, Lixin and Broek, Willem Vanden}, Journal = {Photochemistry and Photobiology}, Year = {1997}, Number = {1}, Pages = {39--46}, Volume = {65}, Doi = {10.1111/j.1751-1097.1997.tb01875.x}, ISSN = {1751-1097}, Owner = {paul}, Publisher = {Blackwell Publishing Ltd}, Timestamp = {2012.02.14} } @Article{Thompson1997a, Title = {Total internal reflection fluorescence: applications in cellular biophysics}, Author = {Nancy L Thompson and B Christoffer Lagerholm}, Journal = {Current Opinion in Biotechnology}, Year = {1997}, Number = {1}, Pages = {58 - 64}, Volume = {8}, Doi = {10.1016/S0958-1669(97)80158-9}, ISSN = {0958-1669}, Owner = {paul}, Timestamp = {2012.02.14} } @Article{Thompson2007, Title = {Total internal reflection with fluorescence correlation spectroscopy}, Author = {Thompson, N. L. and Steele, B. L.}, Journal = {Nat Protoc}, Year = {2007}, Number = {4}, Pages = {878--90}, Volume = {2}, Abstract = {Total internal reflection-fluorescence correlation spectroscopy (TIR-FCS) is an emerging technique that is used to measure events at or near an interface, including local fluorophore concentrations, local translational mobilities and the kinetic rate constants that describe the association and dissociation of fluorophores at the interface. TIR-FCS is also an extremely promising method for studying dynamics at or near the basal membranes of living cells. This protocol gives a general overview of the steps necessary to construct and test a TIR-FCS system using either through-prism or through-objective internal reflection geometry adapted for FCS. The expected forms of the autocorrelation function are discussed for the cases in which fluorescent molecules in solution diffuse through the depth of the evanescent field, but do not bind to the surface of interest, and in which reversible binding to the surface also occurs.}, Doi = {10.1038/nprot.2007.110}, Keywords = {Fluorescent Dyes/analysis Kinetics Ligands Spectrometry, Fluorescence/instrumentation/*methods}, Owner = {paul}, Timestamp = {2014.01.25} } @Article{Toomre2001, Title = {Lighting up the cell surface with evanescent wave microscopy}, Author = {Derek Toomre and Dietmar J. Manstein}, Journal = {Trends in Cell Biology}, Year = {2001}, Number = {7}, Pages = {298 - 303}, Volume = {11}, Doi = {10.1016/S0962-8924(01)02027-X}, ISSN = {0962-8924}, Keywords = {green-fluorescent protein (GFP)}, Owner = {paul}, Timestamp = {2012.02.14} } @Article{Unruh2008, Title = {Analysis of Molecular Concentration and Brightness from Fluorescence Fluctuation Data with an Electron Multiplied CCD Camera}, Author = {Unruh, Jay R. and Gratton, Enrico}, Journal = {Biophysical Journal}, Year = {2008}, Month = dec, Number = {11}, Pages = {5385--5398}, Volume = {95}, Doi = {10.1529/biophysj.108.130310}, ISSN = {0006-3495}, Owner = {paul}, Publisher = {Cell Press}, Refid = {S0006-3495(08)78962-8 DOI - 10.1529/biophysj.108.130310}, Timestamp = {2012.09.21} } @Article{Vacha2009, Title = {Effects of Alkali Cations and Halide Anions on the DOPC Lipid Membrane}, Author = {V\'{a}cha, Robert and Siu, Shirley W. I. and Petrov, Michal and Böckmann, Rainer A. and Barucha-Kraszewska, Justyna and Jurkiewicz, Piotr and Hof, Martin and Berkowitz, Max L. and Jungwirth, Pavel}, Journal = {The Journal of Physical Chemistry A}, Year = {2009}, Number = {26}, Pages = {7235-7243}, Volume = {113}, Doi = {10.1021/jp809974e}, Owner = {paul}, Timestamp = {2012.10.24} } @Article{Wachsmuth2000, Title = {Anomalous diffusion of fluorescent probes inside living cell nuclei investigated by spatially-resolved fluorescence correlation spectroscopy}, Author = {Wachsmuth, M. and Waldeck, W. and Langowski, J.}, Journal = {J Mol Biol}, Year = {2000}, Number = {4}, Pages = {677--89}, Volume = {298}, Abstract = {We have investigated spatial variations of the diffusion behavior of the green fluorescent protein mutant EGFP (F64L/S65T) and of the EGFP-beta-galactosidase fusion protein in living cells with fluorescence correlation spectroscopy. Our fluorescence correlation spectroscopy device, in connection with a precision x-y translation stage, provides submicron spatial resolution and a detection volume smaller than a femtoliter. The fluorescence fluctuations in cell lines expressing EGFP are caused by molecular diffusion as well as a possible internal and a pH-dependent external protonation process of the EGFP chromophore. The latter processes result in two apparent nonfluorescent states that have to be taken into account when evaluating the fluorescence correlation spectroscopy data. The diffusional contribution deviates from ideal behavior and depends on the position in the cell. The fluorescence correlation spectroscopy data can either be evaluated as a two component model with one fraction of the molecules undergoing free Brownian motion with a diffusion coefficient approximately five times smaller than in aqueous solution, and another fraction diffusing one or two orders of magnitude slower. This latter component is especially noticeable in the nuclei. Alternatively, we can fit the data to an anomalous diffusion model where the time dependence of the diffusion serves as a measure for the degree of obstruction, which is large especially in nuclei. Possible mechanisms for this long tail behavior include corralling, immobile obstacles, and binding with a broad distribution of binding affinities. The results are consistent with recent numerical models of the chromosome territory structure in the cell nucleus.}, Doi = {10.1006/jmbi.2000.3692}, Keywords = {Animals COS Cells Cell Line Cell Nucleus/chemistry/*metabolism Cell Survival Cytoplasm/chemistry/metabolism Diffusion Fluorescence Fluorescent Dyes/*metabolism Genetic Vectors/genetics Green Fluorescent Proteins Hydrogen-Ion Concentration Kinetics Luminescent Proteins/chemistry/genetics/metabolism Models, Biological Protein Conformation Protons Recombinant Fusion Proteins/chemistry/genetics/metabolism Solutions Spectrometry, Fluorescence Statistics Transfection}, Owner = {paul}, Timestamp = {2014.01.15} } @Article{Weidemann2013, Title = {Dual-color fluorescence cross-correlation spectroscopy with continuous laser excitation in a confocal setup}, Author = {Weidemann, T. and Schwille, P.}, Journal = {Methods Enzymol}, Year = {2013}, Pages = {43--70}, Volume = {518}, Abstract = {Fluorescence correlation spectroscopy evaluates local signal fluctuations arising from stochastic movements of fluorescent particles in solution. The measured fluctuating signal is correlated in time and analyzed with appropriate model functions containing the parameters that describe the underlying molecular behavior. The dual-color extension, fluorescence cross-correlation spectroscopy (FCCS) allows for a comparison between spectrally well-separated channels to extract codiffusion events that reflect interactions between differently labeled molecules. In addition to solution measurements, FCCS can be applied with subcellular resolution and is therefore a very promising approach for a quantitative biochemical assessment of molecular networks in living cells. To derive thermodynamic and kinetic reaction parameters, the influence of a number of other factors like background noise, illumination intensity profiles, photophysical processes, and cross talk between the channels have to be treated. Here, we provide a roadmap to derive binding reaction data with dual-color FCCS using continuous wave laser excitation, as it is now accessible with many state-of-the-art confocal microscopes.}, Doi = {10.1016/B978-0-12-388422-0.00003-0}, Owner = {paul}, Timestamp = {2014.01.15} } @Book{Weidemann2009, Title = {Fluorescence Correlation Spectroscopy in Living Cells}, Author = {Weidemann, T. and Schwille, P.}, Publisher = {Springer}, Year = {2009}, Address = {Heidelberg}, Series = {Handbook of Single-Molecule Biophysics}, Owner = {paul}, Pages = {648}, Timestamp = {2014.01.15} } @Article{Weidemann2002, Title = {Analysis of Ligand Binding by Two-Colour Fluorescence Cross-Correlation Spectroscopy}, Author = {Weidemann, T. and Wachsmuth, M. and Tewes, M and Rippe, K. and Langowski, J.}, Journal = {Single Mol}, Year = {2002}, Number = {1}, Pages = {49--61}, Volume = {3}, Abstract = {Fluorescence correlation spectroscopy (FCS) is a well-established method for the analysis of freely diffusing fluorescent particles in solution. In a two-colour setup, simultaneous detection of two different dyes allows the acquisition of both the autocorrelation of the signal of each channel and the cross-correlation of the two channels (fluorescence cross correlation spectroscopy, FCCS). The cross-correlation function is related to the amount of diffusing particles carrying both dyes and can be used for monitoring a binding reaction. Here we develop a formalism for a quantitative analysis of ligand binding from a combination of the auto- and the cross-correlation amplitudes. Technical constraints, like the focal geometry, background signal and cross-talk between the detection channels as well as photophysical and biochemical effects which modulate the brightness of the particles are included in the analysis. Based on this framework a comprehensive treatment for the determination of two-component binding equilibria by FCS/FCCS is presented.}, Doi = {10.1002/1438-5171(200204)3:1<49::aid-simo49>3.0.co;2-t}, Keywords = {FCS FCCS receptor-ligand binding protein-DNA interactions}, Owner = {paul}, Timestamp = {2014.01.15} } @Article{Weiss2003, Title = {Anomalous protein diffusion in living cells as seen by fluorescence correlation spectroscopy}, Author = {Weiss, M. and Hashimoto, H. and Nilsson, T.}, Journal = {Biophys J}, Year = {2003}, Number = {6}, Pages = {4043--4052}, Volume = {84}, Abstract = {We investigate the challenges and limitations that are encountered when studying membrane protein dynamics in vivo by means of fluorescence correlation spectroscopy (FCS). Based on theoretical arguments and computer simulations, we show that, in general, the fluctuating fluorescence has a fractal dimension D(0) >or= 1.5, which is determined by the anomality alpha of the diffusional motion of the labeled particles, i.e., by the growth of their mean square displacement as (Deltax)(2) approximately t(alpha). The fractality enforces an initial power-law behavior of the autocorrelation function and related quantities for small times. Using this information, we show by FCS that Golgi resident membrane proteins move subdiffusively in the endoplasmic reticulum and the Golgi apparatus in vivo. Based on Monte Carlo simulations for FCS on curved surfaces, we can rule out that the observed anomalous diffusion is a result of the complex topology of the membrane. The apparent mobility of particles as determined by FCS, however, is shown to depend crucially on the shape of the membrane and its motion in time. Due to this fact, the hydrodynamic radius of the tracked particles can be easily overestimated by an order of magnitude}, Doi = {10.1016/s0006-3495(03)75130-3}, Keywords = {Algorithms ARE Biology Biophysics Cell Membrane Cells Comparative Study COMPLEXES Computer Simulation CORRELATION SPECTROSCOPY Diffusion DYNAMICS Endoplasmic Reticulum Fluorescence FLUORESCENCE CORRELATION SPECTROSCOPY Fractals Germany Golgi Apparatus Hela Cells Humans IN-VIVO LIVING CELLS Membrane Proteins metabolism methods Microscopy,Confocal Models,Biological Molecular Biology Motion N-Acetylgalactosaminyltransferases physiology Protein Protein Transport Proteins Recombinant Fusion Proteins Research Support,Non-U.S.Gov't Spectrometry,Fluorescence spectroscopy Statistics SURFACE Tissue Distribution ultrastructure}, Owner = {paul}, Timestamp = {2014.01.15} } @Article{Widengren1995, Title = {Fluorescence correlation spectroscopy of triplet states in solution: a theoretical and experimental study}, Author = {Widengren, Jerker and Mets, {\"U}lo and Rigler, Rudolf}, Journal = {The Journal of Physical Chemistry}, Year = {1995}, Number = {36}, Pages = {13368-13379}, Volume = {99}, Doi = {10.1021/j100036a009}, Owner = {paul}, Timestamp = {2012.02.20} } @Article{Widengren1998, Title = {Fluorescence correlation spectroscopy as a tool to investigate chemical reactions in solutions and on cell surfaces}, Author = {Widengren, J. and Rigler, R.}, Journal = {Cell Mol Biol (Noisy-le-grand)}, Year = {1998}, Note = {Retrieved from \url{http://europepmc.org/abstract/MED/9764752}}, Number = {5}, Pages = {857--79}, Volume = {44}, Abstract = {Taking advantage of the present day possibilities for ultrasensitive detection by fluorescence, fluorescence correlation spectroscopy (FCS) has over the last ten years emerged as a potentially very powerful technique. In this article we present some results to illustrate the use of FCS for monitoring chemical kinetics on a molecular level and show how, for a wide range of chemical processes, the theoretical treatment can be strongly simplified. The experimental examples given include measurements of ion concentrations and buffer properties, electron transfer reactions, ligand-receptor interactions and diffusion of ligand-receptor complexes in cell membranes. For each of these examples the properties of the FCS technique is discussed in relation to other established techniques used for that particular application. From these examples it is found that FCS can offer important complementary information and, due to the extreme sensitivity of the technique, new information not yet explored by other methods.}, Owner = {paul}, Timestamp = {2014.01.15} } @Article{Widengren1994, Title = {Triplet-state monitoring by fluorescence correlation spectroscopy}, Author = {Widengren, Jerker and Rigler, Rudolf and Mets, {\"U}lo}, Journal = {Journal of Fluorescence}, Year = {1994}, Pages = {255-258}, Volume = {4}, Affiliation = {Department of Medical Biochemistry and Biophysics Karolinska Institute S-171 77 Stockholm Sweden}, Doi = {10.1007/BF01878460}, ISSN = {1053-0509}, Issue = {3}, Keyword = {Biomedizin & Life Sciences}, Owner = {paul}, Publisher = {Springer Netherlands}, Timestamp = {2012.09.24} } @Article{Wohland2001, Title = {The Standard Deviation in Fluorescence Correlation Spectroscopy}, Author = {Wohland, Thorsten and Rigler, Rudolf and Vogel, Horst}, Journal = {Biophysical Journal}, Year = {2001}, Month = jun, Number = {6}, Pages = {2987--2999}, Volume = {80}, Doi = {10.1016/S0006-3495(01)76264-9}, ISSN = {0006-3495}, Owner = {paul}, Timestamp = {2012.09.08} } @Article{Wohland2010, Title = {Single Plane Illumination Fluorescence Correlation Spectroscopy (SPIM-FCS) probes inhomogeneous three-dimensional environments}, Author = {Thorsten Wohland and Xianke Shi and Jagadish Sankaran and Ernst H.K. Stelzer}, Journal = {Optics Express}, Year = {2010}, Month = {May}, Number = {10}, Pages = {10627--10641}, Volume = {18}, Abstract = {The life sciences require new highly sensitive imaging tools, which allow the quantitative measurement of molecular parameters within a physiological three-dimensional (3D) environment. Therefore, we combined single plane illumination microscopy (SPIM) with camera based fluorescence correlation spectroscopy (FCS). SPIM-FCS provides contiguous particle number and diffusion coefficient images with a high spatial resolution in homo- and heterogeneous 3D specimens and live zebrafish embryos. Our SPIM-FCS recorded up to 4096 spectra within 56 seconds at a laser power of 60 \&\#x03BC;W without damaging the embryo. This new FCS modality provides more measurements per time and more, less photo-toxic measurements per sample than confocal based methods. In essence, SPIM-FCS offers new opportunities to observe biomolecular interactions quantitatively and functions in a highly multiplexed manner within a physiologically relevant 3D environment.}, Doi = {10.1364/OE.18.010627}, Keywords = {Fluorescence microscopy; Three-dimensional microscopy; Spectroscopy, fluorescence and luminescence}, Owner = {paul}, Publisher = {OSA}, Timestamp = {2012.11.07} } @Conference{Wright1996, Title = {Direct Search Methods: Once Scorned, Now Respectable}, Author = {Wright, M.H.}, Booktitle = {Numerical Analysis}, Year = {1996}, Editor = {D.F. Griffiths and G.A. Watson}, Pages = {191-208}, Publisher = {Addison Wesley Longman, Harlow, UK}, Owner = {paul}, Timestamp = {2014.03.31} } @Article{Yordanov2009, Title = {Direct studies of liquid flows near solid surfaces by total internal reflection fluorescence cross-correlation spectroscopy}, Author = {Stoyan Yordanov and Andreas Best and Hans-J\"{u}rgen Butt and Kaloian Koynov}, Journal = {Optics Express}, Year = {2009}, Month = {Nov}, Number = {23}, Pages = {21149--21158}, Volume = {17}, Abstract = {We present a new method to study flow of liquids near solid surface: Total internal reflection fluorescence cross-correlation spectroscopy (TIR-FCCS). Fluorescent tracers flowing with the liquid are excited by evanescent light, produced by epi-illumination through the periphery of a high numerical aperture oil-immersion objective. The time-resolved fluorescence intensity signals from two laterally shifted observation volumes, created by two confocal pinholes are independently measured. The cross-correlation of these signals provides information of the tracers' velocities. By changing the evanescent wave penetration depth, flow profiling at distances less than 200 nm from the interface can be performed. Due to the high sensitivity of the method fluorescent species with different size, down to single dye molecules can be used as tracers. We applied this method to study the flow of aqueous electrolyte solutions near a smooth hydrophilic surface and explored the effect of several important parameters, e.g. tracer size, ionic strength, and distance between the observation volumes.}, Doi = {10.1364/OE.17.021149}, Keywords = {Velocimetry; Fluorescence, laser-induced; Spectroscopy, surface}, Owner = {paul}, Publisher = {OSA}, Timestamp = {2012.09.21} } @Article{Yordanov2011, Title = {Note: An easy way to enable total internal reflection-fluorescence correlation spectroscopy ({TIR-FCS}) by combining commercial devices for {FCS} and {TIR} microscopy}, Author = {Stoyan Yordanov and Andreas Best and Klaus Weisshart and Kaloian Koynov}, Journal = {Review of Scientific Instruments}, Year = {2011}, Number = {3}, Pages = {036105}, Volume = {82}, Doi = {10.1063/1.3557412}, Eid = {036105}, Keywords = {fluorescence spectroscopy; optical microscopy}, Numpages = {3}, Owner = {paul}, Publisher = {AIP}, Timestamp = {2012.05.02} } @Article{Zhang2007, Title = {Gaussian approximations of fluorescence microscope point-spread function models}, Author = {Bo Zhang and Josiane Zerubia and Jean-Christophe Olivo-Marin}, Journal = {Applied Optics}, Year = {2007}, Month = {Apr}, Number = {10}, Pages = {1819--1829}, Volume = {46}, Abstract = {We comprehensively study the least-squares Gaussian approximations of the diffraction-limited 2D-3D paraxial-nonparaxial point-spread functions (PSFs)of the wide field fluorescence microscope (WFFM), the laser scanning confocal microscope(LSCM), and the disk scanning confocal microscope (DSCM). The PSFs are expressed using the Debye integral. Under anL$\infty$ constraint imposing peak matching, optimal and near-optimal Gaussian parameters are derived for the PSFs. With anL1 constraint imposing energy conservation, an optimal Gaussian parameter is derived for the 2D paraxial WFFM PSF. We found that (1) the 2D approximations are all very accurate; (2) no accurate Gaussian approximation exists for 3D WFFM PSFs; and (3) with typical pinhole sizes, the 3D approximations are accurate for the DSCM and nearly perfect for the LSCM. All the Gaussian parameters derived in this study are in explicit analytical form, allowing their direct use in practical applications.}, Doi = {10.1364/AO.46.001819}, Keywords = {Numerical approximation and analysis; Microscopy; Confocal microscopy; Fluorescence microscopy; Three-dimensional microscopy}, Owner = {paul}, Publisher = {OSA}, Timestamp = {2012.09.20} } @Book{Rigler:FCSbook, Title = {Fluorescence Correlation Spectroscopy, Theory and Applications}, Editor = {R. Rigler and E.S. Elson}, Publisher = {Springer Berlin Heidelberg}, Year = {2001}, Edition = {1}, HowPublished = {Paperback}, ISBN = {978-3540674337}, Owner = {paul}, Timestamp = {2012.11.02} } PyCorrFit-0.9.9/doc/Images/000077500000000000000000000000001274265304700153765ustar00rootroot00000000000000PyCorrFit-0.9.9/doc/Images/PyCorrFit_Screenshot_CSFCS.png000066400000000000000000003543121274265304700231130ustar00rootroot00000000000000PNG  IHDR ǪbKGD pHYs  tIME+/h7 IDATxwtIH!{P+`oW*E Ҭ4iR ^Oy%B MV3{̞ǽg;șGm șK @3"""""""""g(eJ (a IU1,UĶ_2gS>!"""""""r: @V*럸J:&/bXSƦòۍJ-^|W2pES~ ҙq.L5C@'mLmp- foNou:I7hKgMūٞ^ݮ{peY97 NKF5b$c6-/Ϥ\LG8՚uwN2aɨ}v(L}sLYVdG~M9KU*X]qkx ޽\"'N?ys7.NB,L~~INݹf ,R7m 402IlE~MI1ݳmfe/u<85FPql*\In֍ݣv-iIUYf%ǚEarKeX'F8A| xX&mK;k$~9{U~xj֪MoLs;ۇ/S E=w[CpFS 1%>CS]gW=Bg,f{h\u;7)NxY6}"S&FHbs.ӛ)AgK~P[!_Sof6+l`g^9foYl:_v'pxZ{_lrЮo.:8%+UîxiէC04EUڼt_}lMrVMg².4h8xRN"YE\K;:HvvJ~|Y5odW7pQif-Rߊ"""""""gcC^Ë3,6 ӑnFDEƚbXHyAB&&:-a~G_-'@nF^I Gh.s'M#X=s&@i(BvKHNr8*b3_-BBп/,/rlџ DH4Y/%7' 04MM3ٌЦQYHFGte).0 9Xe8lj5Irmx!($NjvfM"HHےaDAC3~ˡN{Ufom[jAeI,~{ԖkIs׃Z>˘mm@cB Gx\qdSε_J"""""""rvVVf\<e$6B+V?xc雿7.57<I37k盀и4#({?Ll@5sq^lӝƚ>T}qL5whg>6˿?^L#bk;ߘ[grzq:]J80kO8R߳7gJڢxXhlڹu@XxIU7 R[ylQ\BuޡWDcSDDDDDDDiG9WJn`4 P46EDDDDDDtb~`Y>ӇA;q@ټZNi6N'vC:R<< r,Xsg @˲paPXP rq8DFESvB2a'o\NaaeMTtDNU!"""""""g3 &rf  {:EDDDDDDrvC @36"""""""""g$ih%4}uSG,0K)aryN\?~S!t},wNNt59|* Siӹk':6Sgp/3#߿7]ݭ8'`nڳVDDDDDDDq, @7>c֧xaC͎%}L=/,}*EcЬߣ ֭(6Ʈ<63fU|u)؊ef/fm72l":|FڐiZa10~+M ¢I>2 zqo<[O/ ƣ,ܾ=S酀؆]{m@9oy'es.Hr9Upy^{eO{^q"hhs\m?Vl%ߪ,TEu *MMZyKn2]c~e+}, #lGT#<w62h/Cu}pmqz}ފ湯)_V[jq{osk .=z9N p펣>gRVҸS-OIX kҕ&`/Z{d~a?SsӀ1}F ^K3~ Xxg0||kÆ(T)Foբ9}ylm"6FVf Z)u+Ma`ǝS*I9:2jx~ՙkHZ9?r'q}%lUF{4 ٿtI_l(?gu6 l?a8wwMRF@CaQj6^bٳ|j]#Օn#F[АѡCfʨvRֱ<޿N]q~gKlк M`,gUO D?/Ihu +MQEToNf~ߡy6rq|1e#.z'l'֏:& ^xil{s+Ҥ]-<|[϶BHnS`eDDDDDDDDN H9|gڧ\P?j֢sp.w.e7Vm%kgRR`7c~\q$ aԻ}Wkk/c[Vo|)/C@q~Ys =Ѵl_E٢ҫi0! 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soll auch % eins kommen, wenn nicht, schreibe es als Operator: % z.B. $f(x, y) = 3,45$ %Für englische Schriften: \usepackage[english]{babel} \usepackage{siunitx} \usepackage[top = 2cm, left = 2.5cm, right = 2cm, bottom = 2.5cm]{geometry} \usepackage{amsmath} \usepackage{amssymb} \usepackage{array} \usepackage{cite} %Für Zitate und Quellen \usepackage{url} \urlstyle{tt} %\usepackage{longtable} % mehrseitige Tabellen %\usepackage{multirow} %Zusammenfassen von Spalten/Zeilen \usepackage{subfig} % Vereinen von Bildern in gesamte Figure %Schönere Unterschriften für Bilder und Tabellen %Setze captions mit Kommas und Beschriftungen \DeclareCaptionLabelFormat{mycaption}{#1 #2} \DeclareCaptionLabelSeparator{comma}{, } \captionsetup{font=small,labelfont=bf,labelformat=mycaption,labelsep=comma} \setcapindent{0pt} % Zeileneinzug ab zweiter Zeile \newcommand{\mycaption}[2]{\caption[~#1]{\textbf{#1:} #2}} \usepackage{tabularx} \usepackage{textcomp} % Sonderzeichen \usepackage{wrapfig} \usepackage{fancyvrb} \usepackage[svgnames]{xcolor} %Farben wie DarkBlue %% %% %% Definitionen für schöne Links innerhalb des Dokuments %%% graphicx: support for graphics \usepackage[pdftex]{graphicx} \pdfcompresslevel=9 %%% hyperref (hyperlinks in PDF): for more options or more detailed %%% explanations, see the documentation of the hyperref-package \usepackage[% %%% general options pdftex=true, %% sets up hyperref for use with the pdftex program %plainpages=false, %% set it to false, if pdflatex complains: ``destination with same identifier already exists'' % pdfstartview={XYZ 0 0 1.0} , %% Startet das PDF mit 100% Zoom, also Originalgroesse %%% extension options backref, %% adds a backlink text to the end of each item in the bibliography pagebackref=false, %% if true, creates backward references as a list of page numbers in the bibliography colorlinks=true, %% turn on colored links (true is better for on-screen reading, false is better for printout versions) linkcolor=DarkBlue, %% Aendern der Linkfarbe urlcolor=DarkBlue, %% Aendern der Url-Linkfarbe und andere serioese Farben anchorcolor = black, citecolor = DarkGreen, filecolor = black, urlcolor = DarkBlue, breaklinks=false, % %%% PDF-specific display options bookmarks=true, %% if true, generate PDF bookmarks (requires two passes of pdflatex) bookmarksopen=true, %% if true, show all PDF bookmarks expanded bookmarksnumbered=false, %% if true, add the section numbers to the bookmarks %pdfstartpage={1}, %% determines, on which page the PDF file is opened %pdfpagemode=None %% None, UseOutlines (=show bookmarks), UseThumbs (show thumbnails), FullScreen ]{hyperref} %%% provide all graphics (also) in this format, so you don't have %%% to add the file extensions to the \includegraphics-command %%% and/or you don't have to distinguish between generating %%% dvi/ps (through latex) and pdf (through pdflatex) % \DeclareGraphicsExtensions{.pdf} %% %% %\newcommand{\kommentar}[1]{\marginpar{\textcolor{red}{#1}}} % Kommentarkommando %\newcommand{\fehler}[3]{\SI{(#1}{}\SI{\pm #2)}{#3}} % Fehlerkommando %Ort für mögliche Bilddateien (Unterordner) \graphicspath{{bilder/}{messwerte/}{auswertung/}} %Neue Befehle \newcommand{\hyref}[2]{\hyperref[#2]{#1~\ref{#2}}} %Schönerer link statt "link zu Bild \ref{im:bild}" -> "\hyref{link zu Bild}{im:bild}" \newcommand{\mytilde}{\raisebox{-0.9ex}{\~{ }}} \setcounter{page}{1} % Tell latex how to break the program names \hyphenation{Py-Corr-Fit Py-Scan-FCS} % For non-italic greek letters \usepackage{upgreek} \usepackage{doi} \begin{document} \noindent \begin{tabularx}{\linewidth}{Xr} \textbf{PyCorrFit \newline FCS data evaluation} \newline \textit{Software Guide} & \raisebox{-2em}{\includegraphics[angle=0,width=40mm]{Images/PyCorrFit_logo_dark.pdf}} \\ \\ Thomas Weidemann & \\ Max Planck Institute of Biochemistry, Martinsried, Germany & \\ \\ Paul Müller & \\ Biotechnology Center of the TU Dresden, Germany & \\ \\ \today & \\ \end{tabularx} \vspace{2em} \tableofcontents \newpage \graphicspath{{Images/}} \include{PyCorrFit_doc_content} \section*{Acknowledgements} \addcontentsline{toc}{section}{Acknowledgements} I thank André Scholich (TU Dresden, Germany) for initial proof reading of the manuscript. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Literaturverzeichnis \pagestyle{plain} % nur Nummerierung in der Fuzeile \bibliographystyle{plainurl} % Zitierstil: alphadin = [Nam88] apt-get install bibtex-extras \bibliography{Bibliography} % BibTeX-Datei name.bib ohne .bib hier einfgen %\nocite{*} % Listet alle Eintrge der Datei auf, wenn aktiv \end{document} PyCorrFit-0.9.9/doc/PyCorrFit_doc_content.tex000077500000000000000000002253631274265304700211710ustar00rootroot00000000000000\section{Introduction} \label{sec:intro} \subsection{Preface} \label{sec:intro.prefa} \textit{PyCorrFit} emerged from my work in the Schwille Lab\footnote{\url{http://www.biochem.mpg.de/en/rd/schwille/}} at the Biotechnology Center of the TU Dresden in 2011/2012. Since then, the program has been further developed based on numerous input from FCS users, in particular Franziska Thomas, Grzesiek Chwastek, Janine Tittel, and Thomas Weidemann. The program source code is available at GitHub\footnote{\url{https://github.com/FCS-analysis/PyCorrFit}}. Please do not hesitate to sign up and add a feature request. If you you find a bug, please let us know via GitHub.\\ \noindent \textit{PyCorrFit} was written to simplify the work with experimentally obtained correlation curves. These can be processed independently (operating system, location, time). PyCorrFit supports commonly used file formats and enables users to allocate and organize their data in a simple way.\\ \noindent \textit{PyCorrFit} is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 2 of the License, or (at your option) any later version\footnote{\url{http://www.gnu.org/licenses/gpl.html}}. \subsubsection*{What \textit{PyCorrFit} can do} \begin{itemize} \item Load correlation curves from numerous correlators \item Process these curves \item Fit a model function (many included) to an experimental curve \item Import user defined models for fitting \item Many batch processing features \item Save/load entire \textit{PyCorrFit} sessions \item \textbf{\LaTeX} support for data export \end{itemize} \subsubsection*{What \textit{PyCorrFit} is not} \begin{itemize} \item A multiple-$\tau$ correlator \item A software to operate hardware correlators \end{itemize} \subsection{System prerequisites} \label{sec:intro.prere} \subsubsection{Hardware} \label{sec:intro.prere.hardw} This documentation addresses the processing of correlation curves with \textit{PyCorrFit} and was successfully used with the following setups: \begin{itemize} \item[1.] APD: Photon Counting Device from PerkinElmer Optoelectronics, Model: \texttt{SPCM-CD3017}\\ Correlator: Flex02-01D/C from correlator.com with the shipped software \texttt{flex02-1dc.exe}. \item[2.] APD: Photon Counting Device from PerkinElmer Optoelectronics\\ Correlator: ALV-6000 \item[3.] LSM Confocor2 or Confocor3 setups from Zeiss, Germany. \end{itemize} \subsubsection{Software} \label{sec:intro.prere.softw} The latest version of \textit{PyCorrFit} can be obtained from the internet at \url{http://pycorrfit.craban.de}. \begin{itemize} \item \textbf{MacOS X.} Binary files for MacOS X 10.9.5 and later are available from the download page. \item \textbf{Windows.} For Windows XP and later, stand-alone binary executables are available from the download page. \item \textbf{Ubuntu/Debian.} PyCorrFit is available from the Debian repositories and can be installed via the operating systems packaging tool (e.g. \texttt{apt-get install pycorrfit}). \item\textbf{PyPI.} To run \textit{PyCorrFit} on any other operating system, the installation of Python v.2.7 is required. \textit{PyCorrFit} is included in the Python package index (PyPI, \url{http://pypi.python.org/pypi/pip}) and can be installed via\footnote{See also the wiki article at \url{https://github.com/FCS-analysis/PyCorrFit/wiki/Installation_pip}} \texttt{pip~install~pycorrfit$\!$[GUI]}. \item \textbf{Sources.} You can also directly download the source code at any developmental stage\footnote{See also the wiki article at \url{https://github.com/FCS-analysis/PyCorrFit/wiki/Running-from-source}}. \end{itemize} \vspace{1em} \noindent \textbf{\LaTeX .} \textit{PyCorrFit} can save correlation curves as images using matplotlib. It is also possible to utilize \LaTeX to generate these plots. On Windows, installing MiKTeX with ``automatic package download'' will enable this feature. On MacOS X, the MacTeX distribution can be used. On other systems, a latex distribution, Ghostscript, \texttt{dvipng} and the latex packages \texttt{texlive-latex-base} and \texttt{texlive-math-extra} need to be installed. \subsection{Workflow} \label{sec:intro.workf} The following chapter introduces the general idea of how to start and accomplish a fitting project. FCS experiments produce different sets of experimental correlation functions which must be interpreted with appropriate physical models (\hyref{Chapter}{sec:theor}). Each correlation function refers to a single contiguous signal trace or ``run''. In \textit{PyCorrFit}, the user must assign a mathematical model function to each correlation function during the loading procedure. The assignment is irreversible in the sense that within an existing \textit{PyCorrFit} session it cannot be changed. This feature assures the stability of the batch processing routine for automated fitting of large data sets. Nevertheless, the fit of different models to the same data can be explored by loading the data twice or simply by creating two different sessions. Let's briefly discuss a typical example: To determine the diffusion coefficient of a fluorescently labeled protein in free solution, one has to deal with two sets of autocorrelation data: measurements of a diffusion standard (e.g. free dye for which a diffusion coefficient has been published) to calibrate the detection volume and measurements of the protein sample. The protein sample may contain small amounts of slowly diffusing aggregates. While the calibration measurements can be fitted with a one-component diffusion model (T+3D), the protein sample displays two mobility states, monomers and aggregates, which are taken into account by a two-component diffusion model (T+3D+3D). With \textit{PyCorrFit} such a situation can be treated in three ways, having different pros and cons: \begin{enumerate} \item Create separate sessions for each type of sample and assign different model functions. \item Assign a one-component model to the dye measurements and a two-component model to the protein measurements when loading consecutively into the same session. \item Assign a two-component model for all data and, when appropriate, manually inactivate one component by fixing its contribution to 0\%. \end{enumerate} The first approach is straightforward, however, it requires homogeneous diffusion behavior for each data set. The second strategy has the advantage that the dye and the protein curves, as well as the obtained parameters, can be visually compared during the fitting analysis within the same session. In this case, batch fitting is still possible because it discriminates data sets assigned to different models. In the third case, simultaneous batch fitting is also possible. However, for each dye measurement one has to eliminate the second, slow diffusion species manually, which might be laborious. Inactivating components by fixing parameters is nevertheless a common way to evaluate heterogeneous data sets, for example, a protein sample for which only a subgroup of curves requires a second diffusion component due to occasional appearance of aggregates. Such situations are frequently encountered in intracellular measurements. In conclusion, all three strategies or combinations thereof may be suitable. In any case, the user must decide on model functions beforehand, therefore it is advisable to group the data accordingly. The fitting itself is usually explored with a representative data set. Here, the user has to decide on starting parameters, the range in which they should be varied, corrections like background, and other fitting options. Once the fit looks good, the chosen settings can be transferred at once to all other pages assigned to the same model using the \textit{Batch control} tool (\hyref{Section}{sec:menub.tools.batch}). After flipping through the data for visual inspection one may check the parameters across all pages in the \textit{Statistics view} tool and re-visit outliers (\hyref{Section}{sec:menub.tools.stati}). From there, the numerical fit values and example correlation functions can be exported. \subsection{Graphical user interface (GUI)} \label{sec:intro.graph} \begin{figure}[h] \centering \includegraphics[width=\linewidth]{PyCorrFit_Screenshot_Main.png} \mycaption{user interface of PyCorrFit}{Confocal measurement of nanomolar Alexa488 in aqueous solution. To avoid after-pulsing, the autocorrelation curve was measured by cross-correlating signals from two detection channels using a 50 \% beamsplitter. Fitting reveals the average number of observed particles ($n \approx 6$) and their residence time in the detection volume ($\tau_{\mathrm{diff}} = \SI{28}{\mu s})$. \label{fig:mainwin} } \end{figure} Together with a system's terminal of the platform on which \textit{PyCorrFit} was installed (Windows, Linux, MacOS X), the \textit{main window} opens when starting the program. The window title bar contains the version of \textit{PyCorrFit} and, if a session was re-opened or saved, the name of the fitting session. A menu bar provides access to many supporting tools and additional information as thoroughly described in \hyref{Chapter}{sec:menub}. There are three gateways for experimental data into a pre-existing or a new \textit{PyCorrFit} session (\textit{File/Load data}, \textit{File/Open session}, and \textit{Current page/Import data}). When a session has been opened or correlation data have been loaded, each correlation curve is displayed on a separate page of a notebook. For quick identification of the active data set, a tab specifies the page number, the correlated channels (AC/CC), and the run number in cases where multiple correlation runs are combined in a single file. When clicking a little triangle to the far-right, one can use a drop-down list of all page titles to directly access a particular data set. Alternatively, the pages can be toggled by tapping the arrow keys (left/right). There can be only one activated page for which the tab appears highlighted. The active page displaying a correlation function is divided in two panels (\hyref{Figure}{fig:mainwin}). At the left hand side the page shows a pile of boxes containing values or fitting options associated with the current model and data set: \begin{itemize} \item \textit{Data set} specifies the assigned model abbreviation in parentheses and shows a unique identifier for the correlation curve containing the file name, the number of the ``run'', and the data channel. This string is automatically assembled during the loading procedure (\hyref{Section}{sec:menub.filem.loadd}). However, during the session it can be manually edited, thereby allowing to re-name or flag certain data during the fitting analysis. \item \textit{Model parameters} displays the values which determine the current shape of the assigned model function (\hyref{Chapter}{sec:theor}). Initially, starting values are loaded as they were defined in the model description (\hyref{Section}{sec:menub.filem.impor}). Little buttons allow a stepwise increase or decrease in units of 1/10\textsuperscript{th}. It is also possible to directly enter some numbers. A checkbox is used to set the parameter status to ``varied'' (checked) or ``fixed'' (unchecked) during the fitting. At the end, when saving the session, the current set of values together with their indicated names are stored in the *.yaml file (\hyref{Section}{sec:menub.filem.saves}). \item \textit{Amplitude corrections} applies additional rescaling to amplitude related parameters like the number of particles $n$ or amplitude fractions associated with different correlation times ($n_1$, $n_2$, etc.). Experimental values of non-correlated background intensity can be manually entered for each channel. In addition, the correlation curves can be normalized, to facilitate a visual comparison of the decay. \item \textit{Fitting options} offers weighted fitting (a) and a choice for the fit algorithm (b). \begin{itemize} \item[\textbf{a)}] The underlying idea is that data points with higher accuracy should also have a higher impact on model parameters. To derive weights, \textit{PyCorrFit} calculates the variance of the difference between the actual data and a smooth, empiric representation of the curve for a certain neighbourhood. The number of neighbouring data points at each side ($j > 0$) can be set. For such a smooth representation a spline function or the model function with the current parameter set can be used. The default number of knots for the spline function is 5. This number can be manually edited in the dropdown-selector. To view the spline function after each fit, \textit{Preferences/Verbose mode} has to be checked. If the current page is an average, then the standard deviation from the curves that were used to create the average can be used as weights. \item[\textbf{b)}] Several fitting algorithms can be chosen. We recommend to use the Levenberg-Marquardt algorithm (b). For more information, see \hyref{Section}{sec:theor.alg}. \end{itemize} \end{itemize} At the right hand side are two graphics windows. The dimensionless correlation functions $G(\tau)$ are plotted against the lag time ($\tau$) on a logarithmic scale. Below, a second window shows the residuals, the actual numerical difference between the correlation data and the model function. Fitting with appropriate models will scatter the residuals symmetrically around zero ($x$-axis). When weighted fitting was performed, the weighted residuals are shown. A good fit will not leave residuals systematically above or below the $x$-axis at any time scale. The main window can be rescaled as a whole to improve data representation. In addition, to zoom in, one can drag a rectangle within the plot area; a double click then restores the initial scale. Experimental data points are linked by grey lines, the state of the model function is shown in blue. When a weighted fit was applied, the variance of the fit is calculated for each data point and displayed in cyan. \section{The menu bar} \label{sec:menub} The menu bar organizes data management (File), data analysis (Tools), display of correlation functions (Current Page), numerical examples (Model), software settings (Preferences), and software metadata (Help). \subsection{File menu} \label{sec:menub.filem} The File menu organizes the import of theoretical models, experimental correlation data, and opening and saving of entire \textit{PyCorrFit} fitting sessions. However, the numerical fit results are exported from the \textit{Statistics view} panel which can be found under \textit{Tools} (\hyref{Section}{sec:menub.tools.stati}). \subsubsection{File / Import model} \label{sec:menub.filem.impor} Correlation data must be fitted to models describing the underlying physical processes which give rise to a particular time dependence and magnitude of the recorded signal fluctuations. Models are mathematical expressions containing parameters with physical meaning, like the molecular brightness or the dwell time through an illuminated volume. While the most commonly used standard functions are built-in, the user can define new expressions. Some examples can be found at GitHub in the \textit{PyCorrFit} repository, e.g. circular scanning FCS \cite{Petrasek2008} (\hyref{Figure}{fig:csfcs}) or a combination of diffusion and directed flow \cite{Brinkmeier1999}. External model functions are discussed in detail in \hyref{Section}{sec:hacke.extmod}. \subsubsection{File / Load data} \label{sec:menub.filem.loadd} \textit{Load data }is the first way to import multiple correlation data sets into a \textit{PyCorrFit} session. The supported file formats can be found in a drop-down list of supported file endings in the pop-up dialog \textit{Open data files}: \begin{tabular}{l l} \rule{0pt}{3ex} (1) All supported files & default \\ \rule{0pt}{3ex} (2) ALV (*.ASC) & ALV Laser GmbH, Langen, Germany \\ \rule{0pt}{3ex} (3) Correlator.com (*.SIN) & www.correlator.com, USA \\ \rule{0pt}{3ex} (4) PicoQuant (*.pt3) & PicoQuant \\ \rule{0pt}{3ex} (5) Zeiss ConfoCor3 (*.fcs) & AIM 4.2, ZEN 2010, Zeiss, Germany \\ \rule{0pt}{3ex} (6) Matlab ‘Ries (*.mat) & EMBL Heidelberg, Germany \\ \rule{0pt}{3ex} (7) PyCorrFit (*.csv) & Paul Müller, TU Dresden, Germany \\ \rule{0pt}{3ex} (8) PyCorrFit session (*.pcfs) & Paul Müller, TU Dresden, Germany \\ \rule{0pt}{3ex} (9) Zip file (*.zip) & Paul Müller, TU Dresden, Germany \\ \end{tabular} \vspace{3ex} \newline While (2)-(5) are file formats associated with commercial hardware, (6) refers to a MATLAB based FCS evaluation software developed by Jonas Ries in the Schwille lab at TU Dresden, (7) is a text file containing comma-separated values (csv) generated by PyCorrFit via the command \textit{Current Page / Save data}. Zip-files are automatically decompressed and can be imported when matching one of the above mentioned formats. In particular loading of *.pcfs files (which are actually zip files) is a possibility to re-import correlation data from entire \textit{PyCorrFit} sessions. However, these data are treated as raw, which means that all fitting parameters and model assignments are lost. During loading, the user is prompted to assign fit models in the \textit{Choose Models} dialogue window. There, curves are sorted according to channel (for example AC1, AC2, CC12, and CC21, as a typical outcome of a dual-color cross-correlation experiment). For each channel a fit model must be selected from the list (see \hyref{Section}{sec:menub.model}): If a file format is not yet listed, the correlation data could be converted into a compatible text-file (*.csv) or bundles of *.csv files within a compressed archive *.zip. For reformatting correlation data, have a look at \hyref{Section}{sec:hacke.csv}. \subsubsection{File / Open session} \label{sec:menub.filem.opens} This command is the second way to import data into PyCorrFit. In contrast to \textit{Load data}, it opens an entire fitting project, which was previously saved with \textit{PyCorrFit}. Session files are *.zip files named *.pcfs. These files contain all information to restore a session, including comments, model assigned correlation data, and the current state of parameters for each data set (\hyref{Section}{sec:menub.filem.saves}). \subsubsection{File / Comment session} \label{sec:menub.filem.comme} This command opens a window to place text messages that can be used to annotate a fitting session. \subsubsection{File / Clear session} \label{sec:menub.filem.clear} This command closes all pages. The user is prompted to save the current session. \subsubsection{File / Save session} \label{sec:menub.filem.saves} In addition to display and fit individual curves, a strong feature of \textit{PyCorrFit} is to save an entire fitting project as a single session. Sessions allow the user to revisit and explore different models, fitting strategies, and data sets. Importantly the work can be saved at any stage. The number of files bundled in a session varies depending on the number of data sets (pages), the number of used models, and what was done during the fitting. A detailed description can be found in the Readme.txt file attached to each session. For example, the numerical correlation and intensity data are saved separately as *.csv text files. However, in contrast to the \textit{Save data (*.csv)} command of the \textit{Current Page} menu, there are no metadata in the header, just tables containing the numerical values. In sessions, the fitting parameters are stored separately in the human-readable data serialization format *.yaml. \subsubsection{File / Exit} \label{sec:menub.filem.exit} This command closes down \textit{PyCorrFit}. The user is prompted to save the session under the same or a different name. \subsection{Tools menu} \label{sec:menub.tools} The \textit{Tools} menu provides access to a series of accessory panels which extent the capability of the main window. These accessory panels can stay open during the entire analysis. Open panels appear checked in the menu. Most operations can be executed across the entire data set with a single mouse click. \subsubsection{Tools / Data range} \label{sec:menub.tools.datar} This panel limits the range of lag times which are displayed in the main window panel. At the same time it defines the range of points which are used for fitting. For example, this feature can be applied to remove dominant after-pulsing of the avalanche photo diodes (APDs) which may interfere with Triplet blinking at short lag times. The user has the options to \textit{Apply} the channel settings only to the current page or he can \textit{Apply to all pages}. In contrast to \textit{Batch control}, this operation ignores whether the data are assigned to different models. Power user, who frequently load and remove data sets, may take advantage of a checkbox to fix the channel selection for all newly loaded data sets. \subsubsection{Tools / Overlay curves} \label{sec:menub.tools.overl} This window displays the correlation data (not the fit curves) of all pages in a single plot. The curves can be discriminated by color. If only one curve is selected, it appears in red. Curves with ambiguous shape can easily be identified, selected, and removed by clicking \textit{Apply}. A warning dialogue lists the pages which will be kept. Data representation is synchronized with the page display in the \textit{Main window}. For example, narrowing the range of lag times by \textit{Data range }is immediately updated in the \textit{Overlay curves }tool. Likewise, their normalization of the amplitudes to unity. The other way round, some tools directly respond to the selections made in the \textit{Overlay curves} tool: \textit{Global fitting}, \textit{Average curves}, and \textit{Statistics view} allow to perform operations on an arbitrary selection of pages which can be specified by page number. Instead of manually typing their numbers, the curves may be selected within the \textit{Overlay curves} tool. The respective input fields are immediately updated. The tool is closed by the button \textit{Cancel}. All the listed data sets will be kept. However, the selections transferred to the \textit{Global fitting}, \textit{Average curves}, and \textit{Statistics view} tools are kept as well. \subsubsection{Tools / Batch control} \label{sec:menub.tools.batch} By default, the current page is taken as a reference to perform automated fitting. A batch is defined as the ensemble of correlation data sets (pages) assigned to the same model function within a session. A session can therefore have several batches, even for the same data. For fitting, it is crucial to carefully define the starting parameters, whether parameters should be fixed or varied, the range of values which make physically sense, and other options offered within the \textit{Main window}. By executing \textit{Apply to applicable pages}, these settings are transferred to all other pages assigned to the same fit model. Note that this includes the range of lag times (lag time channels) which may have been changed with the \textit{Data range} tool for individual pages. It is possible to prevent the application of fitting parameters to individual pages by checking the \textit{prevent batch modification} checkbox in the \textit{Model parameters} box of a page. The button \textit{Fit applicable pages} then performs fitting on all pages of the same batch. Alternatively, the user can define an external source of parameters as a reference, i.e. the first page of some \textit{Other session} (*.pcfs). However, this assumes a consistent assignment of model functions. \subsubsection{Tools / Global fitting} \label{sec:menub.tools.globa} Global fitting is useful when experimental curves share the same values for certain physical parameters. For example, due to physical constraints in two-focus FCS both autocorrelation curves and cross-correlation curves should adopt the same values for the diffusion time $\tau_\mathrm{diff}$ and the number of particles $n$. A global fit can be applied such that $n$ and $\tau_\mathrm{diff}$ are identical for all data sets. All curves are added to a single array. In contrast to fixing the shared parameters across a batch, in \textit{Global fitting} $\chi^2$ is minimized for all data sets simultaneously (see \hyref{Section}{sec:theor.nonle}). To perform \textit{Global fitting}, a subset of curves has to be selected by typing the numbers into the input field or by highlighting the pages via the \textit{Overlay curves} tool. \subsubsection{Tools / Average data} \label{sec:menub.tools.avera} Often in FCS, the measurement time at a particular spot is divided in several runs. This approach is taken when occasional, global intensity changes are superimposed on the molecular fluctuations of interest. Then the user has to sort out the bad runs. After fitting, one may want to re-combine the data to export a cleaned average correlation function. This can be done with the tool \textit{Average data}, for which a subset of curves has to be selected by typing the numbers into the input field or by highlighting the pages via the \textit{Overlay curves} tool. For averaging, there are constraints: \begin{enumerate} \item Since the correlation curves are averaged point by point this requires the same number of lag time channels. Runs of different length cannot be averaged. \item The tool can only average data sets which are exclusively autocorrelation or cross-correlation. \item The user can check a box to enforce the program to ask for data sets with the same model as the current page. This may help to avoid mistakes when selecting pages. \end{enumerate} The averaged curve is shown on a separate page. The new \textit{Filename/title} receives the entry \textit{Average [numbers of pages]}. The assigned model is by default the same as for the individual pages. However, while averaging, the user can choose a different model from a drop-down list. \subsubsection{Tools / Trace view} \label{sec:menub.tools.trace} FCS theory makes assumptions about the thermodynamic state of the system. Signal fluctuations can only be analyzed when the system is at equilibrium or at a sufficiently stable steady state. Global instabilities on the time scale of the measurement itself, e.g. photo-bleaching, have dramatic effect on the shape of the measured correlation curve. Therefore, it is common practice to check the correlated intensity trace for each curve. Trace view simply displays the signal trace for each correlation function. The window stays open during the session and can be used to revisit and flag ambiguous data sets. \subsubsection{Tools / Statistics view} \label{sec:menub.tools.stati} The goal of a correlation analysis is to determine experimental parameter values with sufficient statistical significance. However, especially for large data sets, it can get quite laborious to check all of the individual values on each page. We designed the \textit{Statistics view} panel to review the state of parameters across the experimental batch (pages assigned to the same model) in a single plot, thereby facilitating to the identification of outliers. The current page is taken as a reference for the type of model parameters which can be displayed. The user can choose different \textit{Plot parameters} from a drop-down list. A subset of pages within the batch can be explicitly defined by typing the page numbers into the input field or by highlighting in the \textit{Overlay curves} tool. Note that page numbers which refer to different models than the current page are ignored. The \textit{Statistics view} panel contains a separate \textit{Export} box, where parameters can be selected (checked) and saved as a comma separated text file (*.csv). Only selected page numbers are included. \subsubsection{Tools / Page info} \label{sec:menub.tools.pagei} Page info is a most verbose summary of a data set. The panel \textit{Page info} is synchronized with the current page. The following fields are listed: \begin{enumerate} \item Version of \textit{PyCorrFit} \item Field values from the main window (filename/title, model specifications, page number, type of correlation, normalizations) \item Actual parameter values (as contained in the model function) \item Supplementary parameters (intensity, counts per particle, duration, background correction, etc.) \item Fitting related information (Chi-square, channel selection, varied fit parameters) . \item Model doc string (\hyref{Section}{sec:menub.model}) \end{enumerate} The content of Page info is saved as a header when exporting correlation functions via the command \textit{Current page / Save data (*.csv)} (\hyref{Section}{sec:menub.curre.saved}). \subsubsection{Tools / Slider simulation} \label{sec:menub.tools.slide} This tool visualizes the impact of model parameters on the shape of the model function of a current page. Such insight may be useful to choose proper starting values for fitting or to develop new model functions. For example, in the case of two parameters that trade during the fitting one may explore to which extent a change in both values produces similar trends. Two variables (A and B) have to be assigned from a drop-down list of parameters associated with the current model function. For each of these, the \textit{Slider simulation} panel shows initially the starting value (x) as a middle position of a certain range (from 0.1*x to 1.9*x). The accessible range can be manually edited and the actual value of the slider position is displayed at the right hand side of the panel. Dragging the slider to lower (left) or higher (right) values changes the entry in the box \textit{Model parameters} of the \textit{Main window} and accordingly the shape of the model function in the plot. By default the checkbox \textit{Vary A and B}\textit{ }is active meaning that both variables during \textit{Slider simulation} can be varied independently. In addition, the variables A and B can be linked by a mathematical relation. For this a mathematical operator can be selected from a small list and the option \textit{Fix relation} must be checked. Then, the variable B appears inactivated (greyed out) and the new variable combining values for A and B can be explored by dragging. \subsection{Current Page} \label{sec:menub.curre} This menu compiles import and export operations referring exclusively to the active page in the main window. \subsubsection{Current Page / Import Data} \label{sec:menub.curre.impor} This command is the third way to import data into a pre-existing session. Single files containing correlation data can be imported as long as they have the right format (\hyref{Section}{sec:menub.filem.loadd}). In contrast to \textit{Load data} from the \textit{File} menu, the model assignment and the state of the parameters remains. The purpose of this command is to compare different data sets to the very same model function for a given set of parameters. After successful import, the previous correlation data of this page are lost. To avoid this loss, one could first generate a new page via the menu \textit{Models} (\hyref{Section}{sec:menub.model}), select a model function and import data there. This is also a possibility to assign the very same data to different models within the same session. \subsubsection{Current Page / Save data (*.csv)} \label{sec:menub.curre.saved} For the documentation with graphics software of choice, correlation curves can be exported as a comma-separated table. A saved \textit{PyCorrFit} text-file (*.csv) will contain a hashed header with metadata from the \textit{Page info} tool (\hyref{Section}{sec:menub.tools.pagei}), followed by the correlation and fitting values in tab-separated columns: \textit{Channel (tau [s])}, \textit{Experimental correlation}, \textit{Fitted correlation}, \textit{Residuals}, and \textit{Weights (fit)}. Below the columns, there are again 5 rows of hashed comments followed by the intensity data in two columns: \textit{Time [s]} and \textit{Intensity trace [kHz]}. Note that there are no assemblies of ``multiple runs'', since \textit{PyCorrFit} treats these as individual correlation functions. A *.csv file therefore contains only a single fitted correlation curve and one intensity trace for autocorrelation or two intensity traces for cross-correlation. \subsubsection{Current Page / Save correlation as image} \label{sec:menub.curre.savec} The correlation curve can be exported as bitmap (e.g. *.png for quick documentation) or as a scalable vector graphic (e.g. *.pdf for post-processing in \textit{Adobe Illustrator} or \textit{Inkscape}). The plot contains a legend and fitting parameters. Note that the variable $\tau$ (= tau) cannot be displayed using Unicode with Windows. A \LaTeX formatted image can be exported when the option \textit{Use Latex} is checked in the \textit{Preferences} menu (\hyref{Section}{sec:menub.prefe}). Furthermore, if \textit{Preferences/Verbose mode} is checked, the plot can be edited before saving\footnote{The plots are generated with matplotlib, \url{http://matplotlib.org/}}. \subsubsection{Current Page / Save trace view as image} \label{sec:menub.curre.savet} An image of the trace can be exported in the same way as for the correlation curve (see above). \subsubsection{Current Page / Close page} \label{sec:menub.curre.close} Closes the page; the data set is removed from the session. The page numbers of all other pages remain the same. The command is equivalent with the closer (x) in the tab. \subsection{Models} \label{sec:menub.model} When choosing a model from the \textit{Models} menu, a new page opens and the model function is plotted according to the set of starting values for parameters as they were defined in the model description. The lists contains all of the implemented model functions, which can be selected during \textit{File / Load data}. The parameters can be manipulated to explore different shapes; the tool \textit{Slider simulation} can also be used. Via \textit{Current page / Import data}, the model may then be fitted to an experimental data set. Standard model functions for a confocal setup are (\hyref{Section}{sec:imple.confo}): \begin{tabular}{l l} %Confocal (Gaussian): 3D \ \ \ \ \ \ [Free diffusion in three dimensions] \rule{0pt}{3ex} - Confocal (Gaussian): T+3D & Triplet blinking and 3D diffusion \\ \rule{0pt}{3ex} - Confocal (Gaussian): T+3D+3D & Triplet with two diffusive components \\ %Confocal (Gaussian): T+3D+3D+3D & [Triplet with three diffusive components] %Confocal (Gaussian): 2D & 2D diffusion, e.g. in membranes \\ \rule{0pt}{3ex} - Confocal (Gaussian): T+2D & Triplet blinking and 2D diffusion \\ \rule{0pt}{3ex} - Confocal (Gaussian): T+2D+2D & Triplet with two diffusive components \\ \rule{0pt}{3ex} - Confocal (Gaussian): T+3D+2D & Triplet with mixed 3D and 2D diffusion \\ \rule{0pt}{3ex} \end{tabular} \noindent There is also a collection of models for FCS setups with TIR excitation (\hyref{Section}{sec:imple.tirfc}): \begin{tabular}{l l} \rule{0pt}{3ex} - TIR (Gaussian/Exp.): 3D & 3D diffusion \\ \rule{0pt}{3ex} - TIR (Gaussian/Exp.): T+3D+3D & Triplet with two diffusive components \\ \rule{0pt}{3ex} - TIR (Gaussian/Exp.): T+3D+2D & Triplet with mixed 3D and 2D diffusion \\ \rule{0pt}{3ex} \end{tabular} \noindent In addition, there are may be user defined model functions which have been imported previously via \textit{File / Import model} (\hyref{Section}{sec:menub.filem.impor}). \subsection{Preferences} \label{sec:menub.prefe} \paragraph*{Use Latex} If the user has a Tex distribution installed (e.g. MikTex for Windows), checking this option will generate \LaTeX formatted plots via the \textit{Current page / Save […] as image} commands. \paragraph*{Verbose mode} If checked, this will cause the \textit{PyCorrFit} to display graphs that would be hidden otherwise. In weighted fitting with a spline, the spline function used for calculating the weights for each data points is displayed\footnote{For obvious reasons, such a plot is not generated when using the iteratively improved \textit{Model function} or the actual \textit{Average} correlation curve for weighted fitting.}. When saving the correlation curve as an image (\hyref{Section}{sec:menub.curre.savec}), the plot will be displayed instead of saved. If ``Use Latex'' is checked, these plots will also be Latex-formatted. The advantage in displaying plots is the ability to zoom or rescale the plot from within \textit{PyCorrFit}. \paragraph*{Show weights} Checking this option will visualize the weights for each data point of the correlation function in the plot, as well as in the exported image. Note that the weights are always exported when using the \textit{Save data (*.csv)} command from the \textit{Current page} menu. \subsection{Help} \label{sec:menub.help} \paragraph*{Documentation.} This entry displays this documentation using the systems default PDF viewer. \paragraph*{Wiki.} This entry displays the wiki of \textit{PyCorrFit} on \textit{GitHub}. Everyone who registers with \textit{GitHub} will be able to make additions and modifications. The wiki is intended for end-users of \textit{PyCorrFit} to share protocols or to add other useful information. \paragraph*{Update} establishes a link to the \textit{GitHub} website to check for a new release; it also provides a few web links associated with \textit{PyCorrFit} \paragraph*{Shell.} This gives Shell-access to the functions of \textit{PyCorrFit}. It is particularly useful for trouble-shooting. \paragraph*{Software.} This lists the exact version of \textit{Python} and the corresponding modules with which \textit{PyCorrFit} is currently running. \paragraph*{About.} Information of the participating developers, the license, and documentation writers. \section{Hacker's corner} \label{sec:hacke} \subsection{External model functions} \label{sec:hacke.extmod} \textit{PyCorrFit} supports the import of your own model functions. If your model function is not implemented, i.e. not available in the \textit{Models} menu, writing a short text file containing a model description is the easiest way to make \textit{PyCorrFit} work. Some examples can be found at GitHub in the \textit{PyCorrFit} repository, e.g. circular scanning FCS \cite{Petrasek2008} (\hyref{Figure}{fig:csfcs}) or a combination of diffusion and directed flow \cite{Brinkmeier1999}. Model functions are imported as text files (*.txt) that must follow a certain format and syntax: \begin{itemize} \item \textbf{Encoding}: \textit{PyCorrFit} can interpret the standard Unicode character set. The model files have to be encoded in \textit{UTF-8}. \item \textbf{Comments}: Lines starting with a hash (\texttt{\#}), empty lines, or lines containing only white space characters are ignored. The only exception is the first line starting with a hash followed by a white space and a short name of the model. This line is evaluated to complement the list of models in the dialogue\textit{ Choose }\textit{model}, when loading the data. Imported models are available from the menu \textit{Models/User}. \item \textbf{Units}: \textit{PyCorrFit} works with internal units for: \begin{itemize} \item Time: \SI{1}{ms} \item Distance: \SI{100}{nm} \item Diffusion coefficient: \SI{10}{\mu m^2s^{-1}} \item Inverse time: \SI{1000}{s^{-1}} \item Inverse area: \SI{100}{\mu m^{-2}} \item Inverse volume: \SI{1000}{\mu m^{-3}} \end{itemize} \item \textbf{Parameters:} To define a new model function, new parameters can be introduced. Parameters are defined by a sequence of strings separated by white spaces containing name, the dimension in angular brackets, the equal sign, and a starting value which appears in the main window for fitting. For example: \texttt{D [\SI{10}{\mu m^ 2 s^{-1}}] = 5.0}. User defined dimensions are only for display; thus mathematical expressions must correctly account for their conversion from internal units of \textit{PyCorrFit}. The parameter names contain only alphabetical (not numerical) characters. \texttt{G}, variables starting with \texttt{g}, as well as the numbers \texttt{e} and \texttt{pi} are already mapped and cannot be used freely. \item \textbf{Placeholder:} When defining composite mathematical expressions for correlation functions, one can use place-holders. Place-holders start with a lower-case ‘g’. For example, the standard, Gaussian 3D diffusion in free solution may be written as \begin{itemize} \item \texttt{gTrp = 1+ T/(1-T)*exp(-tau/tautrip)} \item \texttt{gTwoD = 1/(1+tau/taudiff)} \item \texttt{gThrD = 1/sqrt(1+tau/(taudiff*S**2))} \end{itemize} \end{itemize} The individual parts are then combined in the last line of the *.txt file, where the correlation function is defined starting with upper-case ’G’: \begin{equation} \texttt{G = 1/n * gTrp * gTwoD * gThrD} \notag \end{equation} For reference of mathematical operators, check for example \href{http://www.tutorialspoint.com/python/python_basic_operators.htm}{www.tutorialspoint.com / python / python\_basic\_operators.htm}. To illustrate a more complex example, a model function for circular scanning FCS is shown in \hyref{Figure}{fig:csfcs}. External models will be imported with internal model function IDs starting at $7000$. Models are checked upon import by the Python module sympy. If the import fails, there is most likely syntax error in the model file. \begin{figure} % for case sensitiver Verbatim, we need the package fancyvrb \begin{Verbatim}[frame = single] # CS-FCS T+2D+2D+S (Confocal) # Circular Scanning FCS model function for two 2D-diffusing species # including triplet component. ## Definition of parameters: # First, the parameters and their starting values for the model function # need to be defined. If the parameter has a unit of measurement, then it # may be added separated by a white space before the "=" sign. The starting # value should be a floating point number. Floating point abbreviations # like "1e-3" instead of "0.001" may be used. # Diffusion coefficient of first component D1 [10µm²/s] = 200.0 # Diffusion coefficient of second component D2 [10µm²/s] = 20.0 # Fraction of species one F1 = 1.0 # Half waist of the lateral detection area (w0 = 2*a) a [100nm] = 1.0 # Particle number n = 5.0 # Scan radius R [100nm] = 3.850 # Frequency f [kHz] = .2 # Triplet fraction T = 0.1 # Triplet time tautrip [ms] = 0.001 offset = 0.00001 # The user may wish to substitute certain parts of the correlation function # with other values to keep the formula simple. This can be done by using # the prefix "g". All common mathematical functions, such as "sqrt()" or # "exp()" may be used. For convenience, "pi" and "e" are available as well. gTriplet = 1. + T/(1-T)*exp(-tau/tautrip) gScan1 = exp(-(R*sin(pi*f*tau))**2/(a**2+D1*tau)) gScan2 = exp(-(R*sin(pi*f*tau))**2/(a**2+D2*tau)) gTwoD1 = F1/(1.+D1*tau/a**2) gTwoD2 = (1-F1)/(1.+D2*tau/a**2) # The final line with the correlation function should start with a "G" # before the "=" sign. G = offset + 1./n * (gTwoD1 * gScan1 + gTwoD2 * gScan2) * gTriplet \end{Verbatim} \mycaption{user defined model function for PyCorrFit}{The working example shows a model function for circular scanning FCS (see also its appearance in the \textit{Main window} \hyref{Figure}{fig:csfcsplot} \label{fig:csfcs}} \end{figure} \subsection{Internal model functions} Alternatively, new models can be implemented by programming of the models module of \textit{PyCorrFit}. First, edit the code for \texttt{\_\_init\_\_.py} and then add the script containing the model function. There is no Tutorial yet, but the implemented model files are self-explanatory. If you need help creating a new internal model function and/or want to publish it with \textit{PyCorrFit}, do not hesitate to contact an active developer by creating a new issue on GitHub. \subsection{Correlation curve file format} \label{sec:hacke.csv} PyCorrFit can read correlation data from many file formats. If a file format is not yet listed, the correlation data could be converted into a compatible text-file (*.csv) or bundles of *.csv files within a compressed *.zip archive. For reformatting the following requirements must be fulfilled: \begin{itemize} \item \textbf{Encoding}: \textit{PyCorrFit} uses the standard Unicode character set (UTF-8). However, since no special characters are needed to save experimental data, other encodings may also work. New line characters are \texttt{{\textbackslash}r{\textbackslash}n} (Windows). \item \textbf{Comments}: Lines starting with a hash (\texttt{\#}), empty lines, or lines containing only white space characters are ignored. Exceptions are the keywords listed below. \item \textbf{Units}: PyCorrFit works with units/values for: \begin{itemize} \item Time: \SI{1}{ms} \item Intensity: \SI{1}{kHz} \item Amplitude offset: $G(0) = 0$ (not 1) \end{itemize} \item \textbf{Keywords:}\footnote{Keywords are case-insensitive.} \textit{PyCorrFit} reads the first two columns containing numerical values. The first table (non-hashed) is recognized as the correlation data containing the lag times in the first and the correlation data in the second column. (In case the *.csv file has been generated with \textit{PyCorrFit} up to three additional columns containing the fit function are ignored). The table ends, when the keyword \texttt{\# BEGIN TRACE} appears. Below this line the time and the signal values should be contained in the first two columns. If cross-correlation data have to be imported a second trace can be entered after the keyword \texttt{\# BEGIN SECOND TRACE}. \item \textbf{Tags:}\footnote{Tags are case-insensitive.} Channel information can be entered using defined syntax in a header. The keyword \begin{center} \vspace{-1em} \texttt{\# Type AC/CC Autocorrelation} \vspace{-1em} \end{center} assigns the tag \texttt{AC} and the keyword \begin{center} \vspace{-1em} {\texttt{\# Type AC/CC Crosscorrelation}} \vspace{-1em} \end{center} assigns the tag \texttt{CC} to the correlation curve. These strings are consistently displayed in the user interface of the respective data page in \textit{PyCorrFit}. If no data type is specified, autocorrelation is assumed. Tags may be specified with additional information like channel numbers, e.g. \begin{center} \vspace{-1em} \texttt{\# Type AC/CC Autocorrelation \_01}. \vspace{-1em} \end{center} In this case the tag \texttt{AC\_01} is generated. This feature is useful to keep track of the type of curve during the fitting and when post-processing the numerical fit results. \end{itemize} \subsection{New file format} Alternatively, new file formats can be implemented by programming of the readfiles module of \textit{PyCorrFit}. First, edit the code for \texttt{\_\_init\_\_.py} and then add the script \texttt{read\_FileFormat.py}. There is no Tutorial yet, but the implemented scripts are self-explanatory. If you need help implementing a new file format and/or want to publish it with \textit{PyCorrFit}, do not hesitate to contact an active developer by creating a new issue on GitHub. \section{Theoretical background} \label{sec:theor} In the first place, \textit{PyCorrFit} was designed to evaluate FCS data, therefore we focus on fluorescence. However, the correlation theory could be applied to any other stochastic signal. \subsection{How FCS works} \label{sec:theor.howfc} FCS is a method to determine molecular properties of stochastically moving fluorescent molecules in solutions \cite{Elson1974,Magde1974,Magde1978}. The solution may be a liquid volume (3D) or a lipid membrane (2D) \cite{Widengren1998,Korlach1999,Schwille1999}. The diffusion may be free or anomalous due to barriers or obstacles \cite{Wachsmuth2000,Weiss2003}. The size of the diffusing particles range from synthetic dyes (\SI{800}{Da}) to large complexes or aggregates of labelled macromolecules (several MDa). Particles which differ in size or emission behaviour can be discriminated. Therefore FCS is a powerful tool to study molecular recognition \cite{Bacia2006,Kim2007}. The measurement principle is to illuminate a small open volume within solutions of fluorescent molecules and to detect their molecular transits with a sub-microsecond time resolution by sensitive optics. The stochastic movements and other processes affecting fluorescence emission generate a fluctuating signal. The typical time pattern of these fluctuations can be revealed by a correlation analysis. The shape and time range of the decaying correlation function is defined by the exact geometry of the FCS detection volume in conjunction with the properties of the molecular system at hand. To derive hard numbers, the system must be parametrized as a theoretical model. Once the geometry of the detection volume is characterized, parameters can be determined by comparing the model function with experimental data (mathematical fitting, \hyref{Figure}{fig:mainwin}). \subsection{Framework for a single type of fluorescent dye} \label{sec:theor.frame} The following equations are described in many papers in different ways. Here we follow the notation of one of us \cite{Weidemann2009}. Fluorescence signals are a result of absorption and emission of photons by a fluorophore. Under experimental conditions, the signal depends on the time dependent distribution of fluorescent particles in the sample volume $c(\vec{r},t)$ and the normalized instrumental detection efficiency $W(\vec{r})$. The total intensity, signal $S(t)$, is the sum of all photons emitted from fluorophores at different positions within the detection volume: \begin{equation} \label{eq1} S(t) = q \int W(\vec{r}) c(\vec{r},t) \,dV \end{equation} The factor $q$ combines all the photo-physical quantities associated with fluorescence emission like absorption cross section, quantum yield, and the peak intensity of excitation (laser power). In the following, time averages of observables are indicated by angular brackets. We apply the ergodic theorem (see below). \begin{equation} \label{eq2} \langle S(t) \rangle = \lim_{t\to\ \infty} \int S(t) \,dt = q \int W(\vec{r}) c \,dV = qn \end{equation} \hyref{Equation}{eq2} reveals that $q$ is the instrument dependent molecular brightness (kHz/particle), i.e. the average signal divided by the average number of particles $n$ observed within the effective detection volume $V_{\mathrm{eff}} = \int W(\vec{r}) \,dV$. During FCS measurements the detected signal is correlated by computing a normalized autocorrelation function: \begin{equation} \label{eq3} G(\tau) = \frac{\langle S(t) \cdot S(t+\tau)\rangle}{\langle S(t) \rangle^2}-1 = \frac{\langle \delta S(t) \cdot \delta S(t+\tau)\rangle}{\langle S(t) \rangle^2} = \frac{g(\tau)}{\langle S(t) \rangle^2} \end{equation} Here, $\tau$ denotes the lag time used for correlation and $\delta S(t) = S(t)-\langle S \rangle$ the amplitude of the signal fluctuation for a given time point. \hyref{Equation}{eq3} defines a function with a finite intercept decaying to zero, whereas $g(\tau)$, the non-normalized correlation function, decays to a finite value $\langle S \rangle^{-2}$. To visualize correlation times, the functions $G(\tau)$ are typically plotted against $\log(\tau)$ (\hyref{Figure}{fig:mainwin}). A general way to derive theoretical model functions is to evaluate \hyref{Equation}{eq3} with explicit expressions describing the instrumental detection efficiency $W(\vec{r})$ and the molecular dynamics governing the local fluorophore concentrations $c(\vec{r},t)$. For example, free diffusion of the molecules can be described by the so-called diffusion propagator. \begin{equation} \label{eq4} P_\mathrm{d} \left( \vec{r} \,' | \vec{r},\tau \right) = \frac{1}{\left( 4 \pi D_t \tau \right) ^{3/2}} \exp \left[ - \frac{\left| \vec{r} \,' -\vec{r} \, \right|} {4D_t \tau} \right] \end{equation} The propagator $P_\mathrm{d} \left( \vec{r} \,' | \vec{r},\tau \right)$ is the conditional probability of a particle with diffusion coefficient $D_t$ to move from $\vec{r}$ to $\vec{r \,'}$ within a time period $\tau$. The probability to find a particle inside the volume $d^3r$ is simply the ratio of volumes $d^3r/V$. Such a diffusion propagator leads to a Gaussian shaped probability distribution spreading in time when the molecules successively roam the sample volume $V$. Assuming that the time average and the ensemble average are equivalent (ergodic theorem), the average signal of a single particle is its molecular brightness $q$ normalized to its contribution to the entire volume, because the integral over the detection efficiency function is exactly the effective measurement volume $V_\mathrm{eff}$. \begin{equation} \label{eq5} \langle s(t) \rangle = q \frac{\int W(\vec{r}) \,dV}{V} = \frac{V_\mathrm{eff}}{V} q \end{equation} Accordingly, we can express the average product of two signals separated by $\tau$ as \begin{equation} \label{eq6} \langle s(t) \cdot s(t + \tau) \rangle = \frac{q^2}{V}\iint W(\vec{r}) P_\mathrm{d} \left( \vec{r} \,' | \vec{r},\tau \right) W(\vec{r} \,') dVdV' \end{equation} With the definition of the signal $S(t)$ and its average $\langle S(t) \rangle$ \begin{align} S(t) &= \Sigma_{k=1}^n s_k(t) \\ \langle S(t) \rangle &= \Sigma_{k=1}^n \langle s_k(t) \rangle := N \langle s(t) \rangle \end{align} - where $N$ is the total number of particles in the sample volume $V$ - we can reduce $G(\tau)$ to the sum of individual molecular contributions \begin{align} G(\tau) = \frac{\langle S(t) \cdot S(t+\tau)\rangle}{\langle S(t) \rangle^2}-1 =& \frac{\overbrace{N \langle s(t) \cdot s(t+\tau)\rangle}^{\mbox{\small same particles}} +\overbrace{N(N-1) \langle s(t) \rangle^2}^{\mbox{\small different particles}}}{N^2 \langle s(t) \rangle^2}-1 \notag \\ \approx & \frac{\langle s(t) \cdot s(t+\tau)\rangle}{N \langle s(t) \rangle^2} \label{eq7} \end{align} Inserting \hyref{Equation}{eq5} and \hyref{Equation}{eq6} yields \begin{equation} \label{eq8} G(\tau) = \frac{V}{N} \frac{\iint W(\vec{r}) P_\mathrm{d} \left( \vec{r} \,' | \vec{r},\tau \right) W(\vec{r} \,') dVdV'}{\left( \int W(\vec{r}) \,dV \right)^2} \end{equation} Note that the molecular brightness $q$ cancels; when considering multiple species with different $q$, this is no longer the case. Solving these integrals for the confocal detection scheme yields a relatively simple equation containing the diffusion coefficient $D_t$ (molecular property) and the $1/e^2$ decay lengths $w_0$ and $z_0$ capturing the dimension of the Gaussian detection volume transversal and parallel to the optical axis, respectively (instrumental properties). \begin{equation} \label{eq9} G(\tau) = \frac{1}{n} \left(1+\frac{4 D_t \tau}{w_0^2} \right) ^{-1} \left(1+\frac{4D_t \tau}{z_0^2} \right)^{-1/2} \end{equation} The inverse intercept $(G(0))^{-1}$ is proportional to the total concentration of oberved particles $C = N/V = n/V_{\mathrm{eff}} = n/ (\pi^{3/2}w_0^2z_0)$. It is common to define the diffusion time $\tau_{\mathrm{diff}} = {w_0}^2/4D_t$ and the structural parameter $\textit{SP}=z_0^2/w_0^2$ as a measure of the elongated detection volume. Replacement finally yields the well known autocorrelation function for 3D diffusion in a confocal setup (Model ID 6012) \begin{equation} \label{eq10} G(\tau) \stackrel{\mathrm{def}}{=} G^{\mathrm{D}}(\tau) = \frac{1}{n} \overbrace{ \left(1+\frac{\tau}{\tau_{\mathrm{diff}}} \right) ^{-1}}^{\mathrm{2D}} \overbrace{ \left(1+\frac{\tau}{\textit{SP}^2 \, \tau_{\mathrm{diff}}} \right)^{-1/2}}^{\mathrm{3D}} \end{equation} For confocal FCS, both the detection volume $W(\vec{r})$ and the propagator for free diffusion $P_\mathrm{d}$ are described by exponentials (Gaussian functions). Therefore, spatial relationships can be factorized for each dimension $xyz$. As a result, \hyref{Equation}{eq10} can be written as a combination of transversal (2D) and longitudinal (3D) diffusion. \subsection{Autocorrelation of multiple species} \label{sec:theor.autoc} Very often in FCS, one observes more than one dynamic property. Besides diffusion driven number fluctuations, a fluorophore usually shows some kind of inherent blinking, due to triplet state transitions (organic dyes) or protonation dependent quenching (GFPs) \cite{Widengren1995}. \begin{equation} \label{eq11} G(\tau) \stackrel{\mathrm{def}}{=} G^{\mathrm{T}}(\tau) G^{\mathrm{D}}(\tau) = \left( 1+ \frac{T}{1-T} \exp\left[-\frac{\tau}{\tau_{\mathrm{trp}}} \right] \right)G^{\mathrm{D}}(\tau) \end{equation} Blinking increases the correlation amplitude $G(0)$ by the triplet fraction $1/(1-T)$. Accordingly, the average number of observed particles is decreased $n = (1-T)/G(0)$. In case of GFP blinking, two different blinking times have been described and the rate equations can get quite complicated. Besides photo-physics, the solution may contain mixtures of fluorescent particles with different dynamic properties, e.g. different mobility states or potential for transient binding. Such mixtures show several correlation times in the correlation curve. \hyref{Equation}{eq11} can be derived by considering the correlation functions of an ensemble, which can be built up by the contribution of $n$ single molecules in the sample volume: \begin{equation} \label{eq12} G(\tau) = \frac{g(\tau)}{\langle S(t) \rangle^2} = \frac{\sum_{i=1}^n \sum_{j=1}^n g_{ij}(\tau)}{\langle S(t) \rangle^2} \end{equation} with $g_{ij}(\tau)$ as the pairwise correlation function of identical ($i = j$) or distinguishable ($i \not= j$) particles \begin{equation} \label{eq13} g_{ij}(\tau) = \langle s(t) \cdot s(t + \tau) \rangle = \frac{q_iq_j}{V} \int \int W(\vec{r}) P_{\mathrm{d},ij} \left( \vec{r} \,' | \vec{r},\tau \right) W(\vec{r}\,') dVdV' \end{equation} Note that the diffusion propagator $P_{\mathrm{d},ij}$ is now indexed, since the movement of some particle pairs may depend on each other and therefore show correlations. If particle $i$ and particle $j$ move independently, the mixed terms cancel $g_{ij}(\tau) = 0$. Due to the sums in \hyref{Equation}{eq12}, adding up individual contributions of sub-ensembles is allowed. A frequently used expression to cover free diffusion of similarly labelled, differently sized particles is simply the sum of correlation functions, weighted with their relative fractions $F_k = n_k/n$ to the overall amplitude $G(0) = 1/n$: \begin{equation} \label{eq14} G^{\mathrm{D}}(\tau) = \sum_{k=1}^m F_k G^{\mathrm{D}}(\tau) = \frac{1}{n} \sum_{k=1}^m F_k \left(1+\frac{\tau}{\tau_{{\mathrm{diff}},k}} \right) ^{-1} \left(1+\frac{\tau}{\textit{SP}^2 \, \tau_{{\mathrm{diff}},k}} \right) \end{equation} Up to three diffusion times can usually be discriminated ($m = 3$) \cite{Meseth1999}. Note that this assumes homogenous molecular brightness of the different diffusion species. One of the molecular brightness values $q_k$ is usually taken as a reference ($\alpha_k = q_k/q_1$). Brighter particles are over-represented \cite{Thompson1991} \begin{equation} \label{eq15} G^{\mathrm{D}}(\tau) = \frac{1}{n \left( \sum_k F_k \alpha_k \right)^2} \sum_k F_k \alpha_k^2 G_k^D(\tau) \end{equation} Inhomogeneity in molecular brightness affects both the total concentration of observed particles as well as the real molar fractions $F_k^{\mathrm{cor}}$ \cite{Thompson1991} \begin{equation} \label{eq16} n = \frac{1}{G^{\mathrm{D}}(0)} \frac{\sum_k F_k^{\mathrm{cor}} \alpha_k^2}{\left( \sum_k F_k^{\mathrm{cor}} \alpha_k \right)^2} \quad\mbox {with} \quad F_k^{\mathrm{cor}} = \frac{F_k/\alpha_k^2}{\sum_k F_k/\alpha_k} \end{equation} \subsection{Correcting non-correlated background signal} \label{sec:theor.correc} In FCS, the total signal is composed of the fluorescence and the non-correlated background: $S = F + B$. Non-correlated background signal like shot noise of the detectors or stray light decreases the relative fluctuation amplitude and must be corrected to derive true particle concentrations \cite{Koppel1974,Thompson1991}. In \textit{PyCorrFit}, the background value [kHz] can be manually set for each channel (B1, B2) (\hyref{Figure}{fig:mainwin}). For autocorrelation measurements ($B1 = B2 = B$) the average number of observed particles is then \begin{equation} \label{eq17} n = \frac{1}{G^{\mathrm{D}}(0)} \left( \frac{S-B}{S} \right)^2 = \frac{1}{(1-T)G(0)} \left( \frac{S-B}{S} \right)^2. \end{equation} For dual-channel applications with cross-correlation (next section) the amplitudes must be corrected by contributions from each channel \cite{Weidemann2013} \begin{equation} \label{eq18} G_{\times,\mathrm{cor}}(0) = G_{\times, \mathrm{meas}}(0) \left( \frac{S_1}{S_1-B_1} \right) \left( \frac{S_2}{S_2-B_2} \right) \end{equation} \subsection{Cross-correlation} \label{sec:theor.cross} Cross-correlation is an elegant way to measure molecular interactions. The principle is to implement a dual-channel setup (e.g. channels 1 and 2), where two, interacting populations of molecules can be discriminated \cite{Foo2012,Ries2010,Schwille1997,Weidemann2002}. In a dual-channel setup, complexes containing particles with both properties will evoke simultaneous signals in both channels. Such coincidence events can be extracted by cross-correlation between the two channels. A prominent implementation is dual-colour fluorescence cross-correlation spectroscopy (dcFCCS), where the binding partners are discriminated by spectrally distinct (differently coloured) labels. The formalism is similar to autocorrelation, just the origin of the signals must now be traced \cite{Rippe2000,Weidemann2002,Schwille1997}. \begin{equation} \label{eq19} G_\times (\tau) \stackrel{\mathrm{def}}{=} G_{12} (\tau) = \frac{\langle \delta S_1(t) \delta S_2(t+\tau)\rangle}{\langle S_1(t) \rangle \langle S_2(t) \rangle} \approx \frac{\langle \delta S_2(t) \delta S_1(t+\tau)\rangle}{\langle S_1(t) \rangle \langle S_2(t) \rangle} = G_{21} (\tau) \end{equation} A finite cross-correlation amplitude $G_{12}(0)$ indicates co-diffusion of complexes containing both types of interaction partners. The increase of the cross-correlation amplitude is linear for heterotypic binding but non-linear for homotypic interactions or higher order oligomers. The absolute magnitude of the cross-correlation amplitude must be calibrated because the chromatic mismatch of the detection volumes (different wavelength, different size) and their spatial displacement ($d_\mathrm{x}$, $d_\mathrm{y}$, $d_\mathrm{z}$) constitute instrumental limits, even for 100\% double labelled particles \cite{Weidemann2002}. \hyref{Equation}{eq9} can be extended \begin{equation} \label{eq20} G(\tau) = \frac{1}{n} \left(1+\frac{4 D_t \tau}{w_0^2} \right) ^{-1} \left(1+\frac{4D_t \tau}{z_0^2} \right)^{-1/2} \exp \left(- \frac{d_\mathrm{x}^2 + d_\mathrm{y}^2}{4 D_t \tau + w_{0,\mathrm{eff}}} + \frac{d_\mathrm{z}^2}{4 D_t \tau + z_{0,\mathrm{eff}}} \right) \end{equation} The ratio between cross- and autocorrelation amplitude is used as a readout which can be linked to the degree of binding. Let us consider a heterodimerization, where channel $1$ is sensitive for green labelled particles ($g$) and channel $2$ is sensitive for red labelled particles ($r$), then the ratio of cross- and autocorrelation amplitudes is proportional to the fraction of ligand bound \cite{Weidemann2002} \begin{eqnarray} \label{eq21} CC_1 \stackrel{\mathrm{def}}{=} \frac{G_\times(0)}{G_1(0)} & \propto & \frac{c_{gr}}{c_r} \nonumber \\ CC_2 \stackrel{\mathrm{def}}{=} \frac{G_\times(0)}{G_2(0)} &\propto & \frac{c_{gr}}{c_g} \end{eqnarray} Recently, a correction for bleed-through of the signals between the two channels has been worked out \cite{Bacia2012}. The effect on binding curves measured with cross-correlation can be quite dramatic \cite{Weidemann2013}. To treat spectral cross-talk, the experimenter has to determine with single coloured probes how much of the signal (ratio in \%) is detected by the orthogonal, 'wrong' channel ($BT_{12}, BT_{12}$). Usually the bleed-through from the red into the green channel can be neglected ($BT_{21} = 0$) leaving only a contribution of bleed through from the green into the red channel ($BT_{12}$) \begin{eqnarray} \label{eq22} \langle F_1 \rangle & = & \langle \hat{F}_1 \rangle + \langle \hat{F}_2 \rangle BT_{21} \cong \langle \hat{F}_1 \rangle \nonumber \\ \langle F_2 \rangle & = & \langle \hat{F}_2 \rangle + \langle \hat{F}_1 \rangle BT_{12} \end{eqnarray} Here, the dashed fluorescence signals are the true contributions from single labelled species. Thus, each set of simultaneously recorded auto and cross-correlation curves suffers from a specific fraction of wrong signal in the vulnerable red channel, $X_2 = BT_{12} \langle \hat{F}_1 \rangle/\hat{F}_2 \rangle$, which can be used to back-correct the cross-correlation amplitudes \cite{Bacia2012,Weidemann2013} \begin{subequations} \label{eq23} \begin{align} \frac{c_{gr}}{c_r} & \propto \frac{CC_1-X_2}{\left( 1-X_2 \right)} \label{eq23a} \\ \frac{c_{gr}}{c_g} & \propto \frac{CC_2-X_2 \left( 1-X_2 \right) \frac{G_1^{\mathrm{D}}(0)}{G_2^{\mathrm{D}}(0)}}{1+ X_2 \frac{G_1^{\mathrm{D}}(0)}{G_2^{\mathrm{D}}(0)} - 2 X_2 CC_2} \label{eq23b} \end{align} \end{subequations} As apparent from \hyref{Equations}{eq23}, it is much simpler to use the autocorrelation amplitude measured in the green channel for normalization (\ref{eq23a}) and not the cross-talk affected red channel (\ref{eq23b}). Finally, the proportionality between the fraction ligand bound and the measured cross-correlation ratio depend solely on the effective detection volumes of all three channels (two auto- and the cross-correlation channels) and must be determined with appropriate positive controls (single labelled and double labelled calibration dyes). \subsection{Extensions of the method} \label{sec:theor.exten} Using as confocal alignment for FCS measurements is very successful and widely applied. However, mainly in the context of membrane research, other excitation schemes have been explored. Once the excitation and detection geometry is changed one has to account for it in the model functions. This is usually done by modifying the expressions of the instrumental detection efficiency $W(\vec{r})$ in \hyref{Equation}{eq8}. However, in many cases analytical solutions to the above integrals are not straightforward and approximations have to be made. The following section introduces model functions for different detection symmetries and particle dynamics. \subsubsection{Perpendicular scanning FCS (pSFCS)} \label{sec:theor.exten.perpe} Scanning FCS with a scan path perpendicular to the membrane plane was introduced to measure the lateral diffusion of fluorescent components of a fluid lipid membrane \cite{Ries2006}. Using the linear scan option of a confocal laser scanning microscope (CLSM), the focal spot is moved repeatedly on a straight line through a free standing membrane (typically the equatorial face of a giant vesicle). The fluorescence photons emitted from the detection volume are continuously recorded and stored. The signal originating from the transit through the membrane must be extracted post-measurement, correlated, and evaluated by well-known FCS model functions. Scanning can also be performed with two parallel scan paths perpendicular through the membrane (continuous imaging with two lines). Like in two-focus FCS \cite{Dertinger2007}, the distance between these scan paths can been used to calibrate the diffusion coefficients without further use of diffusion standards. We recently introduced a free, open source software tool (\textit{PyScanFCS}) to perform these steps \cite{Muller2014}. \subsubsection{Circular scanning FCS (cSFCS)} \label{sec:theor.exten.circu} The principle of circular scanning FCS is similar. Here, the laser focus is moved in small circles, several µm in diameter. While pSFCS requires relatively large free standing membranes, cSFCS can be performed in µm sized homogeneous fluorescent regions of supported bilayers, cell membranes, as well as the apical poles of giant vesicles. Once the radius of the circular scan path has been accurately determined (e.g. with a fluorescent grid), the diffusion coefficients can be determined from cross-correlation curves without the need to calibrate the detection volume by diffusion standards \cite{Petrasek2010,Petrasek2008} (See \hyref{Figure}{fig:csfcsplot}). \begin{figure}[h] \centering \includegraphics[width=\linewidth]{PyCorrFit_Screenshot_CSFCS.png} \mycaption{user interface with external model function}{cSFCS curve of DiO diffusing in a reconstituted lipid bilayer on glass support. Fitting yields a diffusion coefficient of \SI{0.28}{\mu m^2s^{-1}} ($F1=1$, so only one component is fitted). The source code of the external model function for this fit is shown in \hyref{Figure}{fig:csfcs}.\label{fig:csfcsplot}} \end{figure} \vspace{1em} \subsubsection{Total internal reflection FCS (TIR-FCS)} \label{sec:theor.exten.total} TIR-FCS was developed to measure transient ligand binding events to receptors in the membrane \cite{Thompson2007}. In contrast to scanning FCS, the detection volume is fixed. However, the geometry is confined to the surface showing an exponential decay of the evanescent field along $z$, whereas the lateral boundaries imposed in $xy$ by a pinhole in the detection path. The situation is notoriously difficult to model and different solutions have been proposed. \paragraph{TIR-FCS with Gaussian-shaped lateral detection volume.} The detection volume is axially confined by an evanescent field and has an effective size of \begin{align} V = \pi R_0^2 d_\mathrm{eva} \end{align} where $R_0$ is the lateral extent of the detection volume and $d_\mathrm{eva}$ is the evanescent field depth\footnote{Where the field has decayed to $1/e$}. From the concentration $C$, the effective number of particles is $n = CV$. The decay constant $\kappa$ is the inverse of the depth $d_\mathrm{eva}$ : \begin{align} d_\mathrm{eva} = \frac{1}{\kappa} \end{align} \paragraph{TIR-FCS with a square-shaped lateral detection volume.} The detection volume is axially confined by an evanescent field of depth $d_\mathrm{eva} = 1 / \kappa$. The lateral detection area is a convolution of the point spread function of the microscope of size $\sigma$, \begin{align} \sigma = \sigma_0 \frac{\lambda}{\mathit{NA}}, \end{align} with a square of side length $a$. \subsection{Fitting} \label{sec:theor.nonle} One can define a distance $d(G,H)$ between two discrete functions $G$ and $H$ with the discrete domain of definition $\tau_1 \dots \tau_n$ as the sum of squares: \begin{equation} d(G,H) = \sum_{i=1}^n \left[ G(\tau_i) - H(\tau_i) \right]^2 \end{equation} The least-squares method minimizes this distance between the model function $G$ and the experimental values $H$ by modifying $k$ additional fitting parameters $\alpha_1, \dots, \alpha_k$: \begin{equation} \chi^2 = \min_{\alpha_1, \dots, \alpha_k} \sum_{i=1}^n \left[ G(\tau_i,\alpha_1, \dots, \alpha_k) - H(\tau_i) \right]^2 \end{equation} The minimum distance $\chi^2$ is used to characterize the success of a fit. Note, that if the number of fitting parameters $k$ becomes too large, multiple values for $\chi^2$ can be found, depending on the starting values of the $k$ parameters. \subsubsection{Weighted fitting} \label{sec:theor.weigh} In certain cases, it is useful to perform weighted fitting with a known variance $\sigma_i^2$ at the data points $\tau_i$. In \textit{PyCorrFit}, weighted fitting is implemented as follows: \begin{equation} \chi^2_\mathrm{weighted} = \min_{\alpha_1, \dots, \alpha_k} \sum_{i=1}^n \frac{\left[ G(\tau_i,\alpha_1, \dots, \alpha_k) - H(\tau_i) \right]^2}{\sigma_i^2} \end{equation} Besides importing the variance alongside experimental data, \textit{PyCorrFit} is able to estimate the variance from the experimental data via several different approaches. A recommended approach is averaging over several curves. Other approaches such as estimation of the variance from spline fits or from the model function (see \hyref{Section}{sec:intro.graph}) cannot be considered unbiased. Note that when performing global fits (see \hyref{Section}{sec:menub.tools.globa}), different types of weights for different correlation curves can strongly influence the result of the fit. Especially mixing curves with and without weights will most likely result in unphysical fits. \subsubsection{Displayed $\chi^2$ values} The displayed value of $\chi^2$ is defined by the type of the performed fit. This value is commonly normalized by the degrees of freedom $\nu = N - n - 1$, where $N$ is the number of observations (data points) and $n$ is the number of fitting parameters. \begin{itemize} \item \textbf{reduced expected sum of squares}: This value is used when there is no variance available for plot normalization. \begin{equation} \chi^2_\mathrm{red,exp} = \frac{1}{\nu}\sum_{i=1}^n \frac{\left[ G(\tau_i,\alpha_\mathrm{min}) - H(\tau_i) \right]^2}{G(\tau_i,\alpha_\mathrm{min})} \end{equation} \item \textbf{reduced weighted sum of squares}: This value is used when the fit was performed with variances $\sigma^2$. \begin{equation} \chi^2_\mathrm{red,weight} = \frac{1}{\nu}\sum_{i=1}^n \frac{\left[ G(\tau_i,\alpha_\mathrm{min}) - H(\tau_i) \right]^2}{\sigma^2} \end{equation} \item \textbf{reduced global sum of squares}: This value is used for global fits. The weights are computed identically to the situation with reduced weights, except that the variance $\sigma_\textrm{glob}^2$ may result in non-physical weighting (hence the emphasis on global). \begin{equation} \chi^2_\mathrm{red,weight} = \frac{1}{\nu}\sum_{i=1}^n \frac{\left[ G(\tau_i,\alpha_\mathrm{min}) - H(\tau_i) \right]^2}{\sigma_\textrm{glob}^2} \end{equation} \end{itemize} \subsubsection{Algorithms} \label{sec:theor.alg} \textit{PyCorrFit} uses the non-linear least-squares fitting capabilities from \texttt{scipy.optimize}. This package contains several algorithms to minimize the sum of the squares. PyCorrFit can utilize several algorithms to perform this minimization. The descriptions of the algorithms listed here are partly copied from the scipy documentation at \url{http://docs.scipy.org/doc/scipy/reference/optimize.html}. \begin{itemize} \item The \textbf{BFGS} method uses the quasi-Newton method of Broyden, Fletcher, Goldfarb, and Shanno (BFGS) \cite{Nocedal2006} (pp. 136). It uses the first derivatives only. BFGS has proven good performance even for non-smooth optimizations. \item The \textbf{Levenberg-Marquardt} algorithm \cite{Levenberg1944} uses the first derivatives and combines the Gauss–Newton algorithm with a trust region approach. It is very robust compared to other algorithms and it is very popular in curve-fitting. \textit{PyCorrFit} uses this algorithm by default. If this algorithm is used, \textit{PyCorrFit} can estimate an error of the fit parameters using the covariance matrix. \item The \textbf{Nelder-Mead} method uses the Simplex algorithm \cite{Nelder1965,Wright1996}. This algorithm has been successful in many applications but other algorithms using the first and/or second derivatives information might be preferred for their better performances and robustness in general. \item The method \textbf{Powell} is a modification of Powell's method \cite{Powell1964, Press} which is a conjugate direction method. It performs sequential one-dimensional minimizations along each vector of the directions set, which is updated at each iteration of the main minimization loop. The function need not be differentiable, and no derivatives are taken. \item \textbf{Sequential Linear Squares Programming} inherently accepts boundaries and thus might behave better than other algorithms for problems with bounded parameters. \end{itemize} \input{PyCorrFit_doc_models} \section{Troubleshooting} If you are having problems with PyCorrFit, you might find the solution in the frequently asked questions\footnote{\url{https://github.com/FCS-analysis/PyCorrFit/wiki/Frequently-Asked-Questions-\%28FAQ\%29}} or on other pages in the \textit{PyCorrFit} wiki\footnote{\url{https://github.com/FCS-analysis/PyCorrFit/wiki}}. There you will also find instructions on how to contact us to file a bug or to request a feature. PyCorrFit-0.9.9/doc/PyCorrFit_doc_models.tex000066400000000000000000000420421274265304700207660ustar00rootroot00000000000000\section{Implemented model functions} \label{sec:imple} This is an overview of all the model functions that are implemented in \textit{PyCorrFit}. To each model a unique model ID is assigned. Most of the following information is also accessible from within \textit{PyCorrFit} using the \textit{Page info} tool. \subsection{Confocal FCS} \label{sec:imple.confo} % 2D diffusion %\noindent \begin{tabular}{lp{.7\textwidth}} %Name & \textbf{2D (Gauß)} \\ %ID & \textbf{6001} \\ %Descr. & Two-dimensional diffusion with a Gaussian laser profile\cite{Aragon1976, Qian1991, Rigler1993}. \\ %\end{tabular} %\begin{align} %G(\tau) = A_0 + \frac{1}{N} \frac{1}{(1+\tau/\tau_\mathrm{diff})} %\end{align} %\begin{center} %\begin{tabular}{ll} %$A_0$ & Offset \\ %$N$ & Effective number of particles in confocal area \\ %$\tau_\mathrm{diff}$ & Characteristic residence time in confocal area \\ %\end{tabular} \\ %\end{center} %\vspace{2em} % 3D diffusion %\noindent \begin{tabular}{lp{.7\textwidth}} %Name & \textbf{3D (Gauß)} \\ %ID & \textbf{6012} \\ %Descr. & Three-dimensional free diffusion with a Gaussian laser profile (eliptical)\cite{Aragon1976, Qian1991, Rigler1993}. \\ %\end{tabular} %\begin{align} %G(\tau) = A_0 + \frac{1}{N} \frac{1}{(1+\tau/\tau_\mathrm{diff})} \frac{1}{\sqrt{1+\tau/(\mathit{SP}^2 \tau_\mathrm{diff})}} %\end{align} %\begin{center} %\begin{tabular}{ll} %$A_0$ & Offset \\ %$N$ & Effective number of particles in confocal volume \\ %$\tau_\mathrm{diff}$ & Characteristic residence time in confocal volume \\ %$\mathit{SP}$ & Structural parameter, describes elongation of the confocal volume \\ %\end{tabular} %\end{center} %\vspace{2em} % 3D diffusion + triplet \noindent \begin{tabular}{lp{.7\textwidth}} Name & \textbf{Confocal (Gaussian) T+3D} \\ ID & \textbf{6011} \\ Descr. & Three-dimensional free diffusion with a Gaussian laser profile (eliptical), including a triplet component\cite{Widengren1994, Widengren1995, Haupts1998}. \\ \end{tabular} \begin{align} G(\tau) = A_0 + \frac{1}{n} \frac{1}{(1+\tau/\tau_\mathrm{diff})} \frac{1}{\sqrt{1+\tau/(\mathit{SP}^2 \tau_\mathrm{ diff})}} \left(1 + \frac{T e^{-\tau/\tau_\mathrm{trip}}}{1-T} \right) \end{align} \begin{center} \begin{tabular}{ll} $A_0$ & Offset \\ $n$ & Effective number of particles in confocal volume \\ $\tau_\mathrm{diff}$ & Characteristic residence time in confocal volume \\ $\mathit{SP}$ & Structural parameter, describes elongation of the confocal volume \\ $T$ & Fraction of particles in triplet (non-fluorescent) state\\ $\tau_\mathrm{trip}$ & Characteristic residence time in triplet \\ \end{tabular} \end{center} \vspace{2em} % 3D+3D diffusion + triplett \noindent \begin{tabular}{lp{.7\textwidth}} Name & \textbf{Confocal (Gaussian) T+3D+3D} \\ ID & \textbf{6030} \\ Descr. & Two-component three-dimensional free diffusion with a Gaussian laser profile, including a triplet component\cite{Elson1974, Aragon1976, Palmer1987}. \\ \end{tabular} \begin{align} G(\tau) &= A_0 + \frac{1}{n (F + \alpha (1-F))²} \left(1 + \frac{T e^{-\tau/\tau_\mathrm{trip}}}{1-T} \right) \times \\ \notag &\times \left[ \frac{F}{(1+\tau/\tau_1)} \frac{1}{\sqrt{1+\tau/(\mathit{SP}^2 \tau_1)}} + \alpha^2 \frac{1-F}{ (1+\tau/\tau_2) } \frac{1}{\sqrt{1+\tau/(\mathit{SP}^2 \tau_2)}} \right] \end{align} \begin{center} \begin{tabular}{ll} $A_0$ & Offset \\ $n$ & Effective number of particles in confocal volume ($n = n_1+n_2$) \\ $\tau_1$ & Diffusion time of particle species 1 \\ $\tau_2$ & Diffusion time of particle species 2 \\ $F$ & Fraction of molecules of species 1 ($n_1 = F n$) \\ $\alpha$ & Relative molecular brightness of particles 1 and 2 ($ \alpha = q_2/q_1$) \\ $\mathit{SP}$ & Structural parameter, describes elongation of the confocal volume \\ $T$ & Fraction of particles in triplet (non-fluorescent) state\\ $\tau_\mathrm{trip}$ & Characteristic residence time in triplet state \\ \end{tabular} \end{center} \vspace{2em} % 2D diffusion + triplett \noindent \begin{tabular}{lp{.7\textwidth}} Name & \textbf{Confocal (Gaussian) T+2D} \\ ID & \textbf{6002} \\ Descr. & Two-dimensional diffusion with a Gaussian laser profile, including a triplet component\cite{Aragon1976, Qian1991, Rigler1993,Widengren1994, Widengren1995, Haupts1998}. \\ \end{tabular} \begin{align} G(\tau) = A_0 + \frac{1}{n} \frac{1}{(1+\tau/\tau_\mathrm{diff})} \left(1 + \frac{T e^{-\tau/\tau_\mathrm{trip}}}{1-T} \right) \end{align} \begin{center} \begin{tabular}{ll} $A_0$ & Offset \\ $n$ & Effective number of particles in confocal area \\ $\tau_\mathrm{diff}$ & Characteristic residence time in confocal area \\ $T$ & Fraction of particles in triplet (non-fluorescent) state\\ $\tau_\mathrm{trip}$ & Characteristic residence time in triplet state \\ \end{tabular} \end{center} \vspace{2em} % 2D+2D diffusion + triplett \noindent \begin{tabular}{lp{.7\textwidth}} Name & \textbf{Confocal (Gaussian) T+2D+2D} \\ ID & \textbf{6031} \\ Descr. & Two-component, two-dimensional diffusion with a Gaussian laser profile, including a triplet component\cite{Elson1974, Aragon1976, Palmer1987}. \\ \end{tabular} \begin{align} G(\tau) = A_0 + \frac{1}{n (F + \alpha (1-F))²} \left[ \frac{F}{1+\tau/\tau_1} + \alpha^2 \frac{1-F}{ 1+\tau/\tau_2 } \right] \left(1 + \frac{T e^{-\tau/\tau_\mathrm{trip}}}{1-T} \right) \end{align} \begin{center} \begin{tabular}{ll} $A_0$ & Offset \\ $n$ & Effective number of particles in confocal area ($n = n_1+n_2$) \\ $\tau_1$ & Diffusion time of particle species 1 \\ $\tau_2$ & Diffusion time of particle species 2 \\ $F$ & Fraction of molecules of species 1 ($n_1 = F n$) \\ $\alpha$ & Relative molecular brightness of particles 1 and 2 ($ \alpha = q_2/q_1$) \\ $T$ & Fraction of particles in triplet (non-fluorescent) state\\ $\tau_\mathrm{trip}$ & Characteristic residence time in triplet state \\ \end{tabular} \end{center} \vspace{2em} % 3D+2D diffusion + triplett \noindent \begin{tabular}{lp{.7\textwidth}} Name & \textbf{Confocal (Gaussian) T+3D+2D} \\ ID & \textbf{6032} \\ Descr. & Two-component, two- and three-dimensional diffusion with a Gaussian laser profile, including a triplet component\cite{Elson1974, Aragon1976, Palmer1987}. \\ \end{tabular} \begin{align} G(\tau) = A_0 + \frac{1}{n (1 - F + \alpha F)²} \left[ \frac{1-F}{1+\tau/\tau_\mathrm{2D}} + \frac{ \alpha^2 F}{ (1+\tau/\tau_\mathrm{3D}) } \frac{1}{\sqrt{1+\tau/(\mathit{SP}^2 \tau_\mathrm{3D})}} \right] \left(1 + \frac{T e^{-\tau/\tau_\mathrm{trip}}}{1-T} \right) \end{align} \begin{center} \begin{tabular}{ll} $A_0$ & Offset \\ $n$ & Effective number of particles in confocal volume ($n = n_\mathrm{2D}+n_\mathrm{3D}$) \\ $\tau_\mathrm{2D}$ & Diffusion time of surface bound particles \\ $\tau_\mathrm{3D}$ & Diffusion time of freely diffusing particles \\ $F$ & Fraction of molecules of the freely diffusing species ($n_\mathrm{3D} = F n$) \\ $\alpha$ & Relative molecular brightness of particle species ($ \alpha = q_\mathrm{3D}/q_\mathrm{2D}$) \\ $\mathit{SP}$ & Structural parameter, describes elongation of the confocal volume \\ $T$ & Fraction of particles in triplet (non-fluorescent) state\\ $\tau_\mathrm{trip}$ & Characteristic residence time in triplet state \\ \end{tabular} \end{center} \vspace{2em} % 3D+3D diffusion + T+T \noindent \begin{tabular}{lp{.7\textwidth}} Name & \textbf{Confocal (Gaussian) T+T+3D+3D} \\ ID & \textbf{6043} \\ Descr. & Two-component three-dimensional free diffusion with a Gaussian laser profile, including two triplet components. The correlation function is a superposition of three-dimensional model functions of the type \textbf{T+3D} (6011). \end{tabular} \vspace{2em} \subsection{TIR-FCS} \label{sec:imple.tirfc} The model functions make use of the Faddeeva function (complex error function)\footnote{In user-defined model functions (\hyref{Section}{sec:hacke.extmod}), the Faddeeva function is accessible through \texttt{wofz()}. For convenience, the function \texttt{wixi()} can be used which only takes $\xi$ as an argument and the imaginary $i$ can be omitted.}: \begin{align} w\!(i\xi) &= e^{\xi^2} \mathrm{erfc}(\xi) \\ \notag &= e^{\xi^2} \cdot \frac{2}{\sqrt{\pi}} \int_\xi^\infty \mathrm{e}^{-\alpha^2} \mathrm{d\alpha} \label{eq:faddeeva} \end{align} The lateral detection area has the same shape as in confocal FCS. Thus, correlation functions for two-dimensional diffusion of the confocal case apply and are not mentioned here. \subsubsection{TIR-FCS with Gaussian-shaped lateral detection volume} % 3D diffusion (Gauß/exp) \noindent \begin{tabular}{lp{.7\textwidth}} Name & \textbf{TIR (Gaussian/Exp.) T+3D} \\ ID & \textbf{6014} \\ Descr. & Three-dimensional free diffusion with a Gaussian lateral detection profile and an exponentially decaying profile in axial direction, including a triplet component\cite{Starr2001, Hassler2005, Ohsugi2006}. \\ \end{tabular} \begin{align} G(\tau) = \frac{1}{C} \frac{ \kappa^2}{ \pi (R_0^2 +4D\tau)} \left(1 + \frac{T e^{-\tau/\tau_\mathrm{trip}}}{1-T} \right) \left( \sqrt{\frac{D \tau}{\pi}} + \frac{1 - 2 D \tau \kappa^2}{2 \kappa} w\!\left(i \sqrt{D \tau} \kappa\right) \right) \end{align} \begin{center} \begin{tabular}{ll} $C$ & Particle concentration in confocal volume \\ $\kappa$ & Evanescent decay constant ($\kappa = 1/d_\mathrm{eva}$)\\ $R_0$ & Lateral extent of the detection volume \\ $D$ & Diffusion coefficient \\ $T$ & Fraction of particles in triplet (non-fluorescent) state\\ $\tau_\mathrm{trip}$ & Characteristic residence time in triplet state \\ \end{tabular} \end{center} \vspace{2em} % 3D+3D+T diffusion (Gauß/exp) \noindent \begin{tabular}{lp{.7\textwidth}} Name & \textbf{TIR (Gaussian/Exp.) T+3D+3D} \\ ID & \textbf{6034} \\ Descr. & Two-component three-dimensional diffusion with a Gaussian lateral detection profile and an exponentially decaying profile in axial direction, including a triplet component\cite{Starr2001, Hassler2005, Ohsugi2006}. \\ \end{tabular} \begin{align} G(\tau) = &A_0 + \frac{1}{n (1-F + \alpha F)^2} \left(1 + \frac{T e^{-\tau/\tau_\mathrm{trip}}}{1-T} \right) \times \\ \notag \times \Bigg[ \,\, & \frac{F \kappa}{1+ 4 D_1 \tau/R_0^2} \left( \sqrt{\frac{D_1 \tau}{\pi}} + \frac{1 - 2 D_1 \tau \kappa^2}{2 \kappa} w\!\left(i \sqrt{D_1 \tau} \kappa\right) \right) + \\ \notag + & \frac{(1-F) \alpha^2 \kappa}{1+ 4 D_2 \tau/R_0^2} \left( \sqrt{\frac{D_2 \tau}{\pi}} + \frac{1 - 2 D_2 \tau \kappa^2}{2 \kappa} w\!\left(i \sqrt{D_2 \tau} \kappa\right) \right) \,\, \Bigg] \end{align} \begin{center} \begin{tabular}{ll} $A_0$ & Offset \\ $n$ & Effective number of particles in confocal volume ($n = n_1+n_2$) \\ $D_1$ & Diffusion coefficient of species 1 \\ $D_2$ & Diffusion coefficient of species 2 \\ $F$ & Fraction of molecules of species 1 ($n_1 = F n$) \\ $\alpha$ & Relative molecular brightness of particle species ($ \alpha = q_2/q_1$) \\ $R_0$ & Lateral extent of the detection volume \\ $\kappa$ & Evanescent decay constant ($\kappa = 1/d_\mathrm{eva}$)\\ $T$ & Fraction of particles in triplet (non-fluorescent) state\\ $\tau_\mathrm{trip}$ & Characteristic residence time in triplet state \\ \end{tabular} \end{center} \vspace{2em} % 2D+3D+T diffusion (Gauß/exp) \noindent \begin{tabular}{lp{.7\textwidth}} Name & \textbf{TIR (Gaussian/Exp.) T+3D+2D} \\ ID & \textbf{6033} \\ Descr. & Two-component, two- and three-dimensional diffusion with a Gaussian lateral detection profile and an exponentially decaying profile in axial direction, including a triplet component\cite{Starr2001, Hassler2005, Ohsugi2006}. \\ \end{tabular} \begin{align} G(\tau) &= A_0 + \frac{1}{n (1-F + \alpha F)^2} \left(1 + \frac{T e^{-\tau/\tau_\mathrm{trip}}}{1-T} \right) \times \\ & \notag \times \left[ \frac{1-F}{1+ 4 D_\mathrm{2D} \tau/R_0^2} + \frac{\alpha^2 F \kappa}{1+ 4 D_\mathrm{3D} \tau/R_0^2} \left( \sqrt{\frac{D_\mathrm{3D} \tau}{\pi}} + \frac{1 - 2 D_\mathrm{3D} \tau \kappa^2}{2 \kappa} w\!\left(i \sqrt{D_\mathrm{3D} \tau} \kappa\right) \right) \right] \end{align} \begin{center} \begin{tabular}{ll} $A_0$ & Offset \\ $n$ & Effective number of particles in confocal volume ($n = n_\mathrm{2D}+n_\mathrm{3D}$) \\ $D_\mathrm{2D}$ & Diffusion coefficient of surface bound particles \\ $D_\mathrm{3D}$ & Diffusion coefficient of freely diffusing particles \\ $F$ & Fraction of molecules of the freely diffusing species ($n_\mathrm{3D} = F n$) \\ $\alpha$ & Relative molecular brightness of particle species ($ \alpha = q_\mathrm{3D}/q_\mathrm{2D}$) \\ $R_0$ & Lateral extent of the detection volume \\ $\kappa$ & Evanescent decay constant ($\kappa = 1/d_\mathrm{eva}$)\\ $T$ & Fraction of particles in triplet (non-fluorescent) state\\ $\tau_\mathrm{trip}$ & Characteristic residence time in triplet state \\ \end{tabular} \end{center} \vspace{2em} \subsubsection{TIR-FCS with a square-shaped lateral detection volume} % 3D TIRF diffusion (□xσ) \noindent \begin{tabular}{lp{.7\textwidth}} Name & \textbf{TIR (□x$\upsigma$/Exp.) 3D} \\ ID & \textbf{6010} \\ Descr. & Three-dimensional diffusion with a square-shaped lateral detection area taking into account the size of the point spread function; and an exponential decaying profile in axial direction\cite{Ries2008, Yordanov2011}. \\ \end{tabular} \begin{align} G(\tau) = \frac{\kappa^2}{C} & \left( \sqrt{\frac{D \tau}{\pi}} + \frac{1 - 2 D \tau \kappa^2)}{2 \kappa} w\!\left(i \sqrt{D \tau} \kappa\right) \right) \times \\ \notag \times \Bigg[ & \frac{2 \sqrt{\sigma^2+D \tau}}{\sqrt{\pi} a^2} \left( \exp\left(-\frac{a^2}{4(\sigma^2+D \tau)}\right) - 1 \right) + \frac{1}{a} \, \mathrm{erf}\left(\frac{a}{2 \sqrt{\sigma^2+D \tau}}\right) \Bigg]^2 \end{align} \begin{center} \begin{tabular}{ll} $C$ & Particle concentration in detection volume \\ $\sigma$ & Lateral size of the point spread function \\ $a$ & Side size of the square-shaped detection area \\ $\kappa$ & Evanescent decay constant ($\kappa = 1/d_\mathrm{eva}$)\\ $D$ & Diffusion coefficient \\ \end{tabular} \\ \end{center} \vspace{2em} % 3D+3D TIRF diffusion (□xσ) \noindent \begin{tabular}{lp{.7\textwidth}} Name & \textbf{TIR (□x$\upsigma$/Exp.) 3D+3D} \\ ID & \textbf{6023} \\ Descr. & Two-component three-dimensional free diffusion with a square-shaped lateral detection area taking into account the size of the point spread function; and an exponential decaying profile in axial direction. \newline The correlation function is a superposition of three-dimensional model functions of the type \textbf{3D (□x$\upsigma$)} (6010)\cite{Ries2008, Yordanov2011}. \\ \end{tabular} \vspace{2em} % 2D TIRF diffusion (□xσ) \noindent \begin{tabular}{lp{.7\textwidth}} Name & \textbf{TIR (□x$\upsigma$) 2D} \\ ID & \textbf{6000} \\ Descr. & Two-dimensional diffusion with a square-shaped lateral detection area taking into account the size of the point spread function\cite{Ries2008, Yordanov2011}\footnote{The reader is made aware, that reference \cite{Ries2008} contains several unfortunate misprints.}. \\ \end{tabular} \begin{align} G(\tau) = \frac{1}{C} \left[ \frac{2 \sqrt{\sigma^2+D \tau}}{\sqrt{\pi} a^2} \left( \exp\left(-\frac{a^2}{4(\sigma^2+D \tau)}\right) - 1 \right) + \frac{1}{a} \, \mathrm{erf}\left(\frac{a}{2 \sqrt{\sigma^2+D \tau}}\right) \right]^2 \end{align} \begin{center} \begin{tabular}{ll} $C$ & Particle concentration in detection area \\ $\sigma$ & Lateral size of the point spread function \\ $a$ & Side size of the square-shaped detection area \\ $D$ & Diffusion coefficient \\ \end{tabular} \\ \end{center} \vspace{2em} % 2D+2D TIRF diffusion (□xσ) \noindent \begin{tabular}{lp{.7\textwidth}} Name & \textbf{TIR (□x$\upsigma$) 2D+2D} \\ ID & \textbf{6022} \\ Descr. & Two-component two-dimensional diffusion with a square-shaped lateral detection area taking into account the size of the point spread function. \newline The correlation function is a superposition of two-dimensional model functions of the type \textbf{2D (□x$\upsigma$)} (6000)\cite{Ries2008, Yordanov2011}. \\ \end{tabular} \vspace{2em} % 3D+2D TIRF diffusion (□xσ) \noindent \begin{tabular}{lp{.7\textwidth}} Name & \textbf{TIR (□x$\upsigma$/Exp.) 3D+2D} \\ ID & \textbf{6020} \\ Descr. & Two-component two- and three-dimensional diffusion with a square-shaped lateral detection area taking into account the size of the point spread function; and an exponential decaying profile in axial direction. \newline The correlation function is a superposition of the two-dimensional model function \textbf{2D (□x$\upsigma$)} (6000) and the three-dimensional model function \textbf{3D (□x$\upsigma$)} (6010)\cite{Ries2008, Yordanov2011}. \end{tabular} \vspace{2em} % 3D+2D+kin TIRF diffusion (□xσ) \noindent \begin{tabular}{lp{.7\textwidth}} Name & \textbf{TIR (□x$\upsigma$/Exp.) 3D+2D+kin} \\ ID & \textbf{6021} \\ Descr. & Two-component two- and three-dimensional diffusion with a square-shaped lateral detection area taking into account the size of the point spread function; and an exponential decaying profile in axial direction. This model covers binding and unbinding kintetics. \newline The correlation function for this model was introduced in \cite{Ries2008}. Because approximations are made in the derivation, please verify if this model is applicable to your problem before using it. \end{tabular} PyCorrFit-0.9.9/doc/README.md000066400000000000000000000012611274265304700154500ustar00rootroot00000000000000This folder contains the TeX-source of the [PyCorrFit documentation](https://github.com/FCS-analysis/PyCorrFit/wiki/PyCorrFit_doc.pdf). If, for some reason, you wish to compile it yourself, you will need a working LaTeX distribution. If you are running a Linux system, make sure that the following packages are installed: ghostscript texlive texlive-base texlive-bibtex-extra texlive-latex-extra texlive-math-extra texlive-science Apply these commands repeatedly (3 times to be on the safe side) to build the documentation: pdflatex -synctex=1 -interaction=nonstopmode PyCorrFit_doc.tex bibtex PyCorrFit_doc.aux PyCorrFit-0.9.9/examples/000077500000000000000000000000001274265304700152425ustar00rootroot00000000000000PyCorrFit-0.9.9/examples/external_model_functions/000077500000000000000000000000001274265304700223345ustar00rootroot00000000000000PyCorrFit-0.9.9/examples/external_model_functions/ExampleFunc_CS_2D+2D+S+T.txt000077500000000000000000000057371274265304700270730ustar00rootroot00000000000000# CS-FCS 2D+2D+S+T (Confocal) # Circular Scanninf FCS model function for two 2D-diffusing species # including triplet component. # Further reading: # Precise Measurement of Diffusion Coefficients using Scanning # Fluorescence Correlation Spectroscopy # Petrasek and Schwille, BiophysJ 2008, 1437-1448 # http://dx.doi.org/10.1529/biophysj.107.108811 # Visit http://fcstools.dyndns.org/pyscanfcs/ for more information. # The first line of this file will be treated as the name of the model # inside PyCorrFit. PyCorrFit will enumerate user imported models with IDs # starting at 7001. You can save a session and the user defined models # like this one will be saved as well. Lines starting with a hash "#" # are treated as comments. Empty lines and lines with only white space # characters are ignored. # Note that if your code does not work, it might be that some variables # have other meaning. This includes using "n" instead of "N". # If you get some Syntax Error it might be that your starting variables # are not set to a reasonable starting function. PyCorrFit is testing the # function with sympy (for safety) and calculates the function for # different values of tau. ## Definition of parameters: # First, define the parameters and their starting values for you model # function. If the parameter has a unit of measurement, then it may be # added separated by a white space before the "=" sign. The starting # value should be a floating point number. You may use abbreviations # like "1e-3" instead of "0.001". # Note that PyCorrFit has it's own unit system: # unit of time : 1 ms # unit of inverse time: 1000 /s # unit of distance : 100 nm # unit of diff.coeff : 10 µm²/s # unit of inverse area: 100 /µm² # unit of inv. volume : 1000 /µm³ # Diffusion coefficient of first component D1 [10 µm²/s] = 200.0 # Diffusion coefficient of second component D2 [10 µm²/s] = 20.0 # Fraction of species One F1 = 1.0 # Half waist of the lateral detection area (w0 = 2*a) a [100 nm] = 1.0 # Particle number n = 5.0 # Scan radius R [100 nm] = 3.850 # Frequency f [kHz] = .2 # Triplet fraction T = 0.1 # Triplet time tautrip [ms] = 0.001 # You may choose to substitute certain parts of the correlation function # with other values for easy reading. This can be done by using the # prefix "g". You may use all common mathematical functions, # such as "sqrt()" or "exp()". For convenience, "pi" and "e" may also # be used. If you are paranoid, you always use float numbers with a dot "." # to be sure the program doesn't accidently do integer division. gTriplet = 1. + T/(1-T)*exp(-tau/tautrip) gScan1 = exp(-(R*sin(pi*f*tau))**2/(a**2+D1*tau)) gScan2 = exp(-(R*sin(pi*f*tau))**2/(a**2+D2*tau)) gTwoD1 = F1/(1.+D1*tau/a**2) gTwoD2 = (1-F1)/(1.+D2*tau/a**2) # The final line with the correlation function should start with a "G" # before the "=" sign. G = 1./n * (gTwoD1 * gScan1 + gTwoD2 * gScan2) * gTriplet PyCorrFit-0.9.9/examples/external_model_functions/ExampleFunc_CS_3D+S+T.txt000077500000000000000000000054241274265304700266240ustar00rootroot00000000000000# CS-FCS 3D+S+T (Confocal) # Circular scanning FCS (3D diffusion with triplet). # Further reading: # Precise Measurement of Diffusion Coefficients using Scanning # Fluorescence Correlation Spectroscopy # Petrasek and Schwille, BiophysJ 2008, 1437-1448 # http://dx.doi.org/10.1529/biophysj.107.108811 # Visit http://fcstools.dyndns.org/pycorrfit/ for more information. # The first line of this file will be treated as the name of the model # inside PyCorrFit. PyCorrFit will enumerate user imported models with IDs # starting at 7001. You can save a session and the user defined models # like this one will be saved as well. Lines starting with a hash "#" # are treated as comments. Empty lines and lines with only white space # characters are ignored. # Note that if your code does not work, it might be that some variables # have other meaning. This includes using "n" instead of "N". # If you get some Syntax Error it might be that your starting variables # are not set to a reasonable starting function. PyCorrFit is testing the # function with sympy (for safety) and calculates the function for # different values of tau. ## Definition of parameters: # First, define the parameters and their starting values for you model # function. If the parameter has a unit of measurement, then it may be # added separated by a white space before the "=" sign. The starting # value should be a floating point number. You may use abbreviations # like "1e-3" instead of "0.001". # Note that PyCorrFit has it's own unit system: # unit of time : 1 ms # unit of inverse time: 1000 /s # unit of distance : 100 nm # unit of diff.coeff : 10 µm²/s # unit of inverse area: 100 /µm² # unit of inv. volume : 1000 /µm³ # Diffusion coefficient D [10 µm²/s] = 200.0 # Structural parameter w = 5.0 # Half waist of the lateral detection area (w0 = 2*a) a [100 nm] = 1.0 # Particle number n = 5.0 # Scan radius R [100 nm] = 5.0 # Frequency f [kHz] = 20.0 # Triplet fraction T = 0.1 # Triplet time tautrip [ms] = 0.001 # You may choose to substitute certain parts of the correlation function # with other values for easy reading. This can be done by using the # prefix "g". You may use all common mathematical functions, # such as "sqrt()" or "exp()". For convenience, "pi" and "e" may also # be used. If you are paranoid, you always use float numbers with a dot "." # to be sure the program doesn't accidently do integer division. gTrip = 1. + T/(1-T)*exp(-tau/tautrip) gScan = exp(-(R*sin(pi*f*tau))**2/(a**2+D*tau)) gTwoD = 1./(1.+D*tau/a**2) gOneD = 1./sqrt(1.+D*tau/(w*a)**2) gThrD = gTwoD * gOneD # The final line with the correlation function should start with a "G" # before the "=" sign. G = 1./n * gThrD * gScan * gTrip PyCorrFit-0.9.9/examples/external_model_functions/ExampleFunc_Exp_correlated_noise.txt000077500000000000000000000005041274265304700315230ustar00rootroot00000000000000# Exponentially correlated noise # Model function for PyCorrFit. # http://fcstools.dyndns.org/pycorrfit/ # This is a test function used to check decay times of exponentially # correlated noise. # Fraction Purity = 0.5 # Exp time tauexp [ms] = 2.0 gTrip = Purity/(1-Purity)*exp(-tau/tauexp) G = gTrip PyCorrFit-0.9.9/examples/external_model_functions/ExampleFunc_SFCS_1C_2D_Autocorrelation.txt000077500000000000000000000012571274265304700322340ustar00rootroot00000000000000# 2D SFCS AC # 2D one component correlation function for for perpendiculat SFCS. # Model function for PyCorrFit. # http://fcstools.dyndns.org/pycorrfit/ # http://fcstools.dyndns.org/pyscanfcs/ # The detection profile is eliptical, as the focus passes the membrane # perpendicular to its axis of symmetry. # The axis ratio / strucure parameter is defined as: # SP = semi-major-axis / semi-minor-axis (wz/w0) ## Parameters # Number of particles Nob = 40.0 # Diffusion time taudiff [ms] = 1.0 # Axis ratio / structural parameter SP = 5 gFirst = 1/sqrt(1+tau/taudiff) gSecond = 1/sqrt(1+tau/(taudiff*SP**2)) # Correlation function G = 1/Nob * gFirst * gSecond PyCorrFit-0.9.9/examples/external_model_functions/ExampleFunc_SFCS_1C_2D_Cross-correlation.txt000077500000000000000000000016131274265304700324660ustar00rootroot00000000000000# 2D SFCS CC # 2D on component correlation function for perpendiculat SFCS. # Model function for PyCorrFit. # http://fcstools.dyndns.org/pycorrfit/ # http://fcstools.dyndns.org/pyscanfcs/ # The detection profile is eliptical, as the focus passes the membrane # perpendicular to its axis of symmetry. # The axis ratio / strucure parameter is defined as: # SP = semi-major-axis / semi-minor-axis (wz/w0) # This model describes the cross-correlation for two-focus FCS ## Parameters # Number of particles Nob = 40.0 # Diffusion time taudiff [ms] = 1.0 # axis ratio / structural parameter SP = 5 # Beam waist radius w0 [100nm] = 2.3 # Distance between the foci d [100nm] = 5.0 gFirst = 1/sqrt(1+tau/taudiff) gSecond = 1/sqrt(1+tau/(taudiff*SP**2)) gac = 1/Nob * gFirst * gSecond # Diffusion coefficient: gD = w0**2/(4*taudiff) gcc = exp(-d**2/(w0**2+4*gD*tau)) G = gac*gcc PyCorrFit-0.9.9/examples/external_model_functions/ExampleFunc_TIRF_zOnly.txt000077500000000000000000000033641274265304700273340ustar00rootroot00000000000000# Axial diffusion (TIRF) # This model function describes fictional one-dimensional diffusion # in TIR-FCS setups. It demonstrates the mathematical functions available # in PyCorrFit. # Visit http://fcstools.dyndns.org/pycorrfit/ for more information. # The first line of this file will be treated as the name of the model # inside PyCorrFit. PyCorrFit will enumerate user imported models with IDs # starting at 7001. You can save a session and the user defined models # like this one will be saved as well. Lines starting with a hash "#" # are treated as comments. Empty lines and lines with only white space # characters are ignored. ## Definition of parameters: # First, define the parameters and their starting values for you model # function. If the parameter has a unit of measurement, then it may be # added separated by a white space before the "=" sign. The starting # value should be a floating point number. You may use abbreviations # like "1e-3" instead of "0.001". # Note that PyCorrFit has it's own unit system: # unit of time : 1 ms # unit of inverse time: 1000 /s # unit of distance : 100 nm # unit of diff.coeff : 10 µm²/s # unit of inverse area: 100 /µm² # unit of inv. volume : 1000 /µm³ D [10 µm²/s] = 5e-5 d [100 nm] = 1.0 # The final line with the correlation function should start with a "G" # before the "=" sign. You may use all common mathematical functions, # such as "sqrt()" or "exp()". For convenience, "pi" and "e" may also # be used. If you need to use the faddeeva function you can do so by # typing "wofz()". A common used version with an imaginary argument is # also available: wofz(i*x) = wixi(x) G = (sqrt(D*tau/pi) - (2*D*tau/d**2 - 1)/(2/d) * wixi(sqrt(D*tau)/d))/d**2 PyCorrFit-0.9.9/examples/external_model_functions/Model_AC_3D+T_confocal.txt000077500000000000000000000005231274265304700270740ustar00rootroot00000000000000# 3D+T (Gauss) # Autocorrelation function for 3D diffusion + Triplet ## Parameters # Particle number n = 10.0 # Triplet fraction T = 0.2 # Triplet time tautrip [ms] = 0.02 # Diffusion time taudiff [ms] = 0.4 # Structural parameter SP = 5 G = 1/( n*(1+tau/taudiff) * sqrt(1 + tau/(SP**2*taudiff)) ) * ( 1+T/(1.-T)*exp(-tau/tautrip) ) PyCorrFit-0.9.9/examples/external_model_functions/Model_Flow_AC_3D_confocal.txt000077500000000000000000000021741274265304700276700ustar00rootroot00000000000000# AC flow 3D (gauss) # Autocorrelation function including flow for confocal setups with # a free 3D diffusing species. # This file was gladly provided by Thomas Kuckert, Schwille Lab, Biotec, # Tatzberg 47-51, 1307 Dresden, Germany. # For more information about this model function, see: # Staroske, Wolfgang: # In Vitro and In Vivo Applications of Fluorescence # Cross-Correlation Spectroscopy, TU Dresden, Diss., June 2010# # # Brinkmeier, M. ; Dörre, K. ; Stephan, J. ; Eigen, M.: Two-beam cross- # correlation: A method to characterize transport phenomena in micrometer- # sized structures. In: Anal Chem 71 (1999), Feb, Nr. 3, 609?616. http://dx. # doi.org/10.1021/ac980820i. ? DOI 10.1021/ac980820i ## Parameters # Diffusion coefficient D [10 µm²/s] = 10.0 # Structural parameter w = 6.5 # Waist of the lateral detection area a [100 nm] = 3.25 # Particle number n = 10.0 #Flow velocity v [100 µm/s] = 0.5 ## Calculation fo correlation function gFlow = exp(-((v**2) * (tau**2))/(a**2+4*D*tau)) gTwoD = 1./(1.+4*D*tau/a**2) gOneD = 1./sqrt(1.+4*D*tau/(w*a)**2) gThrD = gTwoD * gOneD G = 1./n * gThrD * gFlow PyCorrFit-0.9.9/examples/external_model_functions/Model_Flow_CC_Backward_3D_confocal.txt000077500000000000000000000024411274265304700314650ustar00rootroot00000000000000# CC bw flow 3D (gauss) # Backward cross-correlation function including flow for confocal setups with # a free 3D diffusing species. # This file was gladly provided by Thomas Kuckert, Schwille Lab, Biotec, # Tatzberg 47-51, 1307 Dresden, Germany. # For more information about this model function, see: # Staroske, Wolfgang: # In Vitro and In Vivo Applications of Fluorescence # Cross-Correlation Spectroscopy, TU Dresden, Diss., June 2010# # # Brinkmeier, M. ; Dörre, K. ; Stephan, J. ; Eigen, M.: Two-beam cross- # correlation: A method to characterize transport phenomena in micrometer- # sized structures. In: Anal Chem 71 (1999), Feb, Nr. 3, 609?616. http://dx. # doi.org/10.1021/ac980820i. ? DOI 10.1021/ac980820i ## Parameters # Diffusion coefficient D [10 µm²/s] = 10.0 # Structural parameter w = 6.5 # Waist of the lateral detection area a [100 nm] = 3.25 # Particle number n = 10.0 # Focal distance R [100 nm] = 5.0 # Flow velocity v [100 µm/s] = 0.5 #angular difference to Flow for Foci Vector alpha = 0.0000001 ## Calculation fo correlation function gFlowT = (v**2)*(tau**2)+R**2 gAng = 2*R*v*tau*cos(alpha) gC2Flow = exp(-(gFlowT+gAng)/(a**2+4*D*tau)) gTwoD = 1./(1.+D*tau/a**2) gOneD = 1./sqrt(1.+D*tau/(w*a)**2) gThrD = gTwoD * gOneD G = 1./n * gThrD * gC2Flow PyCorrFit-0.9.9/examples/external_model_functions/Model_Flow_CC_Forward_3D_confocal.txt000077500000000000000000000024431274265304700313550ustar00rootroot00000000000000# CC fw flow 3D (gauss) # Forward cross-correlation function including flow for confocal setups with # a free 3D diffusing species. # This file was gladly provided by Thomas Kuckert, Schwille Lab, Biotec, # Tatzberg 47-51, 1307 Dresden, Germany. # For more information about this model function, see: # Staroske, Wolfgang: # In Vitro and In Vivo Applications of Fluorescence # Cross-Correlation Spectroscopy, TU Dresden, Diss., June 2010# # # Brinkmeier, M. ; Dörre, K. ; Stephan, J. ; Eigen, M.: Two-beam cross- # correlation: A method to characterize transport phenomena in micrometer- # sized structures. In: Anal Chem 71 (1999), Feb, Nr. 3, 609?616. http://dx. # doi.org/10.1021/ac980820i. ? DOI 10.1021/ac980820i ## Parameters # Diffusion coefficient D [10 µm²/s] = 10.0 # Structural parameter w = 6.5 # Waist of the lateral detection area a [100 nm] = 3.25 # Particle number n = 10.0 # Focal distance R [100 nm] = 5.0 # Flow velocity v [100 µm/s] = 0.5 # angular difference to Flow for Foci Vector alpha = 0.000001 ## Calculation fo correlation function gFlowT = (v**2)*(tau**2)+R**2 gAng = 2*R*v*tau*cos(alpha) gC1Flow = exp(-(gFlowT-gAng)/(a**2+4*D*tau)) gTwoD = 1./(1.+4*D*tau/a**2) gOneD = 1./sqrt(1.+4*D*tau/(w*a)**2) gThrD = gTwoD * gOneD G = 1./n * gThrD * gC1Flow PyCorrFit-0.9.9/examples/sample_sessions/000077500000000000000000000000001274265304700204515ustar00rootroot00000000000000PyCorrFit-0.9.9/examples/sample_sessions/CSFCS_DiO-in-DOPC.pcfs000066400000000000000000013632031274265304700240610ustar00rootroot00000000000000PK[|D nParameters.yaml͐10 ECr8v81t(SP('i"1b}[` G;Vn.4HNĘP3EpUT &cfk |4%MhcZaVNT-usf۴<>}-BڀG`A8ͯ< ߷eA(TΊi7)[Y PKzpD&`-, 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PyCorrFit will enumerate user imported models with IDs # starting at 7001. You can save a session and the user defined models # like this one will be saved as well. Lines starting with a hash "#" # are treated as comments. Empty lines and lines with only white space # characters are ignored. # Note that if your code does not work, it might be that some variables # have other meaning. Try to use small case letters, e.g. "n" instead of # "N". If you get a Syntax Error it might be that your starting variables # are not set to a reasonable starting function. PyCorrFit is testing the # function with sympy (for safety) and calculates the function for # different values of tau. ## Definition of parameters: # First, define the parameters and their starting values for you model # function. If the parameter has a unit of measurement, then it may be # added separated by a white space before the "=" sign. The starting # value should be a floating point number. You may use abbreviations # like "1e-3" instead of "0.001". # Note that PyCorrFit has it's own unit system: # unit of time : 1 ms # unit of inverse time: 1000 /s # unit of distance : 100 nm # unit of diff.coeff : 10 µm²/s # unit of inverse area: 100 /µm² # unit of inv. volume : 1000 /µm³ # Diffusion coefficient of first component D1 [10µm²/s] = 200.0 # Diffusion coefficient of second component D2 [10µm²/s] = 20.0 # Fraction of species One F1 = 1.0 # Half waist of the lateral detection area (w0 = 2*a) a [100nm] = 1.0 # Particle number n = 5.0 # Scan radius R [100nm] = 3.850 # Frequency f [kHz] = .2 # Triplet fraction T = 0.1 # Triplet time tautrip [ms] = 0.001 offset = 0.00001 # You may choose to substitute certain parts of the correlation function # with other values to obtain a better overview. This can be done by # using the prefix "g". You may use all common mathematical functions, # such as "sqrt()" or "exp()". For convenience, "pi" and "e" may also # be used. If you are paranoid, you always use float numbers with a # dot "." to be sure the program doesn't accidently do integer division. gTriplet = 1. + T/(1-T)*exp(-tau/tautrip) gScan1 = exp(-(R*sin(pi*f*tau))**2/(a**2+D1*tau)) gScan2 = exp(-(R*sin(pi*f*tau))**2/(a**2+D2*tau)) gTwoD1 = F1/(1.+D1*tau/a**2) gTwoD2 = (1-F1)/(1.+D2*tau/a**2) # The final line with the correlation function should start with a "G" # before the "=" sign. G = offset + 1./n * (gTwoD1 * gScan1 + gTwoD2 * gScan2) * gTriplet PK?[|D n Parameters.yamlPK?zpD&`-, Readme.txtPKzpDD1Supplements.yamlPKzpDF ?XX comments.txtPKzpD`II data5.csvPKzpDexternalweights.txtPKzpDHbv v *model_7001.txtPKPyCorrFit-0.9.9/examples/sample_sessions/ConfocalFCS_Alexa488_xcorr.pcfs000066400000000000000000002102761274265304700262110ustar00rootroot00000000000000PK[|DZՂŎBParameters.yaml=0 wNaK7g e 8n$`9߳<ÑN0@dB"dc,H1@.%8όb*Eg&_hQZVS}7볲4=^ZVhXݟ`PK'zpD&`-, Readme.txtVn0}W4^P%(dv*?13Y7Pm33g9kuF PyTkKx}#,/E(ZBغ~#RE Bt bgj1phmukVQBkm k Z* 띏Fq(Cq8;M/ { 6H3A1: v1<.Lg2(:ܛ)l4*1`T7MY9l~~}w=F{9@rfP|$ȏwDf8͔BȆNmJj02z ]';QG c-П^@VcSuȨvbsRUWK[ # 4:H{8#֔|gčZL"W5竗_"zUႝCzE+&Ks $S@Hz'ue.7 yK)gDdӅ\:F eă{d5E4.`_hOXɁO˔2Si[@6)xe/Tyj*yxzlC'tQ0R+ppePock5,'b.ŴOH"7 Jw'sh5/Xg6KQZ:92K.I"ߵ T/_U=m6;AH8W` Ӓ`VsvD҇F:,`~PK!zpD|lSupplements.yaml- - 1 - - [0, 0.002562574558099427] - [1, 0.00019240454744563954] - [3, 3.2761857187177025e-07] - 5.396361880316904e-06 - [] PK!zpD~bNN 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1.19820000000000000000e+05,3.19434782609999992076e+01 1.19980000000000000000e+05,3.22500000000000000000e+01 PK?[|DZՂŎB Parameters.yamlPK?'zpD&`-, Readme.txtPK!zpD|lSupplements.yamlPK!zpD~bNN comments.txtPK!zpDi.+.+ Adata1.csvPK!zpD2externalweights.txtPK!zpD~ikk 2trace1A.csvPK!zpD?9p9p otrace1B.csvPKPyCorrFit-0.9.9/pycorrfit/000077500000000000000000000000001274265304700154455ustar00rootroot00000000000000PyCorrFit-0.9.9/pycorrfit/PyCorrFit.py000066400000000000000000000003111274265304700176730ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ PyCorrFit loader """ from os.path import dirname, abspath, split import sys sys.path = [split(abspath(dirname(__file__)))[0]] + sys.path import pycorrfit pycorrfit.Main() PyCorrFit-0.9.9/pycorrfit/__init__.py000066400000000000000000000007261274265304700175630ustar00rootroot00000000000000#!/usr/bin/env python # -*- coding: utf-8 -*- """ PyCorrFit is a tool to fit correlation curves on a logarithmic scale. """ from . import meta from . import models from . import openfile from . import readfiles from .correlation import Correlation from .fit import Fit from .trace import Trace __version__ = meta.get_version() __author__ = u"Paul Müller" __license__ = "GPL v2" # Import the GUI in the end, because it needs `__version__`. from .gui.main import MainPyCorrFit-0.9.9/pycorrfit/__main__.py000066400000000000000000000001221274265304700175320ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ Runs PyCorrFit """ from .gui import main main.Main() PyCorrFit-0.9.9/pycorrfit/correlation.py000066400000000000000000000442011274265304700203410ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ PyCorrFit data set Classes for FCS data evaluation. """ from __future__ import print_function, division import hashlib import numpy as np import warnings from . import models as mdls from . import fit from .trace import Trace class Correlation(object): """ unifies correlation curve handling """ def __init__(self, backgrounds=[], correlation=None, corr_type="AC", filename=None, fit_algorithm="Lev-Mar", fit_model=6000, fit_ival=(0,0), fit_weight_data=None, fit_weight_type="none", normparm=None, title=None, traces=[], verbose=1): """ Parameters ---------- backgrounds: list of instances of Trace background traces correlation: ndarray of shape (N,2) correlation data (time [s], correlation) corr_type: str type of correlation, e.g. "AC", "AC1", "cc12" filename: str path to filename of correlation fit_algorithm: str valid fit algorithm identifier (Algorithms.keys()) fit_ival: fitting interval of lag times in indices fit_model: instance of FitModel the model used for fitting fit_weight_data: any data for the certain fit_weight_type fit_weight_type: str Reserved keywords or user-defined strings: - "none" : no weights are used - "splineX" : compute weights from spline with X knots and a spread of `fit_weight_data` bins. - "model function" : compute weights from difference to model function - user-defined : other weights (e.g. previously computed averages given in fit_weight_data) normparm: int identifier of normalization parameter title: str user-editable title of this correlation traces: list of instances of Trace traces of the current correlation verbose : int increment to increase verbosity """ # must be created before setting properties self._backgrounds = [] self._correlation = None self._fit_algorithm = None self._fit_model = None self._fit_parameters = None self._fit_parameters_range = None self._fit_parameters_variable = None self._fit_weight_memory = dict() self._lag_time = None self._model_memory = dict() self._traces = [] self._uid = None self.verbose = verbose self.backgrounds = backgrounds self.bg_correction_enabled = True self.correlation = correlation self.corr_type = corr_type self.filename = filename self.fit_algorithm = fit_algorithm self.fit_ival = fit_ival self.fit_model = fit_model # Do not change order: self.fit_weight_type = fit_weight_type self.fit_weight_parameters = fit_weight_data self.normparm = normparm self.title = title self.traces = traces def __repr__(self): if self.is_ac: c = "AC" else: c = "CC" text = "{} correlation '{}' with {} traces".format( c, self.title, len(self._traces)) return text def background_replace(self, channel, background): """ Replace a background. Channel must be 0 or 1. background must be instance of `Trace` """ assert channel in [0, 1] assert isinstance(background, Trace) if self.is_ac: if channel == 1: raise ValueError("Cannot set second background for AC.") self._backgrounds = [background] else: if len(self._backgrounds) == 0: self._backgrounds = [Trace(countrate=0, duration=0), Trace(countrate=0, duration=0)] elif len(self._backgrounds) == 1: self._backgrounds.append(Trace(countrate=0, duration=0)) self._backgrounds[channel] = background @property def backgrounds(self): """ The background trace(s) of this correlation in a list. """ return self._backgrounds @backgrounds.setter def backgrounds(self, value): """ Set the backgrounds. The value can either be a list of traces or instances of traces or a single trace in an array. """ backgrounds = [] if not isinstance(value, list): value = [value] assert len(value) in [0,1,2], "Backgrounds must be list with up to two elements." for v in value: if isinstance(v, np.ndarray): backgrounds.append(Trace(trace=v)) elif isinstance(v, Trace): backgrounds.append(v) else: raise ValueError("Each background must be instance of Trace or ndarray") self._backgrounds = backgrounds @property def bg_correction_factor(self): """ Returns background correction factor for self._correlation Notes ----- Thompson, N. Lakowicz, J.; Geddes, C. D. & Lakowicz, J. R. (ed.) Fluorescence Correlation Spectroscopy Topics in Fluorescence Spectroscopy, Springer US, 2002, 1, 337-378 """ if not self.bg_correction_enabled: # bg correction disabled return 1 if self.is_ac: # Autocorrelation if len(self.traces) == 1 and len(self.backgrounds) == 1: S = self.traces[0].countrate B = self.backgrounds[0].countrate bgfactor = (S/(S-B))**2 else: if self.verbose >= 1: warnings.warn("Correlation {}: no bg-correction". format(self.uid)) bgfactor = 1 else: # Crosscorrelation if len(self.traces) == 2 and len(self.backgrounds) == 2: S = self.traces[0].countrate S2 = self.traces[1].countrate B = self.backgrounds[0].countrate B2 = self.backgrounds[1].countrate bgfactor = (S/(S-B)) * (S2/(S2-B2)) else: warnings.warn("Correlation {}: no bg-correction". format(self)) bgfactor = 1 return bgfactor def check_parms(self, parms): """ Check parameters using self.fit_model.func_verification and the user defined boundaries self.fit_parameters_range for each parameter. """ p = 1.*np.array(parms) r = self.fit_parameters_range for i in range(len(p)): if r[i][0] == r[i][1]: pass elif r[i][0] is None: if p[i] > r[i][1]: p[i] = r[i][1] elif r[i][1] is None: if p[i] < r[i][0]: p[i] = r[i][1] elif p[i] < r[i][0]: p[i] = r[i][0] elif p[i] > r[i][1]: p[i] = r[i][1] return p @property def correlation(self): """the correlation data, shape (N,2) with (time, correlation) """ if self._correlation is not None: corr = self._correlation.copy() return corr @correlation.setter def correlation(self, value): if value is None: warnings.warn("Setting correlation to `None`.") elif not isinstance(value, np.ndarray): raise ValueError("Correlation must be 2d array!") elif not value.shape[1] == 2: raise ValueError("Correlation array must have shape (N,2)!") self._correlation = value @property def correlation_fit(self): """ returns correlation data for fitting (fit_ivald) - background correction - fitting interval cropping """ corr = self.correlation if corr is not None: # perform background correction corr[:,1] *= self.bg_correction_factor # perform parameter normalization return corr[self.fit_ival[0]:self.fit_ival[1],:] @property def correlation_plot(self): """ returns correlation data for plotting (normalized, fit_ivald) - background correction - fitting interval cropping - parameter normalization """ corr = self.correlation_fit if corr is not None: # perform parameter normalization corr[:,1] *= self.normalize_factor return corr @property def is_ac(self): """True if instance contains autocorrelation""" return self.corr_type.lower().count("ac") > 0 @property def is_cc(self): """True if instance contains crosscorrelation""" return not self.is_ac @property def is_weighted_fit(self): """True if a weighted fit was performed""" return self.fit_weight_type != "none" @property def fit_algorithm(self): """The string representing the fitting algorithm""" return self._fit_algorithm @fit_algorithm.setter def fit_algorithm(self, value): # TODO: # - allow lower-case fitting algorithm assert value in list(fit.Algorithms.keys()), "Invalid fit algorithm: "+value self._fit_algorithm = value @property def fit_ival(self): """lag time interval for fitting""" lag = self.lag_time if lag is not None: if self._fit_ival[1] <= 0 or self._fit_ival[1] > lag.shape[0]: self._fit_ival[1] = lag.shape[0] return self._fit_ival @fit_ival.setter def fit_ival(self, value): value = list(value) if value[1] <= 0: if self.lag_time is not None: value[1] = self.lag_time.shape[0] else: # just to be sure warnings.warn("No data available.") value[1] = 10000000000000000 self._fit_ival = value @property def fit_model(self): """instance of a fit model""" return self._fit_model @fit_model.setter def fit_model(self, value): """set the fit model """ if isinstance(value, (int, long)): newmodel = mdls.modeldict[value] elif isinstance(value, mdls.Model): newmodel = value else: raise NotImplementedError("Unknown model identifier") if newmodel != self._fit_model : self._fit_model = newmodel # overwrite fitting parameters self._fit_parameters = self._fit_model.default_values self._fit_parameters_variables = self._fit_model.default_variables self._fit_parameters_range = np.zeros((len(self._fit_parameters), 2)) self.normparm = None @property def fit_weight_data(self): """data of weighted fitting""" try: data = self._fit_weight_memory[self.fit_weight_type] except KeyError: # Standard variables for weights if self.fit_weight_type.count("spline"): # Default area for weighting with spline fit data = 3 else: data = None return data @fit_weight_data.setter def fit_weight_data(self, value): self._fit_weight_memory[self.fit_weight_type] = value @property def fit_parameters(self): """parameters that were fitted/will be used for fitting""" # Do not return `self._fit_parameters.copy()`, because # some methods of PyCorrFit depend on the array being # accessible and changeable with indices. return self._fit_parameters @fit_parameters.setter def fit_parameters(self, value): # must unlock parameters, if change is required value = np.array(value) self._fit_parameters = self.check_parms(value) @property def fit_parameters_range(self): """valid fitting ranges for fit parameters""" model = self.fit_model.boundaries mine = self._fit_parameters_range new = [] for a, b in zip(model, mine): c = [-np.inf, np.inf] if a[0] != a[1]: c[0] = a[0] c[1] = a[1] # user overrides model if b[0] != b[1]: c[0] = b[0] c[1] = b[1] if c[0] is not None and np.isnan(c[0]): c[0] = -np.inf if c[1] is not None and np.isnan(c[1]): c[1] = np.inf new.append(c) return np.array(new) @fit_parameters_range.setter def fit_parameters_range(self, value): value = np.array(value) assert value.shape[1] == 2 assert value.shape[0] == self.fit_parameters.shape[0] self._fit_parameters_range = value @property def fit_parameters_variable(self): """which parameters are variable during fitting""" if self._fit_parameters_variable is None: self._fit_parameters_variable = np.array(self.fit_model.default_variables, dtype=bool) return self._fit_parameters_variable @fit_parameters_variable.setter def fit_parameters_variable(self, value): value = np.array(value, dtype=bool) assert value.shape[0] == self.fit_parameters.shape[0] self._fit_parameters_variable = value @property def lag_time(self): """logarithmic lag time axis""" if self.correlation is not None: return self._correlation[:,0].copy() elif self._lag_time is not None: return self._lag_time else: # some default lag time return 10**np.linspace(-6,8,1001) @lag_time.setter def lag_time(self, value): if self.correlation is not None: warnings.warn("Setting lag time not possible, because of existing correlation") else: self._lag_time = value @property def lag_time_fit(self): """lag time as used for fitting""" return self.lag_time[self.fit_ival[0]:self.fit_ival[1]] @property def modeled(self): """fitted data values, same shape as self.correlation""" # perform parameter normalization lag = self.lag_time modeled = np.zeros((lag.shape[0], 2)) modeled[:,0] = lag modeled[:,1] = self.fit_model(self.fit_parameters, lag) return modeled.copy() @property def modeled_fit(self): """fitted data values, same shape as self.correlation_fit""" toplot = self.modeled[self.fit_ival[0]:self.fit_ival[1], :] return toplot @property def modeled_plot(self): """fitted data values, same shape as self.correlation_fit""" toplot = self.modeled_fit toplot[:,1] *= self.normalize_factor return toplot @property def normalize_factor(self): """plot normalization according to self.normparm""" if self.normparm is None: # nothing to do return 1 if self.normparm < self.fit_parameters.shape[0]: nfactor = self.fit_parameters[self.normparm] else: # get supplementary parameters alt = self.fit_model.get_supplementary_values(self.fit_parameters) nfactor = alt[self.normparm - self.fit_parameters.shape[0]] return nfactor @property def residuals(self): """fit residuals, same shape as self.correlation""" if self.correlation is None: raise ValueError("Cannot compute residuals; No correlation given!") residuals = self.correlation.copy() residuals[:,1] -= self.modeled[:,1] return residuals @property def residuals_fit(self): """fit residuals, same shape as self.correlation_fit""" residuals_fit = self.correlation_fit.copy() residuals_fit[:,1] -= self.modeled_fit[:,1] return residuals_fit @property def residuals_plot(self): """fit residuals, same shape as self.correlation_fit""" cp = self.correlation_plot if cp is not None: residuals_plot = self.correlation_plot.copy() residuals_plot[:,1] -= self.modeled_plot[:,1] return residuals_plot def set_weights(self, type_name, data): """ Add weights for fitting. example: type_name : "Average" data : 1d ndarray with length self.lag_time """ if data is not None: self._fit_weight_memory[type_name] = data @property def traces(self): """ The trace(s) of this correlation in a list. """ return self._traces @traces.setter def traces(self, value): """ Set the traces. The value can either be a list of traces or instances of traces or a single trace in an array. """ traces = [] if not isinstance(value, list): value = [value] assert len(value) in [0,1,2], "Traces must be list with up to two elements." for v in value: if isinstance(v, np.ndarray): traces.append(Trace(trace=v)) elif isinstance(v, Trace): traces.append(v) else: raise ValueError("Each trace must be instance of Trace or ndarray") self._traces = traces if len(self._traces) == 2: if self._traces[0].duration != self._traces[1].duration: warnings.warn("Unequal lenght of traces: {} and {}".format( self._traces[0].duration, self._traces[1].duration)) @property def uid(self): """ unique identifier of this instance This might change when title or filename are updated. """ if self._uid is None: hasher = hashlib.sha256() hasher.update(str(np.random.random())) hasher.update(str(self._correlation)) hasher.update(str(self.filename)) hasher.update(str(self.title)) self._uid = hasher.hexdigest() return self._uid PyCorrFit-0.9.9/pycorrfit/fit.py000066400000000000000000001142411274265304700166040ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ PyCorrFit data set Classes for FCS data evaluation. """ from __future__ import print_function, division import copy import lmfit import numpy as np import scipy.interpolate as spintp import warnings class Constraint(object): """ Class to translate fit constraints to lmfit syntax. """ def __init__(self, constraint, fit_bool, fit_bounds, fit_values): """ Parameters ---------- constraint : list of strings and ints The abstract constraint (e.g. [1, 0, "<", "2.3"]) as used in the model definitions. fit_bool : list of boolean A list of bools indicating which parameters are varied during fitting. fit_bounds : list of lists of two floats The parameter boundaries for fitting. fit_values : list of floats The initial fitting values. Notes ----- - the first item in constraints must be an integer indexing a parameter - the second/third item must be an integer as well or an operator (">", "<") - if the fourth item is omitted, it is assumed to be "0" - the first integer must be larger than the second integer """ if len(constraint) == 3: constraint.append("0") self.constraint = constraint self.fit_bool = fit_bool self.fit_bounds = fit_bounds self.fit_values = fit_values @property def parameters(self): """ Returns list of dict for each parameter. """ parms = [ it for it in self.constraint if isinstance(it, (int, long))] id2 = self.constraint.index(parms[1]) p1 = {"id": parms[0], "bool": self.fit_bool[parms[0]], "sign": +1, "value": self.fit_values[parms[0]] } p2 = {"id": parms[1], "bool": self.fit_bool[parms[1]], "sign": ( +1 if id2==2 else -1), "value": self.fit_values[parms[1]] } return p1, p2 @property def operator(self): strval = [ it for it in self.constraint if not isinstance(it, (int, long))] return strval[0] @property def offset(self): return float(self.constraint[-1]) def update_fit_bounds(self): """ Update the bounds with the given constraint. This only applies if one of the parameters is not varied during fitting. Notes ----- The fitting boundaries are updated in-place (`fit_bounds` variable from `__init__`). """ p1, p2 = self.parameters op = self.operator os = self.offset assert op in ["<", ">"], "Constraint operator not supported" if p1["bool"] and p2["bool"]: # do nothing, this case is handled in `get_lmfit_parameter_kwargs` pass elif p1["bool"]: # only parameter 1 is varied if op == "<": # [3, "<", 1, "0"] -> p1 < p2 # [3, 1, "<", "0"] -> p1 < -p2 # [3, 1, "<", "1.2] -> p1 < -p2 + 1.2 # [3, "<", 1, "1.2] -> p1 < p2 + 1.2 bnd = [-np.inf, p2["sign"]*p2["value"] + os] else: # [3, ">", 1, "0"] -> p1 > p2 # [3, 1, ">", "0"] -> p1 > -p2 # [3, 1, ">", "1.2] -> p1 > -p2 + 1.2 # [3, ">", 1, "1.2] -> p1 > p2 + 1.2 bnd = [p2["sign"]*p2["value"] + os, np.inf] bound = [max(self.fit_bounds[p1["id"]][0], bnd[0]), min(self.fit_bounds[p1["id"]][1], bnd[1]), ] self.fit_bounds[p1["id"]] = bound elif p2["bool"]: # only parameter2 is varied if op == "<": # [3, "<", 1, "0"] -> p2 > p1 # [3, 1, "<", "0"] -> (-)p2 > p1 -> p2 < -p1 # [3, 1, "<", "1.2] -> p2 < -p1 + 1.2 = -(p1-1.2) # [3, "<", 1, "1.2] -> p2 > p1 - 1.2 = +(p1-1.2) if p2["sign"] == -1: bnd = [-np.inf, -(p1["value"] - os)] else: bnd = [(p1["value"] - os), np.inf] else: # [3, ">", 1, "0"] -> p2 < p1 # [3, 1, ">", "0"] -> p2 > -p1 # [3, 1, ">", "1.2] -> p2 > -(p1 - 1.2) # [3, ">", 1, "1.2] -> p2 < p1 - 1.2 if p2["sign"] == -1: bnd = [-(p1["value"] - os), np.inf] else: bnd = [-np.inf, p1["value"] - os] bound = [max(self.fit_bounds[p2["id"]][0], bnd[0]), min(self.fit_bounds[p2["id"]][1], bnd[1]), ] self.fit_bounds[p2["id"]] = bound else: # neither is varied. # Do nothing. pass return self.fit_bounds def get_lmfit_parameter_kwargs(self): """ Using the given constraint, update the list of lmfit parameters. """ p1, p2 = self.parameters op = self.operator ofs = self.offset assert op in ["<", ">"], "Constraint operator not supported" if p1["bool"] and p2["bool"]: if op == "<": #p1 < (-)p2 + 1.2 #-> p1 = (-)p2 - d12 + 1.2 #-> d12 = (-)p2 - p1 + 1.2 #-> d12 > 0 deltaname = "delta_{}_{}".format(p1["id"], p2["id"]) kwdelt = {} kwdelt["name"] = deltaname kwdelt["value"] = p2["bool"]*self.fit_values[p2["id"]] - self.fit_values[p1["id"]] kwdelt["vary"] = True kwdelt["min"] = 0 # note: enforces "<=" (not "<") kwdelt["max"] = np.inf kwp1 = {} kwp1["name"] = "parm{:04d}".format(p1["id"]) # this condition deals with negative numbers kwp1["expr"] = "{MIN} if {COMP} < {MIN} else {MAX} if {COMP} > {MAX} else {COMP}".format( COMP="{}*parm{:04d}-{}+{:.14f}".format(p2["sign"], p2["id"], deltaname, ofs), MIN=self.fit_bounds[p1["id"]][0], MAX=self.fit_bounds[p1["id"]][1]) kwargs = [kwdelt, kwp1] elif op == ">": #p1 > (-)p2 + 1.2 #-> p1 = (-)p2 + d12 + 1.2 #-> d12 = p1 - (-)p2 - 1.2 #-> d12 > 0 deltaname = "delta_{}_{}".format(p1["id"], p2["id"]) kwdelt = {} kwdelt["name"] = deltaname kwdelt["value"] = self.fit_values[p1["id"]] - p2["bool"]*self.fit_values[p2["id"]] kwdelt["vary"] = True kwdelt["min"] = 0 # note: enforces ">=" (not ">") kwdelt["max"] = np.inf #self.fit_bounds[p1["id"]][1] + max(-p2["sign"]*self.fit_bounds[p2["id"]]) - ofs kwp1 = {} kwp1["name"] = "parm{:04d}".format(p1["id"]) # this condition deals with negative numbers kwp1["expr"] = "{MIN} if {COMP} < {MIN} else {MAX} if {COMP} > {MAX} else {COMP}".format( COMP="{}*parm{:04d}+{}+{:.14f}".format(p2["sign"], p2["id"], deltaname, ofs), MIN=self.fit_bounds[p1["id"]][0], MAX=self.fit_bounds[p1["id"]][1]) kwargs = [kwdelt, kwp1] else: kwargs = None return kwargs class Fit(object): """ Used for fitting FCS data to models. """ def __init__(self, correlations=[], global_fit=False, global_fit_variables=[], uselatex=False, verbose=0): """ Using an FCS model, fit the data of shape (N,2). Parameters ---------- correlations: list of instances or instance of `pycorrfit.Correlation` Correlations to fit. global fit : bool Perform global fit. The default behavior is to fit all parameters that are selected for fitting in each correlation. Parameters with the same name in different models are treated as one global parameter. global_fit_variables: list of list of strings Each item contains a list of strings that are names of parameters which will be treated as a common parameter. This breaks the defaul behavior. NOT IMPLEMENTED YET! verbose: int Increase verbosity by incrementing this number. uselatex: bool If verbose > 0, plotting will be performed with LaTeX. """ assert len(global_fit_variables)==0, "not implemented" if not isinstance(correlations, list): correlations = [correlations] self.correlations = correlations self.global_fit_variables = global_fit_variables self.verbose = verbose self.uselatex = uselatex self.is_weighted_fit = False if not global_fit: # Fit each correlation separately for corr in self.correlations: # Set fitting options self.fit_algorithm = corr.fit_algorithm # Get the data required for fitting self.x = corr.correlation_fit[:,0] self.y = corr.correlation_fit[:,1] # fit_bool: True for variable self.fit_bool = corr.fit_parameters_variable.copy() self.fit_parm = corr.fit_parameters.copy() self.fit_bound = copy.copy(corr.fit_parameters_range) self.is_weighted_fit = corr.is_weighted_fit self.fit_weights = Fit.compute_weights(corr, verbose=verbose, uselatex=uselatex) self.fit_parm_names = corr.fit_model.parameters[0] self.func = corr.fit_model.function self.check_parms = corr.check_parms self.constraints = corr.fit_model.constraints # Directly perform the fit and set the "fit" attribute self.minimize() # Run a second time: self.minimize() # update correlation model parameters corr.fit_parameters = self.fit_parm # save fit data in correlation class corr.fit_results = self.get_fit_results(corr) else: # TODO: # - allow detaching of parameters, # i.e. fitting "n" separately for two models # Initiate all arrays self.fit_algorithm = self.correlations[0].fit_algorithm xtemp = [] # x ytemp = [] # y weights = [] # weights ids = [0] # ids in big fitting array cmodels = [] # correlation model info initpar = [] # initial parameters varin = [] # names of variable fitting parameters variv = [] # values of variable fitting parameters varmap = [] # list of indices of fitted parameters varbound = [] # list of fitting boundaries self.is_weighted_fit = None for corr in self.correlations: xtemp.append(corr.correlation_fit[:,0]) ytemp.append(corr.correlation_fit[:,1]) weights.append(Fit.compute_weights(corr)) ids.append(len(xtemp[-1])+ids[-1]) cmodels.append(corr.fit_model) initpar.append(corr.fit_parameters) # Create list of variable parameters varthis = [] for ipm, par in enumerate(corr.fit_model.parameters[0]): if corr.fit_parameters_variable[ipm]: varthis.append(ipm) varin.append(par) variv.append(corr.fit_parameters[ipm]) varbound.append(corr.fit_parameters_range[ipm]) varmap.append(varthis) # These are the variable fitting parameters __, varidx = np.unique(varin, return_index=True) varidx.sort() varin = np.array(varin)[varidx] variv = np.array(variv)[varidx] self.x = np.concatenate(xtemp) self.y = np.concatenate(ytemp) self.fit_bool = np.ones(len(variv), dtype=bool) self.fit_parm = variv self.fit_weights = np.concatenate(weights) self.fit_parm_names = varin self.fit_bound = varbound self.constraints = [] warnings.warn("Constraints are not supported yet for global fitting.") def parameters_global_to_local(parameters, iicorr, varin=varin, initpar=initpar, correlations=correlations): """ With global `parameters` and an id `iicorr` pointing at the correlation in `self.correlations`, return the updated parameters of the corresponding model. """ fit_parm = initpar[iicorr].copy() corr = correlations[iicorr] mod = corr.fit_model for kk, pn in enumerate(mod.parameters[0]): if pn in varin: # edit that parameter fit_parm[kk] = parameters[np.where(np.array(varin)==pn)[0]] return fit_parm def parameters_local_to_global(parameters, iicorr, fit_parm, varin=varin, correlations=correlations): """ inverse of parameters_global_to_local """ corr = correlations[iicorr] mod = corr.fit_model for kk, pn in enumerate(mod.parameters[0]): if pn in varin: # edit that parameter parameters[np.where(np.array(varin)==pn)[0]] = fit_parm[kk] return parameters # Create function for fitting using ids def global_func(parameters, tau, glob2loc=parameters_global_to_local): out = [] # ids start at 0 for ii, mod in enumerate(cmodels): # Update parameters fit_parm = glob2loc(parameters, ii) # return function out.append(mod.function(fit_parm, tau[ids[ii]:ids[ii+1]])) return np.concatenate(out) self.func = global_func # Create function for checking def global_check_parms(parameters, glob2loc=parameters_global_to_local, loc2glob=parameters_local_to_global): for ii, corr in enumerate(self.correlations): # create new initpar fit_parm = glob2loc(parameters, ii) fit_parm = corr.check_parms(fit_parm) # update parameters parameters = loc2glob(parameters, ii, fit_parm) return parameters self.check_parms = global_check_parms # Directly perform the fit and set the "fit" attribute self.minimize() # Update correlations for ii, corr in enumerate(self.correlations): # write new model parameters corr.fit_parameters = parameters_global_to_local(self.fit_parm, ii) # save fit data in correlation class corr.fit_results = self.get_fit_results(corr) def get_fit_results(self, correlation): """ Return a dictionary with all information about the performed fit. This function must be called immediately after `self.minimize`. """ c = correlation d = { "chi2" : self.chi_squared, "chi2 type" : self.chi_squared_type, "weighted fit" : c.is_weighted_fit, "fit algorithm" : c.fit_algorithm, "fit result" : 1*c.fit_parameters, "fit parameters" : 1*np.where(c.fit_parameters_variable)[0], "fit weights" : 1*self.compute_weights(c) } if c.is_weighted_fit: d["weighted fit type"] = c.fit_weight_type if isinstance(c.fit_weight_data, (int, float)): d["weighted fit bins"] = c.fit_weight_data if d["fit algorithm"] == "Lev-Mar" and self.parmoptim_error is not None: d["fit error estimation"] = self.parmoptim_error return d @property def chi_squared(self): """ Calculate displayed Chi² Calculate reduced Chi² for the current class. """ # Calculate degrees of freedom dof = len(self.x) - np.sum(self.fit_bool) - 1 # This is exactly what is minimized by the scalar minimizers if self.chi_squared_type == "reduced expected sum of squares": fitted = self.func(self.fit_parm, self.x) chi2 = np.sum((self.y-fitted)**2/np.abs(fitted)) / dof elif self.chi_squared_type == "reduced weighted sum of squares": fitted = self.func(self.fit_parm, self.x) variance = self.fit_weights**2 chi2 = np.sum((self.y-fitted)**2/variance) / dof elif self.chi_squared_type == "reduced global sum of squares": fitted = self.func(self.fit_parm, self.x) variance = self.fit_weights**2 chi2 = np.sum((self.y-fitted)**2/variance) / dof else: chi2 = self.fit_function_scalar(self.fit_parm, self.x, self.y, self.fit_weights) return chi2 @property def chi_squared_type(self): """ The type of Chi² that currently applies. Returns ------- "reduced" - if variance of data was used for fitting "reduced Pearson" - if variance of data is not available """ if self.is_weighted_fit is None: # global fitting return "reduced global sum of squares" elif self.is_weighted_fit == True: return "reduced weighted sum of squares" elif self.is_weighted_fit == False: return "reduced expected sum of squares" else: raise ValueError("Unknown weight type!") @staticmethod def compute_weights(correlation, verbose=0, uselatex=False): """ computes and returns weights of the same length as `correlation.correlation_fit` `correlation` is an instance of Correlation """ corr = correlation model = corr.fit_model model_parms = corr.fit_parameters ival = corr.fit_ival weight_data = corr.fit_weight_data weight_type = corr.fit_weight_type #parameters = corr.fit_parameters #parameters_range = corr.fit_parameters_range #parameters_variable = corr.fit_parameters_variable cdat = corr.correlation if cdat is None: raise ValueError("Cannot compute weights; No correlation given!") cdatfit = corr.correlation_fit x_full = cdat[:,0] y_full = cdat[:,1] x_fit = cdatfit[:,0] #y_fit = cdatfit[:,1] dataweights = np.ones_like(x_fit) try: weight_spread = int(weight_data) except: if verbose > 1: warnings.warn("Could not get weight spread for spline. Setting it to 3.") weight_spread = 3 if weight_type[:6] == "spline": # Number of knots to use for spline try: knotnumber = int(weight_type[6:]) except: if verbose > 1: print("Could not get knot number. Setting it to 5.") knotnumber = 5 # Compute borders for spline fit. if ival[0] < weight_spread: # optimal case pmin = ival[0] else: # non-optimal case # we need to cut pmin pmin = weight_spread if x_full.shape[0] - ival[1] < weight_spread: # optimal case pmax = x_full.shape[0] - ival[1] else: # non-optimal case # we need to cut pmax pmax = weight_spread x = x_full[ival[0]-pmin:ival[1]+pmax] y = y_full[ival[0]-pmin:ival[1]+pmax] # we are fitting knots on a base 10 logarithmic scale. xs = np.log10(x) knots = np.linspace(xs[1], xs[-1], knotnumber+2)[1:-1] try: tck = spintp.splrep(xs, y, s=0, k=3, t=knots, task=-1) ys = spintp.splev(xs, tck, der=0) except: if verbose > 0: raise ValueError("Could not find spline fit with "+\ "{} knots.".format(knotnumber)) return if verbose > 0: ## If plotting module is available: #name = "spline fit: "+str(knotnumber)+" knots" #plotting.savePlotSingle(name, 1*x, 1*y, 1*ys, # dirname=".", # uselatex=uselatex) # use matplotlib.pylab try: from matplotlib import pylab as plt plt.xscale("log") plt.plot(x, ys, x, y) plt.show() except ImportError: # Tell the user to install matplotlib print("Couldn't import pylab! - not Plotting") ## Calculation of variance # In some cases, the actual cropping interval from ival[0] # to ival[1] is chosen, such that the dataweights must be # calculated from unknown datapoints. # (e.g. points+endcrop > len(correlation) # We deal with this by multiplying dataweights with a factor # corresponding to the missed points. for i in range(x_fit.shape[0]): # Define start and end positions of the sections from # where we wish to calculate the dataweights. # Offset at beginning: if i + ival[0] < weight_spread: # The offset that occurs offsetstart = weight_spread - i - ival[0] offsetcrop = 0 elif ival[0] > weight_spread: offsetstart = 0 offsetcrop = ival[0] - weight_spread else: offsetstart = 0 offsetcrop = 0 # i: counter on correlation array # start: counter on y array start = i - weight_spread + offsetstart + ival[0] - offsetcrop end = start + 2*weight_spread + 1 - offsetstart dataweights[i] = (y[start:end] - ys[start:end]).std() # The standard deviation at the end and the start of the # array are multiplied by a factor corresponding to the # number of bins that were not used for calculation of the # standard deviation. if offsetstart != 0: reference = 2*weight_spread + 1 dividor = reference - offsetstart dataweights[i] *= reference/dividor # Do not substitute len(y[start:end]) with end-start! # It is not the same! backset = 2*weight_spread + 1 - len(y[start:end]) - offsetstart if backset != 0: reference = 2*weight_spread + 1 dividor = reference - backset dataweights[i] *= reference/dividor elif weight_type == "model function": # Number of neighboring (left and right) points to include if ival[0] < weight_spread: pmin = ival[0] else: pmin = weight_spread if x_full.shape[0] - ival[1] < weight_spread: pmax = x_full.shape[0] - ival[1] else: pmax = weight_spread x = x_full[ival[0]-pmin:ival[1]+pmax] y = y_full[ival[0]-pmin:ival[1]+pmax] # Calculated dataweights for i in np.arange(x_fit.shape[0]): # Define start and end positions of the sections from # where we wish to calculate the dataweights. # Offset at beginning: if i + ival[0] < weight_spread: # The offset that occurs offsetstart = weight_spread - i - ival[0] offsetcrop = 0 elif ival[0] > weight_spread: offsetstart = 0 offsetcrop = ival[0] - weight_spread else: offsetstart = 0 offsetcrop = 0 # i: counter on correlation array # start: counter on correlation array start = i - weight_spread + offsetstart + ival[0] - offsetcrop end = start + 2*weight_spread + 1 - offsetstart #start = ival[0] - weight_spread + i #end = ival[0] + weight_spread + i + 1 diff = y - model(model_parms, x) dataweights[i] = diff[start:end].std() # The standard deviation at the end and the start of the # array are multiplied by a factor corresponding to the # number of bins that were not used for calculation of the # standard deviation. if offsetstart != 0: reference = 2*weight_spread + 1 dividor = reference - offsetstart dataweights[i] *= reference/dividor # Do not substitute len(diff[start:end]) with end-start! # It is not the same! backset = 2*weight_spread + 1 - len(diff[start:end]) - offsetstart if backset != 0: reference = 2*weight_spread + 1 dividor = reference - backset dataweights[i] *= reference/dividor elif weight_type == "none": pass else: # This means that the user knows the dataweights and already # gave it to us. weights = weight_data assert weights is not None, "User defined weights not given: "+weight_type # Check if these other weights have length of the cropped # or the full array. if weights.shape[0] == x_fit.shape[0]: dataweights = weights elif weights.shape[0] == x_full.shape[0]: dataweights = weights[ival[0]:ival[1]] else: raise ValueError, \ "`weights` must have length of full or cropped array." return dataweights def fit_function(self, params, x, y, weights=1): """ objective function that returns the residual (difference between model and data) to be minimized in a least squares sense. """ parms = Fit.lmfitparm2array(params) tominimize = (self.func(parms, x) - y) # Check dataweights for zeros and don't use these # values for the least squares method. with np.errstate(divide='ignore'): tominimize = np.where(weights!=0, tominimize/weights, 0) ## There might be NaN values because of zero weights: #tominimize = tominimize[~np.isinf(tominimize)] return tominimize def fit_function_scalar(self, parms, x, y, weights=1): """ Wrapper of `fit_function`. Returns the sum of squares of the input data. """ e = self.fit_function(parms, x, y, weights) return np.sum(e*e) def get_lmfitparm(self): """ Generates an lmfit parameter class from the present data set. The following parameters are used: self.x : 1d ndarray length N self.y : 1d ndarray length N self.fit_weights : 1d ndarray length N self.fit_bool : 1d ndarray length P, bool self.fit_parm : 1d ndarray length P, float """ params = lmfit.Parameters() # First, add all fixed parameters for pp in range(len(self.fit_parm)): if not self.fit_bool[pp]: if self.fit_bound[pp][0] == self.fit_bound[pp][1]: self.fit_bound[pp]=[-np.inf, np.inf] params.add(lmfit.Parameter(name="parm{:04d}".format(pp), value=self.fit_parm[pp], vary=self.fit_bool[pp], min=self.fit_bound[pp][0], max=self.fit_bound[pp][1], ) ) # Second, summarize the constraints in a dictionary, where # keys are the parameter indexes of varied parameters. # The dictionary cstrnew only allows integer keys that are # representing parameter indices. The fact that we are effectively # reducing the number of valid constraints to one per parameter # is a design problem that cannot be resolved here. The constraints # must be defined in such a way, that a parameter with a larger # index number is dependent on only one parameter with a lower # index number, e.g. parm1>parm0, parm3 parm1 < parm0 # [3, ">", 1] -> parm3 > parm1 # or the four-element form: # [1, "<", 0, "2.3"]] -> parm1 < parm0 + 2.3 # [1, 0, "<", "2.3"]] -> parm1 + parm0 < 2.3 for pp in range(len(self.fit_parm)): if self.fit_bool[pp]: inconstr = len([ cc for cc in self.constraints if pp in cc]) kwarglist = [] if inconstr: for cc in self.constraints: con = Constraint(constraint=cc, fit_bool=self.fit_bool, fit_bounds=self.fit_bound, fit_values=self.fit_parm) self.fit_bound = con.update_fit_bounds() if con.parameters[0]["id"] == pp: kws = con.get_lmfit_parameter_kwargs() if kws is not None: kwarglist += kws if len(kwarglist) == 0: # normal parameter kwarglist += [{"name": "parm{:04d}".format(pp), "value": self.fit_parm[pp], "vary": True, "min": self.fit_bound[pp][0], "max": self.fit_bound[pp][1], }] for kw in kwarglist: params.add(lmfit.Parameter(**kw)) return params @staticmethod def lmfitparm2array(parms, parmid="parm", attribute="value"): """ Convert lmfit parameters to a numpy array. Parameters are identified by name `parmid` which should be at the beginning of a parameters. This method is necessary to separate artificial constraint parameters from the actual parameters. Parameters ---------- parms : lmfit.parameter.Parameters or ndarray The input parameters. parmid : str The identifier for parameters. By default this is "parm", i.e. parameters are named like this: "parm0001", "parm0002", etc. attribute : str The attribute to return, e.g. - "value" : return the current value of the parameter - "vary" : return if the parameter is varied during fitting Returns: parr : ndarray If the input is an ndarray, the input will be returned. """ if isinstance(parms, lmfit.parameter.Parameters): items = parms.items() items.sort(key=lambda x: x[0]) parr = [getattr(p[1], attribute) for p in items if p[0].startswith(parmid)] else: parr = parms return np.array(parr) def minimize(self): """ This will run the minimization process """ assert (np.sum(self.fit_bool) != 0), "No parameter selected for fitting." # get all parameters for minimization params = self.get_lmfitparm() # Get algorithm method = Algorithms[self.fit_algorithm][0] methodkwargs = Algorithms[self.fit_algorithm][2] # Begin fitting # Fit a several times and stop earlier if the residuals # are small enough (heuristic approach). nfits = 5 diff = np.inf parmsinit = Fit.lmfitparm2array(params) for ii in range(nfits): res0 = self.fit_function(params, self.x, self.y) result = lmfit.minimize(fcn=self.fit_function, params=params, method=method, kws={"x":self.x, "y":self.y, "weights":self.fit_weights}, **methodkwargs ) params = result.params res1 = self.fit_function(params, self.x, self.y) diff = np.average(np.abs(res0-res1)) if hasattr(result, "ier") and not result.errorbars and ii+1 < nfits: # This case applies to the Levenberg-Marquardt algorithm multby = .5 # Try to vary stuck fitting parameters # the result from the previous fit parmsres = Fit.lmfitparm2array(params) # the parameters that are varied during fitting parmsbool = Fit.lmfitparm2array(params, attribute="vary") # The parameters that are stuck parmstuck = parmsbool * (parmsinit==parmsres) parmsres[parmstuck] *= multby # write changes self.fit_parm = parmsres params = self.get_lmfitparm() warnings.warn(u"PyCorrFit detected problems in fitting, "+\ u"detected a stuck parameter, multiplied "+\ u"it by {}, and fitted again. ".format(multby)+\ u"The stuck parameters are: {}".format( np.array(self.fit_parm_names)[parmstuck])) elif diff < 1e-8: # Experience tells us this is good enough. break # Now write the optimal parameters to our values: self.fit_parm = Fit.lmfitparm2array(params) # Only allow physically correct parameters self.fit_parm = self.check_parms(self.fit_parm) # Compute error estimates for fit (Only "Lev-Mar") if self.fit_algorithm == "Lev-Mar" and result.success: # This is the standard way to minimize the data. Therefore, # we are a little bit more verbose. self.covar = result.covar try: self.parmoptim_error = np.diag(self.covar) except: warnings.warn("PyCorrFit Warning: Error estimate not "+\ "possible, because we could not "+\ "calculate covariance matrix. Please "+\ "try reducing the number of fitting "+\ "parameters.") self.parmoptim_error = None else: self.parmoptim_error = None def GetAlgorithmStringList(): """ Get supported fitting algorithms as strings. Returns two lists (that are key-sorted) for key and string. """ A = Algorithms out1 = [] out2 = [] a = list(A.keys()) a.sort() for key in a: out1.append(key) out2.append(A[key][1]) return out1, out2 # As of version 0.8.3, we support several minimization methods for # fitting data to experimental curves. # These functions must be callable like scipy.optimize.leastsq. e.g. # res = spopt.leastsq(self.fit_function, self.fitparms[:], # args=(self.x), full_output=1) Algorithms = dict() # the original one is the least squares fit "leastsq" Algorithms["Lev-Mar"] = ["leastsq", "Levenberg-Marquardt", {"ftol" : 1.49012e-08, "xtol" : 1.49012e-08, } ] # simplex Algorithms["Nelder-Mead"] = ["nelder", "Nelder-Mead (downhill simplex)", {} ] # quasi-Newton method of Broyden, Fletcher, Goldfarb, and Shanno Algorithms["BFGS"] = ["lbfgsb", "BFGS (quasi-Newton)", {} ] # modified Powell-method Algorithms["Powell"] = ["powell", "modified Powell (conjugate direction)", {} ] # nonliner conjugate gradient method by Polak and Ribiere Algorithms["SLSQP"] = ["slsqp", "Sequential Linear Squares Programming", {} ] PyCorrFit-0.9.9/pycorrfit/gui/000077500000000000000000000000001274265304700162315ustar00rootroot00000000000000PyCorrFit-0.9.9/pycorrfit/gui/__init__.py000066400000000000000000000000001274265304700203300ustar00rootroot00000000000000PyCorrFit-0.9.9/pycorrfit/gui/doc.py000077500000000000000000000112321274265304700173520ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ PyCorrFit Documentation and program specific information """ import sys # This is a fake class for modules not available. class Fake(object): def __init__(self): self.__version__ = "N/A" self.version = "N/A" self.use = lambda x: None try: import matplotlib except: # Create fake opbject for matplotlib matplotlib = Fake() # We do catch warnings about performing this before matplotlib.backends stuff #matplotlib.use('WXAgg') # Tells matplotlib to use WxWidgets import warnings with warnings.catch_warnings(): warnings.simplefilter("ignore") matplotlib.use('WXAgg') # Tells matplotlib to use WxWidgets for dialogs import lmfit import numpy import os import platform import scipy try: import sympy except ImportError: print " Warning: module sympy not found!" sympy = Fake() import wx import yaml import pycorrfit from pycorrfit import readfiles, meta from pycorrfit.readfiles import read_pt3_scripts __version__ = pycorrfit.__version__ def GetLocationOfChangeLog(filename = "ChangeLog.txt"): return meta.get_file_location(filename) def GetLocationOfDocumentation(filename = "PyCorrFit_doc.pdf"): """ Returns the location of the documentation if there is any.""" return meta.get_file_location(filename) def info(version): """ Returns a little info about our program and what it can do. """ textwin = u""" Copyright 2011-2012 Paul Mueller, Biotec - TU Dresden A versatile tool for fitting and analyzing correlation curves. Dimensionless representation: unit of time : 1 ms unit of inverse time: 1000 /s unit of distance : 100 nm unit of Diff.coeff : 10 um^2/s unit of inverse area: 100 /um^2 unit of inv. volume : 1000 /um^3 """ textlin = u""" © 2011-2012 Paul Müller, Biotec - TU Dresden A versatile tool for fitting and analyzing correlation curves. Dimensionless representation: unit of time : 1 ms unit of inverse time: 1000 /s unit of distance : 100 nm unit of Diff.coeff : 10 µm²/s unit of inverse area: 100 /µm² unit of inv. volume : 1000 /µm³ """ if platform.system() != 'Linux': texta = textwin else: texta = textlin one = u" PyCorrFit version "+version+"\n\n" two = u"\n\n Supported file types:" keys = readfiles.Filetypes.keys() keys.sort() for item in keys: if item.split("|")[0] != readfiles.Allsupfilesstring: two = two + "\n - "+item.split("|")[0] lizenz = "" for line in licence().splitlines(): lizenz += " "+line+"\n" return one + lizenz + texta + two def licence(): return """PyCorrFit is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 2 of the License, or (at your option) any later version. PyCorrFit is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program. If not, see . """ def SoftwareUsed(): """ Return some Information about the software used for this program """ text = "Python "+sys.version+\ "\n\nModules:"+\ "\n - cython "+\ "\n - lmfit "+lmfit.__version__+\ "\n - matplotlib "+matplotlib.__version__+\ "\n - NumPy "+numpy.__version__+\ "\n - PyYAML "+yaml.__version__ +\ "\n - SciPy "+scipy.__version__+\ "\n - sympy "+sympy.__version__ +\ "\n - wxPython "+wx.__version__ # Other software text += "\n\nOther software:"+\ "\n - FCS_point_correlator ({})".format(read_pt3_scripts.version) +\ "\n PicoQuant file format for Python by Dominic Waithe" if hasattr(sys, 'frozen'): pyinst = "\n\nThis executable has been created using PyInstaller." text += pyinst if 'Anaconda' in sys.version or "Continuum Analytics" in sys.version: conda = "\n\nPowered by Anaconda" text += conda return text # Standard homepage HomePage = "http://pycorrfit.craban.de/" # Changelog filename ChangeLog = "ChangeLog.txt" StaticChangeLog = GetLocationOfChangeLog(ChangeLog) # Github homepage GitChLog = "https://raw.github.com/FCS-analysis/PyCorrFit/master/ChangeLog.txt" GitHome = "https://github.com/FCS-analysis/PyCorrFit" GitWiki = "https://github.com/FCS-analysis/PyCorrFit/wiki" PyCorrFit-0.9.9/pycorrfit/gui/edclasses.py000066400000000000000000000152131274265304700205530ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ PyCorrFit EditedClasses Contains classes that we edited. Should make our classes more useful. """ # Matplotlib plotting capabilities try: import matplotlib except ImportError: pass # We do catch warnings about performing this before matplotlib.backends stuff #matplotlib.use('WXAgg') # Tells matplotlib to use WxWidgets import warnings with warnings.catch_warnings(): warnings.simplefilter("ignore") try: matplotlib.use('WXAgg') # Tells matplotlib to use WxWidgets for dialogs except: pass import sys import traceback import wx class ChoicesDialog(wx.Dialog): def __init__(self, parent, dropdownlist, title, text): # parent is main frame self.parent = parent #super(ChoicesDialog, self).__init__(parent=parent, # title=title) wx.Dialog.__init__(self, parent, -1, title) ## Controls panel = wx.Panel(self) # text1 textopen = wx.StaticText(panel, label=text) btnok = wx.Button(panel, wx.ID_OK) btnabort = wx.Button(panel, wx.ID_CANCEL) # Dropdown self.dropdown = wx.ComboBox(panel, -1, "", (15, 30), wx.DefaultSize, dropdownlist, wx.CB_DROPDOWN|wx.CB_READONLY) self.dropdown.SetSelection(0) # Bindings self.Bind(wx.EVT_BUTTON, self.OnOK, btnok) self.Bind(wx.EVT_BUTTON, self.OnAbort, btnabort) # Sizers topSizer = wx.BoxSizer(wx.VERTICAL) topSizer.Add(textopen) topSizer.Add(self.dropdown) btnSizer = wx.BoxSizer(wx.HORIZONTAL) btnSizer.Add(btnok) btnSizer.Add(btnabort) topSizer.Add(btnSizer) panel.SetSizer(topSizer) topSizer.Fit(self) #self.Show(True) self.SetFocus() def OnOK(self, event=None): self.SelcetedID = self.dropdown.GetSelection() self.EndModal(wx.ID_OK) def OnAbort(self, event=None): self.EndModal(wx.ID_CANCEL) def save_figure(self, evt=None): """ A substitude function for save in: matplotlib.backends.backend_wx.NavigationToolbar2Wx We want to be able to give parameters such as dirname and filename. """ try: parent=self.canvas.HACK_parent fig=self.canvas.HACK_fig Page = self.canvas.HACK_Page add = self.canvas.HACK_append dirname = parent.dirname filename = self.canvas.get_window_title().replace(" ", "_").lower()+add formats = fig.canvas.get_supported_filetypes() except: dirname = "." filename = "" formats = self.canvas.get_supported_filetypes() parent = self fieltypestring = "" keys = formats.keys() keys.sort() for key in keys: fieltypestring += formats[key]+"(*."+key+")|*."+key+"|" # remove last | fieltypestring = fieltypestring[:-1] dlg = wx.FileDialog(parent, "Save figure", dirname, filename, fieltypestring, wx.FD_SAVE|wx.FD_OVERWRITE_PROMPT) # png is default dlg.SetFilterIndex(keys.index("png")) # user cannot do anything until he clicks "OK" if dlg.ShowModal() == wx.ID_OK: wildcard = keys[dlg.GetFilterIndex()] filename = dlg.GetPath() if not filename.endswith(wildcard): filename += "."+wildcard dirname = dlg.GetDirectory() #savename = os.path.join(dirname, filename) savename = filename try: self.canvas.figure.savefig(savename) except: # RuntimeError: # The file does not seem to be what it seems to be. info = sys.exc_info() errstr = "Could not latex output:\n" errstr += str(filename)+"\n\n" errstr += str(info[0])+"\n" errstr += str(info[1])+"\n" for tb_item in traceback.format_tb(info[2]): errstr += tb_item wx.MessageDialog(parent, errstr, "Error", style=wx.ICON_ERROR|wx.OK|wx.STAY_ON_TOP) else: dirname = dlg.GetDirectory() try: parent.dirname = dirname except: pass class MyScrolledDialog(wx.Dialog): def __init__(self, parent, overtext, readtext, title): wx.Dialog.__init__(self, parent, title=title) overtext = wx.StaticText(self, label=overtext) text = wx.TextCtrl(self, -1, readtext, size=(500,400), style=wx.TE_MULTILINE | wx.TE_READONLY) sizer = wx.BoxSizer(wx.VERTICAL ) btnsizer = wx.BoxSizer() btn = wx.Button(self, wx.ID_OK)#, "OK ") btnsizer.Add(btn, 0, wx.ALL, 5) btnsizer.Add((5,-1), 0, wx.ALL, 5) btn = wx.Button(self, wx.ID_CANCEL)#, "Abort ") btnsizer.Add(btn, 0, wx.ALL, 5) sizer.Add(overtext, 0, wx.EXPAND|wx.ALL, 5) sizer.Add(text, 0, wx.EXPAND|wx.ALL, 5) sizer.Add(btnsizer, 0, wx.EXPAND|wx.ALIGN_CENTER_VERTICAL|wx.ALL, 5) self.SetSizerAndFit(sizer) class MyOKAbortDialog(wx.Dialog): def __init__(self, parent, text, title): wx.Dialog.__init__(self, parent, title=title) overtext = wx.StaticText(self, label=text) sizer = wx.BoxSizer(wx.VERTICAL ) btnsizer = wx.BoxSizer() btn = wx.Button(self, wx.ID_OK)#, "OK ") btnsizer.Add(btn, 0, wx.ALL, 5) btnsizer.Add((5,-1), 0, wx.ALL, 5) btn = wx.Button(self, wx.ID_CANCEL)#, "Abort ") btnsizer.Add(btn, 0, wx.ALL, 5) sizer.Add(overtext, 0, wx.EXPAND|wx.ALL, 5) sizer.Add(btnsizer, 0, wx.EXPAND|wx.ALIGN_CENTER_VERTICAL|wx.ALL, 5) self.SetSizerAndFit(sizer) class MyYesNoAbortDialog(wx.Dialog): def __init__(self, parent, text, title): wx.Dialog.__init__(self, parent, title=title) overtext = wx.StaticText(self, label=text) sizer = wx.BoxSizer(wx.VERTICAL) btnsizer = wx.BoxSizer() btn1 = wx.Button(self, wx.ID_YES) #btn1.Bind(wx.EVT_BTN, self.YES) btnsizer.Add(btn1, 0, wx.ALL, 5) btnsizer.Add((1,-1), 0, wx.ALL, 5) btn2 = wx.Button(self, wx.ID_NO) btnsizer.Add(btn2, 0, wx.ALL, 5) btnsizer.Add((1,-1), 0, wx.ALL, 5) btn3 = wx.Button(self, wx.ID_CANCEL) btnsizer.Add(btn3, 0, wx.ALL, 5) sizer.Add(overtext, 0, wx.EXPAND|wx.ALL, 5) sizer.Add(btnsizer, 0, wx.EXPAND|wx.ALIGN_CENTER_VERTICAL|wx.ALL, 5) self.SetSizerAndFit(sizer) self.SetFocus() self.Show() def YES(self, e): self.EndModal(wx.ID_YES) try: # Add the save_figure function to the standard class for wx widgets. matplotlib.backends.backend_wx.NavigationToolbar2Wx.save = save_figure except (NameError, AttributeError): pass PyCorrFit-0.9.9/pycorrfit/gui/frontend.py000066400000000000000000002331511274265304700204270ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ PyCorrFit - Module "frontend" The frontend displays the GUI (Graphic User Interface). All necessary functions and modules are called from here. """ from distutils.version import LooseVersion # For version checking import os import webbrowser import wx import wx.lib.agw.flatnotebook as fnb # Flatnotebook (Tabs) import wx.py.shell import numpy as np # NumPy import platform import sys # System stuff import traceback # for Error handling import warnings try: # contains e.g. update and icon, but no vital things. import misc except ImportError: print " Some modules are not available." print " Update function will not work." # PyCorrFit modules from . import doc # Documentation/some texts from . import edclasses try: from . import plotting except ImportError: warnings.warn("Submodule `pycorrfit.plotting` will not be "+\ "available. Reason: {}.".format(sys.exc_info()[1].message)) from pycorrfit import models as mdls from pycorrfit import openfile as opf from pycorrfit import readfiles from pycorrfit import meta from . import page from . import tools from . import usermodel ######################################################################## class ExceptionDialog(wx.MessageDialog): """""" def __init__(self, msg): """Constructor""" wx.MessageDialog.__init__(self, None, msg, "Error", wx.OK|wx.ICON_ERROR) ######################################################################## class FlatNotebook(fnb.FlatNotebook): """ Flatnotebook class """ def __init__(self, parent): """Constructor""" style = fnb.FNB_SMART_TABS|fnb.FNB_NO_NAV_BUTTONS|\ fnb.FNB_DROPDOWN_TABS_LIST|fnb.FNB_NODRAG|\ fnb.FNB_TABS_BORDER_SIMPLE|\ fnb.FNB_X_ON_TAB|fnb.FNB_NO_X_BUTTON # Bugfix for Mac if platform.system().lower() in ["windows", "linux"]: style = style|fnb.FNB_HIDE_ON_SINGLE_TAB self.fnb = fnb.FlatNotebook.__init__(self, parent, wx.ID_ANY, agwStyle=style) class MyApp(wx.App): def __init__(self, args): wx.App.__init__(self, args) # Suppress WXDEBUG assertions, as happens by default with wx2.8. # http://anonscm.debian.org/cgit/collab-maint/wx-migration-tools.git/tree/README#n30 self.SetAssertMode(wx.PYAPP_ASSERT_SUPPRESS) def MacOpenFile(self,filename): """ """ if filename.endswith(".pcfs"): stri = self.frame.OnClearSession() if stri == "clear": self.frame.OnOpenSession(sessionfile=filename) elif filename.endswith(".txt"): self.frame.OnAddModel(modfile=filename) else: self.frame.OnLoadBatch(dataname=filename) ########################################################### class MyFrame(wx.Frame): def __init__(self, parent, anid, version): sys.excepthook = MyExceptionHook ## Set initial variables that make sense self.version = version wx.Frame.__init__(self, parent, anid, "PyCorrFit " + self.version) self.CreateStatusBar() # A Statusbar in the bottom of the window self.StatusBar.SetStatusText("Find help and updates online:"+ " 'Help > Update'") ## Properties of the Frame initial_size = (768,700) self.SetSize(initial_size) self.SetMinSize(initial_size) # Set this, so we know in which directory we are working in. # This will change, when we load a session or data file. self.dirname = os.curdir self.filename = None # Session Comment - may be edited and saved later self.SessionComment = "This is a session comment. It will be saved" +\ " as the session is saved." ## Set variables # The model module that can be changed by importing user defined # functions. # These are only for compatibility. # value_set and valuedict only for compatibility! # I should use mdls for anything, since it's globally imported # and modified by this program (e.g. adding new function) self.value_set = mdls.values self.valuedict = mdls.valuedict # Tab Counter self.tabcounter = 1 # Background Correction List # Contains instances of `Trace` self.Background = list() # A dictionary for all the opened tool windows self.ToolsOpen = dict() # A dictionary for all the tools self.Tools = dict() # Range selector (None if inactive) # Fitting parameter range selection # New as of 0.7.9 self.RangeSelector = None ## Setting up the menus. # models, modeldict, modeltypes only for compatibility! # I should use mdls for anything, since it's globally imported # and modified by this program (e.g. adding new function) self.models = mdls.models self.modeldict = mdls.modeldict self.modeltypes = mdls.modeltypes self.modelmenudict = dict() self.MakeMenu() ## Create the Flatnotebook (Tabs Tabs Tabs!) panel = wx.Panel(self) self.panel = panel self.notebook = FlatNotebook(panel) self.notebook.SetRightClickMenu(self.curmenu) #self.notebook.SetAGWWindowStyleFlag(FNB_X_ON_TAB) sizer = wx.BoxSizer(wx.VERTICAL) sizer.Add(self.notebook, 1, wx.ALL|wx.EXPAND, 5) panel.SetSizer(sizer) self.Layout() self.Centre() self.Show() # Notebook Handler self.notebook.Bind(fnb.EVT_FLATNOTEBOOK_PAGE_CLOSED, self.OnFNBClosedPage) self.notebook.Bind(fnb.EVT_FLATNOTEBOOK_PAGE_CHANGED, self.OnFNBPageChanged) # This is a hack since version 0.7.7: # When the "X"-button on a page is pressed, ask the user # if he really wants to close that page. self.notebook._pages.Unbind(wx.EVT_LEFT_UP) self.notebook._pages.Bind(wx.EVT_LEFT_UP, self.OnMyLeftUp) # If user hits the "x", ask if he wants to save the session self.Bind(wx.EVT_CLOSE, self.OnExit) # Set window icon try: self.MainIcon = misc.getMainIcon() wx.Frame.SetIcon(self, self.MainIcon) except: self.MainIcon = None def add_fitting_tab(self, event=None, modelid=None, counter=None, select=False): """ This function creates a new page inside the notebook. If the function is called from a menu, the modelid is known by the event. If not, the modelid should be specified by *modelid*. *counter* specifies which page number we should use for our new page. If it is None, we will simply use *self.tabcounter*. *event* - An event that has event.GetId() equal to a modelid *modelid* - optional, directly set the modelid *counter* - optional, set the "#" value of the page """ if modelid is None: # Get the model id from the menu modelid = event.GetId() # Give the new page focus select = True if counter is not None: # Set the tabcounter right, so the tabs are counted continuously. counterint = int(counter.strip().strip(":").strip("#")) self.tabcounter = max(counterint, self.tabcounter) modelid = int(modelid) counter = "#"+str(self.tabcounter)+": " # Get the model for the page together valuepack = mdls.valuedict[modelid] active_labels = valuepack[0] active_values = 1*valuepack[1] active_fitting = 1*valuepack[2] active_parms = [active_labels, active_values, active_fitting] model = mdls.modeldict[modelid][1] # Create New Tab Newtab = page.FittingPanel(self, counter, modelid, active_parms) #self.Freeze() self.notebook.AddPage(Newtab, counter+model, select=select) if select: # A hack to have the last page displayed in the tab menu: Npag = self.notebook.GetPageCount() self.notebook.SetSelection(Npag-1) #self.Thaw() self.tabcounter = self.tabcounter + 1 # Enable the "Current" Menu self.EnableToolCurrent(True) # ####### # # This is a work-around to prevent a weird bug in version 0.7.8: # The statistics OnPageChanged function is called but the parameters # are displayed double if a new page is created and the statistics # window is open. # Find Tool Statistics # Get open tools #toolkeys = self.ToolsOpen.keys() #for key in toolkeys: # tool = self.ToolsOpen[key] # try: # if tool.MyName=="STATISTICS": # # Call the function properly. # tool.OnPageChanged(Newtab) # except: # pass return Newtab def EnableToolCurrent(self, enabled): """ Independent on order of menus, enable or disable tools and current menu. """ # Tools menu is now always enabled # tid = self.menuBar.FindMenu("&Tools") # self.menuBar.EnableTop(tid, enabled) cid = self.menuBar.FindMenu("Current &Page") self.menuBar.EnableTop(cid, enabled) def MakeMenu(self): self.filemenu = wx.Menu() # toolmenu and curmenu are public, because they need to be enabled/ # disabled when there are tabs/notabs. self.toolmenu = wx.Menu() # curmenu needs to be public, because we want to call it from the right # click menu of a Page in fnb self.curmenu = wx.Menu() modelmenu = wx.Menu() prefmenu = wx.Menu() helpmenu = wx.Menu() # wx.ID_ABOUT and wx.ID_EXIT are standard IDs provided by wxWidgets. # self.filemenu menuAddModel = self.filemenu.Append(wx.ID_ANY, "&Import model", "Add a user defined model.") menuLoadBatch = self.filemenu.Append(wx.ID_ANY, "&Load data", "Loads one or multiple data files") menuOpen = self.filemenu.Append(wx.ID_OPEN, "&Open session", "Restore a previously saved session") self.filemenu.AppendSeparator() self.menuComm = self.filemenu.Append(wx.ID_ANY, "Co&mment session", "Add a comment to this session", kind=wx.ITEM_CHECK) self.filemenu.Check(self.menuComm.GetId(), False) menuClear = self.filemenu.Append(wx.ID_ANY, "&Clear session", "Remove all pages but keep imported model functions.") menuSave = self.filemenu.Append(wx.ID_SAVE, "&Save session", "Save entire Session") self.filemenu.AppendSeparator() menuExit = self.filemenu.Append(wx.ID_EXIT,"E&xit", "Terminate the program") # prefmenu self.MenuUseLatex = prefmenu.Append(wx.ID_ANY, "Use Latex", "Enables/Disables usage of Latex for image saving.", kind=wx.ITEM_CHECK) self.MenuVerbose = prefmenu.Append(wx.ID_ANY, "Verbose mode", "Enables/Disables output of additional information.", kind=wx.ITEM_CHECK) self.MenuShowWeights = prefmenu.Append(wx.ID_ANY, "Show weights", "Enables/Disables displaying weights of fit.", kind=wx.ITEM_CHECK) self.MenuShowWeights.Check() # toolmenu toolkeys = tools.ToolDict.keys() toolkeys.sort() for ttype in toolkeys: for tool in np.arange(len(tools.ToolDict[ttype])): menu = self.toolmenu.Append(wx.ID_ANY, tools.ToolName[ttype][tool][0], tools.ToolName[ttype][tool][1], kind=wx.ITEM_CHECK) self.toolmenu.Check(menu.GetId(), False) # Append tool to list of tools with menu ID self.Tools[menu.GetId()] = tools.ToolDict[ttype][tool] # Bindings # On tool only needs the Id of the wx.EVT_MENU self.Bind(wx.EVT_MENU, self.OnTool, menu) if ttype != toolkeys[-1]: self.toolmenu.AppendSeparator() # curmenu menuImportData = self.curmenu.Append(wx.ID_ANY, "&Import Data", "Import experimental FCS curve") menuSaveData = self.curmenu.Append(wx.ID_ANY, "&Save data (*.csv)", "Save data (comma separated values)") menuSavePlotCorr = self.curmenu.Append(wx.ID_ANY, "&Save correlation as image", "Export current plot as image.") menuSavePlotTrace = self.curmenu.Append(wx.ID_ANY, "&Save trace as image", "Export current trace as image.") self.curmenu.AppendSeparator() menuClPa = self.curmenu.Append(wx.ID_ANY, "&Close Page", "Close Current Page") # model menu # Integrate models into menu keys = mdls.modeltypes.keys() keys.sort() for modeltype in keys: # Now we have selected a type of model # Create a submenu submenu = wx.Menu() modelmenu.AppendMenu(wx.ID_ANY, modeltype, submenu) # Append to menulist self.modelmenudict[modeltype] = submenu for modelid in mdls.modeltypes[modeltype]: # Now we add every model that belongs to that type model = mdls.modeldict[modelid] if platform.system().lower() == "darwin" and hasattr(sys, 'frozen'): ### ### Work-around for freezed mac version ### ### (strange UTF-8 decoding error, ### would work with misc.removewrongUTF8) b = model[1].split("(")[0].strip() c = misc.removewrongUTF8(model[2]) menuentry = submenu.Append(model[0],b,c) else: menuentry = submenu.Append(model[0], model[1], model[2]) self.Bind(wx.EVT_MENU, self.add_fitting_tab, menuentry) # help menu menuDocu = helpmenu.Append(wx.ID_ANY, "&Documentation", "PyCorrFit documentation") menuWiki = helpmenu.Append(wx.ID_ANY, "&Wiki", "PyCorrFit wiki pages by users for users (online)") menuUpdate = helpmenu.Append(wx.ID_ANY, "&Update", "Check for new version"+ " (Web access required)") helpmenu.AppendSeparator() menuShell = helpmenu.Append(wx.ID_ANY, "S&hell", "A Python shell") helpmenu.AppendSeparator() menuSoftw = helpmenu.Append(wx.ID_ANY, "&Software", "Information about the software used") menuAbout = helpmenu.Append(wx.ID_ABOUT, "&About", "Information about this program") # Create the menubar. self.menuBar = wx.MenuBar() # Adding all the menus to the MenuBar self.menuBar.Append(self.filemenu,"&File") self.menuBar.Append(self.toolmenu,"&Tools") self.menuBar.Append(self.curmenu,"Current &Page") self.menuBar.Append(modelmenu,"&Model") self.menuBar.Append(prefmenu,"&Preferences") self.menuBar.Append(helpmenu,"&Help") self.SetMenuBar(self.menuBar) # Adding the MenuBar to the Frame content. self.EnableToolCurrent(False) ## Set events #File #self.Bind(wx.EVT_MENU, self.OnLoadSingle, menuLoadSingle) self.Bind(wx.EVT_MENU, self.OnLoadBatch, menuLoadBatch) self.Bind(wx.EVT_MENU, self.OnAddModel, menuAddModel) self.Bind(wx.EVT_MENU, self.OnCommSession, self.menuComm) self.Bind(wx.EVT_MENU, self.OnClearSession, menuClear) self.Bind(wx.EVT_MENU, self.OnOpenSession, menuOpen) self.Bind(wx.EVT_MENU, self.OnSaveSession, menuSave) self.Bind(wx.EVT_MENU, self.OnExit, menuExit) # Current self.Bind(wx.EVT_MENU, self.OnImportData, menuImportData) self.Bind(wx.EVT_MENU, self.OnSaveData, menuSaveData) self.Bind(wx.EVT_MENU, self.OnSavePlotCorr, menuSavePlotCorr) self.Bind(wx.EVT_MENU, self.OnSavePlotTrace, menuSavePlotTrace) self.Bind(wx.EVT_MENU, self.OnDeletePage, menuClPa) # Preferences self.Bind(wx.EVT_MENU, self.OnLatexCheck, self.MenuUseLatex) # Help self.Bind(wx.EVT_MENU, self.OnSoftware, menuSoftw) self.Bind(wx.EVT_MENU, self.OnAbout, menuAbout) self.Bind(wx.EVT_MENU, self.OnUpdate, menuUpdate) self.Bind(wx.EVT_MENU, self.OnDocumentation, menuDocu) self.Bind(wx.EVT_MENU, self.OnWiki, menuWiki) self.Bind(wx.EVT_MENU, self.OnShell, menuShell) def OnAbout(self, event=None): # Show About Information description = ("PyCorrFit is a data displaying, fitting "+ "and evaluation tool \nfor fluorescence correlation "+ "spectroscopy. \nPyCorrFit is written in Python.") licence = doc.licence() info = wx.AboutDialogInfo() #info.SetIcon(wx.Icon('hunter.png', wx.BITMAP_TYPE_PNG)) info.SetName('PyCorrFit') info.SetVersion(self.version) info.SetDescription(description) info.SetCopyright(u'(C) 2011 - 2012 Paul Müller') info.SetWebSite(doc.HomePage) info.SetLicence(licence) info.SetIcon(misc.getMainIcon(pxlength=64)) info.AddDeveloper(u'Paul Müller') info.AddDocWriter(u'Thomas Weidemann, Paul Müller') wx.AboutBox(info) def OnAddModel(self, event=None, modfile=None): """ Import a model from an external .txt file. See example model functions available on the web. """ # Add a model using the dialog. filters = "text file (*.txt)|*.txt" if modfile is None: dlg = wx.FileDialog(self, "Open model file", self.dirname, "", filters, wx.FD_OPEN) if dlg.ShowModal() == wx.ID_OK: # Workaround since 0.7.5 (dirname, filename) = os.path.split(dlg.GetPath()) #filename = dlg.GetFilename() #dirname = dlg.GetDirectory() self.dirname = dirname # Try to import a selected .txt file else: self.dirname = dlg.GetDirectory() dlg.Destroy() return else: dirname, filename = os.path.split(modfile) self.dirname = dirname NewModel = usermodel.UserModel(self) try: NewModel.GetCode( os.path.join(dirname, filename) ) except NameError: # sympy is probably not installed # Warn the user text = ("SymPy not found.\n"+ "In order to import user defined model\n"+ "functions, please install Sympy\n"+ "version 0.7.2 or higher.\nhttp://sympy.org/") if platform.system().lower() == 'linux': text += ("\nSymPy is included in the package:\n"+ " 'python-sympy'") dlg = wx.MessageDialog(None, text, 'SymPy not found', wx.OK | wx.ICON_EXCLAMATION) dlg.ShowModal() return except: # The file does not seem to be what it seems to be. info = sys.exc_info() errstr = "Unknown file format:\n" errstr += str(filename)+"\n\n" errstr += str(info[0])+"\n" errstr += str(info[1])+"\n" for tb_item in traceback.format_tb(info[2]): errstr += tb_item dlg = wx.MessageDialog(self, errstr, "Error", style=wx.ICON_ERROR|wx.OK|wx.STAY_ON_TOP) dlg.ShowModal() return # Test the code for sympy compatibility. # If you write your own parser, this might be easier. try: NewModel.TestFunction() except: # This means that the imported model file could be # contaminated. Ask the user how to proceed. text = "The model parsing check raised an Error.\n"+\ "This could be the result of a wrong Syntax\n"+\ "or an error of the parser.\n"+\ "This might be dangerous. Procceed\n"+\ "only, if you trust the source of the file.\n"+\ "Try and import offensive file: "+filename+"?" dlg2 = wx.MessageDialog(self, text, "Unsafe Operation", style=wx.ICON_EXCLAMATION|wx.YES_NO|wx.STAY_ON_TOP) if dlg2.ShowModal() == wx.ID_YES: NewModel.ImportModel() else: return else: # The model was loaded correctly NewModel.ImportModel() self.dirname = dirname def OnClearSession(self, e=None, clearmodels=False): """ Clear the entire session Returns: "abort" if the user did not want to clear the session "clear" if the session has been cleared and the user did or did not save the session """ numtabs = self.notebook.GetPageCount() # Ask, if user wants to save current session. if numtabs > 0: dial = wx.MessageDialog(self, 'Do you wish to save this session first?', 'Save current session?', wx.ICON_QUESTION | wx.CANCEL | wx.YES_NO | wx.NO_DEFAULT ) # dial = edclasses.MyYesNoAbortDialog(self, # 'Do you wish to save this session first?', # 'Save current session?') result = dial.ShowModal() dial.Destroy() if result == wx.ID_CANCEL: return "abort" # stop this function - do nothing. elif result == wx.ID_YES: filename = self.OnSaveSession() if filename is None: return "abort" elif result == wx.ID_NO: pass # Delete all the pages self.notebook.DeleteAllPages() # Disable all the dialogs and menus self.EnableToolCurrent(False) self.OnFNBPageChanged() self.tabcounter = 1 self.filename = None self.SetTitleFCS(None) self.SessionComment = "You may enter some information here." self.Background = list() ## Do we want to keep user defined models after session clearing? if clearmodels == True: # Also reset user defined models for modelid in mdls.modeltypes["User"]: mdls.values.remove(mdls.valuedict[modelid]) del mdls.valuedict[modelid] mdls.models.remove(mdls.modeldict[modelid]) del mdls.modeldict[modelid] mdls.modeltypes["User"] = list() # Model Menu menu=self.modelmenudict["User"] for item in menu.GetMenuItems(): menu.RemoveItem(item) return "clear" def OnCommSession(self,e=None): """ Dialog for commenting on session. """ try: self.EditCommentDlg.IsEnabled() except AttributeError: # Dialog is not opened self.EditCommentDlg = tools.EditComment(self) self.EditCommentDlg.Bind(wx.EVT_CLOSE, self.EditCommentDlg.OnClose) self.filemenu.Check(self.menuComm.GetId(), True) else: # Close Dialog self.EditCommentDlg.OnClose() def OnDeletePage(self, event=None): """ This method is based on the flatnotebook demo It removes a page from the notebook """ # Ask the user if he really wants to delete the page. title = self.notebook.GetCurrentPage().tabtitle.GetValue() numb = self.notebook.GetCurrentPage().counter.strip().strip(":") text = "This will close page "+numb+"?\n"+title dlg = edclasses.MyOKAbortDialog(self, text, "Warning") if dlg.ShowModal() == wx.ID_OK: self.notebook.DeletePage(self.notebook.GetSelection()) self.OnFNBClosedPage() if self.notebook.GetPageCount() == 0: self.OnFNBPageChanged() def OnDocumentation(self, e=None): """ Get the documentation and view it with browser""" filename = doc.GetLocationOfDocumentation() if filename is None: # Now we have to tell the user that there is no documentation self.StatusBar.SetStatusText("...documentation not found.") else: self.StatusBar.SetStatusText("...documentation: "+filename) if platform.system().lower() == 'windows': os.system("start /b "+filename) elif platform.system().lower() == 'linux': os.system("xdg-open "+filename+" &") elif platform.system().lower() == 'darwin': os.system("open "+filename+" &") else: # defaults to linux style: os.system("xdg-open "+filename+" &") def OnExit(self,e=None): """ Kindly asks the user if he wants to save the session and then exit the program. """ numtabs = self.notebook.GetPageCount() # Ask, if user wants to save current session. if numtabs > 0: dial = wx.MessageDialog(self, 'Do you wish to save this session first?', 'Save current session?', wx.ICON_QUESTION | wx.CANCEL | wx.YES_NO | wx.NO_DEFAULT ) result = dial.ShowModal() dial.Destroy() if result == wx.ID_CANCEL: return # stop this function - do nothing. elif result == wx.ID_YES: filename = self.OnSaveSession() if filename is None: # Do not exit. The user pressed abort in the session # saving dialog. return # Exit the Program self.Destroy() def OnFNBClosedPage(self,e=None): """ Called, when a page has been closed """ if self.notebook.GetPageCount() == 0: # Grey out tools self.EnableToolCurrent(False) def OnFNBPageChanged(self, e=None, Page=None, trigger=None): """ Called, when - Page focus switches to another Page - Page with focus changes significantly: - experimental data is loaded - weighted fit was done - trigger is a string. For more information read the doc strings of the `tools` submodule. """ if (e is not None and e.GetEventType()==fnb.EVT_FLATNOTEBOOK_PAGE_CHANGED.typeId and trigger is None): trigger = "tab_browse" # Get the Page if Page is None: Page = self.notebook.GetCurrentPage() keys = list(self.ToolsOpen.keys()) for key in keys: # Update the information self.ToolsOpen[key].OnPageChanged(Page, trigger=trigger) # parameter range selection tool for page. if self.RangeSelector is not None: try: self.RangeSelector.OnPageChanged(Page, trigger=trigger) except: pass # Bugfix-workaround for mac: # non-existing tabs are still displayed upon clearing session if platform.system().lower() == "darwin": if self.notebook.GetPageCount() == 0: self.notebook.Hide() else: self.notebook.Show() def OnImportData(self,e=None): """Import experimental data from all filetypes specified in *opf.Filetypes*. Is called by the curmenu and applies to currently opened model. Calls self.ImportData. """ # Open a data file # Get Data SupFiletypes = opf.Filetypes.keys() SupFiletypes.sort() filters = "" for i in np.arange(len(SupFiletypes)): # Add to the filetype filter filters = filters+SupFiletypes[i] if i+1 != len(SupFiletypes): # Add a separator, but not behind the last entry # This is wx widgets stuff. filters = filters+"|" dlg = wx.FileDialog(self, "Open data file", self.dirname, "", filters, wx.FD_OPEN) if dlg.ShowModal() == wx.ID_OK: # The filename the page will get path = dlg.GetPath() # Workaround since 0.7.5 (self.dirname, self.filename) = os.path.split(path) #self.filename = dlg.GetFilename() #self.dirname = dlg.GetDirectory() try: Stuff = readfiles.openAny(self.dirname, self.filename) except: # The file format is not supported. info = sys.exc_info() errstr = "Unknown file format:\n" errstr += str(self.filename)+"\n\n" errstr += str(info[0])+"\n" errstr += str(info[1])+"\n" for tb_item in traceback.format_tb(info[2]): errstr += tb_item wx.MessageDialog(self, errstr, "Error", style=wx.ICON_ERROR|wx.OK|wx.STAY_ON_TOP) return else: dataexp = Stuff["Correlation"] trace = Stuff["Trace"] curvelist = Stuff["Type"] filename = Stuff["Filename"] if "Weight" in Stuff: Weight = Stuff["Weight"] WeightName = Stuff["Weight Name"] else: Weight = [None] * len(Stuff["Type"]) WeightName = [None] * len(Stuff["Type"]) # If curvelist is a list with more than one item, we are # importing more than one curve per file. Therefore, we # need to create more pages for this file. # # We want to give the user the possibility to choose from # several types of input functions. If curvelist contains # more than one type of data, like "AC1", "AC2", "CC1", ... # then the user may wish to only import "AC1" or "AC2" # functions. curvetypes = dict() for i in np.arange(len(curvelist)): try: curvetypes[curvelist[i]].append(i) except KeyError: curvetypes[curvelist[i]] = [i] # Now we have a dictionary curvetypes with keys that name # items in curvelist and which point to indices in curvelist. # We will display a dialog that let's the user choose what # to import. keys = curvetypes.keys() if len(keys) > 1: Chosen = tools.ChooseImportTypes(self, curvetypes) newcurvelist = list() newfilename = list() newdataexp = list() newtrace = list() newWeight = list() newWeightName = list() if Chosen.ShowModal() == wx.ID_OK: keys = Chosen.keys if len(keys) == 0: # do not do anything return for key in keys: # create a new curvelist with the selected curves for index in curvetypes[key]: newcurvelist.append(curvelist[index]) newfilename.append(filename[index]) newdataexp.append(dataexp[index]) newtrace.append(trace[index]) newWeight.append(Weight[index]) newWeightName.append(WeightName[index]) curvelist = newcurvelist filename = newfilename dataexp = newdataexp trace = newtrace Weight = newWeight WeightName = newWeightName else: return Chosen.Destroy() # curvelist is a list of numbers or labels that correspond # to each item in dataexp or trace. Each curvelist/filename # item will be converted to a string and then added to the # pages title. num = len(curvelist) # Show a nice progress dialog: style = wx.PD_REMAINING_TIME|wx.PD_SMOOTH|wx.PD_AUTO_HIDE|\ wx.PD_CAN_ABORT dlg = wx.ProgressDialog("Import", "Loading pages...", maximum = num, parent=self, style=style) # Get current page and populate CurPage = self.notebook.GetCurrentPage() for i in np.arange(num): # Fill Page with data self.ImportData(CurPage, dataexp[i], trace[i], curvetype=curvelist[i], filename=filename[i], weights=Weight[i], weight_type=WeightName[i], curveid=i) # Let the user abort, if he wants to: # We want to do this here before an empty page is added # to the notebok. if dlg.Update(i+1, "Loading pages...")[0] == False: dlg.Destroy() return if i+1 != num: # Create new page. # (Add n-1 pages while importing.) CurPage = self.add_fitting_tab(event=None, modelid=CurPage.corr.fit_model.id, counter=None) # We are finished here: return else: # User pressed "Abort" - do nothing. self.dirname = dlg.GetDirectory() dlg.Destroy() return def OnMyLeftUp(self, event): """ Wrapper for LeftUp: We want to have a wrapper for the page closing event. The code was copied from "flatnotebook.py" Handles the ``wx.EVT_LEFT_UP`` event for L{PageContainer}. :param `event`: a `wx.MouseEvent` event to be processed. """ # Get the page container pc = self.notebook._pages # forget the zone that was initially clicked self._nLeftClickZone = fnb.FNB_NOWHERE where = pc.HitTest(event.GetPosition())[0] FNB_X = 2 FNB_TAB_X = 3 if not pc.HasAGWFlag(fnb.FNB_NO_TAB_FOCUS): # Make sure selected tab has focus self.SetFocus() if where == FNB_X: # Make sure that the button was pressed before if pc._nXButtonStatus != fnb.FNB_BTN_PRESSED: return pc._nXButtonStatus = fnb.FNB_BTN_HOVER self.OnDeletePage(self.notebook.GetCurrentPage()) elif where == FNB_TAB_X: # Make sure that the button was pressed before if pc._nTabXButtonStatus != fnb.FNB_BTN_PRESSED: return pc._nTabXButtonStatus = fnb.FNB_BTN_HOVER self.OnDeletePage(self.notebook.GetCurrentPage()) else: # Call what should have been called. pc.OnLeftUp(event) def ImportData(self, Page, dataexp, trace, curvetype="", filename="", curveid="", run="0", weights=None, weight_type=None, trigger=None): """ Import data into the current page. `trigger` is passed to PlotAll. For more info see the submodule `tools`. """ CurPage = Page # Set name of correlation CurPage.corr.filename = filename # Import traces. Traces are usually put into a list, even if there # is only one trace. The reason is, that for cross correlation, we # have two traces and thus we have to import both. if trace is not None: CurPage.corr.traces = trace # Import correlation function CurPage.corr.correlation = dataexp CurPage.corr.corr_type = curvetype CurPage.OnAmplitudeCheck() # It might be possible, that we want the channels to be # fixed to some interval. This is the case if the # checkbox on the "Channel selection" dialog is checked. #self.OnFNBPageChanged() # Enable Fitting Button CurPage.Fit_enable_fitting() # Set new tabtitle value and strip leading or trailing # white spaces. if run != "": title = "{} r{:03d}-{}".format(filename, int(run), curvetype) else: title = "{} id{:03d}-{}".format(filename, int(curveid), curvetype) CurPage.title = title # set weights if weights is not None: CurPage.corr.set_weights(weight_type, weights) List = CurPage.Fitbox[1].GetItems() if not weight_type in List: List.append(weight_type) CurPage.Fitbox[1].SetItems(List) CurPage.Fitbox[1].SetSelection(len(List)-1) else: listid = List.index(weight_type) CurPage.Fitbox[1].SetSelection(listid) # Plot everything CurPage.OnAmplitudeCheck() CurPage.PlotAll(trigger=trigger) # Call this function to allow the "Channel Selection" window that # might be open to update itself. # We are aware of the fact, that we just did that #self.OnFNBPageChanged() def OnLatexCheck(self,e): """ Checks if we can use latex. If not, create a pop-up window stating so. """ uselatex = self.MenuUseLatex.IsChecked() if uselatex == False: # do nothing return ## Check if we can use latex for plotting: r1 = meta.find_program("latex")[0] r2 = meta.find_program("dvipng")[0] # Ghostscript r31 = meta.find_program("gs")[0] r32 = meta.find_program("mgs")[0] # from miktex r3 = max(r31,r32) if r1+r2+r3 < 3: # Warn the user if platform.system().lower() == 'windows': text = ("Latex plotting features will not work.\n"+ "Please install MiKTeX.\n"+ "http://miktex.org/") elif platform.system().lower() == 'darwin': text = ("Latex plotting features will not work.\n"+ "Please install MacTeX.\n"+ "http://tug.org/mactex/") else: text = ("Latex plotting features will not work.\n"+ "Make sure you have these packages installed:\n"+ " - latex\n"+ " - dvipng\n"+ " - ghostscript\n"+ " - texlive-latex-base\n"+ " - texlive-math-extra\n") dlg = wx.MessageDialog(None, text, 'Latex not found', wx.OK | wx.ICON_EXCLAMATION) dlg.ShowModal() def OnLoadBatch(self, e=None, dataname=None): """ Open multiple data files and apply a single model to them We will create a new window where the user may decide which model to use. """ if dataname is None: ## Browse the file system SupFiletypes = opf.Filetypes.keys() # Sort them so we have "All suported filetypes" up front SupFiletypes.sort() filters = "" for i in np.arange(len(SupFiletypes)): # Add to the filetype filter filters = filters+SupFiletypes[i] if i+1 != len(SupFiletypes): # Add a separator if item is not last item filters = filters+"|" dlg = wx.FileDialog(self, "Open data files", self.dirname, "", filters, wx.FD_OPEN|wx.FD_MULTIPLE) if dlg.ShowModal() == wx.ID_OK: Datafiles = dlg.GetFilenames() # We rely on sorted filenames Datafiles.sort() # Workaround since 0.7.5 paths = dlg.GetPaths() if len(paths) != 0: self.dirname = os.path.split(paths[0])[0] else: self.dirname = dlg.GetDirectory() dlg.Destroy() else: dlg.Destroy() return else: Datafiles = list() if isinstance(dataname, list): for item in dataname: Datafiles.append(os.path.split(item)[1]) self.dirname = os.path.split(Datafiles[0])[0] else: Datafiles.append(os.path.split(dataname)[1]) self.dirname = os.path.split(dataname)[0] Datafiles.sort() ## Get information from the data files and let the user choose ## which type of curves to load and the corresponding model. # List of filenames that could not be opened BadFiles = list() Exceptions = list() # Lists for correlation, trace, type and names Correlation = list() Trace = list() Type = list() Filename = list() # there might be zipfiles with additional name info #Run = list() # Run number connecting AC1 AC2 CC12 CC21 Curveid = list() # Curve ID of each curve in a file Weight = list() WeightName = list() # Display a progress dialog for file import N = len(Datafiles) style = wx.PD_REMAINING_TIME|wx.PD_SMOOTH|wx.PD_AUTO_HIDE|\ wx.PD_CAN_ABORT dlgi = wx.ProgressDialog("Import", "Loading data...", maximum = N, parent=self, style=style) for j in np.arange(N): afile=Datafiles[j] # Let the user abort, if he wants to: if dlgi.Update(j, "Loading data: "+afile)[0] == False: dlgi.Destroy() return #Stuff = readfiles.openAny(self.dirname, afile) try: Stuff = readfiles.openAny(self.dirname, afile) except Exception as excpt: # The file does not seem to be what it seems to be. BadFiles.append(afile) Exceptions.append(excpt) # Print exception trb = traceback.format_exc(excpt) trb = "..." + trb.replace("\n", "\n...") warnings.warn("Problem processing a file."+\ " Reason:\n{}".format(trb)) else: for i in np.arange(len(Stuff["Type"])): Correlation.append(Stuff["Correlation"][i]) Trace.append(Stuff["Trace"][i]) Type.append(Stuff["Type"][i]) Filename.append(Stuff["Filename"][i]) if "Weight" in Stuff: Weight.append(Stuff["Weight"][i]) WeightName.append(Stuff["Weight Name"][i]) else: Weight.append(None) WeightName.append(None) #Curveid.append(str(i+1)) dlgi.Destroy() # Add number of the curve within a file. nameold = None counter = 1 for name in Filename: if name == nameold: Curveid.append(counter) counter += 1 else: counter = 1 nameold = name Curveid.append(counter) counter += 1 # If there are any BadFiles, we will let the user know. if len(BadFiles) > 0: # The file does not seem to be what it seems to be. errstr = "The following files could not be processed:\n" for item, excpt in zip(BadFiles, Exceptions): trb = traceback.format_exc(excpt) trb = " " + trb.replace("\n", "\n ") errstr += " " + item + "\n" + trb dlg = wx.MessageDialog(self, errstr, "Error", style=wx.ICON_WARNING|wx.OK|wx.CANCEL|wx.STAY_ON_TOP) if dlg.ShowModal() == wx.ID_CANCEL: return # Abort, if there are no curves left if len(Type) == 0: return # We want to give the user the possibility to choose from # several types of input functions. If curvelist contains # more than one type of data, like "AC1", "AC2", "CC1", ... # then the user may wish to only import "AC1" or "AC2" # functions. curvetypes = dict() for i in np.arange(len(Type)): try: curvetypes[Type[i]].append(i) except KeyError: curvetypes[Type[i]] = [i] # Fill in the Run information keys = curvetypes.keys() # This part is a little tricky. We assume at some point, that different # types of curves (AC1, AC2) belong to the same run. The only possible # chek/assumtion that we can make is: # If all curvetypes have the same amount of curves, then the curves # from different curvetypes belong together. # Unfortunately, we do not know how the curves are ordered. It could # be like this: # AC1-r1, AC2-r1, CC12-r1, CC21-r1, AC1-r2, AC1-r2, ... # or otherwise interlaced like this: # AC1-r1, AC2-r1, AC1-r2, AC1-r2, ... , CC12-r1, CC21-r1, ... # What we do know is that the first occurence of AC1 matches up with # the first occurences of AC2, CC12, etc. # We create the list/array *Run* whose elements are the run-number # at the position of the curves in *Types*. # Check if the type of curves have equal length lentypes = np.zeros(len(keys), dtype=int) for i in range(len(keys)): lentypes[i] = len(curvetypes[keys[i]]) if len(np.unique(np.array(lentypes))) == 1 and lentypes[0] != 0: # Made sure that AC1 AC2 CC12 CC21 have same length # Create Runs such that they are matched. # We assume that the curves are somehow interlaced and that # the Nth occurence of the keys in Types correspond to the # matching curves. # Also make sure that number starts at one for each selected file. coords = np.zeros(len(keys), dtype=np.int) Run = np.zeros(len(Curveid), dtype=np.int) WorkType = 1*Type for fname in np.unique(Filename): # unique returns sorted file names. for i in range(Filename.count(fname)/len(keys)): for k in range(len(keys)): coords[k] = WorkType.index(keys[k]) WorkType[coords[k]] = None Run[coords] = i + 1 #del WorkType else: Run = [""] * len(Curveid) # Now we have a dictionary curvetypes with keys that name # items in *Type* and which point to indices in *Type*. # We will display a dialog that lets the user choose what # to import. keys = curvetypes.keys() # Start the dialog for choosing types and model functions labels=list() for i in np.arange(len(Filename)): if Run[i] != "": labels.append("{} r{:03d}-{}".format(Filename[i], Run[i], Type[i])) else: labels.append("{} id{:03d}-{}".format(Filename[i], Curveid[i], Type[i])) Chosen = tools.ChooseImportTypesModel(self, curvetypes, Correlation, labels=labels) newCorrelation = list() newTrace = list() newType = list() newFilename = list() modelList = list() newCurveid = list() newRun = list() newWeight = list() newWeightName = list() if Chosen.ShowModal() == wx.ID_OK: keys = Chosen.typekeys # Keepdict is a list of indices pointing to Type or Correlation # of curves that are supposed to be kept. keepcurvesindex = Chosen.keepcurvesindex # modelids is a dictionary with chosen modelids. # The keys of modelids are indices in the *Type* etc. lists. modelids = Chosen.modelids if len(keys) == 0: # do not do anything return for key in keys: # create a new curvelist with the selected curves for index in curvetypes[key]: if keepcurvesindex.count(index) == 1: newCorrelation.append(Correlation[index]) newTrace.append(Trace[index]) newType.append(Type[index]) newFilename.append(Filename[index]) modelList.append(modelids[index]) newCurveid.append(Curveid[index]) newRun.append(Run[index]) newWeight.append(Weight[index]) newWeightName.append(WeightName[index]) Correlation = newCorrelation Trace = newTrace Type = newType Filename = newFilename Curveid = newCurveid Run = newRun Weight = newWeight WeightName = newWeightName else: return Chosen.Destroy() ## Import the data into new pages # curvelist is a list of numbers or labels that correspond # to each item in dataexp or trace. Each curvelist/filename # item will be converted to a string and then added to the # pages title. num = len(Type) # Show a nice progress dialog: style = wx.PD_REMAINING_TIME|wx.PD_SMOOTH|wx.PD_AUTO_HIDE|\ wx.PD_CAN_ABORT dlg = wx.ProgressDialog("Import", "Loading pages..." , maximum = num, parent=self, style=style) for i in np.arange(num): # create a new page CurPage = self.add_fitting_tab(event=None, modelid=modelList[i], counter=None) # Fill Page with data self.ImportData(CurPage, Correlation[i], Trace[i], curvetype=Type[i], filename=Filename[i], curveid=str(Curveid[i]), run=str(Run[i]), weights=Weight[i], weight_type=WeightName[i], trigger="page_add_batch") # Let the user abort, if he wants to: # We want to do this here before an empty page is added # to the notebok. if dlg.Update(i+1, "Loading pages...")[0] == False: break self.OnFNBPageChanged(trigger="page_add_finalize") # If the user did not select curves but chose a model, destroy # the dialog. dlg.Destroy() def OnOpenSession(self, e=None, sessionfile=None): """ Displays a dialog for opening PyCorrFit sessions Optional parameter sessionfile defines the file that shall be automatically loaded (without a dialog). See Also -------- `pycorrfit.openfile.LoadSessionData` """ # We need to clear the session before opening one. # This will also ask, if user wants to save the current session. clear = self.OnClearSession(clearmodels=True) if clear == "abort": # User pressed abort when he was asked if he wants to save # the session. Therefore, we cannot open a new session. return "abort" ## Create user dialog wc = [ "*{}".format(w) for w in opf.session_wildcards ] wcstring = "PyCorrFit session (*.pcfs)|{}".format(";".join(wc)) if sessionfile is None: dlg = wx.FileDialog(self, "Open session file", self.dirname, "", wcstring, wx.FD_OPEN) # user cannot do anything until he clicks "OK" if dlg.ShowModal() == wx.ID_OK: sessionfile = dlg.GetPath() (self.dirname, self.filename) = os.path.split( sessionfile) else: # User did not press OK # stop this function self.dirname = dlg.GetDirectory() return "abort" dlg.Destroy() Infodict = opf.LoadSessionData(sessionfile) ## Check for correct version try: arcv = LooseVersion(Infodict["Version"]) thisv = LooseVersion(self.version.strip()) if arcv > thisv: errstring = "Your version of Pycorrfit ("+str(thisv)+\ ") is too old to open this session ("+\ str(arcv).strip()+").\n"+\ "Please download the lates version of "+\ " PyCorrFit from \n"+doc.HomePage+".\n"+\ "Continue opening this session?" dlg = edclasses.MyOKAbortDialog(self, errstring, "Warning") returns = dlg.ShowModal() if returns == wx.ID_OK: dlg.Destroy() else: dlg.Destroy() return "abort" except: pass self.SetTitleFCS(self.filename) ## Background traces try: self.Background = Infodict["Backgrounds"] except: pass ## Preferences ## if Preferences is Not None: ## add them! # External functions for key in Infodict["External Functions"].keys(): NewModel = usermodel.UserModel(self) # NewModel.AddModel(self, code) # code is a list with strings # each string is one line NewModel.AddModel( Infodict["External Functions"][key].splitlines()) NewModel.ImportModel() # Internal functions: N = len(Infodict["Parameters"]) # Reset tabcounter self.tabcounter = 1 # Show a nice progress dialog: style = wx.PD_REMAINING_TIME|wx.PD_SMOOTH|wx.PD_AUTO_HIDE|\ wx.PD_CAN_ABORT dlg = wx.ProgressDialog("Import", "Loading pages...", maximum = N, parent=self, style=style) for i in np.arange(N): # Let the user abort, if he wants to: if dlg.Update(i+1, "Loading pages...")[0] == False: dlg.Destroy() return # Add a new page to the notebook. This page is created with # variables from models.py. We will write our data to # the page later. counter = Infodict["Parameters"][i][0] modelid = Infodict["Parameters"][i][1] Newtab = self.add_fitting_tab(modelid=modelid, counter=counter) # Add experimental Data # Import dataexp: number = counter.strip(":# ") pageid = int(number) dataexp = Infodict["Correlations"][pageid][1] if Infodict["Parameters"][0][7]: curvetype = "cc" else: curvetype = "ac" # As of 0.7.3: Add external weights to page try: for key in Infodict["External Weights"][pageid].keys(): Newtab.corr.set_weights(key, Infodict["External Weights"][pageid][key]) except KeyError: pass if len(Infodict["Parameters"][i]) >= 6: if Infodict["Parameters"][i][5][0] >= 3: # we have a weighted fit with external weights # these are usually averages. keys = Infodict["External Weights"][pageid].keys() keys = list(keys) keys.sort() key = keys[Infodict["Parameters"][i][5][0]-3] weights = Infodict["External Weights"][pageid][key] weight_type = key else: weight_type = None weights = None self.ImportData(Newtab, dataexp, trace=Infodict["Traces"][pageid], curvetype=curvetype, weights=weights, weight_type=weight_type) # Set Title of the Page try: Newtab.tabtitle.SetValue(Infodict["Comments"][pageid]) except: pass # no page title # Parameters self.UnpackParameters(Infodict["Parameters"][i], Newtab, init=True) # Supplementary data fit_results = dict() fit_results["weighted fit"] = Infodict["Parameters"][i][5][0] > 0 try: Sups = Infodict["Supplements"][pageid] except KeyError: pass else: if Sups.has_key("FitErr"): ervals = list() for errInfo in Sups["FitErr"]: ervals.append(float(errInfo[1])) fit_results["fit error estimation"] = ervals try: if len(Sups["Global Share"]) > 0: fit_results["global pages"] = Sups["Global Share"] except KeyError: pass try: fit_results["chi2"] = Sups["Chi sq"] except: pass # also set fit parameters fit_results["fit parameters"] = np.where(Infodict["Parameters"][i][3])[0] # set fit weights for plotting if fit_results["weighted fit"]: # these were already imported: try: weights = Infodict["External Weights"][pageid] for w in weights.keys(): fit_results["weighted fit type"] = w fit_results["fit weights"] = weights[w] except KeyError: pass Newtab.corr.fit_results = fit_results # Plot everything Newtab.PlotAll(trigger="page_add_batch") # Set Session Comment dlg.Destroy() try: self.SessionComment = Infodict["Comments"]["Session"] except: pass try: prefdict = Infodict["Preferences"] except: pass else: for key in prefdict: if key == "Pages prevent batch modification": for pid in prefdict[key]: for i in np.arange(N): page = self.notebook.GetPage(i) if page.counter.strip("#: ") == pid: page.prevent_batch_modification = True if self.notebook.GetPageCount() > 0: # Enable the "Current" Menu self.EnableToolCurrent(True) self.OnFNBPageChanged(trigger="page_add_finalize") else: # There are no pages in the session. # Disable some menus and close some dialogs self.EnableToolCurrent(False) def OnSaveData(self,e=None): """ Opens a dialog for saving correlation data of a Page Also saves the parameters that are accessible in the Info dialog and the trace(s). """ # What Data do we wish to save? Page = self.notebook.GetCurrentPage() # Export CSV data filename = "#{:04d}_{}".format(int(Page.counter.strip(":# ")), Page.title.strip()) dlg = wx.FileDialog(self, "Save curve", self.dirname, filename, "Correlation with trace (*.csv)|*.csv;*.*"+\ "|Correlation only (*.csv)|*.csv;*.*", wx.FD_SAVE|wx.FD_OVERWRITE_PROMPT) # user cannot do anything until he clicks "OK" if dlg.ShowModal() == wx.ID_OK: path = dlg.GetPath() # Workaround since 0.7.5 if not path.lower().endswith(".csv"): path += ".csv" (self.dirname, self.filename) = os.path.split(path) if dlg.GetFilterIndex() == 0: savetrace = True else: savetrace = False # Collect info on page InfoMan = tools.info.InfoClass(CurPage=Page) PageInfo = InfoMan.GetCurFancyInfo() opf.ExportCorrelation(path, Page.corr, PageInfo, savetrace=savetrace) dlg.Destroy() def OnSavePlotCorr(self, e=None): """ make some output """ # Saving dialog box. uselatex = self.MenuUseLatex.IsChecked() verbose = self.MenuVerbose.IsChecked() show_weights = self.MenuShowWeights.IsChecked() Page = self.notebook.GetCurrentPage() try: plotting.savePlotCorrelation(self, self.dirname, Page, uselatex, verbose, show_weights) except NameError as excpt: trb = traceback.format_exc(excpt) trb = " " + trb.replace("\n", "\n ") raise NameError("Please make sure matplotlib is installed:\n"+trb) def OnSavePlotTrace(self, e=None): """ make some output """ # Saving dialog box. uselatex = 1*self.MenuUseLatex.IsChecked() verbose = 1*self.MenuVerbose.IsChecked() Page = self.notebook.GetCurrentPage() try: plotting.savePlotTrace(self, self.dirname, Page, uselatex, verbose) except NameError as excpt: trb = traceback.format_exc(excpt) trb = " " + trb.replace("\n", "\n ") raise NameError("Please make sure matplotlib is installed:\n"+trb) def OnSaveSession(self,e=None): """ Displays a dialog for saving PyCorrFit sessions Returns ------- - the filename of the session if it was saved - None, if the user canceled the action See Also -------- `pycorrfit.openfile.SaveSessionData` """ # Parameters are all in one dictionary: Infodict = dict() Infodict["Backgrounds"] = self.Background # Background list Infodict["Comments"] = dict() # Session comment "Session" and Pages int Infodict["Correlations"] = dict() # all correlation curves Infodict["External Functions"] = dict() # external model functions Infodict["External Weights"] = dict() # additional weights for the pages Infodict["Parameters"] = dict() # all parameters of all pages Infodict["Preferences"] = dict() # not used Infodict["Supplements"] = dict() # error estimates for fitting Infodict["Traces"] = dict() # all traces # Save each Page N = self.notebook.GetPageCount() # External functions for usermodelid in mdls.modeltypes["User"]: # Those models belong to external user functions. doc = mdls.modeldict[usermodelid][-1].func_doc doc = doc.splitlines() docnew="" for line in doc: docnew = docnew+line.strip()+"\r\n" Infodict["External Functions"][usermodelid] = docnew for i in np.arange(N): # Set Page Page = self.notebook.GetPage(i) counter = int(Page.counter.strip(":# ")) # Apply currently set parameters Page.apply_parameters() # Set parameters Infodict["Parameters"][counter] = self.PackParameters(Page) corr = Page.corr # Set supplementary information, such as errors of fit if hasattr(corr, "fit_results"): Infodict["Supplements"][counter] = dict() if corr.fit_results.has_key("chi2"): Infodict["Supplements"][counter]["Chi sq"] = float(corr.fit_results["chi2"]) else: Infodict["Supplements"][counter]["Chi sq"] = 0 PageList = list() for pagei in Page.GlobalParameterShare: PageList.append(int(pagei)) Infodict["Supplements"][counter]["Global Share"] = PageList # optimization error Alist = list() if (# there is an error key corr.fit_results.has_key("fit error estimation") and # the errors were computed len(corr.fit_results["fit error estimation"]) != 0 and # len(errors) matches len(fit parameters) len(corr.fit_results["fit error estimation"]) == len(corr.fit_results["fit parameters"]) ): for ii, fitpid in enumerate(corr.fit_results["fit parameters"]): Alist.append([int(fitpid), float(corr.fit_results["fit error estimation"][ii]) ]) Infodict["Supplements"][counter]["FitErr"] = Alist # Set exp data Infodict["Correlations"][counter] = [corr.lag_time, corr.correlation] # Also save the trace Infodict["Traces"][counter] = corr.traces # Append title to Comments # #Comments.append(Page.tabtitle.GetValue()) Infodict["Comments"][counter] = Page.tabtitle.GetValue() # Add additional weights to Info["External Weights"] external_weights = dict() for key in corr._fit_weight_memory.keys(): if isinstance(corr._fit_weight_memory[key], np.ndarray): external_weights[key] = corr._fit_weight_memory[key] # also save current weights if hasattr(corr, "fit_results"): if corr.fit_results.has_key("weighted fit type"): fittype = corr.fit_results["weighted fit type"] fitweight = corr.fit_results["fit weights"] external_weights[fittype] = fitweight Infodict["External Weights"][counter] = external_weights # Append Session Comment: Infodict["Comments"]["Session"] = self.SessionComment # Protected pages: protpage = [] for i in np.arange(N): # Set Page Page = self.notebook.GetPage(i) if Page.prevent_batch_modification: protpage.append(Page.counter.strip("#: ")) Infodict["Preferences"]["Pages prevent batch modification"] = protpage # File dialog dlg = wx.FileDialog(self, "Save session file", self.dirname, "", "PyCorrFit session (*.pcfs)|*.pcfs|All files (*.*)|*.*", wx.FD_SAVE|wx.FD_OVERWRITE_PROMPT) if dlg.ShowModal() == wx.ID_OK: # Save everything path = dlg.GetPath() # Workaround since 0.7.5 (self.dirname, self.filename) = os.path.split(path) opf.SaveSessionData(path, Infodict) else: self.dirname = dlg.GetDirectory() self.filename = None # Set title of our window if (self.filename is not None and not self.filename.endswith(".pcfs")): self.filename += ".pcfs" dlg.Destroy() self.SetTitleFCS(self.filename) return self.filename def OnShell(self, e=None): Shell = wx.py.shell.ShellFrame(self, title="PyCorrFit Shell", style=wx.DEFAULT_FRAME_STYLE|wx.FRAME_FLOAT_ON_PARENT, locals=locals()) # Set window icon if self.MainIcon is not None: wx.Frame.SetIcon(Shell, self.MainIcon) Shell.Show(True) def OnSoftware(self, event=None): # Show About Information text = doc.SoftwareUsed() wx.MessageBox(text, 'Software', wx.OK | wx.ICON_INFORMATION) def OnTool(self, event): eid = event.GetId() try: # Check if a tool is open self.ToolsOpen[eid] except KeyError: # eid is not on self.ToolOpen # So we open the dialog and add it to the list self.ToolsOpen[eid] = self.Tools[eid](self) self.ToolsOpen[eid].MyID = eid self.ToolsOpen[eid].Bind(wx.EVT_CLOSE, self.ToolsOpen[eid].OnClose) self.toolmenu.Check(eid, True) else: # We close it then self.ToolsOpen[eid].OnClose() def OnUpdate(self, event): misc.Update(self) def OnWiki(self, e=None): """ Go to the GitHub Wiki page""" webbrowser.open(doc.GitWiki) def PackParameters(self, Page): """ Gets all parameters from a page and returns a list object, that can be used to save as e.g. a safe YAML file """ Page.apply_parameters() # Get Model ID modelid = Page.corr.fit_model.id # Get Page number counter = Page.counter active_numbers = Page.corr.fit_parameters # Array, Parameters active_fitting = Page.corr.fit_parameters_variable crop = Page.corr.fit_ival Parms = [counter, modelid, active_numbers, active_fitting, crop] # Weighting: # Additional parameters as of v.0.2.0 # Splines and model function: # Additional parameters as of v.6.4.0 #self.Fitbox=[ fitbox, weightedfitdrop, fittext, fittext2, # fittextvar, fitspin, buttonfit ] # Some fits like Spline have a number of knots of the spline # that is important for fitting. If there is a number in the # Dropdown, save it. # knots = str(Page.FitKnots) knots = filter(lambda x: x.isdigit(), knots) if len(knots) == 0: knots = None else: knots = int(knots) weighted = Page.weighted_fittype_id weights = Page.weighted_nuvar algorithm = Page.corr.fit_algorithm Parms.append([weighted, weights, knots, algorithm]) # Additional parameters as of v.0.2.9 # Which Background signal is selected? # The Background information is in the list *self.Background*. Parms.append([Page.bgselected, Page.bg2selected]) # Additional parameter as of v.0.5.8 # Is the Experimental data (if it exists) AC or CC? Parms.append(Page.IsCrossCorrelation) # Additional parameter as of v.0.7.8 # The selection of a normalization parameter (None or integer) Parms.append(Page.corr.normparm) # Parameter ranges Parms.append(Page.corr.fit_parameters_range) return Parms def UnpackParameters(self, Parms, Page, init=False): """ Apply the given parameters to the Page in question. This function contains several *len(Parms) >= X* statements. These are used for opening sessions that were saved using earlier versions of PyCorrFit. The `init` variable can be set if fundamental changes are made (loading data). This e.g. might change the type (Autocorrelation/Cross-Correlation) of the page. """ modelid = Parms[1] if Page.corr.fit_model.id != modelid: print "Wrong model: "+str(Page.corr.fit_model.id)+" vs. "+str(modelid) return active_values = Parms[2] active_fitting = Parms[3] # As of version 0.7.0: square pinhole TIR-FCS models # use sigma instead of lambda, NA and sigma_0. This # is for backwards compatibility: changeTIRF = False if modelid in [6000, 6010]: if len(Parms[2]) > len(mdls.valuedict[modelid][0]): lindex = 1 changeTIRF = True elif (modelid == 6022 and len(Parms[2]) == len(mdls.valuedict[modelid][0]) + 1): # Change in verson 0.8.7: TIRF_2D2D model remove d_eva active_values = np.delete(active_values, 4) active_fitting = np.delete(active_fitting, 4) elif modelid in [6020, 6021, 6022, 6023]: if len(Parms[2]) > len(mdls.valuedict[modelid][0]): lindex = 2 changeTIRF = True if changeTIRF: lamb = active_values[lindex] NA = active_values[lindex+1] sigma = 0.21*lamb/NA active_values[lindex] = sigma active_values = np.delete(active_values,lindex+1) active_fitting = np.delete(active_fitting, lindex+1) # Cropping: What part of the correlation should be displayed. Page.corr.fit_ival = Parms[4] # Add parameters and fitting to the created page. # We need to run Newtab.apply_parameters_reverse() in order # for the data to be displayed in the user interface. Page.corr.fit_parameters = active_values Page.corr.fit_parameters_variable = active_fitting # Weighted fitting if len(Parms) >= 6: if len(Parms[5]) == 2: [weighted, weights] = Parms[5] knots = None elif len(Parms[5]) == 3: # We have knots as of v. 0.6.5 [weighted, weights, knots] = Parms[5] else: # We have different fitting algorithms as of v. 0.8.3 [weighted, weights, knots, algorithm] = Parms[5] Page.corr.fit_algorithm = algorithm if knots is not None: # This is done with apply_paramters_reverse: # text = Page.Fitbox[1].GetValue() # text = filter(lambda x: x.isalpha(), text) # Page.Fitbox[1].SetValue(text+str(knots)) Page.FitKnots = int(knots) if weighted is False: weighted = 0 elif weighted is True: weighted = 1 elif len(Page.Fitbox[1].GetItems())-1 < weighted: # Is the case, e.g. when we have an average std, # but this page is not an average. weighted = 0 Page.weighted_fittype_id = weighted Page.weighted_nuvar = weights Page.apply_parameters_reverse() if Page.corr.correlation is not None: Page.Fit_enable_fitting() Page.Fit_WeightedFitCheck() # Set which background correction the Page uses: if len(Parms) >= 7: # causality check: if len(self.Background) > Parms[6][0]: Page.bgselected = Parms[6][0] if len(Parms[6]) == 2: # New in 0.8.1: CC background correction Page.bg2selected = Parms[6][1] # New feature since 0.7.8: BG selection on Page panel Page.OnAmplitudeCheck("init") # Set if Newtab is of type cross-correlation: if len(Parms) >= 8: if Parms[7]: Page.corr.corr_type = "cc" else: Page.corr.corr_type = "ac" Page.OnAmplitudeCheck() if len(Parms) >= 9: # New feature in 0.7.8 includes normalization to a fitting # parameter. Page.corr.normparm = Parms[8] Page.apply_parameters_reverse() Page.OnAmplitudeCheck("init") if len(Parms) >= 10: Page.corr.fit_parameters_range = np.array(Parms[9]) ## If we want to add more stuff, we should do something like: ## if len(Parms) >= 11: ## nextvalue = Parms[10] ## Such that we are compatible to earlier versions of ## PyCorrFit sessions. def SetTitleFCS(self, title): if title is not None and len(title) != 0: title = " {"+title+"}" self.SetTitle('PyCorrFit ' + self.version + title) else: self.SetTitle('PyCorrFit ' + self.version) def MyExceptionHook(etype, value, trace): """ Handler for all unhandled exceptions. :param `etype`: the exception type (`SyntaxError`, `ZeroDivisionError`, etc...); :type `etype`: `Exception` :param string `value`: the exception error message; :param string `trace`: the traceback header, if any (otherwise, it prints the standard Python header: ``Traceback (most recent call last)``. 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""" from __future__ import print_function from distutils.version import LooseVersion import sys class Fake(object): """ Fake module. """ def __init__(self): self.__version__ = "0.0 unknown" self.version = "0.0 unknown" self.use = lambda x: None # Import matplotlib a little earlier. This way some problems with saving # dialogs that are not made by "WXAgg" are solved. try: import matplotlib except ImportError: matplotlib = Fake() # We do catch warnings about performing this before matplotlib.backends stuff #matplotlib.use('WXAgg') # Tells matplotlib to use WxWidgets import warnings with warnings.catch_warnings(): warnings.simplefilter("ignore") matplotlib.use('WXAgg') # Tells matplotlib to use WxWidgets for dialogs import numpy as np # NumPy import os import scipy # Sympy is optional: try: import sympy except ImportError: print("Importing sympy failed! Checking of external model functions") print("will not work!") # We create a fake module sympy with a __version__ property. # This way users can run PyCorrFit without having installed sympy. sympy = Fake() # We must not import wx here. frontend/gui does that. If we do import wx here, # somehow unicode characters will not be displayed correctly on windows. # import wx import yaml ## Continue with the import: sys.path.append(os.path.abspath(os.path.dirname(__file__))) from . import doc from . import frontend as gui # The actual program def CheckVersion(given, required, name): """ For a given set of versions str *required* and str *given*, where version are usually separated by dots, print whether for the module str *name* the required verion is met or not. """ try: req = LooseVersion(required) giv = LooseVersion(given) except: print(" WARNING: Could not verify version of "+name+".") return if req > giv: print(" WARNING: You are using "+name+" v. "+given+\ " | Required: "+name+" "+ required) else: print(" OK: "+name+" v. "+given+" | "+required+" required") ## Start gui def Main(): ## VERSION version = doc.__version__ __version__ = version print(gui.doc.info(version)) ## Check important module versions print("\n\nChecking module versions...") CheckVersion(matplotlib.__version__, "1.0.0", "matplotlib") CheckVersion(np.__version__, "1.5.1", "NumPy") CheckVersion(yaml.__version__, "3.09", "PyYAML") CheckVersion(scipy.__version__, "0.8.0", "SciPy") CheckVersion(sympy.__version__, "0.7.2", "sympy") CheckVersion(gui.wx.__version__, "2.8.10.1", "wxPython") ## Start gui app = gui.MyApp(False) frame = gui.MyFrame(None, -1, version) app.frame = frame # Before starting the main loop, check for possible session files # in the arguments. sysarg = sys.argv for arg in sysarg: if arg.endswith(".pcfs"): print("\nLoading Session "+arg) frame.OnOpenSession(sessionfile=arg) break if arg.endswith(".fcsfit-session.zip"): print("\nLoading Session "+arg) frame.OnOpenSession(sessionfile=arg) break elif arg[:6] == "python": pass elif arg[-12:] == "PyCorrFit.py": pass elif arg[-11:] == "__main__.py": pass else: print("Ignoring command line parameter: "+arg) app.MainLoop() if __name__ == "__main__": Main() PyCorrFit-0.9.9/pycorrfit/gui/misc.py000066400000000000000000000204041274265304700175360ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ PyCorrFit Module misc Non-science related code. """ import codecs from distutils.version import LooseVersion # For version checking import numpy as np import os import tempfile import urllib2 import webbrowser import wx.html import wx.lib.delayedresult as delayedresult from . import doc # Documentation/some texts # The icon file was created with # img2py -i -n Main PyCorrFit_icon.png icon.py from . import icon # Contains the program icon class UpdateDlg(wx.Frame): def __init__(self, parent, valuedict): description = valuedict["Description"] homepage = valuedict["Homepage"] githome = valuedict["Homepage_GIT"] changelog = valuedict["Changelog"] pos = parent.GetPosition() pos = (pos[0]+100, pos[1]+100) wx.Frame.__init__(self, parent, wx.ID_ANY, title="Update", size=(230,240), pos=pos) self.changelog = changelog # Fill html content html = wxHTML(self) string = '' +\ " PyCorrFit
" +\ "Your version: " + description[0]+"
" +\ "Latest version: " + description[1]+"
" +\ "(" + description[2]+")

" if len(homepage) != 0: string = string + 'Homepage
' if len(githome) != 0: string = string + 'Repository
' if len(changelog) != 0: string = string + \ 'Change Log' string = string+'

' html.SetPage(string) self.Bind(wx.EVT_CLOSE, self.Close) # Set window icon ico = getMainIcon() wx.Frame.SetIcon(self, ico) def Close(self, event): if len(self.changelog) != 0: # Cleanup downloaded file, if it was downloaded if self.changelog != doc.StaticChangeLog: os.remove(self.changelog) self.Destroy() class wxHTML(wx.html.HtmlWindow): def OnLinkClicked(self, link): webbrowser.open(link.GetHref()) def parseString2Pagenum(parent, string, nodialog=False): """ Parse a string with a list of pagenumbers to an integer list with page numbers. e.g. "1-3,5,7" --> [1,2,3,5,7] parent is important """ listFull = string.split(",") PageNumbers = list() try: for item in listFull: pagerange = item.split("-") start = pagerange[0].strip() start = int(filter(type(start).isdigit, start)) end = pagerange[-1].strip() end = int(filter(type(end).isdigit, end)) for i in np.arange(end-start+1)+start: PageNumbers.append(i) PageNumbers.sort() return PageNumbers except: if nodialog is False: errstring = "Invalid syntax in page selection: "+string+\ ". Please use a comma separated list with"+\ " optional dashes, e.g. '1-3,6,8'." try: wx.MessageDialog(parent, errstring, "Error", style=wx.ICON_ERROR|wx.OK|wx.STAY_ON_TOP) except: raise ValueError(errstring) else: raise ValueError(errstring) return None def parsePagenum2String(pagenumlist): """ Make a string with dashes and commas from a list of pagenumbers. e.g. [1,2,3,5,7] --> "1-3,5,7" """ if len(pagenumlist) == 0: return "" # Make sure we have integers newlist = list() for num in pagenumlist: newlist.append(int(num)) newlist.sort() # begin string string = str(newlist[0]) # iteration through list: dash = False for i in np.arange(len(newlist)-1)+1: if dash == True: if newlist[i]-1 == newlist[i-1]: pass else: string += "-"+str(newlist[i-1])+", "+str(newlist[i]) dash = False else: if newlist[i]-1 == newlist[i-1]: if newlist[i]-2 == newlist[i-2]: dash = True elif len(newlist) != i+1 and newlist[i]+1 == newlist[i+1]: dash = True else: string += ", "+str(newlist[i]) dash = False else: dash = False string += ", "+str(newlist[i]) # Put final number if newlist[i] == newlist[-1]: if parseString2Pagenum(None, string)[-1] != newlist[i]: if dash == True: string += "-"+str(newlist[i]) else: string += ", "+str(newlist[i]) return string def removewrongUTF8(name): newname = u"" for char in name: try: codecs.decode(char, "UTF-8") except: pass else: newname += char return newname def getMainIcon(pxlength=32): """ *pxlength* is the side length in pixels of the icon """ # Set window icon iconBMP = icon.getMainBitmap() # scale image = wx.ImageFromBitmap(iconBMP) image = image.Scale(pxlength, pxlength, wx.IMAGE_QUALITY_HIGH) iconBMP = wx.BitmapFromImage(image) iconICO = wx.IconFromBitmap(iconBMP) return iconICO def Update(parent): """ This is a thread for _Update """ parent.StatusBar.SetStatusText("Connecting to server...") delayedresult.startWorker(_UpdateConsumer, _UpdateWorker, wargs=(parent,), cargs=(parent,)) def _UpdateConsumer(delayedresult, parent): results = delayedresult.get() dlg = UpdateDlg(parent, results) dlg.Show() parent.StatusBar.SetStatusText("...update status: "+results["Description"][2]) def _UpdateWorker(parent): changelog = "" hpversion = None # I created this TXT record to keep track of the current web presence. try: urlopener = urllib2.urlopen(doc.HomePage, timeout=2) homepage = urlopener.geturl() except: homepage = doc.HomePage try: urlopener2 = urllib2.urlopen(doc.GitHome, timeout=2) githome = urlopener2.geturl() except: githome = "" # Find the changelog file try: responseCL = urllib2.urlopen(homepage+doc.ChangeLog, timeout=2) except: CLfile = doc.GitChLog else: fileresponse = responseCL.read() CLlines = fileresponse.splitlines() # We have a transition between ChangeLog.txt on the homepage # containing the actual changelog or containing a link to # the ChangeLog file. if len(CLlines) == 1: CLfile = CLlines[0] else: hpversion = CLlines[0] CLfile = doc.GitChLog # Continue version comparison if True continuecomp = False try: responseVer = urllib2.urlopen(CLfile, timeout=2) except: if hpversion == None: newversion = "unknown" action = "cannot connect to server" else: newversion = hpversion continuecomp = True else: continuecomp = True changelog = responseVer.read() newversion = changelog.splitlines()[0] if continuecomp: new = LooseVersion(newversion) old = LooseVersion(parent.version) if new > old: action = "update available" elif new < old: action = "whoop you rock!" else: action = "state of the art" description = [parent.version, newversion, action] if len(changelog) != 0: changelogfile = tempfile.mktemp()+"_PyCorrFit_ChangeLog"+".txt" clfile = open(changelogfile, 'wb') clfile.write(changelog) clfile.close() else: changelogfile=doc.StaticChangeLog results = dict() results["Description"] = description results["Homepage"] = homepage results["Homepage_GIT"] = githome results["Changelog"] = changelogfile return results PyCorrFit-0.9.9/pycorrfit/gui/page.py000066400000000000000000001075671274265304700175370ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ PyCorrFit Module frontend The frontend displays the GUI (Graphic User Interface). All functions and modules are called from here. """ import numpy as np # NumPy import warnings import wx # GUI interface wxPython import wx.lib.plot as plot # Plotting in wxPython import wx.lib.scrolledpanel as scrolled from pycorrfit import models as mdls from pycorrfit import fit from pycorrfit import Correlation from . import tools from . import wxutils class FittingPanel(wx.Panel): """ Those are the Panels that show the fitting dialogs with the Plots. """ def __init__(self, parent, counter, modelid, active_parms, tau=None): """ Initialize with given parameters. """ wx.Panel.__init__(self, parent=parent, id=wx.ID_ANY) self.parent = parent self.corr = Correlation(fit_model=modelid) if tau is not None: self.corr.lag_time = tau # active_parameters: # [0] labels # [1] values # [2] bool values to fit self.corr.fit_parameters = active_parms[1] self.corr.fit_parameters_variable = active_parms[2] self._bgselected = None self._bg2selected = None self.FitKnots = 5 # number of knots for spline fit or similiars self.weighted_fittype_id = 0 # integer (drop down item) self.weighted_nuvar = 3 # bins for std-dev. (left and rigth) # The weights that are plotted in the page # This is set by the PlotAll function self.weights_plot_fill_area = None # A list containing page numbers that share parameters with this page. # This parameter is defined by the global fitting tool and is saved in # sessions. self.GlobalParameterShare = [] # Counts number of Pages already created: self.counter = counter # Has inital plot been performed? # Call PlotAll("init") to set this to true. If it is true, then # nothing will be plotted if called with "init" self.InitialPlot = False # Model we are using # Tool statistics uses this list: self.StatisticsCheckboxes = None ### Splitter window # Sizes size = parent.notebook.GetSize() tabsize = 33 size[1] = size[1] - tabsize self.sizepanelx = 270 canvasx = size[0]-self.sizepanelx+5 sizepanel = (self.sizepanelx, size[1]) sizecanvas = (canvasx, size[1]) self.sp = wx.SplitterWindow(self, size=size, style=wx.SP_3DSASH) # This is necessary to prevent "Unsplit" of the SplitterWindow: self.sp.SetMinimumPaneSize(1) ## Settings Section (left side) #self.panelsettings = wx.Panel(self.sp, size=sizepanel) self.panelsettings = scrolled.ScrolledPanel(self.sp, size=sizepanel) self.panelsettings.SetupScrolling(scroll_x=False) ## Setting up Plot (correlation + chi**2) self.spcanvas = wx.SplitterWindow(self.sp, size=sizecanvas, style=wx.SP_3DSASH) # This is necessary to prevent "Unsplit" of the SplitterWindow: self.spcanvas.SetMinimumPaneSize(1) # y difference in pixels between Auocorrelation and Residuals cupsizey = size[1]*4/5 # Calculate initial data self.calculate_corr() # Draw the settings section self.settings() # Load default values self.apply_parameters_reverse() # Upper Plot for plotting of Correlation Function self.canvascorr = plot.PlotCanvas(self.spcanvas) self.canvascorr.setLogScale((True, False)) self.canvascorr.SetEnableZoom(True) self.PlotAll(event="init", trigger="tab_init") self.canvascorr.SetSize((canvasx, cupsizey)) # Lower Plot for plotting of the residuals self.canvaserr = plot.PlotCanvas(self.spcanvas) self.canvaserr.setLogScale((True, False)) self.canvaserr.SetEnableZoom(True) self.canvaserr.SetSize((canvasx, size[1]-cupsizey)) self.spcanvas.SplitHorizontally(self.canvascorr, self.canvaserr, cupsizey) self.sp.SplitVertically(self.panelsettings, self.spcanvas, self.sizepanelx) # Bind resizing to resizing function. wx.EVT_SIZE(self, self.OnSize) @property def active_parms(self): names = self.corr.fit_model.parameters[0] parms = self.corr.fit_parameters bools = self.corr.fit_parameters_variable return [names, parms, bools] @property def IsCrossCorrelation(self): return self.corr.is_cc @property def modelid(self): return self.corr.fit_model.id @property def prevent_batch_modification(self): return self.wxCBPreventBatchParms.GetValue() @prevent_batch_modification.setter def prevent_batch_modification(self, value): self.wxCBPreventBatchParms.SetValue(value) @property def title(self): return self.tabtitle.GetValue() @title.setter def title(self, title): self.tabtitle.SetValue(title.strip()) self.corr.title = title.strip() @property def traceavg(self): warnings.warn("Trace average always set to none!") return None @property def tracecc(self): if self.corr.is_cc and len(self.corr.traces) != 0: return self.corr.traces else: return None @property def bgselected(self): return self._bgselected @bgselected.setter def bgselected(self, value): if value is None: self.corr.backgrounds=[] return # check paren.Background and get id background = self.parent.Background[value] self.corr.background_replace(0, background) self._bgselected = value @property def bg2selected(self): return self._bg2selected @bg2selected.setter def bg2selected(self, value): if value is None: if self.corr.is_cc: self.corr.backgrounds=[] return # check paren.Background and get id background = self.parent.Background[value] self.corr.background_replace(1, background) self._bg2selected = value def apply_parameters(self, event=None): """ Read the values from the GUI form and write it to the pages parameters / correlation class. This function is called when the "Apply" button is hit. """ modelid = self.corr.fit_model.id parameters = list() parameters_variable = list() # Read parameters from form and update self.active_parms[1] for i in np.arange(len(self.spincontrol)): parameters.append(1*self.spincontrol[i].GetValue()) parameters_variable.append(self.checkboxes[i].GetValue()) self.corr.fit_parameters_variable = np.array(parameters_variable, dtype=bool) # As of version 0.7.5: we want the units to be displayed # human readable - the way they are displayed # in the Page info tool. # Here: Convert human readable units to program internal # units parmsconv = mdls.GetInternalFromHumanReadableParm( modelid, np.array(parameters))[1] self.corr.fit_parameters = parmsconv # Fitting parameters self.weighted_nuvar = self.Fitbox[5].GetValue() self.weighted_fittype_id = self.Fitbox[1].GetSelection() fitbox_value = self.Fitbox[1].GetValue() if self.weighted_fittype_id == -1: # User edited knot number Knots = fitbox_value Knots = filter(lambda x: x.isdigit(), Knots) if Knots == "": Knots = "5" self.weighted_fittype_id = 1 self.FitKnots = str(Knots) fit_weight_type = "spline{}".format(self.FitKnots) fit_weight_data = self.weighted_nuvar elif self.weighted_fittype_id == 1: Knots = fitbox_value Knots = filter(lambda x: x.isdigit(), Knots) self.FitKnots = int(Knots) fit_weight_type = "spline{}".format(self.FitKnots) fit_weight_data = self.weighted_nuvar elif self.weighted_fittype_id == 0: fit_weight_type = "none" fit_weight_data = None elif self.weighted_fittype_id == 2: fit_weight_type = "model function" fit_weight_data = self.weighted_nuvar else: # fitbox_selection > 2: fit_weight_type = fitbox_value self.corr.fit_weight_type = fitbox_value fit_weight_data = self.corr.fit_weight_data # Fitting algorithm keys = fit.GetAlgorithmStringList()[0] idalg = self.AlgorithmDropdown.GetSelection() self.corr.fit_algorithm = keys[idalg] self.corr.fit_weight_type = fit_weight_type self.corr.fit_weight_data = fit_weight_data # If parameters have been changed because of the check_parms # function, write them back. self.apply_parameters_reverse() def apply_parameters_reverse(self, event=None): """ Read the values from the pages parameters and write it to the GUI form. """ modelid = self.corr.fit_model.id # # As of version 0.7.5: we want the units to be displayed # human readable - the way they are displayed # in the Page info tool. # # Here: Convert program internal units to # human readable units parameters = mdls.GetHumanReadableParms(modelid, self.corr.fit_parameters)[1] parameters_variable = self.corr.fit_parameters_variable # Write parameters to the form on the Page for i in np.arange(len(self.active_parms[1])): self.spincontrol[i].SetValue(parameters[i]) self.checkboxes[i].SetValue(parameters_variable[i]) # Fitting parameters self.Fitbox[5].SetValue(self.weighted_nuvar) idf = self.weighted_fittype_id List = self.Fitbox[1].GetItems() List[1] = "spline ("+str(self.FitKnots)+" knots)" self.Fitbox[1].SetItems(List) self.Fitbox[1].SetSelection(idf) # Normalization if self.corr.normparm is None: normsel = 0 else: normsel = self.corr.normparm + 1 self.AmplitudeInfo[2].SetSelection(normsel) # Fitting algorithm keys = fit.GetAlgorithmStringList()[0] idalg = keys.index(self.corr.fit_algorithm) self.AlgorithmDropdown.SetSelection(idalg) self.updateChi2() def calculate_corr(self): """ Calculate model correlation function """ return self.corr.modeled def Fit_enable_fitting(self): """ Enable the fitting button and the weighted fit control""" #self.Fitbox = [ fitbox, weightedfitdrop, fittext, fittext2, # fittextvar, fitspin, buttonfit, textalg, # self.AlgorithmDropdown] self.Fitbox[0].Enable() self.Fitbox[1].Enable() self.Fitbox[6].Enable() self.Fitbox[7].Enable() self.Fitbox[8].Enable() def Fit_function(self, event=None, noplots=False, trigger=None): """ Calls the fit function. `noplots=True` prevents plotting of spline fits `trigger` is passed to page.PlotAll. If trigger is "fit_batch", then `noplots` is set to `True`. """ tools.batchcontrol.FitProgressDlg(self, self) def Fit_finalize(self, trigger): """ Things that need be done after fitting """ # Reset list of globally shared parameters, because we are only # fitting this single page now. # TODO: # - also remove this page from the GlobalParameterShare list of # the other pages self.GlobalParameterShare = [] # Update spin-control values self.apply_parameters_reverse() # Plot everything try: self.PlotAll(trigger=trigger) except OverflowError: # Sometimes parameters are just bad and # we still want the user to use the # program. warnings.warn("Could not plot canvas.") # update displayed chi2 self.updateChi2() def Fit_WeightedFitCheck(self, event=None): """ Enable Or disable variance calculation, dependent on "Weighted Fit" checkbox """ #self.Fitbox=[ fitbox, weightedfitdrop, fittext, fittext2, fittextvar, # fitspin, buttonfit ] weighted = (self.Fitbox[1].GetSelection() != 0) # In the case of "Average" we do not enable the # "Calculation of variance" part. if weighted is True and self.Fitbox[1].GetValue() != "Average": self.Fitbox[2].Enable() self.Fitbox[3].Enable() self.Fitbox[4].Enable() self.Fitbox[5].Enable() else: self.Fitbox[2].Disable() self.Fitbox[3].Disable() self.Fitbox[4].Disable() self.Fitbox[5].Disable() def MakeStaticBoxSizer(self, boxlabel): """ Create a Box with check boxes (fit yes/no) and possibilities to change initial values for fitting. Parameters: *boxlabel*: The name of the box (is being displayed) *self.active_parms[0]*: A list of things to put into the box Returns: *sizer*: The static Box *check*: The (un)set checkboxes *spin*: The spin text fields """ modelid = self.corr.fit_model.id box = wx.StaticBox(self.panelsettings, label=boxlabel) sizer = wx.StaticBoxSizer(box, wx.VERTICAL) check = list() spin = list() # # As of version 0.7.5: we want the units to be displayed # human readable - the way they are displayed # in the Page info tool. # labels = mdls.GetHumanReadableParms(modelid, self.corr.fit_parameters)[0] for label in labels: sizerh = wx.BoxSizer(wx.HORIZONTAL) checkbox = wx.CheckBox(self.panelsettings, label=label) # We needed to "from wx.lib.agw import floatspin" to get this: spinctrl = wxutils.PCFFloatTextCtrl(self.panelsettings) sizerh.Add(spinctrl) sizerh.Add(checkbox) sizer.Add(sizerh) # Put everything into lists to be able to refer to it later check.append(checkbox) spin.append(spinctrl) return sizer, check, spin def OnAmplitudeCheck(self, event=None): """ Enable/Disable BG rate text line. New feature introduced in 0.7.8 """ modelid = self.corr.fit_model.id ## Normalization to a certain parameter in plots # Find all parameters that start with an "N" # ? and "C" ? # Create List normlist = list() normlist.append("None") ## Add parameters parameterlist = list() for i in np.arange(len(self.active_parms[0])): label = self.active_parms[0][i] if label[0].lower() == "n": normlist.append("*"+label) parameterlist.append(i) ## Add supplementary parameters # Get them from models supplement = mdls.GetMoreInfo(modelid, self) if supplement is not None: for i in np.arange(len(supplement)): label = supplement[i][0] if label[0].lower() == "n": normlist.append("*"+label) # Add the id of the supplement starting at the # number of fitting parameters of current page. parameterlist.append(i+len(self.active_parms[0])) normsel = self.AmplitudeInfo[2].GetSelection() if normsel in [0, -1]: # init or no normalization selected self.corr.normparm = None normsel = 0 else: self.corr.normparm = parameterlist[normsel-1] if len(parameterlist) > 0: self.AmplitudeInfo[2].Enable() self.AmplitudeInfo[3].Enable() else: self.AmplitudeInfo[2].Disable() self.AmplitudeInfo[3].Disable() # Set dropdown values self.AmplitudeInfo[2].SetItems(normlist) self.AmplitudeInfo[2].SetSelection(normsel) ## Plot intensities # Quick reminder: #self.AmplitudeInfo = [ [intlabel1, intlabel2], # [bgspin1, bgspin2], # normtoNDropdown, textnor] # Signal self.AmplitudeInfo[0][0].SetValue("{:.4f}".format(0)) self.AmplitudeInfo[0][1].SetValue("{:.4f}".format(0)) for i in range(len(self.corr.traces)): S = self.corr.traces[i].countrate self.AmplitudeInfo[0][i].SetValue("{:.4f}".format(S)) if self.corr.is_cc: self.AmplitudeInfo[0][1].Enable() else: self.AmplitudeInfo[0][1].Disable() # Background ## self.parent.Background[self.bgselected][i] ## [0] average signal [kHz] ## [1] signal name (edited by user) ## [2] signal trace (tuple) ([ms], [kHz]) if len(self.corr.backgrounds) >= 1: self.AmplitudeInfo[1][0].SetValue( self.corr.backgrounds[0].countrate) else: self.AmplitudeInfo[1][0].SetValue(0) self.AmplitudeInfo[1][1].SetValue(0) if len(self.corr.backgrounds) == 2: self.AmplitudeInfo[1][1].SetValue( self.corr.backgrounds[1].countrate) else: self.AmplitudeInfo[1][1].SetValue(0) # Disable the second line in amplitude correction, if we have # autocorrelation only. boolval = self.corr.is_cc for item in self.WXAmplitudeCCOnlyStuff: item.Enable(boolval) def OnBGSpinChanged(self, e): """ Calls tools.background.ApplyAutomaticBackground to update background information """ # Quick reminder: #self.AmplitudeInfo = [ [intlabel1, intlabel2], # [bgspin1, bgspin2], # normtoNDropdown, textnor] if self.corr.is_cc: # update both self.bgselected and self.bg2selected bg = [float(self.AmplitudeInfo[1][0].GetValue()), float(self.AmplitudeInfo[1][1].GetValue())] sig = [float(self.AmplitudeInfo[0][0].GetValue()), float(self.AmplitudeInfo[0][1].GetValue())] # Make sure bg < sig for ii in range(len(bg)): if sig[ii] != 0: if bg[ii] > .99*sig[ii]: bg[ii] = .99*sig[ii] self.AmplitudeInfo[1][ii].SetValue(bg[ii]) else: # Only update self.bgselected bg = float(self.AmplitudeInfo[1][0].GetValue()) sig = float(self.AmplitudeInfo[0][0].GetValue()) # Make sure bg < sig if sig != 0: if bg > .99*sig: bg = .99*sig self.AmplitudeInfo[1][0].SetValue(bg) tools.background.ApplyAutomaticBackground(self, bg, self.parent) e.Skip() def OnTitleChanged(self, e): modelid = self.corr.fit_model.id pid = self.parent.notebook.GetPageIndex(self) if self.tabtitle.GetValue() == "": text = self.counter + mdls.modeldict[modelid][1] else: # How many characters of the the page title should be displayed # in the tab? We choose 9: AC1-012 plus 2 whitespaces text = self.counter + self.tabtitle.GetValue()[-9:] self.parent.notebook.SetPageText(pid,text) def OnSetRange(self, e): """ Open a new Frame where the parameter range can be set. Rewrites self.parameter_range Parameter ranges are treated like parameters: They are saved in sessions and applied in batch mode. """ # TODO: # - make range selector work with new class # We write a separate tool for that. # This tool does not show up in the Tools menu. if self.parent.RangeSelector is None: self.parent.RangeSelector = tools.RangeSelector(self) self.parent.RangeSelector.Bind(wx.EVT_CLOSE, self.parent.RangeSelector.OnClose) else: try: self.parent.RangeSelector.OnClose() except: pass self.parent.RangeSelector = None def OnSize(self, event): """ Resize the fitting Panel, when Window is resized. """ size = self.parent.notebook.GetSize() tabsize = 33 size[1] = size[1] - tabsize self.sp.SetSize(size) def PlotAll(self, event=None, trigger=None): """ This function plots the whole correlation and residuals canvas. We do: - Channel selection - Background correction - Apply Parameters (separate function) - Drawing of plots The `event` is usually just an event from buttons or similar wx objects. It can be "init", then some initial plotting is done before the data is handled. The `trigger` is passed to `self.parent.OnFNBPageChanged` so that tools can update their content accordingly. For more information on triggers, have a look at the doctring of the `tools` submodule. """ if event == "init": # We use this to have the page plotted at least once before # readout of parameters (e.g. startcrop, endcrop) # This is a performence tweak. if self.InitialPlot: return else: self.InitialPlot = True ## Enable/Disable, set values frontend normalization self.OnAmplitudeCheck() ## Apply parameters self.apply_parameters() # Calculate correlation function from parameters ## Drawing of correlation plot # Plots corr.correlation_fit and the calcualted correlation function # self.datacorr into the upper canvas. # Create a line @ y=zero: zerostart = self.corr.lag_time_fit[0] zeroend = self.corr.lag_time_fit[-1] datazero = [[zerostart, 0], [zeroend,0]] # Set plot colors width = 1 colexp = "grey" colfit = "blue" colweight = "cyan" lines = list() linezero = plot.PolyLine(datazero, colour='orange', width=width) lines.append(linezero) if self.corr.correlation is not None: if self.corr.is_weighted_fit and \ self.parent.MenuShowWeights.IsChecked(): try: weights = self.corr.fit_results["fit weights"] except: weights = self.corr.fit_weight_data if isinstance(weights, np.ndarray): # user might have selected a new weight type and # presses apply, do not try to display weights # if weights are from average or other, make sure that the # dimensions are correct if weights.shape[0] == self.corr.correlation.shape[0]: weights = weights[self.corr.fit_ival[0]:self.corr.fit_ival[1]] # perform some checks if np.allclose(weights, np.ones_like(weights)): weights = 0 elif weights.shape[0] != self.corr.modeled_fit.shape[0]: # non-matching weigths warnings.warn("Unmatching weights found. Probably from previous data set.") weights = 0 # Add the weights to the graph. # This is done by drawing two lines. w = 1*self.corr.modeled_fit w1 = 1*w w2 = 1*w w1[:, 1] = w[:, 1] + weights w2[:, 1] = w[:, 1] - weights # crop w1 and w2 if corr.correlation_fit does not include all # data points. if np.all(w[:,0] == self.corr.correlation_fit[:,0]): pass else: raise ValueError("This should not have happened: size of weights is wrong.") ## Normalization with self.normfactor w1[:,1] *= self.corr.normalize_factor w2[:,1] *= self.corr.normalize_factor self.weights_plot_fill_area = [w1,w2] lineweight1 = plot.PolyLine(w1, legend='', colour=colweight, width=width) lines.append(lineweight1) lineweight2 = plot.PolyLine(w2, legend='', colour=colweight, width=width) lines.append(lineweight2) else: self.weights_plot_fill_area = None ## Plot Correlation curves # Plot both, experimental and calculated data # Normalization with self.normfactor, new feature in 0.7.8 datacorr_norm = self.corr.modeled_plot linecorr = plot.PolyLine(datacorr_norm, legend='', colour=colfit, width=width) dataexp_norm = self.corr.correlation_plot lineexp = plot.PolyLine(dataexp_norm, legend='', colour=colexp, width=width) # Draw linezero first, so it is in the background lines.append(lineexp) lines.append(linecorr) PlotCorr = plot.PlotGraphics(lines, xLabel=u'lag time τ [ms]', yLabel=u'G(τ)') self.canvascorr.Draw(PlotCorr) ## Calculate residuals resid_norm = self.corr.residuals_plot lineres = plot.PolyLine(resid_norm, legend='', colour=colfit, width=width) # residuals or weighted residuals? if self.corr.is_weighted_fit: yLabelRes = "weighted \nresiduals" else: yLabelRes = "residuals" PlotRes = plot.PlotGraphics([linezero, lineres], xLabel=u'lag time τ [ms]', yLabel=yLabelRes) self.canvaserr.Draw(PlotRes) else: # Amplitude normalization, new feature in 0.7.8 datacorr_norm = self.corr.modeled_plot linecorr = plot.PolyLine(datacorr_norm, legend='', colour='blue', width=1) PlotCorr = plot.PlotGraphics([linezero, linecorr], xLabel=u'Lag time τ [ms]', yLabel=u'G(τ)') self.canvascorr.Draw(PlotCorr) self.parent.OnFNBPageChanged(trigger=trigger) def settings(self): """ Here we define, what should be displayed at the left side of the fitting page/tab. Parameters: """ modelid = self.corr.fit_model.id horizontalsize = self.sizepanelx-10 # Title # Create empty tab title mddat = mdls.modeldict[modelid] modelshort = mdls.GetModelType(modelid) titlelabel = u"Data set {} ({} {})".format( self.counter.strip(" :"), modelshort, mddat[1]) boxti = wx.StaticBox(self.panelsettings, label=titlelabel) sizerti = wx.StaticBoxSizer(boxti, wx.VERTICAL) sizerti.SetMinSize((horizontalsize, -1)) self.tabtitle = wx.TextCtrl(self.panelsettings, value="", size=(horizontalsize-20, -1)) self.Bind(wx.EVT_TEXT, self.OnTitleChanged, self.tabtitle) sizerti.Add(self.tabtitle) # Create StaticBoxSizer box1, check, spin = self.MakeStaticBoxSizer("Model parameters") # Make the check boxes and spin-controls available everywhere self.checkboxes = check self.spincontrol = spin # # As of version 0.7.5: we want the units to be displayed # human readable - the way they are displayed # in the Page info tool. # labels, parameters = mdls.GetHumanReadableParms(modelid, self.active_parms[1]) parameterstofit = self.active_parms[2] # Set initial values given by user/programmer for Diffusion Model for i in np.arange(len(labels)): self.checkboxes[i].SetValue(parameterstofit[i]) self.spincontrol[i].SetValue(parameters[i]) # Put everything together self.panelsettings.sizer = wx.BoxSizer(wx.VERTICAL) self.panelsettings.sizer.Add(sizerti) self.panelsettings.sizer.Add(box1) # checkbox "Protect from batch control" self.wxCBPreventBatchParms = wx.CheckBox(self.panelsettings, -1, "prevent batch modification") box1.Add(self.wxCBPreventBatchParms) # buttons "Apply" and "Set range" horzs = wx.BoxSizer(wx.HORIZONTAL) buttonapply = wx.Button(self.panelsettings, label="Apply") self.Bind(wx.EVT_BUTTON, self.PlotAll, buttonapply) horzs.Add(buttonapply) buttonrange = wx.Button(self.panelsettings, label="Set range") self.Bind(wx.EVT_BUTTON, self.OnSetRange, buttonrange) horzs.Add(buttonrange) box1.Add(horzs) # Set horizontal size box1.SetMinSize((horizontalsize, -1)) ## More info normbox = wx.StaticBox(self.panelsettings, label="Amplitude corrections") miscsizer = wx.StaticBoxSizer(normbox, wx.VERTICAL) miscsizer.SetMinSize((horizontalsize, -1)) # Intensities and Background sizeint = wx.FlexGridSizer(rows=3, cols=3, vgap=5, hgap=5) sizeint.Add(wx.StaticText(self.panelsettings, label="[kHz]")) sizeint.Add(wx.StaticText(self.panelsettings, label="Intensity")) sizeint.Add(wx.StaticText(self.panelsettings, label="Background")) sizeint.Add(wx.StaticText(self.panelsettings, label="Ch1")) intlabel1 = wx.TextCtrl(self.panelsettings) bgspin1 = wxutils.PCFFloatTextCtrl(self.panelsettings) bgspin1.Bind(wx.EVT_KILL_FOCUS, self.OnBGSpinChanged) bgspin1.Bind(wx.EVT_TEXT_ENTER, self.OnBGSpinChanged) sizeint.Add(intlabel1) intlabel1.SetEditable(False) sizeint.Add(bgspin1) chtext2 = wx.StaticText(self.panelsettings, label="Ch2") sizeint.Add(chtext2) intlabel2 = wx.TextCtrl(self.panelsettings) intlabel2.SetEditable(False) bgspin2 = wxutils.PCFFloatTextCtrl(self.panelsettings) bgspin2.Bind(wx.EVT_KILL_FOCUS, self.OnBGSpinChanged) sizeint.Add(intlabel2) sizeint.Add(bgspin2) miscsizer.Add(sizeint) ## Normalize to n? textnor = wx.StaticText(self.panelsettings, label="Plot normalization") miscsizer.Add(textnor) normtoNDropdown = wx.ComboBox(self.panelsettings) self.Bind(wx.EVT_COMBOBOX, self.PlotAll, normtoNDropdown) miscsizer.Add(normtoNDropdown) self.AmplitudeInfo = [ [intlabel1, intlabel2], [bgspin1, bgspin2], normtoNDropdown, textnor] self.WXAmplitudeCCOnlyStuff = [chtext2, intlabel2, bgspin2] self.panelsettings.sizer.Add(miscsizer) ## Add fitting Box fitbox = wx.StaticBox(self.panelsettings, label="Fitting options") fitsizer = wx.StaticBoxSizer(fitbox, wx.VERTICAL) fitsizer.SetMinSize((horizontalsize, -1)) # Add a checkbox for weighted fitting weightedfitdrop = wx.ComboBox(self.panelsettings) self.weightlist = ["no weights", "spline (5 knots)", "model function"] weightedfitdrop.SetItems(self.weightlist) weightedfitdrop.SetSelection(0) fitsizer.Add(weightedfitdrop) # WeightedFitCheck() Enables or Disables the variance part weightedfitdrop.Bind(wx.EVT_COMBOBOX, self.Fit_WeightedFitCheck) # Add the variance part. # In order to do a weighted fit, we need to calculate the variance # at each point of the experimental data array. # In order to do that, we need to know how many data points from left # and right of the interesting data point we want to include in that # calculation. fittext = wx.StaticText(self.panelsettings, label="Calculation of the variance") fitsizer.Add(fittext) fittext2 = wx.StaticText(self.panelsettings, label="from 2j+1 data points") fitsizer.Add(fittext2) fitsizerspin = wx.BoxSizer(wx.HORIZONTAL) fittextvar = wx.StaticText(self.panelsettings, label="j = ") fitspin = wx.SpinCtrl(self.panelsettings, -1, initial=3, min=1, max=100) fitsizerspin.Add(fittextvar) fitsizerspin.Add(fitspin) fitsizer.Add(fitsizerspin) # Add algorithm selection textalg = wx.StaticText(self.panelsettings, label="Algorithm") fitsizer.Add(textalg) self.AlgorithmDropdown = wx.ComboBox(self.panelsettings) items = fit.GetAlgorithmStringList()[1] self.AlgorithmDropdown.SetItems(items) self.Bind(wx.EVT_COMBOBOX, self.apply_parameters, self.AlgorithmDropdown) fitsizer.Add(self.AlgorithmDropdown) self.AlgorithmDropdown.SetMaxSize(weightedfitdrop.GetSize()) # Add button "Fit" and chi2 display fitbuttonsizer = wx.BoxSizer(wx.HORIZONTAL) buttonfit = wx.Button(self.panelsettings, label="Fit") self.Bind(wx.EVT_BUTTON, self.Fit_function, buttonfit) fitbuttonsizer.Add(buttonfit) self.WXTextChi2 = wx.StaticText(self.panelsettings) # this StaticText is updated by `self.updateChi2()` fitbuttonsizer.Add(self.WXTextChi2, flag=wx.ALIGN_CENTER) fitsizer.Add(fitbuttonsizer) self.panelsettings.sizer.Add(fitsizer) # Squeeze everything into the sizer self.panelsettings.SetSizer(self.panelsettings.sizer) # This is also necessary in Windows self.panelsettings.Layout() self.panelsettings.Show() # Make all the stuff available for everyone self.Fitbox = [ fitbox, weightedfitdrop, fittext, fittext2, fittextvar, fitspin, buttonfit, textalg, self.AlgorithmDropdown] # Disable Fitting since no data has been loaded yet for element in self.Fitbox: element.Disable() self.panelsettings.sizer.Fit(self.panelsettings) self.parent.Layout() def updateChi2(self): """ updates the self.WXTextChi2 text control """ label = u"" if hasattr(self.corr, "fit_results"): if "chi2" in self.corr.fit_results: chi2 = self.corr.fit_results["chi2"] chi2str = wxutils.float2string_nsf(chi2, n=3) chi2str = wxutils.nice_string(chi2str) label = u" χ²={}".format(chi2str) # This does not work with wxPython 2.8.12: #self.WXTextChi2.SetLabelMarkup(u"{}".format(label)) self.WXTextChi2.SetLabel(u"{}".format(label)) PyCorrFit-0.9.9/pycorrfit/gui/plotting.py000066400000000000000000000345651274265304700204600ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ PyCorrFit Module plotting Everything about plotting with matplotlib is located here. Be sure to install texlive-science and texlive-math-extra """ import wx # b/c of pyinstaller import codecs import numpy as np import matplotlib # We do catch warnings about performing this before matplotlib.backends stuff import warnings with warnings.catch_warnings(): warnings.simplefilter("ignore") matplotlib.use('WXAgg') # Tells matplotlib to use WxWidgets for dialogs import matplotlib.gridspec as gridspec import matplotlib.pyplot as plt # Text rendering with matplotlib from matplotlib import rcParams import unicodedata # For finding latex tools from pycorrfit.meta import find_program from pycorrfit import models as mdls def greek2tex(char): """ Converts greek UTF-8 letters to latex """ #decchar = codecs.decode(char, "UTF-8") decchar = char repres = unicodedata.name(decchar).split(" ") # GREEK SMALL LETTER ALPHA if repres[0] == "GREEK" and len(repres) == 4: letter = repres[3].lower() if repres[1] != "SMALL": letter = letter[0].capitalize() + letter[1:] return "\\"+letter else: return char def escapechars(string): """ For latex output, some characters have to be escaped with a "\\" """ #string = codecs.decode(string, "UTF-8") escapechars = ["#", "$", "%", "&", "~", "_", "\\", "{", "}"] retstr = ur"" for char in string: if char in escapechars: retstr += "\\" retstr += char elif char == "^": # Make a hat in latex without $$? retstr += "$\widehat{~}$" else: retstr += char return retstr def latexmath(string): """ Format given parameters to nice latex. """ if string == "offset": # prohibit the very often "offset" to be displayed as variables return r"\mathrm{offset}" elif string == "SP": return r"\mathrm{SP}" #string = codecs.decode(string, "UTF-8") unicodechars = dict() #unicodechars[codecs.decode("τ", "UTF-8")] = r"\tau" #unicodechars[codecs.decode("µ", "UTF-8")] = r"\mu" unicodechars[codecs.decode("²", "UTF-8")] = r"^2" unicodechars[codecs.decode("³", "UTF-8")] = r"^3" unicodechars[codecs.decode("₁", "UTF-8")] = r"_1" unicodechars[codecs.decode("₂", "UTF-8")] = r"_2" unicodechars[codecs.decode("₀", "UTF-8")] = r"_0" #unicodechars[codecs.decode("α", "UTF-8")] = r"\alpha" # We need lambda in here, because unicode names it lamda sometimes. unicodechars[codecs.decode("λ", "UTF-8")] = r"\lambda" #unicodechars[codecs.decode("η", "UTF-8")] = r'\eta' unitchars = dict() unitchars[codecs.decode("µ", "UTF-8")] = r"\micro " items = string.split(" ", 1) a = items[0] if len(items) > 1: b = items[1] if b.count(u"µ"): # Use siunitx with the upright µ bnew = ur"[\SI{}{" for char in b.strip("[]"): if char in unitchars.keys(): bnew += unitchars[char] else: bnew += char b = bnew+ur"}]" else: b = "" anew = ur"" for char in a: if char in unicodechars.keys(): anew += unicodechars[char] elif char != greek2tex(char): anew += greek2tex(char) else: anew += char # lower case lcitems = anew.split("_",1) if len(lcitems) > 1: anew = lcitems[0]+"_{\\text{"+lcitems[1]+"}}" return anew + r" \hspace{0.3em} \mathrm{"+b+r"}" def savePlotCorrelation(parent, dirname, Page, uselatex=False, verbose=False, show_weights=True): """ Save plot from Page into file Parameters: *parent* the parent window *dirname* directory to set on saving *Page* Page containing all variables *uselatex* Whether to use latex for the ploting or not. This function uses a hack in misc.py to change the function for saving the final figure. We wanted save in the same directory as PyCorrFit was working and the filename should be the tabtitle. """ # Close all other plots before commencing try: plt.close() except: pass # get data corr = Page.corr dataexp = corr.correlation_plot resid = corr.residuals_plot fit = corr.modeled_plot weights = Page.weights_plot_fill_area tabtitle = Page.tabtitle.GetValue() #fitlabel = ur"Fit model: "+str(mdls.modeldict[Page.modelid][0]) fitlabel = Page.corr.fit_model.name labelweights = ur"Weights of fit" labels, parms = mdls.GetHumanReadableParms(Page.modelid, corr.fit_parameters) if dataexp is None: if tabtitle.strip() == "": fitlabel = Page.corr.fit_model.name else: fitlabel = tabtitle else: if tabtitle.strip() == "": tabtitle = "page"+str(Page.counter).strip().strip(":") if Page.corr.normparm is not None: fitlabel += ur", normalized to "+Page.corr.fit_model.parameters[0][Page.corr.normparm] ## Check if we can use latex for plotting: r1 = find_program("latex")[0] r2 = find_program("dvipng")[0] # Ghostscript r31 = find_program("gs")[0] r32 = find_program("mgs")[0] # from miktex r3 = max(r31,r32) if r1+r2+r3 < 3: uselatex = False if uselatex == True: rcParams['text.usetex']=True rcParams['text.latex.unicode']=True rcParams['font.family']='serif' rcParams['text.latex.preamble']=[r"""\usepackage{amsmath} \usepackage[utf8x]{inputenc} \usepackage{amssymb} \usepackage{siunitx}"""] fitlabel = ur"{\normalsize "+escapechars(fitlabel)+r"}" tabtitle = ur"{\normalsize "+escapechars(tabtitle)+r"}" labelweights = ur"{\normalsize "+escapechars(labelweights)+r"}" else: rcParams['text.usetex']=False # create plot # plt.plot(x, y, '.', label = 'original data', markersize=5) fig=plt.figure() wtit = "Correlation #{:04d}_{}".format(int(Page.counter.strip(":# ")), Page.title.strip()) fig.canvas.set_window_title(wtit) if resid is not None: gs = gridspec.GridSpec(2, 1, height_ratios=[5,1]) ax = plt.subplot(gs[0]) else: ax = plt.subplot(111) # ax = plt.axes() ax.semilogx() # plot fit first plt.plot(fit[:,0], fit[:,1], '-', label=fitlabel, lw=1.5, color="blue") if dataexp is not None: plt.plot(dataexp[:,0], dataexp[:,1], '-', color="black", alpha=.7, label=tabtitle, lw=1) else: plt.xlabel(ur'lag time $\tau$ [ms]') if weights is not None and show_weights is True: plt.fill_between(weights[0][:,0],weights[0][:,1],weights[1][:,1], color='cyan') # fake legend: p = plt.Rectangle((0, 0), 0, 0, color='cyan', label=labelweights) ax.add_patch(p) plt.ylabel('correlation') if dataexp is not None: mind = np.min([ dataexp[:,1], fit[:,1]]) maxd = np.max([ dataexp[:,1], fit[:,1]]) else: mind = np.min(fit[:,1]) maxd = np.max(fit[:,1]) ymin = mind - (maxd - mind)/20. ymax = maxd + (maxd - mind)/20. ax.set_ylim(bottom=ymin, top=ymax) xmin = np.min(fit[:,0]) xmax = np.max(fit[:,0]) ax.set_xlim(xmin, xmax) # Add some nice text: if uselatex == True and len(parms) != 0: text = r"" text += r'\[' #every line is a separate raw string... text += r'\begin{split}' # ...but they are all concatenated # by the interpreter :-) for i in np.arange(len(parms)): text += ur' {} &= {:.3g} \\'.format(latexmath(labels[i]), parms[i]) ## According to issue #54, we remove fitting errors from plots #if errparms is not None: # keys = errparms.keys() # keys.sort() # for key in keys: # text += r' \Delta '+latexmath(key)+r" &= " + str(errparms[key]) +r' \\ ' text += r' \end{split} ' text += r' \] ' else: text = ur"" for i in np.arange(len(parms)): text += u"{} = {:.3g}\n".format(labels[i], parms[i]) ## According to issue #54, we remove fitting errors from plots #if errparms is not None: # keys = errparms.keys() # keys.sort() # for key in keys: # text += "Err "+key+" = " + str(errparms[key]) +"\n" logmax = np.log10(xmax) logmin = np.log10(xmin) logtext = 0.6*(logmax-logmin)+logmin xt = 10**(logtext) yt = 0.3*ymax plt.text(xt,yt,text, size=12) if resid is not None: ax2 = plt.subplot(gs[1]) #ax2 = plt.axes() ax2.semilogx() if Page.corr.is_weighted_fit: if uselatex == True: lb = r"\newline \indent " else: lb = "\n" yLabelRes = "weighted "+ lb +"residuals" else: yLabelRes = "residuals" minx = np.min(resid[:,0]) maxx = np.max(resid[:,0]) miny = np.min(resid[:,1]) maxy = np.max(resid[:,1]) plt.hlines(0, minx, maxx, colors="orange") plt.plot(resid[:,0], resid[:,1], '-', color="black", alpha=.85, label=yLabelRes, lw=1) plt.xlabel(r'lag time $\tau$ [ms]') plt.ylabel(yLabelRes, multialignment='center') ax2.set_xlim(minx, maxx) maxy = max(abs(maxy), abs(miny)) ax2.set_ylim(-maxy, maxy) ticks = ax2.get_yticks() ax2.set_yticks([ticks[0], ticks[-1], 0]) ## Hack # We need this for hacking. See edclasses. fig.canvas.HACK_parent = parent fig.canvas.HACK_fig = fig fig.canvas.HACK_Page = Page fig.canvas.HACK_append = ".png" # Legend outside of plot # Decrease size of plot to fit legend box = ax.get_position() ax.set_position([box.x0, box.y0 + box.height * 0.2, box.width, box.height * 0.9]) if resid is not None: box2 = ax2.get_position() ax2.set_position([box2.x0, box2.y0 + box.height * 0.2, box2.width, box2.height]) ax.legend(loc='upper center', bbox_to_anchor=(0.5, -0.55), prop={'size':9}) if verbose == True: plt.show() else: # If WXAgg is not used for some reason, then our hack does not work # and we must use e.g. TkAgg try: fig.canvas.toolbar.save() except AttributeError: fig.canvas.toolbar.save_figure() # Close all other plots before commencing try: plt.close() except: pass def savePlotTrace(parent, dirname, Page, uselatex=False, verbose=False): """ Save trace plot from Page into file Parameters: *parent* the parent window *dirname* directory to set on saving *Page* Page containing all variables *uselatex* Whether to use latex for the ploting or not. This function uses a hack in misc.py to change the function for saving the final figure. We wanted save in the same directory as PyCorrFit was working and the filename should be the tabtitle. """ # Close all other plots before commencing try: plt.close() except: pass # Trace must be displayed in s timefactor = 1e-3 tabtitle = Page.tabtitle.GetValue() if tabtitle.strip() == "": tabtitle = "page"+str(Page.counter).strip().strip(":") # Intensity trace in kHz may stay the same if len(Page.corr.traces) == 0: return traces = Page.corr.traces labels = list() for ii, tr in enumerate(traces): labels.append("Channel {}: {}".format(ii+1, tr.name)) ## Check if we can use latex for plotting: r1 = find_program("latex")[0] r2 = find_program("dvipng")[0] # Ghostscript r31 = find_program("gs")[0] r32 = find_program("mgs")[0] r3 = max(r31,r32) if r1+r2+r3 < 3: uselatex = False if uselatex == True: rcParams['text.usetex']=True rcParams['text.latex.unicode']=True rcParams['font.family']='serif' rcParams['text.latex.preamble']=[r"\usepackage{amsmath}"] for i in np.arange(len(labels)): labels[i] = ur"{\normalsize "+escapechars(labels[i])+r"}" else: rcParams['text.usetex']=False # create plot # plt.plot(x, y, '.', label = 'original data', markersize=5) fig=plt.figure(figsize=(10,3)) wtit = "Trace #{:04d}_{}".format(int(Page.counter.strip(":# ")), Page.title.strip()) fig.canvas.set_window_title(wtit) ax = plt.subplot(111) for i in np.arange(len(traces)): # Columns time = traces[i][:,0]*timefactor intensity = traces[i][:,1] plt.plot(time, intensity, '-', label = labels[i], lw=1) # set plot boundaries maxy = -np.infty miny = np.infty for tr in traces: maxy = max(np.max(tr[:,1]), maxy) miny = min(np.min(tr[:,1]), miny) ax.set_ylim(miny, maxy) plt.ylabel('count rate [kHz]') plt.xlabel('time [s]') # Legend outside of plot # Decrease size of plot to fit legend box = ax.get_position() ax.set_position([box.x0, box.y0 + box.height * 0.2, box.width, box.height * 0.9]) plt.legend(loc='upper center', bbox_to_anchor=(0.5, -0.35), prop={'size':9}, ) ## Hack # We need this for hacking. See edclasses. fig.canvas.HACK_parent = parent fig.canvas.HACK_fig = fig fig.canvas.HACK_Page = Page fig.canvas.HACK_append = "_trace.png" plt.tight_layout(rect=(.001,.34,.999,1.0)) if verbose == True: plt.show() else: # If WXAgg is not used for some reason, then our hack does not work # and we must use e.g. TkAgg try: fig.canvas.toolbar.save() except AttributeError: fig.canvas.toolbar.save_figure() # Close all other plots before commencing try: plt.close() except: pass # set dpi to 300 matplotlib.rcParams['savefig.dpi'] = 300PyCorrFit-0.9.9/pycorrfit/gui/threaded_progress.py000066400000000000000000000201541274265304700223110ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ PyCorrFit A progress bar with an abort button that works for long running processes. """ import time import threading import traceback as tb import wx import sys class KThread(threading.Thread): """A subclass of threading.Thread, with a kill() method. https://web.archive.org/web/20130503082442/http://mail.python.org/pipermail/python-list/2004-May/281943.html The KThread class works by installing a trace in the thread. The trace checks at every line of execution whether it should terminate itself. So it's possible to instantly kill any actively executing Python code. However, if your code hangs at a lower level than Python, then the thread will not actually be killed until the next Python statement is executed. """ def __init__(self, *args, **keywords): threading.Thread.__init__(self, *args, **keywords) self.killed = False def start(self): """Start the thread.""" self.__run_backup = self.run self.run = self.__run # Force the Thread to install our trace. threading.Thread.start(self) def __run(self): """Hacked run function, which installs the trace.""" sys.settrace(self.globaltrace) self.__run_backup() self.run = self.__run_backup def globaltrace(self, frame, why, arg): if why == 'call': return self.localtrace else: return None def localtrace(self, frame, why, arg): if self.killed: if why == 'line': raise SystemExit() return self.localtrace def kill(self): self.killed = True class WorkerThread(KThread): """Worker Thread Class.""" def __init__(self, target, args, kwargs): """Init Worker Thread Class.""" KThread.__init__(self) self.traceback = None self.target = target self.args = args self.kwargs = kwargs # This starts the thread running on creation, but you could # also make the GUI thread responsible for calling this self.start() def run(self): """Run Worker Thread.""" try: self.target(*self.args, **self.kwargs) except: self.traceback = tb.format_exc() class ThreadedProgressDlg(object): def __init__(self, parent, targets, args=None, kwargs={}, title="Dialog title", messages=None, time_delay=2): """ This class implements a progress dialog that can abort during a function call, as opposed to the stock wx.ProgressDialog. Parameters ---------- parent : wx object The parent of the progress dialog. targets : list of callables The methods that will be called in each step in the progress. args : list The arguments to the targets. Should match length of targets. kwargs : dict or list of dicts Keyword arguments to the targets. If dict, then the same dict is used for all targets. title : str The title of the progress dialog. messages : list of str The message displayed for each target. Should match length of targets. time_delay : float Time after which the dialog should be displayed. The default is 2s, which means that a dialog is only displayed after 2s or earlier, if the overall progress seems to be taking longer than 2s. Arguments --------- aborted : bool Whether the progress was aborted by the user. index_aborted : None or int The index in `targets` at which the progress was aborted. finalize : callable A method that will be called after fitting. Can be overriden by subclasses. Notes ----- The progress dialog is only displayed when `time_delay` is or seems shorter than the total running time of the progress. If the progress is not displayed, then a busy cursor is displayed. """ wx.BeginBusyCursor() if hasattr(targets, "__call__"): targets = [targets] nums = len(targets) if args is None: args = [()]*nums elif isinstance(args, list): # convenience-convert args to tuples if not isinstance(args[0], tuple): args = [ (t,) for t in args ] if isinstance(kwargs, dict): kwargs = [kwargs]*nums if messages is None: messages = [ "item {} of {}".format(a+1, nums) for a in range(nums) ] time1 = time.time() sty = wx.PD_SMOOTH|wx.PD_AUTO_HIDE|wx.PD_CAN_ABORT if len(targets) > 1: sty = sty|wx.PD_REMAINING_TIME dlgargs = [title, "initializing..."] dlgkwargs = {"maximum":nums, "parent":parent, "style":sty } dlg = None self.aborted = False self.index_aborted = None for jj in range(nums): init = True worker = WorkerThread(target=targets[jj], args=args[jj], kwargs=kwargs[jj]) while worker.is_alive() or init: if (time.time()-time1 > time_delay or (time.time()-time1)/(jj+1)*nums > time_delay ) and dlg is None: dlg = wx.ProgressDialog(*dlgargs, **dlgkwargs) wx.EndBusyCursor() init=False time.sleep(.01) if dlg is not None: if len(targets) == 1: # no progress bar but pulse cont = dlg.UpdatePulse(messages[jj])[0] else: # show progress until end cont = dlg.Update(jj+1, messages[jj])[0] if cont == False: dlg.Destroy() worker.kill() self.aborted = True break if self.aborted: self.aborted = True self.index_aborted = jj break if worker.traceback is not None: dlg.Destroy() self.aborted = True self.index_aborted = jj raise Exception(worker.traceback) if dlg is not None: dlg.Hide() dlg.Destroy() wx.EndBusyCursor() wx.BeginBusyCursor() self.finalize() wx.EndBusyCursor() def finalize(self): """ You may override this method in subclasses. """ pass if __name__ == "__main__": # GUI Frame class that spins off the worker thread class MainFrame(wx.Frame): """Class MainFrame.""" def __init__(self, parent, aid): """Create the MainFrame.""" wx.Frame.__init__(self, parent, aid, 'Thread Test') # Dumb sample frame with two buttons but = wx.Button(self, wx.ID_ANY, 'Start Progress', pos=(0,0)) self.Bind(wx.EVT_BUTTON, self.OnStart, but) def OnStart(self, event): """Start Computation.""" # Trigger the worker thread unless it's already busy arguments = [ test_class(a) for a in range(10) ] def method(x): x.arg *= 1.1 time.sleep(1) tp = ThreadedProgressDlg(self, [method]*len(arguments), arguments) print(tp.index_aborted) print([a.arg for a in arguments]) class MainApp(wx.App): """Class Main App.""" def OnInit(self): """Init Main App.""" self.frame = MainFrame(None, -1) self.frame.Show(True) self.SetTopWindow(self.frame) return True class test_class(object): def __init__(self, arg): self.arg = arg app = MainApp(0) app.MainLoop()PyCorrFit-0.9.9/pycorrfit/gui/tools/000077500000000000000000000000001274265304700173715ustar00rootroot00000000000000PyCorrFit-0.9.9/pycorrfit/gui/tools/__init__.py000066400000000000000000000063271274265304700215120ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ PyCorrFit - module "tools" This file contains tools, such as dialog boxes and other stuff, that we need in PyCorrFit. The tools work with triggers on page updates. Every tool has a function `OnPageChanged(self, page, trigger=None)` which is called when something in the frontend chages. In order to minimize user stall time, these functions are not executed for a certain list of triggers that is defined in that function. This e.g. dramatically speeds up tools like "Statistics view" when batch fitting. Recognized triggers: tab_init : initial stuff that is done for a new page tab_browse : the tab has change and a new page is visible fit_batch : the page is batch-fitted right now fit_finalize : a (batch) fitting process is finished parm_batch : parameters are changed in a batch process parm_finalize : finished (batch) changing of page parameters page_add_batch : when many pages are added at the same time page_add_finalize : finished (batch) adding of pages """ # This file is necessary for this folder to become a module that can be # imported by PyCorrFit or other people. import numpy as np # NumPy import sys from . import datarange from . import background from . import overlaycurves from . import batchcontrol from . import globalfit from . import average from . import simulation from . import info from . import statistics from . import trace # Load all of the classes # This also defines the order of the tools in the menu ImpA = [ ["datarange", "SelectChannels"], ["overlaycurves", "Wrapper_Tools"], ["batchcontrol", "BatchCtrl"], ["globalfit", "GlobalFit"], ["average", "Average"], ["background", "BackgroundCorrection"] ] ImpB = [ ["trace", "ShowTrace"], ["statistics", "Stat"], ["info", "ShowInfo"], ["simulation", "Slide"] ] ModuleActive = list() ToolsActive = list() for i in np.arange(len(ImpA)): # We have to add "tools." because this is a relative import ModuleActive.append(__import__(ImpA[i][0], globals(), locals(), [ImpA[i][1]], -1)) ToolsActive.append(getattr(ModuleActive[i], ImpA[i][1])) ModulePassive = list() ToolsPassive = list() for i in np.arange(len(ImpB)): ModulePassive.append(__import__(ImpB[i][0], globals(), locals(), [ImpB[i][1]], -1)) ToolsPassive.append(getattr(ModulePassive[i], ImpB[i][1])) #ModulePassive.append(importlib.import_module("tools."+ImpB[i][0])) #ToolsPassive.append(getattr(ModulePassive[i], ImpB[i][1])) # This is in the file menu and not needed in the dictionaries below. from .chooseimport import ChooseImportTypes from .chooseimport import ChooseImportTypesModel from .comment import EditComment # the "special" tool RangeSelector from .parmrange import RangeSelector ToolDict = dict() ToolDict["A"] = ToolsActive ToolDict["P"] = ToolsPassive # Make the same for Menu Names in Tools NameActive = list() for i in np.arange(len(ImpA)): NameActive.append(ModuleActive[i].MENUINFO) NamePassive = list() for i in np.arange(len(ImpB)): NamePassive.append(ModulePassive[i].MENUINFO) ToolName = dict() ToolName["A"] = NameActive ToolName["P"] = NamePassive PyCorrFit-0.9.9/pycorrfit/gui/tools/average.py000066400000000000000000000377061274265304700213720ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ PyCorrFit Module tools - average Creates an average of curves. """ import numpy as np import wx from pycorrfit import models as mdls from .. import misc # Menu entry name MENUINFO = ["&Average data", "Create an average curve from whole session."] class Average(wx.Frame): # This tool is derived from a wx.frame. def __init__(self, parent): # Define a unique name that identifies this tool # Do not change this value. It is important for the Overlay tool # (selectcurves.py, *Wrapper_Tools*). self.MyName="AVERAGE" # parent is the main frame of PyCorrFit self.parent = parent # Get the window positioning correctly pos = self.parent.GetPosition() pos = (pos[0]+100, pos[1]+100) wx.Frame.__init__(self, parent=self.parent, title="Average curves", pos=pos, style=wx.DEFAULT_FRAME_STYLE|wx.FRAME_FLOAT_ON_PARENT) ## MYID # This ID is given by the parent for an instance of this class self.MyID = None # Page - the currently active page of the notebook. self.Page = self.parent.notebook.GetCurrentPage() ## Content self.panel = wx.Panel(self) self.topSizer = wx.BoxSizer(wx.VERTICAL) textinit = wx.StaticText(self.panel, label="Create an average from the following pages:") self.topSizer.Add(textinit) ## Page selection self.WXTextPages = wx.TextCtrl(self.panel, value="", size=(textinit.GetSize()[0],-1)) self.topSizer.Add(self.WXTextPages) ## Chechbox asking for Mono-Model self.WXCheckMono = wx.CheckBox(self.panel, label="Only use pages with the same model as the first page.") self.WXCheckMono.SetValue(True) self.topSizer.Add(self.WXCheckMono) ## Model selection Dropdown textinit2 = wx.StaticText(self.panel, label="Select a model for the average:") self.topSizer.Add(textinit2) self.WXDropSelMod = wx.ComboBox(self.panel, -1, "", (15,30), wx.DefaultSize, [], wx.CB_DROPDOWN|wx.CB_READONLY) self.topSizer.Add(self.WXDropSelMod) textinit3 = wx.StaticText(self.panel, label="This tool averages only over pages with the same type"+\ "\n(auto- or cross-correlation). Intensity data are"+\ "\nappended sequentially.") self.topSizer.Add(textinit3) # Set all values of Text and Strin self.SetValues() btnavg = wx.Button(self.panel, wx.ID_CLOSE, 'Create average') # Binds the button to the function - close the tool self.Bind(wx.EVT_BUTTON, self.OnAverage, btnavg) self.topSizer.Add(btnavg) self.panel.SetSizer(self.topSizer) self.topSizer.Fit(self) self.SetMinSize(self.topSizer.GetMinSizeTuple()) #Icon if parent.MainIcon is not None: wx.Frame.SetIcon(self, parent.MainIcon) self.Show(True) self.OnPageChanged(self.Page) def OnClose(self, event=None): # This is a necessary function for PyCorrFit. # Do not change it. self.parent.toolmenu.Check(self.MyID, False) self.parent.ToolsOpen.__delitem__(self.MyID) self.Destroy() def OnPageChanged(self, page, trigger=None): """ This function is called, when something in the panel changes. The variable `trigger` is used to prevent this function from being executed to save stall time of the user. Forr a list of possible triggers, see the doc string of `tools`. """ # When parent changes # This is a necessary function for PyCorrFit. # This is stuff that should be done when the active page # of the notebook changes. if trigger in ["parm_batch", "fit_batch", "page_add_batch", "parm_finalize", "fit_finalize"]: return if self.parent.notebook.GetPageCount() == 0: self.panel.Disable() return #idsel = self.WXDropSelMod.GetSelection() self.SetValues() # Set back user selection: #self.WXDropSelMod.SetSelection(idsel) self.panel.Enable() self.Page = page def OnAverage(self, evt=None): strFull = self.WXTextPages.GetValue() PageNumbers = misc.parseString2Pagenum(self, strFull) if PageNumbers is None: # Something went wrong and parseString2Pagenum already displayed # an error message. return pages = list() UsedPagenumbers = list() # Reference page is the first page of the selection! #referencePage = self.parent.notebook.GetCurrentPage() referencePage = None for i in np.arange(self.parent.notebook.GetPageCount()): Page = self.parent.notebook.GetPage(i) if Page.counter.strip(" :#") == str(PageNumbers[0]): referencePage = Page break if referencePage is None: # If that did not work, we have to raise an error. raise IndexError("PyCorrFit could not find the first"+ " page for averaging.") return for i in np.arange(self.parent.notebook.GetPageCount()): Page = self.parent.notebook.GetPage(i) corr = Page.corr model = Page.corr.fit_model j = filter(lambda x: x.isdigit(), Page.counter) if int(j) in PageNumbers: # Get all pages with the same model? if self.WXCheckMono.GetValue() == True: if (model.id == referencePage.corr.fit_model.id and corr.is_cc == referencePage.corr.is_cc): ## Check if the page has experimental data: # If there is an empty page somewhere, don't bother if corr.correlation is not None: pages.append(Page) UsedPagenumbers.append(int(j)) else: if corr.is_cc == referencePage.corr.is_cc: # If there is an empty page somewhere, don't bother if corr.correlation is not None: pages.append(Page) UsedPagenumbers.append(int(j)) # If there are no pages in the list, exit gracefully if len(pages) <= 0: texterr_a = "At least one page with experimental data is\n"+\ "required for averaging. Please check the pages\n"+\ "that you selected for averaging." if self.WXCheckMono.GetValue() == True: texterr_a += " Note: You selected\n"+\ "to only use pages with same model as the first page." wx.MessageDialog(self, texterr_a, "Error", style=wx.ICON_ERROR|wx.OK|wx.STAY_ON_TOP) return # Now get all the experimental data explist = list() # Two components in case of Cross correlation tracetime = [np.array([]), np.array([])] tracerate = [np.array([]), np.array([])] TraceNumber = 0 TraceAvailable = False # turns True, if pages contain traces for page in pages: corr = page.corr # experimental correlation curve # (at least 1d, because it might be None) explist.append(np.atleast_1d(1*corr.correlation)) # trace # We will put together a trace from all possible traces # Stitch together all the traces. trace = corr.traces TraceNumber = len(trace) if TraceNumber > 0: TraceAvailable = True # Works with one or two traces. j = 0 or 1. for j in np.arange(TraceNumber): if len(tracetime[j]) != 0: # append to the trace oldend = tracetime[j][-1] # we assume that the first two points in a # trace are equidistant and we will use their # difference as an offset offset = trace[j][:,0][1] - 2*trace[j][:,0][0] newtracetime = 1.*trace[j][:,0] + offset newtracetime = newtracetime + oldend tracetime[j] = np.append(tracetime[j], newtracetime) del newtracetime tracerate[j] = np.append(tracerate[j], trace[j][:,1]) else: # Initiate the trace tracetime[j] = 1.*trace[j][:,0] tracerate[j] = 1.*trace[j][:,1] # Now check if the length of the correlation arrays are the same: len0 = len(explist[0]) for item in explist[1:]: if len(item) != len0: # print an error message wx.MessageDialog(self, "Averaging over curves with different lengths is not"+\ "\nsupported. When measuring, please make sure that"+\ "\nthe measurement time for all curves is the same.", "Error", style=wx.ICON_ERROR|wx.OK|wx.STAY_ON_TOP) return # Now shorten the trace, because we want as little memory usage as # possible. I used this algorithm in read_FCS_Confocor3.py as well. newtraces = list() if TraceAvailable: for j in np.arange(TraceNumber): tracej = np.zeros((len(tracetime[j]),2)) tracej[:,0] = tracetime[j] tracej[:,1] = tracerate[j] if len(tracej) >= 500: # We want about 500 bins # We need to sum over intervals of length *teiler* teiler = int(len(tracej)/500) newlength = len(tracej)/teiler newsignal = np.zeros(newlength) # Simultaneously sum over all intervals for k in np.arange(teiler): newsignal = \ newsignal+tracej[k:newlength*teiler:teiler][:,1] newsignal = 1.* newsignal / teiler newtimes = tracej[teiler-1:newlength*teiler:teiler][:,0] if len(tracej)%teiler != 0: # We have a rest signal # We average it and add it to the trace rest = tracej[newlength*teiler:][:,1] lrest = len(rest) rest = np.array([sum(rest)/lrest]) newsignal = np.concatenate((newsignal, rest), axis=0) timerest = np.array([tracej[-1][0]]) newtimes = np.concatenate((newtimes, timerest), axis=0) newtrace=np.zeros((len(newtimes),2)) newtrace[:,0] = newtimes newtrace[:,1] = newsignal else: # Declare newtrace - # otherwise we have a problem down three lines ;) newtrace = tracej newtraces.append(newtrace) else: newtraces=[None,None] # Everything is cleared for averaging exparray = np.array(explist) averagedata = exparray.sum(axis=0)[:,1]/len(exparray) # Create a copy from the first page average = 1*exparray[0] # Set average data average[:,1] = averagedata # create new page self.IsCrossCorrelation = self.Page.corr.is_cc interval = self.Page.corr.fit_ival # Obtain the model ID from the dropdown selection. idsel = self.WXDropSelMod.GetSelection() modelid = self.DropdownIndex[idsel] self.AvgPage = self.parent.add_fitting_tab(modelid = modelid, select = True) self.AvgPage.corr.fit_ival = interval self.AvgPage.corr.correlation = average if self.IsCrossCorrelation is False: self.AvgPage.corr.corr_type = "AC average" newtrace = newtraces[0] if newtrace is not None and len(newtrace) != 0: self.AvgPage.corr.traces = [newtrace] else: self.AvgPage.corr.traces = [] else: self.AvgPage.corr.corr_type = "CC average" if newtraces[0] is not None and len(newtraces[0][0]) != 0: self.AvgPage.corr.traces = newtraces else: self.AvgPage.corr.traces = [] self.AvgPage.Fit_enable_fitting() if len(pages) == 1: # Use the same title as the first page newtabti = referencePage.tabtitle.GetValue() else: # Create a new tab title newtabti = "Average ["+misc.parsePagenum2String(UsedPagenumbers)+"]" self.AvgPage.tabtitle.SetValue(newtabti) # Set the addition information about the variance from averaging Listname = "Average" listname = Listname.lower() standarddev = exparray.std(axis=0)[:,1] if np.sum(np.abs(standarddev)) == 0: # The average sd is zero. We probably made an average # from only one page. In this case we do not enable # average weighted fitting pass else: # TODO: # kind of hackish to repeat this three times: # self.AvgPage.corr.set_weights(Listname, standarddev) self.AvgPage.corr.set_weights(listname, standarddev) WeightKinds = self.AvgPage.Fitbox[1].GetItems() # Attention! Average weights and other external weights should # be sorted (for session saving). extTypes = self.AvgPage.corr._fit_weight_memory.keys() # TODO: # find acleaner solution extTypes.remove("none") extTypes.sort() # sorting for key in extTypes: try: WeightKinds.remove(key) except: pass LenInternal = len(WeightKinds) IndexAverag = extTypes.index(listname) IndexInList = LenInternal + IndexAverag for key in extTypes: WeightKinds += [key] self.AvgPage.Fitbox[1].SetItems(WeightKinds) self.AvgPage.Fitbox[1].SetSelection(IndexInList) self.AvgPage.corr.set_weights(listname, standarddev) self.AvgPage.apply_parameters() self.AvgPage.corr.set_weights(listname, standarddev) self.AvgPage.PlotAll() # Keep the average tool open. # self.OnClose() def SetPageNumbers(self, pagestring): self.WXTextPages.SetValue(pagestring) def SetValues(self, e=None): # Text input pagenumlist = list() for i in np.arange(self.parent.notebook.GetPageCount()): Page = self.parent.notebook.GetPage(i) pagenumlist.append(int(filter(lambda x: x.isdigit(), Page.counter))) valstring=misc.parsePagenum2String(pagenumlist) self.WXTextPages.SetValue(valstring) # Dropdown modelkeys = mdls.modeltypes.keys() modelkeys.sort() try: current_model = self.parent.notebook.GetCurrentPage().corr.fit_model.id except: current_model = -1 i = 0 DropdownList = list() self.DropdownIndex = list() # Contains model ids with same index current_index = 0 for modeltype in modelkeys: for modelid in mdls.modeltypes[modeltype]: DropdownList.append(modeltype+": "+mdls.modeldict[modelid][1]) self.DropdownIndex.append(str(modelid)) if str(current_model) == str(modelid): current_index = i i+=1 self.WXDropSelMod.SetItems(DropdownList) self.WXDropSelMod.SetSelection(current_index) PyCorrFit-0.9.9/pycorrfit/gui/tools/background.py000066400000000000000000000612051274265304700220660ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ PyCorrFit Module tools - background perform background correction here. """ import numpy as np import os import sys import traceback # for Error handling import wx from wx.lib.agw import floatspin # Float numbers in spin fields import wx.lib.plot as plot from pycorrfit import openfile as opf # How to treat an opened file from pycorrfit import readfiles from pycorrfit import Trace from .. import misc # Menu entry name MENUINFO = ["&Background correction", "Open a file for background correction."] class BackgroundCorrection(wx.Frame): def __init__(self, parent): self.MyName="BACKGROUND" # Parent is main frame self.parent = parent # Get the window positioning correctly pos = self.parent.GetPosition() pos = (pos[0]+100, pos[1]+100) wx.Frame.__init__(self, parent=parent, title="Background correction", pos=pos, style=wx.DEFAULT_FRAME_STYLE|wx.FRAME_FLOAT_ON_PARENT) ## MYID # This ID is given by the parent for an instance of this class self.MyID = None # Current trace we are looking at self.activetrace = None # Importet trace self.trace = None # Importet trace after user decides to cange radio buttons self.oldtrace = None self.oldfilename = None self.average = None ## Start drawing # Splitter Window self.sp = wx.SplitterWindow(self, style=wx.SP_NOBORDER) ## Controls panel = wx.Panel(self.sp) # text1 backgroundinit = ( "Correct the amplitude for non-correlated background.\n"+ "The background intensity can be either imported\n"+ "from a blank measurement or set manually.") textinit = wx.StaticText(panel, label=backgroundinit) # Radio buttons self.rbtnfile = wx.RadioButton(panel, -1, 'Blank measurement: ', style = wx.RB_GROUP) self.rbtnfile.SetValue(True) self.btnbrowse = wx.Button(panel, wx.ID_ANY, 'Browse ...') self.rbtnhand = wx.RadioButton (panel, -1, 'Manual, [kHz]: ') # Spincontrol self.spinctrl = floatspin.FloatSpin(panel, digits=4, min_val=0, increment=.01) self.spinctrl.Enable(False) # Verbose text self.textfile = wx.StaticText(panel, label="No blank measurement file selected.") textmeanavg = wx.StaticText(panel, label="Average background signal [kHz]: ") self.textmean = wx.StaticText(panel, label="") # name textname = wx.StaticText(panel, label="User defined background name: ") sizeTextn = textname.GetSize()[0] self.bgname = wx.TextCtrl(panel, value="", size=(sizeTextn,-1)) self.bgname.Enable(False) self.btnimport = wx.Button(panel, wx.ID_ANY, 'Import into session') self.btnimport.Enable(False) # Dropdown self.BGlist = ["File/User"] # updated by self.UpdateDropdown() textdropdown = wx.StaticText(panel, label="Show background: ") self.dropdown = wx.ComboBox(panel, -1, "File/User", (15, -1), wx.DefaultSize, self.BGlist, wx.CB_DROPDOWN|wx.CB_READONLY) self.UpdateDropdown() # Radio buttons Channel1 and 2 self.rbtnCh1 = wx.RadioButton (panel, -1, 'Ch1 ', style = wx.RB_GROUP) self.rbtnCh1.SetValue(True) self.rbtnCh2 = wx.RadioButton (panel, -1, 'Ch2') # Apply buttons self.btnapply = wx.Button(panel, wx.ID_ANY, 'Apply') textor = wx.StaticText(panel, label=" or ") self.btnrem = wx.Button(panel, wx.ID_ANY, 'Dismiss') textpages = wx.StaticText(panel, label=" correction for pages: ") self.WXTextPages = wx.TextCtrl(panel, value="") # Initial value for WXTextPages pagenumlist = list() for i in np.arange(self.parent.notebook.GetPageCount()): Page = self.parent.notebook.GetPage(i) pagenumlist.append(int(filter(lambda x: x.isdigit(), Page.counter))) valstring=misc.parsePagenum2String(pagenumlist) self.WXTextPages.SetValue(valstring) textyma = wx.StaticText(panel, label="Shortcut - ") self.btnapplyall = wx.Button(panel, wx.ID_ANY, 'Apply to all pages') textor2 = wx.StaticText(panel, label=" or ") self.btnremyall = wx.Button(panel, wx.ID_ANY, 'Dismiss from all pages') # Bindings self.Bind(wx.EVT_BUTTON, self.OnBrowse, self.btnbrowse) self.Bind(wx.EVT_RADIOBUTTON, self.OnRadioFile, self.rbtnfile) self.Bind(wx.EVT_RADIOBUTTON, self.OnRadioHand, self.rbtnhand) self.Bind(wx.EVT_SPINCTRL, self.SpinCtrlChange, self.spinctrl) self.Bind(wx.EVT_BUTTON, self.OnImport, self.btnimport) self.Bind(wx.EVT_COMBOBOX, self.OnDraw, self.dropdown) self.Bind(wx.EVT_BUTTON, self.OnApply, self.btnapply) self.Bind(wx.EVT_BUTTON, self.OnApplyAll, self.btnapplyall) self.Bind(wx.EVT_BUTTON, self.OnRemove, self.btnrem) self.Bind(wx.EVT_BUTTON, self.OnRemoveAll, self.btnremyall) # Sizers topSizer = wx.BoxSizer(wx.VERTICAL) text1sizer = wx.BoxSizer(wx.HORIZONTAL) text1sizer.Add(self.rbtnfile) text1sizer.Add(self.btnbrowse) text2sizer = wx.BoxSizer(wx.HORIZONTAL) text2sizer.Add(self.rbtnhand) text2sizer.Add(self.spinctrl) textmeansizer = wx.BoxSizer(wx.HORIZONTAL) textmeansizer.Add(textmeanavg) textmeansizer.Add(self.textmean) dropsizer = wx.BoxSizer(wx.HORIZONTAL) dropsizer.Add(textdropdown) droprightsizer = wx.BoxSizer(wx.VERTICAL) dropsizer.Add(droprightsizer) droprightsizer.Add(self.dropdown) #droprightsizer.Add(self.textafterdropdown) applysizer = wx.BoxSizer(wx.HORIZONTAL) applysizer.Add(self.btnapply) applysizer.Add(textor) applysizer.Add(self.btnrem) applysizer.Add(textpages) applysizer.Add(self.WXTextPages) applysizer.Add(self.rbtnCh1) applysizer.Add(self.rbtnCh2) allsizer = wx.BoxSizer(wx.HORIZONTAL) allsizer.Add(textyma) allsizer.Add(self.btnapplyall) allsizer.Add(textor2) allsizer.Add(self.btnremyall) topSizer.Add(textinit) topSizer.Add(text1sizer) topSizer.Add(text2sizer) topSizer.Add(self.textfile) topSizer.Add(textmeansizer) topSizer.Add(textname) topSizer.Add(self.bgname) topSizer.Add(self.btnimport) topSizer.Add(dropsizer) topSizer.Add(applysizer) topSizer.Add(allsizer) panel.SetSizer(topSizer) topSizer.Fit(self) self.SetMinSize(topSizer.GetMinSizeTuple()) self.Show(True) ## Canvas self.canvas = plot.PlotCanvas(self.sp) # Sizes psize = panel.GetBestSize() initial_size = (psize[0],psize[1]+200) self.SetSize(initial_size) sashsize = psize[1]+3 # This is also necessary to prevent unsplitting self.sp.SetMinimumPaneSize(sashsize) self.sp.SplitHorizontally(panel, self.canvas, sashsize) # If there is no page, disable ourselves: self.OnPageChanged(self.parent.notebook.GetCurrentPage()) #Icon if parent.MainIcon is not None: wx.Frame.SetIcon(self, parent.MainIcon) def Apply(self, Page, backgroundid): if self.rbtnCh1.GetValue() == True: Page.bgselected = backgroundid else: Page.bg2selected = backgroundid if Page.IsCrossCorrelation is False: # Autocorrelation only has one background! Page.bg2selected = None def OnApply(self, event): strFull = self.WXTextPages.GetValue() PageNumbers = misc.parseString2Pagenum(self, strFull) if PageNumbers is None: # Something went wrong and parseString2Pagenum already displayed # an error message. return # BG number item = self.dropdown.GetSelection() # Apply to corresponding pages for i in np.arange(self.parent.notebook.GetPageCount()): Page = self.parent.notebook.GetPage(i) j = filter(lambda x: x.isdigit(), Page.counter) if int(j) in PageNumbers: self.Apply(Page, item) Page.OnAmplitudeCheck("init") Page.PlotAll() # Clean up unused backgrounds CleanupAutomaticBackground(self.parent) def OnApplyAll(self, event): self.btnrem.Enable(True) self.btnremyall.Enable(True) N = self.parent.notebook.GetPageCount() item = self.dropdown.GetSelection() for i in np.arange(N): # Set Page Page = self.parent.notebook.GetPage(i) try: self.Apply(Page, item) Page.OnAmplitudeCheck("init") Page.PlotAll() except OverflowError: errstr = "Could not apply background to Page "+Page.counter+\ ". \n Check the value of the trace average and the background." dlg = wx.MessageDialog(self, errstr, "Error", style=wx.ICON_ERROR|wx.OK|wx.STAY_ON_TOP) dlg.ShowModal() Page.bgselected = None Page.bg2selected = None # Clean up unused backgrounds CleanupAutomaticBackground(self.parent) def OnClose(self, event=None): self.parent.toolmenu.Check(self.MyID, False) self.parent.ToolsOpen.__delitem__(self.MyID) self.Destroy() def OnBrowse(self, event): # opf.BGFiletypes is a dictionary with filetypes that have some # trace signal information. SupFiletypes = opf.BGFiletypes.keys() SupFiletypes.sort() filters = "" for i in np.arange(len(SupFiletypes)): # Add to the filetype filter filters = filters+SupFiletypes[i] if i+1 != len(SupFiletypes): # Add a separator filters = filters+"|" dlg = wx.FileDialog(self, "Choose a data file", self.parent.dirname, "", filters, wx.FD_OPEN) if dlg.ShowModal() == wx.ID_OK: # Workaround since 0.7.5 (dirname, filename) = os.path.split(dlg.GetPath()) #filename = dlg.GetFilename() #dirname = dlg.GetDirectory() # Set parent dirname for user comfort self.parent.dirname = dirname try: # [data, trace, curvelist] stuff = readfiles.openAnyBG(dirname, filename) except: # The file does not seem to be what it seems to be. info = sys.exc_info() errstr = "Unknown file format:\n" errstr += str(filename)+"\n\n" errstr += str(info[0])+"\n" errstr += str(info[1])+"\n" for tb_item in traceback.format_tb(info[2]): errstr += tb_item wx.MessageDialog(self, errstr, "Error", style=wx.ICON_ERROR|wx.OK|wx.STAY_ON_TOP) return # Usually we will get a bunch of traces. Let the user select which # one to take. if len(stuff["Filename"]) > 1: choices = list() for i2 in np.arange(len(stuff["Filename"])): choices.append(str(i2)+". " + stuff["Filename"][i2] + " " + stuff["Type"][i2]) dlg = wx.SingleChoiceDialog(self, "Choose a curve", "Curve selection", choices=choices) if dlg.ShowModal() == wx.ID_OK: selindex = dlg.GetSelection() else: return else: selindex = 0 # If we accidentally recorded a cross correlation curve # as the background, let the user choose which trace he wants: channelindex = None if ( len(stuff["Type"][selindex]) >= 2 and stuff["Type"][selindex][0:2] == "CC" ): choices = ["Channel 1", "Channel 2"] label = "From which channel do you want to use the trace?" dlg = wx.SingleChoiceDialog(self, label, "Curve selection", choices=choices) if dlg.ShowModal() == wx.ID_OK: channelindex = dlg.GetSelection() trace = stuff["Trace"][selindex][channelindex] else: return else: trace = stuff["Trace"][selindex] if trace is None: print "WARNING: I did not find any trace data." return # Display filename and some of the directory self.textfile.SetLabel("File: ..."+dirname[-10:]+"/"+filename) name = str(selindex)+". "+stuff["Filename"][selindex]+" "+\ stuff["Type"][selindex] if channelindex is not None: name += " "+str(channelindex+1) self.bgname.SetValue(name) self.trace = trace # Calculate average self.average = self.trace[:,1].mean() # Display average self.textmean.SetLabel(str(self.average)+" kHz") self.spinctrl.SetValue(self.average) # Let the user see the opened file self.dropdown.SetSelection(0) # show trace self.OnDraw() # Enable button and editable name self.bgname.Enable(True) self.btnimport.Enable(True) else: # User pressed "Abort" - do nothing. self.parent.dirname = dlg.GetDirectory() dlg.Destroy() return def OnDraw(self, event=None): item = self.dropdown.GetSelection() if item < 0: # Disable Apply Buttons self.btnapply.Enable(False) self.btnapplyall.Enable(False) # Draw the trace that was just imported if self.trace != None: # Calculate average self.average = self.trace[:,1].mean() self.activetrace = self.trace #self.textafterdropdown.SetLabel(" Avg: "+str(self.average)+ # " kHz") self.textmean.SetLabel(str(self.average)) self.spinctrl.SetValue(self.average) else: # Clear the canvas. Looks better. self.canvas.Clear() # Don't show the average #self.textafterdropdown.SetLabel("") self.textmean.SetLabel("") return else: # Enable Apply Buttons self.btnapply.Enable(True) self.btnapplyall.Enable(True) # Draw a trace from the list self.activetrace = self.parent.Background[item-1].trace # We want to have the trace in [s] here. trace = 1.*self.activetrace trace[:,0] = trace[:,0]/1000 linesig = plot.PolyLine(trace, legend='', colour='blue', width=1) self.canvas.Draw(plot.PlotGraphics([linesig], xLabel='time [s]', yLabel='background signal [kHz]')) def OnImport(self, event): self.parent.Background.append(Trace(trace=self.trace, name=self.bgname.GetValue())) # Next two lines are taken care of by UpdateDropdown #name = "{} ({:.2f} kHz)".format(self.bgname.GetValue(), self.average) #self.BGlist.append(name) self.UpdateDropdown() self.btnremyall.Enable(True) self.btnrem.Enable(True) self.btnapplyall.Enable(True) self.btnapply.Enable(True) self.OnDraw() # Update BG dropdown of each page for i in np.arange(self.parent.notebook.GetPageCount()): self.parent.notebook.GetPage(i).OnAmplitudeCheck() def OnPageChanged(self, page=None, trigger=None): """ This function is called, when something in the panel changes. The variable `trigger` is used to prevent this function from being executed to save stall time of the user. Forr a list of possible triggers, see the doc string of `tools`. """ # We do not need the *Range* Commands here yet. # We open and close the SelectChannelsFrame every time we # import some data. if trigger in ["parm_batch", "fit_batch", "page_add_batch"]: return if len(self.parent.Background) == 0: self.BGlist = list() self.UpdateDropdown() self.dropdown.SetValue("File/User") if self.parent.notebook.GetPageCount() == 0: self.sp.Disable() return self.sp.Enable() if len(self.BGlist) <= 0: self.btnrem.Enable(False) self.btnremyall.Enable(False) self.btnapply.Enable(False) self.btnapplyall.Enable(False) else: self.btnrem.Enable(True) self.btnremyall.Enable(True) self.btnapply.Enable(True) self.btnapplyall.Enable(True) if (self.WXTextPages.GetValue() == "" and self.parent.notebook.GetPageCount() != 0): # Initial value for WXTextPages pagenumlist = list() for i in np.arange(self.parent.notebook.GetPageCount()): Page = self.parent.notebook.GetPage(i) pagenumlist.append(int(filter(lambda x: x.isdigit(), Page.counter))) valstring=misc.parsePagenum2String(pagenumlist) self.WXTextPages.SetValue(valstring) def OnRadioFile(self, event): # Do not let the user change the spinctrl # setting. self.spinctrl.Enable(False) self.btnbrowse.Enable(True) # Restor the old trace self.trace = self.oldtrace if self.oldfilename is not None: self.textfile.SetLabel(self.oldfilename) if self.trace is None: # Disable button and editable name self.bgname.Enable(False) self.btnimport.Enable(False) # Let us draw self.dropdown.SetSelection(0) self.OnDraw() def OnRadioHand(self, event): # Let user enter a signal. self.spinctrl.Enable(True) self.btnbrowse.Enable(False) # save the old trace. We might want to switch back to it. if self.trace is not None: self.oldtrace = 1.*self.trace self.oldfilename = self.textfile.GetLabel() self.SpinCtrlChange() # Do not show the filename self.textfile.SetLabel("No file selected.") # Enable button and editable name self.bgname.Enable(True) self.btnimport.Enable(True) if len(self.bgname.GetValue()) == 0: # Enter something as name self.bgname.SetValue("User") def OnRemove(self, event): strFull = self.WXTextPages.GetValue() PageNumbers = misc.parseString2Pagenum(self, strFull) if PageNumbers is None: # Something went wrong and parseString2Pagenum already displayed # an error message. return # BG number #item = self.dropdown.GetSelection() # Apply to corresponding pages for i in np.arange(self.parent.notebook.GetPageCount()): Page = self.parent.notebook.GetPage(i) j = filter(lambda x: x.isdigit(), Page.counter) if int(j) in PageNumbers: if self.rbtnCh1.GetValue() == True: Page.bgselected = None else: Page.bg2selected = None Page.bgselected = None Page.OnAmplitudeCheck("init") Page.PlotAll() # Clean up unused backgrounds CleanupAutomaticBackground(self.parent) def OnRemoveAll(self, event): N = self.parent.notebook.GetPageCount() for i in np.arange(N): Page = self.parent.notebook.GetPage(i) Page.bgselected = None Page.bg2selected = None Page.OnAmplitudeCheck("init") Page.PlotAll() # Clean up unused backgrounds CleanupAutomaticBackground(self.parent) def SetPageNumbers(self, pagestring): self.WXTextPages.SetValue(pagestring) def SpinCtrlChange(self, event=None): # Let user see the continuous trace we will generate self.average = self.spinctrl.GetValue() self.trace = np.array([[0,self.average],[1,self.average]]) self.textmean.SetLabel(str(self.average)) self.OnDraw() def UpdateDropdown(self, e=None): self.BGlist = list() #self.BGlist.append("File/User") for item in self.parent.Background: self.BGlist.append(item.name) self.dropdown.SetItems(self.BGlist) # Show the last item self.dropdown.SetSelection(len(self.BGlist)-1) def ApplyAutomaticBackground(page, bg, parent): """ Creates an "automatic" background with countrate in kHz *bg* and applies it to the given *page* object. If an automatic background with the same countrate exists, uses it. Input: *page* - page to which the background should be applied *bg* - background that should be applied to that page float or list of 1 or two elements -> if the page is cross-correlation, the second background will be applied as well. *parent* - parent containing *Background* list """ bglist = 1*np.atleast_1d(bg) # minus 1 to identify non-set background id bgid = np.zeros(bglist.shape, dtype=int) - 1 for b in range(len(bglist)): bgname = "AUTO: {:e} kHz \t".format(bglist[b]) # Check if exists: for i in xrange(len(parent.Background)): if (parent.Background[i].countrate == bglist[b] and parent.Background[i].name == bgname): bgid[b] = i if bgid[b] == -1: # Add new background parent.Background.append(Trace(countrate=bglist[b], name=bgname, duration=1)) bgid[b] = len(parent.Background) - 1 # Apply background to page # Last item is id of background page.bgselected = bgid[0] if len(bgid) == 2: page.bg2selected = bgid[1] else: page.bg2selected = None CleanupAutomaticBackground(parent) page.OnAmplitudeCheck("init") page.PlotAll() def CleanupAutomaticBackground(parent): """ Goes through the pagelist *parent.notebook.GetPageCount()* and checks *parent.Background* for unnused automatic backgrounds. Removes these and updates the references to all backgrounds within the pages. """ # Create a dictionary with keys: indices of old background list - # and elements: list of pages having this background BGdict = dict() for i in range(len(parent.Background)): BGdict[i] = list() # Append pages to the lists inside the dictionary for i in range(parent.notebook.GetPageCount()): Page = parent.notebook.GetPage(i) if Page.bgselected is not None: if not BGdict.has_key(Page.bgselected): BGdict[Page.bgselected] = list() BGdict[Page.bgselected].append([Page, 1]) if Page.bg2selected is not None: if not BGdict.has_key(Page.bg2selected): BGdict[Page.bg2selected] = list() BGdict[Page.bg2selected].append([Page, 2]) oldBackground = parent.Background parent.Background = list() bgcounter = 0 for key in BGdict.keys(): if len(BGdict[key]) != 0 or not oldBackground[key].name.endswith("\t"): parent.Background.append(oldBackground[key]) for Page, bgid in BGdict[key]: if bgid == 1: Page.bgselected = bgcounter else: Page.bg2selected = bgcounter bgcounter += 1 # If the background correction tool is open, update the list # of backgrounds. # (self.MyName="BACKGROUND") toolkeys = parent.ToolsOpen.keys() if len(toolkeys) == 0: pass else: for key in toolkeys: tool = parent.ToolsOpen[key] try: if tool.MyName == "BACKGROUND": tool.UpdateDropdown() tool.OnPageChanged() except: pass PyCorrFit-0.9.9/pycorrfit/gui/tools/batchcontrol.py000066400000000000000000000325231274265304700224320ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ PyCorrFit Module tools - batch Stuff that concerns batch processing. """ import numpy as np import os import wx from pycorrfit import openfile as opf # How to treat an opened file from pycorrfit import models as mdls from pycorrfit import Fit from pycorrfit.gui.threaded_progress import ThreadedProgressDlg # Menu entry name MENUINFO = ["B&atch control", "Batch fitting."] class BatchCtrl(wx.Frame): def __init__(self, parent): # Parent is main frame self.parent = parent # Get the window positioning correctly pos = self.parent.GetPosition() pos = (pos[0]+100, pos[1]+100) wx.Frame.__init__(self, parent=parent, title="Batch control", pos=pos, style=wx.DEFAULT_FRAME_STYLE|wx.FRAME_FLOAT_ON_PARENT) ## MYID # This ID is given by the parent for an instance of this class self.MyID = None ## Misc try: self.curpage = self.parent.notebook.GetCurrentPage() except: self.curpage = None ## Controls panel = wx.Panel(self) self.panel = panel #Icon self.Redraw() if parent.MainIcon is not None: wx.Frame.SetIcon(self, parent.MainIcon) self.Show(True) def GetParameters(self): """ The parameters """ # Get the item from the dropdown list item = self.dropdown.GetSelection() if self.rbtnhere.Value == True: # Get parameters from this session if item <= 0: Page = self.parent.notebook.GetCurrentPage() else: Page = self.parent.notebook.GetPage(item-1) # First apply the parameters of the page Page.apply_parameters() # Get all parameters Parms = self.parent.PackParameters(Page) else: # Get Parameters from different session Parms = self.YamlParms[item] return Parms def GetProtectedParameterIDs(self): """ The model parameters that are protected from batch control """ pbool = [ not cb.GetValue() for cb in self.wxParameterCheckBoxes ] return np.array(pbool, dtype=bool) def OnApply(self, event): wx.BeginBusyCursor() Parms = self.GetParameters() modelid = Parms[1] # Set all parameters for all pages for i in np.arange(self.parent.notebook.GetPageCount()): OtherPage = self.parent.notebook.GetPage(i) if (OtherPage.corr.fit_model.id == modelid and OtherPage.corr.correlation is not None): # create a copy of the fitting parameters in # case we want to protect them proparms = OtherPage.corr.fit_parameters self.parent.UnpackParameters(Parms, OtherPage) if OtherPage.prevent_batch_modification: # write back protected parameters OtherPage.corr.fit_parameters = proparms else: # write back only selected parameters pbool = self.GetProtectedParameterIDs() OtherPage.corr.fit_parameters[pbool] = proparms[pbool] OtherPage.apply_parameters_reverse() OtherPage.PlotAll(trigger="parm_batch") # Update all other tools fit the finalize trigger. self.parent.OnFNBPageChanged(trigger="parm_finalize") wx.EndBusyCursor() def OnClose(self, event=None): self.parent.toolmenu.Check(self.MyID, False) self.parent.ToolsOpen.__delitem__(self.MyID) self.Destroy() def OnFit(self, event): item = self.dropdown.GetSelection() if self.rbtnhere.Value == True: if item <= 0: page = self.parent.notebook.GetCurrentPage() else: page = self.parent.notebook.GetPage(item-1) # Get internal ID modelid = page.corr.fit_model.id else: # Get external ID modelid = self.YamlParms[item][1] # Get all pages with right modelid fit_page_list = [] for ii in np.arange(self.parent.notebook.GetPageCount()): pageii = self.parent.notebook.GetPage(ii) if (pageii.corr.fit_model.id == modelid and pageii.corr.correlation is not None): fit_page_list.append(pageii) FitProgressDlg(self, fit_page_list, trigger="fit_batch") def OnPageChanged(self, Page=None, trigger=None): """ This function is called, when something in the panel changes. The variable `trigger` is used to prevent this function from being executed to save stall time of the user. Forr a list of possible triggers, see the doc string of `tools`. """ if self.parent.notebook.GetPageCount() == 0: self.panel.Disable() # nothing to do return else: self.panel.Enable() # Filter triggers if trigger in ["fit_batch", "fit_finalize", "init", "parm_batch", "parm_finalize"]: return oldpage = self.curpage self.curpage = self.parent.notebook.GetCurrentPage() # redraw this tool if necessary if oldpage is not None and not isinstance(oldpage, wx._core._wxPyDeadObject): oldmodelid = oldpage.modelid else: oldmodelid = 0 newmodelid = self.curpage.modelid if oldmodelid != newmodelid: self.RedrawParameterBox() # We need to update the list of Pages in self.dropdown if self.rbtnhere.Value == True: DDlist = list() DDlist.append("Current page") for i in np.arange(self.parent.notebook.GetPageCount()): aPage = self.parent.notebook.GetPage(i) DDlist.append(aPage.counter+aPage.tabtitle.GetValue()) self.dropdown.SetItems(DDlist) self.dropdown.SetSelection(0) def OnRadioHere(self, event=None): self.OnPageChanged(trigger="view") self.RedrawParameterBox() def OnRadioThere(self, event=None): # If user clicks on pages in main program, we do not want the list # to be changed. wc = opf.session_wildcards wcstring = "PyCorrFit session (*.pcfs)|*{};*{}".format( wc[0], wc[1]) dlg = wx.FileDialog(self.parent, "Open session file", self.parent.dirname, "", wcstring, wx.FD_OPEN) # user cannot do anything until he clicks "OK" if dlg.ShowModal() == wx.ID_OK: sessionfile = dlg.GetPath() self.dirname = os.path.split(sessionfile)[0] else: self.parent.dirname=dlg.GetDirectory() self.rbtnhere.SetValue(True) return Infodict = opf.LoadSessionData(sessionfile, parameters_only=True) self.YamlParms = Infodict["Parameters"] DDlist = list() for i in np.arange(len(self.YamlParms)): # Rebuild the list modelid = self.YamlParms[i][1] modelname = mdls.modeldict[modelid][1] DDlist.append(self.YamlParms[i][0]+modelname) self.dropdown.SetItems(DDlist) # Set selection text to first item self.dropdown.SetSelection(0) self.RedrawParameterBox() def Redraw(self, e=None): panel = self.panel for child in panel.GetChildren(): panel.RemoveChild(child) child.Destroy() ## Parameter source selection boxleft = wx.StaticBox(panel, label="Parameter source") self.rbtnhere = wx.RadioButton(panel, -1, 'This session', style = wx.RB_GROUP) self.rbtnhere.SetValue(True) self.rbtnthere = wx.RadioButton(panel, -1, 'Other session') self.dropdown = wx.ComboBox(panel, -1, "Current page", (15, 30), wx.DefaultSize, [], wx.CB_DROPDOWN|wx.CB_READONLY) # Create the dropdownlist text2 = wx.StaticText(panel, label="""Only data sets that have the same model as the parameter source will be affected by batch modification, which includes parameter values as well as settings for fitting and background correction. To prevent batch modification of parameter values for an individual page, check its "prevent batch modification" check box.""") self.Bind(wx.EVT_RADIOBUTTON, self.OnRadioHere, self.rbtnhere) self.Bind(wx.EVT_RADIOBUTTON, self.OnRadioThere, self.rbtnthere) self.Bind(wx.EVT_COMBOBOX, self.RedrawParameterBox, self.dropdown) leftSizer = wx.StaticBoxSizer(boxleft, wx.VERTICAL) leftSizer.Add(self.rbtnhere) leftSizer.Add(self.rbtnthere) leftSizer.AddSpacer(5) leftSizer.Add(self.dropdown) leftSizer.AddSpacer(5) leftSizer.Add(text2) leftSizer.AddSpacer(5) ## Parameter selection boxright = wx.StaticBox(panel, label="Selected parameters") rightSizer = wx.StaticBoxSizer(boxright, wx.VERTICAL) self.parameter_sizer = rightSizer self.RedrawParameterBox() ## Buttons btnapply = wx.Button(panel, wx.ID_ANY, 'Apply to applicable pages') btnfit = wx.Button(panel, wx.ID_ANY, 'Fit applicable pages') # Bindings self.Bind(wx.EVT_BUTTON, self.OnApply, btnapply) self.Bind(wx.EVT_BUTTON, self.OnFit, btnfit) ## Sizers sizer_bag = wx.GridBagSizer(hgap=5, vgap=5) sizer_bag.Add(leftSizer, (0,0)) sizer_bag.Add(rightSizer, (0,1)) horsizer = wx.BoxSizer(wx.HORIZONTAL) horsizer.Add(btnapply) horsizer.Add(btnfit) sizer_bag.Add(horsizer, (1,0), span=wx.GBSpan(1,2)) panel.SetSizer(sizer_bag) sizer_bag.Fit(panel) self.SetMinSize(sizer_bag.GetMinSizeTuple()) # Check if we even have pages. self.OnPageChanged() panel.Layout() sizer_bag.Fit(self) self.mastersizer = sizer_bag self.mastersizer.Fit(self) def RedrawParameterBox(self, e=None): sizer = self.parameter_sizer panel = self.panel for child in sizer.GetChildren(): window = child.GetWindow() panel.RemoveChild(window) sizer.RemoveWindow(window) window.Destroy() text = wx.StaticText(panel, label="""If desired, (de)select individual parameters for batch modification.""") sizer.Add(text) if self.parent.notebook.GetPageCount(): # Get parameters of current page parms = self.GetParameters() modelid = parms[1] ptext, _pval = mdls.GetHumanReadableParms(modelid, parms[2]) ptext = [ p.split()[0] for p in ptext ] self.wxParameterCheckBoxes = [] for p in ptext: cb = wx.CheckBox(panel, label=p) cb.SetValue(True) self.wxParameterCheckBoxes.append(cb) sizer.Add(cb) # Try to set sizes correctly box = sizer.GetStaticBox() boxs = box.GetBestSize() sizs = sizer.GetMinSize() thesize = (max(boxs[0], sizs[0]), sizs[1]) sizer.SetMinSize(thesize) box.SetSize(thesize) try: self.mastersizer.Fit(panel) panel.Layout() self.SetSize(panel.GetSize()) self.mastersizer.Fit(self) except: pass class FitProgressDlg(ThreadedProgressDlg): def __init__(self, parent, pages, trigger=None): """ A progress dialog for fitting in PyCorrFit This is a convenience class that wraps around `ThreadedProgressDlg` and performs all necessary steps for fitting single pages in PyCorrFit. Parameters ---------- parent : wx object The parent of the progress dialog. pages : list of instances of `pycorrfit.gui.page.FittingPanel` The pages with the model and correlation for fitting. trigger : str PyCorrFit internal trigger string. """ if not isinstance(pages, list): pages = [pages] self.pages = pages self.trigger = trigger title = "Fitting data" messages = [ "Fitting page #{}.".format(pi.counter.strip("# :")) for pi in pages ] targets = [Fit]*len(pages) args = [pi.corr for pi in pages] # write parameters from page instance to correlation [ pi.apply_parameters() for pi in self.pages ] super(FitProgressDlg, self).__init__(parent, targets, args, title=title, messages=messages) def finalize(self): """ Do everything that is required after fitting, including cleanup of non-fitted pages. """ if self.aborted: ## we need to cleanup fin_index = max(0,self.index_aborted-1) pab = self.pages[self.index_aborted] pab.fit_results = None pab.apply_parameters() else: fin_index = len(self.pages) # finalize fitting [ pi.Fit_finalize(trigger=self.trigger) for pi in self.pages[:fin_index] ] PyCorrFit-0.9.9/pycorrfit/gui/tools/chooseimport.py000066400000000000000000000227671274265304700224740ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ PyCorrFit Module tools - chooseimport Displays a window that lets the user choose what type of data (AC1, AC2, CC12, CC21) he wants to import. """ import numpy as np import wx from pycorrfit import models as mdls from . import overlaycurves class ChooseImportTypes(wx.Dialog): """ This class is used for importing single files from the "Current" menu. The model function is defined by the model that is in use. """ # This tool is derived from a wx.Dialog. def __init__(self, parent, curvedict): # parent is the main frame of PyCorrFit self.parent = parent # init #super(ChooseImportTypes, self).__init__(parent=parent, # title="Choose types", size=(250, 200)) wx.Dialog.__init__(self, parent, -1, "Choose models") self.keys = list() ## Content self.panel = wx.Panel(self) self.sizer = wx.BoxSizer(wx.VERTICAL) self.boxes = dict() # For the selection of types to import when doing import Data chooseimport = ("Several types of data were found in\n"+ "the chosen file. Please select what\n"+ "type(s) you would like to import.") textinit = wx.StaticText(self.panel, label=chooseimport) self.sizer.Add(textinit) thekeys = curvedict.keys() thekeys.sort() for key in thekeys: label = key + " (" + str(len(curvedict[key])) + " curves)" check = wx.CheckBox(self.panel, label=label) self.boxes[key] = check self.sizer.Add(check) self.Bind(wx.EVT_CHECKBOX, self.OnSetkeys, check) btnok = wx.Button(self.panel, wx.ID_OK, 'OK') # Binds the button to the function - close the tool self.Bind(wx.EVT_BUTTON, self.OnClose, btnok) self.sizer.Add(btnok) self.panel.SetSizer(self.sizer) self.sizer.Fit(self) #Icon if parent.MainIcon is not None: wx.Dialog.SetIcon(self, parent.MainIcon) #self.Show(True) self.SetFocus() def OnClose(self, event=None): # This is a necessary function for PyCorrFit. # Do not change it. self.EndModal(wx.ID_OK) #self.Destroy() def OnSetkeys(self, event = None): self.keys = list() for key in self.boxes.keys(): if self.boxes[key].Value == True: self.keys.append(key) class ChooseImportTypesModel(wx.Dialog): """ This class shows a dialog displaying options to choose model function on import of data """ # This tool is derived from a wx.frame. def __init__(self, parent, curvedict, correlations, labels=None): """ curvedict - dictionary, contains indexes to correlations and labels. The keys are different types of curves correlations - list of correlations labels - list of labels for the correlations (e.g. filename+run) if none, index numbers will be used for labels """ # parent is the main frame of PyCorrFit self.parent = parent # init #super(ChooseImportTypesModel, self).__init__(parent=parent, # title="Choose types", size=(250, 200)) wx.Dialog.__init__(self, parent, -1, "Choose models") self.curvedict = curvedict self.kept_curvedict = curvedict.copy() # Can be edited by user self.correlations = correlations self.labels = labels # List of keys that will be imported by our *parent* self.typekeys = list() # Dictionary of modelids corresponding to indices in curvedict self.modelids = dict() ## Content self.panel = wx.Panel(self) self.sizer = wx.BoxSizer(wx.VERTICAL) self.boxes = dict() labelim = "Select a fitting model for each correlation channel (AC,CC)." textinit = wx.StaticText(self.panel, label=labelim) self.sizer.Add(textinit) curvekeys = curvedict.keys() curvekeys.sort() self.curvekeys = curvekeys # Dropdown model selections: DropdownList = ["No model selected"] # Contains string in model dropdown self.DropdownIndex = [None] # Contains corresponsing model modelkeys = mdls.modeltypes.keys() modelkeys.sort() for modeltype in modelkeys: for modelid in mdls.modeltypes[modeltype]: DropdownList.append(modeltype+": "+mdls.modeldict[modelid][1]) self.DropdownIndex.append(modelid) self.ModelDropdown = dict() dropsizer = wx.FlexGridSizer(rows=len(modelkeys), cols=3, vgap=5, hgap=5) self.Buttons = list() i = 8000 for key in curvekeys: # Text with keys and numer of curves dropsizer.Add( wx.StaticText(self.panel, label=str(key)) ) label=" ("+str(len(curvedict[key]))+" curves)" button = wx.Button(self.panel, i, label) i += 1 self.Bind(wx.EVT_BUTTON, self.OnSelectCurves, button) self.Buttons.append(button) dropsizer.Add(button) # Model selection dropdown dropdown = wx.ComboBox(self.panel, -1, DropdownList[0], (15,30), wx.DefaultSize, DropdownList, wx.CB_DROPDOWN|wx.CB_READONLY) dropsizer.Add( dropdown ) self.ModelDropdown[key] = dropdown self.Bind(wx.EVT_COMBOBOX, self.OnSetkeys, dropdown) self.sizer.Add(dropsizer) btnok = wx.Button(self.panel, wx.ID_OK, 'OK') # Binds the button to the function - close the tool self.Bind(wx.EVT_BUTTON, self.OnClose, btnok) self.sizer.Add(btnok) self.panel.SetSizer(self.sizer) self.sizer.Fit(self) #self.Show(True) self.SetFocus() if parent.MainIcon is not None: wx.Dialog.SetIcon(self, parent.MainIcon) def OnClose(self, event=None): # This is a necessary function for PyCorrFit. # Do not change it. self.keepcurvesindex = list() for key in self.kept_curvedict.keys(): self.keepcurvesindex += self.kept_curvedict[key] for i in np.arange(len(self.keepcurvesindex)): self.keepcurvesindex[i] = int(self.keepcurvesindex[i]) self.EndModal(wx.ID_OK) #self.Show #self.Destroy() def OnSelectCurves(self, buttonevent): # Get the type of curves we want to look at index = buttonevent.GetId() - 8000 self.buttonindex = index key = self.curvekeys[index] # Get correlation curves for corresponding type corrcurves = dict() if self.labels is None: labeldict = None else: labeldict = dict() for i in self.curvedict[key]: corrcurves[str(i)] = self.correlations[int(i)] if self.labels is not None: labeldict[str(i)] = self.labels[int(i)] prev_selected = list() for item in self.kept_curvedict.keys(): prev_selected += self.kept_curvedict[item] overlaycurves.Wrapper_OnImport(self.parent, corrcurves, self.OnSelected, prev_selected, labels=labeldict) def OnSelected(self, keep, remove): # Set new button label for i in np.arange(len(keep)): keep[i] = int(keep[i]) #button = self.Buttons[self.buttonindex] label = " ("+str(len(keep))+" curves)" #button.SetLabel(label) # Add new content to selected key SelectedKey = self.curvekeys[self.buttonindex] #self.kept_curvedict[SelectedKey] = keep # If there are keys with the same amount of correlations, # these are assumed to be AC2, CC12, CC21 etc., so we will remove # items from them accordingly. diff = set(keep).intersection(set(self.curvedict[SelectedKey])) indexes = list() for i in np.arange(len(self.curvedict[SelectedKey])): for number in diff: if number == self.curvedict[SelectedKey][i]: indexes.append(i) for j in np.arange(len(self.curvekeys)): key = self.curvekeys[j] if len(self.curvedict[key]) == len(self.curvedict[SelectedKey]): newlist = list() for index in indexes: newlist.append(self.curvedict[key][index]) self.kept_curvedict[key] = newlist # Also update buttons button = self.Buttons[j] button.SetLabel(label) def OnSetkeys(self, event = None): # initiate objects self.typekeys = list() self.modelids = dict() # iterate through all given keys (AC1, AC2, CC12, etc.) for key in self.curvedict.keys(): # get the dropdown selection for a given key modelindex = self.ModelDropdown[key].GetSelection() # modelindex is -1 or 0, if no model has been chosen if modelindex > 0: # Append the key to a list of to be imported types self.typekeys.append(key) # Append the modelid to a dictionary that has indexes # belonging to the imported curves in *parent* modelid = self.DropdownIndex[modelindex] for index in self.curvedict[key]: # Set different model id for the curves self.modelids[index] = modelid self.typekeys.sort() PyCorrFit-0.9.9/pycorrfit/gui/tools/comment.py000077500000000000000000000052321274265304700214120ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ PyCorrFit Module tools - comment Edit the sessions' comment. """ from __future__ import print_function import wx class EditComment(wx.Frame): """ Little Dialog to edit the comment on the session. """ def __init__(self, parent): ## Variables # parent is main frame self.parent = parent # Get the window positioning correctly pos = self.parent.GetPosition() pos = (pos[0]+100, pos[1]+100) wx.Frame.__init__(self, parent=parent, title="Session comment", pos=pos, style=wx.DEFAULT_FRAME_STYLE|wx.FRAME_FLOAT_ON_PARENT) initial_size = (400,300) initial_sizec = (initial_size[0], initial_size[1]-50) self.SetSize(initial_size) self.SetMinSize((400,300)) ## Content self.panel = wx.Panel(self) self.control = wx.TextCtrl(self.panel, style=wx.TE_MULTILINE, size=initial_sizec, value=self.parent.SessionComment) self.Bind(wx.EVT_TEXT, self.OnTextChanged, self.control) text = wx.StaticText(self.panel, label="Session comments will be saved in the session file.") # buttons btnsave = wx.Button(self.panel, wx.ID_SAVE, 'Save Comment') self.Bind(wx.EVT_BUTTON, self.OnSave, btnsave) #sizers self.topSizer = wx.BoxSizer(wx.VERTICAL) self.topSizer.Add(text) self.topSizer.Add(self.control) self.topSizer.Add(btnsave, 1, wx.RIGHT | wx.EXPAND) self.panel.SetSizer(self.topSizer) self.topSizer.Fit(self) #Icon if parent.MainIcon is not None: wx.Frame.SetIcon(self, parent.MainIcon) self.Show(True) wx.EVT_SIZE(self, self.OnSize) self.text_changed = False def OnSize(self, event): size = event.GetSize() sizec = (size[0], size[1]-50) self.panel.SetSize(size) self.control.SetSize(sizec) def OnClose(self, e=None): self.parent.filemenu.Check(self.parent.menuComm.GetId(), False) if self.text_changed: # ask the user to save or discard. dlg = wx.MessageDialog(self, "Save comment?", "Do you want to save the current changes?", style=wx.YES_NO) if dlg.ShowModal() == wx.ID_YES: self.OnSave() self.Destroy() def OnTextChanged(self, e=None): """ When the user changes the text """ self.text_changed = True def OnSave(self, e=None): self.parent.SessionComment = self.control.GetValue() self.text_changed = False self.OnClose() PyCorrFit-0.9.9/pycorrfit/gui/tools/datarange.py000066400000000000000000000217471274265304700217040ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ PyCorrFit Module tools - datarange Let the user choose time domains. """ import wx import numpy as np # Menu entry name MENUINFO = ["&Data range", "Select an interval of lag times to be used for fitting."] class SelectChannels(wx.Frame): def __init__(self, parent): # parent is main frame self.parent = parent # Get the window positioning correctly pos = self.parent.GetPosition() pos = (pos[0]+100, pos[1]+100) wx.Frame.__init__(self, parent=self.parent, title="Data range selection", pos=pos, style=wx.DEFAULT_FRAME_STYLE|wx.FRAME_FLOAT_ON_PARENT) ## MYID # This ID is given by the parent for an instance of this class self.MyID = None ## Start drawing panel = wx.Panel(self) self.panel = panel # Page self.Page = self.parent.notebook.GetCurrentPage() self.Calc_init(self.Page) text1 = wx.StaticText(panel, label=u"The lag times τ are stored as an "+ u"array of length ") self.textend = wx.StaticText(panel, label="%d." % self.lentau) text2 = wx.StaticText(panel, label=u"You may wish to confine this array. "+ u"This can be done here.") ##Spincontrols: FlexSpinSizer = wx.FlexGridSizer(rows=2, cols=4, vgap=5, hgap=5) FlexSpinSizer.Add(wx.StaticText(panel, label="Channels:")) self.spinstart = wx.SpinCtrl(panel, -1, initial=self.left, min=self.start0, max=self.end0-1) FlexSpinSizer.Add(self.spinstart) FlexSpinSizer.Add(wx.StaticText(panel, label=" - ")) self.spinend = wx.SpinCtrl(panel, -1, initial=self.right, min=self.start0+1, max=self.end0) FlexSpinSizer.Add(self.spinend) FlexSpinSizer.Add(wx.StaticText(panel, label="Times [ms]:")) self.TextTimesStart = wx.StaticText(panel, label="None") FlexSpinSizer.Add(self.TextTimesStart) FlexSpinSizer.Add(wx.StaticText(panel, label=" - ")) self.TextTimesEnd = wx.StaticText(panel, label="None") FlexSpinSizer.Add(self.TextTimesEnd) # Buttons btnapply = wx.Button(panel, wx.ID_ANY, 'Apply') btnapplyall = wx.Button(panel, wx.ID_ANY, 'Apply to all pages') self.ButtonApply = btnapply self.ButtonApplyAll = btnapplyall self.Bind(wx.EVT_BUTTON, self.OnApply, btnapply) self.Bind(wx.EVT_BUTTON, self.OnApplyAll, btnapplyall) self.Bind(wx.EVT_SPINCTRL, self.OnChangeChannels, self.spinend) self.Bind(wx.EVT_SPINCTRL, self.OnChangeChannels, self.spinstart) # Checkbox self.fixcheck = wx.CheckBox(panel, -1, label="Fix current channel selection for all pages.") self.Bind(wx.EVT_CHECKBOX, self.OnCheckbox, self.fixcheck) # Text channelsel = "Leave this window open for a fixed selection." text3 = wx.StaticText(panel, label=channelsel) # Sizer topSizer = wx.BoxSizer(wx.VERTICAL) buttonsizer = wx.BoxSizer(wx.HORIZONTAL) buttonsizer.Add(btnapply, 1) buttonsizer.Add(btnapplyall, 1) text1sizer = wx.BoxSizer(wx.HORIZONTAL) text1sizer.Add(text1) text1sizer.Add(self.textend) topSizer.Add(text1sizer) topSizer.Add(text2) topSizer.AddSpacer(5) topSizer.Add(FlexSpinSizer) topSizer.Add(self.fixcheck) topSizer.Add(text3) topSizer.AddSpacer(5) topSizer.Add(buttonsizer) panel.SetSizer(topSizer) topSizer.Fit(self) self.SetMinSize(topSizer.GetMinSizeTuple()) # Get times. self.OnChangeChannels() #Icon if parent.MainIcon is not None: wx.Frame.SetIcon(self, parent.MainIcon) # Show window self.Show(True) self.OnPageChanged(self.Page) def Calc_init(self, parent): ## Variables # Parent should be the fitting panel - # The tab, where the fitting is done. self.Page = parent if self.Page == None: # dummy info taufull = np.arange(100) self.left = self.right = None self.panel.Disable() else: self.left = self.Page.corr.fit_ival[0] # starting position self.right = self.Page.corr.fit_ival[1] # ending position taufull = self.Page.corr.lag_time self.lentau = len(taufull) self.start0 = 0 # left border of interval # The interval starts at 0! self.end0 = self.lentau - 1 # right border of interval if self.left is None or self.left > self.end0: # This means, that either left = right = None # or the correlation-array is too small self.left = self.start0 if self.right is None: # set the maximum possible value self.right = self.end0 else: self.right -=1 def OnApply(self, event=None): self.SetValues() self.Page.PlotAll() def OnApplyAll(self, event=None): N = self.parent.notebook.GetPageCount() for i in np.arange(N): # Set Page Page = self.parent.notebook.GetPage(i) # Find out maximal length self.SetValues(page=Page) Page.PlotAll() # Page.PlorAll() calls this function. This results in the wrong data # being displayed in an open "View Info" Window. We call it again. self.parent.OnFNBPageChanged() def OnChangeTimes(self, e=None): """ Called, whenever data range in seconds is changed. This updates the data range in channels in the window. This function might be used in later versions of PyCorrFit. """ pass def OnChangeChannels(self, e=None): """ Called, whenever data range in channels is changed. This updates the data range in seconds in the window. """ if self.Page == None: return N = len(self.Page.corr.lag_time) start = self.spinstart.Value end = self.spinend.Value # If the initial boundaries are outside of the experimental # data array of length N, change the start and end variables. start = start*(start < N-2) end = min(end, N-1) t1 = 1.*self.Page.corr.lag_time[start] t2 = 1.*self.Page.corr.lag_time[end] self.TextTimesStart.SetLabel("%.4e" % t1) self.TextTimesEnd.SetLabel("%.4e" % t2) self.OnCheckbox() def OnCheckbox(self, event=None): """ Set the correct value in the spincontrol, if the checkbox is not checked. """ state = self.fixcheck.GetValue() if state == True: self.OnApplyAll() self.ButtonApply.Disable() self.ButtonApplyAll.Disable() else: self.ButtonApply.Enable() self.ButtonApplyAll.Enable() #self.OnPageChanged(self.Page) def OnClose(self, event=None): self.parent.toolmenu.Check(self.MyID, False) self.parent.ToolsOpen.__delitem__(self.MyID) self.Destroy() def OnPageChanged(self, page, trigger=None): # We do not need the *Range* Commands here yet. # We open and close the SelectChannelsFrame every time we # import some data. # if trigger in ["parm_batch", "fit_batch", "page_add_batch"]: return # Check if we have a fixed channel selection if self.parent.notebook.GetPageCount() == 0: self.panel.Disable() else: self.panel.Enable() # There is a page. We may continue. state = self.fixcheck.GetValue() if state: # We do not need to run Calc_init self.SetValues(page=page) page.PlotAll(event="init") else: # We will run it self.Calc_init(page) self.spinstart.SetRange(self.start0, self.end0-1) self.spinstart.SetValue(self.left) self.spinend.SetRange(self.start0+1, self.end0) self.spinend.SetValue(self.right) self.textend.SetLabel("%d." % self.lentau) self.OnChangeChannels() def SetValues(self, page=None): if page is None: page = self.Page # Get interval start = self.spinstart.GetValue() end = self.spinend.GetValue() + 1 # +1, [sic] if start > end: # swap the variables, we are not angry at the user start, end = end, start # Find out maximal length maxlen = len(page.corr.lag_time) # Use the smaller one of both, so we do not get an # index out of bounds error page.corr.fit_ival = [ start*(start < maxlen - 1 ), min(end, maxlen) ]PyCorrFit-0.9.9/pycorrfit/gui/tools/example.py000066400000000000000000000050471274265304700214040ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ PyCorrFit Module tools - example This is an example tool. You will need to edit __init__.py inside this folder to activate it. Add the filename (*example*) and class (*Tool*) to either of the lists *ImpA* or *ImpB* in __init__.py. """ import wx #import numpy as np class Tool(wx.Frame): # This tool is derived from a wx.frame. def __init__(self, parent): # parent is the main frame of PyCorrFit self.parent = parent # Get the window positioning correctly pos = self.parent.GetPosition() pos = (pos[0]+100, pos[1]+100) wx.Frame.__init__(self, parent=self.parent, title="Example tool", pos=pos, style=wx.DEFAULT_FRAME_STYLE|wx.FRAME_FLOAT_ON_PARENT) ## MYID # This ID is given by the parent for an instance of this class self.MyID = None # Page - the currently active page of the notebook. self.Page = self.parent.notebook.GetCurrentPage() ## Content self.panel = wx.Panel(self) btncopy = wx.Button(self.panel, wx.ID_ANY, 'Example button') # Binds the button to the function - close the tool self.Bind(wx.EVT_BUTTON, self.OnClose, btncopy) self.topSizer = wx.BoxSizer(wx.VERTICAL) self.topSizer.Add(btncopy) self.panel.SetSizer(self.topSizer) self.topSizer.Fit(self) self.SetMinSize(self.topSizer.GetMinSizeTuple()) # Icon if parent.MainIcon is not None: wx.Frame.SetIcon(self, parent.MainIcon) self.Show(True) def OnClose(self, event=None): # This is a necessary function for PyCorrFit. # Do not change it. self.parent.toolmenu.Check(self.MyID, False) self.parent.ToolsOpen.__delitem__(self.MyID) self.Destroy() def OnPageChanged(self, page, trigger=None): """ This function is called, when something in the panel changes. The variable `trigger` is used to prevent this function from being executed to save stall time of the user. Forr a list of possible triggers, see the doc string of `tools`. """ # When parent changes # This is a necessary function for PyCorrFit. # This is stuff that should be done when the active page # of the notebook changes. if self.parent.notebook.GetPageCount() == 0: # Do something when there are no pages left. self.panel.Disable() return self.panel.Enable() self.Page = page PyCorrFit-0.9.9/pycorrfit/gui/tools/globalfit.py000066400000000000000000000125221274265304700217100ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ PyCorrFit Module tools - globalfit Perform global fitting on pages which share parameters. """ import wx import numpy as np from pycorrfit import Fit from .. import misc # Menu entry name MENUINFO = ["&Global fitting", "Interconnect parameters from different measurements."] class GlobalFit(wx.Frame): # This tool is derived from a wx.frame. def __init__(self, parent): # Define a unique name that identifies this tool # Do not change this value. It is important for the Overlay tool # (selectcurves.py, *Wrapper_Tools*). self.MyName="GLOBALFIT" # parent is the main frame of PyCorrFit self.parent = parent # Get the window positioning correctly pos = self.parent.GetPosition() pos = (pos[0]+100, pos[1]+100) wx.Frame.__init__(self, parent=self.parent, title="Gobal fitting", pos=pos, style=wx.DEFAULT_FRAME_STYLE|wx.FRAME_FLOAT_ON_PARENT) ## MYID # This ID is given by the parent for an instance of this class self.MyID = None # Page - the currently active page of the notebook. self.Page = self.parent.notebook.GetCurrentPage() ## Content self.panel = wx.Panel(self) self.topSizer = wx.BoxSizer(wx.VERTICAL) textinit = """Fitting of multiple data sets with different models. Parameter names have to match. Select pages (e.g. 1,3-5,7), check parameters on each page and start 'Global fit'. """ self.topSizer.Add(wx.StaticText(self.panel, label=textinit)) ## Page selection self.WXTextPages = wx.TextCtrl(self.panel, value="", size=(330,-1)) # Set initial value in text control pagenumlist = list() for i in np.arange(self.parent.notebook.GetPageCount()): Page = self.parent.notebook.GetPage(i) pagenumlist.append(int(filter(lambda x: x.isdigit(), Page.counter))) valstring=misc.parsePagenum2String(pagenumlist) self.WXTextPages.SetValue(valstring) self.topSizer.Add(self.WXTextPages) ## Button btnfit = wx.Button(self.panel, wx.ID_ANY, 'Global fit') # Binds the button to the function - close the tool self.Bind(wx.EVT_BUTTON, self.OnFit, btnfit) self.topSizer.Add(btnfit) self.panel.SetSizer(self.topSizer) self.topSizer.Fit(self) self.SetMinSize(self.topSizer.GetMinSizeTuple()) self.OnPageChanged(self.Page) # Icon if parent.MainIcon is not None: wx.Frame.SetIcon(self, parent.MainIcon) self.Show(True) def OnClose(self, event=None): # This is a necessary function for PyCorrFit. # Do not change it. self.parent.toolmenu.Check(self.MyID, False) self.parent.ToolsOpen.__delitem__(self.MyID) self.Destroy() def OnFit(self, e=None): # process a string like this: "1,2,4-9,10" strFull = self.WXTextPages.GetValue() PageNumbers = misc.parseString2Pagenum(self, strFull) global_pages = list() if PageNumbers is None: # Something went wrong and parseString2Pagenum already displayed # an error message. return ## Get the correlations corrs = list() for i in np.arange(self.parent.notebook.GetPageCount()): Page = self.parent.notebook.GetPage(i) corr = Page.corr j = filter(lambda x: x.isdigit(), Page.counter) if int(j) in PageNumbers: if corr.correlation is not None: Page.apply_parameters() corrs.append(corr) global_pages.append(int(j)) else: print "No experimental data in page #"+j+"!" if len(corrs) == 0: return # Perform fit fitter = Fit(corrs, global_fit=True) fit_parm_names = [f.split()[0] for f in fitter.fit_parm_names] # update fit results for corr in corrs: corr.fit_results["global parms"] = u", ".join(fit_parm_names) corr.fit_results["global pages"] = u", ".join([str(g) for g in global_pages]) # Plot resutls for i in np.arange(self.parent.notebook.GetPageCount()): Page = self.parent.notebook.GetPage(i) j = filter(lambda x: x.isdigit(), Page.counter) if int(j) in global_pages: Page.apply_parameters_reverse() Page.PlotAll() def OnPageChanged(self, page, trigger=None): """ This function is called, when something in the panel changes. The variable `trigger` is used to prevent this function from being executed to save stall time of the user. Forr a list of possible triggers, see the doc string of `tools`. """ # When parent changes # This is a necessary function for PyCorrFit. # This is stuff that should be done when the active page # of the notebook changes. if trigger in ["parm_batch", "fit_batch", "page_add_batch"]: return if self.parent.notebook.GetPageCount() == 0: self.panel.Disable() return self.panel.Enable() self.Page = page def SetPageNumbers(self, pagestring): self.WXTextPages.SetValue(pagestring) PyCorrFit-0.9.9/pycorrfit/gui/tools/info.py000066400000000000000000000317101274265304700207000ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ PyCorrFit Module tools - info Open a text window with lots of information. """ import wx import numpy as np from pycorrfit import fit from pycorrfit import models as mdls # Menu entry name MENUINFO = ["Page &info", "Display some information on the current page."] class InfoClass(object): """ This class get's all the Info possible from a Page and makes it available through a dictionary with headings as keys. """ def __init__(self, CurPage=None, Pagelist=None ): # A list of all Pages currently available: self.Pagelist = Pagelist # The current page we are looking at: self.CurPage = CurPage def GetAllInfo(self): """ Get a dictionary with page titles and an InfoDict as value. """ MultiInfo = dict() for Page in self.Pagelist: # Page counter includes a whitespace and a ":" which we do not want. MultiInfo[Page.counter[:-2]] = self.GetPageInfo(Page) return MultiInfo def GetCurInfo(self): """ Get all the information about the current Page. Added for convenience. You may use GetPageInfo. """ return self.GetPageInfo(self.CurPage) def GetCurFancyInfo(self): """ For convenience. """ return self.GetFancyInfo(self.CurPage) def GetFancyInfo(self, Page): """ Get a nice string representation of the Info """ InfoDict = self.GetPageInfo(Page) # Version Version = u"PyCorrFit v."+InfoDict["version"][0]+"\n" # Title Title = u"\n" for item in InfoDict["title"]: Title = Title + item[0]+"\t"+ item[1]+"\n" # Parameters Parameters = u"\nParameters:\n" for item in InfoDict["parameters"]: Parameters = Parameters + u" "+item[0]+"\t"+ str(item[1])+"\n" # Supplementary parameters Supplement = u"\nSupplementary parameters:\n" try: for item in InfoDict["supplement"]: Supplement = Supplement + " "+item[0]+"\t"+ str(item[1])+"\n" except KeyError: Supplement = "" # Fitting Fitting = u"\nFitting:\n" try: for item in InfoDict["fitting"]: Fitting = Fitting + " "+item[0]+"\t"+unicode(item[1])+"\n" except KeyError: Fitting = "" # Background Background = u"\nBackground:\n" try: for item in InfoDict["background"]: Background = Background + " "+item[0]+"\t"+str(item[1])+"\n" except KeyError: Background = "" # Function doc string ModelDoc = u"\n\nModel doc string:\n " + InfoDict["modeldoc"][0] # Supplementary variables try: SupDoc = u"\n"+8*" "+InfoDict["modelsupdoc"][0] except: SupDoc = u"" PageInfo = Version+Title+Parameters+Supplement+Fitting+Background+\ ModelDoc+SupDoc return PageInfo def GetPageInfo(self, Page): """ Needs a Page and gets all information from it """ Page.PlotAll("init") # A dictionary with headings as keys and lists of singletts/tuples as # values. If it is a tuple, it might me interesting for a table. InfoDict = dict() # Get Correlation corr = Page.corr # Get model information model = corr.fit_model parms = corr.fit_parameters fct = corr.fit_model.function.__name__ InfoDict["version"] = [Page.parent.version] Title = list() # The tool statistics relys on the string "filename/title". # Do not change it! if len(model[1]) == 0: # Prevent saving no title model[1] = "NoName" Title.append(["filename/title", Page.title]) Title.append(["Model species", model.components]) Title.append(["Model name", model.name]) Title.append(["Model ID", str(model.id)]) Title.append(["Model function", fct]) Title.append(["Page number", Page.counter[1:-2]]) ## Parameters Parameters = list() # Use this function to determine human readable parameters, if possible Units, Newparameters = mdls.GetHumanReadableParms(model.id, parms) # Add Parameters for i in np.arange(len(parms)): Parameters.append([ Units[i], Newparameters[i] ]) InfoDict["parameters"] = Parameters # Add some more information if available # Info is a dictionary or None MoreInfo = mdls.GetMoreInfo(model.id, Page) if MoreInfo is not None: InfoDict["supplement"] = MoreInfo # Try to get the dictionary entry of a model try: # This function should return all important information # that can be calculated from the given parameters. func_info = mdls.supplement[model.id] except KeyError: # No information available pass else: InfoDict["modelsupdoc"] = [func_info.func_doc] ## Fitting if hasattr(corr, "fit_results"): Fitting = list() weightedfit = corr.fit_results["weighted fit"] if corr.correlation is not None: # Mode AC vs CC if corr.is_cc: Title.append(["Type AC/CC", "Cross-correlation" ]) else: Title.append(["Type AC/CC", "Autocorrelation" ]) if "chi2" in corr.fit_results: Fitting.append([ u"χ²", corr.fit_results["chi2"]]) if weightedfit: try: Fitting.append(["Weighted fit", corr.fit_results["weighted fit type"]]) except KeyError: Fitting.append(["Weighted fit", u""+Page.Fitbox[1].GetValue()]) if corr.fit_results.has_key("chi2 type"): ChiSqType = corr.fit_results["chi2 type"] else: ChiSqType = "unknown" Fitting.append([ u"χ²-type", ChiSqType]) Fitting.append([ "Algorithm", fit.Algorithms[corr.fit_algorithm][1]]) if len(Page.GlobalParameterShare) != 0: shared = str(Page.GlobalParameterShare[0]) for item in Page.GlobalParameterShare[1:]: shared += ", "+str(item) Fitting.append(["Shared parameters with Pages", shared]) if corr.fit_results.has_key("weighted fit bins"): Fitting.append(["Std. channels", 2*corr.fit_results["weighted fit bins"]+1]) # Fitting range: t1 = 1.*corr.lag_time[corr.fit_ival[0]] t2 = 1.*corr.lag_time[corr.fit_ival[1]-1] Fitting.append([ "Ival start [ms]", "%.4e" % t1 ]) Fitting.append([ "Ival end [ms]", "%.4e" % t2 ]) # Fittet parameters try: fitparmsid = corr.fit_results["fit parameters"] except: fitparmsid = corr.fit_parameters_variable fitparms = np.array(corr.fit_model.parameters[0])[fitparmsid] fitparms_short = [ f.split()[0] for f in fitparms ] fitparms_short = u", ".join(fitparms_short) Fitting.append(["Fit parm.", fitparms_short]) # global fitting for key in corr.fit_results.keys(): if key.startswith("global"): Fitting.append([key.capitalize(), corr.fit_results[key]]) # Fit errors if corr.fit_results.has_key("fit error estimation"): errors = corr.fit_results["fit error estimation"] for err, par in zip(errors, fitparms): nam, val = mdls.GetHumanReadableParameterDict( model.id, [par], [err]) Fitting.append(["Err "+nam[0], val[0]]) InfoDict["fitting"] = Fitting ## Normalization parameter id to name if corr.normparm is None: normparmtext = "None" elif corr.normparm < len(corr.fit_parameters): normparmtext = corr.fit_model.parameters[0][corr.normparm] else: # supplementary parameters supnum = corr.normparm - len(corr.fit_parameters) normparmtext = MoreInfo[supnum][0] Title.append(["Normalization", normparmtext]) ## Background Background = list() if corr.is_cc: if len(corr.backgrounds) == 2: # Channel 1 Background.append([ "bg name Ch1", corr.backgrounds[0].name]) Background.append([ "bg rate Ch1 [kHz]", corr.backgrounds[0].countrate]) # Channel 2 Background.append([ "bg name Ch2", corr.backgrounds[1].name]) Background.append([ "bg rate Ch2 [kHz]", corr.backgrounds[1].countrate]) InfoDict["background"] = Background else: if len(corr.backgrounds) == 1: Background.append([ "bg name", corr.backgrounds[0].name]) Background.append([ "bg rate [kHz]", corr.backgrounds[0].countrate]) InfoDict["background"] = Background ## Function doc string InfoDict["modeldoc"] = [corr.fit_model.description_long] InfoDict["title"] = Title return InfoDict class ShowInfo(wx.Frame): def __init__(self, parent): # parent is main frame self.parent = parent # Get the window positioning correctly pos = self.parent.GetPosition() pos = (pos[0]+100, pos[1]+100) wx.Frame.__init__(self, parent=self.parent, title="Info", pos=pos, style=wx.DEFAULT_FRAME_STYLE|wx.FRAME_FLOAT_ON_PARENT) ## MYID # This ID is given by the parent for an instance of this class self.MyID = None # Page self.Page = self.parent.notebook.GetCurrentPage() # Size initial_size = wx.Size(650,700) initial_sizec = (initial_size[0]-6, initial_size[1]-30) self.SetMinSize(wx.Size(200,200)) self.SetSize(initial_size) ## Content self.panel = wx.Panel(self) self.control = wx.TextCtrl(self.panel, style=wx.TE_MULTILINE, size=initial_sizec) self.control.SetEditable(False) font1 = wx.Font(10, wx.MODERN, wx.NORMAL, wx.NORMAL, False, u'Monospace') self.control.SetFont(font1) btncopy = wx.Button(self.panel, wx.ID_CLOSE, 'Copy to clipboard') self.Bind(wx.EVT_BUTTON, self.OnCopy, btncopy) self.topSizer = wx.BoxSizer(wx.VERTICAL) self.topSizer.Add(btncopy) self.topSizer.Add(self.control) self.panel.SetSizer(self.topSizer) self.topSizer.Fit(self) #Icon if parent.MainIcon is not None: wx.Frame.SetIcon(self, parent.MainIcon) self.Show(True) wx.EVT_SIZE(self, self.OnSize) self.Content() def Content(self): # Fill self.control with content. # Parameters and models if self.parent.notebook.GetPageCount() == 0: self.control.SetValue("") self.panel.Disable() return self.panel.Enable() Page = self.Page InfoMan = InfoClass(CurPage=Page) PageInfo = InfoMan.GetCurFancyInfo() self.control.SetValue(PageInfo) def OnClose(self, event=None): self.parent.toolmenu.Check(self.MyID, False) self.parent.ToolsOpen.__delitem__(self.MyID) self.Destroy() def OnCopy(self, event): if not wx.TheClipboard.IsOpened(): clipdata = wx.TextDataObject() clipdata.SetText(self.control.GetValue()) wx.TheClipboard.Open() wx.TheClipboard.SetData(clipdata) wx.TheClipboard.Close() else: print "Other application has lock on clipboard." def OnPageChanged(self, page=None, trigger=None): """ This function is called, when something in the panel changes. The variable `trigger` is used to prevent this function from being executed to save stall time of the user. Forr a list of possible triggers, see the doc string of `tools`. """ if trigger in ["parm_batch", "fit_batch", "page_add_batch"]: return # When parent changes self.Page = page self.Content() def OnSize(self, event): size = event.GetSize() sizec = wx.Size(size[0]-5, size[1]-30) self.panel.SetSize(size) self.control.SetSize(sizec) PyCorrFit-0.9.9/pycorrfit/gui/tools/overlaycurves.py000066400000000000000000000375411274265304700226660ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ PyCorrFit Module tools - overlaycurves Let the user choose which correlation curves to use. Contains wrappers for file import and tools. """ try: from matplotlib import cm # @UnresolvedImport except: mpl_available = False else: mpl_available = True import numpy as np import platform import wx import wx.lib.plot as plot # Plotting in wxPython from .. import edclasses from .. import misc # Menu entry name MENUINFO = ["&Overlay curves", "Select experimental curves."] class Wrapper_OnImport(object): """ Wrapper for import function. parent: wx.Frame curvedict: dictionary with curves onselected: external function that is called with two arguments: *kept keys* and *unwanted keys* as lists referring to curvedict. selkeys: preselected values for curves in curvedict """ def __init__(self, parent, curvedict, onselected, selkeys=None, labels=None): self.onselected = onselected self.parent = parent self.Selector = UserSelectCurves(parent, curvedict, wrapper=self, selkeys=selkeys, labels=labels) self.Selector.Show() self.Selector.MakeModal(True) self.Selector.Bind(wx.EVT_CLOSE, self.OnClose) def OnClose(self, event=None): self.Selector.MakeModal(False) self.Selector.Destroy() def OnResults(self, keyskeep, keysrem): """ Here we will close (or disable?) pages that are not wanted by the user. It is important that we do not close pages that do not contain any experimental data (Page.dataeyp is None), because we ignored those pages during import. """ self.OnClose() self.onselected(keyskeep,keysrem) class Wrapper_Tools(object): def __init__(self, parent): """ Wrapper for tools menu. Gets curvedict from parent and starts curve selection. See *UserSelectCurves* class. """ # parent is the main frame of PyCorrFit self.parent = parent ## MYID # This ID is given by the parent for an instance of this class self.MyID = None ## Wrapping curvedict, labels = self.GetCurvedict() self.labels = labels self.Selector = UserSelectCurves(parent, curvedict, wrapper=self, labels=labels) # This is necessary for parent to deselect and select the tool # in the tools menu. self.Bind = self.Selector.Bind if self.parent.notebook.GetPageCount() == 0: self.Selector.sp.Disable() def Disable(self): self.Selector.Disable() def Enable(self, par=True): self.Selector.Enable(par) def GetCurvedict(self, e=None): curvedict = dict() labels = dict() N = self.parent.notebook.GetPageCount() for i in np.arange(N): Page = self.parent.notebook.GetPage(i) key = Page.counter if Page.corr.correlation is not None: curve = 1*Page.corr.correlation_plot curvedict[key] = curve labels[key] = Page.tabtitle.GetValue() return curvedict, labels def OnClose(self, event=None): # This is a necessary function for PyCorrFit. # Do not change it. self.parent.toolmenu.Check(self.MyID, False) self.parent.ToolsOpen.__delitem__(self.MyID) self.Selector.Destroy() def OnPageChanged(self, page=None, trigger=None): """ This function is called, when something in the panel changes. The variable `trigger` is used to prevent this function from being executed to save stall time of the user. Forr a list of possible triggers, see the doc string of `tools`. """ if trigger in ["parm_batch", "fit_batch", "page_add_batch", "tab_init", "tab_browse"]: return # When parent changes # This is a necessary function for PyCorrFit. # This is stuff that should be done when the active page # of the notebook changes. if self.parent.notebook.GetPageCount() == 0: self.Selector.SelectBox.SetItems([]) self.Selector.sp.Disable() else: oldlabels = self.Selector.curvelabels self.Selector.sp.Enable() # Sticky behavior cleaned up in 0.7.8 curvedict, labels = self.GetCurvedict() self.Selector.curvedict = curvedict self.Selector.labels = labels self.Selector.ProcessDict() self.labels = labels if oldlabels != self.Selector.curvelabels: self.Selector.SelectBox.SetItems( self.Selector.curvelabels) for i in np.arange(len(self.Selector.curvekeys)): self.Selector.SelectBox.SetSelection(i) self.Selector.OnUpdatePlot(trigger=trigger) def OnResults(self, keyskeep, keysrem): """ Here we will close (or disable?) pages that are not wanted by the user. It is important that we do not close pages that do not contain any experimental data (Page.dataeyp is None), because we ignored those pages during import. """ if len(keysrem) == 0: self.OnClose() return # warn the user! # First make a list of all pages that need to be removed and then # delete those pages. overtext = "Keep only pages in this list?" textlist = "" for key in keyskeep: textlist += "- "+key+" "+self.labels[key]+"\n" dlg = edclasses.MyScrolledDialog(self.parent, overtext, textlist, "Warning") if dlg.ShowModal() == wx.ID_OK: N = self.parent.notebook.GetPageCount() pagerem = list() for i in np.arange(N): Page = self.parent.notebook.GetPage(i) key = Page.counter if keysrem.count(key) == 1: pagerem.append(Page) for Page in pagerem: j = self.parent.notebook.GetPageIndex(Page) self.parent.notebook.DeletePage(j) self.OnPageChanged() dlg.Destroy() def OnSelectionChanged(self, keylist, trigger=None): if len(keylist) == 0: return # integer type list with page number pagelist = list() N = self.parent.notebook.GetPageCount() for i in np.arange(N): Page = self.parent.notebook.GetPage(i) key = Page.counter if keylist.count(key) == 1: pagelist.append(int(key.strip("#: "))) # Get open tools toolkeys = self.parent.ToolsOpen.keys() if len(toolkeys) == 0: return # Fill string = misc.parsePagenum2String(pagelist) for key in toolkeys: tool = self.parent.ToolsOpen[key] try: tool.SetPageNumbers(string) except: # tool does not have this function and hence does not # need numbers. pass class UserSelectCurves(wx.Frame): # This tool is derived from a wx.frame. def __init__(self, parent, curvedict, wrapper=None, selkeys=None, labels=None): """ *curvedict* is a dictionary that contains the curves. Keys serve as identifiers in the curve selection. e.g. curvelist["#1:"] = np.array[ np.array[0.0,1], np.array[0.0,.971] ...] *parent* is the main frame *wrapper* is the object to which the chosen keys are given back. If it is not None, it must provide a function *OnResults*, accepting a list of keys as an argument. *selkeys* items in the list *curvedict* that are preelected. *labels* dictionary with same keys as *curvelist* - labels of the entries in the list. If none, the keys of *curvedict* will be used. """ # parent is the main frame of PyCorrFit self.parent = parent self.wrapper = wrapper self.curvedict = curvedict self.selkeys = selkeys self.labels = labels # can be None self.curvelabels = None # filled by self.ProcessDict() if self.selkeys is not None: newselkeys = list() for item in self.selkeys: newselkeys.append(str(item)) self.selkeys = newselkeys # Get the window positioning correctly pos = self.parent.GetPosition() pos = (pos[0]+100, pos[1]+100) wx.Frame.__init__(self, parent=self.parent, title="Overlay curves", pos=pos, style=wx.DEFAULT_FRAME_STYLE|wx.FRAME_FLOAT_ON_PARENT, size=(800,500)) ## Pre-process self.ProcessDict() ## Content self.sp = wx.SplitterWindow(self, size=(500,500), style=wx.SP_NOBORDER) self.sp.SetMinimumPaneSize(1) # Top panel panel_top = wx.Panel(self.sp, size=(500,200)) self.upperSizer = wx.BoxSizer(wx.VERTICAL) if platform.system().lower() == 'darwin': ctrl = "Apple" else: ctrl = "Ctrl" text = "By holding down the '"+ctrl+"' key, single curves can be \n" +\ "selected or deselected. The 'Shift' key can be used \n" +\ "to select groups." self.upperSizer.Add(wx.StaticText(panel_top, label=text)) # Bottom Panel self.bottom_sp = wx.SplitterWindow(self.sp, size=(500,300), style=wx.SP_NOBORDER) self.bottom_sp.SetMinimumPaneSize(1) sizepanelx = 250 panel_bottom = wx.Panel(self.bottom_sp, size=(sizepanelx,300)) self.boxSizer = wx.BoxSizer(wx.VERTICAL) # Box selection style = wx.LB_EXTENDED self.SelectBox = wx.ListBox(panel_bottom, size=(sizepanelx,300), style=style, choices=self.curvelabels) for i in np.arange(len(self.curvekeys)): self.SelectBox.SetSelection(i) # Deselect keys that are not in self.selkeys if self.selkeys is not None: for i in np.arange(len(self.curvekeys)): if self.selkeys.count(self.curvekeys[i]) == 0: self.SelectBox.Deselect(i) self.Bind(wx.EVT_LISTBOX, self.OnUpdatePlot, self.SelectBox) self.boxSizer.Add(self.SelectBox, wx.EXPAND) minsx = self.boxSizer.GetMinSize()[0] # Button REMOVE btnrem = wx.Button(panel_bottom, wx.ID_ANY, 'Remove selected') self.Bind(wx.EVT_BUTTON, self.OnPushResultsRemove, btnrem) btnrem.SetMinSize((minsx, -1)) self.boxSizer.Add(btnrem) # Button KEEP btnkep = wx.Button(panel_bottom, wx.ID_ANY, 'Keep selected') self.Bind(wx.EVT_BUTTON, self.OnPushResultsKeep, btnkep) self.boxSizer.Add(btnkep) btnkep.SetMinSize((minsx, -1)) # Button CANCEL btncancel = wx.Button(panel_bottom, wx.ID_ANY, 'Cancel') self.Bind(wx.EVT_BUTTON, self.OnCancel, btncancel) self.boxSizer.Add(btncancel) btncancel.SetMinSize((minsx, -1)) # Finish off sizers panel_top.SetSizer(self.upperSizer) panel_bottom.SetSizer(self.boxSizer) self.upperSizer.Fit(panel_top) self.boxSizer.Fit(panel_bottom) minsize = np.array(self.boxSizer.GetMinSizeTuple()) +\ np.array(self.upperSizer.GetMinSizeTuple()) +\ np.array((300,30)) self.SetMinSize(minsize) #self.SetSize(minsize) #self.SetMaxSize((9999, self.boxSizer.GetMinSizeTuple()[1])) # Canvas self.canvas = plot.PlotCanvas(self.bottom_sp) self.canvas.setLogScale((True, False)) self.canvas.SetEnableZoom(True) # Splitter window self.bottom_sp.SplitVertically(panel_bottom, self.canvas, sizepanelx) sizetoppanel = self.upperSizer.GetMinSizeTuple()[1] self.sp.SplitHorizontally(panel_top, self.bottom_sp, sizetoppanel) self.OnUpdatePlot() # Icon if parent.MainIcon is not None: wx.Frame.SetIcon(self, parent.MainIcon) self.Show(True) def GetSelection(self): keyssel = list() for i in self.SelectBox.GetSelections(): keyssel.append(self.curvekeys[i]) keysnosel = list() for key in self.curvekeys: if keyssel.count(key) == 0: keysnosel.append(key) return keyssel, keysnosel def ProcessDict(self, e=None): # Define the order of keys used. # We want to sort the keys, such that #10: is not before #1: self.curvekeys = self.curvedict.keys() # Sorting key function applied to each key before sorting: page_num = lambda counter: int(counter.strip().strip(":").strip("#")) try: for item in self.curvekeys: page_num(item) except: fstr = lambda x: x else: fstr = page_num self.curvekeys.sort(key = fstr) if self.labels is None: self.curvelabels = self.curvekeys else: # Use given labels instead of curvekeys. self.curvelabels = list() for key in self.curvekeys: self.curvelabels.append("#"+str(key).strip(":# ")+" "+self.labels[key]) def OnCancel(self, e=None): """ Close the tool """ self.wrapper.OnClose() def OnPushResultsRemove(self, e=None): # Get keys from selection keysrem, keyskeep = self.GetSelection() self.wrapper.OnResults(keyskeep, keysrem) def OnPushResultsKeep(self, e=None): # Get keys from selection keyskeep, keysrem = self.GetSelection() self.wrapper.OnResults(keyskeep, keysrem) def OnUpdatePlot(self, e=None, trigger=None): """ What should happen when the selection in *self.SelectBox* is changed? This function will alsy try to call the function *self.parent.OnSelectionChanged* and hand over the list of currently selected curves. This is an addon for 0.7.8 where we will control the page selection in the average tool. If `trigger` is something that occurs during loading of data, then we will not replot everything. """ #if e is not None and e.GetEventType() == 10007: # return # Get selected curves curves = list() legends = list() selection = self.SelectBox.GetSelections() for i in selection: curves.append(self.curvedict[self.curvekeys[i]]) legends.append(self.curvekeys[i]) # Set color map if mpl_available: cmap = cm.get_cmap("gist_rainbow") # Clear Plot self.canvas.Clear() # Draw Plot lines = list() for i in np.arange(len(curves)): if mpl_available: color = cmap(1.*i/(len(curves)), bytes=True) color = wx.Colour(color[0], color[1], color[2]) else: color = "black" line = plot.PolyLine(curves[i], legend=legends[i], colour=color, width=1) lines.append(line) self.canvas.SetEnableLegend(True) if len(curves) != 0: self.canvas.Draw(plot.PlotGraphics(lines, xLabel=u'lag time τ [ms]', yLabel=u'G(τ)')) ## This is an addon for 0.7.8 keyskeep = list() for i in self.SelectBox.GetSelections(): keyskeep.append(self.curvekeys[i]) try: self.wrapper.OnSelectionChanged(keyskeep, trigger=trigger) except: pass PyCorrFit-0.9.9/pycorrfit/gui/tools/parmrange.py000066400000000000000000000140151274265304700217200ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ PyCorrFit Module tools - RangeSelector Select the range in which the parameter should reside for fitting. """ import wx import numpy as np from pycorrfit import models as mdls from .. import wxutils class RangeSelector(wx.Frame): # This tool is derived from a wx.frame. def __init__(self, Page): # parent is the main frame of PyCorrFit self.parent = Page.parent # Get the window positioning correctly pos = self.parent.GetPosition() pos = (pos[0]+100, pos[1]+100) wx.Frame.__init__(self, parent=self.parent, title="Parameter Range", pos=pos, style=wx.DEFAULT_FRAME_STYLE|wx.FRAME_FLOAT_ON_PARENT) # Page - the currently active page of the notebook. self.Page = self.parent.notebook.GetCurrentPage() ## Content self.panel = wx.Panel(self) self.topSizer = wx.BoxSizer(wx.VERTICAL) self.WXboxsizerlist = list() self.WXparmlist = list() self.OnPageChanged(self.Page) #Icon if self.parent.MainIcon is not None: wx.Frame.SetIcon(self, self.parent.MainIcon) self.Show(True) def FillPanel(self): """ Fill the panel with parameters from the page """ corr = self.Page.corr self.parameter_range = np.zeros_like(corr.fit_parameters_range) leftbound = [] for item in corr.fit_parameters_range[:,0]: if item is None: leftbound.append(-np.inf) else: leftbound.append(item) labels, parmleft = mdls.GetHumanReadableParms(corr.fit_model.id, # @UnusedVariable leftbound) rightbound = [] for item in corr.fit_parameters_range[:,1]: if item is None: rightbound.append(np.inf) else: rightbound.append(item) labels, parmright = mdls.GetHumanReadableParms(corr.fit_model.id, rightbound) self.parameter_range[:,0] = np.array(parmleft) self.parameter_range[:,1] = np.array(parmright) # create line self.WXboxsizer = wx.FlexGridSizer(rows=len(labels), cols=4, vgap=5, hgap=5) for i in range(len(labels)): left = wxutils.PCFFloatTextCtrl(self.panel) right = wxutils.PCFFloatTextCtrl(self.panel) left.SetValue(self.parameter_range[i][0]) right.SetValue(self.parameter_range[i][1]) left.Bind(wx.EVT_SPINCTRL, self.OnSetParmRange) right.Bind(wx.EVT_SPINCTRL, self.OnSetParmRange) text = wx.StaticText(self.panel, label=u'< '+labels[i]) text2 = wx.StaticText(self.panel, label=u' <') self.WXboxsizer.Add(left) self.WXboxsizer.Add(text) self.WXboxsizer.Add(text2) self.WXboxsizer.Add(right) self.WXparmlist.append([left, [text, text2], right]) self.WXboxsizer.Layout() self.topSizer.Add(self.WXboxsizer) self.btnapply = wx.Button(self.panel, wx.ID_ANY, 'Apply') self.Bind(wx.EVT_BUTTON, self.OnSetParmRange, self.btnapply) self.topSizer.Add(self.btnapply) self.topSizer.Layout() self.panel.SetSizer(self.topSizer) self.topSizer.Fit(self.panel) self.SetMinSize(self.topSizer.GetMinSizeTuple()) self.topSizer.Fit(self) def OnClose(self, event=None): # This is a necessary function for PyCorrFit. # Do not change it. self.parent.RangeSelector = None self.Destroy() def OnPageChanged(self, page=None, trigger=None): """ This function is called, when something in the panel changes. The variable `trigger` is used to prevent this function from being executed to save stall time of the user. Forr a list of possible triggers, see the doc string of `tools`. """ # When parent changes # This is a necessary function for PyCorrFit. # This is stuff that should be done when the active page # of the notebook changes. if trigger in ["parm_batch", "fit_batch", "page_add_batch"]: return self.Page = page if self.parent.notebook.GetPageCount() == 0: self.panel.Disable() return self.panel.Enable() try: self.btnapply.Destroy() except: pass for i in np.arange(len(self.WXparmlist)): self.WXparmlist[i][0].Destroy() #start self.WXparmlist[i][1][0].Destroy() #pname self.WXparmlist[i][1][1].Destroy() #pname self.WXparmlist[i][2].Destroy() #end del self.WXparmlist for i in np.arange(len(self.WXboxsizerlist)): self.WXboxsizer.Remove(0) self.WXboxsizerlist = list() self.WXparmlist = list() self.FillPanel() def OnSetParmRange(self, e): """ Called whenever something is edited in this frame. Writes back parameter ranges to the page """ corr = self.Page.corr # Read out parameters from all controls for i in range(len(self.WXparmlist)): self.parameter_range[i][0] = self.WXparmlist[i][0].GetValue() self.parameter_range[i][1] = self.WXparmlist[i][2].GetValue() if self.parameter_range[i][0] > self.parameter_range[i][1]: self.parameter_range[i][1] = 1.01*np.abs(self.parameter_range[i][0]) self.WXparmlist[i][2].SetValue(self.parameter_range[i][1]) # Set parameters parm0 = mdls.GetInternalFromHumanReadableParm(corr.fit_model.id, self.parameter_range[:,0])[1] parm1 = mdls.GetInternalFromHumanReadableParm(corr.fit_model.id, self.parameter_range[:,1])[1] corr.fit_parameters_range = np.dstack((parm0, parm1))[0] #self.Page.PlotAll() PyCorrFit-0.9.9/pycorrfit/gui/tools/plotexport.py000066400000000000000000000043211274265304700221630ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ PyCorrFit Module tools - plotexport Let the user create nice plots of our data. --currently not used """ import wx class Tool(wx.Frame): # This tool is derived from a wx.frame. def __init__(self, parent): # parent is the main frame of PyCorrFit self.parent = parent # Get the window positioning correctly pos = self.parent.GetPosition() pos = (pos[0]+100, pos[1]+100) wx.Frame.__init__(self, parent=self.parent, title="Example Tool", pos=pos, style=wx.DEFAULT_FRAME_STYLE|wx.FRAME_FLOAT_ON_PARENT) ## MYID # This ID is given by the parent for an instance of this class self.MyID = None # Page - the currently active page of the notebook. self.Page = self.parent.notebook.GetCurrentPage() ## Content self.panel = wx.Panel(self) btnexample = wx.Button(self.panel, wx.ID_ANY, 'Example button') # Binds the button to the function - close the tool self.Bind(wx.EVT_BUTTON, self.OnClose, btnexample) self.topSizer = wx.BoxSizer(wx.VERTICAL) self.topSizer.Add(btnexample) self.panel.SetSizer(self.topSizer) self.topSizer.Fit(self) self.SetMinSize(self.topSizer.GetMinSizeTuple()) #Icon if parent.MainIcon is not None: wx.Frame.SetIcon(self, parent.MainIcon) self.Show(True) def OnClose(self, event=None): # This is a necessary function for PyCorrFit. # Do not change it. self.parent.toolmenu.Check(self.MyID, False) self.parent.ToolsOpen.__delitem__(self.MyID) self.Destroy() def OnPageChanged(self, page, trigger=None): """ This function is called, when something in the panel changes. The variable `trigger` is used to prevent this function from being executed to save stall time of the user. Forr a list of possible triggers, see the doc string of `tools`. """ # When parent changes # This is a necessary function for PyCorrFit. # This is stuff that should be done when the active page # of the notebook changes. self.Page = page PyCorrFit-0.9.9/pycorrfit/gui/tools/simulation.py000066400000000000000000000422631274265304700221360ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ PyCorrFit Module tools - simulation Enables the user to change plotting parameters and replotting fast. Might be useful for better understanding model functions. """ import wx from wx.lib.agw import floatspin import numpy as np from pycorrfit import models as mdls # Menu entry name MENUINFO = ["S&lider simulation", "Fast plotting for different parameters."] class Slide(wx.Frame): # This tool is derived from a wx.frame. def __init__(self, parent): # parent is the main frame of PyCorrFit self.parent = parent # Get the window positioning correctly pos = self.parent.GetPosition() pos = (pos[0]+100, pos[1]+100) wx.Frame.__init__(self, parent=self.parent, title="Simulation", pos=pos, style=wx.DEFAULT_FRAME_STYLE|wx.FRAME_FLOAT_ON_PARENT) # Starting positions/factors for spinctrls and sliders self.slidemax = 1000 self.slidestart = 500 self.spinstartfactor = 0.1 self.spinendfactor = 1.9 ## MYID # This ID is given by the parent for an instance of this class self.MyID = None # Page - the currently active page of the notebook. self.Page = self.parent.notebook.GetCurrentPage() ## Content self.panel = wx.Panel(self) self.rbtnB = wx.RadioButton (self.panel, -1, 'Vary A and B', style = wx.RB_GROUP) self.rbtnOp = wx.RadioButton (self.panel, -1, 'Fix relation') self.btnreset = wx.Button(self.panel, wx.ID_ANY, 'Reset') # Set starting variables self.SetStart() # Populate panel dropsizer = wx.FlexGridSizer(rows=2, cols=3, vgap=5, hgap=5) dropsizer.Add( wx.StaticText(self.panel, label="Parameter A")) dropsizer.Add( wx.StaticText(self.panel, label="Operator")) dropsizer.Add( wx.StaticText(self.panel, label="Parameter B")) self.droppA = wx.ComboBox(self.panel, -1, self.labelA, (15, 20), wx.DefaultSize, self.parmAlist, wx.CB_DROPDOWN|wx.CB_READONLY) self.droppA.SetSelection(0) self.Bind(wx.EVT_COMBOBOX, self.Ondrop, self.droppA) self.dropop = wx.ComboBox(self.panel, -1, "", (10, 20), wx.DefaultSize, self.oplist, wx.CB_DROPDOWN|wx.CB_READONLY) self.dropop.SetSelection(0) self.opfunc = self.opdict[self.opdict.keys()[0]] self.Bind(wx.EVT_COMBOBOX, self.Ondrop, self.dropop) self.droppB = wx.ComboBox(self.panel, -1, self.labelB, (15, 30), wx.DefaultSize, self.parmBlist, wx.CB_DROPDOWN|wx.CB_READONLY) self.Bind(wx.EVT_COMBOBOX, self.Ondrop, self.droppB) self.droppB.SetSelection(1) dropsizer.Add(self.droppA) dropsizer.Add(self.dropop) dropsizer.Add(self.droppB) textfix = wx.StaticText(self.panel, label="\nEdit intervals and drag the slider.\n") # Parameter A slidesizer = wx.FlexGridSizer(rows=3, cols=5, vgap=5, hgap=5) self.textstartA = wx.StaticText(self.panel, label=self.labelA) slidesizer.Add(self.textstartA) self.startspinA = floatspin.FloatSpin(self.panel, digits=7) slidesizer.Add(self.startspinA) self.sliderA = wx.Slider(self.panel, -1, self.slidestart, 0, self.slidemax, wx.DefaultPosition, (250, -1), wx.SL_HORIZONTAL) slidesizer.Add(self.sliderA) self.endspinA = floatspin.FloatSpin(self.panel, digits=7) slidesizer.Add(self.endspinA) self.textvalueA = wx.StaticText(self.panel, label= "%.5e" % self.valueA) slidesizer.Add(self.textvalueA) # Parameter B self.textstartB = wx.StaticText(self.panel, label=self.labelB) slidesizer.Add(self.textstartB) self.startspinB = floatspin.FloatSpin(self.panel, digits=7) slidesizer.Add(self.startspinB) self.sliderB = wx.Slider(self.panel, -1, self.slidestart, 0, self.slidemax, wx.DefaultPosition, (250, -1), wx.SL_HORIZONTAL) slidesizer.Add(self.sliderB) self.endspinB = floatspin.FloatSpin(self.panel, digits=7) slidesizer.Add(self.endspinB) self.textvalueB = wx.StaticText(self.panel, label= "%.5e" % self.valueB) slidesizer.Add(self.textvalueB) # Result of operation self.textstartOp = wx.StaticText(self.panel, label=self.labelOp) slidesizer.Add(self.textstartOp) self.startspinOp = floatspin.FloatSpin(self.panel, digits=7) slidesizer.Add(self.startspinOp) self.sliderOp = wx.Slider(self.panel, -1, self.slidestart, 0, self.slidemax, wx.DefaultPosition, (250, -1), wx.SL_HORIZONTAL) slidesizer.Add(self.sliderOp) self.endspinOp = floatspin.FloatSpin(self.panel, digits=7) slidesizer.Add(self.endspinOp) self.textvalueOp = wx.StaticText(self.panel, label= "%.5e" % self.valueOp) slidesizer.Add(self.textvalueOp) # Bindings for slider self.Bind(wx.EVT_SLIDER, self.OnSlider, self.sliderA) self.Bind(wx.EVT_SLIDER, self.OnSlider, self.sliderB) self.Bind(wx.EVT_SLIDER, self.OnSlider, self.sliderOp) # Bindings for radiobuttons self.Bind(wx.EVT_RADIOBUTTON, self.OnRadio, self.rbtnB) self.Bind(wx.EVT_RADIOBUTTON, self.OnRadio, self.rbtnOp) self.Bind(wx.EVT_BUTTON, self.OnReset, self.btnreset) # Bindings for spin controls # Our self-made spin controls alread have wx_EVT_SPINCTRL bound to # the increment function. We will call that function manually here. self.startspinA.Unbind(wx.EVT_SPINCTRL) self.startspinB.Unbind(wx.EVT_SPINCTRL) self.startspinOp.Unbind(wx.EVT_SPINCTRL) self.endspinA.Unbind(wx.EVT_SPINCTRL) self.endspinB.Unbind(wx.EVT_SPINCTRL) self.endspinOp.Unbind(wx.EVT_SPINCTRL) self.Bind(wx.EVT_SPINCTRL, self.OnSlider, self.startspinA) self.Bind(wx.EVT_SPINCTRL, self.OnSlider, self.startspinB) self.Bind(wx.EVT_SPINCTRL, self.OnSlider, self.startspinOp) self.Bind(wx.EVT_SPINCTRL, self.OnSlider, self.endspinA) self.Bind(wx.EVT_SPINCTRL, self.OnSlider, self.endspinB) self.Bind(wx.EVT_SPINCTRL, self.OnSlider, self.endspinOp) # Set values self.SetValues() ## Sizers self.topSizer = wx.BoxSizer(wx.VERTICAL) self.topSizer.Add(dropsizer) self.topSizer.Add(self.rbtnB) self.topSizer.Add(self.rbtnOp) self.topSizer.Add(self.btnreset) self.topSizer.Add(textfix) self.topSizer.Add(slidesizer) self.panel.SetSizer(self.topSizer) self.topSizer.Fit(self) self.OnRadio() self.OnPageChanged(self.Page, init=True) #Icon if parent.MainIcon is not None: wx.Frame.SetIcon(self, parent.MainIcon) self.Show(True) def CalcFct(self, A, B, C): if self.rbtnB.Value == True: func = self.opfunc[0] try: C = func(A,B) except ZeroDivisionError: pass else: return B, C else: func = self.opfunc[1] try: B = func(A,C) except ZeroDivisionError: pass else: return B, C def FillOpDict(self): # Dictionaries: [Calculate C, Calculate B) self.opdict["A/B"] = [lambda A,B: A/B, lambda A,C: A/C] self.opdict["B/A"] = [lambda A,B: B/A, lambda A,C: C*A] self.opdict["A*B"] = [lambda A,B: A*B, lambda A,C: C/A] self.opdict["A+B"] = [lambda A,B: A+B, lambda A,C: C-A] self.opdict["A-B"] = [lambda A,B: A-B, lambda A,C: A-C] self.opdict["A*exp(B)"] = [lambda A,B: A*np.exp(B), lambda A,C: np.log(C/A)] self.opdict["B*exp(A)"] = [lambda A,B: B*np.exp(A), lambda A,C: C/np.exp(A)] def OnClose(self, event=None): # This is a necessary function for PyCorrFit. # Do not change it. self.parent.toolmenu.Check(self.MyID, False) self.parent.ToolsOpen.__delitem__(self.MyID) self.Destroy() def Ondrop(self, event=None): self.labelOp = self.oplist[self.dropop.GetSelection()] self.labelA = self.parmAlist[self.droppA.GetSelection()] self.labelB = self.parmBlist[self.droppB.GetSelection()] self.textstartOp.SetLabel(self.labelOp) self.textstartA.SetLabel(label=self.labelA) self.textstartB.SetLabel(self.labelB) self.sliderB.SetValue(self.slidestart) self.sliderOp.SetValue(self.slidestart) self.sliderA.SetValue(self.slidestart) self.SetValues() self.OnSize() def OnPageChanged(self, page=None, trigger=None, init=False): """ This function is called, when something in the panel changes. The variable `trigger` is used to prevent this function from being executed to save stall time of the user. Forr a list of possible triggers, see the doc string of `tools`. 'init' is used by this tool only. """ #if init: # # Get the parameters of the current page. # self.SavedParms = self.parent.PackParameters(self.Page) # When parent changes # This is a necessary function for PyCorrFit. # This is stuff that should be done when the active page # of the notebook changes. if trigger in ["parm_batch", "fit_batch", "page_add_batch"]: return if self.parent.notebook.GetPageCount() == 0: self.panel.Disable() return try: # wx._core.PyDeadObjectError: The C++ part of the FittingPanel # object has been deleted, attribute access no longer allowed. oldcounter = self.Page.counter except: oldcounter = -1 if page is not None: if page.counter != oldcounter: self.Page = page self.SetStart() self.droppA.SetItems(self.parmAlist) self.droppB.SetItems(self.parmBlist) self.droppA.SetSelection(0) self.droppB.SetSelection(1) self.dropop.SetSelection(0) # Set labels self.Ondrop() else: self.Page = page self.panel.Enable() def OnRadio(self, event=None): if self.rbtnB.Value == True: # Parameter B is vaiable self.sliderOp.Enable(False) self.startspinOp.Enable(False) self.endspinOp.Enable(False) self.sliderB.Enable(True) self.startspinB.Enable(True) self.endspinB.Enable(True) else: # Operation result is vaiable self.sliderOp.Enable(True) self.startspinOp.Enable(True) self.endspinOp.Enable(True) self.sliderB.Enable(False) self.startspinB.Enable(False) self.endspinB.Enable(False) self.Ondrop() def OnReset(self, e=None): self.parent.UnpackParameters(self.SavedParms, self.Page) self.Page.apply_parameters_reverse() #self.OnPageChanged(self.Page) self.SetStart() self.Ondrop() def OnSize(self, event=None): # We need this funciton, because contents of the flexgridsizer # may change in size. self.panel.SetSizer(self.topSizer) self.topSizer.Fit(self) self.panel.SetSize(self.GetSize()) def OnSlider(self, event=None): ## Set the slider vlaues idmax = self.sliderA.GetMax() slideA = self.sliderA.GetValue() startA = self.startspinA.GetValue() endA = self.endspinA.GetValue() self.valueA = startA + (endA-startA)*slideA/idmax self.textvalueA.SetLabel( "%.5e" % self.valueA) if self.rbtnB.Value == True: slideB = self.sliderB.GetValue() startB = self.startspinB.GetValue() endB = self.endspinB.GetValue() self.valueB = startB + (endB-startB)*slideB/idmax else: # Same thing slideOp = self.sliderOp.GetValue() startOp = self.startspinOp.GetValue() endOp = self.endspinOp.GetValue() self.valueOp = startOp + (endOp-startOp)*slideOp/idmax self.valueB, self.valueOp = self.CalcFct(self.valueA, self.valueB, self.valueOp) self.textvalueB.SetLabel( "%.5e" % self.valueB) self.textvalueOp.SetLabel( "%.5e" % self.valueOp) self.SetResult() self.OnSize() def SetResult(self, event=None): if self.parent.notebook.GetPageCount() == 0: # Nothing to do return # And Plot idA = self.droppA.GetSelection() idB = self.droppB.GetSelection() # As of version 0.7.5: we want the units to be displayed # human readable - the way they are displayed # in the Page info tool. # Convert from human readable to internal units # The easiest way is to make a copy of all parameters and # only write back those that have been changed: # parms_0 = 1.*np.array(mdls.valuedict[self.modelid][1]) parms_0[idA] = self.valueA # human readable units parms_0[idB] = self.valueB # human readable units parms_i =\ mdls.GetInternalFromHumanReadableParm(self.modelid, parms_0)[1] self.Page.active_parms[1][idA] = parms_i[idA] self.Page.active_parms[1][idB] = parms_i[idB] self.Page.apply_parameters_reverse() self.Page.PlotAll() def SetStart(self): # Sets first and second variable of a page to # Parameters A and B respectively. if self.parent.notebook.GetPageCount() == 0: self.modelid = 6000 ParmLabels, ParmValues = \ mdls.GetHumanReadableParms(self.modelid, mdls.valuedict[6000][1]) else: self.SavedParms = self.parent.PackParameters(self.Page) self.modelid = self.Page.modelid ParmLabels, ParmValues = \ mdls.GetHumanReadableParms(self.modelid, self.Page.active_parms[1]) self.parmAlist = ParmLabels self.parmBlist = ParmLabels # Operators # Calculation of variable A with fixed B self.opdict = dict() self.FillOpDict() self.oplist = self.opdict.keys() self.oplist.sort() self.labelA = self.parmAlist[0] self.labelB = self.parmBlist[1] self.labelOp = self.oplist[0] self.opfunc = self.opdict[self.labelOp] self.valueA = ParmValues[0] self.valueB = ParmValues[1] self.valueB, self.valueOp = self.CalcFct(self.valueA, self.valueB, 0) def SetValues(self, event=None): # Set the values for spin and slider # As of version 0.7.5: we want the units to be displayed # human readable - the way they are displayed # in the Page info tool. # # Parameter A idA = self.droppA.GetSelection() # Parameter B idB = self.droppB.GetSelection() # self.valueB = self.Page.active_parms[1][idB] # self.valueA = self.Page.active_parms[1][idA] if self.parent.notebook.GetPageCount() == 0: self.modelid = 6000 ParmValues = \ mdls.GetHumanReadableParms(self.modelid, mdls.valuedict[6000][1])[1] else: self.modelid = self.Page.modelid ParmValues = \ mdls.GetHumanReadableParms(self.modelid, self.Page.active_parms[1])[1] self.valueA = ParmValues[idA] self.valueB = ParmValues[idB] # Operator idop = self.dropop.GetSelection() #keys = self.opdict.keys() opkey = self.oplist[idop] self.opfunc = self.opdict[opkey] # Parameter A startA = self.valueA*self.spinstartfactor endA = self.valueA*self.spinendfactor self.startspinA.SetValue(startA) self.endspinA.SetValue(endA) # Parameter B startB = self.valueB*self.spinstartfactor endB = self.valueB*self.spinendfactor self.startspinB.SetValue(startB) self.endspinB.SetValue(endB) # Operation result self.valueOp = self.opfunc[0](self.valueA, self.valueB) startOp = self.valueOp*self.spinstartfactor endOp = self.valueOp*self.spinendfactor self.startspinOp.SetValue(startOp) self.endspinOp.SetValue(endOp) # Set text self.textvalueA.SetLabel( "%.5e" % self.valueA) self.textvalueB.SetLabel( "%.5e" % self.valueB) self.textvalueOp.SetLabel( "%.5e" % self.valueOp) self.SetResult() PyCorrFit-0.9.9/pycorrfit/gui/tools/statistics.py000066400000000000000000000702411274265304700221410ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ PyCorrFit Module tools - statistics Provide the user with tab-separated statistics of their curves. Values are sorted according to the page number. """ from __future__ import division import codecs import wx import wx.lib.plot as plot # Plotting in wxPython import wx.lib.scrolledpanel as scrolled import numpy as np import re from pycorrfit import models as mdls from .info import InfoClass from .. import misc # Menu entry name MENUINFO = ["&Statistics view", "Show some session statistics."] def run_once(f): def wrapper(*args, **kwargs): if not wrapper.has_run: wrapper.has_run = True return f(*args, **kwargs) wrapper.has_run = False return wrapper class Stat(wx.Frame): # This tool is derived from a wx.frame. def __init__(self, parent): self.MyName="STATISTICS" # parent is the main frame of PyCorrFit self.boxsizerlist = list() self.parent = parent # Get the window positioning correctly pos = self.parent.GetPosition() pos = (pos[0]+100, pos[1]+100) wx.Frame.__init__(self, parent=self.parent, title="Statistics", pos=pos, size=(700,600), style=wx.DEFAULT_FRAME_STYLE|wx.FRAME_FLOAT_ON_PARENT) ## MYID # This ID is given by the parent for an instance of this class self.MyID = None self.MyName = "STATISTICS" # List of parameters that are plotted or not self.PlotParms = list(["None", 0]) # Page - the currently active page of the notebook. self.Page = self.parent.notebook.GetCurrentPage() # Pagenumbers self.PageNumbers =range(1,1+self.parent.notebook.GetPageCount()) ## Splitter window. left side: checkboxes ## right side: plot with parameters self.sp = wx.SplitterWindow(self, style=wx.SP_3DSASH) # This is necessary to prevent "Unsplit" of the SplitterWindow: self.sp.SetMinimumPaneSize(1) ## Content # We will display a dialog that conains all the settings # - Which model we want statistics on # - What kind of parameters should be printed # (We will get the parameters from the current page) # If on another page, the parameter is not available, # do not make a mess out of it. # Then the user presses a button and sees/saves the table # with all the info. self.panel = scrolled.ScrolledPanel(self.sp) self.panel.SetupScrolling(scroll_y=True) # Parameter settings. if self.parent.notebook.GetPageCount() != 0: self.InfoClass = InfoClass(CurPage=self.Page) else: self.panel.Disable() # A dropdown menu for the source Page: text = wx.StaticText(self.panel, label="Create a table with all the selected\n"+ "variables below from pages with the\n"+ "same model as the current page.") ## Page selection as in average tool Pagetext = wx.StaticText(self.panel, label="Curves ") Psize = text.GetSize()[0]/2 self.WXTextPages = wx.TextCtrl(self.panel, value="", size=(Psize,-1)) # Set number of pages pagenumlist = list() for i in np.arange(self.parent.notebook.GetPageCount()): Page = self.parent.notebook.GetPage(i) pagenumlist.append(int(filter(lambda x: x.isdigit(), Page.counter))) valstring=misc.parsePagenum2String(pagenumlist) self.WXTextPages.SetValue(valstring) ## Plot parameter dropdown box self.PlotParms = self.GetListOfPlottableParms() Parmlist = self.PlotParms DDtext = wx.StaticText(self.panel, label="Plot parameter ") self.WXDropdown = wx.ComboBox(self.panel, -1, "", size=(Psize,-1), choices=Parmlist, style=wx.CB_DROPDOWN|wx.CB_READONLY) self.Bind(wx.EVT_COMBOBOX, self.OnDropDown, self.WXDropdown) self.Bind(wx.EVT_TEXT, self.OnDropDown, self.WXTextPages) self.WXDropdown.SetSelection(0) ## Show Average and SD textavg = wx.StaticText(self.panel, label="Average ") textsd = wx.StaticText(self.panel, label="Standard deviation ") self.WXavg = wx.TextCtrl(self.panel, size=(Psize,-1)) self.WXsd = wx.TextCtrl(self.panel, size=(Psize,-1)) self.WXavg.SetEditable(False) self.WXsd.SetEditable(False) # Create space for parameters self.box = wx.StaticBox(self.panel, label="Export parameters") self.masterboxsizer = wx.StaticBoxSizer(self.box, wx.VERTICAL) self.masterboxsizer.Add(text) self.boxsizer = wx.BoxSizer(wx.HORIZONTAL) self.masterboxsizer.Add(self.boxsizer) self.Checkboxes = list() self.Checklabels = list() if self.parent.notebook.GetPageCount() != 0: self.OnChooseValues() self.btnSave = wx.Button(self.panel, wx.ID_ANY, 'Save') self.Bind(wx.EVT_BUTTON, self.OnSaveTable, self.btnSave) # Add elements to sizer self.topSizer = wx.BoxSizer(wx.VERTICAL) GridAll = wx.FlexGridSizer(rows=4, cols=2, vgap=5, hgap=5) GridAll.Add(Pagetext) GridAll.Add(self.WXTextPages) GridAll.Add(DDtext) GridAll.Add(self.WXDropdown) GridAll.Add(textavg) GridAll.Add(self.WXavg) GridAll.Add(textsd) GridAll.Add(self.WXsd) #Psizer = wx.BoxSizer(wx.HORIZONTAL) #Psizer.Add(Pagetext) #Psizer.Add(self.WXTextPages) #DDsizer = wx.BoxSizer(wx.HORIZONTAL) #DDsizer.Add(DDtext) #DDsizer.Add(self.WXDropdown) #self.topSizer.Add(Psizer) #self.topSizer.Add(DDsizer) self.topSizer.Add(GridAll) self.topSizer.Add(self.masterboxsizer) self.topSizer.Add(self.btnSave) # Set size of window self.panel.SetSizer(self.topSizer) self.topSizer.Fit(self.panel) px = self.topSizer.GetMinSizeTuple()[0] ## Plotting panel self.canvas = plot.PlotCanvas(self.sp) self.canvas.SetEnableZoom(True) self.sp.SplitVertically(self.panel, self.canvas, px+5) ## Icon if parent.MainIcon is not None: wx.Frame.SetIcon(self, parent.MainIcon) self.Show(True) self.OnDropDown() def GetListOfAllParameters(self, e=None, return_std_checked=False, page=None): """ Returns sorted list of parameters. If return_std_checked is True, then a second list with standart checked parameters is returned. """ if page is None: page = self.Page self.InfoClass.CurPage = page # Now that we know our Page, we may change the available # parameter options. Infodict = self.InfoClass.GetCurInfo() # We want to sort the information and have some prechecked # values in the statistics window afterwards. # new iteration keys = Infodict.keys() parms = list() errparms = list() for key in keys: for item in Infodict[key]: if item is not None: if key == "fitting" and item[0].startswith("Err "): errparms.append(item) elif len(item) == 2: parms.append(item) # Separate checkbox for fit errors if len(errparms) > 0: parms.append(("Fit errors", errparms)) Info = Stat.SortParameters(parms) # List of default checked parameters: checked = np.zeros(len(Info), dtype=np.bool) # Fit parameters pbool = page.corr.fit_parameters_variable model = mdls.modeldict[page.corr.fit_model.id] pname = mdls.GetHumanReadableParms(model.id, model.parameters[1])[0] checkadd = np.array(pname)[pbool] for ii, p in enumerate(Info): if p[0] in checkadd: checked[ii] = True # A list with additional strings that should be default checked # if found somewhere in the data. checklist = ["cpp", "duration", "bg rate", "avg.", "Model name", "filename/title"] for i in range(len(Info)): item = Info[i] for checkitem in checklist: if item[0].count(checkitem): checked[i] = True # A list with strings that should not be checked: nochecklist = [] for i in range(len(Info)): item = Info[i] for checkitem in nochecklist: if item[0].count(checkitem): checked[i] = False if return_std_checked: return Info, checked else: return Info def GetListOfPlottableParms(self, e=None, return_values=False, page=None): """ Returns list of parameters that can be plotted. (This means that the values are convertible to floats) If return_values is True, then a second list with the corresponding values is returned. """ if page is None: page = self.Page if self.parent.notebook.GetPageCount() != 0: Info = self.GetListOfAllParameters(page=page) parmlist = list() parmvals = list() for item in Info: if item is not None and len(item) == 2: try: val = float(item[1]) except: pass else: # save the key so we can find the parm later parmlist.append(item[0]) parmvals.append(val) else: parmlist = [""] parmvals = [0] if return_values: return parmlist, parmvals else: return parmlist def GetWantedParameters(self): """ Updates self.SaveInfo with all the information that will be saved to the table. """ strFull = self.WXTextPages.GetValue() PageNumbers = misc.parseString2Pagenum(self, strFull) # Get the wanted parameters from the selection. checked = list() for i in np.arange(len(self.Checkboxes)): if self.Checkboxes[i].IsChecked() == True: checked.append(self.Checklabels[i]) # Collect all the relevant pages pages = list() for i in np.arange(self.parent.notebook.GetPageCount()): Page = self.parent.notebook.GetPage(i) if Page.modelid == self.Page.corr.fit_model.id: # Only pages with same modelid if int(Page.counter.strip("#: ")) in PageNumbers: # Only pages selected in self.WXTextPages pages.append(Page) self.InfoClass.Pagelist = pages AllInfo = self.InfoClass.GetAllInfo() self.SaveInfo = list() # Some nasty iteration through the dictionaries. # Collect all checked variables. pagekeys = AllInfo.keys() # If pagenumber is larger than 10, # pagekeys.sort will not work, because we have strings # Define new compare function cmp_func = lambda a,b: cmp(int(a.strip().strip("#")), int(b.strip().strip("#"))) pagekeys.sort(cmp=cmp_func) #for Info in pagekeys: # pageinfo = list() # for item in AllInfo[Info]: # for subitem in AllInfo[Info][item]: # if len(subitem) == 2: # for label in checked: # if label == subitem[0]: # pageinfo.append(subitem) # # We want to replace the above iteration with an iteration that # covers missing values. This means checking for # "label == subitem[0]" # and iteration over AllInfo with that consition. for ii in pagekeys: pageinfo = list() for label in checked: label_in_there = False for item in AllInfo[ii]: for subitem in AllInfo[ii][item]: if subitem is not None and len(subitem) == 2: if label == subitem[0]: label_in_there = True pageinfo.append(subitem) elif label == "Fit errors" and subitem[0].startswith("Err "): label_in_there = True pageinfo.append(subitem) if label_in_there == False: # No data available pageinfo.append([label, "NaN"]) self.SaveInfo.append(pageinfo) def OnCheckboxChecked(self, e="restore"): """ Write boolean data of checked checkboxes to Page variable *StatisticsCheckboxes*. If e=="restore", then we will attempt to get the info back from the page. """ # What happens if a checkbox has been checked? # We write the data to the Page # (it will not be saved in the session). if e=="restore": checklist = self.Page.StatisticsCheckboxes if checklist is not None: if len(checklist) <= len(self.Checkboxes): for i in np.arange(len(checklist)): self.Checkboxes[i].SetValue(checklist[i]) else: checklist = list() for cb in self.Checkboxes: checklist.append(cb.GetValue()) self.Page.StatisticsCheckboxes = checklist def OnChooseValues(self, event=None): """ Plot the values for the parameter selected by the user. """ Info, checked = self.GetListOfAllParameters( return_std_checked=True) #headcounter = 0 #headlen = len(head) # We will sort the checkboxes in more than one column if there # are more than *maxitemsincolumn* maxitemsincolumn = np.float(19) Sizernumber = int(np.ceil(len(Info)/maxitemsincolumn)) self.boxsizerlist = list() for i in np.arange(Sizernumber): self.boxsizerlist.append(wx.BoxSizer(wx.VERTICAL)) # Start at -1 so the indexes will start at 0 (see below). #itemcount = -1 for i in range(len(Info)): #itemcount += 1 #headcounter += 1 checkbox = wx.CheckBox(self.panel, label=Info[i][0]) #if headcounter <= headlen: # checkbox.SetValue(True) # Additionally default checked items #for checkitem in checklist: # if item[0].count(checkitem): # checkbox.SetValue(True) checkbox.SetValue(checked[i]) # Add checkbox to column sizers sizern = int(np.floor(i/maxitemsincolumn)) self.boxsizerlist[sizern].Add(checkbox) self.Checkboxes.append(checkbox) self.Checklabels.append(Info[i][0]) self.Bind(wx.EVT_CHECKBOX, self.OnCheckboxChecked, checkbox) # Add sizers to boxsizer for sizer in self.boxsizerlist: self.boxsizer.Add(sizer) self.OnCheckboxChecked("restore") self.AllPlotParms = Info def OnClose(self, event=None): # This is a necessary function for PyCorrFit. # Do not change it. self.parent.toolmenu.Check(self.MyID, False) self.parent.ToolsOpen.__delitem__(self.MyID) self.Destroy() def OnDropDown(self, e=None): """ Plot the parameter selected in WXDropdown Uses info stored in self.PlotParms and self.InfoClass """ if self.parent.notebook.GetPageCount() == 0 or self.Page is None: self.canvas.Clear() return # Get valid pages strFull = self.WXTextPages.GetValue() try: PageNumbers = misc.parseString2Pagenum(self, strFull, nodialog=True) except: PageNumbers = self.PageNumbers else: self.PageNumbers = PageNumbers # Get plot parameters DDselid = self.WXDropdown.GetSelection() #[label, key] = self.PlotParms[DDselid] label = self.PlotParms[DDselid] # Get potential pages pages = list() for i in np.arange(self.parent.notebook.GetPageCount()): Page = self.parent.notebook.GetPage(i) if Page.corr.fit_model.id == self.Page.corr.fit_model.id: # Only pages with same modelid if int(Page.counter.strip("#: ")) in PageNumbers: # Only pages selected in self.WXTextPages pages.append(Page) plotcurve = list() for page in pages: pllabel, pldata = self.GetListOfPlottableParms(page=page, return_values=True) # Get the labels and make a plot of the parameters if len(pllabel)-1 >= DDselid and pllabel[DDselid] == label: x = int(page.counter.strip("#: ")) y = pldata[DDselid] plotcurve.append([x,y]) else: # try to get the label by searching for the first # instance for k in range(len(pllabel)): if pllabel[k] == label: x = int(page.counter.strip("#: ")) y = pldata[k] plotcurve.append([x,y]) # Prepare plotting self.canvas.Clear() linesig = plot.PolyMarker(plotcurve, size=1.5, marker='circle') plotlist = [linesig] # average line try: avg = np.average(np.array(plotcurve)[:,1]) maxpage = int(np.max(np.array(plotcurve)[:,0]) +1) minpage = int(np.min(np.array(plotcurve)[:,0]) -1) except: minpage = 0 maxpage = 0 self.WXavg.SetValue("-") self.WXsd.SetValue("-") else: # Plot data plotavg = [[0.5, avg], [maxpage+.5, avg]] #lineclear = plot.PolyLine(plotavg, colour="black") lineclear = plot.PolyMarker(plotavg, colour="black") plotlist.append(lineclear) # Update Text control self.WXavg.SetValue(str(avg)) self.WXsd.SetValue(str(np.std(np.array(plotcurve)[:,1]))) # Draw # This causes a memory leak after this function has been # called several times with the same large data set. # This goes away if only linesig OR lineclear are plotted. # #graphics = plot.PlotGraphics(plotlist, # xLabel='page number', # yLabel=label) graphics = plot.PlotGraphics([linesig], xLabel='page number', yLabel=label) # Correctly set x-axis minticks = 2 self.canvas.SetXSpec(max(maxpage-minpage, minticks)) self.canvas.Draw(graphics, xAxis=(minpage,maxpage)) def OnPageChanged(self, page, trigger=None): """ This function is called, when something in the panel changes. The variable `trigger` is used to prevent this function from being executed to save stall time of the user. Forr a list of possible triggers, see the doc string of `tools`. """ # When parent changes # This is a necessary function for PyCorrFit. # This is stuff that should be done when the active page # of the notebook changes. # filter unwanted triggers to improve speed if trigger in ["parm_batch", "fit_batch", "page_add_batch"]: return elif trigger in ["tab_init"] and page is not None: # Check if we have to replot for a new model if self.Page.corr.fit_model.id == page.corr.fit_model.id: return if (trigger in ["page_add_finalize"] and self.WXTextPages.GetValue() == "1"): # We probably imported data with statistics window open self.PageNumbers = range(1, 1+self.parent.notebook.GetPageCount()) setstring = misc.parsePagenum2String(self.PageNumbers) self.WXTextPages.SetValue(setstring) # # Prevent this function to be run twice at once: # oldsize = self.GetSizeTuple() if self.WXTextPages.GetValue() == "": # Set number of pages pagenumlist = list() for i in np.arange(self.parent.notebook.GetPageCount()): Page = self.parent.notebook.GetPage(i) pagenumlist.append(int(filter(lambda x: x.isdigit(), Page.counter))) valstring=misc.parsePagenum2String(pagenumlist) self.WXTextPages.SetValue(valstring) DDselection = self.WXDropdown.GetValue() self.Page = page self.InfoClass = InfoClass(CurPage=self.Page) self.PlotParms = self.GetListOfPlottableParms() # Make sure the selection stays the same DDselid = 0 for i in range(len(self.PlotParms)): if DDselection == self.PlotParms[i]: DDselid = i Parmlist = self.PlotParms self.WXDropdown.SetItems(Parmlist) self.WXDropdown.SetSelection(DDselid) self.panel.Enable() for i in np.arange(len(self.Checkboxes)): self.Checkboxes[i].Destroy() del self.Checkboxes #self.Checklabels[i].Destroy() # those cannot be destroyed. for i in np.arange(len(self.boxsizerlist)): self.boxsizer.Remove(0) self.boxsizer.Layout() self.boxsizerlist = list() self.Checkboxes = list() self.Checklabels = list() # Disable if there are no pages left if self.parent.notebook.GetPageCount() == 0: self.panel.Disable() self.canvas.Clear() return self.OnChooseValues() self.boxsizer.Layout() self.topSizer.Fit(self) (ax, ay) = self.GetSizeTuple() (px, py) = self.topSizer.GetMinSizeTuple() self.sp.SetSashPosition(px+5) self.SetMinSize((px+400, py)) self.SetSize((np.max([px+400,ax,oldsize[0]]), np.max([py,ay,oldsize[1]]))) # Replot self.OnDropDown() def OnSaveTable(self, event=None): dirname = self.parent.dirname dlg = wx.FileDialog(self.parent, "Choose file to save", dirname, "", "Text file (*.txt)|*.txt;*.TXT", wx.FD_SAVE|wx.FD_OVERWRITE_PROMPT) # user cannot do anything until he clicks "OK" if dlg.ShowModal() == wx.ID_OK: filename = dlg.GetPath() if filename.lower().endswith(".txt") is not True: filename = filename+".txt" dirname = dlg.GetDirectory() openedfile = codecs.open(filename, 'w', encoding="utf-8") # Get Parameterlist of all Pages with same model id as # Self.Page # This creates self.SaveInfo: self.GetWantedParameters() # Write header linestring = u"" for atuple in self.SaveInfo[0]: linestring += u"{}\t".format(atuple[0]) # remove trailing "\t" openedfile.write(u"{}\r\n".format(linestring.strip())) # Write data for item in self.SaveInfo: linestring = u"" for btuple in item: linestring += u"{}\t".format(btuple[1]) openedfile.write(linestring.strip()+u"\r\n") openedfile.close() else: dirname = dlg.GetDirectory() dlg.Destroy() # Give parent the current dirname self.parent.dirname = dirname def SetPageNumbers(self, pagestring): self.WXTextPages.SetValue(pagestring) @staticmethod def SortParameters(parms): u""" Sort a list of tuples according to the first item. The sorting convention was met in issue #113: - at the beginning: avg. countrates and particle numbers - fast components go before slow components: e.g. [τ_trip, τ_diff] - model parameters are sorted logically according to their origin: e.g. [T1, τ_trip1], or [F1, τ_diff1], or [T2, τ_trip2] - if the parameter ends with a number, then we sort it to the logical blocks - includes n1, n2, etc. - at end: other fitting parameters and mode information fitting parameters intensities n tautrip1 T1 tautrip2 T2 tau1 F1 tau2 F2 tau3 F3 alpha SP non-fitting parameter model name chisquare weighted fit interval measurement time ... model id """ startswith_sort = [ u"avg. signal", u"n", u"T", u"τ_trip", u"F", u"C", u"D", u"τ", u"τ_diff", u"alpha", u"SP", ] otherparms = list() # append to this list all parameters that might be in another model. for m in mdls.models: for p in mdls.GetHumanReadableParms(m.id, m.parameters[1])[0]: exists = False for sw in startswith_sort+otherparms: if p.startswith(sw): exists = True if not exists: otherparms.append(p) # sort the other parameters by name otherparms.sort() # special offsets to distinguish "T" and "Type": special_off_start = ["Type", "Fit"] def rate_tuple(item): x = item[0] return rate(x) def rate(x): """ rate a parameter for sorting. lower values are at the beginning of the list. """ x = x.split("[")[0] # start at the top r = 0 # BLOCK OFFSET try: intx = int(x[-1]) except: pass else: # penalty: belongs to block r += 3 + intx # STARTSWITH PENALTY for p in startswith_sort: if x.startswith(p): r += 1 + 3*(startswith_sort.index(p)) break # Block offset integer non_decimal = re.compile(r'[^\d]+') pnum = non_decimal.sub("", x) if len(pnum) > 0: r += int(pnum) if x.count("3D"): r -= 3 if x.count("2D"): r -= 0 # Other Parameters for p in otherparms: if p.startswith(x): r += (otherparms.index(p)) + 3*len(startswith_sort) # Special offsets for p in special_off_start: if x.startswith(p): r += 300 if r==0: r = 10000 return r def compare(x,y): """ rates x and y. returns -1, 0, 1 required for common list sort """ rx = rate_tuple(x) ry = rate_tuple(y) return rx-ry return sorted(parms, cmp=compare)PyCorrFit-0.9.9/pycorrfit/gui/tools/trace.py000066400000000000000000000102121274265304700210350ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ PyCorrFit Module tools - trace Show the trace of a file. """ import numpy as np import wx import wx.lib.plot as plot # Menu entry name MENUINFO = ["&Trace view", "Show the trace of an opened file."] class ShowTrace(wx.Frame): def __init__(self, parent): # parent is main frame self.parent = parent # Get the window positioning correctly pos = self.parent.GetPosition() pos = (pos[0]+100, pos[1]+100) wx.Frame.__init__(self, parent=self.parent, title="Trace view", pos=pos, style=wx.DEFAULT_FRAME_STYLE|wx.FRAME_FLOAT_ON_PARENT) ## MYID # This ID is given by the parent for an instance of this class self.MyID = None # Page self.Page = self.parent.notebook.GetCurrentPage() ## Canvas self.canvas = plot.PlotCanvas(self) self.canvas.SetEnableZoom(True) if self.parent.notebook.GetPageCount() == 0: # We do not need to disable anything here. user input. pass else: self.OnDraw() initial_size = (780,250) self.SetSize(initial_size) self.SetMinSize(initial_size) ## Icon if parent.MainIcon is not None: wx.Frame.SetIcon(self, parent.MainIcon) self.Show(True) def OnClose(self, event=None): self.parent.toolmenu.Check(self.MyID, False) self.parent.ToolsOpen.__delitem__(self.MyID) self.Destroy() def OnDraw(self): traces = self.Page.corr.traces self.canvas.SetEnableLegend(True) if len(traces) == 1: self.trace = 1*traces[0].trace # We want to have the trace in [s] here. self.trace[:,0] = self.trace[:,0]/1000 line = plot.PolyLine(self.trace, legend='{:.2f}kHz'.format(traces[0].countrate), colour='blue', width=1) lines = [line] xmax = np.max(self.trace[:,0]) xmin = np.min(self.trace[:,0]) ymax = np.max(self.trace[:,1]) ymin = np.min(self.trace[:,1]) elif len(traces) == 2: # This means that we have two (CC) traces to plot self.tracea = 1*traces[0].trace self.tracea[:,0] = self.tracea[:,0]/1000 self.traceb = 1*traces[1].trace self.traceb[:,0] = self.traceb[:,0]/1000 linea = plot.PolyLine(self.tracea, legend='channel 1 {:.2f}kHz'.format(traces[0].countrate), colour='blue', width=1) lineb = plot.PolyLine(self.traceb, legend='channel 2 {:.2f}kHz'.format(traces[1].countrate), colour='red', width=1) lines = [linea, lineb] xmax = max(np.max(self.tracea[:,0]), np.max(self.traceb[:,0])) xmin = min(np.min(self.tracea[:,0]), np.min(self.traceb[:,0])) ymax = max(np.max(self.tracea[:,1]), np.max(self.traceb[:,1])) ymin = min(np.min(self.tracea[:,1]), np.min(self.traceb[:,1])) else: self.canvas.Clear() return # Plot lines self.canvas.Draw(plot.PlotGraphics(lines, xLabel='time [s]', yLabel='count rate [kHz]'), xAxis=(xmin,xmax), yAxis=(ymin,ymax)) def OnPageChanged(self, page=None, trigger=None): """ This function is called, when something in the panel changes. The variable `trigger` is used to prevent this function from being executed to save stall time of the user. Forr a list of possible triggers, see the doc string of `tools`. """ if trigger in ["parm_batch", "fit_batch", "page_add_batch"]: return self.Page = page # When parent changes if self.parent.notebook.GetPageCount() == 0: # Nothing to do try: self.canvas.Clear() except: pass return self.OnDraw() PyCorrFit-0.9.9/pycorrfit/gui/usermodel.py000066400000000000000000000247431274265304700206140ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ PyCorrFit Module: user model: When the user wants to use his own functions. We are using sympy as function parser instead of writing our own, which might be safer. We only parse the function with sympy and test it once during import. After that, the function is evaluated using eval()! """ import codecs import numpy as np import scipy.special as sps import sys import warnings try: import sympy import sympy.functions from sympy.core import S from sympy.core.function import Function from sympy import sympify except ImportError: warnings.warn("Importing sympy failed."+\ " Reason: {}.".format(sys.exc_info()[1].message)) # Define Function, so PyCorrFit will start, even if sympy is not there. # wixi needs Function. Function = object import wx from pycorrfit import models as mdls from pycorrfit.models.control import append_model class CorrFunc(object): """ Check the input code of a proposed user model function and return a function for fitting via GetFunction. """ def __init__(self, labels, values, substitutes, funcstring): self.values = values # a --> a # b [ms] --> b self.variables = list() for item in labels: self.variables.append(item.split(" ")[0].strip()) self.funcstring = funcstring for key in substitutes.keys(): # Don't forget to insert the "(" and ")"'s self.funcstring = self.funcstring.replace(key, "("+substitutes[key]+")") for otherkey in substitutes.keys(): substitutes[otherkey] = substitutes[otherkey].replace(key, "("+substitutes[key]+")") # Convert the function string to a simpification object self.simpification = sympify(self.funcstring, sympyfuncdict) self.simstring = str(self.simpification) self.vardict = evalfuncdict def GetFunction(self): # Define the function that will be calculated later def G(parms, tau): tau = np.atleast_1d(tau) for i in np.arange(len(parms)): self.vardict[self.variables[i]] = float(parms[i]) self.vardict["tau"] = tau # Function called with array/list # The problem here might be #for key in vardict.keys(): # symstring = symstring.replace(key, str(vardict[key])) #symstring = symstring.replace("####", "tau") g = eval(self.funcstring, self.vardict) ## This would be a safer way to do this, but it is too slow! # Once simpy supports arrays, we can use these. # # g = np.zeros(len(tau)) # for i in np.arange(len(tau)): # vardict["tau"] = tau[i] # g[i] = simpification.evalf(subs=vardict) return g return G def TestFunction(self): """ Test the function for parsibility with the given parameters. """ vardict = dict() for i in np.arange(len(self.variables)): vardict[self.variables[i]] = sympify(float(self.values[i])) for tau in np.linspace(0.0001, 10000, 10): vardict["tau"] = tau Number = self.simpification.evalf(subs=vardict) if Number.is_Number is False: raise SyntaxError("Function could not be parsed!") class UserModel(object): """ Class for importing txt files as models into PyCorrFit. """ def __init__(self, parent): " Define all important constants and variables. " # Current ID is the last model ID we gave away. # This will be set using self.SetCurrentID self.CurrentID = None # The file to be opened. This is a full path like # os.path.join(dirname, filename) self.filename = None # Imported models # Modelarray = [model1, model2] self.modelarray = [] # String that contains the executable code self.modelcode = None # Parent is main PyCorrFit program self.parent = parent # The string that identifies the user model menu self.UserStr="User" def GetCode(self, filename=None): """ Get the executable code from the file. Optional argument filename may be used. If not self.filename will be used. This automatically sets self.filename """ if filename is not None: self.filename = filename openedfile = open(self.filename, 'r') code = openedfile.readlines() # File should start with a comment #. # Remove everything before that comment (BOM). startfile = code[0].find("#") if startfile != -1: code[0] = code[0][startfile:] else: code[0] = "# "+code[0] # Returncode: True if model was imported, False if there was a problem. # See ModelImported in class CorrFunc self.AddModel(code) openedfile.close() def AddModel(self, code): """ *code* is a list with strings each string is one line. """ # a = 1 # b [ms] = 2.5 # gAlt = 1+tau/b # gProd = a*b # G = 1/gA * gB labels = list() values = list() substitutes = dict() for line in code: # We deal with comments and empty lines # We need to check line length first and then we look for # a hash. line = line.strip() if len(line) != 0 and line[0] != "#": var, val = line.split("=") var = var.strip() if var == "G": # Create a fuction that calculates G funcstring = val.strip() self.FuncClass = CorrFunc(labels, values, substitutes, funcstring) func = self.FuncClass.GetFunction() doc = code[0].strip() # Add whitespaces in model string (looks nicer) for olin in code[1:]: doc = doc + "\n "+olin.strip() func.func_doc = codecs.decode(doc, "utf-8") elif var[0] == "g": substitutes[var] = val.strip() else: # Add value and variable to our lists labels.append(codecs.decode(var, "utf-8")) values.append(float(val)) # Active Parameters we are using for the fitting # [0] labels # [1] values # [2] bool values to fit bools = list([False]*len(values)) bools[0] = True # Create Modelarray active_parms = [ labels, values, bools ] self.SetCurrentID() Modelname = code[0][1:].strip() definitions = [self.CurrentID, Modelname, Modelname, func] model = dict() model["Parameters"] = active_parms model["Definitions"] = definitions self.modelarray.append(model) def ImportModel(self): """ Do everything that is necessarry to import the models into PyCorrFit. """ # Set the model ids of the new model(s) # Normally, there is only one model. for i in np.arange(len(self.modelarray)): self.SetCurrentID() self.modelarray[i]["Definitions"][0] = self.CurrentID # We assume that the models have the correct ID for now append_model(self.modelarray) # Set variables and models # Is this still necessary? - We are doing this for compatibility! self.parent.value_set = mdls.values self.parent.valuedict = mdls.valuedict self.parent.models = mdls.models self.parent.modeldict = mdls.modeldict self.parent.modeltypes = mdls.modeltypes # Get menu menu = self.parent.modelmenudict[self.UserStr] # Add menu entrys for item in self.modelarray: # Get definitions Defs = item["Definitions"] # This is important if we want to save the session with # the imported model. mdls.modeltypes[self.UserStr].append(Defs[0]) menuentry = menu.Append(Defs[0], Defs[1], Defs[2]) self.parent.Bind(wx.EVT_MENU, self.parent.add_fitting_tab, menuentry) def TestFunction(self): """ Convenience function to test self.FuncClass """ self.FuncClass.TestFunction() def SetCurrentID(self): # Check last item or so of modelarray # Imported functions get IDs starting from 7000 theID = 7000 for model in mdls.models: theID = max(theID, model[0]) self.CurrentID = theID + 1 class wixi(Function): """ This is a ghetto solution for using wofz in sympy. It only returns the real part of the function. I am not sure, if the eval's are placed correctly. I only made it work for my needs. This might be wrong! For true use of wofz, I am not using sympy, anyhow. """ nargs = 1 is_real = True @classmethod def eval(self, arg): return None def as_base_exp(self): return self, S.One # @UndefinedVariable def _eval_evalf(self, prec): result = sps.wofz(1j*float(self.args[0])) return sympy.numbers.Number(sympy.functions.re(result)) def evalwixi(x): """ Complex Error Function (Faddeeva/Voigt). w(i*x) = exp(x**2) * ( 1-erf(x) ) This function is called by other functions within this module. We are using the scipy.special.wofz module which calculates w(z) = exp(-z**2) * ( 1-erf(-iz) ) z = i*x """ z = x*1j result = sps.wofz(z) # We should have a real solution. Make sure nobody complains about # some zero-value imaginary numbers. return np.real_if_close(result) sympyfuncdict = dict() sympyfuncdict["wixi"] = wixi evalfuncdict = dict() evalfuncdict["wixi"] = evalwixi evalfuncdict["I"] = 1j scipyfuncs = ['wofz', 'erf', 'erfc'] numpyfuncs = ['abs', 'arccos', 'arcsin', 'arctan', 'arctan2', 'ceil', 'cos', 'cosh', 'degrees', 'e', 'exp', 'fabs', 'floor', 'fmod', 'frexp', 'hypot', 'ldexp', 'log', 'log10', 'modf', 'pi', 'power', 'radians', 'sin', 'sinh', 'sqrt', 'tan', 'tanh'] for func in scipyfuncs: evalfuncdict[func] = eval("sps."+func) for func in numpyfuncs: evalfuncdict[func] = eval("np."+func) PyCorrFit-0.9.9/pycorrfit/gui/wxutils.py000066400000000000000000000114601274265304700203240ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ PyCorrFit Module wxutils """ import numpy as np # NumPy import re import string import wx # GUI interface wxPython def float2string_nsf(fval, n=7): """ Truncate a float to n significant figures and return nice string. Arguments: q : a float n : desired number of significant figures Returns: String with only n s.f. and trailing zeros. """ #sgn=np.sign(fval) try: if fval == 0: npoint=n else: q=abs(fval) k=int(np.ceil(np.log10(q/n))) # prevent negative significant digits npoint = max(0, n-k) string="{:.{}f}".format(fval, npoint) except: string="{}".format(fval) return string def nice_string(string): """ Convert a string of a float created by `float2string_nsf` to something nicer. i.e. - 1.000000 -> 1 - 1.010000 -> 1.010 """ if string.count(".") and len(string.split(".")[1].replace("0", "")) == 0: return "{:d}".format(int(float(string))) else: olen = len(string) newstring = string.rstrip("0") if olen > len(newstring): string=newstring+"0" return string class PCFFloatValidator(wx.PyValidator): def __init__(self, flag=None, pyVar=None): wx.PyValidator.__init__(self) self.flag = flag self.Bind(wx.EVT_CHAR, self.OnChar) def Clone(self): return PCFFloatValidator(self.flag) def Validate(self, win): tc = self.GetWindow() val = tc.GetValue() for x in val: if x not in string.digits: return False return True def OnChar(self, event): """ Filter the characters that are put in the control. TODO: - check for strings that do not make sense - 2e-4.4 - 2e--3 - 3-1+5 """ key = event.GetKeyCode() ctrl = event.GetEventObject() # Get the actual string from the object curval = wx.TextCtrl.GetValue(ctrl) if key < wx.WXK_SPACE or key == wx.WXK_DELETE or key > 255: event.Skip() return char = chr(key) char = char.replace(",", ".") onlyonce = [".", "e", "i", "n", "f"] if char in onlyonce and curval.count(char): # not allowed return if ( char in string.digits or char in ["+", "-"]+onlyonce): event.Skip() return if not wx.Validator_IsSilent(): wx.Bell() # Returning without calling event.Skip eats the event before it # gets to the text control return class PCFFloatTextCtrl(wx.TextCtrl): def __init__(self, *args, **kwargs): wx.TextCtrl.__init__(self, *args, validator=PCFFloatValidator(), size=(110,-1), style=wx.TE_PROCESS_ENTER, **kwargs) self.Bind(wx.EVT_ENTER_WINDOW, self.OnMouseEnter) self.Bind(wx.EVT_LEAVE_WINDOW, self.OnMouseLeave) self._PCFvalue = 0.0 def OnMouseEnter(self, e): self.SetFocus() self.SetSelection(-1,0) def OnMouseLeave(self, e): self.SetSelection(0,0) self.SetInsertionPoint(0) def SetValue(self, value): self._PCFvalue = value string = PCFFloatTextCtrl.float2string(value) wx.TextCtrl.SetValue(self, string) def GetValue(self): string = wx.TextCtrl.GetValue(self) if string == PCFFloatTextCtrl.float2string(self._PCFvalue): # use internal value: more accurate #print("internal", self._PCFvalue) return self._PCFvalue else: # new value #print("external", string) return PCFFloatTextCtrl.string2float(string) @staticmethod def float2string(value): """ inverse of string2float with some tweaks """ value = float2string_nsf(value) value = nice_string(value) return value @staticmethod def string2float(string): """ Remove any characters that are not in [+-{0-9},.] and show a decent float value. """ if string.count("inf"): if string[0]=="-": return -np.inf else: return np.inf # allow comma string = string.replace(",", ".") # allow only one decimal point string = string[::-1].replace(".", "", string.count(".")-1)[::-1] try: string = "{:.12f}".format(float(string)) except: pass # remove letters string = re.sub(r'[^\d.-]+', '', string) if len(string) == 0: string = "0" return float(string) PyCorrFit-0.9.9/pycorrfit/meta.py000066400000000000000000000040001274265304700167370ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ meta data and methods for PyCorrFit """ import os import sys def find_program(program): """ Uses the systems PATH variable find executables""" path = os.environ['PATH'] paths = path.split(os.pathsep) for d in paths: if os.path.isdir(d): fullpath = os.path.join(d, program) if sys.platform[:3] == 'win': for ext in '.exe', '.bat': program_path = fullpath + ext if os.path.isfile(fullpath + ext): return (1, program_path) else: if os.path.isfile(fullpath): return (1, fullpath) return (0, None) def get_file_location(filename): """ Locate non-Python files that are part of PyCorrFit. """ dirname = os.path.dirname(os.path.abspath(__file__)) locations = ["/./", "/pycorrfit_doc/", "/doc/"] locations += [ "/.."+l for l in locations] locations = [ os.path.realpath(dirname+l) for l in locations] for i in range(len(locations)): # check /usr/lib64/32 -> /usr/lib for larch in ["lib32", "lib64"]: if dirname.count(larch): locations.append(locations[i].replace(larch, "lib", 1)) ## freezed binaries: if hasattr(sys, 'frozen'): try: adir = sys._MEIPASS + "/doc/" # @UndefinedVariable except: adir = "./" locations.append(os.path.realpath(adir)) for loc in locations: thechl = os.path.join(loc,filename) if os.path.exists(thechl): return thechl break # if this does not work: return None def get_version(): """ Get the version. """ StaticChangeLog = get_file_location("ChangeLog.txt") # Check if we can extract the version try: clfile = open(StaticChangeLog, 'r') version = clfile.readline().strip() clfile.close() except: version = "0.0.0-unknown" return versionPyCorrFit-0.9.9/pycorrfit/models/000077500000000000000000000000001274265304700167305ustar00rootroot00000000000000PyCorrFit-0.9.9/pycorrfit/models/MODEL_TIRF_1C.py000077500000000000000000000175321274265304700213240ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ This file contains TIR one component models """ import numpy as np # NumPy import scipy.special as sps def wixi(x): """ Complex Error Function (Faddeeva/Voigt). w(i*x) = exp(x**2) * ( 1-erf(x) ) This function is called by other functions within this module. We are using the scipy.special.wofz module which calculates w(z) = exp(-z**2) * ( 1-erf(-iz) ) z = i*x """ z = x*1j wixi = sps.wofz(z) # We should have a real solution. Make sure nobody complains about # some zero-value imaginary numbers. return np.real_if_close(wixi) def CF_Gxy_TIR_square(parms, tau): # Model 6000 u""" Two-dimensional diffusion with a square shaped lateral detection area taking into account the size of the point spread function. *parms* - a list of parameters. Parameters (parms[i]): [0] D Diffusion coefficient [1] σ Lateral size of the point spread function σ = σ₀ * λ / NA [2] a Side size of the square-shaped detection area [3] C_2D Particle concentration in detection area *tau* - lag time Please refer to the documentation of PyCorrFit for further information on this model function. Returns: Normalized Lateral correlation function w/square pinhole. """ D = parms[0] sigma = parms[1] a = parms[2] Conc = parms[3] var1 = sigma**2+D*tau AA = 2*np.sqrt(var1)/(a**2*np.sqrt(np.pi)) BB = np.exp(-a**2/(4*(var1))) - 1 CC = sps.erf(a/(2*np.sqrt(var1)))/a # gx = AA*BB+CC # gxy = gx**2 # g2D = gxy * eta**2 * Conc # F = 1/(eta*Conc) # G = g2D / F**2 G = 1/Conc * (AA*BB+CC)**2 return G # 3D free tir def CF_Gxyz_TIR_square(parms, tau, wixi=wixi): # Model 6010 u""" Three-dimensional diffusion with a square-shaped lateral detection area taking into account the size of the point spread function; and an exponential decaying profile in axial direction. *parms* - a list of parameters. Parameters (parms[i]): [0] D Diffusion coefficient [1] σ Lateral size of the point spread function σ = σ₀ * λ / NA [2] a Side size of the square-shaped detection area [3] d_eva Evanescent penetration depth [4] C_3D Particle concentration in detection volume *tau* - lag time Please refer to the documentation of PyCorrFit for further information on this model function. Returns: 3D correlation function for TIR-FCS w/square pinhole """ D = parms[0] sigma = parms[1] a = parms[2] kappa = 1/parms[3] Conc = parms[4] ### Calculate gxy # Axial correlation x = np.sqrt(D*tau)*kappa w_ix = wixi(x) gz = np.sqrt(D*tau/np.pi) - (2*D*tau*kappa**2 - 1)/(2*kappa) * w_ix # Lateral correlation gx1 = 2/(a**2*np.sqrt(np.pi)) * np.sqrt(sigma**2+D*tau) * \ ( np.exp(-a**2/(4*(sigma**2+D*tau))) -1 ) gx2 = 1/a * sps.erf( a / (2*np.sqrt(sigma**2 + D*tau))) gx = gx1 + gx2 gxy = gx**2 # Non normalized correlation function # We do not need eta after normalization # g = eta**2 * Conc * gxy * gz g = Conc * gxy * gz # Normalization: # F = eta * Conc / kappa F = Conc / kappa G = g / F**2 return G def MoreInfo_6000(parms, countrate=None): u"""Supplementary parameters: For a>>sigma, the correlation function at tau=0 corresponds to: [4] G(τ=0) = 1/(N_eff) * ( 1-2*σ/(sqrt(π)*a) )² Effective detection area: [5] A_eff [µm²] = a² Effective particle number in detection area: [6] N_eff = A_eff * C_2D """ #D = parms[0] #sigma = parms[1] a = parms[2] Conc = parms[3] Info=list() # Detection area: Aeff = a**2 # Particel number Neff = Aeff * Conc # Correlation function at tau = 0 G_0 = CF_Gxy_TIR_square(parms, 0) Info.append(["G(0)", G_0]) # 10 000 nm² = 0.01 µm² # Aeff * 10 000 nm² * 10**(-6)µm²/nm² = Aeff * 0.01 * µm² # Have to divide Aeff by 100 Info.append([u"A_eff [µm²]", Aeff / 100]) Info.append(["N_eff", Neff]) if countrate is not None: # CPP cpp = countrate/Neff Info.append(["cpp [kHz]", cpp]) return Info def MoreInfo_6010(parms, countrate): u"""Supplementary parameters: Molarity: [5] C_3D [nM] = C_3D [1000/µm³] * 10000/6.0221415 Effective detection volume: [6] V_eff = a² * d_eva Effective particle number: [7] N_eff = V_eff * C_3D For a>>σ, the correlation function at τ=0 corresponds to: [8] G(τ=0) = 1/(2*N_eff) * ( 1-2*σ/(sqrt(π)*a) )² """ # 3D Model TIR square # 3D TIR (□xσ/exp),Simple 3D diffusion w/ TIR, fct.CF_Gxyz_square_tir # D [10 µm²/s],σ [100 nm],a [100 nm],d_eva [100 nm],[conc.] [1000 /µm³] #sigma = parms[1] a = parms[2] d_eva = parms[3] conc = parms[4] # Sigma Info = list() # Molarity [nM]: # 1000/(µm³)*10**15µm³/l * mol /(6.022*10^23) * 10^9 n cmol = conc * 10000/6.0221415 # Effective volume [al]: Veff = a**2 * d_eva # Effective particel number Neff = a**2 * d_eva * conc # Correlation function at tau = 0 G_0 = CF_Gxyz_TIR_square(parms, 0) Info.append(["G(0)", G_0]) Info.append(["C_3D [nM]", cmol]) # atto liters # 1 000 000 nm³ = 1 al Info.append(["V_eff [al]", Veff]) Info.append(["N_eff", Neff]) if countrate is not None: # CPP cpp = countrate/Neff Info.append(["cpp [kHz]", cpp]) return Info # 2D Model Square m_twodsq6000 = [6000, u"2D", u"2D diffusion w/ square pinhole", CF_Gxy_TIR_square] labels_6000 = [u"D [10 µm²/s]", u"σ [100 nm]", u"a [100 nm]", u"C_2D [100 /µm²]"] values_6000 = [0.054, 2.3, 7.5, .6] # [D,lamb,NA,a,conc] # For user comfort we add values that are human readable. # Theese will be used for output that only humans can read. labels_human_readable_6000 = [u"D [µm²/s]", u"σ [nm]", u"a [nm]", u"C_2D [1/µm²]"] values_factor_human_readable_6000 = [10, 100, 100, 100] valuestofit_6000 = [True, False, False, True] # Use as fit parameter? parms_6000 = [labels_6000, values_6000, valuestofit_6000, labels_human_readable_6000, values_factor_human_readable_6000] # 3D Model TIR square m_3dtirsq6010 = [6010, u"3D", "Simple 3D diffusion w/ TIR", CF_Gxyz_TIR_square] labels_6010 = [u"D [10 µm²/s]", u"σ [100 nm]", u"a [100 nm]", u"d_eva [100 nm]", u"C_3D [1000 /µm³]"] values_6010 = [0.520, 2.3, 7.5, 1.0, .0216] # For user comfort we add values that are human readable. # Theese will be used for output that only humans can read. labels_human_readable_6010 = [u"D [µm²/s]", u"σ [nm]", u"a [nm]", u"d_eva [nm]", u"C_3D [1/µm³]"] values_factor_human_readable_6010 = [10, 100, 100, 100, 1000] valuestofit_6010 = [True, False, False, False, True] parms_6010 = [labels_6010, values_6010, valuestofit_6010, labels_human_readable_6010, values_factor_human_readable_6010] # Pack the models model1 = dict() model1["Parameters"] = parms_6000 model1["Definitions"] = m_twodsq6000 model1["Supplements"] = MoreInfo_6000 model1["Boundaries"] = [[0, None]]*len(values_6000) model2 = dict() model2["Parameters"] = parms_6010 model2["Definitions"] = m_3dtirsq6010 model2["Supplements"] = MoreInfo_6010 model2["Boundaries"] = [[0, None]]*len(values_6010) Modelarray = [model1, model2] PyCorrFit-0.9.9/pycorrfit/models/MODEL_TIRF_2D2D.py000077500000000000000000000106231274265304700215060ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ This file contains 2D+2D TIR-FCS models. """ import numpy as np # NumPy import scipy.special as sps def wixi(x): """ Complex Error Function (Faddeeva/Voigt). w(i*x) = exp(x**2) * ( 1-erf(x) ) This function is called by other functions within this module. We are using the scipy.special.wofz module which calculates w(z) = exp(-z**2) * ( 1-erf(-iz) ) z = i*x """ z = x*1j wixi = sps.wofz(z) # We should have a real solution. Make sure nobody complains about # some zero-value imaginary numbers. return np.real_if_close(wixi) # model 6022 # 2D + 2D no binding TIRF def CF_Gxy_TIR_square_2d2d(parms, tau, wixi=wixi): u""" Two-component two-dimensional diffusion with a square-shaped lateral detection area taking into account the size of the point spread function. *parms* - a list of parameters. Parameters (parms[i]): [0] D_2D1 Diffusion coefficient of species 1 [1] D_2D2 Diffusion coefficient of species 2 [2] σ Lateral size of the point spread function σ = σ₀ * λ / NA [3] a Side size of the square-shaped detection area [4] C_2D1 Two-dimensional concentration of species 1 [5] C_2D2 Two-dimensional concentration of species 2 [6] α Relative molecular brightness of particle 2 compared to particle 1 (α = q₂/q₁) *tau* - lag time """ D_2D1 = parms[0] D_2D2 = parms[1] sigma = parms[2] a = parms[3] Conc_2D1 = parms[4] Conc_2D2 = parms[5] alpha = parms[6] ## First the 2D-diffusion of species 1 var1 = sigma**2+D_2D1*tau AA1 = 2*np.sqrt(var1)/(a**2*np.sqrt(np.pi)) BB1 = np.exp(-a**2/(4*(var1))) - 1 CC1 = sps.erf(a/(2*np.sqrt(var1)))/a # gx = AA*BB+CC # gxy = gx**2 # g2D = Conc_2D * gxy g2D1 = Conc_2D1 * (AA1*BB1+CC1)**2 ## Second the 2D-diffusion of species 2 var2 = sigma**2+D_2D2*tau AA2 = 2*np.sqrt(var2)/(a**2*np.sqrt(np.pi)) BB2 = np.exp(-a**2/(4*(var2))) - 1 CC2 = sps.erf(a/(2*np.sqrt(var2)))/a # gx = AA*BB+CC # gxy = gx**2 # g2D = Conc_2D * gxy g2D2 = alpha**2 * Conc_2D2 * (AA2*BB2+CC2)**2 ## Finally the Prefactor F = Conc_2D1 + alpha * Conc_2D2 G = (g2D1 + g2D2) / F**2 return G # 2D-2D Model TIR m_tir_2d_2d_mix_6022 = [6022, u"2D+2D","Separate 2D diffusion, TIR", CF_Gxy_TIR_square_2d2d] labels_6022 = [ u"D"+u"\u2081"+u" [10 µm²/s]", u"D"+u"\u2082"+u" [10 µm²/s]", u"σ [100 nm]", u"a [100 nm]", u"C"+u"\u2081"+u" [100 /µm²]", u"C"+u"\u2082"+u" [100 /µm²]", u"\u03b1"+" (q"+u"\u2082"+"/q"+u"\u2081"+")" ] values_6022 = [ 0.01, # D_2D₁ [10 µm²/s] 0.90, # D_2D₂ [10 µm²/s] 2.3, # σ [100 nm] 7.50, # a [100 nm] 0.01, # conc.2D₁ [100 /µm²] 0.03, # conc.2D₂ [100 /µm²] 1 # alpha ] # For user comfort we add values that are human readable. # Theese will be used for output that only humans can read. labels_human_readable_6022 = [ u"D"+u"\u2081"+u" [µm²/s]", u"D"+u"\u2082"+u" [µm²/s]", u"σ [nm]", u"a [nm]", u"C"+u"\u2081"+u" [1/µm²]", u"C"+u"\u2082"+u" [1/µm²]", u"\u03b1"+" (q"+u"\u2082"+"/q"+u"\u2081"+")" ] values_factor_human_readable_6022 = [ 10, # D_2D₁ [10 µm²/s], 10, # D_2D₂ [10 µm²/s] 100, # σ [100 nm] 100, # a [100 nm] 100, # conc.2D₁ [100 /µm²] 100, # conc.2D₂ [100 /µm²] 1 ] valuestofit_6022 = [False, True, False, False, False, True, False] parms_6022 = [labels_6022, values_6022, valuestofit_6022, labels_human_readable_6022, values_factor_human_readable_6022] boundaries = [ [0, np.inf] ] * len(values_6022) model1 = dict() model1["Parameters"] = parms_6022 model1["Definitions"] = m_tir_2d_2d_mix_6022 model1["Boundaries"] = boundaries model1["Constraints"] = [[1, ">", 0]] Modelarray = [model1] PyCorrFit-0.9.9/pycorrfit/models/MODEL_TIRF_3D2D.py000077500000000000000000000114251274265304700215100ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ This file contains 3D+2D TIR-FCS models. """ import numpy as np # NumPy import scipy.special as sps def wixi(x): """ Complex Error Function (Faddeeva/Voigt). w(i*x) = exp(x**2) * ( 1-erf(x) ) This function is called by other functions within this module. We are using the scipy.special.wofz module which calculates w(z) = exp(-z**2) * ( 1-erf(-iz) ) z = i*x """ z = x*1j wixi = sps.wofz(z) # We should have a real solution. Make sure nobody complains about # some zero-value imaginary numbers. return np.real_if_close(wixi) # 3D + 2D no binding TIRF # model 6020 def CF_Gxyz_TIR_square_3d2d(parms, tau, wixi=wixi): u""" Two-component two- and three-dimensional diffusion with a square-shaped lateral detection area taking into account the size of the point spread function; and an exponential decaying profile in axial direction. *parms* - a list of parameters. Parameters (parms[i]): [0] D_3D Diffusion coefficient of freely diffusing species [1] D_2D Diffusion coefficient of surface bound species [2] σ Lateral size of the point spread function σ = σ₀ * λ / NA [3] a Side size of the square-shaped detection area [4] d_eva Evanescent penetration depth [5] C_3D Concentration of freely diffusing species [6] C_2D Concentration of surface bound species [7] α Relative molecular brightness of 3D particle compared to 2D particle (α = q3D/q2D) *tau* - lag time """ D_3D = parms[0] D_2D = parms[1] sigma = parms[2] a = parms[3] kappa = 1/parms[4] Conc_3D = parms[5] Conc_2D = parms[6] alpha = parms[7] ## First the 2D-diffusion at z=0 var1 = sigma**2+D_2D*tau AA = 2*np.sqrt(var1)/(a**2*np.sqrt(np.pi)) BB = np.exp(-a**2/(4*(var1))) - 1 CC = sps.erf(a/(2*np.sqrt(var1)))/a # gx = AA*BB+CC # gxy = gx**2 # g2D = Conc_2D * gxy g2D = Conc_2D * (AA*BB+CC)**2 ## Second the 3D diffusion for z>0 # Axial correlation x = np.sqrt(D_3D*tau)*kappa w_ix = wixi(x) gz = np.sqrt(D_3D*tau/np.pi) - (2*D_3D*tau*kappa**2 - 1)/(2*kappa) * w_ix # Lateral correlation gx1 = 2/(a**2*np.sqrt(np.pi)) * np.sqrt(sigma**2+D_3D*tau) * \ ( np.exp(-a**2/(4*(sigma**2+D_3D*tau))) -1 ) gx2 = 1/a * sps.erf( a / (2*np.sqrt(sigma**2 + D_3D*tau))) gx = gx1 + gx2 gxy = gx**2 # Non normalized correlation function g3D = alpha**2 * Conc_3D * gxy * gz ## Finally the Prefactor F = alpha * Conc_3D / kappa + Conc_2D G = (g3D + g2D) / F**2 return G # 3D-2D Model TIR m_tir_3d_2d_mix_6020 = [6020, u"3D+2D", "Separate 3D and 2D diffusion, 3D TIR", CF_Gxyz_TIR_square_3d2d] labels_6020 = [u"D_3D [10 µm²/s]", u"D_2D [10 µm²/s]", u"σ [100 nm]", u"a [100 nm]", u"d_eva [100 nm]", u"C_3D [1000 /µm³]", u"C_2D [100 /µm²]", u"\u03b1"+" (q3D/q2D)" ] values_6020 = [ 50.0, # D_3D [10 µm²/s] 0.81, # D_2D [10 µm²/s] 2.3, # σ [100 nm] 7.50, # a [100 nm] 1.0, # d_eva [100 nm] 0.01, # conc.3D [1000 /µm³] 0.03, # conc.2D [100 /µm²] 1 # alpha ] # For user comfort we add values that are human readable. # Theese will be used for output that only humans can read. labels_human_readable_6020 = [ u"D_3D [µm²/s]", u"D_2D [µm²/s]", u"σ [nm]", u"a [nm]", u"d_eva [nm]", u"C_3D [1/µm³]", u"C_2D [1/µm²]", u"\u03b1"+" (q3D/q2D)" ] values_factor_human_readable_6020 = [ 10, # D_3D [µm²/s] 10, # D_2D [10 µm²/s] 100, # σ [100 nm] 100, # a [100 nm] 100, # d_eva [100 nm] 1000, # conc.3D [1000 /µm³] 100, # conc.2D [100 /µm²] 1 # alpha ] valuestofit_6020 = [False, True, False, False, False, False, True, False] parms_6020 = [labels_6020, values_6020, valuestofit_6020, labels_human_readable_6020, values_factor_human_readable_6020] model1 = dict() model1["Parameters"] = parms_6020 model1["Definitions"] = m_tir_3d_2d_mix_6020 model1["Boundaries"] = [[0, np.inf]]*len(values_6020) model1["Constraints"] = [[1, "<", 0]] Modelarray = [model1] PyCorrFit-0.9.9/pycorrfit/models/MODEL_TIRF_3D2Dkin_Ries.py000077500000000000000000000361211274265304700231740ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ This file contains a TIR-FCS kineteics model function according to: "Total Internal Reflection Fluorescence Correlation Spectroscopy: Effects of Lateral Diffusion and Surface-Generated Fluorescence" Jonas Ries, Eugene P. Petrov, and Petra Schwille Biophysical Journal, Volume 95, July 2008, 390–399 """ import numpy as np # NumPy import scipy.special as sps import numpy.lib.scimath as nps def wixi(x): """ Complex Error Function (Faddeeva/Voigt). w(i*x) = exp(x**2) * ( 1-erf(x) ) This function is called by other functions within this module. We are using the scipy.special.wofz module which calculates w(z) = exp(-z**2) * ( 1-erf(-iz) ) z = i*x """ z = x*1j wixi = sps.wofz(z) # We should have a real solution. Make sure nobody complains about # some zero-value imaginary numbers. return np.real_if_close(wixi) # Lateral correlation function def CF_gxy_square(parms, tau): u""" 2D free diffusion measured with a square pinhole. For the square pinhole, the correlation function can readily be calculated for a TIR-FCS setup. This function is called by other functions within this module. Attention: This is NOT g2D (or gCC), the non normalized correlation function. g2D = gxy * eta**2 * Conc, where eta is the molecular brightness, Conc the concentration and gxy is this function. *parms* - a list of parameters. Parameters (parms[i]): [0] D Diffusion coefficient [1] sigma lateral size of the point spread function sigma = simga_0 * lambda / NA [2] a side size of the square pinhole *tau* - lag time Returns: Nonnormalized Lateral correlation function w/square pinhole. """ D = parms[0] sigma = parms[1] a = parms[2] var1 = sigma**2+D*tau AA = 2*np.sqrt(var1)/(a**2*np.sqrt(np.pi)) BB = np.exp(-a**2/(4*(var1))) - 1 CC = sps.erf(a/(2*np.sqrt(var1)))/a # gx = AA*BB+CC # gxy = gx**2 return (AA*BB+CC)**2 def CF_gz_CC(parms, tau, wixi=wixi): u""" Axial (1D) diffusion in a TIR-FCS setup. From Two species (bound/unbound) this is the bound part. This function is called by other functions within this module. *parms* - a list of parameters. Parameters (parms[i]): [0] D_3D 3D Diffusion coefficient (species A) [1] D_2D 2D Diffusion coefficient of bound species C [2] sigma lateral size of the point spread function sigma = simga_0 * lambda / NA [3] a side size of the square pinhole [4] d_eva evanescent decay length (decay to 1/e) [5] Conc_3D 3-dimensional concentration of species A [6] Conc_2D 2-dimensional concentration of species C [7] eta_3D molecular brightness of species A [8] eta_2D molecular brightness of species C [9] k_a Surface association rate constant [10] k_d Surface dissociation rate constant *tau* - lag time """ D = parms[0] #D_2D = parms[1] #sigma = parms[2] # a = parms[3] #d_eva = parms[4] Conc_3D = parms[5] # ligand concentration in solution Conc_2D = parms[6] eta_3D = parms[7] eta_2D = parms[8] k_a = parms[9] k_d = parms[10] # Define some other constants: K = k_a/k_d # equilibrium constant Beta = 1/(1 + K*Conc_3D) # This is wrong in the Ries paper #Re = D / d_eva**2 Rt = D * (Conc_3D / (Beta * Conc_2D))**2 Rr = k_a * Conc_3D + k_d # Define even more constants: sqrtR1 = -Rr/(2*nps.sqrt(Rt)) + nps.sqrt( Rr**2/(4*Rt) - Rr ) sqrtR2 = -Rr/(2*nps.sqrt(Rt)) - nps.sqrt( Rr**2/(4*Rt) - Rr ) R1 = sqrtR1 **2 R2 = sqrtR2 **2 # Calculate return function A1 = eta_2D * Conc_2D / (eta_3D * Conc_3D) * Beta A2 = sqrtR1 * wixi(-nps.sqrt(tau*R2)) - sqrtR2 * wixi(-nps.sqrt(tau*R1)) A3 = sqrtR1 - sqrtR2 Sol = A1 * A2 / A3 # There are some below numerical errors-imaginary numbers. # We do not want them. return np.real_if_close(Sol) def CF_gz_AC(parms, tau, wixi=wixi): u""" Axial (1D) diffusion in a TIR-FCS setup. From Two species (bound/unbound) this is the cross correlation part. This function is called by other functions within this module. *parms* - a list of parameters. Parameters (parms[i]): [0] D_3D 3D Diffusion coefficient (species A) [1] D_2D 2D Diffusion coefficient of bound species C [2] sigma lateral size of the point spread function sigma = simga_0 * lambda / NA [3] a side size of the square pinhole [4] d_eva evanescent decay length (decay to 1/e) [5] Conc_3D 3-dimensional concentration of species A [6] Conc_2D 2-dimensional concentration of species C [7] eta_3D molecular brightness of species A [8] eta_2D molecular brightness of species C [9] k_a Surface association rate constant [10] k_d Surface dissociation rate constant *tau* - lag time """ D = parms[0] #D_2D = parms[1] #sigma = parms[2] # a = parms[3] d_eva = parms[4] Conc_3D = parms[5] # ligand concentration in solution Conc_2D = parms[6] eta_3D = parms[7] eta_2D = parms[8] k_a = parms[9] k_d = parms[10] # Define some other constants: K = k_a/k_d # equilibrium constant Beta = 1/(1 + K*Conc_3D) Re = D / d_eva**2 Rt = D * (Conc_3D / (Beta * Conc_2D))**2 Rr = k_a * Conc_3D + k_d # Define even more constants: sqrtR1 = -Rr/(2*nps.sqrt(Rt)) + nps.sqrt( Rr**2/(4*Rt) - Rr ) sqrtR2 = -Rr/(2*nps.sqrt(Rt)) - nps.sqrt( Rr**2/(4*Rt) - Rr ) R1 = sqrtR1 **2 R2 = sqrtR2 **2 # And even more more: sqrtR3 = sqrtR1 + nps.sqrt(Re) sqrtR4 = sqrtR2 + nps.sqrt(Re) #R3 = sqrtR3 **2 #R4 = sqrtR4 **2 # Calculate return function A1 = eta_2D * Conc_2D * k_d / (eta_3D * Conc_3D) A2 = sqrtR4*wixi(-nps.sqrt(tau*R1)) - sqrtR3*wixi(-nps.sqrt(tau*R2)) A3 = ( sqrtR1 - sqrtR2 ) * wixi( nps.sqrt(tau*Re) ) A4 = ( sqrtR1 - sqrtR2 ) * sqrtR3 * sqrtR4 Solution = A1 * ( A2 + A3 ) / A4 # There are some below numerical errors-imaginary numbers. # We do not want them. return np.real_if_close(Solution) def CF_gz_AA(parms, tau, wixi=wixi): u""" Axial (1D) diffusion in a TIR-FCS setup. From Two species (bound/unbound) this is the unbound part. This function is called by other functions within this module. *parms* - a list of parameters. Parameters (parms[i]): [0] D_3D 3D Diffusion coefficient (species A) [1] D_2D 2D Diffusion coefficient of bound species C [2] sigma lateral size of the point spread function sigma = simga_0 * lambda / NA [3] a side size of the square pinhole [4] d_eva evanescent decay length (decay to 1/e) [5] Conc_3D 3-dimensional concentration of species A [6] Conc_2D 2-dimensional concentration of species C [7] eta_3D molecular brightness of species A [8] eta_2D molecular brightness of species C [9] k_a Surface association rate constant [10] k_d Surface dissociation rate constant *tau* - lag time """ D = parms[0] #D_2D = parms[1] #sigma = parms[2] # a = parms[3] d_eva = parms[4] Conc_3D = parms[5] # ligand concentration in solution Conc_2D = parms[6] eta_3D = parms[7] eta_2D = parms[8] k_a = parms[9] k_d = parms[10] # Define some other constants: d = d_eva K = k_a/k_d # equilibrium constant Beta = 1/(1 + K*Conc_3D) Re = D / d_eva**2 Rt = D * (Conc_3D / (Beta * Conc_2D))**2 Rr = k_a * Conc_3D + k_d # Define even more constants: sqrtR1 = -Rr/(2*nps.sqrt(Rt)) + nps.sqrt( Rr**2/(4*Rt) - Rr ) sqrtR2 = -Rr/(2*nps.sqrt(Rt)) - nps.sqrt( Rr**2/(4*Rt) - Rr ) R1 = sqrtR1 **2 R2 = sqrtR2 **2 # And even more more: sqrtR3 = sqrtR1 + nps.sqrt(Re) sqrtR4 = sqrtR2 + nps.sqrt(Re) R3 = sqrtR3 **2 R4 = sqrtR4 **2 # Calculate return function Sum1 = d * nps.sqrt( Re*tau/np.pi ) Sum2 = -d/2*(2*tau*Re -1) * wixi(np.sqrt(tau*Re)) Sum3Mult1 = - eta_2D * Conc_2D * k_d / ( eta_3D * Conc_3D * (sqrtR1 - sqrtR2) ) S3M2S1M1 = sqrtR1/R3 S3M2S1M2S1 = wixi(-nps.sqrt(tau*R1)) + -2*nps.sqrt(tau*R3/np.pi) S3M2S1M2S2 = ( 2*tau*sqrtR1*nps.sqrt(Re) + 2*tau*Re -1 ) * \ wixi(nps.sqrt(tau*Re)) S3M2S2M1 = -sqrtR2/R4 S3M2S2M2S1 = wixi(-nps.sqrt(tau*R2)) + -2*nps.sqrt(tau*R4/np.pi) S3M2S2M2S2 = ( 2*tau*sqrtR2*nps.sqrt(Re) + 2*tau*Re -1 ) * \ wixi(nps.sqrt(tau*Re)) Sum3 = Sum3Mult1 * ( S3M2S1M1 * (S3M2S1M2S1 + S3M2S1M2S2) + S3M2S2M1 * (S3M2S2M2S1 + S3M2S2M2S2) ) Sum = Sum1 + Sum2 + Sum3 # There are some below numerical errors-imaginary numbers. # We do not want them. return np.real_if_close(Sum) # 3D-2D binding/unbinding TIRF def CF_Gxyz_TIR_square_ubibi(parms, tau, gAAz=CF_gz_AA, gACz=CF_gz_AC, gCCz=CF_gz_CC, gxy=CF_gxy_square): u""" Two-component two- and three-dimensional diffusion with a square-shaped lateral detection area taking into account the size of the point spread function; and an exponential decaying profile in axial direction. This model covers binding and unbinding kintetics. *parms* - a list of parameters. Parameters (parms[i]): [0] D_3D Diffusion coefficient of freely diffusing species A [1] D_2D Diffusion coefficient of bound species C [2] σ Lateral size of the point spread function σ = σ₀ * λ / NA [3] a Side size of the square-shaped detection area [4] d_eva Evanescent decay constant [5] C_3D Concentration of species A in observation volume [6] C_2D Concentration of species C in observation area [7] η_3D Molecular brightness of species A [8] η_2D Molecular brightness of species C [9] k_a Surface association rate constant [10] k_d Surface dissociation rate constant *tau* - lag time Returns: 3D correlation function for TIR-FCS w/square pinhole and surface binding/unbinding. Model introduced in: Jonas Ries, Eugene P. Petrov, and Petra Schwille Total Internal Reflection Fluorescence Correlation Spectroscopy: Effects of Lateral Diffusion and Surface-Generated Fluorescence Biophysical Journal, Vol.95, July 2008, 390–399 """ D_3D = parms[0] D_2D = parms[1] sigma = parms[2] a = parms[3] kappa = 1/parms[4] Conc_3D = parms[5] Conc_2D = parms[6] eta_3D = parms[7] eta_2D = parms[8] #k_a = parms[9] #k_d = parms[10] ## We now need to copmute a real beast: # Inter species non-normalized correlation functions # gAA = gAAz * gxy(D_3D) # gAC = gACz * np.sqrt ( gxy(D_3D) * gxy(D_2D) ) # gCC = gCCz * gxy(D_2D) # Nonnormalized correlation function # g = eta_3D * Conc_3D * ( gAA + 2*gAC + gCC ) # Expectation value of fluorescence signal # F = eta_3D * Conc_3D / kappa + eta_2D * Conc_2D # Normalized correlation function # G = g / F**2 ## # Inter species non-normalized correlation functions # The gijz functions take the same parameters as this function # The gxy function needs two different sets of parameters, depending # on the diffusion constant used. # [0] D: Diffusion coefficient # [1] sigma: lateral size of the point spread function # [3] a: side size of the square pinhole parms_xy_2D = [D_2D, sigma, a] parms_xy_3D = [D_3D, sigma, a] # Here we go. gAA = gAAz(parms, tau) * gxy(parms_xy_3D, tau) gAC = gACz(parms, tau) * nps.sqrt( gxy(parms_xy_3D, tau) * gxy(parms_xy_2D, tau) ) gCC = gCCz(parms, tau) * gxy(parms_xy_2D, tau) # Nonnormalized correlation function g = eta_3D * Conc_3D * ( gAA + 2*gAC + gCC ) # Expectation value of fluorescence signal F = eta_3D * Conc_3D / kappa + eta_2D * Conc_2D # Normalized correlation function G = g / F**2 # There are some below numerical errors-imaginary numbers. # We do not want them. return G.real #FNEW = eta_3D * Conc_3D / kappa #GNEW = eta_3D * Conc_3D * gCCz(parms, tau) / FNEW**2 #return GNEW.real # 3D-2D binding Model TIR m_tir_3d_2d_ubib6021 = [6021, u"3D+2D+kin", "Surface binding and unbinding, 3D TIR", CF_Gxyz_TIR_square_ubibi] labels_6021 = [u"D_3D [10 µm²/s]", u"D_2D [10 µm²/s]", u"σ [100 nm]", u"a [100 nm]", u"d_eva [100 nm]", u"C_3D [1000 /µm³]", u"C_2D[100 /µm²]", u"η_3D", u"η_2D", u"k_a [µm³/s]", u"k_d [10³ /s]" ] values_6021 = [ 9.0, # D_3D [10 µm²/s] 0.0, # D_2D [10 µm²/s] 2.3, # σ [100 nm] 7.50, # a [100 nm] 1.0, # d_eva [100 nm] 0.01, # conc.3D [1000 /µm³] 0.03, # conc.2D [100 /µm²] 1, # η_3D 1, # η_2D 0.00001, # k_a [µm³/s] 0.000064 # k_d [10³ /s] ] valuestofit_6021 = [False, True, False, False, False, False, True, False, False, False, False] # For user comfort we add values that are human readable. # Theese will be used for output that only humans can read. labels_human_readable_6021 = [ u"D_3D [µm²/s]", u"D_2D [µm²/s]", u"σ [nm]", u"a [nm]", u"d_eva [nm]", u"C_3D [1/µm³]", u"C_2D [1/µm²]", u"η_3D", u"η_2D", u"k_a [µm³/s]", u"k_d [1/s]" ] values_factor_human_readable_6021 = [10, # "D_3D [µm²/s]", 10, # D_2D [10 µm²/s] 100, # σ [100 nm] 100, # a [100 nm] 100, # d_eva [100 nm] 1000, # conc.3D [1000 /µm³] 100, # conc.2D [100 /µm²] 1, # η_3D 1, # η_2D 1, # k_a [µm³/s] 1000 # k_d [10³ /s] ] parms_6021 = [labels_6021, values_6021, valuestofit_6021, labels_human_readable_6021, values_factor_human_readable_6021] boundaries = [ [0, np.inf] ] * len(values_6021) model1 = dict() model1["Parameters"] = parms_6021 model1["Definitions"] = m_tir_3d_2d_ubib6021 model1["Boundaries"] = boundaries model1["Constraints"] = [[1, "<", 0]] Modelarray = [model1] PyCorrFit-0.9.9/pycorrfit/models/MODEL_TIRF_3D3D.py000077500000000000000000000122521274265304700215100ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ This file contains 3D+3D TIR-FCS models. """ import numpy as np # NumPy import scipy.special as sps def wixi(x): """ Complex Error Function (Faddeeva/Voigt). w(i*x) = exp(x**2) * ( 1-erf(x) ) This function is called by other functions within this module. We are using the scipy.special.wofz module which calculates w(z) = exp(-z**2) * ( 1-erf(-iz) ) z = i*x """ z = x*1j wixi = sps.wofz(z) # We should have a real solution. Make sure nobody complains about # some zero-value imaginary numbers. return np.real_if_close(wixi) # 3D + 3D no binding TIRF # model 6023 def CF_Gxyz_TIR_square_3d3d(parms, tau, wixi=wixi): u""" Two-component three-dimensional free diffusion with a square-shaped lateral detection area taking into account the size of the point spread function; and an exponential decaying profile in axial direction. *parms* - a list of parameters. Parameters (parms[i]): [0] D_3D1 3D Diffusion coefficient (species 1) [1] D_3D2 3D Diffusion coefficient of bound species 2 [2] σ Lateral size of the point spread function σ = σ₀ * λ / NA [3] a Side size of the square-shaped detection area [4] d_eva Evanescent penetration depth [5] C_3D1 Concentration of species 1 [6] C_3D2 Concentration of species 2 [7] α Relative molecular brightness of particle 2 compared to particle 1 (α = q₂/q₁) *tau* - lag time """ D_3D1 = parms[0] D_3D2 = parms[1] sigma = parms[2] a = parms[3] kappa = 1/parms[4] Conc_3D1 = parms[5] Conc_3D2 = parms[6] alpha = parms[7] ## First, the 3D diffusion of species 1 # Axial correlation x1 = np.sqrt(D_3D1*tau)*kappa w_ix1 = wixi(x1) gz1 = np.sqrt(D_3D1*tau/np.pi) - (2*D_3D1*tau*kappa**2 - 1)/(2*kappa) * \ w_ix1 # Lateral correlation gx1_1 = 2/(a**2*np.sqrt(np.pi)) * np.sqrt(sigma**2+D_3D1*tau) * \ ( np.exp(-a**2/(4*(sigma**2+D_3D1*tau))) -1 ) gx2_1 = 1/a * sps.erf( a / (2*np.sqrt(sigma**2 + D_3D1*tau))) gx1 = gx1_1 + gx2_1 gxy1 = gx1**2 # Non normalized correlation function g3D1 = Conc_3D1 * gxy1 * gz1 ## Second, the 3D diffusion of species 2 # Axial correlation x2 = np.sqrt(D_3D2*tau)*kappa w_ix2 = wixi(x2) gz2 = np.sqrt(D_3D2*tau/np.pi) - (2*D_3D2*tau*kappa**2 - 1)/(2*kappa) * \ w_ix2 # Lateral correlation gx1_2 = 2/(a**2*np.sqrt(np.pi)) * np.sqrt(sigma**2+D_3D2*tau) * \ ( np.exp(-a**2/(4*(sigma**2+D_3D2*tau))) -1 ) gx2_2 = 1/a * sps.erf( a / (2*np.sqrt(sigma**2 + D_3D2*tau))) gx2 = gx1_2 + gx2_2 gxy2 = gx2**2 # Non normalized correlation function g3D2 = alpha**2 * Conc_3D2 * gxy2 * gz2 ## Finally the Prefactor F = (Conc_3D1 + alpha * Conc_3D2) / kappa G = (g3D1 + g3D2) / F**2 return G # 3D-3D Model TIR m_tir_3d_3d_mix_6023 = [6023, u"3D+3D", "Separate 3D diffusion, 3D TIR", CF_Gxyz_TIR_square_3d3d] labels_6023 = [u"D"+u"\u2081"+u" [10 µm²/s]", u"D"+u"\u2082"+u" [10 µm²/s]", u"σ [100 nm]", u"a [100 nm]", u"d_eva [100 nm]", u"C"+u"\u2081"+u" [1000 /µm³]", u"C"+u"\u2082"+u" [1000 /µm³]", u"\u03b1"+" (q"+u"\u2082"+"/q"+u"\u2081"+")" ] values_6023 = [ 0.9, # D_3D₁ [10 µm²/s] 9.0, # D_3D₂ [10 µm²/s] 2.3, # σ [100 nm] 7.50, # a [100 nm] 1.0, # d_eva [100 nm] 0.01, # conc.3D₁ [1000 /µm³] 0.03, # conc.3D₂ [1000 /µm³] 1 # alpha ] # For user comfort we add values that are human readable. # Theese will be used for output that only humans can read. labels_human_readable_6023 = [ u"D"+u"\u2081"+u" [µm²/s]", u"D"+u"\u2082"+u" [µm²/s]", u"σ [nm]", u"a [nm]", u"d_eva [nm]", u"C"+u"\u2081"+u" [1/µm³]", u"C"+u"\u2082"+u" [1/µm³]", u"\u03b1"+" (q"+u"\u2082"+"/q"+u"\u2081"+")" ] values_factor_human_readable_6023 = [10, # "D_3D₁ [µm²/s]", 10, # D_3D₂ [10 µm²/s] 100, # σ [100 nm] 100, # a [100 nm] 100, # d_eva [100 nm] 1000, # conc.3D₁ [1000 /µm³] 1000, # conc.3D₂ [1000 /µm³] 1 # alpha ] valuestofit_6023 = [False, True, False, False, False, False, True, False] parms_6023 = [labels_6023, values_6023, valuestofit_6023, labels_human_readable_6023, values_factor_human_readable_6023] model1 = dict() model1["Parameters"] = parms_6023 model1["Definitions"] = m_tir_3d_3d_mix_6023 model1["Boundaries"] = [[0, np.inf]] * len(values_6023) model1["Constraints"] = [[1, ">", 0]] Modelarray = [model1] PyCorrFit-0.9.9/pycorrfit/models/MODEL_TIRF_gaussian_1C.py000077500000000000000000000213401274265304700232060ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ This file contains TIR one component models + Triplet """ import numpy as np # NumPy import scipy.special as sps def wixi(x): """ Complex Error Function (Faddeeva/Voigt). w(i*x) = exp(x**2) * ( 1-erf(x) ) This function is called by other functions within this module. We are using the scipy.special.wofz module which calculates w(z) = exp(-z**2) * ( 1-erf(-iz) ) z = i*x """ z = x*1j wixi = sps.wofz(z) # We should have a real solution. Make sure nobody complains about # some zero-value imaginary numbers. return np.real_if_close(wixi) def CF_Gxyz_TIR_gauss(parms, tau): u""" Three-dimensional free diffusion with a Gaussian lateral detection profile and an exponentially decaying profile in axial direction. x = sqrt(D*τ)*κ κ = 1/d_eva w(i*x) = exp(x²)*erfc(x) gz = κ * [ sqrt(D*τ/π) + (1 - 2*D*τ*κ)/(2*κ) * w(i*x) ] g2D = 1 / [ π (r₀² + 4*D*τ) ] G = 1/C_3D * g2D * gz *parms* - a list of parameters. Parameters (parms[i]): [0] D Diffusion coefficient [1] r₀ Lateral extent of the detection volume [2] d_eva Evanescent field depth [3] C_3D Particle concentration in the confocal volume *tau* - lag time """ # model 6013 D = parms[0] r0 = parms[1] deva = parms[2] Conc = parms[3] # Calculate sigma: width of the gaussian approximation of the PSF Veff = np.pi * r0**2 * deva Neff = Conc * Veff taudiff = r0**2/(4*D) # 2D gauss component # G2D = 1/N2D * g2D = 1/(Aeff*Conc.2D) * g2D g2D = 1 / ( (1.+tau/taudiff) ) # 1d TIR component # Axial correlation kappa = 1/deva x = np.sqrt(D*tau)*kappa w_ix = wixi(x) # Gz = 1/N1D * gz = kappa / Conc.1D * gz gz = kappa * (np.sqrt(D*tau/np.pi) - (2*D*tau*kappa**2 - 1)/(2*kappa) * w_ix) # gz * g2D * 1/( deva *A2D) * 1 / Conc3D # Neff is not the actual particle number. This formula just looks nicer # this way. # What would be easier to get is: # 1 / (Conc * deva * np.pi * r0) * gz * g2D return 1 / (Neff) * g2D * gz def CF_Gxyz_TIR_gauss_trip(parms, tau): u""" Three-dimensional free diffusion with a Gaussian lateral detection profile and an exponentially decaying profile in axial direction, including a triplet component. x = sqrt(D*τ)*κ κ = 1/d_eva w(i*x) = exp(x²)*erfc(x) gz = κ * [ sqrt(D*τ/π) + (1 - 2*D*τ*κ)/(2*κ) * w(i*x) ] g2D = 1 / [ π (r₀² + 4*D*τ) ] triplet = 1 + T/(1-T)*exp(-τ/τ_trip) G = 1/C_3D * g2D * gz * triplet *parms* - a list of parameters. Parameters (parms[i]): [0] D Diffusion coefficient [1] r₀ Lateral extent of the detection volume [2] d_eva Evanescent field depth [3] C_3D Particle concentration in the confocal volume [4] τ_trip Characteristic residence time in triplet state [5] T Fraction of particles in triplet (non-fluorescent) state 0 <= T < 1 *tau* - lag time """ # model 6014 D = parms[0] r0 = parms[1] deva = parms[2] Conc = parms[3] tautrip=parms[4] T=parms[5] # Calculate sigma: width of the gaussian approximation of the PSF Veff = np.pi * r0**2 * deva Neff = Conc * Veff taudiff = r0**2/(4*D) # 2D gauss component # G2D = 1/N2D * g2D = 1/(Aeff*Conc.2D) * g2D g2D = 1 / ( (1.+tau/taudiff) ) # 1d TIR component # Axial correlation kappa = 1/deva x = np.sqrt(D*tau)*kappa w_ix = wixi(x) # Gz = 1/N1D * gz = kappa / Conc.1D * gz gz = kappa * (np.sqrt(D*tau/np.pi) - (2*D*tau*kappa**2 - 1)/(2*kappa) * w_ix) ### triplet if tautrip == 0 or T == 0: triplet = 1 else: triplet = 1 + T/(1-T) * np.exp(-tau/tautrip) # Neff is not the actual particle number. This formula just looks nicer # this way. # What would be easier to get is: # 1 / (Conc * deva * np.pi * r0) * gz * g2D return 1 / (Neff) * g2D * gz * triplet def MoreInfo_6013(parms, countrate=None): u"""Supplementary variables: Beware that the effective volume is chosen arbitrarily. Correlation function at lag time τ=0: [4] G(τ=0) Effective detection volume: [5] V_eff = π * r₀² * d_eva Effective particle concentration: [6] C_3D [nM] = C_3D [1000/µm³] * 10000/6.0221415 """ #D = parms[0] r0 = parms[1] deva = parms[2] Conc = parms[3] Info=list() # Detection area: Veff = np.pi * r0**2 * deva Neff = Conc * Veff # Correlation function at tau = 0 G_0 = CF_Gxyz_TIR_gauss(parms, 0) Info.append(["G(0)", G_0]) Info.append(["V_eff [al]", Veff]) Info.append(["C_3D [nM]", Conc * 10000/6.0221415]) if countrate is not None: # CPP cpp = countrate/Neff Info.append(["cpp [kHz]", cpp]) return Info def MoreInfo_6014(parms, countrate=None): u"""Supplementary variables: Beware that the effective volume is chosen arbitrarily. Correlation function at lag time τ=0: [6] G(τ=0) Effective detection volume: [7] V_eff = π * r₀² * d_eva Effective particle concentration: [8] C_3D [nM] = C_3D [1000/µm³] * 10000/6.0221415 """ #D = parms[0] r0 = parms[1] deva = parms[2] Conc = parms[3] Info=list() # Detection area: Veff = np.pi * r0**2 * deva Neff = Conc * Veff # Correlation function at tau = 0 G_0 = CF_Gxyz_TIR_gauss(parms, 0) Info.append(["G(0)", G_0]) Info.append(["V_eff [al]", Veff]) Info.append(["C_3D [nM]", Conc * 10000/6.0221415]) if countrate is not None: # CPP cpp = countrate/Neff Info.append(["cpp [kHz]", cpp]) return Info def get_boundaries_6014(parms): # strictly positive boundaries = [[0, None]]*len(parms) boundaries[5] = [0, 1] return boundaries def get_boundaries_6013(parms): # strictly positive boundaries = [[0, None]]*len(parms) return boundaries # 3D Model TIR gaussian m_3dtirsq6013 = [6013, "3D","Simple 3D diffusion w/ TIR", CF_Gxyz_TIR_gauss] labels_6013 = [u"D [10 µm²/s]", u"r₀ [100 nm]", u"d_eva [100 nm]", u"C_3D [1000/µm³)"] values_6013 = [2.5420, 9.44, 1.0, 0.03011] # For user comfort we add values that are human readable. # Theese will be used for output that only humans can read. labels_human_readable_6013 = [u"D [µm²/s]", u"r₀ [nm]", u"d_eva [nm]", u"C_3D [1/µm³]"] values_factor_human_readable_6013 = [10, 100, 100, 1000] valuestofit_6013 = [True, False, False, True] parms_6013 = [labels_6013, values_6013, valuestofit_6013, labels_human_readable_6013, values_factor_human_readable_6013] # Pack the models model1 = dict() model1["Parameters"] = parms_6013 model1["Definitions"] = m_3dtirsq6013 model1["Supplements"] = MoreInfo_6013 model1["Boundaries"] = get_boundaries_6013(values_6013) # 3D Model TIR gaussian + triplet m_3dtirsq6014 = [6014, "T+3D","Simple 3D diffusion + triplet w/ TIR", CF_Gxyz_TIR_gauss_trip] labels_6014 = [u"D [10 µm²/s]", u"r₀ [100 nm]", u"d_eva [100 nm]", u"C_3D [1000/µm³)", u"τ_trip [ms]", u"T"] values_6014 = [2.5420, 9.44, 1.0, 0.03011, 0.001, 0.01] labels_human_readable_6014 = [u"D [µm²/s]", u"r₀ [nm]", u"d_eva [nm]", u"C_3D [1/µm³]", u"τ_trip [µs]", u"T"] values_factor_human_readable_6014 = [10, 100, 100, 1000, 1000, 1] valuestofit_6014 = [True, False, False, True, False, False] parms_6014 = [labels_6014, values_6014, valuestofit_6014, labels_human_readable_6014, values_factor_human_readable_6014] # Pack the models model2 = dict() model2["Parameters"] = parms_6014 model2["Definitions"] = m_3dtirsq6014 model2["Supplements"] = MoreInfo_6014 model2["Boundaries"] = get_boundaries_6014(values_6014) Modelarray = [model1, model2] PyCorrFit-0.9.9/pycorrfit/models/MODEL_TIRF_gaussian_3D2D.py000077500000000000000000000156121274265304700234040ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ This file contains a 3D+2D+T TIR FCS model. """ from __future__ import division import numpy as np import scipy.special as sps def wixi(x): """ Complex Error Function (Faddeeva/Voigt). w(i*x) = exp(x**2) * ( 1-erf(x) ) This function is called by other functions within this module. We are using the scipy.special.wofz module which calculates w(z) = exp(-z**2) * ( 1-erf(-iz) ) z = i*x """ z = x*1j wixi = sps.wofz(z) # We should have a real solution. Make sure nobody complains about # some zero-value imaginary numbers. return np.real_if_close(wixi) # 3D + 2D + T # model 6033 def CF_Gxyz_3d2dT_gauss(parms, tau): u""" Two-component, two- and three-dimensional diffusion with a Gaussian lateral detection profile and an exponentially decaying profile in axial direction, including a triplet component. The triplet factor takes into account blinking according to triplet states of excited molecules. Set *T* or *τ_trip* to 0, if no triplet component is wanted. kappa = 1/d_eva x = sqrt(D_3D*τ)*kappa w(i*x) = exp(x²)*erfc(x) gz = kappa * [ sqrt(D_3D*τ/pi) + (1 - 2*D_3D*τ*kappa²)/(2*kappa) * w(i*x) ] g2D3D = 1 / [ 1+4*D_3D*τ/r₀² ] particle3D = α*F * g2D3D * gz particle2D = (1-F)/ (1+4*D_2D*τ/r₀²) triplet = 1 + T/(1-T)*exp(-τ/τ_trip) norm = (1-F + α*F)² G = 1/n*(particle2D + particle3D)*triplet/norm + offset *parms* - a list of parameters. Parameters (parms[i]): [0] n Effective number of particles in confocal volume (n = n2D+n3D) [1] D_2D Diffusion coefficient of surface bound particles [2] D_3D Diffusion coefficient of freely diffusing particles [3] F Fraction of molecules of the freely diffusing species (n3D = n*F), 0 <= F <= 1 [4] r₀ Lateral extent of the detection volume [5] d_eva Evanescent field depth [6] α Relative molecular brightness of freely diffusing compared to surface bound particles (α = q3D/q2D) [7] τ_trip Characteristic residence time in triplet state [8] T Fraction of particles in triplet (non-fluorescent) state 0 <= T < 1 [9] offset *tau* - lag time """ n=parms[0] D2D=parms[1] D3D=parms[2] F=parms[3] r0=parms[4] deva=parms[5] alpha=parms[6] tautrip=parms[7] T=parms[8] off=parms[9] ### 2D species taud2D = r0**2/(4*D2D) particle2D = (1-F)/ (1+tau/taud2D) ### 3D species taud3D = r0**2/(4*D3D) # 2D gauss component g2D3D = 1 / ( (1.+tau/taud3D) ) # 1d TIR component # Axial correlation kappa = 1/deva x = np.sqrt(D3D*tau)*kappa w_ix = wixi(x) # Gz = 1/N1D * gz = kappa / Conc.1D * gz gz = kappa * (np.sqrt(D3D*tau/np.pi) - (2*D3D*tau*kappa**2 - 1)/(2*kappa) * w_ix) particle3D = alpha**2*F * g2D3D * gz ### triplet if tautrip == 0 or T == 0: triplet = 1 else: triplet = 1 + T/(1-T) * np.exp(-tau/tautrip) ### Norm norm = (1-F + alpha*F)**2 ### Correlation function G = 1/n*(particle2D + particle3D)*triplet/norm return G + off def get_boundaries(parms): # strictly positive boundaries = [[0, np.inf]]*len(parms) # F boundaries[3] = [0, .9999999999999] # T boundaries[8] = [0, .9999999999999] boundaries[-1] = [-np.inf, np.inf] return boundaries def MoreInfo(parms, countrate=None): u"""Supplementary parameters: Effective number of freely diffusing particles in 3D: [10] n3D = n*F Effective number particles diffusing on 2D surface: [11] n2D = n*(1-F) Value of the correlation function at lag time zero: [12] G(0) Effective measurement volume: [13] V_eff [al] = π * r₀² * d_eva Concentration of the 2D species: [14] C_2D [1/µm²] = n2D / ( π * r₀² ) Concentration of the 3D species: [15] C_3D [nM] = n3D/V_eff """ # We can only give you the effective particle number n=parms[0] #D2D=parms[1] #D3D=parms[2] F=parms[3] r0=parms[4] deva=parms[5] #alpha=parms[6] Info=list() # The enumeration of these parameters is very important for # plotting the normalized curve. Countrate must come out last! Info.append([u"n3D", n*F]) Info.append([u"n2D", n*(1.-F)]) # Detection area: Veff = np.pi * r0**2 * deva C3D = n*F / Veff C2D = n*(1-F) / ( np.pi*r0**2 ) # Correlation function at tau = 0 G_0 = CF_Gxyz_3d2dT_gauss(parms, 0) Info.append([u"G(0)", G_0]) Info.append([u"V_eff [al]", Veff]) Info.append([u"C_2D [1/µm²]", C2D * 100]) Info.append([u"C_3D [nM]", C3D * 10000/6.0221415]) if countrate is not None: # CPP cpp = countrate/n Info.append([u"cpp [kHz]", cpp]) return Info # 3D + 3D + T model gauss m_gauss_3d_2d_t = [6033, "T+3D+2D", "Separate 3D and 2D diffusion + triplet w/ TIR", CF_Gxyz_3d2dT_gauss] labels = [ u"n", u"D_2D [10 µm²/s]", u"D_3D [10 µm²/s]", u"F_3D", u"r₀ [100 nm]", u"d_eva [100 nm]", u"\u03b1"+" (q_3D/q_2D)", u"τ_trip [ms]", u"T", u"offset" ] values = [ 25, # n 0.51, # D2D 25.1, # D3D 0.45, # F3D 9.44, # r0 1.0, # deva 1.0, # alpha 0.001, # tautrip 0.01, # T 0.0 # offset ] # Human readable stuff labelshr = [u"n", u"D_2D [µm²/s]", u"D_3D [µm²/s]", u"F_3D", u"r₀ [nm]", u"d_eva [nm]", u"\u03b1"+u" (q_3D/q_2D)", u"τ_trip [µs]", u"T", u"offset" ] valueshr = [ 1., # n 10., # D2D 10., # D3D 1., # F3D 100., # r0 100., # deva 1., # alpha 1000., # tautrip 1., # T 1. # offset ] valuestofit = [True, True, True, True, False, False, False, False, False, False] parms = [labels, values, valuestofit, labelshr, valueshr] model1 = dict() model1["Parameters"] = parms model1["Definitions"] = m_gauss_3d_2d_t model1["Boundaries"] = get_boundaries(values) model1["Supplements"] = MoreInfo model1["Constraints"] = [[2, ">", 1]] Modelarray = [model1] PyCorrFit-0.9.9/pycorrfit/models/MODEL_TIRF_gaussian_3D3D.py000077500000000000000000000166031274265304700234060ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ This file contains a 3D+3D+T TIR FCS model. """ from __future__ import division import numpy as np import scipy.special as sps def wixi(x): """ Complex Error Function (Faddeeva/Voigt). w(i*x) = exp(x**2) * ( 1-erf(x) ) This function is called by other functions within this module. We are using the scipy.special.wofz module which calculates w(z) = exp(-z**2) * ( 1-erf(-iz) ) z = i*x """ z = x*1j wixi = sps.wofz(z) # We should have a real solution. Make sure nobody complains about # some zero-value imaginary numbers. return np.real_if_close(wixi) # 3D + 3D + T # model 6034 def CF_Gxyz_3D3DT_gauss(parms, tau): u""" Two-component three-dimensional diffusion with a Gaussian lateral detection profile and an exponentially decaying profile in axial direction, including a triplet component. The triplet factor takes into account blinking according to triplet states of excited molecules. Set *T* or *τ_trip* to 0, if no triplet component is wanted. w(i*x) = exp(x²)*erfc(x) taud1 = r₀²/(4*D₁) κ = 1/d_eva x1 = sqrt(D₁*τ)*κ gz1 = κ * [ sqrt(D₁*τ/π) + (1 - 2*D₁*τ*κ)/(2*κ) * w(i*x1) ] g2D1 = 1 / [ 1+τ/taud1 ] particle1 = F₁ * g2D1 * gz1 taud2 = r₀²/(4*D₂) x2 = sqrt(D₂*τ)*κ gz2 = κ * [ sqrt(D₂*τ/π) + (1 - 2*D₂*τ*κ)/(2*κ) * w(i*x2) ] g2D2 = 1 / [ 1+τ/taud2 ] particle2 = α*(1-F₁) * g2D2 * gz2 triplet = 1 + T/(1-T)*exp(-τ/τ_trip) norm = (1-F₁ + α*F₁)² G = 1/n*(particle1 + particle2)*triplet/norm + offset *parms* - a list of parameters. Parameters (parms[i]): [0] n Effective number of particles in confocal volume (n = n₁+n₂) [1] D₁ Diffusion coefficient of species 1 [2] D₂ Diffusion coefficient of species 2 [3] F₁ Fraction of molecules of species 1 (n₁ = n*F₁) 0 <= F₁ <= 1 [4] r₀ Lateral extent of the detection volume [5] d_eva Evanescent field depth [6] α Relative molecular brightness of particle 2 compared to particle 1 (α = q₂/q₁) [7] τ_trip Characteristic residence time in triplet state [8] T Fraction of particles in triplet (non-fluorescent) state 0 <= T < 1 [9] offset *tau* - lag time """ n=parms[0] D1=parms[1] D2=parms[2] F=parms[3] r0=parms[4] deva=parms[5] alpha=parms[6] tautrip=parms[7] T=parms[8] off=parms[9] kappa = 1/deva ### 1st species tauD1 = r0**2/(4*D1) # 2D gauss component g2D1 = 1 / ( (1.+tau/tauD1) ) # 1d TIR component # Axial correlation x1 = np.sqrt(D1*tau)*kappa w_ix1 = wixi(x1) # Gz = 1/N1D * gz = kappa / Conc.1D * gz gz1 = kappa * (np.sqrt(D1*tau/np.pi) - (2*D1*tau*kappa**2 - 1)/(2*kappa) * w_ix1) particle1 = F * g2D1 * gz1 ### 2nd species tauD2 = r0**2/(4*D2) # 2D gauss component g2D2 = 1 / ( (1.+tau/tauD2) ) # 1d TIR component # Axial correlation x2 = np.sqrt(D2*tau)*kappa w_ix2 = wixi(x2) # Gz = 1/N1D * gz = kappa / Conc.1D * gz gz2 = kappa * (np.sqrt(D2*tau/np.pi) - (2*D2*tau*kappa**2 - 1)/(2*kappa) * w_ix2) particle2 = alpha**2*(1-F) * g2D2 * gz2 ### triplet if tautrip == 0 or T == 0: triplet = 1 else: triplet = 1 + T/(1-T) * np.exp(-tau/tautrip) ### Norm norm = (F + alpha*(1-F))**2 ### Correlation function G = 1/n*(particle1 + particle2)*triplet/norm return G + off def get_boundaries(parms): # strictly positive boundaries = [[0, np.inf]]*len(parms) # F boundaries[3] = [0, .9999999999999] # T boundaries[8] = [0, .9999999999999] # offset boundaries[-1] = [-np.inf, np.inf] return boundaries def MoreInfo(parms, countrate=None): u"""Supplementary parameters: Effective number of particle species 1: [10] n₁ = n*F₁ Effective number of particle species 2: [11] n₂ = n*(1-F₁) Value of the correlation function at lag time zero: [12] G(0) Effective measurement volume: [13] V_eff [al] = π * r₀² * d_eva Concentration of particle species 1: [14] C₁ [nM] = n₁/V_eff Concentration of particle species 2: [15] C₂ [nM] = n₂/V_eff """ # We can only give you the effective particle number n=parms[0] #D1=parms[1] #D2=parms[2] F=parms[3] r0=parms[4] deva=parms[5] #alpha=parms[6] Info=list() # The enumeration of these parameters is very important for # plotting the normalized curve. Countrate must come out last! Info.append([u"n\u2081", n*F]) Info.append([u"n\u2082", n*(1.-F)]) # Detection area: Veff = np.pi * r0**2 * deva C1 = n*F / Veff C2 = n*(1-F) / Veff # Correlation function at tau = 0 G_0 = CF_Gxyz_3D3DT_gauss(parms, 0) Info.append(["G(0)", G_0]) Info.append(["V_eff [al]", Veff]) Info.append(["C"+u"\u2081"+" [nM]", C1 * 10000/6.0221415]) Info.append(["C"+u"\u2082"+" [nM]", C2 * 10000/6.0221415]) if countrate is not None: # CPP cpp = countrate/n Info.append(["cpp [kHz]", cpp]) return Info # 3D + 3D + T model gauss m_gauss_3d_3d_t = [6034, "T+3D+3D", "Combined 3D diffusion + triplet w/ TIR", CF_Gxyz_3D3DT_gauss] labels = [u"n", u"D"+u"\u2081"+u" [10 µm²/s]", u"D"+u"\u2082"+u" [10 µm²/s]", u"F"+u"\u2081", u"r₀ [100 nm]", u"d_eva [100 nm]", u"\u03b1"+" (q"+u"\u2082"+"/q"+u"\u2081"+")", u"τ_trip [ms]", u"T", u"offset" ] values = [ 25, # n 0.9, # D1 25.0, # D2 0.5, # F1 9.44, # r0 1.0, # deva 1.0, # alpha 0.001, # tautrip 0.01, # T 0.0 # offset ] # Human readable stuff labelshr = [ u"n", u"D"+u"\u2081"+u" [µm²/s]", u"D"+u"\u2082"+u" [µm²/s]", u"F"+u"\u2081", u"r₀ [nm]", u"d_eva [nm]", u"\u03b1"+" (q"+u"\u2082"+"/q"+u"\u2081"+")", u"τ_trip [µs]", u"T", u"offset" ] valueshr = [ 1., # n 10., # D1 10., # D2 1., # F1 100., # r0 100., # deva 1., # alpha 1000., # tautrip 1., # T 1. # offset ] valuestofit = [True, True, True, True, False, False, False, False, False, False] parms = [labels, values, valuestofit, labelshr, valueshr] model1 = dict() model1["Parameters"] = parms model1["Definitions"] = m_gauss_3d_3d_t model1["Boundaries"] = get_boundaries(values) model1["Supplements"] = MoreInfo model1["Constraints"] = [[2, ">", 1]] Modelarray = [model1] PyCorrFit-0.9.9/pycorrfit/models/__init__.py000066400000000000000000000215451274265304700210500ustar00rootroot00000000000000# -*- coding: utf-8 -*- u"""PyCorrFit - module "models" Define all models and set initial parameters. Each model has a unique ID. This ID is very important: 1. It is a wxWidgets ID. 2. It is used in the saving of sessions to identify a model. It is very important, that model IDs do NOT change in newer versions of PyCorrFit, because it would not be possible to restore older PyCorrFit sessions. Dimensionless representation: unit of time : 1 ms unit of inverse time: 10³ /s unit of distance : 100 nm unit of Diff.coeff : 10 µm²/s unit of inverse area: 100 /µm² unit of inv. volume : 1000 /µm³ """ # This file is necessary for this folder to become a module that can be # imported from within Python/PyCorrFit. import copy import numpy as np import sys import warnings from .classes import Model from .control import values, valuedict, models, modeldict, modeltypes, supplement, boundaries, shorttype def GetHumanReadableParms(model, parameters): """ From a set of parameters that have internal units e.g. [100 nm], Calculate the parameters in human readable units e.g. [nm]. Uses modeldict from this module. *model* - an integer ID of a model *parameters* - a list/array of parameters (all parameters of that model) Returns: New Units, New Parameters """ stdparms = valuedict[model] if len(stdparms) == 5: # This means we have extra information on the model # Return some human readable stuff OldParameters = 1.*np.array(parameters) Facors = 1.*np.array(stdparms[4]) NewParameters = 1.*OldParameters*Facors NewUnits = stdparms[3] return NewUnits, NewParameters else: # There is no info about human readable stuff, or it is already human # readable. return stdparms[0], parameters def GetHumanReadableParameterDict(model, names, parameters): """ From a set of parameters that have internal units e.g. [100 nm], Calculate the parameters in human readable units e.g. [nm]. Uses modeldict from this module. In contrast to *GetHumanReadableParms* this function accepts single parameter names and does not need the full array of parameters. *model* - an integer ID of a model *name* - the names of the parameters to be translated, order should be same as in *parameters* - a list of parameters Returns: New Units, New Parameters """ stdparms = valuedict[model] if len(stdparms) == 5: # This means we have extra information on the model # Return some human readable stuff # Check for list: if isinstance(names, basestring): names = [names] parameters = [parameters] retstring = True else: retstring = False # Create new lists NewUnits = list() NewParameters = list() for i in np.arange(len(stdparms[0])): for j in np.arange(len(names)): if names[j] == stdparms[0][i]: NewUnits.append(stdparms[3][i]) NewParameters.append(stdparms[4][i]*parameters[j]) if retstring == True: NewUnits = NewUnits[0] NewParameters = NewParameters[0] return NewUnits, NewParameters else: # There is no info about human readable stuff, or it is already human # readable. return names, parameters def GetInternalFromHumanReadableParm(model, parameters): """ This is the inverse of *GetHumanReadableParms* *model* - an integer ID of a model *parameters* - a list/array of parameters Returns: New Units, New Parameters """ stdparms = valuedict[model] if len(stdparms) == 5: # This means we have extra information on the model # and can convert to internal values OldParameters = 1.*np.array(parameters) Facors = 1./np.array(stdparms[4]) # inverse NewParameters = 1.*OldParameters*Facors NewUnits = stdparms[0] return NewUnits, NewParameters else: # There is no info about human readable stuff. The given # parameters have not been converted befor using # *GetHumanReadableParms*. return stdparms[0], parameters def GetModelType(modelid): """ Given a modelid, get the type of model function (Confocal, TIR-Conf., TIR-□, User) """ if modelid >= 7000: return u"User" else: for key in modeltypes.keys(): mlist = modeltypes[key] if mlist.count(modelid) == 1: try: return shorttype[key] except: warnings.warn("No shorttype defined for `{}`.".format(key)) return key def GetModelFunctionFromId(modelid): return modeldict[modelid][3] def GetModelParametersFromId(modelid): return valuedict[modelid][1] def GetModelFitBoolFromId(modelid): return valuedict[modelid][2] def GetMoreInfo(modelid, Page): """ This functino is called by someone who has already calculated some stuff or wants to know more about the model he is looking at. *modelid* is an ID of a model. *Page* is a wx.flatnotebook page. Returns: More information about a model in form of a list. """ # Background signal average bgaverage = None # Get the parameters from the current page. parms = Page.active_parms[1] Info = list() corr = Page.corr if corr.is_ac: if len(corr.traces)==1: countrate = corr.traces[0].countrate else: countrate = None ## First import the supplementary parameters of the model ## The order is important for plot normalization and session ## saving as of version 0.7.8 # Try to get the dictionary entry of a model # Background information if len(corr.backgrounds)==1: bgaverage = corr.backgrounds[0].countrate # Now set the correct countrate # We already printed the countrate, so there's no harm done. if countrate is not None and bgaverage is not None: # might be that there is no countrate. relativecountrate = countrate - bgaverage else: relativecountrate = countrate # In case of cross correlation, we don't show this kind of # information. try: # This function should return all important information # that can be calculated from the given parameters. # We need the relativecountrate to compute the CPP. func_info = supplement[modelid] data = func_info(parms, relativecountrate) for item in data: Info.append([item[0], item[1]]) except KeyError: # No information available pass if countrate is not None: # Measurement time duration = corr.traces[0].duration/1000 Info.append(["duration [s]", duration]) # countrate has to be printed before background. # Background might overwrite countrate. Info.append(["avg. signal [kHz]", corr.traces[0].countrate]) else: ## Cross correlation curves usually have two traces. Since we ## do not know how to compute the cpp, we will pass the argument ## "None" as the countrate. ## First import the supplementary parameters of the model ## The order is important for plot normalization and session ## saving as of version 0.7.8 # Try to get the dictionary entry of a model try: # This function should return all important information # that can be calculated from the given parameters. func_info = supplement[modelid] data = func_info(parms, None) for item in data: Info.append([item[0], item[1]]) except KeyError: # No information available pass if len(corr.traces)==2: # Measurement time duration = corr.traces[0].duration/1000 Info.append(["duration [s]", duration]) # countrate has to be printed before background. # Background might overwrite countrate. Info.append(["avg. signal A [kHz]", corr.traces[0].countrate]) Info.append(["avg. signal B [kHz]", corr.traces[1].countrate]) if len(Info) == 0: # If nothing matched until now: return None else: return Info def GetPositionOfParameter(model, name): """ Returns an integer corresponding to the position of the label of a parameter in the model function """ stdparms = valuedict[model] for i in np.arange(len(stdparms[0])): if name == stdparms[0][i]: return int(i) PyCorrFit-0.9.9/pycorrfit/models/classes.py000066400000000000000000000104151274265304700207400ustar00rootroot00000000000000# -*- coding: utf-8 -*- from __future__ import division, print_function import copy import numpy as np import warnings class Model(object): """General class for handling FCS fitting models""" def __init__(self, datadict): """datadict is an item in Modelarray""" self._parameters = datadict["Parameters"] self._definitions = datadict["Definitions"] if "Supplements" in list(datadict.keys()): self._supplements = datadict["Supplements"] else: self._supplements = lambda x, y: [] if "Boundaries" in list(datadict.keys()): self._boundaries = datadict["Boundaries"] else: # dummy verification function self._boundaries = [[-np.inf, np.inf]]*len(self._parameters[1]) if "Constraints" in list(datadict.keys()): # sort constraints such that the first value is always # larger than the last. newcc = [] for cc in datadict["Constraints"]: if cc[0] < cc[2]: if cc[1] == ">": cc = [cc[2], "<", cc[0]] elif cc[1] == "<": cc = [cc[2], ">", cc[0]] newcc.append(cc) self._constraints = newcc else: self._constraints = [] def __call__(self, parameters, tau): return self.function(parameters, tau) def __getitem__(self, key): """Emulate old list behavior of models""" return self._definitions[key] def __repr__(self): text = "Model {} - {}".format( self.id, self.description_short) return text def apply(self, parameters, tau): """ Apply the model with `parameters` and lag times `tau` """ return self.function(parameters, tau) @property def boundaries(self): return self._boundaries @property def constraints(self): """ fitting constraints """ return copy.copy(self._constraints) @property def components(self): """how many components does this model have""" return self._definitions[1] @property def default_values(self): """default fitting values""" return np.array(self._parameters[1]).copy() @property def default_variables(self): """indexes default variable fitting (bool)""" return np.array(self._parameters[2]).copy() @property def description_long(self): """long description""" return self._definitions[3].__doc__ @property def description_short(self): """short description""" return self._definitions[2] @property def function(self): return self._definitions[3] @property def func_supplements(self): return self._supplements @property def func_verification(self): warnings.warn("`func_verification is deprecated: please do not use it!") return lambda x: x def get_supplementary_parameters(self, values, countrate=None): """ Compute additional information for the model Parameters ---------- values: list-like of same length as `self.default_values` parameters for the model countrate: float countrate in kHz """ return self.func_supplements(values, countrate) def get_supplementary_values(self, values, countrate=None): """ Returns only the values of self.get_supplementary_parameters Parameters ---------- values: list-like of same length as `self.default_values` parameters for the model countrate: float count rate in Hz """ out = list() for item in self.get_supplementary_parameters(values, countrate): out.append(item[1]) return out @property def id(self): return self._definitions[0] @property def name(self): return self.description_short @property def parameters(self): return self._parameters @property def type(self): if len(self._definitions) < 5: return None else: return self._definitions[4]PyCorrFit-0.9.9/pycorrfit/models/control.py000066400000000000000000000170711274265304700207700ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ pycorrfit.models.control Controls which fitting models are imported an in which order. """ from __future__ import division import numpy as np from .classes import Model def append_model(modelarray): """ Append a new model from a modelarray. *Modelarray* has to be a list whose elements have two items: [0] parameters [1] some info about the model See separate models for more information """ global values global valuedict global models global modeldict global supplement global boundaries global modeltypes if not isinstance(modelarray, list): modelarray = [modelarray] for datadict in modelarray: # We can have many models in one model array amod = Model(datadict) models.append(amod) if amod.id in modeldict: raise ValueError("Model with same is already exists: \n {} vs. {}". format(amod, modeldict[amod.id])) modeldict[amod.id] = amod values.append(amod.parameters) valuedict[amod.id] = amod.parameters # Supplementary Data might be there supplement[amod.id] = amod.func_supplements # Check functions - check for correct values boundaries[amod.id] = amod.boundaries # Add model type to internal type list. if amod.type is not None: if not amod.type in modeltypes: modeltypes[amod.type] = [] modeltypes[amod.type].append(amod.id) def model_setup(modelid, name, comp, mtype, fctn, par_labels, par_values, par_vary=None, par_boundaries=None, par_constraints=None, par_hr_labels=None, par_hr_factors=None, supplementary_method=None, ): u""" This helper method does everything that is required to make a model available for PyCorrFit. The idea is that this method can be called from anywhere and thus we do not need to do the tedious work of adding models in the __init__.py file. Parameters ---------- modelid : int Model identifier. name : str Name of the Model. comp : str Description of components of the model, e.g. "T+3D+2D" mtype : str Type of model, e.g. "Confocal (Gaussian)" fctn : callable The method that computes the model function. It must take two arguments. The first is of shape `par_values` and the second is a 2D array containing lag time and correlation. par_labels : list-like, strings The labels of each parameter in PyCorrFit dimensionless representation, i.e. unit of time : 1 ms unit of inverse time: 1000 /s unit of distance : 100 nm unit of Diff.coeff : 10 µm²/s unit of inverse area: 100 /µm² unit of inv. volume : 1000 /µm³ par_values : list-like, floats The parameter values in PyCorrFit dimensionless units. par_vary : list-like, bools or None A list describing which parameters should be varied during fitting. If not given, only the first element is set to `True`. par_boundaries : list-like, floats The parameter boundaries - two values for each parameter. Examples: [[0, np.inf], [0,1]] par_constraints : list of lists Constraints between parameters. For example, make sure parameter 2 is always larger than parameter 1 and parameter 5 is always smaller than parameter 1: [[2, ">", 1], [5, "<", 1]] Parameter count starts at 0. par_hr_labels : list-like, strings User-defined human readable labels of the parameters. If this is set, `par_hr_factors` is also required. par_hr_factors : list-like, floats The multiplicative factors to get from `par_labels` to `par_hr_labels`. supplementary_method : callable A method that takes the parameters `par_values` and the countrate of the experiment as an argument and returns a dictinoary of supplementary information. """ # Checks assert len(par_labels) == len(par_values) for p in [par_vary, par_boundaries, par_hr_labels, par_hr_factors, ]: if p is not None: assert len(p) == len(par_values), "Number of parameters must match!" if par_hr_factors is None or par_hr_labels is None: assert par_hr_factors is None, "human readable requires two parameter" assert par_hr_labels is None, "human readable requires two parameter" if par_vary is None: # Set par_vary par_vary = np.zeros(len(par_values), dtype=bool) par_vary[0] = True if par_hr_factors is None: # Set equal to labels par_hr_labels = par_labels par_hr_factors = np.ones_like(par_values) model={} model["Parameters"] = [par_labels, par_values, par_vary, par_hr_labels, par_hr_factors] model["Definitions"] = [modelid, comp, name, fctn, mtype] if supplementary_method is not None: model["Supplements"] = supplementary_method if par_boundaries is not None: model["Boundaries"] = par_boundaries if par_constraints is not None: model["Constraints"] = par_constraints append_model(model) # Pack all variables values = list() # Also create a dictionary, key is modelid valuedict = dict() # Pack all models models = list() # Also create a dictinary modeldict = dict() # A dictionary for supplementary data: supplement = dict() # A dictionary containing model boundaries boundaries = dict() # shorttypes are used by the GUI to abbreviate the model type shorttype = dict() shorttype[u"Confocal (Gaussian)"] = u"CFoc" shorttype[u"Confocal (Gaussian) and triplet"] = u"CFoc" shorttype[u"Confocal (Gaussian) with double triplet"] = u"CFoc" shorttype[u"TIR (Gaussian/Exp.)"] = u"TIR CFoc" shorttype[u"TIR (□xσ/Exp.)"] = u"TIR □xσ" # Create a list for the differentiation between the models # This should make everything look a little cleaner modeltypes = {} modeltypes[u"User"] = [] # The order of the import matters! # These models perform the integration by themselves using the `model_setup` method. from . import model_confocal_3d from . import model_confocal_3d_3d from . import model_confocal_2d from . import model_confocal_2d_2d from . import model_confocal_3d_2d from . import model_confocal_t_3d from . import model_confocal_t_3d_3d from . import model_confocal_t_2d from . import model_confocal_t_2d_2d from . import model_confocal_t_3d_2d from . import model_confocal_t_3d_3d_3d from . import model_confocal_t_3d_3d_2d from . import model_confocal_tt_3d_3d from . import model_confocal_tt_2d_2d from . import model_confocal_tt_3d_2d # These lines can be removed once all models are converted # from `MODEL_*` to `model_` syntax. modeltypes[u"TIR (Gaussian/Exp.)"] = [6014, 6034, 6033] modeltypes[u"TIR (□xσ/Exp.)"] = [6010, 6023, 6000, 6022, 6020, 6021] ## Models from . import MODEL_TIRF_gaussian_1C from . import MODEL_TIRF_gaussian_3D2D from . import MODEL_TIRF_gaussian_3D3D from . import MODEL_TIRF_1C from . import MODEL_TIRF_2D2D from . import MODEL_TIRF_3D2D from . import MODEL_TIRF_3D3D from . import MODEL_TIRF_3D2Dkin_Ries # Load all models from the imported "MODEL_*" submodules # These are the models that were not imported using the `model_setup` method. for g in list(globals().keys()): if g.startswith("MODEL_") and hasattr(globals()[g], "Modelarray"): append_model(globals()[g].Modelarray) PyCorrFit-0.9.9/pycorrfit/models/cp_confocal.py000066400000000000000000000004231274265304700215470ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ Confocal fitting model components. """ from __future__ import division import numpy as np def threed(tau, taudiff, SP): return 1/((1 + tau/taudiff) * np.sqrt(1+tau/(taudiff*SP**2))) def twod(tau, taudiff): return 1/((1 + tau/taudiff))PyCorrFit-0.9.9/pycorrfit/models/cp_mix.py000066400000000000000000000044331274265304700205650ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ Mixed components for fitting models. """ from __future__ import division def double_pnum(n, F1, alpha, comp1, kwargs1, comp2, kwargs2, ): u""" Double component models where the particle number is given in the model i.e. for confocal diffusion models. Parameters ---------- n : float Total particle number F1 : float Fraction of particle species 1 alpha : float Relative molecular brightness of particle 2 compared to particle 1 (α = q₂/q₁) comp1, comp2 : callables The model functions for each of the components. kwargs1, kwargs2 : dicts The keyword arguments for `comp1` and `comp2` """ norm = (F1 + alpha*(1-F1))**2 g1 = F1 * comp1(**kwargs1) g2 = alpha**2 * (1-F1) * comp2(**kwargs2) G = 1/n * (g1 + g2) / norm return G def triple_pnum(n, F1, F2, alpha21, alpha31, comp1, kwargs1, comp2, kwargs2, comp3, kwargs3 ): u""" Double component models where the particle number is given in the model i.e. for confocal diffusion models. Parameters ---------- n : float Total particle number F1, F2 : float Fraction of particle species 1 and 2. This infers that F3 = 1 - F1 - F2 alpha21 : float Relative molecular brightness of particle 2 compared to particle 1 (α₂₁ = q₂/q₁) alpha31 : float Relative molecular brightness of particle 3 compared to particle 1 comp1, comp2, comp3 : callables The model functions for each of the components. kwargs1, kwargs2, kwargs3 : dicts The keyword arguments for `comp1`, `comp2`, and `comp3`. """ alpha11 = 1 F3 = 1 - F1 - F2 if F3 < 0: F3 = 0 norm = (F1*alpha11 + F2*alpha21 + F3*alpha31)**2 g1 = alpha11**2 * F1 * comp1(**kwargs1) g2 = alpha21**2 * F2 * comp2(**kwargs2) g3 = alpha31**2 * F3 * comp3(**kwargs3) G = 1/n * (g1 + g2 + g3) / norm return GPyCorrFit-0.9.9/pycorrfit/models/cp_triplet.py000066400000000000000000000003761274265304700214550ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ Triplet components. """ from __future__ import division import numpy as np def trip(tau, tautrip, T): if tautrip == 0 or T == 0: AA = 1 else: AA = 1 + T/(1-T) * np.exp(-tau/tautrip) return AAPyCorrFit-0.9.9/pycorrfit/models/model_confocal_2d.py000066400000000000000000000033321274265304700226340ustar00rootroot00000000000000# -*- coding: utf-8 -*- from __future__ import division import numpy as np from .control import model_setup from .cp_confocal import twod # 2D simple gauss def CF_Gxy_gauss(parms, tau): u""" Two-dimensional diffusion with a Gaussian laser profile. G(τ) = offset + 1/( n * (1+τ/τ_diff) ) Calculation of diffusion coefficient and concentration from the effective radius of the detection profile (r₀ = 2*σ): D = r₀²/(4*τ_diff) Conc = n/(π*r₀²) *parms* - a list of parameters. Parameters (parms[i]): [0] n Effective number of particles in confocal area [1] τ_diff Characteristic residence time in confocal area [3] offset *tau* - lag time """ n = parms[0] taudiff = parms[1] dc = parms[2] BB = twod(tau=tau, taudiff=taudiff) G = dc + 1/n * BB return G def supplements(parms, countrate=None): # We can only give you the effective particle number n = parms[0] Info = list() if countrate is not None: # CPP cpp = countrate/n Info.append(["cpp [kHz]", cpp]) return Info parms = [4.0, 0.4, 0.0] ## boundaries boundaries = [[0, np.inf]]*len(parms) boundaries[-1] = [-np.inf, np.inf] model_setup( modelid=6001, name="2D diffusion (confocal)", comp="2D", mtype="Confocal (Gaussian)", fctn=CF_Gxy_gauss, par_labels=[ u"n", u"τ_diff [ms]", u"offset"], par_values=parms, par_vary=[True, True, False], par_boundaries=boundaries, supplementary_method=supplements ) PyCorrFit-0.9.9/pycorrfit/models/model_confocal_2d_2d.py000066400000000000000000000067571274265304700232370ustar00rootroot00000000000000# -*- coding: utf-8 -*- from __future__ import division import numpy as np from .control import model_setup from .cp_confocal import twod from .cp_mix import double_pnum # 2D + 2D Gauß # Model 6037 def CF_Gxyz_gauss_2D2D(parms, tau): u""" Two-component, two-dimensional diffusion with a Gaussian laser profile, including a triplet component. The triplet factor takes into account blinking according to triplet states of excited molecules. Set *T* or *τ_trip* to 0, if no triplet component is wanted. particle1 = F₁/(1+τ/τ₁) particle2 = α²*(1-F₁)/(1+τ/τ₂) norm = (F₁ + α*(1-F₁))² G = 1/n*(particle1 + particle2)/norm + offset *parms* - a list of parameters. Parameters (parms[i]): [0] n Effective number of particles in confocal area (n = n₁+n₂) [1] τ₁ Diffusion time of particle species 1 [2] τ₂ Diffusion time of particle species 2 [3] F₁ Fraction of molecules of species 1 (n₁ = n*F₁) 0 <= F₁ <= 1 [4] α Relative molecular brightness of particle 2 compared to particle 1 (α = q₂/q₁) [5] offset *tau* - lag time """ n=parms[0] taud1=parms[1] taud2=parms[2] F=parms[3] alpha=parms[4] off=parms[5] g = double_pnum(n=n, F1=F, alpha=alpha, comp1=twod, comp2=twod, kwargs1={"tau":tau, "taudiff":taud1}, kwargs2={"tau":tau, "taudiff":taud2}, ) G = off + g return G def supplements(parms, countrate=None): u"""Supplementary parameters: [6] n₁ = n*F₁ Particle number of species 1 [7] n₂ = n*(1-F₁) Particle number of species 2 """ # We can only give you the effective particle number n = parms[0] F1 = parms[3] Info = list() # The enumeration of these parameters is very important for # plotting the normalized curve. Countrate must come out last! Info.append([u"n\u2081", n*F1]) Info.append([u"n\u2082", n*(1.-F1)]) if countrate is not None: # CPP cpp = countrate/n Info.append(["cpp [kHz]", cpp]) return Info parms = [ 25, # n 5, # taud1 1000, # taud2 0.5, # F 1.0, # alpha 0.0 # offset ] ## Boundaries # strictly positive boundaries = [[0, np.inf]]*len(parms) # F boundaries[3] = [0,.9999999999999] boundaries[-1] = [-np.inf, np.inf] model_setup( modelid=6037, name="Separate 2D diffusion (confocal)", comp="2D+2D", mtype="Confocal (Gaussian)", fctn=CF_Gxyz_gauss_2D2D, par_labels=[ u"n", u"τ"+u"\u2081"+u" [ms]", u"τ"+u"\u2082"+u" [ms]", u"F"+u"\u2081", u"\u03b1"+u" (q"+u"\u2082"+"/q"+u"\u2081"+")", u"offset" ], par_values=parms, par_vary=[True, True, True, True, False, False], par_boundaries=boundaries, par_constraints=[[2, ">", 1]], supplementary_method=supplements ) PyCorrFit-0.9.9/pycorrfit/models/model_confocal_3d.py000066400000000000000000000043261274265304700226410ustar00rootroot00000000000000# -*- coding: utf-8 -*- from __future__ import division import numpy as np from .control import model_setup from .cp_confocal import threed # 3D simple gauss def CF_Gxyz_gauss(parms, tau): # Model 6012 u""" Three-dimanesional free diffusion with a Gaussian laser profile (eliptical). G(τ) = offset + 1/( n*(1+τ/τ_diff) * sqrt(1 + τ/(SP²*τ_diff)) ) Calculation of diffusion coefficient and concentration from the effective radius of the detection profile (r₀ = 2*σ): D = r₀²/(4*τ_diff) Conc = n/( sqrt(π³)*r₀²*z₀ ) r₀ lateral detection radius (waist of lateral gaussian) z₀ axial detection length (waist of axial gaussian) D Diffusion coefficient Conc Concentration of dye *parms* - a list of parameters. Parameters (parms[i]): [0] n Effective number of particles in confocal volume [1] τ_diff Characteristic residence time in confocal volume [2] SP SP=z₀/r₀ Structural parameter, describes the axis ratio of the confocal volume [3] offset *tau* - lag time """ n = parms[0] taudiff = parms[1] SP = parms[2] off = parms[3] BB = threed(tau, taudiff, SP) G = off + 1/n * BB return G def supplements(parms, countrate=None): # We can only give you the effective particle number n = parms[0] Info = list() if countrate is not None: # CPP cpp = countrate/n Info.append(["cpp [kHz]", cpp]) return Info parms = [4.0, 0.4, 5.0, 0.0] boundaries = [[0, np.inf]]*len(parms) boundaries[-1] = [-np.inf, np.inf] model_setup( modelid=6012, name="3D diffusion (confocal)", comp="3D", mtype="Confocal (Gaussian)", fctn=CF_Gxyz_gauss, par_labels=[ u"n", u"τ_diff [ms]", u"SP", u"offset"], par_values=parms, par_vary=[True, True, False, False], par_boundaries=boundaries, supplementary_method=supplements ) PyCorrFit-0.9.9/pycorrfit/models/model_confocal_3d_2d.py000066400000000000000000000071331274265304700232250ustar00rootroot00000000000000# -*- coding: utf-8 -*- from __future__ import division import numpy as np from .control import model_setup from .cp_confocal import twod, threed from .cp_mix import double_pnum # 3D + 2D + T def CF_Gxyz_3d2d_gauss(parms, tau): u""" Two-component, two- and three-dimensional diffusion with a Gaussian laser profile. particle2D = (1-F)/ (1+τ/τ_2D) particle3D = α²*F/( (1+τ/τ_3D) * sqrt(1+τ/(τ_3D*SP²))) norm = (1-F + α*F)² G = 1/n*(particle1 + particle2)/norm + offset *parms* - a list of parameters. Parameters (parms[i]): [0] n Effective number of particles in confocal volume (n = n2D+n3D) [1] τ_2D Diffusion time of surface bound particls [2] τ_3D Diffusion time of freely diffusing particles [3] F Fraction of molecules of the freely diffusing species (n3D = n*F), 0 <= F <= 1 [4] SP SP=z₀/r₀ Structural parameter, describes elongation of the confocal volume [5] α Relative molecular brightness of particle 3D compared to particle 2D (α = q3D/q2D) [6] offset *tau* - lag time """ n=parms[0] taud2D=parms[1] taud3D=parms[2] F=parms[3] SP=parms[4] alpha=parms[5] off=parms[6] g = double_pnum(n=n, F1=1-F, alpha=alpha, comp1=twod, comp2=threed, kwargs1={"tau":tau, "taudiff":taud2D}, kwargs2={"tau":tau, "taudiff":taud3D, "SP":SP}, ) G = off + g return G def supplements(parms, countrate=None): u"""Supplementary parameters: Effective number of freely diffusing particles in 3D solution: [7] n3D = n*F Effective number particles diffusing on 2D surface: [9] n2D = n*(1-F) """ # We can only give you the effective particle number n = parms[0] F3d = parms[3] Info = list() # The enumeration of these parameters is very important for # plotting the normalized curve. Countrate must come out last! Info.append([u"n3D", n*F3d]) Info.append([u"n2D", n*(1.-F3d)]) if countrate is not None: # CPP cpp = countrate/n Info.append([u"cpp [kHz]", cpp]) return Info parms = [ 25, # n 240, # taud2D 0.1, # taud3D 0.5, # F3D 7, # SP 1.0, # alpha 0.0 # offset ] ## Boundaries # strictly positive boundaries = [[0, np.inf]]*len(parms) # F boundaries[3] = [0,.9999999999999] boundaries[-1] = [-np.inf, np.inf] model_setup( modelid=6036, name="Separate 3D and 2D diffusion (confocal)", comp="3D+2D", mtype="Confocal (Gaussian)", fctn=CF_Gxyz_3d2d_gauss, par_labels=[ u"n", u"τ_2D [ms]", u"τ_3D [ms]", u"F_3D", u"SP", u"\u03b1"+" (q_3D/q_2D)", u"offset" ], par_values=parms, par_vary=[True, True, True, True, False, False, False], par_boundaries=boundaries, par_constraints=[[2, "<", 1]], supplementary_method=supplements ) PyCorrFit-0.9.9/pycorrfit/models/model_confocal_3d_3d.py000066400000000000000000000071151274265304700232260ustar00rootroot00000000000000# -*- coding: utf-8 -*- from __future__ import division import numpy as np from .control import model_setup from .cp_confocal import threed from .cp_mix import double_pnum def CF_Gxyz_gauss_3D3D(parms, tau): u""" Two-component three-dimensional free diffusion with a Gaussian laser profile. particle1 = F₁/( (1+τ/τ₁) * sqrt(1+τ/(τ₁*SP²))) particle2 = α²*(1-F₁)/( (1+τ/τ₂) * sqrt(1+τ/(τ₂*SP²))) norm = (F₁ + α*(1-F₁))² G = 1/n*(particle1 + particle2)/norm + offset *parms* - a list of parameters. Parameters (parms[i]): [0] n Effective number of particles in confocal volume (n = n₁+n₂) [1] τ₁ Diffusion time of particle species 1 [2] τ₂ Diffusion time of particle species 2 [3] F₁ Fraction of molecules of species 1 (n₁ = n*F₁) 0 <= F₁ <= 1 [4] SP SP=z₀/r₀, Structural parameter, describes elongation of the confocal volume [5] α Relative molecular brightness of particle 2 compared to particle 1 (α = q₂/q₁) [6] offset *tau* - lag time """ n=parms[0] taud1=parms[1] taud2=parms[2] F=parms[3] SP=parms[4] alpha=parms[5] off=parms[6] g = double_pnum(n=n, F1=F, alpha=alpha, comp1=threed, comp2=threed, kwargs1={"tau":tau, "taudiff":taud1, "SP":SP}, kwargs2={"tau":tau, "taudiff":taud2, "SP":SP}, ) G = off + g return G def supplements(parms, countrate=None): u"""Supplementary parameters: [7] n₁ = n*F₁ Particle number of species 1 [8] n₂ = n*(1-F₁) Particle number of species 2 """ # We can only give you the effective particle number n = parms[0] F1 = parms[3] Info = list() # The enumeration of these parameters is very important for # plotting the normalized curve. Countrate must come out last! Info.append([u"n\u2081", n*F1]) Info.append([u"n\u2082", n*(1.-F1)]) if countrate is not None: # CPP cpp = countrate/n Info.append(["cpp [kHz]", cpp]) return Info parms = [ 25, # n 5, # taud1 1000, # taud2 0.5, # F 5, # SP 1.0, # alpha 0.0 # offset ] ## Boundaries # strictly positive boundaries = [[0, np.inf]]*len(parms) # F boundaries[3] = [0,.9999999999999] boundaries[-1] = [-np.inf, np.inf] model_setup( modelid=6035, name="Separate 3D diffusion (confocal)", comp="3D+3D", mtype="Confocal (Gaussian)", fctn=CF_Gxyz_gauss_3D3D, par_labels=[ u"n", u"τ"+u"\u2081"+" [ms]", u"τ"+u"\u2082"+" [ms]", u"F"+u"\u2081", u"SP", u"\u03b1"+" (q"+u"\u2082"+"/q"+u"\u2081"+")", u"offset" ], par_values=parms, par_vary=[True, True, True, True, False, False, False], par_boundaries=boundaries, par_constraints=[[2, ">", 1]], supplementary_method=supplements ) PyCorrFit-0.9.9/pycorrfit/models/model_confocal_t_2d.py000066400000000000000000000054221274265304700231610ustar00rootroot00000000000000# -*- coding: utf-8 -*- from __future__ import division import numpy as np from .control import model_setup from .cp_confocal import twod from .cp_triplet import trip # 2D simple gauss def CF_Gxy_T_gauss(parms, tau): u""" Two-dimensional diffusion with a Gaussian laser profile, including a triplet component. The triplet factor takes into account a blinking term. Set *T* or *τ_trip* to 0, if no triplet component is wanted. triplet = 1 + T/(1-T)*exp(-τ/τ_trip) G(τ) = offset + 1/( n * (1+τ/τ_diff) )*triplet Calculation of diffusion coefficient and concentration from the effective radius of the detection profile (r₀ = 2*σ): D = r₀²/(4*τ_diff) Conc = n/(π*r₀²) *parms* - a list of parameters. Parameters (parms[i]): [0] n Effective number of particles in confocal area [1] τ_diff Characteristic residence time in confocal area [2] τ_trip Characteristic residence time in triplet state [3] T Fraction of particles in triplet (non-fluorescent) state 0 <= T < 1 [4] offset *tau* - lag time """ n = parms[0] taudiff = parms[1] tautrip = parms[2] T = parms[3] dc = parms[4] triplet = trip(tau=tau, tautrip=tautrip, T=T) BB = twod(tau=tau, taudiff=taudiff) G = dc + 1/n * BB * triplet return G def supplements(parms, countrate=None): # We can only give you the effective particle number n = parms[0] Info = list() if countrate is not None: # CPP cpp = countrate/n Info.append(["cpp [kHz]", cpp]) return Info parms = [4.0, 0.4, 0.001, 0.01, 0.0] ## Boundaries # strictly positive boundaries = [[0, np.inf]]*len(parms) # F boundaries[3] = [0,.9999999999999] boundaries[-1] = [-np.inf, np.inf] model_setup( modelid=6002, name="2D diffusion with triplet (confocal)", comp="T+2D", mtype="Confocal (Gaussian) and triplet", fctn=CF_Gxy_T_gauss, par_labels=[ u"n", u"τ_diff [ms]", u"τ_trip [ms]", u"T", u"offset"], par_values=parms, par_vary=[True, True, True, True, False], par_boundaries=boundaries, par_constraints=[[2, "<", 1]], par_hr_labels=[ u"n", u"τ_diff [ms]", u"τ_trip [µs]", u"T", u"offset"], par_hr_factors=[1., 1., 1000., 1., 1.], supplementary_method=supplements ) PyCorrFit-0.9.9/pycorrfit/models/model_confocal_t_2d_2d.py000066400000000000000000000117611274265304700235510ustar00rootroot00000000000000# -*- coding: utf-8 -*- from __future__ import division import numpy as np from .control import model_setup from .cp_confocal import twod from .cp_triplet import trip from .cp_mix import double_pnum # 2D + 2D + Triplet Gauß # Model 6031 def CF_Gxyz_gauss_2D2DT(parms, tau): u""" Two-component, two-dimensional diffusion with a Gaussian laser profile, including a triplet component. The triplet factor takes into account blinking according to triplet states of excited molecules. Set *T* or *τ_trip* to 0, if no triplet component is wanted. particle1 = F₁/(1+τ/τ₁) particle2 = α²*(1-F₁)/(1+τ/τ₂) triplet = 1 + T/(1-T)*exp(-τ/τ_trip) norm = (F₁ + α*(1-F₁))² G = 1/n*(particle1 + particle2)*triplet/norm + offset *parms* - a list of parameters. Parameters (parms[i]): [0] n Effective number of particles in confocal area (n = n₁+n₂) [1] τ₁ Diffusion time of particle species 1 [2] τ₂ Diffusion time of particle species 2 [3] F₁ Fraction of molecules of species 1 (n₁ = n*F₁) 0 <= F₁ <= 1 [4] α Relative molecular brightness of particle 2 compared to particle 1 (α = q₂/q₁) [5] τ_trip Characteristic residence time in triplet state [6] T Fraction of particles in triplet (non-fluorescent) state 0 <= T < 1 [7] offset *tau* - lag time """ n=parms[0] taud1=parms[1] taud2=parms[2] F=parms[3] alpha=parms[4] tautrip=parms[5] T=parms[6] off=parms[7] g = double_pnum(n=n, F1=F, alpha=alpha, comp1=twod, comp2=twod, kwargs1={"tau":tau, "taudiff":taud1}, kwargs2={"tau":tau, "taudiff":taud2}, ) tr = trip(tau=tau, T=T, tautrip=tautrip) G = off + g*tr return G def supplements(parms, countrate=None): u"""Supplementary parameters: [8] n₁ = n*F₁ Particle number of species 1 [9] n₂ = n*(1-F₁) Particle number of species 2 """ # We can only give you the effective particle number n = parms[0] F1 = parms[3] Info = list() # The enumeration of these parameters is very important for # plotting the normalized curve. Countrate must come out last! Info.append([u"n\u2081", n*F1]) Info.append([u"n\u2082", n*(1.-F1)]) if countrate is not None: # CPP cpp = countrate/n Info.append(["cpp [kHz]", cpp]) return Info parms = [ 25, # n 5, # taud1 1000, # taud2 0.5, # F 1.0, # alpha 0.001, # tautrip 0.01, # T 0.0 # offset ] ## Boundaries # strictly positive boundaries = [[0, np.inf]]*len(parms) # F boundaries[3] = [0,.9999999999999] # T boundaries[6] = [0,.9999999999999] boundaries[-1] = [-np.inf, np.inf] model_setup( modelid=6031, name="Separate 2D diffusion with triplet (confocal)", comp="T+2D+2D", mtype="Confocal (Gaussian) and triplet", fctn=CF_Gxyz_gauss_2D2DT, par_labels=[ u"n", u"τ"+u"\u2081"+u" [ms]", u"τ"+u"\u2082"+u" [ms]", u"F"+u"\u2081", u"\u03b1"+u" (q"+u"\u2082"+"/q"+u"\u2081"+")", u"τ_trip [ms]", u"T", u"offset" ], par_values=parms, par_vary=[True, True, True, True, False, False, False, False], par_boundaries=boundaries, par_constraints=[[2, ">", 1], [5, "<", 1]], par_hr_labels=[ u"n", u"τ"+u"\u2081"+u" [ms]", u"τ"+u"\u2082"+u" [ms]", u"F"+u"\u2081", u"\u03b1"+" (q"+u"\u2082"+"/q"+u"\u2081"+")", u"τ_trip [µs]", u"T", u"offset" ], par_hr_factors=[ 1., # "n", 1., # "τ"+u"\u2081"+" [ms]", 1., # "τ"+u"\u2082"+" [ms]", 1., # "F"+u"\u2081", 1., # u"\u03b1"+" (q"+u"\u2082"+"/q"+u"\u2081"+")", 1000., # "τ_trip [µs]", 1., # "T", 1. # "offset" ], supplementary_method=supplements ) PyCorrFit-0.9.9/pycorrfit/models/model_confocal_t_3d.py000066400000000000000000000057621274265304700231710ustar00rootroot00000000000000# -*- coding: utf-8 -*- from __future__ import division import numpy as np from .control import model_setup from .cp_confocal import threed from .cp_triplet import trip def CF_Gxyz_blink(parms, tau): u""" Three-dimanesional free diffusion with a Gaussian laser profile (eliptical), including a triplet component. The triplet factor takes into account a blinking term. Set *T* or *τ_trip* to 0, if no triplet component is wanted. G(τ) = offset + 1/( n*(1+τ/τ_diff) * sqrt(1 + τ/(SP²*τ_diff)) ) * ( 1+T/(1.-T)*exp(-τ/τ_trip) ) Calculation of diffusion coefficient and concentration from the effective radius of the detection profile (r₀ = 2*σ): D = r₀²/(4*τ_diff) Conc = n/( sqrt(π³)*r₀²*z₀ ) *parms* - a list of parameters. Parameters (parms[i]): [0] n Effective number of particles in confocal volume [1] T Fraction of particles in triplet (non-fluorescent) state 0 <= T < 1 [2] τ_trip Characteristic residence time in triplet state [3] τ_diff Characteristic residence time in confocal volume [4] SP SP=z₀/r₀ Structural parameter, describes the axis ratio of the confocal volume [5] offset *tau* - lag time """ n = parms[0] T = parms[1] tautrip = parms[2] taudiff = parms[3] SP = parms[4] off = parms[5] AA = trip(tau, tautrip, T) BB = threed(tau, taudiff, SP) G = off + 1/n * AA * BB return G def supplements(parms, countrate=None): # We can only give you the effective particle number n = parms[0] Info = list() if countrate is not None: # CPP cpp = countrate/n Info.append(["cpp [kHz]", cpp]) return Info parms = [4.0, 0.2, 0.001, 0.4, 5.0, 0.0] ## Boundaries boundaries = [[0, np.inf]]*len(parms) # T boundaries[1] = [0,.9999999999999] boundaries[-1] = [-np.inf, np.inf] model_setup( modelid=6011, name="3D diffusion with triplet (confocal)", comp="T+3D", mtype="Confocal (Gaussian) and triplet", fctn=CF_Gxyz_blink, par_labels=[ u"n", u"T", u"τ_trip [ms]", u"τ_diff [ms]", u"SP", u"offset"], par_values=parms, par_vary=[True, True, True, True, False, False], par_boundaries=boundaries, par_constraints=[[3, ">", 2]], par_hr_labels=[ u"n", u"T", u"τ_trip [µs]", u"τ_diff [ms]", u"SP", u"offset"], par_hr_factors=[1., 1., 1000., 1., 1., 1.], supplementary_method=supplements ) PyCorrFit-0.9.9/pycorrfit/models/model_confocal_t_3d_2d.py000066400000000000000000000124421274265304700235470ustar00rootroot00000000000000# -*- coding: utf-8 -*- from __future__ import division import numpy as np from .control import model_setup from .cp_confocal import twod, threed from .cp_triplet import trip from .cp_mix import double_pnum # 3D + 2D + T def CF_Gxyz_3d2dT_gauss(parms, tau): u""" Two-component, two- and three-dimensional diffusion with a Gaussian laser profile, including a triplet component. The triplet factor takes into account blinking according to triplet states of excited molecules. Set *T* or *τ_trip* to 0, if no triplet component is wanted. particle2D = (1-F)/ (1+τ/τ_2D) particle3D = α²*F/( (1+τ/τ_3D) * sqrt(1+τ/(τ_3D*SP²))) triplet = 1 + T/(1-T)*exp(-τ/τ_trip) norm = (1-F + α*F)² G = 1/n*(particle1 + particle2)*triplet/norm + offset *parms* - a list of parameters. Parameters (parms[i]): [0] n Effective number of particles in confocal volume (n = n2D+n3D) [1] τ_2D Diffusion time of surface bound particls [2] τ_3D Diffusion time of freely diffusing particles [3] F Fraction of molecules of the freely diffusing species (n3D = n*F), 0 <= F <= 1 [4] SP SP=z₀/r₀ Structural parameter, describes elongation of the confocal volume [5] α Relative molecular brightness of particle 3D compared to particle 2D (α = q3D/q2D) [6] τ_trip Characteristic residence time in triplet state [7] T Fraction of particles in triplet (non-fluorescent) state 0 <= T < 1 [8] offset *tau* - lag time """ n=parms[0] taud2D=parms[1] taud3D=parms[2] F=parms[3] SP=parms[4] alpha=parms[5] tautrip=parms[6] T=parms[7] off=parms[8] g = double_pnum(n=n, F1=1-F, alpha=alpha, comp1=twod, comp2=threed, kwargs1={"tau":tau, "taudiff":taud2D}, kwargs2={"tau":tau, "taudiff":taud3D, "SP":SP}, ) tr = trip(tau=tau, T=T, tautrip=tautrip) G = off + g*tr return G def supplements(parms, countrate=None): u"""Supplementary parameters: Effective number of freely diffusing particles in 3D solution: [9] n3D = n*F Effective number particles diffusing on 2D surface: [10] n2D = n*(1-F) """ # We can only give you the effective particle number n = parms[0] F3d = parms[3] Info = list() # The enumeration of these parameters is very important for # plotting the normalized curve. Countrate must come out last! Info.append([u"n3D", n*F3d]) Info.append([u"n2D", n*(1.-F3d)]) if countrate is not None: # CPP cpp = countrate/n Info.append([u"cpp [kHz]", cpp]) return Info parms = [ 25, # n 240, # taud2D 0.1, # taud3D 0.5, # F3D 7, # SP 1.0, # alpha 0.001, # tautrip 0.01, # T 0.0 # offset ] ## Boundaries # strictly positive boundaries = [[0, np.inf]]*len(parms) # F boundaries[3] = [0,.9999999999999] # T boundaries[7] = [0,.9999999999999] boundaries[-1] = [-np.inf, np.inf] model_setup( modelid=6032, name="Separate 3D and 2D diffusion with triplet (confocal)", comp="T+3D+2D", mtype="Confocal (Gaussian) and triplet", fctn=CF_Gxyz_3d2dT_gauss, par_labels=[ u"n", u"τ_2D [ms]", u"τ_3D [ms]", u"F_3D", u"SP", u"\u03b1"+" (q_3D/q_2D)", u"τ_trip [ms]", u"T", u"offset" ], par_values=parms, par_vary=[True, True, True, True, False, False, False, False, False], par_boundaries=boundaries, par_constraints=[[2, "<", 1], [6, "<", 2]], par_hr_labels=[ u"n", u"τ_2D [ms]", u"τ_3D [ms]", u"F_3D", u"SP", u"\u03b1"+" (q_3D/q_2D)", u"τ_trip [µs]", u"T", u"offset" ], par_hr_factors=[ 1., # "n", 1., # "τ_2D [ms]", 1., # "τ_3D [ms]", 1., # "F_3D", 1., # "SP", 1., # u"\u03b1"+" (q_3D/q_2D)", 1000., # "τ_trip [µs]", 1., # "T", 1. # "offset" ], supplementary_method=supplements ) PyCorrFit-0.9.9/pycorrfit/models/model_confocal_t_3d_3d.py000066400000000000000000000123141274265304700235460ustar00rootroot00000000000000# -*- coding: utf-8 -*- from __future__ import division import numpy as np from .control import model_setup from .cp_confocal import threed from .cp_triplet import trip from .cp_mix import double_pnum def CF_Gxyz_gauss_3D3DT(parms, tau): u""" Two-component three-dimensional free diffusion with a Gaussian laser profile, including a triplet component. The triplet factor takes into account a blinking term. Set *T* or *τ_trip* to 0, if no triplet component is wanted. particle1 = F₁/( (1+τ/τ₁) * sqrt(1+τ/(τ₁*SP²))) particle2 = α²*(1-F₁)/( (1+τ/τ₂) * sqrt(1+τ/(τ₂*SP²))) triplet = 1 + T/(1-T)*exp(-τ/τ_trip) norm = (F₁ + α*(1-F₁))² G = 1/n*(particle1 + particle2)*triplet/norm + offset *parms* - a list of parameters. Parameters (parms[i]): [0] n Effective number of particles in confocal volume (n = n₁+n₂) [1] τ₁ Diffusion time of particle species 1 [2] τ₂ Diffusion time of particle species 2 [3] F₁ Fraction of molecules of species 1 (n₁ = n*F₁) 0 <= F₁ <= 1 [4] SP SP=z₀/r₀, Structural parameter, describes elongation of the confocal volume [5] α Relative molecular brightness of particle 2 compared to particle 1 (α = q₂/q₁) [6] τ_trip Characteristic residence time in triplet state [7] T Fraction of particles in triplet (non-fluorescent) state 0 <= T < 1 [8] offset *tau* - lag time """ n=parms[0] taud1=parms[1] taud2=parms[2] F=parms[3] SP=parms[4] alpha=parms[5] tautrip=parms[6] T=parms[7] off=parms[8] g = double_pnum(n=n, F1=F, alpha=alpha, comp1=threed, comp2=threed, kwargs1={"tau":tau, "taudiff":taud1, "SP":SP}, kwargs2={"tau":tau, "taudiff":taud2, "SP":SP}, ) tr = trip(tau=tau, T=T, tautrip=tautrip) G = off + g*tr return G def supplements(parms, countrate=None): u"""Supplementary parameters: [9] n₁ = n*F₁ Particle number of species 1 [10] n₂ = n*(1-F₁) Particle number of species 2 """ # We can only give you the effective particle number n = parms[0] F1 = parms[3] Info = list() # The enumeration of these parameters is very important for # plotting the normalized curve. Countrate must come out last! Info.append([u"n\u2081", n*F1]) Info.append([u"n\u2082", n*(1.-F1)]) if countrate is not None: # CPP cpp = countrate/n Info.append(["cpp [kHz]", cpp]) return Info parms = [ 25, # n 5, # taud1 1000, # taud2 0.5, # F 5, # SP 1.0, # alpha 0.001, # tautrip 0.01, # T 0.0 # offset ] ## Boundaries # strictly positive boundaries = [[0, np.inf]]*len(parms) # F boundaries[3] = [0,.9999999999999] # T boundaries[7] = [0,.9999999999999] boundaries[-1] = [-np.inf, np.inf] model_setup( modelid=6030, name="Separate 3D diffusion with triplet (confocal)", comp="T+3D+3D", mtype="Confocal (Gaussian) and triplet", fctn=CF_Gxyz_gauss_3D3DT, par_labels=[ u"n", u"τ"+u"\u2081"+" [ms]", u"τ"+u"\u2082"+" [ms]", u"F"+u"\u2081", u"SP", u"\u03b1"+" (q"+u"\u2082"+"/q"+u"\u2081"+")", u"τ_trip [ms]", u"T", u"offset" ], par_values=parms, par_vary=[True, True, True, True, False, False, False, False, False], par_boundaries=boundaries, par_constraints=[[2, ">", 1], [6, "<", 1]], par_hr_labels=[ u"n", u"τ"+u"\u2081"+" [ms]", u"τ"+u"\u2082"+" [ms]", u"F"+u"\u2081", u"SP", u"\u03b1"+" (q"+u"\u2082"+"/q"+u"\u2081"+")", u"τ_trip [µs]", u"T", u"offset" ], par_hr_factors=[ 1., # n 1., # taud1 1., # taud2 1., # F 1., # SP 1., # alpha 1000., # tautrip [µs] 1., # T 1. # offset ], supplementary_method=supplements ) PyCorrFit-0.9.9/pycorrfit/models/model_confocal_t_3d_3d_2d.py000066400000000000000000000152361274265304700241410ustar00rootroot00000000000000# -*- coding: utf-8 -*- from __future__ import division import numpy as np from .control import model_setup from .cp_confocal import twod, threed from .cp_triplet import trip from .cp_mix import triple_pnum def CF_Gxyz_gauss_3D3D2DT(parms, tau): u""" Two three-dimensional and one two-dimensional free diffusion with a Gaussian laser profile, including a triplet component. The triplet factor takes into account a blinking term. Set *T* or *τ_trip* to 0, if no triplet component is wanted. F₃ = 1-F₁-F₂ particle1 = F₁/( (1+τ/τ₁) * sqrt(1+τ/(τ₁*SP²))) particle2 = α₂₁² * F₂/( (1+τ/τ₂) * sqrt(1+τ/(τ₂*SP²))) particle3 = α₃₁² * F₃/( (1+τ/τ₃)) triplet = 1 + T/(1-T)*exp(-τ/τ_trip) norm = (F₁ + α₂₁*F₂ + α₃₁*F₃)² G = 1/n*(particle1 + particle2 + particle3)*triplet/norm + offset *parms* - a list of parameters. Parameters (parms[i]): [0] n Effective number of particles in confocal volume (n = n₁+n₂+n₃) [1] τ₁ Diffusion time of particle species 1 (3D) [2] τ₂ Diffusion time of particle species 2 (3D) [3] τ₃ Diffusion time of particle species 3 (2D) [4] F₁ Fraction of molecules of species 1 (n₁ = n*F₁) 0 <= F₁ <= 1 [5] F₂ Fraction of molecules of species 2 (n₂ = n*F₂) 0 <= F₂ <= 1 [6] SP SP=z₀/r₀, Structural parameter, describes elongation of the confocal volume [7] α₂₁ Relative molecular brightness of particle 2 compared to particle 1 (α = q₂/q₁) [8] α₃₁ Relative molecular brightness of particle 3 compared to particle 1 (α = q₃/q₁) [9] τ_trip Characteristic residence time in triplet state [10] T Fraction of particles in triplet (non-fluorescent) state 0 <= T < 1 [11] offset *tau* - lag time """ n=parms[0] taud1=parms[1] taud2=parms[2] taud3=parms[3] F1=parms[4] F2=parms[5] SP=parms[6] alpha21=parms[7] alpha31=parms[8] tautrip=parms[9] T=parms[10] off=parms[11] g = triple_pnum(n=n, F1=F1, F2=F2, alpha21=alpha21, alpha31=alpha31, comp1=threed, comp2=threed, comp3=twod, kwargs1={"tau":tau, "taudiff":taud1, "SP":SP}, kwargs2={"tau":tau, "taudiff":taud2, "SP":SP}, kwargs3={"tau":tau, "taudiff":taud3}, ) tr = trip(tau=tau, T=T, tautrip=tautrip) G = off + g*tr return G def supplements(parms, countrate=None): u"""Supplementary parameters: [12] n₁ = n*F₁ Particle number of species 1 (3D) [13] n₂ = n*F₂ Particle number of species 2 (3D) [14] n₃ = n*F₃ Particle number of species 3 (2D; F₃ = 1-F₁-F₂) """ # We can only give you the effective particle number n = parms[0] F1 = parms[4] F2 = parms[5] Info = list() # The enumeration of these parameters is very important for # plotting the normalized curve. Countrate must come out last! Info.append([u"n\u2081", n*F1]) Info.append([u"n\u2082", n*F2]) Info.append([u"n\u2083", n*(1-F1-F2)]) if countrate is not None: # CPP cpp = countrate/n Info.append(["cpp [kHz]", cpp]) return Info parms = [ 25, # n 5, # taud1 1000, # taud2 11000, # taud3 0.5, # F1 0.01, # F2 5, # SP 1.0, # alpha21 1.0, # alpha31 0.001, # tautrip 0.01, # T 0.0 # offset ] ## Boundaries # strictly positive boundaries = [[0, np.inf]]*len(parms) # F boundaries[4] = [0,.9999999999999] boundaries[5] = [0,.9999999999999] # T boundaries[10] = [0,.9999999999999] boundaries[-1] = [-np.inf, np.inf] model_setup( modelid=6082, name="Twofold 3D and one 2D diffusion with triplet (confocal)", comp="T+3D+3D+2D", mtype="Confocal (Gaussian) and triplet", fctn=CF_Gxyz_gauss_3D3D2DT, par_labels=[ u"n", u"τ"+u"\u2081"+" [ms]", u"τ"+u"\u2082"+" [ms]", u"τ"+u"\u2083"+" [ms]", u"F"+u"\u2081", u"F"+u"\u2082", u"SP", u"\u03b1\u2082\u2081", u"\u03b1\u2083\u2081", u"τ_trip [ms]", u"T", u"offset" ], par_values=parms, par_vary=[True, True, False, True, False, True, False, False, False, False, True, False], par_boundaries=boundaries, par_constraints=[[2, ">", 1], [3, ">", 2], [9, "<", 1], [5, 4, "<", "1"]], par_hr_labels=[ u"n", u"τ"+u"\u2081"+" [ms]", u"τ"+u"\u2082"+" [ms]", u"τ"+u"\u2083"+" [ms]", u"F"+u"\u2081", u"F"+u"\u2082", u"SP", u"\u03b1\u2082\u2081", u"\u03b1\u2083\u2081", u"τ_trip [µs]", u"T", u"offset" ], par_hr_factors=[ 1., # n 1., # taud1 1., # taud2 1., # taud3 1., # F1 1., # F2 1., # SP 1., # alpha21 1., # alpha31 1000., # tautrip [µs] 1., # T 1. # offset ], supplementary_method=supplements ) PyCorrFit-0.9.9/pycorrfit/models/model_confocal_t_3d_3d_3d.py000066400000000000000000000152501274265304700241360ustar00rootroot00000000000000# -*- coding: utf-8 -*- from __future__ import division import numpy as np from .control import model_setup from .cp_confocal import threed from .cp_triplet import trip from .cp_mix import triple_pnum def CF_Gxyz_gauss_3D3D3DT(parms, tau): u""" Three-component three-dimensional free diffusion with a Gaussian laser profile, including a triplet component. The triplet factor takes into account a blinking term. Set *T* or *τ_trip* to 0, if no triplet component is wanted. F₃ = 1-F₁-F₂ particle1 = F₁/( (1+τ/τ₁) * sqrt(1+τ/(τ₁*SP²))) particle2 = α₂₁² * F₂/( (1+τ/τ₂) * sqrt(1+τ/(τ₂*SP²))) particle3 = α₃₁² * F₃/( (1+τ/τ₃) * sqrt(1+τ/(τ₃*SP²))) triplet = 1 + T/(1-T)*exp(-τ/τ_trip) norm = (F₁ + α₂₁*F₂ + α₃₁*F₃)² G = 1/n*(particle1 + particle2 + particle3)*triplet/norm + offset *parms* - a list of parameters. Parameters (parms[i]): [0] n Effective number of particles in confocal volume (n = n₁+n₂+n₃) [1] τ₁ Diffusion time of particle species 1 [2] τ₂ Diffusion time of particle species 2 [3] τ₃ Diffusion time of particle species 3 [4] F₁ Fraction of molecules of species 1 (n₁ = n*F₁) 0 <= F₁ <= 1 [5] F₂ Fraction of molecules of species 2 (n₂ = n*F₂) 0 <= F₂ <= 1 [6] SP SP=z₀/r₀, Structural parameter, describes elongation of the confocal volume [7] α₂₁ Relative molecular brightness of particle 2 compared to particle 1 (α = q₂/q₁) [8] α₃₁ Relative molecular brightness of particle 3 compared to particle 1 (α = q₃/q₁) [9] τ_trip Characteristic residence time in triplet state [10] T Fraction of particles in triplet (non-fluorescent) state 0 <= T < 1 [11] offset *tau* - lag time """ n=parms[0] taud1=parms[1] taud2=parms[2] taud3=parms[3] F1=parms[4] F2=parms[5] SP=parms[6] alpha21=parms[7] alpha31=parms[8] tautrip=parms[9] T=parms[10] off=parms[11] g = triple_pnum(n=n, F1=F1, F2=F2, alpha21=alpha21, alpha31=alpha31, comp1=threed, comp2=threed, comp3=threed, kwargs1={"tau":tau, "taudiff":taud1, "SP":SP}, kwargs2={"tau":tau, "taudiff":taud2, "SP":SP}, kwargs3={"tau":tau, "taudiff":taud3, "SP":SP}, ) tr = trip(tau=tau, T=T, tautrip=tautrip) G = off + g*tr return G def supplements(parms, countrate=None): u"""Supplementary parameters: [12] n₁ = n*F₁ Particle number of species 1 [13] n₂ = n*F₂ Particle number of species 2 [14] n₃ = n*F₃ Particle number of species 3 (F₃ = 1-F₁-F₂) """ # We can only give you the effective particle number n = parms[0] F1 = parms[4] F2 = parms[5] Info = list() # The enumeration of these parameters is very important for # plotting the normalized curve. Countrate must come out last! Info.append([u"n\u2081", n*F1]) Info.append([u"n\u2082", n*F2]) Info.append([u"n\u2083", n*(1-F1-F2)]) if countrate is not None: # CPP cpp = countrate/n Info.append(["cpp [kHz]", cpp]) return Info parms = [ 25, # n 5, # taud1 1000, # taud2 11000, # taud3 0.5, # F1 0.01, # F2 5, # SP 1.0, # alpha21 1.0, # alpha31 0.001, # tautrip 0.01, # T 0.0 # offset ] ## Boundaries # strictly positive boundaries = [[0, np.inf]]*len(parms) # F boundaries[4] = [0,.9999999999999] boundaries[5] = [0,.9999999999999] # T boundaries[10] = [0,.9999999999999] boundaries[-1] = [-np.inf, np.inf] model_setup( modelid=6081, name="Threefold 3D diffusion with triplet (confocal)", comp="T+3D+3D+3D", mtype="Confocal (Gaussian) and triplet", fctn=CF_Gxyz_gauss_3D3D3DT, par_labels=[ u"n", u"τ"+u"\u2081"+" [ms]", u"τ"+u"\u2082"+" [ms]", u"τ"+u"\u2083"+" [ms]", u"F"+u"\u2081", u"F"+u"\u2082", u"SP", u"\u03b1\u2082\u2081", u"\u03b1\u2083\u2081", u"τ_trip [ms]", u"T", u"offset" ], par_values=parms, par_vary=[True, True, False, True, False, True, False, False, False, False, True, False], par_boundaries=boundaries, par_constraints=[[2, ">", 1], [3, ">", 2], [9, "<", 1], [5, 4, "<", "1"]], par_hr_labels=[ u"n", u"τ"+u"\u2081"+" [ms]", u"τ"+u"\u2082"+" [ms]", u"τ"+u"\u2083"+" [ms]", u"F"+u"\u2081", u"F"+u"\u2082", u"SP", u"\u03b1\u2082\u2081", u"\u03b1\u2083\u2081", u"τ_trip [µs]", u"T", u"offset" ], par_hr_factors=[ 1., # n 1., # taud1 1., # taud2 1., # taud3 1., # F1 1., # F2 1., # SP 1., # alpha21 1., # alpha31 1000., # tautrip [µs] 1., # T 1. # offset ], supplementary_method=supplements ) PyCorrFit-0.9.9/pycorrfit/models/model_confocal_tt_2d_2d.py000066400000000000000000000131241274265304700237300ustar00rootroot00000000000000# -*- coding: utf-8 -*- from __future__ import division import numpy as np from .control import model_setup from .cp_confocal import twod from .cp_triplet import trip from .cp_mix import double_pnum # 2D + 2D + TT Gauß # Model 6044 def CF_Gxyz_gauss_2D2DTT(parms, tau): u""" Two-component three-dimensional free diffusion with a Gaussian laser profile, including two triplet components. The triplet factor takes into account a blinking term. Set *T* or *τ_trip* to 0, if no triplet component is wanted. particle1 = F₁/(1+τ/τ₁) particle2 = α²*(1-F₁)/(1+τ/τ₂) triplet1 = 1 + T₁/(1-T₁)*exp(-τ/τ_trip₁) triplet2 = 1 + T₂/(1-T₂)*exp(-τ/τ_trip₂) norm = (F₁ + α*(1-F₁))² G = 1/n*(particle1 + particle2)*triplet1*triplet2/norm + offset *parms* - a list of parameters. Parameters (parms[i]): [0] n Effective number of particles in confocal volume (n = n₁+n₂) [1] τ₁ Diffusion time of particle species 1 [2] τ₂ Diffusion time of particle species 2 [3] F₁ Fraction of molecules of species 1 (n₁ = n*F₁) 0 <= F₁ <= 1 [4] α Relative molecular brightness of particle 2 compared to particle 1 (α = q₂/q₁) [5] τ_trip₁ Characteristic residence time in triplet state [6] T₁ Fraction of particles in triplet (non-fluorescent) state 0 <= T < 1 [7] τ_trip₂ Characteristic residence time in triplet state [8] T₂ Fraction of particles in triplet (non-fluorescent) state 0 <= T < 1 [9] offset *tau* - lag time """ n=parms[0] taud1=parms[1] taud2=parms[2] F=parms[3] alpha=parms[4] tautrip1=parms[5] T1=parms[6] tautrip2=parms[7] T2=parms[8] off=parms[9] g = double_pnum(n=n, F1=F, alpha=alpha, comp1=twod, comp2=twod, kwargs1={"tau":tau, "taudiff":taud1}, kwargs2={"tau":tau, "taudiff":taud2}, ) tr1 = trip(tau=tau, T=T1, tautrip=tautrip1) tr2 = trip(tau=tau, T=T2, tautrip=tautrip2) G = off + g * tr1 * tr2 return G def supplements(parms, countrate=None): u"""Supplementary parameters: [10] n₁ = n*F₁ Particle number of species 1 [11] n₂ = n*(1-F₁) Particle number of species 2 """ # We can only give you the effective particle number n = parms[0] F1 = parms[3] Info = list() # The enumeration of these parameters is very important for # plotting the normalized curve. Countrate must come out last! Info.append([u"n\u2081", n*F1]) Info.append([u"n\u2082", n*(1.-F1)]) if countrate is not None: # CPP cpp = countrate/n Info.append(["cpp [kHz]", cpp]) return Info parms = [ 25, # n 5, # taud1 1000, # taud2 0.5, # F 1.0, # alpha 0.001, # tautrip1 0.01, # T1 0.002, # tautrip2 0.01, # T2 0.0 # offset ] ## Boundaries # strictly positive boundaries = [[0, np.inf]]*len(parms) # F boundaries[3] = [0,.9999999999999] # T boundaries[6] = [0,.9999999999999] boundaries[8] = [0,.9999999999999] boundaries[-1] = [-np.inf, np.inf] model_setup( modelid=6044, name="Separate 2D diffusion with double triplet (confocal)", comp="T+T+2D+2D", mtype="Confocal (Gaussian) with double triplet", fctn=CF_Gxyz_gauss_2D2DTT, par_labels=[ u"n", u"τ"+u"\u2081"+" [ms]", u"τ"+u"\u2082"+" [ms]", u"F"+u"\u2081", u"\u03b1"+" (q"+u"\u2082"+"/q"+u"\u2081"+")", u"τ_trip₁ [ms]", u"T₁", u"τ_trip₂ [ms]", u"T₂", u"offset" ], par_values=parms, par_vary=[True, True, True, True, False, False, False, False, False, False], par_boundaries=boundaries, par_constraints=[[2, ">", 1], [5, "<", 1], [7, ">", 5]], par_hr_labels=[ u"n", u"τ₁ [ms]", u"τ₂ [ms]", u"F₁", u"\u03b1"+u" (q₂/q₁)", u"τ_trip₁ [µs]", u"T₁", u"τ_trip₂ [µs]", u"T₂", u"offset" ], par_hr_factors=[ 1., # n 1., # taud1 1., # taud2 1., # F 1., # alpha 1000., # tautrip1 [µs] 1., # T1 1000., # tautrip2 [µs] 1., # T2 1. # offset ], supplementary_method=supplements ) PyCorrFit-0.9.9/pycorrfit/models/model_confocal_tt_3d_2d.py000066400000000000000000000140261274265304700237330ustar00rootroot00000000000000# -*- coding: utf-8 -*- from __future__ import division import numpy as np from .control import model_setup from .cp_confocal import twod, threed from .cp_triplet import trip from .cp_mix import double_pnum # 3D + 2D + TT Gauß # Model 6045 def CF_Gxyz_gauss_3D2DTT(parms, tau): u""" Two-component three-dimensional free diffusion with a Gaussian laser profile, including two triplet components. The triplet factor takes into account a blinking term. Set *T* or *τ_trip* to 0, if no triplet component is wanted. particle2D = (1-F)/ (1+τ/τ_2D) particle3D = α²*F/( (1+τ/τ_3D) * sqrt(1+τ/(τ_3D*SP²))) triplet1 = 1 + T₁/(1-T₁)*exp(-τ/τ_trip₁) triplet2 = 1 + T₂/(1-T₂)*exp(-τ/τ_trip₂) norm = (F₁ + α*(1-F₁))² G = 1/n*(particle2D + particle3D)*triplet1*triplet2/norm + offset *parms* - a list of parameters. Parameters (parms[i]): [0] n Effective number of particles in confocal volume (n = n2D+n3D) [1] τ_2D Diffusion time of surface bound particls [2] τ_3D Diffusion time of freely diffusing particles [3] F₁ Fraction of molecules of species 1 (n₁ = n*F₁) 0 <= F₁ <= 1 [4] SP SP=z₀/r₀, Structural parameter, describes elongation of the confocal volume [5] α Relative molecular brightness of particle 2 compared to particle 1 (α = q₂/q₁) [6] τ_trip₁ Characteristic residence time in triplet state [7] T₁ Fraction of particles in triplet (non-fluorescent) state 0 <= T < 1 [8] τ_trip₂ Characteristic residence time in triplet state [9] T₂ Fraction of particles in triplet (non-fluorescent) state 0 <= T < 1 [10] offset *tau* - lag time """ n=parms[0] taud2D=parms[1] taud3D=parms[2] F=parms[3] SP=parms[4] alpha=parms[5] tautrip1=parms[6] T1=parms[7] tautrip2=parms[8] T2=parms[9] off=parms[10] g = double_pnum(n=n, F1=1-F, alpha=alpha, comp1=twod, comp2=threed, kwargs1={"tau":tau, "taudiff":taud2D}, kwargs2={"tau":tau, "taudiff":taud3D, "SP":SP}, ) tr1 = trip(tau=tau, T=T1, tautrip=tautrip1) tr2 = trip(tau=tau, T=T2, tautrip=tautrip2) G = off + g * tr1 * tr2 return G def supplements(parms, countrate=None): u"""Supplementary parameters: Effective number of freely diffusing particles in 3D solution: [11] n3D = n*F Effective number particles diffusing on 2D surface: [12] n2D = n*(1-F) """ # We can only give you the effective particle number n = parms[0] F3d = parms[3] Info = list() # The enumeration of these parameters is very important for # plotting the normalized curve. Countrate must come out last! Info.append([u"n3D", n*F3d]) Info.append([u"n2D", n*(1.-F3d)]) if countrate is not None: # CPP cpp = countrate/n Info.append([u"cpp [kHz]", cpp]) return Info parms = [ 25, # n 240, # taud2D 0.1, # taud3D 0.5, # F3D 5, # SP 1.0, # alpha 0.001, # tautrip1 0.01, # T1 0.002, # tautrip2 0.01, # T2 0.0 # offset ] ## Boundaries # strictly positive boundaries = [[0, np.inf]]*len(parms) # F boundaries[3] = [0,.9999999999999] # T boundaries[7] = [0,.9999999999999] boundaries[9] = [0,.9999999999999] boundaries[-1] = [-np.inf, np.inf] model_setup( modelid=6045, name="Separate 3D and 2D diffusion with double triplet (confocal)", comp="T+T+3D+2D", mtype="Confocal (Gaussian) with double triplet", fctn=CF_Gxyz_gauss_3D2DTT, par_labels=[ u"n", u"τ_2D [ms]", u"τ_3D [ms]", u"F_3D", u"SP", u"\u03b1"+" (q_3D/q_2D)", u"τ_trip₁ [ms]", u"T₁", u"τ_trip₂ [ms]", u"T₂", u"offset" ], par_values=parms, par_vary=[True, True, True, True, False, False, False, False, False, False, False], par_boundaries=boundaries, par_constraints=[[2, "<", 1], [6, "<", 2], [8, ">", 6]], par_hr_labels=[ u"n", u"τ_2D [ms]", u"τ_3D [ms]", u"F_3D", u"SP", u"\u03b1"+" (q_3D/q_2D)", u"τ_trip₁ [µs]", u"T₁", u"τ_trip₂ [µs]", u"T₂", u"offset" ], par_hr_factors=[ 1., # n 1., # "τ_2D [ms]", 1., # "τ_3D [ms]", 1., # "F_3D", 1., # "SP", 1., # u"\u03b1"+" (q_3D/q_2D)", 1000., # tautrip1 [µs] 1., # T1 1000., # tautrip2 [µs] 1., # T2 1. # offset ], supplementary_method=supplements ) PyCorrFit-0.9.9/pycorrfit/models/model_confocal_tt_3d_3d.py000066400000000000000000000140011274265304700237250ustar00rootroot00000000000000# -*- coding: utf-8 -*- from __future__ import division import numpy as np from .control import model_setup from .cp_confocal import threed from .cp_triplet import trip from .cp_mix import double_pnum # 3D + 3D + Triplet Gauß # Model 6043 def CF_Gxyz_gauss_3D3DTT(parms, tau): u""" Two-component three-dimensional free diffusion with a Gaussian laser profile, including two triplet components. The triplet factor takes into account a blinking term. Set *T* or *τ_trip* to 0, if no triplet component is wanted. particle1 = F₁/( (1+τ/τ₁) * sqrt(1+τ/(τ₁*SP²))) particle2 = α*(1-F₁)/( (1+τ/τ₂) * sqrt(1+τ/(τ₂*SP²))) triplet1 = 1 + T₁/(1-T₁)*exp(-τ/τ_trip₁) triplet2 = 1 + T₂/(1-T₂)*exp(-τ/τ_trip₂) norm = (F₁ + α*(1-F₁))² G = 1/n*(particle1 + particle2)*triplet1*triplet2/norm + offset *parms* - a list of parameters. Parameters (parms[i]): [0] n Effective number of particles in confocal volume (n = n₁+n₂) [1] τ₁ Diffusion time of particle species 1 [2] τ₂ Diffusion time of particle species 2 [3] F₁ Fraction of molecules of species 1 (n₁ = n*F₁) 0 <= F₁ <= 1 [4] SP SP=z₀/r₀, Structural parameter, describes elongation of the confocal volume [5] α Relative molecular brightness of particle 2 compared to particle 1 (α = q₂/q₁) [6] τ_trip₁ Characteristic residence time in triplet state [7] T₁ Fraction of particles in triplet (non-fluorescent) state 0 <= T < 1 [8] τ_trip₂ Characteristic residence time in triplet state [9] T₂ Fraction of particles in triplet (non-fluorescent) state 0 <= T < 1 [10] offset *tau* - lag time """ n=parms[0] taud1=parms[1] taud2=parms[2] F=parms[3] SP=parms[4] alpha=parms[5] tautrip1=parms[6] T1=parms[7] tautrip2=parms[8] T2=parms[9] off=parms[10] g = double_pnum(n=n, F1=F, alpha=alpha, comp1=threed, comp2=threed, kwargs1={"tau":tau, "taudiff":taud1, "SP":SP}, kwargs2={"tau":tau, "taudiff":taud2, "SP":SP}, ) tr1 = trip(tau=tau, T=T1, tautrip=tautrip1) tr2 = trip(tau=tau, T=T2, tautrip=tautrip2) G = off + g * tr1 * tr2 return G def supplements(parms, countrate=None): u"""Supplementary parameters: [11] n₁ = n*F₁ Particle number of species 1 [12] n₂ = n*(1-F₁) Particle number of species 2 """ # We can only give you the effective particle number n = parms[0] F1 = parms[3] Info = list() # The enumeration of these parameters is very important for # plotting the normalized curve. Countrate must come out last! Info.append([u"n\u2081", n*F1]) Info.append([u"n\u2082", n*(1.-F1)]) if countrate is not None: # CPP cpp = countrate/n Info.append(["cpp [kHz]", cpp]) return Info parms = [ 25, # n 5, # taud1 1000, # taud2 0.5, # F 5, # SP 1.0, # alpha 0.001, # tautrip1 0.01, # T1 0.002, # tautrip2 0.01, # T2 0.0 # offset ] ## Boundaries # strictly positive boundaries = [[0, np.inf]]*len(parms) # F boundaries[3] = [0,.9999999999999] # T boundaries[7] = [0,.9999999999999] boundaries[9] = [0,.9999999999999] boundaries[-1] = [-np.inf, np.inf] model_setup( modelid=6043, name="Separate 3D diffusion with double triplet (confocal)", comp="T+T+3D+3D", mtype="Confocal (Gaussian) with double triplet", fctn=CF_Gxyz_gauss_3D3DTT, par_labels=[ u"n", u"τ"+u"\u2081"+" [ms]", u"τ"+u"\u2082"+" [ms]", u"F"+u"\u2081", u"SP", u"\u03b1"+" (q"+u"\u2082"+"/q"+u"\u2081"+")", u"τ_trip₁ [ms]", u"T₁", u"τ_trip₂ [ms]", u"T₂", u"offset" ], par_values=parms, par_vary=[True, True, True, True, False, False, False, False, False, False, False], par_boundaries=boundaries, par_constraints=[[2, ">", 1], [6, "<", 1], [8, ">", 6]], par_hr_labels=[ u"n", u"τ₁ [ms]", u"τ₂ [ms]", u"F₁", u"SP", u"\u03b1"+u" (q₂/q₁)", u"τ_trip₁ [µs]", u"T₁", u"τ_trip₂ [µs]", u"T₂", u"offset" ], par_hr_factors=[ 1., # n 1., # taud1 1., # taud2 1., # F 1., # SP 1., # alpha 1000., # tautrip1 [µs] 1., # T1 1000., # tautrip2 [µs] 1., # T2 1. # offset ], supplementary_method=supplements ) PyCorrFit-0.9.9/pycorrfit/openfile.py000066400000000000000000000754731274265304700176400ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ PyCorrFit - Module openfile This file contains definitions for opening PyCorrFit sessions and saving PyCorrFit correlation curves. """ import codecs import csv import numpy as np import os import shutil import tempfile import yaml import zipfile import warnings # These imports are required for loading data from .readfiles import Filetypes # @UnusedImport from .readfiles import BGFiletypes # @UnusedImport from .trace import Trace from . import meta __version__ = meta.get_version() def LoadSessionData(sessionfile, parameters_only=False): """ Load PyCorrFit session data from a zip file (.pcfs) Parameters ---------- sessionfile : str File from which data will be loaded parameters_only : bool Only load the parameters from the YAML file Returns ------- Infodict : dict Infodict may contain the following keys: "Backgrounds", list: contains the backgrounds "Comments", dict: "Session" comment and int keys to Page titles "Correlations", dict: page numbers, all correlation curves "External Functions", dict: modelids to external model functions "External Weights", dict: page numbers, external weights for fitting "Parameters", dict: page numbers, all parameters of the pages "Preferences", dict: not used yet "Traces", dict: page numbers, all traces of the pages "Version", str: the PyCorrFit version of the session """ Infodict = dict() # Get the version Arc = zipfile.ZipFile(sessionfile, mode='r') readmefile = Arc.open("Readme.txt") # e.g. "This file was created using PyCorrFit version 0.7.6" Infodict["Version"] = readmefile.readline()[46:].strip() readmefile.close() # Get the yaml parms dump: yamlfile = Arc.open("Parameters.yaml") # Parameters: Fitting and drawing parameters of correlation curve # The *yamlfile* is responsible for the order of the Pages #i. Infodict["Parameters"] = yaml.safe_load(yamlfile) yamlfile.close() if parameters_only: Arc.close() return Infodict # Supplementary data (errors of fit) supname = "Supplements.yaml" try: Arc.getinfo(supname) except: pass else: supfile = Arc.open(supname) supdata = yaml.safe_load(supfile) Infodict["Supplements"] = dict() for idp in supdata: Infodict["Supplements"][idp[0]] = dict() Infodict["Supplements"][idp[0]]["FitErr"] = idp[1] if len(idp) > 2: # As of version 0.7.4 we save chi2 and shared pages -global fit Infodict["Supplements"][idp[0]]["Chi sq"] = idp[2] Infodict["Supplements"][idp[0]]["Global Share"] = idp[3] ## Preferences: Reserved for a future version of PyCorrFit :) prefname = "Preferences.yaml" try: Arc.getinfo(prefname) except KeyError: pass else: yamlpref = Arc.open(prefname) Infodict["Preferences"] = yaml.safe_load(yamlpref) yamlpref.close() # Get external functions Infodict["External Functions"] = dict() key = 7001 while key <= 7999: # (There should not be more than 1000 functions) funcfilename = "model_"+str(key)+".txt" try: Arc.getinfo(funcfilename) except KeyError: # No more functions to import key = 8000 else: funcfile = Arc.open(funcfilename) Infodict["External Functions"][key] = funcfile.read() funcfile.close() key=key+1 # Get the correlation arrays Infodict["Correlations"] = dict() for i in np.arange(len(Infodict["Parameters"])): # The *number* is used to identify the correct file number = str(Infodict["Parameters"][i][0]).strip().strip(":").strip("#") pageid = int(number) expfilename = "data"+number+".csv" expfile = Arc.open(expfilename, 'r') readdata = csv.reader(expfile, delimiter=',') dataexp = list() tau = list() if str(readdata.next()[0]) == "# tau only": for row in readdata: # Exclude commentaries if (str(row[0])[0:1] != '#'): tau.append(float(row[0])) tau = np.array(tau) dataexp = None else: for row in readdata: # Exclude commentaries if (str(row[0])[0:1] != '#'): dataexp.append((float(row[0]), float(row[1]))) dataexp = np.array(dataexp) tau = dataexp[:,0] Infodict["Correlations"][pageid] = [tau, dataexp] del readdata expfile.close() # Get the Traces Infodict["Traces"] = dict() for i in np.arange(len(Infodict["Parameters"])): # The *number* is used to identify the correct file number = str(Infodict["Parameters"][i][0]).strip().strip(":").strip("#") pageid = int(number) # Find out, if we have a cross correlation data type IsCross = False try: IsCross = Infodict["Parameters"][i][7] except IndexError: # No Cross correlation pass if IsCross is False: tracefilenames = ["trace"+number+".csv"] else: # Cross correlation uses two traces tracefilenames = ["trace"+number+"A.csv", "trace"+number+"B.csv" ] thistrace = list() for tracefilename in tracefilenames: try: Arc.getinfo(tracefilename) except KeyError: pass else: tracefile = Arc.open(tracefilename, 'r') traceread = csv.reader(tracefile, delimiter=',') singletrace = list() for row in traceread: # Exclude commentaries if (str(row[0])[0:1] != '#'): singletrace.append((float(row[0]), float(row[1]))) singletrace = np.array(singletrace) thistrace.append(singletrace) del traceread del singletrace tracefile.close() if len(thistrace) != 0: Infodict["Traces"][pageid] = thistrace else: Infodict["Traces"][pageid] = None # Get the comments, if they exist commentfilename = "comments.txt" try: # Raises KeyError, if file is not present: Arc.getinfo(commentfilename) except KeyError: pass else: # Open the file commentfile = Arc.open(commentfilename, 'r') Infodict["Comments"] = dict() for i in np.arange(len(Infodict["Parameters"])): number = str(Infodict["Parameters"][i][0]).strip().strip(":").strip("#") pageid = int(number) # Strip line ending characters for all the Pages. Infodict["Comments"][pageid] = commentfile.readline().strip() # Now Add the Session Comment (the rest of the file). ComList = commentfile.readlines() Infodict["Comments"]["Session"] = '' for line in ComList: Infodict["Comments"]["Session"] += line commentfile.close() # Get the Backgroundtraces and data if they exist bgfilename = "backgrounds.csv" try: # Raises KeyError, if file is not present: Arc.getinfo(bgfilename) except KeyError: pass else: # Open the file Infodict["Backgrounds"] = list() bgfile = Arc.open(bgfilename, 'r') bgread = csv.reader(bgfile, delimiter='\t') i = 0 for bgrow in bgread: bgtracefilename = "bg_trace"+str(i)+".csv" bgtracefile = Arc.open(bgtracefilename, 'r') bgtraceread = csv.reader(bgtracefile, delimiter=',') bgtrace = list() for row in bgtraceread: # Exclude commentaries if (str(row[0])[0:1] != '#'): bgtrace.append((np.float(row[0]), np.float(row[1]))) bgtrace = np.array(bgtrace) newbackground = Trace(trace=bgtrace, name=str(bgrow[1]), countrate=np.float(bgrow[0])) Infodict["Backgrounds"].append(newbackground) i = i + 1 bgfile.close() # Get external weights if they exist WeightsFilename = "externalweights.txt" try: # Raises KeyError, if file is not present: Arc.getinfo(WeightsFilename) except: pass else: Wfile = Arc.open(WeightsFilename, 'r') Wread = csv.reader(Wfile, delimiter='\t') Weightsdict = dict() for wrow in Wread: Pkey = wrow[0] # Page of weights pageid = int(Pkey) # Do not overwrite anything try: Weightsdict[pageid] except: Weightsdict[pageid] = dict() Nkey = wrow[1] # Name of weights Wdatafilename = "externalweights_data"+Pkey+"_"+Nkey+".csv" Wdatafile = Arc.open(Wdatafilename, 'r') Wdatareader = csv.reader(Wdatafile) Wdata = list() for row in Wdatareader: # Exclude commentaries if (str(row[0])[0:1] != '#'): Wdata.append(np.float(row[0])) Weightsdict[pageid][Nkey] = np.array(Wdata) Infodict["External Weights"] = Weightsdict ## Preferences preferencesname = "preferences.cfg" try: # Raises KeyError, if file is not present: Arc.getinfo(preferencesname) except: pass else: prefdict = {} with Arc.open(preferencesname) as fd: data = fd.readlines() for line in data: line = line.strip() if len(line) == 0 or line.startswith("#"): continue key, value = line.split("=") key = key.strip() value = value.strip() if value.count(","): value = [ v.strip() for v in value.split(",")] prefdict[key] = value Infodict["Preferences"] = prefdict Arc.close() return Infodict def SaveSessionData(sessionfile, Infodict): """ Session PyCorrFit session data to file. Parameters ---------- sessionfile : str The suffix ".pcfs" is automatically appended. Infodict : dict Infodict may contain the following keys: "Backgrounds", list: contains the backgrounds "Comments", dict: "Session" comment and int keys to Page titles "Correlations", dict: page numbers, all correlation curves "External Functions, dict": modelids to external model functions "External Weights", dict: page numbers, external weights for fitting "Parameters", dict: page numbers, all parameters of the pages "Preferences", dict: fixed page parameters "Traces", dict: page numbers, all traces of the pages The version of PyCorrFit is written to Readme.txt """ (dirname, filename) = os.path.split(sessionfile) # Sometimes you have multiple endings... if filename.endswith(".pcfs") is not True: filename += ".pcfs" # Change working directory returnWD = os.getcwd() tempdir = tempfile.mkdtemp() os.chdir(tempdir) # Create zip file Arc = zipfile.ZipFile(filename, mode='w') # Only do the Yaml thing for safe operations. # Make the yaml dump parmsfilename = "Parameters.yaml" # Parameters have to be floats in lists # in order for yaml.safe_load to work. Parms = Infodict["Parameters"] ParmsKeys = Parms.keys() ParmsKeys.sort() Parmlist = list() for idparm in ParmsKeys: # Make sure we do not accidently save arrays. # This would not work correctly with yaml. # Parameters Parms[idparm][2] = np.array(Parms[idparm][2],dtype="float").tolist() # Parameter varied Parms[idparm][3] = np.array(Parms[idparm][3],dtype="bool").tolist() # Channel selection Parms[idparm][4] = np.array(Parms[idparm][4],dtype="int").tolist() # Background selection for ii in range(len(Parms[idparm][6])): if Parms[idparm][6][ii] is not None: Parms[idparm][6][ii] = int(Parms[idparm][6][ii]) # Plot normalization if Parms[idparm][8] is not None: Parms[idparm][8] = int(Parms[idparm][8]) # Fit parameter range Parms[idparm][9] = np.array(Parms[idparm][9],dtype="float").tolist() Parmlist.append(Parms[idparm]) try: # We would like to perform safe_dump, because in the # Windoes x64 version, some integers are exported # like this: `!!python/long '105'` using `yaml.dump`. with open(parmsfilename, "wb") as yamlfd: yaml.safe_dump(Parmlist, yamlfd) except yaml.representer.RepresenterError: # `RepresenterError: cannot represent an object: 0` # In this case, we choose to use the normal dump # and pray. # However, this should not happen, because in the above # for-loop we set the correct dtype for each parameter . if os.path.exists(parmsfilename): os.remove(parmsfilename) with open(parmsfilename, "wb") as yamlfd: yaml.dump(Parmlist, yamlfd) Arc.write(parmsfilename) os.remove(os.path.join(tempdir, parmsfilename)) # Supplementary data (errors of fit) errsfilename = "Supplements.yaml" Sups = Infodict["Supplements"] SupKeys = Sups.keys() SupKeys.sort() Suplist = list() for idsup in SupKeys: error = Sups[idsup]["FitErr"] chi2 = Sups[idsup]["Chi sq"] globalshare = Sups[idsup]["Global Share"] Suplist.append([idsup, error, chi2, globalshare]) yaml.safe_dump(Suplist, open(errsfilename, "wb")) Arc.write(errsfilename) os.remove(os.path.join(tempdir, errsfilename)) # Save external functions for key in Infodict["External Functions"].keys(): funcfilename = "model_"+str(key)+".txt" funcfile = codecs.open(funcfilename, 'w', encoding="utf-8") funcfile.write(Infodict["External Functions"][key]) funcfile.close() Arc.write(funcfilename) os.remove(os.path.join(tempdir, funcfilename)) # Save (dataexp and tau)s into separate csv files. for pageid in Infodict["Correlations"].keys(): # Since *Array* and *Parms* are in the same order (the page order), # we will identify the filename by the Page title number. number = str(pageid) expfilename = "data"+number+".csv" expfile = open(expfilename, 'wb') tau = Infodict["Correlations"][pageid][0] exp = Infodict["Correlations"][pageid][1] dataWriter = csv.writer(expfile, delimiter=',') if exp is not None: # Names of Columns dataWriter.writerow(['# tau', 'experimental data']) # Actual Data # Do not use len(tau) instead of len(exp[:,0])) ! # Otherwise, the experimental data will not be saved entirely, # if it has been cropped. Because tau might be smaller, than # exp[:,0] --> tau = exp[startcrop:endcrop,0] for j in np.arange(len(exp[:,0])): dataWriter.writerow(["%.20e" % exp[j,0], "%.20e" % exp[j,1]]) else: # Only write tau dataWriter.writerow(['# tau'+' only']) for j in np.arange(len(tau)): dataWriter.writerow(["%.20e" % tau[j]]) expfile.close() # Add to archive Arc.write(expfilename) os.remove(os.path.join(tempdir, expfilename)) # Save traces into separate csv files. for pageid in Infodict["Traces"].keys(): number = str(pageid) # Since *Trace* and *Parms* are in the same order, which is the # Page order, we will identify the filename by the Page title # number. if Infodict["Traces"][pageid] is not None and len(Infodict["Traces"][pageid]) != 0: if Parms[pageid][7] is True: # We have cross correlation: save two traces ## A tracefilenamea = "trace"+number+"A.csv" tracefile = open(tracefilenamea, 'wb') traceWriter = csv.writer(tracefile, delimiter=',') time = Infodict["Traces"][pageid][0][:,0] rate = Infodict["Traces"][pageid][0][:,1] # Names of Columns traceWriter.writerow(['# time', 'count rate']) # Actual Data for j in np.arange(len(time)): traceWriter.writerow(["%.20e" % time[j], "%.20e" % rate[j]]) tracefile.close() # Add to archive Arc.write(tracefilenamea) os.remove(os.path.join(tempdir, tracefilenamea)) ## B tracefilenameb = "trace"+number+"B.csv" tracefile = open(tracefilenameb, 'wb') traceWriter = csv.writer(tracefile, delimiter=',') time = Infodict["Traces"][pageid][1][:,0] rate = Infodict["Traces"][pageid][1][:,1] # Names of Columns traceWriter.writerow(['# time', 'count rate']) # Actual Data for j in np.arange(len(time)): traceWriter.writerow(["%.20e" % time[j], "%.20e" % rate[j]]) tracefile.close() # Add to archive Arc.write(tracefilenameb) os.remove(os.path.join(tempdir, tracefilenameb)) else: # Save one single trace tracefilename = "trace"+number+".csv" tracefile = open(tracefilename, 'wb') traceWriter = csv.writer(tracefile, delimiter=',') time = Infodict["Traces"][pageid][0][:,0] rate = Infodict["Traces"][pageid][0][:,1] # Names of Columns traceWriter.writerow(['# time', 'count rate']) # Actual Data for j in np.arange(len(time)): traceWriter.writerow(["%.20e" % time[j], "%.20e" % rate[j]]) tracefile.close() # Add to archive Arc.write(tracefilename) os.remove(os.path.join(tempdir, tracefilename)) # Save comments into txt file commentfilename = "comments.txt" commentfile = codecs.open(commentfilename, 'w', encoding="utf-8") # Comments[-1] is comment on whole Session Ckeys = Infodict["Comments"].keys() Ckeys.sort() for key in Ckeys: if key != "Session": commentfile.write(Infodict["Comments"][key]+"\r\n") commentfile.write(Infodict["Comments"]["Session"]) commentfile.close() Arc.write(commentfilename) os.remove(os.path.join(tempdir, commentfilename)) ## Save Background information: Background = Infodict["Backgrounds"] if len(Background) > 0: # We do not use a comma separated, but a tab separated file, # because a comma might be in the name of a bg. bgfilename = "backgrounds.csv" bgfile = open(bgfilename, 'wb') bgwriter = csv.writer(bgfile, delimiter='\t') for i in np.arange(len(Background)): bgwriter.writerow([str(Background[i].countrate), Background[i].name]) # Traces bgtracefilename = "bg_trace"+str(i)+".csv" bgtracefile = open(bgtracefilename, 'wb') bgtraceWriter = csv.writer(bgtracefile, delimiter=',') bgtraceWriter.writerow(['# time', 'count rate']) # Actual Data time = Background[i][:,0] rate = Background[i][:,1] for j in np.arange(len(time)): bgtraceWriter.writerow(["%.20e" % time[j], "%.20e" % rate[j]]) bgtracefile.close() # Add to archive Arc.write(bgtracefilename) os.remove(os.path.join(tempdir, bgtracefilename)) bgfile.close() Arc.write(bgfilename) os.remove(os.path.join(tempdir, bgfilename)) ## Save External Weights information WeightedPageID = Infodict["External Weights"].keys() WeightedPageID.sort() WeightFilename = "externalweights.txt" WeightFile = open(WeightFilename, 'wb') WeightWriter = csv.writer(WeightFile, delimiter='\t') for pageid in WeightedPageID: number = str(pageid) NestWeights = Infodict["External Weights"][pageid].keys() # The order of the types does not matter, since they are # sorted in the frontend and upon import. We sort them here, anyhow. NestWeights.sort() for Nkey in NestWeights: WeightWriter.writerow([number, str(Nkey).strip()]) # Add data to a File WeightDataFilename = "externalweights_data"+number+\ "_"+str(Nkey).strip()+".csv" WeightDataFile = open(WeightDataFilename, 'wb') WeightDataWriter = csv.writer(WeightDataFile) wdata = Infodict["External Weights"][pageid][Nkey] for jw in np.arange(len(wdata)): WeightDataWriter.writerow([str(wdata[jw])]) WeightDataFile.close() Arc.write(WeightDataFilename) os.remove(os.path.join(tempdir, WeightDataFilename)) WeightFile.close() Arc.write(WeightFilename) os.remove(os.path.join(tempdir, WeightFilename)) ## Preferences preferencesname = "preferences.cfg" with codecs.open(preferencesname, 'w',encoding="utf-8") as fd: for key in Infodict["Preferences"]: value = Infodict["Preferences"][key] if isinstance(value, list): value = " ".join("{}".format(it) for it in value) else: value = "{}".format(value) fd.write("{} = {}\n".format(key, value)) Arc.write(preferencesname) os.remove(os.path.join(tempdir, preferencesname)) ## Readme rmfilename = "Readme.txt" rmfile = codecs.open(rmfilename, 'w', encoding="utf-8") rmfile.write(ReadmeSession) rmfile.close() Arc.write(rmfilename) os.remove(os.path.join(tempdir, rmfilename)) # Close the archive Arc.close() # Move archive to destination directory shutil.move(os.path.join(tempdir, filename), os.path.join(dirname, filename) ) # Go to destination directory os.chdir(returnWD) os.rmdir(tempdir) def ExportCorrelation(exportfile, correlation, page_info, savetrace=True): """ Write correlation data (as displayed in PyCorrFit) to a file Parameters ---------- exportfile : str Absolute file name to save the data to correlation : PyCorrFit "Correlation" object Contains all correlation data page_info : module A multi-line string containing information on the correlation that will be written to the file as a comment savetrace : bool Append the trace to the file Notes ----- Note that this method exports the plotted data: - Correlation.correlation_plot - Correlation.residuals_plot - Correlation.modeled_plot which means that the data could be normalized to, for instance, the total particle number `n`. """ openedfile = codecs.open(exportfile, 'w', encoding="utf-8") ## First, some doc text openedfile.write(ReadmeCSV.replace('\n', '\r\n')) # The info for line in page_info.splitlines(): openedfile.write(u"# "+line+"\r\n") openedfile.write(u"#\r\n#\r\n") # Get all the data we need from the Page # Modeled data corr = correlation mod = corr.modeled_plot[:,1] if corr.correlation is not None: # Experimental data tau = corr.correlation_plot[:,0] exp = corr.correlation_plot[:,1] res = corr.residuals_plot[:,1] # Plotting! Because we only export plotted area. if corr.is_weighted_fit: weightname = corr.fit_weight_type try: weight = corr.fit_results["fit weights"] except KeyError: weight = corr.fit_weight_data if weight is None: pass elif len(weight) != len(exp): text = "Weights have not been calculated for the "+\ "area you want to export. Pressing 'Fit' "+\ "again should solve this issue. Weights will "+\ "not be saved." warnings.warn(text) weight = None else: weight = None weightname = None else: tau = corr.lag_time_fit exp = None res = None # Include weights in data saving: # PyCorrFit thinks in [ms], but we will save as [s] timefactor = 0.001 tau = timefactor * tau ## Now we want to write all that data into the file # This is for csv writing: ## Correlation curve dataWriter = csv.writer(openedfile, delimiter='\t') if exp is not None: header = '# Lag time [s]'+"\t"+ \ 'Experimental correlation'+"\t"+ \ 'Fitted correlation'+ "\t"+ \ 'Residuals'+"\r\n" data = [tau, exp, mod, res] if corr.is_weighted_fit and weight is not None: header = "{} \t Weights [{}] \r\n".format( header.strip(), weightname) data.append(weight) else: header = '# Lag time [s]'+"\t"+ \ 'Correlation function'+"\r\n" data = [tau, mod] # Write header openedfile.write(header) # Write data for i in np.arange(len(data[0])): # row-wise, data may have more than two elements per row datarow = list() for j in np.arange(len(data)): rowcoli = "{:.10e}".format(data[j][i]) datarow.append(rowcoli) dataWriter.writerow(datarow) ## Trace # Only save the trace if user wants us to: if savetrace: # We will also save the trace in [s] # Intensity trace in kHz may stay the same if len(corr.traces) > 0: # Mark beginning of Trace openedfile.write('#\r\n#\r\n# BEGIN TRACE\r\n#\r\n') # Columns time = corr.traces[0][:,0]*timefactor intensity = corr.traces[0][:,1] # Write openedfile.write('# Time [s]'+"\t" 'Intensity trace [kHz]'+" \r\n") for i in np.arange(len(time)): dataWriter.writerow(["{:.10e}".format(time[i]), "{:.10e}".format(intensity[i])]) if len(corr.traces) > 1: # We have some cross-correlation here: # Mark beginning of Trace B openedfile.write('#\r\n#\r\n# BEGIN SECOND TRACE\r\n#\r\n') # Columns time = corr.traces[1][:,0]*timefactor intensity = corr.traces[1][:,1] # Write openedfile.write('# Time [s]'+"\t" 'Intensity trace [kHz]'+" \r\n") for i in np.arange(len(time)): dataWriter.writerow(["{:.10e}".format(time[i]), "{:.10e}".format(intensity[i])]) openedfile.close() session_wildcards = [".pcfs", ".pycorrfit-session.zip", ".fcsfit-session.zip"] ReadmeCSV = u"""# This file was created using PyCorrFit version {}. # # Lines starting with a '#' are treated as comments. # The data is stored as CSV below this comment section. # Data usually consists of lag times (channels) and # the corresponding correlation function - experimental # and fitted values plus resulting residuals. # If this file is opened by PyCorrFit, only the first two # columns will be imported as experimental data. # """.format(__version__) ReadmeSession = u"""This file was created using PyCorrFit version {}. The .zip archive you are looking at is a stored session of PyCorrFit. If you are interested in how the data is stored, you will find out here. Most important are the dimensions of units: Dimensionless representation: unit of time : 1 ms unit of inverse time: 10³ /s unit of distance : 100 nm unit of Diff.coeff : 10 µm²/s unit of inverse area: 100 /µm² unit of inv. volume : 1000 /µm³ From there, the dimension of any parameter may be calculated. There are a number of files within this archive, depending on what was done during the session. backgrounds.csv - Contains the list of backgrounds used and - Averaged intensities in [kHz] bg_trace*.csv (where * is an integer) - The trace of the background corresponding to the line number in backgrounds.csv - Time in [ms], Trace in [kHz] comments.txt - Contains page titles and session comment - First n lines are titles, rest is session comment (where n is total number of pages) data*.csv (where * is (Number of page)) - Contains lag times [ms] - Contains experimental data, if available externalweights.txt - Contains names (types) of external weights other than from Model function or spline fit - Linewise: 1st element is page number, 2nd is name - According to this data, the following files are present in the archive externalweights_data_*PageID*_*Type*.csv - Contains weighting information of Page *PageID* of type *Type* model_*ModelID*.txt - An external (user-defined) model file with internal ID *ModelID* Parameters.yaml - Contains all Parameters for each page Block format: - - '#(Number of page): ' - (Internal model ID) - (List of parameters) - (List of checked parameters (for fitting)) - [(Min channel selected), (Max channel selected)] - [(Weighted fit method (0=None, 1=Spline, 2=Model function)), (No. of bins from left and right), (No. of knots (of e.g. spline)), (Type of fitting algorithm (e.g. "Lev-Mar", "Nelder-Mead")] - [B1,B2] Background to use (line in backgrounds.csv) B2 is always *null* for autocorrelation curves - Data type is Cross-correlation? - Parameter id (int) used for normalization in plotting. This number first enumerates the model parameters and then the supplemental parameters (e.g. "n1"). - - [min, max] fitting parameter range of 1st parameter - [min, max] fitting parameter range of 2nd parameter - etc. - Order in Parameters.yaml defines order of pages in a session - Order in Parameters.yaml defines order in comments.txt Readme.txt (this file) Supplements.yaml - Contains errors of fitting Format: -- Page number -- [parameter id, error value] - [parameter id, error value] - Chi squared - [pages that share parameters] (from global fitting) trace*.csv (where * is (Number of page) | appendix "A" or "B" point to the respective channels (only in cross-correlation mode)) - Contains times [ms] - Contains countrates [kHz] """.format(__version__) PyCorrFit-0.9.9/pycorrfit/readfiles/000077500000000000000000000000001274265304700174035ustar00rootroot00000000000000PyCorrFit-0.9.9/pycorrfit/readfiles/__init__.py000066400000000000000000000240631274265304700215210ustar00rootroot00000000000000# -*- coding: utf-8 -*- u""" PyCorrFit - module "readfiles" Import correlation data from data files. """ # This file is necessary for this folder to become a module that can be # imported by PyCorrFit. import csv import numpy as np import os import sys import tempfile import yaml import warnings import zipfile # To add a filetype add it here and in the # dictionaries at the end of this file. from .read_ASC_ALV import openASC from .read_CSV_PyCorrFit import openCSV from .read_SIN_correlator_com import openSIN from .read_FCS_Confocor3 import openFCS from .read_mat_ries import openMAT from .read_pt3_PicoQuant import openPT3 def AddAllWildcard(Dictionary): wildcard = "" keys = Dictionary.keys() N = len(keys) i = 0 for key in keys: newwc = key.split("|")[1] wildcard = wildcard + newwc i = i + 1 if i != N: wildcard = wildcard + ";" Dictionary[Allsupfilesstring+"|"+wildcard] = openAny return Dictionary # To increase user comfort, we will now create a file opener thingy that # knows how to open all files we know. def openAny(path, filename=None): """ Using the defined Filetypes and BGFiletypes, open the given file Parameters ---------- path : str Full path to file or directory containing `filename` filename : str The name of the file if not given in path (optional). """ if filename is None: dirname, filename = os.path.split(path) else: dirname = path wildcard = filename.split(".")[-1] for key in Filetypes.keys(): # Recurse into the wildcards wildcardstring = key.split("|") # We do not want to recurse if wildcardstring[0] != Allsupfilesstring: otherwcs = wildcardstring[1].split(";") for string in otherwcs: if string.strip(" .*") == wildcard: return Filetypes[key](dirname, filename) # If we could not find the correct function in Filetypes, try again # in BGFiletypes: return openAnyBG(dirname, filename) ## For convenience in openZIP #return None # already in openAnyBG def openAnyBG(dirname, filename): wildcard = filename.split(".")[-1] for key in BGFiletypes.keys(): wildcardstring = key.split("|") # We do not want to recurse if wildcardstring[0] != Allsupfilesstring: otherwcs = wildcardstring[1].split(";") for string in otherwcs: if string.strip(" .*") == wildcard: return BGFiletypes[key](dirname, filename) # For convenience in openZIP return None def openZIP(dirname, filename): """ Get everything inside a .zip file that could be an FCS curve. Will use any wildcard in Filetypes dictionary. """ # It's a rather lengthy import of the session file. The code is copied # from openfile.OpenSession. The usual zip file packed curves are # imported on the few code lines after the else statement. ## Open the archive: Arc = zipfile.ZipFile(os.path.join(dirname, filename), mode='r') Correlations = list() # Correlation information Curvelist = list() # Type information Filelist = list() # List of filenames corresponding to *Curvelist* Trace = list() # Corresponding traces ## First test, if we are opening a session file sessionwc = [".fcsfit-session.zip", ".pcfs"] if ( filename.endswith(sessionwc[0]) or filename.endswith(sessionwc[1]) ): # Get the yaml parms dump: yamlfile = Arc.open("Parameters.yaml") # Parms: Fitting and drawing parameters of the correlation curve # The *yamlfile* is responsible for the order of the Pages #i. # The parameters are actually useless to us right now. Parms = yaml.safe_load(yamlfile) yamlfile.close() # Get the correlation arrays ImportedNum = list() for i in np.arange(len(Parms)): # The *number* is used to identify the correct file number = str(Parms[i][0]) expfilename = "data"+number[1:len(number)-2]+".csv" expfile = Arc.open(expfilename, 'r') readdata = csv.reader(expfile, delimiter=',') dataexp = list() if str(readdata.next()[0]) == "# tau only": # We do not have a curve here pass else: Filelist.append(filename+"/#"+number[1:len(number)-2]) for row in readdata: # Exclude commentaries if (str(row[0])[0:1] != '#'): dataexp.append((float(row[0]), float(row[1]))) dataexp = np.array(dataexp) Correlations.append(dataexp) ImportedNum.append(i) del readdata expfile.close() # Get the Traces for i in ImportedNum: # Make sure we only import those traces that had a corresponding # correlation curve. (ImportedNum) # # The *number* is used to identify the correct file number = str(Parms[i][0]) # Find out, if we have a cross correlation data type IsCross = False try: IsCross = Parms[i][7] except IndexError: # No Cross correlation pass if IsCross is False: tracefilenames = ["trace"+number[1:len(number)-2]+".csv"] Curvelist.append("AC") else: # Cross correlation uses two traces tracefilenames = ["trace"+number[1:len(number)-2]+"A.csv", "trace"+number[1:len(number)-2]+"B.csv" ] Curvelist.append("CC") thistrace = list() for tracefilename in tracefilenames: try: Arc.getinfo(tracefilename) except KeyError: # No correlation curve, but add a None pass else: tracefile = Arc.open(tracefilename, 'r') traceread = csv.reader(tracefile, delimiter=',') singletrace = list() for row in traceread: # Exclude commentaries if (str(row[0])[0:1] != '#'): singletrace.append((float(row[0]), float(row[1]))) singletrace = np.array(singletrace) thistrace.append(singletrace) del traceread del singletrace tracefile.close() if len(thistrace) == 1: Trace.append(thistrace[0]) elif len(thistrace) == 2: Trace.append(thistrace) else: Trace.append(None) else: # We are not importing from a session but from a zip file with # probably a mix of all filetypes we know. This works # recursively (e.g. a zip file in a zipfile). allfiles = Arc.namelist() # Extract data to temporary folder tempdir = tempfile.mkdtemp() rmdirs = list() for afile in allfiles: apath = Arc.extract(afile, path=tempdir) if os.path.isdir(apath): rmdirs.append(apath) continue ReturnValue = openAny(tempdir, afile) if ReturnValue is not None: Correlations += ReturnValue["Correlation"] Trace += ReturnValue["Trace"] Curvelist += ReturnValue["Type"] fnames = ReturnValue["Filename"] Filelist += [ filename+"/"+fs for fs in fnames ] # Delte file try: os.remove(os.path.join(tempdir, afile)) except: warnings.warn("{}".format(sys.exc_info()[1])) for rmd in rmdirs: try: os.removedirs(rmd) except: warnings.warn("{}".format(sys.exc_info()[1])) try: os.removedirs(tempdir) except: warnings.warn("{}".format(sys.exc_info()[1])) Arc.close() dictionary = dict() dictionary["Correlation"] = Correlations dictionary["Trace"] = Trace dictionary["Type"] = Curvelist dictionary["Filename"] = Filelist return dictionary def get_supported_extensions(): """ Returns list of extensions of currently supported file types. """ extlist = [] for kf in list(Filetypes.keys()): ext = kf.split("|")[-1] ext = ext.split(";") ext = [ e.lower().strip("*. ") for e in ext] ext = list(np.unique(ext)) extlist += ext extlist = list(np.unique(extlist)) extlist.sort() return extlist # The string that is shown when opening all supported files # We add an empty space so it is listed first in the dialogs. Allsupfilesstring = " All supported files" # Dictionary with filetypes that we can open # The wildcards point to the appropriate functions. Filetypes = { "Correlator.com (*.SIN)|*.SIN;*.sin" : openSIN, "ALV (*.ASC)|*.ASC;*.asc" : openASC, "PyCorrFit (*.csv)|*.csv" : openCSV, "Matlab 'Ries (*.mat)|*.mat" : openMAT, "PicoQuant (*.pt3)|*.pt3" : openPT3, "Zeiss ConfoCor3 (*.fcs)|*.fcs" : openFCS, "Zip file (*.zip)|*.zip" : openZIP, "PyCorrFit session (*.pcfs)|*.pcfs" : openZIP } # For user comfort, add "All supported files" wildcard: Filetypes = AddAllWildcard(Filetypes) # Dictionary with filetypes we can open that have intensity traces in them. BGFiletypes = { "Correlator.com (*.SIN)|*.SIN;*.sin" : openSIN, "ALV (*.ASC)|*.ASC" : openASC, "PyCorrFit (*.csv)|*.csv" : openCSV, "PicoQuant (*.pt3)|*.pt3" : openPT3, "Zeiss ConfoCor3 (*.fcs)|*.fcs" : openFCS, "Zip file (*.zip)|*.zip" : openZIP, "PyCorrFit session (*.pcfs)|*.pcfs" : openZIP } BGFiletypes = AddAllWildcard(BGFiletypes) PyCorrFit-0.9.9/pycorrfit/readfiles/read_ASC_ALV.py000066400000000000000000000433621274265304700220700ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ methods to open ALV .ASC files """ from __future__ import division import os import csv import numpy as np def openASC(dirname, filename): """ Read data from a ALV .ASC files. """ path = os.path.join(dirname, filename) with open(path, 'r') as openfile: Alldata = openfile.readlines() # Open special format? filetype = Alldata[0].strip() if filetype in ["ALV-7004/USB"]: return openASC_ALV_7004_USB(path) else: # last resort return openASC_old(path) def openASC_old(path): """ Read data from a .ASC file, created by some ALV-6000 correlator. ALV-6000/E-WIN Data Date : "2/20/2012" ... "Correlation" 1.25000E-004 3.00195E-001 2.50000E-004 1.13065E-001 3.75000E-004 7.60367E-002 5.00000E-004 6.29926E-002 6.25000E-004 5.34678E-002 7.50000E-004 4.11506E-002 8.75000E-004 4.36752E-002 1.00000E-003 4.63146E-002 1.12500E-003 3.78226E-002 ... 3.35544E+004 -2.05799E-006 3.77487E+004 4.09032E-006 4.19430E+004 4.26295E-006 4.61373E+004 1.40265E-005 5.03316E+004 1.61766E-005 5.45259E+004 2.19541E-005 5.87202E+004 3.26527E-005 6.29145E+004 2.72920E-005 "Count Rate" 1.17188 26.77194 2.34375 26.85045 3.51563 27.06382 4.68750 26.97932 5.85938 26.73694 7.03125 27.11332 8.20313 26.81376 9.37500 26.82741 10.54688 26.88801 11.71875 27.09710 12.89063 27.13209 14.06250 27.02200 15.23438 26.95287 16.40625 26.75657 17.57813 26.43056 ... 294.14063 27.22597 295.31250 26.40581 296.48438 26.33497 297.65625 25.96457 298.82813 26.71902 1. We are interested in the "Correlation" section, where the first column denotes tau in ms and the second row the correlation signal. Values are separated by a tabulator "\t" (some " "). 2. We are also interested in the "Count Rate" section. Here the times are saved as seconds and not ms like above. 3. There is some kind of mode where the ALV exports five runs at a time and averages them. The sole correlation data is stored in the file, but the trace is only stored as average or something. So I would not recommend this. However, I added support for this. PyCorrFit then only imports the average data. ~ Paul, 2012-02-20 Correlation data starts at "Correlation (Multi, Averaged)". Returns: [0]: An array with tuples containing two elements: 1st: tau in ms 2nd: corresponding correlation signal [1]: Intensity trace: 1st: time in ms 2nd: Trace in kHz [2]: An array with N elements, indicating, how many curves we are opening from the file. Elements can be names and must be convertible to strings. """ filename = os.path.basename(path) with open(path, 'r') as openfile: Alldata = openfile.readlines() # End of trace EndT = Alldata.__len__() ## Correlation function # Find out where the correlation function is for i in np.arange(len(Alldata)): if Alldata[i].startswith('Mode'): mode = Alldata[i][5:].strip(' ":').strip().strip('"') single_strings = ["a-ch0", "a-ch1", "auto ch0", "auto ch1", "fast auto ch0", "fast auto ch1", ] if (mode.lower().count('single') or mode.lower().strip() in single_strings): single = True channel = mode.split(" ")[-1] else: # dual single = False # accc ? if mode.lower().count("cross") == 1: accc = "CC" else: accc = "AC" if Alldata[i].startswith('"Correlation'): # This tells us if there is only one curve or if there are # multiple curves with an average. if (Alldata[i].strip().lower() == '"correlation (multi, averaged)"' ): multidata = True else: multidata = False if Alldata[i].startswith('"Correlation"'): # Start of correlation function StartC = i+1 if Alldata[i].startswith('"Correlation (Multi, Averaged)"'): # Start of AVERAGED correlation function !!! # There are several curves now. StartC = i+2 if Alldata[i].replace(" ", "").lower().strip() == '"countrate"': # takes care of "Count Rate" and "Countrate" # End of correlation function EndC = i-1 # Start of trace (goes until end of file) StartT = i+1 if Alldata[i].startswith('Monitor Diode'): EndT = i-1 # Get the header Namedata = Alldata.__getslice__(StartC-1, StartC) ## Define *curvelist* curvelist = csv.reader(Namedata, delimiter='\t').next() if len(curvelist) <= 2: # Then we have just one single correlation curve curvelist = [""] else: # We have a number of correlation curves. We need to specify # names for them. We take these names from the headings. # Lag times not in the list curvelist.remove(curvelist[0]) # Last column is empty curvelist.remove(curvelist[-1]) ## Correlation function Truedata = Alldata.__getslice__(StartC, EndC) readdata = csv.reader(Truedata, delimiter='\t') # Add lists to *data* according to the length of *curvelist* data = [[]]*len(curvelist) # Work through the rows in the read data for row in readdata: for i in np.arange(len(curvelist)): if len(row) > 0: data[i].append( (np.float(row[0]), np.float(row[i+1])) ) ## Trace # Trace is stored in two columns # 1st column: time [s] # 2nd column: trace [kHz] # Get the trace Tracedata = Alldata.__getslice__(StartT, EndT) timefactor = 1000 # because we want ms instead of s readtrace = csv.reader(Tracedata, delimiter='\t') trace = list() trace2 = list() # Work through the rows for row in readtrace: # time in ms, countrate trace.append(list()) trace[0].append((np.float(row[0])*timefactor, np.float(row[1]))) # Only trace[0] contains the trace! for i in np.arange(len(curvelist)-1): trace.append(list()) trace[i+1].append((np.float(row[0])*timefactor, 0)) if not single: k = len(curvelist)/2 if int(k) != k: print "Problem with ALV data. Single mode not recognized." # presumably dual mode. There is a second trace # time in ms, countrate trace2.append(list()) trace2[0].append((np.float(row[0])*timefactor, np.float(row[2]))) # Only trace2[0] contains the trace! for i in np.arange(len(curvelist)-1): trace2.append(list()) trace2[i+1].append((np.float(row[0])*timefactor, 0)) # group the resulting curves corrlist = list() tracelist = list() typelist = list() if single: # We only have several runs and one average # split the trace into len(curvelist)-1 equal parts if multidata: nav = 1 else: nav = 0 splittrace = mysplit(trace[0], len(curvelist)-nav) i = 0 for t in range(len(curvelist)): typ = curvelist[t] if typ.lower()[:7] == "average": typelist.append("{} average".format(channel)) corrlist.append(np.array(data[t])) tracelist.append(np.array(trace[0])) else: typelist.append("{} {}".format(accc, channel)) corrlist.append(np.array(data[t])) tracelist.append(splittrace[i]) i += 1 elif accc == "AC": # Dual mode, autocorrelation # We now have two averages and two different channels. # We now have two traces. # The data is assembled in blocks. That means the first block # contains an average and the data of channel 0 and the second # block contains data and average of channel 1. We can thus # handle the data from 0 to len(curvelist)/2 and from # len(curvelist)/2 to len(curvelist) as two separate data sets. # CHANNEL 0 if multidata: nav = 1 else: nav = 0 channel = "CH0" splittrace = mysplit(trace[0], len(curvelist)/2-nav) i = 0 for t in range(int(len(curvelist)/2)): typ = curvelist[t] if typ.lower()[:7] == "average": typelist.append("{} average".format(channel)) corrlist.append(np.array(data[t])) tracelist.append(np.array(trace[0])) else: typelist.append("{} {}".format(accc, channel)) corrlist.append(np.array(data[t])) tracelist.append(splittrace[i]) i += 1 # CHANNEL 1 channel = "CH1" splittrace2 = mysplit(trace2[0], len(curvelist)/2-nav) i = 0 for t in range(int(len(curvelist)/2),int(len(curvelist))): typ = curvelist[t] if typ.lower()[:7] == "average": typelist.append("{} average".format(channel)) corrlist.append(np.array(data[t])) tracelist.append(np.array(trace2[0])) else: typelist.append("{} {}".format(accc, channel)) corrlist.append(np.array(data[t])) tracelist.append(splittrace2[i]) i += 1 elif accc == "CC": if multidata: nav = 1 else: nav = 0 # Dual mode, cross-correlation channel = "CC01" splittrace = mysplit(trace[0], len(curvelist)/2-nav) splittrace2 = mysplit(trace2[0], len(curvelist)/2-nav) i = 0 for t in range(int(len(curvelist)/2)): typ = curvelist[t] if typ.lower()[:7] == "average": typelist.append("{} average".format(channel)) corrlist.append(np.array(data[t])) tracelist.append([np.array(trace[0]), np.array(trace2[0]) ]) else: typelist.append("{} {}".format(accc, channel)) corrlist.append(np.array(data[t])) tracelist.append([splittrace[i], splittrace2[i]]) i += 1 # CHANNEL 1 channel = "CC10" i = 0 for t in range(int(len(curvelist)/2),int(len(curvelist))): typ = curvelist[t] if typ.lower()[:7] == "average": typelist.append("{} average".format(channel)) corrlist.append(np.array(data[t])) # order must be the same as above tracelist.append([np.array(trace[0]), np.array(trace2[0]) ]) else: typelist.append("{} {}".format(accc, channel)) corrlist.append(np.array(data[t])) # order must be the same as above tracelist.append([splittrace[i], splittrace2[i]]) i += 1 else: print "Could not detect data file format for: {}".format(filename) corrlist = np.array(data) tracelist = np.array(trace) typelist = curvelist dictionary = dict() dictionary["Correlation"] = corrlist dictionary["Trace"] = tracelist dictionary["Type"] = typelist filelist = list() for i in curvelist: filelist.append(filename) dictionary["Filename"] = filelist return dictionary def openASC_ALV_7004_USB(path): """ Opens ALV file format with header information "ALV-7004/USB" This is a single-run file format. - data is identified by 4*"\t" - count rate is identified by string (also "countrate") - allzero-correlations are removed "Correlation" 2.50000E-005 -9.45478E-001 -1.00000E+000 5.22761E-002 3.05477E-002 5.00000E-005 6.73734E-001 -2.59938E-001 3.17894E-002 4.24466E-002 7.50000E-005 5.30716E-001 3.21605E-001 5.91051E-002 2.93061E-002 1.00000E-004 3.33292E-001 1.97860E-001 3.24102E-002 3.32379E-002 1.25000E-004 2.42538E-001 1.19988E-001 4.37917E-002 3.05477E-002 1.50000E-004 1.86396E-001 1.23318E-001 5.66218E-002 2.25806E-002 1.75000E-004 1.73836E-001 8.53991E-002 4.64819E-002 3.46865E-002 2.00000E-004 1.48080E-001 9.35377E-002 4.37917E-002 4.17223E-002 [...] 1.00663E+004 2.80967E-005 -2.23975E-005 -7.08272E-005 5.70470E-005 1.09052E+004 9.40185E-005 2.76261E-004 1.29745E-004 2.39958E-004 1.17441E+004 -2.82103E-004 -1.97386E-004 -2.88753E-004 -2.60987E-004 1.25829E+004 1.42069E-004 3.82018E-004 6.03932E-005 5.40363E-004 "Count Rate" 0.11719 141.83165 81.54211 141.83165 81.54211 0.23438 133.70215 77.90344 133.70215 77.90344 0.35156 129.67148 74.58858 129.67148 74.58858 0.46875 134.57133 79.53957 134.57133 79.53957 [...] 29.29688 143.78307 79.06236 143.78307 79.06236 29.41406 154.80135 82.87147 154.80135 82.87147 29.53125 187.43013 89.61197 187.43013 89.61197 29.64844 137.82655 77.71597 137.82655 77.71597 [...] """ filename = os.path.basename(path) with open(path, 'r') as openfile: Alldata = openfile.readlines() # Find the different arrays # correlation array: " " # trace array: " " Allcorr = list() Alltrac = list() i=0 intrace = False for item in Alldata: if item.lower().strip().strip('"').replace(" ", "") == "countrate": intrace = True i += 1 if item.count("\t") == 4: if intrace: it = item.split("\t") it = [ float(t.strip()) for t in it ] Alltrac.append(it) else: ic = item.split("\t") ic = [ float(c.strip()) for c in ic ] Allcorr.append(ic) Allcorr = np.array(Allcorr) Alltrac = np.array(Alltrac) # Allcorr: lag time, ac1, ac2, cc12, cc21 # Alltrac: time, trace1, trace2, trace1, trace2 assert np.allclose(Alltrac[:,1], Alltrac[:,3], rtol=.01), "unknown ALV file format" assert np.allclose(Alltrac[:,2], Alltrac[:,4], rtol=.01), "unknown ALV file format" guesstypelist = ["AC1", "AC2", "CC12", "CC21"] typelist = list() corrlist = list() tracelist = list() filelist = list() lagtime = Allcorr[:,0] time = Alltrac[:,0]*1000 trace1 = np.dstack((time, Alltrac[:,1]))[0] trace2 = np.dstack((time, Alltrac[:,2]))[0] for i, typ in enumerate(guesstypelist): corr = np.dstack((lagtime, Allcorr[:,i+1]))[0] if not np.allclose(corr[:,1], np.zeros_like(lagtime)): # type typelist.append(typ) # correlation corrlist.append(corr) # trace if typ.count("CC"): tracelist.append([trace1, trace2]) elif typ.count("AC1"): tracelist.append([trace1]) elif typ.count("AC2"): tracelist.append([trace2]) else: raise ValueError("Unknown ALV file format") # filename filelist.append(filename) dictionary = dict() dictionary["Correlation"] = corrlist dictionary["Trace"] = tracelist dictionary["Type"] = typelist dictionary["Filename"] = filelist return dictionary def mysplit(a, n): """ Split a trace into n equal parts by interpolation. The signal average is preserved, but the signal variance will decrease. """ if n <= 1: return [np.array(a)] a = np.array(a) N = len(a) lensplit = np.int(np.ceil(N/n)) # xp is actually rounded -> recalculate xp = np.linspace(a[:,0][0], a[:,0][-1], N, endpoint=True) # let xp start at zero xp -= a[:,0][0] yp = a[:,1] # time frame for each new curve #dx = xp[-1]/n # perform interpolation of new trace x, newstep = np.linspace(0, xp[-1], lensplit*n, endpoint=True, retstep=True) # interpolating reduces the variance and possibly changes the avg y = np.interp(x,xp,yp) data = np.zeros((lensplit*n,2)) data[:,0] = x + newstep # make sure that the average stays the same: data[:,1] = y - np.average(y) + np.average(yp) return np.split(data,n) PyCorrFit-0.9.9/pycorrfit/readfiles/read_CSV_PyCorrFit.py000066400000000000000000000152441274265304700233520ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ methods to open CSV files """ import os import csv import numpy as np def openCSV(dirname, filename): """ Read relevant data from a file looking like this: [...] # Comment # Data type: Autocorrelation [...] 1.000000e-006 3.052373e-001 1.020961e-006 3.052288e-001 1.042361e-006 3.052201e-001 1.064209e-006 3.052113e-001 1.086516e-006 3.052023e-001 1.109290e-006 3.051931e-001 [...] # BEGIN TRACE [...] 10.852761 31.41818 12.058624 31.1271 13.264486 31.27305 14.470348 31.33442 15.676211 31.15861 16.882074 31.08564 18.087936 31.21335 [...] The correlation part could also look like this: # Channel (tau [s]) Experimental correlation Fitted correlation Residuals Weights [model function] 2.0000000000e-07 1.5649271000e-01 1.5380094370e-01 2.6917663029e-03 7.3158300646e-03 4.0000000000e-07 1.4751239000e-01 1.5257959602e-01 -5.0672060199e-03 5.8123579098e-03 6.0000000000e-07 1.5145113000e-01 1.5137624642e-01 7.4883584881e-05 8.5622019656e-03 8.0000000000e-07 1.5661088000e-01 1.5019053433e-01 6.4203456659e-03 6.8098486549e-03 1.0000000000e-06 1.5456273000e-01 1.4902210818e-01 5.5406218229e-03 7.2476381023e-03 1.2000000000e-06 1.3293905000e-01 1.4787062503e-01 -1.4931575028e-02 6.9861494246e-03 1.4000000000e-06 1.4715790000e-01 1.4673575040e-01 4.2214960494e-04 6.9810206017e-03 1.6000000000e-06 1.5247520000e-01 1.4561715797e-01 6.8580420325e-03 6.6680066656e-03 1.8000000000e-06 1.4703974000e-01 1.4451452937e-01 2.5252106284e-03 6.3299717550e-03 In that case we are also importing the weights. Data type: If Data type is "Cross-correlation", we will try to import two traces after "# BEGIN SECOND TRACE" 1st section: First column denotes tau in seconds and the second row the correlation signal. 2nd section: First column denotes tau in seconds and the second row the intensity trace in kHz. Returns: 1. A list with tuples containing two elements: 1st: tau in ms 2nd: corresponding correlation signal 2. None - usually is the trace, but the trace is not saved in the PyCorrFit .csv format. 3. A list with one element, indicating, that we are opening only one correlation curve. """ # Check if the file is correlation data csvfile = open(os.path.join(dirname, filename), 'r') firstline = csvfile.readline() if firstline.lower().count("this is not correlation data") > 0: csvfile.close() return None csvfile.close() # Define what will happen to the file timefactor = 1000 # because we want ms instead of s csvfile = open(os.path.join(dirname, filename), 'r') readdata = csv.reader(csvfile, delimiter=',') data = list() weights = list() weightname = "external" trace = None traceA = None DataType="AC" # May be changed numtraces = 0 prev_row = None for row in readdata: if len(row) == 0 or len(str(row[0]).strip()) == 0: # Do nothing with empty/whitespace lines pass # Beware that the len(row) statement has to be called first # (before the len(str(row[0]).strip()) ). Otherwise some # error would be raised. elif str(row[0])[:12].lower() == "# Type AC/CC".lower(): corrtype = str(row[0])[12:].strip().strip(":").strip() if corrtype[:17].lower() == "cross-correlation": # We will later try to import a second trace DataType="CC" DataType += corrtype[17:].strip() elif corrtype[0:15].lower() == "autocorrelation": DataType="AC" DataType += corrtype[15:].strip() elif str(row[0])[0:13].upper() == '# BEGIN TRACE': # Correlation is over. We have a trace corr = np.array(data) data=list() numtraces = 1 elif str(row[0])[0:20].upper() == '# BEGIN SECOND TRACE': # First trace is over. We have a second trace traceA = np.array(data) data = list() numtraces = 2 # Exclude commentaries elif str(row[0])[0:1] != '#': # Read the 1st section # On Windows we had problems importing nan values that # had some white-spaces around them. Therefore: strip() ## As of version 0.7.8 we are supporting white space ## separated values as well if len(row) == 1: row = row[0].split() data.append((np.float(row[0].strip())*timefactor, np.float(row[1].strip()))) if len(row) == 5: # this has to be correlation with weights weights.append(np.float(row[4].strip())) if weightname == "external": try: weightname = "ext. "+prev_row[0].split("Weights")[1].split("[")[1].split("]")[0] except: pass prev_row = row # Collect the rest of the trace, if there is any: rest = np.array(data) if numtraces == 0: corr = rest elif numtraces >= 1: trace = rest del data ## Remove any NaN numbers from thearray # Explanation: # np.isnan(data) # finds the position of NaNs in the array (True positions); 2D array, bool # any(1) # finds the rows that have True in them; 1D array, bool # ~ # negates them and is given as an argument (array type bool) to # select which items we want. corr = corr[~np.isnan(corr).any(1)] # Also check for infinities. corr = corr[~np.isinf(corr).any(1)] csvfile.close() Traces=list() # Set correct trace data for import if numtraces == 1 and DataType[:2] == "AC": Traces.append(trace) elif numtraces == 2 and DataType[:2] == "CC": Traces.append([traceA, trace]) elif numtraces == 1 and DataType[:2] == "CC": # Should not happen, but for convenience: Traces.append([trace, trace]) else: Traces.append(None) dictionary = dict() dictionary["Correlation"] = [corr] dictionary["Trace"] = Traces dictionary["Type"] = [DataType] dictionary["Filename"] = [filename] if len(weights) != 0: dictionary["Weight"] = [ np.array(weights)] dictionary["Weight Name"] = [weightname] return dictionary PyCorrFit-0.9.9/pycorrfit/readfiles/read_FCS_Confocor3.py000066400000000000000000000404171274265304700233040ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ methods to open Confocor .fcs files """ import os import csv import numpy as np import warnings from . import util def openFCS(dirname, filename): """ Load data from Zeiss Confocor3 Data is imported sequenially from the file. PyCorrFit will give each curve an id which corresponds to the position of the curve in the .fcs file. The AIM software can save data as multiple or single data files. The type is identified by the first line of the .fcs file. This works with files from the Confocor2, Confocor3 (AIM) and files created from the newer ZEN Software. This function is a wrapper combining *openFCS_Single* and *openFCS_Multiple* """ openfile = open(os.path.join(dirname, filename), 'r') identitystring = openfile.readline().strip()[:20] openfile.close() if identitystring == "Carl Zeiss ConfoCor3": return openFCS_Multiple(dirname, filename) else: return openFCS_Single(dirname, filename) def openFCS_Multiple(dirname, filename): """ Load data from Zeiss Confocor3 Data is imported sequenially from the file. PyCorrFit will give each curve an id which corresponds to the position of the curve in the .fcs file. This works with files from the Confocor2, Confocor3 (AIM) and files created from the newer ZEN Software. """ ### TODO: # match curves with their timestamp # (actimelist and cctimelist) # openfile = open(os.path.join(dirname, filename), 'r') Alldata = openfile.readlines() # Start progressing through the file. i is the line index. # We are searching for "FcsDataSet" sections that contain # all the information we want. # index i for linenumber i = 0 # A parameter to check whether we are in a "FcsDataSet" section # and should import something fcsset = False # The names of the traces aclist = list() # All autocorrelation functions cclist = list() # All cross-correlation functions # The intensity traces traces = list() # we use "AcquisitionTime" to match up curves thistime = None actimelist = list() cctimelist = list() # The correlation curves ac_correlations = list() cc_correlations = list() while i <= len(Alldata)-1: if Alldata[i].count("FcsDataSet") == 1: # We are in a "FcsDataSet" section fcsset = True gottrace = False if fcsset == True: if Alldata[i].partition("=")[0].strip() == "AcquisitionTime": thistime = Alldata[i].partition("=")[2].strip() if Alldata[i].partition("=")[0].strip() == "Channel": # Find out what type of correlation curve we have. # Might be interesting to the user. FCStype = Alldata[i].partition("=")[2].strip() FoundType = False for chnum in np.arange(4)+1: if FCStype == "Auto-correlation detector "+str(chnum): FoundType = "AC"+str(chnum) aclist.append(FoundType) elif FCStype == "Auto-correlation detector Meta"+str(chnum): FoundType = "AC"+str(chnum) aclist.append(FoundType) else: for ch2num in np.arange(4)+1: if FCStype == "Cross-correlation detector "+\ str(chnum)+" versus detector "+\ str(ch2num): FoundType = "CC"+str(chnum)+str(ch2num) cclist.append(FoundType) elif FCStype == "Cross-correlation detector Meta"+\ str(chnum)+" versus detector Meta"+\ str(ch2num): FoundType = "CC"+str(chnum)+str(ch2num) cclist.append(FoundType) if FoundType is False: # Jump out of this set. We will continue at # the next "FcsDataSet"-section. print "Unknown channel configuration in .fcs file: "+FCStype fcsset = False elif FoundType[:2] == "CC": cctimelist.append(thistime) elif FoundType[:2] == "AC": actimelist.append(thistime) if Alldata[i].partition("=")[0].strip() == "CountRateArray": # Start importing the trace. This is a little difficult, since # traces in those files are usually very large. We will bin # the trace and import a lighter version of it. tracelength = \ int(Alldata[i].partition("=")[2].strip().partition(" ")[0]) if tracelength != 0: tracedata = Alldata.__getslice__(i+1, i+tracelength+1) # Jump foward in the index i = i + tracelength readtrace = csv.reader(tracedata, delimiter='\t') trace = list() for row in readtrace: # tau in ms, trace in kHz # So we need to put some factors here trace.append( (np.float(row[3])*1000, np.float(row[4])/1000) ) trace = np.array(trace) # If the trace is too big. Wee need to bin it. newtrace = util.downsample_trace(trace) # Finally add the trace to the list traces.append(newtrace) if FoundType[:2] != "AC": # For every trace there is an entry in aclist print "Trace data saved in CC section."+ \ "I cannot handle that." gottrace = True if Alldata[i].partition("=")[0].strip() == "CorrelationArraySize": # Get the correlation information corrlength = int(Alldata[i].partition("=")[2].strip()) if corrlength !=0: # For cross correlation or something sometimes # there is no trace information. if gottrace == False and FoundType[:2] =="AC": # We think we know that there is no trace in CC curves traces.append(None) corrdata = Alldata.__getslice__(i+2, i+corrlength+2) # Jump foward i = i + corrlength readcorr = csv.reader(corrdata, delimiter='\t') corr = list() for row in readcorr: # tau in ms, corr-function corr.append( (np.float(row[3])*1000, np.float(row[4])-1) ) if FoundType[:2] == "AC": ac_correlations.append(np.array(corr)) elif FoundType[:2] == "CC": cc_correlations.append(np.array(corr)) else: # There is no correlation data in the file # Fill in some dummy data. These will be removed. if FoundType[:2] == "AC": # append a dummy correlation curve ac_correlations.append(None) if gottrace == False: # append a dummy trace traces.append(None) elif FoundType[:2] == "CC": # append a dummy correlation curve # cc_correlations do not have traces cc_correlations.append(None) # We reached the end of this "FcsDataSet" section. fcsset = False i = i + 1 # finished. openfile.close() # We now have: # aclist: a list of AC curve names mentioned in the file. # cclist: a list of CC curve names mentioned in the file. # traces: All traces corresponding to non-"None"-type entries in # ac_correlations. Not in cc_correlations, # because cross-correlations are not saved with traces.? # # Identifiers: # actimelist: aquisition times according to aclist # cctimelist: aquisition times according to cclist # # Correlations: # ac_correlations: AC-correlation data in list. # cc_correlations: CC-correlation data in list. # # ac_correlations or cc_correlations can have items that are "None". # These item come from averaging inside the Confocor software and # do not contain any data. # These "None" type items should be at the end of these lists. # If the user created .fcs files with averages between the curves, # the *traces* contains *None* values at those positions. ## We now create: # curvelist: All actually used data # tracelist: Traces brought into right form (also for CCs) # corrlist: Correlation curves # Index in curvelist defines index in trace and correlation. curvelist = list() tracelist = list() corrlist = list() # match up curves with their timestamps # (actimelist and cctimelist) knowntimes = list() for tid in actimelist: if tid not in knowntimes: knowntimes.append(tid) #n = actimelist.count(tid) actids = np.where(np.array(actimelist) == tid)[0] cctids = np.where(np.array(cctimelist) == tid)[0] if len(actids) == 0: warnings.warn("File {} timepoint {} has no AC data.". format(filename, tid)) elif len(actids) == 1: # single AC curve if ac_correlations[actids[0]] is not None: curvelist.append(aclist[actids[0]]) tracelist.append(1*traces[actids[0]]) corrlist.append(ac_correlations[actids[0]]) else: if traces[actids[0]] is not None: warnings.warn("File {} curve {} does not contain AC data.".format(filename, tid)) elif len(actids) == 2: # Get AC data if aclist[actids[0]] == "AC1": acdat1 = ac_correlations[actids[0]] trace1 = traces[actids[0]] acdat2 = ac_correlations[actids[1]] trace2 = traces[actids[1]] elif aclist[actids[0]] == "AC2": acdat1 = ac_correlations[actids[1]] trace1 = traces[actids[1]] acdat2 = ac_correlations[actids[0]] trace2 = traces[actids[0]] else: warnings.warn("File {} curve {}: unknown AC data.".format(filename, tid)) continue if acdat1 is not None: #AC1 curvelist.append("AC1") tracelist.append(trace1) corrlist.append(acdat1) if acdat2 is not None: #AC2 curvelist.append("AC2") tracelist.append(trace2) corrlist.append(acdat2) if len(cctids) == 2: # Get CC data if cclist[cctids[0]] == "CC12": ccdat12 = cc_correlations[cctids[0]] ccdat21 = cc_correlations[cctids[1]] elif cclist[cctids[0]] == "CC21": ccdat12 = cc_correlations[cctids[1]] ccdat21 = cc_correlations[cctids[0]] else: warnings.warn("File {} curve {}: unknown CC data.".format(filename, tid)) continue tracecc = [trace1, trace2] if ccdat12 is not None: #CC12 curvelist.append("CC12") tracelist.append(tracecc) corrlist.append(ccdat12) if ccdat21 is not None: #CC21 curvelist.append("CC21") tracelist.append(tracecc) corrlist.append(ccdat21) dictionary = dict() dictionary["Correlation"] = corrlist dictionary["Trace"] = tracelist dictionary["Type"] = curvelist filelist = list() for i in curvelist: filelist.append(filename) dictionary["Filename"] = filelist return dictionary def openFCS_Single(dirname, filename): """ Load data from Zeiss Confocor3 files containing only one curve. This works with files from the Confocor2, Confocor3 (AIM) and files created from the newer ZEN Software. """ openfile = open(os.path.join(dirname, filename), 'r') Alldata = openfile.readlines() # Start progressing through the file. i is the line index. # We are searching for "FcsDataSet" sections that contain # all the information we want. # index i for linenumber i = 0 # Indicates if trace or FCS curve should be imported in loop fcscurve = False tracecurve = False while i <= len(Alldata)-1: if Alldata[i].partition("=")[0].strip() == "##DATA TYPE": # Find out what type of correlation curve we have. # Might be interesting to the user. Type = Alldata[i].partition("=")[2].strip() if Type == "FCS Correlogram": fcscurve = True tracecurve = False elif Type == "FCS Count Rates": tracecurve = True fcscurve = False else: raise SyntaxError("Unknown file syntax: "+Type) i = i + 1 if tracecurve == True: if Alldata[i].partition("=")[0].strip() == "##NPOINTS": # Start importing the trace. This is a little difficult, since # traces in those files are usually very large. We will bin # the trace and import a lighter version of it. tracelength = int(Alldata[i].partition("=")[2].strip()) # Trace starts 3 lines after this. i = i + 3 if tracelength != 0: tracedata = Alldata.__getslice__(i, i+tracelength) # Jump foward in the index i = i + tracelength readtrace = csv.reader(tracedata, delimiter=',') trace = list() for row in readtrace: # tau in ms, trace in kHz # So we need to put some factors here trace.append( (np.float(row[0])*1000, np.float(row[1])) ) trace = np.array(trace) # If the trace is too big. Wee need to bin it. newtrace = util.downsample_trace(trace) tracecurve = False if fcscurve == True: if Alldata[i].partition("=")[0].strip() == "##NPOINTS": # Get the correlation information corrlength = int(Alldata[i].partition("=")[2].strip()) i = i + 2 if corrlength !=0: corrdata = Alldata.__getslice__(i, i+corrlength) # Jump foward i = i + corrlength readcorr = csv.reader(corrdata, delimiter=',') corr = list() for row in readcorr: # tau in ms, corr-function corr.append( (np.float(row[0]), np.float(row[1])-1) ) corr = np.array(corr) fcscurve = False # Check for correlation at lag-time zero, which lead to a bug (#64) # on mac OSx and potentially affects fitting. if corr[0][0] == 0: corr = corr[1:] openfile.close() dictionary = dict() dictionary["Correlation"] = [corr] dictionary["Trace"] = [newtrace] dictionary["Type"] = [""] dictionary["Filename"] = [filename] return dictionary PyCorrFit-0.9.9/pycorrfit/readfiles/read_SIN_correlator_com.py000066400000000000000000000327361274265304700245060ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ methods to open correlator.com .sin files """ import os import csv import numpy as np def openSIN(dirname, filename): """ D """ path = os.path.join(dirname, filename) with open(path) as fd: data = fd.readlines() for line in data: line = line.strip() if line.lower().startswith("mode"): mode = line.split("=")[1].strip().split() # Find out what kind of mode it is # The rationale is that when the mode # consists of single characters separated # by empty spaces, then we have integer mode. if len(mode) - np.sum([len(m) for m in mode]) == 0: return openSIN_integer_mode(path) else: openSIN_old(path) def openSIN_integer_mode(path): """ Integer mode file format of e.g. flex03lq-1 correlator This is a file format where the type (AC/CC) of the curve is determined using integers in the "Mode=" line, e.g. Mode= 2 3 3 2 0 1 1 0 which means the first correlation is CC23, the second CC32, the third CC01, and the fourth CC10. Similarly, Mode= 1 1 2 2 0 4 4 4 would translate to AC11, AC22, CC04, and AC44. """ openfile = open(path, 'r') data = openfile.readlines() # get mode (curve indices) for line in data: line = line.strip() if line.lower().startswith("mode"): mode = line.split("=")[1].strip().split() mode = [ int(m) for m in mode ] assert len(mode) % 2 == 0, "mode must be multiples of two" # build up the lists corr_func = [] intensity = [] section = "" # loop through lines for line in data: line = line.strip().lower() if line.startswith("["): section = line continue elif (len(line) == 0 or line.count("=")): continue if section.count("[correlationfunction]"): corr_func.append(line.split()) elif section.count("[intensityhistory]"): intensity.append(line.split()) # corr_func now contains lag time, and correlations according # to the mode parameters. corr_func = np.array(corr_func, dtype=float) intensity = np.array(intensity, dtype=float) timefactor = 1000 # because we want ms instead of s timedivfac = 1000 # because we want kHz instead of Hz intensity[:,0] *= timefactor corr_func[:,0] *= timefactor intensity[:,1:] /= timedivfac # correlator.com correlation is normalized to 1, not to 0 corr_func[:,1:] -= 1 # Now sort the information for pycorrfit correlations = [] traces = [] curvelist = [] for ii in range(len(mode)//2): modea = mode[2*ii] modeb = mode[2*ii+1] if modea == modeb: # curve type AC curvelist.append("AC{}".format(modea)) # trace atrace = np.zeros((intensity.shape[0],2), dtype=float) atrace[:,0] = intensity[:, 0] atrace[:,1] = intensity[:, modea+1] traces.append(atrace) else: # curve type CC curvelist.append("CC{}{}".format(modea,modeb)) # trace modmin = min(modea, modeb) modmax = max(modea, modeb) tracea = np.zeros((intensity.shape[0],2), dtype=float) tracea[:,0] = intensity[:, 0] tracea[:,1] = intensity[:, modmin+1] traceb = np.zeros((intensity.shape[0],2), dtype=float) traceb[:,0] = intensity[:, 0] traceb[:,1] = intensity[:, modmax+1] traces.append([tracea, traceb]) # correlation corr = np.zeros((corr_func.shape[0],2), dtype=float) corr[:,0] = corr_func[:, 0] corr[:,1] = corr_func[:, ii+1] correlations.append(corr) dictionary = {} dictionary["Correlation"] = correlations dictionary["Trace"] = traces dictionary["Type"] = curvelist filelist = list() for _i in curvelist: filelist.append(os.path.basename(path)) dictionary["Filename"] = filelist return dictionary def openSIN_old(path): """ Parses the "old" sin file format using an "old" implementation. Read data from a .SIN file, usually created by the software using correlators from correlator.com. FLXA Version= 1d [Parameters] ... Mode= Single Auto ... [CorrelationFunction] 1.562500e-09 0.000000e+00 3.125000e-09 0.000000e+00 4.687500e-09 0.000000e+00 ... 1.887435e+01 1.000030e+00 1.929378e+01 1.000141e+00 1.971321e+01 9.999908e-01 2.013264e+01 9.996810e-01 2.055207e+01 1.000047e+00 2.097150e+01 9.999675e-01 2.139093e+01 9.999591e-01 2.181036e+01 1.000414e+00 2.222979e+01 1.000129e+00 2.264922e+01 9.999285e-01 2.306865e+01 1.000077e+00 ... 3.959419e+02 0.000000e+00 4.026528e+02 0.000000e+00 4.093637e+02 0.000000e+00 4.160746e+02 0.000000e+00 4.227854e+02 0.000000e+00 4.294963e+02 0.000000e+00 [RawCorrelationFunction] ... [IntensityHistory] TraceNumber= 458 0.000000 9.628296e+03 9.670258e+03 0.262144 1.001358e+04 9.971619e+03 0.524288 9.540558e+03 9.548188e+03 0.786432 9.048462e+03 9.010315e+03 1.048576 8.815766e+03 8.819580e+03 1.310720 8.827210e+03 8.861542e+03 1.572864 9.201050e+03 9.185791e+03 1.835008 9.124756e+03 9.124756e+03 2.097152 9.059906e+03 9.029389e+03 ... 1. We are interested in the "[CorrelationFunction]" section, where the first column denotes tau in seconds and the second row the correlation signal. Values are separated by a tabulator "\t". We do not import anything from the "[Parameters]" section. We have to subtract "1" from the correlation function, since it is a correlation function that converges to "1" and not to "0". 2. We are also interested in the "[IntensityHistory]" section. If we are only interested in autocorrelation functions: An email from Jixiang Zhu - Correlator.com (2012-01-22) said, that "For autocorrelation mode, the 2nd and 3 column represent the same intensity series with slight delay. Therefore, they are statistically the same but numerically different." It is therefore perfectly fine to just use the 2nd column. Different acquisition modes: Mode [CorrelationFunction] [IntensityHistory] Single Auto 2 Colums (tau,AC) 1 significant Single Cross 2 Colums (tau,CC) 2 Dual Auto 3 Colums (tau,AC1,AC2) 2 Dual Cross 3 Colums (tau,CC12,CC21) 2 Quad 5 Colums (tau,AC1,AC2,CC12,CC21) 2 Returns: [0]: N arrays with tuples containing two elements: 1st: tau in ms 2nd: corresponding correlation signal [1]: N Intensity traces: 1st: time in ms 2nd: Trace in kHz [2]: A list with N elements, indicating, how many correlation curves we are importing. """ openfile = open(path, 'r') Alldata = openfile.readlines() # Find out where the correlation function and trace are for i in np.arange(len(Alldata)): if Alldata[i][0:4] == "Mode": Mode = Alldata[i].split("=")[1].strip() if Alldata[i][0:21] == "[CorrelationFunction]": StartC = i+1 if Alldata[i][0:24] == "[RawCorrelationFunction]": EndC = i-2 if Alldata[i][0:18] == "[IntensityHistory]": # plus 2, because theres a line with the trace length StartT = i+2 if Alldata[i][0:11] == "[Histogram]": EndT = i-2 curvelist = list() correlations = list() traces = list() # Get the correlation function Truedata = Alldata.__getslice__(StartC, EndC) timefactor = 1000 # because we want ms instead of s readcorr = csv.reader(Truedata, delimiter='\t') # Trace # Trace is stored in three columns # 1st column: time [s] # 2nd column: trace [Hz] # 3rd column: trace [Hz] - Single Auto: equivalent to 2nd # Get the trace Tracedata = Alldata.__getslice__(StartT, EndT) # timefactor = 1000 # because we want ms instead of s timedivfac = 1000 # because we want kHz instead of Hz readtrace = csv.reader(Tracedata, delimiter='\t') openfile.close() # Process all Data: if Mode == "Single Auto": curvelist.append("AC") corrdata = list() for row in readcorr: # tau in ms, corr-function minus "1" corrdata.append((np.float(row[0])*timefactor, np.float(row[1])-1)) correlations.append(np.array(corrdata)) trace = list() for row in readtrace: # tau in ms, corr-function minus "1" trace.append((np.float(row[0])*timefactor, np.float(row[1])/timedivfac)) traces.append(np.array(trace)) elif Mode == "Single Cross": curvelist.append("CC") corrdata = list() for row in readcorr: # tau in ms, corr-function minus "1" corrdata.append((np.float(row[0])*timefactor, np.float(row[1])-1)) correlations.append(np.array(corrdata)) trace1 = list() trace2 = list() for row in readtrace: # tau in ms, corr-function minus "1" trace1.append((np.float(row[0])*timefactor, np.float(row[1])/timedivfac)) trace2.append((np.float(row[0])*timefactor, np.float(row[2])/timedivfac)) traces.append([np.array(trace1), np.array(trace2)]) elif Mode == "Dual Auto": curvelist.append("AC1") curvelist.append("AC2") corrdata1 = list() corrdata2 = list() for row in readcorr: # tau in ms, corr-function minus "1" corrdata1.append((np.float(row[0])*timefactor, np.float(row[1])-1)) corrdata2.append((np.float(row[0])*timefactor, np.float(row[2])-1)) correlations.append(np.array(corrdata1)) correlations.append(np.array(corrdata2)) trace1 = list() trace2 = list() for row in readtrace: # tau in ms, corr-function minus "1" trace1.append((np.float(row[0])*timefactor, np.float(row[1])/timedivfac)) trace2.append((np.float(row[0])*timefactor, np.float(row[2])/timedivfac)) traces.append(np.array(trace1)) traces.append(np.array(trace2)) elif Mode == "Dual Cross": curvelist.append("CC12") curvelist.append("CC21") corrdata1 = list() corrdata2 = list() for row in readcorr: # tau in ms, corr-function minus "1" corrdata1.append((np.float(row[0])*timefactor, np.float(row[1])-1)) corrdata2.append((np.float(row[0])*timefactor, np.float(row[2])-1)) correlations.append(np.array(corrdata1)) correlations.append(np.array(corrdata2)) trace1 = list() trace2 = list() for row in readtrace: # tau in ms, corr-function minus "1" trace1.append((np.float(row[0])*timefactor, np.float(row[1])/timedivfac)) trace2.append((np.float(row[0])*timefactor, np.float(row[2])/timedivfac)) traces.append([np.array(trace1), np.array(trace2)]) traces.append([np.array(trace1), np.array(trace2)]) elif Mode == "Quad": curvelist.append("AC1") curvelist.append("AC2") curvelist.append("CC12") curvelist.append("CC21") corrdata1 = list() corrdata2 = list() corrdata12 = list() corrdata21 = list() for row in readcorr: # tau in ms, corr-function minus "1" corrdata1.append((np.float(row[0])*timefactor, np.float(row[1])-1)) corrdata2.append((np.float(row[0])*timefactor, np.float(row[2])-1)) corrdata12.append((np.float(row[0])*timefactor, np.float(row[3])-1)) corrdata21.append((np.float(row[0])*timefactor, np.float(row[4])-1)) correlations.append(np.array(corrdata1)) correlations.append(np.array(corrdata2)) correlations.append(np.array(corrdata12)) correlations.append(np.array(corrdata21)) trace1 = list() trace2 = list() for row in readtrace: # tau in ms, corr-function minus "1" trace1.append((np.float(row[0])*timefactor, np.float(row[1])/timedivfac)) trace2.append((np.float(row[0])*timefactor, np.float(row[2])/timedivfac)) traces.append(np.array(trace1)) traces.append(np.array(trace2)) traces.append([np.array(trace1), np.array(trace2)]) traces.append([np.array(trace1), np.array(trace2)]) else: raise NotImplemented( "'Mode' type '{}' in {} not supported by this method!". Mode, format(path)) dictionary = {} dictionary["Correlation"] = correlations dictionary["Trace"] = traces dictionary["Type"] = curvelist filelist = list() for i in curvelist: filelist.append(os.path.basename(path)) dictionary["Filename"] = filelist return dictionary PyCorrFit-0.9.9/pycorrfit/readfiles/read_mat_ries.py000066400000000000000000000163001274265304700225530ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ method to open .mat files (Ries) """ from __future__ import print_function import numpy as np # On the windows machine the matlab binary import raised a warning. # We want to catch that warning, since importing ries's files works. import warnings with warnings.catch_warnings(): warnings.simplefilter("ignore") try: # scipy.io might not work on OSX (wrong architecture) from scipy.io.matlab.mio5_params import mat_struct from scipy.io import loadmat except: print(" Error: import error in scipys 'matlab' submodule.") print(" Try upgrading python-scipy or ignore this") print(" error if you are not using .mat files that") print(" were generated by programs by Jonas Ries.") import os def openMAT(dirname, filename): """ Read mat files that Jonas Ries used in his programs. For opening .mat files, this helped a lot: http://stackoverflow.com/questions/7008608/ scipy-io-loadmat-nested-structures-i-e-dictionaries The structure has been derived from "corrSFCS.m" from the SFCS.m program from Jonas Ries. """ # initiate lists correlations = list() traces = list() curvelist = list() # Import everything inside the mat file as big iterated dictionary f = os.path.join(dirname, filename) alldata = loadmat(f) # Correlation functions are stored in "g" g = alldata["g"] # Get all Autocorrelation functions try: # ac for autocorrelation ac = g["ac"] except KeyError: pass else: # Workaround for single ACs, they are not stored in a separate list, # but directly inserted into g["ac"]. We put it in a list. # This is not the case for the trace averages. # There are a maximum of 4 autocorrelation functions in one file, # as far as I know. if len(ac) > 4: ac = [ac] g["act"] = [g["act"]] for i in np.arange(len(ac)): corr = ac[i] try: times = g["act"][i] except KeyError: pass else: # Another workaround # Sometimes, there's just one curve, which # means that corr[0] has no length. if len( np.atleast_1d(corr[0]) ) == 1: final = np.zeros((len(corr), 2)) final[:,0] = times final[:,1] = corr correlations.append(final) curvelist.append("AC"+str(i+1)) try: # only trace averages are saved traceavg = g["trace"][i] except: # No trace traces.append(None) else: trace = np.zeros((2,2)) trace[1,0] = 1.0 trace[:,1] = traceavg traces.append(trace) elif len(corr) == len(times): for j in np.arange(len(corr[0])): final = np.zeros((len(corr), 2)) final[:,0] = times final[:,1] = corr[:,j] correlations.append(final) curvelist.append("AC"+str(i+1)) try: # only trace averages are saved traceavg = g["trace"][i][j] except: # No trace traces.append(None) else: trace = np.zeros((2,2)) trace[1,0] = 1.0 trace[:,1] = traceavg traces.append(trace) # Get dc "dual color" functions try: dc = g["dc"] except KeyError: pass else: for i in np.arange(len(dc)): corr = dc[i] try: times = g["dct"][i] except KeyError: pass else: if len(corr) == len(times): for j in np.arange(len(corr[0])): final = np.zeros((len(corr), 2)) final[:,0] = times final[:,1] = corr[:,j] correlations.append(final) curvelist.append("CC dual color "+str(i+1)) traces.append(None) # Get twof "two focus" functions try: twof = g["twof"] except KeyError: pass else: for i in np.arange(len(dc)): corr = twof[i] try: times = g["twoft"][i] except KeyError: pass else: if len(corr) == len(times): for j in np.arange(len(corr[0])): final = np.zeros((len(corr), 2)) final[:,0] = times final[:,1] = corr[:,j] correlations.append(final) curvelist.append("CC two foci "+str(i+1)) traces.append(None) # Get dc2f "dual color two focus" functions try: g["dc2f"] except KeyError: pass else: for i in np.arange(len(dc)): corr = twof[i] try: times = g["dc2ft"][i] except KeyError: pass else: if len(corr) == len(times): for j in np.arange(len(corr[0])): final = np.zeros((len(corr), 2)) final[:,0] = times final[:,1] = corr[:,j] correlations.append(final) curvelist.append("CC dual color two foci "+str(i+1)) traces.append(None) dictionary = dict() dictionary["Correlation"] = correlations dictionary["Trace"] = traces dictionary["Type"] = curvelist filelist = list() for i in curvelist: filelist.append(filename) dictionary["Filename"] = filelist return dictionary def loadmat(filename): ''' this function should be called instead of direct scipy.io.loadmat as it cures the problem of not properly recovering python dictionaries from mat files. It calls the function check keys to cure all entries which are still mat-objects ''' data = loadmat(filename, struct_as_record=False, squeeze_me=True) return _check_keys(data) def _check_keys(adict): ''' checks if entries in dictionary are mat-objects. If yes todict is called to change them to nested dictionaries ''' for key in adict: if isinstance(adict[key], mat_struct): adict[key] = _todict(adict[key]) return adict def _todict(matobj): ''' A recursive function which constructs from matobjects nested dictionaries ''' adict = {} for strg in matobj._fieldnames: elem = matobj.__dict__[strg] if isinstance(elem, mat_struct): adict[strg] = _todict(elem) else: adict[strg] = elem return adict PyCorrFit-0.9.9/pycorrfit/readfiles/read_pt3_PicoQuant.py000066400000000000000000000073621274265304700234510ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ Wrapper for Loading PicoQuant .pt3 data files Wraps around FCS_point_correlator by Dominic Waithe https://github.com/dwaithe/FCS_point_correlator """ import numpy as np import os from .read_pt3_scripts.correlation_objects import picoObject from . import util class ParameterClass(): """Stores parameters for correlation """ def __init__(self): #Where the data is stored. self.data = [] self.objectRef =[] self.subObjectRef =[] self.colors = ['blue','green','red','cyan','magenta','yellow','black'] self.numOfLoaded = 0 self.NcascStart = 0 self.NcascEnd = 25 self.Nsub = 6 self.winInt = 10 self.photonCountBin = 25 def getTrace(picoObject, number): """ Extracts trace `number` from a `picoObject`. Parameters ---------- picoObject: instance of picoObject The data retreived from a pt3 file number: The id of the trace, can be 1 or 2. """ attrint = "timeSeries{}".format(number) attrtime = "timeSeriesScale{}".format(number) # binned photon counts intensity = np.array(getattr(picoObject, attrint)) # Time in ms for each bin time = np.array(getattr(picoObject, attrtime)) # time delta deltat = np.abs(time[2]-time[1]) trace = np.zeros((intensity.shape[0],2)) trace[:,0] = time # ms trace[:,1] = intensity / deltat # kHz # If the trace is too big. Wee need to bin it. newtrace = util.downsample_trace(trace) return newtrace def openPT3(dirname, filename): """ Retreive correlation curves from PicoQuant data files This function is a wrapper around the PicoQuant capability of FCS_Viewer by Dominic Waithe. """ par_obj = ParameterClass() pt3file = picoObject(os.path.join(dirname, filename), par_obj, None) po = pt3file auto = po.autoNorm # lag time [ms] autotime = po.autotime.reshape(-1) corrlist = list() typelist = list() tracelist = list() # Some data points are zero for some reason id1 = np.where(autotime!=0) # AC0 - autocorrelation CH0 corrac0 = auto[:,0,0] if np.sum(np.abs(corrac0[id1])) != 0: typelist.append("AC0") # autotime,auto[:,0,0] corrlist.append(np.hstack( (autotime[id1].reshape(-1,1), corrac0[id1].reshape(-1,1)) )) tracelist.append([getTrace(po, 1)]) # AC1 - autocorrelation CH1 corrac1 = auto[:,1,1] if np.sum(np.abs(corrac1[id1])) != 0: typelist.append("AC1") # autotime,auto[:,1,1] corrlist.append(np.hstack( (autotime[id1].reshape(-1,1), corrac1[id1].reshape(-1,1)) )) tracelist.append([getTrace(po, 2)]) # CC01 - Cross-Correlation CH0-CH1 corrcc01 = auto[:,0,1] if np.sum(np.abs(corrcc01[id1])) != 0: typelist.append("CC01") # autotime,auto[:,0,1] corrlist.append(np.hstack( (autotime[id1].reshape(-1,1), corrcc01[id1].reshape(-1,1)) )) tracelist.append([getTrace(po, 1), getTrace(po, 2)]) # CC10 - Cross-Correlation CH1-CH0 corrcc10 = auto[:,1,0] if np.sum(np.abs(corrcc10[id1])) != 0: typelist.append("CC10") # autotime,auto[:,1,0] corrlist.append(np.hstack( (autotime[id1].reshape(-1,1), corrcc10[id1].reshape(-1,1)) )) tracelist.append([getTrace(po, 1), getTrace(po, 2)]) filelist = [filename] * len(typelist) dictionary = dict() dictionary["Correlation"] = corrlist dictionary["Trace"] = tracelist dictionary["Type"] = typelist dictionary["Filename"] = filelist return dictionary PyCorrFit-0.9.9/pycorrfit/readfiles/read_pt3_scripts/000077500000000000000000000000001274265304700226535ustar00rootroot00000000000000PyCorrFit-0.9.9/pycorrfit/readfiles/read_pt3_scripts/LICENSE000066400000000000000000000431531274265304700236660ustar00rootroot00000000000000GNU GENERAL PUBLIC LICENSE Version 2, June 1991 Copyright (C) 1989, 1991 Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA Everyone is permitted to copy and distribute verbatim copies of this license document, but changing it is not allowed. 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PyCorrFit-0.9.9/pycorrfit/readfiles/read_pt3_scripts/README000066400000000000000000000006241274265304700235350ustar00rootroot00000000000000The following files were copied from the repository https://github.com/dwaithe/FCS_point_correlator - correlation_methods.py - correlation_objects.py - fitting_methods.py - import_methods.py The following changes were performed: - fib4.pyx A doc string was inserted. - correlation_objects.py Line 7 was commented out: #from lmfit import minimize, Parameters,report_fit,report_errors, fit_report PyCorrFit-0.9.9/pycorrfit/readfiles/read_pt3_scripts/__init__.py000066400000000000000000000000401274265304700247560ustar00rootroot00000000000000from .__version__ import versionPyCorrFit-0.9.9/pycorrfit/readfiles/read_pt3_scripts/__version__.py000066400000000000000000000001201274265304700254770ustar00rootroot00000000000000# This file contains the GitHub hash of the file versions version = "8399ff7401"PyCorrFit-0.9.9/pycorrfit/readfiles/read_pt3_scripts/correlation_methods.py000066400000000000000000000112131274265304700272670ustar00rootroot00000000000000import numpy as np import fib4 import time import thread """FCS Bulk Correlation Software Copyright (C) 2015 Dominic Waithe This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA. """ def tttr2xfcs (y,num,NcascStart,NcascEnd, Nsub): """autocorr, autotime = tttr2xfcs(y,num,10,20) Translation into python of: Fast calculation of fluorescence correlation data with asynchronous time-correlated single-photon counting. Michael Wahl, Ingo Gregor, Matthias Patting, Jorg Enderlein """ dt = np.max(y)-np.min(y) y = np.round(y[:],0) numshape = num.shape[0] autotime = np.zeros(((NcascEnd+1)*(Nsub+1),1)); auto = np.zeros(((NcascEnd+1)*(Nsub+1), num.shape[1], num.shape[1])).astype(np.float64) shift = float(0) delta = float(1) for j in range(0,NcascEnd): #Finds the unique photon times and their indices. The division of 'y' by '2' each cycle makes this more likely. y,k1 = np.unique(y,1) k1shape = k1.shape[0] #Sums up the photon times in each bin. cs =np.cumsum(num,0).T #Prepares difference array so starts with zero. diffArr1 = np.zeros(( k1shape+1)); diffArr2 = np.zeros(( k1shape+1)); #Takes the cumulative sum of the unique photon arrivals diffArr1[1:] = cs[0,k1].reshape(-1) diffArr2[1:] = cs[1,k1].reshape(-1) #del k1 #del cs num =np.zeros((k1shape,2)) #Finds the total photons in each bin. and represents as count. #This is achieved because we have the indices of each unique time photon and cumulative total at each point. num[:,0] = np.diff(diffArr1) num[:,1] = np.diff(diffArr2) #diffArr1 = []; #diffArr2 = []; for k in range(0,Nsub): shift = shift + delta lag = np.round(shift/delta,0) #Allows the script to be sped up. if j >= NcascStart: #Old method #i1= np.in1d(y,y+lag,assume_unique=True) #i2= np.in1d(y+lag,y,assume_unique=True) #New method, cython i1,i2 = fib4.dividAndConquer(y, y+lag,y.shape[0]) #If the weights (num) are one as in the first Ncasc round, then the correlation is equal to np.sum(i1) i1 = i1.astype(np.bool); i2 = i2.astype(np.bool); #Now we want to weight each photon corectly. #Faster dot product method, faster than converting to matrix. auto[(k+(j)*Nsub),:,:] = np.dot((num[i1,:]).T,num[i2,:])/delta autotime[k+(j)*Nsub] =shift; #Equivalent to matlab round when numbers are %.5 y = np.ceil(np.array(0.5*y)) delta = 2*delta for j in range(0, auto.shape[0]): auto[j,:,:] = auto[j,:,:]*dt/(dt-autotime[j]) autotime = autotime/1000000 #Removes the trailing zeros. autotime = autotime[autotime != 0] auto = auto[autotime != 0,:,:] return auto, autotime def delayTime2bin(dTimeArr, chanArr, chanNum, winInt): decayTime = np.array(dTimeArr) #This is the point and which each channel is identified. decayTimeCh =decayTime[chanArr == chanNum] #Find the first and last entry firstDecayTime = 0;#np.min(decayTimeCh).astype(np.int32) tempLastDecayTime = np.max(decayTimeCh).astype(np.int32) #We floor this as the last bin is always incomplete and so we discard photons. numBins = np.floor((tempLastDecayTime-firstDecayTime)/winInt) lastDecayTime = numBins*winInt bins = np.linspace(firstDecayTime,lastDecayTime, int(numBins)+1) photonsInBin, jnk = np.histogram(decayTimeCh, bins) #bins are valued as half their span. decayScale = bins[:-1]+(winInt/2) #decayScale = np.arange(0,decayTimeCh.shape[0]) return list(photonsInBin), list(decayScale) PyCorrFit-0.9.9/pycorrfit/readfiles/read_pt3_scripts/correlation_objects.py000066400000000000000000000665071274265304700272750ustar00rootroot00000000000000import numpy as np import os, sys from correlation_methods import * from import_methods import * import time from fitting_methods import equation_ #from lmfit import minimize, Parameters,report_fit,report_errors, fit_report import csv import copy """FCS Bulk Correlation Software Copyright (C) 2015 Dominic Waithe This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA. """ class picoObject(): #This is the class which holds the .pt3 data and parameters def __init__(self,filepath, par_obj,fit_obj): #parameter object and fit object. If self.par_obj = par_obj self.fit_obj = fit_obj self.type = 'mainObject' #self.PIE = 0 self.filepath = str(filepath) self.nameAndExt = os.path.basename(self.filepath).split('.') self.name = self.nameAndExt[0] self.ext = self.nameAndExt[-1] self.par_obj.data.append(filepath); self.par_obj.objectRef.append(self) #Imports pt3 file format to object. self.unqID = self.par_obj.numOfLoaded #For fitting. self.objId1 = None self.objId2 = None self.objId3 = None self.objId4 = None self.processData(); self.plotOn = True; def processData(self): self.NcascStart = self.par_obj.NcascStart self.NcascEnd = self.par_obj.NcascEnd self.Nsub = self.par_obj.Nsub self.winInt = self.par_obj.winInt self.photonCountBin = self.par_obj.photonCountBin #File import if self.ext == 'pt3': self.subChanArr, self.trueTimeArr, self.dTimeArr,self.resolution = pt3import(self.filepath) if self.ext == 'csv': self.subChanArr, self.trueTimeArr, self.dTimeArr,self.resolution = csvimport(self.filepath) #If the file is empty. if self.subChanArr == None: #Undoes any preparation of resource. self.par_obj.data.pop(-1); self.par_obj.objectRef.pop(-1) self.exit = True self.par_obj.image_status_text.showMessage("Your sample is not in the correct format.") self.par_obj.fit_obj.app.processEvents() return #Colour assigned to file. self.color = self.par_obj.colors[self.unqID % len(self.par_obj.colors)] #How many channels there are in the files. self.numOfCH = np.unique(np.array(self.subChanArr)).__len__()-1 #Minus 1 because not interested in channel 15. #Finds the numbers which address the channels. self.ch_present = np.unique(np.array(self.subChanArr[0:100])) #Calculates decay function for both channels. self.photonDecayCh1,self.decayScale1 = delayTime2bin(np.array(self.dTimeArr),np.array(self.subChanArr),self.ch_present[0],self.winInt) if self.numOfCH == 2: self.photonDecayCh2,self.decayScale2 = delayTime2bin(np.array(self.dTimeArr),np.array(self.subChanArr),self.ch_present[1],self.winInt) #Time series of photon counts. For visualisation. self.timeSeries1,self.timeSeriesScale1 = delayTime2bin(np.array(self.trueTimeArr)/1000000,np.array(self.subChanArr),self.ch_present[0],self.photonCountBin) unit = self.timeSeriesScale1[-1]/self.timeSeriesScale1.__len__() #Converts to counts per self.kcount_CH1 = np.average(self.timeSeries1) raw_count = np.average(self.timeSeries1) #This is the unnormalised intensity count for int_time duration (the first moment) var_count = np.var(self.timeSeries1) self.brightnessNandBCH0=(((var_count -raw_count)/(raw_count))/(float(unit))) if (var_count-raw_count) == 0: self.numberNandBCH0 =0 else: self.numberNandBCH0 = (raw_count**2/(var_count-raw_count)) if self.numOfCH == 2: self.timeSeries2,self.timeSeriesScale2 = delayTime2bin(np.array(self.trueTimeArr)/1000000,np.array(self.subChanArr),self.ch_present[1],self.photonCountBin) unit = self.timeSeriesScale2[-1]/self.timeSeriesScale2.__len__() self.kcount_CH2 = np.average(self.timeSeries2) raw_count = np.average(self.timeSeries2) #This is the unnormalised intensity count for int_time duration (the first moment) var_count = np.var(self.timeSeries2) self.brightnessNandBCH1= (((var_count -raw_count)/(raw_count))/(float(unit))) if (var_count-raw_count) == 0: self.numberNandBCH1 =0 else: self.numberNandBCH1 = (raw_count**2/(var_count-raw_count)) #Calculates the Auto and Cross-correlation functions. self.crossAndAuto(np.array(self.trueTimeArr),np.array(self.subChanArr)) if self.fit_obj != None: #If fit object provided then creates fit objects. if self.objId1 == None: corrObj= corrObject(self.filepath,self.fit_obj); self.objId1 = corrObj.objId self.fit_obj.objIdArr.append(corrObj.objId) self.objId1.param = copy.deepcopy(self.fit_obj.def_param) self.objId1.name = self.name+'_CH0_Auto_Corr' self.objId1.ch_type = 0 #channel 0 Auto self.objId1.siblings = None self.objId1.prepare_for_fit() self.objId1.kcount = self.kcount_CH1 self.objId1.autoNorm = np.array(self.autoNorm[:,0,0]).reshape(-1) self.objId1.autotime = np.array(self.autotime).reshape(-1) self.objId1.param = copy.deepcopy(self.fit_obj.def_param) if self.numOfCH == 2: if self.objId3 == None: corrObj= corrObject(self.filepath,self.fit_obj); self.objId3 = corrObj.objId self.objId3.param = copy.deepcopy(self.fit_obj.def_param) self.fit_obj.objIdArr.append(corrObj.objId) self.objId3.name = self.name+'_CH1_Auto_Corr' self.objId3.ch_type = 1 #channel 1 Auto self.objId3.siblings = None self.objId3.prepare_for_fit() self.objId3.kcount = self.kcount_CH2 self.objId3.autoNorm = np.array(self.autoNorm[:,1,1]).reshape(-1) self.objId3.autotime = np.array(self.autotime).reshape(-1) self.objId3.param = copy.deepcopy(self.fit_obj.def_param) if self.objId2 == None: corrObj= corrObject(self.filepath,self.fit_obj); self.objId2 = corrObj.objId self.objId2.param = copy.deepcopy(self.fit_obj.def_param) self.fit_obj.objIdArr.append(corrObj.objId) self.objId2.name = self.name+'_CH01_Cross_Corr' self.objId2.ch_type = 2 #01cross self.objId2.siblings = None self.objId2.prepare_for_fit() self.objId2.autoNorm = np.array(self.autoNorm[:,0,1]).reshape(-1) self.objId2.autotime = np.array(self.autotime).reshape(-1) self.objId2.param = copy.deepcopy(self.fit_obj.def_param) if self.objId4 == None: corrObj= corrObject(self.filepath,self.fit_obj); self.objId4 = corrObj.objId self.objId4.param = copy.deepcopy(self.fit_obj.def_param) self.fit_obj.objIdArr.append(corrObj.objId) self.objId4.name = self.name+'_CH10_Cross_Corr' self.objId4.ch_type = 3 #10cross self.objId4.siblings = None self.objId4.prepare_for_fit() self.objId4.autoNorm = np.array(self.autoNorm[:,1,0]).reshape(-1) self.objId4.autotime = np.array(self.autotime).reshape(-1) self.objId4.param = copy.deepcopy(self.fit_obj.def_param) self.fit_obj.fill_series_list() self.dTimeMin = 0 self.dTimeMax = np.max(self.dTimeArr) self.subDTimeMin = self.dTimeMin self.subDTimeMax = self.dTimeMax self.exit = False del self.subChanArr del self.trueTimeArr del self.dTimeArr def crossAndAuto(self,trueTimeArr,subChanArr): #For each channel we loop through and find only those in the correct time gate. #We only want photons in channel 1 or two. y = trueTimeArr[subChanArr < 3] validPhotons = subChanArr[subChanArr < 3 ] #Creates boolean for photon events in either channel. num = np.zeros((validPhotons.shape[0],2)) num[:,0] = (np.array([np.array(validPhotons) ==self.ch_present[0]])).astype(np.int32) if self.numOfCH ==2: num[:,1] = (np.array([np.array(validPhotons) ==self.ch_present[1]])).astype(np.int32) self.count0 = np.sum(num[:,0]) self.count1 = np.sum(num[:,1]) t1 = time.time() auto, self.autotime = tttr2xfcs(y,num,self.NcascStart,self.NcascEnd, self.Nsub) t2 = time.time() #Normalisation of the TCSPC data: maxY = np.ceil(max(self.trueTimeArr)) self.autoNorm = np.zeros((auto.shape)) self.autoNorm[:,0,0] = ((auto[:,0,0]*maxY)/(self.count0*self.count0))-1 if self.numOfCH == 2: self.autoNorm[:,1,1] = ((auto[:,1,1]*maxY)/(self.count1*self.count1))-1 self.autoNorm[:,1,0] = ((auto[:,1,0]*maxY)/(self.count1*self.count0))-1 self.autoNorm[:,0,1] = ((auto[:,0,1]*maxY)/(self.count0*self.count1))-1 #Normalisaation of the decay functions. self.photonDecayCh1Min = self.photonDecayCh1-np.min(self.photonDecayCh1) self.photonDecayCh1Norm = self.photonDecayCh1Min/np.max(self.photonDecayCh1Min) if self.numOfCH == 2: self.photonDecayCh2Min = self.photonDecayCh2-np.min(self.photonDecayCh2) self.photonDecayCh2Norm = self.photonDecayCh2Min/np.max(self.photonDecayCh2Min) return class subPicoObject(): def __init__(self,parentId,xmin,xmax,TGid,par_obj): #Binning window for decay function self.TGid = TGid #Parameters for auto-correlation and cross-correlation. self.parentId = parentId self.par_obj = par_obj self.NcascStart = self.parentId.NcascStart self.NcascEnd = self.parentId.NcascEnd self.Nsub = self.parentId.Nsub self.fit_obj = self.parentId.fit_obj self.ext = self.parentId.ext self.type = 'subObject' #Appends the object to the subObject register. self.par_obj.subObjectRef.append(self) self.unqID = self.par_obj.subNum self.parentUnqID = self.parentId.unqID #self.chanArr = parentObj.chanArr #self.trueTimeArr = self.parentId.trueTimeArr #self.dTimeArr = self.parentId.dTimeArr self.color = self.parentId.color self.numOfCH = self.parentId.numOfCH self.ch_present = self.parentId.ch_present self.photonCountBin = self.par_obj.photonCountBin self.filepath = str(self.parentId.filepath) self.xmin = xmin self.xmax = xmax self.nameAndExt = os.path.basename(self.filepath).split('.') self.name = 'TG-'+str(self.unqID)+'-xmin_'+str(round(xmin,0))+'-xmax_'+str(round(xmax,0))+'-'+self.nameAndExt[0] self.objId1 = None self.objId2 = None self.objId3 = None self.objId4 = None self.processData(); self.plotOn = True def processData(self): self.NcascStart= self.par_obj.NcascStart self.NcascEnd= self.par_obj.NcascEnd self.Nsub = self.par_obj.Nsub self.winInt = self.par_obj.winInt #self.subChanArr, self.trueTimeArr, self.dTimeArr,self.resolution = pt3import(self.filepath) if self.ext == 'pt3': self.subChanArr, self.trueTimeArr, self.dTimeArr,self.resolution = pt3import(self.filepath) if self.ext == 'csv': self.subChanArr, self.trueTimeArr, self.dTimeArr,self.resolution = csvimport(self.filepath) #If the file is empty. #if self.subChanArr == None: #Undoes any preparation of resource. # self.par_obj.subObjectRef.pop(-1) #self.exit = True # return self.subArrayGeneration(self.xmin,self.xmax) self.dTimeMin = self.parentId.dTimeMin self.dTimeMax = self.parentId.dTimeMax self.subDTimeMin = self.dTimeMin self.subDTimeMax = self.dTimeMax #Time series of photon counts. For visualisation. self.timeSeries1,self.timeSeriesScale1 = delayTime2bin(np.array(self.trueTimeArr)/1000000,np.array(self.subChanArr),self.ch_present[0],self.photonCountBin) unit = self.timeSeriesScale1[-1]/self.timeSeriesScale1.__len__() self.kcount_CH1 = np.average(self.timeSeries1) if self.numOfCH == 2: self.timeSeries2,self.timeSeriesScale2 = delayTime2bin(np.array(self.trueTimeArr)/1000000,np.array(self.subChanArr),self.ch_present[1],self.photonCountBin) unit = self.timeSeriesScale2[-1]/self.timeSeriesScale2.__len__() self.kcount_CH2 = np.average(self.timeSeries2) #Adds names to the fit function for later fitting. if self.objId1 == None: corrObj= corrObject(self.filepath,self.fit_obj); self.objId1 = corrObj.objId self.fit_obj.objIdArr.append(corrObj.objId) self.objId1.param = copy.deepcopy(self.fit_obj.def_param) self.objId1.name = self.name+'_CH0_Auto_Corr' self.objId1.ch_type = 0 #channel 0 Auto self.objId1.siblings = None self.objId1.prepare_for_fit() self.objId1.kcount = self.kcount_CH1 self.objId1.autoNorm = np.array(self.autoNorm[:,0,0]).reshape(-1) self.objId1.autotime = np.array(self.autotime).reshape(-1) self.objId1.param = copy.deepcopy(self.fit_obj.def_param) if self.numOfCH == 2: if self.objId3 == None: corrObj= corrObject(self.filepath,self.fit_obj); self.objId3 = corrObj.objId self.fit_obj.objIdArr.append(corrObj.objId) self.objId3.param = copy.deepcopy(self.fit_obj.def_param) self.objId3.name = self.name+'_CH1_Auto_Corr' self.objId3.ch_type = 1 #channel 1 Auto self.objId3.siblings = None self.objId3.prepare_for_fit() self.objId3.kcount = self.kcount_CH2 self.objId3.autoNorm = np.array(self.autoNorm[:,1,1]).reshape(-1) self.objId3.autotime = np.array(self.autotime).reshape(-1) self.objId3.param = copy.deepcopy(self.fit_obj.def_param) if self.objId2 == None: corrObj= corrObject(self.filepath,self.fit_obj); self.objId2 = corrObj.objId self.objId2.param = copy.deepcopy(self.fit_obj.def_param) self.fit_obj.objIdArr.append(corrObj.objId) self.objId2.name = self.name+'_CH01_Cross_Corr' self.objId2.ch_type = 2 #channel 01 Cross self.objId2.siblings = None self.objId2.prepare_for_fit() self.objId2.autoNorm = np.array(self.autoNorm[:,0,1]).reshape(-1) self.objId2.autotime = np.array(self.autotime).reshape(-1) self.objId2.param = copy.deepcopy(self.fit_obj.def_param) if self.objId4 == None: corrObj= corrObject(self.filepath,self.fit_obj); self.objId4 = corrObj.objId self.objId4.param = copy.deepcopy(self.fit_obj.def_param) self.fit_obj.objIdArr.append(corrObj.objId) self.objId4.name = self.name+'_CH10_Cross_Corr' self.objId4.ch_type = 3 #channel 10 Cross self.objId4.siblings = None self.objId4.prepare_for_fit() self.objId4.autoNorm = np.array(self.autoNorm[:,1,0]).reshape(-1) self.objId4.autotime = np.array(self.autotime).reshape(-1) self.fit_obj.fill_series_list() del self.subChanArr del self.trueTimeArr del self.dTimeArr def subArrayGeneration(self,xmin,xmax): if(xmax=xmin, self.dTimeArr<=xmax).astype(np.bool) self.subChanArr[np.invert(photonInd).astype(np.bool)] = 16 self.crossAndAuto() return def crossAndAuto(self): #We only want photons in channel 1 or two. validPhotons = self.subChanArr[self.subChanArr < 3] y = self.trueTimeArr[self.subChanArr < 3] #Creates boolean for photon events in either channel. num = np.zeros((validPhotons.shape[0],2)) num[:,0] = (np.array([np.array(validPhotons) ==self.ch_present[0]])).astype(np.int) if self.numOfCH == 2: num[:,1] = (np.array([np.array(validPhotons) ==self.ch_present[1]])).astype(np.int) self.count0 = np.sum(num[:,0]) self.count1 = np.sum(num[:,1]) #Function which calculates auto-correlation and cross-correlation. auto, self.autotime = tttr2xfcs(y,num,self.NcascStart,self.NcascEnd, self.Nsub) maxY = np.ceil(max(self.trueTimeArr)) self.autoNorm = np.zeros((auto.shape)) self.autoNorm[:,0,0] = ((auto[:,0,0]*maxY)/(self.count0*self.count0))-1 if self.numOfCH ==2: self.autoNorm[:,1,1] = ((auto[:,1,1]*maxY)/(self.count1*self.count1))-1 self.autoNorm[:,1,0] = ((auto[:,1,0]*maxY)/(self.count1*self.count0))-1 self.autoNorm[:,0,1] = ((auto[:,0,1]*maxY)/(self.count0*self.count1))-1 return class corrObject(): def __init__(self,filepath,parentFn): #the container for the object. self.parentFn = parentFn self.type = 'corrObject' self.filepath = str(filepath) self.nameAndExt = os.path.basename(self.filepath).split('.') self.name = self.nameAndExt[0] self.ext = self.nameAndExt[-1] self.autoNorm=[] self.autotime=[] self.model_autoNorm =[] self.model_autotime = [] self.datalen= [] self.objId = self; self.param = [] self.goodFit = True self.fitted = False self.checked = False self.clicked = False self.toFit = False self.kcount = None self.filter = False def prepare_for_fit(self): if self.parentFn.ch_check_ch0.isChecked() == True and self.ch_type == 0: self.toFit = True if self.parentFn.ch_check_ch1.isChecked() == True and self.ch_type == 1: self.toFit = True if self.parentFn.ch_check_ch01.isChecked() == True and self.ch_type == 2: self.toFit = True if self.parentFn.ch_check_ch10.isChecked() == True and self.ch_type == 3: self.toFit = True #self.parentFn.modelFitSel.clear() #for objId in self.parentFn.objIdArr: # if objId.toFit == True: # self.parentFn.modelFitSel.addItem(objId.name) self.parentFn.updateFitList() def residual(self, param, x, data,options): A = equation_(param, x,options) residuals = data-A return residuals def fitToParameters(self): #self.parentFn.updateParamFirst() #self.parentFn.updateTableFirst() #self.parentFn.updateParamFirst() #Populate param for lmfit. param = Parameters() #self.def_param.add('A1', value=1.0, min=0,max=1.0, vary=False) for art in self.param: if self.param[art]['to_show'] == True: param.add(art, value=float(self.param[art]['value']), min=float(self.param[art]['minv']) ,max=float(self.param[art]['maxv']), vary=self.param[art]['vary']); #Find the index of the nearest point in the scale. data = np.array(self.autoNorm).astype(np.float64).reshape(-1) scale = np.array(self.autotime).astype(np.float64).reshape(-1) self.indx_L = int(np.argmin(np.abs(scale - self.parentFn.dr.xpos))) self.indx_R = int(np.argmin(np.abs(scale - self.parentFn.dr1.xpos))) #Run the fitting. res = minimize(self.residual, param, args=(scale[self.indx_L:self.indx_R+1],data[self.indx_L:self.indx_R+1], self.parentFn.def_options)) #Repopulate the parameter object. for art in self.param: if self.param[art]['to_show'] == True and self.param[art]['calc'] == False: self.param[art]['value'] = param[art].value self.param[art]['stderr'] = float(param[art].stderr) #Extra parameters, which are not fit or inherited. #self.param['N_FCS']['value'] = np.round(1/self.param['GN0']['value'],4) self.residualVar = res.residual output = fit_report(param) print 'residual',res.chisqr if(res.chisqr>0.05): print 'CAUTION DATA DID NOT FIT WELL CHI^2 >0.05',res.chisqr self.goodFit = False else: self.goodFit = True self.fitted = True self.chisqr = res.chisqr self.localTime = time.asctime( time.localtime(time.time()) ) #self.parentFn.updateTableFirst(); self.model_autoNorm = equation_(param, scale[self.indx_L:self.indx_R+1],self.parentFn.def_options) self.model_autotime = scale[self.indx_L:self.indx_R+1] #self.parentFn.on_show() #self.parentFn.axes.plot(model_autotime,model_autoNorm, 'o-') #self.parentFn.canvas.draw(); def load_from_file(self,channel): tscale = []; tdata = []; int_tscale =[]; int_tdata=[]; if self.ext == 'fcs': corrObj = self text =[0] r_obj = csv.reader(open(corrObj.filepath, 'rb'),delimiter='\t') title = r_obj.next() line = r_obj.next() line = r_obj.next() name = line[1].split(' = ')[1] read = True while read == True: corrObj = corrObject(self.filepath,self.parentFn); self.parentFn.objIdArr.append(corrObj) corrObj.name = name line = r_obj.next() text =[] for part in line: if part != '': text.append(part) #Reads to first correlation array text. while text[0].split(' = ')[0] != 'CorrelationArray' or text[0].split(' = ')[1] == int(0): line = r_obj.next() text =[] for part in line: if part != '': text.append(part) line = r_obj.next() tdata = [] tscale = [] while text[0].split(' = ')[0] != 'PhotonCountHistogramArraySize': try: line = r_obj.next() except: read = False break; text =[] for part in line: if part != '': text.append(part) if text.__len__() >1: tscale.append(float(text[0])) tdata.append(float(text[1])) if tdata.__len__() == 0: corrObj = [] self.parentFn.objIdArr.pop(-1) break; channel = 0 corrObj.siblings = None corrObj.autoNorm= np.array(tdata).astype(np.float64).reshape(-1) corrObj.autotime= np.array(tscale).astype(np.float64).reshape(-1)*1000 corrObj.name = corrObj.name+'-CH'+str(channel) corrObj.ch_type = channel; corrObj.param = copy.deepcopy(self.parentFn.def_param) self.parentFn.fill_series_list() if self.ext == 'SIN': self.parentFn.objIdArr.append(self.objId) proceed = False for line in csv.reader(open(self.filepath, 'rb'),delimiter='\t'): if proceed =='correlated': if line ==[]: proceed =False; else: tscale.append(float(line[0])) tdata.append(float(line[channel+1])) if proceed =='intensity': if line ==[]: proceed=False; elif line.__len__()> 1: int_tscale.append(float(line[0])) int_tdata.append(float(line[channel+1])) if (str(line) == "[\'[CorrelationFunction]\']"): proceed = 'correlated'; elif (str(line) == "[\'[IntensityHistory]\']"): proceed = 'intensity'; self.siblings = None self.autoNorm= np.array(tdata).astype(np.float64).reshape(-1) self.autotime= np.array(tscale).astype(np.float64).reshape(-1)*1000 self.name = self.name+'-CH'+str(channel) self.ch_type = channel; #self.prepare_for_fit() #Average counts per bin. For it to be seconds (Hz), must divide by duration. unit = int_tscale[-1]/(int_tscale.__len__()-1) #And to be in kHz we divide by 1000. self.kcount = np.average(np.array(int_tdata)/unit)/1000 self.param = copy.deepcopy(self.parentFn.def_param) self.parentFn.fill_series_list() #Where we add the names. if self.ext == 'csv' or self.ext =='CSV': r_obj = csv.reader(open(self.filepath, 'rb')) line_one = r_obj.next() if line_one.__len__()>1: if float(line_one[1]) == 2: version = 2 else: print 'version not known:',line_one[1] else: version = 1 if version == 1: self.parentFn.objIdArr.append(self) c = 0 for line in csv.reader(open(self.filepath, 'rb')): if (c >0): tscale.append(line[0]) tdata.append(line[1]) c +=1; self.autoNorm= np.array(tdata).astype(np.float64).reshape(-1) self.autotime= np.array(tscale).astype(np.float64).reshape(-1) self.name = self.name+'-CH'+str(0) self.ch_type = 0 self.datalen= len(tdata) self.param = copy.deepcopy(self.parentFn.def_param) self.parentFn.fill_series_list() if version >= 2: numOfCH = float(r_obj.next()[1]) if numOfCH == 1: self.parentFn.objIdArr.append(self) self.type =str(r_obj.next()[1]) self.ch_type = int(r_obj.next()[1]) self.name = self.name+'-CH'+str(self.ch_type) line = r_obj.next() while line[0] != 'Time (ns)': if line[0] == 'kcount': self.kcount = float(line[1]) if line[0] == 'numberNandB': self.numberNandB = float(line[1]) if line[0] == 'brightnessNandB': self.brightnessNandB = float(line[1]) if line[0] == 'CV': self.CV = float(line[1]) if line[0] == 'carpet pos': carpet = int(line[1]) if line[0] == 'pc': pc_text = int(line[1]) if line[0] == 'pbc_f0': self.pbc_f0 = float(line[1]) if line[0] == 'pbc_tb': self.pbc_tb = float(line[1]) line = r_obj.next() if pc_text != False: self.name = self.name +'_pc_m'+str(pc_text) tscale = [] tdata = [] null = r_obj.next() line = r_obj.next() while line[0] != 'end': tscale.append(line[0]) tdata.append(line[1]) line = r_obj.next() self.autoNorm= np.array(tdata).astype(np.float64).reshape(-1) self.autotime= np.array(tscale).astype(np.float64).reshape(-1) self.siblings = None self.param = copy.deepcopy(self.parentFn.def_param) self.parentFn.fill_series_list() if numOfCH == 2: corrObj2 = corrObject(self.filepath,self.parentFn); corrObj3 = corrObject(self.filepath,self.parentFn); self.parentFn.objIdArr.append(self) self.parentFn.objIdArr.append(corrObj2) self.parentFn.objIdArr.append(corrObj3) line_type = r_obj.next() self.type = str(line_type[1]) corrObj2.type = str(line_type[1]) corrObj3.type = str(line_type[1]) line_ch = r_obj.next() self.ch_type = int(line_ch[1]) corrObj2.ch_type = int(line_ch[2]) corrObj3.ch_type = int(line_ch[3]) self.name = self.name+'-CH'+str(self.ch_type) corrObj2.name = corrObj2.name+'-CH'+str(corrObj2.ch_type) corrObj3.name = corrObj3.name+'-CH'+str(corrObj3.ch_type) line = r_obj.next() while line[0] != 'Time (ns)': if line[0] == 'kcount': self.kcount = float(line[1]) corrObj2.kcount = float(line[2]) if line[0] == 'numberNandB': self.numberNandB = float(line[1]) corrObj2.numberNandB = float(line[2]) if line[0] == 'brightnessNandB': self.brightnessNandB = float(line[1]) corrObj2.brightnessNandB = float(line[2]) if line[0] == 'CV': self.CV = float(line[1]) corrObj2.CV = float(line[2]) corrObj3.CV = float(line[3]) if line[0] == 'carpet pos': self.carpet_position = int(line[1]) if line[0] == 'pc': pc_text = int(line[1]) if line[0] == 'pbc_f0': self.pbc_f0 = float(line[1]) corrObj2.pbc_f0 = float(line[2]) if line[0] == 'pbc_tb': self.pbc_tb = float(line[1]) corrObj2.pbc_tb = float(line[2]) line = r_obj.next() if pc_text != False: self.name = self.name +'_pc_m'+str(pc_text) corrObj2.name = corrObj2.name +'_pc_m'+str(pc_text) corrObj3.name = corrObj3.name +'_pc_m'+str(pc_text) null = r_obj.next() line = r_obj.next() tscale = [] tdata0 = [] tdata1 = [] tdata2 = [] while line[0] != 'end': tscale.append(line[0]) tdata0.append(line[1]) tdata1.append(line[2]) tdata2.append(line[3]) line = r_obj.next() self.autotime= np.array(tscale).astype(np.float64).reshape(-1) corrObj2.autotime= np.array(tscale).astype(np.float64).reshape(-1) corrObj3.autotime= np.array(tscale).astype(np.float64).reshape(-1) self.autoNorm= np.array(tdata0).astype(np.float64).reshape(-1) corrObj2.autoNorm= np.array(tdata1).astype(np.float64).reshape(-1) corrObj3.autoNorm= np.array(tdata2).astype(np.float64).reshape(-1) self.siblings = [corrObj2,corrObj3] corrObj2.siblings = [self,corrObj3] corrObj3.siblings = [self,corrObj2] self.param = copy.deepcopy(self.parentFn.def_param) corrObj2.param = copy.deepcopy(self.parentFn.def_param) corrObj3.param = copy.deepcopy(self.parentFn.def_param) self.parentFn.fill_series_list() PyCorrFit-0.9.9/pycorrfit/readfiles/read_pt3_scripts/fib4.pyx000066400000000000000000000027441274265304700242500ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ PicoQuant functionalities from FCS_viewer This file contains a fast implementation of an algorithm that is very important (yes I have no clue about the structure of pt3 files) for importing *.pt3 files: `dividAndConquer`. The code was written by Dr. Dominic Waithe Wolfson Imaging Centre. Weatherall Institute of Molecular Medicine. University of Oxford https://github.com/dwaithe/FCS_viewer See Also: The wrapper: `read_pt3_PicoQuant.py` The wrapped file: `read_pt3_PicoQuant_original_FCSViewer.py`. """ import cython cimport cython import numpy as np cimport numpy as np DTYPE = np.float64 ctypedef np.float64_t DTYPE_t @cython.boundscheck(False) @cython.wraparound(False) @cython.nonecheck(False) def dividAndConquer(arr1b,arr2b,arrLength): """divide and conquer fast intersection algorithm. Waithe D 2014""" cdef np.ndarray[DTYPE_t, ndim=1] arr1bool = np.zeros((arrLength-1)) cdef np.ndarray[DTYPE_t, ndim=1] arr2bool = np.zeros((arrLength-1)) cdef np.ndarray[DTYPE_t, ndim=1] arr1 = arr1b cdef np.ndarray[DTYPE_t, ndim=1] arr2 = arr2b cdef int arrLen arrLen = arrLength; cdef int i i = 0; cdef int j j = 0; while(i 1: if float(line_one[1]) == 2: version = 2 else: print 'version not known:',line_one[1] if version == 2: type =str(r_obj.next()[1]) if type == "pt uncorrelated": Resolution = float(r_obj.next()[1]) chanArr = [] trueTimeArr = [] dTimeArr = [] line = r_obj.next() while line[0] != 'end': chanArr.append(int(line[0])) trueTimeArr.append(float(line[1])) dTimeArr.append(int(line[2])) line = r_obj.next() return np.array(chanArr), np.array(trueTimeArr), np.array(dTimeArr), Resolution else: print 'type not recognised' return None, None,None,None def pt3import(filepath): """The file import for the .pt3 file""" f = open(filepath, 'rb') Ident = f.read(16) FormatVersion = f.read(6) CreatorName = f.read(18) CreatorVersion = f.read(12) FileTime = f.read(18) CRLF = f.read(2) CommentField = f.read(256) Curves = struct.unpack('i', f.read(4))[0] BitsPerRecord = struct.unpack('i', f.read(4))[0] RoutingChannels = struct.unpack('i', f.read(4))[0] NumberOfBoards = struct.unpack('i', f.read(4))[0] ActiveCurve = struct.unpack('i', f.read(4))[0] MeasurementMode = struct.unpack('i', f.read(4))[0] SubMode = struct.unpack('i', f.read(4))[0] RangeNo = struct.unpack('i', f.read(4))[0] Offset = struct.unpack('i', f.read(4))[0] AcquisitionTime = struct.unpack('i', f.read(4))[0] StopAt = struct.unpack('i', f.read(4))[0] StopOnOvfl = struct.unpack('i', f.read(4))[0] Restart = struct.unpack('i', f.read(4))[0] DispLinLog = struct.unpack('i', f.read(4))[0] DispTimeFrom = struct.unpack('i', f.read(4))[0] DispTimeTo = struct.unpack('i', f.read(4))[0] DispCountFrom = struct.unpack('i', f.read(4))[0] DispCountTo = struct.unpack('i', f.read(4))[0] DispCurveMapTo = []; DispCurveShow =[]; for i in range(0,8): DispCurveMapTo.append(struct.unpack('i', f.read(4))[0]); DispCurveShow.append(struct.unpack('i', f.read(4))[0]); ParamStart =[]; ParamStep =[]; ParamEnd =[]; for i in range(0,3): ParamStart.append(struct.unpack('i', f.read(4))[0]); ParamStep.append(struct.unpack('i', f.read(4))[0]); ParamEnd.append(struct.unpack('i', f.read(4))[0]); RepeatMode = struct.unpack('i', f.read(4))[0] RepeatsPerCurve = struct.unpack('i', f.read(4))[0] RepeatTime = struct.unpack('i', f.read(4))[0] RepeatWait = struct.unpack('i', f.read(4))[0] ScriptName = f.read(20) #The next is a board specific header HardwareIdent = f.read(16) HardwareVersion = f.read(8) HardwareSerial = struct.unpack('i', f.read(4))[0] SyncDivider = struct.unpack('i', f.read(4))[0] CFDZeroCross0 = struct.unpack('i', f.read(4))[0] CFDLevel0 = struct.unpack('i', f.read(4))[0] CFDZeroCross1 = struct.unpack('i', f.read(4))[0] CFDLevel1 = struct.unpack('i', f.read(4))[0] Resolution = struct.unpack('f', f.read(4))[0] #below is new in format version 2.0 RouterModelCode = struct.unpack('i', f.read(4))[0] RouterEnabled = struct.unpack('i', f.read(4))[0] #Router Ch1 RtChan1_InputType = struct.unpack('i', f.read(4))[0] RtChan1_InputLevel = struct.unpack('i', f.read(4))[0] RtChan1_InputEdge = struct.unpack('i', f.read(4))[0] RtChan1_CFDPresent = struct.unpack('i', f.read(4))[0] RtChan1_CFDLevel = struct.unpack('i', f.read(4))[0] RtChan1_CFDZeroCross = struct.unpack('i', f.read(4))[0] #Router Ch2 RtChan2_InputType = struct.unpack('i', f.read(4))[0] RtChan2_InputLevel = struct.unpack('i', f.read(4))[0] RtChan2_InputEdge = struct.unpack('i', f.read(4))[0] RtChan2_CFDPresent = struct.unpack('i', f.read(4))[0] RtChan2_CFDLevel = struct.unpack('i', f.read(4))[0] RtChan2_CFDZeroCross = struct.unpack('i', f.read(4))[0] #Router Ch3 RtChan3_InputType = struct.unpack('i', f.read(4))[0] RtChan3_InputLevel = struct.unpack('i', f.read(4))[0] RtChan3_InputEdge = struct.unpack('i', f.read(4))[0] RtChan3_CFDPresent = struct.unpack('i', f.read(4))[0] RtChan3_CFDLevel = struct.unpack('i', f.read(4))[0] RtChan3_CFDZeroCross = struct.unpack('i', f.read(4))[0] #Router Ch4 RtChan4_InputType = struct.unpack('i', f.read(4))[0] RtChan4_InputLevel = struct.unpack('i', f.read(4))[0] RtChan4_InputEdge = struct.unpack('i', f.read(4))[0] RtChan4_CFDPresent = struct.unpack('i', f.read(4))[0] RtChan4_CFDLevel = struct.unpack('i', f.read(4))[0] RtChan4_CFDZeroCross = struct.unpack('i', f.read(4))[0] #The next is a T3 mode specific header. ExtDevices = struct.unpack('i', f.read(4))[0] Reserved1 = struct.unpack('i', f.read(4))[0] Reserved2 = struct.unpack('i', f.read(4))[0] CntRate0 = struct.unpack('i', f.read(4))[0] CntRate1 = struct.unpack('i', f.read(4))[0] StopAfter = struct.unpack('i', f.read(4))[0] StopReason = struct.unpack('i', f.read(4))[0] Records = struct.unpack('i', f.read(4))[0] ImgHdrSize =struct.unpack('i', f.read(4))[0] #Special Header for imaging. if ImgHdrSize > 0: ImgHdr = struct.unpack('i', f.read(ImgHdrSize))[0] ofltime = 0; cnt_1=0; cnt_2=0; cnt_3=0; cnt_4=0; cnt_Ofl=0; cnt_M=0; cnt_Err=0; # just counters WRAPAROUND=65536; #Put file Save info here. syncperiod = 1e9/CntRate0; #outfile stuff here. #fpout. #T3RecordArr = []; chanArr = [0]*Records trueTimeArr =[0]*Records dTimeArr=[0]*Records #f1=open('./testfile', 'w+') for b in range(0,Records): T3Record = struct.unpack('I', f.read(4))[0]; #T3RecordArr.append(T3Record) nsync = T3Record & 65535 chan = ((T3Record >> 28) & 15); chanArr[b]=chan #f1.write(str(i)+" "+str(T3Record)+" "+str(nsync)+" "+str(chan)+" ") dtime = 0; if chan == 1: cnt_1 = cnt_1+1;dtime = ((T3Record >> 16) & 4095);#f1.write(str(dtime)+" ") elif chan == 2: cnt_2 = cnt_2+1;dtime = ((T3Record >> 16) & 4095);#f1.write(str(dtime)+" ") elif chan == 3: cnt_3 = cnt_3+1;dtime = ((T3Record >> 16) & 4095);#f1.write(str(dtime)+" ") elif chan == 4: cnt_4 = cnt_4+1;dtime = ((T3Record >> 16) & 4095);#f1.write(str(dtime)+" ") elif chan == 15: markers = ((T3Record >> 16) & 15); if markers ==0: ofltime = ofltime +WRAPAROUND; cnt_Ofl = cnt_Ofl+1 #f1.write("Ofl "+" ") else: cnt_M=cnt_M+1 #f1.write("MA:%1u "+markers+" ") truensync = ofltime + nsync; truetime = (truensync * syncperiod) + (dtime*Resolution); trueTimeArr[b] = truetime dTimeArr[b] = dtime #f1.write(str(truensync)+" "+str(truetime)+"\n") f.close(); #f1.close(); return np.array(chanArr), np.array(trueTimeArr), np.array(dTimeArr), ResolutionPyCorrFit-0.9.9/pycorrfit/readfiles/util.py000066400000000000000000000034251274265304700207360ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ utility functions for reading data """ from __future__ import division import numpy as np def downsample_trace(trace, bestlength=500): """ Reduces the length of a trace so that there is no undersampling on a regular computer screen and the data size is not too large. Downsampling is performed by averaging neighboring intensity values for two time bins and omitting the first time bin. """ # The trace is too big. Wee need to bin it. if len(trace) >= bestlength: # We want about 500 bins # We need to sum over intervals of length *teiler* teiler = int(np.floor(len(trace)/bestlength)) newlength = int(np.floor(len(trace)/teiler)) newsignal = np.zeros(newlength) # Simultaneously sum over all intervals for j in np.arange(teiler): newsignal = \ newsignal+trace[j:newlength*teiler:teiler][:,1] newsignal = 1.* newsignal / teiler newtimes = trace[teiler-1:newlength*teiler:teiler][:,0] if len(trace)%teiler != 0: # We have a rest signal # We average it and add it to the trace rest = trace[newlength*teiler:][:,1] lrest = len(rest) rest = np.array([sum(rest)/lrest]) newsignal = np.concatenate((newsignal, rest), axis=0) timerest = np.array([trace[-1][0]]) newtimes = np.concatenate((newtimes, timerest), axis=0) newtrace=np.zeros((len(newtimes),2)) newtrace[:,0] = newtimes newtrace[:,1] = newsignal else: # Declare newtrace - # otherwise we have a problem down three lines ;) newtrace = trace return newtracePyCorrFit-0.9.9/pycorrfit/trace.py000066400000000000000000000067511274265304700171260ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ PyCorrFit data set Classes for FCS data evaluation. """ from __future__ import print_function, division import hashlib import numpy as np import scipy.integrate as spintg class Trace(object): """ unifies trace handling """ def __init__(self, trace=None, countrate=None, duration=None, name=None): """ Load trace data Parameters ---------- trace : ndarray of shape (N, 2) The array contains time [ms] and count rate [kHz]. coutrate : float Average count rate [kHz]. Mandatory if `trace` is None. duration : float Duration of measurement in milliseconds. Mandatory if `trace` is None. name : str The name of the trace. """ self._countrate = None self._duration = None self._trace = None self._uid = None if trace is None: self.countrate = countrate self.duration = duration else: self.trace = trace if name is None: name = "{:.2f}kHz, {:.0f}s".format(self.countrate, self.duration/1000) self.name = name def __getitem__(self, idx): return self.trace[idx] def __repr__(self): text = "Trace of length {:.3f}s and countrate {:.3f}kHz".format( self.duration/1000, self.countrate) return text @property def countrate(self): if self._countrate is None: #self._countrate = np.average(self._trace[:,1]) # Take into account traces that have arbitrary sampling self._countrate = spintg.simps(self._trace[:,1], self._trace[:,0]) / self.duration return self._countrate @countrate.setter def countrate(self, value): assert value is not None, "Setting value with None forbidden!" assert self._trace is None, "Setting value impossible, "+\ "if `self.trace` is set." self._countrate = value @property def duration(self): if not hasattr(self, "_duration") or self._duration is None: self._duration = self._trace[-1,0] - self._trace[0,0] return self._duration @duration.setter def duration(self, value): assert value is not None, "Setting value with None forbidden!" assert self._trace is None, "Setting value impossible, "+\ "if `self.trace` is set." self._duration = value @property def uid(self): if self._uid is None: hasher = hashlib.sha256() hasher.update(str(np.random.random())) hasher.update(str(self.trace)) hasher.update(self.name) self._uid = hasher.hexdigest() return self._uid @property def trace(self): if self._trace is None: self._trace = np.array([ [0, self.countrate], [self.duration, self.countrate] ]) return self._trace @trace.setter def trace(self, value): assert value is not None, "Setting value with None forbidden!" assert isinstance(value, np.ndarray), "value must be array!" assert value.shape[1] == 2, "shape of array must be (N,2)!" self._trace = value # self.countrate is set automagically PyCorrFit-0.9.9/setup.cfg000066400000000000000000000001221274265304700152400ustar00rootroot00000000000000[aliases] test=pytest [egg_info] tag_build = tag_date = 0 tag_svn_revision = 0 PyCorrFit-0.9.9/setup.py000066400000000000000000000104521274265304700151400ustar00rootroot00000000000000#!/usr/bin/env python # -*- coding: utf-8 -*- # To just compile the cython part in-place: # python setup.py build_ext --inplace # To create a distribution package for pip or easy-install: # python setup.py sdist # To create wheels package and upload securely # pip install wheel twine # python setup.py bdist wheel from __future__ import print_function from setuptools import setup, Extension import sys from os.path import join, dirname, realpath, exists from warnings import warn # The next three lines are necessary for setup.py install to include # ChangeLog and Documentation of PyCorrFit from distutils.command.install import INSTALL_SCHEMES for scheme in INSTALL_SCHEMES.values(): scheme['data'] = scheme['purelib'] # We don't need cython if a .whl package is available. # Try to import cython and throw a warning if it does not work. try: from Cython.Distutils import build_ext import numpy as np except ImportError: print("Cython or NumPy not available. Building extensions "+ "with this setup script will not work:", sys.exc_info()) EXTENSIONS = [] build_ext = None else: EXTENSIONS = [Extension("pycorrfit.readfiles.read_pt3_scripts.fib4", ["pycorrfit/readfiles/read_pt3_scripts/fib4.pyx"], libraries=[], include_dirs=[np.get_include()] ) ] # Download documentation if it was not compiled Documentation = join(dirname(realpath(__file__)), "doc/PyCorrFit_doc.pdf") webdoc = "https://github.com/FCS-analysis/PyCorrFit/wiki/PyCorrFit_doc.pdf" if not exists(Documentation): print("Downloading {} from {}".format(Documentation, webdoc)) import urllib #testfile = urllib.URLopener() urllib.urlretrieve(webdoc, Documentation) # Get the version of PyCorrFit from the Changelog.txt StaticChangeLog = join(dirname(realpath(__file__)), "ChangeLog.txt") try: clfile = open(StaticChangeLog, 'r') version = clfile.readline().strip() clfile.close() except: warn("Could not find 'ChangeLog.txt'. PyCorrFit version is unknown.") version = "0.0.0-unknown" # Parameters author = u"Paul Müller" authors = [author] description = 'Scientific tool for fitting correlation curves on a logarithmic plot.' name = 'pycorrfit' year = "2014" setup( author=author, author_email='paul.mueller@biotec.tu-dresden.de', classifiers= [ 'Operating System :: OS Independent', 'Programming Language :: Python :: 2.7', 'Topic :: Scientific/Engineering :: Visualization', 'Intended Audience :: Science/Research' ], data_files=[('pycorrfit_doc', ['ChangeLog.txt', 'doc/PyCorrFit_doc.pdf'])], description=description, long_description=open('README.rst').read() if exists('README.rst') else '', include_package_data=True, keywords=["fcs", "fluorescence", "correlation", "spectroscopy", "tir", "fitting" ], license="GPL v2", name=name, platforms=['ALL'], url='https://github.com/FCS-analysis/PyCorrFit', version=version, # data files packages=['pycorrfit', 'pycorrfit.models', 'pycorrfit.readfiles', 'pycorrfit.gui', 'pycorrfit.gui.tools', ], package_dir={'pycorrfit': 'pycorrfit', 'pycorrfit.models': 'pycorrfit/models', 'pycorrfit.readfiles': 'pycorrfit/readfiles', 'pycorrfit.gui': 'pycorrfit/gui', 'pycorrfit.gui.tools': 'pycorrfit/gui/tools', }, # cython ext_modules=EXTENSIONS, cmdclass={'build_ext': build_ext}, # requirements extras_require = { # If you need the GUI of this project in your project, add # "thisproject[GUI]" to your install_requires # Graphical User Interface 'GUI': ["wxPython", "matplotlib >= 1.1.0", "sympy >= 0.7.2"], }, install_requires=[ "NumPy >= 1.5.1", "SciPy >= 0.8.0", "PyYAML >= 3.09", "lmfit >= 0.9.2", ], setup_requires=["cython", 'pytest-runner'], tests_require=["pytest", "urllib3", "simplejson"], # scripts entry_points={ "gui_scripts": ["{name:s}={name:s}:Main".format( **{"name":name})] } ) PyCorrFit-0.9.9/tests/000077500000000000000000000000001274265304700145665ustar00rootroot00000000000000PyCorrFit-0.9.9/tests/README.md000066400000000000000000000003071274265304700160450ustar00rootroot00000000000000### Test Scripts Execute all tests using `setup.py` in the parent directory: python setup.py test ### Running single tests Directly execute the scripts, e.g. python test_simple.py PyCorrFit-0.9.9/tests/data_file_dl.py000066400000000000000000000130611274265304700175300ustar00rootroot00000000000000#!/usr/bin/env python # -*- coding: utf-8 -*- """ Download experimental data files from https://github.com/FCS-analysis/FCSdata This module establishes """ from __future__ import division, print_function import os from os.path import abspath, dirname, join, exists import simplejson as json import urllib3 import warnings # Download path root raw_origin = "https://github.com/FCS-analysis/FCSdata/raw/master/" # GitHub API root api_origin = "https://api.github.com/repos/FCS-analysis/FCSdata/git/" # Download directory dldir = join(dirname(abspath(__file__)), "data") # Pool Manager handles all requests pool_manager = urllib3.PoolManager() _fcs_data_tree = None def dl_file(url, dest, chunk_size=6553, http=pool_manager): """ Download `url` to `dest`. Parameters ---------- url : str Full download URL dest : str Full download path. Directory will be created if non-existent. chunk_size : int Chunk size of download (download buffer size). http : instance of `urllib3.PoolManager` Manages all connections. Must implement the `request` method. """ if not exists(dirname(dest)): os.makedirs(dirname(dest)) r = http.request('GET', url, preload_content=False) with open(dest, 'wb') as out: while True: data = r.read(chunk_size) if data is None or len(data)==0: break out.write(data) def get_data_file(filename, dldir=dldir, pool_manager=pool_manager, api_origin=api_origin, raw_origin=raw_origin): """ Return first occurrence of `filename` in the data file tree. """ _f, ext = os.path.splitext(filename) assert ext != "", "filename has no extension!" files = get_data_files_ext(extension=ext, dldir=dldir, pool_manager=pool_manager, api_origin=api_origin, raw_origin=raw_origin) files = [ f for f in files if f.count(filename) ] assert len(files) != 0, "filename not found" return files[0] def get_data_files_ext(extension, dldir=dldir, pool_manager=pool_manager, api_origin=api_origin, raw_origin=raw_origin): """ Get all files in the repository `origin` that are in the folder `extension` and have a file-ending that matches `extension` (case-insensitive). The files are downloaded and local paths in the `dldir` directory are returned. If no internet connection is available, previously downloaded files will be used. Parameters ---------- extension : str A file extension such as `fcs` or `sin`. dldir : str Path to download directory. http : instance of `urllib3.PoolManager` Manages all connections. Must implement the `request` method. raw_origin : str Web root for downloads, e.g. "https://raw.github.com/FCS-analysis/FCSdata" api_origin : str GitHub api URL, e.g. "https://api.github.com/repos/FCS-analysis/FCSdata/git/" Notes ----- The files in the remote location must be sorted according to file extionsion. E.g. all `*.sin` files must be located in a folder in the root directory named `sin`. """ ext = extension.lower() if not ext.startswith("."): ext = "."+ext try: # Get file list and download files = get_data_tree_remote(pool_manager=pool_manager, api_origin=api_origin) extfiles = [ f for f in files if f.lower().startswith(ext[1:]+"/") and f.lower().endswith(ext)] extfiles.sort() dl_files = [] for f in extfiles: dest = join(dldir, f) if not exists(dest): dl_file(join(raw_origin, f), dest) dl_files.append(dest) except urllib3.exceptions.MaxRetryError: # e.g. no internet connection warnings.warn("Non connection, using previuously downloaded files only.") files = get_data_tree_local(dldir=dldir) dl_files = [ f for f in files if f.lower().endswith(ext)] return dl_files def get_data_tree_local(dldir=dldir): """ Returns FCSdata repository tree of local files. """ loc_files = [] for root, _dirs, files in os.walk(dldir): # sorting convention: the folder names the extension relpath = os.path.relpath(root, dldir) ext = os.path.basename(relpath[::-1])[::-1] for f in files: if f.lower().endswith(ext): loc_files.append(os.path.join(root, f)) return loc_files def get_data_tree_remote(pool_manager=pool_manager, api_origin=api_origin): """ Returns FCSdata repository tree. The tree is saved in the global variable `_fcs_data_tree` to reduce number of GitHub API requests. """ global _fcs_data_tree if _fcs_data_tree is None: url = api_origin+"trees/master?recursive=1" # headers headers = {'User-Agent': __file__} # GitHub API token to prevent rate-limits # Key is generated with # # gem install travis # travis encrypt GH_READ_API_TOKEN=secret-token # # Add the result to env in travis.yml. if "GH_READ_API_TOKEN" in os.environ: headers["Authorization"] = "token {}".format(os.environ["GH_READ_API_TOKEN"]) r = pool_manager.request("GET", url, headers=headers) jd = json.loads(r.data) tree = jd["tree"] _fcs_data_tree = [ t["path"] for t in tree ] return _fcs_data_treePyCorrFit-0.9.9/tests/test_constraints.py000066400000000000000000000072621274265304700205550ustar00rootroot00000000000000#!/usr/bin/env python # -*- coding: utf-8 -*- """ Test if constraints work with model functions. """ from __future__ import division, print_function import sys from os.path import abspath, dirname, split import numpy as np import os # Add parent directory to beginning of path variable sys.path.insert(0, dirname(dirname(abspath(__file__)))) import data_file_dl import pycorrfit as pcf def test_fit_constraint_simple_inequality(): """ Check "smaller than" relation during fitting. """ dfile = data_file_dl.get_data_file("019_cp_KIND+BFA.fcs") data = pcf.readfiles.openAny(dfile) corr = pcf.Correlation(correlation=data["Correlation"][0], traces=data["Trace"][0], corr_type=data["Type"][0], filename=os.path.basename(dfile), title="test correlation", fit_model=6035 # confocal 3D+3D) ) corr.fit_parameters_variable = [True, True, True, True, False, False, False] # crop triplet data corr.fit_ival[0] = 8 pcf.Fit(corr) assert corr.fit_parameters[1] <= corr.fit_parameters[2] # -> deliberately reverse everything and try again corr.fit_parameters[1], corr.fit_parameters[2] = corr.fit_parameters[2], corr.fit_parameters[1] corr.fit_parameters[3] = 1-corr.fit_parameters[3] pcf.Fit(corr) # This tests also for equality assert corr.fit_parameters[1] <= corr.fit_parameters[2] if corr.fit_parameters[1] == corr.fit_parameters[2]: print("found identity of fit parameters - multiplying by two to see if relation holds") corr.fit_parameters[2] *= 2 pcf.Fit(corr) assert corr.fit_parameters[1] < corr.fit_parameters[2] def test_fit_constraint_sum_smaller_one(): """ Check "a+b deliberately reverse everything and try again corr.fit_parameters[4], corr.fit_parameters[5] = corr.fit_parameters[5], corr.fit_parameters[4] pcf.Fit(corr) # This tests also for equality assert corr.fit_parameters[4] + corr.fit_parameters[5] < 1 if __name__ == "__main__": # Run all tests loc = locals() for key in list(loc.keys()): if key.startswith("test_") and hasattr(loc[key], "__call__"): loc[key]() PyCorrFit-0.9.9/tests/test_file_formats.py000066400000000000000000000020441274265304700206510ustar00rootroot00000000000000#!/usr/bin/env python # -*- coding: utf-8 -*- """ Test if pycorrfit can open all file formats. """ from __future__ import division, print_function import sys from os.path import abspath, dirname, split import numpy as np # Add parent directory to beginning of path variable sys.path.insert(0, dirname(dirname(abspath(__file__)))) import data_file_dl import pycorrfit # Files that are known to not work exclude = [] def test_open(): """ Try to open all files supported files """ # get list of supported file extensions for ext in pycorrfit.readfiles.get_supported_extensions(): files = data_file_dl.get_data_files_ext(ext) for f in files: if len([ex for ex in exclude if f.endswith(ex) ]): continue print(f) dn, fn = split(f) pycorrfit.readfiles.openAny(dn, fn) if __name__ == "__main__": # Run all tests loc = locals() for key in list(loc.keys()): if key.startswith("test_") and hasattr(loc[key], "__call__"): loc[key]() PyCorrFit-0.9.9/tests/test_fit_model_gaussian.py000066400000000000000000000341011274265304700220320ustar00rootroot00000000000000#!/usr/bin/env python # -*- coding: utf-8 -*- """ Check known model parameters and cross-check across models. """ from __future__ import division, print_function import sys from os.path import abspath, dirname, split import numpy as np # Add parent directory to beginning of path variable sys.path.insert(0, dirname(dirname(abspath(__file__)))) import pycorrfit from pycorrfit import models as mdls # GLOBAL PARAMETERS FOR THIS TEST: TAU = 1.468e-6 def test_6001(): #2D model = mdls.modeldict[6001] parms = [4.874, 0.2476, 0.015] assert abs(model(parms, tau=TAU) - 0.22016907491127263) < 1e-14 def test_6002(): #T+2D model = mdls.modeldict[6002] # n τ_diff τ_trip T offset parms = [4.891, 0.853, 0.00141, 0.0121, 0.034] assert abs(model(parms, tau=TAU) - 0.24095843709396209) < 1e-14 model2 = mdls.modeldict[6001] parms2 = [4.891, 0.853, 0.034] parms1 = [4.891, 0.853, 0.0, 0.0, 0.034] assert abs(model(parms1, tau=TAU) - model2(parms2, tau=TAU)) < 1e-14 def test_6031(): #T+2D+2D model = mdls.modeldict[6031] parms = [ 2.487, # n 3.4325, # taud1 2534, # taud2 0.153, # F .879, # alpha 0.00123, # tautrip 0.0314, # T 0.00021 # offset ] assert abs(model(parms, tau=TAU) - 0.41629799102222742) < 1e-14 model2 = mdls.modeldict[6002] parms2 = [4.891, 0.853, 0.0012, 0.108, 0.034] parms1 = [ 4.891, # n 0.853, # taud1 1.0, # taud2 1.0, # F 1.0, # alpha 0.0012, # tautrip 0.108, # T 0.034 # offset ] assert abs(model(parms1, tau=TAU) - model2(parms2, tau=TAU)) < 1e-14 def test_6011(): #T+3D model = mdls.modeldict[6011] # n T τ_trip τ_diff SP offset parms = [2.168, 0.1682, 0.00028, 0.54, 5.864, 0.0053] assert abs(model(parms, tau=TAU) - 0.55933660640533278) < 1e-14 model2 = mdls.modeldict[6012] parms2 = [2.168, 0.54, 5.864, 0.0053] parms1 = [2.168, 0, 1.0, 0.54, 5.864, 0.0053] assert abs(model(parms1, tau=TAU) - model2(parms2, tau=TAU)) < 1e-14 def test_6012(): #3D model = mdls.modeldict[6012] parms = [2.168, 0.54, 5.864, 0.0053] assert abs(model(parms, tau=TAU) - 0.46655334038750634) < 1e-14 def test_6030(): #T+3D+3D model = mdls.modeldict[6030] parms = [ 2.153, # n 5.54, # taud1 1532, # taud2 0.4321, # F 4.4387, # SP 0.9234, # alpha 0.002648, # tautrip 0.1151, # T 0.008 # offset ] assert abs(model(parms, tau=TAU) - 0.53367456244118261) < 1e-14 model2 = mdls.modeldict[6011] # n T τ_trip τ_diff SP offset parms2 = [2.168, 0.1682, 0.00028, 0.54, 5.864, 0.0053] parms1 = [ 2.168, # n 0.54, # taud1 1.0, # taud2 1.0, # F 5.864, # SP 0.9234, # alpha 0.00028, # tautrip 0.1682, # T 0.0053 # offset ] assert abs(model(parms1, tau=TAU) - model2(parms2, tau=TAU)) < 1e-14 def test_6032(): #T+3D+2D model = mdls.modeldict[6032] parms = [ 1.58, # n 3548, # taud2D 0.351, # taud3D 0.345, # F3D 4.984, # SP 0.879, # alpha 0.0014, # tautrip 0.108, # T 0.008 # offset ] assert abs(model(parms, tau=TAU) - 0.72001694812574801) < 1e-14 #->T+3D model2 = mdls.modeldict[6011] # n T τ_trip τ_diff SP offset parms2 = [2.168, 0.1682, 0.0028, 0.54, 5.864, 0.0053] parms1a = [ 2.168, # n 1.0, # taud2D 0.54, # taud3D 1.0, # F3D 5.864, # SP 0.879, # alpha 0.0028, # tautrip 0.1682, # T 0.0053 # offset ] assert abs(model(parms1a, tau=TAU) - model2(parms2, tau=TAU)) < 1e-14 #->T+2D model3 = mdls.modeldict[6002] # n τ_diff τ_trip T offset parms3 = [4.891, 0.853, 0.00141, 0.0121, 0.034] parms1b = [ 4.891, # n 0.853, # taud2D 1.0, # taud3D 0.0, # F3D 1.0, # SP 0.879, # alpha 0.00141, # tautrip 0.0121, # T 0.034 # offset ] assert abs(model(parms1b, tau=TAU) - model3(parms3, tau=TAU)) < 1e-14 def test_6043(): # TT+3D+3D model = mdls.modeldict[6043] parms = [ 1.452, # n 4.48, # taud1 8438, # taud2 0.425, # F 5.43, # SP 0.876, # alpha 0.0012, # tautrip1 0.0101, # T1 0.0021, # tautrip2 0.0102, # T2 0.00004 # offset ] assert abs(model(parms, tau=TAU) - 0.70599013426715551) < 1e-14 #->T+3D+3D model2 = mdls.modeldict[6030] parms2 = [ 2.153, # n 5.54, # taud1 1532, # taud2 0.4321, # F 4.4387, # SP 0.9234, # alpha 0.002648, # tautrip 0.1151, # T 0.008 # offset ] parms1 = [ 2.153, # n 5.54, # taud1 1532, # taud2 0.4321, # F 4.4387, # SP 0.9234, # alpha 0.002648, # tautrip1 0.1151, # T1 0.0021, # tautrip2 0.0, # T2 0.008 # offset ] assert abs(model(parms1, tau=TAU) - model2(parms2, tau=TAU)) < 1e-14 def test_6044(): # TT+2D+2D model = mdls.modeldict[6044] parms = [ 1.452, # n 4.48, # taud1 8438, # taud2 0.425, # F 0.876, # alpha 0.0012, # tautrip1 0.0101, # T1 0.0021, # tautrip2 0.0102, # T2 0.00004 # offset ] assert abs(model(parms, tau=TAU) - 0.70599013619282502) < 1e-14 #->T+2D+2D model2 = mdls.modeldict[6031] parms2 = [ 2.153, # n 5.54, # taud1 1532, # taud2 0.4321, # F 0.9234, # alpha 0.002648, # tautrip 0.1151, # T 0.008 # offset ] parms1 = [ 2.153, # n 5.54, # taud1 1532, # taud2 0.4321, # F 0.9234, # alpha 0.002648, # tautrip1 0.1151, # T1 0.0021, # tautrip2 0.0, # T2 0.008 # offset ] assert abs(model(parms1, tau=TAU) - model2(parms2, tau=TAU)) < 1e-14 def test_6045(): #TT+3D+2D model = mdls.modeldict[6045] parms = [ 25.123, # n 240.123, # taud2D 0.1125, # taud3D 0.3512, # F3D 5.312, # SP 0.87671, # alpha 0.0021987, # tautrip1 0.032341, # T1 0.0013243, # tautrip2 0.014341, # T2 0.12310 # offset ] assert abs(model(parms, tau=TAU) - 0.16498917764250026) < 1e-14 #->T+3D+2D model2 = mdls.modeldict[6032] parms2 = [ 25.123, # n 240.123, # taud2D 0.1125, # taud3D 0.3512, # F3D 5.312, # SP 0.87671, # alpha 0.0021987, # tautrip1 0.032341, # T1 0.12310 # offset ] parms1a = [ 25.123, # n 240.123, # taud2D 0.1125, # taud3D 0.3512, # F3D 5.312, # SP 0.87671, # alpha 0.0021987, # tautrip1 0.032341, # T1 0.1, # tautrip2 0.0, # T2 0.12310 # offset ] parms1b = [ 25.123, # n 240.123, # taud2D 0.1125, # taud3D 0.3512, # F3D 5.312, # SP 0.87671, # alpha 0.1, # tautrip1 0.0, # T1 0.0021987, # tautrip2 0.032341, # T2 0.12310 # offset ] assert abs(model(parms1a, tau=TAU) - model2(parms2, tau=TAU)) < 1e-14 assert abs(model(parms1b, tau=TAU) - model2(parms2, tau=TAU)) < 1e-14 def test_6081(): # T+3D+3D+3D model = mdls.modeldict[6081] parms = [ 1.412, # n 4.498, # taud1 245, # taud2 2910, # taud3 0.123, # F1 0.321, # F3 5.12, # SP 0.876, # alpha21 0.378, # alpha31 0.0021, # tautrip 0.021, # T -0.0004 # offset ] assert abs(model(parms, tau=TAU) - 0.85970140411643392) < 1e-14 #->T+3D+3D model2 = mdls.modeldict[6030] parms2 = [ 2.153, # n 1.120, # taud1 30.120, # taud2 0.4321, # F 4.4387, # SP 0.4321, # alpha 0.002, # tautrip 0.1151, # T 1.2008 # offset ] parmsa = [ 2.153, # n 1.120, # taud1 30.120, # taud2 100.00, # taud3 0.4321, # F1 1-0.4321, # F2 4.4387, # SP 0.4321, # alpha21 1, # alpha31 0.002, # tautrip 0.1151, # T 1.2008 # offset ] parmsb = [ 2.153, # n 1.120, # taud1 10.000, # taud2 30.120, # taud3 0.4321, # F1 0, # F2 4.4387, # SP 1, # alpha21 .4321, # alpha31 0.002, # tautrip 0.1151, # T 1.2008 # offset ] assert abs(model(parmsa, tau=TAU) - model2(parms2, tau=TAU)) < 1e-14 assert abs(model(parmsb, tau=TAU) - model2(parms2, tau=TAU)) < 1e-14 def test_6082(): # T+3D+3D+2D model = mdls.modeldict[6082] parms = [ 1.412, # n 4.498, # taud1 245, # taud2 2910, # taud3 0.123, # F1 0.321, # F3 5.12, # SP 0.876, # alpha21 0.378, # alpha31 0.0021, # tautrip 0.021, # T -0.0004 # offset ] assert abs(model(parms, tau=TAU) - 0.85970140411789908) < 1e-14 #->T+3D+2D model2 = mdls.modeldict[6032] parms2 = [ 2.153, # n 30.120, # taud1 1.234, # taud2 0.4321, # F 4.4387, # SP 1.341, # alpha 0.002, # tautrip 0.1151, # T 1.2008 # offset ] parmsa = [ 2.153, # n 1.234, # taud1 1, # taud2 30.120, # taud3 0.4321, # F1 0, # F2 4.4387, # SP 1., # alpha21 1/1.341, # alpha31 0.002, # tautrip 0.1151, # T 1.2008 # offset ] parmsb = [ 2.153, # n 1, # taud1 1.234, # taud2 30.120, # taud3 0, # F1 0.4321, # F2 4.4387, # SP 1, # alpha21 1/1.341, # alpha31 0.002, # tautrip 0.1151, # T 1.2008 # offset ] assert abs(model(parmsa, tau=TAU) - model2(parms2, tau=TAU)) < 1e-14 assert abs(model(parmsb, tau=TAU) - model2(parms2, tau=TAU)) < 1e-14 if __name__ == "__main__": # Run all tests loc = locals() for key in list(loc.keys()): if key.startswith("test_") and hasattr(loc[key], "__call__"): loc[key]() PyCorrFit-0.9.9/tests/test_fit_models.py000066400000000000000000000105201274265304700203220ustar00rootroot00000000000000#!/usr/bin/env python # -*- coding: utf-8 -*- """ Go through each model, vary one parameter and fit it back to the default value of that model. """ from __future__ import division, print_function import sys from os.path import abspath, dirname, split import numpy as np # Add parent directory to beginning of path variable sys.path.insert(0, dirname(dirname(abspath(__file__)))) import pycorrfit from pycorrfit.correlation import Correlation from pycorrfit.fit import Fit # GLOBAL PARAMETERS FOR THIS TEST: TAUMIN = 1e-3 TAUMAX = 1e6 TAULEN = 100 FITALG = "Lev-Mar" def fit_single_parameter(modelid, fullparms, parmid, parmval, noise=False): """ Use the full parameter set `fullparms` and leave a single parameter `parmid` variable during the fit. Returns the fitted value of the parameter with index `parmid` """ corr = Correlation(fit_model=modelid, fit_algorithm=FITALG, verbose=0) tau = np.exp(np.linspace(np.log(TAUMIN),np.log(TAUMAX), TAULEN)) # Create artificial data by using the current fit_model data = corr.fit_model(fullparms, tau) if noise: if noise is True: deltanoise = (np.max(data)-np.min(data))/20 else: deltanoise = (np.max(data)-np.min(data))*noise anoise = (np.random.random(data.shape[0])-.5)*deltanoise data += anoise # Add artificial data to data set corr.correlation = np.dstack((tau, data))[0] # Set variable parameters fit_bool = np.zeros(fullparms.shape[0]) fit_bool[parmid] = True corr.fit_parameters_variable = fit_bool fullparms_edit = fullparms.copy() fullparms_edit[parmid] = parmval corr.fit_parameters = fullparms_edit Fit(corr) return corr.fit_parameters[parmid] def deviate_parameter(model, parmid): """ Returns a deviated version of the parameter with id `parmid`. Performs model checks to ensure the new value is physical. """ val = model.default_values[parmid] if val==0: val+=.1 else: val*=.9 return val def test_fit_single_parameter(): """ Deviate a single parameter and fit it back. """ allow_fail = [ [6082, "SP"], ] faillist=list() for model in pycorrfit.models.models: fullparms = model.default_values for ii, val in enumerate(fullparms): newval = deviate_parameter(model, ii) fitval = fit_single_parameter(model.id, fullparms, ii, newval, noise=False) #print(val-fitval) if not np.allclose([val], [fitval]): if not [model.id, model.parameters[0][ii]] in allow_fail: faillist.append([model.id, model.parameters[0][ii], val, fitval]) if len(faillist) != 0: raise ValueError("Model tests failed for:\n", faillist) def fit_single_parameter_with_noise(noise=0.005): """ Deviate a single parameter and fit it back. """ faillist=list() succlist=list() for model in pycorrfit.models.models: fullparms = model.default_values for ii, val in enumerate(fullparms): newval = deviate_parameter(model, ii) fitval = fit_single_parameter(model.id, fullparms, ii, newval, noise=noise) if not np.allclose([val], [fitval], atol=.1, rtol=.1): faillist.append([model.id, model.parameters[0][ii], val, fitval]) else: succlist.append([model.id, model.parameters[0][ii], val, fitval]) return succlist, faillist def test_fit_single_parameter_with_noise_one_permille(): succlist, faillist = fit_single_parameter_with_noise(noise=0.001) if len(faillist)/len(succlist) > .01: raise ValueError("Model tests failed for:\n", faillist) def test_fit_single_parameter_with_noise_two_percent(): succlist, faillist = fit_single_parameter_with_noise(noise=0.02) if len(faillist)/len(succlist) > .05: raise ValueError("Model tests failed for:\n", faillist) def test_fit_single_parameter_with_noise_five_percent(): succlist, faillist = fit_single_parameter_with_noise(noise=0.05) if len(faillist)/len(succlist) > .10: raise ValueError("Model tests failed for:\n", faillist) if __name__ == "__main__": # Run all tests loc = locals() for key in list(loc.keys()): if key.startswith("test_") and hasattr(loc[key], "__call__"): loc[key]() PyCorrFit-0.9.9/tests/test_global_fit.py000066400000000000000000000032111274265304700202760ustar00rootroot00000000000000#!/usr/bin/env python # -*- coding: utf-8 -*- import sys from os.path import abspath, dirname import numpy as np # Add parent directory to beginning of path variable sys.path.insert(0, dirname(dirname(abspath(__file__)))) from pycorrfit.correlation import Correlation from pycorrfit.fit import Fit def create_corr(): n = 2.4 taud = 10 SP = 3.3 tau = np.exp(np.linspace(np.log(1e-3),np.log(1e6), 10)) corr1 = Correlation(fit_model=6002) corr1.lag_time = tau # 0: n # 1: τ_diff [ms] p1a = corr1.fit_parameters.copy() p1b = p1a.copy() p1b[0] = n p1b[1] = taud # write values and return to original corr1.fit_parameters = p1b corr1.correlation = corr1.modeled_fit.copy() corr1.fit_parameters = p1a corr1.fit_parameters_variable = [True, True, False, False, False] corr2 = Correlation(fit_model=6011) corr2.lag_time = tau # 0: n # 3: τ_diff [ms] # 4: SP p2a = corr2.fit_parameters.copy() p2b = p2a.copy() p2b[0] = n p2b[3] = taud p2b[4] = SP # write values and return to original corr2.fit_parameters = p2b corr2.correlation = corr2.modeled_fit.copy() corr2.fit_parameters = p2a corr2.fit_parameters_variable = [True, False, False, True, True, False] corrs = [corr1, corr2] initparms = np.array([n, taud, SP]) return corrs, initparms def test_globalfit(): corrs, initparms = create_corr() # commence global fit globalfit = Fit(correlations=corrs, global_fit=True) assert np.allclose(globalfit.fit_parm, initparms), "Global fit failed" if __name__ == "__main__": test_globalfit() PyCorrFit-0.9.9/tests/test_simple.py000066400000000000000000000030131274265304700174650ustar00rootroot00000000000000#!/usr/bin/env python # -*- coding: utf-8 -*- import sys from os.path import abspath, dirname import matplotlib.pylab as plt import numpy as np # Add parent directory to beginning of path variable sys.path.insert(0, dirname(dirname(abspath(__file__)))) import pycorrfit # @UnusedImport from pycorrfit.correlation import Correlation from pycorrfit.fit import Fit def create_corr(): corr = Correlation() tau = np.exp(np.linspace(np.log(1e-3),np.log(1e6), 10)) data = corr.fit_model(corr.fit_parameters, tau) noise = (np.random.random(data.shape[0])-.5)*.0005 data += noise corr.correlation = np.dstack((tau, data))[0] return corr def test_simple_corr(): corr = create_corr() oldparms = corr.fit_parameters.copy() temp = corr.fit_parameters temp[0] *= 2 temp[-1] *= .1 Fit(corr) res = oldparms - corr.fit_parameters assert np.allclose(res, np.zeros_like(res), atol=0.009) if __name__ == "__main__": corr = create_corr() fig, (ax1, ax2) = plt.subplots(2,1) ax1.set_xscale("log") ax2.set_xscale("log") print(corr.fit_parameters) temp = corr.fit_parameters temp[0] *= 2 temp[-1] *= .1 ax1.plot(corr.correlation_fit[:,0], corr.correlation_fit[:,1]) ax1.plot(corr.modeled_fit[:,0], corr.modeled_fit[:,1]) print(corr.fit_parameters) Fit(corr) print(corr.fit_parameters) ax2.plot(corr.correlation_fit[:,0], corr.correlation_fit[:,1]) ax2.plot(corr.modeled_fit[:,0], corr.modeled_fit[:,1]) plt.show()