multipletau-0.1.6/000077500000000000000000000000001274076530500140745ustar00rootroot00000000000000multipletau-0.1.6/.travis.yml000066400000000000000000000015241274076530500162070ustar00rootroot00000000000000language: python python: - '2.7' - '3.4' - '3.5' env: global: - GH_REF: github.com/FCS-analysis/multipletau.git - secure: IVoAJNYKGjWbHUGPpe8oOTLhltGrhu0F+xCaRVGs1tTut34BixSSeDgranlRiXZ0wlVOzBGrDHLkoLxFSRRy43BN4TSiv05WLBZba7ypGYBbDrLrG5nFPnT6n9d4ZgFHHHwyvI2ymdSs6/EJwZRXmr2Ehm0HzetA27FB1/Q3kc0= notifications: email: false install: - travis_retry pip install coverage - travis_retry pip install coveralls - python setup.py develop - pip freeze script: - coverage run --source=multipletau ./setup.py test - coverage report -m after_success: - coveralls --verbose - git config credential.helper "store --file=.git/credentials" - echo "https://${GH_TOKEN}:@github.com" > .git/credentials - if [[ $TRAVIS_PYTHON_VERSION == 3.4 ]]; then pip install numpydoc sphinx; fi - if [[ $TRAVIS_PYTHON_VERSION == 3.4 ]]; then python doc/deploy_ghpages.py; fi multipletau-0.1.6/CHANGELOG000066400000000000000000000011521274076530500153050ustar00rootroot000000000000000.1.6 - also compute correlation for zero lag time (`G(tau==0)`) - support NumPy 1.11 - add tests to complete code coverage - bugfixes: - wrong normalization for cplx array `v` in `correlate` if `normalize==True` - wrong normalization in `correlate_numpy` if `normalize==False` 0.1.5 - update documentation - support Python 3 0.1.4 - integer and boolean input types are now automatically converted to floats - `multipletau.correlate` now works with complex data types - `multipletau.correlate` now checks if input data are same objects - documentation now contains examples 0.1.3 - first non-cython implementation multipletau-0.1.6/LICENSE000066400000000000000000000027071274076530500151070ustar00rootroot00000000000000Copyright (c) 2014 Paul Müller Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. 3. Neither the name of multipletau nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL INFRAE OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. multipletau-0.1.6/MANIFEST.in000066400000000000000000000003321274076530500156300ustar00rootroot00000000000000include CHANGELOG include LICENSE include README.rst include tests/*.py include tests/*.md include tests/data/*.npy include examples/*.py include doc/*.py include doc/*.rst include doc/*.md include doc/extensions/*.py multipletau-0.1.6/README.rst000066400000000000000000000041021274076530500155600ustar00rootroot00000000000000multipletau =========== |PyPI Version| |Build Status| |Coverage Status| Multipe-tau correlation is computed on a logarithmic scale (less data points are computed) and is thus much faster than conventional correlation on a linear scale such as `numpy.correlate `__. Installation ------------ ``multipletau`` supports Python 2.6+ and Python 3.3+ with a common codebase. The only requirement for ``multipletau`` is `NumPy `__ (for fast operations on arrays). Install ``multipletau`` from the Python package index: :: pip install multipletau Documentation ------------- A full code reference including examples is available `here `__. Usage ----- .. code:: python import numpy as np import multipletau a = np.linspace(2,5,42) v = np.linspace(1,6,42) multipletau.correlate(a, v, m=2) array([[ 0. , 569.56097561], [ 1. , 549.87804878], [ 2. , 530.37477692], [ 4. , 491.85812017], [ 8. , 386.39500297]]) Citing ------ The multipletau package should be cited like this (replace "x.x.x" with the actual version of multipletau that you used and "DD Month YYYY" with a matching date). Paul Müller (2012) *Python multiple-tau algorithm* (Version x.x.x) [Computer program]. Available at ``__ (Accessed DD Month YYYY) You can find out what version you are using by typing (in a Python console): .. code:: python >>> import multipletau >>> multipletau.__version__ '0.1.4' .. |PyPI Version| image:: http://img.shields.io/pypi/v/multipletau.svg :target: https://pypi.python.org/pypi/multipletau .. |Build Status| image:: http://img.shields.io/travis/FCS-analysis/multipletau.svg :target: https://travis-ci.org/FCS-analysis/multipletau .. |Coverage Status| image:: https://img.shields.io/coveralls/FCS-analysis/multipletau.svg :target: https://coveralls.io/r/FCS-analysis/multipletau multipletau-0.1.6/doc/000077500000000000000000000000001274076530500146415ustar00rootroot00000000000000multipletau-0.1.6/doc/README.md000066400000000000000000000003171274076530500161210ustar00rootroot00000000000000multipletau documentation ========================= Install [numpydoc](https://pypi.python.org/pypi/numpydoc): pip install numpydoc To compile the documentation, run python setup.py build_sphinx multipletau-0.1.6/doc/conf.py000066400000000000000000000231241274076530500161420ustar00rootroot00000000000000# -*- coding: utf-8 -*- # # project documentation build configuration file, created by # sphinx-quickstart on Sat Feb 22 09:35:49 2014. # # This file is execfile()d with the current directory set to its # containing dir. # # Note that not all possible configuration values are present in this # autogenerated file. # # All configuration values have a default; values that are commented out # serve to show the default. import sys import os # If extensions (or modules to document with autodoc) are in another directory, # add these directories to sys.path here. If the directory is relative to the # documentation root, use os.path.abspath to make it absolute, like shown here. #sys.path.insert(0, os.path.abspath('.')) sys.path.insert(0, os.path.abspath(os.path.join(os.path.abspath( os.path.dirname(__file__)), '../'))) sys.path.append(os.path.abspath('extensions')) # include examples sys.path.append(os.path.abspath(os.path.dirname(__file__)+"/../examples")) # There should be a file "setup.py" that has the property "version" from setup import author, authors, description, name, version, year projectname = name projectdescription = description # -- General configuration ------------------------------------------------ # If your documentation needs a minimal Sphinx version, state it here. #needs_sphinx = '1.0' # Add any Sphinx extension module names here, as strings. They can be # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom # ones. #extensions = [ # 'sphinx.ext.autodoc', # 'sphinx.ext.doctest', # 'sphinx.ext.coverage', # 'sphinx.ext.pngmath', # 'sphinx.ext.viewcode', #] extensions = [ # 'matplotlib.sphinxext.mathmpl', # 'matplotlib.sphinxext.only_directives', # 'matplotlib.sphinxext.plot_directive', # 'sphinx.ext.viewcode', # 'ipython_directive', 'sphinx.ext.intersphinx', 'sphinx.ext.autosummary', 'sphinx.ext.autodoc', # 'sphinx.ext.doctest', # 'ipython_console_highlighting', # 'sphinx.ext.pngmath', 'sphinx.ext.mathjax', # 'sphinx.ext.viewcode', # 'sphinx.ext.todo', # 'inheritance_diagram', 'numpydoc', 'myviewcode', # 'hidden_code_block', ] # Add any paths that contain templates here, relative to this directory. templates_path = ['_templates'] # The suffix of source filenames. source_suffix = '.rst' # The encoding of source files. #source_encoding = 'utf-8-sig' # The master toctree document. master_doc = 'index' # General information about the project. project = projectname copyright = year+", "+author # The version info for the project you're documenting, acts as replacement for # |version| and |release|, also used in various other places throughout the # built documents. # # The short X.Y version. # The full version, including alpha/beta/rc tags. release = version # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. #language = None # There are two options for replacing |today|: either, you set today to some # non-false value, then it is used: #today = '' # Else, today_fmt is used as the format for a strftime call. #today_fmt = '%B %d, %Y' # List of patterns, relative to source directory, that match files and # directories to ignore when looking for source files. # The reST default role (used for this markup: `text`) to use for all # documents. #default_role = None # If true, '()' will be appended to :func: etc. cross-reference text. #add_function_parentheses = True # If true, the current module name will be prepended to all description # unit titles (such as .. function::). #add_module_names = True # If true, sectionauthor and moduleauthor directives will be shown in the # output. They are ignored by default. #show_authors = False # The name of the Pygments (syntax highlighting) style to use. #pygments_style = 'default' # A list of ignored prefixes for module index sorting. #modindex_common_prefix = [] # If true, keep warnings as "system message" paragraphs in the built documents. #keep_warnings = False # -- Options for HTML output ---------------------------------------------- # The theme to use for HTML and HTML Help pages. See the documentation for # a list of builtin themes. html_theme = 'classic' # Theme options are theme-specific and customize the look and feel of a theme # further. For a list of options available for each theme, see the # documentation. html_theme_options = {"stickysidebar": True} # Add any paths that contain custom themes here, relative to this directory. #html_theme_path = [] # The name for this set of Sphinx documents. If None, it defaults to # " v documentation". #html_title = None # A shorter title for the navigation bar. Default is the same as html_title. #html_short_title = None # The name of an image file (relative to this directory) to place at the top # of the sidebar. #html_logo = None # The name of an image file (within the static path) to use as favicon of the # docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32 # pixels large. #html_favicon = None # Add any paths that contain custom static files (such as style sheets) here, # relative to this directory. They are copied after the builtin static files, # so a file named "default.css" will overwrite the builtin "default.css". #html_static_path = ['_static'] # Add any extra paths that contain custom files (such as robots.txt or # .htaccess) here, relative to this directory. These files are copied # directly to the root of the documentation. #html_extra_path = [] # If not '', a 'Last updated on:' timestamp is inserted at every page bottom, # using the given strftime format. #html_last_updated_fmt = '%b %d, %Y' # If true, SmartyPants will be used to convert quotes and dashes to # typographically correct entities. #html_use_smartypants = True # Custom sidebar templates, maps document names to template names. #html_sidebars = {} # Additional templates that should be rendered to pages, maps page names to # template names. #html_additional_pages = {} # If false, no module index is generated. #html_domain_indices = True # If false, no index is generated. #html_use_index = True # If true, the index is split into individual pages for each letter. #html_split_index = False # If true, links to the reST sources are added to the pages. #html_show_sourcelink = True # If true, "Created using Sphinx" is shown in the HTML footer. Default is True. #html_show_sphinx = True # If true, "(C) Copyright ..." is shown in the HTML footer. Default is True. #html_show_copyright = True # If true, an OpenSearch description file will be output, and all pages will # contain a tag referring to it. The value of this option must be the # base URL from which the finished HTML is served. #html_use_opensearch = '' # This is the file name suffix for HTML files (e.g. ".xhtml"). #html_file_suffix = None # Output file base name for HTML help builder. htmlhelp_basename = projectname+'doc' # -- Options for LaTeX output --------------------------------------------- latex_elements = { # The paper size ('letterpaper' or 'a4paper'). #'papersize': 'letterpaper', # The font size ('10pt', '11pt' or '12pt'). #'pointsize': '10pt', # Additional stuff for the LaTeX preamble. #'preamble': '', } # Grouping the document tree into LaTeX files. List of tuples # (source start file, target name, title, # author, documentclass [howto, manual, or own class]). latex_documents = [ ('index', projectname+'.tex', projectname+' Documentation', author, 'manual'), ] # The name of an image file (relative to this directory) to place at the top of # the title page. #latex_logo = None # For "manual" documents, if this is true, then toplevel headings are parts, # not chapters. #latex_use_parts = False # If true, show page references after internal links. #latex_show_pagerefs = False # If true, show URL addresses after external links. #latex_show_urls = False # Documents to append as an appendix to all manuals. #latex_appendices = [] # If false, no module index is generated. #latex_domain_indices = True # -- Options for manual page output --------------------------------------- # One entry per manual page. List of tuples # (source start file, name, description, authors, manual section). man_pages = [ ('index', projectname, projectname+' Documentation', authors, 1) ] # If true, show URL addresses after external links. #man_show_urls = False # -- Options for Texinfo output ------------------------------------------- # Grouping the document tree into Texinfo files. List of tuples # (source start file, target name, title, author, # dir menu entry, description, category) texinfo_documents = [ ('index', projectname, projectname+u' Documentation', author, projectname, projectdescription, 'Numeric'), ] # Documents to append as an appendix to all manuals. #texinfo_appendices = [] # If false, no module index is generated. #texinfo_domain_indices = True # How to display URL addresses: 'footnote', 'no', or 'inline'. #texinfo_show_urls = 'footnote' # If true, do not generate a @detailmenu in the "Top" node's menu. #texinfo_no_detailmenu = False # ----------------------------------------------------------------------------- # intersphinx # ----------------------------------------------------------------------------- _python_doc_base = 'http://docs.python.org/2.7' intersphinx_mapping = { _python_doc_base: None, 'http://docs.scipy.org/doc/numpy': None, 'http://docs.scipy.org/doc/scipy/reference': None, } multipletau-0.1.6/doc/deploy_ghpages.py000066400000000000000000000063071274076530500202130ustar00rootroot00000000000000#!/usr/bin/env python # -*- coding: utf-8 -*- """ Publish the documentation on GitHub Pages. Prerequisites ------------- 1. Create empty gh-pages branch: git branch gh-pages git checkout gh-pages git symbolic-ref HEAD refs/heads/gh-pages rm .git/index git clean -fdx 2. Setup sphinx. python setup.py build_sphinx should create a build/sphinx/html folder in the repository root. 3. Create GitHub repo token and encrypt it gem install travis travis encrypt GH_TOKEN="" --add 4. Add the encrypted token to .travis.yml env: global: - GH_REF: github.com//.git - secure: "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" 5. Add the deploy command to .travis.yml after_success: - git config credential.helper "store --file=.git/credentials" - echo "https://${GH_TOKEN}:@github.com" > .git/credentials - if [[ $TRAVIS_PYTHON_VERSION == 3.4 ]]; then pip install numpydoc sphinx; fi - if [[ $TRAVIS_PYTHON_VERSION == 3.4 ]]; then python doc/deploy_ghpages.py; fi """ from __future__ import print_function import os from os.path import dirname, abspath import subprocess as sp # go to root of repository os.chdir(dirname(dirname(abspath(__file__)))) # build sphinx sp.check_output("python setup.py build_sphinx", shell=True) # clone into new folder the gh-pages branch sp.check_output("git config --global user.email 'travis@example.com'", shell=True) sp.check_output("git config --global user.name 'Travis CI'", shell=True) sp.check_output("git config --global credential.helper 'store --file=.git/credentials'", shell=True) sp.check_output("echo 'https://${GH_TOKEN}:@github.com' > .git/credentials", shell=True) sp.check_output("git clone --depth 1 -b gh-pages https://${GH_TOKEN}@${GH_REF} gh_pages", shell=True) # copy everything from ./build/sphinx/html to ./gh_pages #sp.check_output("cp -r ./build/sphinx/html/* ./gh_pages/", shell=True) sp.check_output("rsync -rt --del --exclude='.git' --exclude='.nojekyll' ./build/sphinx/html/* ./gh_pages/", shell=True) # commit changes os.chdir("gh_pages") sp.check_output("echo 'https://${GH_TOKEN}:@github.com' > .git/credentials", shell=True) sp.check_output("git add --all ./*", shell=True) try: # If there is nothing to commit, then 'git commit' returns non-zero exit status errorcode = sp.check_output("git commit -a -m 'travis bot build {} [ci skip]'".format(os.getenv("TRAVIS_COMMIT")), shell=True) print("git commit returned:", errorcode) except: pass else: sp.check_output("git push --force --quiet origin gh-pages", shell=True) multipletau-0.1.6/doc/extensions/000077500000000000000000000000001274076530500170405ustar00rootroot00000000000000multipletau-0.1.6/doc/extensions/myviewcode.py000066400000000000000000000213211274076530500215640ustar00rootroot00000000000000""" sphinx.ext.viewcode ~~~~~~~~~~~~~~~~~~~ Add links to module code in Python object descriptions. :copyright: Copyright 2007-2015 by the Sphinx team, see AUTHORS. :license: BSD, see LICENSE for details. Edited by Paul Mueller to support imports from submodules. Uses the importlib library. Changes marked with "## EDIT". 2015-02-22 """ ## EDIT import importlib ## import traceback from six import iteritems, text_type from docutils import nodes import sphinx from sphinx import addnodes from sphinx.locale import _ from sphinx.pycode import ModuleAnalyzer from sphinx.util import get_full_modname from sphinx.util.nodes import make_refnode from sphinx.util.console import blue def _get_full_modname(app, modname, attribute): try: return get_full_modname(modname, attribute) except AttributeError: # sphinx.ext.viewcode can't follow class instance attribute # then AttributeError logging output only verbose mode. app.verbose('Didn\'t find %s in %s' % (attribute, modname)) return None except Exception as e: # sphinx.ext.viewcode follow python domain directives. # because of that, if there are no real modules exists that specified # by py:function or other directives, viewcode emits a lot of warnings. # It should be displayed only verbose mode. app.verbose(traceback.format_exc().rstrip()) app.verbose('viewcode can\'t import %s, failed with error "%s"' % (modname, e)) return None def doctree_read(app, doctree): env = app.builder.env if not hasattr(env, '_viewcode_modules'): env._viewcode_modules = {} def has_tag(modname, fullname, docname, refname): entry = env._viewcode_modules.get(modname, None) try: analyzer = ModuleAnalyzer.for_module(modname) except Exception: env._viewcode_modules[modname] = False return if not isinstance(analyzer.code, text_type): code = analyzer.code.decode(analyzer.encoding) else: code = analyzer.code if entry is None or entry[0] != code: analyzer.find_tags() entry = code, analyzer.tags, {}, refname env._viewcode_modules[modname] = entry elif entry is False: return _, tags, used, _ = entry if fullname in tags: used[fullname] = docname return True for objnode in doctree.traverse(addnodes.desc): if objnode.get('domain') != 'py': continue names = set() for signode in objnode: if not isinstance(signode, addnodes.desc_signature): continue modname = signode.get('module') fullname = signode.get('fullname') refname = modname if env.config.viewcode_import: modname = _get_full_modname(app, modname, fullname) if not modname: continue fullname = signode.get('fullname') ## EDIT fullname, modname = find_modname(fullname, modname) ## if not has_tag(modname, fullname, env.docname, refname): continue if fullname in names: # only one link per name, please continue names.add(fullname) pagename = '_modules/' + modname.replace('.', '/') onlynode = addnodes.only(expr='html') onlynode += addnodes.pending_xref( '', reftype='viewcode', refdomain='std', refexplicit=False, reftarget=pagename, refid=fullname, refdoc=env.docname) onlynode[0] += nodes.inline('', _('[source]'), classes=['viewcode-link']) signode += onlynode def env_merge_info(app, env, docnames, other): if not hasattr(other, '_viewcode_modules'): return # create a _viewcode_modules dict on the main environment if not hasattr(env, '_viewcode_modules'): env._viewcode_modules = {} # now merge in the information from the subprocess env._viewcode_modules.update(other._viewcode_modules) ## EDIT def find_modname(fullname, modname): mod = importlib.import_module(modname) if hasattr(mod, fullname): func = getattr(mod, fullname) modname = func.__module__ fullname = func.__name__ return fullname, modname ## def missing_reference(app, env, node, contnode): # resolve our "viewcode" reference nodes -- they need special treatment if node['reftype'] == 'viewcode': return make_refnode(app.builder, node['refdoc'], node['reftarget'], node['refid'], contnode) def collect_pages(app): env = app.builder.env if not hasattr(env, '_viewcode_modules'): return highlighter = app.builder.highlighter urito = app.builder.get_relative_uri modnames = set(env._viewcode_modules) # app.builder.info(' (%d module code pages)' % # len(env._viewcode_modules), nonl=1) for modname, entry in app.status_iterator( iteritems(env._viewcode_modules), 'highlighting module code... ', blue, len(env._viewcode_modules), lambda x: x[0]): if not entry: continue code, tags, used, refname = entry # construct a page name for the highlighted source pagename = '_modules/' + modname.replace('.', '/') # highlight the source using the builder's highlighter highlighted = highlighter.highlight_block(code, 'python', linenos=False) # split the code into lines lines = highlighted.splitlines() # split off wrap markup from the first line of the actual code before, after = lines[0].split('
')
        lines[0:1] = [before + '
', after]
        # nothing to do for the last line; it always starts with 
 anyway
        # now that we have code lines (starting at index 1), insert anchors for
        # the collected tags (HACK: this only works if the tag boundaries are
        # properly nested!)
        maxindex = len(lines) - 1
        for name, docname in iteritems(used):
            type, start, end = tags[name]
            backlink = urito(pagename, docname) + '#' + refname + '.' + name
            lines[start] = (
                '
%s' % (name, backlink, _('[docs]')) +
                lines[start])
            lines[min(end - 1, maxindex)] += '
'
        # try to find parents (for submodules)
        parents = []
        parent = modname
        while '.' in parent:
            parent = parent.rsplit('.', 1)[0]
            if parent in modnames:
                parents.append({
                    'link': urito(pagename, '_modules/' +
                                  parent.replace('.', '/')),
                    'title': parent})
        parents.append({'link': urito(pagename, '_modules/index'),
                        'title': _('Module code')})
        parents.reverse()
        # putting it all together
        context = {
            'parents': parents,
            'title': modname,
            'body': (_('
Source code for %s
') % modname +
                     '\n'.join(lines)),
        }
        yield (pagename, context, 'page.html')

    if not modnames:
        return

    html = ['\n']
    # the stack logic is needed for using nested lists for submodules
    stack = ['']
    for modname in sorted(modnames):
        if modname.startswith(stack[-1]):
            stack.append(modname + '.')
            html.append('
    ')
        else:
            stack.pop()
            while not modname.startswith(stack[-1]):
                stack.pop()
                html.append('
')
            stack.append(modname + '.')
        html.append('
  • %s
    • \n' % (
            urito('_modules/index', '_modules/' + modname.replace('.', '/')),
            modname))
    html.append('' * (len(stack) - 1))
    context = {
        'title': _('Overview: module code'),
        'body': (_('
    All modules for which code is available
    ') +
                 ''.join(html)),
    }

    yield ('_modules/index', context, 'page.html')


def setup(app):
    app.add_config_value('viewcode_import', True, False)
    app.connect('doctree-read', doctree_read)
    app.connect('env-merge-info', env_merge_info)
    app.connect('html-collect-pages', collect_pages)
    app.connect('missing-reference', missing_reference)
    # app.add_config_value('viewcode_include_modules', [], 'env')
    # app.add_config_value('viewcode_exclude_modules', [], 'env')
    return {'version': sphinx.__display_version__, 'parallel_read_safe': True}multipletau-0.1.6/doc/index.rst000066400000000000000000000010331274076530500164770ustar00rootroot00000000000000multipletau reference
=====================

General
:::::::
.. automodule:: multipletau
   :members:

Methods
:::::::

Summary:

.. autosummary:: 
    autocorrelate
    correlate
    correlate_numpy

For a quick overview, see :ref:`genindex`.

Autocorrelation
---------------
.. autofunction:: autocorrelate

Cross-correlation
-----------------
.. autofunction:: correlate

Cross-correlation (NumPy)
-------------------------
.. autofunction:: correlate_numpy


Examples
========
.. automodule:: compare_correlation_methods
   :members:

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# -*- coding: utf-8 -*-
""" 
Comparison of correlation methods
---------------------------------
Comparison between the :py:mod:`multipletau` correlation methods
(:py:func:`multipletau.autocorrelate`, :py:func:`multipletau.correlate`) and :py:func:`numpy.correlate`.

.. image:: ../examples/compare_correlation_methods.png
   :align:   center

Download the 
:download:`full example <../examples/compare_correlation_methods.py>`.    
"""
from __future__ import print_function

import numpy as np
import os
from os.path import abspath, dirname, join
import sys
import time

sys.path.insert(0, dirname(dirname(abspath(__file__))))

from noise_generator import noise_exponential, noise_cross_exponential
from multipletau import autocorrelate, correlate, correlate_numpy


def compare_corr():
    ## Starting parameters
    N = np.int(np.pi*1e3)
    countrate = 250. * 1e-3 # in Hz
    taudiff = 55. # in us
    deltat = 2e-6 # time discretization [s]
    normalize = True

    # time factor
    taudiff *= deltat

    if N < 1e5:
        do_np_corr = True
    else:
        do_np_corr = False

    ## Autocorrelation
    print("Creating noise for autocorrelation")
    data = noise_exponential(N, taudiff, deltat=deltat)
    data -= np.average(data)
    if normalize:
        data += countrate
    # multipletau
    print("Performing autocorrelation (multipletau).")
    G = autocorrelate(data, deltat=deltat, normalize=normalize)
    # numpy.correlate for comparison
    if do_np_corr:
        print("Performing autocorrelation (numpy).")
        Gd = correlate_numpy(data, data, deltat=deltat,
                             normalize=normalize)
    else:
        Gd = G
    
    ## Cross-correlation
    print("Creating noise for cross-correlation")
    a, v = noise_cross_exponential(N, taudiff, deltat=deltat)
    a -= np.average(a)
    v -= np.average(v)
    if normalize:
        a += countrate
        v += countrate
    Gccforw = correlate(a, v, deltat=deltat, normalize=normalize) # forward
    Gccback = correlate(v, a, deltat=deltat, normalize=normalize) # backward
    if do_np_corr:
        print("Performing cross-correlation (numpy).")
        Gdccforw = correlate_numpy(a, v, deltat=deltat, normalize=normalize)
    
    ## Calculate the model curve for cross-correlation
    xcc = Gd[:,0]
    ampcc = np.correlate(a-np.average(a), v-np.average(v), mode="valid")
    if normalize:
        ampcc /= len(a) * countrate**2
    ycc = ampcc*np.exp(-xcc/taudiff)

    ## Calculate the model curve for autocorrelation
    x = Gd[:,0]
    amp = np.correlate(data-np.average(data), data-np.average(data),
                       mode="valid")
    if normalize:
        amp /= len(data) * countrate**2
    y = amp*np.exp(-x/taudiff)


    ## Plotting
    # AC
    fig = plt.figure()
    fig.canvas.set_window_title('testing multipletau')
    ax = fig.add_subplot(2,1,1)
    ax.set_xscale('log')
    if do_np_corr:
        plt.plot(Gd[:,0], Gd[:,1] , "-", color="gray", label="correlate (numpy)")
    plt.plot(x, y, "g-", label="input model")
    plt.plot(G[:,0], G[:,1], "-",  color="#B60000", label="autocorrelate")
    plt.xlabel("lag channel")
    plt.ylabel("autocorrelation")
    plt.legend(loc=0, fontsize='small')
    plt.ylim( -amp*.2, amp*1.2)
    plt.xlim( Gd[0,0], Gd[-1,0])

    # CC
    ax = fig.add_subplot(2,1,2)
    ax.set_xscale('log')
    if do_np_corr:
        plt.plot(Gdccforw[:,0], Gdccforw[:,1] , "-", color="gray", label="forward (numpy)")
    plt.plot(xcc, ycc, "g-", label="input model")
    plt.plot(Gccforw[:,0], Gccforw[:,1], "-", color="#B60000", label="forward")
    plt.plot(Gccback[:,0], Gccback[:,1], "-", color="#5D00B6", label="backward")
    plt.xlabel("lag channel")
    plt.ylabel("cross-correlation")
    plt.legend(loc=0, fontsize='small')
    plt.ylim( -ampcc*.2, ampcc*1.2)
    plt.xlim( Gd[0,0], Gd[-1,0])
    plt.tight_layout()

    savename = __file__[:-3]+".png"
    if os.path.exists(savename):
        savename = __file__[:-3]+time.strftime("_%Y-%m-%d_%H-%M-%S.png")

    plt.savefig(savename)
    print("Saved output to", savename)


if __name__ == '__main__':
    # move mpl import to main so travis automated doc build does not complain
    from matplotlib import pylab as plt
    compare_corr()
multipletau-0.1.6/examples/noise_generator.py000066400000000000000000000065701274076530500214570ustar00rootroot00000000000000#!/usr/bin/python
# -*- coding: utf-8 -*-
"""
This module contains methods for correlated noise generation.

"""

from __future__ import division
from __future__ import print_function

import numpy as np

__all__ = ["noise_exponential", "noise_cross_exponential"]

def noise_exponential(N, tau=20, variance=1, deltat=1):
    """
       Generate exponentially correlated noise.
       
       Parameters
       ----------
       N : integer
          Total number of samples
       tau : float
          Correlation time of the exponential in `deltat`
       variance : float
          Variance of the noise
       deltat : float
          Bin size of output array, defines the time scale of `tau`
       
       Returns
       -------
       a : ndarray
          Exponentially correlated noise.
    """
    # time constant (inverse of correlationtime tau)
    g = deltat/tau
    # variance
    s0 = variance
    
    # normalization factor (memory of the trace)
    exp_g = np.exp(-g)
    one_exp_g = 1-exp_g
    z_norm_factor = np.sqrt(1-np.exp(-2*g))/one_exp_g
    
    # create random number array
    # generates random numbers in interval [0,1)
    randarray = np.random.random(N)
    # make numbers random in interval [-1,1)
    randarray = 2*(randarray-0.5)
    
    # simulate exponential random behavior
    a = np.zeros(N)
    a[0] = one_exp_g*randarray[0]
    b = 1* a
    for i in np.arange(N-1)+1:
        a[i] = exp_g*a[i-1] + one_exp_g*randarray[i]
    
        # Solving the equation iteratively leads to this equation:
        #j = np.arange(i)
        #a[i] = a[0]*exp_g**(i) + \
        #       one_exp_g)*np.sum(exp_g**(i-1-j)*randarray[1:i+1])
        
    a = a * z_norm_factor*s0
    return a


def noise_cross_exponential(N, tau=20, variance=1, deltat=1):
    """
       Generate exponentially cross-correlated noise.
       
       Parameters
       ----------
       N : integer
          Total number of samples
       tau : float
          Correlation time of the exponential in `deltat`
       variance : float
          Variance of the noise
       deltat : float
          Bin size of output array, defines the time scale of `tau`
       
       Returns
       -------
       a, randarray : ndarrays
          Array `a` has positive exponential correlation to the 'truly'
          random array `randarray`.
    """
    # length of mean0 trace
    N_steps = N
    # time constant (inverse of correlationtime tau)
    g = deltat/tau
    # variance
    s0 = variance
    # normalization factor (memory of the trace)
    exp_g = np.exp(-g)
    one_exp_g = 1-exp_g
    z_norm_factor = np.sqrt(1-np.exp(-2*g))/one_exp_g
    
    # create random number array
    # generates random numbers in interval [0,1)
    randarray = np.random.random(N)
    # make numbers random in interval [-1,1)
    randarray = 2*(randarray-0.5)
    
    # simulate exponential random behavior
    a = np.zeros(N)
    a[0] = one_exp_g*randarray[0]
    
    b = np.zeros(N)
    b[0] = one_exp_g*randarray[0]
    # slow
    #for i in np.arange(N-1)+1:
    #    for j in np.arange(i-1):
    #        a[i] += exp_g**j*randarray[i-j]
    #    a[i] += one_exp_g*randarray[i]
    # faster
    j = np.arange(N+5)
    for i in np.arange(N-1)+1:
        a[i] += np.sum(exp_g**j[2:i+1] * randarray[2:i+1][::-1])
        a[i] += one_exp_g*randarray[i]
   
    a *= z_norm_factor*s0
    randarray = randarray * z_norm_factor*s0
    
    return a, randarray

multipletau-0.1.6/multipletau/000077500000000000000000000000001274076530500164415ustar00rootroot00000000000000multipletau-0.1.6/multipletau/__init__.py000066400000000000000000000043001274076530500205470ustar00rootroot00000000000000#!/usr/bin/env python
# -*- coding: utf-8 -*-
u""" 
This package provides a multiple-τ algorithm for Python 2.7 and 
Python 3.x and requires the package :py:mod:`numpy`.

Multipe-τ correlation is computed on a logarithmic scale (less
data points are computed) and is thus much faster than conventional
correlation on a linear scale such as  :py:func:`numpy.correlate`.

Recommended literature 
----------------------

- Klaus Schaetzel and Rainer Peters; *Noise on multiple-tau photon
  correlation data*. Proc. SPIE 1430, Photon Correlation
  Spectroscopy: Multicomponent Systems, 109 (June 1, 1991);    
  http://doi.org/10.1117/12.44160
  
- Thorsten Wohland, Rudolf Rigler, and Horst Vogel; *The Standard 
  Deviation in Fluorescence Correlation Spectroscopy*. Biophysical
  Journal, 80 (June 1, 2001);  
  http://dx.doi.org/10.1016/S0006-3495(01)76264-9

Obtaining multipletau 
---------------------
If you have Python and :py:mod:`numpy` installed, simply run

   pip install multipletau

The source code of multipletau is available at
https://github.com/FCS-analysis/multipletau.


Citing multipletau
------------------
The multipletau package should be cited like this (replace "x.x.x"
with the actual version of multipletau that you used and "DD Month YYYY"
with a matching date).

.. topic:: cite

    Paul Müller (2012) *Python multiple-tau algorithm* (Version x.x.x)
    [Computer program].
    Available at https://pypi.python.org/pypi/multipletau/
    (Accessed DD Month YYYY)


You can find out what version you are using by typing
(in a Python console):


    >>> import multipletau
    >>> multipletau.__version__
    '0.1.4'


Usage
-----
The package is straightforward to use. Here is a quick example:


    >>> import numpy as np
    >>> import multipletau
    >>> a = np.linspace(2,5,42)
    >>> v = np.linspace(1,6,42)
    >>> multipletau.correlate(a, v, m=2)
    array([[   0.        ,  569.56097561],
           [   1.        ,  549.87804878],
           [   2.        ,  530.37477692],
           [   4.        ,  491.85812017],
           [   8.        ,  386.39500297]])

"""
from ._multipletau import *
from ._version import version as __version__

__author__ = u"Paul Müller"
__license__ = "BSD (3 clause)"
multipletau-0.1.6/multipletau/_multipletau.py000077500000000000000000000401671274076530500215320ustar00rootroot00000000000000#!/usr/bin/python
# -*- coding: utf-8 -*-
""" 
A multiple-τ algorithm for Python 2.7 and 3.x.

Copyright (c) 2014 Paul Müller

Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are
met:

  1. Redistributions of source code must retain the above copyright
     notice, this list of conditions and the following disclaimer.
   
  2. Redistributions in binary form must reproduce the above copyright
     notice, this list of conditions and the following disclaimer in
     the documentation and/or other materials provided with the
     distribution.

  3. Neither the name of multipletau nor the names of its contributors
     may be used to endorse or promote products derived from this
     software without specific prior written permission.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL INFRAE OR
CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
"""
from __future__ import division

import numpy as np
import warnings

__all__ = ["autocorrelate", "correlate", "correlate_numpy"]


def autocorrelate(a, m=16, deltat=1, normalize=False,
                  copy=True, dtype=None):
    """ 
    Autocorrelation of a 1-dimensional sequence on a log2-scale.

    This computes the correlation similar to 
    :py:func:`numpy.correlate` for positive :math:`k` on a base 2
    logarithmic scale.

        :func:`numpy.correlate(a, a, mode="full")[len(a)-1:]`
    
        :math:`z_k = \Sigma_n a_n a_{n+k}`


    Parameters
    ----------
    a : array-like
        input sequence
    m : even integer
        defines the number of points on one level, must be an
        even integer
    deltat : float
        distance between bins
    normalize : bool
        normalize the result to the square of the average input
        signal and the factor :math:`M-k`.
    copy : bool
        copy input array, set to ``False`` to save memory
    dtype : object to be converted to a data type object
        The data type of the returned array and of the accumulator
        for the multiple-tau computation.


    Returns
    -------
    autocorrelation : ndarray of shape (N,2)
        the lag time (1st column) and the autocorrelation (2nd column).

    Notes
    -----
    .. versionchanged :: 0.1.6
       Compute the correlation for zero lag time.

    The algorithm computes the correlation with the convention of the
    curve decaying to zero.

    For experiments like e.g. fluorescence correlation spectroscopy,
    the signal can be normalized to :math:`M-k` by invoking ``normalize = True``.           

    For normalizing according to the behavior of :py:func:`numpy.correlate`,
    use ``normalize = False``.

    For complex arrays, this method falls back to the method
    :func:`correlate`.


    Examples
    --------
    >>> from multipletau import autocorrelate
    >>> autocorrelate(range(42), m=2, dtype=np.float)
    array([[  0.00000000e+00,   2.38210000e+04],
           [  1.00000000e+00,   2.29600000e+04],
           [  2.00000000e+00,   2.21000000e+04],
           [  4.00000000e+00,   2.03775000e+04],
           [  8.00000000e+00,   1.50612000e+04]])
    """
    assert isinstance(copy, bool)
    assert isinstance(normalize, bool)

    if dtype is None:
        dtype = np.dtype(a[0].__class__)
    else:
        dtype = np.dtype(dtype)

    # Complex data
    if dtype.kind == "c":
        # run cross-correlation
        return correlate(a=a,
                         v=a,
                         m=m,
                         deltat=deltat,
                         normalize=normalize,
                         copy=copy,
                         dtype=dtype)
    elif dtype.kind != "f":
        warnings.warn("Input dtype is not float; casting to np.float!")
        dtype = np.dtype(np.float)

    # If copy is false and dtype is the same as the input array,
    # then this line does not have an effect:
    trace = np.array(a, dtype=dtype, copy=copy)

    # Check parameters
    if m//2 != m/2:
        mold = m
        m = np.int((m//2 + 1) * 2)
        warnings.warn("Invalid value of m={}. Using m={} instead"
                      .format(mold, m))
    else:
        m = np.int(m)

    N = N0 = trace.shape[0]

    # Find out the length of the correlation function.
    # The integer k defines how many times we can average over
    # two neighboring array elements in order to obtain an array of
    # length just larger than m.
    k = np.int(np.floor(np.log2(N/m)))

    # In the base2 multiple-tau scheme, the length of the correlation
    # array is (only taking into account values that are computed from
    # traces that are just larger than m):
    lenG = m + k*(m//2) + 1

    G = np.zeros((lenG, 2), dtype=dtype)

    normstat = np.zeros(lenG, dtype=dtype)
    normnump = np.zeros(lenG, dtype=dtype)

    traceavg = np.average(trace)

    # We use the fluctuation of the signal around the mean
    if normalize:
        trace -= traceavg
        assert traceavg != 0, "Cannot normalize: Average of `a` is zero!"
    
    # Otherwise the following for-loop will fail:
    assert N >= 2*m, "len(a) must be larger than 2m!"

    # Calculate autocorrelation function for first m+1 bins
    # Discrete convolution of m elements
    for n in range(0, m+1):
        G[n, 0] = deltat * n
        # This is the computationally intensive step
        G[n, 1] = np.sum(trace[:N - n] * trace[n:])
        normstat[n] = N - n
        normnump[n] = N
    # Now that we calculated the first m elements of G, let us
    # go on with the next m/2 elements.
    # Check if len(trace) is even:
    if N%2 == 1:
        N -= 1
    # Add up every second element
    trace = (trace[:N:2] + trace[1:N:2]) / 2
    N //= 2
    # Start iteration for each m/2 values
    for step in range(1, k + 1):
        # Get the next m/2 values via correlation of the trace
        for n in range(1, m//2 + 1):
            npmd2 = n + m//2
            idx = m + n + (step - 1) * m//2
            if len(trace[:N - npmd2]) == 0:
                # This is a shortcut that stops the iteration once the
                # length of the trace is too small to compute a corre-
                # lation. The actual length of the correlation function
                # does not only depend on k - We also must be able to
                # perform the sum with respect to k for all elements.
                # For small N, the sum over zero elements would be
                # computed here.
                #
                # One could make this for-loop go up to maxval, where
                #   maxval1 = int(m/2)
                #   maxval2 = int(N-m/2-1)
                #   maxval = min(maxval1, maxval2)
                # However, we then would also need to find out which
                # element in G is the last element...
                G = G[:idx - 1]
                normstat = normstat[:idx - 1]
                normnump = normnump[:idx - 1]
                # Note that this break only breaks out of the current
                # for loop. However, we are already in the last loop
                # of the step-for-loop. That is because we calculated
                # k in advance.
                break
            else:
                G[idx, 0] = deltat * npmd2 * 2**step
                # This is the computationally intensive step
                G[idx, 1] = np.sum(trace[:N - npmd2] *
                                   trace[npmd2:])
                normstat[idx] = N - npmd2
                normnump[idx] = N
        # Check if len(trace) is even:
        if N%2 == 1:
            N -= 1
        # Add up every second element
        trace = (trace[:N:2] + trace[1:N:2]) / 2
        N //= 2

    if normalize:
        G[:, 1] /= traceavg**2 * normstat
    else:
        G[:, 1] *= N0 / normnump

    return G


def correlate(a, v, m=16, deltat=1, normalize=False,
              copy=True, dtype=None):
    """ 
    Cross-correlation of two 1-dimensional sequences
    on a log2-scale.

    This computes the cross-correlation similar to
    :py:func:`numpy.correlate` for positive :math:`k`  on a base 2
    logarithmic scale.

        :func:`numpy.correlate(a, v, mode="full")[len(a)-1:]`

        :math:`z_k = \Sigma_n a_n v_{n+k}`

    Note that only the correlation in the positive direction is computed.
    To obtain the correlation for negative lag times swap the input variables
    ``a`` and ``v``.

    Parameters
    ----------
    a, v : array-like
        input sequences with equal length
    m : even integer
        defines the number of points on one level, must be an
        even integer
    deltat : float
        distance between bins
    normalize : bool
        normalize the result to the square of the average input
        signal and the factor :math:`M-k`.
    copy : bool
        copy input array, set to ``False`` to save memory
    dtype : object to be converted to a data type object
        The data type of the returned array and of the accumulator
        for the multiple-tau computation.


    Returns
    -------
    cross_correlation : ndarray of shape (N,2)
        the lag time (1st column) and the cross-correlation (2nd column).


    Notes
    -----
    .. versionchanged :: 0.1.6
       Compute the correlation for zero lag time and correctly normalize
       the correlation for a complex input sequence `v`.

    The algorithm computes the correlation with the convention of the
    curve decaying to zero.

    For experiments like e.g. fluorescence correlation spectroscopy,
    the signal can be normalized to :math:`M-k` by invoking ``normalize = True``.           

    For normalizing according to the behavior of :py:func:`numpy.correlate`,
    use ``normalize = False``.


    Examples
    --------
    >>> from multipletau import correlate
    >>> correlate(range(42), range(1,43), m=2, dtype=np.float)
    array([[  0.00000000e+00,   2.46820000e+04],
           [  1.00000000e+00,   2.38210000e+04],
           [  2.00000000e+00,   2.29600000e+04],
           [  4.00000000e+00,   2.12325000e+04],
           [  8.00000000e+00,   1.58508000e+04]])

    """
    assert isinstance(copy, bool)
    assert isinstance(normalize, bool)
    # See `autocorrelation` for better documented code.
    traceavg1 = np.average(v)
    traceavg2 = np.average(a)
    if normalize:
        assert traceavg1 != 0, "Cannot normalize: Average of `v` is zero!"
        assert traceavg2 != 0, "Cannot normalize: Average of `a` is zero!"

    if dtype is None:
        dtype = np.dtype(v[0].__class__)
        dtype2 = np.dtype(a[0].__class__)
        if dtype != dtype2:
            if dtype.kind == "c" or dtype2.kind == "c":
                # The user might try to combine complex64 and float128.
                warnings.warn("Input dtypes not equal; casting to np.complex!")
                dtype = np.dtype(np.complex)
            else:
                warnings.warn("Input dtypes not equal; casting to np.float!")
                dtype = np.dtype(np.float)
    else:
        dtype = np.dtype(dtype)

    if not dtype.kind in ["c", "f"]:
        warnings.warn("Input dtype is not float; casting to np.float!")
        dtype = np.dtype(np.float)

    trace1 = np.array(v, dtype=dtype, copy=copy)

    # Prevent traces from overwriting each other
    if a is v:
        # Force copying trace 2
        copy = True

    trace2 = np.array(a, dtype=dtype, copy=copy)

    assert trace1.shape[0] == trace2.shape[0], "`a`,`v` must have same length!"

    # Complex data
    if dtype.kind == "c":
        np.conjugate(trace1, out=trace1)

    # Check parameters
    if m//2 != m/2:
        mold = m
        m = np.int(m//2 + 1) * 2
        warnings.warn("Invalid value of m={}. Using m={} instead"
                      .format(mold, m))
    else:
        m = np.int(m)


    N = N0 = trace1.shape[0]
    # Find out the length of the correlation function.
    # The integer k defines how many times we can average over
    # two neighboring array elements in order to obtain an array of
    # length just larger than m.
    k = np.int(np.floor(np.log2(N/m)))

    # In the base2 multiple-tau scheme, the length of the correlation
    # array is (only taking into account values that are computed from
    # traces that are just larger than m):
    lenG = m + k * m//2 + 1

    G = np.zeros((lenG, 2), dtype=dtype)
    normstat = np.zeros(lenG, dtype=dtype)
    normnump = np.zeros(lenG, dtype=dtype)

    # We use the fluctuation of the signal around the mean
    if normalize:
        trace1 -= np.conj(traceavg1)
        trace2 -= traceavg2

    # Otherwise the following for-loop will fail:
    assert N >= 2*m, "len(a) must be larger than 2m!"

    # Calculate autocorrelation function for first m+1 bins
    for n in range(0, m + 1):
        G[n, 0] = deltat * n
        G[n, 1] = np.sum(trace1[:N - n] * trace2[n:])
        normstat[n] = N - n
        normnump[n] = N
    # Check if len(trace) is even:
    if N%2 == 1:
        N -= 1
    # Add up every second element
    trace1 = (trace1[:N:2] + trace1[1:N:2]) / 2
    trace2 = (trace2[:N:2] + trace2[1:N:2]) / 2
    N //= 2

    for step in range(1, k + 1):
        # Get the next m/2 values of the trace
        for n in range(1, m//2 + 1):
            npmd2 = (n + m//2)
            idx = m + n + (step - 1) * m//2
            if len(trace1[:N - npmd2]) == 0:
                # Abort
                G = G[:idx - 1]
                normstat = normstat[:idx - 1]
                normnump = normnump[:idx - 1]
                break
            else:
                G[idx, 0] = deltat * npmd2 * 2**step
                G[idx, 1] = np.sum(
                    trace1[:N - npmd2] * trace2[npmd2:])
                normstat[idx] = N - npmd2
                normnump[idx] = N

        # Check if len(trace) is even:
        if N%2 == 1:
            N -= 1
        # Add up every second element
        trace1 = (trace1[:N:2] + trace1[1:N:2]) / 2
        trace2 = (trace2[:N:2] + trace2[1:N:2]) / 2
        N //= 2

    if normalize:
        G[:, 1] /= traceavg1 * traceavg2 * normstat
    else:
        G[:, 1] *= N0 / normnump

    return G


def correlate_numpy(a, v, deltat=1, normalize=False,
                    dtype=None, copy=True):
    """
    Convenience function that wraps around :py:func:`numpy.correlate` and
    returns the correlation in the same format as :func:`correlate` does.


    Parameters
    ----------
    a, v : array-like
        input sequences
    deltat : float
        distance between bins
    normalize : bool
        normalize the result to the square of the average input signal
        and the factor :math:`M-k`. The resulting curve follows
        the convention of decaying to zero for large lag times.
    copy : bool
        copy input array, set to ``False`` to save memory
    dtype : object to be converted to a data type object
        The data type of the returned array.


    Returns
    -------
    cross_correlation : ndarray of shape (N,2)
        the lag time (1st column) and the cross-correlation (2nd column).


    Notes
    -----
    .. versionchanged :: 0.1.6
       Removed false normalization when `normalize==False`.
    """
    ab = np.array(a, dtype=dtype, copy=copy)
    vb = np.array(v, dtype=dtype, copy=copy)

    assert ab.shape[0] == vb.shape[0], "`a`,`v` must have same length!"

    avg = np.average(ab)
    vvg = np.average(vb)

    if normalize:
        ab -= avg
        vb -= vvg
        assert avg != 0, "Cannot normalize: Average of `a` is zero!"
        assert vvg != 0, "Cannot normalize: Average of `v` is zero!"

    Gd = np.correlate(ab, vb, mode="full")[len(ab) - 1:]

    if normalize:
        N = len(Gd)
        m = N - np.arange(N)
        Gd /= m * avg * vvg
    
    G = np.zeros((len(Gd), 2), dtype=dtype)
    G[:, 1] = Gd
    G[:, 0] = np.arange(len(Gd)) * deltat
    return G
multipletau-0.1.6/multipletau/_version.py000066400000000000000000000104631274076530500206430ustar00rootroot00000000000000#!/usr/bin/env python
"""
Determine package version for git repositories.

Each time this file is imported it checks if the ".git" folder is
present and if so, obtains the version from the git history using
`git describe`. This information is then stored in the file
`_version_save.py` which is not versioned by git, but distributed
along with e.g. pypi.
"""
from __future__ import print_function

# Put the entire script into a `True` statement and add the hint
# `pragma: no cover` to ignore code coverage here.
if True: # pragma: no cover
    import imp
    import os
    from os.path import join, abspath, dirname
    import subprocess
    import sys
    import time
    import traceback
    import warnings

    def git_describe():
        """
        Returns a string describing the version returned by the
        command `git describe --tags HEAD`.
        If it is not possible to determine the correct version,
        then an empty string is returned.
        """
        # make sure we are in a directory that belongs to the correct
        # repository.
        ourdir  = dirname(abspath(__file__))
        
        def _minimal_ext_cmd(cmd):
            # construct minimal environment
            env = {}
            for k in ['SYSTEMROOT', 'PATH']:
                v = os.environ.get(k)
                if v is not None:
                    env[k] = v
            # LANGUAGE is used on win32
            env['LANGUAGE'] = 'C'
            env['LANG'] = 'C'
            env['LC_ALL'] = 'C'
            out = subprocess.Popen(cmd, stdout = subprocess.PIPE, env=env).communicate()[0]
            return out
    
        # change directory
        olddir = abspath(os.curdir)
        os.chdir(ourdir)
        
        try:
            out = _minimal_ext_cmd(['git', 'describe', '--tags', 'HEAD'])
            GIT_REVISION = out.strip().decode('ascii')
        except OSError:
            GIT_REVISION = ""
            
        # go back to original directory
        os.chdir(olddir)
        
        return GIT_REVISION
    
    
    def load_version(versionfile):
        """ load version from version_save.py
        """
        longversion = ""
        try:
            _version_save = imp.load_source("_version_save", versionfile)
            longversion = _version_save.longversion
        except:
            try:
                from ._version_save import longversion
            except:
                try:
                    from _version_save import longversion
                except:
                    pass
    
        return longversion
    
    
    def save_version(version, versionfile):
        """ save version to version_save.py
        """
        data="#!/usr/bin/env python\n"+\
             "# This file was created automatically\n"+\
             "longversion='{VERSION}'"
        try:
            with open(versionfile, "w") as fd:
                fd.write(data.format(VERSION=version))
        except:
            warnings.warn("Could not write package version to {}.".format(versionfile))
    
    versionfile = join(dirname(abspath(__file__)), "_version_save.py")
    
    ## Determine the accurate version
    longversion = ""
    
    # 1. git describe
    try:
        # Get the version using `git describe`
        longversion = git_describe()
    except:
        pass
    
    # 2. previously created version file
    if longversion == "":
        # Either this is this is not a git repository or we are in the
        # wrong git repository.
        # Get the version from the previously generated `_version_save.py`
        longversion = load_version(versionfile)
    
    # 3. last resort: date
    if longversion == "":
        print("Could not determine version. Reason:")
        print(traceback.format_exc())
        ctime = os.stat(__file__)[8]
        longversion = time.strftime("%Y.%m.%d-%H-%M-%S", time.gmtime(ctime))
        print("Using creation time to determine version: {}".format(longversion))
    
    if not hasattr(sys, 'frozen'):
        # Save the version to `_version_save.py` to allow distribution using
        # `python setup.py sdist`.
        # This is only done if the program is not frozen (with e.g. pyinstaller),
        if longversion != load_version(versionfile):
            save_version(longversion, versionfile)
    
    # PEP 440-conform development version:
    version = ".dev".join(longversion.split("-")[:2])
multipletau-0.1.6/setup.cfg000066400000000000000000000001221274076530500157100ustar00rootroot00000000000000[aliases]
test=pytest

[egg_info]
tag_build = 
tag_date = 0
tag_svn_revision = 0

multipletau-0.1.6/setup.py000066400000000000000000000031031274076530500156030ustar00rootroot00000000000000#!/usr/bin/env python
# -*- coding: utf-8 -*-
# To create a distribution package for pip or easy-install:
# python setup.py sdist
from os.path import exists, dirname, realpath
from setuptools import setup
import sys


author = u"Paul Müller"
authors = [author]
description = 'A multiple-tau algorithm for Python/NumPy.'
name = 'multipletau'
year = "2013"


sys.path.insert(0, realpath(dirname(__file__))+"/"+name)
try:
    from _version import version
except:
    version = "unknown"


if __name__ == "__main__":
    setup(
        name=name,
        author=author,
        author_email='paul.mueller@biotec.tu-dresden.de',
        url='https://github.com/FCS-analysis/multipletau',
        version=version,
        packages=[name],
        package_dir={name: name},
        license="BSD (3 clause)",
        description=description,
        long_description=open('README.rst').read() if exists('README.rst') else '',
        install_requires=["NumPy >= 1.5.1"],
        keywords=["multiple", "tau", "FCS", "correlation", "spectroscopy",
                  "fluorescence"],
        extras_require={'doc': ['sphinx']},
        setup_requires=['pytest-runner'],
        tests_require=["pytest"],
        classifiers= [
            'Operating System :: OS Independent',
            'Programming Language :: Python :: 2.7',
            'Programming Language :: Python :: 3.3',
            'Programming Language :: Python :: 3.4',
            'Topic :: Scientific/Engineering :: Visualization',
            'Intended Audience :: Science/Research'
                     ],
        platforms=['ALL']
        )

multipletau-0.1.6/tests/000077500000000000000000000000001274076530500152365ustar00rootroot00000000000000multipletau-0.1.6/tests/README.md000066400000000000000000000003061274076530500165140ustar00rootroot00000000000000### 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_basic.py



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.@s6D$Ɛ#Ё0@>}D"Ea!32@DTI4@kkD.ɳ#6@2;쉴Dl68@$߱D&$6i:@\sDJ<@ΪDq>2>@:eAUDp@@*"ߺӣDB@tPDpzD@6~~DBXJ@t7SttD7^L@OriD|>N@V38`DADP@|8SD0.YZmultipletau-0.1.6/tests/test_ac_cc.py000066400000000000000000000075371274076530500177130ustar00rootroot00000000000000#!/usr/bin/env python
# -*- coding: utf-8 -*-
""" Tests correlation-autocorrelation identity
"""
from __future__ import division, print_function

import numpy as np
import os
from os.path import abspath, basename, dirname, join, split, exists
import platform
import sys
import warnings
import zipfile

# Add parent directory to beginning of path variable
DIR = dirname(abspath(__file__))
sys.path = [split(DIR)[0]] + sys.path

import multipletau

from test_autocorrelate import get_sample_arrays


def test_ac_cc_simple():
    myframe = sys._getframe()
    myname = myframe.f_code.co_name
    print("running ", myname)
    
    arrs = get_sample_arrays()
    
    rescc = []
    for a in arrs:
        r = multipletau.correlate(a=a, v=a,
                                  m=16,
                                  deltat=1,
                                  normalize=False,
                                  copy=True,
                                  dtype=np.float)
        rescc.append(r)
    
    rescc = np.concatenate(rescc)

    resac = []
    for a in arrs:
        r = multipletau.autocorrelate(a=a,
                                      m=16,
                                      deltat=1,
                                      normalize=False,
                                      copy=True,
                                      dtype=np.float)
        resac.append(r)
    
    resac = np.concatenate(resac)
    
    assert np.all(resac==rescc)


def test_ac_cc_normalize():
    myframe = sys._getframe()
    myname = myframe.f_code.co_name
    print("running ", myname)
    
    arrs = get_sample_arrays()
    
    res = []
    for a in arrs:
        r = multipletau.autocorrelate(a=a,
                                      m=16,
                                      deltat=1,
                                      normalize=True,
                                      copy=True,
                                      dtype=np.float)
        res.append(r)
    
    res = np.concatenate(res)

    rescc = []
    for a in arrs:
        r = multipletau.correlate(a=a, v=a,
                                  m=16,
                                  deltat=1,
                                  normalize=True,
                                  copy=True,
                                  dtype=np.float)
        rescc.append(r)
    
    rescc = np.concatenate(rescc)

    assert np.all(res==rescc)


def test_ac_cc_m():
    myframe = sys._getframe()
    myname = myframe.f_code.co_name
    print("running ", myname)
    
    arrs = get_sample_arrays()

    ms = [8, 16, 32, 64, 128]
    a = np.concatenate(arrs)

    res = []    
    for m in ms:
        r = multipletau.autocorrelate(a=a,
                                      m=m,
                                      deltat=1,
                                      normalize=False,
                                      copy=True,
                                      dtype=np.float)
        res.append(r)
    res = np.concatenate(res)

    rescc = []    
    for m in ms:
        r = multipletau.correlate(a=a, v=a,
                                  m=m,
                                  deltat=1,
                                  normalize=False,
                                  copy=True,
                                  dtype=np.float)
        rescc.append(r)
        # test minimal length of array
        _r2 = multipletau.correlate(a=a[:2*m], v=a[:2*m],
                                    m=m,
                                    deltat=1,
                                    normalize=False,
                                    copy=True,
                                    dtype=np.float)
    
    rescc = np.concatenate(rescc)
    assert np.all(res==rescc)


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]()
multipletau-0.1.6/tests/test_autocorrelate.py000066400000000000000000000174251274076530500215310ustar00rootroot00000000000000#!/usr/bin/env python
# -*- coding: utf-8 -*-
""" Tests autocorrelation algorithm
"""
from __future__ import division, print_function

import numpy as np
import os
from os.path import abspath, basename, dirname, join, split, exists
import platform
import sys
import warnings
import zipfile

# Add parent directory to beginning of path variable
DIR = dirname(abspath(__file__))
sys.path = [split(DIR)[0]] + sys.path

import multipletau


def get_reference_data(funcname, pyfile):
    adir = os.path.dirname(pyfile)+"/data/"
    aname = os.path.basename(pyfile)+"_"+funcname+".npy"
    return np.load(adir + aname)


def get_sample_arrays():
    a = [-4.3,   1,    9, -99.2, 13]
    b = [9921, 281, 23.5,   5.3, 77]
    l = [  33,  92,   47,    54, 99]
    r = [   0,   1,   12,     4,  0] 
    p = [   1,   4,   .5,     2,  3]
    arrs = []
    
    for ai, bi, li, ri, pi in zip(a,b,l,r,p): 
        x = np.linspace(ai,bi,li)
        arr = (x*np.roll(x,ri))**pi
        arrs.append(arr)
    
    return arrs


def test_ac_simple():
    myframe = sys._getframe()
    myname = myframe.f_code.co_name
    print("running ", myname)
    
    arrs = get_sample_arrays()
    
    res = []
    for a in arrs:
        r = multipletau.autocorrelate(a=a,
                                      m=16,
                                      deltat=1,
                                      normalize=False,
                                      copy=True,
                                      dtype=np.float)
        res.append(r)
    
    res = np.concatenate(res)
    #np.save(os.path.dirname(__file__)+"/data/"+os.path.basename(__file__)+"_"+myname+".npy", res)
    ref = get_reference_data(myname, __file__)

    assert np.all(res==ref)


def test_ac_normalize():
    myframe = sys._getframe()
    myname = myframe.f_code.co_name
    print("running ", myname)
    
    arrs = get_sample_arrays()
    
    res = []
    for a in arrs:
        r = multipletau.autocorrelate(a=a,
                                      m=16,
                                      deltat=1,
                                      normalize=True,
                                      copy=True,
                                      dtype=np.float)
        res.append(r)
    
    res = np.concatenate(res)
    #np.save(os.path.dirname(__file__)+"/data/"+os.path.basename(__file__)+"_"+myname+".npy", res)
    ref = get_reference_data(myname, __file__)

    assert np.all(res==ref)


def test_ac_m():
    myframe = sys._getframe()
    myname = myframe.f_code.co_name
    print("running ", myname)
    
    arrs = get_sample_arrays()

    ms = [8, 16, 32, 64, 128]
    a = np.concatenate(arrs)

    res = []    
    for m in ms:
        r = multipletau.autocorrelate(a=a,
                                      m=m,
                                      deltat=1,
                                      normalize=False,
                                      copy=True,
                                      dtype=np.float)
        res.append(r)

        # test minimal length of array
        _r2 = multipletau.autocorrelate(a=a[:2*m],
                                        m=m,
                                        deltat=1,
                                        normalize=False,
                                        copy=True,
                                        dtype=np.float)
    
    res = np.concatenate(res)
    #np.save(os.path.dirname(__file__)+"/data/"+os.path.basename(__file__)+"_"+myname+".npy", res)
    ref = get_reference_data(myname, __file__)

    assert np.all(res==ref)


def test_ac_copy():
    myframe = sys._getframe()
    myname = myframe.f_code.co_name
    print("running ", myname)
    
    arrs = get_sample_arrays()

    res1 = []
    for a in arrs:
        r = multipletau.autocorrelate(a=a,
                                      m=16,
                                      deltat=1,
                                      normalize=True,
                                      copy=True,
                                      dtype=np.float)
        res1.append(r)

    res2 = []
    for a in arrs:
        r = multipletau.autocorrelate(a=a,
                                      m=16,
                                      deltat=1,
                                      normalize=True,
                                      copy=False,
                                      dtype=np.float)
        res2.append(r)
    
    # simple test if result is the same
    assert np.all(np.concatenate(res1) == np.concatenate(res2))

    arrs = np.concatenate(arrs)
    refarrs = np.concatenate(get_sample_arrays())

    # make sure the copy function really changes something
    assert not np.all(arrs == refarrs)

    
def test_ac_dtype():
    myframe = sys._getframe()
    myname = myframe.f_code.co_name
    print("running ", myname)
    
    a = np.round(get_sample_arrays()[0])


    # integer
    rf = multipletau.autocorrelate(a=a,
                                   m=16,
                                   deltat=1,
                                   normalize=True,
                                   copy=True,
                                   dtype=np.float)

    ri = multipletau.autocorrelate(a=a,
                                   m=16,
                                   deltat=1,
                                   normalize=True,
                                   copy=True,
                                   dtype=np.uint)

    ri2 = multipletau.autocorrelate(a=np.array(a, dtype=np.uint),
                                   m=16,
                                   deltat=1,
                                   normalize=True,
                                   copy=True,
                                   dtype=None)
    
    assert ri.dtype == np.dtype(np.float), "if wrong dtype, dtype should default to np.float"
    assert ri2.dtype == np.dtype(np.float), "if wrong dtype, dtype should default to np.float"
    assert np.all(rf == ri), "result should be the same, because input us the same"
    assert np.all(rf == ri2), "result should be the same, because input us the same"


def test_ac_m_wrong():
    myframe = sys._getframe()
    myname = myframe.f_code.co_name
    print("running ", myname)
    
    a = get_sample_arrays()[0]

    # integer
    r1 = multipletau.autocorrelate(a=a,
                                   m=16,
                                   deltat=1,
                                   normalize=True,
                                   copy=True,
                                   dtype=np.float)

    r2 = multipletau.autocorrelate(a=a,
                                   m=15,
                                   deltat=1,
                                   normalize=True,
                                   copy=True,
                                   dtype=np.float)

    r3 = multipletau.autocorrelate(a=a,
                                   m=15.5,
                                   deltat=1,
                                   normalize=True,
                                   copy=True,
                                   dtype=np.float)

    r4 = multipletau.autocorrelate(a=a,
                                   m=14.5,
                                   deltat=1,
                                   normalize=True,
                                   copy=True,
                                   dtype=np.float)

    r5 = multipletau.autocorrelate(a=a,
                                   m=16.,
                                   deltat=1,
                                   normalize=True,
                                   copy=True,
                                   dtype=np.float)
    assert np.all(r1==r2)
    assert np.all(r1==r3)
    assert np.all(r1==r4)
    assert np.all(r1==r5)


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]()
    
multipletau-0.1.6/tests/test_basic.py000066400000000000000000000024361274076530500177350ustar00rootroot00000000000000#!/usr/bin/python
# -*- coding: utf-8 -*-
"""
basic tests also available in the function docs 
"""
import numpy as np
from os.path import abspath, dirname, join
import sys

sys.path.insert(0, dirname(dirname(abspath(__file__))))

from multipletau import autocorrelate, correlate


def test_ac():
    ist = autocorrelate(range(42), m=2, dtype=np.dtype(float))
    soll = np.array([[  0.00000000e+00,   2.38210000e+04],
                     [  1.00000000e+00,   2.29600000e+04],
                     [  2.00000000e+00,   2.21000000e+04],
                     [  4.00000000e+00,   2.03775000e+04],
                     [  8.00000000e+00,   1.50612000e+04]])
    assert np.allclose(soll, ist)


def test_cc():
    ist = correlate(range(42), range(1,43), m=2, dtype=np.dtype(float))
    soll = np.array([[  0.00000000e+00,   2.46820000e+04],
                     [  1.00000000e+00,   2.38210000e+04],
                     [  2.00000000e+00,   2.29600000e+04],
                     [  4.00000000e+00,   2.12325000e+04],
                     [  8.00000000e+00,   1.58508000e+04]])
    assert np.allclose(soll, ist)

    
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]()
    multipletau-0.1.6/tests/test_correlate.py000066400000000000000000000221601274076530500206300ustar00rootroot00000000000000#!/usr/bin/env python
# -*- coding: utf-8 -*-
""" Tests correlation algorithm
"""
from __future__ import division, print_function

import numpy as np
import os
from os.path import abspath, basename, dirname, join, split, exists
import platform
import sys
import warnings
import zipfile

# Add parent directory to beginning of path variable
DIR = dirname(abspath(__file__))
sys.path = [split(DIR)[0]] + sys.path

import multipletau

from test_autocorrelate import get_reference_data

def get_sample_arrays_cplx():
    a = [-4.3,   1,    9, -99.2, 13]
    b = [9921, 281, 23.5,   5.3, 77]
    c = [  12,   0,    2,   1.3, 33]
    d = [  32,  .1,   -2,   6.3, 88]
    l = [  33,  92,   47,    54, 99]
    r = [   0,   1,   12,     4,  0] 
    p = [   1,   4,   .5,     2,  3]
    arrs = []
    
    for ai, bi, ci, di, li, ri, pi in zip(a,b,c,d,l,r,p): 
        x = np.linspace(ai,bi,li)
        y = np.linspace(ci,di,li)
        arr = (x*np.roll(x,ri))**pi + 1j*y
        arrs.append(arr)
    
    return arrs


def test_cc_simple():
    myframe = sys._getframe()
    myname = myframe.f_code.co_name
    print("running ", myname)
    
    arrs = get_sample_arrays_cplx()
    
    res = []
    for a in arrs:
        r = multipletau.correlate(a=a,
                                  v=a,
                                  m=16,
                                  deltat=1,
                                  normalize=False,
                                  copy=True,
                                  dtype=np.complex)
        res.append(r)
    res = np.concatenate(res)

    #np.save(os.path.dirname(__file__)+"/data/"+os.path.basename(__file__)+"_"+myname+".npy", res)
    ref = get_reference_data(myname, __file__)

    assert np.all(res==ref)

    # also check result of autocorrelate
    res2 = []
    for a in arrs:
        r = multipletau.autocorrelate(a=a,
                                      m=16,
                                      deltat=1,
                                      normalize=False,
                                      copy=True,
                                      dtype=np.complex)
        res2.append(r)
    res2 = np.concatenate(res2)

    assert np.all(res==res2)


def test_cc_normalize():
    myframe = sys._getframe()
    myname = myframe.f_code.co_name
    print("running ", myname)
    
    arrs = get_sample_arrays_cplx()
    
    res = []
    for a in arrs:
        r = multipletau.correlate(a=a.real,
                                  v=a.imag,
                                  m=16,
                                  deltat=1,
                                  normalize=True,
                                  copy=True,
                                  dtype=np.float)
        res.append(r)
    
    res = np.concatenate(res)
    #np.save(os.path.dirname(__file__)+"/data/"+os.path.basename(__file__)+"_"+myname+".npy", res)
    ref = get_reference_data(myname, __file__)

    assert np.all(res==ref)


def test_cc_m():
    myframe = sys._getframe()
    myname = myframe.f_code.co_name
    print("running ", myname)
    
    arrs = get_sample_arrays_cplx()

    ms = [4, 8, 10, 16, 20, 64, 128]
    a = np.concatenate(arrs)

    res = []    
    for m in ms:
        r = multipletau.correlate(a=a,
                                  v=a,
                                  m=m,
                                  deltat=1,
                                  normalize=False,
                                  copy=True,
                                  dtype=np.complex)
        res.append(r)

        # test minimal length of array
        _r2 = multipletau.correlate(a=a[:2*m],
                                    v=a[:2*m],
                                    m=m,
                                    deltat=1,
                                    normalize=False,
                                    copy=True,
                                    dtype=np.complex)
    
    res = np.concatenate(res)
    #np.save(os.path.dirname(__file__)+"/data/"+os.path.basename(__file__)+"_"+myname+".npy", res)
    ref = get_reference_data(myname, __file__)

    assert np.all(res==ref)


def test_cc_copy():
    myframe = sys._getframe()
    myname = myframe.f_code.co_name
    print("running ", myname)
    
    arrs = get_sample_arrays_cplx()

    res1 = []
    for a in arrs:
        r = multipletau.correlate(a=a,
                                  v=a,
                                  m=16,
                                  deltat=1,
                                  normalize=True,
                                  copy=True)
        res1.append(r)

    res2 = []
    for a in arrs:
        r = multipletau.correlate(a=a,
                                  v=a,
                                  m=16,
                                  deltat=1,
                                  normalize=True,
                                  copy=False)
        res2.append(r)
    
    # simple test if result is the same
    assert np.all(np.concatenate(res1) == np.concatenate(res2))

    arrs = np.concatenate(arrs)
    refarrs = np.concatenate(get_sample_arrays_cplx())

    # make sure the copy function really changes something
    assert not np.all(arrs == refarrs)

    
def test_cc_dtype():
    myframe = sys._getframe()
    myname = myframe.f_code.co_name
    print("running ", myname)
    
    a = np.round(get_sample_arrays_cplx()[0].real)


    # integer
    rf = multipletau.correlate(a=a,
                               v=a,
                               m=16,
                               deltat=1,
                               normalize=True,
                               copy=True,
                               dtype=np.float)

    ri = multipletau.correlate(a=a,
                               v=a,
                               m=16,
                               deltat=1,
                               normalize=True,
                               copy=True,
                               dtype=np.uint)

    ri2 = multipletau.correlate(a=np.array(a, dtype=np.uint),
                                v=np.array(a, dtype=np.uint),
                                m=16,
                                deltat=1,
                                normalize=True,
                                copy=True,
                                dtype=None)
    
    assert ri.dtype == np.dtype(np.float), "if wrong dtype, dtype should default to np.float"
    assert ri2.dtype == np.dtype(np.float), "if wrong dtype, dtype should default to np.float"
    assert np.all(rf == ri), "result should be the same, because input us the same"
    assert np.all(rf == ri2), "result should be the same, because input us the same"


def test_cc_dtype2():
    myframe = sys._getframe()
    myname = myframe.f_code.co_name
    print("running ", myname)
    
    a = np.round(get_sample_arrays_cplx()[0])

    print("this should issue a warning of unequal input dtypes, casting to complex")
    rf = multipletau.correlate(a=a.real,
                               v=a,
                               m=16,
                               deltat=1,
                               normalize=True,
                               copy=True)
    assert np.dtype(rf.dtype) == np.dtype(np.complex)

    print("this should issue a warning of unequal input dtypes, casting to float")
    rf2 = multipletau.correlate(a=a.real,
                               v=np.array(a.imag, dtype=np.int),
                               m=16,
                               deltat=1,
                               normalize=True,
                               copy=True)
    assert np.dtype(rf2.dtype) == np.dtype(np.float)


def test_cc_m_wrong():
    myframe = sys._getframe()
    myname = myframe.f_code.co_name
    print("running ", myname)
    
    a = get_sample_arrays_cplx()[0]

    # integer
    r1 = multipletau.correlate(a=a,
                               v=a,
                               m=16,
                               deltat=1,
                               normalize=True,
                               copy=True)

    r2 = multipletau.correlate(a=a,
                               v=a,
                               m=15,
                               deltat=1,
                               normalize=True,
                               copy=True)

    r3 = multipletau.correlate(a=a,
                               v=a,
                               m=15.5,
                               deltat=1,
                               normalize=True,
                               copy=True)

    r4 = multipletau.correlate(a=a,
                               v=a,
                               m=14.5,
                               deltat=1,
                               normalize=True,
                               copy=True)

    r5 = multipletau.correlate(a=a,
                               v=a,
                               m=16.,
                               deltat=1,
                               normalize=True,
                               copy=True)

    assert np.all(r1==r2)
    assert np.all(r1==r3)
    assert np.all(r1==r4)
    assert np.all(r1==r5)


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]()
    
multipletau-0.1.6/tests/test_ref_numpy.py000066400000000000000000000135471274076530500206650ustar00rootroot00000000000000#!/usr/bin/python
# -*- coding: utf-8 -*-
"""
Compare to numpy data.
"""
import numpy as np
from os.path import abspath, dirname, join
import sys

sys.path.insert(0, dirname(dirname(abspath(__file__))))

import multipletau

from test_correlate import get_sample_arrays_cplx



def test_corresponds_ac():
    myframe = sys._getframe()
    myname = myframe.f_code.co_name
    print("running ", myname)
    
    a = np.concatenate(get_sample_arrays_cplx()).real
    m=16

    restau = multipletau.autocorrelate(a=1*a,
                                       m=m,
                                       copy=True,
                                       normalize=True,
                                       dtype=np.float128)

    reslin = multipletau.correlate_numpy(a=1*a,
                                         v=1*a,
                                         copy=True,
                                         normalize=True,
                                         dtype=np.float128)

    idx = np.array(restau[:,0].real, dtype=int)[:m]

    assert np.allclose(reslin[idx, 1], restau[:m,1])


def test_corresponds_ac_nonormalize():
    myframe = sys._getframe()
    myname = myframe.f_code.co_name
    print("running ", myname)
    
    a = np.concatenate(get_sample_arrays_cplx()).real
    m=16

    restau = multipletau.autocorrelate(a=1*a,
                                       m=m,
                                       copy=True,
                                       normalize=False,
                                       dtype=np.float128)

    reslin = multipletau.correlate_numpy(a=1*a,
                                         v=1*a,
                                         copy=True,
                                         normalize=False,
                                         dtype=np.float128)

    idx = np.array(restau[:,0].real, dtype=int)[:m+1]

    assert np.allclose(reslin[idx, 1], restau[:m+1,1])


def test_corresponds_cc():
    myframe = sys._getframe()
    myname = myframe.f_code.co_name
    print("running ", myname)
    
    a = np.concatenate(get_sample_arrays_cplx())
    m=16

    restau = multipletau.correlate(a=a,
                                   v=a.imag+1j*a.real,
                                   m=m,
                                   copy=True,
                                   normalize=True,
                                   dtype=np.complex256)

    reslin = multipletau.correlate_numpy(a=a,
                                         v=a.imag+1j*a.real,
                                         copy=True,
                                         normalize=True,
                                         dtype=np.complex256)

    idx = np.array(restau[:,0].real, dtype=int)[:m+1]

    assert np.allclose(reslin[idx, 1], restau[:m+1,1])


def test_corresponds_cc_nonormalize():
    myframe = sys._getframe()
    myname = myframe.f_code.co_name
    print("running ", myname)
    
    a = np.concatenate(get_sample_arrays_cplx())
    m=16

    restau = multipletau.correlate(a=a,
                                   v=a.imag+1j*a.real,
                                   m=m,
                                   copy=True,
                                   normalize=False,
                                   dtype=np.complex256)

    reslin = multipletau.correlate_numpy(a=a,
                                         v=a.imag+1j*a.real,
                                         copy=True,
                                         normalize=False,
                                         dtype=np.complex256)

    idx = np.array(restau[:,0].real, dtype=int)[:m+1]

    assert np.allclose(reslin[idx, 1], restau[:m+1,1])
    

def test_corresponds_ac_first_loop():
    """
    numpy correlation:
    G_m = sum_i(a_i*a_{i+m})
    
    multipletau correlation 2nd order:
    b_j = (a_{2i} + a_{2i+1} / 2)
    G_m = sum_j(b_j*b_{j+1})
        = 1/4*sum_i(a_{2i}   * a_{2i+m}   +
                    a_{2i}   * a_{2i+m+1} +
                    a_{2i+1} * a_{2i+m}   +   
                    a_{2i+1} * a_{2i+m+1}
                    )
    
    The values after the first m+1 lag times in the multipletau
    correlation differ from the normal correlation, because the
    traces are averaged over two consecutive items, effectively
    halving the size of the trace. The multiple-tau correlation
    can be compared to the regular correlation by using an even
    sized sequence (here 222) in which the elements 2i and 2i+1
    are equal, as is done in this test.
    """
    myframe = sys._getframe()
    myname = myframe.f_code.co_name
    print("running ", myname)
    
    a = [ arr / np.average(arr) for arr in get_sample_arrays_cplx() ]
    a = np.concatenate(a)[:222]
    # two consecutive elements are the same, so the multiple-tau method
    # corresponds to the numpy correlation for the first loop.
    a[::2] = a[1::2]
    
    for m in [2,4,6,8,10,12,14,16]:
        restau = multipletau.correlate(a=a,
                                       v=a.imag+1j*a.real,
                                       m=m,
                                       copy=True,
                                       normalize=False,
                                       dtype=np.complex256)
        
        reslin = multipletau.correlate_numpy(a=a,
                                             v=a.imag+1j*a.real,
                                             copy=True,
                                             normalize=False,
                                             dtype=np.complex256)
        
        idtau = np.where(restau[:,0]==m+2)[0][0]
        tau3 = restau[idtau, 1] #m+1 initial bins
    
        idref = np.where(reslin[:,0]==m+2)[0][0]
        tau3ref = reslin[idref, 1]
        
        assert np.allclose(tau3, tau3ref)


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]()