diff --git a/.github/dependabot.yml b/.github/dependabot.yml new file mode 100644 index 0000000..dfd0e30 --- /dev/null +++ b/.github/dependabot.yml @@ -0,0 +1,10 @@ +# Set update schedule for GitHub Actions + +version: 2 +updates: + + - package-ecosystem: "github-actions" + directory: "/" + schedule: + # Check for updates to GitHub Actions every week + interval: "weekly" diff --git a/.github/workflows/build_wheels.yml b/.github/workflows/build_wheels.yml new file mode 100644 index 0000000..b1b547e --- /dev/null +++ b/.github/workflows/build_wheels.yml @@ -0,0 +1,163 @@ +name: Build wheels and publish to pypi + +on: + push: + branches: + - master + tags: + - '*' + +jobs: + build_wheels: + name: ${{ matrix.os }} • ${{ matrix.python }} + runs-on: ${{ matrix.os }} + strategy: + fail-fast: false + matrix: + os: [ubuntu-latest, windows-latest, macos-15-intel, macos-latest] + python: [cp310, cp311, cp312, cp313, cp314, pp311] + exclude: + # Skip PyPy on Windows + - os: windows-latest + python: pp311 + steps: + - uses: actions/checkout@v5 + - name: Set version suffix for TestPyPI + id: set_version + run: | + if [[ ! "${{ github.ref }}" =~ ^refs/tags/ ]]; then + BUILD_NUM=${{ github.run_number }} + VERSION=$(grep '^version = ' pyproject.toml | sed -E 's/version = "(.*)"/\1/') + sed -i -e "s/^version = \"${VERSION}\"/version = \"${VERSION}.dev${BUILD_NUM}\"/" pyproject.toml + fi + shell: bash + - name: Build wheels + uses: pypa/cibuildwheel@v3.2 + env: + CIBW_BUILD: ${{ matrix.python }}-* + CIBW_ARCHS_MACOS: auto + with: + package-dir: . + output-dir: wheelhouse + config-file: "{package}/pyproject.toml" + - name: Verify clean directory + if: startsWith(github.ref, 'refs/tags/') + run: git diff --exit-code + shell: bash + - uses: actions/upload-artifact@v5 + with: + name: artifact-${{ matrix.os }}-${{ matrix.python }} + path: ./wheelhouse/*.whl + + build_sdist: + name: Build source distribution + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v5 + - name: Set version suffix for TestPyPI + id: set_version + run: | + if [[ ! "${{ github.ref }}" =~ ^refs/tags/ ]]; then + BUILD_NUM=${{ github.run_number }} + VERSION=$(grep '^version = ' pyproject.toml | sed -E 's/version = "(.*)"/\1/') + sed -i -e "s/^version = \"${VERSION}\"/version = \"${VERSION}.dev${BUILD_NUM}\"/" pyproject.toml + fi + - name: Build sdist + run: pipx run build --sdist + + - uses: actions/upload-artifact@v5 + with: + name: artifact-source + path: dist/*.tar.gz + + merge_artifacts: + name: Merge artifacts + needs: [build_wheels, build_sdist] + runs-on: ubuntu-latest + steps: + - name: Merge artifacts + uses: actions/upload-artifact/merge@v5 + with: + name: all-files + pattern: artifact-* + + publish-to-pypi: + name: >- + Publish Python distribution to PyPI + if: startsWith(github.ref, 'refs/tags/') # only publish to PyPI on tag pushes + needs: [merge_artifacts] + runs-on: ubuntu-latest + environment: + name: pypi + url: https://pypi.org/p/melizalab-pyspike + permissions: + id-token: write # IMPORTANT: mandatory for trusted publishing + steps: + - name: Download all the dists + uses: actions/download-artifact@v6 + with: + name: all-files + path: dist/ + - name: Publish distribution to PyPI + uses: pypa/gh-action-pypi-publish@release/v1 + + github-release: + name: >- + Sign the Python distribution with Sigstore + and to GitHub Release + needs: [publish-to-pypi] + runs-on: ubuntu-latest + permissions: + contents: write # IMPORTANT: mandatory for making GitHub Releases + id-token: write # IMPORTANT: mandatory for sigstore + steps: + - name: Download all the dists + uses: actions/download-artifact@v6 + with: + name: all-files + path: dist/ + - name: Sign the dists with Sigstore + uses: sigstore/gh-action-sigstore-python@v3.1.0 + with: + inputs: >- + ./dist/*.tar.gz + ./dist/*.whl + - name: Create GitHub Release + env: + GITHUB_TOKEN: ${{ github.token }} + run: >- + gh release create + '${{ github.ref_name }}' + --repo '${{ github.repository }}' + --notes "" + - name: Upload artifact signatures to GitHub Release + env: + GITHUB_TOKEN: ${{ github.token }} + # Upload to GitHub Release using the `gh` CLI. + # `dist/` contains the built packages, and the + # sigstore-produced signatures and certificates. + run: >- + gh release upload + '${{ github.ref_name }}' dist/** + --repo '${{ github.repository }}' + + publish-to-testpypi: + name: Publish Python distribution to TestPyPI + needs: [merge_artifacts] + runs-on: ubuntu-latest + environment: + name: testpypi + url: https://test.pypi.org/p/melizalab-pyspike + permissions: + id-token: write # IMPORTANT: mandatory for trusted publishing + steps: + - name: Download all the dists + uses: actions/download-artifact@v6 + with: + name: all-files + path: dist/ + - name: Publish distribution 📦 to TestPyPI + uses: pypa/gh-action-pypi-publish@release/v1 + with: + repository-url: https://test.pypi.org/legacy/ + verbose: true diff --git a/.github/workflows/python-package.yml b/.github/workflows/python-package.yml deleted file mode 100644 index bda4a6c..0000000 --- a/.github/workflows/python-package.yml +++ /dev/null @@ -1,51 +0,0 @@ -# This workflow will install Python dependencies, run tests and lint with a variety of Python versions -# For more information see: https://help.github.com/actions/language-and-framework-guides/using-python-with-github-actions - -name: Python package - -on: - push: - branches: [ master, develop ] - pull_request: - branches: [ master, develop ] - -jobs: - build: - - runs-on: ubuntu-latest - strategy: - fail-fast: false - matrix: - python-version: ["3.7", "3.8", "3.9", "3.10"] - cython: ['python -m pip install -q cython', 'echo "No Cython"'] - - steps: - - uses: actions/checkout@v2 - - name: Set up Python ${{ matrix.python-version }} - uses: actions/setup-python@v2 - with: - python-version: ${{ matrix.python-version }} - - name: Install dependencies - run: | - sudo apt-get update - sudo apt-get install libblas-dev - sudo apt-get install liblapack-dev - sudo apt-get install gfortran - python -m pip install --upgrade pip - python -m pip install flake8 pytest nose numpy scipy - if [ -f requirements.txt ]; then pip install -r requirements.txt; fi - - name: Install Cython - run: | - ${{ matrix.cython }} - - name: Install package - run: | - python SetupNoPrompt.py build_ext --inplace - # - name: Lint with flake8 - # run: | - # # stop the build if there are Python syntax errors or undefined names - # flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics - # # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide - # flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics - - name: Test with PyTest - run: | - python -m pytest diff --git a/.github/workflows/python_tests.yml b/.github/workflows/python_tests.yml new file mode 100644 index 0000000..135827f --- /dev/null +++ b/.github/workflows/python_tests.yml @@ -0,0 +1,28 @@ +# This workflow will install Python dependencies, run tests and lint with a variety of Python versions +# For more information see: https://help.github.com/actions/language-and-framework-guides/using-python-with-github-actions + +name: Python tests + +on: [push, pull_request] + +jobs: + build: + strategy: + fail-fast: false + matrix: + os: [macos-15-intel, macos-latest, ubuntu-latest, windows-latest] + python-version: ["3.10", "3.11", "3.12", "3.13", "3.14", "pypy3.10", "pypy3.11"] + runs-on: ${{ matrix.os }} + + steps: + - uses: actions/checkout@v5 + - name: Install uv + uses: astral-sh/setup-uv@v7 + with: + enable-cache: true + version: "latest" + python-version: ${{ matrix.python-version }} + - name: Install dependencies + run: uv sync --frozen + - name: Run tests on python ${{ matrix.python-version }} + run: uv run pytest diff --git a/.gitignore b/.gitignore index 04fef3d..6b58301 100644 --- a/.gitignore +++ b/.gitignore @@ -1,59 +1,8 @@ -HELP.md -.gradle -build/ -!gradle/wrapper/gradle-wrapper.jar -!**/src/main/**/build/ -!**/src/test/**/build/ - -### STS ### -.apt_generated -.classpath -.factorypath -.project -.settings -.springBeans -.sts4-cache -bin/ -!**/src/main/**/bin/ -!**/src/test/**/bin/ - -### IntelliJ IDEA ### -.idea -*.iws -*.iml -*.ipr -out/ -!**/src/main/**/out/ -!**/src/test/**/out/ - -### NetBeans ### -/nbproject/private/ -/nbbuild/ -/dist/ -/nbdist/ -/.nb-gradle/ - -### VS Code ### -.vscode/ - -# Emacs backups -*~ -.#* -\#* - -# Log files -*.log - -# MacOS does this -.DS_Store -~* - -# Jacoco generated -*.exec - -# KDiff3 backup files -*.orig - -# C files are always generated by Cython *.c -*.C +*.so +.DS_Store +.coverage +__pycache__/ +build/ +dist/ +melizalab_pyspike.egg-info/ \ No newline at end of file diff --git a/MANIFEST.in b/MANIFEST.in index aed0ae0..a942d0f 100644 --- a/MANIFEST.in +++ b/MANIFEST.in @@ -1,7 +1,6 @@ include *.rst include *.txt -include pyspike/cython/*.c -include directionality/cython/*.c +recursive-include pyspike *.pyx *.pxd recursive-include examples *.py *.txt recursive-include test *.py *.txt recursive-include doc * diff --git a/Readme.rst b/Readme.rst index 80c8088..26d4d19 100644 --- a/Readme.rst +++ b/Readme.rst @@ -1,10 +1,22 @@ PySpike ======= -.. image:: https://badge.fury.io/py/pyspike.png - :target: http://badge.fury.io/py/pyspike -.. image:: https://travis-ci.org/mariomulansky/PySpike.svg?branch=master - :target: https://travis-ci.org/mariomulansky/PySpike +|ProjectStatus|_ |Version|_ |BuildStatus|_ |License|_ |PythonVersions|_ + +.. |ProjectStatus| image:: https://www.repostatus.org/badges/latest/active.svg +.. _ProjectStatus: https://www.repostatus.org/#active + +.. |Version| image:: https://img.shields.io/pypi/v/melizalab-pyspike.svg +.. _Version: https://pypi.python.org/pypi/melizalab-pyspike/ + +.. |BuildStatus| image:: https://github.com/melizalab/melizalab-pyspike/actions/workflows/python_tests.yml/badge.svg +.. _BuildStatus: https://github.com/melizalab/melizalab-pyspike/actions/workflows/python_tests.yml + +.. |License| image:: https://img.shields.io/pypi/l/melizalab-pyspike.svg +.. _License: https://opensource.org/license/bsd-3-clause/ + +.. |PythonVersions| image:: https://img.shields.io/pypi/pyversions/melizalab-pyspike.svg +.. _PythonVersions: https://pypi.python.org/pypi/melizalab-pyspike/ PySpike is a Python library for the numerical analysis of spike train similarity. Its core functionality is the implementation of the ISI_\-distance [#]_ and SPIKE_\-distance [#]_, SPIKE-Synchronization_ [#]_, as well as their adaptive generalizations [#]_. @@ -13,7 +25,7 @@ All computation intensive parts are implemented in C via cython_ to reach a comp PySpike provides the same fundamental functionality as the SPIKY_ framework for Matlab, which additionally contains spike-train generators, more spike train distance measures and many visualization routines. -All source codes are available on `Github `_ and are published under the BSD_License_. +All source codes are available on `Github `_ and are published under the BSD_License_. `This fork `_ is maintained by the Meliza Lab for PEP518-compatible builds and CI-generated `wheels `_. Citing PySpike ---------------------------- diff --git a/SetupNoPrompt.py b/SetupNoPrompt.py deleted file mode 100644 index 90c3270..0000000 --- a/SetupNoPrompt.py +++ /dev/null @@ -1,5 +0,0 @@ -## interlude to force answer to input('Abort?'): - -import io, sys -sys.stdin = io.StringIO('N\n') -import setup diff --git a/doc/conf.py b/doc/conf.py index 1842be2..460e41e 100644 --- a/doc/conf.py +++ b/doc/conf.py @@ -18,233 +18,230 @@ # 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('../pyspike')) +sys.path.insert(0, os.path.abspath("../pyspike")) + def skip(app, what, name, obj, skip, options): if name == "__init__": return False return skip + def setup(app): app.connect("autodoc-skip-member", skip) + # -- General configuration ------------------------------------------------ # If your documentation needs a minimal Sphinx version, state it here. -#needs_sphinx = '1.0' +# 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.coverage', - 'sphinx.ext.mathjax', - 'sphinx.ext.viewcode', + "sphinx.ext.autodoc", + "sphinx.ext.coverage", + "sphinx.ext.mathjax", + "sphinx.ext.viewcode", ] # Add any paths that contain templates here, relative to this directory. -templates_path = ['_templates'] +templates_path = ["_templates"] # The suffix of source filenames. -source_suffix = '.rst' +source_suffix = ".rst" # The encoding of source files. -#source_encoding = 'utf-8-sig' +# source_encoding = 'utf-8-sig' # The master toctree document. -master_doc = 'index' +master_doc = "index" # General information about the project. -project = u'PySpike' -copyright = u'2014-2017, Mario Mulansky' +project = "PySpike" +copyright = "2014-2017, Mario Mulansky" # 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. -version = '0.8' +version = "0.8" # The full version, including alpha/beta/rc tags. -release = '0.8.0' +release = "0.8.0" # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. -#language = None +# language = None # There are two options for replacing |today|: either, you set today to some # non-false value, then it is used: -#today = '' +# today = '' # Else, today_fmt is used as the format for a strftime call. -#today_fmt = '%B %d, %Y' +# today_fmt = '%B %d, %Y' # List of patterns, relative to source directory, that match files and # directories to ignore when looking for source files. -exclude_patterns = ['_build'] +exclude_patterns = ["_build"] # The reST default role (used for this markup: `text`) to use for all # documents. -#default_role = None +# default_role = None # If true, '()' will be appended to :func: etc. cross-reference text. -#add_function_parentheses = True +# 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 +# add_module_names = True # If true, sectionauthor and moduleauthor directives will be shown in the # output. They are ignored by default. -#show_authors = False +# show_authors = False # The name of the Pygments (syntax highlighting) style to use. -pygments_style = 'sphinx' +pygments_style = "sphinx" # A list of ignored prefixes for module index sorting. -#modindex_common_prefix = [] +# modindex_common_prefix = [] # If true, keep warnings as "system message" paragraphs in the built documents. -#keep_warnings = False +# 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 = 'default' +html_theme = "default" # 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 = {} +# html_theme_options = {} # Add any paths that contain custom themes here, relative to this directory. -#html_theme_path = [] +# html_theme_path = [] # The name for this set of Sphinx documents. If None, it defaults to # " v documentation". -#html_title = None +# html_title = None # A shorter title for the navigation bar. Default is the same as html_title. -#html_short_title = None +# 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 +# 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 +# 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'] +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 = [] +# 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' +# 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 +# html_use_smartypants = True # Custom sidebar templates, maps document names to template names. -#html_sidebars = {} +# html_sidebars = {} # Additional templates that should be rendered to pages, maps page names to # template names. -#html_additional_pages = {} +# html_additional_pages = {} # If false, no module index is generated. -#html_domain_indices = True +# html_domain_indices = True # If false, no index is generated. -#html_use_index = True +# html_use_index = True # If true, the index is split into individual pages for each letter. -#html_split_index = False +# html_split_index = False # If true, links to the reST sources are added to the pages. -#html_show_sourcelink = True +# html_show_sourcelink = True # If true, "Created using Sphinx" is shown in the HTML footer. Default is True. -#html_show_sphinx = True +# html_show_sphinx = True # If true, "(C) Copyright ..." is shown in the HTML footer. Default is True. -#html_show_copyright = 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 = '' +# html_use_opensearch = '' # This is the file name suffix for HTML files (e.g. ".xhtml"). -#html_file_suffix = None +# html_file_suffix = None # Output file base name for HTML help builder. -htmlhelp_basename = 'PySpikedoc' +htmlhelp_basename = "PySpikedoc" # -- 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': '', + # 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', 'PySpike.tex', u'PySpike Documentation', - u'Mario Mulansky', 'manual'), + ("index", "PySpike.tex", "PySpike Documentation", "Mario Mulansky", "manual"), ] # The name of an image file (relative to this directory) to place at the top of # the title page. -#latex_logo = None +# latex_logo = None # For "manual" documents, if this is true, then toplevel headings are parts, # not chapters. -#latex_use_parts = False +# latex_use_parts = False # If true, show page references after internal links. -#latex_show_pagerefs = False +# latex_show_pagerefs = False # If true, show URL addresses after external links. -#latex_show_urls = False +# latex_show_urls = False # Documents to append as an appendix to all manuals. -#latex_appendices = [] +# latex_appendices = [] # If false, no module index is generated. -#latex_domain_indices = True +# 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', 'pyspike', u'PySpike Documentation', - [u'Mario Mulansky'], 1) -] +man_pages = [("index", "pyspike", "PySpike Documentation", ["Mario Mulansky"], 1)] # If true, show URL addresses after external links. -#man_show_urls = False +# man_show_urls = False # -- Options for Texinfo output ------------------------------------------- @@ -253,19 +250,25 @@ def setup(app): # (source start file, target name, title, author, # dir menu entry, description, category) texinfo_documents = [ - ('index', 'PySpike', u'PySpike Documentation', - u'Mario Mulansky', 'PySpike', 'Measure of spike train synchrony.', - 'Miscellaneous'), + ( + "index", + "PySpike", + "PySpike Documentation", + "Mario Mulansky", + "PySpike", + "Measure of spike train synchrony.", + "Miscellaneous", + ), ] # Documents to append as an appendix to all manuals. -#texinfo_appendices = [] +# texinfo_appendices = [] # If false, no module index is generated. -#texinfo_domain_indices = True +# texinfo_domain_indices = True # How to display URL addresses: 'footnote', 'no', or 'inline'. -#texinfo_show_urls = 'footnote' +# texinfo_show_urls = 'footnote' # If true, do not generate a @detailmenu in the "Top" node's menu. -#texinfo_no_detailmenu = False +# texinfo_no_detailmenu = False diff --git a/examples/averages.py b/examples/averages.py index 8b405d0..d5a4d75 100644 --- a/examples/averages.py +++ b/examples/averages.py @@ -1,4 +1,4 @@ -""" averages.py +"""averages.py Simple example showing how to compute averages of distance profiles @@ -7,12 +7,10 @@ Distributed under the BSD License """ -from __future__ import print_function import pyspike as spk -spike_trains = spk.load_spike_trains_from_txt("PySpike_testdata.txt", - edges=(0, 4000)) +spike_trains = spk.load_spike_trains_from_txt("PySpike_testdata.txt", edges=(0, 4000)) f = spk.isi_profile(spike_trains[0], spike_trains[1]) diff --git a/examples/distance_matrix.py b/examples/distance_matrix.py index 0e4d825..9add196 100644 --- a/examples/distance_matrix.py +++ b/examples/distance_matrix.py @@ -1,4 +1,4 @@ -""" distance_matrix.py +"""distance_matrix.py Simple example showing how to compute the isi distance matrix of a set of spike trains. @@ -9,8 +9,6 @@ """ -from __future__ import print_function - import matplotlib.pyplot as plt import pyspike as spk @@ -22,17 +20,17 @@ plt.figure() isi_distance = spk.isi_distance_matrix(spike_trains) -plt.imshow(isi_distance, interpolation='none') +plt.imshow(isi_distance, interpolation="none") plt.title("ISI-distance") plt.figure() spike_distance = spk.spike_distance_matrix(spike_trains, interval=(0, 1000)) -plt.imshow(spike_distance, interpolation='none') +plt.imshow(spike_distance, interpolation="none") plt.title("SPIKE-distance, T=0-1000") plt.figure() spike_sync = spk.spike_sync_matrix(spike_trains, interval=(2000, 4000)) -plt.imshow(spike_sync, interpolation='none') +plt.imshow(spike_sync, interpolation="none") plt.title("SPIKE-Sync, T=2000-4000") plt.show() diff --git a/examples/merge.py b/examples/merge.py index b4437a3..f9b0f10 100644 --- a/examples/merge.py +++ b/examples/merge.py @@ -1,4 +1,4 @@ -""" merge.py +"""merge.py Simple example showing the merging of two spike trains. @@ -7,10 +7,9 @@ Distributed under the BSD License """ -from __future__ import print_function -import numpy as np import matplotlib.pyplot as plt +import numpy as np import pyspike as spk @@ -21,9 +20,8 @@ print(merged_spike_train.spikes) -plt.plot(spike_trains[0], np.ones_like(spike_trains[0]), 'o') -plt.plot(spike_trains[1], np.ones_like(spike_trains[1]), 'x') -plt.plot(merged_spike_train.spikes, - 2*np.ones_like(merged_spike_train), 'o') +plt.plot(spike_trains[0], np.ones_like(spike_trains[0]), "o") +plt.plot(spike_trains[1], np.ones_like(spike_trains[1]), "x") +plt.plot(merged_spike_train.spikes, 2 * np.ones_like(merged_spike_train), "o") plt.show() diff --git a/examples/multivariate.py b/examples/multivariate.py index e9579a5..dcedb82 100644 --- a/examples/multivariate.py +++ b/examples/multivariate.py @@ -1,48 +1,46 @@ -""" Example for the multivariate spike distance +"""Example for the multivariate spike distance Copyright 2014, Mario Mulansky """ -from __future__ import print_function -import time + +import timeit + import pyspike as spk def time_diff_in_ms(start, end): - """ Returns the time difference end-start in ms. - """ - return (end-start)*1000 + """Returns the time difference end-start in ms.""" + return (end - start) * 1000 -t_start = time.clock() +t_start = timeit.default_timer() # load the data -time_loading = time.clock() -spike_trains = spk.load_spike_trains_from_txt("PySpike_testdata.txt", - edges=(0, 4000)) -t_loading = time.clock() +time_loading = timeit.default_timer() +spike_trains = spk.load_spike_trains_from_txt("PySpike_testdata.txt", edges=(0, 4000)) +t_loading = timeit.default_timer() print("Number of spike trains: %d" % len(spike_trains)) -num_of_spikes = sum([len(spike_trains[i]) - for i in range(len(spike_trains))]) +num_of_spikes = sum([len(spike_trains[i]) for i in range(len(spike_trains))]) print("Number of spikes: %d" % num_of_spikes) # calculate the multivariate spike distance f = spk.spike_profile(spike_trains) -t_spike = time.clock() +t_spike = timeit.default_timer() # print the average avrg = f.avrg() print("Spike distance from average: %.8f" % avrg) -t_avrg = time.clock() +t_avrg = timeit.default_timer() # compute average distance directly, should give the same result as above spike_dist = spk.spike_distance(spike_trains) print("Spike distance directly: %.8f" % spike_dist) -t_dist = time.clock() +t_dist = timeit.default_timer() print("Loading: %9.1f ms" % time_diff_in_ms(t_start, t_loading)) print("Computing profile: %9.1f ms" % time_diff_in_ms(t_loading, t_spike)) diff --git a/examples/performance.py b/examples/performance.py index 30691f8..f079973 100644 --- a/examples/performance.py +++ b/examples/performance.py @@ -7,31 +7,31 @@ """ -from __future__ import print_function -import pyspike as spk -from datetime import datetime import cProfile import pstats +from datetime import datetime + +import pyspike as spk # in case you dont have the cython backends, disable the warnings as follows: # spk.disable_backend_warning = True -M = 100 # number of spike trains -r = 1.0 # rate of Poisson spike times -T = 1E3 # length of spike trains +M = 100 # number of spike trains +r = 1.0 # rate of Poisson spike times +T = 1e3 # length of spike trains -print("%d spike trains with %d spikes" % (M, int(r*T))) +print("%d spike trains with %d spikes" % (M, int(r * T))) spike_trains = [] t_start = datetime.now() -for i in range(M): +for _i in range(M): spike_trains.append(spk.generate_poisson_spikes(r, T)) t_end = datetime.now() -runtime = (t_end-t_start).total_seconds() +runtime = (t_end - t_start).total_seconds() -sort_by = 'tottime' +sort_by = "tottime" # sort_by = 'cumtime' print("Spike generation runtime: %.3fs" % runtime) @@ -39,33 +39,33 @@ print("================ ISI COMPUTATIONS ================") print(" MULTIVARIATE DISTANCE") -cProfile.run('spk.isi_distance(spike_trains)', 'performance.stat') -p = pstats.Stats('performance.stat') +cProfile.run("spk.isi_distance(spike_trains)", "performance.stat") +p = pstats.Stats("performance.stat") p.strip_dirs().sort_stats(sort_by).print_stats(5) print(" MULTIVARIATE PROFILE") -cProfile.run('spk.isi_profile(spike_trains)', 'performance.stat') -p = pstats.Stats('performance.stat') +cProfile.run("spk.isi_profile(spike_trains)", "performance.stat") +p = pstats.Stats("performance.stat") p.strip_dirs().sort_stats(sort_by).print_stats(5) print("================ SPIKE COMPUTATIONS ================") print(" MULTIVARIATE DISTANCE") -cProfile.run('spk.spike_distance(spike_trains)', 'performance.stat') -p = pstats.Stats('performance.stat') +cProfile.run("spk.spike_distance(spike_trains)", "performance.stat") +p = pstats.Stats("performance.stat") p.strip_dirs().sort_stats(sort_by).print_stats(5) print(" MULTIVARIATE PROFILE") -cProfile.run('spk.spike_profile(spike_trains)', 'performance.stat') -p = pstats.Stats('performance.stat') +cProfile.run("spk.spike_profile(spike_trains)", "performance.stat") +p = pstats.Stats("performance.stat") p.strip_dirs().sort_stats(sort_by).print_stats(5) print("================ SPIKE-SYNC COMPUTATIONS ================") print(" MULTIVARIATE DISTANCE") -cProfile.run('spk.spike_sync(spike_trains)', 'performance.stat') -p = pstats.Stats('performance.stat') +cProfile.run("spk.spike_sync(spike_trains)", "performance.stat") +p = pstats.Stats("performance.stat") p.strip_dirs().sort_stats(sort_by).print_stats(5) print(" MULTIVARIATE PROFILE") -cProfile.run('spk.spike_sync_profile(spike_trains)', 'performance.stat') -p = pstats.Stats('performance.stat') +cProfile.run("spk.spike_sync_profile(spike_trains)", "performance.stat") +p = pstats.Stats("performance.stat") p.strip_dirs().sort_stats(sort_by).print_stats(5) diff --git a/examples/plot.py b/examples/plot.py index a0e04da..1b40069 100644 --- a/examples/plot.py +++ b/examples/plot.py @@ -1,4 +1,4 @@ -""" plot.py +"""plot.py Simple example showing how to load and plot spike trains and their distance profiles. @@ -9,34 +9,30 @@ """ -from __future__ import print_function - -import numpy as np import matplotlib.pyplot as plt +import numpy as np import pyspike as spk - -spike_trains = spk.load_spike_trains_from_txt("PySpike_testdata.txt", - edges=(0, 4000)) +spike_trains = spk.load_spike_trains_from_txt("PySpike_testdata.txt", edges=(0, 4000)) # plot the spike times -for (i, spike_train) in enumerate(spike_trains): - plt.scatter(spike_train, i*np.ones_like(spike_train), marker='|') +for i, spike_train in enumerate(spike_trains): + plt.scatter(spike_train, i * np.ones_like(spike_train), marker="|") # profile of the first two spike trains f = spk.isi_profile(spike_trains, indices=[0, 1]) x, y = f.get_plottable_data() plt.figure() -plt.plot(x, np.abs(y), '--k', label="ISI-profile") +plt.plot(x, np.abs(y), "--k", label="ISI-profile") print("ISI-distance: %.8f" % f.avrg()) f = spk.spike_profile(spike_trains, indices=[0, 1]) x, y = f.get_plottable_data() -plt.plot(x, y, '-b', label="SPIKE-profile") +plt.plot(x, y, "-b", label="SPIKE-profile") print("SPIKE-distance: %.8f" % f.avrg()) diff --git a/examples/profiles.py b/examples/profiles.py index 8412ffb..2018610 100644 --- a/examples/profiles.py +++ b/examples/profiles.py @@ -1,4 +1,4 @@ -""" profiles.py +"""profiles.py Simple example showing some functionality of distance profiles. @@ -8,12 +8,9 @@ """ -from __future__ import print_function - import pyspike as spk -spike_trains = spk.load_spike_trains_from_txt("PySpike_testdata.txt", - edges=(0, 4000)) +spike_trains = spk.load_spike_trains_from_txt("PySpike_testdata.txt", edges=(0, 4000)) ##### ISI PROFILES ####### diff --git a/examples/spike_sync.py b/examples/spike_sync.py index 13ca0ce..b29d31e 100644 --- a/examples/spike_sync.py +++ b/examples/spike_sync.py @@ -1,17 +1,17 @@ -from __future__ import print_function import matplotlib.pyplot as plt import pyspike as spk -spike_trains = spk.load_spike_trains_from_txt("../test/SPIKE_Sync_Test.txt", - edges=(0, 4000)) +spike_trains = spk.load_spike_trains_from_txt( + "../test/SPIKE_Sync_Test.txt", edges=(0, 4000) +) plt.figure() f = spk.spike_sync_profile(spike_trains[0], spike_trains[1]) x, y = f.get_plottable_data() -plt.plot(x, y, '--ok', label="SPIKE-SYNC profile") +plt.plot(x, y, "--ok", label="SPIKE-SYNC profile") print(f.x) print(f.y) print(f.mp) @@ -22,7 +22,7 @@ f = spk.spike_profile(spike_trains[0], spike_trains[1]) x, y = f.get_plottable_data() -plt.plot(x, y, '-b', label="SPIKE-profile") +plt.plot(x, y, "-b", label="SPIKE-profile") plt.axis([0, 4000, -0.1, 1.1]) plt.legend(loc="center right") @@ -33,16 +33,16 @@ f = spk.spike_sync_profile(spike_trains) x, y = f.get_plottable_data() -plt.plot(x, y, '-b', alpha=0.7, label="SPIKE-Sync profile") +plt.plot(x, y, "-b", alpha=0.7, label="SPIKE-Sync profile") x1, y1 = f.get_plottable_data(averaging_window_size=50) -plt.plot(x1, y1, '-k', lw=2.5, label="averaged SPIKE-Sync profile") +plt.plot(x1, y1, "-k", lw=2.5, label="averaged SPIKE-Sync profile") plt.subplot(212) f_psth = spk.psth(spike_trains, bin_size=50.0) x, y = f_psth.get_plottable_data() -plt.plot(x, y, '-k', alpha=1.0, label="PSTH") +plt.plot(x, y, "-k", alpha=1.0, label="PSTH") print("Average:", f.avrg()) diff --git a/examples/spike_train_order.py b/examples/spike_train_order.py index 3a42472..41de730 100644 --- a/examples/spike_train_order.py +++ b/examples/spike_train_order.py @@ -1,20 +1,19 @@ -import numpy as np from matplotlib import pyplot as plt -import pyspike as spk +import pyspike as spk st1 = spk.generate_poisson_spikes(1.0, [0, 20]) st2 = spk.generate_poisson_spikes(1.0, [0, 20]) d = spk.spike_directionality(st1, st2) -print "Spike Directionality of two Poissonian spike trains:", d +print("Spike Directionality of two Poissonian spike trains:", d) E = spk.spike_train_order_profile(st1, st2) plt.figure() x, y = E.get_plottable_data() -plt.plot(x, y, '-ob') +plt.plot(x, y, "-ob") plt.ylim(-1.1, 1.1) plt.xlabel("t") plt.ylabel("E") @@ -25,11 +24,11 @@ M = 20 -spike_trains = [spk.generate_poisson_spikes(1.0, [0, 100]) for m in xrange(M)] +spike_trains = [spk.generate_poisson_spikes(1.0, [0, 100]) for m in range(M)] F_init = spk.spike_train_order(spike_trains) -print "Initial Synfire Indicator for 20 Poissonian spike trains:", F_init +print("Initial Synfire Indicator for 20 Poissonian spike trains:", F_init) D_init = spk.spike_directionality_matrix(spike_trains) @@ -37,7 +36,7 @@ F_opt = spk.spike_train_order(spike_trains, indices=phi) -print "Synfire Indicator of optimized spike train sorting:", F_opt +print("Synfire Indicator of optimized spike train sorting:", F_opt) D_opt = spk.permutate_matrix(D_init, phi) diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000..0fdb6b2 --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,87 @@ +[build-system] +requires = ["setuptools>=61.0", "wheel", "numpy>=2.0.2", "Cython>=3.0"] +build-backend = "setuptools.build_meta" + +[project] +name = "melizalab-pyspike" +version = "0.8.1" +description = "A Python library for the numerical analysis of spike train similarity - forked for PEP-518" +readme = "Readme.rst" +requires-python = ">=3.10" +license = { text = "BSD-3-Clause" } +authors = [ + {name = "Mario Mulansky", email = "mario.mulansky@gmx.net"}, +] +maintainers = [ + {name = "C Daniel Meliza", email = "dan@meliza.org"}, +] +classifiers = [ + "Development Status :: 4 - Beta", + "Intended Audience :: Science/Research", + "Topic :: Scientific/Engineering", + "Topic :: Scientific/Engineering :: Information Analysis", + "Programming Language :: Python", + "Programming Language :: Python :: 3.10", + "Programming Language :: Python :: 3.11", + "Programming Language :: Python :: 3.12", + "Programming Language :: Python :: 3.13", + "Programming Language :: Python :: 3.14", + "Programming Language :: Python :: Implementation :: CPython", + "Programming Language :: Python :: Implementation :: PyPy", + "Operating System :: Unix", + "Operating System :: POSIX :: Linux", + "Operating System :: MacOS :: MacOS X", + "Operating System :: Microsoft :: Windows", + "Natural Language :: English" +] +dependencies = [ + "numpy>=2.0.2", +] + +[dependency-groups] +dev = [ + "pytest>=7.4.4", + "pytest-cov>=4.1.0", + "ruff>=0.14.3", + "scipy>=1.13.1; platform_python_implementation == 'CPython'", +] +examples = [ + "matplotlib>=3.9.4", +] + +[project.urls] +homepage = "https://github.com/melizalab/PySpike" + +[tool.setuptools] +packages = ["pyspike", "pyspike.cython"] + +[tool.pytest.ini_options] +python_files = ["test_*.py", "*_test.py"] +addopts = "-v --cov=pyspike --cov-report=term-missing" +testpaths = ["test"] + +[tool.ruff] +line-length = 88 +target-version = "py310" +extend-exclude = ["build", "attic"] + +[tool.ruff.lint] +extend-select = [ + "F", # pyflakes + "B", # flake8-bugbear + "I", # isort + "PGH", # pygrep-hooks + "RUF", # Ruff-specific + "UP", # pyupgrade + "NPY201", # numpy 2.0 +] + +[tool.cibuildwheel] +skip= "cp314t-*" +test-requires = "pytest pytest-cov" +test-command = "pytest {project}/test" +manylinux-x86_64-image = "manylinux2014" +manylinux-i686-image = "manylinux2014" +# enable pypy builds - no longer on by default +enable = "pypy" + diff --git a/pyspike/DiscreteFunc.py b/pyspike/DiscreteFunc.py index 6f9cc48..cdfc7a9 100644 --- a/pyspike/DiscreteFunc.py +++ b/pyspike/DiscreteFunc.py @@ -2,22 +2,22 @@ # Copyright 2014-2015, Mario Mulansky # Distributed under the BSD License -from __future__ import absolute_import, print_function -import numpy as np import collections.abc + +import numpy as np + import pyspike ############################################################## # DiscreteFunc ############################################################## -class DiscreteFunc(object): - """ A class representing values defined on a discrete set of points. - """ +class DiscreteFunc: + """A class representing values defined on a discrete set of points.""" def __init__(self, x, y, multiplicity): - """ Constructs the discrete function. + """Constructs the discrete function. :param x: array of length N defining the points at which the values are defined. @@ -31,14 +31,14 @@ def __init__(self, x, y, multiplicity): self.mp = np.array(multiplicity) def copy(self): - """ Returns a copy of itself + """Returns a copy of itself :rtype: :class:`DiscreteFunc` """ return DiscreteFunc(self.x, self.y, self.mp) def almost_equal(self, other, decimal=14): - """ Checks if the function is equal to another function up to `decimal` + """Checks if the function is equal to another function up to `decimal` precision. :param other: another :class:`DiscreteFunc` @@ -46,13 +46,15 @@ def almost_equal(self, other, decimal=14): False otherwise :rtype: bool """ - eps = 10.0**(-decimal) - return np.allclose(self.x, other.x, atol=eps, rtol=0.0) and \ - np.allclose(self.y, other.y, atol=eps, rtol=0.0) and \ - np.allclose(self.mp, other.mp, atol=eps, rtol=0.0) + eps = 10.0 ** (-decimal) + return ( + np.allclose(self.x, other.x, atol=eps, rtol=0.0) + and np.allclose(self.y, other.y, atol=eps, rtol=0.0) + and np.allclose(self.mp, other.mp, atol=eps, rtol=0.0) + ) def get_plottable_data(self, averaging_window_size=0): - """ Returns two arrays containing x- and y-coordinates for plotting + """Returns two arrays containing x- and y-coordinates for plotting the interval sequence. The optional parameter `averaging_window_size` determines the size of an averaging window to smoothen the profile. If this value is 0, no averaging is performed. @@ -77,55 +79,53 @@ def get_plottable_data(self, averaging_window_size=0): # the first value in self.mp contains the number of averaged # profiles without any possible extra multiplicities # (by implementation) - expected_mp = (averaging_window_size+1) * int(self.mp[0]) + expected_mp = (averaging_window_size + 1) * int(self.mp[0]) y_plot = np.zeros_like(self.y) # compute the values in a loop, could be done in cython if required for i in range(len(y_plot)): - if self.mp[i] >= expected_mp: # the current value contains already all the wanted # multiplicity - y_plot[i] = self.y[i]/self.mp[i] + y_plot[i] = self.y[i] / self.mp[i] continue # first look to the right y = self.y[i] mp_r = self.mp[i] - j = i+1 + j = i + 1 while j < len(y_plot): - if mp_r+self.mp[j] < expected_mp: + if mp_r + self.mp[j] < expected_mp: # if we still dont reach the required multiplicity # we take the whole value y += self.y[j] mp_r += self.mp[j] else: # otherwise, just some fraction - y += self.y[j] * (expected_mp - mp_r)/self.mp[j] - mp_r += (expected_mp - mp_r) + y += self.y[j] * (expected_mp - mp_r) / self.mp[j] + mp_r += expected_mp - mp_r break j += 1 # same story to the left mp_l = self.mp[i] - j = i-1 + j = i - 1 while j >= 0: - if mp_l+self.mp[j] < expected_mp: + if mp_l + self.mp[j] < expected_mp: y += self.y[j] mp_l += self.mp[j] else: - y += self.y[j] * (expected_mp - mp_l)/self.mp[j] - mp_l += (expected_mp - mp_l) + y += self.y[j] * (expected_mp - mp_l) / self.mp[j] + mp_l += expected_mp - mp_l break j -= 1 - y_plot[i] = y/(mp_l+mp_r-self.mp[i]) - return 1.0*self.x, y_plot + y_plot[i] = y / (mp_l + mp_r - self.mp[i]) + return 1.0 * self.x, y_plot else: # k = 0 - - return 1.0*self.x, 1.0*self.y/self.mp + return 1.0 * self.x, 1.0 * self.y / self.mp def integral(self, interval=None): - """ Returns the integral over the given interval. For the discrete + """Returns the integral over the given interval. For the discrete function, this amounts to two values: the sum over all values and the sum over all multiplicities. @@ -141,11 +141,10 @@ def integral(self, interval=None): multiplicity = 0.0 def get_indices(ival): - """ Retuns the indeces surrounding the given interval""" - start_ind = np.searchsorted(self.x, ival[0], side='right') - end_ind = np.searchsorted(self.x, ival[1], side='left') - assert start_ind > 0 and end_ind < len(self.x), \ - "Invalid averaging interval" + """Retuns the indeces surrounding the given interval""" + start_ind = np.searchsorted(self.x, ival[0], side="right") + end_ind = np.searchsorted(self.x, ival[1], side="left") + assert start_ind > 0 and end_ind < len(self.x), "Invalid averaging interval" return start_ind, end_ind if interval is None: @@ -155,9 +154,10 @@ def get_indices(ival): multiplicity = np.sum(self.mp[1:-1]) else: # check if interval is as sequence - assert isinstance(interval, collections.abc.Sequence), \ + assert isinstance(interval, collections.abc.Sequence), ( "Invalid value for `interval`. None, Sequence or Tuple \ expected." + ) # check if interval is a sequence of intervals if not isinstance(interval[0], collections.abc.Sequence): # find the indices corresponding to the interval @@ -173,7 +173,7 @@ def get_indices(ival): return (value, multiplicity) def avrg(self, interval=None, normalize=True): - """ Computes the average of the interval sequence: + """Computes the average of the interval sequence: :math:`a = 1/N \\sum f_n` where N is the number of intervals. :param interval: averaging interval given as a pair of floats, a @@ -187,14 +187,14 @@ def avrg(self, interval=None, normalize=True): val, mp = self.integral(interval) if normalize: if mp > 0: - return val/mp + return val / mp else: return 1.0 else: return val def add(self, f): - """ Adds another `DiscreteFunc` function to this function. + """Adds another `DiscreteFunc` function to this function. Note: only functions defined on the same interval can be summed. :param f: :class:`DiscreteFunc` function to be added. @@ -205,21 +205,23 @@ def add(self, f): # cython version try: - from .cython.cython_add import add_discrete_function_cython as \ - add_discrete_function_impl + from .cython.cython_add import ( + add_discrete_function_cython as add_discrete_function_impl, + ) except ImportError: pyspike.NoCythonWarn() # use python backend - from .cython.python_backend import add_discrete_function_python as \ - add_discrete_function_impl + from .cython.python_backend import ( + add_discrete_function_python as add_discrete_function_impl, + ) - self.x, self.y, self.mp = \ - add_discrete_function_impl(self.x, self.y, self.mp, - f.x, f.y, f.mp) + self.x, self.y, self.mp = add_discrete_function_impl( + self.x, self.y, self.mp, f.x, f.y, f.mp + ) def mul_scalar(self, fac): - """ Multiplies the function with a scalar value + """Multiplies the function with a scalar value :param fac: Value to multiply :type fac: double @@ -229,7 +231,7 @@ def mul_scalar(self, fac): def average_profile(profiles): - """ Computes the average profile from the given ISI- or SPIKE-profiles. + """Computes the average profile from the given ISI- or SPIKE-profiles. :param profiles: list of :class:`PieceWiseConstFunc` or :class:`PieceWiseLinFunc` representing ISI- or @@ -242,6 +244,6 @@ def average_profile(profiles): avrg_profile = profiles[0].copy() for i in range(1, len(profiles)): avrg_profile.add(profiles[i]) - avrg_profile.mul_scalar(1.0/len(profiles)) # normalize + avrg_profile.mul_scalar(1.0 / len(profiles)) # normalize return avrg_profile diff --git a/pyspike/PieceWiseConstFunc.py b/pyspike/PieceWiseConstFunc.py index 450e59a..e0a3c0e 100644 --- a/pyspike/PieceWiseConstFunc.py +++ b/pyspike/PieceWiseConstFunc.py @@ -2,21 +2,22 @@ # Copyright 2014-2015, Mario Mulansky # Distributed under the BSD License -from __future__ import absolute_import, print_function -import numpy as np import collections.abc + +import numpy as np + import pyspike ############################################################## # PieceWiseConstFunc ############################################################## -class PieceWiseConstFunc(object): - """ A class representing a piece-wise constant function. """ +class PieceWiseConstFunc: + """A class representing a piece-wise constant function.""" def __init__(self, x, y): - """ Constructs the piece-wise const function. + """Constructs the piece-wise const function. :param x: array of length N+1 defining the edges of the intervals of the pwc function. @@ -28,38 +29,39 @@ def __init__(self, x, y): self.y = np.array(y) def __call__(self, t): - """ Returns the function value for the given time t. If t is a list of + """Returns the function value for the given time t. If t is a list of times, the corresponding list of values is returned. :param: time t, or list of times :returns: function value(s) at that time(s). """ - assert np.all(t >= self.x[0]) and np.all(t <= self.x[-1]), \ + assert np.all(t >= self.x[0]) and np.all(t <= self.x[-1]), ( "Invalid time: " + str(t) + ) - ind = np.searchsorted(self.x, t, side='right') + ind = np.searchsorted(self.x, t, side="right") if isinstance(t, collections.abc.Sequence): # t is a sequence of values # correct the cases t == x[0], t == x[-1] ind[ind == 0] = 1 - ind[ind == len(self.x)] = len(self.x)-1 - value = self.y[ind-1] + ind[ind == len(self.x)] = len(self.x) - 1 + value = self.y[ind - 1] # correct the values at exact spike times: there the value should # be the at half of the step # obtain the 'left' side indices for t - ind_l = np.searchsorted(self.x, t, side='left') + ind_l = np.searchsorted(self.x, t, side="left") # if left and right side indices differ, the time t has to appear # in self.x - ind_at_spike = np.logical_and(np.logical_and(ind != ind_l, - ind > 1), - ind < len(self.x)) + ind_at_spike = np.logical_and( + np.logical_and(ind != ind_l, ind > 1), ind < len(self.x) + ) # get the corresponding indices for the resulting value array val_ind = np.arange(len(ind))[ind_at_spike] # and for the arrays self.x, y1, y2 xy_ind = ind[ind_at_spike] # use the middle of the left and right ISI value - value[val_ind] = 0.5 * (self.y[xy_ind-1] + self.y[xy_ind-2]) + value[val_ind] = 0.5 * (self.y[xy_ind - 1] + self.y[xy_ind - 2]) return value else: # t is a single value # specific check for interval edges @@ -70,18 +72,18 @@ def __call__(self, t): # check if we are on any other exact spike time if sum(self.x == t) > 0: # use the middle of the left and right ISI value - return 0.5 * (self.y[ind-1] + self.y[ind-2]) - return self.y[ind-1] + return 0.5 * (self.y[ind - 1] + self.y[ind - 2]) + return self.y[ind - 1] def copy(self): - """ Returns a copy of itself + """Returns a copy of itself :rtype: :class:`PieceWiseConstFunc` """ return PieceWiseConstFunc(self.x, self.y) def almost_equal(self, other, decimal=14): - """ Checks if the function is equal to another function up to `decimal` + """Checks if the function is equal to another function up to `decimal` precision. :param other: another :class:`PieceWiseConstFunc` @@ -89,12 +91,13 @@ def almost_equal(self, other, decimal=14): False otherwise :rtype: bool """ - eps = 10.0**(-decimal) - return np.allclose(self.x, other.x, atol=eps, rtol=0.0) and \ - np.allclose(self.y, other.y, atol=eps, rtol=0.0) + eps = 10.0 ** (-decimal) + return np.allclose(self.x, other.x, atol=eps, rtol=0.0) and np.allclose( + self.y, other.y, atol=eps, rtol=0.0 + ) def get_plottable_data(self): - """ Returns two arrays containing x- and y-coordinates for immeditate + """Returns two arrays containing x- and y-coordinates for immeditate plotting of the piece-wise function. :returns: (x_plot, y_plot) containing plottable data @@ -106,18 +109,18 @@ def get_plottable_data(self): plt.plot(x, y, '-o', label="Piece-wise const function") """ - x_plot = np.empty(2*len(self.x)-2) + x_plot = np.empty(2 * len(self.x) - 2) x_plot[0] = self.x[0] x_plot[1::2] = self.x[1:] x_plot[2::2] = self.x[1:-1] - y_plot = np.empty(2*len(self.y)) + y_plot = np.empty(2 * len(self.y)) y_plot[::2] = self.y y_plot[1::2] = self.y return x_plot, y_plot def integral(self, interval=None): - """ Returns the integral over the given interval. + """Returns the integral over the given interval. :param interval: integration interval given as a pair of floats, if None the integral over the whole function is computed. @@ -127,38 +130,42 @@ def integral(self, interval=None): """ if interval is None: # no interval given, integrate over the whole spike train - a = np.sum((self.x[1:]-self.x[:-1]) * self.y) + a = np.sum((self.x[1:] - self.x[:-1]) * self.y) else: - if interval[0]>interval[1]: + if interval[0] > interval[1]: raise ValueError("Invalid averaging interval: interval[0]>=interval[1]") - if interval[0]self.x[-1]: + if interval[1] > self.x[-1]: raise ValueError("Invalid averaging interval: interval[0] end_ind: # contribution from between two closest edges - a = (self.x[start_ind]-self.x[end_ind]) * self.y[end_ind] + a = (self.x[start_ind] - self.x[end_ind]) * self.y[end_ind] # minus the part that is not within the interval - a -= ((interval[0]-self.x[end_ind])+(self.x[start_ind]-interval[1])) * self.y[end_ind] + a -= ( + (interval[0] - self.x[end_ind]) + (self.x[start_ind] - interval[1]) + ) * self.y[end_ind] else: - assert start_ind > 0 and end_ind < len(self.x), \ + assert start_ind > 0 and end_ind < len(self.x), ( "Invalid averaging interval" + ) # first the contribution from between the indices - a = np.sum((self.x[start_ind+1:end_ind+1] - - self.x[start_ind:end_ind]) * - self.y[start_ind:end_ind]) + a = np.sum( + (self.x[start_ind + 1 : end_ind + 1] - self.x[start_ind:end_ind]) + * self.y[start_ind:end_ind] + ) # correction from start to first index - a += (self.x[start_ind]-interval[0]) * self.y[start_ind-1] + a += (self.x[start_ind] - interval[0]) * self.y[start_ind - 1] # correction from last index to end - a += (interval[1]-self.x[end_ind]) * self.y[end_ind] + a += (interval[1] - self.x[end_ind]) * self.y[end_ind] return a def avrg(self, interval=None): - """ Computes the average of the piece-wise const function: - :math:`a = 1/T \int_0^T f(x) dx` where T is the length of the interval. + """Computes the average of the piece-wise const function: + :math:`a = 1/T \\int_0^T f(x) dx` where T is the length of the interval. :param interval: averaging interval given as a pair of floats, a sequence of pairs for averaging multiple intervals, or @@ -170,15 +177,16 @@ def avrg(self, interval=None): """ if interval is None: # no interval given, average over the whole spike train - return self.integral() / (self.x[-1]-self.x[0]) + return self.integral() / (self.x[-1] - self.x[0]) # check if interval is as sequence - assert isinstance(interval, collections.abc.Sequence), \ + assert isinstance(interval, collections.abc.Sequence), ( "Invalid value for `interval`. None, Sequence or Tuple expected." + ) # check if interval is a sequence of intervals if not isinstance(interval[0], collections.abc.Sequence): # just one interval - a = self.integral(interval) / (interval[1]-interval[0]) + a = self.integral(interval) / (interval[1] - interval[0]) else: # several intervals a = 0.0 @@ -190,7 +198,7 @@ def avrg(self, interval=None): return a def add(self, f): - """ Adds another PieceWiseConst function to this function. + """Adds another PieceWiseConst function to this function. Note: only functions defined on the same interval can be summed. :param f: :class:`PieceWiseConstFunc` function to be added. @@ -201,19 +209,21 @@ def add(self, f): # cython version try: - from .cython.cython_add import add_piece_wise_const_cython as \ - add_piece_wise_const_impl + from .cython.cython_add import ( + add_piece_wise_const_cython as add_piece_wise_const_impl, + ) except ImportError: pyspike.NoCythonWarn() # use python backend - from .cython.python_backend import add_piece_wise_const_python as \ - add_piece_wise_const_impl + from .cython.python_backend import ( + add_piece_wise_const_python as add_piece_wise_const_impl, + ) self.x, self.y = add_piece_wise_const_impl(self.x, self.y, f.x, f.y) def mul_scalar(self, fac): - """ Multiplies the function with a scalar value + """Multiplies the function with a scalar value :param fac: Value to multiply :type fac: double diff --git a/pyspike/PieceWiseLinFunc.py b/pyspike/PieceWiseLinFunc.py index 1195e9a..e474c91 100644 --- a/pyspike/PieceWiseLinFunc.py +++ b/pyspike/PieceWiseLinFunc.py @@ -2,10 +2,11 @@ # Copyright 2014-2015, Mario Mulansky # Distributed under the BSD License -from __future__ import absolute_import, print_function -import numpy as np import collections.abc + +import numpy as np + import pyspike @@ -13,10 +14,10 @@ # PieceWiseLinFunc ############################################################## class PieceWiseLinFunc: - """ A class representing a piece-wise linear function. """ + """A class representing a piece-wise linear function.""" def __init__(self, x, y1, y2): - """ Constructs the piece-wise linear function. + """Constructs the piece-wise linear function. :param x: array of length N+1 defining the edges of the intervals of the pwc function. @@ -31,46 +32,46 @@ def __init__(self, x, y1, y2): self.y2 = np.array(y2) def __call__(self, t): - """ Returns the function value for the given time t. If t is a list of + """Returns the function value for the given time t. If t is a list of times, the corresponding list of values is returned. :param: time t, or list of times :returns: function value(s) at that time(s). """ + def intermediate_value(x0, x1, y0, y1, x): - """ computes the intermediate value of a linear function """ - return y0 + (y1-y0)*(x-x0)/(x1-x0) + """computes the intermediate value of a linear function""" + return y0 + (y1 - y0) * (x - x0) / (x1 - x0) - assert np.all(t >= self.x[0]) and np.all(t <= self.x[-1]), \ + assert np.all(t >= self.x[0]) and np.all(t <= self.x[-1]), ( "Invalid time: " + str(t) + ) - ind = np.searchsorted(self.x, t, side='right') + ind = np.searchsorted(self.x, t, side="right") if isinstance(t, collections.abc.Sequence): # t is a sequence of values # correct the cases t == x[0], t == x[-1] ind[ind == 0] = 1 - ind[ind == len(self.x)] = len(self.x)-1 - value = intermediate_value(self.x[ind-1], - self.x[ind], - self.y1[ind-1], - self.y2[ind-1], - t) + ind[ind == len(self.x)] = len(self.x) - 1 + value = intermediate_value( + self.x[ind - 1], self.x[ind], self.y1[ind - 1], self.y2[ind - 1], t + ) # correct the values at exact spike times: there the value should # be the at half of the step # obtain the 'left' side indices for t - ind_l = np.searchsorted(self.x, t, side='left') + ind_l = np.searchsorted(self.x, t, side="left") # if left and right side indices differ, the time t has to appear # in self.x - ind_at_spike = np.logical_and(np.logical_and(ind != ind_l, - ind > 1), - ind < len(self.x)) + ind_at_spike = np.logical_and( + np.logical_and(ind != ind_l, ind > 1), ind < len(self.x) + ) # get the corresponding indices for the resulting value array val_ind = np.arange(len(ind))[ind_at_spike] # and for the values in self.x, y1, y2 xy_ind = ind[ind_at_spike] # the values are defined as the average of the left and right limit - value[val_ind] = 0.5 * (self.y1[xy_ind-1] + self.y2[xy_ind-2]) + value[val_ind] = 0.5 * (self.y1[xy_ind - 1] + self.y2[xy_ind - 2]) return value else: # t is a single value # specific check for interval edges @@ -81,22 +82,20 @@ def intermediate_value(x0, x1, y0, y1, x): # check if we are on any other exact spike time if sum(self.x == t) > 0: # use the middle of the left and right Spike value - return 0.5 * (self.y1[ind-1] + self.y2[ind-2]) - return intermediate_value(self.x[ind-1], - self.x[ind], - self.y1[ind-1], - self.y2[ind-1], - t) + return 0.5 * (self.y1[ind - 1] + self.y2[ind - 2]) + return intermediate_value( + self.x[ind - 1], self.x[ind], self.y1[ind - 1], self.y2[ind - 1], t + ) def copy(self): - """ Returns a copy of itself + """Returns a copy of itself :rtype: :class:`PieceWiseLinFunc` """ return PieceWiseLinFunc(self.x, self.y1, self.y2) def almost_equal(self, other, decimal=14): - """ Checks if the function is equal to another function up to `decimal` + """Checks if the function is equal to another function up to `decimal` precision. :param other: another :class:`PieceWiseLinFunc` @@ -104,13 +103,15 @@ def almost_equal(self, other, decimal=14): False otherwise :rtype: bool """ - eps = 10.0**(-decimal) - return np.allclose(self.x, other.x, atol=eps, rtol=0.0) and \ - np.allclose(self.y1, other.y1, atol=eps, rtol=0.0) and \ - np.allclose(self.y2, other.y2, atol=eps, rtol=0.0) + eps = 10.0 ** (-decimal) + return ( + np.allclose(self.x, other.x, atol=eps, rtol=0.0) + and np.allclose(self.y1, other.y1, atol=eps, rtol=0.0) + and np.allclose(self.y2, other.y2, atol=eps, rtol=0.0) + ) def get_plottable_data(self): - """ Returns two arrays containing x- and y-coordinates for immeditate + """Returns two arrays containing x- and y-coordinates for immeditate plotting of the piece-wise function. :returns: (x_plot, y_plot) containing plottable data @@ -121,7 +122,7 @@ def get_plottable_data(self): x, y = f.get_plottable_data() plt.plot(x, y, '-o', label="Piece-wise const function") """ - x_plot = np.empty(2*len(self.x)-2) + x_plot = np.empty(2 * len(self.x) - 2) x_plot[0] = self.x[0] x_plot[1::2] = self.x[1:] x_plot[2::2] = self.x[1:-1] @@ -131,7 +132,7 @@ def get_plottable_data(self): return x_plot, y_plot def integral(self, interval=None): - """ Returns the integral over the given interval. + """Returns the integral over the given interval. :param interval: integration interval given as a pair of floats, if None the integral over the whole function is computed. @@ -141,60 +142,79 @@ def integral(self, interval=None): """ def intermediate_value(x0, x1, y0, y1, x): - """ computes the intermediate value of a linear function """ - return y0 + (y1-y0)*(x-x0)/(x1-x0) + """computes the intermediate value of a linear function""" + return y0 + (y1 - y0) * (x - x0) / (x1 - x0) if interval is None: # no interval given, integrate over the whole spike train - return np.sum((self.x[1:]-self.x[:-1]) * 0.5*(self.y1+self.y2)) + return np.sum((self.x[1:] - self.x[:-1]) * 0.5 * (self.y1 + self.y2)) # find the indices corresponding to the interval - start_ind = np.searchsorted(self.x, interval[0], side='right') - end_ind = np.searchsorted(self.x, interval[1], side='left')-1 - assert start_ind > 0 and end_ind < len(self.x), \ - "Invalid averaging interval" + start_ind = np.searchsorted(self.x, interval[0], side="right") + end_ind = np.searchsorted(self.x, interval[1], side="left") - 1 + assert start_ind > 0 and end_ind < len(self.x), "Invalid averaging interval" if start_ind > end_ind: print(start_ind, end_ind, self.x[start_ind]) # contribution from between two closest edges - y_x0 = intermediate_value(self.x[start_ind-1], - self.x[start_ind], - self.y1[start_ind-1], - self.y2[start_ind-1], - interval[0]) - y_x1 = intermediate_value(self.x[start_ind-1], - self.x[start_ind], - self.y1[start_ind-1], - self.y2[start_ind-1], - interval[1]) + y_x0 = intermediate_value( + self.x[start_ind - 1], + self.x[start_ind], + self.y1[start_ind - 1], + self.y2[start_ind - 1], + interval[0], + ) + y_x1 = intermediate_value( + self.x[start_ind - 1], + self.x[start_ind], + self.y1[start_ind - 1], + self.y2[start_ind - 1], + interval[1], + ) print(y_x0, y_x1, interval[1] - interval[0]) integral = (y_x0 + y_x1) * 0.5 * (interval[1] - interval[0]) print(integral) else: # first the contribution from between the indices - integral = np.sum((self.x[start_ind+1:end_ind+1] - - self.x[start_ind:end_ind]) * - 0.5*(self.y1[start_ind:end_ind] + - self.y2[start_ind:end_ind])) + integral = np.sum( + (self.x[start_ind + 1 : end_ind + 1] - self.x[start_ind:end_ind]) + * 0.5 + * (self.y1[start_ind:end_ind] + self.y2[start_ind:end_ind]) + ) # correction from start to first index - integral += (self.x[start_ind]-interval[0]) * 0.5 * \ - (self.y2[start_ind-1] + - intermediate_value(self.x[start_ind-1], - self.x[start_ind], - self.y1[start_ind-1], - self.y2[start_ind-1], - interval[0])) + integral += ( + (self.x[start_ind] - interval[0]) + * 0.5 + * ( + self.y2[start_ind - 1] + + intermediate_value( + self.x[start_ind - 1], + self.x[start_ind], + self.y1[start_ind - 1], + self.y2[start_ind - 1], + interval[0], + ) + ) + ) # correction from last index to end - integral += (interval[1]-self.x[end_ind]) * 0.5 * \ - (self.y1[end_ind] + - intermediate_value(self.x[end_ind], self.x[end_ind+1], - self.y1[end_ind], self.y2[end_ind], - interval[1] - )) + integral += ( + (interval[1] - self.x[end_ind]) + * 0.5 + * ( + self.y1[end_ind] + + intermediate_value( + self.x[end_ind], + self.x[end_ind + 1], + self.y1[end_ind], + self.y2[end_ind], + interval[1], + ) + ) + ) return integral def avrg(self, interval=None): - """ Computes the average of the piece-wise linear function: - :math:`a = 1/T \int_0^T f(x) dx` where T is the interval length. + """Computes the average of the piece-wise linear function: + :math:`a = 1/T \\int_0^T f(x) dx` where T is the interval length. :param interval: averaging interval given as a pair of floats, a sequence of pairs for averaging multiple intervals, or @@ -208,15 +228,16 @@ def avrg(self, interval=None): if interval is None: # no interval given, average over the whole spike train - return self.integral() / (self.x[-1]-self.x[0]) + return self.integral() / (self.x[-1] - self.x[0]) # check if interval is as sequence - assert isinstance(interval, collections.abc.Sequence), \ + assert isinstance(interval, collections.abc.Sequence), ( "Invalid value for `interval`. None, Sequence or Tuple expected." + ) # check if interval is a sequence of intervals if not isinstance(interval[0], collections.abc.Sequence): # just one interval - a = self.integral(interval) / (interval[1]-interval[0]) + a = self.integral(interval) / (interval[1] - interval[0]) else: # several intervals a = 0.0 @@ -228,7 +249,7 @@ def avrg(self, interval=None): return a def add(self, f): - """ Adds another PieceWiseLin function to this function. + """Adds another PieceWiseLin function to this function. Note: only functions defined on the same interval can be summed. :param f: :class:`PieceWiseLinFunc` function to be added. @@ -244,20 +265,23 @@ def add(self, f): # cython version try: - from .cython.cython_add import add_piece_wise_lin_cython as \ - add_piece_wise_lin_impl + from .cython.cython_add import ( + add_piece_wise_lin_cython as add_piece_wise_lin_impl, + ) except ImportError: pyspike.NoCythonWarn() # use python backend - from .cython.python_backend import add_piece_wise_lin_python as \ - add_piece_wise_lin_impl + from .cython.python_backend import ( + add_piece_wise_lin_python as add_piece_wise_lin_impl, + ) self.x, self.y1, self.y2 = add_piece_wise_lin_impl( - self.x, self.y1, self.y2, f.x, f.y1, f.y2) + self.x, self.y1, self.y2, f.x, f.y1, f.y2 + ) def mul_scalar(self, fac): - """ Multiplies the function with a scalar value + """Multiplies the function with a scalar value :param fac: Value to multiply :type fac: double diff --git a/pyspike/SpikeTrain.py b/pyspike/SpikeTrain.py index 19f2419..4f451a2 100644 --- a/pyspike/SpikeTrain.py +++ b/pyspike/SpikeTrain.py @@ -5,11 +5,11 @@ import numpy as np -class SpikeTrain(object): - """ Class representing spike trains for the PySpike Module.""" +class SpikeTrain: + """Class representing spike trains for the PySpike Module.""" def __init__(self, spike_times, edges, is_sorted=True): - """ Constructs the SpikeTrain. + """Constructs the SpikeTrain. :param spike_times: ordered array of spike times. :param edges: The edges of the spike train. Given as a pair of floats @@ -28,12 +28,12 @@ def __init__(self, spike_times, edges, is_sorted=True): try: self.t_start = float(edges[0]) self.t_end = float(edges[1]) - except: + except TypeError: self.t_start = 0.0 self.t_end = float(edges) def __getitem__(self, index): - """ Returns the time of the spike given by index. + """Returns the time of the spike given by index. :param index: Index of the spike. :return: spike time. @@ -41,19 +41,18 @@ def __getitem__(self, index): return self.spikes[index] def __len__(self): - """ Returns the number of spikes. - + """Returns the number of spikes. + :return: Number of spikes. """ return len(self.spikes) def sort(self): - """ Sorts the spike times of this spike train using `np.sort` - """ + """Sorts the spike times of this spike train using `np.sort`""" self.spikes = np.sort(self.spikes) def copy(self): - """ Returns a copy of this spike train. + """Returns a copy of this spike train. Use this function if you want to create a real (deep) copy of this spike train. Simple assignment `t2 = t1` does not create a copy of the spike train data, but a reference as `numpy.array` is used for storing @@ -69,7 +68,6 @@ def get_spikes_non_empty(self): of empty spike trains. """ if len(self.spikes) < 1: - return np.unique(np.insert([self.t_start, self.t_end], 1, - self.spikes)) + return np.unique(np.insert([self.t_start, self.t_end], 1, self.spikes)) else: return self.spikes diff --git a/pyspike/__init__.py b/pyspike/__init__.py index 4e31120..bfbfc8f 100644 --- a/pyspike/__init__.py +++ b/pyspike/__init__.py @@ -4,65 +4,101 @@ Distributed under the BSD License """ -from __future__ import absolute_import +__all__ = [ + "DiscreteFunc", + "PieceWiseConstFunc", + "PieceWiseLinFunc", + "SpikeTrain", + "filter_by_spike_sync", + "generate_poisson_spikes", + "import_spike_trains_from_time_series", + "isi_distance", + "isi_distance_matrix", + "isi_distance_multi", + "isi_profile", + "isi_profile_multi", + "load_spike_trains_from_txt", + "psth", + "spike_directionality", + "spike_directionality_matrix", + "spike_distance", + "spike_distance_matrix", + "spike_profile", + "spike_sync", + "spike_sync_matrix", + "spike_sync_profile", + "spike_sync_profile_multi", + "spike_train_order", + "spike_train_order_profile", + "spikes", +] -__all__ = ["isi_distance", "spike_distance", "spike_sync", "psth", - "spikes", "spike_directionality", "SpikeTrain", - "PieceWiseConstFunc", "PieceWiseLinFunc", "DiscreteFunc"] +from importlib.metadata import PackageNotFoundError, version +from .DiscreteFunc import DiscreteFunc +from .isi_distance import ( + isi_distance, + isi_distance_matrix, + isi_distance_multi, + isi_profile, + isi_profile_multi, +) from .PieceWiseConstFunc import PieceWiseConstFunc from .PieceWiseLinFunc import PieceWiseLinFunc -from .DiscreteFunc import DiscreteFunc -from .SpikeTrain import SpikeTrain - -from .isi_distance import isi_profile, isi_distance, isi_profile_multi,\ - isi_distance_multi, isi_distance_matrix -from .spike_distance import spike_profile, spike_distance, spike_profile_multi,\ - spike_distance_multi, spike_distance_matrix -from .spike_sync import spike_sync_profile, spike_sync,\ - spike_sync_profile_multi, spike_sync_multi, spike_sync_matrix,\ - filter_by_spike_sync from .psth import psth - -from .spikes import load_spike_trains_from_txt, save_spike_trains_to_txt, \ - spike_train_from_string, import_spike_trains_from_time_series, \ - merge_spike_trains, generate_poisson_spikes - -from .spike_directionality import spike_directionality, \ - spike_directionality_values, spike_directionality_matrix, \ - spike_train_order_profile, spike_train_order_profile_bi, \ - spike_train_order_profile_multi, spike_train_order, \ - spike_train_order_bi, spike_train_order_multi, \ - optimal_spike_train_sorting, permutate_matrix - -# define the __version__ following -# http://stackoverflow.com/questions/17583443 -from pkg_resources import get_distribution, DistributionNotFound -import os.path +from .spike_directionality import ( + optimal_spike_train_sorting, + permutate_matrix, + spike_directionality, + spike_directionality_matrix, + spike_directionality_values, + spike_train_order, + spike_train_order_profile, + spike_train_order_profile_multi, +) +from .spike_distance import ( + spike_distance, + spike_distance_matrix, + spike_distance_multi, + spike_profile, + spike_profile_multi, +) +from .spike_sync import ( + filter_by_spike_sync, + spike_sync, + spike_sync_matrix, + spike_sync_multi, + spike_sync_profile, + spike_sync_profile_multi, +) +from .spikes import ( + generate_poisson_spikes, + import_spike_trains_from_time_series, + load_spike_trains_from_txt, + merge_spike_trains, + save_spike_trains_to_txt, + spike_train_from_string, +) +from .SpikeTrain import SpikeTrain try: - _dist = get_distribution('pyspike') - # Normalize case for Windows systems - dist_loc = os.path.normcase(_dist.location) - here = os.path.normcase(__file__) - if not here.startswith(os.path.join(dist_loc, 'pyspike')): - # not installed, but there is another version that *is* - raise DistributionNotFound -except DistributionNotFound: - __version__ = 'Please install this project with setup.py' -else: - __version__ = _dist.version + __version__ = version("pyspike") +except PackageNotFoundError: + __version__ = "Please install this project with setup.py" disable_backend_warning = False + def NoCythonWarn(): - """ Warn exactly once - (called when an import of one of the cython_...so modules failed) + """Warn exactly once + (called when an import of one of the cython_...so modules failed) """ global disable_backend_warning # initialized False in __init__.py if not disable_backend_warning: - print("Warning: Cython implementation not found." + - " Make sure that PySpike is installed by running\n" + - " 'python setup.py build_ext --inplace'\n" + - "Falling back to slow python backend.\n") + print( + "Warning: Cython implementation not found." + + " Make sure that PySpike is installed by running\n" + + " 'python setup.py build_ext --inplace'\n" + + "Falling back to slow python backend.\n" + ) disable_backend_warning = True diff --git a/pyspike/cython/directionality_python_backend.py b/pyspike/cython/directionality_python_backend.py index 91217fd..4e2926d 100644 --- a/pyspike/cython/directionality_python_backend.py +++ b/pyspike/cython/directionality_python_backend.py @@ -1,4 +1,4 @@ -""" directionality_python_backend.py +"""directionality_python_backend.py Collection of python functions that can be used instead of the cython implementation. @@ -10,41 +10,44 @@ """ import numpy as np + from pyspike.cython.python_backend import get_tau + ############################################################ # spike_train_order_python ############################################################ -def spike_directionality_profile_python(spikes1, spikes2, t_start, t_end, - max_tau, MRTS=0.): +def spike_directionality_profile_python( + spikes1, spikes2, t_start, t_end, max_tau, MRTS=0.0 +): true_max = t_end - t_start if max_tau > 0: - true_max = min(true_max, 2*max_tau) - + true_max = min(true_max, 2 * max_tau) + N1 = len(spikes1) N2 = len(spikes2) i = -1 j = -1 - d1 = np.zeros(N1) # directionality values - d2 = np.zeros(N2) # directionality values + d1 = np.zeros(N1) # directionality values + d2 = np.zeros(N2) # directionality values while i + j < N1 + N2 - 2: - if (i < N1-1) and (j == N2-1 or spikes1[i+1] < spikes2[j+1]): + if (i < N1 - 1) and (j == N2 - 1 or spikes1[i + 1] < spikes2[j + 1]): i += 1 tau = get_tau(spikes1, spikes2, i, j, true_max, MRTS) - if j > -1 and spikes1[i]-spikes2[j] < tau: + if j > -1 and spikes1[i] - spikes2[j] < tau: # coincidence between the current spike and the previous spike # spike in first spike train occurs after second d1[i] = -1 d2[j] = +1 - elif (j < N2-1) and (i == N1-1 or spikes1[i+1] > spikes2[j+1]): + elif (j < N2 - 1) and (i == N1 - 1 or spikes1[i + 1] > spikes2[j + 1]): j += 1 tau = get_tau(spikes1, spikes2, i, j, true_max, MRTS) - if i > -1 and spikes2[j]-spikes1[i] < tau: + if i > -1 and spikes2[j] - spikes1[i] < tau: # coincidence between the current spike and the previous spike # spike in second spike train occurs after first d1[i] = +1 d2[j] = -1 - else: # spikes1[i+1] = spikes2[j+1] + else: # spikes1[i+1] = spikes2[j+1] # advance in both spike trains j += 1 i += 1 @@ -57,11 +60,12 @@ def spike_directionality_profile_python(spikes1, spikes2, t_start, t_end, ############################################################ # spike_train_order_python ############################################################ -def spike_train_order_profile_python(spikes1, spikes2, t_start, t_end, - max_tau, MRTS=0.): +def spike_train_order_profile_python( + spikes1, spikes2, t_start, t_end, max_tau, MRTS=0.0 +): true_max = t_end - t_start if max_tau > 0: - true_max = min(true_max, 2*max_tau) + true_max = min(true_max, 2 * max_tau) N1 = len(spikes1) N2 = len(spikes2) @@ -69,30 +73,30 @@ def spike_train_order_profile_python(spikes1, spikes2, t_start, t_end, j = -1 n = 0 st = np.zeros(N1 + N2 + 2) # spike times - a = np.zeros(N1 + N2 + 2) # coincidences - mp = np.ones(N1 + N2 + 2) # multiplicity + a = np.zeros(N1 + N2 + 2) # coincidences + mp = np.ones(N1 + N2 + 2) # multiplicity while i + j < N1 + N2 - 2: - if (i < N1-1) and (j == N2-1 or spikes1[i+1] < spikes2[j+1]): + if (i < N1 - 1) and (j == N2 - 1 or spikes1[i + 1] < spikes2[j + 1]): i += 1 n += 1 tau = get_tau(spikes1, spikes2, i, j, true_max, MRTS) st[n] = spikes1[i] - if j > -1 and spikes1[i]-spikes2[j] < tau: + if j > -1 and spikes1[i] - spikes2[j] < tau: # coincidence between the current spike and the previous spike # both get marked with 1 a[n] = -1 - a[n-1] = -1 - elif (j < N2-1) and (i == N1-1 or spikes1[i+1] > spikes2[j+1]): + a[n - 1] = -1 + elif (j < N2 - 1) and (i == N1 - 1 or spikes1[i + 1] > spikes2[j + 1]): j += 1 n += 1 tau = get_tau(spikes1, spikes2, i, j, true_max, MRTS) st[n] = spikes2[j] - if i > -1 and spikes2[j]-spikes1[i] < tau: + if i > -1 and spikes2[j] - spikes1[i] < tau: # coincidence between the current spike and the previous spike # both get marked with 1 a[n] = 1 - a[n-1] = 1 - else: # spikes1[i+1] = spikes2[j+1] + a[n - 1] = 1 + else: # spikes1[i+1] = spikes2[j+1] # advance in both spike trains j += 1 i += 1 @@ -102,17 +106,17 @@ def spike_train_order_profile_python(spikes1, spikes2, t_start, t_end, a[n] = 0 mp[n] = 2 - st = st[:n+2] - a = a[:n+2] - mp = mp[:n+2] + st = st[: n + 2] + a = a[: n + 2] + mp = mp[: n + 2] st[0] = t_start - st[len(st)-1] = t_end + st[len(st) - 1] = t_end if N1 + N2 > 0: a[0] = a[1] - a[len(a)-1] = a[len(a)-2] + a[len(a) - 1] = a[len(a) - 2] mp[0] = mp[1] - mp[len(mp)-1] = mp[len(mp)-2] + mp[len(mp) - 1] = mp[len(mp) - 2] else: a[0] = 1 a[1] = 1 diff --git a/pyspike/cython/python_backend.py b/pyspike/cython/python_backend.py index 3e03781..c096691 100644 --- a/pyspike/cython/python_backend.py +++ b/pyspike/cython/python_backend.py @@ -1,4 +1,4 @@ -""" python_backend.py +"""python_backend.py Collection of python functions that can be used instead of the cython implementation. @@ -8,100 +8,114 @@ Distributed under the BSD License """ + import numpy as np + ############################################################ # isi_distance_python ############################################################ -def isi_distance_python(s1, s2, t_start, t_end, MRTS=0.): - """ Plain Python implementation of the isi distance. - Out: spike_events - times from s1 and s2 merged, - except beginning and end reflects t_start and t_end - isi_values - ISI distance between consecutive elements of spike_events +def isi_distance_python(s1, s2, t_start, t_end, MRTS=0.0): + """Plain Python implementation of the isi distance. + Out: spike_events - times from s1 and s2 merged, + except beginning and end reflects t_start and t_end + isi_values - ISI distance between consecutive elements of spike_events """ N1 = len(s1) N2 = len(s2) # compute the isi-distance - spike_events = np.empty(N1+N2+2) + spike_events = np.empty(N1 + N2 + 2) spike_events[0] = t_start # the values have one entry less - the number of intervals between events isi_values = np.empty(len(spike_events) - 1) if s1[0] > t_start: # edge correction - nu1 = max(s1[0] - t_start, s1[1] - s1[0]) if N1 > 1 else s1[0]-t_start + nu1 = max(s1[0] - t_start, s1[1] - s1[0]) if N1 > 1 else s1[0] - t_start index1 = -1 else: - nu1 = s1[1] - s1[0] if N1 > 1 else t_end-s1[0] + nu1 = s1[1] - s1[0] if N1 > 1 else t_end - s1[0] index1 = 0 if s2[0] > t_start: # edge correction - nu2 = max(s2[0] - t_start, s2[1] - s2[0]) if N2 > 1 else s2[0]-t_start + nu2 = max(s2[0] - t_start, s2[1] - s2[0]) if N2 > 1 else s2[0] - t_start index2 = -1 else: - nu2 = s2[1] - s2[0] if N2 > 1 else t_end-s2[0] + nu2 = s2[1] - s2[0] if N2 > 1 else t_end - s2[0] index2 = 0 isi_values[0] = abs(nu1 - nu2) / max([nu1, nu2, MRTS]) index = 1 - while index1+index2 < N1+N2-2: + while index1 + index2 < N1 + N2 - 2: # check which spike is next - from s1 or s2 - if (index1 < N1-1) and (index2 == N2-1 or s1[index1+1] < s2[index2+1]): + if (index1 < N1 - 1) and (index2 == N2 - 1 or s1[index1 + 1] < s2[index2 + 1]): index1 += 1 spike_events[index] = s1[index1] - if index1 < N1-1: - nu1 = s1[index1+1]-s1[index1] + if index1 < N1 - 1: + nu1 = s1[index1 + 1] - s1[index1] else: # edge correction - nu1 = max(t_end-s1[N1-1], s1[N1-1]-s1[N1-2]) if N1 > 1 \ - else t_end-s1[N1-1] - - elif (index2 < N2-1) and (index1 == N1-1 or - s1[index1+1] > s2[index2+1]): + nu1 = ( + max(t_end - s1[N1 - 1], s1[N1 - 1] - s1[N1 - 2]) + if N1 > 1 + else t_end - s1[N1 - 1] + ) + + elif (index2 < N2 - 1) and ( + index1 == N1 - 1 or s1[index1 + 1] > s2[index2 + 1] + ): index2 += 1 spike_events[index] = s2[index2] - if index2 < N2-1: - nu2 = s2[index2+1]-s2[index2] + if index2 < N2 - 1: + nu2 = s2[index2 + 1] - s2[index2] else: # edge correction - nu2 = max(t_end-s2[N2-1], s2[N2-1]-s2[N2-2]) if N2 > 1 \ - else t_end-s2[N2-1] + nu2 = ( + max(t_end - s2[N2 - 1], s2[N2 - 1] - s2[N2 - 2]) + if N2 > 1 + else t_end - s2[N2 - 1] + ) else: # s1[index1 + 1] == s2[index2 + 1] index1 += 1 index2 += 1 spike_events[index] = s1[index1] - if index1 < N1-1: - nu1 = s1[index1+1]-s1[index1] + if index1 < N1 - 1: + nu1 = s1[index1 + 1] - s1[index1] else: # edge correction - nu1 = max(t_end-s1[N1-1], s1[N1-1]-s1[N1-2]) if N1 > 1 \ - else t_end-s1[N1-1] - if index2 < N2-1: - nu2 = s2[index2+1]-s2[index2] + nu1 = ( + max(t_end - s1[N1 - 1], s1[N1 - 1] - s1[N1 - 2]) + if N1 > 1 + else t_end - s1[N1 - 1] + ) + if index2 < N2 - 1: + nu2 = s2[index2 + 1] - s2[index2] else: # edge correction - nu2 = max(t_end-s2[N2-1], s2[N2-1]-s2[N2-2]) if N2 > 1 \ - else t_end-s2[N2-1] + nu2 = ( + max(t_end - s2[N2 - 1], s2[N2 - 1] - s2[N2 - 2]) + if N2 > 1 + else t_end - s2[N2 - 1] + ) # compute the corresponding isi-distance - isi_values[index] = abs(nu1 - nu2) / \ - max([nu1, nu2, MRTS]) + isi_values[index] = abs(nu1 - nu2) / max([nu1, nu2, MRTS]) index += 1 # the last event is the interval end - if spike_events[index-1] == t_end: + if spike_events[index - 1] == t_end: index -= 1 else: spike_events[index] = t_end # use only the data added above # could be less than original length due to equal spike times - return spike_events[:index + 1], isi_values[:index] + return spike_events[: index + 1], isi_values[:index] ############################################################ # get_min_dist ############################################################ def get_min_dist(spike_time, spike_train, start_index, t_start, t_end): - """ Returns the minimal distance |spike_time - spike_train[i]| + """Returns the minimal distance |spike_time - spike_train[i]| with i>=start_index. """ d = abs(spike_time - t_start) @@ -121,27 +135,29 @@ def get_min_dist(spike_time, spike_train, start_index, t_start, t_end): else: return d_temp + ############################################################ # dist_at_t ############################################################ def dist_at_t(isi1, isi2, s1, s2, MRTS, RI): - """ Compute instantaneous Spike Distance - In: isi1, isi2 - spike time differences around current times in each trains - s1, s2 - weighted spike time differences between trains - Out: the Spike Distance + """Compute instantaneous Spike Distance + In: isi1, isi2 - spike time differences around current times in each trains + s1, s2 - weighted spike time differences between trains + Out: the Spike Distance """ - meanISI = .5*(isi1+isi2) + meanISI = 0.5 * (isi1 + isi2) limitedISI = max(MRTS, meanISI) if RI: - return .5*(s1+s2)/limitedISI + return 0.5 * (s1 + s2) / limitedISI else: - return .5*(s1*isi2 + s2*isi1)/(meanISI*limitedISI) + return 0.5 * (s1 * isi2 + s2 * isi1) / (meanISI * limitedISI) + ############################################################ # spike_distance_python ############################################################ -def spike_distance_python(spikes1, spikes2, t_start, t_end, MRTS=0., RI=False): - """ Computes the instantaneous spike-distance S_spike (t) of the two given +def spike_distance_python(spikes1, spikes2, t_start, t_end, MRTS=0.0, RI=False): + """Computes the instantaneous spike-distance S_spike (t) of the two given spike trains. The spike trains are expected to have auxiliary spikes at the beginning and end of the interval. Use the function add_auxiliary_spikes to add those spikes to the spike train. @@ -150,7 +166,7 @@ def spike_distance_python(spikes1, spikes2, t_start, t_end, MRTS=0., RI=False): - t_start, t_end: edges of the spike train Returns: - spike_events - merged times from spikes1, spikes2 (with edge corrections) - - y_starts, y_ends + - y_starts, y_ends In the interval (spike_events[i], spike_events[i+1]) the SPIKE-sync value goes from y_starts[i] to y_ends[i], linearly """ @@ -162,17 +178,17 @@ def spike_distance_python(spikes1, spikes2, t_start, t_end, MRTS=0., RI=False): N1 = len(t1) N2 = len(t2) - spike_events = np.empty(N1+N2+2) + spike_events = np.empty(N1 + N2 + 2) - y_starts = np.empty(len(spike_events)-1) - y_ends = np.empty(len(spike_events)-1) + y_starts = np.empty(len(spike_events) - 1) + y_ends = np.empty(len(spike_events) - 1) t_aux1 = np.zeros(2) t_aux2 = np.zeros(2) - t_aux1[0] = min(t_start, t1[0]-(t1[1]-t1[0])) if N1 > 1 else t_start - t_aux1[1] = max(t_end, t1[N1-1]+(t1[N1-1]-t1[N1-2])) if N1 > 1 else t_end - t_aux2[0] = min(t_start, t2[0]-(t2[1]-t2[0])) if N2 > 1 else t_start - t_aux2[1] = max(t_end, t2[N2-1]+(t2[N2-1]-t2[N2-2])) if N2 > 1 else t_end + t_aux1[0] = min(t_start, t1[0] - (t1[1] - t1[0])) if N1 > 1 else t_start + t_aux1[1] = max(t_end, t1[N1 - 1] + (t1[N1 - 1] - t1[N1 - 2])) if N1 > 1 else t_end + t_aux2[0] = min(t_start, t2[0] - (t2[1] - t2[0])) if N2 > 1 else t_start + t_aux2[1] = max(t_end, t2[N2 - 1] + (t2[N2 - 1] - t2[N2 - 2])) if N2 > 1 else t_end t_p1 = t_start if (t1[0] == t_start) else t_aux1[0] t_p2 = t_start if (t2[0] == t_start) else t_aux2[0] @@ -183,7 +199,7 @@ def spike_distance_python(spikes1, spikes2, t_start, t_end, MRTS=0., RI=False): t_f1 = t1[0] dt_f1 = get_min_dist(t_f1, t2, 0, t_aux2[0], t_aux2[1]) dt_p1 = dt_f1 - isi1 = max(t_f1-t_start, t1[1]-t1[0]) if N1 > 1 else t_f1-t_start + isi1 = max(t_f1 - t_start, t1[1] - t1[0]) if N1 > 1 else t_f1 - t_start # s1 = dt_p1*(t_f1-t_start)/isi1 s1 = dt_p1 index1 = -1 @@ -192,7 +208,7 @@ def spike_distance_python(spikes1, spikes2, t_start, t_end, MRTS=0., RI=False): t_f1 = t1[1] if N1 > 1 else t_end dt_p1 = get_min_dist(t_p1, t2, 0, t_aux2[0], t_aux2[1]) dt_f1 = get_min_dist(t_f1, t2, 0, t_aux2[0], t_aux2[1]) - isi1 = t_f1-t1[0] + isi1 = t_f1 - t1[0] s1 = dt_p1 index1 = 0 if t2[0] > t_start: @@ -200,7 +216,7 @@ def spike_distance_python(spikes1, spikes2, t_start, t_end, MRTS=0., RI=False): t_f2 = t2[0] dt_f2 = get_min_dist(t_f2, t1, 0, t_aux1[0], t_aux1[1]) dt_p2 = dt_f2 - isi2 = max(t_f2-t_start, t2[1]-t2[0]) if N2 > 1 else t_f2-t_start + isi2 = max(t_f2 - t_start, t2[1] - t2[0]) if N2 > 1 else t_f2 - t_start # s2 = dt_p2*(t_f2-t_start)/isi2 s2 = dt_p2 index2 = -1 @@ -208,68 +224,74 @@ def spike_distance_python(spikes1, spikes2, t_start, t_end, MRTS=0., RI=False): t_f2 = t2[1] if N2 > 1 else t_end dt_p2 = get_min_dist(t_p2, t1, 0, t_aux1[0], t_aux1[1]) dt_f2 = get_min_dist(t_f2, t1, 0, t_aux1[0], t_aux1[1]) - isi2 = t_f2-t2[0] + isi2 = t_f2 - t2[0] s2 = dt_p2 index2 = 0 y_starts[0] = dist_at_t(isi1, isi2, s1, s2, MRTS, RI) index = 1 - while index1+index2 < N1+N2-2: + while index1 + index2 < N1 + N2 - 2: # print(index, index1, index2) - if (index1 < N1-1) and (t_f1 < t_f2 or index2 == N2-1): + if (index1 < N1 - 1) and (t_f1 < t_f2 or index2 == N2 - 1): index1 += 1 # first calculate the previous interval end value - s1 = dt_f1*(t_f1-t_p1) / isi1 + s1 = dt_f1 * (t_f1 - t_p1) / isi1 # the previous time now was the following time before: dt_p1 = dt_f1 - t_p1 = t_f1 # t_p1 contains the current time point + t_p1 = t_f1 # t_p1 contains the current time point # get the next time - if index1 < N1-1: - t_f1 = t1[index1+1] + if index1 < N1 - 1: + t_f1 = t1[index1 + 1] else: t_f1 = t_aux1[1] spike_events[index] = t_p1 - s2 = (dt_p2*(t_f2-t_p1) + dt_f2*(t_p1-t_p2)) / isi2 - y_ends[index-1] = dist_at_t(isi1, isi2, s1, s2, MRTS, RI) + s2 = (dt_p2 * (t_f2 - t_p1) + dt_f2 * (t_p1 - t_p2)) / isi2 + y_ends[index - 1] = dist_at_t(isi1, isi2, s1, s2, MRTS, RI) # now the next interval start value - if index1 < N1-1: + if index1 < N1 - 1: dt_f1 = get_min_dist(t_f1, t2, index2, t_aux2[0], t_aux2[1]) - isi1 = t_f1-t_p1 + isi1 = t_f1 - t_p1 s1 = dt_p1 else: dt_f1 = dt_p1 - isi1 = max(t_end-t1[N1-1], t1[N1-1]-t1[N1-2]) if N1 > 1 \ - else t_end-t1[N1-1] + isi1 = ( + max(t_end - t1[N1 - 1], t1[N1 - 1] - t1[N1 - 2]) + if N1 > 1 + else t_end - t1[N1 - 1] + ) # s1 needs adjustment due to change of isi1 # s1 = dt_p1*(t_end-t1[N1-1])/isi1 # Eero's correction: no adjustment s1 = dt_p1 # s2 is the same as above, thus we can compute y2 immediately y_starts[index] = dist_at_t(isi1, isi2, s1, s2, MRTS, RI) - elif (index2 < N2-1) and (t_f1 > t_f2 or index1 == N1-1): + elif (index2 < N2 - 1) and (t_f1 > t_f2 or index1 == N1 - 1): index2 += 1 # first calculate the previous interval end value - s2 = dt_f2*(t_f2-t_p2) / isi2 + s2 = dt_f2 * (t_f2 - t_p2) / isi2 # the previous time now was the following time before: dt_p2 = dt_f2 - t_p2 = t_f2 # t_p1 contains the current time point + t_p2 = t_f2 # t_p1 contains the current time point # get the next time - if index2 < N2-1: - t_f2 = t2[index2+1] + if index2 < N2 - 1: + t_f2 = t2[index2 + 1] else: t_f2 = t_aux2[1] spike_events[index] = t_p2 - s1 = (dt_p1*(t_f1-t_p2) + dt_f1*(t_p2-t_p1)) / isi1 - y_ends[index-1] = dist_at_t(isi1, isi2, s1, s2, MRTS, RI) + s1 = (dt_p1 * (t_f1 - t_p2) + dt_f1 * (t_p2 - t_p1)) / isi1 + y_ends[index - 1] = dist_at_t(isi1, isi2, s1, s2, MRTS, RI) # now the next interval start value - if index2 < N2-1: + if index2 < N2 - 1: dt_f2 = get_min_dist(t_f2, t1, index1, t_aux1[0], t_aux1[1]) - isi2 = t_f2-t_p2 + isi2 = t_f2 - t_p2 s2 = dt_p2 else: dt_f2 = dt_p2 - isi2 = max(t_end-t2[N2-1], t2[N2-1]-t2[N2-2]) if N2 > 1 \ - else t_end-t2[N2-1] + isi2 = ( + max(t_end - t2[N2 - 1], t2[N2 - 1] - t2[N2 - 2]) + if N2 > 1 + else t_end - t2[N2 - 1] + ) # s2 needs adjustment due to change of isi2 # s2 = dt_p2*(t_end-t2[N2-1])/isi2 # Eero's adjustment: no correction @@ -284,81 +306,88 @@ def spike_distance_python(spikes1, spikes2, t_start, t_end, MRTS=0., RI=False): dt_p1 = 0.0 dt_p2 = 0.0 spike_events[index] = t_f1 - y_ends[index-1] = 0.0 + y_ends[index - 1] = 0.0 y_starts[index] = 0.0 - if index1 < N1-1: - t_f1 = t1[index1+1] + if index1 < N1 - 1: + t_f1 = t1[index1 + 1] dt_f1 = get_min_dist(t_f1, t2, index2, t_aux2[0], t_aux2[1]) isi1 = t_f1 - t_p1 else: t_f1 = t_aux1[1] dt_f1 = dt_p1 - isi1 = max(t_end-t1[N1-1], t1[N1-1]-t1[N1-2]) if N1 > 1 \ - else t_end-t1[N1-1] - if index2 < N2-1: - t_f2 = t2[index2+1] + isi1 = ( + max(t_end - t1[N1 - 1], t1[N1 - 1] - t1[N1 - 2]) + if N1 > 1 + else t_end - t1[N1 - 1] + ) + if index2 < N2 - 1: + t_f2 = t2[index2 + 1] dt_f2 = get_min_dist(t_f2, t1, index1, t_aux1[0], t_aux1[1]) isi2 = t_f2 - t_p2 else: t_f2 = t_aux2[1] dt_f2 = dt_p2 - isi2 = max(t_end-t2[N2-1], t2[N2-1]-t2[N2-2]) if N2 > 1 \ - else t_end-t2[N2-1] + isi2 = ( + max(t_end - t2[N2 - 1], t2[N2 - 1] - t2[N2 - 2]) + if N2 > 1 + else t_end - t2[N2 - 1] + ) index += 1 # the last event is the interval end - if spike_events[index-1] == t_end: + if spike_events[index - 1] == t_end: index -= 1 else: spike_events[index] = t_end s1 = dt_f1 # *(t_end-t1[N1-1])/isi1 s2 = dt_f2 # *(t_end-t2[N2-1])/isi2 - y_ends[index-1] = dist_at_t(isi1, isi2, s1, s2, MRTS, RI) + y_ends[index - 1] = dist_at_t(isi1, isi2, s1, s2, MRTS, RI) # use only the data added above # could be less than original length due to equal spike times - return spike_events[:index+1], y_starts[:index], y_ends[:index] - + return spike_events[: index + 1], y_starts[:index], y_ends[:index] def get_tau(spikes1, spikes2, i, j, max_tau, MRTS): - """ Compute coincidence window - In: spikes1, spikes2 - times of two spike trains - i, j - indices into spikes1, spikes2 to compare - max_tau - maximum size of MRTS - MRTS - adaptation parameter - out: combined coincidence window (Eq 19 in reference) + """Compute coincidence window + In: spikes1, spikes2 - times of two spike trains + i, j - indices into spikes1, spikes2 to compare + max_tau - maximum size of MRTS + MRTS - adaptation parameter + out: combined coincidence window (Eq 19 in reference) """ ## "distances" to neighbor: F/P=future/past, 1/2=N in spikesN. mF1 = max_tau mP1 = max_tau mF2 = max_tau mP2 = max_tau - - if i < len(spikes1)-1 and i > -1: - mF1 = spikes1[i+1]-spikes1[i] - if j < len(spikes2)-1 and j > -1: - mF2 = spikes2[j+1]-spikes2[j] + + if i < len(spikes1) - 1 and i > -1: + mF1 = spikes1[i + 1] - spikes1[i] + if j < len(spikes2) - 1 and j > -1: + mF2 = spikes2[j + 1] - spikes2[j] if i > 0: - mP1 = spikes1[i]-spikes1[i-1] + mP1 = spikes1[i] - spikes1[i - 1] if j > 0: - mP2 = spikes2[j]-spikes2[j-1] + mP2 = spikes2[j] - spikes2[j - 1] - mF1, mF2, mP1, mP2 = mF1/2., mF2/2., mP1/2., mP2/2. + mF1, mF2, mP1, mP2 = mF1 / 2.0, mF2 / 2.0, mP1 / 2.0, mP2 / 2.0 MRTS /= 4 def Interpolate(a, b, t): - """ thresholded interpolation - If t small, return min(a,b) - if t big, return b - in between, return t + """thresholded interpolation + If t small, return min(a,b) + if t big, return b + in between, return t """ - mab = min(a,b) - if t b: return b - return t # interpolation - - if i<0 or j<0 or spikes1[i] <= spikes2[j]: + mab = min(a, b) + if t < mab: + return mab + if t > b: + return b + return t # interpolation + + if i < 0 or j < 0 or spikes1[i] <= spikes2[j]: s1F = Interpolate(mP1, mF1, MRTS) s2P = Interpolate(mF2, mP2, MRTS) return min(s1F, s2P) @@ -368,18 +397,16 @@ def Interpolate(a, b, t): return min(s1P, s2F) - - ############################################################ # coincidence_python ############################################################ -def coincidence_python(spikes1, spikes2, t_start, t_end, max_tau, MRTS=0.): - """ python version of logic for bivariate SPIKE-Sync profile - UNUSED - replaced by coincidence_single_python() +def coincidence_python(spikes1, spikes2, t_start, t_end, max_tau, MRTS=0.0): + """python version of logic for bivariate SPIKE-Sync profile + UNUSED - replaced by coincidence_single_python() """ true_max = t_end - t_start if max_tau > 0: - true_max = min(true_max, 2*max_tau) + true_max = min(true_max, 2 * max_tau) N1 = len(spikes1) N2 = len(spikes2) @@ -387,31 +414,31 @@ def coincidence_python(spikes1, spikes2, t_start, t_end, max_tau, MRTS=0.): j = -1 n = 0 st = np.zeros(N1 + N2 + 2) # spike times - c = np.zeros(N1 + N2 + 2) # coincidences - mp = np.ones(N1 + N2 + 2) # multiplicity - + c = np.zeros(N1 + N2 + 2) # coincidences + mp = np.ones(N1 + N2 + 2) # multiplicity + while i + j < N1 + N2 - 2: - if (i < N1-1) and (j == N2-1 or spikes1[i+1] < spikes2[j+1]): + if (i < N1 - 1) and (j == N2 - 1 or spikes1[i + 1] < spikes2[j + 1]): i += 1 n += 1 tau = get_tau(spikes1, spikes2, i, j, true_max, MRTS) st[n] = spikes1[i] - if j > -1 and spikes1[i]-spikes2[j] < tau: + if j > -1 and spikes1[i] - spikes2[j] < tau: # coincidence between the current spike and the previous spike # both get marked with 1 c[n] = 1 - c[n-1] = 1 # BUG?: n-1 is unrelated to this i,j pair. - elif (j < N2-1) and (i == N1-1 or spikes1[i+1] > spikes2[j+1]): + c[n - 1] = 1 # BUG?: n-1 is unrelated to this i,j pair. + elif (j < N2 - 1) and (i == N1 - 1 or spikes1[i + 1] > spikes2[j + 1]): j += 1 n += 1 tau = get_tau(spikes1, spikes2, i, j, true_max, MRTS) st[n] = spikes2[j] - if i > -1 and spikes2[j]-spikes1[i] < tau: + if i > -1 and spikes2[j] - spikes1[i] < tau: # coincidence between the current spike and the previous spike # both get marked with 1 c[n] = 1 - c[n-1] = 1 # same BUG - else: # spikes1[i+1] = spikes2[j+1] + c[n - 1] = 1 # same BUG + else: # spikes1[i+1] = spikes2[j+1] # advance in both spike trains j += 1 i += 1 @@ -421,17 +448,17 @@ def coincidence_python(spikes1, spikes2, t_start, t_end, max_tau, MRTS=0.): c[n] = 2 mp[n] = 2 - st = st[:n+2] - c = c[:n+2] - mp = mp[:n+2] + st = st[: n + 2] + c = c[: n + 2] + mp = mp[: n + 2] st[0] = t_start - st[len(st)-1] = t_end + st[len(st) - 1] = t_end if N1 + N2 > 0: c[0] = c[1] - c[len(c)-1] = c[len(c)-2] + c[len(c) - 1] = c[len(c) - 2] mp[0] = mp[1] - mp[len(mp)-1] = mp[len(mp)-2] + mp[len(mp) - 1] = mp[len(mp) - 2] else: c[0] = 1 c[1] = 1 @@ -442,40 +469,40 @@ def coincidence_python(spikes1, spikes2, t_start, t_end, max_tau, MRTS=0.): ############################################################ # coincidence_single_profile_python ############################################################ -def coincidence_single_python(spikes1, spikes2, t_start, t_end, max_tau, MRTS=0.): - """ python version of logic for bivariate SPIKE-Sync profile - In: spikes1, spikes2 - lists of sorted spike times - t_start, t_end - range of times to consider - max_tau - max window coincidence length - MRTS - Minimum Relvant Time Scale (or 0 if none) - Out: st - spike times - c - coincidences - mp - multiplicity +def coincidence_single_python(spikes1, spikes2, t_start, t_end, max_tau, MRTS=0.0): + """python version of logic for bivariate SPIKE-Sync profile + In: spikes1, spikes2 - lists of sorted spike times + t_start, t_end - range of times to consider + max_tau - max window coincidence length + MRTS - Minimum Relvant Time Scale (or 0 if none) + Out: st - spike times + c - coincidences + mp - multiplicity """ true_max = t_end - t_start if max_tau > 0: - true_max = min(true_max, 2*max_tau) + true_max = min(true_max, 2 * max_tau) N1 = len(spikes1) N2 = len(spikes2) j = -1 - c = np.zeros(N1) # coincidences + c = np.zeros(N1) # coincidences for i in range(N1): - while j < N2-1 and spikes2[j+1] < spikes1[i]: + while j < N2 - 1 and spikes2[j + 1] < spikes1[i]: # move forward until spikes2[j] is the last spike before spikes1[i] # note that if spikes2[j] is after spikes1[i] we dont do anything j += 1 tau = get_tau(spikes1, spikes2, i, j, true_max, MRTS) - if j > -1 and abs(spikes1[i]-spikes2[j]) < tau: + if j > -1 and abs(spikes1[i] - spikes2[j]) < tau: # current spike in st1 is coincident c[i] = 1 - if j < N2-1 and (j < 0 or spikes2[j] < spikes1[i]): + if j < N2 - 1 and (j < 0 or spikes2[j] < spikes1[i]): # in case spikes2[j] is before spikes1[i] it has to be the first or # the one right before (see above), hence we move one forward and # also check the next spike j += 1 tau = get_tau(spikes1, spikes2, i, j, true_max, MRTS) - if abs(spikes2[j]-spikes1[i]) < tau: + if abs(spikes2[j] - spikes1[i]) < tau: # current spike in st1 is coincident c[i] = 1 return c @@ -485,25 +512,25 @@ def coincidence_single_python(spikes1, spikes2, t_start, t_end, max_tau, MRTS=0. # add_piece_wise_const_python ############################################################ def add_piece_wise_const_python(x1, y1, x2, y2): - """ Add piecewise constant functions - In: x1,y1 - first function [y(x) = y1(i) for x(i)<=x x2[index2+1]: + elif x1[index1 + 1] > x2[index2 + 1]: index2 += 1 x_new[index] = x2[index2] else: # x1[index1+1] == x2[index2+1]: @@ -512,60 +539,61 @@ def add_piece_wise_const_python(x1, y1, x2, y2): x_new[index] = x1[index1] y_new[index] = y1[index1] + y2[index2] # one array reached the end -> copy the contents of the other to the end - if index1+1 < len(y1): - x_new[index+1:index+1+len(x1)-index1-1] = x1[index1+1:] - y_new[index+1:index+1+len(y1)-index1-1] = y1[index1+1:] + y2[-1] - index += len(x1)-index1-2 - elif index2+1 < len(y2): - x_new[index+1:index+1+len(x2)-index2-1] = x2[index2+1:] - y_new[index+1:index+1+len(y2)-index2-1] = y2[index2+1:] + y1[-1] - index += len(x2)-index2-2 + if index1 + 1 < len(y1): + x_new[index + 1 : index + 1 + len(x1) - index1 - 1] = x1[index1 + 1 :] + y_new[index + 1 : index + 1 + len(y1) - index1 - 1] = y1[index1 + 1 :] + y2[-1] + index += len(x1) - index1 - 2 + elif index2 + 1 < len(y2): + x_new[index + 1 : index + 1 + len(x2) - index2 - 1] = x2[index2 + 1 :] + y_new[index + 1 : index + 1 + len(y2) - index2 - 1] = y2[index2 + 1 :] + y1[-1] + index += len(x2) - index2 - 2 else: # both arrays reached the end simultaneously # only the last x-value missing - x_new[index+1] = x1[-1] + x_new[index + 1] = x1[-1] # the last value is again the end of the interval # x_new[index+1] = x1[-1] # only use the data that was actually filled - return x_new[:index+2], y_new[:index+1] + return x_new[: index + 2], y_new[: index + 1] ############################################################ # add_piece_lin_const_python ############################################################ def add_piece_wise_lin_python(x1, y11, y12, x2, y21, y22): - """ Add piecewise constant functions - In: x1,y11,y12 - first function - x2,y21,y22 - second function - Out: returns x,y1,y2 - the summed function + """Add piecewise constant functions + In: x1,y11,y12 - first function + x2,y21,y22 - second function + Out: returns x,y1,y2 - the summed function """ x_new = np.empty(len(x1) + len(x2)) - y1_new = np.empty(len(x_new)-1) + y1_new = np.empty(len(x_new) - 1) y2_new = np.empty_like(y1_new) x_new[0] = x1[0] y1_new[0] = y11[0] + y21[0] index1 = 0 # index for self index2 = 0 # index for f - index = 0 # index for new - while (index1+1 < len(y11)) and (index2+1 < len(y21)): + index = 0 # index for new + while (index1 + 1 < len(y11)) and (index2 + 1 < len(y21)): # print(index1+1, x1[index1+1], self.y[index1+1], x_new[index]) - if x1[index1+1] < x2[index2+1]: + if x1[index1 + 1] < x2[index2 + 1]: # first compute the end value of the previous interval # linear interpolation of the interval - y = y21[index2] + (y22[index2]-y21[index2]) * \ - (x1[index1+1]-x2[index2]) / (x2[index2+1]-x2[index2]) + y = y21[index2] + (y22[index2] - y21[index2]) * ( + x1[index1 + 1] - x2[index2] + ) / (x2[index2 + 1] - x2[index2]) y2_new[index] = y12[index1] + y index1 += 1 index += 1 x_new[index] = x1[index1] # and the starting value for the next interval y1_new[index] = y11[index1] + y - elif x1[index1+1] > x2[index2+1]: + elif x1[index1 + 1] > x2[index2 + 1]: # first compute the end value of the previous interval # linear interpolation of the interval - y = y11[index1] + (y12[index1]-y11[index1]) * \ - (x2[index2+1]-x1[index1]) / \ - (x1[index1+1]-x1[index1]) + y = y11[index1] + (y12[index1] - y11[index1]) * ( + x2[index2 + 1] - x1[index1] + ) / (x1[index1 + 1] - x1[index1]) y2_new[index] = y22[index2] + y index2 += 1 index += 1 @@ -580,42 +608,43 @@ def add_piece_wise_lin_python(x1, y11, y12, x2, y21, y22): x_new[index] = x1[index1] y1_new[index] = y11[index1] + y21[index2] # one array reached the end -> copy the contents of the other to the end - if index1+1 < len(y11): + if index1 + 1 < len(y11): # compute the linear interpolations values - y = y21[index2] + (y22[index2]-y21[index2]) * \ - (x1[index1+1:-1]-x2[index2]) / (x2[index2+1]-x2[index2]) - x_new[index+1:index+1+len(x1)-index1-1] = x1[index1+1:] - y1_new[index+1:index+1+len(y11)-index1-1] = y11[index1+1:]+y - y2_new[index:index+len(y12)-index1-1] = y12[index1:-1] + y - index += len(x1)-index1-2 - elif index2+1 < len(y21): + y = y21[index2] + (y22[index2] - y21[index2]) * ( + x1[index1 + 1 : -1] - x2[index2] + ) / (x2[index2 + 1] - x2[index2]) + x_new[index + 1 : index + 1 + len(x1) - index1 - 1] = x1[index1 + 1 :] + y1_new[index + 1 : index + 1 + len(y11) - index1 - 1] = y11[index1 + 1 :] + y + y2_new[index : index + len(y12) - index1 - 1] = y12[index1:-1] + y + index += len(x1) - index1 - 2 + elif index2 + 1 < len(y21): # compute the linear interpolations values - y = y11[index1] + (y12[index1]-y11[index1]) * \ - (x2[index2+1:-1]-x1[index1]) / \ - (x1[index1+1]-x1[index1]) - x_new[index+1:index+1+len(x2)-index2-1] = x2[index2+1:] - y1_new[index+1:index+1+len(y21)-index2-1] = y21[index2+1:] + y - y2_new[index:index+len(y22)-index2-1] = y22[index2:-1] + y - index += len(x2)-index2-2 + y = y11[index1] + (y12[index1] - y11[index1]) * ( + x2[index2 + 1 : -1] - x1[index1] + ) / (x1[index1 + 1] - x1[index1]) + x_new[index + 1 : index + 1 + len(x2) - index2 - 1] = x2[index2 + 1 :] + y1_new[index + 1 : index + 1 + len(y21) - index2 - 1] = y21[index2 + 1 :] + y + y2_new[index : index + len(y22) - index2 - 1] = y22[index2:-1] + y + index += len(x2) - index2 - 2 else: # both arrays reached the end simultaneously # only the last x-value missing - x_new[index+1] = x1[-1] + x_new[index + 1] = x1[-1] # finally, the end value for the last interval - y2_new[index] = y12[-1]+y22[-1] + y2_new[index] = y12[-1] + y22[-1] # only use the data that was actually filled - return x_new[:index+2], y1_new[:index+1], y2_new[:index+1] + return x_new[: index + 2], y1_new[: index + 1], y2_new[: index + 1] ############################################################ # add_discrete_function_python ############################################################ def add_discrete_function_python(x1, y1, mp1, x2, y2, mp2): - """ Add two functions defined on a finite point set - In: x1,y1,mp1 - discrete function, with multiplicities - x2,y2,mp2 - second function - Out: x, y, mp - the sum - Note: Depends on floating point ==, so might not - return expected answer + """Add two functions defined on a finite point set + In: x1,y1,mp1 - discrete function, with multiplicities + x2,y2,mp2 - second function + Out: x, y, mp - the sum + Note: Depends on floating point ==, so might not + return expected answer """ x_new = np.empty(len(x1) + len(x2)) y_new = np.empty_like(x_new) @@ -624,16 +653,16 @@ def add_discrete_function_python(x1, y1, mp1, x2, y2, mp2): index1 = 0 index2 = 0 index = 0 - N1 = len(x1)-1 - N2 = len(x2)-1 - while (index1+1 < N1) and (index2+1 < N2): - if x1[index1+1] < x2[index2+1]: + N1 = len(x1) - 1 + N2 = len(x2) - 1 + while (index1 + 1 < N1) and (index2 + 1 < N2): + if x1[index1 + 1] < x2[index2 + 1]: index1 += 1 index += 1 x_new[index] = x1[index1] y_new[index] = y1[index1] mp_new[index] = mp1[index1] - elif x1[index1+1] > x2[index2+1]: + elif x1[index1 + 1] > x2[index2 + 1]: index2 += 1 index += 1 x_new[index] = x2[index2] @@ -647,20 +676,20 @@ def add_discrete_function_python(x1, y1, mp1, x2, y2, mp2): y_new[index] = y1[index1] + y2[index2] mp_new[index] = mp1[index1] + mp2[index2] # one array reached the end -> copy the contents of the other to the end - if index1+1 < N1: - x_new[index+1:index+1+N1-index1] = x1[index1+1:] - y_new[index+1:index+1+N1-index1] = y1[index1+1:] - mp_new[index+1:index+1+N1-index1] = mp1[index1+1:] - index += N1-index1 - elif index2+1 < N2: - x_new[index+1:index+1+N2-index2] = x2[index2+1:] - y_new[index+1:index+1+N2-index2] = y2[index2+1:] - mp_new[index+1:index+1+N2-index2] = mp2[index2+1:] - index += N2-index2 + if index1 + 1 < N1: + x_new[index + 1 : index + 1 + N1 - index1] = x1[index1 + 1 :] + y_new[index + 1 : index + 1 + N1 - index1] = y1[index1 + 1 :] + mp_new[index + 1 : index + 1 + N1 - index1] = mp1[index1 + 1 :] + index += N1 - index1 + elif index2 + 1 < N2: + x_new[index + 1 : index + 1 + N2 - index2] = x2[index2 + 1 :] + y_new[index + 1 : index + 1 + N2 - index2] = y2[index2 + 1 :] + mp_new[index + 1 : index + 1 + N2 - index2] = mp2[index2 + 1 :] + index += N2 - index2 else: # both arrays reached the end simultaneously - x_new[index+1] = x1[-1] - y_new[index+1] = y1[-1] + y2[-1] - mp_new[index+1] = mp1[-1] + mp2[-1] + x_new[index + 1] = x1[-1] + y_new[index + 1] = y1[-1] + y2[-1] + mp_new[index + 1] = mp1[-1] + mp2[-1] index += 1 y_new[0] = y_new[1] @@ -668,4 +697,4 @@ def add_discrete_function_python(x1, y1, mp1, x2, y2, mp2): # the last value is again the end of the interval # only use the data that was actually filled - return x_new[:index+1], y_new[:index+1], mp_new[:index+1] + return x_new[: index + 1], y_new[: index + 1], mp_new[: index + 1] diff --git a/pyspike/generic.py b/pyspike/generic.py index 7b45026..ecfac00 100644 --- a/pyspike/generic.py +++ b/pyspike/generic.py @@ -7,23 +7,24 @@ Distributed under the BSD License """ -from __future__ import division -from pyspike.isi_lengths import default_thresh -from pyspike.spikes import reconcile_spike_trains, reconcile_spike_trains_bi import numpy as np +from pyspike.isi_lengths import default_thresh +from pyspike.spikes import reconcile_spike_trains + + def resolve_keywords(**kwargs): - """ resolve keywords - In: kwargs - dictionary of keywords - out: MRTS - Minimum Relevant Time Scale, default 0. - RI - Rate Independent Adaptive distance, default False + """resolve keywords + In: kwargs - dictionary of keywords + out: MRTS - Minimum Relevant Time Scale, default 0. + RI - Rate Independent Adaptive distance, default False """ - if 'MRTS' in kwargs: - MRTS = kwargs['MRTS'] + if "MRTS" in kwargs: + MRTS = kwargs["MRTS"] else: - MRTS = 0. # default - if 'RI' in kwargs: - RI = kwargs['RI'] + MRTS = 0.0 # default + if "RI" in kwargs: + RI = kwargs["RI"] else: RI = False # default return MRTS, RI @@ -33,7 +34,7 @@ def resolve_keywords(**kwargs): # _generic_profile_multi ############################################################ def _generic_profile_multi(spike_trains, pair_distance_func, indices=None, **kwargs): - """ Internal implementation detail, don't call this function directly, + """Internal implementation detail, don't call this function directly, use isi_profile_multi or spike_profile_multi instead. Computes the multi-variate distance for a set of spike-trains using the @@ -49,33 +50,30 @@ def _generic_profile_multi(spike_trains, pair_distance_func, indices=None, **kwa Returns: - The averaged multi-variate distance of all pairs """ - if kwargs.get('Reconcile', True): + if kwargs.get("Reconcile", True): spike_trains = reconcile_spike_trains(spike_trains) - kwargs['Reconcile'] = False + kwargs["Reconcile"] = False - MRTS, RI = resolve_keywords(**kwargs) + MRTS, _RI = resolve_keywords(**kwargs) if isinstance(MRTS, str): - kwargs['MRTS'] = default_thresh(spike_trains) + kwargs["MRTS"] = default_thresh(spike_trains) def divide_and_conquer(pairs1, pairs2): - """ recursive calls by splitting the two lists in half. - """ + """recursive calls by splitting the two lists in half.""" L1 = len(pairs1) if L1 > 1: - dist_prof1 = divide_and_conquer(pairs1[:L1//2], - pairs1[L1//2:]) + dist_prof1 = divide_and_conquer(pairs1[: L1 // 2], pairs1[L1 // 2 :]) else: - dist_prof1 = pair_distance_func(spike_trains[pairs1[0][0]], - spike_trains[pairs1[0][1]], - **kwargs) + dist_prof1 = pair_distance_func( + spike_trains[pairs1[0][0]], spike_trains[pairs1[0][1]], **kwargs + ) L2 = len(pairs2) if L2 > 1: - dist_prof2 = divide_and_conquer(pairs2[:L2//2], - pairs2[L2//2:]) + dist_prof2 = divide_and_conquer(pairs2[: L2 // 2], pairs2[L2 // 2 :]) else: - dist_prof2 = pair_distance_func(spike_trains[pairs2[0][0]], - spike_trains[pairs2[0][1]], - **kwargs) + dist_prof2 = pair_distance_func( + spike_trains[pairs2[0][0]], spike_trains[pairs2[0][1]], **kwargs + ) dist_prof1.add(dist_prof2) return dist_prof1 @@ -83,21 +81,22 @@ def divide_and_conquer(pairs1, pairs2): indices = np.arange(len(spike_trains)) indices = np.array(indices) # check validity of indices - assert (indices < len(spike_trains)).all() and (indices >= 0).all(), \ + assert (indices < len(spike_trains)).all() and (indices >= 0).all(), ( "Invalid index list." + ) # generate a list of possible index pairs - pairs = [(indices[i], j) for i in range(len(indices)) - for j in indices[i+1:]] + pairs = [(indices[i], j) for i in range(len(indices)) for j in indices[i + 1 :]] L = len(pairs) if L > 1: # recursive iteration through the list of pairs to get average profile - avrg_dist = divide_and_conquer(pairs[:len(pairs)//2], - pairs[len(pairs)//2:]) + avrg_dist = divide_and_conquer( + pairs[: len(pairs) // 2], pairs[len(pairs) // 2 :] + ) else: - avrg_dist = pair_distance_func(spike_trains[pairs[0][0]], - spike_trains[pairs[0][1]], - **kwargs) + avrg_dist = pair_distance_func( + spike_trains[pairs[0][0]], spike_trains[pairs[0][1]], **kwargs + ) return avrg_dist, L @@ -105,9 +104,10 @@ def divide_and_conquer(pairs1, pairs2): ############################################################ # _generic_distance_multi ############################################################ -def _generic_distance_multi(spike_trains, pair_distance_func, - indices=None, interval=None, **kwargs): - """ Internal implementation detail, don't call this function directly, +def _generic_distance_multi( + spike_trains, pair_distance_func, indices=None, interval=None, **kwargs +): + """Internal implementation detail, don't call this function directly, use isi_distance_multi or spike_distance_multi instead. Computes the multi-variate distance for a set of spike-trains using the @@ -123,39 +123,41 @@ def _generic_distance_multi(spike_trains, pair_distance_func, Returns: - The averaged multi-variate distance of all pairs """ - if kwargs.get('Reconcile', True): + if kwargs.get("Reconcile", True): spike_trains = reconcile_spike_trains(spike_trains) - kwargs['Reconcile'] = False + kwargs["Reconcile"] = False - MRTS, RI = resolve_keywords(**kwargs) + MRTS, _RI = resolve_keywords(**kwargs) if isinstance(MRTS, str): - kwargs['MRTS'] = default_thresh(spike_trains) - + kwargs["MRTS"] = default_thresh(spike_trains) + if indices is None: indices = np.arange(len(spike_trains)) indices = np.array(indices) # check validity of indices - assert (indices < len(spike_trains)).all() and (indices >= 0).all(), \ + assert (indices < len(spike_trains)).all() and (indices >= 0).all(), ( "Invalid index list." + ) # generate a list of possible index pairs - pairs = [(indices[i], j) for i in range(len(indices)) - for j in indices[i+1:]] + pairs = [(indices[i], j) for i in range(len(indices)) for j in indices[i + 1 :]] avrg_dist = 0.0 - for (i, j) in pairs: - one_dist = pair_distance_func(spike_trains[i], spike_trains[j], - interval, **kwargs) + for i, j in pairs: + one_dist = pair_distance_func( + spike_trains[i], spike_trains[j], interval, **kwargs + ) avrg_dist += one_dist - return avrg_dist/len(pairs) + return avrg_dist / len(pairs) ############################################################ # generic_distance_matrix ############################################################ -def _generic_distance_matrix(spike_trains, dist_function, - indices=None, interval=None, **kwargs): - """ Internal implementation detail. Don't use this function directly. +def _generic_distance_matrix( + spike_trains, dist_function, indices=None, interval=None, **kwargs +): + """Internal implementation detail. Don't use this function directly. Instead use isi_distance_matrix or spike_distance_matrix. Computes the time averaged distance of all pairs of spike-trains. Args: @@ -166,28 +168,29 @@ def _generic_distance_matrix(spike_trains, dist_function, - a 2D array of size len(indices)*len(indices) containing the average pair-wise distance """ - if kwargs.get('Reconcile', True): + if kwargs.get("Reconcile", True): spike_trains = reconcile_spike_trains(spike_trains) - kwargs['Reconcile'] = False - - MRTS, RI = resolve_keywords(**kwargs) + kwargs["Reconcile"] = False + + MRTS, _RI = resolve_keywords(**kwargs) if isinstance(MRTS, str): - kwargs['MRTS'] = default_thresh(spike_trains) + kwargs["MRTS"] = default_thresh(spike_trains) if indices is None: indices = np.arange(len(spike_trains)) indices = np.array(indices) # check validity of indices - assert (indices < len(spike_trains)).all() and (indices >= 0).all(), \ + assert (indices < len(spike_trains)).all() and (indices >= 0).all(), ( "Invalid index list." + ) # generate a list of possible index pairs - pairs = [(i, j) for i in range(len(indices)) - for j in range(i+1, len(indices))] + pairs = [(i, j) for i in range(len(indices)) for j in range(i + 1, len(indices))] distance_matrix = np.zeros((len(indices), len(indices))) for i, j in pairs: - d = dist_function(spike_trains[indices[i]], spike_trains[indices[j]], - interval, **kwargs) + d = dist_function( + spike_trains[indices[i]], spike_trains[indices[j]], interval, **kwargs + ) distance_matrix[i, j] = d distance_matrix[j, i] = d return distance_matrix diff --git a/pyspike/isi_distance.py b/pyspike/isi_distance.py index a04adc2..cda9c24 100644 --- a/pyspike/isi_distance.py +++ b/pyspike/isi_distance.py @@ -1,23 +1,26 @@ -""" isi_distance.py - Module containing several functions to compute the ISI profiles and distances - Copyright 2014-2015, Mario Mulansky - Distributed under the BSD License +"""isi_distance.py +Module containing several functions to compute the ISI profiles and distances +Copyright 2014-2015, Mario Mulansky +Distributed under the BSD License """ -from __future__ import absolute_import - import pyspike -from pyspike import PieceWiseConstFunc -from pyspike.generic import _generic_profile_multi, _generic_distance_multi, \ - _generic_distance_matrix, resolve_keywords +from pyspike.generic import ( + _generic_distance_matrix, + _generic_distance_multi, + _generic_profile_multi, + resolve_keywords, +) from pyspike.isi_lengths import default_thresh -from pyspike.spikes import reconcile_spike_trains, reconcile_spike_trains_bi +from pyspike.PieceWiseConstFunc import PieceWiseConstFunc +from pyspike.spikes import reconcile_spike_trains_bi + ############################################################ # isi_profile ############################################################ def isi_profile(*args, **kwargs): - """ Computes the isi-distance profile :math:`I(t)` of the given + """Computes the isi-distance profile :math:`I(t)` of the given spike trains. Returns the profile as a PieceWiseConstFunc object. The ISI-values are defined positive :math:`I(t)>=0`. @@ -54,7 +57,7 @@ def isi_profile(*args, **kwargs): # isi_profile_bi ############################################################ def isi_profile_bi(spike_train1, spike_train2, **kwargs): - """ Specific function to compute a bivariate ISI-profile. This is a + """Specific function to compute a bivariate ISI-profile. This is a deprecated function and should not be called directly. Use :func:`.isi_profile` to compute ISI-profiles. @@ -66,30 +69,33 @@ def isi_profile_bi(spike_train1, spike_train2, **kwargs): :rtype: :class:`.PieceWiseConstFunc` """ - if kwargs.get('Reconcile', True): - spike_train1, spike_train2 = reconcile_spike_trains_bi(spike_train1, spike_train2) - kwargs['Reconcile'] = False + if kwargs.get("Reconcile", True): + spike_train1, spike_train2 = reconcile_spike_trains_bi( + spike_train1, spike_train2 + ) + kwargs["Reconcile"] = False - MRTS,RI = resolve_keywords(**kwargs) + MRTS, _RI = resolve_keywords(**kwargs) if isinstance(MRTS, str): MRTS = default_thresh([spike_train1, spike_train2]) - kwargs['MRTS'] = MRTS + kwargs["MRTS"] = MRTS # load cython implementation try: - from .cython.cython_profiles import isi_profile_cython \ - as isi_profile_impl + from .cython.cython_profiles import isi_profile_cython as isi_profile_impl except ImportError: pyspike.NoCythonWarn() # use python backend - from .cython.python_backend import isi_distance_python \ - as isi_profile_impl - - times, values = isi_profile_impl(spike_train1.get_spikes_non_empty(), - spike_train2.get_spikes_non_empty(), - spike_train1.t_start, spike_train1.t_end, - MRTS) + from .cython.python_backend import isi_distance_python as isi_profile_impl + + times, values = isi_profile_impl( + spike_train1.get_spikes_non_empty(), + spike_train2.get_spikes_non_empty(), + spike_train1.t_start, + spike_train1.t_end, + MRTS, + ) return PieceWiseConstFunc(times, values) @@ -97,7 +103,7 @@ def isi_profile_bi(spike_train1, spike_train2, **kwargs): # isi_profile_multi ############################################################ def isi_profile_multi(spike_trains, indices=None, **kwargs): - """ Specific function to compute the multivariate ISI-profile for a set of + """Specific function to compute the multivariate ISI-profile for a set of spike trains. This is a deprecated function and should not be called directly. Use :func:`.isi_profile` to compute ISI-profiles. @@ -109,9 +115,10 @@ def isi_profile_multi(spike_trains, indices=None, **kwargs): :returns: The averaged isi profile :math:`` :rtype: :class:`.PieceWiseConstFunc` """ - average_dist, M = _generic_profile_multi(spike_trains, isi_profile_bi, - indices, **kwargs) - average_dist.mul_scalar(1.0/M) # normalize + average_dist, M = _generic_profile_multi( + spike_trains, isi_profile_bi, indices, **kwargs + ) + average_dist.mul_scalar(1.0 / M) # normalize return average_dist @@ -119,7 +126,7 @@ def isi_profile_multi(spike_trains, indices=None, **kwargs): # isi_distance ############################################################ def isi_distance(*args, **kwargs): - """ Computes the ISI-distance :math:`D_I` of the given spike trains. The + """Computes the ISI-distance :math:`D_I` of the given spike trains. The isi-distance is the integral over the isi distance profile :math:`I(t)`: @@ -160,7 +167,7 @@ def isi_distance(*args, **kwargs): # _isi_distance_bi ############################################################ def isi_distance_bi(spike_train1, spike_train2, interval=None, **kwargs): - """ Specific function to compute the bivariate ISI-distance. + """Specific function to compute the bivariate ISI-distance. This is a deprecated function and should not be called directly. Use :func:`.isi_distance` to compute ISI-distances. @@ -174,26 +181,32 @@ def isi_distance_bi(spike_train1, spike_train2, interval=None, **kwargs): :returns: The isi-distance :math:`D_I`. :rtype: double """ - if kwargs.get('Reconcile', True): - spike_train1, spike_train2 = reconcile_spike_trains_bi(spike_train1, spike_train2) - kwargs['Reconcile'] = False + if kwargs.get("Reconcile", True): + spike_train1, spike_train2 = reconcile_spike_trains_bi( + spike_train1, spike_train2 + ) + kwargs["Reconcile"] = False - MRTS, RI = resolve_keywords(**kwargs) + MRTS, _RI = resolve_keywords(**kwargs) if isinstance(MRTS, str): MRTS = default_thresh([spike_train1, spike_train2]) - kwargs['MRTS'] = MRTS + kwargs["MRTS"] = MRTS if interval is None: # distance over the whole interval is requested: use specific function # for optimal performance try: - from .cython.cython_distances import isi_distance_cython \ - as isi_distance_impl - - return isi_distance_impl(spike_train1.get_spikes_non_empty(), - spike_train2.get_spikes_non_empty(), - spike_train1.t_start, spike_train1.t_end, - MRTS) + from .cython.cython_distances import ( + isi_distance_cython as isi_distance_impl, + ) + + return isi_distance_impl( + spike_train1.get_spikes_non_empty(), + spike_train2.get_spikes_non_empty(), + spike_train1.t_start, + spike_train1.t_end, + MRTS, + ) except ImportError: # Cython backend not available: fall back to profile averaging return isi_profile_bi(spike_train1, spike_train2, **kwargs).avrg(interval) @@ -206,7 +219,7 @@ def isi_distance_bi(spike_train1, spike_train2, interval=None, **kwargs): # isi_distance_multi ############################################################ def isi_distance_multi(spike_trains, indices=None, interval=None, **kwargs): - """ Specific function to compute the multivariate ISI-distance. + """Specific function to compute the multivariate ISI-distance. This is a deprecfated function and should not be called directly. Use :func:`.isi_distance` to compute ISI-distances. @@ -219,15 +232,16 @@ def isi_distance_multi(spike_trains, indices=None, interval=None, **kwargs): :returns: The time-averaged multivariate ISI distance :math:`D_I` :rtype: double """ - return _generic_distance_multi(spike_trains, isi_distance_bi, indices, - interval, **kwargs) + return _generic_distance_multi( + spike_trains, isi_distance_bi, indices, interval, **kwargs + ) ############################################################ # isi_distance_matrix ############################################################ def isi_distance_matrix(spike_trains, indices=None, interval=None, **kwargs): - """ Computes the time averaged isi-distance of all pairs of spike-trains. + """Computes the time averaged isi-distance of all pairs of spike-trains. :param spike_trains: list of :class:`.SpikeTrain` :param indices: list of indices defining which spike trains to use, @@ -240,6 +254,6 @@ def isi_distance_matrix(spike_trains, indices=None, interval=None, **kwargs): :math:`D_{I}^{ij}` :rtype: np.array """ - return _generic_distance_matrix(spike_trains, isi_distance_bi, - indices=indices, interval=interval, - **kwargs) + return _generic_distance_matrix( + spike_trains, isi_distance_bi, indices=indices, interval=interval, **kwargs + ) diff --git a/pyspike/isi_lengths.py b/pyspike/isi_lengths.py index 78171a2..b7cb61f 100644 --- a/pyspike/isi_lengths.py +++ b/pyspike/isi_lengths.py @@ -1,4 +1,4 @@ -""" isi_lengths.py +"""isi_lengths.py Support for automatic threshold determination @@ -6,49 +6,58 @@ Distributed under the BSD License """ + import numpy as np + def isi_lengths(spike_times, t_start, t_end): - """ Plain Python implementation of logic to extract ISI lengths - In: spike_times - spike times - t_start, t_end - interval for ISI calculation - Out: isi_lengths - ISI distance between consecutive elements of spike_events + """Plain Python implementation of logic to extract ISI lengths + In: spike_times - spike times + t_start, t_end - interval for ISI calculation + Out: isi_lengths - ISI distance between consecutive elements of spike_events - Note: the only complexities are with the edges and N==1 + Note: the only complexities are with the edges and N==1 """ N = len(spike_times) if N == 0: - return [t_end-t_start] + return [t_end - t_start] if spike_times[0] > t_start: - del_start = max(spike_times[0] - t_start, spike_times[1] - spike_times[0])\ - if N > 1 else spike_times[0] - t_start + del_start = ( + max(spike_times[0] - t_start, spike_times[1] - spike_times[0]) + if N > 1 + else spike_times[0] - t_start + ) i_start = 0 else: - del_start = spike_times[1] - spike_times[0]\ - if N > 1 else t_start - spike_times[0] + del_start = ( + spike_times[1] - spike_times[0] if N > 1 else t_start - spike_times[0] + ) i_start = 1 if spike_times[-1] < t_end: - del_end = max(t_end - spike_times[-1], spike_times[-1] - spike_times[-2])\ - if N > 1 else t_end - spike_times[0] + del_end = ( + max(t_end - spike_times[-1], spike_times[-1] - spike_times[-2]) + if N > 1 + else t_end - spike_times[0] + ) i_end = N else: - del_end = spike_times[-1] - spike_times[-2]\ - if N > 1 else spike_times[0] - t_end - i_end = N-1 + del_end = spike_times[-1] - spike_times[-2] if N > 1 else spike_times[0] - t_end + i_end = N - 1 - dels = [spike_times[i+1]-spike_times[i] for i in range(i_start, i_end-1)] + dels = [spike_times[i + 1] - spike_times[i] for i in range(i_start, i_end - 1)] - isi_lengths = [del_start] + dels + [del_end] + isi_lengths = [del_start, *dels, del_end] return isi_lengths + def default_thresh_(train_list, t_start, t_end): - """ Implements default_thresh() - In: train_list - list of list of spike times - t_start, t_end - begin and end times for spikes - Out: threshold + """Implements default_thresh() + In: train_list - list of list of spike times + t_start, t_end - begin and end times for spikes + Out: threshold """ spike_pool = [] for t in train_list: @@ -56,20 +65,21 @@ def default_thresh_(train_list, t_start, t_end): spike_pool = np.array(spike_pool) sum_squares = np.sum(spike_pool * spike_pool) - ss_avg = sum_squares/len(spike_pool) + ss_avg = sum_squares / len(spike_pool) return np.sqrt(ss_avg) + def default_thresh(spike_train_list): - """ Computes a default threshold for a list of spike trains - In: spike_train_list - list of list of SpikeTrain object - Out: threshold as specified in section 2.4 of - "Measures of spike train synchrony for data with multiple time scales" + """Computes a default threshold for a list of spike trains + In: spike_train_list - list of list of SpikeTrain object + Out: threshold as specified in section 2.4 of + "Measures of spike train synchrony for data with multiple time scales" """ if len(spike_train_list) == 0: return 0 - + st = spike_train_list[0] train_list = [st.spikes.tolist() for st in spike_train_list] - return default_thresh_(train_list, st.t_start, st.t_end) \ No newline at end of file + return default_thresh_(train_list, st.t_start, st.t_end) diff --git a/pyspike/psth.py b/pyspike/psth.py index 7cf1140..c7303e8 100644 --- a/pyspike/psth.py +++ b/pyspike/psth.py @@ -3,12 +3,13 @@ # Distributed under the BSD License import numpy as np + from pyspike import PieceWiseConstFunc # Computes the peri-stimulus time histogram of a set of spike trains def psth(spike_trains, bin_size): - """ Computes the peri-stimulus time histogram of a set of + """Computes the peri-stimulus time histogram of a set of :class:`.SpikeTrain`. The PSTH is simply the histogram of merged spike events. The :code:`bin_size` defines the width of the histogram bins. @@ -17,18 +18,15 @@ def psth(spike_trains, bin_size): :return: The PSTH as a :class:`.PieceWiseConstFunc` """ - bin_count = int((spike_trains[0].t_end - spike_trains[0].t_start) / - bin_size) - bins = np.linspace(spike_trains[0].t_start, spike_trains[0].t_end, - bin_count+1) + bin_count = int((spike_trains[0].t_end - spike_trains[0].t_start) / bin_size) + bins = np.linspace(spike_trains[0].t_start, spike_trains[0].t_end, bin_count + 1) # N = len(spike_trains) combined_spike_train = spike_trains[0].spikes for i in range(1, len(spike_trains)): - combined_spike_train = np.append(combined_spike_train, - spike_trains[i].spikes) + combined_spike_train = np.append(combined_spike_train, spike_trains[i].spikes) vals, edges = np.histogram(combined_spike_train, bins, density=False) - bin_size = edges[1]-edges[0] + bin_size = edges[1] - edges[0] return PieceWiseConstFunc(edges, vals) # /(N*bin_size)) diff --git a/pyspike/spike_directionality.py b/pyspike/spike_directionality.py index d62463a..d558257 100644 --- a/pyspike/spike_directionality.py +++ b/pyspike/spike_directionality.py @@ -3,23 +3,24 @@ # Copyright 2015, Mario Mulansky # Distributed under the BSD License -from __future__ import absolute_import + +from functools import partial import numpy as np + import pyspike from pyspike import DiscreteFunc -from functools import partial from pyspike.generic import _generic_profile_multi, resolve_keywords from pyspike.isi_lengths import default_thresh from pyspike.spikes import reconcile_spike_trains, reconcile_spike_trains_bi - ############################################################ # spike_directionality_values ############################################################ + def spike_directionality_values(*args, **kwargs): - """ Computes the spike directionality value for each spike in + """Computes the spike directionality value for each spike in each spike train. Returns a list containing an array of spike directionality values for every given spike train. @@ -34,7 +35,7 @@ def spike_directionality_values(*args, **kwargs): spike_directionality_values(spike_trains, indices=[0, 1]) # use only the spike trains # given by the indices - Additonal arguments: + Additonal arguments: :param max_tau: Upper bound for coincidence window (default=None). :param indices: list of indices defining which spike trains to use, if None all given spike trains are used (default=None) @@ -47,9 +48,10 @@ def spike_directionality_values(*args, **kwargs): return _spike_directionality_values_impl(args, **kwargs) -def _spike_directionality_values_impl(spike_trains, indices=None, - interval=None, max_tau=None, **kwargs): - """ Computes the multi-variate spike directionality profile +def _spike_directionality_values_impl( + spike_trains, indices=None, interval=None, max_tau=None, **kwargs +): + """Computes the multi-variate spike directionality profile of the given spike trains. :param spike_trains: List of spike trains. @@ -61,10 +63,10 @@ def _spike_directionality_values_impl(spike_trains, indices=None, coincidence window has no upper bound. :returns: The spike-directionality values. """ - if kwargs.get('Reconcile', True): + if kwargs.get("Reconcile", True): spike_trains = reconcile_spike_trains(spike_trains) ## get the keywords: - MRTS, RI = resolve_keywords(**kwargs) + MRTS, _RI = resolve_keywords(**kwargs) if isinstance(MRTS, str): MRTS = default_thresh(spike_trains) @@ -74,45 +76,53 @@ def _spike_directionality_values_impl(spike_trains, indices=None, indices = np.arange(len(spike_trains)) indices = np.array(indices) # check validity of indices - assert (indices < len(spike_trains)).all() and (indices >= 0).all(), \ + assert (indices < len(spike_trains)).all() and (indices >= 0).all(), ( "Invalid index list." + ) # list of arrays for resulting asymmetry values asymmetry_list = [np.zeros_like(spike_trains[n].spikes) for n in indices] # generate a list of possible index pairs - pairs = [(indices[i], j) for i in range(len(indices)) - for j in indices[i+1:]] + pairs = [(indices[i], j) for i in range(len(indices)) for j in indices[i + 1 :]] # cython implementation try: - from .cython.cython_directionality import \ - spike_directionality_profiles_cython as profile_impl + from .cython.cython_directionality import ( + spike_directionality_profiles_cython as profile_impl, + ) except ImportError: pyspike.NoCythonWarn() # use python backend - from .cython.directionality_python_backend import \ - spike_directionality_profile_python as profile_impl + from .cython.directionality_python_backend import ( + spike_directionality_profile_python as profile_impl, + ) if max_tau is None: max_tau = 0.0 for i, j in pairs: - d1, d2 = profile_impl(spike_trains[i].spikes, spike_trains[j].spikes, - spike_trains[i].t_start, spike_trains[i].t_end, - max_tau, MRTS) + d1, d2 = profile_impl( + spike_trains[i].spikes, + spike_trains[j].spikes, + spike_trains[i].t_start, + spike_trains[i].t_end, + max_tau, + MRTS, + ) asymmetry_list[i] += d1 asymmetry_list[j] += d2 for a in asymmetry_list: - a /= len(spike_trains)-1 + a /= len(spike_trains) - 1 return asymmetry_list ############################################################ # spike_directionality ############################################################ -def spike_directionality(spike_train1, spike_train2, normalize=True, - interval=None, max_tau=None, **kwargs): - """ Computes the overall spike directionality of the first spike train with +def spike_directionality( + spike_train1, spike_train2, normalize=True, interval=None, max_tau=None, **kwargs +): + """Computes the overall spike directionality of the first spike train with respect to the second spike train. :param spike_train1: First spike train. @@ -124,9 +134,11 @@ def spike_directionality(spike_train1, spike_train2, normalize=True, coincidence window has no upper bound. :returns: The spike train order profile :math:`E(t)`. """ - if kwargs.get('Reconcile', True): - spike_train1, spike_train2 = reconcile_spike_trains_bi(spike_train1, spike_train2) - MRTS, RI = resolve_keywords(**kwargs) + if kwargs.get("Reconcile", True): + spike_train1, spike_train2 = reconcile_spike_trains_bi( + spike_train1, spike_train2 + ) + MRTS, _RI = resolve_keywords(**kwargs) if isinstance(MRTS, str): MRTS = default_thresh([spike_train1, spike_train2]) @@ -134,28 +146,35 @@ def spike_directionality(spike_train1, spike_train2, normalize=True, # distance over the whole interval is requested: use specific function # for optimal performance try: - from .cython.cython_directionality import \ - spike_directionality_cython as spike_directionality_impl + from .cython.cython_directionality import ( + spike_directionality_cython as spike_directionality_impl, + ) + if max_tau is None: max_tau = 0.0 - d = spike_directionality_impl(spike_train1.spikes, - spike_train2.spikes, - spike_train1.t_start, - spike_train1.t_end, - max_tau, MRTS) + d = spike_directionality_impl( + spike_train1.spikes, + spike_train2.spikes, + spike_train1.t_start, + spike_train1.t_end, + max_tau, + MRTS, + ) c = len(spike_train1.spikes) except ImportError: pyspike.NoCythonWarn() # use profile. - d1, x = spike_directionality_values([spike_train1, spike_train2], - interval=interval, - max_tau=max_tau, - MRTS=MRTS) + d1, _x = spike_directionality_values( + [spike_train1, spike_train2], + interval=interval, + max_tau=max_tau, + MRTS=MRTS, + ) d = np.sum(d1) c = len(spike_train1.spikes) if normalize: - return 1.0*d/c + return 1.0 * d / c else: return d else: @@ -166,9 +185,10 @@ def spike_directionality(spike_train1, spike_train2, normalize=True, ############################################################ # spike_directionality_matrix ############################################################ -def spike_directionality_matrix(spike_trains, normalize=True, indices=None, - interval=None, max_tau=None, **kwargs): - """ Computes the spike directionality matrix for the given spike trains. +def spike_directionality_matrix( + spike_trains, normalize=True, indices=None, interval=None, max_tau=None, **kwargs +): + """Computes the spike directionality matrix for the given spike trains. :param spike_trains: List of spike trains. :type spike_trains: List of :class:`pyspike.SpikeTrain` @@ -180,7 +200,7 @@ def spike_directionality_matrix(spike_trains, normalize=True, indices=None, coincidence window has no upper bound. :returns: The spike-directionality values. """ - if kwargs.get('Reconcile', True): + if kwargs.get("Reconcile", True): spike_trains = reconcile_spike_trains(spike_trains) MRTS, RI = resolve_keywords(**kwargs) if isinstance(MRTS, str): @@ -189,17 +209,24 @@ def spike_directionality_matrix(spike_trains, normalize=True, indices=None, indices = np.arange(len(spike_trains)) indices = np.array(indices) # check validity of indices - assert (indices < len(spike_trains)).all() and (indices >= 0).all(), \ + assert (indices < len(spike_trains)).all() and (indices >= 0).all(), ( "Invalid index list." + ) # generate a list of possible index pairs - pairs = [(indices[i], j) for i in range(len(indices)) - for j in indices[i+1:]] + pairs = [(indices[i], j) for i in range(len(indices)) for j in indices[i + 1 :]] distance_matrix = np.zeros((len(indices), len(indices))) for i, j in pairs: - d = spike_directionality(spike_trains[i], spike_trains[j], normalize, - interval, max_tau=max_tau, - MRTS=MRTS, RI=RI, Reconcile=False) + d = spike_directionality( + spike_trains[i], + spike_trains[j], + normalize, + interval, + max_tau=max_tau, + MRTS=MRTS, + RI=RI, + Reconcile=False, + ) distance_matrix[i, j] = d distance_matrix[j, i] = -d return distance_matrix @@ -209,7 +236,7 @@ def spike_directionality_matrix(spike_trains, normalize=True, indices=None, # spike_train_order_profile ############################################################ def spike_train_order_profile(*args, **kwargs): - """ Computes the spike train order profile :math:`E(t)` of the given + """Computes the spike train order profile :math:`E(t)` of the given spike trains. Returns the profile as a DiscreteFunction object. Valid call structures:: @@ -223,7 +250,7 @@ def spike_train_order_profile(*args, **kwargs): spike_train_order_profile(spike_trains, indices=[0, 1]) # use only the spike trains # given by the indices - Additonal arguments: + Additonal arguments: :param max_tau: Upper bound for coincidence window, `default=None`. :param indices: list of indices defining which spike trains to use, if None all given spike trains are used (default=None) @@ -242,9 +269,8 @@ def spike_train_order_profile(*args, **kwargs): ############################################################ # spike_train_order_profile_bi ############################################################ -def spike_train_order_profile_bi(spike_train1, spike_train2, - max_tau=None, **kwargs): - """ Computes the spike train order profile P(t) of the two given +def spike_train_order_profile_bi(spike_train1, spike_train2, max_tau=None, **kwargs): + """Computes the spike train order profile P(t) of the two given spike trains. Returns the profile as a DiscreteFunction object. :param spike_train1: First spike train. @@ -256,40 +282,47 @@ def spike_train_order_profile_bi(spike_train1, spike_train2, :returns: The spike train order profile :math:`E(t)`. :rtype: :class:`pyspike.function.DiscreteFunction` """ - if kwargs.get('Reconcile', True): - spike_train1, spike_train2 = reconcile_spike_trains_bi(spike_train1, spike_train2) - MRTS, RI = resolve_keywords(**kwargs) + if kwargs.get("Reconcile", True): + spike_train1, spike_train2 = reconcile_spike_trains_bi( + spike_train1, spike_train2 + ) + MRTS, _RI = resolve_keywords(**kwargs) if isinstance(MRTS, str): MRTS = default_thresh([spike_train1, spike_train2]) # check whether the spike trains are defined for the same interval - assert spike_train1.t_start == spike_train2.t_start, \ + assert spike_train1.t_start == spike_train2.t_start, ( "Given spike trains are not defined on the same interval!" - assert spike_train1.t_end == spike_train2.t_end, \ + ) + assert spike_train1.t_end == spike_train2.t_end, ( "Given spike trains are not defined on the same interval!" + ) # cython implementation try: - from .cython.cython_directionality import \ - spike_train_order_profile_cython as \ - spike_train_order_profile_impl + from .cython.cython_directionality import ( + spike_train_order_profile_cython as spike_train_order_profile_impl, + ) except ImportError: # raise NotImplementedError() pyspike.NoCythonWarn() # use python backend - from .cython.directionality_python_backend import \ - spike_train_order_profile_python as spike_train_order_profile_impl + from .cython.directionality_python_backend import ( + spike_train_order_profile_python as spike_train_order_profile_impl, + ) if max_tau is None: max_tau = 0.0 - times, coincidences, multiplicity \ - = spike_train_order_profile_impl(spike_train1.spikes, - spike_train2.spikes, - spike_train1.t_start, - spike_train1.t_end, - max_tau, MRTS) + times, coincidences, multiplicity = spike_train_order_profile_impl( + spike_train1.spikes, + spike_train2.spikes, + spike_train1.t_start, + spike_train1.t_end, + max_tau, + MRTS, + ) return DiscreteFunc(times, coincidences, multiplicity) @@ -297,9 +330,8 @@ def spike_train_order_profile_bi(spike_train1, spike_train2, ############################################################ # spike_train_order_profile_multi ############################################################ -def spike_train_order_profile_multi(spike_trains, indices=None, - max_tau=None, **kwargs): - """ Computes the multi-variate spike train order profile for a set of +def spike_train_order_profile_multi(spike_trains, indices=None, max_tau=None, **kwargs): + """Computes the multi-variate spike train order profile for a set of spike trains. For each spike in the set of spike trains, the multi-variate profile is defined as the sum of asymmetry values divided by the number of spike trains pairs involving the spike train of containing this spike, @@ -315,18 +347,19 @@ def spike_train_order_profile_multi(spike_trains, indices=None, :rtype: :class:`pyspike.function.DiscreteFunction` """ prof_func = partial(spike_train_order_profile_bi, max_tau=max_tau) - average_prof, M = _generic_profile_multi(spike_trains, prof_func, - indices, **kwargs) + average_prof, _M = _generic_profile_multi( + spike_trains, prof_func, indices, **kwargs + ) return average_prof - ############################################################ # _spike_train_order_impl ############################################################ -def _spike_train_order_impl(spike_train1, spike_train2, - interval=None, max_tau=None, **kwargs): - """ Implementation of bi-variatae spike train order value (Synfire Indicator). +def _spike_train_order_impl( + spike_train1, spike_train2, interval=None, max_tau=None, **kwargs +): + """Implementation of bi-variatae spike train order value (Synfire Indicator). :param spike_train1: First spike train. :type spike_train1: :class:`pyspike.SpikeTrain` @@ -336,27 +369,32 @@ def _spike_train_order_impl(spike_train1, spike_train2, coincidence window has no upper bound. :returns: The spike train order value (Synfire Indicator) """ - MRTS, RI = resolve_keywords(**kwargs) + MRTS, _RI = resolve_keywords(**kwargs) if isinstance(MRTS, str): MRTS = default_thresh([spike_train1, spike_train2]) if interval is None: # distance over the whole interval is requested: use specific function # for optimal performance try: - from .cython.cython_directionality import \ - spike_train_order_cython as spike_train_order_func + from .cython.cython_directionality import ( + spike_train_order_cython as spike_train_order_func, + ) + if max_tau is None: max_tau = 0.0 - c, mp = spike_train_order_func(spike_train1.spikes, - spike_train2.spikes, - spike_train1.t_start, - spike_train1.t_end, - max_tau, MRTS) + c, mp = spike_train_order_func( + spike_train1.spikes, + spike_train2.spikes, + spike_train1.t_start, + spike_train1.t_end, + max_tau, + MRTS, + ) except ImportError: # Cython backend not available: fall back to profile averaging - c, mp = spike_train_order_profile(spike_train1, spike_train2, - max_tau=max_tau, - MRTS=MRTS).integral(interval) + c, mp = spike_train_order_profile( + spike_train1, spike_train2, max_tau=max_tau, MRTS=MRTS + ).integral(interval) return c, mp else: # some specific interval is provided: not yet implemented @@ -367,7 +405,7 @@ def _spike_train_order_impl(spike_train1, spike_train2, # spike_train_order ############################################################ def spike_train_order(*args, **kwargs): - """ Computes the spike train order (Synfire Indicator) of the given + """Computes the spike train order (Synfire Indicator) of the given spike trains. Valid call structures:: @@ -381,7 +419,7 @@ def spike_train_order(*args, **kwargs): spike_train_order(spike_trains, indices=[0, 1]) # use only the spike trains # given by the indices - Additonal arguments: + Additonal arguments: - `max_tau` Upper bound for coincidence window, `default=None`. - `normalize` Flag indicating if the reslut should be normalized by the number of spikes , default=`False` @@ -400,9 +438,10 @@ def spike_train_order(*args, **kwargs): ############################################################ # spike_train_order_bi ############################################################ -def spike_train_order_bi(spike_train1, spike_train2, normalize=True, - interval=None, max_tau=None, **kwargs): - """ Computes the overall spike train order value (Synfire Indicator) +def spike_train_order_bi( + spike_train1, spike_train2, normalize=True, interval=None, max_tau=None, **kwargs +): + """Computes the overall spike train order value (Synfire Indicator) for two spike trains. :param spike_train1: First spike train. @@ -414,18 +453,22 @@ def spike_train_order_bi(spike_train1, spike_train2, normalize=True, coincidence window has no upper bound. :returns: The spike train order value (Synfire Indicator) """ - c, mp = _spike_train_order_impl(spike_train1, spike_train2, interval, max_tau, **kwargs) + c, mp = _spike_train_order_impl( + spike_train1, spike_train2, interval, max_tau, **kwargs + ) if normalize: - return 1.0*c/mp + return 1.0 * c / mp else: return c + ############################################################ # spike_train_order_multi ############################################################ -def spike_train_order_multi(spike_trains, indices=None, normalize=True, - interval=None, max_tau=None, **kwargs): - """ Computes the overall spike train order value (Synfire Indicator) +def spike_train_order_multi( + spike_trains, indices=None, normalize=True, interval=None, max_tau=None, **kwargs +): + """Computes the overall spike train order value (Synfire Indicator) for many spike trains. :param spike_trains: list of :class:`.SpikeTrain` @@ -447,32 +490,32 @@ def spike_train_order_multi(spike_trains, indices=None, normalize=True, indices = np.arange(len(spike_trains)) indices = np.array(indices) # check validity of indices - assert (indices < len(spike_trains)).all() and (indices >= 0).all(), \ + assert (indices < len(spike_trains)).all() and (indices >= 0).all(), ( "Invalid index list." + ) # generate a list of possible index pairs - pairs = [(indices[i], j) for i in range(len(indices)) - for j in indices[i+1:]] + pairs = [(indices[i], j) for i in range(len(indices)) for j in indices[i + 1 :]] e_total = 0.0 m_total = 0.0 - for (i, j) in pairs: - e, m = _spike_train_order_impl(spike_trains[i], spike_trains[j], - interval, max_tau, MRTS=MRTS, RI=RI) + for i, j in pairs: + e, m = _spike_train_order_impl( + spike_trains[i], spike_trains[j], interval, max_tau, MRTS=MRTS, RI=RI + ) e_total += e m_total += m if m == 0.0: return 1.0 else: - return e_total/m_total - + return e_total / m_total ############################################################ # optimal_spike_train_sorting_from_matrix ############################################################ def _optimal_spike_train_sorting_from_matrix(D, full_output=False): - """ Finds the best sorting via simulated annealing. + """Finds the best sorting via simulated annealing. Returns the optimal permutation p and A value. Not for direct use, call :func:`.optimal_spike_train_sorting` instead. @@ -487,15 +530,16 @@ def _optimal_spike_train_sorting_from_matrix(D, full_output=False): p = np.arange(N) - T_start = 2*np.max(D) # starting temperature - T_end = 1E-5 * T_start # final temperature - alpha = 0.9 # cooling factor + T_start = 2 * np.max(D) # starting temperature + T_end = 1e-5 * T_start # final temperature + alpha = 0.9 # cooling factor try: from .cython.cython_simulated_annealing import sim_ann_cython as sim_ann - except ImportError: - raise NotImplementedError("PySpike with Cython required for computing spike train" - " sorting!") + except ImportError as err: + raise NotImplementedError( + "PySpike with Cython required for computing spike train sorting!" + ) from err p, A, total_iter = sim_ann(D, T_start, T_end, alpha) @@ -508,13 +552,14 @@ def _optimal_spike_train_sorting_from_matrix(D, full_output=False): ############################################################ # optimal_spike_train_sorting ############################################################ -def optimal_spike_train_sorting(spike_trains, indices=None, interval=None, - max_tau=None, full_output=False, **kwargs): - """ Finds the best sorting of the given spike trains by computing the spike +def optimal_spike_train_sorting( + spike_trains, indices=None, interval=None, max_tau=None, full_output=False, **kwargs +): + """Finds the best sorting of the given spike trains by computing the spike directionality matrix and optimize the order using simulated annealing. For a detailed description of the algorithm see: `http://iopscience.iop.org/article/10.1088/1367-2630/aa68c3/meta` - + :param spike_trains: list of :class:`.SpikeTrain` :param indices: list of indices defining which spike trains to use, if None all given spike trains are used (default=None) @@ -529,16 +574,22 @@ def optimal_spike_train_sorting(spike_trains, indices=None, interval=None, :return: (p, F) - tuple with the optimal permutation and synfire indicator. if `full_output=True` , (p, F, iter) is returned. """ - D = spike_directionality_matrix(spike_trains, normalize=False, - indices=indices, interval=interval, - max_tau=max_tau, **kwargs) + D = spike_directionality_matrix( + spike_trains, + normalize=False, + indices=indices, + interval=interval, + max_tau=max_tau, + **kwargs, + ) return _optimal_spike_train_sorting_from_matrix(D, full_output) + ############################################################ # permutate_matrix ############################################################ def permutate_matrix(D, p): - """ Helper function that applies the permutation p to the columns and rows + """Helper function that applies the permutation p to the columns and rows of matrix D. Return the permutated matrix :math:`D'[n,m] = D[p[n], p[m]]`. :param D: The matrix. diff --git a/pyspike/spike_distance.py b/pyspike/spike_distance.py index 84064f5..7a7caf6 100644 --- a/pyspike/spike_distance.py +++ b/pyspike/spike_distance.py @@ -2,21 +2,24 @@ # Copyright 2014-2015, Mario Mulansky # Distributed under the BSD License -from __future__ import absolute_import import pyspike from pyspike import PieceWiseLinFunc -from pyspike.generic import _generic_profile_multi, _generic_distance_multi, \ - _generic_distance_matrix, resolve_keywords +from pyspike.generic import ( + _generic_distance_matrix, + _generic_distance_multi, + _generic_profile_multi, + resolve_keywords, +) from pyspike.isi_lengths import default_thresh -from pyspike.spikes import reconcile_spike_trains, reconcile_spike_trains_bi +from pyspike.spikes import reconcile_spike_trains_bi ############################################################ # spike_profile ############################################################ def spike_profile(*args, **kwargs): - """ Computes the spike-distance profile :math:`S(t)` of the given + """Computes the spike-distance profile :math:`S(t)` of the given spike trains. Returns the profile as a PieceWiseConstLin object. The SPIKE-values are defined positive :math:`S(t)>=0`. @@ -52,7 +55,7 @@ def spike_profile(*args, **kwargs): # spike_profile_bi ############################################################ def spike_profile_bi(spike_train1, spike_train2, **kwargs): - """ Specific function to compute a bivariate SPIKE-profile. This is a + """Specific function to compute a bivariate SPIKE-profile. This is a deprecated function and should not be called directly. Use :func:`.spike_profile` to compute SPIKE-profiles. @@ -64,8 +67,10 @@ def spike_profile_bi(spike_train1, spike_train2, **kwargs): :rtype: :class:`.PieceWiseLinFunc` """ - if kwargs.get('Reconcile', True): - spike_train1, spike_train2 = reconcile_spike_trains_bi(spike_train1, spike_train2) + if kwargs.get("Reconcile", True): + spike_train1, spike_train2 = reconcile_spike_trains_bi( + spike_train1, spike_train2 + ) MRTS, RI = resolve_keywords(**kwargs) if isinstance(MRTS, str): @@ -73,20 +78,21 @@ def spike_profile_bi(spike_train1, spike_train2, **kwargs): # cython implementation try: - from .cython.cython_profiles import spike_profile_cython \ - as spike_profile_impl + from .cython.cython_profiles import spike_profile_cython as spike_profile_impl except ImportError: pyspike.NoCythonWarn() # use python backend - from .cython.python_backend import spike_distance_python \ - as spike_profile_impl + from .cython.python_backend import spike_distance_python as spike_profile_impl times, y_starts, y_ends = spike_profile_impl( spike_train1.get_spikes_non_empty(), spike_train2.get_spikes_non_empty(), - spike_train1.t_start, spike_train1.t_end, - MRTS, RI) + spike_train1.t_start, + spike_train1.t_end, + MRTS, + RI, + ) return PieceWiseLinFunc(times, y_starts, y_ends) @@ -95,7 +101,7 @@ def spike_profile_bi(spike_train1, spike_train2, **kwargs): # spike_profile_multi ############################################################ def spike_profile_multi(spike_trains, indices=None, **kwargs): - """ Specific function to compute a multivariate SPIKE-profile. This is a + """Specific function to compute a multivariate SPIKE-profile. This is a deprecated function and should not be called directly. Use :func:`.spike_profile` to compute SPIKE-profiles. @@ -107,9 +113,10 @@ def spike_profile_multi(spike_trains, indices=None, **kwargs): :rtype: :class:`.PieceWiseLinFunc` """ - average_dist, M = _generic_profile_multi(spike_trains, spike_profile_bi, - indices, **kwargs) - average_dist.mul_scalar(1.0/M) # normalize + average_dist, M = _generic_profile_multi( + spike_trains, spike_profile_bi, indices, **kwargs + ) + average_dist.mul_scalar(1.0 / M) # normalize return average_dist @@ -117,7 +124,7 @@ def spike_profile_multi(spike_trains, indices=None, **kwargs): # spike_distance ############################################################ def spike_distance(*args, **kwargs): - """ Computes the SPIKE-distance :math:`D_S` of the given spike trains. The + """Computes the SPIKE-distance :math:`D_S` of the given spike trains. The spike-distance is the integral over the spike distance profile :math:`D(t)`: @@ -157,7 +164,7 @@ def spike_distance(*args, **kwargs): # spike_distance_bi ############################################################ def spike_distance_bi(spike_train1, spike_train2, interval=None, **kwargs): - """ Specific function to compute a bivariate SPIKE-distance. This is a + """Specific function to compute a bivariate SPIKE-distance. This is a deprecated function and should not be called directly. Use :func:`.spike_distance` to compute SPIKE-distances. @@ -172,9 +179,11 @@ def spike_distance_bi(spike_train1, spike_train2, interval=None, **kwargs): :rtype: double """ - if kwargs.get('Reconcile', True): - spike_train1, spike_train2 = reconcile_spike_trains_bi(spike_train1, spike_train2) - kwargs['Reconcile'] = False + if kwargs.get("Reconcile", True): + spike_train1, spike_train2 = reconcile_spike_trains_bi( + spike_train1, spike_train2 + ) + kwargs["Reconcile"] = False MRTS, RI = resolve_keywords(**kwargs) if isinstance(MRTS, str): MRTS = default_thresh([spike_train1, spike_train2]) @@ -183,28 +192,31 @@ def spike_distance_bi(spike_train1, spike_train2, interval=None, **kwargs): # distance over the whole interval is requested: use specific function # for optimal performance try: - from .cython.cython_distances import spike_distance_cython \ - as spike_distance_impl - return spike_distance_impl(spike_train1.get_spikes_non_empty(), - spike_train2.get_spikes_non_empty(), - spike_train1.t_start, - spike_train1.t_end, - MRTS, RI) + from .cython.cython_distances import ( + spike_distance_cython as spike_distance_impl, + ) + + return spike_distance_impl( + spike_train1.get_spikes_non_empty(), + spike_train2.get_spikes_non_empty(), + spike_train1.t_start, + spike_train1.t_end, + MRTS, + RI, + ) except ImportError: # Cython backend not available: fall back to average profile - return spike_profile_bi(spike_train1, spike_train2, - **kwargs).avrg(interval) + return spike_profile_bi(spike_train1, spike_train2, **kwargs).avrg(interval) else: # some specific interval is provided: compute the whole profile - return spike_profile_bi(spike_train1, spike_train2, - **kwargs).avrg(interval) + return spike_profile_bi(spike_train1, spike_train2, **kwargs).avrg(interval) ############################################################ # spike_distance_multi ############################################################ def spike_distance_multi(spike_trains, indices=None, interval=None, **kwargs): - """ Specific function to compute a multivariate SPIKE-distance. This is a + """Specific function to compute a multivariate SPIKE-distance. This is a deprecated function and should not be called directly. Use :func:`.spike_distance` to compute SPIKE-distances. @@ -218,15 +230,16 @@ def spike_distance_multi(spike_trains, indices=None, interval=None, **kwargs): :returns: The averaged multi-variate spike distance :math:`D_S`. :rtype: double """ - return _generic_distance_multi(spike_trains, spike_distance_bi, indices, - interval, **kwargs) + return _generic_distance_multi( + spike_trains, spike_distance_bi, indices, interval, **kwargs + ) ############################################################ # spike_distance_matrix ############################################################ def spike_distance_matrix(spike_trains, indices=None, interval=None, **kwargs): - """ Computes the time averaged spike-distance of all pairs of spike-trains. + """Computes the time averaged spike-distance of all pairs of spike-trains. :param spike_trains: list of :class:`.SpikeTrain` :param indices: list of indices defining which spike trains to use, @@ -239,5 +252,6 @@ def spike_distance_matrix(spike_trains, indices=None, interval=None, **kwargs): :math:`D_S^{ij}` :rtype: np.array """ - return _generic_distance_matrix(spike_trains, spike_distance_bi, - indices, interval, **kwargs) + return _generic_distance_matrix( + spike_trains, spike_distance_bi, indices, interval, **kwargs + ) diff --git a/pyspike/spike_sync.py b/pyspike/spike_sync.py index 50bb98a..c974abb 100644 --- a/pyspike/spike_sync.py +++ b/pyspike/spike_sync.py @@ -3,22 +3,28 @@ # Copyright 2014-2015, Mario Mulansky # Distributed under the BSD License -from __future__ import absolute_import -import numpy as np from functools import partial + +import numpy as np + import pyspike -from pyspike import DiscreteFunc, SpikeTrain -from pyspike.generic import _generic_profile_multi, _generic_distance_matrix, resolve_keywords +from pyspike.DiscreteFunc import DiscreteFunc +from pyspike.generic import ( + _generic_distance_matrix, + _generic_profile_multi, + resolve_keywords, +) from pyspike.isi_lengths import default_thresh from pyspike.spikes import reconcile_spike_trains, reconcile_spike_trains_bi +from pyspike.SpikeTrain import SpikeTrain ############################################################ # spike_sync_profile ############################################################ def spike_sync_profile(*args, **kwargs): - """ Computes the spike-synchronization profile S_sync(t) of the given + """Computes the spike-synchronization profile S_sync(t) of the given spike trains. Returns the profile as a DiscreteFunction object. In the bivariate case, he S_sync values are either 1 or 0, indicating the presence or absence of a coincidence. For multi-variate cases, each spike in the set @@ -56,7 +62,7 @@ def spike_sync_profile(*args, **kwargs): # spike_sync_profile_bi ############################################################ def spike_sync_profile_bi(spike_train1, spike_train2, max_tau=None, **kwargs): - """ Specific function to compute a bivariate SPIKE-Sync-profile. This is a + """Specific function to compute a bivariate SPIKE-Sync-profile. This is a deprecated function and should not be called directly. Use :func:`.spike_sync_profile` to compute SPIKE-Sync-profiles. @@ -70,31 +76,39 @@ def spike_sync_profile_bi(spike_train1, spike_train2, max_tau=None, **kwargs): :rtype: :class:`pyspike.function.DiscreteFunction` """ - if kwargs.get('Reconcile', True): - spike_train1, spike_train2 = reconcile_spike_trains_bi(spike_train1, spike_train2) + if kwargs.get("Reconcile", True): + spike_train1, spike_train2 = reconcile_spike_trains_bi( + spike_train1, spike_train2 + ) - MRTS, RI = resolve_keywords(**kwargs) + MRTS, _RI = resolve_keywords(**kwargs) if isinstance(MRTS, str): MRTS = default_thresh([spike_train1, spike_train2]) # cython implementation try: - from .cython.cython_profiles import coincidence_profile_cython \ - as coincidence_profile_impl + from .cython.cython_profiles import ( + coincidence_profile_cython as coincidence_profile_impl, + ) except ImportError: pyspike.NoCythonWarn() # use python backend - from .cython.python_backend import coincidence_python \ - as coincidence_profile_impl + from .cython.python_backend import ( + coincidence_python as coincidence_profile_impl, + ) if max_tau is None: max_tau = 0.0 - times, coincidences, multiplicity \ - = coincidence_profile_impl(spike_train1.spikes, spike_train2.spikes, - spike_train1.t_start, spike_train1.t_end, - max_tau, MRTS) + times, coincidences, multiplicity = coincidence_profile_impl( + spike_train1.spikes, + spike_train2.spikes, + spike_train1.t_start, + spike_train1.t_end, + max_tau, + MRTS, + ) return DiscreteFunc(times, coincidences, multiplicity) @@ -103,7 +117,7 @@ def spike_sync_profile_bi(spike_train1, spike_train2, max_tau=None, **kwargs): # spike_sync_profile_multi ############################################################ def spike_sync_profile_multi(spike_trains, indices=None, max_tau=None, **kwargs): - """ Specific function to compute a multivariate SPIKE-Sync-profile. + """Specific function to compute a multivariate SPIKE-Sync-profile. This is a deprecated function and should not be called directly. Use :func:`.spike_sync_profile` to compute SPIKE-Sync-profiles. @@ -118,8 +132,9 @@ def spike_sync_profile_multi(spike_trains, indices=None, max_tau=None, **kwargs) """ prof_func = partial(spike_sync_profile_bi, max_tau=max_tau) - average_prof, M = _generic_profile_multi(spike_trains, prof_func, - indices, **kwargs) + average_prof, _M = _generic_profile_multi( + spike_trains, prof_func, indices, **kwargs + ) # average_dist.mul_scalar(1.0/M) # no normalization here! return average_prof @@ -128,48 +143,55 @@ def spike_sync_profile_multi(spike_trains, indices=None, max_tau=None, **kwargs) # _spike_sync_values ############################################################ def _spike_sync_values(spike_train1, spike_train2, interval, max_tau, **kwargs): - """" Internal function. Computes the summed coincidences and multiplicity + """ " Internal function. Computes the summed coincidences and multiplicity for spike synchronization of the two given spike trains. Do not call this function directly, use `spike_sync` or `spike_sync_multi` instead. """ - MRTS, RI = resolve_keywords(**kwargs) + MRTS, _RI = resolve_keywords(**kwargs) if isinstance(MRTS, str): MRTS = default_thresh([spike_train1, spike_train2]) if interval is None: # distance over the whole interval is requested: use specific function # for optimal performance try: - from .cython.cython_distances import coincidence_value_cython \ - as coincidence_value_impl + from .cython.cython_distances import ( + coincidence_value_cython as coincidence_value_impl, + ) + if max_tau is None: max_tau = 0.0 - c, mp = coincidence_value_impl(spike_train1.spikes, - spike_train2.spikes, - spike_train1.t_start, - spike_train1.t_end, - max_tau, MRTS) + c, mp = coincidence_value_impl( + spike_train1.spikes, + spike_train2.spikes, + spike_train1.t_start, + spike_train1.t_end, + max_tau, + MRTS, + ) return c, mp except ImportError: # Cython backend not available: fall back to profile averaging - return spike_sync_profile_bi(spike_train1, spike_train2, - max_tau, **kwargs).integral(interval) + return spike_sync_profile_bi( + spike_train1, spike_train2, max_tau, **kwargs + ).integral(interval) else: # some specific interval is provided: use profile - return spike_sync_profile_bi(spike_train1, spike_train2, - max_tau, **kwargs).integral(interval) + return spike_sync_profile_bi( + spike_train1, spike_train2, max_tau, **kwargs + ).integral(interval) ############################################################ # spike_sync ############################################################ def spike_sync(*args, **kwargs): - """ Computes the spike synchronization value of the given spike + """Computes the spike synchronization value of the given spike trains. The spike synchronization value is the computed as the total number of coincidences divided by the total number of spikes: - .. math:: SYNC = \sum_n C_n / N. + .. math:: SYNC = \\sum_n C_n / N. Valid call structures:: @@ -201,7 +223,7 @@ def spike_sync(*args, **kwargs): # spike_sync_bi ############################################################ def spike_sync_bi(spike_train1, spike_train2, interval=None, max_tau=None, **kwargs): - """ Specific function to compute a bivariate SPIKE-Sync value. + """Specific function to compute a bivariate SPIKE-Sync value. This is a deprecated function and should not be called directly. Use :func:`.spike_sync` to compute SPIKE-Sync values. @@ -218,21 +240,23 @@ def spike_sync_bi(spike_train1, spike_train2, interval=None, max_tau=None, **kwa :rtype: `double` """ - if kwargs.get('Reconcile', True): - spike_train1, spike_train2 = reconcile_spike_trains_bi(spike_train1, spike_train2) - kwargs['Reconcile'] = False + if kwargs.get("Reconcile", True): + spike_train1, spike_train2 = reconcile_spike_trains_bi( + spike_train1, spike_train2 + ) + kwargs["Reconcile"] = False c, mp = _spike_sync_values(spike_train1, spike_train2, interval, max_tau, **kwargs) if mp == 0: return 1.0 else: - return 1.0*c/mp + return 1.0 * c / mp ############################################################ # spike_sync_multi ############################################################ def spike_sync_multi(spike_trains, indices=None, interval=None, max_tau=None, **kwargs): - """ Specific function to compute a multivariate SPIKE-Sync value. + """Specific function to compute a multivariate SPIKE-Sync value. This is a deprecated function and should not be called directly. Use :func:`.spike_sync` to compute SPIKE-Sync values. @@ -249,43 +273,45 @@ def spike_sync_multi(spike_trains, indices=None, interval=None, max_tau=None, ** :rtype: double """ - if kwargs.get('Reconcile', True): + if kwargs.get("Reconcile", True): spike_trains = reconcile_spike_trains(spike_trains) - kwargs['Reconcile'] = False - MRTS, RI = resolve_keywords(**kwargs) + kwargs["Reconcile"] = False + MRTS, _RI = resolve_keywords(**kwargs) if isinstance(MRTS, str): - kwargs['MRTS'] = default_thresh(spike_trains) + kwargs["MRTS"] = default_thresh(spike_trains) if indices is None: indices = np.arange(len(spike_trains)) indices = np.array(indices) # check validity of indices - assert (indices < len(spike_trains)).all() and (indices >= 0).all(), \ + assert (indices < len(spike_trains)).all() and (indices >= 0).all(), ( "Invalid index list." + ) # generate a list of possible index pairs - pairs = [(indices[i], j) for i in range(len(indices)) - for j in indices[i+1:]] + pairs = [(indices[i], j) for i in range(len(indices)) for j in indices[i + 1 :]] coincidence = 0.0 mp = 0.0 - for (i, j) in pairs: - c, m = _spike_sync_values(spike_trains[i], spike_trains[j], - interval, max_tau, - **kwargs) + for i, j in pairs: + c, m = _spike_sync_values( + spike_trains[i], spike_trains[j], interval, max_tau, **kwargs + ) coincidence += c mp += m if mp == 0.0: return 1.0 else: - return coincidence/mp + return coincidence / mp ############################################################ # spike_sync_matrix ############################################################ -def spike_sync_matrix(spike_trains, indices=None, interval=None, max_tau=None, **kwargs): - """ Computes the overall spike-synchronization value of all pairs of +def spike_sync_matrix( + spike_trains, indices=None, interval=None, max_tau=None, **kwargs +): + """Computes the overall spike-synchronization value of all pairs of spike-trains. :param spike_trains: list of :class:`pyspike.SpikeTrain` @@ -303,8 +329,9 @@ def spike_sync_matrix(spike_trains, indices=None, interval=None, max_tau=None, * """ dist_func = partial(spike_sync_bi, max_tau=max_tau) - ShouldBeSync = _generic_distance_matrix(spike_trains, dist_func, - indices, interval, **kwargs) + ShouldBeSync = _generic_distance_matrix( + spike_trains, dist_func, indices, interval, **kwargs + ) # These elements are not really distances, but spike-sync values # The diagonal needs to reflect that: for i in range(ShouldBeSync.shape[0]): @@ -315,14 +342,20 @@ def spike_sync_matrix(spike_trains, indices=None, interval=None, max_tau=None, * ############################################################ # filter_by_spike_sync ############################################################ -def filter_by_spike_sync(spike_trains, threshold, indices=None, max_tau=None, - return_removed_spikes=False, **kwargs): - """ Removes the spikes with a multi-variate spike_sync value below +def filter_by_spike_sync( + spike_trains, + threshold, + indices=None, + max_tau=None, + return_removed_spikes=False, + **kwargs, +): + """Removes the spikes with a multi-variate spike_sync value below threshold. """ - if kwargs.get('Reconcile', True): + if kwargs.get("Reconcile", True): spike_trains = reconcile_spike_trains(spike_trains) - MRTS, RI = resolve_keywords(**kwargs) + MRTS, _RI = resolve_keywords(**kwargs) if isinstance(MRTS, str): MRTS = default_thresh(spike_trains) N = len(spike_trains) @@ -331,14 +364,14 @@ def filter_by_spike_sync(spike_trains, threshold, indices=None, max_tau=None, # cython implementation try: - from .cython.cython_profiles import coincidence_single_profile_cython \ - as coincidence_impl + from .cython.cython_profiles import ( + coincidence_single_profile_cython as coincidence_impl, + ) except ImportError: pyspike.NoCythonWarn() # use python backend - from .cython.python_backend import coincidence_single_python \ - as coincidence_impl + from .cython.python_backend import coincidence_single_python as coincidence_impl if max_tau is None: max_tau = 0.0 @@ -348,15 +381,18 @@ def filter_by_spike_sync(spike_trains, threshold, indices=None, max_tau=None, for j in range(N): if i == j: continue - coincidences += coincidence_impl(st.spikes, spike_trains[j].spikes, - st.t_start, st.t_end, max_tau, MRTS) - filtered_spikes = st[coincidences > threshold*(N-1)] - filtered_spike_trains.append(SpikeTrain(filtered_spikes, - [st.t_start, st.t_end])) + coincidences += coincidence_impl( + st.spikes, spike_trains[j].spikes, st.t_start, st.t_end, max_tau, MRTS + ) + filtered_spikes = st[coincidences > threshold * (N - 1)] + filtered_spike_trains.append( + SpikeTrain(filtered_spikes, [st.t_start, st.t_end]) + ) if return_removed_spikes: - removed_spikes = st[coincidences <= threshold*(N-1)] - removed_spike_trains.append(SpikeTrain(removed_spikes, - [st.t_start, st.t_end])) + removed_spikes = st[coincidences <= threshold * (N - 1)] + removed_spike_trains.append( + SpikeTrain(removed_spikes, [st.t_start, st.t_end]) + ) if return_removed_spikes: return [filtered_spike_trains, removed_spike_trains] else: diff --git a/pyspike/spikes.py b/pyspike/spikes.py index e2b6475..1af0f50 100644 --- a/pyspike/spikes.py +++ b/pyspike/spikes.py @@ -3,14 +3,15 @@ # Distributed under the BSD License import numpy as np -from pyspike import SpikeTrain + +from pyspike.SpikeTrain import SpikeTrain ############################################################ # spike_train_from_string ############################################################ -def spike_train_from_string(s, edges, sep=' ', is_sorted=False): - """ Converts a string of times into a :class:`.SpikeTrain`. +def spike_train_from_string(s, edges, sep=" ", is_sorted=False): + """Converts a string of times into a :class:`.SpikeTrain`. :param s: the string with (ordered) spike times. :param edges: interval defining the edges of the spike train. @@ -27,10 +28,15 @@ def spike_train_from_string(s, edges, sep=' ', is_sorted=False): ############################################################ # load_spike_trains_from_txt ############################################################ -def load_spike_trains_from_txt(file_name, edges, - separator=' ', comment='#', is_sorted=False, - ignore_empty_lines=True): - """ Loads a number of spike trains from a text file. Each line of the text +def load_spike_trains_from_txt( + file_name, + edges, + separator=" ", + comment="#", + is_sorted=False, + ignore_empty_lines=True, +): + """Loads a number of spike trains from a text file. Each line of the text file should contain one spike train as a sequence of spike times separated by `separator`. Empty lines as well as lines starting with `comment` are neglected. The `edges` represents the start and the end of the @@ -47,23 +53,25 @@ def load_spike_trains_from_txt(file_name, edges, :returns: list of :class:`.SpikeTrain` """ spike_trains = [] - with open(file_name, 'r') as spike_file: + with open(file_name) as spike_file: for line in spike_file: if not line.startswith(comment): # ignore comments if len(line) > 1: # ignore empty lines - spike_train = spike_train_from_string(line, edges, - separator, is_sorted) + spike_train = spike_train_from_string( + line, edges, separator, is_sorted + ) spike_trains.append(spike_train) - elif not(ignore_empty_lines): + elif not (ignore_empty_lines): # add empty spike train spike_trains.append(SpikeTrain([], edges)) return spike_trains -def import_spike_trains_from_time_series(file_name, start_time, time_bin, - separator=None, comment='#'): - """ Imports spike trains from time series consisting of 0 and 1 denoting +def import_spike_trains_from_time_series( + file_name, start_time, time_bin, separator=None, comment="#" +): + """Imports spike trains from time series consisting of 0 and 1 denoting the absence or presence of a spike. Each line in the data file represents one spike train. @@ -77,21 +85,22 @@ def import_spike_trains_from_time_series(file_name, start_time, time_bin, """ data = np.loadtxt(file_name, comments=comment, delimiter=separator) - time_points = start_time + time_bin + np.arange(len(data[0, :]))*time_bin + time_points = start_time + time_bin + np.arange(len(data[0, :])) * time_bin spike_trains = [] for time_series in data: - spike_trains.append(SpikeTrain(time_points[time_series > 0], - edges=[start_time, - time_points[-1]])) + spike_trains.append( + SpikeTrain( + time_points[time_series > 0], edges=[start_time, time_points[-1]] + ) + ) return spike_trains ############################################################ # save_spike_trains_to_txt ############################################################ -def save_spike_trains_to_txt(spike_trains, file_name, - separator=' ', precision=8): - """ Saves the given spike trains into a file with the given file name. +def save_spike_trains_to_txt(spike_trains, file_name, separator=" ", precision=8): + """Saves the given spike trains into a file with the given file name. Each spike train will be stored in one line in the text file with the times separated by `separator`. @@ -99,18 +108,18 @@ def save_spike_trains_to_txt(spike_trains, file_name, :param file_name: The name of the text file. """ # format string to print the spike times with given precision - format_str = "{0:.%de}" % precision - with open(file_name, 'w') as spike_file: + format_str = "{0:.%de}" % precision # noqa: UP031 + with open(file_name, "w") as spike_file: for st in spike_trains: s = separator.join(map(format_str.format, st.spikes)) - spike_file.write(s+'\n') + spike_file.write(s + "\n") ############################################################ # merge_spike_trains ############################################################ def merge_spike_trains(spike_trains): - """ Merges a number of spike trains into a single spike train. + """Merges a number of spike trains into a single spike train. :param spike_trains: list of :class:`.SpikeTrain` :returns: spike train with the merged spike times @@ -119,15 +128,14 @@ def merge_spike_trains(spike_trains): # empty spike trains merged_spikes = np.concatenate([st.spikes for st in spike_trains]) merged_spikes.sort() - return SpikeTrain(merged_spikes, [spike_trains[0].t_start, - spike_trains[0].t_end]) + return SpikeTrain(merged_spikes, [spike_trains[0].t_start, spike_trains[0].t_end]) ############################################################ # generate_poisson_spikes ############################################################ def generate_poisson_spikes(rate, interval): - """ Generates a Poisson spike train with the given rate in the given time + """Generates a Poisson spike train with the given rate in the given time interval :param rate: The rate of the spike trains @@ -143,53 +151,55 @@ def generate_poisson_spikes(rate, interval): try: T_start = interval[0] T_end = interval[1] - except: + except TypeError: T_start = 0 T_end = interval # roughly how many spikes are required to fill the interval - N = max(1, int(1.2 * rate * (T_end-T_start))) - N_append = max(1, int(0.1 * rate * (T_end-T_start))) - intervals = np.random.exponential(1.0/rate, N) + N = max(1, int(1.2 * rate * (T_end - T_start))) + N_append = max(1, int(0.1 * rate * (T_end - T_start))) + intervals = np.random.exponential(1.0 / rate, N) # make sure we have enough spikes while T_start + sum(intervals) < T_end: # print T_start + sum(intervals) - intervals = np.append(intervals, - np.random.exponential(1.0/rate, N_append)) + intervals = np.append(intervals, np.random.exponential(1.0 / rate, N_append)) spikes = T_start + np.cumsum(intervals) spikes = spikes[spikes < T_end] return SpikeTrain(spikes, interval) + def reconcile_spike_trains(spike_trains): - """ make sure that Spike trains meet PySpike rules - In: spike_trains - a list of SpikeTrain objects - Out: spike_trains - same list with some fixes: - 1) t_start and t_end are the same for every train - 2) The spike times are sorted - 3) No duplicate times in any train - 4) spike times outside of t_start,t_end removed + """make sure that Spike trains meet PySpike rules + In: spike_trains - a list of SpikeTrain objects + Out: spike_trains - same list with some fixes: + 1) t_start and t_end are the same for every train + 2) The spike times are sorted + 3) No duplicate times in any train + 4) spike times outside of t_start,t_end removed """ ## handle sorting and uniqueness first (np.unique() does a sort) - spike_trains = [SpikeTrain(np.unique(s.spikes), - [s.t_start, s.t_end], - is_sorted=True) for s in spike_trains] + spike_trains = [ + SpikeTrain(np.unique(s.spikes), [s.t_start, s.t_end], is_sorted=True) + for s in spike_trains + ] ## find global start and end times - Starts = [s.t_start for s in spike_trains] + Starts = [s.t_start for s in spike_trains] tStart = min(Starts) - Ends = [s.t_end for s in spike_trains] + Ends = [s.t_end for s in spike_trains] tEnd = max(Ends) ## remove spike times outside of the bounds - Eps = 1e-6 #beware precision change + Eps = 1e-6 # beware precision change for s in spike_trains: - s.spikes = [t for t in s.spikes if t > tStart-Eps and t < tEnd+Eps] + s.spikes = [t for t in s.spikes if t > tStart - Eps and t < tEnd + Eps] ## Apply start and end times to every train return [SpikeTrain(s.spikes, [tStart, tEnd], is_sorted=True) for s in spike_trains] + def reconcile_spike_trains_bi(spike_train1, spike_train2): - """ fix up a pair of spike trains""" + """fix up a pair of spike trains""" trains_in = [spike_train1, spike_train2] trains_out = reconcile_spike_trains(trains_in) - return trains_out[0], trains_out[1] \ No newline at end of file + return trains_out[0], trains_out[1] diff --git a/setup.cfg b/setup.cfg deleted file mode 100644 index c855aaa..0000000 --- a/setup.cfg +++ /dev/null @@ -1,2 +0,0 @@ -[metadata] -description_file = Readme.rst diff --git a/setup.py b/setup.py index b52cf8b..08b61de 100644 --- a/setup.py +++ b/setup.py @@ -1,131 +1,38 @@ -""" setup.py - -to compile cython files: -python setup.py build_ext --inplace - +"""setup.py Copyright 2014-2017, Mario Mulansky Distributed under the BSD License """ -from setuptools import setup, find_packages -from distutils.extension import Extension -import os.path - -try: - from Cython.Distutils import build_ext -except ImportError: - use_cython = False -else: - use_cython = True - - -class numpy_include(os.PathLike): - """Defers import of numpy until install_requires is through""" - def __str__(self): - import numpy - return numpy.get_include() - - def __fspath__(self): - return str(self) - -if os.path.isfile("pyspike/cython/cython_add.c") and \ - os.path.isfile("pyspike/cython/cython_get_tau.c") and \ - os.path.isfile("pyspike/cython/cython_profiles.c") and \ - os.path.isfile("pyspike/cython/cython_distances.c") and \ - os.path.isfile("pyspike/cython/cython_directionality.c") and \ - os.path.isfile("pyspike/cython/cython_simulated_annealing.c"): - use_c = True -else: - use_c = False - -if not use_cython and not use_c: - print('Cython not installed. Programs will be slow.') - # Ans = input('Abort? (Y/N)\n') - # if len(Ans)>0 and (Ans[0]=='Y' or Ans[0]=='y'): - # print("\nAborting\n") - # raise RuntimeError('User termination') - -cmdclass = {} -ext_modules = [] - -if use_cython: # Cython is available, compile .pyx -> .c - ext_modules += [ - Extension("pyspike.cython.cython_add", - ["pyspike/cython/cython_add.pyx"]), - Extension("pyspike.cython.cython_get_tau", - ["pyspike/cython/cython_get_tau.pyx"]), - Extension("pyspike.cython.cython_profiles", - ["pyspike/cython/cython_profiles.pyx"]), - Extension("pyspike.cython.cython_distances", - ["pyspike/cython/cython_distances.pyx"]), - Extension("pyspike.cython.cython_directionality", - ["pyspike/cython/cython_directionality.pyx"]), - Extension("pyspike.cython.cython_simulated_annealing", - ["pyspike/cython/cython_simulated_annealing.pyx"]) - ] - cmdclass.update({'build_ext': build_ext}) -elif use_c: # c files are there, compile to binaries - ext_modules += [ - Extension("pyspike.cython.cython_add", - ["pyspike/cython/cython_add.c"]), - Extension("pyspike.cython.cython_get_tau", - ["pyspike/cython/cython_get_tau.c"]), - Extension("pyspike.cython.cython_profiles", - ["pyspike/cython/cython_profiles.c"]), - Extension("pyspike.cython.cython_distances", - ["pyspike/cython/cython_distances.c"]), - Extension("pyspike.cython.cython_directionality", - ["pyspike/cython/cython_directionality.c"]), - Extension("pyspike.cython.cython_simulated_annealing", - ["pyspike/cython/cython_simulated_annealing.c"]) - ] -# neither cython nor c files available -> automatic fall-back to python backend +from setuptools import setup, Extension +from Cython.Build import cythonize +from Cython.Compiler import Options +import numpy + +Options.docstrings = True +Options.annotate = False + +ext_modules = [ + Extension("pyspike.cython.cython_add", ["pyspike/cython/cython_add.pyx"]), + Extension("pyspike.cython.cython_get_tau", ["pyspike/cython/cython_get_tau.pyx"]), + Extension("pyspike.cython.cython_profiles", ["pyspike/cython/cython_profiles.pyx"]), + Extension( + "pyspike.cython.cython_distances", ["pyspike/cython/cython_distances.pyx"] + ), + Extension( + "pyspike.cython.cython_directionality", + ["pyspike/cython/cython_directionality.pyx"], + ), + Extension( + "pyspike.cython.cython_simulated_annealing", + ["pyspike/cython/cython_simulated_annealing.pyx"], + ), +] setup( - name='pyspike', - packages=find_packages(exclude=['doc', 'test*']), - version='0.8.0', - cmdclass=cmdclass, - ext_modules=ext_modules, - include_dirs=[numpy_include()], - description='A Python library for the numerical analysis of spike\ -train similarity', - author='Mario Mulansky', - author_email='mario.mulansky@gmx.net', - license='BSD', - url='https://github.com/mariomulansky/PySpike', - install_requires=['numpy'], - keywords=['data analysis', 'spike', 'neuroscience'], # arbitrary keywords - classifiers=[ - # How mature is this project? Common values are - # 3 - Alpha - # 4 - Beta - # 5 - Production/Stable - 'Development Status :: 4 - Beta', - - # Indicate who your project is intended for - 'Intended Audience :: Science/Research', - 'Topic :: Scientific/Engineering', - 'Topic :: Scientific/Engineering :: Information Analysis', - - 'License :: OSI Approved :: BSD License', - - 'Programming Language :: Python :: 3', - 'Programming Language :: Python :: 3.7', - 'Programming Language :: Python :: 3.8', - 'Programming Language :: Python :: 3.9', - 'Programming Language :: Python :: 3.10', - ], - package_data={ - 'pyspike': ['cython/cython_add.c', - 'cython/cython_profiles.c', - 'cython/cython_get_tau.c', - 'cython/cython_distances.c', - 'cython/cython_directionality.c', - 'cython/cython_simulated_annealing.c'], - 'test': ['Spike_testdata.txt'] - } + name="pyspike", + ext_modules=cythonize(ext_modules), + include_dirs=[numpy.get_include()], ) diff --git a/test/numeric/test_regression_random_spikes.py b/test/numeric/test_regression_random_spikes.py index f38de8f..6d92d14 100644 --- a/test/numeric/test_regression_random_spikes.py +++ b/test/numeric/test_regression_random_spikes.py @@ -1,28 +1,39 @@ -""" regression benchmark +"""regression benchmark Copyright 2015, Mario Mulansky Distributed under the BSD License """ -from __future__ import print_function + import os + import numpy as np -from scipy.io import loadmat -import pyspike as spk +HAS_SCIPY = True +try: + from scipy.io import loadmat +except ImportError: + HAS_SCIPY = False +import pytest from numpy.testing import assert_almost_equal +import pyspike as spk + spk.disable_backend_warning = True max_trr_trials = 100 # speed things up -def test_regression_random(): +pytestmark = pytest.mark.skipif(not HAS_SCIPY, reason="scipy not available") + +def test_regression_random(): spike_file = os.path.join("test", "numeric", "regression_random_spikes.mat") spikes_name = "spikes" result_name = "Distances" - result_file = os.path.join("test", "numeric", "regression_random_results_cSPIKY.mat") + result_file = os.path.join( + "test", "numeric", "regression_random_results_cSPIKY.mat" + ) spike_train_sets = loadmat(spike_file)[spikes_name][0] results_cSPIKY = loadmat(result_file)[result_name] @@ -43,25 +54,32 @@ def test_regression_random(): spike_sync = spk.spike_sync_multi(spike_trains) spike_sync_prof = spk.spike_sync_profile_multi(spike_trains).avrg() - assert_almost_equal(isi, results_cSPIKY[i][0], decimal=14, - err_msg="Index: %d, ISI" % i) - assert_almost_equal(isi_prof, results_cSPIKY[i][0], decimal=14, - err_msg="Index: %d, ISI" % i) - - assert_almost_equal(spike, results_cSPIKY[i][1], decimal=14, - err_msg="Index: %d, SPIKE" % i) - assert_almost_equal(spike_prof, results_cSPIKY[i][1], decimal=14, - err_msg="Index: %d, SPIKE" % i) - - assert_almost_equal(spike_sync, spike_sync_prof, decimal=14, - err_msg="Index: %d, SPIKE-Sync" % i) - - -def check_regression_dataset(spike_file="benchmark.mat", - spikes_name="spikes", - result_file="results_cSPIKY.mat", - result_name="Distances"): - """ Debuging function """ + assert_almost_equal( + isi, results_cSPIKY[i][0], decimal=14, err_msg="Index: %d, ISI" % i + ) + assert_almost_equal( + isi_prof, results_cSPIKY[i][0], decimal=14, err_msg="Index: %d, ISI" % i + ) + + assert_almost_equal( + spike, results_cSPIKY[i][1], decimal=14, err_msg="Index: %d, SPIKE" % i + ) + assert_almost_equal( + spike_prof, results_cSPIKY[i][1], decimal=14, err_msg="Index: %d, SPIKE" % i + ) + + assert_almost_equal( + spike_sync, spike_sync_prof, decimal=14, err_msg="Index: %d, SPIKE-Sync" % i + ) + + +def check_regression_dataset( + spike_file="benchmark.mat", + spikes_name="spikes", + result_file="results_cSPIKY.mat", + result_name="Distances", +): + """Debuging function""" np.set_printoptions(precision=15) spike_train_sets = loadmat(spike_file)[spikes_name][0] @@ -83,14 +101,14 @@ def check_regression_dataset(spike_file="benchmark.mat", spike = spk.spike_distance_multi(spike_trains) # spike_sync = spk.spike_sync_multi(spike_trains) - if abs(isi - results_cSPIKY[i][0]) > 1E-14: + if abs(isi - results_cSPIKY[i][0]) > 1e-14: print("Error in ISI:", i, isi, results_cSPIKY[i][0]) print("Spike trains:") for st in spike_trains: print(st.spikes) err = abs(spike - results_cSPIKY[i][1]) - if err > 1E-14: + if err > 1e-14: err_count += 1 if err > err_max: err_max = err @@ -106,12 +124,14 @@ def check_regression_dataset(spike_file="benchmark.mat", def check_single_spike_train_set(index): - """ Debuging function """ + """Debuging function""" np.set_printoptions(precision=15) spike_file = os.path.join("test", "numeric", "regression_random_spikes.mat") spikes_name = "spikes" result_name = "Distances" - result_file = os.path.join("test", "numeric", "regression_random_results_cSPIKY.mat") + result_file = os.path.join( + "test", "numeric", "regression_random_results_cSPIKY.mat" + ) spike_train_sets = loadmat(spike_file)[spikes_name][0] @@ -138,7 +158,6 @@ def check_single_spike_train_set(index): if __name__ == "__main__": - test_regression_random() check_regression_dataset() check_single_spike_train_set(4) diff --git a/test/test_MRTS.py b/test/test_MRTS.py index c250205..91d2845 100644 --- a/test/test_MRTS.py +++ b/test/test_MRTS.py @@ -1,4 +1,4 @@ -""" test_MRTS.py +"""test_MRTS.py Tests the MRTS logic in all distances Also, test automatic generation of the threshold @@ -6,211 +6,234 @@ Copyright 2023, Thomas Kreuz Distributed under the BSD License """ + import numpy as np + import pyspike as spk from pyspike import SpikeTrain from pyspike.isi_lengths import default_thresh + def test_MRTS(): - """ single testcase for all 4 algorithms changing MRTS: - """ - v1 = [12.0000, 16.0000, 28.0000, 32.0000, 44.0000, 48.0000, 60.0000, 64.0000, 76.0000, 80.0000, ]; - v2 = [7.5376, 19.9131, 24.2137, 35.7255, 40.0961, 51.7076, 55.9124, 68.1017, 71.9863, 83.5994, ]; - edges=[0, 300] - max_tau=1000 + """single testcase for all 4 algorithms changing MRTS:""" + v1 = [ + 12.0000, + 16.0000, + 28.0000, + 32.0000, + 44.0000, + 48.0000, + 60.0000, + 64.0000, + 76.0000, + 80.0000, + ] + v2 = [ + 7.5376, + 19.9131, + 24.2137, + 35.7255, + 40.0961, + 51.7076, + 55.9124, + 68.1017, + 71.9863, + 83.5994, + ] + edges = [0, 300] + max_tau = 1000 sp1 = spk.SpikeTrain(v1, edges) sp2 = spk.SpikeTrain(v2, edges) ## SPIKE-SYNC - Results1 = {14:0., 15:.3, 16:.6, 17:.9, 18:1.} + Results1 = {14: 0.0, 15: 0.3, 16: 0.6, 17: 0.9, 18: 1.0} for r in Results1: c = spk.spike_sync(sp1, sp2, MRTS=r) np.testing.assert_almost_equal(c, Results1[r]) ## SPIKE Results2 = { - 0 : 0.12095, - 1 : 0.12095, - 2 : 0.12095, - 3 : 0.12095, - 4 : 0.12095, - 5 : 0.12095, - 6 : 0.12095, - 7 : 0.12095, - 8 : 0.12039, - 9 : 0.11434, - 10 : 0.10900, - 11 : 0.10464, - 12 : 0.10064, - 13 : 0.09418, - 14 : 0.08833, - 15 : 0.08326, - 16 : 0.07882, - 17 : 0.07491, - 18 : 0.07143, - 19 : 0.06832, - 20 : 0.06551, - 21 : 0.06298, - 22 : 0.06067, - 23 : 0.05857, - 24 : 0.05664, - 25 : 0.05487, - 26 : 0.05323, - 27 : 0.05171, - 28 : 0.05030, - 29 : 0.04899, - 30 : 0.04777, - 31 : 0.04662, - 32 : 0.04555, - 33 : 0.04454, - 34 : 0.04359, - 35 : 0.04270, - 36 : 0.04185, - 37 : 0.04105, - 38 : 0.04030, - 39 : 0.03958, - 40 : 0.03890, - 41 : 0.03825, - 42 : 0.03763, - 43 : 0.03704, - 44 : 0.03648, - 45 : 0.03594, - 46 : 0.03542, - 47 : 0.03493, - 48 : 0.03446, - 49 : 0.03401, - 50 : 0.03357, + 0: 0.12095, + 1: 0.12095, + 2: 0.12095, + 3: 0.12095, + 4: 0.12095, + 5: 0.12095, + 6: 0.12095, + 7: 0.12095, + 8: 0.12039, + 9: 0.11434, + 10: 0.10900, + 11: 0.10464, + 12: 0.10064, + 13: 0.09418, + 14: 0.08833, + 15: 0.08326, + 16: 0.07882, + 17: 0.07491, + 18: 0.07143, + 19: 0.06832, + 20: 0.06551, + 21: 0.06298, + 22: 0.06067, + 23: 0.05857, + 24: 0.05664, + 25: 0.05487, + 26: 0.05323, + 27: 0.05171, + 28: 0.05030, + 29: 0.04899, + 30: 0.04777, + 31: 0.04662, + 32: 0.04555, + 33: 0.04454, + 34: 0.04359, + 35: 0.04270, + 36: 0.04185, + 37: 0.04105, + 38: 0.04030, + 39: 0.03958, + 40: 0.03890, + 41: 0.03825, + 42: 0.03763, + 43: 0.03704, + 44: 0.03648, + 45: 0.03594, + 46: 0.03542, + 47: 0.03493, + 48: 0.03446, + 49: 0.03401, + 50: 0.03357, } for r in Results2: d = spk.spike_distance(sp1, sp2, MRTS=r) np.testing.assert_almost_equal(d, Results2[r], decimal=5) - ## RI Results3 = { - 0 : 0.12094, - 1 : 0.12094, - 2 : 0.12094, - 3 : 0.12094, - 4 : 0.12094, - 5 : 0.12094, - 6 : 0.12094, - 7 : 0.12094, - 8 : 0.12038, - 9 : 0.11432, - 10 : 0.10899, - 11 : 0.10463, - 12 : 0.10063, - 13 : 0.09417, - 14 : 0.08832, - 15 : 0.08325, - 16 : 0.07882, - 17 : 0.07490, - 18 : 0.07142, - 19 : 0.06831, - 20 : 0.06551, - 21 : 0.06297, - 22 : 0.06067, - 23 : 0.05856, - 24 : 0.05664, - 25 : 0.05486, - 26 : 0.05322, - 27 : 0.05171, - 28 : 0.05030, - 29 : 0.04899, - 30 : 0.04776, - 31 : 0.04662, - 32 : 0.04555, - 33 : 0.04454, - 34 : 0.04359, - 35 : 0.04269, - 36 : 0.04185, - 37 : 0.04105, - 38 : 0.04029, - 39 : 0.03957, - 40 : 0.03889, - 41 : 0.03824, - 42 : 0.03762, - 43 : 0.03703, - 44 : 0.03647, - 45 : 0.03593, - 46 : 0.03542, - 47 : 0.03493, - 48 : 0.03446, - 49 : 0.03400, - 50 : 0.03357, + 0: 0.12094, + 1: 0.12094, + 2: 0.12094, + 3: 0.12094, + 4: 0.12094, + 5: 0.12094, + 6: 0.12094, + 7: 0.12094, + 8: 0.12038, + 9: 0.11432, + 10: 0.10899, + 11: 0.10463, + 12: 0.10063, + 13: 0.09417, + 14: 0.08832, + 15: 0.08325, + 16: 0.07882, + 17: 0.07490, + 18: 0.07142, + 19: 0.06831, + 20: 0.06551, + 21: 0.06297, + 22: 0.06067, + 23: 0.05856, + 24: 0.05664, + 25: 0.05486, + 26: 0.05322, + 27: 0.05171, + 28: 0.05030, + 29: 0.04899, + 30: 0.04776, + 31: 0.04662, + 32: 0.04555, + 33: 0.04454, + 34: 0.04359, + 35: 0.04269, + 36: 0.04185, + 37: 0.04105, + 38: 0.04029, + 39: 0.03957, + 40: 0.03889, + 41: 0.03824, + 42: 0.03762, + 43: 0.03703, + 44: 0.03647, + 45: 0.03593, + 46: 0.03542, + 47: 0.03493, + 48: 0.03446, + 49: 0.03400, + 50: 0.03357, } for r in Results3: d = spk.spike_distance(sp1, sp2, MRTS=r, RI=True) - #print('%d : %.5f,'%(r, d)) + # print('%d : %.5f,'%(r, d)) np.testing.assert_almost_equal(d, Results3[r], decimal=5) ## ISI Results4 = { - 0 : 0.10796, - 1 : 0.10796, - 2 : 0.10796, - 3 : 0.10796, - 4 : 0.10796, - 5 : 0.10796, - 6 : 0.10796, - 7 : 0.10796, - 8 : 0.10796, - 9 : 0.10796, - 10 : 0.10796, - 11 : 0.10796, - 12 : 0.10704, - 13 : 0.10103, - 14 : 0.09547, - 15 : 0.09065, - 16 : 0.08643, - 17 : 0.08271, - 18 : 0.07940, - 19 : 0.07644, - 20 : 0.07378, - 21 : 0.07137, - 22 : 0.06918, - 23 : 0.06718, - 24 : 0.06534, - 25 : 0.06366, - 26 : 0.06210, - 27 : 0.06066, - 28 : 0.05932, - 29 : 0.05807, - 30 : 0.05691, - 31 : 0.05582, - 32 : 0.05480, - 33 : 0.05384, - 34 : 0.05294, - 35 : 0.05209, - 36 : 0.05128, - 37 : 0.05052, - 38 : 0.04980, - 39 : 0.04912, - 40 : 0.04847, - 41 : 0.04785, - 42 : 0.04727, - 43 : 0.04671, - 44 : 0.04617, - 45 : 0.04566, - 46 : 0.04517, - 47 : 0.04470, - 48 : 0.04425, - 49 : 0.04382, - 50 : 0.04341, - } + 0: 0.10796, + 1: 0.10796, + 2: 0.10796, + 3: 0.10796, + 4: 0.10796, + 5: 0.10796, + 6: 0.10796, + 7: 0.10796, + 8: 0.10796, + 9: 0.10796, + 10: 0.10796, + 11: 0.10796, + 12: 0.10704, + 13: 0.10103, + 14: 0.09547, + 15: 0.09065, + 16: 0.08643, + 17: 0.08271, + 18: 0.07940, + 19: 0.07644, + 20: 0.07378, + 21: 0.07137, + 22: 0.06918, + 23: 0.06718, + 24: 0.06534, + 25: 0.06366, + 26: 0.06210, + 27: 0.06066, + 28: 0.05932, + 29: 0.05807, + 30: 0.05691, + 31: 0.05582, + 32: 0.05480, + 33: 0.05384, + 34: 0.05294, + 35: 0.05209, + 36: 0.05128, + 37: 0.05052, + 38: 0.04980, + 39: 0.04912, + 40: 0.04847, + 41: 0.04785, + 42: 0.04727, + 43: 0.04671, + 44: 0.04617, + 45: 0.04566, + 46: 0.04517, + 47: 0.04470, + 48: 0.04425, + 49: 0.04382, + 50: 0.04341, + } for r in Results4: d = spk.isi_distance(sp1, sp2, MRTS=r) np.testing.assert_almost_equal(d, Results4[r], decimal=5) - print('OK1') + print("OK1") + def test_autoThresh(): - """ Automatic determination of MRTS - """ + """Automatic determination of MRTS""" edges = [0, 1000] spikes1 = SpikeTrain([64.88600, 305.81000, 696.00000, 800.0000], edges) spikes2 = SpikeTrain([67.88600, 302.81000, 699.00000], edges) @@ -219,17 +242,41 @@ def test_autoThresh(): spike_train_list = [spikes1, spikes2, spikes3, spikes4] Thresh = default_thresh(spike_train_list) - print('default_thresh got %.4f'%Thresh) - np.testing.assert_almost_equal(Thresh, 325.4342, decimal=4, err_msg="default_thresh") + print("default_thresh got %.4f" % Thresh) + np.testing.assert_almost_equal( + Thresh, 325.4342, decimal=4, err_msg="default_thresh" + ) c1 = spk.spike_sync(spikes1, spikes2, MRTS=Thresh) - c2 = spk.spike_sync(spikes1, spikes2, MRTS='auto') + c2 = spk.spike_sync(spikes1, spikes2, MRTS="auto") np.testing.assert_almost_equal(c1, c2, err_msg="spike_sync") # apply it to the first example avove - v1 = [12.0000, 16.0000, 28.0000, 32.0000, 44.0000, 48.0000, 60.0000, 64.0000, 76.0000, 80.0000, ]; - v2 = [7.5376, 19.9131, 24.2137, 35.7255, 40.0961, 51.7076, 55.9124, 68.1017, 71.9863, 83.5994, ]; - edges=[0, 300] + v1 = [ + 12.0000, + 16.0000, + 28.0000, + 32.0000, + 44.0000, + 48.0000, + 60.0000, + 64.0000, + 76.0000, + 80.0000, + ] + v2 = [ + 7.5376, + 19.9131, + 24.2137, + 35.7255, + 40.0961, + 51.7076, + 55.9124, + 68.1017, + 71.9863, + 83.5994, + ] + edges = [0, 300] sp1 = spk.SpikeTrain(v1, edges) sp2 = spk.SpikeTrain(v2, edges) @@ -238,29 +285,30 @@ def test_autoThresh(): ## Look at all 4 algorithms c1 = spk.spike_sync(sp1, sp2, MRTS=t) - c2 = spk.spike_sync(sp1, sp2, MRTS='auto') + c2 = spk.spike_sync(sp1, sp2, MRTS="auto") np.testing.assert_almost_equal(c1, c2, err_msg="spike_sync2") - print('SS thresh %.3f, results %.3f'%(t,c1)) + print("SS thresh %.3f, results %.3f" % (t, c1)) # compare with: {14:0., 15:.3, 16:.6, 17:.9, 18:1.} c1 = spk.spike_distance(sp1, sp2, MRTS=t) - c2 = spk.spike_distance(sp1, sp2, MRTS='auto') + c2 = spk.spike_distance(sp1, sp2, MRTS="auto") np.testing.assert_almost_equal(c1, c2, err_msg="spike_distance") c1 = spk.spike_distance(sp1, sp2, MRTS=t, RI=True) - c2 = spk.spike_distance(sp1, sp2, MRTS='auto', RI=True) + c2 = spk.spike_distance(sp1, sp2, MRTS="auto", RI=True) np.testing.assert_almost_equal(c1, c2, err_msg="RI") c1 = spk.isi_distance(sp1, sp2, MRTS=t) - c2 = spk.isi_distance(sp1, sp2, MRTS='auto') + c2 = spk.isi_distance(sp1, sp2, MRTS="auto") np.testing.assert_almost_equal(c1, c2, err_msg="ISI") c1 = spk.spike_directionality(sp1, sp2, MRTS=t) - c2 = spk.spike_directionality(sp1, sp2, MRTS='auto') + c2 = spk.spike_directionality(sp1, sp2, MRTS="auto") np.testing.assert_almost_equal(c1, c2, err_msg="directionality") - print('OK2') + print("OK2") + if __name__ == "__main__": test_MRTS() - test_autoThresh() \ No newline at end of file + test_autoThresh() diff --git a/test/test_auto_thresh.py b/test/test_auto_thresh.py index 5bda443..68c4f26 100644 --- a/test/test_auto_thresh.py +++ b/test/test_auto_thresh.py @@ -1,42 +1,45 @@ -import numpy as np from numpy.testing import assert_allclose + import pyspike as spk from pyspike import SpikeTrain from pyspike.isi_lengths import default_thresh + def gen_spike_trains(): - """ generate spike trains - """ + """generate spike trains""" t1 = SpikeTrain([0.2, 0.4, 0.6, 0.7], 1.0) t2 = SpikeTrain([0.3, 0.45, 0.8, 0.9, 0.95], 1.0) t3 = SpikeTrain([0.2, 0.4, 0.6], 1.0) t4 = SpikeTrain([0.1, 0.4, 0.5, 0.6], 1.0) return [t1, t2, t3, t4] -def auto_test(profile_func, profile_func_multi, dist_func_multi, dist_func_matrix, **kwargs): - """ verify that MRTS='auto' works for the non-pair interfaces - In: profile_func, profile_func_multi, dist_func_multi, dist_func_matrix - -- functions to test for a particular distance - asserts on error + +def auto_test( + profile_func, profile_func_multi, dist_func_multi, dist_func_matrix, **kwargs +): + """verify that MRTS='auto' works for the non-pair interfaces + In: profile_func, profile_func_multi, dist_func_multi, dist_func_matrix + -- functions to test for a particular distance + asserts on error """ spike_trains = gen_spike_trains() Thresh = default_thresh(spike_trains) - Thresh2 = Thresh/1000 + Thresh2 = Thresh / 1000 if profile_func is not None: r1 = profile_func(spike_trains, MRTS=Thresh, **kwargs) - r2 = profile_func(spike_trains, MRTS='auto', **kwargs) + r2 = profile_func(spike_trains, MRTS="auto", **kwargs) r1.almost_equal(r2) if profile_func_multi is not None: r1 = profile_func_multi(spike_trains, MRTS=Thresh, **kwargs) - r2 = profile_func_multi(spike_trains, MRTS='auto', **kwargs) + r2 = profile_func_multi(spike_trains, MRTS="auto", **kwargs) r1.almost_equal(r2) if dist_func_multi is not None: r1 = dist_func_multi(spike_trains, MRTS=Thresh, **kwargs) - r2 = dist_func_multi(spike_trains, MRTS='auto', **kwargs) + r2 = dist_func_multi(spike_trains, MRTS="auto", **kwargs) assert_allclose(r1, r2) r3 = dist_func_multi(spike_trains, MRTS=Thresh2, **kwargs) try: @@ -44,37 +47,41 @@ def auto_test(profile_func, profile_func_multi, dist_func_multi, dist_func_matri except: pass else: - raise Exception('dist_func_multi ignores Thresh') + raise Exception("dist_func_multi ignores Thresh") if dist_func_matrix is not None: r1 = dist_func_matrix(spike_trains, MRTS=Thresh, **kwargs) - r2 = dist_func_matrix(spike_trains, MRTS='auto', **kwargs) + r2 = dist_func_matrix(spike_trains, MRTS="auto", **kwargs) assert_allclose(r1, r2) + if __name__ == "__main__": """ driver for testing MRTS='auto' for non-pair interfaces goes through the various distances """ - auto_test(spk.isi_profile, - spk.isi_profile_multi, - spk.isi_distance_multi, - spk.isi_distance_matrix) - auto_test(spk.spike_profile, - spk.spike_profile_multi, - spk.spike_distance_multi, - spk.spike_distance_matrix) - auto_test(spk.spike_sync_profile, - spk.spike_sync_profile_multi, - None, - spk.spike_sync_matrix) - auto_test(spk.spike_profile, - spk.spike_profile_multi, - spk.spike_distance_multi, - spk.spike_distance_matrix, - RI=True) - auto_test(None, - None, - None, - spk.spike_directionality_matrix) - - \ No newline at end of file + auto_test( + spk.isi_profile, + spk.isi_profile_multi, + spk.isi_distance_multi, + spk.isi_distance_matrix, + ) + auto_test( + spk.spike_profile, + spk.spike_profile_multi, + spk.spike_distance_multi, + spk.spike_distance_matrix, + ) + auto_test( + spk.spike_sync_profile, + spk.spike_sync_profile_multi, + None, + spk.spike_sync_matrix, + ) + auto_test( + spk.spike_profile, + spk.spike_profile_multi, + spk.spike_distance_multi, + spk.spike_distance_matrix, + RI=True, + ) + auto_test(None, None, None, spk.spike_directionality_matrix) diff --git a/test/test_directionality.py b/test/test_directionality.py index 6230a49..c5536ad 100644 --- a/test/test_directionality.py +++ b/test/test_directionality.py @@ -1,4 +1,4 @@ -""" test_directionality.py +"""test_directionality.py Tests the directionality functions @@ -9,29 +9,25 @@ """ import numpy as np -from numpy.testing import assert_equal, assert_almost_equal, \ - assert_array_equal +from numpy.testing import assert_almost_equal, assert_array_equal import pyspike as spk -from pyspike import SpikeTrain, DiscreteFunc +from pyspike import DiscreteFunc, SpikeTrain + def test_spike_directionality(): - st1 = SpikeTrain([100, 200, 300], [0, 1000]) st2 = SpikeTrain([105, 205, 300], [0, 1000]) - assert_almost_equal(spk.spike_directionality(st1, st2), 2.0/3.0) - assert_almost_equal(spk.spike_directionality(st1, st2, normalize=False), - 2.0) + assert_almost_equal(spk.spike_directionality(st1, st2), 2.0 / 3.0) + assert_almost_equal(spk.spike_directionality(st1, st2, normalize=False), 2.0) # exchange order of spike trains should give exact negative profile - assert_almost_equal(spk.spike_directionality(st2, st1), -2.0/3.0) - assert_almost_equal(spk.spike_directionality(st2, st1, normalize=False), - -2.0) + assert_almost_equal(spk.spike_directionality(st2, st1), -2.0 / 3.0) + assert_almost_equal(spk.spike_directionality(st2, st1, normalize=False), -2.0) st3 = SpikeTrain([105, 195, 500], [0, 1000]) assert_almost_equal(spk.spike_directionality(st1, st3), 0.0) - assert_almost_equal(spk.spike_directionality(st1, st3, normalize=False), - 0.0) + assert_almost_equal(spk.spike_directionality(st1, st3, normalize=False), 0.0) assert_almost_equal(spk.spike_directionality(st3, st1), 0.0) D = spk.spike_directionality_matrix([st1, st2, st3], normalize=False) @@ -54,11 +50,10 @@ def test_spike_train_order(): f = spk.spike_train_order_profile(st1, st2) - assert f.almost_equal(DiscreteFunc(expected_x12, expected_y12, - expected_mp12)) - assert_almost_equal(f.avrg(), 2.0/3.0) + assert f.almost_equal(DiscreteFunc(expected_x12, expected_y12, expected_mp12)) + assert_almost_equal(f.avrg(), 2.0 / 3.0) assert_almost_equal(f.avrg(normalize=False), 4.0) - assert_almost_equal(spk.spike_train_order(st1, st2), 2.0/3.0) + assert_almost_equal(spk.spike_train_order(st1, st2), 2.0 / 3.0) assert_almost_equal(spk.spike_train_order(st1, st2, normalize=False), 4.0) expected_x23 = np.array([0, 105, 195, 205, 300, 500, 1000]) @@ -70,11 +65,10 @@ def test_spike_train_order(): assert_array_equal(f.x, expected_x23) assert_array_equal(f.y, expected_y23) assert_array_equal(f.mp, expected_mp23) - assert f.almost_equal(DiscreteFunc(expected_x23, expected_y23, - expected_mp23)) - assert_almost_equal(f.avrg(), -1.0/3.0) + assert f.almost_equal(DiscreteFunc(expected_x23, expected_y23, expected_mp23)) + assert_almost_equal(f.avrg(), -1.0 / 3.0) assert_almost_equal(f.avrg(normalize=False), -2.0) - assert_almost_equal(spk.spike_train_order(st2, st3), -1.0/3.0) + assert_almost_equal(spk.spike_train_order(st2, st3), -1.0 / 3.0) assert_almost_equal(spk.spike_train_order(st2, st3, normalize=False), -2.0) f = spk.spike_train_order_profile_multi([st1, st2, st3]) diff --git a/test/test_distance.py b/test/test_distance.py index 8fa719c..6ed08d8 100644 --- a/test/test_distance.py +++ b/test/test_distance.py @@ -1,4 +1,4 @@ -""" test_distance.py +"""test_distance.py Tests the isi- and spike-distance computation @@ -8,16 +8,19 @@ """ -from __future__ import print_function -import numpy as np +import os from copy import copy -from numpy.testing import assert_allclose, assert_almost_equal, \ - assert_array_almost_equal + +import numpy as np +from numpy.testing import ( + assert_allclose, + assert_almost_equal, + assert_array_almost_equal, +) import pyspike as spk from pyspike import SpikeTrain -import os TEST_PATH = os.path.dirname(os.path.realpath(__file__)) @@ -28,13 +31,24 @@ def test_isi(): # pen&paper calculation of the isi distance expected_times = [0.0, 0.2, 0.3, 0.4, 0.45, 0.6, 0.7, 0.8, 0.9, 0.95, 1.0] - expected_isi = [0.1/0.3, 0.1/0.3, 0.05/0.2, 0.05/0.2, 0.15/0.35, - 0.25/0.35, 0.05/0.35, 0.2/0.3, 0.25/0.3, 0.25/0.3] + expected_isi = [ + 0.1 / 0.3, + 0.1 / 0.3, + 0.05 / 0.2, + 0.05 / 0.2, + 0.15 / 0.35, + 0.25 / 0.35, + 0.05 / 0.35, + 0.2 / 0.3, + 0.25 / 0.3, + 0.25 / 0.3, + ] expected_times = np.array(expected_times) expected_isi = np.array(expected_isi) - expected_isi_val = sum((expected_times[1:] - expected_times[:-1]) * - expected_isi)/(expected_times[-1]-expected_times[0]) + expected_isi_val = sum( + (expected_times[1:] - expected_times[:-1]) * expected_isi + ) / (expected_times[-1] - expected_times[0]) f = spk.isi_profile(t1, t2) @@ -51,12 +65,13 @@ def test_isi(): t2 = SpikeTrain([0.1, 0.4, 0.5, 0.6], [0.0, 1.0]) expected_times = [0.0, 0.1, 0.2, 0.4, 0.5, 0.6, 1.0] - expected_isi = [0.1/0.3, 0.1/0.3, 0.1/0.3, 0.1/0.2, 0.1/0.2, 0.0/0.5] + expected_isi = [0.1 / 0.3, 0.1 / 0.3, 0.1 / 0.3, 0.1 / 0.2, 0.1 / 0.2, 0.0 / 0.5] expected_times = np.array(expected_times) expected_isi = np.array(expected_isi) - expected_isi_val = sum((expected_times[1:] - expected_times[:-1]) * - expected_isi)/(expected_times[-1]-expected_times[0]) + expected_isi_val = sum( + (expected_times[1:] - expected_times[:-1]) * expected_isi + ) / (expected_times[-1] - expected_times[0]) f = spk.isi_profile(t1, t2) @@ -78,14 +93,24 @@ def test_spike(): assert_allclose(f.x, expected_times) # from SPIKY: - y_all = np.array([0.000000000000000000, 0.555555555555555580, - 0.222222222222222210, 0.305555555555555580, - 0.255102040816326536, 0.000000000000000000, - 0.000000000000000000, 0.255102040816326536, - 0.255102040816326536, 0.285714285714285698, - 0.285714285714285698, 0.285714285714285698]) - - #assert_array_almost_equal(f.y1, y_all[::2]) + y_all = np.array( + [ + 0.000000000000000000, + 0.555555555555555580, + 0.222222222222222210, + 0.305555555555555580, + 0.255102040816326536, + 0.000000000000000000, + 0.000000000000000000, + 0.255102040816326536, + 0.255102040816326536, + 0.285714285714285698, + 0.285714285714285698, + 0.285714285714285698, + ] + ) + + # assert_array_almost_equal(f.y1, y_all[::2]) assert_array_almost_equal(f.y2, y_all[1::2]) assert_almost_equal(f.avrg(), 0.186309523809523814, decimal=15) @@ -96,23 +121,48 @@ def test_spike(): # pen&paper calculation of the spike distance expected_times = [0.0, 0.2, 0.3, 0.4, 0.45, 0.6, 0.7, 0.8, 0.9, 0.95, 1.0] - s1 = np.array([0.1, 0.1, (0.1*0.1+0.05*0.1)/0.2, 0.05, (0.05*0.15 * 2)/0.2, - 0.15, 0.1, (0.1*0.1+0.1*0.2)/0.3, (0.1*0.2+0.1*0.1)/0.3, - (0.1*0.05+0.1*0.25)/0.3, 0.1]) - s2 = np.array([0.1, (0.1*0.2+0.1*0.1)/0.3, 0.1, (0.1*0.05 * 2)/.15, 0.05, - (0.05*0.2+0.1*0.15)/0.35, (0.05*0.1+0.1*0.25)/0.35, - 0.1, 0.1, 0.05, 0.05]) + s1 = np.array( + [ + 0.1, + 0.1, + (0.1 * 0.1 + 0.05 * 0.1) / 0.2, + 0.05, + (0.05 * 0.15 * 2) / 0.2, + 0.15, + 0.1, + (0.1 * 0.1 + 0.1 * 0.2) / 0.3, + (0.1 * 0.2 + 0.1 * 0.1) / 0.3, + (0.1 * 0.05 + 0.1 * 0.25) / 0.3, + 0.1, + ] + ) + s2 = np.array( + [ + 0.1, + (0.1 * 0.2 + 0.1 * 0.1) / 0.3, + 0.1, + (0.1 * 0.05 * 2) / 0.15, + 0.05, + (0.05 * 0.2 + 0.1 * 0.15) / 0.35, + (0.05 * 0.1 + 0.1 * 0.25) / 0.35, + 0.1, + 0.1, + 0.05, + 0.05, + ] + ) isi1 = np.array([0.2, 0.2, 0.2, 0.2, 0.2, 0.1, 0.3, 0.3, 0.3, 0.3]) isi2 = np.array([0.3, 0.3, 0.15, 0.15, 0.35, 0.35, 0.35, 0.1, 0.05, 0.05]) - expected_y1 = (s1[:-1]*isi2+s2[:-1]*isi1) / (0.5*(isi1+isi2)**2) - expected_y2 = (s1[1:]*isi2+s2[1:]*isi1) / (0.5*(isi1+isi2)**2) + expected_y1 = (s1[:-1] * isi2 + s2[:-1] * isi1) / (0.5 * (isi1 + isi2) ** 2) + expected_y2 = (s1[1:] * isi2 + s2[1:] * isi1) / (0.5 * (isi1 + isi2) ** 2) expected_times = np.array(expected_times) expected_y1 = np.array(expected_y1) expected_y2 = np.array(expected_y2) - expected_spike_val = sum((expected_times[1:] - expected_times[:-1]) * - (expected_y1+expected_y2)/2) - expected_spike_val /= (expected_times[-1]-expected_times[0]) + expected_spike_val = sum( + (expected_times[1:] - expected_times[:-1]) * (expected_y1 + expected_y2) / 2 + ) + expected_spike_val /= expected_times[-1] - expected_times[0] print("SPIKE value:", expected_spike_val) @@ -122,8 +172,7 @@ def test_spike(): assert_array_almost_equal(f.y1, expected_y1, decimal=15) assert_array_almost_equal(f.y2, expected_y2, decimal=15) assert_almost_equal(f.avrg(), expected_spike_val, decimal=15) - assert_almost_equal(spk.spike_distance(t1, t2), expected_spike_val, - decimal=15) + assert_almost_equal(spk.spike_distance(t1, t2), expected_spike_val, decimal=15) # check with some equal spike times t1 = SpikeTrain([0.2, 0.4, 0.6], [0.0, 1.0]) @@ -132,28 +181,27 @@ def test_spike(): expected_times = [0.0, 0.1, 0.2, 0.4, 0.5, 0.6, 1.0] # due to the edge correction in the beginning, s1 and s2 are different # for left and right values - s1_r = np.array([0.1, (0.1*0.1+0.1*0.1)/0.2, 0.1, 0.0, 0.0, 0.0, 0.0]) - s1_l = np.array([0.1, (0.1*0.1+0.1*0.1)/0.2, 0.1, 0.0, 0.0, 0.0, 0.0]) + s1_r = np.array([0.1, (0.1 * 0.1 + 0.1 * 0.1) / 0.2, 0.1, 0.0, 0.0, 0.0, 0.0]) + s1_l = np.array([0.1, (0.1 * 0.1 + 0.1 * 0.1) / 0.2, 0.1, 0.0, 0.0, 0.0, 0.0]) # s2_r = np.array([0.1*0.1/0.3, 0.1*0.3/0.3, 0.1*0.2/0.3, # 0.0, 0.1, 0.0, 0.0]) # s2_l = np.array([0.1*0.1/0.3, 0.1*0.1/0.3, 0.1*0.2/0.3, 0.0, # 0.1, 0.0, 0.0]) # eero's edge correction: - s2_r = np.array([0.1, 0.1*0.3/0.3, 0.1*0.2/0.3, - 0.0, 0.1, 0.0, 0.0]) - s2_l = np.array([0.1, 0.1*0.3/0.3, 0.1*0.2/0.3, 0.0, - 0.1, 0.0, 0.0]) + s2_r = np.array([0.1, 0.1 * 0.3 / 0.3, 0.1 * 0.2 / 0.3, 0.0, 0.1, 0.0, 0.0]) + s2_l = np.array([0.1, 0.1 * 0.3 / 0.3, 0.1 * 0.2 / 0.3, 0.0, 0.1, 0.0, 0.0]) isi1 = np.array([0.2, 0.2, 0.2, 0.2, 0.2, 0.4]) isi2 = np.array([0.3, 0.3, 0.3, 0.1, 0.1, 0.4]) - expected_y1 = (s1_r[:-1]*isi2+s2_r[:-1]*isi1) / (0.5*(isi1+isi2)**2) - expected_y2 = (s1_l[1:]*isi2+s2_l[1:]*isi1) / (0.5*(isi1+isi2)**2) + expected_y1 = (s1_r[:-1] * isi2 + s2_r[:-1] * isi1) / (0.5 * (isi1 + isi2) ** 2) + expected_y2 = (s1_l[1:] * isi2 + s2_l[1:] * isi1) / (0.5 * (isi1 + isi2) ** 2) expected_times = np.array(expected_times) expected_y1 = np.array(expected_y1) expected_y2 = np.array(expected_y2) - expected_spike_val = sum((expected_times[1:] - expected_times[:-1]) * - (expected_y1+expected_y2)/2) - expected_spike_val /= (expected_times[-1]-expected_times[0]) + expected_spike_val = sum( + (expected_times[1:] - expected_times[:-1]) * (expected_y1 + expected_y2) / 2 + ) + expected_spike_val /= expected_times[-1] - expected_times[0] f = spk.spike_profile(t1, t2) @@ -161,8 +209,7 @@ def test_spike(): assert_array_almost_equal(f.y1, expected_y1, decimal=14) assert_array_almost_equal(f.y2, expected_y2, decimal=14) assert_almost_equal(f.avrg(), expected_spike_val, decimal=16) - assert_almost_equal(spk.spike_distance(t1, t2), expected_spike_val, - decimal=16) + assert_almost_equal(spk.spike_distance(t1, t2), expected_spike_val, decimal=16) def test_spike_sync(): @@ -177,16 +224,13 @@ def test_spike_sync(): assert_array_almost_equal(f.x, expected_x, decimal=16) assert_array_almost_equal(f.y, expected_y, decimal=16) - assert_almost_equal(spk.spike_sync(spikes1, spikes2), - 0.5, decimal=16) + assert_almost_equal(spk.spike_sync(spikes1, spikes2), 0.5, decimal=16) # test with some small max_tau, spike_sync should be 0 - assert_almost_equal(spk.spike_sync(spikes1, spikes2, max_tau=0.05), - 0.0, decimal=16) + assert_almost_equal(spk.spike_sync(spikes1, spikes2, max_tau=0.05), 0.0, decimal=16) spikes2 = SpikeTrain([3.1], 4.0) - assert_almost_equal(spk.spike_sync(spikes1, spikes2), - 0.5, decimal=16) + assert_almost_equal(spk.spike_sync(spikes1, spikes2), 0.5, decimal=16) spikes2 = SpikeTrain([1.1], 4.0) @@ -198,28 +242,28 @@ def test_spike_sync(): assert_array_almost_equal(f.x, expected_x, decimal=16) assert_array_almost_equal(f.y, expected_y, decimal=16) - assert_almost_equal(spk.spike_sync(spikes1, spikes2), - 0.5, decimal=16) + assert_almost_equal(spk.spike_sync(spikes1, spikes2), 0.5, decimal=16) spikes2 = SpikeTrain([0.9], 4.0) - assert_almost_equal(spk.spike_sync(spikes1, spikes2), - 0.5, decimal=16) + assert_almost_equal(spk.spike_sync(spikes1, spikes2), 0.5, decimal=16) spikes2 = SpikeTrain([3.0], 4.0) - assert_almost_equal(spk.spike_sync(spikes1, spikes2), - 0.5, decimal=16) + assert_almost_equal(spk.spike_sync(spikes1, spikes2), 0.5, decimal=16) spikes2 = SpikeTrain([1.0], 4.0) - assert_almost_equal(spk.spike_sync(spikes1, spikes2), - 0.5, decimal=16) + assert_almost_equal(spk.spike_sync(spikes1, spikes2), 0.5, decimal=16) spikes2 = SpikeTrain([1.5, 3.0], 4.0) - assert_almost_equal(spk.spike_sync(spikes1, spikes2), - 0.4, decimal=16) + assert_almost_equal(spk.spike_sync(spikes1, spikes2), 0.4, decimal=16) spikes1 = SpikeTrain([1.0, 2.0, 4.0], 4.0) spikes2 = SpikeTrain([3.8], 4.0) - spikes3 = SpikeTrain([3.9, ], 4.0) + spikes3 = SpikeTrain( + [ + 3.9, + ], + 4.0, + ) expected_x = np.array([0.0, 1.0, 2.0, 3.8, 4.0, 4.0]) expected_y = np.array([0.0, 0.0, 0.0, 1.0, 1.0, 1.0]) @@ -236,8 +280,8 @@ def test_spike_sync(): f.add(f2) i12 = f.integral() - assert_allclose(i1[0]+i2[0], i12[0]) - assert_allclose(i1[1]+i2[1], i12[1]) + assert_allclose(i1[0] + i2[0], i12[0]) + assert_allclose(i1[1] + i2[1], i12[1]) def check_multi_profile(profile_func, profile_func_multi, dist_func_multi): @@ -269,7 +313,7 @@ def check_multi_profile(profile_func, profile_func_multi, dist_func_multi): f = copy(f12) f.add(f13) f.add(f23) - f.mul_scalar(1.0/3) + f.mul_scalar(1.0 / 3) f_multi = profile_func_multi(spike_trains, [0, 1, 2]) assert f_multi.almost_equal(f, decimal=14) d = dist_func_multi(spike_trains, [0, 1, 2]) @@ -279,35 +323,35 @@ def check_multi_profile(profile_func, profile_func_multi, dist_func_multi): f.add(f14) f.add(f24) f.add(f34) - f.mul_scalar(1.0/6) + f.mul_scalar(1.0 / 6) f_multi = profile_func_multi(spike_trains) assert f_multi.almost_equal(f, decimal=14) def test_multi_isi(): - check_multi_profile(spk.isi_profile, spk.isi_profile_multi, - spk.isi_distance_multi) + check_multi_profile(spk.isi_profile, spk.isi_profile_multi, spk.isi_distance_multi) def test_multi_spike(): - check_multi_profile(spk.spike_profile, spk.spike_profile_multi, - spk.spike_distance_multi) + check_multi_profile( + spk.spike_profile, spk.spike_profile_multi, spk.spike_distance_multi + ) def test_multi_spike_sync(): # some basic multivariate check - spikes1 = SpikeTrain([100, 300, 400, 405, 410, 500, 700, 800, - 805, 810, 815, 900], 1000) - spikes2 = SpikeTrain([100, 200, 205, 210, 295, 350, 400, 510, - 600, 605, 700, 910], 1000) - spikes3 = SpikeTrain([100, 180, 198, 295, 412, 420, 510, 640, - 695, 795, 820, 920], 1000) - assert_almost_equal(spk.spike_sync(spikes1, spikes2), - 0.5, decimal=15) - assert_almost_equal(spk.spike_sync(spikes1, spikes3), - 0.5, decimal=15) - assert_almost_equal(spk.spike_sync(spikes2, spikes3), - 0.5, decimal=15) + spikes1 = SpikeTrain( + [100, 300, 400, 405, 410, 500, 700, 800, 805, 810, 815, 900], 1000 + ) + spikes2 = SpikeTrain( + [100, 200, 205, 210, 295, 350, 400, 510, 600, 605, 700, 910], 1000 + ) + spikes3 = SpikeTrain( + [100, 180, 198, 295, 412, 420, 510, 640, 695, 795, 820, 920], 1000 + ) + assert_almost_equal(spk.spike_sync(spikes1, spikes2), 0.5, decimal=15) + assert_almost_equal(spk.spike_sync(spikes1, spikes3), 0.5, decimal=15) + assert_almost_equal(spk.spike_sync(spikes2, spikes3), 0.5, decimal=15) f = spk.spike_sync_profile_multi([spikes1, spikes2, spikes3]) # hands on definition of the average multivariate spike synchronization @@ -315,12 +359,14 @@ def test_multi_spike_sync(): # (np.sum(f1.mp[1:-1])+np.sum(f2.mp[1:-1])+np.sum(f3.mp[1:-1])) expected = 0.5 assert_almost_equal(f.avrg(), expected, decimal=15) - assert_almost_equal(spk.spike_sync_multi([spikes1, spikes2, spikes3]), - expected, decimal=15) + assert_almost_equal( + spk.spike_sync_multi([spikes1, spikes2, spikes3]), expected, decimal=15 + ) # multivariate regression test spike_trains = spk.load_spike_trains_from_txt( - os.path.join(TEST_PATH, "SPIKE_Sync_Test.txt"), edges=[0, 4000]) + os.path.join(TEST_PATH, "SPIKE_Sync_Test.txt"), edges=[0, 4000] + ) # extract all spike times spike_times = np.array([]) for st in spike_trains: @@ -342,12 +388,11 @@ def test_multi_spike_sync(): sts.append(SpikeTrain([], [0, 10])) sts.append(SpikeTrain([], [0, 10])) - assert_almost_equal(spk.spike_sync_multi(sts), 1.0/6.0, decimal=15) - assert_almost_equal(spk.spike_sync_profile_multi(sts).avrg(), 1.0/6.0, - decimal=15) + assert_almost_equal(spk.spike_sync_multi(sts), 1.0 / 6.0, decimal=15) + assert_almost_equal(spk.spike_sync_profile_multi(sts).avrg(), 1.0 / 6.0, decimal=15) -def check_dist_matrix(dist_func, dist_matrix_func, Diagonal=0.): +def check_dist_matrix(dist_func, dist_matrix_func, Diagonal=0.0): # generate spike trains: t1 = SpikeTrain([0.2, 0.4, 0.6, 0.7], 1.0) t2 = SpikeTrain([0.3, 0.45, 0.8, 0.9, 0.95], 1.0) @@ -367,7 +412,7 @@ def check_dist_matrix(dist_func, dist_matrix_func, Diagonal=0.): for i in range(4): assert_allclose(Diagonal, f_matrix[i, i]) for i in range(4): - for j in range(i+1, 4): + for j in range(i + 1, 4): assert_allclose(f_matrix[i, j], f_matrix[j, i]) assert_allclose(f12, f_matrix[1, 0]) assert_allclose(f13, f_matrix[2, 0]) @@ -386,7 +431,7 @@ def test_spike_matrix(): def test_spike_sync_matrix(): - check_dist_matrix(spk.spike_sync, spk.spike_sync_matrix, Diagonal=1.) + check_dist_matrix(spk.spike_sync, spk.spike_sync_matrix, Diagonal=1.0) def test_regression_spiky(): @@ -397,7 +442,7 @@ def test_regression_spiky(): isi_dist = spk.isi_distance(st1, st2) assert_almost_equal(isi_dist, 9.0909090909090939e-02, decimal=15) isi_profile = spk.isi_profile(st1, st2) - assert_allclose(isi_profile.y, 0.1/1.1 * np.ones_like(isi_profile.y)) + assert_allclose(isi_profile.y, 0.1 / 1.1 * np.ones_like(isi_profile.y)) spike_dist = spk.spike_distance(st1, st2) assert_allclose(spike_dist, 0.211058782487353908) @@ -408,13 +453,14 @@ def test_regression_spiky(): # multivariate check spike_trains = spk.load_spike_trains_from_txt( - os.path.join(TEST_PATH, "PySpike_testdata.txt"), (0.0, 4000.0)) + os.path.join(TEST_PATH, "PySpike_testdata.txt"), (0.0, 4000.0) + ) isi_dist = spk.isi_distance_multi(spike_trains) # get the full precision from SPIKY assert_almost_equal(isi_dist, 0.17051816816999129656, decimal=15) spike_profile = spk.spike_profile_multi(spike_trains) - assert_allclose(len(spike_profile.y1)+len(spike_profile.y2), 1252) + assert_allclose(len(spike_profile.y1) + len(spike_profile.y2), 1252) spike_dist = spk.spike_distance_multi(spike_trains) # get the full precision from SPIKY @@ -431,11 +477,24 @@ def test_regression_spiky(): f = spk.spike_profile(st1, st2) expected_times = np.array([0.0, 0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6.0]) - y_all = np.array([0.271604938271605, 0.271604938271605, 0.271604938271605, - 0.617283950617284, 0.617283950617284, 0.444444444444444, - 0.285714285714286, 0.285714285714286, 0.444444444444444, - 0.617283950617284, 0.617283950617284, 0.271604938271605, - 0.271604938271605, 0.271604938271605]) + y_all = np.array( + [ + 0.271604938271605, + 0.271604938271605, + 0.271604938271605, + 0.617283950617284, + 0.617283950617284, + 0.444444444444444, + 0.285714285714286, + 0.285714285714286, + 0.444444444444444, + 0.617283950617284, + 0.617283950617284, + 0.271604938271605, + 0.271604938271605, + 0.271604938271605, + ] + ) expected_y1 = y_all[::2] expected_y2 = y_all[1::2] @@ -446,7 +505,8 @@ def test_regression_spiky(): def test_multi_variate_subsets(): spike_trains = spk.load_spike_trains_from_txt( - os.path.join(TEST_PATH, "PySpike_testdata.txt"), (0.0, 4000.0)) + os.path.join(TEST_PATH, "PySpike_testdata.txt"), (0.0, 4000.0) + ) sub_set = [1, 3, 5, 7] spike_trains_sub_set = [spike_trains[i] for i in sub_set] diff --git a/test/test_empty.py b/test/test_empty.py index f82203e..42a308c 100644 --- a/test/test_empty.py +++ b/test/test_empty.py @@ -1,4 +1,4 @@ -""" test_empty.py +"""test_empty.py Tests the distance measure for empty spike trains @@ -8,10 +8,13 @@ """ -from __future__ import print_function import numpy as np -from numpy.testing import assert_allclose, assert_almost_equal, \ - assert_array_equal, assert_array_almost_equal +from numpy.testing import ( + assert_allclose, + assert_almost_equal, + assert_array_almost_equal, + assert_array_equal, +) import pyspike as spk from pyspike import SpikeTrain @@ -22,11 +25,21 @@ def test_get_non_empty(): spikes = st.get_spikes_non_empty() assert_array_equal(spikes, [0.0, 1.0]) - st = SpikeTrain([0.5, ], edges=(0.0, 1.0)) + st = SpikeTrain( + [ + 0.5, + ], + edges=(0.0, 1.0), + ) spikes = st.get_spikes_non_empty() # assert_array_equal(spikes, [0.0, 0.5, 1.0]) # spike trains with one spike don't get edge spikes anymore - assert_array_equal(spikes, [0.5, ]) + assert_array_equal( + spikes, + [ + 0.5, + ], + ) def test_isi_empty(): @@ -37,25 +50,45 @@ def test_isi_empty(): prof = spk.isi_profile(st1, st2) assert_allclose(d, prof.avrg()) assert_array_equal(prof.x, [0.0, 1.0]) - assert_array_equal(prof.y, [0.0, ]) + assert_array_equal( + prof.y, + [ + 0.0, + ], + ) st1 = SpikeTrain([], edges=(0.0, 1.0)) - st2 = SpikeTrain([0.4, ], edges=(0.0, 1.0)) + st2 = SpikeTrain( + [ + 0.4, + ], + edges=(0.0, 1.0), + ) d = spk.isi_distance(st1, st2) - assert_allclose(d, 0.6*0.4+0.4*0.6) + assert_allclose(d, 0.6 * 0.4 + 0.4 * 0.6) prof = spk.isi_profile(st1, st2) assert_allclose(d, prof.avrg()) assert_array_equal(prof.x, [0.0, 0.4, 1.0]) assert_array_equal(prof.y, [0.6, 0.4]) - st1 = SpikeTrain([0.6, ], edges=(0.0, 1.0)) - st2 = SpikeTrain([0.4, ], edges=(0.0, 1.0)) + st1 = SpikeTrain( + [ + 0.6, + ], + edges=(0.0, 1.0), + ) + st2 = SpikeTrain( + [ + 0.4, + ], + edges=(0.0, 1.0), + ) d = spk.isi_distance(st1, st2) - assert_almost_equal(d, 0.2/0.6*0.4 + 0.0 + 0.2/0.6*0.4, decimal=15) + assert_almost_equal(d, 0.2 / 0.6 * 0.4 + 0.0 + 0.2 / 0.6 * 0.4, decimal=15) prof = spk.isi_profile(st1, st2) assert_allclose(d, prof.avrg()) assert_array_almost_equal(prof.x, [0.0, 0.4, 0.6, 1.0], decimal=15) - assert_array_almost_equal(prof.y, [0.2/0.6, 0.0, 0.2/0.6], decimal=15) + assert_array_almost_equal(prof.y, [0.2 / 0.6, 0.0, 0.2 / 0.6], decimal=15) def test_spike_empty(): @@ -66,37 +99,69 @@ def test_spike_empty(): prof = spk.spike_profile(st1, st2) assert_allclose(d, prof.avrg()) assert_array_equal(prof.x, [0.0, 1.0]) - assert_array_equal(prof.y1, [0.0, ]) - assert_array_equal(prof.y2, [0.0, ]) + assert_array_equal( + prof.y1, + [ + 0.0, + ], + ) + assert_array_equal( + prof.y2, + [ + 0.0, + ], + ) st1 = SpikeTrain([], edges=(0.0, 1.0)) - st2 = SpikeTrain([0.4, ], edges=(0.0, 1.0)) + st2 = SpikeTrain( + [ + 0.4, + ], + edges=(0.0, 1.0), + ) d = spk.spike_distance(st1, st2) - d_expect = 2*0.4*0.4*1.0/(0.4+1.0)**2 + 2*0.6*0.4*1.0/(0.6+1.0)**2 + d_expect = ( + 2 * 0.4 * 0.4 * 1.0 / (0.4 + 1.0) ** 2 + 2 * 0.6 * 0.4 * 1.0 / (0.6 + 1.0) ** 2 + ) assert_almost_equal(d, d_expect, decimal=15) prof = spk.spike_profile(st1, st2) assert_allclose(d, prof.avrg()) assert_array_equal(prof.x, [0.0, 0.4, 1.0]) - assert_array_almost_equal(prof.y1, [2*0.4*1.0/(0.4+1.0)**2, - 2*0.4*1.0/(0.6+1.0)**2], - decimal=15) - assert_array_almost_equal(prof.y2, [2*0.4*1.0/(0.4+1.0)**2, - 2*0.4*1.0/(0.6+1.0)**2], - decimal=15) - - st1 = SpikeTrain([0.6, ], edges=(0.0, 1.0)) - st2 = SpikeTrain([0.4, ], edges=(0.0, 1.0)) + assert_array_almost_equal( + prof.y1, + [2 * 0.4 * 1.0 / (0.4 + 1.0) ** 2, 2 * 0.4 * 1.0 / (0.6 + 1.0) ** 2], + decimal=15, + ) + assert_array_almost_equal( + prof.y2, + [2 * 0.4 * 1.0 / (0.4 + 1.0) ** 2, 2 * 0.4 * 1.0 / (0.6 + 1.0) ** 2], + decimal=15, + ) + + st1 = SpikeTrain( + [ + 0.6, + ], + edges=(0.0, 1.0), + ) + st2 = SpikeTrain( + [ + 0.4, + ], + edges=(0.0, 1.0), + ) d = spk.spike_distance(st1, st2) s1 = np.array([0.2, 0.2, 0.2, 0.2]) s2 = np.array([0.2, 0.2, 0.2, 0.2]) isi1 = np.array([0.6, 0.6, 0.4]) isi2 = np.array([0.4, 0.6, 0.6]) - expected_y1 = (s1[:-1]*isi2+s2[:-1]*isi1) / (0.5*(isi1+isi2)**2) - expected_y2 = (s1[1:]*isi2+s2[1:]*isi1) / (0.5*(isi1+isi2)**2) + expected_y1 = (s1[:-1] * isi2 + s2[:-1] * isi1) / (0.5 * (isi1 + isi2) ** 2) + expected_y2 = (s1[1:] * isi2 + s2[1:] * isi1) / (0.5 * (isi1 + isi2) ** 2) expected_times = np.array([0.0, 0.4, 0.6, 1.0]) - expected_spike_val = sum((expected_times[1:] - expected_times[:-1]) * - (expected_y1+expected_y2)/2) - expected_spike_val /= (expected_times[-1]-expected_times[0]) + expected_spike_val = sum( + (expected_times[1:] - expected_times[:-1]) * (expected_y1 + expected_y2) / 2 + ) + expected_spike_val /= expected_times[-1] - expected_times[0] assert_almost_equal(d, expected_spike_val, decimal=15) prof = spk.spike_profile(st1, st2) @@ -117,7 +182,12 @@ def test_spike_sync_empty(): assert_array_equal(prof.y, [1.0, 1.0]) st1 = SpikeTrain([], edges=(0.0, 1.0)) - st2 = SpikeTrain([0.4, ], edges=(0.0, 1.0)) + st2 = SpikeTrain( + [ + 0.4, + ], + edges=(0.0, 1.0), + ) d = spk.spike_sync(st1, st2) assert_allclose(d, 0.0) prof = spk.spike_sync_profile(st1, st2) @@ -125,8 +195,18 @@ def test_spike_sync_empty(): assert_array_equal(prof.x, [0.0, 0.4, 1.0]) assert_array_equal(prof.y, [0.0, 0.0, 0.0]) - st1 = SpikeTrain([0.6, ], edges=(0.0, 1.0)) - st2 = SpikeTrain([0.4, ], edges=(0.0, 1.0)) + st1 = SpikeTrain( + [ + 0.6, + ], + edges=(0.0, 1.0), + ) + st2 = SpikeTrain( + [ + 0.4, + ], + edges=(0.0, 1.0), + ) d = spk.spike_sync(st1, st2) assert_almost_equal(d, 1.0, decimal=15) prof = spk.spike_sync_profile(st1, st2) @@ -134,8 +214,18 @@ def test_spike_sync_empty(): assert_array_almost_equal(prof.x, [0.0, 0.4, 0.6, 1.0], decimal=15) assert_array_almost_equal(prof.y, [1.0, 1.0, 1.0, 1.0], decimal=15) - st1 = SpikeTrain([0.2, ], edges=(0.0, 1.0)) - st2 = SpikeTrain([0.8, ], edges=(0.0, 1.0)) + st1 = SpikeTrain( + [ + 0.2, + ], + edges=(0.0, 1.0), + ) + st2 = SpikeTrain( + [ + 0.8, + ], + edges=(0.0, 1.0), + ) d = spk.spike_sync(st1, st2) assert_almost_equal(d, 0.0, decimal=15) prof = spk.spike_sync_profile(st1, st2) diff --git a/test/test_function.py b/test/test_function.py index 2e870b2..0332483 100644 --- a/test/test_function.py +++ b/test/test_function.py @@ -1,4 +1,4 @@ -""" test_function.py +"""test_function.py Tests the PieceWiseConst and PieceWiseLinear functions @@ -7,12 +7,16 @@ Distributed under the BSD License """ -from __future__ import print_function -import numpy as np from copy import copy + +import numpy as np import pytest -from numpy.testing import assert_allclose, assert_almost_equal, \ - assert_array_equal, assert_array_almost_equal +from numpy.testing import ( + assert_allclose, + assert_almost_equal, + assert_array_almost_equal, + assert_array_equal, +) import pyspike as spk @@ -29,12 +33,14 @@ def test_pwc(): assert_allclose(f(1.0), 0.25) assert_allclose(f(2.0), 0.5) assert_allclose(f(2.25), 1.5) - assert_allclose(f(2.5), 2.25/2) + assert_allclose(f(2.5), 2.25 / 2) assert_allclose(f(3.5), 0.75) assert_allclose(f(4.0), 0.75) - assert_array_equal(f([0.0, 0.5, 1.0, 2.0, 2.25, 2.5, 3.5, 4.0]), - [1.0, 1.0, 0.25, 0.5, 1.5, 2.25/2, 0.75, 0.75]) + assert_array_equal( + f([0.0, 0.5, 1.0, 2.0, 2.25, 2.5, 3.5, 4.0]), + [1.0, 1.0, 0.25, 0.5, 1.5, 2.25 / 2, 0.75, 0.75], + ) xp, yp = f.get_plottable_data() @@ -43,29 +49,33 @@ def test_pwc(): assert_array_almost_equal(xp, xp_expected, decimal=16) assert_array_almost_equal(yp, yp_expected, decimal=16) - assert_almost_equal(f.avrg(), (1.0-0.5+0.5*1.5+1.5*0.75)/4.0, decimal=16) + assert_almost_equal( + f.avrg(), (1.0 - 0.5 + 0.5 * 1.5 + 1.5 * 0.75) / 4.0, decimal=16 + ) # interval averaging a = f.avrg([0.5, 3.5]) - assert_almost_equal(a, (0.5-0.5+0.5*1.5+1.0*0.75)/3.0, decimal=16) + assert_almost_equal(a, (0.5 - 0.5 + 0.5 * 1.5 + 1.0 * 0.75) / 3.0, decimal=16) a = f.avrg([1.5, 3.5]) - assert_almost_equal(a, (-0.5*0.5+0.5*1.5+1.0*0.75)/2.0, decimal=16) + assert_almost_equal(a, (-0.5 * 0.5 + 0.5 * 1.5 + 1.0 * 0.75) / 2.0, decimal=16) a = f.avrg([1.0, 2.0]) - assert_almost_equal(a, (1.0*-0.5)/1.0, decimal=16) + assert_almost_equal(a, (1.0 * -0.5) / 1.0, decimal=16) a = f.avrg([1.0, 3.5]) - assert_almost_equal(a, (-0.5*1.0+0.5*1.5+1.0*0.75)/2.5, decimal=16) + assert_almost_equal(a, (-0.5 * 1.0 + 0.5 * 1.5 + 1.0 * 0.75) / 2.5, decimal=16) a = f.avrg([1.0, 4.0]) - assert_almost_equal(a, (-0.5*1.0+0.5*1.5+1.5*0.75)/3.0, decimal=16) + assert_almost_equal(a, (-0.5 * 1.0 + 0.5 * 1.5 + 1.5 * 0.75) / 3.0, decimal=16) a = f.avrg([0.0, 2.2]) - assert_almost_equal(a, (1.0*1.0-0.5*1.0+0.2*1.5)/2.2, decimal=15) + assert_almost_equal(a, (1.0 * 1.0 - 0.5 * 1.0 + 0.2 * 1.5) / 2.2, decimal=15) # averaging over multiple intervals a = f.avrg([(0.5, 1.5), (1.5, 3.5)]) - assert_almost_equal(a, (0.5-0.5+0.5*1.5+1.0*0.75)/3.0, decimal=16) + assert_almost_equal(a, (0.5 - 0.5 + 0.5 * 1.5 + 1.0 * 0.75) / 3.0, decimal=16) # averaging over multiple intervals a = f.avrg([(0.5, 1.5), (2.2, 3.5)]) - assert_almost_equal(a, (0.5*1.0-0.5*0.5+0.3*1.5+1.0*0.75)/2.3, decimal=15) + assert_almost_equal( + a, (0.5 * 1.0 - 0.5 * 0.5 + 0.3 * 1.5 + 1.0 * 0.75) / 2.3, decimal=15 + ) def test_pwc_add(): @@ -91,7 +101,7 @@ def test_pwc_add(): f1.add(f2) # same x, but y doubled assert_array_almost_equal(f1.x, f2.x, decimal=16) - assert_array_almost_equal(f1.y, 2*f2.y, decimal=16) + assert_array_almost_equal(f1.y, 2 * f2.y, decimal=16) def test_pwc_mul(): @@ -101,9 +111,9 @@ def test_pwc_mul(): f.mul_scalar(1.5) assert_array_almost_equal(f.x, x, decimal=16) - assert_array_almost_equal(f.y, 1.5*np.array(y), decimal=16) - f.mul_scalar(1.0/5.0) - assert_array_almost_equal(f.y, 1.5/5.0*np.array(y), decimal=16) + assert_array_almost_equal(f.y, 1.5 * np.array(y), decimal=16) + f.mul_scalar(1.0 / 5.0) + assert_array_almost_equal(f.y, 1.5 / 5.0 * np.array(y), decimal=16) def test_pwc_avrg(): @@ -123,6 +133,7 @@ def test_pwc_avrg(): assert_array_almost_equal(f1.x, x_expected, decimal=16) assert_array_almost_equal(f1.y, y_expected, decimal=16) + def test_pwc_integral(): # some random data x = [0.0, 1.0, 2.0, 2.5, 4.0] @@ -130,22 +141,23 @@ def test_pwc_integral(): f1 = spk.PieceWiseConstFunc(x, y) # test full interval - full = 1.0*1.0 + 1.0*-0.5 + 0.5*1.5 + 1.5*0.75; + full = 1.0 * 1.0 + 1.0 * -0.5 + 0.5 * 1.5 + 1.5 * 0.75 assert_allclose(f1.integral(), full) - assert_allclose(f1.integral((np.min(x),np.max(x))), full) + assert_allclose(f1.integral((np.min(x), np.max(x))), full) # test part interval, spanning an edge - assert_allclose(f1.integral((0.5,1.5)), 0.5*1.0 + 0.5*-0.5) + assert_allclose(f1.integral((0.5, 1.5)), 0.5 * 1.0 + 0.5 * -0.5) # test part interval, just over two edges - assert_almost_equal(f1.integral((1.0-1e-16,2+1e-16)), 1.0*-0.5, decimal=14) + assert_almost_equal(f1.integral((1.0 - 1e-16, 2 + 1e-16)), 1.0 * -0.5, decimal=14) # test part interval, between two edges - assert_allclose(f1.integral((1.0,2.0)), 1.0*-0.5) - assert_allclose(f1.integral((1.2,1.7)), (1.7-1.2)*-0.5) + assert_allclose(f1.integral((1.0, 2.0)), 1.0 * -0.5) + assert_allclose(f1.integral((1.2, 1.7)), (1.7 - 1.2) * -0.5) # test part interval, start to before and after edge - assert_allclose(f1.integral((0.0,0.7)), 0.7*1.0) - assert_allclose(f1.integral((0.0,1.1)), 1.0*1.0+0.1*-0.5) + assert_allclose(f1.integral((0.0, 0.7)), 0.7 * 1.0) + assert_allclose(f1.integral((0.0, 1.1)), 1.0 * 1.0 + 0.1 * -0.5) # test part interval, before and after edge till end - assert_allclose(f1.integral((2.6,4.0)), (4.0-2.6)*0.75) - assert_allclose(f1.integral((2.4,4.0)), (2.5-2.4)*1.5+(4-2.5)*0.75) + assert_allclose(f1.integral((2.6, 4.0)), (4.0 - 2.6) * 0.75) + assert_allclose(f1.integral((2.4, 4.0)), (2.5 - 2.4) * 1.5 + (4 - 2.5) * 0.75) + def test_pwc_integral_bad_bounds_inv(): with pytest.raises(ValueError): @@ -153,7 +165,8 @@ def test_pwc_integral_bad_bounds_inv(): x = [0.0, 1.0, 2.0, 2.5, 4.0] y = [1.0, -0.5, 1.5, 0.75] f1 = spk.PieceWiseConstFunc(x, y) - f1.integral((3,2)) + f1.integral((3, 2)) + def test_pwc_integral_bad_bounds_oob_1(): with pytest.raises(ValueError): @@ -161,7 +174,8 @@ def test_pwc_integral_bad_bounds_oob_1(): x = [0.0, 1.0, 2.0, 2.5, 4.0] y = [1.0, -0.5, 1.5, 0.75] f1 = spk.PieceWiseConstFunc(x, y) - f1.integral((1,6)) + f1.integral((1, 6)) + def test_pwc_integral_bad_bounds_oob_2(): with pytest.raises(ValueError): @@ -169,7 +183,8 @@ def test_pwc_integral_bad_bounds_oob_2(): x = [0.0, 1.0, 2.0, 2.5, 4.0] y = [1.0, -0.5, 1.5, 0.75] f1 = spk.PieceWiseConstFunc(x, y) - f1.integral((-1,3)) + f1.integral((-1, 3)) + def test_pwl(): x = [0.0, 1.0, 2.0, 2.5, 4.0] @@ -181,14 +196,16 @@ def test_pwl(): assert_allclose(f(0.0), 1.0) assert_allclose(f(0.5), 1.25) assert_allclose(f(1.0), 0.5) - assert_allclose(f(2.0), 1.1/2) + assert_allclose(f(2.0), 1.1 / 2) assert_allclose(f(2.25), 1.5) - assert_allclose(f(2.5), 2.25/2) - assert_allclose(f(3.5), 0.75-0.5*1.0/1.5) + assert_allclose(f(2.5), 2.25 / 2) + assert_allclose(f(3.5), 0.75 - 0.5 * 1.0 / 1.5) assert_allclose(f(4.0), 0.25) - assert_array_equal(f([0.0, 0.5, 1.0, 2.0, 2.25, 2.5, 3.5, 4.0]), - [1.0, 1.25, 0.5, 0.55, 1.5, 2.25/2, 0.75-0.5/1.5, 0.25]) + assert_array_equal( + f([0.0, 0.5, 1.0, 2.0, 2.25, 2.5, 3.5, 4.0]), + [1.0, 1.25, 0.5, 0.55, 1.5, 2.25 / 2, 0.75 - 0.5 / 1.5, 0.25], + ) xp, yp = f.get_plottable_data() @@ -197,24 +214,26 @@ def test_pwl(): assert_array_almost_equal(xp, xp_expected, decimal=16) assert_array_almost_equal(yp, yp_expected, decimal=16) - avrg_expected = (1.25 - 0.45 + 0.75 + 1.5*0.5) / 4.0 + avrg_expected = (1.25 - 0.45 + 0.75 + 1.5 * 0.5) / 4.0 assert_almost_equal(f.avrg(), avrg_expected, decimal=16) # interval averaging a = f.avrg([0.5, 2.5]) - assert_almost_equal(a, (1.375*0.5 - 0.45 + 0.75)/2.0, decimal=16) + assert_almost_equal(a, (1.375 * 0.5 - 0.45 + 0.75) / 2.0, decimal=16) a = f.avrg([1.5, 3.5]) - assert_almost_equal(a, (-0.425*0.5 + 0.75 + (0.75+0.75-0.5/1.5)/2) / 2.0, - decimal=16) + assert_almost_equal( + a, (-0.425 * 0.5 + 0.75 + (0.75 + 0.75 - 0.5 / 1.5) / 2) / 2.0, decimal=16 + ) a = f.avrg((1.0, 3.5)) - assert_almost_equal(a, (-0.45 + 0.75 + (0.75+0.75-0.5/1.5)/2) / 2.5, - decimal=16) + assert_almost_equal( + a, (-0.45 + 0.75 + (0.75 + 0.75 - 0.5 / 1.5) / 2) / 2.5, decimal=16 + ) a = f.avrg([1.0, 4.0]) - assert_almost_equal(a, (-0.45 + 0.75 + 1.5*0.5) / 3.0, decimal=16) + assert_almost_equal(a, (-0.45 + 0.75 + 1.5 * 0.5) / 3.0, decimal=16) # interval between support points a = f.avrg([1.1, 1.5]) - assert_almost_equal(a, (-0.5+0.1*0.1 - 0.45) * 0.5, decimal=14) + assert_almost_equal(a, (-0.5 + 0.1 * 0.1 - 0.45) * 0.5, decimal=14) # starting at a support point a = f.avrg([1.0, 1.5]) @@ -223,10 +242,10 @@ def test_pwl(): # start and end at support point a = f.avrg([1.0, 2.0]) assert_almost_equal(a, (-0.5 - 0.4) * 0.5, decimal=14) - + # averaging over multiple intervals a = f.avrg([(0.5, 1.5), (1.5, 2.5)]) - assert_almost_equal(a, (1.375*0.5 - 0.45 + 0.75)/2.0, decimal=16) + assert_almost_equal(a, (1.375 * 0.5 - 0.45 + 0.75) / 2.0, decimal=16) def test_pwl_add(): @@ -242,10 +261,22 @@ def test_pwl_add(): f2 = spk.PieceWiseLinFunc(x, y1, y2) f1.add(f2) x_expected = [0.0, 0.75, 1.0, 2.0, 2.5, 2.7, 4.0] - y1_expected = [1.5, 1.0+1.0+0.5*0.75, -0.5+1.0-0.8*0.25/1.25, 1.5-0.25, - 0.75, 1.5+0.75-0.5*0.2/1.5] - y2_expected = [0.8+1.0+0.5*0.75, 1.5+1.0-0.8*0.25/1.25, -0.4+0.2, 1.5-1.0, - 0.75-0.5*0.2/1.5, 2.25] + y1_expected = [ + 1.5, + 1.0 + 1.0 + 0.5 * 0.75, + -0.5 + 1.0 - 0.8 * 0.25 / 1.25, + 1.5 - 0.25, + 0.75, + 1.5 + 0.75 - 0.5 * 0.2 / 1.5, + ] + y2_expected = [ + 0.8 + 1.0 + 0.5 * 0.75, + 1.5 + 1.0 - 0.8 * 0.25 / 1.25, + -0.4 + 0.2, + 1.5 - 1.0, + 0.75 - 0.5 * 0.2 / 1.5, + 2.25, + ] assert_array_almost_equal(f1.x, x_expected, decimal=16) assert_array_almost_equal(f1.y1, y1_expected, decimal=16) assert_array_almost_equal(f1.y2, y2_expected, decimal=16) @@ -258,8 +289,8 @@ def test_pwl_add(): f1.add(f2) # same x, but y doubled assert_array_almost_equal(f1.x, f2.x, decimal=16) - assert_array_almost_equal(f1.y1, 2*f2.y1, decimal=16) - assert_array_almost_equal(f1.y2, 2*f2.y2, decimal=16) + assert_array_almost_equal(f1.y1, 2 * f2.y1, decimal=16) + assert_array_almost_equal(f1.y2, 2 * f2.y2, decimal=16) def test_pwl_mul(): @@ -270,11 +301,11 @@ def test_pwl_mul(): f.mul_scalar(1.5) assert_array_almost_equal(f.x, x, decimal=16) - assert_array_almost_equal(f.y1, 1.5*np.array(y1), decimal=16) - assert_array_almost_equal(f.y2, 1.5*np.array(y2), decimal=16) - f.mul_scalar(1.0/5.0) - assert_array_almost_equal(f.y1, 1.5/5.0*np.array(y1), decimal=16) - assert_array_almost_equal(f.y2, 1.5/5.0*np.array(y2), decimal=16) + assert_array_almost_equal(f.y1, 1.5 * np.array(y1), decimal=16) + assert_array_almost_equal(f.y2, 1.5 * np.array(y2), decimal=16) + f.mul_scalar(1.0 / 5.0) + assert_array_almost_equal(f.y1, 1.5 / 5.0 * np.array(y1), decimal=16) + assert_array_almost_equal(f.y2, 1.5 / 5.0 * np.array(y2), decimal=16) def test_pwl_avrg(): @@ -289,10 +320,32 @@ def test_pwl_avrg(): f2 = spk.PieceWiseLinFunc(x, y1, y2) x_expected = [0.0, 0.75, 1.0, 2.0, 2.5, 2.7, 4.0] - y1_expected = np.array([1.5, 1.0+1.0+0.5*0.75, -0.5+1.0-0.8*0.25/1.25, - 1.5-0.25, 0.75, 1.5+0.75-0.5*0.2/1.5]) / 2 - y2_expected = np.array([0.8+1.0+0.5*0.75, 1.5+1.0-0.8*0.25/1.25, -0.4+0.2, - 1.5-1.0, 0.75-0.5*0.2/1.5, 2.25]) / 2 + y1_expected = ( + np.array( + [ + 1.5, + 1.0 + 1.0 + 0.5 * 0.75, + -0.5 + 1.0 - 0.8 * 0.25 / 1.25, + 1.5 - 0.25, + 0.75, + 1.5 + 0.75 - 0.5 * 0.2 / 1.5, + ] + ) + / 2 + ) + y2_expected = ( + np.array( + [ + 0.8 + 1.0 + 0.5 * 0.75, + 1.5 + 1.0 - 0.8 * 0.25 / 1.25, + -0.4 + 0.2, + 1.5 - 1.0, + 0.75 - 0.5 * 0.2 / 1.5, + 2.25, + ] + ) + / 2 + ) f1.add(f2) f1.mul_scalar(0.5) @@ -315,21 +368,21 @@ def test_df(): assert_array_almost_equal(xp, xp_expected, decimal=16) assert_array_almost_equal(yp, yp_expected, decimal=16) - assert_almost_equal(f.avrg(), 2.0/5.0, decimal=16) + assert_almost_equal(f.avrg(), 2.0 / 5.0, decimal=16) # interval averaging a = f.avrg([0.5, 2.4]) - assert_almost_equal(a, 2.0/3.0, decimal=16) + assert_almost_equal(a, 2.0 / 3.0, decimal=16) a = f.avrg([1.5, 3.5]) - assert_almost_equal(a, 1.0/3.0, decimal=16) + assert_almost_equal(a, 1.0 / 3.0, decimal=16) a = f.avrg((0.9, 3.5)) - assert_almost_equal(a, 2.0/5.0, decimal=16) + assert_almost_equal(a, 2.0 / 5.0, decimal=16) a = f.avrg([1.1, 4.0]) - assert_almost_equal(a, 1.0/3.0, decimal=16) + assert_almost_equal(a, 1.0 / 3.0, decimal=16) # averaging over multiple intervals a = f.avrg([(0.5, 1.5), (1.5, 2.6)]) - assert_almost_equal(a, 2.0/5.0, decimal=16) + assert_almost_equal(a, 2.0 / 5.0, decimal=16) if __name__ == "__main__": @@ -342,4 +395,3 @@ def test_df(): test_pwl_mul() test_pwl_avrg() test_df() - diff --git a/test/test_generic_interfaces.py b/test/test_generic_interfaces.py index 553f3f4..042d1a1 100644 --- a/test/test_generic_interfaces.py +++ b/test/test_generic_interfaces.py @@ -1,4 +1,4 @@ -""" test_generic_interface.py +"""test_generic_interface.py Tests the generic interfaces of the profile and distance functions @@ -8,7 +8,6 @@ """ -from __future__ import print_function from numpy.testing import assert_allclose import pyspike as spk @@ -16,9 +15,10 @@ class dist_from_prof: - """ Simple functor that turns profile function into distance function by + """Simple functor that turns profile function into distance function by calling profile.avrg(). """ + def __init__(self, prof_func): self.prof_func = prof_func @@ -32,8 +32,7 @@ def __call__(self, *args, **kwargs): def check_func(dist_func): - """ generic checker that tests the given distance function. - """ + """generic checker that tests the given distance function.""" # generate spike trains: t1 = SpikeTrain([0.2, 0.4, 0.6, 0.7], 1.0) t2 = SpikeTrain([0.3, 0.45, 0.8, 0.9, 0.95], 1.0) diff --git a/test/test_reconcile.py b/test/test_reconcile.py index 7e73113..43f26c7 100644 --- a/test/test_reconcile.py +++ b/test/test_reconcile.py @@ -1,32 +1,34 @@ + import numpy as np from numpy.testing import assert_allclose + from pyspike import SpikeTrain from pyspike.spikes import reconcile_spike_trains -import pdb + def test_reconcile(): ##input: - tr1 = np.array([1,3,2,5]) - tr2 = np.array([1,4,4,10]) + tr1 = np.array([1, 3, 2, 5]) + tr2 = np.array([1, 4, 4, 10]) - edges1=[0,5] - edges2=[3,9] + edges1 = [0, 5] + edges2 = [3, 9] ##expected output: - edges=[0,9] - trOut = [np.array([1,2,3,5]), - np.array([1,4])] + edges = [0, 9] + trOut = [np.array([1, 2, 3, 5]), np.array([1, 4])] - spike_trains = [SpikeTrain(tr1, edges1), SpikeTrain(tr2,edges2)] + spike_trains = [SpikeTrain(tr1, edges1), SpikeTrain(tr2, edges2)] st_fixed = reconcile_spike_trains(spike_trains) assert len(st_fixed) == 2 - assert(st_fixed[0].t_start==edges[0]) - assert(st_fixed[0].t_end ==edges[1]) + assert st_fixed[0].t_start == edges[0] + assert st_fixed[0].t_end == edges[1] for i in range(2): assert_allclose(st_fixed[i].spikes, trOut[i]) assert_allclose(st_fixed[i].t_start, 0) assert_allclose(st_fixed[i].t_end, 9) + if __name__ == "__main__": - test_reconcile() \ No newline at end of file + test_reconcile() diff --git a/test/test_regression/test_regression_15.py b/test/test_regression/test_regression_15.py index 81b5bb0..8cbf67c 100644 --- a/test/test_regression/test_regression_15.py +++ b/test/test_regression/test_regression_15.py @@ -1,4 +1,4 @@ -""" test_regression_15.py +"""test_regression_15.py Regression test for Issue #15 @@ -8,15 +8,16 @@ """ -from __future__ import division + +import os import numpy as np -from numpy.testing import assert_allclose, assert_almost_equal, \ - assert_array_almost_equal +from numpy.testing import ( + assert_allclose, +) import pyspike as spk -import os TEST_PATH = os.path.dirname(os.path.realpath(__file__)) TEST_DATA = os.path.join(TEST_PATH, "..", "SPIKE_Sync_Test.txt") @@ -30,13 +31,13 @@ def test_regression_15_isi(): dist_mat = spk.isi_distance_matrix(spike_trains) assert_allclose(dist_mat.shape, (N, N)) - ind = np.arange(N//2) + ind = np.arange(N // 2) dist_mat = spk.isi_distance_matrix(spike_trains, ind) - assert_allclose(dist_mat.shape, (N//2, N//2)) + assert_allclose(dist_mat.shape, (N // 2, N // 2)) - ind = np.arange(N//2, N) + ind = np.arange(N // 2, N) dist_mat = spk.isi_distance_matrix(spike_trains, ind) - assert_allclose(dist_mat.shape, (N//2, N//2)) + assert_allclose(dist_mat.shape, (N // 2, N // 2)) def test_regression_15_spike(): @@ -48,13 +49,13 @@ def test_regression_15_spike(): dist_mat = spk.spike_distance_matrix(spike_trains) assert_allclose(dist_mat.shape, (N, N)) - ind = np.arange(N//2) + ind = np.arange(N // 2) dist_mat = spk.spike_distance_matrix(spike_trains, ind) - assert_allclose(dist_mat.shape, (N//2, N//2)) + assert_allclose(dist_mat.shape, (N // 2, N // 2)) - ind = np.arange(N//2, N) + ind = np.arange(N // 2, N) dist_mat = spk.spike_distance_matrix(spike_trains, ind) - assert_allclose(dist_mat.shape, (N//2, N//2)) + assert_allclose(dist_mat.shape, (N // 2, N // 2)) def test_regression_15_sync(): @@ -66,13 +67,13 @@ def test_regression_15_sync(): dist_mat = spk.spike_sync_matrix(spike_trains) assert_allclose(dist_mat.shape, (N, N)) - ind = np.arange(N//2) + ind = np.arange(N // 2) dist_mat = spk.spike_sync_matrix(spike_trains, ind) - assert_allclose(dist_mat.shape, (N//2, N//2)) + assert_allclose(dist_mat.shape, (N // 2, N // 2)) - ind = np.arange(N//2, N) + ind = np.arange(N // 2, N) dist_mat = spk.spike_sync_matrix(spike_trains, ind) - assert_allclose(dist_mat.shape, (N//2, N//2)) + assert_allclose(dist_mat.shape, (N // 2, N // 2)) if __name__ == "__main__": diff --git a/test/test_save_load.py b/test/test_save_load.py index 33249b3..02bd62a 100644 --- a/test/test_save_load.py +++ b/test/test_save_load.py @@ -1,4 +1,4 @@ -""" test_save_load.py +"""test_save_load.py Tests saving and loading of spike trains @@ -8,18 +8,16 @@ """ -from __future__ import print_function -from numpy.testing import assert_array_equal - -import tempfile import os.path +import tempfile + +from numpy.testing import assert_array_equal import pyspike as spk def test_save_load(): - file_name = os.path.join(tempfile.mkdtemp(prefix='pyspike_'), - "save_load.txt") + file_name = os.path.join(tempfile.mkdtemp(prefix="pyspike_"), "save_load.txt") N = 10 # generate some spike trains @@ -34,8 +32,7 @@ def test_save_load(): spike_trains_loaded = spk.load_spike_trains_from_txt(file_name, [0, 100]) for n in range(N): - assert_array_equal(spike_trains[n].spikes, - spike_trains_loaded[n].spikes) + assert_array_equal(spike_trains[n].spikes, spike_trains_loaded[n].spikes) if __name__ == "__main__": diff --git a/test/test_spikes.py b/test/test_spikes.py index eec1d64..52ba841 100644 --- a/test/test_spikes.py +++ b/test/test_spikes.py @@ -1,4 +1,4 @@ -""" test_load.py +"""test_load.py Test loading of spike trains from text files @@ -7,13 +7,13 @@ Distributed under the BSD License """ -from __future__ import print_function +import os + import numpy as np from numpy.testing import assert_allclose import pyspike as spk -import os TEST_PATH = os.path.dirname(os.path.realpath(__file__)) TEST_DATA = os.path.join(TEST_PATH, "PySpike_testdata.txt") @@ -26,9 +26,24 @@ def test_load_from_txt(): assert len(spike_trains) == 40 # check the first spike train - spike_times = [64.886, 305.81, 696, 937.77, 1059.7, 1322.2, 1576.1, - 1808.1, 2121.5, 2381.1, 2728.6, 2966.9, 3223.7, 3473.7, - 3644.3, 3936.3] + spike_times = [ + 64.886, + 305.81, + 696, + 937.77, + 1059.7, + 1322.2, + 1576.1, + 1808.1, + 2121.5, + 2381.1, + 2728.6, + 2966.9, + 3223.7, + 3473.7, + 3644.3, + 3936.3, + ] assert_allclose(spike_times, spike_trains[0].spikes) # check auxiliary spikes @@ -38,12 +53,13 @@ def test_load_from_txt(): def test_load_time_series(): - spike_trains = spk.import_spike_trains_from_time_series(TIME_SERIES_DATA, - start_time=0, - time_bin=1) + spike_trains = spk.import_spike_trains_from_time_series( + TIME_SERIES_DATA, start_time=0, time_bin=1 + ) assert len(spike_trains) == 40 - spike_trains_check = spk.load_spike_trains_from_txt(TIME_SERIES_SPIKES, - edges=(0, 4000)) + spike_trains_check = spk.load_spike_trains_from_txt( + TIME_SERIES_SPIKES, edges=(0, 4000) + ) # check spike trains for n in range(len(spike_trains)): @@ -57,7 +73,7 @@ def check_merged_spikes(merged_spikes, spike_trains): all_spikes = np.array([]) for spike_train in spike_trains: all_spikes = np.append(all_spikes, spike_train) - indices = np.zeros_like(all_spikes, dtype='bool') + indices = np.zeros_like(all_spikes, dtype="bool") # check if we find all the spike events in the original spike trains for x in merged_spikes: i = np.where(all_spikes == x)[0][0] # first axis and first entry @@ -73,26 +89,27 @@ def test_merge_spike_trains(): merged_spikes = spk.merge_spike_trains([spike_trains[0], spike_trains[1]]) # test if result is sorted - assert((merged_spikes.spikes == np.sort(merged_spikes.spikes)).all()) + assert (merged_spikes.spikes == np.sort(merged_spikes.spikes)).all() # check merging - check_merged_spikes(merged_spikes.spikes, [spike_trains[0].spikes, - spike_trains[1].spikes]) + check_merged_spikes( + merged_spikes.spikes, [spike_trains[0].spikes, spike_trains[1].spikes] + ) merged_spikes = spk.merge_spike_trains(spike_trains) # test if result is sorted - assert((merged_spikes.spikes == np.sort(merged_spikes.spikes)).all()) + assert (merged_spikes.spikes == np.sort(merged_spikes.spikes)).all() # check merging - check_merged_spikes(merged_spikes.spikes, - [st.spikes for st in spike_trains]) + check_merged_spikes(merged_spikes.spikes, [st.spikes for st in spike_trains]) + def test_merge_empty_spike_trains(): # first load the data spike_trains = spk.load_spike_trains_from_txt(TEST_DATA, edges=(0, 4000)) # take two non-empty trains, and one empty one - empty = spk.SpikeTrain([],[spike_trains[0].t_start,spike_trains[0].t_end]) + empty = spk.SpikeTrain([], [spike_trains[0].t_start, spike_trains[0].t_end]) merged_spikes = spk.merge_spike_trains([spike_trains[0], empty, spike_trains[1]]) # test if result is sorted - assert((merged_spikes.spikes == np.sort(merged_spikes.spikes)).all()) + assert (merged_spikes.spikes == np.sort(merged_spikes.spikes)).all() # we don't need to check more, that's done by test_merge_spike_trains diff --git a/test/test_sync_filter.py b/test/test_sync_filter.py index 4267a1e..8630da9 100644 --- a/test/test_sync_filter.py +++ b/test/test_sync_filter.py @@ -1,4 +1,4 @@ -""" test_sync_filter.py +"""test_sync_filter.py Tests the spike sync based filtering @@ -8,10 +8,10 @@ """ -from __future__ import print_function import numpy as np -from numpy.testing import assert_allclose, assert_almost_equal, \ - assert_array_almost_equal +from numpy.testing import ( + assert_allclose, +) import pyspike as spk from pyspike import SpikeTrain @@ -24,45 +24,46 @@ def test_single_prof(): # cython implementation try: - from pyspike.cython.cython_profiles import \ - coincidence_single_profile_cython as coincidence_impl + from pyspike.cython.cython_profiles import ( + coincidence_single_profile_cython as coincidence_impl, + ) except ImportError: - from pyspike.cython.python_backend import \ - coincidence_single_python as coincidence_impl + from pyspike.cython.python_backend import ( + coincidence_single_python as coincidence_impl, + ) - sync_prof = spk.spike_sync_profile(SpikeTrain(st1, 5.0), - SpikeTrain(st2, 5.0)) + sync_prof = spk.spike_sync_profile(SpikeTrain(st1, 5.0), SpikeTrain(st2, 5.0)) coincidences = np.array(coincidence_impl(st1, st2, 0, 5.0, 0.0)) print(coincidences) for i, t in enumerate(st1): - assert_allclose(coincidences[i], sync_prof.y[sync_prof.x == t], - err_msg="At index %d" % i) + assert_allclose( + coincidences[i], sync_prof.y[sync_prof.x == t], err_msg="At index %d" % i + ) coincidences = np.array(coincidence_impl(st2, st1, 0, 5.0, 0.0)) for i, t in enumerate(st2): - assert_allclose(coincidences[i], sync_prof.y[sync_prof.x == t], - err_msg="At index %d" % i) + assert_allclose( + coincidences[i], sync_prof.y[sync_prof.x == t], err_msg="At index %d" % i + ) - sync_prof = spk.spike_sync_profile(SpikeTrain(st1, 5.0), - SpikeTrain(st3, 5.0)) + sync_prof = spk.spike_sync_profile(SpikeTrain(st1, 5.0), SpikeTrain(st3, 5.0)) coincidences = np.array(coincidence_impl(st1, st3, 0, 5.0, 0.0)) for i, t in enumerate(st1): - assert_allclose(coincidences[i], 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