diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml new file mode 100644 index 0000000..9c94c88 --- /dev/null +++ b/.github/workflows/main.yml @@ -0,0 +1,94 @@ +name: CI/CD Workflow + +on: + push: + branches: ['*'] + pull_request: + branches: [ main ] + +jobs: + type-check: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v2 + - name: Set up Python + uses: actions/setup-python@v2 + with: + python-version: '3.12.2' + - name: Install Poetry + run: pip install poetry + - name: Install dependencies + run: poetry install + - name: Perform type checking + run: poetry run mypy . + run-tests: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v2 + - name: Set up Python + uses: actions/setup-python@v2 + with: + python-version: '3.12.2' + - name: Install Poetry + run: pip install poetry + - name: Install dependencies + run: poetry install + - name: Execute tests + run: poetry run pytest + + code-style-check: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v2 + - name: Set up Python + uses: actions/setup-python@v2 + with: + python-version: '3.12.2' + - name: Install Poetry + run: pip install poetry + - name: Install dependencies + run: poetry install + - name: Check code style + run: poetry run ruff check + + release: + needs: [run-tests, code-style-check, type-check] + runs-on: ubuntu-latest + if: github.ref == 'refs/heads/main' && github.event_name == 'push' + steps: + - uses: actions/checkout@v2 + with: + fetch-depth: 0 + - name: Set up Python + uses: actions/setup-python@v2 + with: + python-version: '3.12.2' + - name: Install Dependencies + run: | + pip install poetry + poetry install + - name: Semantic Release + run: poetry run semantic-release publish + env: + GH_TOKEN: ${{ secrets.GITHUB_TOKEN }} + #PYPI_TOKEN: ${{ secrets.PYPI_API_TOKEN }} + + + # publish: + # needs: [run-tests, code-style-check, type-check] + # runs-on: ubuntu-latest + # steps: + # - uses: actions/checkout@v2 + # - name: Set up Python + # uses: actions/setup-python@v2 + # with: + # python-version: '3.12.2' + # - name: Install Poetry + # run: pip install poetry + # - name: Install dependencies + # run: poetry install + # - name: Publish package + # uses: pypa/gh-action-pypi-publish@master + # with: + # user: __token__ + # password: ${{ secrets.PYPI_API_TOKEN }} diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml new file mode 100644 index 0000000..96efe0b --- /dev/null +++ b/.pre-commit-config.yaml @@ -0,0 +1,48 @@ +ci: + skip: [pytest] + +default_language_version: + python: python3.12 + +repos: + # general checks (see here: https://pre-commit.com/hooks.html) + - repo: https://github.com/pre-commit/pre-commit-hooks + rev: v4.5.0 + hooks: + - id: check-yaml + args: [--allow-multiple-documents] + - id: end-of-file-fixer + - id: trailing-whitespace + + # black - formatting + - repo: https://github.com/psf/black + rev: 24.2.0 + hooks: + - id: black + name: black + args: + - "--config" + - "./pyproject.toml" + + # ruff - linting + - repo: https://github.com/astral-sh/ruff-pre-commit + rev: "v0.3.0" + hooks: + - id: ruff + name: ruff + + # mypy - lint-like type checking + - repo: https://github.com/pre-commit/mirrors-mypy + rev: v1.8.0 + hooks: + - id: mypy + name: mypy + + - repo: local + hooks: + - id: pytest + name: pytest + entry: poetry run pytest --cov=src tests + language: system + types: [python] + pass_filenames: false \ No newline at end of file diff --git a/docs/Makefile b/docs/Makefile deleted file mode 100644 index a4c27c0..0000000 --- a/docs/Makefile +++ /dev/null @@ -1,20 +0,0 @@ -# Minimal makefile for Sphinx documentation -# - -# You can set these variables from the command line. -SPHINXOPTS = -SPHINXBUILD = sphinx-build -SPHINXPROJ = pyPTE -SOURCEDIR = source -BUILDDIR = build - -# Put it first so that "make" without argument is like "make help". -help: - @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) - -.PHONY: help Makefile - -# Catch-all target: route all unknown targets to Sphinx using the new -# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). -%: Makefile - @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) diff --git a/docs/make.bat b/docs/make.bat deleted file mode 100644 index 882d24d..0000000 --- a/docs/make.bat +++ /dev/null @@ -1,36 +0,0 @@ -@ECHO OFF - -pushd %~dp0 - -REM Command file for Sphinx documentation - -if "%SPHINXBUILD%" == "" ( - set SPHINXBUILD=sphinx-build -) -set SOURCEDIR=source -set BUILDDIR=build -set SPHINXPROJ=pyPTE - -if "%1" == "" goto help - -%SPHINXBUILD% >NUL 2>NUL -if errorlevel 9009 ( - echo. - echo.The 'sphinx-build' command was not found. Make sure you have Sphinx - echo.installed, then set the SPHINXBUILD environment variable to point - echo.to the full path of the 'sphinx-build' executable. Alternatively you - echo.may add the Sphinx directory to PATH. - echo. - echo.If you don't have Sphinx installed, grab it from - echo.http://sphinx-doc.org/ - exit /b 1 -) - -%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% -goto end - -:help -%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% - -:end -popd diff --git a/docs/source/about.rst b/docs/source/about.rst deleted file mode 100644 index a42bfcf..0000000 --- a/docs/source/about.rst +++ /dev/null @@ -1,23 +0,0 @@ -.. =========== -.. About pyPTE -.. =========== - -pyPTE is an open-source implementation of the Phase Transfer Entropy method based on the publications [Lobier2014]_ and [Hillebrand2016]_. It is fully implemented in Python and requires only the Python libraries NumPy and SciPy. - -This implementation estimates the Phase Transfer Entropy which is defined for two given time-series X and Y with a known analysis lag delta as: - -.. math:: - {PTE}_{X \rightarrow Y} &=& H(\theta_Y(t), \theta_Y(t-\delta) \\ - &+& H(\theta_Y(t-\delta),\theta_X(t-\delta)) \\ - &-& H(\theta_Y(t-\delta)) \\ - &-& H(\theta_Y(t), \theta_Y(t-\delta), \theta_X(t-\delta)) -, where the entropy terms H are defined as: - -.. math:: - H(\theta_Y(t), \theta_Y(t)) = - \sum p(\theta_Y(t),\theta_Y(t-\delta)) log p(\theta_Y(t), \theta_Y(t-\delta)) \\ - H(\theta_Y(t-\delta), \theta_X(t-\delta)) = -\sum p(\theta_Y(t),\theta_X(t-\delta)) log p(\theta_Y(t), \theta_X(t-\delta)) \\ - H(\theta_Y(t-\delta)) = -\sum p(\theta_Y(t)) log p(\theta_Y(t)) \\ - H(\theta_Y(t-\delta), \theta_Y(t-\delta), \theta_X(t-\delta)) = - \sum p(\theta_Y(t),\theta_Y(t-\delta), \theta_X(t-\delta)) log p(\theta_Y(t), \theta_Y(t-\delta), \theta_X(t-\delta)) \\ -.. [Lobier2014] Muriel Lobier, Felix Siebenhühner, Satu Palva and J. Matias Palva. Phase transfer entropy: A novel phase-based measure for directed connectivity in networks coupled by oscillatory interactions. *NeuroImage*, 2014. `doi:10.1016/j.neuroimage.2013.08.056 `_ - -.. [Hillebrand2016] Arjan Hillebrand, Preejaas Tewarie, Edwin van Dellen, Meichen Yu, Ellen W. S. Carbo, Linda Douw, Alida A. Gouw, Elisabeth C.W. van Straaten and Cornelies J. Stam. Direction of information flow in large-scale resting-state networks is frequency-dependent. *Proceedings of the National Academy of Sciences of the United States of America*, 113(14):3867-72, 2016. `doi:10.1073/pnas.1515657113 `_ diff --git a/docs/source/conf.py b/docs/source/conf.py deleted file mode 100644 index c82efe5..0000000 --- a/docs/source/conf.py +++ /dev/null @@ -1,181 +0,0 @@ -# -*- coding: utf-8 -*- -# -# Configuration file for the Sphinx documentation builder. -# -# This file does only contain a selection of the most common options. For a -# full list see the documentation: -# http://www.sphinx-doc.org/en/master/config - -# -- Path setup -------------------------------------------------------------- - -# 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. -# -import os -import sys -sys.path.insert(0, os.path.abspath('../../pyPTE/')) -import sphinx_readable_theme - -html_theme_path = [sphinx_readable_theme.get_html_theme_path()] -html_theme = 'readable' - -# -- Project information ----------------------------------------------------- - -project = 'pyPTE' -copyright = '2018, Patryk Mikulski' -author = 'Patryk Mikulski' - -# The short X.Y version -version = '' -# The full version, including alpha/beta/rc tags -release = '0.1dev' - - -# -- General configuration --------------------------------------------------- - -# If your documentation needs a minimal Sphinx version, state it here. -# -# needs_sphinx = '1.0' - -# Add any Sphinx extension module names here, as strings. They can be -# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom -# ones. -extensions = [ - 'sphinx.ext.autodoc', - 'numpydoc', - 'sphinx.ext.doctest', - 'sphinx.ext.intersphinx', - 'sphinx.ext.todo', - 'sphinx.ext.coverage', - 'sphinx.ext.ifconfig', - 'sphinx.ext.viewcode', - 'sphinx.ext.githubpages', - 'sphinx.ext.mathjax' -] - -# Add any paths that contain templates here, relative to this directory. -templates_path = ['_templates'] - -# The suffix(es) of source filenames. -# You can specify multiple suffix as a list of string: -# -# source_suffix = ['.rst', '.md'] -source_suffix = '.rst' - -# The master toctree document. -master_doc = 'index' - -# The language for content autogenerated by Sphinx. Refer to documentation -# for a list of supported languages. -# -# This is also used if you do content translation via gettext catalogs. -# Usually you set "language" from the command line for these cases. -language = None - -# List of patterns, relative to source directory, that match files and -# directories to ignore when looking for source files. -# This pattern also affects html_static_path and html_extra_path . -exclude_patterns = [] - -# The name of the Pygments (syntax highlighting) style to use. -pygments_style = 'sphinx' - - -# -- 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 = 'alabaster' - -# 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 = {} - -# 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'] - -# Custom sidebar templates, must be a dictionary that maps document names -# to template names. -# -# The default sidebars (for documents that don't match any pattern) are -# defined by theme itself. Builtin themes are using these templates by -# default: ``['localtoc.html', 'relations.html', 'sourcelink.html', -# 'searchbox.html']``. -# -# html_sidebars = {} - - -# -- Options for HTMLHelp output --------------------------------------------- - -# Output file base name for HTML help builder. -htmlhelp_basename = 'pyPTEdoc' - - -# -- 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': '', - - # Latex figure (float) alignment - # - # 'figure_align': 'htbp', -} - -# 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 = [ - (master_doc, 'pyPTE.tex', 'pyPTE Documentation', - 'Patryk Mikulski', 'manual'), -] - - -# -- Options for manual page output ------------------------------------------ - -# One entry per manual page. List of tuples -# (source start file, name, description, authors, manual section). -man_pages = [ - (master_doc, 'pypte', 'pyPTE Documentation', - [author], 1) -] - - -# -- Options for Texinfo output ---------------------------------------------- - -# Grouping the document tree into Texinfo files. List of tuples -# (source start file, target name, title, author, -# dir menu entry, description, category) -texinfo_documents = [ - (master_doc, 'pyPTE', 'pyPTE Documentation', - author, 'pyPTE', 'One line description of project.', - 'Miscellaneous'), -] - - -# -- Extension configuration ------------------------------------------------- - -# -- Options for intersphinx extension --------------------------------------- - -# Example configuration for intersphinx: refer to the Python standard library. -intersphinx_mapping = {'https://docs.python.org/': None} - -# -- Options for todo extension ---------------------------------------------- - -# If true, `todo` and `todoList` produce output, else they produce nothing. -todo_include_todos = True diff --git a/docs/source/devdoc.rst b/docs/source/devdoc.rst deleted file mode 100644 index e97461a..0000000 --- a/docs/source/devdoc.rst +++ /dev/null @@ -1,13 +0,0 @@ -========================= -Developer's documentation -========================= - -To-do -===== -Different approaches to estimate the predicition delay used for the Phase Transfer Entropy method shall be implemented and evaluated. -Especially, a more sophisticated estimation as suggested by [Wibral2013]_ is desirable. - -.. [Wibral2013] Wibral, Michael and Pampu, Nicolae and Priesemann, Viola and Siebenhühner, Felix and Seiwert, Hannes and Lindner, Michael and Lizier, Joseph T and Vicente, Raul. Measuring Information-Transfer Delays. *PLOS ONE 8(2): e55809*, 2013. `doi:10.1371/journal.pone.0055809 `_ - - - diff --git a/docs/source/documentation.rst b/docs/source/documentation.rst deleted file mode 100644 index 9d89971..0000000 --- a/docs/source/documentation.rst +++ /dev/null @@ -1,2 +0,0 @@ -Documentation -============= diff --git a/docs/source/examples.rst b/docs/source/examples.rst deleted file mode 100644 index 1d23eed..0000000 --- a/docs/source/examples.rst +++ /dev/null @@ -1,38 +0,0 @@ -examples package -================ - -Submodules ----------- - -examples\.kuramoto\_global module ---------------------------------- - -.. automodule:: examples.kuramoto_global - :members: - :undoc-members: - :show-inheritance: - -examples\.kuramoto\_local module --------------------------------- - -.. automodule:: examples.kuramoto_local - :members: - :undoc-members: - :show-inheritance: - -examples\.neural\_mass\_model module ------------------------------------- - -.. automodule:: examples.neural_mass_model - :members: - :undoc-members: - :show-inheritance: - - -Module contents ---------------- - -.. automodule:: examples - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/source/examples_text.rst b/docs/source/examples_text.rst deleted file mode 100644 index da60f48..0000000 --- a/docs/source/examples_text.rst +++ /dev/null @@ -1,23 +0,0 @@ -======== -Examples -======== - -Currently the pyPTE package delivers three examples which shall demonstrate the usage of the phase transfer entropy method. - -Standard Kuramoto model -======================= - -The standard Kuramoto model is a globally coupled system of linear differential equations of first order. It represents the phase behaviour of a set of coupled oscillators with respect to their intrinsinc frequencies and a global coupling strength. - -Neural mass model -================= -This example incorporates an implementation of the stochastic non-linear dynamics of coupled cortical columns based on [Jansen1995]_ and [Wendling2000]_ - -.. [Jansen1995] Ben H. Jansen and Vincent G. Rit. Electroencephalogram and visual evoked potential generation in a mathematical model of coupled cortical columns. *Biological Cybernetics*, 73:357-366, 1995 `doi:10.1007/BF00199471 `_ - -.. [Wendling2000] Wendling, F. and Bellanger, J. J. and Bartolomei, F. and Chauvel, P. Relevance of nonlinear lumped-parameter models in the analysis of depth-EEG epileptic signals. *Biological Cybernetics*. 2000 `doi:10.1007/s004220000160 `_ - -mne-python sample data set -========================== - -This example illustrates how to extract data from a mne raw object, which can be fed into pyPTE. If you want to incorporate data from other software packages than MNE, please refer to the MNE documentation how to import raw data from other fileformats. diff --git a/docs/source/handle_multi.rst b/docs/source/handle_multi.rst deleted file mode 100644 index a4d0b24..0000000 --- a/docs/source/handle_multi.rst +++ /dev/null @@ -1,7 +0,0 @@ -handle\_multi module -==================== - -.. automodule:: handle_multi - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/source/index.rst b/docs/source/index.rst deleted file mode 100644 index e305ce1..0000000 --- a/docs/source/index.rst +++ /dev/null @@ -1,25 +0,0 @@ -.. pyPTE documentation master file, created by - sphinx-quickstart on Mon May 14 18:03:03 2018. - You can adapt this file completely to your liking, but it should at least - contain the root `toctree` directive. - -pyPTE documentation! -================================= -.. include:: about.rst - -.. toctree:: - :maxdepth: 2 - - - install - usage - examples_text - devdoc - about - -Indices and tables -================== - -* :ref:`genindex` -* :ref:`modindex` -* :ref:`search` diff --git a/docs/source/install.rst b/docs/source/install.rst deleted file mode 100644 index f8538fe..0000000 --- a/docs/source/install.rst +++ /dev/null @@ -1,49 +0,0 @@ -================== -Installation Guide -================== - -The prerequisites for this package are: - -Mandantory: - -- a working Python installation, version 3.6 or higher -- git -- NumPy -- SciPy - -Optional: - -- mne-python -- pandas -- seaborn - -Recommended: - -To prevent Python module incompatibilities using a virtual environment like - -- conda -- pyenv - -is highly recommendable. If you are planning to use mne-python, an Anaconda3 installation is mandandory. - -Step 1: Download pyPTE via GitHub -================================= - -Clone into the public GitHub repository using: -:: - > git clone https://github.com/patrk/pyPTE.git - -Step 2: Build pyPTE -=================== -Build the pyPTE package and make it available to your Python interpreter by: -:: - > cd pyPTE - > python setup.py install - -Step 3: Test pyPTE -================== -To test the installation of pyPTE simply run: -:: - > cd test - > py.test - diff --git a/docs/source/modules.rst b/docs/source/modules.rst deleted file mode 100644 index 9c35cf9..0000000 --- a/docs/source/modules.rst +++ /dev/null @@ -1,7 +0,0 @@ -pyPTE -===== - -.. toctree:: - :maxdepth: 4 - - pyPTE diff --git a/docs/source/phase_transfer_entropy.rst b/docs/source/phase_transfer_entropy.rst deleted file mode 100644 index ad4dfd1..0000000 --- a/docs/source/phase_transfer_entropy.rst +++ /dev/null @@ -1,7 +0,0 @@ -phase\_transfer\_entropy module -=============================== - -.. automodule:: phase_transfer_entropy - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/source/pyPTE.examples.models.rst b/docs/source/pyPTE.examples.models.rst deleted file mode 100644 index cd1e171..0000000 --- a/docs/source/pyPTE.examples.models.rst +++ /dev/null @@ -1,38 +0,0 @@ -pyPTE\.examples\.models package -=============================== - -Submodules ----------- - -pyPTE\.examples\.models\.kuramoto\_global module ------------------------------------------------- - -.. automodule:: pyPTE.examples.models.kuramoto_global - :members: - :undoc-members: - :show-inheritance: - -pyPTE\.examples\.models\.kuramoto\_local module ------------------------------------------------ - -.. automodule:: pyPTE.examples.models.kuramoto_local - :members: - :undoc-members: - :show-inheritance: - -pyPTE\.examples\.models\.neural\_mass\_model module ---------------------------------------------------- - -.. automodule:: pyPTE.examples.models.neural_mass_model - :members: - :undoc-members: - :show-inheritance: - - -Module contents ---------------- - -.. automodule:: pyPTE.examples.models - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/source/pyPTE.examples.rst b/docs/source/pyPTE.examples.rst deleted file mode 100644 index c2f8bb0..0000000 --- a/docs/source/pyPTE.examples.rst +++ /dev/null @@ -1,22 +0,0 @@ -pyPTE\.examples package -======================= - -Submodules ----------- - -pyPTE\.examples\.mne\_demo module ---------------------------------- - -.. automodule:: pyPTE.examples.mne_demo - :members: - :undoc-members: - :show-inheritance: - - -Module contents ---------------- - -.. automodule:: pyPTE.examples - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/source/pyPTE.rst b/docs/source/pyPTE.rst deleted file mode 100644 index 8bf44ee..0000000 --- a/docs/source/pyPTE.rst +++ /dev/null @@ -1,30 +0,0 @@ -pyPTE package -============= - -Submodules ----------- - -pyPTE\.handle\_multi module ---------------------------- - -.. automodule:: pyPTE.handle_multi - :members: - :undoc-members: - :show-inheritance: - -pyPTE\.pyPTE module -------------------- - -.. automodule:: pyPTE.pyPTE - :members: - :undoc-members: - :show-inheritance: - - -Module contents ---------------- - -.. automodule:: pyPTE - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/source/pyPTE.utils.rst b/docs/source/pyPTE.utils.rst deleted file mode 100644 index 0d7d788..0000000 --- a/docs/source/pyPTE.utils.rst +++ /dev/null @@ -1,30 +0,0 @@ -pyPTE\.utils package -==================== - -Submodules ----------- - -pyPTE\.utils\.mne\_tools module -------------------------------- - -.. automodule:: pyPTE.utils.mne_tools - :members: - :undoc-members: - :show-inheritance: - -pyPTE\.utils\.stats module --------------------------- - -.. automodule:: pyPTE.utils.stats - :members: - :undoc-members: - :show-inheritance: - - -Module contents ---------------- - -.. automodule:: pyPTE.utils - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/source/usage.rst b/docs/source/usage.rst deleted file mode 100644 index e05b0b3..0000000 --- a/docs/source/usage.rst +++ /dev/null @@ -1,39 +0,0 @@ -===== -Usage -===== - -The main functionality of this package, calculating the Phase Transfer Entropy (PTE) for a set of time-series is accessible via the following functions: - -If you are using MNE_ for analyzing EEG or MEG recordings an mne.io.Raw_ object can be passed to: - -.. _MNE: https://www.martinos.org/mne/stable/index.html -.. _mne.io.Raw: https://martinos.org/mne/dev/generated/mne.io.Raw.html - -.. code-block:: python - - from pyPTE.utils.mne_tools import PTE_from_mne - - dPTE, rPTE = PTE_from_mne(raw) - -which returns a tuple of the normalized dPTE, containing information about the directionality and the raw PTE matrix, whereas the matrices are pandas DataFrames indexed by the channel names from the mne.io.Raw_ object. - -In other domains the PTE calculation can be called directly by either passing a pandas.DataFrame_ to: - -.. _pandas.DataFrame: https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.html - -.. code-block:: python - - from pyPTE import pyPTE - dPTE, rPTE = pyPTE.PTE_from_dataframe(dataframe) - -or by passing a (m x n) numpy.ndarray_, where m is the number of samples and n is the number of time-series: - -.. _numpy.ndarray: https://docs.scipy.org/doc/numpy-1.14.0/reference/generated/numpy.ndarray.html -.. code-block:: python - - from pyPTE import pyPTE - dPTE, rPTE = pyPTE.PTE(timeseries) - -where the returned tuple consists of the above mentioned dPTE, rPTE matrices as (n x n) numpy.ndarray objects ordered in the same way as the input object. - -If you are interested in further aspects of the implementation see Developer's documentation. diff --git a/docs/source/utils.rst b/docs/source/utils.rst deleted file mode 100644 index f0be025..0000000 --- a/docs/source/utils.rst +++ /dev/null @@ -1,38 +0,0 @@ -utils package -============= - -Submodules ----------- - -utils\.dict\_helpers module ---------------------------- - -.. automodule:: utils.dict_helpers - :members: - :undoc-members: - :show-inheritance: - -utils\.network\_helpers module ------------------------------- - -.. automodule:: utils.network_helpers - :members: - :undoc-members: - :show-inheritance: - -utils\.visualize module ------------------------ - -.. automodule:: utils.visualize - :members: - :undoc-members: - :show-inheritance: - - -Module contents ---------------- - -.. automodule:: utils - :members: - :undoc-members: - :show-inheritance: diff --git a/pyPTE/examples/__init__.py b/examples/__init__.py similarity index 100% rename from pyPTE/examples/__init__.py rename to examples/__init__.py diff --git a/pyPTE/examples/mne_demo.py b/examples/mne_demo.py similarity index 80% rename from pyPTE/examples/mne_demo.py rename to examples/mne_demo.py index 23b5aef..756a52f 100644 --- a/pyPTE/examples/mne_demo.py +++ b/examples/mne_demo.py @@ -1,6 +1,10 @@ import os.path as op import mne +import seaborn as sns + +from pyPTE.adapters import mne_adapter + # from mne.datasets.brainstorm import bst_raw data_path = op.join(mne.datasets.sample.data_path(), 'MEG', 'sample') @@ -8,17 +12,11 @@ preload=True) # raw.plot() -from pyPTE.utils import mne_tools - picks = mne.pick_types(raw.info, meg=True, exclude='bads') t_idx = raw.time_as_index([10., 20.]) data, times = raw[picks, t_idx[0]:t_idx[1]] - -dPTE, rPTE = mne_tools.PTE_from_mne(raw) - -import seaborn as sns -from matplotlib import pyplot as plt +dPTE, rPTE = mne_adapter.PTE_from_mne(raw) sns.heatmap(rPTE) diff --git a/pyPTE/examples/models/__init__.py b/examples/models/__init__.py similarity index 100% rename from pyPTE/examples/models/__init__.py rename to examples/models/__init__.py diff --git a/pyPTE/examples/models/kuramoto_global.py b/examples/models/kuramoto_global.py similarity index 92% rename from pyPTE/examples/models/kuramoto_global.py rename to examples/models/kuramoto_global.py index 0efce50..680e35e 100644 --- a/pyPTE/examples/models/kuramoto_global.py +++ b/examples/models/kuramoto_global.py @@ -1,10 +1,17 @@ import numpy as np +import seaborn as sns +from matplotlib import pyplot as plt +from sdeint import itoint + +from pyPTE.core import pyPTE + def kuramoto(omega_vector, K, N, sigma): def f(theta_vector, t): #theta_vector = np.atleast_2d(theta_vector) theta_vector = np.atleast_2d(theta_vector) - d_theta_vector = omega_vector + K/N * np.sum(np.sin(theta_vector - theta_vector.T), 1) + d_theta_vector = omega_vector + K/N * np.sum( + np.sin(theta_vector - theta_vector.T), 1) p = np.random.normal(0, sigma, N) return d_theta_vector + p def G(v, t): @@ -19,10 +26,11 @@ def G(v, t): omega = np.ones_like(theta0)*8 f, G = kuramoto(omega, 1, 10, 0.1) -from sdeint import itoint + tspan = np.linspace(0, 1, 1000) solution = itoint(f, G, theta0, tspan) -from matplotlib import pyplot as plt + + plt.plot(tspan, solution) plt.show() @@ -35,7 +43,8 @@ def G(v, t): plt.plot(tspan, solution) plt.show() -from pyPTE import pyPTE + + # phase = np.swapaxes(solution, 0, 1) phase = solution delay = pyPTE.get_delay(phase) @@ -46,7 +55,7 @@ def G(v, t): dPTE, raw_PTE = pyPTE.compute_dPTE_rawPTE(dphase, delay) print(dPTE) -import seaborn as sns + cmap = sns.diverging_palette(10, 220, sep=80, n=7) sns.heatmap(dPTE, cmap=cmap, center=0.5) plt.show() diff --git a/pyPTE/examples/models/kuramoto_local.py b/examples/models/kuramoto_local.py similarity index 95% rename from pyPTE/examples/models/kuramoto_local.py rename to examples/models/kuramoto_local.py index 0e809ab..2f04da9 100644 --- a/pyPTE/examples/models/kuramoto_local.py +++ b/examples/models/kuramoto_local.py @@ -1,4 +1,12 @@ +import itertools + import numpy as np +import seaborn as sns +from matplotlib import pyplot as plt +from sdeint import itoint + +from pyPTE.core import pyPTE + def kuramoto(omega_vector, K, N, sigma): def f(theta_vector, t): @@ -35,13 +43,13 @@ def G(v, t): dt = 0.01 t_end = 1 -steps = t_end / dt +steps = int(t_end / dt) # K[:,3] = 100 f, G = kuramoto(omega, K, N, 0.01) -from sdeint import itoint + tspan = np.linspace(0, t_end, steps) solution = itoint(f, G, theta0, tspan) -from matplotlib import pyplot as plt + plt.figure(1) plt.subplot(211) plt.plot(tspan, solution) @@ -52,12 +60,14 @@ def G(v, t): plt.plot(tspan, solution) plt.show() -import itertools + dt = 0.1 t_end = 10 -steps = t_end / dt -from sdeint import itoint +steps = int(t_end / dt) + + + def repetitive_sims(K, N, theta0, omega, Nsims): solutions = list() for _ in itertools.repeat(None, Nsims): @@ -72,7 +82,8 @@ def repetitive_sims(K, N, theta0, omega, Nsims): # Nsim = 10 # solutions = repetitive_sims(K, N, theta0, omega, Nsim) -from matplotlib import pyplot as plt + + # for solution in solutions: # plt.plot(solution) # plt.show() @@ -91,7 +102,7 @@ def repetitive_sims(K, N, theta0, omega, Nsims): # # average_dPTE = np.sum(dPTEs) / len(dPTEs) -from pyPTE import pyPTE + # phase = np.swapaxes(solution, 0, 1) phase = solution delay = pyPTE.get_delay(phase) @@ -104,7 +115,8 @@ def repetitive_sims(K, N, theta0, omega, Nsims): -import seaborn as sns + + plt.figure(1) cmap = sns.diverging_palette(240, 10, as_cmap=True, n=7) diff --git a/pyPTE/examples/models/neural_mass_model.py b/examples/models/neural_mass_model.py similarity index 94% rename from pyPTE/examples/models/neural_mass_model.py rename to examples/models/neural_mass_model.py index 0b8b6d2..8cf02c6 100644 --- a/pyPTE/examples/models/neural_mass_model.py +++ b/examples/models/neural_mass_model.py @@ -3,7 +3,8 @@ def jansen_rit(g1 = 135, C=None): """ - Neural mass model which returns a dynamical system F and Wiener process G for a given coupling matrix C + Neural mass model which returns a dynamical system F and Wiener process G + for a given coupling matrix C This function defines: all fixed parameters for the Jansen-Rit model @@ -18,7 +19,8 @@ def jansen_rit(g1 = 135, C=None): Model parameter which determines what kind of rhythm will be generated default : 135 for alpha-waves C : ndarray - coupling matrix must be quadratic. values of the coupling matrix correspond to the coupling strength between neural mass models + coupling matrix must be quadratic. values of the coupling matrix correspond to + the coupling strength between neural mass models Returns ------- diff --git a/pyPTE/utils/__init__.py b/examples/utils/__init__.py similarity index 100% rename from pyPTE/utils/__init__.py rename to examples/utils/__init__.py diff --git a/pyPTE/utils/stats.py b/examples/utils/stats.py similarity index 84% rename from pyPTE/utils/stats.py rename to examples/utils/stats.py index ff1e91c..0cce3e4 100644 --- a/pyPTE/utils/stats.py +++ b/examples/utils/stats.py @@ -1,6 +1,7 @@ -from scipy.stats import wilcoxon import numpy as np import pandas as pd +from scipy.stats import wilcoxon + def matrix_wilcoxon(x_matrices, 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Missing channels in a raw file can be interpolated - by passing the mne.io.Raw object subject to interpolation and a reference object which contains all channels. - This is achieved by copying channels, replacing its channel information by the reference channels, - marking it as bad and finally utilizing mne.io.Raw.interpolate_bads() + This is a utility function which circumvents the issue that MNE allows to + interpolate only channels, which are present and marked as bad channels. + Missing channels in a raw file can be interpolated by passing the mne.io.Raw object + subject to interpolation and a reference object which contains all channels. + This is achieved by copying channels, replacing its channel information by + the reference channels, marking it as bad and finally utilizing + mne.io.Raw.interpolate_bads() Parameters ---------- @@ -18,7 +21,8 @@ def interpolate_mne(raw, raw_reference): Returns ------- d : mne.io.Raw - New object containing all information from the original raw object and interpolated channels + New object containing all information from the original raw object and + interpolated channels """ ref_channels = raw_reference.ch_chnames @@ -30,19 +34,20 @@ def interpolate_mne(raw, raw_reference): d = raw.copy() if len(diff) > 0: - b.pick_channels(b.ch_names[:len(diff)]) + b.pick_channels(b.ch_names[: len(diff)]) ac = raw.copy().pick_channels(diff) b.info = ac.info d.add_channels([b]) - d.info['bads'] = diff + d.info["bads"] = diff d.interpolate_bads(verbose=False) return d + def PTE_from_mne(mne_raw): """ - This is a wrapper which allows calculating dPTE,PTE matrices by passing an mne.io.Raw object - and calling pyPTE.pyPTE.PTE_from_dataframe(). + This is a wrapper which allows calculating dPTE,PTE matrices by passing an + mne.io.Raw object and calling pyPTE.pyPTE.PTE_from_dataframe(). Parameters ---------- @@ -52,11 +57,11 @@ def PTE_from_mne(mne_raw): Returns ------- result : pandas.DataFrame - The wrapper returns a tuple of (dPTE, PTE) matrices, which are stored as a pandas.DataFrame and indexed by - the channels names of the input file. This allows convenient analysis of the results. + The wrapper returns a tuple of (dPTE, PTE) matrices, which are stored as a + pandas.DataFrame and indexed by the channels names of the input file. + This allows convenient analysis of the results. """ - data_frame = mne_raw.to_data_frame() - return pyPTE.PTE_from_dataframe(data_frame) \ No newline at end of file + return PTE_from_dataframe(data_frame) diff --git a/pyPTE/adapters/pandas_adapter.py b/pyPTE/adapters/pandas_adapter.py new file mode 100644 index 0000000..29f39c5 --- /dev/null +++ b/pyPTE/adapters/pandas_adapter.py @@ -0,0 +1,29 @@ +import pandas as pd + +from pyPTE.core import pyPTE + + +def PTE_from_dataframe(data_frame): + """ + This is a wrapper which allows calculating dPTE,PTE matrices by passing a + pandas.DataFrame + + Parameters + ---------- + data_frame : pandas.DataFrame + This object contains time-series data where pandas.DataFrame.index corresponds + to the time samples and pandas.DataFrame.columns represents the + individual channels + + Returns + ------- + (dPTE_df, rPTE_df) : tuple of pandas.DataFrame objects + The results from pyPTE.pyPTE.PTE are stored as pandas.DataFrames, while it is + indexed in two dimensions by pandas.DataFrame.columns of the input + + """ + time_series = data_frame.as_matrix() + dPTE, rPTE = pyPTE.PTE(time_series) + dPTE_df = pd.DataFrame(dPTE, index=data_frame.columns, columns=data_frame.columns) + rPTE_df = pd.DataFrame(rPTE, index=data_frame.columns, columns=data_frame.columns) + return dPTE_df, rPTE_df diff --git a/pyPTE/core/__init__.py b/pyPTE/core/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/pyPTE/handle_multi.py b/pyPTE/core/handle_multi.py similarity index 50% rename from pyPTE/handle_multi.py rename to pyPTE/core/handle_multi.py index 208586b..80b8e52 100644 --- a/pyPTE/handle_multi.py +++ b/pyPTE/core/handle_multi.py @@ -1,7 +1,13 @@ from multiprocessing import Pool, cpu_count -from pyPTE import pyPTE +from typing import Any, Dict, List, Tuple -def _PTE_process(item): +import numpy.typing as npt + +from pyPTE.core import pyPTE + + +def _PTE_process(item: Tuple[Any, npt.ArrayLike]) ->( + Dict[Any, Tuple[npt.NDArray, npt.NDArray]]): """ Multi processing pool worker for PTE computation - wraps PTE method @@ -19,15 +25,13 @@ def _PTE_process(item): key, value = item print(key) - # dPTE, rawPTE = phase_transfer_entropy(value, method='myPTE') - raw_PTE = pyPTE.PTE(value, method='myPTE') - result = dict() - # result[key] = dPTE, rawPTE - result[key] = raw_PTE + (dPTE, raw_PTE) = pyPTE.PTE(value) + result = {key: (dPTE, raw_PTE)} return result -def multi_process(measurements): +def multi_process(measurements: Dict[Any, npt.ArrayLike]) -> ( + List[Dict[Any, Tuple[npt.NDArray, npt.NDArray]]]): """ Parameters @@ -41,10 +45,6 @@ def multi_process(measurements): dict: key, (dPTE, rawPTE) tuple """ - results = [] - print(cpu_count()) - pool = Pool(processes=cpu_count()) - r = pool.map_async(_PTE_process, list(measurements.items()), callback=results.append) - r.wait() - pool.close() - return results \ No newline at end of file + with Pool(processes=cpu_count()) as pool: + result_dicts = pool.map(_PTE_process, list(measurements.items())) + return result_dicts diff --git a/pyPTE/pyPTE.py b/pyPTE/core/pyPTE.py similarity index 56% rename from pyPTE/pyPTE.py rename to pyPTE/core/pyPTE.py index f9d5d22..9f8c582 100644 --- a/pyPTE/pyPTE.py +++ b/pyPTE/core/pyPTE.py @@ -1,9 +1,11 @@ +from typing import Tuple + import numpy as np -import pandas as pd +import numpy.typing as npt from scipy.signal import hilbert -def get_delay(phase): +def get_delay(phase: npt.NDArray) -> int: """ Computes the overall delay for a all given channels @@ -18,17 +20,19 @@ def get_delay(phase): """ phase = phase m, n = phase.shape - c1 = n*(m-2) + c1 = n * (m - 2) r_phase = np.roll(phase, 2, axis=0) - m = np.multiply(phase, r_phase)[1:-1] - c2 = (m < 0).sum() - delay = int(np.round(c1/c2)) + phase_product = np.multiply(phase, r_phase)[1:-1] + c2 = (phase_product < 0).sum() + delay = int(np.round(c1 / c2)) + return delay -def get_phase(time_series): +def get_phase(time_series: npt.ArrayLike) -> npt.NDArray: """ - Computes phase from time series using a hilbert transform and computing the angles between the real and imaginary part for each sample + Computes phase from time series using a hilbert transform and computing the angles + between the real and imaginary part for each sample Parameters ---------- @@ -45,7 +49,8 @@ def get_phase(time_series): phase = np.angle(complex_series) return phase -def get_discretized_phase(phase, binsize): + +def get_discretized_phase(phase: npt.NDArray, binsize: float) -> npt.NDArray: """ Discretizes the phase series to rectangular bins @@ -66,7 +71,7 @@ def get_discretized_phase(phase, binsize): return d_phase -def get_binsize(phase, c = 3.49): +def get_binsize(phase: npt.NDArray, c: float = 3.49) -> float: """ Computes the bin size for the phase binning @@ -86,7 +91,8 @@ def get_binsize(phase, c = 3.49): binsize = c * np.mean(np.std(phase, axis=0, ddof=1)) * n ** (-1.0 / 3) return binsize -def get_bincount(binsize): + +def get_bincount(binsize: float) -> int: """ Get bin count for the interval [0, 2*pi] for given binsize @@ -104,19 +110,22 @@ def get_bincount(binsize): return bincount -def compute_PTE(phase, delay): +def compute_PTE(phase: npt.NDArray, delay: int) -> npt.NDArray: """ - For each channel pair (x, y) containing the individual discretized phase, which is obtained by pyPTE.pyPTE.get_discretized_phase, - this function performs the entropy estimation by counting the occurences of phase values in x, y and y_predicted, - which is achieved by slicing the x, y to consider delay x samples in the past and delay samples in the future. + For each channel pair (x, y) containing the individual discretized phase, + which is obtained by pyPTE.pyPTE.get_discretized_phase, + this function performs the entropy estimation by counting the occurences of + phase values in x, y and y_predicted, which is achieved by slicing the x, y + to consider delay x samples in the past and delay samples in the future. Parameters ---------- phase : numpy.ndarray m x n ndarray : m: number of channels, n: number of samples delay : int - This is the analysis delta, which is the number of samples in the past to be considered for x and y - Momentarily delay is estimated by pyPTE.pyPTE.get_delay(). A custom delay estimation can be used as well. + This is the analysis delta, which is the number of samples in the past + to be considered for x and y. Momentarily delay is estimated by + pyPTE.pyPTE.get_delay(). A custom delay estimation can be used as well. Returns ------- @@ -124,7 +133,7 @@ def compute_PTE(phase, delay): m x m matrix containing the PTE value for each channel pair """ m, n = phase.shape - PTE = np.zeros((m,m), dtype=float) + PTE = np.zeros((m, m), dtype=float) for i in range(0, m): for j in range(0, m): @@ -133,36 +142,41 @@ def compute_PTE(phase, delay): y = phase[:-delay, j] x = phase[:-delay, i] - P_y = np.zeros([y.max() +1]) + P_y = np.zeros([y.max() + 1]) np.add.at(P_y, [y], 1) - P_ypr_y = np.zeros([ypr.max()+1, y.max()+1]) - np.add.at(P_ypr_y, [ypr, y], 1) + max_dim_ypr_y = max(ypr.max(), y.max()) + 1 + P_ypr_y = np.zeros([max_dim_ypr_y, max_dim_ypr_y]) - P_y_x = np.zeros([y.max()+1, x.max()+1]) - np.add.at(P_y_x, [y, x], 1) + max_dim_y_x = max(y.max(), x.max()) + 1 + P_y_x = np.zeros([max_dim_y_x, max_dim_y_x]) - P_ypr_y_x = np.zeros([ypr.max()+1, y.max()+1, x.max()+1]) - np.add.at(P_ypr_y_x, [ypr, y, x], 1) + max_dim_ypr_y_x = max(ypr.max(), y.max(), x.max()) + 1 + P_ypr_y_x = np.zeros([max_dim_ypr_y_x, max_dim_ypr_y_x, max_dim_ypr_y_x]) - P_y /= (m-delay) - P_ypr_y /= (m-delay) - P_y_x /= (m-delay) - P_ypr_y_x /= (m-delay) + P_y /= m - delay + P_ypr_y /= m - delay + P_y_x /= m - delay + P_ypr_y_x /= m - delay - Hy = -np.nansum(np.multiply(P_y,np.log2(P_y))) - Hypr_y = - np.nansum(np.nansum(np.multiply(P_ypr_y, np.log2(P_ypr_y)))) + Hy = -np.nansum(np.multiply(P_y, np.log2(P_y))) + Hypr_y = -np.nansum(np.nansum(np.multiply(P_ypr_y, np.log2(P_ypr_y)))) Hy_x = -np.nansum(np.nansum(np.multiply(P_y_x, np.log2(P_y_x)))) - Hypr_y_x = -np.nansum(np.nansum(np.nansum(np.multiply(P_ypr_y_x, np.log2(P_ypr_y_x))))) + Hypr_y_x = -np.nansum( + np.nansum(np.nansum(np.multiply(P_ypr_y_x, np.log2(P_ypr_y_x)))) + ) PTE[i, j] = Hypr_y + Hy_x - Hy - Hypr_y_x return PTE -def compute_dPTE_rawPTE(phase, delay): + +def compute_dPTE_rawPTE( + phase: npt.NDArray, delay: int +) -> Tuple[npt.NDArray, npt.NDArray]: """ - This function calls pyPTE.pyPTE.compute_PTE to obtain a PTE matrix and - performs a normalization yielding dPTE to easily investigate directionality information. - Technically it could be a function which computes the normalization for a given PTE matrix, but it appears to be - more convenient to obtain both matrices in one call + This function calls pyPTE.pyPTE.compute_PTE to obtain a PTE matrix and performs a + normalization yielding dPTE to easily investigate directionality information. + Technically it could be a function which computes the normalization for a given + PTE matrix, but it appears to be more convenient to obtain both matrices in one call Parameters ---------- @@ -171,8 +185,9 @@ def compute_dPTE_rawPTE(phase, delay): The discretized phase is computed by pyPTE.pyPTE.get_discretized_phase delay : int - This is the analysis delta, which is the number of samples in the past to be considered for x and y - Momentarily delay is estimated by pyPTE.pyPTE.get_delay(). A custom delay estimation can be used as well. + This is the analysis delta, which is the number of samples in the past to be + considered for x and y. Momentarily delay is estimated by + pyPTE.pyPTE.get_delay(). A custom delay estimation can be used as well. Returns ------- @@ -183,17 +198,19 @@ def compute_dPTE_rawPTE(phase, delay): raw_PTE = compute_PTE(phase, delay) tmp = np.triu(raw_PTE) + np.tril(raw_PTE).T - with np.errstate(divide='ignore',invalid='ignore'): - dPTE = np.triu(raw_PTE/tmp,1) + np.tril(raw_PTE/tmp.T,-1) + with np.errstate(divide="ignore", invalid="ignore"): + dPTE = np.triu(raw_PTE / tmp, 1) + np.tril(raw_PTE / tmp.T, -1) return dPTE, raw_PTE -def PTE(time_series): + +def PTE(time_series: npt.ArrayLike) -> Tuple[npt.NDArray, npt.NDArray]: """ This function performs the whole procedure of calculating the PTE: - 1. Compute the phase by applying the Hilbert transform on the time-series and calculate the angle between - the real and imaginary part. The phase is defined on the interval [-pi, pi[ + 1. Compute the phase by applying the Hilbert transform on the time-series and + calculate the angle between the real and imaginary part. + The phase is defined on the interval [-pi, pi[ 2. Estimate the analysis delay - 3. For ease of binning shift the phase along the ordinate so there are no negative values + 3. For binning, shift the phase along the ordinate so there are no negatives values 4. Calculate the binsize in number of samples 5. Bin the phase data 6. Compute the dPTE and raw_PTE @@ -216,30 +233,3 @@ def PTE(time_series): d_phase = get_discretized_phase(phase_inc, binsize) return compute_dPTE_rawPTE(d_phase, delay) - -def PTE_from_dataframe(data_frame): - """ - This is a wrapper which allows calculating dPTE,PTE matrices by passing an pandas.DataFrame - - Parameters - ---------- - data_frame : pandas.DataFrame - This object contains time-series data where pandas.DataFrame.index corresponds to the time samples and - pandas.DataFrame.columns represents the individual channels - - Returns - ------- - (dPTE_df, rPTE_df) : tuple of pandas.DataFrame objects - The results from pyPTE.pyPTE.PTE are stored as pandas.DataFrames, while it is indexed in two dimensions by - pandas.DataFrame.columns of the input - - """ - time_series = data_frame.as_matrix() - dPTE, rPTE = PTE(time_series) - dPTE_df = pd.DataFrame(dPTE, index=data_frame.columns, columns=data_frame.columns) - rPTE_df = pd.DataFrame(rPTE, index=data_frame.columns, columns=data_frame.columns) - return dPTE_df, rPTE_df - - - - diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000..def5e35 --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,71 @@ +[tool.poetry] +name = "pyPTE" +version = "0.1.0" +description = "Pythonic implementation of the Phase Transfer Entropy using NumPy and SciPy" +authors = ["Patryk Mikulski"] +license = "GPL-3.0-or-later" +readme = "README.md" +keywords = ["PTE", "phase transfer entropy", "neuroscience", "signal processing"] +classifiers = [ + "Development Status :: 3 - Alpha", + "Intended Audience :: Developers", + "Intended Audience :: Science/Research", + "License :: OSI Approved :: GNU General Public License v3 or later (GPLv3+)", + "Operating System :: OS Independent", + "Programming Language :: Python :: 3", + "Programming Language :: Python :: 3.8", + "Topic :: Scientific/Engineering", +] + +[tool.poetry.dependencies] +python = "3.12.2" +numpy = "1.26.4" +pandas = "2.2.1" +scipy = "1.13.0" +mne = "1.6.1" + +[tool.poetry.group.dev.dependencies] +pytest = "^8.1.1" +black = "^24.3.0" +mypy = "^1.9.0" +ruff = "^0.3.0" +python-semantic-release = "^9.4.0" + +[tool.poetry.group.extras.dependencies] +seaborn = "^0.13.2" +matplotlib = "^3.8.4" +sdeint = "^0.3.0" + +[tool.pytest.ini_options] +pythonpath = ["."] + +[tool.ruff] + line-length = 88 + # pyflakes, pycodestyle, isort + select = ["F", "E", "W", "I001"] + +[tool.black] +line-length = 88 +target-version = ['py312'] + +[tool.mypy] +check_untyped_defs = true +ignore_missing_imports = true +warn_unused_ignores = true +warn_redundant_casts = true +warn_unused_configs = true + +[[tool.mypy.overrides]] +module = ["scipy", "mne", "seaborn", "sdeint"] +ignore_missing_imports = true + +[tool.semantic_release] +version_variable = "pyPTE/__init__.py:__version__" +branch = "main" +upload_to_pypi = "false" + +[build-system] +requires = ["poetry-core>=1.9"] +build-backend = "poetry.core.masonry.api" + + diff --git a/requirements.txt b/requirements.txt deleted file mode 100644 index e137c5e..0000000 --- a/requirements.txt +++ /dev/null @@ -1,4 +0,0 @@ -numpy==1.26.0 -pandas==1.3.3 -scipy==1.11.1 -mne==0.23.0 \ No newline at end of file diff --git a/setup.py b/setup.py deleted file mode 100644 index 89f6147..0000000 --- a/setup.py +++ /dev/null @@ -1,13 +0,0 @@ -from setuptools import setup - -setup( - name='pyPTE', - version='0.1dev', - #packages=['pyPTE, pyPTE.examples, pyPTE.utils, pyPTE.viz,], - packages=['pyPTE'], - url='https://github.com/patrk/pyPTE', - license='GNU GENERAL PUBLIC LICENSE', - author='Patryk Mikulski', - author_email='mikulski@patryk.xyz', - description='Pythonic implementation of the Phase Transfer Entropy using NumPy and SciPy' -) diff --git a/tests/test_pyPTE.py b/tests/test_pyPTE.py new file mode 100644 index 0000000..3190862 --- /dev/null +++ b/tests/test_pyPTE.py @@ -0,0 +1,60 @@ +import numpy as np + +from pyPTE.core.pyPTE import ( + PTE, + compute_PTE, + get_binsize, + get_discretized_phase, + get_phase, +) + + +def test_get_phase_zeros_input(): + time_series = np.array([[0, 0, 0], [0, 0, 0]]) + expected_phase_result = np.array([[0, 0, 0], [0, 0, 0]]) + np.testing.assert_almost_equal( + get_phase(time_series), + expected_phase_result, decimal=5) + +def test_get_discretized_phase(): + phase_data = np.array([np.pi/4, np.pi/2, 3*np.pi/4]) + binsize = np.pi/4 + expected_discretized_phase = np.array([1, 2, 3]) + discretized_phase = get_discretized_phase(phase_data, binsize) + np.testing.assert_array_equal( + discretized_phase, + expected_discretized_phase, + "Discretized phase did not match expected values.") + +def test_PTE_integration(): + time_series = np.random.rand(5, 100) + dPTE, raw_PTE = PTE(time_series) + assert dPTE.shape == (5, 5), ( + f"Expected dPTE shape (5, 5), got {dPTE.shape}") + assert raw_PTE.shape == (5, 5), ( + f"Expected raw_PTE shape (5, 5), got {raw_PTE.shape}") + +def test_PTE_with_independent_signals(): + signal_length = 1000 + s1 = np.random.normal(0, 1, signal_length) + s2 = np.random.normal(0, 1, signal_length) + + # Combine signals into a matrix + signals = np.vstack([s1, s2]) + + phase = get_phase(signals) + phase_inc = phase + np.pi + binsize = get_binsize(phase_inc) + d_phase = get_discretized_phase(phase_inc, binsize) + + pte_matrix = compute_PTE(d_phase, 1) + + # Check off-diagonal elements for significant PTE + # (s1 -> s2 and s2 -> s1 should be low) + assert np.isclose(pte_matrix[0, 1], 0, atol=1e-2) and np.isclose( + pte_matrix[1, 0], + 0, + atol=1e-2), "PTE between independent signals should be negligible." + + +