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diff --git a/python-pyriemann.spec b/python-pyriemann.spec new file mode 100644 index 0000000..8ce44fe --- /dev/null +++ b/python-pyriemann.spec @@ -0,0 +1,612 @@ +%global _empty_manifest_terminate_build 0 +Name: python-pyriemann +Version: 0.4 +Release: 1 +Summary: Biosignals classification with Riemannian geometry +License: BSD (3-clause) +URL: https://pyriemann.readthedocs.io +Source0: https://mirrors.nju.edu.cn/pypi/web/packages/ee/5e/ac8347d876bb0c04498196b33fbf25f2a21eace9fe8f791292598773508a/pyriemann-0.4.tar.gz +BuildArch: noarch + + +%description +# pyRiemann + +[](https://img.shields.io/badge/python-3.7%20%7C%203.8%20%7C%203.9-blue) +[](https://badge.fury.io/py/pyriemann) +[](https://github.com/pyRiemann/pyRiemann/actions) +[](https://codecov.io/gh/pyRiemann/pyRiemann) +[](http://pyriemann.readthedocs.io/en/latest/?badge=latest) +[](https://doi.org/10.5281/zenodo.593816) +[](https://pepy.tech/project/pyriemann) + +pyRiemann is a Python machine learning package based on [scikit-learn](http://scikit-learn.org/stable/modules/classes.html) API. +It provides a high-level interface for processing and classification of multivariate time series +through the Riemannian geometry of symmetric positive definite (SPD) matrices. + +pyRiemann aims at being a generic package for multivariate time series classification +but has been designed around multichannel biosignals (like EEG, MEG or EMG) manipulation applied to brain-computer interface (BCI), +transforming multichannel time series into covariance matrices, and classifying them using the Riemannian geometry of SPD matrices [[1]](#1). + +For BCI applications, studied paradigms are motor imagery [[2]](#2) [[3]](#3), event-related potentials (ERP) [[4]](#4) and steady-state visually evoked potentials (SSVEP) [[5]](#5). +Using extended labels, API allows transfer learning between sessions or subjects [[6]](#6). + +This code is BSD-licenced (3 clause). + +## Documentation + +The documentation is available on http://pyriemann.readthedocs.io/en/latest/ + +## Install + +#### Using PyPI + +``` +pip install pyriemann +``` +or using pip+git for the latest version of the code : + +``` +pip install git+https://github.com/pyRiemann/pyRiemann +``` + +Anaconda is not currently supported, if you want to use anaconda, you need to create a virtual environment in anaconda, +activate it and use the above command to install it. + +#### From sources + +For the latest version, you can install the package from the sources using the setup.py script + +``` +python setup.py install +``` + +or in developer mode to be able to modify the sources. + +``` +python setup.py develop +``` + +## How to use it + +Most of the functions mimic the scikit-learn API, and therefore can be directly used with sklearn. +For example, for cross-validation classification of EEG signal using the MDM algorithm described in [[2]](#2), it is easy as: + +```python +import pyriemann +from sklearn.model_selection import cross_val_score + +# load your data +X = ... # EEG data, in format n_epochs x n_channels x n_times +y = ... # labels + +# estimate covariance matrices +cov = pyriemann.estimation.Covariances().fit_transform(X) + +# cross validation +mdm = pyriemann.classification.MDM() + +accuracy = cross_val_score(mdm, cov, y) + +print(accuracy.mean()) + +``` + +You can also pipeline methods using sklearn pipeline framework. +For example, to classify EEG signal using a SVM classifier in the tangent space, described in [[3]](#3): + +```python +from pyriemann.estimation import Covariances +from pyriemann.tangentspace import TangentSpace + +from sklearn.pipeline import make_pipeline +from sklearn.svm import SVC +from sklearn.model_selection import cross_val_score + +# load your data +X = ... # EEG data, in format n_epochs x n_channels x n_times +y = ... # labels + +# build your pipeline +covest = Covariances() +ts = TangentSpace() +svc = SVC(kernel='linear') +clf = make_pipeline(covest, ts, svc) + +# cross validation +accuracy = cross_val_score(clf, X, y) + +print(accuracy.mean()) + +``` + +**Check out the example folder for more examples !** + +# Contribution Guidelines + +The package aims at adopting the [scikit-learn](http://scikit-learn.org/stable/developers/contributing.html#contributing-code) +and [MNE-Python](https://mne.tools/stable/install/contributing.html) conventions as much as possible. +See their contribution guidelines before contributing to the repository. + +# Testing + +If you make a modification, run the test suite before submitting a pull request + +``` +pytest +``` + +# How to cite + +```bibtex +@software{pyriemann, + author = {Alexandre Barachant and + Quentin Barthélemy and + Jean-Rémi King and + Alexandre Gramfort and + Sylvain Chevallier and + Pedro L. C. Rodrigues and + Emanuele Olivetti and + Vladislav Goncharenko and + Gabriel Wagner vom Berg and + Ghiles Reguig and + Arthur Lebeurrier and + Erik Bjäreholt and + Maria Sayu Yamamoto and + Pierre Clisson and + Marie-Constance Corsi}, + title = {pyRiemann/pyRiemann: v0.3}, + month = jul, + year = 2022, + publisher = {Zenodo}, + version = {v0.3}, + doi = {10.5281/zenodo.7547583}, + url = {https://doi.org/10.5281/zenodo.7547583} +} +``` + +# References + +<a id="1">[1]</a> +M. Congedo, A. Barachant and R. Bhatia, "Riemannian geometry for EEG-based brain-computer interfaces; a primer and a review". +Brain-Computer Interfaces, 4.3, pp. 155-174, 2017. [link](https://hal.science/hal-01570120/document) + +<a id="2">[2]</a> +A. Barachant, S. Bonnet, M. Congedo and C. Jutten, "Multiclass Brain-Computer Interface Classification by Riemannian Geometry". +IEEE Transactions on Biomedical Engineering, vol. 59, no. 4, pp. 920-928, 2012. [link](https://hal.archives-ouvertes.fr/hal-00681328) + +<a id="3">[3]</a> +A. Barachant, S. Bonnet, M. Congedo and C. Jutten, "Classification of covariance matrices using a Riemannian-based kernel for BCI applications". +Neurocomputing, 112, pp. 172-178, 2013. [link](https://hal.archives-ouvertes.fr/hal-00820475/) + +<a id="4">[4]</a> +A. Barachant and M. Congedo, "A Plug&Play P300 BCI Using Information Geometry". +Research report, 2014. [link](http://arxiv.org/abs/1409.0107) + +<a id="5">[5]</a> +EK. Kalunga, S. Chevallier, Q. Barthélemy, K. Djouani, E. Monacelli and Y. Hamam, "Online SSVEP-based BCI using Riemannian geometry". +Neurocomputing, 191, pp. 55-68, 2014. [link](https://hal.science/hal-01351623/file/Kalunga-Chevallier-Barthelemy-Online%20SSVEP-based%20BCI%20using%20Riemannian%20Geometry-Neurocomputing-16.pdf) + +<a id="6">[6]</a> +PLC. Rodrigues, C. Jutten and M. Congedo, "Riemannian Procrustes analysis: transfer learning for brain-computer interfaces". +IEEE Transactions on Biomedical Engineering, vol. 66, no. 8, pp. 2390-2401, 2018. [link](https://hal.archives-ouvertes.fr/hal-01971856) + + +%package -n python3-pyriemann +Summary: Biosignals classification with Riemannian geometry +Provides: python-pyriemann +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +%description -n python3-pyriemann +# pyRiemann + +[](https://img.shields.io/badge/python-3.7%20%7C%203.8%20%7C%203.9-blue) +[](https://badge.fury.io/py/pyriemann) +[](https://github.com/pyRiemann/pyRiemann/actions) +[](https://codecov.io/gh/pyRiemann/pyRiemann) +[](http://pyriemann.readthedocs.io/en/latest/?badge=latest) +[](https://doi.org/10.5281/zenodo.593816) +[](https://pepy.tech/project/pyriemann) + +pyRiemann is a Python machine learning package based on [scikit-learn](http://scikit-learn.org/stable/modules/classes.html) API. +It provides a high-level interface for processing and classification of multivariate time series +through the Riemannian geometry of symmetric positive definite (SPD) matrices. + +pyRiemann aims at being a generic package for multivariate time series classification +but has been designed around multichannel biosignals (like EEG, MEG or EMG) manipulation applied to brain-computer interface (BCI), +transforming multichannel time series into covariance matrices, and classifying them using the Riemannian geometry of SPD matrices [[1]](#1). + +For BCI applications, studied paradigms are motor imagery [[2]](#2) [[3]](#3), event-related potentials (ERP) [[4]](#4) and steady-state visually evoked potentials (SSVEP) [[5]](#5). +Using extended labels, API allows transfer learning between sessions or subjects [[6]](#6). + +This code is BSD-licenced (3 clause). + +## Documentation + +The documentation is available on http://pyriemann.readthedocs.io/en/latest/ + +## Install + +#### Using PyPI + +``` +pip install pyriemann +``` +or using pip+git for the latest version of the code : + +``` +pip install git+https://github.com/pyRiemann/pyRiemann +``` + +Anaconda is not currently supported, if you want to use anaconda, you need to create a virtual environment in anaconda, +activate it and use the above command to install it. + +#### From sources + +For the latest version, you can install the package from the sources using the setup.py script + +``` +python setup.py install +``` + +or in developer mode to be able to modify the sources. + +``` +python setup.py develop +``` + +## How to use it + +Most of the functions mimic the scikit-learn API, and therefore can be directly used with sklearn. +For example, for cross-validation classification of EEG signal using the MDM algorithm described in [[2]](#2), it is easy as: + +```python +import pyriemann +from sklearn.model_selection import cross_val_score + +# load your data +X = ... # EEG data, in format n_epochs x n_channels x n_times +y = ... # labels + +# estimate covariance matrices +cov = pyriemann.estimation.Covariances().fit_transform(X) + +# cross validation +mdm = pyriemann.classification.MDM() + +accuracy = cross_val_score(mdm, cov, y) + +print(accuracy.mean()) + +``` + +You can also pipeline methods using sklearn pipeline framework. +For example, to classify EEG signal using a SVM classifier in the tangent space, described in [[3]](#3): + +```python +from pyriemann.estimation import Covariances +from pyriemann.tangentspace import TangentSpace + +from sklearn.pipeline import make_pipeline +from sklearn.svm import SVC +from sklearn.model_selection import cross_val_score + +# load your data +X = ... # EEG data, in format n_epochs x n_channels x n_times +y = ... # labels + +# build your pipeline +covest = Covariances() +ts = TangentSpace() +svc = SVC(kernel='linear') +clf = make_pipeline(covest, ts, svc) + +# cross validation +accuracy = cross_val_score(clf, X, y) + +print(accuracy.mean()) + +``` + +**Check out the example folder for more examples !** + +# Contribution Guidelines + +The package aims at adopting the [scikit-learn](http://scikit-learn.org/stable/developers/contributing.html#contributing-code) +and [MNE-Python](https://mne.tools/stable/install/contributing.html) conventions as much as possible. +See their contribution guidelines before contributing to the repository. + +# Testing + +If you make a modification, run the test suite before submitting a pull request + +``` +pytest +``` + +# How to cite + +```bibtex +@software{pyriemann, + author = {Alexandre Barachant and + Quentin Barthélemy and + Jean-Rémi King and + Alexandre Gramfort and + Sylvain Chevallier and + Pedro L. C. Rodrigues and + Emanuele Olivetti and + Vladislav Goncharenko and + Gabriel Wagner vom Berg and + Ghiles Reguig and + Arthur Lebeurrier and + Erik Bjäreholt and + Maria Sayu Yamamoto and + Pierre Clisson and + Marie-Constance Corsi}, + title = {pyRiemann/pyRiemann: v0.3}, + month = jul, + year = 2022, + publisher = {Zenodo}, + version = {v0.3}, + doi = {10.5281/zenodo.7547583}, + url = {https://doi.org/10.5281/zenodo.7547583} +} +``` + +# References + +<a id="1">[1]</a> +M. Congedo, A. Barachant and R. Bhatia, "Riemannian geometry for EEG-based brain-computer interfaces; a primer and a review". +Brain-Computer Interfaces, 4.3, pp. 155-174, 2017. [link](https://hal.science/hal-01570120/document) + +<a id="2">[2]</a> +A. Barachant, S. Bonnet, M. Congedo and C. Jutten, "Multiclass Brain-Computer Interface Classification by Riemannian Geometry". +IEEE Transactions on Biomedical Engineering, vol. 59, no. 4, pp. 920-928, 2012. [link](https://hal.archives-ouvertes.fr/hal-00681328) + +<a id="3">[3]</a> +A. Barachant, S. Bonnet, M. Congedo and C. Jutten, "Classification of covariance matrices using a Riemannian-based kernel for BCI applications". +Neurocomputing, 112, pp. 172-178, 2013. [link](https://hal.archives-ouvertes.fr/hal-00820475/) + +<a id="4">[4]</a> +A. Barachant and M. Congedo, "A Plug&Play P300 BCI Using Information Geometry". +Research report, 2014. [link](http://arxiv.org/abs/1409.0107) + +<a id="5">[5]</a> +EK. Kalunga, S. Chevallier, Q. Barthélemy, K. Djouani, E. Monacelli and Y. Hamam, "Online SSVEP-based BCI using Riemannian geometry". +Neurocomputing, 191, pp. 55-68, 2014. [link](https://hal.science/hal-01351623/file/Kalunga-Chevallier-Barthelemy-Online%20SSVEP-based%20BCI%20using%20Riemannian%20Geometry-Neurocomputing-16.pdf) + +<a id="6">[6]</a> +PLC. Rodrigues, C. Jutten and M. Congedo, "Riemannian Procrustes analysis: transfer learning for brain-computer interfaces". +IEEE Transactions on Biomedical Engineering, vol. 66, no. 8, pp. 2390-2401, 2018. [link](https://hal.archives-ouvertes.fr/hal-01971856) + + +%package help +Summary: Development documents and examples for pyriemann +Provides: python3-pyriemann-doc +%description help +# pyRiemann + +[](https://img.shields.io/badge/python-3.7%20%7C%203.8%20%7C%203.9-blue) +[](https://badge.fury.io/py/pyriemann) +[](https://github.com/pyRiemann/pyRiemann/actions) +[](https://codecov.io/gh/pyRiemann/pyRiemann) +[](http://pyriemann.readthedocs.io/en/latest/?badge=latest) +[](https://doi.org/10.5281/zenodo.593816) +[](https://pepy.tech/project/pyriemann) + +pyRiemann is a Python machine learning package based on [scikit-learn](http://scikit-learn.org/stable/modules/classes.html) API. +It provides a high-level interface for processing and classification of multivariate time series +through the Riemannian geometry of symmetric positive definite (SPD) matrices. + +pyRiemann aims at being a generic package for multivariate time series classification +but has been designed around multichannel biosignals (like EEG, MEG or EMG) manipulation applied to brain-computer interface (BCI), +transforming multichannel time series into covariance matrices, and classifying them using the Riemannian geometry of SPD matrices [[1]](#1). + +For BCI applications, studied paradigms are motor imagery [[2]](#2) [[3]](#3), event-related potentials (ERP) [[4]](#4) and steady-state visually evoked potentials (SSVEP) [[5]](#5). +Using extended labels, API allows transfer learning between sessions or subjects [[6]](#6). + +This code is BSD-licenced (3 clause). + +## Documentation + +The documentation is available on http://pyriemann.readthedocs.io/en/latest/ + +## Install + +#### Using PyPI + +``` +pip install pyriemann +``` +or using pip+git for the latest version of the code : + +``` +pip install git+https://github.com/pyRiemann/pyRiemann +``` + +Anaconda is not currently supported, if you want to use anaconda, you need to create a virtual environment in anaconda, +activate it and use the above command to install it. + +#### From sources + +For the latest version, you can install the package from the sources using the setup.py script + +``` +python setup.py install +``` + +or in developer mode to be able to modify the sources. + +``` +python setup.py develop +``` + +## How to use it + +Most of the functions mimic the scikit-learn API, and therefore can be directly used with sklearn. +For example, for cross-validation classification of EEG signal using the MDM algorithm described in [[2]](#2), it is easy as: + +```python +import pyriemann +from sklearn.model_selection import cross_val_score + +# load your data +X = ... # EEG data, in format n_epochs x n_channels x n_times +y = ... # labels + +# estimate covariance matrices +cov = pyriemann.estimation.Covariances().fit_transform(X) + +# cross validation +mdm = pyriemann.classification.MDM() + +accuracy = cross_val_score(mdm, cov, y) + +print(accuracy.mean()) + +``` + +You can also pipeline methods using sklearn pipeline framework. +For example, to classify EEG signal using a SVM classifier in the tangent space, described in [[3]](#3): + +```python +from pyriemann.estimation import Covariances +from pyriemann.tangentspace import TangentSpace + +from sklearn.pipeline import make_pipeline +from sklearn.svm import SVC +from sklearn.model_selection import cross_val_score + +# load your data +X = ... # EEG data, in format n_epochs x n_channels x n_times +y = ... # labels + +# build your pipeline +covest = Covariances() +ts = TangentSpace() +svc = SVC(kernel='linear') +clf = make_pipeline(covest, ts, svc) + +# cross validation +accuracy = cross_val_score(clf, X, y) + +print(accuracy.mean()) + +``` + +**Check out the example folder for more examples !** + +# Contribution Guidelines + +The package aims at adopting the [scikit-learn](http://scikit-learn.org/stable/developers/contributing.html#contributing-code) +and [MNE-Python](https://mne.tools/stable/install/contributing.html) conventions as much as possible. +See their contribution guidelines before contributing to the repository. + +# Testing + +If you make a modification, run the test suite before submitting a pull request + +``` +pytest +``` + +# How to cite + +```bibtex +@software{pyriemann, + author = {Alexandre Barachant and + Quentin Barthélemy and + Jean-Rémi King and + Alexandre Gramfort and + Sylvain Chevallier and + Pedro L. C. Rodrigues and + Emanuele Olivetti and + Vladislav Goncharenko and + Gabriel Wagner vom Berg and + Ghiles Reguig and + Arthur Lebeurrier and + Erik Bjäreholt and + Maria Sayu Yamamoto and + Pierre Clisson and + Marie-Constance Corsi}, + title = {pyRiemann/pyRiemann: v0.3}, + month = jul, + year = 2022, + publisher = {Zenodo}, + version = {v0.3}, + doi = {10.5281/zenodo.7547583}, + url = {https://doi.org/10.5281/zenodo.7547583} +} +``` + +# References + +<a id="1">[1]</a> +M. Congedo, A. Barachant and R. Bhatia, "Riemannian geometry for EEG-based brain-computer interfaces; a primer and a review". +Brain-Computer Interfaces, 4.3, pp. 155-174, 2017. [link](https://hal.science/hal-01570120/document) + +<a id="2">[2]</a> +A. Barachant, S. Bonnet, M. Congedo and C. Jutten, "Multiclass Brain-Computer Interface Classification by Riemannian Geometry". +IEEE Transactions on Biomedical Engineering, vol. 59, no. 4, pp. 920-928, 2012. [link](https://hal.archives-ouvertes.fr/hal-00681328) + +<a id="3">[3]</a> +A. Barachant, S. Bonnet, M. Congedo and C. Jutten, "Classification of covariance matrices using a Riemannian-based kernel for BCI applications". +Neurocomputing, 112, pp. 172-178, 2013. [link](https://hal.archives-ouvertes.fr/hal-00820475/) + +<a id="4">[4]</a> +A. Barachant and M. Congedo, "A Plug&Play P300 BCI Using Information Geometry". +Research report, 2014. [link](http://arxiv.org/abs/1409.0107) + +<a id="5">[5]</a> +EK. Kalunga, S. Chevallier, Q. Barthélemy, K. Djouani, E. Monacelli and Y. Hamam, "Online SSVEP-based BCI using Riemannian geometry". +Neurocomputing, 191, pp. 55-68, 2014. [link](https://hal.science/hal-01351623/file/Kalunga-Chevallier-Barthelemy-Online%20SSVEP-based%20BCI%20using%20Riemannian%20Geometry-Neurocomputing-16.pdf) + +<a id="6">[6]</a> +PLC. Rodrigues, C. Jutten and M. Congedo, "Riemannian Procrustes analysis: transfer learning for brain-computer interfaces". +IEEE Transactions on Biomedical Engineering, vol. 66, no. 8, pp. 2390-2401, 2018. [link](https://hal.archives-ouvertes.fr/hal-01971856) + + +%prep +%autosetup -n pyriemann-0.4 + +%build +%py3_build + +%install +%py3_install +install -d -m755 %{buildroot}/%{_pkgdocdir} +if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi +if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi +if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi +if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi +pushd %{buildroot} +if [ -d usr/lib ]; then + find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/lib64 ]; then + find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/bin ]; then + find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/sbin ]; then + find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst +fi +touch doclist.lst +if [ -d usr/share/man ]; then + find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst +fi +popd +mv %{buildroot}/filelist.lst . +mv %{buildroot}/doclist.lst . + +%files -n python3-pyriemann -f filelist.lst +%dir %{python3_sitelib}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Thu Mar 09 2023 Python_Bot <Python_Bot@openeuler.org> - 0.4-1 +- Package Spec generated |