%global _empty_manifest_terminate_build 0 Name: python-neurora Version: 1.1.6.9 Release: 1 Summary: A Python Toolbox for Multimodal Neural Data Representation Analysis License: MIT License URL: https://github.com/ZitongLu1996/NeuroRA Source0: https://mirrors.nju.edu.cn/pypi/web/packages/f0/2a/8e345b2b0e998a314e8e29f597624409d4df745e93b73ffe2c400a28be6e/neurora-1.1.6.9.tar.gz BuildArch: noarch Requires: python3-numpy Requires: python3-scipy Requires: python3-mne Requires: python3-nibabel Requires: python3-matplotlib Requires: python3-nilearn Requires: python3-scikit-learn Requires: python3-scikit-image %description ![ ](img/logo.jpg " ") #NeuroRA **A Python Toolbox of Representational Analysis from Multimodal Neural Data** ## Overview **Representational Similarity Analysis (RSA)** has become a popular and effective method to measure the representation of multivariable neural activity in different modes. **NeuroRA** is an easy-to-use toolbox based on **Python**, which can do some works about **RSA** among nearly all kinds of neural data, including **behavioral, EEG, MEG, fNIRS, sEEG, ECoG, fMRI and some other neuroelectrophysiological data**. In addition, users can do **Neural Pattern Similarity (NPS)**, **Spatiotemporal Pattern Similarity (STPS)**, **Inter-Subject Correlation (ISC)**, **Classification-based EEG Decoding** and **a novel cross-temporal RSA (CTRSA)** on **NeuroRA**. ## Installation > pip install neurora ## Paper Lu, Z., & Ku, Y. (2020). NeuroRA: A Python toolbox of representational analysis from multi-modal neural data. Frontiers in Neuroinformatics. 14:563669. doi: 10.3389/fninf.2020.563669 ## Website & How to use See more details at the [NeuroRA website](https://zitonglu1996.github.io/NeuroRA/). You can read the [Documentation here](https://neurora.github.io/documentation/index.html) or download the [Tutorial here](https://zitonglu1996.github.io/NeuroRA/neurora/Tutorial.pdf) to know how to use NeuroRA. ## Required Dependencies: - **[Numpy](http://www.numpy.org)**: a fundamental package for scientific computing. - **[SciPy](https://www.scipy.org/scipylib/index.html)**: a package that provides many user-friendly and efficient numerical routines. - **[Scikit-learn](https://scikit-learn.org/stable/#)**: a Python module for machine learning. - **[Matplotlib](https://matplotlib.org)**: a Python 2D plotting library. - **[NiBabel](https://nipy.org/nibabel/)**: a package prividing read +/- write access to some common medical and neuroimaging file formats. - **[Nilearn](https://nilearn.github.io/)**: a Python module for fast and easy statistical learning on NeuroImaging data. - **[MNE-Python](https://mne.tools/)**: a Python software for exploring, visualizing, and analyzing human neurophysiological data. ## Features - Calculate the Neural Pattern Similarity (NPS) - Calculate the Spatiotemporal Neural Pattern Similarity (STPS) - Calculate the Inter-Subject Correlation (ISC) - Calculate the Representational Dissimilarity Matrix (RDM) - Calculate the Cross-Temporal RDM (RDM) - Calculate the Representational Similarity based on RDMs - One-Step Realize Representational Similarity Analysis (RSA) - Conduct Cross-Temporal RSA (CTRSA) - Conduct Classification-based EEG decoding - Conduct Statistical Analysis - Save the RSA result as a NIfTI file for fMRI - Plot the results ## Demos There are several demos for NeuroRA, and you can see them in /demos/.. path (both .py files and .ipynb files are provided). | | Run the Demo | View the Demo | | - | --- | ---- | | Demo 1 | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ZitongLu1996/NeuroRA/blob/master/demo/NeuroRA_Demo1_colab.ipynb) | [![View the notebook](https://img.shields.io/badge/render-nbviewer-orange.svg)](https://nbviewer.jupyter.org/github/ZitongLu1996/NeuroRA/blob/master/demo/NeuroRA_Demo1.ipynb) | | Demo 2 | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ZitongLu1996/NeuroRA/blob/master/demo/NeuroRA_Demo2_colab.ipynb) | [![View the notebook](https://img.shields.io/badge/render-nbviewer-orange.svg)](https://nbviewer.jupyter.org/github/ZitongLu1996/NeuroRA/blob/master/demo/NeuroRA_Demo2.ipynb) | | Demo 3 | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ZitongLu1996/NeuroRA/blob/master/demo/NeuroRA_Demo3_colab.ipynb) | [![View the notebook](https://img.shields.io/badge/render-nbviewer-orange.svg)](https://nbviewer.jupyter.org/github/ZitongLu1996/NeuroRA/blob/master/demo/NeuroRA_Demo3.ipynb) | ## About NeuroRA **Noteworthily**, this toolbox is currently only a **test version**. If you have any question, find some bugs or have some useful suggestions while using, you can email me and I will be happy and thankful to know. >My email address: >zitonglu1996@gmail.com / zitonglu@outlook.com >My personal homepage: >https://zitonglu1996.github.io %package -n python3-neurora Summary: A Python Toolbox for Multimodal Neural Data Representation Analysis Provides: python-neurora BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip %description -n python3-neurora ![ ](img/logo.jpg " ") #NeuroRA **A Python Toolbox of Representational Analysis from Multimodal Neural Data** ## Overview **Representational Similarity Analysis (RSA)** has become a popular and effective method to measure the representation of multivariable neural activity in different modes. **NeuroRA** is an easy-to-use toolbox based on **Python**, which can do some works about **RSA** among nearly all kinds of neural data, including **behavioral, EEG, MEG, fNIRS, sEEG, ECoG, fMRI and some other neuroelectrophysiological data**. In addition, users can do **Neural Pattern Similarity (NPS)**, **Spatiotemporal Pattern Similarity (STPS)**, **Inter-Subject Correlation (ISC)**, **Classification-based EEG Decoding** and **a novel cross-temporal RSA (CTRSA)** on **NeuroRA**. ## Installation > pip install neurora ## Paper Lu, Z., & Ku, Y. (2020). NeuroRA: A Python toolbox of representational analysis from multi-modal neural data. Frontiers in Neuroinformatics. 14:563669. doi: 10.3389/fninf.2020.563669 ## Website & How to use See more details at the [NeuroRA website](https://zitonglu1996.github.io/NeuroRA/). You can read the [Documentation here](https://neurora.github.io/documentation/index.html) or download the [Tutorial here](https://zitonglu1996.github.io/NeuroRA/neurora/Tutorial.pdf) to know how to use NeuroRA. ## Required Dependencies: - **[Numpy](http://www.numpy.org)**: a fundamental package for scientific computing. - **[SciPy](https://www.scipy.org/scipylib/index.html)**: a package that provides many user-friendly and efficient numerical routines. - **[Scikit-learn](https://scikit-learn.org/stable/#)**: a Python module for machine learning. - **[Matplotlib](https://matplotlib.org)**: a Python 2D plotting library. - **[NiBabel](https://nipy.org/nibabel/)**: a package prividing read +/- write access to some common medical and neuroimaging file formats. - **[Nilearn](https://nilearn.github.io/)**: a Python module for fast and easy statistical learning on NeuroImaging data. - **[MNE-Python](https://mne.tools/)**: a Python software for exploring, visualizing, and analyzing human neurophysiological data. ## Features - Calculate the Neural Pattern Similarity (NPS) - Calculate the Spatiotemporal Neural Pattern Similarity (STPS) - Calculate the Inter-Subject Correlation (ISC) - Calculate the Representational Dissimilarity Matrix (RDM) - Calculate the Cross-Temporal RDM (RDM) - Calculate the Representational Similarity based on RDMs - One-Step Realize Representational Similarity Analysis (RSA) - Conduct Cross-Temporal RSA (CTRSA) - Conduct Classification-based EEG decoding - Conduct Statistical Analysis - Save the RSA result as a NIfTI file for fMRI - Plot the results ## Demos There are several demos for NeuroRA, and you can see them in /demos/.. path (both .py files and .ipynb files are provided). | | Run the Demo | View the Demo | | - | --- | ---- | | Demo 1 | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ZitongLu1996/NeuroRA/blob/master/demo/NeuroRA_Demo1_colab.ipynb) | [![View the notebook](https://img.shields.io/badge/render-nbviewer-orange.svg)](https://nbviewer.jupyter.org/github/ZitongLu1996/NeuroRA/blob/master/demo/NeuroRA_Demo1.ipynb) | | Demo 2 | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ZitongLu1996/NeuroRA/blob/master/demo/NeuroRA_Demo2_colab.ipynb) | [![View the notebook](https://img.shields.io/badge/render-nbviewer-orange.svg)](https://nbviewer.jupyter.org/github/ZitongLu1996/NeuroRA/blob/master/demo/NeuroRA_Demo2.ipynb) | | Demo 3 | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ZitongLu1996/NeuroRA/blob/master/demo/NeuroRA_Demo3_colab.ipynb) | [![View the notebook](https://img.shields.io/badge/render-nbviewer-orange.svg)](https://nbviewer.jupyter.org/github/ZitongLu1996/NeuroRA/blob/master/demo/NeuroRA_Demo3.ipynb) | ## About NeuroRA **Noteworthily**, this toolbox is currently only a **test version**. If you have any question, find some bugs or have some useful suggestions while using, you can email me and I will be happy and thankful to know. >My email address: >zitonglu1996@gmail.com / zitonglu@outlook.com >My personal homepage: >https://zitonglu1996.github.io %package help Summary: Development documents and examples for neurora Provides: python3-neurora-doc %description help ![ ](img/logo.jpg " ") #NeuroRA **A Python Toolbox of Representational Analysis from Multimodal Neural Data** ## Overview **Representational Similarity Analysis (RSA)** has become a popular and effective method to measure the representation of multivariable neural activity in different modes. **NeuroRA** is an easy-to-use toolbox based on **Python**, which can do some works about **RSA** among nearly all kinds of neural data, including **behavioral, EEG, MEG, fNIRS, sEEG, ECoG, fMRI and some other neuroelectrophysiological data**. In addition, users can do **Neural Pattern Similarity (NPS)**, **Spatiotemporal Pattern Similarity (STPS)**, **Inter-Subject Correlation (ISC)**, **Classification-based EEG Decoding** and **a novel cross-temporal RSA (CTRSA)** on **NeuroRA**. ## Installation > pip install neurora ## Paper Lu, Z., & Ku, Y. (2020). NeuroRA: A Python toolbox of representational analysis from multi-modal neural data. Frontiers in Neuroinformatics. 14:563669. doi: 10.3389/fninf.2020.563669 ## Website & How to use See more details at the [NeuroRA website](https://zitonglu1996.github.io/NeuroRA/). You can read the [Documentation here](https://neurora.github.io/documentation/index.html) or download the [Tutorial here](https://zitonglu1996.github.io/NeuroRA/neurora/Tutorial.pdf) to know how to use NeuroRA. ## Required Dependencies: - **[Numpy](http://www.numpy.org)**: a fundamental package for scientific computing. - **[SciPy](https://www.scipy.org/scipylib/index.html)**: a package that provides many user-friendly and efficient numerical routines. - **[Scikit-learn](https://scikit-learn.org/stable/#)**: a Python module for machine learning. - **[Matplotlib](https://matplotlib.org)**: a Python 2D plotting library. - **[NiBabel](https://nipy.org/nibabel/)**: a package prividing read +/- write access to some common medical and neuroimaging file formats. - **[Nilearn](https://nilearn.github.io/)**: a Python module for fast and easy statistical learning on NeuroImaging data. - **[MNE-Python](https://mne.tools/)**: a Python software for exploring, visualizing, and analyzing human neurophysiological data. ## Features - Calculate the Neural Pattern Similarity (NPS) - Calculate the Spatiotemporal Neural Pattern Similarity (STPS) - Calculate the Inter-Subject Correlation (ISC) - Calculate the Representational Dissimilarity Matrix (RDM) - Calculate the Cross-Temporal RDM (RDM) - Calculate the Representational Similarity based on RDMs - One-Step Realize Representational Similarity Analysis (RSA) - Conduct Cross-Temporal RSA (CTRSA) - Conduct Classification-based EEG decoding - Conduct Statistical Analysis - Save the RSA result as a NIfTI file for fMRI - Plot the results ## Demos There are several demos for NeuroRA, and you can see them in /demos/.. path (both .py files and .ipynb files are provided). | | Run the Demo | View the Demo | | - | --- | ---- | | Demo 1 | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ZitongLu1996/NeuroRA/blob/master/demo/NeuroRA_Demo1_colab.ipynb) | [![View the notebook](https://img.shields.io/badge/render-nbviewer-orange.svg)](https://nbviewer.jupyter.org/github/ZitongLu1996/NeuroRA/blob/master/demo/NeuroRA_Demo1.ipynb) | | Demo 2 | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ZitongLu1996/NeuroRA/blob/master/demo/NeuroRA_Demo2_colab.ipynb) | [![View the notebook](https://img.shields.io/badge/render-nbviewer-orange.svg)](https://nbviewer.jupyter.org/github/ZitongLu1996/NeuroRA/blob/master/demo/NeuroRA_Demo2.ipynb) | | Demo 3 | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ZitongLu1996/NeuroRA/blob/master/demo/NeuroRA_Demo3_colab.ipynb) | [![View the notebook](https://img.shields.io/badge/render-nbviewer-orange.svg)](https://nbviewer.jupyter.org/github/ZitongLu1996/NeuroRA/blob/master/demo/NeuroRA_Demo3.ipynb) | ## About NeuroRA **Noteworthily**, this toolbox is currently only a **test version**. If you have any question, find some bugs or have some useful suggestions while using, you can email me and I will be happy and thankful to know. >My email address: >zitonglu1996@gmail.com / zitonglu@outlook.com >My personal homepage: >https://zitonglu1996.github.io %prep %autosetup -n neurora-1.1.6.9 %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-neurora -f filelist.lst %dir %{python3_sitelib}/* %files help -f doclist.lst %{_docdir}/* %changelog * Fri May 05 2023 Python_Bot - 1.1.6.9-1 - Package Spec generated