%global _empty_manifest_terminate_build 0 Name: python-e3nn Version: 0.5.1 Release: 1 Summary: Equivariant convolutional neural networks for the group E(3) of 3 dimensional rotations, translations, and mirrors. License: MIT URL: https://e3nn.org Source0: https://mirrors.nju.edu.cn/pypi/web/packages/a1/e5/fd5a05004fa4367511bc05b573773fe59031e20d7eb1a21743fda3eb5db5/e3nn-0.5.1.tar.gz BuildArch: noarch Requires: python3-sympy Requires: python3-scipy Requires: python3-torch Requires: python3-opt-einsum-fx Requires: python3-pytest Requires: python3-pre-commit %description # Euclidean neural networks [![Coverage Status](https://coveralls.io/repos/github/e3nn/e3nn/badge.svg?branch=main)](https://coveralls.io/github/e3nn/e3nn?branch=main) [![DOI](https://zenodo.org/badge/237431920.svg)](https://zenodo.org/badge/latestdoi/237431920) **[Documentation](https://docs.e3nn.org)** | **[Code](https://github.com/e3nn/e3nn)** | **[ChangeLog](https://github.com/e3nn/e3nn/blob/main/ChangeLog.md)** | **[Colab](https://colab.research.google.com/drive/1Gps7mMOmzLe3Rt_b012xsz4UyuexTKAf?usp=sharing)** The aim of this library is to help the development of [E(3)](https://en.wikipedia.org/wiki/Euclidean_group) equivariant neural networks. It contains fundamental mathematical operations such as [tensor products](https://docs.e3nn.org/en/stable/api/o3/o3_tp.html) and [spherical harmonics](https://docs.e3nn.org/en/stable/api/o3/o3_sh.html). ![](https://user-images.githubusercontent.com/333780/79220728-dbe82c00-7e54-11ea-82c7-b3acbd9b2246.gif) ## Installation **Important:** install pytorch and only then run the command ``` pip install --upgrade pip pip install --upgrade e3nn ``` For details and optional dependencies, see [INSTALL.md](https://github.com/e3nn/e3nn/blob/main/INSTALL.md) ### Breaking changes e3nn is under development. It is recommanded to install using pip. The main branch is considered as unstable. The second version number is incremented every time a breaking change is made to the code. ``` 0.(increment when backwards incompatible release).(increment for backwards compatible release) ``` ## Help We are happy to help! The best way to get help on `e3nn` is to submit a [Question](https://github.com/e3nn/e3nn/issues/new?assignees=&labels=question&template=question.md&title=%E2%9D%93+%5BQUESTION%5D) or [Bug Report](https://github.com/e3nn/e3nn/issues/new?assignees=&labels=bug&template=bug-report.md&title=%F0%9F%90%9B+%5BBUG%5D). ## Want to get involved? Great! If you want to get involved in and contribute to the development, improvement, and application of `e3nn`, introduce yourself in the [discussions](https://github.com/e3nn/e3nn/discussions/new). ## Code of conduct Our community abides by the [Contributor Covenant Code of Conduct](https://github.com/e3nn/e3nn/blob/main/code_of_conduct.md). ## Citing ``` @misc{e3nn_paper, doi = {10.48550/ARXIV.2207.09453}, url = {https://arxiv.org/abs/2207.09453}, author = {Geiger, Mario and Smidt, Tess}, keywords = {Machine Learning (cs.LG), Artificial Intelligence (cs.AI), Neural and Evolutionary Computing (cs.NE), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {e3nn: Euclidean Neural Networks}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } @software{e3nn, author = {Mario Geiger and Tess Smidt and Alby M. and Benjamin Kurt Miller and Wouter Boomsma and Bradley Dice and Kostiantyn Lapchevskyi and Maurice Weiler and Michał Tyszkiewicz and Simon Batzner and Dylan Madisetti and Martin Uhrin and Jes Frellsen and Nuri Jung and Sophia Sanborn and Mingjian Wen and Josh Rackers and Marcel Rød and Michael Bailey}, title = {Euclidean neural networks: e3nn}, month = apr, year = 2022, publisher = {Zenodo}, version = {0.5.0}, doi = {10.5281/zenodo.6459381}, url = {https://doi.org/10.5281/zenodo.6459381} } ``` ### Copyright Euclidean neural networks (e3nn) Copyright (c) 2020, The Regents of the University of California, through Lawrence Berkeley National Laboratory (subject to receipt of any required approvals from the U.S. Dept. of Energy), Ecole Polytechnique Federale de Lausanne (EPFL), Free University of Berlin and Kostiantyn Lapchevskyi. All rights reserved. If you have questions about your rights to use or distribute this software, please contact Berkeley Lab's Intellectual Property Office at IPO@lbl.gov. NOTICE. This Software was developed under funding from the U.S. Department of Energy and the U.S. Government consequently retains certain rights. As such, the U.S. Government has been granted for itself and others acting on its behalf a paid-up, nonexclusive, irrevocable, worldwide license in the Software to reproduce, distribute copies to the public, prepare derivative works, and perform publicly and display publicly, and to permit others to do so. %package -n python3-e3nn Summary: Equivariant convolutional neural networks for the group E(3) of 3 dimensional rotations, translations, and mirrors. Provides: python-e3nn BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip %description -n python3-e3nn # Euclidean neural networks [![Coverage Status](https://coveralls.io/repos/github/e3nn/e3nn/badge.svg?branch=main)](https://coveralls.io/github/e3nn/e3nn?branch=main) [![DOI](https://zenodo.org/badge/237431920.svg)](https://zenodo.org/badge/latestdoi/237431920) **[Documentation](https://docs.e3nn.org)** | **[Code](https://github.com/e3nn/e3nn)** | **[ChangeLog](https://github.com/e3nn/e3nn/blob/main/ChangeLog.md)** | **[Colab](https://colab.research.google.com/drive/1Gps7mMOmzLe3Rt_b012xsz4UyuexTKAf?usp=sharing)** The aim of this library is to help the development of [E(3)](https://en.wikipedia.org/wiki/Euclidean_group) equivariant neural networks. It contains fundamental mathematical operations such as [tensor products](https://docs.e3nn.org/en/stable/api/o3/o3_tp.html) and [spherical harmonics](https://docs.e3nn.org/en/stable/api/o3/o3_sh.html). ![](https://user-images.githubusercontent.com/333780/79220728-dbe82c00-7e54-11ea-82c7-b3acbd9b2246.gif) ## Installation **Important:** install pytorch and only then run the command ``` pip install --upgrade pip pip install --upgrade e3nn ``` For details and optional dependencies, see [INSTALL.md](https://github.com/e3nn/e3nn/blob/main/INSTALL.md) ### Breaking changes e3nn is under development. It is recommanded to install using pip. The main branch is considered as unstable. The second version number is incremented every time a breaking change is made to the code. ``` 0.(increment when backwards incompatible release).(increment for backwards compatible release) ``` ## Help We are happy to help! The best way to get help on `e3nn` is to submit a [Question](https://github.com/e3nn/e3nn/issues/new?assignees=&labels=question&template=question.md&title=%E2%9D%93+%5BQUESTION%5D) or [Bug Report](https://github.com/e3nn/e3nn/issues/new?assignees=&labels=bug&template=bug-report.md&title=%F0%9F%90%9B+%5BBUG%5D). ## Want to get involved? Great! If you want to get involved in and contribute to the development, improvement, and application of `e3nn`, introduce yourself in the [discussions](https://github.com/e3nn/e3nn/discussions/new). ## Code of conduct Our community abides by the [Contributor Covenant Code of Conduct](https://github.com/e3nn/e3nn/blob/main/code_of_conduct.md). ## Citing ``` @misc{e3nn_paper, doi = {10.48550/ARXIV.2207.09453}, url = {https://arxiv.org/abs/2207.09453}, author = {Geiger, Mario and Smidt, Tess}, keywords = {Machine Learning (cs.LG), Artificial Intelligence (cs.AI), Neural and Evolutionary Computing (cs.NE), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {e3nn: Euclidean Neural Networks}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } @software{e3nn, author = {Mario Geiger and Tess Smidt and Alby M. and Benjamin Kurt Miller and Wouter Boomsma and Bradley Dice and Kostiantyn Lapchevskyi and Maurice Weiler and Michał Tyszkiewicz and Simon Batzner and Dylan Madisetti and Martin Uhrin and Jes Frellsen and Nuri Jung and Sophia Sanborn and Mingjian Wen and Josh Rackers and Marcel Rød and Michael Bailey}, title = {Euclidean neural networks: e3nn}, month = apr, year = 2022, publisher = {Zenodo}, version = {0.5.0}, doi = {10.5281/zenodo.6459381}, url = {https://doi.org/10.5281/zenodo.6459381} } ``` ### Copyright Euclidean neural networks (e3nn) Copyright (c) 2020, The Regents of the University of California, through Lawrence Berkeley National Laboratory (subject to receipt of any required approvals from the U.S. Dept. of Energy), Ecole Polytechnique Federale de Lausanne (EPFL), Free University of Berlin and Kostiantyn Lapchevskyi. All rights reserved. If you have questions about your rights to use or distribute this software, please contact Berkeley Lab's Intellectual Property Office at IPO@lbl.gov. NOTICE. This Software was developed under funding from the U.S. Department of Energy and the U.S. Government consequently retains certain rights. As such, the U.S. Government has been granted for itself and others acting on its behalf a paid-up, nonexclusive, irrevocable, worldwide license in the Software to reproduce, distribute copies to the public, prepare derivative works, and perform publicly and display publicly, and to permit others to do so. %package help Summary: Development documents and examples for e3nn Provides: python3-e3nn-doc %description help # Euclidean neural networks [![Coverage Status](https://coveralls.io/repos/github/e3nn/e3nn/badge.svg?branch=main)](https://coveralls.io/github/e3nn/e3nn?branch=main) [![DOI](https://zenodo.org/badge/237431920.svg)](https://zenodo.org/badge/latestdoi/237431920) **[Documentation](https://docs.e3nn.org)** | **[Code](https://github.com/e3nn/e3nn)** | **[ChangeLog](https://github.com/e3nn/e3nn/blob/main/ChangeLog.md)** | **[Colab](https://colab.research.google.com/drive/1Gps7mMOmzLe3Rt_b012xsz4UyuexTKAf?usp=sharing)** The aim of this library is to help the development of [E(3)](https://en.wikipedia.org/wiki/Euclidean_group) equivariant neural networks. It contains fundamental mathematical operations such as [tensor products](https://docs.e3nn.org/en/stable/api/o3/o3_tp.html) and [spherical harmonics](https://docs.e3nn.org/en/stable/api/o3/o3_sh.html). ![](https://user-images.githubusercontent.com/333780/79220728-dbe82c00-7e54-11ea-82c7-b3acbd9b2246.gif) ## Installation **Important:** install pytorch and only then run the command ``` pip install --upgrade pip pip install --upgrade e3nn ``` For details and optional dependencies, see [INSTALL.md](https://github.com/e3nn/e3nn/blob/main/INSTALL.md) ### Breaking changes e3nn is under development. It is recommanded to install using pip. The main branch is considered as unstable. The second version number is incremented every time a breaking change is made to the code. ``` 0.(increment when backwards incompatible release).(increment for backwards compatible release) ``` ## Help We are happy to help! The best way to get help on `e3nn` is to submit a [Question](https://github.com/e3nn/e3nn/issues/new?assignees=&labels=question&template=question.md&title=%E2%9D%93+%5BQUESTION%5D) or [Bug Report](https://github.com/e3nn/e3nn/issues/new?assignees=&labels=bug&template=bug-report.md&title=%F0%9F%90%9B+%5BBUG%5D). ## Want to get involved? Great! If you want to get involved in and contribute to the development, improvement, and application of `e3nn`, introduce yourself in the [discussions](https://github.com/e3nn/e3nn/discussions/new). ## Code of conduct Our community abides by the [Contributor Covenant Code of Conduct](https://github.com/e3nn/e3nn/blob/main/code_of_conduct.md). ## Citing ``` @misc{e3nn_paper, doi = {10.48550/ARXIV.2207.09453}, url = {https://arxiv.org/abs/2207.09453}, author = {Geiger, Mario and Smidt, Tess}, keywords = {Machine Learning (cs.LG), Artificial Intelligence (cs.AI), Neural and Evolutionary Computing (cs.NE), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {e3nn: Euclidean Neural Networks}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } @software{e3nn, author = {Mario Geiger and Tess Smidt and Alby M. and Benjamin Kurt Miller and Wouter Boomsma and Bradley Dice and Kostiantyn Lapchevskyi and Maurice Weiler and Michał Tyszkiewicz and Simon Batzner and Dylan Madisetti and Martin Uhrin and Jes Frellsen and Nuri Jung and Sophia Sanborn and Mingjian Wen and Josh Rackers and Marcel Rød and Michael Bailey}, title = {Euclidean neural networks: e3nn}, month = apr, year = 2022, publisher = {Zenodo}, version = {0.5.0}, doi = {10.5281/zenodo.6459381}, url = {https://doi.org/10.5281/zenodo.6459381} } ``` ### Copyright Euclidean neural networks (e3nn) Copyright (c) 2020, The Regents of the University of California, through Lawrence Berkeley National Laboratory (subject to receipt of any required approvals from the U.S. Dept. of Energy), Ecole Polytechnique Federale de Lausanne (EPFL), Free University of Berlin and Kostiantyn Lapchevskyi. All rights reserved. If you have questions about your rights to use or distribute this software, please contact Berkeley Lab's Intellectual Property Office at IPO@lbl.gov. NOTICE. This Software was developed under funding from the U.S. Department of Energy and the U.S. Government consequently retains certain rights. As such, the U.S. Government has been granted for itself and others acting on its behalf a paid-up, nonexclusive, irrevocable, worldwide license in the Software to reproduce, distribute copies to the public, prepare derivative works, and perform publicly and display publicly, and to permit others to do so. %prep %autosetup -n e3nn-0.5.1 %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-e3nn -f filelist.lst %dir %{python3_sitelib}/* %files help -f doclist.lst %{_docdir}/* %changelog * Tue May 30 2023 Python_Bot - 0.5.1-1 - Package Spec generated