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|
%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
[](https://coveralls.io/github/e3nn/e3nn?branch=main)
[](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).
## 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
[](https://coveralls.io/github/e3nn/e3nn?branch=main)
[](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).
## 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
[](https://coveralls.io/github/e3nn/e3nn?branch=main)
[](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).
## 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 <Python_Bot@openeuler.org> - 0.5.1-1
- Package Spec generated
|
