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|
%global _empty_manifest_terminate_build 0
Name: python-nessai
Version: 0.8.1
Release: 1
Summary: Nessai: Nested Sampling with Artificial Intelligence
License: MIT License
URL: https://github.com/mj-will/nessai
Source0: https://mirrors.aliyun.com/pypi/web/packages/9b/0d/ca46e471480ba7854ec929e8dbfb3d0724239c2ca46574c712ba866519a1/nessai-0.8.1.tar.gz
BuildArch: noarch
Requires: python3-numpy
Requires: python3-pandas
Requires: python3-matplotlib
Requires: python3-seaborn
Requires: python3-scipy
Requires: python3-torch
Requires: python3-tqdm
Requires: python3-glasflow
Requires: python3-h5py
Requires: python3-pre-commit
Requires: python3-ray[default]
Requires: python3-corner
Requires: python3-sphinx
Requires: python3-sphinx-rtd-theme
Requires: python3-numpydoc
Requires: python3-sphinx-autoapi
Requires: python3-lalsuite
Requires: python3-bilby
Requires: python3-astropy
Requires: python3-nflows
Requires: python3-pytest
Requires: python3-pytest-cov
Requires: python3-pytest-timeout
Requires: python3-pytest-rerunfailures
Requires: python3-pytest-integration
%description
[](https://doi.org/10.5281/zenodo.4550693)
[](https://pypi.org/project/nessai/)
[](https://anaconda.org/conda-forge/nessai)
[](https://nessai.readthedocs.io/en/latest/?badge=latest)
[](https://codecov.io/gh/mj-will/nessai)
# nessai: Nested Sampling with Artificial Intelligence
``nessai`` (/ˈnɛsi/): Nested Sampling with Artificial Intelligence
``nessai`` is a nested sampling algorithm for Bayesian Inference that incorporates normalisings flows. It is designed for applications where the Bayesian likelihood is computationally expensive.
## Installation
``nessai`` can be installed using ``pip``:
```console
pip install nessai
```
or via ``conda``
```console
conda install -c conda-forge -c pytorch nessai
```
### PyTorch
By default the version of PyTorch will not necessarily match the drivers on your system, to install a different version with the correct CUDA support see the PyTorch homepage for instructions: https://pytorch.org/.
### Using ``bilby``
As of `bilby` version 1.1.0, ``nessai`` is now supported by default but it is still an optional requirement. See the [``bilby`` documentation](https://lscsoft.docs.ligo.org/bilby/index.html) for installation instructions for `bilby`
See the examples included with ``nessai`` for how to run ``nessai`` via ``bilby``.
## Documentation
Documentation is available at: [nessai.readthedocs.io](https://nessai.readthedocs.io/)
## Contributing
Please see the guidelines [here](https://github.com/mj-will/nessai/blob/master/CONTRIBUTING.md).
## Acknowledgements
The core nested sampling code, model design and code for computing the posterior in ``nessai`` was based on [`cpnest`](https://github.com/johnveitch/cpnest) with permission from the authors.
The normalising flows implemented in ``nessai`` are all either directly imported from [`nflows`](https://github.com/bayesiains/nflows/tree/master/nflows) or heavily based on it.
Other code snippets that draw on existing code reference the source in their corresponding doc-strings.
The authors also thank Christian Chapman-Bird, Laurence Datrier, Fergus Hayes, Jethro Linley and Simon Tait for their feedback and help finding bugs in ``nessai``.
## Citing
If you find ``nessai`` useful in your work please cite the DOI for this code and our paper:
```bibtex
@software{nessai,
author = {Michael J. Williams},
title = {nessai: Nested Sampling with Artificial Intelligence},
month = feb,
year = 2021,
publisher = {Zenodo},
version = {latest},
doi = {10.5281/zenodo.4550693},
url = {https://doi.org/10.5281/zenodo.4550693}
}
@article{PhysRevD.103.103006,
title = {Nested sampling with normalizing flows for gravitational-wave inference},
author = {Williams, Michael J. and Veitch, John and Messenger, Chris},
journal = {Phys. Rev. D},
volume = {103},
issue = {10},
pages = {103006},
numpages = {19},
year = {2021},
month = {May},
publisher = {American Physical Society},
doi = {10.1103/PhysRevD.103.103006},
url = {https://link.aps.org/doi/10.1103/PhysRevD.103.103006}
}
```
%package -n python3-nessai
Summary: Nessai: Nested Sampling with Artificial Intelligence
Provides: python-nessai
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-nessai
[](https://doi.org/10.5281/zenodo.4550693)
[](https://pypi.org/project/nessai/)
[](https://anaconda.org/conda-forge/nessai)
[](https://nessai.readthedocs.io/en/latest/?badge=latest)
[](https://codecov.io/gh/mj-will/nessai)
# nessai: Nested Sampling with Artificial Intelligence
``nessai`` (/ˈnɛsi/): Nested Sampling with Artificial Intelligence
``nessai`` is a nested sampling algorithm for Bayesian Inference that incorporates normalisings flows. It is designed for applications where the Bayesian likelihood is computationally expensive.
## Installation
``nessai`` can be installed using ``pip``:
```console
pip install nessai
```
or via ``conda``
```console
conda install -c conda-forge -c pytorch nessai
```
### PyTorch
By default the version of PyTorch will not necessarily match the drivers on your system, to install a different version with the correct CUDA support see the PyTorch homepage for instructions: https://pytorch.org/.
### Using ``bilby``
As of `bilby` version 1.1.0, ``nessai`` is now supported by default but it is still an optional requirement. See the [``bilby`` documentation](https://lscsoft.docs.ligo.org/bilby/index.html) for installation instructions for `bilby`
See the examples included with ``nessai`` for how to run ``nessai`` via ``bilby``.
## Documentation
Documentation is available at: [nessai.readthedocs.io](https://nessai.readthedocs.io/)
## Contributing
Please see the guidelines [here](https://github.com/mj-will/nessai/blob/master/CONTRIBUTING.md).
## Acknowledgements
The core nested sampling code, model design and code for computing the posterior in ``nessai`` was based on [`cpnest`](https://github.com/johnveitch/cpnest) with permission from the authors.
The normalising flows implemented in ``nessai`` are all either directly imported from [`nflows`](https://github.com/bayesiains/nflows/tree/master/nflows) or heavily based on it.
Other code snippets that draw on existing code reference the source in their corresponding doc-strings.
The authors also thank Christian Chapman-Bird, Laurence Datrier, Fergus Hayes, Jethro Linley and Simon Tait for their feedback and help finding bugs in ``nessai``.
## Citing
If you find ``nessai`` useful in your work please cite the DOI for this code and our paper:
```bibtex
@software{nessai,
author = {Michael J. Williams},
title = {nessai: Nested Sampling with Artificial Intelligence},
month = feb,
year = 2021,
publisher = {Zenodo},
version = {latest},
doi = {10.5281/zenodo.4550693},
url = {https://doi.org/10.5281/zenodo.4550693}
}
@article{PhysRevD.103.103006,
title = {Nested sampling with normalizing flows for gravitational-wave inference},
author = {Williams, Michael J. and Veitch, John and Messenger, Chris},
journal = {Phys. Rev. D},
volume = {103},
issue = {10},
pages = {103006},
numpages = {19},
year = {2021},
month = {May},
publisher = {American Physical Society},
doi = {10.1103/PhysRevD.103.103006},
url = {https://link.aps.org/doi/10.1103/PhysRevD.103.103006}
}
```
%package help
Summary: Development documents and examples for nessai
Provides: python3-nessai-doc
%description help
[](https://doi.org/10.5281/zenodo.4550693)
[](https://pypi.org/project/nessai/)
[](https://anaconda.org/conda-forge/nessai)
[](https://nessai.readthedocs.io/en/latest/?badge=latest)
[](https://codecov.io/gh/mj-will/nessai)
# nessai: Nested Sampling with Artificial Intelligence
``nessai`` (/ˈnɛsi/): Nested Sampling with Artificial Intelligence
``nessai`` is a nested sampling algorithm for Bayesian Inference that incorporates normalisings flows. It is designed for applications where the Bayesian likelihood is computationally expensive.
## Installation
``nessai`` can be installed using ``pip``:
```console
pip install nessai
```
or via ``conda``
```console
conda install -c conda-forge -c pytorch nessai
```
### PyTorch
By default the version of PyTorch will not necessarily match the drivers on your system, to install a different version with the correct CUDA support see the PyTorch homepage for instructions: https://pytorch.org/.
### Using ``bilby``
As of `bilby` version 1.1.0, ``nessai`` is now supported by default but it is still an optional requirement. See the [``bilby`` documentation](https://lscsoft.docs.ligo.org/bilby/index.html) for installation instructions for `bilby`
See the examples included with ``nessai`` for how to run ``nessai`` via ``bilby``.
## Documentation
Documentation is available at: [nessai.readthedocs.io](https://nessai.readthedocs.io/)
## Contributing
Please see the guidelines [here](https://github.com/mj-will/nessai/blob/master/CONTRIBUTING.md).
## Acknowledgements
The core nested sampling code, model design and code for computing the posterior in ``nessai`` was based on [`cpnest`](https://github.com/johnveitch/cpnest) with permission from the authors.
The normalising flows implemented in ``nessai`` are all either directly imported from [`nflows`](https://github.com/bayesiains/nflows/tree/master/nflows) or heavily based on it.
Other code snippets that draw on existing code reference the source in their corresponding doc-strings.
The authors also thank Christian Chapman-Bird, Laurence Datrier, Fergus Hayes, Jethro Linley and Simon Tait for their feedback and help finding bugs in ``nessai``.
## Citing
If you find ``nessai`` useful in your work please cite the DOI for this code and our paper:
```bibtex
@software{nessai,
author = {Michael J. Williams},
title = {nessai: Nested Sampling with Artificial Intelligence},
month = feb,
year = 2021,
publisher = {Zenodo},
version = {latest},
doi = {10.5281/zenodo.4550693},
url = {https://doi.org/10.5281/zenodo.4550693}
}
@article{PhysRevD.103.103006,
title = {Nested sampling with normalizing flows for gravitational-wave inference},
author = {Williams, Michael J. and Veitch, John and Messenger, Chris},
journal = {Phys. Rev. D},
volume = {103},
issue = {10},
pages = {103006},
numpages = {19},
year = {2021},
month = {May},
publisher = {American Physical Society},
doi = {10.1103/PhysRevD.103.103006},
url = {https://link.aps.org/doi/10.1103/PhysRevD.103.103006}
}
```
%prep
%autosetup -n nessai-0.8.1
%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-nessai -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Tue Jun 20 2023 Python_Bot <Python_Bot@openeuler.org> - 0.8.1-1
- Package Spec generated
|
