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diff --git a/python-nessai.spec b/python-nessai.spec new file mode 100644 index 0000000..49cb888 --- /dev/null +++ b/python-nessai.spec @@ -0,0 +1,367 @@ +%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 |