%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 [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.4550693.svg)](https://doi.org/10.5281/zenodo.4550693) [![PyPI](https://img.shields.io/pypi/v/nessai)](https://pypi.org/project/nessai/) [![Conda Version](https://img.shields.io/conda/vn/conda-forge/nessai.svg)](https://anaconda.org/conda-forge/nessai) [![Documentation Status](https://readthedocs.org/projects/nessai/badge/?version=latest)](https://nessai.readthedocs.io/en/latest/?badge=latest) ![license](https://anaconda.org/conda-forge/nessai/badges/license.svg) ![tests](https://github.com/mj-will/nessai/actions/workflows/tests.yml/badge.svg) ![int-tests](https://github.com/mj-will/nessai/actions/workflows/integration-tests.yml/badge.svg) [![codecov](https://codecov.io/gh/mj-will/nessai/branch/main/graph/badge.svg?token=O7SN167SK6)](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 [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.4550693.svg)](https://doi.org/10.5281/zenodo.4550693) [![PyPI](https://img.shields.io/pypi/v/nessai)](https://pypi.org/project/nessai/) [![Conda Version](https://img.shields.io/conda/vn/conda-forge/nessai.svg)](https://anaconda.org/conda-forge/nessai) [![Documentation Status](https://readthedocs.org/projects/nessai/badge/?version=latest)](https://nessai.readthedocs.io/en/latest/?badge=latest) ![license](https://anaconda.org/conda-forge/nessai/badges/license.svg) ![tests](https://github.com/mj-will/nessai/actions/workflows/tests.yml/badge.svg) ![int-tests](https://github.com/mj-will/nessai/actions/workflows/integration-tests.yml/badge.svg) [![codecov](https://codecov.io/gh/mj-will/nessai/branch/main/graph/badge.svg?token=O7SN167SK6)](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 [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.4550693.svg)](https://doi.org/10.5281/zenodo.4550693) [![PyPI](https://img.shields.io/pypi/v/nessai)](https://pypi.org/project/nessai/) [![Conda Version](https://img.shields.io/conda/vn/conda-forge/nessai.svg)](https://anaconda.org/conda-forge/nessai) [![Documentation Status](https://readthedocs.org/projects/nessai/badge/?version=latest)](https://nessai.readthedocs.io/en/latest/?badge=latest) ![license](https://anaconda.org/conda-forge/nessai/badges/license.svg) ![tests](https://github.com/mj-will/nessai/actions/workflows/tests.yml/badge.svg) ![int-tests](https://github.com/mj-will/nessai/actions/workflows/integration-tests.yml/badge.svg) [![codecov](https://codecov.io/gh/mj-will/nessai/branch/main/graph/badge.svg?token=O7SN167SK6)](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 - 0.8.1-1 - Package Spec generated