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%global _empty_manifest_terminate_build 0
Name: python-autofit
Version: 2023.3.27.1
Release: 1
Summary: Classy Probabilistic Programming
License: MIT License
URL: https://github.com/rhayes777/PyAutoFit
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/64/4c/00845d44fd8eaf8cfe8f3dbf52e24fc0b3022a26f25a6198323de26565c5/autofit-2023.3.27.1.tar.gz
BuildArch: noarch
Requires: python3-corner
Requires: python3-decorator
Requires: python3-dill
Requires: python3-dynesty
Requires: python3-typing-inspect
Requires: python3-emcee
Requires: python3-matplotlib
Requires: python3-numpydoc
Requires: python3-pyprojroot
Requires: python3-pyswarms
Requires: python3-h5py
Requires: python3-SQLAlchemy
Requires: python3-scipy
Requires: python3-astunparse
Requires: python3-xxhash
Requires: python3-autoconf
%description
|binder| |Tests| |Build| |RTD| |JOSS|
`Installation Guide <https://pyautofit.readthedocs.io/en/latest/installation/overview.html>`_ |
`readthedocs <https://pyautofit.readthedocs.io/en/latest/index.html>`_ |
`Introduction on Binder <https://mybinder.org/v2/gh/Jammy2211/autofit_workspace/release?filepath=introduction.ipynb>`_ |
`HowToFit <https://pyautofit.readthedocs.io/en/latest/howtofit/howtofit.html>`_
_ One day make these BOLD with a colon like my fellowsahip proposa,s where the first is Model Composition & Fitting: Tools for composing a complex model and fitting it with dynesty...
PyAutoFit is a Python based probabilistic programming language for the fully Bayesian analysis of extremely large
datasets which:
- Makes it simple to compose and fit multi-level models using a range of Bayesian inference libraries, such as `emcee <https://github.com/dfm/emcee>`_ and `dynesty <https://github.com/joshspeagle/dynesty>`_.
- Handles the 'heavy lifting' that comes with model-fitting, including model composition & customization, outputting results, model-specific visualization and posterior analysis.
- Is built for *big-data* analysis, whereby results are output as a sqlite database which can be queried after model-fitting is complete.
**PyAutoFit** supports advanced statistical methods such as `graphical and hierarchical models <https://pyautofit.readthedocs.io/en/latest/features/graphical.html>`_, `model-fit chaining <https://pyautofit.readthedocs.io/en/latest/features/search_chaining.html>`_, `sensitivity mapping <https://pyautofit.readthedocs.io/en/latest/features/sensitivity_mapping.html>`_ and `massively parallel model-fits <https://pyautofit.readthedocs.io/en/latest/features/search_grid_search.html>`_ .
%package -n python3-autofit
Summary: Classy Probabilistic Programming
Provides: python-autofit
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-autofit
|binder| |Tests| |Build| |RTD| |JOSS|
`Installation Guide <https://pyautofit.readthedocs.io/en/latest/installation/overview.html>`_ |
`readthedocs <https://pyautofit.readthedocs.io/en/latest/index.html>`_ |
`Introduction on Binder <https://mybinder.org/v2/gh/Jammy2211/autofit_workspace/release?filepath=introduction.ipynb>`_ |
`HowToFit <https://pyautofit.readthedocs.io/en/latest/howtofit/howtofit.html>`_
_ One day make these BOLD with a colon like my fellowsahip proposa,s where the first is Model Composition & Fitting: Tools for composing a complex model and fitting it with dynesty...
PyAutoFit is a Python based probabilistic programming language for the fully Bayesian analysis of extremely large
datasets which:
- Makes it simple to compose and fit multi-level models using a range of Bayesian inference libraries, such as `emcee <https://github.com/dfm/emcee>`_ and `dynesty <https://github.com/joshspeagle/dynesty>`_.
- Handles the 'heavy lifting' that comes with model-fitting, including model composition & customization, outputting results, model-specific visualization and posterior analysis.
- Is built for *big-data* analysis, whereby results are output as a sqlite database which can be queried after model-fitting is complete.
**PyAutoFit** supports advanced statistical methods such as `graphical and hierarchical models <https://pyautofit.readthedocs.io/en/latest/features/graphical.html>`_, `model-fit chaining <https://pyautofit.readthedocs.io/en/latest/features/search_chaining.html>`_, `sensitivity mapping <https://pyautofit.readthedocs.io/en/latest/features/sensitivity_mapping.html>`_ and `massively parallel model-fits <https://pyautofit.readthedocs.io/en/latest/features/search_grid_search.html>`_ .
%package help
Summary: Development documents and examples for autofit
Provides: python3-autofit-doc
%description help
|binder| |Tests| |Build| |RTD| |JOSS|
`Installation Guide <https://pyautofit.readthedocs.io/en/latest/installation/overview.html>`_ |
`readthedocs <https://pyautofit.readthedocs.io/en/latest/index.html>`_ |
`Introduction on Binder <https://mybinder.org/v2/gh/Jammy2211/autofit_workspace/release?filepath=introduction.ipynb>`_ |
`HowToFit <https://pyautofit.readthedocs.io/en/latest/howtofit/howtofit.html>`_
_ One day make these BOLD with a colon like my fellowsahip proposa,s where the first is Model Composition & Fitting: Tools for composing a complex model and fitting it with dynesty...
PyAutoFit is a Python based probabilistic programming language for the fully Bayesian analysis of extremely large
datasets which:
- Makes it simple to compose and fit multi-level models using a range of Bayesian inference libraries, such as `emcee <https://github.com/dfm/emcee>`_ and `dynesty <https://github.com/joshspeagle/dynesty>`_.
- Handles the 'heavy lifting' that comes with model-fitting, including model composition & customization, outputting results, model-specific visualization and posterior analysis.
- Is built for *big-data* analysis, whereby results are output as a sqlite database which can be queried after model-fitting is complete.
**PyAutoFit** supports advanced statistical methods such as `graphical and hierarchical models <https://pyautofit.readthedocs.io/en/latest/features/graphical.html>`_, `model-fit chaining <https://pyautofit.readthedocs.io/en/latest/features/search_chaining.html>`_, `sensitivity mapping <https://pyautofit.readthedocs.io/en/latest/features/sensitivity_mapping.html>`_ and `massively parallel model-fits <https://pyautofit.readthedocs.io/en/latest/features/search_grid_search.html>`_ .
%prep
%autosetup -n autofit-2023.3.27.1
%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-autofit -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Sun Apr 23 2023 Python_Bot <Python_Bot@openeuler.org> - 2023.3.27.1-1
- Package Spec generated
|
