automatic import of python-autofit

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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
+* Tue Apr 11 2023 Python_Bot <Python_Bot@openeuler.org> - 2023.3.27.1-1
+- Package Spec generated