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@@ -0,0 +1 @@ +/autofit-2023.3.27.1.tar.gz diff --git a/python-autofit.spec b/python-autofit.spec new file mode 100644 index 0000000..b8ac0c5 --- /dev/null +++ b/python-autofit.spec @@ -0,0 +1,121 @@ +%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 @@ -0,0 +1 @@ +a49c7340e9e26ed291c7965e37d45160 autofit-2023.3.27.1.tar.gz |