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%global _empty_manifest_terminate_build 0
Name: python-dit
Version: 1.5
Release: 1
Summary: Python package for information theory.
License: BSD
URL: http://dit.io
Source0: https://mirrors.aliyun.com/pypi/web/packages/9a/44/4c9ca52132947daafccf1f52f5b0cebc7f1bb62e34ae7723943682b5e686/dit-1.5.tar.gz
BuildArch: noarch
Requires: python3-boltons
Requires: python3-debtcollector
Requires: python3-lattices
Requires: python3-networkx
Requires: python3-numpy
Requires: python3-PLTable
Requires: python3-scipy
Requires: python3-pypoman
%description
Information theory is a powerful extension to probability and statistics, quantifying dependencies
among arbitrary random variables in a way that is consistent and comparable across systems and
scales. Information theory was originally developed to quantify how quickly and reliably information
could be transmitted across an arbitrary channel. The demands of modern, data-driven science have
been coopting and extending these quantities and methods into unknown, multivariate settings where
the interpretation and best practices are not known. For example, there are at least four reasonable
multivariate generalizations of the mutual information, none of which inherit all the
interpretations of the standard bivariate case. Which is best to use is context-dependent. ``dit``
implements a vast range of multivariate information measures in an effort to allow information
practitioners to study how these various measures behave and interact in a variety of contexts. We
hope that having all these measures and techniques implemented in one place will allow the
development of robust techniques for the automated quantification of dependencies within a system
and concrete interpretation of what those dependencies mean.
%package -n python3-dit
Summary: Python package for information theory.
Provides: python-dit
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-dit
Information theory is a powerful extension to probability and statistics, quantifying dependencies
among arbitrary random variables in a way that is consistent and comparable across systems and
scales. Information theory was originally developed to quantify how quickly and reliably information
could be transmitted across an arbitrary channel. The demands of modern, data-driven science have
been coopting and extending these quantities and methods into unknown, multivariate settings where
the interpretation and best practices are not known. For example, there are at least four reasonable
multivariate generalizations of the mutual information, none of which inherit all the
interpretations of the standard bivariate case. Which is best to use is context-dependent. ``dit``
implements a vast range of multivariate information measures in an effort to allow information
practitioners to study how these various measures behave and interact in a variety of contexts. We
hope that having all these measures and techniques implemented in one place will allow the
development of robust techniques for the automated quantification of dependencies within a system
and concrete interpretation of what those dependencies mean.
%package help
Summary: Development documents and examples for dit
Provides: python3-dit-doc
%description help
Information theory is a powerful extension to probability and statistics, quantifying dependencies
among arbitrary random variables in a way that is consistent and comparable across systems and
scales. Information theory was originally developed to quantify how quickly and reliably information
could be transmitted across an arbitrary channel. The demands of modern, data-driven science have
been coopting and extending these quantities and methods into unknown, multivariate settings where
the interpretation and best practices are not known. For example, there are at least four reasonable
multivariate generalizations of the mutual information, none of which inherit all the
interpretations of the standard bivariate case. Which is best to use is context-dependent. ``dit``
implements a vast range of multivariate information measures in an effort to allow information
practitioners to study how these various measures behave and interact in a variety of contexts. We
hope that having all these measures and techniques implemented in one place will allow the
development of robust techniques for the automated quantification of dependencies within a system
and concrete interpretation of what those dependencies mean.
%prep
%autosetup -n dit-1.5
%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-dit -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Thu Jun 08 2023 Python_Bot <Python_Bot@openeuler.org> - 1.5-1
- Package Spec generated
|
