From e488a3365b7f7e97868c350dedae41be0791977e Mon Sep 17 00:00:00 2001 From: CoprDistGit Date: Wed, 10 May 2023 04:37:19 +0000 Subject: automatic import of python-weightedstats --- .gitignore | 1 + python-weightedstats.spec | 141 ++++++++++++++++++++++++++++++++++++++++++++++ sources | 1 + 3 files changed, 143 insertions(+) create mode 100644 python-weightedstats.spec create mode 100644 sources diff --git a/.gitignore b/.gitignore index e69de29..d4c4927 100644 --- a/.gitignore +++ b/.gitignore @@ -0,0 +1 @@ +/weightedstats-0.4.1.tar.gz diff --git a/python-weightedstats.spec b/python-weightedstats.spec new file mode 100644 index 0000000..783234b --- /dev/null +++ b/python-weightedstats.spec @@ -0,0 +1,141 @@ +%global _empty_manifest_terminate_build 0 +Name: python-weightedstats +Version: 0.4.1 +Release: 1 +Summary: Mean, weighted mean, median, weighted median +License: MIT +URL: https://github.com/tinybike/weightedstats +Source0: https://mirrors.nju.edu.cn/pypi/web/packages/da/a5/f5c0e601a610e4618316be3155febbbec98994788fcc0e9d8080369266ec/weightedstats-0.4.1.tar.gz +BuildArch: noarch + + +%description +Python functions to calculate the mean, weighted mean, median, and weighted median. +Installation +^^^^^^^^^^^^ +The easiest way to install WeightedStats is to use pip:: + $ pip install weightedstats +Usage +^^^^^ +WeightedStats includes four functions (mean, weighted_mean, median, weighted_median) which accept lists as arguments, and two functions (numpy_weighted_mean, numpy weighted_median) which accept either lists or numpy arrays. +Example: + import weightedstats as ws + my_data = [1, 2, 3, 4, 5] + my_weights = [10, 1, 1, 1, 9] + # Ordinary (unweighted) mean and median + ws.mean(my_data) # equivalent to ws.weighted_mean(my_data) + ws.median(my_data) # equivalent to ws.weighted_median(my_data) + # Weighted mean and median + ws.weighted_mean(my_data, weights=my_weights) + ws.weighted_median(my_data, weights=my_weights) + # Special weighted mean and median functions for use with numpy arrays + ws.numpy_weighted_mean(my_data, weights=my_weights) + ws.numpy_weighted_median(my_data, weights=my_weights) +Tests +^^^^^ +Unit tests are in the test/ directory. + +%package -n python3-weightedstats +Summary: Mean, weighted mean, median, weighted median +Provides: python-weightedstats +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +%description -n python3-weightedstats +Python functions to calculate the mean, weighted mean, median, and weighted median. +Installation +^^^^^^^^^^^^ +The easiest way to install WeightedStats is to use pip:: + $ pip install weightedstats +Usage +^^^^^ +WeightedStats includes four functions (mean, weighted_mean, median, weighted_median) which accept lists as arguments, and two functions (numpy_weighted_mean, numpy weighted_median) which accept either lists or numpy arrays. +Example: + import weightedstats as ws + my_data = [1, 2, 3, 4, 5] + my_weights = [10, 1, 1, 1, 9] + # Ordinary (unweighted) mean and median + ws.mean(my_data) # equivalent to ws.weighted_mean(my_data) + ws.median(my_data) # equivalent to ws.weighted_median(my_data) + # Weighted mean and median + ws.weighted_mean(my_data, weights=my_weights) + ws.weighted_median(my_data, weights=my_weights) + # Special weighted mean and median functions for use with numpy arrays + ws.numpy_weighted_mean(my_data, weights=my_weights) + ws.numpy_weighted_median(my_data, weights=my_weights) +Tests +^^^^^ +Unit tests are in the test/ directory. + +%package help +Summary: Development documents and examples for weightedstats +Provides: python3-weightedstats-doc +%description help +Python functions to calculate the mean, weighted mean, median, and weighted median. +Installation +^^^^^^^^^^^^ +The easiest way to install WeightedStats is to use pip:: + $ pip install weightedstats +Usage +^^^^^ +WeightedStats includes four functions (mean, weighted_mean, median, weighted_median) which accept lists as arguments, and two functions (numpy_weighted_mean, numpy weighted_median) which accept either lists or numpy arrays. +Example: + import weightedstats as ws + my_data = [1, 2, 3, 4, 5] + my_weights = [10, 1, 1, 1, 9] + # Ordinary (unweighted) mean and median + ws.mean(my_data) # equivalent to ws.weighted_mean(my_data) + ws.median(my_data) # equivalent to ws.weighted_median(my_data) + # Weighted mean and median + ws.weighted_mean(my_data, weights=my_weights) + ws.weighted_median(my_data, weights=my_weights) + # Special weighted mean and median functions for use with numpy arrays + ws.numpy_weighted_mean(my_data, weights=my_weights) + ws.numpy_weighted_median(my_data, weights=my_weights) +Tests +^^^^^ +Unit tests are in the test/ directory. + +%prep +%autosetup -n weightedstats-0.4.1 + +%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-weightedstats -f filelist.lst +%dir %{python3_sitelib}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Wed May 10 2023 Python_Bot - 0.4.1-1 +- Package Spec generated diff --git a/sources b/sources new file mode 100644 index 0000000..4addd42 --- /dev/null +++ b/sources @@ -0,0 +1 @@ +f5cac13564b15e49a4eee9ca6d195ca4 weightedstats-0.4.1.tar.gz -- cgit v1.2.3