automatic import of python-upsetplot

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+/UpSetPlot-0.8.0.tar.gz
diff --git a/python-upsetplot.spec b/python-upsetplot.spec
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+%global _empty_manifest_terminate_build 0
+Name:		python-UpSetPlot
+Version:	0.8.0
+Release:	1
+Summary:	Draw Lex et al.'s UpSet plots with Pandas and Matplotlib
+License:	BSD-3-Clause
+URL:		https://upsetplot.readthedocs.io
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/25/62/f9ab73c23da63d77e8498253b043d03c65c259f4d0358309b37f56cdf5cd/UpSetPlot-0.8.0.tar.gz
+BuildArch:	noarch
+
+
+%description
+|version| |licence| |py-versions|
+|issues| |build| |docs| |coverage|
+This is another Python implementation of UpSet plots by Lex et al. [Lex2014]_.
+UpSet plots are used to visualise set overlaps; like Venn diagrams but
+more readable. Documentation is at https://upsetplot.readthedocs.io.
+This ``upsetplot`` library tries to provide a simple interface backed by an
+extensible, object-oriented design.
+There are many ways to represent the categorisation of data, as covered in
+our `Data Format Guide <https://upsetplot.readthedocs.io/en/stable/formats.html>`_.
+Our internal input format uses a `pandas.Series` containing counts
+corresponding to subset sizes, where each subset is an intersection of named
+categories.  The index of the Series indicates which rows pertain to which
+categories, by having multiple boolean indices, like ``example`` in the
+following::
+    >>> from upsetplot import generate_counts
+    >>> example = generate_counts()
+    >>> example
+    cat0   cat1   cat2
+    False  False  False      56
+                  True      283
+           True   False    1279
+                  True     5882
+    True   False  False      24
+                  True       90
+           True   False     429
+                  True     1957
+    Name: value, dtype: int64
+Then::
+    >>> from upsetplot import plot
+    >>> plot(example)  # doctest: +SKIP
+    >>> from matplotlib import pyplot
+    >>> pyplot.show()  # doctest: +SKIP
+makes:
+And you can save the image in various formats::
+    >>> pyplot.savefig("/path/to/myplot.pdf")  # doctest: +SKIP
+    >>> pyplot.savefig("/path/to/myplot.png")  # doctest: +SKIP
+This plot shows the cardinality of every category combination seen in our data.
+The leftmost column counts items absent from any category. The next three
+columns count items only in ``cat1``, ``cat2`` and ``cat3`` respectively, with
+following columns showing cardinalities for items in each combination of
+exactly two named sets. The rightmost column counts items in all three sets.
+Rotation
+We call the above plot style "horizontal" because the category intersections
+are presented from left to right.  `Vertical plots
+<http://upsetplot.readthedocs.io/en/latest/auto_examples/plot_vertical.html>`__
+are also supported!
+Distributions
+Providing a DataFrame rather than a Series as input allows us to expressively
+`plot the distribution of variables
+<http://upsetplot.readthedocs.io/en/latest/auto_examples/plot_boston.html>`__
+in each subset.
+Loading datasets
+While the dataset above is randomly generated, you can prepare your own dataset
+for input to upsetplot.  A helpful tool is `from_memberships`, which allows
+us to reconstruct the example above by indicating each data point's category
+membership::
+    >>> from upsetplot import from_memberships
+    >>> example = from_memberships(
+    >>> example
+    cat0   cat1   cat2
+    False  False  False      56
+                  True      283
+           True   False    1279
+                  True     5882
+    True   False  False      24
+                  True       90
+           True   False     429
+                  True     1957
+    dtype: int64
+See also `from_contents`, another way to describe categorised data, and
+`from_indicators` which allows each category to be indicated by a column in
+the data frame (or a function of the column's data such as whether it is a
+missing value).
+
+%package -n python3-UpSetPlot
+Summary:	Draw Lex et al.'s UpSet plots with Pandas and Matplotlib
+Provides:	python-UpSetPlot
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-UpSetPlot
+|version| |licence| |py-versions|
+|issues| |build| |docs| |coverage|
+This is another Python implementation of UpSet plots by Lex et al. [Lex2014]_.
+UpSet plots are used to visualise set overlaps; like Venn diagrams but
+more readable. Documentation is at https://upsetplot.readthedocs.io.
+This ``upsetplot`` library tries to provide a simple interface backed by an
+extensible, object-oriented design.
+There are many ways to represent the categorisation of data, as covered in
+our `Data Format Guide <https://upsetplot.readthedocs.io/en/stable/formats.html>`_.
+Our internal input format uses a `pandas.Series` containing counts
+corresponding to subset sizes, where each subset is an intersection of named
+categories.  The index of the Series indicates which rows pertain to which
+categories, by having multiple boolean indices, like ``example`` in the
+following::
+    >>> from upsetplot import generate_counts
+    >>> example = generate_counts()
+    >>> example
+    cat0   cat1   cat2
+    False  False  False      56
+                  True      283
+           True   False    1279
+                  True     5882
+    True   False  False      24
+                  True       90
+           True   False     429
+                  True     1957
+    Name: value, dtype: int64
+Then::
+    >>> from upsetplot import plot
+    >>> plot(example)  # doctest: +SKIP
+    >>> from matplotlib import pyplot
+    >>> pyplot.show()  # doctest: +SKIP
+makes:
+And you can save the image in various formats::
+    >>> pyplot.savefig("/path/to/myplot.pdf")  # doctest: +SKIP
+    >>> pyplot.savefig("/path/to/myplot.png")  # doctest: +SKIP
+This plot shows the cardinality of every category combination seen in our data.
+The leftmost column counts items absent from any category. The next three
+columns count items only in ``cat1``, ``cat2`` and ``cat3`` respectively, with
+following columns showing cardinalities for items in each combination of
+exactly two named sets. The rightmost column counts items in all three sets.
+Rotation
+We call the above plot style "horizontal" because the category intersections
+are presented from left to right.  `Vertical plots
+<http://upsetplot.readthedocs.io/en/latest/auto_examples/plot_vertical.html>`__
+are also supported!
+Distributions
+Providing a DataFrame rather than a Series as input allows us to expressively
+`plot the distribution of variables
+<http://upsetplot.readthedocs.io/en/latest/auto_examples/plot_boston.html>`__
+in each subset.
+Loading datasets
+While the dataset above is randomly generated, you can prepare your own dataset
+for input to upsetplot.  A helpful tool is `from_memberships`, which allows
+us to reconstruct the example above by indicating each data point's category
+membership::
+    >>> from upsetplot import from_memberships
+    >>> example = from_memberships(
+    >>> example
+    cat0   cat1   cat2
+    False  False  False      56
+                  True      283
+           True   False    1279
+                  True     5882
+    True   False  False      24
+                  True       90
+           True   False     429
+                  True     1957
+    dtype: int64
+See also `from_contents`, another way to describe categorised data, and
+`from_indicators` which allows each category to be indicated by a column in
+the data frame (or a function of the column's data such as whether it is a
+missing value).
+
+%package help
+Summary:	Development documents and examples for UpSetPlot
+Provides:	python3-UpSetPlot-doc
+%description help
+|version| |licence| |py-versions|
+|issues| |build| |docs| |coverage|
+This is another Python implementation of UpSet plots by Lex et al. [Lex2014]_.
+UpSet plots are used to visualise set overlaps; like Venn diagrams but
+more readable. Documentation is at https://upsetplot.readthedocs.io.
+This ``upsetplot`` library tries to provide a simple interface backed by an
+extensible, object-oriented design.
+There are many ways to represent the categorisation of data, as covered in
+our `Data Format Guide <https://upsetplot.readthedocs.io/en/stable/formats.html>`_.
+Our internal input format uses a `pandas.Series` containing counts
+corresponding to subset sizes, where each subset is an intersection of named
+categories.  The index of the Series indicates which rows pertain to which
+categories, by having multiple boolean indices, like ``example`` in the
+following::
+    >>> from upsetplot import generate_counts
+    >>> example = generate_counts()
+    >>> example
+    cat0   cat1   cat2
+    False  False  False      56
+                  True      283
+           True   False    1279
+                  True     5882
+    True   False  False      24
+                  True       90
+           True   False     429
+                  True     1957
+    Name: value, dtype: int64
+Then::
+    >>> from upsetplot import plot
+    >>> plot(example)  # doctest: +SKIP
+    >>> from matplotlib import pyplot
+    >>> pyplot.show()  # doctest: +SKIP
+makes:
+And you can save the image in various formats::
+    >>> pyplot.savefig("/path/to/myplot.pdf")  # doctest: +SKIP
+    >>> pyplot.savefig("/path/to/myplot.png")  # doctest: +SKIP
+This plot shows the cardinality of every category combination seen in our data.
+The leftmost column counts items absent from any category. The next three
+columns count items only in ``cat1``, ``cat2`` and ``cat3`` respectively, with
+following columns showing cardinalities for items in each combination of
+exactly two named sets. The rightmost column counts items in all three sets.
+Rotation
+We call the above plot style "horizontal" because the category intersections
+are presented from left to right.  `Vertical plots
+<http://upsetplot.readthedocs.io/en/latest/auto_examples/plot_vertical.html>`__
+are also supported!
+Distributions
+Providing a DataFrame rather than a Series as input allows us to expressively
+`plot the distribution of variables
+<http://upsetplot.readthedocs.io/en/latest/auto_examples/plot_boston.html>`__
+in each subset.
+Loading datasets
+While the dataset above is randomly generated, you can prepare your own dataset
+for input to upsetplot.  A helpful tool is `from_memberships`, which allows
+us to reconstruct the example above by indicating each data point's category
+membership::
+    >>> from upsetplot import from_memberships
+    >>> example = from_memberships(
+    >>> example
+    cat0   cat1   cat2
+    False  False  False      56
+                  True      283
+           True   False    1279
+                  True     5882
+    True   False  False      24
+                  True       90
+           True   False     429
+                  True     1957
+    dtype: int64
+See also `from_contents`, another way to describe categorised data, and
+`from_indicators` which allows each category to be indicated by a column in
+the data frame (or a function of the column's data such as whether it is a
+missing value).
+
+%prep
+%autosetup -n UpSetPlot-0.8.0
+
+%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-UpSetPlot -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Tue Apr 11 2023 Python_Bot <Python_Bot@openeuler.org> - 0.8.0-1
+- Package Spec generated
diff --git a/sources b/sources
new file mode 100644
index 0000000..d958128
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+16cff9af79ce0ab28eaed323edfb443c  UpSetPlot-0.8.0.tar.gz