automatic import of python-xarray

3 files changed, 184 insertions, 0 deletions

diff --git a/.gitignore b/.gitignore
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+/xarray-2023.2.0.tar.gz
diff --git a/python-xarray.spec b/python-xarray.spec
new file mode 100644
index 0000000..e9c62d8
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+%global _empty_manifest_terminate_build 0
+Name:		python-xarray
+Version:	2023.2.0
+Release:	1
+Summary:	N-D labeled arrays and datasets in Python
+License:	Apache-2.0
+URL:		https://github.com/pydata/xarray
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/01/be/ef024d1f3ecac9e8924165e4c5a4e948a08b051036021863548653b97eb5/xarray-2023.2.0.tar.gz
+BuildArch:	noarch
+
+Requires:	python3-numpy
+Requires:	python3-pandas
+Requires:	python3-packaging
+Requires:	python3-scipy
+Requires:	python3-bottleneck
+Requires:	python3-numbagg
+Requires:	python3-flox
+Requires:	python3-netCDF4
+Requires:	python3-h5netcdf
+Requires:	python3-scipy
+Requires:	python3-zarr
+Requires:	python3-fsspec
+Requires:	python3-cftime
+Requires:	python3-rasterio
+Requires:	python3-cfgrib
+Requires:	python3-pooch
+Requires:	python3-bottleneck
+Requires:	python3-numbagg
+Requires:	python3-flox
+Requires:	python3-dask[complete]
+Requires:	python3-matplotlib
+Requires:	python3-seaborn
+Requires:	python3-nc-time-axis
+Requires:	python3-pydap
+Requires:	python3-netCDF4
+Requires:	python3-h5netcdf
+Requires:	python3-scipy
+Requires:	python3-zarr
+Requires:	python3-fsspec
+Requires:	python3-cftime
+Requires:	python3-rasterio
+Requires:	python3-cfgrib
+Requires:	python3-pooch
+Requires:	python3-bottleneck
+Requires:	python3-numbagg
+Requires:	python3-flox
+Requires:	python3-dask[complete]
+Requires:	python3-matplotlib
+Requires:	python3-seaborn
+Requires:	python3-nc-time-axis
+Requires:	python3-sphinx-autosummary-accessors
+Requires:	python3-sphinx-rtd-theme
+Requires:	python3-ipython
+Requires:	python3-ipykernel
+Requires:	python3-jupyter-client
+Requires:	python3-nbsphinx
+Requires:	python3-scanpydoc
+Requires:	python3-pydap
+Requires:	python3-netCDF4
+Requires:	python3-h5netcdf
+Requires:	python3-scipy
+Requires:	python3-zarr
+Requires:	python3-fsspec
+Requires:	python3-cftime
+Requires:	python3-rasterio
+Requires:	python3-cfgrib
+Requires:	python3-pooch
+Requires:	python3-pydap
+Requires:	python3-dask[complete]
+Requires:	python3-matplotlib
+Requires:	python3-seaborn
+Requires:	python3-nc-time-axis
+
+%description
+Multi-dimensional (a.k.a. N-dimensional, ND) arrays (sometimes called
+"tensors") are an essential part of computational science.
+They are encountered in a wide range of fields, including physics, astronomy,
+geoscience, bioinformatics, engineering, finance, and deep learning.
+In Python, NumPy_ provides the fundamental data structure and API for
+working with raw ND arrays.
+However, real-world datasets are usually more than just raw numbers;
+they have labels which encode information about how the array values map
+to locations in space, time, etc.
+xarray doesn't just keep track of labels on arrays -- it uses them to provide a
+powerful and concise interface. For example:
+-  Apply operations over dimensions by name: ``x.sum('time')``.
+-  Select values by label instead of integer location: ``x.loc['2014-01-01']`` or ``x.sel(time='2014-01-01')``.
+-  Mathematical operations (e.g., ``x - y``) vectorize across multiple dimensions (array broadcasting) based on dimension names, not shape.
+-  Flexible split-apply-combine operations with groupby: ``x.groupby('time.dayofyear').mean()``.
+-  Database like alignment based on coordinate labels that smoothly handles missing values: ``x, y = xr.align(x, y, join='outer')``.
+-  Keep track of arbitrary metadata in the form of a Python dictionary: ``x.attrs``.
+
+%package -n python3-xarray
+Summary:	N-D labeled arrays and datasets in Python
+Provides:	python-xarray
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-xarray
+Multi-dimensional (a.k.a. N-dimensional, ND) arrays (sometimes called
+"tensors") are an essential part of computational science.
+They are encountered in a wide range of fields, including physics, astronomy,
+geoscience, bioinformatics, engineering, finance, and deep learning.
+In Python, NumPy_ provides the fundamental data structure and API for
+working with raw ND arrays.
+However, real-world datasets are usually more than just raw numbers;
+they have labels which encode information about how the array values map
+to locations in space, time, etc.
+xarray doesn't just keep track of labels on arrays -- it uses them to provide a
+powerful and concise interface. For example:
+-  Apply operations over dimensions by name: ``x.sum('time')``.
+-  Select values by label instead of integer location: ``x.loc['2014-01-01']`` or ``x.sel(time='2014-01-01')``.
+-  Mathematical operations (e.g., ``x - y``) vectorize across multiple dimensions (array broadcasting) based on dimension names, not shape.
+-  Flexible split-apply-combine operations with groupby: ``x.groupby('time.dayofyear').mean()``.
+-  Database like alignment based on coordinate labels that smoothly handles missing values: ``x, y = xr.align(x, y, join='outer')``.
+-  Keep track of arbitrary metadata in the form of a Python dictionary: ``x.attrs``.
+
+%package help
+Summary:	Development documents and examples for xarray
+Provides:	python3-xarray-doc
+%description help
+Multi-dimensional (a.k.a. N-dimensional, ND) arrays (sometimes called
+"tensors") are an essential part of computational science.
+They are encountered in a wide range of fields, including physics, astronomy,
+geoscience, bioinformatics, engineering, finance, and deep learning.
+In Python, NumPy_ provides the fundamental data structure and API for
+working with raw ND arrays.
+However, real-world datasets are usually more than just raw numbers;
+they have labels which encode information about how the array values map
+to locations in space, time, etc.
+xarray doesn't just keep track of labels on arrays -- it uses them to provide a
+powerful and concise interface. For example:
+-  Apply operations over dimensions by name: ``x.sum('time')``.
+-  Select values by label instead of integer location: ``x.loc['2014-01-01']`` or ``x.sel(time='2014-01-01')``.
+-  Mathematical operations (e.g., ``x - y``) vectorize across multiple dimensions (array broadcasting) based on dimension names, not shape.
+-  Flexible split-apply-combine operations with groupby: ``x.groupby('time.dayofyear').mean()``.
+-  Database like alignment based on coordinate labels that smoothly handles missing values: ``x, y = xr.align(x, y, join='outer')``.
+-  Keep track of arbitrary metadata in the form of a Python dictionary: ``x.attrs``.
+
+%prep
+%autosetup -n xarray-2023.2.0
+
+%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-xarray -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Thu Mar 09 2023 Python_Bot <Python_Bot@openeuler.org> - 2023.2.0-1
+- Package Spec generated
diff --git a/sources b/sources
new file mode 100644
index 0000000..c6d5f6b
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+4f09559aecb2d61791bcc9c13db1d477  xarray-2023.2.0.tar.gz