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@@ -0,0 +1 @@ +/xarray-2023.2.0.tar.gz diff --git a/python-xarray.spec b/python-xarray.spec new file mode 100644 index 0000000..e9c62d8 --- /dev/null +++ b/python-xarray.spec @@ -0,0 +1,182 @@ +%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 @@ -0,0 +1 @@ +4f09559aecb2d61791bcc9c13db1d477 xarray-2023.2.0.tar.gz |