%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 - 2023.2.0-1 - Package Spec generated