f445ddbc82
- This release brings a few important performance improvements, a wide range of:
- usability upgrades, lots of bug fixes, and some new features. These include:
- a plugin API to add backend engines, a new theme for the documentation,:
- curve fitting methods, and several new plotting functions.:
- Many thanks to the 38 contributors to this release: Aaron Spring, Alessandro Amici,:
- Alex Marandon, Alistair Miles, Ana Paula Krelling, Anderson Banihirwe, Aureliana Barghini,:
- Baudouin Raoult, Benoit Bovy, Blair Bonnett, David Trémouilles, Deepak Cherian,:
- Gabriel Medeiros Abrahão, Giacomo Caria, Hauke Schulz, Illviljan, Mathias Hauser, Matthias Bussonnier,:
- Mattia Almansi, Maximilian Roos, Ray Bell, Richard Kleijn, Ryan Abernathey, Sam Levang, Spencer Clark,:
- Spencer Jones, Tammas Loughran, Tobias Kölling, Todd, Tom Nicholas, Tom White, Victor Negîrneac,:
- Xianxiang Li, Zeb Nicholls, crusaderky, dschwoerer, johnomotani, keewis:
- New Features:
- apply ``combine_attrs`` on data variables and coordinate variables when concatenating
and merging datasets and dataarrays (:pull:`4902`).
By `Justus Magin <https://github.com/keewis>`_.
- Add :py:meth:`Dataset.to_pandas` (:pull:`5247`)
By `Giacomo Caria <https://github.com/gcaria>`_.
- Add :py:meth:`DataArray.plot.surface` which wraps matplotlib's `plot_surface` to make
surface plots (:issue:`2235` :issue:`5084` :pull:`5101`).
By `John Omotani <https://github.com/johnomotani>`_.
- Allow passing multiple arrays to :py:meth:`Dataset.__setitem__` (:pull:`5216`).
By `Giacomo Caria <https://github.com/gcaria>`_.
- Add 'cumulative' option to :py:meth:`Dataset.integrate` and
:py:meth:`DataArray.integrate` so that result is a cumulative integral, like
:py:func:`scipy.integrate.cumulative_trapezoidal` (:pull:`5153`).
By `John Omotani <https://github.com/johnomotani>`_.
- Add ``safe_chunks`` option to :py:meth:`Dataset.to_zarr` which allows overriding
checks made to ensure Dask and Zarr chunk compatibility (:issue:`5056`).
By `Ryan Abernathey <https://github.com/rabernat>`_
OBS-URL: https://build.opensuse.org/package/show/devel:languages:python:numeric/python-xarray?expand=0&rev=48
106 lines
3.4 KiB
RPMSpec
106 lines
3.4 KiB
RPMSpec
#
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# spec file for package python-xarray
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#
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# Copyright (c) 2021 SUSE LLC
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#
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# All modifications and additions to the file contributed by third parties
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# remain the property of their copyright owners, unless otherwise agreed
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# upon. The license for this file, and modifications and additions to the
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# file, is the same license as for the pristine package itself (unless the
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# license for the pristine package is not an Open Source License, in which
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# case the license is the MIT License). An "Open Source License" is a
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# license that conforms to the Open Source Definition (Version 1.9)
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# published by the Open Source Initiative.
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# Please submit bugfixes or comments via https://bugs.opensuse.org/
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#
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%{?!python_module:%define python_module() python-%{**} python3-%{**}}
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%define skip_python2 1
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# NEP 29: Numpy 1.20 dropped support for Python 3.6, python36-numpy is removed from Tumbleweed. xarray will follow on next release
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%define skip_python36 1
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Name: python-xarray
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Version: 0.18.0
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Release: 0
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Summary: N-D labeled arrays and datasets in Python
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License: Apache-2.0
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URL: https://github.com/pydata/xarray
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Source: https://files.pythonhosted.org/packages/source/x/xarray/xarray-%{version}.tar.gz
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BuildRequires: %{python_module numpy >= 1.15}
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BuildRequires: %{python_module numpy-devel >= 1.14}
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BuildRequires: %{python_module pandas >= 0.25}
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BuildRequires: %{python_module setuptools_scm}
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BuildRequires: %{python_module setuptools}
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BuildRequires: fdupes
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BuildRequires: python-rpm-macros
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Requires: python-numpy >= 1.15
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Requires: python-pandas >= 0.25
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Provides: python-xray = %{version}
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Obsoletes: python-xray < %{version}
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BuildArch: noarch
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Suggests: python-dask-all
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# SECTION extras accel
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Recommends: python-scipy >= 1.3
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Recommends: python-bottleneck
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Recommends: python-numbagg
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# /SECTION
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# SECTION extras viz
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Suggests: python-matplotlib
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Suggests: python-seaborn
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Suggests: python-nc-time-axis
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#/SECTION
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# SECTION extras io
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Suggests: python-netCDF4
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Suggests: python-h5netcdf
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Suggests: python-scipy
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Suggests: python-pydap
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Suggests: python-zarr
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Suggests: python-fsspec
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Suggests: python-cftime
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Suggests: python-rasterio
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Suggests: python-cfgrib
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#/SECTION
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# SECTION tests
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BuildRequires: %{python_module dask-dataframe}
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BuildRequires: %{python_module pytest-xdist}
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BuildRequires: %{python_module pytest}
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BuildRequires: %{python_module scipy}
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# /SECTION
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%python_subpackages
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%description
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xarray (formerly xray) is a python-pandas-like and pandas-compatible
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toolkit for analytics on multi-dimensional arrays. It provides
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N-dimensional variants of the python-pandas labeled data structures,
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rather than the tabular data that pandas uses.
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The Common Data Model for self-describing scientific data is used.
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The dataset is an in-memory representation of a netCDF file.
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%prep
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%autosetup -p1 -n xarray-%{version}
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chmod -x xarray/util/print_versions.py
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%build
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%python_build
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%install
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%python_install
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%python_expand %fdupes %{buildroot}%{$python_sitelib}
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%check
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if [ $(getconf LONG_BIT) -eq 32 ]; then
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# precision errors on 32-bit
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donttest="(test_interpolate_chunk_advanced and linear)"
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fi
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%pytest -n auto ${donttest:+ -k "not ($donttest)"} xarray
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%files %{python_files}
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%doc README.rst
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%license LICENSE licenses/
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%{python_sitelib}/xarray
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%{python_sitelib}/xarray-%{version}*-info
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%changelog
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