- Update to 2025.12.0

* This release rolls back the default engine for HTTP urls, adds
    support for DataTree objects in combine_nested and contains
    numerous bug fixes.
- Release 2025.11.0
  * This release changes the default for keep_attrs such that
    attributes are preserved by default, adds support for DataTree
    in top-level functions, and contains several memory and
    performance improvements as well as a number of bug fixes.
- Release 2025.10.0 and 2025.10.1
  * These releases revert a breaking change to Xarray's preferred
    netCDF backend.
- Release 2025.09.1
  * This release contains improvements to netCDF IO and the
    DataTree.from_dict() constructor, as well as a variety of bug
    fixes. In particular, the default netCDF backend has switched
    from netCDF4 to h5netcdf, which is typically faster.
- Release 2025.09.0
  * This release brings a number of small improvements and fixes,
    especially related to writing DataTree objects and netCDF files
    to disk.
  ## New Features
  * Support rechunking by SeasonResampler for seasonal data
    analysis (GH10425, PR10519). By Dhruva Kumar Kaushal.
  * Add convenience methods to Coordinates (PR10318) By Justus
    Magin.
  * Added load_datatree() for loading DataTree objects into memory
    from disk. It has the same relationship to open_datatree(), as
    load_dataset() has to open_dataset(). By Stephan Hoyer.
  * compute=False is now supported by DataTree.to_netcdf() and

OBS-URL: https://build.opensuse.org/package/show/devel:languages:python:numeric/python-xarray?expand=0&rev=114
This commit is contained in:
2025-12-23 15:18:29 +00:00
committed by Git OBS Bridge
commit ed0f425347
9 changed files with 4420 additions and 0 deletions

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## Default LFS
*.7z filter=lfs diff=lfs merge=lfs -text
*.bsp filter=lfs diff=lfs merge=lfs -text
*.bz2 filter=lfs diff=lfs merge=lfs -text
*.gem filter=lfs diff=lfs merge=lfs -text
*.gz filter=lfs diff=lfs merge=lfs -text
*.jar filter=lfs diff=lfs merge=lfs -text
*.lz filter=lfs diff=lfs merge=lfs -text
*.lzma filter=lfs diff=lfs merge=lfs -text
*.obscpio filter=lfs diff=lfs merge=lfs -text
*.oxt filter=lfs diff=lfs merge=lfs -text
*.pdf filter=lfs diff=lfs merge=lfs -text
*.png filter=lfs diff=lfs merge=lfs -text
*.rpm filter=lfs diff=lfs merge=lfs -text
*.tbz filter=lfs diff=lfs merge=lfs -text
*.tbz2 filter=lfs diff=lfs merge=lfs -text
*.tgz filter=lfs diff=lfs merge=lfs -text
*.ttf filter=lfs diff=lfs merge=lfs -text
*.txz filter=lfs diff=lfs merge=lfs -text
*.whl filter=lfs diff=lfs merge=lfs -text
*.xz filter=lfs diff=lfs merge=lfs -text
*.zip filter=lfs diff=lfs merge=lfs -text
*.zst filter=lfs diff=lfs merge=lfs -text

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.osc

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<multibuild>
<package>test</package>
</multibuild>

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Index: xarray-2025.03.0/xarray/tutorial.py
===================================================================
--- xarray-2025.03.0.orig/xarray/tutorial.py
+++ xarray-2025.03.0/xarray/tutorial.py
@@ -164,9 +164,11 @@ def open_dataset(
downloader = pooch.HTTPDownloader(headers=headers)
# retrieve the file
- filepath = pooch.retrieve(
- url=url, known_hash=None, path=cache_dir, downloader=downloader
- )
+ fname = pathlib.Path(cache_dir, path).expanduser()
+ if not fname.exists():
+ fname = None
+ filepath = pooch.retrieve(url=url, fname=fname, known_hash=None, path=cache_dir)
+
ds = _open_dataset(filepath, engine=engine, **kws)
if not cache:
ds = ds.load()

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Index: xarray-2025.03.0/pyproject.toml
===================================================================
--- xarray-2025.03.0.orig/pyproject.toml
+++ xarray-2025.03.0/pyproject.toml
@@ -296,8 +296,6 @@ ban-relative-imports = "all"
addopts = [
"--strict-config",
"--strict-markers",
- "--mypy-only-local-stub",
- "--mypy-pyproject-toml-file=pyproject.toml",
]
# We want to forbid warnings from within xarray in our tests — instead we should

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#
# spec file for package python-xarray
#
# Copyright (c) 2025 SUSE LLC and contributors
#
# All modifications and additions to the file contributed by third parties
# remain the property of their copyright owners, unless otherwise agreed
# upon. The license for this file, and modifications and additions to the
# file, is the same license as for the pristine package itself (unless the
# license for the pristine package is not an Open Source License, in which
# case the license is the MIT License). An "Open Source License" is a
# license that conforms to the Open Source Definition (Version 1.9)
# published by the Open Source Initiative.
# Please submit bugfixes or comments via https://bugs.opensuse.org/
#
%global flavor @BUILD_FLAVOR@%{nil}
%if "%{flavor}" == "test"
%bcond_without test
%define psuffix -test
%else
%bcond_with test
%define psuffix %{nil}
%endif
%{?sle15_python_module_pythons}
Name: python-xarray%{psuffix}
Version: 2025.12.0
Release: 0
Summary: N-D labeled arrays and datasets in Python
License: Apache-2.0
URL: https://github.com/pydata/xarray
Source: https://github.com/pydata/xarray/archive/refs/tags/v%{version}.tar.gz#/xarray-%{version}-gh.tar.gz
# PATCH-FEATURE-UPSTREAM local_dataset.patch gh#pydata/xarray#5377 mcepl@suse.com
# fix xr.tutorial.open_dataset to work with the preloaded cache.
Patch0: local_dataset.patch
# PATCH-FIX-OPENSUSE: skip dependency on pytest-mypy
Patch1: no-mypy-test-plugin.patch
BuildRequires: %{python_module base >= 3.11}
BuildRequires: %{python_module pip}
BuildRequires: %{python_module setuptools_scm}
BuildRequires: %{python_module setuptools}
BuildRequires: %{python_module wheel}
BuildRequires: fdupes
BuildRequires: python-rpm-macros
Requires: python-numpy >= 1.26
Requires: python-packaging >= 24.1
Requires: python-pandas >= 2.2
Obsoletes: python-xray <= 0.7
BuildArch: noarch
%if %{with test}
BuildRequires: %{python_module hypothesis}
BuildRequires: %{python_module pytest-asyncio}
BuildRequires: %{python_module pytest-env}
BuildRequires: %{python_module pytest-timeout}
BuildRequires: %{python_module pytest-xdist}
BuildRequires: %{python_module pytest}
BuildRequires: %{python_module ruff >= 0.8.0}
BuildRequires: %{python_module xarray-complete = %{version}}
%endif
# /SECTION
%python_subpackages
%description
xarray (formerly xray) is a python-pandas-like and pandas-compatible
toolkit for analytics on multi-dimensional arrays. It provides
N-dimensional variants of the python-pandas labeled data structures,
rather than the tabular data that pandas uses.
The Common Data Model for self-describing scientific data is used.
The dataset is an in-memory representation of a netCDF file.
%package accel
Summary: The python xarray[accel] extra
Requires: python-Bottleneck
Requires: python-numba >= 0.62
Requires: python-opt-einsum
Requires: python-scipy >= 1.13
Requires: python-xarray = %{version}
# not available yet
Recommends: python-flox
Recommends: python-numbagg >= 0.8
%description accel
The [accel] extra for xarray, N-D labeled arrays and datasets in Python
Except flox and numbagg, because they are not packaged yet.
Use `pip --user install flox numbagg` to install from PyPI, if needed.
%package complete
Summary: The python xarray[complete] extra
Requires: python-xarray = %{version}
Requires: python-xarray-accel = %{version}
Requires: python-xarray-io = %{version}
Requires: python-xarray-parallel = %{version}
Requires: python-xarray-viz = %{version}
%description complete
The [complete] extra for xarray, N-D labeled arrays and datasets in Python
%package io
Summary: The python xarray[io] extra
Requires: python-cftime
Requires: python-fsspec
Requires: python-h5netcdf
Requires: python-netCDF4 >= 1.6
Requires: python-pooch
Requires: python-scipy >= 1.13
Requires: python-xarray = %{version}
Requires: python-zarr >= 2.18
# Not available yet
Recommends: python-pydap
%description io
The [io] extra for xarray, N-D labeled arrays and datasets in Python
%package parallel
Summary: The python xarray[parallel] extra
Requires: python-dask-complete
Requires: python-xarray = %{version}
%description parallel
The [parallel] extra for xarray, N-D labeled arrays and datasets in Python
%package viz
Summary: The python xarray[viz] extra
Requires: python-matplotlib >= 3.8
Requires: python-seaborn
Requires: python-xarray = %{version}
# Not available yet
Recommends: python-cartopy >= 0.23
Recommends: python-nc-time-axis
%description viz
The [viz] extra for xarray, N-D labeled arrays and datasets in Python
Except nc-time-axis and cartopy, because they're not packaged yet.
Use `pip --user install nc-time-axis cartopy` to install from PyPI, if needed.
%prep
%autosetup -p1 -n xarray-%{version}
chmod -x xarray/util/print_versions.py
%build
%if !%{with test}
%pyproject_wheel
%endif
%install
%if !%{with test}
%pyproject_install
%python_expand %fdupes %{buildroot}%{$python_sitelib}
%endif
%if %{with test}
%check
# obs file open race conditions?
donttest="(test_open_mfdataset_manyfiles and (h5netcdf or netCDF4))"
if [ $(getconf LONG_BIT) -eq 32 ]; then
# https://github.com/pydata/xarray/issues/5341
# https://github.com/pydata/xarray/issues/5375
# still precision problems in 2022.11.0
donttest="$donttest or (test_interpolate_chunk_advanced and linear)"
# tests for 64bit types
donttest="$donttest or TestZarrDictStore or TestZarrDirectoryStore or TestZarrWriteEmpty"
donttest="$donttest or test_repr_multiindex or test_array_repr_dtypes_unix or test_asi8"
donttest="$donttest or (test_datatree and TestRepr and test_doc_example)"
fi
# h5py was built without ROS3 support, can't use ros3 driver
donttest="$donttest or TestH5NetCDFDataRos3Driver"
# NetCDF4 fails with these unsupported drivers
donttest="$donttest or (TestNetCDF4 and test_compression_encoding and (szip or zstd or blosc_lz or blosc_zlib))"
%pytest -n auto -rsEf -k "not ($donttest)" xarray
%endif
%if !%{with test}
%files %{python_files}
%doc README.md
%license LICENSE licenses/
%{python_sitelib}/xarray
%{python_sitelib}/xarray-*.dist-info
%files %{python_files accel}
%doc README.md
%license LICENSE
%files %{python_files complete}
%doc README.md
%license LICENSE
%files %{python_files io}
%doc README.md
%license LICENSE
%files %{python_files parallel}
%doc README.md
%license LICENSE
%files %{python_files viz}
%doc README.md
%license LICENSE
%endif
%changelog

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