/
setup.cfg
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setup.cfg
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[metadata]
name = xarray
author = xarray Developers
author_email = xarray@googlegroups.com
license = Apache
description = N-D labeled arrays and datasets in Python
long_description_content_type=text/x-rst
long_description =
**xarray** (formerly **xray**) is an open source project and Python package
that makes working with labelled multi-dimensional arrays simple,
efficient, and fun!
xarray introduces labels in the form of dimensions, coordinates and
attributes on top of raw NumPy_-like arrays, which allows for a more
intuitive, more concise, and less error-prone developer experience.
The package includes a large and growing library of domain-agnostic functions
for advanced analytics and visualization with these data structures.
xarray was inspired by and borrows heavily from pandas_, the popular data
analysis package focused on labelled tabular data.
It is particularly tailored to working with netCDF_ files, which were the
source of xarray's data model, and integrates tightly with dask_ for parallel
computing.
.. _NumPy: https://www.numpy.org
.. _pandas: https://pandas.pydata.org
.. _dask: https://dask.org
.. _netCDF: https://www.unidata.ucar.edu/software/netcdf
Why 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``.
Learn more
----------
- Documentation: `<http://xarray.pydata.org>`_
- Issue tracker: `<http://github.com/pydata/xarray/issues>`_
- Source code: `<http://github.com/pydata/xarray>`_
- SciPy2015 talk: `<https://www.youtube.com/watch?v=X0pAhJgySxk>`_
url = https://github.com/pydata/xarray
classifiers =
Development Status :: 5 - Production/Stable
License :: OSI Approved :: Apache Software License
Operating System :: OS Independent
Intended Audience :: Science/Research
Programming Language :: Python
Programming Language :: Python :: 3
Programming Language :: Python :: 3.6
Programming Language :: Python :: 3.7
Topic :: Scientific/Engineering
[options]
packages = find:
zip_safe = False # https://mypy.readthedocs.io/en/latest/installed_packages.html
include_package_data = True
python_requires = >=3.6
install_requires =
numpy >= 1.15
pandas >= 0.25
setuptools >= 38.4 # For pkg_resources
setup_requires =
setuptools >= 38.4
setuptools_scm
[options.extras_require]
io =
netCDF4
h5netcdf
scipy
pydap
zarr
fsspec
cftime
rasterio
cfgrib
## Scitools packages & dependencies (e.g: cartopy, cf-units) can be hard to install
# scitools-iris
accel =
scipy
bottleneck
numbagg
parallel =
dask[complete]
viz =
matplotlib
seaborn
nc-time-axis
## Cartopy requires 3rd party libraries and only provides source distributions
## See: https://github.com/SciTools/cartopy/issues/805
# cartopy
complete =
%(io)s
%(accel)s
%(parallel)s
%(viz)s
docs =
%(complete)s
sphinx-autosummary-accessors
sphinx_rtd_theme
ipython
ipykernel
jupyter-client
nbsphinx
scanpydoc
[options.package_data]
xarray =
py.typed
tests/data/*
static/css/*
static/html/*
[tool:pytest]
python_files = test_*.py
testpaths = xarray/tests properties
# Fixed upstream in https://github.com/pydata/bottleneck/pull/199
filterwarnings =
ignore:Using a non-tuple sequence for multidimensional indexing is deprecated:FutureWarning
markers =
flaky: flaky tests
network: tests requiring a network connection
slow: slow tests
[flake8]
ignore =
E203 # whitespace before ':' - doesn't work well with black
E402 # module level import not at top of file
E501 # line too long - let black worry about that
E731 # do not assign a lambda expression, use a def
W503 # line break before binary operator
exclude=
.eggs
doc
[isort]
profile = black
skip_gitignore = true
force_to_top = true
default_section = THIRDPARTY
known_first_party = xarray
# Most of the numerical computing stack doesn't have type annotations yet.
[mypy-affine.*]
ignore_missing_imports = True
[mypy-bottleneck.*]
ignore_missing_imports = True
[mypy-cdms2.*]
ignore_missing_imports = True
[mypy-cf_units.*]
ignore_missing_imports = True
[mypy-cfgrib.*]
ignore_missing_imports = True
[mypy-cftime.*]
ignore_missing_imports = True
[mypy-cupy.*]
ignore_missing_imports = True
[mypy-dask.*]
ignore_missing_imports = True
[mypy-distributed.*]
ignore_missing_imports = True
[mypy-h5netcdf.*]
ignore_missing_imports = True
[mypy-h5py.*]
ignore_missing_imports = True
[mypy-iris.*]
ignore_missing_imports = True
[mypy-matplotlib.*]
ignore_missing_imports = True
[mypy-Nio.*]
ignore_missing_imports = True
[mypy-nc_time_axis.*]
ignore_missing_imports = True
[mypy-numbagg.*]
ignore_missing_imports = True
[mypy-numpy.*]
ignore_missing_imports = True
[mypy-netCDF4.*]
ignore_missing_imports = True
[mypy-netcdftime.*]
ignore_missing_imports = True
[mypy-pandas.*]
ignore_missing_imports = True
[mypy-pint.*]
ignore_missing_imports = True
[mypy-PseudoNetCDF.*]
ignore_missing_imports = True
[mypy-pydap.*]
ignore_missing_imports = True
[mypy-pytest.*]
ignore_missing_imports = True
[mypy-rasterio.*]
ignore_missing_imports = True
[mypy-scipy.*]
ignore_missing_imports = True
[mypy-seaborn.*]
ignore_missing_imports = True
[mypy-setuptools]
ignore_missing_imports = True
[mypy-sparse.*]
ignore_missing_imports = True
[mypy-toolz.*]
ignore_missing_imports = True
[mypy-zarr.*]
ignore_missing_imports = True
# version spanning code is hard to type annotate (and most of this module will
# be going away soon anyways)
[mypy-xarray.core.pycompat]
ignore_errors = True
[aliases]
test = pytest
[pytest-watch]
nobeep = True