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setup.py
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setup.py
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#!/usr/bin/env python
import sys
import versioneer
from setuptools import find_packages, setup
DISTNAME = 'xarray'
LICENSE = 'Apache'
AUTHOR = 'xarray Developers'
AUTHOR_EMAIL = 'xarray@googlegroups.com'
URL = 'https://github.com/pydata/xarray'
CLASSIFIERS = [
'Development Status :: 4 - Beta',
'License :: OSI Approved :: Apache Software License',
'Operating System :: OS Independent',
'Intended Audience :: Science/Research',
'Programming Language :: Python',
'Programming Language :: Python :: 3',
'Programming Language :: Python :: 3.5',
'Programming Language :: Python :: 3.6',
'Programming Language :: Python :: 3.7',
'Topic :: Scientific/Engineering',
]
PYTHON_REQUIRES = '>=3.5'
INSTALL_REQUIRES = ['numpy >= 1.12', 'pandas >= 0.19.2']
needs_pytest = {'pytest', 'test', 'ptr'}.intersection(sys.argv)
SETUP_REQUIRES = ['pytest-runner >= 4.2'] if needs_pytest else []
TESTS_REQUIRE = ['pytest >= 2.7.1']
if sys.version_info[0] < 3:
TESTS_REQUIRE.append('mock')
DESCRIPTION = "N-D labeled arrays and datasets in Python"
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
""" # noqa
setup(name=DISTNAME,
version=versioneer.get_version(),
cmdclass=versioneer.get_cmdclass(),
license=LICENSE,
author=AUTHOR,
author_email=AUTHOR_EMAIL,
classifiers=CLASSIFIERS,
description=DESCRIPTION,
long_description=LONG_DESCRIPTION,
python_requires=PYTHON_REQUIRES,
install_requires=INSTALL_REQUIRES,
setup_requires=SETUP_REQUIRES,
tests_require=TESTS_REQUIRE,
url=URL,
packages=find_packages(),
package_data={'xarray': ['tests/data/*']})