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setup.py
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setup.py
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#!/usr/bin/env python
import os
import re
import sys
import warnings
from setuptools import setup, find_packages
from setuptools import Command
MAJOR = 0
MINOR = 9
MICRO = 6
ISRELEASED = False
VERSION = '%d.%d.%d' % (MAJOR, MINOR, MICRO)
QUALIFIER = ''
DISTNAME = 'xarray'
LICENSE = 'Apache'
AUTHOR = 'xarray Developers'
AUTHOR_EMAIL = '[email protected]'
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 :: 2',
'Programming Language :: Python :: 2.7',
'Programming Language :: Python :: 3',
'Programming Language :: Python :: 3.4',
'Programming Language :: Python :: 3.5',
'Programming Language :: Python :: 3.6',
'Topic :: Scientific/Engineering',
]
INSTALL_REQUIRES = ['numpy >= 1.7', 'pandas >= 0.15.0']
TESTS_REQUIRE = ['pytest >= 2.7.1']
DESCRIPTION = "N-D labeled arrays and datasets in Python"
LONG_DESCRIPTION = """
**xarray** (formerly **xray**) is an open source project and Python package
that aims to bring the labeled data power of pandas_ to the physical sciences,
by providing N-dimensional variants of the core pandas data structures.
Our goal is to provide a pandas-like and pandas-compatible toolkit for
analytics on multi-dimensional arrays, rather than the tabular data for which
pandas excels. Our approach adopts the `Common Data Model`_ for self-
describing scientific data in widespread use in the Earth sciences:
``xarray.Dataset`` is an in-memory representation of a netCDF file.
.. _pandas: http://pandas.pydata.org
.. _Common Data Model: http://www.unidata.ucar.edu/software/thredds/current/netcdf-java/CDM
.. _netCDF: http://www.unidata.ucar.edu/software/netcdf
.. _OPeNDAP: http://www.opendap.org/
Important links
---------------
- HTML 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
"""
# Code to extract and write the version copied from pandas.
# Used under the terms of pandas's license, see licenses/PANDAS_LICENSE.
FULLVERSION = VERSION
write_version = True
if not ISRELEASED:
import subprocess
FULLVERSION += '.dev'
pipe = None
for cmd in ['git', 'git.cmd']:
try:
pipe = subprocess.Popen(
[cmd, "describe", "--always", "--match", "v[0-9]*"],
stdout=subprocess.PIPE)
(so, serr) = pipe.communicate()
if pipe.returncode == 0:
break
except:
pass
if pipe is None or pipe.returncode != 0:
# no git, or not in git dir
if os.path.exists('xarray/version.py'):
warnings.warn("WARNING: Couldn't get git revision, using existing xarray/version.py")
write_version = False
else:
warnings.warn("WARNING: Couldn't get git revision, using generic version string")
else:
# have git, in git dir, but may have used a shallow clone (travis does this)
rev = so.strip()
# makes distutils blow up on Python 2.7
if sys.version_info[0] >= 3:
rev = rev.decode('ascii')
if not rev.startswith('v') and re.match("[a-zA-Z0-9]{7,9}", rev):
# partial clone, manually construct version string
# this is the format before we started using git-describe
# to get an ordering on dev version strings.
rev = "v%s.dev-%s" % (VERSION, rev)
# Strip leading v from tags format "vx.y.z" to get th version string
FULLVERSION = rev.lstrip('v')
else:
FULLVERSION += QUALIFIER
def write_version_py(filename=None):
cnt = """\
version = '%s'
short_version = '%s'
"""
if not filename:
filename = os.path.join(
os.path.dirname(__file__), 'xarray', 'version.py')
a = open(filename, 'w')
try:
a.write(cnt % (FULLVERSION, VERSION))
finally:
a.close()
if write_version:
write_version_py()
setup(name=DISTNAME,
version=FULLVERSION,
license=LICENSE,
author=AUTHOR,
author_email=AUTHOR_EMAIL,
classifiers=CLASSIFIERS,
description=DESCRIPTION,
long_description=LONG_DESCRIPTION,
install_requires=INSTALL_REQUIRES,
tests_require=TESTS_REQUIRE,
url=URL,
packages=find_packages(),
package_data={'xarray': ['tests/data/*', 'plot/default_colormap.csv']})