#!/usr/bin/env python
"""
Parts of this file were taken from the pyzmq project
(https://github.com/zeromq/pyzmq) which have been permitted for use under the
BSD license. Parts are from lxml (https://github.com/lxml/lxml)
"""
import os
from os.path import join as pjoin
import pkg_resources
import sys
import shutil
from distutils.version import LooseVersion
from setuptools import setup, Command, find_packages
# versioning
import versioneer
cmdclass = versioneer.get_cmdclass()
def is_platform_windows():
return sys.platform == 'win32' or sys.platform == 'cygwin'
def is_platform_linux():
return sys.platform == 'linux2'
def is_platform_mac():
return sys.platform == 'darwin'
min_cython_ver = '0.24'
try:
import Cython
ver = Cython.__version__
_CYTHON_INSTALLED = ver >= LooseVersion(min_cython_ver)
except ImportError:
_CYTHON_INSTALLED = False
min_numpy_ver = '1.9.0'
setuptools_kwargs = {
'install_requires': [
'python-dateutil >= 2.5.0',
'pytz >= 2011k',
'numpy >= {numpy_ver}'.format(numpy_ver=min_numpy_ver),
],
'setup_requires': ['numpy >= {numpy_ver}'.format(numpy_ver=min_numpy_ver)],
'zip_safe': False,
}
from distutils.extension import Extension # noqa:E402
from distutils.command.build import build # noqa:E402
from distutils.command.build_ext import build_ext as _build_ext # noqa:E402
try:
if not _CYTHON_INSTALLED:
raise ImportError('No supported version of Cython installed.')
try:
from Cython.Distutils.old_build_ext import old_build_ext as _build_ext # noqa:F811,E501
except ImportError:
# Pre 0.25
from Cython.Distutils import build_ext as _build_ext
cython = True
except ImportError:
cython = False
if cython:
try:
try:
from Cython import Tempita as tempita
except ImportError:
import tempita
except ImportError:
raise ImportError('Building pandas requires Tempita: '
'pip install Tempita')
_pxi_dep_template = {
'algos': ['_libs/algos_common_helper.pxi.in',
'_libs/algos_take_helper.pxi.in',
'_libs/algos_rank_helper.pxi.in'],
'groupby': ['_libs/groupby_helper.pxi.in'],
'join': ['_libs/join_helper.pxi.in', '_libs/join_func_helper.pxi.in'],
'reshape': ['_libs/reshape_helper.pxi.in'],
'hashtable': ['_libs/hashtable_class_helper.pxi.in',
'_libs/hashtable_func_helper.pxi.in'],
'index': ['_libs/index_class_helper.pxi.in'],
'sparse': ['_libs/sparse_op_helper.pxi.in'],
'interval': ['_libs/intervaltree.pxi.in']}
_pxifiles = []
_pxi_dep = {}
for module, files in _pxi_dep_template.items():
pxi_files = [pjoin('pandas', x) for x in files]
_pxifiles.extend(pxi_files)
_pxi_dep[module] = pxi_files
class build_ext(_build_ext):
def build_extensions(self):
# if builing from c files, don't need to
# generate template output
if cython:
for pxifile in _pxifiles:
# build pxifiles first, template extension must be .pxi.in
assert pxifile.endswith('.pxi.in')
outfile = pxifile[:-3]
if (os.path.exists(outfile) and
os.stat(pxifile).st_mtime < os.stat(outfile).st_mtime):
# if .pxi.in is not updated, no need to output .pxi
continue
with open(pxifile, "r") as f:
tmpl = f.read()
pyxcontent = tempita.sub(tmpl)
with open(outfile, "w") as f:
f.write(pyxcontent)
numpy_incl = pkg_resources.resource_filename('numpy', 'core/include')
for ext in self.extensions:
if (hasattr(ext, 'include_dirs') and
numpy_incl not in ext.include_dirs):
ext.include_dirs.append(numpy_incl)
_build_ext.build_extensions(self)
DESCRIPTION = ("Powerful data structures for data analysis, time series,"
"and statistics")
LONG_DESCRIPTION = """
**pandas** is a Python package providing fast, flexible, and expressive data
structures designed to make working with structured (tabular, multidimensional,
potentially heterogeneous) and time series data both easy and intuitive. It
aims to be the fundamental high-level building block for doing practical,
**real world** data analysis in Python. Additionally, it has the broader goal
of becoming **the most powerful and flexible open source data analysis /
manipulation tool available in any language**. It is already well on its way
toward this goal.
pandas is well suited for many different kinds of data:
- Tabular data with heterogeneously-typed columns, as in an SQL table or
Excel spreadsheet
- Ordered and unordered (not necessarily fixed-frequency) time series data.
- Arbitrary matrix data (homogeneously typed or heterogeneous) with row and
column labels
- Any other form of observational / statistical data sets. The data actually
need not be labeled at all to be placed into a pandas data structure
The two primary data structures of pandas, Series (1-dimensional) and DataFrame
(2-dimensional), handle the vast majority of typical use cases in finance,
statistics, social science, and many areas of engineering. For R users,
DataFrame provides everything that R's ``data.frame`` provides and much
more. pandas is built on top of `NumPy <http://www.numpy.org>`__ and is
intended to integrate well within a scientific computing environment with many
other 3rd party libraries.
Here are just a few of the things that pandas does well:
- Easy handling of **missing data** (represented as NaN) in floating point as
well as non-floating point data
- Size mutability: columns can be **inserted and deleted** from DataFrame and
higher dimensional objects
- Automatic and explicit **data alignment**: objects can be explicitly
aligned to a set of labels, or the user can simply ignore the labels and
let `Series`, `DataFrame`, etc. automatically align the data for you in
computations
- Powerful, flexible **group by** functionality to perform
split-apply-combine operations on data sets, for both aggregating and
transforming data
- Make it **easy to convert** ragged, differently-indexed data in other
Python and NumPy data structures into DataFrame objects
- Intelligent label-based **slicing**, **fancy indexing**, and **subsetting**
of large data sets
- Intuitive **merging** and **joining** data sets
- Flexible **reshaping** and pivoting of data sets
- **Hierarchical** labeling of axes (possible to have multiple labels per
tick)
- Robust IO tools for loading data from **flat files** (CSV and delimited),
Excel files, databases, and saving / loading data from the ultrafast **HDF5
format**
- **Time series**-specific functionality: date range generation and frequency
conversion, moving window statistics, moving window linear regressions,
date shifting and lagging, etc.
Many of these principles are here to address the shortcomings frequently
experienced using other languages / scientific research environments. For data
scientists, working with data is typically divided into multiple stages:
munging and cleaning data, analyzing / modeling it, then organizing the results
of the analysis into a form suitable for plotting or tabular display. pandas is
the ideal tool for all of these tasks.
"""
DISTNAME = 'pandas'
LICENSE = 'BSD'
AUTHOR = "The PyData Development Team"
EMAIL = "pydata@googlegroups.com"
URL = "http://pandas.pydata.org"
DOWNLOAD_URL = ''
CLASSIFIERS = [
'Development Status :: 5 - Production/Stable',
'Environment :: Console',
'Operating System :: OS Independent',
'Intended Audience :: Science/Research',
'Programming Language :: Python',
'Programming Language :: Python :: 2',
'Programming Language :: Python :: 3',
'Programming Language :: Python :: 2.7',
'Programming Language :: Python :: 3.5',
'Programming Language :: Python :: 3.6',
'Programming Language :: Cython',
'Topic :: Scientific/Engineering']
class CleanCommand(Command):
"""Custom distutils command to clean the .so and .pyc files."""
user_options = [("all", "a", "")]
def initialize_options(self):
self.all = True
self._clean_me = []
self._clean_trees = []
base = pjoin('pandas', '_libs', 'src')
dt = pjoin(base, 'datetime')
src = base
util = pjoin('pandas', 'util')
parser = pjoin(base, 'parser')
ujson_python = pjoin(base, 'ujson', 'python')
ujson_lib = pjoin(base, 'ujson', 'lib')
self._clean_exclude = [pjoin(dt, 'np_datetime.c'),
pjoin(dt, 'np_datetime_strings.c'),
pjoin(src, 'period_helper.c'),
pjoin(parser, 'tokenizer.c'),
pjoin(parser, 'io.c'),
pjoin(ujson_python, 'ujson.c'),
pjoin(ujson_python, 'objToJSON.c'),
pjoin(ujson_python, 'JSONtoObj.c'),
pjoin(ujson_lib, 'ultrajsonenc.c'),
pjoin(ujson_lib, 'ultrajsondec.c'),
pjoin(util, 'move.c'),
]
for root, dirs, files in os.walk('pandas'):
for f in files:
filepath = pjoin(root, f)
if filepath in self._clean_exclude:
continue
if os.path.splitext(f)[-1] in ('.pyc', '.so', '.o',
'.pyo',
'.pyd', '.c', '.orig'):
self._clean_me.append(filepath)
for d in dirs:
if d == '__pycache__':
self._clean_trees.append(pjoin(root, d))
# clean the generated pxi files
for pxifile in _pxifiles:
pxifile = pxifile.replace(".pxi.in", ".pxi")
self._clean_me.append(pxifile)
for d in ('build', 'dist'):
if os.path.exists(d):
self._clean_trees.append(d)
def finalize_options(self):
pass
def run(self):
for clean_me in self._clean_me:
try:
os.unlink(clean_me)
except Exception:
pass
for clean_tree in self._clean_trees:
try:
shutil.rmtree(clean_tree)
except Exception:
pass
# we need to inherit from the versioneer
# class as it encodes the version info
sdist_class = cmdclass['sdist']
class CheckSDist(sdist_class):
"""Custom sdist that ensures Cython has compiled all pyx files to c."""
_pyxfiles = ['pandas/_libs/lib.pyx',
'pandas/_libs/hashtable.pyx',
'pandas/_libs/tslib.pyx',
'pandas/_libs/index.pyx',
'pandas/_libs/algos.pyx',
'pandas/_libs/join.pyx',
'pandas/_libs/indexing.pyx',
'pandas/_libs/interval.pyx',
'pandas/_libs/hashing.pyx',
'pandas/_libs/missing.pyx',
'pandas/_libs/testing.pyx',
'pandas/_libs/window.pyx',
'pandas/_libs/skiplist.pyx',
'pandas/_libs/sparse.pyx',
'pandas/_libs/parsers.pyx',
'pandas/_libs/tslibs/ccalendar.pyx',
'pandas/_libs/tslibs/period.pyx',
'pandas/_libs/tslibs/strptime.pyx',
'pandas/_libs/tslibs/np_datetime.pyx',
'pandas/_libs/tslibs/timedeltas.pyx',
'pandas/_libs/tslibs/timestamps.pyx',
'pandas/_libs/tslibs/timezones.pyx',
'pandas/_libs/tslibs/conversion.pyx',
'pandas/_libs/tslibs/fields.pyx',
'pandas/_libs/tslibs/offsets.pyx',
'pandas/_libs/tslibs/frequencies.pyx',
'pandas/_libs/tslibs/resolution.pyx',
'pandas/_libs/tslibs/parsing.pyx',
'pandas/io/sas/sas.pyx']
def initialize_options(self):
sdist_class.initialize_options(self)
def run(self):
if 'cython' in cmdclass:
self.run_command('cython')
else:
for pyxfile in self._pyxfiles:
cfile = pyxfile[:-3] + 'c'
msg = ("C-source file '{source}' not found.\n"
"Run 'setup.py cython' before sdist.".format(
source=cfile))
assert os.path.isfile(cfile), msg
sdist_class.run(self)
class CheckingBuildExt(build_ext):
"""
Subclass build_ext to get clearer report if Cython is necessary.
"""
def check_cython_extensions(self, extensions):
for ext in extensions:
for src in ext.sources:
if not os.path.exists(src):
print("{}: -> [{}]".format(ext.name, ext.sources))
raise Exception("""Cython-generated file '{src}' not found.
Cython is required to compile pandas from a development branch.
Please install Cython or download a release package of pandas.
""".format(src=src))
def build_extensions(self):
self.check_cython_extensions(self.extensions)
build_ext.build_extensions(self)
class CythonCommand(build_ext):
"""Custom distutils command subclassed from Cython.Distutils.build_ext
to compile pyx->c, and stop there. All this does is override the
C-compile method build_extension() with a no-op."""
def build_extension(self, ext):
pass
class DummyBuildSrc(Command):
""" numpy's build_src command interferes with Cython's build_ext.
"""
user_options = []
def initialize_options(self):
self.py_modules_dict = {}
def finalize_options(self):
pass
def run(self):
pass
cmdclass.update({'clean': CleanCommand,
'build': build})
try:
from wheel.bdist_wheel import bdist_wheel
class BdistWheel(bdist_wheel):
def get_tag(self):
tag = bdist_wheel.get_tag(self)
repl = 'macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64'
if tag[2] == 'macosx_10_6_intel':
tag = (tag[0], tag[1], repl)
return tag
cmdclass['bdist_wheel'] = BdistWheel
except ImportError:
pass
if cython:
suffix = '.pyx'
cmdclass['build_ext'] = CheckingBuildExt
cmdclass['cython'] = CythonCommand
else:
suffix = '.c'
cmdclass['build_src'] = DummyBuildSrc
cmdclass['build_ext'] = CheckingBuildExt
lib_depends = ['reduce', 'inference']
def srcpath(name=None, suffix='.pyx', subdir='src'):
return pjoin('pandas', subdir, name + suffix)
if suffix == '.pyx':
lib_depends = [srcpath(f, suffix='.pyx', subdir='_libs/src')
for f in lib_depends]
lib_depends.append('pandas/_libs/src/util.pxd')
else:
lib_depends = []
plib_depends = []
common_include = ['pandas/_libs/src/klib', 'pandas/_libs/src']
def pxd(name):
return os.path.abspath(pjoin('pandas', name + '.pxd'))
# args to ignore warnings
if is_platform_windows():
extra_compile_args = []
else:
extra_compile_args = ['-Wno-unused-function']
lib_depends = lib_depends + ['pandas/_libs/src/numpy_helper.h',
'pandas/_libs/src/parse_helper.h',
'pandas/_libs/src/compat_helper.h']
np_datetime_headers = ['pandas/_libs/src/datetime/np_datetime.h',
'pandas/_libs/src/datetime/np_datetime_strings.h']
np_datetime_sources = ['pandas/_libs/src/datetime/np_datetime.c',
'pandas/_libs/src/datetime/np_datetime_strings.c']
tseries_depends = np_datetime_headers + ['pandas/_libs/tslibs/np_datetime.pxd']
# some linux distros require it
libraries = ['m'] if not is_platform_windows() else []
ext_data = {
'_libs.algos': {
'pyxfile': '_libs/algos',
'pxdfiles': ['_libs/src/util', '_libs/algos', '_libs/hashtable'],
'depends': _pxi_dep['algos']},
'_libs.groupby': {
'pyxfile': '_libs/groupby',
'pxdfiles': ['_libs/src/util', '_libs/algos'],
'depends': _pxi_dep['groupby']},
'_libs.hashing': {
'pyxfile': '_libs/hashing'},
'_libs.hashtable': {
'pyxfile': '_libs/hashtable',
'pxdfiles': ['_libs/hashtable', '_libs/missing', '_libs/khash'],
'depends': (['pandas/_libs/src/klib/khash_python.h'] +
_pxi_dep['hashtable'])},
'_libs.index': {
'pyxfile': '_libs/index',
'pxdfiles': ['_libs/src/util', '_libs/hashtable'],
'depends': _pxi_dep['index'],
'sources': np_datetime_sources},
'_libs.indexing': {
'pyxfile': '_libs/indexing'},
'_libs.interval': {
'pyxfile': '_libs/interval',
'pxdfiles': ['_libs/hashtable'],
'depends': _pxi_dep['interval']},
'_libs.join': {
'pyxfile': '_libs/join',
'pxdfiles': ['_libs/src/util', '_libs/hashtable'],
'depends': _pxi_dep['join']},
'_libs.lib': {
'pyxfile': '_libs/lib',
'pxdfiles': ['_libs/src/util',
'_libs/missing',
'_libs/tslibs/conversion'],
'depends': lib_depends + tseries_depends},
'_libs.missing': {
'pyxfile': '_libs/missing',
'pxdfiles': ['_libs/src/util'],
'depends': tseries_depends},
'_libs.parsers': {
'pyxfile': '_libs/parsers',
'depends': ['pandas/_libs/src/parser/tokenizer.h',
'pandas/_libs/src/parser/io.h',
'pandas/_libs/src/numpy_helper.h'],
'sources': ['pandas/_libs/src/parser/tokenizer.c',
'pandas/_libs/src/parser/io.c']},
'_libs.tslibs.period': {
'pyxfile': '_libs/tslibs/period',
'pxdfiles': ['_libs/src/util',
'_libs/lib',
'_libs/tslibs/timedeltas',
'_libs/tslibs/timezones',
'_libs/tslibs/nattype'],
'depends': tseries_depends + ['pandas/_libs/src/period_helper.h'],
'sources': np_datetime_sources + ['pandas/_libs/src/period_helper.c']},
'_libs.properties': {
'pyxfile': '_libs/properties',
'include': []},
'_libs.reshape': {
'pyxfile': '_libs/reshape',
'depends': _pxi_dep['reshape']},
'_libs.skiplist': {
'pyxfile': '_libs/skiplist',
'depends': ['pandas/_libs/src/skiplist.h']},
'_libs.sparse': {
'pyxfile': '_libs/sparse',
'depends': _pxi_dep['sparse']},
'_libs.tslib': {
'pyxfile': '_libs/tslib',
'pxdfiles': ['_libs/src/util',
'_libs/tslibs/conversion',
'_libs/tslibs/timedeltas',
'_libs/tslibs/timestamps',
'_libs/tslibs/timezones',
'_libs/tslibs/nattype'],
'depends': tseries_depends,
'sources': np_datetime_sources},
'_libs.tslibs.ccalendar': {
'pyxfile': '_libs/tslibs/ccalendar'},
'_libs.tslibs.conversion': {
'pyxfile': '_libs/tslibs/conversion',
'pxdfiles': ['_libs/src/util',
'_libs/tslibs/nattype',
'_libs/tslibs/timezones',
'_libs/tslibs/timedeltas'],
'depends': tseries_depends,
'sources': np_datetime_sources},
'_libs.tslibs.fields': {
'pyxfile': '_libs/tslibs/fields',
'pxdfiles': ['_libs/tslibs/ccalendar',
'_libs/tslibs/nattype'],
'depends': tseries_depends,
'sources': np_datetime_sources},
'_libs.tslibs.frequencies': {
'pyxfile': '_libs/tslibs/frequencies',
'pxdfiles': ['_libs/src/util']},
'_libs.tslibs.nattype': {
'pyxfile': '_libs/tslibs/nattype',
'pxdfiles': ['_libs/src/util']},
'_libs.tslibs.np_datetime': {
'pyxfile': '_libs/tslibs/np_datetime',
'depends': np_datetime_headers,
'sources': np_datetime_sources},
'_libs.tslibs.offsets': {
'pyxfile': '_libs/tslibs/offsets',
'pxdfiles': ['_libs/src/util',
'_libs/tslibs/conversion',
'_libs/tslibs/frequencies',
'_libs/tslibs/nattype'],
'depends': tseries_depends,
'sources': np_datetime_sources},
'_libs.tslibs.parsing': {
'pyxfile': '_libs/tslibs/parsing',
'pxdfiles': ['_libs/src/util']},
'_libs.tslibs.resolution': {
'pyxfile': '_libs/tslibs/resolution',
'pxdfiles': ['_libs/src/util',
'_libs/khash',
'_libs/tslibs/frequencies',
'_libs/tslibs/timezones'],
'depends': tseries_depends,
'sources': np_datetime_sources},
'_libs.tslibs.strptime': {
'pyxfile': '_libs/tslibs/strptime',
'pxdfiles': ['_libs/src/util',
'_libs/tslibs/nattype'],
'depends': tseries_depends,
'sources': np_datetime_sources},
'_libs.tslibs.timedeltas': {
'pyxfile': '_libs/tslibs/timedeltas',
'pxdfiles': ['_libs/src/util',
'_libs/tslibs/nattype'],
'depends': np_datetime_headers,
'sources': np_datetime_sources},
'_libs.tslibs.timestamps': {
'pyxfile': '_libs/tslibs/timestamps',
'pxdfiles': ['_libs/src/util',
'_libs/tslibs/ccalendar',
'_libs/tslibs/conversion',
'_libs/tslibs/nattype',
'_libs/tslibs/timedeltas',
'_libs/tslibs/timezones'],
'depends': tseries_depends,
'sources': np_datetime_sources},
'_libs.tslibs.timezones': {
'pyxfile': '_libs/tslibs/timezones',
'pxdfiles': ['_libs/src/util']},
'_libs.testing': {
'pyxfile': '_libs/testing'},
'_libs.window': {
'pyxfile': '_libs/window',
'pxdfiles': ['_libs/skiplist', '_libs/src/util']},
'io.sas._sas': {
'pyxfile': 'io/sas/sas'}}
extensions = []
for name, data in ext_data.items():
sources = [srcpath(data['pyxfile'], suffix=suffix, subdir='')]
pxds = [pxd(x) for x in data.get('pxdfiles', [])]
if suffix == '.pyx' and pxds:
sources.extend(pxds)
sources.extend(data.get('sources', []))
include = data.get('include', common_include)
obj = Extension('pandas.{name}'.format(name=name),
sources=sources,
depends=data.get('depends', []),
include_dirs=include,
extra_compile_args=extra_compile_args)
extensions.append(obj)
# ----------------------------------------------------------------------
# msgpack
if sys.byteorder == 'big':
macros = [('__BIG_ENDIAN__', '1')]
else:
macros = [('__LITTLE_ENDIAN__', '1')]
msgpack_include = ['pandas/_libs/src/msgpack'] + common_include
msgpack_suffix = suffix if suffix == '.pyx' else '.cpp'
unpacker_depends = ['pandas/_libs/src/msgpack/unpack.h',
'pandas/_libs/src/msgpack/unpack_define.h',
'pandas/_libs/src/msgpack/unpack_template.h']
packer_ext = Extension('pandas.io.msgpack._packer',
depends=['pandas/_libs/src/msgpack/pack.h',
'pandas/_libs/src/msgpack/pack_template.h'],
sources=[srcpath('_packer',
suffix=msgpack_suffix,
subdir='io/msgpack')],
language='c++',
include_dirs=msgpack_include,
define_macros=macros,
extra_compile_args=extra_compile_args)
unpacker_ext = Extension('pandas.io.msgpack._unpacker',
depends=unpacker_depends,
sources=[srcpath('_unpacker',
suffix=msgpack_suffix,
subdir='io/msgpack')],
language='c++',
include_dirs=msgpack_include,
define_macros=macros,
extra_compile_args=extra_compile_args)
extensions.append(packer_ext)
extensions.append(unpacker_ext)
# ----------------------------------------------------------------------
# ujson
if suffix == '.pyx':
# undo dumb setuptools bug clobbering .pyx sources back to .c
for ext in extensions:
if ext.sources[0].endswith(('.c', '.cpp')):
root, _ = os.path.splitext(ext.sources[0])
ext.sources[0] = root + suffix
ujson_ext = Extension('pandas._libs.json',
depends=['pandas/_libs/src/ujson/lib/ultrajson.h',
'pandas/_libs/src/numpy_helper.h'],
sources=(['pandas/_libs/src/ujson/python/ujson.c',
'pandas/_libs/src/ujson/python/objToJSON.c',
'pandas/_libs/src/ujson/python/JSONtoObj.c',
'pandas/_libs/src/ujson/lib/ultrajsonenc.c',
'pandas/_libs/src/ujson/lib/ultrajsondec.c'] +
np_datetime_sources),
include_dirs=(['pandas/_libs/src/ujson/python',
'pandas/_libs/src/ujson/lib',
'pandas/_libs/src/datetime'] +
common_include),
extra_compile_args=(['-D_GNU_SOURCE'] +
extra_compile_args))
extensions.append(ujson_ext)
# ----------------------------------------------------------------------
# util
# extension for pseudo-safely moving bytes into mutable buffers
_move_ext = Extension('pandas.util._move',
depends=[],
sources=['pandas/util/move.c'])
extensions.append(_move_ext)
# The build cache system does string matching below this point.
# if you change something, be careful.
setup(name=DISTNAME,
maintainer=AUTHOR,
version=versioneer.get_version(),
packages=find_packages(include=['pandas', 'pandas.*']),
package_data={'': ['data/*', 'templates/*'],
'pandas.tests.io': ['data/legacy_hdf/*.h5',
'data/legacy_pickle/*/*.pickle',
'data/legacy_msgpack/*/*.msgpack',
'data/html_encoding/*.html']},
ext_modules=extensions,
maintainer_email=EMAIL,
description=DESCRIPTION,
license=LICENSE,
cmdclass=cmdclass,
url=URL,
download_url=DOWNLOAD_URL,
long_description=LONG_DESCRIPTION,
classifiers=CLASSIFIERS,
platforms='any',
**setuptools_kwargs)