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#!/usr/bin/env python
# Do not add setuptools here; use setupegg.py instead. Nose still has problems running
# tests inside of egg packages, so it is useful to be able to install without eggs as needed.
from __future__ import print_function
from numpy.distutils.misc_util import Configuration
from numpy.distutils.system_info import get_info
import os, sys
#try:
# from googlecode import upload2google
#except:
# def upload2google():
# raise "This commmand is only available with the subversion development version."
config = Configuration('pymc',parent_package=None,top_path=None)
dist = sys.argv[1]
# ==============================
# = Compile Fortran extensions =
# ==============================
# If optimized lapack/ BLAS libraries are present, compile distributions that involve linear algebra against those.
# Otherwise compile blas and lapack from netlib sources.
lapack_info = get_info('lapack_opt',1)
f_sources = ['pymc/flib.f','pymc/histogram.f', 'pymc/flib_blas.f', 'pymc/blas_wrap.f', 'pymc/math.f', 'pymc/gibbsit.f', 'cephes/i0.c',
'cephes/c2f.c','cephes/chbevl.c']
if lapack_info:
config.add_extension(name='flib',sources=f_sources, extra_info=lapack_info, f2py_options=['skip:ppnd7'])
if not lapack_info or dist in ['bdist', 'sdist']:
##inc_dirs = ['blas/BLAS','lapack/double']
print('No optimized BLAS or Lapack libraries found, building from source. This may take a while...')
for fname in os.listdir('blas/BLAS'):
# Make sure this is a Fortran file, and not one of those weird hidden files that
# pop up sometimes in the tarballs
if fname[-2:]=='.f' and fname[0].find('_')==-1:
f_sources.append('blas/BLAS/'+fname)
## for fname in os.listdir('lapack/double'):
## if fname[-2:]=='.f':
## inc_dirs.append('lapack/double/'+fname)
for fname in ['dpotrs','dpotrf','dpotf2','ilaenv','dlamch','ilaver','ieeeck','iparmq']:
f_sources.append('lapack/double/'+fname+'.f')
config.add_extension(name='flib',sources=f_sources)
# TODO Convert Pyrex to Cython
# ============================
# = Compile Pyrex extensions =
# ============================
config.add_extension(name='LazyFunction',sources=['pymc/LazyFunction.c'])
config.add_extension(name='Container_values', sources='pymc/Container_values.c')
config_dict = config.todict()
try:
config_dict.pop('packages')
except:
pass
# ===========================================
# = Compile GP package's Fortran extensions =
# ===========================================
# Compile linear algebra utilities
if lapack_info:
config.add_extension(name='gp.linalg_utils',sources=['pymc/gp/linalg_utils.f','pymc/blas_wrap.f'], extra_info=lapack_info)
config.add_extension(name='gp.incomplete_chol',sources=['pymc/gp/incomplete_chol.f'], extra_info=lapack_info)
else:
raise RuntimeError("Debian build requires system-wide presence of blas/lapack")
if not lapack_info or dist in ['bdist', 'sdist']:
print('No optimized BLAS or Lapack libraries found, building from source. This may take a while...')
f_sources = ['pymc/blas_wrap.f']
for fname in os.listdir('blas/BLAS'):
if fname[-2:]=='.f':
f_sources.append('blas/BLAS/'+fname)
for fname in ['dpotrs','dpotrf','dpotf2','ilaenv','dlamch','ilaver','ieeeck','iparmq']:
f_sources.append('lapack/double/'+fname+'.f')
config.add_extension(name='gp.linalg_utils',sources=['pymc/gp/linalg_utils.f'] + f_sources)
config.add_extension(name='gp.incomplete_chol',sources=['pymc/gp/incomplete_chol.f'] + f_sources)
# Compile covariance functions
config.add_extension(name='gp.cov_funs.isotropic_cov_funs',\
sources=['pymc/gp/cov_funs/isotropic_cov_funs.f'],\
extra_info=lapack_info)
config.add_extension(name='gp.cov_funs.distances',sources=['pymc/gp/cov_funs/distances.f'], extra_info=lapack_info)
if __name__ == '__main__':
from numpy.distutils.core import setup
setup( version="2.2",
description="Markov Chain Monte Carlo sampling toolkit.",
#maintainer="David Huard",
#maintainer_email="david.huard@gmail.com",
author="Christopher Fonnesbeck, Anand Patil and David Huard",
author_email="fonnesbeck@gmail.com ",
url="github.com/pymc-devs/pymc",
#download_url="",
license="Academic Free License",
classifiers=[
'Development Status :: 5 - Production/Stable',
'Environment :: Console',
'Operating System :: OS Independent',
'Intended Audience :: Science/Research',
'License :: OSI Approved :: Academic Free License (AFL)',
'Programming Language :: Python',
'Programming Language :: Fortran',
'Topic :: Scientific/Engineering',
],
requires=['NumPy (>=1.6)',],
long_description="""
Bayesian estimation, particularly using Markov chain Monte Carlo (MCMC),
is an increasingly relevant approach to statistical estimation. However,
few statistical software packages implement MCMC samplers, and they are
non-trivial to code by hand. ``pymc`` is a python package that implements the
Metropolis-Hastings algorithm as a python class, and is extremely
flexible and applicable to a large suite of problems. ``pymc`` includes
methods for summarizing output, plotting, goodness-of-fit and convergence
diagnostics.
``pymc`` only requires ``NumPy``. All other dependencies such as ``matplotlib``,
``SciPy``, ``pytables``, ``sqlite`` or ``mysql`` are optional.
""",
packages=["pymc", "pymc/database", "pymc/examples", "pymc/examples/gp", "pymc/tests", "pymc/gp", "pymc/gp/cov_funs"],
#cmdclass={'upload2google':upload2google},
**(config_dict))
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