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setup.py
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setup.py
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import os
import pathlib
import shutil
from setuptools import setup, Extension
from setuptools.command.build_ext import build_ext as build_ext_orig
class CMakeExtension(Extension):
def __init__(self, name, sources = []):
super().__init__(name, sources = sources)
class build_ext(build_ext_orig):
def run(self):
for ext in self.extensions:
self.build_cmake(ext)
super().run()
def build_cmake(self, ext):
cwd = pathlib.Path().absolute()
# these dirs will be created in build_py, so if you don't have
# any python sources to bundle, the dirs will be missing
build_temp = pathlib.Path(self.build_temp)
build_temp.mkdir(parents = True, exist_ok = True)
extdir = pathlib.Path(self.get_ext_fullpath(ext.name))
print("EXTDIR: ", extdir)
extdir.mkdir(parents = True, exist_ok = True)
# example of cmake args
config = 'Debug' if self.debug else 'Release'
cmake_args = [
'-DCMAKE_BUILD_TYPE=' + config,
'-DPYTHON_OUTPUT_DIR=' + os.path.join(str(cwd), ".")
]
# example of build args
build_args = [
'--config', config,
'--', '-j4'
]
os.chdir(str(build_temp))
self.spawn(['cmake', str(cwd)] + cmake_args)
if not self.dry_run:
self.spawn(['cmake', '--build', '.', '--target', 'invlib_cpu_float'] + build_args)
self.spawn(['cmake', '--build', '.', '--target', 'invlib_cpu_double'] + build_args)
self.spawn(['cmake', '--build', '.', '--target', 'invlib_mpi_float'] + build_args)
self.spawn(['cmake', '--build', '.', '--target', 'invlib_mpi_double'] + build_args)
os.chdir(str(cwd))
import glob
invlib_files = glob.glob("src/**/*.h", recursive = True)
invlib_files += glob.glob("src/**/*.cpp", recursive = True)
setup(
name = 'invlib',
version = '0.1.0',
packages = ['invlib', 'invlib.api'],
ext_modules = [CMakeExtension('invlib', sources = invlib_files)],
cmdclass = {'build_ext': build_ext},
author = "Simon Pfreundschuh",
author_email = "simon.pfreundschuh@chalmers.se",
description = "Maximum a posteriori estimators for Bayesian inverse " \
" problems.",
long_description = "This package is an interface to the invlib library "
" which provides efficient maximum a posteriori (MAP) solvers for "\
" Bayesian inverse problems in remote sensing, also known as the optimal "\
"estimation method (OEM) or XDVAR.",
license = "PSF",
install_requires = ["numpy", "scipy"],
keywords = "Bayesian, MAP, OEM, remote sensing, inverse problems",
url = "https://github.com/simonpf/invlib",
project_urls = {
"Source Code": "https://github.com/simonpf/invlib",
},
classifiers = ['Development Status :: 3 - Alpha',
'Intended Audience :: Science/Research',
'Topic :: Scientific/Engineering :: Atmospheric Science',
'License :: OSI Approved :: MIT License',
'Programming Language :: Python :: 3.8'],
python_requires='>=3'
)