site.cfg.example

# This file provides configuration information about non-Python dependencies for
# numpy.distutils-using packages. Create a file like this called "site.cfg" next
# to your package's setup.py file and fill in the appropriate sections. Not all
# packages will use all sections so you should leave out sections that your
# package does not use.

# To assist automatic installation like pip, the user's home directory
# will also be checked for the file ~/.numpy-site.cfg .

# The format of the file is that of the standard library's ConfigParser module.
# No interpolation is allowed; the RawConfigParser class is being used to load it.
#
#   https://docs.python.org/library/configparser.html
#
# Each section defines settings that apply to one particular dependency. Some of
# the settings are general and apply to nearly any section and are defined here.
# Settings specific to a particular section will be defined near their section.
#
#   libraries
#       Comma-separated list of library names to add to compile the extension
#       with. Note that these should be just the names, not the filenames. For
#       example, the file "libfoo.so" would become simply "foo".
#           libraries = lapack,f77blas,cblas,atlas
#       This setting is available for *all* sections.
#
#   library_dirs
#       List of directories to add to the library search path when compiling
#       extensions with this dependency. Use the character given by os.pathsep
#       to separate the items in the list. Note that this character is known to
#       vary on some unix-like systems; if a colon does not work, try a comma.
#       This also applies to include_dirs and src_dirs (see below).
#       On UN*X-type systems (OS X, most BSD and Linux systems):
#           library_dirs = /usr/lib:/usr/local/lib
#       On Windows:
#           library_dirs = c:\mingw\lib,c:\atlas\lib
#       On some BSD and Linux systems:
#           library_dirs = /usr/lib,/usr/local/lib
#
#   include_dirs
#       List of directories to add to the header file search path.
#           include_dirs = /usr/include:/usr/local/include
#
#   src_dirs 
#       List of directories that contain extracted source code for the
#       dependency. For some dependencies, numpy.distutils will be able to build
#       them from source if binaries cannot be found. The FORTRAN BLAS and
#       LAPACK libraries are one example. However, most dependencies are more
#       complicated and require actual installation that you need to do
#       yourself.
#           src_dirs = /home/username/src/BLAS_SRC:/home/username/src/LAPACK_SRC
#
#   search_static_first
#       Boolean (one of (0, false, no, off) for False or (1, true, yes, on) for
#       True) to tell numpy.distutils to prefer static libraries (.a) over
#       shared libraries (.so). It is turned off by default.
#           search_static_first = false
#
#   runtime_library_dirs/rpath
#       List of directories that contains the libraries that should be 
#       used at runtime, thereby disregarding the LD_LIBRARY_PATH variable.
#       See 'library_dirs' for formatting on different platforms.
#           runtime_library_dirs = /opt/blas/lib:/opt/lapack/lib
#       or equivalently
#           rpath = /opt/blas/lib:/opt/lapack/lib
#
#   extra_compile_args
#       Add additional arguments to the compilation of sources.
#       Split into arguments in a platform-appropriate way.
#       Provide a single line with all complete flags.
#           extra_compile_args = -g -ftree-vectorize
#
#   extra_link_args
#       Add additional arguments when libraries/executables
#       are linked.
#       Split into arguments in a platform-appropriate way.
#       Provide a single line with all complete flags.
#           extra_link_args = -lgfortran
#

# Defaults
# ========
# The settings given here will apply to all other sections if not overridden.
# This is a good place to add general library and include directories like
# /usr/local/{lib,include}
#
#[ALL]
#library_dirs = /usr/local/lib
#include_dirs = /usr/local/include
#

# ATLAS
# -----
# ATLAS is an open source optimized implementation of the BLAS and LAPACK
# routines. NumPy will try to build against ATLAS by default when available in
# the system library dirs. To build NumPy against a custom installation of
# ATLAS you can add an explicit section such as the following. Here we assume
# that ATLAS was configured with ``prefix=/opt/atlas``.
#
# [atlas]
# library_dirs = /opt/atlas/lib
# include_dirs = /opt/atlas/include

# OpenBLAS
# --------
# OpenBLAS is another open source optimized implementation of BLAS and LAPACK
# and can be seen as an alternative to ATLAS. To build NumPy against OpenBLAS
# instead of ATLAS, use this section instead of the above, adjusting as needed
# for your configuration (in the following example we installed OpenBLAS with
# ``make install PREFIX=/opt/OpenBLAS``.
# OpenBLAS is generically installed as a shared library, to force the OpenBLAS
# library linked to also be used at runtime you can utilize the 
# runtime_library_dirs variable.
#
# **Warning**: OpenBLAS, by default, is built in multithreaded mode. Due to the
# way Python's multiprocessing is implemented, a multithreaded OpenBLAS can
# cause programs using both to hang as soon as a worker process is forked on
# POSIX systems (Linux, Mac).
# This is fixed in OpenBLAS 0.2.9 for the pthread build, the OpenMP build using
# GNU openmp is as of gcc-4.9 not fixed yet.
# Python 3.4 will introduce a new feature in multiprocessing, called the
# "forkserver", which solves this problem. For older versions, make sure
# OpenBLAS is built using pthreads or use Python threads instead of
# multiprocessing.
# (This problem does not exist with multithreaded ATLAS.)
#
# https://docs.python.org/library/multiprocessing.html#contexts-and-start-methods
# https://github.com/xianyi/OpenBLAS/issues/294
#
# [openblas]
# libraries = openblas
# library_dirs = /opt/OpenBLAS/lib
# include_dirs = /opt/OpenBLAS/include
# runtime_library_dirs = /opt/OpenBLAS/lib

# BLIS
# ----
# BLIS (https://github.com/flame/blis) also provides a BLAS interface.  It's a
# relatively new library, its performance in some cases seems to match that of
# MKL and OpenBLAS, but it hasn't been benchmarked with NumPy or SciPy yet.
#
# Notes on compiling BLIS itself:
#   - the CBLAS interface (needed by NumPy) isn't built by default; define
#     BLIS_ENABLE_CBLAS to build it.
#   - ``./configure auto`` doesn't support 32-bit builds, see gh-7294 for
#     details.
# Notes on compiling NumPy against BLIS:
#   - ``include_dirs`` below should be the directory where the BLIS cblas.h
#     header is installed.
#
# [blis]
# libraries = blis
# library_dirs = /home/username/blis/lib
# include_dirs = /home/username/blis/include/blis
# runtime_library_dirs = /home/username/blis/lib

# MKL
#---- 
# Intel MKL is Intel's very optimized yet proprietary implementation of BLAS and 
# LAPACK. Find the latest info on building NumPy with Intel MKL in this article:
# https://software.intel.com/en-us/articles/numpyscipy-with-intel-mkl
# Assuming you installed the mkl in /opt/intel/compilers_and_libraries_2018/linux/mkl, 
# for 64 bits code at Linux: 
# [mkl] 
# library_dirs = /opt/intel/compilers_and_libraries_2018/linux/mkl/lib/intel64
# include_dirs = /opt/intel/compilers_and_libraries_2018/linux/mkl/include 
# libraries = mkl_rt
# 
# For 32 bit code at Linux: 
# [mkl]
# library_dirs = /opt/intel/compilers_and_libraries_2018/linux/mkl/lib/ia32
# include_dirs = /opt/intel/compilers_and_libraries_2018/linux/mkl/include 
# libraries = mkl_rt
# 
# On win-64, the following options compiles NumPy with the MKL library 
# dynamically linked. 
# [mkl] 
# include_dirs = C:\Program Files (x86)\IntelSWTools\compilers_and_libraries_2018\windows\mkl\include
# library_dirs = C:\Program Files (x86)\IntelSWTools\compilers_and_libraries_2018\windows\mkl\lib\intel64
# libraries = mkl_rt

# Accelerate
# ----------
# Accelerate/vecLib is an OSX framework providing a BLAS and LAPACK implementation.
#
# [accelerate]
# libraries = Accelerate, vecLib
# #libraries = None

# UMFPACK
# -------
# The UMFPACK library is used in scikits.umfpack to factor large sparse matrices. 
# It, in turn, depends on the AMD library for reordering the matrices for
# better performance.  Note that the AMD library has nothing to do with AMD
# (Advanced Micro Devices), the CPU company.
#
# UMFPACK is not used by NumPy.
#
#   https://www.cise.ufl.edu/research/sparse/umfpack/
#   https://www.cise.ufl.edu/research/sparse/amd/
#   https://scikit-umfpack.github.io/scikit-umfpack/
#
#[amd]
#libraries = amd
#
#[umfpack]
#libraries = umfpack

# FFT libraries
# -------------
# There are two FFT libraries that we can configure here: FFTW (2 and 3) and djbfft.
# Note that these libraries are not used by NumPy or SciPy.
#
#   http://fftw.org/
#   https://cr.yp.to/djbfft.html
#
# Given only this section, numpy.distutils will try to figure out which version
# of FFTW you are using.
#[fftw]
#libraries = fftw3
#
# For djbfft, numpy.distutils will look for either djbfft.a or libdjbfft.a . 
#[djbfft]
#include_dirs = /usr/local/djbfft/include
#library_dirs = /usr/local/djbfft/lib