# 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 easy_install, 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, RawConfigParser class being used to load it.

#

#   http://docs.python.org/3/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

#

#   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/rkern/src/BLAS_SRC:/home/rkern/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.

#       Simple variable with no parsing done. 

#       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.

#       Simple variable with no parsing done. 

#       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.)

#

# http://docs.python.org/3.4/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 

# mkl_libs = mkl_rt 

# lapack_libs =  

# 

# 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 

# mkl_libs = mkl_rt 

# lapack_libs =  

# 

# 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

# mkl_libs = mkl_rt 

# lapack_libs =

# 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.

#

#   http://www.cise.ufl.edu/research/sparse/umfpack/

#   http://www.cise.ufl.edu/research/sparse/amd/

#   http://scikits.appspot.com/umfpack

#

#[amd]

#amd_libs = amd

#

#[umfpack]

#umfpack_libs = 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 for numpy or scipy.

#

#   http://fftw.org/

#   http://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