site.cfg.example - external/github.com/numpy/numpy - Git at Google

 # 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
 #----
 # MKL is Intel's very optimized yet proprietary implementation of BLAS and
 # Lapack.
 # For recent (9.0.21, for example) mkl, you need to change the names of the
 # lapack library. Assuming you installed the mkl in /opt, for a 32 bits cpu:
 # [mkl]
 # library_dirs = /opt/intel/mkl/9.1.023/lib/32/
 # lapack_libs = mkl_lapack
 #
 # For 10.*, on 32 bits machines:
 # [mkl]
 # library_dirs = /opt/intel/mkl/10.0.1.014/lib/32/
 # lapack_libs = mkl_lapack
 # mkl_libs = mkl, guide
 #
 # On win-64, the following options compiles numpy with the MKL library
 # dynamically linked.
 # [mkl]
 # include_dirs = C:\Program Files (x86)\Intel\Composer XE 2015\mkl\include
 # library_dirs = C:\Program Files (x86)\Intel\Composer XE 2015\mkl\lib\intel64
 # mkl_libs = mkl_core_dll, mkl_intel_lp64_dll, mkl_intel_thread_dll
 # lapack_libs = mkl_lapack95_lp64


 # 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
	# 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
	#----
	# MKL is Intel's very optimized yet proprietary implementation of BLAS and
	# Lapack.
	# For recent (9.0.21, for example) mkl, you need to change the names of the
	# lapack library. Assuming you installed the mkl in /opt, for a 32 bits cpu:
	# [mkl]
	# library_dirs = /opt/intel/mkl/9.1.023/lib/32/
	# lapack_libs = mkl_lapack
	#
	# For 10.*, on 32 bits machines:
	# [mkl]
	# library_dirs = /opt/intel/mkl/10.0.1.014/lib/32/
	# lapack_libs = mkl_lapack
	# mkl_libs = mkl, guide
	#
	# On win-64, the following options compiles numpy with the MKL library
	# dynamically linked.
	# [mkl]
	# include_dirs = C:\Program Files (x86)\Intel\Composer XE 2015\mkl\include
	# library_dirs = C:\Program Files (x86)\Intel\Composer XE 2015\mkl\lib\intel64
	# mkl_libs = mkl_core_dll, mkl_intel_lp64_dll, mkl_intel_thread_dll
	# lapack_libs = mkl_lapack95_lp64


	# 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