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 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.
#
#   http://www.python.org/doc/current/lib/module-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 earch 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

# 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}
#
#[DEFAULT]
#library_dirs = /usr/local/lib
#include_dirs = /usr/local/include

# Optimized BLAS and LAPACK
# -------------------------
# Use the blas_opt and lapack_opt sections to give any settings that are
# required to link against your chosen BLAS and LAPACK, including the regular
# FORTRAN reference BLAS and also ATLAS. Some other sections still exist for
# linking against certain optimized libraries (e.g. [atlas], [lapack_atlas]),
# however, they are now deprecated and should not be used.
#
# These are typical configurations for ATLAS (assuming that the library and
# include directories have already been set in [DEFAULT]; the include directory
# is important for the BLAS C interface):
#
#[blas_opt]
#libraries = f77blas, cblas, atlas
#
#[lapack_opt]
#libraries = lapack, f77blas, cblas, atlas
#
# If your ATLAS was compiled with pthreads, the names of the libraries might be
# different:
#
#[blas_opt]
#libraries = ptf77blas, ptcblas, atlas
#
#[lapack_opt]
#libraries = lapack, ptf77blas, ptcblas, atlas


# 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 needed for numpy or scipy.
#
#   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 needed 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
 
 
# MKL
#----
# 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