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Diffstat (limited to 'community/py3-numpy/site.cfg')
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diff --git a/community/py3-numpy/site.cfg b/community/py3-numpy/site.cfg new file mode 100644 index 0000000000..effb46f110 --- /dev/null +++ b/community/py3-numpy/site.cfg @@ -0,0 +1,157 @@ +# 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 + +# 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``. +# +# **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 = /usr/lib +include_dirs = /usr/include + +# 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 + +# 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 |