a02aeb236c
Former-commit-id:06a8b51d6d[formerly 64fa9254b946eae7e61bbc3f513b7c3696c4f54f] Former-commit-id:9f19e3f712
135 lines
4.9 KiB
Text
Executable file
135 lines
4.9 KiB
Text
Executable file
# This file provides configuration information about non-Python dependencies for
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# numpy.distutils-using packages. Create a file like this called "site.cfg" next
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# to your package's setup.py file and fill in the appropriate sections. Not all
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# packages will use all sections so you should leave out sections that your
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# package does not use.
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# To assist automatic installation like easy_install, the user's home directory
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# will also be checked for the file ~/.numpy-site.cfg .
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# The format of the file is that of the standard library's ConfigParser module.
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#
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# http://www.python.org/doc/current/lib/module-ConfigParser.html
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#
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# Each section defines settings that apply to one particular dependency. Some of
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# the settings are general and apply to nearly any section and are defined here.
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# Settings specific to a particular section will be defined near their section.
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#
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# libraries
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# Comma-separated list of library names to add to compile the extension
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# with. Note that these should be just the names, not the filenames. For
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# example, the file "libfoo.so" would become simply "foo".
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# libraries = lapack,f77blas,cblas,atlas
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#
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# library_dirs
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# List of directories to add to the library search path when compiling
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# extensions with this dependency. Use the character given by os.pathsep
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# to separate the items in the list. On UN*X-type systems (Linux, FreeBSD,
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# OS X):
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# library_dirs = /usr/lib:/usr/local/lib
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# On Windows:
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# library_dirs = c:\mingw\lib,c:\atlas\lib
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#
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# include_dirs
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# List of directories to add to the header file earch path.
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# include_dirs = /usr/include:/usr/local/include
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#
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# src_dirs
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# List of directories that contain extracted source code for the
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# dependency. For some dependencies, numpy.distutils will be able to build
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# them from source if binaries cannot be found. The FORTRAN BLAS and
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# LAPACK libraries are one example. However, most dependencies are more
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# complicated and require actual installation that you need to do
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# yourself.
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# src_dirs = /home/rkern/src/BLAS_SRC:/home/rkern/src/LAPACK_SRC
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#
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# search_static_first
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# Boolean (one of (0, false, no, off) for False or (1, true, yes, on) for
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# True) to tell numpy.distutils to prefer static libraries (.a) over
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# shared libraries (.so). It is turned off by default.
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# search_static_first = false
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# Defaults
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# ========
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# The settings given here will apply to all other sections if not overridden.
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# This is a good place to add general library and include directories like
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# /usr/local/{lib,include}
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#
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#[DEFAULT]
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#library_dirs = /usr/local/lib
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#include_dirs = /usr/local/include
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# Optimized BLAS and LAPACK
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# -------------------------
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# Use the blas_opt and lapack_opt sections to give any settings that are
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# required to link against your chosen BLAS and LAPACK, including the regular
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# FORTRAN reference BLAS and also ATLAS. Some other sections still exist for
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# linking against certain optimized libraries (e.g. [atlas], [lapack_atlas]),
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# however, they are now deprecated and should not be used.
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#
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# These are typical configurations for ATLAS (assuming that the library and
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# include directories have already been set in [DEFAULT]; the include directory
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# is important for the BLAS C interface):
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#
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#[blas_opt]
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#libraries = f77blas, cblas, atlas
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#
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#[lapack_opt]
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#libraries = lapack, f77blas, cblas, atlas
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#
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# If your ATLAS was compiled with pthreads, the names of the libraries might be
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# different:
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#
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#[blas_opt]
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#libraries = ptf77blas, ptcblas, atlas
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#
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#[lapack_opt]
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#libraries = lapack, ptf77blas, ptcblas, atlas
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# UMFPACK
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# -------
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# The UMFPACK library is used to factor large sparse matrices. It, in turn,
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# depends on the AMD library for reordering the matrices for better performance.
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# Note that the AMD library has nothing to do with AMD (Advanced Micro Devices),
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# the CPU company.
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#
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# http://www.cise.ufl.edu/research/sparse/umfpack/
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# http://www.cise.ufl.edu/research/sparse/amd/
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#
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#[amd]
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#amd_libs = amd
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#
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#[umfpack]
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#umfpack_libs = umfpack
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# FFT libraries
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# -------------
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# There are two FFT libraries that we can configure here: FFTW (2 and 3) and djbfft.
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#
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# http://fftw.org/
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# http://cr.yp.to/djbfft.html
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#
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# Given only this section, numpy.distutils will try to figure out which version
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# of FFTW you are using.
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#[fftw]
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#libraries = fftw3
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#
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# For djbfft, numpy.distutils will look for either djbfft.a or libdjbfft.a .
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#[djbfft]
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#include_dirs = /usr/local/djbfft/include
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#library_dirs = /usr/local/djbfft/lib
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# MKL
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#----
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# For recent (9.0.21, for example) mkl, you need to change the names of the
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# lapack library. Assuming you installed the mkl in /opt, for a 32 bits cpu:
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# [mkl]
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# library_dirs = /opt/intel/mkl/9.1.023/lib/32/
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# lapack_libs = mkl_lapack
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#
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# For 10.*, on 32 bits machines:
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# [mkl]
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# library_dirs = /opt/intel/mkl/10.0.1.014/lib/32/
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# lapack_libs = mkl_lapack
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# mkl_libs = mkl, guide
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