| # 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 |