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                """
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                Print information about various resources (libraries, library
                directories, include directories, etc.) in the system on which
                NumPy was built.

                See Also
                --------
                get_include : Returns the directory containing NumPy C
                              header files.

                Notes
                -----
                1. Classes specifying the information to be printed are defined
                   in the `numpy.distutils.system_info` module.

                   Information may include:

                   * ``language``: language used to write the libraries (mostly
                     C or f77)
                   * ``libraries``: names of libraries found in the system
                   * ``library_dirs``: directories containing the libraries
                   * ``include_dirs``: directories containing library header files
                   * ``src_dirs``: directories containing library source files
                   * ``define_macros``: preprocessor macros used by
                     ``distutils.setup``
                   * ``baseline``: minimum CPU features required
                   * ``found``: dispatched features supported in the system
                   * ``not found``: dispatched features that are not supported
                     in the system

                2. NumPy BLAS/LAPACK Installation Notes

                   Installing a numpy wheel (``pip install numpy`` or force it
                   via ``pip install numpy --only-binary :numpy: numpy``) includes
                   an OpenBLAS implementation of the BLAS and LAPACK linear algebra
                   APIs. In this case, ``library_dirs`` reports the original build
                   time configuration as compiled with gcc/gfortran; at run time
                   the OpenBLAS library is in
                   ``site-packages/numpy.libs/`` (linux), or
                   ``site-packages/numpy/.dylibs/`` (macOS), or
                   ``site-packages/numpy/.libs/`` (windows).

                   Installing numpy from source
                   (``pip install numpy --no-binary numpy``) searches for BLAS and
                   LAPACK dynamic link libraries at build time as influenced by
                   environment variables NPY_BLAS_LIBS, NPY_CBLAS_LIBS, and
                   NPY_LAPACK_LIBS; or NPY_BLAS_ORDER and NPY_LAPACK_ORDER;
                   or the optional file ``~/.numpy-site.cfg``.
                   NumPy remembers those locations and expects to load the same
                   libraries at run-time.
                   In NumPy 1.21+ on macOS, 'accelerate' (Apple's Accelerate BLAS
                   library) is in the default build-time search order after
                   'openblas'.

                Examples
                --------
                >>> import numpy as np
                >>> np.show_config()
                blas_opt_info:
                    language = c
                    define_macros = [('HAVE_CBLAS', None)]
                    libraries = ['openblas', 'openblas']
                    library_dirs = ['/usr/local/lib']
                """
                from numpy.core._multiarray_umath import (
                    __cpu_features__, __cpu_baseline__, __cpu_dispatch__
                )
                for name,info_dict in globals().items():
                    if name[0] == "_" or type(info_dict) is not type({}): continue
                    print(name + ":")
                    if not info_dict:
                        print("  NOT AVAILABLE")
                    for k,v in info_dict.items():
                        v = str(v)
                        if k == "sources" and len(v) > 200:
                            v = v[:60] + " ...\n... " + v[-60:]
                        print("    %s = %s" % (k,v))

                features_found, features_not_found = [], []
                for feature in __cpu_dispatch__:
                    if __cpu_features__[feature]:
                        features_found.append(feature)
                    else:
                        features_not_found.append(feature)

                print("Supported SIMD extensions in this NumPy install:")
                print("    baseline = %s" % (','.join(__cpu_baseline__)))
                print("    found = %s" % (','.join(features_found)))
                print("    not found = %s" % (','.join(features_not_found)))

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