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133 lines
5.1 KiB
133 lines
5.1 KiB
# This file is generated by numpy's setup.py
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# It contains system_info results at the time of building this package.
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__all__ = ["get_info","show"]
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import os
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import sys
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extra_dll_dir = os.path.join(os.path.dirname(__file__), '.libs')
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if sys.platform == 'win32' and os.path.isdir(extra_dll_dir):
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os.add_dll_directory(extra_dll_dir)
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blas_armpl_info={}
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blas_mkl_info={}
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blas_ssl2_info={}
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blis_info={}
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openblas_info={}
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accelerate_info={}
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atlas_3_10_blas_threads_info={}
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atlas_3_10_blas_info={}
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atlas_blas_threads_info={}
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atlas_blas_info={'language': 'c', 'define_macros': [('HAVE_CBLAS', None), ('NO_ATLAS_INFO', -1)], 'libraries': ['f77blas', 'cblas', 'atlas', 'f77blas', 'cblas'], 'library_dirs': ['/usr/lib/arm-linux-gnueabihf']}
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blas_opt_info={'language': 'c', 'define_macros': [('HAVE_CBLAS', None), ('NO_ATLAS_INFO', -1)], 'libraries': ['f77blas', 'cblas', 'atlas', 'f77blas', 'cblas'], 'library_dirs': ['/usr/lib/arm-linux-gnueabihf']}
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lapack_armpl_info={}
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lapack_mkl_info={}
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lapack_ssl2_info={}
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openblas_lapack_info={}
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openblas_clapack_info={}
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flame_info={}
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atlas_3_10_threads_info={}
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atlas_3_10_info={}
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atlas_threads_info={}
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atlas_info={'language': 'f77', 'libraries': ['lapack', 'f77blas', 'cblas', 'atlas', 'f77blas', 'cblas'], 'library_dirs': ['/usr/lib/arm-linux-gnueabihf'], 'define_macros': [('NO_ATLAS_INFO', -1)]}
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lapack_opt_info={'language': 'f77', 'libraries': ['lapack', 'f77blas', 'cblas', 'atlas', 'f77blas', 'cblas'], 'library_dirs': ['/usr/lib/arm-linux-gnueabihf'], 'define_macros': [('NO_ATLAS_INFO', -1)]}
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def get_info(name):
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g = globals()
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return g.get(name, g.get(name + "_info", {}))
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def show():
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"""
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Show libraries in the system on which NumPy was built.
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Print information about various resources (libraries, library
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directories, include directories, etc.) in the system on which
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NumPy was built.
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See Also
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--------
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get_include : Returns the directory containing NumPy C
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header files.
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Notes
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-----
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1. Classes specifying the information to be printed are defined
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in the `numpy.distutils.system_info` module.
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Information may include:
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* ``language``: language used to write the libraries (mostly
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C or f77)
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* ``libraries``: names of libraries found in the system
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* ``library_dirs``: directories containing the libraries
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* ``include_dirs``: directories containing library header files
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* ``src_dirs``: directories containing library source files
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* ``define_macros``: preprocessor macros used by
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``distutils.setup``
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* ``baseline``: minimum CPU features required
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* ``found``: dispatched features supported in the system
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* ``not found``: dispatched features that are not supported
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in the system
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2. NumPy BLAS/LAPACK Installation Notes
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Installing a numpy wheel (``pip install numpy`` or force it
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via ``pip install numpy --only-binary :numpy: numpy``) includes
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an OpenBLAS implementation of the BLAS and LAPACK linear algebra
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APIs. In this case, ``library_dirs`` reports the original build
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time configuration as compiled with gcc/gfortran; at run time
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the OpenBLAS library is in
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``site-packages/numpy.libs/`` (linux), or
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``site-packages/numpy/.dylibs/`` (macOS), or
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``site-packages/numpy/.libs/`` (windows).
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Installing numpy from source
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(``pip install numpy --no-binary numpy``) searches for BLAS and
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LAPACK dynamic link libraries at build time as influenced by
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environment variables NPY_BLAS_LIBS, NPY_CBLAS_LIBS, and
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NPY_LAPACK_LIBS; or NPY_BLAS_ORDER and NPY_LAPACK_ORDER;
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or the optional file ``~/.numpy-site.cfg``.
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NumPy remembers those locations and expects to load the same
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libraries at run-time.
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In NumPy 1.21+ on macOS, 'accelerate' (Apple's Accelerate BLAS
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library) is in the default build-time search order after
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'openblas'.
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Examples
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--------
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>>> import numpy as np
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>>> np.show_config()
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blas_opt_info:
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language = c
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define_macros = [('HAVE_CBLAS', None)]
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libraries = ['openblas', 'openblas']
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library_dirs = ['/usr/local/lib']
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"""
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from numpy.core._multiarray_umath import (
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__cpu_features__, __cpu_baseline__, __cpu_dispatch__
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)
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for name,info_dict in globals().items():
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if name[0] == "_" or type(info_dict) is not type({}): continue
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print(name + ":")
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if not info_dict:
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print(" NOT AVAILABLE")
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for k,v in info_dict.items():
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v = str(v)
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if k == "sources" and len(v) > 200:
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v = v[:60] + " ...\n... " + v[-60:]
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print(" %s = %s" % (k,v))
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features_found, features_not_found = [], []
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for feature in __cpu_dispatch__:
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if __cpu_features__[feature]:
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features_found.append(feature)
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else:
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features_not_found.append(feature)
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print("Supported SIMD extensions in this NumPy install:")
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print(" baseline = %s" % (','.join(__cpu_baseline__)))
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print(" found = %s" % (','.join(features_found)))
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print(" not found = %s" % (','.join(features_not_found)))
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