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Code Editor : config.py

# This file is generated by numpy's setup.py
# It contains system_info results at the time of building this package.
__all__ = ["get_info","show"]

import os
import sys

extra_dll_dir = os.path.join(os.path.dirname(__file__), '.libs')

if sys.platform == 'win32' and os.path.isdir(extra_dll_dir):
    if sys.version_info >= (3, 8):
        os.add_dll_directory(extra_dll_dir)
    else:
        os.environ.setdefault('PATH', '')
        os.environ['PATH'] += os.pathsep + extra_dll_dir

blas_mkl_info={}
blis_info={}
openblas_info={}
accelerate_info={}
atlas_3_10_blas_threads_info={}
atlas_3_10_blas_info={}
atlas_blas_threads_info={}
atlas_blas_info={}
blas_info={'libraries': ['blas', 'blas'], 'library_dirs': ['/usr/lib/x86_64-linux-gnu'], 'include_dirs': ['/usr/local/include', '/usr/include'], 'language': 'c', 'define_macros': [('HAVE_CBLAS', None)]}
blas_opt_info={'define_macros': [('NO_ATLAS_INFO', 1), ('HAVE_CBLAS', None)], 'libraries': ['blas', 'blas'], 'library_dirs': ['/usr/lib/x86_64-linux-gnu'], 'include_dirs': ['/usr/local/include', '/usr/include'], 'language': 'c'}
lapack_mkl_info={}
openblas_lapack_info={}
openblas_clapack_info={}
flame_info={}
atlas_3_10_threads_info={}
atlas_3_10_info={}
atlas_threads_info={}
atlas_info={}
lapack_info={'libraries': ['lapack', 'lapack'], 'library_dirs': ['/usr/lib/x86_64-linux-gnu'], 'language': 'f77'}
lapack_opt_info={'libraries': ['lapack', 'lapack', 'blas', 'blas'], 'library_dirs': ['/usr/lib/x86_64-linux-gnu'], 'language': 'c', 'define_macros': [('NO_ATLAS_INFO', 1), ('HAVE_CBLAS', None)], 'include_dirs': ['/usr/local/include', '/usr/include']}

def get_info(name):
    g = globals()
    return g.get(name, g.get(name + "_info", {}))

def show():
    """
    Show libraries in the system on which NumPy was built.

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
    -----
    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

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)))

Urban Research Lab

Urban Research Lab

The Urban Research Lab (URL) at IIIT-Delhi was established in 2020, during a time when the pandemic brought the complexities of urban life more visible than ever. What started as an initiative by faculty and students has grown into a collaborative space for critically exploring and reimagining the urban questions through research, dialogue, and interdisciplinary engagement.