shell bypass 403
UnknownSec Shell
:
/
proc
/
self
/
root
/
opt
/
cloudlinux
/
venv
/
lib64
/
python3.11
/
site-packages
/
numpy
/ [
drwxr-xr-x
]
upload
mass deface
mass delete
console
info server
name :
__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): os.add_dll_directory(extra_dll_dir) openblas64__info={'libraries': ['openblas64_', 'openblas64_'], 'library_dirs': ['/usr/local/lib'], 'language': 'c', 'define_macros': [('HAVE_CBLAS', None), ('BLAS_SYMBOL_SUFFIX', '64_'), ('HAVE_BLAS_ILP64', None)], 'runtime_library_dirs': ['/usr/local/lib']} blas_ilp64_opt_info={'libraries': ['openblas64_', 'openblas64_'], 'library_dirs': ['/usr/local/lib'], 'language': 'c', 'define_macros': [('HAVE_CBLAS', None), ('BLAS_SYMBOL_SUFFIX', '64_'), ('HAVE_BLAS_ILP64', None)], 'runtime_library_dirs': ['/usr/local/lib']} openblas64__lapack_info={'libraries': ['openblas64_', 'openblas64_'], 'library_dirs': ['/usr/local/lib'], 'language': 'c', 'define_macros': [('HAVE_CBLAS', None), ('BLAS_SYMBOL_SUFFIX', '64_'), ('HAVE_BLAS_ILP64', None), ('HAVE_LAPACKE', None)], 'runtime_library_dirs': ['/usr/local/lib']} lapack_ilp64_opt_info={'libraries': ['openblas64_', 'openblas64_'], 'library_dirs': ['/usr/local/lib'], 'language': 'c', 'define_macros': [('HAVE_CBLAS', None), ('BLAS_SYMBOL_SUFFIX', '64_'), ('HAVE_BLAS_ILP64', None), ('HAVE_LAPACKE', None)], 'runtime_library_dirs': ['/usr/local/lib']} 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 ----- 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)))
© 2025 UnknownSec