show()

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

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'.

Show libraries in the system on which NumPy was built.

See Also

get_include

Returns the directory containing NumPy C header files.

Examples

This example is valid syntax, but we were not able to check execution

>>> 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']

See :

Local connectivity graph

Hover to see nodes names; edges to Self not shown, Caped at 50 nodes.

Using a canvas is more power efficient and can get hundred of nodes ; but does not allow hyperlinks; , arrows or text (beyond on hover)

SVG is more flexible but power hungry; and does not scale well to 50 + nodes.

All aboves nodes referred to, (or are referred from) current nodes; Edges from Self to other have been omitted (or all nodes would be connected to the central node "self" which is not useful). Nodes are colored by the library they belong to, and scaled with the number of references pointing them

---

GitHub : /numpy/distutils/__config__.py#27
type: <class 'function'>
Commit: