FM / numpy

/opt/alt/python37/lib64/python3.7/site-packages/numpy/

/opt/alt/python37/lib64/python3.7/site-packages/numpy

Name	Size	Mode	Actions
compat/	-	0755	rm
core/	-	0755	rm
distutils/	-	0755	rm
doc/	-	0755	rm
fft/	-	0755	rm
lib/	-	0755	rm
linalg/	-	0755	rm
ma/	-	0755	rm
matrixlib/	-	0755	rm
polynomial/	-	0755	rm
random/	-	0755	rm
testing/	-	0755	rm
tests/	-	0755	rm
__pycache__/	-	0755	rm
add_newdocs.py	228205	0644	edit dl rm
ctypeslib.py	14669	0644	edit dl rm
dual.py	1864	0644	edit dl rm
matlib.py	9584	0644	edit dl rm
setup.py	898	0644	edit dl rm
version.py	261	0644	edit dl rm
_distributor_init.py	331	0644	edit dl rm
_globals.py	1851	0644	edit dl rm
_import_tools.py	13256	0644	edit dl rm
__config__.py	1782	0644	edit dl rm
__init__.py	6274	0644	edit dl rm

Edit: /opt/alt/python37/lib64/python3.7/site-packages/numpy/dual.py (1864B)

"""
Aliases for functions which may be accelerated by Scipy.

Scipy_ can be built to use accelerated or otherwise improved libraries
for FFTs, linear algebra, and special functions. This module allows
developers to transparently support these accelerated functions when
scipy is available but still support users who have only installed
NumPy.

.. _Scipy : http://www.scipy.org

"""
from __future__ import division, absolute_import, print_function

# This module should be used for functions both in numpy and scipy if
#  you want to use the numpy version if available but the scipy version
#  otherwise.
#  Usage  --- from numpy.dual import fft, inv

__all__ = ['fft', 'ifft', 'fftn', 'ifftn', 'fft2', 'ifft2',
           'norm', 'inv', 'svd', 'solve', 'det', 'eig', 'eigvals',
           'eigh', 'eigvalsh', 'lstsq', 'pinv', 'cholesky', 'i0']

import numpy.linalg as linpkg
import numpy.fft as fftpkg
from numpy.lib import i0
import sys

fft = fftpkg.fft
ifft = fftpkg.ifft
fftn = fftpkg.fftn
ifftn = fftpkg.ifftn
fft2 = fftpkg.fft2
ifft2 = fftpkg.ifft2

norm = linpkg.norm
inv = linpkg.inv
svd = linpkg.svd
solve = linpkg.solve
det = linpkg.det
eig = linpkg.eig
eigvals = linpkg.eigvals
eigh = linpkg.eigh
eigvalsh = linpkg.eigvalsh
lstsq = linpkg.lstsq
pinv = linpkg.pinv
cholesky = linpkg.cholesky

_restore_dict = {}

def register_func(name, func):
    if name not in __all__:
        raise ValueError("%s not a dual function." % name)
    f = sys._getframe(0).f_globals
    _restore_dict[name] = f[name]
    f[name] = func

def restore_func(name):
    if name not in __all__:
        raise ValueError("%s not a dual function." % name)
    try:
        val = _restore_dict[name]
    except KeyError:
        return
    else:
        sys._getframe(0).f_globals[name] = val

def restore_all():
    for name in _restore_dict.keys():
        restore_func(name)

""" Aliases for functions which may be accelerated by Scipy. Scipy_ can be built to use accelerated or otherwise improved libraries for FFTs, linear algebra, and special functions. This module allows developers to transparently support these accelerated functions when scipy is available but still support users who have only installed NumPy. .. _Scipy : http://www.scipy.org """ from __future__ import division, absolute_import, print_function # This module should be used for functions both in numpy and scipy if # you want to use the numpy version if available but the scipy version # otherwise. # Usage --- from numpy.dual import fft, inv __all__ = ['fft', 'ifft', 'fftn', 'ifftn', 'fft2', 'ifft2', 'norm', 'inv', 'svd', 'solve', 'det', 'eig', 'eigvals', 'eigh', 'eigvalsh', 'lstsq', 'pinv', 'cholesky', 'i0'] import numpy.linalg as linpkg import numpy.fft as fftpkg from numpy.lib import i0 import sys fft = fftpkg.fft ifft = fftpkg.ifft fftn = fftpkg.fftn ifftn = fftpkg.ifftn fft2 = fftpkg.fft2 ifft2 = fftpkg.ifft2 norm = linpkg.norm inv = linpkg.inv svd = linpkg.svd solve = linpkg.solve det = linpkg.det eig = linpkg.eig eigvals = linpkg.eigvals eigh = linpkg.eigh eigvalsh = linpkg.eigvalsh lstsq = linpkg.lstsq pinv = linpkg.pinv cholesky = linpkg.cholesky _restore_dict = {} def register_func(name, func): if name not in __all__: raise ValueError("%s not a dual function." % name) f = sys._getframe(0).f_globals _restore_dict[name] = f[name] f[name] = func def restore_func(name): if name not in __all__: raise ValueError("%s not a dual function." % name) try: val = _restore_dict[name] except KeyError: return else: sys._getframe(0).f_globals[name] = val def restore_all(): for name in _restore_dict.keys(): restore_func(name)