Created starter files for the project.

This commit is contained in:
Batuhan Berk Başoğlu 2020-10-02 21:26:03 -04:00
commit 73f0c0db42
1992 changed files with 769897 additions and 0 deletions

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# Default ignored files
/shelf/
/workspace.xml

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<?xml version="1.0" encoding="UTF-8"?>
<module type="PYTHON_MODULE" version="4">
<component name="NewModuleRootManager">
<content url="file://$MODULE_DIR$">
<excludeFolder url="file://$MODULE_DIR$/venv" />
</content>
<orderEntry type="inheritedJdk" />
<orderEntry type="sourceFolder" forTests="false" />
</component>
</module>

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<component name="InspectionProjectProfileManager">
<settings>
<option name="USE_PROJECT_PROFILE" value="false" />
<version value="1.0" />
</settings>
</component>

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<?xml version="1.0" encoding="UTF-8"?>
<project version="4">
<component name="ProjectRootManager" version="2" project-jdk-name="Python 3.6 (Vehicle Anti-Theft Face Recognition System)" project-jdk-type="Python SDK" />
</project>

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<?xml version="1.0" encoding="UTF-8"?>
<project version="4">
<component name="ProjectModuleManager">
<modules>
<module fileurl="file://$PROJECT_DIR$/.idea/Vehicle Anti-Theft Face Recognition System.iml" filepath="$PROJECT_DIR$/.idea/Vehicle Anti-Theft Face Recognition System.iml" />
</modules>
</component>
</project>

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<?xml version="1.0" encoding="UTF-8"?>
<project version="4">
<component name="VcsDirectoryMappings">
<mapping directory="$PROJECT_DIR$" vcs="Git" />
</component>
</project>

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# This is a sample Python script.
# Press Shift+F10 to execute it or replace it with your code.
# Press Double Shift to search everywhere for classes, files, tool windows, actions, and settings.
def print_hi(name):
# Use a breakpoint in the code line below to debug your script.
print(f'Hi, {name}') # Press Ctrl+F8 to toggle the breakpoint.
# Press the green button in the gutter to run the script.
if __name__ == '__main__':
print_hi('PyCharm')
# See PyCharm help at https://www.jetbrains.com/help/pycharm/

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import sys
import os
import re
import importlib
import warnings
is_pypy = '__pypy__' in sys.builtin_module_names
def warn_distutils_present():
if 'distutils' not in sys.modules:
return
if is_pypy and sys.version_info < (3, 7):
# PyPy for 3.6 unconditionally imports distutils, so bypass the warning
# https://foss.heptapod.net/pypy/pypy/-/blob/be829135bc0d758997b3566062999ee8b23872b4/lib-python/3/site.py#L250
return
warnings.warn(
"Distutils was imported before Setuptools, but importing Setuptools "
"also replaces the `distutils` module in `sys.modules`. This may lead "
"to undesirable behaviors or errors. To avoid these issues, avoid "
"using distutils directly, ensure that setuptools is installed in the "
"traditional way (e.g. not an editable install), and/or make sure "
"that setuptools is always imported before distutils.")
def clear_distutils():
if 'distutils' not in sys.modules:
return
warnings.warn("Setuptools is replacing distutils.")
mods = [name for name in sys.modules if re.match(r'distutils\b', name)]
for name in mods:
del sys.modules[name]
def enabled():
"""
Allow selection of distutils by environment variable.
"""
which = os.environ.get('SETUPTOOLS_USE_DISTUTILS', 'stdlib')
return which == 'local'
def ensure_local_distutils():
clear_distutils()
distutils = importlib.import_module('setuptools._distutils')
distutils.__name__ = 'distutils'
sys.modules['distutils'] = distutils
# sanity check that submodules load as expected
core = importlib.import_module('distutils.core')
assert '_distutils' in core.__file__, core.__file__
def do_override():
"""
Ensure that the local copy of distutils is preferred over stdlib.
See https://github.com/pypa/setuptools/issues/417#issuecomment-392298401
for more motivation.
"""
if enabled():
warn_distutils_present()
ensure_local_distutils()
class DistutilsMetaFinder:
def find_spec(self, fullname, path, target=None):
if path is not None:
return
method_name = 'spec_for_{fullname}'.format(**locals())
method = getattr(self, method_name, lambda: None)
return method()
def spec_for_distutils(self):
import importlib.abc
import importlib.util
class DistutilsLoader(importlib.abc.Loader):
def create_module(self, spec):
return importlib.import_module('setuptools._distutils')
def exec_module(self, module):
pass
return importlib.util.spec_from_loader('distutils', DistutilsLoader())
def spec_for_pip(self):
"""
Ensure stdlib distutils when running under pip.
See pypa/pip#8761 for rationale.
"""
if self.pip_imported_during_build():
return
clear_distutils()
self.spec_for_distutils = lambda: None
@staticmethod
def pip_imported_during_build():
"""
Detect if pip is being imported in a build script. Ref #2355.
"""
import traceback
return any(
frame.f_globals['__file__'].endswith('setup.py')
for frame, line in traceback.walk_stack(None)
)
DISTUTILS_FINDER = DistutilsMetaFinder()
def add_shim():
sys.meta_path.insert(0, DISTUTILS_FINDER)
def remove_shim():
try:
sys.meta_path.remove(DISTUTILS_FINDER)
except ValueError:
pass

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__import__('_distutils_hack').do_override()

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MIT License
Copyright (c) Olli-Pekka Heinisuo
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

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import importlib
import os
import sys
from .cv2 import *
from .data import *
# wildcard import above does not import "private" variables like __version__
# this makes them available
globals().update(importlib.import_module("cv2.cv2").__dict__)
ci_and_not_headless = False
try:
from .version import ci_build, headless
ci_and_not_headless = ci_build and not headless
except:
pass
# the Qt plugin is included currently only in the pre-built wheels
if (
sys.platform == "darwin" or sys.platform.startswith("linux")
) and ci_and_not_headless:
os.environ["QT_QPA_PLATFORM_PLUGIN_PATH"] = os.path.join(
os.path.dirname(os.path.abspath(__file__)), "qt", "plugins"
)
# Qt will throw warning on Linux if fonts are not found
if sys.platform.startswith("linux") and ci_and_not_headless:
os.environ["QT_QPA_FONTDIR"] = os.path.join(
os.path.dirname(os.path.abspath(__file__)), "qt", "fonts"
)

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import os
haarcascades = os.path.join(os.path.dirname(__file__), "")

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opencv_version = "4.4.0.44"
contrib = False
headless = False
ci_build = True

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import os; var = 'SETUPTOOLS_USE_DISTUTILS'; enabled = os.environ.get(var, 'stdlib') == 'local'; enabled and __import__('_distutils_hack').add_shim();

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"""Run the EasyInstall command"""
if __name__ == '__main__':
from setuptools.command.easy_install import main
main()

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pip

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----
This binary distribution of NumPy also bundles the following software:
Name: OpenBLAS
Files: extra-dll\libopenb*.dll
Description: bundled as a dynamically linked library
Availability: https://github.com/xianyi/OpenBLAS/
License: 3-clause BSD
Copyright (c) 2011-2014, The OpenBLAS Project
All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are
met:
1. Redistributions of source code must retain the above copyright
notice, this list of conditions and the following disclaimer.
2. Redistributions in binary form must reproduce the above copyright
notice, this list of conditions and the following disclaimer in
the documentation and/or other materials provided with the
distribution.
3. Neither the name of the OpenBLAS project nor the names of
its contributors may be used to endorse or promote products
derived from this software without specific prior written
permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE
USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
Name: LAPACK
Files: extra-dll\libopenb*.dll
Description: bundled in OpenBLAS
Availability: https://github.com/xianyi/OpenBLAS/
License 3-clause BSD
Copyright (c) 1992-2013 The University of Tennessee and The University
of Tennessee Research Foundation. All rights
reserved.
Copyright (c) 2000-2013 The University of California Berkeley. All
rights reserved.
Copyright (c) 2006-2013 The University of Colorado Denver. All rights
reserved.
$COPYRIGHT$
Additional copyrights may follow
$HEADER$
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are
met:
- Redistributions of source code must retain the above copyright
notice, this list of conditions and the following disclaimer.
- Redistributions in binary form must reproduce the above copyright
notice, this list of conditions and the following disclaimer listed
in this license in the documentation and/or other materials
provided with the distribution.
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The copyright holders provide no reassurances that the source code
provided does not infringe any patent, copyright, or any other
intellectual property rights of third parties. The copyright holders
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THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
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Name: GCC runtime library
Files: extra-dll\*.dll
Description: statically linked, in DLL files compiled with gfortran only
Availability: https://gcc.gnu.org/viewcvs/gcc/
License: GPLv3 + runtime exception
Copyright (C) 2002-2017 Free Software Foundation, Inc.
Libgfortran is free software; you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
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any later version.
Libgfortran is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
Under Section 7 of GPL version 3, you are granted additional
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3.1, as published by the Free Software Foundation.
You should have received a copy of the GNU General Public License and
a copy of the GCC Runtime Library Exception along with this program;
see the files COPYING3 and COPYING.RUNTIME respectively. If not, see
<http://www.gnu.org/licenses/>.
Name: Microsoft Visual C++ Runtime Files
Files: extra-dll\msvcp140.dll
License: MSVC
https://www.visualstudio.com/license-terms/distributable-code-microsoft-visual-studio-2015-rc-microsoft-visual-studio-2015-sdk-rc-includes-utilities-buildserver-files/#visual-c-runtime
Subject to the License Terms for the software, you may copy and
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C:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\redist
You may not distribute the contents of the following folders:
C:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\redist\debug_nonredist
C:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\redist\onecore\debug_nonredist
Subject to the License Terms for the software, you may copy and
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VC\atlmfc\lib\mfcmifc80.dll
VC\atlmfc\lib\amd64\mfcmifc80.dll
Name: Microsoft Visual C++ Runtime Files
Files: extra-dll\msvc*90.dll, extra-dll\Microsoft.VC90.CRT.manifest
License: MSVC
For your convenience, we have provided the following folders for
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no change to the folder name. You may also redistribute all the
files (*.dll and *.manifest) within a folder, listed below the
folder for your convenience, as an entire set.
\VC\redist\x86\Microsoft.VC90.ATL\
atl90.dll
Microsoft.VC90.ATL.manifest
\VC\redist\ia64\Microsoft.VC90.ATL\
atl90.dll
Microsoft.VC90.ATL.manifest
\VC\redist\amd64\Microsoft.VC90.ATL\
atl90.dll
Microsoft.VC90.ATL.manifest
\VC\redist\x86\Microsoft.VC90.CRT\
msvcm90.dll
msvcp90.dll
msvcr90.dll
Microsoft.VC90.CRT.manifest
\VC\redist\ia64\Microsoft.VC90.CRT\
msvcm90.dll
msvcp90.dll
msvcr90.dll
Microsoft.VC90.CRT.manifest
----
Full text of license texts referred to above follows (that they are
listed below does not necessarily imply the conditions apply to the
present binary release):
----
GCC RUNTIME LIBRARY EXCEPTION
Version 3.1, 31 March 2009
Copyright (C) 2009 Free Software Foundation, Inc. <http://fsf.org/>
Everyone is permitted to copy and distribute verbatim copies of this
license document, but changing it is not allowed.
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----
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How to Apply These Terms to Your New Programs
If you develop a new program, and you want it to be of the greatest
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state the exclusion of warranty; and each file should have at least
the "copyright" line and a pointer to where the full notice is found.
<one line to give the program's name and a brief idea of what it does.>
Copyright (C) <year> <name of author>
This program is free software: you can redistribute it and/or modify
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This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
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You should have received a copy of the GNU General Public License
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Also add information on how to contact you by electronic and paper mail.
If the program does terminal interaction, make it output a short
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<program> Copyright (C) <year> <name of author>
This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
This is free software, and you are welcome to redistribute it
under certain conditions; type `show c' for details.
The hypothetical commands `show w' and `show c' should show the appropriate
parts of the General Public License. Of course, your program's commands
might be different; for a GUI interface, you would use an "about box".
You should also get your employer (if you work as a programmer) or school,
if any, to sign a "copyright disclaimer" for the program, if necessary.
For more information on this, and how to apply and follow the GNU GPL, see
<http://www.gnu.org/licenses/>.
The GNU General Public License does not permit incorporating your program
into proprietary programs. If your program is a subroutine library, you
may consider it more useful to permit linking proprietary applications with
the library. If this is what you want to do, use the GNU Lesser General
Public License instead of this License. But first, please read
<http://www.gnu.org/philosophy/why-not-lgpl.html>.

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The NumPy repository and source distributions bundle several libraries that are
compatibly licensed. We list these here.
Name: Numpydoc
Files: doc/sphinxext/numpydoc/*
License: BSD-2-Clause
For details, see doc/sphinxext/LICENSE.txt
Name: scipy-sphinx-theme
Files: doc/scipy-sphinx-theme/*
License: BSD-3-Clause AND PSF-2.0 AND Apache-2.0
For details, see doc/scipy-sphinx-theme/LICENSE.txt
Name: lapack-lite
Files: numpy/linalg/lapack_lite/*
License: BSD-3-Clause
For details, see numpy/linalg/lapack_lite/LICENSE.txt
Name: tempita
Files: tools/npy_tempita/*
License: MIT
For details, see tools/npy_tempita/license.txt
Name: dragon4
Files: numpy/core/src/multiarray/dragon4.c
License: MIT
For license text, see numpy/core/src/multiarray/dragon4.c

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Metadata-Version: 2.1
Name: numpy
Version: 1.19.2
Summary: NumPy is the fundamental package for array computing with Python.
Home-page: https://www.numpy.org
Author: Travis E. Oliphant et al.
Maintainer: NumPy Developers
Maintainer-email: numpy-discussion@python.org
License: BSD
Download-URL: https://pypi.python.org/pypi/numpy
Project-URL: Bug Tracker, https://github.com/numpy/numpy/issues
Project-URL: Documentation, https://numpy.org/doc/1.19
Project-URL: Source Code, https://github.com/numpy/numpy
Platform: Windows
Platform: Linux
Platform: Solaris
Platform: Mac OS-X
Platform: Unix
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved
Classifier: Programming Language :: C
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Programming Language :: Python :: Implementation :: CPython
Classifier: Topic :: Software Development
Classifier: Topic :: Scientific/Engineering
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX
Classifier: Operating System :: Unix
Classifier: Operating System :: MacOS
Requires-Python: >=3.6
It provides:
- a powerful N-dimensional array object
- sophisticated (broadcasting) functions
- tools for integrating C/C++ and Fortran code
- useful linear algebra, Fourier transform, and random number capabilities
- and much more
Besides its obvious scientific uses, NumPy can also be used as an efficient
multi-dimensional container of generic data. Arbitrary data-types can be
defined. This allows NumPy to seamlessly and speedily integrate with a wide
variety of databases.
All NumPy wheels distributed on PyPI are BSD licensed.

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How to Apply These Terms to Your New Programs
If you develop a new program, and you want it to be of the greatest
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free software which everyone can redistribute and change under these terms.
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state the exclusion of warranty; and each file should have at least
the "copyright" line and a pointer to where the full notice is found.
<one line to give the program's name and a brief idea of what it does.>
Copyright (C) <year> <name of author>
This program is free software: you can redistribute it and/or modify
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This program is distributed in the hope that it will be useful,
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MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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Also add information on how to contact you by electronic and paper mail.
If the program does terminal interaction, make it output a short
notice like this when it starts in an interactive mode:
<program> Copyright (C) <year> <name of author>
This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
This is free software, and you are welcome to redistribute it
under certain conditions; type `show c' for details.
The hypothetical commands `show w' and `show c' should show the appropriate
parts of the General Public License. Of course, your program's commands
might be different; for a GUI interface, you would use an "about box".
You should also get your employer (if you work as a programmer) or school,
if any, to sign a "copyright disclaimer" for the program, if necessary.
For more information on this, and how to apply and follow the GNU GPL, see
<http://www.gnu.org/licenses/>.
The GNU General Public License does not permit incorporating your program
into proprietary programs. If your program is a subroutine library, you
may consider it more useful to permit linking proprietary applications with
the library. If this is what you want to do, use the GNU Lesser General
Public License instead of this License. But first, please read
<http://www.gnu.org/philosophy/why-not-lgpl.html>.

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@ -0,0 +1,77 @@
# 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={'library_dirs': ['D:\\a\\1\\s\\numpy\\build\\openblas_info'], 'libraries': ['openblas_info'], 'language': 'f77', 'define_macros': [('HAVE_CBLAS', None)]}
blas_opt_info={'library_dirs': ['D:\\a\\1\\s\\numpy\\build\\openblas_info'], 'libraries': ['openblas_info'], 'language': 'f77', 'define_macros': [('HAVE_CBLAS', None)]}
lapack_mkl_info={}
openblas_lapack_info={'library_dirs': ['D:\\a\\1\\s\\numpy\\build\\openblas_lapack_info'], 'libraries': ['openblas_lapack_info'], 'language': 'f77', 'define_macros': [('HAVE_CBLAS', None)]}
lapack_opt_info={'library_dirs': ['D:\\a\\1\\s\\numpy\\build\\openblas_lapack_info'], 'libraries': ['openblas_lapack_info'], 'language': 'f77', 'define_macros': [('HAVE_CBLAS', None)]}
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``
Examples
--------
>>> np.show_config()
blas_opt_info:
language = c
define_macros = [('HAVE_CBLAS', None)]
libraries = ['openblas', 'openblas']
library_dirs = ['/usr/local/lib']
"""
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))

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@ -0,0 +1,903 @@
# NumPy static imports for Cython < 3.0
#
# If any of the PyArray_* functions are called, import_array must be
# called first.
#
# Author: Dag Sverre Seljebotn
#
DEF _buffer_format_string_len = 255
cimport cpython.buffer as pybuf
from cpython.ref cimport Py_INCREF
from cpython.mem cimport PyObject_Malloc, PyObject_Free
from cpython.object cimport PyObject, PyTypeObject
from cpython.buffer cimport PyObject_GetBuffer
from cpython.type cimport type
cimport libc.stdio as stdio
cdef extern from "Python.h":
ctypedef int Py_intptr_t
cdef extern from "numpy/arrayobject.h":
ctypedef Py_intptr_t npy_intp
ctypedef size_t npy_uintp
cdef enum NPY_TYPES:
NPY_BOOL
NPY_BYTE
NPY_UBYTE
NPY_SHORT
NPY_USHORT
NPY_INT
NPY_UINT
NPY_LONG
NPY_ULONG
NPY_LONGLONG
NPY_ULONGLONG
NPY_FLOAT
NPY_DOUBLE
NPY_LONGDOUBLE
NPY_CFLOAT
NPY_CDOUBLE
NPY_CLONGDOUBLE
NPY_OBJECT
NPY_STRING
NPY_UNICODE
NPY_VOID
NPY_DATETIME
NPY_TIMEDELTA
NPY_NTYPES
NPY_NOTYPE
NPY_INT8
NPY_INT16
NPY_INT32
NPY_INT64
NPY_INT128
NPY_INT256
NPY_UINT8
NPY_UINT16
NPY_UINT32
NPY_UINT64
NPY_UINT128
NPY_UINT256
NPY_FLOAT16
NPY_FLOAT32
NPY_FLOAT64
NPY_FLOAT80
NPY_FLOAT96
NPY_FLOAT128
NPY_FLOAT256
NPY_COMPLEX32
NPY_COMPLEX64
NPY_COMPLEX128
NPY_COMPLEX160
NPY_COMPLEX192
NPY_COMPLEX256
NPY_COMPLEX512
NPY_INTP
ctypedef enum NPY_ORDER:
NPY_ANYORDER
NPY_CORDER
NPY_FORTRANORDER
NPY_KEEPORDER
ctypedef enum NPY_CASTING:
NPY_NO_CASTING
NPY_EQUIV_CASTING
NPY_SAFE_CASTING
NPY_SAME_KIND_CASTING
NPY_UNSAFE_CASTING
ctypedef enum NPY_CLIPMODE:
NPY_CLIP
NPY_WRAP
NPY_RAISE
ctypedef enum NPY_SCALARKIND:
NPY_NOSCALAR,
NPY_BOOL_SCALAR,
NPY_INTPOS_SCALAR,
NPY_INTNEG_SCALAR,
NPY_FLOAT_SCALAR,
NPY_COMPLEX_SCALAR,
NPY_OBJECT_SCALAR
ctypedef enum NPY_SORTKIND:
NPY_QUICKSORT
NPY_HEAPSORT
NPY_MERGESORT
ctypedef enum NPY_SEARCHSIDE:
NPY_SEARCHLEFT
NPY_SEARCHRIGHT
enum:
# DEPRECATED since NumPy 1.7 ! Do not use in new code!
NPY_C_CONTIGUOUS
NPY_F_CONTIGUOUS
NPY_CONTIGUOUS
NPY_FORTRAN
NPY_OWNDATA
NPY_FORCECAST
NPY_ENSURECOPY
NPY_ENSUREARRAY
NPY_ELEMENTSTRIDES
NPY_ALIGNED
NPY_NOTSWAPPED
NPY_WRITEABLE
NPY_UPDATEIFCOPY
NPY_ARR_HAS_DESCR
NPY_BEHAVED
NPY_BEHAVED_NS
NPY_CARRAY
NPY_CARRAY_RO
NPY_FARRAY
NPY_FARRAY_RO
NPY_DEFAULT
NPY_IN_ARRAY
NPY_OUT_ARRAY
NPY_INOUT_ARRAY
NPY_IN_FARRAY
NPY_OUT_FARRAY
NPY_INOUT_FARRAY
NPY_UPDATE_ALL
enum:
# Added in NumPy 1.7 to replace the deprecated enums above.
NPY_ARRAY_C_CONTIGUOUS
NPY_ARRAY_F_CONTIGUOUS
NPY_ARRAY_OWNDATA
NPY_ARRAY_FORCECAST
NPY_ARRAY_ENSURECOPY
NPY_ARRAY_ENSUREARRAY
NPY_ARRAY_ELEMENTSTRIDES
NPY_ARRAY_ALIGNED
NPY_ARRAY_NOTSWAPPED
NPY_ARRAY_WRITEABLE
NPY_ARRAY_UPDATEIFCOPY
NPY_ARRAY_BEHAVED
NPY_ARRAY_BEHAVED_NS
NPY_ARRAY_CARRAY
NPY_ARRAY_CARRAY_RO
NPY_ARRAY_FARRAY
NPY_ARRAY_FARRAY_RO
NPY_ARRAY_DEFAULT
NPY_ARRAY_IN_ARRAY
NPY_ARRAY_OUT_ARRAY
NPY_ARRAY_INOUT_ARRAY
NPY_ARRAY_IN_FARRAY
NPY_ARRAY_OUT_FARRAY
NPY_ARRAY_INOUT_FARRAY
NPY_ARRAY_UPDATE_ALL
cdef enum:
NPY_MAXDIMS
npy_intp NPY_MAX_ELSIZE
ctypedef void (*PyArray_VectorUnaryFunc)(void *, void *, npy_intp, void *, void *)
ctypedef struct PyArray_ArrayDescr:
# shape is a tuple, but Cython doesn't support "tuple shape"
# inside a non-PyObject declaration, so we have to declare it
# as just a PyObject*.
PyObject* shape
ctypedef struct PyArray_Descr:
pass
ctypedef class numpy.dtype [object PyArray_Descr, check_size ignore]:
# Use PyDataType_* macros when possible, however there are no macros
# for accessing some of the fields, so some are defined.
cdef PyTypeObject* typeobj
cdef char kind
cdef char type
# Numpy sometimes mutates this without warning (e.g. it'll
# sometimes change "|" to "<" in shared dtype objects on
# little-endian machines). If this matters to you, use
# PyArray_IsNativeByteOrder(dtype.byteorder) instead of
# directly accessing this field.
cdef char byteorder
cdef char flags
cdef int type_num
cdef int itemsize "elsize"
cdef int alignment
cdef dict fields
cdef tuple names
# Use PyDataType_HASSUBARRAY to test whether this field is
# valid (the pointer can be NULL). Most users should access
# this field via the inline helper method PyDataType_SHAPE.
cdef PyArray_ArrayDescr* subarray
ctypedef class numpy.flatiter [object PyArrayIterObject, check_size ignore]:
# Use through macros
pass
ctypedef class numpy.broadcast [object PyArrayMultiIterObject, check_size ignore]:
cdef int numiter
cdef npy_intp size, index
cdef int nd
cdef npy_intp *dimensions
cdef void **iters
ctypedef struct PyArrayObject:
# For use in situations where ndarray can't replace PyArrayObject*,
# like PyArrayObject**.
pass
ctypedef class numpy.ndarray [object PyArrayObject, check_size ignore]:
cdef __cythonbufferdefaults__ = {"mode": "strided"}
cdef:
# Only taking a few of the most commonly used and stable fields.
# One should use PyArray_* macros instead to access the C fields.
char *data
int ndim "nd"
npy_intp *shape "dimensions"
npy_intp *strides
dtype descr # deprecated since NumPy 1.7 !
PyObject* base # NOT PUBLIC, DO NOT USE !
ctypedef unsigned char npy_bool
ctypedef signed char npy_byte
ctypedef signed short npy_short
ctypedef signed int npy_int
ctypedef signed long npy_long
ctypedef signed long long npy_longlong
ctypedef unsigned char npy_ubyte
ctypedef unsigned short npy_ushort
ctypedef unsigned int npy_uint
ctypedef unsigned long npy_ulong
ctypedef unsigned long long npy_ulonglong
ctypedef float npy_float
ctypedef double npy_double
ctypedef long double npy_longdouble
ctypedef signed char npy_int8
ctypedef signed short npy_int16
ctypedef signed int npy_int32
ctypedef signed long long npy_int64
ctypedef signed long long npy_int96
ctypedef signed long long npy_int128
ctypedef unsigned char npy_uint8
ctypedef unsigned short npy_uint16
ctypedef unsigned int npy_uint32
ctypedef unsigned long long npy_uint64
ctypedef unsigned long long npy_uint96
ctypedef unsigned long long npy_uint128
ctypedef float npy_float32
ctypedef double npy_float64
ctypedef long double npy_float80
ctypedef long double npy_float96
ctypedef long double npy_float128
ctypedef struct npy_cfloat:
double real
double imag
ctypedef struct npy_cdouble:
double real
double imag
ctypedef struct npy_clongdouble:
long double real
long double imag
ctypedef struct npy_complex64:
float real
float imag
ctypedef struct npy_complex128:
double real
double imag
ctypedef struct npy_complex160:
long double real
long double imag
ctypedef struct npy_complex192:
long double real
long double imag
ctypedef struct npy_complex256:
long double real
long double imag
ctypedef struct PyArray_Dims:
npy_intp *ptr
int len
int _import_array() except -1
# A second definition so _import_array isn't marked as used when we use it here.
# Do not use - subject to change any time.
int __pyx_import_array "_import_array"() except -1
#
# Macros from ndarrayobject.h
#
bint PyArray_CHKFLAGS(ndarray m, int flags) nogil
bint PyArray_IS_C_CONTIGUOUS(ndarray arr) nogil
bint PyArray_IS_F_CONTIGUOUS(ndarray arr) nogil
bint PyArray_ISCONTIGUOUS(ndarray m) nogil
bint PyArray_ISWRITEABLE(ndarray m) nogil
bint PyArray_ISALIGNED(ndarray m) nogil
int PyArray_NDIM(ndarray) nogil
bint PyArray_ISONESEGMENT(ndarray) nogil
bint PyArray_ISFORTRAN(ndarray) nogil
int PyArray_FORTRANIF(ndarray) nogil
void* PyArray_DATA(ndarray) nogil
char* PyArray_BYTES(ndarray) nogil
npy_intp* PyArray_DIMS(ndarray) nogil
npy_intp* PyArray_STRIDES(ndarray) nogil
npy_intp PyArray_DIM(ndarray, size_t) nogil
npy_intp PyArray_STRIDE(ndarray, size_t) nogil
PyObject *PyArray_BASE(ndarray) nogil # returns borrowed reference!
PyArray_Descr *PyArray_DESCR(ndarray) nogil # returns borrowed reference to dtype!
int PyArray_FLAGS(ndarray) nogil
npy_intp PyArray_ITEMSIZE(ndarray) nogil
int PyArray_TYPE(ndarray arr) nogil
object PyArray_GETITEM(ndarray arr, void *itemptr)
int PyArray_SETITEM(ndarray arr, void *itemptr, object obj)
bint PyTypeNum_ISBOOL(int) nogil
bint PyTypeNum_ISUNSIGNED(int) nogil
bint PyTypeNum_ISSIGNED(int) nogil
bint PyTypeNum_ISINTEGER(int) nogil
bint PyTypeNum_ISFLOAT(int) nogil
bint PyTypeNum_ISNUMBER(int) nogil
bint PyTypeNum_ISSTRING(int) nogil
bint PyTypeNum_ISCOMPLEX(int) nogil
bint PyTypeNum_ISPYTHON(int) nogil
bint PyTypeNum_ISFLEXIBLE(int) nogil
bint PyTypeNum_ISUSERDEF(int) nogil
bint PyTypeNum_ISEXTENDED(int) nogil
bint PyTypeNum_ISOBJECT(int) nogil
bint PyDataType_ISBOOL(dtype) nogil
bint PyDataType_ISUNSIGNED(dtype) nogil
bint PyDataType_ISSIGNED(dtype) nogil
bint PyDataType_ISINTEGER(dtype) nogil
bint PyDataType_ISFLOAT(dtype) nogil
bint PyDataType_ISNUMBER(dtype) nogil
bint PyDataType_ISSTRING(dtype) nogil
bint PyDataType_ISCOMPLEX(dtype) nogil
bint PyDataType_ISPYTHON(dtype) nogil
bint PyDataType_ISFLEXIBLE(dtype) nogil
bint PyDataType_ISUSERDEF(dtype) nogil
bint PyDataType_ISEXTENDED(dtype) nogil
bint PyDataType_ISOBJECT(dtype) nogil
bint PyDataType_HASFIELDS(dtype) nogil
bint PyDataType_HASSUBARRAY(dtype) nogil
bint PyArray_ISBOOL(ndarray) nogil
bint PyArray_ISUNSIGNED(ndarray) nogil
bint PyArray_ISSIGNED(ndarray) nogil
bint PyArray_ISINTEGER(ndarray) nogil
bint PyArray_ISFLOAT(ndarray) nogil
bint PyArray_ISNUMBER(ndarray) nogil
bint PyArray_ISSTRING(ndarray) nogil
bint PyArray_ISCOMPLEX(ndarray) nogil
bint PyArray_ISPYTHON(ndarray) nogil
bint PyArray_ISFLEXIBLE(ndarray) nogil
bint PyArray_ISUSERDEF(ndarray) nogil
bint PyArray_ISEXTENDED(ndarray) nogil
bint PyArray_ISOBJECT(ndarray) nogil
bint PyArray_HASFIELDS(ndarray) nogil
bint PyArray_ISVARIABLE(ndarray) nogil
bint PyArray_SAFEALIGNEDCOPY(ndarray) nogil
bint PyArray_ISNBO(char) nogil # works on ndarray.byteorder
bint PyArray_IsNativeByteOrder(char) nogil # works on ndarray.byteorder
bint PyArray_ISNOTSWAPPED(ndarray) nogil
bint PyArray_ISBYTESWAPPED(ndarray) nogil
bint PyArray_FLAGSWAP(ndarray, int) nogil
bint PyArray_ISCARRAY(ndarray) nogil
bint PyArray_ISCARRAY_RO(ndarray) nogil
bint PyArray_ISFARRAY(ndarray) nogil
bint PyArray_ISFARRAY_RO(ndarray) nogil
bint PyArray_ISBEHAVED(ndarray) nogil
bint PyArray_ISBEHAVED_RO(ndarray) nogil
bint PyDataType_ISNOTSWAPPED(dtype) nogil
bint PyDataType_ISBYTESWAPPED(dtype) nogil
bint PyArray_DescrCheck(object)
bint PyArray_Check(object)
bint PyArray_CheckExact(object)
# Cannot be supported due to out arg:
# bint PyArray_HasArrayInterfaceType(object, dtype, object, object&)
# bint PyArray_HasArrayInterface(op, out)
bint PyArray_IsZeroDim(object)
# Cannot be supported due to ## ## in macro:
# bint PyArray_IsScalar(object, verbatim work)
bint PyArray_CheckScalar(object)
bint PyArray_IsPythonNumber(object)
bint PyArray_IsPythonScalar(object)
bint PyArray_IsAnyScalar(object)
bint PyArray_CheckAnyScalar(object)
ndarray PyArray_GETCONTIGUOUS(ndarray)
bint PyArray_SAMESHAPE(ndarray, ndarray) nogil
npy_intp PyArray_SIZE(ndarray) nogil
npy_intp PyArray_NBYTES(ndarray) nogil
object PyArray_FROM_O(object)
object PyArray_FROM_OF(object m, int flags)
object PyArray_FROM_OT(object m, int type)
object PyArray_FROM_OTF(object m, int type, int flags)
object PyArray_FROMANY(object m, int type, int min, int max, int flags)
object PyArray_ZEROS(int nd, npy_intp* dims, int type, int fortran)
object PyArray_EMPTY(int nd, npy_intp* dims, int type, int fortran)
void PyArray_FILLWBYTE(object, int val)
npy_intp PyArray_REFCOUNT(object)
object PyArray_ContiguousFromAny(op, int, int min_depth, int max_depth)
unsigned char PyArray_EquivArrTypes(ndarray a1, ndarray a2)
bint PyArray_EquivByteorders(int b1, int b2) nogil
object PyArray_SimpleNew(int nd, npy_intp* dims, int typenum)
object PyArray_SimpleNewFromData(int nd, npy_intp* dims, int typenum, void* data)
#object PyArray_SimpleNewFromDescr(int nd, npy_intp* dims, dtype descr)
object PyArray_ToScalar(void* data, ndarray arr)
void* PyArray_GETPTR1(ndarray m, npy_intp i) nogil
void* PyArray_GETPTR2(ndarray m, npy_intp i, npy_intp j) nogil
void* PyArray_GETPTR3(ndarray m, npy_intp i, npy_intp j, npy_intp k) nogil
void* PyArray_GETPTR4(ndarray m, npy_intp i, npy_intp j, npy_intp k, npy_intp l) nogil
void PyArray_XDECREF_ERR(ndarray)
# Cannot be supported due to out arg
# void PyArray_DESCR_REPLACE(descr)
object PyArray_Copy(ndarray)
object PyArray_FromObject(object op, int type, int min_depth, int max_depth)
object PyArray_ContiguousFromObject(object op, int type, int min_depth, int max_depth)
object PyArray_CopyFromObject(object op, int type, int min_depth, int max_depth)
object PyArray_Cast(ndarray mp, int type_num)
object PyArray_Take(ndarray ap, object items, int axis)
object PyArray_Put(ndarray ap, object items, object values)
void PyArray_ITER_RESET(flatiter it) nogil
void PyArray_ITER_NEXT(flatiter it) nogil
void PyArray_ITER_GOTO(flatiter it, npy_intp* destination) nogil
void PyArray_ITER_GOTO1D(flatiter it, npy_intp ind) nogil
void* PyArray_ITER_DATA(flatiter it) nogil
bint PyArray_ITER_NOTDONE(flatiter it) nogil
void PyArray_MultiIter_RESET(broadcast multi) nogil
void PyArray_MultiIter_NEXT(broadcast multi) nogil
void PyArray_MultiIter_GOTO(broadcast multi, npy_intp dest) nogil
void PyArray_MultiIter_GOTO1D(broadcast multi, npy_intp ind) nogil
void* PyArray_MultiIter_DATA(broadcast multi, npy_intp i) nogil
void PyArray_MultiIter_NEXTi(broadcast multi, npy_intp i) nogil
bint PyArray_MultiIter_NOTDONE(broadcast multi) nogil
# Functions from __multiarray_api.h
# Functions taking dtype and returning object/ndarray are disabled
# for now as they steal dtype references. I'm conservative and disable
# more than is probably needed until it can be checked further.
int PyArray_SetNumericOps (object)
object PyArray_GetNumericOps ()
int PyArray_INCREF (ndarray)
int PyArray_XDECREF (ndarray)
void PyArray_SetStringFunction (object, int)
dtype PyArray_DescrFromType (int)
object PyArray_TypeObjectFromType (int)
char * PyArray_Zero (ndarray)
char * PyArray_One (ndarray)
#object PyArray_CastToType (ndarray, dtype, int)
int PyArray_CastTo (ndarray, ndarray)
int PyArray_CastAnyTo (ndarray, ndarray)
int PyArray_CanCastSafely (int, int)
npy_bool PyArray_CanCastTo (dtype, dtype)
int PyArray_ObjectType (object, int)
dtype PyArray_DescrFromObject (object, dtype)
#ndarray* PyArray_ConvertToCommonType (object, int *)
dtype PyArray_DescrFromScalar (object)
dtype PyArray_DescrFromTypeObject (object)
npy_intp PyArray_Size (object)
#object PyArray_Scalar (void *, dtype, object)
#object PyArray_FromScalar (object, dtype)
void PyArray_ScalarAsCtype (object, void *)
#int PyArray_CastScalarToCtype (object, void *, dtype)
#int PyArray_CastScalarDirect (object, dtype, void *, int)
object PyArray_ScalarFromObject (object)
#PyArray_VectorUnaryFunc * PyArray_GetCastFunc (dtype, int)
object PyArray_FromDims (int, int *, int)
#object PyArray_FromDimsAndDataAndDescr (int, int *, dtype, char *)
#object PyArray_FromAny (object, dtype, int, int, int, object)
object PyArray_EnsureArray (object)
object PyArray_EnsureAnyArray (object)
#object PyArray_FromFile (stdio.FILE *, dtype, npy_intp, char *)
#object PyArray_FromString (char *, npy_intp, dtype, npy_intp, char *)
#object PyArray_FromBuffer (object, dtype, npy_intp, npy_intp)
#object PyArray_FromIter (object, dtype, npy_intp)
object PyArray_Return (ndarray)
#object PyArray_GetField (ndarray, dtype, int)
#int PyArray_SetField (ndarray, dtype, int, object)
object PyArray_Byteswap (ndarray, npy_bool)
object PyArray_Resize (ndarray, PyArray_Dims *, int, NPY_ORDER)
int PyArray_MoveInto (ndarray, ndarray)
int PyArray_CopyInto (ndarray, ndarray)
int PyArray_CopyAnyInto (ndarray, ndarray)
int PyArray_CopyObject (ndarray, object)
object PyArray_NewCopy (ndarray, NPY_ORDER)
object PyArray_ToList (ndarray)
object PyArray_ToString (ndarray, NPY_ORDER)
int PyArray_ToFile (ndarray, stdio.FILE *, char *, char *)
int PyArray_Dump (object, object, int)
object PyArray_Dumps (object, int)
int PyArray_ValidType (int)
void PyArray_UpdateFlags (ndarray, int)
object PyArray_New (type, int, npy_intp *, int, npy_intp *, void *, int, int, object)
#object PyArray_NewFromDescr (type, dtype, int, npy_intp *, npy_intp *, void *, int, object)
#dtype PyArray_DescrNew (dtype)
dtype PyArray_DescrNewFromType (int)
double PyArray_GetPriority (object, double)
object PyArray_IterNew (object)
object PyArray_MultiIterNew (int, ...)
int PyArray_PyIntAsInt (object)
npy_intp PyArray_PyIntAsIntp (object)
int PyArray_Broadcast (broadcast)
void PyArray_FillObjectArray (ndarray, object)
int PyArray_FillWithScalar (ndarray, object)
npy_bool PyArray_CheckStrides (int, int, npy_intp, npy_intp, npy_intp *, npy_intp *)
dtype PyArray_DescrNewByteorder (dtype, char)
object PyArray_IterAllButAxis (object, int *)
#object PyArray_CheckFromAny (object, dtype, int, int, int, object)
#object PyArray_FromArray (ndarray, dtype, int)
object PyArray_FromInterface (object)
object PyArray_FromStructInterface (object)
#object PyArray_FromArrayAttr (object, dtype, object)
#NPY_SCALARKIND PyArray_ScalarKind (int, ndarray*)
int PyArray_CanCoerceScalar (int, int, NPY_SCALARKIND)
object PyArray_NewFlagsObject (object)
npy_bool PyArray_CanCastScalar (type, type)
#int PyArray_CompareUCS4 (npy_ucs4 *, npy_ucs4 *, register size_t)
int PyArray_RemoveSmallest (broadcast)
int PyArray_ElementStrides (object)
void PyArray_Item_INCREF (char *, dtype)
void PyArray_Item_XDECREF (char *, dtype)
object PyArray_FieldNames (object)
object PyArray_Transpose (ndarray, PyArray_Dims *)
object PyArray_TakeFrom (ndarray, object, int, ndarray, NPY_CLIPMODE)
object PyArray_PutTo (ndarray, object, object, NPY_CLIPMODE)
object PyArray_PutMask (ndarray, object, object)
object PyArray_Repeat (ndarray, object, int)
object PyArray_Choose (ndarray, object, ndarray, NPY_CLIPMODE)
int PyArray_Sort (ndarray, int, NPY_SORTKIND)
object PyArray_ArgSort (ndarray, int, NPY_SORTKIND)
object PyArray_SearchSorted (ndarray, object, NPY_SEARCHSIDE, PyObject *)
object PyArray_ArgMax (ndarray, int, ndarray)
object PyArray_ArgMin (ndarray, int, ndarray)
object PyArray_Reshape (ndarray, object)
object PyArray_Newshape (ndarray, PyArray_Dims *, NPY_ORDER)
object PyArray_Squeeze (ndarray)
#object PyArray_View (ndarray, dtype, type)
object PyArray_SwapAxes (ndarray, int, int)
object PyArray_Max (ndarray, int, ndarray)
object PyArray_Min (ndarray, int, ndarray)
object PyArray_Ptp (ndarray, int, ndarray)
object PyArray_Mean (ndarray, int, int, ndarray)
object PyArray_Trace (ndarray, int, int, int, int, ndarray)
object PyArray_Diagonal (ndarray, int, int, int)
object PyArray_Clip (ndarray, object, object, ndarray)
object PyArray_Conjugate (ndarray, ndarray)
object PyArray_Nonzero (ndarray)
object PyArray_Std (ndarray, int, int, ndarray, int)
object PyArray_Sum (ndarray, int, int, ndarray)
object PyArray_CumSum (ndarray, int, int, ndarray)
object PyArray_Prod (ndarray, int, int, ndarray)
object PyArray_CumProd (ndarray, int, int, ndarray)
object PyArray_All (ndarray, int, ndarray)
object PyArray_Any (ndarray, int, ndarray)
object PyArray_Compress (ndarray, object, int, ndarray)
object PyArray_Flatten (ndarray, NPY_ORDER)
object PyArray_Ravel (ndarray, NPY_ORDER)
npy_intp PyArray_MultiplyList (npy_intp *, int)
int PyArray_MultiplyIntList (int *, int)
void * PyArray_GetPtr (ndarray, npy_intp*)
int PyArray_CompareLists (npy_intp *, npy_intp *, int)
#int PyArray_AsCArray (object*, void *, npy_intp *, int, dtype)
#int PyArray_As1D (object*, char **, int *, int)
#int PyArray_As2D (object*, char ***, int *, int *, int)
int PyArray_Free (object, void *)
#int PyArray_Converter (object, object*)
int PyArray_IntpFromSequence (object, npy_intp *, int)
object PyArray_Concatenate (object, int)
object PyArray_InnerProduct (object, object)
object PyArray_MatrixProduct (object, object)
object PyArray_CopyAndTranspose (object)
object PyArray_Correlate (object, object, int)
int PyArray_TypestrConvert (int, int)
#int PyArray_DescrConverter (object, dtype*)
#int PyArray_DescrConverter2 (object, dtype*)
int PyArray_IntpConverter (object, PyArray_Dims *)
#int PyArray_BufferConverter (object, chunk)
int PyArray_AxisConverter (object, int *)
int PyArray_BoolConverter (object, npy_bool *)
int PyArray_ByteorderConverter (object, char *)
int PyArray_OrderConverter (object, NPY_ORDER *)
unsigned char PyArray_EquivTypes (dtype, dtype)
#object PyArray_Zeros (int, npy_intp *, dtype, int)
#object PyArray_Empty (int, npy_intp *, dtype, int)
object PyArray_Where (object, object, object)
object PyArray_Arange (double, double, double, int)
#object PyArray_ArangeObj (object, object, object, dtype)
int PyArray_SortkindConverter (object, NPY_SORTKIND *)
object PyArray_LexSort (object, int)
object PyArray_Round (ndarray, int, ndarray)
unsigned char PyArray_EquivTypenums (int, int)
int PyArray_RegisterDataType (dtype)
int PyArray_RegisterCastFunc (dtype, int, PyArray_VectorUnaryFunc *)
int PyArray_RegisterCanCast (dtype, int, NPY_SCALARKIND)
#void PyArray_InitArrFuncs (PyArray_ArrFuncs *)
object PyArray_IntTupleFromIntp (int, npy_intp *)
int PyArray_TypeNumFromName (char *)
int PyArray_ClipmodeConverter (object, NPY_CLIPMODE *)
#int PyArray_OutputConverter (object, ndarray*)
object PyArray_BroadcastToShape (object, npy_intp *, int)
void _PyArray_SigintHandler (int)
void* _PyArray_GetSigintBuf ()
#int PyArray_DescrAlignConverter (object, dtype*)
#int PyArray_DescrAlignConverter2 (object, dtype*)
int PyArray_SearchsideConverter (object, void *)
object PyArray_CheckAxis (ndarray, int *, int)
npy_intp PyArray_OverflowMultiplyList (npy_intp *, int)
int PyArray_CompareString (char *, char *, size_t)
int PyArray_SetBaseObject(ndarray, base) # NOTE: steals a reference to base! Use "set_array_base()" instead.
# Typedefs that matches the runtime dtype objects in
# the numpy module.
# The ones that are commented out needs an IFDEF function
# in Cython to enable them only on the right systems.
ctypedef npy_int8 int8_t
ctypedef npy_int16 int16_t
ctypedef npy_int32 int32_t
ctypedef npy_int64 int64_t
#ctypedef npy_int96 int96_t
#ctypedef npy_int128 int128_t
ctypedef npy_uint8 uint8_t
ctypedef npy_uint16 uint16_t
ctypedef npy_uint32 uint32_t
ctypedef npy_uint64 uint64_t
#ctypedef npy_uint96 uint96_t
#ctypedef npy_uint128 uint128_t
ctypedef npy_float32 float32_t
ctypedef npy_float64 float64_t
#ctypedef npy_float80 float80_t
#ctypedef npy_float128 float128_t
ctypedef float complex complex64_t
ctypedef double complex complex128_t
# The int types are mapped a bit surprising --
# numpy.int corresponds to 'l' and numpy.long to 'q'
ctypedef npy_long int_t
ctypedef npy_longlong long_t
ctypedef npy_longlong longlong_t
ctypedef npy_ulong uint_t
ctypedef npy_ulonglong ulong_t
ctypedef npy_ulonglong ulonglong_t
ctypedef npy_intp intp_t
ctypedef npy_uintp uintp_t
ctypedef npy_double float_t
ctypedef npy_double double_t
ctypedef npy_longdouble longdouble_t
ctypedef npy_cfloat cfloat_t
ctypedef npy_cdouble cdouble_t
ctypedef npy_clongdouble clongdouble_t
ctypedef npy_cdouble complex_t
cdef inline object PyArray_MultiIterNew1(a):
return PyArray_MultiIterNew(1, <void*>a)
cdef inline object PyArray_MultiIterNew2(a, b):
return PyArray_MultiIterNew(2, <void*>a, <void*>b)
cdef inline object PyArray_MultiIterNew3(a, b, c):
return PyArray_MultiIterNew(3, <void*>a, <void*>b, <void*> c)
cdef inline object PyArray_MultiIterNew4(a, b, c, d):
return PyArray_MultiIterNew(4, <void*>a, <void*>b, <void*>c, <void*> d)
cdef inline object PyArray_MultiIterNew5(a, b, c, d, e):
return PyArray_MultiIterNew(5, <void*>a, <void*>b, <void*>c, <void*> d, <void*> e)
cdef inline tuple PyDataType_SHAPE(dtype d):
if PyDataType_HASSUBARRAY(d):
return <tuple>d.subarray.shape
else:
return ()
#
# ufunc API
#
cdef extern from "numpy/ufuncobject.h":
ctypedef void (*PyUFuncGenericFunction) (char **, npy_intp *, npy_intp *, void *)
ctypedef class numpy.ufunc [object PyUFuncObject, check_size ignore]:
cdef:
int nin, nout, nargs
int identity
PyUFuncGenericFunction *functions
void **data
int ntypes
int check_return
char *name
char *types
char *doc
void *ptr
PyObject *obj
PyObject *userloops
cdef enum:
PyUFunc_Zero
PyUFunc_One
PyUFunc_None
UFUNC_ERR_IGNORE
UFUNC_ERR_WARN
UFUNC_ERR_RAISE
UFUNC_ERR_CALL
UFUNC_ERR_PRINT
UFUNC_ERR_LOG
UFUNC_MASK_DIVIDEBYZERO
UFUNC_MASK_OVERFLOW
UFUNC_MASK_UNDERFLOW
UFUNC_MASK_INVALID
UFUNC_SHIFT_DIVIDEBYZERO
UFUNC_SHIFT_OVERFLOW
UFUNC_SHIFT_UNDERFLOW
UFUNC_SHIFT_INVALID
UFUNC_FPE_DIVIDEBYZERO
UFUNC_FPE_OVERFLOW
UFUNC_FPE_UNDERFLOW
UFUNC_FPE_INVALID
UFUNC_ERR_DEFAULT
UFUNC_ERR_DEFAULT2
object PyUFunc_FromFuncAndData(PyUFuncGenericFunction *,
void **, char *, int, int, int, int, char *, char *, int)
int PyUFunc_RegisterLoopForType(ufunc, int,
PyUFuncGenericFunction, int *, void *)
int PyUFunc_GenericFunction \
(ufunc, PyObject *, PyObject *, PyArrayObject **)
void PyUFunc_f_f_As_d_d \
(char **, npy_intp *, npy_intp *, void *)
void PyUFunc_d_d \
(char **, npy_intp *, npy_intp *, void *)
void PyUFunc_f_f \
(char **, npy_intp *, npy_intp *, void *)
void PyUFunc_g_g \
(char **, npy_intp *, npy_intp *, void *)
void PyUFunc_F_F_As_D_D \
(char **, npy_intp *, npy_intp *, void *)
void PyUFunc_F_F \
(char **, npy_intp *, npy_intp *, void *)
void PyUFunc_D_D \
(char **, npy_intp *, npy_intp *, void *)
void PyUFunc_G_G \
(char **, npy_intp *, npy_intp *, void *)
void PyUFunc_O_O \
(char **, npy_intp *, npy_intp *, void *)
void PyUFunc_ff_f_As_dd_d \
(char **, npy_intp *, npy_intp *, void *)
void PyUFunc_ff_f \
(char **, npy_intp *, npy_intp *, void *)
void PyUFunc_dd_d \
(char **, npy_intp *, npy_intp *, void *)
void PyUFunc_gg_g \
(char **, npy_intp *, npy_intp *, void *)
void PyUFunc_FF_F_As_DD_D \
(char **, npy_intp *, npy_intp *, void *)
void PyUFunc_DD_D \
(char **, npy_intp *, npy_intp *, void *)
void PyUFunc_FF_F \
(char **, npy_intp *, npy_intp *, void *)
void PyUFunc_GG_G \
(char **, npy_intp *, npy_intp *, void *)
void PyUFunc_OO_O \
(char **, npy_intp *, npy_intp *, void *)
void PyUFunc_O_O_method \
(char **, npy_intp *, npy_intp *, void *)
void PyUFunc_OO_O_method \
(char **, npy_intp *, npy_intp *, void *)
void PyUFunc_On_Om \
(char **, npy_intp *, npy_intp *, void *)
int PyUFunc_GetPyValues \
(char *, int *, int *, PyObject **)
int PyUFunc_checkfperr \
(int, PyObject *, int *)
void PyUFunc_clearfperr()
int PyUFunc_getfperr()
int PyUFunc_handlefperr \
(int, PyObject *, int, int *)
int PyUFunc_ReplaceLoopBySignature \
(ufunc, PyUFuncGenericFunction, int *, PyUFuncGenericFunction *)
object PyUFunc_FromFuncAndDataAndSignature \
(PyUFuncGenericFunction *, void **, char *, int, int, int,
int, char *, char *, int, char *)
int _import_umath() except -1
cdef inline void set_array_base(ndarray arr, object base):
Py_INCREF(base) # important to do this before stealing the reference below!
PyArray_SetBaseObject(arr, base)
cdef inline object get_array_base(ndarray arr):
base = PyArray_BASE(arr)
if base is NULL:
return None
return <object>base
# Versions of the import_* functions which are more suitable for
# Cython code.
cdef inline int import_array() except -1:
try:
__pyx_import_array()
except Exception:
raise ImportError("numpy.core.multiarray failed to import")
cdef inline int import_umath() except -1:
try:
_import_umath()
except Exception:
raise ImportError("numpy.core.umath failed to import")
cdef inline int import_ufunc() except -1:
try:
_import_umath()
except Exception:
raise ImportError("numpy.core.umath failed to import")
cdef extern from *:
# Leave a marker that the NumPy declarations came from this file
# See https://github.com/cython/cython/issues/3573
"""
/* NumPy API declarations from "numpy/__init__.pxd" */
"""

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"""
NumPy
=====
Provides
1. An array object of arbitrary homogeneous items
2. Fast mathematical operations over arrays
3. Linear Algebra, Fourier Transforms, Random Number Generation
How to use the documentation
----------------------------
Documentation is available in two forms: docstrings provided
with the code, and a loose standing reference guide, available from
`the NumPy homepage <https://www.scipy.org>`_.
We recommend exploring the docstrings using
`IPython <https://ipython.org>`_, an advanced Python shell with
TAB-completion and introspection capabilities. See below for further
instructions.
The docstring examples assume that `numpy` has been imported as `np`::
>>> import numpy as np
Code snippets are indicated by three greater-than signs::
>>> x = 42
>>> x = x + 1
Use the built-in ``help`` function to view a function's docstring::
>>> help(np.sort)
... # doctest: +SKIP
For some objects, ``np.info(obj)`` may provide additional help. This is
particularly true if you see the line "Help on ufunc object:" at the top
of the help() page. Ufuncs are implemented in C, not Python, for speed.
The native Python help() does not know how to view their help, but our
np.info() function does.
To search for documents containing a keyword, do::
>>> np.lookfor('keyword')
... # doctest: +SKIP
General-purpose documents like a glossary and help on the basic concepts
of numpy are available under the ``doc`` sub-module::
>>> from numpy import doc
>>> help(doc)
... # doctest: +SKIP
Available subpackages
---------------------
doc
Topical documentation on broadcasting, indexing, etc.
lib
Basic functions used by several sub-packages.
random
Core Random Tools
linalg
Core Linear Algebra Tools
fft
Core FFT routines
polynomial
Polynomial tools
testing
NumPy testing tools
f2py
Fortran to Python Interface Generator.
distutils
Enhancements to distutils with support for
Fortran compilers support and more.
Utilities
---------
test
Run numpy unittests
show_config
Show numpy build configuration
dual
Overwrite certain functions with high-performance Scipy tools
matlib
Make everything matrices.
__version__
NumPy version string
Viewing documentation using IPython
-----------------------------------
Start IPython with the NumPy profile (``ipython -p numpy``), which will
import `numpy` under the alias `np`. Then, use the ``cpaste`` command to
paste examples into the shell. To see which functions are available in
`numpy`, type ``np.<TAB>`` (where ``<TAB>`` refers to the TAB key), or use
``np.*cos*?<ENTER>`` (where ``<ENTER>`` refers to the ENTER key) to narrow
down the list. To view the docstring for a function, use
``np.cos?<ENTER>`` (to view the docstring) and ``np.cos??<ENTER>`` (to view
the source code).
Copies vs. in-place operation
-----------------------------
Most of the functions in `numpy` return a copy of the array argument
(e.g., `np.sort`). In-place versions of these functions are often
available as array methods, i.e. ``x = np.array([1,2,3]); x.sort()``.
Exceptions to this rule are documented.
"""
import sys
import warnings
from ._globals import ModuleDeprecationWarning, VisibleDeprecationWarning
from ._globals import _NoValue
# We first need to detect if we're being called as part of the numpy setup
# procedure itself in a reliable manner.
try:
__NUMPY_SETUP__
except NameError:
__NUMPY_SETUP__ = False
if __NUMPY_SETUP__:
sys.stderr.write('Running from numpy source directory.\n')
else:
try:
from numpy.__config__ import show as show_config
except ImportError:
msg = """Error importing numpy: you should not try to import numpy from
its source directory; please exit the numpy source tree, and relaunch
your python interpreter from there."""
raise ImportError(msg)
from .version import git_revision as __git_revision__
from .version import version as __version__
__all__ = ['ModuleDeprecationWarning',
'VisibleDeprecationWarning']
# Allow distributors to run custom init code
from . import _distributor_init
from . import core
from .core import *
from . import compat
from . import lib
# NOTE: to be revisited following future namespace cleanup.
# See gh-14454 and gh-15672 for discussion.
from .lib import *
from . import linalg
from . import fft
from . import polynomial
from . import random
from . import ctypeslib
from . import ma
from . import matrixlib as _mat
from .matrixlib import *
# Make these accessible from numpy name-space
# but not imported in from numpy import *
# TODO[gh-6103]: Deprecate these
from builtins import bool, int, float, complex, object, str
from .compat import long, unicode
from .core import round, abs, max, min
# now that numpy modules are imported, can initialize limits
core.getlimits._register_known_types()
__all__.extend(['__version__', 'show_config'])
__all__.extend(core.__all__)
__all__.extend(_mat.__all__)
__all__.extend(lib.__all__)
__all__.extend(['linalg', 'fft', 'random', 'ctypeslib', 'ma'])
# These are added by `from .core import *` and `core.__all__`, but we
# overwrite them above with builtins we do _not_ want to export.
__all__.remove('long')
__all__.remove('unicode')
# Remove things that are in the numpy.lib but not in the numpy namespace
# Note that there is a test (numpy/tests/test_public_api.py:test_numpy_namespace)
# that prevents adding more things to the main namespace by accident.
# The list below will grow until the `from .lib import *` fixme above is
# taken care of
__all__.remove('Arrayterator')
del Arrayterator
# Filter out Cython harmless warnings
warnings.filterwarnings("ignore", message="numpy.dtype size changed")
warnings.filterwarnings("ignore", message="numpy.ufunc size changed")
warnings.filterwarnings("ignore", message="numpy.ndarray size changed")
# oldnumeric and numarray were removed in 1.9. In case some packages import
# but do not use them, we define them here for backward compatibility.
oldnumeric = 'removed'
numarray = 'removed'
if sys.version_info[:2] >= (3, 7):
# Importing Tester requires importing all of UnitTest which is not a
# cheap import Since it is mainly used in test suits, we lazy import it
# here to save on the order of 10 ms of import time for most users
#
# The previous way Tester was imported also had a side effect of adding
# the full `numpy.testing` namespace
#
# module level getattr is only supported in 3.7 onwards
# https://www.python.org/dev/peps/pep-0562/
def __getattr__(attr):
if attr == 'testing':
import numpy.testing as testing
return testing
elif attr == 'Tester':
from .testing import Tester
return Tester
else:
raise AttributeError("module {!r} has no attribute "
"{!r}".format(__name__, attr))
def __dir__():
return list(globals().keys() | {'Tester', 'testing'})
else:
# We don't actually use this ourselves anymore, but I'm not 100% sure that
# no-one else in the world is using it (though I hope not)
from .testing import Tester
# Pytest testing
from numpy._pytesttester import PytestTester
test = PytestTester(__name__)
del PytestTester
def _sanity_check():
"""
Quick sanity checks for common bugs caused by environment.
There are some cases e.g. with wrong BLAS ABI that cause wrong
results under specific runtime conditions that are not necessarily
achieved during test suite runs, and it is useful to catch those early.
See https://github.com/numpy/numpy/issues/8577 and other
similar bug reports.
"""
try:
x = ones(2, dtype=float32)
if not abs(x.dot(x) - 2.0) < 1e-5:
raise AssertionError()
except AssertionError:
msg = ("The current Numpy installation ({!r}) fails to "
"pass simple sanity checks. This can be caused for example "
"by incorrect BLAS library being linked in, or by mixing "
"package managers (pip, conda, apt, ...). Search closed "
"numpy issues for similar problems.")
raise RuntimeError(msg.format(__file__))
_sanity_check()
del _sanity_check
def _mac_os_check():
"""
Quick Sanity check for Mac OS look for accelerate build bugs.
Testing numpy polyfit calls init_dgelsd(LAPACK)
"""
try:
c = array([3., 2., 1.])
x = linspace(0, 2, 5)
y = polyval(c, x)
_ = polyfit(x, y, 2, cov=True)
except ValueError:
pass
import sys
if sys.platform == "darwin":
with warnings.catch_warnings(record=True) as w:
_mac_os_check()
# Throw runtime error, if the test failed Check for warning and error_message
error_message = ""
if len(w) > 0:
error_message = "{}: {}".format(w[-1].category.__name__, str(w[-1].message))
msg = (
"Polyfit sanity test emitted a warning, most likely due "
"to using a buggy Accelerate backend. "
"If you compiled yourself, "
"see site.cfg.example for information. "
"Otherwise report this to the vendor "
"that provided NumPy.\n{}\n".format(
error_message))
raise RuntimeError(msg)
del _mac_os_check
# We usually use madvise hugepages support, but on some old kernels it
# is slow and thus better avoided.
# Specifically kernel version 4.6 had a bug fix which probably fixed this:
# https://github.com/torvalds/linux/commit/7cf91a98e607c2f935dbcc177d70011e95b8faff
import os
use_hugepage = os.environ.get("NUMPY_MADVISE_HUGEPAGE", None)
if sys.platform == "linux" and use_hugepage is None:
# If there is an issue with parsing the kernel version,
# set use_hugepages to 0. Usage of LooseVersion will handle
# the kernel version parsing better, but avoided since it
# will increase the import time. See: #16679 for related discussion.
try:
use_hugepage = 1
kernel_version = os.uname().release.split(".")[:2]
kernel_version = tuple(int(v) for v in kernel_version)
if kernel_version < (4, 6):
use_hugepage = 0
except ValueError:
use_hugepages = 0
elif use_hugepage is None:
# This is not Linux, so it should not matter, just enable anyway
use_hugepage = 1
else:
use_hugepage = int(use_hugepage)
# Note that this will currently only make a difference on Linux
core.multiarray._set_madvise_hugepage(use_hugepage)

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'''
Helper to preload windows dlls to prevent dll not found errors.
Once a DLL is preloaded, its namespace is made available to any
subsequent DLL. This file originated in the numpy-wheels repo,
and is created as part of the scripts that build the wheel.
'''
import os
import glob
if os.name == 'nt':
# convention for storing / loading the DLL from
# numpy/.libs/, if present
try:
from ctypes import WinDLL
basedir = os.path.dirname(__file__)
except:
pass
else:
libs_dir = os.path.abspath(os.path.join(basedir, '.libs'))
DLL_filenames = []
if os.path.isdir(libs_dir):
for filename in glob.glob(os.path.join(libs_dir,
'*openblas*dll')):
# NOTE: would it change behavior to load ALL
# DLLs at this path vs. the name restriction?
WinDLL(os.path.abspath(filename))
DLL_filenames.append(filename)
if len(DLL_filenames) > 1:
import warnings
warnings.warn("loaded more than 1 DLL from .libs:\n%s" %
"\n".join(DLL_filenames),
stacklevel=1)

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"""
Module defining global singleton classes.
This module raises a RuntimeError if an attempt to reload it is made. In that
way the identities of the classes defined here are fixed and will remain so
even if numpy itself is reloaded. In particular, a function like the following
will still work correctly after numpy is reloaded::
def foo(arg=np._NoValue):
if arg is np._NoValue:
...
That was not the case when the singleton classes were defined in the numpy
``__init__.py`` file. See gh-7844 for a discussion of the reload problem that
motivated this module.
"""
__ALL__ = [
'ModuleDeprecationWarning', 'VisibleDeprecationWarning', '_NoValue'
]
# Disallow reloading this module so as to preserve the identities of the
# classes defined here.
if '_is_loaded' in globals():
raise RuntimeError('Reloading numpy._globals is not allowed')
_is_loaded = True
class ModuleDeprecationWarning(DeprecationWarning):
"""Module deprecation warning.
The nose tester turns ordinary Deprecation warnings into test failures.
That makes it hard to deprecate whole modules, because they get
imported by default. So this is a special Deprecation warning that the
nose tester will let pass without making tests fail.
"""
ModuleDeprecationWarning.__module__ = 'numpy'
class VisibleDeprecationWarning(UserWarning):
"""Visible deprecation warning.
By default, python will not show deprecation warnings, so this class
can be used when a very visible warning is helpful, for example because
the usage is most likely a user bug.
"""
VisibleDeprecationWarning.__module__ = 'numpy'
class _NoValueType:
"""Special keyword value.
The instance of this class may be used as the default value assigned to a
deprecated keyword in order to check if it has been given a user defined
value.
"""
__instance = None
def __new__(cls):
# ensure that only one instance exists
if not cls.__instance:
cls.__instance = super(_NoValueType, cls).__new__(cls)
return cls.__instance
# needed for python 2 to preserve identity through a pickle
def __reduce__(self):
return (self.__class__, ())
def __repr__(self):
return "<no value>"
_NoValue = _NoValueType()

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"""
Pytest test running.
This module implements the ``test()`` function for NumPy modules. The usual
boiler plate for doing that is to put the following in the module
``__init__.py`` file::
from numpy._pytesttester import PytestTester
test = PytestTester(__name__).test
del PytestTester
Warnings filtering and other runtime settings should be dealt with in the
``pytest.ini`` file in the numpy repo root. The behavior of the test depends on
whether or not that file is found as follows:
* ``pytest.ini`` is present (develop mode)
All warnings except those explicitly filtered out are raised as error.
* ``pytest.ini`` is absent (release mode)
DeprecationWarnings and PendingDeprecationWarnings are ignored, other
warnings are passed through.
In practice, tests run from the numpy repo are run in develop mode. That
includes the standard ``python runtests.py`` invocation.
This module is imported by every numpy subpackage, so lies at the top level to
simplify circular import issues. For the same reason, it contains no numpy
imports at module scope, instead importing numpy within function calls.
"""
import sys
import os
__all__ = ['PytestTester']
def _show_numpy_info():
import numpy as np
print("NumPy version %s" % np.__version__)
relaxed_strides = np.ones((10, 1), order="C").flags.f_contiguous
print("NumPy relaxed strides checking option:", relaxed_strides)
class PytestTester:
"""
Pytest test runner.
A test function is typically added to a package's __init__.py like so::
from numpy._pytesttester import PytestTester
test = PytestTester(__name__).test
del PytestTester
Calling this test function finds and runs all tests associated with the
module and all its sub-modules.
Attributes
----------
module_name : str
Full path to the package to test.
Parameters
----------
module_name : module name
The name of the module to test.
Notes
-----
Unlike the previous ``nose``-based implementation, this class is not
publicly exposed as it performs some ``numpy``-specific warning
suppression.
"""
def __init__(self, module_name):
self.module_name = module_name
def __call__(self, label='fast', verbose=1, extra_argv=None,
doctests=False, coverage=False, durations=-1, tests=None):
"""
Run tests for module using pytest.
Parameters
----------
label : {'fast', 'full'}, optional
Identifies the tests to run. When set to 'fast', tests decorated
with `pytest.mark.slow` are skipped, when 'full', the slow marker
is ignored.
verbose : int, optional
Verbosity value for test outputs, in the range 1-3. Default is 1.
extra_argv : list, optional
List with any extra arguments to pass to pytests.
doctests : bool, optional
.. note:: Not supported
coverage : bool, optional
If True, report coverage of NumPy code. Default is False.
Requires installation of (pip) pytest-cov.
durations : int, optional
If < 0, do nothing, If 0, report time of all tests, if > 0,
report the time of the slowest `timer` tests. Default is -1.
tests : test or list of tests
Tests to be executed with pytest '--pyargs'
Returns
-------
result : bool
Return True on success, false otherwise.
Notes
-----
Each NumPy module exposes `test` in its namespace to run all tests for
it. For example, to run all tests for numpy.lib:
>>> np.lib.test() #doctest: +SKIP
Examples
--------
>>> result = np.lib.test() #doctest: +SKIP
...
1023 passed, 2 skipped, 6 deselected, 1 xfailed in 10.39 seconds
>>> result
True
"""
import pytest
import warnings
# Imported after pytest to enable assertion rewriting
import hypothesis
module = sys.modules[self.module_name]
module_path = os.path.abspath(module.__path__[0])
# setup the pytest arguments
pytest_args = ["-l"]
# offset verbosity. The "-q" cancels a "-v".
pytest_args += ["-q"]
# Filter out distutils cpu warnings (could be localized to
# distutils tests). ASV has problems with top level import,
# so fetch module for suppression here.
with warnings.catch_warnings():
warnings.simplefilter("always")
from numpy.distutils import cpuinfo
# Filter out annoying import messages. Want these in both develop and
# release mode.
pytest_args += [
"-W ignore:Not importing directory",
"-W ignore:numpy.dtype size changed",
"-W ignore:numpy.ufunc size changed",
"-W ignore::UserWarning:cpuinfo",
]
# When testing matrices, ignore their PendingDeprecationWarnings
pytest_args += [
"-W ignore:the matrix subclass is not",
"-W ignore:Importing from numpy.matlib is",
]
if doctests:
raise ValueError("Doctests not supported")
if extra_argv:
pytest_args += list(extra_argv)
if verbose > 1:
pytest_args += ["-" + "v"*(verbose - 1)]
if coverage:
pytest_args += ["--cov=" + module_path]
if label == "fast":
# not importing at the top level to avoid circular import of module
from numpy.testing import IS_PYPY
if IS_PYPY:
pytest_args += ["-m", "not slow and not slow_pypy"]
else:
pytest_args += ["-m", "not slow"]
elif label != "full":
pytest_args += ["-m", label]
if durations >= 0:
pytest_args += ["--durations=%s" % durations]
if tests is None:
tests = [self.module_name]
pytest_args += ["--pyargs"] + list(tests)
# This configuration is picked up by numpy.conftest, and ensures that
# running `np.test()` is deterministic and does not write any files.
# See https://hypothesis.readthedocs.io/en/latest/settings.html
hypothesis.settings.register_profile(
name="np.test() profile",
deadline=None, print_blob=True, database=None, derandomize=True,
suppress_health_check=hypothesis.HealthCheck.all(),
)
# run tests.
_show_numpy_info()
try:
code = pytest.main(pytest_args)
except SystemExit as exc:
code = exc.code
return code == 0

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"""
Compatibility module.
This module contains duplicated code from Python itself or 3rd party
extensions, which may be included for the following reasons:
* compatibility
* we may only need a small subset of the copied library/module
"""
from . import _inspect
from . import py3k
from ._inspect import getargspec, formatargspec
from .py3k import *
__all__ = []
__all__.extend(_inspect.__all__)
__all__.extend(py3k.__all__)

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"""Subset of inspect module from upstream python
We use this instead of upstream because upstream inspect is slow to import, and
significantly contributes to numpy import times. Importing this copy has almost
no overhead.
"""
import types
__all__ = ['getargspec', 'formatargspec']
# ----------------------------------------------------------- type-checking
def ismethod(object):
"""Return true if the object is an instance method.
Instance method objects provide these attributes:
__doc__ documentation string
__name__ name with which this method was defined
im_class class object in which this method belongs
im_func function object containing implementation of method
im_self instance to which this method is bound, or None
"""
return isinstance(object, types.MethodType)
def isfunction(object):
"""Return true if the object is a user-defined function.
Function objects provide these attributes:
__doc__ documentation string
__name__ name with which this function was defined
func_code code object containing compiled function bytecode
func_defaults tuple of any default values for arguments
func_doc (same as __doc__)
func_globals global namespace in which this function was defined
func_name (same as __name__)
"""
return isinstance(object, types.FunctionType)
def iscode(object):
"""Return true if the object is a code object.
Code objects provide these attributes:
co_argcount number of arguments (not including * or ** args)
co_code string of raw compiled bytecode
co_consts tuple of constants used in the bytecode
co_filename name of file in which this code object was created
co_firstlineno number of first line in Python source code
co_flags bitmap: 1=optimized | 2=newlocals | 4=*arg | 8=**arg
co_lnotab encoded mapping of line numbers to bytecode indices
co_name name with which this code object was defined
co_names tuple of names of local variables
co_nlocals number of local variables
co_stacksize virtual machine stack space required
co_varnames tuple of names of arguments and local variables
"""
return isinstance(object, types.CodeType)
# ------------------------------------------------ argument list extraction
# These constants are from Python's compile.h.
CO_OPTIMIZED, CO_NEWLOCALS, CO_VARARGS, CO_VARKEYWORDS = 1, 2, 4, 8
def getargs(co):
"""Get information about the arguments accepted by a code object.
Three things are returned: (args, varargs, varkw), where 'args' is
a list of argument names (possibly containing nested lists), and
'varargs' and 'varkw' are the names of the * and ** arguments or None.
"""
if not iscode(co):
raise TypeError('arg is not a code object')
nargs = co.co_argcount
names = co.co_varnames
args = list(names[:nargs])
# The following acrobatics are for anonymous (tuple) arguments.
# Which we do not need to support, so remove to avoid importing
# the dis module.
for i in range(nargs):
if args[i][:1] in ['', '.']:
raise TypeError("tuple function arguments are not supported")
varargs = None
if co.co_flags & CO_VARARGS:
varargs = co.co_varnames[nargs]
nargs = nargs + 1
varkw = None
if co.co_flags & CO_VARKEYWORDS:
varkw = co.co_varnames[nargs]
return args, varargs, varkw
def getargspec(func):
"""Get the names and default values of a function's arguments.
A tuple of four things is returned: (args, varargs, varkw, defaults).
'args' is a list of the argument names (it may contain nested lists).
'varargs' and 'varkw' are the names of the * and ** arguments or None.
'defaults' is an n-tuple of the default values of the last n arguments.
"""
if ismethod(func):
func = func.__func__
if not isfunction(func):
raise TypeError('arg is not a Python function')
args, varargs, varkw = getargs(func.__code__)
return args, varargs, varkw, func.__defaults__
def getargvalues(frame):
"""Get information about arguments passed into a particular frame.
A tuple of four things is returned: (args, varargs, varkw, locals).
'args' is a list of the argument names (it may contain nested lists).
'varargs' and 'varkw' are the names of the * and ** arguments or None.
'locals' is the locals dictionary of the given frame.
"""
args, varargs, varkw = getargs(frame.f_code)
return args, varargs, varkw, frame.f_locals
def joinseq(seq):
if len(seq) == 1:
return '(' + seq[0] + ',)'
else:
return '(' + ', '.join(seq) + ')'
def strseq(object, convert, join=joinseq):
"""Recursively walk a sequence, stringifying each element.
"""
if type(object) in [list, tuple]:
return join([strseq(_o, convert, join) for _o in object])
else:
return convert(object)
def formatargspec(args, varargs=None, varkw=None, defaults=None,
formatarg=str,
formatvarargs=lambda name: '*' + name,
formatvarkw=lambda name: '**' + name,
formatvalue=lambda value: '=' + repr(value),
join=joinseq):
"""Format an argument spec from the 4 values returned by getargspec.
The first four arguments are (args, varargs, varkw, defaults). The
other four arguments are the corresponding optional formatting functions
that are called to turn names and values into strings. The ninth
argument is an optional function to format the sequence of arguments.
"""
specs = []
if defaults:
firstdefault = len(args) - len(defaults)
for i in range(len(args)):
spec = strseq(args[i], formatarg, join)
if defaults and i >= firstdefault:
spec = spec + formatvalue(defaults[i - firstdefault])
specs.append(spec)
if varargs is not None:
specs.append(formatvarargs(varargs))
if varkw is not None:
specs.append(formatvarkw(varkw))
return '(' + ', '.join(specs) + ')'
def formatargvalues(args, varargs, varkw, locals,
formatarg=str,
formatvarargs=lambda name: '*' + name,
formatvarkw=lambda name: '**' + name,
formatvalue=lambda value: '=' + repr(value),
join=joinseq):
"""Format an argument spec from the 4 values returned by getargvalues.
The first four arguments are (args, varargs, varkw, locals). The
next four arguments are the corresponding optional formatting functions
that are called to turn names and values into strings. The ninth
argument is an optional function to format the sequence of arguments.
"""
def convert(name, locals=locals,
formatarg=formatarg, formatvalue=formatvalue):
return formatarg(name) + formatvalue(locals[name])
specs = [strseq(arg, convert, join) for arg in args]
if varargs:
specs.append(formatvarargs(varargs) + formatvalue(locals[varargs]))
if varkw:
specs.append(formatvarkw(varkw) + formatvalue(locals[varkw]))
return '(' + ', '.join(specs) + ')'

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@ -0,0 +1,186 @@
"""
Python 3.X compatibility tools.
While this file was originally intended for Python 2 -> 3 transition,
it is now used to create a compatibility layer between different
minor versions of Python 3.
While the active version of numpy may not support a given version of python, we
allow downstream libraries to continue to use these shims for forward
compatibility with numpy while they transition their code to newer versions of
Python.
"""
__all__ = ['bytes', 'asbytes', 'isfileobj', 'getexception', 'strchar',
'unicode', 'asunicode', 'asbytes_nested', 'asunicode_nested',
'asstr', 'open_latin1', 'long', 'basestring', 'sixu',
'integer_types', 'is_pathlib_path', 'npy_load_module', 'Path',
'pickle', 'contextlib_nullcontext', 'os_fspath', 'os_PathLike']
import sys
import os
from pathlib import Path, PurePath
import io
import abc
from abc import ABC as abc_ABC
try:
import pickle5 as pickle
except ImportError:
import pickle
long = int
integer_types = (int,)
basestring = str
unicode = str
bytes = bytes
def asunicode(s):
if isinstance(s, bytes):
return s.decode('latin1')
return str(s)
def asbytes(s):
if isinstance(s, bytes):
return s
return str(s).encode('latin1')
def asstr(s):
if isinstance(s, bytes):
return s.decode('latin1')
return str(s)
def isfileobj(f):
return isinstance(f, (io.FileIO, io.BufferedReader, io.BufferedWriter))
def open_latin1(filename, mode='r'):
return open(filename, mode=mode, encoding='iso-8859-1')
def sixu(s):
return s
strchar = 'U'
def getexception():
return sys.exc_info()[1]
def asbytes_nested(x):
if hasattr(x, '__iter__') and not isinstance(x, (bytes, unicode)):
return [asbytes_nested(y) for y in x]
else:
return asbytes(x)
def asunicode_nested(x):
if hasattr(x, '__iter__') and not isinstance(x, (bytes, unicode)):
return [asunicode_nested(y) for y in x]
else:
return asunicode(x)
def is_pathlib_path(obj):
"""
Check whether obj is a pathlib.Path object.
Prefer using `isinstance(obj, os_PathLike)` instead of this function.
"""
return Path is not None and isinstance(obj, Path)
# from Python 3.7
class contextlib_nullcontext:
"""Context manager that does no additional processing.
Used as a stand-in for a normal context manager, when a particular
block of code is only sometimes used with a normal context manager:
cm = optional_cm if condition else nullcontext()
with cm:
# Perform operation, using optional_cm if condition is True
"""
def __init__(self, enter_result=None):
self.enter_result = enter_result
def __enter__(self):
return self.enter_result
def __exit__(self, *excinfo):
pass
def npy_load_module(name, fn, info=None):
"""
Load a module.
.. versionadded:: 1.11.2
Parameters
----------
name : str
Full module name.
fn : str
Path to module file.
info : tuple, optional
Only here for backward compatibility with Python 2.*.
Returns
-------
mod : module
"""
# Explicitly lazy import this to avoid paying the cost
# of importing importlib at startup
from importlib.machinery import SourceFileLoader
return SourceFileLoader(name, fn).load_module()
# Backport os.fs_path, os.PathLike, and PurePath.__fspath__
if sys.version_info[:2] >= (3, 6):
os_fspath = os.fspath
os_PathLike = os.PathLike
else:
def _PurePath__fspath__(self):
return str(self)
class os_PathLike(abc_ABC):
"""Abstract base class for implementing the file system path protocol."""
@abc.abstractmethod
def __fspath__(self):
"""Return the file system path representation of the object."""
raise NotImplementedError
@classmethod
def __subclasshook__(cls, subclass):
if PurePath is not None and issubclass(subclass, PurePath):
return True
return hasattr(subclass, '__fspath__')
def os_fspath(path):
"""Return the path representation of a path-like object.
If str or bytes is passed in, it is returned unchanged. Otherwise the
os.PathLike interface is used to get the path representation. If the
path representation is not str or bytes, TypeError is raised. If the
provided path is not str, bytes, or os.PathLike, TypeError is raised.
"""
if isinstance(path, (str, bytes)):
return path
# Work from the object's type to match method resolution of other magic
# methods.
path_type = type(path)
try:
path_repr = path_type.__fspath__(path)
except AttributeError:
if hasattr(path_type, '__fspath__'):
raise
elif PurePath is not None and issubclass(path_type, PurePath):
return _PurePath__fspath__(path)
else:
raise TypeError("expected str, bytes or os.PathLike object, "
"not " + path_type.__name__)
if isinstance(path_repr, (str, bytes)):
return path_repr
else:
raise TypeError("expected {}.__fspath__() to return str or bytes, "
"not {}".format(path_type.__name__,
type(path_repr).__name__))

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@ -0,0 +1,10 @@
def configuration(parent_package='',top_path=None):
from numpy.distutils.misc_util import Configuration
config = Configuration('compat', parent_package, top_path)
config.add_subpackage('tests')
return config
if __name__ == '__main__':
from numpy.distutils.core import setup
setup(configuration=configuration)

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@ -0,0 +1,19 @@
from os.path import join
from numpy.compat import isfileobj
from numpy.testing import assert_
from numpy.testing import tempdir
def test_isfileobj():
with tempdir(prefix="numpy_test_compat_") as folder:
filename = join(folder, 'a.bin')
with open(filename, 'wb') as f:
assert_(isfileobj(f))
with open(filename, 'ab') as f:
assert_(isfileobj(f))
with open(filename, 'rb') as f:
assert_(isfileobj(f))

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@ -0,0 +1,107 @@
"""
Pytest configuration and fixtures for the Numpy test suite.
"""
import os
import tempfile
import hypothesis
import pytest
import numpy
from numpy.core._multiarray_tests import get_fpu_mode
_old_fpu_mode = None
_collect_results = {}
# Use a known and persistent tmpdir for hypothesis' caches, which
# can be automatically cleared by the OS or user.
hypothesis.configuration.set_hypothesis_home_dir(
os.path.join(tempfile.gettempdir(), ".hypothesis")
)
# See https://hypothesis.readthedocs.io/en/latest/settings.html
hypothesis.settings.register_profile(
name="numpy-profile", deadline=None, print_blob=True,
)
# We try loading the profile defined by np.test(), which disables the
# database and forces determinism, and fall back to the profile defined
# above if we're running pytest directly. The odd dance is required
# because np.test() executes this file *after* its own setup code.
try:
hypothesis.settings.load_profile("np.test() profile")
except hypothesis.errors.InvalidArgument: # profile not found
hypothesis.settings.load_profile("numpy-profile")
def pytest_configure(config):
config.addinivalue_line("markers",
"valgrind_error: Tests that are known to error under valgrind.")
config.addinivalue_line("markers",
"leaks_references: Tests that are known to leak references.")
config.addinivalue_line("markers",
"slow: Tests that are very slow.")
config.addinivalue_line("markers",
"slow_pypy: Tests that are very slow on pypy.")
def pytest_addoption(parser):
parser.addoption("--available-memory", action="store", default=None,
help=("Set amount of memory available for running the "
"test suite. This can result to tests requiring "
"especially large amounts of memory to be skipped. "
"Equivalent to setting environment variable "
"NPY_AVAILABLE_MEM. Default: determined"
"automatically."))
def pytest_sessionstart(session):
available_mem = session.config.getoption('available_memory')
if available_mem is not None:
os.environ['NPY_AVAILABLE_MEM'] = available_mem
#FIXME when yield tests are gone.
@pytest.hookimpl()
def pytest_itemcollected(item):
"""
Check FPU precision mode was not changed during test collection.
The clumsy way we do it here is mainly necessary because numpy
still uses yield tests, which can execute code at test collection
time.
"""
global _old_fpu_mode
mode = get_fpu_mode()
if _old_fpu_mode is None:
_old_fpu_mode = mode
elif mode != _old_fpu_mode:
_collect_results[item] = (_old_fpu_mode, mode)
_old_fpu_mode = mode
@pytest.fixture(scope="function", autouse=True)
def check_fpu_mode(request):
"""
Check FPU precision mode was not changed during the test.
"""
old_mode = get_fpu_mode()
yield
new_mode = get_fpu_mode()
if old_mode != new_mode:
raise AssertionError("FPU precision mode changed from {0:#x} to {1:#x}"
" during the test".format(old_mode, new_mode))
collect_result = _collect_results.get(request.node)
if collect_result is not None:
old_mode, new_mode = collect_result
raise AssertionError("FPU precision mode changed from {0:#x} to {1:#x}"
" when collecting the test".format(old_mode,
new_mode))
@pytest.fixture(autouse=True)
def add_np(doctest_namespace):
doctest_namespace['np'] = numpy

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@ -0,0 +1,143 @@
"""
Contains the core of NumPy: ndarray, ufuncs, dtypes, etc.
Please note that this module is private. All functions and objects
are available in the main ``numpy`` namespace - use that instead.
"""
from numpy.version import version as __version__
import os
# disables OpenBLAS affinity setting of the main thread that limits
# python threads or processes to one core
env_added = []
for envkey in ['OPENBLAS_MAIN_FREE', 'GOTOBLAS_MAIN_FREE']:
if envkey not in os.environ:
os.environ[envkey] = '1'
env_added.append(envkey)
try:
from . import multiarray
except ImportError as exc:
import sys
msg = """
IMPORTANT: PLEASE READ THIS FOR ADVICE ON HOW TO SOLVE THIS ISSUE!
Importing the numpy C-extensions failed. This error can happen for
many reasons, often due to issues with your setup or how NumPy was
installed.
We have compiled some common reasons and troubleshooting tips at:
https://numpy.org/devdocs/user/troubleshooting-importerror.html
Please note and check the following:
* The Python version is: Python%d.%d from "%s"
* The NumPy version is: "%s"
and make sure that they are the versions you expect.
Please carefully study the documentation linked above for further help.
Original error was: %s
""" % (sys.version_info[0], sys.version_info[1], sys.executable,
__version__, exc)
raise ImportError(msg)
finally:
for envkey in env_added:
del os.environ[envkey]
del envkey
del env_added
del os
from . import umath
# Check that multiarray,umath are pure python modules wrapping
# _multiarray_umath and not either of the old c-extension modules
if not (hasattr(multiarray, '_multiarray_umath') and
hasattr(umath, '_multiarray_umath')):
import sys
path = sys.modules['numpy'].__path__
msg = ("Something is wrong with the numpy installation. "
"While importing we detected an older version of "
"numpy in {}. One method of fixing this is to repeatedly uninstall "
"numpy until none is found, then reinstall this version.")
raise ImportError(msg.format(path))
from . import numerictypes as nt
multiarray.set_typeDict(nt.sctypeDict)
from . import numeric
from .numeric import *
from . import fromnumeric
from .fromnumeric import *
from . import defchararray as char
from . import records as rec
from .records import *
from .memmap import *
from .defchararray import chararray
from . import function_base
from .function_base import *
from . import machar
from .machar import *
from . import getlimits
from .getlimits import *
from . import shape_base
from .shape_base import *
from . import einsumfunc
from .einsumfunc import *
del nt
from .fromnumeric import amax as max, amin as min, round_ as round
from .numeric import absolute as abs
# do this after everything else, to minimize the chance of this misleadingly
# appearing in an import-time traceback
from . import _add_newdocs
# add these for module-freeze analysis (like PyInstaller)
from . import _dtype_ctypes
from . import _internal
from . import _dtype
from . import _methods
__all__ = ['char', 'rec', 'memmap']
__all__ += numeric.__all__
__all__ += fromnumeric.__all__
__all__ += rec.__all__
__all__ += ['chararray']
__all__ += function_base.__all__
__all__ += machar.__all__
__all__ += getlimits.__all__
__all__ += shape_base.__all__
__all__ += einsumfunc.__all__
# Make it possible so that ufuncs can be pickled
# Here are the loading and unloading functions
# The name numpy.core._ufunc_reconstruct must be
# available for unpickling to work.
def _ufunc_reconstruct(module, name):
# The `fromlist` kwarg is required to ensure that `mod` points to the
# inner-most module rather than the parent package when module name is
# nested. This makes it possible to pickle non-toplevel ufuncs such as
# scipy.special.expit for instance.
mod = __import__(module, fromlist=[name])
return getattr(mod, name)
def _ufunc_reduce(func):
from pickle import whichmodule
name = func.__name__
return _ufunc_reconstruct, (whichmodule(func, name), name)
import copyreg
copyreg.pickle(ufunc, _ufunc_reduce, _ufunc_reconstruct)
# Unclutter namespace (must keep _ufunc_reconstruct for unpickling)
del copyreg
del _ufunc_reduce
from numpy._pytesttester import PytestTester
test = PytestTester(__name__)
del PytestTester

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